Conferences and Symposia
THE 12th INTERNATIONAL CONFERENCE OF THE SLOVENIAN SOCIETY FORNON-DESTRUCTIVE TESTINGAPPLICATION OF CONTEMPORARY NON-DESTRUCTIVE TESTING
IN ENGINEERING
4 - 6 September 2013, SLOVENIACONFERENCE PROCEEDINGS
Edited by J. Grum and T. Kek and Published by the Slovenian Society for Non-Destructive
Testing, Aškerčeva 6, 1000 Ljubljana, Slovenia

Articles from the AE Sessions (* indicates a keynote paper)
A LOW FREQUENCY LAMB-WAVES BASED STRUCTURAL HEALTH MONITORING OF AN
AERONAUTICAL CARBON FIBRE REINFORCED POLYMER COMPOSITE,*
M. Carboni, A. Gianneo, M. Giglio
S1
FROM ACOUSTIC EMISSION TESTING (AT) TO ACOUSTIC MONITORING,*
P. Tscheliesnig

S21

THE NEW HIGHER ORDER SPECTRAL TECHNIQUES FOR NON-LINEARITY MONITORING OF
STRUCTURES AND MACHINERY,*
L. Gelman
S28
ASSESSMENT OF INFRASTRUCTURES BY MEANS OF PASSIVE AND POSITIVE ELASTICWAVES APPROACHES,*
T. Shiotani, Y. Kobayashi, S. Momoki
S33
ACTUAL POSSIBILITIES OF DAMAGE EVAULATION OF MACHINES WITH ROTATINGPARTS BY ACUSTIC EMISSION,
P. Mazal, L. Nohal, F. Hort
S52
A STUDY ON THE PERFORMANCE OF ACOUSTIC EMISSION AND LOW FREQUENCYVIBRATION METHODS TO THE REAL-TIME CONDITION MONITORING OF RAILWAY AXLES,
M. Carboni, S. Bruni, D. Crivelli, M. Guagliano, P. Rolek
S60
NEW PROSPECTS TO USE ACOUSTIC EMISSION DURING SCRATCH TESTING FOR PROBING
FUNDAMENTAL MECHANISMS OF PLASTIC DEFORMATION,
A. Danyuk, D. Merson, A. Vinogradov
S68
MONITORING OF AUTONOMOUS HEALING PROCESS OF CONCRETE BY ACOUSTICEMISSION AND DIGITAL IMAGE CORRELATION,
E. Tsangouri, K. Van Tittelboom, N. De Belie, D.G. Aggelis, D. Van Hemelrijck
S76
CLUSTERING OF AE SIGNAL AMPLITUDES BY MEANS OF FUZZY C-MEANS ALGORITHM,
R. Svečko, A. Hančič, D. Kusić, J. Grum
S83
ENHANCEMENT OF EFFICIENCY OF TRIBOLOGICAL TESTING BY USING ACOUSTICEMISSION MEASUREMENTS,
I. Rastegaev, D. Merson, A. Vinogradov
S91
J. Acoustic Emission, 32 (2014)i© 2014 Acoustic Emission Group	
  

	
  
WAVELET BASED APPROACH TO ACOUSTIC EMISSION PHASE PICKING,
E. Pomponi, A. Vinogradov

S98

ARC WELDING PROCESS MONITORING BY AUDIBLE SOUND,
I. Polajnar, Z. Bergant, J. Grum

S103

THE SYSTEM OF AE CONTROL AND DIAGNOSTICS OF TECHNICAL STATUS OF ELECTRIC
SWITCH MECHANISM,
S.V. Altynbaev, V.S. Fadeev, N.A. Semashko
S113
STATISTICAL EVALUATION OF INJECTION MOLDING PROCESS PARAMETERS IMPACT ON
THE DETECTED ACOUSTIC EMISSION EVENTS,
D. Kusić, R. Svečko, A. Hančič, T. Kek, J. GrumS118
TESTING PRESSURE VESSEL WITH ACOUSTIC EMISSION,
G. Gavranić, T. Kek, J. GrumS126

ACOUSTIC EMISSION MONITORING OF FRACTURE PROCESS STAGES IN HYDROGENEMBRITTLED HIGH-CARBON STEEL
E. Merson, D. Merson, A. Vinogradov
S134
EFFECT OF TEMPERATURE ON MICROSTRUCTURE EVOLUTION IN TRIP/TWIP STEELSDURING TENSILE DEFORMATION
M. Linderov, C. Segel, A. Vinogradov, A. Weidner, H. Biermann
S142
INVESTIGATING THE PRESENCE OF CRACKS IN TOOL STEEL INSERTS BY USING
ACOUSTIC EMISSION METHOD
T. Kek, D. Kusić, R. Svečko, A. Hančič, J. Grum

S150

ACOUSTIC EMISSION DURING SHORT-TERM ELECTROCHEMICAL CORROSION TESTING:
LSP EFFECT ON PITTING CORROSION
U. Trdan, J. Grum
S158
DETECTION AND PROCESSING OF SOUND SIGNALS IN STEEL QUENCHING
F. Ravnik, J. Grum

J. Acoustic Emission, 32 (2014)

ii

S165

© 2014 Acoustic Emission Group	
  The 12th International Conference of the Slovenian Society for Non-Destructive Testing»Application of Contemporary Non-Destructive Testing in Engineering«
September 4-6, 2013, Portorož, Slovenia

A LOW FREQUENCY LAMB-WAVES BASED STRUCTURALHEALTH MONITORING OF AN AERONAUTICAL CARBONFIBRE REINFORCED POLYMER COMPOSITE
M. Carboni1*, A. Gianneo1, M. Giglio1
1

Department of Mechanical Engineering – Politecnico di Milano
Via La Masa 1, 20156 Milano, Italy
michele.carboni@polimi.it, andrea.gianneo@mail.polimi.it, marco.giglio@polimi.it
ABSTRACT
Carbon Fiber Reinforced Polymer composites have been developed since the ’60s and allow thedesign of resistant and innovative lightweight primary structures, replacing traditional metallic
materials. However, their intensive structural use remains limited due, between also otherfactors, to several peculiar damage mechanisms able to quickly degrade the mechanicalproperties and requiring high maintenance costs due to service interruptions needed forcarrying out periodical non-destructive testing inspections. One solution, more and moresuggested and proposed in the literature, is then to apply a structural health monitoring
approach. The present paper investigates such possibility focusing on the application of the“Design of Experiment” methodology to the performance of Lamb ultrasonic waves-based
monitoring carried out using piezoelectric transducers bonded on the surface of aeronauticallaminates. The choice of a Design Of Experiment approach is based on the well-known fact thatthe success of an ultrasonic monitoring approach is mostly based on the knowledge of the mostinfluencing factors, but also that the today published research studies usually consider them
individually. A rigorous factorial design method is then applied in order to study the influence ofdifferent operative factors. Since the worst type of damage in a composite structure is adelamination, the research is particularly focused on this kind of defect obtained both
artificially, using Teflon patches, and naturally, by suitable mechanical impacts.
Key words: Design of Experiment, Lamb Ultrasonic Waves, Structural Health Monitoring,
Carbon Fiber Reinforced Polymer Composite
1.

Introduction

Carbon Fiber Reinforced Polymer (CFRP) composites have been developed since the ‘60s andallow the design of resistant and innovative light weight primary structures, replacing traditional
metallic materials, due to their high strength–weight and moduli-weight ratios, excellent fatigue
strength as well as fatigue damage tolerance; another advantage is their non-corroding behaviour.
However, their intensive structural use remains limited due to, among other factors, several
peculiar damage mechanisms, which are able to quickly degrade the mechanical properties andresult in high maintenance costs caused by the service interruptions required for carrying out
periodical non-destructive testing (NDT) inspections. An increasingly proposed solution in the
S1

literature is the application of a structural health monitoring (SHM) approach, which, in the
aeronautical field, has shown, according to Chang [1], the potentiality of decreasing the overall
costs of NDT by 30%. The present paper investigates this possibility by focusing on theapplication of the “Design of Experiment” (DOE) methodology to the performance of Lambultrasonic wave-based SHM, using piezoelectric (PZT) transducers surface bonded onto
aeronautical CFRP laminates. The choice of a DOE approach is based on the well-known factthat the success of an ultrasonic SHM approach requires the knowledge of the most influencing
factors and their mutual influences, which up to now have not been studied, but have been
considered individually in the published research. In particular, a rigorous 2k factorial design
method is applied to study the influence of the following factors on a SHM approach: thefrequency of excitation, the dimension and the position of the defects and the inspection
approach (pulse-echo or pitch-catch). Since the worst type of damage in a composite structure isdelamination, this research particularly focuses on this kind of defect, obtained both artificiallyby using Teflon patches and naturally by mechanical impacts. To this aim, the experimental
elastic moduli of CFRP unidirectional (UD) laminas and of a quasi-isotropic laminate, necessary
for defining the propagation of the elastic wave, are firstly determined and this information issubsequently used to simulate the Lamb wave dispersive propagation properties through thesemi-analytical finite element (SAFE) approach. Information about the dispersive behaviour ofsuch waves is needed to best fit the Lamb wave-based SHM methodology to the target structure
and to thereby allow the design of all aspects of the SHM process, i.e. the way to activate andreceive diagnostic signals, the type of PZT transducers, etc. Finally, the DOE approach is applied
to different composite laminate panels, and the sampled data are analysed by the discrete andcontinuous wavelet transforms. The CFRP system adopted in this research is a quasi-isotropic 17
ply layup [0/+45/0/-45/90/-45/0/+45/90] s , one of the most representative carbon fibre laminatesin the aeronautical field, as Toso et al. [2] show in their EASA framework, made of UD pre-preg
SAATI EH-550/T800S laminas.
2.

Experiments on CFRP UD laminas and the quasi-isotropic laminate

Since the propagative properties of an elastic wave depend on both physical and elastic
properties, the first necessary step is the definition of the effective elastic moduli of the UDlaminas constituting the target quasi-isotropic carbon-fibre laminate. CFRP laminas belong to a
special class of orthotropic materials, called transversely isotropic. In particular, their mechanicalbehaviour 𝝈 = [𝐶]𝜺 requires five independent elastic constants: E 11 , E 22 , G 12 , ν 12 and ν 23 , being
1 the in-plane direction along the fibres, 2 the in-plane direction transverse to the fibre and 3 theout-of plane direction. According to ASTM-D 3039 [3] and 3518 [4], experimental static tensile
tests were carried out on four different types of coupons (each composed by five specimens): UD
0°, UD 90°, UD ±45° and the quasi-isotropic layup. The first three coupon types allowed thedefinition of the in-plane tensile properties 𝐸11 , 𝐸22 , 𝜈12 , 𝜈21 and, in a simplified way, the inplane shear response G 12 . The fourth coupon was used to evaluate the elastic properties of thequasi-isotropic laminate: 𝐸𝑥𝑥 , 𝐸𝑦𝑦 and 𝜈𝑥𝑦 , being x and y the in-plane coordinates of the laminate(x aligned to the 0° lamina and y to the 90° one).
An electro-mechanical tensile test machine MTS-Alliance RT/100 (maximum load 100 kN), an
extensometer and two half Wheatstone bridges, dedicated to the longitudinal and transversal
strains and compensated for temperature effects by “dummy” gages, formed the basis of theexperimental setup. The strain gauges were of the type CEA06250UT350. Figure 1 shows theshape, stress-strain curves (obtained from extensometer data) and the typical failure mode of thedifferent specimens, while Table 1 summarises the experimental elastic moduli of the laminas
constituting the quasi-isotropic laminate. In the latter, µ stands for the mean value, σ for the
standard deviation and CV is defined by the ASTM-D 3039 [3] as the sample coefficient ofvariation (σ/µ), respectively.
S2

a)

b)

c)

d)

Figure 1: Experimental tensile stress-strain curves and the corresponding failure modes: a) UD
0°; b) UD 90°; c) UD ±45°; d) quasi-isotropic layup.
Table 1: Experimental in-plane properties of the UD laminas and the quasi-isotropic laminate.
µσ
CV [%]

3.

𝑬𝟏𝟏 [𝑮𝑷𝒂]
𝝂𝟏𝟐
159,420
0,323
2,471
0,019
1,550
5,795

𝑬𝟐𝟐 [𝑮𝑷𝒂]
𝝂𝟐𝟏
8,120
0,018
0,432
0,001
5,326
7,480

𝑮𝟏𝟐 [𝑮𝑷𝒂]
4,68
0,342
7,309

𝑬𝒙𝒙 [𝑮𝑷𝒂]𝝂𝒙𝒚
70,020
0,371
1,033
0,002
1,475
0,560

Semi-analytical finite element simulation and design of the SHM process

The semi-analytical finite element (SAFE) approach is a useful and powerful method fornumerical modelling of guided wave propagation in composite laminates of arbitrary layup andcross-sectional geometry, where usually the traditional FEM method requires high computationalcosts and may result in numerical failure, especially with very short wavelengths, as discussed
by Bartoli et al. [5]. The SAFE approach adopts a finite element bi-dimensional discretization of
the cross-sectional area (Fig. 2a) since the displacements along the wave propagation direction
are assumed to be in the harmonic-plane way. Moreover, the typical plane geometry of CFRP
laminates allows further simplification in terms of mono-dimensional simulations. Results, interms of multiple modes and dispersion properties, can be obtained in a numerically stablemanner by the eigenvalue and eigenvector problem expressed by Eqs. (1) and (2); more details
can be found in [5]. Two quadratic 1-D finite elements have been used to model each of the 17
laminas constituting the quasi-isotropic laminate, following the convergence studies by Bartoli etal. [5] for dispersive solutions. Each element has three degrees of freedom (dof) per node,associated to the displacements U x , U y , U z (Fig. 2b).

S3

Figure 2: Modelling a composite laminate through SAFE: a) discretization along the thickness,b) degrees of freedom of a 1-D quadratic element.
{[𝐾𝑡𝑜𝑡1 ] + 𝑖𝑘[𝐾𝑡𝑜𝑡2 ] + 𝑘 2 [𝐾𝑡𝑜𝑡3 ] − 𝜔2 [𝑀𝑡𝑜𝑡 ]}𝒖 = 0 ==> {[𝐾𝑡𝑜𝑡 ] − 𝜔2 [𝑀𝑡𝑜𝑡 ]}𝒖 = 0[𝑀𝑡𝑜𝑡 ]−1 [𝐾𝑡𝑜𝑡 ]𝒖 = 𝜔2 [𝐼]𝒖

(1)
(2)

Figure 3 shows the dispersion curves, in terms of phase velocities 𝐶𝑝 , obtained using, as input to
SAFE, the experimental results shown in Table 1. Considering the quasi-isotropic layup and toquantify its anisotropy level, four different directions of Lamb wave propagation (0°, 30°, 60°
and 90° with respect to the x coordinate of the laminate) were simulated. The quasi-isotropic
behaviour of the laminate and an essential parameter for the SHM process can be noted. The
latter is the so-called “cut-off frequency” f cut-off , which outlines the extension of the nondispersive region, where only three fundamental Lamb wave modes can exist (S 0 , A 0 and SH 0 )and whose dispersive behaviour is restrained (i.e. the velocity is almost constant in this range,
apart for the A 0 wave close to the origin). Figures 3a to d show no substantial differences
between the f cut-off parameter, whose value is about 380 kHz, since the quasi-isotropic stacking
sequence minimises the anisotropy level of the laminate. This behaviour is concurrently shownin Figure 4, which shows the phase velocities of the Lamb wave modes in a polar diagram.
S0

S0

SH0

SH0

A0

A0

a)

S0

S0
SH0

SH0
A0

b)

A0

c)

d)

Figure 3: SAFE dispersion curves for the target quasi-isotropic laminate expressed as 𝐂𝐩 (𝐟): a)0°; b) 30°; c) 60°; d) 90°. (Wave propagation with respect to the x coordinate of the laminate)

S4

Figure 4: Polar diagram of the phase velocities, at 100 kHz, of the quasi-isotropic laminate. The
values are normalized with respect to themselves.
Having determined the cut-off frequency, the PZT ceramic that best suits the present Lambwave-based SHM process was chosen and enabled the design of the transducer. In particular, theactuating signal, the expected received Lamb waves, the in-plane and out-of-plane resonant
frequencies and, consequently, the geometrical features of the PZT were chosen so to optimise
both the actuating (f < f cut-off ) and receiving (adequately far away from the resonant frequencies)
stages. The aforementioned properties of the PZT PIC255, finally adopted in the present
research, are reported in Table 2, while Figure 5 shows the chosen piezoelectric transducer.
Table 2: Piezoelectric properties of PZT PIC255 [6].
T curie [°C]𝐤𝐩𝐤𝐭
𝐝𝟑𝟏 [C/N]
𝐝𝟑𝟑 [C/N]

350
0.62
0.47
-180*10^-12400*10^-12

𝐠 𝟑𝟏 [Vm/N]g 33 [Vm/N]
Np [Hzm]
Nt [Hzm]-11.3*10^-3
25*10^-3
2000
2000

Figure 5: Detail of the adopted PZT transducer.
As it will be clear in the following Sections, the interest is here focused onto the fundamental
asymmetrical mode 𝐴0 excited at very low frequency, much below the cut-off frequency, so
Figures 6 and 7 summarise the main propagation features of this particular mode, in a restrained
range of actuation: [0;150] kHz. In particular, Figures 6 and 7 show, respectively, its dispersion
properties expressed as the group velocity, i.e. the wave-packet velocity, and the wavelength λagainst frequency through the well-known expression:
𝜆=

2𝜋
𝑘

S5

(3)

Figure 6: Comparison between the numerical SAFE group velocity values 𝐂𝐠 of the fundamental
𝐀 𝟎 mode and the experimental ones.

Figure 7: Dispersive relationship of A 0 mode expressed as frequency function of wavenumber
for different wave propagation directions (with respect to the 0° aligned fibre).
Additionally, the experimental A 0 group velocity is super imposed to the numerical one in
Figure 6. Top this aim, four PZTs (named PZT1, PZT2, PZT3 and PZT4) were bonded onto a
CFRP laminate (Fig. 8) with a step of 30° with respect to the x coordinate of the laminate. The
received signals, from an actuator positioned in the middle of the laminate, were processed
through the Hilbert transform in order to extract 𝐶𝑔 values according to:
𝐶𝑔 =

∆𝑥
𝑡𝑜𝑓

(4)

where ∆𝑥 is the wave propagation path from the actuator to the sensor and “tof” the related timeof flight. The experimental evidence shows good agreement with numerical values, hence thevalidation of SAFE is achieved given that the first numerical output are k and ω (Eqs. (1) and(2)) values from which the group velocities are defined as
𝐶𝑝 ≝

𝜕𝜔 ∆𝜔
≅
𝜕𝑘 ∆𝑘
S6

(5)

Figure 8: Experimental setup for SAFE validation: four PZT receivers equally spaced of 30° anda central actuator driven @ 30 V peak-to-peak.
4.

A “Design Of Experiment” approach

The DOE approach essentially consists of a preliminary experimental stage and of a secondary
stage dedicated to the design of an experimental plan and the analysis of the ensuingexperimental results.
4.1 Preliminary experimental stage
As Montgomery (2005) said: “the success of an experimental research is founded, more than
90%, on an early stage of laboratory work”. This requires initially a better understanding of thephysical phenomenon, in a composite material, causing the Lamb wave propagation and,
consequently, of the development of the best experimental setup and of the way of conducting
the experiments; regarding the first aspect, Figure 9 displays the developed measurement andacquisition systems.
Out-of-phase excitation ofdual actuator PZTAgilent 33220A
Waveform Generator

Wideband amplifier x10
TTi-WA 301

a)
Ch-1 Digital Oscilloscope
Le-Croy WAVEACE 1001

Sensor PZT

High-pass (fcut=200Hz)
active (+40dB) analogicalfilter KRONHITE 3322

Ch-2 Digital Oscilloscope
Le-Croy WAVEACE 1001b)

Figure 9: Detail of the measurement chain (a) and of the acquisition system (b).
Concerning the latter, a PZT transducer fixed onto a host structure generates simultaneously both
symmetric and anti-symmetric modes, which superimpose and influence each other making theinterpretation, of the diagnostic signal, a troublesome task. At present, most mode selection
approaches are based on the rationale that a desired wave mode can be enhanced while other
undesired modes minimised, by mutual interactions of an array of appropriately placed PZTs, inthe resultant signal. Su and Ye (2004) assert to be able to selectively activate a desired Lamb
S7

mode energising, in-phase (symmetric mode S i ) or out-of-phase (anti-symmetric mode A i ), apair of PZT transducers symmetrically bonded on the upper and lower surfaces of a quasiisotropic composite laminate. Actually, this approach seemed here to just enhance a specificmode at a given frequency, but it wasn’t able to cancel wave modes completely. An alternative
method, proposed here, turns out to be particularly efficacious at low frequency, and it’s basedon the following experimental evidence: actuating a single PZT within [0;50] kHz, a pure A 0
mode seems to be generated (Fig. 10a), whose magnitude can be further enhanced out-of-phaseenergising a couple of PZTs (Fig 10b). At the same time, the S 0 mode is almost hidden within
the electrical noise and tends to become significant as 50 kHz are approached, but still with oneorder of magnitude lower than the A 0 mode. All these preliminary tests have been carried out on
the quasi-isotropic circular laminate shown in Figure 11, as explained in detail by Gianneo(2012).

Fig. 10: Effect of the adopted PZT actuation strategy: a) the signal in response to a pair of PZTs
excited out-of-phase; b) signal response to a single PZT excitation.

Figure 11: Quasi-isotropic circular laminate target for the preliminary experimental stage.
In particular, the aforementioned tuning approach is experimentally validated in Figure 12,A
where the peak-to-peak amplitude ratio � S 0 �
for the different wave propagation paths
0

pk−pk

corresponding to the PZT1, PZT2, PZT3 and PZT4 transducers shown in Figure 8. The higher
values (approximately equal to 50 dB) can be reached, as could be expected, out-of-phaseenergising the PZTs, since a flexural mode is induced into the laminate. However, it’s interesting
to emphasize that, in the low frequency range, the way of actuation always gives a stronger
fundamental anti-symmetrical mode rather than a symmetrical one, even though an in-phaseactuation is applied, unlike what is usually reported in the literature. The peak-to-peak ratio
keeps higher than 0 dB until 50 kHz and, above this point, it’s possible to visualize the common
trend enclosed in a bandwidth around 0 dB. For completeness, Figure 13 reports the difference,S8

in dB and at 50 kHz, between out-of-phase and in-phase actuations, whose maximum is reachedat 60°.

Figure 12: Amplitude ratio of the two fundamentals Lamb wave modes (𝐀 𝟎 , 𝐒𝟎 ) obtained driving
the PZT transducers in-phase and out-of-phase.

Figure 13: Difference between the amplitude ratio of the two fundamentals Lamb wave modes(𝑨𝟎 , 𝑺𝟎 ) energised out-of-phase and in-phase @ 50 kHz, as a function of the wave propagation
direction.

This experimental evidence opens the promising possibility of conducting a NDE and a SHM via
PZT wafers bonded onto the surface of a CFRP laminate only using, as diagnostic wave, thefundamental asymmetrical A 0 , simplifying the interpretation of the signal waves received fromthe PZT sensors. In the following subchapters a DOE is established to verify the performance ofsuch an approach based onto the A 0 mode.
4.2 Design of Experiment for artificial delaminations
With the aim to study the influence of k factors on an SHM process founded onto a pair of PZT
actuators symmetrically bonded to the upper and lower surfaces of the laminate and out-of-phaseenergised in the low frequency range, an experimental screening plane with a factorial 2k design
was chosen, thus taking into account both the principal and interactional effects. The design
factors, established amongst many others classified as constant and noise factors, are: theS9frequency of excitation, the position and the dimension of the delamination defects. In particular,
the 8 and 24 mm defect dimensions were chosen compatibly to the SAFE’s dispersion curves
𝑓(𝑘) of Figure 7, the frequency range of actuation and the well-known necessary (but not
sufficient) relationship suggested in literature to guarantee an interaction between an ultrasonicwave and a defect:
𝑑>

𝜆
4

(6)

where d is a characteristic dimension of the defect and 𝜆 is the wavelength. Since the wavelength
decreases with frequency, it’s immediate to establish, from Figure 7, that a reasonable minimum
detectable defect size is 7.85 mm @ 25kHz, rounded up to 8 mm. The defects were located, via
circular Teflon patches during the manufacturing process, between the first and second ply, andthe sixteenth and seventeenth ply (respectively at a distance of 0.125 mm and 2 mm from theupper surface). Despite the possibility of carrying out a NDE at the lowest excitation frequency
into the aforementioned range, since it’s impossible to achieve a benchmark signal of theartificially delaminated panels, the frequency is set across the upper boundary 50kHz, so at 45
and 60 kHz to avoid the mutual scattering interference between boundaries and defects as well.
Furthermore, as the experimental scatter can potentially hide the significance of the factors underinvestigation, their levels were sufficiently spaced to prevent such circumstance. The chosen
factors were coded, as usually by ANOVA, with a low and a high level so that i.e. 45 kHz is
associated to -1 (low level) and 60 kHz to +1 (high level). Table 3 provides all the adopted
codifications.
Table 3: High and low level codifications used in the ANOVA analysis.
Considered factor
Frequency [kHz]Position from the first ply [mm]Dimension [mm]

Value
45
60
2
0.125
8
24

Code for ANOVA
-1
+1
-1
+1
-1
+1

Two CFRP laminates (“Plate-1” and “Plate-2”), compliant with the data reported in Table 1,
were set up with eight PZT transducers along the diagonal lines of the panels: four dedicated to
the Pulse-Eco (PE) technique and the remaining to the Pitch-Catch (PC) one. Radiographic
testing (28 kV and 5 mA for 30 s) was then performed, on each plate, to check the position andthe dimension, after the manufacturing process, of the Teflon patches with respect to PE and PC
transducers. Figure 14 and Table 4 show the results as regards the example case of Plate-2. It is
worth noticing that knowing the exact position of the artificial delaminations allows to compare
the information to the one coming from PE, hence an accuracy of detection is obtained. On thecontrary, the PC can only provide the interaction with a defect, without any information about its
location.
The designed plan was carried out driving a pair PZT actuators out-of-phase, in agreement with a
5.5 cycles sinusoidal tone burst 30 V peak-to-peak modulated with a Hanning window. Twogroups of time records (45 and 60 kHz) were sampled, one for each of the two artificially
defected composite panels, according to the measurement chain aforementioned in Fig. 9a. The
sampled data were processed using the discrete wavelet transform (DWT, wavelet Daubechies
6th level, db6) for de-noising the signal and the continuous one (CWT, wavelet Morlet, morl) toevaluate the percentage of energy for each coefficient of the adopted wavelet transform. In order
to extract the feature of the diagnostic signal in response to artificial defects, the CWT
S10coefficient corresponding to the maximum energy of the scalogram, along the scale axis, wasthen considered. The extracted signal were represented through their energy envelope, i.e. theHilbert transform, an approach aimed at canvassing a Lamb wave signal in the time domain interms of its energy distribution. Figure 15 gives an example of the complete digital signal
processing performed in the following subchapters.

T-T
a)

b)

c)

d)

Figure 14: Radiographic testing of the artificially-delaminated Plate-2: a) and b) 24 mm patches;c) and d) 8 mm patches. The presence of delamination is highlighted through red ellipses.
Table 4: Evaluated distance from PZT transducers and dimensions of artificial Teflon patches.
Nomenclature according to Figure 14.
RX-1
T-T
T-Delamination
Delamination size
RX-2
T-T
T-Delamination
Delamination size

N° of pixel

Distance [mm]2953.81993
453.8

156.00105.26
23.9

2980
1947
435

157.00
102.6
22.9

RX-3
T-T
T-Delamination
Delamination size
RX-4
T-T
T-Delamination
Delamination size

N° of pixel

Distance [mm]2412.61674.9146.8

155.00107.619.4

2906.4
1942.3141.8

153.00
102.3
7.5

a)

b)

c)
d)
Figure 15: Processing of Lamb Wave signals: a) noisy sampled signal in time domain; b) denoised signal (DWT, db6); c) scalogram wavelet coefficient (CWT, morl) corresponding to thecentral frequency of actuation; d) energy envelope of the extracted CWT coefficient.
S11

4.3 Analysis of experimental results from PE
For the Pulse-Echo method, the evaluation of damage response to the diagnostic signal wasachieved by evaluating its reflection coefficient R, that is the ratio of the largest magnitude of thedamage-reflected first wave component captured by the sensor to that of the incident wave.
Figure 16 gives an example of the acquired PZT response to a 24 mm artificial defect: the blueline stands for the response to an artificial delamination in the radio-frequency domain extracted
from the CWT, the red one represents its Hilbert transform.

a)b)
Figure 16: Pulse-Echo response to a 24 mm delamination positioned between the 16th and the17th ply @45kHz, from Plate-1: a) radio-frequency representation; b) Hilbert transform. Data
from CWT coefficient.
The main effects and interaction plot are a first tool of synthesis: in this case, Figure 17 outlines,
as key factors, the dimension and the interaction frequency-dimension and, in a weaker way, thedimension and the interaction dimension-position. Anyway, these results are subjective and ananalysis of variance (ANOVA) needs to be performed to highlight their impact with respect tothe scatter of the method.
Data Means

Data Means

Frequency

Dimension

-1

1

-1

1
0.24

0.18
0.16

0.16

Mean

0.14

Frequency-1
1

Fr equency

0.12

0.08

0.10

0.24

-1

1

-1

1

Position
0.16

0.18

Dimension-1
1

Dimension

0.16

0.08

0.14
0.12
0.10

-1

1

a)

P ositionb)

Figure 17: Main effects (a) and interaction plot (b) of the studied factors: frequency, dimension
and position of artificial delaminations.
Table 5 summarises the ANOVA analysis of the complete model considering a fixed level ofsignificance (α-value) equal to 10%. It is pointed out that the interaction frequency-dimension
seems to significantly influence the Pulse-Echo SHM process. Moreover, Figure 18 shows the
hypotheses of the 2k factorial plan on the residuals: normality, homogeneity of variance, absenceof structure and independence from run-order are all passed. As a result, a lowered model, only
containing the previously highlighted interaction, could be considered. The results, summarisedin Table 6, fits the experimental evidence better than the complete one since the R2 adj parameter
S12

gets higher. The hypotheses about the residuals normality, the homogeneity of variance and theabsence of any structure, as shown in Figure 19, are Also overcome.
Table 5: ANOVA report of the complete model: response versus frequency, dimension, position.
Estimated Effects and Coefficients for Response (coded units)
Term
Constant
Frequency
Dimension
Position
Frequency*Dimension
Frequency*Position
Dimension*Position
Frequency*Dimension*Position
S = 0.121001
R-Sq = 46.92%

Effect0.086870.02073-0.00903-0.11245-0.03075
0.06505
0.00283

PRESS = 0.468522
R-Sq(pred) = 0.00%

Coef
0.14952
0.043440.01036-0.00451-0.05622-0.01537
0.032520.00141

SE Coef0.030250.030250.030250.030250.030250.030250.030250.03025

T
4.94
1.44
0.34
-0.15
-1.86
-0.51
1.08
0.05

P
0.001
0.189
0.741
0.885
0.100
0.625
0.314
0.964

R-Sq(adj) = 0.48%

Normal Probability Plot

Versus Fits

99
0.10

Residual

Percent

90
50
10
1

0.05
0.00
-0.05
-0.10

-0.2

-0.1

0.0
Residual

0.1

0.2

0.0

0.1
0.2
Fitted Value

Histogram

Versus Order
0.10

3

ResidualFrequency

4

2
1
0

0.3

0.05
0.00
-0.05
-0.10

-0.15 -0.10 -0.05 0.00 0.05
Residual

0.10

0.15

1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16

Observation Order

Figure 18: Check about the residuals hypotheses for the response of the complete 2k factorial
plan.
Table 6: ANOVA report of the lowered model: response versus frequency, dimension and theirinteraction.
Estimated Effects and Coefficients for Response (coded units)
Term
Constant
Frequency
DimensionFrequency*Dimension
S = 0.107314
R-Sq = 37.38%

Effect0.086870.02073-0.11245

Coef
0.14952
0.043440.01036-0.05622

PRESS = 0.245683
R-Sq(pred) = 0.00%

SE Coef0.02683
0.02683
0.02683
0.02683

T
5.57
1.62
0.39
-2.10

P
0.000
0.131
0.706
0.058

R-Sq(adj) = 21.72%

Concerning the PE technique, it can be concluded that the interaction frequency-dimension ishighly significant, since its p-value is lower than the imposed significance level. In addition, also
the frequency, as a stand-alone factor, seems to be significant, but the experimental evidence
isn’t so strong to justify such conclusion due to the p-value higher than the α one. The position
and the interactions between the other factors don’t seem to be significant. Furthermore, on thebase of the coefficients of Table 6 and what is shown in Figure 17b, the PE performance withrespect to a high dimension level (i.e. a big defect) is quite independent from the actuation

S13

frequency, while it can be enhanced working at a low frequency level for small defects (lowdimension level).
Versus Fits
0.2

90

0.1

Residual

Percent

Normal Probability Plot
99

50
10
1

-0.2

-0.1

0.0
Residual

0.1

0.0
-0.1
-0.2

0.2

0.05

0.10

Histogram

0.25

Versus Order

0.1

3.6

ResidualFrequency

0.20

0.2

4.8

2.4
1.2
0.0

0.15
Fitted Value-0.15 -0.10 -0.05 0.00 0.05
Residual

0.10

0.0
-0.1
-0.2

0.15

1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16

Observation Order

Figure 19: Check about the residuals hypothesis for the response of the lowered 2k factorial plan.
4.4 Analysis of experimental results from PC
Regarding the Pitch-Catch configuration, the transmission coefficient T (i.e. the ratio of the A 0
peak wave component propagating across the damage and captured by the sensor to that of theincident wave) is chosen as the relevant response from the delamination. Figure 20 shows anexample of the sampled data: the green line stands for the incident wave without defectinteraction, while the red one represents the response of the wave passing through thedelamination. Their envelopes are also given: the blue dots of Figures 20c and d highlight the A 0
mode shape before and after the defect interaction.
a)

b)

c)

d)
Figure 20: Example of PC response caused by a 24 mm delamination positioned between the 16th
and the 17th ply @45kHz, from Plate-1: a) benchmark diagnostic wave; b) transmitted wave
across the delamination; c) and d) corresponding envelopes.
The main effects and interaction plot of Figure 21 outline a strong dependency from dimension,
with respect to the other main factors and a weak interaction between frequency and position. In
particular, the ANOVA analysis, again with α=10% and reported in Table 7, points out thedimension as the only potential key factor influencing the Pitch-Catch SHM process. Moreover,Figure 22 shows the effectiveness hypothesis of the 2k factorial plan about the residuals: all thechecks resulted to be satisfied.

S14

Frequency-1

Dimension

1

-1

1

0.40

0.4

0.35

Fr equency

Frequency
-1
1

0.3

Mean

0.30
0.2

0.25
-1

-1

1

0.4

1

Position
0.40

Dimension

Dimension
-1
1

0.3

0.35

0.2

0.30
0.25

-1

P osition

1a)b)
Figure 21: Main effects (a) and interaction plot (b) of the studied factors: frequency, dimension
and position of artificial delaminations.
Table 7: ANOVA report of the complete model: response versus frequency, dimension, position.
Estimated Effects and Coefficients for Response (coded units)
Term
Constant
Frequency
Dimension
Position
Frequency*Dimension
Frequency*Position
Dimension*Position
Frequency*Dimension*Position

S = 0.119150
R-Sq = 49.83%

Effect-0.02031-0.14621
0.007140.00579-0.02626
0.074190.01129

PRESS = 0.454296
R-Sq(pred) = 0.00%

Coef
0.33439-0.01016-0.07311
0.00357
0.00289-0.01313
0.037090.00564

SE Coef0.02979
0.02979
0.02979
0.02979
0.02979
0.02979
0.02979
0.02979

T
11.23
-0.34
-2.45
0.12
0.10
-0.44
1.25
0.19

P
0.000
0.742
0.040
0.908
0.925
0.671
0.248
0.854

R-Sq(adj) = 5.92%

Normal Probability Plot

Versus Fits

99
0.10

Residual

Percent

90
50
10
1

0.05
0.00
-0.05
-0.10

-0.2

-0.1

0.0
Residual

0.1

0.2

0.25

0.30
0.35
Fitted Value

0.10

3.6

0.05

2.4
1.2
0.0

0.45

Versus Order

4.8

ResidualFrequency

Histogram

0.40

0.00
-0.05
-0.10

-0.10

-0.05

0.00
Residual

0.05

0.10

1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16

Observation Order

Figure 22: Check about the residuals hypotheses for the response of the complete 2k factorial
plan.
Again, a lowered model was then considered: the ANOVA report of Table 8 fits theexperimental evidence better since the R2 adj parameter gets higher. Moreover, the hypotheses
about the residuals normality, the homogeneity of variance and the absence of any structure, as
shown in Figure 23, are again overcome.
In summary the PC response to an artificial delamination defect is influenced only by its
extension, hence, regardless of the diagnostic wave excitation frequency and the relative positionof the defect along the thickness of a CFRP laminate, abrupt changes in the amplitude response
signal to diagnostic wave occur when the A 0 mode is transmitted through delaminations.
S15

Table 8: ANOVA report of the lowered model: response versus dimension.
Estimated Effects and Coefficients for Response (coded units)
Term
Constant
Dimension

Effect-0.14621

S = 0.100302
R-Sq = 37.78%

Coef
0.33439-0.07311

SE Coef0.025080.02508

PRESS = 0.183962
R-Sq(pred) = 18.73%

T
13.34
-2.92

P
0.000
0.011

R-Sq(adj) = 33.33%

Normal Probability Plot

Versus Fits

99
0.1

Residual

Percent

90
50
10
1

-0.2

-0.1

0.0
Residual

0.1

0.0
-0.1
-0.2
0.25

0.2

0.30

Histogram
0.1

2

ResidualFrequency

0.40

Versus Order

3

1
0

0.35
Fitted Value-0.15 -0.10 -0.05 0.00 0.05
Residual

0.10

0.0
-0.1
-0.2

0.15

1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16

Observation Order

Figure 23: Check about the residuals hypotheses for the response of the lowered 2k factorial plan.
5.

Performance of the suggested SHM process from natural delaminations

After taking into account the factors potentially influencing the performance of the suggested
SHM approach, its performance was then evaluated with respect to natural delaminations,established through drop weight impact tests. In particular, the interest was to perform lowvelocity impacts, since they can cause a significant amount of internal delamination without
evidence at the surface. This type of damage is often referred to Barely Visible Impact Damage(BVID) and can cause a significant degradation of structural properties: if the damaged laminate
is subjected to high compressive loading, buckling failure may occur. Boeing [11] defines BVID
as: “small damages which cannot be found during heavy maintenance general visual inspections
using typical lighting conditions from a distance of five feet”.
Impact tests were performed in accordance to ASTM D7136 [12], using a 25.4 mm diameter
hemispherical striker tip impactor (Fig. 24), whose mass is about 1.2 Kg, guided by an impact
device consisting in the cylindrical tube mechanism shown in Figure 25a. This device isequipped, as shown in Figure 25b, with a laser triangulation Mel M7L sensor in order to measure
the real impact velocity. Figure 26 provides an example of the acquired impactor profile during atest and used to determine the real velocity at the impact.
The target CFRP plate, illustrated in Figure 27, presents four randomised impact locations
according to Table 9, which shows the effective impact energies selected to achieve a BVID. A
detail of the highest energy impacted region is also given. It is worth noticing that none of thecommon visible damaged modes, listed in ASTM D7136 [12], are visible, hence the barely
condition is achieved despite of the 20 J application.

S16

Figure 24: Impactor with hemispherical striker tip whose mass is about 1.2 Kg.

a)b)
Figure 25: Detail of the experimental setup used during drop weight impact testst: a)measurement chain and impact device system; b) laser triangulation for impact velocity
calculation.

Figure 26: Superposition of the impactor shape from laser triangulation to the real one.
S17

Table 9: Effective BVID energies applied to the target CFRP laminate.
Nominal Impact Energy
[J]
5
10
15
20

10J

Drop Height
[m]
0.4
0.8
1.2
1.7

Measured Impact Velocity
[m/s]
2.03
3.7
4.8
5.9

Effective Impact Energy
[J]
2.8
8.1
13.8
20.8

15J
20J

5J

Figure 27: Target CFRP laminate used for the drop weight impact tests: red ellipses identify the
impact zones at different energy.
Once again, the laminate is instrumented with a central pair of PZT actuators out-of-phaseenergised at 45 and 60 kHz and an array of PZT sensors to perform the PC and PE SHM
techniques. The sampled data were then digital processed in the same way as in the previous
Sections and the results obtained from the natural delaminations are summarised in the main
effect and interaction plot shown in Figure 28. In this case, the design factors are limited to theimpact energy and the excitation frequency. This because a natural delamination due to an
impact tends to develop involving more plies and to be a much more complicated damage, so tomake the concept of position (as here defined) meaningless. The comparison of results was thenpossible assimilating the impact energy directly to the extension of damage, as a consequence ofits characteristic dimension. Furthermore, a rigorous ANOVA analysis could not be performed
since the number of replications per factor level were limited to one (since just one is theimpacted CFRP laminate): there were not enough degrees of freedom to estimate theerror/variance.
In any case, some elements of similarity into the response to the same SHM sensor network withrespect to artificial defects could be found. In particular, the Pulse-Echo method underlines thesame kind of interaction frequency-dimension, or correspondingly frequency-impact energy,
from which the response was maximised driving the PZT at high frequency for small defect (lowenergy), whereas it was almost steady interacting with wide defect. The main discrepancy
consisted in the apparent, since the variance of the process couldn’t be estimated, significance ofthe energy and frequency as stand-alone factors. Their influence, onto the reflection factor, could
be attributed to the fact that a BVID damage is much more complicated than a simple
delamination simulated through a Teflon patch, indeed is the combination of three types ofdamage [13]: matrix cracks, fibre fractures and delamination, each of those contribute to theglobal response.
S18

On the contrary, as pointed out through the DOE approach, the Pitch-Catch, characterized by its
transmission factor T, was mainly influenced by the extension of the defect (similarly the impact
energy): the higher the extension, the lower the transmission coefficient. Hence, in that case, thecomplex damage mechanism seems not to affect the final response.
Data Means

Data Means

Energy0.35

Frequency

0.300
0.275

0.25

Mean

0.250

Mean

Frequency
-1
1

0.30

0.225
0.200

0.20
0.15
0.10

0.175

0.05

0.150

0.00
-1

1

-1

1

Data Means
Energy-1a)1Energyb)

Data Means
0.20

Frequency

Frequency
-1
1

0.19

0.17

0.18
0.17

Mean

Mean

0.16
0.15

0.16
0.15
0.14

0.14

0.13

0.13

0.12
0.11
-1

0.12

1

Energyc)d)Figure 28: Impact tests results summarized in the main effects and interaction plot: (a) and (b)reflection factor for Pulse-Echo and (c) and (d) Transmission one for Pitch-Catch.

-1

6.

1

-1

1

Concluding remarks

A new way for tuning Lamb waves, based on the actuation at a very low frequency [0;50] kHz
and able to give back mainly the fundamental A 0 mode, was observed and characterised up to
100 kHz, above which the A 0 mode is no longer the only significant one. This frequency range
simplifies the interpretation of the diagnostic signal waves received from PZT sensors, since
only one mode significantly propagates and interacts with the host structure. Moreover, its
magnitude, with respect to the other modes, and its SNR can be successfully enhanced using apair of PZT actuators symmetrically bonded onto the upper and lower surfaces of the composite
laminate. Further studies are required to verify if a strong mode conversion can occur within thislow frequency range.
The performance and the potentially influencing factors of the proposed SHM process, both in
the Pulse-Echo and in the Pitch-Catch configurations of the PZT transducers, were studied
considering a quasi-isotropic composite laminate prone to artificial defects and naturaldelaminations obtained by low-velocity impact tests. The actuation frequency was chosen acrossthe limit value equal to 50 kHz due to the closest boundary condition able to mask useful
response signals. Anyway, the proposed SHM process could be encouraging for vast structures
since, at low frequency, lower attenuation coefficient are achievable.
Some elements of similarity between the response to an artificial defect and a naturaldelamination could be found: the PC response is almost reliant on the damage extension inside
the laminate, while the PE one seems to recognise the complex damage mechanism induced by
an impact damage. This opens the promising possibility to extract, from the response signal,
additional information about the nature of damage, since a fingerprint could be identified on the
S19differences between the response to a pure delamination (the artificial one) and a natural one
which typically consists of multiple delaminations through the thickness together with matrix
cracks and fibre fractures. Moreover, known the key factors and how they play a role in the
NDE, essential information is here supplied with the aim to design an optimized SHM PZT
sensor network.
7.

Acknowledgements

The authors would like to thank Mr. R. Galeazzi for the active help given to the present research.
8.

References[1]

Chang F.K., 1999, Structural Health Monitoring: A Summary Report, Proceedings of the
2nd International Workshop on Structural Health Monitoring, Stanford, CA, 612-621.
[2] Toso N., Alastair J., 2011, LIBCOS-Significance of Load upon Impact Behavior ofComposite Structure, Research Project EASA 2009/3.
[3] ASTM D3039/D3039M-08, 2008, Standard Test Method for Tensile Properties of Polymer
Matrix Composite Materials.
[4] ASTM D3518/D3518M-94, 2007, Standard Test Method for In-Plane Shear Response ofPolymer Matrix Composite Materials by Tensile Test of a ±45° Laminate.
[5] Bartoli I., Marzani A., Lanza di Scalea F., Viola E., 2006, Modeling wave propagation indumped wave guides of arbitrary cross-section, Journal of Sound Vibration 285, 685-707.
[6] PiCeramic, 2012, Piezo Material Data, www.piceramic.com, accessed 01.10.2012.
[7] Gianneo A., 2012, Analysis and Experimental Design Applied to Structural Monitoring of
CFRP plates by Lamb Waves, MSc. Thesis, Politecnico di Milano, Milano, Italy. [InItalian][8] Mallick P. K., 2007, Fibre reinforced composites, 3rd Edition, Taylor & Francis, London.
[9] Montgomery D. C., 2005, Design and Analysis of Experiment, McGraw-Hill [In Italian][10] Su Z., Ye L., 2005, Selective Generation of Lamb Waves Modes and their Propagation
Characteristics in Defective Composite Laminates, Journal of Materials: Design andApplications 218, 95-110.
[11] Allen J. Fawcett, Gary D. Oakes (ATF), Boeing Composite Airframe Damage Tolerance
and Service Experience Boeing Commercial Airplanes 787 Program[12] ASTM D7136/D7136M-12, 2007, Standard Test Method for Measuring the Damage
Resistance of a Fiber-Reinforced Polymer Matrix Composite to a Drop-Weight Impact
Event[13] Abrate S., Castanié B., Rajapakse Y. D. S., 2013, Dynamic Failure of Composite andSandwich Structures Series: Solid Mechanics and Its Applications, Vol. 192 VIII, 643 p

S20

The 12th International Conference of the Slovenian Society for Non-Destructive Testing»Application of Contemporary Non-Destructive Testing in Engineering«
September 4-6, 2013, Portorož, Slovenia

FROM ACOUSTIC EMISSION TESTING (AT)TO ACOUSTIC MONITORINGPeter Tscheliesnig
TÜV AUSTRIA SERVICES GmbH
Deutschstrasse 10, A-1230 Wien, Austria, peter.tscheliesnig@tuv.at
ABSTRACTTechnical degradation processes like corrosion and fatigue cracks are the most frequent reasonsfor structural failures. To avoid the failure of those structures, maintenance and inspection haveto be carried out adopting different strategies and using various methods. For the common
methods (preventive maintenance, anticipatory maintenance) and the more modern Risk Based
Inspection, AT would be an excellent tool to detect the main degradation processes during theshut down combined with an over-loading of the structure. In the last decades thousands of thesetests were performed successfully. In any way, the results have been dependent on how good thenormal service conditions were simulated by the different kinds of over-loading.
Nowadays an increasingly accepted strategy used to prevent structures from failing is“structural-health-monitoring (SHM)”, which is an integration of sensorial and actuator
elements to determine, analyse, locate and predict the load and damage situation with the aim,that the NDT becomes an integrative part of the structure. SHM integrates all the different
sciences and analysis in terms of life time management of a structure and starting during its
design. As long as we are not able to interfere in all stages of the life time management of astructure we have the opportunity to increase the availability and safety of the structure by using
the gap between safe life and certain fail with a monitoring concept, which is able to detect,locate and determine crack during its sub-critical stage.
The only possible integrative method at the moment is Acoustic Emission, whit all its
imponderability coming from its sensitivity against service and process noise.
Experience during the last years showed clearly, that AT is able to detect sub-critical crack
growth but also different corrosion processes. The new modern AE equipments with the dataacquisition, storage and data evaluation techniques open the field to discriminate the real usefulAE signals from the service and process noise by frequency and/or logical filtering and also
more complex pattern recognition techniques.
Every “monitoring” requires a permanent application of the AE sensors, cabling and dataacquisition and evaluation equipment. By a harsh environment this can lead to extensive
investigations before the application of the sensors. But also the normal service conditions haveto be monitored and evaluated in advance. By the combination of all data in combination with alife time calculation the AE monitoring can determine if the structure can stay in-service or has
to be stopped for a shut down and performance of a regular inspection, including an appropriate
NDT.
Besides the comparison AE testing versus AE monitoring this paper shall present an example for
the AT, preparations for monitoring, part time monitoring at different time spans or service timeand permanent monitoring.
S21

Key words: Acoustic Emission Testing, Acoustic Monitoring, Maintenance Strategies, Structural
Health Monitoring
1.

Introduction

It’s well known, that the main technical degradation processes are corrosion and fatigue cracks.
These two types of processes are most often the main reason for structural failures. To avoid
these failure mechanisms, TÜV AUSTRIA performs since more than 35 years AE Tests during
the normal required service inspections (repetition tests). In the first years the AT was mainly
used for the first pressure test to exclude small irregularities, which later can be the reason for
fatigue cracks. These first pressure tests were performed as hydro-tests with an overpressurisation, like it was done since the 19th century. The information from the hydro-tests
were week, the vessel was good or it became leaky, would be deformed or explode and it had
even the problem, that if a defect was introduced in the wall of the vessels, it wasn’t detected
normally. A further problem was that during the repetition tests the over-pressurisation was donewith water, which led often to a residual humidity in the vessel, when it went back into service.
This humidity led later on to corrosion within the metallic wall.
With the application of AT during these tests we got on-line information about irregularities,
which can later lead to defects within the vessel. Because of the immediate information of
defects occurring in the wall, we were able to make a further step forward. The overpressurisation could be performed with gas, which could avoid introducing humidity into thevessel. Because of the pre-warning availability of AT during the pneumatic loading, these tests
could be performed not only in bunkers, how it was done before. Due to the monitoring of thepressurisation with AE, the over-pressurisation could be performed often with the stock media
themselves, which generated an economic advantage for the customer.
2.

Example for Acoustic Emission Tests (AT)An example was the testing of two natural gas storage spheres with a diameter of 32 meters anda pressure of 1,1 MPa. Before they were put in-service these spheres were tested as a firstpressure test with water. These tests were done as well for a duplication of the NDT of the welds
and to check if defects occur during the pressure test. For this unique loading of the spheres(pressure plus weight of the water), they have been supported by a ring, which was grounded 30m deep, that hindered the sphere in their geometry. The repetition tests were done with naturalgas, avoiding a costly shut-down, and we could demonstrate that the results are comparable(Fig.1).
The AT during such tests shall replace other NDT and detect also the defects, which occur
during the pressure tests. The aim of the AT was also to justify the integrity during the next
service period. Fro this reason, we have to be sure, that the kind of over-pressurisation isrepresentative for the normal service loading, which sometimes has been difficult, especiallywhen the pressure equipments are under cyclic and not static loading during service [1].
Nevertheless with all ambiguities of AE we performed tests on a lot of spheres and reactors
during the following years for the first pressure tests but much more during the required periodic
inspections of pressure equipments.
One very interesting test problem was the repetition tests of a steam drum, which for a normalpressure tests with water, has to be taken out of the paper mill production and has to besupported during the hydro-test, because of the weight of the water compared to steam duringservice [1]. By a pneumatic loading monitored with AE the steams can stay in the structure
without harming the bears and/or the roundness of the drum (Fig. 2).

S22

Fig. 1: Pressure tests with AT of a natural gas storage sphere with a diameter of 32 meters (firstpressurisation and periodic inspection)

Fig. 2: Sensor application on a built-in steam drum for pneumatic loading
3.

Discontinuous AE monitoring for in-service inspection

Compared to this time-span based inspection more modern and sophisticated inspection andmaintenance methodologies were carried out, which shall help over the following years to avoid
the failure of those structures within service. For all these concepts (preventive maintenance,
anticipatory maintenance and the more modern Risk Based Inspection) AT was used. For the
Risk Based Inspection, AT is an excellent tool to detect the most important degradation
processes during any kind of over-loading. The draw back for this methodology was that thepressure equipment goes out of service. The advantage of AT is, that the over-pressurisation
could often be performed with the gas (e.g. nitrogen) or even stock product itself.

S23

The requirement to simulate the service loading during the AT can be fulfilled excellent bymonitoring the pressure equipment during service. But AE is not only excellent for detecting
defects, it is also sensitive for background noise coming from the process (pumps, temperaturechanges etc.). The developments in the data management of new AE system give us theopportunity to distinguish between useful AE and background, by application and use ofanalogue- and/or digital filtering or pattern recognition techniques.
4.

Example for dissentious AE monitoring regarding in-service information on the
integrity of the pressure equipment

An example in this direction are tests on pressure equipment (“Druckwechselabsorber”) of
BASF SE at their plant in Ludwigshafen, where we performed a test programme to determine if
the maintenance strategy, which at the moment is absolutely time driven, can use the Acoustic
Monitoring for the detection of defects on time during the normal service [2].
To verify the AE monitoring different test steps became necessary: measurement of the
background noise, measurement of the different wave propagation parameters, selection of thesuitable sensor positions for localisation, monitoring during the normal service (cycling loading
and de-loading within 15 min up to a pressure of 3 MPa) and at the end for comparison a“conventional” AT during a pressure test.
The results showed that the detection of a defect during a normal pressure test has had a higher
probability than during the normal service, because of the speed of the service-loading and theinfluence of process noise. On the other hand we could demonstrate that the defect (crack)
detection was possible during certain periods of the normal pressure cycles, emphasizing evenmore the advantages of an Acoustic Monitoring during the normal service (Fig. 3). Based on the
results of 3 pressure equipments we could demonstrate that an Acoustic Monitoring for these
specific pressure equipments and this specific process is possible and would be the safest, mostcost-effective and time saving method for their maintenance strategy.
In this case we made a first step from an AT to a discontinuous Acoustic Monitoring and canchange the maintenance strategy from a time driven preventive maintenance to a predictive
maintenance driven by the momentous condition of the structure [3]. Clearly you can’t apply
Acoustic Monitoring everywhere and without basis tests, like it was shown in this example.
5.

Continuous Monitoring with AE (Basics and Examples)

Nowadays a more and more accepted strategy to prevent structures from failures is “structuralhealth-monitoring (SHM)”, which is an integration of sensors and actuators to determine,analyse, locate and predict the load and damage state with the aim, that NDT becomes anintegrative part of the structure. SHM integrates different sciences and analysis regarding the life
time management of a structure, starting during its design. As long as we are not able to interfere
within all stages of the life time management of a structure we have the opportunity to increase
the availability and safety of the structure by using the gap between safe life and certain failure
with a monitoring concept, which is able to detect, locate and determine crack during its subcritical stage [4].

S24

Figure 3: Load cycle of pressure swing absorber in service, pressurisation profile (blue with axison the right) together with the corresponding acoustic emission noise level RMS (green, red andyellow with axis on the left) and acoustic emission signal peak amplitudes (green, red and yellow
with axis on the left) for several sensors at different positions 3, 7, 14 at the shell of the pressure
equipment
A first application for this Acoustic Monitoring was done during an EC funded project for
detection of corrosion and fatigue cracks in transport products (ships, trucks and railway cars)[5]. The project showed a guideline, how this methodology could be inserted into themaintenance strategy of a structure. Although we tried to create a generic solution, each of thesetransport products has to be checked separately. As a basis for further work, we checked in thelab, that the material emits AE under the service condition and/or load (Fig. 4). Because we were
sure that the structure will be harmed by corrosion and fatigue cracks we had to simulate thepossible corrosive environment and also to put the material under cyclic load (type andfrequency). The tests are also necessary to establish the evaluation methodology, digital filtering
plus application of a frequency domain pattern recognition programme. Furthermore it has to be
decided if you are able to monitor the complete structure or only some specific points. We
decided, that we will monitor only hot spots, obvious decision in the case of a ship that can be
300 meter long or even more. But also transportation vessels/tanks of trucks and railway cars
shall be checked on specific parts especially from an economic point of view. The hot spots
could be defined on FE calculation or for very complex structures, like ships, based on
experience with information gathered from the classification society. After the decision wherethese hot spots are the specific structures, without and with natural or artificial flaws, have to betested under normal service conditions (Fig. 5 and 6). These tests define a milestone, where you
have to check a sufficient signal to noise ratio and/or check also the specific evaluation
methodology, which was developed during the lab tests. If the system works the most importanttask is to evaluate the boarders for alarm and/or stop (go out of service). These values have to beascertained and proven with other conventional NDT methods.
A further important task for any monitoring in practice and especially for transportation products
is the AE equipment, which besides its feasibility to perform the AE detection and evaluation,

S25

has to be certified according to the safety norms for the tested structure (intrinsically safe,
explosion etc.).

Figure 4: Corroded sample of a lab tests, representing deck-plates with stiffeners

Figure 5: Oil tanker for AE monitoring (background and natural defects)

Figure 6: Corrosion in a water ballast tank

S26

6.

Conclusions

Acoustic Emission is a physical phenomenon whereby transient elastic waves are generated
within material or by a process. The application of load or harsh environment in a material
produces internal modifications such as local plastic deformation, crack growth, corrosion,
erosion and phase transformations. AE sources also arise from impact, leakage (turbulent flow),
cavitations, electric discharge and friction. All these mechanisms and processes are generallyaccompanied by the generation of elastic waves that propagate in materials or into ambientliquids. The waves therefore contain information on the internal behaviour of the material and/or
structure.
AE is therefore an excellent tool to monitor initial proof or acceptance of pressure test equipment
during the hydro-test or to perform requalification tests after defined service periods duringloading of the structure with its storage products, mainly gas pressure tests. The AE could be a
testing method for the health monitoring of structures during in-service conditions.
With the application of AE as a monitoring technique the following advantages and features ofthe method can be underlined: It is a integral, passive and non-invasive testing method; It allows the on-line monitoring during service independent from the storage medium; It allows detection of AE sources, coming from degradation and leakage under specificconditions; It offers the reduction of inside inspections and reduces the follow up NDT to areas of
AE .
The main difference between AE and most NDT methods is that it is the material itself which
releases the energy in consequence of structural degradation due to different source mechanism.
This is different to detecting and measuring existing geometrical discontinuities in a static
condition. These all together makes AE to an ideal tool for monitoring structures and verifying
their integrity under normal service condition.
The biggest limitation for the method is its sensitivity to in-service or extraneous noise.
Therefore it is necessary to check the presence of potential noise sources before performing any
AE monitoring. Because it is normally not possible to remove the noise source, the applicabilityof AE has to be checked before any kind of application as a monitoring technique during inservice conditions.
7.

References[1]

Tscheliesnig P.: Thirty years experience of industrial applications of Acoustic Emission
Testing (AT) at TÜV Austria, Proceedings of the 6th International Conference on Acoustic
Emission, AEWG, Lake Tahoe (USA), 2007
Lackner G., Hecht A.: Prüfkonzept für Beschaffung und Betrieb vonDruckwechseladsorber unter Einbeziehung der Schallemissionsprüfung, Procedings of the
DACH Jahrestagung, German Society for NDT, St. Gallen (CH), 2008
Tscheliesnig P., Lackner G.: Maintenance driven by Acoustic Emission Monitoring,
Proceedings of the CM 2009, BINT, Dublin (Ireland), 2009
Christian Boller, “Structural Health Monitoring (SHM) als Werkzeug desLebensdauermanagements”, held at 11- Annual conference of the ÖGfZP; Linz (Austria),
March 2009
Tscheliesnig P.: AE-monitoring for Surface Transport Products (Ships, Trucks and Railway
Cars), Proceedings of the 6th European Workshop on Structural Health Monitoring,
German Society for NDT, Dresden (D), 2012

[2]
[3]
[4]
[5]

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The 12th International Conference of the Slovenian Society for Non-Destructive Testing“Application of Contemporary Non-Destructive Testing in Engineering”
September 4-6, 2013, Portorož, Slovenia

THE NEW HIGHER ORDER SPECTRAL TECHNIQUES FOR
NON-LINEARITY MONITORING OF STRUCTURES AND
MACHINERY
L. Gelman
Cranfield University, UK
ABSTRACTThe new higher order spectral techniques, the normalized cross-covariances of complex spectral
components, are proposed for monitoring structure and machinery non-linearity and signal nonGaussianity and estimating the harmonic phase coupling of signals from structures andmachines. Normalization of the proposed techniques is also developed. It is shown by simulationthat the proposed techniques provide effectiveness gain for detection of non-linearity incomparison with the HOS.
1. Introduction
For monitoring of a structure and a machinery non-linearity and non-Gaussianity of signals froma structure and a machinery due to damage and estimating the harmonic phase coupling of
signals from a structure and machinery, an input excitation (e. g. vibration excitation, acousticalexcitation, etc.) excites the resonance oscillations of a structure or a machinery in question andresonance oscillations are processed by the higher order spectra (HOS) [1]. This approach has
been widely investigated for stationary and non-stationary signals [1-13].
For diagnosing structure/machinery nonlinearity, we propose to use the normalised crosscovariances of order n between n complex spectral components. The physical sense of this
proposition is that if complex spectral components have appeared due to nonlinearity (damage),these components have non-zero cross covariances.
It can be easily shown that the widely used normalized HOS: i.e. the bicoherence and theskewness for the HOS of order 3 and the kurtosis and the tricoherence for the HOS of order 4, do
not present the exact normalized cross-covariances between three and four complex spectral
components respectively. It also can be shown that in the general case, the normalised HOS oforder n also do not present the exact normalized cross-covariances between n complex spectral
components.
Therefore, the normalised cross- covariances between three, four and n complex spectral
components cannot be estimated by these traditional normalized HOS.
Thus, the main novelty of this paper is the proposed new higher order spectral techniques thatare the normalized cross-covariances between n complex spectral components.
The purposes of this paper are to:

S28

•
•

propose the new higher order spectral techniques for structure and machinery healthmonitoring: the normalized cross-covariances of n complex spectral components
compare by simulation the proposed techniques with the normalized HOS

2. The new higher order spectral cross-covariances
The novel condition monitoring techniques are proposed here: the higher order spectral
techniques, the normalized cross-covariance of n complex spectral components. For estimating
the proposed techniques, the following steps should be undertaken: the time domain signal
should be divided into overlapping segments by the internal time window, m=1...M, M defines
the total number of overlapping segments in the signals.
The generic expression of the proposed cross-covariances of order n based on the Fouriertransform is as follows:
1

𝑛−1
∗
�
�
𝑛𝑐𝑜𝑣(𝑓1 , 𝑓2 , … , 𝑓𝑛−1 ) = 𝑀 ∑𝑀
𝑚=1�� ∏𝑗=1 𝑋𝑚 �𝑓𝑗 � − 𝑋 �𝑓𝑗 �� ∙ [𝑋𝑚 (𝑓𝑛Σ ) − 𝑋 (𝑓𝑛Σ )] �(1)

where X m ( f j ) is the Fourier transform at frequency f j at segment duration ∆t m of a signal,
n −1

j = 1, n − 1 , f nΣ is the accumulated frequency, f nΣ = ∑ f j ,∗is a symbol of the complex

j =1

conjugate, 𝑋��𝑓𝑗 � is the mean value of variable 𝑋�𝑓𝑗 �.

The proposed cross-covariances (1) are complex valued, estimated by the Fourier transforms of asignal at n frequencies and depend on (n-1) frequencies. Only in the particular case of the zeromean complex spectral components, the functions (1) for order n, 3 and 4 are the classical unnormalized HOS of order n, the classical bispectrum and the classical trispectrum respectively.
In the general case of the non-zero-mean complex spectral components, the proposed functionsof order 3 and 4 are not the bispctrum and the trispectrum. The physical significance of the
proposed functions is that they provide a measure of cross-covariances between n complex
spectral components.
It is known from the classical statistical analysis that the cross-covariances should be normalisedin order to avoid the misleading interpretation.
The standard normalization of the cross-covariances is employed here as follows for order n, 3
and 4 respectively:
𝑛𝑐𝑜𝑣(𝑓1 ,𝑓2 ,…,𝑓𝑛−1 )

(2)

1 𝑀
{𝑋 (𝑓 )−𝑋�(𝑓1 )}∙{𝑋𝑚 (𝑓2 )−𝑋�(𝑓2 )}∙{𝑋𝑚 (𝑓1 +𝑓2 )−𝑋�(𝑓1 +𝑓2 )}∗∑𝑀 𝑚=1 𝑚 1

(3)

𝐶(𝑓1 , 𝑓2 , … , 𝑓𝑛−1 ) =𝑡𝑐𝑜𝑟(𝑓1 , 𝑓2 ) =𝑓𝑐𝑜𝑟(𝑓1 , 𝑓2 , 𝑓3 ) =��∏𝑛−1𝑗=1 𝑣𝑎𝑟�𝑋�𝑓𝑗 ���𝑣𝑎𝑟�𝑋(𝑓𝑛Σ )�

�𝑣𝑎𝑟[𝑋(𝑓1 )] 𝑣𝑎𝑟[𝑋(𝑓2 )] 𝑣𝑎𝑟[𝑋(𝑓1 +𝑓2 )]1 𝑀{𝑋 (𝑓 )−𝑋�(𝑓1 )}∙{𝑋𝑚 (𝑓2 )−𝑋�(𝑓2 )}∙{𝑋𝑚 (𝑓3 )−𝑋�(𝑓3 )}∙{𝑋𝑚 (𝑓1 +𝑓2 +𝑓3 )−𝑋�(𝑓1 +𝑓2 +𝑓3 )}∗∑𝑀 𝑚=1 𝑚 1
�𝑣𝑎𝑟[𝑋(𝑓1 )] 𝑣𝑎𝑟[𝑋(𝑓2 )] 𝑣𝑎𝑟[𝑋(𝑓3 )] 𝑣𝑎𝑟[𝑋(𝑓1 +𝑓2 +𝑓3 )]where var is the symbol of the variance.

(4)

In the general case of order n, the proposed normalised cross-covariances (2) differ from thetraditional normalised HOS of order n. It can be also seen from expressions (3-4), that for orders
3 and 4, the normalised triple and fourth covariances differs from the traditional normalized
HOS, the bicoherence and skewness and the tricoherence and kurtosis respectively. Thisdifference remains even for the zero-mean spectral components.

S29

The normalisations of the proposed cross-covariances allow avoidance of the misleadinginterpretation of the proposed techniques (1) due to variations of the power spectral density of asignal.
The proposed techniques can be also used for non-stationary signals by employing theappropriate time-frequency transforms (e. g. the chirp-Wigner transform [14], the short time
chirp-Fourier transform [15] etc.). This can be done by substituting the appropriate timefrequency transforms for the Fourier transform in equations (1-4).
To demonstrate that the proposed techniques can effectively detect structure non-linearity due todamage and to compare them with the traditional HOS, a simulation test with linear and nonlinear (bilinear) structures was performed.
An input random cosine excitation with constant amplitude, random initial phase and linearlychanged instantaneous frequency in time (i. e. the chirp signal) has been passed via the following
nonlinear (bilinear) system: 
x + 2hx + ωS 2 x = A cos Ω ( t ) , x ≥ 0,


 
2
 x + 2hx + ωC x = A cos Ω ( t ) , x < 0,where x =Xc=ωS
, X is the displacement, h =, h is damping;
2m
m

(5)

kS
=
, ωC
m

kC
, m andmc are the mass and damping coefficient respectively, k S and kC are the stiffness for positive

displacement and stiffness for negative displacement respectively, A =A1, A1 is the constantmamplitude of the input signal, Ω(t ) = ∫ ω (t )dt , Ω(t ) is the instantaneous phase, ω (t ) is thelinearly changed angular frequency.
The initial phase of the each simulated signal has been taken randomly and is uniformly
distributed in the range [0; 2 π ].
The output signal of the bilinear structure is transient, with variable instantaneous frequency;
therefore, the normalised spectral cross-covariance of order 3 based on the chirp-Fouriertransform [15] is employed for non-linearity detection.
The non-stationary random cosine vibration excitation excited the resonance oscillations of astructure. 300 signals from the linear structure and 300 signals from the bilinear structure were
tested for non-linearity detection. The resonance frequencies of the linear and bilinear structuresare 14.1 Hz and 13.9 Hz respectively, the chirp rate is 0.15Hz/s, the stiffness ratio is 0.05.
The proposed normalised cross-covariances of order 3 and the bicoherence based on the chirpFourier transform of the structure resonance oscillations have been employed for detecting
additional level of structure nonlinearity. The cross-covariances and the bicoherence at thefundamental and second harmonics have been employed.
The following parameters have been used for estimating the cross-covariances and thebicoherence: the frequency resolution is 3.7Hz (i.e. segment size is 0.27s), duration of signals is
5s, segment overlapping is 60%, the internal time domain window is the Hamming window, thesampling frequency is 3600Hz.
The Fisher criteria [16] for detection effectiveness are 373 and 260 for the proposed techniqueand the bicoherence. It is known [16] that features with higher values of the Fisher criterion
provide better detection effectiveness.

S30

Thus, the proposed technique provides effectiveness gain 1.43 times in comparison with the
HOS and, therefore, is more effective for non-linearity detection.
3. Conclusions
1. The new higher order spectral techniques, the normalized cross-covariances of n complex
spectral components, are proposed for monitoring structure and machinery non-linearity andsignal non-Gaussianity and estimating the harmonic phase coupling of signals from structuresand machines. Normalization of the proposed techniques is also developed.
2. The proposed techniques differ from the classical higher order spectral techniques. The
proposed un-normalised spectral cross-covariances coincide with the classical un-normalised
higher order spectra only for the particular case of the zero mean spectral components. The
normalised spectral cross-covariances differ from the classical higher order spectral techniqueseven for the particular case of the zero mean spectral components.
3. It is shown by simulation that the proposed techniques provide effectiveness gain 1.43 times
for detection of non-linearity in comparison with the HOS.
4. The proposed techniques could be extended for monitoring of structure and machinery nonlinearity and signal non-Gaussianity due to damage and estimating the harmonic phase couplingof signals from structures and machines for non-stationary signals by employing the appropriate
time-frequency transforms in equations (1-4).
The proposed techniques could be used in mechanical and electrical engineering,telecommunication, underwater acoustics, etc.
4. Acknowledgement
The author is very thankful to MSc student A. Stepien (Cranfield University) for carrying out asimulation and processing of the simulated data.
5. References
1. Fackrell J. W, White P. R, Hammond J. K, Pinnington R. J. The interpretation of thebispectra of vibration signals. Mechanical Systems and Signal Processing 1995; 9(3):267-274.
2. Kim Y. C, Powers E. J. Digital bispectral analysis and its applications to non-linear wave
interactions. IEEE Transactions on Plasma Science 1979; 7 (2): 120-131.
3. Collis W. B, White P. R, Hammond J. K. High order spectra: the bispectrum andtrispectrum. Mechanical Systems and Signal Processing 1998; 12 (3): 375-394.
4. Schreier P. J, Scharf L. L. Higher-order spectral analysis of complex signals. Signal
Processing 2006; 86 (11): 3321-3333.
5. Mendel J. M. Tutorial on higher-order statistics (spectra) in signal processing and systemtheory: theoretical results and some applications. Proc. of the IEEE 1991; 79 (3): 278–
305.
6. Nikias C. L, Mendel J. M. Signal processing with higher-order spectra. IEEE Signal
Processing Magazine 1993; 10 (3): 10–38.
7. McCormick A. C, Nandi A. K. Bispectral and trispectral features for machine condition
diagnosis. IEE Proc. of the Vision, Image and Signal Processing 1999; 146 (5): 229–234.
8. Gelman L, Petrunin I. The new multidimensional time/multi-frequency transform forhigher order spectral analysis. Multidimensional Systems and Signal Processing 2007; 18(4): 317-325.

S31

9. Hanssen A, Scharf L. A theory of polyspectra for nonstationary stochastic processes.
IEEE Transactions on Signal Processing 2003; 51 (5): 1243-1252.
10. Rivola A., White P., Detecting system non-linearities by means of higher order statistics.
Proceedings of the 3rd International Conference on Acoustical and Vibratory
Surveillance Methods and Diagnostic Techniques, 13-15/10/1998, Senlis, France, vol. 1:
263-272.
11. Gelman L., White P., Hammond J., Fatigue crack diagnostics: A comparison of the useof the complex bicoherence and its magnitude. Mechanical Systems and Signal
Processing, 19(4), 2005: 913–918.
12. Hillis A., Neild S, Drinkwater B, Wilcox P., Global crack detection using bispectral
analysis, Proc. Royal Soc. A 8, 2006, 462 (2069): 1515-1530.
13. Hickey D., Worden, K., Platten M., Wright, J., Cooper, J. Higher-order spectra for
identification of nonlinear modal coupling, Mechanical Systems and Signal Processing,23 (4), 2009: 1037-1061.
14. Gelman L., Adaptive time-frequency transform for non-stationary signals with nonlinear
polynomial frequency variation”, Mechanical Systems and Signal Processing, 21(6),2007: 2684-2687.
15. Gelman L., Ottley M., (2006) New processing techniques for transient signals withnonlinear variation of the instantaneous frequency in time”, Mechanical Systems andSignal Processing, 20 (5), 2006, pp.1254-1262.
16. Young T, K.-S Fu K-S., Handbook of pattern recognition and image processing. New
York, Academic, 1986.

S32

The 12th International Conference of the Slovenian Society for Non-Destructive Testing»Application of Contemporary Non-Destructive Testing in Engineering«
September 4-6, 2013, Portorož, Slovenia

ASSESSMENT OF INFRASTRUCTURES BY MEANS OFPASSIVE AND POSITIVE ELASTIC WAVES APPROACHES
Tomoki Shiotani1, Yoshikazu Kobayashi2 and Shohei Momoki3
1

2

Graduate School of Engineering/ Graduate School of Business, Kyoto University
C-Cluster, Katsura Campus, Nishikyo-Ku, Kyoto 615-8540, Japan
shiotani.tomoki.2v@kyoto-u.ac.jp

College of Science and Technology, Department of Civil Engineering, Nihon University
1-8-14, Kanda-Surugadai, Chiyoda-ku, Tokyo 101-8308, Japan
kobayashi.yoshikazu@nihon-u.ac.jp
3

Research Institute of Technology, Tobishima Corporation
5472 Kimagase, Noda, Chiba 270-0222, Japan
shouhei_momoki@tobishima.co.jp
ABSTRACTIn matured countries for infrastructures, the assessment of current condition of infrastructures
is in urgent demand. As a whole of infrastructures are not always maintained in the same level,
each inspection techniques corresponding to the damage scale of interest shall be established.
Elastic waves’ approaches such as ultrasonic wave and acoustic emission have great potential
to assess those wide range of damage as to adjust the adapting frequencies; however, they are
yet to be generalized as there is a discrepancy between the laboratory studies and on-site
applications. For example, damage areas could be extracted based on the active part ofacoustic emission, and an index to quantify the integrity of the infrastructure could bedetermined from an elastic wave feature. For the former, however, as the monitoring procedure
as well as the method of load-application have not been systematically studied, ultrasonictomography has been well conducted supplementarily. For the latter, although such elastic
wave features as wave velocities and frequencies have been applied to define the damage, nodecisive parameter has yet to been generalized. In the paper, conventional damage-associatedelastic wave parameters have been reviewed and new index to assess the damage has been
proposed. The index, namely Q-value has been experimentally studied with specimens including
a variety of artificial damages. And as for the approach to extract the damage-prone areas, anideal and effective method, AE tomography, combining AE monitoring with elastic wave
tomography is detailed through the calculation algorithm, followed by in-situ applications.
Key words: Infrastructures, damage assessment, ultrasonic waves, acoustic emission (AE), AE
tomography, Q-value.
S33

1.

Introduction

There can be no doubt that infrastructures shall be maintained properly through their service
life. For the proper maintenance, however, the strategic procedure not only for the detection ofdamage but also the effective repair method has not been established. The conventional way tomaintain the infrastructure is referred to as ‘reactive maintenance,’ where repair or replacement
have been carried out when the damage becomes remarkable. This concept is effective whensufficient amount of investment can be secured, while for the current situation of the country
where a lot of infra-stocks have been constructed and there a limited budget expected due toshortage of taxation, the existent infrastructure shall be maintained from their initial
tiny-damage expecting to minimal investment. This idea as to maintain the infrastructure fromthe initial damage with minimal investment is referred to as ‘proactive maintenance,’ assuming
the potential to assess the damage from the initial level. At present, nevertheless, there are nodecisive technique to evaluate the initial damage in especial where the damage evolves frominternal area of the infrastructure. Acoustic emission has potential to detect internal damage inprogress as it uses elastic waves, namely AE waves generated due to cracking. As for the
existent damage, where no progressive damage was anticipated, ultrasonic techniques
transmitting and receiving ultrasonic waves might be applicable. In these circumstances theauthors have been studying both of passive acoustic emission (AE) and active ultrasonic (UT)
tomography technique and proved the usefulness to combine the AE and UT tomography toqualify and following identification of the damage for the laboratory specimen [1] and in-situ
concrete bridge deck [2]. In addition to these strategic monitoring procedures, the quantificationof damage is other crucial issue to assess the infrastructure. Ultrasonic wave velocities havebeen studied to relate the quality as well as the strength of the concrete [e.g., 3, 4]; however, thevelocities are not intrinsic values that do not only depend on the quality of concrete but highly
influenced by the frequency excited and eventually propagated distances [5, 6, 7].

2.

Conventional damage related elastic wave parameters

2.1

Velocities

To simulate several amount of damage, a vinyl inclusion of 15 x 15 x 0.5 mm was prepared, andthe cubic specimen with side of 150 mm included in contents of 1%, 5% and 10% of inclusions
were each made [8]. P-wave velocities through the specimens can be found in Fig. 1. The pulse
velocity of material with 10% inclusions is decreased by only 3% compared to plain mortar,
while the material with 1% inclusions exhibits practically the same velocity with plain mortar,
namely 4,000 m/s. It is mentioned that the typical error associated with the digitization sampling
rate is 10 m/s, while the values are the average of 10 different measurements on each specimen.
As a result, the above velocity differences between different materials (within 120 m/s) would
not reveal mentionable quality discrepancies.

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A feature that takes into account the later arrivals of a waveform is ‘group’ velocity. In general,group velocity is a measure of the velocity with which the major part of energy propagates. The
calculated values of group velocity are depicted again in Fig. 1 for different inclusion contents.
This parameter is much more sensitive to damage since the group velocity of material with 10%vinyl is decreased by 70% compared to the sound material’s one, while even material with 1%
inclusions exhibits group velocity slightly decreased compared to the plain mortar.

Fig. 1: Wave velocities with damage contents.

Fig. 2: Normalized amplitude with damage contents.
2.2 Energy
Energy parameters are more sensitive to damage than pulse velocity [9, 10]. To quantify thedifference, the absolute amplitude of the waveform was used as well as the total energy,
calculated by the area under the rectified signal envelope. The results are presented in Fig. 2.
Both energy and amplitude decrease significantly with the inclusion content, even for the caseof 1% inclusions. This loss of energy is mainly attributed to the combination of the following
two reasons. One is the redirection of energy components that never reach the receiver and theother is the material damping that depends on the length of the wave path. Thus, its influence is
S35

smaller for the shortest path (typical for propagation in homogeneous material) and higher for
longer paths (in scattering media).

Average peak frequency (kHz)Fig. 3: Frequency features with damage contents.
140
120
100
80
60
40
20
0

Initialloading

10,000passage

20,000passagein-situ #1 in-situ #2

Fig. 4: AE peak frequency with progress of fatigue damage.
2.3 Frequencies
The inclusions could influence individual wavelengths in a different way. Therefore, except
time domain, valuable information can be obtained by simple analysis in the frequency domain.
In Fig. 3a, the fast Fourier transform (FFT) of typical signals from plain mortar and mortar withinclusions are depicted. The energy difference is evident. The other important feature is thedownshift of the spectrum. The content at higher frequencies (e.g., above 200 kHz) isdiminished more intensively than lower ones, as seen in Fig. 3a. The central frequency of 260
kHz for plain mortar decreases to 150 kHz for ‘‘damaged’’ mortar showing that this is anotherfeature that can enhance the inhomogeneity characterization.
The frequency features of AE have also effective to assess the fatigue damage [11]. Whendealing with the secondary AE activity obtained mainly from identical types of source, namely
friction between existent crack interfaces, the frequency components of detected AE waveforms
are worth studying. In this examination several levels of fatigue had been introduced byrepetition of iron-wheel loads. Details can be found in the literature [11]. Figure 4 shows the
S36average of peak frequencies of detected AE waveforms in each case. Undoubtedly the peakfrequency of AE events decreases with damage progress, and it was confirmed that the peakfrequency of measured AE signals could be a clue to provide the degree of fatigue damage. In
Fig. 4, the AE events obtained from in-situ specimens where serious lateral cracking had been
confirmed are added as for the reference. Low average frequencies were obtained from both of
the in-situ specimens. The average peak frequencies in the in-situ cases are equal to or even lessthan the value of 20,000, suggesting the in-situ specimens were damaged as equivalent or moreto the case of 20,000 passage.

3.
Q-value, new approach for assessing damage with transfer function of AE
waveforms
As shown previously the frequency characteristics of AE waves could imply the damage
condition of infrastructures. In this case, exact quantitative evaluation for the damage is only
possible in case where every AE waves travelled in the compatible distance. As the frequency
characteristics of AE waves are also known that they depend on the resultant propagation
distance, obtained frequency does not always demonstrate the damage condition, providing therough evaluation [12]. Thus transfer functions of propagation media are described here toquantify the damage with AE waveforms [13].
3.1

Conceptual configuration

AE waveforms are obtained as convolution of functions of source, propagation media, sensor
and acquisition system in the time domain, and those frequency responses can be formulated bya simple multiple equation in the frequency domain:X ( f )  U ( f )T ( f ) D( f )S ( f )(1)Here X(f), S(f), D(f), T(f), U(f) are Fourier transforms of detected AE waveforms, AE source,
propagation media, sensor and acquisition system, respectively. D(f) in Eq. 1 is the target toquantify the damage; however, as S(f) is not readily obtained in AE technique, an approach toidentify D(f), being irrespective to source time function is crucial. In AE application, plural
numbers of sensors are employed to locate the AE source, and therefore the comparison of
waveforms detected among different sensors for an AE source could suffice this requirement asin Eq. 2 assuming the frequency responses of all the sensors employed are compatible.
X 2 ( f ) T2 ( f ) D2 ( f ) D2 ( f )
X 1 ( f ) T1 ( f ) D1 ( f ) D1 ( f )

S37

(2)

On the other hand, as D(f) is dependent on propagation media attenuation, Eq. 3 can be defined
as well, where f is a frequency (Hz), V is a P-wave velocity (m/s) and Q is a normalized value
demonstrating attenuation rate.
 f 
D( f )  exp  d  VQ 

(3)

By combining Eq. 2 and Eq. 3, Eq. 4 is obtained.
 fX i ( f ) Di ( f ) exp  
d i 
X 1 ( f ) D1 ( f )
 V Q


(4)

d i  d i  d1 (d i  d ), (i  2,, n)Equation 4 shows that a function of frequency response can be expressed by an exponentialfunction depending on the difference of distance Δd and frequency f.

Fig. 5: Concrete specimen for ultrasonic measurements.
3.2 Preliminary examination
As shown in Fig. 5, concrete specimen of 500 x 500 x 100 mm was prepared placing four AE
sensor of 60-kHz resonance at the four corners on the bottom surface. Ultrasonic wave
excitation was made using the wideband AE sensor (1045S, Fuji Ceramics), where electricpulse was made by PUNDIT (-500 kHz, CNS Electronics) .

To examine the potential influence of heterogeneity of the concrete specimen on the transfer
function in the intact specimen, excitations at the intersectional points in the line between Ch1
and Ch4 sensors were conducted with Ch2 and Ch3 operated as receivers. Switching of sensor
pairs was also used. Figure 6 shows the ratio of Ch1 to Ch4 in frequency response when the
excitations were made along the line between Ch2 and Ch3. As many excitation points were
employed, three lines for the mean value  and  ± standard deviations σ were drawn. Two
important facts can be found: even for intact condition the frequency response showed
S38

difference from the point measured, and the frequency response needs to be discussed in therange of effective frequency response of the sensor employed, i.e., this case the range of 60
to180 kHz.

Fig. 6: Ratios of Ch1 to Ch4 in frequency response.

Fig. 7: Ratios of frequency responses for the differences of propagation distances.
To verify the frequency response corresponding to the propagation distance, other intersectional
points than above were used to excite the signal. The ratios of frequency responses for thedifferences of propagation distances can be found in Fig. 7. The legends of the figure show thedifference of propagation distances. The decrease rate for higher frequency above 100 kHz
becomes more remarkable as the difference of propagation distances becomes larger. As thistrend had a good accordance with Eq. 4, the slope of the ratios, α in Fig. 7 were calculated by theapproximation, where the slope can be defined as Eq. 5.



 d
VQ

S39

(5)

Fig. 8: Relation between the slope α and differences of propagation distances.
Figure 8 shows the relation between the slope α and differences of propagation distances.
Surprisingly the relation can be well approximated by a linear equation, resulting in good
agreement with Eq. 4. Of course this slope α correlates well to the difference of propagation
distance, but more likely to demonstrate the properties of propagation media. This is the very
concept to quantify the damage of the materials with transfer functions using AE waveforms.

Fig. 9: Prism mortar specimens with different height.
3.3

Application to the mortar specimens with several degrees of artificial damage
To verify whether the concept of the ratio of frequency responses is applicable to thedamage assessment of concrete, homogeneous standard mortar with formed polystyrene
spheres simulating damage was prepared as test specimens. As shown in Fig. 9, the prismmortar specimens have dimensions of 150 x 150 mm with different heights: about 100, 200,
300, 400 and 500 mm. In order to simulate the damage, spherical foamed styrene material (6mm in diameter) was used as a false cavity, with four types of volumetric percentages as 0%,
1%, 5% and 10%. Two types of elastic wave excitation were made by pencil-lead break andelectric pulser (PAC) with a wideband AE sensor (1045S, Fuji Ceramics). Piezoelectric
sensors of 60 kHz resonant (R6, PAC) are employed for the receiver. The signals detected by
the sensor were amplified by 40 dB at the amplifier, and processed and recorded by AE
S40

monitoring system (SAMOS, PAC) with 1 MHz rate and 1k words samples. The elastic wave
was excited at the center on the top surface of the specimen, and received by the sensor
installed in the bottom surface.

(a) Pulser(b) Pencil-lead break

Fig. 10: Frequency distributions with different distances (damage content: 5%).
As a typical case, the result of damage content of 5% is shown as in Fig. 10. Figure 10a shows
the frequency distribution for the case of elastic wave excitation by electric pulser, and b showsthat by pencil-lead break. The frequency spectra decrease with the increase of propagation
distance, and the decrease rate is large as the frequency becomes high. It can be thus obviousthat this result accords well to the theoretical formula as shown in Eq. 3 demonstrating the decay
with the propagation distance. As for the difference of propagation distances, the attenuation
trend as a function of frequency becomes remarkable when the propagation distance becomes
longer even in the case of the same degree of damage (5% in this case), suggesting that it is noteasy to evaluate degradation only by the frequency related parameters as the frequency centroid
and the peak-frequency which are well used to relate damage with elastic waves’ parameter.
The ratio of frequency spectrum response is determined based on the cases of at least 100-mm
propagation distance. Specifically, the ratio of frequency response for the 96-mm propagation is
obtained from the spectra of 196-mm and 100-mm propagation distances. Other ratios of thespectral difference of propagation distance for 208 mm, 320 mm and 444 mm are obtained in asimilar manner from the frequency spectra of 200-500 mm in propagation distances (or theheight of specimens). Calculated results (for 96 mm, 208 mm, 320 mm, and 444 mm) as afunction of the frequency can be found in Fig. 26. Two straight lines in the figure show the linear
approximation over the frequency between 0 to 200 kHz for the ratio of frequency responses of
distance difference 96 mm and 444 mm. In this experiment, as the same type of receiving sensor
were used for all the measurement, T2(f)/T1(f) in Eq. 2 can be regarded as 1 in all the frequency
bands. Because of the heterogeneity of the mortar specimen, the ratio of frequency responses
does not form a straight line like a theoretical formula. Therefore, the regression line is used bycarrying out linear approximation of the frequency response ratio.

S41(a) Pulser(b) Pencil-lead break

1.2

1.2

1

1

0.8

a(0%)

0.6

a(1%)

0.4

a(5%)
a(10%)

0.2

0Slopes of ratio of frequency
responses  (x10-5)

Slopes of ratio of frequency
responses  (x10-5)

Fig. 11: Ratio of frequency responses with differences of propagations (damage content 5%).

0

0.1

0.2

0.3

0.4

0.5

Difference of propagation distance (m)

0.8

a(0%)

0.6

a(1%)

0.4

a(5%)
a(10%)

0.2

0

0

0.1

0.2

0.3

0.4

0.5

Difference of propagation distance (m)(a) Pulser(b) Pencil-lead break

Fig. 12: Relation between slopes of the ratio of frequency responses and difference of
propagation distances.
As shown in Fig. 11, the larger the difference of propagation distance, the larger the slopes of
the ratio of frequency responses. Here, it was verified experimentally that the theoreticalformula, Eq. 4, simulates the practical behavior well. By using the slopes obtained from Fig. 11,
the ratio of frequency responses and the relation of a distance difference to each mix rate ofdamage simulation are shown in Fig. 12. It is found that correlations were uniquely acquired inall damage mixture rate and in both excitations. The frequency slope obviously becomes large
with the increase of damage volume.

S42

300

PencilQ value

250

PULSER

200
150
100
50
0

0%

5%

10%

Damage content

Fig. 13: Q-value with damage contents.
Q-value is also determined by using Eq. 4, substituting P-wave velocity, V, which was obtainedby another measurement, and it is shown in Fig. 13. As mentioned previously, the velocity ofP-wave shows the dispersion depending on the frequency, e.g., the velocity becomes large to acertain frequency then decreases, and this tendency appears more noticeable when more damage
was included [14]. In addition, since Q-value also depends on the frequency, showing thedispersion [ 15 ], the value depends on frequency. Q values by two excitation methods(pencil-lead and pulser) were different as shown in Fig. 13; however, the damage could roughly
be evaluated by Q-value, e.g., Q-value showing between 170-270 demonstrates intact conditionof the material, 130 stands for 5%, and about 100 for 10% damage. The dependency of Q valueon the frequency and allowable range of Q-value in regard to damage will be further studied in a
subsequent paper.

4.

AE tomography

Ultrasonic or seismic wave tomography has been carried out to evaluate integrity of infrastructures for past decades. The conventional ultrasonic wave tomography is a technique toreconstruct wave velocity distribution from travel times among each location of excitations andreceivers. In this test, as the travel times must contain the information of source location andexcitation time, the excitation shall be made at the designated point with an accurate time of
excitation records. While for the AE measurement, AE sources could be identified by arrival
time differences among AE sensors assuming a constant velocity in the material of interest. As
one can be understood, AE source identification with AE monitoring and velocity
reconstruction with tomography are each directly-opposed ideas in terms of the velocity.
Pioneering works can be found in some literature [16, 17]. These studies have adopted anassumption that the wave ray-paths among sources and receivers are straight to simplify thecomputational procedure; however, this approach increases error of identified wave velocity
S43

distribution due to its insufficient representation of the ray-paths. Therefore, the authors havebeen studying algorithm which can implement the both of wave ray-trace and AE source
location. In this paper, newly proposed source location algorithms are demonstrated leaving
tomographic procedure to the other literature [18].

Fig. 14: Conventional set of the relay points.

Fig. 15: Revised ray path in consideration of proposed relay points.
4.1 Source location technique
The source location technique is based on ray-trace algorithm [16]. This algorithm ischaracterized by installation of relay points in each cell as illustrated in Fig. 14. Since ray-paths
are formed by segments among nodal points in conventional ray-trace algorithm, its resolution
depends on the mesh, implying that high accuracy source location requires fine mesh. This leads
to increment of the number of degrees of freedom since slowness, a reciprocal of velocity shallbe defined in each cell, and consequently makes the identification procedure more complicated.
In this ray-trace algorithm, the relay points between nodes are proposed and a ray-path is formedby segments among nodal and relay points as shown in Fig. 15. The resolution of ray-trace isincreased without increment of the degrees of freedom by this approach. Besides as role of therelay points is relaying the signals, the relay points shall be distributed uniformly on the surface.
S44

However, it is difficult due to the heterogeneous shape of cross section of concrete structures.
To solve this problem, the relay points are installed by using isoparametric mapping that is usedin the ray-trace algorithm. Since the each cell is mapped into isosceles right triangle, the relay
points can be uniformly installed in the mapped cell as shown in Fig. 16. This algorithm does
not give exactly uniform distribution of relay points if the shape of the cell is skewed; however,the distribution is improved by avoiding use of strongly skewed cells. The source location isestimated by using this ray-trace algorithm.

Fig. 16: Mapping to the global coordinate of set relay points.

Fig. 17: Procedure to estimate the source locations.
The procedure of the estimation of source location is briefly described as in Fig. 17. As for the
first step to estimate the source location, the ray-trace is carried out for a receiver as illustrated inFig. 18. This procedure calculates travel times
from a receiver to all nodal and relay
points that are numbered as . Since first travel timeat receiver is observed, the possible
emission time of the signal
is computed as follow at a nodal or relay point .
(6)
The step is applied for all receivers, and then variance of the the
∑(

S45

)

is computed as follow.
(7)

in which
∑
where

(8)

is number of receivers. For the estimation of the source location, the varianceevaluated. If the slowness distribution is exactly identical to real slowness distribution,
be equal to zero at the source location and

is
must

must be the emission time. Due to thediscretization error of slowness distribution and insufficient resolution of ray-trace, generally
is not zero even at the source location while the identification procedure of seismic tomography.
However, it is predicted thatgets minimum at the source location. Hence, in this paper, thesource location is determined as a nodal or relay point of minimum variance. Additionallyis used as possible emission time. It is noted that the accuracy of the estimation of source
location is controlled by the density of nodal and relay points because the source location is
assigned to a nodal or relay point in the proposed algorithm. Furthermore, by applying thistechnique to the iterative procedure of identification of wave velocity structure, the source
locations are updated in every iterative step, improving the accuracy of the source location. Thisapproach can be applied for not only AE signals, but also signals that are generated by any
excitation point.
.
Fig. 18: Radiation of waveform from a receiver.

Fig. 19: Procedure of seismic tomography with estimation of source location.

S46

4.2

Seismic tomography with estimation of source location

Seismic tomography requires source location, emission time and observed first travel time ofsignals. However, the signals having neither emission time nor source location can be used forseismic tomography if the source location and emission time are correctly identified. It isnoteworthy that the source location and emission time are estimated from only observed firsttravel times and presumed wave velocity distribution by the method that was introduced inprevious section. Based on these facts, a procedure of seismic tomography with estimation ofsource location is introduced. Figure 19 illustrates the procedure of seismic tomography withestimation of source location. In seismic tomography with estimation of source location, thefirst step is estimation of source location and emission time. If the observed first travel times canbe separated into groups that are respectively associated with individual excitation points, theestimation of source location and emission time are carried out for each observed first travel
time group. The second step is applying the ray-trace to all estimated source location. In thisstep, the ray-trace is carried out for the all of estimated source location, and travel time among
the estimated source location to the other nodal or relay points are figured out. Finally by adding
the computed travel time to the estimated emission time, the theoretical first travel times atreceivers are given as follow.
(9)
In the third step, the slowness distribution is updated to eliminate the difference of thetheoretical and observed first travel time by identification technique.
4.3 Application to RC bridge deck of bullet trains
As for one of the applications, AE tomography is applied to RC bridge deck of bullet trains. In
the RC bridge deck, several internal lateral voids have been estimated by impact acoustics tests
except for the area IV detailing in the later session. In the AE tomography, AE activity namely
secondary AE sources form the defects induced by train passage will be utilized [19]. As AE
data obtained includes mechanically induced noise as well as ambient noise which is irrelevantto the secondary AE activity, the extraction of this secondary AE activity which can also play as
a source for tomography testing can be a clue to get a successful result. In addition to the AE
monitoring, Rayleigh wave tomography with using accelerometer of 45 kHz-resonance wasapplied for the comparison.
The AE sensor array and estimated damage areas by the impact acoustics can be found as in Fig.
20, and the same manner of Fig. 20 but for the Rayleigh wave tomography can be confirmed by
Fig. 21. The area of interest is 2.4 m x 2.4 m and 13 AE sensors of 60 kHz- resonance for AE
tomography and 16 accelerometers for Rayleigh wave tomography are each arranged as in those
figures. The estimated damage areas by impact acoustics is denoted by I, II and III, and IV is thearea of which impact acoustics could not identify but estimated by other sophisticatedtechniques (no description for the name of the test could be recorded on site).

S47

For the AE tomography, to set the threshold level ambient noise was first measured without any
train passage and subsequently AE monitoring during train passage was repeated 10 times.
While for the Rayleigh wave tomography, two types of the steel hammer with 11 and 15 mm indiameter for the impact surface have been employed to investigate the different depth of damage[20]. More details of the Rayleigh wave tomography can be found in the literature [20].

Fig. 20: AE sensor arrangement for AE tomography.

Fig. 21: Accelerometers arrangement for Rayleigh wave tomography.
Two types of Rayleigh wave velocity’s distributions can be seen as in Fig. 22. Figure 22a shows
the result of 11mm dia. hammer, equivalent to the wavelength of 18 cm, while 20 cm inwavelength of 15mm dia. hammer as shown in Fig. 22b in the case. As considering both cases
showing low velocity areas, damage area I, II and III could be identified with this Rayleighwave tomography but damage area IV could not confirmed by this technique.
For the AE monitoring, in order to extract the secondary AE activity being different form othernoise related AE activities, AE waveforms exhibiting higher peak frequency of 7 kHz wereextracted. This criterion of peak frequency was successfully obtained when comparison wasmade between AE waveforms without train passage and those with train passage. The result of
AE tomography with this high pass filter can be found as in Fig. 23. Surprisingly the damage
area IV, which could not be evaluated by other inspections, could be revealed leaving slightincompatibility of I, II and III. As there still includes mechanical noises in the extracted AE
activity, visual discrimination was further implemented. Figure 24 exhibits the AE tomography
S48

result after both filters of high pass and this visual check. Although the damage area III could
not well visualized, the damage area I and II can be successfully emerged. Two-dimensional AE
tomography algorithm developed and employed in this analysis might be introduced the above
discrepancy to the estimations of impact acoustics. The limitation of the Rayleigh wave
tomography shall be also mentioned. As it has already reported that the maximum depth ofwhich the Rayleigh wave tomography can be applied is the half depth/ thickness of the plate,e.g., in the case of 50 cm thick plate, more than the deeper area of 25 cm could not be
investigated, while AE tomography has no restriction for the depth of measurement event for thethinner plate. Anyhow as three-dimensional algorithm of AE tomography has been alreadyestablished awaiting the application to in-situ structures, this fact will be elucidated later andmore reliable applications shall be shown up for the follow up paper.

(a)  11 mm(b)  15 mm
Fig. 22: Velocity distributions by Rayleigh wave tomography.

①

3500

⑥

2.4

⑪
Ⅳ⑨

④

3250

1.8

⑦
Ⅱ

②

1.2

⑫

⑤
0.6

⑩

Ⅲ

Ⅰ

③
0
0

0.6

3000

2750

⑬

⑧
1.2

1.8

2.4

2500

(m/s)

Fig. 23: AET result with using high pass filter of 7 kHz.

S49

2.4

①

⑪
Ⅳ⑨

④

1.8

3500

⑥

⑦
Ⅱ

②

1.2

⑫

⑤
0.6

3250

⑩

Ⅲ

Ⅰ

③
0
0

0.6

3000

2750

⑬

1.2
⑧

1.8

2.4

2500

(m/s)

Fig. 24: AE tomography with both HP filter and visual filter.

5.

Conclusions

In the paper, conventional damage-associated elastic wave parameters had been reviewed and
new index, namely Q-values to assess the damage has been proposed. And as for the approach to
extract the damage-prone areas, an ideal and effective method, AE tomography, combining AE
monitoring with elastic wave tomography was detailed through the calculation algorithm,
followed by in-situ applications. As a result, it was clarified that the Q-value could be an index
to quantify the damage of concrete materials. As the frequency dependency of Q-value was stillobtained, it will be further confirmed from a viewpoint of engineering by additional
experimental studies using actual concrete mixture. Two-dimensional AE tomography was
successfully applied to the RC bridge deck as almost damage areas that were estimated laterally
developed revealed with low velocity’s zones. Three-dimensional algorithm of AE tomography
will be further elucidated and more reliable applications shall be shown up for the follow up
paper.

6.

References[1]

Momoki, S., Aggelis, D.G., Hir ama, A., Shiotani, T.: Combined stress wave
techniques for monitoring of fracture in concrete slabs, Engineering Technics Press,
Structural Faults and Repair 2008, 12th International Conference and Exhibition, 2008,
CD-ROM.
Shiotani, T., Aggelis, D.G., Makishima, O.: Global monitoring of large concrete
structures using acoustic emission and ultrasonic techniques: case study, ASCE
Journal of Bridge Engineering Vol. 14, No. 3, 2009, 188-192.
Popovics, S.: Analysis of the Concrete Strength versus Ultrasonic Pulse Velocity
Relationship. Mater Eval 59(2), 2001, 123–130.

[2]
[3]

S50

[4]

Popovics, S., Popovics J.S.: Effect of stresses on the ultrasonic pulse velocity inconcrete. Mater Struct, 24, 1991, 15–23.
[5] Philippidis, T.P., Aggelis D.G.: Experimental study of wave dispersion and attenuationin concrete. Ultrasonics 43, 2005, 584–595.
[6] Chaix J.F., Garnier, V., Corneloup, G.: Ultrasonic wave propagation in heterogeneous
solid media: Theoretical analysis and experimental validation. Ultrasonics 44, 2006,
200-210.
[7] Aggelis D.G, Philippidis, T.P.:Ultrasonic wave dispersion and attenuation in fresh
mortar. NDT & E Int 37(8), 2004, 617–631.
[8] Shiotani, T., Aggelis, D.G.: Wave propagation in cementitious material containing
artificial distributed damage, Materials and Structures 42, 2009, 377-384.
[9] Selleck, S.F., Landis, E.N., Peterson, M.L., Shah, S.P., Achenbach, J.D.: Ultrasonicinvestigation of concrete with dis- tributed damage. ACI Mater J 95(1), 1998, 27–36.
[10] Shah, S.P., Popovics, J.S., Subramanian, K.V., Aldea, C.M.: New directions in concrete
health monitoring technology. J Eng Mech-ASCE 12126(7), 2000, 754–760.
[11] Shiotani, T., Ohtsu, H., Momoki, S., H.K. Chai, Onishi, H., Kamada, T.: Damage
evaluation for concrete bridge deck by means of stress wave techniques, ASCE, Journalof Bridge Engineering, Vol. 17 (6), 2012, 847-856.
[12] Polyzos, D., Papacharalampopoulos, A., Shiotani, T., Aggelis, D.G.: Dependence ofAE pa-rameters on the propagation distance, JSNDI, Progress in Acoustic Emission
XV, 2010, 43-48.
[13] Shiotani, T., Takada, Y., Watanabe, T., Ohtsu, H.; Damage evaluation ofheterogeneous materials by Q-value analysis of AE waveforms, JSNDI, Progress in
Acoustic Emission XV, 2012, 1- 6.
[14] Aggelis D.G., Shiotani, T.: Stress wave scattering: Friend or enemy of non destructive
testing of concrete?, Journal of Solid Mechanics and Materials Engineering, 2008, Vol.
2, No. 4.
[15] Yoshimura, K., Andou, S., Sakashita, S., Bruines, P., Okumura, H., Nishiyama, S.,
Ohnishi, Y.: Experimental study on imaging hydraulic conductivity of rock masses
based on elastic wave velocity dispersion,” BUTSURI-TANSA, Vol. 62, No. 3, 2009,307-318 (in Japanese).
[16] Schubert, F.: Basic principles of acoustic emission tomography, Journal of Acoustic
Emission,Vol. 22, 2004, 147-158.
[17] Schubert, F.: Tomography techniques of acoustic emission monitoring, ECNDT 2006,
We. 3.6.2.
[18] Kobayashi, Y.: Mesh-independent ray-trace algorithm for concrete structures,
Emerging Technologies in Non-Destructive Testing V, Taylor & Francis Group,
2012, 103-109.
[19] Luo, X., Haya, H., Inaba, T., Shiotani, T.: Seismic diagnosis of railway substructures byusing secondary acoustic emission, Elsevier Science, Soil Dynamics and Earthquake
Engineering, Vol. 26, 2006, 1101-1110.
[20] Chai, H.K., Aggelis, D.G., Momoki, S., Kobayashi, Y., Shiotani, T.: Single-side accesstomography for evaluating interior defect of concrete, Elsevier Science, Construction
and Building Materials, 24, 2010, 2411-2418.

S51

The 12th International Conference of the Slovenian Society for Non-Destructive Testing»Application of Contemporary Non-Destructive Testing in Engineering«
September 4-6, 2013, Portorož, Slovenia

ACTUAL POSSIBILITIES OF DAMAGE EVALUATION
OF MACHINES WITH ROTATING PARTSBY ACOUSTIC EMISSION
Pavel Mazal, Libor Nohál, Filip Hort
Institute of Machine and Industrial Design, Faculty of Mechanical Engineering,
Brno University of Technology
Technicka 2, CZ 616 69 Brno, Czech Republic, mazal@fme.vutbr.cz
ABSTRACT
Authors present an overview of the contemporary applications of acoustic emission method forthe diagnosis of non-standard operating conditions of machines with rotating parts. The main
attention is focused on operational diagnostics of axial and radial bearings. In part are reviewed
also options of supplementary use of the AE method for the diagnosis of damage of gears andreal gearboxes. This overview of published results is confronted with the practical experiences
gained during the implementation of several projects at Laboratory of Acoustic Emission of theInstitute of Machine and Industrial Design in Brno University of Technology.
Key words: acoustic emission, diagnostics, bearings, gears, gearbox, damage.
1.

Introduction

Condition monitoring of technical devices with rotating parts forms a very important area of
evaluating of the operational reliability of machinery. Bearings and gearboxes are significant
parts of every means of transport or rotating machinery and the detection of their incipient fault
is very important for the evaluation of a service life time and for safety operation of the wholedevice. Quality monitoring is a crucial element of the reliable and safe operation. In the spinningelements typically uses vibration measurements of the whole system, temperature, noise, etc.
NDT methods, especially acoustic emission (AE), are applied very significantly in condition
monitoring too.
The acoustic emission as a phenomenon can be defined as transient elastic waves resulting fromlocal internal micro-displacements in materials of the tested structures. AE method has become acommon NDT method used mainly for testing of the stationary equipments - tanks, pressure
vessels, reactors, pipelines, bridges, etc. In these applications, the AE method is fully acceptedand standardized. The advantage of AE method is the ability of testing of the entire structure,
locating of potential defects and assessment of their risk for the integrity of the structure. AE
application brings significant economic benefit to all users. The partial disadvantage is thelimited ability of precise identification of the defect type and it is necessary to use other NDT
methods for their identification.

S52

2.

Application of AE testing for bearings

An application of AE method in condition monitoring of rolling bearings has been the subject ofinterest of many authors since the beginning of this method application in engineering.
Compared to monitoring of stationary equipments the monitoring of devices with rotating parts
is much more complex. Rolling bearings consist of many parts which are in mutual contact
during function. This leads to a gradual degradation of the contact surfaces. Very important issue
in this area is the quality of lubrication. An important role is played by temperature changes
during bearing operation. In such devices, there are many sources of AE signal. Nevertheless, thepossibility of continuous monitoring of bearing health is extremely appealing.
Information regarding the use of acoustic emission in this area can be found already in the 70s
and 80s [1], [2], [3]. A significant development of AE applications appeared in the late 20th
Century and continues to the present. Extensive works arise in a number of workplaces. Verysignificant results are mainly associated with the works of staff at the University in Telford [4],[5].
Some work studied the possibility of the detection of artificial defects created on the raceway[6]. Elforjani et al. [4] proved the possibility to detect and localised the natural damage initiationand propagation. The AE method is more sensitivity to detection of this mechanism than thevibration based methods. The incipient damage detection in slow speed bearings using acoustic
emission was studied by Elforjani et al. [7]. This study shows that low-energy transient bursts
generated from incipient fatigue damage are not detectible using vibration monitoring. Nélias etal. [8] described the new methodology of rolling contact fatigue evaluation and studied theinfluence of dents on the endurance limit. In a review study about rolling bearing fault detection,
it is suggested that out of the time domain statistical parameters, the Kurtosis value is the mosteffective one for defect identification [9]. At the initial stage of bearing failure, high-frequency
techniques such as the vibration acceleration envelope analysis, the AE measurements, and theshock pulse method (SPM) have been successfully applied.
AE application for the identification of contact involved damage started in our workplace (BUT
FME) approximately from 2004 [10]. Unlike most of the other work our laboratory deals withreal axial and radial bearings of small size at relatively high speeds. Diagnostics of creation of
the damage in rolling bearings is already quite well developed and this methodology could be
used for evaluation of the durability of bearings. Of course there are still a number of problems,particularly in the identification of the type of damage, localization of defect and potential
automation of damage diagnostic.
Example of a very promising application of this diagnostic method may be can the energy sector- monitoring the bearings of large steam turbines. When we talk about use in the production ofelectricity, one possible application is the monitoring of wind turbines, which uses non-invasive
diagnostics and transmission of the information to the recording of the evaluation centre isprovided by telecommunication technology [11]. One of the non-traditional usages can be theuse for diagnosis of turbines of jet aircraft...
Currently, monitoring and examination of various conditions of loading of bearings and bearing
materials devotes a number of authors, e.g. Tandon et al. [12]. He gave a comparison of several
diagnostic methods for condition monitoring of rolling bearings of induction motor. The authors
used these monitoring techniques: vibration monitoring, stator harmonic currents, acoustic
emission and shock pulse method (SPM). Manifestations of techniques have been observed on
the defect of the outer ring of radial bearings.
AE monitoring was carried out in conjunction with other techniques of measurement from zeroloads to full load. Comparison of different monitoring techniques damage used in the experimentfor different degrees of damage, and load is shown in Fig. 1, where the percentage increase in
response is damaged bearings with respect to the average value of healthy bearings. As shown inthese graphs, the highest share is always the AE method followed by SPM. Velocity
measurement and stator currents are on the third, fourth, respectively. Although the method of
S53

measuring of the stator currents appeared to be the least effective, this method has minimal
equipment requirements and it is sometimes also referred to as non-sensor method.
AE has for its higher sensitivity (in the time domain shooting) better placed to detect emerging
damage sooner and easier than vibrating procedures, which is an important consideration
identification of damage frequency analysis. However, either as a supplement or basis formonitoring of the device status is desirable the vibration monitoring for example, if the fault
source generates low-frequency waves (e.g. unbalance of rotating components).

a)b)Fig. 1: Comparison of monitoring techniques for the minimum size of the defect) and the
maximum size of the defect b) [12].

One of the most active in this area at present is D. Mba and his research group. This teampublished numerous articles dealing with the detection and localization of damage, especially forthe axial bearings [4], [7], and [9]. In the published works authors deal with the application of
AE as a tool to detect the emergence and development of contact damage on samples of bearingsand bearing materials, such as complementary and comparative measurement is used vibration
accelerometers.
An example of standard results of the damage identification which has been obtained in ourlaboratory is shown in Fig. 2, 3 and 4 [13]. The axial bearing was tested in Axmat stand, theapplied load in this test was 2660 N. During the first fifteen minutes of the test, an increase in
AE activity was detected, see figure 2. This corresponds to running-in phase of test, after thisperiod (15 min) count rate levels, RMS, and vibration parameters were constant. It was observedthat after 180 minutes into the test count rate levels began to increase steadily and the vibration
acceleration or temperature do not response. On the termination of the test (500 min) the countrate, RMS and vibration increased to the significant activity. After 180th minute of test AE
counts levels and RMS were slowly rising. This is caused by the initiation and propagation ofpitting in the track on the upper ring (material specimen). Next stage after 420th minute correlatewith the vibration level monitoring shown in Fig. 3. A step-level change is caused by tearing of
the material from the surface and followed by a development of spalling.
The vibration observation and temperature monitoring during the test is presented in Fig. 3. Afterthe running-in stage of specimen N1, the temperature remains steady at 42 °C. The temperaturedrop of 1 or 2 °C in the final phase of testing is caused by lowering the temperature of the airaround the test-rig. In this case, the test was terminated after the creation of small pitting andtherefore was recorded only a slight increase in temperature as a response on final damage. The
vibration level was during running-in stage and steady-state constant. The increase in level ofvibration after 420th minute corresponds to spalling propagation. This was observed also in AE
monitoring.

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Fig. 2: Observation of N1 test – AE count rate (4 levels), RMS [13].

Fig. 3: Vibration level and temperature during test N1 [13].

Fig.4: Comparison of AE signal intensity in different frequencies in the starting stage of test
(upper plot) and in the final stage of test (bottom) - test of radial bearing 6204 [14].
Currently is available already a considerable amount of experimental data and research teams
consideration is focused on finding of suitable parameters of AE signals to distinguish thedifferent types of damage. Example of changes in AE signal intensity in different frequencies(the frequency spectrum) is plotted in Fig. 4. - Higher frequencies were identified in the final
stage of the test [14].
A separate but very important, area of problems forms a question of an effective filtering ofinterfering signals.

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3.

Application of AE method for testing of gearboxes

Much more complicated is the situation in the application of AE method for diagnostics of thestatus and possible damage of gears and gearboxes. Standard gearbox consists of many rotatingparts and evaluation of the obtained data is very difficult due to complex structure.
Many authors dealt with the simple single-stage spur gearbox [15] or single-stage helicalgearbox [16]. Transducers were in these causes placed close the tested gear or directly on thewheel. The various methods were used for damage detection, for example noise emission,vibration levels, and AE technique and temperature measurement. Albers et al. [17] dealt withcomparison the AE with laser vibrometer. The results of this work indicate that the obtained data
can be correlated only in frequency range from 20 to 60 kHz. The others authors dealt withexperiment on the spur gear and for evaluation used parameters such as vibration levels,
temperature and AE parameters – RMS, ring down counts, energy.
Authors of the paper Identification of the acoustic emission source during a comparative study
on diagnosis of a spur gearbox [18] tried to ascertain and validate the applicability of AE toseeded gear defect identification. As a direct consequence of the experimental programme, therelationship between temperature, oil film thickness and AE activity were investigated.
Lixin Gao et al [19] made a basic analysis of the AE data from the faults of on-spot low-speed
heavy-duty gears. The application of redundant second generation wavelet and AE testing ingear fault diagnosis was investigated in this study. Through the analysis of on-spot cases, thefollowing conclusions were obtained: a) AE testing diagnosis could be applied to the fault
diagnosis of on-spot low-speed heavy-duty gears and was a crucial supplement to the vibration
testing diagnosis. b) The length of the approximation signals and detail signals obtained after thedecomposition of redundant second generation wavelet was the same as that of original ones, so
the amount of information was twice that of the original. Such a characteristic of redundantsecond generation wavelet guaranteed that it could achieve an effect better than conventional
wavelet and second generation one. With the denoising technique based on redundant second
generation wavelet, all fault shock information could be preserved, which was unmatchable forother denoising techniques. c) The validity of the algorithm of the redundant second generation
wavelet transform during the processing and denoising of acoustic emission signals was verified.
Chee Keong Tan and David Mba published the study Limitation of Acoustic Emission for
Identifying Seeded Defects in Gearboxes [20]. This paper reviews recent developments inapplication of AE to gear defect diagnosis. Furthermore, experimental results examine andexplore the effectiveness of AE for gear defect diagnosis. It is concluded that application of AE
to artificially seeded gear defect detection is fraught with difficulties, particularly for fault
identification. In addition, the viability of the AE technique for gear defect detection by making
observations from non-rotating components of a machine is called into question. Nevertheless,guidance is offered on applying the technique for monitoring the natural wear of gears.
While vibration analysis on gear fault diagnosis is well established, the application of AE to thisfield is still in its infancy. In addition, there are limited publications on application of AE to gear
fault diagnosis. Siores et al. [21] explored several AE analysis techniques in an attempt tocorrelate all possible failure modes of a gearbox during its useful life. Failures such as excessive
backlash, shaft misalignment, tooth breakage, scuffing, and a worn tooth were seeded during
tests. Siores correlated the various seeded failure modes of the gearbox with the AE amplitude,
RMS. standard deviation and duration. It was concluded that the AE results could be correlated
to various defect conditions. Sentoku [22] correlated tooth surface damage such as pitting to AE
activity. An AE sensor was mounted on the gear wheel and the AE signature was transmitted
from the sensor to data acquisition card across a mercury slip ring. It was concluded that AE
amplitude and energy increased with increased pitting.
This paper has demonstrated that artificially seeded gear defect detection with AE is fraught withdifficulties. Experiments to identify seeded defect identification with AE RMS and energy were
not satisfactory. The influence of oil temperature on AE activity has been presented.
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Some examples of our results are plotted on Fig. 5 and Fig. 6 [23], [24]. In collaboration with theLaboratory of gearboxes testing (BUT FME) we carried out several experiments focused on thepossibility of AE signal sensing from automotive gearbox. The experiments were carried out on
the automobile gearbox used by the Volkswagen Group. It was a gearbox bearing MQ 200 usedin vehicles with engine torque to 200 Nm (e.g. Skoda Fabia). On this transmission was
artificially created damage to the driven wheel (helical wheel) of the fifth gear. For experiments
was used the test station consisting of drive flange for mounting gear, eddy dynamometer withmaximum torque of 500 Nm load simulating. During all tests was used as filling mineral gearoil. We have obtained many interesting results, but due to the complexity of real gearboxes it isstill very difficult to prepare a unified methodology for these parts testing. Examples of AE
signal recording during the load change of gearbox is shown in Fig. 6.
We can say that in general, that the most significant source of AE is the inequality in contact.
This is usually caused by either dents of foreign bodies into the surface of the tooth flank or by
erosion of the material during pitting. The most sensitive AE parameter in damage detecting is
the effective value of the signal (RMS). To extract suitable parameters from the raw signal istherefore necessary to use an advanced signal processing. An example might be an application of
wavelet transform, which improved the ability to detect damage acoustic emissions.

Fig. 5: Gearbox Skoda Fabia and the 3D plot of changes in frequency domain of AE signal
during the load change [23].

Fig. 6 Changes of peak amplitude and rise time of AE events during the different load regimes of
the tested gearbox [24].
The way of transmission of AE signal in this case is too complicated and is subject to significant
or inhibits the occurrence of unwanted noise signal. However, despite these complications AE
method could detect the damage at a level comparable with vibrodiagnostics. Detection
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capability is dependent on the degree of smoothing unevenness in contact obliterated damage
was not possible, as mentioned above, to identify the acoustic emission signal. Examples ofchanges in the frequency of AE signals that occur during changes in load transmission are shownin Fig. 5.
4.

Conclusions

The presented results show the extensive possibilities application of acoustic emission method inthis field. Most of the work is still performed in laboratory conditions. Is relatively well masteredmethodology sensing signal, especially in the area of thrust bearings and partially also radial
bearings. The situation in the diagnosis of gearboxes is still very complicated. It is necessary to
focus on the area of signal processing, the identification of suitable parameters of the AE signal,
which can be used for repeated measurements in real conditions. Very important is thedevelopment of methods for the elimination of interferences. Already now, however, acoustic
emission can be used as a very useful complementary method for vibration diagnostics (AEmethod can signalize the damage creation). However, the potential of AE is large, and certainlyin the near future this method will be among the basic diagnostic procedures in the field ofrotating machinery.
Acknowledgements
Results were created in the framework of the research project NETME Centre under the
Operational Programme Research and Development for Innovation (reg. No. CZ.
1.05/2.1.00/01.0002) supported by European Regional Development Fund and project MPO CR
TIP FR TI4/602 "Diagnostics complex for detection of leaks and material defects.
5.

References[1]

Balerston H.L.: The detection of incipient failure in bearings. Mater. Evaluation, 1969; 27;
p. 121–8.
Roger, L.M.: The application of vibration analysis and acoustic emission source location to
on-line condition monitoring of anti-friction bearings. Tribology Internat., 1979; p. 51-59.
Yoshioka T, Fujiwara T.: Application of acoustic emission technique to detection of rolling
bearing failure. American Society of Mechanical Engineers, Production Engineering
Division publication PED, 1984, 14, 55-76.
Elforjani M., Mba D.: Detecting the onset, propagation and location of nonartificial defects
in a slow rotating thrust bearing with Acoustic Emission, Insight, Vol. 50 No 5, p. 264 –
268, May 2008
Mba D.: The use of AE for estimation of bearing defect size, Journal of Failure Analysis
and Prevention, American Society for Metals (ASM), Vol. 8, No. 2, April, 2008, 188-192.
Bhadeshia H.K.D.H.: Steels for bearings, Progress in Materials Science, Vol. 57, No. 2,
pp. 268-435, February 2012.
Elforjani M., Mba D.: Accelerated natural fault diagnosis in slow speed bearings with AE,
Engineering Fracture Mechanics, Vol. 77, No. 1, pp. 112-127, Januar 2010.
Nélias D., Jacq C., Lormand G., Dudragne G., Vincent A.: New Metodology to Evaluate
the Rolling Contact Fatigue Performance of Bearing Steels With Surface Dents:
Application to 32CrMoV13 (Nitrided) and M50 Steels, ASME Journal of Tribology, 2005,
Vol. 127, pp. 611-622.

[2]
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Al-Dossary, Hamzah R.I.R., Mba D.: Observations of changes in acoustic emission
waveform for varying seeded defect sizes in a rolling element bearing. Applied Acoustics,
Jan 2009, vol. 70, no. 1, p. 58-81.
[10] Mazal P., Zykova L., Pazdera L.: Identification of Contact Fatigue Stages with Acoustic
Emission Method. In 9th ECNDT 2006, Berlín, DGZfP, 2006, p. P4 (8 p.). ISBN 3931381-86-2.
[11] Timmerman H.: Monitoring the Winds of Change. Evolution Business and technology
magazine from SKF [online]. 2006, Vol. 3, no. 3/06, [cit. 2011-03-12].
<http://evolution.skf.com/zino.aspx?articleID=14982>[12] Tandon N., Yadava G.S., Ramakrishna K.M.: A Comparison of Some Condition
Monitoring Techniques for the Detection of Defect in Induction Motor Ball Bearings.
Mechanical systems and signal processing. Vol. 21, No. 1, INDE: ITMME Centre. New
Delphi, 2005, pp. 244-256, ISSN 0888-3270.
[13] Nohal L., Hort F., Mazal P.: An Experimental Study on Damage Monitoring of Rolling
Bearings Using Acoustic Emission Method, The Tenth International Conference on
Condition Monitoring and Machinery Failure Prevention Technologies, Krakov 2013, p.
133 -140.
[14] Mazal P., Hort F., Drab, M., Slunecko T.: Some Possibilities of AE Signal Treatment at
Contact Damage Tests of Materials and Bearings. In: Jour. of AE 2008, vol. 26, p. 189198, AE Group, CA, USA, 2009, ISSN 0730-0050.O2 – 50.
[15] Toutountzakis T. et al.: Application of acoustic emission to seeded gear fault detection. In
NDT &amp; E International, Vol. 38, Issue 1, January 2005, Pages 27-36, ISSN 09638695[16] Eftekharnejad B., Mba D.: Seeded fault detection on helical gears with Acoustic Emission,
Applied Acoustics, 70 Issue: 4, p. 547-555, 2009.
[17] Albers A. et al.: Validation of AE - signals recorded with conventional equipment using
3D Scanning-Laser-Vibrometer. In: 29th Europ. Conf. on AE Testing 2010, Vienna,
Austria, 8 pages, CD edition, NDT.net - The Open Access NDT Database, Germany, ISBN
978-3-200-01956-0.
[18] Tan Ch.K, Mba D.: Identification of the acoustic emission source during a comparative
study on diagnosis of a spur gearbox. In: Tribology Int., Vol. 38, Issue 5, May 2005, p.
469-480.
[19] Lixin Gao, Fenlou Zai, Shanbin Su, Huaqing Wang, Peng Chen and Limei Liu: Study andApplication of Acoustic Emission Testing in Fault Diagnosis of Low-Speed Heavy-Duty
Gears Sensors 2011, 11, 599-611; ISSN 1424-8220[20] Tan Ch.K. and Mba D.: Limitation of Acoustic Emission for Identifying Seeded Defects inGearboxes Journal of Nondestructive Evaluation, Vol. 24, No. 1, March 2005, Springer
Science+Business Media[21] Siores E. and Negro A. A.: Condition monitoring of a gear box using acoustic emission
testing, Material Evaluation pp. 183–187, 1997.
[22] Sentoku H.: AE in tooth surface failure process of spur gears, J. Acoust. Emiss. 16 (1–4),
pp. S19–S24 (1998).
[23] MAZAL P., NOHAL L., HORT F., KOULA: Possibilities of the damage diagnostics ofgearboxes and bearings with acoustic emissions method, 18th World Conference on
Nondestructive Testing, 16-20 April 2012, Durban, South Africa[24] Nohal L., Mazal P.: Identifikace poškození součástí automobilové převodovky metodou
AE. In: NDE for Safety 2011. Brno, ČNDT, 2011. p. 107 - 114. ISBN 978-80-214-4358-7(in Czech).

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The 12th International Conference of the Slovenian Society for Non-Destructive Testing»Application of Contemporary Non-Destructive Testing in Engineering«
September 4-6, 2013, Portorož, Slovenia

A STUDY ON THE PERFORMANCE OF ACOUSTIC EMISSION
AND LOW FREQUENCY VIBRATION METHODS TO THE
REAL-TIME CONDITION MONITORING OF RAILWAY
AXLES
M. Carboni1, S. Bruni1, D. Crivelli1, M. Guagliano1, P. Rolek1
1

Department of Mechanical Engineering – Politecnico di Milano
Via La Masa 1, 20156 Milano, Italy
michele.carboni@polimi.it, stefano.bruni@polimi.it, davide.crivelli@mecc.polimi.it,
mario.guagliano@polimi.it, pawel.rolek@polimi.it
ABSTRACT
Railway axles are safety-critical components whose failure could result in large additional costs
for the infrastructure manager and the railway operator and, in most serious cases, even lead to
unacceptable human losses. For this reason, they are periodically inspected by means of nondestructive techniques usually requiring expensive service interruptions. Considering the special
case of solid axles for freight cars, this paper investigates the feasibility to apply two structural
health monitoring techniques to increase vehicle safety and reliability and, at the same time, todecrease the cost of damage inspection. In particular, the considered structural healthmonitoring techniques are Acoustic Emission and the measurement of low frequency vibrations.
In the present paper, a study about the application of both these techniques to freight railway
axles is introduced and discussed.
Key words: structural health monitoring, freight railway axle, acoustic emission, low frequency
vibrations, full-scale crack propagation test
1.

Introduction

Railway axles are safety-critical components designed against fatigue limit, as defined in
relevant standards like EN13261 [1], EN13103 [2] and EN13104 [3]. Actually, due to their very
long service life, they are prone to service damages, like impacts due to the ballast or corrosive
aggression. These events are able to initiate fatigue cracks leading to service failures, whose
effects entail large additional costs for the infrastructure manager and the railway operator and,in the most serious cases, may be cause of serious accidents and even lead to unacceptable
human losses. Besides, while laboratory tests to assess the fatigue limit of materials andcomponents usually assume 10 million cycles as the run-out condition, it is true that fatigue
failures can happen for longer durations and with applied stress lower than the fatigue limit; thatis to say that to consider the fatigue limit determined on the base of 107 cycles for infinite life
fatigue design is not always a safe criterion, especially for critical parts, like railway axles are.

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For all these reasons, in the framework of a damage tolerant approach for maintaining structural
integrity, as generally defined by Grandt [4] or specifically for axles by Carboni and Beretta [5],railway axles are periodically inspected by means of well-established non-destructive techniques(typically ultrasonic and magnetic particle inspections) usually requiring expensive service
interruptions. Considering the special case of solid axles made of A4T steel grade [1], this paper
investigates the feasibility to apply structural health monitoring (SHM) techniques, which, in the
aeronautical field, have shown, according to Chang [6], the possibility to decrease the costs ofdamage preventive inspection up to 30 % without losing accuracy.
The number of SHM techniques available in the literature and likely applicable to the detectionof fatigue cracks in railway axles is rather large: acoustic emission, low frequency vibrations(nxRev periodicity), high frequency vibrations (changes in natural frequencies), microwave
testing, alternating current field measurements, induced current focusing potential drop, AC
thermography and laser-air coupled hybrid ultrasonic technique. Among all of them, the present
study focuses on acoustic emission (AE) and low frequency vibrations (LFV), as they are theones whose equipment seems to be the less invasive and bulky, a very important and criticalcharacteristic considering the configuration of real axles mounted on running trains.
The chosen techniques were judged, by their application, during a crack propagation full-scale
test carried out on freight solid axles made of A4T steel, one of the standardised European gradesfor production of axles [1]. Before proceeding to the description of the research, a short remindof the basic ideas and concepts lying at the base of the chosen SHM techniques is given in thefollowing.
1.1 Acoustic emission (AE)AE SHM is based on the observation that damage developing in a material releases energy inform of ultrasonic elastic waves, which could be fruitfully detected [7]. These waves (so-called“hits”) are typically short and transient (“burst events”) with bandwidth in the 100-1000 kHz
range, which makes AE quite robust against audible noise and structural vibrations.
Traditionally, the so-called “parametric AE” is used, in which the transient waveforms are used
for the calculation of some traditionally well-established parameters (like peak amplitude,
duration, energy). These parameters are then stored and used for further processing, while thewaveforms are generally discarded to avoid the need of undesirably large storage systems.
AE is traditionally used and standardized as a non-destructive technique to assess the structural
integrity of metallic components (e.g. pressure vessels and pipelines) under static and fatigue
loadings [8], with very few applications to the railway field [9], which usually focus on fixedparts, such as rails and bogies.
1.2 Analysis of low frequency vibrations (LFV)LFV is based on the measure of harmonic components in the axle bending vibration having
periodicity which is an integer sub-multiple of the revolution period. These vibrations are
induced by the “crack-breathing” mechanism and by asymmetry in the bending inertia of theaxle, as produced by a propagating crack. The advantage of this second method consists in thepossibility to use low-frequency vibration, hence using simple, robust and inexpensive
transducers. This method was initially proposed for crack detection in the shaft of turbomachinery [10], and was demonstrated to provide reliable results for this kind of application
based on experiments performed on a laboratory test rig. Crack detection and localisation is
performed by representing the crack as an equivalent excitation, produced by a bending moment
having nxRev periodicity applied on a short section of the shaft. Then, an inverse problem issolved in the frequency domain to derive the most probable location and amplitude of theequivalent excitation. The latter is then translated into a guess of the crack amplitude based on
look-up tables defined using a finite element model of the cracked axle. Aim of this research is
to assess the possibility to extend this method to crack detection in railway axles. Some
additional difficulties, posed by this specific application, must be kept into account:

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i)
ii)
iii)
iv)

2.

effect of disturbance produced by wheel-rail contact (in particular, wheel out ofroundness is also cause of nxRev vibration of the axle, so this effect needs to be
depurated);
limited number of measuring sections available in the railway axle;
in the railway application, the angular speed of the axle is changing according to a more
complicate profile, whereas in a turbo-machine run-up and run-down measurements canbe applied for identification purposes;
in turbo-machines, the method is typically applied to rotors running at high speed, above
the first critical, whereas the railway axle is running at speeds much lower than the firstcritical.
Experimental set-up and plan

The experimental full-scale test was carried out by means of the “Dynamic Test Bench for
Railway Axles” (BDA) available at the labs of Politecnico di Milano – Department of
Mechanical Engineering. The BDA is fully compliant with the relevant standards about thequalification of axles (EN13261 [1], EN13103 [2] and EN13104 [3]) and the test procedure andresults are Quality Certified ISO9001 since 2003. Figure 1 shows the static scheme and a view of
the BDA. In particular, a three point rotating bending is applied to the full-scale specimen via an
actuator group and an electric motor: in this way, both constant amplitude and block loading
fatigue or crack propagation tests can be carried out as explained in [11].

(a)(b)
Fig. 1: Dynamic Test Bench for Railway Axles: a) static scheme; b) view of the test bench.

Fig. 2: Scheme of the full-scale axle adopted for the crack propagation test and applied SHM and
NDT techniques.
The test was carried out considering the special case of a deep-rolled axle and consisted in a
crack propagation test. The scheme of the full-scale specimen is shown in Figure 2. Two starting
notches, located at 180° one from the other and useful for initiating two independent fatigue
cracks in the section of interest of the axle, were introduced by electro-discharge machining(EDM) in the deep-rolled cylindrical part of the axle. These starting notches had a semi-circularshape with radius R = 4 mm. The full-scale specimen was subjected to a block loading sequence
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experimentally derived from the typical service spectra of a tilting train on European lines. Moredetails about the experimental set-up for the full-scale crack propagation test are reported in [12].
AE and LFV signals were acquired to monitor damage development during the application ofmultiple repetitions of the block loading sequence. In particular, the AE probe was applied at one
free end of the axle (Figure 2) using a custom made carter (Figure 3) designed to hold in position
the sensor and its pre-amplifier. The rotating group was then connected to a sliding contact tobring the signals to the AE acquisition system (Vallen AMSY-5, [13]). The custom carter allows
the positioning of the sensor without removing the whole assembly, with the aid of a thin sheet
metal part. It has to be underlined that the choice of leaving the preamplifier on the axle was due
to noise issues, which arose during preliminary tests, mainly due to the low sensor output (whichis in the mV range). The sensor was then coupled to the axle with grease, to avoid the formationof air bubbles and to reduce signal attenuation. The assembly was regularly checked to ensure
the grease was not deteriorated or moved away during the test. The use of a single AE sensor,
due to the particular layout of the test bench, did not allow the use of localization algorithms,
though in a real application this would be easily feasible. A calibration of the AE system was
performed according to the standard pencil lead break test [14]: the signal attenuation along theaxle was found to be negligible. The acquisition was run throughout the entire test. After one
minute of rotation without any load application to assess the noise levels, the noise threshold wasset at 55 dB.

(a)(b)
Fig. 3: Custom made carter to hold the AE sensor and its preamplifier: a) CAD model; b)application at the axle end.
LFV was applied via a laser pointing to the central region of the axle, close to the starting
notches (Figure 2), so to get the highest displacements produced by the applied loads.
With the aim to have a feedback on crack propagation to evaluate the efficiency of the applied
SHM techniques, other NDT methodologies were applied at test interruptions between therepetitions of the block loading sequence. In particular, the surface region around the starting
notches was inspected (Figure 2) by an optical microscope (OM) and through the application ofplastic replicas (PR). The possible under-skin crack growing was, instead, monitored (Figure 2)
by traditional ultrasonic testing (UT by a probe having 45° refraction in steel and 4 MHz
working frequency) and by ultrasonic phased array testing (PA by a probe having 32 active
crystals working at 5 MHz and in S-Scan configuration).
It is, finally, worth noticing that another test is now running considering crack propagation in asecond deep-rolled axle containing two starting semi-circular notches with R = 3 mm. All the
SHM and NDT techniques shown in Figure 2 are again being applied.
3.

Results

The test lasted for 66 repetitions of the block load sequence, corresponding to about 3.5 million
kilometres of service. Figure 4 shows two examples of OM pictures taken at the deepest point of
one of the starting notches after 14 repetitions of the block loading (Figure 4a) and at the surface
of one of the surface edges after 14 repetitions of the block loading (Figure 4b). In the latter case,the observation was also supported by PR.
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(a)
(b)
Fig. 4: Optical microscopy of fatigue cracks: a) deep region; b) surface.

(a)(b)
Fig. 5: Ultrasonic measurements of the growing crack: a) traditional UT; b) phased array UT.
The application of UT and PA allowed estimating the cracks dimensions along the whole test. In
particular, Figure 5a shows the response of UT in terms of the acoustic energy reflected by thegrowing cracks: the trend is slightly decreasing, suggesting a very small increase of cracks
dimensions during the whole test. Figure 5b shows, instead, the estimation of crack depth
obtained using PA and the crack tip diffraction method: total crack growth resulted to be about 1
mm.

Fig. 6: Acoustic Emission energy released compared with measured crack sizes.
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AE data from this test are shown in Figure 6. It can be seen that, during crack propagation and as
measured by UT, the amount of AE energy released increases. In particular, the slope of the AE
curve shows different phases: a first run-in phase, then some high-slope moments (whichcorrespond to the crack propagation seen between repetitions 15 and 20) and a stable slope
phase, coincident with stable crack propagation. AE cumulate energy is shown here, but AE
activity (i.e. events per unit of time) carries almost the same information.
Some results showing the application of the LFV technique to the crack propagation test arereported in Figures 7 and 8. Figure 7 shows the spectral components of the axle bending
vibration measured in the starting phase (Fig. 7a) and in the final stage of the test (Fig. 7b): a
number of nxRev harmonics appear in the measured signal, and the increase in the level ofvibration is evident for some of these harmonics, namely the 1xRev, 2xRev and 7xRev. Thisconfirms, as expected, that a rotating cracked axle produces an increased level of bending
vibration at some integer multiples of the revolution frequency.
Figure 8 compares the trend of the amplitude of the first four harmonics (1xRev to 4xRev) with
the number of cumulated loading blocks in the crack propagation experiment. The 2xRev
component shows the largest increase in amplitude with the progress of the tests (and hence withthe size of the crack) and a nearly monotonically increasing trend. The amplitude of the 1xRev
component is also quite sensitive to the number of cumulated loading blocks, but the trend is lessclear, probably on account of disturbances such as thermal effects that may produce a bow of therotating axle. The 3xRev component shows a lower sensitivity to the number of block loading
repetitions and a non-monotone trend. Finally the amplitude of the 4xRev component remained
very low during the whole crack propagation test. In conclusion, the 2xRev vibration component
appears to be the one best suited to be set in relationship with the presence (and possibly with thesize) of a propagating crack in the axle.

(a)(b)
Fig. 7: Change in nxRev components amplitude during crack propagation test: a) initial stage; b)
at the end of the test.
4.

Concluding remarks

This paper proposes the possible application of two different SHM methods, acoustic emission(AE) and the variation of low frequency vibrations (LFV), to detect the presence of propagatingcracks in deep-rolled freight railway axles made of A4T grade and subjected to service load
spectra.
A full-scale crack propagation test, performed on a railway axle, was used to assess theeffectiveness of the two proposed SHM approaches. The obtained results are encouraging andS65

show the potential of both methods to represent viable approaches to real-time crack monitoringin railway axles.
In more detail, the amplitude of AE hits is well correlated with the load applied to the axle. Moreresearch work is ongoing to further investigate how AE events correlate to crack propagation.
As far as the LFV monitoring technique is concerned, measurements of the bending vibration ofa specimen axle taken across a whole crack propagation test show that the level of vibration
associated with the 1xRev, 2xRev and 7xRev harmonic components is increasing with theprogress of the test, and hence with the size of the crack. Among these components, the 2xRev isthe one showing the better correlation with the number of cumulated block loading repetitions.
Comparisons with the measured size of the crack are in progress.
Future developments of this work will be aimed firstly to derive a more comprehensive set ofconclusions based on more tests carried out on other pre-cracked axles. In case the applicabilityof one or both methods to crack detection will be confirmed, the research will then aim at
solving problems related with transferring the crack detection SHM system from the laboratory
to in-service applications.

Fig. 8: nxRev components amplitude during crack propagation test.
5.

Acknowledgements

The SHM results shown in the present research were obtained in the frame of the SUSTRAIL
Collaborative European Project (Grant Agreement #265740). The full-scale crack propagation
test is carried out in the frame of the MARAXIL Project co-funded by Regione Lombardia (ID16973, Rif. n° MAN-15). The authors would like to thank Prof. S. Beretta and Mr. D. Regazzi(Dept. Mechanical Engineering, Politecnico di Milano) for the useful help and discussions.
6.

References[1]

EN 13261, 2011, Railway applications - Wheelsets and bogies - Axles - Productrequirements, CEN.
EN 13103, 2012, Railway applications - Wheelsets and bogies - Non powered axles Design method, CEN.
EN 13104, 2011, Railway applications - Wheelsets and bogies - Powered axles - Design
method, CEN.

[2]
[3]

S66

[4]

Grandt A. F. Jr.: Fundamentals of Structural Integrity, John Wiley & Sons, Hoboken, New
Jersey, 2004.
[5] Carboni M., Beretta S.: Effect of probability of detection upon the definition of inspection
intervals of railway axles, Proc. Instn Mech. Engrs Part F: J. Rail and Rapid Transit, Vol.
221, 2007, 409-417.
[6] Chang F. K.: Structural Health Monitoring: A Summary Report, Proceedings of the 2nd
International Workshop on Structural Health Monitoring, Stanford, California, 1999.
[7] Grosse C., Ohtsu M.: Acoustic Emission Testing, Springer-Verlag, Berlin Heidelberg,
2008.
[8] Huang M. et al.: Using acoustic emission in fatigue and fracture materials research, JOM,
Vol. 50, 1998, 1–12.
[9] Jiang C., Wen Y., Wang L., Chu M., Zhai N.: Real-time monitoring of axle fracture ofrailway vehicles by translation invariant wavelet, 2005 International Conference on
Machine Learning & Cybernetics, Guangzhou, China, 2005, 2409-2413.
[10] Pennacchi P., Bachschmid N., Vania A.: A model-based identification method oftransverse cracks in rotating shafts suitable for industrial machines, Mechanical Systems
and Signal Processing, Vol. 20, 2006, 2112-2147.
[11] Beretta S., Carboni M.: Variable amplitude fatigue crack growth in a mild steel for railway
axles: experiments and predictive models, Eng. Fract. Mech., Vol. 78, 2011, 848-862.
[12] Regazzi D., Beretta S., Carboni M.: The effect of roll-forming onto fatigue life of railway
axles, Proc. 19th European Conference on Fracture, Kazan, Russia, 2012.
[13] Vallen AMSY-5 product brochure (archive.org September 2011 version) goo.gl/FLkUL
accessed 14/03/2013.
[14] ASTM E976: Standard Guide for Determining the Reproducibility of Acoustic Emission
Sensor Response, ASTM International, West Conshohocken, PA, 2010.

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The 12th International Conference of the Slovenian Society for Non-Destructive Testing»Application of Contemporary Non-Destructive Testing in Engineering«
September 4-6, 2013 Portorož, Slovenia

NEW PROSPECTS TO USE ACOUSTIC EMISSION DURINGSCRATCH TESTING FOR PROBING FUNDAMENTALMECHANISMS OF PLASTIC DEFORMATION
A. Danyuk, D. Merson, A. Vinogradov
Laboratory of Physics of Strength of Materials and Intelligent Diagnostic Systems, Togliatti
State University, Togliatti, Russia, alexey.danyuk@gmail.com
ABSTRACTThe acoustic emission technique has been widely used during indentation or scratch testing. A
common problem is that, when the elementary modes of deformation are of concern, the testing
has to be performed locally. Such a testing model implies inevitably in a very small(microscopic) volume involved into plastic deformation under the indenter tip. Therefore, the AE
generated during the test has a quite small amplitude hardly distinguishable on a background of
the electric noise if the traditional threshold-based acquisition is utilized. With the specific aim
at identifying and characterizing fundamental mechanisms of plastic deformation throughanalysis of AE signals, the present work seeks to offer new strategies in AE acquisition andprocessing during scratch testing of material to get a better insight on the elementary processesof local plastic deformation. The experiments have been performed on the model pure copper
which has been well characterized microstructurally in the local area along the indented pathusing a scanning electron microscopy. The AE technique appears a viable and powerful tool forinvestigations of local deformation mechanisms in situ.
Key words: scratch test, deformation mechanisms, acoustic emission, microscopy.
1.

Introduction

A broad variety of local destructive or non-destructive methods is applied for routine
nondestructive testing as well as for innovative academic research, which benefit strongly fromdevelopment of modern nano-indentation and kinetic/dynamic micro-indentation and scratching
techniques enabling precise characterization of the mechanical behavior of materials andcoatings. These techniques have been enjoying booming popularity due to excellent
reproducibility of results, high accuracy and capacity to measure the properties of geometricallysmall objects or local properties in large samples otherwise not assessable in conventional
mechanical testing in the laboratory. Overall, these features of local loading techniques has
ensured their outstanding reputation and reliability in NDT applications where a rapid and costeffective evaluation of mechanical properties is required in small objects such as individual
grains, grain or phase boundaries. Plenty of valuable information pertinent to the analysis ofdeformation processes is offered by the post-factum analysis of the morphology of the footprintof the indenter. The real-time information concerning the local behavior of the materials during
testing is provided by the kinetic indentation diagram or by the measurements of the friction
S68

coefficient during a scratch-test. However, the temporal resolution of these measurements is notvery high to resolve elementary processes of plastic deformation and fracture. To compensate theobvious deficit of the real-time data and provide a new means for characterization of local plastic
deformation and fracture, the acoustic emission (AE) technique opens a wide avenue for deriving
real time information from a locally deforming object during non-destructive materials
evaluation and quantitative characterization of materials properties. The thorough AE analysis
enables discrimination between different types of deformation mechanisms and estimation theintensity of AE sources in dependence on the microstructural features and loading conditions. In
this way, to turn a modern AE technique to a real quantitative tool for characterization of
deformation mechanisms and local properties, it is essential to establish robust relationships
between the AE signal and elementary deformation processes. To accomplish this fundamental
goal we have chosen a well annealed copper polycrystal with the initial microstructure
characterized by orientation image microscopy (OIM) and scanning electron microscopy (SEM)
The obvious problem which most researchers have faced while applying the AE measurements
to the scratch (indentation) test, is that due to a small deforming volume the cumulative AE
signal is small and hardly distinguishable from a background noise if elementary dislocation
reactions are of concern. The appealing opportunity is however that local loading allows us tofollow the deformation process in its finest details with less interference between different ofsources in the AE signal. It is therefore a challenging task for the present work to discriminate
the features of dislocation behavior under indenter from a low amplitude AE.
2.

Experimental procedure and AE methodology during scratch testing

The samples of technical purity 99.9% polycrystalline copper with dimentions 20x20x2 mm
were cut by spark erosion. They were mechanically polished by emery paper to the grade of#2000 and then annealed in vacuum at 1170K for 1 h and furnace cooled. Finally, the front
surface of the samples was electrolytically polished for microscopic observations prior to andafter the test.
The microstructure was investigated by electron backscattered diffraction (EBSD) measurements
in high-resolution FE-SEM ZEISS SIGMA microscope with field emission gun and EDAX/TSL
Hikari-5 OIM detector.
To correlate the AE signal with the deformation structure along the scratch path, thecrystallographic orientation of grains was determined before test by the EBSD technique, Fig. 1
where the orientation of the grains is inked according to the standard inverse pole figure coding
showed in the inset. Depending on the crystallographic orientation of grains, the slip systems
with the highest Schmidt factor are activated, i.e. the local texture controls the anisotropy ofmechanical properties of the crystallites and the appearance of different slip systems on thesurface.
Scratch tests were performed under controlled conditions on the Nanovea Scratch Tester with a
Berkovich diamond indenter moving linearly along the sample at constant speed of 12 mm/min.
For better quantification of the AE signal during indentation/scratch testing one needs to ensure
as high signal-to-noise ratio (SNR) as high as possible. However, as mentioned in the
Introduction, scribing of ductile materials such as well-annealed pure fcc metals results typicallyin low level AE. The measures have to be taken to increase the SNR by proper tuning of thetesting rig. The tester was adjusted to minimize the mechanical and electrical noise in the AE
recording channels so that the feedback parameters of the mechanical actuator were tuned
precisely to a monotonic smooth scratch test. The normal load during the test was chosen below
1 N to minimize the mechanical friction in the vicinity of the indenter tip according torecommendations given in ref. [1]. The final length of the groove was about 2 mm.
The record the AE from dislocation sources operating in a small deforming volume the following
conditions imposed onto the sensor and amplifier have to be met:S69

- minimum internal electrical noise to provide an acceptable SNR;
- maximum sensitivity in the reasonably wide frequency band to enable spectral analysis forsource recognition. Assuming that the AE sources during scratching of pure copper would be
dislocation segments emerging with a high velocity on a free surface under action of the image
force, this type source can be represented simply as a delta-type force function with a wide
power spectral density.
The 10 mm diameter piezoelectric sensor AE-9000-S-WB (NF Electronics, Japan), with afrequency band of 100–1000 kHz, was attached with coupling oil on the side of the samples. The
signals were passed through low-noise 2/4/6/ pre-amplifier with 60 dB gain and were finallyacquired by means of the PIC-2 board (Physical Acoustics Corporation, USA) operated in acontinuous threshold-less mode at the sampling rate of 5MSPS. The on-board filters were set in
the widest possible range of 20kHz to 2MHz and the additional +6 dB gain was pre-set. The AE
waveforms were continuously recorded and synchronized with with the scratch path.
3.

Experimental results

The inverse pole figure colored OIM of the surface of copper polycrystal and the view of thescratch across the grain structure is shown in Fig. 1. One can clearly see that during testing thescratch passes through several grains with different orientations, overcomes grain boundaries andtriple junctions which well suites the goals of the present work.

Fig. 1: The inverse pole figure colored OIM of the surface of the annealed copper polycrystal
and the view of the scratch across the grain structure .
A typical example of the AE signal recorded during scratching is shown in Figure 2. Although
the SNR of this signal is quite low for conventional AE testing assuming a threshold based
acquisition, it can be considered “reasonably high” for typical scratch testing of a ductile copper
sample. In what follows, we shall demonstrate that using appropriate filtering helps to improve
the SNR and to reveal some important fine futures of AE sources in the rectified signal.
The AE signal processing workflow includes the following steps:
1 A continuous raw stream was divided into frames of 8192 readings.
2 For each frame two parameters were calculated in a time domain - the peak Upeak and rootmean-square voltage Urms, respectively.
3 For each frame the FFT denoising procedure involving forward and inverses FFT was applied,
and two parameters were calculated in the frequency domain - Energy (power), E, as the integral
over the power spectral density and the Median frequency, F median , as the frequency which
separates the low and high frequency spectral domains with equal Energy (power).
4 The obtained time series of respective variables (Upeak, Urms, Energy or F median ) weresmoothed by a low-pass Butterworth filtering procedure with the cut-off frequency of 19 Hz.

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Fig. 2: Continuously recorded raw AE data stream acquired during the scratch test with the viewof the scratch groove on the surface superimposed. The indenter moved from left to right under
1 N applied force.
Figure 3 (left hand side) shows the behavior of the AE spectral parameters - Median frequency
and Energy as well as Upeak and Urms as defined from the original stream. The low-passfiltering applied to these time-series filters out the high frequency components and reveals thetrends, which are shown in Fig 3 (right hand side). The use of this filtering with the cut-off
frequency as low as 19 Hz is justified because the grain structure of the annealed specimen iscoarse and the indenter propagates a reasonably long time within a homogeneous area within thesame grain with the mean size around 0.2 - 0.8 mm. Importantly is that the AE signal reflects
well the alternations in the microstructure encountered by the indenter tip when it crosses thegrain boundaries. When the transition from one grain to the other occurs, the new slip systems
are activated resulting in different hardening, the rate of which depends primarily on thedislocation interaction in the intersecting slip systems. The most pronounced changes in the AE
signal are seen in the Energy behavior.
The non-surprising anisotropy of mechanical properties during the scratch test of a copper single
crystal has long been recognized [2]. Several early studies highlighted a strong AE dependence
on the crystallographic orientation of single crystals [3, 4] and the relation of the AE spectral
parameters to the activation of different slip systems [5]. The strong dependence of AE
parameters on the crystallographic orientation of the grains during the scratch test can be vividly
expressed in a scatter-plot showing the distribution of AE descriptive variables such as Energy
and Median frequency, Fig. 4. The clustered pattern corresponding to AE, which is observed inseveral selected grains (see the inset OIM image), is evident. In this diagram, one can see a set ofcompact well separated clusters with higher or lower energy and/or median frequency. The
position of centroids and the shapes of the clusters depend on the crystallographic orientation, i.e.
on the specific dislocation behavior and hardening in different grains. The blue points
correspond to AE observed during the indenter propagation through the rest of the specimen.

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Fig. 3: Evolution AE parameters during scratching: unfiltered data on the left and low-passfiltered data on the rightFig. 4: AE Energy - F median bi-variate distributions highlighting the changes in the AE properties
with indenter transition from one grain to the other. The grains are numerically labeled on the
OIM map and the corresponding Energy-F median distributions are colored as shown in legend.

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The important feature of the AE waveforms, which are recorded typically during plastic
deformation mediated by dislocation slip in pure metals, is that they commonly manifest
themselves as a continuous noise-like signal, Fig.2. The continuous signal, such as that shown in
Fig. 2, is associated with a huge number of dislocation segments generated here and there within
the loaded solid and moving nearly concurrently across the grains. However, on a microscopic
scale plastic deformation of metals is spatially heterogeneous and temporally intermittent by nature
due to discrete origin of carriers of plasticity - lattice dislocations [6-8] creating clearly separated
fine slip lines on the surface during deformation, cf. Fig.5. This discrete character of dislocations is
the root cause of local fluctuations in stress and strain giving rise to measurable AE. The
detectability of AE is commonly supposed to be quite limited [9]. The detectabilty threshold is ofcourse dependent strongly on the noise level and the capacity of the AE system to discriminate thesignals on a background of the electric noise. The latter in turn is temperature dependent. The
modern signal processing techniques can substantially improve the SNR making use the key
property of the electric noise – its sationarity. The AE signal and noise are additive. Therefore, thestationary spectral characteristics of the noise can be judiciously subtracted from the signal by avariety of methods called commonly “denoising”. Perhaps, the simples workflow aimed at noise
subtraction is based on a Fourier analysis and can be implemented for the continuously
streaming AE record as follows:
1 The signal is digitally amplified by +14 to +20 dB.
2 The noise is identified on the AE stream (usually at the beginning of the end of the stream
when the testing rig is off).
3 The noise Fourier spectral density is calculated, averaged and used as a baseline for subtraction
from the entire data stream.
4 Repeat step 1: the denoised signal is amplified again by +40 to +50 dB.
4 Repeat steps 2 and 3: evaluation of noise average spectrum and use at again subtract the noisefrom data.

Fig. 5: SEM micrograph showing the dislocation slip pattern ahead of indenter tip. The rectangle
indicates approximately the plastic zone where the active sources of AE are seen as individual
slip lines in two highlighted slip systems – primary and secondary.
This kind of rectification procedure was applied to the noise-like continuously looking signal
shown in Fig. 2. The shape the “denoised” waveform is shown in Fig. 6. We should note thatS73

even a superficial juxtaposition of the raw and filtered signals shown in Figs. 2 and 6,
respectively, reveals an obvious differences between these two: (i) the SNR increased
spectacularly, revealing fine temporal features in AE, which correspond well to the position of
the tip of the indenter during motion, and (ii) the magnified outlook of the waveform (cf. theinset) reveals a discrete nature of the AE time-series at a smaller time scale. The latter
observation is particularly important in view of the above discussion on the intermittency of thedislocation glide. The observed small-scale frequent AE bursts. This poses a new challenge forquantification of a real detectability threshold in terms of the number and properties of
dislocation segments which can be detected by the AE technique. We do believe that thecommonly accepted estimates of the number of cooperatively moving dislocation segments
needed for detection of AE signals, which amount to several thousand, are significantly
overestimated. The relevant experimental studies are currently in progress and the results will bereported elsewhere.

Fig. 6: Stream of AE signal after FT denoising.
The dislocations emerging at a free surface have long been recognized as a very powerful sourceof AE [10]. The slip lines ahead of the indenter tip form a clear plastic zone. The size of theplastic zone and the elastic stress distribution in the material under the indenter has been
evaluated by many authors is reviewed [11, 12]. Regardless of details of models and calculations,most of them merge at a point that in the first order approximation the plastic zone can beconsidered semi-spherical with tensile stresses ahead the indenter tip. The radius of the plastic
zone is evaluated roughly to be 1.5 – 2 of the width of the scratch. The size of the plastic area
was estimated in the SEM as shown in Figure 5. The white rectangle encompasses approximately
the elastic-plastic area with easy slip lines it is area of "strongest" sources of acoustic emission
signal. In circles shown exit direction of shear bands of different slip systems.
Finally, the analysis of the data shown in Figs. 3 and 4 allows us to conclude that most powerful
AE (in terms of Urms or Energy) corresponds to the motion of the indenter through the grains
with the crystallographic ordinations favoring multiple slip and highest strain hardening rate infair agreement with former observations performed during tensile deformation of conventional
massive single crystals [5]. To summarize this part observing, the sources of AE signal moveahead relative indenter tip at a distance like stress field.

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4.

Conclusions

Using the annealed model copper specimens with carefully characterized microstructure andpost-mortem observations of the dislocation slip traces in the local area along the path of theindenter during the scratch test we demonstrate that the modern AE technique appears as a viableand powerful tool for investigations of local deformation mechanisms in situ. The capacity of the
AE technique to detect low amplitude events from elementary dislocation slip can besubstantially improved if a threshold-less continuous data acquisition and appropriate filtering isutilized. The optimal “denoising” strategy has yet to be found to quantify the real detectability
limit of the AE technique.
Acknowledgements
Financial support from the Russian Ministry of Education and Science through the grants-in-aid
11.G34.31.0031 and from the Russian Foundation for Basic Research (RFBR) under projectNo. 13-08-00259 is gratefully appreciated.
5.

References[1]

Williams J.A., Analytical hardness models of scratch hardness. Tribology International,Vol. 29, No. 8, 1996, 675–694.
[2] Brookes C.A., Green P., Anisotropy in the Scratch Hardness of Cubic Crystals.
Proceedings of the Royal Society London. Series A, Mathematical and Physical SciencesVol. 368, 1979, 37–57.
[3] Bibik ZI. Acoustic-Emission during Deformation of Pure Aluminum Single-Crystals.
Fizika Metallov I Metallovedenie 1987;63:811.
[4] Sedgwick RT. Acoustic Emission from Single Crystals of LiF and KCl. JOURNAL OFAPPLIED PHYSICS 1968;39:1728.
[5] Vinogradov A, Nadtochiy M, Hashimoto S, Miura S. Acoustic-Emission Spectrum and Its
Orientation Dependence in Copper Single-Crystals. Materials Transactions Jim
1995;36:496.
[6] Zaiser M, Seeger A. Chapter 56 Long-range internal stresses, dislocation patterning andwork-hardening in crystal plasticity. In: Duesbery FRNNaMS, editor. Dislocations in
Solids, vol. Volume 11. Elsevier, 2002. p.1.
[7] Miguel MC, Vespignani A, Zapperi S, Weiss J, Grasso JR. Intermittent dislocation flow inviscoplastic deformation. Nature 2001;410:667.
[8] Richeton T, Weiss J, Louchet F. Breakdown of avalanche critical behaviour inpolycrystalline plasticity. Nature Materials 2005;4:465.
[9] Wadley HNG, Mehrabian R. Acoustic emission for materials processing: a review.
Materials Science and Engineering 1984;65:245.
[10] Merson D, Nadtochiy M, Patlan V, Vinogradov A, Kitagawa K. On the role of free surface
in acoustic emission. Materials Science and Engineering a-Structural Materials Properties
Microstructure and Processing 1997;234:587.
[11] Kolesnikov Yu.V., Morozov E.M.: Contact fracture mechanics, Nauka, Moscow, 1989.
[12] Johnson K.L.: Contact mechanics, Cambridge University Press, Cambridge, 1985.

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The 12th International Conference of the Slovenian Society for Non-Destructive Testing»Application of Contemporary Non-Destructive Testing in Engineering«
September 4-6, 2013, Portorož, Slovenia

MONITORING OF AUTONOMOUS HEALING PROCESS OF
CONCRETE BY ACOUSTIC EMISSION AND DIGITAL IMAGE
CORRELATION
E.Tsangouri1*, K.Van Tittelboom2, N. De Belie2, D. G. Aggelis1 and D. Van
Hemelrijck1
1

Department Mechanics of Materials of Constructions (MeMC), Vrije Universiteit Brussel(VUB), Belgium & SIM program SHE vzw, Technologiepark 935, etsangou@vub.ac.be2
Magnel Laboratory for Concrete Research, Ghent University, Belgium & SIM program SHE
vzw, Technologiepark 935
ABSTRACTIn this study, cracking formation and repair due to autonomous healing of concrete beams,
tested in two stages of bending (loading-reloading) are reviewed. Encapsulated PU-basedhealing agent is embedded into concrete beams and is released when crack formation triggers
and actuates the chemical repair process. The released agent fills the open crack and seals thefractured area. The potential mechanical recovery of the damage is investigated. Advanced
optical and acoustic experimental methods evaluate the mechanical response of the composite
material. Acoustic Emission monitors breakage of the capsules embedded into the specimens andlocates and quantifies the damage occurred. In parallel, Digital Image Correlation visualizes thecontrolled crack opening/closure and reopening due to bending. Correlation of the experimental
results obtained by both techniques gives a full view of the healing efficiency of the proposedsystem. Monitoring of potential differentiations at the formation of the fracture process due tocapsule breakage phenomena is the main aim of this research.
Key words: cracking of concrete, healing response, capsule breakage, digital image correlation,
acoustic emission
1.

Introduction

Nowadays, the damage evaluation of concrete elements in which an embedded healingmechanism actuates the fracture recovery is a hot topic in engineering material science. The
detection of test conditions under which the healing system is activated is the main topic of thisstudy. Monitoring the experimental activity during concrete bending tests by the use of advanced
optical and acoustic methods was done in this study and is a very innovative technique is the key
manner and innovation of our research.
2.

Autonomous healing of concrete samples monitored by Acoustic Emission and Digital
Image Correlation
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2.1 Mode-I fracture testing
Crack formation and propagation in normal concrete is frequently studied in literature. The lastfew decades, different fracture models provide plethora of theories to characterize the damage
mechanisms in concrete. Focusing on the simplest case in which cracking forms under purebending load (Mode-I) the experimental approach to the problem is comprehensively describedby RILEM 50-FMC Technical Committee [1]. Concrete beams with a central notch are loaded
under three- point bending and the energy and general fracture process is estimated. Applicationof loading damages the beam by crack formation, initiating from the pre-cracked notch.
Unloading of the sample finalizes the testing procedure. Subsequent loading of the sample, reopens the existing crack and propagates further the damage centrally across the height of thebeam.
2.2 Encapsulation of healing agent
Encapsulation healing agent is the most promising autonomous self-healing approach. Van
Tittelboom et al. embedded a two- component PU- based healing agent in concrete beams using
tubular glass capsules. Damage occur, ruptures the glass tubes, releasing the healing agent.
Triggering of material polymerization leads to crack filling and finally sealing and/or healing of
the damaged area in short term. Detection of healing actuation during cracking is experimentallyinvestigated by three- point bending tests. A repetition of loading 48 hours after initial crack
formation can validate the healing performance [2].
2.3 Monitoring healing actuation and material recovery
In practice, identifying the damage level at which capsule breakage occurs is a challenging task.
In this research, monitoring of capsule activation is done by Acoustic Emission (i.e. AE) andDigital Image Correlation (i.e. DIC). Both methods are well-established in experimental
mechanics field and are not presented in detail in this paper.
Particularly, in the current experimental configuration, elastic waves released due to capsulebreakage during fracture and crack propagation are captured by AE sensors placed at theconcrete surface. The signal waveform features give a clear indication of damage characteristics.
Furthermore, the location of cracking and capsule rupture is accurately done by using a 3-D
location algorithm application [3]. In parallel, DIC full- field overview of cracking provides all
the information about the fracture process and the volume of damage. Displacement profiles of
the central region of the beam calculate the crack opening and propagation during bending.
Strain concentration initially located at the notched pre-crack and distributed across the height ofthe sample till the end of testing visualize the crack opening-closure (loading stage) andreopening (reloading stage) of the crack [4].
3.

Experimental configuration

3.1 Materials
The concrete mix composition is shown in the following table [4]:Table 1: Concrete beams mixture
MATERIAL
Sand 0/4
Aggregates 4/8
Aggregates 6.3/14
Cement CEM I 52.5 N
Water

VOLUME (kg/ m3)
805
150
850
390
188

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A series of concrete samples are casted into wooden molds for 24 hours. After demolding, thespecimens are cured under water for 14 days before testing. A notch was created in the samples
with dimensions of 840x 100x 100 mm, are pre-cracked by means of a Teflon- slice(dimensions: 10 x 5 mm) standing in the middle of the mold’s bottom [1].
The glass tubes are 50 mm long; their inner diameter is 3 mm. Several capsules are filled byinjection with the PU- prepolymer and another with catalyzing agent which hardens and expands
the prepolymer when the two components come into contact. Pairs of glass capsules are
positioned at different locations into the beam. The capsules are attached to thin, poor in stiffness
metallic wires crossing the length of the beam in order to insure that during mixing their place isaccurately fixed.
The encapsulation system is shown at Fig.1.

Fig. 1: Capsule placement during casting
3.2 Bending test set-up
An Instron bending machine applies displacement controlled loading by means of pin at the
middle of the sample. The beam is placed on supports fixing a bending span of 800 mm. The
displacement rate is 0.04 mm/min [1]. A crack mouth opening device (i.e. CMOD) is attachedover the pre-crack notch at the bottom of the sample. Guided by EN 14651, CMOD gage
mounted to the specimen can provide a precise display of the pre-crack opening.
3.3 Digital Image Correlation test set-up
A DIC speckle pattern is applied by airbrush at one of the white painted sides of the beam. The
region at the middle of the beam above the pre-crack notch is chosen as the DIC Area of Interest.
Two CCD cameras are placed facing the side of the beam, focused and successfully calibrated at
the Area of Interest- AOI. The DIC trigger box synchronizes the images captured every 3
seconds during testing and the data acquisition system stores the images and the load cell values
received by the Instron device. Artificial light is used to increase the light contrast and ispositioned next to the DIC cameras facing the sample.
3.4 Acoustic Emission test set-up
Eight sensors are placed at the sides and the bottom of the concrete sample. R15 resonant
frequency transducers are taped at the surface and mounted by vaseline sealant. During testing,the signals are transferred through pre-amplifiers at the AE computer board and the fullconfiguration of waveform signals is stored. 3D location of AE events and several AE waveform
calculated by AEwin software and Noesis post-processing program provides the cracking andcapsule breakage damage mode. The location of sensors is shown at Table 2.
The test set- up is presented in Fig. 2 and the AE and DIC technical set-up characteristics areshown in Table 2.

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Fig. 2: Test set- up
Table 2: AE and DIC set-up.
channel
1
2
3
4
5
6
7
8

X
(mm)
345
335
455
445
355
335
465
475

Y
(mm)
25
75
50
0
0
50
25
75

Z
(mm)
0
0
0
25
75
100
100
100

AE set- up

DIC set-up

ThresholdPre-amplifier
Location typevelocity

45 dB
40dB
3-D
4.000 m/sec

hits/event

min: 4
max: 8CCD Camera type
LensesResolution
AOI
SubsetSubset spacing

AVT Stingray
23 mm
2456 x 2058
100x100 mm
27x 27
5Strain field size

15

4.

Experimental results

4.1

Digital Image Correlation and Acoustic Emission analysis

The loading response indicates regain in strength and stiffness due to healing process. As it is shown in
Fig. 3, the maximum load reached during reloading is 55% of the maximum loading case. The responseof the healed samples is compared to concrete beams which do not contain healing agent (namelyreference beams). Significant recovery of the mechanical properties needs to be correlated further tofracture of the capsules.
2500

load (N)re-loading

2000

re-reloading
he-loading
he-reloading

1500

1000

500

0

displacement (mm)
0

0,2

0,4

0,6

0,8

1

1,2

1,4

Fig. 3: Loading response comparison between reference (re-) and healed (he-) beams
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Acoustic Emission energy activity provides the criterion to classify capsule breakage. It is shownthat the hits energy released during capsule breakage is higher than 2000.On the other hand,bending fracture releases energy lower than 1000 in most of the cases (crack opening energy isless than 500). The clustering of cracking and capsule breakage is presented in Fig. 4.
cracking

AE hits energy

10000
8000
6000

capsules, cracking

4000
2000
0

0

500

1000
Load (N)

1500

2000

Fig. 4: AE hits energy clustering of capsule breakage and cracking fracture

2500

2000

load (N)As it known from fracture mechanics models, the crack propagates further from the notch whenthe maximum load is reached. In this study, AE energy-based analysis proves that capsules breakat the post-peak stage of loading. Rupture of capsules is monitored at the moment crack
formation is initiated. Further, as crack propagates capsule breakage events appear in different
levels of crack opening. The well- distributed location of AE capsule breakage events can be
related to the fact capsules are placed at several beam’s heights. The aforementioned observation
is graphically shown at the graph of Fig. 5 in which the load and crack opening (CMOD
measurements) at the bottom of the sample correspond to moments AE capsule breakage eventsare detected.

capsule breakage

1500

1000

500

0

crack opening (mm)
0

0,1

0,2

0,3

Fig. 5: Capsule breakage location as crack opening occurs
S80

0,4

100
75

Y (mm)

Projecting in 2-D the AE capsule breakage events across the length of the beam and distributionof events across the height of the sample are results in Fig. 6.An AE location algorithm canprovide the location of capsule rupture events with an accuracy of 5 mm and indicates the regionin which the healing agent is released. The damaged area at the vicinity of AE capsule events
may recover. Focusing at the fracture process in that region, further information about thehealing mechanism can be derived.

50
25
0

X (mm)

0

100

200

300

400

500

600

700

800

Fig. 6: AE hits energy clustering of capsule breakage and cracking fracture
Observing the location graph above, it is noticed that the crack opening measured at the bottomof the sample by CMOD does not correspond accurately to the crack opening at the place where
capsule breakage occurs. Consequently, DIC displacement profiles at the cracked region of thesample contribute to this research. DIC provides information about the crack opening in the fullfield of the sample’s side. Measuring the crack opening displacement in time at the bottom and at
the top of capsule breakage region located by AE, the following (Fig.7) curves are calculated.
Crack opening (DIC)

300
250
200
150

initiation of capsule breakage
main stage of capsule breakage

100
50
0

DIC images
0

50

100

150

200

250

300

350

400

Fig. 7: AE hits energy clustering of capsule breakage and cracking fracture
As it is indicated at the graph above, initial crack fracture appears when the crack is only 25 μm
wide. Finally, the stage of loading in which most of the capsule breakage occurs corresponds to a
crack range of 80-110 μm and 130-160 μm at the bottom and top of the capsule breakage region
respectively.

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5.

Conclusions

It is concluded that advanced optical and acoustic experimental techniques applied on concretebeams with autonomous healing properties create a promising and full-field view set-up tomonitor the fracture and recovery of healing mechanisms. Focusing at the capsule rupture
phenomena occurring during testing, AE and DIC combination provides the information required
to locate and characterize the fracture phenomena.
6.

Acknowledgements

We acknowledge the SIM-SHE program for the financial support and Dr. ing. Kim Van
Tittelboom for preparing and providing encapsulated healing agent.
7.

References[1]

RILEM Technical Committee 50-FMC (1985). Determination of the fracture energy of mortar andconcrete by means of three-point bend tests of notched beams, proposed RILEM draft
recommendations. RILEM, Materials and Structures 18(106), 285–296

[2]
[3]
[4]

Van Tittelboom K., De Belie N., Van Loo D., Jacobs P., 2011. Self-healing efficiency of
cementitious materials containing tubular capsules filled with healing agent, Cement andConcrete Composites, Volume 33, Issue 4, 497–505
Aggelis D. G., 2011. Classification of cracking mode in concrete by acoustic emission
parameters, Mechanics Research Communications 38, 153–157
Sutton M., Orteu J., H Schreier H., 2009. Image correlation for shape, motion anddeformation measurements: Basic Concepts, Theory and Applications

S82

The 12th International Conference of the Slovenian Society for Non-Destructive Testing»Application of Contemporary Non-Destructive Testing in Engineering«
September 4-6, 2013, Portorož, Slovenia

CLUSTERING OF AE SIGNAL AMPLITUDES BY MEANS OFFUZZY C-MEANS ALGORITHM
Rajko Svečko1, Aleš Hančič2, Dragan Kusić2 and Janez Grum3
1

University of Maribor, Faculty of Electrical Engineering and Computer Science
Smetanova 17, 2000 Maribor, Slovenia, rajko.svecko@uni-mb.si
2
TECOS Slovenian Tool and Die Development Centre
Kidričeva 25, 3000 Celje, Slovenia, dragan.kusic@tecos.si, ales.hancic@tecos.si
3
University of Ljubljana, Faculty of Mechanical Engineering
Aškerčeva 6, 1000 Ljubljana, Slovenia, janez.grum@fs.uni-lj.si
ABSTRACT

Clustering of numerical measurement data into clusters offers a great potential to find similar
objects, which are normally neglected when observing a production process. In this paper, theclustering results obtained with the use of basic fuzzy c-means algorithm on acoustic emission(AE) signal amplitudes are presented. AE signal amplitudes were captured during total of six
production cycles of standard polypropylene test specimens under two processing conditions.
The final results revealed that the objective function minimization during iterations is very
dependent on the number of selected clusters. In case of higher number of clusters the fuzzy cmeans algorithm clearly needs more iteration runs to cluster the input dataset.
Key words: Clustering analysis, fuzzy, c-means, centroids, dataset.
1.

Introduction

Clustering analysis is a task in which data is grouped into a set of similar objects. Objects in thesame group are called cluster and are more similar to each other than to those in other groups (orclusters). Similar objects for example can represent a collection of patterns that are normallyformed as a vector of measurements in a multidimensional space. By representing the data withfewer clusters we normally lose certain fine details, but can achieve simplification. Clustering
analysis can be divided into:-

hard clustering, where each object can belong to a cluster but not necessarily,
soft clustering (better known as fuzzy clustering), where each object belongs to eachcluster to a certain degree.

From a practical point of view the clustering analysis plays an important role in data mining
applications such as medical diagnostics, information retrieval, industrial data exploration,
pattern classifications, and many others. Typical cluster analysis activity of patterns involves
only a few easy steps, such as: pattern representation, definition of a distance measure (function

S83

like for example Euclidean distance) that is appropriate for the given data, grouping (clustering),
abstraction of data (not always needed) and output assessment (if its needed).
The ordinary c-means algorithm is one of the most popular clustering algorithms and is still usedin scientific and industrial applications. Each of k clusters Cj=1..k is represented by the mean (orweighted average) cj of its points known as centroid. The sum of differences between a data
point and its centroid, which is expressed through appropriate distance, is used as the objectivefunction. During each iteration the objective function is then minimized until the predefined
stopping criteria is reached.
In fuzzy clustering the data points can belong to more than one cluster. Membership levels areassociated with each data point and are later used for assigning these data points to one or moreclusters. Among many fuzzy clustering algorithms the Fuzzy C-Means (FCM) algorithm, which
was introduced by Bezdek, is most widely used [1-16]. A short description of the fuzzy c-means
algorithm is shown in the next subchapter.
2.

Experimental procedure

2.1 Description of the Fuzzy C-means clustering algorithm
Majority of known fuzzy clustering algorithms are based on optimization of ordinary (basic) cmeans objective function or by its modification. As already previously stated the majority of
today’s fuzzy clustering algorithms is usually based on minimization of the fuzzy c-means
objective function, which is formulated as
c

N

i 1

k 1

J ( Z ;U ,V )    ( ik ) m zk  vi A
2

(1)

where

U   ik   M fc

(2)

is a fuzzy partition matrix of data Z={z1, z2, …zN},

V  v1 , v2 ,..., vc  ,vi Rn(3)

is a vector of cluster center that have to be determined,2
DikA zk  vi A   zk  vi  A  zk  vi 
T

2

(4)

is a squared distance norm A from point zk to center of cluster i, andm  1,  (5)is a parameter that determines the fuzziness of the resulting clusters. The minimization of theobjective function represents a nonlinear optimization problem that can be solved during
iterative minimization or for example, with genetic algorithms. It can be shown that if D2ikA > 0,
for all i,k and m > 1, then U ,V   M fc may minimize the objective function only if

S84

1

ik  c (D / D )
j 1

ikA

jkA

2/  m 1

, 1  i  c, 1  k  N,

(6)

and
N

vi 

 ( ) z
m

ik

k 1
N

 ( )k 1ikk, 1  i  c.

m

(7)

The fuzzy c-means algorithm iterates through the last two Equations 6 and 7. The last equation
gives vi as a weighted mean of the data items that belong to a cluster and the weights are themembership degrees.
A short summary of the fuzzy c-means algorithm can be expressed in a few simple steps:
For a given dataset Z, we first choose the number of clusters 1 < c < N, termination tolerance
ε > 0, norm matrix A and the weighting exponent m > 1. The fuzzy partition matrix is randomly
initialized in such form that U  M fc .
For l = 1,2…max. repeatN-step 1: compute the cluster means with vi

(l )



 (
k 1
N

 (
k 1

-

ik

( l 1) m

) zk

ik

( l 1) m, 1  i  c.

)2step 2: distance computation DikA  zk  vi (l )  A  zk  vi (l )  , 1  i  c, 1  k  N.
T

step 3: Update the partition matrix for k=1:N, and if DikA>0, and i=1:c thenc1
ik (l )  c, otherwise ik (l )  0 if DikA>0,and ik (l )  0,1 with  ik (l )  1 .
i 1
 ( DikA / D jkA )2/m1
j 1

until U(l )U

( l 1) .

As can be seen from the above description the clustering process stops when the maximum
number of iterations is reached or when the objective function improvement between two
consecutive iterations is less than the minimum amount of specified improvement.
2.2 Dataset preparation
For testing the fuzzy c-means algorithm we used the acoustic emission measurement results thatwere obtained during injection molding of standard test specimens. Acoustic emission
measurement system AMSY-5 from Vallen-Systeme GmbH was used for capturing andanalyzing the AE signals. Amplitudes of the acoustic emission signals can be given in the formof a voltage, but it is usually converted into decibels by the following equation

 V (t ) 
AAE  20 log  Vr 

S85(8)

whereas V(t) is the maximal measured voltage of AE signal and Vr is the reference input voltage
on preamplifier, which was in our case equal to 0.001 mV [17-18].
We closely focused just on the AE signal amplitudes dataset (expressed in dB) that wereobtained in two different production runs. Here we used two different set of processing
parameters and each test was repeated 3 times. The melt temperature was set in both test runs to
240 °C same as filling pressure (1100 bar), holding pressure (300 bar). We varied the injection
speed from 40 mm/s to 50 mm/s, the holding time from 3 s to 5 s and cooling time from 10 s to 6
s. In this way during test specimens production the captured AE signal amplitudes dataset was
prepared for clustering analysis, as can be seen in Fig. 1.
AE signal amplitudes dataset
100
90
80
70

Dim.2

60
50
40
30
20
10
0

0

10

20

30

40

50
Dim. 1

60

70

80

90

100

Fig. 1: Preparation of AE signal amplitudes dataset for clustering.
3.

Experimental results

On the given experimental dataset we tested the fuzzy c-means algorithm by specifying two
different cluster numbers. In the first part, we defined 3 clusters and calculated the final centroids
position and surface of the membership function. We set the weighting exponent to 2,
termination tolerance was set to 0.00001 and the maximal iteration number was set to 100. In thesecond part, we defined 6 clusters and tested the algorithm under same predefined values. Before
the start only the fuzzy partition matrix is randomly initialized. The weighting exponent is an
important parameter since it has a large influence on fuzziness of the resulting partition. On
Fig. 2 the final results are presented which were obtained for 3 clusters. The final position ofeach cluster centroid is shown in Table 1.
Table 1: Final centroid positions for 3 clusters.
Cluster centroid number
1
2
3

Dim.1
47.07753.09568.252

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Dim.2
47.02770.16148.731

Final result for 3 clusters
100
cluster 1
90cluster 2

80cluster 3
centroid 1

cluster 2

centroid 2

70

centroid 3

Dim.2

60
50
40
cluster 1cluster 3

30
20
10
0

0

10

20

30

40

50
Dim. 1

60

70

80

90

100

Fig. 2: Final clustering analysis result obtained for 3 clusters.
From Fig. 3 we can see that the objective function was successfully minimized during iterations
and that fuzzy c-means algorithm grouped the clusters finally after 16 iterations. In Fig. 4 themembership function surface obtained at cluster 1 is presented and confirms that objectivefunction represents in fact a nonlinear optimization problem.
46

Minimization of objective function during iterations

x 10

Objective function value

5.5

5

4.5

4

3.5

3
0

5

10

15

20
25
Iteration count

30

35

40

45

Fig. 3: Objective function minimization during iterations (3 clusters).

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1
0.9
0.8

Surface membership function

0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0

X

0.5

1

0.2

0

1

0.8

0.6

0.4
Y

Fig. 4: Example of surface membership function obtained for cluster centroid 1.
In the second phase we selected 6 clusters and the final position of each cluster centroid is shownin Table 2.
Table 2: Final centroid positions for 6 clusters.
Cluster centroid number
1
2
3
4
5
6

4

3.2

Dim.1
45.44145.88882.16753.49960.69961.166

Dim.2
45.51961.84847.46885.87744.63661.027

Minimization of objective function during iterations

x 10

3

Objective function value

2.8
2.6
2.4
2.2
2
1.8
1.6
1.4

0

10

20

30

40
50
Iteration count

60

70

80

Fig. 5: Objective function minimization during iterations (6 clusters).

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Final result for 6 clusters
100
cluster 1cluster 2cluster 3
cluster 4cluster 5cluster 6
centroid 1
centroid 2
centroid 3
centroid 4
centroid 5
centroid 6

90
80
70

Dim.2

60
50
40
30
20
10
0

0

10

20

30

40

50
Dim. 1

60

70

80

90

100

Fig. 6: Final clustering analysis result obtained for 6 clusters.
In Fig. 5 we can notice that the objective function was successfully minimized during iterations
and that fuzzy c-means algorithm grouped the clusters finally after 45 iterations. From Fig. 6 thefinal results for 6 clusters with their centroids are shown. By comparing the results in Fig. 2 andFig. 6 we can state that the defined number of cluster has a strong influence on the fuzzy cmeans performance during iterations. As can be seen in Fig. 3 and Fig. 5 the number of iterations
necessary to achieve the local minimum of the objective function in case of 6 clusters is 3-times
higher.
4.

Conclusions

In this work the results of clustering analysis conducted on AE signal amplitudes with fuzzy cmeans algorithm are presented. Our results confirmed the fact that the preselected number ofclusters has a large impact on the fuzzy c-means algorithm efficiency in case when the rest ofparameters are fixed and not varied, such as fuzziness exponent, termination tolerance and norm
matrix A=I. Overall performance of such clustering algorithms is influenced also by the densityof individual clusters, their spatial relations and their distances. A lack of this algorithm is thefact that it converges to local minimum of the functional. So in cases when a different
initialization is used we obtain often different results. Additional improvements of this algorithm
are possible mainly in the direction of changing the objective function, distance measure and byvarying the above mentioned parameters of this algorithm.
Acknowledgement
The research work was partly financed by the European Union, European Social Fund, and by
the Slovenian Ministry for Higher Education, Science and Technology.

S89

5.

References

Bezdek J.C., Ehrlich R., Full W.: FCM: The fuzzy c-means clustering algorithm,
Computers & Geosciences, Vol. 10, 1984, 191-203.
[2] Bezdek J.C., Chuah S.K., Leep D.: Generalized k-nearest neighbor rules, Fuzzy Sets andSystems, Vol. 18, 1986, 237-256.
[3] Pal N.R., Bezdek J.C., Hathaway R.J.: Sequential Competitive Learning and the Fuzzy cMeans Clustering Algorithms, Neural Networks, Vol. 9, 1996, 787-796.
[4] Flores-Sintas A., Cadenas J., Martin F.: Membership functions in the fuzzy C-means
algorithm, Fuzzy Sets and Systems, Vol. 101, 1999, 49-58.
[5] Liu J., Xu M.: Kernelized fuzzy attribute C-means clustering algorithm, Fuzzy Sets andSystems, Vol. 159, 2008, 2428-2445.
[6] Ji Z., Sun Q., Xia Y., Chen Q., Xia D., Feng D.: Generalized rough fuzzy c-means
algorithm for brain MR image segmentation, Computer Methods and Programs in
Biomedicine, Vol. 108, 2012, 644-655.
[7] Kannan S.R., Ramathilagam S., Devi R., Hines E.: Strong fuzzy c-means in medical image
data analysis, Journal of Systems and Software, Vol. 85, 2012, 2425-2438.
[8] Chuang K.S., Tzeng H.L., Chen S., Wu J., Chen T.J.: Fuzzy c-means clustering withspatial information for image segmentation, Computerized Medical Imaging and Graphics,
Vol. 30, 2006, 9-15.
[9] Wang H., Fei B.: A modified fuzzy C-means classification method using a multiscalediffusion filtering scheme, Medical Image Analysis, Vol. 13, 2009, 193-202.
[10] Huang P., Zhang D.: Locality sensitive C-means clustering algorithms, Neurocomputing,Vol. 73, 2010, 2935-2943.
[11] Yang J.F., Hao S.S., Chung P.S.: Color image segmentation using fuzzy C-means andeigenspace projections, Signal Processing, Vol. 82, 2002, 461-472.
[12] Tsekouras G.E.: On the use of the weighted fuzzy c-means in fuzzy modeling, Advances inEngineering Software, Vol. 36, 2005, 287-300.
[13] Zhang D.Q., Chen S.C.: A novel kernelized fuzzy C-means algorithm with application in
medical image segmentation, Artificial Intelligence in Medicine, Vol. 32, 2004, 37-50.
[14] Goktepe A.B., Altun S., Sezer A.: Soil clustering by fuzzy c-means algorithm, Advances inEngineering Software, Vol. 36, 2005, 691-698.
[15] Mukherjee D.P., Pal P., Das J.: Sodar image segmentation by fuzzy c-means, Signal
Processing, Vol. 54, 1996, 295-301.
[16] Chtioui Y., Bertrand D., Barba D., Dattee Y.: Application of fuzzy C-Means clustering forseed discrimination by artificial vision, Chemometrics and Intelligent Laboratory Systems,
Vol. 38, 1997, 75-87.
[17] Vallen H.: Acoustic Emission Testing: Fundamentals, Equipment, Applications, Vol. 6,
Castell Publication Inc., Wuppertal, 2006.
[18] Miller R. K., Hill E.v.K., Moore P. O.: Nondestructive Testing Handbook, Third edition,
Vol. 6, Acoustic Emission Testing, American Society for Nondestructive Testing, Inc.,
2005.
[1]

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The 12th International Conference of the Slovenian Society for Non-Destructive Testing»Application of Contemporary Non-Destructive Testing in Engineering«
September 4-6, 2013, Portorož, Slovenia

ENHANCEMENT OF EFFICIENCY OF TRIBOLOGICALTESTING BY USING ACOUSTIC EMISSION MEASUREMENTS
I. Rastegaev, D. Merson, A. Vinogradov
Laboratory for the Physics of Strength of Materials and Intelligent Diagnostic Systems,
Togliatti State University, Belorusskaya str., 14, Togliatti, 445667, Russia,
E-mail: RastIgAev@yandex.ru, D.Merson@tltsu.ru, alexei.vino@gmail.com
www.intelligent-lab.ru
ABSTRACTThe last two decades saw an explosive growth of research activities in the non-destructive
testing and quality control of a board variety of tribological materials and units. The substantial
amount of data has been accumulated to date, proving the efficiency of the modern acoustic
emission (AE) technique for tribological examinations of materials and coatings using a singlecontact tribometer. In the present work we review the available knowledge and align it with our
original investigations using standard linear reciprocating tribometers (ball-on-disk, ball-oncylinder, ball-on-plate) and the rotary tribometers (ball-on-disk, ball-on-plate, cylinder-oncylinder and four-ball friction machines).
Key words: friction and wear, deformation mechanisms, health monitoring, real-time analysis.
1.

Introduction

The use of standardized types of contact friction machines and methods, e.g. [1-5] allows
for reasonably interpretable, reproducible and comparable results obtained by different
researchers. A strong demand still exists in accelerated tribological testing routines withoutcompromising the ability of restoration of the history of contact surfaces damage during the test.
One effective way to solve this problem is to replace the "post mortem" microscopic
investigations and weighting studies by the "in-situ" acoustic emission (AE) monitoring.
Several threshold-based AE techniques aimed at reaching this goal are known [6].
However, the AE analysis of AE therein is limited to few most common parameters such as thenumber of oscillations in the signal N, total count NΣ, count rate Ṅ, signal amplitudes A andtheir distribution n (A), and "pseudo" energy / power loosely defined as combinations of theabove parameters as A∙N, A∙N Σ , A∙Ṅ or A2∙Ṅ. These parameters have historically gained their
popularity due to simplicity in their hardware implementation. On the other hand, the use ofsome of them, e.g. A, N and their derivatives, has been physically justified [6] as follows. Under
normal conditions of friction (linear wear law) and permanent influencing factors (load Р, sliding
speed v), area of the damaged layer / lubricant layer S 0 appears to be proportional to N, thenumber of wear particles formed is proportional to the AE amplitude n Σ ~ А, the volume rate ofwear Δṁ is proportional to Ā ∙ Ṅ, and the identification of the source type of AE control the
S91

running, the transition between the different mechanisms of wear and performance of lubricantsis determined by the ΔA, ΔṄ and Δn (A). Identification of the AE source, control of wear-in,transition between different wear mechanisms and lubricant efficiency is related to variations in
respective parameters ΔА, ΔṄ and Δn(A).
From definitions given in ref. [7] it follows that N Σ , N и Ṅ are only indirectly related tothe source power (through the number of counts exceeding the discrimination threshold A th ). To
the contrast, the amplitude А does not deliver any information of the source activity. ParametersĀ∙Ṅ and A2∙Ṅ are strongly threshold dependent. They are hardly to be well calibrated due tolower bond limits threshold limits by the threshold and the upper bond limits by the throughput
rate limits. Besides, due to these limitations of the conventional AE apparatus, the Ā∙Ṅ and A2∙Ṅvalues are crudely evaluated either for the pulses of amplitudes which are significantly higher
than the threshold or for the large averaging time (typically of 1 s) [6].
2. Experimental
From the above preceding section it follows that, the threshold-less strategy of AE data
recording can be most beneficial for maximum efficiency of the integral evaluation of AE duringsliding wear. Thus, in the present work we employed continuous recording of the AE Envelope
Y and the root-mean-square, RMS, obtained in the analogue circuits as:
1 T(1)Y (T ) = ∫ A(t ) dt
T 0

RMS (T ) =1 T
A(t ) 2 dt
∫
0
T

(2)

where T is the integration time and A(t) is the instant voltage at the sensor output as a function oftime. The time T = 0.1 s was empirically chosen to smooth out the electric noise yet to follow thedetails of AE bursts. To characterize the AE waveforms, they were digitally recorded. Eachframe of 1.3 ms duration contained 8192 readings. Frames were triggered periodically by timer
with 19 ms period. Hence, during the time interval T of 6 AE frames were captured. AE
recording was carried out in a frequency range 50÷1000 кГц with total gain 40 dB using a homebuilt system having ADC boards La-1.5PCI-14 and La-N20-12PCI at the core. The maximum
irregularity of the sensor frequency response in this band did not exceed +- 5 dB. The sampling
rate for parametric data and AE envelope and RMS was 1 kHz, while the AE waveform were
samples at 6.25 MHz.
The tribological tests were performed on (i) a four-ball friction tribometer, (ii) universal
tribometer with a rotating and reciprocating stage Nanovea and (ii) an original valve-roller
friction machine designed at AutoVAZ Ltd., Togliatti, Russia. The test schematics for turning,rotation, circular and spiral movement and reciprocal sliding is shown in Fig. 1, where P is theload, w and v are the cyclic and linear velocities, respectively, e is the axis offset. implement:
spinning, rotation, circular and spiral movement and reciprocal sliding. The AE sensor was
securely mounted on a housing in the intimate vicinity to the friction pair.
The following contact materials were used as typical friction pairs: the balls of the bearing
steel ShH-15 of 12.7 (Fig.1a) and 6.0 mm (Fig.1b), the plates of steels 20 and 45 of 30×40×5mm (Fig.1b), cylinder of 40HGNM steel or Aluminum alloy AMG3 of ø 8 мм and rollers madeof cast Iron Gh190 of 50 mm diameter and 5 mm width (Fig.1c). Both dry and lubricatedfriction conditions were used with the following lubricants: water, SHRUS-4M, Renolit IP 1619,
LITOL-24, Unirex-3, FIOL-1, SAE 10W-40 «Lukoil-Standard».

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(a)
(b)
(c)
Fig. 1. Schematics of friction units: (a) four-ball friction tribometer, (b) universal tribometer and(c) valve-roller friction machine.
The standard tribological techniques, which were used in the present work, are mainly
summarized as:
1. Comparative analysis of wear (Δm) at constant influencing factors (P = const, v = const) for agiven constant period of testing time (t[s] = const);
2. Comparative analysis of time t[s] to the appearance of the signatures of critical wear at Р =const and v = const;
3. Step-wise increasing loading (↑Р) at constant linear velocity of the reciprocating motion v =const ) and identification of the load Pk or Pc when scoring or edging sets in, respectively.
Microscopic investigations and the calculation the volume of the edged metal and the areaof the frictional seizure were performed by means of the 3D surface profilometry of the wear
spots. The Olympus LEXT OLS4000 laser confocal microscope was used for this purpose.
3. Results of AE analysis
Despite the differences in test procedures, schedules and types of friction all observed
patterns of the AE Envelope and RMS can be reduced to the two types presented in Fig. 2.

(a)

(b)

Fig. 2. AE patterns during friction testing using (а) four ball tribometer and (b) universal
tribometer with a reciprocating motion.
It was observed that the behavior of the AE envelope Y and RMS does not differ
significantly under similar conditions. In what follows we shall label them by the same symbolŪ . Main experimental findings are summarized as follows.

S933.1. The presence of the groups of AE bursts with amplitudes Ūpeak i = (1.1÷3)∙Ūbg i (whereŪbg i is the background voltage) and Δτ = 0.1÷0.5 s or longer (where Δτ is the time interval
where Ū>Ūbg i ) is accompanied by the appearance of individual points of bonding on the wear
spot with the total area of 20÷30 % (Fig. 2а and 3а).
3.2. Intensive bursts with Ūpeak i = (≥3)∙Ūbg i and Δτ ≈ 0.3÷0.5 s give rise to a large variance inŪ compared to the background. These intensive bursts in all cases signified scoring caused bybonding-debonding mechanism with the total area greater than 50% (Fig. 2b и 3b).
3.3. The steep increase of Ū to the magnitudes Ūpeak i = (≥3)∙Ūbg i with high variance andduration as long as Δτ ≈ 1÷5 s at P ≈ Pk or Δτ = 10÷80 s at P < 0.6∙Pk in all cases accompanied
scoring caused by plastic edging (Fig. 3c). The characteristic AE feature of this process is thatthe lower bond level of Ū is always higher than Ūbg i (Fig. 2a);3.4. Increasing Ūbg i value without clearly pronounced peaks such a s those outlined in 3.1÷3.3
is related to the progressive increase of the area of the wear spot. The average Ūbg i level in thiscase is by a factor of 1.1÷1.5 less than the minimal level of AE accompanying scoring. The wear
type is mainly the abrasive-fatigue resulting in the total area of individual disparate points oflocalized bonding between contacting solid surfaces and their breaks of 15÷20 % of the total
damaged area (Fig. 2b and 3a);3.5. The running-in process during the beginning of friction is accompanied by the backgroundlevel Ūbg i reduction, which levels out during the steady normal wear. During scoring the highAE level Ū is also associated with the increase in the wear spot area though this occurs in a more
complex way than that in 3.4. After scoring has become evident on the AE diagram, thefollowing running-in levels out the AE signal although the background level Ūbg i is 1,1÷3 times
higher than that before scoring. (Fig. 2);
3.6. Scoring can be accompanied by a gradual increase/reduction of the AE level Ū according
to 3.2 и 3.3, which indicates that scoring occurs nearly uniformly over the whole contact area
(Fig. 2a). Several stages can be observed during the increase/reduction of the AE level Ū
resulting in several peaks/valleys of Ū, which reflect the respective stages in the wear spot
growth or in the alternating scoring in the case of the multipoint contact between the friction
surfaces (Fig. 2b);

(a)

(b)

(c)

(d)

Fig. 3. The typical morphology of the wear spots: (a) abrasive-fatigue wear, (b) scoring andразрывом областей контакта, (c) plastic edging, (d) example of 3-D surface map of the wear
spot. Blue color highlights the area of scoring.
The analysis of a bulk volume of experimental data allows us to conclude that the featuresof major wear mechanisms can be reliably identified by the AE technique. Once the AE system
is trained in such a way that a characteristic for a given mechanism AE pattern is recognized (seethe next section), the use of the AE technique enables substantial shortening of the testing time
due eliminating microscopic observations. Moreover, it may help to calculate the mass wear andto establish the moments of transition between dominating mechanisms of damage. In what
follows we shall illustrate this conclusion in the case studies below.
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Case 1. Figure 5a shows experimental AE data obtained during standard testing using thefour-balls tribometer aimed at determination of the loads corresponding to the onset of scoring
Pk and bonding Pc at constant applied loads with different lubricants and friction pairs. Figure 4
shows that the systematic use of the integral AE parameters is quite useful for indetification ofscoring and bonding without microscopic investigations. Importantly is that if one compares theresults of determination of Pk and Pc by measuring the limiting wear and by the AE peaks
denoted as PkAE and PcAE, respectively, the results of both methods appear identical, i.e. Pk =PkAE and Pc = PcAE. Therefore one can propose to shorten the testing time by measurements of
PkAE and PcAE during a stepwise increase of the applied load on one and the same set of balls
and lubricant with the time elapsed between subsequent steps not longer than 2÷5 s. Let theobtained in this express AE method loads be labeled as AE Pk and AE Pc . In this case, due to theformation of the contact area and partial heating during the previous load steps, the values of theload and AE Pk and AE Pc are obtained slightly higher (by 10 ÷ 20 MPa) above Pk and Pc,
respectively, obtained in independent tests. In this regard, one can clarify actual loads Pk and Pc
by standard methods, but only within AE Pk minus 30 MPa and AE Pc minus 30 MPa. The use of
the AE technique has shortened the testing time by a factor of 50 if compared to the standard
method, Fig. 4.

(a)

(b)

Fig. 4. Results of AE measurements during tests performed on the four-ball tribometer: (a)standard loading schedule and (b) the proposed step-wise “continuous” loading scheme.
Case 2. Different tribological testing (especially long-term tests which are accompaniedby scoring) results often in pretty much the same wear spots because the surface artifacts
signifying a certain damage process have been overridden by the routine wear process duringlong-term testing. The ambiguity always arises if the questions are asked when a catastrophic
wear sets in and which mechanisms govern the damage? The measurements of integral AE
characteristics and conclusions 3.1-3.6 allow us to answer these questions quite reliably. Figure 6
shows the AE record disclosing the history of damage evolution on the friction surfaces, which
demonstrate that the contact materials in different thermal conditions do not equally respond to
the load. However, the AE record indicates the start of scoring (Tsc), running-in after scoring(Trun) and points when changes occur in wear mechanisms (Ti).
Case 3. During long term tribology testing it is plausible to record AE periodically bytimer. However, the exact moment of scoring can be missed on the AE diagram in this case.
Notwithstanding, the occurrence of scoring can be reliably recognized with a reasonableaccuracy by increasing Ū level in comparison with normal wear conditions (cf. 3.5 and Fig. 6a).

S95

Fig. 5. Typical example of thew AE records corresponding to three test during reciprocating
motion of the friction pair ShH-15-steel 45, разной поверхностной термообработки (the depthof the damaged layer corresponding to the wear is less than the depth of the hardened layer)

(a)

(b)

Fig. 6. Typical example of AE during a step-wise loading and scoring formation.
As a summary, let us notice that all observations of AE during a broad variety of wear processes
investigated using standard tribological procedures overviewed in this section can be used quite
straightforwardly for a training of a simple automated system aimed at identification of damage
mechanisms and at a cost effective express wear testing under different lubrication conditions.
4. Conclusions
It is shown that real-time AE monitoring and analysis delivers valuable information which
can be crucial for understanding of wear processes and improving the quantitative outcome oftribological testing. In particular, the following common issues can be addresses with an aid
from the AE technique:
1. Determination of the critical regime of friction;
2. Time and area of roughness burnishing; and wear of protective coatings or a hardened layer;
3. Comparison of wear of various lubricants on the basis of the dominant mechanisms, the timing
and sequence of their actions;
4. Acceleration of the tribological tests and reduction of their cost without loss of quality andreliability of the results.
Financial support from the Russian Ministry of Education and Science under grant-in-aid 11.G34.31.0031
is gratefully appreciatedS96

6. References[1] ASTM G99 - 05(2010) Standard Test Method for Wear Testing with a Pin-on-Disk
Apparatus (standard)[2] ASTM G133 - 05(2010) Standard Test Method for Linearly Reciprocating Ball-on-Flat
Sliding Wear (standard)[3] ASTM D2266 - 01(2008) Standard Test Method for Wear Preventive Characteristics of
Lubricating Grease (Four-Ball Method) (standard)[4] ISO 20808 Fine ceramics (advanced ceramics, advanced technical ceramics) - Determinationof friction and wear characteristics of monolithic ceramics by ball-on-disc method(standard)[5] DIN 50324 Tribology; testing of friction and wear model test for sliding friction of solids(ball-on-disc system) (standard)[6] Baranov V., Kudryavtsev E., Sarychev G., Schavelin V.: Acoustic emission in friction,
Tribology and interface engineering series 53, Editor: Briscoe B.J., Elsevier, Great Britain,
2007[7] ISO 12716:2001 Non-destructive testing, Acoustic emission inspection, Vocabulary(standard)[8] Belyi V., Kholodilov O., Sviridyonok A., Acoustic spectrometry as used for the evaluation of
tribological systems, Wear, 1981, V.69, №3, P. 309-319[9] Filatov S. Acoustic emission under the abrasive wear of metals, Journal of Friction and wear,
1982, V. 3, № 3, Р. 559-562[10] RU 979958 Sviridyonok A., Kalmykova T., Kholodilov O., Smurugov V., Method of studyof wear resistance of solids, 1981 (patent of Russian Federation)

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The 12th International Conference of the Slovenian Society for Non-Destructive Testing»Application of Contemporary Non-Destructive Testing in Engineering«
September 4-6, 2013, Ljubljana, Slovenia

Wavelet Based Approach to Acoustic Emission Phase Picking
E. Pomponi, A. Vinogradov
Laboratory for the Physics of Strength and Intelligent Diagnostic Systems, Togliatti State
University, 445667 Togliatti, Russia, eraldo.pomponi@gmail.com – alexei.vino@gmail.com
ABSTRACTOne of the most appealing feature of Acoustic Emission (AE) NDT technique is its ability tospatially locate the sources, using times of arrival of elastic waves emitted by the same source, atthe antenna of the sensors. The most commonly adopted triangulation procedure for source
location relies on the accuracy in the time-localization of the picked phases at each different
sensor. To the authors’ best knowledge, all presently available commercial AE systems adopt a
threshold based phase detection. Although this process is the most simple, chose the thresholdadds another source of uncertainty and it suffers from several major drawbacks, particularly
when the testing environment is noisy. This results in false detections, missed events or incorrecttime-location detection. The aim of this paper is to propose a novel algorithm for phase detection
based on the Wavelet transform. Specifically, we exploit the neighboring concepts that have beenfound capable of considerably improve the de-noise performance of the wavelet shrinkage
method, to construct an Acoustic Emission Activity Detector. Among other beneficial
characteristics of the proposed method, preliminary tests conducted in different working
conditions have shown a better accuracy in the time-localization of the picked phases.
Key words: Acoustic Emission, Phase Picking, Wavelet, De-Noising, Neighboring Concepts.
1.

Motivation

One of the key points in the success of AE as an NDT inspection technique is its capability tolocate the source of an even so that becomes clear where a certain amount of energy has beenreleased. At the base of this capability plays a fundamental role the ability to exactly locate intime the first arrival of the AE wavefield at each sensor, i.e. phase picking. The uncertainty
proper of this procedure fixes a lower bound for the source location accuracy. It is clear that theaim of a phase picker algorithm is to discover when what is acquired by the sensor is no morenoise but a proper signal. In the threshold based approach, the most largely adopted phase picker
in commercial systems, this distinction is done just looking at the AE amplitude considering
noise everything do not overcome the fixed threshold level. Although this method is effective inmany application where the S/N ratio is consistently high (e.g > 10 dB), its false detection rate
and time accuracy lowers down soon when this ratio tends to diminish. Another consistent draw
back of a threshold based phase picker is its completely arbitrary setup: there exist “goodpractice” recommendations that experienced practitioners apply when choosing the thresholdlevel but it still remains an arbitrary choice that will affect the entire acquisition process. Thosefacts motivated us to look for a “data-driven” procedure that could overcome the limitations
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stated above attending mostly to the impact on the time location accuracy for low S/N ratio.
The proposed solution exploits:•••

Time coherence of AE signal
Resonant nature of AE sensors
Wavelet block-thresholding [1] de-noise properties

In what follows, we will be focusing on the description of the basis concepts of our approachleaving the details to a successive in-depth publication.
2.

Wavelet transform, De-Noise and Neighboring concepts

Wavelet transform (WT), in both continuous (CWT) and discrete (DWT) form, is a popular andpowerful tool for analyzing non-stationary data and has been extensively adopted for AE
characterization/de-noise [2–6] and source location [7–11]. What makes it so appealing is not
only the time-localization property but also the opportunity to do a multi-scale analysis that
opens new and sometimes easier paths, to difficult tasks like modal analysis, source
characterization etc. [4,5,12]. Before describing the algorithm, it is necessary to remind few
important reference works about WT properties and how its de-noise capabilities have evolved
from the “classic” wavelet shrinkage [13] to the block-thresholding based shrinkage [1].
In their first seminal paper, Donoho and Johnstone [13] introduced the concept of wavelet
shrinkage as the process of shrinking WT coefficients depending on their amplitude compared toa fixed threshold directly estimated from the data (i.e proportional to the estimated noise power).
They called this method VisuShrink with two possible variant: Hard and Soft thresholding. For
instance, since that time, the same authors and many others have extensively proved [14–19] thatthe wavelet based approach to function approximation/de-noising is optimal in many sense andoutperforms the Fourier transform based one. Another significant step ahead has been done with
the introduction of “neighboring concepts” in the shrinking process, following a simple intuition:
A coefficient in the wavelet transform is expected to describe the noise or the signal not justdepending on its value but also on the values of a certain number of neighboring coefficients (i.e.
time coherence). Here's the introduction of blocks of coefficients in the approximation/de-noise
process as first proposed by Hall et al. [20] and successively refined in [1,19,21,22]. Although
there exist theoretical justified rules for an optimal choice of the block-size for a large class of
signals/functions and affecting noise [1,19], we aim to extend them with something that is
specific for AE signals and their acquisition chain. On the other hand, it is straightforward toenvision our approach applied to signals of different “nature” (e.g. Voice) but this kind ofinvestigation is outside the scopes of the present work.
For our purpose, we will focus on the “NeighBlock” method (i.e. Cai and Silverman [1]),because it is one of major success between the many that have been developed since [13], even if,
potentially, other WT block-based de-noise methods could be adopted as well.
3.

Algorithm description

The algorithm is here described in some details however we want to clearly state that it is not in
the scope of this paper to report an in-depth analysis of all the steps that are involved in the
proposed new method. Indeed, we want to give the reader a precise perception of what have been
the ideas behind this work and their basic assumptions.
Let define minimum time-coherent span the duration of the shortest AE signal that we canacquire (i.e. with a certain acquisition chain). It depends on many factors that, unfortunately, we
cannot control neither measure in most cases (e.g. source properties and medium properties).
However, there exist an upper bound for its “dual” (i.e. in frequency domain) that we can easily

S99

find: the maximum frequency band where we can expect useful information is dictated by thesensor response.
Assuming for simplicity a single resonance sensor and adopting a Gaussian model to describe its
response, it is easy to show that the “rise time” (tr) for that system is related to the bandwidth(𝐵𝑊 ≃ 0.009𝜎) by the approximate relation𝑡𝑟 ≃ 0.35⁄𝐵𝑊 . Applying this formula we have
immediately an estimation of the “minimum” rise time that we can expect for the system underinspection. It is then straightforward to adopt this value to fix a lower bond in the time-coherency(block size) that we impose on the WT transform when applying the block-thresholding. The
resulting mask (i.e. equal to 1 for those blocks retained by the block-thresholding rule and equal
to 0 for those blocks shrunk) obtained for each decomposition level m will be called “Probabilityof Presence” at level m (m-PoP). To get a one-dimensional “Probability of Presence” (PoP) we
recombine point-by-point (in time), all m-PoP probability curves according to the “cone ofinfluence” definition [23] (i.e. defines the time localization of each coefficient at different
decomposition levels) plus this simple assumption: m-PoP are independent for every m and every
time t.
This is the simplest form of the proposed algorithm and we will show an example of applicationin the next section. It must be noticed that, for real problems, the simple Gaussian model doesn't
fit really well with most of the response curves of common AE sensor so it has usually been
adopted a multi-resonance model plus a second order approximation for the rise-time calculation
respect to each resonant poit. Moreover, instead of a (0,1) binary mask (i.e. Hard thresholding)in the block-thresholding algorithm we adopted a continuous mask (i.e. Soft thresholding) that
admits all the values in the interval [0,1]. Abut the choice of the mother wavelet we took theHaar wavelet for its (theoretical) arbitrary time-localization accuracy. All the implication of thosechoices are discussed elsewhere.

Figure 1: (a) Reference AE signal and (b) AE noise.
4.

Application

To prove that the proposed method is effective in the detection of the Time of Arrival (TOA)(a.k.a. Time of Flight) and that it is much less sensible to the S/N ratio respect to other methods,
we constructed a set of testing signals taking one frame (4K samples) of a “real” AE signal,reported in Figure 1(a), and summing “real” AE noise, Figure 1(b), (i.e. real colored noise
acquire during an AE inspection before starting the loading of the specimen but with the sensors

S100

already attached to its surface) controlling the resulting S/N ratio, calculated respect to the entire
frame length. We will consider two examples respectively for 5𝑑𝐵and -5𝑑𝐵S/N ratio. We will
also report the result obtained with the proposed method for -10𝑑𝐵, to confirm its low sensibility
to the S/N ratio. The sample frequency is fs=3.125 Ms/s and we will compare our performance(i.e. the obtained TOAs) respect to a threshold based method with the threshold fixed to 30𝑑𝐵𝜇 ,assuming a pre-amplifier gain of 20𝑑𝐵 and a “dead-time” of 32 𝜇𝑠 (100 samples).

Figure 2: Threshold method S/N 5 dB

Figure 3: Proposed method S/N 5 dB
5.

Remarks & Conclusions

As clearly shown in Figure 2, 3, 4, 5 and 6 mostly, the proposed method outperform thethreshold based phase picker and returns almost the same single TOA (i.e. when chosen between
TOA_0 that indicates the first sample where𝑃𝑜𝑃 ≠ 0and TOA_1 that indicates the first sample
where𝑃𝑜𝑃 = 1) even for low S/N ratios.
Being the computational complexity of a standard DWT O(n) so less than that one of the FFTO(n log n) and being all the other operations in the algorithm linear in n, the proposed method ispotentially capable to work in real-time moreover, without any additional cost, it produces
already the de-noised AE signal.
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Figure 4: Threshold method S/N -5 dB

Figure 5: Proposed method S/N -5 dB

Figure 6: Proposed method S/N -10 dB

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References[1] T.T. Cai, B.W. Silverman, Incorporating information on neighbouring coefficients into
wavelet estimation, Sankhyā: The Indian Journal of Statistics, Series B. 63 (2001) 127 –148.
[2] H. Suzuki, T. Kinjo, Y. Hayashi, M. Takemoto, K. Ono, Wavelet transform of acoustic
emission signals, Journal of Acoustic Emission. 14 (n.d.) 69–84.
[3] E.P. Serrano, M.A. Fabio, Application of the wavelet transform to acoustic emission signals
processing, Signal Processing, IEEE Transactions On. 44 (1996) 1270–1275.
[4] G. Qi, Wavelet-based {AE} characterization of composite materials, {NDT} & E
International. 33 (2000) 133–144.
[5] M.A. Hamstad, A. O’Gallagher, J. Gary, A wavelet transform applied to acoustic emission
signals: part 1: source identification, 20 (2002) 39–61.
[6] C.U. Grosse, F. Finck, J.H. Kurz, H.W. Reinhardt, Improvements of AE technique using
wavelet algorithms, coherence functions and automatic data analysis, Construction and
Building Materials. 18 (2004) 203–213.
[7] M.A. Hamstad, A. O’Gallagher, J. Gary, A wavelet transform applied to acoustic emission
signals: Part 2: Source location, Journal of Acoustic Emission. 20 (2002) 62–82.
[8] J. Jiao, C. He, B. Wu, R. Fei, X. Wang, Application of wavelet transform on modal acoustic
emission source location in thin plates with one sensor, International Journal of Pressure
Vessels and Piping. 81 (2004) 427 – 431.
[9] F. Ciampa, M. Meo, Acoustic emission localization in complex dissipative anisotropic
structures using a one-channel reciprocal time reversal method, The Journal of the
Acoustical Society of America. 130 (2011) 168.
[10] E.M. Lympertos, E.S. Dermatas, Acoustic emission source location in dispersive media,
Signal Processing. 87 (2007) 3218–3225.
[11] N.F. Ince, C.S. Kao, M. Kaveh, A. Tewfik, J.F. Labuz, Averaged acoustic emission events
for accurate damage localization, IEEE International Conference on Acoustics Speech andSignal Processing. 9 (2009) 2201–2204.
[12] a. R. Oskouei, Wavelet-based acoustic emission characterization of damage mechanism incomposite materials under mode I delamination at different interfaces, eXPRESS Polymer
Letters. 3 (2009) 804–813.
[13] D.L. Donoho, I.M. Johnstone, Ideal spatial adaptation by wavelet shrinkage, Journal Of
Comparative Education. 81 (1992) 425–455.
[14] D.L. Donoho, On minimum entropy segmentation, Wavelets: Theory, Algorithms, andApplications. (1994) 1–43.
[15] D.L. Donoho, I.M. Johnstone, G. Kerkyacharian, D. Picard, Wavelet shrinkage:asymptopia?, Journal of the Royal Statistical Society Series B Methodological. 57 (1995)
301–369.
[16] D.L. Donoho, I.M. Johnstone, Adapting to unknown smoothness via wavelet shrinkage,
Journal of the American Statistical Association. 90 (1995) 1200 – 1224.
[17] R.R. Coifman, D.L. Donoho, Translation-invariant de-noising, Time. 103 (1995) 125 – 150.
[18] D.L. Donoho, I.M. Johnstone, Asymptotic minimaxity of wavelet estimators with sampled
data, Statistica Sinica. 9 (1999) 1–30.

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[19] T.T. Cai, Adaptive wavelet estimation: a block thresholding and oracle inequality approach,
The Annals of Statistics. 27 (1999) 898–924.
[20] P. Hall, S. Penev, G. Kerkyacharian, D. Picard, Numerical performance of block thresholded
wavelet estimators, Statistics and Computing. 7 (1997) 115 – 124.
[21] P. Hall, G. Kerkyacharian, D. Picard, On the minimax optimality of block thresholded
wavelet estimators, Statistica Sinica. 9 (1999) 33 – 49.
[22] T.T. Cai, On block thresholding in wavelet regression: Adaptivity, block size, and thresholdlevel, Statistica Sinica. 12 (2002) 1241–1273.
[23] M. Vetterli, J. Kovačević, Wavelets and subband coding, Prentice Hall PTR, Englewood
Cliffs, New Jersey, 1995.

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The 12th International Conference of the Slovenian Society for Non-Destructive Testing»Application of Contemporary Non-Destructive Testing in Engineering«
September 4-6, 2013, Portorož, Slovenia

ARC WELDING PROCESS MONITORINGBY AUDIBLE SOUND
Ivan Polajnar1, Zoran Bergant2, Janez Grum3
1

Welding Institute, Ptujska 19, 1000 Ljubljana, Slovenia
ivan.polajnar@fs.uni-lj.si
2Faculty of mechanical engineering, Aškerčeva 6, SI-1000 Ljubljana, Slovenia
zoran.bergant@fs.uni-lj.si
3Faculty of mechanical engineering, Aškerčeva 6, SI-1000 Ljubljana, Slovenia
janez.grum@fs.uni-lj.si
ABSTRACTThe paper presents the results of experimental gas metal arc welding. Namely, this welding
process is commonly used in the automation and robotization of fusion welding, where adaptive
regulation is indispensable.
In addition to variable welding voltage and current, a distinctive characteristic of arc welding
processes are also the pressure of audible sound phenomena which reflect the variableconditions in the arc to the highest extent. It follows that it is reasonable to monitor these
phenomena and that the results can be used for the implementation of adaptive welding processcontrol.
Key words: Fusion welding, arc, material transfer, mild steel, welding parameters, audible sound.
1. Introduction
A constant increase in the demand for automation and robotization of arc welding and therequirements with regard to the desired quality of welds have led to a logical demand for areliable on-line monitoring and control of welding processes. The prevailing control systems
among the well-established variants are based on continuous measurement of arc voltage andwelding current intensity. These control systems may provide reliable information about thematerial transfer in the arc, but they are considerably less informative with respect to the integral
course of the welding process. For a given type and thickness of base material, joined to form a
product by using a weld joint of some sort, the quality of welding is not determined only by thematerial transfer mode. Instead, the quality depends on the size and shape of the welding pool,
the level of splashing, and the shape and magnitude of remelting of base material. However, it is
impossible to extract all this information only from the recorded signals of welding current andwelding voltage. It follows that a comprehensive evaluation of the welding quality must also
incorporate some other process parameters, absolutely including the audible sound pressure level[1,2].
Several papers have been published on the topic of using the sound in monitoring the welding
process, all of them having something in common – the measurement of sound pressure during
the welding and the frequency analysis after the welding [3,4]. The results of frequency analysis
are commonly bound with time windows related to various physical phenomena [5, 6]. These
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include the arc ignition and burning, which is especially easy detectable when welding withshort-circuit filler material transfer. Furthermore, the accessible literature does not deal with therelations between the results of measurement of audible sound and the results of measurement oflight intensity and welding current intensity [6, 7].
A mathematical model relating the sound pressure to the welding current would provide a new
dimension and added value in identifying various modes of material transfer, and could serve asa foundation for a system using the measured sound pressure for a controlled supply of electricalenergy to the arc. In this way, it would be possible to implement reliable control of welding
process stability for the GMAW processes, and consequentially to control the quality of such
welds.

2. Sound generation during metal transfer
In gas metal arc welding process (GMAW) an electric arc is established between the consumablewire electrode and the melted zone on the welding part. Both are shielded by different gases(inert Ar, active CO2 or gas mixtures containing Ar, CO2 , etc).
Generally, for all arc welding processes, there are many different modes of metal transfer whichare, according to International Institute of Welding (IIW), classified into eight basic groups. In
GMAW, there are only three characteristic modes of metal transfer, short circuit, globular andspray mode, figure 1.
The differences in the way the filler material melts are reflected in the shape of the melt pool,and also in the generation of several different sound phenomena [8, 9, 10]. The prevailing modeof filler material melting depends on the current type and intensity, voltage and the shielding
atmosphere.
v = const
U1 , I1

v = const
U2 , I2v = const
U3 , I3

Fig. 1: Characteristic modes of metal transfer in GMAW, w=const.,
U1<U2<U3, I1<I2<I3, (a) short circuit, (b) globular mode, (c) spray mode
The filler material transfer is reflected to a great extent in the temporal variability of welding
voltage, welding current intensity, light intensity and sound pressure generation [11, 12]. The
temporal variation of current intensity in short-circuit transfer of filler material is both very
illustrative and well defined, figure 2.
The short circuit metal transfer can be established at lower welding current and lower voltage
which is a convenient mode to study the sound phenomena. At lower energy input the shape andthe length of arc is unstable and lead to significant variations in sound pressure.

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Welding wire

2

3

5

4

6

Welding current

1

6

1

2

3

4

5

Time

6

Fig. 2: Description of six different phases of material transfer
during GMAW in the short-circuit mode
The electrical circuit of the welding system (power supply, cable, consumable wire, arc andwelding material) consists only of the basic elements: resistance R in ohm (Ω), inductance L in
Henry (H) and capacitance C in Farad (F). A suitable equation describing the electrical scheme
can be obtained by using the second Kirchhoff's law:
U = RI + L

Ra I
dI
+
dt 1 + jωC a Ra

(1),

where I is the electric current in Amperes (A), U is the equivalent open-circuit voltage supply involts (V), Ra is resistance (Ω), Ca capacitance of the arc in farad (F), and ω is the angularfrequency. The capacitance of the arc Ca in Eq. (1) assumes a very small value and may beneglected, and since the arc is responsible for noise generation, we expressed the arc voltage Ua
explicitly as•

U a = R a I = U − RI − L I = U − U R + U L (2).
It follows from the interpretation that a fast enough capture of data on the changing electricalquantities during welding makes it possible to identify the type of filler material transfer. The
presented research aims to identify the possibilities of making an analytical description of filler
material transfer based on the variation of sound pressure.
3. Welding process as a cybernetic system
GMAW can be treated as an electro-thermo-mechanical system with multiple input and output
quantities – signals. There are input signals that can be controlled and those that cannot be
influenced, but can be measured. The latter may also be termed as the noise signals. A complete
description of GMAW therefore requires a system of differential equations, some of them nonlinear, such as the equation describing the sound generation based on the welding current signal[13]. Systems of non-linear differential equations are sensitive with respect to the accuracy of
their coefficients. If the coefficients are not determined to a certain precision, they do notdescribe the welding process well, and the results may even happen to diverge. The
mathematical model is therefore complex and not absolutely stable, which means that it is notpractical for on-line welding process control.
In real operating conditions, it is therefore only reasonable to treat the GMAW process as asimple system with one input, one output, and one common noise factor, figure 3. To be able toselect the most appropriate input and output quantities, it is reasonable to measure the welding

S107

current, welding voltage and sound intensity at different welding conditions, and to analyze theresults separately [14,15].

Fig. 3: The block diagram of the welding process with input and output variables
4. Experiment and resultsΕxperiments were conducted using St36 mild steel (0.1% C, 0.48% Mn), which is often used in
the metalworking industry and is suitable for welding. The geometry of the work piece wasselected so that the welds were suitable for the preparation of specimens for the strength tests
and metallographic examination of welds. The dimensions of work piece halves were: 10 x 60 x
250 mm.
The experiments were conducted using the experimental setup shown in Fig. 4. Standard
industrial welding equipment was used. The Iskra E-450 power source has a horizontal static
characteristic. The VAC 60 consumable wire electrode with φ = 0.8 mm was used and theshielding gas was pure CO2 [16].
Consumable electrodeVAC 60 φ1.2 mm

Mic
B&K 4134

Shielding gas

Welding
Trolley
E11

Arc

L=constShunt resistor

Welding
power source
ISKRA E450

Welding current
signalU

Acoustic signal

I

Measuring amplifier
B&K 2636

Fig. 4: Experimental setup
4.1 Measurement results
The settings on the welding rectifier and the control unit were chosen so that one of the twoboundary modes of filler material transfer was obtained: short-circuit or spray. Table 1 presents
the rectifier and control unit settings for both materials transfer modes, as well as the mean
measured values of welding voltage and current intensity.

S108

Table 1: Welding parameter settings and the measured values
Settings/measured values
Short-circuit transfer

Spray transfer

Rectifier

1-4

1-6

Control unit

5.5

13

Wire feed rate [m/min]

6.3

13.8

Welding voltage [V, rms]

23

38

Welding current [A, rms]

98

178

The data acquisition was performed with an A/D converter, with a sampling rate of 50 kHz perchannel and with 8-bit data resolution. 2 sec of welding process was recorded for each setting
and stored on the hard drive. The welding current was measured via a shunt resistor. The
Bruel&Kjær type 4134 condenser microphone was fixed to the welding head to maintain aconstant distance. Since the noise spectra appear in the frequency range above 4 kHz, themicrophone could be attached at a shorter distance. All measurements were conducted using thesame 0.35 m distance of the microphone from the arc. During the welding, the temporalvariations of current intensity Iw = I(t), light intensity iw = i(t) and sound pressure ps = p(t) wererecorded simultaneously. The recorded data for typical short-circuit filler material transfer in thetime 10 ms is shown in Figure 5.

Welding current signal

Acoustic signal

Sound impulse
by arc extinction

˝Turbolent˝ noisewith frequency range
above 5 kHz

Sound impulse
by arc ignition
0

10

20

30

Background
noise
40

50
Time /ms

60

70

80

90

Fig. 5: Waveform of welding current and sound pressure
Figure 6 shows a comprehensive overview of measured temporal variation of welding current
intensity, light intensity and sound pressure for both boundary filler material modes, recorded in
0.2 and 2 s time intervals.

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10

6

2

(a) Specimen preparation
Sound intensity p(t)Welding current I(b) Waveform of welding current and sound and photos of weld beadFig. 6: Welded overlay with constant parameters on the weldpiece with geometrical defect
4.2 Findings
After reviewing and comparing the results of the conducted measurements, the main findings canbe summed up as follows:
The current signals are useful especially for discerning the stability of conditions in the arc. In
short-circuit filler material transfer, the current signal also provides information about the
number of short circuits and the mean size of molten droplets. These cannot be established withcertainty in the case of spray transfer mode.
The sound signal is considerably more sensitive to variable conditions in the arc, the materialtransfer mode and the disturbances in the welding process. For example: the sound signal
indicates high-frequency arc oscillations which may lead to process instability, which cannot be
identified from the light signals. The sound signals therefore appear to be considerably more
noisy, and a thorough analysis can extract more useful information from them. It follows that thesound pressure technique is suitable for monitoring the welding process.

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5. Conclusions
The quality of arc welding can be evaluated by detecting the pressure of audible sound,especially the fact that the welders monitor and adaptively control the welding process by using
their sight, hearing and sensing the forces in their hands. The research revealed that the soundpressure measurement during the welding is directly connected with the arc changes and qualityof weld. Modern systems of on-line monitoring and control are generally based on measuring thewelding current and voltage. The information about the process obtained in this way may beadequate, but it is often too inaccurate to ensure a first-class repeatability and quality of welds.
In our research it was found that the sound measurement data represents a valuable contribution
for monitoring of the welding process. The online sound pressure monitoring and signal
processing is proposed as a new approach of quality control of weldments.
The sound signal is extremely sensitive to the disturbances in the stability of welding process.
Objectives of the further investigation of sound pressure signals is to detect other instabilities of
the arc behavior connected to the weld quality, as well as small deviations during materialtransfer. The applicability thereof for the online welding process control has not been madeknown and researched enough. For this reason, we will attempt to upgrade research in the futurewith further evaluation of weld quality.
6. References[1]

Saini D. Floyd S.: An Investigation of Gas Metal Arc Welding Sound Signature for Online Quality Control, Welding Journal, pp. 172-179, April 1998
[2] Arata Y. Inoue K. Futamata M.: Investigation on welding arc sound – report 1, IIW/IIS
doc. VIII-1340-86, 1986
[3] Polajnar I, Bergant Z., Grum J.: Spremljanje MIG/MAG varjenja z detekcijo zvočnega
tlaka. V: GRUM, Janez (ur.), BERGANT, Zoran (ur.). 11. slovenska konferenca z
mednarodno udeležbo z naslovom Uporaba sodobnih neporuštvenih metod v tehniki,
Ljubljana, 14. februar 2013. Zbornik referatov. Ljubljana: Slovensko društvo za
neporušitvene preiskave: Fakulteta za strojništvo, 2013, pp 114-122[4] Jesnitzer et.al.: Apparatus for controlling electric welding process, U.S. Patent No.
3.679.865, 1972
[5] Choi J.H., Lee C.D., Yoo C.D.: Simulation of dynamic behaviour in a GMAW system,
Welding Journal, Vol. 80, pp. 293-245, 2001
[6] Mansoor A.M., Huissoon J.P.: Acoustic identification of the GMAW process, 9thInternational conference on computer technology in welding, NIST special publication,
Detroit 1999
[7] Magori V. Monitoring sensor for the production of welds, U.S. Patent No.: 4,650,958,
1987[8] Rostek W.: Investigation on the connection between welding process sound emission andelectric short circuit in GMA welding, IIW/IIS doc. 212-725-89, 1989
[9] Grad L., Prezelj J., Polajnar I., Grum J.: Weld quality assessment by analysing on line
measured acoustic signals, In: Grum, Janez (ed). 6th International Conference of the
Slovenian Society for Non-Destructive Testing, Portorož, Slovenia, September 13-15,
2001. pp. 185-189, 2001.
[10] A. Fasana and B. A. D. Piombo: Identification of linear mechanical system bydeconvolution techniques, Mechanical Systems and Signal Processing, Volume 11, Issue
3 , 3 May 1997, pp. 351-373[11] Prezelj J., Polajnar I.: Slišni zvok kratkostičnega MAG varjenja, Varilna Tehnika, 51(3),pp. 80-85, 2003
S111

[12] S. M. Kuo: Active Noise Control Systems - Algorithms and DSP implementation, Wileyinterscience, New York, 1996[13] Kovacevic R., Zhang Y.M. and Li L.: Monitoring of weld joint penetration based on weld
pool geometrical appearance. Welding journal pp 317-329s, vol. 75, No. 10, 1996.
[14] Polajnar I., Prezelj J., Grad L., Grum J.: Ocena kakovosti obločnega varjenja z detekcijo
slišnega zvoka in svetlobnega sevanja, In: Grum, Janez (ed). 7th International Conferenceof the Slovenian Society for Non-Destructive Testing, Brnik, Slovenia, November 18,
2004. Conference proceedings. pp. 139-147, 2004.
[15] Langus, Danijel, Kralj, Viljem, Grum, Janez. Optimisation of welding parameters inpulsed MIG/MAG welding width-controlled sine-wave current pulses. Part 1:
Determination of a general synergetic equation and a normalised parametric diagram witha defined parametric welding range. Int. j. mater. prod. technol., 2007, letn. 29, št. 1/2/3/4,
pp 244-254[16] Grum J., Bergant Z., Polajnar I., Monitoring the GMAW process by detection of welding
current, light intensity and sound pressure. V: PAZDERA, Luboš (ur.), MAZAL, Pavel(ur.). NDE for Safety 2012 : proceedings : sborník příspěvků. Brno: Brno University ofTechnology, 2012, pp. 291-299

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The 12th International Conference of the Slovenian Society for Non-Destructive Testing»Application of Contemporary Non-Destructive Testing in Engineering«
September 4-6, 2013, Portorož, Slovenia

THE SYSTEM OF AE CONTROL AND DIAGNOSTICS OFTECHNICAL STATUS OF ELECTRIC SWITCH MECHANISM
S.V. Altynbaev1*, V.S. Fadeev2, N.A. Semashko2
1«STC Information Technologies» Perevedenovskiy str. 13/7, 105082, Moscow,
Russia, s.altynbaev@tmaster.ru
2«STC Information Technologies» Perevedenovskiy str. 13/7, 105082, Moscow,
Russia, infotech.mos@gmail.com
ABSTRACT
Increasing of the speed and passenger traffic intensity on the railways require thecomprehensive approach in organization of the control of the technical status of rolling stock inorder to ensure the safety of trains. In connection with it, it is very relevant to create intelligent
diagnostic systems for technological processes of repair and operation of rolling stock.
The AE method is designed for technical inspection of motor and electric switch
components. The acoustic emission method based on the parameter analysis of the acoustic
eradiation from various working nodes of different devices. The value of acoustic emission
energy is uniquely connected with the state of interacting nodes of the mechanism. The acoustic
system allows to control the technical status of main electric switch components in real working
mode and gives a possibility to determine the residual resource without disassembling andinspecting the electric switch.
Keywords: nondestructive control, railway, ground equipment, point.
1.

Introduction

Increasing of the speed and passenger traffic intensity on the railways require the comprehensive
approach in organization of the control of the technical status of rolling stock in order to ensure
the safety of trains. Losses from passenger trains delays and derailment amount to hundred
million rubles annually. In addition, passenger trains derailment may result in human casualties.
In connection with it, it is very relevant to create intelligent diagnostic systems for technologicalprocesses of repair and operation of rolling stock. These intelligent diagnostic systems have to beunited into a single information space. Today, the main method of controlling the technical statusof the ground equipment elements (nodes), including the electric switches, is the method ofvisual inspection by service regulations.
2. Application of AE method in condition monitoring of point machines (electric switches)
2.1 Discription of the diagnostic method

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The AE method is designed for technical inspection of motor and electric switch components.
The acoustic emission method based on the parameter analysis of the acoustic eradiation fromvarious working nodes of different devices. The value of acoustic emission energy is uniquelyconnected with the state of interacting nodes of the mechanism. The method is successfully used
for diagnozing oil and gas facilities, power plants, in aviation industry and other areas ofengineering.
2.2 System of acoustic control of point machines (SACP)The acoustic system allows to control the technical status of main electric switch components inreal working mode and gives a possibility to determine the residual resource without
disassembling and inspecting the electric switch. There is no need in scheduled repair ofelectrical switch mechanism. Replacement and repairs are performed depending on the actual
state of its elements. The extent of controlled node degradation and its residual life is estimatedby the analysis of the main parameters of the AE radiation from electric switch elements in thereal work. Output information can be implemented in a version with three lights of different
colors, that would allow to use the low-skilled technicians and eliminate the human factor in theprocess of diagnostics.
2.3 Structure of SACPThe AE control system includes two separate blocks, as illustrated in Figure 1 and 2.

Fig. 1: General view of SACP.

Fig. 2: Positions of the sensors on the electric switch.

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The first unit includes sensors, connected to the preliminary AE amplifiers. The sensors
are located directly on the controlled elements of the electric switch (motor, gear, friction clutch,
gate, switches). Preliminary amplifiers are mounted on the inside of the case with an output tothe multi-connector mounted on the outside of the case.
The second block includes a multi-channel system like SACP, which processes the AE
information, analyses and graphically displays all incoming information on controlled objects
with the help of special cables connected to the terminals of the first unit and a personal
computer (PC) with Maestro software.
SACP system is built on the principle of parallel multi-channel digital recording
parameters of AE signals. The system software can display SACP complex parameters of AE
test in real time, as well as provide wide opportunities for processing the obtained information inafteranalysis mode. Comparing the acoustic image of reference elements of a new electric switch
with the that of the real element (Figure 5 a, b) in comparison mode allows to assess the degreeof degradation of the controlled element and its remaining life.

Fig. 3 (a): The energy spectrum of the gearbox in the optimum technical condition.

Fig. 3 (b): The energy spectrum of the gearbox after 10 years of operation.

3. Application of SACP in repair factories
The diagnostics of electric switch has been held on repair factories in Pskov, Armavir andBarnaul, Russia. The position of an electric switch on a testing stand is shown in Figure 4.

S115

Fig. 4: The position of an electric switch on a testing stand.
More than 100 electric switches in different technical condition were diagnosed during
the time of work. The ranges of critical values of acoustic parameters were defined allowing todiagnose the remaining life of the electric switch nodes and the whole mechanism in general.
The SAACP usage methodology for repair factories has been developed for the input quality
control of supplied electric switch nodes and output quality control of the repaired products.
The remote dispatch control system of active acoustic monitoring of point machines(electric switches, further referred to as SAACP) has been developed. The structural scheme isshown in Figure 5.

Fig. 5: The structural scheme of SAACP.
Constant monitoring of electric switch technical condition is necessary for high-speed tracks. It
is crucial to forecast the durability and the remaining life of electrical switch nodes on thesetracks. An electric switch node breakdown can lead to disastrous accidents. At the moment the

S116system is undergoing factory tests and is going to have trial operation on the Russian Railways
objects.
SAACP aroused interest of the Russian Railways train department. The research works
connected with diagnosing locomotive gear boxes are being held at the moment.
4. References[1][2]Акустическая
эмиссия
вэкспериментальном
материаловедении
/Н.А.Семашко,В.И.Шпорт,Б.Н.Марьин и др:М.: Машиностроение, 2002. — С. 8-55.
Волович_Схемотехника аналоговых и аналого-цифровых электронных устройств,Мсоква, Наука, 2005, 110-125

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The 12th International Conference of the Slovenian Society for Non-Destructive Testing»Application of Contemporary Non-Destructive Testing in Engineering«
September 4-6, 2013, Portorož, Slovenia

STATISTICAL EVALUATION OF INJECTION MOLDINGPROCESS PARAMETERS IMPACT ON THE DETECTED
ACOUSTIC EMISSION EVENTS
Dragan Kusić1, Rajko Svečko2, Aleš Hančič1, Tomaž Kek3 and Janez Grum3
1

TECOS Slovenian Tool and Die Development Centre
Kidričeva 25, 3000 Celje, Slovenia, dragan.kusic@tecos.si, ales.hancic@tecos.si
2
University of Maribor, Faculty of Electrical Engineering and Computer Science
Smetanova 17, 2000 Maribor, Slovenia, rajko.svecko@uni-mb.si
3
University of Ljubljana, Faculty of Mechanical Engineering
Aškerčeva 6, 1000 Ljubljana, Slovenia, tomaz.kek@fs.uni-lj.si, janez.grum@fs.uni-lj.si
ABSTRACTThe main objective of this article was to statistically evaluate the impact of process parameterson the detected AE events during production of test specimens. The worst and the best test
specimens were scanned using an optical 3D scanner for accurate measurement of test specimen
dimensions in width and length. The optimal set of parameters that provided minimal number ofdetected AE events during specimens’ production was determined by the Taguchi method. The
most influential parameter was statistically obtained through an analysis of the variance method.
With the help of response surface methodology we explored the relationships between theinfluential parameters on the detected AE events, which were captured with two 150 kHz
piezoelectric acoustic emission sensors. As expected the final statistical results from response
surface methodology confirmed that the most influential parameter (holding pressure) fromanalysis of variance has the largest influence on the detected AE events.
Key words: Acoustic emission, Taguchi, analysis of variance, test specimens, PZT sensors.
1.

Introduction

Acoustic Emission (AE) is a popular non-destructive testing (NDT) method used for monitoring
structures or components to detect actively propagating flaws. By applying stresses to a test
specimen, an active flaw releases acoustic energy as an elastic waveform, which is detected,
located and characterized by the AE measurement system. Acoustic emission is different
compared to other non-destructive testing methods. The reason lies in the fact that AE signal
originates within material and that it can detect movement while most other testing methods
detect existing geometric discontinuities. AE’s most important advantage as an NDT method isthe capability of providing complete flaw site information on the entire volume of the structure.
Therefore it can be used for locating structural discontinuities and flaws without losing time withpoint-to-point search method over the entire structures surface. Sources of AE in case of tool

S118

steel may be different for example, from crack advance, corrosion reactions to deformation
during production etc.
Injection molding is one of the most important polymer manufacturing processes where heatedmolten polymer material is injected into a mold cavity under high pressure. Polymer material
solidifies into a shape inside the mold. This manufacturing process is very popular because of
simple operation steps. An ordinary production cycle starts when the mold closes. After that thepolymer material is injected into the mold cavity. Once the cavity is filled, additional pressurecompensates the material shrinkage. Then the screw turns and feeds the next shot to the front
screw tip. This causes the screw to retract as the next cycle is almost prepared. Once the injectedpart is sufficiently cooled, the mold opens and the produced part is ejected. An example ofdifferent polymer granules (types of polypropylene) is shown in Fig. 1.

Fig. 1: Example of different plastic polymer granules.
2.

Experimental procedure

The polymer material used in the experiments was polypropylene (Isofil H 40 C2 F NAT) frommanufacturer Sirmax SpA. This type of material is widely used because its price is relatively low
and due to its suitability for use in various applications, for instance in automotive industry
usually for dashboard components, fans, ductwork etc. Practical experiments have been carriedout on a KraussMaffei (KM80SP380CX) injection molding machine, which has a 40 mm screw
diameter and maximal clamping force of 800 kN. Acoustic emission signals were captured
during the production cycle of standard test specimens that are used for material shrinkage
evaluation. Acoustic emission measurement system AMSY-5 from Vallen-Systeme GmbH wasused for capturing AE signals and analyzing the AE events. Two piezoelectric AE sensors
VS150-M (resonant at 150 kHz) were mounted with silicone grease with two sensor holders on
the tool steel insert from both sides. Both PZT sensors were connected via two preamplifiers
AEP4 with a fixed gain of 40dB on the first and second channel of AMSY-5 measurement
system. In the evaluation stage of acquired AE signals we focused closely on the number ofdetected AE events on both measurement channels. The main aim was to statistically evaluate
the influence of processing parameters on the measured AE hits (events).
To lower the number of experiments we used the Taguchi method. An ordinary L 18 orthogonalarray was chosen in the experimental study as shown in Table 1. Therefore six processparameters (factors) have been analyzed to determine their influence on the detected AE events.
These factors are melt temperature (A in two levels 230 °C and 240 °C), injection speed (B inthree levels 40 mm/s, 45 mm/s and 50 mm/s), injection pressure (C in three levels 1000 bar,
S119

1100 bar and 1200 bar), holding pressure (D in three levels 300 bar, 400 bar and 500 bar),holding time (E in three levels 3 s, 4s and 5s) and cooling time (F in three levels 6 s, 8s and10 s). After conduction of all experiments we proceeded with the statistical evaluation of theobtained measurement results (AE events).
Table 1: The layout of the L18 Orthogonal Array with obtained AE events.
Trial No. A
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
3.

1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2

B

C

D

E

F

1
1
1
2
2
2
3
3
3
1
1
1
2
2
2
3
3
3

1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3

1
2
3
1
2
3
2
3
1
3
1
2
2
3
1
3
1
2

1
2
3
2
3
1
1
2
3
3
1
2
3
1
2
2
3
1

1
2
3
2
3
1
3
1
2
2
3
1
1
2
3
3
1
2

Number Numberof hitsof hits
at CH 1 at CH 2
103
126
56
64
44
50
102
104
84
99
53
70
123
144
40
48
50
58
71
85
96
126
62
71
56
57
45
55
85
103
77
70
70
101
89
56

Total
hits
229
120
94
206
183
123
267
88
108
156
222
133
113
100
188
147
171
145

Experimental results

We varied the processing parameters in 18 experimental runs according to L 18 orthogonal array.
Analysis of variance (ANOVA) was then used for evaluating the effect of process parameters on
the total number of detected hits (AE events). In Table 2 the ANOVA results are presented.
Table 2: ANOVA results for total number of detected AE events
Source df
SVA
1
102.72102.72B
2146.33
73.17
C
2
9463
4731.5D
2 14646.33 7323.17
E
26277
3138.5F
2 7265.33 3632.67
Residual 6 7443.78 1240.63
Total

17

F
p-value Significance0.0828 0.7832
no0.0589 0.9433
no3.8138 0.0853
no5.9028 0.0383
yes
2.5298 0.1597
no2.9281 0.1296
no45344.5

As can be seen only the factor D (holding pressure) is the most influential factor that affects
greatly the total number of detected AE events. According to this result we used the response
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surface methodology to explore further the relationships between the influential parameters on
the detected AE events. As can be seen from Table 1 during production of test specimen in 7th
experimental run the highest number of total hits was 267. On the other side the test specimen
produced in the 8th experimental run had the smallest number of detected hits only 88. These twotest specimens were scanned with an optical 3D digitizer ATOS II SO. Both scanned specimens
are shown in Fig. 2.

(b)(a)
Fig. 2: Scanned test specimens (a) specimen 7 and (b) specimen 8.
Optical 3D Digitizer is based on the principle of capturing images through the camera, whichthen through an appropriate program prepares a computer model. The accuracy that can beachieved with these digitizers depends largely on the quality of the camera, which records thedesired object. Of course, in a very small precision scale the accuracy itself also depends on the
wavelength of the light. The biggest advantage of 3D digitizer lies in extremely rapid procedureof digitizing and excellent precision (precision declared for very small objects are up to 2
microns). As can be seen from Table 1 and Fig. 2 the number of detected AE events during
production of test specimens 7 and 8, is largely affected by the used set of the process parametersin which the most influential is factor D (holding pressure).
The acoustic emission signal intensity is proportional to signal energy [6-7] and defined by an
integral of signal square∞

2

E AE = ∫ V (t ) dt

(1)0

Amplitudes of the acoustic emission signals can be given in the form of a voltage, but it isusually converted into decibels by the following equation
 V (t ) 
AAE = 20 log  Vr 

(2)whereas V(t) is the maximal measured voltage of AE signal and V r is the reference input voltage
on preamplifier, which was in our case equal to 0.001 mV. Fig. 3 shows the amplitudes and AE
signal energy during the production of test specimens 7 and 8.
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Filling (I) and holding (II) stage during production
90
Specimen 7

80

I.

80

70

AE

[dB]

II.

60

A

[dB]

AE

A

90

50
40

0

5

10
Time [s]

15

70

II.

60

40

20

-6

0

5

10
Time [s]

15

20

15

20

-6

10

Specimen 7
I.

-8

10

Specimen 8-810[V 2s]

II.

-10

E

AE

10

I.
II.

-10

10

E

[V 2s]

I.

50

10

AE

Specimen 8

-12

-12

10

10

-14

10

-14

0

5

10
Time [s]

15

20

10

0

5

10
Time [s](a)(b)
Fig. 3: Amplitude and energy values of AE signals during production of(a) test specimen 7, (b) test specimen 8.
As can be seen from Fig. 3 the obvious difference in total number of detected AE hits is both infilling as well as in holding stage during both test specimens production.
Response surface methodology (RSM) is a collection of mathematical and statistical techniques
for empirical model building. In most RSM problems, the form of the relationship between the
response and the independent variable is unknown. Normally, a low-order polynomial in a regionof the independent variables is employed. We modeled the response by a linear function of two
independent variables, so the approximation function is the first order model defined asy = β 0 + β1 x1 + β 2 x2 + ... + β k xk + ε(3)where y is the response, β’s are parameters to be determined, x k are variables that represents thefactors. In our case we investigated the relationships between two parameters on the output
response (total hits). The most influential parameter D (holding pressure) was investigated firstto parameter E (holding time) and later also to parameter F (cooling time). Normally it isunlikely that a polynomial model will be a good approximation of the parameters relationshipover the entire 2-D space. Therefore our goal was to cover only a small region of parametervalues inside which the model gives good results.

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Fig. 4: Contour plot of a total hits response with factors D and E.
Fig. 4 shows the contour plot of a total hits response in regards to the holding pressure andholding time. The contour was plotted by taking into account (beside levels of factors D and E)
also the following actual factor values:
A-Melt temperature = 235°C
B-Injection speed = 45 mm/s
C-Injection pressure = 1100 bar
F-Cooling time = 6 s
From Fig. 4 we can state that by increasing the amount of holding pressure to the maximal levelof 500 bars and by prolonging the holding time to 5 seconds then the number of total hits can be
minimized (method of steepest descent) to the region of 88 detected AE events. This is inaccordance with the obtained measurement results and theoretical assumptions. The aim of thecontour plot is to lead the experimenter efficiently and quickly to the desired goal, which in ourcase is the minimum of detected AE events at which the test specimens are produced withacceptable quality. Inside the contour plot the lines between two factors represent the constantresponse (number of detected AE events in our case).

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Fig. 5: Contour plot of a total hits response with factors D and F.
Fig. 5 shows similar like previously the contour plot of a total hits response in regards to theholding pressure and holding time. The contour was plotted by taking into account (beside levelsof factors D and F) also the following actual factor values:
Contour for total hits response regarding the holding pressure and cooling time
The actual factors:
A-Melt temperature = 235°C
B-Injection speed = 45 mm/s
C-Injection pressure = 1100 bar
E-Holding time = 5 s
From Fig. 5 we can state that by increasing the amount of holding pressure to the maximal levelof 500 bars and by decreasing the holding time to 6 seconds then the number of total hits can be
minimized to the region of 88 detected AE events by taking into account also the above values ofactual factors. In this way the experimenter is capable to obtain the desired number of AE eventsin the future AE tests. According to his process knowledge obtained from practical situations
during production he can quickly adjust the factors levels or simply keep the factor values at the
optimal level value.

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4.

Conclusions

In this research work we statistically evaluated the measurement results obtained during 18
experimental runs. The goal was to minimize the number of AE hits (events) and to investigate
the impact of different process parameters on the detected AE hits. We found that test specimens
with good quality were produced at lower number of AE hits, which were captured by AMSY-5
measurement system from Vallen-Systeme GmbH. Among 18 produced test specimens wescanned the worst and best test specimen according to the number of detected AE hits during
production cycle. With the help of ANOVA method the most influential parameter (holding
pressure) was found which significantly affects the total number of AE hits. With response
surface methodology we closely explored the relationships between the holding pressure and twoother factors on the total number of detected AE hits. In case of holding time we saw that thisfactor needs to be increased if we want to reach minimal number of AE events. In opposite tothis, the cooling time needs to be decreased for the same goal, which is in accordance withtheoretical assumptions. The RSM results confirmed all our expectations from practicalexperiments. Still an improvement in the final RSM result would be possible to achieve by using
a higher degree of polynomial, for example second order model where optimal point can be
precisely reached.
Acknowledgement
The research work was partly financed by the European Union, European Social Fund, and by
the Slovenian Ministry for Higher Education, Science and Technology.
5.

References[1]

Taguchi G., Chowdhury S., wu Y.: Taguchi's Quality Engineering Handbook, Wiley andSons, New Jersey, 2005.
Montgomery D.C.:Design and analysis of experiments, Wiley, New York, 2001.
Khuri A.I.: Response Surface Methodology And Related Topics, World Scientific,
Singapore, 2006.
Khuri A.I., Cornell J.A.: Response Surfaces - Designs and Analyses, Marcel Dekker Inc.,New York, 1996.
Pukelsheim F.: Optimal Design of Experiments, Wiley and Sons, New York, 1993.
Vallen H.: Acoustic Emission Testing: Fundamentals, Equipment, Applications, Vol. 6,
Castell Publication Inc., Wuppertal, 2006.
Miller R. K., Hill E.v.K., Moore P. O.: Nondestructive Testing Handbook, Third edition,
Vol. 6, Acoustic Emission Testing, American Society for Nondestructive Testing, Inc.,
2005.

[2]
[3]
[4]
[5]
[6]
[7]

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The 12th International Conference of the Slovenian Society for Non-Destructive Testing»Application of Contemporary Non-Destructive Testing in Engineering«
September 4-6, 2013, , Portorož, Slovenia

TESTING PRESSURE VESSEL WITH ACOUSTIC EMISSION
Goran GAVRANIĆ2, Tomaž KEK1 and Janez GRUM1
1

University of Ljubljana, Faculty of Mechanical Engineering, Aškerčeva 6, 1000 Ljubljana,
Slovenia, tomaz.kek@fs.uni-lj.si , janez.grum@fs.uni-lj.si
2Ecoplin Zadel d.o.o., Inspection body for pressure vessels, Žabjek 15, 1294 Višnja Gora,
Slovenia, goran@ecoplin-zadel.si
ABSTRACTThe article presents the results of testing pressure vessels for liquefied petroleum gas (LPG) withacoustic emission. The main objective is to show the most significant impacts which haveinfluence on measured AE-signals, like human error, bad weather conditions and also corrosion
on the pressure. An integral part of the test is pressurization, which is necessary for emergenceof acoustic waves, which are later detected as AE signals. We record this signals with two
resonant 150 kHz piezoelectric sensors, which are mounted directly on the coat of the pressure
vessel. From these measurements we gained valuable information, which help us evaluate thepressure vessel and determine the class of it.
Key words: acoustic emission, pressure vessel, pressurization, AE monitoring
1.

Introduction

Acoustic emission is elastic energy, which is spontaneously released from material, when it isunder stress. In our case we achieve stress in material with pressurization. Bigger the stress isand faster is pressurization, more AE-signals we sense (also depend from characteristic ofsensor). The sound waves produced by the stress (pressurization) in an isotropic material
propagates in concentric circles. When wave propagates, also attenuate [1]. Wave attenuation isimportant when we decide how many sensors we shall put on pressure vessel. Sensor must sense
waves which are generated anywhere on pressure vessel. Today is testing with AE applied for
periodic inspection of pressure vessel. For evaluation of test we used only burst signals and notcontinuous signals [2]. Burst signal is result of formation or propagation of crack. But also thissignal in some cases doesn’t show us the real state of material as we shall see later in this article.
The big advantage of AE compared to other NDT methods is that we can check all volume ofmaterial. AE allow us to check very large surface. Larger the surface is more sensors we need.
We can predict failure of material before it happened. That’s why this method is appropriate fortesting pressure vessel when it’s talk about safety. If we estimate on base of measured signals,
that pressure vessel contain specific defect, which can cause failure of material, we are allowed
to prohibit it further use.
Important effect which can appear during test are Kaiser and Felicity effect. For Kaiser effect it
is typical that AE-signals appears when we reach higher pressure as it was at previous loading.
This effect is normal phenomena when testing with AE. Felicity effect is more dangerous,

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because AE-activity appear before reaching previous load. This show that material havedifficulties to reach stable condition [3].
It is important to know that fluid, which is used for pressurization is the same as it is used forstorage. That prevent any additionally corrosion and contribute to longer life of pressure vessel.
2.

Procedure for testing pressure vessel

We have tested small pressure vessel (<5m3). Before testing we have to gather information about
pressure vessel. Different constructions demands different procedure for testing, also different
sensor location. Following information must be known about pressure vessel:-characteristic of geometry,characteristic of material,
location of welds, legs, valve, identification tablealready known defects or repairs,
extreme conditions during operation
results from previous tests

In table we can see description of measuring equipment which is used for gathering AE-signals
and evaluating them.
Table 1: Description of measuring equipment
SystemType of sensors
Peak sensitivity
Resonant frequency
Operating frequency range
Evaluation threshold

MISTRAS - SAMOS - PCI8
R15I (40 dB integriran predojačevalnik)-65 dB/ R15I
150 kHz / R15I
80-200 kHz / R15I
40 dB

Pressure which we have to reach during test is minimal 12 bars. For 5m3 pressure vessel we use
only two sensors, because we have to satisfy conditions from standards EN 15495:2007, EN
14584:2005 and EN 12817:2010. On fig. 1 we can see location of sensors for two different
types of pressure vessels [5].

Fig. 1: Location of sensors

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The surface below the sensors shall be cleaned to ensure the maximum coupling efficiency. Bad
coupling efficiency can prevent credible evaluation of material. The characteristics of the typeof the acoustic couplant used shall not affect the structure adversely. The sensors are directly
attached to the pressure vessel with magnetic holders [4, 6].

3.

Results of the tests where errors

3.1 Raining during the test
On fig. 2, we can see part of the test of the pressure vessels with volume 2,8m3 and length of
1,8m, during which started to rain. Raining started at time 1100s, which is shown as increased
activity of hits. During 1100s and 1300s we tried to hold down the pressure, to get the mostreliable data, how rain affects on measured signals. As we can see on Fig. 2 measured signals
mostly have low amplitudes (< 60 dB). Around 1250s we can see hits with bigger amplitudes.
That we could attribute to bigger drops which have fallen from the nearest tree.

Amplitude of hits
Pressure

Fig. 2: AE activity versus time and pressure
Fig. 3 show amplitude of the measured AE-event versus clusters (area between two sensors
divided on 9 parts) during the test of pressure vessel. During the test the biggest activity wasmeasured in cluster 9 and the smallest in cluster 4. The biggest amplitude in cluster 9 was 70 dB.

Fig 3: Measured AE-events versus clusters

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On Fig. 4 are shown amplitudes of measured signals versus time and pressure. It was slowly
raining during whole test. As we can see from Fig. 4 we have a lot of hits with low amplitude,
lower than 40 dB if we are precise. We used filter, which we are using in such a cases to delete
signals which were caused by rain drops. Blue squares show hits before using filter, while redsquares present hits after using filter. We can see that during holds of the pressure AE-activity
didn’t decreased significantly. In case of not raining during the test, that would show possible
danger. Amplitudes are quite high for this time interval and achieve also 50 dB. AE-activity is
the biggest at the end of the test cause pressure there achieves maximum limit and rain only
contribute to increased AE-activity.
Hits before filtering
Hits after filtering
Pressure

Fig. 4: AE activity versus time and pressure
On Fig. 5 we can see one of the transient AE-signal caused by rain drop. Amplitude of signal
quickly exceeds the acquisition threshold. Signal is short with typical exponential amplitudedecreasing.

Fig. 5: AE-signal caused by rain

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3.2 Touch of the pressure vessel during the test
We were testing pressure vessel for LPG with volume 1,8m3 and length of 2m.
With ellipse on Fig. 6 we can see hits which were caused by touch of the hand during the test. As
we can see AE-activity significantly increase, mostly with low amplitudes but in so large
number. Number of hits during this touch was increased from 111 to 786 hits.
Amplitudeof hits (dB)Time (s)Fig. 6: AE-activity versus time during the touch of the pressure vessel
On Fig. 7 we can see one of the transient AE-signal caused by touch. Amplitude is extremely
high already at the beginning. It raises high but also drop very quickly and fall under acquisition
threshold.

Fig. 7: AE signal caused by touch
3.3 Močno korodirana tlačna posoda
One goal of acoustic emission testing on the pressure equipment is to diagnose the propagationof localized corrosion or to verify the integrity of the corrosion protective layer on the coat.
Corrosion damage propagate very fast in some cases, if coat of pressure vessel is damaged forexample. AE has been applied, not only to detect or confirm corrosion but also to validate
repairs and evaluate efficiency of corrosion protection. Corrosion cracking is important damage
phenomenon when we talk about pressure vessels. Unpredicted failures can occur in service,with safety, health, economic and environmental consequences. Pressure vessel is often corrodedon external coat, because of weather influence (rain, ice, sun,…). It’s hard to separate signals
generated from corrosion, from others like particle impacts, crystallization, [3].
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We were testing pressure vessel for LPG with volume 5m3 and length of 2,3m.
On Fig. 8 we can see AE-activity during test heavily corroded pressure vessel. We raised up
acquisition threshold from 35 to 40dB because we exceed criteria for beginning ofpressurization. The amplitude range is up to 65dB. Number of hits goes over 1000 and AEevents stop at number 159. We classify pressure vessel in class 2, cause AE-events don’t havehigh amplitudes. From this results we can come to conclusion that corrosion have big influence
on increased AE-activity, with low amplitudes, during the test. That kind of pressure vessel must
be restored and painted with corrosion resistant paint. AE-activity starts increasing duringsecond pressurization, higher pressure. On Fig. 9 we can see number of hits on each channel
through whole test. Number of hits is bigger on second channel, from which we can conclude
there was more corrosion near second sensor.

Amplitudeof hits
Pressure

Fig. 8: AE activity versus time and pressure

Fig. 9: Hits increasing on both channels
3.4 Leakage on probe for measuring pressure
We were testing pressure vessel for LPG with volume 2,5m3 and length of 1,5m.
In some cases leakage appear during the test even though we checked for leakage before starting
the test. On Fig. 10 we can see increased ASL (average signal level), marked with red ellipse,
which can tell us that somewhere on pressure equipment leakage is presented. We found that thatprobe for measuring pressure wasn’t tighten well. We tighten it and continued with test.

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Fig. 10: Increased ASL caused by leaking
3.5 Wrong valve opened
Here we have some similar results as in previous paragraph where we talk about leakage. Fig. 11
shows us what happened if we open wrong valve, with which we control pressurization speed.
Instead of opening gas valve we opened liquid valve. System detected different fluid flow as thistime there was liquid phase in pipe and not gas as usually and this was shown as increased ASL,
from 13 to 22dB. Similar thing happened if we open gas valve to fast.

Fig. 11: Increased ASL caused by opening wrong valve

4.

Conclusion

In this article are shown are represented results which were made by some interruptions during
the tests with AE. Based on the results, we can come to the conclusion that rain affects on resultsof the test as it increases the AE-activity. Amplitudes of hits are mostly low (<60dB), but insome cases there are also some higher amplitudes. At the touch of a hand also come to increased
AE-activity, but when we put hand down from pressure vessel AE-activity dropped rapidly. One
of AE-signal has in that case typical shape, with high amplitude at the beginning, which getlower with time. Leakage during the test of pressure vessels or any sudden change in the gas
flow (to quick pressurization) is detected as an increase of the ASL. With ASL values we cancheck leakage on any other pressure equipments. At corroded pressure vessel we detect anincreased AE-activity already at the beginning of pressurization. The amplitude range is up to 65
dB. During the pressure hold AE-activity decreases, which is quite a normal phenomenon,
because at some time during the pressure hold movements in material stops.

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5.

References[1]

Hartmut Vallen: German Society for NDT, Vallen systeme: Acoustic emission testing,Fundamentals, Equipment, Applications, Volume 6, 2006, 6-48
SIST EN 1330-9 Non-destructive testing –Terminology – Part 9: Terms used in acoustic
emission testing
Ronnie K. Miller, Eric v. K. Hill, Patrick O. Moore: Nondestructive testing handbook,Acoustic emission testing, Volume 6, American Society for Nondestructive Testing, 2005,
13, 38, 415-422
SIST EN 15495:2008 Non-destructive testing – Acoustic emission – Examination of
metallic pressure equipment during proof testing – Zone location of AE sources
SIST EN 12817:2010 LPG equipment and accessories- Inspection and requalification of
LPG tanks up to and including 13 m3
SIST EN 14584:2005 Non-destructive testing- Acoustic emission- Examination of metallic
pressure equipment during proof testing- Planar location of AE sources

[2]
[3]

[4]
[5]
[6]

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The 12th International Conference of the Slovenian Society for Non-Destructive Testing»Application of Contemporary Non-Destructive Testing in Engineering«
September 4-6, 2013, Portorož, Slovenia

ACOUSTIC EMISSION MONITORING OF FRACTURE
PROCESS STAGES IN HYDROGEN EMBRITTLED HIGHCARBON STEEL
E. Merson, D. Merson, A. Vinogradov
Laboratory for the Physics of Strength of Materials and Intelligent Diagnostic Systems,
Togliatti State University, Togliatti, 455667, Togliatti, Russia
mersoned@gmail.com
ABSTRACTThe acoustic emission (AE) technique was used for monitoring of the fracture process duringslow three-point bending of smooth Zn-plated high-carbon steel specimens. It was shown thatelectrolytic Zn-plating results in hydrogen charging of specimens, which excreted a strong effecton the ductility of steel. The primary AE sources during slow monotonic loading of the hydrogen
embrittled specimens, which were revealed with an aid of SEM, were associated with brittle
cracking along austenitic grain boundaries or sulphides. The concurrent AE analysis allowed
distinguishing between five stages of the fracture process: (I) microcracks nucleation during the
incubation period showing a low AE activity; (II) the stage of the increasing rate of AE signals
accumulation and increasing RMS level caused by accelerated damage accumulation; (III) the
overall stable crack growth, followed by damage rate reduction, (IV) the stage preceding thecritical regime corresponding to formation and propagation of a catastrophic crack (V).
Key words: hydrogen embrittlement, fracture, acoustic emission, high-carbon steel
1.

Introduction

Hydrogen embrittlement (HE) of metals and alloys is a well-known scientific andengineering problem. Dissolved hydrogen can cause a serious deleterious effect on service
properties of metallic articles. A degree and form of HE manifestation is dependent on manyfactors such as the microstructure, chemical, physical and mechanical properties of metal,
concentration and state of hydrogen, temperature and strain rate, etc. HE of steels usually resultsin crucial downgrade of their ductility and strength [1], particularly for high-strength steels [2]where even a little concentration of hydrogen (<1ppm) can cause a substantial drop in ductility[3]. One of the most common sources of hydrogen for such steels is electrolytic plating which isoften used as a final technological operation during manufacturing. Hydrogen dissolves
predominantly in the coating and the surface layers of the steel substrate during this process [4].
In addition, the distribution of hydrogen can easily change under the influence of elastic stress,
temperature, electric filed, etc., because of high mobility of hydrogen atoms inside the metal.
Therefore, any elastic distortion inside the metal matrix caused by structural irregularities suchas dislocations, grain and interphase boundaries etc. can serve as potential traps for hydrogen [2].

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The increased concentration of atomic or molecular hydrogen in a local volume of metal can leadto microcrack nucleation and propagation near this region.
Several mechanisms have been proposed to explain the effect of hydrogen on mechanicalproperties of materials. The most commonly cited mechanisms include (i) the hydrogen pressurebuildup mechanism, in which the pressure of hydrogen bubbles provides the stress for theformation and propagation of a crack, (ii) hydrogen adsorption at the crack tip or surface
imperfections, which reduces the surface energy for crack propagation, (iii) hydrogen-induced
reduction of the cohesive strength of the lattice, (iv) hydrogen accumulation at precipitates andsecond-phase particles, which can lead to dislocation generation or nucleation and growth of acrack, (v) formation and fracture of brittle hydrides, (vi) hydrogen-induced reduction in stackingfault energy and (vii) hydrogen-induced enhancement of dislocation mobility, which promotes
dislocation motion at a lower applied stress [5]. However before any mechanism sets in, local
enrichment of metal with hydrogen has to occur up to levels at least 10 times higher than normalmean volume concentration. Thus the most pronounced effect of HE near room temperatureappears at slow strain rates or at static load due to a large enough time for hydrogen diffusion[6]. Unfortunately, such service conditions are most frequently encountered for a wide range ofsteel parts and constructions. That is the reason for many failures caused by hydrogen.
Hence, to prevent crucial consequences of HE the effective non-destructive techniquecapable of monitoring for equipment exposed to HE is required along with the deepened insight
into the processes which occur within a material under the influence of hydrogen. One of themost promising instrument for solving both problems is the acoustic emission (AE) techniquewhich is known to be highly sensitive to HE [7–14]. In case of the HE of a high-strength steels
the intergranular cracking proposed as a main AE source [8,10,15]. Therefore, the amplitude of
the AE events is higher for the high strength steel after hydrogen charging and amount of the AE
events correlates with the number of intergranular cracks [8]. Troiano [16] has shown that under
static load of the high strength steel the crack growth occurs involves three stages: (i) incubation
period interpreted as the time required for a critical amount of hydrogen to diffuse to the point of
crack initiation; (ii) nucleation and slow discontinuous crack growth under macroscopic
diffusion of hydrogen; (iii) unstable rapid crack growth through the material free of hydrogen.
However, because of its low sensitivity, electrical resistance technique cannot measure theresistance change for a very small crack and also it does not give a detailed insight into theprocess of crack initiation and propagation [17]. Therefore the same staging was revealed in [17]with the help of the AE technique.
Despite a significant progress in the field of AE application to the HE phenomena it should
be noted that most of investigations were conducted with notched specimens under static load.
Such conditions allow discovering the kinetics of a single crack growth when the most part ofspecimen material is in a state of elastic deformation. However for the successful non-destructive
testing of the steel parts in service it is important to conduct the AE experiments underconditions close to reality. Thus, the objective of this investigation is to reveal the hydrogenassisted fracture kinetics in the smooth high-carbon steel specimens under slow active loading.
2.

Experimental

High carbon steel 70 in Russian designation (AISI 1070 is a close analogue) was chosen
for the present study because it is wide-spread industrial use as spring steel, which is known tobe prone to premature failure due to HE [12]. The chemical composition of this steel is shown in
Table 1. The samples were machined to have a rectangular shape of 120 mm length, 20 mm
width and 1.8 mm thickness. They were thermally treated using a schedule that is commonlyused for this type steels: 1) quenching from 850±10 ºС in oil; 2) low tempering at temperature of
240±10 ºС for 1 h; 3) primary tempering at 450±10 ºС for 2 h followed by cooling in air. The
hardness after this treatment was of 49±2 HRC.

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Table 1. Chemical composition of steel 70.
С
0.70

Si
0.29

Mn
0.72

Cr
0.03

S
0.006

P
0.013

Cu
0.06

Ni
0.02

Fe
balance

For hydrogen charging a part of the specimen was electrolitically plated with a Zn layer of
15 mm thick from an alkaline solution containing of 110-120 g/l NaOH and 12-14 g/l ZnO. The
plating was performed at electric current density of 8 A/dm2 during 20 min. Reference specimens
were left unplated for comparison.
Using a Bruker AXS G8 gas analyzer with a nominal accuracy ±0,05 ppm the hydrogencontent [H] was measured in a reference and Zn-plated specimens and also in a specimens withremoved Zn coating. The samples of 5×5×1.8 mm dimensions were used for the gas analysis.
Surface layer of ~50 µm depth was mechanically removed with the grinding paper for a part ofZn-plated specimens in order to reveal a hydrogen distribution along the specimen thickness.
Three points bending mechanical testing was performed at room temperature using a screw
driven Instron-type frame. The load was applied at a constant cross head velocity of 0.07
mm/min. The ductility was characterized by the maximum deflection at failure Δ max .
Acoustic emission was recorded using a home-made PC-controlled system with a 12 bits
ADC at the core. A broadband (50-500 kHz) piezoelectric transducer MSAE-L2 (Microsensors
AE Ltd., Russia) with a low noise built-in 27 dB preamplifier was securely mounted on thespecimen using vacuum oil as a couplant. The signal from the sensor’s preamp output wastransferred through a main filter-amplifier MSAE-FA010 with the gain set at 40 dB and thefrequency band set between 50 and 1200 kHz. The laboratory noise did not exceed 30 µV (peakto-peak). The burst AE signals having relatively high amplitude above 1 mV (of 30dB threshold)were recorded and counted synchronously with the load signal. The waveforms of 4096 readings
were stored for the post-mortem analysis: the energy per realization was calculated from thepower spectral density function as described in detail in [18]. The MSAE-FA010 amplifiercontains a built-in circuit allowing precise measurement of the true root-mean-square voltage
U RMS with 100 ms integration time. The U RMS signal was fed to a 14 bit ADC for continuous
acquisition at 1 kHz sampling rate.
The fracture surface was observed by a scanning electron microscope LEO1455VP.
3.

Results and discussion

The gas-analysis has shown that electrolytic Zn-plating results in the substantial increase in
the hydrogen concentration [H] in the steel specimens (c.f. Table 2). It is accompanied by theloss of specimens ductility (Δmax) and the dramatic increase of the number of AE events (N Ʃ ).
For the all specimens AE events have a burst type waveforms (c.f. Fig. 1a) which are usuallyrelates to the crack growth process including hydrogen-assisted crack growth [19]. In case of Znplated specimens with high hydrogen content a part of recorded AE events is performed as asequence of burst waveforms which goes successive with a high frequency (c.f. Fig. 1b).
Table 2: Hydrogen content [H], maximum deflection at failure Δ max and number of AE eventsN Ʃ for tested specimens
Specimen type[H], ppm

Δ max , mm

NƩ

Reference

2,5±0,316,9

317

Zn-plated

32,9±0,6

6,5

5172

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After removing a surface layer from the Zn-plated specimens the hydrogen content drops
to the initial value of 2.3±0.5 ppm corresponding to the reference samples. Thus the most part ofhydrogen introduced into the steel during Zn-plating is preserved within the surface layer of thespecimens and Zn coating. This observation appears to be in agreement with the early works [4].

Fig. 1: Typical AE waveforms in tested specimens: (a) discrete AE “burst”-type signal, (b) ashort time series of overlapping burst-type signals.
The fracture surface of the reference samples exhibits mainly ductile features, Fig. 2a, withseparate brittle facets in a central part, which are presumably caused by the initially dissolvedhydrogen, Fig. 2b. The ductile transcrystalline microcracks can also be seen in a central part of
the fracture surface as characteristic “steps” between the MnS sulfides, Fig. 2с. The X-ray
energy-dispersive analysis, Fig. 2c, revealed an excess amount of S and Mn atoms in the local
area corresponding to the steps clearly visible at the fracture relief. Inclusions, such as MnS, canact as stress raisers attracting diffused hydrogen to the site so that hydrogen-assisted microcracks
usually initiate along the interphase boundaries at inclusions [20]. Decohesion of sulfide
inclusions has long been recognized as a powerful AE source in high strength steels [21,22].
The fracture surface appearance of Zn-plated specimens is distinctly different. Figure 3a
reveals a brittle zone having an approximately elliptic shape in the central part of fracturesurface. Intergranular cracking is the main fracture mechanism in this zone as can be seeing at
higher magnification, Fig. 3b. The small sites of transgranular relief separate intergranular facets,Fig. 3c. The brittle zone evidently appears due to electrolytic hydrogen diffused from the surface
toward center part of the specimen during the test.
A macroscopic crack aligned with the major ellipse axis in the middle part of the brittle
zone can be commonly noticed. This intercrystalline crack extends in the direction normal to thefracture surface, Fig. 3d, across the whole brittle zone, although the signatures of sulfides
decohesion, such as those seen in Figs.2, are rare, Fig.3b, c. Since the macrocrack exists on thefracture surfaces of the both reference and Zn-plated specimens it is not due to hydrogen andpossibly it forms due to features of the stress-strain state. However the mechanisms of it forming
can be significantly changed under action of hydrogen as shown above.
Thus the electrochemical Zn-plating results in substantial increase of the hydrogen content
and ductility reduction in steel 70. The reduced ductility gives rise to the increasing area fractionof brittle intergranular fracture surface, Figs.2 and 3, and, simultaneously, to the increasing
amount of discrete type AE, Table 2. It is therefore plausible to suppose that these AE transient
signals are originated from microscopic brittle fracture processes such as intergranular cracking
along the boundaries separating austenitic grains. However, the AE count reflects only the fact of
AE arrival, i.e. it says only that a certain local structural transformation, e.g. crack, has occurred
within the sample, but it does not say anything about the scale of damage. In the contrast, the AE
amplitude (or rms voltage, or energy) can be related to the size of the defect and its velocity [23].
Therefore, the concurrent analysis of the AE RMS behavior and the AE signals accumulation with
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regard to the loading curve can be useful for distinguishing of the fracture kinetics.
At the initial stage of loading before load P II (table 3, stage I) the “silent” zone is observed
without evident AE, Fig. 4. The incubation period is a well-known feature of the hydrogenassisted cracking during static load testing of the notched specimens [1,16,17]. A start of theloading activates the redistribution process of the diffusible hydrogen in the specimen. The highhydrogen concentration appears in the zone of the maximum triaxial stress ahead of the notchand microcracking process begins. Because of the low scale and velocity of this process the AE
activity is negligible during that period [17].

Fig. 2: Fracture surface of the reference specimens: a) – central part, b) – brittle facets, c) –cracking along sulfides, d) – EDS spectrum taken from one of inclusions marked by A on c).

Fig. 3: Fracture surface of the Zn-plated specimens: a) – central part, b) – , c) – cracking
along sulfides, d) - EDS spectrum taken from one of inclusions marked by A on c).
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Table 3: AE parameters for the different stages of fracture of Zn-plated specimen
Stage
I
II
III
IV
V

Mean rate of event
accumulation, event/s
0.05
3.01
8.30
3.53
1.29

Mean rate of event energy
accumulation, arb.units/s
0.65
624.101716.8579.43
143.16

Mean rate of U RMS
accumulation, mV/s
0.01
15.03
72.49
22.32
44.07

Fig. 4: Stages of Zn-plated specimen fracture.
The “silent” zone, which is observed in the present work, has similar features as the
incubation period in [16]. However, in the present case of active loading of the smooth specimen
having no notch, the multiple microcracks initiates near the natural stress concentrators andhydrogen traps such as grain boundaries or sulfides. Thus, the formation of the main crack
occurs by the coalescence of numerous microcracks. Indeed, the intensive AE appears when acertain critical load P II (stage III) is reached, Fig. 4. As the first signal appeared, the rate of AE
events accumulation increased steeply until the maximum value is attained at P III , Fig. 4. The
increase in U RMS is also observed during this stage, Table 3.
Presumably, the stage II (P II < P < P III ) begins when the stress in a hydrogen enrichedzone becomes high enough to trigger multiple cracking. This process should start near the
specimen surface because of the high hydrogen concentration and the maximum stress in thisregion. The big amount of stress risers surrounded by hydrogen can lead to fracture initiation in
multiple places over the specimen surface. The stress growth down to the specimen thickness
during the loading should result in an increase of both the number of microcracks and thenumber of AE events. Therefore, this can probably explain the dependence of the AE events
accumulation rate on the load on the stage II. As a certain time after the load P III is elapsed (stage
III), the cracking process occurs with a high constant rate independently of the load magnitude.
One can suppose that, in this stage the cracks growth is controlled by diffusible hydrogen in away similar to the metastable crack growth during a delayed failure observed in [16]. The
microcracking events follow each other with a high frequency as reflected by both the AE events
accumulation curve, Fig. 4, Table 3, and the AE waveforms, Fig.1b.
At the load P IV the decline of AE activity occurs (stage IV). The accumulation rate ofnumber and energy of the AE events decrease (fig. 4).

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In fact from the beginning of the loading the deformation of the specimen controlled by
two competing mechanisms – the plastic deformation and destruction (microcracks initiation andgrowth). Apparently accumulation rate of the AE events controlled by the rate of the microcracks
initiation and growth. In this case the uniform plastic deformation does not contribute to AE
because no continues type AE waveforms are found. Steady reduction of the cross-section area
due to destruction result in activation of the plastic deformation between the appeared defects.
Thus the forming of the main crack is junction of the brittle microcracks by the plastic
deformation. This is evidenced by the areas of the ductile relief between the brittle intergranularfacets in the Fig.3c. Simultaneous junction of the microcracks and AE activity reduction wasobserved in [14] for the low-carbon steel.
A final fracture stage V corresponding to the unstable main crack growth, Fig.4, sets in at
the load P V . This stage is featured by the increase of the AE event energy and U RMS
accumulation rate, Table 3, Fig. 4. This fact is explained by the formation of the main crack of
critical size and its fairly rapid growth by a ductile mechanism through the rest of the sample. It
should be noticed that the accumulation rate of the number of AE events still reduces during thisstage but their energy increases, Table 3. Thus the U RMS and the energy of AE signals are theappropriate parameters for the identification of the stage V.
It is shown that fracture kinetics of the smooth hydrogen charged specimens during
monotonic loading has a difference compared with a case of static loading of the notched
specimens in [7]. Due to the notch, the main crack forms immediately after the incubation period(stage I). So stages II and III of the multiple microcracking exist only at loading of the smooth
specimen, Fig. 4. Moreover, if there is no notch on a sample the forming of the main crack andits propagation during stage IV presumably occurs by the ductile junction of the brittle
microcracks of critical size. Because of slight contribution in AE of plastic deformation process
the activity of AE reduces in this stage. But in the case of static load the main crack growth iscontrolled by the diffusion of hydrogen so fracture occurs in a brittle manner. It is accompaniedby the intensive AE as showed in [7] for the stage of metastable crack growth.
4. Conclusions
1. The primary AE source in the high-carbon steel during monotonic three-point bending
is associated with microfracture events such as cracking along the austenitic grain boundaries ornonmetallic inclusions. Thus, the number of the discrete AE events correlates with the deflectionof the samples and brittle area on their fracture surface.
2. Simultaneous analysis of the AE RMS behaviour, the AE signals accumulation kinetics
and the loading curves allowed distinguishing between five stages of the fracture process: (I)microcracks nucleation during the incubation period having a low AE activity; (II) the stage
featured by the increasing rate of AE signals accumulation and increasing RMS level caused byaccelerated damage accumulation; (III) the overall stable crack growth, followed by damage rate
reduction, (IV) the stage preceding the critical regime corresponding to formation andpropagation of a catastrophic crack (V).
Financial support from the Russian Ministry of Education and Science under grant-in-aid
11.G34.31.0031 is gratefully appreciated.
5.

References[1] Bernstein I.M.: The role of hydrogen in the embrittlement of iron and steel, Materials
Science and Engineering. 1970, Vol. 6, No 1, 1–19.
[2] Hirth J.: Effects of hydrogen on the properties of iron and steel, Metallurgical andMaterials Transactions A, 1980, Vol. 11, No 6, 861–890.
S140

[3] Wang M., Akiyama E., Tsuzaki K:. Determination of the critical hydrogen concentration
for delayed fracture of high strength steel by constant load test and numerical calculation,
Corrosion Science, 2006, Vol. 48, No 8, 2189–2202.
[4] Beloglazov S.: Surface layer absorption of hydrogen and embrittlement of high-strengthand low-carbon steels during electrochemical processes, Met. Sci. Heat Treat., 2008, Vol.
50, No 5, 273–277.
[5] Robertson I.M.: The effect of hydrogen on dislocation dynamics, Engineering Fracture
Mechanics. 2001, Vol. 68, No 6, 671–692.
[6] Brown J.T., Baldwin W.M.J.: Hydrogen embrittlement of steels, Jr., Trans AIME, 1954,
Vol. 203, 298–304.
[7] Bhattacharya A.K., Parida N., Gope P.C.: Monitoring hydrogen embrittlement cracking
using acoustic emission technique, J. Mat. Sci., 1992, Vol. 27, No 6, 421–1427.
[8] Carpenter S., Smith D.: The effects of cathodic charging on the acoustic emission
generated by intergranular cracking in sensitized 304 stainless steel, Metallurgical andMaterials Transactions A, 1990, Vol. 21, No 7, 1933–1939.
[9] Caskey G.R.J.: Acoustic emission from hydrogen saturated Type 304L stainless steel,Scr. Metall, 1979, Vol. 13, No 7, Pages: 583–587.
[10]
Padmanabhan R., Suriyayothin N., Wood W.: Grain size-acoustic emission
relationships in hydrogen induced delayed cracking, Metallurgical and Materials
Transactions A, 1983, Vol. 14, No 11, 2357–2362.
[11]
Wang Z.F. et al.: Dependence of acoustic emission for low strength steels upon
the embrittlement and the plastic zone reduction at the crack tip during corrosion fatigue
crack propagation, Fatigue Fract .Eng. Mater. Struc, 1993, Vol. 16, No 4, 441–451.
[12]
Merson E.D. et al.: Effect of strain rate on acoustic emission during hydrogenassisted cracking in high carbon steel, Mater. Sci. Eng. A, 2012, Vol. 550, 408–417.
[13]
Wang Z.F., Zhu Z., Ke W.: Behavior of acoustic emission for low-strength
structural steel during fatigue and corrosion fatigue, Metall. Mater. Trans. A, 1991, Vol.
22, No 11, 2677–2680.
[14]
Zharkova N.A., Botvina L.R., Tyutin M.R.: Damage accumulation stages in a
low-carbon steel during uniaxial tension, Russian Metallurgy, 2007, No 3, 223–229.
[15]
Dedhia D.D., Wood W.E.: Application of acoustic emission analysis to hydrogenassisted cracking, Materials Science and Engineering, 1981, Vol. 49, No 3, 263–273.
[16]
Troiano A.R.: The role of hydrogen and other interstitials in the mechanicalbehavior of metals, Transactions of ASM, 1960, Vol. 52, No 1, 54–80.
[17]
Bhattacharya A.K., Parida N., Gope P.C.: Monitoring hydrogen embrittlement
cracking using acoustic emission technique, J. Mat. Sci, 1992, Vol. 27, No 6, 1421–1427.
[18]
Vinogradov A., Nadtochiy M., Hashimoto S. M.S.: Correlation between spectral
parameters of acoustic emission during plastic deformation of Cu and Cu-Al single andpolycrystals, Materials Transactions, 1995, Vol. 36, No 3, 426–431.
[19]
Kudryavtsev V.N. et al.: Detection of hydrogen embrittlement of a carbon steel by
acoustic emission, Corrosion, 1981, Vol. 37, No 12, 690–695.
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Botvina L., Tetyueva T., Ioffe A.: Stages of multiple fracture of low-alloy steels
in a hydrogen sulfide medium, Metal Sci. Heat Treat., 1998, Vol. 40, No 2, 61–70.
[21]
Teoh H.B., Ono K. Fracture-Induced Acoustic Emission during Slow Bend Testsof A533B Steel, J. Acoustic Emission, 1987, Vol. 6, No 1, 1–12.
[22]
Ono K., Yamamoto M.: Anisotropic Mechanical and Acoustic Emission Behaviorof A533B Steels, Materials Science and Engineering, 1981, Vol. 47, 247–263.
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Wadley H.N.G., Scruby C.B., Sinclair J.E.: Acoustic emission source
characterization, The J. Acoust. Soc. Am., 1980, Vol. 68, No S1, S103–S104.

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The 12th International Conference of the Slovenian Society for Non-Destructive Testing»Application of Contemporary Non-Destructive Testing in Engineering«
September 4-6, 2013, Portorož, Slovenia

EFFECT OF TEMPERATURE ON MICROSTRUCTURE
EVOLUTION IN TRIP/TWIP STEELS DURING TENSILE
DEFORMATION

1

M. Linderov 1, C. Segel 2, A. Vinogradov 1, A. Weidner 2, H. Biermann 2

Laboratory of Physics of Strength of Materials and Intelligent Diagnostic Systems, Togliatti
State University, Togliatti, Russia, dartvi@gmail.com
2
Institute of Materials Engineering, Freiberg Technical Univeristy, Freiberg,
Germany,christian.segel@iwt.tu-freiberg.de
ABSTRACTThe model austenitic stainless TRIP/TWIP steels have an outstanding combination of strengthand ductility, depending on chemical composition and loading conditions. The critical factor,
which strongly affects all deformation-induced processes in metastable steels is temperature. To
get a better insight into the effect of temperature on kinetics of deformation processes in Fe-CrMn-Ni TRIP/TWIP steels with different content of Ni (6 and 9%) controlling the degree ofstability of the initial austenitic structure, an acoustic emission (AE) technique was used during
uniaxial tension at two different temperatures - ambient and 373K. The in-situ AE results are
paired with detailed SEM investigations using the electron backscattered diffraction (EBSD)
technique to indentify the martensitic phase transformation and for visualisation of the deformed
dislocation microstructures. The cluster analysis of the AE “streaming” signal recorded
continuously during tension test has revealed an excellent correlation with synergistic
complexity of involved deformation mechanisms in various combinations: dislocation slip,
martensitic α′ transformation, and twinning.
Key words: TRIP steels, TWIP steel, acoustic emission, cluster analysis, martensitic
transformation, scanning election microscopy.
1.

Introduction

Among other engineering steels, modern high alloyed metastable austenitic steels have an
outstanding combination of mechanical properties including a high strength and ductility, Fig. 1[1]. The deformation behavior CrMnNi-steels will be determined by characteristic deformation
mechanism like the TRIP effect (TRansformation Induced Plasticity), the TWIP effect(TWinning Induced Plasticity) and the gliding and cross slipping of dislocations. All thisS142

deformation mechanism influences significantly the deformation characteristics and mechanicalbehaviour of the steel.

Fig.1. Range of mechanical properties of TRIP/TWIP steels in comparison with othermodern engineering steels (Adapted from [2])The TRIP effect is characterized by a deformation-induced martensitic phasetransformation under loading. The transformation from the metastable austenitic phase γ (FCC)
involves the formation of deformation bands which are characterized by an intermediate
structure with a high density of parallel stacking faults which is often associated with the socalled ε-martensite (HCP) and a α’-martensite (BCC) structure. In the literature the TRIP effectin metastable stainless steels will described by a sequence of the martensitic transformation likeγ  ε  α. The TWIP effect increases significantly the plasticity and the flow stress. The
reason is that twins act as a barrier for gliding dislocations. The susceptibility of steel to TRIP or
TWIP effect can be concerted by alloying which controls the stacking fault energy (SFE) and theaustenitic stability, and thus determines the deformation mechanisms active under different
temperatures and strain rates [3]. For instance, the increase of the temperature results in higher
austenite stability and, thus, reduces the propensity to the martensitic phase transformation.
Using a model family of high-alloyed 16Cr-6Mn-xNi cast steels (composition in wt. %)exhibiting the TRIP and/or TWIP effect [4-6] depending on the amount of Ni x varied between 3
and 9 wt.%, it has been demonstrated that the steel with the low nickel content exhibits profuse
a’-martensite formation whereas the steel with the highest nickel content shows intensive
twinning. Comprehensive understanding of the synergistic interplay between the primarymechanisms involved into complexity of plastic deformation – dislocation slip, twinning andmartensitic transformation - at elevated temperature is still lucking. The present work aims at
reducing this obvious deficit in knowledge.
Aiming at clarifying on the effect of temperature on the deformation behavior of austenitic
stainless steels, we used the same methodology as in the previous paper [7]: the detailed
statistical and cluster analysis of the AE time-series in real time scale is backed bycomprehensive post-mortem characterization of the deformation microstructure by highresolution EBSD (electron back scattering diffraction) technique enabled in modern SEM(scanning electron microscopy) facilities.

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2. Experimental
Flat tensile specimens with a rectangular cross section of 8 × 4 mm² and a gauge length of
35 mm were machined from the plates cast as shown in Fig. 2. Their chemical compositions andSFE γ SF are summarised in Table 1. Here M s is calculated from the chemical composition as
described in the experimentally-based approach in ref. [8, 9]; γ SF is calculated according to [8, 9]
on basis of the stacking fault energy of pure iron (36-42 mJ/m²) taking into account the influenceof the alloying elements.

AE sensor

Thermocouple

Fig. 2. Tensile specimen shape with the PAC WD sensor on a shoulder part.
The details of the AE experimental setup based on the 18 bit PCI-2 (Physical Acoustics
Corp., USA) data acquisition board operated in a threshold-less continuous mode with 2
Msamples/s data rate have been given in [7]. The WD FS63 sensor (PAC, USA) was clamped tothe shoulder part, Fig.2, of the specimen using machine oil as a coupling medium.
The AE signal from the sensor output was amplifier by 60 dB by a low-noise PAC 2/4/6
preamp and passed through the 30-1000 kHz band-pass filter before acquisition. For tests at
the elevated temperature a thermocouple was welded to the specimen in the intimate vicinity
to the gauge part.
Table 1.Chemical composition and the stacking fault energy of the tested Cr-Mn-Ni steels
Chemical composition (wt.-%)Stacking fault energy

Steel

C

Cr

Mn

Ni

Si

Al

N

γ SF (mJ/m²)

16Cr-6Mn-9Ni

0.04

15.3

5.8

8.6

0.9

0.07

0.05

15.4 - 21.4

16Cr-6Mn-6Ni

0.08

16.0

6.0

6.1

1.0

0.05

0.05

9.6 - 15.6

The specimens were deformed in tension using a screw-driven frame (Zwick 1476,
Germany) at room temperature (RT) and 100 °C under crosshead displacement control at thenominal strain rate of 3 × 10-3s-1.
For the post-experimental SEM observations the gauge parts of the specimens weremechanically and vibration polished to a mirror-like finish. Vibro-polishing for 24 h with 0.02
µm grade colloidal silica was employed at the last stage of preparation. The BSE and EBSD
investigations were carried out using a field emission scanning electron microscope (MIRA 3
XMU, TESCAN, Czech Republic) powered by the OIM™-technology from EDAX/TSL.

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3. Results of AE analysis
3.1. Acoustic emission behaviour
Loading diagrams of the model TRIP/TWIP steels at room temperature and at 373 K
synchronized with AE data are shown in Fig. 3 where AE is represented by the energy E (perrealization of 1 ms duration) and median frequency f m of the power spectral density (PSD)
function. The temperature exerts a strong effect on the hardening behaviour. Furthermore, thiseffect is more pronounced in the metastable 16Cr-6Mn-6Ni steel than in the relatively stable
16Cr-6Mn-9Ni steel having a higher stacking fault energy, Table 1.
As can be seen from Fig. 3, AE in both TRIP/TWIP steels consists of high-amplitude bursts,
arising on a background of a noise-like continuous signal, cf. [7]. Typically to pure fcc metals
and alloys where plastic deformation is mediated by dislocation slip, the AE level rapidly
increases after the beginning of loading around the yield stress and then reduces smoothly until
fracture. However, importantly is that the behaviour of the median frequency f m in the deforming
stable and metastable steels, 16Cr-6Mn-9Ni and 16Cr-6Mn-6Ni, respectively, is quite specificand is not similar to what is typically observed during hardening of pure fcc metals. The f mmagnitude exhibits a clearly pronounced maximum on the early deformation stage of both steels
at both testing temperatures, Fig. 3, unlike pure fcc metals such as Cu or Ni where the AE
spectrum commonly shifts to higher frequencies during uniform of deformation where hardening
is mediated primarily (or entirely) by dislocation slip and interaction between dislocations. This
specific behaviour of the AE PSD function is a clear indicator of the operating sources other thandislocation slip. The most obvious candidates for those sources are mechanical twins and phasetransformation the distinction between which can be quantified with a help from an AE cluster
analysis as is outlined in the next section.
3.2. Kinetics of different deformation mechanisms probed by AE cluster analysis
Different AE mechanisms are assumed to generate different waveforms, which then can be, in
principle, distinguished by their characteristic shapes of the Fourier power spectral density (PSD)
function. Based on this idea, a new “evolutionary” clustering algorithm (ASK-adaptive
sequential k-means) has been proposed recently by Pomponi and Vinogradov [10] and proved
effective to classify the AE signals with different power spectral density functions associatedwith different emitting sources [11]. The mathematical details of the method used are given in[10]. Importantly is that the method is data driven and the number of clusters to be created does
not have to be specified in advance. Since the AE acquisition starts a few seconds before theloading starts, the beginning of the AE streaming record includes a considerable amount of data
pertinent to the laboratory noise only. These data are used to tune the algorithm and to verify if itworks correctly. Figure 4 shows the results of application of this approach with a KullbackLeibler divergence used as a measure of inter-cluster distance and a measure of dissimilarity
between different clusters compared by the shapes of their normalized AE power spectral density(PSD). If compared to the results reported in [7] for the same type steels tested under the same
conditions but with another type sensors, it is importantly to notice that despite someunavoidable differences of the power distributions caused by different sensors responses, thegeneral trends in the behaviour of normalized PSDs, Fig. 3, in different samples are quite
comparable and the kinetics of evolution of different AE mechanisms at room temperatureappears to be similar, Fig. 4. One can see that at the beginning of the AE recording a single
cluster corresponding to the background noise is recognized in all specimens. This cluster issubstituted for the AE –pertinent clusters as soon as loading begins (the moment when laoding
starts is indicated by arrows in Fig. 4). These AE-pertinent clusters are however different fordifferent steels under different testing temperatures.

S145

Fig. 3. Stress-strain curves plotted together with the average AE energy E and the medianfrequency f m for three types of austenitic cast steel with 16% Cr 6% Mn and 6 and 9% Ni tested
at room temperature and at 373 K with a strain rate of 3 × 10-3 s-1

Fig. 4. The kinetics of different AE mechanisms during deformation of Fe-Cr-Mn-Ni stainlesssteels, which is reconstructed with a help of the AE clustering procedure ASK developed in [10].

S146

First of all the significant amount of burst type AE with high peak amplitude corresponding tomechanical twinning is observed in the 16Cr-6Mn-9Ni steel and this is correctly recognized by
the clustering procedure used due to fine differences in the AE PSD. In the metastable 16Cr6Mn-6Ni, the phase transformation commences already at small plastic strains at eth beginningof deformation. The transformation starts as a cooperative motion of Shockly partials resulting
the bundles of stacking faults associated with the hcp ε-martensite (see discussion in the next
section). Shortly after the formation of deformation bands consisting of finely spaced stacking
faults, the α’-martensite forms at the intersections between the bands and then extends along thewhole deformation band with progressively increasing volume fraction. Although the elementaryslip of lattice dislocations generates a much weaker AE than the cooperative motion of Shockly
partials and the AE which mechanical twins produce, the contribution of the ordinary
dislocations to resultant plastic strain cannot be overestimated and this contribution is recognizedby the AE technique too quite reasonably: AE due to dislocation slip is featured by lo amplitude
and energy, but due to a huge number of moving dislocation segments the overall energy
associated with the bow amplitude but continuous AE appears to be even higher than that oftwinning or martensitic transformation.
3.3. Microstructural Observations and deformation mechanisms
The detailed SEM observations of the austenitic steels after deformation have been reported on
several occasions [3]. In the present work we focus on the changes which the microstructures
undergo during plastic deformation at the elevated temperature. The phase transformations
responsible of the TRIP effect, or twinning responsible for the TWIP effect, in austenitic steels
under load have been fairly well understood with regard to the effect of Ni content and thetemperature, which have a crucial effect on the SFE and the underlying deformation
mechanisms.
The deformation mechanism, which exerts the most significant impact on the overall mechanicalresponse of the steel 16Cr6Mn9Ni with a reasonably stable microstructure, is the mechanicaltwinning, Figs. 5 and 6. The deformation microstructure of steels with the TWIP effect ischaracterized by arrays of mechanically-induced twin layers with a nanometer thickness.
Bundles of numerous twins are regularly observed in the primary and secondary twinningsystems. The twins in the primary twin system extend across the whole grain from one grainboundary to the other unlike the twins in the secondary system where they are confined to thespace between the primary twins. The number of twins in the bundles of primary twins isappreciably lager than that that in the secondary where the length and thickness of twin bands is
notably smaller that those in the primary system. The phase maps show a nearly completeaustenitic microstructure in the field of view. A little amount of a bcc phase, which highlighted
in blue, Fig. 5a, is associated with the δ-ferrite remaining in the matrix after manufacturing andshould not be mixed up with α’ martensite which is also indexed as a bcc phase on the phase
map in the 16Cr-6Mn-6Ni steel. With increasing temperature to 373 K the significant changes inthe twinning activity are observed, i.e. the density of twins decreases strongly with increasing
temperature in the stable 16Cr-6Mn-9Ni steel but it increases notably in the 16Cr-6Mn-6Ni steel,exhibiting transition from TRIP to TWIP effect. In fact, the deformation of type of the latter
metastable steel is particularly complex, involving a variety of mechanisms operatingconcurrently. During deformation at room temperature, the metastable steel 16Cr-6Mn-6Ni
exhibits a complex combination of a martensitic α’ transformation, which occurs through theintermediate ε phase transformation γ  ε  α', and twinning. Additionally, both thesemechanisms occur on a background of conventional dislocation slip. In the electron backscatter
diffraction (EBSD) micrograph the deformation bands are clearly visible. No directtransformation γ  α' from the austenite phase to α’-martensite was observed in the matrix
aside from deformation bands.

S147

The use of the crystallographic orientation in the inverse pole figure (IPF) colouring on theorientation image map makes a clear distinction between the matrix and the twins having a
specific orientation relation, Fig. 5 (the twin boundaries are highlighted in red) for both steels.
When testing temperature increases to 373 K, the martensitic transformation ceases to occur andthe TRIP effect vanishes. On the other hand, the density of twins increases significantly. With
the increasing deformation temperature to 373 K the metastable steel changed the major
deformation mechanism significantly. At high temperature, the steel has a higher austenitic
stability and thus shows no remarkable martensitic phase transformation at 373 K during tensile
test. The most pronounced deformation mechanism at 373 K is the strain-induced twinning.

Fig. 5. OIM phase map in tw austenitic steels after formation and room temperature and 373 K.
Red –fcc austenitic phase, yellow – hcp phase corresponding to the ε martensite in the 16Cr6Mn-6Ni steel and blue – bcc phase corresponding to the residual δ-ferrite in the 16Cr-6Mn-9Ni
steel and to the α’- martensite in 16Cr-6Mn-6Ni steel, respectively. The loading axis ishorizontal.

Fig. 6. SEM/EBSD images showing the deformation microstructures of the 16Cr-6Mn-9Ni steel(a-d) and 16Cr-6Mn-6Ni (e-h) after tensile testing to fracture at RT (a-b) and (e-f) and 373 K (cd) and (g-h), respectively: image quality maps with highlighted coherent twin boundaries having
a rotation angle of 60° along the {111} plane (red coloured) (a,c,e,f) and the corresponding
inverse pole figure map inked according to the surface normal direction (b,d,f,h). The loading
axis is horizontal.
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4. Conclusions
The acoustic emission technique was used is in the present work to highlight the complexity inkinetics of multiple mutually interacting mechanisms involved into plastic deformation of model
metastable austenitic Fe-16Cr-6Mn-xNi TRIP/TWIP steels with x=6 and 9 wt.% of Ni at room
temperature and 373 K. Different microstructure-related major mechanisms of plastic deformation
are identified as major AE sources with a help of a robust non-supervised signal categorization
technique grouping AE signals with similar power spectral densities. Specifically, it is found that
AE follows the behaviour of dislocation slip and twinning in the stable 16Cr-6Mn-9Ni steel. At
373 K the amount (number fraction) of mechanical twins reduced while the role of thedislocation slip in the overall plastic deformation increased. In the metastable 16Cr-6Mn-9Ni
steel, besides the dislocation slip, the complexity of the involved deformation mechanisms
involves a combination of a martensitic α’ transformation, which occurs through theintermediate ε phase transformation γ  ε  α', and twinning with different proportions at room
temperature and 373 K. The transition between TRIP and TWIP effects is well identified by the
AE technique when temperature increased from ambient to 373 K. All major results of AE
analysis appeared in excellent agreement with microstructural EBSD and ECCI observations.
Financial support from the German Research Foundation (DFG) through the CRC 799 contract and by the
Russian Ministry of Education and Science (grant-in-aid 11.G34.31.0031) is greatly appreciated. One of
the authors (ML) gratefully acknowledges the hospitality of the Institute of Materials Engineering,
Freiberg Technical University. Special thanks go to Mr. G. Schade for his skillful help with experiments.

6. References
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De Cooman BC. et al: High Mn TWIP Steels for Automotive Applications, in: New
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Hickel T. et al.: Advancing density functional theory to finite temperatures: methods andapplications in steel design. J of Phys: Condensed Matter, Vol. 24, 2012, 053202.
Biermann H, Solarek J, Weidner A.: SEM investigation of high-alloyed austenitic
stainless cast steels with varying austenite stability at room temperature and 100°C, Steel
Research International, Vol. 83, No. 6, 2012, 512-520.
Weiß A, et al.: Nichtrostender austenitischer Stahlformguss, Verfahren zu dessen
Herstellung, und seine Verwendung, German patent WO 002008009722A1.
Weiß A, Gutte H, Jahn A, Scheller PR.: Stainless steels with TRIP/TWIP/SBIP effect,
Materialwissenschaft und Werkstofftechnik, Vol. 40, No. 6, 2009, 606-611.
Jahn A, et al.: Mechanical properties of high alloyed cast and rolled CrMnNi TRIP steelswith varying Ni contents, Proceedings of the 8th ESOMAT, Prague, 2009, 05013.
Vinogradov A, et al.: Kinetics of deformation processes in high-alloyed casttransformation-induced plasticity/twinning-induced plasticity steels determined byacoustic emission and scanning electron microscopy: Influence of austenite stability on
deformation mechanisms, Acta Materiala, Vol. 61, No. 7, 2013, 2434-2449.
Jahn A, et. al.: Influence of manganese and nickel on the ά martensite transformation
temperatures of high alloyed Cr-Mn-Ni steels, Steel Res. Int., Vol. 82, No. 9, 2011, 1108.
Dai QX, et al.: Structural parameters of the martensite transformation for austenitic steels,
Materials Characterization, Vol. 49, No. 4, 2013, 367-371.
Pomponi E, Vinogradov A.: A real-time approach to acoustic emission clustering. Mech.
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No. 4, 2013, 532-539.
S149

The 12th International Conference of the Slovenian Society for Non-Destructive Testing»Application of Contemporary Non-Destructive Testing in Engineering«
September 4-6, 2013, Portorož, Slovenia

INVESTIGATING THE PRESENCE OF CRACKS IN TOOLSTEEL INSERTS BY USING ACOUSTIC EMISSION METHODTomaž Kek1, Dragan Kusić2, Rajko Svečko3, Aleš Hančič2 and Janez Grum1
1

University of Ljubljana, Faculty of Mechanical Engineering
Aškerčeva 6, 1000 Ljubljana, Slovenia, tomaz.kek@fs.uni-lj.si, janez.grum@fs.uni-lj.si
2
TECOS Slovenian Tool and Die Development Centre
Kidričeva 25, 3000 Celje, Slovenia, dragan.kusic@tecos.si, ales.hancic@tecos.si
3
University of Maribor, Faculty of Electrical Engineering and Computer Science
Smetanova 17, 2000 Maribor, Slovenia, rajko.svecko@uni-mb.si

ABSTRACTIn daily industrial production of different plastic products we often have to deal with various
errors that practically occur on the mold primarily as a result of material wear and tear,
improper storage and improper settings on the injection molding machine. In the testing phase ofplastic materials we use many times different inserts that are made from standard tool steels,such as OCR12VM. In case of tool steel inserts after some years of usage a few micro-cracks canoccur in the early stage, which can later quickly spread according to the applied loading. With
the help of different non-destructive testing methods we know that we can most certainly detect
possible formation of cracks on the tool steel inserts. Therefore the purpose of this paper is topresent a detailed review of the applicability of acoustic emission (AE) method for the detectionof cracks on the tool steel insert during a regular molding production cycle of standard test
specimens. In this paper, we focused exclusively on the acoustic emission signal acquisition byusing two resonant 150 kHz piezoelectric AE sensors on such tool steel inserts that are alreadyaffected by macro-cracks. On such tool steel insert the obtained acoustic emission results werecompared with those obtained from a brand new tool steel insert. The final obtained acoustic
emission results on the crack defected tool steel insert revealed as expected that the energy andintensity of the captured AE signals is higher compared with the ones that were captured on thebrand new engraving insert under same processing conditions.
Key words: Acoustic emission, tool steel insert, cracks, test specimens, PZT sensors.
1.

Introduction

Injection molding is a well known plastic manufacturing process where heated molten plastic
material is forced into a mold cavity under high pressure. The plastic material solidifies into ashape that has conformed to the contour of the mold. Nowadays it is still regarded as the mostimportant and very popular manufacturing process because of simple operation steps. A typical
production cycle begins when the mold closes, followed by the injection of the plastic into the

S150

mold cavity. Once the cavity is filled, additional pressure compensates the material shrinkage. In
the next step, the screw turns, feeding the next shot to the front screw tip. This causes the screw
to retract as the next cycle is almost prepared. Once the molded part is sufficiently cooled, themold opens and the part is finally ejected. An example of a typical injection molding machine isshown in Fig. 1. Like in all today’s industrial manufacturing processes, injection molding
process can produce plastic parts which have poor quality and therefore are characterized as bad
ones. In the field of injection molding during long term production first signs of micro-cracks
can occur on the mold that usually causes production of bad parts. In such cases if a proper
inspection inside the company is not provided then a whole series of freshly produced parts canbe rejected. For a production company this can lead to a heavy economical loss. Location andadvancement of a possible crack on the tool steel insert can be detected by the use of acoustic
emission technique as already reported by many researchers [1-5]. The primary objective ofmonitoring the crack advancement with acoustic emission method is to obtain useful information
about the quality of tool steel inserts which guarantees good quality of the produced test
specimens.

Fig. 1: Example of a typical injection molding machine.
2.

Experimental procedure

Acoustic emission signals were captured during the production cycle of standard test specimensthat are intended for shrinkage evaluation of various plastic materials. The main aim was toanalyze the influence of a possible crack located in tool steel insert on the captured acoustic
emission signals. After finishing the first experimental part on a tool steel insert with a macrocrack we repeated the experiment under same processing conditions also on the brand new toolsteel insert. In this way the captured acoustic emission signals could be compared. The captured
acoustic emission signals were then correlated with the quality of the produced test specimens.
Acoustic emission measurement system AMSY-5 from Vallen-Systeme GmbH was used for
capturing and analyzing the AE signals. Two piezoelectric AE sensors VS150-M (resonant at
150 kHz) were mounted with silicone grease with two sensor holders on the tool steel insert fromboth sides. Both PZT sensors were connected via two preamplifiers AEP4 with a fixed gain of
40dB on the first and second channel of AMSY-5 measurement system. In the evaluation stageof acquired AE signals we focused closely on their maximal amplitudes and energy values
during filling and packing stage. CAD model of the tool steel insert for production of standard
test specimens is shown in Fig. 2.

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Fig. 2: CAD model of the tool steel insert that was used for experiments.
If we want to produce standard test specimens with good quality then the process parametersmust be correctly set. Before the start of the experiment, it was necessary to select and fix thefollowing process parameters: injection pressure was set to 1100 bar in the course of theexperiment, holding pressure was set to 500 bar. On the new tool steel insert the melt
temperature in cylinder has been set to 230 °C and the injection speed was set to 50 mm/s. On
the tool insert with a macro-crack the melt temperature was set to 240 °C and the injection speed
was 45 mm/s. The main criterion for the quality of produced test specimens was chosen to be thesize of the shrinkage in longitudinal and transverse direction of the melt flow.
3.

Experimental results

In the first part, we carried out the first experiments on a brand new tool steel insert. Later in thesecond part of experiments a tool steel insert with visible sign of macro-crack was used. In bothcases to provide similar processing conditions, we used the same polypropylene material from
Sirmax manufacturer (H40 C2 FNAT), which is mainly used in the automotive industry. Afterthe test specimens were produced, they were scanned with an optical 3D digitizer ATOS II SO
after 24 hours. This industrial 3D optical digitizer has two high-resolution CCD cameras(1280x1024 pixels) as shown in Fig. 3.

Fig. 3: Optical 3D digitizer ATOS II SO with two high-resolution CCD cameras.

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Once the test specimens are digitized the measured data can be saved and used later on. Usually
we are interested in individual measuring values and sections across the test specimen. Larger
deviations and/or dimensional changes compared to the nominal ones are easy to verify and tocontrol. Optical 3D Digitizer is based on the principle of capturing images through the camera,
which then through an appropriate program prepares a computer model. The accuracy that can beachieved with these digitizers depends largely on the quality of the camera, which records thedesired object. Of course, in a very small precision scale the accuracy itself also depends on the
wavelength of the light. The biggest advantage of 3D digitizer from Fig. 3 lies in extremely rapid
procedure of digitizing and excellent precision (precision declared for very small objects are up
to 2 microns). Example of produced and later scanned test specimens on a brand new tool steel
insert and insert with a visible macro-crack is shown in Fig. 4.

(a)(b)
Fig. 4: Scanned test specimens (a) specimen 8 produced on a new tool steel insert,(b) specimen 14 produced on a tool steel insert with visible sign of macro-crack.
As can be seen from Fig. 4b the test specimen 14 has a clearly visible macro-crack. Also bothdimensions are outside of the nominal 60 mm in the longitudinal (1.27 mm) and transverse(1.43 mm) direction. From Fig. 4a we can see that the produced test specimen 8 is within theprescribed tolerances (59.91 mm in length and 59.32 mm in width) and with no visible defects.

(a)
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(b)
Fig. 5: Measuring surface deviations on (a) test specimen 8 produced on new tool steel insert,(b) test specimen 14 produced on tool steel insert with visible sign of macro-crack.
A good advantage of scanning the test specimens is the fact that quality of surface and its
deviation can be compared to the flat (ideal) surface as shown in Fig. 5. By comparing bothscanned test specimens in Fig. 5 we can notice on Fig. 5b that there are significant surface
deviations in regards to the ideal surface, which are within the range of 0.17 mm exactly on theplace of macro-cracks. The acoustic emission signal intensity is proportional to signal energy [67] and defined by an integral of signal square2

E AE   V (t ) dt

(1)0

Amplitudes of the acoustic emission signals can be given in the form of a voltage, but it isusually converted into decibels by the following equation
 V (t ) 
AAE  20log  Vr 

(2)whereas V(t) is the maximal measured voltage of AE signal and Vr is the reference input voltage
on preamplifier, which was in our case equal to 0.001 mV. By measuring the simple waveform
parameters such as energy, amplitude, hits, counts etc. on acquired AE signals, we can obtain
useful information about the AE source intensity and its seriousness. In this way we candetermine if the tool steel inserts quality is still good and if it’s necessary to be replaced in theforthcoming future. Acoustic emission energy measurements are also important especially inthose cases when the measured AE signal amplitudes are low. By squaring the captured AE
signal burst a simple pulse is produced and consequently the hit counting is simplified, which isclearly visible in Fig. 6. The peak AE signal amplitudes are normally related to the intensity of
one or more AE sources, which are in our case macro-cracks located on the tool steel insert. In
the past research works [6] correlations between total counts, count rate and various fracture
mechanics parameters (like for example the stress intensity factor) have been established and canbe expressed by following equation
N  Kn(3)where K is the stress intensity factor, N is the total number of counts and n is a constant value
between 2 and 10. The fatigue crack propagation rate is defined byS154

dN da

dc dc

(4)

where a is the crack size, c is the number of cycles and N is the total number of counts.
Filling phase(1)

Holding phase(2)

Cooling phase, mold opening andspecimen ejection phase (3)AE signal amplitudes - test specimen 8 (new insert)
100

[dB]

60

2

3

AE

A

1
80

40

0

2

4

6

8

10
12
14
16
Time [s]AE signal energy - test specimen 8 (new insert)

18

20

0

2

4

6

8

18

20

-5

[V 2s]

10

-10

E

AE

10

-15

10

a)

10
Time [s]

12

14

16

AE signal amplitudes - test specimen 14 (insert with macro-crack)
100

[dB]

60

2

3

AE

A

1
80

40

0

2

4

6

8

10
12
14
16
18
Time [s]AE signal energy - test specimen 14 (insert with macro-crack)

20

0

2

4

6

8

20

-5

-10

10

E

AE

[V 2s]

10

-15

10

b)

10
Time [s]

12

14

16

18

Fig. 6: Captured AE signals during production of (a) test specimen 8 (new insert),(b) test specimen 14 (insert with macro-crack).

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Figure 6 shows the amplitudes and AE signal energy during the production of test specimens 8
and 14. On the tool steel insert with macro-crack a significant increase in the number of detected
AE signals in filling phase can be seen (Fig. 7a), which is due to the filling of polymer melt
through macro-cracks until the test specimen is filled. Even better, this transition of melt throughmacro-cracks is evident in Fig. 7b, where rapid transitions of AE signal from 0.2 mV (46dB) up
to 0.81 mV (58.2 dB) can be clearly seen.
40

30

Amplitude [mV]

20

10

0

-10

-20

-30

0

0.005

0.01

0.015

0.02

0.025
0.03
Time [s]

0.035

0.04

0.045

0.05

0.035

0.04

0.045

0.05

a)
0.8

0.6

0.4

Amplitude [mV]

0.2

0

-0.2

-0.4

-0.6

-0.8

-1

0

0.005

0.01

0.015

0.02

0.025
0.03
Time [s]b)Fig. 7: Maximal amplitude values of captured AE signals during production of test specimen 14(a) injection phase 1100 bar, (b) holding phase 500 bar.
Based on a detailed comparison of captured AE signals from Fig. 6 on both tool steel inserts, wefound that higher number of AE signals was present during the production of test specimen 14
on tool steel insert with visible signs of macro-cracks. During production of test specimen 14 we
can obviously notice higher amplitude and energy values of captured AE signal in filling andpacking phase (Fig. 7).

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4.

Conclusions

The aim of this research work was to determine to which extent is it possible with AE method todetect the presence of macro-cracks on engraving tool steel inserts by conducting a closer
comparison of captured AE signals obtained from new tool steel insert under same processing
conditions. From our experimental results we were able to obtain useful information about thepresence of macro-cracks during production of standard test specimens. We have found in thefiling stage during production of test specimens on both inserts that there was more apparent
difference in the number of detected AE signals during the injection phase of commercial
polypropylene material in favor of the tool steel insert with visible sign of macro-cracks. The
results clearly show also the difference in the maximum amplitudes during filling and holding
phase, as well as in energies of captured AE signals. As could be seen from the practicallyobtained results from captured AE signals we can successfully use the AE method for detecting
fractures on tool steel inserts, which is one of the important advantages of this nondestructive
testing method.
Acknowledgement
The research work was partly financed by the European Union, European Social Fund, and by
the Slovenian Ministry for Higher Education, Science and Technology.
5.

References[1]

Cao J., Luo H., Han Z.: Acoustic emission source mechanism analysis and crack length
prediction during fatigue crack propagation in 16Mn steel and welds, Procedia
Engineering, Vol. 27, 2012, 1524-1537.
Biancolini M.E., Brutti C., Paparo G., Zanini A.: Fatigue cracks nucleation on steel,acoustic emission and fractal analysis, International Journal of Fatigue, Vol. 28, 2006,
1820-1825.
Moorthy V., Jayakumar T., Raj B.: Influence of micro structure on acoustic emission
behavior during stage 2 fatigue crack growth in solution annealed, thermally aged and weld
specimens of AISI type 316 stainless steel, Materials Science and Engineering: A, Vol.
212, 1996, 273-280.
Mukhopadhyay C.K., Sasikala G., Jayakumar T., Raj B.: Acoustic emission during fracture
toughness tests of SA333 Gr.6 steel, Engineering Fracture Mechanics, Vol. 96, 2012, 294306.
Berkovits A., Fang D.: Study of fatigue crack characteristics by acoustic emission,
Engineering Fracture Mechanics, Vol. 51, 1995, 401-416.
Vallen H.: Acoustic Emission Testing: Fundamentals, Equipment, Applications, Vol. 6,
Castell Publication Inc., Wuppertal, 2006.
Miller R. K., Hill E.v.K., Moore P. O.: Nondestructive Testing Handbook, Third edition,
Vol. 6, Acoustic Emission Testing, American Society for Nondestructive Testing, Inc.,
2005.

[2]
[3]

[4]
[5]
[6]
[7]

S157

The 12th International Conference of the Slovenian Society for Non-Destructive Testing»Application of Contemporary Non-Destructive Testing in Engineering«
September 4-6, 2013, Portorož, Slovenia

ACOUSTIC EMISSION DURING SHORT-TERM
ELECTROCHEMICAL CORROSION TESTING: LSP EFFECTON PITTING CORROSION
Uroš Trdan1, Janez Grum2
1

Faculty of Mechanical Engineering, Laboratory for Materials Testing and Heat Treatment
Aškerčeva 6, 1000 Ljubljana, Slovenia, uros.trdan@fs.uni-lj.si

2Faculty of Mechanical Engineering, Laboratory for Materials Testing and Heat Treatment
Aškerčeva 6, 1000 Ljubljana, Slovenia, janez.grum@fs.uni-lj.si
ABSTRACT

Acoustic emission (AE) during accelerated potentiostatic corrosion measurements in a 0.6 M
NaCl naturally aerated aqueous solution was used to study localised corrosion phenomena of
non-treated and Laser Shock Peened (LSP) aluminium alloy. Correlations between LSP
parameters, anodic current density and characteristics of AE events were observed andanalysed. Obvious beneficial effect of preliminary LSP treatment was confirmed, with reductionof both, i.e. current density and AE activity, indicating improved corrosion resistance ofinvestigated alloy. Results revealed three different populations of AE events during thepotentiostatic sweep at -700 mV SCE , which differ significantly in the waveform, amplitude, signal
duration and rise time. First population of AE bursts, with low emissivity are the results of theinitiation step of pitting corrosion, whereas the second population appeared due to propagationof pits revealed higher amplitude and longer AE bursts. However, the most emissive source wasfound to be due to evolution of hydrogen bubbles (population 3), with highest amplitude and thelongest duration of AE signals. Results confirmed the important potential of AE in the detection
and characterization of localised corrosion even at extremely accelerated corrosion tests.
Key words: Acoustic emission (AE), Pitting corrosion, Laser Shock Peening (LSP), Al alloy.
1.

Introduction

Corrosion damage represents one of the most important problems in existing structural parts.
Although aluminium alloys covers a broad field of various applications owing to low weight,
good mechanical properties and high corrosion resistance, in aggressive environment thesealloys undergo a localized corrosion [1, 2] and stress corrosion cracking [3]. However, localized
forms of corrosion are rather complex and consists various steps and electrochemical reactions.
In this aspect, implementation of in-situ corrosion with non-destructive testing (NDT) methods
enables early detection with ameliorated understanding of corrosion phenomena [4]. For the lasttwo decades Acoustic Emission (AE) has been used to monitor the severity of corrosion.
Compared to the conventional NDT methods, it is less intrusive and has the advantage of real-

S158

time measurement [5]. Acoustic emission is based on the rapid release of energy inside thematerial (film rupture, corrosion propagation, etc.) which in-turn generates transient elastic(acoustic) waves that are detected by a piezoelectric sensor and then further analysed.
AE has already been proved as a powerfully tool for early-stage detection of various localised
corrosion modes, such as exfoliation corrosion [6], pitting corrosion [7, 8] and stress corrosion
cracking [9, 10].
It is worth-noting that even aluminium parts exposed to the demanding conditions frequently
requires an adequate surface treatment. Recently, laser shock peening (LSP) has attracted a
global interest and has been accepted as the substitute of conventional Shot Peening. LSP surface
treatment is a versatile, fast emerging and promising technique which involves underwater
sample irradiation with laser pulses of short duration at a level of nanoseconds and a power
density of several GW/cm2, with high laser pulse repetition rate. Specific condition enables
plasma generation at the moment of the interaction of laser light and propagation of shock
impact waves in the material [2, 11]. It was confirmed that LSP has the ability to improve both,i.e. mechanical properties [11, 12] and corrosion resistance [1, 13].
However, until now there is little report on the study of different LSP parameters on AE
activity during electrochemical corrosion process. Thus, this work is focused to obtaincorrelation among AE burst and anodic current density in order to establish the impact of various
LSP treatment parameters’ on corrosion resistance during accelerated potentiostatic tests and to
identify population of specific AE sources.
2.

Experimental procedure

2.1 Specimen preparation & Laser Shock Peening set-up
In this study commercial aluminium alloy AA6082-T651 was used. Prior to LSP treatment
specimens were cut along the longitudinal (L) and long-transverse (LT) directions from a 10 mmthick rolled plate and thoroughly degreased with acetone and then rinsed with deionised water.
Laser shock Peening was performed using a Q-switched Nd:YAG laser operating at 10 Hz with awavelength of 1064 nm in a water confinement regime, without ablative coating (LSPwC).
FWHM of the pulses was 10 ns, where a convert lens system delivered 2.8 J. In this study, theinfluence of different pulse density and power density was evaluated. Pulse density wascontrolled with a 2D motion velocity system and was set to three levels, i.e. 900, 1600 and 2500
pulses/cm². Power density was controlled with a spot diameter, i.e. 1.5 mm at 15.8 GW/cm², 2.0mm at 8.9 GW/cm² and 2.5 mm at 5.7 GW/cm², respectively. First numeral of specimen
designation is relating to pulse density and the second one power density; for example LSP 2-3
denotes the specimen treated with 1600 pulses/cm² and 5.7 GW/cm².

Fig. 1: Schematic presentation of the measurement system.

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2.2 Electrochemical Potentiostatic and Acoustic Emission monitoring
In-situ detection of Acoustic Emission (AE) during potentiostatic (PS) experiments were
performed prior to and after LSP surface pre-treatments. The experimental set-up is shown on
Fig. 1. All the results reported here were conducted in a freshly prepared naturally aerated, near
natural (pH= 6.9±0.2; T= 21±1 °C) 0.6 M NaCl solution, prepared with deionized water. The
applied potential of – 700 mV SCE (SCE=+ 242 mV vs. SHE) was controlled by a PAR Versastat4 potentiostat/galvanostat/ZRA. A classic three-electrode electrochemical cell was used, where asaturated calomel electrode (SCE) was employed as the reference electrode. In order to avoid
acoustic noise induced by hydrogen evolution (bubbles) during potentiostatic corrosion tests,
corrosion cell set-up was altered. For this purpose the counter electrode was connected to the
corrosion cell via a salt bridge. Immediately, prior to each corrosion experiment, specimens were
carefully cleaned and degreased. Procedure of preparation steps were the same as described inref. [1], with EIS evaluation. The exposed working electrode, i.e. specimen was equal to 1 cm².
Instrumentation for AE detection consisted of an acquisition device AMSY5 (Vallen Systeme),
pre-amplifier (AEP4, gain 40 dB) and a contact piezoelectric transducer (PZT) VS150-M, with afrequency range from 100 kHz to 450 kHz. On the basis of the preliminary test the threshold for
all AE measurements was set to 40 dB.
Taking into consideration the investigations by Bellenger et al. [6] and Kim et al [14]: following
AE features were studied:number of events – N AE : number of AE signals detected (discontinuous emission),
Amplitude – A AE [dB]: maximal (peak) amplitude of the considered AE burst,Rise time – t R,AE : time interval between first and last threshold crossing,Counts number – CNTS: number of threshold crossings for a given AE burst,
Duration – t AE : time interval between first and last threshold crossing.
3.

Results and discussion

Evolution of detected acoustic emission events (peak amplitude) and anodic current density
during the PS test for the specimens treated with highest power density (15.8 GW/cm2) andvarious pulse densities (1–900 p/cm2; 2–1600 p/cm2; 3–2500 p/cm2) is shown on Fig. 2.

Fig. 2. Evolution of AE activity and anodic current density during potentiostatic tests in a 0.6 M
NaCl near-natural naturally aerated solution (Note: y-axis scales are not unified).

S160

a)

No LSP

10000

65193
LSP 1-1

10000
1000
100

177.8
81.7 80.1 73.2
30.1

1124
860
999
410.9

3090

LSP 3-3
2015

1000
100

LSP 1-2

16
100

10

LSP 3-2
1114

10
1

16
LSP 1-3

220

5
112

1

LSP 3-1

292

LSP 2-1No
L
LS S PP1
LS -1
P
LS 1-2
P
1
LS -3
P
2
LS -1
P
2
LS -2
P
LS 2-3
P
3
LS -1
P
3
LS -2
P
33

2

Current density - i [µA/cm ]100000

LSP 2-3b)

LSP 2-2

Fig. 3. Comparison of: a) anodic current density – i [µA/cm2] and b) number of events – N AE .
During the first sequence of the test, corresponding to OCP test (t ≤ 120 s) AE activity is
expectedly very low. It seems that in the case when no external polarisation is involved,
aluminium oxide/passive film offers more or less effective barrier against corrosion attack in theaggressive chloride environment. On the contrary, the second sequence (120 s ≤ t ≤ 420 s),
during external polarisation at potentiostatic test at – 700mV SCE , revealed a strong correlation
among detected AE events and anodic current density. From the results obtained it is obviousthat the untreated specimen exhibits the highest AE activity with highest anodic current density,
suggesting lower corrosion resistance compared to the LSP treated specimens.
Results of anodic current density and number of detected AE events during PS test are shown in
Fig. 3a and 3b, respectively. The results confirmed significant influence of LSP treatment
parameters.
No LSPOM
LSP 1-1
LSP 1-2
LSP 1-3

1000
100
14 -0.7 AAE,L

NAE = 1.2 10 e

10-0.18 AAE,L

NAE = 26 103e1-0.54 AAE,L

NAE = 3.8 1010e

0.1
35

a)

10000

-0.35 AAE,L

NAE = 3.87 109 e

40

45

50

55

60

65

Number of events - N AE [/]1000-0.21 AAE,L-0.27 AAE,L

NAE = 3.7 105e

10

NAE = 9 106e

1
-0.11 AAE,L

NAE = 202.3 e

0.1
35

40

45

50

55

60

65

Amplitudne level - A AE,L [dB]-0.35 AAE,L

No LSPOM
LSP 3-1
LSP 3-2
LSP 3-31000
100

No LSPOM
LSP 2-1
LSP 2-2
LSP 2-3100

NAE = 3.87 109 e-0.33 AAE,L

NAE = 5 108e

10-0.44 NAE,L

NAE = 9 109e

-0.39 AAE,L

NAE = 9 109e

1
0.1
35

c)

-0.35 AAE,L

NAE = 3.87 109 eb)

Amplitudne level - A AE,L [dB]
10000

Number of events - N AE [/]Number of events - N AE [/]

10000

40

45

50

55

60

65

Amplitudne level - A AE,L [dB]Fig. 4. Number of AE events inside specific amplitude levels during PS corrosion test; a)Untreated (0) & LSP - 900 p/cm2, b) 0 & LSP - 1600 p/cm2 and c) 0 & LSP - 2500 p/cm2.
S161

One should note that analysis of current density (Fig. 3a) confirmed the lowest value of 30.1
µA/cm2 with LSP 2-2 specimen. Compared to the untreated specimen (65193 µA/cm2) resultspresents current density reduction of factor 2166! However, analysis confirmed that insidespecific LSP group, the highest anodic current density values are obtained with the specimens
treated with highest pulse density (2500 pulses/cm2 → LSP 3-X). These is probably associatedwith the highest degree of surface micro-roughening and largest LSP-affected craters due toablation, plasma generation and mechanical hardening effect of induced shock waves.

Fig. 5. Evolution of AE durations of untreated and LSP treated specimens which had the lowest
AE activity within LSP group (unified pulse density).
From the number of detected AE events, good correlation with current density is observed. It canbe noted that the number of detected AE events was the highest for the untreated specimen andwas equal to 3090. This is followed by specimen LSP 3-3 with 2015 AE bursts. The lowest AE
activity was once again achieved with specimen LSP 2-2, with only 5 detected AE bursts.
Amplitude distributions of detected AE events, for various surface pre-treatments are presented
in Fig. 4. The amplitudes of AE events from 40 dB to 60 dB were classified into 5 amplitudelevels A AE,L , increments being equal to 5 dB. Thus, the number of AE events at amplitude levelof 40 dB represents the total number of AE events equal or higher to 45 dB and lower than 50 dB(45 ≤ A AE [dB] < 50).
In the investigation by Kek et al. [15] dependence of the number of AE events (N AE ) and theamplitude levels A AE,L was successfully described with an exponential function.
Measurement results in this study also confirmed an overall trend, i.e. lower distance of an
exponential trend line from the starting-point of the coordinate system, indicating improved
corrosion resistance (lower AE activity) of LSP treated specimens.
Results once again confirm the beneficial effect of preliminary LSP treatment on improvementof corrosion resistance of investigated aluminium alloy. According to our previous publications[1, 16] it has been shown that LSP clearly enables more difficult penetration of water molecules,
oxygen and Cl¯ ions due to the development of compressive residual stresses and morechemically stable passive/oxide and more compact film on the specimen surface due to surface
ablation, plasma generation and hardening effect of shock waves.

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In order to characterize and to evaluate population of AE source more precisely, complementary
analyses of the amplitude, duration and waveforms were performed.
Fig. 5 shows evolution of AE activity, i.e. duration of specific AE events of untreated and three
LSP treated specimen which had the lowest AE activity within unified pulse density,
respectively. It is worth nothing that the untreated specimens’ exhibits much larger number ofAE events within each population, compared to the LSP treated ones, indicating the most severe
corrosion attack. Furthermore, the results of the AE duration during PS tests demonstrates thatthe highest differences among untreated and LSP treated specimens exists within the second
population of AE events, i.e. active material dissolution.
Results of the present study confirmed the presence of three (3) populations of AE events, which
distinguishes in amplitude - A AE , duration - t AE , rise time – t R,AE and threshold crossings - CNTS:
I.

II.
III.

The first population, noted -1-, consists of AE signals with short duration (0.1 - 2µs),
low distribution and of low amplitude, i.e. in the range 40 dB – 44 dB. This
population of AE events appeared to be related to the initiation step of localizedpitting corrosion, or to say the rupture of the protective passive film.
Second population -2- are related to the 2nd step of corrosion, i.e. formation andpropagation of corrosion pits. These AE events are of grater amplitude, with longer
duration, in the range from 2.5 µs to 300 µs.
Signals related to population -3- (Fig. 6) appeared as the most emissive AE source,with the highest amplitude, short rise time and exceptionally long duration (t≤ 5 ms).
This AE signals are associated with the evolution of hydrogen bubbles inside thecorrosion pits.

Fig. 6. Evolution of AE burst waveform (on left) and spectrograph (right) of AE population -3-.
Analysis of specific population of AE sources confirmed that the population distinguish
especially in the duration time and waveforms. This effect is attributed to the different acoustic
emission sources, or to say different direction of energy release. Furthermore, the results
obtained indicate that the most vivid difference between untreated and LSP treated specimens arein the cumulative number of AE burst belonging to population -2-.
Such results are understandable due to the fact that untreated specimen suffered from the highest
corrosion attack and anodic current density during PS test. During this step corrosion in the formof active dissolution of Al matrix in the proximity of cathodic precipitates of Al x (Fe,Mn)occurred [1, 16].
Analysis revealed that the emissivity of AE events sources are in the following order (from lower
to higher): Population -1- (passive film rupture/corrosion initiation) < Population -2- (corrosion
initiation) < Population -3- (hydrogen evolution /release of hydrogen bubbles), of which
waveform and spectrograph are presented in Fig. 6.
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Furthermore, it has been demonstrated that almost perfect correlation exists between acoustic
activity and current density during accelerated potentiostatic tests at – 700 mV SCE .
However, in order to characterize more precisely corrosion pit morphology evolution anddetected AE signals, further experiments with in-situ observation of the specimens by high speed
camera/microscope during electrochemical corrosion testing would be highly recommend.
4.

Conclusions

Simultaneous (in-situ) use of acoustic emission (AE) during accelerated electrochemical
potentiostatic (PS) tests was found to be a reliable method for early detection of pitting corrosionof investigated Al alloy and for general monitoring of corrosion activity.
AE activity showed good correlation between this activity (N AE & A AE ) and advance of pitting
corrosion by the transients of current density. The analysis revealed that general correlation
exists, i.e. more of AE bursts (N AE ) detected ↔ higher the current density (lower corrosion
resistance).
Analysis of AE signal characteristics revealed three explicit populations of detected AE
bursts. Furthermore, detailed analysis has confirmed that the most energetic, i.e. the mostemissive AE source belongs to hydrogen evolution, which had the amplitude of up to 47 dB andAE signal duration of almost 5 ms. Nevertheless, due to the complexity associated withhydrogen evolution and nature of the acoustic emission, additional visual observation of thespecimens during corrosion testing would be strongly recommended.
5.

References[1] Trdan U., Grum, J., Corrosion Science, Vol. 59, 2012, 324–333.
[2] Trdan U., Ocaña J.L., Grum J., J. Mech. Eng., Vol. 57, No. 5, 2011,385–393.
[3] Yue T.M., Yan L.J., Chan C.P., Applied Surface Science, Vol. 252, 2006, 5026–5034.
[4] Boinet M., Bernard J., Chatenet M., Dalard F., Maximovitch S., Electrochimica Acta, Vol.
55, 2010, 3454–3463.
[5] Prateepasen A.: Pitting Corrosion Monitoring Using Acoustic Emission, Pitting Corrosion,(Bensalah N., ed.), ISBN: 978-953-51-0275-5, InTech, 2012.
[6] Bellanger F., Mazille H., Idrissi H., NDT&E International Vol.35, 2002, 385–392.
[7] Fregonese M., Idrissi H., Mazille H., Renaud L., Cetre Y., Corrosion Science, Vol. 43, No. 4,
2001, 627–641.
[8] Mazille H., Rothea R., Tronel C., Corrosion Science, Vol. 37, No. 9, 1995, 1365–1375.
[9] Kovac J., Alaux C., Marrow T.J., Govekar E., Legat A. Corrosion Science, Vol. 52, 2010,2015–2025[10] Lapitz P., Ruzzante J., Alvarez M.G., Corrosion Science, Vol. 49, 2007, 3312–3325[11] Trdan U., Porro J.A., Ocaña J.L., Grum J., Surface & Coatings Technology, Vol. 208, 2012,
109–116.
[12] Altenberger I., Nalla R.K., Sano Y., Wagner L., Ritchie R.O., International Journal of
Fatigue, Vol. 44, 2012, 292–302.
[13] Krawiec H., Vignal V., Amar H., Peyre P., Corrosion Science, Vol. 53, 2011, 3215–3221.
[14] Kim, Y.P. Fregonese M., Mazille H., Feron D., Santarini D., Corrosion Science Vol. 48,
2006, 3945–3959.
[15] Kek T., Grum J., Int. Journal of Machine Tools & Manufacture, Vol. 49, 2009, 8–12.
[16] Trdan U., Žagar S., Grum J., Ocaña J.L., Int. Journal of Struct. Integ., Vol. 2, 2011, 9–21.

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The 12th International Conference of the Slovenian Society for Non-Destructive Testing»Application of Contemporary Non-Destructive Testing in Engineering«
September 4-6, 2013, Portorož, Slovenia

DETECTION AND PROCESSING OF SOUND SIGNALSIN STEEL QUENCHING
Franc Ravnik*, Janez Grum
University of Ljubljana, Faculty of Mechanical Engineering, Aškerčeva 6, 1000 Ljubljana
franc.ravnik@fs.uni-lj.si; janez.grum@fs.uni-lj.si
ABSTRACTIn the production of machine components a manufacturing process often ends with heat
treatment such as quenching by tempering. Generation of internal stresses during heat treatment
and residual stresses present thereafter frequently result in non-uniform hardness and depth of aquenched layer, which produces distortion of a machine component and generation of residual
stresses. The choice of optimum parameters of a quenching process and monitoring of theprocess itself ensures the achievement of the specified hardness and residual stress at the surfaceof the machine component. The paper treats an experimental setup comprising detection of
acoustic emission in the course of quenching. Due to heat transfer from a specimen to aquenching medium, film boiling and nucleate boiling occur round a heated object, whichstrongly affects quenching. An investigation of acoustic emission in the quenching process was
carried out with cylindrical specimens made of chrome-molybdenum heat-treatment steel
42CrMo4. The cylindrical specimens of two shapes were quenched in five different
recommended quenching media. Sound-pressure signals demonstrated by different amplitudes
depending on time at different frequencies are shown in 3D diagrams. It has turned out that an
analysis of acoustic-emission signals can provide useful information that confirms differences
occurring in quenching with different quenching media and under different quenchingconditions. The quenching results obtained from acoustic-emission signals during the quenchingprocess can be confirmed by variations of residual stresses and hardness in the cross section.
The results confirm the applicability of the new approach to the control of steel quenching.
Keywords: Acoustic emission, Acoustic spectrograph, Cooling rate, Polymeric solution,
Quenching, Sound pressure level, Vapour film
1.

Introduction

A purpose of quenching is to provide a cooling rate higher than the upper critical cooling rate ina certain depth of a machine component in order to obtain desired mechanical properties. The
mechanical properties obtained are then verified by hardness measurement at the surface, i.e. bymeasuring through-depth hardness of the machine component. Critical cooling rates with carbon
steels and low-alloy steels can be achieved by cooling a machine component from the austenitic
microstructure, which is obtained by immersion into fluid quenching media such as water, oil,polymeric water solutions, and emulsions.

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During the quenching process in liquids the occurrence of three phases of heat transport of theheated machine component, which is conditioned by quenching parameters and workpiece
properties, is characteristic.
In the initial phase of machine-component immersion, a stable vapour film of a fluid quenching
medium forms around the workpiece, so that the heat transfer to the quenching medium isconsiderably slowed down. In further cooling the vapour film gradually decays so that a phase of
nucleate boiling occurs at some parts of the workpiece surface having a temperature lower thanthe Leidenfrost temperature. In nucleate boiling, a surface section wets with the liquid quenching
medium and the heat transfer from the workpiece increases. At lower temperatures, a third phaseof convective cooling occurs at the interface. The three phases are characterized by different
modes of heat transfer, which contributes to different cooling intensities.

Temperature T [°C]

Vapourblanket stage

Fig. 1. Nukiyama boiling curve and different
stages of boiling (heat transfer).

Nucleateboiling stage

Maximum
cooling rate

Convective
heat transfer stage

Time t [sec] / Cooling rate vc [°C/s]

Fig. 2. Cooling rate and temperatureduring quenching.

With exacting workpiece shapes it often occurs during heat treatment, i.e. during workpiececooling, that all the three phases occur simultaneously, which, however, produces considerable
thermal stresses and also an explicit influence on microstructural stresses, which leads todistortion, or even to distortion and cracking, of the workpiece. Steel quenching starts at theaustenitizing temperature of a given steel, the quenching process in fluid quenching media
always being accompanied by an evaporation phase of the medium in contact with the workpiecesurface, since a boiling point of most of quenching media ranges between 100 °C and 300 °C at
normal pressure.
Liberation of steam bubbles is a function of their size, which produces sound phenomena in theliquid, which are the strongest at the transition between film boiling and nucleate boiling of thequenching medium. Detection of acoustic signals and their analysis can, therefore, provide useful
information on the quenching process.
The Minnaert model of frequency of bubble formation during immersion quenching of theworkpiece indicates a relation between a bubble size and the frequency of bubble formation. In
the initial phase of nucleate boiling, bubbles of smaller diameters form and their frequency ishigher, and vice versa, when nucleate boiling of the quenching medium is nearing its end. Such
conditions can be achieved in quenching in water and in polymeric water solutions of different
concentrations.
Minnaert's frequency of oscillation bubble:
M 

1
RO

3  p 0

[1]

where:
p0 [N/m2] .... is hydrostatic pressure of the liquid around the bubble in conditions of static
equilibrium,S166χ [ ] ............. polytrophic coefficient,
RO [m] ........ radius of the bubble in the equilibrium and [kg/m3] .... the density of the liquid20
depth in water 100 mm
depth in water 150 mm

Frequency [kHz]15

frequency range
10

5

0
0

Fig. 3.

0.5

1.0

1.5

Bubble’s diameter [mm]

2.0

2.5

Expected frequency of nucleate formation as a function of nucleate size with chosen
immersion depth of specimen

Consequently, a measuring setup is adapted to the expected frequency of sound phenomena.
Thus it should be known which frequencies of bubble formation and decay occur predominantlyin the range of hearing and which slightly below this threshold.
Sound phenomena depend to a large extent on the temperature occurring at the workpiecesurface during the quenching process and a momentary temperature of the quenching medium.
Thus it is necessary that a signal detected with a hydrophone during the quenching process
follows the Minnaert model of formation of bubbles and sound signals in the quenching medium.
The conditions at which the signals were detected can be monitored with temperature
measurement in the specimen during the quenching process. The phenomena occurring at theworkpiece/medium interface should then be logically interrelated in film boiling, including
additional sound effects due to material failure.
2.

Experimental procedure

Because of an influence of the workpiece surface temperature on the occurrence of film boilingand nucleate boiling, an experimental procedure was elaborated for temperature monitoring in
the cylindrical specimens and on-line detection of sound signals in the entire temperature rangeof the quenching process.
2.1. Experimental setup
The designed experimental setup comprises two independent setups, i.e.: a setup for monitoring the temperatures at the surface and in the core during quenching
and monitoring of the temperature of the medium itself (Fig. 4);
 a setup for detection and processing acoustic-emission signals (Fig. 4).
The experimental setup for detecting AE signals in wetting processes is to be independent fromthe type of quenching medium used and of the quenching mode. Although the quenching process
takes some seconds or even up to several minutes, the experimental system should register
individual events sensed up to 0.1 second or less.
Fig. 4 shows the experimental setup for detecting AE signals during quenching. It comprises avertically adjustable hydrophone carrier providing a stable position perpendicular to thespecimen surface, and a small wire basket enabling fast and simple putting of a specimen in astable position to provide quenching free of additional heat removal. The specimen/basket
contact is realized in three points only so that the unwanted heat transfer to the cold basket isS167

prevented. A heated specimen is transferred from a quenching furnace with special pliers as fast
as possible so that heat transfer is as small as possible. The specimen is set in a measuring
position with regard to the hydrophone position in the quenching medium. The entire equipment
is immersed in a container having the form of a glass aquarium having convex walls and filledwith the quenching medium. A preliminary test showed that a 6 mm thick soft rubber lining,which considerably reduces reflection of sound signals from the container walls, considerably
reduces noise.
The measuring setup for detection and processing of AE signals comprises: A hydrophone suitable for laboratory and industrial applications. Its high sensibility
permits sound capturing in liquids, in frequency ranges from 0.1 Hz to 180 kHz, in awide range of working temperatures between 40 °C and 80 °C (for short periods even up
to 120 °C).
 A multi-channel measuring amplifier/pre-amplifier which is suitable for measuring soundemission and is also used in vibration measurements.
 A two-channel Sound Blaster card enables analogous/digital transformation of the signal
captured and permits 16- or 24-bit sampling of velocity up to 96 kHz.
 The program package SpectraLAB, specially designed for the detection, recording andprocessing of sound signals in their original and digitized form, permits subsequentevaluation and monitoring of events in the specimen during quenching.
 A notebook with Intel® Pentium® M Processor 1.6 MHz, and 760 MB RAM. Operating
system: MS Windows XP Professional 2002.
In addition to acoustic emission, temperature variations at the specimen surface and in the core,and quenching-medium temperatures prior to, during and after quenching were monitored too.
Each of the ten specimens was equipped with two thermocouples, the first being 2 mm below thesurface and the second one in the core centre, i.e. in the specimen axis.
The measuring setup for monitoring temperature variations at the specimen surface and in its
comprised: Thermocouples, mounted near the surface and in the core, for measuring temperature
variations during quenching; SCIXI 16-bit USB I/O platform for signal processing which enables the detection oftemperature signals with a 16-bit resolution and sampling rate of up to 200 kHz; Software for monitoring time-dependent temperature.
Within the experimental setup for temperature measurements in the specimen, a temperature of
the quenching medium prior to, during, and after quenching was measured too, using a digital
thermometer, model 30, a product of Macken Instruments Inc.
Digitalthermometer
Specimen

Hydrophone&
carrierPreamplifier&
Amplifier

SCXI
SignalConditioning
DeviceSoundblasterPC&
softwareResults

Temperature/time TW / t
Start/end temperature Tqm

BathSound pressurelevel/time L P/t

Thermocouple T1 /surface
Thermocouple T2 /coreFig. 4. Experimental setup for detection of temperature measurement signals during quenchingand experimental setup for detection of AE signals during quenching

S1682.2. Specimen material and size
Low-alloy Cr-Mo heat-treatable steel AISI 4140 was selected for the specimens. This steel
shows high hardness after heat treatment of even up to 57 HRC. It is characterized by good
through-hardening and high strength after heat treatment [Grum et al, 1998]. For this reason, it is
widely used in the production of static- and dynamical-load components of vehicles, motors, andmachine components with a large cross section.
Specimen form
Test specimens had a form of a cylinder with diameters of 45 mm and 53.5 mm and a height thatprovides the same mass, i.e. 0.92 kg. A ratio between the specimen volume and area was taken
into account since it affects the cooling rate with the same specimen mass. Table 1 shows two
different cylindrical specimens (A and B) with different P/V ratios.
Table 1:

Specimen form and size

Spec. Diameter Length Mass
type D [mm] L [mm] M [kg]Area
P [cm2]V [cm ]

P/V--

Volume3A45.0

75.0

0.92

138

119

1.16

B

 53.5

53.5

0.92

132

119

1.11

2.3. Quenching medium
For the steel chosen an adequate quenching medium and a suitable quenching mode ensuring a
cooling rate slightly higher than the critical one should be used. Thus the required throughspecimen hardness and strength can be obtained, and internal stresses can be optimized in thespecimen during quenching and distortion with residual stresses as a result of quenching. In
accordance with steel manufacturer's recommendations, five types of quenching media werechosen for quenching steel 42CrMo4; they are given in Table 2.
Table 2:

Quenching medium

Quenching medium
Pure water
5% polymeric water solution
10% polymeric water solution
15% polymeric water solution"Olmakal Rapid 90" oil

Ratio
Aquatensid BW
-5%
10 %
15 %
--

Water
100 %
95 %
90 %
85 %
--

"Olmakal Rapid 90"----100 %2.4. Procedure
A cold specimen was introduced in the furnace preheated to a temperature around 600 °C. Then
progressive heating of the specimen to the austenitizing temperature, i.e. 850 °C, followed inaccordance with manufacturer's recommendations. When the temperature became stable, thespecimen was quickly moved with the pliers to a quenching bath to be quenched in the different
quenching media.

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During the experiment the specimen was fixed in a stable position to ensure identical quenchingconditions for all the specimens and to reduce noise that could generate due to stirring of thequenching medium. The initial quenching-medium temperature amounted to 18 °C, whereas the
final temperature varied due to the different specific heat of the quenching media used. It variedin dependence of the quenching medium used. It was the lowest with pure water and highest withoil. The volume of the quenching medium in the container was always the same, i.e. 14 l.
At the IBM notebook two program packages, i.e. "SpectraLAB" for sound-signal processing and"Temperature" program for temperature-signal processing, were running simultaneously at
platform LabView. The captured sound signals were subsequently evaluated with the same
program package, i.e. "SpectraLAB", and the temperature signals with MS EXCEL.
3.

Results

Fig. 5
Free convection

Nucleate boilingTransition boiling

Film boiling

The results on the sound pressure are represented in diagrams showing the varying soundpressure level Lp in dependence of frequency and time.
The sound power at a certain point indicates the relation between the instantaneous pressure andthe ambient pressure. The sound pressure (p) is expressed in [Pa]. A reference value of thesound-pressure for liquids pref is equal to 10-6 Pa. The sound-pressure level Lp is defined by:
p2
p
[dB]
(1)
L p  10  log 2  20  log
p ref
p ref

Typical time series diagram of sound pressure signals.

The diagram of time intervals displays the raw digitized audio data with the amplitude
shown on the vertical axis and time on the horizontal axis in linear view. The amplitude
ratio between maximum level at the start of the quenching process and background noiselevel at the very end of the process is sufficient to identify different stages of the quenchingprocess (Fig. 5).
Signal processing software enables spectrogram view as calculated time record of soundsignals. Digitized audio signal is passed through a math algorithm known as a Fast Fourier
Transform (FFT) which converts the signal from the time domain (amplitude-vs-time) tothe frequency domain (amplitude-vs-frequency). This view displays the spectral data overtime with the amplitude shown in colour in which frequency is seen as the ordinate value
and signal intensity as color intensity: cold - blue color → low intensity; hot - red color →strong intensity (Fig. 7b).

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

The calculated average spectrum shows relative amplitude level dependent on thefrequency. This view is a two-dimensional plot of the spectrum. The horizontal axis shows
the frequency and the vertical axis shows the amplitude of each frequency line (Fig. 6A,
Fig. 6B).

A Quenched in water.

B Quenched in salt solution.

Fig. 6 The average spectrum of type A samples.
3.1. Comparison of spectrograms
The comparison of spectrograms obtained in quenching of the specimens of different forms andthe same mass in different quenching media indicates an important characteristic of thespectrograms, a differentiation in sound intensity. The differences of sound emission intensityand duration during quenching distinctively depend on the specimen mass and the type ofquenching medium used (Fig. 8). As the specimen-surface wetting occurs simultaneously at
different sections of the specimen surface, all regions of Nukiyama boiling curve from the
beginning to the end of cooling can be differentiated. The Nukiyama curve in Fig. 7a shows theheat flow as a function of the temperature difference between the specimen temperature TW andthe quenching-medium temperature TF.
6

6

10

q

10

qm a x

D

B

C

A
10

4

10

2

2

q [W/m ] [W/m K]

5

5

10

4

10

3

10

3

10



2

10

2

10

3

10

2

10

1

10

T=TW-T F [K]B – nucleate boilingD – film boilinga)

SPL (dB rms)

Frequency (Hz)A – free convection
C – transition boiling-1

10Time (10 sec/div)

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b)

Fig. 7.

Comparison between Nukiyama boiling curve (a) and spectrogram
during cooling (b) at specimen of 53.5 mm quenched in 10% polymericwater solution

3.2. Analysis of an acoustic spectrum
A spectrogram is a time record of sound signals, from which a frequency is evident as a value atthe ordinate and signal intensity as colour intensity (cold, blue colour - low intensity, hot, red
colour – strong intensity).
The spectrogram of the quenching process shows the presence of low frequencies at the
beginning of the process, i.e. in the first 10 seconds, when using water as a quenching medium.
Intense sound pulses occur due to the formation and decay of the vapour film. Their frequency
gradually reduces due to a decreasing cooling rate.
3

3
8

1

7

A

1

2

6

after quenching in pure water

Fig. 8.

2

6

5

5

4

4

B

after quenching in"Olmakal Rapid 90" oil

Acoustic signal obtained in quenching of specimen of 45 mm in pure water (A) and"Olmakal Rapid 90" oil (B).

The analysis of the acoustic spectrum obtained during quenching comprises detection of soundemission and transformation into a voltage signal. It provides detailed information on individual
events in the quenching medium used. Fig. 4 shows two records of voltage signals detected withthe hydrophone and shown in the spectrogram giving the frequency of events during nucleateboiling. Although the signal of the 45 mm specimen quenched in pure water and the one
quenched in the "Olmakal Rapid 90" oil (Fig. 8) differ considerably, following may beconcluded: The beginning of quenching is described by the signals of different frequencies which intheir amplitude differ from the environmental noise (Fig. 8, area 1);
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








The end of quenching is described by the signal having intensity comparable to that of
the environmental noise, which means that the number and intensity of the signals withregard to the frequency and their intensity reduces. Determination of the end of cooling isof subjective nature and cannot be made by means of the spectrogram. (Fig. 8, area 2);
The process duration is the difference between the beginning and end of quenching;wherefrom an average cooling rate can be established (Fig. 8, area 3);
From the voltage signal detected with the hydrophone and from the spectrogram, twocharacteristic areas can be determined:* an area of lower frequencies, i.e. up 1 kHz in water quenching, and up to 2 kHz in oil
quenching, where only the amplitude is changing (Fig. 8, area 4), and* an area of higher frequencies, i.e. up 18 kHz in water quenching, and up to 20 kHz in
oil quenching, where the signal frequency is changing as well (Fig. 8, area 5).
Individual peaks of higher sound-emission intensity given by a voltage signal of higher
frequencies (up to 6 kHz in water quenching, and up to 14 kHz in oil quenching) due tosurface oxidation and oxide cracking (scale) at the surface (Fig. 8, area 6) are identified.
In sound emission there are also short-time peaks of high intensity with frequencies of up
to 12 kHz in water quenching as a result of too high a cooling rate (Fig. 8, area 7).
The individual peaks of high intensity with lower frequencies are a result of steam
bubbles escaping from a drillhole of 4 mm in diameter at the upper specimen plane,
which are particularly distinct in oil quenching (Fig. 8, area 8).

In general, it can be concluded that characteristic changes at the specimen/quenching-medium
interface could be determined from the detected sound emission during quenching.
3.3. Temperature measurement
Fig. 9 shows cooling curves at the surface for type A specimen (45 mm) quenched in all fivequenching media. The temperature was measured near the surface and in the core, but only
surface temperature was taken into account when describing the interface phenomena.
Water quenching is more intense, which means that the cooling from the quenching temperature
to the boiling point of water only takes a few seconds (4.5 seconds at the surface and 6.5 seconds
in the core), whereas the cooling to a temperature of 40 °C at the surface takes 52 seconds, thecore temperature being lower by approximately 10 °C.
Oil quenching is slower, i.e. cooling from the quenching temperature to a temperature of 400°C
takes approximately 10 seconds (10.8 seconds at the surface and 11.8 seconds in the core),
whereas cooling to 50 °C at the surface takes 322 seconds, the core temperature being lower byapproximately 2 °C.

Fig. 9Temperature variation at specimen surface of 45 mm indiameter during quenching in different quenching media.

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4.

Conclusions

The paper treats the signals captured through sound emission measurements which occur during
the steel quenching in different quenching media. The sound pressure signal, represented as theamplitude and duration of the signal during quenching, was captured by the hydrophone. During
the recording of the sound pressure the signal shape changes as the vapour film phase on thespecimen surface is formed and the nucleate boiling phase on the specimen/quenching-medium
interface enters. Sound emission analysis provides a new approach to processing, evaluation andoptimization of the quenching process. The sound-pressure signals described with the voltagesignal amplitude were detected with the hydrophone and shown in diagrams as time-dependent
amplitude. The analysis results confirm that there are important differences in the amplitude andfrequency of the detected signals, indicated by the formation and decay of the vapour film at the
specimen surface as well the occurrence of nucleate boiling at the specimen/quenching-medium
interface during quenching. The sound emission occurring during quenching is foundpredominantly in the hearing section of the spectrum; therefore, detecting emission is efficientsince the phenomena at the interface can be monitored. The phenomena at the interface play animportant role in the heat removal from the workpiece, which means that quenching effects withdeviations in hardness and residual stresses in the cylindrical specimens can also be taken into
account. The investigation performed shows that the proposed experimental system for detecting
acoustic emission and temperature in the specimen provides useful information from the voltage
signal even though the quenching phenomena happen quickly. The analysis of the results offers
an interesting new approach to evaluation and, more importantly, to monitoring, controlling andoptimizing the entire quenching process.
5.

References[1.] Tensi, H. M. (1992): Wetting kinematics. Eds: Liščić, B.; Tensi, H. M.; Luty, W.: Theory
and Technology of Quenching, A handbook, Springer-Verlag, Berlin, 481 pp.
[2.] Tensi, H.M., and G.E. Totten (1996): "Development of the Understanding of the Influenceof Wetting Behaviour on Quenching and the Merits in these Developments of Prof. Imao
Tamura'', Proceedings of the 2nd International Conference on Quenching and the Control
of Distortion, Nov. 4-7, 1996.
[3.] Grum J., Božič S. (2003): Acoustic emission during quenching of 42CrMo4 steel,
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Liščić, H.M. Tensi, Prague, Czech Republic, 530-541.
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[8.] Grum J., Božič S., Lavrič R. (1998): Influence of Mass of Steel and a Quenching Agent on
Mechanical Properties of Steel, Heat Treating, Proceedings of the 18th Conference,

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Macherauch E., Vohringer O. (1992): Residual Stresses After Quenching, Eds.: B. Liščić,H.M. Tensi, W. Luty: Theory and Technology of Quenching, A Handbook, SpringerVerlag Berlin, Heidelberg, 117-181.

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