JOURNAL OF
ACOUSTIC EMISSION
2017
Volume 34(First Installment)

Editors

M.A. Hamstad and G. Manthei

Published byAcoustic Emission Group, Encino, CA USA

JOURNAL OF ACOUSTIC EMISSION
Contents
Volume 34, 201734-001Through-Transmission Characteristics of AE Sensor Couplants
Kanji Ono34-012

An Experimental Study of Acoustic Emission Waveguides
Kanji Ono34-052Clustering of Fiber-Break Related Events in Carbon Fiber Reinforced Polymer
Composites Using Acoustic Emission
T. Whitlow and M. Sundaresan,

Selected Papers from PROGRESS IN ACOUSTIC EMISSION XVIII (2016)
Review of Acoustic Emission Source Mechanisms on Large Movable Structures, David E.
Kosnik, S01-S09
On Acoustic Emission Sensor Characterization, Kanji Ono, S10-S19
Classification of acoustic emission signals using wavelets and Random Forests: application to
localized corrosion, N. Morizet, N. Godin, J. Tang, M. Fregonese, B. Normand, S20-S25
Damage Evaluation of RC Bridge Deck under Wheel Loading Test by Means of AE
Tomography, T. Nishida, T. Shiotani, H. Asaue, T. Maeshima and Y. Kobayashi, S26-S31
Source of AEs from IG-SCC of Face Centered Cubic Metals under Static or Dynamic Straining,
Mikio Takemoto, Shuichi Ueno and Motoaki Nakamura, S32-S37
Detection of Acoustic Emission Signals with the Fabry-Perot Interferometer Type Optical Fiber
SensorKazuki Tada1) and Hironobu Yuki, S38-S41
Remote imaging of plate-like structures with E-camera, Takahiro Hayashi, S42-S45
High-precision source location of AE event using automatic error correction of signal risingtime, Kaita Ito and Manabu Enoki, S46-S49
Influence of pre-compression on tensile behavior in wrought AZ31 studied by the acoustic
emission technique, Daria Drozdenko, Patrik Dobro, Jan Bohlen, Sangbong Yi and František
Chmelík, S50-S53

i

Visualization of internal damage in RC slab with single side access attenuation tomography,
Clément Granier, Tomoki Shiotani, Katsufumi Hashimoto and Takahiro Nishida, S54-S58
Acoustic Emission modeling from the source to the detected signal: model validation andidentification of relevant descriptors, Thomas Le Gall, Nathalie Godin, Thomas Monnier,
Claudio Fusco and Zeina Hamam, S59-S64
Evaluation of Irrigation Response using AE Method for Management of High-Frequency
Irrigation of Hydroponic Miniature-Tomato, Kensuke Kageyama and Takenobu Sakai, S65-S70
In-situ investigation of deformation mechanisms in various magnesium alloys by X-ray
diffraction and acoustic emission, Kristián Máthis, Gerardo Garcés, Jan Čapek and Alexei
Vinogradov, S71-S77
Acoustic emission and damage evolution at mean temperature under air of a SiC/[Si-B-C]
composite subjected to cyclic and static loading : towards lifetime prediction, E. Racie, N.
Godin, P. Reynard, M. R'Milli and G. Fantozzi, S78-S84.
Contents of the Proc. of IAES XXIIIii

Through-Transmission Characteristics of AE Sensor Couplants
Kanji Ono
Department of Materials Science and Engineering,
University of California, Los Angeles (UCLA),
Los Angeles, CA 90095
Abstract
This study examined the behavior of AE sensor couplants for through-transmission of acoustic
signals or out-of-plane motion. In order to provide controlled conditions, face-to-face transmitterreceiver arrangement was utilized. All liquid and gel couplants are found to give satisfactory
service with careful installation, producing the minimum couplant thickness of 5 to 8 µm. Theirperformance is nearly identical to 1.2 MHz. With normal installation procedures, however, the loss
can be 3 to 5 dB with viscous resins and gels, as couplant thickness can become 10 to 15 µm andhigh frequency components are lost more than the peak amplitude reduction. Honey turns out tobe an excellent couplant because of its higher acoustic impedance, while commonly used silicone
grease can be a poor choice at higher frequencies. Higher frequency transmission loss increases
with thickness as predicted by theory of through-gap transmission with approximately linear
dependence on frequency.
Keywords: Sensors, Couplants, Through-transmission, Attenuation, Frequency effects
Introduction
In all acoustic emission (AE) tests, a sensor must be mounted on a test object unless a more
expensive non-contact sensor is used. Sensor couplants have been discussed in several articles over
the years [1-6]. These were preceded by discussion of ultrasonic couplants for contact testing [710]. ASTM E650 provides comprehensive guidelines for AE sensor mounting [11]. In selecting a
specific type of AE sensor couplant, Theobald [12] provided the best available guidelines atpresent and summarized the features of various couplants. His group also showed some spectral
transmission characteristics of couplants [5].
We have recently evaluated sensor characteristics and assessed couplant behavior as a part ofthe sensor studies [13]. Here, our studies are limited to the conditions for through transmission of
acoustic signals or out-of-plane motion, using face-to-face transmitter-receiver arrangements.
Quite often, AE applications detect in-plane motion of guided waves. The present results ignore
such AE signals since their detection critically relies on the sensor size, signal frequency spectra
as well as the vibration modes. The use of controlled condition also precludes effects of surface
roughness and curvatures, which often add to coupling loss. While most liquid couplants provide
satisfactory results with careful usage, conventional mounting practices can lead to loss of
sensitivities, especially at higher frequencies above 1 MHz. Results are given below.
Experimental
Couplants are characterized using face-to-face sensor arrangements. Both wideband andresonant sensors are utilized. Most sensors have an almina face plate (except Dunegan S140B and
AET AC175L with epoxy facing) and were used in cleaned state without any special surface
J. Acoustic Emission, 34 (2017)1©2017 Acoustic Emission Grouppreparation. Transmitter is excited using short (<2 µs duration) high-voltage pulses. Received
signals are digitized at 2 ns interval and analyzed using FFT with using the data length of 262,144,
providing 1.9 kHz resolution. The signal length was mostly 10 µs (5,000 points) for broadband
sensor pairs and up to 100 µs (50,000 points) with a resonant sensor. The FFT routine of Noesis(Enviroacoustics, ver. 5.8) was used with the pre-set parameters and Hamming window. All tests
were conducted at 21±1˚C.
Following couplants are examined. Water, motor oil (SAE 5-30 grade), honey, Vaseline, Highvacuum (HV) silicone grease (Dow Corning), Stopcock silicone grease (Dow Corning), Nonaq
stopcock grease (S-530, Fisher Sci.), Couplant resin (SC-6, AET), epoxy resin (quick-setting, HFT
or Harbor Freight Tools), ultrasonic shear gel (54-T04, Sonotech) and a plastic film as dry couplant(Saran wrap). Of these, Nonaq and AET resin are no longer available commercially.
Results and Discussion
1. Best practice
When the viscosity of couplants is high, it takes time to establish ideal couplant thickness.
During mounting with such couplants, a sensor must be pressed using 20-50 N force and be rotatedto squeeze out excess couplant. With Vaseline and honey, it takes 5 to 10 min, while more viscous(like SC-6) and stiffer (HV grease) couplants require 20 to 30 min. In contrast, water and motor
oil provide the maximum transmission from start, though these thinner couplants require somepressure (several N force) to maintain good contact.

Fig. 1 FFT magnitude spectra of received signals through couplant, or t vs. frequency, f. Vaseline:
dark blue curve, motor oil: red, Nonaq: blue, High vacuum grease: purple, Stopcock grease: brown,
SC-6 resin: green. V103 transmitter and FC500 receiver.
Using a 1-MHz transducer (13-mm diameter, Olympus V103) as a transmitter and a wideband
sensor (FC500, AET) as a receiver in a face-to-face sensor arrangement, the output frequency
spectra are shown in Fig. 1. These also represent transmission spectra, t. Six couplants are used
and best contact was achieved for each. Data spread is less than 1 dB to 1.2 MHz, but three curves2

start to deviate lower. Differences between the spectra or ∆t are plotted in Fig. 2, where Vaseline
data is used as reference. Two curves for oil (red) and Nonaq (blue) deviate positively >1.2 MHz,
while three become lower. Of the three, the purple curve for HV silicone grease is the lowest,
being ~2 dB lower than Vaseline >1.5 MHz. This is surprising as this is a commonly used couplant
and the peak values differ only 0.5 dB (Vaseline result is higher than HV silicone). Anothersilicone grease is comparable to viscous resin couplant (SC-6) with both ~1 dB lower than Vaseline.

Fig. 2 Differences in transmission spectra, ∆t, of received signals through couplant using Vaseline
spectrum as reference. Motor oil: red, Nonaq: blue, High vacuum grease: purple, Stopcock grease:brown, SC-6 resin: green. V103 transmitter and FC500 receiver.

Fig. 3 Differences in transmission spectra, ∆t, of received signals through couplant using Vaseline
spectrum as reference up to 4 MHz. Motor oil: red, Nonaq: blue, High vacuum grease: purple,
Stopcock grease: brown. NDT Systems C16 transmitter and FC500 receiver.

3

Another set of best-effort coupling used a 2.25-MHz transducer (13-mm diameter, NDT
Systems C16) as a transmitter and a wideband sensor (FC500, AET) as a receiver. In terms of peakvoltage of the waveform, HV grease was 1.8 dB lower and stockcock grease was 1 dB lower(despite waiting >30 min under 15 N force and 50 N during measurement). Figure 3 shows thedifferences in the spectra, again using the Vaseline result as reference. High frequency loss for thesilicone grease cases was even higher than in the previous tests, reaching 3 to 4 dB >2.5 MHz.
Both oil and Nonaq had better coupling than Vaseline above 2.5 MHz (these used 50 N force). It
is unclear why better coupling could not be made for the silicone grease cases, but at least theseshowed lower peak voltages, indicating poorer performance.
In the best coupling conditions, all the couplants tested performed equally to 1.2 MHz, whilesome have 1 to 2 dB (or 3 – 4 dB) higher transmission loss >1.5 MHz (or >2.5 MHz).

Fig. 4 Differences in transmission spectra of received signals, ∆t, through couplant using Vaseline
spectrum as reference. Non-optimum sensor installation for viscous couplants. Motor oil: red,
Nonaq: blue, Shear gel: dark blue, High vacuum grease: purple, Stopcock grease: brown, SC-6
resin: green, plastic film: black. V103 transmitter and FC500 receiver.
2. Normal coupling
In normal AE testing, we typically mount a sensor within a short time, spending no more than
30 sec. Adequate couplant is applied, squeezed with force in compression and in shear, then fix
the sensor with a spring and/or a holder, or by winding tape over the sensor. With such a method,
viscous couplants typically produce a thicker layer with higher transmission loss. In the next seriesof tests, eight couplants were used. With the best Vaseline coupling as reference, spectral
differences are plotted in Fig. 4. Nonaq (blue), water (red dash) and shear gel (dark blue) achieved
essentially same peak amplitude (–0.1 to –0.6 dB) and the spectra differed less than 1 dB to 2 MHz(except water had a slightly larger loss at 200-600 kHz). Silicone grease (brown and purple curves)
and SC-6 (green) suffered higher losses. In the case of HV grease, the loss reached 10 dB at>1.7 MHz. These three had lower peak amplitude: HV grease showing –6.3 dB, stopcock grease,–3.6 dB and SC-6, –2 dB, respectively. Another one is a plastic film, used as dry couplant. As
4expected, this performed poorly: –7 dB in peak signal amplitude and larger transmission loss
than most liquid/gel couplants except HV grease >1.2 MHz.
Thus, we need to be selective in choosing a couplant when high frequency response is sought.
In particular, the use of high viscosity couplants should be avoided unless one can spend adequate
time to properly achieve good coupling. One remedy is to raise the couplant temperature during
installation, thus lowering their viscosity. Another point of interest is that the shear gel did notprovide any special advantages even though it is expensive. Note also that Nonaq is no longer
marketed commercially. Our bottle is ~50 years old as we rarely used it.
3. Couplant vs. solid bonding
For mechanical stability, solid bonds are superior to liquid couplants. Bonding with glue isalso needed for better shear wave detection. It is assumed intuitively that solid bonds should
provide better wave transmission as the wave velocity increases with curing of thermosetting
polymers, such as epoxy. We examined the wave transmission of epoxy resin during curing. A
quick-setting 2-part epoxy (Harbor Freight Tools) was used. Three sets of curing tests wereconducted; two were face-to-face tests using resonant sensors (R15, S140B and AC175) and oneused 38-mm thick Al plate buffer between FC500 and 1-MHz UT transducer (Automation Ind.,
13-mm diameter). In all cases, the pairs were excited with high voltage pulse and the output
waveforms recorded and analyzed.

Fig. 5 Transmission spectra, t, of received signals through couplant. Vaseline reference: blue curve,
epoxy resin (as mixed): green, epoxy resin (cured for 30 min): red. FC500 transmitter andAutomation Ind. 1 MHz-1/2”.
The peak amplitude of the received signals remained constant from the time of coupling with
mixed epoxy resin to complete curing at 30 min. Some spectral changes can be seen in Fig. 5, but
overall spectra remain essentially constant. This data is for the higher frequency transducer pair,but similar results are found for the pairs of resonant sensors. It is concluded that coupling behavior
is unaffected by epoxy curing, changing liquid resin into solid bond.
5

The solid epoxy bond was stripped from the Al plate after test and its thickness was 13 µm.
The epoxy resins, both before and after mixing, had consistency of honey. From some epoxy
literature, quick setting types have viscosity of about 10 Pa·s (or 10,000 centipoise). Honey islisted as 7 to 10 Pa·s at 20˚C. Thus, we can assume the thickness of moderately viscous liquid
couplant layers to be similar to the hardened epoxy bond, or 13 µm. Note that water has theviscosity of 1 mPa·s and grade-30 motor oil 250 mPa·s. Their couplant layer thickness should be
less.
4. Couplant thickness
Next, we examined effects of couplant thickness. In comparing various couplants (cf. Fig. 4),
we used Vaseline as reference. Upon the initial application of this couplant, the thickness is muchhigher than the minimum we can obtain after applying forces for 5 to 10 min. The received
amplitude is also lower. In the same series of tests for Fig. 4, Vaseline gives only 1.89 V, which
eventually increases to 2.91 V; i.e., the initial value is 3.7 dB lower. For these two tests, thereceived signal spectra and their difference are plotted against frequency in Fig. 6. The difference
linearly increases with frequency, reaching 6.2 dB at 2 MHz. Thus, the loss in peak amplitude isdue mainly to the high frequency components above 500 kHz.

Fig. 6 Transmission spectra, t, of received signals through Vaseline couplant. Vaseline reference:
red curve, Vaseline couplant after normal mounting: blue. Spectral difference, ∆t, between normal
and reference installation is plotted in purple curve, which follows linear frequency dependence inblack dash line. ∆t (dB) = 3.10 f (MHz). FC500 transmitter and Olympus V104 receiver.
Using plastic shims with a 13-mm diameter hole, we examined the thickness effect directly
using honey, water, Vaseline and stopcock grease. Peak output voltage is plotted against the
couplant thickness (in mm) in Fig. 7. From the top curve for honey (purple), water (red), Vaseline(blue) and stopcock grease data are shown. The lower three data follow straight lines in log-log
plotting, indicating the power law behavior. The bottom two lines are nearly identical. The honeydata can be represented by a second-order polynomial. The data show that the peak amplitudedecreases with thickness, but the thickness dependence is not unique. Since honey shows thehighest output below 0.2 mm, the viscosity is not the only factor for the transmission loss. A
6possible cause of honey’s higher response is its higher acoustic impedance, Z. It is at 2.89 E+6
kg/s m2 and is about twice that of water. The sound velocity for Vaseline and stopcock grease were
not found in the literature, so approximate measurements were made. With their published densityof 0.9 and 1.0, their acoustic impedances are found to be 1.72 and 1.87E+6 kg/s m2, which are 15
and 25% higher than that of water. With similar Z values, more steep decrease of signal amplitudein Vaseline and silicone grease appears to come from their higher viscosity. In fact, it is more
appropriate to call them gels.

Fig. 7 Thickness dependence of output voltage of received signals through honey (dark blue),water (green), Vaseline (blue), stopcock grease (red). FC500 transmitter and Olympus V104
receiver.
In Fig. 7, the left-most point for each curve corresponds to the highest output signal level andthe couplant thickness was extrapolated from the best fit curve. For water, this thickness was 5 µm,
while the thickness for the other three was 6 µm. These represent effective minimum couplant
thickness.
From the power law fit for Vaseline, the couplant thickness for the lower amplitude case in
Fig. 6 is estimated to be 14.6 µm. This is comparable to the case of epoxy discussed earlier. Thus,
gels like Vaseline and silicone grease can easily develop couplant layers two to three times that of
their best condition with attendant several dB of transmission loss. The situation also applies toviscous liquids like resins and honey except honey has much better transmission even near 100µm thickness.
While the data in Fig. 7 indicates overall transmission loss, frequency plays a major role indetermining the loss. Figure 8 shows the frequency dependence for two couplants, water and honey.
In both cases, the loss increases with frequency, roughly linear with frequency, but usually with
non-zero intercept. For water with the thickness of 0.04 and 0.13 mm, it reaches 4 and 12 dB at 1
MHz. For honey with 0.05 and 0.28 mm, the corresponding values are 2 and 10 dB at 1 MHz. Thatis, water has approximately twice attenuation over honey and the same thickness or similarattenuation at half the thickness. This effect is more pronounced at lower frequencies.
7Fig. 8a Spectral difference, ∆t, between reference installation and through 0.04 mm (solid) and
0.13 mm (dashed) layer of water. ∆t curves follow nearly linear frequency dependence in blacklines with offset. FC500 transmitter and Olympus V104 receiver.

Fig. 8b Spectral difference, ∆t, between reference installation and through 0.05 mm (solid) and
0.28 mm (dashed) layer of honey. ∆t curves follow nearly linear frequency dependence in blacklines without offset (0.05 mm) or with offset (0.28 mm). FC500 transmitter and Olympus V104
receiver.
Transmission coefficient, T, through a gap filled with liquid (or gas) has been treated since theearly days of ultrasonic testing since this has direct bearing on the success of ultrasonic flaw
detection [7]. For the case of identical materials (of the acoustic impedance Z) on both sides of the
gap and the gap distance, d, being much less than V/2πf (V is the wave speed), or d << V/2πf, wehave an approximate expression for T from Krautkramer [7] as
T = [1 + (π f Z d/ Zg V)2]–0.5(1)where f is the frequency, Zg are the acoustic impedance of the gap medium with Zg << Z. This
expression was derived by considering the interferences of waves repeatedly reflected at the twointerfaces of the gap. That is, it is appropriate for continuous waves.

8

Fig. 9a Transmission coefficient, T, through a gap filled with water vs. gap thickness.
f = 1 MHz (blue curve), 0.5 MHz (red), 0.2 MHz (green), 0.1 MHz (dark blue) and 0.05 MHz(brown).

Fig. 9b Transmission coefficient, T, through a gap filled with honey vs. gap thickness. f = 1 MHz(blue curve), 0.5 MHz (red), 0.2 MHz (green), 0.1 MHz (dark blue) and 0.05 MHz (brown).
Values of transmission coefficient, T, for a gap filled with water and honey are calculated andgiven as Fig. 9a and 9b. Frequencies chosen are 1, 0.5, 0.2, 0.1 and 0.05 MHz and d values are in
the range of 0.003 to 10 mm. The transducer face materials are typically alumina and Z = 38 E+6
kg/s m2 is used. Results show that T starts to decrease at 3 µm to 0.15 mm depending on frequency
and Zg. The value of T reaches at –20 dB (T = 0.1) as d reaches 3 to 10 mm at 50 kHz. At 1 MHz,T drops 20 dB at 0.2 mm for water and 0.5 mm for honey.

9

Fig. 10 Calculated differences in transmission coefficient, ∆T, through a gap filled with water andhoney vs. frequency. Water thickness = 0.04 mm (blue curve), 0.13 mm (blue dash); honeythickness = 0.05 mm (red), 0.28 mm (red dash).
The reduction in transmission coefficient, ∆T, is higher at higher frequencies. Figure 10 gives
the frequency dependence of ∆T for the thicknesses examined in Fig. 8; i.e., 0.04 and 0.13 mm forwater and 0.05 and 0.28 mm for honey. Effects of frequency are not linear, but these curves canbe approximated by straight lines, implying the presence of similar trends found for the data in Fig.
8. The calculated ∆T values are 3 to 5 dB higher than the observed values of transmission loss forwater at 1 MHz: ∆T’s are 7 and 17 dB (4 and 12 dB observed). For honey ∆T’s are 2 and 9 dB (incomparison to 2 and 10 dB observed), so these are in agreement. Differences found for water cases
cannot be attributed to inaccuracies in the gap size as it is hard to make the gap thinner than theshim thickness. The cause is unknown presently.
The classical calculation is thus applicable in the present case probably because pulse transit
time through a 0.3-mm gap is 0.15~0.2 µs and the pulse duration is ~2 µs. Although not visible inmost waveforms examined, reverberation should be possible. When the gap reaches a certain range,
oscillations do occur, but at narrower or wider gap sizes, the waveforms revert to those similar to
the narrow gap cases.
SummaryThis study examined the behavior of AE sensor couplants for through transmission of acoustic
signals or out-of-plane motion, using face-to-face transmitter-receiver arrangement. Results
follow:
1 All liquid and gel couplants can achieve a minimum transmission loss by careful
installation producing the minimum couplant thickness of 5 to 6 µm. At the optimum
condition, the loss is within 0.8 dB of the best couplants (motor oil, Nonaq and water).
2 With normal installation procedures, the loss can be 3 to 5 dB over that of the best case.
With viscous resins and gels, couplant thickness can be 10 to 15 µm and high frequency
attenuation is higher than the peak amplitude reduction.
10

3
4
5

Honey turns out to be an excellent couplant because of its higher acoustic impedance. In
contrast, oft-used silicone grease needs special attention to reduce its thickness as highfrequency loss is substantially higher than others.
Higher frequency transmission loss increases with thickness as predicted by theory ofthrough-gap transmission with approximately linear dependence with frequency.
Coupling behavior is unaffected by epoxy curing, changing liquid resin into solid bond,albeit the solid bond is always preferable for the stability of coupling.

A follow-up study for the detection of guided waves will be beneficial for most practical AE
applications. However, careful consideration is needed to arrange appropriate experimental
conditions as noted in the Introduction.
References
1. A.G. Beattie, Acoustic emission, principles and instrumentation, J. Acoustic Emission,2(1/2), 95-128, 1983.
2. J. Baram and J. Avissar, Low temperature behavior of liquid couplants used in acoustic
emission experiments, J. Acoustic Emission, 1(3), 179-182, 1983.
3. A.G. Beattie, Acoustic emission couplants I: the ASTM survey, J. Acoustic Emission, 2(1/2),
67-68, 1983.
4. A.G. Beattie. J.A. Baron, R.S. Algera and C.C. Feng, Acoustic emission couplants II:
coupling efficiencies of assorted materials, J. Acoustic Emission, 2(1/2), 69-70, 1983.
5. P. Theobald, B. Zeqiri and J. Avison, Couplants and their influence on AE sensor sensitivity,
J. Acoustic Emission, 26, 91-97, 2008.
6. R.K. Miller, E.v.K. Hill, Acoustic Emission Testing, Nondestructive Testing Handbook, 3rded., vol. 6, ASNT, Columbus, OH, 2005.
7. J. Krautkramer and H. Krautkramer, Ultrasonic testing of materials, 2nd ed., Springer, Berlin,
1969.
8. A.S. Birks, R.E. Green, Jr., P. Mclntire, Ultrasonic Testing, Nondestructive Testing
Handbook, 2nd ed. Vol. 7, ASNT, Columbus, OH, 1991.
9. J.L. Rose and P.A. Mayer, Ultrasonic signal-processing concepts for measuring the
thickness of thin layer, Mater. Eval. 32(2), 249-225.1974.
10. Y.H. Kim, S.-J. Song, S.-S. Lee and J.-K. Lee, A study on the couplant effects in contact
ultrasonic testing, Proc. 10th APCNDT, AINDT, 17-21 September 2001, Brisbane, Australia,
ndt.net: Note that some of their equations are missing square-root signs in denominator.
11. ASTM E650-12, Standard guide for mounting piezoelectric acoustic emission sensors,
ASTM International, West Conshohocken, PA, 2012.
12. P. Theobald, Guide on acoustic emission sensor couplants, National Physical Laboratory.
http://www.npl.co.uk/ultrasound-and-underwater-acoustics/ultrasonics/research/guide-onacoustic-emission-sensor-couplants, last updated 1 May 2012.
13. K. Ono, Calibration methods of acoustic emission sensors, Materials, 9, 508 (2016);
doi:10.3390/ma9070508.

11

An Experimental Study of Acoustic Emission Waveguides
Kanji Ono
Department of Materials Science and Engineering, University of California, Los Angeles(UCLA), Los Angeles, CA 90095
Abstract
This study reports the characteristics of acoustic emission (AE) waveguides using displacement pulse of about 1-µs duration from an ultrasonic transducer, driven by a short impulse. By
using one of broadband sensors as a receiver, improved characterization of waveguides is possible over 50 to 2000 kHz. Here, we obtained responses of circular rod waveguides of different
diameters from 1.6 to 12.7 mm with lengths varying from 60 to 900 mm. It is found that the lowfrequency part of L(0,1)-mode rod waves is dominant in thin rod waveguides. This mode is increasingly suppressed above 6-mm diameter. This mode is also responsible for stretching a shortduration (~1 µs) source into a long pulse of more than 100 µs as its velocity is reduced at higher
frequencies. At larger diameters, slower L(0,3)-mode becomes the dominant one, and its peak isfound at its highest velocity over a narrow range of frequency. To keep the pulse duration short,
it is necessary to keep the waveguide diameter below 3 mm, preferably at 1.6 mm or less so thatthe relatively non-dispersive L(0,1)-mode prevails over the frequency range of interest. Diameter
reduction, however, leads to reduced sensitivity unless small aperture sensors are used. Tube
waveguides are found to offer a significant advantage of spectral smoothness. In tubes, L(0,2)
mode plays an important role of providing nearly constant velocity and spectral flatness. Effectsof threading are also evaluated. Three practical waveguide designs are suggested. More extensive
exploration of tubular waveguides is highly recommended.
Keywords: Waveguides, Rod waves, Tube waves, Threaded rods, Dispersion, Reverberation
Introduction
Waveguides have important roles in practical acoustic emission (AE) monitoring since theearliest days. They are widely used in various industries, including petrochemical, electric power
generation, gas and oil transport, among others. Anderson et al. [1] reported their uses for fast
breeder reactors at the first conference on AE in 1971. Rods, tubes and wire bundles were already used in this work. Typical commercial waveguides are of stainless steel rod of 6 to 13 mm
in diameter and 300 mm in length, with or without a sensor mounting conical endpiece. They
may be welded to a structure to be monitored or with a pointed end for pressed contact. They arealso valuable in laboratory for conducting high or low temperature AE testing. For example, platinum wire waveguide was used to detect oxide cracking and spallation at 1000˚C [2]. Various
references on rod wave propagation and mechanical waveguides can be found in literature [1,3-7]. While the basic theory of rod waves was established over a century ago, more waveguiderelated works are continuing [8, 9].
Our previous study evaluated waveguide characteristics of rods and tubes of different sizeand length and concluded that commonly used 6-mm waveguide to be a good compromise interms of attenuation and signal broadening [3]. It was also suggested to keep the diameter smalland stay in the low frequency range to utilize the L(0,1)-rod-wave mode. Sikorska and Pan [10,11] conducted extensive evaluation of short waveguides of several different materials relying on
J. Acoustic Emission, 34 (2017)12©2017 Acoustic Emission Groupwavelet analysis as the main tool and compared their results with rod wave calculations. They
also examined traditional AE features. Perhaps most striking is the dominance of longitudinalresonance due to the length values ranging from 18 to 177 mm. Hamstad [12] examined thinwaveguides of 1.6 mm in diameter using a high-fidelity NIST sensor and reported good reproduction of pencil-lead-break (PLB) induced plate waves through such waveguides, but with 1113 dB attenuation. A thicker (3.2 mm) waveguide produced less attenuation (–5 dB), but alsowith less fidelity in waveform; i.e., more attenuation at higher frequencies. He evaluated platewaves, dominated by low frequency A0-mode and the observed non-dispersive waves are apparently due to the dominance of L(0,1)-rod-wave mode, noted earlier [3]. A more recent study ofZelenyak et al. [13] introduced the finite element modeling (FEM) to the thin waveguide evaluation, extending the Hamstad study and reproducing the essential features [12]. They showed that
FEM can be used in lieu of experiment allowing parametric study of waveguide behavior. Theirresults show that a wave entering a waveguide exits with basically similar features. Attenuation
was low below 100 kHz, but tended to increase to about 10 dB at 1 MHz. It is noted, however,that the frequency dependence of the simulated plate-wave signal of PLB is 40 dB/decade (from0.1 to 1 MHz) after 100-mm propagation. This is twice that of the expected step-function source
used to simulate the PLB (with the rise time of 1 µs). As is well known, the calculated Fouriertransform of a step function has the inverse-frequency dependence (F = 2/j2πf). Accompanying
experimental result showed an even higher value of 50 dB/decade, which is 15 dB/decade higher
than that reported by Hamstad [12] in his comparable experiment. It is unclear why such discrepancy arises from seemingly sound FEM methodology and essentially identical experimental setup, albeit different sensors. Because of the strong A0-mode plate waves, these PLB-based studies
inadvertently focus on the low frequency segment. It is hoped that future FEM work can reveal
the frequency dependence of amplitude attenuation in waveguides quantitatively.
In order to clarify the waveguide behavior under well-defined conditions, qualitative wave
propagation experiments were conducted using waveguides of three different materials, as wellas various diameter and length combinations. Three types of rods are included; solid round rods,
threaded rods and tubes. Previously, the facial displacement of an ultrasonic transducer wascharacterized under pulse input [14]. That work used a step-down pulse, but this time the pulsegenerator output was shunted with 50-ohm load, achieving a shorter impulse output of ~1 µs duration. The displacement waveform was measured using a laser interferometer. This displacement pulse is sent through a waveguide under test and the displacement on the other end is detected using a broadband sensor. The received signal is then analyzed using an FFT routine andChoi-Williams transform to evaluate the waveguide characteristics. This study first reports thetime-frequency distribution of received signal intensity, and identifies the wave mode when it
can be established by a comparison to theoretical dispersion curves of rod and tube waves. It isaimed to provide general view of wave propagation behavior in the size range for practical AE
waveguide applications. Next, the insertion loss (or attenuation) and reverberation (or multiple
reflections) effects are evaluated since these two have to be balanced to obtain workable waveguides. That is, a low-loss waveguide has extended reverberation, limiting it to applications with
low AE event rates. Some practical approaches are suggested.
Experimental
The source of displacement pulse utilized an ultrasonic transducer (AET FC500), which is
identified as 19-mm diameter element at 2.25 MHz center frequency. This was driven by an impulse shown in Fig. 1 a) with the rise time of 50 ns, peak voltage of 225 V, decay time of13

3 µs. We used a laser interferometer (Thales Laser S.A. SH-140) at Aoyama Gakuin University(AGU). It has dc-20 MHz bandwidth as noted previously [14]. The displacement normal to thefront face is shown in Fig. 1 b), with the peak at 7.6 nm, returning to zero within 1.5 µs, followed
by low level oscillations to 30 µs. The rise time of the displacement pulse is 0.26 µs (or 0.16 µs
if 10%-90% value is used). The FFT result for the entire waveform of 30 µs length is given in
Fig. 1 c, showing adequate spectral intensity extending to 1.2 MHz and beyond at reduced intensity. This FFT used Noesis (ver. 5.8) with 64-k points at 100 MHz sampling and the result is given as FFT magnitude spectrum in dB. The original signal was recorded at 500 MHz. Most other
FFT used 256-k points at 500 MHz. Note that when a step pulse is used to drive the FC500, theinitial pulse waveform was identical, but a tail extended for 10 µs (see Fig. 2 c) in [14]). This
produces unwelcome low frequency components in the present experiment. Also used as thesource of displacement pulse were Olympus V103, 1 MHz, 12.7-mm diameter transducer and
KRN BB-PCP conical sensor.

Fig. 1 a) Excitation pulse. b) Displacement output from FC500.
14

Fig. 1 c) FFT magnitude of the displacement output.

Fig. 2 Output waveform from KRN sensor, coupled to FC500 in face-to-face arrangement.
The displacement sensor utilized one of two broadband transducers. The primary receiverwas KRN (BB-PC), which is based on NIST-originated conical element sensor with compact
backing. This is a small aperture sensor of about 1 mm in diameter. Olympus V103 (or NDT
Systems C16. 2.25 MHz) with 12.7-mm element was also used to represent the displacement of
the entire end surface. When the KRN sensor is in contact with the source transducer (FC500), or
the face-to-face arrangement, the output of KRN receiver is shown in Fig. 2. This closely follows
the input waveform of Fig. 1 b) with the main pulse width of 1 µs. There is a low-level tail to 25µs, however. The Choi-Williams transform (CWT) was applied on the KRN sensor output andthe result in a frequency-time-intensity plot or CWT spectrogram is given in Fig. 3. AGU-Vallen
Wavelet software was used for this operation. Peak intensity is found at 0.85 µs and covers therange of 300 to 650 kHz, but signal intensity is present from 50 to 1800 kHz. In the following,
however, the discussion is mostly limited to 1500 kHz as the higher frequency range is usually
not of interest in waveguide applications.

15

Fig. 3 Frequency-time-intensity plot or spectrogram of Choi-Williams transform (CWT) of KRN
output in Fig. 2.
The FFT of the received signal is given in Fig. 4 as KRN sensor response, showing ±6 dB
flatness over 50 to 1200 kHz. When the FC500 spectrum is subtracted from the KRN response, we get KRN sensor response, shown by a green curve in Fig. 4. This part was discussed in a separate report [15], and showed a broadband response over 50 to 1800 kHz(±6 dB). Note that the V103 sensor has a similar response as this KRN sensor [15].
For the characterization of a waveguide, it is placed between the transmitter (FC500) and thereceiver (KRN-PC) except as noted. Vaseline petroleum gel was used as couplant. In the following, we present the Choi-Williams transform applied on the KRN sensor output for each waveguide examined. Such time-frequency spectrograms are compared with the rod wave dispersion
curves, obtained using Disperse software. One example is given here for an aluminum rod of 6.4mm diameter in Fig. 5. It shows the group velocities of ten longitudinal L(0,x) and flexural
F(1,x) modes with x of 1 to 5. This leads to the identification of the wave modes present. Whendifferent values of rod diameter are used, the horizontal scale needs modification. For double thediameter, the frequency should read half the value shown here. For example, L(0,2) peak (green
curve) will be at 400 kHz for 12.8-mm diameter Al rod, instead of 800 kHz shown in Fig. 5. Attenuation can be estimated by comparing the observed spectrum with Fig. 4, although this part isskipped, since the main aim is to find the general propagation behavior of waveguides. For theevaluation of attenuation and signal broadening, it is best to use narrow-band signals as in Ref.
[8].
16

Fig. 4 FFT magnitude of the received signal or KRN sensor output (V) in purple, FC500 displacement output (nm) in red, and KRN sensor response (V/nm) in green vs. frequency.
Another set of dispersion curves are calculated for tube waveguides. Figure 6 shows two ofthem; Fig. 6 a) is for Al (Vp = 6.32 mm/µs, Vs = 3.13 mm/µs) with outer diameter/thickness (D/t)
ratio of 8.0, showing three L-mode velocities. Here, thickness is meant as the wall thickness.
Figure 6 b) is for Fe (Vp = 5.95 mm/µs, Vs = 3.26 mm/µs) with D/t = 4.0. In both cases, L(0,1)
velocity dips sharply near the cut-off frequency for L(0,2) mode. For Al, these values are 0.23
and 0.25 MHz·mm and, for Fe, 0.57 and 0.61 MHz·mm. Initially, L(0,1) velocity is near the rod
velocity, then L(0,2) mode has comparable velocity over a wider range of frequency-thickness(f·t) product. For Fe, tube wave velocity exceeds 5 mm/µs at f·t < 0.26 MHz·mm and for 0.92 <f·t < 1.42 MHz·mm.
Waveguides of aluminum, copper alloys and steels were used. Material composition of mostrods is unknown as cut rod samples typically come without identification or chemical analysis.
Most aluminum rods are believed to be Al 1100, except for 6.4 or 12.7 mm diameter rods of Al
2024. Four brass rods are expected to be C260, but two copper brazing alloys are of unknown
composition. From their densities, the alloy is presumed to be CP-3 (90Cu-2Ag-7P). Thinnersteel rods are of 1080 steel, while some are HSLA and stainless steels of unknown composition.
The longitudinal wave velocity can be obtained from the fastest arrival time along with the rod
wave velocity and mass density from sample weight. Thus, the elastic properties can be estimated when needed. Table 1 lists the materials and dimensions of waveguides used. The diameter
ranges from 1.6 mm to 12.7 mm, while the length varied from 59 mm to 913 mm. Tubes of thesame materials were also included in the study. Many of them were from our previous work [3].
The ends of these waveguides were polished down to 600-grit sandpaper using a jig.
17

Fig. 5 The dispersion curves of group velocity of rod waves in Al rod of 6.4-mm diameter.

Fig. 6 The dispersion curves for tube waveguides of Al and steel with diameter-to-thickness ratio of 8.0 or 4.0, respectively.

18

Table 1 Waveguide materials and shape
Material
Al

Steel

Cu
Brass

Cu brazing

Shape
rod
rod
rod
rod
rod
rod
rod
rod
rod
rod
rod
rod
tube
tube
rod
rod
Rod
rod
rod
rod
rod
rod
rod
rod
rod
rod
rod
rod
tube
tube
tube
tube
rod
tube
tube
rod
rod
rod
rod
rod
tube
tube
tube
tube
rod
rod

Size

Length
.
1.6 mm ø
305 mm2.36 mm ø
305 mm
3.0 mm ø
305 mm
3.2 mm ø
305 mm
4.8 mm ø
303 mm
5.0 mm ø
299 mm
6.0 mm ø
299 mm
6.4 mm ø
305 mm
8.0 mm ø
301 mm
9.6 mm ø
60, 151, 304 mm
10.0 mm ø
302 mm12.7 mm ø150, 305, 611 mm
7.9 mm/1.0 mm
152, 290 mm
10.0 mm/1.1 mm
291 mm
1.6 mm ø
304 mm2.36 mm ø
305 mm
3.2 mm ø
71, 165, 210, 238, 300 (238*) mm
4.0 mm ø
300 mm
6.0 mm ø
300 mm
6.4 mm ø
150, 383 mm
7.8 mm ø59, 648 mm
8.0 mm ø
350, 414 mm
9.5 mm ø
278 mm12.7 mm ø
305 mm4-40 thread
305 mm6-32 thread
307 mm¼”-20 thread
383 (248ª) mm½”-13 thread
306 mm
4.8 mm/0.73 mm
298, 377 mm
6.4 mm/0.8 mm
168 mm
6.4 mm/1.45 mm
90, 393 mm
9.7 mm/1.3 mm with thread of 0.91 mm pitch, 543 mm
2.0 mm ø100, 241, 411 mm
6.4 mm/0.5 mm
88, 375 mm
6.4 mm/0.76 mm
65, 100, 112, 198, 224, 525 (452ª), 626 mm
2.4 mm ø
305 mm
3.2 mm ø
305 mm
4.8 mm ø(235ª) mm
5.0 mm ø
305 mm
5.3 mm ø
200 mm
2.0 mm/0.2 mm
301 mm
4.8 mm/0.4 mm
305 mm
5.6 mm/0.4 mm
305 mm
6.4 mm/0.4 mm
305 mm
1.6 mm ø
200 mm2.36 mm ø
51, 149, 298 and 913 mm

Tube: Outer diameter/wall thickness*: pointed endª: angled end face19

Results and Discussion
1. Aluminum waveguides
Received waveforms from six waveguides (9.6 to 1.6 mm) are shown in Fig. 7. These arenominally 305 mm long. The time scale was chosen to start before the first arrival (50 µs) and to
end at 170 µs before the first reflection at about 180 µs. The initial peak corresponds to L(0,1)mode arrival. It had the maximum height for rods of 4.8 mm or less arriving after traveling at ~5mm/µs and decreased as the diameter increased, as shown in Fig. 8 a). At larger diameters, themaximum height was found corresponding to L(0,3)-mode arrival with~3 mm/µs velocity. The
peak height of the L(0,3)-arrival increased with diameter (see Fig. 8 a). The relative intensity ratio of these two modes was calculated by summing the rms-voltages over 50-70 µs and 85-110µs as follows:
L(0.3) intensity/L(0,1) intensity =

&&'
() (

𝑉#$ )/*')'(

𝑉#$ ).

This ratio increased with the rod diameter, as shown in Fig. 8 b). Changes are large, going from
½ to 2 as the diameter increased from 1.6 mm to 9.6 mm.
The signals are extended to longer than 100 µs duration in a sharp contrast to the 1-µs main
peak duration of the face-to-face condition. The trailing parts beyond the L(0,1)-arrival also havehigher frequency contents. Corresponding Choi-Williams spectrograms are shown in Fig. 9 forthe diameters of 3.2 and 1.6 mm. The time scale is shifted 50 µs and both show the arrival ofL(0,1)-rod-wave mode at ~8 µs. Wave velocity scale is given below the time scale. For the 3.2mm waveguide, high intensity part extends from 50 to 500 kHz and the wave velocity decreases
to 2.5 mm/µs at 850 kHz. For the 1.6-mm waveguide, this goes from 80 to 650 kHz and the velocity is nearly unchanged. For these two waveguides, the diameter ratio is two, so the same velocity of 3.75 mm/µs is found at 700 and 1400 kHz, respectively. The high intensity part thus has
low dispersion. This partly explains why the frequency of the peak intensity shifted down fromthe face-to-face condition (the peak appears at 450 kHz in Fig. 3). For the 4.8-mm waveguide(Fig. 10), strong L(0,1) zone shrinks to below 300 kHz, while the trailing part has low level indications to 1000 kHz at velocities slower than 3.2 mm/µs. This part is barely visible here as wellas in Fig. 9 a), and propagation modes are obscure.
The above observations show that the thin waveguides give good transmission of L(0,1)
mode with low dispersion at lower frequencies. When the diameter is reduced, the low dispersion
zone extends to higher frequencies. Thus, very thin rods provide wide-band transmission, while
moderately thin rods give band-limited transmission with low dispersion.

20

Fig. 7 a-c) Received waveforms from thin waveguides of 9.6 to 4.8 mm diameter (305 mm
length). KRN transmitter and receiver.
21

Fig. 7 d-e) Received waveforms from thin waveguides of 3.2 to 1.6 mm diameter (305 mm
length). KRN transmitter and receiver.
22

Fig. 8 a) Peak height of the initial arriving L(0,1)-mode signal (blue curve) and that of late arriving L(0,3)-mode signal (red curve). b) Ratio of L(0,3) and L(0,1) intensity vs. diameter.

23

Fig. 9 CWT spectrograms for Al waveguides of a) 3.2- and b) 1.6-mm diameter. Time scaleis shifted 50 µs. Group velocity values are given by vertical marks.
24

Fig. 10 CWT spectrogram for Al waveguide of 4.8-mm diameter. Time scale is shifted 50 µs.
Group velocity values are given by vertical marks.
When the rod diameter reaches or exceeds 6.4 mm, higher rod-wave modes become visible,
as shown in Fig. 11. Here, the dispersion curves are shown on the left with the same frequency
scale, but the horizontal scales are different. L(0,1) mode is visible at low frequencies as shown
above and through its velocity minimum at 2.1 mm/µs. A new feature is a strong L(0,3) peak appearing at 3.1 mm/µs in the frequency range of 800 to 1100 kHz. Also visible are weak indications of L(0,2) and L(0,4). These identifications owe to a comparison to the rotated dispersion
curves shown on the left. These features are retained as the diameter is enlarged to 12.7 mm,
while keeping the length at nominally 305 mm (figure omitted). When the length is doubled, as
shown in Fig. 12, L(0,4) mode becomes stronger and L(0,5) is clearly noticeable. For the 6.4-mm
rod, shorter waveguides down to 66 mm showed the same features. However, in 9.6-mm or 12.7mm diameter rods, waveguides shorter than 150 mm produce much weaker L(0,1)-mode zone, as
shown in Fig. 13. Here, Fig. 13 a) is for 12.7-mm diameter rod of 152 mm and the main peak isL(0,3) mode at 400-600 kHz. For the 9.6-mm rod (Fig. 13 b), L(0,3) position was at 550-850
kHz, proportionately higher. The weakening of L(0,1) is understandable as the wavelength at 100
kHz is 50 mm and the rod length is only three times the wavelength. Rod waves are constructedby multiple interactions with the rod surfaces and a minimum length does exist at larger diameters. Figure 14 indicates corresponding signal waveform of Fig. 13 b) and the peak amplitude
occurs at the time of L(0,3) peak. The waveform was omitted in Fig. 10, but the same trend wasobserved. The presence of multiple wave modes contributes to the lengthening of the transmitted
signal as the maximum peak corresponds to the time of L(0,3) arrival. The waveform of 305-mm
long waveguide of the same 9.6-mm diameter was shown earlier in Fig. 7 a). While L(0,1) peakheight remained nearly the same, L(0,3) peak was about double at the half length.

25

Fig. 11 Dispersion curves for 6.4-mm waveguide (left) and observed CWT spectrogram (right).
Time scale is unshifted and group velocity values are given by vertical marks, covering 5 to 2
mm/µs.

Fig. 12 Observed CWT spectrogram for 12.7-mm Al waveguide (611 mm length). Time scale
was shifted by 98 µs for CWT. Original time scale and group velocity values are given by vertical marks, covering 5 to 2 mm/µs.
Thicker waveguides develop higher rod-wave modes, L(0,3) in particular. This mode requires less propagation distance to fully develop than the low-frequency L(0,1) mode, whichneeds more than three-times the wavelength for rods of 9.6 mm or larger diameter. The transmission of L(0,3) mode can be utilized as a narrow-band mechanical filter and may be combinedwith a resonating sensor design. For the range of diameter used here (6.4 – 12.7 mm), the26frequency range of 400 to 1100 kHz can be achieved. For 150-mm long rods, these ranges are:
6.4 mm, 800-1100 kHz; 9.6 mm, 500-750 kHz; 12.7 mm, 400-600 kHz.

Fig. 13 Observed spectrograms for 150-mm long Al waveguides of a) 12.7 and b) 9.6-mm diameter. Group velocity values are given by vertical marks, covering 6 to 2 mm/µs.

Fig. 14 Signal waveform of 9.6-mm waveguide, 152-mm length (Fig. 13 b) The peak amplitude corresponds to L(0,3) peak.
When hollow cylinders or tubes are used as waveguides, the transmission characteristics become different from those of solid rods, as expected from the dispersion curves shown earlier(Fig. 6). Figure 15 shows an example with an Al tube (9.5 mm OD, 7.5 mm ID, 290 mm length:
D/t = 9.5). The dispersion curves for D/t = 8 is shown as Fig. 15 a) for L(0,1) and L(0,2) modes.
(The cut-off frequency for L(0,3) mode is above 1.5 MHz). Results given in Fig. 15 b) matchwith the dispersion curves well, with the peak L(0,2) activity at 350-800 kHz and the strongest
L(0,1) peak occurring at <100 kHz. The latter frequency is lower than the calculated values of120-150 kHz. Extra peaks are also present between the two modes at 200 kHz with 4.3 to 4.827mm/µs velocity. However, this frequency is at the minimum velocity for L(0,1) mode, where
FFT magnitude also shows a dip as will be discussed later. Thus, these peaks appear to be spurious although their origin is unknown. Wave intensity is reduced for slow moving waves: by 17
dB at >3 mm/µs. That is, signal lengthening is limited to <50 µs, a factor of 2 to 3 shorter thanrod waveguides of similar diameters.
In comparison to rod waveguide behavior (see for example, Figs. 11 and 12), the slow
L(0,3)-rod wave is gone. Instead, L(0,2)-tube wave behaves like the L(0,1)-wave for thin rods
with low dispersion. These are also beneficial for waveguide applications.

Fig. 15 a) The dispersion curves of tube waves for D/t = 8 with t = 1.18 mm. b) Observed CWTspectrogram for Al tube (9.5 mm OD, 7.5 mm ID, 290 mm length: D/t = 9.5).
Although ten other tubes of various sizes were examined from a previous work [3], no clear
patterns emerged. L(0,1)-mode was always present and strongest, while L(0,2)-mode was absent
in some conditions. Even when 12.7-mm diameter tubes are used, we did see L(0,1) at low frequency in contrast to solid rod cases where only L(0,3)-rod waves were found. When a low frequency waveguide of large diameter is needed, a tube provides a solution. When L(0,2) is absent,
effects of the spurious peaks are significant and further study is needed to clarify their origin.
2. Steel Waveguides
The rod wave velocities of steel and aluminum are comparable; 5.06 vs. 5.18 mm/µs [5, p.
78]. However, Poisson’s ratio is smaller for iron and steel (0.29) than in aluminum (0.34) andlateral stiffness is higher in steel. This is expected to affect how rod waves develop in steel, specifically, in the transition from the L(0,1)-dominated thin-rod behavior to multi-mode thick-rod
behavior. Dispersion curves for steel of 2.7-mm diameter are given in Figure 16 [15]. Figure 17
gives the CWT spectrogram for 6.4-mm diameter steel rod of 383-mm length, showing the thinrod behavior with mostly L(0,1) mode and weak L(0,3). This L(0,3) essentially disappeared at
496-mm length. In an aluminum waveguide of the same diameter, the L(0,3) mode was alreadywell developed, as was shown in Fig. 11.

28

Fig. 16 Dispersion curves for steel of 2.7-mm diameter from Mijarez (2014).
It is interesting to note that this diameter - length combination is commonly found in industrial applications. Two commercially available waveguides from PAC and AECI share these dimensions – ¼” x 12” in the traditional US unit. From the present results, these waveguides work
best in the range up to 250 kHz, making them suitable for the often used frequency of 150 kHz.
In the previous study [3], we did conclude that this diameter is appropriate for industrial waveguide applications, although we only evaluated aluminum.
When a higher frequency detection is required, thinner waveguides offer the solution. At 4.8mm diameter, one obtains L(0,1) response to 400 kHz and to 550 kHz using 3.2-mm diameter(similar to Fig. 9 a) for 3.2-mm Al). Using 1.6-mm steel rod, the response is comparable to Alcase of the same diameter (Fig. 9 b), except the signal strength drops off slightly faster than in Al.
In this case, low dispersion response to 600 kHz is possible.
When the length was halved to 150 mm for the 6.4-mm rod, the L(0,1) mode failed to develop fully at frequency below 200 kHz (Fig. 18). This is a significant drawback in practical application since oft-used 150-kHz AE sensor’s peak sensitivity is missed. The limitation posed by
the use of a certain length needs to be explored when the diameter approaches the transition tomulti-mode behavior.
When one examines these two spectrograms (Figs. 17 and 18), it is obvious that L(0,1) modein the short waveguide extends to higher frequency (600 kHz compared to 400 kHz) at slow
speed limits observed. This appears to indicate that the calculated dispersion curves, such as theone shown in Fig. 5, are not applicable to short length rods. This is a reflection of inadequate developments of various wave modes. This part requires verification through future FEM calculations.
29Fig. 17 Observed CWT spectrogram for steel waveguide of 6.4-mm diameter (383 mm length).
Time shift = 70 µs.

Fig. 18 Observed CWT spectrogram for a short steel waveguide of 6.4-mm diameter (150 mm
length). Time shift = 25 µs.
Steel waveguides exhibit another behavior during the transition. Multi-mode behavior isfound once the diameter reaches ~8 mm. For 7.8-mm diameter, L(0,3) mode is dominant from ashort rod (59 mm) to a long one (648 mm). At 59 mm length, modes are not as clearly separated,but showing both L(0,1) and L(0,3) as well as possibly L(0,2) spots (Fig. 19). Figure 20 shows
the nearly single L(0,3)-mode behavior that evolved in longer waveguides. This observation
points to the needs of considering the waveguide design from both the diameter, which is ofcourse the main parameter, and also the length. Since the partition of energy transmission into
various modes cannot be theoretically predicted at present, we need to evaluate individual waveguide design to ascertain its performance.
30

Fig. 19 Observed waveform and CWT spectrogram for a short steel waveguide of 7.8-mm diameter (59 mm length). The reflected waves appear starting at 34 µs.

Fig. 20 Observed waveform and CWT spectrogram for a long steel waveguide of 7.8-mm diameter (648 mm length).
31

Fig. 21 a) The dispersion curves of tube waves for D/t = 4 with t = 1.6 mm. b) Observed CWTspectrogram for a steel tube (6.4 mm OD, 3.3 mm ID, 394 mm length: D/t = 3.7). Group velocity
values are given by vertical marks, covering 5 to 3.7 mm/µs.
In testing tube waveguides of steel, four tube sizes were available with the outer diameter of
4.8 and 6.4 mm and D/t ratio of 3.7 to 8. These are of austenitic stainless steel, presumably of
304 type. Main response was L(0.1)-and L(0,3), similar to the case for Al tube (Fig. 15). For along heavy wall 6.4-mm tube (D/t = 3.7; 394 mm length), Fig. 21 shows the CWT spectrogram
together with the calculated dispersion curves. L(0,2) velocity agrees well with the calculated
values, while L(0,1)-mode shows lower frequency at a given velocity. The highest received intensity corresponds to L(0,2) mode here with D/t = 4, while it was L(0,1) mode in the case of Al
with D/t = 8 (cf. Fig. 15). Note that spurious peaks are again seen at 300-500 kHz over 8-10 µs in
this shifted time scale.
When the tube diameter is reduced to 4.8 mm with D/t = 6.6, the dispersion of L(0,2) mode is
reduced as shown in Fig. 22 a). The position of L(0,1) peak is similar to Fig. 21, but its intensity
is stronger. The middle spectrogram (Fig. 22 b) is for the same diameter of 6.4 mm, but with athinner wall or D/t = 8 and at less than a half-length. Here, L(0,1) is absent and only the L(0,2)
mode is found from 400 to 1100 kHz. This is a beneficial effect of a shorter length, which is below the minimum length needed for developing the L(0,1) mode. In Fig. 22 c), a similar result is
exhibited with another short waveguide of 6.4 mm: D/t = 3.7 (the same tube for Fig. 21) and thelength of 90 mm. This effect was also observed in Al cases.

32

Fig. 22 Observed CWT spectrograms for steel tubes. a) tube diameter = 4.8 mm; D/t = 6.6 (377mm length). b) tube diameter = 6.4 mm; D/t = 8 (168 mm length). c) short (90 mm) waveguideof 6.4 mm: D/t = 3.7. Time shift is marked in the figure.
From these cases, we find that tube waveguides are advantageous over rods of smaller diameter in rejecting low frequency components. It appears that a large D/t ratio enhances L(0,2)mode, as was found in Al tube waveguides. The same high-pass filtering effect can be obtainedby using a shorter waveguide as L(0,1) mode requires a certain minimum length to develop. This
length appears to be about 5-times the wavelength.
3. Copper Alloys
For this group, copper rods (of three different lengths), two copper tubes, brass rods (of fivedifferent diameters), brass tubes (of four different diameters) and two copper brazing alloy rods
were used. All the rods are thin, the maximum diameter being 5.3 mm and their wave propagation behavior mostly resembled those of thin Al rods. Poisson’s ratios of aluminum, copper, andbrass (30Zn-70Cu) are in the range of 0.32 to 0.36 and the lateral stiffness is comparable to eachother. The thin-rod behavior is observed for 305-mm length up to 4 mm in diameter and for 5mm-diameter brass rod of 200-mm length, but the multi-mode behavior is found for 5-mm brassrod of 300-mm length.
The copper brazing alloy rods tested were identified by their mass density as CP-3 alloy with
90Cu-2Ag-7P composition. Diameters are 1.6 and 2.4 mm. Thinner 1.6-mm rod is of 200-mm
length, while thicker 2.4-mm rod samples have lengths of 51, 149, 298 and 913 mm. All of themshowed the thin rod behavior of single L(0,1) mode. Two examples are given in Fig. 23.
Of the two, the top graph approximates the condition used by Hamstad and Zelenyak [12, 13],
who used 1.6-mm diameter Cu wire. The observed CWT spectrogram shows that L(0,1) mode
has a nearly constant velocity to 450 kHz, the primary frequency range of their A0-mode platewave. For this range, this wire size gives good reproduction of input waveforms. At higher frequencies, the wave velocity and transmitted amplitude decrease, as was observed in Hamstad’s
experiment. For 2.4-mm wire case, the flat response zone is reduced to less than 250 kHz and34

waveform fidelity is reduced substantially. The high frequency velocity slow down and attenuation also occur, commencing at 200 kHz.

Fig. 23 Observed CWT spectrograms for wires of a copper brazing alloy (type CP-3). a) 1.6 mm
diameter. 200 mm length. b) 2.36 mm diameter. 913 mm length. Group velocity values are given
by vertical marks, covering 4 to 2 mm/µs.
In copper tube testing, common 6.4-mm diameter copper refrigeration tubes were evaluatedas waveguides. The tube is available in wall thickness of 0.5 or 0.76 mm, giving D/t of 12.5 or
8.35. Basic features are identical to those of Al and steel, but Fig. 24 a) shows an example with alarge length (627 mm). This tube’s D/t was 8.35. Because of the length, both modes developed
fully. However, the spurious peaks showed up, centering at 250 kHz, corresponding to a dip in
FFT magnitude over 170-270 kHz. This large length does decrease the peak in waveform by 10
dB in comparison to shorter waveguides of 80 to 150 mm length.
34

A set of brass tubes are also examined. These tubes have the diameter of 4.8 to 6.4 mm andthe wall thickness of 0.4 mm, with D/t of 12 to 16. A representative CWT spectrogram is given
in Fig. 24 b). At higher D/t ratios, L(0,1) is reduced compared to the common 6.4-mm Cu tube.
Due to high D/t, this 4.8-mm tube is useful for 500-1300 kHz range with low dispersion. With alarger diameter, the frequency range moves lower; to 400 to 1100 kHz for 6.4 mm diameter (D/t
= 16). Thus, we again observe with a tube waveguide the low dispersion L(0,2)-propagation
mode and the suppression of low frequency L(0,1) mode, giving a high-pass filtering effect.

Fig. 24 CWT spectrograms of copper and brass tube waveguides. a) copper, 6.4 mm diameter,
D/t = 8.35, 626 mm length. Time shift = 150 µs. b) brass, 4.8 mm diameter, D/t = 12, 305 mm
length. Time shift = 75 µs.
4. Threaded waveguides
Next series of tests are included in order to evaluate effects of surface condition on the propagation of rod waves. Readily available surface modification is the use of threaded rods. The firstexample is shown in Fig. 25. This is an Al rod with ¼”-20 thread (6.2 mm diameter-1.27 mm
pitch thread) of 305 mm length. Corresponding smooth rod CWT spectrogram is given in Fig. 11(right graph), showing multi-mode transmission with strong L(0,1) and L(0,3) intensity. In contrast, Fig. 25 shows strong L(0,1) mode and much weaker L(0,3) mode in the threaded rod. Thus,
this waveguide behaves like a non-threaded rod of smaller diameter. In addition, the velocity ofthe observed L(0,1) mode is approximately 10% slower than that of non-threaded rod: 4.77mm/µs vs. 5.28 mm/µs. A much slower (~2.5 mm/µs) peak is present at the intersection ofL(0,1), F(1,2) and F(1,3). This was also found in Fig. 11, but their origin is unclear.
In contrast, Fig. 25 shows strong L(0,1) mode and much weaker L(0,3) mode in the threadedrod. Thus, this waveguide behaves like a non-threaded rod of smaller diameter. In addition, thevelocity of the observed L(0,1) mode is approximately 10% slower than that of non-threadedrod: 4.77 mm/µs vs. 5.28 mm/µs. A much slower (~2.5 mm/µs) peak is present at the intersection of L(0,1), F(1,2) and F(1,3). This was also found in Fig. 11, but their origin is unclear.
35

Figures 26 and 27 shows a comparable pair of stainless steel. Diameter is again 6.35 mm forsmooth rod and the thread is ¼”-20 with the length of 301 mm (6.2-mm diameter). Fig. 26 shows
the result of the non-threaded rod, indicating the presence of both L(0,1) and L(0,3) modes. Figure 26 is the threaded counterpart. L(0,3) mode was not observed as was the case for Al (cf. Fig.
25). Again, threading of rod suppressed the higher rod-wave mode.
When the rod diameter is increased to 12.7 mm using steel rod (length = 306 mm), thethreading effect was different from the 6.35-mm examples of Figs. 25 and 27. Figure 28 shows
the non-threaded case while Fig. 29 plots the threaded one. Thread is ½”-13 (12.7-mm nominal
diameter with 1.96 mm pitch). Figure 28 shows a strong L(0,3) mode, but weak L(0,1) andL(0,4) are also present. With threading, L(0,3) remains the strongest, but L(0,1) becomes stronger second mode. That is, weakened L(0,3) and higher modes contributed to make L(0,1) more
prominent.
A similar trend is found in five other sets of threaded and non-threaded rods, indicating thatthreaded rods enhance L(0,1) mode at the expense of higher L-modes. In most cases, threading
reduced the L(0,1) mode velocity by about 10%. In one case of brass, the velocity was nearly
unchanged. It is possible that compositions of the alloys are not identical because many brasscompositions are used in commercial applications, and the samples came from different sources.
These observations imply that the primary benefit of threaded rods as waveguides is to promote the lowest L mode while depressing higher modes. This may arise from reduced surface
reflectivity, but is also due to smaller effective diameter of the threaded rods.

Fig. 25. CWT spectrogram of a ¼”-20 threaded Al waveguide of 6.2 mm diameter, 305 mm
length. See Fig. 11 for the corresponding rod waveguide of 6.4 mm diameter.

36

Fig. 26. CWT spectrogram of a 6.4-mm diameter stainless steel waveguide, 305 mm length.
Compare to Fig. 27.

Fig. 27. CWT spectrogram of a ¼”-20 threaded stainless steel waveguide of 6.2 mm diameter,
305 mm length. See Fig. 26 for the corresponding rod waveguide.

Fig. 28. CWT spectrogram of a 12.7-mm diameter steel waveguide, 306 mm length. Compare to
Fig. 29.
37

Fig. 29 CWT spectrogram of a ½”-13 threaded steel waveguide, 306 mm length. See Fig. 28 forthe corresponding rod waveguide.
5. Insertion loss
Three effects reduce the net sensitivity using a waveguide. One is an extra boundary introduced. When best coupling is achieved, the loss is expected to be 1 dB or less below 500 kHz,but the loss can easily be several dB higher [17]. Another effect comes from the mismatch of
acoustic impedance among test piece, waveguide and sensor face. When materials of the waveguide and tested structure are similar, the insertion loss can be ignored. But when an Al waveguide is used between steel structure and a sensor with alumina face plate, it is ~2 dB. Thus, these two loss sources present no major issue when proper sensor mounting practice is used.
The third factor is the diameter of waveguides. From past experience and present results, it isclear that thinner waveguides are conducive to less dispersive transmission of L(0,1)-rod wave.
However, the reduction of waveguide diameter also decreases the wave energy transmitted andreceived. On the source side, the reduction of waveguide size can be useful in preventing waveform averaging effects that cancel parts of non-uniform surface vibration [18]. However, the reduced sensing area on the receiving sensor proportionally decreases its sensitivity relative to thesensitivity from the total sensing area [15]. For example, when a 1.6-mm diameter waveguide isused with a 12.7-mm diameter sensor, the effective sensor sensitivity is reduced by 18 dB (or(12.7/1.6)2 = 67 times) compared to the case of the same sensor receiving plane-wave input on its
entire sensing area.
The size effect is best examined through the use of FFT magnitude spectra of signals that
propagated through a waveguide. The received signals are cut off before the first reflection
reaches the sensor. Thus, signal length was different depending on the waveguide length. These
spectra are naturally reduced from the face-to-face spectrum of the same transducer pair used.
An example of such comparison is given in Fig. 30. Here, Al rods of 10, 8, 6.4 and 3.2 mm diameter, nominally 305 mm length, are used as waveguides. The face-to-face response is the top
curve (purple: V103 to NDT). The next curve (blue dash) is the face-to-face response minus 4.1
dB, corresponding to area reduction to 10-mm diameter. This agrees to the blue curve for 10-mm
waveguide (blue curve) below 200 kHz, but the waveguide response is 3 to 6 dB lower (more atmany dip points). For the three other curves, the area corrections needed are 8.0, 11.9 and 23.9
dB. As the diameter becomes smaller, alignment becomes more difficult and the difference between the value based on the face-to-face spectrum and correction factor and the observed increased. Still, the differences were 6-8 dB even for the thinnest, 3.2-mm waveguide below 800
38

kHz. The main source of the insertion loss (excluding the area correction) is the lack of good
coupling due to poor alignment.
Another prominent feature of these waveguide spectra is the presence of many dips, especiallyabove the frequency corresponding to the velocity minimum of L(0,1)-mode wave. For thesefour curves, the values are 330, 490, 600 and 1200 kHz. These rod spectra are relatively smooth
below the L(0,1) dip. Other large dips correspond to velocity minima of higher propagation
modes. However, many other dips have no clear origins.

Fig. 30 FFT magnitude spectra of signals propagated through Al waveguide of 10 (blue), 8 (red),
6.4 (green) and 3.2 (light blue) mm diameter, 305 mm length. Face-to-face response of V103NDT transducer pair is given by purple curve (top). Its area corrected response (minus 4.1 dB) is
shown by blue dashed curve.
The second example is a comparison between rod and tube waveguides, shown in Fig. 31.
Material is Al, with the same diameter of 8 mm. The red curve (same as the red one in Fig. 30) is
for a rod (8 mm ø x 305 mm), while the blue curve is for a tube (7.9-mm OD x 6 mm ID x 290
mm length). The red and blue dash curves are area-corrected ideal spectra from the face-to-facedata (purple curve). As before, 3 – 6 dB differences are found for the rod, while the tube data differ more above 200 kHz. A large dip exists centering at 240 kHz. This corresponds to the dip invelocity for L(0,1) and the cut-off frequency of L(0,2) mode. Above this dip, the tube data ismuch more smooth to 2 MHz. The flat spectrum zone from 300 to 900 kHz for the tube corresponds to L(0,2) mode, but modes at higher frequencies are unknown at present. Still, this tube
spectrum is smoother than the rod spectrum at the same frequency.
The third example is 3.2-mm steel waveguides of differing length, 71, 165 and 238 mm. The
face-to-face and area corrected spectra are in purple in Fig. 32, while steel data are in blue (short),red (medium) and green (long). The rod data are flat to 1 MHz, but differences are 10-20 dB.
Length effects are not obvious as coupling is more critical. This frequency range corresponds toL(0,1) mode.
39

Fig. 31 FFT magnitude spectra of signals propagated through Al waveguide of 8 mm diameter,
305 mm length (red) and a tube of 7.9-mm OD, 6-mm ID, 290 mm length (blue). Face-to-faceresponse of V103-NDT transducer pair is given by purpl curve (top). Its area corrected responses(minus 8.0 and 11.9 dB) are shown by red and blue dashed curves.

Fig. 32 Output spectra from 3.2-mm steel waveguides of length, 71 (blue), 165 (red) and 238(green) mm. Face-to-face response of V103-NDT transducer pair is given by purple curve (top).
Its area corrected response (minus 19.5 dB) is shown by purple dashed curve.
40

Fig. 33 FFT magnitude spectra of signals propagated through steel waveguide of 6.4 mm diameter, 382 mm length (red) and a tube of 6.4-mm OD, 3.5-mm ID, 393 mm length (blue). Face-toface response of V103-NDT transducer pair is given by purple curve (top). Its area corrected responses (minus 11.9 and 14.4 dB) are shown by red dash and blue dash curves.
The last example is a comparison between steel rod and tube of 6.4 mm diameter. Figure 33
shows the face-to-face and area-corrected spectra as top three curves. The rod (6.4 mm ø x 383mm) is in blue, showing a dip due to the L(0,1) velocity cusp at 0.5 MHz. This is 5 to 8 dB below the blue dash curve of the predicted spectrum. The dotted red curve is for the predicted spectrum for 6.4-mm tube (3.5 mm ID x 393 mm). The observed spectrum is shown in red. These
two match well below 300 kHz before reaching a dip due to L(0,1) dip/L(0,2) cut-off frequency
over 286-404 kHz). Beyond the dip, a differences of 3-6 dB are observed to 2 MHz. This zone
has a smooth spectrum.
These observations are also found in several other sets of rods, tubes, and their mix of different length. Results are comparable over all. However, a few pairs of transducers produced more
attenuation in rods or tubes. The reason for this discrepancy is unknown at present.
6. Reverberation
Reverberation (or multiple reflections) is another important feature of waveguides that has tobe considered in their design. This effect has been recognized from the beginning of their usage.
Earlier waveguide studies [2, 10, 11] observed unwanted signal stretching, sometimes exceeding
10 ms. Sikorska and Pan [10, 11] examined angled face up to 60˚, but only 5 to 8 dB attenuation
was achieved. Ono and Cho [3] evaluated a pointed-end waveguide and found improved pulse
definition, but the suppression of multiple reflections was not addressed.
The first usual step one takes to suppress unwanted reflections is applying damping medium
on such waveguides. Examples are shown in Fig. 34 (using V103 transmitter, NDT receiver).
The top figure (Fig. 34 a) is the output signal from a brass rod (5.3 mm ø x 200 mm length),41

lasting 0.5 ms to 20 dB down. Figure 34 b) shows the same rod, but its 60-mm length in the middle covered with clay (plumber’s putty, a mixture of natural clay and linseed oil). The signal
length is halved to 0.15 ms, while the peak amplitude decreased 2 dB. In both cases, reflections
are buried in the slow-moving signals. The third graph is for a Cu tube waveguide (6.4-mm diameter, 0.76 mm wall thickness, 224 mm length), giving signal length of 0.12 ms. With clay applied in the middle, Fig. 34 d) results, with the same signal length but amplitude reduced beyond
0.04 ms. For these tube waveguides, the initial decay is large and reflections can be easily seen.
Again, clay damped slow-moving waves more than the initial pulse, which only decreased by 1
dB.
These signals can be represented by an exponential decay equation of
V = Vo exp[–a(t – to)],where Vo is the initial peak, a decay constant and to signal arrival time. For these four signals, we
obtain decay constant a of 3.2, 7.6, 5.9 and 7.3 in the unit of ms–1. These show that tube is more
effective in reducing higher mode waves, and obtain better damping effects from surface absorber like clay. As noted before, L(0,3)-rod wave is strong in Fig. 34 a) while L(0,1)-tube wave isdominant in Fig. 34 c).
The increased decay constants resulted from the reduction in slow moving L(0,1) and L(0,3)
modes. The CWT spectrogram of the rod (Fig. 34 a) is similar to Figs. 17 and 26 for 6.35-mm
steel rods, while the rod with clay damper resembles threaded steel rod (Fig. 27). The spectrograms for tubes with or without clay are similar to Fig. 24 a), except the velocity slow-down at
high-frequencies is absent. Their signal spectra are given in Fig. 35, with the red curves showing
the effects of clay damping. The top curves (Fig. 35 a) are for the rod cases, indicating essentially no amplitude loss from clay below 200 kHz but the loss increasing above 300 kHz. The higher
frequency parts are barely present in the CWT spectrograms as they are spread over time. Figure35 b) is for the tube cases. Here, amplitude loss appears only above 1 MHz, reflecting a small
reduction in the peak amplitude (see Fig. 34 c and d). Slower segment of L(0,1) mode is reduced
by clay damper, but this is not shown in the FFT spectra. Again, CWT spectrograms show thehigh frequency parts only at the first arrival. Thus, in these two cases, the decay constants are thebest indicator of damping effects.
Twelve more cases were tested using the same set-up. Table 2 summarizes the values of decay constants, a. The third column lists the results from tests that used two conical transducers(KRN PCP).

42

Fig. 34 Effects of damping. a) Signal waveform for 5.3-mm diameter brass rod (200-mm long),b) Same as a) but with clay covering over 60 mm. c) Signal waveform for 6.4-mm diameter Cu
tube (0.76-mm wall thickness, 224-mm long), d) Same as c) but with same clay covering. V103NDT transducer pair used.
43

Fig. 35 a) FFT magnitude spectra of signals propagated through brass waveguide of 5.3 mm diameter, 200 mm length: blue, no damping, red, with clay damping. b) Same for Cu tube waveguide, 6.4 mm diameter, D/t = 8.4, 224 mm length: blue, no damping, red, with clay damping.
V103-NDT transducer pair used.
Table 2 Decay constants (unit = ms–1)
Material
Sizes
Al rod 1.6 mm ø x 305 mm
3.2 mm ø x 305 mm
6.4 mm ø x 305 mm
Steel 2.38 mm ø x 305 mm
4.0 mm ø x 305 mm
6.4 mm ø x 305 mm
4-40 thread x 305 mm
6-32 thread x 307 mm¼”-20 thread x 383 mm
tube 4.8 x 0.73 mm, 377 mm
Brass 3.2 mm ø x 305 mm
Cu
tube 6.4 x 0.5 mm, 375 mm

V103-NDT
6.1
5.7
5.3
5.3
4.0
2.9
6.0
5.3
2.2
4.0
3.6
2.3

KRN-KRN
2.3
1.1
0.4
0.6
-0.2
1.0
0.7
-0.3
--

The above results show the following trends. Decay constants increase:a) By increasing rod diameter,b) Using threaded rod over smooth rod of the same diameter,c) Using V103-NDT transducer set over KRN transducer set.
44

For trend a), slow-moving wave modes become more dominant with increased rod diameter.
For trend b), threaded rods provide less efficient surface reflections, producing dragging effects
on rod wave propagation. For trend c), the conical transducers have small contact area and lessenergy transfer occurs into them, thus reducing damping effects.
Effects of non-parallel end faces were examined using rods and tubes. With the face anglesof 15 to 30˚, 4 to 6 dB reduction of reflection was obtained, confirming the findings of Sikorska
and Pan [11].
Another variation of waveguide design is the use of a pointed end. One of the steel rods tested (3.2-mm diameter, 238-mm length) was converted to a pointed end, reducing the diameter by
12.5 times (or –22 dB). Results were opposite of anticipated; i.e., the decay constant was reduced
from 8.0 for a straight rod to 2.6 for the rod with a pointed end of 0.9-mm diameter. Signal duration was also lengthened by 2.5 times in the pointed end waveguide. By reducing the contact area,
the energy loss was decreased, increasing reverberation of the rod with a pointed end. The initialidea was to suppress the reflections by reducing the area normal to the wave propagation. However, the size of local shape change is small relative to the wavelength and desired damping ofreflections was not realized. Thus, the benefit of a pointed tip waveguide comes from increased
contact stress, while the reduced contact area decreases the signal intensity and stretches out thesignal duration. Its use should be evaluated carefully.
7. Summary and practical suggestions
The spectral characteristics of rod and tube waveguides are summarized below.
1) In small diameter rods, L(0,1) mode extends to relatively high frequency with low dispersion. This gives good waveform reproduction, but thin rods lack bending stiffness and reduce the signal intensity proportionately.
2) With increasing diameter, L(0,1) mode becomes limited to low frequency region andhigher rod-wave modes, especially L(0,3) mode, become dominant. This contributes toundesirable signal stretching.
3) Tube waveguides possess a low dispersion behavior due to L(0,2) mode, along with low
frequency L(0,1) mode. Larger diameter-to-thickness ratio is preferred.
4) Threaded rods promote lower rod modes, delaying a shift to multi-mode behavior as diameter increases.
5) Low frequency (large wavelength) modes need a minimum distance for their development, being absent in shorter waveguides.
6) Major effect of sensitivity reduction comes from the waveguide cross-sectional area. In
comparison to the face-to-face spectrum of the sensor pair used, the spectra of waveguides are generally lower than the predicted spectra obtained with cross-sectional area
correction. Except at dips, the decrease is typically 3-8 dB, expected from poor coupling.
The latter can be reduced by better mechanical design/finish.
7) For the rod spectra, many dips are present, starting with the dip corresponding to theL(0,1) velocity minimum. Major dips also originate from higher mode velocity minima orat their cut-off frequencies.
8) For the tube spectra, the first dip is at the L(0,1) velocity minimum/L(0,2) cut-off frequency. Spectrum on either side of the dip is smooth unlike the rod spectra.
9) Effects of waveguide length appear in the peak value of received signals, but length effect is small in terms of FFT spectra.
4510) Application of damping materials like clay reduces high frequency components, often of
higher wave modes.
It is notable that the tube spectra are smoother than the rod spectra. When we need to use
spectral features in received signals, this behavior is significant. In terms of amplitude, no advantage was found in an earlier study [3], but this spectral smoothness provides a major impetus
for exploring tube waveguides from now on. Also notable is the emergence of low dispersion
L(0,2) mode in thinner, higher D/t waveguides as discovered in the present study. Reduced
transmission of low frequency L(0,1) mode with shorter tubes gives rise to a high-pass filtering
effect, which can be exploited for selective detection of AE signals from fast mechanical events
over frictional sources, for example.

Fig. 36 CWT spectrogram for #4-40 threaded rod of stainless steel, 305 mm length. V103-NDT
transducer pair used.
From the present study, three improved waveguides are proposed for practical uses. The firstis the use of thin threaded rods, such as #4-40 or #6-32 thread stainless steel rods. These or corresponding metric M2.6 or M3 threaded rods are commonly available to 0.9-1 m length. The advantages are the relative ease in designing connecting jigs and their moderate bending stiffness.
For #4-40 threaded rod, CWT spectrogram is given as Fig. 36. A wide frequency range of transmission is achieved from 50 to 1000 kHz. Over 50 to 500 kHz, the wave velocity is within 5%,
giving a low dispersion. This can be easily interfaced to a sensor like PAC HD50, which has astud of the same thread size. Only L(0,1) mode is present.

46

Fig. 37 CWT spectrogram for ¼”-20 threaded rod of stainless steel, 384 mm length. A faintL(0,3) is visible, but L(0,1) is dominant. V103-NDT transducer pair used.

Fig. 38 CWT spectrogram for 1/8”-IPS threaded tube, 543 mm length. a) No damping. b) With
clay damping applied over 12 cm. V103-NDT transducer pair used.
The second design is a modification of commonly used steel-rod waveguide of 6.4-mm diameter. The change suggested is to use a threaded rod (of ¼”-20 thread) in lieu of smooth rod. A
CWT spectrogram for a basic threaded rod was given in Fig. 27. Here, clay damper is applied
over 8 cm section and its thickness is 2-3 mm. This removes slow moving wave modes and increases the decay constant from 2.2 to 4.2. An improved CWT spectrogram is given as Fig. 37.

47

As can be seen, this gives good transmission from 50 to 450 kHz, the most common AE inspection frequencies.
The third waveguide design combines the threading and tubular elements that we have examined. This design is built on the availability of threaded tubing; two sizes are most commonly
available in the US, known as 1/8” and ¼” iron pipe size. Under the ISO system, these are defined in BS EN ISO 228-1: 2003, based on the British and Japanese standards, as G 1/8 or G ¼.
In the US, these are commonly used for electrical lamps. The tube used has 9.73 mm in (major)
diameter, pitch of 0.907 mm, inner diameter of 7.10 mm, 543 mm length, which is the 1/8” size.
The most prominent feature is the mechanical rigidity of this tube design due to its diameter andwall thickness. In addition, a good transmission behavior is obtained. Figure 38 shows two CWT
spectrograms; a) on the left is without clay damping and b) on the right is with 12 cm segment in
the middle covered with clay. The clay covering reduced the wave amplitude by 12 dB. With noclay, the decay constant was 1.9. This value increased to 2.9 with clay damping added. CWTspectrograms clearly show the shortening of the received signals from 80 µs down to 30 µs. The
wave modes are the same, but, in the damped case, signal strength is absent above 600 kHz. The
FFT magnitude spectra are shown in Fig. 39. These again show the intensity dips around 200
kHz. In Fig. 38, this corresponds to the zone between L(0,1) and L(0,2) at 8 – 30 µs, which isapparently spurious indications of unknown origin. The FFT spectra show that this waveguidecan be used for the popular inspection frequency of 150 kHz without damping applied. The
L(0,2) mode can also add a useful range of 300-700 kHz. Even with damping that reduces thehigher frequency transmission, it is useful in low frequency applications below 120 kHz without
signal stretching. With the decay constant of 2.9, reverberation is in the manageable range.

Fig. 39 FFT magnitude spectra of received signals for the threaded tube (cf. Fig. 38). Red curveis without damping, blue curve is with clay damping.

48

Conclusions
AE waveguides are characterized using a pair of broadband ultrasonic transducers. This allows improved characterization of waveguides over 50 to 2000 kHz. Here, we examined responses of circular rod waveguides of different diameters from 1.6 to 12.7 mm with length varying from 60 to 900 mm. These are of solid, hollow or threaded rods made of Al, Fe and Cu alloys.
In thinner solid rods, the low-frequency part of L(0,1)-mode rod wave is dominant and provides
good transmission behavior with low dispersion. This mode is increasingly reduced above 6-mm
diameter. At larger diameters, slower L(0,3)-mode becomes dominant. In tube waveguides,
L(0,2) mode plays an important role of providing nearly constant velocity and spectral flatness,while threaded rods favor a lower L-mode. In tube waveguides with large D/t ratios, low frequency L(0,1) mode is suppressed and high-pass filtered broadband conditions are achieved, especially with a short length waveguide. Three practical waveguide designs are suggested, butmany more can provide improved signal detection under difficult test conditions. Further exploration of various waveguide geometry is recommended to improve AE detection capability innoisy and harsh environments.
Acknowledgment
The author is grateful to Dr. Hideo Cho of Aoyama Gakuin University, Sagamihara, Japan,
for calculating the dispersion curves of rod and tube waves and to the reviewers, who provided
most meticulous critiques and helped to improve the submitted manuscript.
Appendix
Figure A1 illustrates a set-up of wave propagation experiment. This set-up used two KRN
transducers, which were screwed into threaded holes in the mounting jigs. In this case, contactpressure was adjusted by rotating one of the transducers. For larger diameter rods, vertical arrangements were used. For tubes, small metallic discs were used with the KRN transducers.
The pulser used in this study was built in-house and the core part of its circuit diagram is given in Fig. A2. This design was a modified version of a pulser developed by Suzuki for his doctoral work at Aoyama Gakuin University, Sagamihara, Japan. It uses a high-voltage MOSFET(2SK1758) and usually operates at a repetition frequency of 3 Hz. A more recent MOSFET(STFH10N60M2) has a higher peak current of 30 A and performance was slightly improved.
While the peak pulse voltage was essentially unchanged, the rise time (10% to 90%) was reduced
by a factor of two (from 83 ns to 39 ns) with an FC500 connected. An attempt to reduce the risetime by adding an FET driver produced a desired decrease to under 20 ns, but unwanted oscillations occurred when a transducer is connected. Further work is needed to eliminate these spurious oscillations.

49

Fig. A1. Experimental set-up with two KRN transducers. Receiver is terminated with 10 k-ohms.
A pair of FC500 transducers are in front of the pulser used.

Fig. A2. The circuit diagram of a high-voltage pulser used in this study. Normally, H-setting
for the largest capacitor was used. The high-voltage supply can be switched to 380 V for
low-frequency transducers, but this was unneeded in this work.

50References
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Clustering of Fiber-Break Related Events in Carbon Fiber Reinforced
Polymer Composites Using Acoustic Emission
T. Whitlow and M. Sundaresan
North Carolina A&T State University, Mechanical Engineering Dept., Greensboro, NC 24711
Abstract
The objective of this paper is to develop a technique capable to providing timely warning of
the onset of critical damage events that precede the catastrophic failure of composite specimens.
The sequential failure of numerous adjacent fibers is known to trigger the final fracture ofcomposite structural elements. In this paper, the detection of the formation of such criticalclusters of fiber breaks among the millions of damage events that include matrix cracks,
delamination, and individual fiber breaks, is shown to be feasible through cross-correlation of
acoustic emission waveforms. Acoustic emissions released during static loading of [0/90]3S
cross-ply and [+45/90/-45/0]2S quasi-isotropic specimens were examined in detail for a large
number of specimens.
Keywords: Fiber composites, clustering, critical damage events, cross-correlation
Introduction
Damage initiation and progression in carbon fiber reinforced composites have been wellstudied and is known to occur in stages and involves several failure mechanisms (e.g., matrix
cracking, fiber breakage, delamination) [1 – 6]. The presence of certain mechanisms may give an
indication on where along the failure process the material currently is. Each mechanism has aunique physical process in terms of duration and energy release rate which should produce
distinct AE signals. The subtle differences in AE signals have been a focus for distinguishing the
failure modes. Liu et al. [7] and Mechraoui et al. [8] studied the various failure mechanisms
using primarily AE amplitude with each mechanism occupying a distinct amplitude range but
noted that amplitude values will depend on the material system as well as the source to sensor
distance. Yu et al. [9] looked at specific frequency values to monitor failure in carbon reinforced
composite specimens. Bussiba et al. [10] showed that not only do the different failure modes
generate unique frequencies but they also have noticeably different durations. Fiber breaks arevery short duration events and are known to give rise to relatively high frequency content [11].
However, examining a given parameter may not provide sufficient information regarding thesource [12] since the failure modes may occupy common parameter regimes.
The various failure modes have different effects on the integrity of the structure and canpotentially be classified as either critical or non-critical damage. This ultimately depends on the
make-up of the composite which dictates the failure process. Damage initiation and progressionin fiber reinforced composites have been well studied in the past with several techniques utilizing
micromechanics based models to predict the behavior of the material under certain loadconditions. Ogi et al. [13] developed a probabilistic model to predict transverse matrix crack incross-ply panel and compared it to experimental results. Wharmby et al. [14] related thetransverse crack density to changes of the elastic modulus which is more drastic in the absence of
0˚plies. It was noticed that the majority of the stiffness reduction occurred within the first 10% of
J. Acoustic Emission, 34 (2017)52©2017 Acoustic Emission Groupthe normalized life. Acoustic energy can also be a useful parameter to study damage progression.
High energy events signify large crack growth which can be captured and characterized using
acoustic emission techniques. Using cumulative AE energy was shown to agree well with Cscanning and microscopic analysis when monitoring damage accumulation [15]. Such methods
have been shown to sufficiently capture the material behavior as long as the stress state
information can be determined. However, there has not been a technique that focuses on
identifying the onset of critical damage in real time. Even AE techniques, which attempt todistinguish between the different failure modes in real time, do not differentiate between noncritical and critical damage growth. For this study, critical damage is defined as that whichindicates significant damage or directly precedes final failure.
Filtering out inconsequential signals not only allows for greater emphasis to be placed on
important events but also addresses the data management issue associated with AE in
unidirectional composite materials. Unidirectional composites are profuse emitter of acoustic
emissions and can produce tens of thousands of signals during a simple coupon test. This can becounteracted by raising the trigger threshold to decrease the AE hit rate. Consequently, lower
amplitude signals will be missed as a result. Under the assumption that critical damage signals
start with low energy, ignoring lower amplitude signals may limit the ability to detect the early
onset of critical damage growth. Without a filter, all relevant signals across the entire amplitude
spectrum can be analyzed and also stored for later processing if necessary. The volume of AE
data generated during testing may restrict the acquisition parameters used or the test duration.
Ultimately, this process is a balance between determining the minimum amplitude/energy of
relevant signals and staying below the maximum number of signals capable of being analyzed bypost-processing software.
This work utilizes a cross-correlation technique as a filter to discriminate between noncritical and critical damage in carbon fiber-reinforced polymer (CFRP) panels while accounting
for relevant low energy signals. Cross-ply and quasi-isotropic specimens were cut fromlaminates developed at NASA Armstrong Flight Research Center. The layups of the laminates
were designed to resemble those of carbon fiber composites used in current aerospace structures.
Damage evolution as it transitions from insignificant crack growth to meaningful damage wasinvestigated using AE techniques.
Experimental
Each panel was made by stacking unidirectional tape to achieve a desired layup. Two layups
were manufactured: quasi-isotropic [45/0/-45/90]2S and cross-ply [0/90]3S. After each lamina
were positioned and vacuumed bagged, they were heated 1.1˚C/min to 121˚C, held at thattemperature for one hour then cooled at room temperature. ASTM tension test standard D3039
was used to determine the dimensions of each rectangular specimen which was 25 mm x 275 mm
x 3 mm. The sensor to sensor length was approximately 108 mm and piezo-electric wafer sensors(10 mm wide, 20 mm long, 0.5 mm thick) were bonded adhesively on both ends of the gagesection while damped ultrasonic transducers of 6 mm diameter were attached outside the gage tofunction as guard sensors (12 mm from PZT sensors). The frequency range for the PZT sensors
and ultrasonic transducers was 100 to 700 kHz and 100 to 400 kHz, respectively, and noappreciable resonances were seen. Also, even though the guard sensors were more sensitive tothe out-of-plane mode, their overall sensitivity was good enough to eliminate signals outside thegage. This was confirmed using lead breaks. The polarization direction for the wafer sensors was53along the width direction, which increased sensitivity to signals propagation axially in thespecimen. Wideband PAC 2/4/6 preamplifiers were used and the 40 dB setting was typicallyused for both static and fatigue tests. All AE data was recorded on a 4-channel PCI-2 AE system
and a 35 dBAE threshold was used. A sampling rate of 5 MHz was used to measure waveforms of2048 data points with a pre-trigger of 256 µs. Hit definition and hit lockout time were 100 µs and400 µs, respectively. Five specimen from each layup were tested.
The specimens were loaded in tension using a hydraulic testing machine. Quasi-static tension
tests were done at a rate of 14 MPa/min to ensure a sufficient number of AE events would be
captured to characterize the separate failure regions. With the tensile strengths for the cross-ply
and quasi-isotropic specimens were 965 MPa and 717 MPa respectively, each tensile test was
roughly one hour. Initially, lead breaks were done to ensure each sensor had similar frequency
responses and sensitivity. Also, the time of flight of signal from one sensor to the next can bemeasured and used to calculate the speed of sound (velocity) of the signal, which is useful indetermining source location. A schematic of an instrumented specimen is shown below in Fig. 1.

Extensometer
PZT sensors

Fig. 1. Tensile specimen dimensions (left) showing dimensions of the length and sensor to sensor
distance, specimen during testing.
Methodology
Each static test is capable of generating tens of thousands of signals depending on parameterssuch as threshold value, sensor sensitivity, and failure process of the material. This study utilizeda cross-correlation technique to extract important signals based on the density of signals
generated in a particular area over a set period of time. This approach relates an area where ahigh density of similar AE signals is being generated to critical localized damage growth.
Localized damage growth is considered to be where a given source mechanism continuously
grows or happens within a relatively small area or volume. In such case, that mechanism should
produce repetitive signals as long as there has not been a drastic change in the medium. A focus
54

is placed on critical localized damage, such as fiber breaks, that significantly reduces the strengthof the component. It is known that a fiber breakage causes stress redistribution into neighboring
fibers which may fail if their ultimate strength is exceeded [16]. These neighboring fibers
simultaneously fail in groups, or i-plets, at high load [1, 3, 6]. This behavior has been captured ingreat detail using computed tomography [4]. Capturing the failure of these i-plets in terms of AE
clusters can provide great insight on the damage state of the material in real-time. Also, giventhat composites may initially exhibit random cracking, such a technique may be able todistinguish between separate localized damage areas.
The correlation process relies on raw waveform data captured by the individual sensors todetermine location and various AE parameters (duration, frequency, energy, etc.). Typical AE
software calculates such information but analyzing waveform data using MATLAB was found to
be more effective. The cross-correlation technique xcorr in MATLAB used to compare separate
data sets is shown below in Eqs. (1–3), (MATLAB R2013a, MathWorks Inc., Natick, MA,
2013).
𝑅"# 𝑚 = 𝐸 𝑥()* 𝑦( ∗𝑅"# 𝑚 =

= 𝐸 𝑥( 𝑦(-* ∗1-*-2𝑥()* 𝑦( ∗(34∗

𝑅 "# −𝑚𝑐 𝑚 =𝑅"# 𝑚 − 𝑁

(1)

𝑚≥0
𝑚<0

𝑚 = 1,2, … 2𝑁 − 1

(2)

(3)

If there are two continuous functions representing waveform data, x and y, there will be an
expected value, E, resulting from comparing the two functions. The variable m is the lag or shift,
the output is the correlation array, c, and N is the number of data point in the waveform. Eachcorrelation value corresponds to a lag value and the degree of how similar two waveforms arecan be found by finding the maximum value of c. A visual representation of how this technique
works is shown in Fig. 2. The first signal detected by each sensor serves as the initial reference
signal and the following signals that fall within a specified window, 50 for the data presented
later, are correlated to the reference. For the test data shown here, a window size of 50 yielded
consistently good results. However, the necessary size will vary depending on the rate of the load
applied and sensor sensitivity. This standard window length was used which cut out most of theregions of inactivity. Some events were clearly shorter or longer than this window but nearly all
useful data was capture in either case. Utilizing lead breaks, a 90% correlation factor was seen tobe sufficient in discriminating surface signals 2 mm apart. In the figure on the left, using
waveform 1 as the reference, two red signals that follow in the window indicate they correlate ata high percentage with 1 and form a cluster of 3, including the reference. Next, signal 1 and it’s
two matching waveforms are removed and process repeats using waveform 2 as the reference,
which forms a cluster of 4 of the following waveforms. When clustered signals are removed
from the data set, the remaining signals are shifted to fill the empty spaces and the next iterationof the process begins. Each cluster is independent of one another and is sensitive to localized
damage. This “on-the-fly” technique uses real time data to monitor critical damage growth andhas potential to be used in-situ.

55

Fig. 2. Schematic of clustering process with like signals colored the same. Numbers correspondto the order that the signals were detected.
When attempting to distinguish between the various source mechanisms, there are expectedparameter regimes each mechanism should occupy. However, these parameter values vary withpropagation distance. Using correlation, the effects of the attenuation, dispersion, and otheraspects of wave propagation on the measured waveform can be taken into account. Such as,
signals of the same source type and from the same location should be nearly equally affected by
the aforementioned processes. During the early onset of damage, signals that are captured withina relatively short time that correlate at a high percentage may indicate localized damage growth
and as the damage becomes more pronounced, individual signals travel approximately the same
path within the medium. For a finite time, the medium is assumed to not change due to damage
and the signals that occur within this window that correlate at or above a predetermined
percentage are, therefore, grouped into a clusters. Whereas most models require intrinsic
knowledge about the state of the material to predict failure, here, the rate of cluster formation as
well as cluster size was seen to be an indicator of critical damage for quasi-static tests.
Results
AE Parameters Analysis
The AE data generated during static loading for both the cross-ply and quasi-isotropic
material can be seen below. Cumulative AE events vs stress plots can be seen in Fig. 3 andhighlight the separate regions where matrix cracking, delamination, and fiber breaks occur at ahigh rate. Initially, a sudden increase in the number of events at low stress is seen due to failureof the matrix which generates a high rate of acoustic emissions. This behavior typically happens
around 20-40% of the ultimate stress and saturate shortly after. Matrix cracks saturation isfollowed by an intermediate period characterized by minimal AE signal generation. At this point,
the fibers are completely responsible for carrying load and the drastic increase of AE activity
seen before failure is primarily associated with fiber breakage and delamination.

56

Fig. 3. Cumulative AE hits vs stress for cross-ply (left) and quasi-isotropic (right).
The above cumulative AE plots follow the expected multi-stage failure process [17] but donot show much difference between the two materials. Since the first and last regions containextensive matrix cracking and fiber breaks, respectively, examining the AE parameters may
provide information on the individual failure mechanisms. Splitting of the matrix occurs over acertain area and happens over a particular period of time whereas carbon fibers have a relatively
smaller cross-sectional area and break almost instantaneously. Ideally, this difference in source
duration and energy release of the failure modes should generate unique signals in terms of the
traditional AE parameters.

Fig. 4. Typical average frequency and duration plots seen for the two different lay-ups: cross-ply(left), quasi-isotropic (right).

57

Below, the average frequency and duration plots of the individual AE events, shown as blue
dots in Fig. 4. The average frequency was calculated by estimating the number of cycles within awaveform in a given time window. Ideally, matrix cracking and fiber breaks should generatevery distinct signals but there is only a minimal difference in the frequency characteristics seen
for the two regions. This is seen in both cross-ply and quasi-isotropic material. However, thelater region for the cross-ply seems to have a slightly higher average frequency than the quasiisotropic material. Duration plots for both material as well as any other parameter plots provided
very limited information on the damage development due to signals being affected byattenuation, dispersion, and scattering. The effects of attenuation and dispersion as a function ofmaterial properties and geometry have been well documented. However, the effects ofattenuation and dispersion as a function of damage is not well understood because they dependon the amount of damage a signal encounters on its path to a sensor rather than the amount ofoverall damage.
Correlation of AE signals
Clustering of AE signals usually involves extracting parameters and forming groups orclasses based on waveform characteristics. Some methods utilize un-supervised approaches thatmake use of amplitude or weighted metrics as classification metrics [18-21]. The accuracy ofsuch techniques depends on accurate measurement of distinguishing features while accounting
for effects of wave propagation, which is heavily dependent on distance.
The proposed correlation technique took the same data that generated limited insight from a
traditional AE parameter standpoint and extracted meaningful information in terms of criticaldamage growth. To account for the change in the propagating medium due to damage, a window
was used to only analyze successive signals to find clusters. This window, 50 successive events,
assumes that the material microstructure of a given signal path stays constant or exhibits
insignificant change for a finite period of time or stress. The clusters plots using a 90%
correlation value are shown in Fig. 5 and detail how various cluster sizes grow as a function ofstress. Each line represents an individual cluster size.

Fig. 5. Cumulative cluster growth for cross-ply specimen: sensors 1 (left) and 2 (right). Cluster
sizes of 6 to 10 were plotted. This behavior was seen for quasi-specimens as well (not shown).
The most accurate and non-trivial way to accurately capture the fiber break phenomenon is tophysically count the breaks using high resolution microscopy. Reifsnider and Jamison [1]manually observed fiber breakage in each ply of a cross-ply and unidirectional specimens using
scanning electron microscopy (SEM). A specimen was loaded to a certain percentage of the58

ultimate stress, then unloaded and dissected, and the number of fiber breaks for given area (mm2)
was determined. This was done at multiple ultimate stress percentages. Figure 6 shows acomparison between these findings and the results of this work. The AE data is a cumulative plotof AE clusters that have a size of 10 or greater. After sifting through the data, it was determinedthat 10 was a consistent metric for a minimal cluster size.

Fig. 6. Comparison of AE clusters seen in cross-ply to fiber break measurements using SEM [1].
From the above plot, the trend of the AE data falls between the SEM data as expected since
the lay-up of the AE specimen falls between the layup of the other two. Having such physicalevidence can provide calibration and fine tuning of the AE data analysis, which will allow thisbehavior to be characterized without any labor intensive or computationally heavy processes.
With that being said, the ultimate comparison would involve the same specimen for AE
measurement and SEM imaging.
The waveforms that make up the clusters were also examined and the AE features werestudied. AE parameter analysis of clustered waveforms may provide information not noticeable
when all AE data is analyzed. The frequency content of the reference waveform of each cluster
was plotted with respect to stress, Fig. 7.

Fig 7. Average frequency of cluster signals (red) and all AE data measured in cross-ply
specimen: data from sensor near failure (left), away from failure (right).
59

In the above average frequency plots, the reference signals (red) are superimposed over all
the AE data. Since the clusters are associated with localized damage growth resulting from fiber
breakage, it is expected that the signals contain relatively higher frequency content.
Theoretically, fiber breaks should generate much higher frequencies than what is shown in Fig. 7
but the PZT wafer sensors had an upper frequency limit around 700 kHz. Sensors having abroader frequency response can be used but the influence of attenuation on the signals seems toplay a more significant role in the measurement of higher frequencies. Hence, the reference
signals from a given sensor tend to occupy the upper frequency domain when failure occurs nearor at that sensor.
Location of clusters
The process of identifying clusters started with understanding localized damage growth. In
this section, the origin of AE signals is studied and determined. Location of AE events itself is awell-understood process and has been studied extensively. In the case of transient waves,individual sensors may be triggered by different parts of the same waveform. A detailed
waveform analysis was done to mitigate this problem and help enhance source location of AE
clusters. Such as, when an event is captured by the two sensors, both waveforms are analyzed toinsure that each sensor was triggered by the leading peaks. This provided a safeguard against
measuring incorrect time of arrival which affects the ability to accurately locate the events.
Since clusters are composed of multiple signals, source location only applies to the reference
waveform and the subsequent signals are assumed to have originated from the same location. To
verify that the clusters originated from the same location, random waveforms from various
clusters were chosen and located. The transient nature of the signals makes it difficult to get
precise time of arrival measurements. The extensional mode, given its faster velocity and nondispersive nature over the frequency*thickness range of interest, was used to determine thevelocity of the wave, which was found to be approximately 5000 m/s. However, as in the case ofan aluminum plate, the in-plane surface displacement of the extensional mode is usually
inversely proportional to its frequency, with most of the energy of the in-plane component being
contained near the mid-plane of the material; the opposite effect is seen for the out-of-planecomponent of the extensional mode [22]. The PZT wafer sensors used are designed to be much
more sensitive to the in-plane component but there is only a small percentage of in-plane energy
available for measurement. The internal structure of composite materials is quite different fromisotropic media but this phenomenon may have also influenced AE measurements in this study.
Figure 8 shows the location of raw AE data and the corresponding signal density plots
relative to sensor placement. While it is possible to notice some areas of higher AE activity, thecopious nature of polymer matrix composites can mask useful information due to the sheer
number of events. The density plot highlights regions of high localized activity that is not clearlyvisible in the raw data. The density scale is based on the number of AE clusters per square mm,with dark areas corresponding to regions of high AE activity. These clustered signals are thetargets of the correlation technique and can be indicators of critical damage growth. In addition,
having location information gives the ability to see where the specimen will fail in real time.

60

Fig. 8. Raw AE location data for unclustered data (left) and signal density of the unclustered data(right) plots for cross-ply specimen. S1 and S2 refer to sensors 1 & 2, which were 75 mm apart.
Events are the individual dots. The black area in right plot is due to dense AE activity.
The above plot shows the raw location of AE events as the specimen was loaded to failure.
Tens of thousands of signals were measured but the events were seemingly distributed randomly
along the gage section. Using only location, there was not a relative clustering of signals near the
failure region which would be noticeable in the density plot on the right. However, there was
useful information extracted from the above data using correlation and it was plotted in the formof clusters, seen below.

Fig. 9. Raw AE cluster location for clusters of 10 (left) seen in cross-ply specimen. Right figure
shows the cluster location data transformed into a cluster density plot to highlight the location ofclusters.
Figure 9 shows the results of applying the correlation technique to raw AE data. Whereas theraw data and the AE parameter data gave very limited insight, the cluster based approachedprovided indication of critical damage growth as well as location information. Failure of thespecimen occurred slightly above sensor 2 which corresponds well with the dense region of AE
clusters. On the color spectrum, black indicates an area of high cluster density. The two denseclusters align well with the failure area but slightly less dense clusters appear near sensor 1 rightbefore failure. Ideally, the location of the clusters should indicate where the material will fail.
However, the uncertainty associated with the material in terms of its internal structure, Lamb61

wave behavior, and even the speed of sound right before sudden failure is not well understoodand requires more investigation.
Conclusions
Quasi-static tension tests were performed on cross-ply and quasi-isotropic CFRP specimens.
The traditional acoustic emission parameters, such as frequency and duration, were examined for
information pertaining to the different types of damage growth during testing. While slightdifferences were noticed in the average frequency and duration plots of the matrix cracksaturation region and the fiber break region for the cross-ply material, there was too much scatterin the data to quantify anything useful. The AE parameter analysis for the quasi-isotropic data
was unable to highlight the different failure regions as well.
A correlation technique was developed to extract localized damage growth information
associated with critical damage growth. Using the same AE data that yielded minimal
information from traditional parameter analyses, the correlation process was able to extract
localized clusters which are assumed to be related to fiber failure. The results were consistentand repeatable for both layups and verified on multiple specimens. This technique was able tomonitor the development and growth of various clusters sizes and found that the occurrence oflarge clusters directly precedes sudden failure.
Acknowledgements
The authors acknowledge NASA Armstrong Flight Research Center for supporting this work.
References
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Journal of Fatigue, 1982. 4(4): 187-197.
2. De Rosa, I., C. Santulli, and F. Sarasini, Acoustic emission for monitoring the mechanical behaviourof natural fibre composites: A literature review. Composite: Part A, 2009. 40: 1456-1469.
3. Thionnet, A., H.Y. Chou, and A. Bunsell, Fibre break processes in unidirectional composites.
Composites: Part A, 2014. 65: 148-160.
4. Scott, A., I. Sinclair, M. Spearing, A. Thionnet, and A. Bunsell, Damage accumulation in a
carbon/epoxy composite: Comparison between a multiscale model and computed tomography
experimental results. Composite: Part A, 2012. 43: 1514-1522.
5. Morton, H., P. Reed, A. Scott, I. Sinclair, and M. Spearing, Quantification of carbon fibre/epoxy resin
composite failure processes using synchrotron radiation computed tomography. ECCM15-15th
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6. Foster, G., Tensile and Flexure Strength of Unidirectional Fiber-Reinforced Composites: Direct
Numerical Simulations and Analytic Models (Master's Thesis), in Department of Engineering Scienceand Mechanics. 1998, Virginia Polytechnic Institute and State University.
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10. Bussiba, A., M. Kupiec, S. Ifergane, R. Piat, and T. Bohlke, Damage evolution and fracture events
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11. Jong, H.J., Transverse Cracking in a Cross-ply Composite Laminate - Detection in Acoustic Emission
and Source Characterization. Journal of Composite Materials, 2005. 40(1): 37-69.
12. Gorman, M., Modal AE analysis of fracture and failure in composite materials, and the quality andlife of high pressure composite pressure vessels. Journal of Acoustic Emission, 2011. 29: 1-28.
13. Ogi, K., S. Yashiro, and K. Niimi, A probabilistic approach for transverse crack evolution in acomposite laminate under variable amplitude cyclic loading. Composites Part A: Applied Scienceand Manufacturing, 2010. 41(3): 383-390.
14. Wharmby, A., Observations on damage development in fibre reinforced polymer laminates undercyclic loading. International Journal of Fatigue, 2003. 25(5): 437-446.
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damage parameter for CFRP composites. International Journal of Fatigue, 2007. 29(3): 457-470.
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63Selected Papers from
PROGRESS IN ACOUSTIC EMISSION
XVIII

Proceedings of the 23rd International Acoustic Emission
Symposium, the Inauguration Conference of International Instituteof Innovative Acoustic Emission & the 8th International
Conference on Acoustic Emission- IAES-23, IIIAE2016Kyoto and ICAE-8 December 5-9, 2016, Kyoto TERRSA & Rihga Royal Hotel Kyoto
Kyoto JapanThe Japanese Society for Non-Destructive Inspection (JSNDI) &International Institute of Innovative Acoustic Emission (IIIAE)PROGRESS in ACOUSTIC EMISSION XVIII, JSNDI & IIIAE

Invited Talk

Review of Acoustic Emission Source Mechanisms on Large
Movable Structures
David E. Kosnik1)
1) Consulting engineer, CTLGroup, 5400 Old Orchard Road, Skokie, Illinois 60077, USA

ABSTRACT: This paper reviews and compares three case studies involving acoustic emission (AE)monitoring of anomalous noises on large movement structures. In particular, probable source mechanisms
for the various audible noises observed on the structure and investigated by AE and other measurements
are identified and discussed. The three case studies include a movable stadium roof and two movablebridges, one bascule-type and one swing-type. The stadium roof and bascule bridge both showed AE
correlated with similar stick-slip behaviors, albeit on two very different mechanisms. The swing bridge
showed two distinct noise sources, each associated with different aspects of bridge motion.

15.1

INTRODUCTION

In the course of operation, movable structures such as stadium roofs, movable bridges, and industrial gantries
sometimes emit audible noises loud enough to be disturbing the structure owner and the public. This paper will
review three cases studies in which such loud noises, termed anomalous audible events (AAEs), were observed
during commissioning of movable structures, with particular consideration of probable underlying noise source
mechanisms. In each case, the structure owner needed to know whether the noises were benign or indicative ofsome mechanical (i.e. with the lift machinery) or structural problem. Identification of AAE source locations onlarge steel structures is challenging due to sound propagation and re-radiation through structural members, giving
the impression to the human ear that the sound is coming from all around. It has been shown [e.g. 1,2] that
acoustic emission (AE) monitoring with judicious sensor placement on the structural steel can provide
unambiguous AAE source location results. Consideration of source location results along withstructural/mechanical measurements such as strain, displacement, and rotation can provide insight into theunderlying source mechanism, which in turn informs decision-making by the structure owner.

1.1.1

Overview of Case Studies

Three case studies are considered herein. All of these structures are located in the northern tier of the UnitedStates, in climates with hot summers and cold winters. Deicing salts are used on both bridges. The structures are:1) Movable roof on a baseball stadium2) Rolling bascule bridge carrying a town street over a freshwater commercial waterway
3) Swing bridge carrying a city arterial street over a marine commercial waterway

1.1.2

Considerations for Movable Structures

Movable structures present a variety of engineering challenge not found in static structures. Considerable wear
occurs on bearing surfaces, and members are often subject to large cyclic loads, up to complete stressreversals [3]. In this sense, movable bridges and stadium roofs behave more like large machines than structures.
For example, the primary girders on a bascule-type movable bridge change conceptually from a simple span when
the bridge is closed and locked, to a cantilever span when the bridge first opens, to something like a beam-column
with both bending and axial loads when the bridge is fully open (i.e., the leaf is nearly vertical). Access to
structural and mechanical elements of interest also presents challenges. For example, bearings or raceways in aswing-type structure may be located in a confined space, as in the swing bridge case study. In addition, the size ofthe structure and the relative motion between various structural elements complicates sensor installation and cableconnections to the AE measurement equipment – it is typically necessary to adopt a piecewise approach to㸫S1㸫instrumentation. Finally, a considerable amount of spurious noise (i.e. other than AAEs) is generated during
normal operation from benign processes such as sliding movements on bearings, crushing of oxides and debris on
rolling surfaces, and so forth. These noises must be filtered away to concentrate on AAEs.

15.2

CASE STUDY: STADIUM ROOF

AE monitoring was used to identify AAE sources on the radial retractable roof at the Miller Park baseballstadium in Milwaukee, Wisconsin, USA shortly after the structure opened to the public. The radial roof opens andcloses like a handheld folding fan, as shown in Figure 1. This roof system is both a signature aesthetic feature of
the structure and an essential operating mechanism – unlike most domed stadiums, Miller Park includes a grass(rather than artificial turf) field requiring regular doses of sunlight through the opened roof.

Figure 1: Stadium roof closing sequence showing roof panels pivoting on bearings above home plate (Milwaukee
Journal-Sentinel illustration)
Each of the five movable roof panels is supported by a bearing on a tree-like structure behind home plate. The
tree structure and a typical bearing are shown in annotated photographs in Figure 2. Each panel weighs between
1,590 and 2,270 metric tons (3.5-5.0 million pounds), with approximately 40% of the load supported by the pivot
bearings [4]. The remainder of the load is supported by motorized bogies on the outfield wall, which also provide
the tractive effort to move the panels along a railroad-style track. The movable roof panels span approximately
180 m (600 ft) from the pivot bearings to the track [5].

(a)

(b)

Figure 2: (a) Annotated photograph showing one side of tree-like structure supporting the five roof panel pivot
bearings; (b) close-up of typical roof panel bearing showing location of AE transducer
Instrumentation and monitoring of the roof panels was described by Prine [2]. Each of the five pivot bearings
was instrumented in turn and tested by moving the corresponding roof panel. A non-contact laser displacement
sensor measured the position of the roof panel truss as the truss pivoted about the bearing. The laser displacement
sensor had micrometer sensitivity but limited range - approximately 6 mm. Thus, displacement data were㸫S2㸫available for only the first 6 mm of travel. Nevertheless, plots of acoustic emission peak amplitude and roof truss
rotation revealed correlation between AAEs and changes in the time rate of displacement, as shown in Figure 3.

Figure 3: Time history of AE amplitude (green dots) and roof panel truss displacement (red line) showing
correlation between AE and changes in displacement rate. Dashed lines indicate times of AE events, forcomparison to displacement data.
It is clear from Figure 3 that acoustic emission hits are correlated with momentary pauses in change indisplacement followed by resumption of movement – that is, stick-slip behavior. Due to the long span of the roof
panel trusses, very small displacements/rotations at the bearing correspond to large movements at the drive bogies
along the outfield wall. When the home plate bearing sticks, considerable strain energy may accumulate in theroof truss until the bearing slips. This undesirable behavior indicated that the selected bearing design was notsuitable to the application. When the bearings were replaced with thrust bearings designed to accommodate thedownward load associated with the weight of the roof panels, the AAEs ceased [5]. The stadium roof remains inservice and is operated frequently during baseball season.

15.3

CASE STUDY: ROLLING BASCULE BRIDGE

AE monitoring was used to characterize audible noises generated during opening and closing of a Scherzertype rolling bascule bridge over a busy waterway. Rolling bascule bridges open by rocking along a toothed trackplate at the heel of the bascule girder, as shown in elevation view sketch and photograph in Figure 4. During
operation, as well as when the bridge is locked in the closed position, the entire weight of the movable leaf
(approximately 450 metric tons) is supported by a relatively small contact area on the lower track plate. This
contact area moves along the upper and lower track plates during bridge movement.

Figure 4: Sketch of bascule bridge leaf and photograph of bascule girder heel showing moving contact areas

㸫S3㸫

Instrumentation and data reduction (including development of post-processing filters) for the rolling basculebridge is described in a previous paper [2]. After the drive machinery had been ruled out by first-hit analysis,
linear and planar location analyses were employed to determine whether AAEs corresponded to particular contact
locations during lift cycles. Linear location analysis along the top and bottom track plates showed that AE events
associated with AAEs followed the rolling contact point between those plates. Planar analysis, in which an arrayof AE transducers was deployed to cover a wide area of the heel of the bascule girder, yielded unambiguous
results indicating that AE events associated with AAEs occurred exclusively along the bottom edge of the basculegirder heel, as shown in Figure 5.

Figure 5: Planar location results showing events along bottom flange of bascule girder heel. AE transducer
locations are indicated by the numbered red dots, and located AE events by green dots.
While the source location of the AAEs was evident from the planar location results, the specific source
mechanism remained unclear. A toothed track plate is bolted to the bottom flange of the bascule girder. This
curved upper track plate mates with the flat lower track plate as the bridge moves. The bolted connections consistof groups of bolts on the bottom flange (on either side of the bascule girder web; each group of bolts was
separated from the groups on either side by a radial stiffener. These long and short radial stiffeners are visible inthe drawing in Figure 5.
After the bolts themselves were eliminated as potential AE sources by a close-up transducer array, it was
hypothesized that the AAEs may originate elsewhere along the bascule girder-upper track plate interface. An
orthogonal pair of eddy-current type non-contact displacement sensors was deployed to measure any displacement
or deformation along or perpendicular to the interface – in other words, quasi-shear or normal deformation. A
nearby tilt sensor nearby measured the angle of the bridge, from which the track plate contact position could be
calculated. These sensor locations and orientations are shown in Figure 6.
The measured displacements – particularly the quasi-shear displacements – presented a distinct stepwise
pattern during portions of the bridge lift cycle. Timestamps of acoustic emission events associated with AAEs aresuperimposed on the quasi-shear displacement time histories in Figure 7. While the total change in displacement
during the lift cycle is quite small (64 mm or 0.025 inch), it is evident, particularly in Figure 7b, that AAEs arecorrelated almost one-to-one with stepwise jumps in displacement.
Because the apparent stick-slip movements were very small and occurred away from the bolts, the bridge
designer judged the AAEs and the underlying stick-slip mechanism to be benign. Anecdotal reports indicated that
the AAEs subsided during the first year of regular operation of the bridge. These reports suggest that the stickslip action on the bascule girder-track plate interface was related to “break-in” initial wear on the interface. The
bridge remains in regular operation, opening tens of times daily during the summer months.

㸫S4㸫

Tilt sensor

Displ.
sensor(a)(b)

Figure 6: Photograph and sketch showing displacement sensor (a) location and (b) orientation (exaggerated scale)on bascule girder - upper track plate interface

Figure 7: Correlation of AAEs with displacement along the interface between the bascule girder bottom flange
and the curved upper track plate during bridge closing, showing (a) entire closing and (b) close-up of 10
second period of greatest movement

15.4

CASE STUDY: SWING BRIDGE

Anomalous audible events of two distinct types – one described as “clicking”, the other as “popping” or“booming” – were reported during operation of a swing bridge carrying a city arterial street across a busy marine
waterway. AE monitoring was employed to characterize the two types of AAEs. The bridge owner wasparticularly curious whether AAEs were associated with any particular bridge position or rotational speed.
Instrumentation and data reduction (including development of post-processing filters) for the swing bridge is
described in a previous paper [2]. Following preliminary tests to rule out other areas of the bridge, the center pivot
and outer drive pinion areas were identified as likely noise sources. Locations on the center pivot area of thebridge are described using the clock face system shown in Figure 8a. In this scheme, the 9 o’clock-3 o’clock axis
represents the longitudinal axis of the bridge when closed, and the 12 o’clock-6 o’clock axis represents thebridge’s fully open position. A rotary encoder on the center gear recorded bridge position.

㸫S5㸫

Figure 8: (a) Plan view of swing bridge with clock face representation of bridge angle superimposed; (b)Elevation view of sketch of AE transducer layout on pivot
Testing revealed that clicking-type AAEs occurred only while the bridge was in motion. When the bridge wasmoving at approximately constant speed, AAEs occurred at regular five second intervals and were not generally
associated with bridge starts or stops. It was hypothesized that a particular “hot spot” along the rotation arc might
be the source of the periodic clicking AAEs. Planar location analysis was conducted on the plate at the top of thepivot bearing (AE channels 1-4 in Figure 8b) to test the hot spot hypothesis. The results, shown in Figure 9, wereconsistent with an AAE hot spot at the 5 o’clock position.

Figure 9: Arc of located events (divided into 20-second time bins) in pivot area during rotation of swing bridge
Specifically, when locatable events are distributed into bins based upon the time within the bridge movement
at which they occurred, it is evident that earlier AAEs tend to occur toward the 5 o’clock position, and thesubsequent AAEs generally tend to occur clockwise of that position, subtending an arc from 5 o’clock to 8
o’clock. This distribution is generally consistent with the motion of the bridge as it moves from fully closed to
fully open; thus it may be reasonably concluded that a single source at the 5 o’clock position on the pivot basecould cause the clicking-type AAEs.

㸫S6㸫

The “popping” or “booming”-type AAEs on the swing bridge were found to be fundamentally different thanthe clicking type. While the clicks were strongly periodic and associated with steady motion, the booms occurredalmost exclusively during stops and starts, as shown in the distribution of AAEs by bridge angle in Figure 10.

Figure 10: Stacked histogram of AAEs (n = 257) with bridge angle for selected clockwise (red) andcounterclockwise (blue) bridge movements, showing clusters of AAEs at starts and stops, including a pause at+52 degrees during clockwise motion. Figure courtesy of Daniel Marron (unpublished data).
Linear and planar location analyses showed that the booms originated along the box girders supporting thedrive pinions. The drive pinions (with drive shafts oriented vertically) engage a rack on a large-diameter ring
about the center pivot point. Locatable events were concentrated near the groups of bolts that tie the pinion
bearings (supporting the vertical pinion shafts) to the box girder, as illustrated by the annotated histogram in
Figure 11.

Figure 11: Distribution of AAE source locations along pinion girder. Pinion bearing/box girder through-bolt
groups are highlighted in yellow. Figure courtesy of Daniel Marron (unpublished data).

㸫S7㸫

15.5

DISCUSSION AND CONCLUSIONSThe three case studies demonstrate that AE may be successfully applied to localization and characterization of
anomalous acoustic events on large movable structures, and that the AE method can identify events with various
underlying acoustic source mechanisms. AAEs may be produced by movable structures in several ways. Despite
differences in structure purpose and geometry, similar micro-level source mechanisms are present. In the case of
the swing bridge, two distinct source mechanisms were identified, each with particular relationships to bridge
motion (i.e. constant speed vs start/stop).

1.1.3

Stick-Slip Behavior

The baseball stadium roof and the rolling bascule bridge present clear examples of stick-slip behavior leading
to AAEs. Displacement during the slip was quite small, on the order of micrometers; however, the slip occurrednearly instantaneously, resulting in a sudden energy release. Typically, processes producing audible sounds (e.g.
AAEs) emit acoustic energy over very broad frequency spectrum [6,7]; thus it is expected that the AAEs would bereadily detectable by both the human ear (sensitive from roughly 20 Hz to 20 kHz) and commercially-available
AE transducers (sensitive from roughly 150 kHz to 675 kHz).
Both structures emitted AAEs throughout movement cycles, as opposed to emission during start/stop
acceleration only. In both cases, stick-slip behavior is almost certainly related to the high normal forces on thesurface. On the stadium roof, approximately half the weight of the roof panel acted normally to the bearing
surfaces; on the bascule bridge, it is believed that the vertical stiffeners played a role in “focusing” loads toparticular areas.

1.1.4

Localized Movement Around Bolts

The “popping” AAEs observed on the swing bridge appear to be related to localized movement around thebolts in the pinion girder. However, similar tests to monitor bolted connections between the track place andbascule girder on the rolling bascule bridge yielded no significant acoustic emissions. The difference between
these two observations is likely related to the stiffness of the thick bascule girder flange versus the relatively thinwalls of the swing bridge pinion box girder. Close-in displacement measurements showed that the pinion box
girder does deflect during starts and stops, indicating a possible “oil can” deflection mode at moments of highstress from the pinion torque during movement starts and stops. Thus it seems that in areas of relatively lower
stiffness, localized movement around bolts can be a source of AAEs.

1.1.5“Hot Spots”

Both bridges showed “hot spots”, areas of concentrated AE activity associated with AAEs. On the basculebridge, planar location analysis showed that most AE events occurred along a line (i.e., the interface between thebascule girder heel and the curved upper track plate). On the swing bridge, the “clicking” AAEs were shown tooriginate from the center pivot area; planar location vs. time analysis suggests that the AAEs may be associatedwith a particular point on the center pivot bearing. It is by no means surprising that acoustic emission phenomena
would be concentrated in particular high-stress regions

1.1.6

Limitations and Opportunities

Uniqueness of large civil structures, as opposed to manufactured systems, presents some challenges forscientific analysis of acoustic emission behavior associated with AAEs. Each structure in the case studies was
designed for and constructed at a specific location. Even if similar structures were available for comparison
testing, site-specific design elements, site conditions, construction details, and operating practices affect theboundary conditions for the structure itself as well as the dynamic processes associated with movements. This
uniqueness of constructed facilities stands in contrast to the repeatability found in manufactured systems, which
are built in large volumes under controlled conditions. Furthermore, the scope of investigations for movablestructures is limited by access methods and time available for testing. Since structures are typically closed to the
public during testing, structure owners are motivated to conclude testing as soon as actionable information is
obtained, rather than conduct additional work to fully characterize the problem. Thus, opportunities for detailed
in-situ investigation of the mechanics underlying AAE phenomena are limited. It is hoped that building a library㸫S8㸫

of case studies in the literature will provide sufficient information to inform faithful simulations and laboratory
investigations. In addition, guidance from movable structure case studies may help forge additional connections
laboratory-scale phenomena in the existing AE literature and the experiences of full-scale field observations.

ACKNOWLEDGEMENTS
This work was carried out while the author was with the Northwestern University Infrastructure Technology
Institute, a multidisciplinary transportation research center funded by the US Department of Transportation. In
addition to mentoring in the author in acoustic emission monitoring, David W. Prine kindly provided access to hisdata and reports from the baseball stadium case study for consideration herein. Data acquisition and analysis
contributions of Daniel R. Marron to all three case studies, and particularly to the pinion area of the swing bridgecase study, are gratefully acknowledged.

REFERENCES[1] Prine, D. W. (2004). Localization of noise sources in large structures using AE. In Proceedings, 26th
European Conference on Acoustic Emission Testing (Berlin, Germany). European Working Group on
Acoustic Emission.
[2] Kosnik, D.E. (2014). “Brief review of acoustic emission monitoring on two movable bridges”. In Progress in
Acoustic Emission XVII: Proc. 22nd Int’l Acoustic Emission Symposium, pp. 61-66. Japanese Society for NonDestructive Inspection.
[3] Koglin, T. L. (2003). Movable Bridge Engineering. John Wiley & Sons, Hoboken, New Jersey, USA.
[4] Gimblette, J.A. (2008). “Miller Park – Closing the chapter on retractable roof problems”. In Proc., Heavy
Movable Structures 12th Biennial Symposium. Heavy Movable Structures, Inc.
[5] Pinto, C., Abruzzo, J., DeScenza, R. (2004), “Pivot points: new pivot bearings for Miller Park’s moveableroof hit a home run”. Modern Steel Construction, May 2004. American Institute for Steel Construction.
[6] Drouillard, T. F. (1987). “Introduction to acoustic emission technology.” In Nondestructive Testing
Handbook, Vol. 5: Acoustic Emission Testing. American Society for Nondestructive Testing, Columbus,
Ohio, USA. 2nd edition.
[7] Hopwood, T. and McGogney, C. (1987). “Acoustic emission applications in civil engineering.” In
Nondestructive Testing Handbook, Vol. 5: Acoustic Emission Testing, chapter 10. American Society for
Nondestructive Testing, Columbus, Ohio, USA. 2nd edition.

㸫S9㸫PROGRESS in ACOUSTIC EMISSION XVIII, JSNDI & IIIAE

Invited Talk

On Acoustic Emission Sensor Characterization
Kanji Ono
Department of Materials Science and Engineering, University of California, Los Angeles (UCLA),
Los Angeles, CA 90095, United States
ABSTRACT: We examined calibration methods for acoustic emission (AE) sensors. In spite of the self-evident
needs of reliable calibration, the current state is deplorable globally. The only primary standard at NIST (US) is
non-operational, yet no other standard has emerged. Widely practiced face-to-face calibration methods have novalidated foundation. Reciprocity calibration methods are invalid for the lack of reciprocity and sensor dependent
reciprocity parameters. This work provides three workable solutions based on laser-based displacement
measurement, which leads to “direct” method using the face-to-face arrangement. This leads to the second “indirect”
method of mutually consistent determination of transmitting and receiving sensitivities of sensors/transducers. For
all ultrasonic and AE sensors examined, their receiving and transmitting sensitivities are found to be always
different and non-reciprocal. Displacement vs. velocity calibration terminology is clarified, correlating the “V/bar”
reference to laser-based calibration. We demonstrate the validity of the direct and indirect methods and the third
one based on Hill-Adams equation, called Tri-Transducer method. This uses three transducers as in reciprocity
method, but incorporates experimentally determined reference sensor sensitivities ratio without a transfer block andcan get both transmitting and receiving sensitivities. These three methods provide consistent calibration results forover 30 AE sensors.

1. INTRODUCTION
In acoustic emission (AE) testing, sensors are essential components of a nondestructive test system. Theircharacterization attracted much interest over the years. Notable reviews on AE sensor calibration appeared in the
1980s [1-3], but only established standards are two ASTM standards for the primary and secondary calibration of
AE sensors [4, 5]. These are based on seismic pulse on a large transfer block, developed at NIST [6] and are for thecalibration of the surface wave sensitivity. Currently, they are dormant. Burks and Hamstad [7, 8] reexamined the
NIST procedures and suggested revisions. One of them is to use measured displacement, rather than analytical
calculation since the capillary-break source is elliptical, rather than a point. No ASTM standard exists for AE sensor
calibration for normal incidence waves.
AE sensor manufacturers typically provide a sensitivity curve based on face-to-face calibration. This calibration
procedure has been treated as proprietary information and described only inadequately [9]. Calibration curves areusually in reference to the reference level of 1 V/bar, but this reference remains undefined. ASTM E976 standard
guide [10] is often cited as the basis, but E976 specifically excludes the face-to-face procedure. This widelypracticed face-to-face calibration methods presently have no validated foundation. It does benefit from the ease of
set-up, reasonably good repeatability and the ability to handle long-duration signals of high-sensitivity (undamped
or minimally damped) AE sensors. Commonly used AE sensors reverberate beyond 1 ms when free and often over
200 s even coupled to a metal block, enlarging the needed size of a transfer block to inconvenient sizes of over 1
m. Thus, it is desirable to give it physics-based validation.
Reciprocity calibration methods have been well established for acoustic transducers and hydrophones. In
ultrasonic transducer calibration, more advanced methods have appeared and the frequency range extended to 20
MHz routinely [11] and to 100 MHz in laboratory [12-16]. These include uses of direct measurement of particle
velocity in water, time-delay spectroscopic methods, an optical multilayer hydrophone as reference and pulse-echowith a reflector. This is in sharp contrast to the unsatisfactory state of AE sensor calibration. In general cases ofdiffering transmitting and receiving sensitivities, Hill and Adams [17] showed that the reciprocity calibration
method requires the independently determined ratio of transmitting and receiving sensitivities of an auxiliarytransducer. This condition of non-reciprocity prevails in all the piezoelectric transducers examined [18]. We also
showed [18] that reciprocity parameters are sensor-pair dependent. Reciprocity calibration methods for AE sensors[19-23] are thus invalid for the lack of reciprocity for AE and UT transducers when used in contact with solid
medium.
In well-damped transducers, laser techniques provided satisfactory results for surface displacement/velocity
measurement as discussed extensively by [24]. In the present study, we will utilize laser interferometry as the basis
for sensor/transducer characterization [25] and consider three sensor calibration methods.

㸫S10㸫

Let us denote the transmission output of the i-th transducer due to an electrical pulse as Ti = ti  Vi, where ti isthe transmit transfer function and Vi is the FFT spectrum of the pulse input. Here, the transducer output is measured
in displacement. Using a laser interferometer, we can experimentally determine Ti or ti. Next, Rj is the receiving
displacement sensitivity spectrum of the j-th transducer. That is, voltage output for a given displacement input on
the face of the receiver. When transducer i is coupled face-to-face with transducer j, this constitutes a transmissionreception (T-R) test. We get its output ETij with the above notation (without separating the input spectrum) as
ETij = Ti  Rj = ti  Vi  Rj

(1)

Knowing Ti from laser interferometry, Rj can be obtained. Actual calculation relies on the spectral division
procedure using FFT magnitude spectra of ETij, Ti and Rj. This is designated as “Direct” method. Using the direct
method, we can obtain the Rj spectra for any sensors (which need not be transmitting) and transducers (capable oftransmitting as well). This method also applies to sensors with integrated preamplifiers.
Using transducers as transmitters in combination with sensors of known Rj, we can also determine theirtransmitting sensitivities, Ti via the same equation (1). Combining a transducer needing Ti with the sensors Rj, we
determine multiple spectra of ETij with T-R tests. By spectral division with known Rj, Ti is determined. To minimize
the scatter expected, it is normal to obtain several spectra for a particular transducer’s transmitting sensitivity andaverage them. This is “Indirect” method. This method can also utilize the transmitting sensitivity thus obtained forgetting receiving sensitivity spectra of still other sensors. Again, multiple spectra are averaged to minimize thescatter. Consistency of the present calibration procedures can be verified through the reconstruction of T-R tests
comparing Ti and Rj thus obtained with experimentally measured ETij. Good consistency was found. Results of theindirect method are also compared with those of direct method. As shown later, two methods produced good
agreement as well.
Since AE sensors are always used in contact with solid medium, it is desirable to evaluate them using a transfer
block. With the above notation (without separating the input pulse spectrum) plus the transfer function of thetransfer medium Xij, a T-R test for transducers i and j leads to
Eij = Ti  Xij  Rj = ETij  Xij(1a)

The transfer function Xij can be obtained from equations (1) and (1a); Face-to-face T-R experiment provides
ETij, while Eij in (1a) comes from a T-R test through a transfer block. This Xij function is dependent on a particularpairing of transmitter and receiver, requiring subscripts. Generally, T-R tests are not reversible; E ij  Eji.
Hill and Adams [3, 17] analyzed generalized cases of reciprocity calibration, in which Ti and Ri of transducers
differ and the law of reciprocity is no longer satisfied. Three-transducer set-up is used as in classic reciprocity
calibration methods. One of the three (auxiliary transducer) is used as a reference needing both Ti and Ri functions,while another acts as a transmitter. The third is the target of calibration and can be receiving only. Here, transducer
pairs of 1 and 2, 1 and 3, and 2 and 3 are selected and transducer 3 is the auxiliary transducer while 2 is the target
transducer of calibration. We have
E12 = T1X12R2, E13 = T1X13R3, and E32 = T3X32R2.

(2)

Taking ratio of the first two equations, E12 / E13 = (X12R2)/(X13R3), we get R2 = (E12X13R3)/ (E13X12), and thethird one yields: R2 = E32 /(X32T3). Combining, we have for pairs coupled through the transfer block
R2 = [(X13E12E32 /X12X32E13) (R3 /T3)]1/2.

(3)

There are six unknowns of T and R for transducers 1 to 3, but T2 was unused and T1 cancels out in getting
equation (3). We can thus obtain R2 by getting from experiment six Eij values that provide three Xij and the ratio
R3/T3, determined separately from these six T-R experiments. Notice that no transducer reciprocity is required in
the above derivation.
We deviate here from [17] by using the transmission parameters Xij. In Hill-Adams analysis, X was defined asa unique function of the transfer medium, because X is based on the Green’s function for point to point transmission.
Because of diffraction effects, each transducer pair of finite sizes has a unique Xij. Also the size of the transfer block㸫S11㸫

is a factor, as well as the receiver size. For some pairs, X13 /X12 can be treated as unity over a limited frequency
range and only X32 is needed. When X13 /X12 is unity, equation (3) reduces to Hill-Adams equation [17], given as
R2 = [(E12E32 /XE13) (R3 /T3)]1/2.

(3a)Reciprocity calibration cannot provide the ratio of Ti and Ri of the reference transducer, required in equation (3)or (3a) expressing the receiving sensitivity, R2. This problem can be overcome when Ti sensitivities are obtainedusing the laser interferometry, which in turn provide Ri sensitivities using the direct and indirect methods discussed
above.
It is noticed that Eij = ETij  Xij from equation (1a). Upon substitution into equation (3), we obtain
R2 = [(ET12ET32 /ET13) (R3 /T3)]1/2.

(3b)This means that Xij and the use of transfer block are not required. Face-to-face arrangement can replacecalibration procedures with a transfer block. We designate this approach without a transfer block based on equation(3b) as Tri-Transducer (TT) method.
We examine here the inadequacy of sensitivity calibration methods for AE sensors available today and provide
three workable solutions based on physically measureable quantities. Other related issues are also discussed
including front-loading effects and bar reference.

2. TRANSDUCER SENSITIVITY CALIBRATION – TRANSMISSION AND RECEPTION
The use of laser interferometry for transducer calibration is straightforward [24, 25]. Commercial
interferometers of various design are now available, although their uses have been limited due to high cost. Twoworks [21, 22] made significant advances using laser interferometry to verify the displacement of surface pulse andobtained the sensitivity for surface-wave reception of PAC -80 and UT1000 sensors. This approach is a good
supplement to the NIST method.
In our studies that have focused on normal incident waves, we have used a displacement-sensitive laserinterferometer (Thales, LH140, 20 MHz bandwidth with the sensitivity of 0.1 V/nm at Aoyama Gakuin University,
Sagamihara, Japan; Dr. H. Cho graciously conducted measurement). Three typical transmission curves with a fast
initial rise and slower decay with several oscillations are shown for broadband ultrasonic transducers (Olympus
V101, V103 and V104) in Fig. 1a with their corresponding FFT magnitude in dB scale (Fig. 1b). The peak
displacement values are 10 - 12 nm. These are shifted in time and level as noted. The raw FFT spectral datacontained noise and it was reduced by using 25-point smoothing Savitzky-Golay algorithm [26]. The observed
displacement waveforms are basically of mono-polar shape with trailing oscillations. This feature results from theforward radiation at the front face and the presence of absorber behind piezoelectric element, as predicted by theory[27, 28]. The peak displacement value is approximately 50 pm/V, which is expected from a typical lead-metaniobate
piezoelectric element. The other half of piezoelectric displacement is radiated backward into the absorber andmostly damped.
The FFT magnitude transmitting spectrum is relatively smooth for V104 (Fig. 1b). The spectra of V101 andV103 shows many peaks and dips, showing strong fluctuation. Some of them are extraneous vibrations as thetransducer front face is free, only facing air. With face-to-face arrangements, these apparently disappear as thesetransducers produced smooth spectral curves in T-R tests. Front-face loading effectively suppressed extraneous
vibrations. It appears that the low frequency oscillations coming from radial resonance are not suppressed
adequately. Comparing six Ti spectral curves, we selected to use V104 as the reference transducer and use theremaining five for confirmation by avoiding the range where irregular changes are observed.
The transmission characteristics of V104 are shown in Fig. 2 up to 5 MHz, though higher range is less importantin AE. The HV pulse spectrum is curve 1. The displacement transmitting spectra of V104 are curves 2 and 4 (unit= nm). The latter is corrected by subtracting the HV FFT spectrum, indicating much less frequency dependence ofV104 transmission and showing a broad peak at 2.6 MHz. By multiplying the angular frequency, 2f, these curves
are converted to show the velocity response of V104 transmission, curves 3 and 5 (unit = m/s), with or without the
HV electrical pulse spectrum.

㸫S12㸫

Fig. 1 a. Displacement waveforms of three transducers.

b. FFT displacement spectra, re. 0 dB at 1 nm.
Once TV104 of the reference transducer is obtained, we
determine Ri of other transducers by conducting T-R test
in a face-to-face arrangement of the reference against asensor under test (SUT) using equation (1). This provides
ET for the transducer-sensor pair. The RSUT is thendetermined by RSUT = ET – TV104. The 0 dB reference for
the RSUT is 1 V/nm with frequency in kHz.
Fig. 2 Transmission spectra of V104. 1. HV pulse only,
2. Displacement (nm), 3. Velocity (m/s), 4. 2 less HVpulse (nm/V), 5. 3 less HV pulse ((m/s)/V).

Fig. 3 Receiving sensitivities of 5 UT transducers.

Fig. 4 Receiving sensitivities of 3 AE sensors.

Using V104 transmitter, we first examine the response of a conical PZT sensor, home-made using the Proctor
design [29]. The conical FFT spectrum minus T V104 spectrum has broad distribution, reaching the maximum at -15
dB, in good agreement with the value of -14 dB Proctor reported for his conical sensors [29]. Next, using the direct
method and five UT transducers, all well damped, we obtain the RSUT curves (Fig. 3). Three 2.25 MHz transducers
show similar smoothly varying sensitivities except V101 and V103 have major dips at 1.1 and 2.3 MHz. These allshow the peak of -2 to +5 dB sensitivity and large fluctuations at low frequencies.
The direct method used for broadband UT transducers applies to general-use AE sensors as well. These are
typically of higher sensitivity and most have more than a single resonance. Three receiving spectra are shown in
Fig. 4. PAC R15a is most sensitive here, showing a peak displacement sensitivity of 13 dB at 162 kHz. PAC R15
shows a similar spectral shape, but shows a little lower sensitivity. PAC R6a is a newer sensor, but this is designed
for low frequency (60 kHz) surface-wave detection: here the peak is near 300 kHz. All of these general-use AE
sensors have the peak sensitivity of 0 to 13 dB or about 1 to 4.5 V/nm.
Next, we used nine UT transducers as transmitters and receivers. By using multiple Ti and Ri sensitivity spectrathus obtained, the Ti and Ri sensitivity spectra of any transducer can be determined. This is the “Indirect” method.
In order to get Ti of transducer A, couple it to transducers B, C, D, etc. with known R i spectra. Since results vary㸫S13㸫slightly, they are averaged to finalize the TA sensitivity of transducer A. Even though the laser-based Ti sensitivities
showed some irregular peaks and dips, the averaged spectra are generally smooth, indicating the front-face loaded
Ti sensitivities have smooth spectra. An example of the Ti sensitivity of NDT thus obtained is shown in Fig. 5 andcompared to that due to laser interferometry. These two curves differ by 0.7 dB on average over 22-1400 kHz. For
the indirect spectra, the absence of two large dips at 1.5-2 MHz indicates that the front-face loading removed
extraneous vibration through the coupling of a receiving sensor. In the present case, both front face materials arealumina plates with matching acoustic impedance. The indirect method can be applied in reverse to determine theRi spectra of a transducer. Three such examples are shown in Fig. 6. These have the peak sensitivity of about 1
V/nm. Results of the two methods, direct and indirect, generally agree better than 0.5 dB on average to 2 MHz.
Maximum difference reaches 5 dB at low frequencies.

Fig. 5 TNDT by laser-based and indirect methods.

Fig. 6 Ri spectra of 3 transducers by various methods.

Finding both Ti and Ri spectra of a transducer, we can verify these calibration results. This is done byconstructing the combined Ti and Rj (i jspectrum for any combination of transducers and comparing it with thecorresponding T-R experiment. An example is given in Fig. 7, where V189 and V195 were paired. TV195 spectrum
was obtained from other transducers’ Rj spectra and is marked V195 T (red). RV189 is the bottom curve. The sumof these two is plotted as T + R in purple and is compared to face-to-face output, marked T R (exp) in green curve.
These two agree well over 150-1200 kHz and 1.3-1.7 MHz, but poorer near the dip of RV189 and above 1.7 MHz.
For twelve cases examined in detail, spectral comparison yielded better than this example. The average discrepancy
was typically about 1 dB except below 200 kHz or above 2 MHz, where discrepancy is slightly higher. For
validation purpose, we can also utilize laser-based transmitting sensitivity (but avoiding the frequency range thatshows irregular peaks and dips: >250 kHz for V101, >650 kHz for V103, >1400 kHz for NDT C16). These wereset aside in preference of using V104, but they do provide a back-up. Here, agreement was moderate.

Fig. 7 Combined TV195 + RV189 vs. TR test spectrum.

In the above comparative procedure of the combined
Ti and Rj sensitivity versus directly measured T+R
sensitivity, it is necessary to account for the area of areceiving sensor when it is larger than the transmitter it ispaired with. Assuming that the receiving sensitivity is
uniform over the entire area, one adds 4.99, 7.04 and12.04 dB for the diameter ratio of 1.333, 1.5 and 2,
respectively. The present results of good matching of theexperimental combined spectrum and one deduced fromthe calibration of Ti and Rj spectra indicate the uniformity
assumption is valid. We can thus determine mutuallyconsistent transmitting and receiving sensitivities oftransducers and sensors.

The ratios of the Ri sensitivity and ti sensitivity (excluding the HV pulse spectrum) were obtained for 17transducers including both broadband and resonance types. Surprisingly, the general spectral shapes of Ri/ti spectra
were similar except for shift in values. Four examples are shown in Fig, 8. Below about 600 kHz, the spectral
difference exhibits the dependence is f1.8 for V101, and f1.33 for the rest. All show a peak around 800 kHz and start
decreasing at higher frequencies. It is also strange that the middle two curves for V103 and V104 are essentially
identical despite their non-matching sensitivity spectra. All other curves are between the top and bottom (except
V195). This observation on Ri/ti -ratios can be related to the spectral difference between a half-sine mono-polar

㸫S14㸫

displacement pulse and a full-cycle sinewave pulse (approximating a Gaussian pulse and its derivative). The
difference of their FFT spectra is also plotted in Fig. 8 as a blue (dash-dot) curve. The lower frequency part is linearwith frequency until it approaches the peak at 900 kHz. The transmission pulse shapes from damped transducers
are usually mono-polar, just like a half-cycle sinewave (see Fig. 1a). In contrast, the received signals tend to havean oscillatory, bi-polar shape. The differing transmission and reception behaviors originate from the mechanismsof pulse generation of a piezoelectric element [27] and produce the observed spectral ratio.
This section demonstrates that 1) the direct method of
calibration is successfully used with the face-to-facearrangement, providing receiving sensitivity of various
sensors/transducers, 2) the indirect method is used todetermine both transmitting sensitivity and receiving
sensitivity, 3) the indirect method suppressed extraneous
oscillations of transmitters that may occur in free space,4) calibration results of the two methods agree well, 5)mutual consistency of transmitting and receiving
sensitivities is verified and 6) the receiving and transmitting sensitivities of a transducer always differ while
their ratio shows similarity among various transducers.
Fig. 8 Spectral ratios of transducers and model waves.

3. VELOCITY RESPONSE OF A TRANSDUCER
We have shown that piezoelectric sensors generate output voltages responding to displacement input. It can still
be described in terms of the time-derivative of displacement input, namely, particle velocity. The standard approachstarted with ASTM E1106 [4], which treats a sensor output to be proportional to the displacement function with atypical unit of V/nm. However, its FFT magnitude can be converted to express the sensitivity in reference to the
input velocity function. This is accomplished by the multiplication of 2f factor to the input function according toan identity in Fourier transform theory. When one divides the sensor output function by the velocity input function,
the velocity sensitivity spectrum is obtained with a typical unit of V/m/s or Vs/m. The Ti spectra in terms ofdisplacement can be treated in this manner, as shown in Fig. 5, curve 3. Here, the unit was changed from nm to m,corresponding to 180 dB subtraction in dB-scale. A note of caution: one cannot multiply 2f to a receiving
displacement sensitivity spectrum in an attempt to get the velocity response. Instead, you need an opposite
operation: Divide by 2f and add a factor of 10+9.
In the AE field, it is common to find the use of bar in place of m/s as the unit of velocity. This originated from
Dunegan’s use in 1968 of hydrophone calibration scheme in characterizing a reference transducer [30]. In 1968,
Dunegan obtained calibration up to 400 kHz. Today, the frequency limit for miniature ultrasonic hydrophonecalibration is extended to 20 MHz [11]. The physical meaning of 1 bar reference pressure has not been articulated
in any AE standard documents. When an AE sensor receives the pressure wave, most of the wave is reflected backinto water as the the acoustic impedance of the sensor facing or sensing element is usually much higher than thatof water. The transmitted pressure wave generates particle velocity in the sensor facing or the sensing element.
However, this pressure cannot be measured. Thus, it is impractical to use it as the basis for calibration. In a recent
study, Burks and Hamstad [9] concluded that the conversion procedure of sensor response to V/bar reference is
illogical and arbitrary unless one measures “the transient output pressure as a function of frequency from the driving
transducer”.
The most logical interpretation of V/bar reference is that of pressure in water, as practiced in the underwater
acoustics field for the hydrophone calibration [31]. In the immersion tank where a hydrophone is calibrated, theacoustic pressure field is known as a function of frequency. The acoustic pressure in water is defined as the productof the acoustic impedance of water (1.48 MPa/(m/s)) and particle velocity. Thus, the pressure of 1 bar (= 0.1 Pa)
in water corresponds to 67.6 nm/s. By placing a reference transducer at the position of known acoustic pressure, it
can be calibrated as a function of frequency. This can then be combined with a broadband transmitter in face-toface arrangement, from which the transmitter output can be calibrated in reference to equivalent acoustic pressure
with the unit of bar. Subsequently, a sensor under test is substituted for the reference transducer and calibrated interms of V/bar reference. With this interpretation, the commonly used reference of AE sensors, 1 V/bar, can be
related to physically based reference of 1 V s /m. That is, 0 dB (ref. 1 V/bar) is 143.4 dB in reference to 0 dB at 1
V s/m. Alternately, xx dB in ref. to 1 V s/m = xx – 143.4 dB in ref. to 1 V/bar.

㸫S15㸫

Some Ri sensitivity curves for common AE sensors are shown in Fig. 4. These have the peak sensitivity ofaround 0~15 dB in reference to 0 dB at 1 V/nm. After converting their response to volts per unit velocity of 1 m/s,
we have the velocity response curves for one of them (R15a) shown in Fig. 9. Curves plotted on the lower side arefurther converted in reference to the scale of 0 dB at 1 V/bar. For PAC R15a, the calibration provided by the
manufacturer is plotted as green curve, just above the lower velocity spectrum. Their values are typically 10-20 dB
above our calibration for >0.1 MHz and the shape of this curve matches better with the displacement calibration.
This discrepancy exists for almost all other sensors examined, not just from this manufacturer, implying a possible
existence of industry-wide systematic error. An interim solution is to add 72-74 dB to manufacturer’s calibration,
thereby obtaining the displacement calibration in reference to 0 dB at 1 V/nm.
It is worthwhile to consider direct differentiation of
displacement waveforms from transmitters to velocity
waveforms. This procedure uses the Savitzky-Golay
algorithm [26] and offers physical insight to velocity
output. The waveform changes from monopolar to
bipolar and the rise time was halved. As expected, theFFT spectra are comparable with the 2f-multiplication
curves. The above discussion shows that it is unproductive to classify a sensor to displacement-response or
velocity-response without specifying the frequency rangeor the flatness of response. NIST-type conical sensors
with a large mass backing clearly have a broad range offlat displacement response, while typical accelerometers
Fig. 9 Velocity calibration curves of R15a sensor.
are designed to produce nearly flat wideband response
in acceleration below the resonance frequency. Common AE sensors have been designed for resonance-based peaksensitivity, while newer designs start to broaden the peak sensitivity ranges.
This section leads to the clarification of pressure calibration references in terms of 1 V/(m/s) and 1 V/bar anddemonstration of equivalence of two conversion methods to velocity.

4. RECIPROCITY AND TRI-TRANSDUCER CALIBRATION METHODS
Our laser-based calibration method has demonstrated that the Ti and Ri sensitivities of UT and AE transducers
differ. Hill and Adams [17] analyzed the classic reciprocity methods for the cases appropriate for contact
piezoelectric transducers. In their analysis of reciprocity calibration methods, Ti and Ri sensitivities are not required
to be identical; that is, non-reciprocal. This exactly fits the experimental reality we have established. Their analysis
led to the receiving sensitivity of sensor 2, R2, to be given by Hill-Adams equation (3a) for pairs coupled throughthe transfer block, where X is the point-wise transfer function of the propagating medium. This result leads to thedemise of reciprocity calibration methods for piezoelectric contact transducers since there are six unknowns andonly three equations. You cannot also express Ti sensitivity from Eij and X. Thus, without transducer reciprocity,
current reciprocity calibration methods lack the foundation [19-23].
We have obtained Ti, Rj and Rj/Tj spectra for over 25 transducers. The ratio, R3 /T3, needed in Hill and Adams
equation, is frequency dependent, increasing with frequency, as shown in Fig. 10. Some are similar, but usually thespectra match poorly. Another parameter needed in Hill and Adams equation is X, which corresponds to theattenuation of longitudinal wave as it passes the transfer block. Experimentally, we find this to be dependent on aparticular pair of transducers. A few examples are shown in Fig. 11. In our testing, a transfer block was of Al 7075
alloy (300 x 300 x 156 mm) with waves traveling normal to the broad faces. While X ij between two UT transducers
is relatively smooth, those including a resonant sensor (R15) have large fluctuations. Diffraction effects and sensor
resonances contribute to the prominent features. Overall trend is consistent, however. Deriving X ij functions frompoint-wise transfer function is a complex task, especially when one must also include resonance effects. Now, thisis a moot point as Xij parameters have canceled out in equation (3b) and are no longer required in the Tri-Transducer(TT) method.
Before realizing that equation (3) reduces to (3b), eliminating the need of Xij parameters, we evaluated Ri for 20
combinations using the extended Hill-Adams equation (3). These results are identical to those using equation (3b),or TT method. Fig. 12 shows the case for 1) 1 MHz UT, 2) FC500-2 and 3) FC500-1. The spectra for three Eij (topgroup), R2 (FC500-2) and the same from laser method (middle), and R3 /T3 for FC500-1 (bottom curve) are plotted.

㸫S16㸫

Fig. 11 Parameter Xij for V104, R15 and V103.

Fig. 10 Spectral ratio, Ri/Ti, for four transducers.

The values of R2 by the TT and direct methods agree quite well below 1.5 MHz with the average difference of 0.34
dB. The dip in the two Eij curves are from #1, but it has no effect on the outcome. Even when two resonant sensors
are in the group of 3 (not shown), the values of R2 for R15a by the two methods are close, with the average differenceof 1 dB below 600 kHz. Considering many peaks and dips in the spectra, this is a good match. These two cases
show that the choice of transducer 1 is non-critical: just needed to transmit in the frequency range of interest.
However, size correction is needed when receiver (SUT) is larger than the other two or reference transducer 3 islarger than the transmit-only transducer 1. Two more results of the TT method are given in Fig. 6. Two transducers,
V103 and V104, show good to excellent agreement with the direct and indirect methods. With V103, average
differences between the three methods to 2 MHz were 0.06, 0.25 and 0.19 dB. V104 results gave slightly higher
average differences of 0.32, 1.36 and 1.04 dB. These values are for a set of experiment. By averaging, multiple set
testing should further improve the performance. It is also necessary to explore the sources of errors.

Fig. 12 Spectra for RFC500-2 with a tri-transducer test.

Fig. 13 TV104 by direct (laser) and TT methods.

This Tri-Transducer method developed from Hill-Adams analysis is beneficial for its reliance on the sensitivity
ratio and avoiding direct use of displacement transmission reference alone. Reception from another transmitter is
included so we can avoid potential problems that may arise from a particular combination of transducers. Still, thisapproach does require the determination of R3 /T3 ratio of reference transducer 3 by laser interferometry. The TTmethod has been used for over 20 combinations of three-transducers. Average difference (over 22 kHz to 2 MHz)
with the result of direct method is typically less than 0.5 dB, although a few cases show values of 1 to 2 dB.
The TT method can be used to obtain the transmitting sensitivity, T 1, similarly to the indirect method. We have
T1 = [(ET12ET13/ET32) (T3 / R3)]1/2.

(4)

An example is plotted in Fig. 13. Three transducers used are V104, NDT and V103. Each of ETij curves peaks
at 0.5-1 MHz. The transmitting spectra for V104 are obtained using the TT method with equation (4) (red) andcompared to the result of the direct method (green). Except for oscillations below 200 kHz or above 2.3 MHz,excellent agreement is seen. The average difference in TV104 was 0.12 dB over 22 kHz to 2 MHz. Three additional
TT tests (including PAC Pico and S9220) produced comparable TV104. The averaged TV104 spectrum with the TT
method differed from the direct method by 0.42 dB in the frequency range to 2 MHz. In addition, three TV103
spectra with the TT method agreed better than 0.2 dB with that from the indirect method. With this method forgetting Ti, the use of some resonant sensors as a receiver produced poor results and should be avoided. Thisapproach provides a new means of verifying the laser interferometry and the front loading effects.

㸫S17㸫

Conclusions from this section are:
1) With demonstrated differences in the transmitting and receiving sensitivities of UT and AE transducers,
current reciprocity calibration methods for AE sensors is invalid. Experimentally obtained longitudinal wave
reciprocity parameter X varies depending on transducer pair used and not invariant as the reciprocity theory
requires.
2) Equation (3) derived by extending Hill and Adams equation reduces to equation (3b). Needs for X parameter
vanished, but physically measured ratio of R3 /T3 is still required for Tri-Transducer method.
3) Tri-Transducer method is tested for over 20 combinations and shows good to excellent agreement with thosefrom direct or indirect methods. This provides both transmitting and receiving sensitivities.

5. CONCLUSIONS
We examined outstanding issues of sensitivity calibration methods for ultrasonic and acoustic emission
transducers. Determining spectral sensing properties is of utmost importance, especially in AE sensors, but recent
research activities in this area have been low. In addition, today’s emphasis in this field has been to model the
sensor behavior using lumped parameter approach so that it can be integrated into systems modeling. On the other
hand, physics-based analysis of piezoelectric sensing has been limited until recently. With new tools available today,
laser interferometers and advance modeling methods, we are closer to the goal of finding a suitable and workableapproach to transducer calibration and clarifying underlying sensing mechanisms.
Laser-based displacement measurement leads to the determination of transmitting sensitivities of transducers.
This is Direct method. While simple in concept, some transducers generate extraneous vibrations on the front
surface when it is free from solid contact. This issue was overcome by a suitable selection of a transmitter and byusing an indirect method through the use of receiving sensitivities of other transducers. It was then possible toobtain mutually consistent transmitting and receiving sensitivities. This is Indirect method. The results alsoestablish the foundation for face-to-face calibration methods, which were beset by the uncertainty of input
parameters without access to the transmitter face. Good to excellent agreement has been observed between resultsof the direct and indirect methods using 30 sensors. Further, it is discovered that the receiving and transmitting
sensitivities of over 20 transducers are always different, while their ratios exhibit unexpected similarity. The lattercharacteristics is traced to mono-polar pulse generation of damped piezoelectric transducers as a transmitter and,as a receiver, bi-polar received signals due to the reflection on the back face. This occurs even in transducers withgood backing, likely from electrical impedance mismatch and charge transfer during elastic wave motion.
The observed difference in the receiving and transmitting sensitivities of a transducer leads to the invalidationof reciprocity calibration methods for piezoelectric contact transducers. The issue was raised in 1979 [17], but usersof reciprocity calibration have ignored the fact that a separate measurement of ratio of the transmitting and receiving
sensitivities is required. We have also measured the reciprocity parameters X in the case of through-transmission
and found this to be dependent on transducer pairings, sizes, frequency, etc. in direct conflict with its definition in
the reciprocity calibration methods. These are also not reversible in cases of different sized pairs.
After following Hill-Adams derivation, we found that parameters Xs cancel out, simplifying the expression for
the receiving sensitivity, i.e., equation (3b). This is the basis for Tri-Transducer method, which has been tested forover 20 combinations. Results show good to excellent agreement with those from direct or indirect methods. The
TT method can also provide the transmitting sensitivities of transducers, allowing a check of the laser method.
Displacement vs. velocity calibration terminology is examined, in view of ill-defined “V/bar” reference usedin commercial reporting of sensor property. Returning to the origin of its introduction by Dunegan [30], definition
from hydrophone calibration standards clarified the reference. Procedure is given for converting between thevelocity sensitivities in reference to unit velocity of 1 m/s and to acoustic pressure of 1 bar.

ACKNOWLEDGMENT
The author wish to thank Dr. Hideo Cho of Aoyama Gakuin University, Sagamihara, Japan for running laserinterferometer measurement. He also appreciates valuable discussion over many years with Dr. Al Beattie, Mr. Hal
Dunegan, Prof. M.A. Hamstad, Dr. A.A. Pollack, Dr. Markus Sause as well as with his colleagues at UCLA, Profs.
Ajit Mal and Vijay Gupta.

㸫S18㸫

REFERENCES[1] Sachse, W. and Hsu, N.N.; Ultrasonic transducers for materials testing and their characterizations, PhysicalAcoustics, 1979, 14, Academic Press, New York. pp. 277-406.
[2] Hsu, N.N.; Breckenridge, F.; Characterization of acoustic emission sensors. Mater. Eval., 1981, 39, 60–68.
[3] Hill, R.; Reciprocity and Other AE Transducer Calibration Techniques, J. Acoust. Emiss., 1982, 1, 73-80.
[4] ASTM E1106-12 Standard method for primary calibration of acoustic emission sensors, 2016, ASTM
International, West Conshohocken, PA, USA, 12 p.
[5] ASTM E1781-13 Standard Practice for Secondary Calibration of Acoustic Emission Sensors, 2016, ASTM
International, West Conshohocken, PA, USA, 7 p.
[6] Breckenridge, F.R.; Tscheigg, C.; Greenspan, M.; Acoustic emission; some applications of Lamb’s Problem.
J. Acoust. Soc. Am., 1975, 57, 626–631.
[7] Burks, B.; Re-Examination of NIST Acoustic Emission Sensor Calibration: Part I - Modeling the loading fromglass capillary fracture, J. Acoust. Emiss., 2011, 29, 167-174.
[8] Hamstad, M.A.; Re-Examination of NIST Acoustic Emission Sensor Calibration: Part II - Finite element
modeling of acoustic emission signal from glass capillary fracture, J. Acoust. Emiss., 2011, 29, 175-183.
[9] Burks, B.; Hamstad, M.A.; An Experimental-numerical Investigation of the Face-to-face Sensor Characterization Technique, Mat. Eval., 2015, 73, 414-423.
[10] ASTM E976-10 Standard Guide for Determining the Reproducibility of Acoustic Emission Sensor Response,
2016, ASTM International, West Conshohocken, PA, USA, 7 p.
[11] http://www.npl.co.uk/acoustics/underwater-acoustics/products-and-services/calibration-of-hydrophones-andprojectors Last updated on Mar. 25, 2010.
[12] Esward, T.J.; Robinson, S.P.; Extending the frequency range of the NPL primary standard laser inteferometerfor hydrophone calibration to 60 MHz, IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 1999, 46(3), 737–744.
[13] Theobald, P.D.; Robinson, S.P.; Thompson, A.D.; Preston, R.C.; Lepper, P.A.; Wang, Y.; Technique for thecalibration of hydrophones in the frequency range 10 to 600 kHz using a heterodyne interferometer and an
acoustically compliant membrane, J. Acoust. Soc. Am., 2005, 118, 3110-3116.
[14] Ludwig, G.; Brendel, K.; Calibration of hydrophones based on reciprocity and time delay spectrometry, IEEE
Trans. Ultrason. Ferroelectr. Freq. Control, 1988, 35(2), 168–174.
[15] Radulescu, E.G.; Lewin, P.A.; Wojcik, J.; Nowicki, A.; Calibration of ultrasonic hydrophone probes up to 100
MHz using time gating frequency analysis and finite amplitude waves, Ultrasonics, 2003, 41, 247–254.
[16] Wilkins, V.; Koch, C.; Amplitude and phase calibration of hydrophones up to 70 MHz using broadband pulse
excitation and an optical reference hydrophone, J. Acoust. Soc. Am., 2004, 115, 2892-2903.
[17] Hill, R.; Adams, N. L.; Reinterpretation of the Reciprocity Theorem for the Calibration of Acoustic Emission
Transducers Operating on a Solid, Acustica, 1979, 43, 305-312.
[18] Ono, K.; Calibration Methods of Acoustic Emission Sensors, Materials, (submitted for publication), 2016.
[19] Hatano, H.; Watanabe, T.; Reciprocity calibration of acoustic emission transducers in Rayleigh-wave andlongitudinal-wave sound fields. J. Acoust. Soc. Am., 1997, 101, 1450–1455.
[20] NDIS-2109-91 Method for absolute calibration of acoustic emission transducers by reciprocity techniques,
1991, revised 2004, Japanese Soc. for Non-destructive Inspection, Tokyo, Japan.
[21] Goujon L.; Baboux J C.; Behaviour of acoustic emission sensors using broadband calibration techniques, Meas.
Sci. Technol., 2003, 14, 903–908.
[22] Keprt, J.; Benes, P.; A comparison of AE sensor calibration methods, Proc. The 28th European Conf. on
Acoustic Emission Testing, 2008, Cracow Univ. Tech., Krakow, Poland. pp. 19-24.
[23] Monnier, T.; Dia, S.; Godin, N.; Zhang, F.; Primary Calibration of Acoustic Emission Sensors by the Methodof Reciprocity, Theoretical and Experimental Considerations, J. Acoust. Emiss., 2012, 30, 152-166.
[24] Scruby, C.; Drain, L.E.; Laser Ultrasonics; Techniques and Applications. 1990, Taylor & Francis, London.
[25] Ono, K.; Cho, H.; Matsuo, T.; Transfer functions of AE sensors, J. Acoust. Emiss., 2008, 26, 72-90.
[26] Savitzky, A.; Golay, J.E.; Smoothing and Differentiation of Data by Simplified Least Squares Procedures,
Analytical Chem., 1964, 36, 1627-1639.
[27] Redwood, M.; Transient performance of a piezoelectric transducer, J. Acoust. Soc. Am., 1961, 33(4); 327-334.
[28] Kossoff, G.; The Effects of Backing and Matching on the Performance of Piezoelectric Ceramic Transducers,
IEEE Trans. Sonics and Ultrasonics, 1966, SU-13, 20-30.
[29] Proctor, T.; An improved piezoelectric AE transducer, J. Acoust. Soc. Am., 1982, 71, 1163–1168.
[30] Dunegan, H.L.; private communication, 2008.
[31] ANSI SI.20-1972, Procedures for Calibration of Underwater Electroacoustic Transducers and IEC 62127-2,
Ultrasonics-Hydrophones.

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PROGRESS in ACOUSTIC EMISSION XVIII, JSNDI & IIIAE

Classification of acoustic emission signals using wavelets andRandom Forests : application to localized corrosion
N. Morizet, N. Godin, J. Tang, M. Fregonese, B. Normand
INSA de Lyon, MATEIS Laboratory -UMR CNRS 5510. 7, Avenue Jean-Capelle 69621 Villeurbanne Cedex,
France.
ABSTRACT: This work aims at proposing a novel approach to classify acoustic emission (AE) signals derivingfrom corrosion damage, even if embedded into a noisy environment. Tests involving noise and crevice corrosion
are conducted, by preprocessing the waveforms including wavelet denoising and extracting a rich set of features as
input of the Random Forest algorithm. To this end, a software called RF-CAM has been developed. Results show
this approach is very efficient on ground truth data and is also very promising on real data, especially for its
reliability, performance and speed, which are serious criteria for the chemical industry.

1 GENERAL SPECIFICATIONSConsidering the crevice corrosion process, emitted gas (i.e. bubbles) coming from chemical reactions generate AE
activity, which can be recorded by sensors located on the surface of the specimen. Since AE signals associated tocrevice corrosion are characterized by low energy content, it is very difficult to separate those signals from theenvironmental noise [1, 2]. Thus, an in-depth work has been realized to preprocess the corresponding waveforms
and a major motivation was to find the most relevant set of features. Chosen classification algorithm must be fast,
reliable and not very sensitive to a mislabeled learning database (due to real-time and reliability industrial
constraints). Moreover, it is preferable to provide a confidence level for the final decision. A whole approachcombining the waveform preprocessing and Random Forest supervised classification has been implemented.
To validate this new methodology, synthetic data were first used throughout an in-depth analysis, comparing
Random Forests (RF) to the k-Nearest Neighbor (k-NN) algorithm, in terms of accuracy and speed processing.
Then, tests on real cases involving noise and crevice corrosion were conducted. In order to build up various data
sets, pH, temperature, NaCl concentration and H2O2 addition were controlled to obtain controlled crevice
corrosion for some experiments and no corrosion for the others. The purpose of the classification is to isolate AE
signals from corrosion to noise.

2 WAVEFORM PROCESSING
This important preliminary step is performed on waveforms directly acquired from sensors. The motivation here is
to normalize those AE signals for consistent comparison. It is possible to discard useless information, numericallystore the waveforms for further analysis and denoise them. The waveform preprocessing consists in the threefollowing steps : Pre-trigger removing, tail cutting, Shape Preserving Interpolation (SPI) resampling.
Tail cutting resides in dynamically cutting the end of the waveform according to an energy criterion. For each pointin the waveform, the cumulative energy computed from the beginning is compared to the energy contained in a 10
Ps length window following that point. If this energy is less than a certain threshold T (in %) of the cumulative
energy, then the corresponding point represents the end of the signal.
Wavelet denoising [3] can also be performed and uses the wden function from the Matlab Wavelet Toolbox. Specificparameters have been set: the universal threshold of Donoho [4] is used to select the wavelet coefficients incombination with a soft thresholding being rescaled using level dependent estimation of level noise. Decomposition
is made at level 3 with the symmlet8 as the mother wavelet.

3 FEATURES EXTRACTION AND RANDOM FOREST CLASSIFICATION
Thus, each waveform is turned into a compact representation through a set of 30 features, in time, frequency andwavelet domains (Tab. 1). Besides common features such as amplitude, duration, energy, rise time, partial powers
or peak frequency, other features derive from speech recognition and sound description studies. Wavelet features
are actually a specific set of features using wavelet packet energy [5]. The energy percentage of the terminal nodesof the wavelet packet tree is computed, leading to 8 wavelet packet energy features.

㸫S20㸫The supervised classification is based on Random Forests which are an ensemble learning method that operate by
constructing a multitude of decision trees during training, each capable of producing a response (vote) whenpresented with a new set of features during testing. The algorithm was originally developed by Leo Breiman and
Adele Cutler in 2001 [6]. During the testing phase, each AE signal is ran down each tree of the Forest, leading to T
votes. The final decision can be obtained two different ways. The first one is simply the usual majority voting (MV)rule. In this work, another 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 70% of the total number of trees voted for that class. The wholeapproach combining the waveform preprocessing and the RF supervised classification has been implemented into
the software RF-CAM (“Random Forests Classification for Acoustic emission Monitoring”).
Table 1 : The set of the 30 features. Those features are recalculated from waveforms. “L” = Low-pass filter, “H” =High-pass filter. Thus, “LLH” consists in cascading two low-pass filters and one high-pass filter.

4 VALIDATION ON GROUND TRUTH DATA

Ground truth data come from the synthetic dataset collected in [7]. Those data represent four clearly identifiedclasses (2000 signals per class) and are described with a set of M = 9 features. A Training Set is built, comprised of
70% of those data (5600 signals taken at random), the remaining 30% (2400 signals) constitute the Testing Set. In
order to test the robustness of both algorithms regarding mislabeled data and the introduction of noise and outliers,
a new evaluation tool called the alter-class matrix (ACM) is used [8]. The ACM is a particular n-square matrix
designed to alter the original training set in order to simulate uncertainty on labeled data for supervised classification.
For example, the class C1 becomes C1alter, composed of 79% of waveforms from C1, 2% from C2, 15% from C3
and 4% from C4. Figure 1 shows the alteration of the class C1 from the Training Set, for different values of the trust

㸫S21㸫

factor (100, 90 and 60). Forcing the data to be mislabeled using the ACM can also be seen as a random andprogressive introduction of noise and outliers in the original dataIn all these tests, RF is compared to the widely used and efficient k-Nearest Neighbor (k-NN) algorithm [9]. The
following parameters have been set for the algorithms:- Random Forests: The number of trees T = 200 has been set, the number of randomly selected features has been
set to the recommended value for classification, i.e. m = ¥M where M is the total number of features.
- k-NN: The optimal value of k = 15 derives from the leave-one-out cross-validation (LOOCV) method [10], where
the usual Euclidean distance is used. Recognition rates are computed for both RF and k-NN algorithms. They
correspond to the ratio of the number of predicted labels to the number of true labels. Globally, RF outperforms kNN up to 10% and is less sensitive to a slightly mislabeled library. For example, the recognition rates on synthetic
data for both RF and k-NN algorithms are respectively 96,1 % and 88,9 for a trust factor of 70. Moreover, k-NN isglobally linearly sensitive to the increase of the number of waveforms and the number of features whereas RF is
almost not influenced.


Figure 1: Alteration of the class C1 from the Training Set, for different values of the trust factor (100, 90 and 60).


5 APLLICATION TO THE REAL CORROSION DATAExperiments are conducted on 304L stainless steel. Samples are immersed in a corrosive solution (with different
values of NaCl (2g/L or 35g/L), pH (6.7, 8.3 or 10.5) and temperature (25 °C or 50 °C). In average, each corrosion
experiment has been conducted twice under the same conditions. The pre-treatment (before immersion) of samples
is performed step by step as follows: grinding to 400#, rinsing, chemical passivation (20 vol.% HNO3 during 1 hour,at room temperature), drying in air. Stainless steel sheet is then assembled by two formers made by polymethyl
methacrylate (PMMA). This latter device allows two confined areas on both sides of the specimens in order toenhance crevice corrosion (Fig. 2). The open circuit corrosion potential (OCP) is continuously recorded owing to asaturated calomel electrode (SCE) as a reference (Fig. 2).

㸫S22㸫

Figure 2 : Experimental device.
The AE acquisition system is a Mistras AEDSP embedded computer board. The sensors (R15) are applied on thesurface of the specimen, outside the corrosive solution (distance sensors/sample = 40mm). R15 sensors have beenchosen because of their good frequency sensitivity around 150 kHz. Sensor coupling is performed using vacuum
grease. To ensure the repeatability of the results, assembly torque is controlled using a dynamometric key and set
to 3Nm. Acquisition parameters were set as follows: peak definition time (PDT) = 200 Ps, hit definition time (HDT)= 400 Ps, hit lockout time (HLT) = 200 Ps. Acquisition threshold depends on environmental noise, thus varying
from 19dB to 28dB. An optimal sampling frequency equals to 4MSPS (and 4K points per waveform) has been set
as a very good compromise between the size of the data to process (real-time constraint) and the robustness of theextracted features (reliability constraint). Then, a set of 30 features are extracted from recorded waveforms (Tab. 1).
Some experiments are performed with the addition of H2O2 in order to accelerate corrosion.
The OCP drop shows that crevice initiates as soon as H2O2 is added. For the sake of illustration, experiments
presenting no corrosion and performed at a higher temperature are depicted in Figure 3. The specimen
exhibiting no corrosion presents higher activity than the corroded one because the corresponding test is
conducted at 50°C, which induces a high acoustic activity due to the dilatation of the device and the generationof bubbles, together considered as noise. In order to build various data sets, pH and temperature values, NaCl
concentration and H2O2 addition are controlled to obtain crevice corrosion for some experiments and no
corrosion for the others. 13 out of 17 experiments (almost 75%) are used for the training set. In absence ofcorrosion, OCP does not decrease (Fig. 3a). However, even if there is no corrosion, some AE activity is observed
all along the experiment. These signals are attributed to noise (i.e. dilatation phenomena and bubbles evolution
within the liquid due to the temperature of the test (50 °C)). A first class (denoted as NC) is built from 1200
signals. Regarding the experiments involving crevice corrosion (Fig. 3b), AE activity starts prior to the additionof H2O2, and is mainly assigned to noise. After H2O2 addition, gathered signals are mainly assigned to corrosion
and constitute a second class (denoted as CC), composed of 1167 signals. It has to be noticed that this CC class is
not pure and also contains noise signals. The previous study on the use of the ACM for altered data shows thatrecognition results are satisfactory even if classes are altered up to 20%. Besides, since there is no class-privilege
in real conditions, a special attention has been paid to build a well-balanced training set, for a total of 2367
waveforms. The remaining four experiments are used to construct the different testing sets. Two of thempresent no corrosion (namely NCa and NCb), the other two show crevice corrosion (namely CCa and CCb),for a total of 1311 waveforms. Preprocessing and features extraction have been applied to all waveforms.

㸫S23㸫

E

a)

b)
Figure 3: OCP and AE monitoring of crevice corrosion for a) a specimen without corrosion and b) a specimen withcorrosion.
The percentage of signals for the Majority Voting (MV) rule is given regarding the original total number of signalsof the test set. The percentage of signals for the Security Voting (SV) rule is given regarding the number ofremaining signals, after the security threshold of 70% is applied.
For each test (Table 2), the proper majority class has been recognized and results show that using SV leads to areinforcement of the usual MV decision, when it comes to assign signals to a specific class. Moreover, for each test
case, most of the signals corresponding to the minority class are discarded, thus strengthening the trend of the
majority class.
Table 2: RF classification results of the proposed method for NC and CC experiments.

㸫S24㸫([SHULPHQW
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NCb
CCa
CCb

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179 (76.8%)
103 (92.8%)459 (95.6%)
410 (98.3%)
152 (34.9%)
89 (29.3%)
69 (42.3%)
40 (37%)

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54 (23.2%)
8 (7.2%)21 (4.4%)
7 (1.7%)283 (65.1%)
215 (70.7%)
69 (42.3%)
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MV
SV
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
6 CONCLUSIONSResults show that this approach performed the best on ground truth data and was also very promising on real data,
especially for its reliability, performance and speed, which are serious criteria for corrosion monitoring in thechemical industry. The results associated to the usual majority voting (MV) rule were satisfactory in terms of finding
the proper majority class. In order to take into account the industrial reliability constraint, another decision rulecalled security voting (SV) has been implemented as a confidence level (set to 70%), and reinforced the final
decision process taken by the MV.
Future prospects to be considered are the enlargement of the learning library in order to identify other corrosion
mechanisms such as pitting corrosion. Finally, this methodology which was developed and validated at thelaboratory scale can be applied at the industrial scale, providing the construction of a consistent new learning library.
ACKNOWLEDGEMENTS
The authors wish to thank the French Ministry of Economy, Finance and Industry alongside the AXELERA
competitiveness cluster for their financial support, within the FUI IREINE project (”Innovation for the REliabilityof INdustrial Equipments”). The authors are also grateful to Solvay, IFPEN, Arkema and Mistras industrial partners
for their technical collaboration. The authors are also thankful to R. Di Folco for his technical support regarding thecorrosion experiments.
REFERENCES[1] Y. Kim, M. Fregonese, H. Mazille, D. Feron, G. Santarini (2006), Study of oxygen reduction on stainless steel
surfaces and its contribution to acoustic emission recorded during corrosion processes, Corrosion Science 48
3945–3959[2] Y. Kim, M. Fregonese, H. Mazille, D. Feron, G. Santarini (2003), Ability of acoustic emission techniquefor detection and monitoring of crevice corrosion on 304l austenitic stainless steel, NDT&E International
36 553–562.
[3] D. Donoho (1994), I. Johnstone, Ideal spatial adaptation by wavelet shrinkage, Biometrika 81 425–455[4] D. Donoho (1995), De-noising by soft-thresholding, IEEE Transactions on Information Theory 41 613–627.
[5] R. Coifman, Y. Meyer, S. Quake, V. Wickerhauser (1994), Signal processing and compression withwavelet packets, in: J. S. B. et al. (Ed.), Wavelets and Their Applications, volume 442, Springer, pp. 363–
379.
[6] L. Breiman (1996), Bagging predictors, Machine Learning 24 123–140.
[7] A. Sibil, N. Godin, M. R’Mili, E. Maillet, G. Fantozzi (2012), Optimization of acoustic emission dataclustering by a genetic algorithm method, Journal of nondestructive evaluation 31 169–180.
[8] N. Morizet, N. Godin, J. Tang, E. Maillet , M. Fregonese, B.Normand (2016), Classification of acoustic
emission signals using wavelets and Random Forests : application to localized corrosionMechanical
Systems and , Signal Processing, Volumes 70–71, 1026-1037[9] T. Cover, P. Hart, Nearest neighbor pattern classification (1967), IEEE Transactions on Information
Theory 13 21–27.
[10] S. Arlot, A. Celisse (2010), A survey of cross-validation procedures for model selection, Statistics
Surveys 4 40–79.

㸫S25㸫

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㸫S31㸫

PROGRESS in ACOUSTIC EMISSION XVIII, JSNDI & IIIAE

Source of AEs from IG-SCC of Face Centered Cubic Metals under Staticor Dynamic Straining
Mikio Takemoto, Shuichi Ueno and Motoaki Nakamura
Kanmeta Engineering Co. Ltd,Nakanocho-Higashi,2-3-54, Tondabayashi, Osaka, 584-0022,JapanABSTRACT: Stress Corrosion Cracking (SCC) of Face Centered Cubic (FCC) metals such as sensitized Type -304 and -316 by
tetra-thionic acids and 70Cu/30Zn brass by ammonia vapor are intergranular (IG) type. These SCCs produce no AE when static
SCC test are done. However, when we employ a dynamic strain test such as strain increasing test, these SCCs produce AEs even if
it is done in non-corrosive environment. Detail SEM observation of fracture surfaces suggests that the coincident twin/austenitic
grain boundaries with low energy are broken mechanically by dynamic loading and produce AEs. This paper discusses
relationship between the extrusions observed on the fracture surface and the coincident grain boundaries.

1 INTRODUCTION
Among the environmental assisted cracking, active path corrosion type stress corrosion cracking (APC-SCC) has been recognized
to emit no AE[1][2][3]. Typical example of the APC-SCC is the chloride SCC of austenitic stainless steel and transgranular (TG)type cracking. Though a very few AE can often be monitored for a limited material -environment combination, these AEs aremostly from the cracking of the corrosion products in the SCC, and not the primary AE from the initiation and propagation of theSCC[4]. Independently on the crack types (TG or IG) , we never monitor the primary AEs as long as a static loading such as bent
beam testing is utilized. Authors have, however, reported that the primary AEs can be monitored for the IG-SCC when acontinuously increasing load such as CERT (constant extension rate testing) is employed. We do not necessary need the CERT for
producing the primary AEs, rather the dynamic loading or stepwise or continuously increasing loading is enough. The authors havecalled these AE behaviors as the unique AE so far[5]. However we do not understand the source mechanism of the unique AE.Once Takemoto estimated that the AE are possibly produced b\IUDFWXUHRIWKH³ZHDNO\ERQG intact SRUWLRQ´RIWKHJUDLQERXQGDU\
for the IG-SCC of Type-304 steel[6]. He also reported that WKH³H[WUXVLRQOLNHSURMHFWLRQV´ZHUHRIWHQREVHUYHGRQWKHJUDLQboundary. However we could not understand what is the extrusion and how the extrusion is produced by what mechanism.
This research aims to study the possible source mechanism of AEs which are monitored during the crack opening operation or
dynamic loading of the member which previously suffered IG-SCC. We estimate the source mechanism of the AEs, based on thedetail fractographic observation of the fresh fracture surface produced by the crack opening operation. We insist an importance ofthe twin/austenite low energy grain boundary or coincident grain boundary in producing AEs..

EXPERIMENTAL
Two kinds of material/environment combinations, sensitized stainless steels: Type-304 and -316 strips(2~3mmT-20mmW-67mmL) in 1 mass% tetra-thionic acid solution ( pH=2 ) and as-received 70Cu-30Zn brass in ammonia vapor(mainly 8% vapor) are tested. SCCs were induced into these strips by three point bending (static loading) SCC test and thenwashed by an ultrasonic washer three times and dried by hot air at being loaded. Some strips were kept in a desiccator for more
drying. These strips were then submitted to a crack opening operation in air mainly, but for special aims in dry nitrogen gas or the
corrodant. The crack opening aims to open the closed cracks in front of open SCC, and was done by turning the central bolt of thebending device continuously. Thus the crack opening operation is dynamic or continuously load increasing type. AEs weremonitored by the two resonant type small sensors (PAC PICO) attached on the convex surface of the bent strip, and amplified to
40dB by a preamplifier and stored in a personal computer as digital data. Signals were analyzed by a home-made ADAS system.

㸫S32㸫

3 RESULTS AND CONSIDERATION
3.1 IG-SCC of sensitized Type-304Steel by tetra-thionic acid
Using the procedure of Figure 1, we first studied how long the closed IG-SCC was. Here the closed SCC implies the gain
boundaries which could not be recognized as the crack by transverse microscopic observation.
The SCC strip tested by three point bending at arc height(AH) of 0.6 mm was longitudinally cut into two pieces by wire discharge

cutting. One strip(A) was submitted to the AE
monitoring and transverse microscopic observation after thecrack open operation, and another one(B) to microscopic
observation without opening operation. Figure 2 compares thecrack depth before and after the opening operation. It can beseen that closed crack length reaches to 0.42 mm in this case.
Figure 3 compares the SEMs of open (the left) and closed
crack. The surface of newly produced by the opening
operation are free from thick film (tarnish film of FeS) whilethat of the open crack possesses thick film. We can not obtainany fractographic information from the open crack but can fromclear fracture surface. Figure 4 shows AE behavior during the static SCC test and the crack opening operation. We monitored no
AE during 22 hours of the static SCC test (three point bending test), but 37 signals during the opening operation in air. Above thearc height (AH) of 1.3 mm, we again observed no AE since the attacked grains (closed crack) was completely opened. Figure 5

㸫S33㸫shows SEM of transverse surface after the opening operation (Specimen A of Fig.1). There observed both the deeply attacked grainboundary and not attacked boundary. The former grain boundary corresponds to the random grain boundary or high energy
boundary. Contrary, non-attacked portion corresponds tothe coincident grain boundary with low energy. It isreported that the coincident boundaries are often
produced at twin/austenite grain boundary[7][8]. Thisboundary seems to correspond WRWKH³LQWDFWERXQGDU\´
in authors previous report [6]. We also observe grain
rotation and steps between the grains. We monitored
fluctuation of the strain using the strain gage mounted
over the cracks, and found rapid fluctuation with strain
amplitude of 100x10-6 frequently. This means that thegrains in the closed crack region actively move androtate and produce AEs when the intact portion or thecoincident grain boundary was mechanically fracture. Figures 6,7and 8 are SEMs of the fracture surface produced by the opening
operation.
As the Figure 6 is the fractography of the grain±falling from the random grain boundaries, we do not observe any extrusion.
Contrary, Figure 7 of the new fracture surface produced by the crack opening operation, we observe many extrusions. These
extrusions are considered to be produced by tearing-off of the twins from the austenitic grain boundaries. We also observe the open

sub-cracks or the open crack to the fracture surface, aroundthe grains with the extrusions. This is because the grain colony
with many extrusions are mechanically separated from theneighboring grains. Figure 8 shows small size cracks andin-tact portion (size in few micrometers) on the grainboundary. We observe many extrusions on the grain a.
We next studied the fluctuation of corrosion potential during
the step-wise load increase at the end of static SCC test in thetetra-thionic solution. As shown in Figure 9, we observed both
the RD (Rapid drop) type corrosion and AE generation during
the step increasing of AH. This strongly implies that newelectrochemically active fresh surfaces are produced bymechanical breaking of the coincident grain. Amplitude of the corrosion drop reaches 8mV.

㸫S34㸫

3.2 IG-SCC of sensitized Type-316 by tetra-thionic acid
Sensitized Type-316 strip (700 Υ x 2 hours), as well the cold-rolled and then sensitized ones, show higher resistance against the
tetra-thionic SCC than the Type-304 steel. However we observes the same AE behavior as those for the Type-304, ie., no AE
during the static SCC test , but AEs of 30 to 60 counts during the crack opening operation. Here the authors show only the SEMsof the opened fracture surface. Figure 10 is SEM of the sensitized Type-316 of as-received strip and shows more star-fish typeextrusions than the Type-304. Higher resistance of the Type-316 seems to be due to the frequent extrusions or the low energy
boundaries. We can produce only one IG-SCC for the cold rolled (35%) and sensitized (700Υx2 hours) strips in pH=1.8 after
456 hours. We did not monitor AE during static SCC test but detected AEs during the crack opening operation. Figure 11 shows anumber of extrusions in newly produced fracture surface, but no extrusion in the static SCC surface. The coincident grain boundaryof the twins produced by cold working seems to be resistant to the IG-SCC.

3.3 IG-SCC of as-received 70Cu/30Zn brass in ammonia vapor
Among the brasses, 70%Cu/30%Zn brass shows IG-SCC in ammonia vapor from 2.5 mass % to 8 mass % at room temperature.
We mainly studied IG-SCC of 70Cu/30 Zn brass in 8% ammonia vapor. Contrary the 60Cu/40Zn brass does not suffer TG-SCC
and emit no AE during both the static SCC test and crack opening operation.
Figure 12 compares the crack length before and after the opening operation. The crack opening operation extends the SCC depth
from 0.94 mm to 1.65 mm. Fractographic observation, Figure 13, revealed that the fracture surface by the static SCC is covered bythick corrosion products, but that by the opening operation is free from the products which makes detail SEM observation possible.

㸫S35㸫

Figure 14 shows AE behavior during the static SCC test andcrack opening operation. We also did not monitor AE during the static SCC test but monitored much AE signals during the crack
opening. There fund to be two types of AEs, i.e., high frequency signals with frequency components from 300 to 500 kHz and low
frequency component below 150 kHz, as shown in Figure15. Number of high frequency signals is slightly larger than those of low
frequencies.
Examples of SEM of the new fracture surface are shown in both Figures 16 and 17. Figure 16 represents extrusions on the grains
and open sub-crack around the grain with extrusions. Detail SEM observation revealed that there are another type of fracture asshown in Figure 17. We observe twin at the center of the photo, and quasi-cleavage fracture in the size of less than 25 ȣm. As thebrass is much brittle than the austenitic stainless steel, this alloy is likely to suffer transgranular brittle fracture. This type of fracture
is considered to correspond the mechanical cleavage fracture, suggested by the Film Induced Cracking (FIC)[9]. We did notobserve the cleavage fracture started by brittle de-alloyed film during static loading, however, there is a possibility of the cleavage
brittle fracture when we use dynamic strain increasing. We also monitored AE signals during the crack opening operation inammonia vapor. It is not clear which signal with high frequency or low frequency is produced by which type of fracture shownin Figures 16 and 17.

Figure 18 represents another example of brittle fracture whichcontains both the extrusion like projection and transgranular (TG)cleavage fracture. TG cleavage fracture suggests that theammonia vapor impregnates into the cleavage plane andweakened the cohesion strength.
The AE behavior shown in Figure 14 is not limited to theammonia SCC of the 70/30 brass. In the Mattson¶s solution㸫S36㸫

(pH=7.2, NH4OH+(NH4)2SO4+CuSO4 soluti0on, recommended
method by ASTM ), as shown in Figure 19, the 70Cu/30Zn brassemits no AE during the static SCC test but emits AEs during thecrack opening operation.

5 CONCLUSIONS
Acoustic emission behavior is studied for the inter-granular
SCC of sensitized Type-304 and -316 stainless steels in
tetra-thionic acid and 70Cu/30Zn brass and ammonia vapor.
Conclusions are summarized below:
1) We monitored no AE during static bending SCC test for
both material/environment combinations, however monitored AEs during the crack opening operation (continuously loadincreasing or step wise load increasing) in air and in corrosive environment.
2) AEs from APC-SCC can be monitored only the dynamic loading to the IG-SCC. We can not monitor any primary AEs fromtransgranular type SCC.
3) Detail SEM observation suggests that the AEs during the crack opening operation are produced by mechanical fracture of thecoincident grain boundary in case of both austenitic stainless steels and brass, and possibly by the quasi-cleavage transgranular fracture for the 70Cu/30Zn brass.
4) The intact grain boundary in our previous paper is considered to be the portion of low energy coincident grain boundary, suchas the twin band/austenite grain boundary. The extrusion-like projections observed on the separated grain surfaces in
non-corrosive environment appears to be the fracture trace of the coincident grain boundary.

5 REFERENCES[1]Hideya Okada, Ken-ichi Yukawa and Hideo Tamura, Application of Acoustic Emission Technique to Study of Stress Corrosion
Cracking in Distinguishing Between Active Path Corrosion and Hydrogen Embrittlement, Corrosion, 30-7(1974)pp. 253-255[2]Kaita Ito, Hisashi Yamawaki, Hiroyuki Masuda, Mitsuharu Shiwa and Manabu Enoki, SCC Monitoring of Chloride Dropletson Thin SUS304 Plate Specimens by Analysis of Continuous Recorded AE Waveform,  Materials Transaction,
51-8,(12010)pp.1409-1413[3]M.Shiwa, H.Yamawaki, H.Matsuda, K.Ito and M.Enoki, Continuous And Burst AE Signal Analyses During Chloride Droplet
SCC on Thin Plate of SUS304 Progress in Acoustic Emission XIV, 2008, pp.129-134[4]M.Takemoto, H.Cho and K.Ono, Acoustic Emission during Stress Corrosion Cracking Tests, Duetsche Gesellshaft fur
Zerstorungsfreie Prufung, 26th Europian Conference on Acoustic Emission Testing, (2004)pp. 617-629[5] Mikio Takemoto㸪Motoaki Nakamura and Shuichi Ueno, Unique AE from Ammonia Stress Corrosion Cracking ofBrass, Progress in Acoustic Emission XVI , (2012), pp139-144.
[6] Mikio Takemoto, Contribution of Acoustic Emission to Stress Corrosion Cracking Research, Advances in Acoustic
Emission,(2007)pp.414-422[7]M.Michiuchi, H.Kokawa, Z.J.Wang , Y.S.Sato and K.Sakai, Acta Materialia, 54, pp.5179-5184(2006)[8]Hiroyuki Kokawa, Grain boundary Engineering, Application to Austenitic stainless steel II, Materia Japan, 52-2(2013)pp. 64-67[9]K.Sieradzki and R.C.Newman, Brittle Behaviour of Ductile Metals During Stress Corrosion Cracking, Philosophical
Magazine A,.51-1㸪(1985)pp.95-132

㸫S37㸫

PROGRESS in ACOUSTIC EMISSION XVIII, JSNDI & IIIAE

Detection of Acoustic Emission Signals with the Fabry-Perot
Interferometer Type Optical Fiber Sensor

Kazuki Tada1) and Hironobu Yuki1)
1) The University of Electro-Communications, 1-5-1, Chofugaoka, Chofu-shi, Tokyo, 182-8585, Japan
ABSTRACT: In order to improve the convenience in handling for measuring acoustic emission (AE) signals with
optical fibers, an application of the Fabry-Perot interferometer to optical fiber AE sensor was investigated. The
Fabry-Perot interferometer made of a pair of fiber Bragg gratings was adhered to the inside of a cylindricalsensing block for detecting the out-of-plane component of the displacement caused by AE waves. It wasdemonstrated that the proposed sensor could detect artificial AE signals due to steel ball dropping. Characteristicsof the sensor were discussed by comparing with the result of the piezoelectric sensor.

1 INTRODUCTION

Various type of optical fiber acoustic emission (AE) sensors that are known to have advantages of robustness
against electromagnetic noise, resistance to water, applicability in high temperature, etc. have been proposed inorder to overcome difficulties in using conventional piezoelectric AE sensors [1-9]. AE measurement withinterferometer type sensors is performed by paying attention to the change of the light intensity so that only thelight source and the optical detector are required to construct the monitoring system, while the sensors based onother principles sometimes need equipment to examine wavelength shift, traveling time of the light, etc. One of
the weak points of optical fiber sensors in comparison with the conventional piezoelectric sensors is the cost of theequipment for operation. Therefore, interferometer type sensors seem to be suitable from this point of view.
We have developed the Mach-Zehnder interferometer type optical fiber AE sensor with a cylindrical sensing
block for the purpose of usability, such as detecting component and portability, same as conventional piezoelectric
AE sensors [7]. However, handling of a reference fiber which composes the interferometer significantly affects
the stability of sensing so that the ingenuity of fiber holding or the extra equipment avoiding the influence ofdisturbance are sometimes required. Since the Fabry-Perot interferometer is constructed without the reference
fiber, the sensor based on the Fabry-Perot interferometer is preferable from the viewpoint of the sensing stability
due to fiber handling. Although some Fabry-Perot interferometer type AE sensors have been already proposed[1,3], those sensors seem to be not always convenient because of requiring precise assembly to construct theinterferometer. In this study, the Fabry-Perot interferometer made of fiber Bragg gratings (FBGs) was applied to
the AE sensor in order to improve the convenience in handling, and the proposed sensor was used to detect
artificial AE signals caused by dropping the steel ball.

2 PRINCIPLE AND STRUCTURE OF THE SENSOR
2.1 Principle of the Fabry-Perot interferometer

The schematic of the Fabry-Perot interferometer type optical fiber sensor is shown in Figure 1. Some power oflight provided into the fiber is reflected at the point where made a half mirror, while the rest power of light istransmitted to the endpoint of the fiber and is reflected by a full mirror. Therefore, the interference is occurredcaused by the phase difference in accordance with the change of the distance between the two mirrors. The
intensity of the interference light is described in Equation 1.
‫ ܫ‬ൌ ‫ܫ‬ଵ ൅ ‫ܫ‬ଶ ൅ ʹඥ‫ܫ‬ଵ ‫ܫ‬ଶ ቀͶߨο݈ൗߣቁ;ϭͿ
where I: Intensity of the interference light [mW], ‫ܫ‬ଵ : Intensity of the reflected light at the half mirror [mW], ‫ܫ‬ଶ :Intensity of the reflected light at the full mirror [mW], ο݈: Change of the distance between the half mirror and thefull mirror [nm], ߣ: Wavelength of the light [nm]Figure 1: Schematic of the Fabry-Perot interferometer type optical fiber sensor

㸫S38㸫

This means that the AE sensor can be made by preparing a structure that the distance between the half mirror andthe full mirror is changed according to AE waves.

2.2 Structure of the sensor

In order to prepare the mirrors of the Fabry-Perot interferometer, it is known that FBGs, diffraction gratings
that only reflect the light of the specific wavelength called Bragg wavelength, are applicable. In this study, thesingle mode UV coated silica fiber of 0.25 mm in diameter introducing FBGs at the position where keep a
distance of 2 mm from the end of the fiber as shown in Figure 2 was used. Each length of the FBG is 1 mm andthe distance between two FBGs is 1 mm. The reflectivity of the FBG located at the connector side was set to 50 %,therefore that acts as the half mirror. On the other hand, the other FBG located at the pigtail side acts as the fullmirror because the reflectivity was set to 91 %. The Bragg wavelength and the bandwidth of the reflection
represented by the parameter called the full width at half maximum (FWHM) of both FBGs were 1548.94 nm andapproximately 0.35 nm, respectively.

Figure 2: Fabry-Perot interferometer made of FBGs
A pair of polycarbonate blocks as shown in Figure 3 that were longitudinally halved the cylindrical geometry
was used for making the sensing block. The interferometer part of the fiber mentioned above was adhered to thegrooved block (Figure 3(a)) with cyanoacrylate adhesive by putting the fiber on a half-round groove. Then, thisblock and the non-grooved block (Figure 3(b)) were tightly bonded facing the flat surfaces with cyanoacrylateadhesive. By attaching the bonded block to the specimen, AE waves are propagated to the block in thelongitudinal direction by way of the bottom of the block so that the out-of-plane component of the displacement
caused by AE waves can be detected. Since the fiber related sensing was settled on the central axis of thecylindrical geometry, the sensor is expected to have no directivity. It is also notable that the sensor is convenient
for handling since the whole interferometer part is embedded in the block. Figure 4 shows the appearance of the
proposed sensor.

(a) Grooved block(b) Non-grooved block

Figure 3: Components of the sensing block

Figure 4: Appearance of the proposed optical fiber AE sensor

㸫S39㸫

3 DETECTION OF ARTIFICIAL AE SIGNALS
In order to examine the validity of the strategy for sensing described in Chapter 2, we attempted to measureartificial AE signals by using the proposed sensor. The sensor was attached to the center of an aluminum alloy
plate (120 x 120 x 20 mm) with high vacuum silicone grease and a steel ball (ࢥ10 mm, 4.2 g) was dropped from40 mm height above the opposite side of the plate. The light transmitting in the optical fiber was provided by a
DFB light source (Appointech, B1000; central wavelength: 1549 nm, spectral width: 0.4 nm, output power: 0dBm) with an isolator avoiding the influences of the Fresnel reflection, and the signals of the interference light
were recorded to a digital storage oscilloscope by the sampling rate of 1 s via an O/E converter (Thorlabs,
PDA10CF). The experiment replacing the conventional wide band type piezoelectric AE sensor (NF Corporation,
AE-900S-WB) in the experimental setup was also conducted for the purpose of comparison with the results.
An example of the AE signal detected by the proposed Fabry-Perot interferometer type sensor is shown in
Figure 5. It is found that the remarkable change in the amplitude is observed around the time of 150 s. This fact
implies that the proposed sensor could successfully detect the AE signal. Figure 6 shows result of the experimentusing the piezoelectric sensor. It is found that there is a tendency of the waveform to change in a period of several
hundreds s after arrival of the initial motion of the AE wave. Similar behavior is also found in Figure 5 so thatthe proposed sensor could be used to examine the same phenomenon detected by the piezoelectric sensor. The
amplitude of the waveform detected by the proposed sensor, however, is considerable low rather than that of thepiezoelectric sensor. One of the reasons for low amplitude signals detected by the proposed sensor is due to thefact that the Fabry-Perot interferometer made of FBGs is only workable to the narrow bandwidth filtered lightaround the Bragg wavelength. Further discussion on improving sensitivity is required to apply the proposed sensor
for monitoring AE associated with fracture. On the other hand, the Fabry-Perot interferometer is preferable fromthe viewpoint of sending stability due to fiber handling because the sensor has no reference fiber, while the MachZehnder interferometer type sensor requires the reference fiber which should be fixed in order to avoid thevibration caused by either of AE signals and disturbance. Consequently, it is reasonable to conclude that the
Fabry-Perot interferometer type sensor made by proposed method can improve usability of the interferometer typeoptical fiber AE sensors.


Figure 5: AE signal detected by the proposed Fabry-Perot interferometer type optical fiber sensor

Figure 6: AE signal detected by the piezoelectric sensor

㸫S40㸫

4 CONCLUSION
The Fabry-Perot interferometer type optical fiber AE sensor was proposed for the purpose of improvement theconvenience in handling. The Fabry-Perot interferometer was made of a pair of FBGs and it was adhered to theinside of a cylindrical sensing block in order to detect the out-of-plane component of the displacement caused by
AE waves and have no directivity. It was demonstrated that the proposed sensor could detect the artificial AE
signals caused by dropping the steel ball form the result of the experiment comparing with the piezoelectric sensor.

REFERENCES[1] Shiga, M., Suzuki, G., Yamada, M., Kitahara, M. and Niitsuma, H., (2002) “A Fiber-Optic Micro Sensor for
AE Measurement Fabricated by Micromachining”, Progress in Acoustic Emission XI, pp.152-159.
[2] Matsuo, T. and Takemoto, M., (2002) “Development of AE Monitoring System Using Optical Fibers”,
Progress in Acoustic Emission XI, pp.168-175.
[3] Duke, J.C., Jr., Cassino, C.D., Childers, B.A. and Prosser, W.H., (2003) “Characterization of an Extrinsic
Fabry-Perot Interferometric Acoustic Emission Sensor”, Materials Evaluation, Vol.61, No.8, pp.935-940.
[4] Ohsawa, I., Kageyama, K., Tsuchida, Y. and Kanai, M., (2003) “Development of a Novel Optical Fiber
Sensor for AE Detection in Composites”, Journal of Acoustic Emission, Vol.21, pp.176-186.
[5] Cho, H., Arai, R. and Takemoto, M., (2005) “Development of Stabilized and High Sensitive Optical Fiber
Acoustic Emission System and Its Application”, Journal of Acoustic Emission, Vol.23, pp.72-80.
[6] Yuki, H. and Mine, Y., (2006) “Simultaneous Measurement of Acoustic Emission and Strain Using a Hybrid
Optical Fiber Sensor”, Progress in Acoustic Emission XIII, pp.11-18.
[7] Yuki, H. and Kai, T., (2012) “Structure and Characteristics of the Mach-Zehnder Interferometer Type OpticalFiber AE Sensor with High Portability”, Journal of the Japanese Society for Non-Destructive Inspection,
Vol.61, No.9, pp.488-494 (in Japanese).
[8] Takuma, M., Hisada, S., Saitoh, K., Takahashi, Y., Kobayashi, Y., Kadono, A., Murata, A., Iwata, S. and
Sasaki, T., (2014) “Acoustic Emission Measurement by Fiber Bragg Grating Glued to Cylindrical Sensor
Holder”, Advances in Materials Science and Engineering, Vol. 2014, Article ID 274071,
doi:10.1155/2014/274071.
[9] Wu, Q., Yu, F., Okabe, Y. and Kobayashi, S., (2015) “Application of a Novel Optical Fiber Sensor to
Detection of Acoustic Emissions by Various Damages in CFRP Laminates”, Smart Materials and Structures,
Vol.24, doi:10.1088/0964-1726/24/1/015011.

㸫S41㸫

PROGRESS in ACOUSTIC EMISSION XVIII, JSNDI & IIIAE

Remote imaging of plate-like structures with E-camera
Takahiro Hayashi
Kyoto University, C3-c3S02, Nishikyo-ku, Kyoto, 615-8540, Japan.

ABSTRACT: This study presented the principle of defect imaging technique from a remote location andits experimental results. The experimental system consists of a fiber laser for generating narrow bandburst waves and a laser Doppler vibrometer for detecting flexural vibration in a plate-like structure.
Because a defect image can be obtained at an extremely high speed using the system, we call this Ecamera. The experimental results showed that defect images for aluminum alloy plate and pipe withcomplex geometries can be obtained in the measurements from the distances about 2.3 meters and 6.0
meters.

1 INTRODUCTION
Thin walled large structures are widely used for pipes, bridges, wings and body of aircrafts, and automobile
bodies. To maintain such large structures, in addition to visual inspection, non-destructive testing with X-ray andultrasonic is periodically implemented for essential parts. In ultrasonic non-destructive testing, ultrasonic pulse
echo technique is generally used, in which plate thickness is measured using a contact ultrasonic transducer by theflight time of pulse echo signal. Although the conventional widely-used technique is reliable and highly accurate
in thickness measurements, it is not suitable to inspect whole areas of large structures due to its small
measurement area underneath a transducer.
Guided wave inspection has been considered recently as an effective inspection technique for such large platelike structures. Guided waves are ultrasonic modes propagating along such waveguides as plates, pipes, bars, andrailway rails. Guided wave inspection has been highly expected as an efficient technique for long range
inspection. However, there still have some issues, one of which is that defect echoes cannot be obtained for a
defect in a desired size due to the use of low -frequency range for long-range propagation.
Therefore, the authors have developed defect imaging for plate-like structures using a scanning laser source
technique [1-3]. Because the imaging technique is established with a non-contact and fast measurement and has apotential to be a very easy imaging technique like optical and infrared cameras, we call it E-camera. This paper
describes the principle of defect imaging with E-camera first and introduces some results of the defect imaging.

2 PRINCIPLE OF DEFECT IMAGING WITH E-CAMERAIn E-camera, a pulse fibre laser and a laser Doppler vibromater (LDV) are used for generating elastic waves
and for detecting the waves, respectively. The laser source of elastic wave is rastered by galvano mirror scanners
and then distributions of an amplitude peak or a Fourier spectrum peak are obtained from the waveforms at all
rastering points. Then a phenomenon that generation energy of flexural vibration is enhanced when a laser source
is located on a defect or in the vicinity of a defect was observed and distributions of the vibration energy
correspond to a defect image. The authors analysed the energy enhancement using semi-analytical finite element
calculations and normal mode expansion theory [1, 3]. Figure 1 shows the schematic figure of the principle: (a)for a plate with a widely spread defect like erosion and corrosion and (b) for a plate with a non-volume crack
stretching in the thickness direction. In (a), when the laser beam is irradiated on an intact thick area, small flexuralvibration is generated. On the other hand, if the laser source is located at a thin defected area, the detected signals
at the LDV become large. It is shown in ref. [1] that this phenomenon appears at the frequency range below the
cut-off frequency of A1 mode. In (b), if laser beam is irradiated in the vicinity of a reflective object like a crack,
the interaction of a pair of evanescent waves from the laser source and the reflection object generates extra
vibration energy. On the other hand, if the laser source locates far from the reflection object, the interaction of theevanescent waves becomes small and the energy enhancement cannot be seen. The energy enhancement caused by
the interaction of evanescent modes was discussed in ref. [3].

㸫S42㸫Laser= Elastic wave source

LDV at a fixed point

Thick areaÆSmall signal

Small interaction
Evanescent m

Thin areaÆLarge signal

Laser source
Reflective
objectThick areaÆSmall signal

Energy enhancement
due to interaction of evanesc

Amplitude distributi= Defect indication

Amplitude distribution= Thickness distribution(a) A widely spread defect(b) A non-volume crack
Figure 1 Principle of defect imaging with E-camera.

3 EXPERIMENTAL SET-UP
Figure 2 shows experimental set-up used in this study. A fiber laser was used for generating elastic wave
instead of an Nd: YAG laser that is generally used for elastic wave generation. The fiber laser equipment canradiate laser pulses at a high repetition rate and the laser pulses can be controlled by modulation signals provided
to the laser equipment. In this study, narrowband burst waves were generate using a fiber laser with external
modulation signals in order to improve signal to noise ratio (SNR) in the frequency domain. Using the highrepetition fiber laser in the scanning laser source technique, surface damage by laser radiation can be avoided aswell as the improvement of SNR.
The elastic waves generated by the laser radiation propagated through various paths in the plate-like structure
and were detected by the LDV. An appropriate band-path filter and Fourier transform were performed to thereceived signals in a personal computer, and then frequency peak distributions were obtained. Here, modulation
signals consisting of three rectangular signals at three different frequencies were used, and three distributions
were obtained for the frequency bands. Then, spurious images were reduced by taking the average of the three
distributions [2].

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4 RESULTS
Figures 3 (a) - (c) show defect images obtained by laser irradiation over the region of 80 mm  80 mm
behind the artificial defect at 1 mm increment, and the plate specimen used is illustrated in (d). Schematic figuresof modulation signals used are also shown. The distance between the test plate and the experimental system wasabout 2.3 m. (a) is a defect image for the modulation signals at the repetition rate of 200 Hz (the time interval of 5ms). The modulation signals consisted of three rectangular waves with the time duration of 1 ms connecting inseries. The defect image was an averaged distribution of the three distributions at three frequency bands. The
measurement time resulted in about 33 s (=5 ms (200 Hz) × 81 × 81).
Increasing the repetition rate provides shorter measurement time. (b) and (c) are defect images for therepetition rates of 600 Hz and 1000 Hz, respectively. Although the measurement time became about (b) 11 s and(c) 6.6 s, the images were more largely distorted at the higher repetition rate. Two reasons can be considered in
the image distortions. The first one is reduction of SNR. Increasing the repetition rate resulted in the use of shortertime duration of the modulation signals, which caused the reduction of SNR in frequency range. The second oneis the superposition of waves at previous steps on the wave at the current step. Because waves generated at theprevious steps cannot be reduced sufficiently in the short time interval of modulation signals, reverberations in aplate were received by the LDV. For example, the vertical lines in (b) and (c) near the left edge were caused by
the waves generated at the right side.
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Figure 3 Images of an artificial defect in a plate at different repetition rates.

㸫S44㸫

Next, Figures 4 (a)-(c) show the images for an aluminum alloy branch pipe for different receiving points as in
Figure 4 (d). The distance between the measurement system and the branch pipe was about 6.0 m. Rastering at therepetition rate of 80 Hz and in 2 mm increment resulted in the measurement time of about 12 minutes. The
artificial pipe thinning was appeared in dark regions, which proved that the E-camera can visualize defects in sucha pipe with complex geometries.

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5HFHLYLQJSRLQW(a) Upper receiving point(b) Right end receiving point

5HFHLYLQJSRLQW(c) Left end receiving point(d) A pipe specimen used
Figure 4 Images of a branch pipe for different receiving points.

5 SUMMERYThis study described the principle of E-camera that enables to obtain defect images from a remote position,and then presented experimental results for a plate and a branch pipe. In both cases, artificial defects created on
the back surface of a laser emission region were well visualized.
Because this technique does not require scanning a receiving laser, the measurements for defect imaging are
relatively easy once a receiving point is determined. Therefore automatic inspection and fast imaging like opticalcamera are expected in the future.

ACKNOWLEDGEMENTS
This work was supported by JSPS KAKENHI Grant Number 26282094 and Chubu Electric Power Co., Inc.

REFERENCES[1] Hayashi T., Murase M., and Salim M. N., (2009)³5DSLGWKLFNQHVVPHDVXUHPHQWVXVLQJJXLGHGZDYHVIURPD
scanning laser source´Journal of the Acoustical Society of America, Vol. 126, No. 3, pp. 1101±1106.
[2] Hayashi T., (2016)³,PDJLQJGHIHFWVLQDSODWHZLWKFRPSOH[JHRPHWULHV´Applied Physics Letters, Vol. 108,081901.
[3] Hayashi T., Fukuyama M., and Ishihara K³9LEUDWLRQHQHUgy analysis of a plate for defect imaging with ascanning laser source technique.´to be published in Journal of the Acoustical Society of America.

㸫S45㸫

PROGRESS in ACOUSTIC EMISSION XVIII, JSNDI & IIIAE

High-precision source location of AE event using automatic
error correction of signal rising timeKaita Ito and Manabu Enoki
The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan.
ABSTRACT: Correspondence between the signal arrival time and ultrasonic wave mode is often unclear undernoisy environment. In this study, a novel signal analysis method for high-precision source location of AE event isdeveloped. Multiple arrival times are detected by AIC picker. Correspondence between the detected arrival timeand ultrasonic wave modes is established in the consideration of experimental conditions. Contribution ratio of
each data of signal arrival time to the total error is checked. Low quality data including large error is eliminatedand miss correspondence between the signal arrival time and ultrasonic wave mode is corrected.

1 INTRODUCTION
Determination of arrival times of AE wave to each sensor is essential for source location of AE events. In thesimplest method, trigger time of AE hit, i.e. the time when AE waveform crosses a preset threshold voltage can be
regarded as the arrival time. However, this method is not so precise in many cases. Meanwhile, manual
determination enables better precision than the threshold crossing method. However, automatization is impossible
in this method because the diagnostic criterion of the arrival times is obscure. Here, Akaike’s Information
Criterion (AIC) picker is one of solutions of this problem, i.e. realization of precise and automatic arrival
detection. Maeda et al. developed a direct computation method of AIC value from seismic waveform [1]. Kurz et
al. applied Maeda’s method to AE waveform [2]. Sedlak developed two-steps AIC picker for better precision [3].
In Sedlak’s method, AIC picker is applied twice around the trigger time. In the first step, AIC picker is applied to
AE waveform before and after several tens of microseconds of the trigger time. Then, in the second step, AIC
picker is applied again to a smaller range of waveform around the detected arrival time in the first step. Current
version of our in-house developed AE measurement system “Continuous Wave Memory” (CWM) includes the
Maeda’s and Sedlak’s algorithms.
AE wave is an elastic wave which propagates in solid materials. Therefore, multiple modes of waves withdifferent velocities are included in AE waveform. For example, longitudinal wave, transversal wave and some
types of surface waves are observed in bulk specimen. Lamb wave is dominant in thin plate. Lamb wave includes
symmetric (S) and asymmetric (A) modes where the top and bottom surfaces vibrate symmetrically andasymmetrically, respectively. Therefore, discrimination of these modes is important for precise AE source
location. When the signal-to-noise ratio (SNR) is sufficiently high, a sharp rising edge can be observed inwaveform and the earliest signal arrival can be detected easily. However, often SNR is low in practical AE
measurement and precise detection of arrival times becomes difficult. Therefore in this study, a novel arrival
detection method with automatic error detection and correction was developed for high-precision source locationof AE events.

2 SIGNAL PROCESSING METHODS
2.1 Detection of multiple arrival times of AE wave with AIC picker
Waveforms from AE sensors via amplifiers were continuously sampled and recorded to hard disk drives by
previously mentioned CWM system. A noise reduction filter was applied in frequency domain, i.e. spectrum
subtraction method. At first, the filtered continuous waveforms were scanned to detect AE hits and events bycross-trigger method. Then, an arrival time t0 was decided by two-steps AIC picker. The first AIC picker detected
a tentative arrival time t0* between -60 s and +40 s of the trigger time. After that, t0* was revised by the second
AIC picker between -30 s and +20 s of t0*. Furthermore, two more arrival times t- and t+ were detected by twosteps AIC pickers before and after 100 s of t0, respectively. These default values may be appropriate for the mostcases, but they can be changed as needed. In conclusion, three arrival times, i.e. t-, t0 and t+ were automaticallydetected around the trigger time for one AE hit. An example of this result is shown in figure 1. Figure 1(a) showeda waveform after applying of noise filter. Figure 1(b) and (c) showed the first and second picking of arrival times,
respectively.
After that, some connection between three arrival times and modes of AE wave is needed. In some cases,
different modes of AE wave have different frequency characteristics and the arrival times can be easily assigned
to certain modes. However, this becomes difficult when a resonant type of sensor is used. In such case, modes of
AE wave and area of AE source were limited by reasonable estimation. Then, minimum and maximum timedifferences of arrival times of two modes can be calculated by each sensor.

㸫S46㸫

1000

(b)

(c)

Norm. AIC Norm. AIC Voltage / mV

(a)

0

-1000
1.0

0.0
1.0

0.0
-20

1st step
2nd step

W0

W0

1st step
2nd step

W+
20

40

Time / s

60

80

Figure 1: An example of detection of three arrival times of AE waveform with two-steps AIC pickers, (a) noise
reduced waveform, (b) primary arrival detection by two-steps AIC picker, (c) additional arrival detection by twosteps AIC picker

2.2 Automatic error detection and correction
In this study, source location of an AE event was conducted by this equation( tij – t ) vj = | SL – S |(1)

where i is the number of sensor, j is mode of wave, tij is an arrival time of wave, vj is a wave velocity and pi is
position of a sensor, respectively. These variables are known. Meanwhile, there are unknown variables: t is thegeneration time of AE and S is the location of the AE event. The values of t and S are decided by least-squares
method, i.e. AE event source was located at the position with least mean square error. However, signal processing
in chapter 2.1 may include several types of errors. For example, the detection of signal arrival is sometimes
inaccurate. At least one of the three arrival times is ineffective and points a fluctuation of noise because only twomodes are detectable at most in many cases. Furthermore, there are some miss connection among the arrival times
and wave modes.
Therefore, a novel error detection and correlation process was introduced in this study. When the mean squareerror is larger than a preset threshold value, each contribution ratio of tij to the total error is checked and one
sensor which causes largest error is chosen. Then, a different connection among the arrival times and wave modes
are tried to diminish the error. If the error is not diminished in any connection pattern, this tij data is removed fromthe calculation of source location. Consequently, low quality data with large error on detection of the signal
arrival time is eliminated. Miss estimation of correspondence between the signal arrival time and ultrasonic wave
mode is corrected.

3 EXPERIMENTAL PROCEDURE
AE monitoring during friction stir welding (FSW) of magnesium alloy was conducted as a verification
experiment of the error detection and correction process in the previous section. A schematic diagram of thespecimen and welding is shown in figure 2. The center 100 mm on the long side of two plates was welded with
800 mm/min speed and 1000 rpm of the welding tool. These welding conditions were chosen to generate welding
defects and AE events should be concentrated at the welding point. Four of AE sensors (type M304A, Fuji
Ceramics Corporation, Japan) were attached on the top surface of the specimen. AE waveforms were amplified 86
dB by a head internal amplifier of the sensor and an external amplifier (type A2001, Fuji Ceramics Corporation,
Japan). The amplified waveforms were continuously recorded to hard disk drives by CWM system with 10 MHz
of sampling frequency, 12-bit resolution and s5 V range. A high pass filter with 100 kHz of cut-off frequency
was applied by the internal software of CWM system to reduce noise caused by vibration. AE events were㸫S47㸫

detected witth four thresshold voltagees (100, 200,, 400 and 80
00 mV) to detectdmore nnumber of AE
A events inn
fluctuate noiise level enviironment duriing FSW.
Three arrrival times werew
detecteed automaticaally for one AE hit. Thee detection mmethod was the same ass
mentioned inn the previouus section. Th
he mainly dettected modes of AE wave were assumeed to S0 and A0 modes off
Lamb wave because the specimen waas a thin platee. Then, the wavew
velocitiies of S0 andd A0 modes ofo lamb waveecass 5.2×103 m//s and 3.1×103 m/s lambb from the mmaterial, thickness of theein this speccimen were calculated
specimen annd the frequenncy of Lamb wave. In thi s study, the frequencyfof Lamb wave w
was assumed
d as 200 kHz,,
which was tthe same as the resonant frequency oof the sensorss. Then, the assumed tim
me difference between theearrivals of S
S0 and A0 moodes was 4.2 to 17.6 s, w
when the AE sourceslocatioon was limiteed on the weld
ding line, i.e.
-50  x  +550 mm and y = 0 mm. Theerefore, conneection among
g the three arrrival times (t-, t0 and t+) an
nd two modess(S0 and A0 modes of laamb wave) was
w conducteed. Therefore, maximum eightedata (44 ch × 2 moddes) of wavee
orrection wass conducted as previouslly mentionedd
propagation was obtaineed. After thaat, error deteection and cone sensor of the biggest faactor of error was checkedd
method. Whhen the mean square error was over thaan 2.0 s2, on
and correcteed. At least fourfdata musst be remaineed to make 2D
2 location ofo event. Whhen the mean square errorr
cannot be sm
maller than 2.0
2 s2 after thist processinng, this eventt will be igno
ored. The deffault value off threshold off
the mean squuare error maay be approprriate for the mmost AE evennts, but they canc be changeed as needed.

mdururing FSW
Figure 2: Scchematic diaggram of AE monitoring

4 RESULLTS AND DISCUSSIOD
ON
A continnuous axial defect was observed on thee welding lin
ne from the startspoint to the end poin
nt. Therefore,,many AE evvents are expeected to be lo
ocated at the welding poinnt during FSW
W. The resultt of source loocation of AE
Eevents was sshown in figuure 3. In this figure, verticcal and horizo
ontal axes aree position on the welding line and timee
in welding, rrespectively. Each bubble plot represennts one AE ev
vent and its radiusrreflectss the maximu
um amplitudee.
Furthermoree, the obliquee line in the figure show
ws the weldinng points during the exper
eriment. The algorithm off
source location is the sam
me in the conv
ventional meethod withoutt error detection and correcction (figure 3(a)) and theend correction (figure 3(b))). Location dispersion arround the weelding pointss
new methodd with error detection an
became smaaller in the newnmethod. Standard deeviation of sourceslocatioon, i.e. root mean squaree of distancee
between the location of ana AE event anda the weldiing position ata the time off AE generatioon were abou
ut 22 mm anddb), respectivelly. It shows th
he effect of th
he error detecction and corrrection.
8 mm in the case of figurre 3(a) and (b㸫S48㸫

Figure 3: Reesult of source location of AE events (aa) without andd (b) with errror detection aand correctioon.


5 CONCL
LUSIONS
A novel signal analyysis method forf high-preccision source location of AE event is developed. ThreeT
arrivallC picker. Corrrespondencee between the detected arriival time and ultrasonic wave
wmodes iss
times are detected by AIC
measurementt conditions. Contributionn ratio of eacch data of siignal arrival time to the total error iss
considered m
checked. Loow quality data with large error is elim
minated and missm correspon
ndence betweeen the signal arrival timee
and ultrasonnic wave modde is correcteed. All signall processing is
i automatic anda is done iin real-time. The
T effect off
this new signnal analysis methodm
was verifiedv
by deefect monitoriing during FS
SW test of maagnesium allo
oy plates.

ACKNOWWLEDGEME
ENTS
We are pparticularly gratefulgfor the assistancee given by Mr.
M Takuya Kawaguchi
K
annd Mr. Qi-W
Wei Wang onn
verification experiments,, and this wo
ork is supportted by MEXT
T/JSPS KAKKENHI Grantt #26820302 and researchhM
Schoolarship Founndation.
subsidizing pprogram by Moritani

REFERENNCES[1] N. Maeeda, (1985). “A method fo
or reading annd checking phase times in autoproceessing system
m of seismicc
wave ddata”, Zisin=JJishin, Vol. 38,
3 pp. 365-3379.
[2] J. H. K
Kurz, C. U. Grosse and H-W Reinhhardt, (20055). “Strategiees for reliablle automaticc onset timee
pickingg of acoustic emission and of ultrasouund signals inn concrete”, Ultrasonics,, Vol. 43, pp. 538-546.
[3] P. Sedllak, Y. Hirosse, S. A. Khaan, M. Enoki and J. Sikura, (2009).“New automattic localizatio
on techniqueeof acouustic emissionn signals in thintmetal pllates”, Ultrassonics, Vol. 49,
4 pp. 254-2262.

㸫S49㸫

PROGRESS in ACOUSTIC EMISSION XVIII, JSNDI & IIIAE

Influence of pre-compression on tensile behavior in wrought
AZ31 studied by the acoustic emission technique 
Daria Drozdenko1), Patrik DobroĖ1), Jan Bohlen2), Sangbong Yi2) and František Chmelík1)
1) Charles University in Prague, Ke Karlovu 5, Prague 2, 12116, Czech Republic.
2) Helmholtz-Zentrum Geesthacht, Max-Planck-Straße 1, Geesthacht, D21502, Germany
ABSTRACT: Deformation mechanisms in extruded AZ31 Mg alloy during one cycle loading (pre-compression
followed by tension) are discussed in term of the acoustic emission (AE) response. Due to a fiber texture of thealloy, the level of pre-compression stress significantly influences the subsequent tensile behavior resulting in Sshape of deformation curve. The obtained AE results are correlated to the deformation curves and the differencesin the AE count rate were used to reveal changes in underlying deformation mechanisms. Twinning during thepre-compression was followed by detwinning during the tensile loading.

1 INTRODUCTION

Wrought Mg alloys have been intensively studied due to their low density and high damping capacity. Therefore,
they can fulfill requirements of many applications in the automotive and aerospace industry. However, their use isoften limited due to inherent anisotropy of mechanical properties caused by their hcp lattice, texture andhomogeneity of materials. Specific crystallographic textures are developed during formation processes, e.g.
extrusion, rolling and forging. The strong basal texture of extruded Mg alloys, having basal planes almost parallelto the extrusion direction (ED), favor the occurrence of {10-12}<-101-1> twins during compression along ED [1].
Thus, a distinct tension-compression asymmetry at the yield strength (YS) during loading along the extrusion
direction (ED) in Mg alloys is linked with the formation of those twins [2]. Thus, twinning is a key mechanism ofplastic deformation in Mg alloys. Twins significantly influences the material behavior especially during cyclicloading, when twinning – detwinning mechanism induces important changes in the deformation behavior [2].
Tensile loading of the pre-compressed Mg alloy leads to disappearing of {10-12} twins due to an easier activationof the detwinning process than the twinning one [2]. Extensive experimental research was focused on the role oftwinning and detwinning also in fatigue behaviour [3,4]. It is, therefore, essential to get more insight into
individual mechanisms of deformation (dislocation slip, twinning, twinning - detwinning) during a single cyclicloading with respect to given texture and homogeneity of material. Obtained results can contribute to analyze
deformation behavior during repeated loading for several cycles and strain path changes, when twin-twinjunctions can form, and they have been shown to play a crucial role in increasing strain hardening and controlling
microstructure evolution.
The acoustic emission (AE) studies can be performed concurrently with the deformation tests, allowing the realtime monitoring of active deformation mechanisms. The differences in AE signal characteristics can be used to
distinguish different types of deformation processes. For example, in [5] a comprehensive set of AE data for basal
slip and twinning was obtained during uniaxial compression of Mg single crystals along <11-22х and <11-20>
axis respectively.
The main idea of the paper is to study an influence of pre-compression stress on subsequent tensile behavior of
extruded AZ31 alloy during a single cyclic test. Active deformation mechanisms during loading are discussed interm of the AE response. Obtained results for Mg single crystals [5] are used for an interpretation of the AE signal
in polycrystalline Mg alloy.

2 EXPERIMENTAL PROCEDURES

Wrought Mg alloy AZ31 (Mg + 3 wt%Al + 1 wt%Zn + 0.3 wt%Mn) was fabricated using indirect extrusion at
300°C with an extrusion rate (profile exit speed) of 5 m/min. The extrusion ratio was 1:30, which resulted inround bars with a diameter of 17 mm. The extruded profile exhibits a bimodal microstructure with an averagegrain size of 20 ± 1 ȝm. There are larger grains elongated in ED, along with a distinct fraction of grains withsmaller sizes. The investigated alloy is characterized by a prismatic fiber texture with the highest intensity at the<10-10> pole. Thus, a distinct alignment of basal planes parallel to ED, that is, the c-axis is perpendicular to ED,
is a characteristic feature of this texture.
Samples (gauge length of 15 mm, diameter of 8 mm) with screw heads on both ends were machined from theround extruded bar parallel to ED. Deformation tests (pre-compression followed by tension, i.e., one cycle tests)
were carried out using the universal testing machines Zwick Z50 at room temperature (RT) and at an initial strain
rate of 10-3 s-1.
The AE activity during mechanical testing was monitored by a computer-controlled PCI-2 device, supplied by
Physical Acoustic Corporation (PAC). AE was acquired using a miniaturized MST8S (Dakel-ZD Rpety, Czech
Republic) piezoelectric transducer with a diameter of 3 mm and with a flat response in the frequency band from100 to 600 kHz. The sensor was attached to the sample surface using silicon grease and a spring. A preamplifier

㸫S50㸫

with a gain of 40 dB was used. The full scale of the A/D converter was ±10 V (100 dB). To obtain acomprehensive set of AE parameters, a threshold level detection of 26 dB was applied.

3 RESULTS
To study an influence of the pre-loading level on subsequent tensile test, the samples were firstly pre-compressed
up to a stress of 130 MPa, 150 MPa and 200 MPa, respectively, and then subjected to tensile loading up to
fracture. For sample pre-compressed up to 200 MPa the resulting stress and the concurrently measured AE signal
voltage are plotted against time (Figure 1). Data for whole test is presented in Figure 1a, whereas detail of thetensile part is presented in Figure 1b. As AE response for all three samples is similar the results for samples precopressed up to 130 MPa and 150 MPa are not presented here. It can be seen that yielding occurs at a compressive
stress of 124 MPa. The plastic flow continues with an increasing (plotted negative) slope of the deformation
curve. After the pre-compression and unloading, the tensile curves exhibit a characteristic sigmoidal shape (Sshape), which is typical for compression test of extruded Mg alloys. The deformation curve is correlated to the
concurrent AE measurement trough measuring time. The AE signal is very strong during pre-compression,
especially in the region of the compressive yield strength (CYS). In contrast, during tensile loading, smaller AE
amplitudes were observed (Figure 1).

Figure 1 Stress and the AE signal voltage vs. time during one-cyclic loading (a) and its detail for tensile part (b).
Results of tensile parts of the tests after pre-compression up to various level of the stress are shown in Figure 2. It
can be seen that the higher the compressive stress is the more pronounced is the S-shape of the tensile curve, see
Figure 2a. The AE streaming data were parameterized and represented as the AE count rate – the count number
per time unit [6] at a given threshold voltage level. Such parameterization offers important information aboutcollective dynamic processes which occur during plastic deformation. In order to find the link between theparticular deformation stages and the AE response, the true stress vs. true strain and AE count rates vs. time arecorrelated and presented in the same plot (Figure 2b-d for samples pre-compressed up to 130 MPa, 150 MPa,
200 MPa, respectively). During pre-compression, the AE count rates exhibit similar maximum at the CYS in all
three cases (not presented here). During subsequent unloading from pre-compression level of stress to zero stressany detectable AE is not produced. In contrast, during the tensile loading, the AE count rate exhibits distinct
changes with increasing strain (Figure 2b-d). Evolutions of the AE count rate are different with respect to different
level of pre-loading. Changes in the AE activity could be related to the inflection points on the deformation
curves. In all three cases of pre-compression, the maximum of the AE count rate occurs when stress-strain
dependences are saturated. With applying higher pre-compression stress, an additional AE count rate peak, related
to the plateau after yielding (S-shape), is observed. The AE count rate peaks become more pronounced withincreasing level of pre-loading (Figure 2c-d). At the terminate stage of the deformation test a strong decrease in
the AE count rate is observed in all three cases.

4 DISCUSSIONIn Mg alloys plastic deformation starts in grains favorably oriented for <a> dislocation glide in the basal andprismatic plane [1]. Thus, collective movement of dislocations produces detectable AE signals even before
achieving the macroscopic YS (Figure 1a). To retain the compatibility of plastic deformation during thecompression test, with respect to very high CRSS for the activation of non-basal slip systems [1], the occurrenceof twins is required. Due to strong basal texture of extruded AZ31 Mg alloy, where the basal planes are almost
parallel to ED, the {10-12}<-101-1> twin system activates with applying the compression stress along ED. The
plastic deformation proceeds by basal slip in grains reoriented due to twinning and, after reaching CRSS for㸫S51㸫

activation of non-basal slip systems, it proceeds also by <c+a> dislocation slip. Therefore, the twin nucleation was
found as the main deformation mechanism at the macroscopic yield, and it significantly influences the YS. Thus,
the observed low CYS and the strong AE signal are unequivocal signs of the activity of this mechanism. This is
supported by the investigations conducted on Mg single crystals [5], where twinning was associated with burst AE
signals with high amplitudes and basal slip was accompanied by a low amplitude AE signal. Moreover, in [7] it
was concluded that the twin nucleation is an excellent source of AE, contrary to the twin growth, which does notcontribute to the AE response. Other combined studies, provided by EBSD technique and light microscopy [8],AE with neutron diffraction [9], have shown that {10-12}<-101-1> twins nucleate at the beginning of plastic
deformation.

Figure 2 True stress vs. true strain (a) curves for the tensile loading of pre-compressed AZ31 Mg alloy.
Deformation curves correlated with the acoustic emission count rate for the tensile loading of AZ31 Mg alloy
after pre-compression up to 130 MPa (b), 150 MPa (c), 200 MPa (d).
The AE signal, observed shortly after the YS, has significantly lower amplitudes. This drop in the AE signal
amplitudes indicates a change in the dominant deformation mechanism, and it is connected with the transition
from the twin nucleation to dislocation slip and twin growth. Nevertheless, a few twins could still nucleate during
increase in the compression load.
Unloading from the pre-loading compressive stress (cf. Figure 1a) to zero stress does not produce any detectable
AE signals, which corresponds to the closing of dislocation sources. Furthermore, twin thinning may be an active
relaxation mechanism during unloading. Similar to the growth or thickening of twins, detwinning or thinning isbasically a movement of twin boundaries, and therefore no detectable AE signal [7] is expected as a result of thismechanism.
The subsequent tensile loading re-opens dislocation sources, and therefore, collective dislocation motion produces
detectable AE signals. The AE response during entire tensile loading is significantly lower than during precompression (Figure 1). Basically, two opposite processes influence the AE activity during tensile loading.
Namely, the increasing number of detwinned grains supports the AE activity through the rise in the flight distanceand the free length of moving dislocations. On the other hand, the increasing dislocation density implies a stronger
barrier for their movement, and therefore generally reduces the AE signal in the tensile part of the test.
According to Christian and Mahajan [10], a higher stress is required for nucleation than for the propagation oftwins. Therefore, during reverse loading, detwinning is easily activated due to the already existing twin

㸫S52㸫

boundaries. Based on this, the lower YS for the reverse tension than that for the pre-compression could be
explained (Figure 1a for sample pre-compressed up to 200 MPa). Unlike the usual shape of the tensile curve, after
pre-compression, the deformation curve for the tensile part is very similar to the compression part of the curve andit has S-shape. Twinning and detwinning, due to their strong polar nature, result in large reorientations of thecrystal lattice (86.3°), which macroscopically gives rise to the characteristic S-shape stress–strain behavior,preceding the terminate strain-hardening region. A similar behavior was also observed during cyclic testing in the
textured AZ31 sheet [2].
From analysis of tensile deformation curve and AE responses, it can be seen that detwinning process is moresignificant in case of high pre-compression (up to 200 MPa) and negligible for sample pre-compressed up to
130 MPa just achieving CYS. It is suggested that in last case, twin volume fraction is too small for massive
detwinning process and it can not significantly influence shape of the deformation curve. Therefore, an additional
AE count rate peak related to S-shape of deformation curve (Figure 2c-d) is associated with collective dislocation
motion in grains after detwinning.
For all three cases the upcoming decrease in the AE count rates at terminate stage of deformation is connectedwith the decrease in the free path of moving dislocation due to an increase of dislocation density resulting in thestrain hardening of the material. Similar effect can be seen for Mg single crystal compressed along <11-20> axis
[5].

5 CONCLUSIONSTo study in detail possible deformation mechanisms, such as dislocation slip and twinning, twinning-detwinning,
during cyclic loading rather than damage process a single cyclic test consisting of pre-compression followed bytension were performed. Especially the influence of pre-compression stress of subsequent tensile loading wasanalyzed. The transition between different deformation mechanisms, such as dislocation slip, twin nucleation,
twin growth and detwinning, at different stages of the deformation test were discussed in term of the AE response.
Detwinning has a similar influence on the deformation behavior to twinning: S-shape of the deformation curve.
During twin growth and detwinning, the AE response shows events with lower amplitudes than during twin
nucleation. Thus, the AE activity during tensile loading is a result of collective dislocation processes, while
neither thickening nor thinning of twins obviously do not contributes to the AE response. For samples precompressed up to 150 MPa and 200 MPa an increase in the free length of moving dislocations during twin
thinning (detwinning) leads to an increase in potential dislocation movement in detwinned grain fraction;
therefore, an additional AE count rate peak is observed.
ACKNOWLEDGEMENTS
This work received support by the Czech Science Foundation under the grant 13 - 19812S, by the Grant Agencyof Charles University under the grant 1882314 (DD) and also by the grant SVV-2016- 260322.
REFERENCES[1]
P.G. Partridge, (1967) “The crystallography and defaormation modes of hexagonal close-packed metals”,
Metallurgical Reviews, Vol. 12, pp.169-194.
[2]
X.Y. Lou, M. Li, R.K. Boger, S.R. Agnew, R.H. Wagoner, (2007) “Hardening evolution of AZ31B Mg
sheet”, International Journal of Plasticity, Vol 23, pp. 44-86.
[3]
S. Begum, D.L. Chen, S. Xu, A.A. Luo, (2009) “Low cycle fatigue properties of an extruded AZ31
magnesium alloy”, International Journal of Fatigue, Vol. 31, pp. 726-735.
[4]
J. Koike, N. Fujiyama, D. Ando, Y. Sutou, (2010) “Roles of deformation twinning and dislocation slip in
the fatigue failure mechanism of AZ31 Mg alloys”, Scripta Materialia, Vol. 63, pp. 747-750.
[5]D. Drozdenko, J. Bohlen, F. Chmelík, P. Lukáþ, P. DobroĖ, (2016) “Acoustic emission study on theactivity of slip and twin mechanisms during compression testing of magnesium single crystals”, Materials Scienceand Engineering: A, Vol. 650, pp. 20-27.
[6]Standard Practice for Acoustic Emission Examination of Fiberglass Reinforced Plastic Resin (FRP)
Tanks/vessels (2011).
[7]
C.R. Heiple, S.H. Carpenter., (1987) “Acoustic emission produced by deformation of metals and alloys A review: Part II”, Journal of Acoustic Emission, Vol. 6, pp. 215-237.
[8]
A. Ghaderi, M.R. Barnett., (2011) “Sensitivity of deformation twinning to grain size in titanium andmagnesium”, Acta Materialia, Vol. 59, pp. 7824-7839.
[9]
O. Muransky, M.R. Barnett, D.G. Carr, S.C. Vogel, E.C. Oliver., (2010) “Investigation of deformation
twinning in a fine-grained and coarse-grained ZM20 Mg alloy: Combined in situ neutron diffraction and acoustic
emission”, Acta Materialia, Vol. 58, pp. 1503-1517.
[10]J.W. Christian, S. Mahajan., (1995) “Deformation twinning”, Progress in Materials Science, Vol. 39,
pp. 1-157.

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PROGRESS in ACOUSTIC EMISSION XVIII, JSNDI & IIIAE

Visualization of internal damage in RC slab with single side
access attenuation tomography
Clément Granier1), Tomoki Shiotani2), Katsufumi Hashimoto 2) and Takahiro Nishida 2)
1) School of Architecture, civil and environmental engineering, École Polytechnique Fédérale de Lausanne, Routecantonale, 1015 Lausanne, Switzerland.
2) Graduate school of Engineering, Kyoto University, C3-b4S14, Kyoto-Daigaku-Katsura, Nishikyo-ku, Kyoto
615-8540, Japan.
ABSTRACT: An innovative non-destructive testing method is developed and introduced for inspecting the interiorof concrete. The method is based on the phenomenon of attenuation, which corresponds to the gradual loss inamplitudes of waves traveling through a heterogeneous medium. In the case of concrete, the attenuation isparticularly high in the presence of defects, such as cracks or air voids. Ingeniously, single-side measurements ofacoustic emission (AE) activity are employed for reconstructing distribution of the wave attenuation rate in thetested concrete and thus locating damaged areas. The present method is successfully applied to a panel of areinforced concrete slab of a bridge in service.

1 INTRODUCTION
Acoustic emission (AE) testing is one of non-destructive testing (NDE) methods available for investigating theinternal features of civil engineering structures. AE techniques have been extensively used for bridge examination[1-3]. AE phenomena correspond to the release of internal energy in the form of elastic waves when cracks are
nucleated and extend (referred to as primary AE activity) or internal fretting on cracked surfaces (referred to as
secondary AE activity) [4]. A sensing system for recording AE activity has been already published [5]. Sources ofAE events, namely cracks, are located from the measured arrival times of their associated waves [6, 7]. Their
cracking modes can be determined through the moment tensor analysis [8, 9].
Nowadays, AE source location is ingeniously combined with a travel-time tomography to reconstruct the wave
velocity distribution inside concrete [10]. Indeed, the decrease in the wave velocity substantially corresponds tothe presence of such defects as cracks or air voids [11].
Amplitudes of elastic waves tend to decrease while they are traveling through media such as concrete. Thisphenomenon is well known as an attenuation, resulting from heterogeneous nature of concrete and from wave
characteristics of diffraction, dispersion and scattering at the boundaries in addition to geometrical spreading [12].
The presence of defects in concrete, such as cracks or air voids, could enhance the attenuation [13]. It has beenshown that the attenuation was more sensitive to the presence of defects than the velocity distribution [14]. H.K.
Chai et al. [15] employed the attenuation of ultrasounds for imaging the interior of concrete. Their method has been
proven to be effective for locating damages in concrete. However, it has one disadvantage to require an access toat least two sides of the tested specimen. In the case of bridge slabs, it is generally not possible to measure at thetwo sides because of traffic. Accordingly surface waves have been employed to investigate the interior damage ofconcrete [16] ; however, specific depths could not so far be identified with this technique.
In the present paper, an innovative method is developed for reconstructing distribution of the attenuation rate insidea reinforced concrete element from single-side measurements of AE activity. Applied results of this method to apanel of a reinforced concrete (RC) bridge slab is discussed.

2 ALGORITHM FOR ATTENUATION TOMOGRAHY
2.1 General principle

The attenuation rate in a particular medium can be defined as the amplitude decay undergone by an elastic wave
traveling in this medium, per unit of distance. It is generally expressed in decibels per meter (dB/m). The increasein the attenuation rate generally indicates the presence of defects in concrete, such as cracks or air voids.
For computing the attenuation tomography, the method combines a conventional AE source location algorithm withan algorithm of tomographic reconstruction, known as a SIRT algorithm (Simultaneous Iterative Reconstruction
Technique).
The process is summarized in Figure 1. It is noted that AE activity can be measured from only one side of the tested
member. First, the source locations of all AE events are used as input, where a constant wave velocity across thetested member is assumed for calculation. Second, the amplitude at the source of the considered AE event is㸫S54㸫

estimated on the basis of the amplitudes recorded by the sensors. Third, the attenuation rates along all the wave
paths between the source and the receiving sensors are computed.

Figure 1: Flow chart to compute attenuation tomography

2.2 Source location algorithm
7KHDOJRULWKPIRU$(VRXUFHORFDWLRQLVEDVHGRQWKH,QJODGD¶VPHWKRGZKLFKLVXVHGLQVHLVPLFHQJLQHHULQJIRU
locating the epicenter of earthquakes [6, 7]. By assuming a constant wave velocity inside the tested specimen, thesource location of an AE event is determined from the arrival times of their associated elastic waves at the locationsof several sensors.
2.3 Estimation of the peak amplitude at the source
For each AE event, the peak amplitude of the elastic wave at the source is unknown. Consequently, it must be
approximated to calculate the attenuation rate along the considered wave paths. In the tomography algorithm, theattenuation rate is generally estimated from a relation represented in Figure 2. First, the peak amplitude of thesignal recorded by each sensor is plotted as a function of the distance between the source and the sensors. Second,a linear regression between the peak amplitude of the elastic wave and the distance from the source is computed.
The peak amplitude at the source is referred to as equal to the value for the case that the distance is equal to zero.

Figure 2: Estimation of the peak amplitude of the elastic wave at the source

2.4 SIRT algorithm for attenuation tomography
In the tomography based on the attenuation, the area of interest and analyzed must be divided into mesh elementscharacterized by their own attenuation rates. Then, a first estimate on distribution of the attenuation rates is to beprovided as input. By comparing the measured attenuation rate along each wave path to its calculated value ofthe assumed distribution, the SIRT algorithm could lead to proper distribution of the attenuation rates.
First, the measured attenuation rate along each ray path is estimated from Equation 1.

㸫S55㸫

 ୫ୣୟୱ୳୰ୣୢǡ୧ ൌ୅౩౥౫౨ౙ౛ ି୅౩౛౤౩౥౨ǡ౟(1)ొσౠ ౟ ୢ౟ǡౠ 

Where:
 ୫ୣୟୱ୳୰ୣୢǡ୧: measured average attenuation rate along the wave path from the source to the ith sensor
ୱ୭୳୰ୡୣ: estimated peak amplitude of the elastic wave associated to the considered AE event at its source୰ୣୡୣ୧୴ୣ୰ǡ୧ : peak amplitude of the elastic wave measured at the ith sensor
୧ ǣ mesh number of elements crossed by the wave path from the source to the ith sensor
୧ǡ୨ : length of the wave path from the source to the ith sensor in the jth element
Second, the attenuation rate along each wave path based on distribution of the attenuation rates in the meshelements is computed by Equation 2.

 ୡୟ୪ୡ୳୪ୟ୲ୣୢǡ୧ ൌσ౉ౠ ୅ୖౠ ήୢ౟ǡౠ(2)σ౉ౠ ୢ౟ǡౠ 

Where:
 ୡୟ୪ୡ୳୪ୟ୲ୣୢǡ୧: calculated average attenuation rate along the wave path from the source to the ith sensor (dB/m) ୨: attenuation rate in the jth element (dB/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
path by using Equation 3.
(3)ο ୧ ൌ  ୫ୣୟୱ୳୰ୣୢǡ୧ െ  ୣୱ୲୧୫ୟ୲ୣୢǡ୧In a similar manner to Equation 11, the differences of the attenuation rates on all the wave paths are estimated by
Equation 4.

ο ୨ ൌσొ౟సభ ο୅ୖ౟ ήୢ౟ǡౠσొ౟సభ ୢ౟ǡౠ(4)

Where N is the number of wave paths crossing the jth element. The attenuation rate in each element is thenupdated with Equation 5.

 ୨ǡ୳୮ୢୟ୲ୣୢ ൌ  ୨ ൅ ο ୨(5)

The procedure from Equation 2 through Equation 5 is repeated until the convergence is reached.

3 EXPERIMENT AND MEASUREMENT PROCEDURE
3.1 Sensor array

Figure 3 shows the plan view of the bridge and the location of the investigated panel. The panel of dimensions 3750
mm x 2650 mm x 235 mm was tested. The bottom surface of the slab segment is given in Figure 6. As seen, a
few cracks are observed. Therefore, it was estimated that the panel (slab segment) was not severely deteriorated
according to the visual inspection.
Fifteen AE sensors were placed on the bottom surface of the panel. Figure 7 shows their arrangement. The resonant
frequency of AE sensors employed were 30 kHz. The signals detected were amplified by 40 dB by sensor-integrated
preamplifier and the signals exceed the threshold of 53 dB were recorded. AE activity under normal traffic loads
was monitored for seven days, resulting in total AE events of 53,800,520. The wave velocity for AE source location
was initially set at 3300 m/s, which was the average of experimentally measured values at the investigated panel.

Figure 3: Location overview of the investigated panel and sensor array㸫S56㸫

3.2 Panel removal and core sampling
RC slabs studied herein were fully replaced in November 2015 as other panels in the vicinity of the tested one
were so deteriorated as to be replaced. In the course of renovation process, they were cut into parallelepiped
pieces. The slab segment corresponding to the investigated panel was preserved for further investigation.
4 RESULTS AND DISCUSSION

The attenuation rate distribution in the investigated panel were computed within the area covered by the sensor
array. Parallelepiped-shaped elements of dimensions 375 mm x 350 mm x 60 mm are applied for the meshes.
Results are presented in Figure 4 in comparison with the core samples.

Figure 4: Comparison between the attenuation rate distribution computed and the core samples.
It can be observed that the attenuation rate at the center of the panel is the highest. It suggests that the central partof the panel suffers larger and/or denser cracks than other areas. Furthermore, it appears that the attenuation rates
are higher than 40 dB/m at the area, where the core samples have large horizontal cracks (C4, C5, C6 and C7). In
contrast, the attenuation rates are lower than 40 dB/m at the area, where the uncracked core samples (C2, C3, C8
and C9) are taken out. These results show that single-side attenuation tomography can be applied to identify
severely cracked portions and areas in RC slabs of the bridge in service.

5 SUMMARY

In this paper, an innovative non-destructive method for inspecting the interior of concrete is introduced and applied
to the RC slab of a bridge in service. The method, referred to as single-side attenuation tomography, which is basedon the principle that the elastic waves traveling through concrete are to impinge on the cracks. It could provide thetomogram on the attenuation 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 damages.
If the tomograms cannot be used, in the present state, for directly assessing the structural safety of the tested
specimen, they provide essential information for guiding further investigation.
ACKNOWLEDGEMENTS
A part of this study is commissioned and supported by New Energy and Industrial Technology Development
Organization (NEDO). And, we would like express our sincere appreciation and deep gratitude to Dr. Masayasu
Ohtsu, Professor Emeritus of Kyoto University, for his prominent suggestions in interpreting the significance of theresults of this study.
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[2] L. Golaski et al., (2002) ³'LDJQRVWLFVRIUHLQIRUFHGFRQFUHWHEULGJHVE\DFRXVWLFHPLVVLRQ´-RXUQDORI
Acoustic Emission, Vol, 20, pp 83-98.

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[3] T. Shiotani et al., (2007) ³*OREDOPRQLWRULQJRIFRQFUHWHEULGJHXVLQJDFRXVWLFHPLVVLRQ´-RXUQDORI
Acoustic Emission, Vol. 25, pp 308-315.
[4] A. Behnia et al., (2014) ³$GYDQFHGVWUXFWXUDOKHDOWKPRQLWRULQJRIFRQFUHWHVWUXFWXUHVZLWKWKHDLGRIDFRXVWLFHPLVVLRQ´&RQVWUXFWLRQDQG%XLOGLQJ0DWerials, No. 65, pp 282-302.
[5] M. Ohtsu,  ³Acoustic Emission Testing: Basics for Research -Applications in Civil Engineering´ pp
19-40.
[6] V. Salinas et al., (2010) ³/RFDOL]DWLRQDOJRULWKPIRUDFRXVWLFHPLVVLRQ´3K\VLFV3Uocedia, Vol. 3, pp 863871.
[7] M. Ge, (2003) ³$QDO\VLVRIVRXUFHORFDWLRQDOJRULWKPV´, Journal of Acoustic Emission, Vol. 21, pp 14-28.
[8] M. Ohtsu, (2007) ³$FRXVWLFHPLVVLRQWHFKQLTXHVVWDQGDUGL]HGIRUFRQFUHWHVWUXFWXUHV´-RXUQDORI$FRXVWLF
Emission, Vol. 25, pp 21-32.
[9] M. Ohtsu, (1995) ³$FRXVWLFHPLVVLRQWKHRU\IRUPRPHQWWHQVRUDQDO\VLV´5HVHDUFKLQ1RQ-Destructive
Evaluation, No. 6, pp 169-184.
[10] F. Schubert, (2004) ³%DVLFSULQFLSOHVRIDFRXVWLFHPLVVLRQWRPRJUDSK\´-RXUQDORI$FRXVWLF(PLssion, Vol.
22, pp 147-158.
[11] A. Behnia et al., (2014) ³,QWHJUDWHGQRQ-destructive assessment of concrete structures under flexure by
DFRXVWLFHPLVVLRQDQGWUDYHOWLPHWRPRJUDSK\´Construction and Building Materials, Vol. 67, pp 202-215.
[12] M. Berthelot et al., (1993) ³6WXG\RIZDYHDWWHQXDWLRQLQFRQFUHWH´-RXUQDORI0DWHULDOV5HVHarch, Vol. 8, pp
2344-2353.
[13] T.P. Philippidis and D.G. Aggelis, (2005) ³([SHULPHQWDOVWXG\RIZDYHdispersion and attenuation in
FRQFUHWH´8OWUDVonics, Vol. 43, pp 584-595.
[14] D.G. Aggelis and T. Shiotani, (2008) ³(IIHFWRILQKRPRJHQHLW\SDUDPHWHUVRQZDYHSURSDJDWLRQLQ
FHPHQWLWLRXVPDWHULDOV´$&,0DWHULDOV-RXUQDOSS-193.
[15] H.K. Chai et al., (2011) ³7RPRJUDSKLFUHFRQVWUXFWLRQIRUFRQFUHWHXVLQJDWWHQXDWLRQRIXOWUDVRXQG´1'7 (International, Vol. 44, pp 206-2015.
[16] H.K. Chai et al., (2010) Single-side access tomography for evaluating interior defect of concrete,
Construction and Buildings Materials 24, 2411-2418.



㸫S58㸫

PROGRESS in ACOUSTIC EMISSION XVIII, JSNDI & IIIAE

Acoustic Emission modeling from the source to the detectedsignal: model validation and identification of relevant
descriptors
Thomas Le Gall1), Nathalie Godin1), Thomas Monnier2), Claudio Fusco1) and Zeina Hamam1)
1) Univ Lyon, INSA de Lyon, MATEIS UMR 5510, F-69621 Villeurbanne, France.
2) Univ Lyon, INSA-Lyon, LVA EA677, F-69621, Villeurbanne, France.
ABSTRACT: The interpretation of data measured by Acoustic Emission (AE) is largely based on empiricalcorrelations between the respective characteristics of the source and the measured signal. The main limitation is thatchanges due to the acquisition chain are not well known. Therefore, the aim of this work is to build a quantitative
relationship between the AE sources and the detected signal by unravelling the effect of the different stages of theacquisition chain. For this purpose, an AE modelling, in which each part is considered individually, is carried out.
This will serve to understand the effects of different parameters on the signal waveform, such as the type of damage,the geometry of the specimen and the effect of the piezoelectric sensor.

1 INTRODUCTION
Acoustic Emission (AE) is a method for non-destructive evaluation allowing real time detection of damage
mechanisms. As a consequence, it appears as a well-suited technique to retrace damage evolution in metals andcomposite materials. In fact, microscopic events occurring in the material during damage are at the origin ofultrasonic waves, which propagate up to the surface. Using AE sensors, it is possible to measure the surface
vibrations due to the propagated waves. The analysis of the recorded signal gives some information about the source
mechanisms, which are responsible for the wave generation. The common practice consists in describing themeasured signal through some appropriate parameters (rise time, peak amplitude, centroid frequency, peakfrequency, etc.), and regrouping signals into clusters thanks to classification algorithms [1]. The objective is
to identify separate clusters corresponding to a different source mechanism. However, the wave originating fromthe source is altered during propagation. Hence, the main limitation of the AE techniques is that changes dueto the acquisition chain are not well known. First, it is modified by the propagation medium itself (phasedisplacement, reflections, dispersion) [2] and then, by the recording system. All these transformations make theinterpretation of the signals very difficult. In other words, we would like to d i s c e r n t h e r o l e o f t h e s ourcefrom the effect of the transformations due to the propagation medium and the recording system on the measured
signal. An experimental study alone cannot provide a satisfying answer.
Therefore, the aim of this work is to build a quantitative relationship between the AE sources and the detected signal
by unravelling the effect of each stage of the acquisition chain, namely the source, the propagation medium and thedetection system. For this purpose, an AE modelling is carried out using the finite element method (FEM) code
ABAQUS®. In this case each stage of the acquisition chain is considered individually.
In this paper, once we validate our FE Model for wave propagation, we study the effects of the geometry, of thetype of sources and finally of the sensor.

2 MODEL VALIDATION

For obvious reasons, we first perform validation tests of the FE model by comparing experimental and numericalresults. In the experimental setup, pencil lead breaks are introduced onto the surface of aluminium specimens having
three different shapes shown in Figure 1, the thickness for all specimens being 3.7 mm. The EPL16 specimen has
the exact shape of the dumbbell test pieces typically used in the laboratory for material characterisation in generaland AE tests in particular. Other shouldered test bars EPL3 and EPL80 were especially designed to mimic the
behaviour of a thin rod and a plate respectively, for which analytical solutions of guided propagating modes areeasily calculable. The signals are detected by means of a laser vibrometer, which measures the out-of-plane velocity
on a surface point. Here we have not used a piezoelectric sensor in order to avoid its effect on the signal waveform,in particular the so-called aperture effect, so that we can isolate the signal recorded from the transformations due tothe sensor.

㸫S9㸫Figure 1: The three Aluminum geometries (EPL3, EPL16, and EPL80) used in all studies of this paper
Concerning the FE modelling, loads by the pencil lead breaks are modelled as displacement sources according to[3]. A 3D geometry has been built for each specimen (EPL3, EPL16 and EPL80), where the material is consideredhomogeneous, isotropic and perfectly elastic (Young modulus = 180GPa, Poisson ratio = 0.49 and density = 2170
kg/m3), the damping is taken into account by Rayleigh parameters. The dumbbells have been meshed with 3D
hexagonal C3D8R elements. The average size of one element respects the following criterion:leO min with
O min
R

CR
f max

(1)

Where C R : the Rayleigh wave speed in the material, f max : the maximum frequency, which is usually set at 1 MHz
in AE, Omin : the minimum wavelength and l e : the average size of one element. The resolution parameter R was set
to 10 for the elements located around the source, to 6 in the gauge section and to 4 in the shoulders.
The measured velocity has been compared to the simulated out-of-plane velocity, a good agreement is observed
between the two curves for each case as shown on Figure 2. The green lines indicate the time at which oscillationsof simulated signals start, while the red lines indicate the time up to which experimental and simulated signals arecomparable.

a)

b)

c)
Figure 2: Simulated and experimental velocity : a) EPL80 specimen, b) EPL3 specimen, c) EPL16 specimen

㸫S0㸫

This validation allows us to use the model of propagation medium for other numerical studies. So the next point
treated in this paper is the impact of the geometry on the recorded waveform.

3 EFFECT OF GEOMETRY

In the three geometries (EPL3, EPL16 and EPL80), the same displacement history (with a chirp signal showed
in Figure 3) is introduced on the surface in order to rather equally stimulate all frequencies up to 1.2 MHz. This
simulation allows us to identify the different modes of propagation by presenting the 2D Fast Fourier Transform(FFT2D). Those simulated modes are compared to theoretical modes calculated for plates and beams in the case of
EPL80 and EPL3 specimen respectively.

Figure 3: the Chirp source in terms of time and frequency
The results are presented as 2D Fast Fourier Transform shown in Figure 4; this way to present the results allows us
to identify the excited modes so that we can compare the simulated results to theoretical calculation. Here the results
show that the propagation medium geometry affects the propagated signals. Knowing that, in these geometries, thesignal propagates only following specific modes, we deduce that for the large specimen (EPL80) the plate modes
are stimulated, while for the thin one (EPL3) the beam modes are stimulated. In the intermediate specimen (EPL16),
for which the FFT2D is more complex, and the identification of modes is practically impossible: this geometry
cannot be assigned neither to a plate nor to a beam.

a)

b)

c)
Figure 4: FFT2D for the three geometries excited by the same source simulated on the surface, a) the large specimen(width = 80mm), b) the fine specimen (width=3mm) and c) the medium specimen (width=16mm)
This study shows us that AE tests done on a classical specimen e.g. EPL16 (Figure 1) in the laboratory cannot
replace an industrial case from a geometry perspective.
In the following part, we will study the effect of the type of source on the recorded signal.

㸫S61㸫

4 EFFECT OF TYPE OF SOURCES

In this part, we are interested in simulating physical sources. Three sources corresponding to three different
damage mechanisms have been modelled as buried point sources, according to [4], in three identical specimens (ofequal thickness = 3.7 mm). The sources are located in the gauge section, in the median plan and the centre of thespecimen width. They are modelled as dipole forces as shown in Figure 5, representing the expansion, fracture inplane shear mode and the fracture-opening mode. The sources rise time is 1s. the particle velocity is calculated on
the section of the specimen from the epicentre to its end. The results are presented in the form of FFT2D (wavenumber in terms of frequency).

Figure 5: The modeled sources: expansion, fracture in-plane shear mode and the fracture-opening mode
The Figure 6 shows the FFT2D results for the different types of damage. The stimulated modes are not the same in
the three cases neither the stimulated frequency range. These results highlight an acoustic signature for each kindof source.

Figure 6: FFT2D of the same geometry stimulated by different type of sources; a) expansion, b) fracture in-plan
shear mode and c) fracture opening mode
All studies above are done without considering the piezoelectric AE sensor. This latter, depending on its type, isknown by having resonant frequencies and limited bandwidth. So, in the last part we will take into account thesensor effect on the recorded signals.

㸫S62㸫

5 EFFECT OF PIEZOELECTRIC SENSOR

The last study in this paper concerns the effects of the sensor on the signals. The strategy is to study the AE
signals without considering the sensor, then adding the sensor effect and compare results.
The results without sensor are based on simulation results. The sensor is taken into account by its transfer function
experimentally determined by the reciprocity method [5]. This transfer function is added in the post-processing
phase in the Fourier domain, following the equation 2:
S sensor ( f )M ( f ) u S surf ( f )(2)

Where S sensor ( f ) : the signal spectrum, M ( f ) : the sensitivity function and S surf ( f ) : the spectrum of the simulated
signal retrieved from the surface representing the sensor’s sole at the sensor position.
In this part, the analysis is based on a traditional study of AE signals i.e. descriptor analysis. To obtain several AE
signatures as results, different kind of sources and different geometries are considered.
- Sources (Figure 7): the fracture opening mode (S1) and fracture in-plan shear mode (S2)- Geometries (Figure 1) : EPL3, EL16 and EPL80

Figure 7: Sources S1: fracture opening mode and S2: fracture in-plan shear mode applied to the three geometries
In order to have a good EA descriptors analysis, we selected relevant descriptors. The criteria for this selection are:- the descriptor is constant regardless of the distance from the source- it is independent of specimen geometry- its value depends on the characteristics of the source
Based on those criteria, the pertinent descriptors selected are the zero-crossing rate (temporal descriptor) andfrequency centroid.

The left panel in Figure 8 – left shows the results of classification in terms of the pertinent descriptors without sensor.
We can clearly identify two classes. Each class is assigned to a different source. However the right panel of thesame figure shows the results of classification after considering the sensor (in this case: a typical commercial

㸫S63㸫PACȝ80 sensor from Euro Physical Acoustics). We observe an overlapping of classes, which highlights thetransformation of classification results due to the piezoelectric sensor.

Figure 8: Classification result without considering sensor (left panel) and after considering the 80 sensor effect(right panel)
Finally, this last result presents what we see in our screen as recorded signal when using sensor but the result without
sensor presents what kind of improvement in terms of clustering could actually be obtained when deconvolving thesensor effect on the signals.

CONCLUSIONS
The aim of using the modeling of AE is to understand the transformations of the signal from the source to theacquisition system. Firstly, comparing simulated results to experimental ones validates the propagation medium
model. Then the model is used to study the effect of the geometry, the effect of sources and the effect of sensors.
Each of these parameters has its own intervention on the signal and this paper showed how they affect therecorded signals.
This paper basically treats the case of isotropic homogenous material. It is considered as a first step in the modelingof Acoustic Emission, before addressing the problem of anisotropic materials. The modeling of AE in composite
material is ongoing in view of its significance to understand the different type of sources generally observed during
the degradation of such more complex materials.

REFERENCES[1] Sibil, A., Godin, N., R’Mili, M., Maillet, E., & Fantozzi, G. (2012). Optimization of acoustic emission
data clustering by a genetic algorithm method. Journal of Nondestructive Evaluation, 31(2), 169-180.
[2] Aggelis, D. G., & Matikas, T. E. (2012). Effect of plate wave dispersion on the acoustic emission parameters
in metals. Computers & Structures, 98, 17-22.
[3] Sause, M. G. (2011). Investigation of pencil-lead breaks as acoustic emission sources. Journal of Acoustic
Emission, 29, 184-196.
[4] Scruby, C. B. (1984). Quantitative acoustic emission techniques.
[5] Dia, S., Monnier, T., Godin, N., & Zhang, F. (2012, September). Primary calibration of Acoustic Emission
sensors by the method of Reciprocity: theoretical and experimental considerations. In 30th European
Conference on Acoustic Emission Testing and 7th International Conference on Acoustic Emission (pp. 1-8).

㸫S64㸫

PROGRESS in ACOUSTIC EMISSION XVIII, JSNDI & IIIAE

Evaluation of Irrigation Response using AE Method forManagement of High-Frequency Irrigation of Hydroponic
Miniature-Tomato

Kensuke Kageyama1) and Takenobu Sakai1)1) Saitama University, 255, Shimo-okubo, Saitama, 338-8570, Japan.
ABSTRACT: AEs in living plants are related to the drought stress which induces the cavitation. The irrigation
management is important factor to cultivate the tomato plants under suitable drought stress. In this study, theresponse to irrigation of miniature-tomato plants was evaluated using AE method. We proposed that the change ofAE occurrence rate before and after the irrigation RD represented the stress response to the change of the drought
stress. The AE measurement of hydroponic miniature-tomato with high-frequency irrigation revealed that the RD
was influenced by the irrigation schedule. Furthermore, the average value of RD was deeply related to the yield andthe sugar content of the fruits.

1 INTRODUCTION
AEs in plants are accompanied by cavitation events that occur in xylem elements [1]. Cavitation accompanies
ultrasonic AEs, which can be detected by an AE sensor attached to the stem or trunk. Many studies on diagnosis ofliving tree has been done using AE measurement owing cavitation [2,3]. Qiu and Okushima [4], furthermore,
analyzed the relationship between AE and transpiration rate of tomato plant by measurement of AE, leaf temperatureand transpiration rate. They found that the influence of the change of transpiration rate on the AE behavior depended
on the drought stress level. Takakura et al. also proposed the concept of the speaking plant approach (SPA) toevaluate dynamic responses of plants to changes in their environment and examined developed a chlorophyll
fluorescence imaging system for tomato plants cultivated in greenhouses [5]. Such a combination of several sensing
techniques, i.e. a spectrum of a leaf, transpiration rate (weight of water in a pot) and AE at stem, was successful toevaluate water or drought stress response of plants to environmental changes. The measurements of the spectrumsof leaves and the weight of the pots, however, are not suitable for the practical use in agricultural greenhouse becauseof high cost and delicate handling. The AE method is widely used for monitoring of industrial structures because
the low-cost and robust devices have been developed. Additionally, the stress response of the plants could be
evaluated by only AE method if the environmental changes can be detected. The AE and irrigation is goodcombination to evaluate the stress response of a plant because the irrigation drastically eases the drought stress ofthe plant while the AE behavior is deeply related to the drought stress.
The authors, therefore, studied the AE behavior of miniature tomato plants to develop a technique for the healthmonitoring of plants using AE measurement and irrigation control [6]. Consequently, irrigation control of theminiature tomato using the AE change ratio before and after irrigation RD (response to drought stress change) was
successful for deficit irrigation of the miniature tomato

where   







 



and 



  represent AE events before and after irrigation, respectively.

  

Irrigation  
AE rate

AE rate  



Irrigation



Figure 1: Schematic of evaluation of irrigation response using RD.

㸫S65㸫

RD can be treated as the irrigation response of a plant as shown in figure 1. The plant shows a forward responsewhen RD<0 because both of the drought stress and AE rate were decreased. In our model, the forward responsemeans that the plant is well irrigated and the drought stress is moderate because the occurrence of cavitation wasrestrained. In contrast, the backward response (RD<0) means that the plant needs water and the drought stress issevere because the irrigation accelerated the occurrence of cavitation. Drought stress significantly influences on theyield and the sugar content of fruits at tomato cultivation. Hence, RD could be used for the prediction of yield andsugar content of the fruits because the RD is deeply related to the drought stress of tomato plants [7]. In this study,
the hydroponic cultivation system for tomato plants with high frequency irrigation was fabricated to investigate theinfluence of the irrigation condition on the RD behavior. The AEs at the stem of tomato plants were continuously
detected during cultivation and the response of RD against the change of the irrigation time was discussed.
2 EXPERIMENTAL PROCEDURES
2.1 Experimental material
A miniature tomato (Chika, Takii Seed) was used in this study. The seeds were germinated in a tray and mixed withvermiculite. The plants were placed in a temperature-controlled room where the temperature was set at 24 and 20ºC for day and night, respectively. The lighting intensity was set to 240 molm-2s-1 at the base of the shoot; thelighting cycle ran for 12 h each day. Well-grown plants at one month after transplanting the seedlings were examined
and measured. All lateral shoots were removed when they appeared during the measurement, and the plants werepinched above the third truss with two true leaves over the truss. The seedlings were planted in a small pot andgrown using a hydroponic cultivation system, as shown in Figure 2. Irrigation was done by spraying the nutrient
solution to the roots through mist nozzles using a water pump from a tank below the shoots. Root cutting was downat the interval of 10-14 days for a root-zone restriction. The plants were cultivated for 75-88 days while AE
measurement.
2.2 AE measurement
A high-precision vibration sensor (VS-BV203, NEC TOKIN) was selected as an AE sensor because of reducing
cost. A pair of AE sensors were attached to the same stem of a miniature tomato to measure AE owing to cavitationin xylem tissues. The center-to-center distance between each sensor was set to 2-5 cm. The AE sensor was fixedusing a stainless-steel hose clamp. AE signals were detected by an oscilloscope (PicoScope 4424, Pico Technology)
with a sampling rate of 10 MHz, and the waveforms were recorded using a PC. The thresholds for AE measurement
were set to 3 mV for the AE sensor. RD was evaluated every hour by following procedures. Firstly, sums of  and    up to 1 hour before were calculated when  was set to 10 min or  /2 if  <20 min as shown in Figure
3. Then RD was calculated using sums of   and    and equation 1 (RD was treated as being lost when  +   =0).

Figure 2: Experimental setup

㸫S66㸫




 

   Figure 3: Schematic of definition of Ti and TAE.
Table 1: Experimental conditions for hydroponic culture

Irrigation control was carried out by a PC and a relay circuit as shown in figure 2. Constant and fluctuated section
were selected for the irrigation condition of the plants. For the constant section, the irrigation interval and time were
fixed to 30 and 15 min, respectively. For the fluctuated section, the irrigation time or interval was fluctuated using
a fuzzy control. The concept of the irrigation control was to cultivate the tough plants against change of drought
stress, i.e. RD=0. Then the program was designated to increase and decrease the irrigation frequency when RD>0
and RD<0, respectively. Unfortunately, the fuzzy control in this study did not work properly for several causes(electrical troubles, computer freezing up and bugs of the program) although the fuzzy control was programmed tochange the parameter according to the value of RD. The irrigation condition was treated as being randomly changed
for the fluctuated section in this study. The irrigation condition was listed in table 1. The irrigation time and interval
time were changed for F1 and F2, respectively.

3 RESULTS AND DISCUSSION
Figure 4 to 6 show the behaviors of AE occurrence rate and irrigation time of C, F1 and F2 section. In fluctuatedsections F1 and F2, the relationship between the AE occurrence rate and the amount of irrigation time was notrecognized though the both were largely fluctuated. Hourly behavior of RD of F1 was significantly scattered asshown in figure 7 because the number of the detected AE was not enough to calculate the precise value of RD. Then,an average value of RD  and a sum of the irrigation time ITT were calculated by separating for each period T forthe entire measurement period. Figure 8 shows the behaviors of  and ITT of F1 section when T=24 h. The 
showed slight similar behavior with the ITT (same rising peaks at 44, 70 and 80 d were observed).
   












  
















 










 

(a)(b)
Figure 4: AE behavior and irrigation fluctuation for C section

㸫S67㸫



                   













                  


















 

 

(a)(b)
Figure 5: AE behavior and irrigation fluctuation for F1 section   












                  


















 

 
















  






















 








 







   



Figure 8: Behavior of  and ITT at T=24 h for F1 section 








Figure 7: RD behavior for F1 section     



(a)
(b)
Figure 6: AE behavior and irrigation fluctuation for F2 section 

















 





 

(a)
(b)
Figure 9: Relationship between T and the average of  when ITT was increased (ITT>0) and decreased(ITT<0) at (a) F1 and (b) F2 section.

㸫S68㸫

To identify the influence of the irrigation change on the RD, the change of  against the fluctuation of irrigation
time was investigated when T was varied from 1 to 168 h. Firstly, the differential value of  and ITT from theprevious period in each section  and ITT was calculated. Then, the average values of forITT>0 andITT<0 were derived, respectively to find the trend of against the fluctuation of ITT. The behaviors of theaverage value of  whenITT>0 andITT<0 in F1 and F2 sections were shown in figure 9.
The  showed the reverse polarity to the ITT when T<12 h in F1 section while the polarity of coincidedwith that ofITT regardless of T in F2 section. The cavitation rate (AE rate) is decreased usually after the irrigation
when the drought stress is eased because the amount of the irrigation is increased. Hence, a well-irrigated plantshould demonstrate negative value of whenITT>0 in short period as shown in F1 section. Furthermore, theaverage values of the irrigation time of C, F1 and F2 sections were 709, 347 and 54 min/d, respectively. F2 section,
therefore, suffered the severe deficit irrigation. These results suggest that the plants of F2 suffered severe drought
stress and eventually the behavior of was different between F1 and F2 in short term.
On the other hand, figure 9 also indicate that there is the positive relation between the RD and the amount ofirrigation in long period for both of F1 and F2. Such a tendency might be related to the growth of the examinedplants because the increase of the amount of irrigation (fertilizer) should promote the plant’s growth following theincrease of transpiration rate which increases the cavitation vulnerability.
The was related to the yield YD and the average sugar contend AS of the fruits after experiment as shown infigure 10. The YD was decreased compared with F1 section while AS was not changed as shown in figure 10.
Consequently, these results suggest that  could be controlled by the irrigation control if the polarity of againstITT was inverted in short period as observed in F1 section. Furthermore, the control of  can be expected
to optimize of YD and AS of tomato plants. The effect of irrigation control using RD on the improvement of yieldand sugar content of fruits in tomato cultivation, therefore, will be studied in future works.








  










   







     



Figure 10: Influence of  on yield (YD) and average sugar content (AS) of fruits when T is whole measurement
period. Circles, squares and triangle represent C, F1 and F2, respectively.

4 CONCLUSTIONSThis study investigated the influence of the irrigation condition on the behavior of the AE change ratio before andafter irrigation RD as the irrigation response of a tomato plant. The hydroponic cultivation system for tomato plants
with high frequency irrigation was fabricated and the AEs at the stem of tomato plants was continuously weredetected during cultivation. The daily average value of RD was slightly related to the irrigation time though thehourly value of RD was largely scattered. The change of  showed the same polarity to the fluctuation of irrigation
time when calculating period T was longer than 12 h when the irrigation time was fluctuated (F1 and F2 sections).
On the other hand, The change of  showed the different tendency between the F1 and F2 sections while the yieldof the fruits of F2 was decreased compared with F1. Consequently, RD could be controlled by fluctuating theirrigation condition unless the plants suffers severe deficit-irrigation and might be useful parameter to optimize theyield and sugar content of the fruits of tomato plants.
REFERENCES[1] Milburn, J.A., (1966) “The Conduction of Sap. I. Water Conduction and Cavitation in Water Stressed Leaves”,
Planta, Vol.65, pp.34–42.

㸫S69㸫

[2] Mazal, P., Cerny, M., Vlasic, F., and Nohal, L., (2012) “Correlation of Sap Flow Changes in Trees with Signalof Acoustic Emission During Field Measurements”, Proc. 30th European Conference on Acoustic Emission
Testing (CD-ROM).
[3] Jackson, G. E. and Grace, J., (1996) “Field measurements of xylem cavitation: are acoustic emissions useful?”,Journal of Experimental Botany, Vol.47, pp.1643-1650.
[4] Kacia, M., and Okushima, L., (2005) “Plant Response-Based Sensing for Control Strategies in Sustainable
Greenhouse Production”, Journal of Agricultural Meteorology, Vol 61, pp.15-22.
[5] Takayama, K., Nishina, H., Iyoki, S., Arima, S., Hatou, K., Ueka, Y., and Miyoshi, Y., (2011) “Early detectionof drought stress in tomato plants with chlorophyll fluorescence imaging”, Proceedings of the 18th World
Congress The International Federation of Automatic Control, Milano, pp.1785-1790.
[6] Kageyama, K., and Kurita, T., (2013) “Deficit Irrigation of Miniature Tomato Based on Estimation of
Embolism Risk by Measurements of Acoustic Emission and Stress Wave at Stem”, Journal of JSEM, Vol.13,
pp.s85–s91.
[7] Kageyama, K., Inoue, Y., and Kato, H., (2009) “Estimation for Embolism Risk of Tomato Using Acoustic
Emission Response to Increased Drought Stress”, Environment Control in Biology, Vol.47, pp.127–136.

㸫S70㸫

PROGRESS in ACOUSTIC EMISSION XVIII, JSNDI & IIIAE

In-situ investigation of deformation mechanisms invarious magnesium alloys by X-ray diffraction andacoustic emission
Kristián Máthis1), Gerardo Garcés2), Jan Čapek1) and Alexei Vinogradov3)
1)
2)
3)

Charles Univeristy in Prague, Department of Physics of Materials, Ke Karlovu 5, Prague, 121 16,
Czech Republic.
Department of Physical Metallurgy, CENIM-CSIC, Avenida Gregorio del Amo 8, Madrid, E-28040
Spain.
Norwegian University of Science and Technology, Department of Engineering Designs andmaterials, 220 Richard Birkelandsvei 2B, Trondheim, N-7491, Norway.

ABSTRACT: The influence of the LPSO (long period stacking ordered) phase orientation on the
deformation mechanisms of magnesium alloys has been investigated by X-ray diffraction (XRD) andacoustic emission (AE) measurements. The adaptive sequential k-means analysis (ASK) method, offering
identification of the dominant deformation process (basal, non-basal slip, twinning) in a given time
period, has been used for AE data evaluation. The results indicate that the kinking mechanism exhibits asignificant dependence on the orientation of the LPSO phase with respect to the loading axis.

1 INTRODUCTION
1.1 Current development in field of high-strength magnesium alloys
Magnesium alloys, having excellent strength to weight ratio, represent a highly interesting material fortransportation industry, where the fuel saving achieved through weight reduction belongs to the most importanttasks. Nevertheless, their widespread application is limited by several factors, as generally low formability andyield strength at ambient temperature, strong degradation of mechanical properties above 200 °C. There areseveral ways for improving the mechanical performance of magnesium alloys, including grain size refining andalloying, which leads precipitation formation, which enhance the stability of the microstructure.
The Mg-Zn-RE LPSO (LPSO – long period stacking ordered) structures are fundamentally long-period stacking variants of a hexagonal-closed-packed (hcp) structure of the Mg crystal. The recent investigations shows, thatyield strength exceeding 300 MPa can be achieved using technologically simple hot-extrusion process for thisclass of materials [1]. The high-temperature properties of Mg/LPSO alloys are also very promising – only a slightdecrease of strength was found in the temperature range of 20 - 300 °C.
Despite the efforts of the scientific community in the last decades the elucidation of the deformation mechanisms of magnesium alloys with LPSO phase is still a challenging task. There is a general agreement that there are
two main processes besides the dislocation slip, which contribute to the plasticity: formation of “deformation
kinks” and twinning.
Deformation kinks form, when a large stress acts parallel to basal slip planes. Owing to the low critical resolved shear stress (CRSS) for basal slip, avalanche-like motion of (0001) basal dislocations takes place, accompanied by elastic buckling of planes. During the ongoing loading, basal dislocations with opposite sign nucleate.
They start to move against the dislocations from the first “wave”, which leads to the kink formation.
The kink formation significantly depends on materials parameters (e.g. shape and orientation of LPSO phase,grain size, texture) and experimental conditions (loading direction, temperature etc.). The role of the non-basal
slip in the kink formation has not yet been clarified. However, the activation of {10 10} 1120 prismatic slip has
been reported, but rather at higher testing temperatures [2].
Deformation twinning is the main mechanism accommodating deformation out of the basal plane, particularlyat temperatures below 200 °C. In magnesium, the {10 12} -type extension twinning, associated with extension
along the c-axis and reorientation of the lattice by 86.3° and {10 11} -type compression twinning, resulting incontraction along the c-axis and tilting of the lattice by 56° are the most often reported and studied twinning

㸫S71㸫

mechanisms . In the case of Mg/LPSO alloys, twinning was found to operate for particular sample compositions
and texture. However, revealing the conditions for its activation needs further investigation.

1.2 Experimental methods exploitable for investigation of Mg/LPSO alloys
Owing to their complex microstructure the experimental study of the deformation mechanisms in Mg/LPSO alloys is rather difficult. The deformation kinks, slip bands and twinning are usually studied by light optical microscopy (LOM). In addition, there are several investigations of the dislocation structure performed by transmission
electron microscopy (TEM). However, the study of less populated dislocation types (e.g. prismatic or 2nd order pyramidal ones) by TEM is not an easy task. The SEM experiments are usually limited to documenting of the microstructure by means of secondary and/or back-scattered electrons; the orientation mapping using electronbackscattered diffraction (EBSD) technique is scarce. Generally, the microscopic methods usually examine only asmall volume of the material and the results are not always representative for the whole specimens.
Statistically representative data about the dislocation structure and twinning in magnesium alloys can be obtained from both diffraction experiments and acoustic emission (AE) tests [3]. The total twinned volume is determined from the intensity variations of particular peaks, caused by the crystal lattice reorientation during the twinning [4]. The diffraction measurements can take place both in ex-situ and in-situ regime. In the latter case, thecombination of the diffraction experiments with the AE testing can result in a complex characterization of theplasticity [5].
The acoustic emission (AE) is an efficient experimental tool for in-situ testing of deformation mechanisms inmagnesium alloys . The main advantages of the method are a fine time resolution and sensitivity to twin nucleation and collective dislocation motion. Nevertheless, the differentiation between the particular signal types is a difficult task due to their simultaneous appearance. Various statistical methods (k-means, fuzzy c-means, neuron
analysis) have been worked out to analyse the AE data. The recent one, the so called adaptive sequential k-means
analysis (ASK) by Pomponi and Vinogradov [6] appears very promising for determination of the dominant deformation mechanisms at the various stages of deformation in magnesium alloys.

2 EXPERIMENTAL
2.1 Material
The Mg97Y2Zn1(at.%) alloy was fabricated by melting in an electric resistance furnace using high purity Mg
and Zn elements and a Mg-Y master alloy. Ingots were cast by pouring the liquid metal into a cylindrical steel
mould of diameter 42 mm. Cast billets were extruded to flat bar at 400 ºC with a reduction ratio of 18:1. This
rectangular bar was sectioned to 6x4x4 mm3 sized specimen, which were used for compression tests.

2.2 Microstructural characterization
Prior the optical microscopy, the specimens were sequentially ground on SiC grinding papers (800, 1200,
2000, 4000) with water. Then coarse polishing was performed using 3-µm and 1-µm diamond abrasive. In thenext step the specimens were etched with nital 5% and electrolytically polished in a solution of picric acid (100 ml
ethanol, 18 ml H2O, 6 ml glacial acetic acid, 12 g picric acid 98%) for 7s. Texture analysis was performed by the
back-Schulz reflection method, using a SIEMENS TM Kristalloflex D5000 x-ray diffractometer equipped with an
Eulerian cradle.

2.3 In-situ acoustic emission (AE) and synchrotron diffraction (SD)The deformation test has been performed in compression at room temperature, using a strain rate of 10-3 s-1. In
the case of ED the test was stopped after reaching 20% of strain in order to avoiding the destroying of the AE sensor. A miniature (2 mm in diameter) broadband AE sensor (flat frequency response in the range 100 – 500 kHz)
from Dakel company was mounted on the specimen using vacuum grease and a clamp. The AE was amplified by
40 dB in the frequency range 100 – 1200 kHz using a 2/4/6-type preamplifier manufactured by Mistras Corporation. The AE acquisition (PCI-2 board from Mistras Corp.) took place in a so-called data streaming regime, where
the data were recorded continuously with 18 bits amplitude resolution and 2 MHz sampling rate.

㸫S72㸫

In-situ synchrotron diffraction experiments were carried out during separate compression test on the beamline
EDDI at BESSY, Berlin, Germany. The energy range of the synchrotron white beam was from 10 to 135 keV.
The diffraction angle was around 2θ = 9.74º. The resulting gauge, determined by slit setting, was a rhomboid
prism of 0.5x0.5x5.6 mm. The gauge volume was always positioned in the centre of the cylinder. The sample wastilted within the scattering plane between ψ = 0º (axial direction) and ψ = 90º (radial direction), where ψ is defined as the angle between the scattering vector and the extrusion axis. The use of a white beam allowed the entire
diffraction pattern to be collected for each ψ angle. Individual diffraction peaks were obtained for each diffraction
pattern and fitted with a Gaussian curve to determine the peak position and peak intensity.

3 RESULTS AND DISCUSSION
3.1 Initial microsturcture
Figure 1 shows the microstructure in the extrusion direction (ED) and transverse direction (TD). The LPSO
phase, having long fiber shape, has deformed during the extrusion process. Its volume fraction was estimated as
19%. The magnesium matrix exhibits a bimodal grain structure, consisting of dynamically-recrystallized (DRX)
grains and non-recrystallized grains elongated along the extrusion direction.

Figure 1: Typical microstructure of the initial state in a) extrusion direction (ED); b) transversal direction (TD).
The loading direction is parallel with the vertical axis of the figure.
Figure 2 shows the recalculated (0002) and (10 10) pole figures of the magnesium phase measured in the section perpendicular to the extrusion direction. The alloy shows a strong fiber texture with the basal plane parallel tothe extrusion direction. This texture component is given mainly by non-recrystallised grains, as it has been shownby several authors [1].

Figure 2: (0002) and (10 10) pole figures of the initial specimen showing a strong fiber texture
The deformation curves for the particular directions and the corresponding AE response are plotted in Fig. 3. It
is obvious that the yield strength is higher for the ED specimen. Similarly the AE signal is more intensive in thisdirection. In order to reveal the background of this behavior, ASK analysis was applied on the continuously recorded AE data stream.

㸫S73㸫

Figure 3: Deformation curves and corresponding AE response as measured for a) ED; b) TD specimens.
Raw AE signals recorded during compression tests were further analyzed using the adaptive sequential kmeans (ASK) procedure. The main steps of the procedure can be summarized as follows:• The recorded waveform streaming data are sectioned into consecutive frames. In our case a single frame
corresponds to a 2 ms long “time window”. (see schematic drawing in Fig. 4)• For each frame, the Fast Fourier Transformation (FFT) of the signal is done, which is used for the calculation of the Power spectral density (PSD) function.
• PSDs in the consecutive frames are analyzed one-by-one. The features of the PSD in the first frame define Cluster 1. All other clusters are defined with respect to this initial cluster. The conditions for new
cluster forming are based on k-means method.

Figure 4: Schematic drawing of ASK procedure
When the clustering procedure is completed, a dominant AE source mechanism is assigned to each cluster.
This assignment consists of two basic steps:
1. Checking the time of the appearance of the elements in a given cluster. The elements in Cluster 1 belong
to the background noise, since the recording of AE data is always launched before the starting of the deformation test.
2. Checking characteristic features of the PSDs, as energy, frequency distribution etc.
Finally, the increment of the number of elements with time (Cumulative Number of Elements) can be plotted,
which describes the activity of particular deformation mechanisms in time.

Figure 5: Energy – median frequency cross plot for a) ED; b) TD. The colors represent various AE source
mechanisms

㸫S74㸫

Based on the above mentioned algorithm, 4 main clusters have been identified. In order to facilitate the cluster
assignment to the deformation mechanisms, we constructed the Energy vs. Median Frequency cross-plot (Fig. 5).
It is obvious that for both specimens orientations the characteristic features of the clusters are similar. The evolution of the number of elements in the particular clusters is plotted in Fig. 6.

Figure 6: Time evolution of the number of elements in particular clusters, having different AE source mechanisms for a) ED; b) TD specimen.
Using information given by Figs. 5 and 6, the following mechanisms can be assigned to the clusters:• Cluster 1 – Background noise (color code in figures – black)
Since the elements in this cluster appear before launching of the straining, this cluster naturally belongs to thebackground noise. They are typically low energy, broad frequency signals, as it was reported by several authors[7].
• Cluster 2 – Dislocation slip (color code in figures – red)
The number of events in this cluster starts to increase from the very beginning of the deformation. This behavior corresponds to the basal slip, which has the lowest critical resolved shear stress (CRSS) [8]. As the deformation progresses, further dislocation mechanisms (prismatic and pyramidal slip) contributes to this cluster. However, this analysis could not distinguish them properly. The dislocation cluster has a characteristic tear shape,
which is given by shortening of the mean free path for dislocation movement at higher stresses, where the dislocation density is larger than that at the beginning.
• Cluster 3 – Twinning (color code in figures – green)
The elements in this cluster have high energy and a relatively narrow frequency range. Such a feature is characteristic for burst-type twinning signal. Also the appearance of this cluster in the early stage of the straining indicates the twinning origin of elements, since the CRSS of {10 12} -type extension twinning is as low as several
MPa [8].
• Cluster 4 – Kinking (color code in figures – blue)
The average energy level of the signals is slightly higher compared to cluster 2 (dislocation slip). The cluster
has a tear shape, but the frequency range is narrower than that for the dislocation slip. This cluster becomes dominant in the frequency spectrum from approximately 40 MPa. These features indicate the kinking-origin of thiscluster. As it is mentioned in the introduction, the kinking is caused by avalanche-like motion of basal dislocations. Such a mechanisms produces a relatively high energy signals [9], which is in good agreement with our experimental data. Owing to the structure of the LPSO phase, the initiation of kinking requires higher stresses [1].
If we compare the time evolution of the number of elements in the particular clusters (Fig. 6), we can see thatnoise and dislocation slip clusters behave similarly. The dominancy of background noise terminates as the deformation starts. The dislocation slip has a large jump at the onset of the straining, as a consequence of the basal slip.
There is a further jump on this curve in the vicinity of the macroscopic yield, which is caused by activation ofnon-basal <a>-slip in the prismatic and pyramidal planes [5]. The twinning is more significant in ED. This feature
is evident also from Fig. 7, where the AE data are plotted together with diffraction data. It is obvious that there are
more twins nucleated in ED (cf. AE data – green line) and their volume is significantly larger (cf. diffraction data
– red scatter). This behavior can be elucidated with the initial texture, where in ED both large and small grains arefavorably oriented for twinning. In contrast, only the non-recrystallized large grains can undergo twinning in TD.
Consequently, both number and volume fraction of twins are larger in ED. There is also a difference in kinking
activity. Since the stiff, elongated LSPO phase is aligned with the ED, load transfer takes place from the matrix to
the LSPO during compression of ED specimens. This mechanism is less effective in TD case [10]. After reaching

㸫S75㸫

a certain level of stress, kinking takes place. Again, owing to the orientation of the LPSO phase, kinking requires
lower stress in ED, and it progresses gradually. In TD, kinking can be observed form approx. 80 MPa and its onset is more dramatic (Fig. 7).

Figure 7: Change of the intensity of (0002) diffraction peak and evolution of number of twinning elements as afunction of true stress for a) ED; b) TD specimen

4 CONCLUSIONS
The dependence of the deformation mechanisms with respect to the specimen orientation was investigated in
Mg97Y2Zn1(at.%) alloy having LPSO phase. The following conclusions can be drawn:• Higher yield stress and strength was observed for ED - which is given by more effective load transferfrom matrix to LPSO phase.
• There is a significant difference in the twinning activity – owing to the initial texture more grains are involved in twinning process in ED. Consequently, both the twinned volume and the number of nucleatedtwins is larger for this specimen. In contrast, the dislocation slip seems to be similar for both directions.
• The onset of kinking is shifted to higher stresses in TD.

ACKNOWLEDGEMENTS
The authors are grateful for the financial support of the Czech Science Foundation under the contract 1612075S.

REFERENCES[1] Kawamura, Y. andYamasaki, M.,(2007).
"Formation and mechanical properties of
Mg97Zn(1)RE(2) alloys with long-period stacking ordered structure". Materials Transactions Vol. 48
pp. 2986-2992.
[2] Hagihara, K., Fukusumi, Y., Yamasaki, M., Nakano, T. and Kawamura, Y., (2013). "Non-Basal
Slip Systems Operative in Mg12ZnY Long-Period Stacking Ordered (LPSO) Phase with 18R and 14H
Structures". Materials Transactions Vol. 54 pp. 693-697.
[3] Máthis, K., Beran, P., Čapek, J. and Lukáš, P., (2012). "In-situ neutron diffraction and acoustic
emission investigation of twinning activity in magnesium". Journal of Physics: Conference Series Vol.
340 pp. Art. No.: 012096.
[4] Gharghouri, M.A., Weatherly, G.C., Embury, J.D. and Root, J., (1999). "Study of the mechanicalproperties of Mg-7.7at.% Al by in-situ neutron diffraction". Philosophical Magazine A Vol. 79 pp.
1671-1695.
[5] Čapek, J., Máthis, K., Clausen, B., Straska, J., Beran, P. and Lukáš, P., (2014). "Study of theloading mode dependence of the twinning in random textured cast magnesium by acoustic emission andneutron diffraction methods". Materials Science and Engineering a-Structural Materials Properties
Microstructure and Processing Vol. 602 pp. 25-32.
[6] Pomponi, E. and Vinogradov, A., (2013). "A real-time approach to acoustic emission clustering".
Mechanical Systems and Signal Processing Vol. 40 pp. 791-804.

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[7] Vinogradov, A., Lazarev, A., Linderov, M., Weidner, A. and Biermann, H., (2013). "Kinetics ofdeformation processes in high-alloyed cast transformation-induced plasticity/twinning-induced plasticity
steels determined by acoustic emission and scanning electron microscopy: Influence of austenite stability on
deformation mechanisms". Acta Materialia Vol. 61 pp. 2434-2449.
[8] Chapuis, A. and Driver, J.H., (2011). "Temperature dependency of slip and twinning in planestrain compressed magensium single crystals". Acta Materialia Vol. 59 pp. 1986-1994.
[9] Vinogradov, A., Orlov, D., Danyuk, A. and Estrin, Y., (2013). "Effect of grain size on themechanisms of plastic deformation in wrought Mg-Zn-Zr alloy revealed by acoustic emission
measurements". Acta Materialia Vol. 61 pp. 2044-2056.
[10] Farkas, G., Choe, H., Máthis, K., Száraz, Z., Noh, Y., Trojanová, Z. and Minarik, P., (2015). "In
Situ Investigation of Deformation Mechanisms in Magnesium-Based Metal Matrix Composites". Metals
and Materials International Vol. 21 pp. 652-658.

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PROGRESS in ACOUSTIC EMISSION XVIII, JSNDI & IIIAE

Acoustic emission and damage evolution at mean temperature
under air of a SiC/[Si-B-C] composite subjected to cyclic andstatic loading : towards lifetime prediction
E. Racie, N. Godin, P. Reynard, M. R'Milli and G. Fantozzi
INSA de Lyon, laboratoire MATEIS, 7 avenue Jean Capelle, 69621 Villeurbanne cedex
ABSTRACT͗The low density and the high tensile strength of Ceramic Matrix Composites (CMC) make them agood technical solution to design aeronautical structural components. To fully understand damage mechanisms
and be able to design components, its behavior has to be analyzed during fatigue tests. The aim of the present
study is to consider the possibility of predicting rupture time from acoustic emission monitoring. New indicatorsof damage are defined, based on acoustic energy. These indicators highlight critical times or characteristic times
allowing an evaluation of the remaining lifetime.

1 INTRODUCTIONCeramic matrix composites (CMC) seem to be adequate materials to design new generation of civil aircraft engines
because of their low density and high tensile strength at high temperature. That is why damage mechanisms have tobe identified, through cyclic fatigue tests.
The aim of the study consists in analyzing and comparing material behavior under cyclic and static fatigue loadings,
at the same temperature and under air, to determine the effects of a cyclic loading on damage and lifetime. CMCs
seem to be promising material for aeronautical applications, even if their constituent materials are brittle, the strain
at failure is rather high due to considerable matrix cracking and cracks deflection at interfaces [1]. However, thebehavior of these materials is affected by oxidation of interphases, fibers and the ultimate failure is governed byslow crack growth in fibers [2]. Self-healing material has been developed to protect fibers against oxidation, which
increased largely the lifetime of material. Nevertheless, under air and for temperature under 550°C, self-healing is
QRWVLJQLILFDQWHQRXJKWRIXOO\SURWHFWWKHPDWHULDO,W¶VWKHUHIRUHLPSRUWDQWWRXQGHUVWDQGWKe material behavior for
those temperatures. As specified above, the lifetimes under these types of loadings are rather long, which makes it
hard to realize tests on laboratory equipment. This kind of studies needs to be done with a limited number of tests,
thus the use of different techniques to monitor the damage in real time is mandatory. Acoustic emission (AE) appears
to be a good candidate in this case. The aim of AE is to record transient elastic waves on the material created bydamage mechanisms. There are several studies referring to this kind of method for different types of CMCs under
tensile tests [3-5]. For fatigue tests, damage can be analyzed from different points of view, first by linking eachacoustical event to the damage mechanism which generated it [5-6]. This process needs clustering algorithms [7].
Another approach consists in considering the evolution of released energy [8-10]. It is generally accepted that theenergy of an AE signal is related to the energy released by the source. Consequently, AE energy gives information
about material damage; it is then possible to point out precursory elements to ultimate failure or to simulate AEenergy evolution with a power law to determine lifetime.
A main purpose of this paper is to consider the possibility of predicting rupture time of CMC from damage evolution
recorded by AE technique. The objective of this approach is to propose a method based on acoustic energy in orderto evaluate the remaining lifetime during long-term-mechanical tests. The approach is based on the determinationof energy released and identification of a critical point in energy release during mechanical test. So beyond thischaracteristic point, the criticality can be modeled with a power-law in order to evaluate time to failure.
In this study, new indicators of damage have been defined, based mainly on acoustic energy analysis. These
indicators highlight critical times or characteristic times allowing an evaluation of the remaining lifetime. Moreover,the clustering of acoustic emission, using a supervised clustering method based on random forest approach [11],makes possible to get a real-time detection of each damage phenomena and to identify the mechanism responsible
for this critical time.

2 EXPERIMENTAL PROCEDURE
2.1 Materials and mechanical tests

Fatigue tests have been realised at a temperature of 450°C under air. The material is a ceramic matrix composite
reinforced with Nicalon SiC fibers, coated with PyC and embedded in a self-healing [Si-B-C] matrix. The
reinforcement architecture is a stack of several layers of 2D satin fabrics linked together by strands of fibers in thethird direction. In this study all the specimens have a dog-bone shape with a thickness of 4 mm and a gauge section

㸫S78㸫

of 60 mm x 16 mm. For static fatigue tests, specimens were first loaded at constant loading rate of 1kN/min, andperiodically (every 6 or 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 with constant
amplitude and a frequency of 2 Hz. Strain is measured using an extensometer. In the case of static fatigue tests, theimposed stress increases every Ti (time for 18% of Nf, number of cycles to failure) with a step corresponding to 6 %of tensile strength. At the same time cyclic fatigue tests are realized with imposed stress oscillating at a frequencyof 2 Hz between 0 and a constant maximum value which is incremented of 6 % every 18% of Nf (Fig. 1). Moreover,cyclic fatigue tests with constant amplitude were conducted under a tensile/tensile sinusoidal loading and afrequency of 2 Hz until composite fracture͘



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ď͘

ʍ

ʍ
0.06
ıZf 
Ϭ͕Ϭϲ͘ʍ

0.06
ıfZ
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0.06
ıfZ
Ϭ͕Ϭϲ͘ʍ

RI1ϭϲϬŚ I

0.06
ıfZ
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ϲ

dĞŵƉƐTime 

RI1ϭϲϬŚIϲRI1
RI1ϭϬĐǇĐůĞƐI ϭϬĐǇĐůĞƐI

ǇĐůĞƐ

Figure 1. Applied stress for a. Incremental static fatigue test b. Incremental cyclic fatigue test

2.2 Acoustic emission monitoring

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 tothe data acquisition system (PCI2, Mistras Group) via a preamplifier with a 40 dB gain and 20-1200 kHz bandwidth(Mistras Group).
During tests, AE is recorded with 2 piezoelectric sensors, one on each side of the reduced section. The position ofa detected source can be determined linearly knowing the wave velocity in the material.
The AE wave velocity has been calibrated before the test Ce0, according to a pencil lead break procedure: several
breaks were performed on the specimen at several locations x between the two sensors. The velocity Ce(H) of an
extensional wave in a thin plate is proportional to the square root of the elastic modulus E of the material. Since E
decreases as damage occurs in the material, it is important to take into account the evolution of Ce during themechanical test in order to better evaluate the location of the AE sources. As proposed by Morsher [4], the initialmodulus during unloading E(H) was measured during a cycled tensile test, where hysteresis loops were obtained at
different strains. The velocity Ce(H) was then determined by using equation 1:

Ce(H )E (H )

Ce0E0
2.3 Damage indicators












;ϭͿ

The energy of the recorded AE events represents a part of the elastic energy released by damages of CMC specimens.
Thus, the evolution of elastic energy released by analysing the energy of AE events may be investigated. The sourceenergy E(n) is then defined as the square root of the product of the amounts of energy received at both sensors Ei(n)for each source:


E (n)

E1 (n) u E2 (n) 









㸫S79㸫


;ϮͿ

The first indicator is based on the coupling of mechanical energy (U m) and acoustic energy (UAE) denoted
Sentry Function (F), initially introduced by Minak [12, 13]:

ln

F

Um
U AE

(3)

¦

where Um is strain energy and UAE the cumulated acoustical energy ( E n ). F can be function of time or
strain. It is defined as soon as the first acoustical event is recorded. Strain energy is calculated measuring thearea under the force-displacement curve. To determine only the effects of tension, unloading/loading loads
are not taken into consideration. The function is calculated every k acoustic sources (k~0.1% of number ofsignals).
The coefficient of emission RAE is defined as the increment of energy 'E recorded during an increment of time 't,
divided by the total energy emitted during the initial loading of the sample:

RAE (t )1 ǻ(
E loading ǻW(4)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 @.

In this study the energy rather than the signal strength is used to define the severity. It is defined as theaverage energy.

Sr

1 mJ
¦ Em (n)J m1(5)

J is an empiral constant.
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.

ܴ௅௎ ൌοா೗೚ೌ೏೔೙೒
οாೠ೙೗೚ೌ೏೔೙೒

 







(6)3 RESULTS AND DISCUSSION
Results of fatigue tests are summarized in Table 1. To compare the results of static and cyclic fatigue tests, thebehavior of the material during a tensile test is used as a reference. Thereafter, ͵5 and ͧ5 will respectively represent
ultimate tensile strength and strain at failure. For both types of loading, stress at failure is lower than ͵5, nevertheless
it is twice as high for static loading than for cyclic loading which means that cyclic loading has an effect on thematerial lifetime. Considering the strain at failure, the value is also lower for a cyclic loading than for a static loading.
In addition, the elastic modulus decreases greatly before ultimate fracture of the composite while it tends to an
asymptotic value for a static loading and does not reach the limit value (IYI. Moreover, the mean distance between
matrix cracks in the longitudinal yarns is higher under cyclic fatigue, showing to saturation of matrix cracks could
occur at a lower interfacial shear stress, decreased probably by progressive wear induced by cyclic loading.
Under cyclic fatigue (Tab. 1), a greater proportion of recorded signals and a more significant release of energy wereobtained at a given time than under static fatigue for the same values of temperature and maximum load. Thus thisremark may point out an increase of damage due to loading cycles.
The figures 2a and 2b represent the evolution of the sentry function (F) during static and cyclic loading. In bothcases, a third part appears for an average time of 90% of the total test duration. The function is then decreasing. The
signals which have created this decrease are probably generated by fibers breakages since they are localized in thearea where the specimen failed. The evolution of the sentry function during the cyclic fatigue test shows a part (partI.b) where the function is decreasing significantly. This is caused by the important increase of acoustic energy,
whereas the strain remains constant. It appears that cyclic loading generates different types of damage since the

㸫S80㸫

function does not show this type of variation for a static loading or a tensile test. The variations of this function indifferent cases exhibit different levels of damage on the composite: matrix cracking and fibers breakages for fatigue
tests. Furthermore it shows that a cyclic loading has more critical damage effect than a static loading when the
maximum stress is higher than 36 RIıR. In addition, during incremental fatigue test, this indicator reveals thefatigue limit (Fig. 2).
Now a focus is done on results obtained for cyclic loading at constant amplitude just behind this critical value.
The evolution of RAE coefficient versus time is given in Fig. 3. For the cyclic fatigue tests at constant amplitude,
the evolution of the coefficient RAE is very different from that observed in static fatigue. In both cases for the static
fatigue, RAE decreases first, down to a minimum value, and then increases up to the failure of the composite. On
average, the minimum of RAE appeared at 55% of the rupture time. The minimum of the coefficient RAE indicatesthe beginning of the critical damage phase and provides an estimate of the remaining lifetime. In previous studies,
the restart of activity prior to final rupture is attributed to the avalanche fibers ruptures. For the cyclic fatigue, aminimum value is not observed but a significant change of slope is visible for all mechanical tests at approximately
20-25% of the total test duration. This characteristic time could certainly be used in order to evaluate time to failure.
Table 1. Results of static and cyclic incremental fatigue tests
Stress at failure
Strain at failure
Elastic modulus at failure
Number of AE sources
Cumulated acoustic energy
matrix cracks spacing /matrix cracks spacing after tensile test

Static fatigue (x2)RIıRİR
Ef. vf9500
2.2.108 (attoJ)

Cyclic fatigue (x3)36-RIıR
RIİR
1.5 Ef. vf
2.57.1066.4.1010 (attoJ)

1

1.6




Ultimate failure

Part. IIIPart II

Part IWĂƌƚ/ďPart. IIIPart II
Part IďͿ㸫S81㸫

Ultimate failure areaĂͿFigure 2 : Coupling of mechanical energy and acoustic energy (Sentry function) for a) an incremental cyclic fatigue
test and b) an incremental static fatigue test



1XPEHU RI F\FOHVa)b)
Figure 3 : Evolution of the coefficient RAE a) for static fatigue test at constant stress with time b) for cyclic fatigue
tests at constant amplitude with the number of cyclesƌŝƚŝĐĂůƚŝŵĞ
loading

UnloadingEƵŵďĞƌ ŽĨĐǇĐůĞƐ



Figure 4: a) Evolution of the coefficient RLU during cyclic fatigue at constant amplitude and highlight of two
characteristic times at 25 % and 47 % of the lifetime duration (LT lifetime) on SiCf/[Si-B-C] composites, b)
Evolution of the severityMoreover, it is interesting to notice that the coefficient RLU highlights two characteristic times before 50% of rupture
time (Fig. 4a), 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 increase of the severity of the signals recorded during
the loading part (Fig. 4b). 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.
A supervised classification technique can also be used to analyze AE signals recorded during fatigue of
CMC composites in order to establish a link with the damage mechanism. This technique requires a data base of
signals that have been labelled: the training set. This training set was created by merging data collected during
tensile test. As described in the previous study, the analysis of AE signals, observation of microstructures andanalysis of the mechanical behaviour of the composite led to the identification 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 chronologically well separated:seal coat cracking and tow breaks,
Class B: cluster B contains also signals from two damage mechanisms which are chronologically well
separated: longitudinal matrix cracking and individual fibre breaks in the fracture zone just before failure,
Class C: Cluster C contains signals with relatively short duration, short rise time and low amplitude whencompared to the others: transversal matrix cracking,㸫S82㸫

Class D: this cluster is the last one to be activated and it becomes more active as strain increases and the Dtype signals have a longer rise time when compared to other signals: sliding at fibre/matrix interfaces, fibre/
matrix debonding.
An additional class denoted E was introduced into the library. It corresponds to signals recorded under fatigue,
during unloading steps of cycles after the cycle 2000e and for applied stresses lower than Vmax/2 -10 MPa. Thesesignals are associated to the friction generated in cyclic fatigue.
In order to establish the training set of labelled signals for the supervised analysis, the same amount of signals(2000 signals) in each class (A, B, C, D and E) was used. The cluster analysis with RFCAM algorithm pointed out
different damage mechanisms generated by cyclic loading, which are mainly debonding and friction at
matrix/fiber and matrix/matrix interfaces. Moreover, with this analysis a link is established between thecharacteristic time at 25 % and the beginning of the matrix cracking.

4 CONCLUSIONS

Comparison between static and cyclic tests shows that cyclic loading with an amplitude higher than 36 % of ıR has
significant effects on damage and on material lifetimes. Some new damage indicators are defined based on AE
energy activity and dissipated mechanical energy. These new indicators show the evolution of damage during longterm tests and can be used to propose new predictive laws of lifetimes. A link is established between a characteristic
time at 25 % and the beginning of the matrix cracking for the cyclic fatigue tests.
ACKNOWLEDGEMENTS
The collaboration with Snecma and Herakles is gratefully acknowledged. This work was supported under the PRC
Composites, French research project funded by DGAC, involving SAFRAN Group, ONERA and CNRS.

REFERENCES[1] J. Lamon (2005), CVI SiC/SiC composites NP. Bansal (Ed.), Handbook of ceramics composites, Springer.
[2] O. Loseille, J. Lamon, (2010) Prediction of lifetime in static fatigue at high temperatures for ceramic matrix
composites, J. Adv. Mat. Res., 112:129-40.
[3] GN. Morscher, (1999) Modal acoustic emission of damage accumulation in a woven SiC/SiC composite.
Compos Sci Technol, 59, 687±697.
[4] GN. Morscher, AL. Gyekenyesi (2002), The velocity and attenuation of acoustic emission waves in SiC/SiC
composites loaded in tension. Compos Sci Technol, 62 pp 1171±1180.
[5] M. Moevus, N. Godin, M. R'Mili, D. Rouby, P. Reynaud, G. Fantozzi and G. Farizy (2008), Analysis ofdamage mechanisms and associated acoustic emission in two SiCf/[Si-BC] composites exhibiting different
tensile behaviours. Part II: Unsupervised acoustic emission data clustering, Compos Sci Techno, 68(6): 12581265.
[6] S. MomRQ 1 *RGLQ 3 5H\QDXG 0 5¶0LOL DQG * )DQWR]]L (2012), Unsupervised and supervisedclassification of AE data collected during fatigue test on CMC at high temperature, Composites Part A, 43(2):254-260.
[7] ( 0DLOOHW 1 *RGLQ 0 5¶0LOL 3 Reynaud, G. Fantozzi, J. Lamon (2014), Damage monitoring andidentification in SiC/SiC minicomposites using combined acousto-ultrasonics and acoustic emission,Composites Part A, 57 (8):15.
[8] 60RPRQ00RHYXV05¶0LOL35H\QDXG*)DQWR]]LDQG*. Fayolle (2010), Acoustic emission andlifetime prediction during static fatigue tests on ceramic-matrix-composite at high temperature under air,Composites Part A, 41(7): 913-918.
[9] (0DLOOHW1*RGLQ05¶0LOL35H\QDXG*)DQWR]]LDQG-/DPRQ (2014), Real-time evaluation of energy
attenuation: A novel approach to acoustic emission analysis for damage monitoring of ceramic matrix
composites. Journal of European Ceramic Society, Volume: 34 Issue: 7, 1673-1679.
[10] (0DLOOHW1*RGLQ05¶0LOL35H\QDXG-/DPRQ and G. Fantozzi (2012), Analysis of Acoustic Emission
energy release during static fatigue tests at intermediate temperatures on Ceramic Matrix Composites: Towards
rupture time prediction, Compos Sci Techno, 72, 1001±1007.
[11] N. Morizet, N. Godin, J. Tang, E. Maillet, M. Fregonese, B. Normand (2016), Classification of acoustic
emission signals using wavelets and Random Forests : Application to localized corrosion. Mechanical Systems
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[12] G. Minak, P. Morelli, And A. Zucchelli (2009), Fatigue residual strength of circular laminate graphite±epoxy
composite plates damaged by transverse load, Compos Sci Techno, vol. 69, no 9, 1358-1363.

㸫S83㸫

[13] G. Minak, And A. Zucchelli (2008), Damage evaluation and residual strength prediction of CFRP laminates
by means of acoustic emission techniques, Composite Materials Research Progress. Nova Science Publishers,
New York, 167-209.

㸫S84㸫

Contents
The papers in the present proceedings except for the “Invited Talk” were reviewed by the Peer
Review Committee.

AE & Related NDT 1
Acoustical Emission Propagation in a Prismatic Guided Wave: Simulations Using Lattice
Discrete Element Method
E. B. Groth, G. Sumajer, I. Iturrioz, L. Kostesky and T. R. Clarke

1

Development of Leak Detection Technology due to AE Method Using Gas Pipe
K. Yoshida and K. Kikugawa

7

Source Location of Impact Damages in Multi-Layered Composite Vessel by Acoustic Emission
Parametric Zone Technology
D. Yoon, B. Han, C. Park and I. Kwon

13

Evaluation of Acoustic Emission Generation Behavior of Cathodic Hydrogen Charged andNon-charged A7075-T651 and A6061-T6 Aluminum Alloys in Fatigue Testing underAtmospheric Environment
H. Cho, N. Shoji and H. Ito

17

Evaluation of Grouting Condition of Post-tensioned Concrete by using Spectral Ratio
S. Nagase, T. Shiotani, T. Nishida, H. Asaue and A. Sagradyan

23Industries[Invited Taik] The Survey of Underground Technological Pipelines of Crude Oil of Fuel Storage
Depots by Acoustic Emission Method
I. J. Baran, I. Lyasota and K. Skrok

27

Structural Health Monitoring of Blast Furnace in Steel Mill Using Acoustic Emission Technique
D. H. Kim, S. B. Lee, B. S. Yang and D. M. Bae

37

Corrosion Detection for Ferritic Structures
P. Tscheliesnig, A. Jagebnbrein and G. Lackner

43

Monitoring Power Transformer Tap-changers with Acoustic Emission
O. G. S. Filho49



Machine Engineering
A Study on AE Signals during Micro Cutting by In Situ Observation of Metal Cutting
A. Hase55

AE Monitoring by High Sensitivity and Portable Optical Fiber AE System
I. Shinozaki, G. Nakayama, Y. Sakakibara, T. Ohtsuka, H. Konda and T. Hattori

61

Estimation of the Mechanism of Stress Corrosion Crack Propagation during a Crevice Bent
Beam Test in Sensitized SUS304 Steel by Using an Optical Fiber AE Sensor
T. Matsuo, K. Sano, I. Shinozaki and G. Nakayama

67

AE Generation Mechanism of Rotating Component during Bending Fatigue Test by 71
Non-contact AE Monitoring System
D. Hatanaka and T. Matsuo
Analytical and Experimental Comparison of Basalt and Carbon Fiber Composites
Overwrapping of Highly Pressurized Steel Cylinders
J. C. Schmidt, T. K. Nowak and I. J. Baran

77

Medical Science
Development of Non-invasive Diagnosis Method for Knee Osteoarthritis by Using Knee Joint
Angle and Acoustic Information
T. Sakai, K. Kodama, S. Wakayama, K. Kageyama, K. Hase and S. Ota

83

AE Characterization of Microdamage in Tendon under Different Loading Condition
F. Matsuoka, S. Wakayama, T. Sakai and E. Yamamoto

89

The Advancement of AE Technique in Biomechanics Research in Our Laboratory
J. Li, M. Theiss and G. Qi

93

Civil Engineering 1
Features of the AE Method Use in Monitoring of Bridge Structures
S. Elizarov, V. Bardakov, V. Barat, D. Terentyev and D. Chernov

99

Forecasting of Concrete Strength During the Hardening Process by Means of Acoustic
Emission Method
V. Bardakov and A. Sagaidak

105

Damage Evaluation of RC Bridge Deck under Wheel Loading Test by Means of AE
Tomography
T. Nishida, T. Shiotani, H. Asaue, T. Maeshima and Y. Kobayashi

111

Acoustic Emission Properties of Concrete Slab during Fatigue Testing by Load on Wheel
M. Shiwa, Z. Li, T. Maeshima, H. Koda and Y. Tanaka

117

Delamination between Concrete and Steel Plate in RC Deck Slab Reinforced with Steel Plate
Bonding by Elastic-Wave Technique
K. Ohno, T. Saito, K. Uji, A. Ueno and M. Sekiguchi

123

Monitoring of Crack Growth in Masonry with Acoustic Emission and Fibre Optic Sensors
E. Verstrynge and M. Wevers

129Materials Science 1[Invited Taik] Characterization of Aluminum Microstructural Damage under Tensile Loading
using a Data-enabled Approach
S. F. Wayne, G. Qi, J. Li and L. Zhang

135

Study of the Acoustic Emission Method by Tensile Test of the CFRP Specimen
M. Takizawa, H. Kawasaki, S. Oomori and T. Sasaki

141

Estimation and Controlling of Crack Generation in Thermal Barrier Coatings during
Suspension Plasma Spraying by AE Method
N. Yoneda, K. Ito, H. Araki, X. Chen, S. Kuroda and M. Enoki

145

Evaluation of the Effect of Laser Induced Breakdown on Laser Shock Peening
T. Takata and M. Enoki

149



AE & Related NDT 2Recent Advances in Structural Health Monitoring using Acoustic Emission
O. Ley, R. Gostautas and V. F. Godinez-Azcuage

153

Development of an Internal Concrete Damage Evaluation Technique for Steel Plate-Bonded
Slabs
N. Ogura, H. Yatsumoto, T. Nishida and T. Shiotani

157

Evaluation of Grouting Condition of Prestressing Reinforcement by Means of Wave Velocity
Tomography
A. Sagradyan, T. Shiotani, T. Nishida, H. Asaue and Y. Kobayashi

163

Fracture Behavior of Deteriorated FRP by UV Radiation
D. Jung, Y. Mizutani, A. Todoroki and Y. Suzuki

169

Measuring of Impact Force by Sensor Integrated Protector for KARATE
Y. Mizutani, K. Kokubo, K., Sato, G. Taga, T. Ashizawa, I. Suzuki, M. Kudaka and A. Kuroda

175



Geo-Resource Engineering[Invited Taik] Moment Tensor Inversion on Acoustic Emission Events in Rock in Much
Differing Scale
G. Manthei

181

Study of AE due to Domain Structure Relaxation in Triglycine Sulfate Single Crystals
S. Azimov, V. Petukhov, A. Leksovskii and B. Baskin

191

Acoustic Emission Characteristics of Rocks Subject to Uniaxial Compressive and Splitting
Loads
X. Liu, J. Wang, X. Li and M. Pan

197

Thermal Facture Source Localization in Rocks Using the Efficient Closed-form Solution(ECS) for Locating AE Sources
L. Dong, D. Sun, Z. Wang, X. Shang and W. Shu

205

Spatio-temporal Variation in Propagation Characteristics of Elastic Waves in a Sand Soil
during a Water Injection Test
M. Nakayama, H. Kawakata, I. Doi and N. Takahashi

211

Signal Processing 1
Acoustic Emission Source Localization through Excitability Prediction and Dispersion 217
Removal Technique
K. Grabowski, M. Gawronski, W. J. Staszewski, T. Uhl and P. Packo
Localization of Acoustic Emission Sources in Complex Three Dimensional Fiber Composites 221
Using Artificial Neural Networks
S. Kalafat, M. Bornschlegl and M. G. R. Sause
High-precision Source Location of AE Event Using Automatic Error Correction of Signal 227
Rising Time
K. Ito and M. Enoki
Efficient Damage Inspection of Deteriorated RC Bridge Deck with rain-induced AE Activity
H. Takamine, K. Watabe, H. Miyata, H. Asaue, T. Nishida and T. Shiotani

231

Sensitivity of Ultrasonic Waves to Surface-breaking Cracks in Reinforced Concrete Beams
L. P. Pahlavan, S. Pirskawetz, G. Hüsken and G. Blacquière237

AE & Related NDT 3[Invited Taik] Concrete Specimens under Four-point Bending: Correlation Between Acoustic 243
Emission Signals and Modal Frequency Variation
A. Carpinteri, G. Lacidogna and G. Piana
Estimation of Fracture Behavior in a Pressured Al-Mg-Si Alloy Heat Exchanger Based on AE 251
Pattern Recognition Technique
K. Sashi, H. Ito, H. Cho, T. Ueda, Y. Niidome and M. Yanagida
Three-dimensional Q-value AE-tomography and ITS Verification on Numerical Investigations
Y. Kobayashi and T. Shiotani

257

The Magneto Acoustic Emission Characteristics of Q235 Mild SteelY. Shen, G. Shen and W. Ke

263

Calibration of Acoustic Emission Sensors with Laser Ultrasound and Multi-Physics Modeling
L. Zhang and D. Ozevin269

Civil Engineering 2[Invited Taik] Additive Manufacturing: the Next Industrial Revolution?
D. V. Hemelrijck and M. Strantza

275

Updating Cracks within a Heterogeneous Numerical Model Based on Estimated AE Source 281
Locations
S. Gollob and T. Vogel
Acoustic Emission Analysis for Identification of Damage Mechanisms in Fiber-reinforced 287
Polymer Composites and Structural Integrity Assessment: Selected Examples and Challenges
A. J. BrunnerContinuous AE Monitoring of Fresh Concrete
S. N. Iliopoulos, E. Dzaye, Y. E. Khattabi, G. D. Schutter and D. G. Aggelis

293

Acoustic Emission Study on 50 Years Old Reinforced Concrete Beams under Bending and 299
Shear Tests
Y. Yang, D. A. Hordijk and A. D. Boer

Materials Science 2
Modeling of Acoustic Emission Sources in Fiber Reinforced Composites
M. G. R. Sause

305

Acoustic Emission Monitoring during Laser Surface Quenching to the Carbon Steels
T. Yasuda, K. Nishimoto and Y. Okumoto

311

Study of the Effect of Weave Architecture on the Mechanical Behavior of Geotextiles Used for 315
Coastal Recovery by Using AE Technique
C. R. Rios-Soberanis, J. Rodriguez-Laviada and E. Perez-Pacheco
Acoustic Emission and Damage Evolution at mean Temperature under Air of a SiC/[Si-B-C] 321
Composite Subjected to Cyclic and Static Loading: Towards Lifetime Prediction
E. Racle, N. Godin, P. Reynaud, M. Rmili and G. Fantozzi
Source of AEs from IG-SCC of Face Centered Cubic Metals under Static or Dynamic 329
Straining
M. Takemoto, S. Ueno and M. NakamuraSensor & System 1
Remote Imaging of Plate-like Structures with E-camera
T. Hayashi

335

Elastic Wave Measurement Using a MEMS AE Sensor
T. Omori, T. Usui, K. Watabe, M. D. Nguyen, K. Matsumoto and I. Shimoyama339

Detection of Acoustic Emission Signals with the Fabry-perot Interferometer Type Optical 345
Fiber Sensor
K. Tada and H. Yuki
Effect of Compressive Pre-strain on the Mechanical Properties of LPSO-Mg Alloys
K. Tamura, T. Shiraiwa and M. Enoki

349

Development of a New Piezoelectric Transducer Usable as a Seismograph
H. Kawakata and H. Inaba

355



Civil Engineering 3[Invited Taik] Predicting the Ultimate Load Capacity of Concrete Bridge Beams from the 359"Relaxation Ratio" Analysis of AE Signals
M. C. Forde, S. Colombo, I. G. Main, M. Ohtsu and M. Shigeishi
Characterization of Mechanical Behaviour of Masonry under Compression Using Acoustic 365
Emission
N. Shetty, G. Livitsanos, D. V. Hemelrijck, D.G. Aggelis, M. Wevers and E. Verstrynge
Numerical Simulation of Wave Propagation and AE Characterization of Fracture Mode in 371
Masonry
G. Livitsanos, N. Shetty, E. Verstrynge, M. Wevers, D. V. Hemelrijck and D.G. Aggelis
Influence of Pipe Material on the Transmission of Leak Vibro-acoustic Signals in Water 377
Distribution Systems
J. D. Butterfield, R. Collins and S. Beck
Visualization of Internal Damage in RC Slab with Single Side Access Attenuation 383
Tomography
C. Granier, T. Shiotani, K, Hashimoto and T. NishidaMaterials Science 3[Invited Taik] AE Parameter-based Characterization of Hybrid Materials’ Fracture
D. G. Aggelis, S. Verbruggen, S. D. Sutter and T. Tysmans

389

In-situ Investigation of Deformation Mechanisms in Various Magnesium Alloys by X-ray 399
Diffraction and Acoustic Emission
K. Mathis, J. ýapek, A. Vinogradov and G. Garcés
Damage Characterization in Quasi-static Mode of Ti-6Al-4V Additive Manufacture by 407
Acoustic Emission Method
R. Carmi, E. Tiferet, E. Chackotay, G. Gutman, R. Shneck and A. Bussiba
Acoustic Emission and Neutron Diffraction Measurement during Loading and Unloading of 413
Magnesium Aluminum Binary Alloys
J. ýapek and K. MathisAE & Related NDT 4
Investigation on Acoustic Emission Characteristics of Roller Coaster Track
J. Zhang, G. Shen, Z. Wu and Y. Yuan

419

A New Methodology for Detecting the First Arrival Time of an Acoustic Wave
A. K. Das and C. K. Y. Leung

425

Evaluation of Irrigation Response using AE Method for Management of High-frequency 431
Irrigation of Hydroponic Miniature-tomato
K. Kageyama and T. Sakai
Evaluation of Water Leakage Repair by One Side Access Elastic Wave Tomography Using 437
Rayleigh Wave
H. Asaue, T. Shiotani, T. Nishida, C. Huang and Y. Kobayashi
Influence of an Optical Fiber Embedded on Unidirectional CFRP Laminates Evaluated with 441
the Acoustic Emission and 3D Digital Image Correlation Techniques
E. Suarez, M. G. R. Sause and A. Gallego


Civil Engineering 4[Invited Taik] Review of Acoustic Emission Source Mechanisms on Large Movable Structures
D. Kosnik

447

Detection of Elastic Waveform Parameter to Evaluate Deterioration of ConcreteT. Watanabe, Y. Yamaguchi, N. Mori, C. Hashimoto and T. Shiotani

457

Application of Acoustic Emission Analysis of Crushable Material
S. Luo, A. Diambra and E. Ibraim

461

Investigation of Acoustic Emission Source Locations in Reinforced Concrete Beams under 467
Cyclic Loading
R. Lee, J. Ongpeng, A. Oreta and S. Hirose
Evaluations of Crack due to Drying Shrinkage in Mortar and Concrete by AE Method
K. Nishiyama, T. Watanabe, K. Nakashima, C. Hashimoto and T. Shiotani473



Signal Processing 2[Invited Taik] Super-acoustic Sensor for Bridge Health Monitoring
I. Shimoyama and M. D. Nguyen

479

Damage Evaluation for In-field RC Bridge Deck by AE Tomography
M. Fukuda, T. Shiotani, T. Nishida, H. Asaue and Y. Kobayashi

481

Identification of Similar Seismic Events Using Phase-only Correlation Technique
H. Moriya

487

Amplitude of AE Propagating in Laminated FRP Plates
Y. Mizutani, T. Miki, Y. Suzuki and A. Todoroki

491

Obtaining Wave-path Information from Waveform Data of the Sensor Coupling Test
H. Vallen, T. Thenikl and H. Trattnig

497



Sensor & System 2[Invited Taik] On Acoustic Emission Sensor Characterization
K. Ono505

Development of Elastic Wave Tomography Method for Evaluation of Internal Concrete Using 515
Drilling Holes
H. Yatsumoto, T. Nishida, T. Shiotani, C. Huang, N. Ogura and Y. Kobayashi
About the Verification of acoustic emission (AE) Instrumentation Characteristics
H. Vallen, T. Thenikl and H. Trattnig

523



Civil Engineering 5[Invited Taik] AE Energy in Reinforced Concrete Specimens Subjected to Different Loading 529
Patterns
A. Gallego, F. Sagasta, A. B. Climent and G. Cortes
Evaluation of Ultrasonic Propagation Properties in Reinforced Concrete that Reproduced 539
Rebar Corrosion by Artificial Defect and Chloride Damage
H. Fukutomi, T. Watanabe, C. Hashimoto, K. Miyazaki and K. Ishimaru
Behavior of Reinforced Concrete Slab Monitored by Acoustic Emission
Y. Kan and K. Pei

545

A Basic Study of an Elastic-wave Based Inspection Technique for Evaluating Rebar-concrete 551
Interface Conditions
N. Okude, T. Nishida and T. Shiotani
Materials Science 4Classification of Acoustic Emission Signals Using Wavelets and Random Forests: Application 555
to Localized Corrosion
N. Morizet, N. Godin, M. Fregonese, J. Tang and B. Normand
Acoustic Emission Modelling from the Source to the Detected Signal: Model Validation and 561
Identification of Relevant Descriptors
T. Le, N. Godin, T. Monnier, C. Fusco and Z. Hamam
Three-dimensional Source Location by Water Propagating Waves in the Hydraulic Test of 567
CFRP Pressure Vessel
S. Fukumoto, S. Oomori, M. Takizawa, H. Kawasaki and T. Nishido

Influence of Pre-compression on Tensile Behavior in Wrought AZ31 Studied by the Acoustic 573
Emission Technique
D. Drozdenko, P. Dobron, J. Bohlen, S. Yi and F. ChmelíkAuthor’s Name Index

577

List of Poster Presentations

581

