Contents

Selected Papers from EWGAE 2018
Topical Editors: C. Herve and G. Manthei

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Title preceded with Paper Number
Contents
19 - Combination of vibration analysis & Acoustic Emission measurements to bettercharacterize damage and mechanical behavior of aerospace high speed gear box
Y. Hebrard, A. Proust, M. Batel
35 - Acoustic Emission for in situ monitoring of laser processing
S. Shevchik, T. Le Quang, B. Meylan, K. Wasmer
72 - Monitoring of stamping by Acoustic Emission (AE)A. Saidoun, C. Hervé
14 - Acoustic Emission monitoring of adhesively bonded wood joints underquasistatic and cyclic fatigue mode II flexure loads using end-notch-flexurespecimens
A. J. Brunner, G. Clerc, P. Niemz
17 - Acoustic Emission analysis during bending tests of timber beams reinforced
with carbon fiber composite
F. Rescalvo, C. Abarkane, E. Suárez, A. Gallego
23- Frequency-Amplitude class of Acoustic Emission for different fracture
mechanisms in C/SiC composite
D. Xiao, Y. Gao, L. Jin, F. Meng, N. Li, X. Zhou, Z. Tong, B. Jiang, T. He
29 - Damage monitoring on a steel truck trailer using parameter-based analysis of
Acoustic Emissions
J. V. Pimentel, R. Klemm, M. Dalgic, A. Irretier, K.-L. Krieger
49 - Structural health monitoring using Acoustic Emission on metallic components
in industrial plants
G. Lackner, H. Marihart, G. Schauritsch
83 - Novel AE monitoring of hydrogen induced damaged vessel and real time
alarms. A case study
D. Papasalouros, K. Bollas, I. Ladis, E. Aerakis, A. Anastasopoulos, D. Kourousis
55 - The influence of source depth and source-to-sensor distance on the AE signal
characteristics of damaging events in thin-walled CFRP laminates
B. Kelkel, J. Vogtmann, M. Gurka
57 - Acoustic Emission behaviour on CFRP with curved fibers
Y. Mizutani, T. Ashizawa, N. Toyama, A. Todoroki
73 - Use of AE for characterization of damage mechanisms in composite
accumulator
O. Colas, C. Briançon, C. Hervé, A. Houssais
40 - Continuous recording and wireless transmission of AE waveforms by battery
powered sensor nodes
K. Ito, M. Enoki
01- Modal Acoustic Emission analysis of mode-I and mode-II fracture of adhesivelybonded joints
A. Crawford, M. Ghazi Droubi, N. Faisal
02- On use of signal features for Acoustic Emission source identification in fiberreinforced composites
M. G. R. Sause
03- Damage diagnostic and lifetime prognosis for ceramic matrix composite with
i

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Acoustic Emission during long-term mechanical tests at intermediate temperature
N. Godin, P. Reynaud, G. Fantozzi
04- Combination of synchrotron computed tomography and Acoustic Emission
measurements for cyclic loading of fiber-reinforced composites
P. Potstada, S. Rosini, M. Mavrogordato, I. Sinclair, S. M. Spearing, M. G. R. Sause
48 - Structural Health Monitoring of composites structures by Acoustic Emission
analysis
L. Schubert, E. Schulze, R. Neubeck, B. Weihnacht
68 - Structural Health Monitoring of composite aerospace structures with Acoustic
Emission
M. Lehmann, A. Bueter, O. Schwarzhaupt, C. Contell Asins
53 - Mechanical and Acoustic Emission characterization of lime-based masonry
constituents
N. Shetty, G. Livitsanos, D. Aggelis, D. Van Hemelrijck, M. Wevers, E. Verstrynge
22 - Characterization of particle impacts using Acoustic Emission
E. Serris, L. Carrillo, A. Valentini
32 - The sound of metal: Acoustic Emission during the deformation of commerciallypure titanium
N. Goossens, M. Seefeldt, M. Wevers
38- Analysis of kinking and twinning behavior in mg alloys by AE clustering method
M. Enoki, K. Tamura, T. Shiraiwa
39- Non-threshold Acoustic Emission analysis of damage evolution in pipe segmentsof steel S355J2H under bending load
F. Baensch, W. Baer, P. Wossidlo, A. Habib
71 - Galling detection by Acoustic Emission (AE) according to ASTM G98
A. Saidoun, C. Hervé, Y. Chen, T. Lesage
11-AE monitoring and three-dimensional observation of stress corrosion cracking
from corrosion pits
K. Wu, K. Ito, I. Shinozaki, M. Enoki
18 - Application of Acoustic Emission measurements to monitor the initiation andthe propagation of single or multiple Stress Corrosion Cracks (SCC)
O. Al Haj, A. Proust, J. Bolivar, M. Fregonese
64 - Acoustic Emission from gas bubble nucleation and breaking away from a fl t
surfaceV. Kietov, M. Mandel, L. Krüger, A. Vinogradov
75 - New edition of GEA guideline for Acoustic Emission Testing of pressure
equipments
C. Di Giulio, C. Hervé
15 - Use of Acoustic Emission for inspection of various composite pressure vessels
subjected to mechanical impact
F. Dahmene, O. Bardoux, S. Bittendiebel, P. Blanc-Vannet, N. Alexandre, F. Nony,
M. Barcikowski, A. Maldachowska, M. Panek, P. Sheggem, A. Echtermeyer, K. Lasn
30 - Detection and identifi ation of low-amplitude Acoustic Emissions during plastic
deformation under indentation and scratch testing
A. Danyuk, D. Merson, A. Vinogradov
33 - When AE (Acoustic Emission) meets AI (Artificial Intelligence) II
K. Wasmer, F. Saeidi, B. Meylan, T. Le Quang, S. A. Shevchik
65 - AE fracturing modes classifi ation by means of shear tensile source model
M. Petruzalek, T. Lokajicek, T. Svitek, P. Kolar, Z. Jechumtalova, J. Sileny, P.
Adamova
21 - AE sensor sensitivity verifi ation using a stimulated motion verifi by laserii

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iv
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vibrometry
H. Vallen
76 - Method for assessing the likelihood of fatigue crack detecting
V. Barat, D.V. Chernov, S.V. Elizarov
78 - Statistical non-parametrical algorithm for Acoustic Emission impulses detection
adapted on the basis of modal analysis approach
V. Barat, D.A. Terentyev, S. Elizarov
09 - Acoustic Emission measurements during a tensile fatigue test in reinforced
concrete
G. Manthei, M. Koob, J. Minnert
77 - Test of bending reinforced concrete structures by means of Acoustic Emission
methodS.V. Elizarov, V. Bardakov, A.I. Sagaidak
81 - Assessment of infrastructures by rainy induced AE tomography with wave
velocity and attenuation rate
T. Shiotani, K. Hashimoto, N. Okude, C. Granier, K. Watabe, H. Takamine
34 - Contouring geodetically accurate Acoustic Emission sources via kernel
density estimates
P. Gális, V. Kůs
41 - Analysis of AE source location precision for general sensor configurations
M. Chlada, J. Kober, Z. Prevorovsky
54 - Time reversal localization of continuous and burst AE sources under noise
Z. Prevorovsky, J. Krofta, J. Kober, M. Chlada, M. Mracko
06 Modelling of fiber break as acoustic emission source in single fiber
fragmentation test: comparison with experimental results
Z. Hamam, N. Godin, C. Fusco and T. Monnier
Conference Proceedings Content
Conference Proceedings Authors Index

iii

19
- Combinationof vibration
analysis
Acoustic Emission
Emission
Combinationof vibration
analysis
and&Acoustic

measurements
measurementsto
tobetterbettercharacterizecharacterizedamage
damageandandmechanicalmechanicalbehavior of
behaviour
ofaerospace
aerospacehighhighspeed
speedgear
gearboxbox.
Hebrard Yoann1, Proust Alain2, Batel Mehdi2,
1

SKF Aerospace, 22 rue Brillat Savarin, 26958 Valence, FRANCE
yoann.hebrard@skf.com
2MISTRAS SAS, 27 rue Magellan 94370 Sucy en Briealain.proust@mistrasgroup.com

Abstract:
Designed to break the paradigm for efficiency, the new generation of engines promises double-digit
reductions in fuel burn, as well as an unparalleled single-leap improvement in emissions and lower noise
to fulfil societal environmental objectives for a more sustainable future. The end-use consumer andenvironmental policy requirements for aircrafts of the next generation translate into components withhigher temperature and speed. Furthermore, new instrumentation techniques are needed to closely
monitor rolling contact during testing of the next generation of aero engine bearing to check its behavior
under the new application condition. Vibration analysis for condition assessment and fault diagnostics
is widely used nevertheless interpretation and correlation of collected data is often cumbersome. That
is why combination of both techniques giving different types information in two different frequency band
can help to understand the behavior of new gear box. This study proposes a correlation between low
and high frequency signals with different strategy of signal acquisition and processing. Real timetransient analysis with feature extraction can be done in parallel with streaming acquisition. Then patternrecognition of individual AE signal is possible and can be correlated with more traditional analysis basedon “multiple chocks” vibration analysis. Continuous monitoring of an aging gear box is giving genuine
information on no stationary regime and also time of stabilization. Long term experiments are conducted
on damaged and defect free gear boxes at several rotating speed and loading level.

Keywords: Rolling contact monitoring, vibration monitoring, EHL conditions

1. Introduction
Many studies are dealing about the use of vibration to detect fault in gear box and rolling
bearings. Some of them are focusing on the use of Acoustic Emission (AE) and vibration for
better characterization of the gear box default type. Based on vibration technology theacquisition of raw signal is done by a partial acquisition of the signal at random. AE technology
is more focusing on the detection of transient above a predefined threshold in a narrow
bandwidth [1,2]. According to the progress of the acquisition system, this study proposes a
combination of all these types of acquisition. Wide band sensors offer richer bunch of data
allowing us to investigate new methods of processing and default characterization.
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The default Characterization we propose will be more than a statistic acquisition but acontinuous monitoring.
Acoustic emission (AE) is defined as transient elastic waves generated from a rapid
release of strain energy caused by a deformation or damage within or on the surface of amaterial [4]. This technique is widely used as a non-destructive testing technique for fitness forservice evaluation in industrial field. AE is also a powerful tool to characterize and understanddamage initiation and propagation. Most of all microscopic mechanisms has been studied andcorrelated with AE signals as fretting [3]. Many developments in AE technology, mainly
developments in AE instrumentation, have occurred in the past ten years.
In this particular investigation, AE appears as the transient elastic waves generated by theinteraction of two surfaces in relative motion. The interaction of surface asperities andimpingement of the bearing rolling elements over the seeded defect on the outer race will
generate AE hits. Due to the high frequency content of the AE transients typical mechanicalnoise (less than 20kHz) is eliminated.

Figure 1: AE signal from asperity in rolling contact

There have been numerous investigations reported on applying AE to bearing defectdiagnosis. Roger [5] utilized the AE technique for monitoring slow rotating anti-friction slew
bearings on cranes employed for gas production. In addition, successful applications of AE tobearing diagnosis for extremely slow rotational speeds have been reported [6, 7]. Yoshioka
and Fujiwara [8, 9] have shown that selected AE parameters identified bearing defects before
they appeared in the vibration acceleration range. Hawman et al [10] reinforced Yoshioka’s
observation and noted that diagnosis of defect bearings was accomplished due to modulationof high frequency AE hits at the outer race defect frequency. The modulation of AE signaturesat bearing defect frequencies has also been observed by other researchers [11, 12, 13].
Morhain et al [14] showed successful application of AE to monitor split bearings with seeded
defects on the inner and outer races.
This paper investigates the relationship between AE signals for a range of defectconditions, offering a more comparative study than is presently available in the public domain.
Moreover, comparisons with vibration analysis are presented. The source of AE from seeded
defects on bearings, which has not been investigated to date, is presented showing
conclusively that the dominant AE source mechanism for defect conditions is asperity contact.

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2. Experimental setup
The bearing test rig employed for this study had an operational speed range of 5000 to
15000 rpm with a maximum load capability of 50kN via a hydraulic ram. The test bearing
employed was 3 points contact ball bearing. This bearing type was selected as it allowed
defects to be seeded onto the races, furthermore, assembly and disassembly of the bearing
was accomplished with minimum disruption to the test sequence. Five calibrated dents weredone on the bearing inner ring (rotating). Pure axial loading is applied to the rolling bearing.
The defects are located along the predicted rolling raceway path inside the hertz contact zone.
Dent length against the rolling velocity are around 200µm. The acquisition is performed via a
multichannel last generation Mistras AE acquisition system: Express 8.
We have use all capability of this system to record the maximum of information. First, weuse continuous energy summation without threshold, which is much accurate than traditional
RMS or ASL (RMS with log scale) integration. Then, acquisition of transients based on smart
threshold allows us to avoid triggering on continuous signal. It can guaranty a significant hits
rate whatever is the level of background continuous AE (fig.2). Manual change of the trigger is
not any more require. Transient is characterized by hit and waveform, it can be feed in Noesis,
Mistras pattern recognition software for multiparametric evaluation. Express 8 offer also thecapability to record streaming (acquisition of raw signal at very high rate and with quasi
unlimited buffer) to apply more traditional signal processing often use in vibration but with lower
sampling rate.

Figure 2: acquisition with smart threshold (above graph) and waveform and hit correspondence (graph below).

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The streaming has been also used in parallel without any reduction of performance oftraditional AE acquisition. Streaming is a synchronized acquisition of the 4 channels without
any threshold. The windows length can be arbitrary of defined like shown in figure 3.

Figure 3: streaming acquisition during 5 sec at 2 Mega sample per second on the four channels.

Three different wideband AE sensors (WD, S9208 and micro 80) are used in parallel with astandard accelerometer Bruel et Kjaer type 4374 (bandwidth 1 to 26KHz 0,5 pC ms-2). The
calibration curves of these sensors are given in figure 4.

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Frequency response of S9208

Frequency response of WD

Frequency response of Micro 80

Figure 4: calibration curves of sensors S9203, WD and micro 80.

5S5The sensor must be held in place for the duration of the test. Dry contact between the sensor
and the structure does not meet the goal for appropriate wave transmission. For the AE
sensors coupling, we used an adhesive tape developed for aerospace industry called“KAPTON” on which the sensor is glued with cyanoacrylate or cement glue. (figure 5). Thissolution has been successfully tested with cyanoacrylate glue during test space telescope for
ASTRIUM under high energetic vibration [15]. The reduction of measured amplitude compare
to a traditional grease coupling is less than one dB for Hsu Nielsen source (NF EN1330-9).
Accelerometer: channel 4

WD : channel 1

S9208 : channel
3

Micro 80 : channel 2

Figure 5: picture of the different sensors used for the monitoring and coupling mode.

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3. Results
We performed the same loading sequence for all bearings, see table 1.
Speed (Rpm)
Load (kN)

3000

5

6000

5

11500

5

11500

10

11500

15

11500

20

11500

25

6000

23,5

Table 1: loading sequence for bearing the colors are used to separate the loading sequences.

The four sensors are compared and the WD (channel 1) and micro 80 (channel 2) are giving better
results than accelerometer and S9208 to characterized default (fig.6).
Good bearing
Good bearing

default

default

Good bearing
Good bearing

default

default
Figure 6: evolution of ASL and frequency centroid versus time with and without default. On the top graphchannel 1 (WD) at the bottom channel 2 (Micro 80).

A clear difference of behavior can be seen above 6000 rpm at 5 kN and it enhance at 10 kN
on the energy of the AE signals on transients. Also, the center mass of the frequency spectrum
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(called frequency centroïd) increases a lot for the bearing with default. It exhibits the best powerof discrimination using our pattern recognition software. The stabilization of the AE signal takes
at least 2 minutes after the loading condition change.
Considering a more standard acquisition mode for channel 1 (WD), on the bearing with default,
it can be seen a new pic at 27 kHz at 11500 tr/min and above speed. For another side, thewidth of the FFT increases as the load increases (fig. 7).

Figure 7 : Fourier transform on 5s files on channel 1 for different cases of load levels and rotating speed.

4. Discussion
The source of AE for seeded defects is attributed to material protrusions above the surface
roughness of the outer race. This was established as the smooth defect could not be
distinguished from the no-defect condition. However, for all other defects where the material
protruded above the surface roughness, AE transients associated with the defect frequency
were observed. As the defect size increased, AE ASL, maximum amplitude and kurtosis values
increased, however, observations of corresponding parameters from vibration measurements
were disappointing. Although the vibration RMS and maximum amplitude values did showchanges with defect condition, the rate of such changes highlighted the greater sensitivity of
the AE technique to early defect detection.
Again, unlike vibration measurements, the AE transient could be related to the defectsource whilst the frequency spectrum of vibration readings failed in the majority of cases to
identify the defect frequency or source. Also evident from this investigation is that AE levels
increase with increasing speed and load. It should be noted that further signal processing could
be applied to the vibration data in an attempt to enhance defect detection.

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Techniques such as demodulation, band pass filtering, etc, could be applied though thesewere not employed for this particular investigation. The main reason for not applying further
signal processing to the vibration data was to allow a direct comparison between the acquired
AE and vibration signature. From the results presented two important features were noted:•

firstly, AE was more sensitive than vibration to variation in defect size•

secondly, that no further analysis of the AE response was required in relating the defectsource to the AE response, which was not the case for vibration signatures.

The relationship between defect size and AE hit duration is a significant finding. In the longer
term, and with further research, this offers opportunities for prognosis. AE hit duration was
directly correlated to the seeded defect length (along the race in the direction of the rolling
action) whilst the ratio of hit amplitude to the underlying operational noise levels was directly
proportional to the seeded defect width.

5. Conclusion
It has been shown that the fundamental source of AE in seeded defect tests was due tomaterial protrusions above the mean surface roughness. Also, AE maximum amplitude has
been shown to be more sensitive to the onset and growth of defects than vibration
measurements.
A relationship between the AE hit duration and the defect length will be established in further
posttreatment.

References:[1] Raad. A. Sidahmed, B; Early detection of the gear fault – advantage of the acoustic
emission technique 25th European conference, Acoustic emission testing; EWGAE; 2002;
Prague; 2; II/125-II/130[2] Lekou, D.J., Mouzakis, F., Anastasopoulos, A. A., Kourousis, D., Fused Acoustic
Emission and Vibration Techniques for Health Monitoring of Wind Turbine Gearboxes andBearings; Scientific Proceedings of EWEC 2009, pp. 78-82., At Marseille[3] Boinet, M. Proust, A., Lenain, J.C., Yantio Njankeu Sabeya, G.R., Paris, J.-Y., Denape, J.,
Analyse par Emission Acoustique de la dégradation d’un contact sollicité en fretting,
Journées Francophone de Tribologie Poitiers, 22–23 mai 2007.
[4] Mathews, J. R. Acoustic emission, Gordon and Breach Science Publishers Inc., New
York. 1983, ISSN 0730-7152.
[5] Roger, L. M., The application of vibration analysis and acoustic emission source location
to on-line condition monitoring of anti-friction bearings. Tribology International, 1979; 51-59.

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[6] Mba, D., Bannister, R.H., and Findlay, G.E. Condition monitoring of low-speed rotatingmachinery using stress waves: Part’s I and II. Proceedings of the Instn Mech Engr 1999;
213(E): 153-185[7] N. Jamaludin, Dr. D. Mba, Dr. R. H. Bannister Condition monitoring of slow-speed
rolling element bearings using stress waves. Journal of Process Mechanical Engineering, I
Mech E. Pro. Inst. Mech Eng., 2001, 215(E):, Issue E4, 245-271.
[8] Yoshioka T, Fujiwara T. New acoustic emission source locating system for the study ofrolling contact fatigue, Wear, 81(1), 183-186.
[9] Yoshioka T, Fujiwara T. Application of acoustic emission technique to detection of rolling
bearing failure, American Society of Mechanical Engineers, Production Engineering Divisionpublication PED, 1984, 14, 55-76.
[10] Hawman, M. W., Galinaitis, W. S, Acoustic Emission monitoring of rolling element
bearings, Proceedings of the IEEE, Ultrasonics symposium, 1988, 885-889[11] Holroyd, T.J. and Randall, N., (1993), Use of Acoustic Emission for Machine Condition
Monitoring, British Journal of Non-Destructive Testing, 1993, 35(2), 75-78.
[12] Holroyd, T. Condition monitoring of very slowly rotating machinery using AE techniques.
14th International congress on Condition monitoring and Diagnostic engineering
management (COMADEM'2001), Manchester, UK, 4-6 September 2001, 29, ISBN
0080440363[13] Bagnoli, S., Capitani, R. and Citti, P. Comparison of accelerometer and acoustic
emission signals as diagnostic tools in assessing bearing. Proceedings of 2nd International
Conference on Condition Monitoring, London, UK, May 1988, 117-125.
[14] Morhain, A, Mba, D, Bearing defect diagnosis and acoustic emission Journal ofEngineering Tribology, I Mech E, Vol 217, No. 4, Part J, p 257-272, 2003. ISSN 1350-6501.
[15] Levalllois, F., Sobeczko, A., Proust, A., Marlot, D., Lenain J.C. Non-destructive testingof GAIA frame by means of acoustic emission monitoring during launch simulation tests.,
30th European Conference on Acoustic Emission Testing & 7th International Conference on
Acoustic Emission University of Granada, 12-15 September 2012.

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35ACOUSTIC- Acoustic EMISSION
Emission forin situmonitoringof laserOF
processing
FOR
IN SITU
MONITORINGLASER
PROCESSING

Sergey SHEVCHIK1, Quang Tri LE 1, Bastian MEYLAN1, Kilian WASMER1Laboratory for Advanced Materials Processing, Empa - Swiss Federal Laboratories
for Materials Science and Technology, Feuerwerkerstrasse 39, CH – 3602 Thun,
Switzerland; Phone: +41 58 762 62 71; Email: kilian.wasmer@empa.ch1

Abstract:
Laser processing is an important technology in industrial manufacture. Today, a standard quality
monitoring of the processed workpieces mainly relies on costly X-ray or time consuming post mortem
methods. The commercially available in situ and real-time monitoring units are also accessible andrely on optical measurements or high resolution imaging of the process zone. Unfortunately, theirperformance is greatly affected by the plume, formed during the overheating of the material. This limits
the detection of defects (e.g. pores) formed inside the workpiece. To bypass the aforementioned limits,
we focus on acoustic measurements that are the derivatives of the shockwaves, generated inside theworkpiece directly during processing. The measurements are conducted using a high sensitive piezo
sensor. The acoustic signals are analysed further by state-of-the-art signal processing. This included acompartment of wavelets and Fourier decompositions, followed by machine learning. The developed
methodology is realized in a hardware unit that operates in pseudo real-time. The unit was tested onreal welds with various materials. The welding experiments were performed using spot weld, SteplessHigh-speed Accurate and Discrete One-pulse Weld (SHADOW). The tests were carried out at different
laser powers that are closely related to the weld quality. The in situ quality control unit demonstrated
the capability to classify the weld quality with a confidence level ranged between 82% and 95%. Dueto the similar light-matter interaction phenomena, the presented approach and apparatus can also be
applied for monitoring other laser-processing technologies, namely: ablation, cutting, drilling andadditive manufacturing.

1. Introduction
Laser welding is a rapidly developing technology that becomes an essential part inautomobile [1,2], medical [3,4], aerospace [5,6,8] industries and heavy machinery production[8,9]. The advantages of this technology are in relatively low running costs, high processing
speed and quality in terms of the mechanical properties of the welded joints [1,5,7]. The
growing demand for laser welding requires the development of a reliable in situ quality
monitoring and control that remains an open topic until today [1]. The challenge in developing
such a system is the high complexity in laser-matter interactions [1,5,7]. This happens due tomultiple physical phenomena that are present at different time scales affecting the process[5,7]. This complexity may be a reason for the deviations of the real quality from the desired
one even in well controlled conditions [1,5,7]. One of the possible causes is the occurrenceof pores that are a hidden threat for the mechanical properties of the processed workpieces[1,5,7]. At present, the diagnostic of pores in industrial environment is carried out with several
well established methods.

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Analysis of the cross-section of the workpieces is one of the most reliable techniques thatallows visually inspect the processing results [1]. This method is destructive and time
consuming that is obviously not applicable to every single piece in mass production. X-ray
imaging is a non- destructive visualization method that allows to search the hidden pores
directly inside the workpiece medium [1,10]. The drawback of this approach is theinvolvement of expensive and very complex equipment. In addition, rather complex image
processing algorithms are needed for the search and interpretation of the obtained data. This
leads to long computational time of the acquired images. These factors put technical limits on
the adaption of this technology for in situ control. At present, this method is only applied forpost mortem analysis of workpieces that require long manufacturing time.
The development of a real-time in situ quality control is still pushed by an industrial demandand some new approaches exist but are still at development stage. Image analysis is one of
the mostly visible directions due to the availability of the cheap hardware and minimal
modifications needed for already existing commercial laser welding systems. In this setup,
the images of the process zone are registered in infra-red or visible spectral ranges [1]. The
quality inside the workpiece medium is correlated with the surface temperature distributions
and/or the geometry of the process zone [1]. The drawback of this approach is due to only
surface measurements, while the inside processes remains hidden. In this situation, theaforementioned complexity of the laser-matter interactions [5,7] is, sometimes, responsiblefor deviations between the surface behavior and the expected behavior inside the medium,thus reducing the precision of this method.
Other methods based on spectroscopy of the evaporated materials from the process zone
showed a potential but only for a limited number of applications [1]. High efficiency indetecting the plume and spatter was shown in a number of works [1]. However, thosephenomena take place after overheating of the material [11], thus post-factum indicating an
uncontrollable development of the process outside the acceptable quality range.
To sum up, the existing approaches for in situ monitoring have forenamed limits. Taking this
into account, the present study is based on acoustic emission (AE) that has someadvantages due to its nature. AE is a derivative of the elastic shockwaves deformations thattake place inside the workpiece [1] and grab the information about the momentary pressureof the liquid/gas material phases inside the process zone onto the solid surrounding [1,7].
This implies direct volumetric measurements of the material behavior inside the processzone. The drawbacks of this approach are in the complexity of the AE signal interpretation.
At present, AE proved itself to be a robust method for detection of the plume and spatter [1,5]that are characterized by a higher output intensity of the AE. Those events indicate extreme
undesirable process behavior and the detection of the earlier quality critical events, which
provide with more decision time, is an open topic. This work is a feasibility study for thedetection of the momentary critical events in the AE signals and correlates those with thequality. To do so, we employ the combination of a highly sensitive AE sensor for datacollection, and machine learning (ML) for data interpretation. The attraction of ML is in thepossibility to create efficient correlation models to recover the links in intricate data, which isthe case for laser welding processing inducing AE [1,5]. One additional motivation is given by
the successful application of ML in a number of practical applications [12]. In the context of
this work, ML was employed as a classification framework of the momentary quality events
following the real welding process. The output of the method is a quality map of the entire
welded joint with a high spatial resolution.

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The hard/soft-ware realization of our approach is a full-fledged prototype of the quality
monitoring system. In our setup, additional software was attached to the commerciallyavailable laser welding components. The demonstrator operates in pseudo real-time andrequires no interventions inside the commercial parts. The data processing proposes anumber of unique ML algorithms, the efficiency of which can be tested on the fly, adapting
the algorithmic parameters to a specific application.

2. Hardware description
The hardware included a single mode fiber laser source (Fiber laser – StarFiber 150P/300P– long pulse fiber laser systems, Coherent, Switzerland) with a 1070 nm wavelength andpeak output light power of 300 kW. The laser source operated in pulse mode with a repetition
rate varied in the range of 0.01 – 20 Hz with a spatial Gaussian energy distribution. The
output of the laser source was transmitted through a 12 µm core diameter single – mode
optical fiber to a laser head. The laser head was a customized version of LASAG PH-10
Wobble (Coherent, Switzerland). Its objective had a focal length of 170 mm that focused thelaser light on the sample surface into a spot of 30 µm diameter. The laser head was also
equipped with a photodiode that measured the back reflected laser light from the surface of
the workpiece. The photodiode was a Ge based with a spectral sensitivity ranged between(1100-1700) nm. Additionally, a narrow band optical filter was placed in front of thephotodiode for selective transmittance of the back reflected laser light.
Plates from titanium (Ti) grade 5 with size 2x20x50 mm were used as workpieces. The plates
were fixed in an aluminum workpiece-holder, which was placed on a U-521 PILine linear
positioning Stage (Physik Instrumente GmbH, Germany). The stage provided the shifts of theworkpieces in the direction perpendicular to the laser beam with a tunable velocity between 1
mm/s and 100 mm/s. In this work, the velocity was varied to achieve different welding
strategies and qualities and the details are given below.
The laser welding process induced AE, which was recorded by a piezoelectric sensor Pico
(Physical Acoustics, USA). The sensitivity range of the AE sensor was in the range of
50 - 1850 kHz. The AE sensor was firmly clamped to the aluminium workpiece-holder in astationary position throughout all the experiments. The recorded AE signals were digitized bya data acquisition unit from Vallen (Vallen GmbH, Germany) with a sampling rate of 10 MHz.
The redundancy in sampling rate was removed later during the signal processing. The dataacquisition was triggered by the Ge photodiode of the processing head. At the same time,
the acquisition duration was adjusted to the duration of the laser pulse that illuminated theworkpiece.
The digitized signals were transmitted to PC where they were stored using the software fromVallen. In addition, the same computer included a specially developed software with themachine learning framework. This software processed the stored AE signals and output theresults as a quality map of the weld joint. The quality map was formed as the time orderedsequence of the spatially localized momentary welding events within a single weld. Thosemomentary events were established before the processing. The general view of the entire
setup and the interface of the software with the processing output are shown in Fig. 1.

3

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Figure 1: A general view of the setup : 1) the general view of the customized laser head; 2) the
optical fiber input from the laser source; 3) the photodiode; 4)the general view of the workpieceplacement in the welding chamber; 5) piezo sensor; 6) workpiece from Ti; 7) the moving stage; 8)
the interface of the specialized software; 9) the interface panel with the list of the pre-defined
momentary welding events and their colour encoding; 10) interface panel with the current
momentary classification results; 11) interface panel with the quality statistics; 12) colour encoded
quality map of the weld.

The described system allowed realising several welding strategies that are widely used in theindustry, namely: i) spot welding and ii) Stepless High-speed Accurate and Discrete Onepulse Welding (SHADOW). The settings of the power of the laser irradiation for both regimes
was varied to provoke distinctive welding qualities and the corresponding settings for the
power of the laser irradiation were taken from a previous work [11].
Spot welding is the exposure of separate surface areas with short laser pulses that provided
deep weld but with a narrow process zone [1,5,11]. To create this regime, the laser pulse
duration was fixed at 5 ms whereas the laser power was set to 20W, 40W, 80W and 120W.
The sample was moved on a distance of 0.4 mm after each pulse to avoid any overlaps ofthe heat affected zones. Thirty five pulses for each laser power condition were applied andthe corresponding AE signals were used for training of the machine learning classifier.
The welding in a SHADOW regime produces quasi continuous welds from a single pulse. It isdistinguished by the lower power consumption while operating during long time periods andprovides with less crystallographic changes in heat affected zone [13]. During experiments
the welds of 5 mm in length were produced. The irradiated workpieces were shifted at a
speed of 100 mm/s under the laser beam and were exposed with a laser pulse duration of
50 ms. The laser power was set to the discrete values of 50W, 100W, 150W, 200W and
250W to provoke different qualities. For SHADOW welding, five welds for each condition
were made and used for the training of the machine learning framework. For both welding
regimes, the classification tests were performed using real welds that followed after training.

3. Signal processing
All acquired AE signals were recorded in the PC and were processed with a delay of two
seconds compare to each laser pulse without data loss. The saved AE signals wererecovered and analyzed with our specially developed software one by one according to thetime order thus following the actual welding process. For quality classification during spot
welding, the entire AE signal from each individual pulse was considered as a single
momentary welding event and the AE signals from those were processed entirely. For
SHADOW welding regime, the recorded AE signals were evidently of a longer duration. In
4

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this case, those signals were scanned with a running window following the process andsplitting the AE signals into a sequence of separate patterns. The processing procedure thatis described below was applied to each of such patterns. The typical example of runningwindow processing for SHADOW welding is shown in Fig. 2,a. The time span of the runningwindow was estimated empirically during an exhaustive experimental search. On the one
hand, the shorter time span provided a higher spatial resolution in the localization of the local
particularities of the weld joint. On the other hand, those were more affected by the noises
thus reducing the classification accuracy. It was experimentally estimated that the optimalcompromise between those contradicting conditions was with the time span of 1 ms. This
time span allowed classifying the weld quality for each 0.1 mm of the weld joint.
The processing of the signals was made in two steps: i) pre-processing and ii) classification.
The pre-processing included the computation of two types of features that were comparedwith each other inside a common machine learning framework. The magnitude spectra ofwindow fast Fourier transform (WFFT) was computed using a Hann window, decomposing
the given AE signals in the frequency domain [14]. Then, the wavelet sonograms werecomputed decomposing the signals in the time-frequency domain [14]. The interest in
wavelet application towards this problem was stipulated by wavelet advantages when
operating with non-stationary data as compared to the Fourier transforms [14]. The wavelet
decomposition is sensitive to the choice of the base (or mother) wavelets. In this study, thesearch of the suitable base wavelet was performed via an extensive search among thestandard wavelets families, including: Daubechies, Symlets, Mexiacan hat, Coiflets,biorthogonal wavelets. The wavelet approximation errors of the collected AE signals wereanalyzed to make the final choice. Finally, the Daubechies wavelet with 5 vanishing moments
was chosen for further analysis as it had the minimum approximation errors.
The construction of the wavelet sonograms was carried out using wavelet packets [14] andthe general scheme of this transform is presented in Fig. 2,b. In wavelet transform, the signal
is gated through low (h0 in Fig. 2,c) and high (h1 in Fig. 2,c) pass filters [14], resulting theextraction of the low and high frequency content. The multi-resolution analysis was achievedby including several decomposition levels, where the same operation is applied to the resultsof the previous split in the pyramidal way [14] according to the scheme in Fig. 2,c. The
relative energies of the low and high frequency components after each such split were������computed as: ρ�������� �, where E��� � ������ ���� �� � � ∑������ � is the energy of each��

split and dj,m are the coefficients that are the products of each split and m is thedecomposition level. The computed relative energies of narrow frequency bands are
localized in the time-frequency domain [14]. The time-frequency ordering of those was used
to build the sonograms of the recorded AE signals. An example of such sonogram can beseen in Fig. 2,d.
Two tests were carried out in which the magnitude spectra of the Fourier transform and thewavelets sonograms were the input to the classifier.

The task of the machine learning framework in the context of the present study was to findthe unique acoustic signatures of different power regimes. In our setup each power regime
was closely related to the quality and the explanations are given in the next paragraph. The
classification was made using random forest (RF), which is the state-of-the-art inclassification / regression tasks [15,16]. The main attraction of this algorithm towards our
problem is its robust operation in the presence of noises, possibilities to build hyperplanes ona data with a complex mutual configuration, insensitivity to outliers and overfitting [16]. RF is5

S15

an extension of a bigger family of machine learning algorithms based on decision trees [16]which provides the aforementioned processing advantages. The architecture of decision
trees incorporates a number of nodes. Each node splits the data into smaller portions andthe need for further splits is decided if the current split fulfils some pre-defined criteria. RF
employs the so-called CART (classification and regression trees) [16] in which the gini
impurity criteria at each next split is compared with the one of the parent node, thus deciding
the needed number of splits for each particular case [15,16].
The general RF architecture includes an ensemble of decision trees, unified into a forest.
The processing is carried out by forwarding the input data into a common root of the forest,
where it is sub-sampled on each tree individually [15,16]. The final decision is made byvoting among all the trees inside the forest after the data propagation through the forest. The
outstanding performance of RF is gained due to several novelties that were introduced in its
architecture as compared to other ML algorithms. This makes it efficient when operating withreal life data. RF does not require any cross validation tests to estimate the tests errors. The
corresponding errors are the subject of internal computations (so-called out-of-bag
estimates). The algorithm operates well with big dimensions of input variables and, in thiscase, only important dimensions are selected and further considered for analysis. The
individual splits on each tree that followed the gini impurity criteria and the same splits are
inhibited for different samples of the datasets. All this preserve RF from overfitting, maximallyautomatizing the operation of the algorithm and still keeping all the inner parameters
accessible.

Figure 2: a) The AE signal from the SHADOW welding regime, where the solid and dashed markers
show the present and next positions of the running window during processing; b) is the magnitude
spectra of the pattern, bounded by the solid marker in a); c) is the wavelet packet transform scheme,
where h0 and h1 are low and high pass filters correspondently; d) wavelet sonogram, obtained from apattern, bounded by a solid marker in a).

4. Discussion
The relation of the welding quality to the laser power, provided in our setup, can be observed
from the light microscopic images of the cross-sections in Fig. 3. The laser power was
modulated during both, spot and SHADOW welds with the discrete laser power mentioned
above and the corresponding quality variation, verified in earlier work [11]. The examples are
given in Fig. 3,a and b. The microscopic images of the cross-sections for spot and SHADOW
regimes were made post-mortem. As seen from the figures, the quality variations are in thewelding penetration depth and pores presence. In the images from the figure the melt zone
can be observed as a lighter color area inside the material with some textural differences. It
is additionally bordered by white solid line in Fig. 3,a for better contrast. As seen for spot
welding (Fig. 3,a), the powers of laser of 20W and 40 W corresponded to conduction welding
with shallow penetration depths. With the powers of 80W and 120 W, the formation of vapor
channel was created (so called keyhole) that resulted deeper penetration depth. The
6

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presence of the key hole can be verified from the aspect ratio of the heat affected zone
(HAZ) as is described in [11]. None of the cases in spot welding provoked the formation ofpores. For SHADOW welding, the laser powers of 200 W and above were found to be thelimit for the creation of pores and evidence of this is seen Fig. 3,b. The detection of pores ischallenging as they are located at some depth under the surface. Those are not observablefrom the surface and carry a hidden threat to the mechanical properties of the workpieces.
The laser powers below 200 W were characterized by different penetration depth of themelted material inside the workpiece. The experimental setup presented here provided therepeatability of the results in Fig. 3 during all experiments.

Figure 3: Cross-sections of the workpiece after a) spot and b) SHADOW welding regimes. The
markers denote the power of the laser irradiation

The training of the classifier was performed using the preliminary collected dataset. The tests
were done afterwards running real welding of workpieces. The results of the tests for spot
and SHADOW welds are presented in Tables 1 and 2. The classification results in rows are
given versus ground truth in columns. The match of the test results with the ground truth areplaced in diagonal cells and highlighted in grey, while the structure of the classification errors
is in the non-diagonal row cells.
The classification accuracy for the spot welds ranged from 85 % to 99 % for wavelets (inbold) and from 91 % to 99 % for WFFT [in brackets]. As seen from Table 1, the errors weremainly due to the overlap of the regimes with the neighbored laser power. It is interesting tonote that the laser powers below 40 W (co called conduction welding) were classified with ahigh accuracy. In contrast, the welding regimes with laser power 80 W and 120 W (so called
keyhole welding) showed lower accuracies. As seen from Table 1, both categories with highpowers showed an overlap between each other’s. One explanation can be in the emittanceof AE in a broader frequency range while material overheating at higher laser powers thatbrings to more complex frequency configurations, thus complicating the separation of thedata.
The classification accuracy for SHADOW welds varied between 75 % and 95 % for wavelets
and 87 % and 98% for WFFT. The lower accuracy as compared to spot welding may be
explained by the absorbed energies fluctuations inside a single weld caused by the local
optical non-uniformities of the material that caused temperature drops. The errors structure
presented in Table 2 showed an overlap between the neighbored values of the laser power
which is identical to the situation in Table 1. Also, the greater overlap between 100 W and
150 W can be explained by a smooth border between the two regimes as observed inFig. 3,b. The overlaps between the regimes with pores formation at 200 W and 250 W inindustrial applications do not make a big difference as both are dangerous for the mechanicalproperties of the processed workpiece. In contrast, the overlap between the laser power withpores at 200 W with the one without at 150 W is relatedly low and is only 5% [-]. This
relatively good separation between both regimes with a critical impact on quality
demonstrates the applicability of acoustic emissions for quality monitoring. Generally this7

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processing scheme potentially allows avoiding non-desirable phenomena. However, further
optimizations of the algorithms are planned to increase the classification accuracy.
Table 1. Classification accuracy for spot
weld, where GT is the ground truth.
GT
20 W
40 W
80 W
120 W
Test
20 W 99 [99]
1 [1]
40 W
1 [1]
99 [99]80 W
85 [92] 15 [8]120 W
15 [9] 85 [91]Table 2. Classification accuracy for SHADOW weld,
where GT is the ground truth*
GT
50 W
100 W
150 W 200 W 250 W
Test
50 W
95 [99]5 [1]100 W
75 [ 87] 25 [13]150 W
15 [11]85 [89]200 W
5 [-]75 [89] 15 [11]
250 W
5 [2]
95 [98]*the results are given in the format: wavelets [WFFT]

5. Conclusion
This work is a usage of acoustic emission (AE) and machine learning for laser welding
quality monitoring. The frequency and time-frequency domains of the AE signals were
exploited for signals representations and were the input of the classifier. The test results onreal welds showed that this approach is technically feasible for the monitoring of the quality incommercially available laser welding machines. It was approbated on spot and SHADOW
welds. The corresponding accuracy rates varied in the ranges of 85 % to 99 % and 75 % to95 % for both welding regimes respectively. The possibilities to detect potentially dangerous
welding regimes that cause the pores formations were shown. The greater error rates wereobserved as overlaps between the categories at higher laser powers. Mainly those regimes
caused the occurrence of overheated material vapors (keyhole). Due to this, the emittance of
AE is in a broader frequency range brought to more overlaps between the categories.
However, it is important to underline that the regimes with and without pores are stillseparable with an acceptable precision.
The improvements of the classification confidence may be done with some optimizations of
the processing algorithms. Those could be the usage of preliminary preprocessing to capture
the training data structure. This is feasible, for example, with spectral graph methods. The
extension of the classifier operation in recursive way may bring to higher efficiency asprovides with better transients analysis. All this is planned as a future work.

References:[1]
Stavridis, J., Papacharalampopoulos, A., Stavropoulos, P., Quality assessment inlaser welding: a critical review, International Journal of Advanced Manufacturing Technology,
Vol. 94, 2018, pp. 1825–1847, https://doi.org/10.1007/s00170-017-0461-4[2]
Kyung-Min, H., Yung, C. S., Prospects of laser welding technology in the automotive
industry: A review, Journal of Materials Processing Technology, Vol. 245, 2017, pp. 46-69,
https://doi.org/10.1016/j.jmatprotec.2017.02.008[3]Perveen A., Molardi C.,, Fornaini C., Applications of laser welding in dentistry: a state
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art
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[4]
Besnea D., Dontu O., Spanu A., Rizescu C., Pascu T., Study on laser welding ofstainless steel/copper dissimilar materials, IOP conference series: Materials andengineering, Vol.147, 2016, article 012047, https://doi:10.1088/1757-899X/147/1/012047[5]
Behzad F., Steven F. Wayne, G. L., Ebrahim A., A Review on Melt-Pool
Characteristics in Laser Welding of Metals, Advances in Materials Science and Engineering,Vol. 2018, 2018, article ID 4920718, https://doi.org/10.1155/2018/4920718[6]Blackburn, J., Laser welding of metals for aerospace and other applications, In
Woodhead Publishing Series in Welding and Other Joining Technologies, edited by M.C.
Chaturvedi,, Woodhead Publishing, 2012, pp. 75-108, Welding and Joining of Aerospace
Materials, ISBN 9781845695323, https://doi.org/10.1533/9780857095169.1.75[7]
Svenungsson J., Choquet I., Kaplan A.F.H., Laser Welding Process – A Review of
Keyhole Welding Modelling, Physics Procedia, Vol. 78, 2015, pp. 182-191, ISSN 1875-3892,
https://doi.org/10.1016/j.phpro.2015.11.042[8]
Reitemeyer D., Schultz V., Syassen F., Seefeld T., Vollertsen F., Laser Welding ofLarge Scale Stainless Steel Aircraft Structures, Physics Procedia, Vol. 41, 2013, pp. 106111, ISSN 1875-3892, https://doi.org/10.1016/j.phpro.2013.03.057.
[9]Viherva T., Viherva T., Riikonen H., Kujanpaa V., Welding of ship structural steel A36
using Nd:Yag laser and gas-metal arc welding, Journal of laser applications, Vol.12, 5, 2000,
pp.185-188, https://doi.org/10.2351/1.1309549[10]
Heider A., Sollinger J., Abt F., Boley M., Weber R., Graf T., High-Speed X-Ray
Analysis of Spatter Formation in Laser Welding of Copper, Physics Procedia, vol. 41, 2013,
pp. 112-118, https://doi.org/10.1016/j.phpro.2013.03.058.
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Vakili-Farahani F., Lungershausen J., Wasmer K., Wavelet analysis of light emission
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Shivakumara P.G., Lia H., Knightb K., Georgiou P., Learning from Past Mistakes:
Improving Automatic Speech Recognition Output via Noisy-Clean Phrase Context Modeling,
February, 2018, arXiv:1802.02607v1[13]
Kramer T., Olowinsky A.M., Durand F., SHADOW: a new welding technique,
Proceedings of the SPIE, vol. 4637, 2002, pp. 545-554[14]Mallat S., A wavelet tour of signal processing, 1999, pp. 795[15]
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72 - Monitoringof stampingBY
byACOUSTIC
Acoustic Emission(AE)(AE)MONITORING
OF STAMPINGEMISSION
Abdelkrim SAIDOUN 1, Catherine HERVE 1
1
CETIM Senlis

Abstract:
The optimization of the shaping processes (stamping, fast cutting, forming, spot welding, machining ...)and the quality of the production are very important in the field of industry. CETIM has been increasingly
asked to propose solutions to improve the quality of production while not slowing it down. In this context,
MOST “Monitoring of Stamping” has been developed, an online acoustic emission control system thatguarantees the quality of production in real time.
The main defects that occurred during the stamping process are the sheet tears. To test the reliabilityof the MOST system for the detection of this type of defects, various feasibility tests have been carriedout on the presses of industrial stamping.
In this study, we show the results of the detection in real time of some representative defects that havebeen created voluntarily by manufacturers (either by stopping lubrication or by changing the depth punch/ mesh …) by using MOST system.
Key words: stamping, acoustic emission, monitoring of stamping.

Introduction
In a globalized world where the competition is more and more intense, the optimization of theprocesses of shaping (stamping, fast cutting, trimming, welding by point, ....) and the quality ofthe production are the principal preoccupation for the industrialists.
The sheet metal stamping process is one of the most employed manufacturing processes in
the automobile and aerospace industries to produce a variety of parts in terms of shape andsize. On a mechanical press, the sheet is pressed between the die and the punch is pressedto obtain the required shape. Shearing, bending and drawing are incorporated in the stamping
process to produce larger scale components [1]. However, the stability of the metal forming
process depends on the parameters of the material and the process. A slight change in aprocess parameter, such as material properties or lubrication conditions, may result in a defectthat could damage the tool, and consequently produce defective parts. For this reason, themonitoring of the manufacturing processes is essential to ensure the safety of the tools as wellas the quality of the products.
To monitor manufacturing processes, sensors have been incorporated to monitor mainly thetool wear [2-6]. Considerable condition monitoring studies have also been conducted in the
1

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stamping process using different sensors like strain, audio, borescope, thermocouple, force
etc. [7-11]. Sah et al. [12] integrated force sensors into the die to study the contact pressure
on the die radius during the forming process. However, high noise produced during thestamping process made the tool wear study complex.
In recent times, authors have attempted to use acoustic emissions to study wear because of
its ability to work in the high frequency range (20kHz-2MHz) [13]. Skåre et al. [14] studied thewear and frictional behaviour of high strength steel using acoustic emission. Using AE, Sindi
et al. [15] monitored the galling phenomena occurring in forming process on a tribo-test setup.
Hase et al. [16] monitored the abrasive and adhesive wear phenomena using acoustic
emission on a tribo-test setup.
Unlike the studies mentioned above, which are concerned with the detection of tool wear,
MOST (the system developed by the CETIM) use the acoustic emission for the real time
detection of tears in stamped parts. In this paper, we present the results of detection feasibility
in real time of some representative defects that have been created voluntarily by manufacturers(either by stopping lubrication or by changing the depth punch / mesh …).

1. Presentation of MOST system
MOST is a monitoring system for industrial processes of forming based on acoustic emission.
The tears of sheet metal during the shaping operation are the main defects sought.
MOST can detect the appearance of defects on a production line whose rate varies between
10 and 120 strokes / minute. On the considered presses, the stamped materials vary fromaluminum to steel for thicknesses of a few millimeters.

Figure 1. Representation of MOST system

The control protocol via the most system is as follows:➢ Implantation of acoustic emission sensors:
The ideal is to place the acoustic emission sensors closer to the stamped part, in order
to benefit from maximum detection sensitivity.
2

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Figure 2. Implantation of acoustic emission sensor closer to the stamped part

➢ Determination of the angular window of acquisition:
The acquisition of the acoustic emission data is not done continuously over the cycle,
only the acoustic signature of stamping + stripping is recorded in order to limit the noise.
Most system is suitable for external triggering via the cam signal.
➢ Acquisition and analysis of acoustic emission data
Once the trigger is correctly set, it is then possible to record the acoustic emission dataof the strike as shown in the figure 3. From this record, a processing phase is thenperformed in order to extract statistical parameters:••

RMS, this parameter reflects the acoustic noise level.
KTS, this parameter makes it possible to highlight a pulse event of a continuous
background. noise

Figure 3. Acoustic emission signal of stamping + stripping➢ Determination of a criterion of nonconformity
The nonconformity criterion is determined following a learning phase where the level ofthe two parameters (KTS and RMS) is defined. If one of the two parameters are
exceeded, MOST system sends a stop signal to the press automaton.

2. On-line detection of stamping defects by the MOST system
2.1

Industrial feasibility 1

Figure 4.a shows the first case of feasibility by the MOST system. It is an impact observed on
the stamped parts. this impact is related to tool wear.

3

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Figure 4.b shows the representative defect voluntarily generated by the manufacturer bycorrecting the punch in order to increase the penetration.
The parameters of the tests are grouped in table 1:
Material

Inox 1,4307, e = 1,5 mm

Production rate

40 strokes / minute

Press

250T, 13 step tracking tool, punched: Z160
tempered to 58 HRC coated (Same as matrix)

AE sensor

Freq ≈ 200 kHz

Preamplifier

40 dB

Table 1. parameters of the first feasibility testabReal defectDefect generated voluntarily

Figure 4.a. part with a real defect caused by the tool. b. part with a defect generated voluntarily bycorrecting the punch

Figure 5 shows the implantation of the AE sensor close to the stamped parts as explain
previously.

AE sensor

Figure 5. Implantation of AE sensor close to the stamped parts

4

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Figure 6 present the evolution in real time of the KTS as function of the number of stamped
parts. A learning phase (test 1 and test 2) was initially carried out on 250 good parts todetermine the criterion of nonconformity. The maximum value of the KTS parameter in thisphase is 43. The significant increase of the KTS on the series 3 and 4 correspond to the parts
produced after rectification of the punch.
MOST was able to detect defective parts of the series (3 and 4) by a significant increase in
KTS value.

Nonconformity
Conformity

test 1

test 2

Test 3

KTS

Nonconformity threshold

Test 4

Numbre of stamped parts
Figure 6. Evolution of KTS as function of the number of stamped parts

2.2

Industrial feasibility 2

Figure 7.a shows the second case of feasibility by the MOST system. It is a tear of the sheet
caused by galling.
Figure 7.b shows the representative defect voluntarily generated by the manufacturer bygenerating impacts on the sheet before the stamping phase.
The parameters of the tests are grouped in table 2.

5

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a

b

Defect generated voluntarily

Real defectFigure 7.a. part with a real defect caused by galling. b. part with a defect generated voluntarily by
impacting the sheet before the stamping

Material

Standard steel Re= 980 MPa, e = 1 mm

Production rate

18 strokes / minute

Press

300T

AE sensor

Freq ≈ 200 kHz

Preamplifier

40 dB

Table 2. parameters of the second feasibility test

Figure 8 present the evolution in real time of the RMS parameter as function of the number ofstamped parts. A learning phase of 50 good parts was carried out to determine the criterion of
nonconformity. The maximum value of the RMS parameter in this phase is 29 dB. The
significant increase of the RMS on the pieces 58 and 61 correspond to the two defective
pieces.
MOST was able to detect defective parts (58 and 61) by a significant increase in RMS value.

6

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35

Nonconformity

34

Piece 61

33

Piece 58

32

RMS (dB)

31

Conformity

30
29
28
27
26
25
24
23

0

5

10

15

20

25

30

35

40 45 50 55 60 65
Number of stamped parts

70

75

80

85

90

Figure 8. Evolution of RMS as function of the number of stamped parts

2.3

Industrial feasibility 3

The defects observed for this case are shown in figure 9. The factor causing this type of defectis the lack of lubrication in the stamping process. In this case, usually the first defect thatappears is the stiction in the stamped parts as shown in the figure 9.a. Then, gradually, thecontact between the sheet and the punch / matrix becomes more and more dry which leads tothe tearing of the sheet as shown in the figure 9.b.
The aim of the feasibility 3 is to reproduce this defect by stopping lubrication in order to evaluate
MOST's ability to detect these defects in real time.
The AE sensor has been installed on the upper part of the tool, close to the stamping section
as shown in figure 10.
The parameters of the tests are grouped in table 3.
The different tests carried out in this study are summarized in the table4. The four first tests
aim to determine the threshold of non-conformity and at the same time to verify the
reproducibility of the measurements. The lubrification was stopped in the last test in order togenerate the defects.
Figure 11 shows the evolution of the RMS parameter (mV) as function of the number ofstamped parts for all the series carried out:

7

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-

The measurements made on the series (2,3,4,5) corresponding to the production
without defects have made it possible to set the reference level of the RMS value (RMS
≈ 10 mV);-

It should be noted that measurements made during the learning phase are relatively
reproducible;-

The evolution of the RMS for series 6 which corresponds to the phase of generation of
defects presents three important steps:•

Step 1: corresponds to the first 15 good pieces. The level of the RMS value isequivalent to the reference threshold. This could be related to the sufficient levelof lubricant in the tool mainly in the lubrication rollers;•

Step 2: This is the longest step (835 good pieces approximately). There is asignificant increase in the level of RMS (RMS≈23 mV). This result could be
explained by a lack of lubrication in the stamping process. Thus, the friction isimportant between the punch-part-matrix and therefore the acoustic noise ishigher;•

Step 3: corresponds to the last 28 strikes. It is also the number of pieces
cracked. The level of the RMS is even more important (RMSmax≈43 mV).

In this study, most allowed not only to detect tears on the stamped parts, but it also allowed togive an indication on the lack of lubricant in the tool according to the first increase of the valueof the RMS
a

b

Tear
Striction

Figure 9. defects caused by a lack of lubrification of the tool. a. stiction which appears at fist b. Tear of
the the sheet at the second time

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Figure 10. Implantation of AE sensor close to the stamped parts

Material

Steel DC04+ZE25, e = 0.6 mm

Production rate

53 strokes / minute

Press

400T

AE sensor

Freq ≈ 200- 900 kHz

Preamplifier

40 dB

Table 3. parameters of the second feasibility test

Conformity of theserie

Numbre of parts

Objectif

Serie 2

No defects

200

Determination of thethreshold of
nonconformity

Serie 3

No defects

200

Test of reproducibility

Serie 4

No defects

200

Test of reproducibility

Serie 5

No defects

200

Test of reproducibility

Serie 6

No defects

878

Detection of defects

Table 4. summarizing of the tests carried out in this study

9

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Numbre of parts
Figure 11. Evolution of the RMS (mV) as function of the number of stamped parts

3. Conclusion
In this paper, the feasibility of detecting defects on stamped parts by the MOST system wasstudied. Three types of representative defects were analyzed, tool wear, galling and lack of
the lubrification.
The results of this study show that the MOST system was able to detect in real time the three
types of defects.
The results show also that the KTS parameter is more sensitive to defects related to the tool
wear. while the RMS parameter is more sensitive to defects related to galling or a lack oflubrication.
In view of this study, a long-term monitoring test will be conducted to test the robustness of the
MOST system under production conditions.

10

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References:[1] Sekar, K.V. ed., 2014. Manuf. Eng. and Tech. (p. 913). Pearson.

[2] Govekar, E., Gradišek, J. and Grabec, I., 2000. Ultrasonic, 38(1), pp.598-603.
[3] Grzesik, W. and Bernat, P., 1998. In International conference AC. 98, pp. 77-84.
[4] Li, X., 2002. Int. J. of Mac. Tools and Manu. 42(2), pp.157-165.
[5] Garcia, C., 2005. J. Mat. Pro. Tech. 164, pp.1351-1357.
[6] Burek, J. and Babiarz, R., 1998. In Int. Conf. on Monitoring and Auto. Supervisionin Manuf.-AC. 98, pp. 41-44.
[7] Doolan, M., Kalyanasundaram, S., Cardew-Hall, M. and Hodgson, P., 2003. Jour.
of Manuf. Sci. and Eng., 125(2), pp.363-368.
[8] Ubhayaratne, I., Pereira, M.P., Xiang, Y. and Rolfe, B.F., 2017. Mech. Systems
and Signal Proc. 85, pp.809-826.
[9] Zhang, G.C., Ge, M., Tong, H., Xu, Y. and Du, R., 2002. Eng. App. of Artificial Int.,
15(1), pp.97-104.
[10] Pereira, M.P. and Rolfe, B.F., 2014. Jour. of Mat. Proc. Tech. 214(8), pp.17491758.
[11] Hamedon, Z., Mori, K. and Abe, Y., 2014. Jour. of Mat. Proc. Tech., 214(4),pp.945-950.
[12] Sah, S. and Gao, R.X., 2011. The Int. Jour. of Adv. Manuf. Tech. 55(1-4),
pp.121-132.
[13] Fan-Bean Wu, Jenq-Gong Duh. (2002). Surf. and Coat. Tech. Vol 162. pp 106–112.
[14] Skåre, T. and Krantz, F., 2003. Wear, 255(7), pp.1471-1479.
[15] Sindi, C.T., Najafabadi, M.A. and Salehi, M., 2013. Tribo. Lett. 52(1), pp.67-79.
[16] Hase, A., Mishina, H. and Wada, M., 2012. Wear, 292, pp.144-150.

11

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14 - Acoustic
EmissionmonitoringOF
of ADHESIVELY
adhesively bonded
ACOUSTIC
EMISSIONMONITORING
BONDED
jointsunderQUASISTATIC
quasistatic andcyclicfatiguemode II MODE
WOODwood
JOINTSUNDERANDCYCLICFATIGUE
flexureloadsUSING
using end-notch-flexurespecimens
II FLEXURE
LOADS
END-NOTCH-FLEXURE
SPECIMENS
A.J. Brunner1, G. Clerc2, P. Niemz2
1Empa, Swiss Federal Laboratories for Materials Science and Technology,
Laboratory for Mechanical Systems Engineering, Überlandstrasse 129,
CH-8600 Dübendorf, Switzerland
2
BFH, Bern University of Applied Sciences, Department of Architecture, Wood andCivil Engineering, Solothurnstrasse 102, CH-2500 Biel/Bienne, Switzerland

Abstract:
Adhesive bonding is becoming more and more important for manufacturing structures or structuralelements made from wood. For designing such structures, the performance of adhesives has to beevaluated under quasi-static and cyclic fatigue loading as well as under corresponding fracture loads.
The performance of wood glued beams under quasi-static loading is fairly well understood, but their
cyclic fracture behavior remains still largely unknown. There are indications from standard tensile
shear tests with several types of adhesive that the performance ranking of adhesives under cyclic
fatigue loading may differ compared with that observed in the same test under quasi-static loads.
Hence, it is of interest whether this is also the case for quasi-static and cyclic fatigue mode II shear
fracture. For the mode II shear fracture tests, adhesively bonded joints with wood adherends areprepared with two different adhesives, one a rather brittle (phenol resorcinol formaldehyde) systemand one a rather ductile (one component polyurethane) system. These are then compared for theirperformance under quasi-static and cyclic fatigue mode II in-plane shear fracture loads. Those
adhesives are presenting different failure mechanism, for the ductile adhesive the failure willpropagate in the interface between wood and adhesive and for the brittle one the crack will mainly run
in the wood. For the fracture tests a set-up with four-point end notched flexure specimens is being
used, analogous to testing of adhesively bonded polymer composites joints and polymer composite
laminates. Selected fracture tests were monitored by acoustic emission for assessing damage
evolution due to the mode II in-plane shear fracture loads. The acoustic emission monitoring initiallyalso contributed to identifying shortcomings of a first test set-up that was subsequently modified. The
discussion will focus on the comparison between two different types of adhesive and between quasistatic and cyclic fatigue fracture loads.

1. Introduction
Adhesively bonded joints play an increasingly important role in construction with composite
materials [1], and, hence, specifically also in civil engineering structures made of wood [2].
Characterizing and comparing the performance of different types of adhesives under various
load cases is essential for designing safe and durable wooden structures. A recent
comparison between four types of adhesives used in glued lap joints (as defined in [3]) had
indicated that cyclic tensile shear fatigue loads yielded differences among the four adhesivesthat had not become apparent in quasi-static tensile shear tests [4]. Therefore, fracture1

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mechanics tests under quasi-static and cyclic fatigue loads are performed for a range ofdifferent adhesives. The so-called Four-point End-Notched Flexure (4-ENF) test set-up waschosen for comparing quasi-static and cyclic fatigue fracture of adhesively bonded wood
joints under in-plane shear (mode II) loading [5]. In this contribution, selected test results will
be discussed with an emphasis on acoustic emission (AE) monitoring of the fracture tests.

2. Description of work
The first 4-ENF test set-up (Figure 1 left) was modified in several steps to remove problems
observed in preliminary quasi-static and cyclic fatigue tests. While this first set-up worked forquasi-static mode II loading, it was not suitable for cyclic fatigue, since one of the top loadingrollers tended to lift-off the wood specimen during the test, also noted acoustically by ear andby periodic AE signal generation. This noise source was removed by introducing a rotating
joint to the top loading bar. Later, this was further improved by implementing a universal joint
between top loading bar and load cell (Figure 1 right) instead of the uniaxial rotating joint inorder to reduce possible moments on the load cell.

Figure 1: Photographs of two versions of the 4-ENF type test-rig, (left) upper loading bar rigidly
mounted to the load cell, (right) upper loading bar with rotating joint, the AE sensors are mounted withmetal springs.

Beech wood (Fagus sylvatica L.) with a density of 714 kg/m3 at a wood moisture content of
12% was used for the tests. The lumber has no defects such as knots and grain deviation.
The length, the width and the height of the specimens correspond, respectively, to thelongitudinal, tangential and radial wood orientation (maximal radial orientation of 30°). The
planks were first planed to a thickness of 10 mm, a width of 150 mm and a length of 700 mm.
The planks were stored for at least two weeks in a climate of 20°C and 65% relative humidity
before planing to a thickness of 5 mm and then cut in half. Before the adhesive bonding, a 15
µm thick fluoropolymer (ETFE230N) foil was applied between the lamellae on the first 120
mm to simulate a starter crack. Two adhesives are compared in the tests, the first a relatively
brittle phenol resorcinol formaldehyde (PRF, trade name «Aerodux 185»), the second a moreductile one-component polyurethane (PUR, trade name «HB110»). The gluing of the twolamellae was done approximately 3-4 hours after the planing according to the gluing
parameters described in Table 1 below. Once cured, the front-position of the foil wasreferenced as position of the crack tip and the crack length was set to 110 mm. The samples
2

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were then cut to a width of 20 mm and a length of 317mm. The adhesively bonded wood
joints were stored for several days in the test climate of 23°C and 50% relative humidity
before testing.

Adhesive

Hardenener

HB110
Aerodux
185

-

Glue
Spread[g/m2]
180

HRP 155

340

Mixture ratio[Adhesive/Hardener]

Pressing
time [h]Pressure
[MPa]

-

10

0.8

Closedassembly
time [min]10

100/20

10

0.8

30

Table 1: Processing parameters used in manufacturing the adhesively bonded beech wood joints.

In order to get additional information on the initiation and propagation of the mode II
delamination in the adhesively bonded joints, AE monitoring was performed using AE
equipment (type AMSY-6 from Vallen Systeme GmbH) with resonant AE sensors (type SE
150-M from Dunegan Engineering Corp.) as well as one broad-band AE sensor (type S9208
from Physical Acoustics Corp.). The AE sensors were connected to the data acquisition via
preamplifiers (type AEP-3 from Vallen Systeme GmbH) with a hardware high-pass frequency
filter of 30 kHz, a low-pass of 1’000 kHz and a gain of 34 dB. Figure 2 shows a schematic of
the 4-ENF test specimen with the geometry and dimensions used in the test series, including
the positions of the seven AE sensors for monitoring the damage development during thefracture tests. Please note that the distance between the top loading rollers (100 mm) of the
4-ENF test rig did not correspond to either one third or one half of the distance between thebottom support rollers, i.e., were different from the spans usually recommended for the tests[5]. This was due to the fact that the top loading bar had to leave space for the AE sensors
mounted on the adhesive joint. A silicone-free vacuum grease was used as coupling agent
and the AE sensors were mounted by metal clamps (shown in Figure 1 right). AE dataacquisition settings were: threshold 40 dBAE, duration discrimination time 400 s, and rearmtime 1 ms (except for preliminary tests with rearm time of 3.23 ms). For the acquisition of fullwaveforms (with the broad-band AE sensor only) sampling at 5 MHz and 4’096 samples(corresponding to roughly 820 microseconds) with a pretrigger of 200 samples (40microseconds) was chosen.

Figure 2: Schematic of the 4-ENF type specimen for mode II (in-plane shear) quasi-static and cyclic
fatigue fracture testing of adhesively bonded wood joints, The positions of the AE sensors and thespecimens dimensions are also shown (H = 10 mm, atot = 110 mm, a0 approximately 70 mm).

The load tests were performed on a servo-hydraulic test machine (type 1237 from Instron)
equipped with a 1 kN load cell (mounted below the 25 kN load cell). The quasi-static tests
3

S33

were performed under displacement control at 1 mm/min and the cyclic fatigue tests also
under displacement control at 5 Hz; starting from a displacement level selected from theload-displacement curve by setting a displacement ratio of 0.1 between upper and lower
limit). Load and displacement signals from the test machine were also recorded
synchronously with the AE data (sampling-rate around 40 Hz). There were preliminary,quasi-static compressive tests at 0.1 mm/min with limited displacements for determining thecompliance of the test set-up in which the wood joint was replaced by a steel bar, andcalibration tests with different length of the starter crack between the bottom support rollers
for determining the delamination length from specimen compliance. A travelling optical
microscope was used to visually observe the delamination during the fracture tests.

3. Results
Focusing on the information from the AE monitoring of the tests, the evaluation of thefracture toughness for the comparison between the adhesives (the main goal of the tests) will
not be discussed here. Figure 3 shows examples of the linear AE signal source location plots
from quasi-static flexure testing, one for each adhesive type. The auto-calibration signals of
the AE equipment were used to determine the signal speed for the linear AE signal source
location by adjusting the signal speed until the location of the recorded signals matched the
known sensor positions. The values of the signal speed obtained varied between about 400
and 500 cm/ms with an average around 425 cm/ms.

Figure 3: AE linear signal source location (color-coded for AE signal amplitude) of quasi-static tests(under displacement control at 1 mm/min) using the AE sensors on the compression side (top) and theload curve for the 4-ENF specimens with (left) PRF and (right) HB110 adhesive.

It can be noted that the non-linear point in the load curves indicating the initiation of thedelamination corresponds fairly well to the onset of significant AE. As a trend, the more brittle
adhesive (PRF) yields more AE signal source locations than the more ductile one (HB110).
For the PRF-type joint (Figure 3 left), the AE signal amplitudes above 60 dBAE roughly
indicate the position of the delamination tip as a function of time. These positions are also
consistent with the visual observation of the delamination propagation on the edge of thespecimens. For the more ductile adhesive (HB110), the AE signal source locations do nothighlight the tip of the delamination as clearly as for the more brittle adhesive. The AE signal
amplitudes recorded during delamination propagation in this case (Figure 3 right) areessentially all below 60 dBAE. For the PRF-type joint, the cluster of AE signal source locations
extending over most of the distance between the two sensors at about 740-750 s is likely due
4

S34

to tensile damage in the (bottom) wood beam occurring before the loading was stopped(Figure 4). This tensile damage in the wood also yielded audible cracking noise. The
respective AE signal source cluster is even more pronounced when plotting the signal source
location data from the tensile, i.e., the bottom side of the joint. The located signals yield evenhigher AE signal amplitudes than the locations from the compressive (top) side. The
scattered AE signal source locations beyond the tip of the delamination after about 500 s(Figure 3 left) are likely due to precursors of the macroscopic tensile damage. Similar AE
signal source locations beyond the tip of the delamination for the HB110 adhesive joint
possibly indicate that some damage is also initiating there. However, tensile damage was notnoted and was not visible when inspecting the specimen after the test. It has to be noted,
however, that contributions from friction signals (e.g., from the contact between test-rig andjoint or from slight relative movement between sensor and coupling surface due to specimen
bending) to both location plots cannot be excluded.

Figure 4: Photographs of the PRF-type joint showing signs of tensile failure (highlighted by black zigzag-type marks) on the bottom side after quasi-static testing, on the edge, the delamination is visible
as well.

Since the 4-ENF setup inevitably involves some friction between the support and loadingrollers and the wood joint, the AE signals are further analyzed using selected AE signal
parameters. Figure 5 shows plots of AE signal duration (logarithmic scale) versus AE signal
amplitude (linear scale), again for both types of adhesive. This plot has been shown to allow
for rough identification of AE signal source mechanisms in fiber-reinforced polymer
composites [6], such as, e.g., mechanical rubbing (low amplitude, broad range of durations),
delamination (intermediate amplitudes, high durations), fiber breakage (high amplitude range
and high durations), electromagnetic interference (intermediate amplitude, short durations).
This approach has also been used by Schubert et al. [7] for identifying AE signal source
mechanisms in a full-scale wind rotor blade test. Even though the AE signal parameter areas
for different mechanisms may overlap to some extent, it is likely that “low” amplitude signals(e.g., about 40-45 dBAE) for all durations observed (up to about 1’000 s in Figure 5) indicate
friction or rubbing. The other parameter ranges are then attributed to crack or delamination
propagation and fiber breaks (in the case of laminates). That possibly also holds for wood as
5

S35

a natural fiber-reinforced composite material, if the delamination damage develops in theadherend rather than in the adhesive or at the interface between wood and adhesive.

Figure 5: AE signal parameter plots (duration versus amplitude) of quasi-static tests using the AE
sensors located near the starter crack tip of the 4-ENF specimens with (left) PRF and (right) HB110
adhesive (following [6]).

Figure 6: AE signal parameter plots (counts versus duration) of quasi-static tests using the 150 kHz
resonant AE sensors located in the delamination propagation zone of the 4-ENF specimens (between
the top loading rollers) for (left) PRF and (right) HB110 adhesive.

As already seen in the AE signal source location plots (Figure 4), the HB110 adhesive joint
yields less source locations and less AE signals (Figure 5) than the PRF adhesive joint. Thiscan be attributed to the difference in adhesive properties, the more ductile adhesive (HB110)
yields less signals and/or lower amplitude signals when failing, resulting in fewer data being
recorded. Figure 6 shows another set of AE signal parameters (counts versus duration) that
also yields information on the type of source mechanism. Typically, signals with low numberof counts at relatively long duration correspond to signals with significant low frequency
partial power in their Fourier Transform power spectra. The example for the PRF adhesive(Figure 6 left) has much more AE signals with durations between about 200 and 600
microseconds and less than 20 counts than that for the HB110 adhesive (Figure 6 right).
Please note that the plots in Figure 6 show channels 3 and 4, i.e., sensors located in the
delamination propagation zone. The same parameter plots for the other sensor pairs(channels 1 and 2 located in the starter crack range; and channels 5 and 6 located in theuncracked part of the adhesive joint) show less signals in this parameter range (low number
6

S36

of counts and long duration). Comparing the two types of adhesive, these plots are
consistent with the signal parameter analysis shown in Figure 5, i.e., a larger amount offriction signals for the PRF-type joint.
The dynamic load-signal (cycling at 5 Hz under displacement control) as a function of test
duration shown in Figure 7 on the right indicates a decreasing trend of the maximum
compressive load with time. This is due to the increasing compliance of the specimen, i.e., areduction in flexural rigidity with increasing delamination length.

Figure 7: (left) AE linear signal source location (color-coded for AE signal amplitude) of dynamic 4ENF tests at 5 Hz using the AE sensors on the compression side and (right) load curve for the 4-ENF
specimen with the PRF adhesive.

Figure 8: (left) AE linear signal source location (color-coded for AE signal amplitude) of dynamic 4ENF tests at 5 Hz using the AE sensors on the compression side and (right) load curve for the 4-ENF
specimen with the HB110 adhesive.

The duration versus amplitude plots for the two types of adhesive joints tested under mode II
cyclic fatigue fracture load at 5 Hz (Figure 9) differ less in the amount of signals than for therespective quasi-static tests (Figure 5). Interpreting the signals with amplitudes below 45
dBAE again as being mainly due to friction, the HB110 adhesive joint yields more friction
signals and with durations extending up to 2-3 ms rather than 1 ms for the PRF type joint,
and this in spite of the lower total number of cycles applied (PRF 50’000 cycles, HB110
40’000 cycles).
7

S37

Figure 9: AE signal parameter plots (duration versus amplitude) of quasi-static tests using the AE
sensors located near the starter crack tip of the 4-ENF specimens with (left) PRF and (right) HB110
adhesive (following [6]).

Figure 10: AE signal parameter plots (duration versus amplitude) of quasi-static tests using the broadband AE sensor in the propagation zone of the delamination for (left) PRF and (right) HB110 adhesive(following [6]).

4. Discussion
The AE monitoring of the quasi-static and cyclic mode II (in-plane shear load) fatigue fracture
tests yields additional information for interpreting the damage induced in adhesively bonded
wood joints under quasi-static and cyclic fatigue in-plane shear (mode II) fracture loads.
Linear AE signal source location yields indications of delamination propagation with time forcomparison with the visual observation on the edge of the joints. From this, the averagedelamination propagation speed can be calculated. AE signal source location also yields
indications of damage initiation in the wood adherends of the adhesive joints beyond oroutside the propagating delamination that cannot be detected by visual observations.
The rather dense set of AE signal locations toward the undamaged end of the HB110-type
specimen in Figure 8 is tentatively attributed to friction from slight movement of the specimenon the support during cyclic loading. For confirming this, AE signal source mechanisms
should be identified.
8

S38

Figure 11: Selected AE waveforms and Fast Fourier Transform power spectra for one PRF adhesive
joint from the broad-band AE sensor mounted in the propagation zone of the delamination.

Therefore, for further analysis of the possible different source mechanisms, recorded AE
waveforms are briefly discussed as well. Figure 10 shows the duration versus amplitude
plots for the broad-band AE sensor (channel 7) for the two types of adhesive. As for thesignals recorded with resonant type AE sensor, the more ductile adhesive (HB110) yields
significantly less signals than the more brittle one (PRF). Figure 11 shows selected AE
waveforms and Fourier Transform power spectra of signals recorded with the broad-band AE
sensor on the PRF adhesive joint. As expected, the AE signals with low amplitude or low
counts, and intermediate duration, respectively show larger low frequency partial power
contributions than AE signals with higher amplitude and counts for comparable signal
duration.
In the cyclic fatigue fracture loading tests, likely, noise sources such as, e.g., friction between
test-rig and specimen, seem to dominate the recorded AE. It is planned to investigate this in
more detail by applying pattern recognition approaches, e.g., as described in [8,9].

5. Conclusion
Examples of AE monitoring and analysis of mode II fracture tests on adhesively bonded
wood joints indicate that the adhesive type has a significant effect on the amount as well ason the parameters of the recorded AE signals. AE further provides a perspective for9

S39

comparison between quasi-static and cyclic fatigue fracture behavior of adhesively bonded
wood joints made with different adhesive types. Differences observed in the power spectrafrom AE waveforms recorded with a broad-band AE sensor during quasi-static tests suggestthat pattern recognition approaches (e.g., similar to those described in [8,9]) should be allow
to discriminate between different AE signal source mechanisms, possibly also for the cyclic
fatigue fracture tests. This will be further investigated.

Acknowledgment
Contributions of Ms. Magdalena Pérez-Galmés to the design and modification of the 4-ENF test rig aswell as to data analysis; and test machine setup and control by Mr. Daniel Völki are gratefully
acknowledged.

References:[1] Vassilopoulos, A.P., Fatigue and fracture of adhesively bonded composite joints:
behavior, experiments, simulation and modeling, Woodhead Publishing, Series in
Composites Science and Engineering, Number 52, 2015, ISBN 978-0-857098-06-1.
[2] Vallée, T., Tannert, Th., Fecht, S., Adhesively bonded connections in the context oftimber engineering – A Review, The Journal of Adhesion, Vol. 93(4), 2017, pp. 257-287.
[3] DIN-EN-302-1 Adhesives for load-bearing timber structures – Test methods – Part 1:
Determination of longitudinal tensile shear strength (German version), 2013, pp. 1-13.

[4] Bachtiar, E.V., Clerc, G., Brunner, A.J., Kaliske, M., Niemz, P., Static and dynamic
tensile shear test of glued lap wooden joint with four different types of adhesives,
Holzforschung, Vol. 71(5), 2017, pp. 391-396.
[5] Yoshihara H., Mode II R-curve of wood measured by4-ENF test, Engineering Fracture
Mechanics, Vol. 71, 2004, pp. 2065–2077.
[6] Gostautas, R.S., Identification of Failure Prediction Criteria using Acoustic Emission
Monitoring and Analysis of GFRP Bridge Deck Panels, Kansas Department ofTransportation, Report No. K-TRAN: KU-02-1, 2007, pp. 1-71.
[7] Schubert, L., Schulze, E., Frankenstein, B., Source mechanisms of acoustic emissions
as an indicator of injury during a full scale fatigue test of a wind turbine rotor blade,
Proceedings of the 8th International Conference on Structural Dynamics, EURODYN
2011, G. De Roeck, G. Degrande, G. Lombaert, G. Müller (eds.) ISBN 978-90-760-19314, 2011, pp. 3462-3468.
[8] Sause, M.G.R., Gribov, A., Unwin, A.R., Horn, S., Pattern recognition approach toidentify natural clusters of acoustic emission signals, Pattern Recognition Letters, Vol.
33(1), 2012, pp. 17-23.
[9] Sause, M.G.R., Horn, S., Quantification of the Uncertainty of Pattern Recognition
Approaches Applied to Acoustic Emission Signals, Journal of Nondestructive Evaluation,
Vol. 32, 2013, pp. 242–255.

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17 - Acoustic Emission analysis during bending tests

Acoustic emission analysis during bending tests of timberof timberbeams reinforced with carbon fiber composite
beams reinforced with carbon fiber composite
Francisco J. Rescalvo1, Chihab Abarkane1, Elisabet Suarez1, Antolino Gallego1
1 University of Granada, Dept. of Applied Physics, Building Engineering School,

Granada, Spain, antolino@ugr.es

Abstract:
This paper proposes the monitoring of old timber beams with natural defects (knots, grain
deviations, fissures and wanes), reinforced with carbon composite materials (CFRP).
Reinforcement consisted of the combination of a CFRP laminate strip and a CFRP fabricdiscontinuously wrapping the timber element. Monitoring considered the use and comparison of
two types of sensors: strain gauges and multi-resonant acoustic emission (AE) sensors. AE data
analysis propose two main steps: 1) A filtering of signals based on the root mean squared of the
waveforms; 2) The use of partial spectral ratio corrected by the attenuation characteristics of thematerial, to identify damage mechanisms. Results demonstrate that: 1) the mechanical behaviourof the beams can be considerably improved by means of the use of CFRP (160% in bending load
capacity and 90% in stiffness; 2) Acoustic emission sensors provide valuable information fordamage assessment and its location during operation in real wood structures.

1. Introduction

The use of FRP for retrofitting and repair of wooden structures as an alternative material
to steel has been booming for several years. Its high strength and deformability and thelow weight added to the structure are its main attractions. In particular, the use of carbon
fiber pultruded laminates and fabrics (CFRP), already at reasonable and competitive
prices in the market, provide excellent results in terms of strength, stiffness and ductility.
Numerous experimental, analytical and numerical works have demonstrated the highcapacity for retrofitting and repair conferred by different layouts of CFRP [1-14].
However, at present, the use of codes and standards for the calculation and design of
FRP reinforcement systems on timber beams, unlike in the case of concrete, are still
scarce or nonexistent. This gap generates in the professionals of architecture and civilengineering and in the owners of wooden structures, both private and administrations
that own many historic buildings, certain reluctance to use them.
To generate confidence about its operation in situ, as well as to do a continuous followup that establishes the pertinent structural alerts, the in-situ and real-time monitoring ofstructures [15] is an efficient strategy (Structural Health Monitoring), which can be ofgreat utility to the case of FRP-retrofitted or repaired timber structures. In particular,
acoustic emission (AE) method is an efficient technology for structural monitoring
especially suitable for this purpose [16-18]. There are just one, as far as the authors of
this work know, previous publications about the AE monitoring of timber structures
reinforced with CFRP [19]. The method has been, however, widely used for wood and
CFRP, individually [20-25]. There are also some works on the AE monitoring of concreteelements retrofitted with CFRP [26-30].
In the case of wood, however, two problems arise when applying this technique: a) Its
high heterogeneity, which confers a very high distortion to the waves. Multiple reflections
and other propagation phenomena of the wave, alter in a very considerable way theshape of the wave, making its ulterior analysis very complicated and unreliable; b) The1

S41

high attenuation of the material, which entails a closer location of the sensors, and thealmost loosing of information at higher frequencies.
To overcome both inconveniences, this work proposes a double strategy for the AE data
analysis. Firstly, all the AE data analysis is done only on a very narrow window of thesignal, just after the threshold crossing, including only one cycle of the waveform. Thisaction largely avoids the effect of reflections and other propagation phenomena.
Secondly, the proposed method includes a correction of the signal in the frequency
domain, which attempts to compensate the attenuation losses.
Using this double strategy, the work proposes the use of signlals whos spectral energy
is predominant within a band of high frequencies, correlating these signals as comingfrom the resin-wood failure (precursor of CFRP-wood delamination). The appearance,
detection and location of these AE signals serve as a real-time alert of final element
failure. The proposed methodology has been validated on two different retrofitting
layouts, providing successful AE results in both cases.

2. Description of work

All the timber beams used in this work were extracted from the last rehabilitation carriedout on the roof of the Faculty of Law of the University of Granada. They are pinus
sylvestris beams from the forests of the south of Spain, which had been in service formore than 200 years. They were cut and sanded with a final cross-section of (147 ± 11)
x (222 ± 6) mm2 and a length of 4500 ± 2.4 mm.
In particular, six beams were reinforced with CFRP by using two different layouts named
as LR (longitudinal) and BR (braided), respectively. They are represented in Fig. 1.
Furthermore, two beams without reinforcement (NR) were used as control specimens.
As is shown in Fig. 1, the width of the CFRP lamella for both layouts was set at 𝑤𝑤𝑤𝑤𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 =100 mm. In the case of the BR layout, the CFRP fabric height was set at ℎ𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 = 150 mm
and the length of each piece of fabric was set at 𝑙𝑙𝑙𝑙𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 = 100 mm.

Fig. 1. LR (left) and BR (right) reinforcement layouts

AE data analylsis
The acoustic attenuation of the six beams was measured before the mechanical tests.
To do that, one AE sensor was placed at the center of six intervals of length [-60, 60] cm.
Pencil lead break tests (PLBs, using 0.5 mm 2H leads) were carried out at each particularpoint every 20 cm on the central line of the lateral face of the beam, according with thestandard ASTM E976. Three PLBs were done at each point, and the peak amplitude in
dB of each recorded AE signal was obtained. Fig. 2 describes the attenuation tests.

2

S42

Fig 2. Attenuation tests procedure

A Vallen Systeme AMSY-5 equipment was used to acquire the AE signals. Multiresonant VS45-H sensors with a sensitivity within the frequency range 25-500 kHz (Fig.
3), were used. As it can be seen, two of the main resonances are located around 100
kHz (low frequency) and 300 kHz (high frequency), respectively. The sampling frequency
for the waveforms recording was established at 5 MHz, and the number of samples was
set at 4096 with a pre-trigger of 300 samples.
Big attenuation values and very relevant differences between all the specimens is
obtained. It is basically due to the big differences in density and number and position of
defects of each specimen. Similarly, a no symmetric attenuation pattern is obtained
respect to the central position of the sensor, demonstrating the enormous heterogeneityof the used wood.
For ulterior data analysis of the AE signals during the mechanical tests (Section 3), it
was required to evaluate and quantify the attenuation in the two particular frequency
bands, B1 and B2. To do that, the RMS for each band was calculated. Only the first cycleof the signal just after the first threshold crossing, W0: [0 - 20] μs, was used for the
calculation of the spectral energy. This very narrow temporal window was selected inorder to avoid as much as possible the influence of the different reflections and otherpropagation effects of the wave on the calculation of the spectral energy. This strategy
was already follow for some of the authors in a previous work [31]. More precisely, theused bands werea) Band (B1): [60 - 120] kHz. Low frequencies. RMS0 (B1).
b) Band (B2): [270 - 330] kHz. High frequencies. RMS0 (B2).
AE data analysis was carried in four steps, described as follows.
Step 1. Filtering of miss-recorded signals. Due to the avalanche of generated AE
signals, some of them were miss-recorded. Basically, they were signals recorded with alarge period of low amplitude before the arrival time. In order to filter automatically the
miss-recorded signals, two temporal windows were considered in each signal. Then, theroot mean squared (RMS) of each signal in both temporal windows W 1 (RMS(W 1)) andW2 (RMS(W 2)) was calculated. Using the difference between RMS(W 1) and RMS(W 2),
only the signals fulfilling the RMS12 criterion were considered as properly acquired andpassing the filter for ulterior data analysis in step 2.

3

S43Step 2. Source linear location. By means of the classical AE source location theory[16-18], linear location of the passing signals was carried by using the 6 used sensors.
Thus, for each particular AE event the coordinate x between two sensors was obtained.
Step 3. Attenuation correction. For the first-hit of the located events in Step 2, thespectral energy at each frequency band, RMS0 (B1) and RMS0 (B2) in decibels wascalculated. Using the location of each event, x, and the attenuation fitting curves, foreach frequency band, f-(x)B1 and f+(x)B1 for B1 and f-(x)B2 and f+(x)B2 for B2, a corrected
spectral energy was calculated. The corrected RMS was named as RMSC0 (B1) and
RMSC0 (B2), respectively.
Step 4. Classification of events. During the last step, a classification of passing signals
in two groups was carried out, according to the relative importance of its corrected
spectral energy. More precisely, passing signals were classified in Group 1 aspredominant low-frequency signals and in Group 2 as predominant high-frequency
signals, respectively (Eqs. 1-2), i.e.
𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 1 → 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅0 (𝐵𝐵𝐵𝐵1 ) > 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅0 (𝐵𝐵𝐵𝐵2 )𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 2 → 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅0 (𝐵𝐵𝐵𝐵2 ) > 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅0 (𝐵𝐵𝐵𝐵1 )

(1)
(2)

Mechanical testing with acoustic emission monitoring

All the beams were subjected to monotonic 3-point bending test until failure.
Displacement rate was 1.5 mm/min. Tests were carried out on a hydraulic machine fromthe company SERVOSIS S.L., model CH4·ST·100, with a span between supports set at
4000 mm.
In addition, all specimens were continuously monitored by the acoustic emission method,by using six multi-resonant VS45-H sensors. For these tests, the pre-trigger was
established at 500 samples. The acquisition threshold was set at 32.1 dB and a 34 dBgain preamplifier was used. The AE sensor was coupled with silicone grease and fixedwith a magnetic holder to the specimen.

3. Results and discussion

Fig. 3 shows the stress as a function of time during the bending test on the eight
monitored specimens. In the case of non-retrofitted beams (NR) the final failure wastotally brittle. For the case of the beams with LR retrofitting layout, the stress curve showsa slight plastic range, although the final failure was also brittle. On the other hand, thethree beams retrofitted with the BR layout had a much more ductile behavior, with a
gradual and stepped decreasing of the loading capacity. For LR beams, although theknots still remain as sensitive areas, the final failure occurs due to delamination between
the CFRP laminate and wood. In the case of the BR layout, the purpose of the fabric is
to prevent the resin-wood failure and the subsequent delamination. Therefore, when aparticular stress level is reached, the fabric begins to work, thus avoiding thedelamination. The failure that causes the first loading decay occurs when this adhesion
fails, i.e. when wood-resin breakage occurs.

4

S44

Fig. 3. Stress versus time for all the tested beams. Red: Non-reinforced beams (NR). Blue:
Longitudinal reinforced beams (LR). Black: Braided reinforced beams (BR).

Fig. 4 represents the cumulative AE events grouped as explained in Section 2, for threeparticular beams of each group (NR, LR and BR). In all cases, it is observed that Group
1 events (in which low frequencies predominate) are much more numerous than Group
2 events (in which high frequencies predominate). It is also observed that the events ofGroup 1 do not show any special change when the element is close to reach the
maximum load. However, in many cases, the events of Group 2 show a substantial
increase when the load is close to its maximum value. This indicates that Group 2 events
can be relevant to predict the final failure of the element in advance.

Fig. 4. Cumulative AE events versus time for an example of non-reinforced (NR), longitudinal
reinforced (LR) and braided reinforced (BR) beam.

For this reason, Fig. 5 shows the location results of the Group 2 AE events along eachtested specimen. The red dots represent the position of the sensors. Each blue dot in
the graphs represents a localized event. The figures represent the peak amplitude ofeach localized event versus its position along the monitored area of the beam (thecoordinate origin is at the center of the beam). The green lines correspond to the cracks
in the wood, whereas the areas where breakage between wood and resin was observed
are shown in orange color. The black areas correspond to the CFRP laminate that did
not delaminate, while gray areas with black dots correspond to the zones where CFRPwood delamination occurred.
In general, fewer events and shorter pick amplitudes are observed for the non-reinforced
beams (NR) than for the reinforced beams (LR and BR). This indicates that the eventsof Group 2 can be mainly associated with resin-wood breakage (precursors of CFRPwood delamination).
In the case of the beams retrofitted with the CFRP laminate (LR beams), it is observedthat Group 2 events (especially those with a greater peak amplitude) are mostly andmore clearly located in the area where the resin-wood breakage occurred. This area was5

S45

where the final CFRP-wood delamination began, leading to the fragile failure of theelement.

Fig. 5. AE linear location of events (Group 2) during the bending test for an example of nonreinforced (NR), longitudinal reinforced (LR) and braided reinforced (BR) beam.

A similar result is observed for beams reinforced with laminate and fabric (BR). For thethree beams, the cracks of the wood were concentrated at the central part of the element,
subjected to greater stresses. It can be clearly seen that the AE events are located mainlyin this central area of the beam, which is an excellent correlation between AE locationand damage location. In addition, the highest concentration of events for the beam BR3
is located in the interval [-20, 0] cm, just at the resin-wood breakage area and precursorof lamella CFRP-wood delamination.

4. Conclusion

The work proposes the use of acoustic emission technique for the early detection ofdelamination between CFRP and wood, applicable to the case of retrofitted or repaired
wooden beams with CFRP material. This analysis consisted on the following steps:-Elimination of erroneously recorded AE signals, by comparing the temporal RMSof the signals in two different time windows.
- Calculation of the spectral RMS in a very narrow time window of the AE signals
in two particular frequency bands. This minimizes the influence of the reflectionsof the acoustic waves and other propagation effects.
-Correction of the spectral energy of the AE signals by means the attenuation
curves empirically determined.
-Clustering of signals into two groups, according to the value of the corrected
spectral energy in two frequency bands: Group 1 (low frequencies are
predominant) and Group 2 (high frequencies predominant). It has been shown, that
if the attenuation correction is not carried out, no signals of Group 2 are found, due
to the strong attenuation in wood material.
It has been shown that Group 1 events undergo virtually no change when the specimen
is close to the final failure. However, in general, the events of Group 2 suffer aconsiderable increase. It has been corroborated that the location of the Group 2 events
correlates very well with the location of the wood-resin break zones, which are theprecursors of delamination failure between the CFRP sheet and the wood. This checkhas been made both on reinforced beams only with CFRP laminate (in which
delamination is a critical aspect) and beams reinforced also with CFRP fabric.
6

S46

References:[1] Parisi MA, Piazza M, Restoration and strengthening of timber structures: Principles,
criteria, and examples, Pract Period Struct Des Constr, Vol 12(4), 2007, pp: 177-85.
[2] de Jesus AM, Pinto JM, Morais JJ, Analysis of solid wood beams strengthened with
CFRP laminates of distinct lengths, Constr Build Mater, Vol 35, 2012, pp: 817-28.
[3] Nowak TP, Jasieńko J, Czepiżak D, Experimental tests and numerical analysis ofhistoric bent timber elements reinforced with CFRP strips, Constr Build Mater, Vol 40,
2013, pp: 197-206.
[4] de la Rosa García P, Escamilla AC, García MNG, Bending reinforcement of timber
beams with composite carbon fiber and basalt fiber materials, Compos Pt B-Eng, Vol 55,
2013, pp: 528-36.
[5] Yang Y-l, Liu J-w, Xiong G-j, Flexural behavior of wood beams strengthened with
HFRP, Constr Build Mater, Vol 43, 2013, pp: 118-24.
[6] Solarov R, Glišić M, Glulam beams reinforced with FRP strips and their application inarchitecture, Spat, Vol 32, 2014, pp: 1-6.
[7] D’Ambrisi A, Focacci F, Luciano R, Experimental investigation on flexural behavior oftimber beams repaired with CFRP plates, Compos Struct, Vol 108, 2014, pp: 720-8.
[8] Ansell MP, Hybrid wood composites–integration of wood with other engineering
materials, Wood Compos, 2015, pp: 411-26.
[9] Biscaia HC, Cruz D, Chastre C, Analysis of the debonding process of CFRP-to-timberinterfaces, Constr Build Mater, Vol 113, 2016, pp: 96-112.
[10] Vanerek J, Smak M, Kusak I, Misak P, Durability of FRP/wood bonds glued withepoxy resin, Mater Tehnol, Vol 51(6), 2017, pp: 889-95.
[11] Biscaia HC, Chastre C, Cruz D, Viegas A, Prediction of the interfacial performanceof CFRP laminates and old timber bonded joints with different strengthening techniques,Compos Pt B-Eng, Vol 108, 2017, pp: 1-17.
[12] Rescalvo FJ, Valverde-Palacios I, Suarez E, Gallego A, Experimental Comparisonof Different Carbon Fiber Composites in Reinforcement Layouts for Wooden Beams ofHistorical Buildings, Materials, Vol, 10 (10), 2017, pp: 1113.
[13] Rescalvo FJ, Valverde-Palacios I, Suarez E, Gallego A, Experimental and analyticalanalysis for bending load capacity of old timber beams with defects when reinforced withcarbon fiber strips, Compos Struct, Vol 186, 2018, pp: 29-38.
[14] Rescalvo FJ, Suarez E, Abarkane C, Cruz-Valdivieso A, Gallego A, Experimental
validation of a CFRP laminated/fabric hybrid layout for retrofitting and repairing timber
beams, Mech Adv Mater Struct, 2018, pp: 1-8.
[15] Rescalvo FJ, Valverde-Palacios I, Suarez E, Roldán A, Gallego A, Monitoring ofcarbon fiber-reinforced old timber beams via strain and multiresonant acoustic emission
sensors, Sensors, Vol 8(4), 2018, pp: 1224.
[16] Ibrahim M, Nondestructive testing and structural health monitoring of marine
composite structures, Marine Applications of Advanced Fibre-Reinforced Composites:
Elsevier; 2016, pp, 147-83.
[17] Grosse CU, Ohtsu M, Acoustic emission testing: Springer Science & BusinessMedia, 2008.
[18] Mizutani Y, Practical acoustic emission testing, Tokyo2016.
[19] Ono K, Application of acoustic emission for structure diagnosis, Diagnostyka, Vol 2(58), 2011, pp: 3-18.
[20] Degala S, Rizzo P, Ramanathan K, Harries KA, Acoustic emission monitoring of
externally bonded FRP-reinforced concrete, In: Kundu T, editor, Health Monitoring ofStructural and Biological Systems 2008.
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[21] Pullin R, Eaton MJ, Pearson MR, Pollard C, Holford KM, Assessment of bonded
patch bridge repairs using acoustic emission and acousto-ultrasonics, In: Uhl T, editor,
Structural Health Monitoring, 2012, pp: 57-65.
[22] Ono K, Gallego A, Research and applications of AE on advanced composites, 30th
European Conference on Acoustic Emission Testing, Granada 2012.
[23] Dahmene F, Yaacoubi S, El Mountassir M, Bendaoud N, Langlois C, Bardoux O, On
the modal acoustic emission testing of composite structure, Compos Struct, Vol 140,
2016, pp: 446-52.
[24] Diakhate M, Angellier N, Pitti RM, Dubois F, On the crack tip propagation monitoring
within wood material: Cluster analysis of acoustic emission data compared withnumerical modelling, Constr Build Mater, Vol 156, 2017, pp: 911-20.
[25] Kolanu NR, Raju G, Ramji M, Experimental and numerical studies on the bucklingand post-buckling behavior of single blade-stiffened CFRP panels, Compos Struct, 2018.
[26] Degala S, Rizzo P, Ramanathan K, Harries KA, Acoustic emission monitoring of
CFRP reinforced concrete slabs, Constr Build Mater, Vol 23 (5), 2009, pp: 2016-26.
[27] Verbruggen S, Aggelis DG, Tysmans T, Wastiels J, Bending of beams externallyreinforced with TRC and CFRP monitored by DIC and AE, Compos Struct, Vol 112, 2014,
pp: 113-21.
[28] Choi W-C, Yun H-D, Acoustic emission activity of CFRP-strengthened reinforced
concrete beams after freeze–thaw cycling, Cold Reg Scienc Tech, Vol 110, 2015; pp:47-58.
[29] Diakhaté M, Bastidas-Arteaga E, Pitti RM, Schoefs F, Cluster analysis of acoustic
emission activity within wood material: Towards a real-time monitoring of crack tip
propagation, Eng Fract Mech, Vol 180, 2017, pp: 254-67.
[30] Tayfur S, Alver N, Tanarslan HM, Ercan E, Identifying CFRP strip width influence on
fracture of RC beams by acoustic emission, Constr Build Mater, Vol 164, 2018, pp: 86476.
[31] Martínez-Jequier J, Gallego A, Suárez E, Juanes FJ, Valea Á, Real-time damage
mechanisms assessment in CFRP samples via acoustic emission Lamb wave modal
analysis, Compos Pt B-Eng, Vol 68, 2015, pp: 317-26.

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23 - Frequency-Amplitude class of Acoustic Emission for different
Frequency-Amplitude class of acoustic emission for different
fracture mechanisms in C/SiC composite
fracture mechanisms in C/SiC composite

Xiao Denghong1, Gao Yong1,2, Jin Liang1, Meng Fanchao1, Li Naitian1, Zhou
Xiaohong1, Tong Zongkai1,He Tian2 Jiang Bo3
1
Beijing Electro-Mechanical Engineering Institute, Beijing 10074, P. R. China
2
Beihang University, Beijing 10079, P. R. China
3
Shenyang Aerospace Xinguang Group Co., LTD

Abstract:
Acoustic emission (AE) registration is a useful methodology, which allows ‘‘hearing’’ and registeringdamage during loading of a specimen or a part. It is a challenge to determine connection between
acoustic emission (AE) events and the corresponding damage modes. In the present study AE events
registered during static loading of a C/SiC laminate are correlated to actual damage. The friction andcracks in the C/SiC laminate and localized delimitations are distinguished. AE events are classified
according to the amplitude and centroid frequency of the signal into high frequency ,low frequency –low amplitude and low frequency – high amplitude clusters. The first (high frequency) AE events are
assumed to be connected to friction. The second (low frequency – low amplitude) are assumed to beconnected to formation and propagation of cracks, and the later (low frequency – high amplitude) are
assumed to be connected to fiber breakage. The study validates use of Frequency-Amplitude class of
AE for identification of damage models in C/SiC laminates and find out the load when each damage
model first emerge.

1. Introduction
Due to its high temperature resistance, C/SiC is widely used in aerospace applications,especially in some high temperature resistant structures [1], such as the spacecraft rudder
surface [2]. Due to lack of plastic deformation, ceramic matrix composites have highbrittleness and may cause sudden damage under external loads. The use of suitable nondestructive testing methods to detect the damage of the C/Sic structure will play a key role.
NASA puts advanced nondestructive testing methods in a very important position in its
advanced ceramic matrix composite structural plans [3]. C/Sic composite Damage will lead tostrong acoustic emission [4]. Acoustic emission signals contain important information about
structural damage, such as the location of damage, severity and type of damage [5].
Therefore, for certain C/Sic structure, this paper uses the relationship between the frequency
and amplitude of the center of mass of acoustic emission signals received to classify andrecognize acoustic damage.

2. Structural static test and data collection
Here, the acoustic emission monitoring technology is applied to the damage analysis of the
C/SiC structure during the static loading process. Six acoustic emission sensors were1

S49

arranged on the upper surface of the structure and two sensors are arranged on the lower
surface of the structure. The location is shown in Figure 1.
y4

AE Sensor 1

320 mm

200 mm

2

120 mm

5

90 mm

50 mm

x

380 mm

190 mm

7
280 mm

3
6

y

50 mm

o

AE Sensor

8
290 mm

o

x

(a)

(b)

Fig. 1 Arrangement of acoustic emission sensors: (a) upper surface; (b) lower surface

The threshold is set to 200 mV, and the resulting cumulative energy of the acoustic emission
signals received by the eight sensors is shown in Table 1. It can be seen that theaccumulative energy of the acoustic emission signal received by the No. 8 sensor is thelargest, and the damage position of the test piece is closest to the No. 8 sensor. Therefore,
the following section mainly analyzes the acoustic emission signal received by the No. 8
sensor.
Table 1 cumulative energy of acoustic emission signals received by eight sensors

Sensors number

cumulative energy

1
0.12

2
1.30

3
9.54

4
36.26

5
10.17

6
5.17

7
26.20

8
39.77

3. Acoustic emission data analysis
The cumulative energy and loading load of the acoustic emission signal received by the No.
8 sensor over time are shown in FIG. 2 . It can be seen from Fig. 2 that the accumulative
energy of the acoustic emission signal received by the No. 8 sensor gradually increases with
the increase of the loading load, and the accumulated energy suddenly increases to the
maximum slope when the applied load causes the test piece to be destroyed. Therefore, themoment of destruction of the test piece can be judged based on the cumulative energychange of the acoustic emission signal. However, the cumulative energy of the acoustic
emission signal can only be seen at a time when the degree of damage of the test piece isrelatively large, and it cannot be judged when the damage has just started and the type ofdamage. Therefore, the relationship between the amplitude of the acoustic emission signal
and the center-of-mass frequency is studied in this paper to solve the problem of determining
the type of damage and the moment of initial damage.
The relationship between the centroid frequency and amplitude of the acoustic emission
signal received by the No. 8 sensor is shown in Fig. 3. It can be seen from Fig. 3 that therelationship between the mass center frequency and amplitude can be clearly divided into
three categories, namely the high-frequency sound at 600 kHz. The transmitted signal is a
low-frequency, low-amplitude acoustic emission signal and a low-frequency, high-amplitude
acoustic emission signal with an amplitude of 7000 mV. The resulting changes in the centroid
frequency of the three types of acoustic emission signals over time are shown in Fig. 4.
2

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It can be seen from Figure 4 that the first type of high-frequency AE signals is mainly
concentrated in the later stage of the test, and the frequency will be higher during the period
when the test pieces are destroyed. This article observes the time-domain diagram of
acoustic emission signals during the destruction stage of the test pieces. It has been foundthat there are a large number of signals with high center-of-gravity frequencies. At this time,since the damage expansion is severe, a large number of fibers break, and the acoustic
emission band of fiber breakage is high, so the center-of-gravity frequency of the damage
acoustic emission signal increases at this time. The second type of low-frequency and lowamplitude acoustic emission signals appeared at the beginning of the test and existed
throughout the entire test. Therefore, such acoustic emission signals can be regarded as thegeneration of micro-cracks in the test piece. The third type of low-frequency, high-amplitude
acoustic emission signals are generated in large quantities before the test piece is destroyed.
Therefore, such acoustic emission signals can be regarded as a matrix fracture. At this time,
the center-of-focus frequency of the acoustic emission is not high, but the energy of theacoustic emission event is significantly increased. high. Then find the time when the three
types of damage begin to occur. The time for the first type of large-scale fiber breakage
signal to be generated is 964s, and the corresponding load is 120%; the time for the second
kind of microcrack signal to start is 98s, corresponding to The load is 50%; the time for thethird type of matrix fracture signal to start generating is 829s, and the corresponding load is80%.

Fig. 2 The cumulative energy by No. 8 sensor during Loading process

3

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Fig. 3 Relationship between centroid frequency and amplitude of acoustic emission signal
received by No. 8 sensor

Figure 4 Occurrence time of three types of acoustic emission events

4. Conclusion
In this paper, we use acoustic emission to study the damage characteristics of a C/Sic
composite structure during static experiments. Based on the relationship between thefrequency and amplitude of the center of the acoustic emission signal, acoustic emission
events can be divided into three categories: high frequency with low amplitude, lowfrequency with low-amplitude and Low-frequency. The main damage characteristics of the4

S52three types of acoustic emission events are determined. The research results of this paper
can provide reference for the damage monitoring of this kind of structure.

References:[1] Xiang Y., Chen Z. H., Cao F., High-temperature protective coatings for C/SiC composites,
Journal of Asian Ceramic Societies, Vol. 2 , 2014, pp.305–309.
[2] Natalie D. S., High-temperature modal survey of a hot-structure control surface, 27th
International Congress of The Aeronautical Sciences, 2010.
[3] Advanced Ceramics Technology Roadmap—Charting Our Course, Sponsored by UnitedStates, Advanced Ceramic Association, U.S. Department of Energy, Prepared by Energetics,
Incorporated and Richerson and Associates, 2000.
[4] Chomiot N. C., Fleischmann P., Fougeres R., Fiber Break Characterization by Acoustic
Emission in a Single SiC Aluminum Matrix Model Composite, Progress in acoustic emission
VI, The Japanese Society for NDI, 1992: 567-574.
[5] Madaras E. I., Winfree W. P., Prosser W. H., Nondestructive Evaluation for the Space
Shuttle’s Wing Leading Edge, 41st AIAA/ASME/SAE/ASEE Joint Propulsion Conference &Exhibit 10-13 July 2005, Tucson, Arizona

5

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29 - Damage
monitoringa steelTRUCK
truck trailer
using
DAMAGEMONITORINGON AonSTEELTRAILER
USING
parameter-based
analysisof
Acoustic
Emissions
PARAMETER-BASED ANALYSIS OF ACOUSTIC EMISSIONSJoão Vitor Pimentel1, Rolf Klemm1, Munip Dalgic2, Andree Irretier2, Karl-Ludwig
Krieger1
1Institute of Electrodynamics and Microelectronics (ITEM),

University of Bremen, Bremen, Germany

2Leibniz Institute for Materials Engineering (IWT),

University of Bremen, Germany

Abstract:
The structural health of a solid body can be monitored by detecting acoustic emissions (AE) thatindicate the occurrence and/or propagation of fractures. Continuous monitoring is possible withvibroacoustic sensors that generate electric signals in response to structure-borne sound. However,damage detection and identification in real time imposes performance challenges on the sensor
system that must be solved so that it can acquire and process the signal of interest.
In this work, piezoelectric sensors were connected a prototyping unit to continuously monitor a steel
semitrailer structure subjected to fatigue tests. From previous tests on sample structures, signals infrequencies of up to several hundred kHz were expected to be associated with the emergence of
cracks. At the required sampling frequencies, it was not possible to record, display or transmit themeasurement data for external or post-processing. A parametric analysis was therefore carried out,
whereby the vibrations were processed in 5 ms bursts. In such an implementation, the raw measured
data is never made available. It could nevertheless be processed in real-time in order to calculate
parameters that enabled damage monitoring.
The paper describes the parametric analysis used for online monitoring of the structure under test.
The hardware implementation of the sensor system is described and results are compared with postprocessed results of raw data to validate the online analysis.

1. Introduction
Structural health monitoring techniques based on acoustic emission (AE) detection are used
in a wide range of applications, from buildings to aircraft. Such monitoring relies on thedetection of AE events caused by crack formation or propagation in the material. The firstchallenge to the success of a quasi-passive AE-based condition monitoring system is toproperly acquire relevant vibroacoustic data, since AE signals can have very low amplitudes
relative to unavoidable background noise. In general, one-time events (such as catastrophic
failures) generate stronger and clearer signals than progressive damage (such as caused by
fatigue). Moreover, the characteristics of an AE signal, both in time and in the frequency
domain, depend on the type of event that caused it as well as on the geometry andmechanical properties of the medium where it propagated. The second challenge is,therefore, to properly identify an AE event within the vibroacoustic data, which is particularly
1

S54

difficult if there is no prior knowledge of where a fracture is likely to emerge, or of what kindof damage may occur in the structure.
The work presented here was carried out in the scope of the research project TraZu(Electronic Condition Monitoring System for Predictive Maintenance of Semitrailers). The
project focuses on semitrailers used in the transportation industry, which saw in 2016 anincrease of 3.5 billion tonnes in the amount of goods transported by road within Germany
alone [1]. There is a constant demand for ever-lighter structures, to be in operation as often
as possible, carrying as close to its load limit as permitted, and often above it. Making theload-bearing structures of transport vehicles lighter can increase payload, but also the risk ofdamage to which these vehicles are already subjected in normal operation [2,3]. In Figure 1
examples are shown of damages to a semitrailer frame which most likely occurred
progressively, but too fast to be found by regular periodic maintenance [4].

Figure 1. Damages to semitrailers at junction plate (left) and transverse beam (right)

The goal of the research project is a predictive maintenance system (illustrated in Fig. 2), a
crucial part of which is the damage detection based on AE detections by decentralised
sensor units. Specific parts of the semitrailer were chosen for monitoring taking into accountthe likelihood of structural damage occurring in their vicinity and on previous studies of the
propagation of structure-borne sound on the frame [5]. The electrical signal from each sensor
first goes through a signal conditioning stage (including pre-filtering and analog-to-digitalconversion). The digital data is then delivered to a sensor controller unit, which performs thedamage detection. These three stages comprise a decentralized sensor unit (DSU). EachDSU reports its results to the central electronics unit, which interprets these data taking into
consideration complementary data received from the telematics unit – environmental data,
vehicle information such as speed and load, etc. The end user (e.g. the driver or the fleet
management) can also configure the damage detection via the telematics user interface.
SensingElement

SensorElectronics

SensorController

Decentralized Sensor Unit

CentralElectronics

Telematics

Service
CentralService
Workshop

Central Trailer Service Unit

Interconnected Trailer Condition Monitoring System

Maintenance Planning System

Figure 2. Overview of the predictive maintenance system

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In previous works, the authors have demonstrated the validity of the AE-based damage
detection strategies used in preliminary examinations of test specimens in controlledenvironments (static tensile tests and fatigue tests) and how they can be correlated todamage in a real semitrailer structure in operation [6–9]. Among the results of these firststudies was the determination of a range of frequencies corresponding to the signals ofinterest (i.e. acoustic emissions due to fractures in the material). It became clear that for the
relatively high frequencies involved (ca. 150–500 kHz), real-time processing of the raw data
would impose severe performance challenges to the equipment. Therefore, a parametricanalysis was favoured for the damage monitoring.
In parametric damage monitoring, a probable AE event is typically detected when themonitored signal exceeds a certain voltage threshold. A sample of a certain length of thesignal is then quickly processed so that predefined parameters, also called “features”, arecalculated. The raw measurement data is discarded and only the values of the features
obtained is used for further analysis of the event, e.g. to confirm whether it corresponds to an
acoustic emission or to identify its source. Classical features of an AE signal (see 10)
include:•Maximum – peak amplitude within the sampled window;•

Rise time – time from the first crossing of the threshold until the maximum is reached;•

Fall time – time from the maximum until the last threshold crossing within the window;•

Duration – time between first and last threshold crossings, equals rise + fall times;•

Counts – number of times the threshold is crossed during the sampled window.

Amplitude (V)These are illustrated in Fig. 3 over a sample of a real detection captured during preliminary
tests. The parameter counts is omitted for visual clarity.

Time (ms)Figure 3. Classical features in AE testing

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The classical features listed above were used in this work, as described in the following
sections. In the system, these are called “low-end” features, to differentiate them from thesecondary or “high-end” features calculated later. For brevity, they will be referred to here
only as “features”, since further stages of the damage monitoring system fall out of the scopeof this publication.

2. Description of work
During the development of the damage detection algorithm, Hsu-Nielsen tests were used tosimulate acoustic emissions on samples of various geometries, all out of S700 MC steel, 4mm thick. The algorithms were then tested during the fatigue test of a larger longitudinal
section of semitrailer chassis (length 3.5 m, width 2.5 m) comprising the region
corresponding to all three axles. The second (middle) axle was mounted. The testingmachine varied the force applied to the axle between a minimum FL and a maximum FH =10∙FL at a frequency of 2 Hz. The forces were progressively increased in different phases of
the test, with FH in the range 46–110 kN, until a crack could be cleary seen and its
propagation observed. Structure borne-sound was measured using four VS150-RIC
piezoceramic sensors (Vallen Systeme), placed on the longitudinal beams on either side of
the semitrailer structure, adjacent to crossbeam junctions near the first and second axles.
Measurement data was acquired for post-processing using a PicoScope 5444B (picoTechnology). For the online monitoring, a MicroLabBox (dSPACE) was used as a real-timedata processing unit.
Models were built in MATLAB/Simulink so the algorithms developed until then could be
implemented in a real-time application on the MicroLabBox. The analog-to-digital converter(ADC) class 1 of the MicroLabBox has a fixed vertical resolution of 16 bits and a configurable
sampling rate of up to 1 Msps. Considering the range of frequencies of interest, the upper
limit was desired. However, although the ADC can sample the signal within 1 µs, the timeneeded for data from the ADC to be made available to the running application can be close
to 3 µs. Hence it was not possible to implement the algorithms developed for post-processing
in an online manner.
A solution was to build the application using the burst conversion mode, whereby the ADC
samples at 1 Msps and after 5 ms (i.e. 5000 samples) sends a hardware interrupt signal andmakes the whole 5000-sample burst available to the application as a vector X of values xi
indexed from 1 to 5000. Both the interrupt block and the ADC subsystem, which delivers the
sampled vector as well as a binary status signal, are part of the group Input on Fig. 4.
Additionally, a switch signal is used to determine whether detections will be triggered basedon an amplitude threshold value þa or on a variance threshold þv. The trigger switch andthreshold values can be set by the user in real-time, and are thus part of the group Input.
The interrupt signal is used in the MATLAB/Simulink model to trigger a subsystem (in thegroup Detection), which takes the newly available data and compares it to the chosen
threshold þ. If xi ≥ þa for any i, or if Var(X) ≥ þv, depending on the chosen threshold, adetection signal is set to 1; otherwise, it is set to 0. This signal is used to trigger other parts ofthe model. The parameter counts can be directly obtained as the number of indices i where xi≥ þa. Furthermore, the first and last such indices, denoted as a and z respectively, are carriedover to the subsystem in the Low-End Features group and used to determine the duration
feature of the signal. With the index m of the maximum value within the vector, the rise andfall times also follow as r = m - a and f = z - m respectively. In the model, duration, rise and4

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fall are given by an integer number of samples, instead of a floating-point value of time; witha predefined sample rate, the actual times can be calculated directly. Although not strictly
necessary, the calculated features are then set as model outports, so they can be easily
visualised in a real-time application running on the dSPACE software in a host-PC connectedto the MicroLabBox. To save the generated features (and some additional control signals) for
later analysis, the USB Flight Recorder functionality is used (group USB Recorder). Chosen
variables are saved automatically and periodically to a USB mass storage device connectedto the MicroLabBox according to the sample time of their tasks in the model. The application
can still run with no USB connected, but then no data is saved. The USB flight recorder iss
used only as a means to analyse the results a posteriori, and has no corresponding
functionality in the planned monitoring system described above.

Detection

Input
ADC

Low-End Features

Detection

ADC CL1 HWINT
Channel: 1 Interrupt
Interrupt: Data readydouble0

double

DetSw

Switch[ADC]
ADC_ReadydoubledoubleSwitchbooleanVariance

Detection

Switchdouble0.2

double

ThrAi

ThrA

doubleDet

HitCntXa

ThresholdA

ThresholdA
doubledouble

ThrVi

ThrV

doubleXz

ThresholdV

ThresholdV

Data

Count

XCnt

ADC1_Data_Outboolean[ADC]

Data_In

TriggerBlock[Data]

Write toUSB Flight Recorder

3
Variance

Var

Write toUSB Flight Recorder2
Detection

DetCnt4
HitCnt

Max

Rise

7
Rise

L-End Features

ADC_1

Rise
Dur
Fall

Fall

Write toUSB Flight Recorderboolean
uint8

Det

5
Max

Duration[Data][ADC]

1
ADC_Status

Ind_X_last

SetMethodThreshold

ADC1_Status_Out boolean

uint32

Outputs

Max
Ind_X_first

5e-3

USB RecorderClassical Features

6
Duration
8
Fall

XCnt
Thr1

uint32
uint32

singleuint16

uint16

uint16

uint32

singleWrite toUSB Flight RecorderWrite toUSB Flight RecorderWrite toUSB Flight RecorderWrite toUSB Flight RecorderWrite toUSB Flight RecorderWrite toUSB Flight RecorderWrite toUSB Flight RecorderFigure 4. Simulink model of the first stages of the detection algorithm

In accordance with the system structure, the features generated so far are then packed into
CAN messages (configured in a separate .dbc file and controlled by pre-built Simulink
blocks) and sent out along with a detection ID. The CAN communication is out of the scopeof this paper. However, it should be said that these messages are not periodic, but rather
triggered by the detection signal.
The graphic user interface developed for the online detection monitoring is shown in Fig. 5.
The GUI allows the user to configure the damage detection in real time via adjustable valuesof threshold as well as the input gain of the ADC and other visualization tools, e.g. the LEDs
that show when a certain number N of events was detected within a certain period T. Theinterface reports detections clearly as they happen. An “event detected” means that the set
threshold was overcome, and does not take into account the features calculated. These areshown to the user (although not readable in Fig. 5) along with with the status of theirrespective CAN messages, to confirm that they were sent to the next stage of the monitoring
system.

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Figure 5. Graphic User Interface for the AE event detection in the MicroLabBox

3. Results and Discussion
The results of AE monitoring during the fatigue test of the larger semitrailer structure,described in the previous section, was compared to the outcome obtained with postprocessing of the corresponding measurement data. Moreover, visual inspection of thetrailer, as well as strain gauge data, were used to assess the online feature-based
monitoring. Due to the length of the test, selected periods were considered for thisevaluation. The results in Fig. 6 correspond to one of the last phases of the fatigue test, with
an applied force of 110 kN. Towards the end of the period shown (at a point in time
highlighted in red) the crack was already visible. The left-hand y-axis corresponds to thenumber of events detected during that phase (plotted in blue), while the right-hand y-axis
corresponds to the counts parameter in each burst. From the figure it is already apparent that
periods of rapid increase in the number of events correlated to high values of counts in each
event. A rapid increase in event detections can be associated to either crack initiation or,
especially, rapid crack propagation [9,11]. Therefore, this result indicates a significance ofcounts as a valid analysis parameter in the system presented.
Another example of validation of the feature generation was obtained later, after themacroscopic crack had already been verified. Crack propagation gauges were placed overthe fracture so that its progression could be tracked in 100-µm steps. While the rate ofpropagation was roughly 100 µm/h most of the time, there were “jumps” during which thecrack grew by several hundred microns within an hour. In Fig. 7, the maximum voltage in the
burst is plotted against the total number of load/unload cycles of the fatigue test until thatmoment. The parameter was saved periodically in the USB flight recorder. For the large timescale displayed, it is not possible to differentiate between subsequent data points, especiallysince at this stage in the test AE events were frequent, resulting in a rapid variation between
high and low values of the maximum parameter. Nevertheless, it can be seen that the high6

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Total detections

value is relatively stable at ca. 1.8 V, except for a significant rise between approx. 4.579 and4.581 million cycles, as well as a few spikes around the same time. Nearly all significant
variations in the maximum value occur within a period (highlighted in green) when the crack
propagation rate rose from ca. 100 µm/h to 336 µm/h. Fig. 7 is thus shown as anotherexample of a clear change in the value of the features that can be strongly correlated to
crack formation or propagation verified by other means. The sudden drop in the maximum
value at the point highlighted in red marks the time at which the testing machine was turned
off and the test was stopped.

Time
Figure 6. Blue (left-hand y axis): cumulative number of events detected.
Green (right-hand y axis): counts per burst.

Figure 7. Maximum voltage in a burst for the last hours of the test. Area highlighted in green
corresponds to an increase in the crack propagation rate. Red dashed line indicates test stopped.

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4. Conclusion
The first stages of a parametric damage monitoring system for semitrailer structures basedon acoustic emission were implemented on a MicroLabBox. Due to the technical impossibilityof delivering raw measurement data to the further stages of the system, the structure-borne
sound acquired by the sensors were digitized using burst conversion mode, which resulted insome restrictions to the signal processing. The approaches taken to implement the AE
detection were described in this work, including the generation of classical features. The
applicability of the features to the desired application was verified during fatigue tests, theresults of which were analysed by various methods. Other stages of the damage monitoring
system will be presented in future work.

References:[1] Federal Federal Statistical Office, Goods transport in 2016: volume of transport at record
high again, Press Release 057, 2017.
[2] Bathias, C., There is no infinite fatigue life in metallic materials, Fatigue Fract Eng Mater
Struct, Vol. 22, 1999, pp 559–565[3] Fatemi, A. and Yang, L., Cumulative fatigue damage and life prediction theories: asurvey of the state of the art for homogeneous materials, Int J Fatigue, Vol. 20, 1998, pp
9-34[4] Gesamtvorhabenbeschreibung Elekttronisches Zustandsüberwachung zur
vorbeugenden Wartung von Trailern (TraZu), 2015
[5] Klemm, R., Harmstorf, J., Pimentel, J.V., and Krieger, K.-L., Untersuchung derKörperschallausbreitung auf einem LKW-Trailer mit Hilfe von Hsu-Nielsen Tests, Proc.
Deutsche Jahrestagung für Akustik (DAGA), 2018[6] Pimentel, J.V., Klemm, R., Dalgic, M., Irretier, A., and Krieger, K.-L., Automatic detectionof fractures during tensile testing using vibroacoustic sensors, Proc. 3rd International
Electronic Conference on Sensors and Applications (ECSA), 2016[7] Pimentel, J.V., Klemm, R., Dalgic, M., Irretier, A., Zoch, H.-W., and Krieger, K.-L.,
Monitoring crack formation in metal structures with a percussion-inspired detection
method, Proc. IXth NDT in Progress, pp 67-74, 2017[8] Klemm, R., Pimentel, J.V., Krieger, K.-L., Irretier, A., and Dalgic, M., Entwicklung eines
echtzeitfähigen Sensorsystems zur frühzeitigen Erkennung von Rissen an
Trailerrahmen, Proc. 5th International Commercial Vehicle Technology Symposium(CVT), 2018
[9] Klemm, R., Pimentel, J.V., Dalgic, M., Irretier, A., and Krieger, K.-L., Early detection of
fatigue cracks in truck trailer structures by acoustic emission testing, Proc. 12th
European conference on Non-Destructive Testing (ECNDT), 2018[10] Shiotani, T., Parameter Analysis, in: Grosse, C.U., and Ohtsu, M. (Eds), Acoustic
Emission Testing: Basics for Research - Applications in Civil Engineering, SpringerVerlag, 2008, pp. 41-51[11] Kohn, D.H., and Ducheyne, P., Acoustic emission during fatigue of Ti-6Al-4V: incipient
fatigue crack detection limits and generalized data analysis methodology, J Mat Sci, Vol.
27, 1992, pp. 3133-3142

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49 - Structural health monitoring using Acoustic Emission

Structural health monitoring using acoustic emission on metallic
on metallic components in industrial plants
components in industrial plants
Heribert Marihart 1, Gerald Lackner 1, Gert Schauritsch1
1

TÜV AUSTRIA, Austria, tph@tuv.at

Abstract:
The Structural health monitoring (SHM) is a method to safeguard the integrity of metallic components
or component assemblies during service. This process involves a continuous or periodic
measurement, the extraction or selection of degradation-relevant data and the statistical analysis todetermine the actual state of the system condition. Acoustic emission (AE) monitoring detects andlocates potential defects in plant components like piping and pressure vessels. An essential
advantage of acoustic emission is that it allows assessing the components regarding evolving defects,
leaks or even active corrosion during operation. Therefore, acoustic emission qualifies excellently as atool for structural health monitoring.
The sensors of the monitoring system have to be mounted at preselected positions of the metallic
structure and a tailored data analysis programme evaluates the incoming signals to provide a reliablestatement regarding the structural health. Individually designed alarm and warning criteria enable
corrective actions to prevent that the observed degradation evolves to failure.
In order to meet the requirements of customers from the oil and gas industry or the energy generationindustry, TÜV AUSTRIA developed a monitoring technique for hot and cold surfaces including amethodology for collecting, transferring, storing and evaluating the measuring data.
This paper contains case studies where structural health monitoring with acoustic emission is used toprovide an alternative to the regular requalification procedure of the monitored metallic pressure
equipment.

1. Introduction
The performance of a conventional water pressure test indicates total failure in the form ofvisible deformation or leakage. A conventional acoustic emission testing (AT) is used as aperiodic inspection method for the condition assessment of metallic pressure equipment.
Accompanying pneumatic or hydraulic pressure tests are generally performed up to 1.1 timesof the design pressure of metallic pressure equipment, while the acoustic emission testing
provides a statement regarding the structural integrity of the item under test and can also
detect early failure mechanisms. A relevant issue of AT is that the pressurization is
performed with the operating medium. In contrast to hydrostatic testing no hazardous waste
will be produced. Furthermore residual humidity may induce or facilitate failure mechanism.
Therefore plant operators avoid complex cleaning and drying procedures which would be
required after completion of the test. Acoustic emission testing is appealing for industry andenables the plant operators to optimize the plant availability with low operating costs for therecommissioning of the pressure equipment after the inspection test.
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Structural health monitoring by means of acoustic emission is the next milestone in AT
history. The pressure equipment operational parameters, such as working pressure,temperature or cycles should be recorded during the performance of the pressure test with
AE. The monitoring period for SHM can either be discontinuous (e.g. 3 days 4 times peryear) or continuously.
In general information regarding the current conditions of the metallic components inindustrial plants is of vital importance for every plant operator. Therefore, one main task ofthe maintenance department is to take proper actions for ensuring secure operation. As an
illustration, there are a lot of different methods on the non-destructive testing sector, toestimate the duration of a service period for pressure equipment, which are provided by TÜV
AUSTRIA. Hence, there is increased attention on structural health monitoring using acoustic
emission. Precise information regarding the actual conditions of the metallic components
would enable the maintenance department to point out the components which should be
repaired or when they should be replaced. That would give an economic advantage by
preventing a shutdown of entire industrial plants and enable an appropriate repair.

2. Acoustic Emission Monitoring Concept
The main function of structure health monitoring is to merge the AE-data with the operating
parameter (pressure, temperature, volumetric flow rate) in the measuring system (seefigure 1). Subsequently these monitoring data will be stored and can be analysed withdedicated software (e.g. Vallen Systeme “Automation Manager”). The automation manager
has an online access to the measurement data. For example, if certain criteria are exceeded,an alarm message is sent if necessary per email and a test report is created over apredefined inspection period.

Figure 1: Acoustic emission monitoring concept

Two kinds of sensors will be applied in these use cases. Standard sensors VS150-RIC fromVallen Systeme with a temperature range up to 60 °C are mounted on the metallic surface ofthe pressure equipment. For a temperature, up to 180 °C, VS150-BD-V01 sensors are2

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mounted directly on the tested surface. For temperature ranges above 180 °C, a specificallydesigned waveguide device is applied between the test surface and the acoustic emission
sensor. The VS150-BD-V01 sensor is certified for hazardous areas and is connected to the
AMSY-6 measuring system with a 20-meter hazardous-certified coax cable and an AEP3BD-V01 preamplifier.
The sensitivity of each measuring chain is checked with the Hsu-Nielsen source before thetest. The AE system is an AMSY-6, which can acquire AE data and waveform data
simultaneously for each channel. It has an internal pulsing unit, which sends on request an
electric pulse to a sensor. That electric pulse is transformed into an acoustical pulse by thesensor. The other sensors may detect the sent pulse after propagation in the metallic
structure. The AE system measures the time period from the emitting to the receiving sensor
and that together with the distance between emitter and receiver gives the speed of sound.
This pulsing function, defined as “Auto Pulsing”, will also be used to check the functionality
and sensitivity of the measuring chain before, during and after the 24h-AE-measurements.
The software for data acquisition, visualisation and frequency analysis is also provided byVallen Systeme. With VisualAETM evaluation data files can be created concerning to themetallic pressure equipment, the respective relevant operational parameters (pressure,temperature, strain, volumetric flow rate...), cluster evaluation factor (CEF) [1] and external
data (weather data).
“Automation Manager”, the software used for structural health monitoring with acoustic
emission is also provided by Vallen Systeme. For a better comparison 24h measurements
will be saved with previously configured data acquisition and visualisation files. With the“Auto Pulsing” function mentioned before the measuring chain is checked at certain intervals.
If a previously defined deviation of AE parameters occurs, an alarm should be sent as aresult [2]. Similarly, previously set limits of the acoustic emission results can trigger alarms.
Consequently, digital inspection reports will be created. The structure health monitoring could
be performed discontinuous or continuous.

3. Examples of monitored metallic Components•

Urea plant (passivation air cylinder, figure 2)

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Figure 2: AET application on passivation air cylinder (urea plant)

The entirety of the structure of the passivation air cylinder can be monitored by an applicationof acoustic emission testing. As an example, for a simple standard sensor arrangement, 3sensors must be applied linear on the 660-mm diameter cylinder in figure 2. The used sensor
is the VS150-RIC, which has a resonant frequency of 150 kHz. The sensitivity of eachmeasuring chain is checked with the Hsu-Nielsen source before the test. The “Auto Pulsing”
function of the Vallen Systeme was executed before and after a 24h-AE-measurement andperiodically in a 6 hours cycle. This functional check of the AE measuring chain wasappended to each 24h-AE-measurement inspection report (see figure 3). The AE system is
an AMSY-6, which can acquire AE data and waveform data simultaneously for each channel
and is able to intercommunicate with the “Automation Manager” software.

Figure 3: Auto Pulsing on passivation air cylinder

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While the pressure equipment was in operation the 24h-AE measurement was recorded withthe operation pressure as external parameter. For the passivation air cylinder SHM usedacoustic emission was done discontinuous 3 days 4 times per year.
•

Urea plant (Nitrogen cylinders, figure 4)

The same sensor arrangement as the passivation air cylinder was applied for this both
nitrogen cylinders. Those metallic pressure equipment are exposed to environmental
impacts. Thus, the external parameters were enhanced with the data of the weather station
for a profound analysis. The monitoring time was also the same: discontinuous 3 days 4times per year.
•

Preheater for fuel gas in gas-fired power station (figure 5)

Figure 4: SHM on nitrogen cylinders•

Figure 5: SHM on preheater for fuel gas in gas-fired power station

Main steam distributor (figure 6)

The main steam distributor should give an example of the application of an acoustic emission
monitoring system during operation at temperatures up to 170 °C. A sensor arrangementwith 5 sensors must be applied linear on the 450-mm diameter cylinder in figure 6. Whereas
3 sensors were mounted directly with magnet holder and 2 further sensors were mounted
between those 3 sensors with a waveguide technique. For this example, external parameters
are temperature, pressure and volumetric flow rate. The electrical signal of these 3parameters was fed directly into the acoustic emission system.

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Figure 6: SHM on main steam distributor

4. Results
The verification of the structural integrity while the metallic component is in operation was
implemented by the evaluation of a 24h-AE-measurement and provides further information:
Existence of active AE-sources caused by the act of defect-mechanism in the structure,grading of the intensity of the AE-sources during the applied pressurisation and localisationof the AE-sources in space of the whole structure.
Moreover, in respect of safety-relevant measures the early recognition potential of AET incontrast to alternative test methods is from vital importance, especially to the common
hydraulic pressure test (water pressure test). This also includes an early detection at
approximately 30 % of the load of the component failure for ductile materials. Also, possible
defect-mechanisms can be observed in a microscopic range.
Figure 7a) shows the cluster evaluation factor (CEF) and the pressure versus time of
measuring. This graph gives conclusions about the load profile; it shows minimum- &maximum- working-pressure, and pressure intervals during operation of the metallic
component. The cluster evaluation factor is applied for on-line evaluation of located events.
The CEF calculation is based on prototype data acquired on structures with known defects.
The evaluation range of CEF start from 0 (no located events = 0 severity) up to 4 (very highseverity).
At the example of the passivation air cylinder the traceable, active AE-sources are locatedamong the sensors xd4 and xd5 at the range between 380 and 400 cm (see figure 7b).

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a)
b)
Figure 7: Evaluation of passivation air cylindera) CEF and pressure vs. time, b) CEF vs. X-Location.

Exactly on this position the mounting ring is fitted. Therefore, this area was verified on thecylinder with an ultrasonic test. This inspection method exhibits no noticeable indications.
Hence, the result suggests that the activity of the acoustic emission was caused bymechanical friction between the mounting ring and the passivation air cylinder.
The important advantage of the monitoring inspection report is the history of those 12 x 24hAE-measurements which could be compared easy. An equally important reason to realise
SHM is the adequately long monitoring period of about 290 h compared to one hour of aconventional acoustic emission test. The project target was the extension of the service
period till to next inspection (one year).

5. Conclusion
Structure health monitoring using acoustic emission on metallic components will become anincreasingly role on non-destructive testing sector. The system effectiveness of new
industrial plants is growing and the knowing of the actual conditions of the metallic
components in industrial plants is of vital importance for every plant operator.
Conventional acoustic emission tests will be more popular because they are performed withthe process medium, but this method must take a requalification. The test performance must
realise during a planned shut-down and usually on pressure equipment in ambientconditions. Often the prevailing test requirements do not represent the operating conditions
or only in a limited way. Also, the material is sometimes not sufficiently loaded as inoperation, for example material properties modify with temperature.
In case if points which mention above should be relevant, acoustic emission monitoring could
be the solution. However, the degradation of the material happens normally during operation
nevertheless not during testing. Consequently, structure health monitoring using AET isrecording during operation and as a result it is a measurement of the degradation process.
Considering this analytic data, the “Automation Manager” makes it possible to send awarning if defect-mechanism occurs as early as possible. Moreover, for a measurement
during operation, no further time must be spent for testing in a shut-down. Additionally, in
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case of severe degradation, the scheduled operation time can be observed with acoustic
emission monitoring to safeguard the time till to replacement of the metallic component.
This enables further improvements of customer specific implementation of SHM using
acoustic emission, which shall foster a broad acceptance of acoustic emission by equipment
users/operators.

References:[1] TSCHELIESNIG, Peter; SCHAURITSCH, Gert: Inspection of LPG vessels with AE
examination. Journal of Acoustic Emission, Vol. 18, 2000, p. 138 – 143[2] G. Schauritsch, “Schallemissionsprüfung an einem rissbehafteten Flüssiggaslagerbehälter
–Ergebnisse zum Nachweis der Verfahrenseignung”, Proceedings of 18. Kolloquium
Schallemission, Vortrag #03, 2011.

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83
- Novelmonitoring of
induceddamagedvesselNOVELAEAEMONITORING
OFhydrogen
HYDROGENINDUCEDDAMAGEDtime
alarms.
A case A
study
VESSELand
ANDrealREALTIME
ALARMS.
CASE STUDY

Dimitrios Papasalouros1, Konstantinos Bollas1, Ioannis Ladis1, Eleftherios Aerakis1,
Dr. Athanasios Anastasopoulos1, Dimitrios Kourousis1
1

Mistras Group Hellas ABEE, Athens, Greece

Abstract
Hydrogen induced cracking (HIC) damage is a common damage mechanism, expected typically invessels in severe wet H2S service. It can occur at relatively low temperatures, as the atomic hydrogenis concentrated at the impurities within the steel. The occurring damage is mostly cumulative and it
can be manifested in various types such as blistering, step-wise cracking or stress-oriented cracking.
As such, the qualitative and quantitative evaluation of structural integrity of any vessel possibly
suffering from wet hydrogen damage is of great concern from a safety, environmental and financial
point of view. In the present paper, results of the application of real-time AE monitoring of a HIC
damaged area of a thick-walled amine absorber in severe wet H2S service are presented. The
damaged area was found during a routine UT inspection. UT and AE trials were performed in order tofine-tune and maximize the effectiveness of the application. This resulted in a unique correlation
database from which monitoring criteria were developed, that greatly maximized the AE location
performance and minimized the false alarms due to the noisy environment. Major benefits of the AE
monitoring application include an overall increase of health and safety, as well as minimum down-time
for the refinery until a full vessel replacement becomes possible.

1. Introduction
Acoustic Emission [1][2] has been successfully applied for the integrity assessment of static
structures such as pressure vessels and storage tanks during both in-service and hydrostatic
tests for a wide range of manufacturing materials. Extensive testing of such equipment has
led to the development of AE testing procedures, evaluation criteria and international
standards [3][4]. In addition to that, industry applied procedures, such as MONPAC & IPAC
[9][10] for pressure vessels, extended the codes’ pass-fail assessment to quantitativeevaluation of fault severity and criticality, providing the industry with a tool for 100%
evaluation of the vessel, capable of giving early warning of developing defects, increasing,thus, the operational safety.
In areas with high stress concentration and where local strength may lead to crack
propagation, as in the case of step wise cracking HIC or SOHIC (Figure 1), local monitoring
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can be performed. Many variables, like temperature, contaminations, microstructure etc.,
may affect and thus differentiate the final damage.
Although surface breaking cracks are visible, their detection may be performed with various
other NDT techniques like UT (TOFD-PA), EC, PT or RT. Many of these methods may also
provide information about the crack size, position and orientation.
In cases where the primary interest is the crack growth and its criticality in relation with thevariation of operating conditions, continuous AE may be used by utilizing the stochastic
stimulation of the structure (varying pressure, temperature variations etc.) [6]. In this paper, anovel adaptation of the method and application of real-time alarm criteria over a noisy
environment is presented. This was possible due to the unique AE & UT findings correlation
database. The continuous monitoring was able to provide an early indication of possible
damage accumulation in severely damaged areas.

(a)

(b)

Figure 1 – (a) Stepwise Indication - HIC (b) Through Thickness Indication in HAZ – SO-HIC [6]2. Case Study: HIC Monitoring of Amine Absorber
During a routine internal inspection, a large number of surface breaking blisters was found on
the internal surface of the 91mm-thick column (Figure 2) with an operational temperature of60οC. These indications appeared just above the operational water level, on the internalsurface of the 2nd course of the column. Subsequent base metal and weld ultrasonic (UT)
inspection with phased arrays (PA), as indicated by [6] were performed in sampled areas of
the column. UT inspection showed unexpected scattered indications in both areas (HAZ &base metal).
The indications that were discovered were ranging from planar to step-wise (Figure 3a).
Some larger clustered axial-oriented indications were also discovered that appeared as
through-thickness damage (Figure 3b). The presence of these indicated that the damage
was already in an advanced state.
To this respect, the planning for continuous monitoring was established due to the criticalityof the column regarding its structural integrity condition as well as its impact on the generalsafety during subsequent operation, until its replacement.

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Figure 2 –Internal Surface Condition

(a)

(b)
Figure 3 – (a) Step-wise damage (b) Through-thickness damage indication

3. Setup
Eight areas (1–8) with larger clusters of identified damage were selected for monitoring as
being the most critical. Area 6, was selected as a reference since it was damage-free (Figure
4a).

(a)

(b)

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Figure 4 – (a) Monitoring areas 1-6 (b) Sensor layout, PAC R15i-AST, probes frequency
response

In total, twenty-two (22) R15i AE sensors, with a 150 kHz resonant frequency and integral
preamplifier, were mounted around the 2nd course in triangular formation (Figure 4b), where
the first and last four sensors were used as guards, in order to exclude operational or otheractivity not correlated with the damaged areas. These were mounted after the column was
brought back to service.
The sensors were mounted with high-temperature couplant and magnetic holders (Figure5a). The connection to the more centralized junction boxes (Figure 5a) was performed via
coaxial cables that were shielded inside elastic tubing (Figure 5b).

(a)

(b)

(c)

Figure 5 – (a) Sensor mounting (b) Junction box (c) Cable routing and μSamos Express-8 AE
monitoring workstation

From each of the junction boxes the coaxial cables were routed to a nearby shielded isobox
where the physical connection with the system (Figure 5c) was established. All routing to thesystem was performed via larger diameter elastic tubing to shield the cables against theelements.

4. Attenuation Measurements & Location Verification
AE signal attenuation measurements (Figure 6a) were performed along the axial direction,
circumferential direction and 45ο angle from the circumferential direction. As is often
observed in thick vessels, the signal attenuation was relatively low (65 dBae at 500cm froman AE probe). In this respect, it was expected that the predefined tight array of Acoustic
Emission probes would greatly increase the event detection capability of the resulting setup.

4

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(a)

(b)

Figure 6 – (a) Attenuation measurements in 3 directions (b) Hsu-Nielsen sources location
results(a)(b)

Figure 7 – (a) Event due to a HN source (b) Resulting waveforms from the first 8 hits acquired

This was verified with the application of Hsu-Nielsen sources along the axial direction of eachof the areas of interest. The raw results are shown in Figure 6b. There is a clear distinction
between the sources located at area 1 and 2 which shows that the effective spatial resolutionof the setup was at least 10cm (distance between Areas 1 & 2). Moreover, the effectives andperformance of the resulting setup during the noisy service time was maximized by utilizing ahigher amount of AE hits for the location algorithm [21].

5. System Functionality and Sensitivity Verification
For any monitoring scheme, safeguards and system checks must be enforced in order tomaintain a high confidence level for the functionality of the system. For these reasons, daily
routines were performed in order to ensure that the system was functioning properly and thesensitivity of the probes was not degraded due to the operation of the column or exposure to
the elements.
This was achieved by using the Auto Sensor Test (AST) feature of the MISTRAS μSamosExpress-8 boards in predefined and/or random intervals and the results were crossreferenced with the reference AST results acquired when the probes were initially mounted.
The AST feature data comparison shows of the difference of AE features between two data
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sets. The comparison of the newer AST measurements (when performed) with the reference
AST measurements was automatically performed by the system each time new AST data
was available. Graphs from AST trials showing typical AE features for each probe are shownin Figure 8a. In addition, for system performance, the Average Signal Level (ASL - Figure 8b)of each channel was continuously monitored in order to have clear indications about any
abrupt operational changes or external noise that could contaminate the acquired data.

(a)

(b)

Figure 8 – (a) Example of AST measurements for all 22 mounted AE probes (b) ASL
measurements

In the rare case that a sensor’s response was degraded from its average behavior andsensitivity, local crew was performing the necessary adjustments.

6. Data Evaluation and Reporting AE Results Grading
Daily evaluation and reporting of acquired data was decided due to the criticality of thesituation and the massive amount of acquired data. AE and UT phased array trials were
performed in order to develop a unique correlation database. Upon the completion of trials
and by using the resulting database the real-time alarm criteria were greatly improved andfine-tuned in order to be used in real-time alarms. The number of false alarms duringcontinuous monitoring was greatly reduced, thus maximizing the effectiveness of themonitoring. A confidential list of Events Cluster Activity, Amplitude and Energy Criteria andtime compensation/normalization was agreed with the client, upon the completion of initial
trials. The typical daily report contained the cluster activity grading of the monitored areas
assigned into 4 categories (Table 1a).

(a)

(b)

Table 1 – (a) Cluster activity grading categories (b) Daily/weekly reported cluster activity

In addition, a summary weekly report was generated at the end of each week (Table 1b).

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7. Monitoring and Real Time Alarms
24/7 remote access to the μSamos Express-8 AE Monitoring Workstation was available
through a high speed internet connection. Daily reports of operational parameters of thecolumn and weather conditions were correlated in order to filter the data. AEWIN software
was used for automated located data clustering during acquisition (Figure 9). The alarm
generation according to the aforementioned categories and criteria was subsequently
triggered.

(a)

(b)

Figure 9 – (a) Example of automated clustering (b) Real-time automated clustering

An interesting observation is that there is significant located AE event clustering, in the areasof interest.

8. Discussion & Conclusions
In general, this novel AE monitoring method of verified HIC damaged areas, offers theunique opportunity for asset owners to be informed rapidly and continuously, with minimum
interference, about the condition of the structural integrity of critical assets that need tooperate at their design limits.
Results of the test can be given almost immediately, upon the alarm triggering, with fullevaluation within hours after, thus providing a great assistance to the general operations
regarding the asset.
More specifically, safety and operational risks are minimized and operational efficiency is
increased, therefore minimum downtime is guaranteed.
Location optimization using high-density AE probe arrays shows a highly promising
contribution to the early detection of damage areas even in case that were not identified

7

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previously. This was shown in (Figure 9b) where the area between probes 6, 7 and 11 was
not initially selected for monitoring. However from the initiation of AE monitoring the resulted
large AE event cluster that was located, change the criticality of this area. and additional UTPA follow up that was performed revealed many smaller critical indications.
Finally, all the above advantages can be further exploited by installing permanent AE
monitoring systems with remote access functionality, in order to assess the structural
integrity of assets during in-service operation and/or prolonged time periods. Typical systemsof this magnitude are already successfully monitoring critical assets, thus greatly promoting
the minimization of safety risks and the increase of operational efficiency.

References[1] Pollock A. A., 1989, “Acoustic Emission Inspection”, Metals Handbook, 9th Edition, Vol.
17, ASM International, pp. 278-294.
[2] Vahaviolos S. J., 1996, “Acoustic Emission: A new but sound NDE technique and not apanacea”, in NDT, eds. D. Van Hemelrijck & A. Anastasopoulos, Balkema, pp. 101-115.
[3] ASME, Section V, Article 12, 1988, “Acoustic Emission Examination of Metallic Vessels
During Pressure Testing”.
[4] ASTM E 569, 2002, “Standard Practice for Acoustic Emission Monitoring of Structures
During Controlled Stimulation”.
[5] Fowler T. J., Blessing J. A., Conlisk P. J. and Swanson T. L., 1989, “The MONPAC
System”, Journal of Acoustic Emission, Vol. 8, Number 3.
[6] American Petroleum Institute (API) 571-1, FFS-1, 2016[7] Fixed Equipment in the Refining Industry RECOMMENDED PRACTICE 571, FIRSTEDITION, 2003
[8] MONPAC Procedure for Acoustic Emission Testing of Metal Tanks/Vessels, August 1992.
[9] “Selection of 5 papers on MONPAC technology and applications”, Journal of Acoustic
Emission, 1989, Vol. 8, No.3 July-September, pp.1-33.
[10] ASME, Section V, Article 12, “Acoustic Emission Examination of Metallic Vessels During
Pressure Testing”, 2015.
[11] ASTM E569-13, “Standard Practice for Acoustic Emission Monitoring of Structures
During Controlled Stimulation”.
[12] EN 15495:2007, “Non Destructive testing - Acoustic emission - Examination of metallic
pressure equipment during proof testing - Zone location of AE sources”.
[13] EN 14584:2013, “Non-destructive testing - Acoustic emission - Examination of metallic
pressure equipment during proof testing - Planar location of AE sources”.

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[14] C. R. Heiple and S. H. Carpenter, “Acoustic Emission Produced by Deformation ofMetals and Alloys – A Review”, Journal of Acoustic Emission, Volume 6, Number 3, 1987.
[15] Anastasopoulos A. and Tsimogiannis A., 2004, “Evaluation of Acoustic Emission Signals
During Monitoring Of Thick-Wall Vessels Operating At Elevated Temperatures”, J. of
Acoustic Emission, Vol. 22, pp 59-70.
[16] Carlos M. F., Wang D., Vahaviolos S., Anastasopoulos A., 2003, “Advanced acoustic
emission for on-stream inspection of petrochemical vessels”, in “Emerging Technologies in
NDT”, Editors D. Van Hemelrijck, A. Anastasopoulos, N. Melanitis, Proceedings 3rd
International Conference On Emerging Technologies In Non Destructive Testing, 26–28 May
2003, Thessaloniki, A. A. Balkema, Netherlands 2004, pp. 167-172.
[17] Anastasopoulos A., 2004, “Pattern Recognition Techniques for Acoustic Emission based
Condition Assessment of Static Equipment in the Refining Industry”, Progress in Acoustic
Emission XII, JSNDT, pp.411-418.
[18] Tsimogiannis A., Anastasopoulos A., Kouroussis D., 2002, “Stainless Steel Vessel, Tank
and Pressure Equipment Structural Integrity Evaluation”, 2nd Balkan Conference of
B.S.N.D.T. Athens, November 2, 2002.
[19] M.F.Carlos, D.Wang, S.Vahaviolos, A. Anastasopoulos, “Advanced Acoustic Emission
for On-stream Inspection of Petrochemical Vessels”, Proceedings of the 3rd International
Conference on Emerging Technologies in NDT, A. A. Balkema, Netherlands 2004, ISBN 90
5809 645 9 (Volume)-. 90 5809 645 7(CD), pp. 167-172.
[20] Noesis V9, Pattern Recognition Software, Mistras Group Hellas ABEE.
[21] Aewin Software for Express-8 E5.90, Mistras Group Inc., U.S.A.

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55influence- The influenceof source
depth
and source-to-sensor
Theof source
depth andsource-to-sensor
distance on
distance
on thecharacteristics
AE signal characteristics
damaging
eventsthe AE signalof damagingof
eventsin thin-walledin thin-walled
CFRP laminates
CFRP laminates
Benjamin Kelkel1, Julia Vogtmann1, Martin Gurka1
1 Institut für Verbundwerkstoffe GmbH, Kaiserslautern, Erwin-Schrödinger-Straße,

67663 Kaiserslautern

Abstract:
The identification of damaging events in fiber-reinforced plastics (FRP) based on their acoustic
emissions (AE) has been a vivid field of research for more than 25 years. Damage classification has
been carried out by correlating damage patterns with single or multiple features from the time andfrequency domain of the corresponding AE. Features such as maximum amplitude, peak frequency andWeighted peak frequency were used as indicators to distinguish between the various damaging eventsin FRP. However, classification results among authors partly contradicted each other and transferabilityof results has therefore been questioned. Besides the differences in measurement equipment (e.g.
sensor, amplifier) and specimen design (e.g. geometry, layup) the effect of source depth and source-tosensor distance on the resulting signal characteristics has been pointed out as another possible cause
for the ambiguity in classification results. In this study, acoustic emissions are systematically producedin various depths and distances to the AE sensors during quasi-static tensile testing of bidirectional
CFRP laminates with 0/90/0 and 90/0/90 layup in order to investigate the influence of source depth andsource-to-sensor distance on the signal characteristics of fiber and matrix fracture. The results underline
the impact of source depth and source-to-sensor distance on the resulting AE features and visualize thelimitations of AE for damage classification in CFRP.

1. Introduction
Multiaxial carbon fiber reinforced plastics (CFRP) show gradual failure characteristics with highcomplexity due to their inherent heterogeneous structure and the various types of damaging
mechanisms. Understanding this failure process is the key for exploiting the full potential of
CFRP for structural lightweight applications. Acoustic emission (AE) analysis has been utilized
to characterize damage progression through the detection and analysis of released acoustic
emissions from crack initiation and development during mechanical testing. Source
identification has been realized through the correlation of single or multiple features from thetime and frequency domain of released acoustic emissions with the occurring damaging
mechanisms [1-7]. In this context, unsupervised and supervised clustering methods such as
k-means or self-organizing maps have been commonly utilized to find similarities in extracted
features in a multidimensional feature space and thereby identify the acoustic signatures ofdamaging mechanisms [8,9]. However, the transferability of the findings has been questioned
since ambiguous results for the acoustic signatures of damaging modes such as fiber andmatrix fracture have been published [10]. Recent studies pointed out the importance of thesource-to-sensor distance and source depth on the acoustic footprint of matrix cracks in thinwalled CFRP [10-12]. They proved that matrix cracks can excite low- as well as high-frequent1

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acoustic emissions depending on the source depth. This contradicts the general believe that
only fiber cracks will produce acoustic emissions with high frequency content. The cause liesin the excitation of the fundamental guided wave modes and their characteristics. Whereas aspontaneous in-plane deformation in the center plane of a plate results in a symmetric wave
mode, an in-plane excitation outside the plane of symmetry will yield a dominant antisymmetricwave mode. Since both modes vary in attenuation, wavelength and group velocity, AE signal
analysis with a constant time window will result in variations in frequency content and extracted
parameters. Hamstad et. al simulated this change in modal content in an aluminum plate andthereby proved the importance of source depth on the resulting acoustic wave [13]. Maillet et.
al pointed out that limitations have to be given regarding the source-to-sensor distance in order
to allow for valid damage classification in CFRP [11]. The strong attenuation of the acoustic
waves in CFRP has a major impact on the frequency content of the detected waves andthereby impedes source identification.
This study focusses on the influence of source-to-sensor distance and source depth on the
acoustic waves and damage classification. Specimens are designed to systematically produce
fiber and matrix fracture during quasi-static tensile loading in various depths and distances tothe sensors.

2. Description of work
Specimen
Mechanical testing was performed on CFRP laminates with 0/90/0 and 90/0/90 layup that were
manufactured with SGL Prepreg T700 12K 50C/EP322 according to the recommended cure
cycle. The layup was chosen to systematically produce fiber break and transverse matrix
cracking in different depths during tensile loading in 0° direction. Based on the design from[14] the specimens were cut on a CNC milling machine to the dimensions shown in figure 1.
The tailored shape thereby promotes the occurrence of damaging events and respective
acoustic emissions in the mid-section. The nominal thickness of the specimens was 0.73 ±0.03 mm with a fiber volume content of around 53%. Tapered (+45/-45)s GFRP laminates wereused as end taps in order to avoid early failure in the gripping area.
Experimental Setup
Mechanical testing was performed on a universal testing machine with hydraulic clamps. The
specimens were fixed with a clamping length of 215 mm and loaded in tension with a rate of 1
mm/min. AE analysis was performed with Vallen AMSY-5 system and four AE Sensors. The
inner VS900 sensors (1 & 2) were used for evaluation while the outer VS150 sensors (3 & 4)
served as guards to filter noise from the gripping area. The AEP4 was set up as preamplifier
for every channel with a gain of 34 dB. The sensors were coupled with silicon grease and fixedwith spring clamps.
For data acquisition, a sample rate of 10 MS/s (0.1 µs) with 4096 samples per set and 1024
pre-trigger samples was selected. Hit definition was based on a fixed Threshold of 60dB anda duration discrimination and rearm time of 100 µs. Frequency evaluation was constrained
through a bandpass filter from 25 to 850 kHz.

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Figure 1: Specimen dimensions and experimental setup for the quasi-static tensile testing of CFRP
coupons

Data Evaluation
Acoustic events were defined based on differences in time of arrival between triggeringchannels. For event localization, the threshold-based arrival time were adjusted through the
Akaike Information criterion (AIC) picker [15]. The S0 mode was assumed to be the triggeringmode of each event with an estimated velocity of 2500 and 5000 m/s in the 0° direction for the
90/0/90 and 0/90/0 specimen respectively. Data evaluation was constrained to the area
between the inner sensors. Acoustic emissions outside this area were filtered out by
considering only those events that were localized in this area and triggered one of the innersensors (1 & 2) first. Feature extraction was performed with the remaining events in thefrequency domain based on the Fast Fourier Transform of the time signal in a fixed evaluation
window that spanned 1536 samples, starting 512 samples before the trigger and ending 1024
samples after. The features are based on Sause et. al [7] and are listed in table 1. In order to
identify the damaging mechanisms, a cluster analysis was performed in the AE feature spaceby fitting a variable number of gaussian mixed models to the data set. The definition of thenumber of gaussians was based on the results of former work [7,8].

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AE Features

Abbreviation

Unit

Partial Power 1, 0 to 200 kHz

PP1-Partial Power 2, 200 to 400 kHz

PP2-Partial Power 3, 400 to 600 kHz

PP3-Partial Power 4, 600 to 800 kHz

PP4-Partial Power 5, 800 to 1000 kHz

PP5-Frequency centroid

fc

kHz

Peak frequency

fp

kHz

Weighted peak frequency

fwp

kHz

Table 1: Extracted Features from the frequency domain of AE signals (See Reference [7])3. Results
Influence of source depth
Among the extracted AE features, the combination of Partial Power 1 with Weighted peakfrequency was found to visualize patterns in the data sets the best way. The results of thecluster analysis for the data sets of both specimen types can be seen in figure 2. Only thefeatures of the first hit of each event were selected for analysis. Three clusters could be
identified for the 0/90/0 specimen, whereas four clusters were visible in the 90/0/90 data set.
However, after investigating the time and frequency domain of signals from cluster 3, it was
found that low frequency noise and electromagnetic interference were responsible for theformation of this additional cluster of 90/0/90. Cluster 3 was therefore not considered for further
analysis.
(a) 0/90/0(b) 90/0/90

Figure 2: Signal classification through AE Features Partial Power 1 (-) and Weighted peak frequency(kHz) of 1st Hit of events for specimen 0/90/0 (a) and 90/0/90 (b) with color coded classes

4

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The partitions of both data sets resemble each other. The shapes as well as the positions ofclusters 1, 2 and 4 are similar for specimen 0/90/0 and 90/0/90. However, the quantity of AE
data from each cluster differs as can be seen in figure 3 on a logarithmic scale. Whereas
specimen 90/0/90 shows a higher number of events in classes 1 and 2, there are more eventsof class 4 found for specimen 0/90/0.

Figure 3: Cumulative number of events in the classes 1, 2 and 4 for the 90/0/90 and 0/90/0 specimen

In the next step, the frequency spectra of all three classes were inspected furthermore. For
comparability, a representative spectrum for each class was created by standardizing every
single spectrum by its maximum value and averaging these spectra class wise. The results
can be seen in figure 4 where the average spectra for each class (blue line) are visualizedalong with their standard deviation (black dotted line) for both specimens.
Classes 1 and 2 of both specimens show a similar frequency distribution. However, specimen
0/90/0 shows more magnitude in the frequency region above 800 kHz in both classes. This ishighlighted furthermore in figure 5, where Partial Power 5 is displayed against Weighted peakfrequency. In the frequency spectra of class 4 additional dissimilarities can be found between
both specimens. Whereas some peaks can be found in both spectra, specimen 90/0/90 shows
one pronounced peak between 550 and 600 kHz that is not as prominent in specimen 0/90/0.
Furthermore, there is no hard drop in magnitude after the peak at around 700 kHz for the
90/0/90 specimen. Instead, it seems like there are multiple peaks following the major peak at
700 kHz where there is only one around 800 kHz for the 0/90/0 specimen.

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(a) Class 1, 0/90/0(d) Class 1, 90/0/90(b) Class 2, 0/90/0(e) Class 2, 90/0/90(c) Class 4, 0/90/0(f) Class 4, 90/0/90

Figure 4: Standardized and averaged Fourier transforms of acoustic emission signals (blue line) fromclasses 1, 2 and 4 from specimen 0/90/0 (a,b,c) and 90/0/90 (d,e,f) along with their standard deviation(black dotted line)

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(a) 0/90/0(b) 90/0/90

Figure 5: Partial Power 5 (-) and Weighted peak frequency (kHz) of 1st Hit of events for specimen
0/90/0 (a) and 90/0/90 (b) with color coded classes

Quasi-static tensile testing – Influence of source-to-sensor distance
The influence of source-to-sensor distance on the ability to identify clusters in AE features anddistinguish between damaging mechanism can be seen in figure 6 where Partial Power 1 andWeighted peak frequency are displayed for the first and second Hit of an event that differ in
100 mm propagation path at maximum. Except for cluster 1, the shape and position of theclusters varies. An increase in Partial Power 1 and a decrease in Weighted peak frequency
can be observed for the AE events of cluster 2 and 4. As a result, separation between theclusters gets lost and events would have gotten misclassified if AE features from the 2 nd Hit ofeach event were taken for cluster analysis.
For a closer look on the development of AE features the change in Weighted peak frequency
as a function of source-to-sensor distance is illustrated in figure 7. For both specimens adecrease in Weighted peak frequency can be observed for events from the classes 2 and 4
with increasing source-to-sensor distance. These charts allow the estimation of a maximum
source-to-sensor distance for proper cluster separation. In the case of the 90/0/90 specimen,
this critical distance lies between 60 and 80 mm. For the 0/90/0 specimen, the critical distanceis hard to estimate due to the lack of data points between 20 and 80 mm. In contrast to theevents from other classes, events from class 1 don’t show a trend in the change of Weighted
peak frequency in relation to source-to-sensor distance.

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(a) 1st Hit of Event, 0/90/0(c) 1st Hit of Event, 90/0/90(b) 2nd Hit of Event, 0/90/0(d) 2nd Hit of Event, 90/0/90

Figure 6: Signal classification through AE Features Partial Power 1 and Weighted peak frequency of
1st and 2nd Hit of events for specimen 0/90/0 (left side) and 90/0/90 (right side)(a) 0/90/0(b) 90/0/90

Figure 7: Change in Weighted peak frequency as a function of source-to-sensor distance for specimen
0/90/0 (left side) and 90/0/90 (right side). The colors represent the classes

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4. Discussion
Influence of source depth
According to several authors the source depth influences modal as well as frequency content
of the resulting acoustic emissions and should therefore affect the acoustic signature of fiber
break and matrix cracking in the selected specimen layups [10-13]. Regarding class 1 and 2 ahigher frequency content was observed for the events from 0/90/0 compared to 90/0/90. If weassign cluster 1 with matrix fracture as suggested by Sause et. al [7], the change in frequency
content of cluster 1 could be attributed to the variation in source depth of transverse matrix
cracking. The reason for an increase in frequency content could be found in the change ofmodal content. According to Hamstad et. al, an in-plane excitation outside of the axis ofsymmetry leads to the formation of a dominant antisymmetric wave mode whereas an in-plane
excitation in the plane of symmetry results in a purely symmetric mode [13]. Since transverse
matrix cracking represents an in-plane source for wave excitation, the resulting acoustic
emission should carry a dominant antisymmetric mode in the 90/0/90 and a dominantsymmetric mode in the 0/90/0 specimen. By looking at the continuous wavelet transforms of
exemplary events from class 1 of both specimens, one can see the differences in modal
content.
(a) 0/90/0(b) 90/0/90

S0
A0

A0

Figure 8: Continuous wavelet transforms of exemplary signals of class 1 from specimen 0/90/0 (leftside) and 90/0/90 (right side)

A change in modal content doesn’t have to result in a change in frequency content. However,there are three reasons for it:
1. Both modes show differences in dispersion behavior which affect the temporal durationof the wave package as a function of propagation path. When taking a constant time
window for AE evaluation and feature extraction, the result will depend on the excitedmode and the source-to-sensor distance, leading to differences in frequency content
and related features.
2. There are differences in the attenuation behavior of the symmetric and antisymmetricwave mode that will lead to variations in frequency content depending on the sourceto-sensor distance and the propagation direction in CFRP [16-18]
3. Both modes show a different wavelength at a given frequency, which leads to variationsin the sensors response due to the aperture effect [19]. Since the wavelength is in theorder of the sensor diameter this can have a significant effect on the frequency content
and the resulting features.
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In general, the combination of all three effects will determine the resulting changes in frequency
content when modal content of the excited wave varies.
In this study, transverse matrix cracking in the 0/90/0 specimen lead to a higher magnitude
above 800 kHz compared to the 90/0/90 specimen. These changes in frequency content could
result from the formation of a strong S0 mode that is excited through transverse matrix cracking
in the center layer of the 0/90/0 specimen. Since the S0 mode shows a lower attenuation in
CFRP compared to the A0 mode [16-18], the high frequency content in the acoustic signal ismore likely to be sustained over the propagation path. Furthermore, the S0 mode shows ahigher wavelength at a given frequency compared to the A0 mode which results in a higher
critical frequency for signal attenuation due to the aperture effect of the piezoelectric sensor.
Regarding the events of cluster 4, we also observed changes in the frequency content between
both specimen. If we assign cluster 4 to fiber breaking, as suggested by Sause et. al [7], we
can explain the changes in frequency content again with a change in modal content that resultsfrom the variation of source depth of fiber breaking. For the 90/0/90 specimen fiber breaking
takes place predominantly in the middle layer (the plane of symmetry), promoting the formationof a symmetric wave mode whereas fiber breaking in the 0/90/0 specimen should excite amore prominent antisymmetric wave mode. Again, differences in the attenuation factors aswell as in wavelengths of both modes are responsible for the observed changes in frequency
content.
Due to the aperture effect and the differences in wavelength of both modes, different peaks inthe frequency spectra can form from a broadband signal that can be assigned to the S0 and
A0 mode respectively. This was observed in figure 4 and can be validated by looking at
exemplary signals in more detail. If the sensor dimensions and the wavelength of the modes
are known, the characteristic peaks could be determined and serve as indicators for the S0 to
A0 ratio that could again yield information about the source depth.
Influence of source-to-sensor distanceIn figure 7, events from classes 2 and 4 showed a decreasing trend in Weighted peakfrequency with increasing source-to-sensor distance. After around 60 to 80 mm of propagation
path in the 90/0/90 specimen, acoustic events from the two classes could not be separated
from events of class 1 anymore. This could result from the strong attenuation of lamb waves
in CFRP compared to metallic structures [16-18].
The Weighted peak frequency of events from each class is affected differently by the sourceto-sensor distance. The highest loss in Weighted peak frequency can be observed for eventsfrom class 4 for both specimen. This is due to the higher frequency content in acoustic signalsof class 4 because attenuation increases with frequency. The events of class 1 seemed to beunaffected by the source-to-sensor distance. This behavior can be explained with the strong
resonance of the AE sensors around 140 kHz. The low-frequency events of class 1 are mainly
characterized by this resonance which represents a stable peak frequency over the source-tosensor distance. Hence, frequency centroid shows only slight changes as well so that theWeighted peak frequency of events from class 1 stays stable over the source-to-sensor
distance.

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To quantify the reduction in Weighted peak frequency as a function of source-to-sensor
distance and thereby define limitations for source identification in CFRP, more specimens will
be included in future analysis.

5. Conclusion
In this study, the influence of source depth and source-to-sensor distance on acoustic signal
characteristics of damaging events in CFRP was studied. The specimens were designed topromote the generation of fiber breaking and transverse matrix cracking during quasi-static
tensile loading in various depths and distances to the sensors. The authors could show thatthe source depth as well as the source-to-sensor distance have a significant impact on thefrequency content of the acoustic signals. Regarding the influence of source depth, a variationof S0 to A0 ratio in relation to source depth was suggested to be the cause for the observedchanges in frequency spectra. Both modes vary in group velocity, wavelength and attenuation
behavior and therefore yield different frequency spectra. The role of the aperture effect wasunderlined in this context. In terms of source-to-sensor distance, the authors could show that
propagation distances of 60 to 80 mm are sufficient for misclassification of AE signals in the
CFRP specimens under investigation. In the future work, the limitations for damage
identification will be investigated in more detail in order to increase the awareness of theinfluences of source depth and source-to-sensor distance in AE analysis.

References:[1] Crivelli, D., Guagliano, M., Eaton, M., Pearson, M.R., Al-Jumaili, S.K., Holford, K.M., Pullin,
R., Localisation and identification of fatigue matrix cracking and delamination in a carbon fibre
panel by acoustic emission, Composites: Part B, Vol. 74, 2015, pp. 1-12
[2] McCrory, J.P., Al-Jumaili, S.K., Crivelli, D., Pearson, M.R., Eaton, M., Featherston, C.A.,
Guagliano, M., Holford, K.M., Pullin, R., Damage classification in carbon fibre composites
using acoustic emissions: A comparison of three techniques, Composites: Part B, Vol. 68,
2015, pp. 424-430[3] Gutkin, R., Green, C.J., Vangrattanachai, S., Pinho, S.T., Robinson, P., Curtis, P.T., On
acoustic emission for failure investigation in CFRP: Pattern recognition and peak frequency
analyses, Mechanical Systems and Signal Processing, Vol. 25, 2011, pp. 1393-1407[4] Kempf, M., Skrabala, O., Altstädt, V., Acoustic emission analysis for characterization ofdamage mechanisms in fibre reinforced thermosetting polyurethane and epoxy, Composites:
Part B, Vol. 56, 2014 pp. 477-483[5] Liu, P.F., Chu, J.K., Liu, Y.L., Zheng, J.Y., A study on the failure mechanisms of carbon
fiber/epoxy composite laminates using acoustic emission, Materials and Design, Vol. 37, 2012,
pp. 228-235[6] Martínez-Jequier, J., Gallego, A., Suárez, E., Juanes, F.J., Valea, Á, Real-time damage
mechanisms assessment in CFRP samples via acoustic emission Lamb wave modal analysis,
Composites: Part B, Vol. 68, 2015, pp. 317-326
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[7] Sause, M.G.R., Müller, T., Horoschenkoff, A., Horn, S., Quantification of failure
mechanisms in mode-I loading of fiber reinforced plastics utilizing acoustic emission analysis,
Composites Science and Technology, Vol. 72, 2012, pp. 167-174[8] Sause, M.G.R., Gribov, A., Unwin, A.R., Horn, S., Pattern recognition approach to identify
natural clusters of acoustic emission signals, Pattern Recognition Letters, Vol. 33, 2012, pp.
17-23[9] Godin, N., Huguet, S., Gaertner, R., Integration of the Kohonen’s self-organising map andk-means algorithm for the segmentation of the AE data collected during tensile tests on crossply composites, NDT&E International, Vol. 38, 2005, pp. 299-309[10] Baker, C., Morscher, G.N., Pujar, V.V., Lemanski, J.R., Transverse cracking in carbon
fiber reinforced polymer composites: Modal acoustic emission and peak frequency analysis,
Composites Science and Technology, Vol. 116, 2015, pp. 26-32[11] Maillet, E., Baker, C., Morscher, G.N., Pujar, V.V., Lemanski, J.R., Feasibility andlimitations of damage identification in composite materials using acoustic emission,Composites: Part A, Vol. 75, 2015, pp. 77-83[12] Oz, F.E., Ersoy, N., Lomov, S.V., Do high frequency acoustic emissions events always
represent fibre failure in CFRP laminates?, Composites: Part A, Vol. 103, 2017, pp. 230-235[13] Hamstad, M.A., O’ Gallagher A., Gary J., A wavelet transform applied to acoustic
emissions signals : Part 1 : Source identification, Journal of Acoustic Emission, Vol. 20, 2002,
pp. 39-61[14] AVK Expert task force: Standardization of continuous fibre reinforced thermoplastics[15] Kurz, J.H., Grosse, C.U., Reinhardt, H.W., Strategies for reliable automatic onset time
picking of acoustic emissions and of ultrasound signals in concrete, Ultrasonics, Vol. 43, 2005,
pp. 538-546[16] Neau, G., Lamb waves in anisotropic viscoelastic plates. Study of the wave fronts andattenuation, Phd Thesis, 2003[17] Calomfirescu, M., Lamb Waves for Structural Health Monitoring in Viscoelastic Composite
Materials, Phd Thesis, 2008[18] Su, Z., Ye, L., Identification of Damage Using Lamb Waves, Lecture Notes and Applied
Computational Mechanics, Vol. 48, 2009[19] Miller, R.K., McIntire, P., American Society for Nondestructive Testing, Nondestructive
testing handbook (2nd Edition), Vol. 5, 1987

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57
- Acoustic
Emission BEHAVIOUR
behaviour onON
CFRP
withWITH
curvedfibersACOUSTIC
EMISSION
CFRP
CURVEDFIBERS

Yoshihiro MIZUTANI1, Takeshi ASHIZAWA1, Nobuyuki TOYAMA2, Akira
TODOROKI1
1

School of Engineering, Department of Mechanical Engineering, Tokyo Institute ofTechnology, JAPAN2 Research Institute of Measurement and Analytical Instrumentation, National Instituteof Advanced Industrial Science and Technology (AIST), JAPAN
Abstract:
Application of CFRP to the industrial field is expanding and acoustic emission testing (AT) is utilized
for some applications. In this paper, possible problems that may cause when AE testing is applied to
CFRP components with curved fibers are discussed based on the previous author’s paper about
ultrasonic propagation in CFRPs. It was concluded that the AE amplitude has a potential to be highly
affected by the location of AE sensors on the components. It is also noted that general AE source
location algorithm cannot be applied for this kind of materials in some cases since shortest path fromAE source to AE sensor are varied with the position on the components

1. Introduction
Carbon fiber reinforced plastics (CFRP) are widely used for structural components inindustrial fields. Acoustic emission testing (AT) is sometimes used for structural healthassessment of CFRP components together with other inspection methods. As CFRP is
anisotropic material, AE propagation behavior is different from that in isotropic material.
Figure 1 shows propagation direction of phase velocities and group velocities of AE in
CFRPs when AE propagates as bulk waves. Relationship between phase velocities andgroup velocities of bulk wave can be found in the basic acoustic textbook, such as reference
1. Rhee et al. [2] reported that this relationship could be applied for lamb waves when Fd(Frequency×Thickness) value is less than 0.7 for S0-mode, and between 0.2-0.7 for A0mode. The authors investigated amplitude of Lamb wave AE that propagates in CFRP
according to this relationship [3].

AE wave
Fiber direction
Figure 1 Propagation direction of phase velocities and group velocities in CFRPs. Dotted arrowshows phase velocities direction, Solid arrow shows group velocities direction

1

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Figure 2 shows normalized amplitude distributions of Lamb wave AE generated with dipole
source in Cross-ply CFRP [0/90]2s with Fd=0.64 for S0-mode and =0.32 for A0-mode. Material
properties used for the calculation are shown in Table 1. As shown in the figures, amplitudesof Lamb wave AE highly change with propagation direction and this phenomenon is different
from that in isotropic materials.
1.2

1.0

0.8
0.6
0.4
0.2
0.0

0 10 20 30 40 50 60 70 80 90

Normalized amplitude

Normalized amplitude

1.2

1.0
0.8
0.6
0.4

0.2
0.0

0 10 20 30 40 50 60 70 80 90

Propagation direction [deg]Propagation direction [deg]Figure 2 Relationship between normalized amplitude and propagation direction of Lamb wave AE incross-ply CFRP generated with dipole source. (Left: Fd=0.64 for S0-mode,Right: Fd =0.32 for A0mode)[3]Table 1 Stiffness Matrix used for calculating amplitude distributions

IM7/977-3
T300/914
T700GC/M21

C11
178.2
143.8
123.44

C22
14.44
13.3
11.54

C33
14.44
13.3
11.54

C23,C32
8.119
6.5
6.35

C31,C13
8.347
6.2
5.55

C12,C21
8.347
6.2
5.55

C44
3.161
3.6
2.6

C55,C66
6.1
5.7
4.6
Unit: GPaOn the other hand, there are curved structures made of CFRPs as shown in Figure 3. There
are several published papers discussing elastic wave propagation behavior in CFRP withcurved fibers [4-6]. However, these studies focused on guided waves and the CFRP
specimens are curved with curvature of structures. Then the elastic waves are affected not
only by curved fibers but also by curved boundaries of structures. The authors investigatedelastic wave behavior of thick CFRP with curved fiber by both the numerical analysis and by
the experiment [7]. In this paper, the results from reference 7 are introduced and the effects
for the AE testing are discussed.

AE sensor？？
AE
Figure 3 Thick CFRP component with curved carbon fibers

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2. Numerical simulation in reference 7
In the reference 7, 2D analyses of bulk elastic wave propagation in CFRP with curved fibers
were conducted using the finite difference method. A plane strain condition was assumed.
The numerical simulations were performed in the cylindrical (r-θ) coordinate system toincorporate the curvature of the principal axis. The fiber direction is correspond with theθaxis of the cylindrical coordinate system. Figure 4 shows the analysis model. r1, r2, △r, △θ in the figure are 0.1[m], 1[m], 1.75×10-3[m] and 1.75×10-3[rad] respectively. Figure 5
shows description of AE source used for the simulation. Detailed condition of the simulationis shown in reference 7.

Figure 4 Analysis model of elastic wave in the cylindrical coordinate system [7]Figure 5 Discription the quasi-point source of sound [7]3. Amplitude of AE
Figure 6 and 7 shows simulation result from reference 7. The figures show the absolutevalue of displacement in color. Figure 6 shows results for isotropic aluminum specimen and
7 for CFRP with curved fibers which explained in chapter 2. Direction of continuous fibers
are shown in the figure. In the original our paper, only the wave propagation was focused
and acoustic emission issue was not taken into account. Then, in this paper, we assume
the point sources in the figures as the source of AE and discuss the amplitude dependence
to the location of AE sensors.
3

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As shown in the figure 6, when the material is isotropic aluminum, AE propagate
concentrically from the source. As a result, when the AE is detected by AE sensors, AE
amplitude (dBAE) is same when the distance between the source and AE sensor is constant.

AE Sensor(a) 8000 steps, 26.6 μs(b) 18000 steps, 59.8 μs

Figure 6 The result of the numerical simulation for aluminum. The point sound source was applied at
the center of the model [7]On the other hand, as shown in Fig. 7, when the material is CFRP with curved fibers, AE
generated from the source propagates under the influence of the curved fiber and does notpropagate concentrically. Though, AE amplitude detected by AE sensor highly influenced by
the location of AE sensor.

AE Sensor(a) 8000 steps, 26.6 μs(b) 18000 steps, 59.8 μs

Figure 7 The result of the numerical simulation for CFRP, T700GC/M21. The point sound source wasapplied at the center of the model [7]When the material is an isotropic material as shown in Fig. 6, the AE signal amplitudedepends only by the magnitude of the damage at the source and the distance from thesource to the AE sensor, so the AE signal amplitude is used as an important AE parameterfor evaluating scale of fractures at the source. However, when the material is an anisotropic
material as shown in Fig. 7, the AE signal amplitude varies greatly depending not only by the4

S94

propagation distance but also by the position of the AE sensor, so special caution must be
taken when using the AE signal amplitude for evaluating the damage scale.

It is also noted that, special care is required to detemine the location of AE sensor since AE
is difficult to transmit in directions other than along the fibers. For example, when
considering a CFRP component having curved fibers with drilled hole as shown in Fig.8, it isreccomended to attache the AE sensor at position A or B rather than position C and D as AE
propagates along fibers. Such consideration is particularly necessary when the direction of
the fiber does not follow the outer shape of the structure, and it is assumed that it will
become more important issue in the future as the number of continious fiber CFRP products
made with 3D printers increases [8].
Even the stacking sequence of CFRP components are quasi-isotropic, such as [0i/45i/90i/45i]sym., similar consideration may nesessary when the wavelengh of the AE is muchsmaller than the thickness of the components as AE propagates only the upper surface ofthe components.

C
AE
D

A

B

Figure 8 Suitable AE sensor locations (A and B) on CFRP with curved fibers

4. Source location of AE
Source location of AE is important issue for AE testing. Even the fibers in CFRP are straight,
special consideration is required for the accurate source location [9][10]. When the CFRP
components includes curved fibers, propagation path for the minimum arrival time becomes
not the straight line between AE source and AE sensors. It makes difficult to conduct source
location in such kind of the components. As in the previous chapter, consider a CFRP in
which fibers are arranged concentrically around the origin. Figure 9 comes from reference 11
from author’s research group and shows wave front of elastic waves when the waves are5

S95

generated not from the origin of the fibers. AE sensor and path-A and –B in the figure are
overwrapped with original figures. In this paper, the source of the elastic waves (indicated as“Point source of sound” in the figure) is considered as the source of AE. Dotted line in the
figure shows shortest path of AE from the source of AE.

0.5 m

When AE sensor is attached at the position in the figure and the material is isotropic, shortest
path to arrive AE sensor is path-A in the figure. In such case, it is easy to conduct the source
location by traditional source location algorithm. However, in this case (material is CFRP withcurved fibers), path-B becomes the shortest path for the AE. Since the shortest path fromthe source to the AE sensor changes complicatedly depending on the combination of thesetwo parameters, it is difficult to estimate the AE source accurately.

6KRUWHVWSDWK:DYHIURQW
3RLQWVRXUFH
RIVRXQG2ULJLQ

Path B

Path AAE Sensor
Figure 9 Shortest path for the AE in the CFRP with curved fibers [11]5. Conclusion
In this paper, based on the previous author’s paper of elastic wave propagation analysis in
CFRP with curved fibers, we raised the potential problems when the AE testing is conducted
for such kind of materials. Followings are main possible problems for the AE testing. It may
be required to solve these problems when we apply AE testing for CFRP with curved fibers
adequately.
1) AE signal amplitude depends not only by the magnitude of damage occurrence at the AE
source but also by the positional relationship between the AE sensor and the source2) For an isotropic material, the shortest path that AE reaches from AE source to AE sensors
is straight line connecting these two points, although, in a CFRP with curved fibers, shortest
path becomes curved line and the pass changes in complicated manner by the position
between the AE sensor and the AE source. Though, traditional source location algorism
cannot be applied in this case.
6

S96

References:[1] Auld, B. A., Acoustic fields and waves in solids, 1, 2: John Wiley&Sons Inc., 1973

[2] Rhee, S. H., Lee, J. K. and Lee, J. J., The group velocity variation of Lamb wave
in fiber reinforced composite plate, Ultrasonics, Vol. 47, 2007, pp. 55-63.
[3] Mizutani, Y., Miki, T., Suzuki, Y. and Todoroki, A., Amplitude of AE propagating inlaminated FRP plates, Progress in Acoustic Emission XVIII, 2016, pp.491-496.
[4] Towfighi, S., Kundu T. and Ehsani M., Elastic wave propagation in circumferential
direction in anisotropic cylindrical curved plates, J. Appl. Mech., Vol. 69, No.3, 2002,
pp.283-291.
[5] Leutenegger, T. and Dual J., Detection of defects in cylindrical structures using
time reverse method and a finite-difference approach, Ultrasonics, Vol. 40, 2002,
pp.721-725.
[6] Gsell, D. and Dual J., Non-destructive evaluation of elastic material properties inanisotropic circular cylindrical structures, Ultrasonics, Vol. 43, 2004, pp.123-132.
[7] Ashizawa, T., Mizutani. Y., Toyama, N., Todoroki, A. and Suzuki, Y., Numericalanalysis and experimental observation of ultrasonic wave propagation in CFRP withcurved fibers, Composite Structures, Vol. 194, 2018, pp.316-327[8] Matsuzaki, R., Ueda, M, Namiki, M., Jeong, T, Asahara, H, Horiguchi, K,
Nakamura, T., Todoroki, A. and Hirano, Y., Three-dimensional printing of continuousfiber composites by in-nozzle impregnation, Scientific reports, 2016, doi:
10.1038/srep23058[9] Yamada, H., Mizutani, Y., Nishino, H., Takemoto, M., Ono, K., Lamb wave source
location of impact on anisotropic plates, Journal of AE, Vol. 18, 2000, pp.51-60[10] Kurokawa, Y., Mizutani, Y., Mayuzumi, M., Real-Time Executing Source Location
System Applicable to Anisotropic Thin Structures, Journal of Acoustic Emission,
Vol.23, 2005, pp.224-232[11] Ashizawa, T., Numerical simulation to analyze ultrasonic waves in CFRP having
concentric fibers, NIMS Week 2017, academic symposium, 2017, T83

7

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73 - Use of AE for characterization of damage mechanisms

USE OF AE FOR CHARACTERIZATION OF DAMAGE MECHANISMS
in composite accumulator
IN COMPOSITE ACCUMULATOR
Oriane COLAS1, Catherine HERVE2, Christophe BRIANCON1, Alain HOUSSAIS3
1

CETIM, 74 route de la Jonelière, 44000 NANTES
2 CETIM, 52 Avenue Félix Louat, 60300 SENLIS

3 PARKER OLAER, 16 Rue de Seine, 92700 COLOMBESAbstract:
This study presents the results of a collective study performed at CETIM for industrials of the hydraulictransmissions profession. The purpose is to develop an accumulator made of composite material,
carbon fiber and thermosetting resin, and to characterize the behavior of this accumulator by both
static and cyclic pressure tests.
Knowledge of the damage mechanisms evolution occurring during these tests is studied by Acoustic
Emission monitoring combined by strain gage measurements.
The AE monitoring results are compared to the failure criteria used in the design model in order tocorrelate the different damage mechanisms occurring in each layer with the detected AE signals.
Further mechanical tests were carried out on samples taken from accumulators tested at differentpressure levels. Visual observations by video camera combined with AE data recording during thesetests validated the identification and the progression of the different damage mechanisms in different
layers.

1. Introduction
In the scope of hydraulic transmission needs, a study is currently conducted at CETIM tobetter understand damage mechanisms of accumulators made from carbon fiber-reinforced
thermosetting matrix. For that purpose, several accumulators and more specifically theirpressure envelopes, have been designed, manufactured and tested. This paper is mainly
focused on the results of the static tests. The tests were divided in two parts; operational
tests with proof testing at different pressure levels and residual mechanical characterization
with tensile tests on samples cut from tested accumulator. The aim of the tests analysis is tocharacterize the behavior of the structure during the proof test through AE monitoring andstrain measurement combined with ultrasonic inspection performed before and after the
proof test. The mechanical tests are then performed in order to give the residual strength inaxial direction and the damage mechanisms are observed with a camera combined with AE
monitoring.

1

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2. Description of work
2.1

Accumulator design

The accumulator is composed of a 5 mm plastic liner, a 6 mm layer of composite materialand 2 metallic nozzles. The working pressure of the accumulator is 300 bar. The composite
design is made up of 6 different orientation layers (circumferential and helicoïdal layers)
containing various ply number. Each ply is composed of epoxy resin and carbon fiber with afiber volume fraction of 50%.
Six accumulators have been manufactured by filament winding process.
2.2

Proof tests

Different proof pressure levels have been applied in order to analyze their influence on theaccumulator damage mechanisms. The tests have been performed at Ps (300bar), 1,5Ps (450bar) and 2Ps (600bar) (Table 1). The test device consists of a water booster with a range of
0-1000 bar. The proof test cycle used is presented Figure 1.
Accumulator reference

Proof test pressure (bars)

ESP8
ESP9
ESP12
ESP13

300
450
600
450
Table 1: Proof test description

Figure 1: Pressure test cycle

A strain measurement has been performed with 4 unidirectional strain gages located arounda circumference on the center of the accumulator (90° apart). The strain measured is thecircumferential strain.

2

S99

All the proof tests are monitored by Acoustic emission. Ten sensors are located on theaccumulator as described in Figure 2. They are 150kHz resonant sensors, coupled bycement to the composite surface.

Figure 2: Sensor implantation

Non-destructive inspection is performed before and after the proof test by phased arrayultrasonics.

2.3

Mechanical tests

The tensile mechanical tests are performed to determine the residual mechanicalcharacteristics in axial direction on tested accumulators. To do this, specimens are takenfrom the central part along the axis of the accumulator, their location being determined fromAE and ultrasonic inspection results .
The tensile tests are realized with a tensile test machine equipped with self-fastening
hydraulic clamps. Tabs are glued on the specimens to the specimens so as to obtain a flat
surface in contact with the hydraulic jaws. The test velocity is 1 mm/min according to theISO527-4, the strain measurement is performed by an extensometer and there is amonitoring by acoustic emission (Figure 3).
These tests do not stress the composite in the main direction of stress induced by internalpressure, i.e. in the circumferential direction. Nevertheless, they provide valuable informationon the pressure damage induced in the helicoïdal layers.

3

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Figure 3: Tensile test configuration

3. Results
3.1
3.1.1

Proof tests results
acoustic emission analysis

The first step of the analysis is to evaluate the AE signal evolution during the tests in terms ofactivity (hit number) and intensity (hit amplitude or energy).
-

Global analysis with acoustic energy parameter

It can be seen on Figure 4 that signal energy for high amplitudes signals (superior to 85 dB)
increases regularly with the pressure with local acceleration around 150 to 200 bar and after
400 bars (cumulative energy slope changes suddenly after these steps).

Figure 4: AE cumulative energy for high amplitude signals

4

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These punctual accelerations can be linked to the progression of the damages during thepressure holds. The acceleration for high amplitude signal is sudden compare to lower
amplitude signals acceleration (Figure 5). This can be explained by the fact that highamplitude signals correspond to high energy damage phenomena such as fiber strand
breaks, which occur randomly.
Another observation linked to the signal amplitude is that for the 4 accumulator the signal
amplitude doesn’t exceed the following values:

Figure 5: AE cumulative energy for medium (brown) and low(green) amplitude signals- 70dB for the 100 bar increase- 80 à 85dB for the 200 bar increase- 90dB for the 300 bar increase- 95dB for the 400 bar increase
These characteristics have been observed on all the 4 accumulators. What can be also
noted is that the accumulators have the amount of acoustic emission activity related to thesame pressure level. The results for 400 bars are shown below in Figure 6.
At 400bar :18.10^7 [Energy Unit]
3.10^7 [EU]At 400bar :15.10^7 [EU]2.10^7 [EU]At 400bar :24.10^7 [EU]2.10^7 [EU]Figure 6: AE cumulative energy for low and medium amplitude signals

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Given the energy evolution and the signal amplitude progression, these two parameters
could be linked in order to define a damage starting point. This will have to be consolidated
by further tests leading to critical damages and their impact on signal energy level.
-

Local analysis

Zonal analysis makes it possible to identify the areas where acoustic emission isconcentrated. In the conducted tests, several areas where identified, principally on thebottom shell junctions and on the center of the shell (Figure 7). These observations helped
us to select the mechanical tests and micrographic observation samples.

Figure 7: Zonal analysis of the accumulator ESP12

Pressure holds analysis
The AE activity during the different holds(10 min for intermediate holds every 100 bar and 30
min for the final hold) has been analyzed. From the 400 bar steps, the acoustic activity
doesn’t decrease during the holds but has a stable level. No activity increase was detectedduring the holds, which means that there is no significant damage progression occuringwhen the accumulator is stabilized in pressure.
3.1.2

ultrasonic testing analysis

The ultrasonic inspection of the 4 accumulators showed that there was no significant
evolution of the indications initially detected, knowing that the purpose of the inspection is todetect defects such as delamination. Moreover, no significant defects created by the proof

S103

test was detected, which means that proof test until 600 bar doesn’t lead to severe damages
between the composite layers.
3.1.3

micrographic analysis

For the accumulator tested at 300 bar, matrix cracking is observed in the helicoidal layers. A
more severe damage is noted in the layer ±10° compare to the ±30° and ±55° layers. The±10° layer presents over the thickness cracks whereas discontinuous cracks are in the ±30°
and ±55° layers (Figure 8).
For the accumulators tested at 450 bars, the cracking state of the ±10° layer is more severe
than for the 300 bar accumulator, particularly due to cracks appearing through all the
thickness (Figure 8). The cracks from the ± 30° et ±55° seem also more developed in lengthand density.
As for the 600 bar accumulator, the cracking in the ±10° layer is comparable to the one
present in the 450 bar accumulator. The cracks in the ± 30° ±55° layer are also moredevelopped.
± 55°

Micrographic observations location

a

± 30°

b

± 10° cFigure 8: Micrographic observations

3.2

3.2.1

Mechanical tests results

residual strength

The tensile tests show a decrease of 19-22% of material stiffness after the tests at 300 and
450 bar. This can be explained by the transverse cracking observed in the helicoidal layers.
7

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The stiffness decrease doesn’t generate a decrease on the residual strength which meansthat the high energy AE signals detected before 450 bars don’t affect the breaking load.
3.2.2

damage mechanisms

During the tensile tests, a monitoring by AE and a camera gave us information about thechronology of damage occurring during the load increase.
What has been observed thanks to the camera is that the onset of damage occurs withtransverse cracking in the ±90° plies around 57MPa (13kN) (Figure 9). Then these cracks
multiplicate and at 271MPa (65kN) the damage of intern plies oriented at ±10° begins.
Internal damage continued to propagate until the final failure at 340MPa (82kN). Then, adelamination propagates along the oriented plies. All the specimen tested (x21) have asimilar behavior.

Figure 9 : Tensile test damage mecanisms Top:damage visual observation Bottom: loadincrease

The AE data have been analyzed in parallel and what can be noted is that before the final
failure of the specimen, two AE accelerations occur. The first one happens around 10kN andthe second one between 70 and 80kN (Figure 10). These two accelerations could be linked
with the initiation of damages in the ±90° and ±10° layers as described above.

8

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Figure 10 : AE energy cumulate for tensile tests

4. Discussion

The different results obtained by the tests leaded were brought together with the design
criteria, the Hashin criteria. Hashin criteria is used to determine the criticity of tensile loads
for unidirectional fibers [reference?]. The criteria allow to differentiate the damage
mechanisms of:-tensile fibre breakage-

compression fibre breakage-

tensile matrix breakage-

compression matrix breakage

Once the value of the criteria reaches 1 for each damage mechanism, the break is likely tooccur. For 300 and 450 bars levels an evaluation of this criteria has been realized.
At 300 bar, this criteria shows the following:
The helicoidal ply ±10° is in a breaking state which means that the fibre transversal strain
exceed the layer transversal strain. The ±30° layer has a high probability of throughthickness cracks. The ±55° has also a probability of cracks presence.

9

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Figure 11 : Overview of accumulator layers with Hashin criteria evaluation for a proof test pressure of 300 bar

This Hashin criteria observations can be linked to the experimental results. Indeed, the AE
data showed that an acceleration of acoustic activity took place around 200 bar which meansthat some damage mechanisms have already appeared and are propagating until reaching
the proof test pressure. In addition to that, micrographic observations validate the fact that
there is damage in the ±10° layer but also in the ±30° and ±55° layers.

At 450 bar, the criteria has evolved:
The helicoidal ply ±10° must have several through thickness cracks, and the first throughthickness cracks appear in the ±30° layer. In the ±55° layer the cracks are longer anddenser.

Figure 12: Overview of accumulator layers with Hashin criteria evaluation for a proof test pressure of 450 bar

In the case of the accumulator tested at 450 bar, the experimental results can also be linked
with the Hashin criteria. In micrographic observations cracks through the thickness of thelayer at ±10° are more numerous. In the ±30° and ±55° layers, cracks are denser and have ahigher length than in the accumulator tested et 300 bar. Moreover, AE signal amplitude are
getting higher which may correspond to the multiplication of through thickness cracks.

5. Conclusion
The present project has given us an approach on how to study the behavior under pressureof a composite material accumulator . As far for the Acoustic Emission analysis is concerned,
the energy criteria have given us important information about the evolution of the damages
during the proof tests. Moreover, the possibility to locate the areas where the activity was themost intense was also an asset to evaluate the zones with the highest stress and to analyze
10

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them after with micrographic observations and tensile tests. AE provides valuable information
for the manufacturer in evaluating the safety and the residual life of this pressure equipment.
The micrographic observations were useful to link the AE observations with the Hashin
design criteria evaluation and so to the actual damage mechanisms occurred during theproof tests. (It gave a good information of what had been induced by the proof tests).
The possibility of performing tensile tests allowed to give information of the criticality of thedamage present in the accumulators.

11

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40 - ContinuousrecordingwirelessCON
NTINUOU
US RECOORDING
ANDandWIIRELESSS transmission
TRANS
SMISSION
N OF
of AE FORMS
waveforms
battery
poweredsensornodes
AE WAVETERY POOWEREDD
SENSO
OR NODE
ES
BY by
BATT
Kaita Itoo1, Manabu
u Enoki2
1

Nattional Instittute for Ma
aterials Science
2
The U
University ofo Tokyo

Abstraact:
AE streaaming i.e. continuouscreecording of AE waveforrm is effectiv
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environm
ment such ass materials processing
pb
because an optimized digital noise fiilter can be designed
from the real measurrement result. For real-tim
me denoisingg of AE strea
ams, a PC-leevel high perfformance
CPU andd a number of cables arre needed fo
or power sup
pply and com
mmunicationn for conventtional AE
streaminng. Howeverr, complicate
ed cabling aamong the measuremenmt equipmentt, amps andd sensorsoften disturb the materialsmproocessing. Th
herefore, a wireless AE
E streamingg system is strongly
demandeed. In this study,ssuch a wireless ssystem was developed. A battery-poowered sma
all sensor
node continuously acquiresa
AE waveform aand transmits
s it to a base station viaa broadband wirelessnetwork such as IEE
EE802.11ac (Wi-Fi).
(
The whole wavefform analysis
s is conducteed in the bas
se station
which inncludes a hiigh performa
ance compu
uter. An in-p
process defe
ect monitorinng during friiction stir
welding (FSW) was conducted asa a test of tthe develope
ed system. AE
A sensors were attached to the
FSW maachine and moved on the specime
en. Four cha
annels AE streamswith 4 MHz of sampling
frequenccy and 14 bitt resolution was
w acquiredd, transmitted
d and analyz
zed in real-tim
me. AE evennts due tojoining d
defect were successfullyy detected aand located.. The measu
urement noiise in the developed
wireless sensor nodee was much lower than tthe conventio
onal wired syystem becauuse the sens
sor nodes
were ele
ectrically insu
ulated.

1. IntroductionnSince thhe AE methhod detects the occurreence of mic
crofracture inside the mmaterial in reeal time,
it is an effective methodm
not only for hhealth monitoring of sttructures buut also for processmonitorring of mateerial manufa
acturing. Co
ontinuous acquisition i.e. streaminng of AE wa
aveforms
is effecttive for noisse reductionn and AE e
event detection under noisy enviroronments. For
F morethan a decade, th
he authors have deve
eloped “Conntinuous Wave
W
Memoory” (CWM)) system
which sspecialized for measurrement and analysis ofo continuou
us AE waveeforms, connsideringthe importance of streamingsinn process mmonitoring.[[1][2] Furthermore, it hhas been ap
pplied tovarious materials manufacturing processses includ
ding thermal sprayingg [2] and welding.
Howeveer, since the equipmen
nt for mateerial manufa
acturing pro
ocess oftenn has manyy moving
parts annd large mo
onitoring ob
bject, it is t roublesome
e to handle a large nuumber of ca
ables forpower supply andd signal traansmission between the AE measuring ddevices, am
mps andsensorss. Therefore
e, the authors have devveloped wireless CWM
M.
Although the wireless AE meeasuring deevices alrea
ady exist, th
hey were ddifficult to dealdwithcontinuo
ous wavefo
orms. In recent years , calculation efficiencyy (performaance per watt)
w
and
1

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wirelesss communiccation perfo
ormance of small computers for IoT (Interneet of Thingss) device
are dram
matically im
mproving. However, an alysis of co
ontinuous AE
A waveform
ms is still tooo heavy
load forr such small computerr. Therefore
e, in order tot realize wirelessw
CW
WM, perform
mance ofhigh sppeed wirele
ess communication wa
as expecte
ed. The wirreless senssor node i.e. slave
machinee only perfo
orms contin
nuous wave
eform meas
surement and separateed from thee master
unit withh sufficient computing performancce and stora
age capacityy.

2. Developmen
nt of senso
or node off wireless CWM
AE streeaming is rooughly divid
ded into thrree steps asa acquiringg, recordingg, and anallyzing ofcontinuo
ous wavefo
orms. Since the AE wa
ave generally includes signal com
mponents off about 1
MHz at the maximum, at leasst 2 MHz off sampling frequencyfiss required. The resolution of atypical A
A/D (analo
og-to-digital)) converter is 10 to 16
1 bits i.e. one samplle data is 2 bytes,
thereforre the requuired data rate is 4 M
MB/s/ch. Th
his data ra
ate can be sufficiently
y written
continuo
ously even with an inte
ernal storagge of a sens
sor node likees microSD
DHC card. However,H
it is nott practical tot store con
ntinuous AE
E waveform
ms with sev
veral hundreeds of giga
abytes in
one meeasurementt in memory cards fo
or each seensor nodee. Next, coonsider ana
alysis ofcontinuo
ous AE wa
aveform. Wh
hen digital noise filtering with a large amouunt of calcu
ulation isconductted by thee built-in CPUCof thee sensor node, the powerp
conssumption becomesbunaccep
ptably largee. On the otherohand, power con
nsumption canc be reduuced by using DSP(Digital Signal Proccessor) or FPGAF
(Fieldd-Programm
mable Gate Array) for ffiltering. How
wever, itis difficu
ult for thesee processors to changge the anallysis metho
od frequentlly. Finally, considerctransmisssion of con
ntinuous AE
E waveform
ms. The receent high spe
eed Wi-Fi (I EEE 802.11
1ac) canobtain aan effective
e speed of 25
2 MB/s (2200 Mbps) evenewith a low theoreetical speed
d of 433
Mbps. S
Since four accessapoin
nts with no frequency bands overrlapping aree allowed inn Japan,continuo
ous transm
mission of several teens of channels of continuousc
AE wavefforms is
estimate
ed to be possible.
From thhe above consideratio
on, the wi reless sensor node onlyoacquirres the continuous
waveforrm. Wavefo
orms are traansmitted too the master unit in rea
al time via W
Wi-Fi, record
ding andanalysiss are performed in this master u nit. By usin
ng the conv
ventional C
CWM as thee masterunit, thee developm
ment man-hhours are kkept down and high compatibilitycy with conv
ventional
wired CCWM system
m is maintained. An IoT
T board ST
TEMlab 125-14 board bby Red Pita
aya team
was adoopted as thee sensor noode. Figure 1 shows a schematic diagram of the data flo
ow of theo of the wirreless senso
wirelesss CWM system, figure 2 is a photoor node andd table 1 shhows thespecifications of th
he wireless sensor nod
de. Because
e the A/D convertercis high resolu
ution (14bits) annd the volta
age range cancbe narrrow as ± 1 V, preamp
plifier was nnot necessa
ary. The
highest sampling frequency
fofo the built--in A/D con
nverter of STEMlabS
1225-14 is 12
25 MHz.
Howeveer, this is the instantaaneous highhest speed which valid only for a short tim
me burst
transferr. Then, a preliminary
y experime
ent was co
onducted too investigaate the upp
per limit
frequency for continuous meeasurementt without anyadata loost. As a reresult, the effective
maximuum was abo
out 4 MHz (1125 MHz / 332). The sampling data
a was acquiired from th
he FPGAby ARM
M CPU andd transmitte
ed to the mmaster unit by IEEE 80
02.11ac Wii-Fi. At thiss time, a
microSDDHC card was used as a cacche in the sensor noode to preepare for temporalt
destabillization of wirelesswcom
mmunicatioon in enviro
onments witth many meetal equipm
ment andother W
Wi-Fi devices like factory and labo
oratory. Thee acquired continuousc
AE waveform wererecordeed and analyyzed with In
ntel CPU onn the master unit.

2

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Figure 1. Schemattic diagram oof the data flo
ow of the wirreless CWM system

Figu
ure 2. Photoo of the wirele
ess sensor noden
(STEMlab125-14 booard)
Table 1.
1 Specificattions of the wirelessw
sens
sor node

ChannelsMax. Sampling
S
Freq.
F
for conntinuous reccording
Voltage
V
rang
ge
A/D
A resolutioonPow
wer consum ption
Footprint
Weighht (without bbattery)

3

S111

2 ch
about 4 M
MHz
±1 V or ±±20 V
14 bitt
3.5 W
107 mm × 660 mm
90 g

3. Demonstratiion Experiiments of wireless CWM
CFor demonstrationn of the wirelessw
CW
WM, AE measuremenmnt during F
FSW (Fricttion Stir
Weldingg) was perfo
ormed. Fig
gure 3 show
ws a schema
atic diagramm of the FS
SW experiment. The
specimeen was two flat plates of flame-ressistant mag
gnesium allo
oy with 2000 mm long × 70 mm
wide × 2.0 mm thick. A rotating steel too
ol was inse
erted into the specimenn and moveed along
the buttting line. During
D
the FSWF
processs, specime
en softenedd in the soolid phase andawasjoined b
by plastic flo
ow. At this time, if therre was exce
ess or defic
ciency in heeat input, a welding
defect ooccurred an
nd AE was generated.. Because ofo the heat due to thee welding, fo
our heat
resistannt AE sensoors (type AE
E254SMH17
77 by Fuji Ceramics)
C
werew
placedd so as to surrounds
the wellding part. These sennsors were fixed to th
he FSW toool holder aand moved
d on thespecimeen while addsorbed to thet sample by magnetic force of thet neodym
mium magne
et. Ch. 1
and ch. 2 were connnected to thet conventtional wired
d CWM via the type 99913 preamp
plifier (20dB ampplification) manufacturemed by NF ccorporation,, ch. 3 and ch. 4 weree connecte
ed to thewirelesss sensor noode. The sampling freq uency was set to abou
ut 2 MHz (1 25 MHz / 64)
6 and itwas weell above thee sensor's resonantr
freequency of 250 kHz. AfterA
the coontinuous waveformw
recordinng, the samme high-pas
ss filter withh a cutoff freequency of 100 kHz w
was applied to eachwaveforrm on the wiredw
and wiireless sidees.
A typica
al waveform
m of the AE event part is shown inn figure 4. AlthoughAth e absolute value ofthe ampplitude variied depend
ding on the presence or absence
e of the am
mplifier, alm
most thesame w
waveform was
w acquired. A notablle differenc
ce between the wired and wireles
ss sideswas thee presence or absence
e of electrica
al noise. In wired measurement, eelectrical nooise waslikely too intrude intto the A/D convertercb ecause thee all electric
cal devices i.e. sensorss, amps,
PC for CWM and even the FSW
F
mach ine were electricallyeconducted
cvvia the com
mmercial
power ssupply line. In additionn, when ele
ectric noise invaded the AE sensoor and vibrated thepiezoele
ectric elemeent, noise withw a wavveform simillar to an AE event thaat rapidly roserandgradually attenuate
ed was gen
nerated. Onn the other hand, in th
he wireless measurem
ment, thesensor node was touching on the specim
men via the sensor, butt no electriccal noise wa
as foundsince thhere was noo electrical pathpthroughh the senso
or node.

Figure 3.
3 Schematicc diagram of the FSW experiment

4

S112

F
Figure 4. Typpical waveforrm of an AE event recordded by (a) wired and (b) w
wireless CW
WM

4. Co
onclusionAbbattery-poweered wireless sensor nnode with AE
A streaminng function was develo
oped. All
wavveforms witth 16 MB/s of data ratee (2 ch, 4 MHz,M
2 Bytes/sample) per node could
c
be
tran
nsferred to thet master unit via Wi--Fi.



Monitoring of FSW
F
process could bee performed
d with high S/N ratio ssince the deevelopedwireeless senso
or node wass not easily invaded byy electrical noise.
nAcknow
wledgeme
ent:
This ressearch wass supportedd by JSPS KAKENHI (Grant-in-A
Aid for scienntific research) No.
17K148820 and ressearch grants from NE
EDO, the MO
ORITANI Foundation aand the Lig
ght Metal
Education’s Found
dation. Alsoo, the autho
ors are gra
ateful to Mr. Makoto Katayama and Mr.
Kazuki Takahashii of the UniversityUoof Tokyo and Dr. Eitaro
E
Yukuutake of Industrial
Technology Innova
ation Centerr of Ibaraki Prefecture

Refereences:[1] Ito, K., Enoki, M.,
M Acquisition and An alysis of Co
ontinuous Acoustic
A
Emiission Wave
eform forClassificcation of Dam
mage Sources in Ceram
mic Fiber Ma
at, Materials Transactionns, vol. 48, 2007,2
pp.
1221-1226.
[2] Ito, K
K., Kuriki, H., Araki, H., Kuroda, S., Enoki, M., Detection
Dof Segmentatioon Cracks inn the Top
Coat of T
Thermal Barrrier Coatingss during Plassma Spraying by Non-Co
ontact Acousstic Emissionn Method,
Science and Techno
ology of Adva
anced Materiials, vol. 15, 2014, pp.035
5007.

5

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01 - Modal Acoustic Emission analysis of mode-I

MODAL ACOUSTICOF MODE-I
AND MODE-IIand mode-IIEMISSION
fracture ofANALYSIS
adhesively-bonded
jointsFRACTURE OF ADHESIVELY-BONDED JOINTS
Alasdair R. Crawford, Mohamad G. Droubi, Nadimul H. Faisal
SCHOOL OF ENGINEERING, ROBERT GORDON UNIVERSITY, GARTHDEE
ROAD, ABERDEEN, AB10 7GJ, UK

Abstract:
Acoustic emission (AE) testing has previously been demonstrated to be well suited to detecting failurein adhesively-bonded joints. In this work, the relationship between the fracture-mode of adhesivelybonded specimens and the acoustic wave-modes excited by their failure is investigated. AE
instrumented Double-Cantilever-Beam (Mode-I fracture) and Lap-Shear (Mode-II fracture) tests are
conducted on similar adhesively-bonded aluminium specimens. Linear source-location is used to
identify the source-to-sensor propagation distance of each recorded hit, theoretical dispersion curves
are used to identify regions of the signal corresponding to the symmetric and asymmetric wave modes,
and peak wavelet-transform coefficients for the wave-modes are compared between the two fracturemodes. It is demonstrated that while both fracture-modes generate AE dominated by the asymmetricmode, the symmetric mode is generally much more significant during Mode-II fracture than Mode-I.
While significant scatter and overlap in results prevents the ratio of peak-wavelet transform coefficientsfrom being a robust single classifier for differentiation between fracture-modes in most cases, othermodal analysis methods, or integration of this parameter into multi-parameter methods in future work
may result in more reliable differentiation. Understanding of the wave-modes excited by the different
fracture-modes also has implications for source-location, as identification of the correct modes is criticalfor selection of suitable wave velocities.

1. Introduction
Structural adhesive bonding is increasingly being utilized across a wide range of industries,
such as the aerospace, renewable energy, marine and automotive industries. Adhesives offer
a variety of advantages over more conventional mechanical fastening methods, including
improved stress distribution, low weight, corrosion resistance, damping properties and theability to join dissimilar materials and composites. They are however susceptible to defects
introduced throughout manufacture and service life which can lead to catastrophic failure.
These defects can include disbonds or weak “kissing” bonds, introduced by surface
contamination of the adherends, voids, due to inadequate quantities of adhesive or air
entrapment during lay-up, porosity, due to volatiles or entrained air, cracks, due to thermal
shrinkage or applied stressed in service, and poor cure, occurring from improper mixing or
inadequate thermal exposure [1].
Due to these potential defects, non-destructive testing (NDT) and condition monitoring is vitalif adhesives are to be used in safety critical applications. Acoustic Emission (AE) testing has
been well proven in its ability to detect adhesive failure and, particularly for large structures,the ability to provide continuous real-time monitoring over a large area is advantageous andmakes AE an appealing technique to complement more conventional NDT techniques, suchas ultrasound and resonant-frequency based methods.
1

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Multiple studies of adhesively bonded joints have demonstrated the ability of AE to detect
initiation and propagation of debonding and adhesive cracking through the correspondence
between AE and drops in load during various fracture tests [2-5], while studies such as thoseby Croccolo and Cuppini [6] have demonstrated the ability to predict the final failure load of a
joint, based on the acoustic emissions at lower load. Differentiation between debonding andadhesive cracking has also been achieved by Galy et al. [3] through clustering based on typical
AE parameters, while work by Bak and Kalaichelvan [7] used peak-frequency analysis todifferentiate between the failure mechanisms of adhesive failure, light fiber tear failure andfiber tear failure during lap-shear tests of composite specimens.
Dzenis and Saunders [8] successfully utilized the statistical pattern recognition software Vallen
VisualClass to differentiate between Mode-I (crack opening), Mode-II (shear) and Mixed-mode
fatigue failures of adhesively bonded joints. While it was demonstrated that differentiation
between fracture-modes was possible, the method used, and results reported provided little
insight into the fundamental differences between the recorded waveforms. The differentiation
between fracture-modes is extremely valuable in the case of adhesive bonds, as there is vastdisparity in the strength of bonds dependent on the loading orientation. For this reason, mostadhesively bonded joints are designed to be loaded predominantly in Mode-II, and applicationof unexpected Mode-I loadings may therefore lead to catastrophic failure. The study will lookfurther into the differences in AE occurring from different fracture-modes.
It has been demonstrated by Gorman [9], amongst others, that the orientation of an AE source
affects the amplitudes of the wave-modes propagating from it. In-plane sources are seen to
create a greater extensional/symmetric wave-mode, while out-of-plane sources are seen to
create a greater flexural/asymmetric wave-mode. This finding has been previously used inmodal AE analysis of composites to aid in differentiation between delamination (out-of-plane)
and fiber-breakage or matrix cracking (in-plane) [10-11]. Understanding of the wave-modes
generated is also crucial for accurate source location. Due to differing propagation velocities,
it is critical that velocities used in calculating source-location correspond to the wave-modes
for which arrival times have been detected.
The aim of this study was to investigate the wave-modes generated by Mode-I and Mode-II
fracture of adhesively-bonded joints and to identify whether modal analysis has the potential
to discriminate between fracture-modes in a similar manner to which it has been used todiscriminate between failure mechanisms of composites. To achieve this, AE instrumented
Double Cantilever Beam (DCB) (Mode-I) and Lap-Shear (LS) (Mode-II/Mixed-mode) tests
have been conducted on adhesively-bonded aluminum specimens and the resulting AE
analysed, using continuous wavelet-transforms and theoretical dispersion curves to identify
the resulting wave-modes.

2. Experimental Setup
2.1

Specimen preparation

Both specimen types were manufactured from 3.175 mm x 50 mm HE30TF aluminium bar.
Adherends were cut to 300 mm long for the DCB test, and 360 mm long for the lap shear test.
Specimen widths and lengths were chosen to be as large as was practical for the available
test equipment, to minimize the effects of edge-reflections in the results, and to allow significant
propagation distance for the wave modes to separate through dispersion. The adhesive
bonding process consisted of surface preparation, adhesive application and curing. The
specimens were initially rinsed with acetone, before being abraded with P400 grade abrasive2

S115

paper, rinsed with acetone again, and then cleaned with Loctite SF 7063. Silicone grease wasthen carefully applied to a 60 mm long region of the DCB specimens to prevent bonding andthus create a pre-crack. Loctite EA 3430, a relatively brittle two-part epoxy adhesive, was thenapplied through a mixer-nozzle to the bond areas of one adherend for each of the specimens.
The bond area for the DCB specimens covered the entire specimen, aside from the pre-crack,
while the bond area for the shear specimens was a 50 mm x 50 mm square, located 70 mm
from the ends of the adherends. Small 0.5 mm thick aluminium shims were then added into
the adhesive to maintain a uniform bond thickness as the other adherends were placed on top.
Once assembled, weights totaling 4 kg were added on top of each specimen and they wereleft to cure for a minimum of five days, at an average temperature of 19°C and humidity of
about 20%.
2.2

Mechanical testing

Both types of specimen were tested using an Instron 3382 universal testing machine (UTM),
controlled through BlueHill 3 software. Loading blocks were bonded to the DCB specimens toallow them to be mounted to the machine in custom made yokes. Lap shear specimens hadtabs bonded to each end and were then clamped into the machine using 50 mm mechanicaljaws. The loading rates used were 0.5 mm/min, based on ASTM D5528 – 01 [12], and 1.3
mm/min, based on ASTM D1002-10 [13], for the DCB and lap shear specimens respectively.
DCB tests were run up to a crosshead displacement of 10 mm, while lap shear tests were run
until complete failure was achieved. Each test was conducted four times to ensure
repeatability.
2.3

Acoustic emission setup

Two Physical Acoustics Micro80D differential sensors were used. These were connected to a
PC with a NI PCI-6115 DAQ through; Physical Acoustics 2/4/6 variable-gain pre-amplifiers (setto 60dB), an in-house built signal-conditioning unit (providing an additional 12dB gain), and a
NI BNC-2120 shielded connector block. The system was operated through LabVIEW software,with the signals being recorded continuously at 2.5 MHz and saved in *.tdms format. Signal
processing and analysis was conducted after testing, using MATLAB.
The sensor locations used were both on the same sides of the specimens. On the DCB
specimens, as indicated in Figure 1, one sensor was located at the end of the pre-crack, andthe other 10 mm from the end of the specimen. On the lap shear specimens, illustrated in
Figure 2, the sensors were located either side of the bond area, at distances of 90 mm and
200 mm away from the center of the bond area. Sensors were coupled to the specimens witha layer of silicone grease and secured using aluminium adhesive tape. As well as recording
AE, a video camera was used to record crack propagation for the DCB test to verify the positionof the crack-front. This approach could not be used effectively for the shear specimens due tothe speed of their failure.

3

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Figure 1. Double Cantilever Beam experimental schematic(not to scale)Figure 2. Lap Shear experimental
schematic (not to scale)

3. Signal processing
As the system was recording continuously, the initial step was to isolate hits for further analysis
and to discard the noise. This was achieved by averaging the RMS value of the signal over
200 data points, to create something similar in form to the upper wave envelope, and thenapplying upper and lower thresholds. Hits of significant amplitude were identified by crossingsof the upper threshold. The start and end of the hit were then identified by the nearest crossingsof the lower threshold before and after this point. Threshold values were set at 0.05 V and 0.15
V, chosen based on the level of noise present in the recorded signals.
The identified hits were transformed into the time-frequency domain by continuous wavelet
transform. In this case the Gabor wavelet was used, as this provides the best combination oftime and frequency resolution. An example of this transformation from time to time-frequency
domain is illustrated in the upper panels of Figure 3. Arrival times, corresponding to the A0
mode at 300 kHz, were determined as the first peak in the 300 kHz band of the wavelet
transform to exceed 70% of the maximum WT coefficient. These arrival times, and theseparation distance between the sensors, were used to estimate the linear source location ofeach hit, and therefore the propagation distance from the source to each of the sensors. Hits
located as occurring from outwith the potential bond regions of the specimens were excluded
from further analysis.
Based on the identified propagation distances, the arrival times of both the S0 and A0 wavemodes are calculated using the theoretical dispersion curves for the adherends (generated byVallen Dispersion software). The central panel of Figure 3 shows these dispersion curves,modified by the propagation distance, overlaid on the wavelet transform plot of the signal. This
allows certain peaks in the wavelet transform plot to be attributed to these wave-modes.
To allow quantitative analysis of the contributions of each wave-mode, the corresponding
peaks within a certain frequency band were extracted. The frequency band around 300 kHz
was chosen as it is close to the resonant peak of the sensor and contains significant content
from both wave-modes. There is also significant enough dispersion at this frequency todifferentiate between the wave-modes in the time-domain. The lower panel of Figure 3 shows
4

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the WT coefficients in the 300 kHz band with the S0 and A0 peaks marked. The ratio between
the amplitudes of these peaks was then used to investigate the difference between thefracture-modes. In a small number of hits the wave-modes could not be clearly identified or
separated due to factors such as overlapping of hits, in such cases the hits were excluded fromfurther analysis.

Figure 3. Example of signal-processing method. Top: Original AE signal. Middle: Wavelet Transform
plot with overlaid dispersion curves indicating symmetric (S0) and asymmetric (A0) wave-modes.
Bottom: Wavelet Transform coefficients for 300 kHz frequency band, with S0 and A0 peaks marked.

4. Results
4.1

Load results

Typical examples of the loading curves and AE source location results for the tests are shownin Figure 4. As expected, the loading characteristics of the two specimen types varysignificantly. The DCB tests result in an approximately linear increase in load as the adherends
deflect elastically in the pre-crack region until the maximum load is reached. This is followedby multiple small drops in load as the adhesive fails in sections, and small rises in load between
these, as the adherends elastically deform again. The lap-shear tests however, exhibit an5

S118

approximately linear region of elastic deformation up to their maximum load, followed bysudden complete failure in which the adherends completely separate. The maximum loads
withstood by the two specimens should be noted, as the DCB specimens withstood loads inthe region of only 50 N, while maximum loads applied to the lap-shear specimens were in theregion of 2000 N. This disparity highlights the potential importance of being able to discriminate
between fracture-modes during condition monitoring.
4.2

Failure mechanisms

The main failure mechanism observed in all specimens was adhesive failure, with the adhesive
layer separating from one adherend as the bond between adhesive layer and adherend failed,while remaining bonded to the other adherend. In the DCB specimens, and to a lesser extent
in two of the lap-shear specimens, some adhesive cracking was also found. In some regions
the adhesive failure would occur at the interface with the upper adherend, and in other regions
it would occur at the lower adherend. The result being that the adhesive layer cracked between
these regions, allowing sections of the adhesive to remain attached to either adherend. Figure
5 shows annotated examples of the failure mechanisms present in both types of specimen.
4.3

Acoustic emission source locations

In both specimen types, a small number of hits were recorded during the initial linear portionof the loading. Most hits however, correspond to significant drops in load as the adhesive fails.
In the DCB specimen hits are spread throughout the test as crack slowly propagates throughthe specimen, whereas in the lap-shear test, AE activity is concentrated around the momentof final failure. The AE source locations identified in the DCB tests generally correspond wellwith the visually observed crack-front recorded with the video-camera, with the hits initiallybeing centered around the tip of the 60 mm pre-crack and then progressing further along thespecimens as the crack opens. While the AE source location results generally correspond well,there is some variation and scatter which is believed to be due to a combination of the following
factors; the crack-front will not be uniform due to the inhomogeneous bond quality, so thelocation of the crack-front recorded at one side will not necessarily be accurate through theentire specimen width. The use of a linear source-location method, as opposed to 2D or 3D,
will also generally result in a small level of error as not all hits will occur directly between thesensors. In the lap-shear specimens hits are concentrated within the bond area, as would be
expected, although some hits are identified as occurring out with the bond area. This may
again be due to the limitations of linear source-location but may also be due in some cases tothe incorrect identification of arrival times due to interference between overlapping hits, aproblem which can also occur in the DCB specimens but is much more prominent in the lapshear tests due to the limited time in which the hits all occur.

6

S119

Lap-Shear (Mode-II)a.

b.

AE Source Location

Load

Double-Cantilever-Beam (Mode-I)c.
d.
Figure 4. Examples of loading curves and source locations. (a) Loading of DCB test. (b) Loading of
lap-shear test. (c) AE source location and visually observed crack length for DCB test. (d) AE source
location for lap-shear test.

Figure 5. Example of failure mechanisms observed. Left: DCB specimen showing adhesive failure andcracking of the adhesive layer. Right: Lap-shear specimen showing only adhesive failure.

4.4

Modal acoustic emission analysis

The resulting ratios of the peak wavelet-transform coefficients corresponding to the S0 and A0
wave-modes are presented in Figure 6. Both fracture-modes can result in a wide range of S0/A0
ratios being generated. For the DCB tests, values range from 0.0169 to 0.4178 with an overall
7

S120

mean and standard deviation of 0.085 and 0.0848 respectively. Lap-shear tests produced
values ranging from 0.0616 to 0.7197 and with an overall mean and standard deviation of
0.1902 and 0.1425.

Figure 6. Ratios of peak wavelet-transform coefficients at 300 kHz corresponding to S0 and A0 wavemodes. (a) All hits analysed. (b) Mean and std. deviation for each test. (c) Overall mean and std.
deviation for the two test types.

5. Discussion
The WT coefficient peak ratios show that in both tests the A0 mode is dominant and that there
is significant overlap between the sets of results. There is however a clear trend indicating that
the S0 mode is generally greater in the Mode-II lap-shear tests than in the Mode-I DCB tests.
This result appears to be in line with previous work, as Mode-I failure creates a clear out-ofplane source, very similar to the delamination of composites, which has previously been shownto create a dominant A0 component.
While the loading in the Mode-II test is applied in-plane with respect to the adherends, any
failure occurring at the interface with the adhesive is occurring at the surface of the adherend
rather than near the mid-plane, as can be the case for other in-plane sources previously
investigated, such as fiber-failure or matrix cracking in composites. It has been previously
demonstrated by Hamstad et al. [14] that while a signal generated by an in-plane source
located on the mid-plane will be dominated by the symmetric mode, the same source, appliedaway from the mid-plane, can create a signal dominated by the asymmetric mode. This
provides some explanation as to why both fracture-modes result in signals dominated by theasymmetric mode, despite the loading orientation. Additionally, a lap-shear test is technicallynot a pure Mode-II test, while the loading is predominantly in shear, bending of the adherends
can result in a small Mode-I crack-opening component, making it Mixed-mode and potentiallycontributing further to the generation of the A0 mode.

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As both fracture-modes have a significantly higher amplitude A0 than S0 mode, a suitablychosen threshold can be used to consistently select the arrival time of the A0 mode, without
risk of accidental selection of the S0 arrival time. If the Mode-II tests had resulted in a greater
S0 component, then a more sophisticated method would be necessary to select arrival times
and calculate source locations.
The results presented by Dzenis and Saunders [8], analysed using Vallen VisualClass, clearly
demonstrated the possibility to differentiate between fracture-modes using AE, they did not
however provide much insight into the fundamental differences in the signals which allowed
this differentiation. The results presented in this work indicate that it is likely that the differencein wave-modes excited during their tests will have been one of the significant factors
contributing to the differentiation which was achieved, while other differences may have also
occurred from features such as the specimen geometries causing variation in attenuation andreflections. An increased understanding of these factors allowing differentiation will bebeneficial if attempts are made to utilize these techniques on full scale structures, rather thansmall laboratory specimens, as any method used will need to suitably account for thedispersion, attenuation and reflection which will be present in larger structures with potentiallyirregular geometries.
The use of the WT peak ratio as a classifier to differentiate between fracture-modes may befeasible when considering multiple hits, i.e. an entire test, however due to significant variation
between hits, and the overlap between tests, it would not be possible in most cases to identify
fracture-mode based on a single hit. Future work should therefore consider either; othermethods to assess the modal content of the signals which may yield clearer discrimination, or,
combining this parameter with others to form a more robust method of discrimination.
Differentiation between hits occurring from the adhesive failure and cracking of the adhesive
layer has not yet been attempted within this study, and it is recognized that results for eachtest may include hits from both failure mechanisms which may exhibit different characteristics.
Future work should address this issue by conducting tests capable of isolating each of thesefailure mechanisms to identify the defining characteristics of AE occurring

6. Conclusion
The aim of this study was to investigate, for the first time, the AE wave-modes generated byMode-I and Mode-II fracture of adhesively-bonded joints (aluminium metal-to-metal) and to
identify whether modal analysis has the potential to discriminate between fracture-modes in asimilar manner to which it has been used to discriminate between failure mechanisms ofcomposites. Differentiation between fracture-modes is particularly important in adhesive joints
due to the vast disparity in strength between joints in Mode-I and Mode-II loading, assessmentof loading conditions through AE could therefore provide a very useful tool for structural healthmonitoring. Understanding of the wave-modes generated by different fracture-modes is also
important for accurate source location. Due to the differing propagation velocities of the modes,
it is critical that the velocity used in source location calculations corresponds to the wave-mode
for which arrival times have been detected. From the work conducted, the following has beenconcluded:

Use of linear source location to identify propagation distances and theoreticaldispersion curves to identify arrival times, has successfully identified regions of thetime-frequency domain corresponding to the fundamental S0 and A0 modes.
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

Modal analysis, based on investigation of the amplitude-ratio of peaks in thecontinuous wavelet-transform corresponding to the S0 and A0 modes, has revealed
clear differences between Mode-I and Mode-II/Mixed-mode fracture. While signals
from both fracture-modes are dominated by the A0 mode, the S0 mode is generally
greater in the Mode-II tests than in the Mode-I.

As the amplitude of the A0 mode is consistently higher than that of the S0 mode, a
suitably chosen threshold can be used as a reliable method to select the arrival
time of the A0 mode for the purposes of source location.

Analysis of the amplitude-ratio of wavelet-transform peaks corresponding to thewave-modes has been demonstrated to reveal differences between the fracturemodes when considering each test. However, due to the variation between hits
within each test, and the overlap between results from the two test types, it isgenerally not possible to distinguish between fracture-modes based on a single hit.
Future work should therefore focus on investigation of other methods to assess
modal content of the signals or on utilizing the wavelet peak ratio in combination
with other parameters to provide a more robust classifier.

References:[1] R. D. Adams, "Nondestructive Testing," in Handbook of Adhesion Technology, Oxford,
Springer, 2011, pp. 1050-1067.
[2] M. G. Droubi, A. Stuart, J. Mowat, C. Noble, A. K. Prathuru and N. H. Faisal, "Acoustic
emission method to study fracture (Mode-I, II) and residual strength characteristics incomposite-to-metal and metal-to-metal adhesively bonded joints," The Journal of
Adhesion, 2017.
[3] J. Galy, J. Moysan and A. Y. N. M. N. El Mahi, "Controlled reduced-strength epoxyaluminium joints validated by ultrasonic and mechanical measurements," International
Journal of Adhesion and Adhesives, vol. 72, pp. 139-146, 2017.
[4] M. G. Droubi, J. Mcafee, H. R. C, S. Walker, C. Klaassen, A. Crawford, A. K. Prathuru
and N. H. Faisal, "Mixed-mode fracture characteristics of metal-to-metal adhesively
bonded joints: experimental and simulation methods," in Structural Integrity Procedia,
Funchal, 2017.
[5] K. Senthil, A. Arockiarajan and R. Palaninathan, "Experimental determination of fracture
toughness for adhesively bonded composite joints," Engineering Fracture Mechanics, no.
154, pp. 24-42, 2016.
[6] D. Croccolo and R. Cuppini, "A methodology to estimate the adhesive bonding defects
and the final releasing moments in conical joints based on the acoustic emissions
technique," International Journal of Adhesion & Adhesives, no. 26, pp. 490-497, 2006.
[7] M. K. Bak and K. Kalaichelvan, "Evaluation of Failure Modes of Pure Resin and Single
Layer of Adhesively Bonded Lap Joints Using Acoustic Emission Data," Transactions of
the Indian Institute of Metals, no. 68, pp. 73-82, 2015.
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[8] A. Dzenis and I. Saunders, "On the possibility of discrimination of mixed mode fatigue
fracture mechanisms in adhesive composite joints by advanced acoustic emission
analysis," International Journal of Fracture, no. 117, pp. 23-28, 2002.
[9] M. R. Gorman, "AE Source Orientation by Plate Wave Analysis," Journal of Acoustic
Emission, vol. 9, no. 4, pp. 283-288, 1991.
[10] J. Martínez-Jequier, A. Gallego, E. Suárez, F. J. Juanes and Á. Valea, "Real-timedamage mechanisms assessment in CFRP samples via acoustic emission Lamb wave
modal analysis," Composites Part B: Engineering, vol. 68, pp. 317-326, 2015.
[11] M. Surgeon and M. Wevers, "Modal analysis of acoustic emission signals from CFRP
laminates," NDT & E International, no. 32, pp. 311-322, 1999.
[12] ASTM International, "D5528-01 Standard Test Method for Mode I Interlaminar Fracture
Toughness of Unidirectional Fiber-Reinforced Polymer Matrix Composites," ASTM
International, West Conshohocken, 2007.
[13] ASTM International, "D1002-10 Standard Test Method for Apparent Shear Strength ofSingle-Lap-Joint Adhesively Bonded Metal Specimens by Tension Loading (Metal-toMetal)," ASTM International, West Conshohocken, 2010.
[14] M. A. Hamstad, A. O'Gallagher and J. Gary, "A wavelet transform applied to acoustic
emission signals: Part 1: Source Identification," Journal of Acoustic Emission, no. 20, pp.
39-61, 2002.

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02
On usesignal FEATURES
features for FOR
Acoustic
EmissionsourceON -USE
OF of
SIGNAL
ACOUSTIC
EMISSION

identification in fibre-reinforced
composites
SOURCE IDENTIFICATION
IN FIBRE-REINFORCEDCOMPOSITES
Markus G. R. Sause1
1

University of Augsburg, Institute for Materials Resource Management, MechanicalEngineering, D-86135 Augsburg

Abstract:
In the past, many approaches were proposed to perform the task of acoustic emission based source
identification in fibre-reinforced composites. Almost all identification attempts make use of feature values
to act as representation of the recorded acoustic emission signals. The typical features are classified in
two primary categories, one to express the intensity / energy of the signal and one to describe thefrequency characteristic of the signal. Both categories are used to classify signals into microscopic
failure mechanisms such as matrix cracking, fibre breakage and many more. To this end, various
approaches used either energetic or frequency features or a mix of both. This contribution takes a closer
look at the relationship between acoustic emission signals and their feature values and assesses theirrelation to acoustic emission source mechanics. This provides guidance on the reliability of acoustic
emission features for source identification procedures and points out some key aspects for successfulclassification attempts.

1. Introduction
Failure of composite materials during mechanical loading is a complex phenomenon startingon the microscopic level. At exposure to increased load, small flaws start to grow into larger
ones, escalating over several orders of magnitude, which finally coalescent into macroscopic
failure. Each microscopic (and macroscopic) crack progression generates acoustic emission(AE). The multitude of different cracks occurring in composites have been categorized andlisted several times (i.e. [1], [2]). In the context of AE, it is most noteworthy that all of thesedifferent cracks result in detectable AE signals. Among other things, the detectability will
depend on the distance between source position and the sensor position. One intrinsic
challenge is to deal with the variety of terminology used in the past to categorize the different
failure mechanisms in composites. Still there is no consensus on how many different AE source
mechanisms can be distinguished in a particular test setup. However, in the past decades
there have been many attempts to distinguish between the different AE source mechanism as
occurring in composites [3-20]. Basic distinction is made between work that has applied model
specimens to establish prototype signals for each source type, approaches that are using
validation of signal classes by microscopic evidence and approaches using numerical or
analytical modelling to assist in the interpretation.
The aim of this contribution is to review the reliability of particular AE parameters (features)
applied to perform source identification in fibre-reinforced composites.

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2. Experimental
As described in more detail in [10], [12], a four-point bend test was combined with multiresonant type WD sensors to detect the AE generated during damage initiation andprogression. For acoustic coupling Baysilone silicone grease of medium viscosity was used,
the mechanical contact was provided by two clamps. To decrease detection of friction noise abandpass ranging from 20 kHz up to 3 MHz was used. The AE waveforms were recorded usinga PCI-2 data acquisition system with 40 dBAE preamplification and the software AEWin(Physical Acoustic Ltd.) with a threshold of 40dBAE and a sampling rate of 10 MS/s. This will
provide some representative AE data for further discussion and only acoustic emission sources
located between the two upper supports are considered for the following analysis.
To analyse the crack formation inside the material, high-resolution computed tomography wasused. The Nanotom m system (General Electric) was applied with 50 kV tube voltage and200 µA tube current using a voxel resolution of 5.2 µm. This provides a way of volumetricmeasurement of the typical crack dimensions found after four-point bending in a composite
material.
After extraction of classical signal features out of the first 100 µs after signal arrival (definitionof features see Appendix C in [2]), the feature dataset is ready for analysis by patternrecognition methods. In figure 1, a typical result from a hierarchical clustering using theagglomerative tree linkage [21] is shown for one representative dataset resulting from loading
the specimens to failure. This statistical data analysis method applies the Ward-algorithm (alsoknown as “sort algorithm” or “agglomerative tree linkage”) and groups the AE features
according to their similarity based on a minimum variance criterion [19], [22]–[25]. Highnumerical values indicate an equal relevance to describe the underlying data structure. In theexample of figure 1, the feature “energy” and “signal strength” link at a value of approximately
one, so they essentially provide the same information, i.e. they are highly correlated.
For composite materials, this feature dendrogram (example see figure 1) often splits in two
branches that link at relatively low levels. One branch is primarily composed of features related
to the “energetic quantities”, such as energy, amplitude, signal strength, but it also includes
features related to the amplitude. One such example is the duration of the signal (louder signals
take longer to fall below threshold). Similar, the rise time and the counts to peak correlate withthe strength of the signals as increased amplitudes may cause an earlier onset above thresholdlevel.
The other branch of the feature dendrogram typically holds the features, related to frequency
information of the AE signals. Besides the obvious frequency measures, this chart reveals thehigh correlation between counts and the average frequency, as the latter is based on theformer value (see Appendix C in [2] for precise definition).
As this kind of behaviour seems to appear in many AE datasets of composites, this indicatesthat there is a distinct difference in the descriptive capabilities of both branches. Therefore, thismotivates the organization of this work in two sections, one dealing with a review of “energetic”
AE features and one dealing with a discussion of “frequency” AE features.
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Frequency Centroid

Partial Power 4

Partial Power 3

Initiation Frequency

Partial Power 2

Partial Power 1

Peak Frequency

Weigthed Peak Frequency

Reverberation Frequency

Average Frequency

CountsDuration

Counts to Peak

Rise Time

Amplitude

Absolute Energy

Signal Strength

Energy

1.00
0.82
0.70
0.55
0.37

0.16

complete link clustering

Figure 1. Hierarchical clustering of dataset using the Ward-algorithm (agglomerative tree linkage).

3. Energetic AE signal features
Several fundamental acoustic emission theories aim to establish a relationship between the
AE source type, the properties of the fracturing homogeneous isotropic solids and the resulting
AE signal [2]. Composite materials are, dependent on the observation scale, significantly
different to that, i.e. they are only homogeneous on the macroscopic scale where they may beapproximated as homogeneous anisotropic solids. Nevertheless, the key principles of these
theories are still found to be still valid for composite materials [2].
One of the common results of all the theories mentioned above is that they expect a
proportionality between the size of a crack growth 𝑎𝑎 and the AE signal amplitude 𝑈𝑈. The theoryof Lysak would propose 𝑈𝑈 ∝ 𝑎𝑎3⁄2 for the case of a “crack-through” and 𝑈𝑈 ∝ 𝑎𝑎 for an internal“penny-shaped” crack [22]. Green and Zerna reported a 𝑈𝑈 ∝ 𝑎𝑎 relationship for a crack-throughprocess [26]. Investigations by finite element modeling recently confirmed the relationship 𝑈𝑈 ∝𝑎𝑎3⁄2 for a crack-through process in a homogeneous material, while results from internal cracks
apparently obey the 𝑈𝑈 ∝ 𝑎𝑎 relationship [2].
The theory of Ohtsu and Ono uses the moment tensor representation for AE sources, which
generally states 𝑈𝑈 ∝ Δ𝑉𝑉, with Δ𝑉𝑉 being the internal volume produced by the crack growth [23],[24]. Similar, the theory of Scruby and Wadley would predict the proportionality 𝑈𝑈 ∝ Δ𝑉𝑉, given
the dynamics of the crack process are unchanged when comparing different crack sizes [2],[27], [28].
3.1. Matrix cracking and interfacial failure
In a composite, the expected variation of crack sizes for “matrix cracking” ranges from fairly
small length scales in the order of 1 µm to 10 µm (cf. figure 2a) to reasonably macroscopic
sizes such as several 100 µm to some 1 mm (cf. figure 2b). In [29] polymer fracture was evenmeasured to generate one huge signal for final fracture at several centimetres of length scale.
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The theories quoted above allow estimating an increase of the corresponding acoustic
emission signal amplitude by three orders of magnitude. This corresponds to a dynamic rangeof the corresponding AE signal amplitudes of at least 60 dB (1µm to 1mm internal cracks).
Furthermore, for a typical composite, there is no expectation of distinct crack sizes, as larger“matrix cracks” do not necessarily need to grow at once, but may show several intermediate
rests. This will result in more than one AE signal for one such macroscopic crack. Accordingly,
the distribution of crack lengths for source types such as “matrix cracking” and “interfacial
failure” is not discrete. This is different in some cases, when the typical extent is geometricallylimited in one more directions (e.g. because of ply structure, bundle distances, or other). Thus,in [30] it was demonstrated, that the average signal amplitude of matrix cracking is related tothe thickness of the failing plies. Similar, findings in [31] indicate, that matrix cracking in fibre
yarns causes characteristic acoustic emission energies.
(a)

(c)

> 1.0 mm

200 µm

100 µm

0.6 mm

0.4 mm

0.23 mm(b)
50 µm< 10 µm0.20 mm

0.5 mmFigure 2. Computed tomography image of crack network on microscale (a and b) and on macroscale(c) taken from damage region of four-point bending sample of [10], [12].

Nevertheless, for the majority of composites, there is no general discretization of crack
dimensions for matrix cracking and interfacial failure, but a homogeneous distribution of crack
length sizes. Hence, it appears invalid to detect the occurrence of either “matrix cracking” or“interfacial failure” based on a particular amplitude range unless the microstructure explicitly
forces discrete size distributions.
3.2. Fibre breakage
Another type of failure mechanism frequently interpreted in terms of its expected AE signal
amplitude is fibre breakage. Essentially, contradicting proposals have been made that either
concluded 𝑈𝑈𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 ≫ 𝑈𝑈𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 or 𝑈𝑈𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 < 𝑈𝑈𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 .

Based on the theories mentioned in section 3.1, the decisive factor for the size of the AE
amplitude is the Δ𝑉𝑉-value. Due to the fibre diameters, the crack area is well defined for carbon
fibres (5 µm to 10 µm) or glass fibres (10 µm to 50 µm). For fibres failing under tensile load,
the additional contribution to Δ𝑉𝑉 is the extension along the fibre axis. The amount of thisextension in load direction relates to two factors, namely (i) the amount of elastic energy stored
before fibre failure and (ii) the confinement effect of the surrounding fibres and matrix. The first4

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contribution implicitly relates the amount of extension to such factors as the fibres tensile
strength (or their fracture toughness). Hence, a larger Δ𝑉𝑉 expects for a stronger fibre. In
addition, a stronger AE signal is expected for a 10 µm diameter cracking fibre vs. a 10 µm
diameter cracking matrix polymer. Assuming typical fibre and matrix failure strength values(50 MPa polymer vs. 5 GPa fibre) and applying the theoretical relationships above, for thesame cross-sectional area this results in 100 times larger AE signal amplitude andaccordingly 𝑈𝑈𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 < 𝑈𝑈𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 . Note that this description does not take into account potential
additional contributions to Δ𝑉𝑉 due to matrix cracking and debonding surrounding the fibre
filament. This might well lead to additional contributions to the fracture surface normal to thetensile load (i.e. factor of 2 – 4 as estimated from computed tomography images, cf. figure 3).
A primary difference to the discussion of amplitude distribution of section 3.1 is the discrete
size distribution expected for fibre breakage. As demonstrated e.g. by [32] the single filament
failure initiates long before ultimate failure and does not necessarily result in cascades of fibres
failing simultaneously. For constant cross-section and constant fibre strength, a discrete AEenergy release is theoretically expected. However, this discrete energy smoothens out due tothe cross-section distribution of the fibre filaments and the Weibull strength distribution of thefilaments.
Single filament testing sometimes is used to demonstrate that fibre breakage AE signals havevery strong amplitudes. As sketched in figure 3, the confinement of the failing filament issubstantially different in single filament testing vs. a real composite. In the case of the freefilament, the crack faces may move independently in each direction, reaching many millimetresor even centimetres. For fibre failure inside a composite material, the surrounding fibres andmatrix (in case they do not fail simultaneously) confine the movement of the breaking fibre. In
this case the resulting movement along the axis could be quantified using in-situ computed
tomography analysis to be less than 5 µm as seen in figure 3-b [33]. Thus, the resulting Δ𝑉𝑉 is
more than three orders of magnitude smaller than for single filament testing. Based on thetheories stated above this would result in a corresponding reduction of AE signal amplitudes
of up to 90 dBAE. The same change in energy release was also consistently found in finite
element modelling for the case of free fibre filaments in [33] and for confined fibres in [2].
(a)single filament testing

DV

(b)

z

x
y

DV
x

y

y

y

25 µm

y
x

matrix cracking

z
25 µm

composite testing

Figure 3. Schematic difference in Δ𝑉𝑉 for single filament testing vs. fibres confined within composite
material (a) and experimental measurement of Δ𝑉𝑉 using high-resolution computed tomography at
0.4 µm voxel size (b).

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Other indications that fibre breakage signals from composites (confined condition) show
relatively low amplitudes were recently provided by Lomov et al. and Swolfs et al. [30], [31].
They compared theoretical predictions for first fibre filament failure based on classical Weibull
theory to the corresponding onset of AE signals. Overall, the predicted onset of the weakest
fibre filaments systematically preceded the measured AE onset. This indicates that the AE
signals of the weakest fibres possibly fall below the experimentally used detection threshold.
This was additionally verified by finite element modelling of fibre breakage with varying tensile
strength following a Weibull-type strength distribution in [2].
As baseline of all these considerations above, the dominating factors for the AE signal
amplitude (and hence energy) are the size of the crack as well as the amount of elastic energy
stored immediately before fracture.

4. Frequency AE signal features
In contrast to the energetic AE signal features, there are several established ways to compute
frequency information to represent the recorded AE signals. The three different features“Initiation-“, “Reverberation-“ and “Average-Frequency” are used to provide an estimate of thecharacteristic frequency before and after the peak-maximum and of the complete signal (seeAppendix C in [2] for precise definition). These features are no exact frequencies since they
rely on the number of signal threshold crossings, which is a discrete and often error-prone
count, thus resulting in relatively inaccurate nominal frequencies.
A significantly better approach is to compute the Fast-Fourier-Transformation (FFT) of the AE
signal. The “Peak-frequency” 𝑓𝑓𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 is the frequency value of maximum intensity in the
spectrum. The “Frequency Centroid” 𝑓𝑓𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 characterizes the frequency content of an AE
signal in a similar way as the centre of mass describes the properties of geometric object withuniform density. Thus, it is an independent evaluation of the characteristic average frequencyof the signal and is generally not equal to the “Peak-frequency”. Another means of representing
the signals frequency spectrum is the definition of different “Partial Power” levels. They
measure the signals frequency contribution within a given interval and represent the frequency
distribution of the AE signal by more than only one characteristic value. Hence, they usuallyare defined for subdivisions of a certain frequency range of interest, e.g. 150 kHz intervals
ranging from 𝑓𝑓𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 = 0 kHz to 𝑓𝑓𝑒𝑒𝑒𝑒𝑒𝑒 = 1200 kHz (see Appendix C in [2]).

Another sophisticated approach is to extract the frequency information from time-frequency
transformations. Using such concepts based on wavelets [34] or kernel convolution procedures
such as those proposed by Choi and Williams [35] this allows to obtain additional frequency
features. For the discrete wavelet transformation, the intensity of the decomposition levels has
been proposed as features to describe the AE signal [36], [37].
Similar as for the energetic features, established theories allow raising some expectations forthe resultant frequency spectrum of a particular source mechanism in a fibre reinforced
composite. All of the established theories assume a source function, which is due to a rapid
internal displacement [19], [22]–[24] and has been recently confirmed by fracture mechanics
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based numerical modelling [33]. Typically, this is a step function as seen exemplarily in figure
4-a for two typical rise-times. In this simple source representation, the governing factor to judge
on the source dynamics is the rise-time of the internal displacement. However, the rise-time is
not identical to the time the crack needs to propagate. In [2], [33] the duration for crack growth
was found to be substantially shorter than the rise-time of the source function with theadditional dynamics caused by inertia effects and by the vibration of the crack faces, as wellas propagating surface waves. Nevertheless, a simple FFT of the two exemplary source
functions of figure 4-a reveals the corresponding bandwidth of the AE source and is plotted infigure 4-b. Naturally, faster internal displacement generates broader bandwidth, i.e. causes
excitation of higher frequencies. Similar, the slower internal displacement causes reduced
bandwidth of the source.
(b)norm. FFT displacement [arb. units]

(a)

1.0

displacement [arb. units]

1.0
rise time 2µs
rise time 10µs

0.8

0.6

-3dB

0.6

0.4

0.4

0.2

0.0

rise time 2µs
rise time 10µs

0.8

spectralband-width

0.2

0

2

4

6

8

time [µs]

10

12

0.0

14

0

200

400

600

frequency [kHz]

800

1000

Figure 4. Scheme of source function (a) and corresponding spectral bandwidth estimated from -3dB
drop (b).

4.1 Dynamics of source mechanisms in composites
Based on these considerations, the discussion now focuses on the expected dynamics fordifferent source mechanisms. In general, the duration for a singular crack event (from initiation
to rest) links to two factors. This is (i) the ductility/brittleness of the material and (ii) thegeometry/extension of the crack growth. The first item directly links to the crack propagation
speed. As has been discussed by Scruby [25], depending on the applied load and the material
properties, cracks may potentially reach their ultimate propagation speed limit, which is theRayleigh wave speed. The second item relates to the geometric boundary conditions posed
for the crack growth. At constant speed, larger crack length will need longer to form thanshorter crack length, hence adding to the overall rise-time of the source. Potentially there is a
fixed upper limit to this duration of growth, as cracks in composites can be geometricallyconfined due to sizes of the plies or due to the overall dimension of the sample (width,
thickness, …).
For failure mechanisms such as matrix cracking or delamination the basic expectation forbroad crack length distributions has already been discussed in section 3.1. Based on the above
considerations, this would expect a broad distribution of source rise-times for these cases.

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Likewise, for discrete crack dimensions as for fibre breakage, a discrete rise-time distribution
is expected.
However, reinforcement fibres such as glass or carbon fail more brittle than typical matrix
polymers and also exhibit up to one order of magnitude higher Rayleigh velocities (e.g. glass
3000 m/s, carbon 5000 m/s, polymer 500 m/s) as can be calculated from the materials Young’s
modulus, density and Poisson’s number when using the Bergman approximation [38].
Hence, generally the rise-time of the source function is significantly shorter for failure of thereinforcement fibres than for a comparable failure size of a matrix polymer. Accordingly,
nominally larger crack increments as for matrix cracking and interfacial failure will reduce thebandwidth further compared to the fibre breakage bandwidth.
4.2 Transfer of frequency information from source to sensor
Although, the source dynamics govern the bandwidth of the source and the spectrum itself
seems to be fairly flat with frequency (cf. figure 4-b), there are several factors which affect the
propagation of frequency information from source to sensor. One example obtained from FEM
calculus to demonstrate the severity of these effects shows figure 5.

Figure 5. Transmission characteristics of sensor, material and propagation path and theirinfluence on generation of AE signal spectrum in relation to source spectrum.

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The first non-linear transmission is the formation of guided waves in a typical thin-walled
composite plate. As discussed in [2], the formation of plate waves comes with particularfrequency regions, that allow preferential transmission of the signal. Correspondingly, somefrequency ranges transmit badly, resulting in a significant change of the frequency spectrum.
The particular frequency values for preferential transmission depend mostly on the thicknessof the plate and the material properties. This guided wave propagation also leads to formationof some of the characteristic peaks in the FFT spectrum of the final AE signal. Anotherimportant source of non-linear transmission is the sensing system used. Especially for the
typical commercial sensor systems (resonant or multi-resonant designs), this results inadditional reduction of bandwidth or may cause characteristic peaks in the frequency
spectrum, which are owed to the internal sensor resonances (cf. figure 5).
(a)

(b)

fpeak

300000<fpeak>250000

FFT-magnitude [V]FFT-magnitude [V]fcentroid

200000150000100000

0

(c)
12000

400

600

frequency [kHz]800

fcentroid

6000

4000

0

1000

200

400

600

frequency [kHz]fcentroid

16000

8000
6000
4000
2000

800

1000

fpeak

18000

<fpeak>

10000

0

(d)

FFT-magnitude [V]FFT-magnitude [V]

200

fpeak

0

<fpeak>

8000

2000

50000
0

fpeak

10000

<fpeak>14000

fcentroid

12000
10000
8000
6000
4000
2000

0

200

400

600

frequency [kHz]

800

0

1000

0

200

400

600

frequency [kHz]

800

1000

Figure 6. Comparison of frequency representation for four AE signals (a-d) using Peakfrequency 𝑓𝑓𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 , Frequency Centroid 𝑓𝑓𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 and Weighted Peak-Frequency 〈𝑓𝑓𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 〉.

As seen in figure 5, the frequency information of the AE source maps not well to the acquired
electrical AE signal. In particular, features such as the “Peak-frequency” usually link either tothe sensor resonances or to the frequency ranges of preferential transmission of the guided
waves. Thus, it does not seem appropriate to use such features to distinguish failure
mechanisms based on their absolute frequency values. Typically, a choice to distinguish amechanism of e.g. ≶ 137 kHz is either due to the sensor used or due to the material
investigated.
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To remove the sensor and material bias implicit to the frequency features, the use of relative
features, such as the “Weighted Peak-Frequency” 〈𝑓𝑓𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 〉 or “Partial Powers” was proposed[11]. As the aim of all the frequency features is to best represent the frequency spectrum,
consider the four examples shown in figure 6. Visually, one would state close similarity
between the spectra of figure 6-a and 6-b and high dissimilarity between spectra of figure 6-c
and 6-d. Accordingly, frequency parameters should be representative numerical values for thisobservation. Using the 𝑓𝑓𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 feature this works well for the bottom case, resulting in adifference of 600 kHz. However, the upper case results in a difference of 240 kHz for visuallysimilar spectra. Accordingly, absolute values of the 𝑓𝑓𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 feature do not suffice in this case.

The 𝑓𝑓𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 feature is less susceptible to the occurrence of peaks in the spectrum as it isobtained from an averaging of the full spectrum. However, the disadvantage of this is the
reduced sensitivity to relative changes of frequency spectra. Although the example spectra of
figure 6-a and 6-b are similar, they still do have different weight at low and high frequencies.
Still the 𝑓𝑓𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 is numerically almost identical for both spectra. This lack of sensitivity of the
𝑓𝑓𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 feature is quite typical as the basic “finger-print” of the spectrum originates to asignificant extent from constant contributions, such as sensor characteristics and guided wave
propagation (cf. figure 5). Therefore, the 𝑓𝑓𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 feature by itself is not very useful to capturesmall changes relative to the average frequency content.
As result of these observations, the combination of both features into the 〈𝑓𝑓𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 〉 feature wasproposed [11]. As square-root combination of both other features, it retains the good
discriminative capability of the 𝑓𝑓𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 , but is also susceptible to small changes in frequency
spectrum (cf. figure 6-a and 6-b) without being too susceptible to the resonance frequenciesof AE sensor or the material.

5. Conclusion
Based on the presented considerations it hardly seems applicable to perform source
identification in composite materials based on energetic features unless there is a discrete size
distribution expected for one mechanism. In specific cases this can be justified (i.e. matrix
cracks all growing the full height and width within a ply at once). In a general situation, there is
no expectation for a discrete distribution of AE signal amplitudes for one of the failure
mechanisms in a composite. Instead, especially for “matrix cracking” and “interfacial failure” awide distribution of AE signal amplitudes is expected, which cover several orders of magnitude.
Thus, the AE signal amplitude and energetic features should be interpreted more in terms ofstrength or severity of the failure mechanism other than a feature to distinguish failure
mechanisms.
In order to perform source identification, frequency features were found to provide better
discriminative capabilities. The AE signals bandwidth directly relates to the duration of thecrack growth. In fibre-reinforced composites, the different failure mechanisms show
characteristic crack durations, which originates from the speed of the crack propagation andthe typical crack length of the different mechanisms (specifically for fibre breakage). However,
10

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the transfer of the information of the source bandwidth to corresponding AE features suffersfrom resonances of the structure and the sensor. To overcome the associated issues in
evaluation of the frequency spectra the “weighted Peak-Frequency” was proposed. In
combination with other frequency features such as the “Partial Powers” and multi-variante data
analysis (e.g. by pattern recognition methods [12]) this appears as best approach to meaningful
AE source identification in fibre-reinforced composites, as the reliability of a single AE feature
is not significant enough to allow this complex task.

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[17] V. Kostopoulos, T. Loutas, A. Kontsos, G. Sotiriadis, and Y. Pappas, “On the identification of the
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03 - Damage
diagnostic
and lifetime
prognosis
for ceramic
Damagediagnostic
and lifetime
prognosis
for ceramic
matrixmatrixcompositewith Acoustic
Emission
duringcompositewithacoustic emission
duringlong-termmechanicallong-term mechanicaltests at intermediate
temperature
tests at intermediate
temperature
Nathalie Godin, Pascal Reynaud, Gilbert Fantozzi
University of Lyon, INSA Lyon, MATEIS, UMR 5510, 69621 Villeurbanne cedex
Abstract:
Damage of composite materials is a key factor for the durability in service. It is therefore essential todefine the most suitable damage indicators and to develop models to estimate the Remaining Useful
Lifetime from analysis of precursor events resulting from damage. Acoustic emission is relevant to thedevelopment of the Prognostic and Health Management because it allows knowing the state of damageof a composite structure in real time. This work is dedicated to lifetime prediction using AE for long-term
tests on CMC during static and cyclic fatigue tests. New indicators of damage have been defined, based
mainly on acoustic energy analyses. These indicators highlight critical times (around 50 % of thecomposite lifetime) allowing an evaluation of the remaining lifetime. A linear correlation is observed
between these critical times and the lifetime duration. For a prognostic phase, the results obtained withthis empirical law are compared to those obtained with a power law such as a Benioff law. Moreover,the clustering of acoustic emission, using a random forest approach, makes possible to identify themechanism responsible for this critical time. For the static fatigue test, the critical time around 50 %correspond to the delayed failure of fibres.

1. Introduction
Ceramic matrix composites (CMCs) are very attractive candidates for civil aircrafts applications
because of their excellent creep resistance, high-temperature strength and lightweight [1-2].
Even though both fibre and matrix are made of brittle materials (mostly Carbon and Silicon
Carbide), CMCs can reach relatively high strain-to-failure due to energy dissipation throughmultiple matrix cracking, deviation of cracks at the fibre/matrix interface or propagation into a
multi-layered matrix. Matrix cracking is then the first main damage mechanism occurring.
However, the composite failure is driven by physicochemical degradation of the fibres that
includes both effects of stress redistribution during damaging of matrix and interface andeffects of oxidation. The various phenomena that are involved in the damage process makethe lifetime prediction of this type of material a very challenging work. Here, the objective is todevelop a new approach based on acoustic emission monitoring that would allow real-time
prediction of the Remaining Useful Lifetime (RUL) of CMCs during fatigue tests. The aim ofprognostics is to make remaining useful lifetime previsions [3-6]. Prognostics is a naturalextension of structural health monitoring where the user is not only concerned with detecting,locating and identifying damage but also determining the RUL long before failure. The RUL or
the TTF (Time To Failure) can be defined as duration between the time t (after detection of thedamage) and the time when the damage will reach its critical threshold. It is a continuous
process and predictions must be regularly updated as the system health state changes (Fig.1).
A diagnosis phase and a prognostic phase can thus be distinguished. The diagnosis phasemust be able to detect, locate, identify the damage and assess its severity. The prognosis
1

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phase, based on the results of the diagnosis and on models, makes it possible to evaluate theresidual lifetime. Acoustic emission allows reaching this objective, as this technique allows thehealth of an entire structure regardless of its size to be monitored. The analysis of the collected
data can be used to discriminate the sources of damage (matrix cracks, fibre breaks,
fibre/matrix decohesion, delamination...) [7-9] and to measure the kinetics of the various
degradation mechanisms. In addition, an estimate of the composite materials remaining
lifetime can be considered based on a real-time tracking of the damage recorded by EA. In thiscontext, two approaches based on two complementary analyses of acoustic activity aredeveloped:(1) a collective and cooperative analysis of all the collected signals. The idea is to predict the
lifetime of a part in service by analysing its behaviour by monitoring the acoustic emission
during the load. The rupture can then be predicted by looking at the cooperative aspect,
synthesizing all the information contained in the events in a global way.
(2) an individual analysis of the signals: the objective of this analysis is to associate each EA
signal with the generated damage mechanism. This allows, in real time, identifying the type ofdamage that occurs and to quantify its severity.

Figure 1: Prediction of lifetime of Ceramic Matrix Composite during long-term mechanical tests

2. Description of work
2.1 Materials, mechanical tests and AE monitoring
Fatigue tests have been realized at a temperature of 450°C or 500 °C under air. The material
is a ceramic matrix composite reinforced with Nicalon SiC fibres, coated with PyC andembedded in a self-healing [Si-B-C] matrix, manufactured by SAFRAN CERAMICS. The
reinforcement architecture is a stack of several layers of 2D satin fabrics linked together by
strands of fibres in the third direction. In this study, all the specimens have a dog-bone shapewith a thickness of 4 mm and a gauge section of 60 mm x 16 mm. For static fatigue tests,
specimens were first loaded at constant loading rate of 1kN/min, and periodically (every 6 or
2

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12 hours) unloading-reloading cycles were carried out in order to determine the secant elastic
modulus. Cyclic fatigue tests were conducted under a tensile/tensile sinusoidal loading withconstant amplitude and a frequency of 2 Hz. Strain is measured using by an extensometer.
Two piezoelectric sensors (Micro80, Mistras Group) are maintained on the specimen surface.
Medium viscosity vacuum grease is used to ensure a good coupling between the specimen
and sensors. Each sensor is connected to the data acquisition system (PCI2, Mistras Group)
via a preamplifier with a 40 dB gain and 20-1200 kHz bandwidth (Mistras Group). The positionof a detected source can be determined linearly knowing the wave velocity in the material.
2.2 Damage indicators
Most of the damage indicators are based on the investigation of the AE energy. It is generally
accepted that the energy of an AE signal includes the energy released by the source at crack
initiation. The following equation is proposed to describe the energy of recorded AE signals(for instance, at sensor 1) received from the source n [10-11]:

E1 (n) = Es (n).A1 .e-B(L +x(n))(1)

Es(n) is the energy released at source n in the form of elastic waves. Ai is the proportion ofsource energy that is recorded by sensor i. It is a constant characteristic of sensor. L + x (n) is
the distance of propagation from source n to sensor 1 (2L being the distance between sensors).
The attenuation coefficient B is related to the propagation medium, which is subjected tochanges due to damage evolution.
The source energy is then defined as the square root of the product of the amounts of energy

received at both sensors for each source:
𝐸𝐸𝑒𝑒𝑒𝑒 = √𝐸𝐸1 × 𝐸𝐸2 = 𝐸𝐸𝑠𝑠 (𝑛𝑛). 𝐴𝐴1 . 𝐴𝐴2 . 𝑒𝑒 −𝐵𝐵𝐵𝐵(2)

where L is the distance between sensors. B is related to the propagation medium but it is
supposed independent of the source energy.

2.2.1 Identification of attenuation parameters
To evaluate energy attenuation [10], the ratio of AE signal energies recorded at both sensors
is calculated for each source n. For an easier identification of attenuation coefficient B, X(n) is
defined as the natural logarithm of this ratio. From Equation 1 for the both sensors, it comes:

X(n) = log

E1 (n)
A= log 1 - 2.B.x(n)E2 (n)
A2(3)

Estimation of attenuation coefficient B is performed as follows. Attenuation is evaluated for agiven time interval using the median values of X(n) in every space interval (width: 10 mm,
overlapping: 5 mm). Each median value of X(n) corresponds to a few hundred AE sources
located in the same space-time interval. The linear approximation is repeated in consecutive
3

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time intervals. Each time interval corresponds to 2000 AE sources. At every increment of 500
sources, a new time interval is considered. The overlapping is set in order to accurately monitor
evolution of both attenuation parameters.
2.2.2

Coefficient of emission RAE

The energy of the recorded AE events represents a part of the elastic energy released
by CMC specimens. Thus, the evolution of elastic energy released by analysing the energy ofAE events may be investigated [12]. The coefficient of emission RAE is defined as theincrement of energy E recorded during an increment of time t, divided by the total energy
emitted during the initial loading of the sample:

𝑅𝑅𝐴𝐴𝐴𝐴 =

1

∆𝐸𝐸

(4)

𝐸𝐸𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 ∆𝑡𝑡

where Eloading is the cumulative AE energy for all the signals recorded during the initial loading
up to the nominal load of the test, E is the cumulative AE energy for all signals recorded
during the interval t; t  t .
2.2.3

The severity

In this study, the energy rather than the signal strength is used to define the severity. It isdefined as the average energy.

Sr 

1 m J Em (n)
J m1

(5)

J is an empirical constant.
2.2.4 The coefficient Rlu
The indicator denoted RLU, is defined by the ratio of the liberated energy during the loading
part of a cycle and the energy recorded during the unloading part of this cycle [13].
2.2.5 Coupling between mechanical data and AE data
AE data and mechanical information can be analysed independently. However, combinationof mechanical energy (Um) and acoustic energy (UAE) denoted Sentry Function (F), initiallyintroduced by Minak [14], can be used in order to obtain a better damage characterization:

𝐹𝐹 = 𝑙𝑙𝑙𝑙

𝑈𝑈𝑚𝑚 (𝑥𝑥)(6)𝑈𝑈𝐴𝐴𝐴𝐴 (𝑥𝑥)where Um(x), UAE(x) and x are respectively the strain energy, the cumulated acoustical energy
and the strain of the tested material. It is defined as soon as the first acoustical event isrecorded. Strain energy is calculated by measuring the area under the force-displacement
curve. To determine only the effects of tension, unloading/loading loads are not taken into
account. This global function is calculated every k acoustic sources (k~0.1% of number of4

S140signals). Sudden drops of the function may be related to the occurrence of a significant internalmaterial damage.
2.3 Power law : Time to failure estimation
Benioff’s law [15] (the sum of the square root of the energy released for sequential
earthquakes) has been suggested for precursory phenomena of large earthquakes, increasing
as an inverse power law of time before a main shock.
N (t )

(t )  Ei  R  C (tR  t )1

(7)

i 1

where Ei is the seismic energy release of the ith precursory earthquake and N(t) is the number
N (t )of precursory earthquakes considered up to time t.  R is the value of (t )  i 1when t = tR, tR is the failure time.

C 


1 

is negative, 1  

Eiis an exponent and  isa constant. Based on Eq. (7), the increase of AE collected during fatigue is analysed. Ei is theenergy of the ith AE signal detected and N(t) is the number of AE signals recorded and locatedalong the gauge length until time t.

2.4. Supervised Classification with a random forest algorithm: identification of the acoustic
signature
A supervised classification technique can also be used to analyse AE signals recorded during
fatigue of CMC composites. This technique requires a database of signals that have beenlabelled: the training set. This training set was created by merging data collected during tensile
test. As described in the previous paper [16], the analysis of AE signals, observation of
microstructures and analysis of the mechanical behaviour of the composite led to theidentification of 4 types of AE signals and to the following labelling of classes:
Class A: cluster A contains signals from two damage mechanisms, which are chronologicallywell separated: seal coat cracking and tow breaks,
Class B: cluster B contains also signals from two damage mechanisms, which arechronologically well separated: longitudinal matrix cracking and individual fibre breaks in thefracture zone just before failure,
Class C: Cluster C contains signals with relatively short duration, short rise time and low
amplitude when compared to the others: transversal matrix cracking,

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Class D: this cluster is the last one to be activated, it becomes more active as strain increases,
and the D-type signals have a longer rise time when compared to other signals: sliding atfibre/matrix interfaces, fibre/matrix debonding.
An additional class denoted E was introduced into the library [13]. It corresponds to signals
recorded under fatigue, during unloading steps of cycles after the cycle 2000 and for appliedstresses lower than max/2 -10 MPa. These signals are associated to the friction generated
in cyclic fatigue.
In order to establish the training set of labelled signals for the supervised analysis, the sameamount of signals (2000 signals) in each class (A, B, C, D and E) was used. This training set
included all the damage modes that may operate in this composite. The supervisedclassification is based on a random forest algorithm [17]. During the testing phase, each AE
signal runs down each tree of the forest, leading to T votes. The final decision can be obtainedby two different ways. The first one is simply the usual majority voting (MV) rule. In this work,a second decision rule is introduced called security voting (SV) rule. In this special rule, one
given AE signal is assigned to a specific class if more than 51% of the total number of trees
voted for that class and the second class the most represented following the votes has togather no more than 30 % of the votes [13].

3Results

3.1 Identification of a critical time during static fatigue test
The evolution of RAE coefficient versus time is given in Fig. 2a. RAE coefficient decreases first,
down to a minimum value, and then increases up to the failure of the composite [12]. On
average, the minimum of RAE appeared at 55% of the rupture time. The representation of AE
sources localization during the static fatigue tests reveals that the energy is mainly locatednear the failure zone after 55 % of the total test duration certainly due to the beginning of fibre
breaks (Fig. 2b). The colour scale corresponds to the recorded acoustical energy for the events
recorded in a mesh defined in the plane “position” versus “time”. During the first part of thestatic fatigue test, the recorded energy is distributed along the gauge length certainly
associated to the evolution of interfaces. The restart of activity prior to final rupture may be
attributed to the avalanche fibres ruptures, controlled by the oxidation of fibres and by therecession of interfaces.
Attenuation coefficient B [11] increases significantly during the first half of tests and reaches aplateau value at approximately 40% of the rupture time (Fig. 3a). At the same time, a slightincrease of the damage parameter D can be observed. Fig. 3b shows the evolution of
attenuation coefficient B during the tensile test carried out at 450°C. A slight increase isobserved until 0.2 % certainly due to the matrix cracking. Thus, attenuation coefficient B
remains relatively constant until 0.4 %. Behind this value, a significant increase is observed
certainly due to the cracks opening and the evolution of interphases. Moreover, attenuation
coefficient B decreases during unloading and increases back to its previous maximum value
at the end of reloading. Therefore, attenuation coefficient B, during static loading, is mainly
6

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sensitive to matrix crack opening. The plateau observed on the evolution of attenuation
coefficient B indicates that matrix crack opening gets to an equilibrium state around 50% of the
rupture time. The significant increase of matrix crack opening observed before 50% of the
rupture time is attributed to oxidation of carbon in the interphase causing an increase in lengthof the debonded region of fibres in the vicinity of matrix cracks. Beyond 50% of the rupture
time, the oxygen flux, determined by the degree of matrix crack opening, controls the rate at
which fibres break by subcritical crack growth. The sentry function (Fig 3c) mainly sensitive tothe matrix cracking does not bring further information.

a)

7

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b)

Figure 2 : a) Evolution of the coefficient RAE and b) Location of the acoustic emission signals and therecorded energy versus time for a static fatigue test at 500 °C and applied/r=0.44, the colour scale
represents the recorded energy in a mesh.

8

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a)

b)

c)
Figure 3: a) Evolution of the coefficient of attenuation B and the damage parameter D versus time
during a static fatigue test at 500 °C (applied/r=0.44). b) Evolution of the coefficient B versus strain
during a tensile test at 450°C and c) Stress/Strain curve and evolution of the sentry function versus
strain during a static fatigue test at 500 °C (applied/r=0.44).

9

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3.2 Identification of a critical time during cyclic fatigue test
The evolution of the coefficient RAE is very different during cyclic fatigue test from that observedin static fatigue (Fig.4). A minimum value. Is not observed. A significant change of slope takes
place for all mechanical tests at approximately 20 % of the total test duration [13]. This
characteristic time could certainly be used in order to evaluate time to failure. Moreover, it isinteresting to notice that the coefficient RLU highlights two characteristic times before 50% of
rupture time (Fig. 4), the coefficient RLU is again upper to 1 after the last characteristic time.
After this characteristic time, the increase of the coefficient RLU is linked to a significant increaseof the severity of the signals recorded during the loading part (Fig. 4). While for the signals
recorded during the unloading phases, the severity remains constant. Moreover, the coefficient
RLU goes through a minimum around 25 % of rupture time. It is interesting to notice that thecoefficient RLU highlights two characteristic times around 25 % and 50% of rupture time. Indeed
the coefficient RLU is again upper to 1 beyond this characteristic time [13].

Figure 4: Evolution with the number of cycles of the coefficient RAE, the coefficient RLU. (LT lifetime)
and the severity for two different cyclic fatigue tests at for a cyclic fatigue test at constant amplitude(max/r=0.36).

10

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3.3 Link with the damage mechanism
The coefficient RAE is calculated for the several damage mechanisms identified with clustering
analysis of AE data for the static fatigue tests (Fig. 5). The coefficients obtained for the twoclasses A and B go through a minimum, contrary to those of classes C and D. It may be noticed
that the minimum of the coefficient RAE is observed only for clusters A and B corresponding tofibres breaks during the second part of the test around 55 % of the rupture time.
The cluster analysis with random forest algorithm pointed out various damage mechanisms
generated by cyclic loading during the loading phase (Fig. 6). First with the cluster E, it ispossible to eliminate friction. With the supervised clustering, the cluster A is mainly recordedat the end of the test around 90 % of the total test duration and the signals are attributed to thebreaks of fibres. The cluster B associated with the matrix cracking in the longitudinal yarns is
significantly activated very early towards 25 %. Hence, with this analysis a link is establishedbetween the characteristic time at 25 % and the beginning of the matrix cracking [13].
Nevertheless, it is not possible with this analysis to make a link with the second characteristic
time around 50 % and the damage mechanisms. It may be due to the fact that only 50 % of
the signals are kept after the application of the security voting.

Figure 5 : Evolution of the RAE coefficient for the four clusters identified during static fatigue test

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Figure 6 : Evolution of the activity of several damage mechanisms during the loading phase of a cyclic
fatigue test.

3.4 Remaining Useful Lifetime obtained with acoustic emission for the static fatigue
tests
Beyond this critical time, the criticality of the liberated energy can be modelled by a power law
such as the Benioff law in order to evaluate the time to failure for the static fatigue test [12].
For this estimation, it is necessary to fit all the data collected between the time t RAE and therupture time in order to evaluate the exponent m. In fact, this value is very scattered. In thiscontext, it is very difficult to calibrate the law with a specific test (table 1).
The time tRAE presents a very interesting correlation with the lifetime duration of the test (Fig.
7) also for the tests conducted at 500 °C and 450 °C. The slope of the linear fit is equal to 1.49.
Another time may be introduced, based on the investigation of the evolution of the coefficient
B denoted tB. It corresponds to the time at which a significant change is observed in theevolution of B during the static loading. A second linear correlation may also be observed
between this time tB and the lifetime duration. Nevertheless, this time is very difficult to detectin real time and its evaluation presents uncertainty.

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tR (exp)

T (°C)

appliedr

500
500
500
500
450
450

0.95
0.79
0.67
0.44
0.71
0.62

Experimental
values
(h)

42
136
195
480
372
591

m
0.49
0.35
0.27
0.53
0.50
0.52

Table 1 : Exponent (m) for the Benioff law for the static fatigue tests conducted at 450 °C and 500°C.

Figure 7 : Correlation between the characteristic time tRAE, obtained with the minimum of the coefficientof emission, and the experimental time to failure.

The linear correlation confirms [6] that the characteristics time indicates the beginning of theavalanche fibres ruptures, controlled by the oxidation of the fibres and by the recession ofinterfaces, slowed down by the self-healing matrices. With this characteristic time, it is possible
to estimate the remaining lifetime with the following equation:𝑡𝑡𝑟𝑟(𝑅𝑅𝑅𝑅𝑅𝑅) = 1.49𝑡𝑡𝑅𝑅𝑅𝑅𝑅𝑅 + 26

(8)

The results are summarized in the Table 2. The estimated TTF are denoted tR(RAE).The
correlation between the experimental values and the predicted values is good when the ratioappliedR is lower than 0.7 whereas large inaccuracies can occur for high appliedRratio.
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T (°C)appliedr

500
500
500
500
500
450
450

0.95
0.71
0.79
0.67
0.44
0.71
0.62

tR (exp)
Experimental
values
(h)
42
109
136
195
480
372
591

Lifetime estimation (h)tr(RAE)(eq. 8)
61
115
131
206
425
386
571

Table 1 : Comparison between the estimated time to failure and the experimental time to failure
obtained with the two methods.

3.5 Limitation of this approach :In these studies, the wave from the source is altered during propagation and this aspect is nottaken into account. Indeed, the signal is modified during propagation (mode conversion,reflections, dispersion) and then by the acquisition system. Aggelis [18] shows that the
separation distance of the sensors is a paramount importance and it should be taken into
account. The analyzed parameters are highly dependent on the material properties, thestructure geometries, the sensor, the detection and the analysis system. In addition, the stateof damage of the material can affect AE signals [19-20] and the recorded energy. The geometryof the sample is also an important parameter [21]. These aspects need to be included in amodelling work in order to establish a quantitative link between the acoustic energy and theenergy released by the source.

4 Conclusion
This paper focuses on the investigation of the damage during fatigue (static and cyclic) testsof CMC at intermediate temperature. Some new damage indicators are defined based on AE
energy activity. These new AE indicators show the evolution of damage during long-term tests.
AE is a powerful method to identify critical times and to predict the remaining lifetime. An
empirical relation based on the critical time tRAE is defined, which allows prediction of the timeto-rupture. These results offer very interesting perspectives. Now, it is necessary to investigate
the effects of the size of the specimens in order to apply this method to real parts. Future work
will focus on the introduction of new predictive laws beyond this characteristic time in order toevaluate the remaining lifetime.
5. Acknowledgments
The collaboration with SAFRAN Ceramics is gratefully acknowledged.
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References:[1] Dalmaz A, Reynaud P, Rouby D, Fantozzi G & Abbe F. Mechanical behaviour anddamage evolution during fatigue at high temperature of a 2,5D Cf/SiC composite. Composite
Science and Technology 1998; 58: 693-699.
[2] Lamon J. Micromechanics-based approach to the mechanical behavior of brittle matrix
composites. Composites Science and Technology 2002; 58 : 2259-2272.
[3] Loutas T, Eleftheroglou N, Zarouchas D. A data-driven probabilistic framework towards
the in-situ prognostics of fatigue life of composites based on acoustic emission data.
Composites structures Composite Structures 2017 ;161 : 522–529.
[4] Suresh Kumar C, Arumugam V, Sengottuvelusamy R et al. Failure strength prediction ofglass/epoxy composite laminates from acoustic emission parameters using artificial neuralnetwork, Applied Acoustics 2017; 115 : 32-41.
[5] Eleftheroglou N; Loutas T. Fatigue damage diagnostics and prognostics of composites
utilizing structural health monitoring data and stochastic processes; Structural Health
Monitoring 2016; 15 (4) : 473-488.
[6] Godin N, Reynaud P, Fantozzi G. Contribution of AE analysis in order to evaluate time to
failure of ceramic matrix composites Engineering Fracture Mechanics
https://doi.org/10.1016/j.engfracmech.2018.03.006[7] Godin N, Reynaud P, Fantozzi G. Acoustic emission and durability of composites
materials, Book published by ISTE-Wiley editions, N° ISBN : 9781786300195, 2018.
[8] Sause M., “In situ monitoring of fiber-reinforced composites : Theory, basic concepts,
methods, and applications” Springer series in materials science 242, 2016.
[9] Ramasso E., Placet V., Boubakar L. , Unsupervised Consensus Clustering of Acoustic
Emission Time-Series for Robust Damage Sequence Estimation in Composites, IEEE
Transactions on Instrumentation and Measurement, vol 64, p. 3297-3307, 2015.
[10] Maillet E, Godin N, R'Mili M, et al., Real-time evaluation of energy attenuation: A novel
approach to acoustic emission analysis for damage monitoring of ceramic matrix composites,
Journal of the European Ceramic Society. 2014, 34 (7) :1673-1679.
[11] Godin N, Reynaud P, R’Mili M, Fantozzi G. Identification of a Critical Time with Acoustic
Emission Monitoring during Static Fatigue Tests on Ceramic Matrix Composites: Towards
Lifetime Prediction, Appl. Sci. 2016; 6 (2) : article number 43[12] Momon S, Moevus M, Godin N, R’Mili M, Reynaud P, Fantozzi G, Fayolle G. Acoustic
emission and lifetime prediction during static fatigue tests on ceramic matrix composite athigh temperature under air. Comp: Part A 2010;41:913-18.

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[13] Racle E, Godin N, Reynaud P and Fantozzi G. Fatigue Lifetime of Ceramic Matrix
Composites at Intermediate Temperature by Acoustic Emission, Materials 2017; 10 (6) article
number 657.
[14] Minak G; Morelli P; Zucchelli A. Fatigue residual strength of circular laminate graphite–
Epoxy composite plates damaged by transverse load. Compos. Sci. Technol. 2009; 69:1358–1363.
[15] Bufe CG, Varnes DG. Predictive modeling of the seismic cycle of the Greater San
Francisco Bay Region; Journal of Geophysical Research 1993; 98 : 9871-83[16] Moevus M, Godin N, R’Mili M, Rouby D, Reynaud P, Fantozzi G, Fayolle G. Analyse ofdamage mechanisms and associated acoustic emission in two SiC/[Si-B-C] composites
exhibiting different tensile curves. Part II: Unsupervised acoustic emission data clustering.
Comp Sci Technol. 2008; 68 (6): 1258-1265.
[17] Morizet N., Godin N., Tang J., Maillet E., Fregonese M., Normand B., Classification of
acoustic emission signals using wavelets and Random Forests : Application to localized
corrosion. Mechanical Systems and Signal Processing, 2016, Volumes 70–71, pp. 10261037[18] Aggelis D.G. Shiotani T. Papacharalampopoulos A. and Polyzos D. The Influence of
propagation path on elastic waves as measured by acoustic emission parameters. Structural
Health Monitoring 11(3), 359-366 (2011)[19] Kharrat M., Placet V., Ramasso E. and Boubakar L., “Influence of damage accumulation
under fatigue loading on the AE-based health assessment of composite material: Wave
distortion and AE-features evolution as a function of damage level”. Composites Part A:
Applied Science and Manufacturing, in press,
http://doi.org/10.1016/j.compositesa.2016.03.020, 2016[20] Carpinteri A., Lacidogna G., Accornero F., Mpalaskas A.C, Matikas T.E., Aggelis D.G.,“Influence of damage in the acoustic emission parameters”, Cement and concrete
composites 44, p. 9-16, 2013.
[21] Le Gall T., Godin N., Monnier T., Fusco C. and Hamam Z. “Acoustic Emission modelingfrom the source to the detected signal: model validation and identification of relevant
descriptors, Journal of Acoustic Emission, vol 34, 2016

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04 - Combinationof synchrotron COMPUTED
computed tomography
COMBINATIONOF SYNCHROTRONTOMOGRAPHY
and Acoustic
Emissionmeasurements
AND ACOUSTICEMISSION
MEASUREMENTS
FOR CYCLIC
forcyclicloadingoffibre-reinforced
composites
LOADING OF FIBRE-REINFORCED COMPOSITES
Philipp Potstada1, Sebastian Rosini2, Mark Mavrogordato2, Ian Sinclair2, S. Mark
Spearing2, Markus G. R. Sause1
1 University of Augsburg, Institute for Materials Resource Management, MechanicalEngineering, D-86135 Augsburg

2 University of Southampton, Faculty of Engineering and the Environment,

Abstract:
During incremental load-unload cycles of fibre-reinforced materials, the re-initiation of acoustic emission
below the previously reached load is frequently observed. This is known as the Felicity effect and its
physical origin has been discussed in the community for several decades. The principal explanations
for this effect are the rubbing together of existing crack faces which thus act as tribological acoustic
emission sources or the initiation of new damage because of material stress-relaxation during theunloading phase. We conducted combined acoustic emission measurements and high-resolution
synchrotron computed tomography experiments on a carbon fibre / epoxy prepreg system. Samples
were prepared as double-edge notched tensile specimens with [±45 0 ±45], [0 903 0] and [90 03 90]
layups. At a voxel resolution of 1.625µm, we were able to visualize the growth of damage as a functionof applied load/unload steps in a sequence of volume images. With the simultaneously acquired acoustic
emission signals, this allows identification of the origin of the source volumetrically, as three volumes
were taken per load step in situ. These investigations are part of an ongoing measurement campaign
and represent a first interpretation towards the physical origin of the early acoustic emission onset infibre-reinforced materials. This provides a first step to reveal the origin of the Felicity effect in fibrereinforced materials.

1. Introduction
Fibre-reinforced polymer composites are used in transport applications because of theirstrength/weight and stiffness/weight ratios to reduce component weight, and hence to provide
greater fuel efficiency and lower emissions. However, predicting damage formation andprogression remains challenging, so conservative design levels are used and safety-relevant
components need to be re-qualified frequently.
Acoustic emission (AE) sensing represents a core non-destructive testing technology forcomposite materials in transport applications, in which transient elastic waves, linked tosudden stress redistribution phenomena (such as micro-cracking, interface sliding) are used
to detect and quantify damage processes. In many safety-critical applications of fibrereinforced polymers such as pressure vessels, aerospace structures and similar, qualification
and re-qualification of components makes use of the measurement of the so-called Felicity
ratios (FR) as introduced by Timothy Fowler [1].
In his pioneering work, Joseph Kaiser demonstrated for metallic materials, that AE usually
starts after exceeding the previous load level [2]. However, as first pointed out by Timothy
Fowler, in fibre-reinforced polymers, the AE signals usually initiate at lower load levels than1

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those previously achieved [1]. After almost 40 years of scientific debate [3]–[10], no convincing
experimental evidence has hitherto been provided that clearly explains the source of such early
AE signals in FRPs and thus explains why these materials apparently violate the Kaiser effect[1].
In the scientific community there are essentially two principal ways of interpreting this early
onset of AE:
1. Microscopic relaxation, viscoelastic behaviour and “settling” of the material cause local
stress levels, that are sufficient to initiate new damage at macroscopic load levels,
lower than those previously reached;
2. Stick-slip friction (rubbing) of existing crack walls causes AE, which is not related toany microscopic or macroscopic damage formation.
As stick-slip friction is a well-known source of AE, it cannot be excluded, that all or part of theearly AE originates from this source. Revealing that such non-damage related AE acts as anindicator for the structural integrity of a fibre-reinforced polymer would immediately question
the validity of current testing routines, as accept / reject decisions are then taken based on
friction signals, not damage growth. In addition, the formation of new microscopic damage as
consequence of local stress redistribution is another aspect, which is rarely taken into accountin structural analysis and definition of test procedures.
Previous work using synchrotron radiation computed tomography (SRCT) has demonstrated
that damage mechanisms, such as individual fibre fractures, matrix cracking, and otherdamage mechanisms can be reliably located and quantified [11], [12]. Thus, with the ability toobserve visually the initiation and growth of new damage at low load levels and to distinguish
this versus the relative movement of crack walls due to stick-slip friction we expect to make a
key contribution to this scientific and engineering question.
This paper presents initial data demonstrating that it is possible to acquire AE-signals during
an in situ SRCT loading experiment at the European Synchrotron Radiation Facility (ESRF) in
Grenoble France. In this study, we present first results to correlate the AE signals recorded
during load/unload cycles of a carbon/epoxy material with the occurrence of microscopic
damage identified by SRCT.

2. Experimental

For the experiments at the ESRF, the microtomography beamline ID19 was chosen in orderto provide optimal image fidelity at the resolution required. For the in situ acquisition, a
combination of a MISTRAS PCI-2 AE-system and a modified DEBEN CT5000 tensile stage
proven in previous studies was used [13], [14]. The AE measurement chain consists of aminiature multi-resonant sensor HD2WD from MISTRAS. Two AE sensors were used to allow
a one-dimensional AE source localization. The acquired signals were amplified by a
MISTRAS 2/4/6 pre-amplifier and were subject to a built-in analogue 20-1200 kHz band-passfilter. Preamplifiers were mounted on the rotation table close to the in situ tensile load stage.
The amplified AE signal was led outside the experimental hutch using a standard RG58 coaxial
cable, which connects to the PCI-2 acquisition card. The acquired signals were recorded with
the MISTRAS software AEwinTM.
The load was applied by a commercial CT5000 DEBENTM U.K. in situ tensile stage connected
via the associated controller box to a standard PC using software provided by DEBENTM U.K.
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While the drive unit and the load cell of the tensile stage remained unmodified relative to thecommercial setting, the load introduction concept of the specimen was modified (see Figure1).

top cap

slits

finger grip

PMMA tube

specimenbase

Figure 1. Modified part of the in situ tensile stage with finger grip clamping system and PMMA tube
with two inserts for acoustic emission sensors cables.

The load was introduced via a finger grip system, which ensures a simple assembly of thespecimen and a proper load transfer into the specimen after reaching a slight pre-load. The
upper finger grip was statically fixed in a top-cap, which transferred the load via a PMMA tube
into a custom-made frame of the tensile stage. To ease assembly of the PMMA tube, it washeld in position with three screws. For synchronous evaluation of mechanical load and acoustic
emission data, the current force values were fed into the AE system by a proportional analogue
voltage signal.
Based on the explanations in Section 1, to reveal the origin of the Felicity effect, it is necessary
to induce step-wise crack growth and potential friction effects during load-unload experiments.
Accordingly, we used three different multi-angle layups, which allow growth of a crack network
before ultimate failure of the specimen. In addition, based on the layup we also expect a certain
likelihood of mutual crack interaction and the occurrence of crack surface friction. Based onearlier investigations by Wright et al. [15], we prepared double edge notched tensile specimens
via waterjet cutting from [±45 0 ±45], [0 903 0] and [90 03 90] laminate plates. All laminates
were produced from carbon/epoxy prepreg, SigrafilTM CE1250-230-39 following the material
supplier’s specifications. T-shaped aluminium tabs were adhesively bonded to the specimen
specimen ends. A grinding and cleaning phase on the contact surface between tabs andcoupons took place before application of fast curing methyl-methacrylate adhesive. To ensure
adhesion and mechanical stability of the AE sensors on the specimens, holders were
fabricated out of polylactide by additive manufacturing and attached tightly to the specimens
using rubber rings. To obtain a repeatable and correct positioning of the support structure, theholders include four integrated dowel pins (as indicated in Figure 2).

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At the beginning of each test, three pencil lead breaks (PLB) were performed at the base of
the tensile stage to ensure a sufficient coupling of the AE-sensors to the specimen. The settingused for the AE-acquisition for both sensors were a threshold of 35dBAE at 40dBAE preamplification and a Peak Definition Time (PDT) of 10µs, a Hit Definition Time (HDT) of 80µs
and a Hit Lockout Time (HLT) of 300µs. The AE signals were acquired with a sampling rate of
10 MS/s.
After a satisfactory pre-check, the AE-acquisition was started. A first SRCT scan was collected
to record the initial (undamaged) specimen volume. To ensure a proper alignment of thespecimen in the beam path, a load of 15 N (representing a small percentage of the sample
ultimate tensile strength) was applied. At this load level, no pre-damage was observed. The
drive unit was switched off and the cross-head position was kept constant by the self-locking
mechanics of the loading stage. The scan was performed continuously with beam energy of
19.5 keV. During each tomographic scan, 2996 projections with an exposure time of 50 ms
were collected at a detector distance of 50 mm. The voxel size was 0.65 µm.

upper fingergrip

SR window

PMMA tube

dowel pins

AE-sensor holder
with HD2WD

notched area
rubber ringAE-sensor holder
with HD2WD
lower fingergrip

Drive unit with load cell
Figure 2. In situ tensile stage with installed specimen and AE sensors ready for testing.

For the evaluation of the Felicity effect, load/unload experiments are carried out at a constantdisplacement rate of 0.2 mm/min. Typically, the load is increased to some nominal target value
𝐿𝐿1 , then reduced and increase again to a higher value 𝐿𝐿2 . This is continued for increasing loads
until the material fails at a value 𝐿𝐿𝑚𝑚𝑚𝑚𝑚𝑚 . At the first occurrence of significant acoustic emission
during the reload cycle, the load level 𝐵𝐵2 is defined. This allows calculation of the Felicity ratio
FR for the second load cycle according to 𝐹𝐹𝐹𝐹 = 𝐵𝐵2 ⁄𝐿𝐿1. In order to reveal the origin of the
Felicity effect, we follow a slightly modified load schedule in this investigation. Other than predefined step-wise increased loading, test were interrupted after the occurrence of a sequenceof acoustic emission signals (e.g. > 5 signals). After reaching this peak load, the specimens
are unloaded by 10% of the peak load. Two more load/unload cycles are carried out with thesame peak load as seen in the examples of Figures 6-8.This allows the virgin Felicity ratio tobe determined at each load level and to distinguish this from subsequent loading to the same
4

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load level. At each plateau phase seen in Figures 6-8, additional SR scans are taken to assess
the occurrence of damage progression. Two more peak load increments are carried out foreach sample to induce a certain amount of damage progression. For the three different sample
types, the ultimate tensile strength was calculated as 577 MPa for [±45 0 ±45], 736 MPa for [0903 0] and 1046 MPa for [90 03 90] layups.
2.1. Acoustic emission evaluation
For the acquired AE signals, a feature extraction routine was performed. Besides the common
AE feature values of amplitude, energy, duration, also the partial powers of six equal frequency
ranges of 150 kHz width, ranging from 0 MHz to 1 MHz were calculated out of the first 35 µs
after the arrival time. The arrival time was calculated using the Akaike information criterion(AIC) [16]. In addition, spectral feature values, such as the weighted peak frequency andfrequency centroid were calculated from FFT spectra of the signals (see [17] for precise
definition of features). A first data reduction was carried out to remove erroneous AE signals
resulting from driver motor control signals. These were easily identified based on their uniquefrequency characteristics and their time of occurrence of the direction of load reversal.
Furthermore, AE sources were localized using a classical t-based algorithm considering the
given geometry of the specimen and the sensor positions. The velocity of sound was measured
for each material using time-of-flight tests on the raw material plates before cutting the test
geometries. Only the AE-signals localized within 50±3 mm around the notch centre were used
for the following evaluation. This limitation is necessary to conform with the observation volumeof the SR scans, i.e. to reach an equivalent data basis for both methods.
A pattern recognition approach following [18] was performed using a Gustafson-Kessel
algorithm allowing a maximum number of 𝑃𝑃 = 6 clusters and a minimum number of 𝑀𝑀 = 4
features for 12 previously selected list of frequency features 𝐾𝐾. The selected features arereverberation frequency, average frequency, initiation frequency, partial power 1-6, peakfrequency, frequency centroid and weighted peak frequency. Similar to other specimens tested
in this experimental setting [19], two clusters are identified in this optimization procedure.
Based on conclusions of earlier investigations [17], [20], [21] and direct validation within [19],these are ascribed to the occurrence of fibre breakage (high frequency cluster) and to theoccurrence of matrix cracking and interfacial related mechanisms (low frequency cluster).
For the evaluation of the Felicity ratio, a modified procedure originally published in [4], [22] wasproposed to stabilize the numerical values [23]. In contrast to this, for this investigation, we use
the most conservative approach and take the load level of the first acoustic emission signal as
the onset value for the definition of the Felicity ratio. Additionally, we distinguish between
signals of both clusters, e.g. we calculate separate Felicity ratios for fibre breakage and matrix
cracking / interfacial failure related AE signals.
2.2. SRCT evaluation
Ten scans of the same region of interest (showed in Figure 4) were collected, while thespecimens were increasingly loaded (and strains increased). To ensure at a micro-metre scalethat the same region could be investigated while damage progressed, a registration of thescanned volumes was made by visual inspection. The user was asked to recognise a uniquefeature (e.g. a particular pattern in the top view of the fibres) and to record the position of thisfeature in all the scanned volumes. Based on this, all the volumes were cropped and aligned
to a same position along the specimen length. All the scans were loaded onto the software
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VGStudio MAXTM and the volume recorded at the highest strains was observed first. Cracks
were visually detected and segmented using a growth segmentation tool available within thesoftware. The user was able to identify the most developed cracks and reduce the time needed
to investigate the volumes at lower strains as the centroids of the segmented cracks had
approximately the same coordinates in all the registered volumes. By visual check and use of
the automated growth segmentation tool it was then possible to record the evolution of aparticular crack. An example of a crack detected with the automated tool is shown in Figure 3
were both the contours of the less and more developed crack are shown respectively in dark
and lighter cyan.

a)

50 µm

b)

15 µm

Figure 3. a) Crack evolution as identified on a single slice of the highest strains scanned volumeb) zoomed region showing in black the crack at the highest strains with its contours in light cyan. In
darker cyan, the contours of the same less developed crack as detected with the growth segmentation
tool in a scanned volume at lower strains.

inter-fibre crack
inter-fibre crackzzx

500 µmy

500 µmFigure 4. Frontal (left) and lateral (right) view of the region of interest, kept the same for all thescanned volumes. The inter-fibre crack is shown in red as identified in the highest strains scans. The
combined use of a visual approach and an automated growth segmentation tool on VGStudio MAXTM
allowed the user to identify the crack evolution from lower to higher strains.

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3. Results
In the following, the results of the in situ investigations are presented, using combined AE and
SRCT measurements, distinguished based on the stacking sequence used.
3.1. Layup [0 903 0]
As the first case, a cross-ply stacking with a predominantly 90° fibre orientation relative to theload axis is studied. With increasing load, the occurrence of transverse matrix cracks (interfibre cracks) is observed, see Figure 5. In addition, fibre breakage is observed to occur in the
0° plies. From SRCT observations, no growth of delamination between the 0° and 90° plies
was observed. Figure 6 shows the acoustic emission results of one specimen. Based on thepattern recognition results, cumulative hits for both signal classes are superimposed on theload-time curves. Each cycle with occurrence of acoustic emission additionally holds thecorresponding Felicity ratios for each signal class. For this cross-ply stacking, all Felicity ratios
were > 0.99, rendering it impossible to discuss the origin of acoustic emission signals at Felicity
ratios ≪ 1.00. However, there is a first indication, that Felicity ratios of fibre breakage related
signals are typically larger than those of matrix cracking / interfacial failure related signals. This
is in accordance with observations on macroscopic specimens [17].

Azycrack region

x
B

z
x

C

400 µmFigure 5. Visualization of inter-fibre crack and volumetric evaluation at three load levels (A-C) as
noted in Figure 6.

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Figure 6. Example of layup [0 903 0] including Felicity Ratios evaluated for fibre breakage signals (red)and matrix cracking / interfacial failure related signals (blue).

3.2. Layup [±45 0 ±45]
As second case study, a ±45° dominated stacking sequence was investigated. In theevaluation of acoustic emission signals, two cycles result in Felicity ratios significantly lower
than unity (i.e. FR = 0.85 and FR = 0.84). Until reaching a load level of 57% ultimate tensile
strength, in SRCT the only significant damaged region is observed close to the notch positions.
As seen in the SRCT slice example for load level A in Figure 7, part of this damage is alreadypresent in the beginning of the experiment (centre break, bright zone on right). Accordingly, it
is likely, that this damage is a residue of the drilling process used to introduce the notches. At
increased load levels (example at load level B) the number of cracks appears to grow.
However, in all cases inspected, indications of the presence of these cracks is already foundin the first (unloaded) SRCT scan. With applied load, the cracks are seen much better than in
the unloaded case due to crack opening, resulting in the appearance of crack growth. However,the potential occurrence of additional damage growth in this area cannot be excluded. This is
likely causing the numerous acoustic emission signals recorded in the experiment.
Unfortunately, with no other evidence of crack growth from SRCT inspection, this does notallow a dedicated interpretation of the recorded Felicity ratios for this stacking sequence.
3.3. Layup [90 03 90]
As third case, a cross-ply stacking sequence with predominant fibre orientation in 0° was
tested. Other than the test case of Section 3.1, this apparently results in less acoustic emission
activity as seen in Figure 8. However, the Felicity ratios for this case are also consistently> 0.99, resulting in the same difficulty of interpretation of early onset acoustic emission signals.
Similar to the previous cases, fibre breakage related signals apparently tend to result in slightly
higher Felicity ratio values. In SRCT scans, we observed the occurrence of multiple fibre
breaks. In the example shown in Figure 8, fibre breaks are visible at load level B located in
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close proximity to a high-density inclusion (probably a dust particle introduced during specimen
manufacture). Apart from that, no matrix cracks or delamination are visible in the SRCT scans.

Figure 7. Example of layup [±45 0 ±45] including Felicity Ratios evaluated for fibre breakage signals(red) and matrix cracking / interfacial failure related signals (blue) and SRCT slices at two load levels(A-B).

Figure 8. Example of layup [90 03 90] including Felicity Ratios evaluated for fibre breakage signals(red) and matrix cracking / interfacial failure related signals (blue) and SRCT slices at two load levels(A-B).

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4. Conclusion
The combination of high-resolution SRCT and acoustic emission measurements has beensuccessfully carried out during step-wise cyclic loading of fibre-reinforced materials. The
overall experimental approach appears promising to reveal the origin of the Felicity effect infibre-reinforced materials. However, in the present test campaign the vast majority of cases
results in Felicity ratios ~ 1.0. Based on the tensile load and the layup used in theseexperiments, the fracture surfaces observed in SRCT are mode I dominated. No fracturesurfaces were generated that are expected to undergo high tribological load during load/unload
cycles. The only cases with Felicity ratios significantly below zero were the [±45 0 ±45]
samples. Based on this layup, the expectation is a delamination damage growth between plies,
resulting in mixed mode loading. However, the SRCT observation do not indicate suchdelamination growth in the experiments. Accordingly, the present data basis does not allow a
final judgement on the true origin of the Felicity effect. However, a general trend of fibre
breakage Felicity ratios being larger than for matrix cracking and interfacial failure related
signals has been observed. This potentially indicates a difference in the origin of signal onset
in these failure modes. Accordingly, future test campaigns should take into account different
load concepts and layups to force the occurrence of shear loaded fracture surfaces. This might
result in a higher number of test cases with low Felicity ratios.

Acknowledgments
The authors would like to acknowledge the European Synchrotron Radiation Facility forprovision of synchrotron radiation facilities and would like to thank Dr. Lukas Helfen and Ms.
Elodie Boller for assistance in using beamline ID19. The authors also gratefully acknowledge
the µ-VIS X-Ray Imaging Centre at the University of Southampton for provision of tomographic
imaging facilities, supported by EPSRC grant EP-H01506X.

References:
[1]

[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]

T. J. Fowler, “Acoustic Emission Testing of Fiber Reinforced Plastics,” Proc. Pap. J.
Tech. Counc. ASCE, vol. 105(TC2), pp. 281–289, 1977.
J. Kaiser, “Untersuchungen über das Auftreten von Geräuschen beim Zugversuch,”
Dissertation, Technische Hochschule München, 1950.
J. Awerbuch, M. R. Gorman, and M. Madhukar, “Monitoring Acoustic Emission During
Quasi-static Laoding-Unloading Cycles of Filament-wound Graphic-Epoxy Laminate
Coupons,” Mater. Eval., vol. 43, no. 6, pp. 754–764, 1985.
K. S. Downs and M. A. Hamstad, “Acoustic Emission from Depressurization to
Detect/Evaluate Significance of Impact Damage to Graphite/Epoxy Pressure Vessels,”
J. Compos. Mater., vol. 32, no. 3, pp. 258–307, Feb. 1998.
W. Haselbach and B. Lauke, “Acoustic emission of debonding between fibre andmatrix to evaluate local adhesion,” Compos. Sci. Technol., vol. 63, no. 15, pp. 2155–
2162, Nov. 2003.
M. R. Gorman, “Acoustic Emission in 2-D Carbon-Carbon Coupons in Tension,” J.
Compos. Mater., vol. 25, no. 6, pp. 703–714, Jun. 1991.
K. Ono, “Structural integrity evaluation using acoustic emission techniques,” J Acoust.
Emiss., vol. 25, pp. 1–20, 2007.
K. Ono and A. Gallego, “Research and Applications of AE on Advanced Composites,”
J Acoust. Emiss., vol. 30, pp. 180–229, 2012.
M. A. Hamstad, “A review: Acoustic emission, a tool for composite-materials studies,”
Exp. Mech., vol. 26, no. 1, pp. 7–13, 1986.
M. A. Hamstad, “Thirty years of advances and some remaining challenges in the
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application of acoustic emission to composite materials,” in Acoustic Emission Beyond
the Millennium, S. Y. T. Kishi, M. Ohtsu, Ed. Amsterdam: Elsevier Science, 2000, pp.
77–91.
P. Wright, X. Fu, I. Sinclair, and S. M. Spearing, “Ultra High Resolution Computed
Tomography of Damage in Notched Carbon Fiber--Epoxy Composites,” J. Compos.
Mater., vol. 42, no. 19, pp. 1993–2002, Oct. 2008.
A. E. Scott, M. Mavrogordato, P. Wright, I. Sinclair, and S. M. Spearing, “In situ fibre
fracture measurement in carbon-epoxy laminates using high resolution computed
tomography,” Compos. Sci. Technol., vol. 71, no. 12, pp. 1471–1477, Aug. 2011.
S. C. Garcea, M. N. Mavrogordato, A. E. Scott, I. Sinclair, and S. M. Spearing,“Fatigue micromechanism characterisation in carbon fibre reinforced polymers usingsynchrotron radiation computed tomography,” Compos. Sci. Technol., vol. 99, pp. 23–
30, Jul. 2014.
S. C. Garcea, I. Sinclair, and S. M. Spearing, “Fibre failure assessment in carbon fibre
reinforced polymers under fatigue loading by synchrotron X-ray computed
tomography,” Compos. Sci. Technol., vol. 133, pp. 157–164, Sep. 2016.
P. Wright, A. Moffat, I. Sinclair, and S. M. Spearing, “High resolution tomographic
imaging and modelling of notch tip damage in a laminated composite,” Compos. Sci.
Technol., vol. 70, no. 10, pp. 1444–1452, 2010.
H. Akaike, “Markovian representation of stochastic process and its application to theanalysis of autoregressive moving average processes,” Ann. Inst. Stat. Math., vol. 26,
pp. 363–387, 1974.
M. G. R. Sause, In Situ Monitoring of Fiber-Reinforced Composites, vol. 242. Cham:
Springer International Publishing, 2016.
M. G. R. Sause, A. Gribov, A. R. Unwin, and S. Horn, “Pattern recognition approach toidentify natural clusters of acoustic emission signals,” Pattern Recognit. Lett., vol. 33,
no. 1, pp. 17–23, Jan. 2012.
P. Potstada, S. Rosini, M. Mavrogordato, I. Sinclair, M. Spearing, and M. G. R. Sause,“Cross-validation of single filament failure by acoustic emission and high-resolution
synchrotron computed tomography,” in ECCM18 - 18th European Conference on
Composite Materials, 2018, pp. 1–8.
M. G. R. Sause, T. Müller, A. Horoschenkoff, and S. Horn, “Quantification of failure
mechanisms in mode-I loading of fiber reinforced plastics utilizing acoustic emission
analysis,” Compos. Sci. Technol., vol. 72, no. 2, pp. 167–174, Jan. 2012.
M. G. R. Sause and S. Richler, “Finite Element Modelling of Cracks as Acoustic
Emission Sources,” J. Nondestruct. Eval., vol. 34, no. 1, p. 4, Mar. 2015.
K. S. Downs and M. A. Hamstad, “Correlation of Acoustic Emission Felicity Ratios andHold-Based Rate Moments with Burst Strngths of Spehrical Graphite/Epoxy Pressure
Vessels,” J. Acoust. Emiss., vol. 13, no. 3/4, pp. 45–56, 1995.
M. G. R. Sause, S. Schmitt, and S. Kalafat, “Failure load prediction for fiber-reinforced
composites based on acoustic emission,” Compos. Sci. Technol., vol. 164, pp. 24–33,
Aug. 2018.

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48 - Structural
HealthMonitoringof composite
compositesstructures
structuresby
Structural
HealthMonitoringof fibreby
AcousticEmission
EmissionAnalysis
analysis
Acoustic
Lars Schubert1, Eberhard Schulze1, Robert Neubeck1 *, Bianca Weihnacht1
1

Fraunhofer Institute for Ceramic Technologies and Systems, Dresden, Germany

Abstract:
Fiber reinforced composites are often used in actual light weight structures applications likestructural components for automotive or rotor blades for wind turbine power applications. The
reliability of those components are of great importance for safety relevant areas. The lifetimeof the components are affected by damages like fibre cracking or delaminations which canlead to early failure of the overall structure. The acoustic emission testing is well accepted forstatic or dynamic testing of fiber composite components since fiber breaks and delamination
processes cause high energetic acoustic emissions. The challenge lies in the application of
the method during field measurement tests like filtering any indications caused by the noisy
environment and the development of special condition monitoring hardware which allows the
instrumentation of rotating wind turbines. Damage evaluation parameters like the weighted
peak frequency were evaluated during tensile lab test and full-scale fatique tests andsubsequently transferred to real operation of wind turbines. Results from field operation ofseveral systems on wind turbine power blades will be presented and discussed.

1. Introduction
Fibre composites as used for the construction of rotor blades are constructed based on theprinciple of material science especially continuum mechanics and the well-known material
characteristics of the single components. Nevertheless, a pre-damage or an actively
introduced failure during operation may shorten the life time significantly and question theoriginal material design.
The non-destructive testing of these materials is challenging. Despite to the standard visual
testing, the proposed technique of acoustic emission testing (AET) may also detect damages
in the material which are not visible from the outside. Specific acoustic emission parameters
may serve as early warning factors since especially fibre breakage and delamination course
strong acoustic emissions.
The technique of AET is based on the fact, that dynamic dislocations in the nm range can bedetected on the surface of loaded objects by highly sensible piezoelectric sensors in thefrequency range of 10 kHz – 1 MHz. The dislocations on the surface are caused by acoustic
waves by short time and very small material dislocations due to micro crack development,
crack growth, friction, delamination, matrix cracking etc.

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Sensors have to be adapted to the characteristics of the material. Delaminations can becommonly detected up to 150 kHz, fibre debonding or cracks show significant higher
frequencies. Any noise has to be filtered by band pass filtering. To determine electromagnetic
noise, several sensors have to be applied to the testing object to differentiate between the data
signal and noise.

2. Description of work
Tensile bending test
The 14 probes were manufactured at the project partner cp.max Rotortechnik and refer to realmaterials from rotor blade manufacturers regarding the layer design. During the experiments,
the analogue signals of the force were monitored continuously during the experiment. The
probes were instrumented with sensors developed at the Fraunhofer IKTS KAR601S which
are adapted to the needed frequency range. Four sensors were positioned at the upper andlower border of the probe (as shown in Figure 1) to get redundant information in order to
exclude noise. Furthermore, during the experiments an in-house developed measurement
system MAS (Multi-Channel-Acoustic-System) was used for the data acquisition. Theseexperiments were conducted at the project partner IMA Dresden.

Figure 1: Tensile testing at the moment of failure - Probe CP01 [3]

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Full scale rotor blade test
Beside the lab testing, also a full scale dynamic fatique test took place. These experiments
were conducted at the project partner IMA Dresden with an optical fibre measurement system(Teletronic Rossendorf) which can also be used at a rotating wind mill despite the lightning
issues.
The rotor blade was instrumented with 24 acoustic sensors as shown in Figure 2.

Figure 2: Spatial distribution of 24 acoustic sensors in the rotorblade

3. Discussion
Tensile tests
Figure 3 shows the characteristics of the location of the traverse during the experiment which
increases due to weakening of the material. The size of the circles of the events in magenta is
proportional to the energy of the burst signals. The diagram also shows the curves of the
cumulated number of events in black and the cumulated burst energy in blue. The green curves
allow for the estimation of the burst energy at the single experiment time intervals. As it can beseen, the experiment was stopped. During failure, the location of the traverse and also theburst energy increase significantly.
Figure 4 shows a summary of all measured probes which allows a clustering and therefore an
evaluation of the damage mechanisms. The clusters in the parameter space of weighted peakfrequency and partial power (150-300 kHz) display various source mechanisms and failure
types. Low values of WPF <150 kHz are mainly caused by matrix failure / delaminations /3

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friction processes, whereas high values of WPF >150 kHz are mainly caused by fibre matrix
failure and fibre breakage [1,2].

Figure 3: Results for the Probe CP01 - Displacement (red), Source location (magenta) [3]

Full scale rotor blade test
During the fatique test, the rotor blade failed completely due to a transversal crack at R=18m
Figure 5. The located events are shown in Figure 6. At the location of failure, little activity isshown. Therefore, clear indications are presented on both sides of the crack (diagonal
structures). They indicate that those areas have to compensate the missing load bearing
capacity in the failure area and loaded beyond the load they are designed for. To give a
better insight, the following diagrams (Figure 7, Figure 8) show all events and the absolute
power occurring at certain radii. It can be seen, that in the crack area which is marked with avertical red line, no indications are shown. Nevertheless, at the radii of the diagonal failurezones, clear indications are found. Looking at the time scale, the failure can be determined atabout 34000 cycles since the energy increases there significantly.

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Figure 4: Partial power of the burst signals against their the weighted peak frequency (WPF)[3]Figure 5: Medium chamber: transverse crack and failure of both spar caps at R=18,1 m

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Figure 6: Overall view of the rotor blade with the cumulated event distribution depending on the load
cycles (purple: early, red: late during the experiment)

Figure 7: Events located over the radius and the load cycles

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Figure 8: Absolut energy located over the radius and the load cycles

4. Conclusions
The paper deals with investigations on GFRP structures like rotor blade materials. Tensile tests
have been carried out on 14 tensile probes. The results give the possibility to differentiate
various failure types. Only a medium cluster cannot be clearly correlated.
Furthermore, a full size dynamic fatigue test has been carried out on a 37,5 m rotor blade
which failed during the experiment. The results help to understand the failure mechanisms. It
can be seen, that there are hardly any AE indications in the damaged area but in the areas
which have to compensate the failure regarding the load.
These experiments help to understand the complicated failure mechanism in GFRP structures.
They open the possibility for an online monitoring and may give clear indications of the single
acoustic emissions. Nevertheless, more data is needed to develop a standard procedure forstandardized rotor blade monitoring.

5. Acknowledments
The presented investigations were funded by the Sächsische Aufbaubank in the project
QuantSHM (Nr. 100207022). The authors thank the project partners cp.max Rotortechnik and
IMA Dresden for very good cooperation.

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References:[1] Hönig, U., Holder, U., Pietzsch, A., Schulze, E., Frankenstein, B., Schubert, L.: Definitionof requirements for reference experiments to determine and evaluate various damage
mechanisms in fibre composites by acoustic emission, 19th World Conference on Non
Destructive Testing, München, Paper, 2016[2] Holder, U., Schreiber, C., Schulze, E., Schubert, L.: Zuordnung vonSchallemissionsparametern zu spezifischen Schadensarten in CFK-Werkstoffen, Fulda, 21.
Schallemissionskolloquium 2017[3] Weihnacht, B., Schulze, E., Pietzsch, A., Schubert, L., Vandenbrande, A., Cerbe, B.,
Borchers, K.-W.: Schallemissionsmessungen während der Durchführung von Zugversuchen
an GFK-Proben, Leipzig, DGZfP Jahrestagung 2018

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- Structural
HealthMonitoringaerospaceof composite
Structural68
HealthMonitoringof composite
structures
aerospacestructureswith
Acoustic Emission
withAcoustic
Emission
Martin Lehmann, Andreas Bueter, Oliver Schwarzaupt, Conchin Contell Asins
Fraunhofer LBF, Darmstadt Germany
Abstract:
While composite materials have many advantages due to their lightweight potential impact events or
Foreign Object Damage (FOD) is critical for composite and many other lightweight and thin aerospacestructures. FOD can lead to unscheduled maintenance in the cases of hail damage, tool drop, rampthrash or even loss in the cases of tire debris (Concorde Accident July 25 , 2000) or insulation debrisst(Columbia Space Shuttle Feb. 1 , 2003). Impact damage reduces the static load capability and thefatigue life of a structure. The cost of an aircraft on ground is about 40 k$ per hour. Thus a decision onunscheduled inspection and repair or a return to flight must be taken fast and responsibly. Nondestructive inspection combined with numerical analysis is the state of the art. A detailed numericalanalysis of predamaged parts may take several days or weeks. Novel fast Structural Health Monitoring(SHM) and predictive maintenance tools can support the necessary decision making process.
Impact damage generates characteristic acoustic signals that can be detected and analyzed byacoustic emission systems during the event. This was investigated in the Clean Sky Program in theGreen Regioanl (GRA) platform* by Fraunhofer LBF (Laboratory for “Betriebsfestigkeit” – structural
durability). A fast but simple analytical model was developed that can analyze certain extracted
acoustic features. This model was trained with 50 composite plates clamped to simulate a stringer bay
each. The specimen were subjected to different impact energies and locations and corresponding
Acoustic Emission (AE) features, damage sizes as well as the compression load after impact were
derived from these tests. After this training the system could analyze impact events in near-real-timeand present estimations on impact energy levels, location, damage size, mechanical properties,
delamination growth as well as the remaining fatigue life under a given load level.
The project closed the loop from data acquisition with a commercial AE system, via the assessment of
the structural properties based on sensor records to the prognosis of this structural health and its
presentation in near real-time after impact events. Unfortunately the high scatter of results regarding
impact testing as well as of the extracted acoustic signals affects the reliability of the system so far butwith approaches from big data methods AE may become an interesting sensor type for predictive
maintenance of sporadic failure types.

*Research leading to these results has received funding from the European Union (FP7/20072013) for the Clean Sky Joint Technology Initiative, rel. grant agreement.

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1. Introduction

Two famous accidents in aerospace resulted after impact events. The loss of a Concorde after tyreparts damaged the tank marked the end of this type. The Space Shuttle Columbia was lost on re-entry
after the leading edge had been damaged during the launch by rather lightweight foam-insulation
debris. While a Structural-Health-Monitoring (SHM)-system would not have saved the Concorde, it
may have given the crew of the Columbia an early warning of the damage of their vehicle. That is why
NASA investigated SHM technologies for applications in the remaining Shuttle-Fleet after the accident.
In the search for the source of the damage to the leading edge, impact tests were made with foam
segments on a spare Reinforced Carbon-Carbon (RCC) leading-edge. During the tests acoustic
emission sensors could detect the impact of the foam segment and it was possible to locate thedamage with these sensors. [1,2,3]Different SHM-approaches were investigated in the Clean Sky program and a similar approach wasinvestigated on aircraft structures in the work package described here. An impact event on composite
structures results in impact induced damage (e.g. delaminations) and thus affects the structurescompressive strength after impact (see figure1). Also possible is the delamination growth dependingon the kinetic energy level of the impact event and the design of the structure. The primary reason for
preliminary compressive failure of an impacted specimen is the lower buckling stiffness in the impact
induced delamination area. The structural performance is investigated in compression strength after
impact tests (CAI). The possible growth of delamination is investigated in compression after impact
and fatigue (CAIF) tests. Potential delamination growth depends on the impact energy, the load leveland of course the design and mechanical properties of the structure.

Figure 1: Impact induced delamination in composites and delamination growth by fatigue while barely
visible (right) (Fraunhofer LBF)
The result of impact events can be divided in different levels based on certain energy levels andresulting structural damage.

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The first threshold is when the impact damage has no detectable influence on the global compression
strength of the composite.
The next threshold is the Barely Visible Impact Damage (BVID) upon which the dent of the impact
damage becomes just visible. Unfortunately this does not necessarily correlate with the delaminatedarea as this depends e.g. on the material (matrix- and fiber type and layup) and its thickness. In figure
1 the very small contact area (much smaller than the diameter of the ball of 25 mm) led to adelamination of about 47 mm x 42 mm. Those delaminations can grow under further loading
depending on boundary conditions.
The next threshold is the penetration level after which the effect of the impact induced damage on thecompression strength remains nearly constant as the diameter of the resulting hole remains constant.
The following figure 2 shows the typical curve of the remaining compressive strength over the damage
size with a certain "Allowable Damage Limits ADL", "Critical Damage Threshold CDT" in relation to the
Design Limit (LL) respectively the Design Ultimate Load (UL).

Figure 2: Residual Strength versus damage size Relationship [4]The following diagram shows the need for a fast assessment of impact induced damages based onthese remaining load capabilities of the part.

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Figure 3: Levels of Damage Tolerance Assessments [4]The diagram shows the need for a system that can assist to detect and classify impact events andassess their effects on the structure rapidly. In simplified terms the effect of impact induced damages
can either lead to a grounded aircraft for immediate unscheduled repairs (very expensive) or thedamaged plane may continue flying until a repair can be scheduled with upcoming maintenancecycles and prepared by detailed simulation. The type of approach depends on the extent of damage
and if this can be expected to grow.
There are several possible monitoring systems that can be used to detect impact events anddamages. One famous example for active systems is the Stanford Multi Actuator Receiver
Transduction (SMART)-layer [5]. Here only passive systems based on AE-systems are investigated.
Similar activities have been published [6], [7].
A future system for acoustic impact monitoring based on current Acoustic Emission technology
combined with a structural analysis needs to fulfil the following requirements:-detect impact events in-flight above a certain threshold using a very high sampling rate-locate the position of the impact event-categorize the impact event intensity and correlate a predicted impact induced damage magnitude(see above) based on the acoustic signature of the impact events and the calibration of the systemwith experimental tests. It will not be necessary to calculate an actual impact energy in Joule.
-estimate and predict the residual strength of the monitored part immediately after the impact event.

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-support the prognosis of the remaining lifetime and damage growth of the predamaged structure
under further loading (for a calculation of the repair urgency). A more detailed numerical analysis canbe done later if the damage allows a later repair.
In order to achieve these requirements mature equipment in the AE-field was used. Speaking in the
SHM terms of preprocessing, feature extraction and classification based on sensor signals, theseelements of the monitoring system are processed mainly by a modified mature system. The
corresponding hardware and software are combined with fast numerical tools for a quick structural
analysis of sensor data.
The following flow chart (figure 4) shows the approach for SHM of composite aerospace structureswith AE. An impact event results in an electrical sensor signal which is analysed using Acoustic
Emission hardware and software with minor modifications only. The estimated position and magnitude
information of the impact event is then analysed in a special software using information about
geometry and material data of the structure.

Figure 4: Flow chart of the approach for Structural Health Monitoring (SHM) of composite aerospacestructures with Acoustic Emission (source: Fraunhofer LBF)
The optimization of sensor placement, the characterisation of the acoustic behaviour by impact(correlation of impact energy and resulting electrical signal) as well as the structural behaviour by
impact (correlation of the impact energy and the resulting damage size) is done in empirical test
campaigns for the necessary database.

2. Description of work
Test Campaign
For the localisation of impact events several sensors are used to triangulate the origin of acoustic
emissions excited by the impact event and/ or impact induced damages to the composite structure.
The sensors need to be placed at strategically optimal loactions to cope with different sound velocities
and the certain attenuation behaviour of composites. At the time of the project there was no

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compensation for the special effects of composite material on acoustic waves included in the systemyet.
The sound velocities in composite materials depend on the fiber orientation and differ by the stiffness
differences in matrix and fiber dominated directions. That is why an empirical approach was used to
identify all sound velocities first. In Acoustic Emission testing the Hsu-Nielsen Pencil Lead Break test
(PLB) is used to calibrate systems with reproducable acoustic events. In this case the velocity ishigher by a factor of roughly 2 for 0° (predominate fiber direction) compared to the 90° direction. No
detailed information on the material data can be given here unfortunately.
The solution to compensate for this effect was to modify the position of real sensor positions on thepart to virtual ones in the software in order to be able to calculate the correct location. To compensate
for the lower velocity 90° to the fiber direction the real position of the sensors is multiplied by the factor
two in that direction in the software while the position in 0° direction (used as sound velocity) was notchanged. By this way the system exports correct x,y coordinates on the part with a calculated level of
uncertainty (LUCY) of 0.2-3 mm for panels of 300 mm x 200 mm in repeated tests. The LUCY value isthe standard deviation of the calculated sensor distances to the source position and the distanceaccording to the measured arrival time difference.
As the system showed good results for localising impact events, the next step for the identifcation of
the impact energy was tested with a great number of clamped panels with symmetrical ‘’mid-bay’’
impacts only to get the maximum number of results.
The effect of potentially different amplitude levels at the position of the sensors due to an excentric
impact can be compensated by considering the mean value of the signal of the four sensors as thefeature for the identification of the impact energy. This is one reason why this feature was investigatedmostly for the identification of the impact energy.
A number of different tests were necessary for the identification chain
The results of the test campaign are discussed briefly only. Altogether 50 Carbon Fiber Reinforce
Plastic (CFRP) panels of roughly 300 mm x 200 mm were tested in 50 impact tests accompanied by
AE measurements. Afterwards the panels were cut down to 150 mm x 100 mm for standardized
mechanical compression after impact (CAI) testing. The tests are discussed next very briefly.

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Figure 5: Overview of the panels for CAI (left) and the original size for Impact tests (right)
Impact testing: The impact energy is preset with the drop height but the panels absorb only a part ofthis energy depending on the clamping conditions and energy level. The absorbed impact energy is
calculated by the Impact Testing Machine Instron Dynatup 9250 HV. While the initial impact energy
may be 10 J as one example only 6 J are absorbed. The difference is the elastic energy which reflects
the impactor back to a secondary drop height which is measured but a second drop is prevented.
According to the general point of view only the absorbed energy causes damage. The effects ofdifferent rates of elastic and absorbed energy on acoustic emissions are much less understood. Only
the absorbed energy was considered in further examinations.
AE-Recording: The Vallen AMSY 5 System recorded acoustic signals in realtime during the 50 impact
tests (see figure 6). Certain features were analysed afterwards. Only two results are discussed here.
Other features may deliver better results in other cases. The development of more elaborate advanced
signal analysis and filtering methods was not the intention of this work package so the AE-features"acoustic energy" and "counts" were used for correlating the electrical signals to the damage size.

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Figure 6: Overview of the test rig with the Impact Test rig in the back and the Vallen AE-system in thefront
Damage Analysis: The correlation of damage size to impact energy levels was done with a Lock-InThermography system for NDT and a special scanner system for detecting the delamination size.
Water based ultrasonic test methods are unfavourable as these may influence the result of thesubsequent compression after impact investigation. An observation grid printed on a specialtransparent film enabled an investigation of the damaged area in the relevant thermographic image
with the highest visible area. A special scanner software was used to calculate the damaged area insquare millimetres for each impact. Figures 7and 8 show some images of delaminations of the panels.

Figure 7: Lock-In-Thermography System and Screenshots with some delmainations under different
Energy levels

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Figure 8: Microscopic analysis of delaminations.
The results of the tests are shown in the next figure
structural properties after impact
3500
damage size [mm²]

3000
2500

thickness blue >thickness red

2000
1500
1000
500
0

0

10

20

30

40

absorbed impact energy (J)Figure 9: Damage Size over absorbed impact energy
Mechanical Analysis: Compression strength after impact testing of the panels to get results for the
structural behaviour after impact for the simulation model. The result is shown next.

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60

3000

50

damage size [mm²]

3500

2500

40

2000

30

1500

20

1000

10

500
0

Results for one
thickness only

CSAI [kN]

structural properties after impact

0

10

20

30

40

0

absorbed impact energy (J)Figure 10: Compressive strength after impact and damage size over absorbed impact energy
Finally a correlation of the acoustic signal with the resulting delamination size and other values ispossible by linking all the results (see figures 11-13). It was decided to use the calculated acoustic
signal strength which is calculated as the Root Mean Square (RMS) result by integrating the absolutevalue of the electrical signal as a mean AE-signal. This value is given in energy units (eu) andmeasured in nVS (1eu = 1 nVs).
comparison of damage size and AE signal
5000

2000
1800

4500
4000

1400
1200

3500

1000

3000

800
600

2500

400

2000

200
0

AE signal [nVs]damage size [mm²]1600

Results for the
thickermaterial

0

10

20

30

40

absorbed impact energy (J)1500

Figure 11: Comparison of damage size and AE signal over the absorbed impact energy for the thicker
panel

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comparison of damage size and AE signal
4000

3500
3000

Results forthe thinnermaterial

3500

3000

2000
1500

2500

1000
2000

500
0

AE signal [nVs]damage size [mm²]

2500

0

10

20

30

40

absorbed impact energy (J)1500

Figure 12: Comparison of damage size and AE signal over the absorbed impact energy for the thinner
panThe following figure shows the correlation of damage size and the AE signal
The most important result of the experimental test campaign that contained 50 sensorized impact tests
and subsequent NDT evaluations on the reference material was the following correlation between the
acoustic signal and the impact induced delamination size. It was decided to use a simple linear
approach instead of a neural network as the scatter of results does not justify a better mathematicalinterpretation of the results. Though not very reliable due to the high scatter of results the simple linear
equation y (damage size in mm²) = 0.5274 x (AE energy in nVs) - 484.11 was used here for thethinner material. For a higher reliability of such a system based on AE much more tests and a more
systematic analysis of factors leading to scatter would be necessary.
correlation of damage size and feature mean AE energy

damage size [mm²]2500

Thinnermaterial in red

2000

1500

1000

500

0
1000

1500

2000

2500

3000

3500

4000

4500

5000

mean AE Energy [nVs]

Figure 13: Correlation of damage size and acoustic mean energy (AE signal) delivered by the AE
At best these results make it possible to correlate acoustic signals in this case in an area of about 10 J
to 25 J. The penetration threshold was at around 35 J leading to high acoustic emissions and an
override and errors in measurement at higher impact energies with this feature.

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It is clear by looking at the scatter of results that it will be very hard to interpret the sensor signal andcorrelate a damage size with a high degree of certainty using this feature alone.
An alternative feature that may be used for detecting higher impact energies is the number of counts(see the following figure 14). The following figure shows a screenshot of a test series. While there is
no relevant difference between the Amplitudes in the top left picture the number of counts rises rapidly
in the big screen when the area of impact penetration is reached. This may make it possible to detect
critical impacts with an additional feature where the feature acoustic energy becomes unrelaible due toa sensor override.

Figure 14: Possible features of acoustic signals [2]

5J

10J

15J

20J

25J

30J

35J

Figure15: comparison of “counts” for different impact energy levels
The mean AE energy and location coordinates are the only variables to be exported by the AE-system
for subsequent analysis of the post impact behaviour in the next step.
The interface-software is using the Vallen "Embedded Code Processor" Software (ECP) which isinstalled together with the Vallen "Visual AE and TR software" on the same laptop as the subsequent
MATLAB software "Delamination Damage Evaluator" (DDE).
The interface software module is using a fast Lua script that generates and exports a file containing
location and acoustic energy data as soon as an impact event is registered by the AE-system. The
output of the Vallen "AE/TR software" are the location coordinates and a mean acoustic energy levelcalculated using four acoustic sensors.

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The Calculation Tool for Delamination Damage Evaluation (DDE)The software tool is used for a fast, simple calculation of damage caused by an impact event. A
growth of damage could be measured by AE as well via e.g. the felicity-effect or Kaiser-effect but thisis not considered here yet.
The numerical models of the DDE are based on previous research done by de Jonge at Fraunhofer
LBF [8-38] but there is also the opportunity to add new or modify calculation models.
In the model, the plate is divided in sub-areas. Each of the sublaminates, created by a delamination, issuch an area as well. The model idealizes the damage and starts from the assumption that thesublaminates are rectangular and in the middle of the plate (see Figure 16a). Then the load
deformation behaviour of each of the areas is represented by a spring. In this manner, the whole plateis described by a system of parallel and in-series springs (see Figure 16b).

Figure 16: a) Idealization of the characteristic state of damage after impact. b) Areas representing
springs in one-dimensional spring model (source: Fraunhofer LBF)
Once the load on a sublaminate has exceeded its buckling load, it will not behave like a normal spring
anymore. The load a sublaminate can take after buckling equals the buckling load of the sublaminate.
In this way, the load-deformation curve for a plate with certain damage can be calculated. By
determining the released energy between two subsequent damage configurations from these curves,the expected life can be calculated with methods from fracture mechanics (see Figure 17).

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1. Idealized characteristic of damage
after impact

Loading
Conditions

Idealized growth
behaviourdA/dN = C (G)^p

2. Geometry of structure
3. Material properties for each ply

Represent
plate as asystem ofin-series andparallelsprings

Consider subsequentdamage s states

Calculateload-deformation
curve of the wholeplate

Calculate load-deformation
curves for these damage
statesCalculate Bucklingloads for all
sublaminates

Calculate G

I. Buckling Load

II. Load Deformation BehaviourIII.

Calculate Life, N

Figure 17: Schematical representation of the theoretical model that predicts damage growth (source:
Fraunhofer LBF)
The prediction model uses fracture mechanics to describe the damage growth. For this, it is necessary
to calculate the Strain Energy Release Rate (SERR), between two subsequent states of damage,before and after growth. The quantities needed for the calculation of SERR from the analysis of theload-deformation behaviour are the potential energy and the deformation at a Fatigue Load Level(FLL). The load-deformation curve for a damaged plate is calculated by representing different areas ofthe plate as a system of parallel and in-series springs, which will be referred to as a 'spring-model'. If aplate is loaded the sublaminates will start to buckle one after the other. Every time a sublaminate
buckles, the stiffness of the plate changes a little and the load-deformation curve will show a change inslope.

Figure 18: Spring Model (one half of the plate) (source: Fraunhofer LBF)
Performing this procedure iteratively and keeping in mind that the curve ends when the strain in theparallel spring is the critical (fracture) strain, gives the load-deformation curve for a plate with a certain

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damage. As described above, the important parameters needed for the next part of the prediction
method are the deformation at Fatigue Load level (FLL) and the potential energy of the overall plate at
FLL.
The DDE is divided in three parts of calculation:I:buckling loads

II

load deformation behaviour

IIIfracture mechanics

The computer program, based on the model, is able to make the calculations for each of the threeparts. The first “off-line” part focused on modifications of material data by test results while the fast
online tool to be developed is optimized on rapid calculation. (figure 19)

Figure 19: Two-Dimensional Spring Model (one half of the plate) (source: Fraunhofer LBF)
The “off-line” software was used for testing and optimised using experimental compression after
impact results (see figure20).

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Numerical Buckling Load (kN)

25

20

15

10
Sublaminat 1
Sublaminat 2

5

Total
ideal

0
0

5

10

15

20

25

Experim ental Buckling Load (kN)

Figure 20: Buckling Loads Total and Sublaminate Buckling (source: Fraunhofer LBF)
The resulting online software is triggered automatically when the file with the variables is generated by
the AE system via the fast Lua-script. Constants like material properties for the laminate, layup anddimensions, loads, mechanical and calculation parameters are defined beforehand.
The next figure 21 shows a flowchart of the DDE software. If, depending on the Delamination Size,
Fatigue Load Level and material constants, a delamination growth is calculated 50-500 loops with
gradually increasing delamination sizes resulting in gradually decreasing buckling loads are used togenerate a graphical output of the Damage size over load cycles and Buckling loads over load cycles
respectively.

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Figure 21: Flowchart of the DDE software

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7. Discussion

Several blind tests were done with the system after the software was optimized to demonstrate thefast calculation on the different impact levels. As mentioned the reliability of the system is affected by
the high scatter of signals.
Depending on the Impact Energy level and thus the estimated delamination size there are four
possible warning levels (Figures 22-24) and context sensitive outputs the software creates when thecalculation is completed about one second after the impact event as a pop up.
a) uncritical damage when no damage is estimated as the mean AE energy level is below thecritical threshold that was established by experiments

Fig 22: Output Window for Impact Events when no damage is estimated based on the mean
acoustic energy levelb) critical damage “Impact detected” when no delamination growth is estimated by the DDEsoftware tool. Here the damage size and the Estimated Remaining Buckling loads arepresented (Fig. 19) so that they can be compared to maximum or minimum allowables(Allowable Damage Limits (ADL), Maximum Design Damage (MDD), Critical Damage
Thresholds (CDT), Design LL, Design UL etc.).

Fig. 23: Output Window for Impact Events when delamination damage is estimated based on themean acoustic energy level and a delamination size is estimated based on the correlation of theseproperties. A remaining buckling load is calculated by the DDE software in this case.
c) critical damage “Impact detected” where delamination growth is estimated by the DDEsoftware tool. Additionally the estimated trends of the delamination growth and the static
compression strength of sublaminates and laminates over the remaining lifetime is calculated
in a way that a residual lifetime can be read until minimum or maximum allowables (seeabove) will probably be reached under the preset Fatigue Load level (Fig. 20). The chosen
Paris constants needed for the calculation of damage growth could not be determined withexperiments using the Cytec material but have been estimated based on earlier tests. Any

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new material chosen for the monitoring with this system would require a certain minimum
number of tests to gather realistic data on material constants.

Fig. 24: Output Window for Impact Events when delamination damage is estimated based on themean acoustic energy level and a delamination size is estimated based on the correlation of theseproperties. A remaining buckling load and a trend for sublaminate and laminate buckling loads(left) is calculated by the DDE software and the delamination growth (right) is calculated in thiscase.
d) Penetration level impact where the impact energy was so high that a penetration of the panel
is estimated. A different acoustic feature may be needed to detect penetration level impact
events without doubt. Due to the high scatter of acoustic signals using the current acoustic
feature “mean acoustic energy” there is a limitation to discriminate penetration levels fromother strong impacts. This would have to be implemented in the future by putting in a “second
path acoustic feature” in the Delamination Estimator.
3. Conclusion
In the scope of this WP in Clean Sky Fraunhofer LBF was able to close the loop from hardware tosoftware and from data acquisition to presentation of the remaining structural health of a simplified
composite part after impact events in near realtime.
The different hard- and software elements were sucessfully linked together with the speciallydeveloped DDE software and the monitoring system was tested in impact tests.
The calculation time was measured with about one second. There are impact induced damages wherea fast evaluation of the incident is needed and preferable to a more sophisticated but more time
consuming FE analysis (see figure 4). For some situations an onboard and online system may bebenefitial to quickly differentiate between the severities of foreign object damages and advice theappropriate action. Some progress was made with the presented approach but future projects
including a larger number of tests to acount for the scatter would be necessary.
Contact :martin.lehmann@lbf.fraunhofer.de , andreas.bueter@lbf.fraunhofer.de , www.lbf.Leichtbau.de

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4. References[1] COLUMBIA ACCIDENT INVESTIGATION BOARD Reporthttps://www.nasa.gov/columbia/home/CAIB_Vol1.html[2] W.H. Prosser1, S. G. Allison , S.E. Woodard, R. A. Wincheski1, E.G. Cooper, D.C. Price,
M. Hedley, M. Prokopenko, D. A. Scott, A. Tessler and J. L. Spangler
STRUCTURAL HEALTH MANAGEMENT FOR FUTURE AEROSPACE VEHICLES

http://www.prokopenko.net/Publications/Agents/Australasian_SHM_Prosser.pdf[3] Eric I. Madaras, William P. Winfree, William H. Prosser,Russell A. Wincheski, and K. Elliot Cramer,
Nondestructive Evaluation for the Space Shuttle’s Wing Leading Edge, NASA Langley ResearchCenter, Hampton, VA 23681, 41st AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit 10 13 July 2005, Tucson, Arizona[4] H. Razi and S. Ward, PRINCIPLES FOR ACHIEVING DAMAGE TOLERANT PRIMARYCOMPOSITE AIRCRAFT STRUCTURES, The Boeing Company Boeing Commercial Airplane Group
Seattle, Washington, 11th DoD/FAA/NASA Conf. On Fibrous Composites in Structural Design Page
Fort Worth, TX, August 1996
[5] Simone Mancini, Giorgio Tumino and Paolo Gaudenzi, Structural Health Monitoring for FutureSpace Vehicles, ,Journal of Intelligent Material Systems and DOI:
10.1177/1045389X06059077,Journal of Intelligent Material Systems and Structures 2006; 17;577,http://jim.sagepub.com/cgi/content/abstract/17/7/577[6] Cheng-Shung Wang, Yi Zhao and Tuan-Khoi D. Nguyen, Prediction of After-Impact Compressive
Strength for Laminated Composites
http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6TWT-3VRYHW4-J1&_cdi=5571&_user=843885&_orig=search&_coverDate=12%2F31%2F1996&_sk=999439993&view=c&wchp=dGLbVzWzSkWA&md5=a4db394f84ef3d7e4de0127c1312d90b&ie=/sdarticle.pdf[7] D. Tsamtsakis, M. Wevers, P. de Meester,Acoustic Emission from CFRP Laminates during fatigue
loading,Journal of Reinforced Plastics and Composites 1998; 17; 1185
[8] Gerharz, J.J. and Idelberger, H.:A method to predict risidual strength and fatigue life of Impact
damaged carbon epoxylaminates,Proceedings of the International colloquium of durability of polymer
based composite systems for structural applications, held in Brussels, Belgium, 27.-31.8.1990, ed. by
A. H. Cardon and G. Vercery, Elsevier Science Publisher, London (19901), pp. 445 – 454[9] Gerharz, J. J.; Idelberger, H. and Gumnior, P., Impact-Damage Tolerance Untersuchungen an
ebenen Faserverbundplatten-Konstruktionen, LBF-Report 6202, July, 1990
[10]Whitehead, R.S., Certification of Primary Composite Aircraft Structures in:New Materials andthFatigue Resistant Aircraft Design, ed. David L. Simpson, Proceedings of the 14 Symposium of theICAF, June 1987, Ottawa Canada

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[11] Clark, G., Modeling of impact damage in composite laminates, Composites Vol. 20, Number 3,
May 1989[12] Robert M. Bader, An, Post-Impact Composite Compression Strength Prediction, in Review of
Research on Aeronautical Fatigue in the United States, 1987-1989, ICAF Meeting June 1989 in
Jerusalem Israel[13] Shivakumar,K.N. and Whiteomb, J.D.:Buckling 01 a Sublaminate in a quasi-Isotropie Composite
Laminate, J. 01 C. Mat., Vol. 19, 1985, pp. 2-18
[14] Whiteomb, John D.:Three-Dimensional Analysis 01 a Postbuckled Embedded Delamination, J. 01
C. Mat., Vol. 23, 1989, pp. 862-889[15] Whiteomb, John D.:Mechanics 01 Instability-Related Delamination Growth, ASTM STP 1059,
1990, pp. 215-230[16] Chai, H., Babeock, C.D., and Knauss, W.G.:One-Dimensional Modelling of Failure in LaminatedPlates By Delamination Buckling, Int. J. Solids Structures, Vol. 17, 1981, pp. 1069-1083[17] Chai, H. and Babeock, C.C.: Two-Dimensional Modelling of Compressive Failure in DelaminatedLaminates, J. of C. Mat., Vol. 19, 1985, pp. 67-97[18] Chai, H.:Buekling and Post-Buckling Behavior of Elliptieal Plates: Part I - Analysis
J. of Appl. Meeh., Vol. 57,1990, pp. 981-988[19] Chai, H.: Buekling and Post-Buckling Behavior of Elliptieal Plates: Part 11 - Results
J. 01 Appl. Mech., Vol. 57,1990, pp. 989-994[20] Flanagan, G.:Two-Dimensional Delamination Growth in Composite Laminates Under
Compression Loading, ASTM STP 972, pp.180-190[ 21 ] Peck, S.O. and Springer, G.:The Behavior of Delaminations in Composite Plates-Analytical andExperimental Results, J. 01 C. Mat., Vol. 25, 1991, pp. 907-929[22] Lagace, Paul A. and Jensen, David W., and Fineh, Dougles C.:Buckling of UnsymmetricComposite Laminates, Composite Structures 5, 1986, pp. 101-123[ 23 ] Lagace, AL, and Vizzini, A.J., The Sandwich Column as a Compressive Characterization
Specimen for Thin Laminates, ASTM STP 972,1988, pp. 143-160[24] Vizzini, A.J., and Lagace, PA:The Buekling of aDelaminated Sublaminate on an Elastie
Foundation,J. of C. Mat., Vol. 21,1987, pp. 1106-1117[ 25] Jensen, D.W., and Lagace, P.A.:Inlluenee of Mechanieal Couplings on the Buekling andPostbuckling 01 Anisotropie Plates, AIAA Journal, Vol. 26, 1988, pp. 1269-1277- 47-[ 26] Vizzini, A.J., and Lagace, PA: The Buckling of aDelaminated Sub laminate on an Elastic
Foundation, J. of C. Mat., Vol. 21,1987, pp. 1106-1117[ 27] Yin, Wan-Lee:The Effects 01 Laminated Structure on Delamination Buckling and
Growth, J. of C. Mal., Vol. 22,1988, pp. 502-517
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[ 28] Rohwer, K.: Einfluss der Biege-Torsionskopplung auf die Beullasten symmetrisch geschichteter
Platten, Vortrag DGLR-Symposium, Berlin, 14-15 mai 1987
[ 29] Rohwer, K.:Nachbeulverhalten von Platten und Schalenfeldern aus Faserverbundwerkstoff, ZFW
10,1986, Heft 4, pp. 228-232[ 30][ Davidson, B. D.:Delamination Buckling: Theory and Experiment, J. of C. Mat., Vol. 25,1991, pp.
1351-1378[ 31] Rothschilds, R.J., and Gillespie, J.W., and Carlsson, L.A.: Instability-Related Delamination
Growth in Thermoset and Thermoplastic Composites, ASTM STP 972,1988, pp. 161-179[ 32] Ashton, J.E., and Whitney, J.M.: Theory of Laminated Plates Technomic, 1970
[ 33 ] Luftfahrttechnisches Handbuch Struktur-Berechnung HSB, LTH Structural Analysis (IASB)
working group https://www.lth-online.de/en/working-groups/iasb-structural-analysis.html[ 34] Timoshenko, and Gere: 'Theory of Elastic Stability' McGraw-Hill; New York (1961)[ 35] Little, G.H.:An efficient computer program for the large deflection analysis of rectangularorthotropic plates, Comp. & Strucl., Vol. 27, 1987, No.4, pp. 467-482[ 36] Little, G.H.:Large deflections of orthotropic plates: some extensions to the computer program'Eldarop', Comp. & Struct., Vol. 37, 1990, No.4. pp. 621-624[37] Popp, P. : Experimentelle Studienarbeit zur dreidimensionalen Erfassung und Darstellung von
Impactschaden in Faserverbundplatten, Institut für Werkstoffkunde der Technische HochschuleDarmstadt und Fraunhofer-Institut für Betriebsfestigkeit (LBF), Darmstadt, 1990
[38] Hackbusch, D.: Einsatz von Faserverbundwerkstoffen bei Knickstäben als Federelemente
Diplomarbeit FH und Fraunhofer-Institut für Betriebsfestigkeit (LBF), Darmstadt

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53 - Mechanicaland Acoustic
Emissioncharacterization OF
MECHANICALAND ACOUSTICEMISSION
CHARACTERIZATION
of lime-based
masonry
constituents
LIME-BASEDMASONRY
Naveen Shetty1, Georgios Livitsanos3, Dimitrios G. Aggelis3, Danny Van Hemelrijck3,
Martine Wevers2, Els Verstrynge1
1 Building Materials and Building Technology Division, Department of Civil

Engineering, KU Leuven, Kasteelpark Arenberg 40, box 2448, 3001 Heverlee,
Belgiume-mail: naveen.shetty@kuleuven.be
2 Department of Materials Engineering, KU Leuven, Kasteelpark Arenberg 44, box

2450, 3001 Heverlee, Belgium

3 Department of Mechanics of Materials and Constructions, Vrije Universiteit Brussel,

Pleinlaan 2, 1050 Elsene, Brussels, Belgium

Abstract:
This paper deals with an improved representation of the progression of damage in lime-based masonryunder cyclic loads using the Acoustic Emission (AE) technique. Stress-strain relations are a familiar
concept in engineering which detail the elastic-plastic behaviour of the material. During AE monitoring,
AE activates with the increase of stress and initiation of micro fracture due to local changes in thematerial structure. The population of AE events registered at an increasing stress levels provides
information on the structure’s integrity. In the present study, masonry couplets and walls were built withclay bricks in combination with two different mortar types: hybrid lime-cement (LC) and lime hydratemortar (LH). One set of experiments focuses on masonry couplets and another set of experiments
focuses on masonry walls, both under compression. The AE-based damage quantification is validated
by a comparative analysis with strain measurements from Linear Variable Differential Transformers(LVDTs). In conclusion, results indicated that at initial stages the AE trend was closely related to axial
strain for LC specimens and lateral strain for LH specimens. Regardless of size effects with respect tomasonry couplets and walls, there was no significant difference in damage progress.

1. Introduction
Over many centuries, the traditional binder in historical monuments is found to be lime. Even
today, lime mortar is primarily used in most repair and restoration work of historic buildings [13]. Lately, the combination of lime with cement is used because it is quick to set and has highcompressive strength. Mortars are bound with the bricks to form masonry and behave in acomplex manner. Mechanical properties are well documented for lime-based masonry inliterature [4-6] but there continues to be a gap in understanding the behaviour of masonryunder compression more precisely. This leads to a search for a non-destructive technique(NDT) to monitor the structure in real-time analysis.

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Acoustic emission (AE) is an NDT which detects the release of high frequency elastic waves
from a localized source generated by a crack within a material [7]. AE has been recognized for
its ability in monitoring the damage progression in materials [8-10] and masonry structures[11,12]. Quantification of damage in lime-based masonry due to time-dependent progressive
deterioration has been addressed in [13].
This paper contributes to the understanding of the mechanical behaviour of lime-cement andlime hydrate masonry under cyclic compressive loads using AE technique. From the literature[14], it is important to know whether the effect of size has an impact on the mechanicalbehaviour and progression of damage in masonry. Hence, in this study, we analyze the size
effects of masonry couplets (combination of two bricks and one mortar layer) and masonrywalls (seven bricks in height with running bond). This study shows how the degradation
process in lime-based masonry will effectively be assessed by strain measurements and by
AE data.

2. Methods and materials
2.1 Materials
Red clay bricks were used with dimensions 188x88x63 mm3 having an average compressive
strength of 11.8 N/mm2 (Standard deviation SD = 2.41 N/mm2) and average Young’s modulusof 1238 N/mm2 (SD = 208 N/mm2). Two different types of mortar were chosen, the compositionof the mortar is as shown in Table 1.
Table 1. Mortar composition in mass percentage.
Lime-cement1

Lime hydrate2

Sand

70 %

74 %

Binder

15 %

9%

Water

15 %

17 %

The binder for the preparation of:
1 hybrid lime-cement mortar is a mixture of 66.7 mass-% of calcic lime [CL90S] and 33.3 mass-% of
cement [CEM1 42.5R]
2 lime hydrate mortar is calcic lime, CL90S

2.2 Methods and equipment
Two types of masonry specimens (couplets and walls) were tested under cyclic compressive
loads to investigate the progressive damage by means of AE. Couplets were built out of two
stacked bricks with overall dimensions 188x88x138 mm3, see Figure 1(a). Walls were built withseven courses in height with overall dimensions 388x88x515 mm3, see Figure 1(b). In all the
specimens, an average mortar joint thickness of 12 mm was maintained. Two samples for eachtype of masonry were tested at the age of 28 days. The lime hydrate (LH) specimens were too‘young’ at the time of testing as hardening relies solely on carbonation.
Cyclic compression tests were performed on the specimens using a hydraulic press testingmachine ‘Instron’ with a capacity of 2,500 kN. The loading rate was 0.25 kN/sec. A thin layerof gypsum was applied on top and bottom of the specimens and slightly pressed under theplatens of the hydraulic press to make it a flat surface for an equal distribution of load. Up to2

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7-9 loading and unloading cycles were done. In every new loading phase, a higher peak load
value was reached. The peak load was maintained by 1.5 minutes and the successive cycles
were separated by a time of approximately 1.5 minutes. In the final loading step, the load wasincreased until the ultimate failure of the specimen. Linear Variable Differential Transformers(LVDT) were used to measure the deformations. For masonry walls, the displacement wasrecorded across two mortar joints in vertical direction at three locations (3A-3B; 4A-4B; 2A-2B)
and one mortar joint in horizontal direction (1A-1B). For masonry couplets, the displacement
was recorded across only one mortar joint in vertical direction (1A-1B).
For masonry walls, AE data was recorded by a Vallen AMSY-6 system with six channels in a
ASIP-2 board connected to a PC, with a sampling rate of 10 MHz. For masonry couplets, AE
data was recorded by a Vallen AMSY-4 system with four channels. In both cases, piezoelectric
sensors with a frequency bandwidth 100-450 kHz were attached to the specimens by meansof a hot-melt glue. The set-up of the sensors was symmetrical with respect to the mid-sectionof the specimens as shown in Figure 1. The built-in band pass filter had a frequency range
from 50-850 kHz for signal processing. Pre-amplifiers AEP5 with 34 dB gain were used. A
threshold of 38.5 dB was set to eliminate the background noise.

(b)(a)
Figure 1. Test setup for compression test on masonry (a) couplet and (b) wall.

During the fracture process, AE hits exceeding the threshold are registered and subsequently
grouped into AE events. For progressive damage evaluation in masonry, the Japanese Society
for Non-Destructive Inspection proposed two AE indices in order to estimate the criticaldamage levels [15,16]. The first parameter is called the ‘Load ratio’. It is calculated as the ratio
between the load at the onset of AE activity in the subsequent loading step to the maximal load
reached at the previous cycle. When the Load ratio is above 1, Kaiser effect is present, whichindicates ‘stress’ memory in a material for the previously applied maximum load level. Whenthe Load ratio is below 1, Felicity effect is present, which evidences the occurrence of microstructural damage before previous maximum load. In this study, to calculate the Load ratio, theonset of AE activity is determined by the occurrence of at least 10 events in a time interval of
10 seconds. The second parameter is called the ‘Calm ratio’. It is calculated as the ratio of AE
3

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events registered during unloading to the AE events registered during the whole loadingunloading cycle.

3. Results and discussion
3.1 Stress-strain
Figure 2 shows representative stress-strain curves from the compression tests on masonrycouplets and masonry walls. Two interesting observations can be made by comparing the typeand size of masonry. Firstly, the lime-cement masonry has higher compressive strength thanthe lime hydrate masonry. Secondly, masonry couplets present a higher compressive strengthcompared to masonry walls.

Figure 2. Stress-strain curves for compression tests on masonry couplets and walls.

3.2 Mechanical behaviour and AE trends
AE data obtained for cyclic compressive tests on both masonry types with different size(couplet and wall) are analysed. For this analysis, AE events generated in each cycle andlocalized all over the specimen were considered. The normalised cumulative AE events inrelation to the normalised load and time is shown in Figure 3(a) and (b). The cumulative AE
events is the total number of AE events received from the beginning of the test to a certain
moment.
In Figure 3(a), the load levels for the LC wall are very low compared to the LC couplet, which
results from an underestimation of the strength of the LC walls prior to testing. In Figure 3(b),for the LH couplet and wall, a good agreement between the load levels can be observed. In
general, AE events registered for LC specimen are much higher than LH specimen. The reason
for such a behaviour in AE activity is due to two reasons. Firstly, the LH specimen has lower
mechanical strength and less brittle nature of cracking compared to LC specimen. Secondly,at higher load levels the formation of micro fracture in LH specimen largely increases AE
attenuation. As can be seen in Figure 3(a) and (b), the AE activity increased progressively forsuccessive load cycles. In both cases, irrespective of size effects, the cumulative AE events
4

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remain similar apart from some scatter in the trend. Vertical dotted lines in Figure 3(a) and (b)are drawn at those moments when the macro-crack was visually observed.

(a)(b)
Figure 3. Results of a cyclic test with AE monitoring on a representative sample of (a) lime-cement
masonry and (b) lime hydrate masonry.

In Figure 4(a) and (b), the measured stiffness (E) for each load cycle normalised by the initialstiffness (Eo) of the first load cycle vs. the number of AE events is shown. The stiffness
reduction with the increase in AE events for the successive load steps is observed. In thisstudy, the elastic modulus (E) was calculated at the stress level in a current cycle that exceeds
the previously applied maximum stress, and corresponding AE events were considered. For
the correlation, a logarithmic fitting function is used.
In Figure 4(a), for the first load cycles, the number of AE events is less than 5,000. In the lastload cycles, the number of AE events is reaching almost 15,000 to 20,000 for the LC wall andcouplet with the stiffness modulus decreasing by approximately 30 to 40%. Similarly, in Figure4(b), the number of AE events is reaching almost 8,000 to 11,000 for the LH wall and couplet
with the stiffness modulus decreasing by approximately 20 to 30%. In both cases, as the stressapproaches the ultimate strength of the masonry, an increase in AE activity at the cycle (norm.
load = 80%) prior to failure is observed. This is due to the signals registered from spalling of
the mortar. In the last loading cycle (norm. load = 95%), the macro-cracks followed by crushingof masonry leads to a huge drop in stiffness and an increased number of AE events.
In general, as observed from these results for couplets and walls, LH specimen quickly
deteriorates compared to LC specimen, which is dependent on the stiffness characteristics ofthe masonry constituents. However, in recent studies [17,18] this hypothesis has been highly
investigated in different combinations of lime-based masonry. From the stress-strain graph(see Figure 2), LH specimen has low stiffness and large deformability compared to LC
specimen. As can be seen in Figure 4, LC specimen has a better correlation between the
stiffness and AE events compared to LH specimen. Another observation indicates that the
couplet have high stiffness and large number of AE events compared to the walls. However,
the masonry couplets and walls of the same mortar type have the same shape of trend
irrespective of size effect.
In Figure 5(a) and (b), the normalised load vs. the number of AE events is shown. The
increasing load values leads to higher AE activity which is due to more damage induced by
5

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crack initiation and propagation. These trends illustrate that the correlation between thenormalised load and number of AE events is slightly better than the correlation between the
stiffness loss and the AE events.

(a)(b)
Figure 4. Stiffness modulus vs. number of AE events for (a) lime-cement masonry and (b) lime hydrate
masonry.

(a)(b)
Figure 5. Normalised load vs. number of AE events for (a) lime-cement masonry and (b) lime hydrate
masonry.

3.3 AE activity during cyclic loading
In Figure 6(a) and (b), the trend represents the AE events accumulated during the loading andunloading phase of each loading cycle. The number of AE events is shown with respect to thenormalised load during each loading step. Here, AE activities are grouped into two categories
namely ‘primary AE activity’ and ‘secondary AE activity’. As can be seen from the results, AE
events were registered during the loading phase of subsequent loading cycles due to crack
formation under compressive loading referred to as primary AE activity. Similarly, AE eventswere registered during the unloading phase of subsequent loading cycles due to friction
between the existing crack surfaces referred to as secondary AE activity.
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For LC specimen and LH specimen, the loading phase of the first cycles has higher AE activity
which indicates the nucleation phases of micro-cracking in masonry. Upon continuing of thetest, a sudden increase in secondary AE activity is observed by the change of slope in thetrend (arrows in Figure 6). On approaching the ultimate strength of the masonry, the secondary
AE activity is found to be dominant as a consequence of deterioration of the material’s
microstructure. Hence, the AE activity during the unloading phase appears to be an interesting
characteristic to further investigate the fracture growth.

(a)(b)
Figure 6. AE activity during loading and unloading phase for (a) lime-cement masonry and (b) lime
hydrate masonry.

3.4 AE and strain-based damage quantification
Load ratio and Calm ratio are applied to quantify the damage progress in masonry under cyclic
compressive loading. Load ratio characterizes the damage based on the stress level at theonset of AE activity. Calm ratio is applied to assess the damage during unloading due to crackclosure and rubbing. As previously explained, these two AE indices are important as they
characterize the progression of damage during the loading and unloading phases. By
combining both AE indices, Load and Calm ratio, this scheme classifies the damage into threezones: minor, intermediate and heavy damage [15,16]. The damage quantification limits for
Load ratio (0.9) and Calm ratio (0.05) were developed from AE evaluation criterion in bending
load for RC beams [16]. However, the same limits are used here to investigate theirapplicability for identifying the progress of damage from sub critical to critical stage in masonryunder compression.
In this study, two different research questions are addressed. Firstly, the accuracy of AE-based
damage quantification has to be validated. Secondly, most recorded AE signals during
unloading are attributed to the damage development. These questions are addressed bycomparing AE-based damage quantification with deformation measurements. A comparative
analysis was performed with the measured deformation from LVDTs in terms of strain values
with the Calm ratios, as shown in Figure 7(a) and (b).The axial strains are obtained from one
7

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LVDT in couplets and the average of three LVDTs in walls. The lateral strains are obtained
from one LVDT in walls. Results from AE and LVDTs present a clear trend representing thedamage progress. Irrespective of masonry type and size effects, the trend shifts from the soundzone (phase I) to the heavy damage zone (phase III) with little or no accumulation at theintermediate damage zone (phase II). There is no major difference observed in the trajectories
to reach the heavy damage region.

(a)(b)
Figure 7. AE and strain-based damage quantification for (a) lime-cement masonry and (b) lime hydrate
masonry.

From the comparative analysis of the results of the lime-cement masonry, following
observations are made, see Figure 7(a): For the LC couplet, AE is in good correlation with the axial strain throughout the experiment,
except the last load cycle.
 For the LC wall, in the initial stage, AE is in good correlation with the axial strain. As thetest continues, AE shifts apart from the axial strain. Nevertheless, AE is more close to axial
strain rather than lateral strain.
For lime hydrate masonry, following observations are made, see Figure 7(b): For the LH couplet, from the initial to final stage, the AE-based trend line remains lower
than the axial strain.
 For the LH wall, in the initial stage, AE is in good correlation with the lateral strain. In thelast two load cycles, AE shifts apart from the lateral strain. Nevertheless, AE is in between
lateral and axial strain.
The horizontal LVDTs were not installed in the couplets and therefore AE was not comparedwith lateral strains. In general, the interesting observation from these results is the shifting
behaviour of AE in different masonry types. It can be seen that the AE trend either follows theaxial strain or lateral strain or remains intermediate. Such behaviour is likely to be related with
the AE activity in connection to the mode of cracking in the specimen. Usually, tensile type ofcracks (mode I) are generated during the nucleation of cracks upon stress increase. Upon
approaching the failure, the frictional activities in the crack surfaces generate shear type of AE
8

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signals (mode II). The shifting behaviour of AE may be related to the change in the fracturemode of cracking from the tensile type to shear type of fracture. However, a further analysis isneeded to confirm this hypothesis. On the other hand, the damage quantification limits seemto be too low and overestimate the damage grade for masonry under axial compression.
Hence, in future, the Load-Calm ratio critical limits have to be calibrated with other NDTtechniques. In this way, it provides the basis for damage quantification in masonry forlaboratory and on-site applications.

4. Conclusion
In this study, the mechanical and acoustic emission behaviour of two different types of masonrywith lime-based mortar is examined under cyclic compression. Walls as well as couplets arestudied to investigate size effects. Firstly, the study focuses on comparing the AE andmechanical trends for lime-cement and lime hydrate masonry types. AE activity is strongly
related to successive load levels, and a good correlation to the stiffness degradation of thematerial is observed. Secondly, the study focuses on the AE activity during cyclic loading. The
presence of secondary activity has been indicated by the registered AE events during theunloading phase of a loading cycle.
Furthermore, the Load-Calm ratio in relation to strain values from LVDTs are applied to quantify
damage. Irrespective of masonry type and size effects, the trend indicates similar trajectories
to reach the heavy damage state. Comparison with strain measurements indicates that thedamage quantification limits are to be revised. The trends of Load-Calm ratio from AE were ina reasonable agreement with the strain values from LVDTs. In general, AE is closely related
to axial strain for LC specimens and lateral strain for LH specimens. Results demonstrate someinteresting findings and raise questions about the mode of cracking for the two different typesof masonry. A detailed study of the crack classification using the RA value and averagefrequency is a part of further work to investigate this hypothesis. Lastly, for masonry couplets
and walls with respect to size effects, there was no significant difference in AE damage
progress, apart from the registered number of AE events.

ACKNOWLEDGEMENTS
The Research Fund - Flanders (FWO) is acknowledged for funding the FWO project “AEFracMasS: advanced Acoustic Emission analysis for Fracture mode identification in Masonry
Structures” (G.0C38.15)

References:[1] RILEM TC 203-RHM : Repair mortars for historic masonry: Performance requirements for
renders and plasters. Materials and Structures, Vol. 45, 2012, pp. 1277-1285.
[2] M.R. Veiga, A. Fragata, A.L. Velosa, A.C. Magalhães, M.G. Margalha (2010). Lime-basedmortars: Viability for use as substitution renders in historical buildings. International Journal ofArchitectural Heritage, April-June 2010, Vol. 4(2), pp. 177-195. ISSN 1558-3058.
[3] L. Schueremans, Ö. Cizer, E. Janssens, G. Serré, K. Van Balen (2011). Characterizationof repair mortars for the assessment of their compatibility in restoration projects: Research andpractice. Construction and Building Materials, December 2011, Vol. 25(12), pp. 4338-4350.
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[4] A. Costigan, S. Pavía, O. Kinnane (2015). An experimental evaluation of prediction models
for the mechanical behaviour of unreinforced, lime-mortar masonry under compression.
Journal of Building Engineering, December 2015, Vol. 4, pp. 283-294.
[5] A. Drougkas, P. Roca, C. Molins (2016). Compressive strength and elasticity of pure lime
mortar masonry. Materials and Structures, March 2016; Vol. 49(3), pp. 983-999.
[6] R. Hayen, K. Van Balen, D. Van Gemert (2004). The mechanical behaviour of mortars intriaxial compression. Arch Bridges ARCH’04. P. Roca, E. Oňate (Eds.), © CIMNE, Barcelona.
[7] C.U. Grosse, M. Ohtsu (Eds.) (2008). Acoustic emission testing - basics for research applications in civil engineering. Springer, Berlin.
[8] D.G. Aggelis, N.-M. Barkoula, T.E. Matikas, A.S. Paipetis (2013). Acoustic emission as atool for damage identification and characterization in glass reinforced cross ply laminates.
Applied Composite Materials, August 2013, Vol. 20(4), pp. 489-503.
[9] E. Eberhardt, D. Stead, B. Stimpson (1999). Quantifying progressive pre-peak brittle
fracture damage in rock during uniaxial compression. International Journal of Rock Mechanics
and Mining Sciences, January 1999, Vol. 36, pp. 361-380.
[10] K. Andreev, N. Shetty, E. Verstrynge (2018). Acoustic emission based damage limits andtheir correlation with fatigue resistance of refractory materials. Construction and Building
Materials, March 2018, Vol. 165, pp. 639-646. DOI: 10.1016/j.conbuildmat.2018.01.062[11] A. Carpinteri, S. Invernizzi, G. Lacidogna (2007). Structural assessment of a 17th-century
masonry vault with acoustic emissions and numerical techniques. International Journal ofArchitectural Heritage: Conservation, Analysis and Restoration, Feb 2007, Vol. 1(2), pp. 214226. ISSN: 1558-3058. DOI: 10.1080/15583050701287649[12] S. De Santis, A.K. Tomor (2013). Laboratory and field studies on the use of acoustic
emission for masonry bridges, NDT&E International, January 2013, Vol. 55, pp. 64-74.
[13] E. Verstrynge, L. Schueremans, D. Van Gemert, M. Wevers (2009). Monitoring andpredicting masonry’s creep failure with the acoustic emission technique. NDT&E International,
September 2009, Vol. 42(6), pp. 518-523. DOI: 10.1016/j.ndteint.2009.03.001[14] K.S. Gumaste, K.S. Nanjunda Rao, B.V. Venkatarama Reddy, K.S. Jagadish (2007).
Strength and elasticity of brick masonry prisms and wallettes under compression. Materials
and Structures, March 2007, Vol. 40(2), pp. 241-253. DOI: 10.1617/s11527-006-9141-9[15] NDIS 2421 (2000). Recommended practice for in-situ monitoring of concrete structures by
AE. Japanese Society for Non-destructive Inspection, 2000.
[16] M. Ohtsu (2006). Quantitative AE techniques standardized for concrete structures.
Advanced Materials Research, Feb 2006, Vol. 13-14, pp. 183-192.
[17] N. Shetty, G. Livitsanos, D. Aggelis, D. Van Hemelrijck, M. Wevers, K. Van Balen, E.
Verstrynge (2018). Visualization of crack propagation in brick masonry couplets undercompression. In G. Milani, A. Taliercio, S. Garrity (Eds.), 10th International Masonry
Conference, July 9-11, 2018; Milan, Italy.
[18] G. Livitsanos, N. Shetty, D. Hündgen, E. Verstrynge, M. Wevers, D. Van Hemelrijck,
D.G. Aggelis (2018). Acoustic emission characteristics of fracture modes in masonry materials.
Construction & Building Materials, Feb 2018; Vol.162, pp. 914-922.

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22 - Characterization of particle impacts using Acoustic Emission
Characterization of particle impacts using acoustic emission
Lorenzo CARRILLO1, Eric SERRIS1
1

Mines Saint-Etienne, SPIN EMSE, CNRS UMR 5307, LGF, CS 62362, F-42023 St
Etienne, France

Abstract:
Acoustic emission has been successfully applied for monitoring a large variety of solids elaboration
processes. In order to know the applicability of this technique in an industrial process such as grinding,
mixing or also fluidized bed, we need to initially study the impacts of particles on different material
surfaces. Namely, these will aim to simulate the industrial vessel walls. A wide range of powders andimpact surfaces has been examined and the Hertz law has been applied to characterize the impact ofparticles. By considering only elastic deformations, the law has been simplified obtaining several
parameters, such as the total displacement or the total load applied, which describe the impact. The
influence on the acoustic emission of particle size and speed has also been explored in this study. A
quasi-linear correlation has been found between these variables with a slope, which depends frommaterial properties. Relevant differences have been observed within impact surfaces, especially inenergy, caused by a different attenuation capacity in surface’s material. These differences are evident
in waveforms obtained during experimental phase. Regarding other parameters such as amplitude a
correlation has been found with elastic properties of particles and surfaces. In particular, sugar
particles have produced higher amplitudes caused by plastic deformations. Finally, centroids
frequencies have shown values completely different, allowing understanding the composition of thesurface. In conclusion, surface’s properties influence frequency and energy fields, on the contrary
particles elastic properties seem to affect amplitude values.

1. Introduction
Acoustic emissions have found several applications regarding material integrity and in a wide
range of industrial processes. This technique must allow the monitoring of different process
conditions. The main advantage is its non-intrusive applicability to sensor longer life
processes. Several problems are linked to the interpretation of the transmitted signal and the
big large amount of collected data. Different studies show that it is possible to elaborate
prediction models using a statistical or physical interpretation of obtained parameters withdata processing [1,2]. Numerous constraints are also related to the physical explanation of
the impact between the particle and the surface. A characterization of this phenomenon
could be given with the elastic impact theory and the Hertz law. Assuming a circular or
elliptical impact surface, Hertz equations could be used to predict forces and interactions
between particles and surfaces [3]. Several examples of this interesting application could befound in literature. Namely, fluidized bed reactors show issues related to the time ofresidence that could cause undesirable events like sintering or particles agglomeration [4].
This kind of phenomena can be prevented studying particles speed and bed level using
acoustic emission [5]. Acoustic emissions could also be used to evaluate distribution ofparticles in cyclones in order to prevent problems as surging. Predictive models concerning1

the particle size and the speed can be developed [6]. A good example of correlation between
acoustic parameters and Hertz equations could be seen in Pecorari work [7]. Partial power
could be related with the force generated by the impact. Hertz theory shows that the totalload applied generated by the impact depends on elastic properties such as Young’s
modulus and Poisson coefficient. The dependence between amplitude of the waveform andparticle’s speed is widely demonstrated. In particular, it is proportional to the particle’s
speed, in the low frequency range, for example, when wavelength is greater than the body of
impact thickness. Even increasing impact speed and incurring in plastic deformation, whichmeans greater energy dispersion, amplitude continue to increase. However, in the last casea shift of amplitude distribution could be seen through higher frequencies. In conclusion, lowvelocity impacts produce signal with little extensional mode and large flexural mode. On thecontrary, extensional mode becomes larger and flexural mode becomes shorter in highvelocity impacts, due to the penetration of the particle in the surface of impact (plastic
deformation) [8]. In this study, several correlations have been found between particles’ speed
and acoustic parameters. Furthermore amplitudes, energies and frequency centroid show
certain dependence from surface elastic properties. This dependency could be seen inrecorded waveform allowing a better explanation of acoustic wave transmission andattenuation phenomena.

2. Theoretical Model
When two solids with different geometry collide, they initially touch in a single pointcausing a deformation in its proximity. This causes a contact between the two areas, which
depends on the collision force between the two bodies, their geometry and the material
properties such as the Poisson ratio, and the Young’s modulus. Regarding two solids ofgeneral shape, for convenience, a Cartesian coordinate system is placed in the point ofcontact. The collision causes a normal compressive load into the two solids, which isconsidered as all applied in the contact point. Hertz introduced some simplification to contact
mechanics. He considered an elliptical contact surface and he also considered the twobodies as two half-spaces. This means that stresses in the two surfaces could be studied
separately.
However, the contact area has to be really small compared with bodies’ surface andwith radii of curvature. Finally, Hertz assumed that two bodies are frictionless and theirsurfaces are perfectly smooth. The most notable case considers two solids of revolution withcurvature radii R. Under these simplifications Hertz theory allows to define some interesting
parameter, such as the contact area a and the total displacement δ[3]:
𝑎𝑎 =
𝛿𝛿 =
with

!

!!"

(1a)

!! !

(1b)

!! ∗

!

!

!∗

!"!" ∗!

=

!!!!!
!!

!!! !

+ ! ! , a mechanical constant that considers the Poisson ratios and the!

Young’s moduli of the two bodies (1 for the particle, 2 for the surface) and P the applied load.
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If the particle is falling under the gravitational force, from the energy balance the applied load
could be expressed as:!

𝐹𝐹 = 𝐹𝐹! + 2𝐹𝐹! + 2[𝐹𝐹! 𝐹𝐹! + 𝐹𝐹! ]! (2)Where Fo is the gravitational force due to the mass particle and FS are the surface forces,
expressed by
𝐹𝐹! = 2∆𝛾𝛾𝛾𝛾𝑟𝑟!! (!

!! ! ∗(3))Δγ is the adhesion surface energy per m2, rt is the total projected radius and Fm is the totalmechanical force. For rt and Fm, it is possible to define two contributions according to theelastic and plastic deformation. [9]. However, considering the particle falling on a perfectly
flat surface and assuming the impact as perfectly elastic, the applied load (2) could be
simplified as follows [7], with ρ the density of the falling particle and v the velocity of theparticle:
𝐹𝐹!" = 8,23𝜋𝜋 ! 𝜌𝜌 ! 𝑣𝑣 ! !/! 𝑅𝑅 ! 𝐸𝐸 ∗!/!

(4)

3. Materials and Methods
During the experimental phase, three kinds of particles of different nature are used. Siliconparticles are provided by Elkem Silicones, the sugar powder is provided by Seppic and theglass bead are provided by Prolabo. Their properties are resumed in Table 1 (E Young’s
modulus, ν Poisson ratio, ρ density).

Sugar
SiliconGlass

Diameter[µm]
1100 305
1090 325
995 - 354

E
[GPa]
4,5

ν
0,3

ρ
[Kg/m3]
1600

160

0,27

2330

70

0,22

2600

Table 1- Particles' properties

To characterize the impact, particles have been dropped in free-fall from four different
heights. (Table 2) on three different surfaces (Table 3). A simple model neglecting the effectof air viscosity was used to calculate the speed.

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Height [cm]
2
10
50
90

Speed [m/s]
0,63
1,40
3,13
4,20

Table 2 - Fall speeds

PMMA
Steel
Ceramic

Width
[mm]
10
20
5

E
[GPa]
3
200
87

ν
0,33
0,26
0,17

ρ
[Kg/m3]]
1150
7850
2300

Table 3 - Surfaces' properties

Once particle drops on the surface, signals are recorded by using two piezoelectric sensors
F-15α (pass band 100 - 450 kHz) and WD (pass band 50 - 350 kHz). Signals were also
amplified with 2 preamplifiers model 2/4/6-Mistras and digitalized with 2 channels acquisition
card AEDSP. Finally, data streaming and acquisition were possible by using the software
AEWIN. Mistras Group provided all the equipment.

Figure 1 - Sensor F-15α on steel surface. Coupling is insured with silicon grease.

The test was performed 10 times in order to a good quantity of data. The sensor F-15α wasplaced at about 9 cm from the region where impacts occurred. The distance between the 2sensors was about 39 cm. A 30-dB filter was used to screen background noise. Besides, theproper functioning of the sensor was assured with the Hsu – Nielsen Test.

4. Results
AEWIN software splits the signal in bursts every time it passes a threshold value. Thus,
amplitudes in every burst have a similar size. Bursts are characterized by duration,
amplitude, energy, average frequency, etc. The most interesting variables analysed are theenergies, the amplitudes and the centroids frequencies.

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Energies as function of particle’s speed are resumed in figures 2 and 3.
Silicon particles

1E+04

Energy [aJ]

1E+03

1E+02

1E+01

1E+00

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

Speed [m/s]Ceramic - 1000 µm
Steel - 350 µm
Plastic - 350 µm

Steel - 1000 µm
Plastic - 1000 µm
Ceramic - 350 µm

Figure 2 - Energy as function of particle's speed for silicon particles
Steel surface

Energy [aJ]

1E+04

1E+03

1E+02

0

0,5

1

Silicon - 1000 µm
Sugar - 1000 µm

1,5

2 2,5 3 3,5 4 4,5Speed [m/s]
Glass - 1000 µm
Silicon - 350 µm

Figure 3- Energy as function of particle's speed for steel surface

Each point represents the average energy for a fixed speed. The graphics have beenobtained by considering a single type of particle and a single type of surface. Anyway, same
results could be observed by using all the other particles and the other surfaces. A linear
trend between energy and particle’s speed could be observed for each kind of particle
Regarding amplitudes, they could be represented as well as function of particles’ speed, as
shown in figures 4.

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Steel surface
95
90

Amplitude [dB]

85
80
75
70
65
60
55
50

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

Speed [m/s]
Silicon - 1000 µm
Sugar - 1000 µm
Glass - 350 µm

Glass - 1000 µm
Silicon - 350 µm
Sugar - 350 µm

Figure 4 - Amplitude as function of particle's speed for steel surface.

Once again, an essentially linear relationship could be seen between amplitude and particle’s
speed. In this case the amplitude highest values may appear to correspond to those of theglass particles. However, the sugar ones presents high amplitudes as well probably becauseof plastic deformation, which causes exceeding dispersion energy.
By fixing the particle’s speed at 3,13 m/s, Hertz theory of contacts’ parameters could becorrelated with acoustic variables. Glass and silicon particles have the same behaviour.
However, the sugar shows a trend completely different. These differences could be observedby plotting the energy as function of total displacement δ and of E*. Furthermore, sugar
particles show a bigger total displacement because of their smaller stiffness. These particles
are supposed to incur in plastic deformation, even if their energies are weaker than theothers.
1E+04

Energy [aJ]

Glass
Sugar
Silicon

1E+03

1E+02

0

5

10

15

20

Delta [um]Figure 5 - Energy as function of total displacement. The 3 points for each series represent the 3
surfaces (steel, ceramic and plastic).

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Energy [aJ]

1E+04

1E+03

1E+02

Glass
Sugar
Silicon
025

50
E* [GPa]

75

100

Figure 6 - Energy as function of E*. The 3 points of each series represent the 3 surfaces (steel,ceramic and plastic).

At the end, centroid frequencies have been analysed. For each surface, there is a limited
range, except for glass particles, as shown in table 4.
Centroid frequencies [kHz]Steel Ceramic Plastic
Glass
205
210
42
Silicon
102
190
39
Sugar
135
210
54
Average147
203
45
Table 4 - Average centroid frequencies

In the last part, Hertz theory’s parameters δ and E* have been correlated with the energies.
Figures 5 and 6 show the same tendencies for silicon and glass particles. In particular, forhigh values of δ, energies get weaker. δ shows the displacement caused by elastic andplastic deformations. Thus, it seems that plastic deformation reduces the energy collected by
the sensors. In fact, in this case a part of particle’s kinetic energy should be dissipated asenergy necessary to plastic deformation.
In figure 6 a direct relationship could be observed. E* increases energy collected for eachkind of particle. However, it must take in account surfaces attenuation capacity.
It would be interesting to evaluate plastic deformations, in order to understand the ratio
between the two contributions to energy and amplitude. This was not possible in this study.
Attenuation coefficient and propagation’s velocity of the acoustic wave have also to beconsidered.

5. Discussion
This study shows some correlations existing between acoustic emissions and mechanics
contacts. How it has been observed, a quasi-linear relationship exists between energy (andamplitudes) and particles velocity.
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Figure 7 - Waveforms for steel surface (left) and plastic surface (right). Silicon particles were used andtheir speed was fixed to 3,13 m/s. The different attenuation of the signal could be easily observed.

It could be noticed that the steel surface produce higher energies if compared to the others.
Through the examination of signals streaming, this phenomenon could be better understood.
The plastic surface and the steel surface have been compared in figure 7. For the first one
higher amplitudes could be observed even if the signal seems to get weaker in a shortperiod. On the contrary, the steel surface shows amplitudes smaller but at the same time agreater number of oscillations in the unit of time. Furthermore, the duration of the signal islonger and it leads to exceeding energies. Thus, a key role in acoustic emission is played by
the attenuation coefficient, which differs in each material.
Regarding particles, the main differences are caused by the diameter. Generally, a smaller
diameter causes lower energies. Collected amplitudes always show high values, according
to Prosser et al [8].
Glass particles produce the highest energies and the highest amplitudes. The impact seems
to be independent of particles’ density. However, by calculating the stiffness of particles andsurfaces an inverse relationship could be observed. Stiffness is the resistance of an elastic
body to deflection or deformation by an applied force and it is calculated as the ratio between
the elastic force Fel and the total displacement δ.

Sugar
SiliconGlass

Ceramic
Steel
Plastic
Stiffness [N/m]
2,76E+06 3,52E+06 3,74E+05
4,03E+06 5,94E+06 4,13E+05
4,89E+05 5,01E+05 2,52E+05

Table 5 - Stiffness calculated for each particle impacting with the different surfaces. Particles velocity
was fixed to 3,13 m/s.

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In conclusion, the amplitudes and the energies are linked to the elastic contribution and aplastic contribution. The first one depends on the stiffness, which takes in account Young’s
moduli of the surface and the particle, but also their geometric properties. Thus, glass
particles allow higher elastic deformations than the others.

6. Conclusion
A simple model proposed by Hertz was used to characterize mechanical impact. A sphericalparticle was launched under free fall on a smooth plane. The impact was considered as
totally elastic. Thanks to these simplifications, energies and amplitudes have been evaluated.
Essentially, linear correlations with particle’s speed have been found in every case.
Furthermore, attenuation coefficient plays an important role in the characterization of these
impacts. Energies are strongly linked to this parameter and waveforms could help us tobetter characterize this phenomenon.
On the other hand, particles produce high energetic acoustic emissions when they have agreat stiffness. Thus, greater energy values could be observed when particles deform
elastically more than plastically. However, amplitude seems to have an inverse correlation.
This aspect should be better explored in order to understand how plastic deformation affectthe mechanic of the contact.

References:[1] Vahaviolos S: Acoustic Emission: Standards and Technology Update [Internet], 100 Barr
Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, ASTM International,
1999. DOI: 10.1520/STP1353-EB[2] Boyd JWR, Varley J: The uses of passive measurement of acoustic emissions fromchemical engineering processes. Chem Eng Sci 2001;56:1749–1767.
[3] Johnson KL: Contact Mechanics [Internet], Cambridge, Cambridge University Press,
1985. DOI: 10.1017/CBO9781139171731[4] Jiang XJ, Wang JD, Jiang BB, Yang Y, Hou LX: Study of the power spectrum of acoustic
emission (AE) by accelerometers in fluidized beds. Ind Eng Chem Res 2007;46:6904–6909.
[5] Jingdai W, Congjing R, Yongrong Y: Characterization of flow regime transition andparticle motion using acoustic emission measurement in a gas-solid fluidized bed. AIChE J
2010;56:1173–1183.
[6] Spencer SJ: Monitoring of dense medium cyclone performance and condition by acoustic
emission 2004

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[7] Pecorari C: Characterizing particle flow by acoustic emission. J Nondestruct Eval
2013;32:104–111.
[8] Prosser WH, Gorman MR, Humes DH: Acoustic emission signals in thin plates produced
by impact damage. J Acoust Emiss 1999;17:29–36.
[9] Wall S, John W, Wang HC, Goren SL: Measurements of kinetic energy loss for particles
impacting surfaces. Aerosol Sci Technol 1990;12:926–946.

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32 - The sound of metal: Acoustic Emission during

THE SOUND OF METAL: ACOUSTIC EMISSION DURING THE
the deformation of commercially pure titanium
DEFORMATION OF COMMERCIALLY PURE TITANIUM
Nick Goossens1, Marc Seefeldt1, Martine Wevers1

1

Department of Materials Engineering, KU Leuven, Kasteelpark Arenberg 44, 3001
Leuven, Belgium

Abstract:
Acoustic emission (AE) was monitored in situ during in-plane tensile deformation of a commerciallypure titanium plate. Different load orientations were considered: parallel to the rolling direction (RD),transversal direction (TD) and under an angle of 45 degrees (45D) with RD. The AE event rate, peak
amplitude, signal duration, rise time and ring-down counts were monitored during deformation.
Orientation dependent mechanical behavior, AE signal characteristics and microstructural evolution
was observed and correlated to the difference in texture and twinning phenomena. The intensity andcharacteristics of AE signals in RD and 45D do not change considerably during deformation, which
was attributed to the continuous nucleation and growth of compression twins. Anomalous AE behavior
in TD was observed, showing two distinct peaks. All signal parameters show significantly decreased
values during a first peak, which was correlated to massive nucleation of small tensile twins. Thesetensile twins disappear with increasing strain, indicating the end of the first peak. The second peak iscomparable to the plateau behavior in RD and 45D and occurs during necking. Compression twinningis dominant, but limited to the necking zone. An unfavorable textural effect in TD has been identified,
which hinders the formation of compression twins during the homogeneous deformation, as opposed
to RD and 45D, resulting in anomalous AE behavior. The acoustic emission technique proves to be acomplementary technique to electron diffraction as it allows monitoring and identifying the twinningmodes of commercially pure titanium in situ.

1. Introduction
Commercially pure titanium (CP-Ti) and titanium alloys have excellent engineering properties
such as a good corrosion resistance, high specific strength and stiffness and a good
biocompatibility. Therefore, titanium is extensively used in cutting-edge applications in thefields of aerospace, automotive and medicine, for instance for engine applications andmedical implants. In all these applications, the material is loaded and the mechanicalresponse has to be known precisely. For this purpose, a complete understanding on the
deformation behavior of titanium is desired. However, titanium is an hexagonal metal andtherefore has only access to three main slip systems: basal, prismatic and pyramidal. The
von Mises criterion states that the activation of five independent slip systems is required toplastically deform polycrystals [1]. The prismatic <a>, pyramidal <a>, basal <a> andpyramidal <a+c> are the most common slip systems in titanium. However, <a> slip systems
are unable to allow strain in the direction of the c-axis and the critical resolved shear stress ofpyramidal <a+c> slip systems is high at room temperature. Therefore, twinning mechanisms
have to complement dislocation slip to fulfill the von Mises criterion and allow plasticity [2,3].
The strong orientation-dependency of the activation of basal slip and particular twinningmodes complicate the explanation of the entire deformation process of titanium.
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Acoustic emissions (AE) are transient elastic waves that are formed by the relaxation ofinternal stresses, which are instantaneously imbalanced by a sudden change in the
microstructure of a material [4]. AE carries information of the dynamic processes, from which
they originated and which lead to plastic deformation. Twinning and the initiation of
dislocation slip are known to be the principal sources of AE in titanium. AE can be measured
in situ during the deformation of a material and therefore allows the extraction of dynamic
information concerning the deformation mechanism. Because of this AE provides a dynamic
complement to the classical static Electron Backscatter Diffraction (EBSD) technique, which
has already been used to investigate the deformation mechanisms in magnesium alloys[5,6].

2. Description of work
A plate of hot-rolled commercially pure titanium grade 2 (0.30 wt.% Fe, 0.25 wt.% O, 0.1
wt.% C, 0.03 wt.% N, 0.015 wt.% H) having a thickness of 1 mm was used. The
microstructure consists of equiaxed grains with a mean grain size of 52 µm and standard
deviation of 20 µm. Two sets of three types of tensile specimens (fig. 1) were prepared withthe load direction parallel to the rolling direction (“RD”), the transversal direction (“TD”) andunder an angle of 45° with respect to the rolling direction (“45D”). One set was used to carryout in situ AE measurements, whereas the other set was used for EBSD measurements. The
tensile tests were performed on an Instron 4505 tensile machine (Instron, United States)
equipped with a 100 kN load cell and a constant displacement rate of 0.5 mm min-1 at room
temperature. The specimens were strained until failure occurred.

Fig. 1. Dimensions of the tensile specimens in mm.

AE was simultaneously monitored using the AMSY-5 system (Vallen Systeme, Germany).
Two B1025 broadband transducers with a flat frequency response between 50 and 2000 kHz
were symmetrically attached on the tensile specimens. The distance between both sensors
was 100 mm. A signal threshold level of 30 dB was set just above the general noise level.
Signals that were not generated in the area between both transducers, such as signals fromthe grips, were considered as noise and discarded by a location filter.
The microstructural evolution up to several different strain levels was investigated with
automated EBSD scans using a scanning electron microscope (Nova 600 Nanolab, Thermo
Fisher Scientific, United States) and TSL OIM Analyze v.8 (EDAX, United States) dataacquisition and analysis software. An acceleration voltage of 20 kV and a step size of 2 µm
were used. The final strain levels were chosen based on the observed AE behavior. All
specimens were cut and mechanically polished after straining. In the last polishing step, a2

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colloidal silica suspension containing 30 vol.% H2O2 was used. All specimens were finallypolished using a VibroMet (Buehler, United States) vibratory polishing apparatus for 2 hours.

3. Results
The true stress-strain curves for the three different specimens are shown in fig. 2. Fig. 3
shows the engineering stress-strain curves together with the corresponding AE event rates(number of events per time interval). Plastic anisotropy is evidenced by a slightly larger yieldstress in TD compared to RD and 45D. The elongation to failure and the rate of strain
hardening (slope of the stress-strain curves) are both the largest in RD and the smallest inTD, suggesting that the plasticity is the highest in RD and the lowest in TD. Anisotropic
stress-strain behavior is well-known for commercially pure titanium and attributed to thetexture, resulting in the activation of different twinning systems [7-10]. Different fracture types
were finally observed: the TD specimen failed by a cup-cone type fracture, while the RD and
45D specimens failed by a shear-type fracture.

Fig. 2. True stress-strain curves of commercially pure titanium at room temperature parallel to therolling direction (RD), transversal direction (TD) and under an angle of 45° with the rolling direction(45D).

The AE event rate curves are determined by burst-type emission, which is likely due to fastand discontinuous deformation phenomena such as twinning. An example of a typical AE
burst signal is given in fig. 4. The AE response in TD differs from the RD and 45D and can becalled anisotropic as well, corresponding to the mechanical behavior. The amount ofdetected acoustic events increases sharply at the onset of plastic deformation. In TD (fig. 3b)
two maxima can now be distinguished: a first maximum around 9% strain when the ultimate
tensile strength (UTS) is reached and a maximum peak around 26% strain, which coincides
with the macroscopic necking of the tensile specimen. The AE response in RD (fig. 3a) and45D (fig. 3c) is much less strain-dependent. The AE event rate increases fast to reach aplateau value, which remains nearly constant until the specimen fractures. The start of thisplateau occurs when the UTS of the material is reached. Besides the shape of the curves,the magnitude of the AE event rate is remarkably different as well. It is the largest in RD,
which corresponds to the outspoken strain hardening behavior. A plateau value of 81 ± 9 s-1
is obtained. In contrast, the lower strain hardening in TD is visible by a reduced AE event
rate. At the first maximum, an event rate of 34 ± 6 s-1 is obtained, which is increased to 68 ±8 s-1 at the second maximum. In 45D an in-between event rate of 60 ± 7 s-1 is obtained at
the plateau.
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Fig. 3. Engineering stress-strain curves and corresponding acoustic emission event rate ofcommercially pure titanium at room temperature in a direction parallel to (a) RD (b) TD and (c) 45D.
The arrows denote the strain levels at which the microstructure is investigated with EBSD.

Fig. 4. Typical AE burst signal. Peak amplitude, signal duration, signal rise-time and ring-down counts
are indicated. The threshold amplitude corresponds to 30 dB.

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Whether the different AE response in TD translates into a different active deformation
mechanism needs further investigation. Indeed, a different deformation mechanism would
cause sound waves with different properties and characteristics. Therefore, the evolution offour signal parameters was monitored as a function of the strain (fig. 5) for the three different
specimen orientations. Peak amplitude, signal duration, signal rise-time and the ring-down
counts were considered.
A physical interpretation of these parameters is shown in fig. 4. The evolution of theseparameters as a function of strain has a similar anisotropy. No outspoken change in the AE
parameters occurs in RD and 45D with increased strain. Only the ring-down count has atendency to decrease during the stage of inhomogeneous plastic deformation. Moreover, thedistribution of the different signal parameters is quantitatively similar in RD and 45D. The
values of the 50th and 80th percentile of the peak amplitude, signal duration, signal rise-timeand the ring-down count distributions are given in table 1 for RD, 45D and TD.
RD

45D

TD

Percentile

50th

90th

50th

90th

50th

90th

Peak amplitude (dB)

40.0

48.7

40.0

48.5

39.9

48.4

Signal duration (µs)

53

267

50

223

43

207

Signal rise-time (µs)

5.0

13.6

5.4

11.3

7.0

13.4

Ring-down counts

12

36

11

33

10

27

Table 1. 50th and 90th percentile values of the distribution of the peak amplitude, signal duration,
signal rise-time and ring-down counts of the acoustic emission measured with the load direction
parallel to the rolling direction (RD), under an angle of 45° with the rolling direction (45D) and parallelto the transversal direction (TD)The numbers for RD and 45D show that although the maximal values of all parameters in fig.
5a and fig. 5c seem to differ and appear to show large scatter, the vast majority of all thesignals considered have considerably lower parameter values, which are comparable in both
RD and 45D. The scatter in the values of these parameters is caused by a very small
percentage of signals, which could be used to distinguish the two orientations: a larger partof the AE in RD can reach larger parameter values than the respective AE in 45D can.
The shapes of the parameter distributions obtained from AE during straining in TD are again
different and show two maxima. The time (or strain) at which these maxima occur, is similar
to the time (or strain) at which the maxima in the AE event rate curve (fig. 3b) occur. The
maxima in the parameter distributions are less pronounced though. A similar quantitative
approach is followed to compare the distributions of the AE signal parameters in TD with theearlier described distributions in the other directions (table 1). Except for the signal rise-time,
all the values are lower than the respective values in the other directions. Again, the scatterin the parameter distribution is predominantly caused by a minority of the signals. To
summarize, the majority of AE signals in TD have the lowest value of peak amplitude, ringdown counts and signal duration and the highest value of signal rise-time compared to thesignals in RD and 45D. The same holds for the ring-down counts and signal rise-time of theminority signals. However, the highest percentile of these minority signals can achieve thelargest values for the peak amplitude and the smallest values for signal rise-time. This
5

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anomaly in the parameter distribution can be better illustrated using crossplots (fig. 6), in
which two independent signal parameters are plotted against each other. In this case, thering-down counts and peak amplitude of the signals are plotted against each other.

Fig. 5. Distributions of the peak amplitude, ring-down counts, signal rise-time and signal duration of the
AE signals in function of time. The AE signals are received upon tensile deformation of commerciallypure titanium at room temperature in a direction parallel to (a) RD (b) TD and (c) 45D.

The color code is related to the three ‘deformation stages’ that determine the mechanicalbehavior of the tensile specimens and were seen to influence the AE behavior in an
anisotropic way. The blue data points correspond to the onset of plastic deformation. The
green data points correspond to the moment when the UTS of the material is reached.
Finally, the red datapoints correspond to the period during which macroscopic necking isobserved. While the green and red region almost completely overlap for RD (fig. 6a) and 45D(fig. 6c) specimens, the red region is clearly stretched out more towards larger peak
amplitudes than the green region for TD (fig. 6b) specimens. This essentially confirms thetime (thus strain) indifference of the parameters in RD and 45D and the large similarities
between those two orientations.
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Fig. 6. Peak amplitude versus ring-down counts cross-plots of the AE during tensile deformation ofcommercially pure titanium at room temperature parallel to the (a) RD (b) TD and (c) 45D. The colors
correspond to different time intervals during the deformation and are linked to mechanical deformation
events. The blue points correspond to the onset of plastic deformation. The green points correspondto the time interval during which the UTS is reached. The red points correspond to the appearance ofmacroscopic necking.

In particular, 88.7% of these signals that have a peak amplitude larger than 50 dB are ‘red’
signals, meaning they originate during macroscopic necking. The ’red’ signals also make up
92.3% of all signals with a peak amplitude larger than 55 dB. Signal localization revealed thatthe origin of nearly all ‘red’ signals is found in a zone that is about 2 cm long, which
demonstrates that the ‘red’ signals are localized in the necking zone and can indeed belinked to the macroscopic necking of the tensile specimen. This necking has caused a
specific AE response in TD as seen in the second peak of fig. 3b and the anomalous
behavior of the minority signal parameters in fig. 5b. However, in the other directions no suchdifferent AE response, nor difference in signal parameters was observed. This anisotropy
can be explained by a deformation mechanism that is only activated during the macroscopic
necking in the TD, which does not yet occur during uniform plastic deformation. Based solely
on the AE results, no such change in deformation mechanism would be expected in RD and
45D. This also implies that the activity of the deformation mechanisms and in particular theirevolution with increasing strain in TD, must be orientation-dependent.
AE cannot reveal what these deformation mechanisms exactly are. In order to couple theseobservations to the microstructural deformation mechanisms, a second set of tensile tests
was carried out to different maximal strain levels. The microstructure at these strain levels isinvestigated with EBSD. These levels are marked with vertical arrows in fig. 3. For the TD
specimens, these strains are chosen to be immediately before and after the expected peakmaxima (fig. 3b) and just before the strain to failure. For the other specimens, the strainlevels were chosen before and after the beginning and the end of the plateaus (fig. 3a and3c) and one strain level in the middle of the plateau. Fig. 7 shows image quality maps of thedeformed tensile specimens in RD, TD and 45D respectively. The twin boundaries aremarked with colored lines.

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8

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Fig. 7. EBSD image quality maps of tensile specimens deformed at room temperature until different
strains with the load direction parallel to the rolling direction (RD), transversal direction (TD) and under
an angle of 45° with the rolling direction in the rolling plane (45D). Twin grain boundaries are colored:
tensile twins in red, compression twins in blue and compression twins in green. Deviations up to 5°
from the theoretical K1 twinning planes and theoretical misorientations according to [3] are allowed.

4. Discussion
Partridge [3] reported that deformation twinning in titanium mainly occurs in the {1012},{1121} and {1122} twinning planes. The magnitude of the twinning shear in these planes are
0.167, 0.638 and 0.225 respectively. These twins have already been observed in different
studies [10-15]. Here, mainly {1012} tensile twins with a misorientation of 90° and {1122}
compression twins with a misorientation of 65° are observed, in agreement with thetheoretical values (94°52’ and 63°58’ respectively) [3]. Small deviations up to 5° from thetheoretical twinning plane and theoretical misorientations are allowed. These deviations canbe attributed to the relative rotation of the twins and other grains during further deformation.
The twinning characteristics seem to be orientation-dependent. In RD and 45D twinning is
dominated {1122} by compression twins, while in TD twinning is dominated by {1012} tensile
twins. This observation is in accordance with other EBSD measurements [10,15,16] andcrystal plasticity simulations [17].
The twinning activity appears to be low in RD and 45D until large strains (> 25%) arereached. Few twins are observed at a time in the microstructure. The twins are exclusively{1122} twins in RD, but a combination of {1122} twins and {1012} twins appears in 45D,
although {1122} twins are the dominant type. Due to the generally low number of twins in RD
and 45D the number of twins in consecutive scans and specimens shows a significant
relative variability. Despite the large relative variations in the twin activity, the absolute
number of twins remains low in general. This statement is backed by the AE data and fits theobservation of the plateaus in fig. 3a and fig. 3c. The slightly larger width of {1122} twins in
RD, compared to 45D and the larger twinning shear of {1122} twins compared to {1012}
twins [11] could explain the higher AE event rate and larger amplitudes in RD. For large
strains (> 25%) the specimens showed macroscopic necking and the microstructure inside
this necking zone was investigated. The microstructure inside the necking zone is composedof elongated grains which show a larger twinning activity. In both RD and 45D more {1012}
twins are present. However, in the AE no change in event rate or signal parameters isobserved in the whole specimen, indicating that the increased {1012} twinning is only
happening locally in the necking zone.
The twinning evolution in TD is significantly different. Even at small deformations (1.3%
strain) extensive twinning is observed. Besides {1122} compression twins, a large fraction of{1012} tensile twins is present. A small fraction of {1121} compression twins is also
distinguished. The twins are smaller in size than the twins in RD and 45D. Because the{1012} twins result in less shear, their contribution to the deformation and, therefore, theirassociated deformation energy is smaller than {1122} twins. The resulting AE signals in TD
have a reduced energy and therefore lower amplitude for the same signal duration ascompared to the signals in RD and 45D (fig. 5). It is likely that a larger fraction of thosesignals cannot exceed the threshold set for the amplitude and are blocked by the noise filter,
resulting in a reduced AE event rate, as seen in fig. 3b. The twinning activity has decreased
significantly at an intermediate strain of 10.3%, which coincides with the first AE peak in fig.
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3b. A transormation has occurred during which the many narrow {1012} twins have been
replaced by a relative low amount of wider {1012} twins. It is unclear whether thedisappearance of certain twins and growth of other twins is coupled (e.g. twin merging) ornot. Detwinning phenomena have been observed in pure titanium under reversed loading
conditions during which {1012} twins were replaced by {1122} twins and vice versa [14,15].
The nucleation of twins requires more shear stress than the subsequent growth [18] and theresulting AE signal amplitude will be larger for twin nucleation than twin growth [19]. An insitu study of detwinning in an AZ31 magnesium alloy showed that the initiation of detwinning
results in higher AE amplitudes when compared to twin shrinkage [20]. However, noreversed loading has been applied during the tensile tests, which renders this option highly
unlikely. The exact mechanism behind the change in twinning conditions remains unknown.
At large strains (>20%) when macroscopic necking occurs, the microstructure inside thenecking zone is dominated by {1122} compression twins and the AE behavior becomessimilar to the behavior observed in RD and 45D, as shown in fig. 6. The change in twinningmode also appears to be strongly localized inside the necking zone. It is suggested that theinitial texture in the TD is unfavorable to the nucleation and growth of {1122} twins. The{1122} twins dominate the microstructure in RD and 45D suggesting that the unfavorable
textural effect is only present when the loading direction is close to the TD. If the angle
between the loading direction and the TD is increased, the unfavorable textural effect seems
to disappear fast. When this angle reaches 45°, which is the case for the 45D specimens, thetextural effect has already disappeared. The appearance of a majority fraction {1122} twins in
the TD in the necking zone can be attributed to the complex triaxial stress state present in
the necking zone. The inhomogeneous deformation causes the local stress state to deviatesignificantly from the homogeneous uniaxial stress state. Therefore, the unfavorably oriented
grains, which initially have low Schmid factors in the uniaxial stress state, can locally havehigher Schmid factors in the triaxial stress state and can accommodate {1122} twinning.
Outside the necking zone the uniaxial stress state and consequently low Schmid factors arepreserved. Therefore {1122} twinning is not observed at all.

The low activity of {1122} twins in RD and 45D corresponds to increased strain hardening
and a large failure strain values, whereas the high activity of {1012} twins in the TD
corresponds to decreased strain hardening and smaller failure strain values in agreement toother experimental work. However, besides the twinning modes, the influence of theanisotropy on the dislocation motion and slip systems [3,8,17] must also be taken into
account to correctly explain the full stress-strain behavior. The crystal-plasticity finite element
simulation of Hama et al. [17] assumes a strong basal texture in which the c-axes of thegrains are tilted between 20° and 40° from the plate normal direction to the TD and simulates
the relative activity of various dislocation gliding mechanisms, {1012} twinning and {1122}
twinning in RD and TD until a true strain of 9% is reached. In RD the relative activity of{1012} twinning is nearly zero during the entire deformation, while the relative activity of{1122} increases to 0.15 at 2% true strain and remains nearly constant during thesubsequent deformation. In TD the relative activity of {1012} increases fast to 0.20 at under
1% true strain, but is seen to decrease with increasing strain. At 6% true strain the relative
activity has dropped under 0.10. The relative activity of {1122} is small but non-zero and isseen to continuously increase, albeit slowly. These simulation results are in agreement with
our data in the simulated strain range, highlighting the dependence of twinning on the
texture.

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5. Conclusion
The deformation mechanisms of commercially pure titanium subjected to uniaxial tensile load
were investigated by combining in-situ AE measurements with EBSD investigations. The
following conclusions could be drawn:(i) A simple plateau-shaped AE response is observed when the tensile load is applied in RD
and 45D. The signal characteristics and intensity remain relatively constant during
the deformation. The deformation microstructure is dominated by {1122}
compression twins.
(ii) Different AE response and twinning mechanisms were observed when the tensile load isapplied in the TD, compared to RD and 45D. Both the AE event rate and signal
parameters follow a continuous upward trend exhibiting two distinct maxima. The firstpeak is associated with the transformation of many thin {1012} tensile twins into a
few broad {1012} twins, while the second peak is associated with the appearance of{1122} twins in the necking zone.
(iii) The difference in twinning mechanisms and the resulting AE responses are closely
related to the texture and stress state. {1122} twinning is unfavorable in the TD as
the texture is unfavorably aligned towards the uniaxial stress state and {1012}
twinning is initiated. Once the loading direction is rotated away from the TD, the{1122} twinning becomes increasingly dominant. The comparable AE and twinningbehavior in the RD and 45D suggest that a small angle between the load direction
and the TD is already sufficient to allow {1122} twinning. Additionally, during
macroscopic necking, the stress state inside the necking zone becomes complex andtriaxial and the disadvantageous texture alignment disappears. Extensive {1122}
twinning is then observed as well in the necking zone, regardless of the (in-plane)loading direction.

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Charact., Vol. 72, 2012, 24-36[14] Chun Y.B., Yu S.H., Semiatin S.L., Hwang S.K., Effect of deformation twinning on
microstructure and texture evolution during cold rolling of CP-titanium, Mat. Sci. Eng. AStruct., Vol. 398, 2005, 209-219[15] Hama T., Nagao H., Kobuki A., Fujimoto H., Takuda H., Work-hardening and twinningbehaviors in a commercially pure titanium sheet under various loading paths, Mat. Sci. Eng.
A-Struct., Vol. 620, 2015, 390–398[16] Yi N., Hama T., Kobuki A., Fujimoto H., Takuda H., Anisotropic deformation behavior
under various strain paths in commercially pure titanium Grade 1 and Grade 2 sheets, Mat.
Sci. Eng. A-Struct., Vol. 655 ,2016, 70-85[17] Hama T., Kobuki A., Takuda H., Crystal-plasticity finite-element analysis of anisotropic
deformation behavior in a commercially pure titanium Grade 1 sheet, Int. J. Plasticity, Vol.
91, 2017, 77-108[18] Pirouz P., Lawlor B.F., Geipel T., Bilde-Sørensen J.B., Heuer A.H., Lagerlo K.P.D., On
Basal slip and basal twinning in sapphire (α-Al2O3)—II. A new model of basal twinning, Acta
Mater., Vol. 44, 1996, 2153-2164[19] Li Y., Enoki M., Evaluation of the Twinning Behavior of Polycrystalline Magnesium atRoom Temperature by Acoustic Emission, Mater. Trans., Vol. 48, 2007, 1215-1220
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[20] Yasutomi T., Enoki M., In-situ evaluation of detwinning behavior in extruded AZ31 Mg
alloy by AE, Mater. Trans., Vol. 53, 2012, 1611-1616

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38 - Analysis of kinking and twinning behavior

ANALYSIS OF KINKING AND TWINNING BEHAVIOR IN MG ALLOYS
in mg alloys by AE clustering method
BY AE CLUSTERING METHODManabu Enoki 1, Kengo Tamura 1, Takayuki Shiraiwa 1
1

Department of Materials Engineering, The University of Tokyo, 7-3-1 Hongo,
Bunkyo-ku, Tokyo 113-8656, Japan

Abstract:
LPSO (Long Period Stacking Ordered Structure)-Mg alloy has both low density and high strength, and
it is expected to be applied to transport equipment. However, it is necessary to evaluate thedeformation and fracture behavior for usage as structural materials. Acoustic emission (AE) signals
were analyzed and classified using several machine learning methods to evaluate the deformation
mechanism of various LPSO-Mg alloys. It is difficult to identify AE signals due to each deformation
mechanism in extruded materials consisting of two phases, α-Mg phase and LPSO phase. In thisstudy, the high-speed camera was used to directly observe the kink deformation in LPSO phase andthe twinning deformation in α-Mg phase. Compression tests were carried out, and AE signals during
tests from sensors attached to the jig were continuously measured and recorded using Continuous
Wave Memory (CWM) developed in our laboratory. Surface motion of specimens was captured by ahigh-speed camera using the AE signal as a trigger. Based on the recorded AE signals corresponding
to kink deformation and twinning, AE behaviors of different extruded LPSO-Mg alloys weresuccessfully classified into the kinking and twinning by machine learning methods.

1. Introduction
Recently, Mg alloy having a long period stacking ordered (LPSO) structure has beendeveloped and lightweight application such as transportation equipment is expected becauseof its high strength [1-4]. It is necessary to evaluate the deformation/fracture behavior to use
the LPSO-Mg alloy as structural materials. We have evaluated the deformation mechanismof LPSO-Mg alloy in both the directionally solidified (DS) specimen and the cast specimen
using the microstructure observation and the clustering analysis of Acoustic Emission (AE)signals during the mechanical test [5-12]. there are also some researches on AE of Mg alloys[13-16]. However, in-situ deformation behaviors of extruded materials consisting of twophases of α-Mg phase and LPSO phase which are expected as practical materials have notyet been elucidated. Therefore, in this study we focus on the twinning deformation and kinkdeformation as the main deformation mechanism in the α-Mg phase and LPSO phase,respectively, and associate each deformation mechanism with the AE signal during thecompression test of the model material. The dynamic deformation/fracture mechanism of
extruded LPSO-Mg alloy containing two phases was evaluated by classifying of AE signals
using the machine learning method with AE of model material as learning data.

2. Experiment and analysis method

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Fig. 1 Observation of twining in pure Mg by high-speed camera.

Pure polycrystalline extruded Mg alloy (model material for twinning deformation), Mg97Zn1Y2
extruded material (B3 material, LPSO phase volume ratio of 25%) and Mg89Zn4Y7 extrusions(B4 Material, LPSO phase volume ratio 85%) with two-phase structure of α-Mg phase and
LPSO were used. In all the extruded materials, the bottom texture is oriented in a direction
parallel to the extrusion direction.
A cubic specimen of 5×5×5 mm was prepared from pure Mg and heat treatment was
performed to increase the particle diameter, and then one side of the specimen was polished.
Using this specimen, a compression test was conducted in a direction in which twin
deformation is likely to occur, that is, a load was applied in the extrusion direction. During thetest, the AE signal was measured by the AE sensor attached to the upper and lower jigs, andsimultaneously the polished side surface was observed using a video microscope. By
operating the high-speed camera with the AE signal as a trigger, in-situ observation was
done at the instant of deformation occurred at a photographing speed of 5,000,000 fps or
10,000,000 fps. A cubic specimen of 5×5×5 mm was also prepared for B3 material and B4
material, and a compression test was conducted. The compression direction was a direction
tilted by 0 ° and 45 ° with respect to the extrusion direction. The AE signal under test wasmeasured in the same manner as pure Mg. Structural observation was carried out using
SEM at each stress.

Fig.2 Evolution of length and growth speed of twin.

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Fig. 3 Normalized average energy spectra of twin and kind for learning data.

AE events were extracted by setting a threshold value from continuous AE waveform, andenergy spectrum of frequency was calculated [17]. In this study, we classified AE eventsusing two kinds of machine learning methods. One is conventional clustering, which is anunsupervised learning method (hereinafter referred to as learning A). All generated AE
events during one test were used as data and classified into two clusters by k-means method.
The other is classification by supervised learning which uses AE waveform corresponding toin-situ observation as learning data (hereinafter referred to as learning B). Twin deformation
observed with pure Mg corresponding to AE waveform and kink deformation observed in
LPSO-Mg alloy of DS material [12] were prepared, and the machine learning was performed
using these signals as learning data. After that, the AE event of B3 material and B4 material
was classified as twinning or kinking as test data. Linear support vector machine (linear
SVM) was used for learning algorithm.

3. Results
AE features due to twin generation were investigated. The characteristics of the AE
waveform corresponding to the high-speed camera observations have the peak frequency
majority of 200 ~ 400 kHz, and those were mainly generated in the initial stage of
deformation. After the timing was adjusted by considering the propagation of the waveform
and the time lag in the camera, the generation times of twin nucleus were consistent with AE
waveform arrival times for almost all the events. Therefore, it was demonstrated that the AE
acquired in this experiment was corresponding to twin nucleus generation.
Twinning was observed in crystal grains in pure Mg. Figure 1 shows an example of twin
generation captured by a high-speed camera. A deformation zone of a certain length was
generated within 0.2 μs of Fig. 1(a) - (b), and then it grew and became as shown in Fig. 1(c).
As shown in Fig. 1(d), another deformation zone was formed at the lower left with a slightdelay in the first deformation zone. Figure 2 shows the evolution of the length of the first
deformation zone and the growth rate. There was a region where the growth rate became
constant between about 1 μs immediately after generation and the growth rate at this timewas 610 m/s. Similar growth rates were measured for other twin crystals, and it was
distributed in the range of 300 to 1000 m/s.

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Fig. 4 AE event rate of twinning and kinking in (a) Mg97Zn1Y2 and (b) Mg89Zn4Y7.
Deformation behavior of extruded LPSO-Mg Alloy was also evaluated. First, the AE events ofthe extruded LPSO-Mg alloy were classified by learning A. In this case, the number of eventsof two clusters showing high energy on the low frequency side and the high frequency side
were almost the same number of events. Next, classification was done by learning B. Figure
3 shows the energy spectra calculated for the twin/kink AE waveforms used for learning andthey were normalized after averaging of the energy spectra. The twin showed the prominent
peak in the low frequency band around 300 kHz with high energy, and the kink showed
relatively high energy in the wide range from 200 to 800 kHz. Machine learning was
performed using this learning data, and the AE events generated during the 0° direction
compression test (B3-0, B4-0, respectively) of B3 material and B4 material were classified.
Figure 4 shows the evolution of the twin and kink AE events. Overall, the ratio of kink tends
to be larger in B4-0, which is considered to reflect that the ratio of LPSO phase is higher.
Comparing learning A learning B, different criteria for each material is necessary to classify
the phenomena in learning A and manually associate the clusters and phenomena. It resultsin the difficulty in the comparison between a plurality of materials. In learning B, the sameclassification model can be used for any material and the correspondence to kink or twin is
automatically made. It is concluded that quantitative evaluation and comparison of
deformation behavior are possible.

4. Conclusion(1) Compression test was performed on pure Mg, and the instant of twin generation wasobserved by high-speed camera observation with the AE signal triggered. It directly indicatesthat the source of AE is twin nucleation. We could clarify the formation and growth rate at thetime of twin generation from observation results.
(2) The AE waveforms by twinning deformation were extracted by making each twin
responsive to AE waveform and the AE waveforms for kink deformation were also prepared.
Finally, we prepared the twin and kink classification models by machine learning. By
classifying the AE of the extruded LPSO-Mg alloy by using these data, the time change of
deformation mechanism and the ratio of twin / kink to ratio of two phases were evaluated.

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(3) Using the machine learning based on the results of in-situ observation by a high-speed
camera, it is possible to classify AE more effectively than the conventional method and toevaluate and compare the deformation behavior quantitatively.

References:[1] Y. Kawamura, K. Hayashi, A. Inoue, T. Masumoto, Rapidly solidified powder metallurgy
Mg97Zn1Y2 Alloys with excellent tensile yield strength above 600 MPa, Mater. Trans. (2001)
1172–1176.
[2] A. Inoue, M. Matsushita, Y. Kawamura, K. Amiya, K. Hayashi, J. Koike, Novel hexagonal
structure of ultra-high strength magnesium-based alloys, Mater. Trans. 43 (2002) 580–584.
[3] T. Itoi, T. Seimiya, Y. Kawamura, M. Hirohashi, Long period stacking structures observedin Mg97Zn 1Y2 alloy, Scr. Mater. 51 (2004) 107–111.
[4] X.H. Shao, Z.Q. Yang, X.L. Ma, Strengthening and toughening mechanisms in Mg-Zn-Y
alloy with a long period stacking ordered structure, Acta Mater. 58 (2010) 4760–4771.
[5] Y. Li, M. Enoki, Deformation and anelastic recovery of pure magnesium and AZ31B alloy
investigated by AE, Mater. Trans. 48 (2007) 2343–2348.
[6] Y. Li, M. Enoki, Evaluation of the twinning behavior of polycrystalline magnesium at room
temperature by acoustic emission, Mater. Trans. 48 (2007) 1215–1220.
[7] Y. Li, M. Enoki, Recovery behaviour of pure magnesium in cyclic compression–quick
unloading-recovery process at room temperature investigated by AE, Mater. Trans. 49(2008) 1800–1805.
[8] Y. Li, M. Enoki, Twinning behavior of pure magnesium quantitatively investigated byacoustic emission, Mater. Sci. Eng. A 536 (2012) 8–13.
[9] Y. Muto, K. Matsumoto, T. Shiraiwa, M. Enoki, Detection of Crack Propagation by AE and
Evaluation of Fracture Toughness in Various Mg Alloys, J. Jpn. Inst. Met. Mater. 78 (2014),
381-387.
[10] T. Yasutomi, M. Enoki, In-situ evaluation of detwinning behavior in extruded AZ31 Mg
alloy by AE, Mater. Trans. 53 (2012) 1611–1616.
[11] Y. Muto, T. Shiraiwa, M. Enoki, A.E. Analysis, of compression test with different loading
direction of unidirectional solidification LPSO-Mg alloys, J. Jpn. Inst. Met. Mater. 80 (2016)
697–701.
[12] Y. Muto, T. Shiraiwa and M. Enoki, Evaluation of the deformation behabior in
directionally solidified Mg-Y-Zn alloys containing LPSO phases by AE analysis, Mater. Sci.
Eng. A. 689 (2017) 157-165.
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[13] E. Pomponi, A. Vinogradov, A real-time approach to acoustic emission clustering, Mech.
Syst. Signal Process. 40 (2013) 791–804.
[14] A. Vinogradov, D. Orlov, A. Danyuk, Y. Estrin, Deformation mechanisms underlying
tension-compression asymmetry in magnesium alloy ZK60 revealed by acoustic emission
monitoring, Mater. Sci. Eng. A 621 (2015) 243–251.
[15] J. Čapek, K. Máthis, B. Clausen, J. Stráská, P. Beran, P. Lukáš, Study of the loading
mode dependence of the twinning in random textured cast magnesium by acoustic emission
and neutron diffraction methods, Mater. Sci. Eng. A 602 (2014) 25–32.
[16] D. Drozdenko, J. Bohlen, F. Chmelík, P. Lukáč, P. Dobroň, Acoustic emission study on
the activity of slip and twin mechanisms during compression testing of magnesium single
crystals, Mater. Sci. Eng. A 650 (2016) 20–27.
[17] K. Ito, M. Enoki, Acquisition and analysis of continuous acoustic emission waveform forclassification of damage sources in ceramic fiber mat, Mater. Trans. 48 (2007) 1221–1226.

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39Non-threshold- Non-threshold
Acoustic
Emissionanalysisdamageevolution
acoustic
emissionanalysisof ofdamageevolutionininpipepipesegments
segmentsof
ofsteelsteel S355J2H
S355J2H under
under bending
bending load
loadFranziska Baensch, Wolfram Baer, Peter Wossidlo, Abdelkarim Habib
Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 87,12205 Berlin, Germany

Abstract:
Four-point bending tests were carried out to analyze the acoustic characteristics of damage
development in pipe segments made of steel, grade S355J2H, being 2.5 m long with an outer diameterof 168 mm and 16 mm wall thickness. To induce stable crack growth, the pipe segments were predamaged by a 90°circumferential notch in the middle of the pipe length. While the pipe was quasistatically loaded, microscopic damage and plastic deformation accumulated to form a macroscopic crackthat grew through the pipe wall until leakage. For acoustic emission (AE) monitoring, four broadband
sensors of type VS 900 M were mounted close to the notch. Continuous AE signal detection was
performed by the non-threshold method. Advantages and disadvantages of the non-threshold AE
monitoring compared to a commonly employed method analyzing only signals exceeding a predefined
threshold are discussed. The results of AE analysis are compared to additional information on the crack
growth detected by the direct current potential drop (DCPD) technique. These studies were carried out
in the course of the interdisciplinary research project AGIFAMOR, Ageing infrastructures - distributed
acoustic monitoring of pipes at BAM.

1. Introduction
In the course of an interdisciplinary research project at BAM (AGIFAMOR, Ageing
Infrastructures – Application of distributed acoustic and fibre optic sensors for continuous
monitoring of pipes, 2015-2018), the potential of distributed acoustic fibre optic sensing
methods (DAS) in terms of a condition monitoring system for pipelines has been investigated.
Therefore, besides studies on the sensitivity of the DAS to noise caused by leakage [1] or topressure shocks causing dynamic circumferential changes of the pipe, also the sensitivity of
the DAS to acoustic emission (AE) caused by crack formation in a pipe wall is investigated.
The latter scenario, object of this contribution, was realized by four-point bending tests carried
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out on steel pipe segments. Originally, a similar experimental setup was designed at BAM in
the 1990’s to study crack initiation and growth on large steel pipes by means of the direct
current potential drop (DCPD) and the AE method [2]. To validate the DAS sensitivity, the testis accompanied by accelerometers and AE measurements. The AE during initial tests wasmonitored using threshold-based approach with threshold settings above 56 dBAE [3]. Analysisof the continuous data recorded by DAS and accelerometers compared to the AE dataset
recorded using threshold-based approach turned out to be insufficient to characterize test
progress at the test object as well as to assign sudden deviations and possibly technicalartefacts due to the employed extensive monitoring instrumentation. Thus, another test was
performed with continuous monitoring by AE, DAS, and accelerometers. First results of AE
analysis in comparison to the detection by the DCPD method are presented in the following.

2. Description of work
To investigate the crack initiation and stable growth of a macroscopic crack, a four-point
bending test was carried out on a pipe segment made of steel grade S355J2H. The pipe
segment was 2.5 m long, had a wall thickness of 16 mm and an outer diameter of 168 mm.
Centered at 1.25 m length, a 90°circumferential notch was shaped into the pipe wall by a CNC
machine. The notch had an opening angle of 90°, a notch root radius of 0.2 mm and a ratio ofwall thicknesses of 0.5. For deflection measurements, six angles were glued to the pipe at the
3 o’clock and 9 o’clock positions as technical aid for the displacement sensors. The bending
test was performed with a 4 MN universal testing machine that operates hydraulically. Test
was driven displacement controlled with a test speed of 0.25 mm min-1.
The test was monitored by non-threshold acoustic emission (AE) measurement as well as by
the method of the Direct Current Potential Drop (DCPD), DAS and accelerometers (Fig.1).
Herein, exclusively the results of the AE and the DCPD measurements are focused on. For
DCPD, a DC of 300A was injected and the potential drop was measured at five positions close
to the notch. The measured potential U is normalized with its output value U0 and plotted as afunction of the deflection (Fig. 2). Then, the potential data are approximated with a fifth degree
polynomial in a deflection interval from zero to a freely selectable value between maximum
load and load of leakage growth. By doing so, the stable crack growth process is taken into
consideration. Based on the first order derivation of the polynomial fit, information on thecharacteristic development of damage accumulation, crack initiation and stable crack growth
are obtained.

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Figure 1. Experimental setup for monitoring of a four-point bending test of a 2.5 m long pipe segment
by means of DCPD measurement, acoustic emission, accelerometers and fiber optical sensors.

The non-threshold AE monitoring was performed with the digital AE equipment AMSY-6 incontinuous mode with a sampling rate of 3333333 Hz. Four broadband sensors, sensitive inthe range from 100 kHz up to 900 kHz (type VS900-M), were used to ensure the monitoring of
crack events. The response of the sensors is characterized by two peaks at 190 kHz and
350 kHz accompanied by two anti-resonances at 200 kHz and 400 kHz. The AE sensors were
mounted with magnetic holders. Coupling agent was a reusable adhesive (Bostik Prestik madein South Africa). The four sensors were arranged in a square around the notch withapproximately 100 mm distance to the notch. To enable good coupling of all sensors, thecorrosion layers were removed by grinding surface at the appropriate areas along the pipe.
Teflon sheets were inserted in between the pipe wall and the load bearings. Furthermore, PVC
damping plates were assembled into the bearing elements (Fig.1). By this, the acoustic noise
was reduced by approximately -10 dB. For AE analysis the near field behavior of the sensors
with an attenuation of 9…17 dB must be considered. For the in-plane AE wave propagation avelocity of 5225 m s-1 was estimated.
First step in processing the AE data is to reduce the noise by applying a high pass filter at
150 kHz. For AE hit detection, the moving standard deviation of the measured amplitude
values (mV) is calculated with a moving window size of 512 samples. To level low deviations
relative to higher ones, the logarithm of the moved standard deviation is employed. Since thenoise level increased with increasing load, the AE hits are detected by means of a moving
threshold applied to the logarithm from the moved standard deviation of the measured
amplitude values. Only AE events detected by all four sensors are evaluated. To determine
the time shifts of one signal detected by the four sensors, the cross-correlation algorithm isapplied to a time window of 2000 samples comprising the signal’s arrival and its peak
amplitude. According to the coordinates of the notch (AE source localization) and the AE
sensors, the shift of a signal detected in all four sensors must not exceed 2.7E05 s (90samples) to be assigned to crack events at the notch.

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3. Results
The quasi-static bending test of the pipe segment took about 75 minutes (Fig.2). Afterapproximately 2500 s test duration, the piston movement was stopped in order to carry out
corrective actions and checks to the setup. In total, a maximum load of 696 kN at deflection of
11.43 mm was yielded (Tab.1). The initial phase of bending test, the pipe segment undergoes
an elastic deformation and its cross-section becomes ovalized. With progressive loading,mechanisms of plastic deformation set on; a macroscopic crack initiates and grows in a stablemanner in the direction of pipe wall thickness. This is characterized by initial blunting and crosssection necking at the notch, increasing dislocation density as well as by accumulating ductile
damage in the metal microstructure. After reaching its maximum, load decreases while thecrack finally reaches the state of leakage. With progressing testing time, the leakage opens ina stable manner and the crack continues to grow in circumferential direction of the pipe wallon a low level. At this stage, the load curve stagnates due to equilibrium of strain hardening
and crack growth, whereby the deflection increases further.
During the elastic deformation, the DPCD is approximately constant. The significant increasein potential drop indicating the initiation of stable macroscopic crack growth was found at 90%
of maximum load (630 kN) and 56% of maximum deflection (6.5 mm). During the leakage
grows in circumferential direction of the pipe wall, the potential drop increases with lower slope.

Figure 2. Load-deflection graph of the bending loaded pipe segment of steel (S355J2H). By means of
the direct current potential drop (DCPD), the onset of macro cracking at the notch is detected. Leakage
usually arises at the marked stage after maximum load.

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AE data analysis revealed 1042 AE events (EV) detected by all four sensors, 185 of which arelocalized (LEV, located event) close to the notch. First LEV is detected within the elastic range
at 150 kN after 334 s test duration (Fig. 3). 35 EV and 5 LEV occurred, randomly spread overtime, until a significant AE set on. After approximately 1600 s of test duration, the cumulative
curves of peak amplitudes significantly increase (Fig.4). Particularly, the increase in cumulative
peak amplitude of LEV indicates the accumulation and formation of significant crack events atthe notch. The occurrence of distinct AE signals correlates with the crack initiation determinedby the DCPD method (Tab. 1). The development of macro-crack and its growth through thepipe wall thickness generates AE with corrected peak amplitudes up to 90 dBAE. However, theLEV with the corrected maximum peak amplitude (95 dBAE) was not detected close to the
maximum load, but shortly before the load stagnated at 585 kN (3500 s) in the course ofleakage growth. The formation of the leakage results in a steep increase in cumulated peak
amplitude (Fig. 4). The subsequent crack growth in circumferential direction generated AE
activity, since the four AE sensors detect approximately 150 EV. However, localization of the
EV and, hence, the definite identification of AE source events close to the notch failed.
Table 1. Characteristic values from the DCPD and the AE monitoring of the four-point bending test.
Mechanical properties at load peak and deflection peak.
Crack initiation estimated by
DCPD(6 o’clock position)AE

Maximumload

Maximum
deflection

Load (kN)

630

626

696

575

Deflection (mm)

6.5

6.5

11.59

19.09

4. Discussion on AE results
During the test progress, the amplitude of noise in AE data rise with increasing load. This has
a main impact on the sensitivity and accuracy of the AE method using “fictive” constant or
moving threshold during post-processing of streaming signals, because only AE exceeding aredetectable and, furthermore, the estimation of the AE arrival time is affected, too. Noisy signals
range up to approximately 150 kHz in frequency. Furthermore, tracking damage processes
particularly focuses on AE with higher frequency contents. Thus, setting a high pass at 150 kHz
improves the data evaluation. When omitting the de-noising, only 33 LEV with peak amplitudes>60 dBAE were identified, which is less than 20% of the herein presented 185 LEV.

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Figure 3. Load and corrected peak amplitudes of all detected AE events (1042 EV and 185 LEV) plottedvs. duration of the bending test of a pipe segment of steel. Highlighted characteristic states in the test
progress are the crack initiation determined by DCPD, maximum load of 699 kN and leakage formation.

Figure 4. Load and cumulative corrected peak amplitudes of all detected AE events (1042 EV and 185
LEV) plotted vs. duration of the bending test of a pipe segment of steel. Highlighted characteristic statesin the test progress are the crack initiation determined by DCPD, maximum load of 699 kN and leakage
formation.

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By post-processing the non-threshold streamed AE data, the sensitivity of the measurement
system was improved. By applying a moving threshold-based on the standard deviation of
signal amplitude, LEV with peak amplitudes >30 dBAE are identified, even early LEV at low
loading levels. The localization of EV of the subsequent crack growth in circumferential
direction failed due to the limited AE sensors, their arrangement and the AE wave propagationinterrupted by the leakage in the pipe wall.
Determination of the crack initiation by DPCD correlates well with results of the AE analysis.
Analogous to the steep increase in DCPD, while crack finally breaks through the wall (leakage
reached), also the cumulated AE peak amplitude increases even with a steeper slope. To
improve the accuracy in EV localization, more sensors, at least six would be needed. Three
sensors are needed on each side of the notch until leakage. Afterwards, as soon as the crack
grows in circumferential direction, three sensors should be positioned close to both crack tips
in radial direction.

5. Conclusion
AE monitoring by means of non-threshold recording enables true-to-the original replica of thetest procedure. This might be particularly beneficial in the case of elaborate experiments, forexample, to identify sudden deviations that occurred during the test progress and to clarify
technical artefacts and possibly other phenomena retrospectively. The aim of the presented
experiment is to investigate the sensitivity of the DAS. Since DAS and accelerometers record
the acoustic information continuously during the tests, for comparison purpose, the nonthreshold AE monitoring mode provides a sufficient database.
Transient recording with non-threshold mode provides limited information on AE activity in realtime analysis during the test due to the lack of arrival time estimation. The size of recorded
datasets essentially depends on number of sensors, sampling rate and test duration. However,compared to the conventional threshold-based recording mode, large data volumes areproduced. For example, the monitoring of a single bending test (75 min duration, 4 AE sensors,
3 MHz sampling rate in AE) produces a dataset of 110 GB, but only 10 % include relevant
information on AE events due to damage mechanisms. The handling of large data volumes
and the time-consuming post-processing data analysis are not insignificant. However, mostimportant advantage of the non-threshold AE recording is that almost no information is omitted
by digital threshold and frequency filter settings during the active measurement and themeasuring system captures and stores the data with its maximum technical performance.

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Acknowledgements
Pavol Stajanca and Sebastian Chruscicki are acknowledged for the measurements with the
distributed acoustic fibre optic sensing system (DAS), Jens Prager and Tobias Homann for themeasurements with the accelerometers, Margit Weltschev and Dirk Schmidt for the exchangeof experiences in the field of damage to pipelines.

References:[1]

Stajanca, P. et. al, Monitoring of pipes by distributed acoustic fibre optic sensors - A large scale
test on a pipeline under realistic operation condition, Proceedings of the 13th Pipeline Technology
Conference, Berlin, Germany, 2018.

[2]

Aurich, D. et. al, Methoden zur Extrapolation von JR-Kurven für die Vorhersage derRissöffnungsgeometrie unter postulierten Belastungen, Teilprojekt 2 des BMFT-Vorhabens
Analyse und Weiterentwicklung bruchmechanischer Versagenskonzepte Schwerpunkt:Anwendung
fortgeschrittener
zähbruchmechanischer
Konzepte;
Bruchübergang,
Förderkennzeichen 1500 970, BAM-Forschungsbericht 232, ISBN 0938-5533, Berlin, Germany,
1999.

[3]

Baensch, F., et al.: BAM-Projekt AGIFAMOR – Zustandsüberwachung von Rohrleitungen, 21.
Schallemissionskolloquium Fulda, Deutsche Gesellschaft für Zerstörungsfreie Prüfung (DGZfP),
Fulda, Germany, 2017

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71 -detection
Galling detection
by Acoustic
Emission(AE)
Gallingby acoustic
emission(AE) according
toaccording to ASTM G98
ASTM G98

Abdelkrim SAIDOUN1, Catherine HERVE1, Yan Ming CHEN1, Thibault LESAGE2,
Salima BOUVIER2
1

CETIM Senlis

2 Université de Technologie de Compiègne

Abstract
For many mechanical companies, galling detection remains an important issue because it can occur inmany mechanisms (bearings, transmissions ...) and thus lead to serious failures with consequences
both in terms of production and in terms of people and machines safety. The diagnosis and identificationof galling are made through facies observations and profilometry analysis (according to ASTM G98).
When contact surfaces are not visible or easily accessible, it is not possible to actually know the stateof galling. It is therefore important to have a method capable of performing a reliable diagnosis without
having to stop or disassemble considered machines. Acoustic emission is a monitoring method for
detecting the start of this physical phenomenon between two surfaces and also to follow it evolution.
Feasibility tests with CETIM tribology team have demonstrated the potential of this method and also
assess possibilities to develop a detection criterion based on AE features. Experimental principle
consists of applying a load on a pin which is slowly rotate to a fixed plate (360°). The galling thresholdis determined from the pressure at which there is a first material transfer on friction surfaces.
The obtained results show the potential of the AE for the detection of the galling threshold. Furthermore,
the correlation of AE measurements with the EBSD method (graphically showing grain orientation andsize of grains before and after testing) revealed two types of galling: catastrophic and tolerant galling.

Key words: galling, acoustic emission, ASTM G98, EBSD, catastrophic and tolerant galling.

EBSD (Electron Backscattered Diffraction) in a scanning electron microscope has recently become a widelyused technique, especially in metallurgy for the simultaneous characterization of the local microstructure andcrystallographic texture of polycrystalline materials. It provides access not only to orientation cartographies but
also to phase cartographies. In addition, it allows for assessing elastic and plastic deformations, as well as theenergy stored in the grains during deformation
1

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1. AIMS AND SCOPE
One of the objectives is to understand the impact and relevance of different heat treatments
on galling phenomenon in the case of stainless steels and to study their impact on the
microstructure and the phases involved. Thanks to EBSD method (graphically showing grainorientation and size of grains before and after testing), two types of galling were characterized:
catastrophic and tolerant galling. Acoustic emission must make it possible to quantify the rateof galling in a first time and then to differentiate these two types of galling.
This article explains how AE has been used to:- Detect galling from AE data using a frequency analysis.
- Determine a criterion associated to galling phenomenon beginning. Thanks to this criterion,an alarm is generated to communicate and stop the tension compression machine when gallingis detected.
- Quantify the galling severity (catastrophic and tolerant)- Distinguish catastrophic and tolerant gallingThis paper uses EBSD results of the thesis work carried out by T.Lesage in the context of
GRIPAC's thesis: "GRIPpage of stainless steel and its consequences on the degradation offriction components in the agri-food and pharmaceutical industry".

2. PRESENTATION OF STANDARD ASTM G98
The objective of this standard is to determine the susceptibility to galling of a couple ofmaterials.
2.1

Principle

The test principle is a pin-on-disc type of test in which an Ø12.7 mm pin is placed on a flat
surface (figure 1). The principle is to apply a load on a pin which is slowly rotated (Velocity=
10rotations/minute) one revolution 360° relative to a fixed plate. Both the flat surface and thepin are checked for galling. The criterion for either galling occurs or not is the appearance ofspecimens based on unassisted visual examination. The first selected load applied is 350MPa
and then decreased progressively. The load program will depend to contact materials. If there
is no galling of either surface, then the load is decreased and the test repeated. Then the
average of the highest non-galled load and the lowest galled load is calculated (figure 2). Thisis the “galling threshold” at which the paired materials begin to show signs of galling (firstmaterial transfer on friction surfaces).
Nota: 5 pins are required per block for galling threshold determination.

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➢
➢
➢
➢
➢
➢

Tension compression Instron machine (capacity 250kN) “TTKMH3450 250KN”
Load cell: A212-201 model 2511-320 250KN
HBM torque sensor model TB2.
HBM conditioner model: MGC and AB22A with ML10B and BL01 cards.
SEW geared motor brand equipped with a brushless technology
An acquisition station model YOKOGAWA « DL750 » with 8 channels: tension 0-10V andsampling frequency 100 kHz.
Figure 1. Galling machine presentation

Figure 2. Experimental designs suggested by ASTM G98

2.2

Weaknesses of ASTM G98

The current method used to detect galling and to define the galling threshold between twomaterials presents two main disadvantages:

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•

For some cases, raking light is needed to rule if galling occurred or not. The same
sample is presented below. Picture on the right was taken under raking light. If raking
light is not used in this case, it would have been impossible to detect galling according
to ASTM G98•

ASTM G98 is not able to quantify galling which is occurring during the test.

Figure 3. Galling, not galling?

Acoustic emission could be an effective method to improve ASTM G98 by instrumenting thetest machine.

3. ACOUSTIC EMISSION: AN EFFECTIVE METHOD TO UNDERSTANDGALLING PHENOMENON
3.1

Experimental description

The experimental setup is shown in figure4. AE activity is recorded by two different kind of
sensors:
Resonant
sensorResonant AE sensor (MISTRAS)
Resonance frequency: 150 kHz
Number: 1
Preamplifier: AEP3H (Gain 34 dBea)

Wide band
sensorWide Band AE sensor (MISTRAS)
Range frequency: [100; 900 kHz]
Number: 1
Preamplifier AEP3H (Gain 34 dBea)

Figure 4. Implementation of the AE sensors on the disc

4

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Many different physical phenomenons are occurring during the ASTMG98 test (plastification,
friction, galling, …). The previous study [1] showed that the galling phenomenon occurs onlyin the rotation phase during the test. For this purpose, we focus in our analysis only on the
acoustic emission data recorded during the rotation phase.
The previous study showed also that the frequency analysis of the AE signals is well adapted
for the early detection of the galling phenomenon and possibly to characterize it (catastrophic
and tolerant galling). That is why a large band sensor is used. The resonant AE sensor allows
acquiring AE activity linked to galling with a higher sensibility than the wideband sensor.
A large number of tests were carried out on a variety shades of steel. in this paper, three
representative examples are presented with respect to the resulting galling nature (no galling,
tolerant galling and catastrophic galling) as shown in table 1.
No galling

Pin: 316L(K33*)

Disc: 316L

Tolerant galling

Pin: Nit60

Disc: 316L

Catastrophic galling

Pin: 17-4PH

Disc: 316L

350 MPa

K33: is a surface thermo-chemical treatements for a beter resistance to galling

Table 1. Summary of studied galling (tolerant galling, catastrophic galling)

In the next step on this paper, the metallographic characterization of the galling will bepresented in order to explain the difference between the two types of galling (tolerant andcatastrophic). Then the results of the AE analysis relative to the detection of galling will bepresented. Finally, a correlation between the results of AE and those of tribologicalobservations will be presented in order to discuss the possibility of galling characterization byAE.
3.2

Characterization of tolerant and catastrophic galling by the metallographic
examination

One can see easily on the table 2 that for the similar load of 175Mpa, the galling occurring with
Nitronic 60 is less severe than that occurring with the 17-4PH, so it is called’ tolerant’ galling
contrary to ’catastrophic’ galling that will cause a blockage of the components

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Nitronic 60

17-4PH

175 MPa

175 MPa

Table 2. Tow types of galling are generated on Nitronic60 and 17-4PH under the same load 175MPa(tolerant galling, catastrophic galling)

In order to further the analysis of the two cases of galling, the thesis team performed EBSD
scan as shown in the figure below.

Catastrophic Galling
17-4PH brut - 175MPa

Tolerant Galling
Nitronic60 - 350MPa
2 mm

2 mm

100 µmFigure 5. EBSD results (Nitronic60 (tolerant galling) and 17-4PH (catastrophic galling))

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EBSD method is used to characterize the microstructure of the samples before and after
testing. After galling, grain size is greatly reduced for all samples (in the case of catastrophic
galling) apart from Nitronic 60 (tolerant galling, resulting in localized disorientation at the grainboundaries).
3.3

Detection and characterization of galling by AE

In order to study the detection of galling by AE, a frequency analysis of AE signals has been
performed.
Firstly, the no-galling case is analyzed in order to determine the maximum limit of the frequency
content of the signals recorded in this step. Figure 6.a shows the distribution of the frequency
center (of signals recorded by the wide band sensor) as function of the time test for the couple
316LN/316L treated by K33 under a load of 350 MPa. As previously stated, this study focuseson the rotation zone to analyze galling. From this representation we can see that the recorded
frequencies do not exceed 320 KHz. This result is true for other no-galling tests as shown infigure 6.b.
ab316LN/316L (K33)

316L/316L (K33); 316LN/316L (K33);
Nit60/ 316L (K33).

Figure 6.a. Distribution of the centroid frequency as function of the time test under a load of 350 MPa.
a. for non-galling test 316LN/316L (K33). b. for other no-galling tests (316L/316L (K33); 316L/316LN(K33); 316L/Nit60 (K33)

In a second step, the case of catastrophic galling is considered. AE data of the couple 174PH/316L are analyzed. Figure 7 shows a comparison between the results obtained by thetwo used sensors (wide band sensor and resonant sensor).
Unlike the result obtained previously, for both sensors the frequencies obtained for the forces
greater than 11 MPa exceed the limit of 320 KHz.
The wide band sensor has a slower dynamic compared to the resonant one, so the number ofrecorded signals is lower. On the other hand, as its spectrum is wider, the recorded frequency
range is wider compared to the resonant sensor. In this study the wide band sensor is preferredover the frequency analysis.
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a

b

17-4PH/316L (Wide bande sensor)

17-4PH/316L (Resonant sensor)

Figure 7. Distribution of the centroid frequency as function of the time test under different loads (green
points represent frequency <320 KHz; red ones represent frequency >320 KHz).
a. AE recorded by a wide band sensor. b. AE recorded by a resonant sensor.

Tolerant galling: Nit60/316L

Catastrophic galling: 17-4PH/316L

Figure 8. Comparison between tolerant galling and catastrophic one “distribution of the centroid
frequency as function of the time test under different loads (green points represent frequency <320
KHz; red ones represent frequency >320 KHz)

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The final step of this paper concerns the case of tolerant galling. AE data of the couple
Nitronic60/316L are analyzed. Figure 8 shows a comparison between tolerant galling andcatastrophic one, we notice that:-

The common result between the two types of galling is that the frequencies exceed 320
KHz. This result confirms that this frequency represents the threshold of galling
phenomenon.

-

On the other hand, it is noted that in the case of tolerant galling the number of signals
is less compared to the catastrophic galling. In addition, the frequencies obtained
during the tolerant galling (<450 KHz) remain lower than those obtained in the case ofcatastrophic galling.

4. Conclusion
In this study the AE technique was used on ASTMG98 tests. The first objective is to test theearly detection of galling on stainless steel specimens with or without heat treatment. The
second objective is to differentiate AE signals as function of galling severity (tolerant or
catastrophic) in order to complete and clarify the existing standard.
The realized analysis of AE data shows that the frequency parameter is very sensitive to galling
phenomenon.
-

The results show that the AE technique is able to detect early the galling phenomenon
by setting a frequency threshold (320 KHz).

-

Thanks to the work thesis carried out by T.Lesage, two types of galling have been
distinguished: tolerant galling (which is not critical) and the catastrophic galling (which
can generate appalling effects). Using the EBSD method, it has been shown thattolerant galling does not change grain size (only disorientation at the joints), whilecatastrophic galling, leads to grain breakage until 40 micrometers.

-

The first results show that acoustic emission seems to be able to distinguish these two
types of galling, notably by using the centroid frequency of the acoustic emission bursts.
If centroid frequency of a burst is less than 450 kHz, this burst is associated to tolerant
galling

The results presented in this article are therefore encouraging. On the samples analyzed, it
was possible, by acoustic emission, to quantify galling, and to distinguish between the two
types of galling, catastrophic and tolerant. New test campaigns are planned to confirm theseresults.

References:[1] Friedrich, P et al., Galling detection by acoustic emission (AE) according to ASTM G98,
Metallic Materials and processes: Industrial challenges, 25-27 October 2017, France.
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11 - AE monitoring
and three-dimensional
observation OF
AE MONITORING
AND THREEDIMENSIONAL OBSERVATION
of stress
corrosioncracking fromcorrosionpits PITS
STRESS
CORROSION
CRACKING
FROM
CORROSION
Kaige Wu1, Kaita Ito2, Ippei Shinozaki3, Manabu Enoki1
1

Department of Materials Engineering, The University of Tokyo, 7-3-1 Hongo,
Bunkyo-ku, Tokyo 113-8656, Japan
2
National Institute for Materials Science, Tsukuba 305-0047, Japan
3
Research Laboratory, IHI Corporation, Yokohama 235-8501, Japan
Phone: +81 3 5841 7129; fax: +81 3 5841 7181;
E-mail: kaigewu@rme.mm.t.u-tokyo.ac.jp

Abstract:
Stress corrosion cracking (SCC) is a complicated three-dimensional (3D) process involving bothelectrochemical and mechanical phenomena. The source mechanism of acoustic emission (AE)behavior during SCC development is still not well understood. In this work, AE monitoring combinedwith 3D characterization using X-ray computed tomography (X-ray CT) were proposed to investigate
the SCC of an initially smooth sample of SUS420J2 stainless steel exposed to both a 0.6% constantstrain and a neutral sodium chloride droplet at room temperature. Cracks were observed to originate
from corrosion pits. Further 3D observation of the inner geometry of cracks indicated that cracks
preferentially attacked the mouth, rather than the bottom, of the corrosion pit. It was also observed thatby the path of crack propagation existed several interior corrosion pits which may play a critical role incrack growth. AE signals were detected during SCC evolution and characteristics of AE events wereassociated with crack initiation and propagation. K-means clustering analysis grouped the AE events
into three clusters. With in-situ surface observation and 3D images of cracks, hydrogen bubble
evolution, plastic deformation around the crack tip, and the crack propagation were considered as thepossible AE source mechanisms during the SCC.
Keywords: stress corrosion cracking; corrosion pit; clustering; acoustic emission; X-ray CT

1. Introduction
Structural materials, especially stainless steels, are critical for developing and maintaining
infrastructures. However, it can undergo stress corrosion cracking (SCC) by slow,
environmentally induced crack initiation and propagation. The SCC process is concurrent byboth electrochemical and mechanical phenomena that are the results of the synergistic effectof stress and corrosive environment on materials [1]. In many applications, corrosion pit
serves the primary precursor to crack initiation and the general stages of SCC are therefore
recognized as pitting corrosion, pit-to-crack transition and subsequent crack growth [2].
Regarding the crack initiation from corrosion pits, there still exist different arguments.
Conventional viewpoint argues that SCC preferentially initiates at the base of a pit because
the pit base not only serves the local stress-strain concentrator but also retains anaggressive environment [2-3]. Comparatively, another new perspective, based on the three
dimensional (3D) observations with X-ray computed tomography (X-ray CT, or XCT), is put
forward recently that cracks are not necessarily initiated at the pit base during the pit-to-crack
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transition [4]. It suggests that cracks nucleate on the pit walls in relation to the dynamic strainprovided by the growing pit. Subsequently, cracks grow around the pit and finally coalesce toform a complete through-crack. The inconsistency implies further research work is neededon the pit-to-crack transition during the early stage of SCC.
On the other hand, developing in-situ methodologies is important in understanding thecracking mechanism and evaluation of the health condition of metallic structure andcomponents. Acoustic emission (AE) is an in-situ nondestructive evaluation (NDE) methodthat bases on the detection of elastic waves generated by the fast energy release during an
irreversible change within materials and solids. AE method holds great potential in monitoring
the progression of materials degradation and steel corrosion [5-10]. AE method has long
been applied to study the crack initiation and propagation during SCC. In the literature,different damage mechanisms during SCC, including the rupture of pit cover [10], fracture ofmetallic ligaments [11-12], plastic deformation around the crack tip [11, 13-18], ductile
fracture [14], hydrogen bubble evolution [10, 16-21], falling off of the surface grain [20-21],
rupture of corrosion products [20-22], and the cracking process [11-24] have been correlatedwith AE signals. However, the interpretation of the source mechanisms in current reports was
mainly based on speculation of AE evolution coupled with post mortem microstructural
analysis. Therefore, an irrefutable elucidation of the source mechanisms of the detected AE
signals requires more investigation of in-situ visualized linkage. Moreover, the SCC testing
proposed in literature [13-14, 18] is difficult to directly correlate the detected AE signals to
one single step of cracking evolution since multiple-pits and cracks may be initiated todevelop at different steps when the samples were immersed in the corrosive solution.
In order to better understand the crack initiation, we developed an experimental
methodology to study the evolution of one single crack under a NaCl solution micro-droplet.
Simultaneously, AE monitoring accommodated with an in-situ surface observation was
proposed to perform with the SCC testing. XCT was also used to give a 3D morphologicalobservation of the crack and corrosion pit.

2. Experimental procedures
2.1 SCC testing and AE measurements
The material used here is SUS420J2 (C: 0.32 mass%, Si: 0.45 mass%, Mn: 0.54 mass%,
Cr: 13.39 mass%, Ni:0.09 mass%, P: 0.031 mass%, S: 0.005 mass%, Fe : bal.). Three kindsof samples were prepared with different heat treatment sequences as follows. (Sample A):
600°C/1h → 800°C/1h → 1010°C/2.75h → gas cooling → 180°C/3h; (Sample B): 600°C/1h→ 800°C/1h → 1010°C/2h45m →gas cooling; (Sample C): 950°C/2h → gas cooling. The
Vicker’s hardness of sample A, B, and C were HV 593, 611, and 511. Then they were
polished to a mirror-like finish. The SCC samples were fixed in a jig with a curvature of R125
to apply a constant strain of 0.6% to the sample surface. A micro-droplet of 1.0 mass% NaClsolution of 1μL was set in the center of the surface of samples placed inside a thermostaticbath (298K, 99-100%). The bath was capped with a transparent glass cover to accommodate
an in-situ surface observation with a digital microscope (VHX-5000, Keyence Corp., Japan).
Two high-sensitivity R-CAST sensor systems of 200 kHz resonant (M204A, Fuji Ceramics,
Japan) were respectively placed 10 mm and 30 mm away from the droplet site to filter out
the noise outside the corrosion region by source location method. A continuous acquisitionmode named “Continuous Wave Memory” (CWM) which was developed by our group [25]was used to record and analyze the AE signals. The sampling rate and threshold were 52

S251

MHz and 20 mV, respectively. The experimental arrangements are schematically shown in
Fig. 1. Finally, the extracted AE data were analyzed by k-means clustering algorithm [26].
2.2 3D observation of crack morphology
After testing, the SCC samples were both inspected by SEM and XCT. The SCC samples
were polished and cut to a small size of XCT sample, schematically shown in Fig. 2(a), tomeet the requirement of lab XCT scanning. A 3D XCT Scanner (TDM1000-IS, Yamato
Scientific Co., Ltd., Japan) was used for scanning the internal structural information of cracks.
The X-ray computed tomographic process is schematically shown in Fig. 2(b). The XCTsample was fixed on a rotational stage. A rotation step of 0.24 degree and 25 mins per fullrotation was set for high-resolution scanning. The X-ray tube voltage was set to 100 kV and

Fig. 1 Schematic of the AE measurements and SCC test incorporated with an in-situ digital
microscope system.

(a)

(b)

Fig. 2 (a) Schematic of the preparation of XCT sample, (b) 3D X-ray computed tomographic
process of internal view of cracks.

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X-ray tube current was set to 30μA~54μA in dependence of crack size. 2D-slice images werereconstructed using the image processing unit of the X-ray CT system. Finally, the volumetricimage visualization and segmentation of the crack morphology were implemented using
VoTracer software package (Riken, Japan).

3. Results
3.1 Surface and 3D observation of cracks
Fig. 3 shows the surface morphologies of cracks in three samples. First, one single crack
was initiated under the micro-droplet. Regardless of the heat-treatment exposures, thecracks of all samples were observed to stem from the corrosion pits. In detail, the cracks
developed preferentially at the place next to the pit mouth on the sample surface, rather than
the general perspective that cracks develop preferentially and coalesce at the pit base [2-3].

Fig. 3 SEM observations of the crack morphologies of samples (a) A, (b) B, and (c) C.

Fig. 4 Reconstructed tomographic 2D slices and 3D volume rendering iamges of cracks
morphologies of sample A, B, and C.

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Fig. 5 Reconstructed tomographic slices showing an “interior pit” along the crack path of thesample C. The inserted schematic indicates the planes.

Fig. 4 shows the reconstructed slices and volume rendering images. The internal views
provide a direct illustration of the relative position of the crack and corrosion pit, i.e., thecrack is next to but does not cross through the corrosion pit. This observation is close to therecently-proposed viewpoint that the cracks primarily attack the mouth, rather than thebottom, of the corrosion pits [4]. But the different point is that the crack does not finally crossor combine the pit, unlike the formation of through-crack after crack growth rate exceeds thepit growth rate [2-4]. This may elucidate a different role of corrosion pits in the crack initiationin high strength steels. The volume rendering images indicate that the crack morphologies
are not continuous in spatial distribution. The discreteness may imply a competition among
different advancing crack tips which probably depended on the local inhomogeneity of theheat treated material. Furthermore, several interior cavities (named here “interior pit”) wereobserved to situate by the path of the crack propagation. One is shown in Fig. 5 by thereconstructed tomographic 2D slices. The interior pit is apparently an included local that may
play a critical role in the crack growth.
3.2 AE signals and damage identification
Fig. 6 shows the evolution of AE signals obtained during SCC process of three samples.
According to the increase trend and amplitude distribution, the data could be divided into fourstages. Combined with the in-situ observational SCC evolution, the four-stage AE signals areassociated with pitting corrosion, pit-to-crack transition, small crack growth and long crack
propagation. Such observation is consistent with the fundamental steps of overall SCC
development [1]. In stage I, chloride ions locally destroy the passive film surface of stainlesssteel sample, exposing the bare metal to the electrolyte and allowing the activation of
localized corrosion. Later, local acidification by the hydrolysis of dissolving metal cations
within pit lowers the pH value and generates the hydrogen bubbling which was captured inthis work. A sharp increase in AE events in stage III reflects the transition from pit to crack.
Stage II is a quiescence phase for sample A and B, but sharp increase for sample C. This
stage seems to be microstructure dependent and will be further investigated in future work.
Stage IV with the relatively low number of AE events is corresponding with crack propagation.
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70

Stage I

3

10
II

2

10

60

1

10

50
40
4000

0

6000

8000

10000 12000 14000 16000

AE amplitude, dB

90

4

10

90
3

10

II

80
70

2

10

Stage I

60

1

10

40
46000

(Sample-C)
Cumulative AE events
AE amplitude

IV

50

10

Time, t / s

III

III

II

0

48000

50000

Time, t / s

52000

10
54000

Cumulative AE events, N

80

AE amplitude
AE events

100

AE Amplitude, dB

III

Stage I: Pit corrosion.
Stage II: Pit-to-crack transition.
Stage III: Small crack growth.
Stage IV: Crack propagation.

10

IV

Cumulative AE events, N

AE Amplitude, dB

90(Sample-B)

4

AE amplitude
AE events

4

10
IV

80

Cumulative AE events, N(Sample-A)

100

3

10

70

2

10

Stage I

60

1

10
50

0

40
50000

10
100000150000Time, t / s

200000250000Fig. 6 Evolution of cumulative events and amplitude of AE signals obtained during SCC evolutionof samples A, B, and C.

Fig. 7 Identification of AE source mechanisms during SCC from in-situ surface observation of thecrack evolution (sample A).

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To identify the damage mechanism for AE signals, k-means clustering algorithm was used toanalyze the AE data. According to the similarity of AE data, three different AE clusters wereobtained. From in-situ linkage analysis, three AE clusters were associated with hydrogen
bubble evolution, plastic deformation and the cracking process. Fig. 7 shows the partial dataof distribution of three AE clusters and the corresponding observation of a periodic
coalescence phenomenon of two crack branches during crack propagation. Interestingly,
high-amplitude signals were highly correlated with the occurrence of coalescence events of
two branches. Therefore, the cluster 3 (red) is further attributed to the crack-branches
coalescence. Suggestively, the plastic deformation and hydrogen bubble evolution dominate
the detected AE signals during the crack growth. The cracking itself seems to be “silent” until
the coalescence events occur with the concurrent high-amplitude signals. Moreover, thehigh-amplitude signals are not continuous in time series. This may be a response to thediscrete morphologies of cracks shown in Fig. 4.

4. Discussion
The direct optic capture of the hydrogen bubble evolution approaching the point of crack
initiation suggests that the hydrogen embrittlement mechanism may dominate the crack
initiation of the high-strength steels in the present work. First, it is well known that any
process producing hydrogen at a metal surface will induce considerable atomic hydrogen
absorption and rapid diffusion into the metal lattice even at normal temperature [27]. The
hydrogen diffusion phenomenon is more preferable in high-strength steels and enhanced by
the applied tensile stress. The observed surface- and interior-pits can serve the source ofhydrogen through the “local acidity” by hydrolysis of the dissolved metal ions [28]. Also, aprevious report explained the SCC with hydrogen embrittlement mechanism accompanied by
AEs in 13% Cr martensitic stainless steel exposed to 3% NaCl solution [29]. Furthermore,considering the observed zigzag-feature, the crack presents the intergranular type. Hydrogenis but one element that can segregate to grain- or interphase boundaries and its solubility ina metal depends on the applied tensile stress level [30]. This may account for that the cracks
primarily attacked the pit/grain boundary interface on the sample surface, rather than the pit
bottom. As the surface of bent sample suffered the highest stress level, which may cause thesite near surface being the preferential crack nuclei. Furthermore, the final cracks did notcross through or combine with the corrosion pits, suggesting that the critical role of corrosion
pits may be the source of hydrogen in the pit-to-crack transition in high-strength steels.
On the other hand, the distribution of high-amplitude cracking events with the plastic
deformation events can be explained by the Hydrogen-Enhanced Localized Plasticity (HELP)
mechanism [31]. The cracking was followed by the generation and accumulation of
dislocation by the combination of hydrogen atoms and stress. Therefore, the plastic
deformation dominated the AE signals of medium amplitude until the occurrence of crackbranches coalescence events which produced the strongest AE signals. This is consistent
with the conclusions of previous reports [16-17] that the crack extending along the grainboundaries cannot generate detectable AE signals. Further work is required to give a reliable
validation of this explanation.

5. Conclusion
The behavior of one single stress corrosion crack of 13Cr martensitic stainless steel wascontrolled and investigated by AE monitoring and 3D XCT observation. The crack was
7

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observed to primarily originate from the mouth of a corrosion pit. The hydrogen embrittlement
is supposed to account for the mechanism of crack initiation of SUS420J2. The corrosion pit
may serve a critical role of providing the hydrogen in pit-to-crack transition. The evolution of
AE signals were stage-by-stage associated with the crack initiation and propagation. By
direct linkage with in-situ surface observations, the AE signals were attributed to thehydrogen bubble evolution, plastic deformation, and the coalescence events of crackbranches. However, a valid interpretation of the AE signals requires in-situ 3D visualizationsof the crack evolution and quantitative evaluations of the crack behavior with AE data.

Acknowledgements
This work was supported by Council for Science, Technology and Innovation (CSTI), Crossministerial Strategic Innovation Promotion Program (SIP), “Structural Materials for Innovation”(Funding agency: JST). The authors acknowledge the great helps from Prof. Jun Takahashi,
Dr. Mizuki Ohta, Dr. Pornthep Chivavibul, Dr. Takayuki Shiraiwa in X-ray CT scanning andimage processing.

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3466-3485.
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emission in fatigue and fracture materials research., JOM, Vol. 50, 1998, pp. 1-14.
[7] Wu, K.G., Choe, C.Y., Lee, S.M., Yoo, J., Hyun, C.Y., Hong, D.P., Byeon, J.W., Real-time
Monitoring of Degradation of Zirconia Ceramic by Acoustic Emission, Applied Mechanics andMaterials, Vol. 433, 2013, pp. 816-820.
[8] Wu, K., Jung, W.S., Byeon, J.W., Evaluation of corrosion critical variables of 304 stainlesssteel by delay time of acoustic emission, Materials Transactions, Vol. 56, 2015, pp. 398-403.
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pp. 587-592.
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[10] Wu, K., Jung, W.S., Byeon, J.W., In-situ monitoring of pitting corrosion on vertically
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18 - Applicationof Acoustic
measurements
Applicationof Acoustic
EmissionEmission
measurements
to monitor theto monitor
initiation andthe propagationof single
initiationand thethe
propagationof single
or multiple Stress
Corrosionor multiple Stress
Corrosion
Cracks(SCC)Cracks (SCC)
Omar Al Haj1, José Bolivar2, Alain Proust1, Marion Frégonèse2
MISTRAS GROUP S.A.S, Instrumentation, Research and Development,
F-94370 Sucy en Brie2
Univ Lyon, INSA de Lyon, MATEIS UMR 5510 , F-69621 Villeurbanne cedex
1

Abstract:

The aim of this study is to monitor the initiation and the propagation of single or a colony of intergranular
SCC cracks using the acoustic emission technique. The main goal is a better understanding of the
behavior and the propagation of multiple cracks for a better evaluation of the influence of cracks
interactions on the failure process. For nuclear applications, the final aim is to propose new integrity
criteria.
Several experimental tests were performed using mill annealed and sensitized samples of alloy 600,
which were immersed in a 0.01 M of potassium tetrathionate solution acidified at pH 3.0 or 5.3. The
experiments were also monitored by in-situ digital image correlation (DIC) and electrochemical noise(EN) measurements.
Three stages were identified in the propagation and the growth of both a single crack and a colony ofcracks. During stage I, no crack was detected and the main acoustic emission signals were related tointergranular corrosion and/or anodic dissolution.
Stage II was characterized by surface crack propagation, mostly induced by anodic dissolution. The
acoustic emission signals recorded during this phase were generated by intergranular corrosion
processes.
During stage III, the interactions between the cracks became more and more intense (crack
coalescences) with an important implication of plastic strain (macro cracking, plastic deformation,…).
Such processes were identified as the source of the important acoustic emission activity detected during
this last stage.
Finally, the implementation of acoustic emission technique coupled with DIC and EN measurements
contributed to the understanding of the mechanisms involved in short stress corrosion cracks
interactions which was determinant for the modeling of the colony behavior.

1. Introduction
Stress Corrosion Cracking (SCC) is one of the significant ageing degradation processes inindustries operating under extreme conditions, especially in nuclear power plants (pressurized
and boiling water reactors). SCC leads to a premature degradation process of metals andalloys generated by the synergistic effect of static tensile mechanical loading andenvironmental factors [1]. In that context, a better understanding and evaluation of the SCC
process is considered as an important technical issue for the safety and maintenance of theseageing nuclear power plants (NPP) which are reaching a mean operating age of 30 years [23]. A streamlined approach to SCC risk evaluation involves the stage of initiation and the period
of interaction between several cracks (e.g. shielding effects, coalescence and irregularpropagation) [4-8]. For this purpose, acoustic emission technique (AE) represents an efficient
non-invasive and non-destructive evaluation technique allowing the detection and localizationof stress corrosion cracks [9-15]. Literature review about the use of AE technique for studying
corrosion processes, confirms that several types of corrosion can be detected and monitored
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by the mean of AE. Pitting, crevice, corrosion-abrasion in addition to stress corrosion cracking
have been studied and found to be correlated with AE [16-22].
Yuyama [23] actually reported that several processes could liberate acoustic emission signals
during SCC. These processes involve metal dissolution, hydrogen gas evolution, thebreakdown of thick oxide films, fracture or decohesion of precipitate and inclusions, plastic
deformation, martensitic transformation and micro/macro cracking (see Figure 1).
Nevertheless these processes didn’t release the same amount of energy and didn’t present
the same probability of being detected by piezoelectric sensors. Metal dissolution and film
formation are the less energetic processes, in contrast the rupture of thick film, gas release,plastic deformation and cracks propagation represent the most energetic processes and canbe easily detected.

Figure 1: Sources of acoustic emission in SCC [23]The main goal of this work is a better understanding of the behavior and the propagation ofmultiple cracks for a better evaluation of the influence of cracks interactions on the failure
process. The correlation of different techniques: AE (Acoustic Emission), EN (Electrochemical
Noise) and DIC (Digital Image Correlation) allow a better characterization of the behavior of a
SCC colony considering the individual evolution of each crack. The use of these threetechniques will enable the identification of the different stages of the crack colony evolution
and on the mechanisms involved in each stage.

2. Description of work
All the experiments mentioned below have been conducted and performed during the PhD
thesis of Bolivar, J. [24] which was a part of the ANR ECCOFIC project. This project aimed atstudying the behavior of colonies of environmentally assisted short cracks. In the present work
we focus particularly on the AE acquisition and the contribution of this technique in determining
the characteristics of intergranular SCC defects. Otherwise Bolivar [24-26] has reported indetails the technical aspects and results of the two other techniques mentioned in this paper(EN and DIC).
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The experiments were performed on a 2 mm thick plate of Alloy 600. The chemical composition
can be found in Table 1. The specimens were machined out by Electrical Discharge Machining,then heat-treated during 30 h at 600°C and air-cooled.
The specimens were partially covered with a silicon rubber in order to limit the exposed area.
Only the middle section of the samples was exposed to the electrolyte, with an exposed areaof 120 mm2 on each side (15 X 8 mm). The test solution consisted in 0.01 M potassium
tetrathionate acidified at pH 3 or 5.3 by H2SO4 addition.
Table 1. Composition of the tested Inconel 600 alloy

Element

Ni

Fe

Cr

Mn

Cu

Co

Ti + Al

C

S

Wt. %

bal. 9,25 15,52 0,12

0,1

0,1

0,25

0,03 0,002

Samples were immersed in the test solution at least 60 minutes before loading starts. Loading
tests were performed after the immersion step at 80% of the yield stress (σy = 263 MPa) using
an electromechanical tensile machine (LLOYD LR30K Plus).
Figure 2 shows the experimental device used during the tests and allowing the use of DIC, AE
and EN simultaneously.
AE data acquisition was performed using MISTRAS PCI2 acquisition system. The sample rate
was set at 2 MHz, offering a good compromise between measurement accuracy and data filesize. Two piezoelectric sensors (R15D) were used because of their good immunity against the
EMI (Electromagnetic Interference), and allowing the conversion of transitory elastic waves
resulting from corrosion processes into an electrical voltage. The sensors were fixed on the
top and at the bottom of the stressed specimen (working electrode). These piezoelectric
sensors present an operating frequency range between 50 – 400 kHz with a resonant
frequency centered on 150 kHz.
Table 2 gathers the characteristics of the AE acquisition devices. Two 2/4/6 preamplifiers, witha selected gain of 60 dBAE, were used. Furthermore, a band-pass filter was set-up between
100 kHz and 400 kHz, in order to minimize the environmental noise. The threshold was set to
23 dBAE, intended first to get an appropriate sensitivity and then to separate effective signals
from insignificant events.
Table 2. Main Characteristics of AE acquisition devices.

Instrumentation
Sensors
Threshold (dBAE)
System Filter (kHz)Preamplifier – gain (dB)
PDT-HDT-HLT (µs)
Waveform Sample Rate (MSPS)
Waveform Pre-Trigger
Waveform Length

Characteristics
R15D
23
100-400
2/4/6 – gain: 60 dB
300-600-1000
250 pts
1K [512 ms]

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The acquisition chain was computed using MISTRAS AEwin™ software and results wereanalyzed using MISTRAS Noesis™ software. The normalized Hsu–Nielsen test [EN 1330-9]
was carried out to verify the sensor coupling and the whole setup of the AE system.

Figure 2: Experimental device used during the experiments

3. Results
Four room temperature SCC constant load tests were performed in a 10 mM K2S4O6 (acidified
to pH 3 or pH 5.3) solution and loaded until 80 % of the yield stress (tests name: CC-02-15,
CC-03-15, CC-04-15, CC-07-15). One test has been conducted without loading, only underimmersion condition (CI-01-15). Table 3 gathers the details of these different tests.
The experiments CC-02-15, CC-03-15 and CC-07-15 were performed under conditions wherea dense crack colony propagated. Test CC-04-15 was conducted at higher pH (5.3) and asingle IG-SCC defect was detected. It must be highlighted that the test CC-07-15 is different
in comparison to other tests, as this test was not monitored by EN measurements.
Based on the correlation between DIC and EN results, Bolivar [24-26] has reported that thecrack colony evolution could be split into 3 main stages (see Figure 3). Stage I’ isrepresentative of the lapse of time comprised from the moment when the loading started (t0)until the detection of the first cracks by DIC (ta). No cracks were detected by DIC throughout
this initial stage, and the EN measurements reveals an intergranular attack signature(intergranular corrosion and/or anodic dissolution).
Stage II’ was defined as the lapse of time between the end of the stage I’ (ta) and the moment
where the crack density was maximal (tb). This stage is mainly characterized by the detection
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of superficial cracks which propagate mostly in surface. The propagation mechanism is mainly
induced by the anodic dissolution of grain boundaries.
Finally, the stage III was comprised from tb until the end of the test. Macro-cracks wereobserved during this stage after an increase of the cracks interaction (coalescences andshielding). Plastic mechanisms present an important implication throughout this stage (macro
cracking, plastic deformation, rupture).
For AE data analysis, it was chosen to represent separately the data between (t0 – ta), (ta-tb)
and (tb – end of test). These time values were determined, based on DIC and EN results (taequal to 24000 seconds and tb equal to 48000 seconds).
Table 3. Experimental characteristics of the different conducted tests (CC-02-15, CC-03-15, CC-04-15,
CC-07-15 & CI-01-15).

ID testCC-02-15
CC-03-15
CC-04-15
CC-07-15
CI-01-15

Experimental conditions
MAS Alloy 600,
Solution 0.01 M K2S4O6
Loading (0.8 σy)
MAS Alloy 600,
Solution 0.01 M K2S4O6

pH

Immersion time before
loading (h)Test duration (h)

3

4

24

3

1

23

5.3

4

100

3

1

25

3

No loading

113.

Figure 3: Representation of the different times and stages of crack colony evolution according to DIC& EN measurements [24]The first step of the AE data processing was based on the discrimination between noise andsignificant signals. Table 4 presents the characteristics of noise signals (electromagnetic, highfrequencies and mechanical). AE hits related to noise are mainly characterized by a hits
amplitude lower than 27 dBAE and a low number of counts per hit (1 count/hit). These signals
were filtered from the AE analysis for the rest of the study.
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Table 4. Characteristics of AE hits recorded during the different stages of crack colony evolution.

Class ID

Amplitude (dBAE)

Noise

23-26

< 0,1

1

220-320

Stage I’

24-40

0,1-101-20

240-300

Stage II’

24-50

<100

1-100

200-300

Stage III

24-50

<150

1-100

190-310

3.1

Absolute Energy (aJ) Counts Frequency centroid (kHz)Stage I’ (0 – 24000 seconds):

For this stage, AE data processing was performed based on following experiments: (CC-0215, CC-03-15 and CC-03-15). These tests were carried out in similar experimental conditions.

Figure 4: Filtered AE results recorded during the following tests (Stage I’ [0 s - 24000 s]: CC-02-15,
CC-03-15 & CC-07-15)
Plot 1: Cumulated Activity of AE hits as a function of time, Plot 2: Cumulated Intensity of AE hits as afunction of time, Plot 3: AE hits number as a function of their amplitude, Plot 4: AE hits amplitude as afunction of time.

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Figure 4 recapitulates the AE filtered results obtained during the first stage of a crack colonyevolution (stage I’). It shows the presence of an important AE activity at the beginning of thetests, which corresponds to the loading time (80% σy). The main part of these AE signals were
induced by plastic deformation which occurred locally in the material, eventually in grainboundaries (dissolved zone after the immersion phase). The AE activity tends to decrease
after the loading step, indicating a viscoplatic mechanical behavior of loaded specimens.
Based on Figure 4, the test CC-07-15 presents a higher activity and intensity in comparison tothe other tests CC-02-15 and CC-03-15.
3.2

Stage II’ (24000 – 48000 seconds):

For this stage, AE data processing was performed based on following experiments: CC-02-15,
CC-03-15 and CC-03-15 in addition to the immersion test carried out without loading (CI-0115).
AE hits detected throughout this second stage are characterized by low and medium amplitude(24 dBAE - 50 dBAE). The main part of these hits presents an absolute energy lower than 10 aJ.
AE activity remains constant during this stage and comparable to the AE signals detectedduring the constant load part of the first stage (see Figure 5).

Figure 5: Filtered AE results recorded during the following tests(Stage II’ [24000 s - 48000 s]: CC-02-15, CC-03-15, CC-07-15 & CI-01-15)
Plot 1: Cumulated Activity of AE hits as a function of time, Plot 2: Cumulated Intensity of AE hits as afunction of time, Plot 3: AE hits number as a function of their amplitude, Plot 4: AE hits amplitude as afunction of time.

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These signals were compared to the signals detected during the immersion test (CI-01-15) inorder to identify the source of these AE signals. A low AE emissivity was detected during theimmersion test, otherwise this emissivity remains similar to the emissivity recorded during thethree constant load tests (CC-02-15, CC-03-15 & CC-07-15). AE hits characterized by a low
and medium amplitude were detected during these different tests (immersion and constantload tests) (see Table 4 & Figure 5).
The correlation of DIC, EN and AE results indicates that intergranular corrosion and superficial
crack propagation are the emissive mechanisms of this stage with low energy hits.
3.3

Stage III (t>48000 seconds):

For this stage, we first compared the AE results obtained during the following experiments:
CC-02-15 & CC-07-15, these experiments were interrupted before rupture. AE results indicate
a significant increase of the AE activity and intensity, 57000 seconds after the beginning of thetests (see Figure 6). The correlation between DIC, EN and AE results indicates that thisincrease is induced by cracks coalescence and propagation.

Figure 6: Filtered AE results recorded during the following tests (Stage III [t>48000 s]: CC-02-15 &CC-07-15)
Plot 1: Cumulated Activity of AE hits as a function of time, Plot 2: Cumulated Intensity of AE hits as afunction of time, Plot 3: AE hits number as a function of their amplitude, Plot 4: AE hits amplitude as afunction of time.

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The test CC-03-15, was performed until rupture; the AE results obtained during this test arepresented in Figure 7. These results indicate an AE activity increasing after 57000 seconds;
this increase becomes more and more significant after 68000 seconds.

Figure 7: Filtered AE results recorded during the following tests (Stage III [t>48000 s]: CC-03-15),
Plot 1: Cumulated Activity of AE hits as a function of time, Plot 2: Cumulated Intensity of AE hits as afunction of time, Plot 3: AE hits number as a function of their amplitude, Plot 4: AE hits amplitude as afunction of time.

4. Discussion
The correlation of three different techniques (AE, EN and DIC) allows the identification of thedifferent stages of cracks colony evolution and of the mechanisms involved in each stage. This
correlation indicates that the stage I’ of the crack colony evolution is mainly driven by theintergranular corrosion.
The AE activity detected during this stage is mostly induced by plastic deformation which
occurred during the initial loading part. Mechanical tests monitored by AE technique of Inconel
and austenitic alloys, show similar results indicating an important AE activity due to local plasticstrain [14, 17]. Furthermore it must be noticed that AE activity decreases during the constantload phase without any full stabilization of AE, revealing a change in the emissive mechanism.
Once the effect of the load is fully stabilized, AE activity is released by intergranular corrosion.
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Multiparametric pattern recognition analysis of AE signals recorded during Stage II’, indicatesthat these signals were closely similar to the signals recorded during the immersion test (seeFigure 8). These hits were characterized by low and medium amplitude varying between 24
and 50 dBAE. The hits were not very energetic and presented a frequency centroid band
centered on 250 kHz (see Figure 8).
AE analysis was correlated with DIC and EN results, indicating the detection of superficial
cracks during this second stage. Intergranular corrosion and superficial cracks propagation
represent the active and emissive processes during this second stage (ta<time<tb).

Figure 8: Frequency centroid of AE hits as function of their amplitude; Absolute energy of AE hits
versus time (Stage II [24000 s - 48000 s]: Loaded tests CC-02-15, CC-03-15 & CC-07-15 – Immersion
test CI-01-15).

The third stage of the crack colony evolution process is the most emissive stage and it ischaracterized by a significant increase of the AE activity and intensity. This AE emissivity ismainly induced by cracks propagation which results from local plastic deformation. These
results confirm the interest and the sensitivity of AE technique on crack propagation detection.
In order to confirm the AE signature of plastic deformation and macro-cracking, AE results
obtained during the test CC-03-15 (test conducted until rupture with an important colony ofmacro-cracks) and the test CC-04-15 (test with single crack due to an increase of pH) werecompared. Figure 9 shows the single macro-crack observed at the end of the test CC-04-15.

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Figure 9: Optical observations and SEM images of sample surface after SCC tests under Constant
Load (0.8 σy) of a MAS Alloy 600 sample in a 0.01 M tetrathionate solution at different pH.

The comparison of AE results recorded during these two tests (CC-03-15 & CC-04-15), isrepresented in Figure 10, allowing distinguishing macro-cracking signals from plastic
deformation signals. For the single macro-cracking test (CC-04-15), the main part of AE signals
was induced by plastic deformation mechanisms; only a few number of highly energetic AE
signals were related to the macro-cracking propagation. It is notable that these high energy
hits were more detected during the macro-cracking test CC-03-15, where an important colonyof macro-cracks was observed.
Furthermore, it must be noticed that the AE signals recorded during the single-crack sample
were closely similar to the AE signals recorded before the rupture of the multi-cracks sample(signals recorded between 57000 seconds and 80000 seconds). This indicates that thesesignals were released by the same mechanism (plastic deformation).

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Figure 10: Multiparametric analysis and PCA (Principal Component Analysis) of AE filtered hits
recorded during the tests CC-03-15 (multi-cracks) & CC-04-15 (single-crack).

5. Conclusion
Correlations between DIC, EN and AE results allowed characterizing the crack colony
propagation into three different stages.
The first stage was characterized by a low acoustic emission activity and high levels of current(DC part). This result evidences active dissolution and lack of re-passivation during this stage;grain boundary dissolution affects only the surface.
In the second stage, AE activity remains constant with low energy signals. Anodic dissolution
and intergranular corrosion remain the main mechanisms of this stage, in addition to someinteractions between superficial cracks.
Finally, the last stage is the most emissive part of the crack colony evolution process. Highenergetic AE signals were detected during this stage resulting from local plastic deformationand cracks propagation and coalescence.
Associated to complementary techniques, AE is therefore a powerful tool to study and identify
the different propagation steps of SCC, involving either a single crack or a colony.

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Acknowledgements
The authors would like to acknowledge the National French Research Agency (ANR) for its
financial support under contract MATETPRO ANR-12-RMNP-0020 (ECCOFIC project). The
authors would also like to thank the different partners of this project: Institut de la Corrosion,
AREVA, ANDRA and Pierre Combrade for the fruitful discussions and participations.

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[22] Prateepasen, A., Pitting corrosion using Acoustic Emission, Pitting corrosion Nasr Bensalah (Ed.),
InTech, March 2012, pp. 43-60.
[23] Yuyama, S., Fundamental Aspects of Acoustic Emission Applications to the Problems caused bycorrosion, presented at the Corrosion Monitoring in Industrial Plants Using Nondestructive Testing andElectrochemical Methods, Philadelphia, vol. ASTMSTP 908, 1986, pp. 43–74.
[24] Bolivar, J., Experimental study of the behavior of colonies of environmentally-assisted short cracks
by Digital Image Correlation, Acoustic Emission and Electrochemical Noise, INSA de Lyon, MATEIS
laboratory - Université de Lyon, Thesis defended in July 2017.
[25] Bolivar, J., Nguyen, T. T., Shi, Y., Frégonèse, M., Réthoré, J., Adrien, J. King, A., Buffiere, J.Y. and
Huin, N., Multiple cracks interactions in Stress Corrosion Cracking: in-situ observation by Digital Image
Correlation and Phase Field Modeling, Proceedings of the 18th International Conference on
Environmental Degradation of Materials in Nuclear Power Systems – Water Reactors, EDM 2017.
[26] Bolivar, J., Frégonèse, M., Réthoré, J., Duret-Thual, C. and Combrade, P., Evaluation of multiple
stress corrosion crack interactions by in-situ Digital Image Correlation, Corrosion Science, Vol. 128,
2017, pp. 120-129.

14

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64 - Acoustic Emission from gas bubble nucleation

ACOUSTIC EMISSION FROM GAS BUBBLE NUCLEATION AND
and breaking away from a flat surface
BREAKING AWAY FROM A FLAT SURFACE
Volodymyr KIETOV1, Marcel MANDEL1, Lutz KRÜGER1 , Alexei VINOGRADOV2Institute of Materials Engineering, TU Bergakademie Freiberg
Gustav-Zeuner str. 5, D-09599, Freiberg, Germany

1

Departement of Mechanical and Industrial Engineering, Norwegian University of Scienceand Technology - NTNU
Richard Birkelandsvei 2B, N-7491, Trondheim, Norway

2

E-mail of corresponding author Volodymyr.Kietov@iwt.tu-freiberg.de

Abstract:
Gas evolution is an important source of acoustic emissions (AE) in many chemical and electrochemical
reactions. Specifically, AE accompanies gas evolution during uniform and pitting corrosion of metals.
Therefore, monitoring of gas evolution provides valuable information on degradation processes and theirrates during corrosion. In the present work, gas evolution on a steel surface was investigated in detailto get the deeper insight into the properties of AE sources. The polished surface of the plane steel
sample was used as an electrode for water electrolysis. Evolution of hydrogen and oxygen bubbles wasfilmed via a long-distance microscope with the high-speed-camera operating at a frame rate up
to 100 000 fps. The processes of bubbles nucleation, merging and breaking away from the surface wereobserved and analysed frame by frame. The possible sources for acoustic emissions are associatedwith merging and breaking away of gas bubbles. The characteristic futures of the AE generated by gas
evolution were estimated.

1. Introduction
Gas evolution is an important source of acoustic emissions (AE) in many chemical andelectrochemical reactions. Specifically, AE accompanies gas evolution during uniform andpitting corrosion of metals. The acoustic emissions connected with the corrosion processes
have been studied in many works. As a main result, the correlation has been establishedbetween the AE rate and the corrosion rate [1–6]. However, the details of relations between
the AE parameters due to gas evolution are still missing and are of great interest. In several
attempts, AE from gas bubbles formation was characterized by means of various parameterssuch as rise time, amplitude and duration [7–10]. Two types of acoustic signals associatedwith gas evolution were found: the low-amplitude resonant signals with the long duration andrise time and burst-type transients having the short rise time and duration and greater energy.
The first type signals were attributed to the hydrogen formation inside the pits in case of
localized corrosion [7–9], while the second category of signals was linked to the gas formationin case of the uniform corrosion [7,10]. A more elaborated approach implements the analysis
of AE spectral features. It was noticed that evolution of the hydrogen bubbles during pitting
corrosion was accompanied by discrete AE signals with the frequency peak in the range 100– 120 kHz [11,12]. The resonant signals with the frequency of about 150 kHz were observed

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in [13] in relation to the same process. On the other hand, in [14] the frequency maximum wasused for discrimination of the AE sources during corrosion of the austenitic stainless steel.
Acoustic emission produced by hydrogen bubbles formation during pitting corrosion wasobserved in the frequency range of 120 – 350 kHz whereas the signals with peak frequencies
in the 120 – 450 kHz range were recorded during uniform corrosion. Similarly, on the basis of
the frequency peak position the discrimination between different corrosion processes inaluminum was performed in [15,16]. Hydrogen formation was claimed to produce the AE
signals in the range of 200 – 300 kHz. Both hydrogen and oxygen formation in the course of
potentiodynamic polarization of the stainless steel generated the AE signals with the peakfrequency of 100 – 300 kHz [10]. In [17] the AE signals from corrosion pitting of stainless steel
were divided into 4 categories with respect to their waveforms. Some waveforms were similar
to those described above but none was related to gas formation. As a resume, understandingof phenomenology of acoustic emission caused by gas evolution in corrosion processes isincomplete as yet. To get a deeper insight into this phenomenon, gas bubbles formation on
the metal surface has to be investigated in more details.
The self-resonant frequency 𝜈𝜈𝜈𝜈 of the gas bubble inside the liquid was calculated in [18] for the
first time and then was frequently used in relevant publications [12,14,19,20] in the following
form:
1
3𝐾𝐾𝐾𝐾𝑃𝑃𝑃𝑃 1/2
� � 0�𝜌𝜌𝜌𝜌
𝜋𝜋𝜋𝜋𝐷𝐷𝐷𝐷0(1)𝜈𝜈𝜈𝜈 = �where 𝐷𝐷𝐷𝐷0 is the bubble equilibrium diameter, 𝑃𝑃𝑃𝑃0 is a static pressure, 𝜌𝜌𝜌𝜌 is the density of the liquid
and 𝐾𝐾𝐾𝐾 is the so-called polytropic index, which takes values between unity and the ratio of heat
capacities at constant pressure and volume, i.e. is 𝐾𝐾𝐾𝐾 = 1.4 at room temperature andatmospheric pressure.
The resonant frequencies calculated for bubble diameters ranging from 10 µm up to 1 mm,
which are commonly observed in corrosion studies, are shown in Fig. 1.

Fig. 1. Resonance frequencies of the hydrogen bubbles according to Eq.(1)It is evident that the calculated self-frequency range corresponds to the frequency response of
the most common acoustic transducers. The gas bubbles with the diameters smaller than 30µm are rare. Thus, the upper frequency limit of ca. 400 kHz found in literature can bereasonably explained. The lower frequency of the common AE transducers is often limited to
20 kHz. Therefore, according to the calculation given above, the bubbles with the diameter
greater than ca. 400 µm should not produce AE detectable by conventional transducers.

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2. Description of work
Hydrogen bubbles formation occurs on the metal surface during the electrolysis of water. The
austenitic steel plate with the thickness of 3 mm was ground and polished up to surface
roughness of 1µm. The specimen’s surface was covered with the isolating tape so that only asmall area was exposed to the electrolyte. This ensured that all processes occurred within theview field of the high-speed camera used for direct observations of gas production. The
schematics of the measurement unit is represented in Fig. 2.

Fig. 2. Schematics of the measurement unit

Acoustic emissions were acquired using the PCI-2 system by Physical Acoustic Corporation(PAC, USA). The continuous waveform recording mode was implemented with the sampling
frequency of 2 MHz. Two different transducers - WD from PAC, and 1045D from Fujicera were connected to the preamplifier PAC 2/4/6 operated at 60 dB gain. The measurements withdifferent transducers were performed separately.
The high-speed camera (Photron Fastcam SA-Z) operated at the shutter speed of 80000
frames per second with the resolution of 384 x 256 pixels and 108,667 pixel per millimeter.
The field of view was calibrated using the stage micrometer to provide the measurement of theobserved bubbles diameter. Using this way of measurement, the accuracy of bubble dimension
measurements was ±10 µm. The camera’s TTL trigger output was used to synchronize theacoustic stream with the camera record. When the camera started recording, the TTL output
sent the triggering signal to the PCI-2 board and the waveform streaming acquisition started
simultaneously.

3. Results
During the experiments the metal surface was usually covered with gas bubbles. Direct
observations revealed that the predominant process controlling the generation of acoustic
emissions was the merging of bubbles. The breaking away of a gas bubble without preliminary
merging with the other bubble was observed rarely. This event generated the waveform similarthat observed during the bubble merging. Furthermore, it was noticed that the nucleation of
the new bubble on the surface did not produce any detectable AE signal. An example ofmerging of two hydrogen bubbles is shown in Fig. 3a, and the corresponding acoustic signal
in Fig. 3b.

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Fig. 3. Merging two hydrogen bubbles (a), and the corresponding acoustic signal (b), transducer
Fujicera 1045D.

AE signals produced by the bubble merging process had relatively low amplitudes. Importantly
is that the correlation between the amplitude of the signal and the final size of the merged
bubble was not evidently observed, see Fig. 4, for example.

Fig. 4. Amplitude of the acoustic signals and the final size of the merged bubbles:(a) transducer WD by PAC; (b) transducer 1045 by Fujicera.

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4. Discussion
In order to identify the AE parameters resulting from gas evolution, the collected AE signals
were analyzed by means of their spectral characteristics. At first, the peak frequency - thefrequency corresponding to maximum magnitude in the FFT- spectrum, was found. At second,
the median frequency, fm, of the Fourier power spectrum G(f) was computed by definition as:
𝑓𝑓𝑓𝑓

∞

(2)

m
∫0 𝐺𝐺𝐺𝐺(𝑓𝑓𝑓𝑓)d𝑓𝑓𝑓𝑓 = ∫𝑓𝑓𝑓𝑓 𝐺𝐺𝐺𝐺(𝑓𝑓𝑓𝑓)d𝑓𝑓𝑓𝑓
mwhere f is frequency.

The dependence of the median frequency and the peak frequency on the final diameter of themerged bubbles was hardly to found when AE was acquired by the WD transducer, Fig. 5.
Most of the AE events concentrated in two frequency bands, from 100 to 200 kHz and from 20
to of 70 kHz. The frequencies higher than 200 kHz were unlikely to observe. Most of the values
were markedly higher than it could be expected from Eq. (1). It was evident, that the spectral
characteristics of the used transducer affected the results of the measurements quite
substantially.

Fig. 5. (a) frequency peak and (b) median frequency of thetransducer PAC WD.

signals collected by the

When the measurements were repeated under similar conditions, but using the Fujicera
broadband 1045D transducer, unlike the above results, the frequency peak and medianfrequency appeared to be lower for AE signals produced by larger bubbles, Fig. 6. This effectwas more pronounced for the peak frequency than for the median frequency.

Fig. 6. (a) frequency peak and (b) median frequency of the signals collected by thetransducer Fujicera 1045D.

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Nevertheless, the observed characteristic frequencies were markedly higher than those
expected from calculations by Eq. (1). This could be explained by the fact that Eq. (1) describes
the vibrations of the ideal spherical bubble. During merging, the bubbles deform heavily, as
illustrated in Fig. 3. Thus, Eq. (1) provides only a rough approximation of the expected
frequency. On the other hand, the non-flat multiresonant frequency response of AE
transducers has to be taken into account when experimental data are compared withcalculations.

5. Conclusion
The conducted in situ rapid video observations synchronized with AE measurements allowed
to clarify the AE features related to the gas bubbles evolution on the metal surface. Main AE
sources were associated with gas bubble merging and breaking away from the surface. No AE
signals corresponding to the nucleation of a gas bubble were observed in the present work.
The size of the gas bubbles cannot be simply correlated with the AE amplitude. However, thecorrelation does exist between the AE characteristic frequency and the size of bubbles. The
larger bubbles tend to produce the AE signal with the lower peak frequency in the power
spectrum. A strong distortion of the spherical form of bubbles during merging made the exact
prediction of the characteristic frequency impossible. This frequency can be only approximately
estimated from the bubble diameter. The nonlinearity of the transducer frequency response
significantly affects the results of measurements, and therefore the judicious choice of sensor
is of key importance if any quantitative characterization of the gas bubble evolution is aimed.

Acknowledgement
The authors gratefully acknowledge for the financial support of this study by the German
Research Foundation as part of the Collaboration Research Centre 799 “TRIP-Matrix
Composites”, subproject B6.

6. References[1] Kasai N, Utatsu K, Park S, Kitsukawa S, Sekine K. Correlation between corrosion rateand AE signal in an acidic environment for mild steel. Corrosion Science
2009;51(8):1679–84.
[2] Jones RH, Friesel MA. Acoustic Emission During Pitting and Transgranular Crack
Initiation in Type 304 Stainless Steel. Corrosion 1992;48(9):751–8.
[3] Skal’s’kyi VR, Nazarchuk ZТ, Dolins’ka ІY, Yarema RY, Selivonchyk ТV. AcousticEmission Diagnostics of Corrosion Defects in Materials (a Survey). Part. 1. Detection ofElectrochemical Corrosion and Corrosion Fatigue. Mater Sci 2017;53(3):295–305.
[4] Kim Y, Fregonese M, Mazille H, Féron D, Santarini G. Ability of acoustic emission
technique for detection and monitoring of crevice corrosion on 304L austenitic stainlesssteel. NDT & E International 2003;36(8):553–62.
[5] Rettig TW, Felsen MJ. Acoustic Emission Method for Monitoring Corrosion Reactions.
Corrosion 1976;32(4):121–6.

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[6] Seah K, Lim KB, Chew CH, Teoh SH. The correlation of acoustic emission with rate ofcorrosion. Corrosion Science 1993;34(10):1707–13.
[7] Fregonese M, Idrissi H, Mazille H, Renaud L, Cetre Y. Initiation and propagation steps inpitting corrosion of austenitic stainless steels: Monitoring by acoustic emission.
Corrosion Science 2001;43(4):627–41.
[8] Fregonese M, Idrissi H, Mazille H, Renaud L, Cetre Y. Monitoring pitting corrosion of
AISI 316L austenitic stainless steel by acoustic emission technique: Choice ofrepresentative acoustic parameters. Journal of Materials Science 2001;36(3):557–63.
[9] Xu J, Wu X, Han E-H. Acoustic emission during pitting corrosion of 304 stainless steel.
Corrosion Science 2011;53(4):1537–46.
[10] Mandel M, Kietov V, Dubberstein T, Krüger L. The Potentiodynamic Polarisation of aHigh-Alloy Steel—An Analysis by Acoustic Emission Testing and Long-DistanceMicroscopy. Electrochimica Acta 2017;240:350–60.
[11] Calabrese L, Galeano M, Proverbio E, Di Pietro D, Cappuccini F, Donato A. Monitoringof 13% Cr martensitic stainless steel corrosion in chloride solution in presence ofthiosulphate by acoustic emission technique. Corrosion Science 2016;111:151–61.
[12] Wu K, Jung W-S, Byeon J-W. Acoustic Emission of Hydrogen Bubbles on the Counter
Electrode during Pitting Corrosion of 304 Stainless Steel. Mater. Trans. 2015;56 :587–
92.
[13] Wu K, Jung W-S, Byeon J-W. In-situ monitoring of pitting corrosion on vertically
positioned 304 stainless steel by analyzing acoustic-emission energy parameter.
Corrosion Science 2016;105:8–16.
[14] Jirarungsatian C, Prateepasen A. Pitting and uniform corrosion source recognition using
acoustic emission parameters. Corrosion Science 2010;52:187–97.
[15] Bernard J, Boinet M, Chatenet M, Dalard F. Contribution of the Acoustic Emission
Technique to Study Aluminum Behavior in Aqueous Alkaline Solution. Electrochem.
Solid-State Lett. 2005;8:E53.
[16] Boinet M, Bernard J, Chatenet M, Dalard F, Maximovitch S. Understanding aluminum
behaviour in aqueous alkaline solution using coupled techniques. Electrochimica Acta
2010;55(10):3454–63.
[17] Shiwa M, Masuda H, Yamawaki H, Ito K, Enoki M. In-Situ Observation and Acoustic
Emission Analysis for Corrosion Pitting of MgCl2 Droplet in SUS304 Stainless Steel.
Mater. Trans. 2012;53(6):1069–74.
[18] Minnaert M. XVI. On musical air-bubbles and the sounds of running water. The London,
Edinburgh, and Dublin Philosophical Magazine and Journal of Science
2009;16(104):235–48.
[19] Leighton TG, Fagan KJ, Field JE. Acoustic and photographic studies of injectedbubbles. Eur. J. Phys. 1991;12(2):77–85.
[20] Leighton TG, Lingard RJ, Walton AJ, Field JE. Acoustic bubble sizing by combination ofsubharmonic emissions with imaging frequency. Ultrasonics 1991;29(4):319–23.

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69 - Applicationof AE techniqueandthermography FOR
APPLICATIONOF AE TECHNIQUE
AND
THERMOGRAPHY
for assessment
sludge volume
insidecrude
oil storage
tanks
ASSESSMENTOFof
SLUDGEVOLUME
INSIDECRUDE
OIL STORAGE
TANKS
Ireneusz Baran 1, Tomasz Dunaj 1, Marek Nowak 1
1

Office of Technical Inspection (UDT), ul. Szczesliwicka 34, 02-353 Warsaw, Poland
Central Laboratory of Technical Inspection (CLDT)

Abstract:
When crude oil is stored in large tanks, invariably high-molecular-weight organic sediments (paraffin)
are deposited but the resulting sediments contains also solid particles (sand, silt, corrosion products,etc.) and also water. After some period of in-service of tank, these deposits build up to form a sludge,
which causes a reduction in the storage capacity. These high in hydrocarbons a valuable raw material
could be proper recovered and recycled back to the refining process. Therefore, it is significant to know
the sludge characteristics shape and volume to define the best form of treatment (maintenance of tankfor control of level of sludge) and removal method (in case of out of service and opening of tank). The
knowledge about volume and shape of such sludge inside tanks is important for user of tank farm due
to a reasons such as: maintenance of storage tanks and control/monitoring of level of sludge inside them, when conventional treatments are used to remove crude oil sludge, there is a potential for highenvironmental impact, so the choose of the method of cleaning is very essential,
 most of design of tanks is with floating roofs, what in case when inside tank is high level of sludge
could cause problems and/or damage of roof for low level stored product.
In this paper are presented AE and thermography techniques for assess of level, volume and shape ofsludge inside storage tank. Thermography method is used to assess the level of sludge (heights) along
the storage tank shell. AE technique is used in this case in different way than normally purposes. Thereis used specific layout of sensors on the wall and roof of the tank. The sensors generate of AE pulses
according to established algorithm, which allows to record AE signals were caused the waves
propagation and waves reflection (including from sludge) in storage product between sensors. Under
measured differences in time of wave propagation are evaluated heights of sludge in various points
inside on the bottom of tank.
In 2017 Office of Technical Inspection (UDT) opened the project to develop a system for assessing of
the level, volume and shape of sludge inside crude oil storage tanks, using AE technique andthermography. As part of the project, UDT has started cooperation with company PERN S.A. the nationaland regional leader in oil logistics.

keywords: aboveground storage tank, sludge, crude oil, acoustic emission technique, thermography
1. Introduction
When crude oil is stored in large tanks, invariably high-molecular-weight organic
sediments (paraffin) are deposited but the resulting sediments contains also solid
particles (sand, silt, corrosion products, etc.) and also water. After some period of inservice of tank, these deposits build up to form a sludge, which causes a reduction in1

S281

the storage capacity. These high in hydrocarbons a valuable raw material could beproper recovered and recycled back to the refining process. Therefore, it is significant
to know the sludge characteristics shape and volume to define the best form oftreatment (maintenance of tank for control of level of sludge) and removal method (incase of out of service and opening of tank). The knowledge about volume and shapeof such sludge inside tanks is important for user of tank farm due to a reasons suchas:
 maintenance of storage tanks and control/monitoring of level of sludge insidethem, when conventional treatments are used to remove crude oil sludge, there is apotential for high environmental impact, so the choose of the method of cleaning
is very essential,
 most of design of tanks is with floating roofs, what in case when inside tank ishigh level of sludge could cause problems and/or damage of roof for low level
stored product.
In this paper are presented AE and thermography techniques for assess of level,
volume and shape of sludge inside storage tank.
2. Project of the system for assess of the sludge inside storage tanks
The origin and the reasons of the work are based on the following needs:
 expectations from users/owners of tank farms (fuel bases with crude oil) – storage
tanks with crude oil require monitoring and good maintenance practice to keepsludge from becoming the problem,
 AE testing agency/laboratory need to have knowledge about level of sludge
inside storage tank – necessity to assess the level of sludge before starting AE
testing of storage tank bottom according to procedures and standards.
In 2017 Office of Technical Inspection (UDT) opened the project to develop a system
for assessing of the level, volume and shape of sludge inside crude oil storage tanks,
using AE technique and thermography. As part of the project, UDT has started
cooperation with company PERN S.A. the national and regional leader in oil logistics.
The goal of the project is to build up a concept and prototype system to assess thelevel and volume of sludge at the bottom of an crude oil storage tank. In principle, thesystem will use several research methods:
 acoustic emission in various measurement methods, evaluation of temperature field using thermography method and temperature
measurement by non-contact method.
So far in frame of the project, a number of measurement procedures have beendeveloped, i.e. tests using combined measurement methods: acoustic emission
methods and thermographic methods together with contactless temperature
measurement. The purpose of the tests carried out according to these procedures is
to collect measurement data of heights of sludge at points along the wall and on thebottom inside the tank. Collected data in the form of a measurement database allow
2

S282

next for spatial visualization (map of sediments) and assessment of the level andvolume of sludge on the bottom of the storage tank.
3. Thermography and temperature measurement for evaluation of sludge level
The use of thermography to assess the level of sludge along the storage tank shell isalready a known method and described many times in various publications.
Temperature differences are caused of differences in the density of mediums and, asa result, their thermal properties, which allows to determine the transition line fromliquid crude oil to solidified sediments on the bottom. It should be noted that it is notalways possible to use thermographic tests to determine the sludge line. For example,
in the case of double-shell tanks, due to the specific thermal conditions in the interstitialspace, it is sometimes necessary to use conventional methods of wall temperature
measurement (eg, contactless method), combined with the appropriate method of
evaluation of the temperature field. As part of the project, a series of thermographic
and temperature measurements were carried out to determine the sludge line along
the storage tank's wall. In fig. 1 below is presented the example of thermal image oftank with sludge on the bottom.

Fig 1. Thermal image of tank wall with visible sludge line

The measurement of the level of sludge along the wall on the circumference of the tankconsists in assessing the temperature field every 3-5 m, as well as in the places where
equipment (mainly pipelines and mixers) are present on the wall. After determining theheights of sediments at the specified points, a profile of sludge along the wall is drawn
as shown in figure 2. Obtained measurement data of sludge heights are collected in
the measurement database.

3

S283

Fig 2. The line contour of sludge along circumference tank wall

4. AE technique for evaluation of sludge level
AE technique is used in this case in different way than normal purposes. There is used
specific layout of sensors on the wall and roof of the tank (fig.3). The sensors generateof AE pulses according to established algorithm, which allows to record AE signals
were caused the waves propagation and waves reflection (including from sludge –fig.4) in storage product between sensors. Under measured differences in time of wave
propagation are evaluated heights of sludge in various points inside on the bottom oftank (fig.5). Obtained measurement data of sludge heights are collected in themeasurement database. Fig. 3 shows an example of the layout of AE sensors whenusing AE technique for sludget heights measurement. Fig. 4 shows a waveform of the
AE signal with echoes of waves from the bottom and roof of the tank. Fig. 5 presents
the spatial visualization of the measurement data of sludge heights at specific points
on the bottom of the storage tank.

Fig 3. An example of the layout of AE sensors on the tested storage tank

4

S284

Fig 4. The wave propagation and wave reflection in storage product between AE sensors

Fig 5. Spatial visualization of the sludge heights at the measurement points on the bottom of the tank

5. Determination the volume of sludge on the bottom of the tank
All measurement data of heights of sludge in many points, both on the wall and inside
the tank are undergone mathematical polynomial interpolation, what finally give graphof shape of sludge (fig.6). Determination of the volume of sludge on the bottom of thetank, is carried out by the mesh of division into defined unit elements (fig.6) of theobtained spatial shape of the sludge and numerical counting (numerical integration
using the rectangles method) volume of individual elements according to their heights.
The result of the assessment of the sludge on the bottom of the storage tank is thevolume of sludge in cube meters and spatial visualization of sludge.

5

S285

Fig 6. Spatial visualization of the sludge shape on the bottom of the tank with division into defined unit
elements

6. Conclusion
The procedures and measurement methodologies developed at this stage of the
project, as well as computational algorithms allow us to visualize the shape of sludge
on the bottom of the tested storage tank and to determine the volume of sludge.
The use of both AE technique and thermography method proved to be an effective toolfor determining the heights of sludge at specific points inside the tested storage tank.
References:
1. M.Monteiro, V.Svet, D.Sandilands, S.Tsysar: Experimental Investigations of Various
Methods of Sludge Measurements in Storage Oil Tanks, Advances in Remote Sensing,Vol. 4, 2015;
2. H.Hooyberghs: “Pictures” the Emissions of Storage Tanks with the Use of Infrared
Cameras. 6th Annual Aboveground Storage Tank Conference & Trade Show Houston,
Houston, 2013;
3. I.Baran, G. Lackner: Analysis of Corrosion Processes and Leaks in Aboveground Storage
Tanks with AE Monitoring, EWGAE 2012;
4. NDT Handbook - Third Edition Vol.6 - Acoustic Emission Testing, Editors: R.K.Miller,
E.v.K.Hill and P.O.Moor, ASNT 2005.
5. PN-EN 1330-9:2017-09 „Non-destructive testing - Terminology - Part 9: Terms used inacoustic emission testing”.
6. PN-EN 13554:2011 „Non-destructive testing - Acoustic emission testing - Generalprinciples”.
7. PN-EN 15856:2010 „Non-destructive testing - Acoustic emission - General principles of
AE testing for the detection of corrosion within metallic surrounding filled with liquid”.
8. PN-EN ISO 18081:2016-08 “Non-destructive testing - Acoustic emission testing (AT) Leak detection by means of acoustic emission”.

6

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75
- New
editionOF
of GEA
GEA guideline
forFOR
Acoustic
Emission
TestingNEW
EDITIONGUIDELINE
ACOUSTIC
EMISSION
of pressure
equipments
TESTINGOF PRESSURE
EQUIPMENTS
C.DIGIULIO1/C. HERVE2
1Air Liquide France Industrie 152, avenue Aristide Briand 92227 BAGNEUX CEDE X
2CETIM 52 avenue Félix Louat 60304 SENLIS Cedex

Abstract:
To master over time the integrity of the equipment and ensure operational safety, in service inspections
are carried out periodically in compliance with regulatory requirements and / or professional rules ofsupervision.
Acoustic emission now performed industrially for more than thirty years, is currently the only techniquewhich, when applied in a suitable methodology enables to control an equipment in a global way
regardless of the nature of damage that could be identified.
The AFIAP, only national entity bringing together the main parties involved in the pressure equipment:
administration, manufacturers, users, technical centers, notified bodies, NDT provider organizations,mandated to a working group called "Acoustic Emission Group " the development of a" good Practice
Guide for the acoustic emission.
This guide, which is officially recognized by the French administration, incorporates the requirements of
the available standards and the experience gained by the professionals of this method.
It consists of general basis and annexes, each relating to a family of equipment. It is applicable in thecontext of the French legislation for the in-service monitoring of pressure equipment.
Previous editions of this guide were published in 2004 and 2009 with an enlargement to new annexes.
This new edition which is the subject of this presentation, takes into account the feedback over all theseyears of use and integrates new additional annexes, allowing extending the scope of this requalification
method.

1. Introduction
Acoustic emission is a test technique that, when practiced according to a controlled andappropriate methodology, makes it possible to control globally an equipment regardless of its
dimensions and its location. It is practiced now for more than thirty years and has been usedon thousands of pressurized equipment of the oil, chemical, energy, ... The experience gained
allows to have a great confidence in the information provided.
AFIAP - the only national entity including the five parties involved in pressure equipment:
administration, manufacturers, users (represented in particular by UFIP and UIC), technicalcenters, non-destructive testing organizations - has developed, with the support of the French
Administration, and maintains a "Guide to Good Practices for Acoustic Emission" (GBP).
1

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The AFIAP Board of Directors has entrusted the preparation of this guide to a drafting
committee called "Acoustic Emission Group" - GEA - composed of representatives of the five
parties mentioned above.
This guide is approved by the Ministry of Environment, Energy and the Sea before it can beapplied. Its ambition is to facilitate the practice of acoustic emission during requalifications ofspecific equipment by allowing to replace certain hydraulic tests by hydrostatic or pneumaticpressure tests, in order to avoid the opening of the equipment and the hydraulic re-proof test.

2. The practice of acoustic emission on pressure equipment

For more than 30 years acoustic emission tests have been performed on pressure vessels.
They were first selected as a global control method during hydraulic tests and then, on a caseby-case basis, in the context of derogations, during pneumatic tests carried out in substitutionof hydraulic re-proof test.
In the past, the requests made to the administration were made most often when a re-hydraulicproof test leads to heavy constraints on the equipment or when the test was impossible(structures not designed to take over the overloads induced by the hydro proof test, equipment
apparatus that cannot be drained and dried completely, etc.) or when the overall cost induced
by a re-test was industrially, economically excessive. The results obtained were quickly
encouraging for several reasons: the test, as previously reported, makes it possible to obtain
information on the globality of a structure,- the sensitivity of the test is great,- the test can be carried out during a "simple" process or pneumatic fluid pressurization, theoverload being limited to 10% of the maximum pressure seen by the equipment during the last
6 to 12 months of operation, - the test requires the immobilization of the equipment only alimited time most often to one or a few days depending on the complexity of the equipment
and that of the implementation of the test.
However, the practice of acoustic emission has been limited by:- the need for expensive test equipment,- few operators on the market, although now more and more providers are entering the field ofactivity,- the limited experience of his practice a few years ago,
-The absence of implementation benchmarks recognized by the professions and theadministration,- the imposition of passage of applications for exemptions in Central Commission of Pressure
Devices (CCAP).
The oil, gas, chemical and energy industries wished to develop (but not generalize) theapplication of this test method, particularly because of the openness offered by the newpressure equipment regulations relating to the operations to be carried out within theframework of the requalifications: decree of 15 March 2000, title V, article 23 § 7 and 8"Decisions taken after opinion of the CCAP, may provide that the hydraulic test be replaced byanother test resistance under pressure ... that the periodic requalification be replaced by anyother method which guarantees an equivalent level of safety. "

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2.1

Position of the administration

In order to be able to give confidence in the practice of testing the pressure equipment, theadministration wished that a frame of reference covering all the stages that mark its
implementation be drawn up by the actors concerned: Bureau of Risk and Network Equipment(BSERR), manufacturers, users, service providers, technical centers. This led to the creationof the Acoustic Emission Group (GEA) presented in § 1 and the drafting of the "Good Practice
Guide for Acoustic Emission" (GBP) within AFIAP.
The GEA has produced a first version of the guide to the rules of good practice for acoustic
emission in July 2001 (2001 edition) which was the subject of Ministerial Decisions DM-T / PN
32255 of 26 September 2002 and DM-T / PN 32460 of 1 April 2003.
A second version of the guide has been produced (May 2004 edition); it was the subject ofMinisterial Decision DM-T / P N ° 32970 of May 28, 2004. This edition has been translated into
English.
The revised Annex 4, relating to liquefied petroleum gas storage tanks (LPG) called "smallbulk" was approved by the BSEI 05-442 of 23 December 2005. The annex 8 relating to thereactors (vessels in which occur chemical reactions) was approved by BSEI decision n ° 07107 of April 13, 2007.
A third edition of the GBP took place in 2009. It was the subject of the BSEI decision N° 09102
of June 29, 2009 (modified by the BSEI n ° 11110 of October 5, 2011). Since then, annex 10
concerning stainless steel equipment has been validated by the BSEI decision N°13-009 of
17/01/2013 as well as appendix 7 on the equipment in composite materials by the decision
BSEI N ° 14079 of 4 July 2014 and Annex 11 on exchangers by BSEI decision N°14110 of 31
October 2014.
This edition 2009 has been translated in English with the name “Guideline for acoustic
emission testing of pressure equipment”. [1]Recently a new edition has being published in 2016 and released from the beginning of 2018
in computer form. It was validated by the BSERR decision N°16-034 of February 23, 2016.
Thus, any application, filed by an industrialist, to the requalification requirements, such as thereplacement of a hydraulic re-proof test by a pneumatic test with acoustic emission monitoringin accordance with the requirements of the GBP and its annexes, may be treated at the levelof the Authorized Organizations without passing through the DREAL or at the level of theCCAP.

3. The guide to Good Practices for Acoustic Emission
3.1

Constitution of the guide

The guide consists of a general part, applicable to all acoustic emission tests performed on
ESPs, called "body of the guide", then appendices, each applicable to a family of equipmentthat defines the specificities of the Acoustic Emission attached to the family, including criteria
and thresholds for interpretation of recorded data.
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The body of the guide contains the chapters indicated in appendix 1.
3.2

Working methodology for guide development

The GEA is a group of about 30 representatives from the five entities involved: BSERR,
manufacturers, users, service providers, technical centers. This group gives instructions forthe work to be done and validates the documents. It is led by a user representative and its
secretariat is provided by a technical center.
3.3

The body of the guide

It had been written by two "colleges".
- The "college 1" composed of representatives of the administration, the manufacturers, theusers and the CETIM wrote the general chapters and those concerning the service provider /principal / inspection links,- The "college 2" composed of representatives of the service providers, the technical centerswrote the chapters concerning the practice of the test and the interpretations of the recordings
3.4

Annexes

The first two appendices are general:
1. Standards, codes, regulatory texts,
2. Examples of equipment controlled by acoustic emission
Each of the following appendices is applicable to a family of equipment. They complete thebody of the guide as to the modalities of the test and the interpretation of the results.
The available annexes concern respectively:
3 Methodology to be used for the elaboration of a procedure applicable to spheres
4 Methodology to be used to develop a procedure for underground "small bulk" LPG tanks
6 Methodology to be used to develop a procedure for cylindrical ESPs
7 Methodology to be used for the development of a procedure for composite material
equipment
8 Methodology for the development of a reactor procedure
9 Methodology to be used to develop a procedure for autoclaves
10 Methodology for developing a procedure for stainless steel equipment
11 Methodology to be used for the development of a procedure for exchangers
The summary of these appendices is based on that of the guide body.

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3.5

Working methodology for the preparation of an Annex

For a new family of equipment concerned, a first file must have been approved in CCAP. Twoother files made on the same basis must also have been approved by the administration.
These tests are the subject of a feedback analysis.
The decision to create a new annex is made by the GEA in plenary session. For each newannex, a working group of experts composed of representatives of service providers and users
having practiced this type of acoustic emission tests on the family of equipment concerned isconstituted.
Other appendices applicable to new families of equipment may be commissioned by industrial
or professional unions.
The working group, led by a user, develops a draft annex. The convenor of the working group
informs the GEA in plenary session of the progress of work. In the event of a blockage ortechnical difficulty, the assistance of a third-party expert can be requested from the GEA.
The project is then forwarded to the members of the GEA, accompanied with the Acoustic
Emission Guide modification sheet and all supporting documents for approval of this annex.
The GEA plenary examines this project and decides to approve it after incorporating any minor
adjustments or to return it to a working group for finalization.
Once approved by the GEA, the project, the modification sheet and the supporting documents
are forwarded to the BSERR by the AFIAP President, for instruction and transmission to the
CCAP for approval.
Between the various editions of the guide of intermediate evolutions (body of the guide, newappendix or revision of an existing appendix) can be subject to validation by the GEA andrecognition by the Administration. As soon as they are recognized, these changes are madeavailable to the users of the guide.
3.6

New Annexes available

Three new annexes have been included in the new edition of the 2016 guide.
• Annex 7: Methodology to be used for the development of a procedure for composite
equipment
Composite ESPs are generally composed of a strong structure made from resin impregnatedfibers, deposited around a sealing liner.
These structures are intrinsically heterogeneous, and their degree of anisotropy depends on
the orientation of the fibers and the stacking of the various layers constituting the composite
structure. This anisotropy will have an influence on the propagation of acoustic emission
waves.
The acoustic emission produced by a composite material structure depends on many factors
such as material components, laminate composition, manufacturing process, geometry andambient conditions during the test. The interpretation of acoustic emission results will therefore
depend on the knowledge and consideration of these various factors.
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With this observation, it appeared that for a composite materials ESP, the performance of thepressurization tests and the data processing must be adapted to the specificities of thecomposite materials while following the practical recommendations described in the Guide ofGood Practices for the control by Acoustic Emission, 2nd edition 2009 (GBPEA). Specificcriteria were developed based on the experience gained by the members of the working group.
Working Group 7 of the GEA has therefore focused its work on the drafting of a specific annex
for ESP in composite materials.
Initially it was planned to deal with two parts in the scope of the annex:• Annex 7a concerning equipment operating at low pressure (<10 bar) and atmospheric tanks• Annex 7b concerning pressure equipment.
Following the observation of the BSERR that the atmospheric tanks do not fall within theregulatory framework of the decree of March 15, 2000, the WG decided to devote first to thedrafting of the section concerning pressure equipment.
Another point was raised in terms of "transportable" pressure equipment, which is covered by
the amended decree of 29 May 2009 on the transport of dangerous goods by land (known as
the TMD) which refers directly to the texts of international agreements on the transport ofdangerous goods (RID / ADR / ADN). The ADR already provides the possibility to replace thehydraulic test of the periodic regulatory inspection by AE monitoring of a pneumatic test.
Despite the peculiarities of this fixed pressure equipment made of composite materials, thisAnnex 7 is as accurate and homogeneous as the other types of equipment already approved
under GBPEA.
Annex 10: Methodology for the development of a procedure for stainless steel equipment
Tests and expertise show that stainless steels behave similarly to low-alloy steels with a lower
emissivity and a generally higher attenuation.
This appendix for stainless steel equipment therefore proposes that:- the carrying out of the pressurization tests follows the same treatment principle as for nonalloy steel equipment, i.e. the provisions with annexes already approved of the Guide to Good
Practice for Acoustic Emission;-some parameters are adapted to guarantee the objectives of the tests (thresholds andreference criteria).
The logic adopted by the GEA was therefore not to complete the existing annexes but to draft
an annex common to all stainless steel ESPs.
For the development of a procedure for stainless steel ESPs, it is therefore appropriate to use
Annexes 3, 6, 8 and 9 of the guide, respectively supplemented with the recommendations
described in Annex 10.
• Annex 11: Methodology to be used to develop a procedure for interchanges
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The exchangers are similar for the common parts (shells, bottoms, nozzles, etc.) to the otherpressure equipment, the specific parts being the tube plates and the bundle. The tests and theexpertise carried out show that the AE diagnosis is feasible on the tube plates; on the other
hand, as far as the bundle is concerned, the state of the art so far does not completely cover
this part even if information can be used on a case-by-case basis during the test.
As a result, the scope of this annex is limited to the parts covered by acoustic emission without
bundle. In the context of the replacement of the periodic requalification hydraulic test, othermeasures shall be provided by the operator for parts not covered by AE, with the prior approvalof the Administration. These measures are not described in this annex, which only concerns
AE.
With these observations, it appeared that for a given exchanger, the implementation of the AE
should follow for most common parts (shells, funds, nozzles ...), similar recommendations tothose of Annex 8 " reactors "already approved of the Guide of Good Practices for the control
by Acoustic Emission, 2nd edition 2009.
The drafting of this self-supporting appendix deals with the particularity of heat exchangers,
especially for tube plates, and requires the AE test while the shutdown of the equipment.
This appendix is intended to be as accurate and homogeneous as those of the other types ofequipment already approved and allows to frame the AE of the exchangers for the parts
covered by the diagnosis.

4. Organization of feedback
Feedback is managed by Feedback Working Group and is carried out annually using two
sheets to be completed by each user, a summary sheet and a detailed sheet that collects theessential characteristics of equipment controlled according to the GBP guide as well as themain results of the acoustic emission.
The objective is to identify the key points, the possible problems encountered, and the solutions
chosen. Through the lessons learned from the experience described, these sheets cangenerate modification sheets of the guide.
These experience feedback sheets are then grouped by type of annexes concerned in orderto develop a synthesis that is sent each year to the Administration.
Till now, more than 22,000 feedback sheets have been received since the implementation of
the GEA guide. These sheets are distributed in the different annexes as follows:
Annex 3(spheres):30
Annex 4(small bulks):
21 618Annex 6(cylindrical vessels): 266
Annex 7(composites vessels):2
Annex 8(reactors):
83
Annex 9(autoclaves):
341
Annex 10(inox vessels):
60
Annex 11(exchangers):
22
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In parallel, 126 change sheets have been studied and integrated in the last edition of the guide.

5. Conclusion
The working group of the GEA is still active within AFIAP, it meets in plenary session twice ayear to follow the application of the guide and to take into account the feedback. This work led
to the new edition of the 2016 guide which will be available for the first time in electronic
version.
.
An English version will be available soon.

Bibliography:[1] Guideline for acoustic emission testing of pressure equipment - AFIAP - 2009 edition

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15
- Use
Acoustic Emission
forFOR
inspectionof various
composite
USE
OF of
ACOUSTIC
EMISSION
INSPECTION
OF COMPOSITE
pressure
vessels SUBJECTED
subjected to mechanicalimpactIMPACT
PRESSURE
VESSELS
TO MECHANICAL

F. Dahmene1, S. Yaacoubi1, S. Bittendiebel1, O. Bardoux2, P. Blanc-Vannet2, A.
Maldachowska3, M.Barcikowski3, M.Panek3, N.Alexandre4, F.Nony4, K.Lasn5,
A.Echtermeyer5, P.Sigurd Heggem6
1 Institut de Soudure, Plateforme RDI CND, 57970 Yutz, France

2 Air Liquide, Paris-Saclay-Research-Center, Jouy-en-Josas, France.
3 Wroclaw University of Technology, Poland.

4 CEA, DAM, Le Ripault, 37260 Monts, France.

5 Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
6 Hexagon Composites, Raufoss, Norway

Abstract:
Motivated by the emerging hydrogen mobility uses, especially the need for storing a high amount ofenergy in a small and lightweight volume, type IV composite pressure vessels have become a state-ofthe-art technology for high pressure hydrogen. They comprise a non load-bearing liner, generally madeof plastic and assembled with metallic bosses, around which a carbon fibre-epoxy composite iswound. Though such vessels can already be operated safely, there is a lack of knowledge concerning
their residual performance after a mechanical impact. There is a need for associated nondestructive
examination (NDE) methods, able to assess whether a cylinder is still fit for service after impact. The
FCH-JU funded pre-normative research project HYPACTOR was set up to investigate the damage
created in type IV pressure vessels by mechanical impacts, and then the use of Acoustic Emission tobuild rejection criteria during inspection. The methodology was to combine several AE tests on healthy
and impacted vessels and correlate the results with residual performances. The developed criteria wereapplied to a wide database of Type IV composite vessels with different sizes, design specifications andworking pressures. The validation was carried out on 114 hydraulic tests on healthy and impacted
vessels. Results have demonstrated a capacity to differentiate damaged cylinders from healthy ones,
with some small influence of the number of pressure cycles between impact and inspection.

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1. Introduction
Composite overwrapped pressure vessels (COPVs) are widely used for many applications.
The combination of their light weight and high mechanical strength is a major benefit fortransportation of large quantities of gas. Type IV composite pressure vessels have become astate-of-the-art technology for high pressure hydrogen (>350 bar), in transportable and onboard application. They comprise a non load-bearing liner, generally made of plastic andassembled with metallic bosses, around which a carbon fibre-epoxy composite is wound.
Though such vessels can already be operated safely, there is a lack of knowledge concerning
their residual performance after a mechanical impact. There is a need for associatednondestructive examination (NDE) methods, able to assess whether a cylinder is still fit forservice after impact.
Periodic inspection of transportable composite vessels is based on visual examination
combined to hydraulic proof test [1]. This procedure, inherited from metallic vessels (type I) is
not well adapted for composite structures assessment and leads to industrial drawbacks suchas dismounting and handling steps, process disruption, need of drying process after hydraulic
test… In this way, Acoustic Emission Testing, already used for some metallic vessels, may be
a good candidate to perform a periodic inspection of COPVs.
The FCH-JU funded pre-normative research project HYPACTOR was set up to investigate thedamage created in type IV pressure vessels by mechanical impacts, to assess the residual
performances after impact in terms of burst (short-term performance) and cycling (long-term
performance), and then the use of Acoustic Emission to build AE criteria. It has to be notedthat an important work item of Hypactor was the construction of the relationship between
impact, created damage and vessels residual performance to define rejection criteria forinspection purpose.

2. Description of work
The experimental trials were performed mainly on a certified 36L 70MPA type IV cylinders
provided by Hexagon Composites. They are designed according to the on-board storage
regulation EC79/EU406 [2] with a safety factor of 2.25. The liner is a polymeric and thecomposite shell is a carbon fibre reinforced polymer (CFRP) filament wound. Other larger
cylinders 255L and 513L were also tested. One impact per vessel was chosen to assess
residual performance at short term and long term (after further pressure loads in service).
Different levels of impact energy were carried out in order to determine the burst pressure
reduction curve. All impacts were performed using a hemispherical steel impactor in order toproduce less damage to the composite than a sharper one and therefore make damage
detection difficult or barely visible using visual inspection (less favorable situation for
identification of damaged vessels by visual inspection and therefore assess acoustic emission
ability). All impacts were carried out in the cylindrical part, using two different setups dependingon the speed and energy required: a drop tower and a pneumatic canon, displayed in Figure
1.

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Figure 1: The crash set-up used in this study. Left: Using drop tower. Right: Using pneumatic canon.

AE monitoring was performed using an advanced AMSY5/6 systems supplied by VALLEN,
and eight resonant R15α sensors. Six spring-loaded sensors were placed on the cylindricalpart of the COPV by means of three belts, and acoustically coupled with vacuum grease. 2
Sensors were placed on bottoms. The eight sensors are distributed so that the COPV is fully
covered. An example of sensors implementation is shown in Figure 2. The AE signals were
amplified by a 34 dB fixed gain, using an AEP3N preamplifier. A relatively large pass-band [25850] kHz hardware filter was used to detect a wideband of frequencies. A trigger threshold of
40 dB was selected, which was above the background noise level.

Figure 2: Distribution of the 8 used sensors on the COPV.

AE monitoring was performed for both healthy and impacted vessels at short and long term
tests. For long term performance, vessels were monitored by AE after three cycling period inorder to define pass-fail criteria enabling the identification of damaged vessel whatever theperiodicity of control and in particular regardless the time between the occurrences ofimpact/damage to AE control. Vessels were then cycled between 0 and 875 bar which is the
maximum pressure reached during hydrogen filling for a service pressure of 700 bar. Different
cycling periods were carried out combined to AE monitoring: 50 cycles at 875 bar + AE at 1050
bar + 5000 cycles at 875 bar + AE at 1050 bar + 10000 cycles at 875 bar + AE at 1050 bar +burst.
For short term performance, AE test was carried out just after impact and then vessels were
burst. The loading during AE test, should be gradually increased up to the maximum pressure(Pmax), at least 110% of its nominal filling pressure (963 bar) and maximum equal to the test
pressure 150% of its service pressure (1050 bar). In this study the test pressure was reachedduring AE test in order to collect more data related to damage propagation at high pressure.
However, AE criteria were defined for a maximum pressure of 110% of the nominal fillingpressure. The pressure sequence contains various bearings which allow to identify the3S297

damage state and its evolution at each pressure level but also to avoid a sudden burst. To
avoid too sudden stress relaxation, strain rate during depressurization was similar to thatduring pressurization (<5%Pmax) and regular as possible. An example of pressure sequenceis given in Figure 3.

Figure 3: Pressure sequence for 36L vessels.

3. Results and discussions
In order to establish AE pass/fail criteria to be able to identify damaged vessel, the ideal
approach is to record the AE signature of each vessel before and after impact. However, inpractice, this proposal generates a complex pattern of vessel travel as well as a complex trials
plan (since the impact trials and AE trials on short and long term performance were performed
in different countries). To reduce and simplify trials, the solution proposed was to study thelong term performance of 3 healthy vessels from different batches. These vessels wereconsidered as a reference providing that AE dispersion is very low. These data wereconsidered as a baseline of healthy vessels. In the case where scatterings would have beenvery high i.e the AE behaviour of vessels is different, AE tests would have to be performed foreach vessel before and after impact.
As it can be seen in Figure 4, dispersion was evaluated at three conditions (after 100 cycles,
after 5100 cycles and after 15100 cycles) by comparing the AE activity of the three vessels.
The sum of events (first hit channel with Amplitude ≥ 50 dBae) detected by all channels per
vessel was calculated for each holds. Results indicate a very low dispersion between vessels
whatever the cycling period. These AE data can be considered as a baseline signature for all
36L COPV.

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Events during holds

10000
1000
100
10
1
400

600

800

1000

1200

Pressure (bar)Figure 4 : AE activity dispersion evaluuated for three vessels at three cycling periods.

The approach followed to define AE criteria is based on the appendix 7 of the French bestpractices guideline for Acoustic Emission, approved in 2014 by the French authority [3]. AE
criteria are divided into two types, real-time shutdown criteria and post analysis criteria. The
first one aims to detect and reject dangerous vessels in real time during the AE test, in case ofvessels with high loss of performance to avoid a sudden burst. The second one aims to identify
and remove from service vessels with a low loss of performance. Three categories are
possible: for category 1 and 2: vessels are accepted while for category 3, vessels are rejected.
Parameters to take into account for real-time shutdown criteria and post analysis criteria are
as follows:

Setting: The evaluation threshold is set at 50 dBae and the reference threshold at 75
dBae.



Activity and intensity evolution during the test. Activity (number of events and intensity
energy) are assessed throughout the duration of the test.



The number of counts Na, determined by the number of counts generated by the breakof fourteen 0.5mm 2H pencil leads at a distance from the sensor corresponding to an
amplitude of 60 +/- 1 dBae, depending on the fibres direction (main or visible on thesurface) and at 45° to this direction. The Na value is equal to the mean of the numberof counts obtained for each direction multiplied by 10 (value measured from theacquisition threshold).

N2s: Events with Amplitude ≥ 75 dBae, calculated for the entire duration of the testN3s: Activity with Amplitude ≥ 50 dBae during holds



Dm: Number of hits with duration higher than 5 ms



FR: Felicity Ratio calculated during the unload/load ramp

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More details about parameters thresholds are available on the appendix 7 [3]. The first step
was the compliance assessment of criteria defined in this appendix to our vessels. It has beenfound that both criteria were conservative; most of healthy vessels (thus with no loss of
performance) were systematically rejected. An example of zonal and planar analysis for ahealthy vessel after 100 cycles at 875 bar is given in Table 1 and Figure 5. We can note thatall parameters are classified in category 3 except the Felicity Ratio. For zonal analysis inparticular when considering activity during holds, we talk about category 3 when the numberof events exceeds 50 in the case of intermediate holds and 80 for final hold. This threshold islargely exceeded in our case. For planar localization, one red cluster appearance is sufficient
to reject the vessel. A red cluster represents more than 50 located events in a very small area.
This high activity could be related to the high thickness and high working pressure of onboard/transportable vessels. Indeed, the safety factor and design are different compared tostationary vessels. Although the appendix does not cover on-board and transportable vessels(the case of our study), the control protocol and the analysis methodology are still consistentand remain applicable via some modification.
Table 1 : Zonal analysis of a healthy vessel after 100 cycles at 875 bar.

Intensity

Na

A≥ 75 dB

3
3
3
3
3
3
3
3

3
3
3
3
3
3
3
3

3
3
3
3
3
3
3
3

3
3
3
3
3
3
3
3

855
Bar

960 1050
1050 Intermediate
Bar Bar 750Bar Bar
Classe

0
0
0
0
0
0
0
2

162
113
79
165
142
102
89
25

996 1628
416 853356 739745 1342
631 1272
507 973487 838
180 557

13
10
6
11
13
3
10
4

1
0
0
0
0
0
0
0

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943
255
237
427
397
326
369
243

3
3
3
3
3
3
3
3

D>Dm

FR
Felicity
ratio

activity

1
2
3
4
5
6
7
8

535 750
Bar Bar

Final
ClasseHits Nb
D>Dm

activity

Act Int. Na N2S

N3s/Zone (Blue color : 10min hold, analysed from [2min; 10 min]; Red color : 20min holds analysed from [2min; 20 min]

3
3
3
3
3
3
3
3

0,88
0,99
0,96
0,94
0,96
0,97
0,96
0,97

3
3
3
3
3
3
3
3

Figure 5 : Cluster localization of a healthy vessel after 100 cycles at 875 bar.

For confidentiality reasons, values of criteria thresholds are not communicated in this paper.
However, the global approach followed to build and calibrate the AE pass-fail criteria is detailed
hereafter.
Methodology is based on the construction of a burst pressure reduction curve, which give themeasured burst pressure for several vessels with respect to impact energy (Figure ).

Figure 6 : Burst pressure reduction curve, with respect to impact energy.

It was demonstrated that the energy at which residual burst pressure starts to decrease is arelevant threshold to define a critical defect limit (from green to red area) where detectionneeds to be efficient [4, 5, 6].
As a recommendation, Hypactor project proposes to calibrate NDT method using this burst
pressure reduction curve, giving the limit where NDT needs to detect a defect with a highreliability. It means that calibration of AE criteria should be performed for each vessel design,and connected to the burst pressure reduction curve, as well as visual inspection calibration.
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AE test should be performed on “healthy vessels” (without and with impact but no loss of
performance) located in the plateau of the curve of Figure 6 and also on impacted vessel at
different energy levels with a loss of performance (red area).
In order to limit the number of destructive tests, a formula to estimate the threshold of criticalenergy (inflexion point of Figure 6) was given in [4, 5, 6] depending on vessels features.
Eabs/(Pburst*Ri ) = 30.10-6 (units = S.I.)

With:
Eabs = absorbed energy
Eincident = incident energy (impactor total energy)
Pburst = burst pressure of healthy vessel
Ri = inner vessel radius

By using Eabs = 0.65 Eincident (proposal from [5]), the formula becomes:
Eincident = (Pburst*Ri ) . 30.10-6 / 0.65 (units = S.I.)
In terms of damage induced by impacts, testing on empty vessels was found to be moreconservative than on pressurized vessels. However, from the point of view of detection by AE,
impacts on pressurized vessels is recommended because the propagation of damage will needan overpressure and criticity assessment is more difficult to predict by AE compared to empty
condition. The calibration of thresholds of AE criteria is illustrated in the Figure . The
methodology is to define the low limit and high limit for each AE parameter based on the burst
pressure reduction curve (Figure ). Thresholds will depend on vessels features (type, design,
mechanical behavior, impact conditions…) and also on the safety factor to be adopted for the
category 2. The extent of this category will depend on the safety factor adopted by the
stakeholders which could be linked to vessel features and results scatterings.

Figure 7: Burst pressure reduction curve and calibration of AE criteria protocol.

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AE Criteria were first built in the framework of H2E project (Horizon Hydrogène Energie) [7] ontransportable 143L vessels from Stelia Composites. Mechanical impacts have been carriedout using a dedicated bench. The configuration of impact was in the dome area at 45°. The
developed AE criteria have been used in Hypactor even if the design, impact configuration andoperating pressure were different. The aim was to evaluate their application on 36L Hypactor
vessels.
Refined criteria have shown satisfying results despite the high number of parameters (Table 1& Figure 5). However, real time analysis requires a solid experience of the operator. The
alternative proposed by the Institut de Soudure for real time criteria, is based only on two
criteria, one zonal (N2s) and one using localization analysis (Clusters during holds). These
criteria are very fast to analyze and simple to automate.
For healthy vessels, impacted vessels at 1kJ on empty condition and impacted vessels at 3kJ
on 700 bar condition (all those vessels were without any loss of performance), only threevessels were stopped in real time during the AE test:

2 impacted vessels at 1kJ empty condition



1 impacted vessel at 3kJ 700 bar condition

After verification using visual inspection, it has been found that an external damage was
located in the critical area identified by AE. Damage was induced by the rebound of the
impactor after impact. A severe fiber cut was then identified.

b)a)Figure 7 : Vessel impacted at 3kJ@700 bar. Rejection of vessel is due to a harsh surface damage
induced by the projectile rebound. A) Localization of AE sources. B) Damage induced by the rebound.

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All vessels impacted at high energy and presenting a loss of performance were also rejectedby real time and post analysis criteria. An example of classification is given in the following
tables (Table 2, Table 3).
Table 2 : Result of classification obtained by AE on impacted vessels at high energy at empty
condition.

Impacted vessels at high energy @empty
Numberofvessels

Impact conditions

AE testcondition

1

3 kJ@empty

After impact,
after 50 cycles

decrease

Rejected

Rejected

1

4 kJ@empty

After impact,
after 50 cycles

decrease

Rejected

Rejected

1

5 kJ@empty

After impact,
after 50 cycles

decrease

Rejected

Rejected

1

7 kJ@empty

After impact

decrease

Rejected

Rejected

Burst
AE Realpressure time criteria

AE PostanalysisCriteria

Table 3 : Result of classification obtained by AE on impacted vessels at high energy at 700 bar
condition.

Impacted vessels at high energy @700 bar
Numberofvessels

Impact conditions

1

4 kJ@700b

1

5 kJ@700b

1

6 kJ@700b

AE testcondition
After impact,
after 50 cycles
After impact,
after 50 cycles
After impact,
after 50 cycles

Burst
AE Realpressure time criteria

AE PostanalysisCriteria

decrease

Rejected

Rejecteddecrease

Rejected

Rejecteddecrease

Rejected

Rejected

The validation has been carried out on a large data base of 114 vessels from different type,
dimensions, working pressure and manufacturer.

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4. Conclusion
Currently there is no AE standards applicable for transportable COPVs. In FCH-JU Hypactor
project, many AE test have been performed on type IV composite vessels in order to define
AE rejection criteria for periodic inspection.
The methodology was to combine several AE tests on non-impacted and impacted vessels
and correlate the results with vessels residual performances. The approach followed by ISA
was to refine criteria thresholds from the appendix 7 of French Guide to good practice foracoustic emission testing [3] and adapt them for type IV vessels using zonal and cluster
localization analysis. The use of both localization methods offers the possibility to obtainconsistent result. This approach allows a calibration of AE criteria to accept or reject a vessel
during pressurization. The developed criteria were applied to a wide database of Type IV
composite vessels with different sizes, design specifications and working pressures.
Acoustic emission tests (AE) using developed ISA criteria have provided a good assessmentof damage.
More details in www.hypactor.eu

Acknowledgment

The research leading to these results has received funding from the European Union's Seventh
Framework Programme (FP7/2007-2013) for the Fuel Cells and Hydrogen Joint Technology
Initiative under grant agreement n° 621194.

References:[1] ISO 11623 Gas cylinders - Composite construction - Periodic inspection and testing[2] Regulation (EU) No 406/2010 implementing Regulation (EC) No 79/2009 on type-approvalof hydrogen-powered motor vehicles[3] Guide to good practice for acoustic emission testing of pressure equipment. 2nd ed. june
2009 [AFIAP] French association of pressure equipment engineers. edited by Sadave[4] P. Blanc-Vannet, «Burst pressure reduction of various thermoset composite pressure
vessels after impact on the cylindrical part,» Composite Structures, vol. 160, pp. 706-711,
2017[5] P. Blanc-Vannet, «Residual performance of composite pressure vessels submitted tomechanical impacts» International Conference on Hydrogen Safety 2017
[6] FCH-JU HYPACTOR project “Pre-normative research on resistance to mechanical impact
of composite overwrapped pressure vessels” – Final report[7] http://www.horizonhydrogeneenergie.com/programmehorizon-hydrogene-energie.html,
retrieved the 1st November 2015

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30 - Detection
and identificationof low-amplitude
Acoustic Emissions
DETECTION
AND IDENTIFICATION
OF LOW-AMPLITUDE
during plastic
deformation
underPLASTIC
indentationand scratch testing
ACOUSTIC
EMISSIONSDURINGDEFORMATIONUNDER
INDENTATION AND SCRTATCH TESTING
A. Danyuk1, D. Merson1, A. Vinogradov1,2
1
Laboratory of Physics of Strength of Materials and Intelligent Diagnostic Systems,
Togliatti State University, Togliatti, Russia
2
Department of Mechanical and Industrial Engineering, Norwegian University of
Science and Technology – NTNU, Trondheim N-7491, Norway

Abstract
The acoustic emission (AE) technique is unique in that it enables real-rime monitoring of processes ofplastic deformation and fracture in materials by receiving signals originating from the entire volume of
the testing. This capacity is hardly achievable by any other non-destructive methods aimed atcontinuous and/or in-situ monitoring of deformation processes. However, the price paid for the highsensitivity of the AE method is well known – the AE signal is buried into the electric noise: the higher
the gain the higher the noise. This noise is a natural physical limit of the sensitivity of the method. In
this paper, we overview the factors that interfere with the detection and identification of AE signals ofvery low amplitude. For probing these factors, the indentation and scratch testing were used as these
testing methods are gaining popularity in modern laboratory and industrial practice. The low-amplitude
AE arises in these tests from the sources caused by plastic deformation in small volumes loaded
locally in the micrometre scale. Procedures for of analysing and suppressing the external noise andthe noise from testing equipment are proposed and discussed. Results of detection of AE events withlow signal-to-noise ratio (SNR) during scratch testing of pure polycrystalline metals are presented. The
efficiency of proposed algorithms for noise reduction and SNR improvement by AE digital signal
processing is demonstrated on the examples of (1) wavelet-based “Phase Picker” signal detection
algorithm, (2) spectrum noise gating procedure (Audacity), (3) spectral signal analysis. In this way, theAE technique has been proven to be a viable and versatile tool for investigations of local deformation
mechanisms in situ.
Keywords: acoustic emission, scratch test, deformation mechanisms, signals detection, spectrum
analysis.

1. Introduction
The acoustic emission method is highly sensitive to processes of local stress relaxation
occurring in a crystal under external influence. These relaxation processes are associatedwith dynamic changes in the microstructure caused by phase transitions, fracture or plastic
deformation. The latter is most often related to dislocation slip and/or twinning in crystals.
An estimate of the minimum sensitivity limit of the AE method is an interesting andchallenging practical and theoretical task. An analysis of the theoretical sensitivity limit of the
AE method is well presented in [1, 2] for various mechanisms. In particular, the theoreticalsensitivity limit to dislocation motion has been explored in the continuum mechanics
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approach within the Green’s function formalism. The authors substantiate analytically that theelementary dislocation jump at a distance equal to the value of the Burgers vector forms anamplitude response on the surface at the AE sensor location of the order of the regular
background noise level. However, the practical sensitivity threshold is significantly worse.
The main factors complicating the registration of AEs from single dislocation glide events are
related to signal attenuation during propagation and transformation, mechanical andelectrical noise. Increasing the AE sensitivity to the elementary dislocation reactions in solids
is a long standing fundamental and technical problem, the solution of which can bring the AE
method to the foremost positions among other in-situ techniques used in modern materialscience.
We demonstrate a practical approach aiming at determining the actual level the AE method
sensitivity to discrete dislocation events detecting during plastic deformation. The key points
exploited in the present work are the controlled local deformation and the advanced signal
processing algorithms for increasing the practical sensitivity of AE to low-amplitude events inin continuous time series with very low signal-to-noise ratio.

2. Experimental
To address these issues we have developed a versatile threshold-less AE signal recording
and analysing procedure and applied it to the instrumented micro-scratching of copper
polycrystals with the well-characterised initial microstructure.
Pure polycrystalline copper is a model material which deformation is mediated by dislocation
glide at room temperature. Acoustic emission in pure single- or poly-crystalline copper
appears as a powerful continuous wide-band signal under tension or compression [3-5].
Such a signal is formed by a huge number of gliding dislocation. It is not possible to separate
an individual signal from a single dislocation slip source by amplitude, signal duration or any
other AE parameter even if the low-noise equipment and high sensitivity resonant sensors
are used.
The samples of 3N (99,9% Cu) polycrystalline copper with dimensions of 20 × 20 × 2 mm3
were annealed in vacuum at 1170 K for 1 h and electrolytically polished to a mirror-like finish.
The microstructure was investigated by electron backscattered diffraction (EBSD)
measurements in a field-emission scanning electron microscope (SEM) ZEISS-SIGMA
equipped with the EDAX/TSL EBSD detector.
To promote highly localised AE within individual grains, scratch tests were carried out using
the Nanovea tester. The driver of the setup was previously examined by AE and configured
to exclude mechanical noise. It is the noise level that limits the amount of loading and speedof the indenter's movement. To reduce the effect of interfacial friction between stylus andspecimen [6, 7] the Berkovich diamond indenter in the edge-forward orientation was moved
linearly at relatively high constant dragging speed Vs = 12 and 24 mm/min until the scratch
reaches 2 mm length. The normal load F during the test was set constant at the minimum
stable controllable value 1 N and 2 N.
The piezoelectric sensor AE-900S-WB (NF-Electronics, Japan) with a frequency band of
100–1000 kHz was attached to the samples through vacuum oil with a rubber band. The
short distance between indenter to sensor was a few millimetres. The signals were amplified
by 60 dB by the low-noise pre-amplifier and acquired by the 16-bit PCI-2 board (MISTRAS,
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USA) operated in a continuous threshold-less mode at the sampling rate of 5 MSamples/s.
The on-board filters were set in the widest possible range of 20 kHz to 2 MHz and the
additional +6 dB gain was pre-set. The AE waveforms were continuously recorded andsynchronised with the scratch path.
The stationarity of loading conditions and propagation of AE waves is the main advantage ofindentation and scratch-testing in comparison to other mechanical testing modes. During thetest, the shape of the sample does not change and the transfer function for AE waves
propagation is constant. The indentation techniques allow for precise positioning andlocalisation of the deformation zone. However, the local strain rate and the deformed volume
change considerably while the indentation test in progress. In the present work we confine
ourselves to the scratch test, in which the average strain rate remains constant and thestationarity of the deformation process is ensured. This feature of the scratch test makes it
possible to control test duration within a comfortable range.
When a scratch test is performing, the indenter deforms serial crystals (grains) separated byboundaries. Generally, the crystal axes of grains have an arbitrary crystallographic
orientation with respect to the test surface. Therefore, the spatial configuration of slip
systems is different for each grain and is characterised by the Schmidt factor. This difference
in dislocation glide activation in different grains should result in a distinct relaxation responseof the AE signal. To identify the AE of dislocation, three different approaches to signal
detection were tested:
1) The Wavelet-based “Phase Picker” signal detection algorithm proposed in [8]. Thismethod demonstrates an excellent temporal localization of the picked phases even for thesignals with the very low signal-to-noise ratio. This procedure detects the time of arrival of AE
hits with high accuracy. Furthermore, it is efficient in the analysis of distributions ofamplitudes and time intervals between the signals.
2) Spectrum noise gating procedure implemented in the Audacity software [9]. This is anonlinear procedure for reducing noise spectral components and restoring the waveform by
inverse FFT. It can reveal the nonstationary fluctuations inside streaming data, which areassociated with dislocation line growth.
3) Analysis of the spectral parameters of the AE signal is Power and Median frequency
coordinates calculated using a sliding time window technique applied to the continuous
stream [10].
The AE signal processing workflow includes the following steps:- A continuous raw stream was divided into successive frames of 8192 (1.6 ms) readings.
- For each frame, the forward FFT was applied, and two parameters were calculated in thefrequency domain – the Power as the integral over the power spectral density and theMedian frequency as the frequency which separates the low and high frequency spectral
domains with equal power.
- The obtained time series of Power and Median frequency were smoothed by a low-pass
Butterworth filtering procedure with the cut-off frequency, 19 Hz at present tests.

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3. Results
The footprint of indenter tip and the initial AE stream are shown synchronously in figure 1.
The footprint has the uneven width because of anisotropy of deformation in differently
oriented grains in fcc crystals. The variation of crystallographic orientations in neighbouringgrains is shown in figure 1 by the EBSD map.

Figure 1 – Scratch test raw data, load force 1N and scratch velocity 12 mm/min: EBSD scan map of
test surface superimposed with the continuous acoustic emission stream.

The test duration was of 10 seconds. The background noise was recorded before and after
the test and this can be seen in Figure 1. An amplitude of AE signal does not exceed theamplitude of the noise signal significantly so that any amplitude-based threshold detection is
impossible.
The Wavelet-based "Phase Picker" algorithm was applied to detect the signals with the low
signal-to-noise ratio. At the output, one obtains a set of arrival times and signal durations.
The distinct feature of the “Phase Picker” detector is a statistically determined distance a · which is introduced in the algorithm a measure of the difference between the useful signal
and pre-analysed noise. The parameter a is a scale coefficient, and  is a variance of thenoise statistics. For a low-amplitude signals detection 1<a2. The obtained data can be
represented by distributions shown in figure 2. The AE amplitude increases in response to(1) the increasing indenter velocity and (2) increasing volume of the material deformed by the
indenter tip. The longer intervals between the AE events were observed at slower indenter
movement and lower load. As opposes to this, at higher loads (larger material’s volume
involved in the deformation process), the intervals between AE events are shorter.

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Figure 2 – Distributions of AE features during scratch test of a copper polycrystal. Amplitude
distribution (left); Signal duration distribution (top right); Inter-arrival interval distribution (bottom right)

When a small load was applied during the scratch test, the relatively long duration ofintervals between signals was detected. Duration of inter-event intervals was longer than acharacteristic relaxation time of the applied sensor. The intervals between neighbouring
signals are filled with the background noise. We tried to highlight it using the noise-reduction
procedure implemented in the open-access Audacity software. The noise-reduction is basedon the "Spectrum Noise-Gating" algorithm [9]. It consists of a few steps. At the first stage, thesignal was amplified by +40dB. On the second stage, an average spectral characteristic of
the noise signal was determined from the AE record immediately after the test completion.
The final stage is the "Noise Subtraction" procedure was applied with the noise suppressionof 60 dB. One should bear in a mind that the noise reduction procedure is substantially nonlinear. As such, it strongly distorts the frequency spectrum of the original signal.
Figure 3 (inset) shows the signal after noise-reduction. Discrete signals and pauses between
them are clearly visible. Figure 3 also illustrates a fragment of the surface area in front of theindenter tip. The correspondence of distances between the slip lines on sample surface andintervals between transients in the AE time series is evident. It should be noticed that thecoarse and long slip lines correspond to the most powerful signals.

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Figure 3 - Comparison between the surface topology in front of the indenter tip and the AE signal after
noise reduction.

We assume that the sources of recovered AE transients are associated with long slip lines
formed by dislocations escaping from the crystal lattice to a free surface. The “active” zone ofa perfect crystal lattice, where plastic deformation initiates, is located in front of the indentertip as shown on the EBSD map in figure 1. We should expect that all variations in the AE
response stems from variations of the Schmid factor when the indenter passes through grainboundaries. However, the AE response should be stationary as long as the indenter moves
within the grain and as long as the effect of grain boundaries is minimised. To verify this, we
performed the analysis of major spectral parameters of raw AE data using a sliding window.
The result of the behaviour of AE Power and spectral Median frequency (see definitions
given, for example, in [3]) is shown in figure 4. This figure shows the bi-variate distribution of
AE Power vs. Median frequency, where the colours correspond to those of inverse pole
figure colours from the EBSD maps of respective grains. The grey dots indicate the transition
stages when the indenter passed through the boundaries between the grains.

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Figure 4 - Power and Median Frequency of Acoustic Emission for a Scratch Test
(Copper, 12 mm / min, 1N)

4. Discussion
The acoustic emission method is a highly sensitive tool capable of analysing of crystal latticedynamics on a very fine scale corresponding to individual slip lines. A sensitivity of themethod to highly localised deformation of polycrystalline copper is shown in the present
paper. The dislocation slip is a major mechanism of deformation in pure copper at room
temperature. The AE Power and Median frequency exhibit a good response to different
crystallographic orientations of grains. A similar effect was observed in [3, 5] in tensile testingof samples with different crystallographic orientations of loading axes.
During the scratch test, the crystal deforms locally, which allows detecting discrete events of
dislocation glide in the AE signal if the appropriated nose cancelling procedure is used torectify the signal and increase the signal-to-noise ratio. Admittedly, the largest fraction ofelastic energy is released when a dislocation escapes to a free surface [11]. It is therefore
plausible to suppose that the "loudest" events corresponding to dislocation annihilation at thesurface on the early stage of strain hardening resent most of the AE signal. Plastic
deformation commences in the local zone ahead of the indenter where the stress exceeds
the critical resolves shear stress for dislocation nucleation and where long slip lines areformed resulting in the intermittent plastic flow such as that reported in [12] fornanoindentation tests. The slip lines in plastic zone ahead of the indenter are featured by a7

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reasonably larger height, of up to 50 nm (as has been confirmed by scanning white lightinterferometry) in a large grain. This roughly corresponds to 200 dislocation segments in thesame slip line. This can be now seen as a reasonable lower bound estimate of thedetectability threshold for the AE method.

Conclusions
The AE response during a scratch test is highly sensitive to the crystal orientation and thegrain boundary properties. The thresholdless data acquisition technique and the noisereduction signal processing methods demonstrate an outstanding capacity of the AE
technique to reflect the collective dislocation behaviour within individual slip lines emerging toa free surface of a crystal in front of the moving indenter.
Financial support from the State Assignment of the Ministry of Science and Education of
Russia according to the contract N 11.5281.2017/8.9 is gratefully appreciatedReferences:[1] Scruby, C.B. et al., Dynamic elastic displacements at the surface of an elastic half-space
due to defect sources, J. Phys. D. Appl. Phys. 16 (6), 1983, pp. 1069–1083[2] Wadley, H.N.G. and Mehrabian, R., Acoustic emission for materials processing: a review,
Materials Science and Engineering, 65 (2), 1984, pp. 245-263.
[3] Vinogradov, A. et al., Acoustic Emission Spectrum and Its Orientation Dependence in
Copper Single Crystals, Mater. Trans. JIM., 36 (4), 1995, pp. 496–503.
[4] Danyuk, A.V., et al., The effect of stacking fault energy on acoustic emission in puremetals with face-centered crystal lattice, Letters on materials, 7 (4), 2017, pp. 437-441[5] Vinogradov, A. et al., Correlation between Spectral Parameters of Acoustic Emission
during Plastic Deformation of Cu and Cu-Al Single and Polycrystals, Mater. Trans. JIM., 36(3), 1995, pp. 426–431.
[6] Challen, J.M., et al., Plastic Deformation of a Metal Surface in Sliding Contact with a Hard
Wedge: Its Relation to Friction and Wear, Proc. R. Soc. A Math. Phys. Eng. Sci., 394 (1806),
1984. pp. 161–181.
[7] Hamid, R.C., et. al., The effect of Berkovich tip orientations on friction coefficient in
nanoscratch testing of metals, Tribology International, 103, 2016, pp. 25-36.
[8] Pomponi, E., et. al., Wavelet based approach to signal activity detection and phasepicking: Application to acoustic emission, Signal Processing, 115, 2015, pp. 110–119.
[9] How Audacity Noise Reduction Works, 2018,https://wiki.audacityteam.org/wiki/How_Audacity_Noise_Reduction_Works[10] Vinogradov, A. et al., Probing elementary dislocation mechanisms of local plastic
deformation by the advanced acoustic emission technique, Scripta Materialia, 151, 2018, pp.
53–56.
[11] Merson, D., et al., On the role of free surface in acoustic emission, Mater. Sci. Eng. A.
234-236, 1997, pp. 587–590.
[12] Borc, J., et. al., Investigation of pop-in events and indentation size effect on the (001)and (100) faces of KDP crystals by nanoindentation deformation, Materials Science andEngineering: A, 708, 2017, pp. 1-10.

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33 - When AEWHEN(Acousticmeets
AI (Artificial
Intelligence) II
AE Emission)(ACOUSTICEMISSION)MEETS
AI (ARTIFICIAL INTELLIGENCE) II

Kilian WASMER1, Fatemeh SAEIDI1, Bastian MEYLAN1, Quang Tri LE1,
Sergey A. SHEVCHIK1
1

Laboratory for Advanced Materials Processing, Empa - Swiss Federal Laboratories
for Materials Science and Technology, Feuerwerkerstrasse 39, CH – 3602 Thun,
Switzerland; Phone: +41 58 762 62 71; Email: kilian.wasmer@empa.ch

Abstract:
Acoustic emission (AE) is an effective method to monitor and control the quality in different technicalprocesses, including tribology and fracture mechanics. However, in highly dynamic processes, theprocessing of AE signals is very burdensome. At the same time, artificial intelligence (AI) has been
considered as a new and powerful tool to overcome both: the complexity of the large data processing
with a reasonable computational time. In this contribution, we will present two examples fromcompletely different process where AE techniques are combined with AI in order to make a significant
step forward in process monitoring and quality control.
To start with, in additive manufacturing, which has been at the center of attention in recent years, wewill demonstrate that the combination of AE with AI makes possible to detect several types of defects,
including pores..
With the second example, we will prove that the combination of AE with AI is a very promising
approach for in situ and real-time monitoring of electrical discharge inside natural rocks.

1. Introduction
Analysis of acoustic Emission (AE) takes its origin from the middle of the 20th Century andits progress has been closely related to the development of measuring devices [1]. AE based
methods have numerous advantages such as high sensitivity, indifference to structural
resonances and mechanical background noises, real-time capability of monitoring as well as
early and robust detection of defects [2]. In addition, in recent years, the AE machine
manufacturers have made a significant progress to simplify the usage of the hardware fordata recording and storage. This makes AE now has become very much a mainstream.
Consequently, AE techniques are considered by many as being one of the most effective
monitoring methods, in particular for Nondestructive Testing (NDT), Nondestructive
Inspection (NDI), and Nondestructive Evaluation (NDE) [3]. This technique has beensuccessfully applied to a broad range of engineering applications, as well as investigations ofa wide range of materials, material compositions and structures [1]. The industrial
applications of AE also includes the tracking of dynamic processes, such as failure of brittle
components [3] and, more recently, laser welding and/or additive manufacturing (AM) [4, 5].

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Despite all the advantages of the AE, its major drawback is the interpretation of the AE
signals. This is particularly true for complex and dynamic processes. The main reason is that
the AE signals content is generally the result of mix of multiple background physical events,
which take place inside a material at different time scales. Taking this into account, eachspecific application requires an exclusive understanding of the specifics of the AE
generation, ideally relating those with the underlying fundamental physical phenomena.
Unfortunately, in most real-life cases, the combinations of the physical phenomena that takeplace at the same time cannot be recovered with details. One typical example is the AE oflaser-material interaction. The complexity of AE, in this case, is additionally complicated by
the presence of noise that is a vital part of the real-life conditions and industrial
environments. Under such circumstances, a new approach is necessary to bring a robust
analysis of the AE signals into real-life applications. Artificial Intelligence (AI) may be asolution as it possesses numerous of advantages. AI allows bypassing the development ofcomplex physical models for process understanding. It relies on statistics that can be easilycollected in industrial applications. Also, AI allows using highly non-linear data transforms
and automatically reduce the noises. To do so, AI builds complex empirical rules for intricate
big data, thus correlating the acquired AE with the real events. The latest achievements in AI
allow reducing the effect of the non-accuracies in preparations of training sets which results
in reducing the preparation costs. These capabilities of AI were exploited for continuous
crack propagation, laser welding and additive manufacturing [6]. The requirements for AI are
sufficient AE data (input data) that corresponds to the desire outputs such as quality,
quantification of defects, or some other process related phenomena of interest.
In fracture mechanics, the AE signals may have at least the five following origins (1) elastic
interaction & impacts; (2) changes in stress-strain state of a local volume around the crack
tip; (3) plastic deformation and damage at the crack tip; (4) generation, motion andinteraction of dislocation; (5) energy liberation when two new surfaces are created due tocrack initiation and propagation [2].
Additive manufacturing and laser welding are analogous in numerous aspects. Bothprocesses have phenomena such as rapid heating, melting, solidification, and cooling.
Consequently, the content of the AE signal seizes comprise material phase changes,
dynamics inside the melted material and solidification processes. In addition, the AE signals
are altered by the material and surface characteristics (e.g. chemical composition, phasetransformation, optical properties, surface roughness, and environments), machineparameters (e.g. laser type, spot size, wavelength, pulse length, optical components) as wellas the process parameters (laser power, scan speed, and scan line spacing). In addition,
additive manufacturing is known to be prone to various defect types such as pores, balling,
unfused materials, and cracking [7-10]. Under these circumstances, AE is of main interest inprocess monitoring. Obviously, even with the help of a fundamental physical understandingof the laser-material interaction, to correlate the AE signals with the real events is achallenge.
This contribution is an extension of our previous work [6]. Hence, we present two examples
where acoustic emission (AE) was selected as a process monitoring method and therecorded data where analyzed by artificial intelligence (AI). The first case shows how AE
system can be used for quality monitoring of additive manufacturing. The second is related tothe monitoring of the pre-weakening of rocks during electric discharge.

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2. Signal processing - Deep learning
In recent years, deep learning (DL) is actively introduced in many practical applications
becoming an essential part of speech recognition, natural language processing, internet
services and tele-communications [11, 12]. Originally inspired by the biological neuronalsystems [11], today, this technique outperforms other traditional processing approaches inanalysis of big arrays of data even in unsupervised way [11-13]. Such a rapid introduction
from theory into practice in the past decade was possible thanks to the developments in
optimization techniques that allowed involving a greater number of the basic computational
units (neurons, decision trees etc.) into the DL architectures [11-13]. Consequently, this has
given advantages in selection of more relevant information while suppressing the nonrelevant one and made it possible to analyze more intricate data arrays [14]. At present,
further developments in this field turn DL into a ready “on-shelf” tool. Thus, DL also starts to
be involved into more complex scientific problems, for example description of dynamic
systems [15], analysis of DNA [16] and computer vision tasks [11]. Such universality,
combined with the computational power, makes DL attractive for industrial applications, andthe first successful trials are seen in the literature. An interpretation of the acquired data forclassification of the critical states in the same way was also shown in the field of fracture
mechanics, laser welding and additive manufacturing [6]. The aforementioned applications
are distinguished by the extreme operating rates. The complexity of those industrial problems
is in the acquired data that take into account the dynamics of the underlying physicalphenomena. Usually, in real-life, the latter is known for high non-linearity thus making
interpretation in terms of existing physics extremely difficult, but still possible involving DL [6].
Today, DL incorporates a number of additional tools that increase the analytical efficiency
when operating with real-life data and that can be involved to solve the industrial problems.
Obviously, DL has become an attractive framework also for the monitoring of processes inreal-life applications [6, 17-19] thus motivating us to investigate its applicability for various
industrial problems. Under such circumstances, DL has many advantages in particular in
extracting the relevant information [14]. As an example, belief propagation networks collect
the relevant information in analogy with the renormalization that is widely used for the
analysis of complex physical systems [15]. Convolution networks (CN) reach excellent
results involving the convolutions over the given data that is followed by data compression[11]. The strong side of this technique is the recent extension for the adaptive and nonregular convolutions that allow operating on more complex data-grids [11]. Some otheroptimizations on simple feed forward networks also bring to better performance of thosewhen analyzing complex data [11]. All this gives a rich instrumentation for development of
new architectures and methods and adapt those to specific applications.

3. Examples
3.1

Example 1: AE meets AI in the field of AM quality monitoring

The first example shows that AE is strong instrument for monitoring in situ and in real-time
the quality in additive manufacturing. Today the quality monitoring in additive manufacturing(and generally in all laser based processes) is mainly carried out either with temperature
measurements of the melt pool or high resolution imaging of the process zone [7, 20].
Despite all the efforts to bring these technologies to the market, there is a consensus amongscientists and industries that these monitoring techniques still do not provide acceptable
rates in quality reproducibility during production [7, 20].
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Wasmer, Shevchik et al. [17, 18] proposed an approach which combines AE with AI for insitu and real-time monitoring of AM. The results were obtained on real process.
An industrial 3D printer Concept M2 (Concept Laser GmbH, Germany) was used. It is a
powder-bed technology, also known as a selective laser melting (SLM) process. The
machine was equipped with a fiber laser operating in continuous mode at a wavelength of
1071 nm with a beam quality M2 = 1.02 and a spot size of 90 μm. The powder had a particle
size distribution ranging from 10 to 45 μm and was made of CL20ES stainless steel (1.4404 /316L). In addition, an opto-acoustic, a fiber Bragg grating (FBG), operated as an acoustic
sensor. The FBG was mounted in the machine 20 cm away from the process zone.
A workpiece with dimension 10x10x20 mm3
was produced under N2 atmosphere (SeeFig. 1a). During the process, the laser power
P was 125 W, the hatching distance h was
0.105 mm and the layer thickness t was 0.03
mm. Three laser velocities were selected toachieve three porosity contents that
determined three different quality grades. The
three colors visible in Fig. 1a correspond to
the different process parameters, where 800
mm/s are bright regions, 500 mm/s are brown
regions and 300 mm/s are dark/blueish
regions. Typical light microscope images of
the three qualities are shown in Fig. 1b-1d.
The porosity concentration was 0.3 ± 0.18 %(medium quality; 300 mm/s; 132 J/mm3; Fig.
1b), 0.07 ± 0.02 % (high quality; 500 mm/s;
79 J/mm3; Fig. 1c), and 1.42 ± 0.85 % (poorquality; 800 mm/s; 50 J/mm3; Fig.1d). In Fig.
1, the insets show typical defects observed
such as a) tubular, b) small lack-of-fusion andc) large lack-of-fusion defects. The z- and ydirections is based on Fig. 1a.

Figure 1: a) Test workpiece produced with threeporosity contents; b) – d) Typical lightmicroscope cross-section images of the regions
produced with b) 300 mm/s (medium quality), c)
500 mm/s (high quality) and d) 800 mm/s (poorquality).
Reprinted by permission from Elsevier License:
Elsevier, [18].

In terms of signal processing, the main techniques employed are based on Deep Learning(DL), in particular Convolution Neural Networks (CNN) [17] and Spectral Convolution Neural
Networks (SCNN) [18]. In both cases, the search of distinct features is carried out in thetime-frequency domain using wavelet spectrograms. Also, the intensity measurement of the
AE components in spectrograms was taken as the relative energies of the narrow frequency
bands which were the input features for the NN classifiers. The frequency bands wereextracted using several wavelet families such as Daubechies, Symlets, and Coiflets as wellas adaptive M-band wavelets.
The analysis of the acquired AE signals was performed using one or two running windows(RW) that scanned the signal. The choice of the time span for the RW is a compromise
between the spatial resolution in defects detection and classification accuracy. On the one
hand, a short running window increases the spatial resolution in the detection of possible
defect areas within each layer. On the other hand, very short time spans are more affectedby noise and thus are deteriorating the classification efficiency. Several time spans and usingof two RW were investigated.
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Obviously, to achieve the best classification accuracy, there are many decisions that have to
be made. The choices included the selection of the NN (CNN or SCNN), the number ofconvolution layer, number of pooling layer, wavelet family, decomposition level, or type of
adaptive wavelet, the number of running windows, their corresponding time spans, etc…
Wasmer, Shevchik et al. [17, 18] defined that the best choice was provided by the NN andwavelet which showed the minimum approximation errors on the given AE signals. In termsof RW, the time span was selected after an exhaustive search in which the classification
accuracy was evaluated after each retraining using RWs with different time spans.
A typical AE signal from one complete layer of medium quality (300 mm/s) is shown in Fig.
2a. Figure 2a contains two running windows that are delimited by the red and a green box.
They represent a long (LRW – red box) and short running windows (SRW – green box)
scanning the acquired signal. Figure 1b is the reconstructed spectrogram from the relative
energies of the M-band wavelets from a pattern of the signal in Fig.2a that is bounded by the
red box between 0.2 and 0.4 s [17].
In terms of NN used, Fig. 3 displays a scheme of the CNN used in Wasmer et al. [17]. In thisfigure, it is seen that the spectrograms build from the LRW and SRW pass through two
different convolution layers from which a series of perception maps 1 are build. Then, theinformation is combined in the pooling layers 1 before the information from both flows ispushed into a common convolution layer 2 followed by a second pooling operation (seepooling 2 in Fig. 3). The final classification is carried out in fully connected layers. The
classification result/accuracy is the output of the procedure which is often given in a table.

Figure 2: a) A typical AE signal from one complete layer of medium quality (300 mm/s). The red
and green boxes are the long (LRW) and Short Running Windows (SRW) scanning the acquired
signal; b) reconstructed spectrogram for the LRW time span bounded by red lines in Fig. 2, a).
Reprinted by permission from Springer Nature: Springer eBook, [17].

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Figure 3: The structure of the CNN, where LRW and SRW denote the long rang window and shortrange window, respectively.
Reprinted by permission from Springer Nature: Springer eBook , [17].

The main results of these feasibility studies from Wasmer, Shevchik et al [17, 18] aresummarize in Table 1. In this table, the numbers in bold and in italic are the lowest andhighest classification accuracies found to date. The classification accuracies in the table aredefined as the number of true positives divided by the total number of tests for eachcategory. These values are given in the diagonal cells of the table (red cells). The
classification errors are computed as the number of the true negatives divided by the total
number of the tests for each category. These corresponding values are filled in non-diagonalrow cells. For example, the highest accuracy rate for the poor quality was 89%. The
classification error is more or less equal between the medium and high qualities, 4 and 7%,
respectively.
It was found that, by and large, the lowest accuracy rate was found using the Daubechies
wavelet with ten vanishing moments to reconstruct the spectrogram, one running window
and the conventional CNN for classification. In contrast, the best results are mainly obtainedusing also the Daubechies wavelet with ten vanishing moments to reconstruct the
spectrogram, one running window but with the spectral CNN (SCNN) for classification [18].
However, it is important to note that using M-band wavelets, two running windows and theconventional CNN, the classification accuracies are only slightly lower than the highest onespresented in Table 1. Therefore, it shows that there is still some potential improvement byusing M-band wavelets, two running windows and the SCNN as a classifier.
To conclude this example, Wasmer, Shevchik et al [17, 18] could demonstrate that theirinnovative approach that is combining AE with AI has some potential for in situ and real-timemonitoring of additive manufacturing (AM) processes.

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Poor quality

Medium quality

High quality

Table 1: Classification accuracy test results in % for three categories of events*

Poor quality (1.42 ± 0.85 %, 800 mm/s)

62 - 89

19 – 419 – 7

Medium quality (0.3 ± 0.18 %, 300 mm/s)

25 - 553 – 85

22 – 10High quality (0.07 ± 0.02 %, 500 mm/s)

18 - 821 – 961 – 83

Ground truth
Test

Red and light red highlight the classification accuracies and errors, respectively

3.2

Example 2: AE meets AI in the field of fracture mechanics

Pre-weakening of solid materials using electric discharge is a new technique aiming at
reducing the costs and energy consumption of raw materials processing in mining andrecycling industries. However, the absence of an effective pre-weakening quality control
prohibits its introduction into a wide practice. In our previous work [6], we showed thepotential to use AE to monitor this process with transparent materials. The aim of this work is
to show that this process can be applied to real mining materials.
Stones of different sizes wereselected from a copper ore. The
stones were separated by successive
sieving in 4 size categories; (i) 2531mm, (ii) 31-35mm, (iii) 35-40mm
and (iv) 40-45mm. Each stone wasthen submitted to a single pulse
using the same voltage generator as
previously described [6, 23]. Eachstone was placed individually on themetallic conveyor belt that acts as acounter electrode and aligned withthe electrode as shown in Fig. 4. The
gap between the electrode andcounter electrode was fixed at 50 mm
for all the tests and the belt was kept
motionless.

Figure 4: Schematic representation of the setup.

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The stones were treated at 200kV and four different capacitance (10, 15, 22.5 and 37.5 nF),
which correspond to energy of 200, 300, 450 and 750 J per pulse. For the two smaller sizes
categories, 30 stones of each were tested at each energy level. For the two larger stone
sizes, 20 stones of each were tested at each energy level. This gives a total of 400 stones
tested.
For each electrical pulse, the corresponding AE signal was recorded simultaneously. The
detection of the acoustic signals was made directly inside the water filled chamber using an
acoustic hydrophone sensor R30UC. It was placed at a distance of 20 cm from the electrodegap (See Fig. 4). The AE signals were recorded with a 10 MHz sampling rate for 20 ms andthe record was synchronized with the pulse start.
Contrary to our work with transparent samples, the categorization is not straightforward as
the presence of cracks inside the sample cannot be directly observed. What can becharacterized is the presence or absence of discharge and the eventual fragmentation of thestone in pieces. To quantify the fragmentation the weight of the stone before (Wbefore)
treatment is measured and the weight of the larger remaining particle after discharge ismeasured (Wafter). The weight loss is then calculated as ����� � �������� � ������ �⁄������� .
Two examples of stones from the category 25-31 mm are given in Fig. 5. The two stones
have a weight loss of 44 and 6%, respectively.
In this work, we defined 5 categories
based on the weight loss andpresence or absence of discharge.
The absence of discharge is easily
recognizable due to the lack ofdischarge sound and lack of damageon the stone. The AE signal as
shown in Fig. 6a is also very
characteristic. The amplitude andduration of AE is much less than forother categories as presented inFig. 6b and 6c. The next category issurface discharge or weight loss of 01%. In this category, the discharge
occurred mainly in water or at thestone interface and did not fragment
Figure 5: a) and b) Stones of the 25-31mm size category the stone. The next category is ashown before discharge. c) and d) the same stones
weight loss between 1 and 10% asshown after discharge of 200kV and 37.5nF.
illustrated in Fig. 5d with thecorresponding AE signal shown in Fig. 6b. This category is often also considered as surface
discharge as the path is mainly at the surface of the stone and entered the stone only in asmall edge as shown in Fig. 5d.

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Figure 6: a) AE signal for a pulse without discharge, b) AE signal from the stone shown in Figure 2b
and d, corresponding to 6% weight loss, c) Ae signal from the stone shown in Figure 2a and c,corresponding to a 44% weight loss. d), e) and f) are the corresponding sonogram.

The last two categories correspond to various degree of fragmentation, the first with a weightloss of 10-50% and the second with a weight loss of more than 50%. A stone with a weightloss of 44% is shown in Fig. 5c and the corresponding AE signal is in Fig. 6c.
The AE signals for the no discharge case are easily separated from the discharge case withboth a lower intensity and shorter duration of the acoustic emission (see Fig. 6a and 6d).
However, all the other signals where discharge occurred are showing similar characteristic
and similar sonograms (see Fig. 6b, 6c, 6e and 6f). Indeed, the frequency with the highest
response on the sonogram is around 300 kHz and corresponds with the highest sensitivity ofthe sensor. The amplitude and duration of the signals, as well as the frequencies are similar
and advanced processing techniques are needed to separate the signals.
For this work, we used a similar approach to our previous work [6]. We used first wavelet
decomposition in order to extract the features from the AE signals. In this work we used
standard Daubechies wavelet with 10 vanishing moments to decompose the signals in timefrequency domains.
Before classification, only the most informative features are selected using the principal
component analysis (PCA) [24]. The disposal of non-informative features decreases thenoise and additionally reduces the computational complexity [24].
The classification was performed using support vector machine (SVM), a statistical learningtechnique proposed by Cortes and Vapnik [25].
The classification results are presented in Table 2. As expected the classification results of
the no discharge category presents no difficulty to correctly separate. Good news is for thecorrect separation with a 90% accuracy of fragmentation with a weight loss of more than
50%. This category corresponds to discharge with the most damage created and means thatthe process is running correctly. It is important to recognize sample from this category. The
category surface discharge is also relatively well separated with an accuracy of 70%. The
categories in between suffer from a lower accuracy of 53 and 66%. This can come from thefact that the boundary between the category are just arbitrary selected and it is well possiblethat these categories have common features with neighboring categories as the process is
not expected to physically vary between them. It is also well possible that a lot of invisible
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WL 0-1%

WL 1-10%

WL 10-50%

WL >50%Moreover, the process was not run in
optimal conditions as the gap between theelectrodes was kept constant at 50mm. Thisgap is good for big stones that fill most of
the gap and thus limit that amount ofdischarge occurring in the water above thestone. For the small stones, a large portionof the discharge will occur anyway in thewater which means that all the signals will
have common water discharge features andnot only the stone from the 0 - 1% weightloss category.

Table 2: Classification accuracy test resultsin % for three categories of events*
No discharge

cracks are created in the stones without thestone being fragmented, a process known
as pre-weakening. In this case the acoustic
signal is expected to be very similar to acomplete fragmentation. This could explain
why a lot of signals from low weight loss
categories are classified in the more than
50% weight loss category.

Nodischarge

100

-

-

-

-

WL 0-1%

-

70

-

3

27

WL 1-10%

-

7

53

2

38

WL 10-50%

-

8

-

66

26

WL >50%

-

5

-

5

90

Ground
Test

truth*Red and light red highlight the classification
accuracy with the ground truth andclassification mistakes, respectively.

4. Conclusions

Following our first contribution “When AE (Acoustic Emission) meets AI (Artificial
Intelligence)“ presented at the 21. Kolloquium Schallemission – in Fulda, Germany, 2017 [6],we presented two new examples where AE meets AI. The first example is related to quality
monitoring of additive manufacturing (AM) process. To do so, the approach is to use state-ofthe-art opto-acoustic sensors as AE sensors and analyses the recorded AE data with Deep
Learning (DL) techniques such as convolution neural networks (CNN) and spectral neuralnetworks (SCNN). It was found that the classification accuracy between three close
categories varies significantly depending on the selection of the NN (CNN or SCNN), thenumber of convolution layer, number of pooling layer, wavelet family, decomposition level, ortype of adaptive wavelet, the number of running windows, their corresponding time spans,
etc…. However, the best classification accuracies are higher than 83% even though theresults are only based on two feasibilities studies. This demonstrated the potential of thisapproach to be industrialized as in situ and real-time quality monitoring in AM processes.
A second example related to highly dynamic fracture mechanics, in particular for mineindustries. We demonstrated that by combining AE with AI, it is possible to classify withacceptable accuracy the effect of electrical discharge in rocks in terms of weight loss (WL).
This is also an important result as it may allow saving a significant amount of energy in themine industry, especially for pre-weakening of rocks. This technology was patented [26].
Finally, based on the two examples presented, it can be concluded that by combining AE
signals with a state-of-the-art signal processing, it is possible to address highly complex
industrial processes. We demonstrated that, even in very noisy and dirty environments, weare able to achieve reliable classification of investigated events. This makes thismethodology very effective for industrial applications even though it does not explains thenature of the AE signals, but this is planned as the future work.
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References:[1] Grosse, C., Ohtsu, M., Acoustic emission testing basics for research - applications in civilengineering, 1st ed., Springer, Berlin, Heidelberg, 2008, DOI: 10.1007/978-3-540-69972-9[2] Baranov, V., Kudryavtsev, E., Sarychev, G., Schaveli, V., Acoustic emission in friction,
vol. Volume 53. Tribology and Interface Engineering Series. Elsevier, 2007.
[3] The Japanese Society for Non-Destructive Inspection, Practical Acoustic Emission
Testing, 2nd ed. Springer, Tokyo, Japan, 2016, DOI: 10.1007/978-4-431-55072-3[4] Huang, M., Jiang, L., Liaw, P.K., Brooks, C.R., Seeley, R., Klarstrom, D.L., Using
acoustic emission in fatigue and fracture materials research, JOM, Vol. 50, 1998,
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[5] Gu, H., Duley, W.W., A statistical approach to acoustic monitoring of laser welding,
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[6] Wasmer, K., Saeidi, F., Meylan, B., Vakili-Farahani, F., Shevchik, S.A., When AE(Acoustic Emission) Meets AI (Artificial Intelligence)”, Presented at 21. Kolloquium
Schallemission, Fulda, Germany, 9 – 10 March, 2017 and published in NDT.net Vol.22
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[7] Everton, S.K., Hirsch, M., Stravroulakis, P., Leach, R.K., Clare, A.T., Review of in-situ
process monitoring and in-situ metrology for metal additive manufacturing, Materials andDesign, Vol. 95, 2016, pp.431-445.
[8] Zhang, L.C., Attar, H., Selective laser melting of titanium alloys and titanium matrix
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[9] Khairallah, S.A., Anderson, A.T., Rubenchik, A., King, W.E., Spatter formation in powderbed selective laser melting, Acta Materialia Vol. 108, 2016, pp. 36-45.
[10]King, W.E., Barth, H.D., Castillo, V.M., Gallegos, G.F., Gibbs, J.W., Hahn, D.E.,
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Vol. 214, 2016, pp: 2915-2925.
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Voulodimos, A., Doulamis, N., Doulamis, A., Protopapadakis, E., Deep learning forcomputer vision: A brief review, Computational Intelligence and Neuroscience, Vol. 2018,
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Wasmer, K., Kenel, C., Leinenbach, C., and Shevchik, S.A., In situ and real-timemonitoring of powder-bed AM by combining acoustic emission and artificial intelligence,
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Switzerland, pp: 200-209, Springer, 2018, DOI: 10.1007/978-3-319-66866-6_20.
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Additive Manufacturing, Vol. 21, pp: 598-604, 2018, DOI: 10.1016/j.addma.2017.11.012.
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Wasmer, K., Le-Quang, T., Meylan, B., Vakili-Farahani, F., Olbinado, M.P., Rack, A.,
Shevchik, S.A., Laser processing quality monitoring by combining acoustic emission andmachine learning: A high-speed X-ray imaging approach, accepted in Physics Procedia,
LANE 2018 - 10th Conference on Photonic Technologies.
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65 - AE fracturing modes classification by means

AE FRACTURING MODES CLASSIFICATION BY MEANS OF SHEAR
of shear tensile source model
TENSILE SOURCE MODEL
Petruzalek1, M., Lokajicek1, T. Svitek1, T., Jechumtalova2, Z., Kolar2, P., Adamova2, P.
and Sileny2, J.
Institute of Geology of the Czech Academy of Sciences, Prague, Czech Republic

1

Institute of Geophysics of the Czech Academy of Sciences, Prague, Czech Republic2

Abstract:
A shear-tensile crack (STC) is presented as a suitable source model for acoustic emission (AE) events.
The experimental data comes from an uniaxial compression test realized on the Westerly granite
specimen using a 14 channel Vallen Amsy5 AE monitoring system. The advantage of the STC against
the traditional MT is: (i) it is a physical source – contrary to MT, as STC describes straight the simple
fracture modes anticipated inside the loaded sample, namely the shear-slip and both the opening andclosing tensile crack, (ii) it is simpler, described by less parameters (5 instead of 6 for the unconstrainedMT), which is essential in solving the inverse problem. Proposed STC procedure was tested on 38 AE
events selected in the range of 50 – 98% of uniaxial compressive strength. Compare to the MT model,
the STC model showed similar fit of the input data while providing by far smaller confidence regions.
That means more certain determination of mechanism orientation but mainly the highly improved
reliability of the decomposition components. Thus the use of STC model allow, besides other things, tobetter distinguish between tension and shear type of AE events, which may be a crucial in recognizing
approaching failure. In this particular experiment, the application of STC model proved to be useful inrecognizing threshold of unstable microcracking and indicative in determination of failure plainorientation. The AE crack length was found to be slightly above the range of the largest grains. The
principle stresses orientation, inverted from the STC mechanisms, corresponded to the stress conditionsof uniaxial compression test.

1. Introduction

Acoustic emission (AE) is a phenomenon that spans the generation and propagation of
acoustic (elastic) waves caused by sudden irreversible changes in the internal structure of a
type of material. In the case of rock, it is closely connected to the generation, growth, andinteraction of microcracks and leads to a brittle failure [1]. As such, an AE event may beconsidered, on a very small scale, as an earthquake with a size on the order of millimeters in
the case of laboratory experiments [2]. Considering phenomena similarity [3] andapproximately 70 years of seismology, it is not surprising that most of the methods forquantitative AE analysis originally came from seismology [4].
From a rock mechanics point of view, an AE analysis contributes to recognizing
microcracking thresholds [5]: the crack initiation threshold (CI) and the crack damage
threshold (CD). The fracturing process becomes unstable when crossing the CD threshold
and the transition from tension to shear microcracking [6]. While an AE analysis helps
describe the spatio-temporal evolution of damage, studies of AE source mechanisms andtheir dependence on the current stress state of rock enables detailed insight into themicroscale of the fracturing process.

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A simple method that distinguishes between shear, tension, and collapse source types isbased on average first arrival polarity [7]. Using first arrival polarity, [6] illustrated an increasein shearing in the expanse of tension type when specimens were approaching failure intriaxial tests. A more sophisticated approach for examining failure mechanisms is theapplication of the moment tensor (MT) model. [8] used the MT to determine crack type andcrack orientation. Since introduction of the above studies, application of the MT analysis hashelped us determine the fracturing process in all types of laboratory studies. Moment tensor
analyses have revealed that shear cracks are dominant during uniaxial and triaxial loading
experiments [7, 9, 10]. Shear faulting was also determined to be a dominant type of failure ina plane-strain compression test [11] and a three-point-bend fracture test [12].
Although the moment tensor has become a standard for describing seismic sources acrossall scales, it may not provide resolvable information for all data sets. For some applications, it
may be useful to constrain the full MT with the aim of increasing resolution. A suitable
alternative to the MT is the shear-tensile crack (STC) model, a model that possesses thecapacity to discern shear vs. tensile dislocation and to yield a more robust inverse task.
Additional details on this model are provided in the Methods chapter. Until now, applicationsof the STC have only been employed for earthquakes (e.g., Jechumtálová et al., 2014;Šílený et al., 2014). Here, we apply the STC in a laboratory acoustic emission experimentand demonstrate its usefulness as compared to the standard MT approach.

2. Experiment

The specimen of fine grained Westerly granite was uniaxially loaded up to the failure. A 14
channel AE apparatus was used for AE monitoring as well as for periodical ultrasonicsounding (US). More information about the experiment or the rock properties can be found in
Petruzalek et al., (2017, 2018). The determined mechanical properties were: uniaxial
compressive strength (UCS = 226.6 MPa), Young’s modulus (E = 51 GPa), poisson ratio(0.26). Crack initiation threshold (CI = 45% of UCS, the stress level at which stablemicrocracking begins) and the crack damage threshold (CD = 72% of UCS, the stress levelat which unstable microcracking begins) were determined in detail using three independent
methods based on stress-strain data, AE activity, and ultrasonic attenuation [13]. AE eventswere localized by a grid search procedure with anisotropic velocity model. The accuracy was
up to 2 mm [14]. Thirty-eight of the strongest AE events, with very low localization were used
to make the input amplitudes as reliable as possible. Eleven of these AE events were locatedin the portion of stable microcracking and twenty-seven were located in the regime ofunstable microcracking. The 38 selected AE events were further used as a data set for
testing AE source mechanisms. Peak to peak first arrival amplitudes (AM), corrected for theparticle velocity sensitivity (S) of used sensors and for AE sensor directional sensitivity(cos(α), where alfa is the angle of incidence, served as an input (AC) for the source
mechanism inversion: A^C=SA^Mcos(α).

3. Methods

We assessed the reliability of mechanisms for selected microearthquakes by estimating theerror of the evaluation and by comparing the mechanisms retrieved using the two source
models: MT and STC. The MT approach is routine across scales – from earthquake
seismology ranging from mega-earthquakes, to strong and moderate events, to weak
intraplate seismicity, to the rupturing of rock and other samples in the laboratory and thefracturing of materials in civil and mechanical engineering. The STC results from a special
type of constraint applied to the MT, offering enhanced robustness for the retrieved
mechanism.

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The Moment Tensor – MT
The unconstrained MT is the most comprehensive description of shear and non-shear
sources and is the body force equivalent of a rupture that generates the same wave field in acontinuous medium as an actual rupture. Thus, the MT is not a physical source but a
substitute for real processes occurring within the focus. As a system of body forces, the MT
is not a priori convenient for offering simple insight into processes within an earthquake
focus. Therefore, it is generally decomposed into simple sources. The method of
decomposition is not unique. The most common and widely used procedure is one that splits
the general MT into isotropic and deviatoric portions (unique), and then splits the deviatoric
portion into a double couple (DC) and a compensated linear vector dipole (CLVD) with acommon major tension or pressure axis that is ambiguous (e.g., [15]). Thanks to its
generality, the MT is relevant to the fracturing of a solid body, including all of the modes offracturing recognized within fracture mechanics and their combinations. However, since the
MT does not describe the rupture itself but rather body force equivalents of actual rupturing,it also includes mechanisms that do not generally represent realistic physical sources. In
other words, sometime the MT is needlessly general. In addition to rupture mode I (tensile
fracturing) and rupture modes II and III (plane and anti-plane shear slip), combinations offorces that do not correspond to a physically feasible rupturing are also present. Therefore,
for the simple rupturing expected within the foci of tectonic or induced earthquakes, the MT is
unnecessarily complex, leading to more parameters than those relevant to simple rupture
models.
The Shear-Tensile Crack – STC
Among constrained MTs, a particularly convenient model is one that combines modes I andII/III of the fracturing, namely the shear slip and tensile crack. The model’s design dates backto the 1980’s to a manuscript by [16] and follows the applications of [17] and [18]. Later, acombination of shear fault with a tensile crack, leaving a fault tip at various angles, wasstudied by [15]. A special case of geometry amongst the superpositions of a shear fault witha tensile crack is the geometry that occurs when the tensile crack formally coincides with ashear fault and perpendicularly opens to a fault at the same time instant. The concept ofshear slip and simultaneous opening of the same fault originated in [19], afterward appearedin [20], and was revisited by [21, 22]. Although the concept has been differently named byvarious authors, here, we refer to it as the Shear-Tensile Crack (STC). A big advantage incomparison to the MT is the fact that the STC is a physically based concept for the source
mechanism. Simply stated, it is a slip along a fault that may open or close at the same time(i.e. it directly describes a seismoactive process in terms of the rupture occurring within thefocus), Fig. 1. If we fix the values of the material constants, the STC is described by fiveparameters: (i) the dip of the fault; (ii) the strike of the fault; (iii) the rake angle specifying theshear component of the slip; (iv) the angle, α, referred to as the slope, which represents theoff-plane component of the slip vector; and (v) the magnitude.
Fig. 1. The STC source model - a
combination of shear-slip and tensile
crack represented by the slip vector off
the fault plane and the slope angle α (α= 0° is a pure shear slip and α = 90° is
a pure tensile crack).
The number of parameters is the sameas that for the deviatoric MT which isfrequently applied in order to constrain
the MT for the sake of increasing the stability of inversions of teleseismic and regional
records for Global CMT and regional CMTs. On the other hand, the STC also has thecapability for describing a change in volume within the focus. In fact, it is the simplest source
model for incorporating a shear slip and a volume change. In this way, it combines a

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desirable amount of generality with the maximum possible simplicity, ensuring robustness forthe inverse task [23, 24].
Confidence zones
To assess the reliability of the STC solution obtained, we evaluated the confidence zones ofsource model parameters or their combinations. Confidence zones are objects within themodel space that specify the volume in which the solution of the inverse task is contained in
an a priori specified probability, taking into account errors in the data. In other words,
confidence zones describe a distribution for model parameters that yield a good match to thedata. The size and shape of the confidence zones indicates the uncertainty of the estimatedparameters: large confidence zones indicate a poor solution while small confidence zones
suggest a good solution. Both reliability for determining the orientation of the mechanism andits characteristics for the DC and non-DC content, should be estimated. To assess geometricreliability, we constructed confidence zones for the T, P, and N axes of the deviatoric portionof the MT solution corresponding to the retrieved STC, and confidence zones fordecomposition of this type of MT in order to determine the characteristics for both the DC
and non-DC content. For the latter, parallel information is the confidence zone for the slope
angle. For the procedure, we scanned the model space within a regular grid and evaluated amatch to the data and the probability density function (PDF) for the grid points. We thenintegrated the PDF across a trial volume within the model space and searched for a patch inwhich the cumulative probability acquired certain values, generally 0.9, 0.95, and 0.99
(details are provided in [25]). This type of visualization is also relevant if properties of themechanism described by the unconstrained MT are studied.
A comparison of MT and STC
Solving the inverse task requires matching predicted amplitudes to observed data. The
criterion is met by minimizing the quantity expressing the misfit. For this work, we minimized
the root mean square of the differences normalized by the norm of the data, the quantity
nrms (Tab. 1). A perfect fit implies a value of 0 and values below 0.5 are considered
acceptable. Only a few solutions were below 0.15. However, the bulk of solutions (70%) were
below 0.3 and only a single event (event number 34) had the misfit worse than 0.5. Misfit
values did not differ much for the MT and STC inversions, the latter being slightly larger
based on the definition (six parameters in the MT model vs. five parameters for the STC).
The Thanks to the 3-D configuration of the observations, coverage of the focal sphere by
sensor projections was generally good. For events located deep inside the specimen, thisfeature was obvious. The benefit is illustrated on focal sphere coverage for events number 9,5, and 24 (Fig. 2), which are analyzed in this paper in detail.
Figure 2. Examples of the quality ofcoverage for the focal sphere byobservation site for event numbers 9, 5,
and 24. Sensors located above thefocus are displayed as triangles pointing
down, while sensors below the focus are
displayed as triangles pointing up. Colormarks the polarity of the incoming P
wave: blue – compression, red – dilatation. Equal-area projections of the lower focal
hemisphere were applied.
Table 1. Results of the MT andSTC inversion of the 38 events selected from the laboratory
experiment. Source type (STC determined) of AE event: T – tension, S – shear, ST –combined.

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Event nr. MT RMS STC RMS
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38

0.297
0.12
0.229
0.168
0.342
0.074
0.132
0.179
0.211
0.094
0.091
0.143
0.252
0.165
0.159
0.33
0.264
0.201
0.15
0.128
0.065
0.217
0.154
0.241
0.24
0.209
0.151
0.435
0.165
0.48
0.155
0.223
0.151
0.536
0.283
0.156
0.209
0.251

0.306
0.19
0.251
0.171
0.344
0.085
0.149
0.221
0.224
0.111
0.099
0.144
0.267
0.166
0.165
0.349
0.271
0.238
0.19
0.162
0.099
0.223
0.161
0.243
0.25
0.235
0.152
0.47
0.204
0.482
0.16
0.228
0.153
0.582
0.297
0.161
0.218
0.257

Kagan
angle
[°]
5.6
5.2
8.9
3.8
0.9
1.4
17.7
3.3
1.4
4.5
2.7
0.6
3.4
2.6
2
17.8
1.4
11.1
4
11.3
1.4
1.9
3.2
7.2
0.9
1.4
4.1
15.7
1.6
11.7
8
1.6
2.9
13.2
1.8
2.4
1.8
0.4

MT
DC
[%]
34.9
64.2
11.8
22.7
34.7
21.9
14.4
45.6
60.5
13.9
27.7
91.8
27.9
53.5
82.8
5.6
52.7
14.7
17.4
21.2
27.3
73.5
64.3
23.8
24.8
77.6
73.1
29.7
34.6
41.8
19.5
25.7
27.5
17.4
76.6
63.5
15.5
92.2

V
[%]
44.5
2.5
35.3
46.9
38.9
38.7
33.4
52.3
14.6
35.8
33.5
7.6
24.2
23.7
-2.2
48
29.9
29.7
27.5
33.5
35.6
17.7
17.2
48.7
32.2
-1.2
15.1
15.7
41.8
41.1
38
29.2
32.3
37.9
0.1
28.2
39.8
1.2

STC
CLVD
[%]
20.6
-33.3
52.9
30.4
26.4
39.4
52.2
2.1
24.9
50.3
38.8
-0.6
47.9
22.8
15
46.4
17.4
55.6
55.1
45.3
37.1
-8.8
18.5
27.5
43
-21.2
-11.8
54.6
23.6
17.1
42.5
45.1
40.2
-44.7
-23.3
8.3
44.7
6.6

DC
[%]
27.8
95.3
34.4
19.2
33.6
27.4
35.4
12
76.2
37.4
37.9
87
53.1
55.9
98.3
14.8
48
46.3
46.3
36.4
34.5
73
71.9
15.7
40.5
96.9
76.3
69.9
25
44.4
29.4
48.8
38.9
32.6
96.9
51.6
27.4
97

V
[%]
40.1
2.6
36.4
44.9
36.9
40.3
35.9
48.8
13.2
34.8
34.5
7.2
26.1
24.5
0.9
47.3
28.8
29.8
29.8
35.4
36.4
15
15.6
46.8
33.1
-1.7
13.2
16.7
41.6
30.9
39.2
28.4
33.9
37.4
-1.7
26.9
40.3
1.7

CLVD
[%]
32.1
2.1
29.2
35.9
29.5
32.3
28.7
39.2
10.6
27.8
27.6
5.8
20.8
19.6
0.8
37.9
23.2
23.9
23.9
28.2
29.1
12
12.5
37.5
26.4
-1.4
10.5
13.4
33.4
24.7
31.4
22.8
27.2
30
-1.4
21.5
32.3
1.3

Constrain AE sourceed type
type
3
T
1
S
2
T
2
T
2
T
1
T
1
T
3
T
1
S
1
T
1
T
1
S
1
ST
1
ST
1
S
3
T
2
ST
3
ST
2
ST
2
T
1
T
1
S
1
S
3
T
2
ST
1
S
1
S
3
S
3
T
2
ST
2
T
1
ST
1
T
4
T
2
S
1
ST
2
T
1
S

The expectation of obtaining well-determined mechanisms in terms of both the MT and the
STC, thanks to the distribution of sensors surrounding the foci, came true for most eventsfrom the treated set. MT and STC solutions were very similar for the bulk of treated data. The
principal axis deviation angle (the Kagan angle) was determined for 75% of tested events
below 5° and less than 20% of the data set had a Kagan angle of 10-18° (Tab. 1). The rangeof confidence zones was used for comparison of reliability of MT and STC mechanisms. As
an illustration, three events are presented in Fig. 3: (1) a well-constrained mechanism forevent number 9 (constrained Type 1 in Table 1), (2) an inferior solution in terms of the sizeof the confidence zones represented by event number 5 (constrained Type 2 in Table 1),and (3) event number 24, a poorly resolved mechanism for both orientation and DC vs. nonDC content (constrained Type 3 in Table 1). The size of a confidence region directly

S330

depends on the constraint imposed on the source mechanism: the stronger the constraint,
the smaller the confidence region, as is obvious and follows from the definition. From thisviewpoint, the fact that confidence regions for the STC shrink as compared to those related
to the MT is not surprising. The constrained mechanism displays an inferior fit to the data, ascompared to the unconstrained mechanism, but yields a more “certain” solution: itsconfidence region shrinks. The point is that we only dealt with solutions that still fit the datawell (i.e. further investigation was restricted to data that could be considered successful
solutions of the inverse task). How the confidence region shrinks was also considered. It
affects both the orientation of the mechanism and the decomposition. Concerning the former,
the ambiguity for the principal axes position can be removed. Concerning the latter, it may
allow us to determine shear vs. non-shear performance for an examined event. Describing
the orientation, such a scenario was definitely the case for event 5 and, partially, for event
24. For event 5, the confidence regions for all of the principal axes in the MT solution werevery large and, in fact, merged. The result indicates that the orientation of the MT was
entirely uncertain. In contrast, within the STC solution they were markedly smaller: theconfidence region of the T-axis was fairly compact, indicating that its direction was very wellresolved.

Fig. 3. The MT (left) vs. STC (right) mechanisms and estimates of their uncertainty for event
number 9), 5 and 24. The MT panel: Column 1 – a traditional fault-plane solution plot (nodal
lines, zones of compression for P-waves in green; principal axes, red triangles (T-axis,
triangle up; P-axis, triangle right; N-axis, triangle left); Column 2 - the T (in red), P (blue), andN (green) axes and their confidence regions at the 90% (dark color), 95% (medium), and
99% (pale color) probability levels. The STC panel: Columns 1-2 – the fault-plane solution
and the T, P, and N confidence zones similar to the MT solution. Column 3 – the confidencezone (in the form of a histogram) for the slope angle .
For the P and N axes, they were stretched but remained mutually isolated (i.e. although lesscertain than the T-axis, the P and N axes were resolved one from the other, unlike the caseof the MT solution). For event 24, a decrease in the confidence regions for all of the threeS331

principal axes were observed when comparing the STC and MT solutions. When using the
STC, the T-axis becomes well-resolved while the P and N axes remain ambiguous (theymerge together and there even seems to be bimodality in the P-axis position, see the blue
spots on the green background). They are, however, at this point, tightly constrained to theplane perpendicular to the well-determined T-axes.
The above discussion concerned orientation of the mechanism. Equally important is its
decomposition, i.e. the content of individual “elementary mechanisms”, namely the DC, ISO,and CLVD components in mechanisms resolved using the MT and STC approaches.
Obtaining the DC, ISO, and CLVD percentages in the MT approach is straightforward. For
obtaining them using the STC approach, it is necessary to first evaluate the moment tensor
corresponding to the current STC and to then decompose it in the standard way. Using thisapproach, it is possible to directly compare individual components and their percentages andthe uncertainty of their retrieval expressed by their confidence regions. When inspecting
histograms corresponding to the MT and STC solutions in Fig. 4, it is obvious that the latterare narrower than the former. Thus, percentages in the STC solution are better resolved thanthose within the MT solution. Such a result is logical, as previously indicated, because the
STC is one of the constrained MTs. The benefit of using the constrained source model
results from the fact that increased resolution may allow recognition of features within themechanism decomposition that are lost during the unconstrained MT approach. This is thecase for all three of the events in Fig. 4. Event number 9 is largely a DC event (Table 1) andconfidence regions for non-DC portions of the MT approach are rather wide. Both the ISO
and CLVD involve a value of zero. Therefore, by taking into account all of the involved errors,
we can conclude that the ISO or CLVD components, although their best values are non-zero,are zero. However, for the STC solution this is not the case. The solution is even more DC
than the MT solution (76 vs. 61 percent), although the non-DC components were significantly
recovered because their confidence zones were very tight (the event belongs to Group 1 of
the well constrained solutions, see Table 2) and remained well apart from the zero value.
Event number 5 is a part of Group 2 with a bit less of a constrained orientation. Nevertheless,the same conclusion, as for the previous case, can be reached. The event was largely nonDC and the DC content was only around 34 percent. The non-DC components were evenless certain than they were for the previous event. However, the confidence region of the ISO
component remained markedly above zero. As a result, we concluded that the resolved ISO
was significantly positive. The same cannot be said for the CLVD since its confidence region
included zero and the component remained unresolved as for its sign. Similar to the previous
event, the STC solution was markedly more constrained. Confidence regions for all of thethree source components were fairly narrow. Both the ISO and CLVD yielded positive values
far from zero. Therefore, they were significantly resolved as positive and the event may beclassified as a slip with an extension. Worth noting here is that the histograms relevant to the
STC inversion for data subjected to random noise contamination were markedly wider thanhistograms describing the confidence regions, and we, again, assumed this was due to thefact that the noise experiments were not entirely relevant to error estimates consisting ofconfidence region construction since they changed the data.

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Fig. 4. Decomposition of the mechanisms for event number 9, 5 and 24. The top row in thepanel corresponds to the full moment tensor (MT) solution. The bottom row corresponds todecomposition of the moment tensor corresponding to the shear-tensile crack (STC) solution.
Histograms - confidence regions at the 90% (dark color), 95% (medium), and 99% (palecolor) probability levels are shown.
The last example, event number 24, belonged to Group 3 events that were not very wellconstrained for orientation, see the large confidence zones of the principal axes in Fig. 6.
Surprisingly, this property was not transformed into the decomposition and confidence zones
for the source components were approximately the same size as those corresponding toevents 5 and 9. A large quantity of the non-DC content was determined for the mechanism of
event 24. However, the CLVD within the MT solution remained insignificant. In contrast, theISO was significantly positive because its confidence region was entirely located within
positive numbers. Similar to previous events, the STC, by far, yielded much moreconstrained source components and we concluded that the mechanism is significantly
extensional. As previously mentioned, such behavior may contradict a common experiencein which the decomposition is worse resolved than the orientation. However, uncertainty indetermining the orientation here was not universal, and it, in fact, only involves the P and N
axes while the T-axis was very well recovered (see Fig. 5 of the STC solution).

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Event number 9, 5, and 24, representing examples of the mechanisms, were very wellconstrained (with respect to orientation), less well constrained, and rather poorly constrained,
respectively. Considering the MT and STC solutions as two alternative source models, thelatter was preferred because a similar fit was kept for the data but smaller confidence regions
were obtained. The result not only indicates more certain solutions for orientation of themechanism, but also whether or not the focus was simply shear, taking into accountuncertainty in the mechanism determination or a non-shear mechanism (i.e. having either an
extensional or a dilatational component). As a result, we processed the entire data set (all 38
events) in terms of the STC source model.
The STC mechanism in a rock mechanics context
The main goal of this work is not to present a detailed analysis of the fracturing process, but
to test and present an STC approach for AE mechanism determination for a set of 38
selected AE events (from approximately 20,000 registered events). Despite limitations for the
AE data set, discussing how STC obtained mechanisms fit the general idea of the fracturing
process in compression tests is illustrative.
For the case of rock mechanic compression tests, the fracturing process can be divided into
two phases, stable and subsequent unstable microcracking, with the CD threshold being theboundary between the two. Unstable microcracking indicates that after crossing the CD
threshold the sample fails even without additional increased stress above the CD threshold.
That is why CD stress, which is approximately 70-80% of the uniaxial compressive strength,
may be used as an estimate of long term strength. Tension microcracking, with cracks
parallel to the maximum compressive stress, is supposed to be dominant during stablemicrocracking. Shearing, on planes inclined to the maximum compressive stress at angles of
20-30°, plays a significant role throughout unstable microcracking. The mentioned
suppositions were determined by considering the interpretation of stress strain behavior forthe compression tests (e.g. [26, 27] and the microscopic analysis of thin sections obtained
from loaded specimens [28]. The analysis of AE source types (the average first arrival
polarity) corroborated different microcracking mechanisms for both microcracking stages [6].
Based on the STC model decomposition (Tab. 1), the source types of AE events weredivided into three different categories: 1. Shear type (S), DC > 70%; 2. Tension type (T), DC< 40%, CLVD > 25%; and 3. Combined type (ST), DC: 40-56%, CLVD: 19-23%. Of the 38
analyzed AE events, the first 11 belonged to the stable microcracking stage and theremaining 27 to the unstable microcracking stage. Even with the limited AE data set, thesource type distribution well-fit the general fracturing process idea (Fig. 5). Tension
dominated stable microcracking (T: 82%, S: 18%, and ST: 0%). When the axial stresscrossed the CD threshold, shearing began to play a significant role in the fracturing process(T: 33%, S: 33%, and ST: 33%). For comparison, a very similar source type distribution,
determined using just the first arrival polarity, was also reported by [6]. While both of thesource planes from a mathematical standpoint are equivalent, we chose the more probableplane (the red colored plane in Fig. 5) based on principal stresses orientation with respect tothe source type mechanism. The more vertical plane was chosen for the tension source type.
The plane with a closer declination to 60-70° (20-30° from σ1) was chosen to represent a
shear crack plane. Fig. 9a provides the orientation distribution of the poles for chosen crack
planes. From the pole graph one can determine that the tension crack planes have an
inclination of 65-85° and almost a random azimuthal distribution. Most of the cracks for the S
and ST source types had an inclination in the interval of 55-65° (25-35° from σ1). The S andST poles displayed a slight preference for the azimuthal orientation (the two marked clusters
in Fig. 6a).

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Fig. 5. STC mechanisms for the set of 38 selected events displayed using a traditional faultplane solution plot (source lines and zones of compression for P-waves are shown in color;
principal axes, triangles (T-axis, triangle up; P-axis, triangle right; N-axis, triangle left)).
Colors represent different source types: T-dark blue, S-gray, and ST-light blue. Red labels
mark the phase of the uniaxial loading test for CI-stable microcracking and CD-unstablemicrocracking; the numbers mark a constrained type. Bars correspond to the size of theevent.
The strike/dip coordinates of the center points of the clusters (85/30 and 319/30) may
represent the poles of macroscopic failure planes (175/60 and 47/60). The plane 175/60 (thered color in Fig. 6) corresponds rather well with its dip and azimuthal orientation to the only
macrocrack (190/56) found on the surface of the tested specimen (Fig. 6b). However, thelocation of the 38 selected AE events did not display any preferential orientation (Fig. 6c).

S335

Fig. 6. . a) An equal area stereographic projection (lower hemisphere) of poles to theselected crack planes (red in Fig. 5). The set of 38 AE events and the STC determinedmechanisms. Dark blue is the tension source type, gray is the shear source type, and light
blue is the combined source type. Red and green triangles indicate poles and great circles
for the two possible failure planes. b) The WG specimen after testing with its orientation of
failure. c) The location of the 38 selected AE events.

4. Conclusions

As compared to the MT model, the STC model displayed a similar fit for input data
while providing far smaller confidence regions.
The STC results indicate a more certain determination of source mechanism whencompared to MT solution.
The use of STC solution leads to a highly improved the reliability of decomposition
components.
Application of the STC model allowed, in addition to other things, better distinction
between the tension and the shear type of AE events that may be crucial for recognizing anapproaching failure.
Transition from tension to shear microcracking was recognized while crossing the CD
threshold.
Preferential orientation of the shear cracks was determined to coincide withorientation of the failure plane.

Acknowledgement
This study was partially supported by the Czech Science Foundation research grants 1603950S, 18-08826S and by the Czech Academy of Sciences project RVO 67985831.

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21 - AE sensor sensitivity verification using a stimulated motion
verified by laser-vibrometry
Hartmut VALLEN1,
1

Vallen Systeme GmbH, Germany, www.vallen.de

Abstract
This presentation describes work in progress towards a practicable procedure for obtaining the receiving
sensitivity spectra of an AE sensor under test (SUT) using a face-to-face (F2F) setup. Sensitivity isexpressed as the SUT’s output in units of volt per unit of particle motion input, which can be nanometerof particle displacement or millimeter per second of particle velocity. The motion was stimulated by acommercially available transmitter. The transmitter’s surface motion was measured in absolute units ofvelocity, by using a scanning laser vibrometer, at 16 positions per ring over 10 rings covering an active
diameter of 25,4 mm. Two transmitter units of same type were used alternatively with the expectationthat both will lead to almost identical spectra of a SUT’s receiving sensitivity. Differences in the obtained
sensitivities have been obtained and some explained by differences in the uniformity of motion
distribution over the transmitter face.

1. Introduction
An easy-to-use, economic and reproduceable method for sensor sensitivity verification has
been a top desire of many users of acoustic emission phenomena for many years. AE sensor
calibration has been reviewed by Hill and Breckenridge [1,2] and the face to face (F2F) method
was described [1]. Dunegan published no details of his method, which were recorded by Ono’s
interview, reported in [3, 5]. Ono [5] made a systematic study of AE sensor calibration methods
and updated the F2F method using laser interferometry, instead of hydrophone calibration
used by Dunegan. The principle of the F2F setup and the sensitivity reference to “V/µbar”
originated from the hydrophone calibration of a standard transducer used in a F2F verification
by Harold Dunegan in 1968. However, no systematic study of the F2F method was publisheduntil Burks’ and Hamstad’s paper in 2015 [4] and Ono‘s paper in 2016 [5]. Ono clarified thecorrelation between reference scales of V/µbar, V/(m/s) and V/nm, so any one of them isworkable. However, a reference to particle velocity or displacement is preferred for AE sensors
as these are mounted to solid surfaces where particle motion is the appropriate input of thesensor.

2. Description of work
2.1

Block diagram of face-to-face setup

Fig. 2-1 shows the block diagram of the face-to-face setup. The arbitrary function generator(AFG, Keysight 33500) delivers a voltage pulse UT (blue curve in Fig. 2-5) to the transmitter,causing a transient motion in normal direction (90°), which is converted by the sensor under
test (SUT) to the output voltage USUT. Both, USUT and UT, are measured by a transient recorder(Picoscope 5442, set to: 2 chan, 20 MHz BW, 8 MHz low-pass, 15 bit, 50µs/div, 64 kS, for 8ns
sampling interval, later interpolated to 25 ns). The AFG amplitude was set to 4,9 Vpp. Due tothe high impedance of the transmitter, a maximum voltage of 9,8V was measured for UT. See
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Fig. 2-5. A mechanical tool centered transmitter and SUT without touching the active
transmitter face. A coupling agent warranted reproducible acoustic coupling. The SUT waspressed down onto the transmitter face by a 9 N cylindric weight.

Fig. 2-1 Block diagram of face-to-face setup

As transmitter the ultrasonic probe model Olympus V104 was used. The element diameter is25,4 mm, the frequency at maximum amplitude 2,25 MHz. We changed from model V103 with
12,7 mm diameter element to the larger one to achieve a more uniform motion distribution.
Two transmitter units (A and B) were used alternatively with the expectation that both will leadto almost identical spectra of a SUT’s receiving sensitivity.
2.2

LVM Measurements and signal averaging

The velocity of the particle motion normal (90°) to the transmitter’s face was measured by usinga scanning laser vibrometer (Polytec PSV 400), hereafter called “LVM”. Fig. 2-2 shows the
positions of 161 measured points:10 rings of 16 points each, plus one in the center. The
outermost ring covered a circular area of 25,4 mm diameter. For improved signal-to-noise ratio
200 measurements per position were averaged to one signal “velocity vs time”.
Sixteen time-series signals from sixteen positions per ring were averaged to one time-series
signal per ring. From center point (ring 0) outwards up to the ring number of interest (1 to 9),
the ring signals were averaged considering the proper weight per ring. Data of the outermost
ring 10 was not used, due to reduced signal-to-noise ratio. Analyzing data over a varied center
distance shall reveal, if so, a non-uniform motion distribution. For sensitivity verification, thering numbers averaged shall be adopted to the size of the sensitive area of the SUT.

Fig. 2-2 161 Measurement
positionsFig. 2-3 Block diagram of LVM setup

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Fig. 2-3 shows the block diagram of the LVM setup. The
arbitrary function generator (AFG) delivers the same
stimulation signal as used with the F2F setup, but the amplitude
set to 2 Vpp, triggered in burst mode in 10 ms time interval. A
34 dB post-amplifier (Falco Systems WMA-300) was used tosupply up to 100 V and up to 300 mA peak over theamplifier internal 50 Ω impedance to the transmitter. The
transmitter voltage was measured over a 39 to 1 voltage
divider by the LVM’s reference input.
The LVM filter was set to 10 kHz high-pass of “middle
quality”, the average count to 200, trigger to 10% of 200 µs
recording time. The distance LVM head to transmitter face
was 300 mm.
Fig. 2-4 shows the vibrometer while it scans a transmitter’s
face.
2.3

Fig. 2-4 LVM scanning thetransmitter surface

Stimulation signal

Fig. 2-5 Transmitter voltage (blue) and current (red), measured by Picoscope

In the F2F setup the transmitter was stimulated by the signal shown in Fig. 2-5 in blue. The
signal rise was shaped to cause a short unipolar Gaussian current pulse (red curve in
Fig. 2-5, 1 mV corresponds to 1 mA) through the transmitter. The shorter this pulse the higher
the probability that a non-uniform motion over the transmitter’s face, e.g. due to transmitterinternal reflections, can be revealed.
After the fast rise at t=1,2 µs in Fig. 2-5 the amplitude decays exponentially by 50 % per 18 µs.
Neither the peak current nor the spectrum of the current through the transmitter of the usedmodel changed measurably, whether the transmitter is used in free air or coupled to a solidmaterial.

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2.4

Signal interpolation

The LVM model used delivered 2,56 Msamples per second (MS/s) only. Hence, meaningful
LVM data can be expected up to about 1 MHz. For the calculation of a smooth time-series
signal, the 2,56 MS/s data from LVM was interpolated to 40 MS/s by using the “sinc” function(available with Matlab). Fig. 2-6 shows the samples from LVM as blue circles and theinterpolated curve as green line. All graphs shown in this work were produced from 40 MS/s
data. All FFT calculations used a FFT buffer of 32K zero padded samples, resulting in a FFTresolution of 1,2207 kHz (40 000 / 32 768).

Fig. 2-6 Transmitter voltage (normalized) from LVM data (blue circles) and 40 MS/s interpolated
signal (green line) and its 1st derivation du/dt (red line) that represents T-current

2.5

Windowing

Behind the averaging process a FFT windowing takes place for the optional removal of signal
parts before and behind the first main arrival.
Table 2-1 defines seven window signals identified by “W2” to “W8”, Fig. 2-7 shows the window
signal W2 with a flat range from -0,5 µs (pre-peak time) to +1 µs (post-peak time).
The meanings of the table header:Up (0-1):Pre-peak:Post-peak:
Down (1-0):

the number of microseconds the window signal takes for the sine-squared upramp from 0 to 1 before the pre-peak phase begins.
the number of microseconds the window signal stays at 1 before the positive or
negative maximum occurs.
the number of µs the window signal stays at 1 after the positive or negative
maximum.
The number of µs the window signal takes for the sine-squared down-ramp from
1 to 0.

Windowing takes place by multiplication of the input signal with the selected window signal.

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Up
(0-1)
µs
2
2
2
2
2
2
2

Id
µs
W2
W3
W4
W5
W6
W7
W8

Pre- Post- Down
peak peak (1-0)
µs
µs
2
0.5
1
2
0.5
1.5
2
1
2.5
2
1
4
2
1
11
5
1
50
10
2
100
5

1.2
1.0
0.8
0.6

0.4
0.2
0.0
-0.2

-3

-2

-1

0

1

2

3 µs

Fig. 2-7 Window signal W2, maximum at t=0

Table 2-1 Window definitions

300
mV
200
100
0
-100

-200
-300
-400
-500
0

5
10
3FB5W715

20
25
3FB5W630

35
40
3FB5W545

50
55
3FB5W2µs
60Fig. 2-8 Application of 4 FFT windows (W7, W6, W5, and W2, see last two characters in the legend)on the response of a resonant 150 kHz sensor.

Fig. 2-8 shows the response of a resonant 150 kHz sensor. Four window signals were applied:W7 (blue), W6 (brown), W5 (green), W2 (red), with the shorter window signals in foreground.
The maximum amplitude of all windowed signal arrives at t = 4 µs. The return to zero for theW2 signal is 3 µs later.
Fig. 2-9 shows for each signal in Fig. 2-8 the obtained displacement receiving sensitivity. The
shortest window (red) leads to the smoothest FFT. The longest window W7 (blue) leads to thehighest resonance at about 160 kHz, about 10 dB above the red FFT curve. It should be wellunderstood: The 10 dB higher maximum of the blue FFT curve does not indicate that the
maximum response amplitude is larger. The resonance peak of the blue curve in Fig. 2-9 at
160 kHz stems from the larger duration of low-amplitude reverberations fed into the FFT, as
seen in Fig. 2-8.

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dB
10
0
-10
-20
-30
0

100
200
300
400
3Rd FB5W7 dB(V/nm)
3Rd FB5W2 dB(V/nm)

500

600

700

800
900 1000
3Rd FB5W6 dB(V/nm)
3Rd FB5W5 dB(V/nm)

1100

kHz
1200

Fig. 2-9: Displacement receiving sensitivities derived from the time-series signals in Fig. 2-8.

3. Results
3.1

Glossary of signal names

SUT output signals having passed the windowing process are identified by a designation like:“1FA5W2” with the following meaning:“1” stands for a character of [1…6] identifying the concerned SUT type. In this work, only
data of SUT type 1 (V103) and SUT type 3 (VS150) are shown.
“F” stands for a character of [F, N, D] identifying the coupling configuration. In this work, only
data from coupling configuration F are shown.
“A” stands for a character of [A, B] identifying the transmitter involved“5”

stands for a character of [0...9] identifying the highest ring no included in the averaging
process. It shall match the radius of sensitive area of the SUT.
“W2” stands for the applied FFT window.
Velocity signals from LVM having passed the interpolation, averaging and windowing
processes are identified by a sequence of characters, beginning with “V” and followed by e.g.
“FA5W2” (same meaning as with SUT output signal 1FA5W2).
3.2

Comparing velocity signals of different ring nos in configuration F

Fig 3-1 top shows velocity signals from transmitter A, rings 9 to 0, with window W6 applied, intime domain. The legend shows the ring no to which the average is processed and the radiusbelonging to. The lower ring number curves are drawn last and overlay the previous drawn
curves.

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Fig 3-1 top: Velocity signals from transmitter A, rings 9 to 0, window W6 in time domain.
Bottom: the FFTs of the time domain signals in top graph

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Fig 3-2 like Fig. 3-1 but from transmitter B.

Fig. 3-2 shows the same as Fig 3-1 but the data are from transmitter B. It shows on top at
t = 18 µs, 8 µs after the main pulse, an additional spike with a maximum amplitude of 1 mm/s
(pp, 14 % of main pulse amplitude). The spike causes in the FFT (bottom) ripples of up to 5
dB (pp). The amplitude of the spike varies with center distance. It is highest in the center, curve
R0 yellow. That spike clearly indicates for transmitter B a less uniform motion distribution
compared to A.

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Fig 3-3 like Fig. 3-2 but window size shortened from W6 to W5.

Fig. 3-3 shows the application of the shorter window W5 compared to W6 in Fig. 3-2. The spike
is now eliminated from the velocity diagram because the window signal zeros it. However, thespike is still active and will cause a SUT to respond. Hence, a short window must also be
applied to the SUT signal, if the receiving sensitivity from transmitter B shall match that of A.

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3.3 Obtaining the receiving sensitivity of a SUT in Vs/m and V/nm
dB

50

40
30

20
10
0

-10
-20

-30
-40
0

100
200
300
SUT 1FA5W6 dB(mV)
Td FA5W6 dB(nm/V)
UTA(LVM-SUT)W8 dB
Rd KO 1612 dB(V/nm)

400

500
600
700
V FA5W6 dB(mm/s)
Rv FA5W6 dB(Vs/mm)
UTA(SUT) W8 dB(V)800

900 1000 1100 1200
kHz
Tv FA5W6 dB(mm/sV)
Rd FA5W6 dB(V/nm)
Tv KO1710 dB(mm/sV)

Fig. 3-4 Relevant FFT curves for sensitivity determination.

The ten signals shown above in Fig. 3-4 are hereafter described from top to down (at 100 kHzline) with reference to their legend below the graph. Lines in {…} are alternatives but not shownin Fig. 3-4.
SUT 1FA5W6(red) SUT response in dB referred to 1 mV.
Meaning of the code “1FA5W6”:
1: SUT 1 (V103 wideband UT probe, SNo -64),{3: SUT 3 (VS150 resonant sensor SNo -6379 )}FA: laser scanned free surface of transmitter A (V104-55),{FB: dito transmitter B (V104-09)}
5: area of rings 0 to 5 were averaged,
W6: FFT window W6 was applied.

UTA(SUT)(light gray) Voltage spectra at transmitter A during SUT measurement in dB
referred to 1V

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V FA5W6(dark blue) Velocity at free face of transmitter, scanned by laser vibrometer,
81 points averaged over rings 0 to 5 (6,3 mm radius), in dB referred to 1
mm/s.

UTA(LVM-SUT) (orange) Transmitter voltage “UTA during LVM measurement” minus “UTA
during SUT measurement” in dB.
Rv FA5W6(dark gray) Velocity receiving sensitivity in dB referred to 1 Vs/mm.
Rv = SUT 1FA5W6 – V FA5W6 + UTA(LVM-SUT) - 60
( -60 adjusts dB(mV) of SUT to dB(V) ).

Rd FA5W6(light purple) Displacement receiving sensitivity in dB referred to 1 V/nm.
Rd = Rv + 20*log10 (2 * π * f [Hz]) -120.
( -120 dB adjusts mm to nm )Rd KO1612(thin pink) Displacement receiving sensitivity in dB referred to 1 V/nm fromthe setup described in [5] for same SUT 1, provided by Prof. Kanji Ono, forcomparison.

Td FA5W6(light blue) Displacement transmitting sensitivity (of transmitter A) in dB
referred to 1 nm/V. Td = Tv – 20*log10 (2 * π * f [Hz]) -120.
( -120 dB adjusts mm to nm )Tv FA5W6(green) Velocity transmitting sensitivity (of transmitter A) in dB referred to
1 mm/Vs. Tv = V FA5W6 / UTA(LVM).
UTA(LVM) is the transmitter voltage during LVM measurement (not shownin graph). UTA(LVM) = UTA(SUT) + UTA (LVM-SUT), both are shown ingraph.

Tv KO1710(dark purple) Velocity transmitting sensitivity shown in [5] Fig. 5, provided
by Prof. Kanji Ono. This curve shows the transmitting sensitivity curve ofanother V104 sample for comparison. Small deviations between the curves
are normal.

3.4

Obtaining sensitivity differences when using transmitter B instead of A

For Fig. 3-5 data of velocity was taken from V FB5W6 (not shown) and for SUT fromSUT 1FB5W6 (not shown), using transmitter B instead of A.
The “velocity receiving sensitivity difference” is shown as thin red curve (signal “Rv FB-A5W6* 10”, see the legend of Fig.3-5, where “* 10” means the difference is multiplied by 10). It shows
three maxima: 2,2 dB difference at 250 kHz, 1,3 dB difference at 180 kHz and 1 dB difference
at 80 kHz. The difference stays below 1 dB above 300 kHz.
The “displacement transmitting sensitivity difference”, signal “Td FB-A5W6 * 10”, is the thinblue curve in Fig. 3-5 that shows a minimum of -1.5 dB at 250 kHz, several other minima of-1 dB, and several maxima that are all below zero.
One reason for the Td difference was already seen in the additional peak of the time-series
signal at 18 µs in Fig. 3-2. Improvement seems possible when the transmitters are selected
for minimum difference in Tv or Td.

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dB

20
10
0
-10
-20

-30
-40
0

100

200

300

400

Rv FB-A5W6 * 10
Td FB5W6 dB(nm/V)

500

600

700

800

Td FB-A5W6 * 10
Rv FB5W6 dB(Vs/mm)

900

1000

1100

kHz
1200

Tv FB5W6 dB(mm/sV)
Rd FB5W6 dB(V/nm)

Fig. 3-5 Transmitting and receiving sensitivites using transmitter B and deviation to

3.5

Differences in receiving sensitivity of a resonant SUT
5
dB
4
3
2
1
0
-1
-2
-3
-4
-5
-6
0

100
200
300
3Rd W7 B-A Sd: 0.82

400

500
600
700
3Rd W6 B-A Sd: 0.81800

900 1000 1100 1200
kHz
3RD W5 B-A Sd: 0.56

Fig. 3-6: Differences of R-sensitivity of a 150 kHz sensor, obtained by usingtransmitters A and B with windows signals W5 to W7.

Fig. 2-8 and Fig. 2-9 (page 5) show data from a 150 kHz sensor using FFT windows W7, W6,
W5, and W2. Fig. 2-8 shows the time-series signals that exhibits reverberations initiated by the

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main pulse. The peak sensitivity for the main pulse is that shown in red in Fig. 2-9. The peaksensitivities shown in brown and blue stem from the lower amplitude reverberations.
Fig. 3-6 shows the differences in displacement receiving sensitivity, when using different
transmitters (A and B) and different windows (W5 to W7). With W7 we see differences of up to
4 dB in the interesting range of 50 to 300 kHz. With W5 we see only one substantial differenceat the strong resonance dip, outside of the interesting frequency range 50 to 500 kHz.

4. Discussion
4.1

Windowing

Windowing is a tool to separate a SUT’s sensitivity on the short main pulse motion against thesensitivity on the full response comprising main pulse plus reverberations and reflections.
Example: Compare red curve 3Rd FB5W2 in Fig. 2-9, at 160 kHz (+1,4 dB), with light purple
curve Rd FA5W6 in Fig. 3-4 (-14 dB). One would correctly expect that VS150 delivers amaximum amplitude that is about 15 dB above that of V103. If one would compare with VS150
sensitivity 3Rd FB5W7, which is about 11 dB higher than W2 sensitivity, he would overestimate
the expected amplitude from VS150, compared to V103, by 11+15 = 26 dB!
Other purposes to separate the pure main pulse response from the full response:

4.2•

to extract that part of information that is not influenced by any kind of post pulse motion,•

to eliminate the effect of disturbing spikes, e.g. from non-uniform motion distribution(Fig. 3-2).

•

to obtain the true sensitivity of a resonant sensor for the detection of a short main pulse,(the example mentioned above)!

•to allow for measurement of cancellation effects, e.g. the aperture effect, e.g. whenusing a small transfer block, (not treated in this work).

•

To visualize the spectrum that initiates reverberations versus the spectrum that
includes initiation and post pulse motion.
Motion reference

A laser vibrometer measures normal (90°) motion on a free surface. When a sensor is mounted
to that surface, the motion between sensor and surface will be modified by the sensor’s
mechanical load. This raises the question, whether a SUT’s sensitivity shall be scaled to the
motion of the free surface or to the real motion at the SUT’s surface, when it is mounted to thetest block. The most recent standard on primary sensor calibration [6] deals with this topic andstates:“Since AE sensors are used to monitor motion at a free surface of a structure andinteractive effects between sensor and structure are generally of no interest, the freesurface motion is the appropriate input variable. It is, therefore, recommended that theunits of calibration should be voltage per unit of free motion; for example, volts permeter.”
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This justifies to scale the sensor sensitivity to the free motion measured by LVM even though
the motion at the sensor-loaded surface differs.

5. Conclusion
The velocity measurement by using a scanning laser vibrometer allows for obtaining an
absolute and trustworthy sensitivity to the normal (90°) motion of the free surface of atransmitter. Sensitivity is expressed in absolute units of volts per nanometer particle
displacement, or volts per meter per second particle velocity scaled to the free surface motion.
The uniformity of motion distribution over the transmitters’ active face differs between unit A
and B of same transmitter model, what can cause differences of several dB in the obtained
sensitivity of a SUT. Fig.3-5 reports differences of a wideband SUT (V103) of up to 3,5 dB(peak peak), when using window W6, Fig. 3-6 reports differences for a resonant sensor(VS150) of up to 4,6 dB (peak peak), when using window W6. We think for getting wide
acceptance, these differences need to be reduced, e.g. by using better matching transmitter
units.
Main points treated in this work:
A layout for LVM measurement positions for laser vibrometry has been designed, for an optimal
averaging of the motion over a circular array of variable diameter.
A shape of stimulation pulse has been designed, causing an extremely short main responsecomprising frequencies down to less than 10 kHz.
Problems with the LVM digitizer rate of only 2,56 MS/s have been solved by an interpolation
process to 40 MS/s.
The signal windowing can process windows of a few µs up to 100 µs length meaningfully andreproducibly.
The obtained receiving and transmitting sensitivities have been compared with absolutecurves, kindly provided by Prof. K. Ono, and match these.
This reports the current (1807) status of our work towards a practical sensor verification
method.

Acknowledgement
We are thankful to Professor Kanji Ono for his excellent advise and for providing us two curves
included in Fig. 3-4 for comparison with our results, identified in the legend by “Tv KO1612”
and “Rd KO1710”.

References:[1]

R. Hill, “Reciprocity and Other Acoustic Emission Transducer Calibration Techniques,”
JAE 1-73 (1982)

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[2]

F. R. Breckenridge, “Acoustic Emission Transducer Calibration by Means of the Seismic
Surface Pulse,” JAE 1-87 (1982)[3]

K. Ono, H. Cho, T. Matsuo, “Experimental Transfer Functions of Acoustic Emission
Sensors,” JAE 26-72 (2008)[4]

B. Burks, M. A. Hamstad, “An Experimental-numerical Investigation of the Face-to-faceSensor Characterization Technique.” Mat Eval. 2015, 73, 414-423.

[5]

K. Ono, “Calibration methods of acoustic emission sensors,” Materials, 2016, 9, 508.

[6]

ASTM E1106-12 (2017), “Standard Method for Primary Calibration of Acoustic Emission
Sensors,” ASTM International, West Conshohocken, PA, USA, 2016; 12p

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(This paper was revised on February 6, 2019.)

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76 - Method for assessing the likelihood of fatigue crack detecting
V.A. Barat1, D.V. Chernov1, S.V. Elizarov2
1

2

NATIONAL RESEARCH UNIVERSITY "MPEI", MOSCOW, RUSSIAN
FEDERATION

LLC «INTERUNIS-IT», MOSCOW, RUSSIAN FEDERATION INTERUNIS-IT LLC,ENTUZIASTOV SHOSSE 20B, MOSCOW, 111024, PO 140, RUSSIA

Abstract:
An estimation of the probability of defect detection (POD) is an actual task of acoustic emission (AE)testing. Despite the fact that AE testing is the most sensitive method of NDT, the POD is difficult toestimate. The probability depends on a set of random factors also not directly related to the size of thedefect. The probability of detecting defects is currently being studied by many researchers, but the
proposed approaches are not yet applied in practice.
In the frame of this work, the method allowing to estimate the probability of fatigue crack with help of
AE testing method was developed. This method allows to calculate the probability of fatigue defectdetection taking into account the following factors: the stage of defect growing, the distance between
AE source and sensor and the noise level.

1. Introduction
The great numbers of industrial facilities operate under mechanical or thermal cyclicloads. Long-term exploitation under cyclic loads leads to formation and accumulation of
fatigue damages which degrade strength properties and working lifespan of facilities. A
fatigue crack develops unevenly as it goes through initial, stable power growth and unstable
growth stages. The last stage - unstable fatigue crack propagation - results in the rapid
failure of the structure, so precise detection of fatigue crack is a very important task. During
every stage a fatigue crack is a source of acoustic emission and could be efficiently detectedby AE testing. AE of fatigue damages is investigated in many papers [1-5], generally
development of diagnostic model, i.e. correlation between AE parameters and mechanicalproperties, as damage rate and crack growth rate, is considered.
The purpose of this paper is analysis of accuracy for fatigue cracks detection against
noise. Experimental research of AE during cyclic loading of metal specimen till its destruction
was conducted. Experimental investigation of AE during cyclic loading of metal samples
before its destruction was carried out. Specimens made of alloyed low-carbon steel 09G2S
were tested with an Instron Testing System. The amplitude of the loading cycle was varied
from 50 to 70% of the yield point. This loading scheme in some approximation repeats theoperational mode of the industrial structure.

1

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The influence of maximal load in cycle, load rate and stress ratio on AE parameters wasinvestigated. On the basis of experimental AE parameters was calculated and statisticalmodel of AE – source was constructed.
Function of the propagation path was approximately evaluated by means of attenuation
coefficient as a function of distance between the sensor and AE source. Attenuation
coefficient was empirically estimated during calibration measurements on a gas pipeline.
The probability for crack detection on its different stages was calculated in the form of one
and two-dimension POD-diagrams. The calculated POD – diagrams allow to estimate theprobability of detection for fatigue damages on a various growth stages upon specificconditions of AE testing. It also make possible to choose the maximum distance between thesensors and threshold value to provide defined probability of fatigue crack detection.

2. Materials and Materials and methods of research
In order to solve the task, a series of laboratory tests on the fatigue strength of thenotched specimen was carried out. Specimen were made from low-carbon steel 09G2S,
which is used in the production of a wide range of industrial structures, for example, tanks,
vessels and pipelines. The choice of the shape and geometric dimensions of the specimen
was based on the recommendations described in the State Standard [6].

Fig. 1 The sample installed in the clamps of the testing machine

The thickness of the specimen was 3 mm, width 50 mm, and the length – 350 mm.
Fatigue load was carried out on Instron 8801 servo-hydraulic testing machine. To reduce thenoise of the test machine, metal pads with dimensions of 50x110x3 mm with 4 through-holes
were used (Fig. 1). The specimen was loaded according to a specially developed loading
scheme, the parameters of which are similar to the loads of industrial structures – pipelines,
tanks and vessels. Fig. 2 shows the main stages of the loading scheme.

2

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Fig. 2 Loading scheme
The first stage is a single cycle of a trapezoidal shape, including a load lifting and rejection
section, as well as two holding sections of duration t1 = 60 s and t2 = 60 s at maximum(𝑷𝑷𝒎𝒎𝒎𝒎𝒎𝒎) and zero load, respectively. A preliminary loading cycle is necessary to evaluate theacoustic activity of noise sources. It checks the efficiency of the measuring equipment,
clarifies the level of mechanical noise that occurs when the electric drive of the loading
installation is operated [7]. The second stage is a sequence of cycles of a triangular shapewith a frequency from 0.5 to 2 Hz. The maximum load 𝑷𝑷𝒎𝒎𝒎𝒎𝒎𝒎 is taken as it is in operatingstresses arising during operation of main oil pipelines.
The value of 𝑷𝑷𝒎𝒎𝒎𝒎𝒎𝒎 varies from 19 to 23 kN, and the R-ratio from 0 to 0.5. The measuring of
AE was carried out by the industrial system A-Line 32D of «INTERUNIS-IT» LLC with theresonant sensors GT200 and preliminary amplifiers of the electrical signal PAEF-014 with again of 26 dB. Taking into account the noise of the loading machine the threshold value waschosen equal to 40 dB. To determine the length of the fatigue crack during the cyclic loadingof metallic samples, an optical method was used. Using an endoscopic chamber, aphotograph was taken at intervals of 5 seconds from one of the side surfaces of the sample,
and the length of the fatigue crack was determined from the obtained photographs. Totally 45
fatigue experiments were conducted.
3. AE data analysis
In the results of laboratory tests, there is a considerable scatter of both the fatigue
characteristics of the material and the values of standard AE parameters. Fig. 3 shows thedependence of the cracks size on the number of loading cycles.

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crack length, mm

40
35
30
25
20
15
10
5
0

0

2000

4000
6000
cycle number

8000

10000

Fig. 3 Dependence of the cracks size on the number of loading cycle

As shown in Fig. 3, the destruction of a metallic sample under loading by the same scheme
can occur for a different number of cycles from 6500 to 8100. Such a scatter could becaused by the structural heterogeneity of the material (the presence of internal defects,
nonmetallic inclusions, etc.), leading to a change in the kinetics of fatigue crack.

Fig. 4 Typical KDFF, registered during experiment: dl/dN – the crack length l change during N loading
cycles; ΔK – stress intensity factor peak-to-peak range

The fatigue crack growth rate is described by the kinetic diagram of fatigue failure (KDFF) [8],the shape of which is shown in Fig. 4.
The two linear parts of the KDFF correspond to a stable and unstable stage of crack growth.
Monitoring of the stage of crack initiation is impossible due to the limited sensitivity of optic
and acoustic equipment.
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250001000

20000
AE activity

Cummulative hit countAt each stage of development, the fatigue crack is the source of acoustic emission. The
sensitivity of the AE testing system made it possible to detect the impulses that arise during
the development of the fatigue crack already at the stage of stable growth at a length of 300500 μm and a growth rate of the order of 1 μm per cycle.

15000
10000

100
10

5000
0

0

1

2000 4000 6000
Cycle number

0

5000
10000
Cycle numbera.

b.

Fig. 5 Dependence of cumulative hit counts and AE activity vs. loading cycle number

Comparison of KDFF and the trends of AE parameters show the similar consistent patterns.
At the stage of stable growth of the fatigue crack, the acoustic emission process is stationary
and characterized by low activity of the order of 0.5-5 impulses per loading cycle, theaccumulation of impulses occurs predominantly linearly. At the stage of the rapid growth, thenumber of AE impulses increases avalanche-like, the activity value reaches about 100
impulses per cycle. The dependences cumulative hit count vs. cycle number and AE activity
vs. cycle number is shown on the Fig.5 a,b.
The distribution of the amplitudes of AE impulses is shown on Figs.6a, b. Fig 6a corresponds
to the stable crack growth, Fig.6b to the rapid growth stage.
16000

7000

14000

6000

5000

Number of impulses

Number of impulses

12000
10000
8000
6000

3000

2000

4000

1000

2000
0

4000

42

45

48

51

54

Amplitude, dB

57

60

63

0

a.

42

45

48

51

54

57

60

Amplitude, dB

63

66

69

72

75

b.

Fig.6 Histograms of amplitudes distribution at different stages of fatigue crack growth a. for stablestage b. for accelerated growth

A sufficiently low level of amplitudes is noted during the fatigue test for the most specimen no more than 55 dB for the stage of stable growth, not more than 70 dB for the stage ofaccelerated growth.
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It is noted that in all experiments the parameters of the AE are changed in accordance withthe changing of the crack growing stage. At the stage of stable growth, comparatively low
activity and low amplitudes of AE impulses are noted, with the propagation of the fatigue
crack to the final stage of development, the number of impulses and their amplitudes
substantially increase. The averaged AE parameters are given in Table 1
Table 1
Activitystable crack rapid
crack
growth
growth
1.4±0.855.3±6,3
1.2±0.956.5±8,5
0.9±1.1
61.5±5,5
0.8±1.260.8±9,5
1.0±1.4
57.4±4,6
1.1±1.5
52.4+6.5
0.9±0.862.3±7.8

Average amplitude, dB
stable crack rapid
crack
growth
growth
45.8±3.456.5±9.4
46.4±2.4
58.6±5.645.2±3.6
55.6±7.846.0±3.2
57.6±10.444.9±2.561.2±6.8
47.5±2.3
59.9±8.6
44.7±1.858.4±8.7

Experimental conditions
Pmax, kN
f, Hz
R
23
23
23
21
19
21
21

1
2
0,5
1
1
1
1

0
0
0
0
0
0,25
0,5

Analysis of the results shows that the AE parameters have a significant variance in eachseries of experiments. While the maximum cyclic loading, loading frequency and the ratio R
do not significantly affect the average values of the amplitudes and activity of the AE
impulses. For example, the average activity value at the stage of stable crack growth for any
loading parameters is approximately equal to one impulse per cycle. While, the variance of
this parameter is significant and even exceeds the average value. The same trend is also
observed for other parameters.
The variance of the AE data, which is observed in a number of experiments, may be
explained by the influence of the metal microstructure. Fractographic analysis carried out
within the framework of this study allows to suppose the presence of the secondary sourcesof AE, creating additional AE activity. Nonmetallic inclusions, microcracks present in themetal, lateral cracks and stratifications can be a secondary sources of AE impulses
When comparing the results of fractographic analysis and AE data, a number of regularities
were established. In particular, it was noted that the presence of nonmetallic inclusions leads
to a short-term, up to 5-10 cycles of loading, generation of high-amplitude AE impulses, with
an amplitude reaching 80 dB. Fig.7b shows the dependence of the amplitudes of AE
impulses vs. the number of the loading cycle. The moment of the occurrence of highamplitude AE impulses during the 900th and 1100th loading cycles corresponds to the
propagation of the crack front through the nonmetallic inclusion shown on the fractographic
image.

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Amplitude, dB

120
110
100
90
80
70
60
50
40

0

2000
4000
cycle number

6000

Fig. 7 a. Nonmetallic inclusions on the fractographic image; b. Dependence of AE impulse amplitudes
vs. cycle number

In some experiments, activity during the stage of the stable crack growth increases in theseveral times and reaches 10 impulses per cycle. The fractographic and metallographyevaluations of the specimen for which this phenomenon was observed show the presence of
the additional cracks initiated presumably due to non-metallic inclusions. These cracks
whose lengths do not exceed 300 µs are shown at the metallography image (Fig.8a),
dependence rate of AE impulses vs. time with an anomaly increasing of AE activity at thestable stage of the crack growth is shown on the Fig.8b.

Fig. 8 a. Metallographic image of the additional fatigue crack b. increasing of AE activity vs cycle
number due to the presence of additional defects

Another atypical case of the trend of changing AE parameters is the appearance of samples
in the amplitude spectrum of AE impulses, which, firstly significantly exceed the averages
level, and secondly, have practically constant values in a certain time interval. Illustration forthis case is shown on Fig.9. The amplitudes of the part of AE impulses increase until 70 dBin the period from 1500 to 7000 loading cycle.

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Fig. 9 The secondary AE process on the dependence of impulse amplitude vs. time with thesecondary high amplitude AE processThis phenomenon was explained by the influence of the friction effects caused either by the
Elber effect, or the crack face contact, or the effect caused by the falling of nonmetallic
inclusions into the pores and stratifications that have arisen upon the destruction [11]. The
above-described cases of atypical changes in the AE parameters, on the one hand,complicate the interpretation of the AE data and the classification of the AE source, but on
the other hand, contribute to a more reliable identification of fatigue defects due to generation
additional AE impulses.

4. Results
Based on the results of 45 experiments, it is possible to estimate the probability of detecting
fatigue defects by the AE method. The probability of detection depends on multiple factors, inparticular, on the stage of fatigue crack growth, the distance between the sensor and thedefect, on the properties of the propagation path and the noise level.
For defect detection by AE method, it is necessary that the sensitivity of the AE testing
system is sufficient to register the impulses emitted by a defect after the energy dissipation
caused by the passage of AE signals through the propagation path [12].
𝑠𝑠(𝑡𝑡) = 𝑢𝑢𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (𝑡𝑡) ∗ ℎ𝑐𝑐ℎ𝑎𝑎𝑎𝑎𝑛𝑛𝑒𝑒𝑒𝑒 (𝑡𝑡) ∗ ℎ𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (𝑡𝑡)
𝐴𝐴𝐴𝐴_𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 = 𝑇𝑇ℎ𝑟𝑟𝑟𝑟𝑟𝑟ℎ𝑜𝑜𝑜𝑜𝑜𝑜(𝑠𝑠(𝑡𝑡), 𝑡𝑡ℎ𝑟𝑟_𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙)

(1)
(2)

The AE signal that is detected by the sensor can be represented as a result of convolution of
the AE source function usource(t) with the transfer function of the propagation path hchannel(t)and the impulse response of the AE sensor hsensor(t) (1). Further, as a result of thresholdprocessing, detected AE impulses are represented as a number of AE parameters (2).
Within the framework of the study, the assumption is made that a defect is detected if the AE
system measures more than N impulses (N = 10). The probability of detecting a fatigue crack
is equal to the percentage of defects for which the number of AE impulses will be greaterthan N=10 after taking into account attenuation factor and threshold level.

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Number of AE
impulses

100000
10000
1000
100

0

10

20

30

40

50

speciment number
Fig.10 Number of AE impulses measured during the stable crack growth represented in the form of theordered series

The function of AE source was estimated empirically on the basis of the results experiment.
As was shown in the previous section, a fatigue crack in a low-carbon steel is the AE sourcewith relatively low-amplitude impulses, 95% of impulses have an amplitude of less than 55
dB on a stable growing stage. The number of impulses registered during the stable growthstage is characterized by a significant spread of the values observed from the specimen to
the specimen. The empirical distribution of the number of impulses for the stable stage ofcrack growth in the form of the ordered series is shown in Fig.10. The greatest AE activity isobserved in the case of the presence of secondary AE sources lateral cracks, cracks facecontact or the influence of the nonmetallic inclusions. The lowest AE activity is observed in
more homogeneous materials. It should be noted that the influence of the sensor transfer
function has already been taken into account as the AE data was obtained experimentally.

attenuation coefficient, dB

The transfer function of the acoustic channel was evaluated approximately using theattenuation function of the AE waves from the distance until 11 meters in the frequency bandfrom 100 to 500 kHz.
45
40
35
30
25
20
15
10
5
0

0

5

10
distance, m

15

20

Fig.11 The attenuation coefficient depending on the distance between sensor and AE source

The dependence of the attenuation coefficient vs. distance is show on Fig.11. In the nearfield, the attenuation coefficient is 8 dB per meter, in the far field - 1.5 dB per meter.

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The probability of a defect detection was calculated using the following algorithm. The
amplitudes of the AE data obtained as a result of the experiment were recalculated to acertain distance, in accordance with the attenuation parameter of the propagation path
(Fig.11). The influence of noise was taken into account by setting the threshold value. It was
assumed that the noise level did not exceed the threshold value. All impulses which turned
out to be below the threshold were excluded from consideration. The probability of detecting
a defect was determined as the number of defects for which the detection criterion is met
divided by the total number of defects.

probability of detection

The results of calculating the probability of defect detection at different stages of the crack
growth are shown in Figs. 12 and 13. The dependence of Fig.12 shows the probability of adefect detection at different distances between the AE source and the sensor at different
noise levels during the stage of stable growth of the fatigue defect. The same dependencies
for the stage of accelerated growth of the fatigue defect are shown on Fig.13.
1,2
1
0,8
0,6
0,4
0,2
0

0

5

10
distance, mthreshold=40 dB

threshold=45 dB

15

20

threshold=50 dB

probability of detection

Fig.12 Probability of fatigue crack detection on the stable growing stage depending on the distanceand threshold value
1,05
1
0,95
0,9
0,85
0,8
0,75
0,7
0,65
0,6

0

threshold=45 dB

5

10
distance, mthreshold=50 dB

15threshold=55 dB

20

threshold=60 dB

Fig.13 Probability of fatigue crack detection on the unstable growing stage depending on the
distance and threshold value
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As can be seen from the figures, reliable detection of the fatigue defect at the stage of its
stable growth is possible only if the sensor is placed in the immediate vicinity of the source.
With a probability of 90%, a fatigue defect in low-carbon steel can be detected at a noiselevel of 40 dB at a distance of 2.5 m from the source, if the noise level is 45 dB, then the
distance corresponding to the 90% probability decreases to 1.5 m and with noise which does
not exceeded 50 dB to 1 meter.
For the stage of the unstable defect growth, more optimistic results were obtained. At the
final stage of destruction more high-amplitude signals are generated, so the distance of areliable detection of the fatigue defect essentially increases. At a noise level of 45 dB, thefatigue defect is detected with a probability of 90% at a distance of 11 m, with a noise level of
50 and 55 dB at distances of 8 and 4.5 m correspondingly. Even with a noise level of 60 dB,
the fatigue defect is reliably detected at a distance of not more than two meters from thesource.

a.

b.

Fig.14 Two-dimensional representation of the POD diagrams (threshold level = 40 dB) a. POD forstable growing stage b. for the unstable growing stage

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For practical use, the two-dimensional version of POD diagrams is more preferable. Fig. 14
shows the POD diagrams depending on the distances from the sensor located in the centerof the diagram.
5.

Conclusion

The method of calculating the probability of detection of fatigue defects in low-carbon steels
by the AE testing method is considered in this paper. The probability estimation was
conducted taking into account the empirical function of the AE source, the attenuation
parameter of the propagation path, and the noise level.
Despite the fact that not all the influenced factors have been investigated in detail, the results
obtained are of practical importance. The calculated POD – diagrams allow to estimate theprobability of detection for fatigue damages on a various growth stages upon specificconditions of AE testing. It also make possible to choose the maximum distance between thesensors and threshold value to provide defined probability of fatigue crack detection.
Acknowledgment
Research work was carried out during the implementation of the State order project of the
Ministry of Education and Science of Russian Federation in the field of scientific activity №
11.9879.2017/8.9.
References[1]. Ido N., Tagami M., Katou K., Tsuji T. Evaluation of fatigue crack propagation rate byacoustic emission method / 7th International conference on NDE in relation to structural
integrity for nuclear and pressurized components.2009.
[2]. Fady F. Barsoum, Jamil Suleman, Andrej Korcak, Eric V. K. Hill. Acoustic emission
monitoring and fatigue life prediction in axially loaded notched steel specimens // Journal of
acoustic emission. 2009. Vol. 27. P. 40-63.
[3]. Dimitrios G. Aggelis, Evangelios Z. Kordatos, Theodore E. Matikas Monitoring of metal
fatigue damage using acoustic emission and thermography // Journal of acoustic emission.
2011. Vol. 29. P.113-122.
[4]. Claudia Barile, Caterina Casavola, Giovanni Pappalettera, Carmine Pappalettere Fatigue
damage monitoring by means of acoustic emission and thermography in Ti grade 5
specimens // Procedia Engineering. Vol. 114(2015). P. 487-492.
[5]. Zain M.S.M., Jamaludin N., Sajuri Z., Yusof M.F.M., Hanafi Z.H. Acoustic emission studyof fatigue crack growth in rail track material // National Conference in MechanicalEngineering Research and Postgraduate Studies (2nd NCMER 2010) 3-4 December 2010,
Faculty of Mechanical Engineering, UMP Pekan, Kuantan, Pahang, Malaysia; pp. 82-90.

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[6]. GOST 25.506-85. Methods of mechanical testing of metals. Determination of fracture
toughness characteristics (fracture toughness) under static loading.
[7]. Building norms and rules: SNiP 2.05.06-85. Trunk pipelines [Text]: normative andtechnical material. – Moscow, 1985. – 89 p.
[8]. Matvienko Yu.G. Models and criteria for fracture mechanics. - M .. FIZMATLIT, 2006. –328 p.
[9]. Terentyev V.F., Korableva S.A. Fatigue of metals. Moscow: Nauka, 2015. - 479 p.
[10]. Parton V.Z. Mechanics of destruction: From theory to practice. Ed. 3rd. Moscow:
Publishing house LCI, 2010. - 240 p.
[11]. Guohao S., Zhengxing W., Delian K., Mingqing B., Yang Zh., Fengjing Xu., Jiazhen P.
Analysis on acoustic signal characteristics of the fatigue crack in anchor structure of cablestayed bridge stayed bridge tower. Russian Journal of Nondestructive testing, N12, 2012[12]. A. Pollock Probability of detection for acoustic emission./Journal of acoustic emission,
V25, 2007, pp.231-237

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STATISTICAL
ALGORITHM
FOR ACOUSTIC
78 - StatisticalNON-PARAMETRICAL
non-parametrical algorithm
for Acoustic
Emission
EMISSIONIMPULSESDETECTION
ADAPTEDON
THE BASIS
OF
impulsesdetection
adaptedon the basisof modalanalysis
approach
MODAL ANALYSIS APPROACH
V.A. Barat1, D.A. Terentyev1, S.V. Elizarov1
1

INTERUNIS-IT LLC, Entuziastov shosse 20B, Moscow, 111024, PO 140, Russia.
E-mail: tyev@interunis-it.ru

Abstract:
This work is devoted to the study of a nonthreshold method for detecting acoustic emission impulses.
As a method of impulse detection, a statistical ranking algorithm based on comparing the ranks ofsamples in two neighboring sliding windows is considered. To select the setting parameters of thealgorithm and verify its reliability, the modal analysis method is used. The modal analysis is amathematically exact method that leads to a closed integral equation for the elastic fields in the
waveguide and which naturally includes the problem of the selectivity of the mode, offering an idea ofthe physics of waveguide behavior. The non-threshold algorithm developed in the framework of thispaper was tested using 10000 model signals. The results of the research showed its high reliability
and accuracy. A numerical experiment based on model signals showed that using the proposed
nonparametric rank detection algorithm, in comparison with the threshold method, the location error
has decreased by 1.5-2 times.

1. Introduction
Among many methods of nondestructive testing the acoustic emission (AE) is one of themost complicated in the field of data processing. The raw data volume could be as big as
several terabytes whereas the noise part in it is generally about 90-99%. So the upgrading ofAE impulse detection methods is a critical task. Threshold method is a convenient one, as it
is simple for realization, but it has substantial drawbacks, such as inaccuracy of measured
arrival time and other AE parameters. This method easily provides low level of false
detections, i.e. type I statistical errors. But if the noise level is high then it leads to high levelof type II statistical errors, i.e. undetected AE impulses.
Threshold-free algorithm, based on signal detection theory, would allow to substantiallydecrease the level of missed AE impulses as the level of false detections is set. Efficiency isprovided by method intellectualization, as detection of AE impulses is based on its typical
waveforms. Development of such an algorithm has a main problem in great variability of AE
impulse parameters; it is induced in general by unknown acoustic propagation path.

2. Description of work
2.1. Non-parametric algorithm of AE impulse detection

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A statistical non-parametric algorithm for the detection of AE impulses is proposed as analternative to traditional threshold data recording. It allows to detect AE impulses in an
automatic mode without setting a threshold value.
Nonparametric detection methods [1] are effective for detecting AE impulses whose shapeand parameters are a priori unknown. The methods are based on comparing the parametersof the AE signals distribution in the time intervals which can be neighboring, overlapping or inintervals of different time scales. In this case, the difference in the distribution parameters isverified by means of a statistical hypothesis. If the difference in distributions is ascertainedwith a high probability, the AE impulse is recognized.
Nonparametric algorithms are widely used in the practice of AE data processing. An example
of nonparametric analysis can be the use of LTA / STA technology, the Akaike criterion andthe method of higher-order moments as hypothesis testing criteria [2-4]. As works devoted to
non-parametric detection of AE pulses, we can also mention [5] and [6], in which the authors
use the sequential probability ratio test and autoregressive analysis.
In this paper, we investigate a ranking impulse detection method, based on comparing theranks of the sample values of the AE signal in neighboring shifting time intervals. With thecorrect choice of the observation interval, comparison of ranks is a sensitive method of
detecting AE impulses, which are represented by short-time burst parameter changes in thelong-time noise process.
0,004
0,003
0,002

U, V

0,001
0

-0,001-0,002-0,003-0,004

0

1000

2000

3000

4000

5000

6000

time, µs

Fig.1. AE impulse against background noise, two time intervals are marked with color
rectangles
In Fig.1 AE impulse with a characteristic waveform and corresponding to the growth of afatigue defect is shown. The two time intervals allocated for comparing the distribution
parameters are indicated by a red and blue rectangle, respectively. The left window contains
noise samples. The onset of the impulse belongs to the right window. The samples of bothwindows are grouped together, sorted in ascending order and then samples taken from left
window are replaced by zeros and samples taken from right window are replaced by ones.
Fig. 2 shows the result of such operation.

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[0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,1,1,0,0,0,1,1,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]

Fig. 2. The processed ordered series for the samples from the neighboring windows, zeroes
correspond to the left window, ones for the right
Since the samples of the left window are grouped mainly at the left part of the ordered series,
and the samples of the right window mainly at the right part, the probability distributions in
the neighboring time intervals are different. The difference in the nature of the distributions isestablished using rank statistics, such as the Spearman test, the Kendal criterion [1], with acertain level of defined confidence. When implementing the algorithm, the rank statistics waschosen in such a way as to ensure a level of false detections of not more than 1 pulse per
100 s of observation, provided that the noise has a colored spectrum and a distribution isclose to normal.
One of the features of ranking algorithm implementation is the choice of the duration of theobservation interval Twindow in accordance with the parameters of the analyzed signals. If theobservation interval Twindow significantly exceeds the duration of the AE impulse, thecontribution of the impulse count to the overall distribution of the time interval samples may
be insignificant, which can lead to a missed pulse, as well as to a significant error in
determining the beginning of the signal. On the other hand, if the observation interval is too
short, the change in the distribution parameters may be statistically insignificant, which isalso dangerous for the potential missing of the AE impulse.
It should also be noted that in the ranking algorithm, unlike the threshold detection algorithm,
the key parameter of the AE impulse will be not the amplitude, but the rise angle (RA) andrise time (RT), characterizing the rate of the AE impulse leading edge. It is precisely the short
RT and small RA parameters in AE impulses that differ from the majority of acoustic noisesthat are either stationary or characterized by slow growth and decay trends. In previous
studies [7-8], it was shown that the best result of detection of AE pulses is achieved if theduration of the time interval Twindow corresponds to RT/2. However, the high probability ofdetecting AE pulses remains for the relative wide time interval from 0.1 to 2 rise timedepending on the rise angle and noise parameters against which the AE impulse isobserved.

а)b)

Fig.3. a) Continuous friction noise. b) AE signals from the fatigue strange experiment
The correct choice of Twindow makes it possible to detect AE impulses against the nonstationary impulse noise, if these processes correspond to various time scales. Fig. 3ashows the intensive noise process generated by the friction of the rotating mechanisms ofthe dynamic equipment, in Fig. 3b for comparison, a signal containing AE impulses from thegrowing fatigue crack is shown. Impulses corresponding to friction and impulses
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corresponding to the defect do not differ in amplitude, but differ significantly in timeobservation scale. The AE impulses have a relative small RT and RA, while theseparameters for for friction noise process are substantially higher.
Fig. 4 represents a scatterplot with the quantitative analysis result. On the dependences RA
vs RT the points corresponding the noise are marked with the red color, the AE impulses
from the crack growth are marked in blue. As you can see, the parameters RA and RT aresignificantly different and do not overlap on the scatterplot.

Rise angle, ms/V

100

10

1

1

10

100

1000

10000

100000

0,1

0,01

0,001

Rise time, µs

Fig.4. Scatterplot Rise angle vs Rise time for AE signal (blue points) and friction noise (redpoints)Such a difference in parameters makes it possible to detect AE pulses against the friction
noise due to the correct choice of the time interval Twindow. Fig. 5 shows the result of applying
a ranking algorithm to processing a friction noise signal. Since the process is completely
noise, all detected pulses correspond to false detections. Fig. 5 represents the dependenceof the number of false detections on the duration of the observation interval. If the Twindow
value is comparable with the RT of the noise signal, the number of false detections reaches
hundreds. If Twindow parameter decreases by an order in comparison with RT, the number offalse detections also decreases by approximately an order.

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False detection number

400

300

200

100

0

0

128

256

384

512

640

768

896

1024

Twindow, µs
Fig.5 The number of false impulse detections depending on the duration of time interval
Effective detection of AE pulses against the friction noise is achieved, in this case, due to theintellectuality of the non-threshold ranking algorithm of AE impulse detection. This algorithm
allows indirectly, due to the choice of the observation interval, to take into account the
waveform of the impulse, and not only its amplitude.
However, the complexity of the algorithm has not only advantages, but also drawbacks. Oneof them is a greater number of specific settings parameters, which values should be precisely
selected. For example, the correct operation of the ranking algorithm in the case underconsideration is achieved if the value of the Twindow interval, firstly, is compliant with the RT
and RA parameters of typical AE impulses; second, its value should be as small as possible
to ensure a minimum number of false detections.
It is well known that the propagation path of the testing structure has the greatest influenceon the shape of the AE impulse [10]. The greater the distance from AE source to AE sensor,
the greater RT and RA parameters of AE impulse. Therefore, in order to correctly select theparameters of the impulse detection algorithm, it is necessary to know the distribution of the
AE impulse parameters that are expected at certain acoustic properties of the structure, thelength of the propagation path, the presence or absence of isolation, etc. To study the
influence of the propagation path, numerical simulation was used in this paper. The
application of the modeling approach allows to study the main influencing factors, without
resorting to a large number of experiments. As a result of the simulation, a numerous AE
signals corresponding to all possible geometric and acoustic parameters of the testing
structure were obtained.

2.2. Modal analysis approach
Modal analysis approach was used for acceleration of modelling because it is an effective
analytical method based on the expansion of the forcing terms acting in the waveguide into
the set of its proper modes. Modal analysis is a mathematically exact technique that leads toa closed form integral equation for the elastic fields in the waveguide, and which incorporates
in a natural way the issue of mode selectivity, offering insight on the physics of waveguidebehavior.
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The PCDISP package proposed in [9] for analyzing the L-, F-, and T-modes in the waveguidein the framework of the Pochhammer-Chree model was used as the basis for themathematical apparatus. This mathematical package was reworked for calculations related
to Lamb waves in a plate. In general, the calculation of the model signal 𝑢𝑢(𝑧𝑧, 𝑡𝑡) is as follows:where𝑢𝑢(𝑧𝑧, 𝑡𝑡) = 𝐹𝐹 −1 [𝑈𝑈(𝑧𝑧, 𝜔𝜔)],(1)

𝑈𝑈(𝑧𝑧, 𝜔𝜔) = 𝑈𝑈(0, 𝜔𝜔)𝑎𝑎𝑝𝑝 (𝑧𝑧, 𝜔𝜔);

(2)

𝑈𝑈(𝑧𝑧, −𝜔𝜔) = 𝑈𝑈 ∗ (𝑧𝑧, 𝜔𝜔),(3)

𝑈𝑈(0, 𝜔𝜔) contains information about the spectrum of the emitted signal, 𝑎𝑎𝑝𝑝 (𝑧𝑧, 𝜔𝜔) contains boththe frequency response of the AE sensor, and the effects caused by propagation of thesignal in the form of normal waves. One of the main stages of calculations is the computationof phase velocities of normal waves for a certain set of frequencies.
The thickness and material of the plate, the distance between the source of the signal andthe sensor, the emitted Lamb mode, as well as the time dependence of the emitted signal
are used as model parameters.
The limitation of the method is as follows: an infinite plate is modeled without defects andinhomogeneities, damping is not taken into account. In addition, there is no possibility of
studying the dependence of the energy ratio of different Lamb modes and the spectrum of
the emitted signal on the type of defect, its depth and orientation [10-11].
Calculations were carried out for plate thickness values from 3 to 40 mm, at propagation
paths from 0.5 to 12 m. Acoustic parameters of carbon steel were used: cL = 5900 m/s, cT =3100 m/s.
Separately, signals that came in the form of S0 or A0 waves were modeled separately, thensuch signals were used individually or as a sum. The frequency of digitization of thesimulated signal was 1 MHz. The duration of the simulated waveforms was from 4096 to
16384 μs, while the typical calculation time on a computer with a processor with a clock
speed of 3.5 GHz when using one core was from 0.04 to 0.16 s, respectively (without taking
into account the time for recalculation of the dispersion curves required for each new
thickness value or when changing material parameters).
At first, the Morlet wavelet was used as the emitted signal, which made it possible to obtaincompactness both in the time and in the frequency domain. Model AE signals were obtained,
possessing all the characteristic features of real AE signals relating to the shape of theenvelope and frequency filling (Fig. 6). Both in model and real signals, the fastest, but at thesame time low-frequency components of the S0-mode are observed in the initial part of thesignal corresponding to velocities of about 5000 m/s. In the middle of the signal, as a rule,the maximum value of the instantaneous amplitude is observed, caused by the almost
simultaneous arrival of many components of the S0 and A0 modes having different
frequencies, but practically the same velocity of the order of 3000 m/s. In the final part of thesignal, corresponding to velocities of the order of 1500-2000 m/s, acoustic beat can occur
due to the interference of simultaneously arriving two components of the S0-mode having
slightly different frequencies and the same velocities. On the spectrograms [10, 12] of the6

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model signals, the dispersion curves of Lamb waves were found, which lends evidence thecorrectness of the simulation (Fig. 7).

b)a)Fig. 6. a) Model signal for thickness of 17 mm and a distance of 5 m, created on the basis of
the Morlet wavelet with a center frequency of 160 kHz; b) the actual signal received by theGT200 sensor on the 17 mm plate at a distance of 5 m from the source

Fig. 7. Continuous wavelet transform of the model signal shown in Fig. 6a
Hereafter, waveforms of the real signals received by the GT200 sensors (130-200 kHz),
which was located at a distance of 2 cm from a crack, were used as the emitted signals. Thismethod made it possible to take into account in the calculations the spectrum of the AE
source and the impulse response of the AE sensor.
Signals modelled in such a manner had a more complex envelope shape (Fig.8) than thesignals modelled with the use of the Morlet wavelet. This circumstance is associated with acomplicated form of the spectrum of real AE sensor, which affects the distribution of theamplitudes of different frequency components of the signal arriving at different times.

7

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a)

b)

Fig. 8. a) Real signal detected by the GT200 sensor at a distance of 2 cm from a crack; b)modelled signal, which would be registered after passing 12 m along a plate with a thicknessof 17 mm
It was found that with the same source signal and the absence of the attenuation, theenvelope of modelled signals received at different distances is practically identical and differs
in fact only by scaling along the time axis (Fig. 9).

a)b)Fig. 9. Signals modelled on the basis of the same emitted signal, corresponding to the
distances: a) 9 m, b) 12 m
Thus, the modal analysis method has appeared an effective analytical method, as a result of
which it was used to model the order of 104 signals during the development of the algorithm
for automatic threshold-free data recording based on the theory of signal detection.

2.3. The account of attenuation during propagation through an acoustic path
Since the PCDISP model does not include attenuation calculation, the attenuation of AE
waves in the object was calculated using a special algorithm. The algorithm was
implemented as follows, it was assumed that the amplitude of each frequency component of
the signal decreases with the distance from the source in accordance with the power law,with the damping coefficient proportional to the frequency. Therefore, the simulation of
attenuation of AE waves was carried out using a set of digital filters, with a transfer
characteristic (4), of the form
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𝐻𝐻(𝑓𝑓) = 10−𝛼𝛼𝛼𝛼 ,(4)Transient coefficientwhere α – empirical coefficient, which depends on the acoustic properties of the tested
object.

1,2
1
0,8
0,6

2.5 m

0,4

5m

0,2

10 m

0

0

100

200
300
frequency, kHz

400

500

Fig. 10. The transfer function for the attenuation filters
The transfer characteristics of the filters are shown in Fig. 10. To match the filter with thefrequency characteristics of the AE system, the filter (4) is combined with a high-pass filter
with a cutoff frequency of 30 kHz.

3. Results and discussion
As a result of using the reworked PCDISP package and the attenuation algorithm, realistic
models of AE pulses were obtained, with the help of which, without resorting to complex field
experiments, it was possible to determine the correspondence between the numericalparameters of the AE pulses and the physical parameters of the monitored object. Gaussian
noise level of 30 dB was added to all signals having amplitude 60 dB.
Fig. 11a, b shows the dependence of the mean values of the parameters RT and RA on the
distance between the AE source and the sensor. As can be seen, the parameter RT
increases linearly, with a speed of approximately 150 μs/m, RA parameter increases
according to the x·exp(x) law, increasing at a distance form 0.5 to 12 m by approximately twoorders.

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20

2000

Rise angle, ms/V

Rise time, µs

2500

1500

1000
500
0

1

3

15
10
5
0

5 7 9 11 13
distance, m

a)

1

3

5 7 9 11 13
distance, mb)

Fig.11 Rise time and rise angle parameters calculated for the modeling signals depending on
the distance between the defect and sensor
Fig. 12 represents the correlation plane rise time vs. rise angle, which shows the parametersof pulses, the source of which according to the simulation is located at a distance of 1, 5 and10 m from the sensor. As can be seen from fig.12 there is a significant variation in theparameters, which is explained by the fact that the modeling is based on AE data obtained
from a real defect, having an original shape and spectrum. The spread of the parameters is
the greater as the length of the acoustic path is longer.

Rise angle, ms/V

20

15
10 m

10

5 m

5
0

1 m
0

500

1000
1500
Rise time, µs

2000

2500

Fig.12. The scatterplot rise angle vs. rise time for the modelling signals
On the basis of the dependences obtained, it is possible to automatically adapt theparameters of the nonthreshold algorithm for AE impulses detecting. As mentioned above,
the recommended value for the observation interval is a half of RT. For testing structure of ashort extension up to 1 m, it is recommended to select the Twindow parameter not exceeding
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50-100 µs, at long distances it can reach 1000 µs. It is also recommended to use multi-scale
processing algorithms for more reliable impulse detection in the case of large-scale testing
structures.
Models of AE impulses were used for a comparative analysis of the efficiency of the
traditional threshold algorithm and a nonthreshold ranking algorithm. The comparison was
carried out as follows, for each pair of model AE impulses calculated for distances 𝑙𝑙1 and 𝑙𝑙2 ,the arrival time was determined by the threshold (𝑡𝑡1 𝑡𝑡ℎ𝑟𝑟 and 𝑡𝑡2 𝑡𝑡ℎ𝑟𝑟 ) and the nonthresholdautomatic method (𝑡𝑡1 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 and 𝑡𝑡2 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 ), then based on arrival time differences∆𝑡𝑡𝑡𝑡ℎ𝑟𝑟 =(𝑡𝑡1 𝑡𝑡ℎ𝑟𝑟 − 𝑡𝑡2 𝑡𝑡ℎ𝑟𝑟 ) and ∆𝑡𝑡𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 =(𝑡𝑡1 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 − 𝑡𝑡2 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 ) the location of the AE source wasconducted. When calculating the position of the AE source, it was assumed that the first
sensor is located directly nearby the defect, and the coordinate of the second is 𝑙𝑙1 + 𝑙𝑙2 . If thearrival time difference is correctly determined, the result of the location should be equal to 𝑙𝑙1 .
The threshold level was selected 36 dB which is two time higher than the noise level. The
average location errors are shown in Table 1.
Table 1. Average location errors
Average location error, mDistance between sensors, m

1

2

3

4

5

6

7

8

9

10

Threshold method

0.18

0.29

0.34

0.33

0.39

0.34

0.37

0.33

0.35

0.41

Ranking method

0.12

0.16

0.14

0.17

0.16

0.21

0.20

0.18

0.18

0.23

As follows from the Table 1, the error of location for the ranking algorithm is 1.5-2 times
smaller than for threshold. This accuracy is explained by the fact that the ranking algorithm
allows to determine the impulse arrival time with a significantly lower delay than the thresholdalgorithm.
Fig. 13 shows the result of processing the AE pulse models calculated for the same radiatedimpulse and different distances from 1 to 12 m. The arrival times calculated by the rank andthreshold algorithm are shown in Fig. 13 as a function of distance.

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time of arrival, µs

3500
3000
2500
2000
1500
1000
500
0

0

2

4

6
distance, mranking algorithm

8

10

12

threshold algorithm

Fig.13. Time of arrival depending on the distance between the source and the sensor
The times of arrival obtained with the rank algorithm increase linearly depending on the
distance, and therefore correspond to the same velocity of AE waves propagation equal toapproximately 5400 m/s, which corresponds to the fastest component of the Lamb waves the
low-frequency part of the S0 wave. Times of arrival, calculated with the help of the thresholdalgorithm, depend on the propagation distance nonlinearly and consequently correspond todifferent components of the Lamb waves.

4. Conclusion
Modal analysis approach has appeared an effective analytical method for the modelling of
waveforms of AE signals propagated in plates. It was used for the modelling of the order of
104 AE signals received by sensors mounted at different distances from AE sources.
As a result of the conducted researches, a threshold-free automated algorithm for detecting
AE impulses is proposed, that uses the modal analysis approach for selecting setting
parameters. A numerical experiment based on model signals showed that when using the
proposed nonparametric rank detection algorithm, in comparison with the threshold method,
the location error has decreased by 1.5-2 times.
A nonparametric rank detection algorithm for signals was implemented at the hardware levelin the UNISCOPE portable system of the company INTERUNIS-IT LLC [13].
At present, the study of the nonthreshold algorithm for detection of AE impulses is inprogress. It is planned to optimize the choice of rank criteria to minimize type I and type IIerrors. It is also planned to study the results of the algorithm in the case of various types ofnoise, and also to calculate the probability of defects detection using the procedure formodeling of the propagation path.
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References:
1. Shakhtarin B.I. Signal detection. Gelios press (in Russian), 488 p.
2. Karen M Holford and Mark Eaton. Recent Advances in Acoustic Emission.
Proceedings of World conference of Acoustic emission, 2011, Beijing, pp. 58-66.
3. 3. P. Sedlak, Y. Hirose, S.A. Khan, M. Enoki and J. Sikula. New Automatic
Localization Technique of Acoustic Emission Signals in Thin Plates. Ultrasonics, 49,2009, pp. 254-262.
4. 4. T. Lokajicek and K. Klima. A First Arrival Identification System of Acoustic Emission(AE) Signals by Means of a Higher-Order Statistics Approach. Measurement Scienceand Technology. 17, 2006, 2461-66.
5. G. Por, P. Bereczki, Z. Danka, P. Trampus. Detecting Acoustic Events during
Thermal and Mechanical Loading. Proceedings of the 31st Conference of theEuropean Working Group on Acoustic Emission, Dresden, 2014.
6. Rastegaev I.A., Danyuk A.V., Vinogradov A.Y., Merson D.L., Chugunov A.V. Locationof noise-like sources of acoustic emissions using the spectral similarity method.
Russian Journal of Nondestructive Testing. 2013. V. 49. № 10, pp. 553-561.
7. S. Elizarov, V. Barat, A. Shimansky. Nonthreshold Acoustic Emission Data
Registration Principles. Proceedings of the 31st Conference of the European Working
Group on Acoustic Emission, Dresden, 2014.
8. V. Barat, S. Elizarov, I. Bolokhova, E. Bolokhov Application of ICI Principle for AE
Data Processing. J. Acoustic Emission. V. 30, 2012.
9.

F. Seco and A.R. Jiménez, Modelling the generation and propagation of ultrasonicsignals in cylindrical waveguides. In Ultrasonic waves, chapter 1, pp. 1-28, Intech
2012.
Open
AccessPublisher,
http://www.car.upmcsic.es/lopsi/people/fernando.seco/pcdisp/

10. Hamstad M. A., O’Gallagher A., J. Gary. A Wavelet Transform Applied to Acoustic
Emission. J. Acoustic Emission, 20 (2002), pp. 39-82.
11. Gallego A., Martinez-Jequier J., Suárez E., Juanes F. J., Valea A. Real-time
Algorithm to Classify AE Events of Lamb Waves in CFRP. Proceedings of the 31st
Conference of the European Working Group on Acoustic Emission, Dresden, 2014.
12. Suzuki H., Kinjo T., Hayashi Y., Takemoto M., Ono K., Appendix by Hayashi Y.
Wavelet Transform of Acoustic Emission Signals, J. Acoustic Emission, Vol. 14, No.2
(1996, April-June), pp. 69-84.
13. S. Elizarov, V. Barat, A. Shimansky. Intelligent Acoustic Emission System.
Proceedings of the 31st Conference of the European Working Group on Acoustic
Emission, Dresden, 2014.

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09
- AcousticEMISSION
Emission measurements
duringa tensile
fatigue
ACOUSTIC
MEASUREMENTS
DURINGA TENSILE
in reinforced
concrete
FATIGUEtest
TEST
IN REINFORCED
CONCRETE
Gerd Manthei1, Manuel Koob1, Jens Minnert1
1

THM · UNIVERSITY OF APPLIED SCIENCES, GIESSEN, GERMANY
Institute for Structural Engineering (IKT)

Abstract:
In a research project funded by the German Federal Ministry of Education and Research (BMBF)
acoustic emission (AE) measurements were carried out during a tensile fatigue test on a plate-shaped
reinforced concrete specimen to investigate the fatigue behaviour of an embedded steel anchor with aclothoid-shaped form in reinforced concrete. The results of AE measurements show that the AE
activity begins immediately after starting dynamic loading. Due to the limited location accuracy, thelocated AE events are not identified on fracture planes, but are cloud-like distributed in zones of thehigh stresses on the left and right edges of the steel anchor. During the test 9,132 AE events could belocated using the longitudinal (L wave) and transverse (T wave) wave onsets. The locations of the AE
events let suggest that microcracks occur due to the form-closed connection between the concreteand the steel anchor under fatigue stress.

1 Introduction
Today, many constructions activities deal with repair of existing buildings and it will gain in
importance in the future. In order to decide on the necessity the type and scope of aredevelopment measure, the knowledge about the conditions of the existing building isrequired. The current mapping of reinforced concrete structures extends to the visualinspection about damage of the structure surface without technical aids. This appraisal of thestructure is necessary but not always sufficient. Damage inside the building remains hidden
and can only be discovered with a delay.
The use of non-destructive testing methods can provide information on processes inside thestructure. Acoustic emission (AE) testing is a non-destructive testing method, which is used
to detect material damage. Due to the damage of structures like concrete components elastic
waves emit during fracturing of materials under load. The AE signals are detected by AE
sensors, which are mounted on the surface of the component, allowing the state of thematerial and the cause of the deformation to be traced.
The investigation of AE in reinforced concrete started very early. Rüsch (1959) examined infirst tests on concrete pressure prisms (100 mm · 150 mm) the strength, the deformation
behaviour and the AE activity under short-term and continuous load and under cyclic load [1].
He traces out that the so-called “Kaiser effect” on concrete is not valid. The “Kaiser effect” isdefined as the absence of AE at stress level below the previously applied maximal stress [2].
In the 1960s and 1970s this was followed by many further investigations using AE analysis todetect crack formation in concrete. A good overview of literature provides the bibliography
made by Drouillard (1986) [3]. Manthei (2016) gives an overview about the application of the
AE in concrete in the last decades [4]. Further important works about AE in concrete are1

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published by Schechinger (2005), Shiotani et al. (2006) and Grosse & Ohtsu (2008) [5-7].
Pull-out tests of concrete reinforcing bars were examined by Finck (2005) with AE analysis.
The focus of the study was the material behaviour of concrete under static load until failure[8].
At the THM (Germany), Koob et al. (2015) are developing an innovative hybrid frameworkwith low material input in an ongoing research project [9]. The hybrid framework will serve asa supporting structure for the construction of wind turbines. Due to the rotation of the rotor
blades, the building materials are exposed to high dynamic loads, whereby the fatigue limit of
the used materials can be achieved. In this intention anchored concrete dowels are
examined under dynamic tensile stress. The specimen is inspected for stress, marginalforces, cracking and fatigue behaviour. The damage to the transition zone between concrete
and reinforcing bars is preceded by the formation of microcracks in the concrete matrix. AE isbased on the appearance that elastic waves are emitted during deformation or cracking in amaterial. These elastic waves can be detected with sensitive AE sensors. The aim of the AE
measurements is to directly measure microcracking and to locate the AE events. With thelocation of the cracks the progress of damage during dynamic loading is analysed. Changes
in the velocity of the elastic waves are measured using transmission measurements withultrasonic signals. These can also indicate cracking.

2 Testing programme
2.1

Description of the experimental specimen

With the help of pull-out experiments, a concrete dowel anchorage should be checked underdynamic conditions. Koob and Minnert (2018) investigated the pull-out resistance, the slip
development and the failure mechanism of the dowel anchorage [10]. In the plate-shaped
specimen of reinforced concrete with the dimension 146 cm · 45 cm · 16 cm a 61 cm longand 26 cm wide steel anchor with a thickness of 20 mm were embedded into the specimen.
Figure 1 shows a front view (left) and a side view (right) of the specimen with the location of
the embedded steel anchor with a clothoid-shaped form and the reinforcing bars. The arrows
indicate the direction of the tensile load.

Fig. 1: Front view (left) and side view (right) of the specimen of reinforced concretewith position of the embedded steel anchor with a clothoid-shaped form andthe vertical and horizontal reinforcing bars. The arrows indicate the directionof the tensile load.

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The entire specimen mounted on a solid steel plate for attachment to the lower punch of thetesting machine. To initiate the tensile force of the testing machine the concrete dowel
protruded about 41 cm out of the specimen. In the concreted part of the concrete dowel
recesses are cut out in clothoid-shape (Fig. 1). By encasing in concrete, the concrete
hardens in the recesses and it forms a positive anchoring of the dowel in the concrete. In
order to increase the bearing strength, additional reinforcing bars are laid through therecesses.
2.2

Experimental setup for AE measurement

In a previous research project, broadband AE sensors with a measurement frequency of up
to 200 kHz were developed for the detection of microcracks especially for concrete. For thethree-dimensional location of the AE sources, 16 of these AE sensors were attached to thesurface of the plate-shaped specimen. The bottom of the sample was inaccessible because
the steel plate for attaching the specimen was fixed of the testing machine. The arrangementof the AE sensors can be seen in Figure 2 (left).

Fig. 2: Left: Perspective view of the contour of the specimen with the position of the AE
sensors (red dots) and ultrasonic transmitters (yellow dots). Right: Installation
situation of the specimen with the AE sensors and preamplifiers (blue boxes)and displacement sensors in vertical and axial direction.

Six AE sensors (red dots) were placed on the front side and back side, two sensors wereplaced on the top and one sensor on the left and right side of the sample. With the additional
ultrasonic transmitters (yellow dots) ultrasonic measurements could be made in longitudinallyand transversely direction at three points of the specimen. The sensors were mounted to thetest specimen with screws. The couplant between the sensors and the surface of thespecimen was a high-strength vacuum grease to improve the acoustic conductivity. A 16channel system was used for AE and transmission measurement (manufacturer: GMuG).
The measuring system digitized the signals in a very high frequency with a sampling
frequency of 10 MHz and an amplitude resolution of 16 bit. The signals were pre-amplified by
40 dB (factor 100) and bandpass filtered. The lower and upper cut-off frequency of the
bandpass was set to 20 kHz and 200 kHz, respectively. The transmission measurements
were repeated at the beginning of the test and during stopping phase of the testing machine
to measure the wave velocities used for AE location. During a single transmission
measurement eight signals of different shape were transmitted serially through the specimen
to the receiver. There were used a wide band signal (step function) with rise time of 1 µs andseven signals with centre frequencies between 20 and 200 kHz. In order to improve thesignal quality up to 256 stacking (i.e. repetitions with summation) were performed for each3

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measurement. A servo-hydraulic testing machine was used for the test, which was designed
for tensile and compressive forces of up to 2,000 kN and dynamic load up to ±1,600 kN.
2.3

Experimental procedure

Force [kN]

Displacement [mm]The load of the specimen was applied in three phases. After a static preload phase of 80 kN,
the dynamic testing of the specimen was force-controlled carried out. Figure 3 shows thetime course of the tensile load (black continues line) and the vertical displacement of thetesting machine (red continues line). The cyclic load history at 8 Hz is shown in the inlet in
the upper left corner of Figure 3. During a periodic load cycle the applied load oscillates with± 36 kN with a mean tensile force of 44 kN. Thus, the upper and lower tensile force is 80 kN
and 8 kN, respectively. A total of 10 stress cycles were performed with 10,000 cycles each.
After 100,000 load cycles, a residual carrying capacity of about 161 kN was measured.
Furthermore, it can be seen that during the dynamic load the displacement of the testingmachine remains nearly constant at approximately 1 mm. At the ultimate load, thedisplacement is about 3.0 mm and increases up to 30 mm until failure.

Time

Fig. 3: Load (black continues line) and displacement behavior (red continues line)
versus time. Upper left: Reduced representation of the dynamic tensile loadwith oscillation frequency of 8 Hz with a mean tensile force of 44 kN.

3 Results of AE measurement
The main part of the evaluation was the three-dimensional location of the AE sources for the
analysis of the spatial distribution of the cracks in the specimen. For this purpose, anautomatic location programme was used. The location method uses clear discernible onsetsof L and T waves. A location is valid if the location error is below 5 cm and more than five Land T-waves onsets are used for location. The location error is calculated using the traveltime residuals of the L and T waves. We assume that the real location error is two times
greater. An event was rejected when these conditions are not fulfilled.
The waveforms for an event with 16 channels are shown in Figure 4. At the end of the signal
trace the maximum of the amplitude is given in volts. The automatically determined L- and Twaves onsets used for location are marked above the signal by a vertical line. Dashed lines
4

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Channel number

below the signals mark the calculated arrival times of the L and T waves. The considered
event was located using 10 and 2 L-waves and T-waves onsets, respectively. The measured
L-wave velocity is 3.6 mm/µs and T-wave velocity is 2.1 mm/µs, which were used forlocation. The occurrence origin of the considered event is approximately in the middle of thefront of the sample with coordinates x = 61.1 cm, y = 2.8 cm, and z = 24.6 cm. The calculated
location error is about 2.7 cm. The good agreement between the measured and calculated
onsets leads to the expectation that the real location error is in the range of the calculated
error.

Time in µs

Fig. 4: Event originating from the middle of the front of specimen. The picked andcalculated first (L wave) and second (T wave) arrivals are marked by verticalticks above and below the signal traces, respectively.

Force [kN]

Number of located events (cumulative)

Figure 5 shows the cumulated AE events versus time (red continues line) together with thetensile force of the testing machine (black continues line).

Time

Fig. 5: Temporal course of cumulative number of localisations (red continues curve)
and tensile force (black continues curve) during the test.

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The AE activity starts immediately with the dynamic load and increases steadily during thetest. At approximately 12:30 the number of AE events decreases slightly, which is certainly
due to the fact that the damage to the sample has increased and open gaping cracks hinder
the propagation of AE signals. However, a slight increase of the number of the AE events
can be seen at the end of the test, when reaching the residual load capacity. Figure 6 shows
all located events in a lateral view in projection onto the x-z-coordinate plane.

ZxFig. 6: Location of the 9,132 AE events in lateral view on the x-z coordinate plane.

A total of 9,132 events could be located over the test period of approximately 7 hours. Figure
6 shows that the events are preferentially distributed in some accumulations of event at theedge of the embedded concrete dowel. It is noticeable that about 6,700 events are
distributed in two accumulations on the left edge (ovals in Fig. 6).
Figure 7 displays an enlarged part of the specimen together with the contour of the concrete
dowel. This figure illustrates that the above-mentioned accumulations of events are clusteredin zones, where the clothoid-shaped recesses of the concrete dowel influence the stressstate.

Fig. 7: Position of the located events in a projection onto the x-z-plane together withthe contour of the concrete dowel in an enlarged view.

For further verification of the AE results, the located AE events were compared with theresult of a non-linear finite-element calculation of the anchorage in the concrete. The
remaining tensile strength of the concrete after the dynamic load is shown in Figure 8. The
tensile strength is displayed in projection onto a vertical cross section in the middle of theconcrete dowel anchorage in a perspective view. In this figure the red colour is indicating
6

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areas of undisturbed concrete, whereas in the blue collared areas the bearing capacity of theconcrete are significantly decreased. That means in this zones the concrete structure isdamaged and no tensile force can be transmitted. In comparison with the results of the AE
measurements the main part of the AE events occurred in the high damage, where thebearing capacity is very low (blue areas in Fig. 8). According to the results of the AE location,
the damaged zone continues below the clothoid recess, approximately to the lower end of
the concrete dowel.

Fig. 8: Non-linear finite-element calculations of the tensile strength in projection onto
a vertical cross section in the middle of the concrete dowel anchorage in aperspective view.

4 Conclusions
This work demonstrates, that AE is a non-destructive method for localisation of fracture
processes in loaded concrete structures. During the execution of a cyclic pull-out test, thecrack formation in the reinforced concrete specimen could be detected by AE measurement.
The main task of this work was the location of cracks within the specimen. Because of thelimited location accuracy of the AE sources, there is no direct proof that cracking indicated by
AE occurs on planer microscopic cracks. The AE activity starts early in the loading
immediately after beginning of the dynamic load. In the later stages of the test microcracking
occurred in the vicinity of the concrete dowel. In these highly damaged zones, the ultimate
failure occurs. In our experiment it was not possible to follow this development by AE source
location because microscopic open tensile cracks lead to a high attenuation of wave
propagation.
The aim of further investigations is to optimise the evaluation procedure and thus extend the
AE analysis to the application for existing structures with high dynamic loads such as bridges
or wind turbines.

7

S388

Acknowledgment
The described research project in this paper is currently in development by the THM(University of Applied Sciences, Giessen, Germany) together with the precast company
Oberhessisches Spannbetonwerk (Nidda, Germany). This project (FKZ / project-no.
13FH012IX4) is sponsored by the Federal Ministry of Education and Research, Germany.

References[1] Rüsch, H., Physikalische Fragen der Betonprüfung, Zement-Kalk-Gips, Vol. 12, No. 1,
Bauverlag GmbH, Wiesbaden, 1959, pp. 1-9.
[2] Kaiser, J., Untersuchungen über das Auftreten von Geräuschen bei Zugversuchen,
Dissertation at the TU München, 1950, Germany[3] Drouillard, T.F., AE Literature – Concrete, JAE, Vol. 5, No. 2, 1986, pp. 103-109.
[4] Manthei, G., Anwendung der Schallemissionsanalyse im Bauwesen, in: Minnert, J.
(Hrsg.), 13. Giessener Bauforum, Shaker Verlag Aachen, 2016, pp. 19-38.
[5] Finck, F., Untersuchung von Bruchprozessen in Beton mit Hilfe derSchallemissionsanalyse, Dissertation at the University of Stuttgart, 2005, Germany[6] Schechinger, B., Schallemissionsanalyse zur Überwachung der Schädigung vonStahlbeton, Dissertation at ETH Zurich, 2005, Switzerland.
[7] Shiotani, T., Luo, X., Haya. H., Damage Diagnosis of Railway Concrete Structures by
Means of One-Dimensional AE Sources, JAE, Vol. 24, 2006, pp. 205-214[8] Grosse, C., Ohtsu, M., Acoustic Emission Testing, Springer-Verlag Berlin Heidelberg,
2008.
[9] Koob, M., Wolff, D., Minnert, J., Innovative Hybrid-Tower for Onshore Wind Energy
Plants, CPI - Concrete Plant International, Edition 1/2015, pp 152-156.
[10] Koob, M., Minnert, J., Untersuchungen einer Betondübelverankerung unter
dynamischen Einwirkungen für den Einsatz bei Windenergieanlagen, Proceedings 21.
DASt-Kolloquium, Kaiserslautern, Germany, 2018, pp 146-151.

8

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EMISSION
BEHAVIOUR
OFstructures
REINFORCEMENT
77ACOUSTIC- Test of bending
reinforced
concreteby meansof Acoustic
Emission TO
methodCONCRETE BEAMSSUBJECTED
3 POINT BENDING
Bardakov V.V.1,2, Sagaidak A.I.3, Elizarov S.V.1 , Barat V.A. 1,2

1

INTERUNIS-IT LLC, Moscow, Russian Federation, 111024, Shosse Entuziastov
str., 20B
2
National Research University Moscow Power Engineering Institute, Moscow,
Russian Federation, 111250, Krasnokazarmennaya str., 14
3
JSC SIC Construction, Moscow, Russian Federation, 109428, 2nd Institute’s str., 6

Abstract:
The paper presents the results of the conducted experimental studies on three-point bend testing of
reinforced concrete beams with incremental cyclic load, up to destruction, using the acoustic emission
method. Special attention is paid to the investigation of the relationship between destructive processes
occurring during the testing of reinforced concrete beams and the evolution of acoustic emission dataregistered during the test. The analysis described in the article makes it possible, on the basis of
acoustic emission data, recorded during the tests, to distinguish the stages corresponding to thedifferent engineering status of reinforced concrete structures.

1. Introduction
Reliability and security of existing infrastructure facilities (bridges, stadiums, tunnels, etc.)
has recently received more and more attention. This kind of objects is required to haveincreased security, as their destruction threatens human lives and significant financial
damage. One of the key structural elements in these structures is reinforced concrete (RC)elements, which in general are subject to bending loads. RC is an indispensable material inmodern construction due to its high strength, which is ensured by the joint work of concrete
and reinforcement. At the same time, RC like any other material subjected to an externalload needs examination of its integrity and engineering status.
Traditionally, the engineering status of reinforced concrete structures is estimated on thebasis of periodic diagnostic results. As a rule, periodic testing of such objects is based on
visual inspection, which allows fixing large size surface defects and deviations. To detect
internal defects, the most widespread for periodic testing get ultrasonic method implemented,
as a rule, for most critical parts of constructions by means of tomography. In addition, insome cases, periodic testing of RC elements is limited to measuring the strength of individual
sections of construction by mechanical or ultrasonic methods of Non-Destructive Testing(NDT).
However, periodic testing does not allow detecting of sudden changes in the state ofconstruction between inspections, is extremely time-consuming, and also has low reliabilityS390

[1]. In this regard, the actual task is to perform monitoring of such objects. Acoustic emission(AE) method is a promising one, which has proved itself for monitoring of the oil and gas
industry objects due to its remoteness and high sensitivity.
AE testing is a method of NDT, it is based on the phenomenon of stress waves generation
caused by sudden stress redistribution in the material structure. Traditionally, AE testing iscarried out for metal objects. However, the development of AE method for concrete testing is
gaining increasing popularity. AE testing of concrete is more difficult and less studiedcompared with metals. It is associated with complex structure of RC. The typical AE source
in metal is a growing crack. In RC, due to its complex and heterogeneous structure, which
consists of brittle concrete and plastic steel reinforcement, AE sources are more diverse. So,
in addition to the crack formation as AE sources can be debonding and slipping between
concrete and reinforcement and also reinforcement and cement stone deformation.
There are works in which authors proposed criteria that allow evaluate integrity and damage
degree of RC structures based on criteria parameters determined on the basis of AE data
recorded during the testing of RC beams [2-6].
The main purpose of this paper is identification of correlation between AE data recorded
during destruction of RC beams and physical processes which characterized deferent
structural state of construction. Determining of this correlation, in prospect, will allow
estimate the damage degree of RC structural elements.

2. The destruction stages of RC beams in the bending test
Concrete is a brittle material with high compressive and low tensile strength. Low tensile
strength of concrete doesn’t allow its single using in critical building structures. Thisdisadvantage is compensated by addition of steel reinforcement in concrete, which has ahigh plasticity. Using of two materials with different physical and mechanical properties makeit possible compensate disadvantages which materials have individually. Therefore, an
important aspect of RC structures reliability is to test joint operation of reinforcement withconcrete during their deformation.
In accordance with modern views, when RC beams are tested with a consistently increasingload, up to destruction, three characteristic stages of their stress-strain state can be
distinguished [7], each of which is characterized by different engineering status of the RC
beam.
Stage I. Before the appearance of cracks in the concrete of the tension zone. At low loads on
RC, the stress in concrete and reinforcement is small, the concrete deformations are mostly
elastic. The stress in concrete is less than the tension strength and tensile forces areperceived by concrete and reinforcement together. With increasing load on the concrete of
the tension zone, inelastic deformations begin to develop, stresses in concrete approach thetensile strength limit. This characterizes the end of stage I. With further increase in load,cracks are formed in the concrete of the tension zone. There comes a new qualitative state stage II.
Stage II. The stage of permissible operation of RC. In that place of the tension zone where
cracks were formed, the tensile force is perceived by the reinforcement and the concrete
section above the crack. In the areas between the cracks, the adhesion of the reinforcement

S391

to the concrete is not completely disturbed, and the concrete continues to work on stretching.
With further increase in load, the fracture gap continues to grow, shortening the area of theconcrete that is still stretching, the stress in the reinforcement and in the concrete of thecompressed zone increases. The end of this stage is characterized by the onset ofnoticeable inelastic deformations in the reinforcement.
Stage III. Stage of destruction. If the load continues to increase, the stresses in thereinforcement reach a physical (conventional) point of yielding, the stresses in the concreteof the compressed zone reach the value of the compression break down point. The structureis destroyed.
The destruction of reinforced concrete beams during bending can occur according to two
different schemes:- For RC elements with a normal content of tension reinforcement, the fracture begins
with the reinforcement of the tension zone and ends with the crushing of concrete in thecompressed zone. Such destruction is plastic.
- For RC elements with excess content of tension reinforcement, the destruction occurs
over the concrete of the compressed zone and is always brittle in the case of incomplete useof tension reinforcement. The transition from stage II to stage III occurs suddenly.

3. Description of experimental studies
3.1. Experimental samples
The experimental samples of RC beams were made from concrete of 2 classes ofcompressive strength: M60 and M90. In total, 3 reinforced concrete beams were made fromheavy concrete. Compositions of concrete for experimental samples are presented in Table1.
Table 1. Compositions of concrete for experimental samples№

Concrete grade

Cement, kg

Sand, kg

1
2
3

M90
M60
M90

470
350
470

720
840
720

Crushed stone,
kg
970
970
970

Water, l
160
170
155

RC beams were reinforced with cages of various configurations (Fig. 1). Steel rods A500C of
various diameters were used as reinforcement.

a.

b.

c.

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d.
Fig. 1 – Reinforcement cages cross-section view (a) type1, (b) type 2, (c) type 3, (d) end
All the beams were overreinforced, as a result the destruction had a fragile character.
Weld plates (150x200 mm) were placed on the undersides of each frame, which were usedas supports during testing. Concreting was carried out in metal molds with dimensions of
1500x200x150 mm, in which the reinforcing cage was pre-installed.
During the concreting, in addition to the beams, witness samples were prepared in theamount of 10 pcs. from compositions completely analogous to those used for beams. Thesesamples were hardened under conditions similar to beams in plastic molds of 100x100x100
mm size, and were used to perform strength measurements under the press.
Generalized data on the beams participating in the experiments are presented in Table 2.
Table 2. Generalized data on the beams№

Designation

Cage type

1
2
3

M90
M60
M90(2)

2
3
1

Compression
strength, MPa
101.3
80.3
94.3

Beam load to
failure, kgf
10000
13500
10500

At the expiration of the 28 days hardening period, the RC beam was tested for a three-point
bending with incremental cyclic load, up to destruction.
3.2. Test Equipment
INSTRON 8802 system was used as a loading machine. The load was applied to the central
part of the beam from one side. Felt pad was used to reduce the effect of the friction between
loading device and the concrete surface. On the other side the beam was fixed by two
supports, one of which was movable and the other was fixed (Fig. 2).

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Fig. 2 – RC beam testing
The loading scheme was incremental cyclic load and consisted of the next stages - load,load exposure, unloading, and no-load exposure. The loading was carried out to the samevalue of the load for two cycles (), after which the load value was increased. Then theprocess was repeated until the sample was destroyed. As an example, Fig. 3 shows theloading scheme for RC beam M90(2). Loading schemes for other beams were similar anddiffered only in value of the destructive load. In addition to the load, the system also wasrecording the displacement of cross-beam.

Fig. 3 – Loading scheme for RC beam M90(2)S394As a fixed parameter of loading, the duration of each stage - loading / unloading and loading/ unloading exposure - was the same. The duration of the loading / unloading intervals was
40 seconds, and the load / no-load exposure interval was 300 seconds. At the end of eachstage of loading, cracks were observed on the surface of the beam, and the value of their
opening was also recorded. The three-point bending tests were accomplished by AE control.
3.3. Data acquisition system
A-Line 32D DDM system with 10 channels was used as a data acquisition system. Band DIS
30-300 transducers with frequency band of 30-300 kHz were used as the acoustic emission
sensors. Sensors were installed on the surface of a concrete sample through a contact fluidlayer. Sensor fastening was carried out by means of magnetic holders, which werepreviously glued to the surface of the concrete sample with by epoxy glue. The arrangementof AE sensors is shown in Fig. 4.

Fig. 4 – The arrangement of the sensors
The selected arrangement of the sensor made it possible to determine the location of AE
sources using linear and 3D location algorithms.

4. AE data analysis
The AE method has a high sensitivity and allows detecting of destructive processes
occurring in a material subjected to external loading on the basis of an estimation of AE
parameters evolution. Fig. 5 depicts the cumulative hits for most active channel and the load
for the RC beam M90(2) as functions of time.

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Fig. 5 – Cumulative hits and the load for the RC beam M90(2)This dependence characterizes the process of damage accumulation in the RC beam withtime. Based on the change in the AE parameters, it is possible to distinguish the stages
characterizing the structural state of the facility, including the 3 stages of the stress-strainstate described above. Let's consider each of the stages in more detail. Stage I, as alreadymentioned corresponds to low loads. AE activity at this stage is also not high (Fig. 6a) anddoes not exceed tens of hits per second.

а.

b.
Fig. 6 – (а) Cumulative hits and the load for the RC beam, (b) Amplitude distribution forloading less than 5 kN (first loading step)

AE activity occurs at this stage mainly due to the presence of primary concentrators ofinternal stresses arising from the physical and chemical processes of cement stonehardening. Cumulative hits on the load is linear, jumpy in nature, the amplitudes of thedetected signals are low, which is confirmed by the amplitude distribution data (Fig. 6b). At

S396

the end of the first stage, there are no visible cracks on the surface of the beam. The
appearance of the first visible cracks coincides with the beginning of stage II.
Stage II, in its turn, can be divided into 2 sub-stages (stage IIa and stage IIb), each of whichcorresponds to different levels of engineering status of the RC beam. Stage IIa occurs
simultaneously with the moment of occurrence of the first cracks. Formation of the first
cracks occurs in the tension zone (Fig. 7), and their position can be localized using a linear
location algorithm even before they are visible visually.

Fig. 7 – The central area of the RC beam M90(2) under the bending test
The beginning of the crack formation is accompanied by a sudden increase in AE activity(Fig. 8a).

а.

b.
Fig. 8 – (а) Cumulative hits and the load for the RC beam, (b) Amplitude distribution forloading to 15 kN (first loading cycle of the second loading step)

The time dependence of the cumulative hits has a nonlinear (power-law) nature for a linearlyvarying load. This kind of dependence for metals characterizes them as defective [8].
Obviously, this statement is also true for complex structural materials, such as reinforced

S397

concrete. In addition to AE activity increasing, the amplitudes of the detected signals also
increase (Fig. 8b). However, the number of low-amplitude signals is still dominant.
At this stage, there is also a sudden increase in AE activity on exposure to the load. The
onset of AE activity is associated with creep of concrete. The dependence of the cumulative
hits has a logarithmic character of time characterizing the gradual attenuation of this process.
Stage IIa will continue until the crack has reached the reinforcement in the concrete. The
beginning of stage IIb, conditionally, can be considered as the development of a crack in thetension zone above the reinforcement. From this moment, the joint work of concrete andreinforcement is disrupted, which is reflected in turn on the AE data (Fig. 9a).

а.

b.
Fig. 9 – (а) Cumulative hits and the load for the RC beam, (b) Amplitude distribution forloading to 30 kN (first loading cycle of the third loading step)

The dependence shown in Fig. 9a can be divided into two sections: non-linear (power-law)dependence and linear dependence. The section of nonlinear dependence corresponds tothe state of RC beam, in which the reinforcement has not yet begun to deform separatelyfrom the concrete (at the initial stage of stage IIb), or when the values of the previous
deformation (under repeated loading) have not been exceeded. The section of linear
dependence proposed corresponds to the state of the RC beam, in which the reinforcement
begins to deform separately from the concrete. In this case, the slope of the cumulative hits
is reduced. The decrease of the slope is associated with an increase in the durations of therecorded AE signals. The main sources of AE at this stage are friction and adhesion failure,
as well as the formation of microcracks in the area of contact between concrete and steel
reinforcement, which is most active, according to the location data, in the anchoring points of
the reinforcement. In this case, high-amplitude AE signals are generated, which significantly
change the shape of the amplitude distribution (Fig. 9b), in comparison with the previous
stage (Fig. 8b).
At the moment of the inflection of the cumulative hits versus time, a similar inflection can also
be observed on the dependence of the beam deflection versus time.
Stage IIb corresponds to the appearance of significant activity at the unloading of thesample, which corresponds to the friction between fractured surfaces, which at the moment
are sufficiently large and in the no-load stage, which is associated with stress relaxation.
Among other things, it should be noted that the transition from stage IIa to stage IIb
approximately coincides with the moment of inclined cracks formation (Fig. 7). With a further

S398

increase in the load, the nature of the dependencies is similar. In this case, the change of the
non-linear character to linear, with decreasing slope, approximately corresponds to themoment when the amount of deflection (deformations) reached at the previous cycle isexceeded, during the loading stage (due to creep of the structure).
Dependencies view described above is similar for all RC beams used in experiment (fig. 10).

Fig. 10 – Dependences of cumulative hits on time for all RC beams
This kind of dependence of cumulative hits will persist until the collapse of the test specimen,
as it in strongly reinforced structures comes suddenly (fragile destruction) and, at the time ofdestruction, the reinforcement has not yet used its resource. That fact was establishedpreviously by other authors [9]. In the case of normally reinforced structures, the destruction
process accompanies a nonlinear increase of AE parameters.

5. Comparison of the results with NDIS-2421
Classification of the damage level according to the quantitative criterion NDIS-2421 [4] wascarried out for AE data recorded during experiments. That criterion is based on the definitionof two parameters:

S399

In our experiments Calm ratio was calculated as the number of cumulative AE activity underunloading divided into the number of cumulative AE activity under loading, to exclude AE
activity on the load/no-load exposure intervals.
The use of these parameters makes it possible to classify damage levels into minor,
intermediate and heavy for each loading cycle (Fig. 11a). An example of applying of thiscriterion for M90(2) beam is represented in Fig. 11b.

a.

b.
Fig. 11 – Classification of the damage levels (a) theory, (b) results

The number near the point in Fig.11b represents the number of loading step for the first of
two loading cycles (Fig. 3) for the most active channel. The classification levels were
determined as 0.5 for calm ratio (based on the beam deflections experiment results) and as 1for load ratio (based on Kaiser effect).
According to the obtained diagram, damages are classified as heavy beginning with third
loading step. This result corresponds to loading step for which joint operation between
concrete and reinforcement was disturbed, and also corresponds to the transition from Stage
IIa to Stage 2b. That moment was noted earlier based on the change of AE data (Fig. 9).

6. Conclusion
The investigation of RC beams destruction by three-point bending with AE data acquisition
was carried out. The features of AE data which characterized different stages of stress-strain
behavior were selected. Selected features can be used to develop criteria for destruction
state estimation of RC beams.

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7. Acknowledgements
These results are obtained during the execution of the State Assignment of the Ministry of
Education and Science of the Russian Federation in the field of scientific activity (Project
Code 11.9879.2017/8.9)

References:[1] V.V. Bardakov, V.A. Barat, D.A. Terentyev, D.V. Chernov, K.O. Osipov, Osobennosti
primeneniya metoda akusticheskoj emissii pri monitoringe mostovyh konstrukcij, Testing.
Diagnostics. 1(211) (2016) 32-39. (in Russian).
[2] Recommendation of RILEM TC 212-ACD: acoustic emission and related NDE techniques
for crack detection and damage evaluation in concrete. Test method for classification ofactive cracks in concrete structures by acoustic emission. 29 July 2010.
[3] Recommendation of RILEM TC 212-ACD: acoustic emission and related NDE techniques
for crack detection and damage evaluation in concrete. Test method for damage
classification of reinforced concrete beams by acoustic emission. 29 July 2010.
[4] NDIS 2421, Recommended Practice for In-Situ Monitoring of Concrete Structures by AE.
2000.
[5] Michael C Forde, Sabrina Colombo et al, Predicting the Ultimate Load Capacity ofConcrete Bridge Beams from the «Relaxation Ration» Analysis of AE Signals, Progress in
Acoustic Emission XVIII. (2016) 359-364.
[6] A.I. Sagaidak, S.V. Elizarov, Acoustic emission parameters correlated with fracture anddeformation processes of concrete members, CONSTRUCTION AND BUILDING
MATERIALS, 21(3) (2007) 477-482, https://doi.org/10.1016/j.conbuildmat.2006.04.004.
[7] V.N. Baikov, Eh.E. Sigalov, Zhelezobetonnie konstrukcii: obshchij kurs, fifth ed.,Stroyizdat, Moscow, 1991. (in Russian).
[8] H.L. Dunegan, D.O. Harris, Acoustic Emission – A new Non-destructive Testing Tool,
Ultrasonic. 7 (1969) 160-166.
[9] C.U. Grosse, M. Ohtsu, Acoustic Emission Testing, Springer, Berlin, 2008.

S401

81Assessment- Assessmentofofinfrastructures
infrastructuresby
byrainy
rainyinduced
inducedAE
AEtomography
tomography
withwithwave
wavevelocity
velocityand
andattenuation
attenuation rate
rate

Tomoki Shiotani 1, Katsufumi Hashimoto 1, Nobuhiro Okude1, Kazuo Watabe2, 3
and Hidefumi Takamine2, 3
Dept. Civil & Earth Resources Engineering, Kyoto University
Toshiba Corporation Corporate Research and Development Center
3
NMEMS Technology Research Organization
1

2

Abstract:
As damage evaluation of infrastructures to establish an efficient non-destructive testing (NDT) is highly
demanded, an acoustic emission (AE) tomography technique has been developed. It is very important
nowadays to establish economic and appropriate management systems for ageing concrete
infrastructures, in order to guarantee the performance and safety with identifying severe deterioration
through their service life. The AE tomography estimates wave velocity distribution, which is supposed
to be decreased as the damage progresses, inside the reinforced concrete. The AE tomography
combines an iterative AE source location algorithm with travel-time tomography to produce a 3D
visualization of the elastic wave velocity. However, since the computation for the elastic wave ray-trace
algorithm considering all potential detours of elastic waves takes up much time, and in case that only a
few AE signals are detected, AE tomography technique does not always work efficiently. In this paper,
AE signals induced by rain droplets hit on surface of RC deck are utilized as elastic waves’ excitations,and wave velocity and attenuation tomography assuming linear ray paths are performed in conjunction
with AE source locations. Accordingly, rain-induced elastic waves lead to thousands of AE events afterin-situ measurement for an hour. Consequently, the 3D tomography results show accurate and timesaving analysis, compared to the above-mentioned conventional AE tomography technique, forquantifying the damage in RC deck.

1. Introduction
It is generally recognized that preventive and proactive maintenance works are necessary forsuch infrastructure as bridges and tunnels to implement rational maintenance programs. For
reinforced concrete (RC) members, essential issues include establishing a maintenancesystem with the appropriate measures prior to extensive damage and failure.
As a result of budgetary restrictions, preventive and proactive maintenance of infrastructure isdesired, and inspections by non-destructive testing (NDT) methods must be applied. In termsof damage assessment and estimation of repair and retrofit recovery in concrete structures, inaddition to current NDT, innovative methods must be established.
Tomography techniques have been studied based on elastic-wave and acoustic emission (AE)to visualize internal defects in concrete in three dimensions. The applicability of thesetechniques has already been reported as elastic-wave tomography [1][2] and AE tomography[3][4][5].

1

S402

Through the tomography technique, internal distributions are obtained using elastic-wave
parameters such as amplitudes and elastic-wave velocities. In this study, elastic-wave velocity
and attenuation rate are used as the parameters. In elastic-wave tomography, both the locationof the excitation and the excitation time should be known, whereas they are unknown for AEs
induced by random excitations such as steel ball hammering and rain fall hits. Specifically,
tomography can evaluate the elastic-wave velocity and attenuation rate in each set-element
over the structure, which is supposed to be theoretically associated with the modulus ofelasticity. Because of the presence of such internal defects as cracks and voids, the values
would be exhibited as low-velocity and high-attenuation zones.

2. Attenuation rate and wave velocity tomography
2.1 Computation procedure for damage detection
An innovative non-destructive method for inspecting the interior of concrete has been
introduced and applied to the RC slab of a bridge in service [6]. The method, referred to as
single-side attenuation tomography, which is based on the principle that the elastic waves
traveling through concrete are to impinge on the cracks. It could provide the tomogram on theattenuation rates inside the tested specimen. To summarize, single-side attenuation
tomography must be understood as a very practical non-destructive testing method for
identifying the parts of a reinforced concrete member which are very likely to present serious
damage.
Assuming that the propagation path of P-wave is a straight line, the tomography analysis via
the attenuation-rate and the wave-velocity distribution was carried out in order to identify thehorizontal cracks in RC slab. Fig. 1 shows the flow chart to compute the attenuation-rate andthe wave-velocity distribution. The data processing flow is followed so as extracting AE events.
As summarized in the figure, first, the source locations of all AE events are used as input,
where a constant wave velocity (m/s) and attenuation-rate (dB/m) across the tested member
is assumed for calculation. Second, the amplitude and the excitation time at the source of theconsidered AE event is estimated on the basis of the arrival times and the amplitudes recordedby the sensors. Third, the attenuation rates and wave velocities along all straight ray-paths
between the source and the receiving sensors are computed.
2.2 Source location algorithm
The algorithm for AE source location is based on the Inglada’s method, which is used inseismic engineering for locating the epicentre of earthquakes. By assuming a constant wave
velocity inside the tested specimen, the source location of an AE event is determined from thearrival times of their associated elastic waves at the locations of several sensors. In this paper,3000 m/sec was set as the constant value in consideration of averaged speed in intact anddeteriorated concrete.
2.3 Estimation of travel time and peak amplitude at AE source
For each AE event, the peak amplitude of the elastic wave at the source is unknown.
Consequently, it can be approximated to calculate wave velocity and attenuation rate along
the considered wave paths. First, the arrival time and the peak amplitude of the signal recordedby each sensor is plotted as a function of the distance between the source and the sensors.
Second, a linear regression between the arrival time and the peak amplitude of the elastic
2

S403

wave and the distance from the source is computed. The AE dispatched time and the peak
amplitude at the source is referred to as equal to the value for the case that the distance isequal to zero. As shown in Fig. 2, the linear estimation on assumed logarithmic function for therecorded amplitude, f(x), can be re-calculated for linear function, F(x), in order to estimate theamplitude at AE source (x=0).

Fig.1 Flow chart to compute wave-velocity and attenuation-rate tomography

Fig. 2 Linear estimation on travel time amplitude from AE source to sensors

2.4 SIRT algorithm for tomography computation
Tomography computation is carried out based on elastic wave parameters with the velocity
and the attenuation rate in this paper. In the tomography based on the attenuation, the area ofinterest and analysed must be divided into mesh elements characterized by their own wave
velocities and attenuation rates. Then, a first estimation on distribution of the wave velocities
and the attenuation rates is to be provided as input. By comparing those values along eachwave path to its calculated value of the assumed distribution, the SIRT algorithm could lead toproper distribution of the wave velocities and the attenuation rates.
The measured travel time and attenuation rate along each ray path is estimated from eq. (1).
3

S404

AR #$%&'($),+ =-./0123 4-.35./1,6(1):∑8 6 )6,8

Where: ARmeasured,i: measured average attenuation rate along the wave path from the source
to the ith sensor, Asource: estimated peak amplitude of the elastic wave associated to theconsidered AE event at its source, Areceiver,i: peak amplitude of the elastic wave measured at
the ith sensor, Ni: mesh number of elements crossed by the wave path from the source to theith sensor, di,j: length of the wave path from the source to the ith sensor in the jth element
The attenuation rate along each wave path based on distribution of the attenuation rates in
the mesh elements is computed by eq. (2).
AR ;%<;'<%=$),+ =∑@8 ->8 ∙)6,8(2)∑@8 )6,8Where: ARcalculated,i: calculated average attenuation rate along the wave path from the source
to the ith sensor (dB/m), ARj: attenuation rate in the jth element (dB/m), M: mesh number of
elements crossed by the ray path from the source to the ith sensor, Afterwards, the difference
between the measured and the calculated attenuation rates is calculated for each wave pathby using eq. (3).
(3)

∆AR + = AR #$%&'($),+ − AR $&=+#%=$),+In a similar manner to Equation 1, the differences of the attenuation rates on all the wave
paths are estimated by eq. (4).
∆AR C =∑:
6DE ∆->6 ∙)6,8(4)∑:
6DE )6,8

Where N is the number of wave paths crossing the jth element. The attenuation rate in eachelement is then updated with eq. (5).
(5)

AR C,'F)%=$) = AR C + ∆AR C

The procedure from eq. (2) through eq. (5) is repeated until the convergence is reached. As
for wave velocity as the parameter of SIRT algorithm, the same manner as attenuation rate isapplied for computing wave velocity distribution in the area of interest and analyzed.

3. Rain-induced AE detection at exiting RC bridge deckA real RC bridge deck was selected as a study target which has deterioration such as rebar
corrosion, breaking by salt damage, and deck fatigue. AE measurement was carried out in the
RC bridge deck. 15 AE sensors are set on the bottom side of RC bridge decks. Resonant
frequency of AE sensor is 30 kHz. AE sensors arrangement on the RC deck is shown as inFig. 3. Thickness of both RC bridge decks is 235 mm. Large cracks with water leakage trace
were confirmed. Threshold value of LUCY (location uncertainty) is set on 300 mm as half
spacing of two adjacent sensors in this study. LUCY means source location accuracy and is4

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the root-mean-square of the difference between calculated and observed distances between
the source and the sensor [7].

Fig. 3 Sensor arrangements for AE measurement on the real RC bridge decks

AE hits by one hour in the measurement period is shown as in Fig. 4. A lot of AE hits wereacquired from 120 to 130 hours. A rain drop is known as factor of AE generation when it hits
on a solid material [8]. After the consideration of AE activity in the panel between 120 to 130
hours shown in Fig. 4, AE hits can be regarded as hits induced by rain drops on the surface ofRC deck.

Fig. 4 AE activities and relative humidity during measurement

In Fig. 4, an extremely high AE activity is observed at around time 120 hours. Besides relative
humidity at the bridge is also measured and shown in the bottom of Fig. 4 as well. Around 120
hours, the humidity also rises to 100%. In the weather record, precipitation and wind was also
recorded at that period. Thus, the peak of AE activities is considered to be caused by heavy
rainfall. It indicates that the impacts of raindrops on the road surface cause AE activity, and
the AE reaches the sensors attached on the bottom of the deck are recorded. Those
5

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concentrated AE activities are not correlated to the traffic load and not emitted from cracks
inside the bridge decks. Therefore, those signals are considered not to reflect the deteriorationof the deck and should be treated as noise. However, it was found that the rain-induced noise
signals could be used to inspect the inside of the bridge decks.
The data was extracted during the rain peak and analyzed the source locations. Fig. 5 shows
the AE source result of the measured panel. Source locations considered to have low reliability
has been filtered. The extracted data for only 700 seconds indicated enough amounts of AE
sources for analysis are identified. In the figures, relatively low-density areas surrounded bydashed lines can be seen in the panel. Red line shows the sensor arrayed area. This lowdensity area is suspected to be heavily deteriorated as in the following reason.

Fig. 5 Result of AE source location analysis

Since raindrops hit all over the road surface uniformly, the AE source locations should also
show a uniform distribution in the case of the uniformly sound case. On the other hand, if
severe damage like a large horizontal crack is partially existed inside the deck, as depicted in
the figure, the AE generated at the road surface is attenuated, diffracted, or blocked by thecrack. Consequently, AE sensors right under the crack receive fewer AE or don’t receive raininduced AE activity at all, and the source distribution under the crack would be sparse or vacant.
Therefore, by calculating the AE source locations, the density of the AE sources would reflect
the damage condition inside the deck. Since all AE sources generated by raindrops are on thesame plane namely the road surface, the 3D source locations can be readily estimated by
fixing datum point in the depth direction. Additionally, heavy rain generates large amount of
AE hits on the road in a short time so that it is realized that the inspections of serious damage
inside the deck could be carried out with great efficiency.

4. Wave velocity and attenuation rate tomography for rain-induced AE activity
In order to guarantee the applicability of the proposed tomography technique it is necessary toquantitatively clarify that the tomography results are simulated with in-situ measurement
condition. It is investigated in this paper if a measurement condition performs for an adequate
resolution of tomography results, followed by AE measurement.
In the present study, using wave propagation simulation based on visco-elastic wave equation,
the result of AE tomography is numerically investigated. A stand-alone computer software
6

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package that generates solutions to practically any ultrasonic (elastic wave propagation)
problem with simulating received waveforms, was used for wave propagation analysis tonumerically identify the influence of ray path density followed by sensor arrangements (sensorto-sensor distance) in the area of interest on the accuracy of tomography computation results.
The specific acoustic equation that is simulated is given by eq. (6). Fig. 6 shows geometricalinformation and parametrical information provided into the simulation models in this study.
ρ

IJ K
IJ L

I

I

V I

= M𝜇𝜇 + η IL P ∇R 𝑤𝑤 + M𝜆𝜆 + 𝜇𝜇 + 𝜙𝜙 IL + W IL P ∇(∇ ∙ 𝑤𝑤)(6)

Fig. 6 Geometrical information and parametrical information for wave propagation analysis

The wave velocity and the attenuation rate distribution are shown as in Fig. 7. 2500 m/sec and
15 dB/m are the boundary between the intact and deterioration cases in the numerical resultswith any sensor distances, threshold value of wave velocity and attenuation rate for identifying
the presence of horizontal crack are unknown in terms of inspection for existing structures.

Fig. 7 Numerical analysis for tomography results on wave velocity and attenuation rate

As expected and afore-mentioned, a sufficient number of rain-induced AE events respecting
the quality criteria has been found. The wave velocity distribution by means of AE tomography,
as explained above, in this panel has been successfully computed from these data and theresults are presented in Fig. 8 for wave velocity tomography, also they are described in Fig. 9
with a tentative classiﬁcation for using pulse velocity as an indicator of quality known in Table
1 [9]. Accordingly, wave velocity distribution output provided from the proposed tomography
technique is colored in red (poor < 3050m/sec) or blue (intact < 3050m/sec). Fig. 10 shows theresults for attenuation rate tomography respectively, with core sampling results which weretaken out from the deck to verify the results and visually observe the presence of horizontal
7

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crack. Tomography results show top and bottom layer of 117.5mm each in the deck depth(235mm) since 3D computation was performed in this paper.

Fig. 8 Wave velocity distribution

Fig. 9 Wave velocity distribution with quality classification
Table 1 Concrete quality and P-wave velocity

The relatively low-density AE source areas in the panel surrounded by dashed lines in Fig. 5
is supposed to be a largely extended horizontal crack [5]. The horizontal cracks were locatedat 150 mm or less from the surface of the deck (mostly in the top layer) by visual inspection of
the cored samples. Therefore, it is reasonably understood that wave velocities and attenuation
rate in top layer are lower than bottom layer. After all, in the tomography results, the soundnessof concrete and the presence of horizontal crack roughly correspond to the wave velocity, and
it is similarly seen observed for the attenuation rate distribution as well. The wave velocity andattenuation rate inside the core samples presenting large horizontal cracks are generally lower
than 2400-3000 m/s and more than 15-20 dB/m at least over a certain part of their depth andthe wave velocity inside the un-cracked core samples is generally higher than 3000m/s overtheir entire depth.

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Fig. 10 Attenuation distribution

5. Conclusion
The elastic waves generated by the rain hitting the pavement surface can be recorded andused for reconstructing the wave velocity and the attenuation rate distribution inside RC slab.
This method presents the advantage that a large number of AE events can be recorded in avery short period of time. Also, the computation technique assuming linear ray-paths enables
to make the computation time drastically shorter than conventional ray-trace algorithm since
the calculation process is undoubtedly simpler based on linear ray-paths without the detours.
It is additionally believed that wave velocity and attenuation rate distribution can be calculatedwith hundreds or thousands of the rain-induced AE events more precisely than those resultswith using a few internal AE events such as secondary AE induced by traffic loading. Further
investigation is needed to identify the appropriate sensor array and the threshold value ofelastic wave parameters, wave velocity and attenuation rate, to judge the damage in targeted
structure when the proposed tomography technique is applied. It is necessary to pay attention
the AE measurement condition, such as sensor-space distance, when wave velocity is used
as a parameter for acoustic emission tomography on the assumption with linear ray-paths ofelastic wave travels to simplify the computation process.

References:[1] Y. Kobayashi, T. Shiotani and H. Shiojiri, Damage identification using seismic travel time
tomography on the basis of evolutional wave velocity distribution model, Structural Faults
and Repair 2006 (CD-ROM), 2006.
[2] Y. Kobayashi, T. Shiotani, D. G. Aggelis, H. Shiojiri.: Three-Dimensional Seismic
Tomography for Existing Concrete Structures, Proceedings of Second International
Operational Analysis Conference, Vol. 2, pp. 595-600, 2007.
[3] Y. Kobayashi and T. Shiotani, Seismic tomography with estimation of source location for
concrete structure, Structural Faults and Repair 2012, CD-ROM, 2012.
[4] T. Shiotani, N. Okude, S. Momoki and Y. Kobayashi, Proposal of assessment method forinfrastructures by AE tomography, Proceedings of 2011 National Conference on Acoustic
Emission, 39-42, 2011 (in Japanese).
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[5] H. Asuae, T. Shiotani, T. Nishida, K. Watabe, H. Miyata, Applicability of AE Tomography
for Accurate Damage Evaluation in Actual RC Bridge Deck, Structural Faults & Repair
Conference, No.1743, 2016.
[6] C. Granier, Shiotani, T., Hashimoto, K., and Nishida T., Visualization of internal damage
in RC slab with single side access attenuation tomography, Proceedings of IAES-23,
IIIAE2016 Kyoto and ICAE-8, pp. 383-387, 2016.
[7] M.A. Hamstad, Acoustic emission source location in a thick steel plate using lamb modes,
J. Acoustic Emission, Vol. 25, pp. 194-214, 2007.
[8] T. Shiotani, Evaluation of rock slope stability by means of acoustic emission, NDT&E
international, Vol.39, pp. 217-228, 2006.7[9] E. A. Whitehurst, Evaluation of Concrete Properties from Sonic Tests, ACI Monograph 2,
American Concrete Institute, Detroit, MI, pp. 94, 1966.

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34 - Contouring geodetically accurate Acoustic Emission sources

CONTOURING GEODETICALLY ACCURATE ACOUSTIC EMISSION
via kernel density estimates
SOURCES VIA KERNEL DENSITY ESTIMATES
Ing. Petr Gális1, Ing. Václav Kůs1
1

Department of mathematics, FNSPE, Czech Technical University in Prague,
Trojanova 13, Prague 2, CZ (galispet@fjfi.cvut.cz, vaclav.kus@fjfi.cvut.cz)

Abstract:
We deal with numerical model of localization of acoustic emission (AE) sources on real complex solid
bodies. Our approach is based on exact geodesic curves on 3D vessels composed of several
parametrized surfaces. The numerical computations are provided via Finite difference, Newton–
Raphson, and Fixed-point iteration methods applied to geodesic equations acquired from differential
geometry theory. To speed up computations, some technical improvements and optimizations areproposed. The variable propagation velocity and also the case when the geodesic curve has to bypass
a given obstacle there is also included into the model. These techniques are employed in the realexperimental setup on bodies with higher geometrical complexity. The results (localization maps) of
AE localization principle using length (ΔL) or time (ΔT) differences, obtained by means of geodesics,
are then processed through the two-dimensional Kernel probability density estimates executed directlyon the 3-D surfaces, which give us the most probable areas of the AE source positions on the main
body. The placement of piezo-ceramic AE sensors is outside the central part of the vessel because it
can be inaccessible due to possible high temperature or radioactivity, such as in the case of nuclear
power station health monitoring. This outward position of all AE sensors can result in a dispersed AE
wave detected, or attenuated because of welded intersections of different surfaces. Thus, the Changepoint analysis of AE signals is also discussed in order to obtain the most precise arrival times of AE
events, which is crucial for ΔT / ΔL localization.

1. Introduction on geodesics
In our mathematical model of geodesic-based localization of acoustic emission sources we
employ a posteriori evaluation of acquired localization maps. This is carried out by using thestatistical kernel density estimates in 2D, which are applied either in parametric domain of
the surface or directly on the 2D surface embedded in 3D. The resulting geodetic-kernel
localization is applied to two experiments – the ionized iron watering can and steam
reservoir. The method of geodesic localization itself is based on the well-known timedifferences localization principle. Measured time differences are compared (via certain
functional F) with length differences that are computed using geodesic curves. Thesegeodesic curves (geodesics) possess the property of the shortest length curves between two
points and they are obtained from the following system of two non-linear ordinary differential
equations [3] of the second order
𝟏𝟏 𝟐𝟐
𝟏𝟏 𝟐𝟐
𝟏𝟏
𝒖𝒖̈ + 𝚪𝚪𝟏𝟏𝟏𝟏
𝒖𝒖̇ + 𝟐𝟐𝚪𝚪𝟏𝟏𝟏𝟏
𝒖𝒖̇ 𝒗𝒗̇ + 𝚪𝚪𝟐𝟐𝟐𝟐
𝒗𝒗̇
𝟐𝟐 𝟐𝟐
𝟐𝟐 𝟐𝟐
𝟐𝟐
𝒗𝒗̈ + 𝚪𝚪𝟏𝟏𝟏𝟏 𝒖𝒖̇ + 𝟐𝟐𝚪𝚪𝟏𝟏𝟏𝟏 𝒖𝒖̇ 𝒗𝒗̇ + 𝚪𝚪𝟐𝟐𝟐𝟐 𝒗𝒗̇

= 𝟎𝟎,
= 𝟎𝟎,

with a boundary conditions 𝑢𝑢(𝑎𝑎) = 𝛼𝛼1 , 𝑢𝑢(𝑏𝑏) = 𝛼𝛼2 and 𝑣𝑣(𝑎𝑎) = 𝛽𝛽1 , 𝑣𝑣(𝑏𝑏) = 𝛽𝛽2 , where 𝑢𝑢 = 𝑢𝑢(𝑡𝑡)and 𝑣𝑣 = 𝑣𝑣(𝑡𝑡), 𝑡𝑡 ∈ (𝑎𝑎, 𝑏𝑏), are coordinate functions of the geodesic in parametric domain of thesurface with parametrization 𝐱𝐱 = 𝐱𝐱(x(u, v), y(u, v), z(u, v)). The symbols Γ𝑖𝑖𝑖𝑖𝑘𝑘 are called
Christoffel symbols and they describe a metric on the surface. We go through the each point1

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of a surface and numerically compute the geodesics to each sensor. Since many real bodies
cannot be, in general, parametrized by a single parametrization, the algorithms obtaining a
geodesic between two arbitrary points on the body are required [1, 2].

2. Numerical computation of geodesic curves
The above system of geodesic equations is solved via finite difference method, which leads
to the system of algebraic non-linear equations [2], which is solved through the NewtonRaphson iterative method, given by the following principledF(𝝎𝝎𝑘𝑘 ) △ 𝛚𝛚k = −𝐹𝐹(𝝎𝝎𝑘𝑘 ) ,𝝎𝝎𝑘𝑘+1 = 𝝎𝝎𝑘𝑘 +△ 𝛚𝛚k ,with initial root guess 𝛚𝛚𝟎𝟎 as a straight line in parametric domain. To be able to compute
geodesics on real bodies we need to decompose them to elementary surfaces so that we
can describe each of them parametrically and easily obtain its Christoffel symbols. Then wecompute the geodesics on the every sectional surface between start/end points by using aconnection point lying at the intersection of two surface sections (Fig. 1 left). Then we iterate
through each point of intersection and minimize the sum of lengths of partial geodesics. The
computational cost can be very high, mostly when we have to iterate through many
intersections.

Figure 1: Computing geodesics through intersection on compound body.

To fasten the computation through intersections, the Sequential Algorithm (SA) is applied [1].
It is based on bisectional method and iterates through the points of intersection with minimal
sum of length of geodesics (Fig. 1 right). Let (𝑎𝑎0 , 𝑏𝑏0 ) ⊂ 𝐽𝐽 be one part of parametric domain of
the intersection curve. Set 𝛾𝛾0 = (𝑎𝑎0 + 𝑏𝑏0 )/2 and the step 𝜎𝜎0 = |𝑏𝑏0 − 𝑎𝑎0 |. Now we compute
geodesics through the points 𝑎𝑎𝑘𝑘 , 𝑏𝑏𝑘𝑘 , 𝛾𝛾𝑘𝑘 , 𝑘𝑘 ≥ 0,
𝜎𝜎𝑘𝑘
𝑎𝑎𝑘𝑘+1 = 𝛾𝛾𝑘𝑘 + 𝜎𝜎𝑘𝑘+1 ,𝑏𝑏𝑘𝑘+1 = 𝛾𝛾𝑘𝑘 − 𝜎𝜎𝑘𝑘+1 , 𝜎𝜎𝑘𝑘+1 = 
2
𝛾𝛾𝑘𝑘+1 = argmin{𝐿𝐿𝑎𝑎𝑘𝑘+1 , 𝐿𝐿𝑏𝑏𝑘𝑘+1 , 𝐿𝐿𝛾𝛾𝑘𝑘 },where 𝐿𝐿𝑖𝑖 means the length of geodesic through the point 𝑖𝑖. This SA algorithm is very fast but
there may be possible disadvantage that the points, through which the SA iterates, are not
restricted to the interval (𝑎𝑎0 , 𝑏𝑏0 ). Then a simple modification of the algorithm is needed.
There occurs one serious problem when solving geodesic system of equations. In
short, the curve obtained need not be the shortest possible path between two points (Fig. 2).
It happens in practical applications while working with solid vessels whose elementary parts
form closed rotary surfaces. It means that at least one of coordinates u, v of its
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parametrization domain takes values in (0,2π) or (−π,+π). These intervals aremathematically equivalent apart from the discontinuity at 2π ↔ 0 or π ↔ −π, which has an
influence to the geodesic curve solution. Therefore, if |𝑢𝑢𝐴𝐴 − 𝑢𝑢𝐵𝐵 | > 𝜋𝜋, we a priori change thecomputational parametric range of the angular coordinate, where 𝑢𝑢𝐴𝐴 , 𝑢𝑢𝐵𝐵 are angularcoordinates of selected two surface points. The idea of this criterion comes from theobservation that greater angle between two points results in longer arc and consequently
greater length of geodesic curve.

Figure 2: Conditional a priori reparametrization of (0,2π) to (−π,+π).

We also have to deal with computations of geodesic curves around obstructions, e.g. see
Figure 4. It is clear that the exact geodesic computations around obstacles can give more
realistic signal paths and more exact AE source localization [2]. Thus, we have to find out
sets of points for each obstruction through which we minimize the sum of length of all parts ofgeodesic (this may be tricky if we are dealing with multiple obstructions and advanced
graphical methods are needed [4]). This algorithm is considerably time consuming because
we are forced to compute very large number of geodesics in both reparametrizations.

Figure 3: Geodesic curves in cases of path obstructions.

3. Localization of acoustic emission sources
In this part we present results from experiments with watering can and steam reservoir using
the exact geodetic method mentioned above. First, SIC algorithm [5] is used to determine
time arrivals of acoustic signals (produced by pentests) and then the common localization of3

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AE sources is carried out by means of either length differences: 𝐹𝐹𝐿𝐿 = ∑𝑖𝑖<𝑗𝑗 |𝑐𝑐𝑖𝑖𝑖𝑖 Δ𝑡𝑡𝑖𝑖𝑖𝑖 − Δ𝑙𝑙𝑖𝑖𝑖𝑖 |, ortime differences: 𝐹𝐹𝑇𝑇 = ∑𝑖𝑖<𝑗𝑗 |Δ𝑡𝑡𝑖𝑖𝑖𝑖 − Δ𝑇𝑇𝑖𝑖𝑖𝑖 |, where 𝑐𝑐𝑖𝑖𝑖𝑖 denotes the specific velocity between i-thand j-th component of the surface. There may occur the differences between resulting
localization maps because the velocities 𝑐𝑐𝑖𝑖𝑖𝑖 are in general different from the velocities oneach component itself (Figure 4 (right)).

Figure 4: Sensor placement on the watering can (left) and an example of localization map using
length (black+green) or time differences (blue+yellow), the source is red (right).

In the sequel, we use the time differences for our pentest experiments on steam reservoir,
where the localization results are shown in Figure 5. As we can see, the precision of
localization is quite high. This precision was mainly maintained due to incorporation of thevariable velocity (non-constant on the whole surface) and also due to computing geodesics
around obstacles (pouring ladle, welded pipes). We point out here that SIC criterion for
obtaining the AE signal arrivals played a very important role in localization accuracy.

Figure 5: Localization maps on steam reservoir. Black dots with circles denotes pentest areas.

4. Kernel density estimates in 2D
The Kernel density estimate [6] of some unknown density f is defined by
4

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𝑓𝑓̂(𝐱𝐱) =𝑛𝑛
1∑ 𝐾𝐾𝐇𝐇 (𝐱𝐱 − 𝐗𝐗 𝑖𝑖 ) ,𝑛𝑛
𝑖𝑖=1

where 𝐗𝐗 𝑖𝑖 = [𝑢𝑢𝑖𝑖 , 𝑣𝑣𝑖𝑖 ] ∈ ℝ2 represents the i-th localized point in the parametric domain of the2
surface, 𝐱𝐱 ∈ ℝ2 , 𝐇𝐇 = (hij )∈ ℝ2,2 is a symmetric and positive definite smoothing matrix
i,j=1

and 𝐾𝐾𝑯𝑯 is determined through the kernel function K by the expression
𝐾𝐾𝐇𝐇 (𝐱𝐱) = |𝐇𝐇|−1/2 𝐾𝐾(𝐇𝐇 −1/ 2 𝐱𝐱).

𝐇𝐇 plays a similar role as the window width in one dimensional case. The essential thing
about kernel density estimates is that it weakly depends on 𝐾𝐾, however it substantiallydepends on the smoothing matrix 𝐇𝐇. We briefly present two algorithms which determine thematrix 𝐇𝐇 automatically.
Reference density method (RD): If we suppose that the unknown density 𝑓𝑓 belongs to thê , where ∑̂ = (𝑠𝑠𝑖𝑖𝑖𝑖 )2
̂ 𝑅𝑅𝑅𝑅 = 𝑛𝑛−1/3 ∑family of normal densities, then the estimate of 𝐇𝐇 is 𝐇𝐇

stand for the estimate of covariance matrix obtained from measured data set.

𝑖𝑖,𝑗𝑗=1

Iterative method (IT): IT estimate of 𝐇𝐇, based on the family of normal densities again, is
𝑛𝑛

2ℎ11 √𝑆𝑆(𝑠𝑠𝑖𝑖𝑖𝑖 ) ∑ Λ𝐇𝐇 (𝐗𝐗 𝑖𝑖 − 𝐗𝐗𝑗𝑗 ) = 𝑛𝑛 𝑉𝑉(𝐾𝐾),2 )/𝑠𝑠 2𝑆𝑆(𝑠𝑠𝑖𝑖𝑖𝑖 ) = (𝑠𝑠11 𝑠𝑠22 − 𝑠𝑠12
11 ,𝑖𝑖,𝑗𝑗=1,𝑖𝑖≠𝑗𝑗

𝑉𝑉(𝐾𝐾) = ∫ℝ2 𝐾𝐾 2 (𝐱𝐱) d𝐱𝐱, and Λ 𝐇𝐇 (𝐳𝐳) = (𝐾𝐾 ⋆ 𝐾𝐾 ⋆ 𝐾𝐾 ⋆ 𝐾𝐾 − 2 𝐾𝐾 ⋆ 𝐾𝐾 ⋆𝐾𝐾 + 𝐾𝐾 ⋆ 𝐾𝐾)(𝐳𝐳), where ⋆ denotes the convolution. The equation can be solved for example with
Newton-Raphson method. The remaining ℎ12 , ℎ21 , ℎ22 are then obtained from ℎ11 as ℎ𝑖𝑖𝑖𝑖 =ℎ11 𝑠𝑠𝑖𝑖𝑖𝑖 /𝑠𝑠11. Note that 𝐇𝐇 is symmetric and thus ℎ12 = ℎ21 .
with

5. Localization results using kernel density estimates

The localization map from Figure 5 is further processed through kernel density estimate
which tells us the most probable location of AE sources. This kernel density estimate can be
carried out either in parametric domain of the surface and then transformed onto the surface
in 3D, or preferably, can be performed directly on the curved surface 2D object embedded in
3D.

Figure 6: Kernel density estimates on the steam reservoir applying RD procedure.

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Figure 7: Kernel density estimates on the steam reservoir under IT procedure.

The former resulting kernel density estimates can be seen in Figures 6 and 7, where thesignificant difference between IT and RD methods can be observed. The outstretched RD
kernel estimate is caused by its application on quite dispersed pentest data with big variance,
consequently leading to unusable estimates. However, if we treat the data separately foreach localization cluster then the RD method provides practicable estimates. Further, wê , this IT
found out that although the IT algorithm also depends on the covariance matrix ∑method does not suffer from the dispersed estimates.
The second approach based on direct kernel computations on the curved surface in 3D
is presented in Figure 8. It was realized by means of generalized definition of the kernel
estimator using geodesic metric 𝜌𝜌 at the surface and restricted to the window radius ℎ. Thismethod offers wider variability of adjusting magnitude of the kernel window, unlike themethods for automatic estimate of smoothing matrices 𝐇𝐇. For comparison purposes, thedirect surface kernel estimates were also transformed back into the parametric domains of
the surface, see Figure 8 (left).

Figure 8: Kernel estimates computed directly on the steam reservoir with window radius 1 cm.

6. Conclusions
Mathematical model of sources of AE based on time differences principle expanded fromplane to curved surfaces make use of geodesic curves obtained from geodesic equations.
These equations are solved numerically via finite difference method and Newton-Raphson
method. Both the variable propagating velocity of acoustic signal and the geodesics
bypassing obstacles were incorporated into the model. This led, with other support
techniques, to high accuracy of AE localization model. Computation is parallelized for the
possibility of online localization. Resulting model was applied to laboratory experiments on
ionized watering can and steam reservoir. The watering can represents higher geometriccomplexity, whereas the steam reservoir brings computations of geodesics bypassing multiobstacles. Resulting localization maps were processed with 2D kernel density estimates
6

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which provided most probable locations of AE sources. Based on our comparison we pointout the possibility of computing the kernel estimates directly on the 2D surface in 3D, which
can be very useful and more accurate. All the results can be found in [2] in details.
Acknowledgments: The experimental data on steam reservoir was measured by Ing. Milan
Chlada, Ph.D., from the Institute of Thermomechanics, Academy of Sciences of the Czech
Republic. We thank him very much for his considerable help with data handling. This work
was supported by the grants LM2015068 (MYES) and SGS18/188/OHK4/3T/14 (CTU).

References:[1] Kůs, V., Záveský, M., Převorovský, Z., Acoustic Emission Defects Localized by Means of
Geodetic Iterative Procedure - Algorithms, Tests, AE Experiment, In: Proceedings of the 30th
European Conference on Acoustic Emission Testing & 7th International Conference on
Acoustic Emission. Granada: Universidad de Granada, 2012. pp. 536-546.
[2] Gális, P., Geodetic optimization and acoustic emission localization maps processing,
Diploma thesis, Department of mathematics, FNSPE CTU in Prague, Prague, 2018.
[3] Carmo, M.P., Differential Geometry of Curves and Surfaces, Prentice Hall, New Jersey,
1976.
[4] Foley, J., Dam, A., Feiner, S., Hughes, J., Computer Graphics (3rd Edition), AddisonWesley Professional, 2013.
[5] Gupta, A.K., Chien, J., Parametric Statistical Change Point Analysis, Birkhäuser, Basel,
2000.
[6] Silverman, B.W., Density Estimation for Statistics and Data Analysis, Chapman & Hall,New York, 1986.

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41 - Analysis
ofSOURCE
AE sourceLOCATION
location precision
ANALYSIS
OF AE
PRECISION
general SENSOR
sensor configurations
FOR forGENERAL
CONFIGURATIONS
Milan CHLADA1, Petr GALIS2, Zdenek PREVOROVSKY1
1

NDT Laboratory, Institute of Thermomechanics AS CR, v.v.i.,
Prague, Czech Republic;2
Department of Mathematics, FNSPE,
Czech Technical University in Prague, Czech Republic.

Abstract:
Proper sensor placement is the crucial step and a premise for precise AE source location estimate.
Optimal sensor configuration in cases of complex structure shapes is one of AE expert challenges.
It leads to numerical analysis of relations between the signal arrival chronology and the coordinatesof emission sources. Using the algorithm for finding the shortest ways in discretely defined bodies
it is possible to design three parallel tools how to evaluate problematic areas, namely the location
sensitivity, similarity and ambiguity maps, available even for non-continuous or anisotropic materials.
Analogically to Global Positioning System, location of AE sources meets the geometrical dilutionof precision (GDOP) phenomena. Similarly to GDOP parameter, recently introduced sensitivity map
shows critical regions characterized by strong sensitivity of location results to signal arrival timechanges or errors. Remaining two mentioned methods illustrate the topology of arrival time differences
space and possible ambiguities of source location. To check the numerical forecast of location
capabilities of given sensor configuration, theoretical results were reviewed with the data measured
during real experiment.

1. Introduction
To ensure reliable AE source location results in particular structure part, it is needed tochoose proper configuration of sensors, or, conversely, take into account only AE events
coming from “stable” region defined by sensor placement. Improper positions of receivers
towards the source can generally disable location methods using the signal arrival delays.
Similar problem is solved also in the field of satellite navigation. In order to predict theaccuracy of location results, the geometry of the setup (i.e. known position of satellites andthe estimated position of GPS signal receiver) is exploited. The dimensionless parametermodelling expected location accuracy is called Geometric Dilution of Precision (GDOP) [1, 2].
The idea is to state how the measurement errors affect the final location estimation. If smallchanges in measured data (time delays) are not resulting in large location changes, thesolution is not too sensitive to measurement errors.
(1)
In general the GDOP solely depends on the arrangement of sensors and the emitter position.
GDOP serves as a measure of the location errors magnification derived from perturbations
in arrival times. In areas of great GDOP values, especially outside the convex hull of thesensor array, it is not recommended to locate AE sources, due to expected high location
uncertainty and errors.

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Another indicator of expected location precision is the Location Uncertainty (LUCY)
parameter [3]. It describes how well a calculated source position fits the measured
arrival-time-differences and can be used as a filter criterion to separate the sources with poor
location accuracy.
All above mentioned approaches are theoretically ready to be applied for general body
shapes, however, their usage is mostly restricted by essential knowledge of shortest (direct)
paths between the source and sensors. Following chapters deal with forecasting of location
possibilities and precision by application of numerical analysis of particular sensor
configurations. The methodology is formulated for discretely defined bodies and it isdemonstrated for real complex structure, namely the steam storage vessel with embeddedpressure and temperature sensors, waveguides and supporting legs (see photos in fig.1).
The goal is to predict the accuracy of AE source location for selected group of sensors. The“numerical forecast” is then reviewed with nine sets of pencil-lead breaks (PLBs) realizedin nine mesh points on the main body surface.

Fig.1: Experimental body.

2. Arrival-time chronology parametrization
The mostly applied approach to AE source location is the analysis of time chronology(delays), in which the elastic wave arrives at each sensor. However, the arrival-time
information can be furthermore parametrized by various ways. Instead of commonly used
time-differences, let's demonstrate advice methodology of location precision analysis forarrival-time profiles. For group of N sensors, the arrival-time profile (ATP) is a vectorof numbers pi defined as:
N

N

∑

pi =

1
Ti − N
Tj
j=1

T

N

∑

=

1
ti − tS − N(t j − t S )
j=1

T

∑

=

1
ti − N
tj
j=1

T

(2)

where Ti is arrival time period (signal propagation time from source to sensor i) , ts the AE
source inception time, ti signal arrival time and T is a normalization period representing thelength of time interval which AE signal needs to pass between two appropriately selected
points. Since no AE analyser measures the arrival time periods Ti before precise AE source
location is done, the left part of equation (2) can be considered only when computing thedatabase of typical arrival-time profiles in points sufficiently covering the body. Arrival-time

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profiles are independent on elastic wave velocity and structure scale changes and theseparameters can be calculated by real arrival times ti or time periods Ti evaluatedon a physical or numerical model. Normalization period T serves as a calibration time, whichmust be measured on the real structure and computed for corresponding points on the model
body. Finally, arrival-time profile P=(p1,..., pN) represents a kind of arrival-chronology
relativizing (see fig.2).
ARRIVAL TIMESARRIVAL TIME PROFILE

p2

T4

S4

S2

T1

S1
tS

+
+

S3

T2

S2

p4 p3

S4

T3

S3

p1

+
+

S1

t 2 t1 t 4 t3

[s]

-1

1

0

Fig.2: Relativisation of arrival-times by arrival-time profile.

For further computations let's consider the sensor configurations outgoing from a body shapeand sensor placement illustrated in fig.3. Experimental setup includes also the positionsof nine PLBs performed within the most complex part of steam storage vessel. All
computations are done in 2D on unwrapped main body surface (see right part of fig.3).
S2
sensors on waveguides

S1
UNWRAPPED BODY SURFACE

S5z [cm]

p9

on supporting leg

S4

p6

S2

p9

p8

p7

p6

p5

p4

p3

p2

p1

S1

on waveguide

p4

p2

S6

y [cm]

p7

p5

p3

on waveguide

p8

p1

S3
S3
S6
on supporting leg

x [cm]y [cm]
x [cm]

Fig.3: Experimental setup scheme.

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3. Shortest ways finding in discrete bodies
For reliable results of AE source location it is needed to trace elastic wave while propagatingfrom initiation point to sensor and estimate the distance between them. The choice of mostsuitable method for solving the problem depends largely on the kind or form of the body
shape definition. It can be given as a photograph, as 2D or 3D computer sketch, otherwise
as a mathematical model etc.
Due to it's simple implementation and sufficient precision, the method of shortest ways
finding similar to Dijkstra's [4] algorithm was considered as a basic model of the directionof wave propagation. It is suitable for discrete bodies derived from 2D or 3D bitmap pictures[5] and enables tracing of elastic waves propagating in structures of complicated shapes andalso considering the anisotropy, without mathematical models of wave propagation. The
method is inspired by Huygens' principle: each point on advancing wave front may beconsidered as a new point source generating spherical Huygens' wavelets. Such approachenables collecting of sufficient amount of representative data for AE source location
algorithms based on database searching or artificial neural networks.
Having the tool for general finding the distances in incoherent bodies, it is possible to show
the aspects of particular configurations of sensors in accordance with the sensitivityof location results to the changes of arrival-time profiles together with their possible variability
and ambiguity (discussed in next chapters).

4. Sensitivity maps
Analogically to GDOP, let’s define the location sensitivity parameter SM for any pixel XA andits neighbourhood NA in discretely defined body with N placed sensors. The scheme in theright part of the fig.4 shows the detail view of structure part:

X ∈N A{d E (P ( X A ) , P( X ))d E( X A , X )where P( X A ) is ATP for pixel X A}(3)Body pixels
Actual body pixel XA=[xA,yA]

y [pixels]

SM ( X A ) = mean

Neighbourhood NA shape
Pixels to compute … X=[x,y]P( X ) is ATP for pixel X

NA

Out-of-mass pixels

and d E (... ,...) is Euclidean distancex [pixels]

Fig.4: Definition of sensitivity parameter SM.

After computation of SM parameter for all body pixels, it is possible to show resulting
sensitivity map. Higher values predict possible good location precision, while the pooris shown by small numbers caused by significantly higher variance of AE source coordinatescompared to variance of corresponding arrival-time-profile. Following figure illustrates thecase of two selected configurations of three sensors. Problematic areas in a senseof location precision can be expected in areas characterized by small levels of SM (darkregions) and close to the points with more steepest changes of this parameter (sharp lines
in fig.5).

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y [pixels]

y [pixels]

x [pixels]

x [pixels]

Fig.5: Sensitivity maps for two selected combinations of three sensors.

As expected, the best region for location should be in a convex hull of the three sensors.
Some singularities or numerical stability problems can be caused by the holes in material.

5. Similarity of arrival-time profiles
To illustrate how the arrival-time profiles vary within the complicated body, the similarity map
was assorted as well. Each colour distinguishes the regions having in some small tolerance
similar chronology of signal arrivals to sensors. Large regions detect problematic areas andvice versa (see fig.6).
SIMILARITY MAPSIMILARITY MAP

S4
p8

S1

p6

p3

p9

p7
y [pixels]

y [pixels]

p9

p5

p4

p2

p1

p8

p7
S1

p6

p3

p5

p4

p2

p1

S6

S6

x [pixels]

x [pixels]

Fig.6: Similarity maps for two selected combinations of three sensors.

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S3

6. Location ambiguity measure
Another point of view to location problem aspects can be illustrated as finding a crossingof the two hyperboles in localization by time-differences. It is clear, that 2D location of AE
sources using 3 transducers is not unambiguous [6]. Left part of fig.7 shows the problemof possible existing of two different source locations inducing the same time-differences.
Naturally, the number of such coupled-points is not limited.

S2

S3

S1

source location(crossing of hyperboles)

Fig.7: 2-D AE sources location ambiguity.

The right part of fig.7 shows the areas of unambiguous locations for this particular sensor
configuration. Any AE source in dark grey area has the same time-differences
as corresponding source in light grey area and vice versa. The problem is usually solved byadding the fourth sensor, while the area of unambiguous locations is theoretically reduced toinsular points.
The location ambiguity is generally caused by the insufficient number of sensors andconcerns with the arrival-time profiles as well. To detect distant pixels having very similararrival-time chronology, so-called Location Ambiguity Map (LAM) is introduced. For every
body pixel X=(x,y) it is needed to find a set of other pixels Xi that are not direct neighbours
indeed, but have very close arrival-time profile by Euclidean measure. It means that theirdistance is less than a small tolerance tol. For such pixels Xi the resulting number LAM(X)is the mean ratio of distances in arrival-time profile and x-y-coordinate spaces:

LAM ( X ) = MEAN{

d E (P ( X ) , P ( X i ))d E( X , X i)∣

d E ( P ( X ) , P( X i )) <tol.

}(4)

An ambiguity map is the set of numbers LAM(X) enumerated for all body pixels and can beconsidered as a useful tool for finding an adequate setup of sensors. Small numbers detectproblematic areas of pixels for which somewhere exist points with nearly the same
arrival-time chronology. Thus, the estimate of unique AE source location is unable in suchareas.
After analysis by all above mentioned methods, both two sensor configurations (1-4-6 and1-3-6) were tested by real measured arrival-times of AE signals. The goal wasto compare numerical prediction of location precision with resulting locations of sets of PLBs
(see fig. 8 and 9).

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LOCATION MAP

S4y [cm]

y [pixels]

p9

p8

p7

p6

S1

p3

p5

p4

p2

p1

S6

x [cm]

x [pixels]

Fig.8: Location Ambiguity Map versus location results for sensor group 1-4-6.

LOCATION MAP

y [cm]

y [pixels]

p9

p8

p7

p4

p6

S1

p5
p1
p3

p2
S3

S6

x [cm]

x [pixels]

Fig.9: Location Ambiguity Map versus location results for sensor group 1-3-6.

7. Discussion of results
After recording of nine sets of approximately 50 PLBs by six AE channels, the crucial step for
their location was to estimate the signal onsets. While testing the most stable and precise

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approach, the expert signal edge detection method [7] approved as the best. Anotherproblem was to fit the elastic wave velocities for each component of the testing structure with
the numerical pixel model. The sensors were placed directly on the main body surface, on
the waveguide, or the supporting leg, each part made from different materials. As a reference
point for the best fitting, the position of PLB with the most stable location in the meaningof Location Ambiguity Map was selected (position 1 for sensor configuration 1-3-6 and pos.
5 for configuration 1-4-6). The optimal elastic wave velocities approximately measured andthen corrected by gradual fitting proved best around 5 km/s. As the final location estimateof each AE event, the x-y coordinates of pixels with the closest numerically computed
arrival-time profile were established.
Comparing the left and right sides of fig. 8 and 9 it is possible to conclude, that the reliabilityof AE source location can be illustrated very well by Location Ambiguity or Similarity Maps.
Especially in the case of sensor group 1-3-6, the resulting variance and shift of estimated x-y
AE source coordinates matches the envelope of LAM levels. The satisfactory precision was
found for PLB position 1, while for positions 2 and 3 precision subsequently failed. Resulting
small shift at position 4 seems to be caused by location sensitivity detected by certain edge
coming through corresponding pixels of LAM. The results for remaining positions are notdisplayed, because of too high variance and shift. In conclusion, for particular configurationsof sensors it is possible to analyse the location capability of the setup by means of sensitivity,
similarity and ambiguity maps.

Acknowledgements
The work was supported by the Grant Agency of the Czech Republic under the grant no.
GACR 17-22615S, and by Institutional support RVO: 61388998 (CR), which is gratefully
acknowledged.

References:[5] Benneths, J.B., Organisation for Materials, NDT Journal Concept, Vol. 54, 2001, pp.
[1] Zekavat, R., Buehrer R. M., Handbook of Position Location: Theory, Practice andAdvances, Vol. 27. John Wiley & Sons, 2011.
[2] Bar-Shalom, Y., Li, X. R, Kirubarajan, T., Estimation with Applications to Tracking andNavigation: Theory Algorithms and Software, John Wiley & Sons, 2004.
[3] Thenikl, T., Altmann, D., Vallen, H., Quantifying Location Errors, Proc. of EWGAE 2016.
Prague, Czech Society for Nondestructive Testing, ISBN 978-80- 214-5386-9, pp. 495-502.
[4] Dijkstra, E. W., A note on two problems in connexion with graphs, (PDF). Numerische
Mathematik 1: 269–271. doi:10.1007/BF01386390.
[5] Chlada, M., Prevorovsky, Z., Shortest Ways Finding in Discrete Bodies, Proc. of NDT in
Progress 2015. Prague, Czech Society for Nondestructive Testing, ISBN 978-80-214-5262-6,
pp. 69-74.
[6] Blahacek, M., Time Differences Uncertainty Influence on Acoustic Emission Source
Location Accuracy, Proc. of NDT in Progress 2007. Prague, Czech Society for
Nondestructive Testing, ISBN 978-80- 214-3505-6, pp. 15-22.
[7] Chlada, M., Prevorovsky, Z., Expert AE signal arrival detection. International Journal of
Microstructure and Materials Properties 6(3/4), pp. 191-205.

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54 - TimeLOCALIZATION
reversal localizationof continuousAND BURST
TIME REVERSALOF CONTINUOUS
andAE
burst
AE sources
under
noise
SOURCESUNDER
NOISE
Z. Prevorovsky1, J. Krofta1 , J. Kober1, M. Chlada1, M. Mracko1
1

Institute of Thermomechanics CAS, Prague, Czech Republic

Abstract:
Time reversal (TR) processing of acoustic and ultrasonic signals is effective tool for complicatedproblems solution in NDT /E and structural health monitoring. TR enables space-time focusing ofelastic wave and thereby relatively easy location and partial reconstruction of both burst andcontinuous acoustic emission (AE) sources. AE source location problems come up in situations withhigh external noise, wave dispersion in thin-wall structures, and wave velocity changes in complex
structures containing welds, flanges, branches, etc. Localization problems in such cases mostly
require large sensor arrays and sophisticated signal processing and filtration. A new solution withusing TR approach is suggested in this paper. This approach allows planar location of burst AE
sources with only one transducer, and continuous AE sources with two transducers, both under highbackground noise from other sources. TR procedure is in this paper applied to long random noise(leakage) signals for the first time.
Suggested method was experimentally verified by simulated burst and continuous AE sources on a
thick steel plate and thin Al-plate with various holes. Source location results certified high robustnessof suggested approach. Need of detailed scanning of a region around a pre-localized source
constitutes certain disadvantage of that method. Numerical simulations shown that scanning can beomitted by inserting experimental TR signals into the computer model of monitored structure (“digitaltwin”) and perform the source location in the model. Localization accuracy of suggested procedure
proved by LDV is about one mm, which is better than the wavelength and transducer aperture.

1. INTRODUCTION
Reliable and relatively precise location of Acoustic Emission (AE) sources is one of the mostimportant inverse problems of AE monitoring in NDT and SHM of engineering structures andmany industrial processes. A lot of different ways exists today how to determine the AE
source position using one or more AE transducers. Classical algorithms of AE source
location are mostly based on time differences of signal arrival (TDOA) between two or moretransducers, which give satisfactory results only in relatively simple cases using classicaltriangulation method with the aid of reliable first arrival signal ʻpickerʼ, like e.g. AIC (Akaike
Information Criterion ) [1] or expert signal detection [2]. Such localization can completely failunder situations when e.g. wave dispersion and/or high background noise occur. A lot ofdifferent procedures were already proposed to solve these problems [1,3]. Some years ago
we contributed to that by fuzzy-probabilistic approach with extracted signal features [4] andartificial neural networks (ANN) [5]. In addition to more convenient, variously improved
threshold signals arrival detection are widely used alternative approaches based e.g. on
frequency analysis and filtration, cross-correlation functions (CCF) of two signals [6], etc. The
dispersion and/or noise problems are easily solvable when we localize burst type AE
sources.
.

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2. CONTINUOUS AE SOURCE LOCATION
Location of random noise-like AE sources occurred during leaks, plastic deformation, friction
and wear, etc. [7] is much more complicated than location of discrete AE sources underheavy conditions. Conventional triangulation techniques cannot be there used. The arrival
times are not well defined, and only time differences between more channels acquired e.g.
by CCF are available. The standard, but only rough AE method of leakage source location isbased on signal strength defined as RMS or similar energy parameters [8,9]. If we omit
manual searching with single transducer, larger array of transducers is necessary for linear
location e.g. on a simple tube. Various specialized devices and methods for continuous AE
monitoring and localization are mentioned in more US patents (e.g.[10]). The methods
employ e.g. octave filters to determine characteristic frequencies, noise distribution
histograms, floating threshold values to determine time offsets between signal spikes, time
profile changes of AE parameters, etc. Authors [11] used spectral similarity of noise signals,
authors of [12,13] developed intelligent AE locator employing general regression ANN withlearning on prototype sources. They also discussed the use of correlation techniques, andstated that an optimal wave packet with approx. constant velocity along the structure must be
found for dispersive waves. To solve the problem of separation signals from more mixed
sources they used ʻblind source separationʼ technique with independent component analysis(ICA). ANN for AE source location apart from structure size and velocity changes (portabilityof already learned ANN) we used with ʻArrival Time Profilesʼ (ATP) in [5], which is based on
chronology of signal arrivals to sensor array, determined e.g. on precise structure drawings.
For continuous AE location, proposed in the patent [14] were ATPs replaced by ʻPositional
Profilesʼ independent on structure attenuation. The method needs amplitude calibration of avirtual model for ANN learning. Wang et al. [15] recently proposed gas leak location
algorithm using optimally spaced multi-array sensors subjected to correlation and the phasedifferences profiles. Xu Bian et al. [16] proposed time-space correlation method using AE
sensor array for acquiring directional orientation of the leakage source, and Yong et al. [17]presented new technique for gas leaks location with low frequency AE sensors and signal
analysis in both time and frequency domains with empirical mode decomposition and signal
reconstruction to eliminate wave speed changes and attenuation along the pipeline. A
comprehensive survey on gas leak detection and localization techniques published Murvay
and Silea [18]. Most of the above techniques require optimally dispersed large arrays of
sensors and their calibration, optimal signal filtrations, time-frequency decompositions, andknowledge of monitored structure properties (geometry, wave velocities, etc.). Most of them
are based on TDOA acquired by CCF.

2.1

Time reversal source location

Some years ago we elaborated new concept of more precise AE source location andidentification in arbitrary complex structures [19, 20, 21]. That concept is based on the Time
Reversal(TR) signal processing broadly used in many fields (NDT/E and SHM, acoustics,
seismology, telecommunications, medicine, etc.).The background noise is suppressed in TR
operation when its sources are out of the localized source origin[22]. The TR robustness
originates in the waves character and their space-time reciprocity (solution of wave equation
is invariant with respect to TR operation).TR space reciprocity enables also reciprocal TR
processing when the source position is used to rebroadcast TR signals received by theoriginal receiver.TR experiments are accomplished in two steps: 1. forward propagation,
when a source s(t)excites the medium and response uj(t) is recorded by a set of j transducers
at different locations; 2.back propagation, whenthe response signals are reversed in time andrebroadcast into the original structure. This results in wave energy focusing at the original2

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source location and in the (partial) reconstruction of the source function [22]. A partial
reconstruction can be accomplished using only one receiving transducer but summation of
TR responses from more receivers lead to better reconstruction results. Rebroadcast TR
signal, monitored on the body surface, exhibit local peaks, and the largest one correctly
reveals the source location. Smaller side lobe peaks result from reflections and incomplete
coverage by small number of transducers. At the focal time, a strong maximum occurs at thesource location corresponding to the original pulse source signal. After the focus, theincoming waves pass through each other, propagating outward.
AE signalu(r,t) recorded by a transducer placed at the distance r outside the source at r0 canbe in forward propagation generally considered as a convolution of the source functions(r0 ,t0) with the Greenʼs function G(r,r0 ;t,t0 ), which represents wave transfer between thesource and a receiver:u(r,t) = G( r, r0 ; t,t0) * s(r0 ,t0).

(1)Another convolution with transfer functions of device and transmitting and receiving
transducers is omitted in (1). TR operation is described as a transform t→ T − t, where T isduration of TR signal, and a new source function w(t) can be expressed as proportional to
the TR source [23]:w(t) = G(t) ∗ u(T −t) = G(t) ∗ G(T −t) ∗ s(T −t) ≈ s(T −t) ,(2)

where we suppose approximation G(t) ∗ G (T – t) ≈ δ (t) (Diracʼs function) as both signal
directions are equivalent. The fact that resulting signal represents time reversed
reconstruction of the original source function in its position at focal time T is very importantfor AE source location and further analysis. So we can use TR as a robust procedure for
localization also in a case with external noise created by other sources. Precise timealignment of TR signals returned from receivers randomly dispersed around a point-like
pulse source can illustrate principle of TR-based source location [19]. If three signals aredetected by various transducers in times t1, t2, t3 , after their TR are rebroadcast to thesource with delays ∆t1, ∆t2, ∆t3. As ∆t1+t1=t, ∆t2+t2=t, and ∆t3+t3=t, all three TR signals arecoming to the source synchronously with the same delay t, and any small deviation from theoriginal source location or changes in the structure and/or receiver positions impairs theiralignment [20, 24].

3. SOURCE LOCATION EXPERIMENTS
3.1 Simulated leakage noise on a steel plate
In a simpler case of burst AE (pulse) are synchronized signals maxima at the source position
clearly detectable (visible) and only one receiving sensor may be used to source location
with about 1 mm precision. Worse situation is in the case of continuous AE source likeleakage noise, where TR localization was not yet studied as its reconstruction gives again
random noise signal. Hence we performed further documented experiments showing thatone sensor is not enough to reliably localize bounded noise sources and instead CCF of
signals from two sensors should be used.
Source location experiments were performed on a steel plate 500x500x45 mm shown in
Fig.1. Four relatively broadband small piezoelectric AE transducers DAKEL IDK 09 (9 mmdiameter with 6mm contact ceramic wear plate) indicated in Fig.1 as T1 to T4 were glued to3

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the plate with cyanoacrylate glue. Their typical frequency response is shown right in Fig.1.
On the plate are shown distances between receiving sensors and marked are also positionsof artificial AE sources S1 and S2. In position S1 was placed another AE transducer as asource transmitting two different signals simulating a) continuous leakage noise, and b) pentest AE burst. Original random noise signal (a) was recorded on a tube of diameter 160 mm
and 4.5 mm wall thickness during compressed air (6 bar) leak through 0.8 mm orifice. 13ms
long signal from leak detected by AE transducer was input to the arbitrary waveformgenerator (AWG) and emitted as a source signal at S1. Simulated leakage signal with its
spectrum of approx. 200 kHz maximum is plotted in Fig.2. All signals were sampled withfrequency 10 MHz by two Tie-Pie HS5 USB oscilloscopes with AWG.

Fig. 1: Testing steel plate with marked placement of four AE transducers (T1 to T4) and artificial AE
sources (S1, S2) – left. Right is frequency response of the IDK 09 transducers.

Fig. 2: Source signal of leakage noise n(t) (left) and its frequency spectrum (right).

3.2 Simulated pulse location under leakage noise
Burst signal (b) simulating crack extension was recorded at pen-test on the tube surface. We
donʼt know exactly its waveform, so as the burst AE source was used short sine-train pulse
p(t) of 40 µs duration (simulated pen-test), which gives TR reconstruction similar to that ofreal pen-test. Simulated pen-test signal p(t) was emitted by AWG as the burst source at
position S1, and by synchronized second AWG was at position S2 simultaneouslytransmitted leakage noise n(t) shown in Fig.2. Noise signal was before transmitting amplified
up to four levels by power amplifier (max. amplitudes 5,10,15, and 20 V) while transmitted
burst signal p(t) was held on 1V max. amplitude. So the burst signal was -14 to -26 dB below
the noise level. Signal mixtures were recorded by four transducers T1 to T4, then time
reversed and repeatedly sent back from all transducers working in transmit mode.
Transmitted TR signals were recorded by AE transducer DAKEL MDK 17 with magnetic wear
plate (15 mm diameter) at 25 nodes on 25 mm mesh around the source S1 (see Fig.1) andquality of TR reconstructions was evaluated. The same position search ʻscanningʼ was
performed also in tests with localization of continuous noise source alone.
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3.3

Location of two simultaneously emitting different leakage noise sources

Further localization experiments were performed with two different noises (continuous AE
sources) simultaneously acting on the steel plate. The first noise n1 (t) = n(t) was emitted at
position S1, and other noise signal n2 (t) also recorded from leakage with similar
characteristics was emitted by transducer at position S2. The aim of this experiment was toverify the capability of TR procedure to localize ʻleakageʼ AE source under extraneous
random noise from other source, which is in real situations extremely difficult task. Both noise
signals were emitted by two synchronized AWGs and their mixture was recorded by fourtransducers T1 to T4.

3.4 AE source localization on thin Al-plate with holes
Similar experiments as on the steel plate were realized also on an Al plate of dimensions
501x501x5 mm with randomly dispersed 10 circular holes of various diameters (Ø7, Ø15,
Ø20, and Ø30 mm) shown in Fig. 3 (left). Both continuous and burst sources were tested.
Rickerʼs pulse 220 kHz was used as a burst AE source, and continuous random noise n(t)was applied for ʻleakageʼ simulation. The goal of these tests was to prove real possibilities ofsuggested TR source location procedure on more complicated structure with highly
dispersive behavior and many wave propagation obstacles. For more precise source location
has been used detailed noncontact scanning with LDV (Laser Doppler Vibrometer)
POLYTEC OFV-5000 with laser head OFV-505 mounted on 3D scanner STARMANS.
Detailed scanning was performed on area 30x30 mm with 1.5 mm steps around the source.
The experimental setup is shown right in Fig. 3. For synchronized signals generation andrecording was used modular system NI-PXI and before TR operation were recorded signals
amplified by power amplifier. Transmitting and receiving probes were switched by 8-channel
multiplexer.

Fig.3: Left - 5 mm thin Al plate with circular holes and marked placement of four sensors T1 – T4 andSource transducer; right – experimental arrangement with scanning LDV.



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4. DISCUSSION OF AE SOURCE LOCATION RESULTS
4.1

Localization of pulse buried in noise

TR localization of AE quasi-point burst signal sources always gives excellent results with
precision better than wavelength and sensing transducer diameter (up to 1 mm)
independently on dispersion and other influences [9]. The tests with simulated pen-tests
buried in higher leakage noise originated outside the source position approved the sameconclusions up to approx. -40 dB S/N ratios. Following results in Fig.4 illustrate source
location capabilities of TR procedure in two cases: -14 dB and -26 dB S/N. Simulated Pentest signals recorded by T1-T4 transducers are plotted left in part (a) of Fig. 4, mixtures ofburst and noise signals (S/N = -14 dB) are in the middle, and their TR reconstructions atburst source S1 are in the right.
a)

T1
T2
T3
T4

b)

Fig 4: a). Direct burst signals of recorded ʻpen-testsʼ by T1-T4 are plotted left, direct mixed signals
under S/N = -14 dB condition are in the middle, and their TR reconstructions are right;b) Sum of reconstructions with its zoomed central part under S/N = -26 dB condition.

Part b) of Fig. 4 shows result of all four TR signals summation under S/N = -26 dB condition
and the detail of the source reconstruction. TR signal reconstruction lies always just before
the middle of transmitted TR data, which simplifies its search and source location over alarge area. The source position is characterized by much higher absolute amplitude or RMS
in the central part of recorded TR signal over other signal parts (the highest S/N). Length ofthe central part corresponds with double length of reconstructed source signal and can betracked by sudden amplitude enhancement. In the first case of burst AE buried under noiseby -14 dB is the TR amplitude +17 dB over noise maximum, which means +31 dB
improvements due to TR operation in one channel. Further improvement by summation of all
TR channels is of +1 to +3 dB better but eliminates possible faults in some channels (in ourcase weaker T2). TR reconstruction in the worst situation (S/N = -26 dB) resulted in +9 dB
maximum at the source position over the rest, which represents +35 dB improvement, andthe hidden source can be reliably located. Necessity of point–by–point searching is drawbackof this method. Maximal point distance in searching grid is about 20-25 mm in our case [7]but the ʻscanningʼ can be effectively solved as discussed later.

6

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4.2

Precise location of continuous AE noise source on steel plate

Previous section illustrated simpler case with TR reconstruction of hidden burst AE whereexactly synchronized maxima are distinctly visible and precise source location is possibleusing only one transducer. Location of continuous leakage source is more difficult. An
estimate of source location can be accomplished by methods based on signal attenuation
and/or other changes with distance from the source or by cross-correlation function (CCF) of
signals from 2 or more sensors. The first approach gives only rough and unreliable data andsimple CCF approach is also disputable. CCF w(t) between two signals u(t) and v(t) is
generally defined (for discrete signals) as:(3)
CCF is not commutative and associative as a convolution of two signals but sometimes ismore convenient to compute CCF as a convolution, i.e. by multiplication of FFT spectra.
Maximum of CCF indicates time delay (lag) between two similar signals. The lag for two
identical signals is equal to zero and CCF maximum is used as a measure of signal
waveforms similarity. Simple standardized use of CCF for determination time delays
between more channels is not applicable on direct detected noise signals as it is shown in
Fig. 5 for our experiments with leakage noise signal acting on the plate. Left in Fig.5 are four
signals recorded by transducers T1 to T4 from the same leakage noise source n(t) acting at
position S1. There are 6 possibilities how to correlate 4 signals, which are denoted as C12(CCF between signals from T1 and T2), C13, .,C34, and to obtain 6 delays between
transducers (only 3 are necessary to locate source on the plate). All CCFs are plotted in the
middle of Fig. 5 (all time axes are in sample numbers, so divided by 10 gives time in ms). As
all four detected signals originate from the same source, they should be similar, and CCFs
maxima should indicate their time delays, redundant for standard source location.
Nevertheless, when we computed corresponding lags, we got absolutely unrealistic results
illustrating unreliability of standard procedures for random noise source location. Right in Fig.
5 are plotted zoomed central parts of CCFs with many peaks of slightly different amplitudes.
CCF increases correspondingly to accidental peaks in recorded signals, and absolute CCF
maxima are randomly distributed. The reason why recorded noise signals from the same
source must not contain exactly similar segments is caused by their unknown time shiftchanges through the wave propagation and attenuation on the plate and also by different
response characteristics of transducers.

Fig.5: Direct signals from noise source at S1 detected by four transducers T1-T4 (left),six CCFs between all two-signal combinations C12, C13…C34 (middle), and their zoomed centersaround zero lag (right).

7

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The actual time delays, estimated from knowledge of transducer–source distances andmeasured wave propagation celerity (3.04 km/s) were very small (about 2 ms) due to small
distance differences in each transducer pair (6 mm in average). In such situations are errorsof source location using CCF for computing time delays inaccurate and extremely
dependent on measurements precision. It is also the reason why various authors used
optimized spacing of transducers. Up to now we didnʼt find references about TR properties ina case of long random noise signals (except TR noise suppression). Fig. 6 illustrates that
one transducer TR source location in the case of continuous random noise is not applicableas there is no detectable maximum. By comparison three different TR source reconstruction
positions in Fig.6 it follows that the right location of the source reconstructions cannot beeasily determined neither by amplitudes nor by sharp rise near the signal middle, which
occur e.g. in the TR signal coming from T3 in right part of Fig.6 at false location.

Fig.6: Comparison of TR leakage noise signals emitted by T1-T4 and reconstructed at the source
position (left), at 25 mm (middle) and 50 mm (right) positions out of the source.
In the bottom row are TR summations of all 4 channels.

The localizing inability of the simple TR procedure due to random character of reconstructedsignals is clearly visible in Fig. 7 where are plotted TR reconstructions maxima, detected atall 25 nodal regions in the 5x5 matrix grid positions pij around the source (see Fig.1). Noisesource is in 13th point (i,j=3,3) and its marked TR maxima from all emitting transducers T1-T4
are comparable or even lower than at other grid nodes. The same holds also for ʻimprovingʼTR summations plotted right in Fig.7.

Fig. 8: Maxima of TR reconstructions at 25 nodal areas in the 5x5 mesh around the source positionth(13 point p3,3). TR signals maxima emitted from T1-T4 are plotted left and maxima of their

To correct TR localization inability is suggested to use cross-correlation of TR signals fromtwo transducers. Results for all six CCF combinations presented in Fig.8 confirm efficiency ofthis approach summations right.

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Fig. 8: Cross-correlations (CCF) of all TR signal pairs detected at the source point.
Right are CCFs zoomed around zero lag.

Example results for all 25 mesh points around the source are presented in two tables:
Table 1: CCF maxima between TR signals emitted byT1 and T4 (C14ij) and reconstructed at
25 nodal positions p1,1 to p5,5 in the 5x5 grid. Maximum at p3,3 (noise source) is emphasized.
Table 2: Instead of maxima are similarly shown CCF34 lags (L34) representing time shifts (in0.1 ms samples) between TR signals emitted by T3 and T4. Zero lag at the source position
p3,3 means that both TR signals are perfectly synchronized. Zero or very small lags (0.1 up to3 ms) were determined in all CCF combinations at the source position. Lags at the source
have always absolute minima compared to other mesh points and any positions out of thesource. Results from Tab. 1 and 2 better surpass in 3D or color flat surface views in Fig. 9.
Table 1: C14 maxima at grid nodes.

a)Table 2: C34 lags (L34) at nodal points (in 0.1 µs)

b)

c)

d)

Fig. 9: 3D and flat surface mapping of resulting values detected in 5x5 mesh around the source
location: a) C14 Cross Correlation Coefficients (in %), b) negative logarithms of L34 absolute lagvalues (both 3D pictures are 2nd order cubic interpolated), c) flat color map projections of C13
correlation coefficients without interpolation, d) L34 reciprocal time lag minima values.

9

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Reliable source recognition better than CCF maxima in Table 1, and further enhanced S/Nratio over the rest of the plate enable normalized Piersonʼs cross-correlation coefficientsplotted in % values in Fig.9 a). Fig. 9 b) shows interpolated 3D representation of negative
absolute values of lag logarithms from Tab.2 (–log (abs L34)). In part c) is a color map ofTable 1 (without interpolation), and in part d) are reciprocal values of L34 lags from Tab.2(1/(abs L34). Zero lags at the source were replaced by one (i.e.100 ns).
All six CCF combinations exhibit sharp maxima of CCFs and their coefficients at the source
position (30% in average), which means average separation +20 dB, and +28 dB is averageseparation of reciprocal absolute minima lag values (max. 3 ms) overall other points on theplate. CCFs maxima with their lag minima together indicate the precise source location.
4.3 Location of two simultaneously emitting noise sources on the steel plate
Next example illustrates the ability of TR localization procedure to recognize and locate two
independent uncorrelated ʻleakageʼ noise sources simultaneously acting at different placeson the steel plate. Fig. 10 left shows two noise signals emitted at sources S1 and S2 andtheir cross-correlation function. In the right part of Fig. 10 are mixtures of both signals
recorded by transducers T1 to T4.
Noise emitted at S1

1

T1

0.5
0
-0.5
-1

0

2

1

4

6

8

10

Noise emitted at S2

12
4

x 10

T2

0.5
0
-0.5
-1

0

500

2

4

6

8

10

Cross-correlation of S1 and S2

T3

12
4

x 10

T4

0

-500
-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

5

x 10

Fig. 10: Left – noises emitted at S1 and S2, and their cross-correlation.
Right – mixed signals recorded by transducers T1 to T4.

Mixing of both signals may be also interpreted as ʻleakageʼ signal buried in extraneous noise.
TR reconstructions of mixed signals from both sources are plotted for all four transducers in
Fig. 11. Left are TR reconstructions at the source S1, in the middle at the source S2, andright are plotted for a comparison TR reconstructions detected at the mesh node p1,1 outsideof both sources. From these plots is clear that the localization of sources based on TR
signals reconstructions from only one transducer is not possible. In Fig.12 is documented TR
separation of both signals located at their 189 mm distant sources with help of TR
reconstruction cross-correlations (CCF) from two transducers. Left are six CCFs ofreconstructions at the source S1, in the middle at the source S2, and right are CCFs out ofsources. Resulting CCFs from two transducers show positions of both sources reliably by
CCF maxima at near zero lags. More quickly can be results obtained by comparing maxima
10

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of normalized cross-correlation functions, but it is less reliable when CCF maximum is notprecisely at zero lag.
TR at source S1

TR at source S2

TR at mesh node p1,1

Fig. 11: TR reconstructions at the source S1, in the middle at the source S2, and right are plotted for acomparison TR reconstructions detected at the mesh node p1,1 outside of both sources.

CCF of TR signals at S1

CCF of TR signals at S2

CCF of TR signals at point p1,1

Fig. 12: Separation and localization of two noise sources at S1 and S2 using cross-correlation of
TR reconstructions from 2 transducers.

4.4

AE source location on thin Al-plate with holes

The 5 mm thick Al sheet with ten circular holes of various diameters represents a more
complicated structure for precise AE source location tasks than previously tested steel plateof similar planar dimensions. Two main reasons make those tasks difficult: 1. Strong wave
dispersion effects and 2. Geometrical discontinuity substantially complicates wave-paths
between the AE sources and sensing transducers, which holds also for TR backpropagation.
It is well known that each wave reflecting obstacle can enhance TR reconstruction as virtualtransducer [22], which supports the use of TR localization procedure. Nevertheless, there
was a challenge to verify how precise will be source location under such conditions when
standard localization methods are mostly unreliable. Both burst and continuous AE sourceswere tested on that plate. The Rickerʼs pulse 220 kHz was used as a burst, and simulated
11

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leak n(t) as a continuous source. As an example, Fig. 13 illustrates TR reconstruction
capability of burst signal from T1 at the source (left) and at the plate corner (right).

Fig. 13: TR reconstruction of the burst signal from T1 at the source (left) and at the plate corner (right).

Maxima of TR reconstructions from all four transducers T1 to T4 in the region 30x30 mm
around the source are shown in Fig. 14. Remarkable are concentric circles around thesource representing lower maximal amplitudes of reconstruction, which are related to thewavelength distance. Fig.15 shows distribution of maximal amplitudes along horizontal
sections of flat projections in Fig.14. Even more reliable localization results may be obtainedusing cross-correlations of TR reconstructions from two transducers how illustrates Fig. 16.
They are also necessary for location of continuous AE sources.
T1
T2
T3
T4

Fig.14: Distribution of TR reconstructions maxima from transducers T1 to T4 in the region 30x30 mm
around the source (cubic interpolation).

Fig. 15: Distribution of maximal amplitudes along horizontal sections of flat projections in Fig. 15.

1-2

2-3

1-3

1-4

2-4
3-4

Fig. 16: Cross-correlations of TR reconstructions of burst signals from two transducers.

12

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Similar results as in previous sections were obtained on Al plate with holes also forcontinuous AE source emitting signal n(t). Fig. 17 shows distribution of TR reconstruction
maxima of the noise source from individual transducers. It is evident from this figure that only
one transducer cannot be used to random noise source location. Against that, crosscorrelations of two TR reconstructions again allow its precise location, which illustrates Fig.
18. In the part a) are compared cross-correlation functions at the source (left) and at theplate corner (right). In the part b) are 6 distributions of CCFs maxima around the source.
T1

T2

T3

T4

Fig. 17: Distribution of TR reconstructions maxima of the noise source from individual transducers.

a)

b)

1-2

1-3

1-4

2-3

2-4

3-4

Fig. 18: a) Cross-correlations of 2 TR reconstructions at the source (left) and at the plate corner (right),b) Distributions of CCFs maxima at 30x30 mm region around the source.

CONCLUSIONS
AE source location procedures based on time reversal signal processing were tested on asteel plate using simulated burst and continuous AE sources. Compared to other techniques,
the AE signal processing based on TR procedure can fetch the source location mostprecisely. Quasi-point sources can be reliably localized with up to 1 mm precision, which isless than the wavelength and transducer aperture. After TR, any arbitrary time segment of
the signal returns to the source position, and therefore TR methods don't need any timing
features. TR also eliminates problems with wave dispersion, attenuation, reflections, etc. in
relatively simple way with no need of some huge computations and knowledge of structure
geometry and wave celerity. Burst sources buried in out of source ʻleakageʼ noise of higher
amplitude is possible to detect and precisely localize by only one sensor up to -40 dB S/Nratio. Two sensors are necessary to localize continuous AE sources up to the same precision(about 1 mm) using cross correlation of two time-reversed signals. The same procedure canbe used to separate and localize two simultaneously emitting continuous AE sources atdifferent places. Tests with simulated both burst and continuous sources on the thin Al platewith randomly dispersed circular holes verified the same conclusions also for more
complicated structures. Detailed scanning is required for localization in the region aroundroughly pre-localized source in the monitored structure of any geometry. Scanning can be
13

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efficiently realized e.g. by scanning laser interferometer or by mechanical movement of air
coupled transducers or other sensor arrays with the only requirements of partial covering
frequency band of detected signals. Another possibility gives also reciprocal TR withstructure excitation at scanning mesh points by TR signals using e.g. air coupled contactless
instrument. The advantage is that the rebroadcast TR signals can be arbitrarily amplified tocompensate attenuation or extraneous noise of other origin. The new potentiality has TR
signals transfer from the real source onto its physical or reliable computer model, so calledʻdigital twinʼ [23] where the source is localized by numerical searching procedure, which is
however limited by some restrictions more discussed in [25]. Such modelling with TR signaltransfer from real structures is now performed in the frame of currently solved project.
Acknowledgement
This research was supported by the Czech Grant Agency under grant no. GACR 17-22615S,
which is gratefully acknowledged.

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Grosse Ch. U., Ohtsu M.(eds): Acoustic Emission Testing. Basic for Research Application in Civil Engineering, Spriger-verlag, 2008.
ChladaM., PrevorovskyZ.: Expert AE Signal Arrival Detection. Int. J. Microstructure andMaterials Properties, Vol. 6 (3/4), 2011, 191-205.
Kundu T.: Acoustic source localization. Ultrasonics, Vol. 54, 2014, 25–38.
Blahacek M.,Chlada M.,Prevorovsky Z.: Acoustic Emission Source Location Based on
Signal Features. Advanced Materials Research, Vol. 13-14, 2006, 77-82.
Chlada M., PrevorovskyZ., BlahacekM.: Neural network AE source location apartfrom structure size and material. J. Acoustic Emission, Vol. 28(1), 2010, 99-108.
Miller R.K., ed.: NDT Handbook Vol.5: Acoustic Emission. ASNT 1987.
Ono K., Cho H., Takuma M.: The origin of continuous emissions. J. Acoustic Emission,
Vol.23, 2005, 206-214.
ASTM E1211 / E1211M - 12 : Standard Practice for Leak Detection and Location
Using Surface-Mounted Acoustic Emission Sensors. ASTM International, WestConshohocken, PA, 2012, www.astm.org.
ISO 18081International Standard: Non-destructive testing - Acoustic emission testing(AT) - Leak detection by means of acoustic emission. First Edition 2016.
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Rastegaev I. A., Danyuk A. V., Vinogradov A. Yu., Merson D. L., Chugunov A. V.:
Location of Noise-Like Sources of Acoustic Emissions Using the Spectral Similarity
Method. Russian J. of Nondestructive Testing, Vol. 49 (10), 2013, 553–561.
Kosel T., Grabec I.: Intelligent location of two simultaneously active acoustic emission
sources. Part I and II. arXiv: 07040050v1, April 2007.
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Arrays for Gas Leakage Location Based on Correlation of the Time-Space Domain ofContinuous Ultrasound. Sensors, Vol.15, 2015, 8266-8283; doi:10.3390/s150408266.
Yong Yan, Xiwang Cui, Miao Guo,Xiaojuan Han: Localization of a continuous CO2 leakfrom an isotropic flat-surface structure using acoustic emission detection and near-field
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Prevorovsky Z., Krofta J., Chlada M., Farova Z., Kus V.: Progressive Approaches toLocalization and Identification of AE Sources. 30th European Conf. on AE Testing &7thInternat. Conf. on AE, Granada, Spain, 2012, CD Proc., www.ndt.net/EWGAE-ICAE
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sources in complex structures more precisely. 31st Conf. "EWGAE 2014", September,
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Modelling of fiber break as Acoustic Emission Source in Single
Fiber Fragmentation Test: comparison with experimental results
Zeina Hamam1, Nathalie Godin1, Claudio Fusco1 and Thomas Monnier2
Univ Lyon, INSA de Lyon, MATEIS UMR 5510, F-69621 Villeurbanne, France.
2
Univ Lyon, INSA-Lyon, LVA EA677, F-69621, Villeurbanne, France.
1

Abstract:
The objective of this work is to build a quantitative relationship between the fiber break as source ofAcoustic Emission (AE) and the detected signal by unravelling the effect of each stage of the AE
acquisition chain. For this purpose, an AE modelling is carried out using the Finite Element Method(FEM) and then the simulation is compared to experimental results of Single Fiber Fragmentation Test
(SFFT). The SFFT is used in order to produce preferential fiber break. Two different types of transducer
are used in order to gather information on a wider frequency bandwidth. The analysis of detected signals
shows an important dependence of the frequency content of the signals on the propagation distance.
For the modeling part, the entire geometry of the specimen is modelled. The sensor is taken into accountby its transfer function. The FEM model is used to study the effects of different parameters on the signal
such as the propagation medium, propagation distance and effect of sensors.

1. Introduction
The Acoustic Emission (AE) technique is widely used to detect failures in composite material.
The different types of failures such as fiber breakage, interface debonding, matrix cracking anddelamination are identified thanks to their AE signature. This signature is expressed by signal
parameters called descriptors: amplitude, energy, peak frequency, frequency centroid, etc.
Single-parameter analysis or pattern recognition techniques are usually used to classify
signals belonging to the same type of failure [1-5]. In the most of studies that treat failures inpolymer matrix composites, higher frequency and higher amplitude are assigned to fiber
breaks as mode of failure and lower frequency and amplitude are assigned to matrix cracking.
Despite the effectiveness to these analysis methods, they are still considered as qualitative
methods. Besides, in many cases, the results analysis cannot be generalized to other studies
of composite failure [6-8]. Actually, in addition of type of failures, the AE signals depend onother parameters like type of sensor, geometry of the sample and the distance between sourceand sensor. All these parameters affect the signal recorded by AE system [9-12].
In the AE analysis, it is important to discern the role of the AE source from the effect of thetransformations due to the propagation medium and the recording system. Based on
experimental tests only, it is not easy to understand the contribution of the different parameters.
However, modeling of AE using numerical methods allows us to go further and build aquantitative relationship between the EA source and the detected signal by unravelling theeffect of each stage of the acquisition chain, namely the detection system and the propagation
medium. In this context, in various works, AE signals are calculated by analytical approaches
or by finite element simulations [13-23].
In this study, we focus on the identification of the features of fiber break from AE signals using
Finite Element Method. For this purpose we use a model material consisting of an epoxy matrix
containing a single brittle carbon fiber for our experimental tests and numerical simulations.
The modeling of fiber breaks as AE source in polymer matrix composite material is the main
S442

objective of this work. It presents two parts: experimental and numerical. In the experimental
part, a Single Fiber Fragmentation Test (SFFT) is carried out on carbon fiber embedded in theepoxy, in order to produce preferential fiber break. Two types of resonant sensors are used.
Concerning modeling parts, the entire geometry of the specimen is modelled using Finite
Elements Method (FEM) by means of Abaqus software and a dynamic calculation is
performed. The fiber breaks are created in different positions on the specimen. The model has
been validated by comparison with experimental data. Three type of sensors are considered(point-contact and resonant sensors) in order to understand and to quantify the sensor effects
on the signal waveform.

2. Description of work
2.1

Experimental Setup

The specimen is made of a single long carbon fiber T700 embedded in epoxy/amine matrix:
1,4 - Butanedioldiglycidyl ether (DGEBD) / 4,4 ′- Methylenebis(2-methylcyclohexylamine)(3DCM). The dimensions of the specimen are shown in Figure 1.
The AE monitoring is carried out by means of multiple sensors using a PCI-2 data acquisition
system. In order to investigate the effects of the sensors, tensile tests are performed with two
kinds of sensors (nano 30 and PicoHF sensors) located at the same position on each face ofthe specimen 40 mm apart. These two sensors display a good sensitivity in different frequency
ranges, 200 to 900 kHz for nano 30 sensor and 500-1850 kHz for Pico HF sensor (MISTRASGroup data). In all cases, medium viscosity vacuum grease is used as coupling agent.
Each sensor is connected to a preamplifier (gain 40 dB, type 20 H) and AE signals are recorded
by a PCI-2 acquisition system (MISTRAS Group). Each AE signal waveform is digitized andrecorded, with a sampling rate of 2 MSPS or 5 MSPS. The acquisition threshold is set to 35
dB and the acquisition parameters are equal to 25 µs, 50 µs and 1000 µs for the peak definition
time (PDT), the hit definition time (HDT) and the hit lockout time (HLT). All recorded signals
are filtered in the bandwidth [20 - 1200] kHz. The AE wave velocities are measured before thetests by calculating the difference in time of arrival on each sensor of several pencil leadbreaks, generated at well-known positions. The velocity is found to be equal to 3500 m/s.
The SFFT is a tensile test applied on single fiber composite in axial direction, in order to create
fiber breakage. Usually the debonding occurs at the same time of the fiber breaks, but in ourcase, because of a high interfacial strength, debonding does not occur. The SFFT is carriedout using the tensile test machine (type: MTS, capacity 5 kN), at room temperature and with adeformation rate of 0.5 mm/min. The analysis of the experimental results is based on thecalculation of descriptors as defined in [1, 24]. In our work, the main descriptors analyzed areamplitude, energy, frequency centroid (FC) and peak frequency (PF). Moreover, the time
frequency graphs are obtained according to smoothed pseudo Wigner-Ville distribution.

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Figure 1. Dimension of specimen and sensors placement.

2.2

Numerical Setup

The single fiber fragmentation is modeled using FEM by means of Abaqus® software (6.14-5)
with dynamic calculation. We modeled the entire geometry of the specimen: the boundary
conditions are shown in Figure 2. The geometry is meshed using tetrahedral elements (C3D4)
mainly, the element size varies between 0.01 mm and 0.2 mm and the calculation step time is
0.1 µs. The fiber is located in the middle of the specimen.
The main properties of the material are shown in Table 1. Epoxy is modelled as anelastoplastic material with damping properties. The viscoelasticity of the matrix is taken into
account by the Rayleigh parameters (alpha = 50 and beta = 10-6). The fiber breakage ismodelled by instantaneously separating the nodes forming fracture faces and creating a wave.
During the modelling of AE signals, it was assumed that the failure process occurs withoutchange in its rate. The numerical signals are collected on the specimen surface, at sensor
positions.

Figure 2. Scheme of the numerical specimen with the boundary conditions.

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Carbon fiber
DGEBD-3DCM

Table 1 mechanical properties of materials.
Young Modulus (GPa) Poisson’s Ratio Density (kg/m3)
187
0.22
1800
1.41
0.38
1034

Three types of sensor are considered in the modeling part: one point-contact sensor and two
resonant sensors. The first one is considered as perfect sensor, which accurately preserves
the detected signal, namely the out-of-plane velocity calculated on the node corresponding to
the sensor position. The resonant sensors are taken into account by their transfer function
shown in Figure 3, which was experimentally determined in our laboratory by the reciprocity
method [25-28]. The out-of-plane velocity corresponding to the sensor surface contact is
calculated then convolved with the sensor transfer function. The calculated signals then arefiltered on the bandwidth [20 – 1000] kHz with a Butterworth band pass filter.

(a)

(b)

Figure 3. Transfer function of (a) nano30 and (b) picoHF, determined by reciprocity method.

3. Results and discussion
3.1
3.1.1

Experimental results
Localization of AE Sources

After waveform acquisition during SFFT, the source position of the signals is determined. A
good agreement is found between the number of localized sources detected by AE and fiber
breakage observed by means of transmission optical microscope (Figure 4). Therefore, in our
tests, all located sources are ascribed to fiber breaks. In Figure 5, the cumulative number of
localized signals are presented for the types of sensors used in our tests. The sensors have
localized exactly the same sources. So, both are suitable for this test.

S445

Figure 4. Fiber breaks as observed by optical microscopy.

(a)

(b)

Figure 5. Cumulative localized signal during SFFT: (a) sources located by nano30; (b) by
picoHF.

3.1.2

Effect of propagation distanceThe calculation of descriptors shows a clear dependence on distance between the position of
the source (fiber breaks) and the sensor. Table 2 shows a summary of some descriptors for
two tests. The values presented show essentially three points: the tests are reproducible, thetype of sensors and the position of the AE sources affect substantially the descriptors.
Figure 6 and Figure 7 show the evolution of temporal and frequency descriptors with the
distance source/sensor. Each point on the graphs corresponds to a detected signal. The
amplitude, calculated in dB, varies linearly with distance and it loses 25 dB between the nearest
and the furthest fiber break. The energy decreases drastically with distance. The frequency
centroid decreases linearly for 25 mm of propagation distance: from 300 kHz to 150 kHz with
nano30 sensor and from 600 kHz to 200 kHz with picoHF sensor. The peak frequency takes
two main values; the higher is equivalent to the resonant frequency of the sensor.
Figure 8 shows the time-frequency maps calculated with smoothed pseudo Wigner-Ville
distribution for signals ascribed to fiber breaks for four positions (Figure 10-b). For fiber breaks
close to the sensors, the content of high frequency disappears progressively when the sources
are located farther away.
The frequency content of signals varies significantly with propagation distance. Some signals
have only low frequency content. Due to this dependence, these results cannot allow to define
an acoustic signature of fiber breakage based on frequency content.

S446

Table 2 Descriptors calculated for two SFFT (denoted AF02 and AF03)
Descriptors

Energy (attoJ)
Amplitude (dB)Frequency Centroid (kHz)Peak Frequency (kHz)(a)

nano30
Near toFar from
SensorSensor
AF02
AF03
AF02
AF03
2332
320
260
15.8
66.4
64
53.7
48.6
253
259
162
168
251
260
70
53

(b)

picoHF
Near toFar from
SensorSensor
AF02
AF03
AF02
AF03
107
55.1
8.4
5.2
60.8
57.2
43.7
41.2
455
536
157
189
351
256
50
40

(c)

(d)

Figure 6. Temporal descriptors vs. source/sensor distance for signals detected by nano30
sensors: (a) energy in attoJ, (b) amplitude in dB, (c) frequency centroid in kHz and (d) peakfrequency in kHz.

(a)

(b)

(c)

(d)

Figure 7. Temporal descriptors vs. source/sensor distance for signals detected by picoHF
sensors: (a) energy in attoJ, (b) amplitude in dB, (c) frequency centroid in kHz and (d) peakfrequency in kHz.

S447

Figure 8. Time-frequency results for four source positions for signals recorded on sensor
denoted C1 (P1 is the nearest position and P5 the farthest position from the sensor)

3.1.3

Effect of Sensor Type: Comparison signals of PicoHF and Nano30

The nano30 is very sensitive around 300 kHz and does not detect high frequency as from 500
kHz. However, the picoHF is very responsive around 600 kHz and detects high frequencies.
In Figure 9, we compare two signals in the temporal and frequency domain, stemmed from thesame source and detected by nano30 and picoHF sensors. These two sensors are equidistantfrom the source. Having a coefficient of correlation of 25% in the frequency domain, it is clearthat those different sensors do not detect the same information, especially beyond 200 kHz.
Therefore, they give certainly complementary information about the source.

(a)

(b)

Figure 9. Temporal signals (a) and frequency spectra (b) for two types of sensors.

S448

3.2

Simulation results

In this part, five fiber breaks are modeled at five different positions (denoted Pi) shown in the
Figure 10. Each break is simulated for 150 µs, which is sufficient to attenuate the signal. Threedifferent cases corresponding to three different types of sensor are considered: a perfect pointcontact sensor and 2 two cylindrical resonant sensors.

(a)

(b)

Figure 10. Position of fiber breaks and sensors: (a) point-contact sensor; (b) cylindricalsensor

3.2.1

Perfect point-contact sensor

In the model, the signal detected by perfect point-contact sensor, is the out of plane velocity of
the node at sensor position (Figure 10-a). The advantage of studying this kind of sensor is thatthe signal is not altered by the sensitivity of the sensor or its aperture effect of the surface
contact.
Figure 11 shows the time-frequency transforms, their corresponding signals and spectra for
the five fiber breaks (P1 to P5). The frequency content of the signals is dominated by highfrequency but it decreases farther away from the source.

S449

(a)

(b)

(c)

Figure 11. Results of point-contact sensor for five different fiber breaks: (a) Time-frequency
graphs; (b) temporal signals; (c) signals in frequency domain

In order to better understand the evolution of AE signals with distance, the descriptors arecalculated. For one fiber break, signals are calculated at nodes on the red line of Figure 12.
It is equivalent to different sensor positons, therefore, different distance source / sensor. The
descriptor curves are shown in Figure 13. As a first observation, there is a similarity between
the decrease of experimental curves (Figure 6 – Figure 7) and the numerical curves,especially for energy, amplitude and frequency centroid. The main difference is the range of
frequency content; for point-contact sensor, the frequency content is significantly higher (>500 kHz) comparing to signals detected by resonant sensors (<600 kHz).

Figure 12. One fiber break and all point-contact sensor positions

(a)

(b)

S450

(c)

Figure 13. Model: descriptors vs. source/sensor distance for signals detected by perfect
point-contact sensors: (a) energy in attoJ, (b) amplitude in dB, (c) frequency centroid in kHz

The following part is devoted to study signals detected by resonant sensors to better
understand how sensors affect signals.

3.2.2

Cylindrical resonant sensors

The sensors are taken into account by their sensitivity function (Figure 3) by this equation:
𝑉"#$"%& (𝜔) = 𝑉"+&,-.# (𝜔) × 𝑆"#$"%& (𝜔)(1)Where 𝑉"#$"%& is the velocity calculated by considering the effect of the sensor, 𝑉"+&,-.# (𝜔) is
the average of out of plane velocity on the surface and 𝑆"#$"%& (𝜔) is the sensitivity function.
The average of surface velocity 𝑉"+&,-.# (𝜔) is firstly calculated and compared to the velocity
determined at the center of the surface. The results are presented in dB in Figure 14 for two
different sensor radii (𝑅$-$%23 = 3.6 𝑚𝑚 and 𝑅9:.%;< = 2.3 𝑚𝑚) and for two positons of fiber
breaks, the nearest (P1) and the farthest (P5) from the sensor. This figure shows that thesurface average of the velocity, without considering the sensor sensitivity, causes some
information loss, especially in the high frequency part. For the fiber break at P1, the point
contact sensor shows that the peak frequency is around 900 kHz but for the surface contact
sensor the peak frequency is much lower (380 kHz).

(a)

(b)

Figure 14. Velocities spectra for fiber break waves for 3 simulated cases: point-contact sensor
velocity, velocity average on circular surface with 2 different radii (𝑅$-$%23 = 3.6 and 𝑅9:.%;< =2.3): (a) fiber break position P1, (b) fiber break position P5

3.2.3 Comparison Numerical and Experimental Results
In this part, we calculated signals with sensor effects according to Equation (1) for the two
types of sensors (nano30 and picoHF). Then, the simulated an experimental signals arecompared. This comparison is carried out for signals of five fiber breaks at different positions,collected at sensor C1 position (Figure 10-b). The spectra are shown in Figure 15-a fornano30 sensor and Figure 15-b for picoHF. A good agreement is observed between thesimulation and the experiment results despite neglecting some parameters in the calculationsuch as the coupling.
S451

The descriptors corresponding to signals with nano30 sensor are calculated for 22 mm ofpropagation distance. The results are presented in Figure 16. Similarities are observed
between numerical results and experimental results (Figure 6). The energy decreases
exponentially, the amplitude loses 23 dB, the frequency centroid is higher but the rate ofdecrease is close to the experimental results and the peak frequency is close too. The lattercorresponds to the resonance frequency of the nano30 sensor.

!

(a)

(b)

S452

Figure 15. Comparison experimental / simulations results for 5 fiber breaks (a) results with
nano30 sensor; (b) results with picoHF sensor.

(a)

(b)

(c)

Figure 16. Model: descriptors vs. source/sensor distance for signals simulated with nano30
sensors: (a) energy in attoJ, (b) amplitude in dB, (c) frequency centroid in kHz.

4. Conclusion
The objective of this work is to build a quantitative relationship between the fiber break assource of Acoustic Emission and the detected signal by unravelling the effect of the different
stages of the AE acquisition chain. Concerning experimental results, the calculation of classicaldescriptors does not give steady characteristics of this mode of failure. They are very
dependent on the propagation distance. Their dependence is mainly due to the damping
characteristic of material. The two type of sensors used in the tests detect the waves of thesame sources. However, they do not give the same information. Both sensors detect low
frequency (until 200 kHz) but for higher frequency, each sensor detects according to its
sensitivity.
The advantage of simulating perfect point-contact sensor is to get signals not altered by thesensor effects. Therefore, the frequency content proves that the fiber breaks release waves
with very high frequency content in single fiber composite specimen. Contrary to signals
recorded by cylindrical sensor, the frequency reached with this type of sensor, for remotes
sources, is still considered as high frequency.
The simulation results taking into account cylindrical resonant sensors show a good agreementwith experimental results, highlighting the effect of propagation distance and the way that thesensor alters the detected signal. The results of this contribution highlight the limitations of theconventional source identification procedures. Modeling approaches that simulate “primitive”
AE sources can be useful for (1) optimizing sensor selection and placement, (2) quantifying
the wave propagation effects (3) generate training data for machine learning algorithms. With
the numerical approach, it may be possible to enlarge a training set.

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S455

Content
Keynotes

42 - Acoustic Emission method at the integrated structural health monitoring systems -the past, the present, the future
I. Razuvaev

86 - Acoustic Emission work over the last 10 years and relationship with Industry 4.0
M. Cherfaoui

Poster Session - Short presentation

16 - Acoustic Emission energy for condition monitoring of RC waffle slab structuressubjected to bidirectional ground seismic motions
C. Abarkane, E. Suárez, F. Rescalvo, A. Benavent-Climent, A. Gallego
20 - Characterization of failure mechanisms in the wood-CFRP interface by meansof Acoustic Emission during pull-off tests
F. J. Rescalvo, A. Aguilar-Aguilera, C. Abarkane, E. Suárez, A. Gallego

28 - Features of Acoustic Emission in double-walled elements of vessels pressure in thepresence of cracks
I. Rastegaev, A. Chugunov
37 - Multiparametrical big data processing for Acoustic monitoring
A. Samokhvalov

44 - About new AE criteria of cracking in metal objects
I. Razuvaev, E. Suchkov
46 - Damage detection for linear pneumatic actuators using Acoustic Emission
H. Mahmoud, P. Mazal, F. Vlasic, M. Jana

47 - The parameters of Acoustic Emission signal proposed to identification of damaged

and undamaged cylinders
V. Richter, H. Mahmoud, P. Mazal, V. Kratochvilova

58 - Identification of damages in composite materials during registration of Acoustic
Emission signals by piezoelectric and fiber-optic sensors
O. Bashkov, I. Bashkov, A. Bryansky, T. Bashkova
62 - FEM based simulation of Acoustic Emission monitoring on oil offshore pipelines
H. Calás, J. Manuel Martin, M. Pelaez, E. Cabellos
74 - In-situ control of additive manufacturing process by Acoustic Emission

G. Perrin, J. Frechard, P. Verlet, C. Grosjean, D. Maisonnette, J. Delgado, F. Zhang

89 - Features of Acoustic Emission in double-walled elements of vessels pressure in thepresence of cracks
I. Rastegaev, A. ChugunovMonitoring in process

19 - Combination of vibration analysis & Acoustic Emission measurements to bettercharacterize damage and mechanical behavior of aerospace high speed gear box
Y. Hebrard, A. Proust, M. Batel
79 - Advanced condition monitoring system using Acoustic Emission and vibration
analysis technique
D.-H. Kim
35 - Acoustic Emission for in situ monitoring of laser processing
S. shevchik, T. Le Quang, B. Meylan, K. Wasmer

72 - Monitoring of stamping by Acoustic Emission (AE)A. Saidoun, C. Hervé
Advanced composites - I

06 - Modelling of fiber break as Acoustic Emission Source in SFFT: comparison withexperimental results
Z. Hamam, N. Godin, C. Fusco, T. Monnier
14 - Acoustic Emission monitoring of adhesively bonded wood joints under quasistaticand cyclic fatigue mode II flexure loads using end-notch-flexure specimens
A. J. Brunner, G. Clerc, P. Niemz
17 - Acoustic Emission analysis during bending tests of timber beams reinforced withcarbon fiber composite
F. Rescalvo, C. Abarkane, E. Suárez, A. Gallego

23 - Frequency-Amplitude class of Acoustic Emission for different fracture mechanisms
in C/SiC composite
D. Xiao, Y. Gao, L. Jin, F. Meng, N. Li, X. Zhou, Z. Tong, B. Jiang, T. He
24 - Mechanical behavior and Acoustic Emission technique for detecting damage
in C/SiC structure
Y. Gao, L. Jin, B. Jiang, Z. Tong, D. Xiao, X. Zhou, N. Li, F. Meng

Monitoring metallic structures

27 - Study of a steel railway bridge with defects under various load conditions
M. Nowak, I. Lyasota, I. Baran

29 - Damage monitoring on a steel truck trailer using parameter-based analysis
of Acoustic Emissions
J. V. Pimentel, R. Klemm, M. Dalgic, A. Irretier, K.-L. Krieger
31 - Data processing and interpretation at AE monitoring
T. Petersen, A. Samokhvalov, W. Chernigovski, D. Kurnosov
49 - Structural health monitoring using Acoustic Emission on metallic components
in industrial plants
G. Lackner, H. Marihart, G. Schauritsch

51 - Acoustic Emission Testing on a rail bridge

H. Marihart, G. Lackner, M. Posch

83 - Novel AE monitoring of hydrogen induced damaged vessel and real time alarms.
A case study
D. Papasalouros, K. Bollas, I. Ladis, E. Aerakis, A. Anastasopoulos, D. Kourousis

Advanced composites - II

26 - Dynamic Acoustic Emission for the Characterization of the Nonlinear Behaviorof Composite Materials
X. Yu, M. Bentahar, S. Montresor, C. Mechri

55 - The influence of source depth and source-to-sensor distance on the AE signal
characteristics of damaging events in thin-walled CFRP laminates
B. Kelkel, J. Vogtmann, M. Gurka
57 - Acoustic Emission behaviour on CFRP with curved fibers

Y. Mizutani, T. Ashizawa, N. Toyama, A. Todoroki

63 - Experimental investigation of the damage mechanisms in interlock reinforced
composites based on AE data analysis
M. Kharrat, G. Moreau, Z. Aboura
73 - Use of AE for characterization of damage mechanisms in composite accumulator
O. Colas, C. Briançon, C. Hervé, A. Houssais

Signal Processing - Equipment and Software I

40 - Continuous recording and wireless transmission of AE waveforms by battery
powered sensor nodes
K. Ito, M. Enoki
45 - Research the comparative sensitivities of fiber-optic sensors and piezoelectric

sensors for detection of Acoustic Emission waves
H. Htoo Khun, O. Bashkov, V. Zaikov, R. Romashko, I. Bashkov

96 - Phased Array Modal Acoustic Emission (PA-MAETM)
B. Burks

Advanced composites - III

01 - Modal Acoustic Emission analysis of mode-I and mode-II fracture of adhesivelybonded joints
A. Crawford, M. Ghazi Droubi, N. Faisal
02 - On use of signal features for Acoustic Emission source identification in fibre-

reinforced composites
M. G. R. Sause

03 - Damage diagnostic and lifetime prognosis for ceramic matrix composite with
Acoustic Emission during long-term mechanical tests at intermediate temperature
N. Godin, P. Reynaud, G. Fantozzi
04 - Combination of synchrotron computed tomography and Acoustic Emission
measurements for cyclic loading of fibre-reinforced composites
P. Potstada, S. Rosini, M. Mavrogordato, I. Sinclair, S. M. Spearing, M. G. R. Sause
Monitoring composites / Different applications

48 - Structural Health Monitoring of composites structures by Acoustic Emission analysis

L. Schubert, E. Schulze, R. Neubeck, B. Weihnacht

68 - Structural Health Monitoring of composite aerospace structures with Acoustic

Emission
M. Lehmann, A. Bueter, O. Schwarzhaupt, C. Contell Asins36 - Post-harvest Acoustic Emission in angiosperm leaves

S. Rosner

53 - Mechanical and Acoustic Emission characterization of lime-based masonry

constituents
N. Shetty, G. Livitsanos, D. Aggelis, D. Van Hemelrijck, M. Wevers, E. Verstrynge

22 - Characterization of particle impacts using Acoustic Emission
E. Serris, L. Carrillo, A. Valentini

Material characterisation

32 - The sound of metal: Acoustic Emission during the deformation of commerciallypure titanium
N. Goossens, M. Seefeldt, M. Wevers

38 - Analysis of kinking and twinning behavior in mg alloys by AE clustering method
M. Enoki, K. Tamura, T. Shiraiwa

39 - Non-threshold Acoustic Emission analysis of damage evolution in pipe segmentsof steel S355J2H under bending load
F. Baensch, W. Baer, P. Wossidlo, A. Habib

71 - Galling detection by Acoustic Emission (AE) according to ASTM G98
A. Saidoun, C. Hervé, Y. Chen, T. Lesage

84 - Acoustic Emission method in nanoindentation and scratch test
R. Ctvrtlik, J. Tomastik, V. Koula, M. DrabCorrosion

11 - AE monitoring and three-dimensional observation of stress corrosion cracking fromcorrosion pits
K. Wu, K. Ito, I. Shinozaki, M. Enoki
12 - Effects of media degradation on the Acoustic Emission detection

L. Calabrese, M. Galeano, E. Proverbio

18 - Application of Acoustic Emission measurements to monitor the initiation and the
propagation of single or multiple Stress Corrosion Cracks (SCC)
O. Al Haj, A. Proust, J. Bolivar, M. Fregonese
64 - Acoustic Emission from gas bubble nucleation and breaking away from a flat surface
V. Kietov, M. Mandel, L. Krüger, A. Vinogradov
Pressure Vessels

05 - Lifetime tests of composite cylinders monitored by Acoustic Emission Testing
E. Duffner
69 - Application of AE technique and thermography for assessment of sludge volume

inside crude oil storage tanks
I. Baran, T. Dunaj, M. Nowak

75 - New edition of GEA guideline for Acoustic Emission Testing of pressure equipments
C. Di Giulio, C. Hervé
07 - Acoustic Emission study of pressure vessels material degradation processes after
long-term operation in the refinery industry
I. Lyasota, T. Ryncarz, B. Kozub

15 - Use of Acoustic Emission for inspection of various composite pressure vessels

subjected to mechanical impact
F. Dahmene, O. Bardoux, S. Bittendiebel, P. Blanc-Vannet, N. Alexandre, F. Nony,
M. Barcikowski, A. Maldachowska, M. Panek, P. Sheggem, A. Echtermeyer, K. Lasn

Signal Processing - Equipment and Software II

30 - Detection and identification of low-amplitude Acoustic Emissions during plastic
deformation under indentation and scratch testing
A. Danyuk, D. Merson, A. Vinogradov
33 - When AE (Acoustic Emission) meets AI (Artificial Intelligence) II
K. Wasmer, F. Saeidi, B. Meylan, T. Le Quang, S. A. Shevchik

65 - AE fracturing modes classification by means of shear tensile source model

M. Petruzalek, T. Lokajicek, T. Svitek, P. Kolar, Z. Jechumtalova, J. Sileny, P. Adamova

21 - AE sensor sensitivity verification using a stimulated motion verified
by laser-vibrometry
H. Vallen

Signal processing - Equipment and Software III

76 - Method for assessing the likelihood of fatigue crack detecting
V. Barat, D.V. Chernov, S.V. Elizarov

78 - Statistical non-parametrical algorithm for Acoustic Emission impulses detection

adapted on the basis of modal analysis approach
V. Barat, D.A. Terentyev, S. Elizarov

80 - CFRP failure mechanisms detection using traditional and modal AE equipment’s
features
C. Rowland
82 - Application and Analysis of AE signals based on the EEMD-FastICA method
Y. Jiang, F. Xu, Z. Yang, J. Yu, B. Xu

10 - Evaluation of localized corrosion using Acoustic Emission signals: wavelets
denoising and random forests classification
N. Morizet, M. Frégonèse, N. Godin, J. Tang, B. Normand
Civil Engineering

09 - Acoustic Emission measurements during a tensile fatigue test in reinforced concrete
G. Manthei, M. Koob, J. Minnert

59 - AE analysis of particle crushing: towards 3D analysis of soils
S. Luo77 - Test of bending reinforced concrete structures by means of Acoustic Emission
methodS.V. Elizarov, V. Bardakov, A.I. Sagaidak
81 - Assessment of infrastructures by rainy induced AE tomography with wave velocity
and attenuation rate
T. Shiotani, K. Hashimoto, N. Okude, C. Granier, K. Watabe, H. Takamine

Source Location

34 - Contouring geodetically accurate Acoustic Emission sources via kernel density
estimates
P. Gális, V. Kůs
41 - Analysis of AE source location precision for general sensor configurations
M. Chlada, J. Kober, Z. Prevorovsky
54 - Time reversal localization of continuous and burst AE sources under noise
Z. Prevorovsky, J. Krofta, J. Kober, M. Chlada, M. Mracko

Authors
16 - 20ABARKANE Chihab

12 - 13CALABRESE Luigi

06

FUSCO Claudio

63

ABOURA Zoheir

62CALÁS Héctor

12 - 13GALEANO Massimiliano

65

ADAMOVA Petra

22

CARRILLO Lorenzo

34

GALIS Petr

53

AGGELIS Dimitrios

71

CHEN Yanming

16 - 17 - 20 GALLEGO Antolino

20

AGUILAR-AGUILERA
Antonio86

CHERFAOUI mohammed

23 - 24

18 - 19

AL HAJ Omar

31

CHERNIGOVSKI
Wenceslaus

03 - 06 - 10 GODIN Nathalie

15

ALEXANDRE Noemie

76

CHERNOV D.V

83

ANASTASOPOULOS
Athanasios

41 - 54

CHLADA Milan

50

CHOMYAKOV Vladimir

28

CHUGUNOV Alexey

14

CLERC Gaspard

73

COLAS Oriane

68

CONTELL ASINS Conchin

01

CRAWFORD Alasdair

84

CTVRTLIK Radim

15

DAHMENE Fethi

29

DALGIC Munip

30

DANYUK Alexey

71 - 72 - 73 HERVÉ Catherine
74 - 75 - 76
77 - 78 - 79
80 - 82 - 83
85 - 86

74

DELGADO Julien

73

HOUSSAIS Alain

59

DIAMBRA Andrea

45

HTOO KHUN Han

75

DIGIULIO Christophe

59

IBRAIM Erdin

84

DRAB Martin

29

IRRETIER Andree

01

DROUBI Mohamad Ghazi

11 - 40

ITO Kaita

05

DUFFNER Eric

46

JANA Miroslav

69

DUNAJ Tomasz

65

JECHUMTALOVA Zuzana

15

ECHTERMEYER Andreas

23 - 24JIANG Bo

76 – 77 - 78 ELIZAROV Sergey.V.

82

JIANG Yu

11 - 38 - 40 ENOKI Manabu

23 - 24

JIN Liang

01

FAISAL Nadimul

55

KELKEL Benjamin03

FANTOZZI Gilbert

63

KHARRAT Mohamed

74

FRECHARD Jonathan

64

KIETOV Volodymyr

10

FRÉGONÈSE Marion

79

KIM Dong-Hyun

57

ASHIZAWA Takeshi

39

BAENSCH Franziska

39

BAER Wolfram

50

BAKSHEEV Viktor

27 - 69BARAN Ireneusz

76

BARAT Vera

15

BARCIKOWSKI Michal

77

BARDAKOV V.V.

15

BARDOUX Olivier

58 - 45

BASHKOV Ilya

58 - 45

BASHKOV Oleg

58

BASHKOVA Tatiana

19

BATEL Mehdi

16

BENAVENT-CLIMENT
Amadeo26

BENTAHAR Mourad

15

BITTENDIEBEL Sylvie

15

BLANC-VANNET Pierre

18

BOLIVAR José

83

BOLLAS Konstantinos

73

BRIANÇON Christophe

14

BRUNNER Andreas J.

58

BRYANSKY Anton

62

CABELLOS ElenaGAO Yong

32

GOOSSENS Nick81

GRANIER Clement

74

GROSJEAN Christophe

55

GURKA Martin

39

HABIB Abdelkarim

06

HAMAM Zeina

81

HASHIMOTO Katsufumi

19

HEBRARD Yoann

15

HEGGEM Per S

29

KLEMM Rolf

09

MINNERT Jens

07

RYNCARZ Tomasz

41 - 54

KOBER Jan57

MIZUTANI Yoshihiro

33

SAEIDI Fatemeh

65

KOLAR Petr

06

MONNIER Thomas

77

SAGAIDAK A.I.

09

KOOB Manuel

26

MONTRESOR Silvio

71 - 72

SAIDOUN Abdelkrim

84

KOULA Vaclav

63

MOREAU Gurvan

31 - 37SAMOKHVALOV Alexey

83

KOUROUSIS Dimitrios

10

MORIZET Nicolas

02 - 04

SAUSE Markus G. R.

07

KOZUB Barbara

54

MRACKO Michal

49

SCHAURITSCH Gert

47

KRATOCHVILOVA Vendula

48

NEUBECK Robert

48

SCHUBERT Lars

29

KRIEGER Karl-Ludwig

14

NIEMZ Peter48

SCHULZE Eberhard

54

KROFTA Josef

15

NONY Fabien

68

SCHWARZHAUPT Oliver

64KRÜGER Lutz

10

NORMAND Bernard

32

SEEFELDT Marc

31

KURNOSOV Dmitry

27 - 69

NOWAK Marek

22

SERRIS Eric

34

KŮS Václav

81

OKUDE Nobuhiro

53

SHETTY Naveen

49 - 51

LACKNER Gerald

15

PANEK Maciej

33 - 35

SHEVCHIK Sergey A.

15

LASN Kaspar

50

PANIN Vladimir

11

SHINOZAKI Ippei

33 - 35

LE QUANG Tri

83

PAPASALOUROS Dimitrios

81

SHIOTANI Tomoki

68

LEHMANN Martin

62

PELAEZ Miguel

38

SHIRAIWA Takayuki

71

LESAGE Thibault

74

PERRIN Guillaume

50

SHULATOV Aleksandr

23 - 24

LI Naitian

31

PETERSEN Tatiana

65

SILENY Jan

53

LIVITSANOS Georgios

65

PETRUZALEK Matěj

04

SINCLAIR Ian

65

LOKAJICEK Tomas

29

PIMENTEL Joao Vitor

04

SPEARING S. Mark59

LUO Sha49

POSCH Michael

44

SUCHKOV Evgeny

07 - 27

LYASOTA Igor

04

POTSTADA Philipp

16 - 17SUÁREZ Elisabet

46 - 47

MAHMOUD Houssam

41 - 54

PREVOROVSKY Zdenek

65

SVITEK Tomas74

MAISONNETTE Daniel

18

PROUST Alain

81

TAKAMINE Hidefuji

15

MALDACHOWSKA
Agnieszka

12

PROVERBIO Edoardo

38

TAMURA Kengo

64

MANDEL Marcel

33 - 35QUANG Tri Le

10

TANG Junlei

09

MANTHEI Gerd

28

RASTEGAEV Igor

78

TERENTYEV D.A.

49 - 51MARIHART Heribert

42 - 44RAZUVAEV Igor

57

TODOROKI Akira

62

MARTIN José Manuel

16 - 17 - 20 RESCALVO Francisco J.

84

TOMASTIK Jan

04

MAVROGORDATO Mark

03

REYNAUD Pascal

23 - 24TONG Zongkai

46 - 47

MAZAL Pavel

47

RICHTER Vladislav

57

TOYAMA Nobuyuki

26

MECHRI Charfeddine

45

ROMASHKO Roman

22

VALENTINI Andrea

23 - 24

MENG Fanchao

04

ROSINI Sebastian21

VALLEN Hartmut

30

MERSON Dmitry

36

ROSNER Sabine

53

VAN HEMELRIJCK Danny

33 - 35

MEYLAN Bastian80

ROWLAND Chris

74

VERLET Philippe