E-GLEA 1; PRIMER ENCUENTRO DEL GRUPOLATINOAMERICANO DE EMISION ACUSTICA(The First Meeting of Latin American Group on
Acoustic Emission) at Buenos Aires, ARGENTINAJOURNAL OF ACOUSTIC EMISSION
Editor: Kanji Ono
Associate Editors: A. G. Beattie, T. F. Drouillard, F. C. Beall and M. Ohtsu
1.

Aims and Scope of the Journal

Journal of Acoustic Emission is an international journal
designed to be of broad interest and use to both researcher andpractitioner of acoustic emission. It will publish original contributions of all aspects of research and significant engineering
advances in the sciences and applications of acoustic emission.
The journal will also publish reviews, the abstracts of papers
presented at meetings, technical notes, communications and summaries of reports. Current news of interest to the acoustic emission communities. announcements of future conferences andworking group meetings and new products will also be included.
Journal of Acoustic Emission includes the following classesof subject matters;
A. Research Articles: Manuscripts should represent completed original work embodying the results of extensive investigation. These will be judged for scientific and technical merit.
B. Applications: Articles must present significant advancesin the engineering applications of acoustic emission. Material
will be subject to reviews for adequate description of procedures,substantial database and objective interpretation.
C. Technical Notes and Communications: These allow publi,cations of short items of current interest. new or improved experimental techniques and procedures. discussion of published articles
and relevant applications.
D. AE Literature section will collect the titles and abstractsof papers published elsewhere and those presented at meetings'
and conferences. Reports and programs of conferences andsymposia will also be presented.
Reviews. Tutorial Articles and Special Contributions willaddress the subjects of general interest. Nontechnical part will
cover book reviews. significant personal and technical accomplishments, current news and new products.
2,

Endorsement

Acoustic Emission Working Group (AEWG). European
Working Group on Acoustic Emission (EWGAE), Committee on
Acoustic Emission from Reinforced Composites (CARP), andAcoustic Emission Working Group of India have endorsed the
publication of Journal of Acoustic Emission.
3.

Governing Body

The Editor and Associate Editors will implement the editorial
policies described above. The Editorial Board will advise the editors on any major change. The Editor, Professor Kanji Ono. has
the general responsibility for all the mallers. Associate Editors
assist the review processes as lead reviewers. Mr. T.F. Drouillard
is responsible for the AE Literature section. The members of the
Editorial Board are selected for their knowledge and cxperience on
AE and will advise and assist the editors on the publication
policies and other aspects. The Board presently includes thefollowing members:

S.H. Carpenter
D.A. Dornfeld
P. Reischmann
L. Golaski
M.A. HamSlad
H.R. Hardy. Jr.
R. Hill
I.V. Ivanov
P. JaxT. Kishi
O.Y. Kwon
J.C. Lenain
S.L. McBride
W. Morgner
C.R.L. Murthy
H. Niitsuma
A.A. Pollock
T.M. Proctor. Jr.
I. RomanC. Scala
C. ScrubyA. Vary

E. Waschkies
M. Weyers
1. W. Whittaker
B.R.A. Wood
4.

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5.

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6.

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ISSN 0730-0050 Copyright @ 1999 Acoustic Emission Group

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New Format for
Journal of Acoustic Emission
We are pleased to announce a new format for the publication of Journal ofAcoustic
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Founding members of GLEA, Grupo Latinoamericano de Emisi6n Acustica. From I to r: Rosa Piotrkowski,
Tulia Palacios, Jose Ruzzante. M. Isabel L6pez Pumarega. Maria Armeite; front, Pedro Feres Filho, MiguelAngel Sabia Montero. (Missing from the photo is C. 0' Anellis.)

Color plate for Figure 8 on page 9.

"

Classification of Acoustic Emissions
in Metallic Glasses
A. Vinogradov
Abstract

experimental results indirectly showing a possibility of theinhomogeneous flow by the formation and motion of linear
defects with long-range stress fields. Alternative understanding is possible, however, if one takes into account theexistence of a large fraction of the excess free volume inMGs, which can significantly facilitate atomic mobility andprovide strain localization upon loading (Alekhin andKhonik, 1992; Argon. 1979; Spaepen, 1977). Neither of themodels is commonly accepted today and more experimental
evidence is needed for verification of the mechanism ofplastic flow in MGs.

A complex statistical and spectral analysis of acoustic
emission (AE) during the plastic flow of metallic glasses(MGs) is performed with the aim of classification of AE
sources in this kind of disordered media. The ideas of optimization and enhancement of reliability of AE analysis aredescribed in terms of factor and cluster analysis over a wide
range of AE parameters in both time and frequency domains. It is sbown that the AE signals in MGs naturally fall
into two categories (clusters) differed by their amplitude
and spectral characteristics. This allowed to conclude thattwo different mechanisms of inhomogeneous plastic flow,
i.e. two AE sources, exist in MGs. One mechanism is simply associated with shear band formation, while identification of the other mechanism is not quite straightforward. A
possible nature of that unknown mechanism of plastic deformation of MGs is discussed and argued that it could be anucleation of shear bands inside the amorphous body.

Localization of plastic deformation in the narrow shear
bands of less than I mm width at sufficiently low temperatures is typical for most MOs. The structure and propertiesof shear bands have been studied and reviewed in Donovan
and Stobbs (1981) Zielinski and Ast., (1983) and Noskova
et al. (1985). Shear plastic deformation in MGs is accompanied by rather powerful AE as has been found in former
studies (Leksovskii et aI., 1986; Braginskii et aI., 1986;
Vinogradovet al., 1988; Vinogradov et aI., 1989; Vinogradov and Leksovskii, 1996; Khonik et aI., 1997). The present work is directed towards further clarification of AE
origin in amorphous metallic alloys. This is supposed toshed some light on the microscopic mechanism of theirinhomogeneous flow.

1. Introduction
1.1 Plastic Deformation o/MetaUic Glasses
Plastic deformation of metallic glasses (MGs) was thefocus of numerous experimental and theoretical investigations in the last two decades. It was found that depending
on testing conditions one of two flow modes occurs: inhomogeneous flow which is highly localized in the slip bands
similar to those in crystalline metals or homogeneous viscous flow which does not reveal any signs of strain localization (Masumoto and Maddin, 1975; Alekhin and Khonik,
1992). Despite many efforts and a variety of speculativemodels the nature of inhomogeneous plastic deformation of
MGs remains unclear. Difficulties of direct structural investigations have led to a fairly large number of structuralmodels, which are employed to describe a thermal stability,
mechanical behavior and other properties of MGs. One
approach comprises several dislocation-discIination models
and their modifications (Gilman, 1973; Morris, 1979;
Zaichenko and Borisov, 1982). This concept is based on

1.2 AE during Plastic Deformation ofMetals and AE Signal
Processing
Although it has long been recognized that the AE signal contains information about the dynamics of defect motion (Fleischmann and Rouby, 1979), the problem of AE
analysis is still how to extract this information. Early AE
studies related to plastic deformation of metals had a simple
descriptive character. trying to establish some qualitative
correlation between the AE signal and macroscopic stressstrain behavior. The situation is still very puzzling in attempts to relate acoustic emissions and micro-structuralcharacteristics of materials and their defects. A progressachieved in this area is largely due to the works of Kishi etal (1981), Enoki and Kishi (1991) and also Wadley et al.
(1981) and Scruby et al. (1986), for example. They havedeveloped a quantitative approach for AE source characterization by a source function, which was determined by
the deconvolution technique. This way is particularly effective for evaluation of fracture processes. but it has obvious

Received 10 April 1998; in final form. 15 September 1998. The
author was affiliated with Department of Mechanical Systems
Engineering. Kanazawa University. Kanazawa 920-8667. Japan.
He is now with Faculty of Engineering. Osaka City University.
Osaka. Japan.
JowuaJ of Acoustic Emission
VOIWlle 17. Numbers )-2

1

0730-0050199/55.0011700)-013
Copyright<el1999 Acoustic Emissioo Groupacted at significantly different instants of time. Woodwardand Harris (1977) applied statistical treatment to the arrayof AE spectra, showing that there were three distributionsof amplitude spectra in zirconium. This allowed the authors
to conclude about the presence of three kinds of AE mechanism such as twin initiation, twin broadening and slip.

difficulties in application to plastic deformation. where up
to now we have no uniform approaches to both the AE dataprocessing and interpretation.
Fundamental theoretical aspects of AE caused by various dislocation reactions have been considered by Natsik
and Chishko (1972. 1978, 1982) within the framework ofcontinuous mechanics. They have shown that different
sources produce different AE in the wave zone. This could
serve as a fundamental physical basis for source identification. However. precise verification and application of theircalculations is still difficult even when a well calibrated
measuring system is used. because activating a single AE
source in a real crystal is quite rare and the sensitivity of theavailable measuring AE equipment is limited (especially forthe broad-band one).

The Fourier transform is fast, vivid and, therefore. convenient for AE depiction. However. other kinds of spectral
orthogonal expansions or data representation can be equally(or even more) useful to explore the AE structure. During
the past decade. the impressive development of the patternrecognition technique has provided new opportunities forclassification and identification of AE signals. For example, the wavelet transform with further nearest neighborclustering, autoregressive analysis and neural networks applications were shown to be very effective for this problem(Ono and Wu, 1996; Suzuki et aI., 1996; Grabec, 1995;
Grabec and Sachse, 1989).

AE is always a random process (Lucia and Redondi,1976). The arbitrary AE appearance reflects the stochastic
nature of the defect movement during the plastic deformation. This is not surprising. because AE is always associatedwith the movement of a great number of atoms in randomstress fields of microscopically inhomogeneous media.
Thus, it seems obvious that the statistic methods developedin the theory of random processes and random data arenatural for quantitative characterization of AE fluxes. Fourmain quantities are commonly used to describe the basic
properties of random data: mean square values. probability
density functions. auto-correlation functions and power
spectral densities. All theses quantities have been utilized
elsewhere for AE description. However, since AE can be
regarded as a multi-variative process the problem of the"most informative" parameter arises. A total AE count.
peak and root mean square (RMS) voltage, energy andmany other time-domain parameters are often measured
using different equipment that provides insufficient information about AE sources and makes it extremely difficult
to compare and rationalize the results obtained by different
investigators unless the same conditions of data recording
and treatment have been met. More detailed information on
the AE sources can be deriVed. in principle, from the power
spectral density function if the broad-band apparatus is used
and if a special attention is paid to calibration of the meas·
uring system. Although the shape of AE spectra is affectedby many experimental factors such as the specimen geometry. sensor characteristics and location. etc. (Ono andUcisik. 1976). we should always bear in mind that for thestationary data its spectral presentation is equal to the time
domain picture by Wiener-Khintchine theorem (priestley.
1981) and the problems of spectral analysis are just masked
but not solved in time domain. Moreover, many of theabove-mentioned factors can be taken into account by calibration and proper filtering in spectrUm analysis (Lucia andRedondi. 1976). Successful examples of the AE source
discrimination by means of Fourier spectral analysis are
given by Graham and Alers (1974) and Egle at a!. (1981).
In both cited works the sources were a priori known andThus. the purpose of the present work is two-fold. At
first. we focus ourselves on quantitative discrimination
between the AE sources during the plastic deformation ofsuch a complicated material as a metallic glass. anticipating
further identification of emitting defects. Secondly. weaimed at showing that the systematic use of mathematicalstatistics provides a powerful tool for quantitative AE
source discrimination without any a priori assumptions on
the nature of emitting defects. To achieve this purpose weemploy the complex data treatment involving general descriptive statistics, Fourier analysis of individual pulses,
factor and cluster analyses. The details of data processing
are given below.

2. Experimental
2.1 Specimens and Mechanical Testing
Ribbon shaped COS7FesNiloSillBI7 MG (IS J.lm thick,
14 mm width) was obtained by standard melt-spinningtechnique. This glass was chosen because its mechanicalbehavior has been extensively studied in a wide temperature
range (Khonik et al.. 1997). Thin foil transmission electron
microscopy was used to ensure an amorphous structure.
The sample gage length was 120 mm. A total number ofthe specimen tested is 18. The tensile tests were performedon an Instron-type screw-driven testing machine at the constant strain rate (7.0±0.4) x lO's S·I and (373±S) K temperature. This temperature is much lower than the crystallization temperature of the given glass (Khonik et al.. 1997).
The specimen was heated to the testing temperature at a rateof 10 KJmin. The elevated temperature was used to facilitate formation of shear bands during inhomogeneous plastic
flow. As has been reported in Khonik et aI., (1998) for theglass under investigation. the AE activity correlating with anumber of shear bands attains its maximum value at about
373 K. This provides a large enough number of AE
2

events within a single realization; this is important for further statistical treannent. The surface of the deformed
specimens was observed with a help of scanning microscopy.
2.2 AE Apparatus

AE monitoring was carried our using a computercontrolled AE setup. which is schematically shown in Fig.
l. A miniature broadband (50-1000 kHz) AE sensor AE900M (3 mm in diameter) made by NF Electronic Instruments was securely mounted on the specimen surface 15
mm from the lower grip. The sensor frequency response isshown in Fig. 1. The AE signal was amplified by 60 dB
with a low-noise broadband preamplifier followed by the
20-1000 kHz bandpass filter together with the 40-dB main
amplifier and built-in integrating circuit for RMS voltage
measurements.

MAIN

AMPUFIER

A part of installation. which is responsible for acquisition of AE parameters. contains two synchronized channels:
one is for recording slow AE·parameters such as RMS voltage (URMS> and activity m (a number of pulses per unittime) and the other is for high-speed AE sampling.

!g 50,.----------------.,

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l&I
CD

In the first (slow) channel. we used a 12·bit analog-todigital converter (AnC) (Datel PC412A). which also contains a built-in 16-cannel multiplexor and 12-bit digital-toanalog converter (DAC). This ADC was also used for recording of external experimental parameters such as strain.
stress. temperature, etc. The sampling rate could be chosen
by software. but mostly we took a set of 20 samples ofevery parameter at every 0.1 s and stored their mean values.

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It

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20

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Z
W

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0.1----------------500
o

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1500

2000

FREQUENCY. 'I khZ

In the second (fast) channel for high-speed AE sampling. we used a Datel PC414D 12-bit ADC with a sampling rate up to 4 MHz and on-board FIFO memory of 16
ksamples. To gain a pre-triggering mode of operation we
added an extra home-made circuit with an analog comparator. The threshold reference signal for the comparator
is taken from the PC412 DAC and can be changed by software. In the case of continuous AE. which has no pronounced peaks of amplitude. the ADC can be periodicallystarted by a computer timer. The threshold was set of 2 dB
higher than the peak noise level. The system was carefully
grounded to minimize an electrical noise. About lOa noise
events were detected before each test. Calculation of spectral and time-domain parameters followed by statisticaltreatment described below allows to obtain a representative
image of the noise events in terms of its waveform. power
spectral density and cluster of calculated parameters. This
noise image was used during data processing for further
identification of false signals. which must be excluded fromanalysis and for subtraction of noise spectral density fromthe spectra of true AE events. The digital oscilloscope
Kenwood ORI400 is used for a real·time monitoring ofFig. 1 Experimental set-up and AE sensor frequency response.
stress-strain behavior together with AE RMS voltage orother slow parameters.
2.3 Data Processing

The simplified outline of data processing used in thepresent work is shown in Fig. 2. Let us briefly comment on
the basic steps of data analysis. omitting the technical details that can be found elsewhere. As a caution. we shouldstress that the improper use of statistical algorithms can bemisleading and the reliability and limitations of conclusions
drawn from statistical results is of primary importance(Bendat and Piersol. 1966).
1) At first. time histories of various time-domain parameters are obtained and plotted with the stress-strain curve.
By visual evaluation of the recorded realizations one candetermine the type of AE events: continuous. burst or3

mixed. In the case of burst type AE. we plot an activity mand peak amplitude Up(t) as a function of time (or strain)
while for continuous AE the plot of URMS(t) appears to be
an almost unique characteristic of the AE flux in time domain.
2) During the next step. we determine general statisticalproperties of AE fluxes. Since the way of data treatment
depends on the type of process. a test of stationarity should
be conducted. If the process can be regarded as a stationary
one. then it is natural to describe it via probability distribution functions of peak amplitudes h(Up), time intervals between pulses h(t), etc. Otherwise. we should use some special methods developed for non-stationary processes; this ismuch more formidable mathematical task (Lu cia and Redondi. 1976; Priestley. 1981; Bendat and Piersol. 1966).
3) The details of AE spectral analysis have been reported in
Egle et al. (1981) and Vinogradov et a1. (1995). The standard 4096-points digital fast Fourier transform of the AE
signal gives a periodogram. which is not a consistent estimate of the power spectral density G(f). By windowing.
one can significantly decrease the variance of this estimate.
A rectangular smoothing window is used in the present
work with a spectral width from 1 to 20 kHz. The spectral
density of electrical noise is subtracted before calculation ofspectral parameters. From the power spectral density function. the following characteristics are computed numericallyafter noise spectrum subtraction: E is the total energy
measured as an integral of G(f) over a whole frequency
range of 50-1000 kHz. fc is the fundamental frequency corresponding to the maximum of G(f). Gmu == G(fe) is themagnitude of spectral density at the fundamental frequency.
fmod is the median frequency and ferr is the effective width ofspectra. which equals to the width of imaginary rectangular
spectra with the maximum density Gnw. and the total energy
E. i.e. E GIllU·feff• With the time domain parameters suchas Up and URMS they form a complete set of descriptive
variables that we used. In principle. one can expand this set
by adding extra parameters such as a rise time. duration ofpulse. etc.• but this is not necessary for the purpose of thepresent work.
4) Since we measure many parameters to describe the same
AE phenomenon. the problem of how to reduce a numberof variables becomes important. Apparently. AE parameters correlate with each other. This provides the possibility
to choose the less correlated variables for optimal description of AE. It seems impossible to say a priori which variables are to be used. We employ a principal component
analysis as a data reduction method. The fundamentals offactor analysis can be found in Cooley and Lohnes. (1971)
and Harman. (1967). The question is how many factors to
extract? Although the nature of this decision is arbitrary ingeneral case. it is reasonable to suppose that for the purposeof the present work the number of factors should be two
because all variables differ substantially in two respects:
those which are measured in time domain (Up. U RMS ' E. risetime. duration of pulse. etc.) and those. which are related/

Data
Acquisition~/

AE KINETICS.
GENERALDESCRIPTION

00:(>
DESCRIPTIVESTATISTICS"<..7
SPECTRALANALYSIS"<..>
FACTORANALYSIS~>

CLUSTERANALYSIS~>=DESCRIPTIVESTATISTICSFOR
EACH CLUSTER"<..7AE KINETICSFOR
EACH CLUSTER/l
Identificationof AE Sources/

Fig. 2 A flow-chart showing the main steps of data processing.
to the shape of spectrum in frequency domain (Gmu • f mcd • fe•(efT, etc.). Further analysis will confirm this idea because
extracting more than two factors does not significantly
change variability.

45) The goal of clustering is to reduce the amount of data by
categorizing or grouping similar data items together. The
so-called partitional clustering attempts to decompose thedata set into a set of disjoint clusters. The criterion functionthat the clustering algorithm tries to minimize may emphasize the local structure of the data, as by assigning clusters
to peaks in the probability density function, or the global
structure. Typically the global criteria involve minimizing
some measure of dissimilarity in the samples within eachcluster, while maximizing the dissimilarity of different
clusters. A commonly used partitional clustering method is
the so called K-means clustering (MacQueen, 1967; Hartigan, 1975) where the criterion function EK is the averagesquared distance of the data items XI< from their nearest
cluster centroids, m."
EK =~ IXI< - Il\(V12(1)
I<
where c(xJ is the index of the centroid that is closest to xI<'
6) After clustering, one can obtain statistical descriptions
for the populations of signals belonging to different clusters. Estimating the distribution functions and their parameters is supposed to be useful for conclusions about thenature of emitting defects and for further comparison withtheoretical models of AE.
7) Once each signal has been associated with a certain
cluster it is not difficult to separate contributions of signals
from different clusters to resultant AE. One can plot a time
history for individual clusters to see how do different AE
mechanisms behave with time (or strain).

2500

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2000

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1500

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~

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0.005

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STRAIN, t

Fig. 3 Typical AE activity (a) and amplitude (b) as functions of strain in metallic glasses (AE time-history in termsof activity and peak voltage).
140-

3. Experimental Results

3.1 AE Appearance in Metallic Glasses.. ~.....

,

.....

The stress-strain curve of MG under investigation atroom temperature is almost linear. Typical AE time-history
is shown in Fig. 3 as an activity (number of pulses per unittime) (a) and an amplitude (b) versus strain. Although AE
starts long before failure. the stress corresponding to the AE
onset is fairly large. The AE behavior displayed in Fig. 3 iscommon for a wide range of MGs (Leksovskii et al., 1986;
Braginskii et al., 1986; Vinogradov et aI., 1988; Vinogradov et aI., 1989; Vinogradov and Leksovskii, 1996; Khonik
et al., 1997). In contrast to continuous emission in puresingle- and poly-crystals, AE in MGs is of burst type thatreflects a strongly localized character of plastic flow at low
temperatures. Let us note that for any AE time-history in
MGs it is possible to find some parts, which can be treatedas weakly self-stationary processes. All results discussed
below refer to this kind of behavior.

I

--]

....

~I~~~"~~-~'·····~··_~·~~~~::--:=~'":"';";;;1
120.30](40.50)(60.70)180.90)(100.110]•

12~

PEAK AMPUTUDE. Upl 10-&v

Fig. 4 Typical amplitude distribution of AE signals and its
fit by normal law (solid line). No simple fit by ordinary
statistical functions can be accepted for a given AE record.
high-amplitude region. One can suspect that the Up distribution is bimodal and consists of a low-amplitude fraction
and a high-amplitude fraction, which are produced by different sources. In some glasses, the second mode in theamplitude distribution is more clearly pronounced (Braginskii et at., 1986; Vinogradov et aI., 1988). Even so, a simple
amplirude discrimination is not effective enough for source
characterization neither in general case nor in particularcase of metallic glasses because of ambiguity in the discrimination threshold and in the statistical significance. The
features of AE fluxes on different stages of deformation of
MGs have been discussed in details in the above cited pa-

The amplitude distribution is shown in Fig. 4. It does
not differ substantially from that usually observed in MGs(Braginskii et al., 1986). This distribution cannot be fitted
by a simple statistical function because of a long tail in5pers, whereas in the present work we deal predominantlywith the AE sources.

nihilation of network disclinations during the step growth ata free surface while the high frequency component was
assigned to dislocation emergence from the ribbon. This
attractive interpretation was, however. criticized in Vinogradov and Leksovskii, (1996b) where it was underlinedthat a thorough examination of some experimental factors
such as a sample geometry and sensor position is absolutely
required because of their great importance for the shape ofspectrum (Ono and Usicik, 1976). It was shown that the
low-frequency component of 10-30 kHz is due to a longitudinal sample resonance similar to tbat of the stretchedstring. For this reason we limit the lowest frequency of ouranalysis at SO kHz.

3.2 Shear Bands as AE Sources in MGs
Using direct in-situ AE monitoring with simultaneous
SEM image recording we have shown that the shear bands
such as those shown in Fig. 5 produce elastic waves during
inhomogeneous plastic deformation (Vinogradov and Leksovskii, 1996). Formation and growth of these bands areaccompanied by the release of elastic energy from local
plastically deforming regions, resulting in the AE pulse of a
specific shape. The in-situ experiments have also revealed
that some signals appear, while no changes in the surface
morphology could be noticed within the limits of SEM
resolution. Moreover, the shape and amplitude of thesesignals were different than those of the signals coming fromthe shear bands. Taking into account that the first AE
events are usually observed at the stresses much lower thanthose required for the formation of the first shear bands, one
can suppose that two kinds of AE source may operate in
MGs during inhomogeneous plastic flow. Digitizing individual waveforms makes it possible, in principle, to distinguish precisely between these two kinds of signal and to
trace their behaviors separately.

All signals were visually evaluated in both time andfrequency domain. This resulted mainly in two distinct
types of signals. Typical waveforms and power spectra of
AE in MGs are represented in Figs. 6 and 7. The signals do
not overlap usually and the burst type AE is evident. Type1 signals are characterized by a sharp initial rise and decaying waveform of high peak amplitude and clearly pronounced high-frequency components of 640 kHz andhigher. The number of type-I signals is much smaller than
the number of type-2 signals. Compared to the type-l signals, the type-2 events have smaller amplitudes, shorter
duration and more flat noise-like spectra with dominant
low- and medium-frequency components of 100 and 300
kHz. We should notice that although most peaks are attributable to the features of the sensor frequency response, theirrelative heights are different in different kinds of signals.
This results in significant difference in spectral characteristics (median frequency, in particular).

3.3 Shapes and Spectra ofIndividual Signals
Zaichenko and Braginskii (1990) have found two characteristic frequencies in power spectra of MGs: a low frequency maximum of 10-30 kHz and a high frequency peakof 500 kHz. These authors have used a disclinationdislocation model (Zaichenko and Borisov, 1982) to account for their experimental results. They pointed out thatthe low-frequency component could be associated with an

To compare the shapes of spectra, the normalized
spectral density, G*. for each signal was computed as
G*(f) = G(f)IE. The contour map of the normalized power

Fig. 5 Shear bands on the surface of the Co57Fe$Ni,oSi Il B 17 metallic glass deformed intension. These bands are identified to be one source of AE in MGs (type 1).
6related (fmcd-Up). In the calculation of eigenvalues (Table 3,
see Harman, (1967) for details about computational algorithms) for each variable, two factors are to be extractedwith respect to the Kaiser criterion (Harman. 1967). This
Kaiser criterion states that we have to retain only factors
with eigenvalues greater than 1. These two factors cover
78% of total variance (Table 3) and their loadings are presented in Table 2. Factor loadings can be interpreted ascorrelation between the respective variables and factors,
providing most important infonnation for the understandingof factors. Factor 1 is marked by the highest loadings for
amplitude-related parameters such as UP. UR.\tS and E while,in contrast, Factor 2 shows the highest loadings for frequency domain items such as fmcd , fe and fcff and the lowest
for Up, Ums and E. Choosing two variables with the highest
loadings for Factor 1 and Factor 2 correspondingly, we reoduce the number of measured parameters to a pair of mostrepresentative ones (Up.fm• d ), which promises to give theclearest description of the AE structure in MOs. Further. weshall use only this pair for AE characterization. We should
notice that the attempt to call a greater number of variables
for bener AE description does not change the results ofanalysis. but it substantially complicates both calculationand interpretation.

spectral density in time (or strain) -frequency coordinate isshown in Fig. 8. The spectral feamres such as characteristicfrequencies peculiar to individual signals can be seen fromthe color change. No significant evolution of spectral components with time (or strain) is revealed. The shapes ofspectra of individual signals at the end of deformation are
essentially the same as those at the beginning. Thus. theprocess meets requirements of the weakly self-stationary
random process and, therefore, can be simply treated interms of distribution functions. means and variances of respective variables.
Such a visual classification is useful, but it is a time
consuming task, providing only qualitative information in
general case. As we examine a fairly large number of
specimens and signals, the use of semi-automatic data processing (Fig. 2) is more rigorous and convenient.

3.4 Factor and Cluster Analysis
The correlation matrix for variables, which are listed inTable 1, is given in Table 2. It shows that some correlations
are of substantial magnitude (for example. E-URMS are cor·
related at the level of 0.85) while other are just weakly cor
2". . - - - - - - - - - - - - - - - -.

~'lID.

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I~'-#ilw\~!~~W.'-v-"V\lv\'.~"..-..,j
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II

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z

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:

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110

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60

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(b)

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TUAE. \/&1

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VI I

I'I

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·liO

III
~

~ 0.020.02~~

lr 0.00
200

400

&00

800

1000

Ie)
0.00
200

400

&00

800

1000

FAfOUENCY. f I kHzFREQUENCY. II kHz

Fig. 7 Representative waveform (a), (b) and power spectral
density (c) of AE in a metallic glass - a type-2 signal; (a) awhole single realization; (b) a part of realization. Dashedline (c) shows the spectral density prior to noise removal.

Fig. 6 Representative waveform (a), (b) and power spectral
density (c) of AE in a metallic glass - a type.l signal; (a) awhole single realization; (b) a part of realization. Dashedline (c) shows the spectral density prior to noise removal.
7

Table 1 Statistical parameters of AE signals in the metallic glass under load.

Parameter

Total AE, N=167

Cluster 1, N=ll

Cluster 2, N=156

Mean

Std.Dev.

Mean

Std.Dev.

Mean

Std.Dev.

Up,f.LV

94

155

630

118

56

52

UaMS' flV

9.6

32.6

17.1

13.2

6.3

5.6

E, JLV2S

2800

15150

35520

45580

320

1960

fDkHz

324

150

610

130

304

120

futecb kHz

390

84

620

100

374

53

feff, kHz

142

48

170

30

140

47

Table 2 Correlation matrix and factor loadings for some AE parameters.

Variable

Up

E

URMS

fc

~ed

Factor Loadings

f eff

Factor 1

Factor 2

Up

1

0.78

0.65

-0.02

-0.01

-0.59

0.89

-0.25

URMS

0.78

1

0.85

-0.07

-0.06

-0.46

0.81

0.2

E

0.65

0.85

1

-0.17

-0.24

-0.47

0.82

-0.42

fc

-0.02

-0.07

-0.17

1

0.48

0.04

-0.04

0.81

!.ned

-0.01

-0.06

-0.24

0.48

1

0.24

-0.27

0.87

f eff

-0.59

-0.46

-0.47

0.04

0.24

1

-0.71

0.42

Table 3 Eigenvalues for 6 factors (number of factors equals to the number ofvariables measured). Eigenvalues for the factors to be retained are shown in bold.

Factor

Eigenvalue

Cumulative

Variance

Cumulative
Eigenvalue% total

%

1

3.4

57

3.4

57

2

1.3

21

4.7

78

3

0.63

11

5.33

89

4

0.34

6

5.67

95

5

0.21

3

5.88

98

6

0.12

2

6

100

8

•

Fig. 8 Contour map showing the normalized average power spectral density as a function of time (strain). No change in the
averaged spectrum shape is visible with deformation. Arrows I and 2 indicate the map cross sections where the two typicalexamples of AE events have been taken and shown in Figs. 6 and 7, respectively. See Page ii for the same figure with color.

900 ..----'"""'C"'-------,-~_:_:_-~--___"'!"::::::__--,_.--__,
800

> 700

b

:: 600......... ;. .............. -

Il.

::>

W 500

.......................... ;c::>..... 400

:JCL.
::E 300 ...

~---..... Cluster 1'c.... Cluster 2-':···········.l·.. ~~~:··:···:>····t·· ··.......·······;·..········ ..·· .. ··········t·····

:....

<t:

~

..

T'

.

:

:·..·

~

~.

~

.

_

.......

c';

~

~ 200

CL.

. . . . mm'

.
C

.

:

·T

-..
·..··':·..

~

.

;

, m.. . ·E·£,,~,,:.:I. ':0:":::" :::~~.::::::.,,'.:.:..m

100

o
100

13- ••••••.•

200

300

400

500

600

700

800

MEDIAN FREQUENCY, FMED I kHz
Fig. 9 Example of AE scatter-plot in Up·fmocl (peak amplitude versus median frequency) coordinate after K-means clustering.
Cluster 1: filled circles; Cluster 2: open squares. Scattering ellipses. regression lines and confidence bands are shown.
9

40 .......- - - - - - - - - - - - - - - - - - . . . , - 2500
(a)

2000
1500

o

1000
500
O-i'-----i"-"'-..,jl..llIo+-I~olI-jl.....u....jl...ll...lil.llll.llll.lll. ........IIW~~~O

1 2 0 , . . . . - - - - - - - - - - - - - - - - - -....

~ 100

---

(b)

~
u.i 80c_
Cluster 1
c=J Cluster 2

~ 60

::J
Q.

~w 40
~
a:
w
~

20

O+-

--lIl..ll..,jL.jI...II-II...,jI""jI..A-4-11-..II-jI..AIoIjUIUII.L.III.IjUII.+-..IoII--l

0.000

0.005

0.010

0.015

STRAIN. £Fig. 10 AE time-history (Fig. 3) is redrawn in this figure after signal classification into two clusters. The signals of Cluster 2
appear earlier than the signals of Cluster 1.
4. Discussion

Applying K-means clustering algorithm to the array of
AE records from the same specimen. one can find that all
signals naturally fall into two categories with respect totheir peak amplitudes and median frequencies. Clusteredstructure of AE data can be easily recognized from thescatter-plot in Up·fmcd coordinate. as seen in Fig. 9. Cluster
1 consists of signals with relatively high amplitudes andhigh median frequencies whereas Cluster 2 includes low
amplitude signals with high median frequency. Mean values and standard deviations of both clusters are given in
Table 1. Let us notice that the signals are nearly normally
distributed within each cluster as is proved by the Kolmogorov-Smimov test of normality for a fairly large number of AE records. This classification agrees with qualitative visual evaluation by signal and spectral shape discussed
above: the type-1 signals form Cluster 1 and the type-2 signals belong to Cluster 2. The similar. but not the same. classification procedure was adopted earlier by Woodward andHarris (1977) and also by Muravin and co-authors (1989.
1996a,b).

We have experimentally shown that the resultant AE{X(t)} is caused by two random stationary processes{X\(t)} and (X2(t)} characterized by their own distributionfunctions and different shapes of waveforms and power
spectra of their constitutive events. Thus. we deal with atypical example of the additive random process:{X(t)} = {X.(t)} + (X2(t)}.

(2)

Having a certain type of cluster assigned to each signal. we
obtain the AE time history as a sum of these two processes
as shown in Fig. 10. Thus. the structure of the AE flux
shown in Fig. 3 becomes clearer. One can see that flfstly
AE in MGs appears as a flux of type-2 signals. The activityof this flux may attain its maximum and then another process comes into play. The latter process is easy to identify:
Cluster 1 is formed by the signals coming from the shear
bands (see Sees. 3.1 and 3.2). Interpretation of the other10

type of signals (Cluster 2) is much more complicated sinceno direct structural investigations can help resolve thisproblem. For this reason AE appears as a powerful tool,
which may shed some light on the mechanisms of plastic
flow of amorphous metals and alloys. The results of AE
studies undoubtedly show that inhomogeneous plastic deformation of MGs cannot be entirely associated with theshear bands visible on the surface. The other mechanism,
which acts in addition to shear banding is expected. In theliterature. a few arguments exist in favor of possible presence of some plastically deformed regions with significantly smaller scale than the shear bands. For example, inline with our observations. Zielinski and Ast (1983) showedthat irreversible plastic deformation started long before thefirst shear band could be noticed during bending of
Ni75Si8BI7 MG. Rolling MGs up to 30-40% may not reveal any shear bands on relatively wide regions of the surface while the microhardness of these regions is higher thanthat of initial material (Alekhin and Khonik, 1992). The
nature of such a mechanism of inhomogeneous plastic flow
as an additional source of AE is not clear yet and is a subject of further investigation. However. that mechanism mustmeet at least the following requirements:(I) it cannot be identified by direct surface observations;(2) it should be localized and should act during a relatively
shon time to ensure necessary conditions for AE;(3) it should be of a smaller scale than the shear bands.
Presumably, it seems plausible to suppose that the small
shear events may nucleate inside the amorphous ribbon.
This can occur at a relatively low applied stress because thestrong heterogeneity of microscopic structure of multicomponent MGs results in the significant non-uniformity ofinternal stress fields. These stresses are arbitrary distributed
within the sample and as they attain the magnitude highenough for shear band nucleation, the shearing occurs in thesmall local volumes of the ribbon. The stress relaxation
with the rapid latent energy release causes a flexural elastic
wave detected as an acoustic emission. The shearing does
not necessary reach a free surface due to the same structural
heterogeneity. which plays a dual role in plastic deformation: on one hand. it assists shear initiation but. on the other
hand, it provides high stress barriers to be overcome during
shear band propagation. Thus, the propagation of shear requires much higher stresses than nucleation. Indeed, thenucleation precedes propagation and type-I signals appear
always after type-2 signals. The small area swept during
shearing inside the ribbon leads to the smaller amplitudes
and energies of AE signals.

analysis is employed to optimize a set of descriptive variables. The algorithm of AE data processing is described.
Two parameters were found to be most representative forthe purpose of this study: peak amplitude measured in time
domain and median frequency obtained from power spectral density. With a help of cluster analysis it is shown that
the AE signals in MGs fall into two categories (clusters)
differed by their amplitude and spectral characteristics.
This allowed us to conclude that two different mechanisms
of inhomogeneous plastic flow, i.e. two AE sources, operate
in MGs. One mechanism is associated with shear band
fonnation during inhomogeneous deformation, while identification of the other mechanism is incomplete. A possible
nature of that unknown mechanism of plastic deformationof MGs is discussed and argued that it could be a nucleationof shear bands inside the amorphous body.
Finally, let us note that the method of data processing
used in the present work is not limited to metallic glasses
and can be widely used for various materials and engineering constructions both in laboratory experiments and inpractical non-destructive testing. The simplicity and thelack of arbitrary decisions during data processing allows
full automation of the calculations involved. It makes thisapproach effective for AE source identification. For instance, it can be employed for reliable identification ofnoise signals that is of importance in most AE problems
and. particularly, non-destructive inspection.
Acknowledgements
The author is grateful to Professors K. Kitagawa, S.
Hashimoto and V. Khonik for their stimulating interest andencouragement. Financial support is provided by the Ministry of Education, Science and Culture of Japan, grant-inaid 09650715.
References
A.P. Alekhin and V.A. Khonik (1992), Structure andPhysical Regularities of Deformation of Amorphous Alloys,
Metallurgiya, Moscow, (in Russian).
A.S. Argon (1979), Acta Metal!., 27,47.
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A.P. Braginskii, A.Yu. Vinogradov, A.M. Leksovskii andB.M. Medvedev (1986), Sov. Tech. Phys. Len., 12, 459.

5. Summary and Conclusions

A complex statistical and spectral analysis of acoustic
emission (AE) during the plastic flow of metallic glasses(MGs) is performed with the aim of classification of AE
sources. A special attention is paid to quantitative characterization of AE features peculiar to individual sources. AE
is considered as a multivariate stochastic process. Factor

W.W. Cooley and P.R. Lohnes (1971), Multivariate Data
Analysis, Wiley and Son. New York.
P.E. Donovan and W.M. Stobbs (1981), Acta Metal!., 29,
1419.
11

G. Muravin, L. Lezvinsky, B. Muravin and Y. Canetti(19961), Progress in Acoustic Emission VII, eds. T. Kishi,
Y. Mori, Y. Higo and M. Enoki, The Japanese Society for
NDI, Tokyo, p. 255.

D.M. Egle, C.A. Tatro and A.E. Brown (1981), Mater.
Evaluation, 38, 1037.
M. Enoki and T. Kishi (1991), Acoustic Emission. Current
Practice and Future Directions, eds. W. Sachse, J. Rogetand K. Yamaguchi, STP 1077, ASTM, Philadelphia, p. 47.

G. Muravin, L. Lezvinsky and B. Muravin (1996b), Progress in Acoustic Emission VII, eds. T. Kishi, Y. Mori, Y.
Higo and M. Enoki, The Japanese Society for NOI, Tokyo,
p.261.

P. Fleischmann and D. Rouby (1979), Strength 0/ Metals
and Alloys, (cds. P. Haasen, V. Gerold and G. Kostorz) vol.
1, Pergamon Press, New York, p. 559.
J.J. Gilman (1973), J. Appl. Phys., 44, 675.

V.D. Natsik and K.A. Chishko (1972), SOy. Phys. Solid
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I. Grabec (1995), Proc. o/Neural, Parallel and Scientific
Computations, vol. 1, p. 172.

V.D. Natsik and K.A. Chishko (1978), SOy. Phys. Solid
State, 20, 264.

I. Grabec and W. Sachse (1989), J. Acoust. Soc. Am., 85,1226.

V.D. Natsik and K.A. Chishko (1982), SOy. Phys. Acoust,
28,225.

V.A. Khonik, K. Kitagawa, V.M. Mikhailov and A.Yu.
Vinogradov (1998), J. Appl. Phys. (in press).

N.1. Noskova, N.F. Vildanova, Yu.I. Filipov and A.P. Potapav (1985), phys. stat. sol. (a), 87, 549.

LJ. Graham and G.A. Alers (1974), Mater. Evaluation, 32,
31.

K. Ono and H. Ucisik (1976), Mater. Evaluation, 34, 32.

K. Ono and J.-Yu Wu (1996), Progress in Acoustic EmisH.H. Harman (1967), Modern Factor Analysis, Chicago
University Press, Chicago.

sion VII, eds. T. Kishi, Y. Mori, Y. Higo and M. Enoki, The
Japanese Society for NOI, Tokyo, p. 237.

J.A. Hartigan (1975), Clustering Algorithms, Wiley & Sons,
New York.

M.B. Priestley (1981), Spectral Analysis and Time Series.
vol. I, Academic Press, New York.

V.A. Khonik, V.M. Mikhailov and A.Yu. Vinogradov(1997), Scripta Mater., 37, 377.

C.B. Scruby, K.A. Stacey and G.R. Baldwin (1986), J. of
Physics 0,19,1597.

T. Kishi, K. Ohno and K. Kuribayashi (1981), Journal of
JSNDI, 30, 911 (in Japanese).

F. Spaepen (1977), Acta Metal!., 25, 407.
H. Suzuki, T. Kinjo, M. Takemoto and K. Ono (1996),
Progress in Acoustic Emission VII, eds. T. Kishi, Y. Mori,
Y. Higo and M. Enoki, The Japanese Society for NOI, Tokyo, p.47.

A.M. Leksovskii, A.Yu. Vinogradov and V.V. Smirnov,SOy. Tech. Phys. Lett., 12 (1986) 265.
A.C. Lucia and G. Redondi (1976), Trans. ASME Ser. J(USA),98,199.

A. Vinogradov, M. Nadtochiy, S. Hashimoto and S. Miura(1995), Mater. Trans., JIM, 36, 426.

J. MacQueen (1967), Proc. 0/ the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. I, eds.
L.M. Le Cam and J. Neyman, California University Press,
Los Angeles, p. 281.

A.Yu. Vinogradov., A.M. Leksovskii, V.A. Bershtein,
V.M.Egorov and V.V.Smirnov (1988), Sov. Phys. Solid
Stat., 30, 314.

T. Masumoto and R. Maddin (1975), Mat. Sci. & Eng., 19,
1.

A.Yu. Vinogradov, A.M. Leksovskii, Yu.V. Milman and
V.P. Ovcharov (1989), Phys.Met, 11, 66.

R.C. Morris (1979), J. Appl. Phys., 50, 3250.

A. Vinogradov and A. Leksovskii (19961), Materials Science Forum, 210·213, 549.

G.B. Muravin, Ya.V. Simkin and A.1. Mennan (1989), SOY.
J. Nondestruct. Test., 25(4), 239.

A.Yu. Vinogradov and A.M. Leksovskii (l996b), Progressin Acoustic Emission VIl, eds. T. Kishi, Y. Mori, Y. Higo
12

and M. Enoki, The Japanese Society for NDI, Tokyo, p.
153.

R.N.G. Wadley, C.B. Scruby and G. Shrimpton (1981),
Acta Metall., 29, 399.
B. Woodward and R.W. Rarris (1977), Acustica, 37, 190.
S.O. Zaichenko and V.T. Borisov (1982), Doklady AN
SSSR, 263(3), 622 (in Russian).
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12(4), 15 (in Russian).
P.G. Zielinski and D.G. Ast (1983), Phil, Mag. A,48, 811.

13

MEETING CALENDAR
24th EWGAE Meeting (EWGAE 2000)
May 24-26, 2000; at CETIM, Senlis (40 km N of Paris), France: Program Chairs: C. Rigault and M. Cherfaoui(christel.rigault @cetim.fr)
AE in structural and advanced materials (metals, concrete, rock, FRP, ceramics,...)
AE in structures (pressure vessels, pipelines, reactors, aircraft, bridges, offshore platforms, buildings, ...)
AE in manufacturing process monitoring (welding, machining, casting,...)
AE in diagnostics (leak detection and monitoring, plant maintenance, wear, machine condition...)AE instrumentation and signal processing; Standards and codes of practice
Seizmoacoustics (dams, geoteehnics)
Contact the Program Chairs to request Call for Papers.

43rd Meeting of AEWG
July 17-20,2000; Seattle, WA: Program Chair: Allen T. Green (ATGreenl @aol.com)
July 17: 2000 Primer
July 18: Technical Presentations, AEWG business meeting
July 19: Technical Presentations, Awards Banquet
July 20, morning: Tour of Boeing Engineering Labs
July 21-22: ASME Pressure Vessels and Piping Meeting (separate registration needed)

15th International AE Symposium (IAES-15)
September 11-14, 2000; The International House of Japan, Tokyo, Japan; http://home.att.ne.jp/greenliaesI5
Symposium Organizing Committee Co-Chairs: T. Kishi and Y. Higo; Program and Papers Committee Chair: M. Ohtsu;
Secretary Committee Chair: S. Yuyama (e-mail: aeyuyama@shO.po.iijnet.or.jp)(1) Advanced overview: Review and future aspects of AE technology, Standards and codification,(2) Sensors: Calibration, New development, Standardization etc.
(3) AE instrumentation and AE analysis: System calibration, Signal processing, Time- and frequency-domain analysis, etc.
(4) AE in materials and source-materials characterization: Ceramics, FRP, FRM. Metals, Concrete, Rock, Wood, etc.
(5) Applications to structures: Pressure vessels. Reactors, Pipelines, Aerospace, Transportation, Offshore structures, etc.
(6) Manufacturing process monitoring: Wear, Contact, Tribology, Machining, Welding, Casting, etc.
(7) Applications to medical fields: Implant, Heart, etc.
(8) Applications to geo-structures and civil engineering: Landslide, Underground development, Mining, Microseismics,
Geothermal development, Maintenance of infrastructure, etc.
(9) Novel applications:
The deadline for submission of abstract (100 - 200 words with key words in English, 4 copies): January 31, 2000.
March 31, 2000 Notification of acceptance and instructions for authors.
May 31, 2000
Deadline for final text. Registration and remittance of registration fee.
June 30, 2000
Second announcement and program.

14

Acoustic Emission Characteristics of Soil andSand in Response to Simulated Root Growthc. Divaker Durairaj, L. Okushima and S. Sase
Their studies revealed the range of AE signals generated
from root-soil interaction for sand and culture soils. AE due
to root-soil interaction, involve a high degree of complexity, since the medium in question, is so complex in characteristics. Many factors, such as the type and size of soil
particles. the moisture content of the soil, the presence of
humus (Okushima et a1., 1996), the level of compaction,and the limiting stresses of the soil surface boundary, affectthe nature of AE signals generated. The key factor. which isof interest, is the rate at which the root grows and its influence on the pattern of AE hit distribution over time. The
rate of real root growth varies drastically from 0.25 to 1.4
mmIh over different stages of its growth (Shimotashiro etaI., 1998). Our objective is to recognize the pattern of AE
hit occurrence and correlate it with that of the root growthrate. Towards this end, the pattern of AE generated at constant root growth rates need be ascertained first. Since it
may not be possible to maintain a constant root growth rate
in a real plant, our study attempted to simulate the root
growth itself using a mechanized simulator and acquire AE
data for each level of growth rate. The data was ultimately
used for recognizing the pattern of hits generated for eachgrowth rate.

Abstract
Detecting root growth with acoustic emissions (AE)caused by root-soil interaction has potential applications ingreenhouses. In this study, root growth Was simulated bypushing a probe into the soil at preset rates using a mechanical simulator developed for this purpose. The investigation was conducted under noise proof environment on
soil and sand at various moisture levels with the simulated
root growth varied at 3 levels from 0.5 to 1.33 mmIh. AE
were detected by an array of three sensors connected toconventional AE instrumentation. The results showed thatthe emissions were very weak in the soil consisting of 0.4
mm particles, but the growth rate could be inferred basedon the rate, at which these emissions are generated. In sand,wet or dry, the emissions were of high intensity and periodic. Spectral methods were used to recognize the patternof hit distribution. When AE measurements from real root
growth were analyzed using spectral methods. it was possible to detect the change in real root growth rate, by way ofcorrelating it with those of the simulated study.

1. Introduction
The physiological state of plants in greenhouses should
be assessed accurately. so that the in-house environmental
attributes could effectively be controlled to obtain highquality crop production. This, in turn, will lead to just-intime market delivery of produce and conservation of waterand other inputs. From this viewpoint. non-destructive
sensing of individual plants in greenhouses is needed. AE isan interesting method for the said purpose. In particular. thestudy of AE patterns. generated by root growth in soil, is ofgreat interest towards developing a root growth monitoring
system for greenhouses. In an effort to detect AE caused byreal root-soil interaction, Okushima et al. (1994) experimented with different types of sensors. including a simple
condenser microphone and studied their response to thegenerated AE signals from different soils. Okushima et aI.
(1996) showed that under real root growth, sand emitted
higher AE hit counts than a culture soil containing humus.

2. Experimental Setup
The root growth simulator (Fig. 1) was just a mechanism, which pushed a probe into the soil at the required rateof root growth, thus simulating the growing root tip. It consisted of a stepper motor and allied circuitty for generating
the stepper signals. Since the stepper motor is a discrete
device and steps through its rotation, directly driving theprobe by the motor may cause discrete motions to thesimulating probe. Hence, to make the simulations realistic,a gearbox of reduction as high as 2730: 1 was used between
the motor and the probe movement. This way, the stepper
motor could be driven at higher speeds, thus masking its
discrete motion. A sliding barrel receiving motion from thegearbox. converts it to a linear motion. To isolate the simulator's noise and vibration from the soil itself, a hydraulictransmission was added as the final drive element. It was asimple assembly of two injection syringes connected mouth
to mouth through a flexible hose and filled with water devoid of air bubbles. One of these syringes was mounted
beneath the slider so that it pushes on the plunger at the set
rate. The movement gets transmitted to the other syringe's
plunger. which held the probe simUlating the root. The

Received 19 August 1998. C. Divaker Durairaj is affJ1iated with
Department of Farm Machinery, Tamil Nadu Agricultural University, Coimbatore 641 003, INDIA, L. Okushima (limi@nkk.affrc.
go.jp) and S. Sase are with Department of Land Improvement,
National Research Institute of Agricultural Engineering, 2-1-2
Kannondai, Tsukuba 305, JAPAN.
journal ofAcoustic Emission
Volume 17, Numbers 1-2

150730-o0S0/99/S5.00/170I5-021
Copyright!OI999 Acoustic Emission GroupStepper driverboard Belt drive frommotor to gear box

tand~~;~; ;;;;;/~~~i~~~r
Heatmotor
sink
e
for
l, bit driver

signalStepper motor'----\+-Keyed slider
First syringe -+-+--ELEVATION

SIDE VIEW

Fig. 1 Construction of the root simulator.

Line of probe

was made to traverse from point A to B (Fig. 2). The soil
bath itself was a plexiglass (PMMA) cylinder of 50 mm
diameter and 70 mm depth. It had circular marks around its
periphery at equal intervals of 10 mm. so that the soil could
be loaded in layers, ensuring the same level of compaction
allover and maintaining the bulk density of the soil at therequired level. Two root growth media were considered forthe study, which were,i. a fine beach sand consisting of uniformly sized 0.4
mm particles,
ii. Akadama soil (a lower layer soil of Andosols).
which was ground and sieved to acquire particle
sizes ranging from 0.3 to 0.4 mm.

...~travel

.....

'

1= A
+;
lsi i= B

IS' I
!

70

-L 1

I
I
III

50

Sand was tested when it was dry and at 10% moistureand soil was tested at three levels of moistures, namely dry,
20% and 30% moisture. During all the trials, soil and sand,whether dry or moist were loaded into the bath at bulk densities of 1.2 and 1.5 glml, respectively. The travel rate of
the simulator's probe (growth rate) was varied at three levels of 0.5, 1.0 and 1.33 mmIh on each treatment and measurements replicated thrice. AE was logged for 24,12 and 8h, respectively for 0.5, 1.0 and 1.33 mmIh growth rates andhence the net travel made by the probe's tip ('AB' in Fig. 2)
was 12 mm in all the cases. The trials were conducted inside a noise proof growth chamber to eliminate spurious
hits due to external noise. The hits as accumulated overparticular time intervals were analyzed for any possible
patterns of distribution with respect to time. Different timeintervals of 5 min. IS min and 30 min were tried for obtaining the discrete samples of hit data. It was found that the
distributions sampled at larger time intervals were "aliases"of the periodicity of distributions obtained at smaller timeintervals. For convenience and also to obtain a higher degree of freedom for any statistical analysis, the hit distribution sampled over 0.25 hr was considered for pattern recognition. The sampling rate was hence kept constant. while
analyzing all the hit distributions pertaining to different.'

Fig. 2 Arrangement of sensors in the soil bath.
second syringe with the probe was clamped on a stand overIhe soil sutface at Ihe required position. To exactly simulate
root growth, a real root tip itself of 0.8 mm diameter wasused as inserted on to a 0.2-mm metal pin. The bulbous tip
eliminated any abrasion of the metal probe's periphery withsoil, since its diameter is larger than the metal probe itself.
Figure 2 shows the arrangement of sensors in the soilbath. In an effort to study the relationship between the AE
hits received by different sensors placed at different locations, an array of three sensors were used in the soil bath.
This provided for analyzing the influence of the medium
and its condition on the propagation of AB. The sensors
were accelerometers (CR-03C-0030, Fuji Ceramics Co.)
whose main detecting band of frequencies was 3-15 kHzwith resonance at 9 kHz. Preamplifiers of 40 dB gain wereused to amplify the sensor outputs. 40 dB gain and 35 dB
threshold levels were set on the AE monitor (LOCAN 320,
Physical Acoustics Corp.). The probe simulating the root16

Table 1 ANOVA on the number of hits as influenced by probe travel rate and position in dry sand.
Growth rate

Count

0.5mm h· 1

Sum

Average

Variance

122285190.4' 67 6536.992

1.0 mm h"122264

188.6667 4795.879

1.33 mm h"

121702141.8333 5076.697

ANOVA

F

P·vaJue

F critS5

df

MS

72039.64

116549.058 1.328341 0.273815 2.258517

18227.06

29113.528

Error

108465.6

224930.255

Total

198732.3

35

Sourceo!
Variation
Displacementof Probe. mm
Growth rate,

1.84849 0.181112 3.443361

mmh· 1:z:

growth rates. Hence, the Nyquist critical frequency, whichis the reciprocal of twice the sampling time interval, wasconstant for the whole analysis.

0.5 mm h-I

150

50

3. Results and Discussiono+--.l~=:"';'_~;"':";"":'--=::":"";':'.!o.&...l
o
5
10
15
20

3.1 Acoustic Emissions ofthe Sand

The AE activity in dry sand was to a maximum of 125
hits per 0.25 h. To explore whether the number of hits
themselves could be used to deduce the simulated growthrate (probe travel rate), an Analysis of variance (ANOVA)
was attempted on the Dumber of hits, with the displacementof probe (time) itself as one factor and the probe travel rate
as another. The ANOVA (Table 1) on dry sand showed thatboth of the factors were insignificantly affecting the numberof hits detected. This implied that the number of hits as
such, could not be used for correlating it with growth rate.

Time,h

1.0 mm h· 1

150
100
:z:

Top seDsor

50o+-~~~~:¥::~;:::"---r-.....,

o

4

2

6

8

10

12

As seen from the hit distributions, a periodicity waspresent. Since the hits were observed at equal interval oftime, the data was analyzed as a time series. Figure 3 shows
the data series with respect of dry sand, when the probe
rates were 0.5, 1.0 and 1.33 nunIh. The auto-correlation
function (act) of the AE hit series, acquired by the three
sensors, showed that hits received by all the three were almost of the same pattern since they have received the same
hits simultaneously. The acf clearly showed that the data
series was non-stationary. Making Polynomial regressions
to the hit data distribution effected the required de-trending.
The regressions were of the form as detailed below.

Tnne,h1.33 mm h·1

150.,,100•50

oi-..-:;........;;~--r-.--:;.....;:~=:....;:::=:;;;;;.­
o

2

4

6

8

Time,hY =77.94-56.63777x+17.70994x2-2.469692x3+0. 1737567x4-O.00600871 x5+O.000080896x6;
y.2 =42% for 0.5 mmIh

Fig. 3 Distribution of AE hits in dry sand.
17

y=-132.91+229.6816x-102.3444x2+20.93684x3

In wet sand, the hit rates were to a maximum of 400
hits per 0.25 h (Fig. 5). The higher hits were due to thelower acoustic impedance of the wet sand transmitting evenweak hits to the sensors. The ANOVA (Table. 2) showsthat both the factors were significant, which was unlike ofthe dry sand. This provides for the possibility of using thenumber of hits themselves to detect the growth rate of theroot. Since the hit distribution of wet sand also had distinct
periodicity, spectral analysis was carried out The distributions showed the presence of a trend, which was removed
using polynomial regression, which were in the form of,
Y =S1.74-86.46x+43.0799x2-8.122728x3+O.732373Sx4-o.03189654xs+O.OO053952x6 ;47% for 0.5 mmIh
Y 1283.01-1096.99x+426.6945x2-83.19402x3+8.634029x4-o.4549649xs+O.00951093x6;= 82% for 1.0 mmIh
Y =-463.019+1560.2739x-1101.103x2+363.8607x3-61.64478x4+5.145188xs-O.1673926x6 ;=88% for 1.33 mmIh,
where 'Y' is the number of hits and 'x' is the probe displacement.

-2. 151279x4+o. 1087562xs-O.00216329xs ;~ =33% for 1.0 mmIh
Y -S9.635+230.S496x-211.9526x2+97.79892x3-22.31707 4+2.40S202xs-o.0978658lx6 ;=70% for 1.33 mmJh,
where •Y' is the number of hits and •x' is time in hour.

=r

After de-trending the data, it showed that the distribution had periodical seasonality. Therefore, the method of
Dominant Frequencies was used to do spectral analysis on
the distribution as a time series. Rather than using a fast
Fourier transform method, this method uses the maximumentropy (or all poles) method. The maximum-entropy
method represents the data in terms of a finite number (N)of complex poles of discrete frequency. By contrast, thefast Fourier transform essentially fits the power spectrum toa polynomial and is better for data whose spectrum issmoothly varying. Figure 4 shows the power spectrum of
the hit distribution pertaining to the three simulated probe
travel (root growth) rates. The frequency is in units of the
Nyquist critical frequency (the reciprocal of twice the timeinterval between the data points) and the power expressedin arbitrary units. The number of complex poles (N) was 4
for calculating the spectrum. The distinct peaks indicatedthat the AE hit distribution had dominant frequencies of
periodicity in relation to the simulated root growth rate. To
further verify whether the time series had common frequency components, a coherence analysis was made between the time series pertaining to each pair of growth
rates. The coherence analysis proved that the frequencies atwhich the hits occurred were distinctly dissimilar for eachsimulated growth rate.

r=

=

r
r

0.5 mm h-I

100
80
~

60

~40

20

o.,.....-r......,---T"""'--T"-.Di.u..a..--..
o
5
10
15
20
Time, hDry sand

1.0 mm h- I

400

0.5 mm h· 11
-e 0.8'" 0.60.4
A. 0.2

300

= 200
~

t

100
~-""-..~_ 1.0 mm b· 1o+-.......--r-----,.---..---,;;,
o
3
6
9
12

0+---...--.........--......--=-..,....--,

o

0.1

0.2

0.3

0.4

0.5

TIDle, hNyquist frequency

wet sand1.33 mm h-I

400

1
~ 0.8

300

=- 0.6:r= 200~-- 1.33 mm b·1

0.4
0.2

~

100

0.5 mm h· 1

0+---...--.........--......--"""'--'

o

0.1

0.2

0.3

0.4

0.5
2

Nyquist frequency

4

6

8

Tune, hFig. 4 Dominant frequencies at simulated growth rates.

Fig. 5 Distribution of AE hits in wet sand at 10% moisture.
18

Table 2 ANOVA on the number of hits as influenced by probe travel rate and position in wet sand (10%).
Growth rate,
mmh"

Count

Sum

Average

Variance

0.5 mm h"

12 3596299.6667

12163.7

1.0 mm h"

12 7451620.9167 53748.63

1.33 mm h"

12 5695

474.5833 30285.72

MS

F

ANOVA
Source ofSS

df

P-value

F critVariation
1170556.84

5.503394 0.000342

2.258517

620843.4

2310421.7

24.21272 2.76E.Q6

3.443361

Error

282053.32212820.6

Total

1679022

35

Probe position. n6125.2mm
Growth rate,
mmh"

penetration rate consisted of a band of frequencies, which
did not fall into the frequency band of the sensor or they
were being attenuated by the medium completely. Whenthe penetration rate was increased further to 1.33 mmIh, theAE activity was higher. but they occurred at every 3-4 rom
movement of the probe. This rate of movement could havecaused localized compression zones building up pressure.
until released by particle movement at longer intervals.

The power spectrum (Fig. 4) indicated distinct peaks inthe distribution. Here also. the coherence analysis provedthat the frequencies at which the hits occurred were distinctly dissimilar for each simulated growth rate.
3.2 Acoustic Emissions ofSoil

AE hits in soil were not many and the reason was basically due to the absence of abrasive particles (Okushima etal., 1996). Moreover. soil as a porous medium. attenuates
whatever weak AE being emitted. The AE hits as acquired
from loose dry soil of 0.4 mm particle size exhibited veryminimal counts and were practically of no use. However,the AE hits from moist soils, though weak and sparse, weregenerally in the range of 1 to 5 at various intervals of timedepending on the probe travel rate. Increasing the gain of
AE instrumentation, was attempted, but noise and spurious
hits were excessive and the experiment was aborted. So theexperiment was continued with the same gain and the hit
distribution was analyzed as such. Since the hit distributions did not have any periodicity, a spectral analysis could
not be made. However, the intervals. at which the hits occurred in terms of the probe displacement. were analyzed
for an underlying pattern.

When the soil was made 30% moist. the hit distributions (Fig. 7) were different from those of the drier soils. Ata penetration rate of 0.5 mmlh. hits occurred every 0.8 to
1.4 mm movement of the probe. The sensor located atdeeper layers was not registering hits, whereas the sensor at
the top layer was sensing hits. This was specific to the soil
at 30% moisture. There is a possibility that the soil particles. instead of moving locally at the probe tip. rise as single mass at the top. causing AE activity there. When thepenetration rate was taken up to 1.0 mmlh. AE activity decreased very much like in the drier soil with 20% moisture.
The penetration rate of 1.33 mmlh, offered a similar response as that at 0.5 mmIh, but here the hits occurred every
2 • 3 mm movement of the probe. From the analysis, it
seems that there is a possibility of detecting the growth rate
based on the interval. at which the hits occur.

As for the wet soil 20% moist. when the probe travel
was 0.5 mmlh (Fig. 6), the hits occurred at approximately
every 0.6 to 0.8 mm of probe displacement. But when theprobe entered deeper layers, a slight periodicity in hit distribution was observed. However the number· of hits ranged
only up to a maximum of 6. When the penetration rate wasincreased to 1.0 mmIh. AE hits decreased. There was AE
activity only when the probe was directly in front of thesensor. The reason may be that the AE generated at this

3.3 Influence ofthe Medium on Hit Detection

The hit data was also analyzed whether the attenuationof sound in porous media posed problems in detection of
AE generated by root growth. Dry sand, anticipated to be
the most attenuating medium. did not cause any problems.
since the AE activity is at least 10 folds higher than that ofplain soil. But plain soil. especially the dry one. may cause
19Growth rate ·0.5 mmlht

Growth tate - 0.5 mm/ht

Clatllnce of tip trom mid o.noor,mm

tlllltance ottiptrom mid ••noor, mm

Growth rate • 1.0 mmlht
OtOWlhtI'.·'.0 m lIl/hr

I

!

.

,
i

-7

·5

·3

.,

~
'"c

Jl

If,\A
3

5

7

..

Clatulce of tip from mid o.noor, mm

lIlatenc. all" rrom mill •• n.or. III III

Growth rate -1.33 mmlhr
Growth rlt.· 1.33 m m/hr

-8

·8

-4
·2
0
2
4
6
Diatance of lip ftom mid oonoor, mm__ Bottom__ Mid

8

-8

~

4

~

0

2

4

6

8

Dletlnce oftlp from mid oenoor, mm. __ Top

Fig. 6 AE hit distribution of wet soil at 20% moisture.

Fig. 7 AE bit distribution of wet soil at 30% moisture.

AE detection problems. Increasing the gain and threshold of
AE sensor and associated circuitry may cause problems ofnoise and spurious signals being picked up.

tions indicate that the sensor placement becomes criticalwhen the medium is dry.
3.4 AEfrom Real Root Growth

The auto-correlation functions of the hit distributions
against time indicated that all the sensors detected the same
hits. But the hit distribution itself (Fig. 3) showed that thesensor placed near the soil surface (top) logged far lesser
hits than those of the other two. This could only mean thatthe acoustic impedance of the dry sand near the surface washigher than the rest and had mollified the hits arriving atthat sensor. Dry sand seems to vary its impedance based on
its looseness of packing. Even when the probe tip was between the top and the middle sensors. the hit rate was weakon the top sensor. However, this phenomenon was absent inwet sand (Fig. 5). All the sensors detected the hits simultaneously and had more or less the same number of hits perunit time. In regard to soil. the sensor near the soil surface
also had problems detecting the hits, when the medium was
relatively drier (20% moisture). However. the number ofhits observed in soil was very low in all me sensors and me
difference could not be discerned clearly. These observa-

A germinated maize seed with a root length of 2 mm
was sown in the bath (Fig. 2) having wet sand at 10%
moisture. AE caused by real root growth was measured for
3 days. The hits accumulated over 0.25 h interval wereanalyzed. The data series was found to be non-stationary
and carrying periodicity. The 72 h data series was segmented into distributions pertaining to consecutive 12 h
periods and each segment analyzed as a separate time series. Each was in turn de-trended using polynomial regression over time and. dominant frequencies at four complex
poles were determined (Fig. 8). Comparing Figs. 8 and 4,
the dominant frequencies at the first 12 h period of real root
growth were similar to that of the simulated growth at 1.0
mmIh. The dominant frequencies at 12 - 24 h period peaked
at about 0.2 Nyquist frequency (Fig. 8). which was similar
to that at the simulated growth rate of 0.5 mmJh (Fig. 4).
20

First 12 h
1/'

12 - 24 h

0.75

24 - 36 h..: 0.5
CP
3=
0
a..

36 -48 h

..

.Q

ca

48 - 60 h

0.25

60 - 72 h

o
o

0.1

0.2

0.3

0.4

0.5

Nyquist frequency
Fig. 8 Power spectrum of hits caused under real root growth.
Similarly. the 36 - 48 h period showed a drop in the dominant frequency (Fig. 8), which was alike as that of thesimulated growth at 1.33 mmIh. The total root growth in 72
h was 10 mm, as measured after removing the seed out of
the sand. This corresponded approximately with that of thesum of predicted growth proposed by the simulated data.
Though the real root growth rate could not be exactly discerned yet, the analysis showed possibilities of using the hit
distribution for predicting the real root growth rate. This
would be possible if data on more number of simulated
growth rates are available for analysis.

of AE. This would facilitate decisions on the proper placement of the sensor in the real greenhouse environment.

Acknowledgements
This study was supported by a short term fellowship of
the Japanese Science and Technology Agency awarded to
the rarst author, through the Japanese International Scienceand Technology Exchange Center, Tsukuba, Japan. We acknowledge the support offered by the staff of the National
Research Institute of Agricultural Engineering, Tsukuba,
Japan. where the work was carried out.

4. Conclusions

References
In sand, wet or dry, the AE was high at 400 hits per
0.25 h. Since they are periodic, spectral analysis could effectively be used to differentiate the patterns, towards deducing the growth rate. The results showed that the emissions were very weak in soil. but the root position andgrowth rate could still be inferred based on the rate at whichthese emissions are generated. AE measurements from realroot growth were analyzed using spectral methods and possibilities were found to exist for detecting the real root
growth rate, by way of correlating it with those of thesimulated study. But the simulations must be extended toother growth rates also, so that the acquired database could
be used for accurate predictions. The acoustics of the porous medium itself is of much concern towards effective
deduction of the emission. Much more studies should bedirected at the influence of the medium on the attenuation

L. Okushima. M. Shimojo and Y. Higo (1994), "Acoustic
emission sensor for detecting of plant root growth", Progress in Acoustic Emission VII. eds. T. Kishi et al.• Japanese Society for NOI, Tokyo, p. 131.

L. Okushima, M. Shimojo, Y. Higo, T. Ohtani and S. Sase
(1996), "Possibility of root growth measurement underground by Acoustic emission sensor", Progress in Acoustic
Emission VIII. eds. T. Kishi et aI., Japanese Society for
NDI. Tokyo, p. 339.

T. Shimotashiro, S. Inanaga. Y. Sugimoto and H. Dokai(1998), "Non-destructive measuring for distribution of root
number on vertical soil plane using AE method", Plant Production Science, 1, 25-29.
21

Program and Abstracts of the 42nd AEWG Meeting
June 15, 16, 1999 at Nassau Inn, Princeton, NJ; S.J. Vahaviolos, Program Chair.
TUESDAY, JUNE 15, 1999
Detection and Evaluation of Acoustic Emission due to Rock Failure
Tomoki Shiotani. Masayasu Ohtsu and Shigeru Miwa, Research Institute of Technology. Tobishima Corporation
and Department of Civil Engineering and Architecture. Kumamoto University
It is well known that acoustic emission (AE) waves strongly attenuate during propagation inside geotechnicalmaterials. To detect AE waves clearly. therefore. AE sensors of lower frequency characteristics and defonnationrelated wave-guides should be applied to the detection. In the case of monitoring rock failure. it is further difficult tocompletely take into account the state of the pre-existing crack properties. although several surveys to examine rock.
characteristics and crack conditions are performed prior to the monitoring. Thus. it is necessary to develop an
effective method for detecting AE waves which is not dominantly influenced by the existing cracks. One prospective
method for a wave-guide is replacing excavated holes with cementitious materials of the same mechanical properties
as rock. When rock-mass moves along the cracks. the filler replaced in the holes would behave coincidentally withrock, and AE waves produced from the filler are expected to be of the same signal-patterns as those from rock. Then.
it suggests that the local failure of the rock could be observed by AE activity from the filler. By evaluating the AE
activity of various fracture tests in the laboratory. fracture state of the rock could be estimated by AE activity of thefiller. In the case that the filler already contains many cracks. there is another problem to be solved. Because AE
attenuation characteristics of the filler with many cracks are possibly identical to those of actual rock. it may resultin the difficulty to detect AE signals effectively. To solve this problem. a steel bar is installed into an excavated We
with AE sensors. AE signals subsequent to the local motion would be detected until final rock failure. These AE
waves may have no relation with the fracture. because they result from friction between the filler and the steel bar.
A series of studies were conducted for detecting and evaluating of AE signals from this type of wave-guides. In thelaboratory. AE characteristics due to fracture of the filler were experimentally studied by bending and shear load testsThus. AE parameters effectively characterizing failure-state were extracted. In situ slope monitoring was carried out
by applying the proposed techniques. AE source location was performed. and then locations were compared withactual cracks observed by a borehole-camera. AE waves were examined on their parametric characteristics. taking into
account the results laboratory experiments. Thus. the applicability of the technique to detecting and evaluating of AE
signals for rock failure is verified.
Quantitative Evaluation of Fracture Processes in Concrete by the Use of Improved b-Value.
Shigenori Yuyama, NPA; Tomoki Shiotani. Tobishima Corporation; T. Okamoto. Taiheiyo Cement Co.;
Masayasu Ohtsu. Kumamoto University
Improved b-value. an effective parameter to describe state of fractures. has been proposed by the authors. Thisparameter is obtained as gradients of cumulative amplitude distributions calculated for a constant number of the most
updated AE data (hits). determining statically the range of amplitudes. In the present study. the improved b-value
analysis is applied to the fracture processes of a reinforced concrete (RC) beam with a single reinforcement, an RC
rigid frame under repeated loadings. a center notched concrete beam. and the same beam reinforced with CFRP sheet.
Moment tensor analysis. a quantitative AE wavefonn analysis based on the moment tensor modeling of cracks. has
been applied to classify crack types and to determine crack orientations in the fracture processes of these specimens.
Comparison of the results from the moment tensor analysis and those from the improved b-value analysis
demonstrated that the latter is very useful for quantitative evaluation of fracture processes as a practical method basedon the extracted AE features.
Quantitative Analysis. of Fracture Process in RC Column Foundation by Moment Tensor
Analysis of Acoustic Emission. Shegnori Yuyarna. Zheng-wang Li. NPA; Yoshihiro Ito. Masaki Arazoe.
Sato Kogyo Co.

22

Detection of Defects in Gears by Acoustic Emission
Measurements
N. Tandon and S. Mata
Abstract

many years. Clearly there is a large gulf between research
on a test rig under controUed condition and routine applications within industry as part of a predictive maintenancestrategy. However. the signs are that AE has stood the testof time and is poised to emerge as a major player in condition monitoring over the coming years.

Acoustic emission (AE) measurements have been performed on test gears. without defect and with defects ofdifferent diameters in one of the teeth, on a gear test rig.
The defect. simulating a pit. was produced on gear tooth by
spark erosion and the size of this defect was successively
increased after each measurement. Acoustic emission (ringdown) counts. peak amplitude and energy and the distribution of events versus AE counts and peak amplitude were
measured. Overall vibrations of the gear were also measured simultaneously. The results indicated that the AE
measurements were successful in detecting defects in gears
and could detect smaller defect sizes as compared to vibration measurements. The distribution of AE counts and peak
amplitude becomes broader because of the presence of adefect in the gear.

The most common failure modes of gears are pitting
and scoring. Pitting failure is a fatigue effect. which occurs
due to higher Hertzian contact stresses than the surface canwithstand. Gears under load produce repeated surface andsub-surface stresses and if the loads are high enough.
chunks of metal will fatigue from the surface producing
pits. Initial pitting usually occurs near the pitch line. in theregion where the oil film is thin. Vibration and noise analysis is the most common means of gear monitoring and diagnostics (Umezawa et al.. 1982; Fujita et aI., 1983; Nicks.
1990; Muralidharan et a1.. 1994; Choy et aI., 1996). Gear
vibration is affected by the faults in gear teeth. It has beenshown (Fujita et a1.. 1983) that pitting failures and toothbreakage due to pitting failures could be predicted bymonitoring the change in tooth profile and changes of dynamic characteristics such as vibration and noise and also
the tooth root strains.

1. Introduction
The wear caused by mechanical deterioration in rotating machines almost always manifests itself in terms ofchanges in the levels of vibrations and acoustic emissions at
different points of machine. As the machine deteriorates it
vibrates more and more. and the mechanical stresses andstrains have a tendency to cause further deterioration.
Acoustic emissions (AE) are in the frequency range from100 kHz to 2 MHz as opposed to vibrations which are in 020 kHz range. The classic source of acoustic emissions are
defect-related deformations. The sudden movements at thesource produce stress waves. which radiate out into thestructure. As the stress in the material is raised. many ofthese emission are generated. The most commonly used AE
signal measurement parameters are AE events, AE (ringdown) counts. energy and peak amplitude. The distributionof events versus any other of these parameters is also
sometimes measured. Acoustic emission measurements
have been applied to the condition monitoring of machinery
for at least a quarter of a century and has consistently beenfound to be sensitive to distress and degradation in a wide
range of machinery. The application of AE to condition
monitoring has been growing steadily. albeit slowly forSome studies on the AE measurements for the detection of defects in bearings have been carried out (Tandon
and Nakra, 1990; Yoshioka and Fujiwara. 1984). Acoustic
emission is also expected to increase when a defect in theform of a pit is present in a tooth of a gear. So in the present
investigation. AE measurements were performed on gears
without defect and with intentionally introduced defects byspark erosion. The size of these defects. simulating pits,
was also changed to determine the smallest defect size that
could be detected by AE measurements for the gears tested.
2. Experimental Setup

2.1 Test Rig and Gears
The investigation was carried out on lAB gear lubricant
testing machine. A schematic diagram of the gear test rig isshown in Fig. 1. The machine basically consists of test headand power return gearbox mounted on a bed plate and coupled together by a torque shaft. The test head assembly consists of two test gear spindles mounted on anti-friction

Received 2 April 1998. The authors are affiliated with ITMME
Centre, Indian Institute of Technology. Hauz Khas. New Delhi
110016. India. (ntandon@itmmec.iitd.emet.in)

Journal of Acoustic Emission
Volume 17. Numbefs 1·2

230730-00S0J99/SS.00'17023..()27
Copyright~ 1999 Acoustic Emission GroupPower gears

Loadingarrangement

Torque shaft

Driving pulley
Detachable loading arm

LoadSide-view of loading arrangement
Fig. 1 A schematic diagram of the gear test rig.
bearings. The front cover of the test head assembly is easily
removable and behind this cover is the spigot for locating
the test gears. The test gears are lubricated by a jet of oil.
Load on the test gears can be applied by an arm onto whichweights can be added.

the main amplifier can be displayed on an oscilloscope. A
microcomputer is interfaced with the mainframe processor
for recording the AE data.
Burst type acoustic emission event is usually separated
from the continuous activity by the use of a threshold. Only
AE events or AE counts that exceed thresholds are allowed
to be processed. Since the noise level may vary, a floating
or automatic (rather than fixed) threshold is used in order toimprove the resolution of the burst event. Each AE signal ismeasured by hardware circuits, which are contained in themainframe and measured parameters are passed through thecentral microcomputer to a disc file of signal descriptions.
During or after the data recording, the main system extracts
the data for graphic display.

The measurements were performed on hardened andground spur gears supplied along with the test rig. The
gears had involute profile with a pressure angle of 20·. The
face width of the gears was 5.1 nun (0.2") and diametral
pitch 5. The gear and the pinion had 16 and IS teeth, respectively.

2.2 Measuring Instruments
Acoustic emissions were detected using a piezoelectrictransducer (AET AC375). The acoustic emission transducer
had a resonant frequency of 375 kHz and was followed by apreamplifier (AET 160) with an appropriate plug-in filter(FL-25). The preamplifier had a gain of 60 dB. The signal
was post-amplified before being analyzed in AET 5000
mainframe AE processor. The first elements in the mainframe are main amplifiers and thresholds, which are adjusted to determine the test sensitivity. The main amplifier
provides additional gain up to 40 dB to boost the signal
further prior to analysis. The analog wave that comes out of

Overall vibration acceleration was measured using an
accelerometer (B&K 4368) and a charge amplifier (B&K
2635). The undamped natural frequency of the accelerometer is 39 kHz and its sensitivity is 4.30 mVs-/m. Overallrms vibrations in 0.2 Hz to 10kHz frequency range were
measured.

24

140.----------------..,

100
80

c
o

c

III

.~ 8°L

0
III
III==F==i==,,*-~----+

UJ

60

-5
lJ

60

~

ILl

lJ

•

I II

I II

40

:3
0

lJ

(J

<t

<t

20
Ol....--........- - " " - - - . . L - . -........_--l"----'
4
o
2
10
8
6
12

500

Load, Kg
~R.O.C.l~R.D.C.2--+-P.A.l(dB)-If-P.A.2(dB)

10001500 2000 2500

Defect Size. mi eron----#- Energy 1-6--Energy 2

Fig. 2 Repeatability of AE measurements on a defective
gear. ROC = AE counts; PA =peak amplitude.

~ Energy-o-Peak amplitude~ Ringdown

countsFig. 4 AE measurements of gears at 6 kg load.

100-----------------.

...

100~-----------

c

o

--

'iii

III

III

80

I II

'E

~ 60
c
.2

ILl

lJ

-e

I II

:3

o

lJ

GI
GI

<t

40

lJ
lJ

<t

2

4

6

8

10

20

12

Load. Kg---M- Energy

O.....- ......--..a....---I.--~--'o--6- Peak amplitude---.- Ringdown counts

500

1000

1500

2000 2500

Defect Size, micronFig. 3 AE measurements of non-defective gears.

Fig. 5 Overall vibrations of gears at 6 kg load.

3. Measurement Method

J.UIl diameters with a constant depth of 500 Jlm were produced.

The AE signal was measured close to the bearings ofthe test gear box. Vibrations (generated mainly at gear teeth
mesh frequency and its harmonics) were also measured
simultaneously, for the purpose of comparison. All the tests
were carried out at 1000 rpm. The AE measurements were
performed when there was no defect in the gear and when
the gear was removed and a defect in the form of a pit was
produced on a tooth on the gear pitch line, by spark erosion.
The defect diameter was increased after each test on thesame gear. Defects of 250, 350,450, 550, 1100 and 2200

In order to provide maximum discrimination between
the signal and the background noise, and at the same time,
to maintain the sensitivity of the AE monitoring system,
appropriate threshold level of detection and the level of post
amplification were set up. These levels were kept constantfor all the measurements. The AE parameters measured
were energy, peak amplitude and AE counts. The distribution of AE events versus AE counts and peak amplitude
was also measured. The repeatability of AE measurements
25

100

100

Q)

-

UJ

UJ

III

III

cQ):

c

>

>

250

250

4055

Ringdown counts

Peak amplitude (dB)

(a)

(Q)

100'---"

100

GI

-

UJ

UJ

III

III

c:

c:

QI

:>

>

250

85

45

250

100

Ringdown counts

Peak amplitude (dB)

( b)

(b)

Fig. 6 The distribution of AE events vs. AE counts for thegood gear (a) and for the gear with 11()()..J1m size defect (b)at 6 kg load.

Fig. 7 The distribution of AE events vs. peak amplitude forthe good gear (a) and for the gear with llOO-J1M size defect(b) at 6 kg load.

was also checked on a defective (having some pitting
marks) gear set.

reference to acoustic emissions at zero defect. AE (ringdown) counts show slightly better results as compared toenergy and peak amplitude. The vibration measurements,
which were performed simultaneously for the purpose ofcomparison, indicate that distinct increase in vibration levels is measured when the defect size is more than 1000 J1m(Fig. 5). This indicates that the AE measurements are better
than overall vibration measurements for the detection of
defects in gears, because they can detect defects earlier(smaller defects) as compared to overall vibration levels.

4. Results and Discussion
The AE parameters - AE (ring-down) counts. peak amplitude and energy were measured at two different times ona gear, which had some pitting marks. to study the repeatability of these measurements. The results are shown in Fig.
2. All the three parameters measured show quite good repeatability. The variation of acoustic emission parameterswith load was investigated on the test gear set without defect. The results shown in Fig. 3 indicate that there is someincrease in acoustic emissions with an increase in load.

The distribution of AE events and AE counts and peak
amplitude for the good gear and for the gear with 1100-).tDl
size defect. at 6 kg load, are shown in Figs. 6 and 7. It isseen that, in general, the distribution becomes broader because of the presence of defect in the gear. The AE event
count goes to zero for AE counts of 20 and 85 for the gearwithout defect and with llDO-J.UI1 size defect. respectively(Fig. 6). Figure 7 shows that for the maximum events, thepeak amplitude is centered around 50 dB for good gear andaround 75 dB for gear with II00-J1m size defect.

The variation in AE parameters measured for different
sizes of defects in test gear tooth for 6 kg load are shown in
Fig. 4. The results show that there is an increase in the AE
parameters measured, with increase in defect size. Thisshows that AE measurements can be used for condition
monitoring of gears. The increase in AE is marked when
the defect size is around 500 J1m or greater in diameter with26

S. Conclusions
The AE measurements can be successfully used for the detection of defects in gears. The AE measurement results are
better than overall vibration measurements for the detectionof defects in gears because the defects of smaller size could
be detected by AE measurements. The distribution of AE
events versus AE (ring-down) counts and peak amplitude
becomes broader with the presence of defect in the gear
tooth.
References
F.K. Choy, V. Polyshchuk and D.P. Townsend (1996),"Analysis of the effect of surface pitting and wear on thevibration of a gear transmission system", Tribology International, 29. 77-83.
K. Fujita, A. Yoshida and K. Ota (1983), "On the possibility of early detection of gear tooth failures by noise andvibration", Proceedings Sixth World Congress On TheoryofMachine and Mechanisms, pp. 668-672.
1. Nicks (1990), "Evaluation of vibration analysis for fault
detection using gear test rig", Proceedings 1. Mech. E., First

Intemational Conference on Gear Box Noise and Vibration,
pp. 165-172.

M.K. Muralidharan, N. Tandon and B.C. Nakra (1994),"Condition monitoring of gears by noise analysis", Proceedings Inti. Con! COMADEM 94, New Delhi, pp. 427439.
N. Tandon and B.C. Nakra (1990), "Defect detection inrolling element bearings by acoustic emission method" J.
Acoustic Emission, 9, 25-28.
K. Umezawa. K. Handa, H. Kawarada and H. Kamei(1982), "On prognosis of gear surface failures using soundof gear". Bull. JSME, 25,834-841.
T. Yoshioka and T. Fujiwara (1984), "Application of
acoustic emission technique to detection of rolling bearing
failure", Acoustic Emission Monitoring and Analysis in
Manufacturing. ed. D. A. Dornfeld, ASME. New York, pp.

55- 75.

27

42nd AEWG Meeting

After the great Hanshin earthquake. numerous studies have been made to evaluate damage levels. toughness anddefonnation characteristics of reinforced concrete(RC) columns exposed to the earthquake shock. In the present study,
RC columns are subjected to simulated seismic lateral loading under different axial loads. Acoustic emission (AE) ismonitored to characterize the seismic behavior of column foundation. AE is shown to be very sensitive to detectcracks generated in the foundation. An AE moment tensor analysis is applied to analyze the fracture process
quantitatively. Experimental results demonstrate different fracture behavior, depending on the different axial loads. It
is also shown that the moment tensor analysis is very useful for quantitative evaluation of the fracture process.
PipeJine Testing Experience in Russia. Victor V. Shemyakin. PAC-Russia (No abstract available)
Cold Header Machine Process Monitoring Using an Acoustical Approach.
Henrique Reis. D. Brad Cook, and Aaron C. Voegele. NOT and E Research Lab., University of Dlinois
In cold heading manufacturing processes. a partial fracture of the punch pin recess tip leads to production ofout-of-tolerance parts. A process monitoring system was developed to assure that out-of-tolerance parts do notcontaminated the batch of acceptable parts. A four-channel data acquisition system was assembled to collect and store
the acoustic signal during the manufacturing process. A genetic algorithm was designed to select the smallest subset
of waveform features necessary to develop a robust artificial neural network that could differentiate among thevarious cold heading machine conditions. The developed monitoring system is able to tenninate production within
seconds of punch failure using only four waveform features. Both complete and partial fracture modes can be detectedusing the developed process monitoring system.
Acoustic Emission Monitoring of Plasma Sprayed Ceramic Thermal Barrier Coatings
David W. Prine. Jennifer R. Mawdslcy. and Katherine T. Faber. Northwestern University
Acoustic emission monitoring techniques are providing a practical tool to evaluate the mechanical performance ofplasma sprayed ceramic coatings. Test specimens are fabricated with Small Particle Plasma Sprayed 7% (weight)
yttria partially stabilized zirconia deposited on 1018 carbon steel substrates to a thickness of 3 to 6 mils. Thesematerials show considerable promise as thermal barrier coatings that can survive severe mechanical deformation.
AE is being used to monitor performance of the test specimens in a 4-point bend test. Both single cycles that exceed
the elastic limit of the substrate and fatigue tests with several thousand cycles have been monitored. Other workers
in the field have attempted to apply AE to this problem but have typically not given sufficient attention to theelimination of non-valid AE resulting from either the test machine or the test fixture. For these tests a simple threechannel linear array with source location is used to suppress background noise from the test fixture. Both narrow
band resonant and broad band sensors are being used. Special sensor coupling shoes were constructed to eliminate
coupling variations resulting from extreme changes in curvature of the test specimen as it undergoes bending.
Conventional event based AE as well as digital waveform data is recorded. This paper will discuss in detail theexperimental results obtained to-date. The specimens are remarkable for their lack of AE. The presence of porosityand microcracking in the coating apparently prevents or suppresses the growth of large brittle failures. Large
numbers of very low amplitude events that probably result from frictional and microcraek sources characterizes the
AE from a good specimen. The waveform data clearly shows several classes of AE events distinguishable in both thetime and frequency domains. Good correlation has been obtained between AE and changes in the process paranleters.
Waveform Evaluation of Acoustic Emission Signais from Electroplating
Kanji Ono. UCLA and Tsutomu Morikawa. Osaka Pref. Industrial Research Inst.
Takano and Ono reported 25 years ago that AE signals from crack formation during electroplating can be detected andare distinguishable from those due to hydrogen bubble evolution and collapse. Few studies have been done since,
although AE is now a standard technique in monitoring the adhesion of plated/coated films in conjunction withscratching. We recorded AE signals during electroplating of copper, nickel and chromium on copper and stainlesssteel sheets. by varying the conditions of plating (solution composition, pH level. temperature and current density).
AE waveforms (using a PAC WD sensor and a MISTRAS board) exhibit distinct plate-wave characteristics. withcracks giving strong extensional modes and hydrogen bubbles having mostly flexural waves. Waveform- or
frequency-based discrimination can be easily performed to separate the two signal types. Signal amplitude can also beAcoustic Emission Signals in Thin Plates Produced by
Impact Damage
William H. Prosser, Michael R. Gorman and Donald H. Humes
Abstract

damage including birds, projectiles used against military aircraft, and collisions with other aircraft or spacecraft. For

Acoustic emISSion (AE) signals created by impact
sources in thin aluminum and graphite/epoxy composite
plates were analyzed. Two different impact velocity regimes
were studied. Low-velocity (less than 0.21 kmls) impacts
were created with an airgun firing spherical steel projectiles(4.5 mm diameter). High-velocity (1.8 to 7 kmls) impacts
were generated with a two-stage light-gas gun firing smaIJ
cylindrical nylon projectiles (1.5 nun diameter). Both theimpact velocity and impact angle were varied. The impacts
did not penetrate the aluminum plates at either low or highvelocities. For high-velocity impacts in composites, there
were both impacts that fully penetrated the plate as well as
impacts that did not. AIJ impacts generated very large amplitude AE signals (1-5 V at the sensor), which propagatedas plate (extensional andlor flexural) modes. In the lowvelocity impact studies, the signal was dominated by a large
flexural mode with only a small extensional mode component detected. As the impact velocity was increased within
the low velocity regime, the overall amplitudes of both theextensional and flexural modes increased. In addition, a
relative increase in the amplitude of high-frequency components of the flexmal mode was also observed. Signals
caused by high-velocity impacts that did not penetrate theplate contained both a large extensional and flexural mode
component of comparable amplitudes. The signals also
contained components of much higher frequency and were
easily differentiated from those caused by low-velocity impacts. An interesting phenomenon was observed in that thelarge flexural mode component, seen in every other case,
was absent from the signal when the impact particle fully
penetrated through the composite plates.

spacecraft. there is another hazard, which is the impact of
micrometeoroids and space debris. These on-orbit collisions
occur at velocities exceeding 10 kmIs, and at these velocities. even very small particles can create significant damage.
The seriousness of the threat of this type of impact damage
to spacecraft is reflected in the titles of two government
reports; "Space Program: Space Debris is a Potential
Threat to Space Station and Shuttle" (GAO. 1990) and"Space Station: Delays in Dealing with Space Debris May
Reduce Safety and Increase Costs." (GAO, 1992)
Acoustic emission (AE) monitoring has been proposed
as one method for structural health monitoring to detect,locate, and assess impact damage. Lempriere (1987) and
Nelson and Lempriere (1987) discussed such applications of
AE for spacecraft. The advantages of such a monitoring
system are that the number of post-flight inspections can be
minimized or eliminated. This can lead to a tremendous
cost savings while at the same time providing enhanced
safety. For long-tenn orbiting space platfonns such as the
proposed Space Station. routine inspections for impact
damage are particularly difficult. Extra-vehicular activity(EVA) is expensive and dangerous. and there are limited
space-tested tools and techniques for damage inspection andassessment. A recent example demonstrating this point is
the inability to find the leak in a Mir Space Station Module, caused by a collision with another spacecraft, even after
several EVA inspections.
In this research. AE signals from impact sources inplates were detected with broad band, high fidelity sensors
and digitized for analysis. Impact events were studied inboth isotropic aluminum, and anisotropic graphite/epoxy.
Both materials are widely used on aircraft and spacecraft.
Low-velocity impacts were created with a pump airgun,
firing spherical steel projectiles at velocities less than 0.21
kmls. Varying the number of times the airgun was pumped
made it possible to study the effect of velocity variation
within the low velocity regime. The effect of the angle ofthe plate on the observed signal was also evaluated. Highvelocity impacts were generated in these plates with a twostage light-gas gun. The velocity was varied over the rangeof 1.8 to 7 kmIs. A smaller nylon projectile was used.
Again, the angle of impact was varied. For the composite
materials. by varying the velocity in the high-velocity re-

1. Introduction
Damage from impact is a serious hazard to aircraft arxi
spacecraft. During takeoff and landing, impact threats include runway debris and birds. Ice created on spacecraft fromthe exposure of humid air to the cold of cryogenic propellants may break off during launch and cause impact damage. There are also numerous in-flight sources of impact
Received 30 June 1998. William H. Prosser (w.h.prosser@
express.larc.nasa.gov) and Donald H. Humes are affiliated with
NASA Langley Research Center, Hampton. VA 23681-0001,
and Michael R. Gorman is with Digital Wave Corp.• Englewood. CO 80111.
JouroaJ of Acoustic Emission
Volume 17. Numbers 1-2

29

073O-0050J99I$S.OO/17029.()36
CopyrigbtCi>1999 Acoustic Emission Groupgime, impacts that fully penetrated the plate were produced
as well as those that did not.
For all conditions of impact studied in this research inboth the aluminum and graphite/epoxy composite plates,
the detected AE signals were of very large amplitude. At
the relatively short distances of propagation (less than 20em) in this work, attenuators, rather than preamplifiers,
were used in some cases. This was necessary to reduce thesignal amplitudes to a sufficiently low level for digitization.
All signals contained propagating plate mode (extensional
andlor flexural) components. Although location results are
not presented in this paper, the large amplitude signals (mithus large signal-t~noise ratios) made it possible to obtain
excellent impact location agreement with that measured
directly on the plate.
For low-velocity impacts, the flexural plate mode waspredominant. As the projectile velocity was increased
within the low-velocity regime, the overall amplitude of themodes increased and the amplitude of higher frequency components in the flexural mode increased. Previous research(Gorman, 1990; Gorman and Prosser, 1991; Prosser, 1991)
showed the effect of the direction of source motion on therelative amplitudes of plate modes. This was the motivation for varying the angle of impact. In contrast to the previous studies, no clear effect of varying the trajectory on therelative amplitude of plate modes was observed.
For high-velocity impacts, the extensional mode amplitude was much larger than for low-velocity impacts arx1
comparable to that of the flexural mode. The signals contained much higher frequency content. Again, the effect ofvarying the impact angle produced no change in the relative
amplitudes of the plate modes. The flexural mode was entirely absent in every case where full penetration of thecomposite plate occurred. Further study of this effect iswarranted as this could provide a useful discriminator for thedetection of spacecraft-hull penetration.

In previous research, attempts were made to use acoustic detectors to characterize the micrometeoroid environment. Examples of this work are described by Konstantinov et al. (l969), Alexander et al. (1962), and Kapinsinsky(1978). Laboratory experiments were used to characterize
the sensitivity of the devices and such acoustic detectors
were widely flown on balloons, sounding rockets, satellites,
and lunar and d~space probes. However, much like AE
testing of structures, the reliability of the data from theseon-board acoustic sensors has often been questioned because
extraneous noise was not identified or eliminated. Thus, therates of impacts determined from acoustic signals were unrealistically high in comparison to other measurement systems such as capacitive discharge detectors. The extraneous
noise signals were attributed to both electromagnetic interference and noise caused by thermal expansion of the ~craft.

Laminated composite materials offer considerable
weight savings for aircraft and spacecraft, but are particularly susceptible to impact damage. As such, there has been
considerable study on acoustic waves generated by impact
sources (Gardiner and Pearson, 1985; Weems et al., 1991;
Takeda et at, 1981). However, these studies have focused
primarily on impact sources of very low velocity such as
dropped projectiles or weights dropped onto impact tups.
The signals acquired in these experiments are in agreementwith the results from the low-velocity impact testing in thisstudy in that small amplitude extensional mode components
followed by much larger flexural mode components can be
identified. In addition, a number of authors including Moon
(1973), Sun and Lai (1974), Datta et aI. (1992), Karim(1991), and Mal and Lih (1992) have investigated theoretical
approaches for predicting acoustic waves in composite
laminates generated by impact sources.
More recently, Nelson and Lempriere (1987) evaluatedAE signals generated by impact sources in metallic plates
for the development of AE technology for the Space Station. Although they successfully demonstrated the detection
and location of impacts, an AE monitoring system was notincluded in later plans for the Space Station. It is believedthat the infant state of the waveform-based technology used,combined with severely limited development funds, led tothe curtailment. In their study, they detected and digitizedsignals with broadband sensors for low-velocity impacts
created with an air gun and spherical projectiles. They alsocollected signals from a very limited number of highvelocity impacts, but the results were not discussed in <if>tail. AE signals propagating as plate modes were also observed in both cases and considerable discussion focused on
the ability to locate sources with these dispersive waves.
However, they tested only metal specimens representative of
space-station materials and only considered normal impact.
They also suffered problems with their digitizing instrumentation causing high frequency spikes that distorted somesignals. Furthermore, differences between penetrating andnon-penetrating high-velocity impacts were not evaluated.
Thus, a more extensive study was conducted and the results
are detailed below.

2. Low-Velocity Impact
A pump air gun (Daisy model 880) was used to produce
low-velocity impacts. Spherical, steel balls with a diameterof 4.5 mm were used. The velocity of the projectiles was
not measured in these experiments. The nominal maximum
muzzle velocity for this model gun, as obtained from the
manufacturer, is 0.21 kmls when the gun is pumped to its
full capacity of 10 pumping. At this velocity. the estimated kinetic energy (mv2l2) of the projectile is 8.2 J.

The target plate was 2024 aluminum sheet with athickness 00.175 mm and 50.8 cm square. For safety, thetarget plate with attached sensors was placed inside a chamber of 76-mm thick high density foam. The plate wasplaced on a fixture that allowed the angle of impact to bevaried from nonnal to the plate (90·) down to 10· grazing
incidence in 10· steps.

were all located at a nominal distance of 15.2 cm from theimpact. In repeated test firing, the impact position did notvary more than 1.27 cm in any direction from the desired
impact position. As the amplitudes of the AE signals produced by these impacts were so large, no preamplifiers wereused. The output of the sensors were put directly into atransient recorder (LeCroy model 6810) which had a 12-bit
vertical resolution. A sampling frequency of 2 MHz wasused and 1024 points were acquired for each waveform.

Heavily damped, 3.5 MHz ultrasonic transducers for
thickness gaging (panametrics model V182) were used todetect the signals produced by these low-velocity impacts.
Operated far off their resonance in the low-frequency AE
range (less than 1 MHz), these sensors provide flat withfrequency, high-fidelity, displacement response. This wasdemonstrated (Prosser, 1991) by comparison of their response against that of a calibrated laser interferometer. In
addition, Papadakis (1980) discussed why such transducers
make ideal high-fidelity AE sensors. This sensor has a diameter of 1.27 cm. This large aperture leads to problems
with phase cancellation across the sensor face at higher frequencies. To reduce this effect, a smaller diameter sensor(Digital Wave Corp., model BI025) was used in the highvelocity impact studies, which were conducted later. Even
though these two sensors have similar response curves as afunction of frequency except for the apenure effects, exact
comparisons of the low- and high-velocity impact generated
AE signals are not made. This is particularly true for thehigher-frequency extensional mode and the higher-frequency
component of the flexural mode. However, gross differences of these signals, particularly of the relative amplitudesof the plate modes between low-velocity and high-velocity
impacts are noted and will be discussed.

A typical signal captured from an impact, in which thegun had been pumped five times is shown in Fig. 1a. The
extensional and flexural mode components of this signal are
identified in this figure. The extensional mode propagateswith a faster velocity and suffers little dispersion, while theflexural mode travels more slowly with dispersion such thathigher frequencies arrive earlier in the signal. The peak
amplitude of the signal is quite large, and again, this iswithout any amplification. The flexural mode is dominant
and the extensional mode is barely observable in comparison. This is as expected since the impact produces a large
out-of-plane source motion and thus generates a large bending moment. For these impacts, the projectile did not penetrate the plate but did produce a significant crater. The crater
diameter was approximately the same diameter as thespherical projectile.
A signal from an impact, in which the gun was only
pumped twice is shown in Fig. lb. The velocity (and energy) of the projectile for this case is smaller, which leads
to a smaller peak amplitude. Again, the flexural mode isdominant with very little extensional mode signal present.
Not only is the amplitude different for the signals from different impact velocities, but the frequency content changes
as well. As can be seen in the time domain signals of Fig.

For these experiments, four sensors were placed in arectangular array around the anticipated impact spot. They

Flexmal

Mode

10

~4

........
~

~ 5

]_2

.g

.g

.......

.E 0 ~--,..~-'\{\-aI~ -5 Extensional.ec.. 0 ~--"--'V
~ -2

Mode

-10 1-----+----+----1

o

50
100
Time (lJSec.)-4 t - - - - + - - - - t - - - - t

o

150

100
50
Time (J.lsec.)

150

b)

a}

Fig. 1 AE signals produced by low-velocity impact on aluminum plate with: a) air gun pumped 5 times, and b) air gun
pumped 2 times.

31

250

250

200

200
C)

C)

] 150

"0

.~

.-=

flOO

) 100

50
0

150

50
0

Fig. 2 Magnitude of FFT of AE signals in Fig. 1 produced by low-velocity impact on aluminum plate with: a) air gun
pumped 5 times, and b) air gun pumped twice.

I, and even more clearly in their frequency response curves
in Fig. 2, the high-frequency content increased in the signal
from the higher-velocity impact.

hydrogen until the high pressure ruptureS a polymer membrane at the muzzle end of the first stage. The piston iscaught in a large steel capture block prior to reaching theend of the fD'St stage. The pressurized hydrogen then pro.
pels a much smaller projectile down an evacuated chamber
at velocities. for this gun. up to 7 kmls. More details
about the operation of such guns are given by Crozier am
Hume (1957) and more recently by Schneider and Stilp(1990).

No effect of impact-angle variation on the relative amplitudes of the two plate modes was observed. As mentioned previously, earlier research (Gorman, 1990; Gonnan
and Prosser, 1991; Prosser, 1991) had demonstrated a strong
relationship between the directionality of the source motion
and the relative amplitudes of plate modes. In this previous
work, the simulated AE sources (pencil-lead breaks) were allapplied at the mid-plane of the plate as the source angle wasvaried. Impact sources, however, occur on. or near the surface of the plate. This off-mid-plane source always creates alarge bending moment, and thus large flexural mode. The
variation in the angle of impact produced frequency content
and overall amplitude (of both modes) changes similar to
those produced by varying the velocity. This is because thenormal component of the impact velocity changes, as theangle is varied. These effects. as well as signal dispersion
effects due to slight variation in the propagation distancefrom shot to shot. all contributed to further mask any possible effect on relative amplitudes of plate modes that might
have been caused by impact angle variations. Further study
is needed to determine the feasibility of impact trajectoryassessment through AE signal analysis.

The velocity is controlled by a number of factors including the projectile size and weight, the amount of powdercharge in the first stage, and the original pressure of thehydrogen gas in this stage. For these experiments. the impact projectiles were nylon cylinders with diameter of 1.5
nun and length of 1.25 mm. The velocity was measured
with an optical system. Using mirrors, the path of a He-Ne
laser beam was manipulated so that it passed twice throughthe intended path of the projectile at known. fixed distances
between passes. The laser beam then impinged on a photodetector. As the impact projectile traveled down the chamber, each time it passed through the laser beam. it temporarily blocked the light from arriving at the photo detector.
The photo-detector output signal could then be used to cbtermine the time required for the particle to travel the distance between the two beam paths. This time, with the
known distance. was then used to determine the velocity ofthe projectile. which in our experiments ranged from 1.8 to
7 kmIs. Over this velocity range. the calculated kinetic
energy of the projectile ranges from 4.1 J to 61.7 J. Thus.
even though the velocities are much higher than those usedin the low-velocity study, the energy of the projectile is of
the same order of magnitude.

3. High-Velocity Impact
A two-stage light-gas gun was used for high-velocity
impact experiments. These high-velocity impacts cause
damage. which more closely simulates that caused by hypervelocity impacts of rnicrometeoroids and space debris on
spacecraft.. The first stage of this gun consisted of a Swift
5.59-mm caliber rifle. which fires a nylon piston. The barrel for this first stage is pressurized with hydrogen prior to
the shot. As the 5.59-mm diameter nylon piston is fired
down the barrel in the first stage, it further compresses theBoth aluminum and graphite/epoxy composite plates
were impacted at high velocities. The 2024 aluminum
plates had the same nominal thickness and lateral dimensions as those used for low-velocity experiments. Two32

GlA) was used to provide 2O-dB attenuation. For the signals presented in this paper, the amplitudes have been multiplied by 10 to correct for this attenuation and provide thevoltage output from the sensor. This allows direct comparison of these signals against those recorded from the lowvelocity impact study. However, the differences in type of
sensors and filtering must be considered when making suchcomparisons. Thus, only significant differences in wave
shape and modal content will be discussed between these
two impact conditions.

different graphite/epoxy composites were used. The fust
was 8 plies of IM7/8552, which had a nominal thickness of
1.27 nun. The lateral dimensions of these plates varied but
was 25.4 x 25.4 em or larger. The second was 24 plies of
fM71977-2 with a nominal thickness of 3.56 nun. The
lateral dimensions of these plates were 35.6 x 35.6 em.
Both laminates were quasi-isotropic lay-ups, the first being[0. 45. 90, -45]5 and the second being [0, 45. 90. -45]35'
For the composites, only impact normal to the plate wasstudied. while for the aluminum, the impact angle was varied again from normal (90') down to 10° in 10° steps.

Figure 4a shows the signal produced in an aluminum
plate from a high-velocity impact. The velocity of the impact particle was 4.2 kmls and the aluminum plate was not
penetrated. In fact, for the range of velocities tested, no
projectiles penetrated the aluminum plates. The propagation distance to the sensor was 14.7 em. Because of thesignificant high-frequency components in this signal, it ismore difficult to clearly identify the extensional and flexural
mode components. A 500 kHz low-pass filter was appliedwith the results shown in Fig. 4b. Here the plate mode
components are more readily identified. In comparing thesesignals to the low-velocity impact signals in Fig. 1. it isnoted that the shift in arrival times of these signals, arxl
others to be presented later. are due to differences in trigger
timing. Two significant differences are readily apparent
when comparing signals caused by high-velocity impact tothose caused by low-velocity impact. The first, which was
clearly demonstrated by the need to use a low-pass filter to
identify the modes, is that there is much higher frequency
content in signals produced by high-velocity impact. It issuspected, although not verified by signal analysis, thathigher order Lamb modes may be present in this highfrequency signal. This higher frequency content is not unexpected due to the source mechanism at much higher velocity. An increase in high-frequency content was also observed even for the slight velocity increase in low-velocity
impact (cf. Fig. 2).

As mentioned previously, different sensors (Digital
Wave Corp. model BI02S) were used for the high-velocity
experiments. These were similar to those used in the lowvelocity impact study in that they are highly damped ultrasonic sensors operating far off resonance to provide a flat
frequency response. However. they have a smaller diameter.
which minimizes aperture effects thus providing better fidelity at the higher frequencies in the extensional mode and theearly portion of the flexural mode. Again, four sensors
were used in an array around the impact site. However, in
this case, they were not equidistant from the impact site.
For the aluminum plates. the nominal sensor positions
were as shown in Fig. 3. For the composite plates, similarsensor spacing was maintained, but because the lateral dimensions of the plate were different, the positions relative
to the plate edges were different. Also, as illustrated in thisfigure, there was slightly larger Variability of the impact
site from shot to shot.

13~.7

Tt
508

1

t7 6.2 l

o 0

c

/

228.6

1

ImpactRegion0o,~152.4-i76.21

The second significant difference between the low- arxl
high-velocity impact signals is the much larger extensionalmode component for high-velocity impacts. It has an evenlarger amplitude than the flexural mode for this signal. A
qualitative explanation for this difference can be offeredbased on an examination of the type of deformation produced by impacts at these different velocities. As discussed
previously, low-velocity impact produces a crater. which isof similar diameter to the projectile diameter. For highvelocity impact, however. the diameter of the crater is several times larger than the diameter of the projectile. Thus.
there is significant in-plane defonnation as the crater expands in the high-velocity impact event. It seems likelythat this in-plane deformation from crater expansion accounts for the large extensional plate mode component inhigh-velocity impact signals.

Sensors508

Fig. 3 Illustration of sensor positions for high-velocity
impact in aluminum plate. Dimensions in mm.
A different signal recording system (Digital Wave
Corp. model 4012 Fracture Wave Detector) was used forthese experiments. It also had 12-bit vertical resolution,
but the sampling frequency was 10 MHz. The signal was
bandpass filtered from 20 kHz to 1.5 MHz. Because theinput range of this digitizer was limited to ±O.5 V, an attenuating preamplifier (Digital Wave Corp. model 2040
33

Flexural

3

3

-~ 2

-2

~
~ 1

! I
]

0 I------~I]

0 1-------'1111:a-I
E:aE -1< -2< -2-3 1-----1-.....:..--1----1
o
50
100
ISO

-3 1---+----+--------1

o

SO
100
Time (Jlsec.)b)

Time(~.)

a)150

Fig. 4 a) AE signal produced by high velocity impact on aluminum plate; b} same signal as a) except after filtering with
Sao-kHz low-pass filter.
Again, no clear difference in the relative amplitude of
the plate-mode components was detected as a function of theangle of impact. As the angle was decreased from nonnal
impact, overall decreases in signal amplitude were observed
as the normal component of the velocity decreased. However, the larger variations in the actual impact spot relative
to the sensor positions made it· difficult to investigate thiseffect. The apparent amplitudes of the modes change withpropagation distance, especially for the flexural mode because of its high dispersion. Further, more controlled tests,
in which either the propagation distance varied less, or amplitude changes due to propagation distance variations arecompensated, are required. This will allow a better determination of whether information about impact trajectory is
also contained in AE signals.

tion of 51.4" with respect to the O"-fiber direction. For thissignal, the flexural mode component is not present. The
signal is composed only of the extensional mode and its
reflections from the plate edges. A similar result is shownin Fig. 6b for the 24-ply laminate. The velocity in thiscase was 5.2 kmIs. The propagation distance was 13.0 cm
along a 54.3" propagation direction.

2..-.. 1.5

~
o

1
~ 0.5
.g 0 I---~:::l

For high-velocity impacts in composite plates, theimpact projectile fully penetrated the plate in most cases,
except when the projectile was at the low end of the velocity spectrUm (less than 2 kmIs). Figure 5 shows the signal
from an impact event, in which the projectile did not penetrate the composite plate. The velocity for this impact was
1.8 kmIs. The plate in this experiment was the 8-ply
IM7/85S2 material. The distance of propagation was only
7.2 cm in a direction at a small angle (approximately 1.5°)
from the a·-fiber direction in this quasi-isotropic material.
Again, similar to the signal from high-velocity impact inaluminum, there are large amplitude extensional and flexural
mode components. However, the very high-frequency components that were present in Fig. 4a, are missing. This ismost likely due to the much higher attenuation at higher
frequencies that is typical of composites.
A different result was obtained when the composite
plale was penetrated. Figure 6a shows a signal for such acase in the 8-ply material where the projectile velocity was
6.6 kmIs. The propagation distance was 14.1 em in a direc-

~-0.5

E

-1

<-1.5
-2 ..---+---+--+--~
o
100
150
50
200
Time (J.l.sec.)
Fig. 5 AE signal produced by non-penetrating high-velocity
impact on 8-ply graphite/epoxy composite plate.

4. Summary
In this study, AE signals generated by impact weredetected with broadband. high fidelity sensors. A wide variety of impact conditions were studied including both lowand high-velocity impact, and varied impact trajectory inmetal and composite targets. In the composite plates athigh velocities. both penetrating and non-penetrating impacts were generated. In all cases. the AE signals had verylarge amplitude and propagated as plate modes.

33'U)' 2~ 2~....... 1-~ 1.......

.g 0 1----lL~.g 0 t - - - - - 'is. -1

'~-1

E

.-=:

~ -2

E

< -2

-3 1---+---+--+---1
o
50
100
150
200
lune (lJ.Sec.)-3 1----+...;.:...--11--_-+_---1
o
50
100
150
200
Time (J.lsec.)a)b)Fig. 6 AE signals produced by penetrating impact in composite plates; a) 8-ply laminate. and b) 24 ply laminate.
AE signals from low- and high-velocity impacts were
easily differentiated from their frequency content and modal
analysis. Low-velocity impacts produced signals withlittle extensional mode and large flexural mode components. For variations in velocity over the low-velocity
regime. effects on both the amplitude and frequency con·tent of the AE signals were observed. High-velocity. nonpenetrating impacts also produced AE signals with large
flexural modes. However. these signals had large extensional mode components of comparable amplitude to theflexural mode. The large extensional modes may be dueto the in-plane crater deformation caused by impacts atthese velocities. When high-velocity impacts fully penetrated the composite plates. the flexural mode was absent
from the AE signal. Variations in the impact trajectory
had no discernible effect on the modal content of the AE
signals at either high or low velocities. The only signal
variation was a change in overall amplitude. which was
attributed to simple changes in the normal velocity component as a function of impact angle.
These results demonstrate the feasibility for using AE
to not only detect and locate impact events on aircraft andspacecraft. but also provide information about the natureof the damage. The very large signal amplitudes for the
impacts srudied indicate that relatively large sensor spacing might be used for practical. economical. implementation. This. of course. depends on a number of factors suchas the minimum allowable undetected impact damage. ard
the structural complexity of the region to be monitored,
which can significantly affect the propagation of AE
waves. The observed variation in signal characteristics
with impact parameters such as velocity suggests thatquantitative information about the impact parameters
could be obtained. Such information could be used toremotely assess damage, which is of particular importancefor long-term orbiting spacecraft. Likewise. the ability todifferentiate penetrating and non-penetrating impact eventswould be critical for assessing damage in spacecraft, particularly on presswized components such as habitat modules andtanks.
However. funher study will be necessary to develop andimplement a full-scale AE impact-monitoring system for agiven application. A wider range of impact velocities, projectile size and materials, and propagation distances, which are
representative of those expected for the particular application
should be tested. Also, realistic specimen geometrY again
representative of the aircraft or spacecraft to be monitored
should be studied. This will allow the effects of wave propagation such as reflections or attenuation through complex
geometrY to be evaluated. Such effects can be significant andare often ignored when attempting to extend laboratory AE
results to monitor real structures. And finally, as with any
AE application, considerable attention needs to be paid toevaluating and eliminating potential noise sources. The ~velopment of accurate models to predict AE signals from impact sources will also be of tremendous benefit Such simulations could help minimize the high expense required forexperimentally testing a wide variety of impact conditions.
and can be used to help understand and interpret results from
actual events.

References
W. M. Alexander. W. W. McCracken. L. Secretan. and O. E.
Berg (1962). ''Review of Direct Measurements of Interplanetary Dust from Satellites and Probes," NASA TN 0-1669.
pp.39-60.
W. D. Crozier. and W. Hume (1957), "High Velocity Light
Gas Gun," J. Appl. Pbys., 28, 892·894.

S. K. Dana. T. H. Ju, R. L. Bratton, and A. H. Shah
(1992), "Transient Response of a Laminated Composite
Plate, " Review of Progress in Quantitative NondeslJ'Uctive Evaluation, 11, 145-151.

E. P. Papadakis (1980), "Broadband Flaw Detection Tramducers: Application to Acoustic Emission Pulse Shape and SpectrUm Recording Based on Pulse Echo Response Spectrum
Corrected for Beam Spreading," Acoustica, 46,293-298.

GAO (1990), "Space Program: Space Debris is a Potential Threat to Space Station and Shuttle," GAO Report #GAOIIMTEC-90-18.

W. H. Prosser (1991), "The Propagation Characteristics of
the Plate Modes of Acoustic Emission Waves in Thin Aluminum Plates and Thin GraphitelEpoxy Composite Plates
and Tubes," NASA Technical Memorandum 104187.

GAO (1992), "Space Station: Delays in Dealing withSpace Debris May Reduce Safety and Increase Costs,"
GAO Report It GAOIIMTEC-92-15.
D. S. Gardiner and L. H. Pearson (1985), "AcousticEmission Monitoring of Composite Damage Occurring
Under Static and Impact Loading," Experimental Techniques, 9, 22-28.
M. R. Gorman (1990), "Plate Wave Acoustic Emission,"
1. Acoust. Soc. Am., 90(1), 358-364.

M. R. Gorman and W. H. Prosser (1991), "AE Source
Orientation by Plate Wave Analysis," Journal of Acoustic
Emission, 9(4), 283-288.
I. Kapinsinsky (1978), "00 the Reliability of the Acoustic Method of Micrometeoroid Detection," Bulletin of the
Astronomical Institutes of Czechoslovakia, 29(6), 326330.
M. R. Karim (1991), "Impact Response of a Unidireclional Composite Laminate," JSME International Journal,
34(4), 496-504.
B. P. Konstantinov, M. M. Bredov, E. P. Mauts, V. N.
Panov, R. L. Aptekar, S. V. Golenetskii, Y. G. Guryan,
and V. N. IJ'inskii, (1969), "Micrometeors in Circumterrestrial Space Observed by Kosmos-163," Translated from
Kosmicheskie Issledovaniya, 7(6), 817-821.

B. M. Lempriere (1989), "Nondestructive Evaluation ofComposite Structures in Space," Boeing Report # D18031598-1.
A. K. Mal and S. S. Lib (1992), "Wave Field Produced ina Composite Laminate by a Concentrated Surface Force,"
Review of Progress in Quantitative Nondestrut:tive
Evaluation, Vol. 11, 137-144.
F. C. Moon (1973), ''Wave Surfaces Due to Impact on
Anisotropic Plates," Journal of Composite Materials, 7,
62-79.
J. M. Nelson and B. M. Lempriere (1987), "Space Station
Integrated Wall Design and Penetration Damage Control,"Final Report for NASA Contract NAS8-36426.

E. Schneider and A. Stilp (1990), "Acceleration of Particles
in Light Gas Guns for MicrometeoroidiSpace Debris Simulation," Proceedings of the International Symposium on Environmental Testing for Space Programs - Test Facilities cnJ
Methods, Sponsored by ESA, eds. T. GuyeMe and J. Hunt,
Noordwijk, Netherlands, June 22-29, pp. 385-388.

C. T. Sun and R. Y. S. Lai (1974), "Exact and Approximate
Analyses of Transient Wave Propagation in an Anisotropic
Plate," AIAA Journal, 12(10), 1415-1417.
N. Takeda. R. L. Sierakowski, and L. ·E. Malvern (1981),''Wave Propagation Experiments on Ballistically Impacted
Composite Laminates," Journal of Composite Materials, IS,
157-174.
D. Weems, H. T. Hahn, E. Granlund, and I. G. Kim (1991),"Impact Detection in Composite Skin Panels Using Piezoelectric Sensors," American Helicopter Society 47th Annual
Forum Proceedings, I, 643-652.

Reflections of AE Waves in Finite Plates: Finite Element
Modeling and Experimental Measurements

w. H. Prosser, M. A. Hamstad, J. Gary and A. O'Gallagher
Abstract

aperture were also evaluated in this work. Later, Hamstad
et aI. (1996) extended this approach in developing a threedimensional DFEM for predicting AE signals in thickplates. Again. experimental measurements with a calibrated wideband sensor were used to confirm the finite
element models. More recently, Harnstad et aI. (1998a) and(1998b) have applied the DFEM for predicting AE signals
from more realistic source configurations such as buried
dipole sources. Prosser et al. (1998) also compared the
DFEM approach to plate theory predictions of AE signals
for both isotropic and anisotropic plates. In all of this previous work on predicting AE waveforms with the DFEM.
only the direct signal arrivals have been studied.

We investigated the capability of a three-dimensional
dynamic finite element method for predicting far-field
acoustic emission (AE) signals in thin plates of finite lateralextent. including their reflections from the plate edges. A
lead-break (Hsu-Neilsen) source to simulate AE was modeled and used in the experimental measurements. For thethin plate stUdied. the signals were primarily composed ofthe lowest order symmetric (So) and antisymmetric (Ao)Lamb modes. Experimental waveforms were detected with
an absolutely calibrated. wideband. conical element transducer. The conditions of lead fractures both on the surfaceof the plate as well as on the edge of the plate were investigated. Surface lead breaks preferentially generate the Aomode while edge lead breaks generate the So mode. Reflections of developed plate waves from both normal andoblique incidence angles were evaluated. Particularly interesting for the case of the lead break on the plate edge were
So waves produced by the interaction of a Rayleigh wave
with the plate comer and those produced by a bulk shear
wave mode that converted at the side edge. The Rayleighwave. in this case. propagated along the specimen edge.
For all cases considered. the experimental measurements
were in good agreement with the predictions of lateral plateboundaries.

However. a major advantage of the DFEM in comparison to other methods for predicting AE signals is the ability
to model AE signals in realistic specimen geometries. This,of course, includes predicting reflections from lateral plateboundaries. Because of the complexity of the problem.
most theoretical treatments of Lamb waves for AE or ultrasonics assume the plate to be of infinite lateral extent. Examples of this are given by Guo et al. (1996) and references
contained therein. Exceptions to this are works by Gorman
and Prosser (1996). Prosser et al. (1998). and Huang(1998). However. the normal mode solution to plate theory
used in these studies is applicable only to limited simple
geometries such as rectangular and circular plates. Also.
these plate theories predict only the extensional plate (lowest order symmetric Lamb) mode and flexural plate (lowest
order anti-symmetric Lamb) modes and thus are not usefulwhen the AE signals contain higher order Lamb modes.

1. Introduction
Gary and Ramstad (1994) previously validated a dynamic finite element method (DFEM) for predicting simulated AE signals in thin plates. Experimental measurements
were shown to be in excellent agreement with predictions
from a two-dimensional. cylindrically symmetric. finite
element model. The effect of varying finite element parameters such as cell size was investigated. Other variables
such as source rise time and diameter. as well as sensor

This research validated the capability of the DFEM topredict AE signals in finite plates including reflection components. Simulated AE sources (lead breaks or HsuNeilsen sources) were modeled in rectangular aluminum
plates. 3.175 mm thick. Experimental measurements ofwaveforms from lead-break sources were then obtainedwith an absolutely calibrated, wideband, conical element
sensor for comparison. The lead-break source was positioned on either the plate surface or the plate edge near the
mid-plane. As discussed by Gorman (1991) and Gorman
and Prosser (1991). lead breaks on the surface preferentiallygenerate the Ao mode while lead breaks on the plate edge
generate signals with dominant So-mode components. For
these two source configurations. plate specimens with different source and receiver positions were used to examine

Received on 30 June 1998. W. H. Prosser is afflJiated with NASA
Langley Research Center, Hampton. VA 23681-0001 (w.h.prosser@express.Iarc.nasagov). M.A. Hamstad is with University ofDenver. Denver. CO 80208 and National Institute of Standards
and Technology. Boulder. CO 80303. and J. Gary. and A.
O'Gallagher are with National Institute of Standards and Technology. Boulder. CO 80303. This is a contribution of the U.S. National Institute of Standards and Technology; not subject to copyright in US.
Journal of Acoustic Emission

Volume 17. Nwnbers 1-2

37

073Q-OOS0J99/SS.0QI17037-047
Copyrigbtet999 Acoustic Emission Groupsignals containing reflections from both nonnal and obliqueangles of incidence.

were invened in this study for comparison to the theoreticalpredictions of DFEM.

For the lead break on the plate edge, two particularlyinteresting reflection signals were theoretically predicted
and observed experimentally. The flfst was due to a Rayleigh wave, which was generated by the edge lead-breaksource and propagated along the plate edge. After interacting with the plate comer, it propagated back to the receiver on the plate surface with a velocity corresponding to
the So mode. Another apparent mode conversion was due
to a wave propagating to the plate edge at the bulk-mode
shear velocity. mode converting with a change in angle
appropriate for shear-to-longitudinal mode conversion, andthen returning to the receiver as the So mode. For all cases
considered, including these apparent mode converted reflections, excellent agreement between DFEM predictions
and experimental measurements was observed.

In order to produce situations, in which one or two reflections could be observed without significant superposition with the arrival of the direct signal or other reflections,plates with relatively large lateral dimensions were required
for the finite-element model and experiment. Plates withtwo different lateral dimensions were used. The first was
26.7 x 63.5 em and the second was 38.1 x 50.8 em. Because of these large lateral dimensions and the large memory requirements of the three-dimensional DFEM, theminimum finite-element cell dimension that could be used
was 1/12 of the plate thickness or approximately 0.26 DUD.
The cells had an aspect ratio of unity. This relatively large
cell size led to a source diameter that was much larger thanexperimental conditions. For the experiment, a 0.3 rom
lead was used. In reality, the source diameter is probably
much smaller than this as the lead was held at an angle withrespect to the surface. causing a smaller point of contact.
Gary and Hamstad (1994) demonstrated that for a stepfunction input source in the DFEM. the source diametermust be at least four times larger than the cell dimension to
avoid high frequency numerical transient noise. In thesemodels with the lead-break source, which has a slower risetime and a smoother start and finish, a source diameter of
two times the cell dimension was used. No high frequency
numerical transients were observed and good agreementwith experimental measurements was obtained.

2. Dynamic Finite Element Method
The DFEM used in this research has been reported by

Gary and Hamstad (1994) and Hamstad et al. (1996). Only
the details relevant to this study are repeated herein. Both a
two-dimensional, cylindrically symmetric model and athree-dimensional model have been developed. The twodimensional model. although requiring less memory andcomputational time, bas limited applications. It can be used
only for isotropic materials and limited source/specimen
geometries (round plate with axisymmetric source at thecenter). The three-dimensional model was required for thiswork to predict reflections in plates with noncircular geometries. and to model the in-plane lead-break source on aplate edge.

3. Experimental Measurements
Simulated AE signals were produced by the fracture of
0.3-mm, Pentel 2H lead on 6061-T6 aluminum plates. To
preferentially generate So-mode AE signals, the lead was
fractured on a plate edge near the plate mid-plane. For theAo mode. the lead was fractured on the plate surface. In
agreement with the DFEM models, the two plates were
3.175 mID thick with one having lateral dimensions of 26.7
x 63.5 cm while the second was 38.1 x 50.8 cm. A National Institute of Standards and Technology (NISn standard reference AE sensor was used to detect the simulated
AE signals. It was coupled to the aluminum plate specimens with Apiezon M grease. This sensor was a wideband,
absolutely calibrated, conical element type sensor. The
response of this sensor is flat with frequency from nearly 20
kHz to above 1 MHz. The calibration factor used to convert the voltage output of the sensor to surface displacement
was 5.623 nmIV.

In the fmite element method, a leapfrog approximationin time and linear elements in space was used. Stress-freeboundary conditions were assumed along the top and bottom surfaces as well as along the outer edges of the plate.
A source function with temporal variation to approximatethat of a lead break as determined by Breckenridge et al.
(1990) was used. An amplitude of 1 N was used. which isin good agreement with that produced by the fracture of a
O.3-mm diameter Pentel 2H lead. A density of 2700 kglm3,
longitudinal elastic wave speed of 6320 mls. and shear
wave speed of 3100 mis, which were obtained from Kolsky(1953) for aluminum, were used in the model calculations.
For all cases, a plate thickness was taken to be 3.175 mm.
As discussed previously by Gary and Ramstad (1994),
the DFEM also models the source as the application of aforce with the time history of a lead break at the position ofinterest. However, the force condition for the experimental
lead break is actually the release of a force with that time
response at the given position. Thus, the measured signal is180' out of phase with that theoretically predicted. In
agreement with this previous work, the experimental signals

The theoretical and experimental signals were bandpass filtered by digital signal processing software with fourpole, Bessel filters. However, the low-pass and high-pass
cutoff frequencies were different in the surface lead breakexperiments from those used in the edge lead break experiments. Two factors necessitated this filtering scheme toallow a comparisons of the DFEM predictions with experi38

distance for the signal reflected off of the plate back edge
(path 2) was 25.4 em. The DFEM and experimental signals
are shown in Fig. lb. Both were bandpass filtered from 50
to 1000 kHz. For the Ao mode. out to the 150 J.1S time period shown in Fig. Ib, only the direct arrival and the backwall reflection are observed. Excellent agreement between
the theoretical prediction and experimental measurement
was obtained for the Ao mode and its reflection.

ment. The first was that the sensor used for experimental
measurements was flat with frequency only from around 20
kHz to just above I MHz. The second motivation for filtering was so that the reflected signals of interest could be
more clearly observed. For the Ao mode created by surface
lead-break sources, the amplitude of the signals becomesincreasingly larger at lower frequencies, which arrive laterin the signal because of their slower velocity. In this case,the signals reflected from the plate edges were superimposed on the low-frequency, large-amplitude components inboth the theoretical and experimental signals. A 50-kHz
high-pass cutoff frequency was used for the studies of Aomode to reduce these component of much lower frequencies
and to more clearly show the reflected signals. A l()()()..kHz
low-pass cutoff frequency was used because of the limited
sensor response above this frequency.

Even though the surface lead-break source preferentially generated the Ao mode, a very small So-mode component is seen in Fig. Ib prior to the anti-symmetric mode
arrival. Because of the faster velocity of the So mode, thesignals also contain multiple reflections of this mode.
However, they are much smaller in amplitude than thedominant anti-symmetric mode and were not examined indetail. The So mode and its reflections were studied withedge lead-break sources, which are discussed later in thispaper.

For the edge lead-break sources, even though great care
was used in positioning the source, it was impossible toexactly center it at the mid-plane of the plate. Thus, a component of the Ao mode was always generated, which was
not present in the DFEM predictions. Since this mode
contains predominantly lower-frequency components thanthe So mode, the discrepancy between theory and experiment was minimized with the high-pass filtering. A highpass cutoff frequency value of 100 kHz was necessary toadequately reduce the Ao mode. Also, in contrast to the Aomode, the So mode has very high frequency componentsthat travel more slowly and superimpose with the reflectedsignals. A lower value of low-pass cutoff frequency of 750
kHz was used to reduce these high-frequency components
to more clearly see the reflections.

~ 25.4cm---1T

8.89 ClIl+) Receiver<DSawce

7.62cm-L

For comparing the amplitudes of the experimental signal and the theoretical prediction of DFEM. only the NlSTsensor calibration factor was used. However, the experimental signal had to be adjusted in time for comparison
with the model because the experimental data acquisition
was not triggered by the lead-break source. The arrival of
the wave at the experimental sensor location triggered dataacquisition, with digital pre-trigger acquisition used to record the earliest arrival of the signal. In all cases, the experimental waveform was shifted in time so that the fIrSt
peak of the symmetric mode arrival coincided with the
DFEM prediction

38.lcm1

SO.8cm
a)

0.6

E

.s

il
Ii

I...
J
B

4. Surface Lead-break Source

:r.

~

0.4

g-

!!

Exp.

0.2

0

£

<D

·0.2

TbeoJy

-0.4

c
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;a
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Several different AE source and receiver positions
were used to investigate AE signals of Ao-Lamb mode andtheir reflections generated by pencil-lead breaks on theplate surface. For the first case, the source and receiver
were positioned as shown in Fig. la to allow observation of
the direct arrival and normal incidence reflection of the Aomode. As illustrated in that figure, the 38.1 x 50.8 cm plate
was used and the propagation distance for the direet arrival(illustrated as path I) was 7.62 em. The total propagation

0

.50

100

150

1ime(ps)b)

Fig. I Direct arrival and normal incidence reflection of AoLamb-mode AE signal generated by surface lead-breaksource; a) Plate geometry and source/receiver locations, b)Filtered experimental and DFEM-predicted waveforms.

39

Ijj

20

10

15

5

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0

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r-

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~

100
1ime{jU)

19.OScm

n

::I

i
=
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3

n

Theory

-0.4

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-0.2

-0.6

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I

a)

ut

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0.4

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0.2

1

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0.2

.

m

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0

Exp.

'"
;;

'"

[

-0.2

is

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·0.4

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-0.6

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100

50

100
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ISO

200

Fig. 4 Theoretical and experimental signals from Fig. 3bover longer time scale showing agreement for arrival of
multiple reflections.

-t- 12.7cm-t-- 19.OScm-l

1

I

0

o

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T

~

=
e.
0
~

01

IS.B7Scm

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Fig. 2 Unfiltered theoretical and experimental signals forplate, source, receiver geometry shown in Fig. la.

S

0

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u

tr-

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..s 0.4
B

-20

-10

E

0.2

ISO

'Dmc(PS)b)

Fig. 3 Direct arrival and oblique incidence reflection of
Ao-mode AE signal generated by surface pencil lead-breaksource; a) Plate geometry and source/receiver locations, b)Filtered experimental and DFEM-predicted waveforms.

The necessity of filtering is illustrated by examining
the unflllered theoretical and experimental signals from Fig.
1b which are shown in Fig. 2. Because of the increasing
amplitude of the Ao mode at longer times, which corresponds to the lower frequency arrivals, the reflections are
not even apparent. The SO-kHz high-pass filter enables theobservation of the reflections. The lack of agreement between the experimental signal and the DFEM prediction at
longer times in Fig. 2 is caused by the lack of lowfrequency response of the sensor below 20 kHz.
The case of oblique incident reflections of the Ao mode
generated by a surface lead break was studied next The
source, receiver, and plate geometry are shown in Fig. 3a.
These were chosen such that. after filtering, the direct arrival (path 1) and reflected signals (path 2) were not superimposed. Again, the 38.1 x 50.S em plate was used. The
source to receiver distance was 12.7 em, and they wereequidistant (15.9 em) from the plate edge. The total propagation distance for the reflected signal was calculated to be
34.2 em. As illustrated in the figure, the angle of incidence(and reflection) with respect to a normal to the plate edge
was 21.S". The bandpass filtered DFEM and experimental
waveforms are shown in Fig. 3b. Again, good agreement
was observed. Figure 4 shows the comparison of the theoretical and experimental signals at even longer times. Additional reflections from the other plate edges begin to arrive after 145 J.ls. The agreement is still quite good forthese superimposed reflections.
The final surface lead-break case considered was again
oblique incident reflection. However, in this case, the dimensions were chosen such that the direct arrival and thereflected signal were superimposed. Figure Sa shows theplate geometry, and the source and receiver positions. The
26.7 x 63.5 cm plate was used and the source to receiver40

I- 21.59cm

21.59cm

---f

T

T

7.62cmt

SourceCD

Receiver26.67cm

19.05 em

1

1
63.Scma)

0.6

0.2.......

trl

g
t:!
g 0.2
0.4

oR

~

-a
ea

0

~

§.
n

i.

CD

i5

~
(,)
'::2

0

Exp.

4)

-0.2

i§a
n

a

Theory
-0.4

-0.2

-~

-0.6

o

50

100
'lime (~)
b)

150

200

Fig. 5 Direct arrival and oblique incident reflection of Ao-lamb-mode AE signal generated by surface lead-break source;a) Plate geometry and source/receiver locations. b) Filtered experimental and finite element predicted waveforms.

s. Edge Lead-break Source

distance was 20.3 em. The source and receiver were positioned 7.62 cm from the plate edge, which produced apropagation distance of 25.4 em for the reflected signal.
The angle of incidence and reflection was 53.1·, The theoretical and experimental signals are shown in Fig. 5b. In
this figure, the direct arrival and reflected signals are indicated wirh arrows labeled 1 and 2, respectively, Additionalreflected signals from other plate edges appear in rhe signal
beyond 135~. Again. there is good agreement between
the theoretical and experimental waveforms.

Edge lead-break sources were used to preferentiallygenerate AE signals of So-Lamb mode and to investigate
their reflections. As before, two different plate dimensions
were used along wirh different source and receiver positions
to observe the particular reflections of interest. The firstcase studied was a nonnal incident reflection. The 26.7 x63.5 cm plate was used with the source positioned midplate along the longer edge. The positions of the source andreceiver with respect to the plate dimensions are shown in
Fig. 6a. Likewise, the ray paths for the direct and reflected4131.75 emT

26.67 em(. Receiver1

4. Source
63.5 em
a)

0.08

-~
5

I

0.04

Antisymmebic
Mode

0.06

0.02

0.04

0

0.02

-0.02

is

'B

'fi

tr1

"8§.

1
Q

-

~
~

0

~ -0.02e-0.04~

Theory

§
-0.06

-0.04

'-'

-0.08

o

20

40

60

80

100

120

Tune (tLs)b)
Fig. 6 Direct arrival and DOnnaJ incident reflection of So-Lamb-mode AE signal generated by edge lead-break source;a) Plate geometry and source/receiver locations. b) Filtered experimental and DFEM-predicted wavefonns.
scale so that the excellent agreement of the reflected components is more clearly seen.

signal are indicated in this figure as 1 and 2, respectively.
The propagation distance for the direct signal was 11.4 em,with the propagation distance from the backwall reflection
being 41.9 em. The bandpass filtered (100 - 750 kHz) theoretical and experimental signals are shown in Fig. 6b withthe direct and reflected signal arrivals indicated by 1 and 2,
respectively. As can be seen, the signals are in good agreement with the exception of the circled Ao component in theexperimental wavefonn, which is discussed below. Figure
7 shows the same two waveforms with an expanded time

As mentioned previously, it was impossible to exactly
center the edge lead-break source at the mid-plane of theplate. A magnifying glass and a fine rule scale on the plateedge were used to more closely position the source at the
mid-plane. However. an Ao-mode component was always
detected in the experimental signal. A number of leadbreaks were performed for all of the edge lead-break ex42

0.02
0.015

0.005

5
e

0

~

-0.005~Experiment

0.01

-

!...

DFEM

!

M~M

;
I

.

j

I

f"f

.: ~
"
Ili·

-

'

·f:Mt.j .
.,

~

'\ ~

t.
Y

'

~

i

is

t

-0.01
-0.015
-0.02

90

80

100

110

120

Tune (J,1s)
Fig. 7 Signals from Fig. 6 with expanded time scale to show comparison of reflected signal.

0.3
0.1
-.

!...c

0.2

§

0.1

tr1
x

'8

v

Exp.

e

-....Ii-

0

~

0

=
E_.
O

-0.1

Q

"5'0

§.

0

S
3
::s
...

en
00

-a
0

Theory-0.2~......,

-0.1

0

50

100

-0.3
150

Tune (J,1s)
Fig. 8 Theoretical and experimenlal waveforms of Fig. 6 without bandpass filtering.
periments and ones chosen with a minimum of this antisymmetric mode component. The IOO-kHz high-pass filtering then eliminated most of this mode. However. as seenin the circled region of the experimental waveform in Fig.

6b. the Ao mode does contain higher frequencies whichwere not filtered. These are superimposed on the direct
arrival of So mode. Other signals from edge lead-breaksources shown later in this paper also show this effect.
43

I- 15.24 -I
ClIl

38.1 em

1
0.04

O.oz
0

f.
ft

Eo

-O.oz oal?-0.04
-0.06

-0.04

-o.OS
o

W

~

~

~

~

W

W

-0.04

~

TIDIC(IU)

o

W

m

~

ro

fa

!

-O.os
100

T_(IU)

h)bl

Fig. 9 Direct arrival and oblique incident reflection ofsymmetric Lamb-mode AE signal generated by edge leadbreak source; a) Plate geometry and source/receiver locations, b) Filtered experimental and DFEM-predicted wave·
forms.

Fig. 10 a) Plate geometry, source/receiver locations, andpropagation paths for mode-converted Rayleigh and shear
waves, b) Filtered experimental and DFEM-predictedwaveforms.
are shown in Fig. 9b. This figure shows the signals out to apoint in time. during which only these two arrivals (direct
wave and sidewall reflection) are present. Very good
agreement between the DFEM and experimental waveforms
is demonstrated. The discrepancy of the presence of somehigher frequency components of the Ao mode of the experimental signal is again noted.

Since me DFEM allowed me source to be positioned exactly at the mid-plane, a corresponding anti-symmetricmode component in me theoretical signal is not observed.
Figure 8 shows the unfiltered experimental and theoretical
waveforms for the source, receiver, and plate geometry
shown in Fig. 6a. The unfiltered, larger-amplitude Aomode in the experimental signal is clearly seen.

When the time scale for Fig. 9b is expanded as shownin Fig. lOb, two additional signal arrivals are noted and arelabeled 3 and 4. The arrival times for signals 3 and 4 were
found to be too early to have been created by reflections
from either the back edge or the far side edge of the plate.
These two signal arrivals are particularly interesting in thatthey appear to be reflected signals with paths as shown in
Fig. lOa, which have been mode-converted at the edge andcomer of the specimen, respectively. Several aspects of the
DFEM signals were examined to reach this conclusion.
These included the arrival times and amplitudes of signals 3
and 4 at different sensor locations as well as the in-plane
displacement components for positions along the propagation paths to the edge and the comer.

For me case of oblique incident reflection of me SoLamb mode, we used an edge lead-break source on the 38.1
x 50.8 cm plate. As shown in Fig. 9a, the source was positioned at 15.2 cm from one comer. This position was used
so that the reflection from only one side could be obtained
unobstructed by reflections from the backwall or other side.
Also shown in Fig. 9a are the positions of the source andthe ray paths for the direct (1) and reflected (2) signals.
The source-to-receiver distance for the direct arrival was
7.62 em and for the reflected signal, it was 31.4 cm. The
angle of incidence and reflection for this reflection withrespect to the normal to the plate edge was 14". The bandpass-filtered experimental and DFEM predicted waveforms
44/ - 15.24 CIll ~I1

38.1 emT
7.62cm
~

3)

-:

.:;.

gl

6

I~

~~

4

+

ot--+--+--+---+---+--~

8

0.06

-J

I 0.02
!

E

.s.
ii

0.04

0

'§.

0.02

-0.02

0

-0.04

I

is

1!
-=

J

.

m

11:So
:I

n

"Eo
t::'
i
R
:I
!!

::.

S

-0.06

-0.02
-0.04

0

20

40

60

80

!

-0.08
100

TIIllC (\lS)

propagation path and modes. which gave such a constantarrival-time difference between calculated and measured
values for the different sensor locations. was that of a bulk
shear wave propagating out to the edge. mode-converting toa longitudinal wave with the appropriate change in angle.
and then returning to the receiver as the So mode. Figure
11b shows the time difference between the measured peakof signal 3 arrival time, and the calculated arrival time forthis mode-converted reflection. Other possible paths considered include (a) shear and Rayleigh waves propagating
along the edge. which affects the mode conversion at thecomer. and returning as the So mode. and (b) a shear mode.
which is mode-converted at the edge without the expectedchange in angle of reflection. None of these gave expectedarrival times consistent with those measured for signal 3 atthe different sensor locations.

+

l~
~Q 2

6

1 0.04

Fig. 12 Filtered experimental and DFEM-predicted waveforms for source. receiver. and plate geometry as in Fig.
lOa except with receiver positioned at 10.2 em propagation
distance from source.
+

+

0.08

10
12
14
16
Source 10 Sensor Dislance (em)18b)

Fig. II a) Source and receiver positions for arrival timemeasurements to evaluate propagation paths for arrivals ofmode-converted reflection. b) Difference in arrival time
between that determined from peak of signal 3 (DFEM) andcalculated from shear/longitudinal mode-converted propagation path using shear and extensional plate velocities.
The sensor locations. for which the arrival times andamplitudes were measured from the DFEM signals (equivalent to those of signals 3 and 4 in Fig. lOb), are shown in
Fig. Ila. They were such that the direct path from thesource to the sensor ranged from 7.62 cm to 17.8 cm in 2.54
cm intervals. Because of the smaller amplitude of arrivals 3
and 4 and their superposition with higher frequency components of other weaker signals, it was not possible to determine their exact first arrival time. Instead, the arrival
times of the peaks of signals 3 and 4 were measured at thedifferent locations. These arrival times were compared tothose calculated using the known shear (3100 m1s) andRayleigh velocities (2894 m1s). and the plate theory extensional velocity (5403 m1s) which serves as an approximation for the velocity of So-mode fust arrival. In comparison with the calculated arrival times. it was expected thatthe values measured from the peaks of the DFEM signals 3
and 4 would be slightly later. However. this time difference
between measured and calculated arrival times should beconstant for the different sensor locations. For signal 3, theIn addition to the arrival time, the amplitude of thissignal component was also measured at the various sourceto-sensor distances. These were examined and compared to
expected changes in amplitude for mode-converted longitudinal waves as a function of the angles of incidence of theshear mode as discussed by Graff (1991). At the 7.62-cm
distance used for the signals in Fig. lOb. the angle of incidence for the shear mode with respect to the normal to theedge is 9.34°, and the angle of reflection for the modeconverted wave is 18.55·. The total propagation distance is31.5 em. The amplitude of this mode-converted reflection
is quite small and barely noticeable. However, when thesignal is examined with the sensor at a distance of 10.2 emfrom the source on the edge. the amplitude of this reflection
is larger as shown in Fig. 12. In this case, the angle of incidence is 12.37· and the angle of reflection is 24.10" with a
total propagation distance of 32.3 em. Although not shown
here. it was confirmed that the amplitude of this modeconverted signal continues to increase when the sensor-to45

source distance, and thus angle of incidence of shear mode,
is increased. This increase of signal amplitude as a functionof increasing angle of incidence is consistent with the amplitude relations for the mode conversions from bulk shear
waves to bulk longitudinal waves. Another factor, whichmight also be contributing to this increase in amplitude formore distant sensor positions, is the shape of the radiation
patterns for shear modes from a point monopole source, as
discussed by Scruby (1985).

0.5

I

0

J

-0.5

~

is

For such a mode conversion to occur at the plate edge,
it is noted that the shear wave must be polarized verticallywith respect to the plate edge. This shear wave is also p'olarized horizontally with respect to the plane of the plate.
The in-plane displacements, perpendicular to the propagation direction, were also examined in the DFEM calculation
to evaluate the existence of a shear mode with horizontal(with respect to the plane of the plate) polarization. These
were examined at the modeled sensor locations, as well as
at positions along the path of the shear wave, which would
propagate out to the plate edge, be mode-converted andreturn to the sensor at 7.62 em distance from the source.
For the DFEM signals, which propagated along a direct
path to the modeled sensor locations. no transverse, inplane displacements corresponding to a shear mode arrival
were observed. This is to be expected if the radiation patterns, as discussed by Scruby (1985), for shear modes froma point monopole type source are considered. Shear modes
from such a source radiate out at angles with respect to thedirection of the monopole with no component propagating
directly along the direction of the monopole force (Le., directly ahead of the source). The transverse, in-plane displacements for propagation along the direction of the shear
wave to the edge did show an arrival that corresponded tothe shear wave arrival time. However, analysis of these inplane displacement components was complicated becauseof other shear modes with different polarizations, and theirinteractions with the plate surfaces.

B
'I:!

-t

J

-t.5

-2 L....-_.l...-_L....----'L....-_L....----'L-~""__1

o

5

10

t5

20

2S

30

35

1tme(IlS)

Fig. 13 Theoretical in-plane displacement for position at
mid-plane of plate edge at a distance of 7.62 em from edge
break source showing the arrival of Rayleigh wave component.
6. Summary and Conclusions
The results of this study validate a three-dimensional
dynamic fmite element method (DFEM) for predicting AE
waveforms in finite plates including reflection components.
Simulated AE sources (lead breaks) were modeled and used
for the experimental confirmation. Both lead breaks on thesurface and the edge of thin aluminum plates were considered. In thin plates, surface lead breaks preferentially generate the Ao-Lamb mode while lead breaks on the edge near
the mid-plane of the plate preferentially generate the So
mode. It was demonstrated theoretically and experimentallythat the edge break source also generates a Rayleigh wave,
which propagates along the plate edge. This Rayleigh wave
interacts at the plate comer to produce a mode converted So
wave. Also observed theoretically and experimentally wasa mode-converted reflection caused by shear waves generated by the edge lead-break source. These waves weremode.converted at the sides of the plate upon reflection tolongitudinal waves, which then propagated through the thinplate as the So mode. An absolutely calibrated, wideband
sensor was used for all experimental measurements. In allcases, good agreement was obtained between the DFEM
predictions and experimental measurements.

A similar analysis of the arrival times of the waves
designated as signal 4 in Fig. lOb and 12 at different propagation directions was completed. From this, the path andmodes of signal 4 were found to be consistent with aRayleigh wave propagating away from the source on theplate edge, which was mode.converted at the plate comer
and propagated to the modeled sensor location as the So
mode. The in-plane (and normal to the plate edge) displacement from the DFEM model was examined at multiple
locations along the plate edge to verify the existence of aRayleigh wave. Figure 13 shows the in-plane displacement
component for a position at the mid-plane of the plate andon the edge at a distance of 7.62 em from the edge leadbreak source. The large amplitude Rayleigh wave is clearlypresent in this signal. The dynamics of this mode conversion at the comer are not as well understood. However, theagreement between the DFEM and experiment is again
good for these mode-converted reflections.

The validation of the DFEM for predicting the reflections of AE signals in plates is an important step towardmaking it a useful tool for predicting AE wavefonns in realpractical structures. In such structures, signal reflections
are often a significant contribution to the waveform becauseof structural complexities such as holes, free edges. welds,
joints, etc. The effect of reflections on AE waveforms iseven more pronounced in laboratory specimens such as
coupons, which usually have very small lateral dimensions.
Further work is necessary, however, to validate the model
for predicting waveforms in other practical situations that46

include specimens with thickness changes, welds, varying
and/or anisotropic material properties.

Methods for Predicting Acoustic Emission Waveforms,"
Submitted to the Journal of Nondestructive Evaluation.

References

C. B. Scruby (1985), "Quantitative Acoustic Emission
Techniques," Nondestructive Testing, Vol. 8, Academic
Press, Inc., London, pp. 141-208.

F. Breckenridge, T. Proctor, N. Hsu, S. Fick, and D. Eitzen(1990), "Transient Sources for Acoustic Emission Work,"
Progress in Acoustic Emission V, eds. K. Yamaguchi et al.,
JSNDl, Tokyo, pp. 20-37.
J. Gary and M. A. Hamstad (1994), "On the Far-field Structure of Waves Generated by a Pencil Lead Break on a Thin
Plate," J. Acoustic Emission, 12(3-4),157-170.
D. Guo. A. Mal, and K. Ono (1996), "Wave Theory of
Acoustic Emission in Composite Laminates," J. Acoustic
Emission, 14(3-4), 519-S46.
M. R. Gorman (1991), "Plate Wave Acoustic Emission," J.
Acoust. Soc. Am., 90(1), 358-364.
M. R. Gorman and W. H. Prosser (1991), "AE Source Orientation by Plate Wave Analysis," J. Acoustic Emission,
9(4),283-288.
M. R. Gorman and W. H. Prosser (1996), "Application ofNormal Mode Expansion to Acoustic Emission Waves in
Finite Plates," J. Appl. Mech., 63(2),555-557.
K. F. Graff (1991), Wave Motion in Elastic Solids, Dover
Publications Inc., New York.
M. A. Hamstad, J. Gary, and A. O'Gallagher (1996), "Farfield Acoustic Emission Waves by Three-Dimensional Finite Element Modeling of Pencil-Lead Breaks on a Thick
Plate," J. Acoustic Emission, 14(2),103-114.
M. A. Hamstad, J. Gary, A. O'Gallagher (l998a), "On
Wideband Acoustic Emission Displacement Signals as a
Function of Source Rise-Time and Plate Thickness," J.
Acoustic Emission, 16, 5251-5259.
M. A. Hamstad, J. Gary, A. O'Gallagher (1998b), "Modeling of Buried Acoustic Emission Monopole and Dipole
Sources with a Finite Element Technique," to be submitted.
W. Huang (1998), "Application of Mindlin Plate Theory toAnalysis of Acoustic Emission Waveforms in Finite
Plates," to be published in the Proceedings of the Review ofProgress in Quantitative Nondestructive Evaluation, 17.
H. Kolsky (1953), Stress Waves in Solids, Dover Publications Inc., New York.
W. H. Prosser, M. A. Hamstad, J. Gary, and A. O'Gallagher(1998), "Comparison of Finite Element and Plate Theory47

42nd AEWG Meetingused to discriminate weak AE signals from the bubbles. which were originally detected during corrosion by Rettig

and Felson.
Zonal and Database Match Approach: A Potential Method for AE Source Location on Water
Filled Tank, H. Warren Shen and Adrian A. Pollock. Physical Acoustics Corporation
The traditional zonal approach assumes that the AE source should fall within the zonal area where the first triggered
sensor is located. However, in the railroad tank test that involves water, the above is not always true. We found in
our study that many sensors were first triggered by water-borne AE signals generated on the other side of the tank.
The Zonal and Database Match approach looks at the entire tank for possible sources of the triggering patternobserved from the 12 channels.
The tank surface is first divided into 12 hexagons where each hexagon contains a sensor. Each hexagon is further
divided into 6 triangles (A. B, C, D. E, F) and each triangle contains 2 median triangles (Left and Right). Four small
right triangles (1, 2. 3, and 4) are then further divided from a Left and a Right median triangle respectively. In thatcase, the entire tank car has 576 small triangles. and each small triangle can now be uniquely identified. For example
4DL I indicates Sensor #4, D Triangle, L median triangle, small triangle # 1. Each one of the 576 small triangles
should cover an area ranging from 313 in 2 (for a 10.000-gallon Railroad Tank Car) to 634 in (for a 29,OOO-gallon
Railroad Tank Car).
The Zonal and Database Match approach considers each of these 576 triangles as a possible source zone for an
observed AE hit-set. For each zone, the computer can calculate the possible range in arrival times of four kinds ofwaves (extensional, flexural. water-bome direct and waterborne reflected) at each of the twelve sensors. This
theoretical pattern of arrival time range is unique for each small triangle. When an AE signal hits the system. thecomputer will try to compare the real hit pattern with each of the theoretical patterns and locate one small triangle
out of the 576 with the best match. In simple terms. we will have the computer calculate the arrival time range
patterns for all the 576 small. triangles, and then in order to process an AE hit-set the computer will look as its
arrival time pattern and see which of the 576 small triangles gives a good fit. This is the Zonal and Database Matchapproach.
AE Source Location with Dual-wave Mode Method, Maochen Qe. GE Acoustics Technology
The dual-wave mode method refers to an AE source location method that utilizes two different types of wave arrivals
with distinctive travel velocities. From a practical point of view, extensional and flexural arrivals are the mainconcern of these two different wave types. In the case of liquid filled tanks, they may be metal borne extensional
waves and liquid borne waves. The need of the dual-wave mode method is due to a number of practical and theoreticalreasons. First, the arrivals that can be used for AE source location is highly mixed. Practical experiences haveindicated that it is almost equally possible for both extensional and flexural wave triggering in an automated arrival
timing system. The traditional AE location methods are limited to handle a single type of arrivals and. therefore.
have to face one of two following choices: assuming all arrivals as extensional waves or simply using one type ofarrivals. In the first case, it would introduce large and systematical errors into the input data, which has been a
primary reason responsible for the poor AE location accuracy in the past. In the second case, it would lead to atremendous loss of original data.
With the dual-wave mode method, one could use both extensional and flexural wave arrivals from the same sensor.
Therefore. the second advantage of the dual-wave mode is that one could double the location information with thesame hardware configuration. This is particularly important when a monitoring system consists of the very limited
number of channels or when the manual analysis is needed to analyze those critical AE events.
The third important reason to use a dual-wave mode method is due to the location mechanisms. The location
mechanisms for methods using one type and two types of arrivals are different. In general, the methods using asingle type of arrivals have a good accuracy control in the central areas between sensors, but poor control in the areas
near the sensors and the areas outside the array. However. when two types of arrivals are used simultaneously, it adds
another location mechanism by increasing the accuracy control in the radial direction. Therefore. one would normally
have the better location accuracy with a dual-wave mode method. In this presentation. ample practical cases will be
used to demonstrate how our dual-wave mode method has dramatically improved AE source location accuracy.
4B

Classification of Acoustic Emission Signatures
Using a Self-organization Neural Network
Tinghu Yan, Karen Holford, Damian Carter and John Brandon
Abstract

clair et al., 1977; Bassim, 1987; Bassim et al., 1994).
However, the successful application of these quantitative
results in situ relies on the reliable detection and identification of AE of interest, i.e., elimination of extraneous noisefrom sources other than fatigue crack growth.

Acoustic emission (AE) testing is a promising technique for use in structural health monitoring. A criticalfactor for the successful implementation of this technique inboth laboratory and field is the ability to achieve reliable
source identification, which can be best achieved throughadoption of pattern recognition techniques. In particular,
there is a need for discrimination of AE signals fromfatigue crack growth and extraneous AE in order toquantitatively evaluate fatigue crack growth. This paper
presents a waveform descriptor-based classification of AE
signals during laboratory fatigue testing of a full-scale steel
bridge girder using a self-organization neural network. The
pitfalls of AE signal classification based on automaticallyextracted waveform features are demonstrated through theconsideration of typical signals from individual sources andthe complications during the AE signal acquisition andwaveform feature extraction process. Careful interpretationof the classification results is emphasized in order to
identify the likely origin of AE data contained within eachclass.

Perhaps noise is the most difficult problem of AE
monitoring both in the laboratory and in the field (Gong etal., 1992; Hamstad and McColskey, 1997). It has beenreported that extraneous AE can make up more than 98% ofdetected AE when monitoring a steel railroad bridge in thefield (Gong et al., 1992). A wide range of potential noise
sources may occur in practical AE monitoring, which
include mechanical noise, such as load causing vibration
and rubbing of structural components; environmental noise,such as rain. wind, and air-borne acoustic noises from otheracoustic activities; and electrically induced noise, such as
electromagnetic interference (EMI). Using modern
integrated pre-amplifiers, differential transducers. proper
grounding and screening of AE equipment. the problem of
EM! has been vinually eliminated. For continuous monitoring of large structures in the field, environmental noise stillpresents a problem. to which careful attention must be paid
(Gong et al., 1992).

1. Introduction

Mechanical noises of vibration generally present AE
events of long rise time, which are located in a lower frequency band. This noise may be eliminated by the properchoice of operating frequency to attenuate low frequencies.
or filtered out through time-domain feature analysis as wellas through high-pass filters. However, it has been observedthat mechanical noises from rubbing of moving parts andfretting of crack faces present high-frequency signals, which
are similar to the AE signals emitted from fatigue crack
growth (Gong et aI., 1992). It is then difficult to eliminate
this type of noise through frequency filtering without losing
the AE signals of interest from crack propagation.

Acoustic emissions (AE) or stress waves are generated
during irreversible processes either external or internal to amaterial. The stress waves can be detected and monitored
by sensors to yield information about source location andsource characteristics, which in tum can aid damage assessment The detection and evaluation of fatigue crack growthin steel structures by the AE technique has been of greatinterest for practical non-destructive testing (NOT) applications since the early 1970s (pollock and Smith, 1972; Gong
et al., 1992; Sison et al., 1996; Yoon et al., 1997). Many
approaches have been proposed to correlate fatigue crack
growth behavior with AE parameters, including AE events,
counts, amplitude and energy. AE count rate has beenfound to be quite useful for modeling fatigue crack growth(Harris and Dunegan, 1974; Monon et al.• 1974; Sin-

Mechanical noises, such as vibration and rubbing ofmoving parts. may also be stopped at the source duringlaboratory testing by using damping materials between
specimen and jig contact point, or eliminated by space discrimination through the use of guard sensors and spatial
filtering. A guard sensor is an AE transducer placed at alocation outside the mounting area. Any signal that firstintercepts this sensor wiIl be considered an invalid signal
and will be discarded. For spatial filtering, two sensors

Received 9 January 1999. Tinghu Van is affiliated with Department of Mechanical Engineering, Southeast University, Nanjing
210096, P. R. China. Karen Holford (Holford@cardiff.ac.uk),
Damian Carter and John Brandon are with Cardiff School ofEngineering, University of Wales Cardiff. Queen's Buildings, The
Parade, PO Box 685, Cardiff CF2 3TA, UK.
Journal of Acoustic Emission
Volume 17, Numbers 1-2

49

0730-0050/99/$5.00/17049-059
Copyrigh~1999 Acoustic Emission Group2. Experimental Procedure

are placed so that if the signal is emitted from a crack at aknown location, the differences of the arrival time at two
sensors would fall in a particular "window" and allplausible background noise sources would give arrival-timedifferences outside the "window". These techniques arequite successful where a noise source is separated from the
known monitoring area. In addition, meticulous mountingof AE sensors and careful attachment of cables also help
reducing other types of mechanical noises caused by thevibration of electrical connections and cables.

The measurement of AE signals was conducted using a
LOCAN 320, which is a multiple channel commercial AE
system from PAC (Physical Acoustics Corp., USA).
Resonant transducers R15D and R151 (PAC) of 150 kHz
nominal center frequency were mounted on the web of afull-scale steel bridge girder, coupled with silicon grease.
Pre-amplification of the sensor signal with 40 dB gain wasmade with integral internal preamplifiers for the RISI
sensors and using external PAC 1220A preamplifiers for
the RI5D sensors. The threshold used for AE signal
acquisition was 45 dB, and system timing parameters; PeakDefmition Time (pDT), Hit Definition Time (HOT), and
Hit Lockout Time (ID..T) of 500, 1000 and 1000 J,ls,
respectively, were implemented. Three-point bending
cyclic fatigue testing was conducted at a stress range of 10262 MPa, load range of 10-230 leN, and frequency of 1.5
Hz, using a Dartee 500 hydraulic fatigue machine.

For laboratory cyclic fatigue testing, a combination ofsource location and load-controlled gating techniques
provide a further means for discrimination of AE fromfatigue crack activities (including both crack propagationand crack surface fretting) and other extraneous noises. Asa crack is a concentrated source from a fixed location, only
AE signals originating from a tightly clustered locationneed be considered, while randomly scattered AE signals
can be ignored. In addition, fatigue crack growth usuallyoccurs only under peak tension loading cycles. In this case,the load-controlled gating technique can be used to record
AE signals only when the load approaches its peak level,thus eliminating a large amount of noise events producedby frictional rubbing at the crack surfaces during
intennediate loading cycles.

Figure I shows the geometry of the girder and theexperimental set-up and Fig. 2 shows the photograph of atypical girder after testing. The girder was fabricated from
grade 43C steel plate by metal-inert gas (MIG) welding
with approximately 6 mm fillet welds. To minimize
distortion and residual stresses, the components of thegirder (flanges, webs and stiffeners) were initially tackwelded at the comers and centers of each panel. Continuous
welds were then run between the tacks, following as closely
as possible the methods used typically in the bridge
construction industry.

In the field, the elimination of extraneous AE signals
other than those from fatigue crack growth is much more
complicated. The location of a crack, if it exists, isunknown prior to testing, and due to the dispersion of the
AE waves originating from either fatigue crack growth or
extraneous AE sources at various distances and the poor
accuracy of arrival time-based source location techniques
used in a typical fIXed-threshold AE system, the means ofguard sensors, spatial filtering, andlor source location may
not be so effective. Furthermore, in field testing, theunpredictable fatigue-load spectrum rather than smoothsinusoidal pattern used in the laboratory, makes it difficult
to apply the load-gating technique to record AE signals
only during the peak load

Seven AE signal parameters as well as time, load andchannel number were measured in real time by the PAC
LOCAN 320 AE data acquisition system, and the AE signal
waveforms were recorded using a PAC TRA 212 with the
PAC LOCAN 320. A sampling rate of 5 MHz and data
length of 4096 was used for acquisition of signals fromcrack activities, and 2048 for EM! signals. The AE
waveform descriptors examined in this study include Risetime (RT, J,lS), AE Counts (CNTS), Energy (ENRY),
Duration (DUR, J,ls), Peak amplitude (AMP, dB), Averagefrequency (AVF, kHz), and Counts-to-peak (CN1P).

In this paper, waveform descriptor-based classification
using a self-organization neural network for AE signals
from EMI, fatigue crack growth, and crack surface fretting
during a cyclic tensile loading of a full-scale bridge girder
in a laboratory environment is investigated. The pitfalls of
AE signal classification based on automatically extracted
wavefonn features are demonstrated through an importantconsideration of the typical signals from individual sources.
The complications during AE signal acquisition andwaveform feature extraction process are reported, resulting
in a recommendation for careful interpretation of the
waveform descriptor-based classification results.

3. Pattern Recognition Analysis of AE Signals
AE signals provide information concerning the sourceof the emission and the material or structure under
examination and generally one can recognize visually theexistence of certain waveform categories and correlatethem to underlying source mechanisms (Ono, 1997).
Although this can be very time consuming as the number ofAE signals is often large. especially in continuous
monitoring, manual waveform classification has to be donein some cases (Hamstad and McColskey, 1997). With
modem pattern recognition analysis techniques, the50

This side wasconstrained to ensure
no failure occurred.

1-4: R15D, LOCAN
5-6: RI5I, TRA 212

800mm

3

1150 mm~I

Fig. 1 The experimental set-up.

F "I
.~

Fig. 2 Photograph of typical tested girder.
algorithms has been successfully applied for characterizing
different failure mechanisms of glass fiber/epoxy specimens during tensile loading through the classification of
detected AE signals (Anastassopoulos and Philippidis.
1995), and more recently, a clustering algorithm based onlearning vector quantizer (essentially a self-organizationneural network) has been employed for recognizing thedifferent damage modes of carbon/carbon laminates during
tensile loading through the clustering of similar AE signals(Philippidis et aI., 1998). A number of other authors havealso investigated the potential of neural networks in AE
source identification. An adaptive Resonance Theory(ARn family of neural networks has been utilized toanalyze AE signatures of loose parts within a nuclear
reactor coolant system and to classify the patterns of AE
signals associated with different masses and impact energies of the loose parts (Keyvan and Nagaraj. 1996; Keyvanclassification of AE signals has become less labor
intensive.
Most recently, commercial software packages designed
for classification or for noise discrimination of AE signals
have become available, such as ICEPAK (Tektrend International Inc., Canada) and PAC-PARS (physical Acoustics
Corp., USA). which include standard statistical and error
back-propagation neural network classifiers; and VisualClass™ (Vallen Systeme GmbH, Germany), which performs classification based on multiple FFr (Fast Fourier
Transform) of the signals in staggered time windows. All ofthese are very useful for classification of AE signals and, of
these methods, the k-nearest neighbor classifier has been
proved most effective (Ono, 1997). In addition, in composite material testing, a clustering method based on the
combination of max-min distance and modified Forgy51

terization of transient signals than the power density spectra
obtained from the traditional Fourier transform. WT has
been successfully applied to classify the AE signals emined
during tensile loading of a longitudinal glass-fiber reinforced composite specimen and four types of visually observed WT spectrograms have been correlated to the known
fracture modes excellently (Suzuki et al.• 1996). Furthermore. in order to classify the WT spectrograms automatically, Kinjo et al. (1997) examined three 3-D image
classification methods, i.e.• classical matched filtering ofgiven images (MF). matched filtering of Laplacian images(MF-LI), and Fourier phase correlation (FPC). and it was
found that the FPC method was superior to the other two
methods in its higher classification counts and discrimination capability.

and Pickard, 1997). An error back-propagation neuralnetwork has been utilized to solve the inverse problem in
AE. i.e.. determining the information of the AE source
waveform, which is independent of the propagation media.
the AE sensor and the AE measuring system, from thedetected AE signal. It was demonstrated that the appropriate source waveform associated with mode I crack
extension can be effectively detennined by a trained multilayer neural network (Yuki and Homma, 1996). Again. it
has been demonstrated that an error back-propagationneural network can locate AE sources in the aluminum
plate and bolted channel structures (with and without
damping material between the joints) far more accurately
than the classical time difference method. especially in
structures with discontinuities where the wave velocity is
no longer constant in all directions (Venkatesh andHoughton. 1996).

The current study focuses on the investigation of
waveform-descriptors based classification for AE signals
using a self-organization neural network, while the AE
waveform-based analysis using wavelet transform fordiscrimination of extraneous AE signals during the fatigue
testing is investigated further in the forthcoming
publication by the authors.

However. classification and interpretation of AE signals
based on AE waveform descriptors must be conductedcarefully, as many factors may influence the waveformfeatures of AE signals. These include the characteristics ofsource itself and material. the path and distance from thesource to the AE transducer. geometrical factors of thestructural members, frequency response of the AE sensor, as
well as the set-up of AE measuring system, such as
threshold and system timing parameters. Thus, the waveform features of AE signals from a certain source may
change in both the frequency and the time domain, depending on the structural factors and processing methods. This
makes unsupervised classification methods more suitable
than supervised classification methods for AE signal
classification in practical structure monitoring, as the trained
patterns for supervised classification methods are usuallyobtained from testing of model specimens. In doing so, one
presumes the existence of similarity between the generated
AE signals and the propagation characteristics of eachfailure mechanism in the model specimen and the actual
structure in situ. and the coherence of AE sensors, preamplifiers. the AE measuring system as well as theircorresponding set-ups.

4. Self-organization Neural Network
The self-organization neural network was introduced
by Kohonen in 1981 (Kohonen, 1989). It consists of two
fully connected layers (input-output) of neurodes, and theconnections arriving to neurode j in the output layer bearweights that collectively fonn the coordinates for thecluster center at neurOOe j. The input layer is commonly
arranged in a linear or rectangular array. while the output
layer may be configured in rectangular or hexagonal arrays.
The geometry chosen to represent the output layer
determines the configuration of the "neighborhood" of each
oUlput neurode (Fig. 3). The topology of self-organizationneural network (SONN) used in this paper is iUustrated inFig. 4.
Initial cluster center coordinates are randomly picked
through the generation of random connection weights.
When an input pattern is presented to the input layer, each
Euclidean distance between the input pattern and thecorresponding connection weight vector from the input
neurodes to a particular output neurode j is calculated andcompared to produce a single winning output neurode withminimum Euclidean distance. The winning output neurode
indexes the cluster center. whose coordinate weight vector
matches the input pattern most closely.

Another aspect of AE signal classification is that it isbeing recognized that waveform-based analysis of AE
signals obtained using either resonant or wideband sensors
offers advantages to traditional AE waveform descriptors
based classification methods for discrimination of extraneous AE (Hamstad and McColskey, 1997; Keyvan andPickard, 1997; Suzuki et at.• 1996; Kinjo et al., 1997). andthat the wavelet transform (WT) is becoming anotherpowerful tool for AE signal discrimination. WT allows thedetermination of the frequency spectrum as a function oftime with optimized resolutions both in time and frequency
domains using width-variable wavelets as the basic functions. The resultant wavelet transform coefficients in thetime-frequency plane provides more informative charac-

Once the winning output neurode is found. the weightsof each connection arriving to output neurodes in theneighborhood of the winner are adjusted by
AWji =TI(t) ~i(1)
where TI(t) is a learning rate decreasing with time, O<T)(t)
52

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

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0

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in the neighborhood of the winner is repeated for an
appropriate predefined number of iterations. The result isthat the values of the weights form clusters. which reflect
the probability density of the input patterns. while theoutput neurodes that are in topologically near each otherreact similarly to similar input patterns (Eberhart andDobbins. 1990).

0osingle~neurode0neighborhood

5. Results and Discussion
In the classification experiment. 122 samples fromsensor 5. including 58 crack growth and 64 crack surface
fretting AE. were extracted using a combination of information on source location. load cycle occurrence. expertiseof the general signal characteristics from different sources
and their associated waveforms; another 21 spurious AE
signals from electromagnetic interference (EMI) were introduced by interfering with the transducer cables without
cyclic loading. The 143 samples were amalgamated in random order to a single data file for subsequent classification
bytheSONN.

Fig. 3 Neighborhood of neurode in rectangular array.

0

0

0

0
0

0

0
0

0

0

0

output layer

0

Figure 5 shows the SONN classification topology
result with 9 output neurodes. The figure shows how four
significant classes of data are recognised by the SONN.
containing 16. 56. 41, and 30 input patterns, respectively.
The typical waveform feature data characteristics of thesefour classes are listed in Table 1.

connection
weightinput layer
Fig. 4 Self-organization (!Cohonen) neural network (SONN)
topology.

11(t)=110(1 - tff)Go<1; i =O.I ..... n. n is the number of input neurodes. j>O. theindex of output neurodes; t is the iteration number increasing upon the introduction of all input patterns; and Wji is thei-th coordinate difference between the corresponding
weight vector of output neurode j and the input pattern. The
learning rate function is generally defined as

III

o

o

o

(2)

where 110 is the initial learning rate constant. T is the timeinterval. during which the learning rate decreases to zero.

o

All input patterns are sequentially presented to the
input layer of SONN. and the process of calculating Euclidean distances. finding the winning output neurode. aswell as adjusting the connection weights of output neurodes

Fig.5 SONN classification topology result for 143 AEwith 9 output neurodes.

Table 1 Typical characteristics of the four classes of AE identified by the SONN
Class
No
I
II
ill
IV

Patterns

RT

CNTS

ENRY

DUR

AMP

AVF

(US)

(dB)

(kHz)

1-71
1-36
98-119105-123

(V·s)
2-4
1-42
230-706
255-576

2-86
12-358756-819818-819

45-56
52-80
80-95
80-87

278-82248-167125-154128-150

(11S)

16
56
41
30

1-27
5-210
26-204354-756

53

CNTP
1-13
1·23
3-27
51-106quency may be misleading and adversely affect theclassification process. It is clearly unreliable for further
identification based on the extracted waveform features.

Consideration of the relative associated waveformfeature data from the four classes can provide a hypothesis
as to their likely identities and corresponding waveformcharacteristics. However, identification and interpretationof AE waveform descriptor-based SONN classification
results may not be so simple. Determination of the source
contents of these classes is less straightforward and requires
an understanding of the nature of the individual source
events and the implications of the AE signal acquisition andwaveform feature extraction process. The complications are
best demonstrated through a consideration of typical signals
from the individual sources.

However if the PDT. HOT and HLT are set at more
appropriate values to cope with the type of cracking AE. forexample. HDT=200 I.I.s. then this may corrupt the measurement of waveform features from longer duration signals.
Figure 7 shows two typical signals from crack surface
fretting. Two observations are significant. Firstly. bothsignals are relatively "flat" in comparison with crack
growth emissions. consequently their peaks are less welldefined and the two waveforms will yield significantly
different measurements of rise time and counts-to-peak,which may affect classification reliability. Secondly. both
waveforms occupy the entire window length. which. inhindsight. is inappropriately short for fretting signals andlimits the maximum duration to approximately 820 f.I.S due
to the adopted sampling rate (5 MHz) and data length

AE from fatigue crack growth can arise from a multiplicity of sources, including plastic zone expansion. inclusion fracture and ductile tearing. Figure 6 illustrates four
typical AE signals associated with fatigue crack growth andhighlights several critical considerations. The flJ'St point tonote is that the emission category broadly termed "crack
growth" can embrace two basic types of source behaviour.
The waveform in Fig. 6(a) illustrates a "classic" crack
growth event. which has well defined peak and decay
characteristics. and is of short duration and fast rise time.
The lower signals in Figs. 6(b). (c) and (d) also show three
waveforms from crack growth and demonstrates anapparently unique property; its ability to produce cascaded
bursts of emission in rapid temporal succession with individual bursts having similar waveform feature properties tothose of the isolated crack growth event. The "rapid cascaded burst" phenomenon is highly significant since it
presents an opportunity for crack growth source identification based on an event rate criterion. and the time lapse
between bursts may also offer an insight into the activity
level of a crack. However. this relies On the assumption that
the AE hardware and software is capable of resolving suchrapid bursts of AE and correctly identifying the signal as
being composed of separate hits of AE.

(4096).

Evidently. there is a problem in defining these hit
timing parameters. In order to identify and describe theburst-type crack growth AE. short HOT and lfi..T aredesirable; however, this adversely affects measurement of
the long-duration fretting signals.
The complications of AE data acquisition and feature
extraction process are not confined to crack growth andfretting signals. Figure 8(a) illustrates a typical EM! signal.
while Fig. 8(b) shows a rapid and intense EM! signal.
which can produce high measured values in energy. counts,
duration as well as amplitude. Another important point tonote is that. although crack growth is a potentially uniquemechanism of rapid burst emission. it is not the only sourcethat can give rise to the detection of ''rapid burst" phenomena. Figures 8(c) and (d) illustrate that EM! may also
replicate this behavior and produce similar waveform features to the AE signals arising from crack growth.

In defining a hit, the AE system uses three userpredefined timing parameters for AE waveform measurement. i.e.. Peak Definition Time (PDT). Hit Definition
Time (HDT). and Hit Lockout Time (lR.,T). Consider thecase where the PDT. HOT and lll..T were set at 500, 1000
and 1000 flS. the AE signals of Fig. 6(b). (c) and (d) will berecorded as a single hit respectively by the AE system andtreated accordingly by the waveform feature extraction
routine. This can result in measurement of erroneous waveform feature values, since under the current system timing
parameters set-up. the software cannot unambiguously
isolate the individual bursts into separate hits. Consequently. measured values of rise time. counts, duration.
energy. peak amplitude. counts-to-peak and average fte-Thus. it would not be always reliable to classify AE
signals based on automatically extracted waveform features. The limitations behind the technique to produce thesewaveform feature data must be considered. especially as.
due to inappropriate values of HOT and m...T. rapid burstsof cracking AE or EM! signals may be recorded as a single
hit. resulting in measurement of erroneous waveformfeature values and adverse affects on the classification.
Returning to the waveform descriptor-based classification
results of the SONN. it is evident that some confusion of
the number and identity of sources can occur.

54

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55

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Time (usee)Fig. 7(a) and (b) Crack surface fretting AE (1) and (2).
The 16 patterns of class I can readily be identified as
EM! signals due to their low measured values of energy andamplitude and, most significantly, their very high averagefrequency. Unlike other AE signals. EM! is not the result of
excitation of the resonant transducer element via a mechanical coupling. but is the result of an induced COlTent in thetransducer-to-AE-system cables and is therefore not bounded by resonant behavior of the element. yielding measured
values of average frequency, which cannot be attained by
the narrow-band resonant transducers used in this experimenl. This phenomenon also produces low amplitudes
since the EM! signals may not be amplified by the integrated preamplifiers inside the AE sensors. However, due tothe complexity of EM! signals as shown in Fig. 8, themeasured values of rise time, counts, duration and countsto-peak may not be small.

resonant transducer element and thus the measured valuesof their average frequency may be bounded around thetransducer resonance frequency (in this experiment, i.e.•
ISO kHz). Another important phenomenon is that the
duration of this category of signal may be as long as theuser predefined HDT. Although crack surface fretting
signals are mainly contained in class ill and IV, someoverlap with rapid burst-type crack growth andlor intense
EM! signals may also occur according to their waveforms
as shown in Figs. 6 and 8, as these signals may produce
much higher measured values of rise time, counts, energy,
and duration than a single "classic" crack growth or atypical EM! signal.
Careful attention must be paid to the interpretation of
the 56 patterns contained within class n. It is most likelythat this class is primarily comprised of "classic" and"burst-type" crack growth AE. although some overlap with
EM! may also occur. Further examination of the waveformfeature data associated with their corresponding waveforms
reveals that three subclasses of AE contained within class II
and their typical waveform feature data characteristics arelisted in Table 2.

The 41 patterns of class m and 30 patterns of class IV
can be identified as mainly crack surface fretting signals.
due to their very high measured values of duration, energy,
counts and amplitude. Whilst the measured values of risetime of this emission category may occupy a wide range asshown in Fig. 7(a) and (b). so may the measured values ofcounts-to-peak. An important phenomenon of this categoryof signal is that they are the result of excitation of the56

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~

...
;
. _
......._

:·

-_~~

. _
...._

~~

61.5

.
......_ _

~~

163

~~

266

368

Time (usee)
2.9990~~"1.2908..
·.
................................
:............... !

!

rw ~

~

III

. :j ..J

0.4999·..··.. ·T..·..··

..

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.:
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·..!

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i

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.:

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.

·..· ···1 ···..·· ..
··~ ..·~.

T..·..· ·T·..· ·.. ·~ ..·~::'ii -8.4989

E<t -1.2909

Time (usee)

.
..
..
··
.
.
.
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.......····.···!··············.
. .;:···············i···············i···············I···············j············...
··
.
.
.
.
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.
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.
.
..:! .........•...•..
.
.
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8.8188 ............... ·:
.
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.
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l
;
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:
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::
·1···..···.. ·····1·. ..·..····.... ·1····. .. ·..·.. ···;·. .. ··· ..··..·..1.. .... ···· .. ·.. ···;....·..·..·....
'ii -8.99n::::.............. :
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<t -8.9188 ........... ···f···············~···············~···············~··~;~.
... .........;';; .
.

8 . 8 3 1 3 . - - - - -.. . . - - - - . - - - - . - - - - - . - - - - - . . .- - - - - - - - .---.....,

61.5

163

"

266

~

-

-

~

~

~

__

=

"

~

~

~

~

~

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-

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"

-49.S

"

~

-8 .9313 L....

368

Time (usee)
8 . 1 2 5 . . . . - - - - . . . - - - -....-....- - - - - - .-.

---

..

~

~

.a

- - - - - . -.- - . . . . . ,.

~~~~~~~~ ~~: ~ ~~~ ·~~· ~~ ~~: ~~~~ ~ ~~~ ~ ..~~~~~~:~~~ ~~ ~~:~~::~:~ ..~: ~ ~:: ~ ~~~:~~:: ~ 1::... ~~: ~ ~~ ~:: ~: ~ ~ ...~::::::: ~ ~~~: ~ ~~.... ~:::~ ~~ ~:: ~~~:::~~::~:~~:~
...
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···
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.
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.
..............................................................................................................................
..
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..
···.. ~: .. ~.:.: .. ~· 1· ~; · i · ~jj..
8.825 ........... I.;·!·
0.975

rr

:

'ii -0.925 .............. !

:!

..............'
:·
:..
E
.
.; <t -8.975 .................... ;····....
.................!
~

1'1

:

:

.:
.;
...

.:
.;.. .
... ,

!

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!

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:

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.

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.;..... .
..
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:.

.:
..

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~

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-8 • 1_2851L..~8---·""'--~1-:=2~~~----'·---=3='2~;--- · --~5=O:3~2~'- - -

!

.

.

;

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';'.

.
.

---:7:":3~7

Time (usee)Fig. 8 EMI signals. (a) Typical EM! signal (top). (b) Rapid and intense EMI signal.
(e) and (d) Rapid bursts ofEMI (I) and (2).

57

Table 2 Typical characteristics ofwavefonn feature data of the three subclasses in class II.
Patterns & Identity

RT

CNTS

ENRY

DUR

AMP

AVF

(us)

(dB)

(kHz)

294
12-276207-358

54
52-80
61-76

24
80-16748-122

(us)

I
5-84
10-2103EMI
45 classic crack growth AE
8 burst-tvoe crack 2I'Owth AE

7
1-32
13-36

2
1-42
7-29

CN1P
1
1-13
1-23

Table 3 Distribution of input panerns in each identified class.
Identity of
input patterns

EMI
Crack growth
FrettinR

SONN identified classes
I
16
0
0

n

ill

3
53
0

0
3
38

Obviously, due to the inherent disadvantages associated with wavefonn descriptor-based classification, some
confusion of the number and identity of sources was
unavoidable, although the SONN is able to give good value
classification results. Table 3 summarizes the distributionof original 143 AE signals of known source identities ineach SONN class identified, based on the waveform feature
data in this classification experiment.

IV
2
2
26

Total
number21
58
64Structural and Mechanical Engineering is also gratefully
acknowledged.
References
A.A. Anastassopoulos and T.P. Philippidis (1995),"Clustering Methodology for the Evaluation of Acoustic
Emission from Composites", Journal of Acoustic Emission,
13(112), 11-22.

6. Conclusions
M.N. Bassim (1987), "Macroscopic Origins of Acoustic
Emission", Non-destructive Testing Handbook, 2nd ed., Vol.
5, Acoustic Emission Testing, eds. R.K. Miller and P.
McIntire, American Society for Non-destructive Testing,
Columbus. OH, pp. 46-61.

It is shown that difficulty exists in unambiguously
identifying individual AE sources by consideration of
wavefonn-descriptor feature values alone, unless considerable care is taken over AE waveform acquisition andfeature extraction, which requires adequate knowledge ofthe behavior of AE sources in the specimen or structure
under investigation. Absolute feature values are highly
specific to a particular test configuration, AE source andspecimen geometry, and this factor can seriously prejudice
the validity of supervised classification procedures in arbitrary applications. However, if these considerations are
duly addressed, significant potential for AE signal classification by the self-organization neural network (SONN)
technique exists; for example. in assisting the practitioner
in establishing the likely number and identity of AE sources
present, and careful consideration of the relative propertiesof distinct classes identified by the SONN technique may
provide a useful insight into the origin of acoustic emission
in an arbitrary application.

M.N. Bassim, S.S. Lawrence and C.D. Liu (1994),"Detection of the Onset of Fatigue Crack Growth in Rail
Steels Using Acoustic Emission", Engineering Fracture
Mechanics. 47(2), 207-214.
C. Eberhart and R.W. Dobbins (1990), Neural Network PC
Tools, Academic Press, London.

Z. Gong. E.O. Nyborg and G. Oommen (1992), "Acoustic
Emission Monitoring of Steel Railroad Bridges", Materials
Evaluation, 50(7), 883-887.
M.A. Hamstad and J.D. McColskey (1997), "Wideband andNarrowband Acoustic Emission Waveforms from Extraneous Source During Fatigue of Steel Samples", Journal of
Acoustic Emission. 15(1-4). 1-18.

Acknowledgements
The authors wish to express their gratitude to HEFCW
for their financial support of this research and to the Royal
Society for its support of the Royal Fellowship programme.
Laboratory testing was performed in the StructuralEngineering Laboratory. Cardiff School of Engineering.
University of Wales Cardiff. The assistance of Dr. A.W.
Davies and the technical staff of the divisions of Civil,

D.O. Harris and H.L. Dunegan (1974), "Continuous
Monitoring of Fatigue Crack Growth by Acoustic Emission
Techniques", Experimental Mechanics, 14(2), 71-81.

s. Keyvan and J. Nagaraj (1996), "Panern Recognition of
Acoustic Signatures Using ART2 - a Neural Network".
Journal of Acoustic Emission. 14(2). 97-102.
58

S. Keyvan and R.G. Pickard (1997), "Feature Extraction ofMetal Impact Acoustic Signals for Pattern Classification by
Neural Network", Journal of Acoustic Emission, 15(1-4),
79-87.
T. Kinjo, H. Suzuki, N. Saito. M. Takemoto and K. Ono(1997), "Fracture-mode Classification Using Wavelet
Transformed AE Signals from Composite", Journal of
Acoustic Emission, 15(1-4). 19-32.
T. Kohonen (1989), Self-organization and Associative
Memory. 3rd ed.• Springer-Verlag. Berlin, New York.
T.M. Morton. S. Smith, and R.M. Harrington (1974),"Effect of Loading Variables on the Acoustic Emissions of
Fatigue Crack Growth", Experimental Mechanics, 14(5),208-213.

K. Ono (1997). "Recent Development in Acoustic Emission", Journal of Acoustic Emission. 15(1-4), S95-S102.
T.P. Philippidis. V.N. Nikolaidis and A.A. Anastassopoulos(1998). "Damage Characterisation of Carbon/Carbon
Laminates Using Neural Network Techniques on AE
Signals". NDT&E International, 31(5),329-340.
A.A. Pollock and B. Smith (1972), "Stress-wave Emission
Monitoring of a Military Bridge". Nondestructive Testing
International, 5(6), 348-353.
A.C.E. Sinclair. D.C. Connors and C.L. Formby (1977),"Acoustic Emission Analysis During Fatigue Crack Growthin Steel". Materials Science and Engineering, 28(2), 263273.
M. Sison, J.C. Duke, G. Clemens and M.G. Lozev (1996).
"Acoustic Emission: a Tool for the Bridge Engineer",
Materials Evaluation, 54(8), 888-900.
H. Suzuki, T. Kinjo, Y. Hayashi, M. Takemoto. K. Ono and
Y. Hayashi (1996). "Wavelet Transform of Acoustic
Emission Signals", Journal of Acoustic Emission. 14(2),69-84.
V. Venkatesh and J.R. Houghton (1996), "Neural Network
Approach to Acoustic Emission Source Location", Journalof Acoustic Emission, 14(2), 61-68.
D.J. Yoon, 8.S. Lee. P. Park. S.H. Kim, S.H. Lee and, Y.l.
Park (1997). "Acoustic Emission Monitoring of the Fatigue
Crack Activity in Steel Bridge Members", Journal of
Acoustic Emission. 15{l-4), SII-SI8.
H. Yuki and K. Homma (1996). "Estimation of Acoustic
Emission Source Waveform of Fracture Using a Neural
Network". NDT&E International, 29(1), 21-25.
59

42nd AEWG Meeting

Source Location Studies on a Tank Car, Yajai Promboon, The University of Texas at Austin
The paper presents preliminary results from a study of source location methods for a steel tank car. Two types ofartificial sources were used in the study; pencil lead breaks and bending of a steel bar welded to the tank. For the
initial part of the study, wide band sensors were used to capture the waveform. Tests were perionned on an empty
tank car and then on the tank car filled with water. On an empty tank, source location based on the frequency at thegroup velocity yielded very promising results. On the full tank, source location is accomplished by using a low
frequency signal. The paper also presents the algorithm used to triangulate the source called simplex. optimization.
Current work is focussing on use of a sensor with multiple resonant frequencies, and on source location for tanks
under pressure. Results from a pressure test with air will be presented. Research continues on the more difficult
problem of a liquid filled tank.
A New Method for Lamb-Wave Source Location of Impact on CFRP Plates.
Hirokazu Yamada, Yoshihiro Mizutani, Hideo Nishino, Mikio Takemoto. Aoyama Gakuin University and Kanji
Ono. UCLA.
This study reports the development of a new source location method using Lamb waves of impact on carbon-fiber
reinforced plastic (CFRP) plates. We monitored Lamb waves produced by pencil-lead breaks and steel-ball impacts
using four resonant-type AE transducers mounted on 300-mm square comers within 5()().mm-square plates anddetermined arrival time of 65 kHz Ao-Lamb waves, extracted by using a wavelet transform. Source location was firstestimated into one of four quadrants by the arrival time differences of the Lamb waves. Next, the source location in
the previously identified quadrant was determined by iteration. Here, the measured arrival-time differences werematched with those computed by moving a virtual source position in 0.5 mm steps. We developed an algorithm
providing automatic determination of arrival times and coordinate computation. It takes 17 s for each source-location
identification. Orientation dependence of Lamb waves for CFRPs are experimentally measured and included in thealgorithm. Accuracy of the source location for a unidirectional-CFRP plate is less than 10 mm within the 300 x 300
mm square. The maximum error of 6.5 mm and the average error of 2.1 mm were obtained for a (0/45/-45)s CFRP
plate.
PLENARY INVITED LECTURE
Quantitative Analysis of AE Source Parameters by SIGMA Procedure.
Masayasu Ohtsu and Mitsuhiro Shigeishi, Kumamoto University
Previously, the source characterization of AE was performed by the deconvolution analysis, which could provide
information only on crack kinetics. Recently, it is realized that the moment tensor analysis of AE can provide
quantitative information on kinematics of crack motion in materials. By applying SIGNIA procedure developed. AE
sources are classified into tensile cracks and shear cracks. The direction ofcrack motion is derived from theeigenvectors of the moment tensor.
In the present study, to characterize AE sources further. a relation between the damage parameter in the damage
mechanics and the moment tensor is investigated. Thus, a procedure to determine the damage parameter from the
SIGMA-AE analysis is developed. In uniaxial compression tests of the plate specimens with a center slit. thedamage evolution is estimated as the accumulation of the damage parameter for concrete and mortar materials.
Further, the procedure leads to quantitative estimation of the crack volumes in the case that absolutely calibrated
sensors are employed. As a result, the crack volumes are quantitatively estimated in concrete and mortar plates. Fromthese results, it is found that both the damage parameter and crack volume of mortar are larger than those of concrete.
It demonstrates the effect of crack arrest due to aggregate in the process of microfracturing in cementitious materials.
AEWG Business MeetinglBanquet
Wednesday, June 16, 1999
Martensitic Transformation Dynamics of Cu-AI-Ni Shape Memory Alloy Single Crystals by
Acoustic Emission Methods. Kenichi Yoshida. S. Kihara. K. Sakamaki. Tokushima University
Cu-AI-Ni shape memory alloy exhibits a super-elasticity which shows perfect recovery of plastic deformation. The
super-elasticity used in this paper results from a perfectly reversible martensitic transformation. The martensiticmDiscussion of the Log-Normal Distribution of
Amplitude in Acoustic Emission Signals
M. I. Lopez Pumarega, R. Piotrkowski and J. E. Ruzzante
Abstract

Earlier, L6pez Pumarega et al (1996) presented results
concerning the analysis of AE signals coming from steel
tubes. The log-normal distribution is shown to be adequate
to describe the results. In the present paper, we continued
the analysis of these signals and the results are explained
according to the Central Limit Theorem (CLT).

In this paper. we study from a statistical point of view,
the acoustic emission (AE) produced by deformation andcrack propagation in steel samples from seamless tubes.
The most convincing result was obtained by correlating thehistogram of burst amplitudes with the log-normal distribution. This result is interpreted according to the Central
Limit Theorem. It can be assumed that in the process ofplastic flow and crack propagation, the incremental damage
sustained between any two stage of the process is a random
proportion of the total damage existing at the preceding
stages. Because the measurable characteristic of the material. in this case the deformation arriving at the AE sensor,
is the product of a large number of underlying causes,
which are independent random variables. the Central Limit
Theorem applies.

2. Procedures

2.1 Materials and Methods
Test samples were rings obtained by transversal cut
from seamless steel tubes, with 14 em outside diameter, 1
em thickness and 2 em width. A section with a chord
length of 6 cm was eliminated from each ring in order tointroduce it in a device. which. by opening it, introduced a
distribution of stresses. Force was manually exerted througha screw. Figure 1 shows a photograph of the experimental
setup and Fig. 2 shows the block diagram of the detection
system.

1. Introduction
The acoustic emission (AE) waves represent the propagation of elastic energy coming from different sources, suchas deformation and crack propagation in materials, when
they are submitted to stresses. The elastic waves are detected at the surface by means of piezoelectric transducers
that transform the mechanical pulses into electric signals.

A total of 11 samples were employed, 10 of them corresponding to the same steel (type 1) in Table I, while thecomposition of the remaining one (type 2), was slightly
different and its thickness was greater so as to be able tobear a fatigue-produced initial crack.
Table 1 Metallurgical and geometrical characteristics oftype 1 samples.

The electric signals are discrete packets. named bursts,
immersed in a background of continuous noise. The bursts
are asymmetric and are characterized by their amplitude(A), duration (D) and rise-time (R). Due to the complex
nature of the whole phenomenon, the subject is usuallytreated statistically. Up to now, the property, for which
better results have been obtained, is the amplitude. Nevertheless, different distributions have been employed by several authors; such as power law models, log-normal andWeibull distributions (Erlenkamper. 1979; Pollock. 1973.
1981; Ono et aI., 1978).

Composition (in wt. %): C 0.16. P 0.042. S 0.001, Si 0.26,
Mn 0.67, V<.05, Nb<0.03. Ti<0.05
Hardness:
75 Rockwell B scale
15 JUD
Grain size:
Thickness: 1 cm; Width: 2 cm; Outside Diameter: 14 cm
Three AE transducers were attached to each sample.
Two of them with a resonance frequency of 200 kHz, acted
as guard sensors to eliminate the noise produced at theholding points. The third one. put on the top of the ring,where the maximum degree of deformation occurred. was a
wideband (300-800 kHz) sensor. A wideband sensor was
selected in order to perform the frequency analysis of thesignals in future work. The signals were pre.amplified andthen amplified and processed with the AE AEDOS system.
The force to open the ring was applied by hand and wasmeasured with a load cell. The end opening of the ring was

Received 20 July 1998. M.I. Lopez Pumarega (Iopezpum@cnea.
edu.ar) and J.E. Ruzzante are affiliated with Grupo Ondas Elaticas, U. A. ENDE, Comisi6n Nacional de Energy At6mica(CNEA), CAC. Av. Oral. paz 1498, (1650) Pcia. de Buenos Aires.
Argentina. R. Piotrkowski (pioukow@cnea.edu.ar) is with Escuela de Ciencia y Tecnologia. Universidad Nacional de General
San Martin, Calle 91, No. 3391, (1652) Pcia. de Buenos Aires.
Argentina.
Jourual or Acoustic Emission
Volwne 17. Numbers 1-2

61

0730-00501991$5.00117061-067
Copyrigh~1999 Acoustic Emission GroupcFig. 1 Experimental setup. A: sample, B: wideband sensor, C: guard sensors, D: screw, E: load cell..
Table 2 Characteristics of experiments
Name

Acoustic
Emission
System:Load c:eu

AEDOS

MatcIIAJ

1lIInning

Oxide

cas

1

Yos

-

-

eEL9

1

Yes

-

CEL11

1

Yea

-

CELlO

1

Yes

CEL12

1

-

CEL13

1

CEL14

-

IMilII

Expaimenl

cradt

ttPe

-

B
B
B

-

Yes

-

B,

Ya

-

-

a

-

Yes

-

-

a

1

-

YI!$

-

-

a

CELlS

1

-

Yos

Yes

-

a,

CELlS

1

Yes

Yes

-

a,

CEL17

1

Yes

Yes

-

a,

AROFl

2

-

-

Yes

T

Potentiometer- Acoustic Emission
signal parameters:
Amplitude, Duration,
Rlsetime.

Noldl

Yes

measured with a potentiometer. Both the force and theopening were recorded by the AEDOS system.
Four different types of rings were prepared from the 10
type-l samples. Some of them had a triangular inside notchat the top in order to promote crack propagation; others
were thinned out in the same region in order to favor de-- Force- Digitized signal

Fig. 2 Block diagram of the detection system.
62

formation. The tubes were oxidized in their natural state.
Some samples were tested with their oxide layer and inothers. this layer was eliminated. Table 2 shows the characteristics of experiments.

log-normal distributions could not be rejected; besides.
looking at the histograms themselves. with log-normal distribution we obtained the best fits. In Figs. 3-5, three examples of fits for both Weibull and log-normal distributions
are shown; the figures themselves are self explanatory. Thisoccurred for all the experiments that are in Table 2, whichmeans that the involved phenomena share a common essential property.

In the CEL12, CELl3 and CELl4 experiments, (atype), due to the previous thinning, the fundamental process
induced by stresses is deformation. In the CELIS, CEL16
and CELl7 experiments (ai-type) added to deformation is
the oxide layer break-down. In the eELS, CEL9. CELlI
experiments (~type) and AROFI. (y-type). due to the priornotch and initial crack. the crack propagation process is
expected to play the fundamental role. In the CELIO (PItype) experiment the oxide breakdown is added.

:II
U
C

•cr
...•
~

2.2 Experimental Results

L

The mean values of the parameters A. D and R together
with the total number of AE events (N) for each experimentwere obtained. The results are summarized in Table 3,
which shows that for the oxidized samples (a l -. PI-type)both the number of bursts and the mean value of the amplitude (A) take high values and those of rise-time (R) takelow values. The mean D and R values are very different for
the a- and p-type experiments. For this reason, they can beused to identify the events in a general experiment.

o

Q

a1

II

pi

4

6

8
(X 1000)

AROF1.AMPLXTUO(mV)
24

~

1
J

Table 3 Typical values of the N. A, D and R parameters.
Type

2

I

20

1

16

i

12

i1

y

:II

u

c

•cr
...
~

N

21·27

141-332

17~

214

64

GI
L

]

i

a

ACmV)
970·12652425-3390

703·1213

1629

4

1800

0

D

CjIlI)

21·143203-266229-376

102

ns

2

6

8

(X 1090)
AROF1.AMPLITUO(mV)R
fjlll)

4

15-35

16-23

59-85

19

258

Fig. 3 Comparison of the amplitude histograms with lognormal (above) and Weibull (below) distribution for
AROFI sample.

2.3 Statistical Analysis ofSignals
2.4 Influence ofthe Detection System
Statistical studies were performed on the signal parameters and the total number of events for each experiment. With this aim. the distribution function. which betterfits the histograms. was selected. Until now. the more convincing results refer to the amplitudes data. As the histograms are asymmetric. the exponential, Gamma, Chisquared, Weibull and log-normal distributions were compared. Assuming a significance level of 0.05, and using
Pearson Chi-square and Kolmogoroff goodness-of-fit tests.

A burst is the transducer response r(t) to the mechanical excitation s(1), that arrives coming from different points.

where accumulated elastic deformation energy was produced. In the following. we analyze the influence of thedetection system on the output signal in two extreme caseswith (a) resonant transducer, and (b) wideband transducer.
In both cases we assume that the amplification stage behaves linearly.
63

HI

la

S

s
:II

:II
U

c

•
...•

U

6

c

•
::l
lJ'

::l
lJ'

L

6

...L•

4

4

2

2

a
o

1/1

5G"1I laa0 1599 2SS0 2599

S09 UUIIS 15S0 201110 2500
CELll.AMPLITUD(mV)

CELll.AMPLITUD(mV)

Fig.4 Comparison of the amplitude histograms with log-normal (left) and Weibull (right) distribution for CELlI sample.
12
10
JI

u

B

c

•

6

...•

4

::l
lJ'

L

i~

2
0
a

1

2

4

3

(X leee)

CEL13. AMPLITUD (mV)
12

11/1
JI
U
C
Gl
::l
lJ'
Gl

...

S
6

L

4

2

a

3

~r

I

t
0

~
2

1

CEL13.AMPLITUD(mUI

3

J
4(X leae>Fig.5 Comparison of the amplitude histograms with log-normal (left) and Weibull (right) distribution for CELl3 sample.
64

Following Ono et al. (1978), we suppose that rectangular pulses with amplitude S and duration T reach the sen-

These are very important results. The proportionality
between both amplitudes, S (mechanical impulse) and A(AE signal Amplitude). permits to infer that the statistical
distribution of both parameters is the same.

sor:set) = S[u(t) - u(t-T)](I)

In another work of our group. Perez et al. (1997) presented a mathematical model to simulate the AE events.
The detection system was adequately represented by afourth-order differential equation. If impulses enter thesystem. burst signals emerge. characterized by A. D. and R
parameters. where A is proportional to the input amplitude.
The output signals for impulse inputs. with amplitude lognormally distributed. were burst type with the same amplitudes distribution. Moreover. the output signals resulted
similar to the experimental events. including D and R parameters. This shows that D and R parameters are strongly
influenced by the detection system, and this is not the casefor the A parameter, for which a linear relation holds. In theactual case. the input signals are not impulses; neverthelessthe referred results show the strong and complicated influence of the detection system on the D and R parameters, forwhich proportionality between input and output values cannot be postulated.

where u(t) is the unit step function. The transducer response to this stimulus, ret), is the convolution of set) withthe transducer response vet) to a mechanical impulse cS(t),r(I)= Js(~

)V(I-~ )d~=s(I)ev(t)(2)

For a resonant damped transducer with resonant frequency

c.oo and damping decay constant ~ (case (a», Ono et al.
(1978) found that:vet) =expH3t) exp(i root) u(t)(3)

from which we obtain for t ~ T the solution:r(l) =

S (e fJ T - i O)oT - 1) -fJ I + iO)o I
efJ - iO)o(4)

When ret) is expanded in Taylor series for t = T, and~T«I, the result is that the maximum ret) value is proportional to S. putting thus in evidence the proportionality between the amplitude of the transducer response A. and theamplitude of the arrived mechanical excitation S.

In the following. the proportionality between the amplitudes of the input mechanical impulse and output electric
signals (AE) is assumed.

2.5 Log-normal Distribution

For a wide band transducer (case (b». considered as a
low-pass filter with cut-off frequency O)e' it holds:_{e
H(O)- 0It»! < c1t»!>O)co

We apply to our case the analysis performed by Cramer(1961) to decide the conditions leading to the fact that agiven random function can be described by the log-normal
distribution.

0)

-t41I

(5)

where is the transducer transference function and to is anarbitrary initial value.

If the causes that originate a given amplitude of deformation. s. inside the material do not act by simple addition
but are sequentially connected. they would give rise to an
asymmetric distribution.

From (2)F[r(t»)=F[v(t)] x F[s(t») =H(ro) x F[s(t)](6)

Let us suppose that there are n stress values (~I"'''' ;,,)
that act in the order indicated by their sub-indexes. These
stresses are then considered as independent random variables. We designate as Sj the total deformation produced by
the set of stresses (~I" .... ;i)' It can be supposed that theadditional deformation produced by the following stress.
~+I' is proportional to both ~I+I and to a certain function of
the deformation reached until the prior step. h(s;):

where F means Fourier Transform. So we obtain
T.

F(r(I)] = H(w) x Sx Jexp(-it»l)dl = g(0)
o

and
ret)

=F [g(CI)].
1

(7)

(8)

To solve (8) it is necessary to expand the exponential.
Assuming for simplicity. to O. and (t - n « 1/(I)e. weobtain=ret) =Sro.The.

(10)

(9)

from which

Therefore. the amplitude of the transducer response is again
proportional to the amplitude of the mechanical excitation~S"I-S,~I+·"+~. =~ h(s,)

S.

65

(11)

ing to the CLT. The underlying physical meaning is that thedeteCted AE signal is the complex consequence of a great
number of random independent sources, to which individual
effects are linked.

If each stress gives place to a minute contribution of
the total deformation, the transition to continuos calculation
can be performed and the sum is replaced by an integral.
~I+"·+';.

where s, dt

=I. . h(t) = /(s)(12)

There are several antecedents of using asymmetric distribution functions, specially log-normal and Weibull distributions, in materials science.

=sQ represents the total deformation.
;1'....'

As by hypothesis
I;" are independent variables
and n is a large number, it can be deduced that the function
I(s) of the random variable s, which appears in the second
member of (14) follows a normal distribution. If the simplest case, h(t) t is considered, it holds:

Stranadel and Jonsta (1994) found Weibull distribution
to represent the carbide size distribution in spheroidized
steels. Ono et al. (1978) found that the distribution of AE
amplitudes in HSLA steels could be described by the
Weibull distribution, and assigned the AE source to thedecohesion of a great number of MaS inclusions present inthese steels, sizes of which are Weibull distributed. In thatcase, samples were round tensile specimen submitted totensile tests, and they had near twice nominal Mn composition compared with our samples. The effect of shorttransverse tensile stress orientation made the decohesion of
the inclusions the most important source of AE comparedwidt deformation.

=

(13)

In order to gain generality we consider In (s - a) instead of In (s), "a" being the initial deformation value. We
also consider 50 =1, which means that deformations aremeasured in So units. From (IS), it is In (s - a), which
follows a normal distribution, with m and a parameters, andthe result is:(In (s-o»=g• • III(In ($ -0) _m)lt:L cxp ()tT1/2tr
2o'z

Satora (1978) showed that carbide size distributions insteels fit the log-normal distribution. The log-normal function is frequently used to describe the distribution of grain
sizes in polycrystals. While the normal distribution occurs
as a result of the additive effect of random events, the lognormal distribution results from effects that are multiplicative. This has been used by Vaz and Fortes (1988) as ajustification for the applicability of the log-normal function
to describe the sizes of recrystallized grains, which nucleate
at random and grow at a rate that depends on their size.
Bury (1975) stated that the incremental damage produced
between any two stages of plastic flow or crack propagation, is a random proportion of the total damage existing at
the preceding stage. and the magnitude of the relevant observable failure criterion (percentage elongation, rupture
strength, etc.) is then log-normally distributed. Since damage processes are irreversible, their states are functions oftime. It follows that the statistical models of material properties should be based on a theory of stochastic processes intime and space relating external conditions to atomic andmolecular changes at various locations in the solid.

(14)

which is valid for s>a, while g =0 for s<a. It then holds
that the variable s follows a log-normal distribution f:If •. ,,(s)= tT(s-arfiji exp (-(In (s-a)-m)z )
20'2

(15)

As stated previously, the "a" parameter represents theshift of the curve along the abscissa; that is to say, the initial value of deformation. The parameter m represents thearithmetic mean of the variable In (s - a). So, from the lastvalue, the geometric mean value of (s - a) can be obtained.

As a conclusion, the distribution of amplitudes of thedeformation pulses that arrive at the detection system is
log-normal, and this result is according to the CLT.

3. Discussion and Conclusions
The mean values of the parameters A, D and R seem toact as indicators of the main source of AE signal; that is tosay, if they are originated in crack propagation, deformationor layer breakdown, as can be deduced from Table 3. Thisis an encouraging result and it induces the performing of alarger number of future experiments. In particular, the difference between the mean values of the parameters D and R
for the experiments a and 13 is outstanding. The N and A
values are very high for the oxidized samples. All thesefacts deserve an adequate theoretical explanation.

Summarizing, it is possible to assume that only two
important mechanisms exist as sources of AE signals: creation and propagation of dislocations, and creation of new
surfaces by cracks or inclusions decohesion. Plastic flow
and crack propagation essentially constitute irreversible
damage processes. It can be assumed that damage is amultiplicative process; that is, the incremental damage sustained between any two stages of the process is a random
proportion of the total damage existing at the preceding
stages.

A good fit was obtained between the amplitudes histograms and the log-normal distribution for all our experiments in this work. This result could be explained accord66

If we suppose that the measurable characteristic of amaterial is the result of causes that cannot be readily measured themselves, and if:i) the measurable characteristic is a multiplicative effect of underlying causes,ii) the causes are independent random variables, andiii) there is a large number of causes,
then the Central Limit Theorem (CLT) is applicable.

K. Satora (1988), "Size distribution and carbides dispersion
in bearing steel", Kutnik (Katowice), 55(11-12), 319-321.
B. Stranadel and Z. Jonsta (1994),"Distribution of dimple
sizes of the fracture surface of spheroidized steel in thetransition region", Eng. Fract. Mech., 48(6),863-871.
M. Vaz and M.A. Fortes (1988), "Grain Size Distribution:
The Log-Normal and the Gamma Distribution Functions",
Scripta Metallurgica, 22, 35-40.

In our case, the log-nonnal distribution was adequate todescribe the total number of experiments. This could be
because the phenomena of dislocation movements andcrack creation and propagation present in different proportion in all experiments have the same behavior from thestatistical point of view.
Acknowledgements
The authors would like to record their appreciation of
the very helpful discussions with Dr. Kanji Ono during his
visit to their Laboratory.
References

K.V. Bury (1975), Statistical Models in Applied Science,John Wiley & Sons, New York. pp. 295-591.
S. Erlenkamper (1979), "Time and Amplitude of Acoustic
Emission Signals in Fracture Mechanism Experiments",
Acoustic Emission, Proc. Deutsche Gessellschaft fUr Metallkunde eV, pp. 165-188.

M.1. L6pez Pumarega and J.E. Ruzzante (1996), "Acoustic
Emission Signal Analysis from Steel Tubes", Proc. 14th
World Conference on Non-Destructive Testing (14th
WCNDT), Vol. 4, pp. 2493-2496, New Delhi, India, (December 1996).
K. Ono, R. Landy and C. Ouchi (1978), "On the Amplitude
Distribution of Burst Emission Due to MnS Inclusions in
HSLA Steels", Proc. The Fourth AE Symposium, High
Pressure Institute of Japan, Tokyo, pp. 4-33/4-45.
L.V. Perez, M. Anneite, M.I. L6pez Pumarega and J.E.
Ruzzante (1997), "Modelo Matematico para la Simulacionde Eventos de Emision Acostica", Actas Congreso Regionalde Ensayos No Destructivos, CORENDE, pp. 299-303,
Mendoza. Argentina, 27-30 Oct.
A.A. Pollock (1973), "Acoustic emission amplitUdes", Nondestructive Testing, October, pp. 264-269.
A.A. Pollock (1981), "Acoustic Emission Amplitude Distributions", International Advances in Non-Destructive
Testing,7,215-239.

67

42nd AEWG Meetingtransformation used is from 003 structure to 18R transformation which can be observed in-situ by an optical
microscope during tensile deformation at room temperature. Therefore, the dynamics of martensite plate such as thenucleation and the growth was examined in detail. Acoustic emission behaviors during the stress-induced martensitic
transformation have been investigated using two types of specimens ofCu-Al-Ni shape memory alloy single crystal([100)(001) crystallographic orientation), each of which have different stress concentration coefficients, Kt. 1.01 inlarge round shaped notches and 2.65 in rectangular notches. The difference of dynamical martensitic transformation
behavior has been compared with that of acoustic emission behavior using these specimens. Small martensites occur
far before yielding near the notched bottom of the latter specimen. The generation of these martensites which consistof two or three-kinds of habit planes has bee n considered as the sources of lower amplitude, shorter rise time andshorter duration time acoustic emission. On the other hand, sporadic martensites occur near yielding near the bottomof the former specimen. The acoustic emission activity also is lower and more sporadic. As a result, it is made clearthat the acoustic emission event rate and the amplitude distribution are associated with the nucleation and the growthof martensite plate during loading or the disappearance and the shrinkage of them during unloading. After nucleation
at the center of the specimens, the martensite plates grow to three directions, that is, length, width and thickness
directions. Comparatively higher amplitude acoustic emission occurs during growing to the length and the thickness
directions. When the coalescence of the martensite plates and the growth passing through the specimen thickness
occur, we can observe the highest amplitude acoustic emission. Otherwise, it was made clear that acoustic emission
total event counts generated during martensitic transformation d id not depend on the specimen volume, sequentialchange of the acoustic emission waveforms during deformation was different in two types of specimens because ofdifference of these martensite plate shapes, and the martensitic transformation rate obtained by acoustic emission
waveform analysis increased with increase offatigue damages.

Acoustic Emission Monitoring of Noisy Materials.
Timothy J. Fowler, The University of Texas at Austin
Composites, concrete, some aged metals, and carbon are among a group of noisy materials that give copious
emission on initial loading. The pattern of emission is unpredictable. Sometimes it occurs as bursts, withintervening periods of low emission, other times it is continuous. Emission may start early in the load cycle, reacha peak rate, and then decrease at higher loads. Other times the rate of emission will increase with increasing load.
The large amount of noise on first loading makes it difficult to conduct meaningful acoustic emission tests on new
equipment and structures. In addition, monitoring the first loading may create the misleading impression that thestructure is defective, when it is not.
The problem caused by the large quantity of emission on first loading is well recognized. Most of the standard test
procedures specify less stringent evaluation criteria for emission from rust loading than for emission from
subsequent loadings. Some test procedures, such as the American Society of Mechanical Engineers Code for fiber
reinforced plastic (FRP) pressure vessels, allow data from the first loading to be disregarded. Other procedures, suchas the American Society for Nondestructive Testing Recommended Practice for balsa core FRP highway tankers,
compare emission from multiple loadings.
A large quantity of emission during initial loading is not necessarily bad. Rather it is a consequence of the internal
structure of the material. The presentation will discuss the problem of noisy materials, the source of this noise, andvarious approaches for dealing with it. Data from recent experimental work will be used to illustrate some of theevaluation techniques used to interpret emission from noisy materials.

Acoustic Emission Testing of Hoist Brake Components at the Waste Isolation Pilot ProjecL
Alan Beattie, PAC
The Waste Isolation Pilot Plant (WIPP) was developed to store low level radioactive waste in chambers carved out ofrock salt. The storage area is some 2000 ft. below the floor of the desert in south eastern New Mexico near the townof Carlsbad.. Three separate hoists are operated at the site to convey cargo and passengers to and from the storage
area Safety considerations dictate that two of the hoists be operational at all times. These hoists are of different sizes
with passenger capacities ranging from four to over sixty persons. The two smaller hoist systems are quite old, themiddle sized one having been built around 1925. All hoists have emergency brakes on the cable drums to stop thecages in the event of system failures. The emergency braking systems on the two smaller hoists have had theircomponents tested with AE for the last six years. These tests are novel in that the actual testing time isapproximately one second. The rods driving the emergency brakes are instrumented with AE sensors and the cage isE8Unsupervised Pattern Recognition Techniques for
the Prediction of Composite Failure
T. P. Philippidis, V. N. Nikolaidis and J. G. Kolaxis
Abstract

problems one has to deal with in an eventual application of
acoustic emission (AE) monitoring on such a structure is
the identification of AE signals emitted from various
sources, Le. damage modes exhibited by different layers.
Furthermore. it is of great practical importance to correlate
clusters of identified similar signals to specific load stages.
or life fractions for fatigue loading, thus enabling failure
prediction of the structural component.

An unsupervised pattern recognition procedure is presented for the discrimination of acoustic emission (AE)signals from glass-fiber/polyester (GRP) coupons stressedin tension to failure. The laminated composites consist ofunidirectional and bidirectional stitched glass fabrics
stacked at various sequences. The purpose of the analysis is
to identify AE from specific layers in a laminate and to relate clusters of similar signals to characteristic load stages.
thus enabling failure prediction of laminates of any stacking
sequence. The multivariate unsupervised classification
method presented in this work. based mainly on artificial
neural system (ANS) algorithms. achieves classifications
that can be used as a means to predict coupon failure by
performing clustering on the union of several AE data sets
produced in different experiments and using principal component projection. Class size ratios. of selected classifications. are used to create criteria linked to coupon failure andthe schema is stabilized via a simple supervised classifier toapply the criteria on unknown coupon AE data.

The materials investigated in this study consist of GRP
constructions typical of wind-turbine rotor blades. Morespecifically. twO basic building layers are used; a unidirectional (tID) and a bidirectional (BD) layer stitched glass
fabric consisting of an equal proportion of fibers oriented at±45°. The two basic layers are laminated in two different
stacking sequences. A number of tensile tests is perfonned
on coupons made either of lID or BD layers and. based on
AE data processing results from these tests. identification of
signals emitted during loading of multidirectional (MD)laminates made of the aforementioned layers is attempted.
An unsupervised ANS scheme similar to the Learning
Vector Quantizer (LVQ)-based methodology used successfully in carbon/carbon (C1C) composite coupon AE data
(Philippidis et al.. 1998). is applied on AE data recorded
during experiments of GRP coupons under tension. Fourgroups of GRP specimens are considered. varying on fiber
lay-up specifications.

1. Introduction
For practical applications of AE monitoring where
there is no a priori knowledge of signal characteristics andsimulation of AE generating mechanisms is difficult. e.g.
failure modes of composite materials. unsupervised patternrecognition (UPR) techniques (Anastassopoulos et al..
1995) were introduced to assist in discriminating signals
from various different sources. In a series of projects. UPR
procedures were used to successfully identify signals emitted from different damage mechanisms of various composite systems based either to conventional PR algorithms(Anastassopoulos et al.. 1996; Anastassopoulos et at.
1998a. 1998b) or to artificial neural systems (ANS).
(Philippidis et aI.• 1998). The method was successfully applied to material coupons and structures as well.

While recorded under similar experimental constraints.
specific GRP AE differs from ClC AE in several ways.
Even at low sensitivity settings, the GRP coupons produce amuch larger number of AE records. Typical data sets contain an average of 11250 AE records. up to the maximum
load reached. for each MD and BD coupon. 25030 for eachUD coupon. and 17630 for MD coupons of different stacking sequence (MON). Being much larger than the average
3670 records recorded per CtC coupon experiment. thedata-set sizes themselves call for a different classification
approach. Furthermore. unlike CtC AE where extremevalued records could be used to predict failure. in GRP experiments a significant number of records with high AE
signal characteristics consistently appear during all loadstages.

Typical primary composite strUctures made of glassfiber reinforced polyester matrix (GRP) composites consistof a number of basic building layers. which are stacked inlaminates of varying thickness and content. One of theReceived 15 September 1998. The authors are affiliated with Mechanical & Aeronautics Engineering Dept.• University of Patras.
P.O. Box 1401. Patras 265 00. Greece. (philippidis@tech.mech.
upatras.gr)
JoUJ1l31 of Acoustic Emission

Volume 17. Numbers }·2

The original method of performing classification on
AE data from each experiment separately, (AnastaSsopou
69

073~~/S5.~}7069-o81

Copyrighte1999 Acoustic Emission Groupweighs 450 glm2 in total and contains equal proportion ofcontinuous fibers in the (+45°) and (-45°) directions withrespect to the loading axis stitched together. The stacking
sequences of the four laminate types are [03lY • [(±45hh •[OI(±45)l0Jy , and [(±45)1~/(±45)h, for UD, BD, MD, and
MDN, respectively (see Table 1).

los et at.. 1995). handicaps the results in respect to theirgeneralization power. since the produced classes are boundto be unique for the experiment. Classes of particular interest need to appear in all classifications; otherwise. the information provided by the class properties cannot be used
in a generalized remaining·life prediction schema. Being
affected by singularities in each data set, such as outliers or
different number of "natural" clusters. similar classes
within different classifications are difficult to identify,
methods such as the Rand statistic (Rand, 1971) are used. ifthey exist at all.

From each laminate, four straight edge coupons of 250

rom length and 25 rom width are cut using a diamond wheel
and are prepared in accordance to ASTM D 3039-76. Aluminum tabs are glued to all coupon ends. resulting on 160
rom of free coupon length. Double edge notches of 5 rom
length each are cut normal to the loading axis at the coupon's mid-length. Sixteen tensile tests. four from eachlaminate type. are carried out using a screw-type tensile
machine under displacement control mode. In all experiments, displacement rate is kept constant at 1 mmlmin.

The above constraints indicate that a different approachneeds to be taken in order to achieve classifications of any
explorative value on GRP AE data, i.e. classifications thatcan be used as a means to predict failure. The methodology
discussed here involves clustering performed on the unionof several AE data sets (produced in different experiments)using principal component projection. Questions about theuse of mathematical clustering evaluation criteria for selecting the best classification amongst several are raised andan alternative approach based on amplitude distributions issuggested. With the number of natural clusters in the data
being unknown. the clustering algorithm needs to be applied for a different number of requested classes, and thusclassification selection is of major importance in unsupervised AE data exploration. Based on selected classifications. class-size ratio criteria are created and linked to the
coupon's remaining life. Finally, it is shown that the resultsof the unsupervised analysis can be used to train a simple
supervised classifier enabling the application of the prediction criteria to new AE data.

During all tests described above. AE activity wasmonitored using a SPARTAN 2000AT of Physical Acoustics
Corporation (PAC). A single AE transducer PAC R15.
resonant at 150 kHz, is fumly attached to each coupon at a
45 rom distance from the notches, using ultrasonic couplant(Krautkraemer 20). AE signals are amplified by a PAC
1220A filter-preamplifier set at 40 dB pre-amplification and100-300 kHz band-pass filtering. The SPARTAN signal
analyzer settings are set at 45 dB threshold. 20 dB gain. 50).LS peak definition time (pDT), 150 ).LS hit definition time(HDT), and 300 ).LS hit lock-out time (lILT). Data sets consisting of SPARTAN AE records containing seven signal
descriptors are recorded during each experiment. The descriptors are Rise Time (RT). Counts (CNTS). Energy(ENE). Duration (OUR), Amplitude (AMP). Average Frequency (AVF>, and Counts-ta-Peak (CNP).

2. Experimental Procedure

Four E-glass fiber reinforced polyester laminates ofdifferent stacking sequence are considered. All laminates
are INTERCHEM CHEMPOL 80 THIX polyester resin handlay-ups. E-glass from AHLSTROM GLASSFIBRE. cured atroom temperature. Two fabrics are used in manufacturing
the above mentioned laminates. The first is mainly unidirectional. of 700 g/m'1. total weight, with parallel continuous
fibers. A number of glass fiber strands. 30 glm2, are laidtransversely to the main portion of fibers and stitched together to allow fabric handling. The second glass fabric

3. Data Preprocessing
Twelve out of the sixteen tensile experiments performed are used for the AE analysis via the unsupervisedclassification schema. Experiments using MDN coupons
are excluded from the unsupervised classification stage
since they will be used as unknown data in the supervised
schema to be presented later. For each of the twelve experiments. data items recorded past the last maximum load
value recorded are dropped out of further consideration. A

Table 1 Stacking sequences for the four laminate types.
Stacking Sequence

Namberor
Experimeats

Lamillllte
Abbreviation

Laminate
Description

4

UD

Unidirectional

0°

0°

0°

N/A

4

BD

Bi-directional

±4So

±4So

±45°

N/A

4

MD

Multidirectionall

0°

±4So

±45°

00

4

MDN

Multidirectional 1

±4So

0°

0°

±45°

70

4. Clustering Method

series of steps is then taken to form the final data set. on
which clustering will be performed: Each data set is filtered
to eliminate outliers and false recordings. These filters
eliminate records for which CNTS $ CNP. DUR $ RT.
CNTS ;:: 1000 and AVF ;:: 500. The last two filters are justified by data plots indicating that only a negligible numberof records have these properties and the values are far fromthe recorded average.

Work presented here uses a classification schema basedon Learning Vector Quantizer artificial neural system (LVQ
ANS). The LVQ is a two-layer (input-output) ANS presented by Kohonen in 1981. The unsupervised single winner LVQ ANS (Kohonen. 1988; Lippmann, 1987; Simpson,1990). upon which this clustering schema is based, determines cluster reference vectors. centers, by a series of iterative steps that classify each input vector, via an embedded parallel Euclidean nearest-neighbor classifier. to one of
the initial cluster centers and then uses the input-to-center
distance to adjust the winner cluster center itself. The cluster-center adjustment is inversely proportional to the number of iterations performed forcing the algorithm to stabilize. The maximum number of encoding iterations to be
performed is user-defined, the iteration counter increases
upon the presentation of the entire data set, and no otherconverge criterion is used to terminate the encoding (cluster-center adjustment) stage.

Two additional "empirical" filters are applied on thedata. The first eliminates records x, for whichI(d - <d))/sl ~ 70.
where d the Euclidean distance of x to the mean vector of
the set, <d) the mean of all such distances. and s the standard deviation of all such distances. Similar in concept, thesecond filter eliminates all records, for which some descriptor x takes values satisfying I(x - <x> )/sl ~ 50, where <x>and s the descriptor's mean and standard deviation in theset, respectively. Being applied last and with very highthreshold, the two filters did not eliminate more than a few
records in each data set.

In this form. the LVQ suffers from non-deterministic
behavior caused by the initialization of cluster centers to
random values and the effect of elata-item encoding order inthe cluster centers created. Taking these disadvantages into
account, a modified LVQ schema (M-LVQ) was previously
introduced for classification of AE records from C1C composites (Philippidis et al., 1998). The M-LVQ selects initialcluster centers from a small subset of the data set that contains most-distant vectors. The pair of vectors of greater inbetween distance is first added to the initially empty set,
then the vector. whose total distance from those already inthe set is maximum. This step is repeated until the set size
becomes equal to the number of requested classes and thus
contains enough vectors to initialize the LVQ. This selection and initialization method first detects extreme vectors.
lying usually at the ends of the data envelope and then, possibly, vectors inside the data envelop. which progressively
splits the data in zones. To fix the presentation order during
the encoding stage of the M-LVQ scheme, data vectors
were presented according to their Euclidean distance fromthe elata set's mean, with vectors closest to mean were presented first.

The union of the filtered sets fonns a new set, which isstandardized by subtracting the mean value and dividing by
the standard deviation of each corresponding descriptor in
the data set. Data is then projected on a new set of axes
found via Principal Component Analysis (PeA). (Anderberg, 1973; Johnson et aI., 1992). which allows the reduction of the data set dimension by utilizing statistical properties of the data. The method implemented here calculates
the eigenvectors of covariance matrix to expose a set oforthogonal axes (principal components - PCs) on which thedata reaches maximum variance. Using PCs, the clustering
can be performed on a space of limited dimensionality, towhich all descriptors have contributed according to theirseparability power. The PC direction can also be used to
select the most powerful subset of the original data set descriptors in an approach similar to the original (Anastassopoulos et al., 1995). where dimensionality reduction is
performed by dropping descriptors of high correlation via
Wilk's Aor complete link criteria. This, however. is not theapproach taken here, where data is projected and clustering
is performed on PC axes. In analyzing the results of clustering performed in PC-space. the one-to-one correspondence of original and projected vectors is used to determine
class properties in tenns of physical signal characteristics.
Combined with the above data normalization, the covariance matrix of the standardized set equals the correlation
matrix of the original, while its eigenvalues equal the newvariances of the data projected to the corresponding newaxes (Anderberg, 1973). To select the number of new axes
used. a degree of fit is given by the ratio of the sum of selected new variances with the sum of all new variances. For
the clustering results presented later. this ratio was valued at
0.98 for 5 PCs.

The M-LVQ applied on individual AE data sets from
C/C composite experiments successfully identified small
critical classes at the very last stage of the coupon's life. At
the same time. the scheme produced classifications rating
very low on the R separation measure (Davies, 1979;
Anastassopoulos et aI., 1995), which is assumed to be asuitable method for selecting classifications on this type of
data. However, the M-LVQ-scheme presentation ordering
step proved too time-consuming for the size of the GRP AE
data sets analyzed in this work. and was not used.

71

18

1.3

~
D

----- ID.__.-.... UD

12

II:

1.0
0.9

0.8

o
40

50

60

70

80

0.7 l.---'-_~__'--""""_Jo---.l._""""_Jo-"""'_"""---'
024
6
8
ro u
u a ~ nw

100

Amplitwle (dB)OasscsFig. 2 R-criterion values for RM-LVQ classifications of2
to 20 classes.

Fig. 1 Amplitude distributions for all data files used in UPR
analysis.
Summarizing. the LVQ-based schema used for GRP
AE clustering had a leaming rate constant set to 0.8. run for2500 encoding iterations, with initial cluster-reference
vectors set to the most distant data-set items. and operated
on a 5-PC space. No algorithmic ordering of the data wasapplied for encoding. This "relaxed" M-LVQ schema will
be referred to as RM-LVQ. The data set was reordered sothat experiments from MD coupons are presented first at
each encoding cycle. followed by BD and UD. This is anarbitrary decision, partially justified by the fact that, unlike
UD and BO, MDs contain both 0 0 and ±45° fiber layers, but
did result in the most interesting classifications that will beshown below. The clustering schema run for 2 to 20 classes.
Classification results are to be discussed later.

However, common mathematical clustering evaluation
criteria such as the R-criterion, which has proven somereliability with respect to AE clustering (Anastassopoulos etal, 1995; Philippidis et al., 1998), cannot take into accountsuch qualifying factors. Indeed. for evaluating the set of
RM-LVQ classifications of variable output size, number ofclasses. in the 2 to 20 range. the R separability measure isof little use since it does not produce any significant minimization, which would indicate a particularly "good" clustering, from the engineering point of view.
More specifically, the minimal R value, taken for the
preprocessed items in principal component space and valued at 0.781537 (Fig. 2). is found at the 4 class classification. which produced classes sized 40, 1630. 145952. and
130 records each. This clustering placed most AE signals in
one class, Class#2. and created two classes too small to beof any interest. The remaining class, labeled Class#l andsized 1630 records, whose amplitude distribution (Fig. 3),
indicates to be of some significance, contains very bighamplitude records. around 85 dB, from all experiments.
However. with the GRP coupons producing high amplitude
signals at early load, the class is distributed around all loadstages and thus cannot be used to make assumptions about
the coupon's remaining strength or life. Figure 4 shows the
distribution of AE records classified in this class over theload normalized per experiment.

5. Results and Discussion
5.1 Clustering Selection
The final. preprocessed, data set consists of 147752 records projected into PCs. The set is a union of all AE signal
descriptor vectors, recorded during 4 MD, 4 BD, and 4 UD
coupon tensile experiments. Focusing on the amplitude
distributions of signals recorded on each coupon type, it isseen that the presence of 00 fibers, i.e. in the loading direction, gives a distinguishable signature on the frequency distribution plots. as shown in Fig. 1. The MD and UD data
sets exhibit a multi-modal distribution of amplitudes peaking around 52, 66, and 74 dB, while BD amplitudes aremostly distributed around 52 dB. Amplitude distribution
analysis has been a trusted tool in detecting AE sources(pollock, 1981). Practical AE approaches suggest that failure modes produce mostly unimodal amplitude distributions. thus in this case at least three amplitude-separable
failure modes take place in the experiments. Therefore,
when evaluating a clustering of such data. it would be desirable that these modes are distinguishable as individual
clusters preferably having a unimodal amplitude distribution.

to-Class RM-LYO Classification Results
Conversely, by moving on to classifications rating
higher. worse on the R-criterion. the 10-class classificationshows some interesting properties. The clustering placed
69% of the entire AE data set to Class#7, a class of records
with mostly low signal characteristics. as seen in Table 2.
However. the mean counts, energy. duration and amplitude
values for Class#7 are very close to the mean recorded values of BD experiments; see Table 3. Additionally. the overall amplitude distribution of the class. as shown in Fig. 5, is
72

12

600

l'

1\\

9

~
.!!

::III

CIISSOCIISSICIISS 2
OassJ

I
I
I
I
I
I
I
I
I
I
I
I

6

<

J

\

\

'\
'\

1\
, ..,I ..."

40

'\
'\
'\

60

0.0

0.2

I

~

=
<
III

\

,,

70

200

"-

....,

o

.):»1

so

Oassl

400

,

"

0

1-

80

90

100

0.1

0.3

0.4

0.5

0..6

0.7

o.a

0.9

1.0

Normalized Load

Amplilvcle (dB)

Fig. 4 Load distribution for c1ass#1 of RM-LVQ 4-c1ass
classification.

Fig. 3 Amplitude distribution for RM-LVQ 4-class classification.

Table 2 Descriptor ranges for RM-LVQ lO-class case.

m

Size

RT

CNTS

ENE

DUR

AMP

AVF

CNP

0

31

1
min
99
max
mean 18.38
min1
max 177
mean 33.24
0
min
76
max
mean 24.42
min
14
max
231
mean 65.29
min1
max 105
mean 37./4
min
1

447
844

361
2129

3438
7550

81
100

106
157

1
13

649.70/350.774822.61

96.58

135.03

6.41

67
265

48
436

594
2522

65
100

73

238

1
28

129.23201.071047.2886.76

124.04

6.88

26
127

17
222

173
1119

55
100

45
205

1
14

49.06

65.52

374.9578.50

133.27

4.33

3
221

3
182

46
1182

45

2
39

25.00

15.88

141.99

11.54

9
119

5
226

179.4066

96
6fJ.23

16
307

1048

52
91

48
277

1
15

35.71

3/,50

252.0569.99

145.17

7.78

3
75

3
62

9
490

57
100

52
333

2
13

29.60

22.79

212,86

8.36

104
138

1358
3765

80,38
64

/45.44

156
396

82
169

1
22

259.77447.682058.28

6.99

1
47

5
634

91.16
44

126.92

2
51

8

100

444

1
13

/275

7.83

97.07

56,70

134.30

4.05

236
339

818
1027

1904
3240

98

41
51

287.5

9225

2572.0

99
98.5

104
123
IJ3.5

46.0

304
596

352
1140

2319
4965

78
100

112
159

1
23

414.89756.653035.3693,31/37.17

6.95

I

752

2

12104

3

11688

4
5

19858
931

max
6
7

8

9

113
1022262
47

48

mean 8.91
1
min
max 143
mean 27.35
0
min
85
max
mean 20.95
246
min
max 312
meD1t 279.0
1
min
max 104
mean 26.14

similar to lhe one displayed by individual BO data subsets,
given in Fig. 1.

100

dB, and Class#2 peaking at 83 dB. The latest can be used to
identify the presence of 0°-fibers in the coupons. Containing records with above-average valued counts, energy, duration, and relatively high amplitude, Class#2 contains 8.6% of all un AE, 13.2 % of all MD. and only 0.89 % of BD
AE. Similarly, focusing on two of lhe smaller-sized classes,
namely 0 and 9, both are characterized by high energy andduration records. Each of these classes contains AE from aU

These are indications lhat lhe AE classified in this classis caused by mechanisms not related to the presence of 0°fibers. Classes#2, 3 and 4 are also of significant size. These
classes show a trend towards unimodal amplitude distribution. with Class#4 peaking at the originally predicted 74
73

shown in Table 4. The twelve smaller classes were notanalyzed further since they did not contain AE from all
experiments. Figure 7 shows the distribution of the larger
classes on load. normalized independently for each experiment. Once again, due to the nature of GRP AE, mostclasses contain AE that was recorded at all load stages, andcannot directly be used for failure prediction. The percentage, however, of records gained by some of the classes appears to vary depending on the load stage.

10....:>t

:..

6

,,

.~

=
III

ClGssO

I'
I\
I \
I \
I
\ClMs7
I
\
\
I
\
I
\
I
\
A

8

4

~

'

,
I
,

2

/

0

,

Cass2

Cad
0lISS4

alllSsClISS60lISS7

V\
\

Cass8

" C1ass4)... ...
/ \ \Clius 3 , / \)~--'" C1D.Js 2~ "',.........- ....,~.;:".::____....':"...... ~; ......=T>n

ClISS9

70

100

50

40

a_I

60

90

80

This property can be exploited to predict the remaining
life of the coupon via ratios of class sizes per load stage, i.e.
ratios of the number of AE records recorded during the period under consideration and classified in the classes usedby the criteria. Indeed, application of such criteria gave
interesting results, and proved quite fit to purpose, especially in cases where 0 0 fibers were used. i.e. UD and MD
coupons.

Amplitude (dB)

Fig. 5 Amplitude distributions for RM-LVQ lO-Class classification.
MD and un experiments, with no BD record classified inthem.

One of the criteria created splits the experiment in threestages: not critical, less critical, and critical, The stages arecharacterized by three score numbers nt, nl' as follows:
Not Critical (1)n. Ss/(S4 + s'S)

2Q-Class &M-LVa C1assjfication Results
Following the same procedure, the 2Q-Class RM-LVQ
classification rated worse on the R-criterion. The trend towards mostly unimodal amplitude distributions per classcontinues in this classification as well. as shown in Fig. 6.
The clustering consists of eight larger sized classes, as"3=Less Critical (2)n2 = sJ{l + s's)

Critical (3)03=(5) + S4 + slS)1 (1 + SIS).........
.........

4._._ .. _..

Class 11{"...'u
,'"

I

\

r,

ClaSS 5

, \1 \
I
t \\
, f'
,t
I
'1'\
\
,I :1• \'\\3

I :/ \'.
,

:/

I'

2

'//

I •• I

1:/
I : I,I .: II:,I ,1

, :J.....

o

\

,\~

_4'''

40

50

\'
•

"

...

'

._ ... _....

---_.

.........
.........
----.........
.........
..............

\

,

,

(\

'flO

..........

~jj\\ ,.. • ,\ '. i'\ '.,.
\ "'/
\,/
...
\ I.
\.

.........

;, .\.
\.

.........

./"'

\.

I: I
\\
':J. I v,.y;.~

, "

.,',

,'.

I

,\

--------..........

Ii Class 3\.........

'

\

\
\
\

'

'

~
,1\ .........

"\,

"'.
\·f.. i\ \~'<>o "'. ~\\....

':"" ".,\.., 1\ . J
~.
~
-

'..

60

.........

I' I \

"';

,....

,,,,,

", ~

'.-0

lJt:.... "" .~:~.~.~~. ~ . .,........

~..:eA

70

80

90

Amplitude (dB)
Fig. 6 Amplitude distributions for RM-LVQ 2o-class classification.

74

100

Class 0Class 1
Class 1
Class 3
Clau4
Class 5Class'Class 7
ClauS
Class 9Class 10Class 11Class 12Class 13Class 14Class 15Class 16Class 17Class ISClass 19

Table 3 Signal characteristic values recorded during GRP tension experiments.

RT

CNTS

ENE

DUR

AMP

AVF

CNP

24.93

20.48

18.16

150.0861.88

139.66

5.06

std. deY. 15.24

19.74

32.92

151.12

9.93

34.33

2.82

mean

29.20

13.66

8.12

97.93

54.64

137.255.24

std. deY. 18.99

11.54

11.30

78.62

6.61

41.22

3.52

mean

29.34

28.11

27.32

219.08 64.07

128.095.52

std. dev. 18.44

29.93

55.24

229.39

1\.08

31.89

3.01

mean

26.92

20.93

18.32

156.20 60.90

136.295.21

std. deY. 17.08

22.02

37.56

168.56

35.65

3.03

AESource(Coupon Type)

mean

UD

BD

MD

All Types(MDUBDUUD)

10.21

Table 4 Descriptor ranges for the larger-sized classes of RM-LVQ 2O-class clustering.

m

Size

1

8625

3

4

5

10

11

15
18

27994

24848

38283

8713

22828

7053
5276

ENE

DUR

AMP

AVF

CNP

5

5

123

63
768

46
96

36
307

4
36

68.84

113
28.17

61.18

26

SO

/43./6
103

/1.47

5
65

/8.7/
3

200.26

0
63

59

87

298

1
16

30./8
0

27.20

/9.22

401
173.5866.65/58.96

6.84

2
39

1
26

8
215

44
83

129
280

1
12

9.04
3

4.97

53.78

54.02

17/.05

3.26

2

61

39
8.07

24
541

45
76

16
166

1
14

//9.1356.93/24.56

532

173

57
100

64
201

79./4
44

/33.09

RT

min
max
meon
min
max
mean
min
max
mean
min
max
mean
min
max
mean
min
max
mean
min
max
mean
min
max
mean

22
211

45
J6.04

0
97

CNTS

30.45
0

14.76

45

113
48.54

66.78

1163
369.26

2
55

1
27

14
615

5.63
25

4./2

81

12
147

42.40

52

2
57

/0.56

23.43

22.86
0

75
14.13
23
85
37.07
0

26

18
222

1

81

8
142

67.58

50.84

90.40

11
3.99
I
8
1.04

181
479

60

91

89
208

1
13

47.99

303.3674.72

140.28

7.J6

2

14
840

56
100

18
166

1
12

208.3572./4

JJ3.28

4.70

64
/8.03

where Sk is the number of items classified in class k for theperiod under consideration. The stage can then be determined by comparing the above listed ratios for a selectedload period and finding the largest. If, for example, at any
given load stage, z, only AE signals recorded in the period
O... Z are considered, the overall n-scores are shown in Fig.
8a, and 8b. The values displayed in Fig. 8a are sums of thecalculated n-score values of each experiment with load
normalized to the experiment's maximum load. Figure 8b

shows the percentage of each n-score over the sum of all nscores for the period under consideration. The overall trend
shows the prevalence of n. values in up to 13% of criticalload, 02 in 13% to 60%, and n3 in the last 40% of the coupon's life. To verify that this still applies to AE from eachindividual experiment, Figs. 9 and 10 show the n-score percentage for each MD and UD AE data separately. Despiresome fluctuation. the figures do show that the schema generally performs properly.
75

5
4

-::

.-.

3

"$.

lID3

CIoaI
CIoa3
CIoa4
CIoa5
CIoaIO
Oaoll
CIoal5
CIoaII

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:2

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al

",/.

0113
~Ib

91'/.

I I
I !
J ;'
Ii,'
If,'

~

~

/

29'4
0'10
Q.O

IT" •.'/'"/.1,' ,/,': I

cu

04/.,.'/,/',"",~:;'--'I

-

oI

,

,

,

0,0

0.1

0.2

~

,

1

0.3

"

0,4

__

!

05

0.6

!

~ft .n
••v
,

I

Q.O

,.
;;

cu

04

~

(\

f

\.

'f

6

o.a

1.0

0.6

o.s

1.0

'7S%
91'4

\,

l

~

"",...........

,f

c:

10710

''',,

1

lL6

OOS

I

- - - DJ- - DJ! \,~,

1.0

25'10

Fig. 7 Load distribution of RM-LVQ 20-class classification.

9

o.a

91'4

0'/.

Nonnallzed Load

11

0.6

'7S%

u.a v•.,

0.7

1.0

100'4

.' ~

,.w;:-::-;~........

o.a

UD4

.-:

<L':a;=;;~~~.~.:;;'~_ ··<'···"
__ ~_

lL6

0'4
)

,
V-

0t

I

!

-'

0.0

Q.O

006
,

,

,

0.1

,

0.4

'

0.6

,

!

0.8

1.0

",4
91'10

(a)

29'4

2S'Io

iii~ii~~~~~~~~!!!!!!1111111111 ~

0"10
Q.O

lU

04

Q6

o.a

cu

04

Normalized Load
Fig. 9 n·scores, perfonnance on tID coupon AE: AE period

0'4
QO

04

10710

Nol'III&III.ed Load

:::1

cu

=(O..z).

1.0

NonllllliJ4cl Load

The overall trend can by verified by looking at the individual AE subsets from each experiment separately, as
shown in Figs. 12 and 13 for UD and MD experiments,
respectively. The figures give strong evidence that by using
AE recorded at a later stage in the experiment, enough infonnation is still revealed to enable a remaining-life orstrength prediction mechanism.

(b)Fig. 8 (a) and (b) n-scores and overall perfonnance for entire AE data set: AE period (O.. z).

=

The same n-score definitions are applicable even if
only AE recorded during a shorter period, not the entire AE
history, of the experiment is considered. For example,
splitting each experiment in 30 load periods, and using only
AE recorded in each period, the n-scores can still be used tohint about the loading history of the coupon. The overall
performance of the schema is, in this case, shown in Figs.
11 a, and II b. Once again, the sum of n-scores for AE recorded in periods equal to 1/30-th of each experiment's
load is displayed in Fig. Ila, while Fig. lib shows the percentage over the sum of all such scores for the given period.

Notably, the above described scheme is only one ofseveral possible. Another similar criterion uses:n = (S3 + SI5)/(5 1 + Sll)to determine if the coupon has entered the last half of its
remaining life. The criterion can be applied for the problem
of prediction using AE acquired during particular load stage
periods. Once again, periods equal to 1I30-th of the totalload per experiment are used for the evaluation. Overall, ntakes values shown in Fig. 14, indicating that n> I can beassociated with load around 50% of the failure load.
76

fied by NNC, thus no new information is revealed by thealgorithm. Here, the NNC class-prototype vectors are set
equal to the mean vectors of RM-LVQ classes viewed inregular descriptor space; Le., with AE data filtered andstandardized but not projected in PC-space.

MD3
tOO'/.~

7.5"/0

DI

~

!

Olb

$"/.

~Ib

2S%

Performing only on-line capable operations, the new
AE is standardized using the mean and standard deviationof the original RM-LVQ data set, and all filters except empirical ones are applied. For each new AE data set, NNC isused to classify the AE data recorded during a limited loadstage and the class-size criteria are calculated. Overall, the
NNC classification of new data creates classes with similar
physical properties to those produced by RM-LVQ, which
is verified by Fig. IS. In testing the online applicability of
the criteria, two cases are considered:

0'/0
0.0

CU

0.4

0.6

0.8

l.O

Q6

0.8

l.O

MD4

=lIm
25%

0'/0 '
0.0

CU

0.4
MUS

(a) using NNC-created classes of all AE recorded from the
Do-damage state to current load.
This involves NNe classification of all AE from the
beginning of the test. At any point, the NNC classes can beused to calculate the n-score criteria, which would predictthe level of damage that has occurred so far. The overall
evolution of the first n-score criteria calculated in suchmanner can be visualized by Figs. 16a, and 16b where thesum of n-scores over the load normalized to the maximum
reached, per experiment, is displayed. Once again, at any

100%7.5"/0
$"10

25'%
0'/.
0.0

CU

Q6

0.4

0.8

l.O

MD6
100'/.

7.5"1.

~
I

16

$"/.

.,f;
tl

25"/.

- - - III- •••- III

.!
it

12

• • • • • • • • • • ',',',',',',',','.',',','.',',';',
0'/. I ••"I "'''."",
QA
0.0
cu
0.6
0.8
l.O

•

i

1\
.

':'\'

i'

:

1\1 \"1\ii"8Normalized Load

Fig. 10 n-scores, performance on MD coupon AE: AE period = (Ouz).

~

~

t

,,,'

.f

4

tI~

I " :.

,,-'

r·

~~
I ':;1' .._

\ ...

0.0

In an attempt to evaluate the aforementioned criteria
schemes in an online-like situation where AE is recorded
and failure criteria are applied while the test is in progress,
four new tension experiments were performed using materials similar, but not identical, to the ones already used for
the RM-LVQ data set. The four coupons, identified MON,
are MD-type specimens with [±4S/~/±4Sh glass fiber layup, different from the [O/(±4S)2/Oh MD, whose AE was
included in the RM-LVQ data set.

,I

-

0.2

o.A5

0.4

'.-

'"

~""'~ '\ ;"'........

"'v~-'-~o

5.2 Applying the Criteria on Unknown AE Data.

,.. : \ ~,/- ", \,
,'.1'
...,

,
~

0.8

1.0

Nllrmaliud Lad(a)~ ~ ~ !!I! IIIIIIm!!!!! ~
0'/.

0.0

The schema created uses an Euclidean distance nearest
neighbor classifier (NNC) to classify incoming AE to 20
classes of similar propenies to those created by RM-LVQ
while repeatedly applying the criteria to determine the load
criticality. The I-NNC, (Tou and Gonzales, 1974), is thesimplest form of supervised classifier and requires a set ofpre-calculated fixed class prototypes to compare with incoming AE vectors. These cluster centers are never modi-

CU

OA

Q6

QB

1.0

Nllrmaliud Load(b)

Fig. l1(a) and (b) n-values and overall performance for
entire AE data set: AE period (113Q..th).

=

77

UD3

MD3

1lJJ'/.

[i;J

7.W.
9J%

DI

100'10Olb'15"10

~Ib

5rh

25'/.

DI

Olb
(;]
~Ib

25'/0

cr/. I • ,',',U,',',',',',',',',',M,W,',Q,W,N,Y,',U,H.",H,W,W,W,U,.,
QIi
0.0
QA
oa
cu
LO

0'.;'0 I .',',',',',',',',',',',',',',',',',' jill,',',',',',",",.,',

0.0

cu

CIA

VD4

1lJJ'/.

!II%25%

cr/. I ,',',.,',',',',',',',',',',',.,-,',',-,
0.0
QA
QIi
cu

o.a

100%
'l5"'{'

91'10

25"/.

LO

I

li!~!III!!!II!II"'11II

0'/.; I' ,',.,' ,',',','.',' ,',' " I I I I , ' , ' , ' , ' , ' , ' , ' I I I' II , ' , ' " I' ,',

LO

0.0

cu

QA

VDS

QIi

o.a

LO

MDS

100%

lOO'/o'lS%'15"/0

!D'I.

91'10

25"10

2S"/o

0'/. I .',',' ',',',',',',',',',',',',',',',',' ',',',',',',',',',".

cu

0.0

CIA

Cl.6

oa

O'IG f i',',',',',',',',',',',',',',',',',',",',',',"',',',;,'",',',',

LO

0.0

cu

CIA

VD6

QIi

oa

LO

MD6

1lJJ'/.

IIlJYo

".1.

'lS%

91'/.

91%

25"1.

J5%

.........•••••••"I.

erAt I ili·,I,I,·tUtU ,.,',',',',',', I • , , • , , , , • i • i • , ,
0.0

OJ

QA

QIi

o.a

LO

I ,', I ' ' , . , ' , ' , ' , . , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' , ' ' , ' •0.0Normalized Load

cu

0.4

D.6

o.a

to

Normalized Load

Fig. 12 n-scores. perfonnance on un coupon AE: AE period (l13Q.th).

Fig. 13 n-scores, performance on MD coupon AE: AE period (1130-th).

==load stage. the classes used to calculate the n-scores depicted in the figures consist of AE from 0 to current loadstage. The overall trend is repeated for individual experiments as is evident in Fig. 17. showing that the criteria
could still be applied on an on-line mode of operation.

1.8

l.2

i

oa

MD4

I ~ml !IU

".,{,

Cl.6

(b) usjng NNC-created classes of a Ijmjted number of Past
AErecords.
This case simulates a situation where pre-loading has
been applied to the coupon but no AE history is available.
Based on very limited AE records (whose rate of emission
varies with load), this is the hardest of all cases considered.
To test the applicability of the criteria-based schema, the
NNC is applied to sequential subsets of 300 AE records andthe criteria are calculated using the classification of theserecords only. The classes which are created and used to

Q.6

0.0

lU

0.4

0.6

0.8

1.0

Normalized Load

Fig. 14 Overall values for second criterion (AE period =(l/30-th).

78

ClutOClass IClassZClass 3
Class 4
Class 5Class 6Class 7
Class 8Class 9Class 10Class 11
C1assUC1us13
Class 14Class 15Class 16Class 17Class 18
Class 19

Class 11

4

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80

70~·.!...1:;""

90

.'.

..:~

100

Amplitude (dB)
Fig. 15 Amplitude distributions for NNe 2G-c1ass classification (overall MDN AE data).

6r~1- - - DI,calculate the class size ratios are subsets of the 300 AE record sets. With such a small number of AE records considered, there are periods where very few elements are classified in the classes used for the criteria; this creates discontinuities in the ratios and makes results fluctuate. Still, as
indicated by Fig. 18 with results from coupon MDN2.
showing the progress of n-score values for the criteria applied on MDN coupons and the average normalized load forthe period, whose AE is used, n J and n2 still generally prevail during the less critical periods of the experiment whilen3 tends to dominate the last loading stages.

,,"

""

- - - - DS

,,
,,

4

",

f

¥
c

\

2

""

"

\7C=-".,",--......................

0
\

,

•

0.0

,

•

0.2

,

•

U

"

, !

0.6

0.8

1.0

N_alized Load

6. Conclusions(a):::1 iiil~~~~~~~~~~~~!!!!!!11111111 ~An AE study of GRP tensile failure behavior whensubjected to loading is presented along with an unsupervised, ANS-based, pattern recognition procedure, which
can be used to predict remaining strength or life of the

15%

0'/0
0.0

0.2

0.6

OA

o.a

GRP.

1.0

The principal-component projection proved useful inthe clustering of similar signals, eliminating the need forextensive experimentation in descriptor selection. Classifi
cation performed on a union of AE from experiments of all
different coupon types allowed the creation of classes with

Normalized Load(b)

Fig. 16(a) and (b) n-scores using NNe. overall performanceon all MDN AE: AE period =(O.. z).
79

properties of unifonn validity. Focusing on classificationsof classes with unimodal amplitude distributions also
proved suitable.

MDNI
IGJ'~
".~

!1I'1oThe unsupervised classification procedure presented inthis work shows that for these AE data sets such clustering
is advantageous, producing valid results and raising questions about the use of mathematical clustering-evaluation
criteria as a method of selecting the best classification
amongst several - as needs to be done since the number ofnatural clusters in the data is unknown - and suggests analternative approach based on class amplitude distributions.

2S'1t
0'10
0.0

Q.6

OA

LD

MDNI
l~

15%

SI'Y.

Working along these lines, interesting classifications
are revealed. There is strong evidence that a classification
exists, in which grouping of AE caused by 00 fibers and AE
unrelated to 0 0 fibers occurs in two amplitudedistinguishable classes. Furthermore, the classes tend toobtain AE records at different rates depending on the load
criticality. This observation, expressed as a set of threeclass-size ratios, is used to fonn a criterion linking AE to
the percentage of remaining coupon strength or life. The
criterion proved successful in aU coupons tested, allowing
the creation of a supervised, trained, schema where loadstage is successfully predicted using new AE not consideredin the original unsupervised analysis.

J5%
0'1.
0.0

CU

o.a

Q.6

OA

1.0

MDN3
1~

",1.

91%
19'1.

or.
0.0

CU

CL6

OA

LO

MDN4

100%

Although the study is limited to small coupon testing,where damage occurs very close to the AE sensor, it verifies the strong potential of unsupervised AE analysis.

",1.

lIl'/.

J5'1o

0'10
0.0

CU

OA

o.a

CL6

All preprocessing, classification and pattern recognition methods used in this work were performed using acustom pattern recognition software package, VPR, runningunder Win32. Text-only versions of the software are available for other operating systems. The program is available
from the authors upon request.

LO

Normalized Load

Fig. 17 n-scores performance using NNe classifications on
MDN coupon AE: AE period = (O..z).
1.0 r

'C
\II

.3
'C

~

30

u[

I'

0.6 l

E
0.4
=

I

I'
I ,

,

l

~

n

I

,
,

:~
I,

\

, I

I,
1'\ I
\,.
"
'-" \

I ,
• ,

LI

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,

7

"

I

I,

,\ l'!:'

Z

10

'I

0.2

o
0.0

0

2.000

4000

6000

8000

10000

AE Hits

Fig. 18 n-scores and performance on MDN2 coupon AE: AE period=300 Hits.
80

References
A.A. Anastassopoulos and T. P. PhiJippidis (1995), "Clustering Methodologies for the Evaluation of AE from Composites", J. Acoustic Emission. 13(112), 11-22.
A.A. Anastassopoulos, T. P. Philippidis and S. A. Paipetis(1996), "Failure Mechanism Identification in Composite
Materials by Means of Acoustic Emission. Is it Possible?",Non Destructive Testing, eds Van Hemelrijck and A.A.
Anastassopoulos, Balkema, Rotterdam, pp. 143-149.
A.A. Anastassopoulos and T. P. Philippidis (1998a), "Pattern Recognition Analysis of AE from Composites", Proc.
23rd European Conference on Acoustic Emission Testing,
Vienna, Austria. pp. 15-20.
A.A. Anastassopoulos, S. J. Vahaviolos and J. C. Lenain(l998b). "Signature Recognition of Acoustic Emission
from FRP Structures", Proc. 7th European Conference on
Non Destructive Testing (EeNDT), Copenhagen, Denmark.
M. R. Anderberg (1973), Cluster Analysis For Applications. Academic Press, New York.
D. L. Davies and D. W. Bouldin (1979), "A Cluster Separation Measure", IEEE Transactions on Pattern Analysis
and Machine Intelligence PAMI-l, pp. 224-227.
R. A. Johnson and D.W. Wichern (l992), Applied Multivariate Statistical Analysis, Prentice-Hall International,
New York.
T. Kohonen (l988), Self-Organization and Associative
Memory, Springer-Verlag, Berlin.
R. P. Lippmann (l987), "An Introduction to Computing
with Neural Nets", IEEE ASSP Magazine, 14. 4-22.
T. P. Philippidis. V. N. Nikolaidis and A. A. Anastassopoulos (1998), "Damage Characterization of Carbon/Carbon Laminates Using Neural Network Techniques on AE
Signals", NDT&E International, 31(5). 329-340.
A.A. Pollock (1981), Acoustic Emission Amplitude Distributions, International Advances in Nondestructive Testing,
7,215-239.
W.M. Rand (1971), "Objective criteria for the evaluation ofclustering methods", J. Am. Stat. Association, 66, 846-850.
P. K. Simpson (1990), Artificial Neural Systems, Pergamon
Press, New York.
J. T. Tou and R. C. Gonzalez (1974). Pattern Recognition
Principles, Addison-Wesley, Reading, MA.
81

42nd AEWG Meetingallowed to go into free fall. When the maximum allowed velocity is reached. the emergency brakes are applied. All
detected AE is carefully examined and the approximate source locations determined. A wrinen procedure with failure
criteria has been established. For the first five years, no AE indications of flaws were seen. On a test in October
1998. a large AE burst was seen at the peak load. A retest with sensors concentrated in the suspected region
confirmed the finding. The braking system was disassembled and an inclusion inside a main pivot pin was found in
the general position indicated by the AE analysis.
Fast Fracture Source for Quantitative AE Simulation: Laser break-down of Silicone in a
Crack. Kanji Ono, UCLA; Ryuji Masaki, Mikio Takemoto, Aoyama Gakuin University
We propose a new artificial fracture source for quantitative acoustic emission (AE) characterization. The source
utilizes dipole forces produced by the break.ciown of silicone (or other liquids) placed inside a slit (or a crack). We
irradiated a focused Q-switched Nd- Y AG laser beam (S ns duration, 20 to 90 mJ) on the silicone in a 0.3-mm wideslit. The slit was machined on the distal plane of a 70-mm diameter cylindrical block. The out-of-plane displacement
produced by the resultant bulk waves was monitored on the outside surface using a heterodyne-type laserinterferometer. Quantitative source simulation to the displacement revealed that the silicone break-down produces
dipole forces to slit walls (equivalent to Mode-I fracture with the direction of motion normal to the wall). The
source had a rise time of 0.18 us, which was unaffected by the level of laser beam energy. However, the source
opening volume increased with laser beam energy. This source has the rise time shorter than that of a pencil-leadbreak source and allows the calibration of a sensor-structure system to several MHz range. Next, this break.ciown
source was employed to determine the transfer function of a broadband AE transducer and the propagation medium.
Using the transfer function determined, we estimated the source function of a compression-type PZT element coupled
to two half-cylinders and excited by a pulse. The source function agrees with that measured directly by a laserinterferometer, confirming the utility of the break-down source. This experiment also verifies that such anarrangement of a PZT element and structural shapes can be a good AE source for simulation and calibration. Other
combinations of sources, structural shapes and AE transducers are also examined and results will be discussed. For
example, we monitored AE signals from delayed fracture in a dual-phase stainless steel and successfully studied thesource kinetics by the waveform simulation technique developed here.
Studying AE from Carbon Fiber Reinforced Silicon Carbide for use in re-entry Component
Health Monitoring System (HMS). Richard Nordstrom, Hartmut Vallen, Vallen-Systeme GmbH; Jan
Werner, Andreas R. Meyer, Technicai University Munich; Amd Reutlinger. R. Graue, Kayser-Threde
Carbon fiber/SiC matrix material is being developed for use in the nose of the X-38 as a thennal protection system.
Within this context it is desired to exploit AE generated from the CJSiC as part of a health monitoring system(I"IMS) strategy. The project has begun with material and simulation testing and later will be implemented in actual
flight. The HMS including AE has the status of an experiment on the X-38.
The first steps of this program are laboratory material testing and developing in-flight monitoring capability. The
basic understanding gained from AE measurements on ClSiC with a high end AE system (AMSY4) provides someguidelines for specifying the in-flight system. The in-flight HMS program and X-38 design place more specificconstraints on the in-flight AE monitoring instrument.
Initial testing is described wherein mechanical and thermal material tests were performed. Tests where controlled
variation of loading rate, load program. stress concentration and temperature were carried out. Most tests were cardedout on a laminate consisting of 16 unidirectional plies, however a CJSiC weave was also tested. The ClSiC
composites generated a great deal of AE in all test variations performed. The trend seen first in simple static bend
testing was repeated in various test programs: many AE measurements increase toward failure in a steady process,most notably: peak amplitude, energy. rms!ASL, and number of hits/events. Load program, load rate and specimen
geometry are parameters varied in initial testing. Damage observations are made in-test and microscopy is performed
post test to help characterize damage. Specific results are presented for various test configurations. Transient
recording of AE signals and pattern recognition of AE signal type are further tools used to analyze the initial testing.
The in-flight AE monitoring system is to provide data for the HMS acquisition system. The acquisition speed for all
subsystems is set within the HMS specification (l Sample/ms); the total monitoring time period is set by re-entry
flight characterization. The AE monitoring system is. designed so that multiple AE acquisition modules can be
attached to each AE sensor, each module providing a characteristic of the AE per millisecond. A data reduction
82Real-Time Tool Condition Monitoring in Cold
Heading Machine Processes Using an Acoustic
Approach
Henrique L.M. dos Reis, David B. Cook and Aaron C. Voegele
the raw wire is drawn from a spool and forced to passthrough a series of feed rollers. The wire is fed through a
cut-off bar until it reaches the required length gauge. Thisgauge determines the amount of material in the final part.
The cut-off bar shears the material and a pair of transfer
fingers moves the sheared piece of wire into alignment withthe die; then, a moving ram assembly pushes the work piece
into the die. The first blow punch is rotated into alignment
with the die by the rotating head on the end of the ram.
After the first deformation step, i.e., first punch, is completed. the second deformation step, i.e., second punch,takes place. As the second punch reaches the front deadcenter of its movement, the knock out pin advances to pushout the part as the ram recedes. The finished part is pushed
free of the die and drops to the bottom of the machine into acollection bin. The process repeats itself as the next cut-off
bar comes into alignment with the die. The intermediatestep in the defonnation sequence is similar for a wide variety of panhead fasteners; the cone-shaped head is produced
by impacting the bar blank with a straight punch pin. The
second step, i.e., the second punch, of the process is uniqueto each type of fastener produced; for the panhead partshown in Fig. I-a. the Tamper Resistant Torx T-20 recesspunch pin shown in Fig. 2, is used.

Abstract
In cold heading manufacturing processes. complete orpartial fracture of the punch-pin leads to production of outof·tolerance parts. An on-line process monitoring system
has been developed to assure that out-of-tolerance parts,
caused by failure of the punch pin, do not contaminate thebatch of acceptable parts. A four-channel data acquisition
system was assembled, and corresponding software developed. to collect and store the acoustic signal generated
during the manufacturing process. Waveform features wereextracted from the acoustic signal. A genetic algorithm wasdesigned to select the smallest subset of waveform features
necessary to develop a robust artificial neural network that
could differentiate among the various conditions of thepunch pin. i.e.. no failure. complete fracture. and partial
fracture of the punch pin. For the particular part being
manufactured. the developed monitoring system is able toterminate production within seconds of punch pin failureusing only four wavefonn features of the acoustic signal.

1. Introduction
Cold heading is a manufacturing process used to produce complex shaped components from metal rod or wire.
This process utilizes interchangeable punch and die tools ina versatile high-speed multi-step manufacturing system.
Through a progressive series of deformations. manufacturing of complex parts. once achieved only through multiple
turning. milling. and drilling stages. can now be manufactured in a cold header. The advantages of cold heading
processes over traditional manufacturing methods including
higher strength pans, better surface propenies. higher production speed, and lower manufacturing costs (Carlson.
1987; Dallas. 1980; Browne, 1985). However, the realization of these advantages is dependent on both the reliabilityof the cold heading machine during operation and the skillof the operator during setup and during production of the
manufactured parts (Ghostline, 1992; Crum. 1996; Brankamp. 196; Terzyk. 1996).

To achieve lower costs for cold header manufactured
parts. machine down time. setup time, and tool replacement
frequency needs be minimized; as a result. quality control
has had an increasing economic impact on cold header
manufacturing processes (Terzyk. 1996; Reis and Cook.
1997; Reis and Voegle, 1997). For example, the primarycustomers of the precision fastener sector of cold header
manufacturing use assembly-line robots to attach fasteners
quickly and precisely to their products. The influence of
one out-of-tolerance part stopping the production of an entire automated assembly line has a severe cost penalty. The
four main causes for out-of-tolerance fasteners are materialinconsistencies, inadequate lubrication, punch pin wear, andpunch pin fracture. Each of these four failure modes causes
unacceptable part production. but becomes critical at significantly different rate. The first three causes tend to affectpart quality over a long period of time; this extended
amount of time gives the operator the opportunity to observe the progression to failure. and take corrective action
at the appropriate time. In these slow progressive failuremodes. 'failure' is defined at the discretion of a skilled operator to maximize the efficient operation of the cold heading process. The fourth cause, namely, punch pin fracture.

An example of the manufacturing progression of apanhead fastener produced by a double-blow, single-die
cold heading is shown in Fig. 1. Part production begins as
Received 30 June 1999. The authors are affiliated with Depanmenl of General Engineering. University of Illinois at UrbanaChampaign. Urbana. Illinois 61801. (h-reis@uiuc.edu)
Journal of Acoustic Emission
Volume 17. Numbers 1-2

830730-0050199/$5.00'17083·093
Copyrigb~ 1999 Acoustic Emission GroupTable I Data acquisition parameters.
High Pass(kHz)
ChannellChannel 2
Channel 3
Channel 4

0.3
12
40
90

Low Pass
(kHz)
12
40
90
150

Amplification (dB)

Buffer Length

7
14
24
30

4096
4096
8192
8192

(kHz)

i

BI~nk

~

B-

Fig. I (a) Cold heading progression (left); (b) Cold
header tamper-resistant T-20 Ton recess panhead punchpin (above).

~
•

195
195
781
781

-.

Punch 2

Punch 1

Sampling Rate•iIntermediatePart

FinishedPart

DELL GXMT 5200
COMPUTERD

IOMEGA
1GB JAZ DRIVE

STR-82S AID BOARDKROHN·HJTEMODEL 3988
DUAL OIANNEL
F1LTElVAMPLIF1ERSPOWER
SUPPLYMICRO SWITCH
MODEL MHP·R3IL
OPTICAL POSmON
SENSORDIE BLOCKLED REfLECTORr--

COLD HEADER

L __

RAM

DECI MODEL SE·IOOOHPIEZOELECTRIC
TRANSDUCER

Fig. 2 Hardware components for the four channel data acquisition system.
84

able hard drive. The SE lOOO-H directional transducer exhibits slightly resonant response characteristics near 7 kHz,and has a relatively flat response from approximately 10
kHz to 250 kHz. The transducer was mounted to the coldheader die block. The transducer output signal was divided
into the four channels of the data acquisition system. Four
eight-pole filter/amplifiers (Krohn-Hite Model 3988) wereused to independently adjust the bandpass filtering and thenecessary amplification on each data acquisition channel, as
shown in Table l.

cases an abrupt change in the condition of the cold header,which can not be detected by the operator in time to prevent
poor quality pans from being produced.
A common quality control method used to decrease therisk of unrecognized punch pin fracture consists in placing
part count limits on the life of the punches based on previous failure rates. However. the number of parts produced
before failure of the punch pin varies considerably for different runs; therefore, the pan count limit must be set atvery low percentages of the maximum attainable life.
While pan limits do prevent a large quantity of out-oftolerance parts, the premature replacement of the punch pin
results in significant unrealized production along with increased machine downtime. In an attempt to prevent poor
quality parts from leaving the manufacturing facility, thesequential batch containment is also common practice inindustry; here, a bin is placed at the cold header exit chutewhich collects pans as they are produced. The operator
periodically checks the quality of the part by verifying its
critical dimensions; if the critical dimensions are withintolerance. the operator empties the bin into a finished partcontainer. If the sampled pans do not meet specifications.
the machine is shut down, and the parts contained in the bin
are scrapped.

An optical sensor (Micro Switch Model MHP-R31L).
mounted on the cold header frame near the moving punch.
was triggered using a reflective tape strip mounted to themoving punch ram. The sensor mounting-bracket was adjustable to allow the time between the initiation of the sensor output signal and the physical contact of the punch withthe material blank to be varied. A Pentium personal computer, equipped with an IOMEGA internal 1GB JAZ drive.
was used.
The software for the four-channel data acquisition system was also developed; it included in a command menu,
whish among other options, allowed the user to independently configure the parameters for the two AID data acquisition boards. From all the collected files, the percentage offiles saved pennanently from the fust portion of the test run
depended on the total number of files collected for the run.
If fewer than 400 files were collected, all of them were
saved permanently. If more than 400 but less than 3,000
files were collected, the final 400 were saved sequentiallybut only every other file of the remaining files was saved.
If more than 3,000 but less than 6,000 files were collected,
only every third file was saved. If more than 6,000 but lessthan 1,000 files were collected, only every fourth file of theremaining files was collected. If more than 10,000 but
fewer than 15,000 files were collected, only every sixth fileof the remaining files was saved. This progressive scheme
allowed data collection runs of varying lengths to be saved
in approximately the same storage space, yet containing thevital information recorded at the end of each test run near
the location of the cold header condition variation, including failure of the punch pin. For an in-depth description of
the data acquisition system, the reader is referred to references (Reis and Cook, 1997; Reis and Voegle, 1997).

Current on-line machine condition monitoring methods
attempt to determine when changes occur in the quality ofeither the pan being produced or the production machineduring operation. However. current methods are only partial successful in detecting complete fracture of the punch
pin, and a high incidence of false calls is observed whichleads to unacceptable excessive machine down time (Reisand Cook. 1997; Reis and Voegle, 1997). This paper presents a potentially robust, real-time cold header machinecondition monitoring methodology that would allow the
production of out-of-tolerance pans due to punch pin fracture to be detected before the pans exit the cold header.
Because the punch pin used in the second punch fails more
frequently than the punch pin used in the first punch, failureof punch pin during the second punch is the main cause ofout-of-tolerance parts. As a consequence, monitoring thecondition of the punch pin during the second punch is theprimary goal of this paper. The proposed methodology has
the potential to monitor the cold heading machine process
condition, and automatically stop the manufacturing process at the moment of incipient failure.

The tests were conducted during production runs of thepanhead tamper-resistant Ton fastener, see Fig. l. The
production runs had an average production rate of 2.97
parts per second. Eight production runs were monitored
regarding the condition of the cold header including failureof the punch pin used during the second punch (Reis andCook, 1997; Reis and Voegle, 1997). The parts manufactured during these runs were characterized as well as thetime of failure of the corresponding punch pins used during
the second punch; complete fracture of the punch pin was

2. Experimental Procedure

Figure 2 shows the developed four-channel data acquisition system (Reis and Cook, 1997). The main components of the system include a directional piezoelectrictransducer (DECI, Model SE-lOOOH), four eight-pole dualchannels filter/amplifiers (Krohn-Hite Model 3988). twodual-channel data acquisition AID boards (SONIX, STR825). an optical trigger, a personal computer, and a remov85

~11

Subpartilicn 13-15 :! 0.8it 0.8j

I

~

~

0.4

0.2 T
I

0 ...
1- - - - - - --0.2 -:-0.4 ~-o.6jI-0.8

rFirst Punch

Second Punch..

-1 "-:- - - - - - - . . . - - - - - - - - - ., •
o
10
5
15

20

25

Time (msl

Fig. 3 Location of 128 point time domain sUb-partitions 13. 14. and 15 in the fustchannel for the typical impact sound waveform.
for any delay between actual and detected failure. The regions were defined as three hundred points before and after
the failure gap. as shown in Fig. 4. The procedure involved
the calculation of the average and the standard deviation foreach of the waveform features within the two selectedboundaries. The difference between the lower standard
deviation and the upper standard deviation boundary wasevaluated and used to rank the waveform feature trends; thelarger the difference. the easier the detection and the higher
the rank (Reis and Cook. 1997; Reis and Voegle. 1997), seeFig. 4. Other changes in the stress-wave feature trends.
such as slope changes. were found not to be sensitive topunch failure.

observed during six production runs. and partial failure of
the punch pin was observed during two production runs.
3. Experimental Results and Analysis or Data

For each of the eight rons. waveform features were calculated for the waveforms stored in each of the four channels (one waveform per channel) using a time-based partitioning scheme as shown in Fig. 3. For the first and second
channel. twenty partitions of 128 points long were usedwhile. because of their higher sampling rate. channels threeand four only used the last twelve partitions. A total of 289
waveform features per time domain sub-partition were calculated in the time. frequency. cepstral. auto-correlation.
and crossc-orrelation domains. Furthermore. the ratios
between two features. such as the area under the power
spectral density curve, extracted from two time-equivalent
sub-partitions from two different channels were also calculated. For a complete list of the waveform features (Wuand Du. 1996; Hay and Chan). the reader is referred to references (Reis and Cook. 1997; Reis and Voegle. 1997).
The data was organized into wavefonn feature trends witheach trend containing one waveform feature versus filenumber, i.e.• time.

By keeping trends with positive differences in two or
more trial runs. the large number of waveform feature
trends was reduced to 70. Some of these trends showedlarge variations during the cold header start up. By eliminating trends that displayed significant start up noise orpossessed some type of discontinuity not related to punch
pin failure. the list of waveform features was further reduced to twenty; these remaining waveform features consistently showed changes with respect to failure of thepunch pin for every trial. The waveform feature trends
were then normalized by dividing each of the trend data
points by the average of one hundred points collected at the
beginning of the corresponding run. i.e.• beginning at point
one hundred. Several of these trends showed strong linear
correlation with each other (R> 0.90) indicating a strong
linear dependence. Reducing the number of waveformfeatures, ~, as input would result in significant computational savings. Four of the twenty normalized feature
trends that showed the smallest correlation with each otherwere linearly combined as shown in equation (1) below:

By plotting the amplitude of a single feature versus
time. distinct trends were observed in certain features.
which corresponded to changes in the cold header during
production runs. However. because of the overabundanceof data, an automated procedure was developed to identify
waveform features that were sensitive to punch pin failure.
For each trend. two regions defined by a starting and ending
file number (a file number corresponding to a manufacturing pan) were selected based upon the file collected whenpunch pin failure was detected. A gap of 100 files wasplaced on either side of the recorded failure point to account13=t+P+ll+K-3
86

(1)

9
III

"tl

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~

8

Q.

7

~

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Domain ~

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Domain

(a)

o.
3000

3200

3400

3600

3800

4000

4200

4400

RleNumber9~--------r----r--Y---..---------,

8

I

7

•
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•
I

6

5

Linear Regression

4
3

2

1

Standard ~-"""W1II•
Deviation
Significant Difference

.

oL
3000

(b)

I

-_--L---l:if=:I!!5!!:5L----I
3200

3400

4200

4400

Rle Number

Fig.4 Parameters for typical automated trend analysis showing (a) file domain regions for trend comparison and (b) significant feature amplitude difference between the two me regions.
In equation (1), 't represents the ratio of the normalized energy in time domain sub-partition 13 for the channels one
and two, p represents the ratio of the normalized energy intime domain sub-partition 14 for the channels one and two,
11 is normalized energy in the cepstrum of the time domain
sub-partition IS for the channel one, and K is the normalized rectified area of the auto correlation of the time domain
sub-partition IS for the channel one.

observed for the other trial runs (Reis and Cook, 1997).
The results shown in Fig. 5 indicate that this procedure has
the potential of being used to determine when failure, i.e.,fracture, of the punch pin occurs during the operation of thecold header.
This empirical methodology could serve as a simple
failure criteria for a single cold heading machine. However, the problem is messy in the sense that the selected
waveform feature set will vary for different cold headers,and for each cold header, it will vary for each die/punch
combination that are used for different manufactured parts.
Therefore, there is a need to develop an adaptive failure
criteria methodology for the cold heading manufacturing
processes. The monitoring system needs to be automated inorder to self-adapt to new cold beading machines that may
require different wavefonn feature trends. In order to provide an adaptive process control, an approach based upon a

Figure 5 shows the amplitude variation of the combined waveform feature ~as a function of file number, i.e.,time, for trial run number eight. Figure 5-a shows thevariation of ~ without any filter being used, and Figs. 5-b
and 5·c show the variation of /3 with a three point and a five
point median filter, respectively. These figures show that a
distinct change in the amplitude of dimensionless combined
waveform feature /3 occurs with the experimentally observed failure of the punch pin. Similar results were also
87

40
c::l.

-=
~

co:

~

Tw 0 Lobes of Amch
Fractured (2887)

35
30
25
20

~

15

A_Ie Part

Double Parts
Being A"oducedIaua"'--i

(a)

10
5
0

=

-5

FaDure Threshokl P 0

-10
0

1000

2000

3000

4000

5000

File Number
"V
c::l.

-=
f

Tw 0 Lobes of Punch
Fractured (2887)

35
30

Double Parts
Being A"oduced

I

CI:

~

25

~

20

I

Acceptable Part Quality

'I

15
10
5

I

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.

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0 I.......

T

-5

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-r ... .,.'

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o

1000

3000

2000

4000

5000

File Number40
Tw 0 Lobes of Punch
Fractured (2887)

c::l.35e.:

30

Double Parts
Being A"oduced

I

CI:

~

25
20

Ace_Ie ParlQuaily

15

~

10

... ,

5

o

'I

-5

~

(c)

'1/

1.ll..J_".,., AJa='-"

FaDure Threshokl P 0

J

-

WI'

-10

o

1000

2000

3000

4000

5000

File Number

Fig. 5 Feature f3 for punch test number eight vs. file number with (a) no filter applied, (b) a three point median filter applied,and (c) a five point median filter applied.
genetic algorithm designed neural network monitoring system is proposed (De long, 1975; Goldberg at aL, 1993;
Goldberg, 1996, 1989).

4. The Neural Network Monitoring System
Genetic algorithms are robust search procedures basedon the Darwinian principle of natural selection and genetics
88

F2

o
o

F3

F4

F -- Input

1

o

1 -

StringF3o

F5 Reduced Input

filtering, which was carried out by multiplying the input
feature vector by the fllter vector for each exemplar on eachepoch. leads to a reduced input vector which includes only
the desired features. While the network contains twenty
inputs and all twenty inputs receive a signal, the input
nodes of the excluded features receive a zero signal. The
hidden layer network input features are then calculated bycounting the number of active features. Please note that touse this methodology in other cold heading machine processes. a longer binary string Dlay be needed; the length of
the binary string depends upon the wavefonn features selected by the automated trend analysis to be sensitive topunch pin failure.

5"

Input Layer

Hidden Layer(s)Output Layer

The output layer contains three modes. Le.• no failure,partial failure. and complete failure of the punch pin. However, the process monitoring system requires only theevaluation of system failure or non-failure. This was accomplished by output signal filtering. This filtering was
accomplished by setting the output of a node to zero if theoriginal output signal is less than 0.65. The level of 0.65
was chosen because if a signal output is below 0.65, thenetwork does not have strong confidence in the classification. Furthennore, a second filtering level uses a five-point
minimum filter, which requires a minimum of five failure
calls for the system to identify failure of the punch pin.
This conservative approach significantly reduces the
chances of nuisance shutdowns due to spurious responses.
Using a nonnal production rate of 2.97 parts per second, thenetwork with a five-point minimum filter has an inherent
delay of 1.7 seconds; this requires a conveyor belt to provide the necessary delay time between part production anddeposit in the collection bins. A relatively small conveyor
belt is a small price to pay for nuisance shutdowns (Reisand Voegle, 1997).

Fig. 6 Neural network and genetic algorithm design interface.
(Goldberg, 1989). Genetic algorithms work by relating
binary strings to a perfonnance or fitness estimate (Reis andVoegle. 1997). a single number that summarizes the overall
performance of the designed neural network. The fist step
towards the genetic algorithm designed neural network wasto encode the binary strings for the artificial neural network
simulation (Wilson and Lehr, 1990; Anon., 1994; Zurada,
1992; Masters. 1994; Hassoun, 1995; Haykin, 1994). To
control which impute features were introduced into the neural network. a binary fLIter mechanism was used as shownin Fig. 6. Using one bit assigned to each waveform feature.
a string with a length of twenty bits was used to represent
the impute layer strUcture; to include a feature, the bit was
set to one while a setting of zero removed the input. The

T

1.0
0.9 T
0.8 T

I

0.7 T

= 0.6 t

~ 0.5

I

0.4 I
0.3..,..
0.2 _~;-~

0.1

AP = 10, SP = 0.05, i = 3

L 1Sp

/-'
..
,,,.,.,.'
"--

~r

-

_--'

-----~ AP=IO.SP=O.OS.i=sl

I_J.,; , l I
'
•
f
.
00
00
~
~
~

Average Accurac~
Fig. 7 Accuracy and size penalty relationship.
89

I
~

100

(a) Initial Population

C 6.0
::J

designs with fewer and fewer active inputs. The genetic
algorithm process was found to be robust and insensitive tocrossover, mutation, population. accuracy penalty. and size
penalty parameters, and proved capable of reaching thedesired objectives without human intervention. For a better
discussion of the genetic algorithm designed neural network
the reader is referred to Reis and Voegle (1997).

Distribution

5.0

8c 4.0o~ 3.0:i 2.0a.

~

1.0
0.0+-......-+-f~~1--+--t=
1 2 3 4 5 6 7 6 9

10 11 12 13 14 15

Figure 8-a shows the initial population histogram created by using a random number generator to assign either
the value of zero or one to each bit in every string. Figure
S-b shows the population bistogram for the generation
numbcr twenty-seventh. Figure 8-b shows that by the timc
of the twenty-seventh generation, the algorithm has focusedon networks with a very small number of active input waveform features. The average numbcr of active input waveform features is about five, with several models attempted anumber as small as four or three inputs. The network performance for later generations did not further reduce thenumber of input features; the algorithm continued to workwith similar size distribution of input features (Reis andVoegle, 1997). The early generations provided sporadic
and usually poor results; however, as the number of new
generations progressed, the number of networks with fourand five active features increased. as did the fitness. Aftertwenty-seven generations. see Fig. 8. while there is still
variation, the amount present is far less that in earlier generations. The genetic algorithm is able to introduce new
combinations that maintain accuracy while requiring fewer
input features. For later generations, the genetic algorithm
recommended about half of the networks with input featuresets of five waveform features and the other half of the
networks with input feature sets of four active waveformfeatures.

Number of Ingut Features(b) Population Disttibution
After 27 Generations

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15

Number of Input Features

Fig. 8 (a) Initial population histogram; (b) Twenty-seventh
generation population histogram.
The main objective of the genetic algorithm search
procedure is to select the smallest number of waveformfeatures in the input layer that maintains the required accuracy. Accuracy is evaluated as a direct measure of the percentage of correct calls for each used output class. An accuracy penalty and a size penalty are introduced to allow
one performance measure to be balanced against the other.
Equation (2) shows the used fitness relationship. where AP
and SP represents the accuracy and size penalties, respectively. and the variable Ui" represents the number of active
inputs to the neural network.
f=~AP(AP + IOOk)- Aawl - Aclaa - ...-Act-r

SP(i-1)

In production runs number 6 and 7. complete and partial punch pin fracture. respectively, were observed. Data
collected in these runs was used in the neural network
training. Two hundred data points corresponding to goodpart production, i.e., no punch failure, were collected fromeach of these two runs for a total of four hundred data
points and labeled "no failure." Four hundred data points
corresponding to complete fracture of the punch pin wereextracted from run number 6 and labeled "complete fracture," and seven hundred eighty two data points were extracted from run number 7 and labeled "partial fracture."
These class samples were selected randomly throughout theprocess. Once selected, the data set was combined by randomly sorting the data in the training file, which contained
the classification string and twenty possible inputs for each
exemplar. The network was able to accurately separate all
three classes. Table 2 shows the accuracy resulting from a
set of four waveform featurcs used as impute to the neuralnetwork. The smaller networks, i.e., networks with fourfeature input set, converged quickly and proved to be very
stable; the neural network training always converged in
fewer than 250 epochs. Several sets of four wavefonn fea(2)Equation (2) has two terms; the first term incorporates theaccuracy while the second introduces the size of the network. The denominator adjusts to a wide variety of inputs;
the first term has a maximum value of one when the input
accuracy for each class is 100% and decreases asymptotically to zero. Here, three classes (k=3) were used, namely,
no failure, partial failure. and complete failure of the punch
pin. The accuracy and size penalties act together in order tobalance each other. An accuracy penalty value of 10 and asize penalty value of 0.05 were used in this study, as shownby the middle line in Fig. 7, which shows the effects of thevarious penalty values on fitness for two different size input
sets.
The genetic algorithm was capable of starting with arandom initial population and evolving the network designs
by striving for constant improvement. Artificial innovation
was observed as the algorithm produced neural network
90

Table 2 Training accuracy results for the four-feature input set.

Good Punch
Partial Failure of
Punch
Complete Failureof Punch

1.0 T

t
a T
0.9

~

:s!
Do

Good
Punch

Partial Failure of
Punch

Complete Failure of Punch

Accuracy

394
6

5

1

770

6

98.5%
98.5%

0

2

398

99.5%

Cold Header

Shutdown

0.8
0.71
0.6
0.5 T

t

a! 0.4 TI~ 0.2
0.3 ""t1.7 Second Detecrion
Delay

T

;-.._.......---...

-+-_ _-I-+-_-IL_.;-.

o

20

60

0.1
0.0 +-

40

80

100

120

Time (Minutes)

Fig. 9 Evaluation of Trial 1 - Case of complete failure.

1.0
0.9

a

0.8 -I~ 0.7:s 0.6
!

t-

t

Do

0.5-

!

1

~ 0.3

t

2nd Punch Partial
Fracture.: 0.4 T
0.2

T

T

0.1
0.0
-'-

o

J. 7 Second Delection
Delay

.;..-

.;..-

20

40

...JL

60

80

100

Time (Minutes)

Fig. 10 Evaluation of Trial 8 - Case of partial failure.

5. Process Monitoring System Evaluationtures. including the one used in Equation (1). see Fig. 8-b.
worked well for the input of the neural network. All theselected waveform features were extracted from the later
portion of the waveform; this strongly points to the conclusion that the critical information occurs in the last half ofthe second impact punch (Reis and Voegle. 1997).

The remaining six runs were used to evaluate the neural network. Figure 9 shows the neural network response
for the first trial run. There are three failure predictions
before the second punch pin actually completely fractured.

91

The complete fracture was detected 1.7 seconds after it occurred, which is a result of inherent delay in the system due
to filtering. While, the premature responses at first glance
appear to be false calls by the network, other possibilities
exist for these early failure indications. The first failure
indication correlates to a shutdown cycle. While the second, third, and fourth early failure predictions do not haveany known attributable cause, it is possible that the neuralnetwork may be detecting early partial fractures of thepunch pin that do not change part perfonnance, but lead toeventual punch failure. The second early failure indication
occurs twenty minutes before the complete fracture, and thethird, and fourth indications occur only about ten minutes
before complete fracture.

Aknowledgements

Figure 10 shows the evaluation response for trial number eight, when the punch pin partially fractured after aperiod of time. Figure 10 confmns that the genetic algorithm designed neural network is able to separate the goodfrom the partial failure class. Although the failure prediction is a combined response for both the partial and complete fracture failure, a close examination of the minimum
filtered data shows accurate prediction of failure and thatthe network is capable of differentiate between the two
types of failure, i.e., partial and complete failure. Failure of
the punch pin for the other production runs was successfully detected with similar performance (Reis and Voegle,1997).

T. Browne and N. Tepovich (1985), "Cold Header Rod: A
Quality Approach," Wire Journal International, 18(6), 5561.

The authors would like to acknowledge the suppon
provided by Elco-Textron, Inc. and by Camcar-Textron,
Inc.
References
Anon. (1994), "NeuroSolutions, Manual" vol. I, Version
2.1, NeuroDimension Inc. Gainesville, FL.
K. Brankamp and T. Terzyk (1996), "Monitoring Forces
and Acoustic Emission in Cold Forming:' Wire, 46(5), 334335.

K. Carlson (1987), "More Precision and Variety from Cold
Heading," Machine Design, 59(18),99-103.
T.H. Crum (1996) "Surprising Answers- Results of a Condition Monitoring Program Survey," Proc. Twentieth Annual Meeting of the Vibration Institute, Willowbrook, IL.
pp. 197-201.
D.B. Dallas (1980), "New Innovations in Cold Forming:'
Manufacturing Engineering, pp. 96-99.

6. Conclusions
S. Ghostline and P. Bebick (1992), "Inspection Of Ferromagnetic Wire in A Cold Heading Operation:' Wire Journal International, 25(7), 61-65.

A monitoring system is presented to detect punch pin
failure in cold heading machine processes. Four waveformfeatures were linearly combined to provide an accurate andstable failure detection criterion for the punch pin failure incold heading manufacturing process of a precision fastener.
A robust genetic algorithm designed neural network is alsopresented to serve as the monitoring system. The genetic
algorithm designed neural network, using four waveformfeatures as the input layer, also proved to be able to detectboth partial and complete fracture of the punch pin for acold heading machine process. The proposed genetic algorithm designed neural network methodology allows thedevelopment of monitoring systems that are adaptable todifferent cold heading machines as well as different diepunch combinations within the same cold header. It wasobserved that all the selected waveform features were extracted from the later portion of the waveform, whichstrongly suggests that the critical information appears in thelast half of the second impact punch. The proposed genetic
algorithm designed neural network has the potential of being self-adaptable to other cold heading machine processes.

D.E. Goldberg (1989), Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Publishing, Reading, MA.
D.O. Goldberg, K. Deb, and D. Thierens (1993), "Toward a
Better Understanding of Mixing in Genetic Algorithms:'
Society of Instrument and Control Engineers Journal, 32(1),10-16.
D.E. Goldberg (1996), "Toward a Mechanics of Conceptual
Machines," Proc. Eighteenth Southeastern Conf. on Theoretical and Applied Mechanics, vol. 18, pp. 1-9.
M.H. Hassoun (1995), Fundamental of Artificial Neural
Networks, MIT Press, Cambridge, MA.
R.D. Hay and R.W.Y. Chan (1993), "/CEPAK Intelligent

Classifier Engineering Package Theory of Operations, ..
Techtrend International Inc., Montreal, Quebec.

92

S. Haykin (1994). Neural Networks: A Comprehensive
Fount:illtion. Macmillan, New York, NY.
De Jong, K. A. (1975), "An Analysis of The Behavior of aClass Of Genetic Adaptive Systems," Doctoral Dissertation, University of Michigan, dissertation Abstracts International, 36(10}, 5140B, University Microfilms No. 769381. Ann Harbor, MI.
T. Masters (1994), Advanced Algorithms for Neural Networks: A C++ Sourcebook, John Wiley and Sons. N.Y.
H.L.M. dos Reis and D.B. Cook (1997). "Real-Time Tool
Condition Monitoring in Cold Heading Processes Using an
Acoustic Approach," Technical Report UILU ENG 973003. University of Illinois at Urbana-Champaign, Urbana,
Illinois.
H.L.M. dos Reis and A.C. Voegle (1997), "Cold Header
Process Monitoring Using a Genetic Algorithm Designed
Neural Network Approach." Technical Report UILU ENG
97-3004, University of Illinois at Urbana-Champaign. Urbana, Illinois.
T. Terzyk and F. Oppel (1996) "Force And Acoustic
Monitoring in Cold Forming," Wire Industry, 63(752). 622623.
B. Widrow and M.A. Lehr (1990), "30 Years of Adaptive
Neural Networks: Perceptrons. Madaline. And Backpropagation." Proceedings of the IEEE, 78(9), 1415 - 1442.
Y. Wu and R. Du (1996) "Feature Extraction and Assessment Using Wavelet Packets for Monitoring of Machine
Processes," Mechanical Systems and Signal Processing,10(1),29-53.
J.M. Zurada (1992), Introduction To Artificial Neural Systems, West Publishing, New York, NY.

9342nd AEWG Meetingstrategy for analysis/interpretation goals can then be described based on the monitored values. Details of thismonitoring strategy are subject to change. From here, more realistic simulations are to be performed. Temperature,
fatigue and vibration tests are currently underway. Scheduled frrst in-flight use is the first X-38 re-entry flight in theyear 2001.
Nondestructive Evaluation of Radial Track Tires using AcoustoUitrasonics.
Henrique L. Reis. Nondestructive Testing & Evaluation Research laboratory (No abstract available)
Laboratory Scale Testing in AE. Richard Nordstrom. Vallen-Systeme GmbH
Whereas specimens and loading rigs have been developed specifically for studying elasticity and fracture properties ofmaterials in the laboratory, most laboratory scale testing in Acoustic Emission use no specialized test, rather they
rely on the tests developed for elasticity and fracture mechanics. This is despite the fact that they are designed tocharacterize altogether different physical properties of materials than the quantities that are important for AE in use:
damage indicators. integrity analysis indicators. and the like. There are certain materials tests where single damage
mechanisms predominate, here these tests are called single dominant damage mechanism (SDDM) tests. SDDM tests
performed on composite materials include tests on model composites (e.g. single fiber fragmentation test, SFFT),
single mode fracture tests (e.g., Mode I delamination in the double cantilever beam specimen, DeB), and tests
merely developed to study a specific phenomenon (e.g., matrix splitting in static tensile tests on double-edge notched
specimens. DEN) or specific parameters (e.g., tensile tests to determine the limiting monotonic strength, LMS). AE
from tests beyond coupon testing are considered for comparison. The results of these tests are not analyzed fordamage type which is largely determined, but for quantity. progression and variance within each test type. The
Question of how well each SDDM test can be used to characterize specific types of AE and which features are mostdesired for an AE analysis within laboratory scale testing will be discussed. How different -choices in laboratory
scale testing effect the usefulness of AE results for generalization and scaling will be discussed.
Wavelet-based Acoustic Emission Techniques.
Gary Qi. Department of Mechanical Engineering. University of Memphis
Wavelet transform, introduced in the area of applied mathematics and signal processing in the early 1980's, wasapplied to an arbitrary square summable real function and transformed into a series of shifted and dilated sum of
wavelets. It has been shown to be a powerful tool in all areas dealing with analysis of transient signals. The conceptof wavelet analysis has been studied extensively for the past 15 years. Recent research activities have dealt with
developments of new wavelet basis, performing fast computing algorithmic transforms. and new applications.
In the study of material fracture behavior, carbon and glass fiber composites (CPR. & GFR) are examined- It is foundthat the exponential constant M value (used to determine the relationship between stress and stress intensity factor)
is well correlated with the wavelet level energy compared relatively to the classical fracture mechanics. The
conventional and wavelet-based AE techniques are both presented to show the advantage of wavelet-based methods.
The results verify that the wavelet-based method better approximates residual strength. using classical fracture
mechanics as the base line. Data compression is one of the major applications of wavelet analysis technique. The
AE signal is decomposed into different 'wavelet levels. These levels contain information that can be minimized. To
achieve the maximum compression ratio. the amplitude-based criterion and signal energy are computed andcompared. It is found that the compression ratio can be as high as 7 with the distortion only 0.6% using thethresholding algorithm introduced by the author.

The wavelet analysis is also applied to the failure mode identification of composites. A general trend is observed byinvestigating the energy distribution of decomposed AE signals. This trend indicates that the energy in the AE
signals is concentrated in three levels (levels 7, 8. & 9) representing frequency rages of 50-150 kHz, 150-250 kHz,and 250-310 kHz. respectively. Furthermore. the energy percentages in these levels are 8%, 15%. and 75%.
respectively. These are speculated to be associated with different failure modes of CFR composites. At last, AE bodywave attenuation is studied. The wavelet-based method is able to provide the waveform energy distribution. The
analysis presents that level approximation energy attenuation is dominated by lower scales. whereas, level detailenergy attenuation is dominated by median scales. In summary, the wavelet analysis is powerful tool and it is aneffective method to he applied in the acoustic emission area.

42nd AEWG Meeting

Visualizing Acoustic Emission by Visual AE. Jochen Vallen, Vallen-Systeme GmbH (No abstract
available)
Prototype Multiplexing System for AE Acquisition. W.H. Prosser, Daniel F. Perey, NASA LangleyResearch; Edgar F. Scales. Analytical Services and Materials; Michael R. Gorman, Digital Wave Corporation
Over the last decade, advances in wavefonn based AE instrumentation and Modal AE analysis have led to
improvements in AE technology. More accurate source location, and better noise discrimination and source
identification are now possible. These advances have resulted in a renewed interest in developing AE for in-flight
structural health monitoring of aerospace vehicles. AE is being evaluated for a wide range of aerospace vehicles
including the X-33 and Reusable Launch Vehicle, F-IS, Joint Strike Fighter (JSF), as well as commercial aircraft.
For in-flight monitoring, these programs have all identified a requirement to significantly reduce the cost, mass,size, and power requirements of AE instrumentation.
To help address this requirement. a prototype AE multiplexing (AEMUX) circuit for wavefonn acqUisition was built
and evaluated. This prototype circuit multiplexed the output from eight sensors into two acquisition channels.
eliminating the need for six AID channels. The system was designed such that the eight sensors being monitored
formed a linear array. When a trigger event was detected from anyone of the sensors. the AEMUX rapidly « Ips)
switched. passing the signal from two sensors in the array to the two acquisition channels. The switching was
configured so that the two acquired signals were from the sensors on either side of the trigger sensor. The rationale
for this was to take advantage of the propagation delay so that the earliest arrivals of the signal are still acquired oneach channel. These fltSt signal arrivals are critical for accurate source location. If the signal from the trigger sensor
is acquired. which is the case if the trigger sensor was at either end of the array, then the pre-trigger signal arrivals
are lost. The AEN= circuit also identified which channels were acquired with digital encoding on the signals. The
location of the source could then be detennined relative to positions of the acquired sensor pair and the overall sensor
array. This prototype AEMUX system was evaluated using pencil lead break (Hsu-Neilsen) sources on an aluminum
plate. It perfonned as designed providing rapid, low noise. trigger based switching. with encoded channel
identification. This multiplexing approach is not limited to linear arrays, but can be easily extended to monitor
sensors in planar or three-dimensional arrays. Likewise, the multiplexing ratio is not limited to that of (8:2) used forthis prototype, but can be optimized for the application of interest.
Investigation of Auto Sensor Test-Self Test Mode for AE System Performance Verification.
James R. Mitchell. Peter Finkle, PAC
An elecuonic means to detennine the perfonnance of an AE instrument has been developed. The technique. known as
Auto Sensor Test -Self Test Mode (AST-STM) utilizes a voltage pulse to excite a sensor's crystal and the structure
to which the sensor is attached. After excitation. the sensor output is measured. Instrumentation performance.
including efficiency of the coupling. can be determined by comparing the wavefonn duration and energy to minimum
values established using a sensor and instrumentation channel that is known to be operating properly on the samestructure. The AST·STM method is compared with the pencil lead break method for a variety of sensor frequencies,
couplant types. couplant efficiency factors. structural geometry and plate thickness.
Smart Sensors & Instruments for AE Testing.
Harold L. Dunegan. Dunegan Engineering Consultants Inc. (No abstract available)
Commercial Presentations

AVAILABLE BOOKS ON ACOUSTIC EMISSION
Journal of Acoustic Emission, Vol. 1 ·3 (1982 - 84) $ 50 per volume; Vol. 4 - 8 (1985 - 89) $ 90 per volume;Vol. 9 - 16 (1990, 91192, 93-98) $ 96 per volume (+ shipping of $8 US/$Il others per vol.)Progress in Acoustic Emission IX (1998), The Proc. of the 14th International AE SympJ5th WMAE; $120 + shipping
Available from Acoustic Emission Group, 16350 Ventura Blvd., Encino, CA 91436 USA
Send credit card (VisaIMC) order by FAX to 1-818-990-1686; e-mail: ono@ucla.edu
ASNT NDT HANDBOOK (2nd Edition), VoL 5, Acoustic Emission Testing (1988)
Eds. R.K. Miller and P. Mcintire. 650 pages. $110.25
Third to Sixth Int'l Symposium on AE from Composite Materials (1989,1992,1995,1998)
Available from Book Dept., ASNT, PO. Box 28518, Columbus, OH 43228. Also call (800) 222-2768.
Third-Fifth Com. on AElMicroseismic Activity in Geologic Structures and Materials (1982).
(1989), (1991); Eds. Hardy & Leighton. Available from Trans Tech Publications, 16 Bearskin Neck, MA 01966.
ASTM-STP's:
Acoustic Emission, Current Practices and Future Directions (1991), STP-1077.
Acoustic Emission Testing of Aerial Devices and Associated Equipment used in the Utility Industries (1992),
STP-1139. Available from ASTM, 1916 Race St, Philadelphia. PA 19103.
Progress in Acoustic Emlssion IV (1988), V (1990), VI (1992), VII (1994), VIII (1996),
The Proceedings of the 9th-13th International AE Symposium. These volumes are $150 plus shipping.
Available from JSNDI, Natsume BLDG No.5, 4th fl, Kanda Sakumagashi 67, Chiyoda. Tokyo 101 JAPAN
22nd European Com. on AE Testing. Aberdeen, Scotland, May 1996; Available from Univation, Robert Gordon
University, Kepplestone, Queen's Road, Aberdeen, AB9 2PG, UK; fax (44) 1224263323
Acoustic Emission -- Training Guide; How to Ensure an Accurate and Valid Acoustic Emission Test,
Jim Leaird. ISBN 1-89054S.oo7.1ist price $125. Available through Amazon.com and ASNT.

Short Courses
Acoustic Emission Shon Courses for the requirements of ASNTs TC-IA led by Dr. Richard A. Nordstrom will be offered byAcoustic Technology Group. Dates and venues are:December 6-10,1999: the Oregon Graduate Institute, Hillsboro (ponland suburb), Oregon.
March 20-24. 2000: Northwestern University Infrastructure Technology Institute, Evanston (Chicago suburb),lllinois.

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96

Notes for Contributors
1. General

The Journal will publish contributions from all parts of theworld and manuscripts for publication should be submilled
to the Editor. Send to:Professor Kanji Ono, Editor - JAB
6531 Boelter Hall, MSE Dept.
University of California
Los Angeles, California 90095-1595 USA
FAX (1) 818-99()'1686
e-mail: ono@ucla.edu
Authors of any AE related publications are encouraged tosend a copy for inclusion in the AE Literature section to:
Mr. T.F. Drouillard, Associate Editor - JAE
11791 Spruce Canyon Circle
Golden, Colorado 80403 USA
All the manuscripts will be reviewed upon submission to
the Editor. Only papers not previously published will be
accepted. Authors must agree to transfer the copyright to
the Journal and not to publish elsewhere, the same paper
submilled to and accepted by the Journal.

References to published literature should be quoted in the
text citing authors and the year of publication. These are to
be grouped together at the end of the paper in alphabeticaland chronological order. Journal references should bearranged as below. Titles for journal or book articles arehelpful for readers, but may be omitted.
H.L. Dunegan, D.O. Harris and CA. Tatro (1968), Eng.
Fract. Mech., I. 105-122.
Y. Krampfner, A. Kawamoto, K. Ono and A.T. Green(1975), "Acoustic Emission Characteristics of Cu Alloys
under Low-Cycle Fatigue Conditions," NASA CR-134766,
University of California. Los Angeles and Acoustic Emission Tech. Corp., Sacramento, April.
A.E. Lord, Jr. (1975). Physical Acoustics: Principles andMethods. vol. II, eds. W. P. Mason and R. N. Thurston,Academic Press, New York, pp. 289-353.
Abbreviations of journal titles should follow those used in
the ASM Metals Abstracts. In every case, authors' initials,
appropriate volume and page numbers should be included.
The title of the cited journal reference is optional.

A paper is acceptable if it is a revision of a governmental ororganizational report, or if it is based on a paper publishedwith limited distribution.

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Contents
Page iPage ii

Announcement: New Fonnal for Joumal ofAcoustic Emission
Founding members of OLEA, Grupo Latinoameric31l0 de Ernisi6n Acustica
Color pllue for Figure 8 on p3ge 9.

Page 1-13

Classification of Acoustic Emissions in Metallic Ghtsses

Page 15-21

Acoustic Emission Charncteristics of Soil ilnd Sand in Response to Simulated RoOi
Growth C. Divaker Durairaj, L. Okushima and S. Sase

Page 23-27

Detection of Defects in Gears by Acoustic Emission Measurements
N. Tandon and S. Mata

Page 29-36

Acoustic Emission Signals in Thin Plates Produced by Impact Damage
William H. Prosser. Michael R. Gorman and Donald H. Humes

Page 37-47

Reflections of AE Waves in Finite Plates: Finite Element Modeling and Experimental
Measurements
W. H. Prosser. M. A. Hamstad, J. Gary und A. O'Gallagher

Page 49·59

Classification of Acoustic Emission Signatures Using a Self-organization Neural
Network Tinghu Van, Karen Holford. Damian Caner and John Brandon

Page 61·67

Discussion of the Log-Normal Distribution of Amplitude in Acoustic Emission Signals
M. J. L6pel Pumarega. R. Piotrkowski and 1. E. Ruzzante

Page 69-81

Unsupervised Pallern Recognition Techniques for the Prediction of Composite Failure
T. P. Philippidis, V. N. Nikolaidis llnd J. G. Kolnxis

Page 83-93

Real-Time Tool Condition Monitoring in Cold Heading Machine Processes Using an
Acoustic Approach
Henrique L.M. dos Reis, David B. Cook and Aaron C. Voegele

Page 14
Page 22-95
Page 96

Meeting Calendar
42"" Meeting of The Acoustic Emission Working Group
Available Books on Acoustic Emission/Short Courses

Conferences and Symposia

A. Vinogrildov

Australian AE Testing of Chemical Plant Start-up in lieu of Hydro-test

JOURNAL OF ACOUSTIC EMISSION
Editor: Kanji Ono
Associate Editors: A. G. Beattie, T. F. Drouillard, F. C. BeaU and M. Ohtsu
1. Alms and S~ope of the Journal
Journal of Acoustic Emission is an international journal
designed to be of broad interest and use to both researcher andpractitioner of acoustic emission. It will publish original contributions of all aspects of research and significant engineering
advances in the sciences and applications of acoustic emission.
The journal will also pUblish reviews. the abstracts of papers
presented at meetings. technical notes. communications and summaries of reports. Current news of interest to the acoustic emission communities. announcements of future conferences andworking group meetings and new products will also be inclUded.
Journal of Acoustic Emission includes the following classesof subject matters;
A. Research Articles: Manuscripts should represent completed original work embodying the results of extensive investigation. These will be judged for scientific and technical merit.
B. Applications: Articles must present significant advancesin the engineering applications of acoustic emission. Material
will be subject to reviews for adequate description of procedures.
substantial database and objective interpretation.
C. Technical Notes and Communications: These allow publications of short items of current interest. new or improved experimental techniques and procedures. discussion of published articles
and relevant applications.
D. AE Literature section will collect the titles and abstractsof papers published elsewhere and those presented at meetingsand conferences. Reports and programs of conferences andsymposia will also be presented.
E. AE Program and Data Files: Original program files anddata files that can be read by others and analyzed will be
distributed in CD-ROM.
Reviews. Tutorial Articles and Special Contributions willaddress the subjects of general interest. Nontechnical part will
cover book reviews. significant personal and technical accomplishments. current news and new products.

1. Endorsement
Acoustic Emission Working Group (AEWG). European
Working Group on Acoustic Emission (EWGAE). Committee on
Acoustic Emission from Reinforced Composites (CARP). andAcoustic Emission Working Group of India have endorsed the
publication of Journal of Acoustic Emission.

3. Governing Body
The Editor and Associate Editors will implement the editorial
policies described above. The Editorial Board will advise the editors on any major change. The Editor. Professor Kanji Ono. has
the general responsibility for all the matters. Associate Editors
assist the review processes as lead reviewers. Mr. T.F. Drouillard
is responsible for the AE Literature section. The members of the
Editorial Board are selected for their knowledge and eltperience on

AE and will advise and assist the editors on the publication
policies and other aspects. The Board presently includes thefollowing members:
S.H. Carpenter
D.A. Dornfeld
P. Fleischmann
L. Golaski
M.A. Hamstad
H.R. Hardy. Jr.
R. Hill

P.Ju
T. Kishi
O.Y. Kwon
J.C. Lenain
S.L. McBride
C.R.L. Murthy
H.Niitsuma
A.A. Pollock
T.M. Proctor. Jr.
1. RomanC.ScalaC. ScrubyA. Vary
E. Waschkies
M. Wevers
J.W. Whittaker
B.R.A. Wood

(USA)
(USA)
(France)(Poland)
(USA)
(USA)
(UK)
(Germany)(Japan)(Korea)(France)(Canada)(India)(Japan)(USA)(USA)(Israel)(Australia)
(UK)

(USA)
(Germany)(Belgium)(USA)(Australia)

4. Publication
Journal of Acoustic Emission is published annually ul CDROM by Acoustic Emission Group, PMB Suite 106. 16350
Ventura Blvd. Encino. CA 91436. It may also be reached at 6531
Boelter Hall. University of California, Los Angeles. California
90095-1595 (USA). tel. 310-825-5233. FAX 818-990-1686. email: ono@ucla.edu

s. SubscrIption
Subscription should be sent to Acoustic Emission Group.
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VISA/Me accepted. Inquire for individual (with institutional
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6. Advertisement
No advertisement will be accepted, but annoum:ements forbooks. tmining courses and future meetings on AE will be
included without charge.

ISSN (J730-00s0 Copyright © 1999 Acoustic Emission Group

Coden: JACEDO Printed in U.S.A.

Modeling of Buried Monopole and Dipole Sources of
Acoustic Emission with a Finite Element Technique
M. A. Ramstad, A. O'GaIIagher and J. Gary
Abslraet

2. Finite Element Model Summary

The requirements for dynamic finite-element modelingof the source dynamics and wave propagation of buried
acoustic-emission point sources were examined. Maximum
permissible source and cell sizes for point sources were
determined as a function of the minimum wavelength forfrequencies of interesL These wavelengths were calculated
from the source rise-times. For both buried monopoles anddipoles, finite-element predictions for both plates and halfspaces were compared with published results obtained fromother approaches. The modeled signals were evaluated fromthe epicenter to a distance of 15 plate thicknesses for plates
wiab a thickness of ~ 2S DlDl. The finite-element melbod
provided accurate results and a prcu:tical means to analyze
finite specimens, unlike most alternate approaches.

Since the model has been discussed previously (Garyand Hamstad, 1994), only its new aspects are fully described here. The DFEM method was based on a leapfrog
approximation in time and linear elements in space. Where
the specimen configuration involves surfaces, stress-freeboundary conditions were used. To model a buried dipole,
two closely spaced body forces were applied simultaneously along opposite directions. As a consequence of thefinite aspect of the finite-element (FEM) approach, each ofthese body forces was actually distributed uniformly over asmall region. Thus, the dipole was equivalent to two small
but finite, simultaneous monopole sources in close proximity. The dipole strength was determined by multiplying thefon:e of each of the equal (and opposite) monopoles by thesmall disrance (dipole spacing) between the centers of abe
two monopoles. The temporal fonn of the applied bodyforces was step-like in nature. The exact time dependenceof abe step-like loading was a variable that wilJ be describedin more detail later. The axi-symmeUical code was used formost of this study since it enabled us to increase the resolution without overwhelming our computing resources.

1. Introduetion
This paper is a third publication validating the use of
dynamic finite-element modeling (DFEM) to predict
acoustic emission (AE) displacement fields. The flISt publication (Gary and Hamstad, 1994) reponed the experimenlal
validation of an axi-symmetric two-dimensional code; thesecond (Hamstad et al.. 1996) extended the experimental
validation to a three-dimensional code. In both of these
papers, a short-rise-time surface force was used as the AE
source to accomplish an experimental validation. Since, in
general, a real AE source is a buried transient dipole, theaxi-symmetric DFEM method has been extended to include
this more complicated case in this work. First, we examined the necessaJY resolution and other requirements forconvergence of the DFEM results for buried point sources.
Then, since it was not easy to cany out an experimental
validation, we compared our results with other, previously
published, independent analytical results (Scruby et al.,
1983; Pao et al., 1979; and Hsu, 1985). Since this effort isto be coupled with an experimenlal program, the DFEM
results generally extend to significant distances from thesource (i.e., IS or more plate thicknesses from the source).
Received 5 November 1999. The authors are affiliated with Natiooal Institute of Standuds and Technology, Boulder, CO 80303,
USA. M. A. Hamstad is also with University of Denver, Denver,
CO ~ USA. This is a contribution of the u.s. National Institute of StaDdaIds and Technology; not subject to copyright in theUDi1ed States.
Journal of Acouslic Emission'{~ \1,Numbe1s3J4

3. Buried Source Resolution Requirements
Analytical approaches to modeling AE signals usually
have not been concerned with the source size since, bydefinition, the sources are point sources. To compare
DFEM results with analytical point-source results, it wasnecessary to consider the requirements for a finite source toaccurately approximate a point source. From the literature
dealing with seismology (Aki and Richards, 1980), we
found a requirement that abe wavelengths of the signals ofinterest must be much longer than the source dimensions.
Another requirement was that the point where the signal isobserved must be at a distance D (from the source) that ismuch greater than the source dimensions (Scruby et al.,1983). In addition. DFEM modeling requires that the celJsize must be smaller than the smallest wavelength of interest, and the time step must satisfy a stabiti~ condition
called the COuraDt-Friedricbs-Lewy (CFL) condition(Strikwerda, 1989). In our case the CFL condition required
the time step to be less than the time required for the bulk
longitudinal wave to traverse a single cell. We took thenormalized time to traverse a single cell as I, hence the
CFL value must be < 1.
0'7'30-0050I991$5.00117097-110

CopyrigblCl999 Acoustic Emission Group

1.----...,.----,-===---...,.-----,

1.....----,---"'"'?'T---..,......----,

0.9

0.9

0.8

0.8

(a):

0.7

0.7

u..
<D
~ 0.6
o

u..

-g 0.5-g 0.5

(ij

cu

Q)

~ 0.6

~

LL

.!::!

.!::!

E
0.4
o

o

z

0.3

E0.4

z

F(t) =0 for t < 0

0.3

o
o

1.5

2

2

t ... Rise Time

L--_ _--'-

L--_--'-

for t> 00.1..... Rise Time

olL...._ _......._ _--'o
0.5
1

-

F(t) =- E~( t sqrt[(-1n{0.5)) I «O$r) .)]) .

0.2

for- 0-<= t <= 'to: .

F(t) = 1 for t > t

0.1

F(t) ,; 0 for t < 0f(t) = (0.5"'0.5cos(n tI(t)))

0.2

0.5

' " -_ _........_ _- - '

1

1.5

2

Nondimensional Time, tit

Nondimensional Time, tit

1 ,......---.,.----:;:o~--....,......--...,

focus on dipoles. The minimum wavelength (Am> is one of
the most important parameters, and it must be determined ina consistent manner. We used the equation

0.9

Am = eJf =c.'t •

0.7

<D
~ 0.6
o
u..
-g 0.5

.!::!

(ij

E 0.4

o

Z

=0 for t < 0
F(t) =(2t2 )/(l) fo~'O <~ t .::~- 0:5t
F(t)

0.3

T(t) =0
T(t) 0.5 - O.Scos(ntft)
T(t) 1==.. F(t)= [(0.5t2)-:(t-0.5t-)2] ~ [(0.5t)2].

0.2

for 0.5t < t <=tF(t) 1 for t >: tT •• ; Rise Time

O~----'-------''----'''''''----''

o

0.5

1

1.5

fort<Ofor 0 ~ t ~,;for t> 'to(2)

Figure 1 demonstrates this temporal dependence along withother temporal functions that are used later in this work.
Figure 2 shows as a function of Vs the normalized PeaJc
amplitude (for a constant dipole strength) of the out-ofplane displacement at distances of lb. Sh and ISh (h is plate
thickness) from the epicenter. These are for a midplanevertical dipole in a steel plate 25 mm thick (bulk longitudinal velocity of S940 mls; shear velocity, 3220 m1s; and density, 7800 kglm). Results at the epicenter, 2h. and 10h weresimilar and are not shown. The scale for normalized peak
amplitude was created by setting the peak amplitude equal
to one for the smaUest source size. This plot (Fig. 2) is aconvenient way to examine the convergence of the DFEM

=

0.1

(1)

where c. is the bulk shear velocity, ,; is the soun:e rise time,and f is the maximum frequency (taken as the reciprocal of
the soun:e rise time). To determine how much bigger than
the source size the minimum wavelength must be, we examined the convergence of the DFEM results as a functionof the parameter A.JS, in which s was the dipole soun:e size.
The dipole size was calculated along the axis of the dipole
force from the dimension (height) of eacb monopole (of thedipole) along with the dimension of the cell(s) separating
the two monopoles. For these calculations the temporalsource function, T(t), was a ·cosine bell" given by

0.8u.

. (b) .

2

Nondimensional Time. tit
Fig. 1 Soun:e temporal characteristics: (a) "cosine bell",(b) half-width Gaussian, (c) parabolic.
The DEFM requirements for a dipole to be considereda point soun:e are more difficult (assuming ljmits on computing resOUICeS) 10 meet than those of a monopole becauseof the increased source size. Hence, our considerations

98

Table 1 Finite-element parameters for "A.n/s runs with the two-dimensional code.
Cell size (cs), mm"A.n/(cs)
CFL
Source rise time, J.1S
Run no.
Source size (s), mm

0.031
51.9
0.9
0.5
2506
1.313
9.5
1.23

Minimum* Dis

"AmIs

2505
0.938
13.3
1.72

2499
0.75
16.7
2.15

2498
0.563
22.2
2.86

2268
0.375
33.3
4.29

2278
0.281
44.5
5.73

2492
0.156
80.1
10.32

2493
0.094
133
17.2

• At epicenter where D = 12.5 mm
Table 2 Finite-element parameters for Dt/s runs with the two-dimensional code.
Cell size (cs), mm'-n/(cs)
CFL
Source rise time, J.1S
Run no.
Source size (s), mm
'-n/s

DIs

0.031
20
0.9
0.2
2510
0.531
1.18
3.78

(cosine bell)
2509
2508
0.281
0.156
2.22
4
7.14
12.9

2507
0.094
6.68
21.4

tTbe distance D is calculated from the center of the dipole to the epicenter on thesurface of the half-space.

1.~.-.-

error for DFEM modeling of dipole AE sources for two
reasons: first, the peak amplitude error is only 0.5 dB; second, as Fig. 3 shows, the signal for A."js 2.2 is very similar to that for A."js = 17.2 in both its time and frequency
domains for Ih and ISh. The similar results at other distances are not shown.

Q)-0~ 0.98a..
«E 0.96
.:s:.
co

II

cf 0.94

It

-0
Q)

co
E
~

z

~

0.9
0.88

We also examined the requirement that the distance D
to the observation point be much greater than the source
sizes. In practice, the distance of propagation is almost
always much greater than the source size, but for completeness we wanted to establish a specific value for Dis. We
chose to examine the effect of the Dis ratio on convergence
for the epicenter displacement on a half-space representation that bad the center of the vertical dipole source locatedjust 2 mm below the surface. The rise time of the cosinebell source was reduced to 0.2 Jls (to allow us to moreclearly distinguish bulk arrivals). Figure 4 shows the n~r­
malized out-of-plane peak amplitude of the signal (for constant dipole strength) as a function of the ratio Dis. The
normalized peak amplitude scale was created by setting thepeak amplitude equal to one for the smallest source size.
Table 2 provides a record of other finite-element parameters
for these results. This table confirms that the other requirements for DFEM modeling of point sources were met
or exceeded. The relevant material constants for aluminum(bulk longitudinal velocity of 6250 mis, shear velocity 3125
mis, and density 2700 kglm3) were used. Figure 5 shows
the calculated signals (out-of-plane displacement at the epi-

'I

~ 0.920=o at 1 plate tk.
x at 5 plate tks.
....at 15 plate tks.

I

0

*

5

10
15
A Is, dimensionless

20mFig. 2 Normalized peak amplitude of out-of-plane displacement at l.>plate thickness versus AJs for a 2S-mm
thick steel plate with a midplane vertical dipole.
results as the source size becomes small relative to theminimum wavelength. Table 1 gives other parameters of
the DFEM calculations for the "cosine-bell" sources with
O.'>JAS rise time. This table confirms (in some cases fromnumbers determined later in this paper) that the other requirements for DFEM modeling of point sources were met
or exceeded. From Fig. 2, the value of AJs required forconvergence was determined. We chose a value of A."js ~ 2,
to be satisfactory, even though it leads up to a 5 % error in
the peak amplitudes. We consider this to be an acceptable99Output at 1 Thickness
6000r--------......--------,

Output at 15 Thicknesses
1000r----~-----~----.

'E

~

4000

~

~

2000

-g

0

o
N

500

o1-----rlo<,JNJIv~

~

E

-500

l5 -2000z---J-1000 l-....

-4000 ' - - - - - - - - - - - - - - '
o
20
40
60
80
100

o

6000..-----------.......,

50

150

200

1000..------------.

'E
~

100

'·rrls~2.2

4000

500

Q)

~

a. 2000
o'"
]

0

iii
E

-500

l5 -2000z-4000 '--

o

--1

20

40
60
Time.lJs

80-1000L..---------~-----J

o

100

50

100
Time. IJS

150

200

Fig. 3(a) Comparison of nonnalized out-of-plane displacement signals with Vs = 2.2 versus Vs = 17.2 Note the nonnalized displacement scale has units of mJ(N·m). since the displacement has been divided by the dipole strength.
Spectrum at 1 Thickness

Spectrum at 15 Thicknesses

30.------------.
20

m
'C

.g 10
g 0
Q.

.¥ -10iii.= -20-20tl;-30

-30

-40

-40

~

o

0.5

1.5

0'----0.....5 - - - - - - 1 - . 5 - - - i
2230,...----------20

m

'C

cD

'C

g

Q.

0

ci -10iii.= -20~tl;-30-40-40'------------------J
0.5
1
1.5
2

o

O

Frequency. MHz

0.5

1
1.5
Frequency. MHz

Fig. 3(b) Comparison of the speclIUIllS of nonnalized out-of-plane displacement with A"js100

2

= 22 versus A"js = 17.2.

Table 3 Finite element parameters for ).,/(cs)"restart" IUDS except reference high-resolution run no. 2519.

CFL
Source rise time, f.1S
Dipole strength N.ms,romMinimum· DIs
A,,/S tRun no.
Cell size (cs) tt, mm
A,,/(CS) tt

0.9
I

4.2xl0-2
0.156
75.3
20.6
2519
0.052
62

2544
0.104
31

2523
0.208
15.5

2524
0.313
10.3

2520
0.417

7.7

2525
0.521
6.2

2521
0.625
3.9

• At epicenter when D= 12.5 mm.
t Defmed by high resolution run number 2519.

tt In lowest resolution region.

ply adjust the number of cells in the source to keep thesource size constant Tlius, we used a previously developed
technique (Hamstad et aI., 1996) based upon two separate
two-dimensional code FEM runs in 25-mm thick steel(same material constants as before) plates. In this numericalexperiment, we first carried out a high-resolution IUD for a
midplane vertical dipole source (l f.1S, cosine bell) where
the requirement for 'A.,Js was well satisfied. From the results of this run we extracted the displacements (as a function of time) at an imaginary cylindrical surface in the plate
located at a 75-mm radius from the dipole source. Next, we
computed far-field results for a series of lower-resolution
runs for a cylindrical plate with a disk of 75-mm radiusremoved from the center. For the lower-resolution runs at
each time step, the displacements (u, v) were specified
along the vertical boundary at 75 mm by using the resultsfrom the first higher-resolution JUD. Then, the final resultsfrom "restarted" (lower-resolution) runs were comparedwith a higher-resolution run over the entire domain of interest (solid plate with no hole). This comparison of out-ofplane displacement took place at Sh, 10h, and ISh from thesource. Figure 6 shows the displacement signals at ISh as afunction of 'A.,J(cs); the cell size (cs) of the lower-resolution
domain was used except for the JUD at higher resolution
over the entire domain. The dipole strength was 4.2 x 10.2
N·m. Table 3 shows relevant finite-element parameters forthe runs leading to Fig. 6. This table confmns that the otherrequirements for DFEM modeling of point sources weremet or exceeded. As Fig. 6 shows, a resolution equivalent
to 'A.,J(cs) it 15 is probably acceptable for AE finite-element
modeling since this signal is very close to that with 'A.,J(cs)62. Clearly, for 'A.,J(cs) = 6.2 the signal convergence issignificantly degraded. The results for Sh and lOh lead tosimilar conclusions with respect to the required value of'A.,J(cs). It is interesting to note how 'A.,J(cs) it 15 comparesto the 'A.,Js i!: 2 requirement for the case when the dipole isof minimum size (i.e., 3 cells, 1 each for the monopoles
with 1 cell space between). In this case, substituting s3(cs) results in 'A.,Js = 5, which meets the previously es~
lished criteria.

~ 0.99
::::I

;:

Q..

~ 0.98'0CI)N~ 0.97...oE

z 0.960.95 L . . - . . -...........- - - ' ' - -...........---'2----.....
25

o

10

15
0
DIs
Fig. 4 Normalized out-of-plane peak amplitude at the epicenter versus DIs (run numbers shown).
center) for three of the cases. The vertical scale bas beennormalized by the dipole source strength to result in units ofm/(N·m). The peak amplitude used to create Fig. 4 corresponds approximately to the beginning of a bulk shear wave
arrival (See Fig. 2 in Tirbonod, 1992, for a complete discussion). The small "bump" at about 0.5 f.1S corresponds
approximately to the end of the bulk longitudinal arrival.
These arrivals are only approximations due to the presenceof near-, intermediate- and far-field terms, all contributing
to the displacement field (Aki and Richards, 1980; Tirbonod, 1992) for this model geometry. The reader shouldnote the small degradation in peak amplitude and in theclarity of the initial DbumpD in Fig. 5 as DIs decreases.
Based on the results shown in Figs. 4 and 5, we chose DIs>
7 to be satisfactory for convergence.

=

The study of convergence of FEM results as a functionof the cell size is complicated by the fact that the minimum
source size also changes as the cell size changes. What isneeded is a way to study the effect of cell size (relative tothe minimum wavelength of interest) alone. Due to therange of cell sizes we chose to examine, we could not sim-=101

4,-----.---:::---.....-----.----..,

4

2
3

(a)

.

0

-2
2

.
-4

1

,

,

'

.

4

2
0.5

1.5

0

2

-2
-4

4 , - - - - - r - - - - . - - -.........-----,

4· ..... _(b):.... ".... ",_ ...

E

~

I

2

0

0
E
en
E -2
Ql
0
CIS

••••

·
··
·

I

••••••••••

.
...

• -._

Q.
(/)

••••••••

is

-4

4
0.5

1

1.5

2

2

0
4~--"""T'""---..------.----"'"I.... :···

3(.~)..~

..
.

-2
-4

.

4

2

2 .........

I. ml(cs)=6.2

0
1

:

..

a '-_...e..a
0.5

~

.

1
Time, J.lS

:

.

-2

---'-

.....

-4
50

..

1.5

100

150

200

Time. ~l.S

2Fig. 6 Comparison of out-of-plane displacement signals at
ISh as a function of V(cs) for -restarted ft runs. The casefor V(cs)= 62 is not a ·'restarted" run. Dipole strength
equals 4.2 x 10'1 N-m.

Fig. 5 Out-of-plane epicenter displacement normalized bydipole source strength. Dis equals to (a) 21.4, (b) 7.1, (c)

3.8.
102

Table 4 Conditions of finite-element runs for comparisons with the results of others.
Figure

Run

7
8
9

2586
2607
2639

10(a)

2644,
2645
2643
2646

10(b)
11 (b)

Rise time,
J.ls
0.3
0.5
0.03

I
2.566

Source location
Plate
Cell size Source size (s),thickness,mm(cs), J.U1l
J.U1l
midplane
25
65.1
260.4
25
midplane
65.1
130
25 mm below
6.25
NA*
43.8
surface
midplane
50
25
200
50
66

25
25.4

500
397

Longitudinal and shear
velocities, m/s
5940,3220
5940,3429
6250,3125
5940,3220
5940,3220
5940,3429

midplane
midplane* Not applicable

Table 5 Values of key parameters for the finite-element runs of table 4.
3

Source force, N

7800
7800
2662
7800

Shear
modulus,N/m2
8.09x1O lo
9.17x10 1o
2.6xlO JO
8.09xl0 JO

1

Dipolespacing, m
NA*
NA*
2.5xI0·s
NA*

7800
7800

8.09xI0 Io
9.17xl0 1oI
1

2.6SxlO-4

Run

Am Is

A,,/(CS)

Minimum DIs

CFL

Density, kglm

2586
2607
2639
2644,
2645

3.7
13.2
22
16.1

14.8
26.3
IS
64

192
385
571
250

0.9
0.9
0.9
0.9

2643
2646

6.44
22.1

64
133

100
128

0.9
0.9

I
100

I

3xl0-4* Not applicable

4. Comparison with Results of Other Approaches

where d is actual displacement (m), G is the shear modulus(N/m~ and F is the monopole force (N). The normalized
time, twa, is given by

4.1 Buried Monopoles

twa=tVs/h,

We used Hsu's code (Hsu, 1985) based on a Green'sfunction approach to provide one comparison with the
DFEM. Results from both models were compared for a
vertical monopole AE source located at the midplane of asteel plate 2S mm thick. The source was again a "cosinebell· shape with a rise time of 0.3 JlS. Since such a source
could not be modeled directly using Hsu's code, results
were obtained from the convolution of the results from a"unit step" calculation (obtained with a 12.5 ns time step)with the first derivative of the ·cosine-bell· source function.
All the previously developed requirements for the DFEM
calculation of a point source were meL Tables 4 and 5 list
the parameters for this calculation, identified as Fig. 7, as
well as those for other further comparisons. Figure 7 direedy compares the out-of-plane displacement of the two
approaches at two horizontal distances (measured from theepicenter position) 2b and Sh from the source. In this figure,the normalized displacement, d", is given byd" =1t d G h I F,

(4)

where t (in second) is time and Vs is the shear velocity(m/s).
A second comparison was made with published results(for a plate with a midplane vertical monopole with a step
rise-time. Analytical results based on a ray method (Pao et
aI., 1979) were presented with the restriction that the longitudinal velocity be -v'3 times the shear velocity. The FEM
results were calculated with a ·cosine-ben· source with arise time of 0.5 JlS. For this rise time, all the necessary
DFEM requirements were met for the plate of 2S mm
thickness. Tables 4 and 5 list key OFEM parameters for thiscalculation associated with Fig. 8, which directly compares
Ihe out-of-p1ane (oJ and in-plane (0,.) displacements at 2h,
4h, and 6b from the source. The ordinate axis was nondimensionalized by Ihe same expression as equation 3; theabscissa was normalized by equation 4a,(3)

twa = t VLIb,
where VLis the 10ngiwdinaJ velocity (m/s).

103

(4a)

Green's Function

FEM

E

(c)

0.6

~ 0,6

CIl
(,)

til

0.4

~O.4

is
'0

.~ 0.2

0.2

a;

E

0

Z

0

0
2

0

3

0

1

2

3

0

cCD

E

B
-0.1
til

-0.1

~

is

-0.2

"0 -0.2
CIl.~a;§ -0.3

-0.3

o

z

3

456

-0.4
7

3

Normalized Time456

7

Normalized Time

Fig. 7 Buried monopole comparison of out-of-plane displacement signals at 2b and 5b for FEM versus Green's function code
(Hsu, 1985). Note (a) and (c) at 2h and (b) and (d) at Sh.
For both cases considered above, the ,DFEM results
clearly match the amplitude and shape of the alternate calculations. For the second case, this result is, at first. surprising since the O.S-Jls rise time does not match the step
rise for the other approach. The step rise results were notavailable in a form that would allow convolution to be preformed. However, good agreement was still obtained, as
the signals are dominated by the high amplitude of the lowfrequency displacement owing to the non-equilibIated monopole force. A better comparison of the two approaches
could be made by using a high-pass frequency fdter andthen comparing the results. Since the step source result is
not available in a form that would enable such a comparison, the models were evaluated below for dipoles where theself-equilibIating source forces result in signals without asteady increase in displacement in one direction. Hence, thehigher-frequency aspects can be more directly compared.

plane displacement at the epicenter position 2S mm above
the 0.03-J1S rise time sowce occurring in a half-space(Scruby et aI., 1983). The temporal shape of the source was
one-half a Gaussian curve, as shown in Fig. lb. The DFEM
results were calculated with conditions meeting the necessary requirement (see Tables 4 and 5, Fig. 9). Figure 9shows results that are nearly identical except for a small 2.2
dB difference in peak amplitude. To match the vertical axis
in the previously published result. the out-of-plane displacement in JUD was divided by the dipole strength in N'm.
The abscissa is real time. The DFEM result was frltered by
a 30 MHz (based on the specbUm in Scruby et al., 1983)
lOW-pass six-pole Bessel filter to remove some oscillations
due to the very small rise time.
The second comparison was made by using Hsu's code
(Hsu. 1985) to form the difference of two vertical monopoles centered about the midplane of a 25-mm thickplate. One monopole was centered at 12.65 mm below the
top of the plate and the other at 12.35 DUD. (Note that if
these two monopoles are viewed as a dipole, then the dipole
spacing and the source size are 0.3 mm.) The Green's function for a "unit step" (calculated with a 5 os time step) of
each monopole was convolved with the first derivative of

4.2 Buried Dipoles
Four comparisons were made for buried vertical dipoles where the rise time and shape of the DFEM source
were the same as those for the independent approaches as
described below. The first comparison was for the out-of-104

1.0 ... {a)· .: ....:. - --:--. -: ... :

-1.0

-

-

.
~

.
. . . '.' -

1

c::
Q)

2

. . . . . . . . ... .
'.'

'.'

3

4

.

5

6

7

8

9

10

c:

CD

~

0.5 ....---.---,-.........---.--.....---.--,-.........---.----,

Q.

,.... . ur

g

;".-

~b)

I/)

is

.",,'"._/:

0 .--..........------::==-..:..

CiS

c::
o

E
~
as
.!!!
'0

c:

-g -0.5l-..........- .........----""----""'------'--~ ...........- '

E

E

Z

5

6

(e)

as

1ii

4

0.5

C-

§
'ecn

o

-2.0

7

8

9

10 11 124

5CD

Dimensionless time

:ac:

-0.5

Z

0.5

o

(f)

:(c)

o

~,

6

-0.5 1.--"""""----'-_""'""---'-_ _""""-'---"""""-----'
6 7 8 9 10 11 12 13 14
Dimensionless Time

7

8

9

10 \".~

Dimensionless time
-0.5

Fig. 8 Buried monopole comparison of both in-plane and out-of-plane signals at 2h, 4h, and 6b (top to bottom) for FEM [(a)(c)] versus ray-method [(d)-(f)] approach (Pao et al., 1979). Out-of-plane: liz; in-plane: u..
the "cosine-bell" temporal source with a rise time of 1 Jls.
Then, the result for a dipole (sttength of 0.3 N·mm) was
obtained by subtracting one convolved monopole result
from the other. The corresponding DFEM result was also
formed by adding two vertical monopoles (with opposite
direction forces and 1 Jls cosine bell rise time) also centeredabout the midplane. The FEM calculation satisfied convergence requirements; the pammeters are given in Tables 4
and 5 for Fig. 100a). Figure 100a) compares the results at
2b. The ordinate axis for results of both the Green's function and FEM methods was normalized by using equation 3
with the plate thickness replaced by the plate thickness
squared and the monopole force replaced by the dipole
strength (in N·m). Since the source size for the "summed
dipole" was very small, both summed results met the requirements for a point source when the rise time is 1 Jls.

A third comparison used the previously mentioned
Hsu's code (Hsu, 1985) for a midplane vertical dipole in a
2S-mm thick plate. As in the above case, the rise time was 1
Jls and the shape was the "cosine bell" function. The result
for the dipole was obtained by convolution of the Green's
function result for a ·unit step" source (obtained with as ns
time step) with the first derivative of the ·cosine beD· function with a l-Jls rise time. The results are shown in Fig.
1O(b) at the same position and with the same material pr0perties as those in Fig. 100a). The DFEM midplane vertical
dipole result shown in Fig. 100b) met the required convergence properties with the parameters shown in Tables 4 and
5 for Fig. 100b). The same normalization was used in Fig.
100b) as was used in Fig. 100a).
The final comparison was also made for a midplanevertical dipole in a plate. In this case, the rise time was the

105

o.s

-z
--

(b)

0.3
(a)

~

E

:::L

El
•
~

0.4-

-

0.3-

.--

0.2-

l..

G

0.2

etS

r:l
~

::3

~

C

CIt

=

CD

E

~

CD

0.1
cu

lr)

()

Q

a.

0.1-

(J)

Ci
o -!---!===::::::::I=--~
I=~

O+---+---..------.----r-

2

4

6
8
Time, J.1s

10

2

4

6

8

10

Time, ps

Fig. 9 Comparison of epicenter out-of-plane displacements between (a) alternate approach (Scruby et al.• 1983) and (b) FEM
approach. Displ~ment shown is per unit source strength.
direct code changes regardless of whether the source is asurface fon:e or a buried body force monopole or dipole.
Another evidence that the dipole result obtained by using
Hsu's code <Hsu. 1985) is not correct is the following: theresult of using the Hsu code to calculate a dipole by summing two monopoles does exactly match the DFEM result(i.e.• Fig. 100a) [Green's function] versus 100b) [DFEM]).

'pmaboIic ramp· (see Fig. I(c» originally used in the analytical work (Pao et al., 1979) with a rise time value that
corresponds to 2.56 Jls for the plate thickness of 2S mm
used in the DFEM calculation. Again. the calculated DFEM
result met convergence requirements with the parameters
shown in Tables 4 and 5 for Fig. 11. Figure I I (a) and (b)compare the out-of-plane displacements at ~. 4h, and 6h
positions on the top surface of the plate. The abscissa wasnormalized by using equation 4a. The ordinate normalization is given by

The second case where the DFEM result does notmatch is the dipole result (Pao et al., 1979) shown in Fig.
11. In this case, the major difference (contrary to that iUustrated in Fig. 10) is the scale of the normalized displacement axis. We have not been able to resolve this differencein scale factor. The waveforms match quite closely. particularly at posilions near the epicenter. Further from theepicenter, there is a small difference in one region of the
waveforms calculated by the two methods. Note that (1) the
DFEM result exactly matches the amplitude for the my approach for a buried monopole (by the same authors Pao.et
aI., 1979. see Fig. 8) and that (2) the DFEM code is thesame as that validated previously with eltperimental results(Hamstad and Gary, 1994; Hamslad et al.• 1996).

(5)

where S. is the spacing of the monopoles (m).
Figures 7 through 11 clearly show that the DFEM results nearly match the independent analytical results withtwo exceptions. In the rust of these, the result calculated byusing Hsu's code for the midplane dipole does not match the
DFEM waveform (see Fig. 1O(b», but the peak amplitude isnearly the same. Upon closer examination of the first partof the displacement (DFEM and Hsu results), we determined that the match was exact until the first direct my reflection arrived from the bottom plate surface at the point
where the displacement was calculated (at 10.68 ps in the
Hsu result in Fig. 1O(b». The reflected ray displacements
differed from the DFEM result in sign. This difference
probably leads to the differences observed between the two
approaches, as seen in Fig. 100b). Note that with the DFEM
code the effects of reflections are calculated without any

Recently. another independent study validated the
DFEM approach for buried dipoles (Mal et aI.. 1998). In
this case, at Sh. 10h, and ISh, the DFEM and independent
results matched both in amplimde and in waveform for a
300 ns "cosine-bell" rise-time source. The match was nearly
perfect until shortly after the peak of the AE signal. After106

'E 0.6
CD'E 0.6

Green's Function

FEM

CD

E

E

~ 0.4

CD 0.4~Ci.
en 0.2=c-gr------....----...,...-----,

0.8

0.8.....----.........- - - - - . - - - - - . . ,(a)01-----/\

~ 0.2

:c

"0

Of------.....-./\

CiS

.~
~-0.2

o

o

~

...E -0.2...

z -0.4z -0.4_0.61-.----........------L----'
o
10
20
30

-0.61-.----""""-------------'
o
10
20
30

Time. ~

Time, J.LS

0.8.....----.........- - - - - - . - - - - - - ,0.8 .....----........----...,...----..,

~ 0.6
E
CD

~

Q.

.!!2
"0
"0

.~

1: 0.6
CD
E

Green's Function

FEM

~ 0.4

0.4

Q.

0.2

(b)

0 f - - - - -__"

en
15

al

0.2
Ot------.....-./\.!::!

~ -0.2oz -0.4~... -0.2
oz -0.4-0.6 "--0.6 I-----"""-----.......---~
30
o
10
20

o

Time, ~.......
10..L...-

20

--J

30

Time, J1S

Fig. 10 Midplane vertical dipole comparisons of Green's function approach versus FEM approach. Out~f-plane displacement is shown at 2b for top (a) dipoles fonned by summing two monopoles and bottom (b) dipoles.
the peak. the waveforms differed slighdy owing to the artificial damping present in the independent approach.

tive non-reflecting boundary condition (Higdon, 1990) toeliminate significant reflections for up to 200 J4S. The displacement shown in Fig. 12(a) is the result along 22.5" radiation angle. The result in Fig. 12(a) was the same as thatat 0', 45·,67.5", and 90· radiation angles. These are all in
the first quadrant having the source center located at X
345.4 and y 345.4 mm and being 7.7 mm below the plate
top surface, except for minor changes from the ideal of ISh(due to constraints imposed by the cell size). Figure 12(b)shows the displacement result at the same position with 408
cells (identical to a 204 cell result not shown) across the
thickness for the vertical dipole using the two-dimensional
code with a domain radius of 1067 mm about the cenb'al
source. Looking at two key parameters, A,,/s and A,,/(cs),
we can see reasons for the small difference between those
two runs. For Fig. 12(a), A,,/s is 5 and A,,/(cs) is 14.9; forFig. 12(b), A,,/s is 10 and A,,/(cs) is 119. Clearly, the threedimensional code results could be slightly improved (in theregion of the peak amplitude) if the resolution could be
increased to 101 cells across the thickness, as shown in Fig.

s. Resourc:es Required for Tbree-Dimensional ModeUng
Since in the future we are interested in modeling threedimensional buried-dipole cases in up to 25.~mm thickplates out to ISh. we evaluated whether our current computing capability will be adequate to meet the establishedn:quirements. For this calculation, we used a 2.3-J4s risetime 8cosine be1l 8 source in steel with the same material
CODStants used in Figs. 1 and 2 and our current maximum of
51 cells were distributed across the thickness of a 25.4 mID
x 929 nun x 929 mm domain. The out-of-plane displacement was calculated with the three-dimensional DFEM
code. Result on the top surface of the plate at ISh is shownin Fig. 12(a), while Fig. 13 shows the geometry for thiscalculation. We note that this domain is adequate for one
quadrant, 0 0 to 9()0 of a first quadrant, of the radiation pattern using both the large domain size and a partially effec-

=

107

=

(b)

c
CD

E
o

At2h
0.02

At2h

(a)

CD

J!
DJ!!

"

etl

=

C

.2etl
c

Dimensionless time

CD

-9

E

..

C

-0.02

~

0.01

8as

c:l
~

At4h

~

'i

=

.S
-0.0
fIJ
c:l
~

~

a

•• -O.IH-+-----,r---.....---.----,-----,r-...---r----r-f
Q

CD

-

-0.01
0.01

CD

E

At6h

;

3

4 S

6 7 8

9 10 11 12

Dimensionless time

0.0

~

8as
i
:e

.! 0.0
=:a
fIJ

etl

~

=

~

C
o
'iii

"i
.S -0.0

Dimensionless time

IS

c

CD

E
C

At4h

~

co

C

9 10

fIJ

"

E

2 3 4 S 6 7 8Dimensionless time

0.1.! 0.0
=:a

0.01

Q.
.!!

C

1

~

a -O.OS+---,.---r-,--.,...-....----r---..,-----r~
.•

-0.01

Q

S

6 7 8 9 10 11 12 13 14
Dimensionless ~eFig. 11 Out-of-plane displacement at 2h, 4b, and 6h from a midplane vertical dipole. Analytical results (Pao et al., 1979) are
shown in (a) and FEM results in (b). Regions between arrows show primary differences in signal-shape.
12(c) for the two-dimensional code. In this case, the above
two key ratios are 5 and 29.7.
The cmrent threedimensional case mentioned above leads to a run with themesh of 52 x 1867 x 1867 points for a total of 2651 time
steps. Each time step requires about 480 floating-point
operations per mesh point This problem requires about 4.4
x 10' bytes of memory. The processor used for this computation required about 2.6 ]As to advance each mesh point
one time step. The total computation required about 240
hours of computer time. The code runs at very high efficiency on a parallel computer so an eight-processor machine could reduce the wall clock time to 30 hours. We

believe that this code would run efficieody on a distributedmemory parallel computer with 32 to 128 processors. We
think that the 51 cell results are adeqWlle for initial explorations of three~mensional radiation patterns and more
complicated dipole sources, but we hope our computing
resources will be capable of modeling 101 cells for a 25.4mm thick plate in the not too distant future. This increased
resolution would allow more accurate modeling at the 23]As rise time or similar accuracy modeling but with a risetime of down to 1.15 ]As. In order to most accurately examine three-dimensional cases with rise times less than
1.15 ]As, additional computing capability will be required.

108

However, as we recendy observed (Ramstad et al., 1998),
relating results modeled with small rise times to experimental results requires high sensitivity, wideband sensors
with a nat frequency response to higher frequencies thanthat typically available in current sensors.

0.5
0

e -0.5=- 0.5..:ce 0

6. Contrasts Between DFEM ModeUng and Alternate
Analytical Approaches(b)~

A key difference between DFEM and the altemale
analytical approaches is the fact that with DFEM finite
plate samples with edge reflections can be modeled as wellas the infinite plates, to which the other analytical techniques typically are limited. The DFEM method has beenvalidated experimentally for both nonnal and oblique edge
renections (Prosser et al., 1999). Further, in principle, theDFEM can also model AE in geometry more complex thana simple plate or half-space and provide for both near- andfar-field results within the same computation. Fmally. theDFEM method can provide spatial displacements at selected times after the operation of the AE source.

~

--==
u

CIII

GIl

-0.5
0.5

(c)

0
-0.5
50

80

110

140

170

200
7. Conclusions

Time, J1S
Fig. 12 Out-of-plane displacement at ISh for a 2.3-JlS risetime -cosine ben- dipole source: (a) three-dimensional codewith 51 cells through the thickness; (b) two-dimensional
code with 408 cells through the thickness; (c) twodimensional code with 102 cells through the thickness.
Dotted circles show regions of small differences in the signals.

Receivers along this arc
at ISh (381 nun) on top

I ,..\~

345.4

Comparisons with independent analytical methods
show that the FEM results are accurate. Results also demonstrate that three-dimensional cases (with a 23-]ls risetime and a 25.4-mm thick plate) can be effectively modeledwith the FEM code to obtain displacements up to a distanceof ISh by using our current computing resources.

Ts~Referenees

929

"\

i;

Key finite element parameters have been determined
for finite element modeling of buried monopole and dipole
AE point sources with a "cosine bell" source: "A.,Js and)..J(cs). Here, Am is the minimum wavelength of interest, s
is the source size, and cs is the cell size. Our study indicatedthat adequate results are obtained for )..Js ~ 2 and "A.,J(cs) ~15. Very good results can be obtained if both of these numbers are doubled.

Keiiti Aid and Paul G. Richards (1980), QuantiUllive Seis-

\i

turce

loca~onmology Theory and Methods. Vol. II, W.H. Freeman, New
York. pp. 802-803.

7.7 mn:below top surface

T~345.4

John Gary and Marvin Ramstad (1994), -00 the far-fieldstrocture of waves generated by a pencil break on a thinplate,- J. ACOUSL Emission, 12(3-4),157-170.

,r

I

M.A. Hamstad, J. Gary, and A. O'Gallagher (1996), -Farfield acoustic emission waves by three-dimensional finite
element modeling of pencil-lead breaks on a thick plate," J.
ACOUSL Emission, 14(2), 103-114.

r---929~

Fig. 13. Domain for three-dimensional DFEM result shownin Fig. 12(a). Dimensions in mm.

109MA. Hamstad. J. Gary. and A. O'GaUagher (1998),"Wideband acoustic emission displacement signal~ as afunction of sowce rise-time and plate thickness... Progressin Acoustic Emission IX. Acoustic Emission Wprlcing
Group and Acoustic Emission Group, Los Angeles, CA, pp.
IV-48-IV-S7.

Yih-Hsing Pao, Ralph R Gajeweski, and Ahmat N.
Ceronoglu (1979), "Acoustic emission and transient waves
in an elastic plate," J. AcousL Soc. Amer., 6S( I}, 96-105.
W. H. Prosser, M.A. Hamstad, J. Gary and A. O'Ga1lagher(1999), "Reflections of AE waves in finite plates: finite
element modeling and experimental measurements." J.
Acoust Emission, 17(1-2),37-47.

Robert L. Higdon (1990), "Radiation boundary conditions
for elastic wave propagation," SIAM J. Numer, Anal.,%7(4),831-870.

C.B. Scruby, H.N.G. Wadley, and JJ. Hill (1983). "Dynamic elastic displacements at the surface of an elastic half
space due to defect sources: J. Phys. D: Appl. Phys., 16,

N.N. Hsu (1985), -Dynamic Green's functions of an infinite
plate-a computer program,- NBSIR 85-3234, available
through NTIS. PB86-143856/AS.

1~1083.

John Strikwerda (1989), Finite Difference Schemes andPartial Differential Equations, Wadsworth and Brooks!
Cole, Pacific Grove, CA.

Ajit K. Mal, Dawei Guo, and Marvin A. Hamstad (1998),•AE wavefield calculations in a plate," Progress in Acoustic
Emission IX, Acoustic Emission Working Group andAcoustic Emission Group, Los Angeles, CA, pp. IV-19-IV29.

B. Tirbonod (1992), "Acoustic emission sources modeledby dislocations," International J. of Fracture. 58, 21-53.

110

Numerical Assessment of the Quality of AE Source
Locations
Gang Qi and Jose Pujol
Abstract

form AE source location computations. In their work. notonly the source location was discussed. but the mechanismof the source was studied as well. Similar work was foundalso in Buttle and Scruby (1990) and Scruby et aI. (1986).
Although other researchers used AE location techniques,most of their work used no more than three-channel AE
systems on planar settings. In the practice of AE source
location, there are several factors preventing the advancement of the technique. One likely major factor is the presence of noise in the measured AE signals due to the complex nature of the AE experiments. Another possible factor
is the number and disbibution of sensors recording an
event, which in some cases may DOt be adequate to properly
constrain the locations. In any case, the AE technique has
been criticized as being unpredictable and unreliable. To
address these questions, the authors used a two-step approach. First, they developed a simple but effective location method (Qi et aI.• 2000) using ideas borrowed fromwell-established earthquake location techniques (Lee andStewart, 1981). The method was tested with data generated
in a simple specimen, which allowed a preliminary assessment of the quality of the computed crack locations. In thesecond step, described here, the authors performed a computer simulation of the effects of internal noise, studied
sensor disttibution, and introduced several numerical indicators that allow an objective quantification of the qualityof the locations. These indicators are directly related to the
singular values of the matrix of partial derivatives thatarises when setting the equations for the location problem.
An important aspect of this walk. which also follows ideas
used in earthquake location, is that it allows an assessmentof the quality of the results that can be expected for a given
distribution of sensors when the location of the sources are
assumed to be known. In other words, if there is reason tobelieve that the AE source will have a preferred location,then a proposed distribution of stations can be tested to determine its expected performance.

In this article, we examine the factors affecting thequality of acoustic emission (AE) source locations and introduce numerical indicators that can be used to establish
whether an AE location can be considered reliable or not.
The locations discussed here were computed using location
software based on well-established earthquake location
techniques. Assuming that the specimen, in which AE 0ccurs. is approximately isottopic and homogeneous, the two
major sources of possible location errors are noise and the
distribution of sensors that record an event. The distribution of sensors plays a critical role. This is demonstrated
with results obtained when the recording sensors foan aplanar array, in which case there was a Dblind zone" forevents in a position symmetric with respect to the positionof the sensors. The effect of noise is studied using simulated noisy data generated for known source locations. By
comparison of the locations obtained using these data andthe aetual locations the effect of noise can be assessed. The
effect of the sensor distribution is quantified by the condition number of a matrix of partial derivatives involved in
the computations. Low condition numbers indicate that thelocations are well constrained, while high condition numbers indicate that the locations may be unreliable. Regarding the effect of noise. in our case an error level of 1% orless does not introduce significant distortion, while a S%
error level is close to the maximum acceptable level.
1. Introcluetlon

Although the acoustic eOUSSlon (AE) technique is
widely used in various areas to evaluate the mechanicalbehavior of materials and to provide early warnings andhealth monitoring, algorithms for the determination of thelocation of AE sources (or cracks) involving denoising andnumerical assessment of the reliability of the computed
results have not been investigated. In fact, in reviewing the
published literatUIe, only a limited number of research papers are available in the area of AE source location. For
instaltce, 2D and 3D AE source location IeChniques wereintroduced in the Nondestructive Testing Handbook(ASNT, 1987), while Grosse et al. (1997) developed Hype"! (a derivative of earthquake location software) to per-2. Experimental

2.1 Material

Compact tension specimens were prepared for the tests.
The specimens were made of Palacos R (Smith & Nephew,
Memphis, TN). The composition of the Palaces powder isgiven in Table 1 (Lewis, 1997).

Received 7:1 December 1999. Gang Qi (gan~j@mempbisedu) isaffiliated with Department of MecbaDical Engineering. aDd Jose
Pujol is with Center for EarIIlquake Research aod Information,
The UDiversity of Memphis. Memphis. TN 38152-6576 USA.
JoumaI or ACOUSIie Emission
Volume 17. Numbers 3/4

111

Q'73O.OQSOi99/SS.001l7111-120
Copyright@I999 ACOUSlie Emission GroupTable 1 Constituents ofPalacos cement

BPO

2,02

Chlorophyll

P(MMAIMA)

N,N-OMPT

Hydroquinone

MMA

0.5-0.6

14.85

200 ppm

83.55-84.65

2.13

64 ppmlTl.f!7ppm

Table 2 Test parameter setup.

r -~'Parameter TVDe
Threshold tVDe
Threshold value
Peak Definition time
Hit definition time
Hit lock time
SamDlerate
Filter on board (low)Filter on board (hi2h)Pre-tril!l!er
Hitlen2th
CYcle count threshold
Delta-T front end filter
Front end filter

senscr2

12Sq·~O

l!--IO_.o_.

'2_1_)..l:='--_ _~IIEE---,62mm----:~
Fig. 1 Specimen dimensions and sensor locations.

2.2 Instruments and Test Setup

Instrument set values
Fixed
40 dB
200 us
400 us
300 us2 MHz
10kHz
1.2 MHz
20 us
lk
0.4 V
Yes
No

3. 3-D AE Source Loeatlon Algorithm

The instrumentation used in the tests is listed below.
PAC AEDSP 32/16 PC-based data acquisition board(highest sampling frequency 20 MHz, six-channel
system).
• PAC 1220A preamplifier (100 - 300 kHz).
• PAC PICO wide band piezoelectric AE transducer(bandwidth 100 kHz - 1 MHz).
• Silicon grease couplanL
The parameters for the instrument setup are listed in Table2.

The location algorithm has been introduced in Qi et al.
(2000) and is based on a standard technique used to locate
earthquakes (e.g•• Lee and Stewart. 1981). The basic featuresof the algorithm are as follows:
1) Assume that the medium is homogeneous and isotropic. so that the velocity of wave propagation is a constant.
and that a crack occurs at time To at point (Xo. Yo> 7.0>. The
w~v~ genemted by the crack reacb the sensor j at time~. In
pnnClple. the crack generates both compressional (or P-)
waves and shear (or 5-) waves. and the following equations
apply to both types of waves provided that the appropriate
velocity is used.
2) The origin time to and location (XQ> Yo> zo> of the crack
are derived from the arrival times ~ recorded at N sensors as
follows. The arrival time ~ is equal to•

Figure 1 shows the geometry of the specimen and thelocations of three of the six AE sensors (see also Table 3).
The AE sensors were taped to the specimens l • The acquired signals were conditioned with preamplifiers. The
conditioned signals (with gain of 40 dB) were fed to themain data acquisition boards. Fatigue tests were perfonned with a stress ratio of zero. During the tests. thefollowing AE parameters were monitored and recoIded:
time. AE counts. AE events. amplitude. energy. and fre.
quency. The time sequence AE data were recoIded according to the order of hit time at each individual sensor.
This time sequence AE data was used with the location
algorithm referred to above.

j = 1. 2.....N(1)

where 1j is the tmvel time from (Xo. Yo. ~ to the j-th sensor.
As the system of equations (1) is nonlinear. it is solved in
two steps. First. it is linearized using a Taylor expansion andthen the conesponding system is solved by iteration. Let i
indicate itemtion number. Then. equation (1) is replaced byTji_4= dTo + Vr . dv

(2)_

where rp is the difference (residual) between the observed
arrival time and the anival time computed in the previous
iteration

1 It was found that the sensors became loose from time totime during the fatigue test period of four to five hours.
As a result, the AE signal became temporarily quite noisy.

(3)

112

Table 3 Sensor locations.
SensornumberCoordinates (mm)Y-coordinates
0
0
23
23
0
23

X-coordifUltes

1
2
3
4
5
6

2S
2S
2S
2S

0
0

For the first iteration an initial estimate for the solution
must be given. Symbol V denotes the gradient operator. anddv is the vector (dx.dy.dz)T. In matrix fonn. equation (2)becomes

Noise can have a detrimental effect on the quality of
AE experiments, especially on the computed AE source
locations. Three types of potential AE noise are recognized: environmental. internal and special. Environmental
noise is that generated by external factors such as load train,electronic instrumentation. and other unknown soun:es generated outside of the tested material. The internal noise
originates in waves other than the direct P- and S-waves,such as reflected and refracted waves. Their importancewill depend. to a large extent, on the nature of the specimen
investigated; the more heterogeneous the model the higher
the likelihood that this kind of noise will be a problem.
Special noises are those associated with particular aspectsof mechanical tests and are generated by surface touching
during the tests. An example of this type of noise is thefretting between fatigued surfaces associated with fatigue
tests. The fretting noise is one of the most difficult to beeliminated/reduced.

where
(5)

(6)

A=
1 VTN_l
1 VTN(7)

di - (d'!'o,dvf

Equation 2 is solved using a damped least-square technique.
The iterative process stops when one of the following three
conditions are satisfied:(i) The average root square residual

ARSR -_

Jr::::l
Rl <
N

Er

0
42
0
42
21
21

4.1 Noise

(4)

R-Adi

Z-coordinates

Presently. the techniques to remove noise are limited tothe use of threshold values for quantities such as amplitude,energy. and frequency content Environmental noise bas
been satisfactorily removed in material characterization
tests and usually will not be a significant source of error for
AE source location computation. The special noise. on theother hand, mayor may not introduce errors in AE source
location computations depending on the specific nature ofeach mechanical test For example. in a mechanical fatigue
test, AE signals are commonly associated with fretting
noise. It is not an easy task to remove this noise. Generally. the crack-tip region is the place where most fretting
occurs. As a result, the physical contact between the fatigued surfaces can enhance the AE signal from the regionof interest It must be noted that the special noise is not any
form of stress/strain energy release during loading. Therefore. it is of no interest to include the special noise in the
AE material characteristic evaluation.

(8)

where £r is a cut-off value;(ii) Idd $ e.. and Id1:I $ Ey where e.. and Ey are cut-off values;(iii) the number of iterations exceeds a pre-set value.
This algorithm was implemented in a Matlab toolbox
that includes a search for arrivals times from the same
event The results described here were obtained using thistoolbox and P-wave arrivals. The P-wave velocity wasdetermined using pencil lead breaks (Qi et al.• 20(0). Usually. convergence to a solution required 2 to4 iterations.
4. Factors All'ecting the Quality of the AE Source Loca-

tions
Assuming that the specimen. in which AE occurs isfairly homogeneous. there are two possible major sourcesof error in the computed locations. One is noise and theother is the distribution of the sensors that record an event
They are described below.

In an AE source location test, the internal noise deserves further consideration. First of all, since the internal
noise is in the form of wave energy released during the113Input values:
sensor array
locationacoustic
velocity -fl
initial location
estimates
initial origin
time estimate

Simulate theComputearrival times using
source ~the computed ~locationsource location

Add artificial random errors(10%, 5%, 1%) to thesimulated arrival timeI
Compute the source locatio
again using the simulated
noisy arrival time....

Observe the effects
of noise on the
results

Fig. 2 Computer simulation of noise effect

4.2 Distribution ofSensors

loading process. it is safe to say that the effect of internal
noise on a typical AE material characteristic test or processmonitoring is very limited. However. this type of noise canbe a significant source of eoor when locating AE sources.
In AE location computations. the internal noise win interfere with the direct waves if they arrive at a sensor at approximately the same time. The nature of the interference
can be complicated and will depend on which signal arrives
first and on the polarity and amplitude of the signals. For
example. if the noise arrives before the direct signal. theonset of the latter may be affected. thus introducing someerror in the estimated arrival time. which may affect thequality of the computed locations. In this paper. it is theintention of the authors to limit the discussion to the effectsof the internal noise on the quality of the computed source
locations. It is assumed that the environmental noise can beeliminated or reduced to a level such that it will not havesignificant effects on the detennination of the AE source
location. As in routine AE tests the output of the detection
algorithm is an arrival time. there is no a-priori way to tellwhether it comlSponds to a direct P- or S-wave, or to estimate how much eoor may affect it To find out, it is necessary to search for a group of arrivals to the different sensors
within an expected time window. to use those arrivals to
locate the source. and to use the results of the location algorithm to estimate the quality of the location. In order to investigate the effect of errors in the arrival times. the authors
used simulated times computed for given source locations.
These locations are detennined using actual data and thencomputer generated uniformly distributed random errors areadded to the simulated times to make the signal noisy. The
noisy simulated data are then used with the location algorithm. By comparison of the results obtained with the actual and the simulated data. it is possible to get an idea ofthe effect of noise on the locations. This simulation process
is illustrated in Fig. 2. An example of application of thisapproach is given in the next section

Even for perfect experimental data, if the distributionof the sensors that recorded an AE event is not appropriate,
the location of the event may be affected by a significant
amount of eoor. Interestingly, the average residual (seeequation 8) may be very small and yet the location may begrossly inaccurate. For this reason. we investigated the useof the condition number of the matrix A given in equation 6
to assess the quality of the location. The condition numberof any matrix is the ratio of the largest singular value to thesmallest one (Noble and Daniel, 1977). Condition numbers
are important because they give an indication of the linear
dependence, or independence, of the columns of a matrix.
If one of the singular values is equal to zero. then one of thecolumns of A will be linearly dependent on the other ones
and a unique solution does not exist If the smallest singular value is nonzero but considerably small, the columns
may be close to linearly dependent and the matrix is said tobe ilI-conditioned. As a result, small eoors in the data may
be "magnified" into large errors in the vector solution.
Therefore. AE soun:e locations. for which the condition
number of A matrix is very large, should be considered unreliable even if the average residual is small. Examples
given in the next section illustrate the relation between condition number, sensor distribution and event locations.

S. Results and DiseussioD
The fatigue tests performed in this research were continuously monitored The fll'St 14,400 cycles constitute theinitial stage of the tesL In the second stage. the maximum
load was increased to 800 N. For details. see Qi et aI••
(2000). In this paper. only one time period was selected toinvestigate the factors affecting the accuracy of the computed results. In this section. it will be shown that some of
the computed locations occur near where they are expected,
which makes it possible to estimate location errors. Other
computed locations. on the other hand, are not well con-114

12r······· .,

=-

:

:

··

..

:

.

• ~

..· · 1·..····

•, ..

..l.
1

.

.

...ji
·····..·····1···..·..·.. ···f:···

•
II ~

;

i

+

~

.

. . . . . . L r ..l.

10~ ........ ·· ..

~

i

,-

~

............ ~

·
·j
:

)0

1:

.
.it

~.
~

1

NU~OI SOUJcu.~nts. 1489
~
:

:

~

:

.

.

!t

.. ~

8 ... ·· .. ·······

.

.

~

.

............ _.............•........ , ...•........................................ ,

···
··

7

.
..
~

6'

,

(

,

,

,

I

I

-10

0

10

21

:lO

4:l

50

II)

XCDlldIllllts IIWll

Fig. 3 x-y projection of crack locations for fatigue cycles 17,280 to 17.720 using a location close to sensor 3 as initial esti-mates.
strained as a result of an unfavorable distribution of sensors.
As a consequence. one of the coordinates cannot be determined uniquely. The reason for this fact is discussed indetail later in the paper.

......... "I!D)r .. · .... · .... : .. ··· .. ·····

"

..

,

5.1 Estimated Location Errors

lCXXl,·.... t·....

I

Figure 1 shows a detailed view of the sensor locations.
Notice the location of the tip of the notch. given by x = 19,
0< Y < 23 and z 21.5. It is expected that AE activity willtake place along this tip. Figure 3 shows the computed
source locations for fatigue cycles 17.280 to 17.720. Notethat there is a concentration of activity centered at about y =11.5 and X = 21.5. The corresponding value of z is 21.
Comparison of the coordinates of the tip of the DOtch withthe computed locations shows that the inferred source ofAE activity is close to the expected location. To estimate
the error in the computed locations, the difference between
the coordinates of the crack tip and the coordinates of theaverage of the source locations was computed. This gives
21.5· 19 = 2.5 mm. zero, and 21 - 21.5 = -0.5 DUD in the X-,
y- and z-directions. respectively. Notice that the physicalsize of a PICa sensor is 5 mm in diameter. Therefore, theaccuracy of the computed results is well within the limitations introduced by the instrumentation. There is a slightly
curved line with a length of less than 4 DUD. possibly related
to activity along the crack tip, and several concentrations ofactivity along y U 11.5. The latter have a range of values
for the x coordinate. some of which place the AE sources
well outside of the specimen. This obviously indicates thatat least some of these locations must be incorrecl To investigate the reliability of the locations. the condition number of the matrix of derivatives in the last iteration and the

I

=

.

~

J .

~tO

a

_

.

- . . . wTlw.....!WrWrl
toll _ _
3l
30

41

/ill

Ill)

Fig. 4 Condition number vs. x coordinate for fatigue cycles
17.28010 17.720.
average residual were computed (Figs. 4 and 5). For the
events with xU 21.5 and the events on the curved segment
the condition number is less than 100 and SO. respectively.
These condition numbers can be considered to be relatively
small values and they indicate that the computed locations
are well consttained. As a result, these locations are reliable. On the contrary. for all the other computed locations,about balf of the total computed locations. the condition
numbers are significantly larger, which indicates that thematrices of derivatives are not well conditioned. Therefore.
the corresponding computed locations cannot be considered
reliable. In fact. as discussed next. they might well be part
115

u·r
_10"

W

w

·rwwr

. , :~·:::::::r:::::T·· ,·.·.·. .•:t..·.· :;.....:,

J':w; .wJwl·············
i

I

1"

~
0

10

.

~

1

" WW\WW~oy~' ··········~lmi
rx
_
~
30

.

_,: .........•...........1. '. ....;...
i"·'" ;~ _. .~ .". ····1

30

50

III

Fig. 5 ARSR value vs. x coordinate for fatigue cycles
17,280 to 17,720.

Fig. 6 3D crack locations for fatigue cycles 17.280 to
17,720 using the average sensor array location as the initial
estimates.

of the concentration of events at x= 21.5, although there is
no way to prove, or disprove, this statement

11.5, the locations of the events recorded by these four sensors do not constrain the x coordinate. The locations with x= 25, in particular, were suspected to be an artifact To
verify this hypothesis the locations were recomputed with
the initial value of the x coordinate set equal to 15. The
corresponding final locations for the events recorded only
by sensors 1,2,3, and 4 had x coordinate equal 15. Thesenumerical experiments, as well as several others the authors
carried out, demonstrate that, in a way, this planar foursensor distribution has a "blind zone" along the x-axis. As
can be seen from equation 6, one of the columns of matrix
A is made of ones. If all of the columns of A"t as in theoriginal are not a multiple of the first column, then A will
be well-conditioned and a unique solution will exist Whenone or more of the columns of as in the original 6"t become
linearly dependent or close to multiples of a column ofones, then A will become ill-conditioned. This situation
occurs for a symmetrical planar sensor distribution withrespect to the potential source and parallel to a coordinate
plane (in this case parallel to y-z plane). As a consequence,
there is not enough information to constrain the values ofthe four unknowns and the technique becomes "blind". For
instance, the computed value of x in this paper wiU dependon the initial value of x and there will be a trade-off between the values of x and origin time. A similar trade-off issometimes observed in earthquake seismology, where thelocation of an event (a point in 3-0 space) must be inferredfrom the arrival times recorded by sensors arranged on thesurface of the earth (which can be assumed to be plane for
distances of a few hundreds of kilometers). Generally
speaking, the "blind zone" effect is rarely seen in practicaltests because the geometric shape of the test subjects is notnecessarily symmetrical, as in the case of the standard compact tensile specimen studied here.

5.2 Effect ojSensor Distribution
As a general rule, the distribution of sensors should besuch that it surrounds the potential AE sources, and thenumber of sensors should be as large as possible to provide
sufficient consuaints. However, even in cases where thesensor distribution appears to be adequate, as in the present
experiment, it may happen that the amplitude of the AE
signals is not strong enough to be recorded by all the sensors. This reduces the actual number of sensors hit, andchanges the geometry of the sensor distribution.
The results described in the previous section were obtained with the initial estimate of each source location very
close to the location of sensor 3. The initial origin time foreach event was the earliest arrival time. When the initial
estimate was changed to the average of the locations of thesensors that recorded the event, the computed locations
clustered primarily at x = 21.5 and x = 25, as shown in Fig.
6. Note that there is no activity at x = 25 in Fig. 3. The
locations in Fig. 6 look better than those shown in Fig. 3
because none of them are outside of the specimen and alarge part of them occur where they are expected. However, in view of the large condition numbers discussed
above, it was suspected that the events at the locations otherthan x = 21.5 were not well constIained, and that the dependence of the final locations on the initial locations was
the result of an unfavorable distribution of recording sensors. This was indeed the case, as it was found that these
non-unique locations were associated with the events recorded by sensors 1,2, 3 and 4 only. From Fig. 1 and Table
3, it can be seen that these four sensors form a symmetricplanar anay with center at y = 11.5 and x = 25. As themain source of AE activity happens to occur around y=116

Our results also show lhat the locations of the events
recorded by five or six sensors are not affected by the selection of the initial locations. In general, this fact meansthat the computed locations are well constrained, and can beused as a criterion to establish the reliability of the locations. Therefore, in addition to the condition number discussed above, it is possible to assess the quality of the locations by comparing the results obtained for different initial
locations. However, this approach may not be feasible if
real-time results are expected. in which case the condition
number will be the only quality indicator. The average residual, on the other hand, may have limited value when thenoise level is low, as in the case discussed in this paper, andthe number of recording sensors is small. This can be seenclearly from Figs. 4 and 5; the events with the largest condition numbers have residuals that are among the smallest
of all.

IS .......

~11

I:.. . . . . .• • • • • • r • rrr·•• • • . Ci........... .
7

IS •··· .. ·•• ..

T··.. ····.. ·I..·.. ····.. ·1·.. ··· ··1··.. · . ·····!·····.. ···.. T·.. · ·.. j

12

j

)

,..
:
:

:

:

:, :.,. :.
~

• :

1

)

:

·'j'· · . ··r

9 ·.. ·.. ·..

a .. ·· .. ·..
7 .

)

~

...;

·

,)

j

:

:
:

:

:

t

:

.

.

·;

;

:

:

:

:

. · ·.. ·t ·.!.

110

·t~

)

:

..·l

I

~

~

~

,. :

:

'1'

····1

-. ,. 0;
j

~

~

x_"'"

=

clusters along the x axis at y 11.5 become linear features
along the y direction. The original values of x changed by asmall amount only, although it is clear that the thickness of
the linear features is larger than the width of the clusters.
The net result is a somewhat distorted picture of the actual
size of the source of AE activity. On the other hand, thecurvilinear feature at the expected source location is fairly
well recovered. The condition numbers (Fig. 9) remaincomparable to those obtained in the original computations.
The ARSR value (Fig. 10), however, does not provide
much information. These statements regarding condition
numbers and ARSR also apply when the error level is 10%(Figs. 11 and 12), but not to the computed locations. At
this error level, the resulting pattern is quite different fromthat obtained for the ~vious error level. The clusters
along the x axis are barely recognizable, with a large spreadin the computed locations. Figure 13 shows clearly that thelinear feature exhibited in the ~vious error level no longer
exists. When the error level is increased further the clusters
tend to merge together.

)

·

~
~
~ Nul.d__ L.I49J!
1···j····.. ·······i"··........·..~......·.. ··t·.. · · . ·····1····....··.. ·j··.. ·········1.·

I.. . · · . ··l··.. .·.. . j··....·.... · 1

':

:

'::"

~

.~

~

···j·

.~

·/..·

Fig. 8 Locations obtained using simulated data for the locations shown in Fig. 3. &ror level 5%.

11!

rc'_ct~............r············ or··········· "r' 0.···~As discussed before, the internal noise is a possible
major source of error affecting the accuracy of the AE
sowee location. To investigate the effect of internal noise
the computer simulation process was used as described inSec. 4.1 and Fig. 2. The locations used to compute thesimulated times are those shown in Fig. 3. With these locations it is possible to see the effect of noise under wellconstrained and poorly-constrained conditions. To compare
the results, several levels of noise were studied. The initial
locations were always close to sensor 3.

:

;a.

:

~':-o--O~---:IO!:---::Z=---::lO~--:~;:----=!ill:------:GO

5.3 Effect ojNoise

.

:

"I"

:

:

These results suggest that the intemal noise may indeed
have a significant effect on the accuracy of the computed
AE source location. In our case, the effect will be minimal
when the error level is below 1%. At this error level, !heaocumcy of the noisy signal is close to that of the "clean
signal". Therefore, in our case a 1% error will not affectthe locations significantly, while the 5% enor is probably
the highest that can be acceptable for AE source location.

:1!toL-- _ . . Lo.--....LIO--~31,....---:30':---Cl.l:--~5D--~lID

x _ .....

Fig. 7 Locations obtained using simulated data for the locations shown in Fig. 3. &ror level 1%.

6. Conclusions
Figure 7 shows the results for a noise level of 1%. The
computed locations differ Iiale from the "clean signal".
Correspondingly, the condition number and the ARSR are
almost identical to those in Figs. 4 and S. Figure 8 shows
the results for a noise level of 5%. Comparison of this figure with Fig. 3 shows several differences. First, the original

The 3-D AE source location software package developed by the authors has the capability to assess the reliability of computed locations. The most useful indicator of thequality of a solution is the condition number of the matrixof partial derivatives A. A small condition number cone-117

.. .............................................

3000

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. .............................................

2500

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.

2000

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1000

500

..

0l---IJUJ..--l.

;
I.
..LIJL--....L..Z.

-10

10

0

40

20
30
X coordinates mm

;

Li

50

60

Fig. 9 Condition number YS. x coordinate for the locations shown in Fig. 8.
7
X 104.5 .....

4

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3.5

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3

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go

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20
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X ccoralnates mm----JIL.-_....L....-

40

50

Fig. 10 ARSR value ys. x coordinate for the locations shown in Fig. 8.

118.......

60

4X~~-G

,

3.5

.

:-

3

;

,

:.. .

~

..

:

.

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112.5
!
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1 .......

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-10

0

10

20

30
40
Xcoordinates mm

50

60

70

Fig. II ARSR value vs. x coordinate for the simulated data with an error level of 10%.

1500

1000

.

.
.
.
.
................................................................................

.
.
........................................

500

oI""."L-.&..I--........- - ' - _...
-10

0

10

20

30
40
X coordinates mm

50

60

70

Fig. 12 Condition number vs. It coordinate for the simulated data with an elTOr level of 10%.

119

20

: ........••.. :

~

18

:

:

:

;+-

~ ~:.~~ ~

:
:
...
.
:
: +

14

-:

.

:

.:.:

;

+

.:

+
-4'*"

..

:..

:.
:
.

.

.
.

:

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.

.:;,:~ .. ..:...'+.....
' . . ,..-

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:

t. ···········~··········~·~·iT.:·L
. +.
.

+:

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+ +::i'...........;.
;+.
~+...........:.

:

l..: + ++.+:++.j.··.··.. ····l············~····· .. ·····~··~i+···~····
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+;( :

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-10

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50

0

10

30
40
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60

70

Fig. 13 Locations obtained using simulated data for the locations shown in Fig. 3. Error level 10%.
sponds to a well-constraioed solution, while a large one

D. J. Buttle and C. B. Scruby (1990), "Characterization ofindicates that the solution may not be reliable. A simple

fatigue of alWDinum alloy by acoustic emission, Part I -way to find out whether a solution is reliable or not is tochange the initial estimate. If the computed location depends on the initial estimate, the location is unreliable. The
vector R is a reasonable indicator of solution quality only
when it can be assured that a solution is well constrained.
Large condition numbers are associated with unfavorable
sensor distributions. For the data presented here, the wellconstrained locations agree very well with the expected
location of AE activity. The poorly-constrained locations,on the other hand, are the result of a planar distribution of
sensors in a symmetric position with respect to the point of
AE activity. This resulted in a blind zone for the x coordinate, which could not be detennined. Therefore, when designing an AE experiment it is important to make sure thatthe distribution of sensors is such that the condition numberof matrix A is as small as possible. This can be done before
the experiment is actually carried out using simulated data
generated for expected AE sources locations.

identification of source mechanism", J. Acoustic Emission,
9(4),243.
C. Grosse, H. Reinhardt, and T. Dahm (1997), "Loca1iza·tion and classification of fracture types in concrete withquantitative acoustic emission measurement techniques",
NOT & E International, 30(4), 223.
W. Ue and S. Stewart (1981), Principles and AppUcationsofMICToeartluJuake Networks, Academic Press, New York.
G. Lewis (1997), "Properties of acrylic bone cement stateof the art review", J. Biomedical Materials ReseaIch, 38,
155-182.
B. Noble, and J. Daniel (1977), Applied Linear Algebra,
Prentice-Hall, Upper Saddle River, NJ.
G. Qi, J. Pujol, and Z. F. Fan (2000), "3D AE visualizationof bone cement fatigue crack locations", J. Biomedical
Materials Research. (in press)

Acknowledgement
CER! contribution No. 399.

C. B. Scruby, K. A. Stacey, and G. R. Baldwin (1986), "I)e.
fect characterization in three dimensions by acoustic emission", J. Physics: D Applied Physics, 19, 1597- 1612

References
ASNT (1987), NondestIUCtive Testing Handbook, ~ Edition, American Society for Nondestructive Testing, Columbus,OH.

120

Structural Integrity and Remnant Life Evaluation Using
Acoustic Emission Techniques
Brian R. A. Wood, Robert W. Harris and Elizabeth L. Porter
Abstract

tails of the elastic wave propagation between source andtransducer, and the characteristics of the tnU1Sducer andmonitoring equipment Simple analysis of the detected
pulses may not always give results with a high degree ofcredibility for both source location and identification, so theinterpretative procedures must take into account as manyfactors as possible.

The present industrial community which, in many
countries, bas been permitted to operate under a selfregulation criteria, in some instances, has demonstrated atendency to use economic and convenience consb'aints todetermine their operating scenarios. The desire to makemanufacturing plants and structures more productive hassometimes resulted in them being operated under conditions
which can exceed those envisaged during the initial design.
While these plants and structures still operate efficiently
and safely, the real structural integrity and remnant life
evaluation is really unknown. This report discusses the application of acoustic emission techniques, which can provide an estimate of both present structural integrity and theremaining safe operational life of the structure being monitored.

In an analysis in the frequency domain, the power
spectral density (PSD) of a source can be estimated if someknowledge about the transfer functions operating in thecoon of events between source and transducer is available.
Simple pattern recognition applied to the experimentallymeasured PSD's can provide further information aboutsource attributes, however this does require a wide-band
response and also critical spatial positioning of the transducers, so that the approach is impractical for large transducer separations. The results of the various analysis techniques applied to the detected AE signals form a knowledge
base, which is used to extract information about the natureof these signals.

1. Introduction
It is now in excess of 30 years since modem acoustic
emission (AE) techniques have been generally employed inindustry. In that period, great advances have been made inboth AE instrumentation development and applications
including basic functions such as defectlleaklsource detection and location, and some source identification. Acoustic
emission techniques have been used in a variety of applications in industrial plant The special techniques we developed can provide real time defect location, generally identify processes and procedures damaging pressurized equipment, provide a structural integrity evaluation, and after anumber of tests and analysis operations provide an estimateof remnant life of the structure monitored.

A desirable end-point for all of the processing is to present the data in ways which will readily provide a real time
indication of structural integrity. One parameter called Indicative Energy is used in the analysis procedures. It can bederived from the peak amplitude distributions of the detected pulses. Provided that all the equipment and the transducer locations remain the same, then valid comparisonsboth within a test and between tests are possible on thesame structure. An indication of structural integrity can begained from the indicative energy values if plotted as afunction of an appropriate activity/stimulus factor producing what has been termed a localized energy graph. These
indicative energy values can be plotted as a function of timeto predict maintenance requirements and estimate the remnant life of the structure. Alternatively the data can beplotted and continuously updated during a monitoring period so that areas of probable significant activity can bereadily identified as the test progresses. This is a very valuable tool when monitoring a plant or structure start-up operation.

2. Basic: Theory

When a material or object is placed under stress, any
defect. which is activated. will act as a source of elastic
waves. The resultant surface wave can be detected by anappropriate transducer, which produces an electrical pulsethat may be analyzed to provide a framework to determine
much of the information contained in a signal. The detected
pulse will be different from that generated by a source as aconsequence of: the microstructure of the material; the de

3. Philosophy of AE Monitoring

Received IS November 1999. Authors are afTtliated with Metacoustics Ply. Limited. P.O. Box 6042. Blac:ktown. New South
Wales. 2148. Australia. aeteam@metaeoustics.com.au
Journal of ACOUSlic Emission

Volume 17. Numbers 3/4

The use of AE techniques to monitor structures often
employs a 10 hour monitoring program, which is about
0.02% of the life of a vessel in operation for 5 years. The

121

073Q.00S0J99/SS.00I17121-126
Ccpyrigh~ 1999 Aooustic Emission Groupversus log time graph is given in Fig. 1. The exact nature of
the regions of inflection and the approximately linear sections of the graph will vary for different situations.

results of such a program have been used to provide a current evaluation of structural integrity and so gain certification for future operation. If the monitoring becomes an annual occurrence then the testing time rises to around 0.1 %of the operating period. The use of continuous or periodic
AE monitoring, consideration of the plant material characteristics and test procedures which recognize the Kaiser
effect, make it now feasible to use AE assessment as thebasis for continuous plant operation (no statutoJY shutdowns). This is especially so when coupled with other NOT
techniques and if there are realistic plant operating conditions. Prior to start-up, any plant or structure should be in a
new condition. On start-up the plant or stroeture is placed
under considerable stress, both localized and overall (particularly if it is a high-temperature, high-pressure vessel).
This traumatic experience often results in significant damage being done to the plant or strocture. It is possible thatup to 60% of the operational degradation may be initiatedby transient operating conditions specifically in the first 48
to 72 hours of operation, although other mechanisms suchas fatigue and creep will both initiate and produce defectgrowth during plant operation. The growth of any defect or
AE source will be dependent on the material properties andthe plant operating conditions. Under "nonnaI" plant operation the source/defect activity which may be connected withmechanisms such as fatigue. creep. stress corrosion etc, isusually associated with sustained long-tenn defonnation.
Since any defect/source activity will vary depending on thesurrounding stress it is not possible to get a definite measure of the damage/degradation of the structure. A number ofplant monitoring programs and separate tests where vessels
and samples have been stressed to failure indicate that there
is a relationship between Indicative energy 1 and Indicative
energy 2 which produces a local andlor overall indication ofrelative damage. The Relative Damage Index can be plotted
as a function of a number of parameters including test time,
operating period. or applied load and is usually depicted ona logarithmic scale. A typical fonn of the relative damage

After a prescribed period (typically 3 to 10 years depending on the strocture and its operating conditions), there
will be a statutory shut-down and maintenance program torestore the plant or strocture to a near new or defect-free
condition. The next start-up will again cause degradationand so the cycle continues. AE monitoring during stan-up
and preferably also during operation will highlight and assess the severity of any source activity. so that operational
conditions detrimental to stroctural integrity will be determined. Historical AE data will allow the detennination of acumulative indication of remnant life. This analysis can beapplied to data obtained during a test program and may berelated to both the whole structure. or to specific areas ofinterest for that test program providing a short-tenn evaluation of integrity, stability and failure. It may also use datafrom several tests on the same strocture. employing thesame transducer locations over an extended period of timeto provide a long tenn estimate of the operation, integrity
and remnant life for both specific areas of interest or theentire Stnlcture.
4. Structural Integrity Evaluation

The AE data was analyzed and interpreted with respect
to individual surveillance areas or to structures as complete
units for a number of stroctures. The results of the analyses
were progressively up-dated during the tests so that a continuing series of results were obtained, which also provided
trends related to individual conditions in the strocture. An
evaluation of the stroctural integrity of the areas monitored
provided an indication of the localized conditions at various
stages during the test programs. The Stroctura! Integrity
Index is a computed value from the acoustic emission data
recorded from each transducer. A Structural. Integrity Index

Relative Damage Index

10-----...:..----------------ailure
8,..

10 yr life reduction by returning to ambient

ost Useful lif
Secondary Damage

I

Tertiary Damage

Log Time or Log Stimulus

Fig. 1 Relative damage curve.

122

100

Structural Integrity Index

Material Parameter.:..:....::..:....:..:......:...-.:...:....::--.::.....---------------------:1 100

;.!

I

90

90

00

~OO
i

I

- -l. . .-. . . . .L. . .-.J~- - -. .-. - - L. .- - - '- . . I. . - - - - -'- 17o

70 1l . 1

2

3

4

5

6

7

8

9

10

11

12

Test SequenceFig. 2 Structural integrity index vs. test sequence (time or stimulus)
Structural Integrity Index
Material Paramameter
100 r - - - - - - - - - - - - - - - - - - - - - - - - , 100

90

I,

90

to

80

70
70
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Test SequenceFig. 3 Structural integrity index variations from annual monitoring.

dams. The data is combined with the physical properties ofthese materials and the waveform analysis of AE data re-of 100 is the highest level of struetural integrity. Dependingon the material and the geometty of the structure, a Structural Integrity Index above:
90 for Fiber-reinforced plastics
90 for Geologic/Concrete-type materials and
80 for structural steel,indicates a good structure. An idealized structural integrity
graph is shown in Fig. 2.

corded during the field test to derive the Structural Integrity
Index. The laboratory and field test data also better define
the index value associated with material failure, and thus
the structure's operational safety criteria can be estimated.
Successive test monitoring bas demonstrated the gradual degradation of vesselslstructures, and the associatedmaterial, from the start of service life to the time of current
monitoring. This allows the scheduling of maintenance andidentifies any specific area exhibiting material degradation.
If any repair has been fully successful, the results of subsequent AE tests on the vesselfS1IUCture will demonstr3te as. Typleal Test Results
The analysis techniques are based on laboratory research on various materials used in structures such as pipelines, pipe structures, tanks, pressure vessels, bridges and123

5.3 Continuous PIanl Moniloring

return to a high level of structural integrity and thus safe
service life. Ukewise areas of inadequate repair will beidentified.

Continuous plant monitoring is a very expensive technique and so is not frequently employed. However semicontinuous monitoring. where a plant or a section of plant is
instrumented and monitored both frequently and regularly,
can have very significant advantages to plant owners/operators. Using these techniques, it is possible to plot
the progress of an entire plant or only a small segment of astructure, which may require specific atlention. Many
structures have been taken out of service because a defecthas been identified. Some, but certainly not all. of these
defects may not have been in areas subjected to significant
operating stresses, and the structure may have been capableof continued safe operation without adversely affecting the
integrity of the structure. While this concept may causesome concern, it can also provide significant re-assuranceof a structure's safety for continuing operation while preparations are made for repair and/or replacement of the apparently defective structure. The graph shown in Fig. 5 shows
the results of intensive monitoring of one section of a pressure vessel where a known defect existed. This area wassignificantly stressed during the plant start-up operation.
which is shown early on the graph. From this point on. theknown defect was activated by some plant operating conditions. These were identified and controlled/modified, thus
allowing continued plant operation, in this case for somemonths, while a new piece of equipment was manufactured.
tested and then the defective component was replaced.

This AE test technique has been applied to many different structures and operating conditions. The technique
can be modified to accommodate many test/operating criteria, which include the following conditions usually encountered in modem plant operating programs.

5.1 Regu/Qr Tune-Based Moniloring
A number of vessels were monitored annually over anumber of years. The results shown in Fig. 3 demonstrate
the change in the Structural Integrity Index of one vessel.
initially through normal aging/degradation and then theaccelerated deformation resulting from a detrimental
change in plant operating conditions. The vessel was repaired in 1996 and the return of structural integrity is apparenL

5.2 AE Testing in lieu ofHydro-Testing· During PIanl
Start-up.

There is some debate about the use of a hydro-test onpressure equipment and many alternatives have been explored. Acoustic emission techniques offer a valid alternative and have been successfully used in lieu of a hydro-testin the start-up of a new chemical planL

5.4 Remnant Life Evaluation

The data were recorded and analyzed during the startup operations. and many defects located. Each defect wasclassified and most were repaired. This is shown in Fig. 4
where the Structural Integrity Index falls as there is a stepin the applied stress, and then it levels out as the structurestabilizes.

As structures and plants are being required to operate ator near the design criteria, the need 10 know and understandthe real structural integrity of plant equipment is a necessity. Laboratory tests on the base material used to build

Integrity Index
110,

i
I

100r

j

----'I9080

70
60 l -_ _....L..._ _---!

o5....&...._ _----.l.

- ' - -_ _--l.._ _- '

10
15
20
25
Increasing Start-Up Pressure

Fig. 4 Plant Start-up Monitoring.

124

30

35

Structural Integrity Index

1

1.~IFIil.iii--=--------------i

0.9

0.9

0.8

~0.8
;

I

Itn-upjO'7

0.7 1-

i
0.6 l.-_-..:.-_ _..:...-_-----l._ _-l.-_ _" - _ - - - ' - _ _"'"---_---.

o

30

60

90

120

150
180
Time in days from Start-up

0.6

240

210

Fig. 5 Continuous AE monitoring of a defect area during plant operations.

1---------~ 55000
50000-45000
45000

40000

40000

35000

35000
30000
25000

Peek Amplitude (valls)

Ratio

2 0

Fig. 6 Paaem recognition plot of AE activity from an operating plant
5.5 Pallem Recognition Analysis

a structure are made to determine a series of physical parameters. These pammeters are then used to determine thecritical values reported in section 5 of this report. As the
Structural Integrity Index calculated in each test progmm isevaluated, it is possible to make an indicative prediction of
the time required for the structure integrity index to reachthe calculated critical value of the base material. In theearly stages of the life of a struetureIvessel. the predicted
remnant life will usually be large and approximate. however as the stnlcwreJvessel approaches failure. this estimate
becomes realistic and a valuable safety tool which can also
be used to plan plant maintenance schedules and component
replacement

A goal of any field investigation is to estimate therange of different types of signals being received by thetransducers. and so some form of pattern recognition is Iequired. The investigator requires both uniqueness and r0bustness in the analytical techniques used. which in practicecannot be totally achieved. There also should be somephysical basis behind the choice of pammeters rather thansimply an elegant mathematical algorithm with no relevance to the physics and material science involved. Patterns
in the pammeters derived from the detected signals need to
be determined. so samples of digitiZed raw data are

125

Plant Startup-------

Frequency(Hz)- T25000D

250000'y(200000
200000-\150000£15000~',100000. 50000

o
0.8

0.7

2.5

0.6
0.5
0.4

.'
~,././ ~ .../
2·~ ....·

0.3
0.2

Peak Amplitude

Ratio

1 0

(volts)Fig. 7 Pattern recognition plot from a damaged plantrequired. The first question that should be asked is whether
the samples selected are good representations of the general
population. These samples of digitized raw data generally
exhibit a bias since the larger amplitude signals are preferentially captured. Hence, predominantly events arising fromthe more deleterious SOUIt:es are being stored for analysis,
meaning that a pessimistic view of the mechanical integrity
would be deduced using this data on its own. This in itself
is not an unsatisfactory result since safety of a structure
must always be of supreme importance. When acquiring
data for analysis by sampling and digitizing signals, therestrictions placed by such a process must be considered.
The Nyquist criterion requires that the sampling frequency
should be at least twice the highest frequency inherent in
the signal being sampled. Since the conversion is being
carried out by an analog·to-digital converter having only acertain number of bits in the result, the possibility of quantization elTOrs must be considered. Laboratory and field
investigations have shown that this approach to pattern recognition has greatly assisted in delineating different group.
jngs of events. Figure 6 is a typical plot of data recorded
during plant operation. While this data is a mixture of proc·
ess noise and AE from the structure monitored, the tightsingle cluster is indicative and may be tenned nonnal oper·
ating conditions. Data recorded from an operating plant,
which has both known and unknown defects, produces two
obvious groupings of activity as shown in Fig. 7, whichhighlights material degradation as a totally independent setof AE data due to the plant operation.

the associated stress distribution generating AE pulses. The
cunent technology is biased towards engineering and elec·
tronics and a significant input relating to the physics andmaterials science aspects of AE is needed to advance thetechnology. At present, we have developed a monitoring
and analysis technique which it has been demonstrated canclassify the structural integrity of pressure equipment alongwith other structures and predict the remnant life andmaintenance requirements of the structure or plant item
being monitored. The addition of signal analysis, waveformand pulse analysis to the already viable collection of AE
techniques fonns part of the testing regime required to both
detect and validate AE data. Universally. most of the in·formation available from AE monitoring is not used, be·cause of the short·term economic considerations controllingboth monitoring equipment and techniques. The need tovalidate data and its interpretation has never been so important. The correct detection of AE data and the simulta·
neous analysis of that data using a portfolio of techniques isnecessary to ensure the safe and efficient use of industrial
plant and structures.
Acknowledgments
The authors wish to express their appreciation to theother members of the Metaeoustics Acoustic Emission team
who participated in both the laboratory and field test work
associated with this report.
The data were obtained from a large number of fieldtest programs, which are commercially confidential. However, the assistance and cooperation of the many companies
that worked with Metaeoustics on these projects is both
acknowledged and appreciated.

6. Conclusions
There is still much work required to determine what
happens at a microstructural level and the detailed nature of126Selected Papers fromInternational Conference

Acoustic Emission '99

Held at Bmo, Czech Republic, IS-17 June 1"9In commemoration of the lOOth annivenaryof Bmo Univenity of Technology

International Conference Acoustic Emission '99
Report on International Conference Acoustic Emission '99, at Hmo, Czech
Republic, 15-17 June 1999.
Pavel Mazal and Vaclav Svoboda
On the occasion of celebrations of the l00th anniversary of Brno University of Technology(VUT) (Czech Republic), a successful intemational conference dedicated to the development andpossibilities of using the technology of acoustic emission was organized by the Institute of
Design. The Conference AE'99 took place from 15 to 17 June 1999 at Faculty of MechanicalEngineering, Brno University of Technology. Czech Society for Non-destructive Testing andBrno branch of the Association of Mechanical Engineers of the Czech Republic were coorganizers. The conference was steered by a scientific committee consisting of:Jean-Claude LENAIN, EPA SUCY en Brie, France
Eckhard PRIDO~, FI Dresden, Germany
Lubomir SODOMKA, CVUT Praha, Czech Republic
Vaclav SVOBODA, CNDT Praha, Czech Republic
Josef SIK.ULA, CVUT Brno, Czech Republic

Oldrich TARABA, CVUT Praba, Czech RepublicPeter TSCHELIESNIG, TOV Wien, Austria
More than 80 participants from 10 countries registered at the conference and nearly 50
contributions were presented. They were divided into a session aimed to theoretical problems
and into several sessions solving practical problems connected with AE applications, especiallyin mechanical engineering and civil engineering. The level of most of the contributions was very
high, the evidence of what was a number of wide-ranging discussions. An exhibition ofexperimental technology was a very important part of the conference. The most modern
appliances by Vallen-Systeme, Euro Physical Acoustics, Energovyzkum Brno, AED Laboratory
Brno, ADA Plzen, and others were demonstrated. The participants of the conference had an
opportunity to take part in rich accompanying program, including an evening social party on asteamer at the Brno Lake, a visit of caves in the Moravian Karst National Park and a visit to amuseum at fortress Apilberk, etc.
The international conference provided a very good overview of contemporary state ofdevelopment of AE method and its use in technical practice in the Czech Republic and in a
number of top workplaces abroad. As far as the Czech Republic is concerned, theoreticalproblems of acoustic emission are being developed especially in the Academy of Science of the
Czech Republic (especially the Institute of Thennomechanics and at some Technical Universities(CVUT Praha, VUT Brno, TU Liberec, etc.). As for practical applications, acoustic emission isstandard method used in a number of power stations for analyzing and control of various parts ofenergetic devices (Opatovice Electric, CEZ Detmarovice, JE Temelfn, etc.) and in applications inmechanical engineering (namely, aircraft industry) and in civil engineering. Several domestic
S2

fmns introduced themselves at the conference, that (on commercial basis) ensure non-destructive
testing by help of AE for foreign fums. AE applications by evaluation of the condition ofpressure vessels, containers, and eventually the entire construction (especially energetic) systems
and searching for leaks of various media are quite common. Other potential uses of acoustic
emission are gradually being developed, e.g. in the area of machining, evaluation of mechanicalqualities of materials (especially by cyclic loading of advanced materials). Prompt editing of avery interesting proceedings of all contributions, edited by Pavel Mazal, was positive side of theconference, too. Selected papers are presented in this issue. List of the entire contributions in theproceedings of the conference AF99 is given below (page numbers are listed for the papers
included in this issue.):

J. Blaha, K. Slama, M. Svetlik (ADA Akusticka emise, s.r.o. PIzen, Czech rep.)
Accurate localization of defects in the welds of a header with a set of pipes
T. Boczar (TU Opole, Poland) Identification of fundamental forms of partial discharges 87
based on the results of frequency analysis of their acoustic emission
T. Boczar, M. Lorenc (TV Opole, Poland) Measurements of acoustic emission
generated by basic forms of partial discharges and calibration sources
G. Budenkov, O. Nedzvetskaya (TU Izhevsk, Russia) The calculation programof acoustic fields of growing cracks
G. Budenkov, O. Nedzvetskaya, E. Bulatova (TV Izhevsk, Russia) Technical possibilities of the non-contact acoustic emission method at testing hollow articles integrity

813

K. Chandrashekara, A. Ramachandra (SJ. College of Engineering, Myssore, India)
Experimental investigation and analysis of universal machinability index using AE
M. Cerny, P. Mazal, V. Suba (Mendel UAF Bmo + Bmo UT, Bmo, Czech rep.)
Method of AE and possibilities of corrosion degradation detection

820

N.G. Chausov, S.A. Nedoseka, N.D. Gakh (IPP Kiew, Ukraine) Accelerated strength
check by the acoustic emission method for concrete with additives

F. ChmeIik, P. Lukac (Charles University Praha, Czech rep.) Application of acoustic
emission in metal physics and materials science
S. Constantinescu, O. Mitoseriu, A. Ciocan, T. Radu, A. Chiriac (University of Galati,
Romania) Hot-rolled plates surface defects

J. Crha, K.Wiirfl (Vitkovice, Czech rep.) Life time estimation and quality assessmentby acoustic emission method

F. Dusek (AED Lab. Bmo, Czech rep.) Acoustic emission analyzers of series AFD FfA
J. Dvoracek, J. Petras, D. Kolar (Bmo UT, Czech rep.) Description of AE signals
in the course of the contact fatigue test method
S3829

L. Golaski, P. Gebski, I. Baran, Kanji Ono (Kielce lIT + FRI Krakow, Poland andUniv. of California, USA) Waveform analysis of acoustic emission during
pressurization of glass-fiber composite pipes

837

F. Havlicek, J. Crba (TU Ostrava, Vitkovice a.s. Ostrava, Czech rep.) Acoustic
emission monitoring during solidification processes

S4S

F. Helebrant, J. Ziegler, L. Brabec, J. Dedrle (TU Ostrava, Czech rep.) Certifikace
odbome zpusobilosti osob v ATO CR (in Czech)
M. Korenska, L. Pazdera, J. Smutny (Bmo CVUT, Czech rep.) Determination of
propagation wave velocity in solid state by means of electric discharge spark

A. Kotolomov, G. Budenkov, O. Nedzvetskaya (lzhevsk ru, Russia) Radiation of
B. acoustic emission waves during stress corrosion cracking of the metal

851

M. Landa, M. Chlada, Z. Prevorovsky (rrCAS Praha, Czech rep.) NDE of phasetransformations in Cu based shape memory alloys by ultrasonic techniques

857

J.C. Lenain (EPA Sucy en Brie, France) Structural integrity assessment of pressure
vessels with MONPAClIPAC technology
M. Lorenc (TU Opole, Poland) Selection of calibration method of measuring systems
used in the acoustic method of estimating electric discharges
O. Matal, J. zaloudek, T. Simo (Energovyzkum. spot. s.r.o. Bmo, Czech rep.) WER
steam generators and acoustic emission

865

O. Matal, J. Zaloudek (Energovyzkum, spot. s.r.o. Bmo, Czech rep.) Application of
acoustic emission in valve diagnostics
P. Mazal, J. Richter (Bmo lIT, Czech rep.) Application of acoustic emission techniqueon fatigue testing machine Rumul

870

L. Moraru, A. Ciocab, S. Constantinescu, C. Tudose (University of Galati, Romania)
Crystallite size and microstrain of aluminium solidified in ultrasonic field
O. Nedzvetskaya, G. Budenkov, A. Kotolomov (Izhevsk TU, Russia) DefInition of
growing cracks depth using Rayleigh waves

J. Nuffer, D. Lupascu, J. ROdel (lIT Darmstadt, Germany) Acoustic emission andstate of fatigue of ferroelectric Pb(ZrxTit.J03 ceramics
L. Pazdera, J. Smutny (Brno lIT, Czech rep.) Possibility to location of an acoustic
emission sources

R. Pensec. M. Cherfaoui, C. Herve, A. Laksimi (UT Compiegne and CETIM, France)
Monitoring of defects evolution in pressure vessels. Application to the improvement
S4878

of the AE detection criteria
V. Svoboda, J. Petrasek, A. Proust (Elektramy Opatovice a.s. and Preditest s.r.o. Praha, S83
Czech rep.) Application of AE method at pressure tests of boiler header
V.1. Petrov, V.E. Gromov, I.S. Lobova, N.D. Kalyukina, E.S. Kuchumova, S.N. Gorlova(Siberian TU, Novokuznetsk, Russia) The use of acoustic emission in heat engineering
I. Polajnar (University of Ljubljana, Slovenija) AE during resistance spot welding
V. Poppeova, S. Fogelton, J. Uricek (TU Zilina, Slovakija) Vyuzitie AE pri hodnoteni
procesu obrabania kovov (in Slovak)z. Prevorovsky, M. Chlada, M. Landa, M. Blahacek (Institute of Thermomechanics,
CAS, Czech rep.) Simulation and identification of AE sources in anisotropic structures
F. Rauscher (TOV Vienna, Austria) Acoustic emissions of vessels with partiallypenetrated longitudinal seams

892

L. Sodomka (CTU Praha, Czech rep.) Acoustic emission of composites
L. Sodomka (CTU Praha, Czech rep.) Acoustic emission and mechanoluminescence
L. Sodomka (CTU Praha, Czech rep.) Acoustic emission on dislocation

J. Sikula, B. Koktavy, I. Kosikova. J. Pavelka, T. Lokajfcek (Bmo UT, Czech rep.)
Electromagnetic emission from polycrystalline solids

SIlO

J. Sikula, J. Pavelka, K.Koktavy, K. Hajek. M. Dobes, L. Ostry (Bmo UT. Czech rep.)
Acoustic emission of ballscrews
R. Stastny, C. Holecek (Elektr8rna Detmarovice, Czech rep.) Experience from stationary
equipment operation used for leak detection by means of AE on boiler pressure units of
Detmarovice power stationo. Taraba, M. Murla (ELTEC Praha, Czech rep.) The characteristic of the acoustic
emission generated by leaks when gas or liquid medium is transported
O. Taraba, M. Murla (ELTEC Praha, Czech rep.) The acoustic emission generated by anelectrical polarisation of a dielectric and a magnetic polarization of some magnetic materials
P. Tscheliesnig, J. Uoka (TOV Vienna, Austria and SKODA Plzen, Czech rep.)
The testing of LPG vessels with acoustic emission examination
Z. Weber, M.Korenska, L. pazdera (Bmo ur, Czech rep.) Structural integrity
evaluation of reinforced concrete crossbeam by acoustic emission method
K. Zehnula, P. Benes (Bmo ur, Czech rep.) Utilization of AE for the flow measurement

ss

SI08

Abstracts
M. Dabrowski, K. Karbowski (Cracow ur, Poland) The investigation into wear of TiN coating
on tool steel considering the signal of AE
I. Kolarov (HST, Sofia, Bulgary) A model investigations of AE in rolling bearings
R. Novak (cru Praha, Czech rep.) AE application to monitoring of hard coatings failures
D. Sajdl (VSCHf Praha, Czech rep.) Development of a new laboratory calibration source for AE
J. Zizka, M. Novak, V. Prukner, V. Gabriel (TU Liberec, Czech rep.) AE for cutting processmonitoring
Limited number of Proceedings from the conference is still available at the organizers of theconference and it is possible to buy it for price US$70, (mailing and packing included) from theconference organizer at the address below:

PavelMazal,
Institute of Design,
Bmo University of Technology,
Technick·2,
CZ 616 69 BRNO, Czech Republic
fax: +420-5-4114-3231, e-mail: mazal@cmslme.vutbr.cz

Received IS October 1999. Pavel Mazal is affiliated with Bmo University ofTecbnology. Faculty of MecbaDical EngiJJeeriDg.
Brno, CZ 61669 Czech Republic (mazal@cms.fme.vutbr.cz) and Vtlclav Svoboda is with Preditest, s.r.o., Praha, Czech

Republic.

Next EWGAE Meeting
In 2002, EWGAE meets at Praha, Czech Republic. It is tentatively scheduled for the second
week of September.

Cover Photograph
Metacoustic team of Australia used over 170 transducers simultaneously to monitor a chemicalplant during its start-up in 1997. This test was used successfully in place of the hydro-test that isnormally employed to evaluate such a plant prior to start-up.

S6

International Conference
Smo, 15th _17th June 1999
Czech Republic
Acoustic Emfssfon'gg

IDENTIFICATION OF FUNDAMENTAL FORMS OF PARTIAL DISCHARGES BASEDON THE RESULTS OF FREQUENCY ANALYSIS OF THEIR ACOUSTIC EMISSION
BOCZAR TOMASZDEPARTAMENTOF ELECfRlCAL ENGINEERING AND AUTOMATIC CONTROLTECHNICAL UNIVERSITY OF OPOLE

Abstract
The paper presented the spectral analysis of acoustic emission pulses generated by a sort ofdischarges on indeterminate-potential particles moving in liquid dielectric materials. Amplitude andpower density spectra for the pulses was computed, followed by the derivation of their descriptors
characterizing the discharges in a synthetic way. Some interesting properties of the discharges ware
extracted from the descriptors, contributing to possible use of the method in diagnostics ofinsulating systems in electric power facilities.
Keywords:
AE-acoustic emission, PD - partial discharges

1. PROBLEM STATEMENT
In recent years the development of the AE method has been conditioned by improving
measuring systems employing the achievements of electronics and computer technology, and has
been connected with the application of digital signal processing procedures. Increasingly, theproblem is not the recording of AE generated by PD but the proper analysis and interpretation of theachieved measurement results.
An important issue consists in defming and determining the parameters characterizing AE
pulses, referred to as descriptors, which should be combined with the condition of the measured
insulation and should contain indispensable information for PD form identification. To date, inorder to characterize PD, a group of parameters determined in a course of time run transformationsof the measured AE signals has been mainly used. These variables are not always sufficient tocharacterize the processes that are accompanied by AE activity. Digital processing of AE signals
enables the discrimination of parameters characterizing the spectra paths of the measured AE
pulses. Determining descriptors' values in the time-domain and evaluating them in respect to theirusefulness in detection, location and PD measurement is a current issue and requires further
research. Moreover, the issue is also the possibility of PD form identification, based on theknowledge of frequency spectra generated by this AE pulse discharge. These issues have been
discussed in Refs. [1,2, 3,4, 5J.
In the research conducted, the following two theses have been put forward:a) various PD forms are accompanied by AE generation of characteristic frequency spectraand descriptors connected with these spectra,b) identification of the form of discharges based on the knowledge of frequency spectra of
AE pulses and descriptors connected with them, at the present state of science, is possible only inthe case of single-source discharges ofone kind.
S7

2. SELECTION OF PD GENERATED TYPES AND THEIR CHARACTERISTICS
In publications on PD, depending on the approac~ we can encounter various criteria for theirclassification. The most common is the energy-type criterion. We can also encounter classification
based on the variation ofPD intensity in time. There is also classification based on the geometry of
the flaw and on the sort of dielectric material in which PDs are generated. In this paper,a classification is adopted which makes it possible to differentiate the most important forms of PD
occurring in the insulation of high-power transformers during their operation. We thus classify thedischarges as follows: discharges of the type "spike-plate" in oil, surface discharges in oil,discharges in gas bells generated in oil, discharges on indeterminate-potential particles moving inoil.
Spark gap sketches of each of the listed forms of discharges have been built for thegeneration. They have been characterized in Refs. [1,2,3,4,5].

3. SELECTION OF DESCRIPTORS CHARACTERlZING AE PULSES GENERATED BY PD
In AE measurements it is very important to detennine the descriptors of pulses containing
the most essential information for PD evaluation. So far. when detennining the detrimental effectsof PD on insulation, time~domain descriptors have been used. These variables are not always
sufficient to characterize PD processes, which are accompanied by AE activity. Moreover, they
have not been used to identify generated types of PD. Theoretically. around 50 various descriptors
can be used in the AE method, only 20 of which, however, are of metrological value.. In this paper
have been analyzed and determined the values of the following descriptors, which in the frequencydomain characterize the measured AE pulses, generated by PD: maximum value, frequency for the
maximum value of the spectrum., medium value. RMS value, peak factor, variance. medianfrequency, standard deviation, ranges of dominant frequencies for the assumed discriminationthreshold. Formulas defining the above-listed parameters have been taken from the theory ofsignals. The values of the above-defined descriptors have been calculated separately for theamplitude spectrum, which presents the dependence of the Fourier transfonn module on frequency,
and for the energy density spectrum, determined as a square of the module afthis transfonn. These
values have been determined far all registered AE paths generated by each of the matched PD

fonns.
4. SELECTION OF CONDITIONS OF PD GENERATION, MEASURING SYSTEMS AND
THE MEANS OF DETERMINING THE SPECTRA OF THEIR AE PULSESThe value of voltage of discharge generation, at which the generated AE pulses wereregistered, for all four systems was 80% Up (breakdown voltage). For each of the PD generation
systems the recording of AE pulses was performed. ten times. The time of each measurement was
1s, which ensured the recording of AE pulses in 50 periods. To determine what influence a changeof the value of voltage of discharges generation for PD generated in the system with particles ofindeflnite potential has on the frequency analysis reSUlts. AE pulses measurements at the voltage of
60% and 70% Up were perfonned. In order that the results of measurement and analysis of AE
pulses generated by the selected PD forms may be of general value and be comparable, they were
given as relatives referred to the values of the breakdown voltage UI" To measure and analyze thefrequency spectra of AE pulses generated by the defined PD types, a standard diagnostic system by
the firm Bruel & Kjaer was used that consists of the following parts: a wide-band contactsapiezoelectric transducer (type 8312), an amplifier with a fiher (type 2638), a measuring card
(TAD-OS) provided with a 12-bit AID converter, placed in a computer.
Frequency analysis of AE pulses generated by typical PD fonns was performed for time
periods of various lengths in the range from a few IDS to several dozen J.1S. The sequences of AE
pulses coming from all discharges generated during consecutive half-cycles, and also single
discharges separated from these series were taken into consideration. Frequency analysis was
performed separately for AE impulses emitted during the positive and negative half-cycles of the
supplying voltage.
For each measurement the following AE characteristics have been drawn: time plot,
amplitude spectrum and spectrum ofenergy density that have been presented in Refs. [1,2,3,4, S].

S. ANALYSIS OF THE POSSIBILITIES OF IDENTIFYING PD FORMS BASED ON
A KNOWLEDGE OF THE SPECTRA OF AE PULSES AND DESCRIPTORS THATCHARACTERIZE THEM.
The resuhs of the performed analyses presented in Refs. [1, 2, 3, 4, S] proved that thegeneration of each of the four tested PD forms is accompanied by AE of characteristic plots offrequency spectra. The length of the analyzed time periods and the value of generation vohage inthe range from striking vohage to the value of800/O Up for the tested PD forms did not influence theplots oftheir amplitude spectra and energy densities. In Fig. 1 and 2 the averaged amplitude spectra
plots obtained for the four considered PD fonns have been presented. The characteristics have beendrawn, using spline interpolation, separately for AE impulses generated in positive (Fig. 1) andnegative (Fig.2) half-cycles of the vohage. The length of time for which the characteristics were
designated was 4.096 IDS. In order to distinguish between the plots of particular spectra, letter
assignations have been introduced that indicate consecutively the graphs corresponding with thefollowing forms: a - PD generated in oil in "spike-plate" system, b - PD generated in oil systemwith gas bells, c - surfuce PO in oil, d - PO generated in oil with indeterminate-potential particles.
Comparing the designated frequency characteristics, a clear difference in their plot should be noted,
dependent on the kind ofPO.

d-40 ~0----=1~OO=-----:::2~00=-----3..L0~O---4~OO~---.J500

r 1kHz)Fig. 1 Averaged amplitude spectra plots ofthe four PD forms during the positive half-cycle,
for the time period 4.096 IDS
For each of the analyzed PD fonns, characteristic values of descriptors designated for
frequency spectra of their AE have been received. In Fig. 3 and 4 the average values of medianfrequency have been compiled for both half-cycles, separately for the amplitude spectrum (Fig. 3)
and energy density spectrum (Fig. 4).
S9

Or------r----.,----"""T"""~_-..,._--__rc

A (dB)

a
b
d

-40 0

100

200

r 1kHz)

300

400

500

Fig. 2 Averaged amplitude spectra plots ofthe four PD forms during the negative half-cycle,
for the time period 4.096 ms

Positive haIf-cyele
5;()()

4()()

Negative haIf-eyele-------------------------------------------- -------------------------.-------------.-----.-.....

~----------------------_... _----------------- ------_ .. _._----------------------------------

3()()
Z()()

1()0

o
Fig. 3r r kHz 1
Compilation of average values of median frequency calculated fOT the amplitude spectrum
for the four tested PD forms

Positive half-cyele

Negative haIf-eyele

500 ..........---.t-------------------.-----------------------4()0..........-------------_.----------------------.---_._-

346.4

349.1

3()0

Z()O

100

o
Fig. 4

rrkHzlCompilation ofaverage values of median frequency calculated for the energy density
spectrum fOT the four tested PD forms
810

Explanations:

PZaII -

-f"SSl -Chart I'....

oil discharges in "spike-plate" systemsurface discharges in oil
discharges in oil system with gas bells
discharges generated in oil with indeterminate-potential
particles

Compilation ofranges ofdominance frequencies for the four tested PD forms"~iroBand\Vidth:ofdominance ftequencies [kHz].

the positivehalf-cYcle PD generated

the positive half-cycle
0-30 ; 260 - 450
70 -110; 290 - 350
250 500
30-40; 60 -1000-30; 230 - 370; 420 - 500
60 - 160 ; 260 -500
"flat"
30 - 50; 60 -100.

The designated values are mathematical averages calculated each time from the values
obtained for three time periods. In Chart I are listed the ranges of bandwidths of dominancefrequencies in the spectrum, which have been determined for each of the tested PD forms, at thesame discrimination threshold equal -100 dB. They have been determined separately for the
positive and negative voltage polarization.

6. CONCLUSIONS AND SUMMING UPa) Characteristic frequency spectra plots of AE pulses and descriptor values that characterize them
have been obtained for the tested PD forms.
b) On the evidence of frequency spectra plots, the ranges of bandwidth of dominance frequencies
and the value of the median frequency, compared simultaneously, it is possible to identify PD
forms. However, it refers only to one-point discharges, single and of one type. The open issue
remains the possibility of identifying a few PD forms occurring simultaneously, or discharges of
one type but repeated ones and additionally in more complex insulation systems. Moreover, it is notpossible to uniquely identify PD forms exclusively on the basis of the value of one, selected
descriptor.
c) Frequency analysis of AE pulses from PD should be conducted regarding the polarization of the
supplying voltage.
d) Designated limits (boundaries) of bandwidths of dominance frequencies for particular PD forms
indicate the necessity ofusing of measuring equipment and performing the analysis ofthe measured
AE pulses in the range ofat least 500 kHz.
In this paper the values of descriptors characterizing the measured AE pulses in theS11ftequency-domain have been defined and determined. It has been shown which descripto~ in somecases, can be used to identify the generated PD fonns.
The results of the performed frequency analysis confll111 the possibilities of PD form
identification knowing the frequency spectra and descriptors determined for them. This can be ofpractical significance in evaluating the condition of insulation systems of such electrical power
facilities as high power transformers, high voltage measuring transformers, insulators and power
capacitors. However, PD occurring in industrial conditions have multiple source, are repeated andare ofvarious types, therefore it is necessary to carry out further research, which would supplement
the state of knowledge in Frequency analysis of AE pulses generated by multi-point PD of various
types.
REFERENCES[1]
Boczar T., Analiza widm impuls6w akustycznych emitowanych przez niezupelne
wyJadowania powierzchniowe, ZN PoL Op. Nr 219, z. 44 Elektryka, Opole 1996, str. 125 - 136.
[2]
Boczar T., Analiza c~otliwoSciowa sygnal6w emisji akustycznej generowanej przez
wyJadowania elektryczne, Prace 20 SPETO, 1997, str. 441 - 445.
[3]
Boczar T., MoZliwoSci identyfikacji formy wyladowania na podstawie widma
c~otliwoSciowego jego emisji akustycznej, VI EUI'97 zakopane, 23 - 25.10.1997, str. 35 - 40.
[4]
Boczar T., Badanie widm c~stotliwoSciowych impuls6w emisji akustycznej generowanych
przez wyladowania niezupelne, IV IW - 98, Poznan - Kiekrz, 1998, str. 51-57.
[5]
Skubis J., Boczar T., Spectrum of acoustic emission pulses generated by electric slide
surfilce discharges in oil, Archives ofElectrical Engineering, No 1, Vol. XLVII, 1988, pp.25-38$12International Conference
Brno, 15th _17th June 1999
Czech Republic
Acoustic Emlsslon"gg

TECHNICALPOSSIBILITIES

OF

THE

CONTACTLESS

ACOUSTIC

EMISSION METHOD AT TESTING HOLLOW ARTICLES INTEGRITY
Prof., Dr.Tech.Sc. G.Budenkov.
Ass. Prof.. PhD O.Nedzvctskaya. Ass. Prof. E. Bulatova
Izhevsk State Tcclmical University
Studenchcskaya st., 7, Izhcvsk, Russia, 426069,
E-mail: root imLudmurtia. SU, Tel. (3412) 588852; Fax (3412) 588852
ABSTRACT. Technical possibilities of the contactless acoustic emission method at testing hollow
articles using ultrasonic leak detector «GERMES-2 )) are pre~;ented. The possibilities of thequantitative estimation of leakage output in articles working with gauge pressure and CjIlUlUilative
estimation of the sizes of hollow articles through dejects are ~·hown. The recolllmendations jiJrdevelopmel1l oftechniques and means ofa ultrasonic leak detective method are given.
KEYWORDS: integrity. tigh/lless. conlactless acoustic method, leakage. output. through defects.

INTRODUCTION
In order to insure reliable work of systems of transportation and keeping of liquids and gases.
working both with gauge pressure (pipelines. high pressure vessels). and without it (sectional coolers,cabins and automobile intcriors. the containers for products keeping, etc.). the requirements of tightness
or impenctrability and air-tightness and leak-proofness are necessary. The reason of loss of tightness
arc more oftcn through defects, which look like voids. burn- through. cracks, fusion. local channels.
Tightncss is cstimatcd by leak dctective method. Leak-tcsted methods arc made the rathcr highrequirements on scnsitivity remotability. productivity and maintenance of safety The majority ofmodem leak-testcd mcthods with a high tlucshold of sensitivity (mass-spectrometcr method 510-1J W,
luminescent method - 10-10 W. halogen method - 10-7 W) havc practically up to zero remotability, low,
productivity and require the special preparation of a surface.
In this aspcct thc contacless acoustic leak-tested method is notable for high rcmotability andoperations and can be effectively uscd for the control of articles on tightness at low rcquirements tothreshold of sensitivity.
The conlacless acoustic leak-tested method is based on registration and analysis of parametcrs
of acoustic waves gencrated by leakage into cnviromncntal atmosphere and allows to define a leakage
location and to estimatc its parameters.
$13

CONTACLESS ACOUSTIC LEAK-DETECTOR "GERMES-2»
The portable ultrasonic leak detector «GERMES-2 » (Fig.!) is developed in Izhevsk Statetechnical university. The device is intended for:- Detecting gas leakage in systems with gauge pressure;- Definition of leakage location;- Estimation of leakage output and volume of following gas for a unit of time;- Detecting and estimation of tightness loss places of closed volumes.
- Detecting discharge in power electrical circuits.
The action of the device is based on registrating acoustic waves of an ultrasonic range, which
are radiated during gas outflow through small through holes under pressure large than 0,3 atmospheres,
and also accompany the discharge processes. The frequency spectrum of such waves can extent fromsound range frequencies up to several megahertz and consequently can't be always are heard by theoperator (in absence of the device).
In order to detect site of tightness loss of closed volumes the generator of ultrasonic waves
including as a set of the device with periodically deviating frequency is located inside of the article.
The site of tightness loss are defined with the help of the receiver-display block, recording the
maximum signals in such sites.

Fig.i. Folo of contacless acouslicleak-detector"Germes-2».

The basic technical characteristics ofleak-detector "Germes-2» are listed in table 1.
Table I
Technical characteristics
I. Transmitter operating frequency, kHz
2. Wave stress of the transmitter block, Pa
3. Sensitivity of the receiver-display block (min. sound stress, registered by thedevice on operating frequency), Pa
4. Sensitivity of the the receiver-display block to an airflow in a leak-tested
regime, W
5. Indication:
Sound (dynamic loudspeaker and head telephones), frequency, kHz
Pointer type indicator
6. Power source:
Receiver-display block, V
814

Value
40±2

2
10-5

1,5-3

+6

10
+5
50

Time of continuous work without boost charge t hour
Ultrasound transmitter block t VTime of continuous work without boost charge hour
7. Sizes t mm
Receiver-display block
Ultrasound transmitter block
Weight, kg
Receiver-display block
Ultrasound transmitter block
Article in packingt

250x55x46
120x55x35
Ot 52
0,28
2,5

ESTIMATION OF LEAKAGE OUTPUT BY ACOUSTIC EMISSION METHOD
As was told above, gas (liquid) outflowing through leakage at superfluous pressure isaccompanied by radiation of acoustic waves into an environmental atmosphere or by acoustic emission(AE).

The true gas leakage output W is defined by product of gauge pressure P on volume of outflowing gas V for a time unit t:
(1)

W=PV
/

The energy of acoustic waves, radiated by leakage for unit of time (leakage acoustic output
W,d makes up a part of general output W. The acoustic and true leakage outputs are connected by somecoupling coefficient k:

k=!!:

(2)

W"

Coefficient k depends on a nUplber of the factors (sizes and geometry of the hollow t pressure
differential, temperature etc.). It can be determined from the experimental graph, constructed according
to the technique given in [I]. To estimate true (general) output of leakage quantitatively it is necessary
to know the coupling coefficient k and the output of acoustic waves WA , radiated by leakage into an
atmosphere.
Consider a way of determination of acoustic waves output W" using the leakage detector«GERMES-2» described above, receiving acoustic waves in a narrow frequency bandwidth about 40
kHz.
The threshold intensity in a wave 10 registered by the device receiver (power of sound energy,transferring through an unit area, perpendicular to the propagation direction) is defined as

0)2

1 = Poo

2pc

5where po=2.1U Pa - the threshold of sensitivity of the device on pressure (Table I), pC= 430 Pa*c/m- wave resistance of propagating environment (atmosphere). At such value of receiving pressure thedevice pointer deviates on half of indicator scale. Calculations according with the formula (3) give for
10=-I,6*1U 13 w/l1l. Thus the minimum value of ultrasound intensity (threshold of sensitivity) has beenfound, it can confidently register by the device. The distance r between the leakage source and device815

receiver-display block, on which the indicator pointer deviates on half of scales at the maximum
sensitivity, is the radius of half-sphere, on which the intensity of acoustic wave radiated by the leakage
source is equal 10. (Here the leakage is assumed to be a point source of ultrasound, forming a sphericalwave). Then the complete power of sound energy in a spherical wave, radiated by leakage source inoperating frequency band of the device (acoustic leakage power), is defined:
2
WA=Io.S =Io .2n:r(4)
In the paper [1] the results of measurements of true and acoustic leakage outputs depending ongauge pressure are given, coupling coefficient k and threshold of sensitivity to to gas and gas-liquidleakage is estimated.

r,m
0.8

.__._._--.•.
.. _-

__

0.7
0.6

0.5
0,4
0,3

0,2
0.1

o

~

o

~

~

-----

-

.-:--

u

4

2

~

.--.

8

8

W,Watt

r,m
8

7
8
II
4

3
2

-- -- ~~

.-

~

- --

~

~

0

-----

""

~

,--~
_

o . ..
o

2

3

4

567

8

J.v, Walt

Fig. 2. The dependencies a/leakage output Won the distance r:
a- without using the aUenuator, b- using the altenuator.
The dependencies of leakage output W on the distance r between the leakage source and device
receiver-display block are submitted on Fig.2 as an example of the estimation of leakage output(without using the attenuator - Fig.2a, using the attenuator - Fig. 2b). (The attenuator is used in
conditions of external noises of sound range, and also if necessary to reduce the device sensitivity for
estimating the leakages of great output). The submitted dependencies are received experimentally atgauge pressure 0,1-0,3 MPa.
The accuracy of the estimation of leakages output depends on geometry of the gas outflow
channel and the presence of a moisture and oil in outflow area. The latter can result in essential$16

overestimate of measured leakage output.
The knowledge concerning leakage true output, allows us to make a conclusion about flow rateof the product (gas) outflowing for a certain time interval, that is to estimate product losses, gassing ofindustrial premises and living accommodation, etc.
For example the gas flow rate will make 1,05 mJ/year, if the leakage output is equal to 0,25 W
and gauge pressure is equal to 75 atm.; and the gas flow rate will make 1040 mJ/year, if the leakage
output is equal to lOW and gauge pressure is equal to 3 atm. The lekage with higher output as a ruleradiate waves in a sound frequency range and can be detected by the operator without the device.
Thus;contactless acoustic leak-tested method allows- To detect the leakage in systems with gauge pressure;- To define the leakage location;- To estimate the leakage output and volume of following gas for a unit of time.

TESTING OF HOLLOW ARTICLES INTEGRITY BY CONTACTLESS ACOUSTIC METHOD
The contactless acoustic method testing of hollow articles on the presence of through defects isfollowing. The source of in a frequency range of 30 - 60 KHz is located inside of hollow articles. The
ultrasonic waves, penetrating through a hole with the size more than -0,01 A. (A. is the wave length), canbe fixed by acoustic receiver-display block of the device outside the testing article.
However the walls of the controllable articles can be also transparent for the ultrasonic waves atthe certain angles of incidence, determined by a rule of coincidence
a=arcsin(CvlCm),(5)

where ClI is the wave velocity in air, Cm is the velocity of the Lamb wave mode ao , excited in a wall of
the article. Therefore acoustic devices alongside with a useful signal (ultrasonic waves, penetrated
through the hole), will also register noise (ultrasonic waves caused by the acoustic transparency of thewalls of the testing articles).
The authors investigate experimentally the dependencies of a useful signal and noise on
parameters of through defect, elastic properties of a material and article wall thickness. The contaclessacoustic leak-detector "Germes-2» was used in researches as measuring-acoustic device.
The acoustic fields of holes, simulating through defects, with diameters of 0,08-0,22 mm in asteel and aluminium plates with the thickness 0,8-4 mm, and also acoustic transparency of the plates(noises) are investigated on the base of technique described in [2].
In Fig. 3 the dependencies of acoustic pressure in the wave, penetrating through the hole on thehole cross-section area (Fig 3a) and on the hole length, referred to its diameter (Fig.3b) are shown.
It is assumed that the ultrasonic waves penetrating through the hole are the point source ofultrasound, forming a spherical wave.
Therefore the acoustic pressure in the wave is determined by the formulaA _P=-.e 6tI ,

(6)

ro

where

A P~:',re=

is the distance r between the through hole and device receiver-display block, on

which the indicator pointer deviates on half of scales at the maximum sensitivity, that corresponds topressure in registered wave Po=2.1U5Pa (Table 1); ro is equal for example to 1 cm (the distance817

between the through hole and device receiver-display bloc14 on which signal was reliably registered).
P,Pa

p,Pa
Q3

0,35

•

•

0.3
0,25

Q2)

0,2
0,15
0,1
0,05

Q2

0.15
0.1

0

0.<:6

0

0.01

0,02

0,03

0,04

S, mnr

0
0

4)

8J

dt

b

a

Fig. 3. The dependencies ofacoustic pressure in the wave on the hole cross-sec/ion area (a)and on the hole length, referred to its diameter (b).

P, Pa 0,28

0,20

0,12

0,04

0,08

0,12

0,20

0,25

d, mm

Fig.4. Spatial distributions of wave pressure amplitudes, radiated by a through holes with thediameters of 0,11 mm (curve 1), 0,14 mm (curve 2), 0,15 mm (curve 3), 0,19 mm (curve 4), andradiated by the steel plate with thickness of2,1 mm (curve 5).

As an example experimentally obtained spatial distributions of wave pressure amplitudes,
radiated by the steel plate with thickness of 2,1 nun are represented on Fig. 4. It is the results ofresearch acoustic transparency of the steel plate walls (noise). The lines of acoustic pressure level P in
waves, radiated by a through holes with the diameters of 0,11-0,19 nun are put also there.
From figures 3-4 it is visible, that delectability of the through defects depends on their sizes(diameter and length), and also from acoustic transparency of the controllable article walls. On the one
hand, the amplitude of a useful signal is directly proportional to the through defect area and decreases
with increase of its length (wall thickness). On the other hand, its acoustic transparency (noise)$18

decreases with increase of wall thickness. Thus the relation of the useful signal to noise is increased.
The increase of noise most occurs in directions of the maximal transparency, that arc defined by thecoincidence rule (5) for Lamb wave mode aD.
The method sensitivity may be increased owing to orientation of the acoustic device in fhedirections of the least transparency of the article wall. Besides the acoustic noise at testing is possible

to reduce by damping of article material in the field of testing.
The sensitivity of the method is larger at testing articles made of materials with large
impedance.
The sensitivity of the given meLhod in some cases is rather high. So, in a steel plate of 4 mm
thickness a defect with the diameter of 80 microns has been found. For revealing such a defect bymeans of the echo-pulse method using angle probe (transversal waves) the working frequency 18 Mru,
would be required, no control on which practically is realised.
As the device can be calibrated in absolute values of acoustic pressure the possibility of thequantitative estimation of through defect parameters (length and area) is following from the Fig.3. Thus
estimation of through defect sizes is more unambiguous, than in the case of the estimation of leakage
output.
Thus, the results obtained can be used as a basis for development of techniques of contactless
acoustic emission control of articles on tightness:- for the estimation of leakage output in the systefl1s with gauge pressure;- for the control and estimation of integrity of hollow articles in the systems without pressure.
REFERENCES
I. G.A. Budenkov, a.v. Nedzvetskaya, E.G. Bulatova. Technical possibility of contactless acoustic
method of leakage finding. - Defectoskopiya, 1996, N, 12, p. 48-53 (in Russian).
2. G.A. Budenkov, E.G. Bulatova, a.v. Nedzvetskaya,. Grebenshikov S.P. Contactless acoustic
method for through type defect discovering in hollow articles. - - Defecloskopiya, 1996, N, II, p. 4348 (in Russian).

S19

International Conference
Bmo, 15th _17th June 1999
Czech Republic
Acoustic Emission'99

METHOD OF ACOUSTIC EMISSION AND POSSIBILITIES OF
CORROSION DETECTION DEGRADATION
Micbal Cerny*, Pavel Mazal**, Viktor SUba*** Mendel University of Agriculture and Forestry of Hrno** Brno University ofTecbnology

Introduction

Corrosion is a type of material damage, that is an important problem in both production and userarea of industry. It represents an important heavy drain of the economy and it influences safety,
working life, ecology etc.
For above mentioned reasons, the corrosion laboratory of Institute of Basic Technology and Repair
MZLU in Bmo, together with Institute of Construction vUr FS Bmo decided to start research
program aimed not only to better understanding of parallel phenomenon of corrosion degradation,but especially to early detection of corrosion attack of steel construction materials by the method of
non-destructive control (NDK). Endeavour should be directed to corrosion attack control in situ,
which is not very persuasive by many NDK methods (electrochemical noise measuring,roentgenologic, laser, ultrasonic method). In many cases, it is only stating of surface corrosion
attack, which means that of square of local character. The effort should be aimed to those types of
local corrosion, that are the most feared and the most perfidious - point corrosion, selective
corrosion and intercrystalline corrosion.
AE is comparatively new method of non-destructive contro4 which is quickly developing at present.
It should not only be an efficient laboratory to04 as the results from periodical controls of integrityof equipment and components but even one of the most advantageous methods for analysing andfollowing of equipment degradation during periodical stopping or even by following the damage
during operation. AE enables to make quick diagnosis even in noisy operational conditions. This typeof control represents considerable economic savings taking into consideration that the continuity of
production process is not interrupted.
S20

1 Acoustic emission phenomenon

AE is a phenomenon when, due to internal local micro movement of material, transition elastic
waves appear /1/. It is noticed as an electric signal with help of piezoelectric sensing unit, thattransforms elastic waves emitted by material into electric signal. It is increase~ filtered andprocessed in such a way that it will enable to determine characteristics that were sought after. (Chart
1)

Chart la: A list ofpotential AE sources (incomplete)

Metal materials

Plastic deformation, co-operative movement of dislocation,
twinning, slip on grains bounds, emergence ofLiiders bands...
Fracture of decohesion of precipitates of inclusions appearance
and/or spreading of cracks by static or dynamic stress, fatigue
or stress corrosion, embrittlement by hydrogen
Diffilsionless phase transformation (martenzite)
Local corrosion (point, slit corrosion)

Chart Ib: The influence of application and configuration on AE

Secondary orhydrogen release

external sources

friction ofsidewalls of the crackmechanical shock, friction, wear, abrasion
leakage ofgas or liquidcavitation, boiling, erosion, crystallisationelectric discharge (coronary effect)

S21

Many parameters exist, that can be characterised in order to be able to establish the character of thesource, to localise it and to measure its activity: time delay of signa4 length of signal lasting,transconductance, maximum amplitude, frequency, impulse dose energy, time behaviour of signal
etc. It can be done by registering of changes or maximum values in the cycle batch or by counting ofthem. There are many procedures for their analysis: space analysis (localisation) spectrum analysis,time analysis, statistics, respectively muhivariable analysis.
It is inevitable that the followed mechanism is irreversible. The source has satisfactory capacity andfrequency zone ofthe sensing unit and its sensitivity correspond with the followed phenomenon. The
emissivity of the material depends on many parameters, connected with its characteristics(microstructure, mechanical characteristics, way of possible degradation, structure transformationetc.). The influence of the ,,size" of signal is especially important for measuring in situ due tocompensation o~ in many cases unfavourable, conditions ofacoustic noise background.
2 AE using in the area ofcorrosion
Of many NDK methods, main method of electrochemical noise has spread in last few years. There
are few works that effectively make use of AE and they have not been, except for several of theme.g. 12/ significantly applied in industry. The reason is probably in a low intensity of the source ofemission by corrosion attack (initiation and spreading of point corrosion and intercJYstalline
corrosion) in comparison with other sources during operation of the part. Many works take into
account AE by evaluation of surface corrosion in connection with hydrogen release or with changes
ofpresence ofcorrosion products inside the materiall51.
Mastering measuring technology and computer processing of signa4 that has been gained during
measuring offatigue qualities of construction materials at UK VOT FS was motivation for researchin the area of point and intercrystalline corrosion, that takes into account especially the influence ofgrowth of corrosion products in the amount of material as the source of acoustic voltage surge bypolymorfic distortion ofmaterial lattice.

3. Measuring
Tested material has been chosen with consideration to coincidence of inter-crystalline corrosion andof extended application as well. Steel 17242 (0.25%C,18%Cr,9%Ni,2%Mn) serves the purpose very
well; it represents basic construction material in chemical and manufacturing industry. It has not beenalloyed by titanium and niobium and that is why it is significantly subject to inter-crystalline
corrosion as a result ofdeprivation ofareas on boundaries of grains by Cr, which is connected withcarbide precipitation Cr 23 C6 (Cr7 C2) on boundaries of grains under suitable diffusion conditions(temperature). This perfidious corrosion type, together with embrittlement mean localisation formechanical defect, especia1ly by welded and heat fonned steels.
Tested samples: the shape of sample has been influenced by standard CSN 420321 (for case ofproving existence ofinter-crystalline corrosion by traction test). The dimensions of sample of rolled
metal sheet : 200x2Oxlmm (Figure 1). Etalons, certain for sensing of basic acoustic echo were
carefully polished and passivated. Tested samples were only polished. For general comparison withdifferent type of corrosion, material 11373, which has been exploated paralelly, proves importanttendency to surfilce corrosion.
S22

Corrosion environment: chloride environment is very unsuitable for austenitic steel 18/9; it led to
choice of corrosion solution - IOOJ'o NaCI solution. The temperature of the solution as an activatorand corrosion accelerator was 40°C. The sample was immersed into solution in constant depth ofdraft about SO mID. By measuring 2b, 3b, solution 33% H2SO4 was chosen for clear degradation ofmaterial (Figure2).

Fig. 2 Sample from measuring in H2SO4

Fig. 1 Sample with fixed probe
solution

Measuring system: 2-channel analyser ABD IOC with pre-amplifier without arranged filters has beenused for orientation measuring. In further measuring 1 and by measuring 2, 2B, 3, 3b, the main
configuration was formed by IS-channel analyser ABD FTA 16. Two to three probes in the sametime, pre-amplifier, recording, and analysing unit PC (Figure3).

Fig. 3 Workplace with AEO FfA 16 analyser, with probes, pre-amplifiers and PC

S23

4 Measuring
Measuring itselfhas been done in three stages
First stage - verifying: during these preliminary testst acoustic activity was followed on corroding
samples in time intervals, both on AED 10C and AED FCA 16. It was consequently compared withacoustic activity from passivated etalons. Furthermore, growth of activity by individual measuring ofsteels 17242 and 11373 has been followed.
Second stage- long-lasting measurement has been done in two environments, in solution ofNaCI and
H2SO4.
II a - sample was fully fixed on a board and immersed (sOmm) in l00A! solution of NaCL
Measurement conditions - strengthening 28dB, frequency range 30kHZ - ItS. reference level
100mV. Period of measuring 60s (1440 measuring lday). Measuring was done for 144 hours (Figure
4,S).

u

1,132E+05------r, - - - - · · · - , - - - - - - . - ,- -:3th stage

,
,
,

....:

..

.,

~ 1.130E+05 _______ ._._.~-.---.-- __ .. ~2I\~~.-~-..---.-

=
o

.2S
l! 1,128E+05

I
u

..

----------_.?----._------,------------~--------

1,132E+05··--·-·--T···--I
I
I
I

¥

:t:

~ l,131E+05=!
sl! l,130E+05___ e.

:--'--'--r---~--'--r----

I
•
•
•

I
•
•
I

•
I
I
•

~3fH-~--~--_.-

,

,

t
I

I"""'~::.Yv:.. .. -.••.,-.~-----

I
•

•

~_

,

•

•

I

•

•

,

•

I
I

•
•

I
I

•
I

:

:

:2nd sta~

.
I

-~-_.
•

I
f
•

,

~:I
o

l,126E+05 +----+----1------+--1
181
61
121
time (sec)ul,129E+05 +---t----+--I----+---+-1
61
121
181
241
301
time (sec)Fig. 4: The dependence ofsignal frequency

Fig. S: Frequency ofsignals as a function

on time in NaCI solution

oftime by steel 11373

II b - checking of acoustic signals record in comparison solution H2SO4 - by intensive attack(etching) ofthe surfilce by acid. There is a prerequisite that the degradation from the point of view of
acoustic echo should have been analogy ofsurfilce corrosion (material- steel 11373).
3 stage - measuring for comparison of activity of sources is represented by measuring in NaCI,
H2S04 and in the atmosphere (etalon) and it was aimed to direct comparison offrequency of signals
and the importance of damage followed in the end of the test. AE sensing from etalon enabled toeliminate appeared artefilcts during measuring from the reason ofelectric noise in the net. Analysis ofcorrosion damage has been done by method of light microscopy - following of inter-crystalline
damage on metalographic samples. Samples were processed mechanical - chemically and they were
followed both as polished and with visible structure (structure caused by Vilela-Bain and Nita!
mixture 1:1). Parameters of measuring equipment: amplifYing - pre-amplifier 4OdB, analyser
amplifier 28 dB t reference level, and frequency level are the same. Time 2160 hours.
S24

5 Results
1 stage - measuring proved the importance oftuning of piezoelectric sensing units to the same levelof amplifying for all channels, which must be done by means of characteristics of individual sensing
units, used for identical formula (value oftolerance +-2dB quoted by producer importantly influences
value of sensed dependencies). The dependency in Figure 6 show growth in values of summary
frequency by individual taking offs with time distances 14 to 21 days. Taking into account thecorrosion extent (initial stadium of inter-crystalline corrosion) the growth of frequency is slow.
Infonnation concerning creating of passivating layer (immediately after immersing the sample into
the solution) its failure and initial corrosion development is missing. Completing measuring on steel
11373, that corrodes on the surface shows gradual growth as well The number ofrecorded signals is
more considerable than by steel 17242

cE 6.0E+04. n.-;--.,. _:u . - ---;- .. 2,5E+05,

j

6 4,OE+04 .. -.r -- -~

~

.
.
S t .
~

u

,

~ 2,OE+04 ...... •..
g
u

,,

, .

,

l!

.,

----------r----------~----------~----------~-----·

•

+.
•

t

2.0E+05,,+......
. 1,5E+05
I
I

1,OE+05: : : :

r-·--···..
r....··... r..-·· ....r·...·· 5'OE+041lGiiiii~~~~~~~~~~~~::::, ' . .

O.OE+OO +---+---+-'---+-----+-'1
241
481
721
961

O,OE+OO
1

241

721

481

time (min)

961

1201

1441

time (min)Fig. 6: Growth of frequency by long lastingFig. 7: Share ofindividual levels in total sum

test (17242 - NaCl)

ofsignal (NaCI)II stage - Evaluation has been aimed to search oflevels, which significantly prove on total growth of
signal frequency. Figure 7 shows the highest activity on levels 35, 36 and less on level 37 dB. By
samples degraded in solution H2S04, the activity of levels is divided better-proportioned into
individual levels 35 to 48 and it is naturally bigger (Figure 8). Figure 9 shows gradual growth in AE
frequency in time steps 100 min. By the sample H2S04 the initial growth is steeper and than theactivity corresponds with all-surface degradation ofthe metal by etching (Figure 10).
,., - --,---,,,,,,,

3.0E+06

I 2.5E+06

,,

,,

"

..
,

-,.

.,

't"

o
o

g 2,OE+06

u

-!IiE
:::I
Gl

1,5E+06
1,0E+06

500E+051~~~~~~~~~~~~=

O,OE+OO

1

1441 2881 4321 5761 7201 8641 10081

1

104 207 310 413 516 619 722 825 928 1031

time (min)Fig. 8: Share of individual in total sum

time (min)Fig. 9: Development of inter-crystaline corrosion

oflevels by H2SO4

by steel 17242
S25

-

2,5E+03

.

,,

C

t

•

~ 2,OE+03!c,.......----r------,'~__,.,----.

•

,

••

,,
,,,

•••

~

••

.~

,

.. __ ..
8 1,5E+03-i 1,OE+03 .----.- ...
..••••..•... _.~..,, ..

:::I

o

.

_~---_._--~--_
•
•

I
I

•
•

t

•

•

•

I

•

•
I

•
•

I

...

_--~_.-----~--_

•
•

_.~

:
••••

I

••••• 4--_-~-------

I
I

•

..

..... - ---I
I_-.--~-----_'! 5,OE+02
U

•

•

: .
• • __ • __

·

_

I

I .

:

~._._

.

.
•

•

•

:
•••••••••-_ -- 4_'

""

.,

.
O,OE+OO ------f--1

1441

2881

time (min)Fig. 10: Damage ofsteel surface 11373 by acidm. stage records made in the last days of the test of the third stage show growth of frequency bothin the area of corrosion attack and in its further progress (development) in both environments (NaCl,
H2SO4) (Figurell).The activity of corrosion action inside the material and on the surface of thematerial is in contrast with stabilised emitivity of the etalon (Figure 12). The extent of intercrystalline damage are shown in metalographic snaps (Figures 13-15), where we can see progressive
development of attack and destruction of grain borders by inter-crystalline corrosion and theplacement of carbides Cr by individual take offs. Individual extensions can be, with someapproximity, classified to results from measuring ofacoustic echo of material

4,OE+07. .
.
.. · . . . . ~. ·,, ,, , .
8

I

~

o

--,-~- --r--~---~-

•
•
I
I

~ l,OE+09

'0

I
I
I

I
•
I

•

I

I

-~-1--

•

•

I

•

•

•

I

•

•
I

•
•

•
•

,
•

I
•

______ .. _.... __ .····._ .. __~ 2,OE+07.k:::::;::::C~===d::~~±=t==t=1,, -----r-----,-,,
,,
,

.,

,

,

:Nacl:

O,OE+OO--r----

•

•

,,
,
,

~

...,-

t

.

•

_0-

•
•

I

·

u

I
I
I

.
"

'0

~

.-

., .. -._._._,--_.....
,,
,,
,,
,
,,
,
,
,,
,
,
,

2.0E+09__•.. __ ··4·· •..•...
.
.
,
"

~

•

:NaCI:

O,OE+OO .lL~=~=i:==~=i:=t=::;:=;:=

241 481 721 961 1201 1441 1681 1921
time (min)1

Fig. lla: Growth of frequency in initial phase241 481 721 961 1201 1441 1681 1921
time (min)Fig. lIb: Growth offrequency in ending

ofthird stage by NaCVH2S04

ofthird stage by NaClIH2S04

S26

_

.

40 .

·· ..

".

-

0
0
0
0

0

-- ....

~._._

,,
,,

·

,

0

',.,
0

0

.. .. _.- ..
~

I
•
I .
• •
• •

·

. ..

~._---_.~_,- .. ,_ . . - ·-·f .._--,--.
0,_--~--_._-~-_._-_.~.

•
•
• •
• •
I '

.,

I
I
I
I

I ' .

O+---+---f---+---f---+---l----+181

541
721
time (min)

361

901

1261

1081

Fig. 12: Sleeping average ofsignal frequency byetalon

:

..

..

o

.1

.

.
.
...
'.
/~. _--"'-_..
.
~~.:c.
r~'.
.'. . . ,'f.!, ..
.:,
"

~

~

•

.

~

"""C>

•

•

.......

:~,.' ,,- ....

.

.

.'

.-.

'II>

Fig. 14: Inter-crystalline damage and placing

Fig. 15: Significant inter-crystalline damage

ofcarbides Cr

after about three months ofNaCI
solution perfonnance527

Conclusion

The contnbution represents entry part of research project of above-mentioned workplaces MZLU
and VUT FS in Brno. Described applied method of acoustic emission is in this entry part charged
with insufficient experience with elimination of considerable noise of background. AE sources incourse ofdevelopment ofcorrosion attack have been sufficiently descn1>ed neither in domestic nor inforeign literature so far.
That is why effort to set quantitative relation between the number of recorded signals and the extent
of damage followed in the end of the test (stage ill) is expressed in the introductory part.
Comparison of linear relationship between frequency ofsignals by inter-crystalline corrosion of steel
17242, surfuce corrosion by steel 11373 and "etching" of the same steel in H2S04 is a contnbution.
We can say that AE method seems to be sensitive and reliable enough.
In further research, our attention will be aimed to localisation of damage, time progress of signal inindividual corrosion stadium and the fact, that acoustic emission in corrosion progress probably
appears in sequence waves in connection with further local micro sources. Some areas can become
active again after several hours (days) of latency. Both this and further facts significantly charge theevaluation of short-tenn measuring. The file of AE measurement should mouth into ensming ofcontinual total acoustic activity. Especially degradation is concerned, where no other NDK can beused (in the same price relations). Significant savings can be reached by periodic controls of static
device. Enabling to control it by AE method requires still much research effort.

Literature/1/ Association Fran~aise de Normalisation. Vocabulaire utilise en emission acoustique, AFNOR,
NFA09-350/2/ D. L. Parry. Detection of corrosion pitting in pipelines using acoustic emission
technology. Conference on Non Destructive Evaluation, Proc., April 1983, p. 388-402/3/ T. V. Rettig, M.J. Felsen. Acoustic Emission Method for Monitoring Corrosion Reactions.
Corrosion, Vol. 32, No.4, 1976, p. 121-126/4/ F. Mansfeld, P. J. Stocker. Acoustic Emission from Corroding Electrodes. Corrosion,
Vol 35, No. 12, 1979, p. 514-544/5/ R H. Jones, M. A. Freisel Acoustic Emission during Pitting and Transgranular Crack
Initiation in Type 304 Stainless Steels. Corrosion, Vol. 48, No.9, 1992, p. 751-758

S28

International Conference
Bmo. 15th _17th June 1999
Czech Republic
Acoustic Emlsslon'99

APPLICATION OF ACOUSTIC EMISSION IN METAL PHYSICSAND MATERIALS SCIENCE
FRANTISEK CHMELiK and PAVEL LUKAt
Department of Metal Physics, Charles University, Kc Karlow 5, 121 16 Praha 2, Czech Republic

Abstract

Acoustic emission (AE) is a value ill situ method to study various processes in metals and metal
matrix composites, particularly if combined with other experimental techniques. Few examples are
given, showing the AE applicability to structure changes induced by thermal and/or mechanicalloading. The effect of precipitation on the Portevin - Le Chitelier effect in an AlLi alloy,
microstructure evolution in Mg-based metal matrix composites due to internal thermal stresses andphase transitions in the CuAu system (dental gold) will briefly be discussed.
Key words: acoustic emission, alloys, Portevin - Le Chitelier effect, metal matrix composites,
thermal stresses, phase transitions.
1. INTRODUCTION
Acoustic emission (AE) stems from transient elastic waves generated within a material due to sudden
locaiized irreversible structural changes [1]. In metallic materials, AE responds to the dislocation
glide, twinning and microstructural damage. In metal matrix composites, damage in the ceramicphase and interface decohesion are additional sources of AE. The objective of this paper is to reporton our recent results on AE from metallic materials.
2. EXPERIMENTAL PROCEDURE
The computer controlled DAKEL-LMS-16 AE facility was used to detect AE during various
experiments. A highly sensitive LB lOA transducer (sensitivity 85dB ref. IVfms· l , flat response
between 100 and 500kHz, built-in preamplifier JOdB) was coupled to the quartz rod using silicongrease. The DAKEL AE facility applies a two-threshold-level system of detection and evaluation of
AE signals. The AE signals are filtered, amplified and then evaluated at two threshold levels to obtain
two AE counts denoted NC1 and Nn . The total cou·nt NC1 at the lower threshold (corresponding to a
total gain of 1OOdB) includes all AE signals detected. The burst count No obtained at the upper
threshold level (set difference of 20dB, i. e. total gain of SOdB) includes only the AE signals withlarger amplitudes (AE bursts). This procedure makes it possible to evaluate the character of the AE
signals. i. e. whether they have a small or a large amplitude.
3. EXPERIMENTAL RESULTS AND DISCUSSION
3.1 The Portcvin - Lc ChitcUer Effect ill an AIZ.. Alloy
The Portevin - Le Chatelier (PLC) effect (serrated flow) appears in many AI-based supersaturated
alloys if they are deformed at suitable temperature and strain rate. It is well known that prior ageing
treatment giving rise to changes in precipitation structure can affect the shape of serrations or
determine whether serrations ever appear. Therefore, it is important to study the relation between thealloy precipitation structure and the deformation behaviour. Pink and Kr61 [2] investigated the AS29

· type of serrations in an AI-I O. Swt.%Zn alloy with different size and volume fractions of precipitates.
By measuring a correlation between the load drop amplitudes and the precipitation structure they
have found that coherent precipitates rather prevent serrated flow provided that their amountexceeds a certain value. Large and incoherent precipitates have not been found to affect the A- typeof the PLC effect. Brechet and Estrin [3] have proposed (mainly theoretically) that an alloy hardened
by fine shearable precipitates may exhibit serrated flow with all signs of the PLC effect, (the pseudoPLC effect). even if no dynamic strain ageing due to solute diffusion is present.
The influence of the precipitation structure on the plastic flow. particularly on the PLC effect,
can well be studied by the AE technique. A distinct correlation between AE and the precipitation
structure and also between AE and the PLC effect has convincingly been demonstrated by a numberof authors [1. 4-6]. The objective of our research was an AE study on how the precipitation structure
affects serrated flow in an AI-lO%Zn alloy. This alloy is prone to a rapid age hardening thereby
matching the assumptions ofBrechet and Estrin [3). More detail can be found in [7].
An AI-10.5wt.%Zn alloy (solws temperature at 185°C) was melted. cast and rolled in one
direction to sheets of O.95mm thickness. Tensile samples with a 75mm nominal gauge length were
cut parallel to the direction of rolling. The samples were recrystallized at 270°C for 30 min.• thenhomogenized at 450°C for 1h. quenched in oil or water to be aged 6h at 180 or 20°C. The material
aged at the elevated temperature was typical of a low amount of coherent precipitates. The material
aged at room temperature contained a high amount of coherent precipitates. Immediately after thetreatment, the samples were tested in an Instron 1195 testing machine at room temperature and atconstant crosshead speed giving an initial strain rate of 4.4xlO-6 S·I. To determine the strain ratesensitivity ofthe flow stress. the crosshead speed was increased by certain factors during the tests.
Figure 1a presents the strain dependences of true stress and count rates for the sample with thehigh amount of coherent precipitates. The offset yield stress of 120 MPa is considerably higher
showing significant age hardening. The strain rate sensitivity is positive indicating that the PLC effectleaves its flow controlling role to the precipitate shearing. A detailed analysis shows few load drops
25 ...---~-----,--~----,

200
150
I

~

N

i

100

u

·z

20

il

~

50

.

·z

I

5

o 1-U---L~u...J.JL..I.I'r--l.....L.---r--1

o
2500
~

500

f' 4002000~ 1500.z" 1000 500 ;15~cr 10I.!. 300

1"1\. II.zu 200

l\~~~

100

o ~!!!!!!!L-;----..::..:~r.....~-:;:h<""----1

o
150

200
,..., 100
a
a.
2
b

o

no

....

:I!

b

1:-'0·SO(a),OO ....._ _........._ _..........
0.05
0.10
0.00

_-~

0.15

E

o '---..........- - ' - - - - - - ' - - - - - - !
0.00

0.0:-'

0.10

0.15

0.20

E

Fig. 1. The effect of strain rate increases from 4.4x lO-6 S·1 to 4.4x lO·5 S·1 and further to 4.4x 10-4S·1 onstress-strain curves and on count rates for samples of AI-10%Zn with (a) a high amount of coherent
precipitates. and (b) a low amount of coherent precipitates.
830

with an amplitude of 0.03 MPa. The count rates are much higher than in the sample with the lowamount of coherent precipitates, especially, many AE bursts appear. This increase can be explainedby an intense precipitate shearing that is widely accepted as an effective source of AE [1]. The countrates respond distinctly to strain rate changes by a factor 10 showing an increase by the same factor.
This result supports the idea about the precipitate shearing as the main source of AE because one
may assume that the number of the precipitates sheared per unit strain should be independent ofstrain rate (the stress for shearing is independent of the strain rate). Fig. Ib presents the strain
dependences of true stress and count rates at two threshold levels for the sample with the lowamount of coherent precipitates. The offset yield stress of 26 MPa is consequently low. From thestrain rate changes. a negative strain rate sensitivity is visible giving evidence that the PLC effect isthe controlling mechanism of the plastic deformation. An inspection of load recordings reveals ajerky character of the plastic flow with sudden load drops of 0.2 Mpa. During the deformation, amore significant AE activity is observed at lower threshold level only, at higher threshold level few
AE bursts are found. An increase of the strain rate by a factor ten produces a less distinct response in
the count rates that tend to increase by a factor slightly less than ten, i. e. the AE count per unit strain
decreases. Such a behaviour has also been observed in other alloys exhibiting the PLC effect [4.6]and is related to another well known experimental fact that the amplitude of load drops often
decreases with increasing strain rate.

3.2 Microstructural Changes in Mg-Based Composites Subjected to Thermal Loading
Many Mg-based metal matrix composites (MMCs) have been developed and manufactured during
the last decade as prospective light high-performance constructional materials. The microstructure
and mechanical properties of~Cs are influenced significantly by the interfaces between the matrix
and the reinforcement. When a MMC is subjected to temperature changes, thermal stresses arise at
the interfaces owing to a considerable mismatch of the thermal expansion coefficient of the matrix
and that of the reinforcement. Even moderate temperature changes may produce thermal stresses
which exceed the matrix yield stress, consequently. generating new dislocations at the interfaces
causing microstructural changes and plastic deformation of the matrix. It is noteworthy that the effectof temperature variations is similar to creep under non-stationary loading conditions.
Hitherto, the microstructural response of MMCs on changes in temperature has been
investigated mainly indirectly (post mortem) [8-11]. The generation of new dislocations has been
demonstrated by etch pit technique [8], transmission electron microscopy [9.10] and internal friction[11]. However, it is difficult to assess the evolution of the dynamic structure, i. e. to obtain a direct
correlation of the structure to the temperature using these methods. In situ studies (by transmission
electron microscopy - TEM) [12] can establish such a correlation but that is only of a local characterand the stress state is most probably influenced by the relatively small thickness of TEM specimens.
AE has been observed during thermal cycling of Mg-based MMCs [13]. It is, therefore,possible by AE measurements to identify and to characterize the microstructural changes and tocorrelate them with temperature or other parameters. The combination of AE measurements with thedilatometry technique yields non-destructively, at any instant of the test, information on the structure
and operating processes over the entire volume of the specimen. The object of our research was todevelop combined AE and dilatometry technique and to apply it in a study of structural evolution in
Mg-based MMCs subjected' to thermal cycling (which corresponds to the typical operating
conditions of most structural parts). Detail can be found in [14].
Various short fibre reinforced Mg-based MMCs were prepared by squeeze casting with asubsequent homogenizing treatment followed by quenching in hot water. Thermal cycling for
dilatometric and AE measurements was carried out in a dilatometer equipped with a radiant furnaceand compressed air for controlled heating and cooling. Up to 60 cycles were carried out on eachspecimen depending on the experimental requirement. The specimens were in the form of machinedrods. 50mm long and 5mm in diameter with the reinforcement plane parallel to the longitudinal axis.
831

A specimen and a quartz dummy specimen with the same dimensions as the specimen were mounted
into the specimen holder of the dilatometer. AE was transmitted by the dilatometer quartz rod incontact with the specimen.
Figure 2 depicts the AE count rate and the specimen elongation during a temperature cycle withan upper temperature of 300°C applied to a Mg-21.1 vol. %SaffiI MMC. As can be seen, AE appears
in certain temperature ranges being much more feeble during heating than during cooling. After thecycle, a residual elongation is measured.
1200

300

1000

2~

~

I

·z0800200~600

ISO :a

!Ill

~

~
8 400
~

100 ...

200

SO

o

0

o

300

600

9ClO

TIMEI_!

1200

1500

1800

Fig. 2. AE count rate NCI' temperature T and sample elongation &1 as measured during the second
cycle ofthe Mg-21.1vol.%Saffil MMC.
Measurements show that there is a minimum upper temperature of cycling necessary for theappearance of AE (AETIop)' The AETtop is considerably lower for the commercially pure (cp) MgSaffil MMC than for the Mg a1loys-Saffil MMCs. The cp Mg-SaffiI MMC is the only case where AE
occurs during the heating period. AZ91(Mg-9%A1-1%Zn)- and ZE41(Mg-4.3%Zn-l%Rare Earths)Saffil MMCs exhibit intense AE bursts during cooling. The residual defonnation after the cycle isgenerally an elongation for lower values of the upper temperature and a contraction for higher upper
temperatures. The transition from elongation to contraction takes place for an upper cycle
temperature of about 300°C depending on the matrix composition. The behaviour of the Mg19.5vol.%Saffil MMC during thennal cycling with step by step increase in upper temperature ofcycling Tiop is characterized in Fig. 3. During the entire test an increasing residual elongation isobserved. A significant dependence of the total AE count on T lop is found. When Tlop reaches 140°C
100000o.,-----------------r\.0000l

1000000.1000 S~IOOOO

<I

~

8

l'l

0.0100 ~~ 1000

0..

~

~
~

ti

~

100

0.0010 8~

10I

0.000140 60 80 100 120 140 160 180 200 220 240 260 280 300 320
UPPER CYCLE TEMPERATURE T.. IOCJ

Fig. 3. Total AE count N c and residual expansion &1110 vs. upper cycling temperature Tlop for the Mg19.5vol.%Saffil MMC. The total AE count was evaluated for the entire heating and cooling periods
ofthe cycles.
S32

the total AE count increases by several orders. Above Tlop of 200°C the increase in the total AE
count slows down and reaches almost a constant value.
As a consequence of the fabrication at elevated temperatures, all squeeze-cast Mg M:MCs
exhibit internal thermal stresses at room temperature, whose magnitude should be close to a
threshold stress for creep of.matrix. The matrix is under tensile stresses and the reinforcement isunder compressive stresses. When a M:MC is heated up the tensile stress acting on the matrix
decreases to zero and on further heating compressive stresses build up. On cooling the M:MC theinternal stresses will behave in an opposite manner. Evidently, in certain temperature ranges thethermal stresses may exceed the matrix yield stress and relax by generating dislocations in the matrix,
which give rise to the matrix structural changes and plastic deformation. Hence, the temperaturedependence of the internal thermal stresses during cycling will have the form of assymetric hysteresis
loop. Different deformation mechanisms may appear as dislocation glide, twinning, diffusional creep,and grain boundary sliding. The share of these mechanisms is a result of a complex interplay between
the temperature dependences of the thermal stresses and the matrix yield stress and will vary
according to the temperature and other parameters of thermal cycling.
AE due to plastic deformation is produced by collective dislocation generation and motion.
Hence. a large AE is expected during the deformation of the materials prone to coarse slip and/or
twinning, such as hexagonal metals and alloys deformed at lower temperature. On the contrary,diffusional creep, grain boundary sliding and the dislocation glide at higher temperatures, when thedislocations do not move collectively and tend to annihilate. are not typical oflarge emissions.
When a Mg MMC is cycled (Fig. 2) AE appears in a certain temperature interval during heating.
Its onset correlates with the internal stresses acting on the matrix exceeding the matrix yield stressand being relaxed by collective dislocation generation and motion, i. e. localised strain bursts.
Increasing temperature renders the dislocation generation more homogeneously and the dislocations
do not move collectively and tend to annihilate. Thus. AE disappears before the top cycle
temperature is reached. When the specimen is cooled from the upper cycle temperature, thecompressive stresses diminish rapidly and tensile stresses in the matrix build up. AE should take place
due to relaxation ofthe tensile stresses but only at lower temperatures and should persist down to thelower cycle temperature. The matrix yield stress of a MMC with Mg alloy matrix is considerably
higher (due to alloying) and the initial value of the internal tensile stresses acting on the matrix
should consequently be higher. Hence, during heating, the compressive stresses exceeding the matrix
yield stress will be built up at a higher temperature. and possibly relaxed by deformation mechanismsthat do not produce detectable AE. AE will not appear during heating. However. as in the case of cp
Mg M?vfC, AE occurs at temperatures close to the lower cycle temperature when tensile stresses arerelaxed by collective generation and motion of dislocations. Large AE bursts as observed in the Mg,
ZE41 and AZ9I MMCs may signalise twinning. It is noteworthy that our results imply that the mainchanges of the matrix structure should appear in the temperature intervals of the AE events.

3.3 Phase Transitions in a euAu Alloy
The equiatomic CuAu alloy is a well-known system exhibiting two phase transitions of the first orderin solid state [15]. The equilibrium structure is disordered face centred cubic (AI) with a lattice
parameter a = O.387nm at temperatures higher than TCI = 410°C and below this temperature a longrange order occurs. Between T CI = 410°C and TCl = 385°C the product of ordering is the long
period superlattice CuAuII poase described by a pseudo-orthorhombic cell with lattice parameters a= O.3979nm, b = 0.3963nm and c = O.3678nm. Below To = 385°C the equilibrium state is orderedface centered tetragonal (Llo) CuAuI phase with lattice parameters a = O.395nm, c = O.368nm. The
disordered state may only partly be preserved by quenching (due to spinodal ordering), which
complicates any post mortem investigation. The ordering in the CuAu system is accompanied bylattice shear and volumetric changes. Structure aspects of the ordering mechanisms have beenstudied e.g. in [16-19]. At lower temperatures. homogeneous nucleation is exhibited, as is typical ofS33-..... ,05

~ ,a'.<:,0 ,qJ
,02

'0'

'0'

428

430

434

436

. 438

440

Temperature fCl

Temperature fC]
(c)

432

,a'
45O-36O·C.2.S"cmn

~

~

,qJ

.<:,0 ,0"
10'

o

200

100

300

Temperature FCl

Fig. 4. AE count rate vs. temperature. (a) heating of the polycrystalline specimen (several hours after
quenching) at 4K1min, (b) the second AE peak in Fig. 4a in detail, (c) subsequent cooling of thespecimen at 2,5K1min.
S34