AE Tester for Aircraft Halon Fire-Extinguisher Bottles

JOURNAL OF ACOUSTIC EMISSION
Editor: Kanji Ono
Associate Editors: A. G. Beattie, T. F. Drouillard, F. C. Beall and M. Ohtsu
I.

Alms 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 sum·maries 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 malters:
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. Malerial
will be subjeci 10 reviews for adequale description of procedures.
subslantial database and objective inlerpretation.
Technical Notes and Communications: These allow publications of short items of current interest. new or improved experi·mental techniques and procedures. discussion of published articles
and relevant applications.
D. AE Literature section will collect the titles and abstractsof papers published elsewhere and Ihose presented al meelingsand 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 accom·
plishments. current news and new products.

e.

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 Acouslic Emission.
3.

Governing Body

The Editor and Associate Editors will implement the editorial
policies described above. The Edilorial 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 experience 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. Hamstad
H.R. Hardy. Jr.
R. Hill
I.V. Ivanov
P.Jax
T. Kishi
O.Y. Kwon
LenainS.L. McBride
W. Morgner
C.R.L. Murthy
H. Niitsuma
A.A. Pollock
T.M. Proctor. Jr.
I. RomanC. Scala
C. Scruby
A.VaryE. Waschkies
M. Wevers
J.W. Whittaker
B.R.A. Wood

le.

4.

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ISSN 0730-0050 Copyright CD 1997 Acoustic Emission Group

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Wideband and Narrowband Acoustic Emission Waveforms from Extraneous Sources during Fatigue of SteelSamples
M.A. Hamstad and J.D. McColskey
exttaneous AE (signals from SOUIteS other Ihan cracks) are asignificant problem. Inspectors in the field have reponedthat exttaneous AE can make up more than 98% of deleded
AE when monitoring a steel railroad bridge (Gong et aI.,
1992).

Abstract
An extensive study of acoustic emission (AE) genenued
from extraneous sources during fatigue loading of steel
samples has been performed. AE wavefonns were emphasized instead of traditional AE parameters. Typically. four
resonant sensors and four widebmd high-sensitivity sensors
were used 10 gather eight waveforms for each AE evenL The
fatigue rests were conducted in a servo-hydraulic fatigue rest
machine. using hydraulic grips. Specimen variables included
two different thicknesses and diffesent lateral dimensions of
the plate-like samples. In addition to ~based extraneous
AE sources. artificial extraneous sources were also examined. Acoustic emission signals from both solid- and airuansmiued artificial sources were studied. The extraneous
signals were contrasted to an AE event from a fatigue crack.
A motivation for this research was the fact that applicationsof AE to monitor steel bridges encounter extensive extraneous AE. The waveform database established in this work
indicates that recognition of extraneous AE is not always
easy. This is due to the variety of source types. lite effects
of lItickness changes. and dispersion effects during wave
propagation. Some extraneous signal types could be easily
distinguished from crack signals in the wideband AE daIa.
Oilier extraneous signals are not as easily distinguished.
even with wideband sensors. Using either resonant or wideband sensors. waveform-based AE analysis offers advantages
to fixed-threshold approach for spatial discrimination of
extraneous AE.

In a typical laboratory environment, extraneous sourcesof AE include:(I) friction or fretting in the loading pin holes of pin-typegrips;(2) relative interfacial movement of wedges. specimen, arxl
pistons in hydraulic grips;(3) other test-machine sotueeS such as screw threads, flxturing and hydraulic servo-valves;(4) aiJbome signals from other laboratory activity; and(S) electrically induced AE.
Several approaches have been developed in the laboratory to
remove extraneous signals from the AE of interest. These
include:(l) frequency filtering 10 auenuate low frequencies;(2) load-gating to eliminate AE signals except those near
the peak load;(3) source location and/or guard sensors to eliminate AE
originating outside the crack region; and(4) rise-time analysis to eliminate slow-rise-time signals,
which are considered to be extraneous AE.
Often. combinations of these approaches are used. Thesemethods may allow the acquisition of AE data from fatigue
cracks, which are not significantly contaminated by extraneous sources during careful laboratory experiments.

1. Introduction
Acoustic emission (AE) has been proposed as a tool todetect and locate. fatigue cracks in metal structlD'es such as
steel bridges (Galambos and McGogney. 1975). Thus. AE
monitoring of fatigue cracks has been the subject of numerous laboratory studies and field studies (see references inSison et al., 1996). Both in the laboratory and in the field.

The application of AE to detect and monitor fatigue
cracks in the field is much more complicated. The locationof a crack, if it exists, is often not known ahead of time.
This greatly reduces the usefulness of guard sensors and!crsource location for the elimination of extraneous AE. Also.
field testing usually involves an unpredictable fatigue-load
spectrUm without the smooth variation of a sinusoidal paltern. Maximum stresses do not occur throughout lite strUCture at the same instant of time. This makes it difficult toapply load-gating, since strain values over the whole region
monitored by the AE sensors would not likely be available.
Another difficulty is that aack-indlX:ed AE sources. as well
as exttaneous AE sources. originate at various distances

Received 23 November 1996. M.A. Hamstad (bamstad@boulder.nist.gov) is affiliated with National Instinne of Standards
and Technology and Universil)' of Denver. Denver, CO 80208
and J.D. McColskey is with National Institute of Standards andTechnology. 325 Broadway, Boulder, CO 80303. Contributionof the National Institute of Standards and Technology; Not subject to copyright in the U.s.
Journal of Acoustic Emission
Volume IS, Number 1-4

10730-OOS0I97JSS.OO/1SOOloOl8
Copyright@19c.n Acoustic Emission Group

from the AE sensoIS. The dispersion of the acoustic waves
makes it difficult to apply rise-time analysis techniques.
The number of potential exttaneous AE sources (impacts
generated by vehicular traffic on bridges, friction or fretting
associated with thennal-expansion design fealUreS and normal bridge load-based deflections) is much larger in fieldtesting, as is the number of directions from which the signals propagate to the sensoIS. Finally, the poor accuracy ofsource location of typical fixed-threshold, resonant-sensor
AE systems provides, at best, only the vicinity of the origin of an AE signal. This lack of accuracy makes it difficult
to distinguish between real fatigue cracks and extraneous AE
source regions, when the crack is near such a region.

2. Experimental Conditions
Experiments were conducted in a closed-loop, servohydraulic, fatigue-testing machine with a 1 MN capacity.
The servo valve was isolated from the machine acblator tominimize AE from this valve. The isolation was accomplished by placing the servo valve on a manifold adapter amconnecting high-pressure hydraulic hoses. 1 m long, between the manifold and the actuator. The test specimens
were gripped with hydraulic wedge grips 1hat have a 0.5 MN
load capacity. A hydraulic pressure of 55 MPa was used togrip the specimen before fatigue cycling. Two different
types of grips were used in this study. The first, 102 mm
wide, had diamond-shaped serration that were 1 mm deep (16pitch), and those for the second set were 204 mm wide, withdiamond-shaped serration that were 0.5 mm deep (32 pitch).
Since the majority of the transverse gripping fmces wasapplied before axial loading of the sample. these grips
helped decrease the amount of exttaneous AE genetared during the fatigue test as compared to extraneous AE generated
by non-hydraulic wedge grips. Further, the gripping surfaces
were only lightly serrated, to reduce the "digging-in" of thesenation into the specimen and the resulting generation of
extraneous AE. The grips were aligned according to ASTM
Standanl Practice EI012. Type A, Method I, to minimize
bending sttesses (in the test specimen) and relative motionof the grip wedges and grip body (which generates exttaneous AE).

These difficulties in the field provide encouragement forthe development of wideband, flat-with-frequency, waveform-based AE approacl1es. Such methods provide two important benefits. rU'St. they allow more accurate source location, since the same fixed-velocity of propagation can beused for each waveform hit of a particular AE event This is
in contrast to fixed-threshold AE systems, in which the firstpenetration of the threshold can come from different velocities of propagation for each hit of an event Furthermore,these techniques provide a broader frequency window, which
potentially enhances the distinction of cmck-induced AE
sources from extraneous sources.
Two previous smdies reported wavefonns from eXb'aneous sources. Miller et aI. (1984) documented wavefonns
from a widehand conical sensor. However, they did not filter
the signals to attenuate the very low frequencies which must
be removed to allow a successful AE application. Further,
the extraneous AE waveforms, which they show, often lacksufficient prettigger. Hence, they are difficult to comparewith the results of the current research, as well as with realcrack-induced AE, which has the signal of interest in theinitial region of the AE signal. Friesel (1987) did not report
the frequency response characteristics of the sensor, thebandpass, or the physical locations of the sensoIS. Thus, it
is difficult to compare his results with this research.

Before each test, the interfaces between the grip bodyand wedges and also between the grip piston and wedges
were cleaned. Fresh grease (described below) was then applied to minimize the introduction of foreign particles.
These particles can lead to extraneous signals as a result ofcrushing. Several interfacial lubrication methods were
evaluated, including various greases and different Teflon
sheet thickness. The grease supplied by the gripmanufacturer (a molybdenum-disulfide pastellubricant) was
found to contain graphite spheres, which aid in lubricity.
These spheres can rupmre during fatigue testing, crealing
numerous exttaneous AE sources. An attempt was made touse Teflon sheets (0.5 and 0.25 mm thick) between the grip
body and wedges in place of the grease. The high interfacialpressure defonned the Teflon until it was 100 thin to be
useful. Better success in reducing extraneous sources was
obtained using the Teflon sheet between the wedge bottoms
and the grip piston. In this case it retained sufficient thickness to isolate the grip piston from the wedge bottoms.
Eventually, a combination of a molybdenum disulfide
grease (without graphite spheres) and Teflon sheet, 0.5 mm
thick, between the wedges and grip piston was selected.
Between the wedges and the grip body, only molybdenum
disulfIde grease (without spheres) was used. These exuaneous source suppression techniques were used because thewavefonn-recording il1SlJ'Ul11entation could only recad
events at a rate of about 10 events per second.

In the past, wideband waveform-based testing has been
hindered by insufficient sensor sensitivity. Recently, awideband sensor with sensitivity (or signal-to-noise ratio)
equivalent to that of narrowband resonant sensors, has beendeveloped (Ramstad and FOrblnko, 1995; Ramstad, 1996).
This sensor provides opportunities for the development ofpractical application of waveform-based approaches. The
purpose of this research was to examine waveforms associated with extraneous AE sources during fatigue cracking in alaboratory environment Multiple AE channels which monitored both wideband and narrowband sensors were used. This
examination has the long-range intent to develop robust
field-suited techniques to distinguish extraneous AE fromthe AE signals caused by cracks.

2

Table I Materials and sample configurations
Materia'·

A36
A36
A36
A588
A588
A514

Thickness,mm Width, mm Length, mm Notch length, mm

6.4
25.4
8
22.9
25.4
22.9

178
92
900
197
900
197

406
406
900
406
1200
406

Configuration··

25.4
12.7

C
D

na

st

25.4

A

na

Bt

25.4

A,B

·Hot rolled condition.. See figure 1
t Configuration refers only to sensor spacing relative to center of sample for these specimens

A dial indicator was used to measure the relative vertical motion between the grip body and the wedges, and thegrip body and the grip piston, during fatigue cycling ~tween loads of 15 kN and 292 leN. With molybdenum disulfide grease (without spheres) as a lubricant, a typical peakro-peak vertical displacement value of 6 J1D'I was measured
between the grip body and wedge. Between the grip bodyand grip piston, a measurement of 23 J1D'I was made (withno Teflon between the wedge bottom and grip piston). Respective readings of 5 J.UTI and 10 JJrn were measured with
Teflon between the wedge bottom and grip piston. Presumably. the differences with and without Teflon are due tothe changes in the interfacial frictional conditions and low
modulus of the Teflon. These motions are suspected to be
key factors in the generation of extraneous grip-based AE.

that purpose. The commercial sensors were aaached to thespecimen using silicone-robber pads, aluminum angle bars.
and robber bands. Figure 1 shows sensor locations (fourresonant and four wideband) on the different specimen geometry.
Digital storage oscilloscopes capwred and stored AE
wavefonns sensed by the wideband and resonant sensors.
The recorders had four 12-bit channels. with digitization
rates of up to 10 Msamples/s. The memory depth of thedigitizers was selectable up to 1 Mbyte of memory pee
channel per wavefonD. The experimental waveforms wereacquired at 10 Msamplesls and had a typical length of 4001
data points. The acquired wavefonns, including pre-lrigger
data, were displayed and analyzed using commercially available software.

Table I shows the materials and specimen configurations used in this program. These steels (1eSted in their hotrolled, as-received condition) were chosen because of theiruse in bridges, Some specimens were notched through thelhickness (using an elecbical discharge machine) and thenfatigued to provide a sharp crack, representative of a ClaCk ina bridge member. Some specimens were not notched, toevaluate extraneous sources only.

3. Experiments and Results
Sets of eight simUltaneous wavefonns were gatheredusing one of the resonant sensors as a bigger for all thechannels. The bigger threshold was set at -20 mV. The
eight AE wavefonns included four each from the resonant
narrowband sensors and the non-resonant wideband sensors.
In some cases, an additional waveform of the cwtent km
applied to the sample was also simultaneously digitized.
This load wavefonn was used to determine the load associBled with the set of wavefonns. In the graphs that fonow,
the starting times of the wavefonns were varied so that boththe elecbical background noise and the beginning of the AE
signal were clearly displayed. In all cases. however, the displayed major time divisions were 50 J.IS apart.

Two different types of sensors (typically coupled withvacuum grease) were used in this sWdy. The fust was acommercially available. general-pmpose resonant AE sensor
that, according to the manufacturer. had good sensitivity
within the typical 100-300 kHz bandpass. This sensor ~connected to the commercially supplied preamplifier. Thispreamplifier provided 60 dB of gain and 100-300 kHz bandpass filtering. Recording of the resonant sensor signals took
place after preampUfieation. The second type sensor was abroadband, high-fidelity sensor of high-sensitivity developedat NIST-BouJder (Hamsrad and Fortunko. 1995; Hamstad,1996). It had an integral field-effect transistor and. combinedwith a conditioning preamplifier. had a rotal gain, in thisapplication, of 51 dB. These preamplifier outputs werebandplssed from 50 kHz to 2 MHz with four-pole BUIlerworth bandpass filters. before waveform recording. The
wideband sensors were auaehed to the specimens using a

For the clarity of presentation, the experimental wavefonns have been divided into four groups. The first group
included exttaneous source wavefonns associated with fa.
tigue cycling (0.3 Hz) of specimens containing growing
sharp fatigue cracks or specimens without known cracks.
The next group was composed of wavefonns associated withdeliberately generaled exttaneous AE, while the specimens
were not under significant load The third group was composed of wavefonns generated during fatigue cycling of large
plates without cracks. The fourth consisted of a set of AE

special holder (Hamstad and Fonunko, 1995) designed for3

Rt:sooanl -llRl)

I

I

• !•

RI

@

_WB3

I

.; .

WBI_

I

R2

I
I

(A)

RI---4-.

@•

WBI-

I
I

R3

ff •

12
WB4
WBI---.

....WB2

R4

WB4

(0)

(C)

••

Resonant Sensors

DO @ Wideband Sensors->n~lOOmm
Fig. 1Sample and sensor configurations (front and side views) drawn to me indicated scale.

are not shown, two additional cases of electrical noise

waveforms from real fatigue aacks. The flfSt group was
extracted from data. which contained both extraneous andcrack-induced AE. The crack-induced AE was separated fromme extraneous AE based upon a careful wavefonn examination of the differences of arrival time at the different sensors
using the known locations of the grips. cmck. and sensors
and the appropriale propagation velocities. General results
for fatigue-aack-induced AE will be presented in a subsequent paper.

spikes were observed:(1) on wideband channels only; and(2) on narrowband channels only.

Waveforms from exttaneous AE originating at the ~per grips are shown in Fig. 3. These wavefonns were ~aected on a cracked A36-steel plate with reduced section (6.4mm thick). as shown in Fig. IC. The extraneous source
detennination is based again upon waveform-based arrivaltime analysis. The first and nearly equal signal anivals at
the two top resonant sensors (RI and R2) were followed bya progression of arrivals down the specimen to the two
resonant sensors (R3 and R4) at the bottom of the sample.
The waveforms show short rise times with relatively distinct peak amplitudes at the two top resonant sensors ml
longer rise times with no clearly defined peak amplitudes at
the two bottom resonant sensors. These changes are due inpart to dispersion. The wideband sensors show considerable

3.1 Cyclic Extraneous SignaJsfrom Cracked or Uncracked

SamplesFigure 2 presents the signal waveforms from an electtical noise spike. These waveforms ocx:urred during testing ofa a1clced A36-steel plate with reduced section (6.4 mm
thick). as shown in Fig. IC. The fact that the signal was anelectrical noise spike follows from the observation that thesignal appears on all eight AE channels (as well as the lood
channel) at exactly the same time. In other signals. which

low-frequency content in this extraneous grip source. This
low-frequency content is not apparent in the resonant sensor
4+22 WB I+320 WBI+0-320+320 WB2+0-320+320 WB3
+0

+0

-22
+22 WB4-320+320 WB4

~

+0

Cl

-=:=

-320
+240 Rl

~

=.

,

E

<

h.

+0

+0

I..

r

-45
+45

915

R4

+0

'L

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1

R2

-45
+45 R3-240+240 R4

S

+0

v

+0

>'

Rl

+0

III ..

-240+240 R3

-240
940

-22
+45

-45
+45

-240
+240 R2
+0

+0

r

-45
-70

I

I

1

J

1

l t. ,1, ..lJ

T r

r I

-20

+30

+80

+130 +180

Time (J1s)+50 +100

Fig. 3 Wavefonns from extraneous faIigue-AE soun:e emanating from Ibe top grip. From specimen wilb a redIr;:ed
section (6.4 mm) thick as shown in Fig. Ie. Gains were 51
dB for wideband and 60 dB for resonant sensors (sensor type
and positions labeled on the wavefonns).

Fig. 2 Wavefonns for an eleclrical noise spike during mtigue from wideband sensors (WB) after 51 dB gain arxl
resonant sensors (R) after 60 dB gain. The bouom wavefonn shows Ibe load-cell OutpUL

wavefonns. This difference is particularly interesting whencompared to the signals in Fig. 4. These wavefonns weredetected on a cracked AS14-steel plate of fulllbiclcness (22.9mm thick) (Fig. lA). This figure shows anolber extraneous
source at the upper grip, which fonows the same anival

oS
890

I

l

r'l'"
I

r

I

-ISO -100

I

I I
I

I

-50

+0

I

I

Time (J1s)5

+50 WBI+30 WBI

+0

+0

-50
+50

-30
+30 WB2

WB2

+0

+0
-50
+50 WB3-30+30 WB3+0-50+50 WB4

+0

-30
+30

~

WB4

III

~

.....

!
III

~

::s
'a
E
;::

-<

+0

'-'

+0

::s
~
'a

-50
+75

<

+0

E

-30
+75 RI

RI-75+75 R2+0+0-75+75 R2-75+75 R3+0+0-75+75 R3

-75
+75

+0

+0

-75
+75 R4

-75
-ISO

-100

-50

+0

+50

+100

Time (PS)+0

Fig. 5 Different type of extraneous fatigue-AE from thebottom grip for a specimen (22.9 mm thick) in configuration of Fig. lB. Gains 51 and 60 dB, respectively, for wi&band and resonant sensOI'S.

-75
-100

R4

-50

+0

+50

+100

+150
pattern at Ehe various sensoI'S as in Fig. 3. In this case.
however. the wideband sensors show a much higher ~quency content. while the resonant sensors have a dominantfrequency content that does not increase much from lhat
shown in Fig. 3. Again. the dispersion effects on peak amplitude and rise time of the resonant sensor signals are ap-

Time (J1s)

Fig. 4 Set of wavefonns from extraneous fatigue-AE fromthe bottom grip for a specimen (22.9 mm thick) in configuration of Fig. lB. Gains 51 and 60 dB. respectively. for
wideband and resonant sensors.
6+42 WBI-42+42 WB2

duration of the part of the signal of highest amplitude ismuch longer on most of the wideband sensors. Figure 6
shows that the extended high-frequency extraneous source is
not limited to the thick plates. These wavefonns were generated from a lower-grip extraneous source on a 6.4 mm
thick steel plate of unifonn thickness. as shown in Fig. lB.

+0

3.2 Artificial Extraneous Signals

+0

-42
+42

Figure 7 illustrates the wavefonns from a handclap
about 0.5 m from the front of a steel sample mounted in thefatigue test machine. These wavefonns were detected in a
cmcIced A36-steel plale of reduced section (6.4 mrn thick) as
shown in Fig. lC. This low-frequency airborne source isinteresting because it displays a signal-tQ.noise ratio higher
on the wideband sensors than on the narrowband sensors(noise refers to the background electtonic noise level prior
to the AE signal). It is also interesting that the lowfrequency content of the source is apparent on both sensor
typeS. Figure 8 shows a similar set of handclap wavefonns
derected in an AS14-steel plate of unifonn thickness (22.9mm). as shown in Fig. lB.

WBJ

+0
-42
+42 WB4

~
~

+0

:::=

-42
+75

<

+0

'C

:r

=..E

-75
+75

Rl

Figures 9 and 10 show the higher-frequency content on
the wideband sensors if a shon-rise-time pencil-lead break(0.3 mm. 2H) is applied to the grip body. The mechanicalpencil lead break was conducted with a force parallel to thelength of the specimen. on the botlOm surface of the upper
grip body. These sets of wavefonns were detected in a
cracked ASl4-steel plate of full thickness (22.9 mm) and ina non-cracked A36-steel plate of unifonn thickness (6.4mm) (Figs. IB and IC, respectively). These wavefonns
illustrate that extraneous sources with shon-rise-times cangenerate high-frequency signals. They also illustrate that
propagation across interfaces and longer distances (dispersion and attenuation effects) lengthens the apparent rise-timeof the resulting waveforms for both types of sensors.

R2

+0
-75
+75 RJ
+0

-75
+75

R4

Figures 11 and 12 demonstrate the extraneous source
wavefonns for the thick and thin plates. respectively. due 10
airbome sound waves generated by impacting two hand-heldpieces of metallOgether (steel rod. 32 mm dia. 152 mm
long. and a steel pipe. 51 mm outside dia, 6 mm wall
thickness, 914 mm long). The metal-tQ.metal impact was
carried out at a distance of 4 m from the front of the test
samples. Figures lB and lC. respectively, show the test
configurations. In these cases. only one wideband sensor
was used on the sample. Typically. the wideband sensorlocation number 4 was used for this measuremenL Thissensor was located on the front of the sample. in the middle.
below the crack. It had no internal preamplifier electronics
and its output went directly to the filter and then to the
waveform recorder, without any gain. Sensors with internal
electronics experienced voltage saturation due 10 the highampliblde. low-frequency content of this aiIbome sound. arxl
hence they were not used.

+0
-75
-150

-100

-50

+0

+50

+100

Time (PS)

Fig. 6 Wavefonns from extraneous fatigue-AE from thebottom grip, similar 10 Fig. S. but for a specimen (6.4 mm
thick) in configuration of Fig. IA. Gains 51 and 60 dB,
respectively, for wideband and resonant sensors.
parent. Figure 5 demonstrates an extraneous source emanating from the lower grips with characteristics different fromthose in Fig. 4. These waveforms 0CCUITed in a aa:ked
A588-steel plate of uniform thickness (22.9 mm thick). as
shown in Fig. lA. The same high frequency content in the
wideband wavefonns as shown in Fig. 4 is present, but the7+40 WBI+12 WB 1

+0
-40
+40

-12+==-=------------4+12 WB2

+0
-40
+40
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-40
+40

~

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fol

=

-40
+40

<

+0

~

'-'

'-'

"l:l

~

=-is

cu

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:I

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-12
+38 RI
+0

-40
+40

-31
+3

+0

+0

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+40

-38
+38 R3

+0

+0

-40
+40

-38
+38 R4+0+0-40-l-_---r_ _.,.-_-,~-_,_-__1+30 +80-70-20-170 -120-38 +----r----r---,..----,.......---!
+50 +100
-ISO
-100
-50
0

Time (PS)

R2Time (PS)

Fig. 7 Set of waveforms from handclap at 0.5 m from thefront of the red1x:ed-section sample (6.4 mm thick) in configuration of Fig. Ie. Gains 51 and 60 dB. respectively, forwideband and resonant sensors.

Fig. 8 Waveforms from handclap at 0.5 m position in frontof specimen (22.4 mm thick) in configuration of Fig. lB.
Gains 51 and 60 dB. respectively. for wideband and resonant
sensors.

33 Cyclic Extraneous Signals on Non-craclred Large Samples

The A588·steelrestsample measured 0.9 m x 1.2 m x
25.4 mm, and the A36-steel test sample measured 0.9 m x
0.9 m x 8 mm. The sensor configuration and dimensional
spacing. relative to the center of the plate, were the same as
those used on the smaller plates, as shown in Fig. lB.
These experiments provided exttaneous sources originatingmuch farther from the sensors. Figures 13 and 14 show two

To provide a different perspective and one that mightmore closely represent field-generared exttaneous AE, some
waveforms were gathered from fatigue cycling of two large
non-cracked steel plates. The materials were A588 and A36.
8

+420 WB 1

+475 WB 1+0t----"WI~.'IItJI+O+----...y~"" MI

-475

-420
+
420+---------------4
WB2+475t-::W~B=-2:------~-------J+O+-----~.J"4.11Il+Ot-----r~HIILM-420t+420 WB3-475 t +475 WBJ~-__4+0+---_"+0 4----h.IflNIII\IMJ+420 t-420 WB4--:._ _--:-_ _-f-420++1.1-475 t+475 WB4

~

i-

~

Rl

=ia

+O.O~--"""'N\JUIn.I/IJ"'''IIIIJ-1.1.J+1.1
R2

__4

~

-<

__+

+Ot---""'V'II,MWI.IWIP
__4

-750

+750t--R-3------~------t+O+--------.AIl.JI01J11J1111+o.o~----"'\J\ojrtlW\nnJltMM-1.1-1-+1.1
R4

-475

+750+-::R~I~-------------I+O+----..IIo,~Nnl~+o.o~--y,~~IIM1lj-1.1-1-+1.1 RJ+0+----_'""',"""'.." ,,,-.. I-750 t +750 R2

___I

___I

__+

-750

+750t-~R-4-------------++O·°t-----""oI\JW~MVI~+O+-------..,..,..~""AI\l-750 t - _ - , r - _ - - ,_ _.....,.._ _"""'T"_ _-l+50+100+150 +200-50+0Time (PS)-1.1~_....,..--.,....-__r--_,_-___4+50+100+150 +200-50+0Time (PS)

Fig. 9 Set of wavefonns for lead break (force parallel tolength of sample) on the upper grip housing for specimen(22.4 nun thick) in configuration of Fig. lB. Gains 51 a1Xl
60 dB, respectively. for wideband and resonant sensors.

Fig. 10 Wavefonns from lead break (force parallel to lengthof sample) on upper grip housing for reduced-section specimen (6.4 mm thick) in configuration of Fig. 1C. Gains 51
and 60 dB. respectively, for wideband and resonant sensors.

examples of such extraneous source wavefonns. In bothcases, the soun:e was located in the bottom grip region for
the thicker plate. It is interesting to note (see arrows in Fig.
13), the appearance of a Rayleigh wave (verified by velocity
of propagation between sensors and the obvious high frequency spike) on three of the wideOOnd sensor channels, but
not on the fourth wideband sensor located on the other sideof the plate. In other cases, we observed this Rayleigh wave
only on the backside sensor. Bam on out-of-plane pencillead-break data, we believe the Rayleigh wave was generated
by a grip-based surface source. as will be discussed below.
Figure 13 also shows such Rayleigh wave information is
not available in the resonant sensor waveforms. The exlmneous AE signal in Fig. 14 occurred at the peak cyclic load.
9+1+1 WB4WB4-I3 5 + - - - - - - - - - - - - - - - 1+
Rt-I3 5 + - - - - - - - - - - - - - - - 1
+

-35
+35

-35
+35

RI

R2

~

~
~

~

:II

::
'E.
E

R2

'-'
~

~

:II

::

+0

'a
E

-<

-<

-35
+35

-35
+35

R3

R3

+OWII~lIa.a./V-35+35+---------------4-35+35~--------------tR4R4-35 i----,r---.......,--......,.----r---f-1.10 -1.05
-1.00
-0.95
-0.90 -0.85-35 . f - - - - r - - - r - - - - r - - - - , r - - - f-1.00 -0.95-t.20 -t.t5
-1.10
-1.05

Time (ms)Time (ms)Fig. 11 Set of waveforms from impact of a pipe and bar at
4 m from the front of a 22.9 mm thick specimen in c0nfiguration of Fig. lB. No gain for wideband sensor and 60
dB gain for resonant sensors.

Fig. 12 Waveforms from impact of a pipe and bar at 4 mfrom dIe front of a reduced-section specimen (6.4 mm thick)in configuration of Fig. Ie. No gain for wideband sensor
and 60 dB gain for resonant sensors.

Thus, load gating would have been ineffective in eliminating this signal. Figures IS. 16. and 17 provide examples ofdifferent classes of exttaneous source waveforms observed
during fatigue cycling of dIe large thin plate. The appearance of the Rayleigh wave is not as clear as it was for dIe
thick plate. Figure 17 provides anodIer example. in which

load-gating does not eliminate all extraneous AE, since thissource also 0CCUIIed at peak load.
3.4 Fatigue-Crack-/nduced AE

A comparison of Figs. 18 and 19 illUSlnues the contrast between aack-induced AE and grip-generated signals
10

+85

WBI

....

+13
Rayleigh wave

+0
-85
+85

+0
-13
+13

WB2

+0
-85
+85

+0

-13

+13

WB3

+0

+0

-85
+85 WB4

-13
+13

S;

+0

--'=
=
:=
C.

~

-85
+95

'=

<

+0

E
~

E

"WBI

+0

~

-95
+95

Rl

-13
+55

<

+0

E

-55
+55

R2

+0
-95
+95

=
C.

:=

+0

-55
+55

RJ

+0

+0

-95
+95 R4

-55
+55

+0

+0

-95+-.-_--.-_ _-r-_ _......-_---._ _--1
+90 +140-110 -60-10+40-5S ol--_--._ _--.-_ _- r -_ _..,......_--4

-170

-120

-70

-20

+30

+80

Time (PS)Time (PS)

Fig. 14 Large-plate. different type extraneous faligue-AE
wavefonns from a bottom grip SOUICe. Plate of 25.4 mm
thickness with sensors amnged around the center as shownin the configuration of Fig. lB. Gains 51 and 60 dB. respectively. for the wideband and resonant sensors. Event occurred at the peak load.

Fig. 13 t.arge..plate extraneous fatigue-AE wavefonns frombottom grip source. 25.4-mm-thick plate. Sensors arranged
as shown in the configuration of Fig. lB. Gains 51 dB for
the wideband sensor and 60 dB for the resonant sensor.
Event occurred at 22% of peak load. during rising load.
during fatigue cycling of a aacked A36-steel plate of uniform thickness (25.4 mm). as shown in Fig. 10. The fact
lhat Fig. 18 is for a aack·induced source foUows from thenearly equal values of signal arrival times at all of the sensors. Further. the cnK:k-indtK:ed event was generared at thepeak fatigue load while the lower grip signal in Fig. 19
occurred at 43% of the cyclic peale load.

4. Discussion or Results
The waveforms exhibited in this paper demonstrare lhat
extraneous sources produce a wide variety of wavefonns.

particularly when deIected with wideband sensors. Thus. ingeneral. simple approaches to eliminating exttaneous AE
11

+110

+45 WBI

+0

+0

-110
+110 WB2-45+45 WB2

+0

+0
-45
+45

-110
+110 WB3

WB3

+0

+0

-110
+110 WB4-45+45 WB4

-~
e.I

":l
:l

:=

=..
E

-<

+0

-~

-45
+45 RI

":l
::I

-110
+150

E

+0

e.I

-

=c::.

+0
-45
+45

+0

-<

-150
+150

R2

+0

RI

R2

+0
-150
+150 R3-45+45 R3
+0

+0

-45
+45

-ISO
+150

R4

+0

+0
-45
-ISO

R4

-100

-50
0
Time (PS)

+50

-ISO
-100

+100

-50

+0

+50

+100 +150

Time (PS)

Fig. 15 Set of Jarge--plate extraneous fatigue--AE waveforms
from a bottom grip source. Plate of 8 mm lbickness wilb
sensors arranged aroWld the centec of lbe specimen wilb thesame sensor spacing from lbe center as shown in the configuration of Fig. lB. Gains 51 and 60 dB, respectively, for
the wideband and resonant sensors. Event occurred at 50% of
peak load during decreasing load.

Fig. 16 Anolber soun:e-type set of large-plate exttaneous
fatigue-AE wavefonns from a bottom grip source. Plate of
8 mm thickness with sensors arranged around the center ofthe specimen with the same sensor spacing from the centeras shown in the configuration of Fig. lB. Gains 51 and 60
dB, respectively, for the wideband and resonant sensors.
Event occurred at 14% ofpeak load dming rising load.

12+100 WBI

+18 WBI

+0

+0

-100
+100

-18
+18

WB2

+0
-100
+100

+0
-18
+18

WB3

+0

=
=.
~

E

-<

-18
+18 WB4

+0

u

~

;>

+0

u

-18

!.

-100
+170 Rl~= +32 Rl

~

=.
E

+0
-170
+170

WB3

+0

-100
+100 WB4

~

WB2

-<

-32
+32

R2

+0

+0

-170
+170 R3-32+32 R3

+0

+0

-170
+170 R4-32+32 R4

+0
-170
-100

+0

+0

-50

+0

+50

+100

+150

Time (PS)

-32
-100

-50

+0
+50
Time(p.s)

+100

+150

Fig. 18 Set of wavefonns from crack source at peak mt
during fatigue of plate with 25.4 mm thickness with sensor
configuration shown in Fig. ID. Material was A36-steel
with sharp crack. Gains 51 and 60 dB, respectively. for the
wideband and narrow band sensors.

Fig. 17 Additional sourte-type set of large plate extraneous
fatigue-AE wavefonns from a top grip source. Plate of 8mm thickness with sensors arranged arolDld the center of thespecimen with the same sensor spacing from the center asshown in the configuration of Fig. lB. Gains 51 and 60 dB,
respectively, for the wideband and resonant sensors. Eventoccmred at peak load.

lighted. A focus of the discussion will be on the differencesin the waveforms from the wideband sensors as compared to
the resonant sensors. Since the resonant sensors and the
wideband sensors were not physically located at exactly thesame positions, it might be argued that some of the difI"er-will not be fully successful except in cases where the different typeS of exttaneous AE are very limited. In the discussion that follows. key aspects of the different extraneous
source waveforms. which were observed, will be high13

gJed out between the two types of sensors are real and not a
result of ahe differences cited above.

+17 WB 1
+0
-17
+17

4.1 Extraneous Signals in Large Plates
As demonstrated in Figs. 13 through 17, ahere aremuch wider variations in both the wavefonn shapes and thefrequency content in the wideband-sensor waveforms thanthose of ahe resonant-sensor waveforms for exuaneous gripbased AE. The same, or nearly the same, resooant-&equency
is apparent in all the resonant-sensor wavefonns in thesefigures. Figure 19 also illustrates, for a real aa:k-induced
AE event in a smaller specimen (25.4 mm thick), similar
resonant-frequency content This is in spite of the wide variety of the source-frequency content (as seen in the wideband
waveforms) and the two different plate thicknesses (varied
by a factor of 3.2). Thus, for resonant or narrowband daIa.
simplified post-processing frequency discrimination of extraneous AE does not appear to be a robust method to generally distinguish real aack-induced AE from all fatiguerelated extraneous AE, as has been proposed (Ghorbanpoor,
1985). Furthennore, the cmrent results, showing that res0nant sensor daIa has similar frequency content for both
aack:-induced AE and extraneous AE, is consistent withlater results (Ghorbanpoor and Renuneester, 1993; Gong etal.,1992).

WB2

+0
-17
+17

WB3

+0
-17
+17 WB4~......
Gl

-=

:I

~

=.
E

~

+0
-17
+34 Rl
+0
-34
+34

R2

The wideband-sensor waveforms show much more p0.tential for source identification in both the time and thefrequency domain because of the wide variation of signal
types. For example, the wideband waveforms in Fig. 16 aresimilar to those obtained from an out-of-plane pencil-leadbreak on the plate. In contrast, the wideband waveforms in
Fig. 13 show Rayleigh waves at three of the sensors on thesame side of the plate. This is consistent with the appearance or nonappearance of Rayleigh waves as a result of leal
breaks on the two smfaces of the plate. This result indicatesa means to detect, with wideband sensors, a SU1'fal»based
extraneous source on a thick plate and to deaennine on
which side it originates. Figure 13 shows no evidence of theRayleigh wave in the waveforms from the resonant sensors,even though they were mounted on the same plate surface as
the three wideband sensors which showed the Rayleighwave. Both the 100 to 300 kHz bandpass and the large aperture (about 18 mm) of the resonant sensors would be expected to conttibute to eliminating response to the highfrequency Rayleigh wave.

+0-34+34 R3- 34 t-----,r---""'"""T---r--....,.-----t

-150

-100

-so

0
Time (PS)

+50

+100

Fig. 19 Set of waveforms from exlI3neOUS source fromlower grip during the same fatigue testing for Fig. 18. Extraneous AE event at 43% of peak load during the risingload.

Figures 13, 14, IS, and, to a lesser degree, Fig. 16,
indicate that a long rise time can be associated with the nar·rowband extraneous AE wavefonns. However, the measured
rise-time values will clearly be a function of the particularthreshold used. This clear dependence of memured rise timeon threshold is contrary to other work previously reported(Ghorbanpoor and Renuneester, 1993). These waveforms
indicate that higher thresholds, or lower amplitude extraneous signals of the same type, could result in relatively shortences between the two typeS of sensors could be attributed
to the differences in distances of wave propagation between
sensor locations, effects of radiation pauerns, or differences
between sensors of the same type. To provide the evidence
that this is not true for the factors cited here, all the available wavefonns for each extraneous AE signal are provided.
Thus, readers can assess for themselves that the effects sin14

measured rise times. Figure 16 shows that, potentially, avery short measured rise time (3 10 9 J.1S) could result if thethreshold was above the apparent lower ampliwde extensional mode in the resonant sensor data. Previous work forresonant sensors has claimed that extraneous sources haverise times that measure 50 JIS or more (Ghorbanpoor, 1985)and valid crack sources have shon measured rise times of I
10 IS IJS (Ghorbanpoor and Renuneester. 1993), or less than
2S J.1S (Gong, et aI., 1992). The current results (Fig. 16)
indicale that such approaches are not valid for all ~sources. Furthermore, if the propagation distance is relatively large, dispersion will increase the rise times.

being classified as aa:k-induced. If the resonant sensors
were further away from the asswned ceruer-aack region, theconcept would be expected 10 work more reliably. However,most experiments are not done with the required large samples. If the sensors were closer to the asswned center aaclc.
using spatial location to distinguish the extraneous gripbased event is expected to be worse. This conclusion isbased on the fact that the average time difference for gripbased SOUJt:eS would be smaller when Ihe sensors are closer
to each other. In this case, the errors in arrival time differ·ence would be more likely to show that a signal originared
between the sensors rather than outside the sensor array. A
similar analysis of extraneous grip-based events with Ihelarge, thinner (8 mm thick) plate pnxhx:ed simi1ar scatter in
the arrival time differences, except that in this case, the average time difference no longer corresponded to the expected
time difference for the sensor spacing, based on the bulk
shear velocity. Instead, it was near the Ihin-plate eXleDSional
velocity (5.4 mm/JlS). While identification of extraneous
grip sources by fixed-threshold determination of arrival
times can be poor, resonant waveforms allow easy identification of grip-based sources, by using visual observation of
Ihe set of resonant waveforms.

Visual examination of the resonant-sensor wavefonns
in Figs. 13 through 17 shows whether an extraneous source
originated in the lower or upper grips. Thus, it might besurmised that source location would be a valid approach todistinguish the crack-induced events from the grip-based
events with Ihreshold-based systems. To test this Iheory(assuming a crack was midway between the upper and lower
resonant sensors), the differences in arrival times at Ihe
resonant sensors for the extraneous hits of each of several
exuaneous grip-based events (on a 25.4 mm-thick sample)
were detennined as a function of different thresholds. The
different thresholds leSt Ihe effects of changes in source amplilUdes. Starting at a 40 dB Ihreshold (about 3 dB above Ihebackgro1Dld electronic noise) and successively raising a twosided threshold in 3 dB increments, the time differences 1»
tween the first-hit sensors (upper and lower) and the secondhit sensors (upper and lower) were deIennined. These lime
differences are cornpan:d in Fig. 20. This figure includes aline at the time difference of 47.2 J.lS, which conesponds toIhe 152 mm spacing between the upper and lower resonant
sensors based on the bulk shear-wave velocity for steel of
3.2 mm/J1S. This velocity is commonly used for source
location in conventional AE technology. Clearly, Fig. 20
shows significant errors even Ihough the average delta timedifference of 49.7 IJS is very close to 47.2 JIS. A similar
study of arrival time differences was done for real endinduced AE. The time diffemtces as a function of a twosided lhreshold are shown in Fig. 21 for several cndcinduced events. This figure demonstrates substantial deviations from Ihe expecIed time difference of 0 J.lS (even overlapping those of the extraneous sources in Fig. 20) for Ihese
crack-induced events which had peak signal amplitudes similar to the extraneoUS signals used to generate Fig. 20. Examination of aack-induc:ed events wilh larger amplitudes(up to nearly 100 mV peak amplilUdes) resulted in the sametypes of deviations from the expected 0 J.LS. Hence, the reliability of using source location with fixed thresholds is nOl
always acceptable for the sensor spacing considered here. A
conservative choice (a low value near 0 J.lS) of the maximum time difference allowed to distinguish a crack·induced
source would result in some real aack-induced events beingclassified as extraneous sources, and a non-conservative
maximum lime (a high value near 47 JlS for the case considered above) would result in some extraneous grip sources

As was noted before, exaaneous signals can occur at, or
near, the peak load during cyclic fatigue, as illustrated in
Figs. 14 and 17. Thus, load-gating can be expected to accept
some exaaneous AB as well as real crack-indtx:ed AB. To
account for this fact, olher techniques are required to identify
the extraneous AE that OCClU'S near the peaIc loads.

4.2 Extraneous Sources in Smaller Plalts during Fatigue
Extraneous AE can also be generated by sources, which
are close to the sensors mounted to monitor cracks. To
study such cases, this work also included two Ihicknesses ofsmaller plates. As with the large plates, these results (Figs.
3 through 6) show a similar diversity of extraneous source
waveforms captured with the wideband sensors. The waveforms deu:cted by the resonant sensors are more similar toeach other and to the resonant wavefonns for the real cnainduced source in Fig. 18. Another fealUre demonsttared bythese waveforms was that Ihe rise time measured with athreshold·based system could result in very shan rise times.
This was also observed in the large plates. The resonant
waveforms in Figs. 3 and 4 indicate that relatively shonmeasured rise times (5 to 13 J1S, wilh a threshold slightly
above Ihe initial background noise level for the sensors
nearest Ihe source) would be determined by a threshold-based
system for these extraneous source cases. This result showsthat rise-time techniques are not reliable in eliminating exttaneous AB.

15

200

-y---------------,

50-r---------------..,

o
180

o

40

o

46 dB

iii"
:1.
;- 30
E

160

43 dB
40 dB

i=

S 20

140
_120

'ii

c

40 dB

43 dB

46 dB

49 dB

10

III

..=:
III
.5
100
I-

0+-.g,..+---1f-~~_+_-_+_-_+_-.;-__t_J

o

C

10

15

20

25

30

35

40

Case Number

ea

~

III

5

80

o

Fig. 21 Plot of arrival time differences between top am
bottom sensors (like Fig. 20) for different thresholds for real
aack-induced AE events during fatigue of an A36-steel
plate, 25.4 mm thick. with sensor configuration shown in
Fig. ID. Theoretical time difference expected to be 0 J.IS.

8

4.3 Extraneous Airborne Signals
During field AE monitoring. airborne sound waves
from other nearby mechanical processes can be "inrercepted"
by the metal structure. on which the AE sensors aremounted. Comparison of Figs. 7 and 8 shows that the
thickness of the plate has a substantial effect on the ftequencies observed with the wideband sensors for a handclap
source. Thus. if frequency filtering is used to attenuate such
sources. the filtering frequency range will potentially need
to be altered for different sample thickness. On the otherhand. the frequency content of the resonant sensors increases
only slightly as the plate thickness increases.

o +---+----l---+---+--+---I
75
100
125
150
o 25
50
Case Number

Fig. 20 Plot of amval time differences (for different twosided thresholds applied to recaded wavefonns) between the
top resonant sensor and the bottom resonant sensor on eachside of the sample (sensors on the front smface). Grip-based
extraneous fatigue signals on large plate of 25.4 mm thickness with the resonant sensors in a rectangular array (152mm high by 76 mm across) centered on the sample. Horizontalline at 47.2 J.lS corresponds to propagation time between upper and lower sensors, based on the bulk shear velocity for steel.

The air-transmitted sound created from banging together
two metal pieces did not result in a measurable signal(above background electronic noise) on the wideband sensor(after the 50 kHz to 2 MHz filter) in contrast to the behavior of the resonant sensors. This result is not surprising fa
two reasons. First, measurements have confirmed that !hewideband sensor without the internal electronics loses atleast 13 dB in signal-to-noise ratio or sensitivity (mostly
due to sensor cable capacitance and filter input capacitance).
Second, the signal-to-noise ratio (at the stan of the signal).
for the resonant sensor signals. is only about 8 dB. To
mainrain the sensitivity of the wideband sensors in a continuous. high-amplimde. noisy field environment, will lequire high-pass filtering before introduction of much gain. If
this is not done. then the sensor with internal electronics
may be saturated much of the time.

As was obselved for the large plate case, the largechanges in the frequency content in the wideband extraneous
signals are not reflected in the waveforms of the resonant
sensors. This "frequeD=y effect" can be seen in contrastingFig. 3 with Fig. 5. Figure 3, which has waveforms for a
specimen with the sensors located on a reduced center section is also interesting. This figure shows that the measured
rise time at the two resonant sensors near the top grip (origin of the source) is very fast (about 5 J.lS with a threshold
slightly above the initial background noise level) and, at thetwo bottom resonant sensors. is considerably slower (25 to
30 J.IS). due to dispersion. Again. the distinction as to which
grip was the source of the extraneous AE is readily apparent
from visual examination of the wavefonns of the resonant
sensors.

The extraneous airborne signal presents a different perspective since, as the waveforms for the resonant sensors
show in Figs. 7. 8, 11 and 12. the signals typically arrive16

at aI) the sensors at close to the same time. Thus, spatial
discrimination andIcr guard sensors are not useful to ~criminate this extraneous source as the signal does notpropagate along the leSt sample in a particular direction.
Further, since all the resonant sensors would be hit at asimilar time for a IhreshoId-based system, it might be c0ncluded that an AE source was active near the center of thesensor array. An examination of the melal-lO-meraJ impact
wavefonns (for a time greater than that illusuated in the
figures) shows that this source results in a very long duration signal. They also show continuous near-peak amplilude(presumably due to the ringing of the impacting objects,
which would not occur for the handclap source) that could
be used to eliminate this source, as distinguished from a realmK:k-induced source. Long signal duration bas been recognized by others to indicate an extraneous event (Gong, etal.• 1992: Ghorbanpoor, 1985). But, since the dispersion
and/or reflections can increase the signal duration, care must
be taken in the selection of values used for disaimination.
Otherwise, real aa:k-induced sources may be incorrectly
categorized. For example, if the sensors were on a small
sample where multiple reflections would lengthen the duration of a signal, a large amplimde aack-indlI:ed source
would have a long duration. Since the wideband sensors
would better chanK:terize the low-frequency content of such
extraneous sources, it may be easier to use this feawre torecognize them.

except for the visual sequence of waveform arrival due 10 the
different source locations (grips versus crack). However.
even with the wideband sensors. some extraneous sources(Fig. 4) can resemble a crack-induced source (Fig. 18),
where similar fast rise-time and a short-duration higheramplimde signals are present on the wideband sensors far
both of these source types.

4.6 Electrical Extraneous Signals
The use of 3O().kHz lOW-pass filtering to eliminale
problems with elecuomagnetic intezference bas been pnr
posed (Ghorbanpoor. 1985). As Fig. 2 demonstrates. that
hypothesis is not always D'Ue. Clearly. in this case. the
3QO-kHz low-pass filter allowed a significant noise spike ofamplitude up to 200 mV (66 dB) to appear on the resonant
sensors. This signal is probably present due to the antenna
effect of the sensor cable and/or the cable between the preamplifier and the wavefonn recorder. Since the main threshold-based AE measurement unit includes an additional 300kHz low-pass filter. we anticipate that this signal will beattenuated further before measurement To estimate thisanenualion,the recorded signal (which experiences only thepreamplifier filter) was passed through a six-pole 300-kHz
low-pass Butterworth software filter. This filter reduced thesignal for one channel from about 214 mV to 169 mV,
which was still 18.5 dB above the threshold typically used
to monitor AE in these experiments.

4.4 Extraneous Solid Transmilled
S. Current Bottom-Line Perspectives
A pencil-lead-break source, as demonstrated in Figs. 9
and 10, bas a slow rise time and long duration (typicallyassociated with extraneous AE), after it bas passed throughthe two melal-to-melal interfaces in order to navel from thegrip housing to the test sample. These feawres are present
in the wavefonns from both types of sensors. As expectedfrom the discussion above. the increase in higher frequencycontent is more apparent in the wideband sensors on the
thick plate than the thin plate. As before. there is no discemable change in the resonant sensor's wavefonns as the
specimen thickness is increased. These results show thateven shon-rise-lime SOUICes result in long-rise-time signals
if the waves must propagate across interfaces to reach thesensors. Additionally. these results indicate that aa:kinduced sources may be especially difficult to distinguish if
the AE waves must pass through interfaces or other disc0ntinuities in the propagation path to reach the sensors.

Examination of all the recaded waveforms indicates,for known directions of propagation of extraneous AE. thatvisual spatial discrimination of waveforms of either sensor
class can be very effective in identifying extraneous signals,
except in the case of airborne extraneous AE. Since the
wideband signals provide an opportunity for accurate source
location (Ziola and Gorman. 1991) by an automatic, rather
than a visual, inspection. the wideband-sensor approachseems to be potentially better at the present time. Further.
with the diversity of widebU1d extraneous wavefonn types(and presumably sowte typeS) as compared to the resonantsensor wavefonns, advanced signal processing techniques
may be developed for most wideband cases to identify the
extraneous sources. This potential advantage is of great importanee for cases where the location of real aadc-induced
AE is near the location of extraneous sources. where spatial
discrimination would not likely be useful.

4.5 Contrast oferode-induced AE with Extraneous AE
In the application of resonant AE sensors to monitor an
existing crack, the current results indicate that a visual
wavefonn approach or an equivalent signal-processing
method combined with proper sensor placement would be
effective in eliminating extraneous AE. This "visual observation" takes account of the arrival of a substantial part of
the resonant waveform. not just a single feature. If an AE

Although defailed discussion of aack-induced AE will
be deferred until a later paper. we note some differences between crack-induced AE and extraneous wavefonns. Figures
18 and 19 demonstl3te that differences in wideband AE
waveform can be very large between extraneous grip-based
and real aack-induced sources. while narrowband waveforms
for the same two sources can have almost no differeoce,
17

References

measurement system with resonant sensors and fixed
thresholds is used without visual examination of wave.
fonns, then guard sensors will be effective only if they meplaced along the direction of the propagation of SbUCtureborne extraneous AE at a considerable distance away fromthe crack-monitoring sensors. This approach compensates
for the arrival-time difference errors. which occur with suchapproaches. But this technique would not necessarily eliminate airbome extraneous AE, Wlless the daIa sensors wereoffset by large differences from being cente:red around the

M.A. Friesel (1987), "Application of Pattern Recognition
Techniques to Acoustic Emission from Steel and Aluminum," Review of Progress in Quantilative Nondestructive
Evaluation, Vol. 6A. Plenum Press, New York, pp. 337343.
C.F. Galambos and C.H. McGogney (1975), "Oppommities for NOT of Highway Structures." Materials Evaluation,

crack.

33(7), 168-175. 180.

6. Conclusions

A. Ghorbanpoor (1985), "Acoustic Emission 01aracteriza..
tion of Fatigue Crack Growth in Highway Bridge Components," Ph.D. dissertation, University of Maryland, College
Park, MD.

1. Exttaneous wavefonns from wideband sensors show significant differences in both shape and frequency content fur
different types of sources, whereas relatively small differences in shape and frequency content are seen in resonantsensor wavefonns from the same sources.

A. Ghorbanpoor and A. T. Rentmeester (1993), "Acoustic
Emission Study For Evaluation of Highway Bridges," Final
Report, Study No. 90-05, WI-06-93, University of W1SCQDsin-Milwaukee, MilwuaIcee, WI.

2. Techniques, which rely on using rise-time or frequencyspectrum discrimination 10 distinguish real crack-induced
AE sources from extraneous sources, may be ineffective forresonant sensors.

Z. Gong, E. O. Nyborg and G. Dommen (1992), "Acoustic
Emission Monitoring of Steel Railroad Bridges," Materials
Evaluation, 50(7), 883-887.

3. A crack-induced AE source and an extraneous source cansometimes result in large shape and frequency-content differences in the wideband-sensor waveforms from the twosources, whereas in the rwrowlmld resonant-sensor waveforms, these differences are typically relatively small.

M.A. Hamstad and C.M. Fortunko (1995), "Developmentof Practical Wideband High-Fidelity Acoustic Emission
Sensors," Nondestructive Evaluation of Aging Bridges 3fII
Highways, SPIE Proc., SPIE-2456, ed. Steve Chase, pp.
281-288.

4. Great care must be used in specimen design and spacingof sensors for spatial or guard-sensor discrimination of gripbased AE to be effective, when using Ihreshold-based arrival
times. Visual arrival-time detennination or equivalent signal
processing of recorded wavefonns from resonant sensors canbe used much more effectively for spatial discrimination of
extraneous grip sources.

MA. Hamsrad (1997), "Improved Signal-To-Wldeband
Acoustic! Ultrasonic Contact Displacement Sensors forWood and Polymers," Wood and Fiber Science, 29(3), 239248.

R. 1(. Miller, H. I. Ringermacher, R. S. Williams, and P.
E. Zwicke (1984), "Characferization of Acoustic Emission
Signals (Final Repon)," Report No. FHWA/RD·84/025,
United Technologies Research center, East Hartford, CT.

5. Approaches 10 distinguish extraneous AE from aa:kinduced AE with wideband sensors will need to account for
sample-thickness variations and their effects on wave propa.
galion.

M. Sison, J. C. Duke. Jr., C. Clemens. and M. G. Lazev(1996), "Acoustic Emission: A Tool For The Bridge Engineer," Materials Evaluation, 54(8), 888-900.

6. Extraneous AE occurs in the peak-load region during
fatigue cycling. Hence. load-gating is useful but not 100%
effective in eliminating extraneous sources.

s. M. Ziola and M. R. Gonnan (1991), "Somce Location
In Thin Plates Using Cross-Correlation," J. ACOUSL Soc.
Amer., 90(5), 2551-2556.

Acknowledgments
This research was partially supported by the Federal
Highway Administration (FHWA). The encouragement of
Dr. Richard Livingston, FHWA. and Mr. Charles McCog
ney, FHWA(retired), is gratefully acIcnowledged. Technicalsuppon from Mr. Raymond Santoyo of NlST was essential
to the testing of the samples.
18

Fracture-Mode Classification Using WaveletTransformed AE Signals from a Composite
Tetsuo Kinjo, Hiroaki Suzuki, Naoya Saito, Mikio Takemoto and Kanji Ono
Abstract

The detection and analysis may be simplified if we can d>velop a signal classification method based on AE signals
obtained by using more sensitive resonant sensors.

Wilh lhe aim of establishing a reliable method of AE
signal classification. we examined lhe utility of classicalmatched filtering of given images (MF). matched filteringof Laplacian images (MF-LI) and Fourier phase correlation(FPC) of lhe wavelet transfonned AE signals. Trial classification of lhree model objects revealed that the FPC
method was superior to the olhers. We attempted to classify lhe wavelet-transfonned AE signals from a model UDGFRP by lhe three methods. and examined their discrimination capabilities. Classification was first attempted for 86
AE events, which were recorded using a resonant sensor arxi
whose fracnue modes had been identified by me sowce
simulation analysis of simultaneously recorded displacement
data. The FPC again was lhe best among the three. Detailed
examination of similarity coefficients of the FPC results
showed that 68 events were distinctly classified. Using the
FPC. we classified anolher set of 600 AE events. which had
been recorded wilhout corresponding displacement sensor
data. The relative population of signal types and theirchanges wilh strain were similar to those predicted by thesource simulation analysis.

In our previous studies (Suzuki et al.. 1993. 1996a.b).
we demonstrated the validity of me source simulation analysis for the fracture mode classification in model and actual
unidirectional GFRP specimens. In these specimens. thepresence of four fracture modes is shown. In addition to thisapproach. we recently examined the pauern recognition
analysis by using auterregressive modeling and the wavelet
transfonn (Suzuki el al.• 1996b. c). The wavelet nansform(WT) of AE signals represents lhe mapping in the frequency-time coordinates and therefore is also useful for signal classification. We reponed that the contour map (orvoice print) of wavelet coefficients was closely correlated to
the four fraclure modes classified by the source simulation
analysis. but did not propose at thai time any concrete
method to classify the three-dimensional images of wavelet
transfonned AE signals (bird's-eye view of wavelet coefficients).
We believe that a reliable and automatic method of signal classification of the wavelet tranSfonned AE signals isboth desirable and feasible. As the 3-D projection of wavelet coefficients consists of an enormous amount of discrete
data but also with many near-zero values. it is futile to use
all the pixel data as the feature-characterizing parameters of apattern (or signal types in case of AE). One useful andsimple classification methods is to utilize the reference patterns characteristic of each signal type. The wavelet transformed AE signals can be classified by computing the CI'OSScorrelation (or similarity coefficients) to the reference patterns. which can be correlated to the fracture mode by thesource simulation and P-wave radiation analyses. In thisstudy. we have examined three classification methods. i.e.•classical matched filtering of given image (abbreviated as
MF. hereafter). MF of Laplacian image (MF-LI) and Fourierphase correlation (FPC).

1. Introduction
Reliable AE signal classification is important in identifying lhe sequence of various micro-fracture processes incomposite materials (Ono and Huang. 1994). Successfulclassification of AE signals can elucidate fracture mechanisms and leads to an improved design of advanced composite materials. Quantitative source simulation analysis of me
out-of-plane displacement appears to be the most reliable
signal classification scheme when it is jointly used with the
P-wave radiation pauern analysis (Suzuki et al. 1996a. b).
However. the source simulation analysis utilizes a low
sensitivity displacement transducer with broad bandwidth
signal processing and is vulnerable to noise interference.
Received 19 May 1997; in fmal form. 2 November 1997. Tetsuo Kinjo. Hiroalci Suzuki. Naoya Saito and Mikio Takemoto(takemoto@me.aoyama.ac.jp) are affiliated with Faculty of
Science and Engineering. Aoyama Gakuin University. 6·16·1
Chitosedai. Setagaya, Tokyo 157. Japan and Kanji Ono(ono@ucla.edu) is with University of California. Los Angeles.
405 Hilgard Ave. Los Angeles. CA 90095-1595. USA.

Journal of Acoustic Emission
Volume 15. Number 1-4

We first studied how mese methods classify 3-0 images
using such model objects as circular. square and triangularcolumns. We found that MF-LI and FPC are superior tothe classical MF. Using these methods. we classified 86 AE
events whose frdcwre modes had been identified by thesource simulation analysis. Actually. the fracture modes of
14 more events were identified by the source simulation
analysis. but the resonant sensor data was unavailable. Of
190730-00501911$5.00/15019-032
Copyright<i:lI997 Acoustic Emission Groupthe 86 events, 74 events were classified by the FPC
method, which was better than the other two. Using someof the classified 74 events as reference, we next classified
600 AE signals. which had no corresponding displacement
sensor data. The evolution of relative population of different
fracture modes with strain coincided well with that expectedfrom the source simulation analysis of the 100 AE events.

2. Classification Methods of Three Dimensional
Image

The similarity coefficient gj can be rewritten for i = I
to M by using a vector notation as(3)Here, f and h are vectors in the N x N-dimensional spacewith fjt and hjk as elements. From equation (1), both satisfy
the following two expressions, and a superscript t indicatesa transposed vector.
(4)

2.1 Matched Filtering(5)This method evaluates the similarity between the reference and given images using Cull phase and amplitude.
Measured image, !(x,y), and reference images, hj(x,y) (for i
I to M). which had been normalized by using the sum ofsquared values of each data point. have the unit integral values as equation (1)=II!2(x,y)dxdy =IIhr (x.y)dxdy = 1(1)

The similarity coefficient gj oCthe measured image f(x,y) to
die reference image hj(x,y) is formulated by integrating the
product of corresponding values for i = I to M as
gj =

ff

!(x,y)h j (x,y)dxdy(2)

Variables x and y in these equations represent the twodimensional coordinate (x.y) of an object, but can be replaced by frequency and time for the wavelet transformed AE
signals. In practice, discretized values, sampled at regularintervals. ljkO. k = I to N), are used to represent an image
F (see Fig. 1).

x

("
(,z

The similarity coefficient gj is then given by equation(6), using the angle e between the two vectors f and h in
the N x N-dimensional space.
gj=cos 9(6)j

The similarity coefficient gi takes a value from zero to one,and becomes unity when e is zero, or when a given image
completely matches the reference image.
We examined how simple 3-D images are classified by
the MF. These objects are used as both the reference am
given images, and are a circular column (represented as circle in two dimensional pattern, 0 in tables), a square column (square,
and a triangular column (triangle, a), as
shown in Fig. 2. The factor gj was computed for nine
combinations of these objects. Pixel number used for each
image is 30 x 30 = 900. Table 1 shows the factor gj thus
determined. The gi in the three diagonal elements are unity
as expected. However. the gj for a combination of 0 and ais estimated as 0.748. and 0.875 for 0 and 0 combination.
The gi for a and 0 combination was 0.830. The gi values
for the dissimilar objects is closer to 1.0 rather than zero,
indicating that the discrimination capability of MF is poorin this example.

cnTable I The similarity coefficient gj for simple 3-D images
as classified by the MF.

0
Sample0c.

0Reference image
0

c.

1.000
0.875
0.748

0.875
1.000
0.830

0.748
0.830
1.000

2.2 Matched Filtering of Laplacian Images (MF-U)y[F]

Fig. 1 Vector representation of an image.

f

In this processing, Laplacian image is utilized formatched filtering. Laplacian operation of an image is expressed as equation (7), and emphasizes the edges of me
original image and also islands (isolated small objects am
points). This is known to be useful in classifying similar
images. (Marr and Hildreth, 1980)

2D-lmage

(0)

(B)

y

x
3D-Imagez

Fig. 2 20- and 30 model objeclS used for comparison of classification methods.
3-D Laplacian Imagez

LaplaCIan contour map

yx

Fig. 3 The Laplacian images of the Lhree model objeclS.
21

(7)

Shown in Fig. 3 are the Laplacian images of the threemodel objects. Second order differentiation gives positive
and negative peaks around edges. as shown in the x-z view.
Utilizing lhese processed images. a betler classification is
obtained as shown in Table 2. It is noted that the gi'S for
dissimilar objects become less than 0.2. The MF-LJ appears to be superior in its discrimination capability to theclassical MF method.
Table 2 The similarity coefficient gi for simple 3-D images
as classified by the MF-Ll method.
Reference image

Sample

0
0
~

0

0

~

1.000
0.191
0.049

0.191
1.000
0.103

0.049
0.103
1.000

2.3 Fourier Phflse Correlanon Method (FPC)

The factor gj takes values from -1.0 to + 1.0, and gj=1.0 implies the best matching. Figure 4 shows the Fourier phase images. The top row represents the 3-D Fourierphase images of the model objects. The middle row (x-zview) represents the profile of image amplitude along xaxis. Shown in the bOllom row are the contour maps of theFourier phase images. It can be seen that the outline (periphery) is emphasized for the cylindrical and triangular columns. and the comer for the square column. Classification
result by the FPC is shown in Table 3. We observed aperfect classification of gj =1.0 for the same images (diagonal elements) and almost zero or negative for dissimilar
images. The FPC is superior to the MF and appears to be
betler than the MF-LI method. This is expected to be valuable in classifying complicated wavelet-transformed AE
images.
Table 3 The similarity coefficient gi for simple 3-D images
as classified by the FPC method.
Reference image

SampleHomer and Gianino (1984) and Kawata et al. (1987)
suggested that the phase information is significantly more
imponant than amplitude infonnation in preserving thefeatures of a 3-D image. The FPC method classifies 3-D
images by using only the phase information. Here, thesimilarity coefficient gi is given by equation (8) using theFourier phase images r(x,y) and hj9(x,y).

0
0
~

0

0

~

1.000
-0.016-0.045-0.0161.000
0.020-0.0450.020
1.000

Among three classification methods, the computation
time for one gj value is slightly longer for MF-LI and FPC
methods than that for the MF. but even the FPC requires
only 2 seconds for one pair of classification by using anengineering work station (Sun SPARC-5) for the present
example.

(8)
3. Classification of AE Signals

The Fourier phase images are obtained by inverse Fouriertransform (71) of the phase lenns. cI>~!;,l\) and ~i(!;,l\) byequations (9) and (10).
r(x.y) =7 1 (exp[-j cM!;,l\)]}hjo(x,y)=7 (exp[-j 4lHi(S,l\)]}1(9)

We examine next the classification of acwal AE signals
from a composite specimen under tension using these threemethods. The AE signals are separalely evaluated by thesource simulation analysis and identified according to theirrespective fracture mode.

(10)

3.1 AE Monitoring of a Glass-Fiber Composite Specimen

where j2 = -1 and the Fourier phase images are dermed by
the imaginary parts of the Fourier transformed images
F(!;.l\) and H;(l;,l\); Le.•
F(/;,l\) =JF(!;,Tl)1 exp[-j M/;,Tl)](11)

Hi(!;,Tl) =1H;(I;.Tl)1 exp[-j «PHi(!;.Tl)](12)

The phase images are, as before, normalized according toequation (13)(13)

A specimen containing a bundle of 6400 E-glass fibersof 131Jffi diameter was prepared. A bundle was placed longitudinally at the cenler of the specimen as shown in Fig.
5. This fiber configuration is different from that of the previous specimen (Suzuki et al.. 1996a) and lends to cause
frequent Mode-II fiber disbonding. The epoxy matrix contains 6 parts Epicote 828 and 4 parts Epicote 878 (suppliedby Shell Epoxy Co. Ltd.). We used 8 sensors and a 10channel recording system comprised of two digitizers. Sevensmall resonant sensors with a small aperture diameler of 3
mm (pAC PICO) were used for channel #1 to #7. A conicalelement displacement sensor was used for channel #8. The
displacement sensor was mounted above the reduced section

3-D Fourier phase imagez

x-z view

Fourier Phase Contour map

yx
Fig. 4 The Fourier phase images of the three model objects.

~z
7nmTf~y

crp!i!l bMd !ItA • 0 os

mmIm!nGlasa

Saqlling Interv8I 5Ch-.
polnt 1024

~ oSource simulationI_oP-wave radiation

er-B~ Interval 200nS

r==...po~lnt 2048~_

oWavelet transform(3-0 image classification)

L.-.:l.ICS;;;;;ll!t:::;:::;;5;;s;-'

10nun

EWS

Fig. 5 The specimen configuration and experimental setup for AE signal acquisition.
Z3

while the resonant sensor #1 was placed on the opposite
side. diagonally from the displacement sensor. The outputsof sensors #1 and #8 were recorded on digitizer A, following the amplification by 4O-dB preamplifiers. The digitizer
A operated at 2oo-ns sampling interval with 4096 sampling
points at 8 biL This set of data was used for the wavelet
transform. The outputs from all the sensors (#1 to #8) weredigitized at 50-ns interval with 1024 sampling points at 10
bit by digitizer B. The data set of digitizer B was used forthe source simulation (#8) and P-wave radiation patternanalyses (#1 to #7). The system used in this research isbasically the same as that reported in a previous paper (Suzuki et aI.• 1996b).

Type-1 66 countType-2 16
Type-3 8
Type-4 10o

Signal classification by the presently proposed methods
was attempted following the wavelet transformation of signals from sensor #1 (from digitizer A). Digitizer A recadedover 2.000 events. Not all these events were synchronized
to those recorded by digitizer B. Of the lauer. the fracturemodes of 100 events were identified using the source simulation and radiation pattern analyses. 86 out of the 100
events had corresponding data for the wavelet uansformation. These were examined and classified by the three pl&posed classification methods for 3-D wavelet-transformed
signal. Subsequently. 600 events were sampled from 2000+
events. We have classified these signals using the previously classified events as reference. The results show thesame trend of fracture-mode variation with strain as that of
the 100 events identified by the source simulation and radiation pattern analyses

2
Straln, "-4Fig. 6 The stress-strain curve and cumulative classified AE
events for the 4 types of microfracture.
curve. The four fracture modes are shown in Fig. 7. TypeI event is Mode I fracture of a fiber or the matrix, Type-2 isMode I debonding of the fiber-matrix interface in the transverse direction, Type-3 is Mode I debonding in the thickness
direction, and Type-4 is Mode II debonding, respectively.
Also shown in the figure are the locations of identified AE
sources. Of the 100 events, 66 events were classified asType-l fracture, 16 events as Type-2, 8 events as Type-3
and 10 events as Type-4 fracture.
Figure 8 compares typical waveforms for the four types
detected by the displacement-sensitive sensor (#8). These arecompared with the simulated out-of-plane displacement
waveforms. Each was calculated following the identificationof the fracture mode and source location such that the Pwave rise time (ATpr) and amplitude (AUp) as well as relative peak positions best represent the observed signal.
Here, the source displacement rise time (ATr) and source
strength (Abj ) are adjusted by iteration. These values are
given in Fig. 8.

3.2 AE Signal Classification by the Source Simulation
Using the source simulation and P-wave radiation pattern analyses, 100 events recorded by digitizer B were classified into four types. Results of the classification as a fWIction of strain are given in Fig. 6 along with the stress-strain-§~--ffi!3-m_1_"_I~"'"

1-- . .•.
:'

-

BIll

_I~

_I~

1-- ,

'0

E0
.: 00

II1II

E0
.: 00

·'0 L::::~;::;:-:;::~:;:::::~
4l)·tS.lOoO 0 5 10 t5 III_a-....g

10

'0

-

.-

11l-ml-~n.~.
~ I0 IU- f .~-.-

-

1--

10

•

lo~~cl~•.~.

~:::=::::::;::::;::::::::::::;::.l
I::::=::::::::::::::::=~·'0 L::::~;::;::;::~::::::=:~
x._
x._
x._
x._
~ =11"ft;i(~Wf" ~I ! =IFfiMtJ~~1 ~ =R11BCuPJ$~3 ~ =II<·¥JI!~~-.mr-RI
40 ·tS.tO 00

x 0....I'0 IS III·1040·1$.,0.

0I

10 tS 20

4O~~oOOOtOtS20

11,,,,,"·10·111·1$·10 0$0

0 10 tS III

40·1$.1000 0

4O~~0$01IOtSlIl

40·15·'00$ 0}I .....

...

5 '0 IS III....

5 10 IS III

Fig. 7 Schematic representation of four fracture modes and the source location of the classified AE events.
24

..

-

-TIme , psec

TIme , J.IS8C

llme , IIS8C
Soun:e Polnl (3.03, 2.33, 1.60)
Propagallon DIstance 13.0Snm

SoIllClt Point (3.73, ·1.84, 1.28)
Propegal1on CisIanC8 13.42mm
ATpr -O.55~c
6Up • 7.62 X 1~' m

ATp' -O.S5l15'c
AUp-1.19 X1~'m--

II

SoIR8 Point (7.67, -0.15, 0.01)
Propagation DisIanc8 1U4mm
ATpt .0.60 IIS'-C
AUp - 6.17 X 1~3m

llme , IJS4IC
SoIR8 PoInI (·12.20,0.46, -0.04)
PropagaIIon DIslance 6.SOmm
6Tp' • 0.S5 IlS!'!c
AUp • 1.50 X

ia'm

"

Time , IIS8C
ATr • 1.28 psec
AbJ. ".16 x 1de m'

TIme , jJS8C
ATr • 1.29 J.IS8C
Abj. 2.38 x1dO m'

Fig. 8 Typical wavefonns for the four types detected by the displacement-sensitive sensor (first row). The simulared oot-ofplane displacement waveforms are shown in the second row so that the P·wave rise time (dTpr) and amplitude (dU,> as well
as relative peak positions match those observed. The best fit values of source displacement rise time (dT,) and source
strength (Ab-) are given below the simulated waveforms.
Table 4 Three sets of the P-wave polarity (+ or -) of the 8 sensors for 9 Type-4 events.
Event
No

7461
8245
8999
9560

10771
11114
11137
11265
11899

#1

#2

#3

--

--

Sensor number
#4
#5

#7

#8

- -- --+ - -+++ +++ - -- -- -++ - -- -- - -- - --

- --

#6

--- - -- ++- - -- +-+- -- - -- - -- - -- -- +++ - -- +++- -- - -- - -- - -+++ - -- - -- - ---- - - -- --- -+++ --- +++ --- - ---+ --- - -- - ---- --- -+-+++ --- -+++ --- --- --- +++ ---- ----------The uncertainty in the classification to Type-4 fracuue
is larger than in the others. The most important information in classifying AE events into Type-4 or Mode-II disbonding is the four-quadrant disuibution of P-wave polarity.
We basically classified the events inlD Type-4 fracture if oneof the sensors #1 10 #8 indicaleS the P-wave polarity opposite 10 the other sensors. This is because the polarity
changes depend on the SOlD'CC location and vector directionof displacement discontinuity. However. the determinationof the P-wave polarity of resonant sensor output is oflen
difficult due 10 low SIN ratios. Table 4 lists three sets of
the P-wave polarity (+ or -) of the 8 sensors for 9 Type-4
events. The number in the left column indicates the d>

+++
- --

- --

teeted time in seconds and is used as signal reference number. The polarity was independently detennined by three
individuals. Of the nine events. they agreed completely on
the polarity pattern of only fOUf events. In Ihe remainingcases, some differences were noted; e.g., for event 11114,
only one found the polarity of sensors #2 and #6 as positive. It appears that not all the 9 events in Table 4 arereally from the Mode-II disbanding. Incorrect assignmentsof the P-wave polarity also make an accurate source locationdifficult. and may result in a larger data scattering in Fig. 7.
If these events can be classified correctly by wavelet trans·
formation and 3-D image classification. a significant improvement of the signal classification accuracy is expected.
25

.. ..

10111'

>

j

•

2.
O.
02.

.

.-

>~[Br

.

... ....
,

!.;:
o

40
40
0.0

100

1000

150.0

l1nle • . 0.. . , . - - - - - - - - - .

o

o

ao

•

m

.,

zo

tao

40

eo

eo

tOO

111M. IJMC

1lme. ~"

0104110000100

OZOe.,aDUD

l1nle • . -11me • IJMC

Fig. 9 Typical waveforms detected by sensor # I and their WT images.
The reference images used for the MF method are simi-

0.028
0.039

lar to the contour map of WT coefficients shown in Fig. 9.

The contour maps of Laplacian images are used for the MFLI and these are obtained by the second-oroer differentiationof the averaged images. The phase components are used for
FPC. Examples of the images for the MF-Ll and FPC classification corresponding to AE waveforms shown in Fig. 9
are shown in the middle row and the bottom row of Fig. II.
respectively. Original AE waveforms are again illustrated
in the top row. Comparing the contour maps in Fig. 11
with those in Fig. 9, more small islands or craters are apparent. These may be useful in classifying the fracturemodes.

0.078
N

:J:

0.156

:::E

l; 0.313
c

'"t

It

0.825
1.250

2.500
20

40

60

80

33.2 Classification of the 100 AE Events
100

Tune. IISGC

Fig. 10 The 1792 pixels used for the 3-D classification.

33 Classification of the Wavelet Transformed AE Signals
by the MF. MF-LJ and FPC Methods

33.1 Reference Images
Selection of appropriate reference images is importantin the proposed scheme of signal classification. AE wavefonns (or their WT images) usually change with the source
positions. This requires careful consideration in selecting
the reference images. Typical waveforms and the WT images are shown in Fig. 9. In order to represent a range ofAE signals. we selected five waveforms for each fracturemode, taking into account the time of detection and correspondence with the source simulation results. The reference
images were prepared by averaging the WT images of thesefive. Figure 10 shows the 1792 pixels used for the 3-Dclassification. The pixels consist of 64 division in the timescale and 28 in the frequency scale. This level of representation appears to be adequate in characterizing the image features. although no other set of divisions has been examined.

Classification results of 100 events via source simulalion analysis of the displacement data are given in the left
two columns of Table S. Signal classifications with wr
were achieved for 86 events. For the remaining 14 events,
the corresponding AE waveforms for WT were not recorded.
Results are summarized in Table 5 (three right columns).
The MF method classified 53 events into 37 as Type-I, 7 as
Type-2,4 as Type·3 and 5 as Type-4. Event percentages(shown in the parentheses) agree well with those obtainedby the source simulation. but 33 events were mis-classified
to other types. The MF-LI method classified 55 events, ani
the event counts of each fracwre type are almost identical tothose by MF. The FPC method classified 74 events into 53
as Type-I, 10 as Type-2. 6 as Type-3 and 5 as Type-4. with
12 mis-classified events. Percentages of the classified eventsby the FPC are again similar to those by the source simulation. These results indicate that the FPC method is superior
to the other two methods because it classified more eventsof Types 1 to 3 and has less misclassification.
Even with the FPC method. however. we still have
14% misclassification. With respect to Type 4 events, thesituation is worse. Thus, we funher examined the cone-

---

_M+

*Me

•I E B r . 1 t 1
> 4

i!
a-,

i ....nne ....
I

nne ....
x.Zylgw

Ill.

ISUI

'nN .....

lOOJl'nIw ....

Itrl 8 181:;' 8 i[1ft. B
•

•

III

•

ClCOIllICD

III

Cl

10

III

1111

01D

'nIw . . . .

'nno . . . .

•••

,CID

'nIw._fInne._
x.Zy!gw

IfII:
•

III

..

III

10

'nno . . . .

lSinne._ t 161:' 8

8

o

IlD

III

Cl

10101111

020ClIOIOICD'nIw._

Fig. 11 Original AE waveforms from sensor #1 are in the top row. The images for the MF-L1 and FPC classification corresponding to mese AE waveforms are given in lhe middle row and lhe bouom row.
Table 5 Classification results of 100 events via source simulation analysis of Ihe displacement daaa
and three classification memods.
Sourcesimulation

MF

MF-LI

FPC

Type-l66 (66 %)Type-216 ( 16 %)Type-38 (8 %)
10 ( 10 %)Type-4
Classified events 100 ( 100 %)0
Others *Total
100

37 (70 %)
7 ( 13 %)
4 (8 %)
5 (9 %)

38 (69 %)
7 (13 %)
5 (9 %)
5 (9 %)

53 (72 %)
10 (14 %)
6 (8 %)
5 (6 %)

53 ( 100 %)55 (100 %)
31
86

74 (100 %)

33
86

12
86

* events classified to another fracture type.
spondence between me source simulation and FPC. Table 6
compares ahe correspondence of 86 events classified by boththe source simulation method (in the left column) and FPC
method. The next 4 columns represent how many events,

originally classified by the source simulation, are classified
to another fraclwe mode by the FPC. Fraclwe mode by
FPC is shown in the top row as Type-X*ref with X being 1
to 4. Among 57 events originally classified as Type-l byTable 6 Comparison of the correspondence of 86 events classified by boththe source simulation method (in the left column) and FPC method.
Source simulation
Type-I: 57
Type-2: 13
Type-3: 7
Type-4: 9
Total: 86

I Type-l * ref I Type-2 * ref I Type-3 * ref I Type-4 * ref I
4

53
2
1
3

59

Table 7 The FPC-detennined gj of 57 Type-I events.

5512
6021
6250
0
6990
0
© 7093
()
7149
L::..
7159
7410
0
L::..
7417
L::..
7542
x 8175
O· 8293
()
8528
8792
0
8794
0
@
8915
8948
0
X
9182
9340
0
()
9383
9584
0
9750
0
9768
0
9788
0
@
10132
0 10199x 10220
0 1029110358
00 10477
10568
0

0

R
R
R
R

R

0.591
0.570
0.685
0.597
0.287
0.500
0.418
0.560
0.424
0.406
0.062
0.293
0.379
0.603
0.430
0.657
0.375
0.424
0.409
0.261
0.520
0.703
0.721
0.721
0.368
0.560
0.295
0.576
0.527
0.646
0.340

0.088
0.466
0.654
0.096
0.169
0.024
0.411
0.157
0.412
0.292
0.311
0.030
0.190
0.132
0.082
0.190
0.060
0.441
0.079
0.009
0.405
0.233
0.114
0.114
0.078
0.333
0.357
0.106
0.227
0.128
0.229

0.292
0.428
0.568
0.227
0.128
0.243
0.257
0.204
0.241
0.357
-0.006
0.135
0.196
0.422
0.171
0.289
0.164
0.359
0.122
0.159
0.228
0.283
0.386
0.386
0.130
0.283
0.300
0.268
0.320
0.332
0.257

0
1
0
5
6

Table 7 The FPC-determined gi of 57 Type-I events. (cont.)[}~, .1 ~11 ~~21 ~ 11YPH I
()

0
0
6
1
7

10
0
0
14

[[] TUM,' 1~~11 ~21 ~31 ~~41
0

0.244
0.352
0.351
0.238
0.044
0.334
0.263
0.170
0.267
0.310
-0.003
0.083
0.271
0.112
0.125
0.211
0.058
0.162
0.128
0.020
0.341
0.181
0.133
0.133
0.166
0.384
0.195
0.191
0.210
0.182
0.063

L::..

©
L::..
@

©>
«)
@

0

©
L::..

0
0
@
@
X

©
©
©
«)
()

0
©
©
©
©

10874
10887
11004
11034
11084
11186
11226
11229
11281
11291
11349
11390
11395
11403
11437
11441
11452
11541
11561
11573
11596
11772
11868
11879
11929
1l~44

0.393
0.237
0.538
0.284
0.585
0.694
0.682
0.620
0.644
0.647
0.496
0.508
0.359
0.715
0.672
0.289
0.505
0.755
0.685
0.700
0.521
0.500
0.646
0.608
0.465
0.725

0.276
0.214
0.130
0.067
0.172
0.098
0.112
0.289
0.077
0.379
0.428
0.106
0.114
0.149
0.204
0.475
0.102
0.156
0.282
0.131
0.113
0.401
0.115
0.256
0.322
0.181

0.245
0.068
0.311
0.280
0.304
0.401
0.388
0.299
0.453
0.290
0.314
0.395
0.236
0.458
0.320
0.298
0.340
0.397
0.262
0.408
0.346
0.288
0.341
0.364
0.171
0.406

0.196
0.081
0.194
0.087
0.222
0.154
0.187
0.378
0.190
0.349
0.344
0.147
0.134
0.248
0.260
0.112
0.311
0.181
0.262
0.228
0.302
0.353
0.152
0.204
0.402
0.198

five events remain as Type-4, but 3 events were mis-classified to Type-l and one event to Type-3.
Next, we examined the faclOr gj of all events. because
this data may show the reasons of mis-classification am
establish the discrimination limit of the FPC. Table 7
shows the FPC-determined gi of 57 Type- I events. Letter"R" (for 5 events) in the left-most column implies that thesignals were used as the reference image. The symbols inthe second column indicate the following:

the source simulation (9 more did not have the corresponding AE waveform for WT), 53 events were classified asType-l by the FPC method. but 4 events were misclassified as Type-2. Two or one event belonging to Type2 or -3 was mis-classified to Type-I. Disagreement wasmost apparent for Type-4 (Mode-II fiber disbanding). Only
2B

=

@
well classified;.1 = classified correctly, but gj value for another type isclose;
X = mis-classified to the another type.
Number in the third column indicates the signal number(detection time). The well classified©-events total 47. Six.1-events indicated Type-I. even though their gj values wereclose to those of Type 2 or 3 and the highest gj of these
events were less than 0.5. Four signals indicated by X weremis-classified as Type-2. The waveforms are significantly
distorted and this appears ro be the cause of misclassificaLion. The gj values of 13 Type-2 events are shown in Table
8. Ten © -events give correct classification and two X·
events were mis-classified ro Type-I and one to Type4.
Table 9 for seven Type-3 fracture events indicates that one
X-event was mis-classified ro Type-I, but six others wereclassified correctly. Table 10 for the classification of Type4
events indicates five @-events with correct classification,
while three X-events of Type-I and one X-event as Type-3.

Table 8 The FPC-determined gj of 13 Type-2 events.

Table 9 The FPC-determined gj of 7 Type-3 events.

R ©

0
R 0
R X
R ©

©
R 0

X
X

©
R @
@

R @

©
R

X
()
X

x
R ©
R ©

©

9448
9468
9488
10249
10521
10782
10982
11057
11241
11505
11636
11636
11884

0.276
0.210
0.399
0.222
0.019
0.190
0.737
0.138
0.115
0.679
0.434
0.038
0.107

0.566
0.548
0.487
0.556
0.735
0.355
0.168
0.423
0.335
0.174
0.502
0.665
0.257

0.226
0.168
0.303
0.194
0.064
0.102
0.418
0.087
0.139
0.372
0.286
0.107
-0.019

0.060
0.051
0.126
0.072
0.026
0.044
0.179
0.070
0.350
0.176
0.381
·0.067-0.001The detailed examination of these gj values indicales
more cases of potential misclassification or uncertain classification. Excluding marginal events, 47 Type-I. 10 Type-2.
6 Type-3 and 5 Type-4 events can be unambiguously classified; i.e.• 68 out of 86 events. The main reason appears tobe the small number of events for each type (with the possible exception of Type 1). It is also possible that the results of classification based on the source simulation analysis of the displacement data contain uncertainty. Thus. thelimit of the FPC method needs to be tested with a larger
data population.
In order to eliminate the marginal event classification (as in
the case of six .1-events indicating Type-I), an additional
step of setting the lower limit of acceptable gj values can beused. The ratio of the highest to the second highest

0.436
0.311
0.115
0.433
0.175
0.249
0.312

0.193
0.252
0.112
-0.046
0.110
0.049
0.082

0.500
0.464
0.332
0.345
0.442
0.303
0.701

0.247
0.078
0.199
0.122
0.262
0.110
-0.017

Table 10 The FPC-determined gj of 9 Type4 events.

R 0

()

8438
8762
9006
9917
10823
11393
11679

R ©
R ©
X

R 0
R ©
X

7461
8245
8999
9560
10771
11114
11137
11265
11899

0.598
0.191
0.742
0.231
0.179
0.349
0.114
0.094
0.773

0.144
0.025
0.681
0.043
0.094
0.145
0.084
0.078
0.192

0.281
0.111
0.586
0.210
0.102
0.355
0.042
0.062
0.428

0.248
0.399
0.344
0.685
0.497
0.028
0.306
0.431
0.180

gj values may also be useful. In regan! to the larger scauer
in Type-4 analysis. the UD-GFRP sample used in thisstudy emitted only a limited number of Type-4 events aIl1
with lower SIN ratios. The source positions were also dis·tributed more than the other types. These made correct classification difficult In another study using a UD-GFRP
sample with a different fiber arrangement. we observed more
frequent Type-4 events with higher SIN ratios. In that case.
the proposed FPC method classified all types of events better including Type-4. This result will be reported else·
where.
3.3.3 Classification of 600 Events by FPC Method

From 2000 events recorded by digitizer A. we selecred
600 events by picking one event from every 3 (or 4 when
the third event is evidently noise) events in time sequence.
This is because of the limitation in the signal processing
capability. The same reference images as those used in theprevious section are employed except that for Type-3. As
the signal 9917 (Table 9) was found to be inappropriate as areference pattern, we omitted this signal.
By using the FPC method. these 600 events were classified to 364 Type·I. 151 Type-2. 44 Type-3 and 41 Type.
4 events. Due to the pre-selection process. no noise eventand 2 events for Type-3) should basically be omitted because of their ambiguous discrimination. However, lhese
events constitute only 7% of the classified events and do notaffect lhe general conclusion. The ratio of the maximum tothe nearest gj is usually higher than 1.4 for Types-l to -3.
This implies that the classification for Mode-I fracture isreliably performed by the FPC method. This is expected as
the source positions of Mode-I fracture are more localized.

350
300

§0

c.>

CIS

a..

250W

«

~

200 .~
150

E:::J
E

4. Conclusion

100 :::Jc.>50~~""''''''---!:o--''''''--+--1

In order to classify AE signals from composite materials, we examined their wavelet transforms as images via
three methods; i.e., the matched filtering of a given image(MF), that of the Laplacian image (MF-LI) and that of theFourier phase correlation (FPC). Results are summarized
below:oI) Eighty six (86) AE events from a resonant sensor wereclassified by the three proposed methods. These were compared with the fracture modes determined by the source
simulation analysis, which classified a larger set of 100
events. The FPC method was superior to the others in its
higher classification counts and discrimination capability.
Most AE events were successfully classified to the fracturemodes deduced by the source simulation; however, classification was difficult for Type-4 events with a small population and varying source positions.

was included. Cumulative AE counts of each fracture modeare shown in Fig. 12 as a function of strain. Comparing
this figure with Fig. 6, we find the general strain dependence of each fracture mode is similar to that revealed by the
analysis of 100 events. However, the present FPC analysisof 600 events showed higher occurrence of Type-2 fracture(25% of the total) than previously found (15-16%). This
resulted from the reduction of Type-l (70% down to 60%).
Since the number of events analyzed is larger, the present
results are probably more representative.

2) Classification of 600 events, monitored by a resonant
sensor but had no corresponding recordings of the displacement-monitoring system, revealed that the occurrence ofeach fracture mode agrees reasonably well with that predictedby the source simulation analysis of 100 events. Similarity
coefficients. gi'S, are generally above 0.5 and/or far from thenearestgj's (with ratios of 1.4 or more). The FPC method
was demonstrated to be capable of classifying the complicated fracture modes in composite materials. However, thepresent method needs a faster computer processing speedwith a large memory capacity. Faster wavelet-aansform
algorilhms and more efficient processing of WT data aredesirable for near real-time analysis of AE data.

The variation of gj values for the four types are shownin the percentage histograms of Fig. 13. Histogram peakfor Type-l event is located at higher gi. compared to thoseof other types. We found only 19 Type-l events (5%)
whose gj values were lower than 0.4. For Type 2, the gj
distributed over a wider range from 0.3 to 0.8. Type 3 appears to be well classified because of their relatively higher
gj values. For Type 4, most gj values are lower than 0.5.
This may be due to the fact that the waveform of Type 4(Mode-II disbanding) is significantly affected by the source
location as noted previously.
In classification, the relative gj values are more important than the absolute values; i.e.• how is a given gj value
different from the nearest gj value(s) of other types. Figure
14 compares the change of the maximum gj value for Type4 (the upper solid line) and nearest gj (the lower broken line)with the time of detection. In the figure, the numbers (1 to
3) next to the nearest gj indicate the other fracture types.
Although the absolute value of the maximum gj slightly
decreases with time. the ratio of the maximum gj to thenearest gj is higher than 1.4 for 15 of 41 events. These
events are indicated by 0 and undoubtedly are of Type-4.

Acknowledgment
We gratefully acknowledge the assistance Mr. Y. Haruaka. The authors also thank Showa Highpolymer Co. forproviding us with epoxy resin and to Teitsu Densi Co. for a
special PZT sensor.
References
Joseph L. Homer and Peter D. Gianino (1984), "Phase-only
Matched Filtering", Applied Optics, 23(6), 812-816.

Changes of the gj values for Types-I, -2 and -3 withtest time are shown in Fig. 15. The events with gj values
lower than 0.3 (8 events for Type-I. 10 events for Type-2
3J

40

40

Type-1

Type-2C:=;J( event count )

10

10

(1)

~.o

1.0

0.1

1.0

50

50

'#.
30

rD

l

CD

eCD 20Q.

10

0.8 0.9

1.0

Fig. 13 Percentage histograms of g, for the four fracture modes.

1.0..--------------------,• : ""'" ltIo ratio oJ (mlIlIIimum QI/II8QIOSt QI)1\igIIer than t.4

0.8

Fig. 14 Temporal variation of the maximum value of g. (thick line) and the nearest values for Type-4 fracture.
31

S. Kawala. T. Noda and S. Minami (1987), "SpeclI'al
Searching by Fourier-Phase Correlation", Applied Specuoscopy, 41(7), 1176-1182.

1.0r-----------------,
0.9

Type-t0.8
0.7

D. Marr and E. Hildreth (1980), "Theory of Edge Derection'" Proc. Roy. Soc. London, Ser. B, 8207, 187-217.

0.6

&0.5

HiroaJd Suzuki, Mikio Takemoto and Kanji Ono (1993),"A Study of FraCture Dynamics in a Model Composite by
Acoustic Emission", J. Acoustic Emission, 11(3), 117128.

0.4
0.3
0.2

0.1
O.O'-----""---"-----.......- -.......----'~....J
6000

8000

10000

Time,

12000

Hiroaki Suzuki, Mikio Takemoto and Kanji Ono (19900),"The Fracture Dynamics in a Dissipative Glass-FiberJEpoxy
Model Composite with AE Source Wave Analysis", J.
Acoustic Emission, 14(1), 35-50.

14000

S

1.0r-----------------.
0.9

Type-20.8

Hiroaki Suzuki, Tetsuo Kinjo, Mikio Takemoto and Kanji
Ono (l996b), "Fracture-mode determination of glass-fiber
composites by various AE processing", Progress in Acouslic Emission VIIl, eds. T. Kishi et at., JSNDI, Tokyo, pp.
47-52.

0.7
0.6

QO.5

-----------lI.L.~,l--..:.u.u.

0.4
0.3

0.2

Hiroaki Suzuki, Tetsuo Kinjo, Yasuhisa Hayashi, Mikio
Takemoto and Kanji Ono, with Appendix by Yasuhisa Hayashi (1996c), "Wavelet Transform of Acoustic Emission
Signals", J. ACOUStic Emission, 14(2), 69-84.

0.10.0'-----""---"------.......- -.......- - - - ' ' - - - 1
6000
8000
10000
12000
14000

Time, S

1.0',.-------------------.
0.9

Type-3

0.8
0.7
0.6

& 0.5
0.4

0.3
0.2
0.1
0.0 '-----""
6000"---_ _-'-_ _......._ _---''--.....J
8000
10000
12000
14000

Time.

S

Fig. 15 Change in time of g. values for Types 1-3 fracture.

32

Nondestructive Evaluation of Fiberglass-Reinforced
Plastic Subjected to Localized Heat Damage Using
Acoustic Emission
H. Nayeb-Hashemi, P. Kisnomo and N. Saniei
Abstract

I. Introduction

Fiberglass-reinforced composites are extensively used inelectronic and aerospace applications due to their high specific strength. In these applications, they are often subjected
to localized heat damage due 10 various sources such as electronic over heating, elecbic arching, and laser beams. In
onler to ensure their reliability, it is important to predict
their residual properties using nondestructive evaluation
techniques. Unidirectional fiberglass composite specimens
were manufactured using three layers of a fiber-glass
prepreg. Some of the specimens were subjected to a localized heat damage using a heated copper tip with a diameterof 12.5 mm at 360"C and other specimens were subjected toa laser beam operaIOO at 0.64 W/mm2 for various exposure
time. In addition, the number of Iaser-damaged spots variedamong similar specimens. The specimens were then subjected 10 tension tests while acoustic emission (AE) activities of specimens were collected.

Fiberglass-reinforced plastics have been used extensively in manufacturing components used in aerospace applications, electtonic boards, pressure vessels, etc. In theseapplications they may be subjected to 1Dldesired localizedheat damage. In order to ensure the reliability of these canponents, their residual properties should be investigatedusing an effective nondestructive evaluation technique.
Nondestructive evaluation of fiber composites using
wave propagation has resulted in a variety of techniques for
intrinsic property determination. In addition 10 typical examples of wave propagation velocity and attenuation measurements and the use of these values to compute elastic ani
damping constants (Kraui, 1963; Achenbach, 1973), thework by Vary and coworkers (1979a,b), Vary (1987a.b) am
Nayeb-Hashemi et aI. (1985, 1994) have indicated that because the presence of micro-flaws reduces the wave propagation efficiency of composites, it may be possible to c:onelate efficiency pammeters with strength parameters. Theirinitial efforts have focused on an ultrasonically measured
quantity called stress wave factor. Henneke and co-worlcers(1987, 1990) inttoduced several moments of the fIequeocy
spectrum as wave propagation efficiency. They relared theseparameters to the composite properties.

The AE activity of all specimens showed three distinct
regions. An early activity, followed by a relatively dormant
activity period and a high exponential activity before final
failure. The period of the donnant activity was independentof the contact heat duration of less than 15 min. However,the dormant period for the laser-damaged specimens was afunction of the nwnber of laser-damaged spots. The majority of the early activities for all specimens were re1aIed tomechanisms other than fiber fracture. The activity in the
donnant period for contact heat damage was mainly c0ntrolled by the fiber fracture, while for the tmdamaged andlaser-damaged specimens was still by the interfacial failure.
This could be justified since laser-damaged specimens conrained numerous damaged fibers leading to a significant interfacial shear stress. The failure modes of specimens further
supported this conclusion. The state of the damage in thecomposite was predicted using the AE-sttess delay concepL

Acoustic emission is another nondestructive evaluation
technique, which has been shown to have potential for pedieting the structural integrity of composites. Possible AE
sources are fiber fracture, fiber matrix debonding, fiber/fiber
rubbing, matrix deformation and failure etc. In addition topredicting the residual strength of the composite, acoustic
emission can indicate the source of the energy emission aId
thus the damage mode. Williams and Lee (1978) have pr0vided an extensive review of the literatures on the early AE
sbldies of fiber reinforced plastics. They concluded thatevents corresponding to the fiber fracture have high amplibide with small event dwation. In contrast, events cxmespending to fiber pullout and fiber/fiber rubbing have lowamplibldes with long event duration.

Received 20 May 1997; in fmal form, 20 February 1998. H.
Nayeb-Hashemi (hamid@coe.neu.edu) and P. Kimomo are affiliated with Department of Mechanical. Induslrial and Manufacturing Engineering, Northeastern University, Boston, MA
02115 and N. Saniei is with Department of Mechanical andIndustrial Engineering, Southern illinois University, Edwardsville, Illinois, USA.

IoumaJ of Acoustic Fmission
Volume IS, Number 1-4

Laksimi et al. (1994) investigated the damage and failure mechanisms in a glass-fiber/epoxy composite specimens
with and without a hole under monotonic tensile loading.
The damage initiation and failure mechanisms were studied
33

0730.()()S0/97JSS.00IIS033.()42
Copyright@I997 Acoustic £mission Groupevaluated and found to be 52-57%. The composite plate wasthen cut in the direction of fibers to make specimens of
12.5 mm width. Aluminum end-tabs were auached to thespecimen with a Loctite 404 adhesive.

by means of scanning electron microscopy and AE monitoring. It was found that the amplitude distribution of AE signals is an efficient tool for identifying damage mechanisms
such as micro-cracldng in the resin. fracture and friction of
the interface between fiber and matrix. and fiber pull-out.

To produce the heat damage. some specimens were exposed to 0.64 W/mm2 laser beam for 5 min. This Jl'(Xhx:ed
a damage spot of about 2 mm in diameter. The number oflaser-damaged spots varied from 1 to 5. Some of the specimens were subjected to the contact heat damage using aheated copper tip with the contact diameter of 12.5 mm.
The temperabJJe of the copper tip was maintained duringcontact at 36O'C using a temperature controller. The contact
time was varied from 1 to 60 min. Figure 1 shows a schematic diagram of specimens.

Despite this body of information. there is relatively
little research on the applicability of the above techniques in
evaluating the residual properties of composites subjected toheat damage. Kerr and Haskin (1984) studied the effects ofprolonged aging at temperature of 394-561K on the mechanical properties of unidirectional S3501 graphite/epoxy
and lIT-SnlO graphite polyamide composites. The results
showed that the loss in mechanical properties dwing thermal aging are related to both degradation of the resin matrix
and to a lesser extent to the graphite reinforcing fiber. Since
tensile strength is a fiber.oominated property. a postexposure tensile test was probably not the best choice for
evaluating the effects of thermal aging. A test that measured
the matrix sb'ength would undoubtedly have given results
more indicative of the degree of the material degradation.

Heat damage Spot.---.---.!. I

I~

203.2 - - - - I

1-25.4--f

Carrol et aI. (1993) investigated the effect of I-min.contact exposure at tempera!Ure of 427'-650·C on the flexural properties of a graphite/epoxy composite. FolD' different
techniques were used to evaluate the residual properties of
the composite specimens. It was found that C-scan ultrasonic testing is the most accommodating to variations in
response to heat exposure within a specimen.

Fig. 1 Schematic diagram of composite specimens. All
dimensions in mm

In a recent paper. Fisher et aI. (1995) evaluated thedamage caused by subjecting graphite fiber reinforced polymer composite to high temperatures. using a Iaset-indtx:ed
fluorescence technique. They showed that the heat damaged
material exhibits significam changes in fluorescence intensity and in the wave length of maximum fluorescence intensity. The extent of thermal damage was quantified by measuring the shift in the high intensity wavelength.

22 Acoustic Emission Experiment
Acoustic emission experiments were conducted using
an AET 5500 system. Two AE transducers, each with thecenter frequency of 175 KHz (AC-175L), were utilized todetect the events emitted from the specimen at a distance of
25.4 mm apart with the damage center located at the mid
distance. Each sensor was coupled with the worlcpiecethrough a couplant and held in position with the aid of 4
stiff spring as shown in Fig. 2. Data collection was focusedon the damage region only and all other AE activities werediscaItIed. AE event locations were established using thedifference in the anival times of the signals received by thetwo sensors. Calibration was perl'onned using pencil-leadfracture axxmJing to the ASTM SIaDdard (ASTM. 1994).
Some of the parameters measured during the tensile tests
were as follows: peak ampliblde in decibels, events duration
and rise time in J.1S. AE (ring-down) COWlts. and AE energy.
The detected signals were pre-amplified 60 dB with a total
system gain of 70 dB. A floating threshold of 0.3 V waschosen to eliminate the background noise.

The above investigations have all focused on the degradation of composites when the entire composite was subjected to high temperature. The pmpose of this paper is toevaluate the degradation of the composite specimen subjected to a localized heat damage using both mechanicaltesting and acoustic emission.
2. Experimental Procedures

2.1 Material
The material used in this investigation is an E-glass
fiber-reinforced epoxy provided in the fonn of prepreg by the
3M company. Three layers of prepreg was laid in a mold toform a unidirectional composite plate of 0.64 mm thickness. The composite was cured at 7S'C under 96 kPa pressure for I hr and air cooled undel pressure before being removed from the mold. The volume fraction of the fiber was

All tensile tests were perfonned using a sezva-hydraulictensile machine under stroke control of 2.5 J.UI1/s. This slow
loading rate was in order to increase AE sensitivity and to34

,=.=::.:=--A.
ColT'(:lUler
Oeta Analysis

5ensar1

AET 5500
Sensor 2

Fig. 2 Schematic diagram of AE data acquisition system.

.....

1 em

•

(a)
(b)
Fig. 3 Photograph of two damaged specimens: (8) contact
healed and (b) laser damaged.

I"'lg. 4 ~~M phoro-mu:rograph ot 8 contact heat damaged

specimen. White bar indicates 1 mm.

prevent AE event pile- up. It is possible that several simullaDeous events to appear as one or several subsequent events
to be missed if the loading rate is too high.

3. Results and Discussions
Figure 3 shows a typical composite with Iaser- andconlaCt-heat damage. Although visual examination may
show more discoloration of specimens with the CODlaCt-bealdamage, the scanning electron microscopy of the damDged
area revealed that the damage was mostly confined to thematrix material while for the laser.<Jamaged specimens bothmatrix and fibers were damaged, as shown in Figs. 4 and S.
Specimens subjected to prolonged contact-heat damage
showed extensive char fonnation and blistering, while thiswas not observed in me laser-damaged specimens.

rig.:> ::ibM photo-micrograph 01 a laser-heal aamageo
specimen. White bar indicates 100 JUD.
35

4000

3000

JEl

'"
Iii

.

w
c(

2000

-li
~

e

::0
(,)

1000

500

1500

1000

2000

Tensile Load. N

Fig. 6 Typical initial data of cwnuJative events vs. tensile load with initial exponential rise. followed by a linear behavior.
40000

r - - - - - - , , . - - - - - - " T - - - -......- - - - - - . - - - - - - ,undamaged

30000

~

w

~

~

20000

~

:I
(,)

10000

2000

4000
6000
Tensile Load. N

10000

8000

Fig. 7 Cumulative events vs. applied load for specimens with different number of laser damage spots.
40000

30000

aundamageb 5 min. exposurec 10mln.
d15mln.
e45min.
f 60 min•

e

a

.!l
c
COl

&ii

w
c(

~

20000

!!i'"

(,)

10000

2000

4000
6000
Tensile Load. N

8000

Fig. 8 Cumulative events vs. applied load for specimens with various contact heat duration.
36

10000

A typical initial cumulative AE~vent dam versus abe
tensile load is shown in Fig. 6. This behavior was independent of the damage slate and was similar to those reponed by Fang and Berkovits (1995) and Pocecho et aI.
(1997). The activity rose exponentially with the increase of
the applied load The cumulative events then followed alinear relation until specimen failed. Such a behavior iJldi..
cateS that abe collected data may have been influenced by abe
experimental setup.
'200 , - - - - - - - - - - - - - - - - - - ,IIIQ.
::i: '000...iii:;:)800

ic 600~ 400

In order 10 undersland the micro-mechanisms of damage

~

<II
C

~

200

0
0

2

3

4

5

Number of Laser Damaged Spots

Fig. 9 Tensile suength as function of number of laser damage spots.
1lXXl
III

Q.

::i:

~

:;:)

~
C)
c

r--.

llOO

--

-

llOO

.= 400II)11.
c: 200~~The period of the dormant activity was independent of abe
contact-heat damage of less than 15 min. However, thisperiod was a function of abe number of damage spots in Ihe
Iaser-damaged specimens. The strength of the composite
with the conlaCt heat damage again did not change significandy compared to undamaged specimen, for the CODlaCt
heat damage of less than 15 min. However, the composite
strength was a function of number of wer-damage spots(Figs. 9 and 10). This could be auributed to preserving mI
not preserving the fibers integrity in these two damage
modes. In the Iaser-damaged specimens, a significant number of fibers are damaged, while in the conlact heat no fibers
were damaged. The reducbon of the composite suength afrrr
45 min.-contact-heat damage could be related to the lack ofmatrix malerialto transfer the load unifonnly to fibers.

01.-------

o

10

20

--'
30

40

Contact Heat Duration, min.

Fig. 10 Tensile suength as fWlction of contact heat damage
duration.
Fiber-glass composite has a fairly high AE-event rare
when IeSIed in tension. Such a high rate can easily saturaIe
the AET 5500 bllffer. The AE events will be aben coDected
at the rate of data transferred from abe buffer 10 the hard disk.
which is constanL In order to coDect the total AE activities.
tests were periodically balted and all ~ dam were
b3I1Sfened to the hanl disk. Tests were then reswned withthe full available buffer. Following this procedure, the linear region of the cmnulative events versus applied load did
not appear. Figures 7 and 8 show the cumulative AE events
versus load for composites with Iaser-damaged spots aRI
CODIaCt-heat damages.
Figures 7 and 8 show that the AE activities of thespecimens rose with the applied load. This was followed bya relatively donnant activity period and an exponential increase in the number of AE events before specimen failure.

growth upon tensile loading in these composites, the AE
events corresponding 10 the three regions in the cumulative
event curves were analyzed for abeir peak amplibJde, AE
event duration and AE counts. Figure 11 shows the three

regions med to analyze the events. Figure 12 shows AE
events versus event duration for undamaged composite, laserdamaged and CODlact-heal damaged specimens. Since fiber
fracture produces events with the lowest event duralion,
events with less than 70 ms was asswned to be due to fiber
fracture. This assumption may be justified since the majority
of AE activities are due to fiber fmcbIre with event durationof less than 70 ms in region 3. Figure 12 shows that, inregion I, the majority of AE events have event duralion of
more than 70 ms. Therefore, in region I, the dominant micro-mechanisms of the damage growth is other than fiber
failure. This mechanism could be fiber-mattix debonding.
delamination, matrix cracking. and fiber pull-oUL The majority of the AE activities in region 2 of the conlaCt-heat damaged and undamaged specimens could be awibuled 10 the fiber
fracture, while for laser-damaged specimens they are still due
to interfacial failure. The AE events in region 3 are mainly
controlled by the fiber fiac:ture. These results were further
justified by considering AE events versus AE counts in thethree regions. Events corresponding to the lowest AE counts
were auributed to fiber fiac:wre, as shown in Fig. 13. Figure
14 shows the failure mode of a CODIact-heat-damaged specimen and a laser-damaged specimen. This figure indicates fiber
fracture in the contaet-heal-damaged specimen was the dominant damage mechanism. while the Iasec-damaged specimen
contained numerous interfacial delamination prior to final
failure.
Williams and Lee (1983) have introduced a methodology that allows one to predict the residual suuctmal poperties using AE dala. An AE-delay parameter was defined as aparameter value that was required to produce a specified lowbase-line level of cumulative AE measure. The [parameter]
can be load, suess, strain, temperalUre, etc. Using this c0ncept, Ihe load corresponding to total cumulative AE-events

Undamaged
30000...!:;!I

I

/

20000

~f

J'
10000

rogiOn I

00

roglon II

2000-

rogion III

8000
Tonollo 1.DGd. N

8000

10000

1 Spot Laser Heat Damaged
30000

o 0~----::2=OOO=-....l...--"':':.OOO"!="-...l--""'8000,.".,.".----.J8000
To""llo I.0o<I. N

15 Min. Contact Heat Damaged

30000

r--------.-------.-----..:;:...-------,

rog~O" •roglon III

o O~-=::.....J._::2=OOO=----""':'_==--..l---8000~,....-------l8000
Tonallo Load. N

Fig. 11 Three regions of cumulative events vs. tensile load, used to funher study the damage growth mechanism.

4. Conclusionsof 5000 were collected from Figs. 7 and 8. The composite
damage Slate was defined as
11 = 1 - (UI'S)r /(UTS)ud(1)

Acoustic emission technique was used to assess the
structural integrity of the composite subjecled to JocaJi7P1i
heat damage from contact heating and Iaser beam. It was
fO\Uld thatand the nonnalized load parameter required to produce 5000
AE events relative to \Uldamaged specimen was defined as
NP = (load parameter)d'(load parameter)ud, (2)1) contact heating produced significant discoloration, charand blistering compared to the laset-damaged specimens. Despite this, the strength of the composite dii
not change significantly because fibers were left intact
during contact heating. In contnlSt, the sttength of thelaser-damaged specimens was changed and reduced with
the increase of the laser-beam exposure.

where (UI'S)r is the residual suength of the damaged specimen and (UI'S).uf is the sttenglh of the undamaged specimen. Figures 15 and 16 show the damage Slate versus normalized load parameter for the contact- and lasec-damaged
specimens. The results show that this technique can be med
to predict the residual sttenglh of the laser-damaged specimens. while the data for the contact-heat-damaged specimens
are somewhat scaltered.
38

E"III~_iIlRe9iOnl

I

.s

:~r
120

:

400

1IIl':Lo__.'-....-._-...IID

o10

800

e-eur-....
l5CO •

700-

100

430

560

560

.,i sao
240

120

o

i1

.20

oS

210

140

o

200

400

800

_eur-....

>1000

.00
800
_
EwtIl 0utalJ0n....

>1000

140

70 - - - - - - - - - 200
.00
800
100
>10ll0
E_ Out8lion....

200

400

800

800

'ICOO

E..IllDutllliOn....

EwnI 0ut8li0n DlsIrilIlllion in Region III

5000

2500

4000

2000

2400

1500

• 1_

i
OJ

lCOO

-

!., 1200

1000
500

200

400

800

_

>1000

200

E.enlDlnliDn...'Undamaged

1 Spot laser heatdamaged

400
600
100
E.enl 0utalJ0n....

>1000

15 Min. contact heatdamagedFig. 12 Distribution of events by event duration for an IDldamaged. a laser heat damaged and a contact heat damaged specimen.
Ring Down COo.rlIs III Reglotll
12OO~'--

Ring Down CaurU ~ For ReP> I

••

500

I

960t

.,i .,

;

.,~

240

240

o

0
80

120

RinV lloion CcurlIS

160

i

40

3500! -- ----

1200

t

800

UI

400

0
0

40

80

120

Ring Down CcurlIS

160

'200

~i
0

40

_In

80
120
Ilulg Dawn CClunIs

Il"'G Down CCUtllS2000 I

400

300
200
100

0

'200

I_
OJ

'l!

.,~

430

120

40

·

no

0
160

>200

0

1500

.,i lOGO

·

500
0

80

120

Ring Down Cowlls

160

0

'200

.

~

120

160

'200

in Region lU

6000

-

6000

2000
0

0

Undamaged

60

RJno Down CClunIs
Rklg Down co=s _

OJ

1.1lO
>200

.0

10000

.,I 2800
180

.200

2000

4200

120

160

25ClO

5eOO

80

120

RJno 0Gwn e-u DisIt-.. in ROIIio<lll

Region II

Rklg Down CouI'.Il_1II Region III

Ring Down Ccu...

80

RitO Down COwU

7000 -

40

40

0

40

80

120

Ring Down C4<ltIts

180

1 Spot Laser heat

.200

0

.0

80

120

RJno Down CCUIIII

160

'200

15 Min. Contact heatFig. 13 Distribution of events by AE COlDlt for an undamaged. a laser heat damaged and a contact heat damaged specimen.

s

1 em
(a)

1 em
(b)

Fig. 14 Photograph of the failure modes of composites with (a) contact-heat damaged and (b) laser-heat damaged.
2) Post-damage growth of heat-damaged composites subject to tensile load were evaluated by collecting the AE
activities. The cumulative AE events curve showed
three distinct regions. An early activity. foUowed by arelatively dormant activity, leading to an exponential
increase in AE event activities prior to specimen failure. The period of the donnant activity was independentof contact heat duration, but was a function of numberof Jaser-damaged spots.
3) The early AE activity was related to failure mechanisms
other than fiber fracture. However, the majority of AE
activities in the third region were related to fiber Dacture.

4) Fracture mode of specimens damaged by the contact
heat was different from the specimens having laser heatdamage. In the lasec-bumed specimens, there were significant number of cracks (delamination) running along
the specimens prior to specimen failure. These were absent in the contaet-heat-damaged specimens.
5) AE delay parameter seems to indicate a trend in theresidual strength of composite subjected to heat damage. This trend may be used to predict the residual
strength of the composite.

.......-____,

1.0 ~----_'T------..------___.-------...,..--

a..

0.8

z

..:

*
E

~ 0.6

a..

~
CD

o

w
«
0.4

~E
o

Z

0.2

~0.0

0.2

0.4
0.6
Damage Parameter, 1'1

0.8

10
.

Fig.lS Nonnalized stress delay, NP, as function of damage parameter. 1). for laser heat damaged specimens.
1.0 A---------.----.......-------r-------r---~--____,

~

0.8

..:

~

~

eca

0..

~ 0.6
Q)

o
w
<

~

Cii

g 0.4

Z

0.2
0.00

0.10

0.20
Damage Parameter, 1'1

0.30

0.40

Fig.16 Nonnalized mess delay. NP. as f1D1ction of damage parameter. 1). for contact heat damaged specimens.
References

E. A. Carrol, P. A. Mehrkam and R. Cochran (l993). "Heat
Damage Evaluation of Painted Graphite /Epoxy Composites." Proc. Con/. on Characterization and NDE of Heal
Damage in Graphite Epoxy Composites. April 27-28. Orlando. Florida. pp. 113-124.

J. D. Achenbach (1973). "Wave Propagation in Elastic Solids." North-Holland Publishing Co•• Amsterdam, London.
ASTM (1994), ASTM Standard Guide for Detennining the
Reproducibility of Acoustic Emission Sensor Response.
E976-94, ASTM. Philadelphia, PA.

J. C. Duke. Jr.• E. G. Henneke. M T. Kiernan and P. P.

Grosskopf (1986). "Ultrasonic Stress Wave Characterizationof Composite Materials. NASA CR 4187. May.
1t

41

A. Vary and R.F. Lark (1979a), "Correlation of Fiber
Composites Tensile Strength with Ultrasonic Stress Wave
Propagation Factors," J. of Testing and Evaluation, 7(4),185-191.

D. Fang and A. Berkovits (1995), " Acoustic Emission
During the Tensile Deformation of Incoloy 901 Superalloy," J. of Materials Science, 30, 3552-3560.
W. G. Fisher, J. M. E. Storey, S. L. Sharp, C. J. Janke,
and E. A. Wachter (1995), "Nondestructive Inspection ofGraphite-Epoxy Composites for Heat Damage Using Lm1er-

A. Vary and KJ. Bowles (1979b), "Use of an Ulb'aSOnic
Acoustic Technique for Nondestructive Evaluation of Fiber
Composite Strength," NASA TM 73813, Feb.

Induced Fluorescence," Applied Specaroscopy, 49(9), 12251231.

J. H. Williams and S.S. Lee (1978), •Acoustic Emission
Monitoring of Fiber Composite Materials and Structure.," J.
of Composite Materials, 12, 348-370.

E. G. Henneke (1990), "Ultrasonic Nondestructive Evaluation of Advanced Composites," Nondestructive Testing ofFiber Reinforced Plastic Composites, ed. J. SummerscaIes,
Vol. 2, Elsevier Applied Science, London, pp. 55-159.

J. H. Williams and S. S. Lee (1983), "Acoustic Emission
Characterization Using AE [Parameter] Delay," Materials
EValuation, 41, 961-966.

E. G. Henneke, J. C. Duke, Jr., and R. C. Stiffler (1987),"Characterization of the Damage State of Composite Laminates via Acousto-Ultrasonic Technique," eds. J. D. Achenbach and Y. Rajapakse, Martinus Nijhoff Publishers,
Dordrecht, Netherlands, pp. 217-235.

Appendix
Properties of fiber and matrix used in this study

J. R. Kerr and J. F. Haskins (1984), "Effects of 50,000
Hours of Thermal Aging on Graphite/Epoxy and GraphitelPolyamide Composites," AIAA Journal, 22, 96-102.

Elastic Modulus Tensile Strength Poisson's ratio
GPa (Msi)
GPa (ksi)
Fiber 72.4
Mattix 10.5

A. Kraut (1963), " Advances in the Theory of Anisotropic
Elastic Wave Propagation," Review of Geophysics, 1(3),401-448.
A. Laksimi, X. L. Gong and M. L. Benzeggagh (1994),"Analysis of Damage Mechanism in a Centrally Notched
Glass-Fiber I Epoxy Plale," Composite Science and Technology, 52, 85·91.
H. Nayeb-Hashemi and J. Rossettos (l994), " Nondestructive Evaluation of Adhesively Bonded Joints by AcoustoUltrasonic Technique and Acoustic Emission," J. of Acoustic Emission, 12(1/2), 1-14.
H. Nayeb-Hashemi, M.D. Cohen and T. Enurk (1985),"Evaluation of Fatigue Damage on the Mechanical Properties of Fiber Reinfon::ed Cement Pastes''' J. of Cement andConcrete Research,15, 879-888.
T. Pacheco, H. Nayeb-Hashemi and H. M. Sallam (l996),"Acoustic Emission in a Nextel 440 Fiber Reinforced 6061
At Composite," J. of Acoustic Emission, 14(2), 85-95.
A. Vary (1987a), "Acousto-Ulb'asonic," Nondestructive
Testing of Fiber-Reinforced Plastic Components, Vol. 2,
ed. J. Summerscales, Elsevier Applied Science, London,
pp.25-54.

A. Vary (1987b), "The Acousto-Ulttasonic Approach,"NASA Technical Memorandum 89843, April.

42(10.5)(1.53)

3.45

(500)

0.2
0.28

An Investigation of LLiders Band Deformation and the
Associated Acoustic Emission in AI - 4.5°/~ Mg Alloys
D. L. Armentrout and S. H. Carpenter
breadth ratio of 12 or less and with a breadth of 5 mm or
more, the shear stress in a stiff tensile machine is best accommodated by nucleating a series of Liiders bands characterized by multiple yield points. Under Lomer's stiff
specimen conditions. Luders-band deformation reveals asurface characterized by narrow depression bands that format roughly 45· to the direction of the applied stress. With
each sudden drop in stress or yield point. a single depression band forms. As the sample deforms. many depression
bands form, evenly spaced across the gage section of thesample.

Abstract
We have conducted a detailed investigation of nonuniform plastic deformation and acoustic emission (AE)associated with the initiation and motion of Liiders bands inaluminum-magnesium alloys. A series of Al-4.5%Mg alloys with increasing amounts of Fe, Si and Mn was investigated during deformation in uniaxial tension. The elementsof Fe. Si and Mn are common non-soluble impurity elements that form constituent particles in aluminum alloys.
Increasing the impurity elements concentration produced arefmement of the grain size of the test materials, resulting
in changes in the mechanical properties. All of the samples
tested. irrespective of the impurity concentration, exhibited
unstable Liiders-band deformation, as characterized bymultiple yield points. High-speed digital instrumentation
was used to capture and analyze the stress. strain and AE
behavior in detail during individual yield points. From
careful analysis of these data. the following has been determined: 1) three separate and distinct regions during thedrop in stress associated with a yield point have been identified. 2) a distinct burst of AE activity is associated witheach yield point. 3) the AE activity actually begins a shorttime before any decrease in stress is observed, 4) the strain
rate during a drop in stress of a yield point is estimated to
be as high as 30 S·I and 5) an excellent correlation exists
between the magnitude of the drop in stress and the AE
generated for a given impurity concentration.

A large number of metallurgical variables strongly influence Liiders-band deformation. At least fifteen different
experimental test and material variables have been investigated and reported on in the literature (Armentrout. 1991;
Hall, 1970; Morris. 1974; Sperry. 1963; Pink. 1989; Tabata
et al.. 1980). Of the material variables investigated, thegrain size of the test material appears to have the largest
effect on whether or not Liiders deformation occurs (Hall.
1970). In order to have a well-defined planar front established and propagated uniformly along the gage section, theratio of the minimum specimen dimension to the grain sizemust be greater than 25:1 (Moon, 1971). McReynolds(1949) found the LUders front becomes more distinct as thegrain size decreases. In the present investigation, the concentration of Fe, Si and Mn was increased to produce a refinement of the grain size in the test materials.

1. Introduction

In a sample with multiple yield points, the AE generated during yield deformation can be compared and studied
many times in the deformation of a single sample. Multiple
yield points in the same sample can be more easily compared than a single yield point in multiple samples. At sufficiently low strain rates, LUders bands are distincL Whenthe Liiders band forms. the deformation rate in the band issignificantly higher than the applied strain rate. Digital
signal instrumentation was used to capture and analyze thestress, strain and AE behavior in detail during individual
yield points. The digital data allows enhanced analysis andadditional understanding of the LUders-band deformation
phenomena. Certain characteristics of the generated AE andcharacteristics of the individual stress drops were investigated to provide a better understanding of Lilders deforma·tion. the mechanisms involved and the sources of AE operational during Liiders deformation.

The focus of this investigation was to examine in detailthe non-uniform plastic deformation associated with Liiders
deformation in aluminum-magnesium alloys. In addition to
the mechanical properties. the acoustic emission (AE) generated during Lilders deformation was measured and analyzed to aid in the characterization and understanding of the
non-uniform plastic deformation. Liiders deformation has
different appearances on the surface that are effected by thegrain size and the sample shape. Lomer (1952) showed thatfor fine grain. stiff tensile specimens with a length-toReceived 9 September 1997; in fmal form. 12 December 1997. D.
L. Armenttout is afflliated with Metallurgical and Analytical Instrument Consultant. Golden, CO and S. H. ClUpenter (scarpont@du.edu) is with Physics Department. University of Denver.
Denver, CO 80208.
Journal of Acoustic Emission
Volume IS. Number 1-4

430730.()()S9197/SS.llO/1S043.{)S2
Copyrighl@1997 Acoustic Emission Group

'2, Equipment and Experimental Procedure

both the AE and mechanical test equipment. A DuneganlEndevco S9204 resonant transducer (140 kHz) waschosen primarily because of its high sensitivity. The outputof the pre-amplifier was divided into an amplifier and asignal acquisition module (Acoustic Emission Associates;
also known as a Phoenix system). The amplifier (60 dBgain) also filtered the signal between 100-300 kHz, output
of which was then fed into two rms voltmeters (Hewlett
Packard 3400A), and a digital recorder. The signal acquisition module is a device that measures input signal characteristics and other test parameters, recording the data in apersonal computer. The digital recorder offers a wide selection of voltage ranges and capability of recording over a
million digital values. Two channels of data on the digitalrecorder were used to record the AE signal and the appliedstress. Two completely different setups of the digital recorder required during the investigation to effectively capLUre with sufficient accuracy each of the two different inputs. For a more accurate AE signal measurement, thesetup recorded 514,288 (512K) samples per channel withthe sampling interval of 1 IJ.S. For a complete measurementof a stress serration, the setup was 512K samples per channel with the sampling interval of 22 J.lS.

The materials used in this investigation were obtained
from the Alcoa Aluminum Co. The samples were suppliedoriginally in the H19 condition and were annealed for 1 hr
at 616 K prior to testing. Tensile specimens were machined
to dimension, as shown in Fig. 1 and then deformed inuniaxial tension at a constant displacement rate. Table I
lists the chemical composition and grain size of the test
materials. The sample materials have a relatively constant
Mg concentration (approximately 4.5%) with increasing
concentrations of Fe, Si and Mo. At room temperature, Fe,
Si and Mn are essentially insoluble in aluminum (Moffatt,1995). Optical microscopy of the teSt materials revealed
constituent particles on the order of I IJ.m in diameter separated by distances larger than the average grain size. The
addition of impurity elements caused a refinement of thegrain size through formation of constituent particles, see
Table 1. A linear relationship between the inverse squareroot of the grain size with impurity concentration was foundas shown in Fig. 2.

1.91

--

~

0.6-r------------~.........,

3:
c

--

Fig. 1 Schematic diagram of tensile specimens used in thisinvestigation. All measurements are in centimeters. The
sample thickness was 0.51 mm.

-coa

An MrS testing machine with a mechanical stiffness of

B

~

c

c
o
o

2.5 x lOS N/mm (many times greater than the stiffness ofthe test samples) was used for the uniaxial deformation
tests. A 25-kN load cell was used, which allowed for goodresolution of the load and a quick response time. The
maximum resolution of the load was 0.05 N with an overall
accuracy of better then ±O.3%. A 100-mm-long strain gage
was used with a resolution of at least 3.2 x 10"7. The accuracy of the strain gage was better then ±D. I% over the entire
range of 1.27 x 10"2 after calibration.

0.3

~

'i:

::J

Co

.5

O.O-t--gr:",.".---r--.......---r-----1

200

350
(Grain sizer1/2 m-1/2

Fig. 2 Plot of impurity concentration in wt% versus theinverse square-rool of grain size.

The AE equipment used was standard, commerciallyavailable equipment. Figure 3 shows a block diagram ofTable I Grain Size and Chemical Composition of Alloys by Weight Percent
Samnle tVDe
1
II
11I
IV

Mil
4.56
4.47
4.45
4.47

500

Fe
.00
.00
.11
.28

Si
.00
.000
.06

.13
44

Mn
.00
.14
.15
.15

Grain size (urn)
18.4
11.9

7.9
5.3

Table 2 Properties of Alloys with -4.5%Mg
Sample
type
I

n
III
IV

au

EL

(MPa)

(0/0)

101.6
112.1
144.0
150.4

0.738

%

Mg

impurities

%
4.56
4.47
4.45
4.47

<0.02
.14
.32
.56

d
0·00)

1.13

18.4
11.9

1.94
2.17

5.3

7.9

Strain' Gage
Conditioner
DigitalLoad Cell
Conditioner

Oscilloscope
rms meter 1

HydraulicGrips

rms meter 2

:;

~

~

SignalSen(1)E :::Ja; Acquisition cue.
c:... .5cu~
Module Q.
Fig. 3 Schematic diagram of experimental setup and equipment used in this investigation.
The AE equipment was carefully calibrated before perfonning the uniaxial tensile tests. A measurement of thesystem noise was made using a stainless steel specimen
known to generate essentially no AE, with similar dimensions to the test samples. In all cases. the background andmachine noise were maintained at a low level compared to
the AE generated in the test specimens during deformation.

stress drop for a number of tests of the different alloys.
Table 3 provides a summary of the data displayed in Fig. 5.
The values given under k.6o>l are the average magnitudeof the drop in stress and the values given under <Vrms> arethe average peak values of the nns voltage burst during the
LUders deformation. The linear least-squares fit of the datais plotted in Fig. 5. The slope of the lines and the linear
correlation coefficient, L e • of the fit are also given in Table

3. Experimental Results

3.

Figures 4a-d show the applied stress and the AE. intenns of the nns voltage (voltage referenced to the transducer output). as a function of strain for increasing concentrations of Fe. Si and Mo. Multiple yield points were observed for all samples tested, as seen in the figures. Table 2
lists average mechanical properties of the different test materials. The value au is the average value of the upper yieldstress, EL is average value of the total plastic strain during
Liiders defonnation (Lfiders strain), and d is the averagegrain diameter of the test material. A review of the data
shown in Table 2 indicates that the upper yield strength andthe Liiders strain increase with increasing impurity concentration. The grain size of the test material, as expected,
decreases as the impurity concentration increased, as shown
earlier in Fig. 2. An examination of Fig. 4 reveals that foreach individual yield point there is a corresponding burst of
AE activity. Figure 5 is a plot of the AE, given as the peakvalue of the nns voltage burst. versus the corresponding

The data shown in Fig. 5 and Table 3 indicate a direct
proportionality between the magnitude of the stress drop
and the resulting AE; i.e., the larger the stress drop thelarger the resulting AE. However. the proportionality constant and the scatter in the data between the stress drop andthe AE are strongly effected by increasing impurity concentrations. For the binary AI-Mg alloy (sample I, impurity
concentration <0.02 wt%, Fig. Sa), an excellent correlation
between the AE and me stress drop is observed. The addition of 0.14 wt% Mn increases the magnitude of the suessdrop slightly and decreases the average AE slightly (Fig.
5b). The addition of 0.06 wt% Si and 0.11 wt% Fe produces larger average stress drops and smaller average AE(Fig. 5c). The scatter of the data increases markedly from
Fig. 5a to Sc. Doubling of the Si and Fe concentration has
only a slight effect on the average stress drop. but causes a
decrease in the rms voltage peak and enhances the scatter,as shown in Fig. 5d. A weak correlation between the AE
45

200

60

ao.- Sample Type I

-

150

45

C.

C)

30 co

"6

CD

50

>
rn
:i
15 a:

0

0

"-

CiS

co

c. 100

30

:i
en
en

rn

co

Q.

:5
en
en
CI)
"-

US

CD

:i
en 100en!

->::::1..

C'lS

-

~
CD

C)

as

"6

50

15 >
rn
:5
a:

0

0
Co- Sample Type III->-::::1..

100

30

CD

C)

as

"6

15 >
rn

50

:i

a:

0

0

-

do- Sample Type IV

30

-

CI)
C)

as

en

~

en

!
US

->::::1..

~ 100
:5

0

>

50

15 rn
:5

a:

0-+--0.0

0.5

1.5

100

2.0

2.5

o

Strain (%)
Fig.4 Plots of applied stress and AE versus strain for increasing impurity concentrations: a) type I sample, b) type II sample.
c) type III sample, d) type IV sample.
46

25s.- sample Type I-

b.- sample Type IIc.- sample Type III

d.- sample Type IV

20....~ 15
GI
til

!!
'0

10

>

(I)

:e
II:
5

0

20

>
....:t 15
GI
til

->
III

'0

10

(I)

:e
II:
5

O-t-...,........,....-.....--.....--.,..--.--+---.---r--.---,.--.-.,r--r-r---i1068o 246 8 o 2 4Stress Drop (MPa)Stress Drop (MPa)Fig. 5 Plots of the peak values of the nns voltage versus the magnitude of the corresponding slress drop for increasing impurity concentrations: a} type I sample, b} type II sample, c) type III sample, d} type IV sample.
Table 3 Statistical Propenies of Slress Drops of Alloys with -4.5% Mg
Sampletvne

Mg%

1<l1cr>1<Vrms>

%

(MPa)

(uV)

I
II

4.56
4.47
4.45
4.47

impurities
<0.02
0.14
0.32
0.56

3.91
4.62
6.63
6.60

13.8
13.4
8.95
6.55

III
IV

and magnitude of the stress drop has been reponed in an
earlier work on 3004 Al alloy with higher impurity concentrations (Armenrrout and Carpenter, 1991). Figure 6
shows the percentage change of both the average stressdrop and the associated average nns voltage peak as afunction of total impurity concentration.
Using the digital recording system, the time sequenceof the stress drop and AE behavior can be studied in detail The stress and the resulting AE (nns voltage in reference to the transducer output) are plotted, as a functionof time, for a yield point with a double drop in stress from

Slope

Lc

2.23
1.91
0.75
0.72

0.822
0.652
0.469
0.310

a type III sample in Fig. 7. A double drop in streSS occurredin less than 10% of the stress drops. The signal voltage scaleis enlarged to show details of the signal. The graph insen in
Fig. 7 shows the AE signal plotted on scale. The stress has
been corrected for the time delay, as will be discussed later,
and processed to remove 30 and 60 Hz signals from the loadcell conditioner. Examination of Fig. 7 reveals that the majorpart of the stress drop occurs in less than 10 ms. The higher
frequency oscillations that begins after the steepest portion of
the drop in stress are mechanical resonance of the system
excited by the stress drop.
47

An expanded view of another suess drop from type III
sample is shown in Fig. 8. This stress drop can be separated
into three distinct regions or zones. The ftrSt region. arbiuarily called band initiation. occurs fim over a short interval of time. The initiation region begins as the AE signal
increases above the noise level. The suess slightly decreases in the initiation region. The second region. called
the band-slip region. corresponds to the short time interval
characterized by the rapid decrease in suess. This region
has been called the band-slip region because the primarydeformation mechanism of a stress serration is slip. The
peak amplitude of the AE signal occurs during the band-slip
region. A third region. called the kink-resolution region. ischaracterized by a slower decrease in stress to a minimum
value. For a sample that is constrained at the grips. a stressor kink develops that opposes slip. The kinking force
builds during slip until it is large enough to stop the slip
band. The duration of the AE signal closely corresponds tothe time required for the suess to drop to its minimum
value.

1.8
Ct.

rn

2

1.0

'0
'0

,g

CD

.c

.E

~

.:.:

m

CD

1.4
0.5

CD

Q

a.
CD
.c
.5

CD

C)

C

C
CD

CD

.c
(,)

.c
(,)
::::e
o

:::e
0
1.0 ...----....,...--..---+ 0.0
0.0
0.3
0.6

Impurity concentration (wt %)Fig. 6 A plot of the percent change in the load drop and thepercent change of the peak AE as a function of impurity
concentration.

137...-----------------------

135

131

129

0.5

5

0.4

:>

.5.

0.3

iii 0
c:

CI

0.2

i:i)

:;- 0.1

-5

g

(ij

0.5

0

Time (sec)0c.2l -0.1
en

-0.2
-0.3
-0.4
-0.5

0

0.1

0.2

0.3

0.4

0.5
0.6
Time (sec)

0.7

0.8

0.9

1

Fig. 7 Plot of a double stress drop and associated AE as a function of time for a type III sample. recorded at a sampling interval of 11 JlS.
48

136 ~1>.J1'~-Band Initiation Region

I

I
I
I

Band Slip Region

I

I

134

.,'

I~I----- Kink Resolution Region

130

128

6
4

-

2

>

E

Cij

c:

0

C)

I
I
I
II
I I

US
-2

I I
I I

-4

I I

-6

I I
I I

0

10

20

30

40

50

60

70

80

90

100

Time (ms)Fig. 8 An expanded view of a single suess drop from a type III sample.
load drop decreases as the impurity concentration increases.
A larger drop in stress also means a faster resolution of thekink stress.

Because both the AE signal and the impulse from theload drop can be delayed by a certain amount of time until
they were measured. the relative time delay of the AE signal and impulse needed to be measured. This relative timedelay was measured using a sharp hammer blow to one of
the hydraulic grips to simulate a stress drop. A value of
86O±1SO J.LS was oblained for the time delay of the initialchange in slope of the load cell from the rise out of noise of
the AE transducer signal due to the hammer blow. A correction has been made in Figs. 7 and 8 for the time delay ofthe load cell. Figure 9 shows data for the load drop as afunction of the duration of the load drop for a number oftests recorded using the digital recording system. Noticethat as the concentration increases, the magnitude of theload drop increases, in agreement with data reported earlier(Armentrout and Carpenter, 1991). The duration of the

As shown above, the suess drop takes place rapidly,
and is also confined to a small volume of material. Hence,the suain rate during a stress drop is expected to be muchhigher than the applied strain rate of the test machine. The
volume of material deforming during one suess drop can beinferred from the size of the depression band that fooos on
the surface of the sample. The amount of deformation canbe obtained from the magnitude of the drop in stress. Fromthese data. a rough estimate has been made for the suain
rates occurring during a suess drop. Table 4 provides results from limited transient recorder data for three different
stress drops from two different specimens. The value ~al49

Table 4 Transient Recorder Data and Calculated Suain Raaes
1~G1

Sample
type
III

(MPa)
7.26
7.66
7.69
3.46
8.94
9.19

III

III
IV
IV
IV

Ell<
(v2ns)
181
226
255
109
122
138

lei
(ms)
175.7
109.1
82.4
135.1
67.1
68.4

as
Do
:i

8

Ul

6

-...
a.

",

0

••

0

(/)
(/)

!

4

• Sample II
A sample III@ samplelV

A

f

~.

Ci)

'"

•

•

2

.

200

as

Do

150

-•

140

tn

130

==

-"
en

!

CD

>=...

120

a.

110

•

·112

:)

100
200

300

d- 112 (m

23
30
24

=

ku =0.26 MN I m312

CD

a.

22

=

aUg = 40 MPa

au = aUg + kud

0.17
0.18
0.19
0.26
0.21
0.25

All of the mechanical propenies. i.e.• the upper yieldstrength. Liiders strain and magnitude of the load drop.
were found to increase as the impurity concentration wasincreased. The changes in the mechanical properties could
be due to changes in the grain size or hardening effects dueto the impurities inside the grains. Changes in the upper
yield strength due to changes in the grain size is given by
the HaU-Petch relationship (Hall. 1970) as
l12
0u
00 + k u d(1)where 0u is the upper yield strength. 00 and ku are constantsand d is the grain size. Figure 10 is a plot of equation (1).
A linear fit of the data yields values of ku 0.26 MN/m 312
and 00 =40 MPa. Using the data of Phillips et aI. (1953).
Hall (1970) obtained values of ku =0.26 MN/m 213 and 00 =49 MPa. for an AI-3.5%Mg alloy with the Fe. Si and Mnconcentration similar to the sample IV material tested here.
The value of k u is not very sensitive to the concentration.
but 00 should be concentration dependent. The agreementof our data with that of Phillips et al. (1953) is good. The
agreement of the experimental data to the theoretical linear
line implies that the increase in strength measured in thetest materials is primarily due to the reduction in grain size
and not significantly effected by any hardening mechanism
from increasing the Fe. Si and Mn concentration. The
magnitude of the average streSS drop was found to be linearly dependent on the magnitude of the upper yield stress.
i.e.• the higher the upper yield stress the larger the average
stress drop. A plot of the average stress drop versus theupper yield stress is given in Fig. 11. where an excellent&

Fig. 9 Magnitude of the measured stress drop as a functionof the time duration of the stress drop for sample types II.
m.andIV.

160

18
20

4. Discussion or Results

400
Drop Duration (ms)

0

£k
(S-I)

is the magnitude of the stress drop. td • is the duration of thestress drop. £s is the estimated suain rate during the band
slip region and ~ is the estimated strain rate during kink
resolution. and EO is the relative energy of the AE waveform. The response of the load cell limited the strain rates
that could be measured during the band-slip region. Since
the applied strain rate was 2.6 X10-5 S-I. the values of bandslip strain rate, ranging from 18 to 30 S·I. are six orders ofmagnitude larger than the applied strain rate. The values
for the kink-resolution strain rate are four orders of magniwde larger than the applied strain rate.

10

i

£5
(S-I)

400
0

450

112 )

Fig. 10 Hall-Petch plot of the upper yield stress versus
grain size.

so10-r-----------...,

cations in the individual grains. However, the presence of
the additional constituent particles will present obstaCles tothe avalanche motion of the dislocations and should decrease the average rms voltage peak, as was observed. A
distinct feature of the data shown in Fig. 5 is the increase in
scatter of the data as the impurity concentration is increased. We believe that the increase in scatter is directly
related to the fact that the Luders deformation is localized
to small volumes and to the differences in the local impurity concentration. With no constituent particles to block
the avalanche motion. the AE and the load drops should beclosely correlaled regardless of where the deformation occurs. However, as the concentration of constituent panicles
increases, there will be regions where deformation may
occur and there will be significant obstacles to coordinatedmotion while there will also be localized area with low constituent panicles and no obstacles to avalanche motion.
Hence, the larger deviation of stress drops and AE should
be expected as the constituent particle concentration is in·

0+---.---.....--......---1
80
120
160
Upper yield stress (MPa)Fig. 11 Plot of the average stress drop versus the upper
yield stresscreased.
straight line is obtained with a correlation coefficient of
0.985. This result indicates that the average stress drop is
also detennined primarily by the grain size of the test material.

5. Conclusions
A detailed investigation of non-uniform plastic deformation and AE associated with the initiation and motion of
Luders bands in aluminum-magnesium alloys has been carried oUl. Increasing the concentration of constituent particles produced a refinement of the grain size of the test materials, resulting in changes in the mechanical properties.
Digital instrumentation was used to capture and analyze thestress, strain and AE behavior during individual yield
points. From the data generated and analysis of the same,the following conclusions are possible:
1. It is possible to identify three separate and distinct reo
gions when a stress drop is measured as a function oftime.
2. A distinct burst of AE is generated during every yield
point.
3. Careful time resolution of the data reveals that the AE
activity starts before there is any indication of a drop instress.
4. The calculated strain rate during the stress drop of a yield
6
point was found to be as high as 30 S-I. or 10 times
larger than the applied strain rate.
5. For a given impurity concentration. there exists a correlation between the magnitude of the drops in stress andthe AE generated.
6. As the impurity concentration increases, the correlation
between the stress drop and the magnitude of the AE deteriorates.

Since the stress drop occurs in a short time interval andthe machine displacement is negligible, the plastic deformation in the Liiders band causes elastic relaxation in therest of the sample and the tensile machine. The magnitudeof the stress drop is directly proportional to the amount ofplastic deformation of the Luders band. Smaller grain size
produces higher yield stress and larger magnitude of stressdrops. This, in tum, means that a smaller grain size produces more plastic deformation per Luders band on average. For there to be greater plastic strain, the product of themobile dislocation density and the average dislocation displacement must increase.
The effects of grain size and impurity concentration on
the AE are more difficult to analyze, since there exists a
fundamenlal difference in the dislocation motion required
to produce plastic strain and that required to produce measurable AE (Heiple and Carpenter, 1987). The dislocation
motion required to accommodate a given amount of plasticstrain needs neither be a uniform or coordinated motion nor
needs it to occur in a short interval of time. On the other
hand, the dislocation motion required to produce a measurable AE event requires that the dislocation motion be coordinated and that it must occur in a short duration: Le., anavalanche motion of dislocations. It is reasonable to surmise that a smaller grain size with a higher dislocation density would limit the avalanche motion of the dislocations,
resulting in lower values of the average rms voltage peak,as is observed in the present study.

References
D. L. Armentrout (1991). Ph. D. Dissertation, University ofDenver. Denver, CO.

The above arguments assume that the additional impurities are not effective in the pinning or locking of the dislo51

D.L. Annenuou[ and S.H. Carpemer (1991), Proc. 4th
World Meeting on Acoustic Emission and 1st Imernational Conference on Acoustic Emission in Manufaclur.
ing. ASNT. Columbus. OH. p.50.
E. O. Hall (1970). Yield Point Phenomena in Metals andAlloys, Plenum Press. New York, NY.
C.R. Heiple and S.H. Carpenter (1987), J. Acous. Emis.,
6, 171.
D. Hull (1975), Inlroduction 10 Dislocalions, 2nd ed.,Pergamon Press, New York, NY.
W.M. Lomer (1952), J. Mech. and Phys. of Solid, I, 64.
A.W. McReynolds (1949). Trans A1ME. 185. 32.
W.O. Moffatt (1995). The Handbook of Binary Phase
Diagrams. vol. I, Genium Pub. Corp., Schenectady, NY.
D.W. Moon (1971), Mater. Sci. Eng., 8, 235.
J.O. Morris (1974), Mater. Sci. Eng., 13, 101.
V.A. Phillips, AJ. Swain and R. Eborall (1953), J. lnst.
Met, 81. 625.
E. Pink (1989). Acta Metall., 37,1773.
P. R. Sperry (1963). Acta Metall., 11, 153.
T. Tabata. H. Fujita and Y. Nakajima (1980), Acta Melall.• 28, 795.

52

Modal Analysis of Acoustic Emission Signals

H.L. Duneganfor nondestructive testing (NOT), and the results of thiswork find continuing use today in ultrasonic NDT applications. Researchers in acoustic emission (AE) testing, whobegan a few years later, largely ignored applications of
Lamb's theory to analyzing AE data. One reason this occurred is due to the fact that most of the early AE experiments were conducted on small specimens, where the theory is not applicable. The second reason is that ultrasonicresearchers can transmit a harmonic wave to select themode desired for a given plate thickness, while AE researchers are faced to deal with waves generated by a transient event and therefore such selection is denied.

Abstract
This repon shows the results of a continuing study todetermine the type of waves present in plates of different
thickness created by breaking pencil leads in-plane (IP) andout-of-plane (OOP) on the plates and by detecting the resulting stress waves with a high fidelity acoustic-emission
transducer mounted on the edge and surface of the plates.
The results show that the extensional velocity over therange of plate thickness from 3.13 to 12.5 mm does notchange, and the flexure wave is dispersive in accordancewith plate wave theory. The procedure in this work split thesignal into two frequency ranges, a high frequency range
with a 100 kHz high-pass filter and a low frequency range
with a 20-70 kHz bandpass filter. This frequency split provided an easy method of measuring the velocity of the low
frequency flexure wave. A bulk shear wave, not predictedby plate wave theory was observed along with the extensional wave in the high frequency channel. In order to confirm the presence of the shear wave, two shear plates, onewith particle motion venical to the plane of the plate andthe other parallel to the plane of the plate were attached tothe end of one of the bars. These ttansducers were used asreceivers for IP and OOP pencil-lead breaks. and also as
transmilters excited by a l50-V spike pulse. Both the Sv
and Sh waves, created with pulsing. could be detected witha transducer mounted on the surface with petroleum jelly.
The Sh wave had a much higher amplitude than the Sv
wave. The results show that mode conversion of the SV
wave to a flexure wave occurs, as it propagates down thebar. which panially accounts for its loss of amplitude. From
this study. it is suggested that the crack growth signal detected by most AE tests on large thin-wall slrUctures utilizing 100kHz high-pass filtering, resonant transducers. andlarge transducer spacing. is the Sh wave created by crack
propagation.

Gonnan (1990) discussed the use of plate wave theory
to the AE testing of thin plate-like specimens. According to
the theory, there are two modes of propagation. One iscalled the extensional and the other the flexural mode. The
extensional mode is non-dispersive and the Dexural mode isdispersive. This work and the fonowing work by Gorman
and Prosser (199 I) sparked a great interest on the pan of
the author in starting an experimental program to study thewave modes in plates. Most of the prior published experimental data was obtained from thin composite plate specimens. This author's primary interest was in finding out if
the theory was applicable to thicker plates constructed fromsteel and aluminum. Experiments were conducted on 6.25mm and 12.5 mm thick steel bars, 1.2 m in length (Dunegan. 1996). It was discovered from the stan that a signal
traveling at the shear velocity of steel was present in mostof the data. It was also found in this study that analog filtering of the signal into two frequency bands; 100kHz - 1
MHz and 20 - 70 kHz would allow for separation of theextensional and flexure modes. The extensional-mode wave
and shear wave always appear in the high frequency passband and the flexure wave in the low frequency rassband.
The purpose of this repon is to show results of a more intensive study on the presence and detectability of shear
waves in plates, as well as the results of a study to determine the dispersive nature of plate waves in the thickness
range between 3.13 rom and 12.5 rom.

1. Introduction
Over three decades ago. Worlton (1961) published his
paper, "Experimental Confirmation of Lamb Waves at
Megacycle Frequencies". This work was received withgreat enthusiasm by researchers using ultrasonic techniques

2. Procedures
The specimens chosen for the study (see Fig. I) were
three steel bars 1.05 m in length. by 75 mm in width, withthree different thickness; 12.5 mm, 6.25 mm and 3.13 mm.
The first experiment conducted on these steel bars was tobond a high fidelity broadband transducer (DECI SE WOO-

Received 23 October 1997. The author (bal@deei.eom) is affiliated with Dunegan Engineering Consultants Inc.• PO Box 1749.
San Juan Capistrano, CA 92693.

Journal of Acoustic Emission
Volume IS. Number 1-4

530730-0050J97ISS.lX)JISOS3-06!

Copyrightel997 Acoustic Emission GroupO.3lr.:l' ~::: lea; b'ea~sI;>CC?

7r4:'1so:.::er =~e:: t: er.:it~ he~~::Je_...

:....0------ 1.C-5 :::ete~

T:-::c:kr:t'Ss~C:b.'11----~=----------:c--------~•~~.5:'t!:r.

I

r=L.

6.25...,:n3"13,.,~--ji5r.'!r"1 - -,-:an$dl.--:e: ~ s:.::fa:ecQaptee WI':" ~~:;lJ:'n;e!!(Fig. 1 Schematic of the placement of transducers on steel bars, and location where pencil-lead breaks are made on bars.

1~Trans<!ucer bonded to edge of plate

,

,

;

SOOmm

i

,...

~

!IP&OOP!lead:breaks

,

:.--

.5mm

!thlCk-------------I'"

7COmm

,

.

,,-

.... ".';..-

-'

- -.

.,-

'--":";..

Fig. 3 Horizontal and venical shear elements bonded to theend of a 12.5 mm thick steel bar.

Fig. 2 Schematic of aluminum plate used in experiments.
H) to one end of each bar and make 0.3 mm pencil-leadbreaks both in-plane (IP) and out-of-plane (OOP) at theother end of the bar.

an SE looO-H high fidelity transducer was bonded to thecenter of the 6OO-mm edge and both IP and OOP pencillead breaks were made at the opposite center of the other600-mm edge, giving 700 mm of travel for the simulated
AE signal.

Per prior experimental results (Dunegan, 1996), different modes present in plates can be observed by splitting thesignal from the AE transducer into two frequency components: 100 kHz-I MHz and 20-70 kHz. We used AESMART 2000 instrument designed to split the AE signal
into these two frequency bands. Amplified and filtered AE
signals from these high and low frequency channels wererecorded by a digital oscilloscope and Paintbrush software
was used to present the data. For the 12.5-mm-thickness
plate used in the previous study, the arrival time of thehigh-frequency shear wave very closely matched the arrival
time of the lo,w frequency flexure wave and it was initiallythought that the high frequency wave was simply the highfrequency components of the flexure wave. What seemed tocontradict this scenario was the fact that the high-frequency
wave traveling at the shear velocity was still present when
pencil-lead breaks were made in the exact center of theedge of the plate. The symmetry produced by this condition
does not produce flexure-wave components in the plate.
Consideration was given to the finite width of the bars being used, as a possible reason for the presence of this highfrequency wave. Therefore, another experiment was chosenusing a large aluminum plate. The dimensions of the plateare 600 mm x 700 mm x 5 mm, as shown in Fig. 2. Again,

A third experiment was conducted in order 10 determine the efficiency of a transducer coupled to the surface ofthe plate in detecting shear waves of different polarization
traveling in the plate. Two piezoelectric shear elements
were bonded to the end of a 12.5-mm-thick steel bar, 1.2 m
in length (Fig. 3). The bar had damping material on theboltom side. One of the shear elements was oriented suchthat particle displacement was venical to the plane of thebar and the other with particle displacement paral1el to theplane of the bar. OOP- and IP-type 0.3-mm-pencil-leadbreaks were made at the opposite end of the plate, and thesignals from each of the shear elements were recorded. The
shear elements were then used as transmitters by connecting
a 50-V spike pulse to each in tum. Receiving transducers
were placed at 600 mm on the surface of the bar and coupled with petroleum jelly.

3. Experimental Results
3.J Edge-mounled High-fidelil)l Transduar
Figure 4 shows the data obtained from the high-fidelity
transducer, which was bonded to the end of three steel bars
54

IP high frequency

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Fig. 4 SE lOOO-H transducer bonded to the end of 3 steel bars 1.05 m in length and varying thickness as indicated above. IPand OOP·type 0.3-mm·pencil-Iead breaks were made at the opposite cnds of the bars. Note that a signal arriving at a velocity
of 3,230 m/s is present in all the data regardless of thickness.
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Fig. 5 SE lOOO-H bonded to center 6OO-mm edge of a 600 mm x 700 mm aluminum plate, 5 mm in thickness. IP- and OOP·
type 0.3·mm-pencil·lead breaks at opposite edge from the transducer with 700 mm of wave travel. 60 dB gain, AESMART

2000.
of different thickness. OOP- and IP-type O.3-mm-pencillead breaks were made at the opposite end of each bar. Notc
that some dispersion is observed in the first arrival signal(extensional wave) in the high-frequency in-plane (IP)channel as a fWlction of thickness of the bars. Also note thepresence of a large signal arriving at approximately 325 J.lS
from each of the bars. Since this signal arrives at the sametime regardless of the thickness of the bar and exhibits avelocity equal to the shear velocity of steel, one must as-

sume that it is a shear wave and nOl a high.frequency flexure wave. Note thal only faim evidence of the extensional
wave is present from the OOP high frequency channels,
and, similar to the IP channels, a large signal traveling at
the shear velocity in steel is observed from each of the bars.
An OOP·type pencil-lead break apparently produces a vertically polarized shear wave in addition to the large amplitude flexure wave more clearly observed at lower frequencies. Again, argument for this wave being a shear wave55

2.110

Horizontal Po orization

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Fig. 6 Vertical and horizontal shear elements were bonded to the end of a 1.2 m steel bar, 12.5 mm thick. Out of plane (OOP)
and in-plane (IP) 0.3-mm-pencil-lead breaks were made at the opposite end. Signals recorded in twO frequency ranges,
HF=IOO-IOOO kHz, LF=20-70 kHz. Trigger transducer for the digital scope was adjacent to lead breaks. Note venical scaledifference. 40 dB gain.
damping material. IP- and OOP-type O.3-mm-pencil-leadbreaks were made at the opposite end. A trigger transducer
was located adjacent to the pencil-lead-break region in order to provide a time trace to measure velocities. Figure 6
shows the response of the shear elements to the pencil.lead
breaks. First of all, note the difference in vertical scale. The
horizontally poled element gave more than twice the signal
as the vertically poled element. One reason for this is thedamping material on the bottom of the bar attenuated the Sv
wave, but had minimal influence on the Sh wave as would
be expected. Another reason is that some of the energy ofthis shear wave is mode·converted into a low-frequency
flexure wave as indicated by the large signal deteCted in the
low frequency channel. Note the lack of signal in the low
frequency (IF) channel of the element with horizontal polarization. The OOP source created a low-frequency flexure
wave in the bar, which was detected by the vertically poled
element as would be expected. The IF-OOP channel fromthe horizontally poled crystal was not responsive to thisflexure wave since the particle motion was 90· from thepoling direction. A slight hint of a shear-wave arrival ispresent in born IF-IP channels. Most of the Sh wave energy
is displayed in the high frequency (HF-IP) channel for thehorizontally polarized element. Note that the anti-symmetrical wave (IF-OOP) from the vertically poled element
has a velocity very close to the shear velocity measured byboth the vertically and horizontally poled high frequency
channels. Also note that a small signal traveling at the ex·tensional velocity precedes the arrival of the Sh wave in the
HF-IP horizontally poled channel. It is almost impossible tocomes from the fact that the arrival time of the signal is thesame regardless of the thickness of the bar. If this were ahigh frequency flexure wave, which is dispersive, one
would expect to see arrival times that differ as a function ofplate thickness.
All of the steel bars were 75 nun in width. In order totest whether or not the arrival of what appears to be a shear
wave was not an anifact due to side reflections from thefinite width of the bar, the large aluminum plate shown in
Fig. 2 was utilized. It is wide enough to prevent any side
reflections from reaching the transducer prior to the arrival
time for the shear wave. The data from the aluminum plateis shown in Fig. S. Note that the IP- and OOP-highfrequency channels, and the IP-low-frequency channel
show the arrival times of an extensional wave and the shear
wave. Note the lack of signal strength from the OOP-lowfrequency channel. Apparently, the small aperture, highfidelity transducer (SE lOOO-H) bonded to the edge of theplate is fairly unresponsive to the large low frequency flexure wave generated by an OOP source, but is responsive tothe low frequency components of the shear wave from the
IPsource.
Further confmnation of the presence of a shear wave inthe steel bars, and the ability of AE transducers in detecting
the shear waves of different polarization was the motivation
for conducting experiments on the bar shown in Fig. 3. Twoshear elements were fabricated from PZT-S material andbonded to the end of a 12.5-mm-thick steel bar 1.2 m inlength. The bottom side of this bar was coated with a56

Vertical Polarization

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Frequencies (kHz)

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750 1000 tm 1500 1150 <tooo mo noo <t750 )000

Frequencies (kHz)Fig. 7 Vertically and horizontaUy polarized shear elements were bonded to the end of a 1.2 m steel bar. 12.5 mm thick with
damping material on the bottom side. A high fidelity transducer was coupled to the bar with petroleum jelly at 600 mm distance from the shear elements. Each shear element was excited with a 50-V spike pulse.
fabricate a "pure" shear element from PZT plates. so a certain amount of cross coupling is to be expected.

present in the high frequency channel. The frequency spectrum of this channel shows a predominant peak at 125 kHz.

3.2 Surface-mounted Transducers

The transducer responded to signals from the venicallypoled PZT plate in both the high frequency and low frequency channels. It is apparent that the Sv wave excites thelow frequency flexure wave as would be expected. The datasuggests that the high frequency response is a direct measure of the Sv wave. As seen previously. both the Sv wave
and the low frequency flexure waves have approximately
the same arrival time (or velocity) for this plate thickness.
The frequency spectrum of the low frequency channelshows maximum response in the 50 kHz range. which correlates with previously observed frequencies of the low
frequency flexure wave.

It is shown in Fig. 6 that both IP- and OOP-pencil-leadbreaks produce shear waves in the steel bars. The next stepin the experimental program was to determine how effectively an AE transducer mounted on the surface can detect
shear waves baving polarization parallel to the plate. Againa high fidelity transducer (SE lOOO-H) was coupled withpetroleum jelly at 600 rom on the 1.2-m bar, on which theshear PZT plates were bonded. Each of the shear PZT
plates was pulsed with a 50-V spike pulse and resultant
signals were detected by the SE lOoo-H. Figure 7 shows theresults of this experiment. One of the surprising results ofthis experiment was the presence of the high amplitude signal from the horizontally poled PZT plate. Since the particle
motion is parallel to the surface, on which the transducer ismounted. it was expected that not much signal would couple through the petroleum jelly. The low frequency channel
in Fig. 7 did not detect the Sh wave very effectively. A hintof a signal is present in the low frequency channel witharrival time corresponding to the Sh wave, but it is insignificant compared to the amplitude of the high frequency
Sh wave. Again, a hint of an extensional wave arrival is

The next experiment involved attaching a transducer at
800 mm on each of the three bars of different thickness.
(This longer distance was used in order to obtain more accurate velocity measurements). IP- and OOP-pencil-leadbreaks were made at the ends of each bar. Figure 8 shows
the high and low frequency response on the three bars to
OOP·pencil-lead breaks. A voltage range on the oscilloscope was set at a high sensitivity in order to detect theleading edge of the signals for the purpose of making velocity measurements on the different waves. Note that on
57

OOP Low Frequency Channel

OOP High Frequency Channel

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·100

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·2.00
150

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100
0.50
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•1.50
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Fig. 8 A high fidelity transducer (SE lOOo-H) at 800 mm on three steel bars of varying thickness. Out-of-plane (OOP) 0.3rom-pencil-lead breaks were made at the end of each bar. 40 dB gain.

8.00

plate thickness. This is the type of results one would expectfrom the zeroth order anti-symmetrical Lamb wave.

IP High Frequency ChaJmel

8,...J ..12SK11Z

6.00
4.00

Fig. 9 A high fidelity transducer (SE lOOO-H) at 400 mm onthree different thickness bars. Showing a response to IP 0.3mm-pencil-Iead breaks at the end of the bar. High frequency channel of AESMART 2000. 60 dB gain.

There was also an interest in determining if the primary
frequency content of the IP· and OOP-source signals could
be determined easily. The transducer was re-positioned to
400 mm on the bars and the time base was expanded inorder to show more detail of the leading few cycles of thesignals. Figure 9 shows the response of the high fidelity
transducer to IP-pencil-Iead breaks at the opposite end ofeach bar. One can see from this data that there is no apparent change in the extensional velocity as a function of plate
thickness. There is a definite difference in the frequency
content of the signals. The frequency of the signal was estimated by measuring the time in fls between the ftrst two
positive going peaks and dividing this value into 1. Notethat the thinner the plate, the higher the frequency content,
and the higher the amplitude of the signal of these extensional waves. The primary frequencies measured in thismanner were:
125 kHz for the 12.5 mm bar.
200 kHz for the 6.5 mm bar. and400 kHz for the 3.13 mm bar.

the high frequency channel for each bar a signal with a velocity of 3,200 mls is observed from an three bars. This
constant velocity regardless of thickness along with a valueof 3,200 mls shows these waves to be shear waves. Note in
the high frequency data a small amplitude signal having avelocity of 3,700 m/s. The question mark following thevelocity value indicates that the mode for this wave is unknown. It might possibly be a first order anti-symmetricalLamb wave. The signals observed on the low frequency
channel have a varying velocity, which is dependent on

The same procedure was repeated with the exceptionthat OOP-pencil-Iead breaks were made at the end of thebar in order to create a low frequency flexure wave in eachbar. Again. the time in JlS was measured between the firsttwo positive peaks in order to estimate frequency content.
This data is shown in Fig. 10. Observe that the frequency
content and amplitude of the signals increases as the bar
gets thinner. This is similar 10 results for extensional waves.
The primary difference in the low frequency data is thevelocity dispersion, which was not present in the high fre-

2.00
12.5_ tldck
V .2.00
0.00 - - - - - - - - - -..........A-4,00
5~Mlsec---l
.$.00
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5~

6.00
4.00

1200

2.00

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0.00 - - " - - - - - - -4.00
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58

in Fig. 10 differ from those in Fig. 8 by approximately 200
m/s. but the relative differences remain in the same proportion. Note at this sensitivity that the high frequency precursor for the two thinner plates that was present in Fig. 8 areabsent from this data. The primary frequencies measured
for the data were:
45 kHz for the 12.5 mm bar.
56 kHz for the 6.5 mm bar and91 kHz for the 3.13 rom bar.

OOP Low Frequency Channel

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Figure 11 shows the group velocities for the first threemodes of the symmetrical (So) and anti-symmetrical (Ao)Lamb waves against thickness-frequency in mm-MHz. Below these curves are corresponding values in kHz for thethree steel bars used in these experiments. The smaller
numbers on the ordinate are group velocities measured inthis study (Figs. 8 and 9). These values were projected horizontally until they intersected with the proper wave mode.
Projections (small dotted lines) from these intersections
were then made vertically until intersection was made withthe bar. from which the velocities were measured. Someinteresting observations can be made from this data. Notethat the zeroth order symmetrical mode velocity (So) for all
three thickness plates is constant and itS projection to thethree different plate thickness results in a frequency of approximately 70 kHz for the 12.5 mm plate. 140 kHz for the6.25 mm plate and 280 kHz for the 3.13 rom plate. These
values favorably compare with the measured values in Fig.
9 considering the possible errors present in only measuring
the time between the fust two cycles of the signals. Observethat the zeroth order anti-symmetrical mode (Ao) for the

. ..

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-8.00 .

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.

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.

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Fig. 10 A high fidelity transducer (SE lOoo-H) at 400 mm
on three different thickness steel bars. Response to OOP
0.3-mm-pencil-lead breaks at the end. Low frequency
channel of AESMART 2000. 40 dB gain.
quency extensional wave data. This increase in amplitude
as the bar gets thinner for both the extensional waves andflexure waves is an indicator that the surface displacement
is greater in the thinner bars. The velocity dispersion of thislow frequency wave as a function of thickness. shown in
Fig. 8. is repeated in this data. The velocity measurements
lDlO
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59

same bars decreases in velocity such that projection onto
the respective bars yields a frequency of SO kHz for the12.5 mm bar. 60 kHz for the 6.5 mm bar and 100 kHz forthe 3.13 mm bar. These results also compare favorably withthe measured results in Fig. 10.

It has been observed throughout this study that theshear wave is present for both IP and OOP sources and its
amplitude is much higher than the amplitude of the extensional wave. This leads to the following hypothesis. For IP
sources both Sh and Sv shear waves are launched by the
pencil-lead break at the edge of a plate. The Sv wave willlose energy due to any damping material on the surfaces
and due to transfer of energy through mode conversion to a
flexural wave. The Sh wave. which has particle motion parallel to the plate surface. will propagate without mode conversion. This is indicated by the data in Fig. 6. which showsthat the Sh wave has more than three times the amplitude of
the Sv wave. For the OOP source in Fig. 6. a strong flexural
wave is detected by the vertically poled PZT element in the
low-frequency channel. while no such wave is detected by
the horizontally poled PZT element in the same frequency
band.

4. Discussion of Results
The stress wave produced in a plate by an impulse
source such as breaking of a O.3-mm pencil lead appears topartition the majority of its energy into three primarywaves: The So symmetrical Lamb wave (extensional). theAo anti-symmetrical Lamb wave (flexural) and a shear
wave. The amount of energy carried by the extensional andflexure waves depends strongly on whether or not thesource direction is in-plane or out-of-plane with respect tothe plate dimensions. The amplitude of the shear wave is
not so dependent on source orientation. since it can travel inthe plate with different polarization. For OOP sources. astrong flexure wave is produced with little extensional
component. and for IP sources a strong extensional wave is
produced with lesser amounts of the flexure-wave component depending on the depth in the plate where the source
originates (Hamstad et aI.• 1994).

Further evidence of this partitioning of energy between
modes is shown in Fig. 4. For an IP source at the centeredge of the plate, a partitioning of energy is shown by theIP-high-frequency channel between the extensional andshear waves (no low-frequency flexural wave is created bythis source). The OOP-high-frequency amplitude of theshear wave is less than the IF-high-frequency amplitude due
to energy partition to a low frequency flexural wave (notshown). Therefore. it appears that one can expect higher
amplitude shear waves from IF sources than from OOP
sources. If one assumes that the IP-pencil-lead break truly
represents the growth of a crack, the Sb wave is the strongest high-frequency wave produced and is probably the predominant wave detected in most field tests using AE instrumentation with high-pass filtering above 100kHz. resonant sensors, and large transducer spacing. The data in Fig.
7 shows that it does not matter whether or not a displacement at the surface is present in order to detect stresswaves. a shear wave with polarization parallel (Sh) to thesurface will excite the transducer as well as a displacementof the surface if the coupling material has enough stiffness
to couple the shear wave to the transducer. Apparently athin layer of petroleum jelly is sufficient to transfer the energy to the transducer for higher frequency/shorter wavelength signals but not for signals having frequencies below
100kHz. as indicated by a lack of data in the low frequency
channel. The same transducer was used for the data in Fig.
7 and in Fig. 5 where a low-frequency shear wave was apparently detected. For the situation of Fig. 5 where thetransducer was on the end of the bar as opposed to being on
the surface of the bar (Fig. 7), the detected wave corresponding to the shear-wave velocity could have been modeconverted signals that created a displacement at the end of
the bar.

Figure 4 shows the dependence of the extensional wave
on source orientation. For the OOP case. the extensional
wave components are very weak. but note that the shearwave amplitude is approximately the same for both source
inputs in the high frequency range. Another example of theeffect of orientation is shown by Fig. 8, which shows a lackof extensional component in the OOP-high-frequency range
and a strong shear wave. The OOP-Iow-frequency range forthis figure shows a strong flexural wave and a lack Qf extensional- and shear-wave components. This is further indication that the extensional and shear waves are high frequency phenomenon and do not have frequency components below 100kHz in plates of the thickness used in thisstudy. Note here that the above test utilized a high-fidelity
displacement transducer (SE lOOo-H) coupled to the surface of the plate with petroleum jelly. Surprisingly. thisarrangement detects the displacement due to high-frequency
shear waves.
The above statement is contradicted by the data shownin the IP-Iow-frequency range of Fig. 5. Both extensionaland shear waves having low frequency components weredetected in this example. However, the test utilized a highfidelity displacement transducer (SE JOOO-H) bonded to theedge of the plate. It was capable of detecting low-frequency
extensional waves. and apparently shear waves as well. in
the plate due to its high sensitivity to displacement andbroad frequency response. From a practical standpoint. it
would be rare that access to the edge of a plate would beavailable. since in most instances the transducer is placedon the surface of the plate.

The data in Figs. 4 and 5 show a comparison of thehigh fidelity transducer bonded to the end of three steel bars
and the large aluminum plate. Both sets of data show thearrival of the extensional wave and shear wave. The data60

from the aluminum plate is "cleaner", due to a lack of anyedge reflections prior to approximately 500 Jls. There issome evidence of side reflections from the bar data, but it
occurs after the arrival of the shear wave. Therefore, if one
is only interested in studying the extensional and shear
wave arrival in the high frequency range, and the flexuremode in the low frequency range, the finite width bars are
suitable subjects for investigating plate waves. The widthto-thickness ratio for the three bars used in this study
ranged from 12 for the thicker bar to 48 for the thin bar.
There is no obvious difference in the appearance of the signals in Fig. 4 due to this wide difference in ratio.

on the plate surface while the Sh wave is not affected byeither. It is further shown that the detected shear wave hasvery little frequency content below 100kHz. Therefore, it ispostulated that it is the Sh wave that is detected from crack
growth as well as noise from impact and friction (since it isgenerated by both IP and OOP sources) on large plate-likestructures such as pressure vessels, piping, and bridges by
AE transducers with large spacing. Since the larger aperture
resonant AE transducer has a higher sensitivity in detecting
the high frequency shear wave over a smaller aperture highfidelity sensor (Dunegan, 1995, ]996), its use for fieldtesting of large structures is justified. Since the shear wave
is generated by both OOP (or noise) sources and IP (orcrack growth) sources, the resonant transducer alone will
not provide the type of data needed to separate crack-likesignals from noise. Best identifier of OOP (noise) sources isthe zeroth order anti-s)(mmetrical Lamb wave (Ao). It isfurther shown that finite width bars can be used effectively
for the study of the zeroth order plate modes and the shear
mode without influence from side reflections since they
arrive later than the shear wave in the high frequency channel and higher order Lamb wave modes are not present in
the low frequency channel.

The data in Fig. 9 shows that the extensional wave velocity remains constant for the three thickness bars used inthis study. This is only obvious when increasing sensitivity
to view the leading edge of the signals. When viewing thesignals such that the shear wave signal remains on scale,there is an "apparent" decrease in velocity when going fromthe 6.25 mm to the 12.5 rom bar (Fig. 4). The data in Fig.
10 shows the frequency and velocity variation of the Aowave as a function of bar thickness. The frequency differences are small and are only measurable due to the highfidelity response of the transducer used in these measurements. Favorable correlation of these frequencies to thoseobtained from the measured velocities and projection fromthe group velocity curves to each bar in Fig. 11 further
shows that splitting the signal into a high and low frequency
range prior to making velocity measurements allows a highdegree of accuracy in measuring the velocity of the slower
flexure waves.

References
Harold L. Dunegan (1995), "The Effects of Aperture Size
and Mass Loading on the Response of AE Transducers to
Plate Waves," presented at the 38th AEWG Conference,
NASA Langley Research Center, May 1-4.
Harold L. Dunegan (1996), "Use of Plate Wave Analysis in
Acoustic Emission Testing to Detect and Measure Crack
Growth in Noisy Environments". Proceedings of Structural
Malerials Technology - An NOT Conference, Feb. 20-23,
San Diego, California.

s. Conclusions
Results of this investigation have shown that for transient signals produced by breaking of 0.3-mm pencil leadon plates of thickness ranging from 3 to 12.5 mm, usefulsignals from the acoustic emission standpoint are only present from extensional waves, zeroth order anti-symmetricalLamb waves, and a shear wave. For plate thickness greaterthan 3.1 mm, the first two zeroth order Lamb modes are
predominant at frequencies below 200 kHz. Plate wave
theory only predicts two waves for thin plates: A nondispersive extensional wave, and a dispersive flexure wave.
This study shows that a strong shear wave is generated in aplate by both OOP and IF pencil-lead breaks. This signal
unlike the normal Lamb modes associated with wave
propagation in plates is non-dispersive and therefore travels
at a constant velocity regardless of plate thickness and frequency. It is consistently higher in amplitude than the extensional wave, and is the primary wave detected by AE
transducers in field testing of large structures when working
at frequencies above 100 kHz.

Michael R. Gorman (1991), "Plate Wave Acoustic Emission",1. Acoustical Society of America, 90(1), July, 358-

364.
Michael R. Gorman and William H. Prosser (1991), "AE
Source Orientation by Plate Wave Analysis", J. Acoustic
Emission, 9(4),283-288.
M.A. Hamstad, S.L. Quarles and R.L. Lemaster (1994),"Experimental Far-field Wideband Acoustic Waves in
Wood Rods and Plates", 9th International Symposium on
Nondestructive Testing of Wood, organized by Roy Pellerin
and Kent A. McDonald, Sept 22-23, 1993, Forest Products
Soc., Madison, WI, pp. 30-44.
D.C. Worhon (1961), "Experimental Confirmation of Lamb
Waves at Megacycle Frequencies", J. Applied Physics,
32(4), June, 967-971.

This report has shown that an AE transducer is capableof detecting both Sv and Sh waves and that the Sv waves
lose energy due to mode conversion and damping material
61

Meeting Calendar
Ninth Annual Symposium on Nondestructive Characterization of Materials
June 28 - July 2. 1999, Novotel Hotel at Daring Harbor. Sydney. Australia
Contact: Robert E. Green Jr.• The Johns Hopkins University. Baltimore. MD; Don PriceCSIRO. Sydney, Australia
Abstracts due: December 4, 1998
E-mail cnde@jhu.edu; www.cnde.com; phone (410) 516-7126

Acoustic Ernission '99
An international conference commemorating the 150111 anniversary of Technical University in Brno
Sponsored by Technical University ofBrno and Czech Society for Nondestructive Testing.
CONTACT: Pavel MazaI, Technical University, Faculty of Mech. Engineering, Institute of Design
Technicka 2. Brno, CZ 616 69
Abstracts due: September 30, 1998
E-mail: mazal@cms.fme.vutbr.cz;http:\\cmspro.fme.vutbr.cz

The 41 st AEWG Meeting
September 1999. Princeton. NJ
Contact: S.J. Vahaviolos, sales@pacndt.com

Recent Books on AE
Progress in Acoustic Emission IX
the Proceedings of 14th International Acoustic Emission Symposium & 5th Acoustic Emission
World Meeting, Transitions inAEfor the 21st Century.
Proceedings volume (480 pages, 1998): Published jointly by the Acoustic Emission Working
Group (AEWG) and the Acoustic Emission Group (Suite 106, 16350 Ventura Blvd, Encino, CA
91436 USA). For ordering copies (S120 each + applicable sales tax (CA only, 8.5%) andshipping), contact Acoustic Emission Group (fax 818-990-1686) or send e-mail to ono@ucla.edu.
Shipping: US: S5; All other countries: $7; Air mail to S. America: S18; Air to Europe: S27; Air toother contries: S35.
Proceedings of previous International Acoustic Emission Symposia can be purchased fromJapanese Society for Non-Destructive Inspection, Natsume No.5 Building, 67 KandaSakumagashi, Chiyoda, Tokyo 101, Japan.

Proceedings
22nd European Conference on Acoustic Emission Testing, Aberdeen, Scotland: May 1996
Order to be sent to: Meg Thain. Univation. The Robert Gordon University, Kepplestone. Queen's
Road, ABERDEEN AB9 2PG UK. Cost: £40 including shipping.
Fax: 01224 263323 I Tel: 01224 263322
62

An Acoustic Emission Tester for Aircraft Halon-1301
Fire-Extinguisher Bottles
Alan G. Beattie
Abstractof the wall due to corrosion. not flaws in the bottle. Grosscorrosion has not been a problem in recent years. The few
bottles that have failed the hydrostatic test recently (~from experimental error) exploded during the test Thus, thehydrostatic test has effectively become a g%o-go proof
test Another problem is that the procedure of cutting open
a bottle and aben resealing it by welding removes metal
can introduce new weld defects.

An acoustic emission test for aircraft Halon-1301 bottles has been developed, a prototype acoustic emission test
system constructed. and over 200 used bottles tested at the
repair facilities of the two manufacturers of these bottles.
The system monitors a bottle with six acoustic emission
sensors while its pressure is raised by heating it in an oven.
The sensors are held in position with a fixed relationshipbetween them by a special fixture. This fixture was d>signed to fit spheres with diameters between 127 and 406
mm (5" and 16"). Results of the tests on used bottles indicate that over 95% of the boUJes showed no indication ofsignificant defects. The rest had some indication of flaws acorrosion. However. all bottles tested to dare have passed
the hydrostatic test required by the U.S. Deparunent ofTransportation.

am

The production of Halon 1301 was banned in January
1994 by international agreement due to the ozone depleting
properties of halon. At this time. no substitute material
exists. which combines both HaJon 1301's outstanding fire
extinguishing effectiveness and low toxicity. The U.S. airline industry has been granted an exemption by the DOT tocontinue using Halon 1301 through the year 2000. The costof halon from existing stores has mpidly inaeased eventhough halon from the bottles undergoing a hydrostatic test
is recovered., pmified, and reused. A test of the bottle's integrity, which does not require the opening of the bottle.
would save time and expense for the airline industry as wellas eliminating one source of loss of the world's Halon-1301
supply.

1. Introduction
Many of the world's commercial jet airaaf't use spheri-cal bottles containing pressurized Halon 1301 (CF,Br) toextinguish fires of an engine. an auxiliary power unit. andcargo hold. The regulations of the U.S. Department ofTransportation (DOT) require periodic testing of these bottles for structural integrity. The only test method currently
approved by the DOT regulations is a hydrostatic test fa
inelastic expansion of the bottles.

In the past. the DOT has granted exemptions. whichallow acoustic emission (AE) testing in place of the hy~
static test of gas cylinders for gas transportation trailers(ASTM. 1992). This procedure is now widely usedthroughout the United States. In this test. these cylinders
are individually monitored by an AE system while beingpressurized to 110% of their service pressure. This extra
presswe is applied during the normal filling procedure
bled off after Ihe tesL An extensive testing program wasconducted to detennine the AE failure criteria fa these cylinders. The extension to a similar AE test for aircraft halon
bottles appeared to be a viable solution to the problems
associated with the hydrostatic test

To perform a hydrostatic test. the permanently sealed
bottle is first cut open and the halon removed. The bottle isthen hydrostatically pressurized to twice its marked servicepressure in a water bath. The displacement of the water ismeasured both at the maximum pressure and after the release of the pressure. The elastic and inelastic expansion lie
determined from the data. If the inelastic expansion exceeds
10% of the total expansion, the bottle is rejected. After testing. the bottle is then refilled with Halon 1301, resealed,
and returned to service. The test primarily detects thinningam

2. Principles of Acoustic Emission Testing
An acoustic emission is generated in the form of atransient burst of acoustic energy. Acoustic waves pr0pagate through the material until they reach the surface where
they can be detected by a piezoelectric sensor. In a thinsheet of material. the waves travel along the sheet. generally in one of two different modes of motion. The twomodes are an extensional wave, where the particle motion is

Received 1 March 1998. The author (pacabq@worldneLalLnet)
was affiliated with Department 9742, Sandia National Labora·
tories. Albuquerque, NM 87105-0615; now is at PhysicalAcoustic Corp., PO Box 572. Corrales, NM 87048. This isbased on a U.s. Department of TransponationIFederal Aviation
Administration report. DOTIFAAlAR-9719.

Iournal of Acoustic Emission
Volume IS, Number 1-4

630730-OOS0191/SS.00/1S063.()68
Copyrighlel997 Acoustic Fmission Groupparallel to the propagation direction in the sheet. and a flexural wave, where the particle motion is perpendicular to thedirection of propagation. Both of these modes have frequency dependent velocities and are thus dispersive. The
flexural velocity is about half the extensional velocity alii
both modes are usually seen in an emission burst The dispersion of the two modes results in the shape of the aooustic wavefonn continually changing over time. Both modes
are detected by conventional piezoelectric sensors with theflexural mode usually having the largest amplitude (Beattie,
1983).

8.5
8.0

7.S

~

T.D

I

..•il
·•.

8.5

8.0

110

5.5
5.0
4.S

As the burst signal propagates away from the source,
the locus of maximum energy is an expanding circle in isotropic materials. The wave will excite the surface m01Duedsensors at different times as it passes under them. The
measured time of arrival is proportional to the wave velocity and the distance from source to sensor. Knowing thewave velocity and the sensor locations, one can then use
these anival times to triangulate back to the source and thus
calculate its location. Because the time of origin of the
wave is not known, three sensors are needed for location ona surface and four for location in a solid.

~

~

~

~

~

~

~

~

~

~

~

Temperalure - dellrcc. C

Fig. 1 Pressure - temperature curve for a halon bottle.
A completely quiet AE test of a pressure vessel is ararity. Acoustic emission can be generared by the spattingor cracking of oxides or other corrosion products. Grit inthreads can fracture with a change in the overall strain in thebottle. There are also different types ofelectrical interference
which can be detected as emissions. A source location test
was chosen since emission produced by corrosion or grit
usually will not repeat themselves and do not show several
emissions from the same location. A growing crack willusually show a cluster of sources around one location.

In general, a single AE burst only indicates that something happened in the test object The large number of possible generating mechanisms usually prevents the determination of the source mechanism for a single emission. On
the other hand, several emissions from the same location
usually is evidence of a growing flaw. Another significant
piece of infonnation is the occmrence rate of the deIecIed
emissions as a function of load. If the rate of emissions
from one region increases more than linearly with respect tothe load value, there is a strong probability that unstablecrack growth is occurring and failure is imminent

Most of the Halon-1301 bottles in commercial aircraft
are spheres. The mathematics for the location of a point on
a spherical surface have long been known. If the sensors are
positioned on the ends of the same spherical radii for differ·ent size spheres, the only pmameter which has to bechanged in a location calculation is the value of the radius.
Therefore, a ftxture which runs the sensors in or out on the
tip of a rod located on a radii will allow the use of the samemathematics for a variety of sphere sizes. Such a fixture,
shown in Fig. 2, was designed to hold spheres from 127
and 406 mm (S" and 16'') in diameler and to ftt in an industrial oven.

3. Design or the Acoustic Emissiou Test
The main problem in the design of a safe, practical AE
test for halon bottles was how to apply an overpressure in
the sealed bottles. The most viable method to increase thepressure appeared to be heating the bottle and its contents.
The bottle is filled with a measured chaIge of Halon 1301
and then charged with dry nitrogen gas to a pressure of 2.5,
4.1 or 5.5 MPa (360, 600, or 800 psi). Most of the halon
bottles currently in use have been restricted by their manufacturers to tempenUUreS below noc (160°F). Theoreticalcurves such as the one shown in Fig. 1 show that a temperature of approximately 63°C (14S°F) will produce a pressure in most halon bottles about 30% over the bottle pressure at 43°C (110°F). This is a temperature, which industry
members have estimated that a bottle will seldom exceed in
nonnal commercial service. Therefore, heating a halon bottle to 63°C will exceed the 110% criterion developed for gas
trailer bottles and should provide a valid test

4. Description of the Prototype Test System
The AE system uses the Physical Acoustics Corporation (PAC) AEDSP-32/16 digital AE board. Three boards
are mounted in a 66-MHz 486 computer. These boards have
two separate channels, each containing a digital processor
with a 16-bit word and a maximum digitization frequency of
8 MHz. The large dynamic range of the 16-bit word (up to
90 dB) allows uiggering at a very low signal level without
losing high-amplimde data. The system is triggered at a 25
dB level (17 JJ.V out of the sensor).
The board uses a dedicaled signal processing chip to
calculate various AE signal paramelers in real time from thedigital record. In this application, the following parameters64conuoDer. This required a relatively skilled operator to run
and to constantly monitor the system. Since it was intended
that the system be run by aircraft mechanics in maintenancefacilities, it was decided to automate as much of the system
as possible. A computer program was developed to control
the AE system and the oven. and to decide upon the healthof the boUle in almost all cases. The operator is required
only to mount and dismount the bottle and to enter information about the bottle into the computer. This turnkey
system was med for the second half of the tests at Walrfr
Kidde and all of the tests at Pacific Scientific.
1be system operates as follows. The opeI3lOr firstloads a bottle into the fixture and aaaches the thermocouple
to its lower skin. Next the boUle's identification number isentered into the computer with the keyboard. When this isverified, the computer starts an auto sensor test. Each sensor is excited several times with a voltage pulse. This genemtes an acoustic burst which is received at each of theother five sensors. The peak amplitude of each received signal is measured If the average of the peak amplitudes of all
signals received by each sensor lies within ±4 dB of theaverage of all signals received by all sensors, the test ispassed. This shows that all the electronics are working aliithat each sensor has good acoustic coupling to the bottle. If
the test fails, the operator is iDSb11cted to reseat the sensors
and try again. After the auto sensor test is passed, the system turns on the oven and the circu1ation fan and starts laking data. When the bottle wall IemperabJre reaches 66 C.
the heaters and fan are tmned off and the bottle sits for 5
min. to allow the halon inside to reach equilibrium with thebottle wall. The equilibrium lemperature is usually between
63- and 66 C. Throughout the test, the computer calculates
the locations of every evenL It checks each located event to
see whether it is a member of a cluster or whether to start a
new cluster. If a cluster size exceeds a preset criterion, thecomputer wiD conclude that the bottle has failed the test am
may be close to sttuctural failure. It will shut off the hearer
and circulating fan and signal the operator. The operator
will then open the door to allow the botde to cool befae
removing it from the oven. At the end of a test, the auto
sensor test is again performed to insure that all sensors are
stiD working. A brief report is printed after the test identifying the boule and stating that it has either passed or tailed
the tesL

Fig. 2 Drawing of the fixture.
for each AE hit are recorded: the test time to within 0.25
J!S, the AE count, the peak signal amplitude, the signal risetime, the signal length, the area under the voltage time
curve (called variously signal strength, energy, or MARSE),
and finally, the digitized wavefonn for each hit This digitalrecord contains 2048 words digitized at a 4-MHz rate and isrecorded for every hit on each sensor. This dam set allows atest engineer to examine a boUle test in great detail, including the AE wavefonns, if the test results are not clear. The
detectors are PAC Nano-30 sensors with a response peak
between 300 and 350 kHz. The ~ preamplifiers have afrequency bandpass set at 250 to 1200 kHz. This restriction
to higher frequencies is necessary to reduce the effect of
low-frequency sound waves reverberating through the halon
inside the bottle.

tt

The prototype test system (see cover photograph) combines the AE system described above with a Russell industrial oven having a 730-Iiter internal volume. The OVeD has
a s1ide-out shelf. which supports the test fixture. The fixture supports the bottle and positions the sensors on it The
shelf with the fixtme is shoved back into the oven and thedoor closed to heat the bottle. The oven has encased heating
elements with a surface temperature limit of 427 t C (8oo t F).
This will prevent thermal decomposition of the Halon 1301
in the event of an accidental release. The oven air temperature is limited to a maximum of 93-C (200-F). Relays are
installed in the oven to allow the computer to operate theheaters and circulation fan. The computer reads the skintemperawre of the bottle using a thermocouple raped to thebottom of the bottle.

5. Discussion of Analysis
While the mathematics of location of points on aspherical guface are not new, the problem is complicatedbecause bottles are not smooth spheres. Fill ports, discharge ports, and mounting lugs are welded onto the bottle.
These cause distortions of the acoustic waves travelingalong the boltle wall. The largest amplitude signals areusually flexural waves, which are easily distorted as they
pass under a mounting or positioning lug or through thebase of a pon. The lugs are often mounted on a doubler

Initially, the system was controDed by the operator,
who initiated all operations from the keyboard or the ovenEi5plate which is fillet \\'elded to the smface of the sphere. At
the relatively low signal amplitudes (3S to SO dB) seen inthese spheres, the distortion of the waves can produce triggering on either the extensional or the flexural portion of
the wave, making the calculation of the exact source position difficulL To achieve reasonably accurate location of thesource ofthe detected signals. only over-delennined data setsare used in the calculation (an event where the emitted
acoustic wave excites 4 or more sensors). A nonlinear leastsquares program was written in FORmAN to calculate themost probable location of the event on the sphere. It ~then modified by PAC for their hardware. This location
program first tries the extensional wave velocity (5.21mm/~). If that does not produce a good fit to the data, theflexural velocity (3.00 mm/J1S) is tried. The computer ignores the event if it cannot locate the SOlD'Ce position with arelatively good fit to the daJa by using one of these two
wave velocities. Approximalely 80% of all the events arelocated. This percentage approaches 100% as the peaIc amplitude of the wave exceeds 316 JiV (SO dB) out of the sensor.

ties with AE event rate cmves typical of conosion. Even in
stainless steel boules, a small amount of liquid impurity,
such as water, sitting on the bottom of the sphere for long
periods of time could cause corrosion in a small area if theHalon 1301 decomposed in the liquid. It was decided that asecond failure criterion based upon cluster density might beappropriate, depending upon the results of the bottle tests atthe commercial repair slations.

6. Results of Bottle Tests
Both current manufacturers of these balon bottles maintain test and repair facilities. It was arranged 10 perform Ihe
AE test on used Halon 1301 bottles as they were returned 10these facilities for repair or testing. The test was run on thearriving bottles before emplyjng them for the hydrostatictesL The prototype system was first insla1Jed in the Waller
Kidde testing facility in Wilson, NC. Because these bottles
were owned by customers and some had tight schedulingrequirements, not all ofthe desired NOT procedures could be
perfonned on those questionable bottles found by the AE
tests. However, aU bottles tested with the AE system su~
sequendy passed the hydrostatic test. A total of 147 bottles
were tested at the facility while the prototype system wasthere. The system was then moved 10 Pacific Scientific
Corporation in Duarte, CA. Here another 60 bottles were
tested. This gave a daJa set of 207 botdes. Table 1 shows
the number of the tested bottles made of each material. Table 2 shows the number of the tested bottles as a functionof size. A majority of the bottles were made of Nitronic
steel and had a volume of 10.3 liters (630 cubic in.), but awide selection of bottles were tested.

To estimate the significance of the events, an algorithm was written, which searches for spatial clusters ofevent sources. A cluster is arbitrarily defined here as all
events, which fall within a circle on the surface of thesphere. The radius of this circle is 15· of the arc of a great
circle on the bottle. The 15· radius corresponds to a circle
about 75 mm in diameter on a 280-mm-diameter sphere.
This cluster radius was based on data from a flawed bottle.
Extensive testing indicates that it is a reasonable choice forthe spherical halon bottles used in the aircraft fleet

Table 1 Bottles Tested Versus Composition

Analysis of Ihe Pacific Scientific da1a showed that theusual AE signal parameters (AE count, signal suength,
signal energy or signal length) are not ideal indicators forthe cluster severity. The acoustic impedance ratio between
the Halon 1301 and the bottle wall is different for different
metals. This changes the damping of the acoustic wave,
which affects these parameters. For example, an AE signal
for a titanium bottle has an AE count about SO% greaterthan the same amplimde signal for one made of ferrous alloys. Therefore, it was decided lhat the failure criteria should
be based solely on the density of the locared emissions in
the cluster and the change in slope of the AE curve withtemperature (or pressure). The program keeps a record of thelocation coordinates and the temperature for each member ofa cluster. From the data it was delennined that bottles withidentified flaws showed most of located events above a temperature of 54·C. Here, we calculate the slopes of the located AE event versus temperature curves between 43 and54·C and between 54 and 66·C. We use as the failure criterion, a factor of 3.0 or more increase in the slope above
S4·C in comparison to that below 54·C.

NumberMaterial
Nitronic (21-6-9)
125
40
Almar
4130 Steel
26
7
Titanium
9300 Series Stainless
Table 2 Bottles Tested Versus Volume
Bottle Volume (liters)
1.17
2.45

3.66
6.18
6.87
8.76
10.3
13.1
22.9

While conosion has not appeared to be a problem inrecent years, some of the bottles showed high cluster densi-

esNumber Tested
1
1
23
24
4
14

123
13
5

Table 3. Summary of Acoustic Emission Data for The 207
Bottle Tests
Number of Bottles

Results

7

no AE observed

56
77

random AE hits but no located events
AE hits and located events - no clusters with over 4 located events
clusters with 5 to 17 located eventsat least one cluster with 18 or more
located events

48
19

The acoustic emission results are given in Table 3. Seven
bottles had no emissions, 56 bottles had emissions but nolocated events, and another 77 had located events and possibly small clusters, which contained less than 5 events.
Thus, about 68% of the bottles showed no significant AE.
The rest had clusters of moderate to large number of events.
Based on the daIa from Walter Kidde tests, it appearedthat the bottles could be graded acconiing to whether AE

clusters contained low or high Dumbers of events. However,
25% of the Pacific Scientific bottles showed clusters c0ntaining 18 or more events. although none failed the hydrostatic test. A more thorough examination was then made of..
It

the data. Most pressure vessels. which fail an AE test,
show a distinct knee in the daIa where a relatively sreadyrate of emission as a function of the load suddenly starts toincrease. This is indicative of continuous crack propagation,
which ends in the failure of the vessel if the loading is not
at least stopped, if not reversed. Of the 19 bottles with 18
or more located events in a cluster, 15 of them showed arelatively constant rate of emission between 43'C and theend of the test at 66'C. Only 4 of them showed a muchhigher emission rate between 54' and 66,C than between 43
and 54'C. However 5 of the remaining 15 had 35 or more
located events in a cluster. This high density in a clUSler
with a constant slope of the event versus temperature curve
was probably due 10 the cracking or spalling off of ~sian products at a localized region inside the bottle. After
discussions with the DOT technical staff, it was decided 10
set two failure criteria. FU'St, any boUle with a cluster c0ntaining 35 or more 10caJed events would fail the AE test
Second, any boUle with a cluster containing 18 or more
events and which had a significantly ~ AE mJeabove 43'C would also fail the AE tesL The actual criterion
used in the campuree program was to divide the number ofAE events in the cluster, which occ:urred at temperatures
above 54'C by the number of cluster events, which oc:curredin the range between 43 and 54'C. If this ratio is 3.0 or.

:!

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1ilL.---'-_"""-----''--........._.L-----L_........._J------L_--'

Iill----'-_....L-_"-----L_....&.-_L.----L_....a.-_"-----'

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Fig. 3 Event versus temperature curve and location plOL
The slope of the event curve for the cluster is constant withincreasing temperature.

&

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~

~

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~

~

~

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~.DcpaFig. 4 Event versus temperature curve and location plOL
The slope of the event versus temperature curve for thecluster shows a significant increase at high temperatures.

higher, the boule will fail the AE test. Of the 207 boUles
tested, two failed both criteria, two failed the slope criterion
and 5 failed the cluster density criterion.

7. Conclusions
The AE Halon Bottle Tester has shown its ability todetect smaIl flaws in Halon 1301 bottles. None of the flaws
seen yet has caused the rejection of a bottle by the hydnr
static tesL Conservative AE rejection criteria have been set
for commercial use of the system. The test data reported inthis paper show that over 95% of the tested boUles ~this criteria. There was no indication that any specific boUle
design or construction was more prone to failure by this AE
test than the rest of me test seL

Location diagrams and event versus temperature data me
shown in Figs. 3 and 4. In Fig. 3. a serious appearing cluster containing 29 events is shown on the location plOL
However. the event rate is constant as the temperature. andthus the pressure. increases. In conttast, Fig. 4 shows acluster containing 20 events, but 18 of the 20 events occmred above 54"C. The cluster in Fig. 4 is located partiallyover a doubler plate on the boule. An x-ray of this p1ale
showed a tungsten inclusion in the fiDet weld holding theplate on the bottle. The fact that the emission did not start
until a relatively high temperalUre and the rapid emission
rate above 64 'C is strong evidence that a crack is propagating in the weld, starting from the tungsten inclusion. Because the boule passed a hydrostatic test the crack is probably not in the vessel wall but instead is propagating in the
doubler plate fillet weld parallel to the wall. Thus. it is areal flaw but one. which may not result in the failure of theboule.

Based on the daIa presented here, the Air Transport
Association applied for an exemption, which would allow
its members to use the AE Halon Bottle Tester describedhere in place of the hydrostatic teSL This exemption(OOT-E 11850) was granted on 11 December 1997. The
system is now available commercially.

Acknowledgment
This project was funded by the Federal Aviation Adminisrration (FAA) William J. Hughes Technical Center'and the Air Transport Association (ATA). The author would
like to thank Michael Bueles of American Airlines am
Steve JelUlg of United Airlines for supplying me halon bottles used in abe experimenL Kamran Ghacmmaghmi of federal Express provided both aid and encouragemenL Steve
Erickson, fonnerly of abe ATA. was instrumental in gelting
this project started. Steve Lamb and Junior Bunn of WaIter
Kidde enabusiasticaUy conducted me tests of user bottles attheir facility as did John McNeese at Pacific Scientific Corporation. Finally. Wayne Roney of Physical Acoustics
Corporation (pAC) wOlked many extta long days to adapt
the Sandia program to PAC hardware on schedule.

These results show that 4 of the 207 boUles in the daIa
set produced AE data, which indicared the possibility ofserious flaws. Five more of the boules had cluster densities,
which indicated the possible presence of areas with at least
moderate corrosion products inside the bottles. AU nineboules passed the hydrostatic test, which primarily meansthat they did not blow up at pressures twice the nominalpressure at 21'C. The other 198 boules passed the AE test
based on the failme criteria described above. It was noticed
that two sets of identical bottles with closely grouped serial
numbers contained the majority of moderate to high density
clusters. It is not known whether these belonged to two
distinct barches of old bottles, whether they had been somehow mistreated by their owners or whether this observation
was just a statistical anomaly.

References
ASTM (1992), "Standard Test Method for Examination of
Seamless. Gas Filled Pressure Vessels Using Acoustic
Emission," ASTM Slandard E 1419, Annual Book of
ASTM Standards, Section 3. 1992 and later. ASTM. Philadelphia, PA.

The time that it takes to run a test on a bottle depends
upon the size of the boUIe, its surface condition (paintslows down me heat transfer), and the condition of theoven. Total test times including the five-min. hold at theend. ranged from 15 min. to over an hour. The first test of
the day is always longer due to the heating of the mass ofthe oven. Handling the 66"C boules is no problem if a little care wim gloves and long sleeves are used. The WInaround between tests is quite short, even when the fixture
has to be reset for a new bottle size. At Walter Kidde, theaverage time was about 25 min. per boule. This daIa indicates that an average test time of around 30 min. per boule
should be quite feasible.

A.G. Beattie (1983). "Acoustic Emission, Principles am
Insttumentation" J. Acoustic Emission. 2, 96.
Cover PhotographPrototype test system is depicted in this photograph, withthe top fixture open and a halon bottle placed in abe midcUe.
This demonstration was conducted on 12 December 1995.

68

AE Detection of Cracking in Pipe Socket Welds

Bryan C. Morgan
The end result of the vibration is the initiation andgrowth of weld cracks, potential fluid leakage and ultimately
a pipe break. Figure 2 shows examples of discovered cracks.
In Fig. 2(a), the crack originates at the weld root. Due tothe loose socket fit-up to pipe, the root is prone to exhibit
lack of weld penetration, and correspondingly higher stressconcentration and intensification. Figure 2(b) shows cracks
growing above the weld root pass. However, the figure also
shows a lack of weld root penetration.

Abstract
Small-bore pipe socket welds in fossil and nuclear
power plants are prone to vibration induced cracking. Thisis particularly a problem for small-bore pipe vents anddrains that are cantilevered from large diameter pipes. It has
been found that fluctuations in flow and attached rotating
equipment resonate with the natural frequencies of thesmall-bore pipe cantilever. Typically, the small-bore pipe
is attached to main large-bore pipe using a socket weld.
This type of weld can have a high stress concentration,
which may result in cracking at the weld root. The socket's
geometry makes a non-destructive inspection using either
ultrasonics or radiography very difficult. In the laboratory,
stainless steel pipe specimens with socket weld cracks were
stressed to the plastic yield while monitoring for acoustic
emission (AE). This showed that a significant amount of
AE was generated during elastic bending, and could be used
to differentiate between specimens with cracks and without
cracks. The cumulative AE signal energy at relatively low
stress levels provided a means of identifying the aa:ked
specimens.

Stainless steel small-bore (19 mm and 25 mm (3/4"
and I") nominal diameter) pipe has been particularly of concern for leakage. This is a concern for both safety and forpotential to shut down a power plant. It is particularly an
issue with nuclear power plants where leaks or breaks insocket welds may RqUire immediate shutdown of the plant.
Inspections to identify the cracks are difficult, as manysocket welds are in plant areas not accessible during normal
power operation. In some plants, the access may only beevery 18 months. This provides time for a small crack topropagate to leakage before loss of fluid sounds an alarm.
As such, a small leak can go unnoticed until a pipe breakoccurs.
1. Introduction
Another problem is the difficulty in detecting cracks.
The socket-fitting geometry complicates the inspection.
Radiography will show clearly the fitting material and thegap between the socket fitting and pipe, but generally
misses the root cracking. Likewise, ultrasonics will show
the gap between socket fitting and pipe wall. A shallow
crack will generate a reflector that is obstructed by the gap's
reflector signal. As such, using ultrasonics for inspection
is not likely to detect cracking in the bottom one-third of
the material thickness. Thus, the crack in Fig. 2(b) would
be detected, but not the cracking in Fig. 2(a).

Behavior of power-plant piping systems during dynamic loading, particularly from flow-induced vibration, canlead to a degraded condition and a safety concern. One pipe
component of special concern is the socket weld connecting
a cantilevered small-bore pipe to a large-diameter main run
pipe. The flow drives the low frequency response of thecantilever to generate high stresses in the weld. This isaggravated when the cantilever supports a heavy isolation
valve. Inherently, the socket-weld geometry and any weld
undercutting exhibits high stress intensification. FigureI(a) shows a typical cantilevered pipe with a large valve. In
this case, an instrument tube is connected to the valve, but
it provides little structural support or influence on the cantilever's natural frequency. Figure 1(b) shows another typicalconfiguration, where the cantilevers are dfain lines with eM
caps.

To address these concerns, an inspection procedure wasdeveloped using the detection of acoustic emission (AE) as
an indicator of the socket weld's structural integrity. The
expectation is that a significant amount of AE is generated
by applying a nominally elastic stress level. Degraded andcracked welds will exhibit higher concentrated stress levels
and higher AE activity.

Received 7 October 1997. The author (BXMO.TesMs.Gsv@bangate.pge.com) is affiliated with Technical & Ecological Services Laboratory, Pcific Electric and Gas Company, 3400 Crow
Canyon Road, San Ramon, CA 94583.

2. Mechanical Testing
Laboratory mechanical testing was conducted to quantify the AE generated by socket-welded specimens. Testing

Journal of Acoustic Emission
Volume IS. Number 1-4&9073().()()5OI97/sS.00I1 5069·78
Copyrighl@J997 Acoustic Emission Group

(a)

(b)

Fig. 1 (8) Small-bore (l9-mm diameter) cantilever pipe with instrument connection. The socket weld of concern connects
the socket fitting to the small-bore pipe. [Note, a metal name plate tag is tied around the weld] (b) Three small-bore
cantilever drain lines with straight pipe sections. In many cases, the cantilever bas an elbow between the valve and large-bore
pipe. This tends to increase flexibility and lowers the cantilever's natural frequency; making it more susceptible to re5OD8IlCewith flow-induced vibration frequencies.

(b)

(a)

Fig. 2 (a) Crack initiated and growing from the weld root The crack bas extended almost to the top of the weld root pass.
The socket weld indicated is the weld most of concern for cracking. (b) Service induced cracks above the weld root pass.
Note lack ofweld penetration at root, the typical initiation point for mechanical fatigue cracking.

70

Fig. 3 Pipe specimen in the 3-point bender. Specimen is pipe socket coupling with pipe welded to both sockets. The photos
show two linear mays of sensors, one with a 150 kHz resonance (Physical Acoustics RlS) and the other with a broadband
response (Digital Wave BI025). The broadband response was limited with 100 to 1200 kHz bandpass filtering.

Maximum Load1

(Approx. ~112:-p""ip-e-yi""'eld:"':""":'stre-s-S':"'")

Hold Period(1-10 minutes)

I'

'I

Load

(kips)

Number of hOld periods varied from 1to 5 per test
Time (minutes) - - - .

Fig.4 Load histogram. Ramp loading has hold points at 4.45 kN (l kip) increments.

Fig. 5 - Four socket weld specimens (#3 is 13 mm diameter; all others are 19 mm diameter.)71

was perfonned at Pacific Gas & Electric Company's Technical & Ecological Services Laboratory. Loading was applied in stages up to yielding, in order to determine if thegenerated AE that occurs during elastic loading can act as an
identifier of weld cracks. To be a usable technique the applied loading must be significantly lower than the yield
point to prevent damage to non-cracked specimens.

The broadband sensor's signal wavefonns show a broadfrequency response, with maximum amplitudes at the lowest frequencies within the filtering pass-band. Figure 7
shows an example for a wave originating at the flawed weldof Specimen #2. This was typical of waves generating at
one-half yield stress. At lower loads, signals showed lower
amplitude and a reduced spectral content with just the lowest
frequencies. It was observed that the surface mounted sensors on the pipe OD detected predominately the flexural
wave mode.

A Io-ton hydraulic 3-point bender was used for loading.
13 mm, 19 mm, and 25 mm (1/2", 3/4" and I") nominal
diameter pipe specimens of Type-304 austenitic stainlesssteel were tested. Each specimen consisted of a pipe coupling with a pipe socket welded to each end. Figure 3 showsa typical specimen in the 3-point bender. Flaws were induced in the weld root, by preventing weld penetration in
the weld root pass. The welder could control, to an extent,
the lack of penetration circumferentially around the pipe
length and correspondingly the flaw length. This induced
various flaw lengths in the specimens. Typically for eachspecimen, one weld would contain a flaw; the other weld
would be without any significant flaw.

One reason for comparing the sensor types, broadband
and resonant, was to determine which would be bener in thefield testing. Considering the short distances between weld
and sensor, the lower sensitivity of the broadband sensor
does not appear to be an issue. The primary difference may
be in their ability to deal with background flow noise. Previous experience with power-plant piping noise (Morgan
and Foster, 1997) has suggested that high-pass filtering
above 200 kHz will eliminate a significant amount of flow
noise. As such, when noise is present the broadband sensor
with high-pass filtering should be preferred over a low frequency resonant sensor.

Piezoelectric sensors were attached to the specimen, as
shown in Fig. 3, to provide a linear location array. Bothbroadband (DWe BI025) and resonant (PAC R15) sensors
were used. The array setup provided the pipe axial locationof generated AE: i.e., at the welds, bend points, and otherlocations. AE monitoring was performed for both transient
waveforms and signal feature values utilizing a Vallen
AMS3 system. This setup provided a direct comparison
between emission generated at the flawed and good welds.

The effects of filtering using signal feature values are
displayed by Fig. 8. It shows for a specimen loaded pastyielding, cumulative number of events, energy and counts.
A count filter was used to remove events with very small
number of counts. A comparison of the two plots showsthat the filtering removes events that locate near the good
weld, but not at the flawed weld. It is expected that manyof these events were associated with rubbing from thebender's middle load point, which is located between thetwo welds.

Both tension and compressive loads were applied to thesocket welds across the flaw surfaces. The specimen was
ramp loaded as indicated by Fig. 4, to a maximum loadequivalent to one-half the yield point Then unloaded, eircumferentially rotated 900 and reloaded. The specimen was
loaded four times at each 900 orthogonal direction. The
orientation generating the greatest amount of AE was selected to continue loading to the yield point. Figure 5
shows four specimens after loading.

A major concern exists that water or steam flow in thelarge main pipes will generate a substantial amount ofbackground noise. While pipe flow was not simulated in
the lab, it is known that this type of count filter is also
proficient in removing flow background noise. As such,
this filter was used in the laboratory data evaluation.

Figure 6 shows the cumulative number of AE events,
energy and counts as a function of axial position; for aflawed specimen loaded past yield stress. Energy was ~corded as the true energy of the wavefonn (the integrated
area under the waveform). The figure shows most emission
was located at the flawed weld and at one of the bender support points. Emission was initiated first at the flaw location, and then at the support point. After bending, thespecimen visually showed a localized pipe deformation occurring at the support point. The figure provides two plots
comparing the broadband (top) and resonant (bottom) sensor
responses. These show similar event locations. The majordifference is likely a result of the different circumferential
locations of the two sensor arrays, resulting in dissimilar
wave path lengths.

In comparing the plots, filtering shows differences between cumulative event, energy or counts. Using counts as
an indicator of flaw location is less effective than energy or
number of events. This was found in common for a number of specimens. Overall. using energy as the indicator offlaw location showed the best correlation.
The Specimen #3 (shown in Fig. 8) was loaded past theyield point into gross bending. Figure 9 shows the cumulative AE versus load history for just the specimen's flaw
location. The specimen was initially loaded to approximately one-half yield stress, unloaded then reloaded to checkthe felicity ratio. This showed a ratio of approximately
80%, thus indicating material instability.
72

6IlJ

7

!DI

6

.tnr'l.......

1,..~......--~-...-,

Broadband Sensor(B1025) AlTay

DI••• - -Counts

5-Energy3

4-E¥8nts

D

2

j~

1~

O-h-+-.......~~~~~~~~ ........-+-.::,~~~-+- ...............- .......-+-............+O~

B
III

0

34

"0

36 38 40

~~

~~

I

~1&m
~~

~~

14IJJ ll"".......---~-...-,

15

1D
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IDJ

10

6IlJ
4111
D

5

D-h-+-...............~.............r-t-............-lI-r-.............~ ~ ~ ~.......--+-........- -......+-w.........4D
024

6

8

ro

~

w ~ m ~ ~ ~ ~ ~ ~ ~ 34 36 38 ~LoC8llon (em)Fig. 6 - Axial location ofAE from specimen #2 (1" schedule 80 pipe). Top plot shows linear located AE from the broadband
sensor (BI02S) array. Bottom plot shows linear location from the resoDaDt seDSOr (RIS) array.
lIllY] Chan: 2 18:18:53 855
8.25

Index: 42 P-e-: 32

first hit

8.2
8.16
8.1
8.85
8

-e.•
-e.l
-e. us-e.2-e.25.....~_...._ ..._ ..._ ..._ ..._ . ._ ...
8
Cell]

58

lee

158

288

258

388

3S8

---,~---,~--r_--r_--r_~--l

488

468

688

658

see

658

788

7!58

U5

DSP: F_t Fourier Tr_forll
T1_ IUme.: 8.ee - 489.48 U5
Saples alculated: 2848

24

28
16
12

lee

998

288

usee

1188

1288

kHz

Fig. 7 Transient waveform and FFr spectrum of broadband sensor from specimen #2. Top plot shows waveform withwindowed time period used in FFr spectrum c:alculatiOD. Wave originated at flawed weld location with loading at
approximately one-balfyield stress. Bottom plot shows associated spectrUm.

73

I~on-Filtered

3!iOO
:III)

2!ilXI
~

.

....

35

-Enargy

3J
25

--~s

~

·CllIIlll(l

15

11D1

10 "

9XI0

5

1

~ 't Support Point

31

j

Good Weld~+-.-+---,.....ICQ~4!=j:~..-.;~r-+_"""T'"...,:.......,:...:.:.:.;:::i~IIIIF<~~~'I'I!'.~oaii
10 12 1. 16 18 ~ 22 2. 26
3J 32 34 36 33
42 44 46 48 50
~

O
.,

f

I

19X1

=

'E
o

Data

.a

Support po1ntJ

3!DJ~:III): I Filtered Data I

t"f

.~

Loading Point

19X1

25~
15

~

ro

9XI

5

o
10

12

14

16 18

2Il

22

~

26

4B

3J 32

34

36

3B

.a 42 44 46 48 m

0

LDca1IDD (an)Fig. 8 Filtered vs. non-filtered AE from specimen #3 (112" schedule 80 pipe). Both plots show linearly located AE from theresonant sensor (R.1S) may. Restricting data to first hit sensors ;:: 2S counts provides the filter.

70.00
60.00

!=- 50.00- - -calculated Elastic Struss
I---Ewnts• • • • ·Counts
Energy

7000
6000

---

1

40.00

i 30.00

I

20.00
10.00

-

....... ::::::::::: .

E=====~=:;~~~~--:;;;:--

J!I

5000 §

84000 ..
3000

f

~

2000
1000

0.00 ~~=---+-----I1!!II!!!!l1!l!~~=-----+------l,----+------+----4
0
o
0.5
1
1.5
2
2.5
3
3.5
Load (tons)

Fig. 9 Cumulative events at flaw location from specimen #3. Plot shows emission versus 3-point bender load. Specimen
was loaded to 1.4 1, completely unloaded, and reloaded to 3.3 1. Plot is for filtered ~ 25 count filter) resonant sensor may
data at the flaw (within ± one pipe diameter from flaw location). The specimen's minimum yield stress is stated to be 210
MPa (30 ksi) per the ASTM A312 TP304 standard.
To identify the main load direction that was generating
flaw AE, results from each 90° orthogonal loading wascompared. Tensile strain is created on the side of the pipe
opposite the loading point, and compressive strain on theloading side. Cumulative energy, events, and counts forboth Specimens #2 and 3 were compared in Figs. 10 aOO] ]. Both specimens were loaded to approximately one-halfthe yield stress. They indicate that most emission is being
generated from tensile loading of the flaw. Note the graphfor cumulative counts in Fig. 1], 'which shows a highnumber of counts for the non-flawed IQcations. This isconsistent with the result in Fig. 8, where a high number ofcounts are seen at the good flaw location.
In order to help connnn the presence and circumferential location of the flaws; some flawed specimens were ultrasonically scanned. As noted before. there is difficulty ininterpreting ultrasonic signals at the weld-root area. It
helped that specimens were scanned after loading to better
detect any crack enlargement. Figures 12 and 13 compare
the signal amplitudes of ultrasonic test (tIT) and AE energy
measurements. Figure 12 shows three specimens loaded to
74

~

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4lII
350

lIllO

DI

Gl

J:

j~

150

ZlI

lao
50
2

o

3

2

. . .NIl

Total Events (>25 Ccl.IIlt Filer)

--

l1lO

3

2

3
_Fellll

Total Energy (>25 Count Riel')

Bend
Pel.. 1

Flaw IocatIcln (at Bend Polnt 1)

~

•

I

e.u. CCIuItI. arEnelgy

Bend
Point 4

- At n-dWllldo-

At 0IIler Iocalicns

I

Bend
PoIft 3Fig. 10 Circumferential location of AE from specimen #2. Graphs are for the resonant sensor array data with filtering as
shown. Black bars show data from the flawed weld location. White bars show data from all other locations combined. The
flaw length is short as indicated by the cross-sectional view. Its location is centered at bend point 1. Loading at bend point 3
puts the flaw at bend point 1 in tension.

1lIlII

1&

12

l Gl

10

12IIl

I e

11lIII
aa
a

! 6

.,

&

2

o

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e.a.-_o3

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e.PIlaC

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Total CCIIris

2

--

3

Total Enefvy (>25 Count Rller)
Bend

Paint 1

fIlIW location (Ill Bend Paint 4) ~~ifil~,
Cu!IUlIIMl Evem. COInI. or Energy• • At lIawcl MId

D· AtCllharloc:8Oons
I
Bend

Point 3Fig. 11 Circumferential location of AE from specimen #3. Graphs are for the resonant sensor array data with filtering as
sbown. Black bars show data from the Sawed weld location. White bars show data from aU other locations combined. The
Saw length is short as indicated by the cross-sectional view. Its location is centered at bend point 4. Loading at bend point 2
puts the flaw at bend point 4 in tension.

75

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SIal

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mal

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lllIl

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~

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2'0

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8....

..... 1lIl:Il

f::

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PIatic .... at WClId

U1': IR

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23.ft

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~

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418

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OS WELDS - Pipe Circumference (em.)

Fig. 12 Circumferential location of AE from specimen #2. Graphs are for the resonant sensor array data with filtering as
shown. Black bars show data from the flawed weld location. White bars show data from all other locations combined. The
flaw length is short as indicated by the cross-sectional view. Its location is centered at bend point 1. Loading at bend point 3
puts the flaw at bend point 1 in tension. Top: Upstream welds; Bottom: Downstream welds.

....
....
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llII

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Fig. 13 Correlation of AE and UT data from specimens loaded to one-half yield stress. Format of the figure is the same as
for Fig. 12.

76

\

LOAOOIlEC1lON I

Fig. 14 (a) Torque wrench and clamp loading. (b) Field test ofsocket weld. Bolt heads are provided on
all four side of clamp to allow for torque loading in different orientations without rotating clamp.

3. Field Application

the yield point. Figure 13 shows three different specimens
loaded to just one-half the yield point. As such, specimens
loaded to yielding include the initially induced flaws,
any material damage generated by the yielding.

am

To demonstrate the technique, cantilevered small-bore
socket welds in use at the power plant were tested. To
strain the socket weld, a pipe clamp with a bolt head fortorque wrench loading was attached to the pipe, as shown in
Fig. 14(a). The desired torque wrench load was calculated toprovide a stress level at one-half the yield stress. That load
is a function of the pipe configuration, pipe diameter, loading direction, and the placement point of the clamp.

The figures provide comparisons for both the specimen
welds. The welds are identified as the upstream-side
downstream-side welds. The specimens in Fig. 12 were
loaded to yielding to identify a baseline cumulative energy
level. It was selected to be 7 x 1()6 energy unit (eu) tomatch the energy generated by specimen # 12. [Note, thisenergy unit (eu) is as measured by the Vallen AMS3 AE
system.] The UT peak signal amplitudes are shown as apercentage of the UT DAC, with the identified UT signal
end-points; i.e., distance-amplitude corrected echo. Correlation shows the AE energy peak locations matching the UT
signal end-points. This is expected, as the crack tip extension is a known source for generation of AE. For eachspecimen in Fig. 12, a visible plastic hinge formed.

amFigure 14(b) shows a test in progress. Four ISO-kHz
resonant sensors were attached to the pipe using vacuum
grease and tape. Three sensors are shown in the figure. on
the socket coupling, elbow. and small-bore pipe. Not visible is a sensor on the bottom of the large-bore main run
pipe. These sensors were aligned in a linear array to clearly
distinguish emission from specific locations. Identifying
any emission associated with the pipe clamp rubbing, andwater/steam flow through the main run pipe was particularly of interest. As expected, it was found that the countfilter (~ 25 counts) eliminated most emissions from theclamp and fluid flow. Consequently, a simpler two-sensor
array could have been used.

Specimen #11 is of interest as the visible plastic binge
formed on the downstream weld, where significant AE energy was emitted, but no UT indication was identified.
Generally, plastic yielding is not a good UT reflector. Moretypically, plastic hinges would be expected to form at theinduced flaw locations, as occurred with Specimens #12 arxI
#16.

Inspections were perfonned for three welds. AE was recorded and UT scans were performed. No reportable UT
indications were identified. No acoustic emission was recorded that exceeded the energy level of 7 x 10' eu.

A comparison was made of the AE energy measurements generated by the specimens loaded to one-half yieldstress (Fig. 13) and the yielded specimens (Fig. 12). Theenergy level for the former exceeds.7 x 10' eu (10% of thebaseline level) for all specimens at the flawed weld. Thislevel was found to indicate a flaw in these specimens, aswell as others not shown in the figure. Specimen #21
showed a level that exceeded 100% of the baseline energy
associated with yielding. This specimen demonstrated material instability even at low loads; by continued emission atconstant load hold.

4. Conclusions
The mechanical testing showed that the AE generated
during elastic stressing of the socket weld provided a precursor to identifying flaws. Three classical AE signal features;i.e., cumulative number of events. (true) AE energy. andAE counts, were utilized as an indicator. Best fit was foundwith the measured energy. An energy criteria level wasestablished to separate flawed welds from non-flawed welds.
71However, both lab and field experience demonstrated thatmost good welds generated very little emission when loaded
up to one-half the yield point. certainly much lower thanthe energy criteria level.
Evaluation of the waveform showed the frequency response from the flawed welds to be of broadband allowing
detection by either resonant or broadband sensors. The presence of field background noise and the different susceptibility of the sensors should dictate the best sensor type.
Correlation was shown between AE energy levels andUT scans. The flaw ends, as located by the UT scans, typically crack tips, were found to be strong AE emitters. As
such. the AE monitoring is demonstrated to be a method of
identifying the crack extension point.
Most AE was generated with the flaw under tensileloading. Very little emission was produced during compressive loading. As such, loading direction for detection is
critical. This requires that a tensile load be applied in all
four 90 0 orthogonal directions from the weld's axial direction.

References
B.C. Morgan, and C.L. Foster (1997). "Acoustic Emission
Monitoring of High. Energy Steam Piping, Volume 2: Basis for Acoustic Emission Monitoring", EPRI Repon TR105265-V2.

78

Feature Extraction of Metal Impact Acoustic Signals For
Pattern Classification by Neural Networks
Shahla Keyvan and Rodney G. Pickard
Abstract

The feasibility of using neural networks to recognize

and classify AE signals for improvement of LPMS perfonnance has been and continues to be explored (Olma mi

Acoustic emission (AE) impact signals are created innuclear reactors when a loose part strikes the wall of a pressure vessel. steam generator tube sheet. or other object
within the reactor coolant system (RCS). According to the
US Nuclear Regulatory Commission (NRC) Regulatory
Guide 1.133. any reactor licensed after 1978 must have systems to detect loose parts within reactor vessels and primarycoolant systems. As part of a continuous effort to improve
Loose Parts Monitoring System (LPMS) effectiveness thefeasibility of using artificial neural networks to recognize
and classify AE signals has been and continues to be explored. In searching for the ideal means of exploiting neuralnetwork technology for this particular problem much attention has been devoted to using feature exttaction to preprocess AE signals prior to presenting them to a neuralnetwork. The goal of this research was to perform various
types of feature extraction on AE signals created in the laboratory and examine the effectiveness of a Fuzzy ARTMAP
neural network in classifying these signals. The results ofthis study indicate that feature exttaction do not increase
network effectiveness. However, artificial neural networks
stiJI have a high potential for successful application in
LPMS using raw acoustic data.

Wach.1994; Khan. 1995; Nagaraj. 1995; Pickard. 1997).
Pasl research has already proven neural networks to be acapable tool well suited for the loose parts monitoring problem, but their full potential has yet to be realized (Keyvan
and Nagaraj, 1997).
One postulated means of realizing this potential is to
extraCt certain key features from the raw AE data and using
these features as neural network input data In cases where
numerous independent features can be exttacted from a set ofraw data. this process is proven to be quite effective (Keyvan et aI.• 1997). but extracting independent features fromAE data is a challenge undertaking.
It is a common practice to extract features from acoustic signals for signature analysis (Edmonds et al.. 1992).
Many researchers have even utilized neural networks for
eXtraCting features from acoustic signals (Yang andDumont. 1991; Szyszkko and Payne, 1991).
The objective of this paper is to present the development of representative features from metal-impact AE signal, and examine their effectiveness in pattern classification
using a Fuzzy ARTMAP neural network (Carpenter and
Grossberg. 1992). All principle aspects of this processfrom the generation of the raw data to the testing results
will be discussed.

1. Introduction
The primary purpose of the loose parts monitoring system, as the name implies. is simply to detect the presenceof parts that have become loose (and possibly completely
unattached) within the reactor coolant system (Keyvan mi
King. 1992). Although the purpose is simple enough. it israther difficult to accomplish. Loose parts are detected based
upon their acoustic emission (AE) as they strike other objects. The colossal background noise indigenous to powerreactors inhibits accurate detection of the acoustic signatureof loose parts. and much of the technology used to this cOO
has proven to be inadequate. The result of this unfortunate
pairing is that present loose parts detection methods simply
do not provide adequate identification and diagnosis of loose
parts.

2. Neural Networks and Fuzzy ARTMAP
It is appropriate to define some neural network related
tenninology used in this work.
Features- A feature is a unique measurable attribute of apattern. A feature vector is a collection of features thatcompletely describe a pattern. This feature vector is presented to the artificial neural network as input during thetraining process.
Feature Space- Feature space is a set of feature values that
detennines a pattern class. from which the feature vector is

Received 24 May 1998. The authors are affiliated with Nuclear
Engineering Depanment, University of Missouri-Rolla, 102
Fulton Hall, 1870 Miner Circle, Rolla, MO 65409-0170 (keyvan@umr.edu).
.
Journal of Acoustic Emission
Volume IS. Number I'"

detennined.

79073Q.OO50197/S5JXJl15079·87

Copyrighl@I997 Acoustic Emission GroupVigilance parameter- Vigilance parameter p (0 ~ p ~ I) is
used by the adaptive resonance theory family of neural networks in order to detennine if an input pattern is "similar"
to the existing prototype. The vigilance parameter (p) sets
the criterion for matching, under the exact same conditions,lower vigilance leads to coarser categories and higher vigilance to finer categories. The optimum p is obtained bytrial and error.
Fuzzy ARTMAP (FAM) neural network is selected forthis application due to its capability of handling nonstationary stochastic data. as well as, its supervised learning characteristics. It is beyond the scope of this paper to describe
the intricate details of the functionality of the Fuzzy
ARTMAP algorithm. Nevertheless, it is useful to discuss
briefly the general operation of the FAM network.
Adaptive Resonance Theory (ART) represents a familyof neural networks which self-organize categories in response to arbitrary sequences of input patterns in real time
for pattern recognition (Carpenter and Grossberg, 1988).
ART network architecture consists of two major subsystems called the attentional subsystem and orienting subsystem. The attentional subsystem as shown in Fig. I consists of two layers of processing elements, the Fl layer(input layer) and the F2 layer (output layer). The Gain
Control in ART networks is a mechanism which acts toadjust overall sensitivity to input patterns and to coordinate
the difficult functions of the ART subsystems.
The orienting subsystem acts as a novelty detector.
The orienting subsystem in ART networks is responsible
for controlling the fineness or coarseness of the pattern being recognized. It has only two input signals and one output signal. The two inputs are the input data pattern andthe overall activity in Fl. The single output of the orienting system goes to F2 as a reset wave (see Fig. 1). The
resonance and reset is accomplished in the orienting subsystem through a very important parameter called the vigilance"p". The vigilance is a parameter which points to the degree to which the system discriminates between different
classes of input patterns. It can have any decimal value
between 0 and I.

.---_
..
!

The FuzzyARTa complement coding preprocessor
transfonns the Ma-vector a into a 2Ma-vector A (a, aC) at
the FuzzyARTa field Fg , where aC is the complement ofvector a (i.e., a,c I - a j for each nonnalized element i ofthe vector a). A is the input vector to FuzzyARTa field F;'
The input to F~ like FuzzyARTa is the 2Mb-vector B(b, be), where bC is the complement of vector b. For each
input (I) in the 0 to I range presented to the network, thenet value at the output is compared as

=

=

=

netj (I) = I I 1\ Wj II (a + IWjl)
where a = 0.00001, I = input vector, W. = adaptive weights
or LTM traces (initialized to 1),1\ = theJfuzzy And operator.
The maximum ofnetj (1) is selected. For this node J, it ischecked if the vigilance (p) parameter is met. that is:I I 1\ Wj III I I ~ P

I Gli> COlll ,01:

During the supervised learning the FuzzyARTa module
receives a stream (a) of input patterns and FuzzyARTb reo
ceives a stream (b) of input patterns, where (b) is the correct
prediction given (a). These modules are linked by an associative learning network and an internal controller that en·sures autonomous system operation in real time.

_--_._---------_._--_._--,._-_._--_.~

AIIe.li....1Sut.yllem ~ Orie.li.g
F2 Layer: !llbsySlem

;

••
:

Fuzzy ARTMAP (see Fig. 2) is built with a pair offuzzy ART modules, Fuzzy ARTa and Fuzzy ARTh. These
networks are designed as generalized ART except the settheoretic operation of intersection (n) is replaced by the
fuzzy set theory conjunction (1\). This fuzzy set operator
makes fuzzy ARTMAP capable of handling both analog andbinary data and makes the ART neuro-dynarnic equations
much simpler (Carpenter and Grossberg, 1992). As shownin Fig. 2, the two fuzzy ART modules a and b are connectedwith an inter-ART module called the Mapfield Layer. The
Mapfield associates the fuzzy ARTa recognition categories
with the fuzzy ARTh recognition categories. If a matchoccurs at the Mapfield layer then learning takes place. If amismatch occurs, then the match tracking process is initiated. Match tracking is a method of forcing the mismatched
fuzzy ARTa winning node to reset by incrementing p~ (vigilance parameter) by a small value, such that the current
winning node fails the resonance condition in the subsequent trial.

I

I :•
~:

+

·

Oil $goal:~

resulting in node J being committed, else reset occurs at the
selected output node and is inhibited for further competition
for input 1. This process is repeated until the vigilance cri·
teria equation is satisfied. If the output node satisfies thevigilance criteria then learning takes place using

I

I

Wr

I

·

w

=(I 1\ wjld).

I

I

L

3. Data Acquisition
I n""lVeCIOt

Fig. I General ART network.

The impact signals were generated by striking metal
tubes with small metallic masses. The tubes were open atfield ~b

w·
I

X...

Fuzzy ARTa

X· and Xb represent activity of the FI layer
y. and yb represent activity of the F2 layer
Wj's are two top-down adaptive weights
p is the vigilance parameter
X ab represent the activity of the mapfield layer fob
Fig. 2 Architecture of fuzzy ARTMAP neural network.

Fig. 3 Short tube with pendulum.

one end and plugged at the other with a metal pipe cap. The
striking was accomplished by way of pendulum action inwhich the pendulum was formed by attaching a mass to astring that was in turn attached to the top of the tube itself(see Fig. 3). The pendulum would be held roughly perpendicular to the side of the tube with the string taut and released such that it swung approximately 90° before striking
the side of the tube at its approximate midpoint.

•
•
•
•
•
•
•
•

Long Tube, 100 g Mass, Top Sensor(LTIOOGT)Long Tube, 100 g Mass, Bottom Sensor (LTlOOGB)Long Tube, 50 g Mass, Top Sensor(LTSOGT)Long Tube, 50 g Mass, Bottom Sensor(LTSOOB)
Short Tube, 100 g Mass. Top Sensor(STlOOGT)
Short Tube, 100 g Mass, Bottom Sensor (STlOOGB)
Short Tube, 50 g Mass, Top Sensor(ST5OGT)
Short Tube, 50 g Mass, Bottom Sensor(STSOGB)

The piezoelectric accelerometer output was routed to asignal conditioner which applied a 10 kHz low-pass ruter tothe signal and sent it to a PC-based high-speed data acquisition board. Signals were digitized at a rate of 50 kHz. Examining the plot of each data set revealed that 700 data
points were sufficient to capture the first waveform envelope for each data set. Hence, the first 700 data points ofeach data set were selected for this study.

For each of these eight categories or classes. 60 signals
were generated to produce a net of 480 signals. From thispopulation of signal. 10 from each category were selected
for neural network training, and 10 others from each category were selected for testing. Thus, from this populationof 480 signals, 160 were randomly selected for use in thisproject.

Signals were created using either a long or a short tube
in conjunction with either a lOOg or a 50g mass. These
two variables produce a net of four possible combinations(e.g., long tube, 100g mass; long tube, 50g mass; shorttube. loog mass; and short tube, 50g mass). An additionalvariable was the placement of the sensor that collected thesignals. The sensor was initially placed at the bottom of
the tubes, and then the experiments were repeated with thesensor at the top of the tubes. Introduction of this variable
creates the following eight possible combinations:

For the purpose of these experiments, a certain degreeof randomness in generating the signals was preferable toextreme consistency simply because the varied signals provide a more realistic sorting challenge for the neural network. Thus, only minimal effort was made to consistently
reproduce the set of signals for a certain class. For example,
visual inspection was considered an adequate means of determining that the pendulum was perpendicular to the tube
prior to its release. Similarly, sensor placement was unlikely to be exact when the sensor was moved from top tobottom and tube to tube.
81

4. Feature Extraction

information is certainly contained in some of the other frequency-related features discussed thus far. Average frequency
can often be estimated from knowing the frequency peaks, it
can be easily approximated from a frequency histogram, andit can be calculated exactly from the unabridged frequency
spectrum. Since the point of feature extraction is, among
other things, to provide information to a neural networkthat is directly useable rather than forcing the network to
discover for itself the relevant characteristics of its input
data, calculating the average frequency up-front can be extremely useful. The average frequency for each signal was
calculated similar to the average frequency calculation for ahistogram bin.

Among the more obvious features that describe an
acoustic signal are its frequency components. Figures 4 and5 i1Iustrate the relationship of the frequency to time domain
for a particular signal. Figure 5 is the frequency spectrumof the time domain signal shown in Figure 4. Transforming a time domain signal to the frequency domain produces
a data set that contains no more or no less information than
the time domain, but; for many applications, the frequency
domain signal is more useful. For example, the human ear
is designed to function in the frequency domain. People caneasily recognize familiar voices based upon the frequency
spectrum of the voices.

All other things being equal, impacts of greater energy
produce measurably higher frequency impact signals (Mayoet aI., 1988); so the frequency spectrum may also contain
information that will assist the neural network in sorting
impact events by their impact energy. A more obvious
feature for sorting by impact energy is the root mean square(RMS) amplitude of the entire impact signal. Since, higher
energy impacts will produce larger acoustic signal amplitude. clearly this feature should be valuable for sorting impacts by energy.

The same is true of impact signals. Each region in a
nuclear power plant (steam generators, reactor pressure vessel, etc.) has its own unique set of resonance which, whenstruck by a loose part, manifest themselves in the frequency
spectrum of the resultant acoustic impact signal. Information in the frequency spectrum of impact provides valuable
information for sorting signals by their point of impact.
Using the fine-bin frequency spectrum obtained fromperforming a Fourier transform on the original time-domain
signal as input to a neural network is unlikely to be theoptimal method for extracting the useful information contained therein. Since the aforementioned frequency shifts are
typically macroscopic, the efficiency of using the frequency
spectrum is, in some cases, enhanced by using a coarse histogram of the spectrum as input to the neural network. Of
course, this potentially introduces a new variable into the
optimization process since varying bin widths should resultin varied network performance.

Signal RMS is calculated next. Like the frequency
spectrum, the time-domain signal may be broken into bins
and the RMS value computed for each bin. Doing so enables one to see the macroscopic behavior of the signal overtime. Macroscopically, a metal impact signal is an example of damped harmonic motion. The initial impact sets thematerial in motion. Then, inertia and the restoring force ofthe metal lattice attempt to keep the material in motion; but
the oscillations are exponentially damped by forces which
oppose the motion. Mathematically, this is expressed as:

Figure 6 is an i1Iustration of a frequency histogram. In
this case, the frequency spectrum depicted in Fig. 5 has been
converted into a ten bin histogram. For this project, thehistograms were created by breaking a frequency spectrum
such as the one shown in Fig. 5 into N equal bins. The
average frequency is then calculated for each bin.

RMS(t)=RMSI"oCxp (-At).

(I)

By calculating the signal RMS over equally spacedbins, the results may be used to calculate the exponential
damping factor (A). Because A is a function of the physicalsystem where the impact occurred, this feature also contains
information that is useful for sorting signals by impact
location.

Another means of capturing the macroscopic behaviorof a signal in the frequency domain is to simply note thepeaks in a frequency spectrum. Indeed, this may be a very
effective means of extracting useful information from thefrequency spectrum since it quickly identifies characteristic
resonance and fundamental frequencies specific to an object.
In this project, These peaks are identified by simply locating the largest spikes that appear in a frequency spectrum.
If two peaks are identified and are determined to be too close
together (Le., below the resolution threshold), the peaks aredeliberately identified as the same peak.

Figure 7 illustrates graphically how this damping factor
is calculated. Mathematically, the damping is factored bybreaking the time domain signal into a number of bins. theRMS for each bin is calculated and the logarithm is taken
for each of these RMS values. Finally, a linear leastsquares fit is performed on these values using a standard
least-squares algorithm to find a linear equation that best fits
the log of the RMS values as a function of time. The slopeof this equation is the damping factor, A.

One simple feature that can be useful in sorting AE
signals is to note the mean frequency of the signal. This
82

.:

....

"

..

'c',. F "i:''! :~,.~

,
;:
"

.~

..

..

','

"

.t;:

~ol,

~.

·1',

Ittf-,'H'1'1;11+-

.

, :;

"....

;~:
". ",...

.~:-.;::."'~!/;' -.: I

Time (sec)Fig, 4 Time domain signal.

o

5000

10000

15000

20000

25000

Frequency (hz)Fig. 5 Frequency domain signal.
As a matter of practicality, calculating this damping
factor in the manner described is a rather simple process,but the results can be somewhat misleading if DC bias
was present in the signal. In this case. the entire signal isshifted such that it is no longer centered about the x-axis;
and, as a result. the calculated damping factor, whilemathematically accurate. may no longer describe the signal in
the same manner. Additionally. material anomalies may prevent the oscillations from following equation (1) exactly; and.
in such a case. the signal can be significantly asymmetricwith respect to the x-axis.

u..------------------------r........
i
:0

a

::t

HOO

lOCO

1100

10000

1IlOl

'HOO

"100

IQCOO

bIOI

'MOO

FrcquenC)' (hZ)Fig. 6 Frequency spectrum histogram.
Fortunately, a simple solution was developed, implemented, and found to be quite effective. For any given signal, the damping factor was calculated using the previously
described method. Next, a similar damping factor was calculated using only the positive portion of the signal; and,
finally, a damping factor was calculated using the negative
portion of the signal.

S. Testing
Eight basic tests was run with four different types offeature data set. Generally, a test would use two or four
categories of signals and attempt to sort them into theirappropriate two or four categories. For each test several
sets of signals were used. Ten signals from each of thesecategories were used for training data Similarly. anotherten signals from each category, separate from those used fortraining, were used as test data

Table I List of various features extracted.
Feature DescriDtion
Raw Time-Domain Data
Unabride:ed Freauency Soectrum
Freauency Soectrum Histomun
Freauencv Peaks
Positive DamDine: Constant
Nel!ative DamDinl! Constant
Net DamDin2 ConstantAVera2e Freauencv
Sil!nal RMS

Data Points
700
512
10

The one exception to these generalizations is a particular test that, for reasons that will be discussed later. attempted to sort four categories of signals into only twogroups. Like the others. this test used ten signals fromeach category (for a total of 40 signals) for training and another 40 signals for testing data, but these 40 testing signals were to be classified into two groups. Table 2 summarizes the various tests performed.

5
I
I
I

The types of feature extraction and the abbreviations
that will be used to reference them throughout the remainderof this work are listed below.

I
I

If the signal has no DC bias and is essentially symmetric about the x-axis, the positive damping factor, negative
damping factor. and net damping factor will be nearly equal
in magnitude. For signals with DC bias or appreciableasymmetry, these factors will be significantly different.

•••

Table I provides a summary of the various features usedin this project and the number of data points associated witheach particular feature. While raw time-domain data represents no feature extraction at all, it is included in the tablefor comparison.

•84

Raw Data: No feature extraction at all. just the raw
time domain signal.
Frequency Spectrum: The complete frequency spectrum obtained from converting the time do,:"ain signal
to the frequency domain.
Standard Feature Set: The frequency histogram.
frequency peaks, damping factors. average frequency.
and signal RMS features described in Section 4.
Scaled Feature Set: Same as the standard feature
set. but the feature values are scaled such that all are on
the same order of magnitude.

U)

.5
c:l

g3: 2000
ca
Z
...
~

o> 1500"0~;;;

B 1000

-=u
en
~
c:t::
ca
c

500 .

.!:!l
en
N
0
0
0

c::>

~

0
0

0

\0
N
0
0

0

00
M

0

0

0

0
0

V')

0
0

N
\C
0
0

0

~

r-

0
0

c::>

00
0\

\0

oo
0
C!

0
0

0

0

0
0

0

N
N

M

~

\0

C!

C!

0

0

<:>

~

0

00
V')

0

0

0

N

oo

0\

<:>

<:>

0

r-

0

0

~

0

Time (sec)Fig. 7 Exponential signal damping trend.
Table 2 General tests performed.
Data Sets Used

CateJ!:ories

Test number
Test 1
TeSl2
Test 3
Test 4
Test 5
Test 6LTIOOB
LT50B
STlOOB
ST50B
LTlOOB
LT50B

LT100T
LT50T

STlOOB
ST50B

STlOOTB
ST50TB

4
4

Test 7
Test 8LTlOOB
LTlOOB

LT50B
LT50B

STI OOB
STl OOB

ST50B
ST50B

4
2

Since it has not been previously mentioned, the"scaled feature set" is deserving of some additional description. There was some concern about the wide range ofvalues between different types of features. This concern
seems particularly valid given that a FAM input data file
must be normalized prior to use. This could resuh insmall feature values to loose their significance completely. To remedy this situation, the values of the standard feature set were scaled such that all features have values on the same order of magnitude, thus producing the"scaled feature set".

Similarly, Tests number 5 and 6 call upon the network
to distinguish between lOp and bollom sensor mounting
while simultaneously differentiating between short and longtubes. Test number 7 also calls upon the network to separate
data into four categories, but does not include sensor placement as a variable. Rather, the network is to sort long andshort tubes (as in Tests 5 and 6) as well as sorting by themass of the pendulum. Because Tests number 5-7 have four
possible outcomes, it is to be expected that, all other things
being equal, they represent a greater challenge for the network
than do Tests 1-4.

Recalling the labeling convention discussed in section
3, one can see from Table 2 that in Tests number 1-4 theFuzzy ARTMAP network is called upon to differentiate
between top and bottom sensor mounting.

Test number 8 is comparatively unusual in that four
categories of input data must be sorted into only two categories of output. This test was arranged such that the network
was to sort only by the mass of the pendulum used. In sodoing, the network would be required to ignore the significant
85

Table 3 Success rate summary for each test in %.
TEST # 1-8% success rate
1

2

3

4

5

6

7

8

100

100

100

80

83

85

85

83

80

85

95

75

93

65

53

80

Standard Features

65

85

95

85

78

83

78

70

Scaled Features

100

95

80

75

75

78

78

78

difference between using short and long tubes. It was theintent of this test to observe the ability of the network toignore certain stimuli, because doing so provides anothermeans of gaining insight into network response to various
features. Table 3 summarizes the results achieved for eachfeature set on each test.

Supervised Learning of Analog Multidimensional MAPs".
IEEE Transactions on Neural Networks, 3(5),698-713.

6. Conclusions

M. B. Christopher (1995), Neural Networks for Pattern Recognition, Clarendon Press, Oxford.

From Table 3 it may be concluded that, overall, thebest results were obtained using raw data as input. In
Tests number 1-3, the network actually achieved 100%
correct classification using raw data input.
Although the network performed well when using thescaled feature set, this work does not support the assumption that feature extraction will improve neural network
performance in classifying acoustic signals of this type.
It is, of course, recogniud that new features may be cbveloped that could potentially lead to higher success rates
than those obtained using raw data. Given the challenge
of developing independent features for acoustic signals andthe remarkable results achieved using only raw data, theincentive for developing such features seems less thanoverwhelming.
The results from this study as well as a prior work
(Keyvan and Nagaraj, 1997) indicate that artificial neuralnetworks indeed have high potential for successful application in pattern recognition of loose pan acoustic emission
signals. In addition, there is no need for extra effort offeature extraction in pre-processing of network input data,
the raw acoustic data will do the job.

G. Carpenter and S. Grossberg (1988), "The ART of Adaptive
Pattern Recognition by a Self-Organizing Neural Network",
Computer, 21(3), 77-88.

E. A. Edmonds, L. Y. Pam, and S. M. O'Brien (1992),"Automatic Feature Extraction from Spectrograms for Acoustic-Phonetic analysis", Proceedings ofthe JJth lAPR International Conference on Pattern Recognition. vol. II. pp. 701704.
S. Keyvan and R. King (1992), "The Role of Acoustic Monitoring as a Diagnostic Tool in Nuclear Reactors" Journal of
Acoustic Emission, 10(3-4),91-95.
S. Keyvan and J. Nagaraj (1997), "Pattern Recognition of
Acoustic Signatures Using ART2-A Neural Network". J.
Acoustic Emission. 14(2),97-102.
S. Keyvan, X. Song, and M.L. Kelly (1997), ''Feature Extraction for Artificial Neural Network Application to Fabricated Nuclear Fuel Pellet Inspection," Nuclear Technology.
119, 269-275.
W. Khan (1995), "Application of AI Techniques to the Diagnosis of Nuclear Reactor System", M.S. Thesis, Universityof Missouri-Rolla.

c. W. Mayo, D. P. Bozarth, G. N. Lagerberg and C.L. MaAcknowledgments
This work was supported by the National Science
Foundation (Grant # ECS-9415004).

References
G. A. Carpenter and S. Grossberg (1992), ''Fuzzy ARTMAP: A Neural Network Architecture for Incrementalson (1988), "Loose-Parts Monitoring Systems Improvements", Electric Power Research Institute, EPRI NP-S743.
J. Nagaraj (1995), "A Prototype Diagnostic Monitoring System Using Artificial Neural Networks", M.S. Thesis, University of Missouri-Rolla.
B. Olma and D. Wach (1994), "On-line Classification of
Acoustic Burst Signals by a Neural Network Application to
Loose Parts Monitoring in Nuclear Power Plants", Proceed-ings of the IEEE Conference on Control Applications,Piscataway, NJ, pp. 1133-1138.
R. G. Pickard (1997), "Application of Artificial Neural
Network Technology to Loose Parts Monitoring Systems", M.S. Thesis, University of Missouri-Rolla.
S. Szyszkko, P.A. Payne (1991), "Artificial Neural Networks for Feature Extraction from Acoustic Emission
Signals". Proceedings of lEE Colloquium on measurement, Modeling. and lmagingfor Non-destructive Testing.
6, 1-6.

J. Yang and G.A. Dumont (1991), "Classification of
Acoustic Emission Signals via Hebbian Feature Extraction", Proceedings of IEEE International Joint Conference
on Neural Networks (UCNN), vol. I. pp. 113-118.

Conferences and Symposia
Fourth Far East Conference On NDT (FENDT '97)
October 8-11, 1997, Cheju-Do, Korea, sponsored by Korean Society of Nondestructive Testing.
The following articles are reprinted from its Proceedings.
Source Location in Highly Dispersive Media by Wavelet Transform. of AE Signals
Oh-Yang Kwon and Young-Chan Joo
SI-SIO
Acoustic Emission Monitoring of the Fatigue Crack Activity in Steel Bridge Members
Dong-Jin Yoon, Seung-Seok Lee, Philip Park, Sang-Hyo Kim, Sang-Ho Lee and Young-lin ParkS II-SI8
Acousto-Ultrasonic Evaluation of Adhesively Bonded CFRP-Aluminum Joints
Seung-Hwan Lee and Oh-Yang Kwon
S 19-530
Estimation of Initial Damage in Concrete By Acoustic Emission
Masayasu Ohtsu, Yuichi Tomoda and Taisaku Fujioka

S31-S39

Application of AE to Evaluate Deterioration of Port and Harbor Structures
Kimitoshi Matsuyama, Akichika Ishibashi, Tetsuro Fujiwara, Yasuhiro Kanemoto, Shiro Ohta,
Shigenori Hamada and Masayasu Ohtsu
S40-S49
Acoustic Emission Diagnosis of Concrete-Piles Damaged By Earthquakes
Tomoki Shiotani, Norio Sakaino, Masayasu Ohtsu and Mitsuhiro Shigeishi

S50-S59

Observation of Damage Process in RC Beams under Cyclic Bending by Acoustic Emission
Mitsuhiro Shigeishi, Masayasu Ohtsu, Nobuyuki Tsuji and Daisuke Yasuoka S60-S69
Spectral Response and Acoustic Emission of Reinforced Concrete Members under Fatigue
Bending Yasunori Sakata and Masayasu Ohtsu
S70-S79
Acoustic Emission Behavior during Tensile Defonnation of Welded Steel Joints
1. H. Huh, K. A. Lee and C. S. Lee
880-S88
Acoustic Emission Signal Analysis in CIC Composites
Ja-Ho Koo, Byung-Nam Kim, Manabu Enoki and Teruo Kishi

S90-S94

Congreso Regional de Ensayos No Destructivos y Estructurales
Oct. 27-30, 1997, Mendoza, Argentina, sponsored by Comisi6n Nacional de Energfa At6mica
and Universidad Tecnologica Nacional. The following article is reprinted from its Proceedings.
Recent Development in Acoustic Emission

Kanji Ono

S95-S102

40th AEWG Meeting, Program and Abstracts
81 03-S 11 0
14th International Acoustic Emission Symposium & 5th Acoustic Emission World Meeting,
S I11-S 124
Abstracts of Oral Briefs
6th AECM Symposium, Program
8 125-S 126
S-i

Fourth Far East Conference on NDT (FENDT '97)
Cheju-do, Korea, October 8-11, 1997

SOURCE LOCATION IN HIGHLY DISPERSIVE MEDIABY WAVELET TRANSFORM OF AE SIGNALS
Ob-Yang Kwon and Young-Chan Joo
Department of Mechanical Engineering, Inha University
253 Yonghyun-dong, Inchon 402-751, Korea

ABSTRACTA new technique for the location of acoustic emission(AE) sources in thin plates has beenstudied using wavelet transfonn of AE signals. The detected AE signals were pre-processedusing wavelet transfonn to decompose the low frequency flexural wave and the high frequency
extensional wave. Since the flexural wave is highly dispersive, a decomposed flexural
component has been extracted for a single frequency or within a very narrow frequency bandby the inverse wavelet transfonn. The result of source location has been compared with thoseby the conventional as well as the cross-correaltion method.

INTRODUCTION
Source location can be regarded as the most practical thus important function of AE testingof real structures since it provides a quick or real-time access to the region of interest, damaged
or being damaged. Numerous studies of source location have been done since the application
AE technique to the hydroproof testing of Polaris missile chamber in 1964[1], most of which
are well documented in the literatures[2]. When the AE source location technique had been
applied for the thick-walled structures such as nuclear reactor vessels, the conventional method
based on the threshold crossing may have no problem. On the other hand, when it is applied
for thin-walled structures such as aerospace structures such as composite shells or aluminum
panels, the conventional method cannot be successful any more. Plate wave consists of thenondsipersive extensional wave and the dispersive flexural wave. The large difference inamplitude between the extensional and the flexural mode can cause a serious problem insource location[3,4]. By introducing wavelet transfonn of AE signals, the low-amplitude
extensional mode can be filtered out. The decomposed flexural waves have been selected forthe source location based on threshold crossing. The wavelet transfonn appears to have a great
potential for AE signal·processing[5].
81WAVELET TRANSFORM
The continuous wavelet transform(CWT) is defined as the sum of the signal multiplied byscaled, shifted versions of the wavelet function TJI over all time:C(sca/e, position) = [,f(t)'P(scale, position. t)dt

(I)

The result of the CWT are many wavelet coefficients C, which are a function of scale andposition. If we choose scales and positions based on powers of two which is so called dyadic
scales and positions. then our analysis will be much more efficient and just as accurate. We
obtain such an analysis for actual waveform data from the discrete wavelet transform(DWT).
C(a.b)=CU,k) =:LS(n)gj,k(n)where, (a = 2J ,b = k2 J ,j eN, k e Z)

(2)

noZ

In order to be efficient and useful, a method designed for analysis also has to be able toperform synthesis(an inverse transform). The wavelet method achieves this. The analysis starts
from s and results in the coefficients C(a,b). The synthesis starts from the coefficients C(a,b)
and reconstructs s. Synthesis is the reciprocal operation of analysis. For signals of finite energy,
there is two formulas to perform the inverse wavelet transform:• Continuous synthesis:

S(/) = _1_ HC(a,b)_1 '¥(x -b) da :b
K'f' R
..j;
a
a

(3)

where K'I' is a constant depending on 'P.
• Discrete synthesis:

Set) = L LC(j,k)'Pj,k(t)(4)

je ZlceZ

In the above Eq. 4, let us fix j and sum on k. A detail Dj is nothing more than the Eq. 5. Let us
sum on j. The signal is the sum of all the details: S = J e Z Dj

L

DJ (I) =L CU,k)'P (I)
•
J k

(5)

keZ

(6)

S2

The details have just been defined. Take a reference level called J. There are two sorts ofdetails. Those associated with indices j5J correspond to the scales a = ~ ~ 2J which are the finedetails. The others, which correspond to j>J, are the coarser details. We group these latter
details into the Eq. 6.
An efficient way to implement this scheme using filters was developed in 1988 by

Mallat[6]. The Mallat algorithm is in fact a classical scheme known in the signal processing
community as a two-channel subband coder.
For many signals, the low-frequency content is the most important part since it is whatgives the signal its identity. The high-frequency content, on the other hand, imparts flavor or
nuance. The original signal, S passes through two complementary filters and emerges as two
signals as shown in Fig. 1. The approximation, A is the high-scale, low-frequency componentof the signal. The detail, D is the low-scale, high-frequency component. The filtering process,at its most basic level, looks like this:C§:]1---1!

I
Ill=:>
lowpass

Filters

Lkiia=ss=11

Fig. 1 The filtering process
The decomposition process can be iterated, with successive approximations being decomposedin turn, so that one signal is broken down into many lower-resolution components. This iscalled the wavelet decomposition tree as shown in Fig. 2.

rWlr§]l ~
r~l §)
I c:A3 1

Fig. 2

1.5

{\
!\

D.S

A'J'
- = A.
l + D.
J,I

o I---'~'
·0.5

j

'. ,

."';t

The wavelet decomposition tree

83

\!

V

-1

I cD 31\ !\..3

5

0

2

Fig. 3

Daubechies wavelet

The arrival time can be calculated using one of the decomposed wavelets, Le. anapproximation or a detail. In this study, the detected signals from each sensor were
decomposed by the Daubechies[7] wavelet shown in Fig. 3 at levelS, i.e. the d5 for AI6061
plate and the as for CFRP laminate as shown in Fig. 4.

a5

o:r

-0.1

Fig. 4The original signal(S) from Al6061 plate and the decomposed wavelets

EXPERIMENTS
Source location was carried out on a 1200 x 1000 x 3 mm AI 6061 plate and a 650 x 650 x
4 mm cross-ply CFRP laminate. The elastic waves generated from the simulated source of
pencil lead breaks(HB 0.5 Standard pencil lead; JSNDI-006). Four broadband, high fidelity,
piezoelectric sensors(BI025; Digital Wave) were employed to form double triangular sensor
array. Sensors were mounted on the surface of the plate by high vacuum grease. Detectedsignals were amplified by preamplifiers then fed into the signal conditioning module(F4000).
The module controlled the filtering and gain for AE signal and the trigger level. Signals weredigitized and recorded at PC through AID boards(CS22S; Gage). The conventional thresholdcrossing and the cross-correlation method were perfonned by using FWD96 software in the$4apparatus. Another method of the threshold crossing with wavelet transfonned(WT) signals
was perfonned by using the program contained in the Matlab0 Toolbox. Fig. 5 is a schematic
diagram of the experimental setup.

I=~I I .~1
1:=:1 ~

Signal Conditioning

I~:II .~I

AJO Board(GAGE CS2251250)

IPC~ ~ -r-L-...M_O_d_U_le_(_F_4_00_0_>_---'AE Sensor(81025)WT(wavelet transform)

I---t

PA2040G/A

FWD96

Fig. 5

Schematic diagram of the experimental setup

Table. 1 The coordinates of AE sensor location~

CFRP plate (x, y)

Al6061 plate (x,y)

1

(0,0)

(0,0)

2

(300.0,0)(600.0,0)3( 150.0,259.8 )( 300.0, 519.6)

4( 450.0, 259.8 )(900.0,519.6 )Sensor No.

Table 2 The coordinates of exact AE source~

CFRP plate (x, y)

Al6061 plate (x, y)

1
2( 150.0,86.7)( 187.5, 108.3 )( 300.0, 173.2 )( 375.0, 216.5 )34( 105.0,30.0 )( 180.0, 165.0)( 150.0, -43.5 )(210.0,60.0 )Source No.

5

ss( 360.0, 330.0 )( 300.0, -87.0 )RESULTS AND DISCUSSION
Results of source location in a Al6061 plate based on the conventional threshold crossing,
the cross-correlation and the threshold crossing with wavelet transformed signals are shown in
Fig. 6, Fig. 7 and Fig. 8, respectively. With the conventional method, the result of source
location appeared to be highly affected by the level of threshold voltage. The result with 0.1 V
and 0.2V as the thresholds were fairly successful as shown in Fig. 5(b) and (c). On the other
hand, the results shown in Fig. 5(a) and (d) appeared to be very poor with such a small changeof threshold voltage as ±O.05V.
To get rid of the dependence on threshold voltage level, a new approach was developedand instrumented using cross-correlation.[3] the results shown in Fig.7 were obtained by using
the apparatus in which the source location by cross-correlation is available. Once the frequency
has been chosen properly, the result appeared not to be affected by the gain or threshold levelsettin s.
+- : 80urce

x: GO••cUon

X: dOlocl/on

1303.,.

o : I~U. 1~.3)

0:
178.21
I : 376.9. 220.1
2: 214.9.68.11
3: 361.0.330.
4: 301.2. -75 I

1 : 1_.8. 187.8)

Z: 291.0. -5.4)
3: 410.0. 300.7)
4:

ss~(1H'O'\lFI'It>iI(lli'CDS'ol+ : ,GUrU~Z.O. -155.3)

I
I

I
I

I
I
I
I

........ D: .,*'";:*:: ,,.,.,,,,
~

'1

"

~

............

2:

I........

",

I

82

+4

S2

51
)(

(a) V lh == 0.05V=

F'MlSll (TH 0 M

1

f'A1l!IIl(1ll.0ZoIl1
t:_+- :5_00

1:-

X : C1otaeliOllo: 1303.5. 172.41

1202...

0:
lao.B)
I : 37r.l. zn.41

I : 384.9. 220.11
2 : 1209.0. 55.0)
3: 3&0.8. 333.31

z: 207.0. 85.31

3: 413.1.313.51
4:
-1551

202.4.

4 : 1308.7. -84.

"

52

11

+4
)(

(c) V th =O.2V

Fig. 6(d) Vth = O.25V

Results of source location based on the conventional
threshold crossing method in an AI6061 plate86...........

F\\1JIU 1m! • 1ll7&Hl)t : so:urce
X: dol.eli...
0: 19.7.173.7
I: 375.5.216.6
2 : 9.5. 65.11
3: 357.2. 331.5)
4: 298.7. -29.3)

53

F\\ml ~ • mItI)+ : SDUICO

X: dotneUono, :: fOZ'8'
172.81378.3, 216.61

1

2: 212.2. 62.01
3: 364.1. 329.81
4: 302.2. -83.

~

lit"

"
~

~

'*~', ,1,
I

~

,

~

"'2

~

I
I

~2

52

,,

52

51
~4

+4

(b) f= 200kHz(a) f= 100kHz

Fig. 7

,,

I

X

51

,,

I

~

~

...

I

~
~

Results of source location based on the cross-correlation in an Al6061 plate
53

WT (TH' O.o5V)~ : source$3

WT (THo O.lY)+ : soureo
X: dOlOeUon

X: delllCUono:1295,8, 176'01
1: 313.3. 228.6

o : 1301.4. 178.8l
I : 363.2. 224.2}
2: 210.8. 64.21
3: 369.4.316.51
4: 391.6. -61.2

2: 208.6. 69.51
3: 366.6.321.41
4: 304.1. -99.0

82

S1

81

S2

(a) Vth = O.05V)Vth=O.lV
53

Wf(llt'01flt: lClClICt
X: dtltdiDft....TlTH·O.l5\')t : _Ice
X: cleleclion

0: 1310.0.

'.7.9l,: 318.1.223.8
2: 207.8. 45.91
3: 384.3. m.51o:116.01
1 : 13Cl6.3'
331.5.239.2
2: 212.1,41.S1
3: 363.8. 318.

4: -.-)

4: -,-}

S1

S1

SlI

52

+4

(d) Vth = O.20V(c) V th = O.l5V

Fig. 8Results of source based on the threshold crossing withwavelet transformed signals in an Al6061 plate

Another possibility for the arrival time to be independent of threshold level or gain settings
based on the dispersive flexural wave has been investigated and the results are shown in Fig. 8.
$7

90

3000

80

90

80

70

1500

2000

1000

1000

J

500
10

10

o ,--'-"I"T'"""-----.----~--...,II ao ........-.,.----;,--,--,----.--'Y 0
1500
o
500
1000

o

1000

2000

3000

Velocity (mls]

Velocity (mls)(a) Al6061(b) [0/90]85 CFRP
Fig. 9 Experimentally measured velocity with angle

F\WSIl (IH. 0 0SVl

FVal!IIl(lll=O'1I)

t:SClllrce• : 8Clun:ex: delection
0:
IISO.2. 1!Ill.I
1: 192.8. 10,1.8

X: dOleel/onI4• 159.41
oI:: jI5O.
182.5. 108.8
2: 100.6. 30.8)
3: 115.1. 164.01
4: 149.8. -58.

2: 80.1.142.3)

3: 113.8. 185.1
4: ISO.O. 131.1

.1

11

S2

+4
X

(a) Vth = 0.05V(b) Vth = O.IV
F\\1l9a (TIl: o2Vl

1\\W&(lII'~l\V)+ : SOUICO+: SllllICO

x: dotoeUon

X: dotoeUon
0: jl49.7. 92.11
I: 182.0. IOU)
2: 100.6. 31.3)
3: 188.2. 110.5)

oI :: 1149.7.92.5)
181.8. 108.7)
2: 100.1.32.2)
3: 188.6. 170.3)
4: 149.7. -61.

4: 149.7. -59.

.1

+

.1

4

+

"

(c) Vth = O.lSV

Fig. 104x(d) Vth = 0.20V

Results of source location based the conventional threholdcrossing method in a [0/90]85 CFRP platesa

WT(TH'OC5\/)

FVoml (lH. 0 1~

t : SOIIrcet : COUlee

X: jOI8Cl/on

X: detection
a: 1146.9. 88.2)

0: lSO.4. 159.41
I: 182.5. 108.8
2: 100.6. 30.8)
3: 175.1. 164.0)
4: 149.8. -58.

l: 191.2. 101.7)

2: 101.9. 32.4)
3: 178.0. 164.3)
4: lSO.O. -41.4',.
........

_~

,,'

"

2
:It.
.

:~:
II

~~1

~

,~

......

I
I
I
I

11

11

12

+'*
x

(a) Vth = 0.05V

Vth=O.lV

WT llH' 0 15\1)

wr (lH • D.2Vlt :x : deleclicno:1148.7. 82.2)
I: lSO.O. 104.51

t : SOII'CC
x: delecliono:1149.0. 81.2)
l: 190.4. 104.4)_,ce

2: 108.5. 29.4)
3: 178.2. 164.0)
4: 150.0. -so.

2: 103.3. 26.9)
3: 173.7. 165.9)
4: lSO.O. -58.8)

IZ

Ii

52

Ii

t ..

(d) Vth = 0.2V(c) Vth = 0.I5V

Fig. 11

Results source location based on the threshold crossing withwavelet transformed signals in a [0/90]85 CFRP plate

Although the results are not as excellent as those by the cross-correlation method, they areinsensitive to threshold level and most of the locations fall within the experimental error found.
The wave velocities in Al6061 and CFRP plates were experimentally measured withvarious angles from 0 to 90 degree as shown in Fig. 9. It was a little unexpected but the wave
velocities in the [0/90]8S CFRP laminate appeared not very anisotropic. Similarmeasurements
for source location in the CFRP laminate were carried out as in the case of Al6061 plate. The
wave velocities employed for source location were 2870 mls and 1480 mls for Al6061 and
CFRP plates.
Results of source location in the CFRP plates using the conventional method and thewavelet method are summarized as shown in Fig. 10 and Fig.ll, respectively. As in the case ofsource location in Al6061 plate. the conventional method appeared to be highly affected by thethreshold level whereas the wavelet transform method did not. The result of source location bycross-correlation method was too poor to be presented here.

S9

With the wavelet transformed signals, it is possible to trigger the location clocks on the
flexural component with which we nay apply the method to large real structures. Although the
problem due to anisotropy was not solved, the problem due to dispersion could be overcome
by isolating a single frequency or a very narrow band component using wavelet transform.

CONCLUSION
The source location in a thin plate can be improved by introducing the wavelet transformof AE signals. The accuracy of source location appeared to be independent of threshold levelor gain senings if the wavelet transformed signals are employed for the arrival time differences.
The decomposed flexural wave becomes nondispersive if it has a single or a very narrowfrequency band so that the method can be applied to a large scale real strllcrures, where thehigh amplitude of flexural wave is utilized.

ACKNOWLEDGEMENT
This work was supported by the Internal Research Fund, Inha University, Korea.

REFERENCES
1. A. T. Green, C. S. Lockman, nd R. K. Steele, "Acoustic Verification of Structural Integrityof Polaris Chambers", Modem Plastics, Vol. 41, No. 11, pp. 137-139&178&180, 1964
2. T. F. Drouillard, "Acoustic Emission - A Bibliography with Abstracts", Plenum, New
York, 1979
3. S. M. Ziola and M. R. Gorman, "Acoustic Emission Source Location in Thin Plates Using
Cross-correlation", Proc. of AECM-4, pp 411-417, July 27-31, 1992, Seattle, WA, U.S.A.
4. M. R. Gorman and W. H. Prosser, "AE Source Orientation by Plate Wave Analysis",
Journal of Acoustic Emission, Vol. 9, No.4, pp. 283-288, 1990
5. H. Suzuki, T. Kinjo, Y. Hayashi, M. Takemoto and Kanji Ono, "Wavelet Transform of
Acoustic Emission Signals", J. of Acoustic Emission Vol. 14, No.2, pp. 69-84, 1996
6. S. Mallat "A Theory for multiresolution signal decomposition: the wavelet representation",
IEEE Pattern Anal. and Machine Intell., vol. 11, no. 7, pp. 674-693, 1989
7. I. Daubechies, liTen lecture on wavelets", SIAM, 1992

810

Fourth Far East Conference on NDT (FENDT '97)
Cheju-do, Korea, October 8-11, 1997

ACOUSTIC EMISSION MONITORING OF THE FATIGUECRACK ACTIVITY IN STEEL BRIDGE MEMBERS
Dong-Jin Yoon l , Seung-Seok Lee l , Philip Park2 ,Sang-Byo Kim2 , Sang-Ho Lee2 and Young-Jin Park3
NDE Group, Failure Prevention Research Center
Korea Research Institute of Standards and Science
P.O.BOX 102, Yusong, Taejon 305-600, Korea
2 Department of Civil Engineering, Yonsei University

1

3

Institute of Construction Technology, Kolon Construction Co.

ABSTRACT

Acoustic emission technique was employed for the monitoring of crack activity in steel
bridge members. Laboratory experiment was carried out to identify active fatigue crack
for the several test condition such as a different specimen type, a varying of cyclic load
ratio. The results of this test indicated that the crack propagation characteristics forfatigue cycles did not change significantly with different type of beam. Through all of
tests, load ratio of specimen affected significantly to the results such as the crack growthrate and the amount of generating AE signals. From fracture mechanics point of view,
the straight curve obtained from theoretical calculation by Paris equation was wellcorrelated to the experimental results. AE location events were not early detected
although several AE hits from each sensor were generated in the early of the test.
However the features of three parameters, that is, crack growth length, AE location
events and cumulative AE events, showed almost same trend in their increase with thenumber of cycles. Although there were somewhat scattered location in the vicinity ofupper flange, which were considered as environmental noises, good locations near theactual crack tip were obtained. Post ftltering work was carried out to eliminate theseunwanted location, good results were obtained from filtering work using AE parameters.

INTRODUCTION

The deterioration of steel bridge members is caused by a combination of load andenvironmental factors. The unpredictable rate of deterioration makes it difficult for the$11bridge engineer to plan for repair or replacement.[I] Generally most of steel bridge
members are constantly subjected to cyclic loadings. The materials of structures were
undergone by these fatigue loading then came to failure of structures fmally. Several
previous studies have showed the possibility for the applying acosustic emission
technique to monitor the fatigue crack propagation in the bridge structure materials[2,3,4]
and in the field testing[I,5,6). However an useful guideline or a method of finding crack
growth were not suggested in these results clearly. In addition it is very important toknow and to plan for the applied load in the real structures during acoustic emission
testing. Therefore the works relating to this relationship will be necessary to apply thistechnique to real structures. In this study, laboratory experiments for the I-beam withsame materials of bridge construction were conducted to obtain and to characterize therelationship between acoustic emission signals and fatigue crack propagation. And also, inorder to apply this technique to monitoring of real structures, an AE source location
method to find fatigue cracks will be discussed.

EXPERIMENTS

Specimen preparation
In order to evaluate the effect of different types of structure, two types of specimen
were prepared. Material used in these structures was bridge construction material of
SWS490A. As shown in figure I, web plate with thickness of 20mm were cut into
1200mm, 200mm in length and hight respectively. And then it was welded to I-type
beam using 10mm thickness plate. V-end notch was cut into 100 mm depth in the
middle of plate span. Actual crack in the notch was made by fatigue test for introducingof pre-crack before each test.
.i!0
100

(a)

....------, Jf'1
)j 10~---._---_

I<

300

200

.i!0

IE-------------~ ~o

(b)

200

Figure 1 Preparation of test specimens (a) Type I, (b) Type II (unit : mm)

S12

Load control and strain measurement
Three point bending tests were conducted on a MTS closed loop hydraulic loadingmachine. All of test specimens were subjected to constant amplitude cyclic loading foreach test at load frequency of 3 Hz. In order to eliminate environmental noise such as a
hydraulic noise and rubbing sources between specimen and jig contact point, a soft andtough paper was used between support and specimen as noise insulator. Five specimens
were tested in this study, three of type I and two of type II, as shown in table 1. To
observe the effect of load condition, some different load ratio (min. load / max. load)
and load range (difference between max. load and min. load) were applied to eachspecimen. To measure local average stress of near crack, four strain gauges were used.
Strain measurements for each strain gauge were conducted for both static and dynamic
loading condition
Table 1 Type of Specimens and load condition
Type I

Type of Specimen
No. of Specimen
Load

Range (liP: ton)

condition

Ratio (R)1

2Type II
43

5

3-6(3) 3-6(3) 1.8-6(4.2) 3-5(2)/3-6(3) 1.5-5(3.5)
0.5

0.5

Crack growth measurement
Crack gauges were used to obtain crack
growth length during the experiments. All of
specimens used crack gauge (Model KG-B25,
25mm crack length, KRAK-GAGE®) which isthin film type for continuous crack growthmeasurement. Crack gauges were attached to
web plate properly so that there iscoincidence between the gauge starting crack
tip and the root of the specimen notch. The
output voltage from crack measurement system(Model 1078, FRACTOMAT) was fed into
AE measurement system as a parameter input.
Figure 2 shows AE sensor array and a crackgauge attached in the root of the notch.

0.3

0.6/0.5

0.3

AE SENSOR

~.~
\

•

•

AE SENSOR
IE-'y-+--+--~ AE SENSOR

150m

NOTCH

Figure 2 AE sensor array for triangularsource location and a crack gauge
attached in the root of the notch

AE monitoring
The measurement of AE signals was conducted using multi-channel commercial AE
equipment, SPARTAN 2000 (by PAC). All of tests used triangular source location
method, using three sensors, for monitoring of crack propagation. With all sensors inplace, the traditional pencil lead break test was performed for source location sensor
813

array. The pencil break also checked theaccuracy of source location setup, indirectly
determining the actual acoustic velocity inthis structure. A preamplifier gain of 60dB
plus post gain of 20dB and a fixed
threshold of 48dB were used. Figure 2
shows the position of each sensor aroundpre-crack tip. AE Sensors with resonant
frequency of 300 kHz were used. The
preamplifier output was also fed into 4channel digital oscilloscope, model 9354
(by LeCroy). Then, after AE waveform
acquisition and storage, post-analysis of
waveforms and spectrums was carried out.
The
schematic
diagramof overall
experimental setup is shown in figure 3.

Figure 3 Schematic diagram of overall
experimental setup

RESULTS AND DISCUSSION
Figure 4 shows the crack growth length according to fatigue cycles for specimen No.3
and No.5 that is specimen of Type I and of Type II, respectively. The results of thistest indicated that the crack propagation characteristics for fatigue cycles did not change
significantly with different type of beam. Generally, crack growth rate showed three
typical features that are smooth increase in initial stage, stable increase in second stage,
and rapid increase according to increasing of number of cycles in last stage.
40 r----.-~-----~---,

4 0 , - - - - - - - - - - - - -.....
IR=O.3

RaO.S

~

E
E

-..

30 -- - - - - - - - - -

e
!.

c

~

20

.!

.!!

oil:
U

~

.

---,.-?
~

-

.-

..

20 I--,----~-~

U

III

III

0

--

.c

..c

Cl

30

10

U

5

10

15

20

26

30

35

Cycles(x1000)

10 1---r---,fi1'!"d~o I;l:o 10--'-_..J

20

30

40

50

60

70

Cycles(x1000)

Figure 4 Crack length vs. fatigue
cycle for specimen beam type
514

Figure 5 Crack length vs. fatigue
cycle for loading ratio

Figure 5 shows the crack growth length according to fatigue cycles for specimen No.3
and No.2 which corresponds to R=O.2 and R=O.5 of load ratio, respectively. Through allof tests, load ratio of specimen affected significantly to the results such as the crack
growth rate and the amount of generating AE signals. As shown in this figure, in caseof R=O.3 the number of fatigue cycles was about 2 times compared to in case of R=O.5
up to crack propagation of 35mm. It also could be found that this load ratio was related
to the change of stress intensity factor (flK).
Figure 6 shows the relationship1E-4 ~ - - - - - - - - --,SPECIMEN 1
between the crack growth rate (daldn)
SPECIMEN 2
and the change in stress intensity factor
EN 3x(flK) for all five specimens. The straight
1 E-5 1:-~-':;':";::";:':;:::':'::~---"'"------1
curve
obtainedfromtheoreticalocalculation by Paris equation was wellcorrelated to experimental results. In the cearly works of AE with fatigue test, ~ 1 E-6
most of studies dealt with relating
acoustic emission to crack growth rate
1 E-7 1:-------lfili"'-o--1
and to the stress intensity factor using
several kinds of equations. In this study,
Paris equation was used for theoretical
1E-B L --J
calculation, namely daldn = C(flKt
10100
where flK is the applied stress intensity
Delta-Kfactor and is given as 6K= 60'/ iram;
Figure 6 Crack growth rate vs. Delta-K for
where flo is the magnitude of the each specimen
alternating stress, Gmax- Gmin, and a at. is2S
40
the average crack length. N is the
AE locallon events
number of cycles and C and m are
Total AE eyents
20
constants for a given material.
Crick length30
Figure 7 shows the AE location
III
E
I I:
events, the cumulative AE events and the
E
:
15
CIl
crack
growthlengthrespectively
II:
ell
o
20 II:::
according to the fatigue cycles for
.!!
.2 10
specimen No.1. In this experiment AE...uw
location events were not early detected<.><
10
although several AE hits from eachS
sensor were generated in the early of thetest. However the features of threeo~...-Ai:l;;;;;ii;~==~l.Il!!.J!.,jJJJII1I!lJ:...L......lI!Ill---J 0
parameters, that is, crack growth length,
o
20000
40000
60000
80000
CyclesAE location events and cumulative AE
events, showed almost same trend in
Figure 7 Crack length, AE location events
their increase with the number of cycles.
and cumulative AE events vs. fatigue cycle
SPE~I"

)(

-

-

~

..

815

..

~

Figure 8 shows the result of source location during entire test of specimen No.5.
Each graph shows AE event locations in the cycle range of 10-20 x 103, 20-30 X 103,
30-35 X 103, 35-38 X 103, respectively. As increasing of the number of cycles, a numerous
AE events were located around the crack tip. Although there were somewhat scatteredlocation in the vicinity of upper flange, which were considered as environmental noises,
good locations near the actual crack tip were obtained. As shown in these figures, theseenvironmental noises were considered as the noises at the upper contact point of threepoint contact in case of the early stage of test. On the other hand it was considered as
the random locations by sources at the plastic region around crack tip in case of the laststage of test. Therefore post filtering work was carried out to eliminate these unwanted
location events using several AE parameters. Figure 9 shows the typical results of post
filtering works. AE parameters such as rise time, peak amplitude, event duration, etc.
were used to filter out unwanted location. On the whole good results were obtained
except noise signals having same features with actual crack signals unfortunately. Figure
9 shows the result of filtered output by rise time parameter, representatively. The AE
event locations by noise signals were almost eliminated.

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

a ....' . - - - - - - - - - - - - - - ,......

.......

._.,

-0

•••••

'0

'. "'i:l:J. .

...•.

0.

•

'

•

••

llO
0,

•••••

0.'- .: 0:.:

.. °

"0°

..as-a. : ..
••••••

....
. .

0°

.......

4 ••••.......

•'.' .0- .0:::' "0 .

e.-a-•

a.-. ea-.I.... .c-.PM.",.·a......

...c. . . . . .

a. ••.

Ie.a_.

a.:.Koo...c•• 'I'...... s..... . .._1. . . . . N "'11'1011

Figure 8 (a) The resu~t of AE
source location(10-20 x 10 cycles)

Figure 8 (b) The result of AE
source location (20-30 x 103cycles).......-------------,.._._.. . . : ._.:' .. . .:._,. ,..._.. . __ ..-: ,"'1. ••••, , - - - - - - - - - - - - - ,...... ....

........ --'·~~r~~j~tF·.·;··........

~:o.......

..

.:. ',.: .~:~::.... :.:.::~.:WF~Ji:;:· /.. .

.......

.......

. ~ ~':. '.

..::' .

a.o&a-a' , ' .!"'~ :~. : .... :'..

' !.

•. 4a".

a&-a....t .. "~........
1'1'1_

a....

:L.a..•...

••-I .......
....C&) W "'11'1_ " •• II PGSI'IJ. .

Figure 8 (d) The result of AE
source location (35-38 xl 03cycles)

Figure 8 (c) The result of AE
source location (30-35 X 103cycles)816

a ....... . - - - - - - - - - - - - - ,......

•

",

••• :

• • _._"•.~,.. ":#0 • • • • • • -":":'.

..... ': :.

' • • • •' #..ila•••.:: >: ':'..

,......

....•.. .
.:\..'....-a

~

.~;

...

.......
....

.....

.....

a •••••...c,.. " 10111'1'" _ • • POIn....

....

-•••••

...

.. ~.

a.a..

&.eel) y .oe1'l1Q111 • • • • .-o.I71Cl11

a.aF

Figure 9 (b) The result of AE
source location (after filtering)

Figure 9 (a) The result of AE
source location (before filtering)

In this study it was not reviewed for the relationship between fatigue crack actIvIty
and stress state in order to generate AE in actual structure. Practically it is very
important to know and to plan for the applied load in the real structures during acoustic
emission testing. Therefore further works relating to this will be necessary to apply thistechnique to real structures.

CONCLUSIONS
Acoustic emission technique was employed for the monitoring of crack activity in steel
bridge members. The crack propagation characteristics for fatigue cycles did not change
significantly with different type of beam. Generally, crack growth rate showed three
typical features that are smooth increase in initial stage, stable increase in second stage,
and rapid increase in last stage according to increasing of number of cycles. Through allof tests, load ratio of specimen affected significantly to the results such as the crack
growth rate and the amount of generating AE signals. From fracture mechanics point ofview, the straight curve obtained from theoretical calculation by Paris equation was wellcorrelated to the experimental results.
AE location events were not early detected although several AE hits from each sensor
were generated in the early of the test. However the features of the crack growth length,
AE location events and cumulative AE events, showed almost same trend in theirincrease with the number of cycles.
Although there were somewhat scattered location in the vicinity of upper flange, whichwere considered as environmental noises, good locations near the actual crack tip wereobtained. Post filtering work was carried out to eliminate these unwanted location events,
good results were obtained from filtering work using AE parameters.

S17

REFERENCES

1. M. Sison, J.e. Duke, G. Clemena, and M.G. Lozev, "Acoustic Emission: A Tool for
the Bridge Engineer", Materials Evaluation, August pp.888-900, 1996
2. H.L. Dunegan, "Use of Plate Wave Analysis in Acoustic Emission Testing to Detectand Measure Crack Growth in Noisy Environment", Proc. of Structural Materials
Technology - An NDT Conference, Sandiego, pp.335-341, 1996
3. M.N. Bassim, "Detection of Fatigue Cracking in Metallic Structures with Acoustic
Emission", Proc. of 11 th Intll Acoustic Emission Symposium, Fukuoka, Japan,
pp.347-352, 1992
4. A. Ghorbanpoor and D.W. Vannoy, "Laboratory Acoustic Emission Investigation of
Full Size ASTM A-588 Bridge Beams", The 2nd Int'l Conference on Acoustic
Emission, pp.S307-S31 0, 1985
5. H.L. Chen and R.D. Fultineer, "Study of Fatigue Cracks in Steel Bridge Components
Using Acoustic Emission", Proc. of Structural Materials Technology - An NDTConference, Sandiego, pp.317-322, 1996
6. A. Ghorbanpoor and T. rentmeester, "NDE of Steel Bridges by Acoustic Emission",
Natural Hazards Mitigation, pp.l 008-1 013, 1988

S18

Fourth Far East Conference on NDT (FENDT '97)
Cheju-do, Korea, October 8-11, 1997

ACOUSTO-ULTRASONIC EVALUATION OFADHESIVELY BONDED CFRP-ALUMIMUM JOINTS
Seung-Hwan Lee and Oh-Yang Kwon
Department of Mechanical Engineering, Inha University,
253 Yonghyun-dong, Inchon 402-751, Korea

ABSTRACT
Correlation between the amount of artificial defects in bonded region and the acoustoultrasonic parameters(AUPs) including signal amplitude and then the static strength ofadhesively bonded joints of carbon fiber reinforced plastic(CFRP) laminates and A1606) plates
has been investigated. The effect of the frequency content and the bandwidth of input signals
were studied using 200kHz, 650kHz, )MHz, 2MHz pulses and )MHz tone-burst signals. With
increasing fraction of defects, the signal amplitude and AUP) were decreased whereas AUP2
was increased. This result has been attributed to the energy transfer characteristics of bonded
joints with delamination-type defects and the change of spectral content due to the defects.
Considering the nature of high attenuation, a pulse signal with major frequency content at thethird harmonic of thickness mode resonance, 650kHz for the dimension of specimens used inthis study, has been found optimal for acousto-ultrasonic testing of CFRP-aluminum joints.

INTRODUCTION
Composite materials which have excellent characteristics like high specific strength andmodulus, and good fatigue resistance are used more and more as structural materials in spacestructures, aircrafts and ground vehicles. For such usages as in real mechanical structures, it isdifficult to avoid having parts bonded with similar or dissimilar materials. In general, bonded
areas have inferior mechanical properties than base materials to a great extent because of theexistence of interfacial or transitional region show quite different gradients in chemical and819

microstructral aspects. Therefore, bonded parts can be more critical than base structures todetermine the overall integrity of mechanical structures.[1 ]There are two possible methods for bonding of polymer matrix composites or fiber
reinforced plastics(FRP); One is mechanical bonding using fasteners such as rivets, screws,
and bolts, the other is the adhesive bonding using various adhesives. The latter method has
been widely adopted since the adhesively bonded joints can extend fatigue life, reduce total
weight of structures, and homogeneoulsy transfer stresses. These characteristics play animportant role in highly efficient structure such as aluminum-FRP or aluminum-aluminum
bonding of aircraft structures.
Various nondestructive testing methods such as ultrasonics, acoustic emission(AE),radiographic testing have been used for the detection of defects at bonding areas. Especiallydetailed infonnation about bonding interface can be easily acquired by analyzing signals whichpass through defects, failures or damages of structures using acousto-ultrasonic(AU) method
which inputs ultrasonic source signals and receives AE signals. [2]THEORETICAL BACKGROUND
Definition of Acousto-Ultrasonic Parameters(AUPs)
AUPs has been defined from the power spectral moments, Mn, which are given as:

Mn =!N w(f)fndf

where fN is Nyquist frequency, W(t) is power spectral density function and n is integer. Fromreal digital domain, Mn can be calculated as follows:
M

n

I

=

~o

(Wi.! + Wi) (if).f + f).f )n 6.f2

2

From the above equation, .6fis frequency resolution, which is defined as .6f=(l/j.6t), where.6 t is sampling interval. For example, the zeroth moment(Mo) is defined as follows:
Mo =!N W (f) df or, M o = ~ (Wi +2Wi.! ) f).f

(in digital domain)

The zeroth moment or Mo means the signal energy calculated as the area under spectral
density curve, which is analytically related to the mean square of voltage signals.[3, 4] The
first moment(Ml) means total spectral density for each frequencies, whereas the second
moment(M2) shows the dispersion of profile slope distribution of spectral density function.
The third and the fourth moments are related to skewness and kurtosis, respectively. Therefore,
the odd numbered moments are related to the symmetry, and even numbered ones are to the$20profile curvature of spectral density function. For applying the above theories to AU
experiments, AUPI can be defined as Mo, which means the total energy of received signals.
AUP2 has been defined as Mt/Mo, which means the arithmetic mean value of spectral density
for each frequencies. AUP3 is defined as (M1/Moi, which means the dispersion factor ofspectral density function, whereas AUP4 as (MJM2i' the factor that shows the profile
curvature of spectral density function. The quantities of AUP3 and AUP4 are also known as
the distortion factors and are theoretically the indication of the amount of high frequency
content in a predominantly lower frequency content,[4-6]

EXPERIMENTS
Specimens
Specimens used in this study were made by joining 16-ply uni-directional CFRP
laminates and 3mm-thick AI6061 plates using adhesives(9923A1B, IPCa National Ltd.). The
mating surface of aluminum plates was polished with sandpapers whereas that of CFRP wasjust cleaned using acetone. Specimens were designed in both configuration of the single lap(SL) and the double lap (DL) joints. The bonded area was kept I" x }" for all specimens withpredetermined fraction of artificial defects introduced as shown in Fig. 1.
- Banding Area with 25$ Simulated DeIect

SINGLE LAP JOINT

2,,,,,,·,-

DOUBlE LAP JOINT,
Fig. 1 Dimension and shape

Fig.2

Shape of bonding area withartificial defectsof specimen

To introduce artificial defects, specimens have been designed by adjusting the size,
location and shape of teflon sheets as shown Fig. 2. For single lap joints, the fractions ofdefects per total bonding area were 12.5% and 25%, whereas they were 6.25% and 12.5% for
double lap joints. Specimens with double lap joints had two bonding surfaces; one was made821

in the same way as the single lap joints, the other was made as a perfectly bonded surface.
Therefore, the fraction of defects at double lap joints is a half of that at single lap joints. As thereference of experimental measurements, four specimens with perfectly bonded joints were
included.
Table 1 Number of specimens for each type of bonding area
Bondini! area with artificial defectType of

Fraction of

joint

artificial defects(%)

bonding surface #1

Bonding surface #2

Specimens0

perfectlY bonded

4

4

well

-

perfectlY bonded

Perfectly bonded

4

perfectly bonded

type I ofSL02.5%)

4

perfectly bonded

type II ofSL(l2.5%)

4

perfectlv bonded

type I of SU25%)

4

perfectly bonded

type II of SL(25%)

4Singletype I
12.5

lap
(SL)

type II
type I25

0
Double

6.25

lap
(DL)
12.5

No. of4

4
4

Instrumentation and Data Acquisition
As the specimens with bonded joints were prepared, they were inspected by ultrasonic Cscan method to confirm that specimens were made with correct size and location of artificial
defects. A table-top C-scan system (UltraPac II; PAC) were used for this purpose. Only the
specimens whose size and location of defects were confirmed have been used. A schematic
diagram of experimental set-up is shown in Fig. 3. Source signals were made from an arbitrary
waveform generator (CompuGen 840; Gage) and filtered by a tunable band-pass filter(KH3103A; Krohn-Hite Corp.). The signals were then amplified by 50dB using a broadband
RF amplifier (240L; ENI) to survive in CFRP which generally exhibits high attenuation. Bothtone-burst signals and pulses at 1MHz were employed at first, then pulses of 200kHz, 650kHz
and 2MHz were also employed to investigate the effect of the frequency content and the
bandwidth of input signals. To put input signals into specimens, a O.5MHz ultrasonictransducer (KO.5S; Krautkramer) for 200kHz and 650kHz pulses, an 1MHz transducer(B 1S;
Krautkramer) for IMHz pulses and tone-burst signals and a 2.25MHz transducer(KB-A) for$22

2MHz pulses were used. Signals propagated through the specimens to be detected by abroadband type AE sensor (WD; PAC). Detected signals were amplified by 40dB at preamplifier with bandpass filter( 100kHz-1200kHz) or highpass filter( 100kHz high), and then fed
into AE DSP unit (MISTRAS 200 I; PAC), where they were processed to calculate AUPs.
The bonding strength were obtained from tensile testing of the same specimens that used
for the AU measurement. The correlation between the bonding strength and four AUPs and thefraction of defects has been evaluated using regression analysis.

RF Power
Amplifier(ENI240L>

rs ~loleta Is

Adhesive layer

TunableBandpass Aller(t(H-3103A)

rk

Composites~
L.......-...t

Waveform
Generator(CompuGen 840)~,.......

I

PreamDllfierII

Bandpllss
Alter

I

DlgllalOscilloscope(LeCroy 8514)

f-t
'--0

AE DSP(MISTRAS 2000

Fig. 3

Schematic diagram of experimental set-up

RESULTS AND DISCUSSION
The results shown in Fig. 4 were obtained from the AU measurement for type I specimensof both SL- and DL-joints using an IMHz pulse as the input signal. Amplitude and AUPI
decreased with increasing fraction of defects, whereas AUP2 increased. The decrease of
amplitude and AUPl(Mo) are due to the increased attenuation as the fraction of defects
increased. Similar results were obtained from the measurement for type II specimens of bothSL- and DL-joints. Therefore, this tendency appeared to depend primarily on the amount ofdefects. When a tone-burst signal at IMHz was employed as the input, the results were also
very similar but less significant than that shown in Fig. 4. Pulse signals, which has broader
bandwidth than tone-burst signals, appeared to be more efficient for AU evaluation of lap
joints.

523

50

>

45

34
32 •
>30 •
E 28 '- .... _-._-.......
&26

•
•

,... !'....

.§. 40

.

•

•

Gl
Cl

~35

S 24

•
•
20
••
18.......-"""T"'"".........-......---r-___.--r-.--.
0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14
Fraction of Defect•30

0.00 0.05 0.10 0.15 0.20 0.25
Fraction of Defect(d) Amplitude (DL type I)(a) Amplitude (SL type I)
800

•
700

•

•

.... 600 -~"'---""

;:)

< 500

•

400

--

-_
•

.~~-.

1300
•
1200
1100
1000 ..···•·· ....... _-~900.

.. ~.--.~-

•

"'

•

•

•
•

<

700
600

•

< 860.0 --.-- ..-'

;:)

840.0

.•

~------------._-

•
•

•
-----

•

1.0k

•
•

>
:.
U
C
~ 900.0CTeu. 850.0•r.--···•.---•.--- .. -.-- .•._----_ ..
•

•

•

•

•

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14
Fraction of Defect0.00 0.05 0.10 0.15 0.20 0.25
Fraction of Defect(f) AUP2 (DL type I)(c) AUP2 (SL type I)
Fig.4

•

L--,..----r"--..-~.....__--.--..-~.:.: 950.0
•
•

•

•

".

0.00 0.05 0.10 0.15 0.20 0.25
Fraction of Defect

•

,:,t.

·

(e) AUPI (DL type I)

920.0~900.0••.
•(b) AUPI (SL type I)
940.0

-.

800

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14
Fraction of DefectN880.0Q.

•-" i"'"

~ 22

~

Q.

•

Results of AU measurements for type I specimens of both single-lap
and double-Iapjoints using an 1MHz pulse input

In Fig. 5t the input and the detected AU signals employed to get the above results areshown with the respective power spectrum for each signal. The input was a typical pulse signal
which had relatively wide band characteristics in the vicinity of 1MHz as shown in Fig. 5(b).

824

15

~
CII

10

I

5

I

Cl

=
CII

0

0

>

-5
-10

~r-0

5

15
10
Time(usec)

o

20

234
Frequency(MHz)(a) Input pulse (l MHz)(b) Power spectrum of input signal

o

0

60
40

>E 20

CD

-~
C)

0

ftI

-20
-40
-20

20

40 60 80Tlme(usec)

100 120 140

1

2

3

4

Frequency(MHz)(C) Detected AU signal(d) Power spectrum of detected signal
Fig.5 Waveforms and power spectra of the input and the detected signals

On the other hand, the power spectrum of detected signal has main peak at about 650kHz
and about lMHz as shown in Fig. Sed). Because of this result, a pulse signal whose major
frequency content at about 650kHz was intensively employed in addition to the originallyscheduled frequencies such as 200kHz, 1MHz and 2MHz.
Fig. 6 shows the result obtained from the AU measurement for type II specimens of DLjoints using a 650kHz pulse as the input signal. With increasing fraction of defects, amplitudeand AUPl decreased whereas AUP2 and AUP4 increased. This tendency is similar to but isalso more significant than that observed in Fig. 4.
In order to understand the tendency in terms of the spectral density distribution, frequency
spectra of AU detected signals from specimens with three different fraction of defects wereobtained as shown in Fig. 7. It appeared that with increasing fraction of defects, the magnitude
of relatively low frequency band at about 300-400kHz decreased rapidly, whereas themagnitude at about 500-600kHz was virtually unchanged. The relative change of magnitude
between at lower frequency band(300-400kHz) and at higher frequency band(500-600kHz)
accounts for the increase of AUP2 with increasing fraction of defects.

825

4500
300

•

$' 250 ...•...

-

~200

........•

•

Ii"

a..
""'

...

•

•

:=

~

.

•

......

E
OJ••....~~- .....

~ 3000

••

-

....

•
"'1·, .....

__~-..-.........~---.--.....,
0.000.020.04 0.06 0.08 0.10 0.12 0.14

2000~--'-T"".........,

Fraction of DefectFraction of Defect(a) Amplitude(b) AUPI
•

560.0

•
6.lllc10"

540.0!;'520.0
N

480.0

....

•

2500
0.000.020.04 0.06 0.08 0.10 0.12 0.14

~500.03500....

---......Ii!!•••••.

150

a..

•

4000 .... ~.

./_/

.~

.._-"

•
........
......• ....•//.....--....•...-_.
•
3.lllc1o'"•.....:.

2.lllc1o'" I ..······•........ .:... .....

.

- .
/--

;

......

..... .....

-

•

•

'.lllc1o"'~---,.--.~--"""T""~-,--.........---r-

0.000.020.04 0.06 0.08 0.100.120.14

0.00 0.02 0.04 0.06 0.08 0.10 0.12

Fraction of DefectFraction of Defect(c) AUP2(d) AUP4

Fig.6

..-

.............. .

Results of AU measurement for type II specimens ofdouble-lap joints using a 650kHz pulse input

The increase of AUP4, which is related to the kurtosis, can also be expressed bycomparing three spectra of Fig. 7. As compared with perfectly bonded specimens whichshowed relatively distributed frequency spectrum, specimens with higher fraction of defects
showed spectra more or less concentrated at narrow band. This can be appeared as the increaseof AUP4 since it is based on the kurtosis of spectra. The physical basis for the above
phenomena can be explained in terms of the thickness mode resonant frequencies of lap joints
with and without having defects at bonding area. The resonant frequency, fr can be calculated
by using the equation given as [7]n vf, = 2" D(n=l, 2, 3, ... )(1)

where n is the order of resonance, v is sound wave velocity in the material and D is the
thickness of plates. For the specimens with joints consisted of a metal(AI6061) and acomposite(CFRP). however, it is impossible to calculated the exact frequencies using eq. (l)

$26

GJ

"C

o .025~-o .020

a.

0,015

E

o 01 0

c(

0.005

o 000

~

0.0

0.5
1 .0
1 .5
Frequency(M Hz)

2.0(a) Spectrum of AU detected signals from specimens without defects
GJ

"C
~

0.025

:::

0.020

a.

0.01 5E o .010
C

0.005
0.000

o .0

o .5

1 .0
1 .5
Frequency(M Hz)

2.0(b) Spectrum of AU detected signals from specimens with 6.25% defects
GJ

"C
~

:::
a.
E
C

o .05
o .04
0.03
0.02
0.01
0.00

o .0
(c)

Fig. 7

0.5

1.0

1.5

2.0

Frequency(MHz)Spectrum of AU detected signals from specimens with 12.5% defects
Power spectra of AU detected signals from the measurements for type I
specimens of double-lap joints using a 650kHz pulse input

since the sound velocity, v is different in different materials. Therefore, the resonant frequencyof specimens used in this study was assumed as a value between that of AI-AI joints and thatof CFRP-CFRP joints at equivalent thickness. Table 2 shows natural frequencies of Al6061
and CFRP calculated for 5mm-thick plates.
Frequencies of 200kHz and 650kHz employed in the experiment were assumed as theapparent fundamental and the third-order resonant frequencies, respectively. It was found thatthe tendency of AU parameters with the fraction of defects could be measured the mostefficiently using 650kHz as shown in Fig. 6.
S27

Table 2. Calculated natural frequencies and their harmonics at 5mm-thickness (kHz)Materials

fl*

f2

f]

14

fs

Aluminum

313

616

939

1,252

1,565

Composites

220

440

660

880

1,100

Since the specimens were designed with the artificial, delamination-type defects at thebonding area, the effective thickness of joints actually decreased with increasing fraction
defects. This resulted in the decrease in thickness, D in eq. (I) and eventually resulted in theincrease of resonant frequencies. This accounts for the shift of major peaks in power spectra
toward higher frequency band and further for the increase of AUP2 with increasing fraction of
defects.
The effect of frequency content of input signals on the AU parameters has beensummarized as shown in Fig. 8 for specimens with single-lap joints. Regardless of the fractionof defects, with the increasing frequency of input signals from 200kHz to 650kHz, both
amplitude and AUP 1 increased to the highest value and then significantly decreased to lower
value with further increase in input frequency up to IMHz and 2MHz. The amplitude of AU
detected signals from the measurements using 1MHz pulse as the input signals were alreadyclose to the minimum detectable value in this configuration. Although the bandwidth of digital
AE signal processing unit was limited by 100kHz to 1.2MHz, the input pulse with frequency
contents higher than 1Mz appeared to be not very useful for the purpose.

CONCLUSIONS
1.

The wider band pulse signals appeared to be more efficient than the narrower band toneburst signals for AU evaluation of metal-composites lap joints.
2. The signal energy transfer characteristics was highly affected by the frequency content of
input signals so that the fundamental and the third-order resonant frequencies could be theoptimal input frequencies.
3. The apparent correlation between the amount of defects in bonded region and the acoustoultrasonic parameters(AUPs) is due to the change of frequency contents of AU detectedsignals, which can be attributed to the reduction of the affective thickness of joints withincreasing fraction of delamination-type defects.

S28

250

•

•

4000

r\\

3000

200
>

-

e 150
CD

I')

~ 100

~

50
0.0

0.5

....

•

•

~ 2000•1000
IO+-~-.---..,.....----.-....-~-...

0.0

2.0

-~l:

3000

. Type 2

•
•

150

·· .

~ 50

.... 2000~ 1500

\\
\\

\

\

0.0

0.5

r---.-----.

1.0
1.5
Frequency(MHz)

•

.

>120
100
80

,•

\
\

\

\

\

1-------.

on

OB

1n

1B

20

(e) AUPl change (12.5% defects)
3500-Type 1
. . . . Type 2-Type 1

•

....•

3000
2500

Type 2.... 2000
CL~ 1500

III

60
40
20

\

Frequency(MHz)

'&
>

\\

500+-~-.---..,.....----r---r--

2.0

e

~

-Type 1... Type 2

\

1000

(b) Amplitude change (12.5% defects)
160
140

2.0

1·\

\

100

/\
.

2500

• /" " a\\
1-'/

CD

:!

•,

-Type 1

,
·

0.5
1.0
1.5
Frequency(MHz)(d) AUPI change (0% defects)(a) Amplitude change (0% defects)

200

•

<

1.0
1.5
Frequency(MHz)

250

•
•

•

1--------•

1000
..

500

t--"""T"'--"'---""""'--"'--

0.0

0.5
1.0
1.5
Frequency(MHz)2.0+---......-....-.....,..--....-----r~

0.0

0.5

1.0
1.5
Frequency(MHz)2.0(t) AUPl change (25% defects)
Fig. 8 The effect of frequency content of input signals on AU parametersfrom the measurements for specimens with single-lap joints(C) Amplitude change (25% defects)

829

ACKNOWLEDGEMENT
This study was supported by the Ministry of Education, Government of Korea.

REFERENCES
1. G. B. Chapman, Jr., "A Nondestructive Method of Evaluating Adhesive Bond Strength inFiberglass Reinforced Plastic Assemblies", Joining of Composite Materials, ASTM STP
749, K. T. Kedward, Ed., American Society for Testing and Materials, pp.32-60, 1981
2. A. Vary and K. J. Bowles, "An Ultrasonic-Acoustic Techniques for Nondestructive
Evaluation of Fiber Composite Quality", Polymer Engrg. Science, 19(5), pp.373-376, 1979
3. A. Tiwari and E. G. Henecke, Jr., "Real Time AU-NDE Technique to Monitor Damage in
SiC/CAS Ceramic Composites under Dynamic Loads", ASTM STP 1184, M.R. Mitchelland O. Buck, Eds., ASTM, Philadelphia, PA, pp. 363-375, 1994
4. P.A. Dickstein et al., "Ultrasonic Feature-Based Classification of the Interfacial Condition
in Composite Adhesive Joints", Res. Nondestr. Eval. 2, pp.207-224, 1990
5. Michael T. Kiernan and John C. Duke, Jr., "A Physical Model for the Acousto-Ultrasonic
Method", NASA CR185294, October 1990
6. Grant E. Hearn and Andrew v Metcalfe, "Spectral Analysis in Engineering: Concepts andCases", ARNOLD, 1995
7. Y. H. Son, J. O. Lee and S. H. Lee, "Nondestructive Evaluation of Adhesive Bonding
Quality by Measurements of Peak Amplitude of Simulated Stress Wave", Journal of the
Korean Society for Nondestructive

S30

Fourth Far East Conference on NDT (FENDT '97)
Cheju-do, Korea, October 8-11, 1997

ESTIMATION OF INITIAL DAMAGE IN CONCRETE
BY ACOUSTIC EMISSION
Masayasu Ohtsu 1, Yuichi Tomoda1 and Taisaku Fujioka2
1

2

Department of Civil Engineering and Architecture, Kumamoto University,
2-39-1 Kurokami, Kumamoto 860, Japan
Fukuoka City Office, 2-5-31 Daimyo,Chuou-ku,
Fukuoka 810, Japan

ABSTRACT
An application of acoustic emission (AE) observation to the uniaxial compression test of
concrete-core samples has been proposed and under improvement. Damage of concrete isquantitatively estimated by the rate process analysis of AE generating behaviors undercompression. Because the rate of AE activity is dependent on mixture proportion and thecondition of core samples,the damage is estimated relatively from the rate in comparison
with that of sound concrete.
In the present paper, the damage mechanics is introduced to estimate the damage of concrete
quantitatively. Concrete samples of controlled damage are prepared by freezing and thawing
tests. The initial damages are determined on the basis of Loland's model. It is attempted todetermine the initial damage from AE generating behaviors. correlating with the rate process
analysis. Results show a promise to estimate the initial damage ofconcrete samples without
information on the sound sample.

INTRODUCTION
The damage caused by microcracking is one essential feature of concrete, and is criticallyrelated with the ultimate load-bearing capacity of concrete structures in service. The increaseof aging structures and the disastrous damage due to recent earthquakes lay stress on the needfor repairing and retrofiting procedures for damaged structures. For this purpose, a detailed
inspection of existing concrete structures is required in advance. Core samples are often boredout to measure their uniaxial compressive strengths and Young's moduli. Thus, the damage.831

degree of concrete is estimated from only mechanical properties comparable with thespecification, because concrete properties of the same mixture proportion and of the soundconcrete are not concurrently available.
For the assessment of damaged concrete, recently the measurement of acoustic emission (AE)
activity in uniaxial compression tests of core samples is proposed [1]. This is because AE
generating behavior is closely associated with the presence of microcracks in concrete. To
analyze quantitatively the occurrence of AE events, the rate process theory is introduced [2].
In the present paper, a practical procedure for the assessment of damaged concrete is studied.
The damage evolution process under uniaxial compression is modelled by the damage mechanics[3]. It is attempted to estimated the initial damage from AE generating behaviors without
information on the sound concrete.

CONCRETESA~LESOFCONTROLLEDDAMAGE

To prepare concrete samples of controlled damage, cylindrical specimens of dimension 10 cm
diameter and 20 cm height were cast. Mixture proportion of concrete is given in Table 1. At
28 days after moisture-curing, concrete samples were subjected to freezing and thawing cycles
0from _16 C to +6° C for three hours. Damaged samples due to 100 freezing-thawing cycles,
150 cycles, 200 cycles and 250 cycles were prepared and then loaded uniaxially at 0.65 kN/sec.
Results of uniaxial compression tests are summarized in Table 2. Mechanical properties were
determined from the averaged values of three samples.
Table 1 Mixture Proportion
Maximum
Aggregate
Size (nun)

20

W/C

Unit weight (kg/m1

sfa(%) (%)
55

47

W

C

S

G

179

326

816

1040

Air EnAir
Slumpcontents
trained
(em)
Agent
(%)
(cc)

196

10.2

Table 2 Mechanical properties
Number ofcyclesCompressive
strength (MPa)Young'smodulus(GPa)Poisson's
ratio

Ocycle100cycles
150cycles
200cycles
250cycles

46.5
45.5
44.4
40.0
44.7

35.2
29.5
30.8
29.2
30.0

0.25
0.22
0.22
0.22
0.21

832

7.0

In the uniaxial compression test, strain measurement was conducted by using four strain
gauges in the axial and the lateral directions. For AE measurement, mainly one AE sensor of
150 kHz resonance frequency was attached at the center of the cylindrical sample. AE events
generated under compression was counted up to final failure by AE processor. For eventcounting, the threshold level was set to 120 mV with 60 dB gain, and the duration of thedeadtime was 2 msec. To decrease noise from contact surfaces between the sample and loadingplates, teflon sheets of 0.5 mm thickness were inserted with silicon grease. Total gain of AE
measurement was set 60 dB and the frequency range employed was 10kHz - 300 kHz.
In the course of freezing and thawing cycles, dynamic Young's moduli of concrete were alsomeasured in accordance with the nS-A-1127 code. Variations of all mechanical properties
measured are shown as relative values in Fig. 1. Dynamic moduli decrease only 8 % after 250
freezing-thawing cycles, while static Young's moduli decrease around 15 %. Because concrete
contained 7 % air contents of total volume as seen in Table 1, it is realized that fairly minor
damages were induced into these concrete samples.
As a result, the strengths of thesesamples are comparable to that of sound concrete (at 0 cycle).

10

--~

~
::2
co
>
u

-

9

-c::: 8

.~

co

"i)

7

.....

dYJ!aIDic modulus-II . compressive strength-A- Young's modulus

The number of cycles
Fig. 1 Variation of mechanical properties
To quantify the damage degree induced in the samples, pore distributions were determined.
From the samples, small cylindrical elements of 1 em diameter were drilled at 3 em depth fromthe surface. Then, the porosity was measured. Results are summarized in Fig. 2. It isobserved that the ratio of pores over 0.05 J.lm diameter increases after 100 freezing-thawing
cycles, although the pores over 0.1 fJ.m diameter do not consistently increase with the increaseof freezing-thawing cycles.
This might be a reason why the strengths were comparable
regardless ofthe freezing-thawing cycles. The minor damages of the samples were confirmed.
S33

250 cycles

IKI l -

150 cycles.0.1-1

100 cycles

I§] O. 05-0. I

ocycle--.: - ---;-- .--"11m,-il "
0%

20%

40%

60%

80%

~O. 01-0.05

D -0.01

C.U m)

100%

Volume ratio
Fig. 2 Pore distnbution

RATE PROCESS ANALYSIS
When concrete contains a number of critical microcracks, active AE generation is expected in
the uniaxial compression test due to crack propagation from existing defects. In contrast, AE
activity in sound concrete under compression is considered to be stable and low prior to final
failure. Thus, the presence of critical microcracks in concrete is closely associated with AE
generating behavior under compression [4]. To formulate the AE occurrence under uniaxial
compressive stress, the rate process theory is introduced [2]. The probability function reV) of
AE occurrence from stress level V(%) to V+dV (%) is correlated with the incremental numberof AE events, dN, as follows;f(V) dV = dNIN,(1)where N is the total number of AE events up to stress level V (%), which is normalized by thecompressive strength: 0c. To approximate high AE activity at low stress level, the function
f(V) is assumed in the following hyperbolic function,f(V) = afV +b,(2)

where a and b are empirical constants. Substituting eq. 2 into eq. 1, a relationship between thenumber of total AE events N and stress level V(%) is readily obtained,834

N = C Va exp (bV).

(3)

Here, C is the integration constant.

DAMAGE EVOLUTION UNDER COMPRESSION
According to the damage mechanics [3], the state of damage is represented by introducing
the scalar damage parameter Q.

As Young's modulus of an intact material is E*, YOWlg's

modulus E of a damaged material is expressed as E*(1 -Q).

After the damage evolution by

Loland [5], the damage parameter Q is assumed as a function strain E as,
Q= Qo+Ao EA.,

(4)

where Qo is the initial damage. Ao and ~ are empirical constants. In the uniaxial compression
test, a stress-strain relation is represented as 0 = EE and E = E*(I -Q). Combining with eq. 4,
a= Eo E- E*Ao EA.+l,(5)where Eo is Young's modulus ofan initially damaged material.

Assuming that do/dE = 0 at thepeak stress 0e and the corresponding strain Ee ,Eo = E*Ao(~ + 1) EeA. and 1 - Qo = Ao(~ + 1) E(?-.

(6)

Thus, coefficient ~ can be determined as,(7)

In Table 2, Young's moduli Eo at particular freezing-thawing cycles are given.
corresponding coefficients A are readily determined from eq. 7.

Thus, theFrom stress-strain relations

obtained in the tests, coefficients ~ were obtained. As referring to E* as Young's modulus at 0
cycle, the initial damage levels Qo were also determined and then empirical constants Ao were
From these data, stress-strain relations were modelled. One result at
obtained from eq. 6.
100 freezing-thawing cycles is given in Fig. 3 (a). The uniaxial stress-strain relation in theexperiment is remarkably modelled byeq. 5. In this case, the damage evolution process in eq.
4 is estimated as shown in Fig. 3 (b). It is found that even at the peak strain Ec, the maximum
damage Qc induced is not greater than 0.5.
S35

-5

O.

(a)

100 cycles

(b)

a O.

-4

c
---------------------"""~e 0 .

eao.
t>oo experiment- - analysis-1o-0. 001-0.002

=

~ O.
O. 1

-0.003

o

O. 2

O. 4

Strrain

O. 6
e/

O. 8

Bc

Fig. 3 (a) Stress-strain relation and (b) damage evolution process

DAMAGE ESTIMATION BY AE
All tests were performed for three samples of the same freezing-thawing cycles.

The

strength, Young's modulus. the initial damage Qa. and coefficient A were determined as theaveraged value of three. Thus, the stress-strain relations at particular freezing-thawing cycles
were modelled.
Setting the total number of AE observed in a test as Nc and V = al a e, eq. 3 is rewritten,
N/Nc= (alae)aexp[b(alae-l)].

(8)

Coefficients a and b were also determined as the averaged values of three samples at the same

At a given strain level EI&:, stress level alae was calculated from eq.
freezing-thawing cycles.
5. Then, AE activity NlNc was obtained from eq. 8. These results are plotted in Fig. 4 inthe logarithmic scale. It is found that relations between AE activity and strain are so linear as
being formulated by,
N I Nc = (EI £Cp.

(9)

Introducing the least square method, coefficient m is determined. Then combining with eq. 4,(0 - Oo)/(Oc-Oo) = (NlNc)Alm.

838

(10)

1

-----"'0--~- y - - L.:)UCYCleSZ__

z' - -3

-1-1

-

,

.1JVl,;Yl,;lC~

.I

,..,

,

,
,
,

o

,-~~

.. '

I".

0

0

0

C

.- -2
-1
-3
Fig. 4 Relations between AE counts and strains
As setting (0 - Qo)/NAim = (Oc-Qo)/NcAlm=k, eventually eq. 6 leads to,
Qo = 1 - (A + 1) k NcAirn .

(11)In order to determine the coefficient k, an empirical relation between the value, Oc-Qo, and the

o.
o

a

I

(,)

a

The number of cycles
Fig. 5 Damage evolution under freezing and thawing$37

freezing-thawing cycles is necessary. Here, the two cases were considered. In case 1, thevalue was directly determined from the test results.
In another case 2, the relation wasapproximated as shown in Fig. 5, where a linear relation is assumed.
The initial damages Qo analyzed by eq. 11 were compared with the values Qo actually
determined from 1 - EolE*. Results are given in Fig. 6. It is found that the initial damages arereasonably estimated from eq. 11 in the both cases.
The value, Qc-Qo, however, aredetermined empirically from the test results of damaged concrete. It implies that the procedure
is still to be improved.

Gcase 1. . . . case 2

-]

..

~ 0.1

•••

l::

~

o

a

0.]

0.2

Q 0 ( experiments)
Fig. 6 Initial damage estimated

CONCLUSION
A procedure for the evaluation of damaged concrete is studied. Stress-strain relations ofconcrete under compression were modelled by introducing the damage mechanics.
It isattempted that the damage parameters are determined from the AE rate process analysis.
Concrete samples ofcontrolled damage are prepared by freezing and thawing tests of concrete.
The following conclusions are obtained.
{l) The initial damages are determined on the basis ofLoland's model in the damage mechanics.
Experimentally, the damage is estimated from the initial Young1s modulus of the damaged
concrete.
(2) AE generating behaviors are analyzed by the rate process theory. It is attempted to838

detennine the initial damage without infonnation on the sound concrete. Results show that theinitial damages of concrete samples are reasonably estimated.
In theses analyses, the value, Qc-Qo, are determined empirically from the test results ofdamaged concrete. It implies that the procedure is still to be improved.

REFERENCES
1. M. Ohtsu, II Acoustic Emission Characteristics in Concrete and Diagnostic Applications, II J.
AE, Vol. 6, No.2, pp. 99-108 (1987)
2.M. Ohtsu,"Rate Process Analysis of Acoustic Emission Activity in Core Test of Concrete, II
Concrete Library ofJSCE, No. 20, pp. 143-153 (1992)
3. M. Kachanov,"A Continuum Model for Medium with Cracks," 1. Engineering Mechanics,
ASCE, Vol. 106, No.5, pp. 1039-1051 (1980)
4. M. Ohtsu, K. Ukita and K. Yuno,tlStochastic Analysis of AE Activity in Uniaxial
Compressive Test,1I Proc. JSCE, No. 520N-28,pp. 13-24 (1995)
5. S. P. Shah, S. E. Swartz and C. Ouyang, Fracture Mechanics of Concrete, John Wiley &Sons, Inc., New York, Chapter 11, pp. 425-451, (1995)

S39

Fourtb Far East Conference on NDT (FENDT '97)
Cbeju-do, Korea, October 8-11, 1997

ACOUSTIC EMISSION DIAGNOSIS OF CONCRETEPILES DAMAGED BY EARTHQUAKES
Tomoki Shiotanil , Norio Sakaino l , Masayasu Ohtsu2 and Mitsuhiro Shigeishi2
1

2

Technological Research Institute, Tobishima Corporation,
5472 Kimagase, Sekiyado, Higashi-katsushika, Chiba 270-02, Japan
Department of Civil Engineering and Architecture, Kumamoto University,
2-39-1 Kurokami, Kumamoto 860, Japan

ABSTRACT

Earthquakes often impose unexpected damage on structures. Concerning the soundness ofthe structure, the upper portion is easily estimated by visual observatio~ while the lower portion
located in deep underground is difficult to be estimated. Thus there exist few effective methods
to investigate underground structures. In this paper, a new inspection technique for damage
evaluation of concrete-piles utilizing acoustic emission (AE) is proposed, and is verified by aseries of experiments. Firstly, such basic characteristics as the attenuation and effective waveguides for detecting AE underground, are examined through laboratory tests. Secondary, fracture
tests of full-scale prefabricated concrete piles are conducted, and the characteristics of the AE are
examined. Finally, actual concrete-piles attacked by the 1995 Great Hanshin Earthquake areinvestigated. Results confinn that the estimated damages by the proposed method are in good
agreement with actual damaged locations. Thus, the method is very effective for the diagnosis of
the concrete-piles.

INTRODUCTION
After big earthquakes attacked, it is in urgent demand for infrastructures to be inspected anddiagnosed for their soundness. The upper portions of the structures are comparatively easy to be
estimated by visual observation, while the lower portions, foundations of structures in particularpiles located in deep, are difficult to be estimated. As the techniques to investigate the soundnessof piles, there exist conventionally a few methods. One of them is an impact-echo test commonly
called "Pile Integrity Test (PIT)", that has been worldwide used for the quality control of cast in840

place piles. Because of the simplicity, PIT has many results, although in advance to the survey,
the vicinity of head of the piles has to be exposed for producing stress waves. Boring of the piles
and the borehole observation by CCD camera, are also used for the investigation.
Acoustic Emission (AE) is a stress wave generated due to the deformation and fracture. In afracture process of concrete-piles by earthquakes, when the strain energy is released with crack
propagation, and AE would be generated. In the piles once damaged, it is expected that the AE is
produced due to creep deformation of piles and the increase of skin friction with consolidation
settlement of the ground in loosen sand and cohesive soil. Additionally, it is reported that, inmaterials experienced plastic deformation, Kaiser Effect l ) does not hold2). Hence, considering
these aspects on the characteristics of fracture in piles, "Damage Diagnosis Technique by
Acoustic Emission in Concrete-Piles" have been developed and applied to the investigation of
the piles in actual buildings. In the paper, the concept of the proposed method is reviewed, aseries of the studies on basic issues of the technique is discussed and then the application isshown.

CONCEPT OF THE TECHNIQUE
AE Propagation
When the piles having damage are under external loading, AE from damaged portions is
expected to be generated. For simplicity, we limit our attention to the motion of P-wave incident.
Figure 1 shows a schematic procedure for detecting AE in concrete-piles. An AE wave radiatedwithin piles is firstly propagated into the soil, and approaches to an interface between the soil
and a wave-guide. At the interface, the incident wave is partially reflected, and partiallytransmitted into the wave-guide. The transmitted wave is propagated in the wave-guide, and is
detected by AE sensors.
Estimation of AE sources

AE source location is performed by the one-dimensional source location method. In the right
portion of Figure 1, AE wavefonns detected in AE sensors are schematically shown. Because
AE source exists close to AE sensors #1, the sequence of AE arrival is sensor #1 and sensor #2.
Accordingly, we obtain the arrival time difference &. The location of AE source is calculated
from,

I-vOl

a=-2

where I is the sensor spacing, and v is the wave velocity.
S41

(1)

Radiation
of.~E.-+-~i:3\.

./......

,.

. •.•.
Wavefonn#2

AE propagation-+..

~
Interface between soil
and wave-guideFig. 1 Schematic procedure for detecting AE in concrete-piles

Wave Transmission Loss at Interfaces
An interface is defmed as the boundary between two media with different acoustic
properties. The transmission loss of an incident wave is dependent on the difference between
acoustic impedances Zsoil and Zwave-llIIide which are defined as density times wave velocity. The
amplitudes of the reflected and the transmitted waves, A r and A" are given by

(2)

(3)

where 2 1 is the acoustic impedance of the medium where the wave propagates and is incident to
the interface, 2 2 is the acoustic impedance of the medium where the wave is transmitted from theinterface, and Ai is the amplitude of the incident wave. The ratio of A/A; is the coefficient ofreflection, R,

842

(4)

The traditional and still widely-used wave-guides are made of metal3), 4). It implies that anincident wave is almost reflected at the soil/metal interface, because Z, is even smaller than ~ inequation 4. Therefore, in the case that the wave-guides are applied to the detection of AE
underground, the materials of wave-guides should be examined on their acoustic properties.

CHARACTERISTICS OF AE PROPAGATION IN THE GROUND
A body wave radiated from a simple AE source is known as a spherical wave. In thisradiation process, the AE wave loses its energy with propagation, and the attenuation is normallycalled "geometrical damping", and is represented by(5)

where Ao is the amplitude of source, A is the amplitude at the distance of r from the source, andn : 2 (semi-infinite media); 1(infinite media); 112(swface wave). Furthermore, the AE wave also
attenuates caused by absorption. Absorption is the result of internal friction. Considering
absorption, equation 5 is modified,
if -n-nA = .r~er ,(6)where A. is the ratio of equivalent attenuation to the absorption, and is given by

A. = 211hf ,v(7)

where h is the ratio of absorption, andfis the frequency.
Characteristics of Attenuation in Toyoura Sand

Due to the homogeneous distribution of the grain size, Toyoura sand is known as thestandard sand. Figure 2 shows the result of AE attenuation in Toyoura sand. In the experiment,
an artificial AE is generated by hitting nails of stainless steel, and is detected by AE sensors of
60kHz resonant type. In the figure, "Dry" demonstrates a result of sand of relative density 50%,
S43

"Sat" denotes saturated sand, and "Wet"indicates sand which is drained for 24hours after saturated. From Figure 2, AE
attenuates in proportion to the logarithms
of propagation distance up to a few
centimeters. Beyond 1 meter, AE quickly
decays. Therefore, for detecting AE of
small magnitude produced by microcrack growth, 1 meter would be thestandard length for the effective AE
monitoring.

200 j

, ,

t

r
r

III

;g

~8

l00~·
~

0

i

.i

~

r
[
f

e -\00 lu .

iiii -200 r '.
:I

~

-300

~

.....•...

-400~ ~ -,--""",-~~---",,,--_,-,-,,,,,,,,,,--~~.~,.i'-

EFFECTIVE WAVE-GUIDES

1

10

100

1000

let

Distance (mm)Taking into account characteristics of
AE propagation, new wave-guides areproposed. The wave-guide is combined"Unplasticized Polyvinyl Chloride (PVC)
Pipes" with "water". The PVC pipes isdevised for the decrease of thetransmission loss between soil and theouter portion of wave-guides, and water
is fiJled in the pipes for the decrease of
attenuation in propagation. AE sensors
are set at proper places in the water, andthey detect AE propagated in the water
without notable attenuation.

Fig. 2 Results of AE attenuation in Toyoura sandr-

I

~ PuIser.#1
-.

";~#2

I

~i

"'1

II Pidser.#3

P

-J

~.

f;~#4

iPuIset: #3

-I

A
:...

.:.' -I

PuIser.#4-I
I

Experiment of Wave-guideiPulser:#5

Figure 3 shows an experimental
setup of wave-guides. Materials of waveguides are aluminum and vinyl chloride.
The length of wave-guides is 500mm, thediameters are 70 (vinyl chloride) and78nun (aluminum), and the thicknesses
are 11 (vinyl chloride) and20mm(aluminum), respectively. Two AE
sensors, denoted A and B, are placed in

1€sers:tB
89_-28-..1.• ..-.. -- Gum sheet

Aluminium pipe~~
Vynil chloride pipe

Fig. 3 Experimental setup of wave-guides

S44

Horizontal: Orientationof AE Sensor response

Vertical: Orientation of
AE Sensor response
\

I

Air

Water

Water

...

'V
':'

00
Water

Aluminium or vinyl
chloride pipe
(b)

(a)

Monolithic
Aluminium
(d)

(c)

(e)

Fig. 4 Methods for setup of AE sensors in the wave-guides

the both sides of the wave-guides. Figure 4 shows setting methods of AE sensors in the waveguides: (a)AE sensor on the inter wall of the pipes; (b) AE sensor in water; Cc) AE sensor
changed vertically; Cd) AE sensor suspended in the water; and (e) AE sensors conventionallymounted. The wave-guide is set in Toyoura sand of 50% relative density. Five AE sensors of
60kHz resonant type are arranged at 3mm separated from the wave-guide as shown in Figure 3
from #1 through #5. AE measurements are performed by Pac's Mistras DSP, in which AE waves
are recorded with sampling of 1MHz and 2k words.
120.0

Considered transmission loss and 25an of propagation attenuation
D103.6CD

8:! 100.0 I-------------------~'"

8~

Considered transmission loss
80.0 ~--------------------'~
1il 60.0
:E.
41
'0

::I

iE 40.0«w«.,
1: 20.0'0~

0.0A1um,non-fil~hor

A1um,w-fill.hor

Alum,w-filI,ver

Vynik:hl.w-fill,ver

A1um(ref.)Malerials (main-malerial, filling material, sensor-direction)

Fig. 5 AE amplitude detected by AE sensor of A and B on various wave-guides in Voltage$45

Experimental Results
Figure 5 exhibits the AE amplitude detected by AE sensor of A and B. Figure 5 shows by
decibel term in which artificial AE output referred to 140dB. "Considered transmission loss" inthe figure, shows the average of the amplitude when the artificial AE are generated at pulser # 1
and #2 in Figure 3. The amplitude also includes the effect of the attenuation of AE propagationin the water. "Considered transmission loss and propagation attenuation of 25cm" shows theresults of the average of the amplitude when the artificial AE are generated in the middle of thewave-guides at pulser #3. In the horizontal axis, methods of AE sensor setups and materials ofwave-guide are indicated. "Alum, non-fill, hoI", for example, represents case that the wave-guideof aluminum is filled with nothing, and AE sensors are mounted horizontally. From Figure 5, theproposed wave-guides denoted as "Vmyl-chl, w-fill, ver" shows better characteristics than themonolithic aluminum, and the amplitude is twice as high as those of the aluminwn pipes.
Therefore, it is concluded that the proposed wave-guide combining PVC Pipes with water
reveals excellent characteristic for detecting AE under the ground.

FRACTURE TESTS OF FULL-SCALE PREFABRICATED CONCRETE-PILES. Experimental Outline
Fracture tests of full-scale prefabricated concrete-piles are performed· for examining thecharacteristics of AE in the fracture process. The length of pile is 7m, and the diameter is
400mm. Two types of the fracture mode are adopted. One is bending and the other is sharing. In
this paper, the characteristic of AE in the shearing test is only explained.

Test Procedure

Prefabricatedconcrete-pile

AE measuring
range

Fig. 6 Schematic configuration of the shearing test

S46

Figure 6 illustrates the schematic configuration of the shearing test. Basically, the test issimilar to 4-point bending tests. Placing the supports close to the loading points, the zone of highshear stress is concentrated in the narrow area shown in the figure. AE is monitored by six AE
sensors to cover the measuring range in Figure 6. AE is previously amplified 40dB in preamplifier and is recorded over the threshold of 50dB.
Experimental Results(a) small amplitude AE of the fracture(b) remarkable AE of the fracture

Fig. 7 AE waveforms and frequency spectra

Figure 7 shows the AE waveforms and frequency spectra. Figure 7 (a) shows the waveformand frequency spectrum that is generated by small magnitude of the fracture and observed
frequently until visible crack growth. Figure7 (b) is the waveform caused by the fracture withcrack growth. In these figures, the AE wave due to micro-fracture has the short duration time andparticular peak frequencies, while AE wave due to macro-fracture has the long duration time andthe broad frequency contents ranging from low to high.

APPLICATIONS
Site and The Method for Detecting AE
The investigation was carried out at a foundation of high-rise building damaged by the 1995
Great Hanshin Earthquake located in Hyogo Prefecture. When the following inspections were
performed, the upper structure was already removed. Therefore, loading by heavily machinery
can easily induce AE. Figure 8 shows the plan view of investigated piles. The AE monitoring is
performed by a suspended wave-guide in the monitoring well located in the vicinity of pile A.
The PIT and "Pile head observation by human (PHO)" were examined in piles B, C and D.
S47

Objective pile for AE monitoring.......,.,

AE wave-guide

Range of PIT and PHO/

012345m
••

!

•

,

!

Fig. 8 Plan view of investigated plies

Unfortunately, the piles investigated by AE isdifferent from those by PIT and PHO. Figure 9shows the geological chart of investigatedground and wave-guide configuration. In thisground, a reclamation bed of silty sand exists
from surface to 3m depth, a loose sand bed lays
up to 6m depth, a dense gravel bed over 30 of
N-value is observed from 6 to 7m depth, and asilty sand bed is located below. AE monitoring
was performed by using 5m-Iong aluminumwave-guide that was suspended 10m deep in themonitoring well filling up with the pure water.
Estimated Damage
Monitoring

LocationbyN-valueo 102030.cISOo

1 Silty sane
(Redam alian

2

3

III
rave, ~III

Gravelly sanctl
4g Sandy 9

.s:

!

AE

5

~

I
Sand'
6
I
sandyG rave~ I I Y I
7Sand; I UI I
Gravelly sand. I [! I
8

II

I

9

Silty sandlJ

10~ i II

Si~ II ! I

Figure 10 shows AE events, weighted byring-down counting versus the depth. In the
figure, the damage location was estimated"around head of pile" and "3.5m to 4.5m (6m to Fig. 9 Geological chart of investigated and a
7m in the depth) below the head of pile". wave-guide configuration
Although the objective piles of AE, PIT and
PHO were different, estimated damage location at "head of pile" is well coincident with theresults of PHO, and the fact "3.5m to 4.5m below the head of pile" also well agree with theresults of PIT. It is noted that location estimated by both AE and PIT is identical to a peculiar
stratwn of ground beds. Generally it is reported that the weak portions of piles are introduced by
the hasty change ground beds boundary, hence the estimated damage location by AE isconsidered to be reasonable.
848

GL 0 j~01~------~--------

rn"'·~'-c.,••.... ,~,..... •.

--.;< '"• . • • . • _•..•• " ' .

0,
It)i

.

.,.......

. .•.

~ .-._.,._._.-.1:"- 4ooo~.-----+------I-------1! JfL~~~-~~:~:_~~~~~~:~·,~~. .o51-~-.-~ ~l=JC=======1!=======J
E
E

_.

·1

6000

8000

b

--'--

-----l-

--i

I

.

I
I

10000 i

200

'Q.

ig
8

~

I

--------'---------i.

100

~

C.

+-1- - - - - - -............

oFooting~

300

I

.5
Ul

lU
(.)

Number of AE ringdown~countFig. 10 AE event occurrence weighted by ringdown~ounts

CONCLUSION
A new inspection technique for damage evaluation of concrete-piles is proposed by utilizing
AE, and is verified by a series ofexperiments. It is concluded as follow:
1) For detecting a small magnitude of AE produced by micro-crack growth, 1 meter
would be the standard distance for the effective AE monitoring.
2)
The proposed wave-guide combining PVC Pipes with water reveals excellent
characteristic for detecting AE in the ground.
3)
AE wave with micro-fracture has the short duration time and particular peakfrequencies, while AE wave with macro-fracture has the long duration time and broadfrequency contents ranging from low to high.
4)
In an actual application, estimated damage location by the proposed AE technique isconsidered to be reasonable.

REFERENCESI) J.Kaiser, "Erkentnisse und folgerungen aus der messung von gerauschen bei zugbeanspruchung von metallischen
werkstoffen", Arch. Einsenhiittemvesen 24, pp.43-45, 1953. (in German)2) S. Nagataki, T. Okamoto, T. Ayada and S. Yuyama, "Classification of cracking pattern developed in reinforced
concrete members by Acoustic Emission", Proceedings ofthe Symposium on Nondestructive Evaluation in Civil
Engineering, JSCE. pp.139-144, 1991.(in Japanese)
3) E. Lord, Jr. et ai., "Utilization of steel rods as AE waveguides", Journal of the Geotechnical Engineering
Division. ASCE, Vol. I08, No.OTI, pp.300-305, Feb.• 1982.
4) H.Geginald Hardy, Jr. et aI., "Mechanical waveguides for use in AE/MS geotechnical applications", Progress in
AE IV. JSNDI, pp.292-302, 1988.

849

Fourth Far East Conference on NDT (FENDT '97)
Cheju-do, Korea, October 8-11, 1997

APPLICATION OF AE TO EVALUATE DETERIORATION
OF PORT AND HARBOR STRUCTURES
Kimitoshi Matsuyamal , Akichika Ishibashil , Tetsuro Fujiwara}, Yasuhiro Kanemoto l ,Shiro Ohta l , Shigenori Hamada2 and Masayasu Ohtsu3} Nippon Koei Co., Ltd. Research and Development Center
2304 Takasaki, Kukizaki-cho, Inashiki-gun, Ibaraki 300-12, Japan
2 The Port and Harbour Research Institute, Ministry of Transport
3-1-1 Nagase, Yokosuka-shi, Kanagawa 239, Japan
3 Kumamoto University, Department Civil Engineering and Architecture
2-39-1 Kurokami, Kumamoto 860, Japan

ABSTRACTThe degree of concrete deterioration is normally evaluated by uniaxial compression tests ofcore samples taken from existing concrete structures. The uniaxial compression test can givedetails on concrete strength, elastic modulus and so forth. By using AE (acoustic emission)technique with uniaxial compression tests, we can get more useful information on interior
failures of the core sample. The observation of AE activity was conducted during uniaxial
compression tests of concrete specimens and samples. The specimens were cast with S, 10, 15
and 20% air content. The samples were taken from 2 harbor sites in Japan. Generating behaviorof AE was studied quantitatively on the basis of rate process theory. Distribution of pores in theconcrete could be measured by the porosimeter. The relation between AE activity in the uniaxial
compression test of core samples and concrete property is clarified.

INTRODUCTION
A large amount of concrete structures, in particular built in 1960's, are found to beapproaching their service limit due to severe environmental conditions. Cost of maintenance forthe concrete structures is predicted as very high in the beginning 21st century. Therefore, thedevelopment of diagnostic nondestructive evaluation (NDE) is urgently required.
There are many old structures in port and harbor facilities. These require proper diagnosis andtimely reinforcement or repair for their continuous service. To estimate deterioration of the old
concrete structure, core samples are taken from them and the uniaxial compression test is
normally carried out. The uniaxial compression test can give details on concrete strength, elastic
modulus etc.. However, these informations are still insufficient to evaluate the concrete
deterioration.
SSG

AE (acoustic emission) is one of the NDE techniques that give useful information on internalcracking. By applying AE technique to uniaxial compression tests, information of micro cracks inthe concrete can be obtain. These informations can make us to evaluate deterioration of theconcrete.
In this study, an observation of AE activity was carried out during uniaxial compression
testing of concrete specimens and core samples. For newly cast specimens, entrained air contents
were arranged as 5%, 10%, 15% and 20%. AE measurement was carried out under the uniaxial
compression test and behavior of AE was studied quantitatively on the basis of rate process.
Distnoution of pores in the specimens could be measured by the mercury porosimeter. A relation
between pore volume and result of rate process was investigated. The concrete core samples from
2 harbor sites (port-A and port-B, which were constructed in 1963, 1906 respectively.) in Japan
were tested. AE measurement, the rate process analysis, and the pore distribution were studied. It
is clarified that results of the rate process analysis are strongly related to characteristic of the pore
distribution in deteriorated concrete. Considering the relationship and the mechanism of theconcrete deterioration, AE technique could be available for evaluating deterioration of concrete
structure.

RATE PROCESS ANALYSISThe Kaiser effect is well known as an irreversible AE generating behavior under uniaxial
compression. It results from the fact that cracks nucleated due to prior loading do not propagate
until the load exceeds the previous level in the next loading. However, limited success is reported
in respect to the determination of the previous load level, because it depends highly on theloading path and history. In usual cases, AE activity under uniaxial compressipn test is observed
from low stress level. This is because the loading path is not identical to the previous path. It
leads to a conclusion that some critical micro-cracks in one loading path may propagate due tostress increase. When concrete contains a number of critical micro-cracks, active AE generation
is expected from low stress level due to new crack propagation. In contrast, AE activity inconcrete of few micro-cracks is considered to be stable and low at stress level as in Fig. 1. Thus,
the amount of critical micro-cracks in concrete could be evaluated by monitoring AE activity
under the uniaxial compressive stress.

~I.=:======-----~..-;.....-------(a) Sound Concrete(b) Deteriorated Concrete

-a

cu

Load

Load

Fig.1 AE activities of concrete551

To quantify the AE activity in the uniaxial compression test, the rate process theory was
introduced by Ohtsu [1]. His method is described below;
The probability function f(V) of AE occurrence from stress level V(%) to V(%) + dV(%) is
assumed, as follows:f(V)dV= dNIN

(1)where N is accumulated AE events up to stress level V(%), which is normalized by thestrength. Here, the probability function f(V) is defined as the following hyperbolic function,f(V)= aN+b(2)

where a and b are empirical constants. In the conventional rate process theory, the probability
function f(V) is usually assumed as constant, which is called a rate. Whereas Eq.2 suggests two
possible relations between the probability of AE occurrence and stress, introducing thehyperbolic term, aN. The relation is illustrated in Fig.2. In the case that the value 'a' is positive,
the probability of AE occurrence is high at low stress level. In contrast, the probability is very
low at low stress level when the value 'a' is negative. Since the discrepancy of'a' is considered
to be dependent upon the amount of critical micro-cracks, the deterioration due to micro-cracking
could be quantitatively evaluated on the basis of the value 'a'.
From Eq.l and 2, a relationshipbetween the number of total AE events N
and stress level V(%) is obtained,
N= CV

a

exp (bV)(3)a>O.-.ooc..

where C is an integration constant.
In the case that f(V) is constant, we
obtainc2 b - - - - - - -

...
..-

->.

N=Cexp(bV)

(4)

a<O

.0
C'd

.0

£ o

Eqs. 3 and 4 represent the relation
between AE event and stress level. In theanalysis, experimental data on AE events
versus stress is approximated by Eq.3 orEq.4 and empirical constant a, b, and C
are determined by the least-square error
analysis.

20

40

60

80

100

Stress level : V (%)
Fig.2 Assumed relations: f(V) vs. V(%)

S52

PORE DISTRIBUTION OF CONCRETE
Pore distributions of concrete are summarized in Fig. 3 [2]. Pores of concrete can be classified
into the primary pore and the secondary. The primary pore is generated under construction andhardening of concrete, while the secondary pore is generated after hardening of concrete.
In the primary pore distribution, concrete has several radius of pore as given below:
Gel pores: under 3.0 nm
Capillary pores: 3.0 nm - 10 j.J. m
Entrained air: 10 j.J.m -1.0j.J.m
Entrapped air: over 1.0 j.J.m
At any stage of hydration, the hardened paste consists of hydrates of such compounds as,collectively referred to as gel, crystals of Ca(OH)2' minor components, unhydrated cement, andthe residue of the water-filled spaces in the fresh paste. These voids are called capillary pores, but
within the gel itself there exist interstitial voids called gel pores.
Entrained air in concrete is defined as air intentionally incorporated by means of suitableagent. This air is clearly distinguished from accidentally entrapped air.
In the secondary pore distnbution, concrete has some radius of pore as given below:
Pores by drying shrinkage: 0.005 -1.0j.J.m, Pores by freezing and thawing: 1.0 -100 j.J.m
Mortar cracks: 1 j.J.m - 10j.J.m, Bond cracks: 10 -150 J,J.m
These pore volume in concrete should be changed due to an extent of the concrete
deterioration.

Pore Distribution by
Drying schrinkage
Freezing & thawing
Bond Crack
Mortar Crackr
I( Secondary )Pore Distributionof Concrete(Primary )

JOI
0.001

10 3
0.1

10 4
I

lOS
10

Pore Radius

Fig.3 Pore distributions of concrete

553

10 7
1000

10~

A

10000.', m

BASIC EXPERIMENT
Test Procedure(1) Specimens
Mix proportions employed are indicated in Table 1. Based on the mix proportions of Table 1,
the entrained air contents were arranged as 5%, 10%, 15%, 20% by using air-entrained
admixture. For the uniaxial compression test, cylindrical specimens of 10 cm radius and 20 cm
height were cast and moisture-cured for 90 days.
Table1 Mix proportions of concrete
Unit Weight (kg per 1 m3 of concrete)
Water

CementSand

GravelAir(%)

178

223

855

983

5.0

(2) Experiments
During the uniaxial compression
test, AE events were counted up to
the final failure. AE sensor of 150
kHz resonant frequency was attachedat the center of a core sample. To
decrease noise from contact surfaces
between the specimen and loadingplates, Teflon sheets of 0.5 mm
thickness were inserted Total gain of
AE measurement was set to 60 dB.
The pore distribution of concrete
were measured by means of themercury porosimeter. The porosimeterwith 400 MPa maximum pressure wasused. After vacuum pumping on thespecimens, mercury was let in. Poresize distribution was calculated
according to the Washburn equation
assuming cylindrical pore shape :<I}

AE~en,or

Pef1'onal compu.er~

8"
Loading plate
Load cell

AE mea.<urement cquipmem(SPARTAN AT)

Fig.4 AE test setup

P = -4 g cos d ID
P : pressure, g : surface tension of mercury
d : contact angle mercury-pore
D : radius of pore
S54

Result(1) Pore distribution
Pore distribution of the specimens is shown in Fig. 5. Over 1.0 JJ.m pore radius differencesof pore volume are remarkable. In general, entrained air has typically a radius of about tens tohundreds JJ.m. The pore radius over 1.0j.Lm in Fig. 5 corresponds to the range of pore radiusof general entrained air.
0.018

r;======;--------..,.......------,•.•... air51l6

0.016_._. airl0lli

bO 0.014..........

_ •• - air15lli........

--air20lli

a 0.012~ 0.010 ~=======---------I-!-.::Il~----.j,"0> 0.008 1 - - - - - - - - - - - - - I - ; . . - o P . - . : - -'.- - _ _ 1
(1)

8.

: '

0.006 t----r---m-----+;.-~.~,\=------1
0.004 r---"""'~..-_;__~+---__Jl+,!-----JH'·:--\:·· -----1

\i-'\

.

O. 002 I~~~~e:..-.~~?J.~:.:.-~.-:- ....:.\~-I
o. 000 '------=>..----'---~-'---__t_---'---..-.I
0.001
0.010
0.100
1.000
10.000100.000

pore radius(ttm)
Fig.5 Pore distnbution of the specimens
A relation between pore volume of radius from 3.4 to 5.0 j.L m and strength is shown in Fig.
6. It is found that the relationship is linear. It means that the pore volume of particular radius isone of causes which influence the strength of concrete.

,

0.016
_::I.

~q
-

0.012

Ir)

E l
'i>'~

y = -o.OOO7x + 0.0181
R2 = 0.9985-

E 0.014

0.010

~

""

.eEM
.!a 0.008
o tI:I> .2 0.006

~

0'0

5
e 0 • 004
Q. 0

..&.

0.002

~

0.000
0.0

5.0

10.0

15.0 20.0 25.0

30.0

strength(N/mm2)

Fig.6 Relation between pore volume and strength555

Fig. 7 shows relation
1.2
between 'a' value in Eq. 3
and the pore volume of pore
1.1
radius from 3.4 to 5.0,u.m. Q,)::s
The higher pore volume is, "'a 1.0
the larger 'a' value is >obtained This means that ~0.9high AE activity at low
stress level derives from the
0.8
increase of the pore volume,
0.000
and 'a' value becomeslarger.

0.006

0.012

0.018

pore volume(ml/g)

Fig.7 Relation between 'a' value and pore volume

EXPERIMENT OF CORE SAMPLES FROM STRUCTURES
Test Procedure(1) Objective
AE data was obtained from concrete core samples of two ports - port-A and port-B. Port-A
was constructed in 1963. Concrete core samples were extracted from upper, middle and lower
parts of prestressed concrete bridge located at sea-side and land-side. Port-B was constructed in1906 and was restored in 1924, because of the Kanto earthquake in 1923. Concrete core samples
were extracted at splash zone and under the sea of reinforced concrete caisson. Damage
estimation of port-A and port-B by visual inspection and compressive strength of cores are given
in Table 2.
Table2 Damage estimation at Port-A and Port-B

Name

I

Localions

Sea side

0

0

0

43.5

Land side

0
X
X

X
X
X

42.8

Splash zone

X
X
X

Pon-A

Pon-B

Uniaxial
Compression Test
Crocks of Corrosion of Siriooing Ave. of Compressive
Concrete Reinforcing Bar of Concrete Strenj!th (N/mm~)
Visuallnspcction

Under the sea

!

22.9
29.5

0: Exist X: Not Exist
Although port-A is not older than port-B, some characteristics of deterioration wererecognized at port-A In the port-B, any particular deterioration were not observed by visualinspection.

S56

(2) Experiments
The uniaxial compression test and AE measurement were carried out as the basic experiment.
The mercury porosimeter was applied to evaluate pore distribution.
Result(1) Characteristic of Pore Distnbution
Pore distributions at cover concrete of port-A and port-B are summarized in Fig.8 [3]. The
ratios of the pore volume of 0.1 - 100 fJ.m radius to total pore volume of 0.005 - 100 fJ.m
radius are indicated. In the port-A, sea-side concrete has the pore volume more than the landside concrete. Concrete of middle and lower parts show higher ratio of pore volume thanconcrete of upper part. In the port-B, the ratio of pore volume at splash zone is higher than thatof pore volume in the sea.
The pore volume of 0.1 - 100 fJ.m radius is coincident with the pore radius of bond crack
and mortar crack as shown in Fig. 3 (shade zone).
..-.,
Q)

E 70.0

::;

6~.1

f ".

~9Mt61.5~ 60.0~ ~.... 50.0
<U "Cn
§ .e
40.0
"0

48.3

f

5_.6

48.3

4M.ll

44.2-

-~ 'E" 30.0
,.J

~5.1

~1I.5
42.3

T

38.7

34 .6

32.9

31.1

31.1

~ :::t

I

f'

24.0

g 02. 10.0&. 8 20.0

1'./.5'-.;3& 15 0.0

I

-

<U

C'l

M

Q)

Q)

-0

"0

-0
"U;
-0

.5

...J

(J

E
::;

"0

'0
;>

c

--

'Cii

I

c

~

.r;;

Q)

-0

"l:I

·Cii

..J

CI)

c
1':1

'Q)

4)

0..
0..

~

::>

III

~
"l:I

:E
~

C

1-0
Q)

~

..2

0

N

~
Q)

.c
ell

C!.l

ell

c.. ......:::c
CI:l(Exlructed Part) II l'I'.lPort-A

Port-B

location
Fig.8 Pore distribution (port-A,B)(2) Pore Distribution and 'a' Value
Fig.9 shows a relationship between the ratio of the pore volume of 0.1 - 100 j.J.m radius to
the total pore volume of 0.005 - 100 j.J.m radius and 'a' value calculated by the rate process
analysis. Concrete that has more the pore volume ofO.l - 100 fJ.m radius shows high value of'a'. Comparing between port-A and port-B, the range of 'a' value and the ratio of pore volumeof port-A are varied wider than those of port-B.
S57

• Splash zone (Port-B) l::. Sea side (Port-A)
8.0 .• Under the sea (Port-B) 0 Land side (Port-A)-4.0 '----'------'------"-----'-------'

o

10

20

30

40'

50

rate of pore volume (%)

Fig.9 'a' value and Rate of pore volume

DISCUSSION
Environmental conditions of port-A was clarified as below:(1) Corrosion was seen at sea-side and lower part.

(2) Corrosion was not seen at land-side and upper part.
(3) The minimum temperature is below -10 degree Celsius.
Considering these environmental conditions, 'a' value and the pore ratio, it is considered that
deterioration of port-A concrete occurred due to freezing-thawing process and then corrosion ofreinforced bar was introduced as shown in Fig.IO.
Concrete".

Reinforced bar

Occurense ofreinforced bar's corrosion

Occurence of micro crack

Breaking off the covering concrete

Enlargement of microcracking zone
Growth of crack

Fig.IO Mechanism of concrete deterioration
S58

Environmental conditions of port-B was clarified as below:(1) Cracks and corrosions of reinforced bar did not exist.
(2) Port-B has been used over 70 years.
(3) Climate around port-B is not as cold as port-A
Considering these environmental conditions, 'a' value and the pore ratio, port-B could be
estimated to be slightly influenced by exposure condition.

CONCLUSIONS(1) Increase of the pore volume made AE activity high at low stress level, and 'a' value increasein the experiments of cast specimens.
(2) Freezing-thawing and corrosion result in the high ratio of pore volume of 0.1 - 100 f.J.m
radius in concrete of Port-A(3) There is a consistent relationship between the ratio of the pore volume of 0.1 - 100 f.J.m
radius to the total pore volume of 0.005 -100 f.J.m radius and 'a'" value calculated by the rate
process analysis.
AE measurement under the uniaxial compression gives useful information on micro-cracks inthe core sample. The 'a'" value could be applied to evaluate the extent of deterioration of concrete.
Thus, the rate process analysis of AE measurement and the uniaxial compression test could
supply not only compressive strength and young's modulus but also 'a'" value that could evaluate
the extent of concrete deterioration quantitatively.

REFERENCES[1] M. Ohtsu, "Rate process Analysis of AE activity in Uniaxial Compression Test of Core
Sample", Progress in AE If, JSNDI, 1990, pp.479-486.
[2] J. Yamada, "Intelligible knowledge of cement and concrete", Kajima publisher, 1987, pp.145.
[3]A Ishibashi, "Concrete Core Testing of Harbor Structures using AE Rate Process Analysis",
The First US-Japan Symposium on Advances in NDT, 1996, pp.49-54.

S59

Fourth Far East Conference on NDT (FENDT '97)
Cheju-do, Korea, October 8-11, 1997

OBSERVATION OF DAMAGE PROCESS IN RC BEAMSUNDER CYCLIC BENDING BY ACOUSTIC EMISSION
Mitsuhiro Shigeishi1 , Masayasu Ohtsu1, Nobuyuki Tsuji2 and Daisuke Yasuoka3
Department of Civil Engineering & Architecture, Kumamoto University,
2-39-1 Kurokami, Kumamoto 860, Japan
2 Graduate School of Kumamoto University,
2-39-1 Kurokami, Kumamoto 860, Japan
3 Osaka Branch, Daiho Corporation,
Shinkou Building #2, 2-2-5 Higashi-Tenma, Kita-ku, Osaka 530, Japan
1

ABSTRACTReinforced concrete (RC) structures are generally applied to construction of buildings andbridges, and are imposed on cyclic loading incessantly. It is considered that detected acoustic
emission (AE) waveforms are associated with the damage degree and the fracture mechanisms of
RC structures. Therefor, the cyclic bending tests are applied to damaged RC beam specimens. To
evaluate the interior of the damaged RC beams, the AE source kinematics are determined by'SiGMA' procedure for AE moment tensor analysis. By using 'SiGMA' procedure, AE source
kinematics, such as source locations, crack types, crack orientations and crack motions, can be
identified. The results show the applicability to observation of the fracture process under cyclic
bending load and evaluation the degree of damage ofRC beam.

INTRODUCTION
A phenomenon, acoustic emission (AE) is the propagation of elastic waves generated from asource in an elastic material. AE source is a micro-motion caused by rapid dislocation of mass.
The most typical AE source is the release of internal energy due to propagation of an existing
crack. Further, contact of the faces and friction at the existing crack surfaces are also sources of
AE. AE behavior, activity, waveforms contain its source mechanisms and the conditions of
medium during propagation. Accordingly, in the case that AE waveforms are appropriately
detected and theoretically analyzed, it is expected that the existence and expansion of damaged
domain could be evaluated.
AE technique enables real-time and wide-range surveillance of structures without

seointemusslon. Furthermore, the behavior of a material ! structure could be observed underdynamic loading. In consequence of these advantages of AE technique, research on diagnosis ofinterior condition and behavior of a material! structure by using AE technique has been actively
performed in concrete and structural engineering [1].
Reinforced concrete (RC) beams! slabs are extensively employed in construction of bridges
and structures. RC beams I slabs are liable to be damaged because they are always imposed on
cyclic loading. It is considered that detected AE waveforms and their parameters are associatedwith both the fracture mechanisms of RC structures and the damage degree due to cracking in
concrete body and slipping of reinforcement.
In this research, the fracture process of RC beams under cyclic bending is observed, and then
SiGMA moment tensor analysis is applied to diagnosis of damaged degree ofRC structures.

CYCLIC BENDING TEST OF RC BEAMS
Two RC beam specimens reinforced by a single steel bar are used. Configuration of thespecimen is shown in Fig. 1. These are designed to bear static 44.1 leN bending. The measured
ultrasonic velocity which propagates in concrete is 4 770 mls. The specimen has a square sectionof 100 mm and a length of 400 mm, and one through-thickness notch 1 mm wide is prepared at
the center of span at the bottom surface.
Type A contains a deformed steel bar of 10 mm diameter at depth 15 mm. A round steel
reinforcement of 10 mm diameter is embedded in Type B.
(unit; mm)

400

I~

I

I
I

/

j:

Plane View~:

St;el Bar /~NotCh~Elevation View

<;.

I~~

37_0

J

-

~

-tl.~

Figure 1. Configuration ofRC beam specimen
To examine the applicability of AE waveform analysis using SiGMA procedure, cyclic
bending tests ofRe beams were carried out.AE waveform analysis has already been applied toobservation ofRC beam fracture under cyclic bending [2]. However, semi-static load was simply
applied to specimens intermittently. In this experiment, the low-level cyclic load was applied
continuously. The experimental setup ofthe four-pointed bending test is shown in Fig. 2.

$61

AE Waveform RecorderFigure 2. Experimental setup of cyclic bending test
Before cyclic bending test, the static load (22.05 kN) corresponding to 50 % of bending
strength was applied to both specimens. After pre-load is removed, cyclic load from 0.98 kN to
22.05 leN was applied to RC beam specimens 100 times automatically. The loading speed of thecross head was set to 10 mm/s.
During the test, AE waveforms were continuously recorded at 6-channel AE measurement
system. AE sensor locations are shown in Fig. 3. AE waveforms were detected at 6 AE sensors,which are attached to the top surface (# 1 and # 2), the facing side (# 3), the bottom surface (# 4
and # 5) and the back side (# 6) of the specimen. These sensors have flat response in the wide
frequency-band up to 1 MHz.
(unit; mm)125~---+------,~~:v.
Upper Side
#2

::

#1

125

50

F_r_on_t_S_id_e_+-#_3_;...-#6 (Back)

~]~O
]

L-

50

90

'------+----+------'~~:v.
Lower Side

::

#4

#5

90

Figure 3. Arrangement of AE sensors

S62

When a signal, of which threshold level was over 120 m~ due to arrival via sensor #1, thesystem enforced all channels to start recording AE signals. The high-pass filtering over 3 kHz
was employed for recording AE waveforms. Total amplification of the measurement was 60 dB
gain. AE signal was digitized into 2 048 words in length at 2 MHz sampling rate. Digital data
were stored into the hard disk.

SiGMA MOMENT TENSOR ANALYSIS
Generalized Theory of Acoustic Emission
For quantitative AE waveform analysis, theoretical treatment is summarized as a generalized
theory of AE [3]. Mathematically a micro-cracking of the AE source is modeled by a dislocation
ofintemal surfaces as shown in Fig. 4.

,.."......nCrack surface; F

Figure 4. Dislocation model for micro-crack
The crack surface F is nucleated at location y due to dynamic fracturing. An internal face withnormal vector n virtually consists of two planes in coincident motion. Due to micro-fracturing,
dislocation vector b between the two planes is defined. Dynamic motion of dislocation bgenerates elastic wave which is observed at surface of the body.
When crack motion direction b is parallel to normal vector n, a tensile crack is nucleated.
Otherwise, a mixed-mode or shear crack is occurred. The angle between the direction b of crack
and the normal direction n is not equal to zero and is less than 90 degree.
Thus, crack kinematics are defined by normal vector n and crack motion direction I (b=bl). A
physical quantity defined by two orientations is mathematically called a tensor. Conveniently, thefollowing tensor is defined from the two vectors,(1)

Here Cpqjk are elastic constants. Then, AE waveform U j at location x corresponding crack
motion on crack surface F is represented by using moment tensor mpq ,uj(x,t) = JFGjp,q(x,y,t)mpq*S(t)dS.

(2)

Where GjP•q is the spatial derivative of Green's function and S(t) represents the source-time
S63

function (crack kinetics) and the notation * means the convolution integral.

SiGMA Procedure
Fig. 5 shows a view of AE waveform detected by an AE sensor placed at a boundary surface.

Figure 5. AE waveform observation
To process AE waveform data promptly, a simplified moment tensor inversion procedure isdeveloped by the use of only P wave amplitude [4]. A procedure named as SiGMA (simplified
Green's function for moment tensor analysis) is developed. Selecting the P wave portion fromthe full-space Green's function of homogeneous and isotropic material, Eq. 2 is simplified,

A = C Re(s, r)

y y m
p

~ 19

(3)

Here, C is the material constant, Re(s, r) is the reflection coefficient due to directions s of
sensor sensitivity and direction r of wave incidence from the source as shown in Fig. 5. R is the
distance between the source and the sensor, and r is the direction cosine of r. The moment tensor
mpq can be represented by using the normal n to crack surface and direction 1 of crack
displacement in an isotropic material,

m pq = ')JknkBpq +J.1V pn q +lqn p),(4)

where A. and J.1 are Lame constants.
When elastic wave due to one AE event is detected at more than six sensors, the parametersconcerned with source location and the amplitudes of the first motion are known. Hence, theindependent six component of moment tensor can be determined by solving the simultaneous
equations ofEq. 3 at each observation point.
Information about crack kinematics can be obtained from the eigenvalue analysis of the
moment tensor. The eigenvalues, En and the eigen vectors, en are determined from Eq. 4,

S64

o

(5)

[1 + nix n 1- n]
lknk -1o

1-2v

Note that all vectors are nonnalized in the calculation. Making reference to Fig. 6, original
magnitudes of the vectors can be reproduced from the following equations,

81=~2+21knk·el'

(6)

(7)

(8)

Figure 6. Crack orientations and eigen vectors of moment tensor
Thus, the direction of the normal n to the crack surface and the direction 1of the crack motion
can be recovered,

1=(8, +8 3 )/2,(9)n = (8, -8 3 )/2.

(10)

A quantitative classification of the crack type into shear mode, tensile mode and mixed modeis developed. The eigenvalues of the moment tensor are decomposed into a shear component, adeviatric component (CLVD) and a hydrostatic component [5]. These components are given in
Fig. 7. AE sources consist generally of a mixed mode with tensile and shear components. Setting
the ratio of shear component as ~ that of CLVD component as Y and that of hydrostatic
component as Z with respect to the maximum eigenvalue of the moment tensor, all eigenvalues
865are uniquely decomposed,
1.0=x+y+Z{

E2 / E1 = -O.5Y + Z(11)

E 3 / E 1 = -x - O.5Y + Z .
Intennidiate

Maximum

7

Z

-O.5Y

Z

x
Shear (X)

CLVD(Y)

Hydrostatics ( Z)

Figure 7. Eigenvalue decomposition of the moment tensor
In the case that the moment tensor components are determined from detected AE waveforms,
information on locations, types and directions of cracks is determined by a SiGMA procedure of
AE sources. The experimental verification of this procedure in two-dimensional problem has
already reported [6].
Furthermore, confirmation of the SiGMA procedure solutions is conducted by a post analysis[7]. When a moment tensor and a location of an AE source are known, the first portion of theelastic wave at the observation point can be simulated by Eq. 2. As the verification, the SiGMA
solutions determined from the detected AE waveforms are compared with those calculated fromthe simulated waves. Then, poor solutions are discarded.

DAMAGE PROCESS OBSERVATION
The analyzed results in the pre-loading test are shown in Figs. 8 and 9. An arrow symbol
indicates an AE source classified into a tensile crack ofwhich shear component ratio X is smaller566

than 30 %. The direction of the allow directs the crack opening direction. In contrast, a crosssymbol indicates a shear-type AE source or a mixed-mode AE source of which shear component
ratio X is greater than 30 %. Each line directs either the crack nonnal n or crack displacement I.
The dotted lines and the broken lines show the observed visible crack on the front side (dottedlines) and the rear side (broken lines).
The difference of damage degree resulting from the shape of reinforcement is clearlyobserved. The visible cracks propagate a little and a few of micro-cracks are produced at the tipof notch in Type A (reinforced by defonned steel bar). In contrast, the visible many cracks growand almost reach the top of the specimen in Type B (reinforced by round steel bar). The microcracks (AE sources) are widely distributed around the visible cracks. It could be concluded thatType B is already damaged considerably at this step.
ProntV1eW'I. ""-~l\).

",\,. .".::~'\.\.\\ \\ .t~
l.
\\
1

, ",

Mixed-mode or Shear Crack. ,.

V Tensile Crack

I

I

~

~
Section vie1

seetionVty

).1.

it

."1-

*. r

~

r. 'IX
X 1

X~~ _.IX. X.

- - . Itl-X- - - .. - - .

.X

. __

y

Figure 8. Results of Type A at pre-loading

Figure 9. Results of Type B at pre-loading

Results under the cyclic loading test are shown in Figs. 10, 11, 12 and 13. Until the 15th
loading, micro-cracks are mainly generated is the compressive stress zone at the upper in Type A.
Some micro-cracks are also created near the reinforcement, indicating the slipping off In thecase of Type A, most micro-cracks are generated along the visible cracks after the 15th loading.
Thus, the defonned bar surely contributes to prevent from cracking.
In the case of Type B, before the 15th loading, further propagation of visible cracks is hardly
observed. However some micro-cracks are scattered in the wide area. This means probably thatthe fracture process zone is spreading out step by step. At the final step in Fig. 13, the area,
where the micro-cracks are generated, further spreads out.
$67

FrontVaew,\)

FrontVaew'X It''\I
I

Section Vie,

Sectionliew

~

X

Jr

I

X

~

X

\X

X

XX

x,f

•

X

•

•• __ ~ .X. _ X_"-. - . . -~- . - . . . .; -Of

Figure 10. Results of Type A at 15th loading

Figure 11. Results of Type B at 15th loading

Front View

Front View'"\.

\ t: }\: X /
~.

"

~~,

'fa

_------r.. .,~1----Section VIeW

I

~

~

J~
I I

\

1

•

.X- - - ..... - - - - . - . - Figure 12. Results of Type A at 100th loading

Figure 13. Results of Type Bat 100th loading$68CONCLUSIONSFor the on-site investigation of civil structures, the inspection, which could be performed
without stopping the service, is meaningful as well as non-destructive. Considering theadvantages of AE technique, it is expected that AE technique is applicable to monitoring thedamaging process and estimating the deteriorated degree in RC structures. To examine theapplicability of AE waveform analysis using SiGMA procedure, cyclic bending tests of RC
beams are carried out and dynamic AE measurement is performed. Then, the following
conclusions are obtained:
1) The difference of damage degree resulting from the shape of reinforcement is clearlyobserved.
2) At early step (until 15th loading) in cyclic bending test, micro-cracks are mainly generated
is the compressive stress zone at the upper in RC beam specimen containing a deformed
steel bar (Type A). Some micro-cracks are also created near the reinforcement, indicating
the slipping off. Most micro-cracks are generated along the visible cracks after that. Thus,
the deformed bar surely contributes to prevent from cracking.
3) In the case of RC beam specimen containing a round steel bar (Type B), propagation ofvisible cracks is hardly observed at early step (before the 15th loading). However somemicro-cracks are scattered in the wide area. This means probably that the fracture processzone is spreading out step by step. At the final step, the area, where the micro-cracks are
generated, further spreads out.

REFERENCES
1. M. Ohtsu, "The History and Development of Acoustic Emission in Concrete Engineering",
Concrete Library ofJSCE, No. 25 (1995)
2. S. Yuyama, T. Okamoto, M. Shigeishi and M. Ohtsu, "Quantitative Evaluation and
Visualization of Cracking Process in Reinforced Concrete by a Moment Tensor Analysis ofAE", Material Evaluation, Vol. 53, No.6, pp. 751-756 (1995)
3. M. Ohtsu and K. Ono, "A Generalized Theory of Acoustic Emission and Green's Functions in
a Half Space", Journal ofAE, Vol. 3, No.1, pp. 124-133 (1984)
4. M. Ohtsu, "Determination of Crack Orientation by Acoustic Emission", Material Evaluation,
Vol. 45, No.9, pp. 1070-1075 (1987)
5. L. Knopoff and M. 1. Randall, "The Compensated Linear Vector Dipole: A Possible
Mechanism for Deep Earthquakes", Journal of Geophysical Research, Vol. 75, No. 26, pp.
4957-4963 (1970)
6. M. Shigeishi and M. Ohtsu, "Observation of Mixed-Mode Fracture Mechanics by SiGMA2D", Journal ofAcoustic Emission, Vol. 11, No.4, pp. 57-63 (1994)
7. M. Ohtsu, K. Arao and S. Yuyama, "Post-Analysis of SiGMA Solutions for Error Estimation
in Reinforced Concrete Members", Progress in Acoustic Emission VII, pp. 411-416 (1994)

569

Fourth Far East Conference on NDT (FENDT '97)
Cheju-do, Korea, October 8-11, 1997

SPECTRAL RESPONSE AND ACOUSTIC EMISSION
OF REINFORCED CONCRETE MEMBERS
UNDER FATIGUE BENDING
Yasunori Sakata l and Masayasu Ohtsu2
1

2Faculty of Engineering, Department ofCivil Engineering, Kyushu-Tokai University
9-1-1 Toroku, Kumamoto 862, Japan
Faculty of Engineering, Department of Architecture
and Civil Engineering, Kumamoto University
2-39-1 Kurokami , Kumamoto 860, Japan

ABSTRACT
Durability of concrete structures is an important problem in concrete engineering.
Nondestructive testing to estimate defects in concrete structures is desired tomaintain their durability. The reinforced concrete (RC) structures subjected tofatigue bending is known to gradually deteriorate for a long term.
Several nondestructive testings are applied to estimate the defects in concrete
structures. Only one method, however, could not evaluate readily the deterioration
degree, because the phenomenon is very complicated. Thus, the combined method seems
to be promising to estimate the deterioration degree of RC structures subjected tofatigue.
In this paper, the method to estimate the deterioration degree of RC members under
fatigue bending is studied experimentally, by using ultrasonic spectroscopy andacoustic emission (AE) method. The results show that both methods are useful and the
combined method is promising to estimate the deterioration degree of RC members under
fatigue bendi ng •INTRODUCTION
Conventionally, concrete structures are considered to be durable. But, some concrete
structures recently arise very fast deterioration due to salt attack, alkali-aggregate$70reaction. freezing and thawing, fatigue, and so forth. Therefore, inspection of these
concrete structures is so important as to maintain the durability. To estimate thedefects and the durability of concrete structures. non-destructive testing methods')
have been developed. There are the ultrasonic velocity method. the ultrasonicspectroscopy and the impact echo method, the acoustic emission. the infrared
thermography. the radar, the half cell potential, the X-ray and the r-ray. However.
the estimation of the deterioration degree of structures under fatigue process is noteasy. Because any single method is directly effective, a combined method is to be
developed. Thus. tests of RC beam subject to fatigue bending are performed by using
the ultrasonic spectroscopy21. 31 and the acoustic emission method. 5)

MEASURING SYSTEM
A measuring system 21 to obtain spectral responses is shown in Fig.I. Sinusoidal
sweep-mode signals generated by a signal generator are driven into a transmitter
through an amplifier. These signals are converted into elastic waves that propagate
through a specimen. Arriving at a receiver. elastic waves are converted again into
electric signals. The signals are monitored through a preamplifier by an oscilloscopeand stored in a digital recorder. Transducers employed for both a transmitter and a
receiver are AE sensors of broad band type with 1 KHz resonance frequency. The system
has a smooth and flat response in the low frequency range(See Fig.3). The sensitivity
below 10 kHz is fairly low. The amplitude scales of the spectral responses are
relative. because the amplification is adjusted to keep spectral responses not
saturated in the measuring scale. For AE measurement, a system consisting of AE
sensor. pre-amplifier, and discriminator is employed. For the location of AE events.
two sensors are applied.
( a)

(b)
(2)

(3) (5)(6)(l)Signal generator.(Z}Amplifier.(J}Transaitter.
(4)Speciaen. (S}Receiver. (S}Preamplifier,(7)Discriainator.(8}Oscilloscope.
(9)Signal analyzer.(lO}Printer(1)Speci.en.(Z}&(3)AE censor.(4)l(S)Preamplifer(S}Analyzer.(7}PrinterFig.I Measuring system for spectral responses (a) and acoustic emission (b).
$71£( oj~~~~~g(t;),,
g (t)f (t )[(t):lncident wave due to signal-generator
wv,(t):Transfer function of the amplifier
wt,(t):Transier function of the transmitters(t):Transfer function of the specimenw,z(t):Transfer function of the receiverw.z(t):Transfer function of the pre-amplifir
and discriminatorg(t):Detected wave

Fig.2 Transmission path of ultrasonic waves and corrsponding transfer functions

THEORY ON SPECTRAL RESPONSEZ)
On the basis of the linear system theory, the measuring system is summarized asshown in Fig.2. In the time domain it is represented as,(1)

where a notation * means the convolution integral. All transfer functions are denoted
in Fig.2. The effect of a coupling agent is included in WII(t) and Wtz(t). Response
functions G(f),F(f),Wul(f),W,,(f),S(f),W,z(f) and Wuz(f) are Fourier transforms ofg(t), f(t), WUI(t), WII(t), set), YIZ(t), and Yuz(t), respectively. Then Eq.l istransformed into the frequency domain.
(2)

The aim of this research is to obtain specimen response S(f). To determine S(f) from
G{f), G(f) must be calibrated by Go(f).
S( O=G(O/Go (F)where Go(f)=F(f)·Wu,(f)·Wt,(f)·Wtz(f)·Wuz(f).

(3)
(4)

Examples of the response functions G(f), Go(f) and S(f) are shown in Fig.3, which was
obtained from a plain concrete specimen. Go(f) has a fairly smooth response over thefrequency range, while G(f) has particular peak frequencies. As a result, spectral872

G O(r)

0.

5

-<

o

10

20

50

40

30

Frequency (kHz)Fig.3 Response function G(f), Go(f) and S(f).
response SCf) is not much different from G(f). Thus, ultrasonic spectral responses ofconcrete members are discussed only in term of G(f).

EQUIVALENCE BETWEEN SPECTRAL RESPONSE AND FOURIER SPECTRUM
Wave form in time domainoSpectrum
0S

-<

I..
o

b~

10

20

30

40

50

Frequency (kHz)
Fig.4 Comparison between Fourier spectrum due to impulse imput
and spectral response by sweep-mode input.
873

Sinusoidal signals of sweep-mode and constant amplitude are driven from a signal
generator. An output of a time-amplitude curve is referred to as a spectral response.
The curve is theoretically equivalent to the Fourier spectrum due to the impulse
signal. In eq.1, in the case of f(t) equal to the delta function, Fourier transform
F(f) becomes constant. A comparison between the spectral response obtained by thesweep-mode input and Fourier spectrum by an impulse are shown in Fig.4. In this case,the spectral response and Fourier spectrum are measured for the range 0 ~ 51.2 kHz.
It is known that the spectral response by the sweep-mode input is effected by the
duration time of the sweep-time. To reduce the effect of sweep-time, the sweep-timemust be setlonger than 1 second 2 ) . In this experiment, the durationotime to measure
the spectral responses is 10 seconds.

SUMMARY OF EXPERIMENT3)
The size of specimens is shown in Fig.5. The specimen of dimension 15 x 20 x 120 cm
is reinforced with steel bar of 13 am diameter (SD2~5). Stirrup bars are used of 4 mmdiameter with 10 cm spacing. For concrete, the maximum size of coarse aggregate is 25
mm. Water-cement ratio W/C is 60 %. The strength of concrete is 26.5 MPa. Underloading, the one-dimensional AE location was conducted. Spectral responses were
measured at both the compressive side and the tensile side. The ratios of cyclic loads
to static failure load are 75, 80 and 85 %. AE and spectral responses were measured
at particular cycles from 0 to 2.5 xl0' cycles.

435

330

oa

435

ILoad
i rt<c~T 1 4mm doubt e vi'res stirrrup
rj)

I

/

T
1""""=

tl~

Tr"- D13mm 1T
(b'
a'

85

1030

0

l1@llOCPllOO

I

Di
I

-h

085

(1lIIIl)

T

F-

I ~
3900
150

~

50 T :AE Transducer

1200 (nun):

Fig.5 A sketch of the specimen and the sensor arrangement.

874

RESULTS AND DISCUSSION
The relationships between the load cycles and AE counts observed under loading atthese cycle stages are shown in Fig.5. AE counts are normalized by the number observed
at 1000 cycles. AE counts increase rapidly in the case of high load level while» inloy load level» AE counts are gradually increasing. Those imply that the measuring of
AE counts is availahle to estimate cyclic load level for the structures. In the
distribution of AE location under cyclic loading process» more AE events are
relatively generated near the principal cracks» although many AE events are observedin the whole region. This is because most principal cracks were generated at theearlier stage» and AE events due to friction between steel bar and concrete arecontinuously generated.

100
P85-1 8P75-1
P85-2 -9P75-2
P80-1

en

~

c::
:::s
0

(.)

10'

Cz:l

-<

.<1,)

>

~

a:l
......

1

(1)

Cd

10~

10'

Load cycles
Fig.6 Relationships between AE counts and load cycles.
The examples of the amplitude distribution of AE events are shown in Fig.1. The
cases of broken specimens near load cycles 10 5 are given in Fig.1 (a) and {b}, andFig.1(c) is the case of unbroken specimen even at 2.5xl0 5 cycles. In high load level,some types of amplitude distribution had been observed. In Fig.7(a), the amplitude
distribution shifts to the high amplitude region with increasing of load cycles. The
other side, in Fig.7(b), the amplitude distribution grow up with load cycles in all
range. In the case of low load level, the amplitude distribution does not shift. It isconsidered that the deterioration of RC beam under cyclic load does not develop, andonly AE events due to the friction between steel bar and concrete are generated.
S75

4.------------------------.
(a)
("')

+

-e- E+3+5*E+3-6- £*+4*5*£+4-e- E+53-.... z
C..:I

rn

§
o

u

C..:I

<

90

85

95

100

105

Amplitude (dB)1101500 . . . - - - - - - - - - - - - - - - - - - - - ,B-E+3+ 5*£+3-6- E+4*5*£+4-e- Et5(b)

2 1000
§
o

c..>

~

500

o Ll~~~~~~.,......,II_II__D_II_tI_....
80

85

90

Ampli tude (dB)

4

-....
("')

+

3

Cd

rn

l:::
::l
0

100

-e E+3+5*£+3-fr £+45*£+4-e- £+5.s;z- 5*£+5

95

100

*

Z

95

x £+6

+ 1.5*E+6*2*Ef6).. 2.5*E+6

U
C..:I

<

85

90

Ampli tude (dB)105

Fig.7 Amplitude distributions of AE waves with increase of load cycles.
Therefore, the monitoring of the amplitude distribution is also available to estimate
the deterioration degree of the RC beam subjected to fatigue bending.
Spectral responses in the frequency range 1 ~ 400 kHz with increasing of load
cycles obtained at compressive side of RC beam are shown in Fig.S. Before loading,tberesponses is dominant in high frequency range. The spectral responses in the highfrequency zone gradually decrease with increase of load cycles. This is because
concrete gradually deteriorate due to cyclic bending load. The tendency isparticularly remarkable in the high frequency range. On the other hand. the shifts
S76

\!M,lOc,r ,0 .. ~e, . ,lw..
x ,10'~ .-"iJ",*
Q)"....::s

5

~

I
I

0-

El

-<

400

Frequency (kHz)Fig.8 Spectral responses with increase of load cycles.

of peak frequencies with increase of load cycles were observed in the low frequency
range under 20 kHz. Therefore, the shift of peak frequencies with increase of load
cycles is applicable to estimate the deterioration degree of RC beam subjected tofatigue bending.
Relationships between accumulated ultrasonic energies obtained from integrated
spectral responses and load cycles are shown in Fig.9~ Relative ultrasonic energies
obtained from integrated spectral responses in the frequency range 1 ~ 400 kHz after
calibrating by the response curve of measuring system. In the case of high cyclic load
level, the energies decrease all the way with increase of load cycles. On the other
hand, in the case of low cyclic load level, the energies is decrease in fast stage,
and then keep constant levels.
S77

-

100 .~~PB5-1

ep75-1e PB 5-2 ~P75-2* 80

er PB 5-3 .P75-3*PBO-l.........

~

b.D
t..

(l)

C

(l)

(l)

.....>

60~

a:s......
(l)Q:l

40
20 '-------....L...---.....L...-_ _--.J........L..-_ _--.l-_ _

o

10 7

Load cycles
Fig.9 Relationships between relative energy and load cycles

CONCLUSION
To estimate the deterioration degree of RC beam subjected to fatigue bending, thespectroscopy and the AE method are applied. The results show that the both methods
are available to estimate the deterioration of RC beam subjected to fatigue bending.
Thus, the combined method is in progress.
1) AE counts increase rapidly under the high cyclic load level with increase of load
cycles, while, those in the low cyclic load level keep almost constant.
2) In the high cyclic load level, the amplitude distribution shift to the larger
amplitude region with increase of load cycles. In the low cyclic load level, thedistribution keep constant.
3) The spectral responses obtained from RC beam subjected to fatigue bending decrease
gradually with increase of load cycles.
4) Using the ultrasonic energy, the variation of spectral responses is quantitatively
estimated. In the high cyclic load level, ultrasonic energies decrease rapidly in all
the way of fatigue bending. On the other hand, in the low cyclic load level, theenergies decrease rapidly in the early stage~ but then keep stable. Thus, the tendency
could be available for evaluating the deterioration degree under fatigue.

$78

REFERENCES
I.Special issue on nondestructive testing of concrete.JCI.Concrete Journal,Vol.27.No.3.
1989.3
2.Y.Sakata and K.Ohtsu.Crack Evaluation in Concrete Hembers Based on UltrasonicSpectroscopy.ACI Material Journal/Nov.-Dec.1995.pp.686-699
3.Y.Sakata and H.Ohtsu,Evaluation of Internal Defects in Concrete Structure by
Ultrasonic Spectroscopy.Proceedings of JSCE,1992.2.No.442.V-16,pp.71-80
4.Y.Sakata and M.Ohtsu.Spectral Responses and Acoustic Emission on RC-Kember Subjectedto Fatigue Bending.Proc.JCI.1997.Vol.19.No.1,pp.1339-1344
5.H.Ohtsu.Rate Process Analysis of Acoustic Emission Activity in Core Test of Concrete.
Oroceedings of JECE.1992.2.No.442/V-16,pp.211-218

579

Fourth Far East Conference on NDT (FENDT '97)
Cheju-do, Korea, October 8-11, 1997

ACOUSTIC EMISSION BEHAVIOR DURING TENSILE
DEFORMATION OF WELDED STEEL JOINTS
J. H. Huh, K. A. Lee and C. S. Lee
Center for Advanced Aerospace Materials,
Pohang University of Science & Technology, Pohang 790-784, Korea
ABSTRACTAn investigation has been carried out to study the acoustic emission (AE) behavior
during tensile defonnation of welded steel joints of low-carbon low alloy steel. The results
indicate that the AE characteristics of the base metal is distinctly different from those of theheat affected zone (HAZ) and the weld metal. For the base metal consisting of ferritepearlite microstructure, most of the AE events are produced near the yield point, mainly due
to the dislocation movements during the defonnation. However, For the HAZ and the weld
metal, a second AE peak with higher energy is evident after yielding, in addition to the AE
peak occurring around the yield. point. This second AE peak is attributed to the existence ofmartensite and, its amplitude is proportional to the volume fraction of the martensite. The
sources of the second AE peak were the debonding of ferrite-martensite interfaces and themicro-cracking of brittle martensite plates, as rationalized by the microstructural and thefrequency spectrum analysis.

INTRODUCTION
The structural steels used for the construction of modem buildings have been developed
to improve the strength, ductility and weldability, which enhances the resistance to theearthquakes and strong winds. In recent years the steels treated via the thenno-mechanicallycontrolled process (TMCP) are especially attractive for their high strength and hightoughness due to their flOe grain sizes and low carbon contents [1]. During welding threezones are usually generated in the steel joints, namely, the base metal, the heat affected zone
(HAZ) and the weld metal. Mechanical properties, especially the toughness [2], are seriously
deteriorated in those regions with evidently coarsened grains (typically in the heat-affectedzone (HAZ) and the weld metal) because of coarsened grains and transformed
microstructures.
An acoustic emission (AE) method, one of the non-destructive evaluation techniques.
widely utilized in recent decades to inspect the safety of atomic power plants, bridges andfuel tanks owing to its capability of real time monitoring [3]. Since AE is very sensitive tosome specific micro-events in local areas, e.g. dislocation movement and multiplication,
microcrack initiation and growth, and interfacial cracking, the AE method is useful in
S80

identifying the origin, magnitude and distribution of the source that causes the characteristic
AE events. Advances have been made in studies using the AE method in characterizing thedeformation behavior [4-6], the interfacial debonding/cracking [7-8] and the micro-void
coalescence or crack growth [9] in steels and other non-ferrous alloys. Although several
studies have been made on the mechanical behavior of welds by use of AE [10-11],
information is still lacking for a better understanding of the correlation between the AE
behavior and microstructure in steel welds. This study is aimed to investigate the AE
behavior during tensile defonnation of welded steel joints and to correlate the AE behavior
with the specific microstructures in the base metal, the HAZ and the weld metal.

EXPERIMENTAL PROCEDURESThe TMCP steel (pILAC-BT33) used in this work was supplied by Pohang Iron andSteel Company in the form of 48mm thick plate. The nominal composition of this steel isshown in Table 1. A submerged arc welding (SAW) method was used to weld the steel joints,
with an arc voltage of 36 V, a welding current of 980 A and a heat input of 92 kJ/cm. No
heat-treatment was conducted after welding.
Table 1. Chemical composition ofTMCP steel (PILAC-BT33).
Element
wt%

C
0.14

Si
0.23

Mn
1.23

P
0.0117

S
0.003

Cr
0.033

Ti
0.15

Fe
Bal.

The tensile sheet specimens were prepared according to the relevant ASTM code andthey had a width of 6.25 rom, a thickness of 3 mm and a gauge length of 25 rom. The
specimens of the welded joints were cut from the base metal, the HAZ and the weld metal
regions, respectively, with the tensile axis in the rolling direction. To simulate themicrostructural changes in the HAZ and the weld metal, another batch of samples were
heated to 760°C, 800 °C, 830°C and 860 °C, respectively, for 1.5 hours and then quenched
into water, to produce different amount of transformed martensite. They are hereafterreferred to as HT HT2, HT) and HT4, respectively. The volume fraction of the martensite" the Mossbauer analysis to be 26%, 43%, 61% and 82%, respectively,
was measured using
corresponding to the increase in heating temperatures given above. The tensile tests wereconducted at room temperature using an Instron 1361 testing machine with a cross-head
speed of 0.27 mm/min. Scanning electron microscopy was also utilized for fractographic
analysis.
The AE analysis was carried out using the AE detector (AEDSP-32/16) made by PAC
Co., with the sensor RI5. The data processing was done under the condition of a preamplification of 40 dB plus a main amplification of 40 dB, giving a total amplification of 80
dB. The sensor was coupled to the polished jig by a thin layer of vacuum grease under aconstant pressure. To remove the noise from actuator, etc., a threshold amplitude wasspecified to be 30 dB, which was determined in the preliminary tests by using anoscilloscope. The jig pin was coated with a layer of grease to minimize the friction between
the specimen and the jig. The AE data acquisition was realized by the MISTRAS 2001
program. A schematic diagram of the testing and acoustic emission recording system isshown in Fig. 1.
S81

Load CenI~AEDSP32/16
M1STRAS2001

I~I

Instron1361~ ~Ahnsor_et I-I
Actuator

1

ICO"1J~et I
I

PrinterI

Fig. 1. Schematic diagram showing tensile and AE tests.

RESULTS AND DISCUSSION

Microstructures and tensile properties
The microstructures of the weld joints in different regions are shown in Fig. 2. The basemetal is composed of ferrite and pearlite (Fig. 2a). The microstructure in the HAZ ischaracteristic of very coarse grains (Fig. 2b), which also includes the martensite-austenite(M-A) constituent, upper bainite and martensite formed during the heating-cooling cycle ofwelding. In the region of the weld metal, the microstructural constituents similar to those in
the HAZ may also exist, but typically an area of coarse columnar microstructure is observed(Fig. 2c), which is chiefly due to the temperature gradient formed during the cooling process
while being welded.

Fig. 2. Optical micrographs showing three different regions of welds;(a) base metal (b) HAZ and (c) weld metal.
S82

Table 2 summarizes fundamental tensile properties of specimens representing the basemetal, the HAl and the weld metal, respectively. For the specimens in the HAl, the yieldstrength (Y.S.) is about 9% lower and the elongation is about 15% lower than the base metal,
presumably due to the grain coarsening and the transformed constituents-induced
embrittlement. In the weld metal, the elongation is about 10% lower than the base metal,
which is probably caused by the structural inhomogeneity occurring in the coarse columnar
solidification area or in the segregated central area.
Table 2. Tensile properties of the welds in different regions.

Y.S. (Mpa)
U.T.S. (Mpa)
Elon2ation (%)

HAl
383
477
22.8

Base metal
421
527
26.8

Weld metal
402
498
24.1

The AE characteristics of the welds
As one of main AE parameters, the AE energy rate (frequently represented as a rootmean-square voltage) measured during the tensile tests is shown in Fig. 3 together with thecorresponding tensile curve, for the specimens representative of different regions in thewelded joint. In the base metal (Fig. 3a), most of the AE events occur around the yielding
stage and the energy rate peak is detected around the yield point. By contrast, the AE
behavior in the HAl and the weld metal is quite different from that in the base metal. As isseen in Fig. 3 b - c, the AE events are evidenced not only around the yield point, but also inthe post-yielding stage. A second and higher peak of the AE energy rate is detected in the
post-yielding stage.
Fig. 4 shows the AE amplitude distribution for the tensile specimens taken from different
regions of the welds. Only amplitudes above 30 dB are plotted due to the threshold set at thisvalue. It is found that, for all three regions, most AE events occur around the yield point butwith relatively low amplitudes (30-35 dB). For the specimens in the HAl and the weld metal,
however, the AE events with higher amplitudes (typically around 54 dB) are also evidenced(Fig. 4, b-c), in contrast to the case in the base metal in which the high-amplitude AE events
are basically not detectable. Therefore the specimens in the HAl and the weld metal arecharacteristic of the appearance of the second AE peak with high amplitudes occurring in the
post-yielding stage.
It is found by Scruby [12] that the AE signals due to the dislocation movement reach tothe maximum near the yield point and then gradually decrease. In view of similar AE
features, the AE peak around the yield point for the present cases is also assumed to berelated to the onset of large amount of dislocation movements. On the other hand, the 2nd AE
peak has also been documented in some previous investigations[5, 7-8, 13-14] and its origin
has been identified as the interaction of moving dislocations with precipitates [5], theserrated flow [14] and the debonding of second phase particles or inclusions or precipitatesfrom the matrix [7-8, 13]. Considering the present microstructural constitution, it is highly
probable that the 2nd AE peak appearing in the HAl and the weld metal might be attributed
to the existence of martensite. To examine this assumption, the AE investigations havefurther been conducted on another batch of TMCP steel samples heat-treated to result indifferent amount of martensite.
883

Fig. 3. AE energy rate produced during the tensile defonnation of welds ;(a) base metal (b) HAZ and(c) weld metal.

a

b
5

Ir"

S!

..!!.

i

'l5

z

t

20

30

co

eo

lill

70

eo

20

Amplitude (dB)

40

eo

8)

70

80

ICI

Amplitude (dB)

C

Ir"
0

..!!.

I

i

'l5

2

Z
2ll

30

co

eo

lill

70

eo

90

Amplitude (dB)

Fig. 4. AE amplitude distribution produced during the tensile defonnation of welds;(c) weld metal.
(a) base metal (b) HAZ and
884

The AE amplitude distribution during the tensile tests has been analyzed for the
specimens with different martensite volume fractions of 26%,43%, 61% and 82% and theresults are given in Fig. 5. It is found that all the heat-treated specimens exhibit similar
characteristics with the distribution of HAZ and weld metal (Fig. 4 b-c). As is indicated inthis figure, in addition to the low-amplitude heavy AE activities occurring around the yield
point, there are quite a lot of AE events occurring at amplitudes higher than 50 dB,
characteristic of the second AE peak.. The general trend for the second peak of the heattreated specimens is that the amplitude and the number of events both increase withincreasing the volume fraction of the martensite.

a

b

R"

&

I

'8

.l,S.

-

4

lS

I

2

F;'
0

o

2

Z

Z
«l

50

III

70

III

1IO

fill

AmpOIude (dB)

70

eo

70

III

90

AmplIlude (dB)

c

d

F;'

5

0

~

JJ
c

•J

'8

2

Z
70

eo

90

50

AmpIIIude (dB)

AmpIIlude (dB)

Fig. 5. AE amplitude distribution for heat-treated specimens;(a) HT) (b) HT2 (c) HT3 and (d) HT4•
As an example, Fig. 6 shows the SEM micrographs taken from the tensile specimens of
the heat-treated steel. It is evidenced that microcracks can be found both at the ferritemartensite interface (Fig. 6a) and in the interior of martensite (Fig. 6b).
Combining with the AE characteristics shown above, it is justified that the occurrence ofthe 2nd AE peak is attributed to the existence of the martensite in which the micro-cracking
events occur due to the ferrite-martensite interfacial debonding and/or the martensite platecracking itself. This point is supported by a previous AE investigation on the carburizedsteels [15], which identifies the 2nd AE peak with higher amplitude as originated from theintergranular cracking.
The difference in the strength level between ferrite and martensite is assumed to bemainly responsible for the cracking via the ferrite-martensite interfacial debonding.
Moreover, the residual stresses resulted from the inhomogeneous contraction due to coolingand expansion during martensitic transformation may also contribute to the interfacial
cracking. Therefore the interface is highly potential for the initiation of microcracks andS85

hence, the AE from these regions possesses an evidently higher energy than in the casewhere just plastic defonnation occurs.

Fig. 6. SEM micrographs for the heat-treated specimens after tensile defonnation.
Cracks were observed(a) at interfaces between martensite and ferrite, and (b) inside the martensite phase.
Fast Fourier transformation (FFT) analysis
Using FIT method to analyze the frequency spectrum for the AE waves is importantbecause different defonnation or damage processes usually correspond to different frequency
spectrums [9]. In the present study this method is employed to analyze the AE signals
detected in the specific cases of the ferrite defonnation and the ferrite-martensite interfacial
cracking. The results are given in Fig. 7 as typical examples.

ba..
•.••c. ':::::'::::=~:::~:::. :.::l·.':::~ :f::'::~::::~...........

....

..

~:::::: ~.:~;I~=:~j-:~:::~r::·::~:::E::.~~.l~~~~I::;·-.=F;';~••••,......

.,....

. . . . . . . . . . . . . . . . 'fQ I

• '.M

I.

••·••

.". o.

:.

•

-I'

·i·

.

.. .. • .. .. .. .. .. .. ..

!-

..

.. p

~ _

1

I_ _ i.

:

..!

t

••••

......

.

,~············i··_····_··,••.•• ::::: ::: ::.. ::t::. :::: :: ::: :1: :: :: :::: ::
....

.

Q.t",

•

.,..

.'s ...._~ • •,-....

: : : ~ ~ ~ ~~ ~ ~ ~ :~ ~~..i-:::::::::::
J~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ l:::::::::::
t~ ~ ~ ~ ~ ~~ ~ ~~ ~..

:- •••••••••• - •• - '1•• - •• - ••••.. ... ... ... . ... . . ...

.11'.",....N_.eI:u.",...,aa.................. _ .. ";'••.•• . . . . . . . . . . . . t~I• :~-~:::.:: i :··::·::j:::::-:::::!::::::::::::t:::::=t:···:::I::=::::i:::::::::::

-~

. .

-

-

....

a

.

....

Fig. 7. AE waveform (upper part) and frequency spectrum (lower part) for(a) ferrite deformation and (b) cracking at F/M interfaces.

In the case of ferrite defonnation (Fig. 7a), the AE wave is characterized by long risetime (rise time = the time required to reach the maximum amplitude) and low amplitude.
S86

This is in accordance with the present fractographic observation that the base metal fracturesin a typical ductile mode. The frequency corresponding to the peak is about 175 kHz. By
contrast, in the case of the ferrite-martensite interfacial cracking (Fig. 7b), the AE wave ischaracterized by short rise time and high amplitude (approximately 5 - 10 times that of theformer case). This is also in agreement with the fact that a brittle fracture occurs with a highenergy releasing rate in the steel with the martensitic microstructure. The frequency for the
peak is about 110kHz, obviously lower than in the case of ferrite deformation. It is clarified
that the results on the frequency spectrum analysis further confinn the arguments on the two
distinct types of AE origins stated in earlier sections.

CONCLUSIONS(1) In the base metal region of the weld joint, most AE events are associated with thedislocation movement during the yielding and only one AE peak is found around the yield
point. In the HAZ and the weld metal, in addition to the 151 AE peak around the yield point, a
2nd peak appears in the post-yielding stage and is related to the microcracking events at the
ferrite-martensite interface andlor within the martensite.
(2) The AE behavior of the heat-treated steel with different martensite contents is alsocharacterized by the existence of the 2nd AE peak. The AE amplitude and the number of
events both increase as the volume fraction of martensite increases. Cracks are found both at
the ferrite-martensite interface and in the interior of martensite.
(3) When the ferrite matrix deforms, the AE wave is characteristic of long rise time, lowamplitude. However, when the ferrite-martensite interfacial cracking occurs, the AE wave ischaracteristic of short rise time and high amplitude.

ACKNOWLEDGEMENT
The authors wish to acknowledge the financial support of the Pohang Iron & Steel Co.
Ltd. (POSCO). The authors are also thankful to Dr. Hee-Don Jeong (RIST) for his helpful
discussion of this work.

REFERENCES
1. A. J. DeArdo, "Proc. of IntI. Conf. on Microalloying", Iron and Steel Society, Pittsburgh,
pp. 15 (1995)
2. T. Haze, S. Aihara and H. Mabuchi, "Accelerated Cooling of Rolled Steel", TMS,
Warrendale, (1986)
3. "Non-Destructive Testing Handbook, 2nd Edition", Vol. 5, American Society for NonDestructive Testing, Columbus, (1981)
4. H. N. G. Wadley, C. B. Scruby, P. Lane and 1. A. Hudson, "Influence of microstructureon acoustic emission during deformation and fracture ofFe-3.5Ni-O.21C steel", Met. Sci.,
Vol. 15 pp. 514-524 (1981)
5. C. B. Scruby, H. N. G. Wadley and K. L. Rusbridge, Mater. Sci. Eng., "Origin of acoustic
emission in AI-Zn-Mg alloys II : Copper-containing quaternary alloys", Vol. 59, pp. 169S87

183 (1983)
6. K. A. Lee and C. S. Lee, "Acoustic emIssIon characteristics associated withmicrostructures and plate orientations of an Al-Li 8090 alloy", Mater. Sci. Eng. A, in
print, (1997)
7. C. H. Heiple, S. H. Carpenter and M. Carr, J. Mater. Sci.. 15, pp. 587 (1981)
8. S. M. Cousland and C. M. Scala, Mater. Sci. Eng., "Acoustic emission during the plastic
deformation of aluminium alloys", Vol. 57, pp. 23-29 (1983)
9. B. Raj, B. B. Jha and P. Rodriguez, Acta Metall., "Frequency spectrum analysis of
acoustic emission signal obtained during tensile deformation and fracture of an AISI
316type stainless steel", Vol. 37, pp. 2211-2215 (1989)
10. C. B. Scruby and K. A. Stacey, "Acoustic emission measurements on PWR weldedmaterial with inserted defects", J. of Acoustic Emission, Vol. 7, pp. 81-93 (1988)
11. P. G. Bentley, and M. J. Beesley, J. of Acoustic Emission, "Acoustic emission
measurements on PWR weld material with inserted defects using advanced
instrumentation", Vol. 7, pp. 59-79 (1988)
12. C. Scruby, H. Wadley and J. E. Sinclair, "The origin of acoustic emission during
deformation of aluminum and aluminum-magnesium alloy", Phil. Mag., 44A, pp. 249(1981)
13. M. Wu and S. H. Carpenter, J. Acoustic Emission, 11, pp. 5 (1983)
14. Y. Xu, Z. Zou and F. Du, "Relationship between the second acoustic emission peak. andthe serrated flow of aged AI-Zn-Mg-Cu alloy", Phys. Stat. Sol., A155, pp. 305-319 (1996)
15. B. Raj and T. Tayakumar, J. of Acoustic Emission, "Acoustic emission during tensile
deformation and fracture in austenitic stainless steels", Vol. 7, SI-S 12 (1988)

S88

Fourth Far East Conference on NDT (FENDT '97)
Cheju-do, Korea, October 8-11, 1997

ACOUSTIC EMISSION SIGNAL ANALYSISIN C/C COMPOSITES
Ja-Ho Koo, Byung-Nam Kim, Manabu Enoki and Teruo Kishi
Research Center for Advanced Science and Technology, University of Tokyo,
4-6-1 Komaba, Meguro-ku, Tokyo 153, Japan

ABSTRACTIn the case of materials in which plane wave yelocity varies along the direction of the
propagation, satisfactory algorithm to locate acoustic emission (AE) source has to be developed.
Therefore, for the fIrst, we measured the wave velocity in C/C composites and made empiricalequation for the relation between the direction and the velocity. Anisotropic location algorithm
using this equation and the Successive Over Relaxation (SOR) method were used to find theposition of AE source. Then, we study crack propagation mechanism of 2-D C/C composite byusing AE method during the tensile loading of CT specimen. From these results we found thatthe effect of sub-bundle breaking mechanism is dominant than other mechanisms in 2D C/Ccomposites with 8-hamess satin structure and after maximum stress non-fractured bundles
remained.
KEY WORD: AE, Anisotropic, Location, C/C composites

I . INTRODUCTION
C/C composites have very high resistance of heat transfer, thermal shock and low specifIc
gravi ty ( e.g., 2.25 g1cm3 in the case of graphite single crystal). High strength and stiffness of
C/C composites make them be an attractive material for aerospace industry.[l - 4] However for
the importance of usage, more high reliability is requested than other materials. On the purposeof making the reliability high, various methods are perfonned, for example, prediction of life
ti me, measurement of weibull modulus and development of processing, nondestructive
evaluation, etc. In these vari ous methods nondestructive evaluation method can moni tor the
behavior of propagated or propagating cracks. [5]In this study, 2D C/C composites with 8-harness satin structure has been examined by AE
signal analysis that is one of the nondestructive evaluation methods. In the fIeld of AE sign.al
analysis, one of the im portant technique is caIcul ati ng oflocation of the AE source. But, inthe case of materials in which plane wave velocity varies along direction of the propagation,
S89

satisfactory algorithm has not been developed yet. Therefore, for the first, we made empiricalequation about the relation between direction and velocity and calculated 2-dimension locationof AE source by using the equation and SOR method that had been used by Sato et al [6]. Then,
we study crack propagation mechanism of 2-D C/C composite by using of AE method during
the tensile loading of CT specimen.

n. Two Dimensional Location in Anisotropic Medium
The longitudinal wave velocities in 2D C/C composites with 8-hamess satin structure were
measured from 0° to 45° in the interval of 5°. The results are shown in Fig. I. Because thiscomposites have 45° symmetry in the variation of wave velocity as shown in Fig. 2, only
between 0° and 45° curve fitting was performed. The result equation is as following.
(1)

where & is the angle of the direction of propagation for the longitudinal wave, V is thelongitudinal velocity of the direction ( 8), Vo is the longitudinal velocity of the direction of fiber,a and b are constants.

-(,)~ 8000 ....- - - - - - - - - -..

E 7000

~

~

E
_
~

'0

~ 6000.!2

g 5000CD~ 4000~ 3000== 2000«i.S 1000"C2
0 10-..................._ 1 0 -..................
Cil
0
:c/12
:cIS
1C/4
c:
o
Angle I radian
-I

>

~

8000
7000
6000
5000
4000
3000
20 00 or--..
1000

J

0 ...--t-+-i-+-t--f-+++-l-+--4-.j......I~o

ti
c:

=c
E

Q)

c:
o

-I

61C/4

Angle I radianFig. I Relation between wave velocity and angle. Fig. 2 Symetry of wave velocity and angle.
The denotation for numerical analysis is as followings. (x, y) is location of AE source, tistime of AE occurrence, (Xi> Y J is location of i-th sensor, Tj is arrival time of first longitudinal
wave to i-th sensor, t; is arrival time of frrst longitudinal wave of i-th sensor, Dj is distance
between (x, y) and (Xi> Y 0, V j is longitudinal wave velocity in direction of between (x, y) and(Xi. Yj). For the first, if (x, y) and t are numerical approximated values, t; becomes as following.
(2)
S90

3 0 1:II'I''I'''I''''I'. ...~P'''I'''I''lI'''l''l''I''I''P.....''I''''I'''I~. . . .

3 0 . .~P"'I"'I''I'''I'''I''I'TlP'''l'''l''I'''I''''I''I'''P'Il'''I'T'n''I'''I''T'I.

25

25

20

20

15

15

10

10

5

5

5

10

15

20

25

30

Fig.3 Simulation comparing (a)isotropic algorithm with (b)anisotropic
algorithm (dimension of medium is 30.4 x 30.4 mm, black is the
position of AE sources, grey is the position of output ).
At this time, if the sum of square of (Ti - 10 is larger than given error range, by using the
successive over relaxation method t, x and y are becomes t + tl.t, x + ~ and y + tl.y. Then newvalue of t; can be calculated from corrected t, x, y.
Fig. 3 is the result of comparing isotropic algorithm with anisotropic algorithm. If AE signal
is generated on the black circle in the medium that has the same wave velocity distribution as
sho\VIl in Fig. 2, each arrival time of the signal is different in foure AE sensors. Fig. 3 (a) is theresult of the case inputing the arrival time table to isotropic algorithm and Fig.3 (b) is the case
inputing the arrival time table to anisotropic algorithm. Maximun error in (b) is 0.0164 rom. As
this value is very small, we applyed this anisotropic algorithm to practical CT specimen.

ill. FRACfURE MECHANISM OF 2-D CtC COMPOSITES
1.

Experimental Procedure

In order to apply the location evaluation method mentioned above, thin CT specimen (32 xcomposites with 8-hamess satin structure was selected. The length of
30 x 5, mm) of 2D
notch was 22 mm. The kinds of specimen is four. The misorientation angles of the fiber on thenotch are 0°,15°,30°,45° (to be called each by type 1, type 2, type 3, type 4 ). During tensileloading, load and COD were measured. AE signal was detected by using of AE
analyzer(DCM140, IT!) with 4 AE sensors(M304A, Fuji Ceramics Co.). Two kinds of tests
were perfonned. One of two tests is performed using the pre-amplifier with the gain of 11 dB
and 54 dB.

ctc

2. Results and Discussion

S91

For type 1, in the case of using 54dB pre-amplifier as shownin Fig. 4 (a), too many largeAE
signals ( > 4.5 V ) were generated and saturated the limit of pre-amplifier capacity. The
saturated AE signals are in the hashed range. In order to analizing this area, 11dB pre-amplifier
was used. The result is shown in Fig. 4 (b). When using 54dB pre-amplifier, it is suposed thatAE signals ( < 4.5 V ) is due to matrix cracking, debonding and fiber pull-out but the other AE
signals ( > 4.5 V ) is due to fiber breaking and sub-bundle breaking. When using IldB preamplifier, it is suposed that AE signals ( < 1.5 V ) is due to fiber breaking and AE signals ( >2500

120 . - - - - - - - - - - - -......
100

2000
>.

~

0

80

I1l
::::l

60

....I1l
LL40

5i 1500

c:

::::l
CT

cr

~ 1000

u..

500

20
1

o

2345

01

Amplitude I V

234

Amplitude I V

Fig. 4 AE Amplitude distribution «a) in the case using 54dB pre-amplifier,(b) in the case using 54dB pre-amplifier ).

25o30 r""""""'....,..-...,..-r--"T"""''''"''T'''---r---.,.......,

type 1 (0·)

•

25

20

20

a•

• dO<>

15

10
5

10

•

0

5

20

o

o
o

•

.

.. •

••
o.·0 ••

ow..o...........- ' - '..........r.....-.......-..........................
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type 1 (45· I
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Fig. 6 Stress-COD curves and AE amplitude; (a) type 1 (b) type 2 (c) type 3 (d) type 4.

Fig.7 Fracture surfaces of CT specimen for type 1.
$931.5 V and < 4.5 V ) is due to sub-bundle breaking and AE signals ( > 4.5 V ) is due tosaturated AE. Therefore, in order to analyze large AE signal, we used 11dB pre-amplifier in Fig.
5, Fig. 6 and Fig. 7.
Fig. 5 is the result of location using two dimensional anisotropic algorithm. Four types allshow AE location in front of notch tip. Fig. 6 is the result ofCT Test with AE analyzer. Type 1
has the highest maximum stress and type 4 has the lowest maximum stress. We did AE source
characterization. The radius of source of large ( > 4.5 V) AE signal was calculated by invers
operation. The radius of a fiber is 3.5J.1m but the result radius range is in about from 50J.1m to
200J.1m. The radius of sub-bundle shown in SEM phtograph of Fig. 7 is also in that range.

N. CONCLUSIONS
Two dimensional anisotropic location algorithm using empirical equation for wave velocity
in C/C composites was developed to find the position of AE source. The propriety of application
to practical materials was proved by the simulation checking the algorithm. The maximum error
between the source position and the result position by anisotropic location algorithm was 0.0164
rom in anisotropic medium whose dimension was 30.4 x 30.4 nun . In practical CT test, the good
location result that showed the direction offiber was obtained by the anisotropic algorithm. On
the other hand we found the fact that the effect of sub-bundle ( the radius range is in about from
50J.1m to 200J.1m) breaking mechanism on fracture is dominant than other mechanism in 2D C/Ccomposites with 8-hamess satin structure by AE source characterization and SEM obselVati on.

REFERENCES
1. D. Belitskus, "Fiber and Whisker Reinfrrced Ceramics for Structural Applications", Marcel
Dekker, Inc., pp. 315 (1993)
2. O.L.Blakslee, D.G.Proctor, E.J.Seldin, G.B.Spence and T.Wang, J.Appl.Phys., Vol. 41, 8, pp.
3373 (1979).
3. E.Fitzer and A.Gkogkidis, Prepr.Am.Chem.Soc.Div.Pet.Chem., Vol. 29, pp. 506 (1984)
4. B.Broquere, Proc.4th Int.Conf.SAMPE, pp. 279 (1984)
5.1. Kishi, A. Kama, Y. Komiya, H. Ohkushi, H. Tanoue, M. Ohtsuka, "Evaluation System ofMaterials", Tokyo Univ. Press (1987)
6. Y. Sato, K. Katsuyama, Mining and Preservation ,Vol. 31, No.6, pp. 11 (1985)$94

RECENT DEVELOPMENT IN ACOUSTIC EMISSION
Kanji Ono
Depanment of Materials Science and Engineering. Box 951595
University of California, Los Angeles. California 90095-1595. USA
e-mall: ono@ucla.edu

This review paper presents recent development in the field of acoustic emission (AE). The topics ~include AE source simulation analysis applied to composite samples, guided wave AE, pattern recogmnon
analysis for AE signal discrimination, moment tensor analysis with application to concrete, and newgeneration AE instrumentation.
1. Introductionof a glass capillary. This allowed the development of quanRecently. Drouillard. appraised the hisrory of the first titative comparison of AE experiment with theories of thehalf century of acoustic emission (AE)U). AE field in the wave propagation. Hsu et aI. (5), Sachse and Pao(6). thepast 30 years has evolved from late 1960's main thrust of Harwell group('7,s) and Kishi and cowo~ led theaerospace applications with special attention given to growth of deconvolution (or source inversion) methods tometal welds. In the 1970·s. nuclear applications became determine AE-source characteristics in metals, ceramics
the mainstay and composite applications started ro emerge. and composites. Noise was a serious problem in the
The Three Miles Island accident stymied Ibe nuclear inversion scheme and lhe difficulties in calculating Green's
applications, and composite applications of AE rook off in functions for useful geometries was the fundamental
Ibe 1980·s. The most recent trend appears ro be the obstacle. Ohtsu and OnoUC1,l1,12) have proposed alternate
proliferation of faster digital insauments and the diversity techniques based on source simulation. Takemoro andof applications resulting from user-friendly AE instrumen- coworlcel's(U,14) have combined lhe source simulation c0ntation. General current summary of AE is given in Ono (2). cept with radiation pattern and source location analysis
I examined in 1994(3) the staWS of acoustic emission techniques for the fracture mechanism characterization in
research and applications from papers collected by a dala- composites taking the viscoelastic nature of the matrix.base, known as INSPEC over the period of 1989 to 1993. into accountIn order to obtain an overall piCbUe of AE studies. the
Acoustic emission has been widely used to study
titles and abstracts of about 1000 AE papelS were eval- various types of microfracture in fiber reinforced comuated. Selected papers were summarized to provide ~posites(15,I6). Hsiao et aI.(m experimentally measured thesentative SlaWS of each segment of the field. Breakdown out-of-plane displacements due to a step-wise point unwas: research (600 papers); applications (160 papers); loading (pencil lead breaking) on polyethylene and polysignal detection, generation and processing (ISO papers) methyl methacrylate. and recognized both the response
and olbers (60 papers). It was noted lhat composite and delay and amplitude attenuation. Weaver et al.(I8.19) prrr
ceramic materials were Sb1died actively. but melals were posed a comprehensive method to calculate lhe displaceresearched just as much with emphasis on fIacture and ment component of viscoelastic waves. Here, the wave
fatigue. Fewer applications papers were published. but cer- relaxation was accounled for successfully by including
tain areas had strong activities, as in signal detection and frequency independent relaxation function. R(t). They
signal processing methods.
demonslraled a good agreement between experimental andIn preparation for this presenaation, a similar search, theoretical responses to a monopole excitation.
though wilh less systematic evaluation. was amducted
In order to examine the fracwre modes and dynamics inOverall impression was lhat comparable activities are in fiber-reinforced composites, Suzuki et 31.(13.14) have develprogress (about 950 papers in folD' years) except metals oped an AE monitoring system for the source location andactivities are down. For this review, however, I will coo- for fracture mode analysis based on the radiation pattern ofcenttate on several specific topics; AE source simulation the P-wave. (See Fig. 1) A displacement-sensitive sensor
analysis applied to composite samples, guided wave AB, on one of the channels was utilized to obtain the source
pattern recognition analysis for AB signal disaimination, wavefonn using a computer algorithm in time domain inmoment tensor analysis wilh application to concrete, and corporating a waveform simulation scheme. By combining
new generation AE instrumentation.
the available information. detailed characterization of
microfracture processes can be performed;
2. AE Source Simulation Analysis
In this approach, the method of Ohtsu and Ono°O) was
Breckenridge et al.(4) slandardized the calibration of AE
extended to calculate the displacement component ofsensors using a capacitive transducer to capture the initialviscoelastic waves for dipole problems, taking the concept
anival of a displacement signal JI'Odtx:ed by the fraclUreof Weaver et al. (18.191 into accounL The proposed method

895

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o Wavelet transronn(3-D image classification)m.,

X-Z VlewllOrnm

Digitizer·A

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Fig. 1 Experimental setup for l<khannel AE monitoring during the tensile test of a composite sample(4).
10·\12,-.;....-------..,
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Propagation distance 7.2mm

Source location ( 4.09, 0.96, 0.33 )

E

5

10
15
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6Tr::O.8 Il sec

20

Abl =1.22X10's '"

§ -2
2-

IPoint of dilation I

0-4

o

5

10

15

20

Time. Jlsec
t.Tr=1.101l sec t.V =8.6X10· .i

Fig. 2 Comparison of detected and simulaled waves for Mode-I fiber fracture, Mode-D disbanding and dilatation centerL14).
modified the elastodynamic response by introducing the
waveatt.enualion term, exp(-a(x - x')], and by using therelaxation function. This concept is expressed by thefollowing equation for displacements Ui at (x,t) due to asource at (x',t') in lite half-space as:U.(x,t) = expHx(x - x')].TitX,t; x',t'}*Abtx',t'}*R(t),represent the crack area and discontinuity vector· (Burgers
vector equivalent), respectively. Tij*Abj gives the surface
displacement in a non-dissipative medium. Suzuki et aI. CO)established its validity for fiber fraaure in a model
specimen. This was refined in Suzuki et a1.(14), which
examined the complicated fracwre dynamics in more
realistic glass.fiber reinforced plastics. Figure 2 illustrates

where the symbol * denotes a convolution integral intime. T11(x,t; x',t') represents the Green's function of thesecond kind for an isotropic elastic medium and Abj(x',t')is the source function or the time ttanSient of aackvolume (A being the source area), respectively. A and bj

lite waveforms, bollt experimental and simulated of three
types of fracture lItus identified; namely. Mode-I fiber
fracture (in a longimdina1 fiber-bundle reinforced sample),
Mode-ll disbanding and dilatation (in a transverse fiberbundle sample).

S96

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_...•- . _ 1\..... ......
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0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.2S 2.50\.

Frequency (MHzI•• • ••((

Loo:::~_....L..._.L.----L_-'-_..I----'_~O

0.2

0.4

0.6

0.8

Source rise time • l.l sec

Phase V -GroupV-.s. 1.0 .

Fig. 3 Source parameters for fiber fracture in a unidirectional composite sample(l4).

~

"R

~ 0.5

Figure 3 shows a cross-plot of two source parameleISfor fiber fracIure in a longitudinal fiber-bundle sample.
The source rise time for fiber fracture is in the range of
0.17 to 0.5 J.lS. and the crack volume or Abl , is 0.7 to 5 x
10-16 m3 • Assuming the AE is produced by the fracture of asingle fiber of 10 J.UI1 diameter, aack opening displace-

0.0 L -................................._"'"-..o-.........-.........._ " - -.........- . J
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1,8 2.0 2.2

Frequency (MHt)ment bl becomes 1.0 to 6.4 JUD. This discontinuity CCI'responds 10 1/8 to If]. of the fiber diameter, and suggeststhat fiber pull out does not occur in these composites.
Indeed, we did not absented any AE signals by ~nfiber pull-oUL Slope of b l vs. ATr gives the crack velocity
on the fracture surface, and it reaches the maximum of 22
m/s. As the source function is approximated by a sinusoidal ramp function, peak aack velocities reach about 55

m/s.
3. Guided Wave AE
There has been awareness lhat guided wave propagation must be incorpotated into AE studies (e.g.,
Poll<><:kaol), especially in connection to lhin-walled pressure vessels and waveguides. With more wide-spread usesof AE in aircraft and aerosplK:e SlJ'Uetures, in which thinskins are common, consideration of guided acoustic modes
has become unavoidable in the inlelpretation and analysis
of AE with respect to source-location accuracy and idefi.
tifieation of source mechanisms. GoI'llUUf2l) and Gonnan
and Prosser'22> reported on the effects of guided-wave AE
propagation in plates and pointed out that the two primary
propagation modes in AE signals are the extensional andflexwal plate modes. The predominant particle motion for
the extensional mode is in the plane of the plate, while themain motion of the flexural-mode is out-of-the-plane componenL A source with in-plane dispJacements (especiallysymmetric about the midplane) generates AE signals withstrong extensional components. Mode-I fatigue aacks inmetals, longitudinal fiber fracture in the O' plies and matrix cracks in the 90' plies of a composite laminate are
typical examples. (Also pencil-lead breaks on sample
edges.) Out-of-plane source motion such as delamination

Fig. 4 The phase and group wave velocities vs. frequency
for a unidirectional composite laminate. 1 mm thi~.
The extensional wave (top); The flexural wave (bonom).
or panicle fracwre (or pencil-lead breaks) on the surface
produces AE signals with strong flexwal components.
These wave modes are dispersive and the velocitychanges with frequency as shown in Fig. 4 for a unidirectional composite laminate, 1 mm tmck(23). Over thefrequency range of 10 kHz 10 1 MHz common in AE
experiments, the extensional mode propagates with a fastervelocity and suffers liule dispersion. The flexural mode isslower and is highly dispersive below 500 kHz. A typical
waveform generated by a pencil-lead break and detected in acomposite plate with a broadband sensor identifying these
two modes is shown in Fig. 5(2.4). ~) measured theaaenuation of these two modes as 42 dB/m (81410 kHz)and 83 dB/m (at 85 kHz), respectively. The higher value
for the Oexwal waves resulted primarily from the dispersion effect80

iii' 70

:g.

"60

~

...

-e- EmDsioa.aJ Mode
- a - FIeXDR1 Mode0 ...

...... 0

...

gso
40

3OL..- L-----L_-L._....L...-...J
0.1 0.2 0.3 0.4 O.S 0.6
Distance (m.)

Fig. 5 Peak amplitude versus propagation distance forextensional and flexural modes in an 8-ply quasi-isotropic
carbon fiber composite(2.4).

$97

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r\

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Fig. 6 Typical wavefonns of six signal types from quasi·isottopic carbon fiber samples. A·type: fiber fracture; B: fiberl
mattix fracture; C: transverse matrix fracture; D: delamination; E: splitting; F: angle-ply matrix crack. 41 J.LSIdiv.
Proper uses of the varying velocities improve the accuracy of solD'Ce location. Conventional single-frequency.
threshold-crossing schemes for anival time detenninalion
and source location can lead 10 large location errors whenapplied to thin structures. This is due to the mixing of
two or more modes that travel at different velocities.
Simulation of AE signals using pencil·lead breaks coupledwith the orientation of the breaks assists in identifying the
AE source mechanisms. Ono and HuangC25,26} employed inplane and out.af-plane pencil-lead breaks (together withmicroscopy) to distinguish AE signals from fiber fracture,delamination and longitudinal and transverse matrix cracks.
(cf. Fig. 6) These were then used as the basis for theirpattern recognition analysis. However. the use of pencil·
lead breaks to simulate AE signals should be awroachedwith caution since, in most practical test configuration,
only out-of·plane penci1·lead breaks can be produced. Thisis documented on AE testing of cylindrical composite
vessels by Downs and Hamstad(27).
Guided wave propagation in isotropic plates have beenstudied intensively in the literature using a variety ofteehniquesOS,29l. Wave motion in composite plates is more
complicated due to their anisotropy and inhomogeneity.
Only two-dimensional cases and three-dimensional casewith surface loadingOO.Ju have been studied in detail
Approximate solutions have been developed for wave
propagation in anisotropic plates°2.33l. Retaining only thelowest Lamb-wave modes. approximate Iheories may be
used to analyze AE data at low frequenciesO").
Guo et al.(23) investigated Lamb waves produced byvarious micmfracture sources in unidirectional and cross·ply composite laminates. The experimental measurements
and theoretical calculations are compared and are found tofiber e-kago

~-

0.5

vv

i

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1/

r'M1

1\

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V, VV

-"
·1

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10

To.- (microsoc:)

~~

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20

Fig. 7 Experimental measurements and theoretical calcu·
lations of Lamb waves produced by fiber fracture in aunidirectionallaminate(23).
.
agree well in the time domain as shown in Fig. 7 for fibec
fracture in a unidirectional laminate.

4. Pattern Recognition Analysis
When AE signals are recorded. eilher in analog or indigital fonn, one recognizes visually the existence of
certain wavefonn categories. This can be time consuming
as Ihe nwnber of AE signals is often large, but manual
wavefonn classification has been done for some time.
With digital signal analysis techniques. the procedure forclassifying different wavefonns and correlating them tounderlying miaofracture processes has become less labor
intensive. Review on this topic by 080 and Huan~provides the basic concept and an application to thecharacterization of composite microfracture. They used
Tektrend's lCEPAK software package, which includes fivestatistical and neural network classifiers. Of these, k-

S98

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60

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0

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40

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o

A.type-B-type•0 -typo- - - - E ·type

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0

500

1000
S tre s s

-

30

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20

III

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---0-

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0

U

2000

50

:::.
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(M P a)

GI

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S

...

c:
co

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10

B -tv p e

E -tv p e

Q.

0
0

200

400

600

800

Stress (MPa)

50

~

c:

40

;I

..
....

0

0

Q

20

2

1 0

-

A.tvpe0

C -tv po=o -IV P 0

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--x--

30

~

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8 -tv p e

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o

200
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400

GOO

(MPa)

Fig. 8 Percentage counts of signal types ploned against s~. Top: unidirectional; middle: cross·ply and bouom:
quasi·isolJ"Opic laminates.
nearest neighbor classifier was most effective(25,26}. ICEPAK's neural network classifier was slow at that time elm
to the lack of computational power of 386/486 processors.
but this limitation no longer applies. By using thisanalysis procedure, Ono and Huan~ classified up to8000 events per test in sbldying the evolution of diffeIent
microfracture processes in composite samples. Percenragesof six signal types shown in Fig. 6 varied as the applied
load increased to fracture. The changes are shown for
unidirectional, cross-ply and quasi-isotropic laminates inFig. 8. Note the increase of Type-O events just before thefinal fracture in all three cases.
More recently, three other software packages designed
for classification or for noise discrimination of AE signals
have become available from US and Gennan sources. One(PAC-PARS) has a similar standard approach as ICEPAK.
while the other two use frequency spectral features.
Vallen's VisuaiClass uses staggered time windows for
multiple FFI's and the results are used as features forclassifier. These should be useful for any AE user.;interested in classifying recorded waveforms. However,effects of wave dispersion and attenuation need to beconsidered carefully in employing these procedures whenIhe distance between AE source and sensor varies widely.
Wtdeband (but not necessarily high fidelity) sensors ande1eeuonics are also needed to discriminate differeill signal
fealUreS as waveform and spectral features are drasdcallyaltered with narrow-band sensors.
Another powerful tool for signal discrimination iswavelet transform(3$). Wavelet ttansform allows the determination of frequency specuum as a function of time with
optimized resolutions in time and fiequency using shonwaveform segments or wavelets as the basis functions.
Resultant mapping of wavelet coefficients in the frequency-time coordinate plane provides more informative
chaIacterization of II'ansient signals than the power-density
spectra from usual Fourier transform. Suzuki et al.(3$)
performed wavelet tmnsfonn on AE signals from a longiwdinal glass-fiber reinforced composite sample under ten·sile loading (see e.g., Fig. 9). Obtained speclJ"Ograms YJere
classified into four types and compared to known fracture
dynamics results with excellent correlation.

S. Moment Tensor Analysis
Ohtsu and Ono(l2) recognized that in practical structures
only external surfaces are available for transducer place·

S99

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ftM •• NC

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~.-

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t

0Ji3

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3.54 0

1.0

III 1&0 240 3m 400

3.54 080 111 lMO •«lO

n..JI-

1lme.Il_U4U:O:::!::;IIl;=':;:60=2;::40;:::;:a:IO~COO:;'
nne.p_

ll.54 0

III 160 2CO a:IO 400

n..JI_

Fig. 9 Examples of AE wavefonns and lheir Fourier Transform and Wavelet Transfonn(3S). Types 1 to 4 are AE signals
from a glass-fiber composite sample using a PAC-PICO sensor and represent Mode-I fiber fracture, two kinds of Mode-I
matrix fracture and MOOe-D strain gage exfoliation.
ELEVATION VI!V(1)

SIDE VIEV"it: Sheer
~

(U)

X

~

\

\ TellSUe~ 1,*1\ ilf.£\ Craek

''1

.Jr

"'.Jr

I

~\

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Fig. 10 Crack distribution plot at 5 stages of a concrete
bend test(38). A cross indicates a shear or mixed crack andan 8lTOW a tensile crack. Dotted lines are the crack paths.

ment and that late arriving waves are compromised bymany factors. They fomulated on the basis of thegeneralized theory of Ohtsu and Ono(l1) an approachrelying on the amplitude of initial p-wave arrivals to
obtain the orientation of crack motion, now referred to as
Moment Tensor Analysis. Ohtsu(36,37) developed thisconcept into a StIUcturai monitoring system and along
wilh many coworkers applied to laboratory and full scaleconcrete stIUcwre eva1uation(38'>~. By using at least 4 or 6
sensors. 2-D or 3-D analysis can be perfonned. producing
crack classification based on tensile or shear types.
location, orientation as well as other AE parameters. An
example of the resultant crack distribution plot is given in
Fig. 10. obtained from 4-point bend testing of a notehedsteel-reinforced-concrete beam. In the initial linear Ioaddisplacement segment (below 50% of the maximum load).
many tensile cracks are seen near the crack path. Before orat lhe start of non-linear segment, cracks are mostly sheartype and are ahead of macro-eracks. Here, tensile cracks aredefined as containing less than 40% shear component
This method has been successful in identifying aa:kdevelopment in full-scale concrete structures and should
prove its utility in evaluating civil infrastIUctures.

S100

6. New Generation AE Instrumentation.
In the last few years, improved AE instruments haveappeared in the marketplace from manufacwrers in the US,
Gennany and Japan. One is based on PC plug-in boards
with controlling software (pAC-Mistras). This system hasa digital processing IC on each boaJd. which processes AE
data for two cllannelslboard and provides conventional AE
parameters and wavefonn data. The second instrument(Vallen-AMSY4) similarly provides conventional AE
parcuneters and wavefonn data. but in a derlcued hardware
design with AE pammeters and waveform <lara being pr0cessed in separate modules. Surgeon et al.(4O) eva1ualed
some aspects of the perfonnance of these two systems(albeit one generation older versions) and found that one
excels in speed while the second derives advantages inoperational flexibility from dedicarrd hardware designs.
Both have been improved over the past year. Two othermore specialized instruments are primarily for obtaining
waveform data. One of them (Digital Wave) analyzes thedata on the basis of plate-wave theory. while the second(IT of Japan) evaluates the AE source characteristics of
cracks in compact tension test geomell)'.
These new generation AE equipment has vastly
upgraded the processing capability and throughput By
combining with pauern recognition and other signal
analysis procedures. automated "intelligent" processing ofAE signals should become reality before too long.

6 - Sachse, W. and Y.H. Pao. Ultrasonics IntI 81-Conf
Procs, IPC Science and Tech. Press. Surrey. England.
(1981). pp. 116-122.
7 - Wadley, H.N.G., C.B. Scruby. and G. Shrimpton.
Acta Metallurgica, 29, 399414 (1981).
8 - Scruby. C.B.• GR. Baldwin, and K.A. Stacey, IntI J.
Fracture. 28. 201-222 (1985).
9 - Ohira, T. and T. Kishi. Progress in AB, eds. M. Onoe
et al.• Japan Soc. NOI, Tokyo. pp. 208-217 (1982).
10 - Ohtsu. M. and K. Ono. J. Acoustic Emission. 3. 2740 (1984).
11 - Ohtsu. M. and K. Ono. J. Acoustic Emission. 5.
124-133 (1986).
12 - Ohtsu. M. and K. Ono. NOT International, 21. 143152 (1988).
13 - Suzuki, H., M. Takemoto and K. Ono. J. Acoustic
Emission. 11. 117·128 (1993).
14 - Suzuki, H•• M. Takemoto and K. Ono. J. Acoustic
Emission, 14. 35-50 (1996).
15· Awerbuch, J. M.R. Gorman and M. Mudhukar. Proc.
First Int'l. Symp. on AE from Reinforced Plastics. Soc.
Plastics Industry. New York, Session 2. (1983) pp. 1-30.
16 - Kawamoto, K. and K. Ono. Proc. 3rd Int'l Symp. on
AE from Reinforced Composites, Amer. Soc. Nondestr.
Testing. Columbus, OH. (1989). pp. 230-239.
7. Conclusion
17 - Hsiao. C.P.. R.A. Kline. and K. A. Grasse. Proc.
This brief review highlights some of recent innovation IEEE Ultrasonic Symp., IEEE, New York, (1986), pp.
in the AE field. While the first-principle prediction of AE 1005-1008.
signals is still beyond our reach in most practical applications. some understanding has started to emerge under" 18 - Weaver. R.L., W. Sachse and L. Niu. J. Accust. Soc.
Am., 85. 2255-2261 (1989).
well-.eontrolled conditions.
With new advances in AE haIdware. software and 19 - Weaver. R.L.• W. Sachse and L. Niu, J. Acoost. Soc.
theory-based guidance. the often criticized empirical nature Am., 85, 2262-2267 (1989).
of AE results is fmally changing. These tools will allow 20 - Pollock, A.A., Progress in AE III. Japan Soc. NOI,
one to minimize uncertainty in physical interpretation of Tokyo, (1986), pp. 708-721.
AE phenomena.
21 - Gorman, M. R., J. Acoust. Soc. Am., 90. 358-364(1991).
Acknowledgment
This work was supported by the AFOSR Wlder grant
22 - Gorman. M.R. and W.H. Prosser. J. Acoustic EmisF49620-93-1-Q320 (Ajit MaJ, Principal Investigator) sion. 9, 283-288 (1991).
monitored by Dr. Walter Jones.
23 - Guo. D., A. Mal and K. Ono, J. Acoustic Emission.
14. SI9-S46 (1996).
References
1 - Drouillard. T.F.. J. Acoustic Emission, 14. 1-34 24 - Prosser. W.H., J. Acoustic Emission. 14. Sl-S11,(I9%).

(1996).

2 - Ono. K.• "Chapter 68: Acoustic emission". Encyclopedia of Acoustics. Volume Two. ed. Malcolm J. Crocker.
John Wiley, New York. (1997). pp. 797-809.
3 - Ono. K.• Journal of Acoustic Emission. 12. 177-198(1994).
4 • Breckenridge. F.R.• C.E. Tschiegg. and M. Greenspan.
J. Accust Soc. Am.• 57. 626-631 (1975).
5 - Hsu. N.N.• J.A. Simmons. and S.C. Hardy. Materials
Evaluation. 35. 100-106 (1977).

25 - Ono. K. and Q. Huang, Progress in Acoustic
Emission VII. eds. T. Kishi et aI.. Japan Soc. NOI.
Tokyo, (1994), pp. 69-78.
26 - Ono. K. and Q. Huang, Advanced Structural Fiber
Composites, Proc. Topical Symp. ill on Advanced Structural Fiber Composites. 8th CIMTEC, ed. Vincenzini. P.,
TEClINA. Faenza, Italy, (1995) pp. 551-558.
27 - Downs, K.S., and Marvin A. Hamstad, J. Acoustic
Emission. 14. S61-S73 (19%).
28 - Knopoff. L.• J. Appl. Phys., 29, 661-670 (1958).

S101

29 - Pao. Y. H.. Elastic Waves and Nondestructive
Testing of Materials. ed. Y. H. Pao, AMD Vol. 29.
ASME. New York. (1918). pp. 107-128.
30 - Green. E. R.. J. Appl. Mech.• 60. 217-223 (1993).
31 - Lih. 5.5 .• and A.K. Mal, Composites, 27B, 633641 (1996).
32 - Reddy, J. N.• ASME J. Appl. Mech., 51, 745-752(1984).
33 - Sun, C. T•• and T. M. Tan. J. Astronautical Science.
32. 269-284 (1984).
34 - Lib. S.S.• and AX. Mal. Wave Motion. 21. 17-34(1995).
35 - Suzuki, H.. T. Kinjo. Y. Hayashi. M. Takemoto aM
K. Ono. J. Acoustic Emission, 14.69-84 (1996)
36 - Ohtsu. M., NDT Inrernational, 22(1), 14-20 (1989).
37 - Ohtsu. M.. Res. Nondestr. Eval., 6, 169-184 (1995).
38 - Shigeishi, M. and M. Ohtsu. Progress in AE vm,
eels. T. Kishi et aI., Japan Soc. NOI, Tokyo, (1996), pp.
205-210.
39 - Yuyama. 5., T. Okamoto. M. Shigeisbi and M.
Ohtsu, Materials Evaluation, 53. 347-354 (1995).
40 - Surgeon, M.. M. Wevers, P. De Meester, and K.
Ono. Trends in NOE Science and Technology, Proc. 14th
World Conr. on NOT, vol. I, cds. e.G. Krishnaclas Nairet al., New Delhi, India, (1996). pp. 143-148.

$102

Program and Abstracts of the 40th AEWG Meeting
June 9-11, 1997 at Infrastructure Technology Institute, Northwestern University, Evanston,lllinois; David W. Prine, Program Chair.
MONDAY, JUNE 9, 1997

AEPRIMER
Introduction to AB, Alan G. Beattie, Sandia National Laboratories
Metals and Alloys, Harold L. Dunegan, Dunegan Engineering Consultants, Inc. (DEC!)AE from Composites, Yolanda L. Hinton, NASA Langley Research Center
AU and Its Application to Wood Products, Frank C. Beall, University of California, Forest
Products Lab.
Geotechnical Applications of AE, H. Reginald Hardy, Jr., Penn State University
AE Testing of Pressure Vessels and Piping, Allen T. Green, Acoustic Technology GroupTUESDAY, JUNE 10, 1997

APPLICATIONS #1; SESSION CHAIR: David W. Prine, Infrastructure Technology Institute,
Northwestern Univ.
1a. AE Detection of Cracking in Pipe Socket Welds, Bryan Morgan, Pacific Gas & Electric Co.
Small bore pipe socket welds in fossil and nuclear power plants are prone to vibration induced
cracking. This is particularly a problem for small bore pipe vents and drains cantilevered off large
pipes. Fluid flow fluctuations and attached rotating equipment resonant with fundamental
frequencies from the small bore pipe cantilever. Typically the small bore pipe is attached to themain piping using a socket weld. The weld geometry exhibits a high stress concentration and canresult in cracking at the weld root. The socket geometry makes a non-destructive inspection using
either ultrasonics or radiography very difficult. In the laboratory, stainless steel pipe specimens
with socket weld cracks were stressed to material plastic yield while monitoring for AE. This
showed that a significant amount of AE was generated during elastic bending, and could be used todifferentiate cracked and non-cracked specimens. The cumulative AE signal energy at relatively
low stress levels provided a means of identifying the cracked specimens.
1b. Field Data on Testing of NOV Containers Using Proposed ASTM Standard E070403-95/I,
Roy D. Fultineer, Jr., Spencer Testing Services
Spencer Testing Services has inspected approximately 1500 natural gas vehicle containers over thelast two years comprised of Type II, Type ill, and Type IV containers. These are composite
pressure vessels with metal or plastic liners and working pressures up to 3600 psi. In service
inspection of these containers is not regulated by any federal authority, however, owners of thesecontainers are anxious to reduce the risk of in service failures. We feel we can successfully remove
any structurally damaged container from service with the utilization of ASTM E070403-95/1. This
standard has three levels of container·acceptance criteria: a three year retest, a one year retest, and aremove from service recommendation. A three year retest indicates a cylinder with no apparent
problems. A one year retest indicates a container that produced low levels of acoustic emission(AE) and needs to be retested. A remove from service recommendation indicates that the container
did not meet the minimum requirement to remain in service. We have found that six containers did
not meet the acceptance criteria for a three year retest Two of these containers were recommended
to be taken out of service, and the four remaining containers were recommended for a one year
retest. The two containers removed from service had damage in areas which could not be visually
examined. One of the containers removed from service was taken to the manufacturer for further
evaluation. The container was pressurized and monitored with AE during this reevaluation and thedata received matched almost exactly with the field data It was found that containers mounted in a5103

manner that would expose their composite wraps to degrading factors produced more AE activity
than containers that were fully isolated. It has also been shown through the testing of thesecontainers that AE monitoring is a feasible method for testing the composite wrap for damage
which may result in a loss in structural integrity.
Ie. The Use of Portable Wireless Acoustic Emissions Systems for Bridge Monitoring, Lozev,
M.G., Virginia Transportation Research Council; Washer, G., Federal Highway Association;
Cados, M. and Miller, R., Physical Acoustics Corporation
A portable, battery powered, multi channel, all digital signal processor acoustic emission systemwith built-in real time filtering and wireless communications features has been used for bridge
inspections.
1d. Underground Pipeline Leak Detection Using Acoustic Techniques, Carlyle, J.M., New Jersey
Institute of Technology; Tafuri, A.N., U.S. Environmental Protection Agency; Watts, D.J., New
Jersey Institute of Technology; and Yezzi, J.1, Jr., U.S. Environmental Protection Agency

Volumetric and pressure change techniques work well for detecting the presence of leaks, but they
do not determine the location of leaks (so that they can be repaired). Reacting to this need, theStrategic Environmental Research and Development Program initiated a project to develop passive
acoustic leak location instruments and techniques. The Department of Defense, the Department ofEnergy, and the Environmental Protection Agency are all participating in the building of anexperimental pipeline facility at Edison, NJ, to test new acoustic pipeline leak detection concepts
under realistic field operating conditions.
Research work under this project during the past year has established that acoustic techniques
readily detect small leaks in underground pipelines, and that a variety of acoustic techniques areavailable to meet a number of difficult field circumstances. We have shown that it is possible tolocate a 0.03 gaJlhr leak in a buried 2" diameter steel water line under field conditions using a 25
foot sensor spacing (by enhancing the leak signal using a newly developed method). In addition,
we have characterized the leak source process (finding that turbulence, two phase flow, jetting andgeysering are distinct sources), and have also developed several styles of artificial leak sources.
This paper will provide an overview of some of some of the more important findings of the projectto date, and will also present our plans for future work under the program.
Ie. Waveform-Based AE for Damage Evaluation of FRP-Reinforced Glued-Laminated Wood
Beams
Marsh, K., and Landis, E., Department of Civil and Environmental Engineering, University ofMaine
We are studying damage and failure mechanisms in glulam beams with an FRP reinforcement in
the tension zone. We re considering an energy approach to estimate the relative effects of damage
mechanisms such as interface shear failure, wood tension failure, and wood-FRP interface
debonding on the overall load-deformation response of the beam. Waveform-based AE techniques
are being applied to estimate the energy released by the different damage mechanisms based on thecorresponding energy of the AE source. In order to do this, two basic AE analysis routines arebeing developed. First, AE source location estimation techniques for bulk waves in layered
orthotropic media, and second, a routine to estimate the energy released by the AE source. The
goal is to match specific AE event properties to corresponding changes in the beam's
load-deformation response.

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I f. Acoustic Emission During Rotary Cutting of Coal, Hardy, H.R., Jr., Pennsylvania Mining
and Mineral Resources Research Institute; Shen, H.W., Department of Mineral Engineering,Pennsylvania State University; and Khair, A.W., Mining Engineering Department, West Virginia
University

Studies are presently under way by the authors to investigate the character of the bit-eoal
interaction during coal cutting and its influence on the size and shape distributions of the generated
dust. An important aspect of these studies is the use of acoustic emission (AE) and associatedartificial intelligence techniques for remote monitoring of coal cutting operations. Recent papers by
the authors have provided a general outline of the overall study, and the preliminary results oflinear, single-bit cutting. The present paper will focus on the test facilities, experimental
techniques, and results associated with the rotary, single-bit cutting studies. The cutting studies
were carried out using the West Virginia University laboratory-scale shearer facility in
Morgantown, West Virginia. The coal specimens were initially cast in plaster of Paris. During
testing, a rotating bit was advanced into the coal while AE signals were monitored. Bit rotation
speed and advance rate were controlled. All mechanical operations were carried out within a closed
test chamber to allow a detailed analysis of particle size and shape to be undertaken.
Ig. An Evaluation of the Performance of Acoustic Emission Systems, Surgeon, M., Wevers M.,
and De Meester, P., Katholieke Universiteit Leuven; and Ono, K., University of California - Los
AngelesTwo AE systems were evaluated. These are Vallen AMS-3 and PAC MISTRAS. Characteristicfeatures were compared and, in particular, the data acquisition speed and ease of use were
examined. Strong and weak points based on OUT personal experiences will be reported.
APPLICAnONS #2; SESSION CHAIR: Kanji Ono, University of California, Los Angeles
2a. Analysis of AE Data from a Rocket Motor Case: Key Questions in AE Source Location,
Maochen Ge, Natural Resources Canada, Mineral and Energy Technology
To make AE a credible and practical NOT tool, there are two essential conditions. First, thelocation accuracy must ~ drastically improved from the current level and be predictable for the
given application. Secondly, the location process must be robust and can be automated. Otherwise,
large scale and daily industrial applications would be very difficult, if not impossible.
The priority is to solve two long existed problems, phase association and unsophisticated location
algorithms. Without phase association, serious efforts are introduced into arrival time data whichmakes the accurate location impossible. With the unsophisticated location algorithms, it is difficult
to implement the necessary means to reduce the impact of initial errors. The severe effect of thesetwo problems are vividly demonstrated by the analysis of the AE data from a rocket motor case. It
also demonstrates with the same data that the solutions to these problems are available and that,with these solutions, the AE source location accuracy in the NDT industry will immediately
advance to a new level. A particular advantage of these solutions is that they do not require theadditional hardware technology and are compatible with all existing ones used in the NDTindustry. Therefore, the major improvement of the AE source location accuracy in the NDTindustry can be achieved with a minimum price.
To make the automated AE monitoring truly practical, the other important issue is to develop acomprehensive data analysis and source location package. In addition to those generalcharacteristics requested for a sophisticated source location code, such as efficient in calculation,
no restrictions on sensor locations and numbers, flexible on regression methods, and easy toincorporate different velocity models, four other features are essential based on the study of the AE
data from the rocket motor case and the extensive experience in automated rockburst monitoring.
These features are phase association, which allows one to process and organize arrival time dataand its critical role has been extensively discussed in the paper; effective surface ray tracing
technique, which is the key to be able to use those sophisticated algorithms originally used for810.5

extended media for shell-type structures, reliability assessment on event data and event locations,which allows the AE data to be used confidently and selectively; and a superior convergencecharacter
The achievable source location accuracy in any application is fundamentally governed by thegeometry of sensor arrays. A carefully planned array is, therefore, the single most important factor
which would secure a long term source location accuracy. Array planning also allows one toevaluate whether the projected accuracy is achievable, a problem showing increased importance inmodem AE applications.
2b. Evaluation of Prototype Retrofit of a Steel Bridge Using AE and Strain Gages, David W.
Prine, Infrastructure Technology Institute, Northwestern University
The development of a retrofit design aimed at retarding or eliminating fatigue crack growth in alarge bridge can be a very difficult and expensive procedure. Analytical techniques frequently do
not provide sufficient accuracy when applied to complex structural details. The California
Department of Transportation (CALTRANS) recently awarded a contract to the Infrastructure
Technology Institute (lTI) of Northwestern University to apply experimental state of the art NDE
technology to the Interstate 80 bridge over the Sacramento River near Sacramento California (Bryte
Bend). m researchers installed over 90 strain gages on the structure and monitored the results ofboth live traffic loading and controlled load tests using state of the art field monitoring equipment.
Acoustic emission monitoring was also applied in conjunction with the strain gage monitoring to
aid in characterizing the retrofits effect on existing active fatigue cracks. Tests were run on thebridge prior to and immediately following the installation of two different retrofit designs. Bothlive traffic and test loads applied under bridge closure were utilized for the tests. The load testing
during bridge closure include both static and dynamic loading. The combined test results (strain
and AE) clearly showed that one retrofit design was superior to the other. This paper will discuss
the installation of the gages, the equipment used, the test procedures, and the results obtained. This
work clearly proved the cost effectiveness of utilizing experimental techr:aiques to gain improved
understanding of retrofit performance.
2c. The Role of the Shear Wave in AE Testing of Plate-Like Structures, Harold L. Dunegan,
Dunegan Engineering Consultants, Inc. (DEC!)It has been observed while conducting experiments on bars and plates, using acoustic emission
transducers and instrumentation, that a very strong signal having a characteristic velocity of a shear
wave in the material is always present. It was first assumed that this signal corresponded to thefirst arrival of a dispersive anti-symmetrical lamb wave. It became apparent that this signal was stillpresent for an in-plane source (breaking a pencil lead at the center edge of a plate) that produced
pure symmetrical lamb waves with no evidence of the anti-symmetrical mode. In an attempt tobetter determine the identity of these signals, two shear plates with vertical and horizontal
polarization were bonded to the end of a 12.5 mm. thick bar. They were used as both receivers andtransmitters and AE transducers of different types were used in different orientations to receive
signals from these transmitted shear waves, as well as pencil lead breaks. It is shown that a highfrequency shear wave is created for both out-of-plane (OOP) and in-plane (IP) source inputs to thebar, and that AE transducers mounted to the surface of a plate are effective in detecting shear
waves of both orientations. This paper presents experimental results that confirm these
conclusions.
2d. Attenuation of AE Signals in Composite Plates, William H. Prosser, NASA Langley ResearchCenter
The increased understanding of the nature of AE signal propagation as guided acoustic modes inmany practical testing geometries such as plates, shells, pipes, tubes, and rods has led tosignificantly improved AE analysis capabilities in laboratory testing. This so-called, Modal AE
approach has yielded improved source location accuracy and has also provided the capability todifferentiate signals from different source mechanisms (including extraneous noise) in coupons8106

and small scale structures. However, to apply these concepts to the testing of large structures,
careful consideration must be given to the propagation behavior of the different guided modes overlonger distances of propagation. Attenuation of the different modes must be characterized andcorrections in amplitude measurements made to account for signal losses of the different modes.
This includes signal loss in both the virgin material and that due to structural elements such as
joints, stiffeners, or coatings. As an example, measurements of far field, peak amplitudeattenuation are presented for the extensional and flexural plate modes propagating in two different
thicknesses of a composite material. Even though the flexural mode contains much lower
frequency components, its signal loss is much larger than that of the extensional mode. This is dueto the significant dispersion of the flexural mode which causes a spreading of the signal in timeover increasing propagation distances. The material studied is a candidate for cryogenic hydrogenpropellant tanks on the prototype of a Reusable Launch Vehicle (X-33). As such, the effects on theattenuation of the plate modes due to bonded cryogenic foam insulation, which will be used onthese tanks, were also evaluated. The presence of this cryogenic foam insulation had nomeasurable effect on the propagation of the extensional plate mode. However, it caused further
severe attenuation of the flexural plate mode.
2e. Considerations for Time-of-Arrival Measurements Based on Lamb Wave Theory, Miller,
R.K., Pollock, A.A, and Almeida, A.F., Physical Acoustics InstituteOperating in the very low millimeter-Mega Hertz (mmMHz) range, of the Lamb Wave dispersion
curves, has been shown as one method of separating flexural and extensional wave modes.
Ideally, this method should make the choice of a characteristic velocity easier resulting in accurate
source location. However, this method only focuses on using the group velocities of the firstextensional mode and ignores the reality that this mode may disappear into the normal backgroundnoise one encounters in actual Acoustic Emission (AE) tests. In addition, the method is only usefulfor very thin plates.
An investigation is underway to better understand the effects of both the group and phase velocities
and how best to'make measurements for realistic AE applications. Other methods are being
examined that are more applicable to real-world structures. In particular is the use of. (1) the"Triple Point" (for metals); and (2) very high mmMHz values that allow the user to focus on theRayleigh wave speed as a characteristic velocity.
2f. AE Analysis and Applications Using Moment Tensor, Yuyama, S., Nippon PhysicalAcoustics; and Carlos, M., Physical Acoustics Corporation
Moment Tensor analysis provides insight into the step-like movement associated with a single AE
source event, in both magnitude and direction. Coupled with waveform based arrival time analysis
from each sensor and location detennination, a complete description of location, orientation,
movement direction and source characterization can be accomplished. The purpose of thispresentation is to describe the moment tensor process and several useful applications on structures.
2g. Dispersion Compensation in Acoustic Emission Pipeline Leak Location, Rewerts, L.E.,
Roberts, R.A., and Clark, M.A., The Center for NOE, Iowa State University
This paper addresses leak location in liquid-filled pipelines using acoustic emission time-of-flightmeasurements. Conventional data processing for time-of-flight-based acoustic emission event
location perform less than optimally in this application due to the multiple-mode dispersive
propagation characteristics of liquid filled pipes. This paper will summarize on-going work toanalyze the propagation characteristics of liquid filled pipes, and develop model-based dataprocessing for source location which accounts for the multi-mode dispersive nature of thetransmission line.
Model simulation of pipeline propagation responses will be presented, along with corroborating
experimental measurements. The functioning of cross-correlation source location data processing
will be outlined, and degradation in algorithm performance arising from dispersive pipeline
8107

propagation will be demonstrated. Potential approaches to data processing which incorporate apriori knowledge of the pipeline transmission dispersion characteristics will be presented. .

Particular attention is given to time-space transfonn methods which seek to isolate individual mode
contributions to measured leak noise signals.
WEDNESDAY, JUNE 11, 1997

SIGNAL PROCESSING; SESSION CHAIR: Harold L. Dunegan. Dunegan Engineering
Consultants. Inc. (DECI)
3a. The Use of Neural Networks to Discriminate Defect Signals from Noise. Pollock. A.A,
Almeida, A.F., and Miller, R.K., Physical Acoustics Institute
Neural Networks provide a means of developing classifiers that can potentially discriminate
different types of AE sources. A recent research program provided an excellent opportunity tocollect true defect signals as well as typical background noise during the testing of railroad tankcars. A description of the classifier training and results will be given.
3b. Collecting AE Activity Graphs into an Atlas - A Proposed Format, Nordstrom, R. andBrunner, A.J., EMPA Dubendorf, Switzerland; and Bohse, J., BAM, Germany
Comparison of AE results from different materials is limited by the great deal of variable
dependencies involved in AE testing, including sensor, signal analysis and type of data
representation desired. To find AE results that can be meaningfully and usefully compared, thetypes of AE formats that have been published were reviewed. It was found that a large number of
AE papers contain a graph that represents AE activity, often as a first step in the analysis.
However, there was a large variety of fonnats of these AE activity graphs that made comparison
between materials and tests meaningless. A specific version of the AE activity graph is definedalong with three graphs showing AE intensity. Examples from several materials, mostly
fiber-reinforced composites, are presented from tests run at EMPA and BAM. How this collectionof AE activity and intensity graphs, together with information on test materials and test parameters
can be built into an AE-atlas and what its uses would be are discussed.
3c. Wideband and Narrowband Acoustic Emission Waveforms from Extraneous Noise During
Fatigue of Steel Samples, Hamstad, M.A. and McColskey, J.D., National Institute of Standards
and Technology
An extensive study of extraneous acoustic emission (AE) generated during fatiguing of steel
samples has been peIformed. AE waveforms were emphasized instead of traditional AE
parameters. Typically, four resonant sensors and four wideband high-sensitivity sensors wereused to gather eight waveforms for each AE event. The fatigue tests were conducted in a
servo-hydraulic fatigue test machine, using hydraulic grips. Specimen variables included two
different thicknesses and different lateral dimensions of the plate-like samples. In addition togrip-based extraneous noise sources. artificial noise sources were also examined. Acoustic signals
from both solid and air-transmitted artificial sources were studied. The extraneous AE wascontrasted to an AE event from a sharp fatigue crack.
3d. Wavelet Transform of AE Signals, Suzuki, H. Aoyama Gakuin University; Hayashi, Y.,
Shizuoka University; Takemoto, M., Aoyama Gakuin University; and Ono, K., University ofCalifornia - Los Angelest

A new analysis technique for AE signals will be presented and examples of applications illustrated.
Although computationally intensive, wavelet transfonn allows one to separate different frequency
components more accurately than windowed Fourier transform. Program listing is available foradaptation to different AE applications such as triangulation and power spectra specific parameters.
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3e. Wave Propagation Effects Relative to AE Source Distinction of Wideband AE Signals from a
Composite Pressure Vessel, Downs, K.S., Lockheed Martin Astronautics; and Hamstad, M.A.
University of Denver.
Numerous acoustic emission waveforms were recorded using wideband, non-resonant sensors
during the initial proof pressurization ramp of a graphite/epoxy pressure vessel following a single
controlled impact. The waveforms exhibited a variety of characters and frequency spectra. Some ofthe observed differences will be presented, and potential source distinctions will be discussed.
3f. PolyMODAL® AE Waveform Analysis Application, Van, W., and Carlos, M., PhysicalAcoustics Corporation
PolyMODALTm Waveform software Toolbox combines AE waveform signal processing withclassical wavemode analysis to provide a complete AE test and analysis from wavefonn,acquisition to location analysis and data interpretation. An application example will be presented
and each step in the analysis process.detailed.
3g. AE Instrument, Worldwide Services Certification/Standards Issues, Carlos, M., Mitchell, J.,
Lowenhar, E., and Cole, P., Physical Acoustics Corporation
Standards and certifications in the Acoustic Emission industry are no longer buzzwords. They are
now an essential ingredient to conducting worldwide business in terms of developing and'qualifying AE instruments and carrying out AE testing services. Certifications to be discussed
include ISO-9001, CE Mark, Intrinsic Safety for equipment development and manufacture,
ISO-9OO2, ISO-9712 for Testing Services.
SENSORS/CERTIFICATION; SESSION CHAIR: Maochen Ge, Natural Resources Canada,
Mineral and Energy Technology
4a. Sensitivity and Noise Models for AE Sensors, Vahaviolos, S.1., Carlos, M.P., andLowenhar. E., Physical Acoustics InstituteThe various AE "pick-ups" used for detection of low amplitude short duration and high frequency
mechanical disturbances are presented. It will be shown that in order to optimize the detection of
AE source waves proper impedance matching, crystal orientation and selection of preamplifier
properties are necessary.
Mason's piezoelectric circuit theory models are extended and presented. These circuits are thensimplified for use near or/and resonance of the piezoelectric crystal. For wideband models it isshown that cable impedance is "deadly" under high frequency and high impedance (wide band
high fidelity) AE applications.
The electric current of a piezoelectric crystal is evaluated using various simpJi.fications as based ona practical thin disk operating in a thickness mode. This current is then related to the stress wave
generated by an AE source. The complete noise model of an AE transducer is presented. The effecton the noise from external components is thoroughly investigated. Optimum characteristics are
obtained at resonance using a "peaking" external inductor and an PET OP-AMP as the charge
amplifier. It is shown that the SIN degrades linearly with external electrostatic cable noise charge.
The sensitivity of the detector is presented using the model of the "lattice." It is shown for noisevoltages of 250 J.1Va PZf-5 detector (transducer) could detect minimum displacements ofinteratomic distances. At the end a complete AE detection and processing system is detailed.
Comparisons for sensitivity and noise rejection of an industrial AE sensor and "home made" onewill be shown.

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4b. Improved Signal-to-Noise Wideband AcousticlUltrasonic Contract Displacement Sensors forWood and Polymers, Marvin A. Hamstad. University of Denver - Department of Engineering andNational Institute of Standards and Technology
Research leading to a significant improvement in the signal-to-noise sensitivity of wideband
acoustic/ultrasonic contact displacement sensors for wood and polymers is described. Designprinciples for such high-sensitivity sensors are reviewed. Comparisons of response between
ceramic and polymer piezoelectric elements are made on low modulus specimens. Anew, practicalhigh-sensitivity sensor is characterized and its signal-to-noise sensitivity is compared to that of an
existing commercial wideband. displacement sensor. The comparisons were made for polymer,
maple. and redwood samples. Optimization of the piezoelectric element in the new sensor isconsidered. The typical increased sensitivity of the new sensor is about 30 dB over the existing
commercial sensor.
4c. Automated Sensor Testing (AST) and Low Noise Preamplifiers for AE Testing, Lowenhar, E.,
Carlos, M.P., and Vahaviolos, S.J.• Physical Acoustics Corporation
Automated Sensor Testing (AST) describes a unique sensor testing capability built into an AE
system that allows a user to characterize the pulsing sensor as well as its neighboring sensors, on a
structure, before, during and after an AE test. The use of AST allows the user to detennine
uniquely; if sensor coupling has remained consistent, if the sensor has fallen off, or if the cable has
been removed, on a sensor by sensor basis.
Low noise preamplification of the AE signal from the sensor is extremely important in tenns ofduplicating the exact response from the AE sensor without being affected by external interfering
sources. Low noise design includes maximizing signal-to-noise ratio in terms of boosting signal
by amplification and filtering and reducing background noise, electronic circuit noise and external

electrical noise generated from high Electromagnetic generation sources such as welders and highenergy inductive devices.
Technical details about both of these important AE test factors will be discussed.
COMMERCIAL PRESENTATIONSBUSINESS MEETING
AWARDS BANQUET, Omni Orrington Heritage Ballroom
THURSDA Y, JUNE 12, 1997; ASTM-E07.04 MEETINGFRIDAY, JUNE 13, 1997; ASTM-E07.04 MEETING

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ABSTRACTS OF ORAL BRIEFS
14th International Acoustic Emission Symposium &5th Acoustic Emission World Meeting
August 9 - 14. 1998. Big Island. Hawaii. USA

ORAL BRIEFS #1 Session Chair:
Tokyo Institute of Technology)

Yakichi Higo (Precision And Intelligence Laboratory.

Effect of Fatigue Crack Growth Rate on Detected Acoustic Emission in Bridge Steels
David McColskey (National Institute of Standards and Technology)
Results will be presented on an in-progress acoustic emission (AE) study of fatigue-crack
generated AE conducted on as-received A36. A588. A572. A514 and field-aged A7 bridge
steels and as-received 2024-T351 aluminum. The materials were machined into samples thattypically measured 203mm x 406mm x 25mm thick. A 25mm long notch was cut in the centerof each sample. The samples were fatigue cycled (O.3Hz) at constant DK (stress intensity)
values in order to gather AE data while maintaining constant crack growth rates of 2x 10's
m1cycle (10.6 in/cycle) and 2x10·7 m1cycle (10·s in/cycle). These low fatigue crack growth rates(and lower) are representative of actual fatigue crack growth rates realized on bridge structures.
Acoustic emission signals were monitored using four wideband high-sensitivity sensors
developed at NIST-Boulder and four commercial resonant sensors. coupled to a digital
waveform-based acoustic emission system. Eight digital waveforms were gathered for eachrecorded AE event. Only the AE generated during the peak 10-15 % of the loading cycle wereincorporated in the presented data. The collected AE wavefonns were reviewed to separate thereal crack-based events from the events generated by extraneous sources. The average numberof real crack-based AE events per cycle was determined from the reviewed data. The data forsteel clearly demonstrates that a number (0.01 events per cycle to 0.91 events per cycle) of real,crack-based AE events were generated during the higher fatigue crack growth rate. However.
the lower fatigue crack growth rate data indicated almost no generation of real crack-based AE
events.

Acoustic Emission Monitoring of a Pressure Test of a Model Nuclear Reactor Containment
Vessel.
A. G. Beattie (Sandia National Laboratory. now at Physical Acoustics Corp. Corrales. NM)
A mixed-scale steel model of a boiling water reactor (BWR) containment vessel was pressurized
to failure. The instrumentation included an acoustic emission source location system. The realtime AE monitoring predicted the failure but not its location. Post test analysis showed generalagreement between the location of acoustic emission sources and the regions of distortion andfailure. The analysis also gave the pressure dependence of different emitting regions. The

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emphasis of this talk will be on the post test analysis of the acoustic emission data and the resultsof this analysis. The overall perfonnance of the model itself will not be discussed.

ORAL BRIEFS #2 Session Chair:

Mikio Takemoto (Aoyama Gakuin University)

Acoustic Emission Characterization on Failure in Toughened Resin Carbon Fabric Composite
Toshiyuki Uenoya (Technology Research Institute of Osaka Prefecture, Izumi, Osaka, Japan)
Acoustic emission (AE) technique is used to characterize the progression of internal micro
failure during the deformation of toughened composite materials. Cyclic short-tenn tensile creep
tests were perfonned on two types of plain woven carbon-fiber fabric/epoxy laminate specimens
with a center circular hole notch. During each test, AE activity was monitored using various AE
intensities, which were analyzed in detail. Also, an AE analysis method is utilized to recognize
the fracture mechanism during a tensile straining and to characterize the evolution of internal
failure. Simultaneously, thennoelastic stress analysis (TSA), which can offer 2-dimensional
distribution pattern of the failure, was employed to examine the development of damage. AE
source were active at different locations near the center hole and the majority of the AE waspractically found to originate in damaged regions which were detennined through TSA. AE
location and highly concentrated AE events demonstrated that the damage was bigger in size andmore serious in the unmodified-matrix material than the modified-matrix material.
Felicity effect of AE from the laminated composites appeared at a nonnalized applied stresslarger than about 0.55 for each material whereas the initiation of damage detected by using TSA
technique was at a nonnalized stress larger than about 0.6. Although AE technique can not
illustrate the detailed damage distribution as compared with TSA technique, the AE evolution
responded more sensitively to the damage initiation and development in the composites than thechange of TSA information.
An employed correlation criterion between AE amplitude and rise-time of AE signals is based ona similar concept to a rising-slope criterion proposed by one of the authors. The AE analysis
almost perfectly distinguished various source mechanism of AE signals of the laminatedcomposites, which was favorable to explain the effect of matrix-modification on the AE
characteristics of fracture in the composite material, also the damage evolution due to toughening
matrix material.

Acoustic Emission Observation of Fatigue Damage Development in Laminates Within Loading
CyclesY. A. Dzenis (University of Nebraska-Lincoln, Lincoln, NE 68588-0526)
Fundamental relation between overall damage and failure process in laminates under cyclicloading and damage development within cycles is addressed. Development of damage in an unnotched graphite-epoxy laminate subjected to low-cycle fatigue loading is studied by acoustic
emission analysis. Overall time history of acoustic emission is separated into emission duringloading and unloading portions of the loading cycles. Histories of the locations of the AE sourcesand distributions of the acoustic events over the stress range are analyzed. Qualitatively different
time histories and distributions over the stress range are obtained for the emission generated
during loading and unloading. It is concluded that most of the new damage is developed during
the loading portions of the cycles. The emission during unloading is attributed to friction
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between crack faces, with possible exception for the emission at highest stresses. The location
distribution of the AE sources during loading is fairly random to failure, indicating that thedominating fracture process in an un-notched laminate is scattered damage development. The
time history of the new damage exhibits initial, steady, and final damage development stages.
The new damage is developed over the entire stress range with higher damage rates at higher
stresses. This latter result may ex plain the observed effects of cycle shape on the overallbehavior of laminates under cyclic loading.

ORAL BRIEFS #3: Session Chair:

Masayasu Ohtsu (Kumamoto University)

Application of Acoustic Emission for the Slope Failure Monitoring in Pyroclastic Flow Area
Tetsuro Fujiwara (Nippon Koei Co. Research and Development Center, Ibaraki 300-12 JAPAN),
Keiichi Monma (Public Works Research Institute, Ministry of Construction, Government ofJapan, Tsukuba-eity, Ibaraki 305 JAPAN), and Akichika Ishibashi (Nippon Koei Co.)The Shirasu, which is widely distributed over southern Kyusyu, is characterized by gray softrock derived from pyroclastic flow sediment. In Shirasu area, slope stability is frequently
disturbed by heavy rain, and Shirasu slope failures caused some deaths every year. It has become
very important to predict these disasters. This paper on application of acoustic emission
technique in the field of Shirasu slope failure prediction presents the experimental findings on
the real scale slope (4m x 5m) and laboratory tests.
In applying AE technique to monitor ground motions, so called wave-guides, which are devices
designed to transmit elastic waves are often used. With the aid of wave-guides we haveconducted slope failure on the real scale slope, to study the applicability of AE technique in
detecting ground movements and in predicting slope failures. On the other hand the slope
monitoring by video camera and other sensors (inclination meters and deformation meters) tocompare and analyze AE data was carried out.
The slope failure in this experiment is caused by the slope foot cutting and water penetration. AE
technique could predict the occurrence of slope failure about thirty minutes before it. It was
found that a minute ground motion can be clearly monitored by AE event. This finding identifies
potential of AE for Shirasu slope failure predictions.

Structural Integrity and Remnant Life Evaluation from Acoustic Emission Monitoring
Brian R. A. Wood and Robert W. Harris (Metacoustics Pty. Ltd, Blacktown, New South Wales,
2148, Australia.)
It is now in excess of 30 years since modern acoustic emission techniques (AE) have been
employed in industry. In that period great advances have been made in both AE instrumentation
development and applications including basic functions such as defect/leak/source detection andlocation, and some source identification.
The industrial community which, in many countries, has been permitted to operate under a SelfRegulation Criteria, in some instances has demonstrated a tendency to use economic andconvenience constraints to determine their operating scenarios. The desire to makemanufacturing plants and structures more productive, has resulted in them being operated underconditions which exceed those envisaged during the initial design. While these plants andS113

structures still operate efficiently and safely, the real structural integrity and remnant life
evaluation is unknown.
The changes in operating philosophies have created a need for more precise and sophisticatedmonitoring and analysis techniques to provide specific information about structures,
manufacturing plant, and operating techniques. The application of selected acoustic emission andsignaVwavefonn analysis techniques combined with a range of physical parametermeasurements, has resulted in an analysis technique which can be used for the evaluation of
structural integrity and remnant life of any monitored structure. The technique has beensuccessfully used on civil structures, industrial plants and mining equipment and on metal,
composite and geological materials.
The proposed paper will discuss the bases employed in the monitoring techniques employed, anddescribe a variety of industrial applications and case studies where the approaches have beenused to demonstrate the successful application of the technique.
This paper will present the culmination of results .obtained in over 30 years of research andindustrial application which has resulted in the development of these techniques. While the data
used spans 30 years, the research is current and this is the first time that this infonnation has
been published in the public domain.

Struclurallntegrily Project
Large Fibre Reinforced Plastic Vessel
StruGtural Integrlly Ratio

Uaterlal Parama.utur

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9114~ VesselORAL BRIEFS #4 Session Chair:
University)

Yasuhiko Mori (College of Industrial Technology, Nihon

Reciprocity Calibration of Acoustic Emission Transducers
Hajime Hatano (Department of Applied Electronics, Science University of Tokyo)
The present paper reviews and discusses the background, methodology and application fields ofabsolute calibration of acoustic emission transducers by means of the reciprocity method. In
acoustic emission measurements, transducer calibration is of significant importance, not just forquantitative evaluation, but also for mutual comparison of data obtained by different
laboratories. For characterizing and calibrating acoustic emission transducers, a number ofmethods utilizing various mechanical sound sources, for instance, a falling steel ball, a breaking
pencil lead, a gas jet, a sand blast, and an electric spark, appeared in the literature. However, by
these methods it was difficult to obtain absolute sensitivity, because the characteristics of thesound sources were not clear enough, and the reproducibility from test to test was inadequate. A
seismic surface pulse method was developed by Breckenridge and his collaborators, wherein thefracturing of a glass capillary is employed for the sound source and a capacitive transducer isused for the reference.
Calibration of acoustic emission transducers by the reciprocity method was proposed more than
two decades ago, and has since been employed by transducer manufacturers and laboratories.
The procedure for reciprocity calibration has been specified as NDIS 2109 by the JapaneseSociety for NonDestructive Inspection. Recently, in addition to the magnitude of sensitivity,
phase characteristics and impulse responses were obtained. Frequency characteristics ofamplitude and phase of absolute sensitivity were measured on the basis of the newly derived
complex reciprocity parameters, and the impulse responses were determined through inverse
Fourier transform. An outstanding advantage of the reciprocity method is that absolutesensitivity, including frequency characteristics and impulse responses, both to the Rayleigh wave
and longitudinal wave, can be determined by means of purely electrical measurements without
the use of mechanical sound sources or reference transducers.
Finite Element Prediction and Experimental Measurement of Lamb Wave AE Signal Reflectionsin Finite Plates
W. H. Prosser (NASA Langley Research Center, Hampton, VA), M. A. Hamstad (University ofDenver), J. Gary, and A. O'Gallagher (National Institute of Standards and Technology, Boulder,CO)
A dynamic finite element method (DFEM) has been developed at NIST Boulder which allows
the prediction of acoustic emission (AE) waveforms in plates. Previous research has confirmed
the validity of this technique for predicting the direct arrivals of AE signals in both thin and thickisotropic metal plates. Effects on AE waveforms of a number of variables such as source
characteristics (both rise time and spatial distribution), plate thickness, and finite element celldimensions have been previously investigated. In this current work, the capability of the DFEM
for predicting AE signals in finite plates including the effects of reflections and mode
conversions from lateral boundaries was experimentally confirmed. Both in-plane and out-ofplane simulated AE sources (pencil lead breaks) were modeled which preferentially generate thelowest order symmetric and anti-symmetric Lamb modes, respectively.

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Experimental measurements were made with an absolutely calibrated NIST AE sensor. For theout-of-plane source, several different source, receiver, and plate geometries were consideredsuch that reflections at both normal and oblique incidence angles were obtained. The
superposition of multiple reflections was also studied. For all cases, the DFEM predictedwaveforms were in excellent agreement with experimental measurements. Likewise, for the inplane sources, reflections at normal and oblique incidence angles were considered. In addition,
the DFEM predicted, and experiments confirmed, symmetric Lamb modes which were createdby the mode conversion of shear and Rayleigh waves interacting with the plate edge and comer,
respectively. Again, excellent agreement between the DFEM predictions and experiment was
obtained. This ability to predict AE signals including reflections is a major advantage incomparison to most analytical approaches which can only be used for infinite plate geometries.
Furthennore, AE signals in most realistic test geometries, especially laboratory coupons used formaterials characterization, are heavily influenced by reflected signal components.

Inspection of Steel Bridges with Acoustic Emission
Arup Maji (Department of Civil Engineering, University of New Mexico, Albuquerque, NM
87131)
Acoustic emission (AE) monitoring as a prospective method of inspecting large steel bridges
discussed. The application of Acoustic Emissions for structural inspection has been difficult due
to extraneous noise, attenuation of P-waves, and the subjectivity of various AE parameters used.
A new approach to evaluating AE signals involves the use of dispersive guided plate waves(lamb waves) that can be detected over long distances, and have relatively little attenuation. The
dispersive nature of these can be an indicator of the distance traveled by the wave, and hence
detennine the source location. Laboratory experiments also confirmed that since individualcomponents of a structure tend to vibrate in specific modes, AE sources associated with thosemodes also repeat themselves at the time interval corresponding to those specific frequencies.
Hence, by associating the AE repetitions with the structural modes it may be possible to identify
the areas where the AE signals originate. Laboratory scale bridge tests and field testing on an
existing highway bridge provide valuable insight into the possible use of lamb waves andvibration modes for locating AE sources in large structures.

AE Research in the Petrochemical Industry
Joseph Krynicki (Exxon, Petroleum Environmental Research Forum)
The current industrial practices of acoustic emission testing (AET) have limited reliability foron-stream use in discriminating sources of background noise from corrosion and crack growthenergy release. Therefore, a joint industry program, Advanced Acoustic Emission for On-stream
Inspection", was initiated through PERF (petroleum Environmental Research Forum). The
objective of this program is to develop and implement advanced AET technology in a way thatwill enable reliable use of AET during on-stream inspections. These inspections should identify
corrosion and crack growth of structural integrity significance.
The program is divided into three focus areas: accurate source location, quantitative AElFFS
relationship, and reliable source discrimination. The program is pursuing these efforts withtheoretical and supporting experimental efforts. Issues regarding background noise andS116

monitoring at elevated temperatures (850° F) are also included. This presentation wi1l discuss
program objectives, technical challenges, and invite the participation of technical experts.

ORAL BRIEFS #5 Session Chair:

Karyn Downs (Lockheed Martin Astronautics)

Acoustic Emission Responses of Thermal Barrier Coatings During Four Point Bend Tests
C. K. Lin (Institute of Materials Engineering, National Taiwan Ocean University, Keelung 202,
Taiwan, ROC), U. Senturk (The Thermal Spray Laboratory, Department of Materials Scienceand Engineering, SUNY, Stony Brook, NY 11794-2275), R. Lima, P. Y. Lee, and C. C. Berndt(SUNY at Stony Brook)
Thermal barrier coatings (TBCs) with different preparing conditions (with and without bondcoat, with and without cooling) were air plasma sprayed onto mild steel substrates. The bondcoat and ceramic coating were NiCrAlY and yttria-stabilized zirconia respectively.
In situ acoustic emission (AE) were used to monitor crack growth and propagation of thermal
barrier coatings during the four point bend tests. The load-displacement curve obtained from thefour point bend tests can be separated into three different stages (elastic and plastic regions, andunloading). Meanwhile, AE characteristics such as ring down counts, event duration, peak
amplitude, and energy were recorded and analyzed to evaluate different cracking mechanisms.
It is noticed that most AE activities were detected during the plastic region of the loaddisplacement curve. Most macrocracks presented at this region as well. It is also observed that
TBCs with bond coat prepared without cooling of substrates during spraying exhibited the fewest
AE activities (cracks) during the tests.

Sources Of Acoustic Emission From Rubbing Surfaces
R. J. Boness, S. H. Benabdallah and T. Liu (Royal Military College of Canada, Kingston
Ontario, Canada)This paper is concerned with the measurement and subsequent analysis of acoustic emission(AE) signals obtained during the sliding wear testing of both lubricated and un-lubricatedcombinations of steel and silicon nitride surfaces. In the systematic wear testing of advanced
engineering materials, wear maps are frequently used to determine regions of high levels ofwear. These regions, on a wear map, are normally specified in terms of the operating
parameters, load and sliding speed, and are further identified by the dominant wear process
occurring. However in cases in which more than one wear mechanism is present during agiven test, the transition from one wear process to another may not be detected. In this work
SEM and microscopic examination of the worn surfaces confirm that the time dependent
nature of the acoustic signal is able to detect the predominant wear process occurring and thepresence of wear reducing additives. Further third body abrasive wear tests show that both
wear and acoustic emission signals increase with alumina particle concentration added to the
lubricant. For the steel surfaces a direct empirical relationship between integrated nns signal
and the wear volume removed from the test ball in a ball-on-cylinder test apparatus has beenobtained. Consequently, acoustic emission monitoring offers a novel method for studying thecomplex wear mechanisms that exist between loaded surfaces in relative motion.

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Unique Mineral Engineering Applications Of AElMS Techniques
H. Reginald Hardy, Jr. (The Pennsylvania State University)
For some 30 years the Penn State Rock Mechanics laboratory has been involved in basic andapplied studies involving the application of Acoustic EmissionlMicroseismic (AElMS)
techniques. This paper will first briefly review a number of earlier field related studies,including those associated with the evaluation of the mechanical stability of geologic
structures such as mines, gas storage reservoirs and work slopes. The main thrust of thepresentation, however, will deal with a number of recent, and somewhat unique, mineralengineering applications. These include the study of fluid flow through broken rocks, flotation
control in mineral processing, the Kaiser effect in salt, and the monitoring of coal and rockcutting.

Acoustic Emission Signal Generation During Resistance Spot Welding
Chang-Sheng Chien, Elijah Kannatey-Asibu, Jr. (Dept. of Mechanical Engineering and Applied
Mechanics, The University of Michigan, Ann Arbor, MI 48109-2125)
Resistance spot welding has been used for several years as a reliable technique for joining sheet
metal. It is the primary method by which sheet metal parts are joined in the automotive industry.
The quality of the resulting welds can be monitored using non-destructive testing (NOT)
techniques such as ultrasonic detection, radiography, and acoustic emission (AE). AE is theelastic energy that is spontaneously released by materials when they undergo deformation. AE
can also be thought of as the naturally generated ultrasound created by local instabilities within amaterial. Acoustic emission diffefS from other non-destructive testing methods in two significant
respects. First, the energy that is detected is released from within the material rather than being
supplied by the NDT method. Second, AE is capable of detecting the dynamic, real-time
response of processes.
Earlier work involving AE monitoring of resistance spot weld quality has been based primarily
on total AE counts and threshold, for expulsion detection. To date, no analytical work has been
done to model AE signal generation during resistance spot welding (RSW). This paper models
AE signal generation during resistance spot welding. It focuses on analysis of AE generation
during phase transformation, nugget expansion, and expulsion, and is based on a onedimensional heat flux for both constant and sinusoidal input currents. With this as basis, a model
is developed for the nugget pressure. This is then used to model AE generation during nugget
growth and expulsion. Since AE is capable of detecting the dynamic, real-time response ofprocesses, the signal modeled can be used as a basis for weld quality control.
The results show that with sinusoidal input, the initial stages of nugget growth is discontinuous.
The discontinuous melting is due to the fact that the heat is initially dissipated faster than is
supplied. Once the average temperature is raised to a level that the heat dissipation is slower thanthe heat input, continuous melting begins to occur. The initial discontinuous melting results inseveral AE signals from melting being generated.

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Amplitude and Frequency Characteristics of Simulated Acoustic Emission Signals as a Functionof Propagation Distance in Thick-Walled Composite Tubes
David McColskey (National Institute of Standards and Technology, Boulder, CO)
Results will be presented from an in-progress study of certain aspects of propagation changes of
acoustic emission (AE) signals in.filament wound thick walled E-glass/graphite-epoxy hybrid
composite tubes. The tubes have an outside diameter of approximately 30 cm, a wall thickness ofapproximately 1.6 cm and lengths of up to 3 m. Acoustic emission was monitored with 6-8
wideband high-sensitivity sensors connected to digital storage oscilloscopes or to a digital
waveform-based acoustic emission system. The AE signals were bandpassed from 15 kHz to 500
kHz or from 50 kHz to 1 MHz, in the two different systems. Characteristics of simulated AE
were measured on the tubes under several conditions: 1) without nitrile rubber liner, 2) withnitrile rubber liner, 3) with nitrile rubber liner and water filled, and 4) with nitrile rubber liner,
water filled and internally pressurized. Acoustic emission signals were generated on the tube
surface using the standard pencil lead break method. The generated signals were captured andstored for analysis. Initial results indicate an increase in the attenuation of AE with increasing
distance from the source as well as with increase in internal pressure. The multiple paths to thesensors modify normal distance attenuation characteristics. Typical waveform signals andfrequency spectra will be presented to demonstrate changes in characteristics of signals as afunction of propagation distance and internal conditions.

ORAL BRIEFS #6 Session Chair:

Roger Hill (The Nottingham Trent University)

Investigation of Crack Formation in Salt RockTh. Spies (Federal Institute for Geosciences and Natural Resources), J. EisenbUitter and Gerd
Manthei (Gesellschaft fUr Materialpriifung und Geophysik)
The suggested paper deals with the application of acoustic emission measurements in salt mining
especially in the case of the disposal of hazardous waste underground. Salt rocks reveal a large
variety of geomechanical behavior. Anhydrite and salt clay are examples for brittle behavior
resulting in the formation of fracture. On the other side, rock salt and some potash minerals arecapable of creep deformation without occurrence of fracture in a large range of the conditions ofstate. This is one reason, why rock salt is considered as a favorable host rock for the safe disposalof hazardous waste underground. Nevertheless, creep deformation of rock salt will beaccompanied by dilatancy due to cracking if a certain level of deviatoric loading has beencrossed. These so-called 'microcracks' are small fractures of single grains or grain boundaries.
As a consequence of increasing density of microcracks, creep failure, e.g. the formation of larger
fractures, can occur and permeability can increase in the long-range. For the assessment of thestability of underground cavities and also for the assessment of the hydraulic integrity of the rocksalt, dilatant material behavior in the rock has to be identified.
For this purpose a variety of geotechnical investigation methods can be applied but for the direct
detection of cracks, acoustic emission mesurements in situ are necessary. Event rates and signal
amplitudes can be determined using one probe only. This can characterize the temporaldevelopment of crack formation in a very limited part of a mine, for instance in a single pillar.
To monitor larger areas multichannel-equipment is used. The spatial and temporal variations of
the event rates, the locations of acoustic emission events and their magnitudes are determined tocharacterize crack formation more precisely.
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A 24-channel equipment operating in the frequency range from 1 to 100kHz will be briefly
described. The results to be discussed have been obtained in German salt mines. The
determination of event magnitudes using maximum signal amplitudes will be emphasized. It is
usually observed that acoustic emission events are located in the vicinity of the cavities in therock salt showing larger accumulations in pillars and in the roof beween cavities. In presence ofanhydrite layers in the rock, acoustic emission acitivity is also observed farer away from thecavities, in the rock salt as well as in the anhydrite. For both types of activity differences in thespatial-temporal variations of locations and magnitudes will be outlined and the geomechanicalimplications will be discussed.

Absolute Measurement of Transient Dynamic Nonnal Velocity of Surface by Broad Band
Velocity Sensor
Byoung-Geuk Kim and Sekyung Lee (Korea Research Institute of Standards and Science),
Manabu Enoki and Teruo Kishi (The University of Tokyo)
Several broad-band sensing technologies have been developed so that surface motions can bedetected without any deformation. So far, any technology for broad-band sensing of the transient
dynamic normal velocity of surface, in particular for direct sensing without additional
processing, has not been reported. In the present work, broad-band and direct sensing of thetransient dynamic normal velocity of an object surface and evaluating its absolute value wererealized by using a sensor fabricated with a piezoelectric polyvinylidene fluoride (PVDF) film
and a polyvinyl chloride (PVC) back-load and a PVC wear plate. The transient output signal
from the sensor obtained by test employing well-defined steplike force l at the epicenter of a steel
plate, showed very good agreements with the dynamic normal velocity calculated by using a
Green's function and a simulated source function. The output was proportional to the dynamic
normal velocity of surface, and the frequency-dependent sensitivity for the velocity was flatwithin the deviation of - 3.8 dB for the average in the frequency range up to 2 MHz. The
transient dynamic normal velocity of surface could be absolutely determined by using the sensor
calibrated by theoretical one.

Set Of Parameters For Acoustic Emission Instruments
O.B.Tarutin (The Institute For High Pressure Physics RAS, Russia)
The passive acoustic emission measurement is one of the popular method of non-destructive
diagnostics. History of creation of the instruments for AE testing brings us a lot of the AE signal
parameters. Several of them are common for different instruments, but another change from one
to another. The philosophy of the parameters introducing reflects both attempts to measurephysically based values and, for another hand, technological level of corresponding years, which
limited the possibilities to realize parameters desired. As a result, there are no universal method
for AE testing, there are no common parameters set for AE instruments, and AE diagnostics
itself is a mixture of science and art. Last years give us new possibilities to process AE signal,so, it is a time to reconsider parameters of AE signal, moving from technologically restricted tomore physically grounded ones. Permanent acoustic emission is connected with leaks throughdefects like holes, splits. Its intensity is characterized by the average amplitude of permanent AE
signal. First devices rectified both halves of the signal's oscillation and averaged it with the helpof RC-chain. This type of averaging gives mean amplitude. In electrical engineering, anotherS120

method of averaging is used. It is based on the equality of power dissipation for AC signal andso-called effective DC one. In this case one must calculate mean of squared AC signal andextract square root. Such method is known as RMS (Root Mean Squares). But, designers ofseveral AE instruments named with RMS another procedure. So, now we have an expression"True RMS" in several instruments to underline the Root Mean Squares algorithm. Third usefulaveraging algorithm is mean logarithm of fully rectified AE signal. Sometimes it is called ASL.
All three methods are used in AE instruments. In analog systems, the mean amplitude takes
minimum electronic components. From physical viewpoint, RMS is proportional to square rootof the signal's energy, but the mean amplitude and ASL have no direct physical sense. In general,
RMS lies closer to peaks of AC signal, ASL - closer to X axis and the mean amplitude lies
between them.
The averaging algorithm tightly connected with practically used characteristic, the noise level.
When preparing the AE instrument for measurement, user adjusts so-called level to be above thenoise, but not far from it in order to sense even pure AE signals. User decreases the level until
the instrument begins to indicate random noise peaks, ones per several seconds. This noise value
is often presented in the AE instrument's manual. Physically this level strictly depends on thenoise value, frequency range and noise distribution. The RMS lies closer to the noise level, thenother two average values: the mean amplitude and ASL. So, there are two arguments to establish
the RMS as common parameter for AE instruments.
Pulse acoustic emission is associated with cracks in material of objects investigated. Acoustic
wave propagates from its source to sensors and reaches its in different arrival time. When arrival
time for each sensor is known, one can calculate a location of the source. Usually the arrival timeis detected, when electric signal from the sensor first crosses the threshold level up. It is clear
and corresponds to most direct way of the acoustic wave propagation. But, two factors darkens
the situation. In order to increase sensitivity, so-called resonant sensors are used for acoustic
pulses measurement. According to physical resonance, the sensor selects oscillations withnarrow frequency range from wide frequency range of acoustic wave and it provides better signal
to noise ratio, because the noise is proportional (not directly) to the frequency bandwidth. Due toresonance, an amplitude of electric signal does not directly correspond to the acoustic vibration
velocity amplitude, but the amplitude increases step by step, pumped by the vibration. The better
resonant characteristics, the better sensitivity and, unfortunately, the longer time interval fromacoustic pulse start to maximum amplitude of electric signal. Moreover, there is no directconnection between acoustic and electric maximums. Second factor is accuracy dependence on
propagation length. The more distance, the lower amplitude of acoustic wave. Taking into
account, that the level for all sensors approximately the same, we obtain, that electric signal on
closest to the source sensor·will cross the level just after pulse's beginning. but on the farthest
sensor it will cross the level closer to maximum of amplitude, several oscillations after pulse's
beginning. It will cause certain error in the location calculation. To obtain more accurate
calculations, it is worth to recommend to use arrival time of the maximum of electric signal.
The maximum of electric signal is one of the most physically based and simply measured
parameter. There is one remark. When the signal's amplitude is comparable with noise, an errorof amplitude measurement arises because of the noise and signal superposition. To minimize theerror, it is worth to subtract the noise from data measured, but take into account a nonlinear ruleof addition random noise with the signal. Several AE instruments provide registration of noise
just before the pulse beginning, it can help to subtract the noise. A problem of noise
superposition with the signal touches not only the maximum amplitude, but every parameter oflow signal, depended on the amplitude: so-called MARSE and energy.

5121

In first AE devices, an integral parameter (amplitude by duration) of the acoustic pulse wasmeasured as a square of an "envelop" of electric signal in axes amplitude vs time. The envelope
was created by RC-chain and approximately equal to the integral of amplitude by time. Now thisparameter is known as MARSE. It is easy to measure MARSE, but this parameter has no direct
physical sense. Advanced technology allows to measure more physically based parameter, trueenergy: integral of squared amplitude by time. It is directly proportional to the acoustic pulse
energy. Nevertheless, MARSE can be used to compare pulses from the same source with same
shape. MARSE depends on the shape of pulse at the same energy. So, there is a reason to choose
a "true energy" as an integral parameter in future devices.
Traditionally, the timing points of acoustic pulse (crest) are measured with digital counts
technique, based on the points, where the signal crosses the level. Well known are "Peaktimeout", SCETO, "dead time". Despite of its simplicity, this method gives good results. Deadtime blocks the measurement in a channel after previous pulse execution for two reasons, toallow an acoustic ring to die out and to initialize an electronic environment of the channel to beready to next pulse. In one AE instrument the dead time is called rearm time. In contemporary
instruments, there are no technical reason to allocate time for the rearm, so, it is possible todecrease the dead time down to zero and to do continuous measurements.
There are pulse counting parameters in several AE instruments: peak count from start tomaximum, from start to end and so on. When using resonant sensor, such counts reflects
resonant frequency of the sensor itself and practically no more. With wide-band sensor (nonresonant) counts can present dominant frequency of the acoustic pulse. But, from contemporary
point of view, it is better to digitize the wave and calculate frequency distribution with Fast
Fourier Transform. Many parameters and technical characteristics of AE instruments depend on
type of the sensors used. Resonant one has up to 40 dB peaks and pits on a response-frequency
plot. It makes several parameters of AE instruments unusable and does not use, for example,
flatness of amplitude-frequency characteristic of an amplifier. Wide band sensors can inspire
physical sense into most parameters and can use good electrical characteristics of the
instruments. We can eXPect new types of sensors, based on another physical principles, which
could combine good acoustic to electric response with lower noise, than it is possible today.

An Investigation Of The Thermal Damage In Aluminum Alloy By Laser Ultrasonic And
Conventional NOT TechniquesA. Maslouhi, R. D. Leblanc (Universite de Moncton) and D. S. Forsyth, A. Fahr (Institute for
Aerospace Research NRC)
Aluminum alloy (AA7050-T7451) has applications in aircraft structures that can be subjected toabnormaUy high temperatures as a result of accidental hot exhaust leaks or engine fires. The
conventional method of assessing the mechanical properties of heat-damaged parts requires both
hardness and electrical conductivity measurements and can not be used on in-service parts
because of indentations created by hardness testing procedures. The state of in-service parts iscurrently determined by assuming that the alloy is in the funy-annealed condition, andreplacement is indicated if the conductivity value falls outside the manufacturer-specified range
for AA7050-T7451. The cost of repair and replacement under this criterion is extremely high. A
non-destructive inspection technique is necessary to properly assess the extent of the heatdamage, thus providing a scientific basis for repair or replacement of the affected zones.
The effect of exposure of 7050 aluminum alloy to high temperatures has been studied using
ultrasonic techniques. Contact, immersion, and laser ultrasonic techniques were used. The
S122

relationships of parameters of ultrasonic propagation with static material properties and material
microstructure were evaluated. Pattern recognition techniques were used in conjunction withimmersion ultrasonic measurements to distinguish between different heat treatments. It was
shown that attenuation measurements at high frequencies can provide an indication of material
microstructure and thus mechanical properties of 7050 aluminum.
The results show that ultrasonic wave attenuation measurements using laser generation anddetection at high frequencies are sensitive to precipitate phase changes in the AA7050 alloy, andtherefore can provide some indication of mechanical degradation due to high temperature
exposure. Overall, laser ultrasonic measurements provide valuable information that cancompliment electrical conductivity tests in characterizing heat-affected zones of aluminum
aircraft structures

Oral Briefs Dot presented at the Conference
Identification of Acoustic Emission Sources Using a Neural Network
Xi-qiang Liu (University of Science and Technology of China, Beijing, China), Zhizhen Zheng,
Ping Shen, Xuanhui Yang (Center for Earthquake Data and Information, State Seismological
Bureau, Beijing, China)
An artificial neural network (ANN) algorithm has been applied to the automatic picking of local
acoustic emission P and S phase. For a set of local three-component acoustic emission data, avariety of features for signal detection and phases identification were analyzed in terms ofsensitivity and efficiency. To compare the performance of these attributes in discriminating the
local P and S phases, eight attributes are discussed in this study. They are Rectilinearity(Jurkevics, 1988), Planarity (Jurkevics, 1988), Long-axis incidence angle (Jurkevics, 1988),
Short-axis incidence angle (Jurkevics, 1988), Degree of polarization (Cichowicz, 1993), Ratio ofhorizontal power to total power, Ratio of short-term average (STA) to long-tenn average (LTA)(Allen, 1978), Changes of autoregressive (AR) coefficients (Haykin, 1991). Comparing theperformance of each feature indiscriminating the local P and S phases, five features wereselected as input attributes of the ANN P and S phases picker: (1) degree of polarization, (2) the
ratio between short-term average and long-term average, (3) the ratio between horizontal power
and total power, (4)auto-regressive model coefficients, and (5) the short-axis incidence angle ofpolarization ellipsoid. The five attributes were calculated in the frequency band of 2 to 8 Hz witha 2.5-sec moving window. This choice of frequency band and window length is appropriate for
local micro-acoustic emission monitoring. The result of preliminary training and testing with aset of local acoustic emission recording show that the ANN P and S phases picker can achieve agood performance in identification and onset-time estimation for local P and S phases. In overall
result, 83% correct rate of phases identification has been achieved by the trained ANN P and S
phases picker, 75% of them are precisely picked with less than 0.10 sec onset time error. We
believe that the method presented here is a promising approach to automatic phases
identification and onset-time estimation.
A genetic algorithm has been applied to the automatic location of local marine array full wave
train acoustic log. On the basis of identified phases and known 3-D velocity structure, we
calculate theory travel time in 3-D velocity structure. In the process of conversion, we use thegenetic algorithms. Considering large parameter space in the initial iterations and small
parameter space in the final iterations, this paper gives mutation probability which is a large
value in the initial iterations and small value in the final iterations. The formation of the mutation
probability is linear and exponential with iterations. It is found that the exponential function is a
5123

more effective method. Using the exponentially reduced mutation probability, the acoustic
emission location is obtained through genetic algorithms. The residual of the source location isvery small.
The objective of the work described in this article is to implement the artificial neural network
for the P and S phases picking and genetic algorithms for accurate location, which is an
important task for local location of acoustic emission source.

Acoustic Emissions During Anelastic Strain Recovery of Cores from Deep Boreholes
Lawrence W. Teufel (Sandia National Laboratories and New Mexico Institute of Mining andTechnology, Socorro, New Mexico 87801)
Several studies have proposed that microcracks in rock may. in some manner. be related to thepre-existing in situ stress field at depth, and that the distribution of microcracks in a rock sample
may be related to its unloading history. This hypothesis was tested by monitoring acoustic
emissions and compressional wave velocity measurements during anelastic (time-dependent)
strain recovery measurements on sandstone. siltstone, and chalk cores. immediately after thecores were retrieved from 2.7 to 3.2 km deep boreholes. Strain recovery occurred in all of thecores, and acoustic emissions were always detected. The acoustic emission rate decreased
exponentially with time, similar to the strain relaxation behavior of the cores. The cumulative
number of acoustic emission events was linear with volume expansion of the cores. No
significant decrease in velocity occurred during strain relaxation, monitored 6-52 hours after thecores were cut. because the strains were small (less than 300 microstrains). However, laboratory
measurements of compressional velocity on relaxed, air-dried cores show a well-defined
anisotropy in low porosity cores, which has a strong preferred orientation of the principal
recovery strains. The slowest velocity was in the direction of maximum expansion. The field andlaboratory results suggest that a statistically aligned microcrack population evolves during strain
relaxation, with microcracks opening more in the direction of maximum strain recovery. It is
proposed that the degree of anisotropy is dependent on the relative difference in the magnitude of
the in situ stresses and corresponding strain relaxation, as well as the degree of heterogeneity of
the rock. In high porosity rocks the fractional increase in porosity due to relaxation microcracks
is much smaller. Therefore, differential expansion of a core. due to opening of relaxation
microcracks with preferred orientations, does not produce a measurable anisotropy.

Pattern Recognition of Artificial Neural Network to Waveform Data
Shifeng Lin (Nondestructive Testing Lab, Mechanical Engineering Department, Tsinghua
University, Beijing 100084, P. R. China)
Pattern recognition of artificial neural network is being applied for some waveform data. The
data were taken from acoustic emission monitoring of pressure vessels and underground
pipelines. from acousto-ultrasonic testing of composite materials. The characteristic data for the
input of neural network were obtained from original waveform data by the various methods
respecting to the different individual data characteristics. The pre-processing methods include
some advanced modem spectrum analysis methods. The neural networks are basically BP
networks with some significant modifications such as single data group training for the data typeof shortage of good quality data.

5124

AECM·6 Symposium Program
I JUNE 1998: EDUCATIONAL SEMINAR, Introduction to Acoustic Emission in Composite Materials
2 - 4 JUNE 1998: San Antonio, Texas
CARP CODES AND PROCEDURES
John Teti Plenary Lecture: Acoustic Emission: A Mature NDT Technology with Worldwide Applications
and Standards S. Vahaviolos, Physical Acoustics Corporation
Acoustic Emission Testing of Carbon Fiber Composite Offshore Drilling Risers
C. Barnes, G. Ramirez, University of Texas at Austin

Acoustic Emission Testing of ASME Section X FRP Pressure Vessels
M. Droge, Dupont
Recommended Procedure for Determination of Design Strain Limit in FRP Specimens
P. Ziehl, T. Fowler, University of Texas at Austin
Standard Test Method for Acoustic Emission Examination of FRP Fan Blades
T. Crump, DupontRESEARCH AND DEVELOPMENT IN COMPOSITE TESTING
Acoustic Emission Source Simulation for Integrity Evaluation of UD-GRFP's with Different
FilterlMatrix Interfacial Quality
K. Ono, University of California at Los Angeles; T. Kinjo, H. Suzuki, M. Takemoto, Aoyama Gakuin
University (Japan)
APPLICAnONS OF CARP TESTING
Keynote Speaker: Waveform Analysis of Acoustic Emission in Composites
William Prosser, NASA Langley Research Center
Neural Network Burst Pressure Prediction in FiberglasstEpoxy Pressure Vessels Using Acoustic
Emission E. Hill, Embry-Riddle Aeronautical University; M. Fisher, Lockheed Martin
Acoustic Emission Testing - A Composite Tank Manufacturer's Experiences
J. Richter, Tankinetics
Design and Acoustic Emission Testing of FRP Equipment
C. Garza, Dow Chemical
Acoustic Emission Evaluation ofField Tests on FRP Materials Using the Three Way System
S. Botten, Independent Testing Lab
Acoustic Emission of Large Scale Composite Aerospace Structures
P. Cole, Physical Acoustics Ltd (United Kingdom)
ACOUSTIC EMISSION DEVELOPMENT IN APPLICATIONSFracture Mode Classification in Locally Loaded Cross-Ply CFRP Coupons using Wavelet
Transform
Y. Mizutani, K. Nagashima, M. Takemoto, Aoyama Gakuin University (Japan); K. Ono.
.
University of California at Los Angeles
5125

Acoustic Emission Testing of GRP Composites Progressively Damaged by Fatigue andEnvironmental Exposure
J. Hale, G. Kotsikos, University of Newcastle upon Tyne (United Kingdom)
ACOUSTIC EMISSION IN CONCRETE APPLICATIONS
Keynote Speaker: Quantitative Evaluation of Crack and Damage in Concrete by Acoustic
Emission M. Ohtsu, Kumamoto University (Japan)
Acoustic Emission Application for Diagnosis of Deteriorated Concrete of Harbor Structures
A. Ishibashi, K. Matsuyama, Nippon Koei Ltd (Japan)
Crack Extension in Concrete by SiGMA
M. Munwam, M. Ohtsu, Kumamoto University (Japan)
Acoustic Emission Characteristics of Full-Scale Concrete-Piles Under Bending and Shear Load
T. Shiotani, Tobishima (Japan)
Bedrock Observation by Captured Acoustic Emission
T. Shiotani, Tobishima (Japan); K. Monma, Public Works Research Institute (Japan)
Acoustic Emission Monitoring of Reinforced Concrete Structures
C. Barnes, University of Texas at Austin
Acoustic Emission Monitoring of Prestressed Concrete Girders
T. Fowler, University of Texas at Austin; L. Yepez, Universidad Catolica de Guayaquil (Ecuador)
Evaluation Criteria for Acoustic Emission Examination of Reinforced Concrete Structures
M. Ohtsu, Kumanmoto University (Japan); S. Yuyama, Nippon Physical Acoustics (Japan);
T. Okamoto, Nihon Cement (Japan); T. Kishi, The University of Tokyo (Japan)
Property Evaluation of Thermal-Sprayed Metallic Coatings for Acoustic Emission Analysis
A. Ishida, M. Takemoto, Aoyama Gakuin University (Japan); K. Ono, University of California at Los
Angeles
Panel Session: Discussion on CARP subjects
1997 SUBSCRIPTION RATE

for Volume 15

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Publication Date of This Issue: 14 September 1998.

5126

Index to Volume 15
Contents
Page 1-18

Page 19-32

Page 33-42

Page 43-52

Page 53-61
Page 63-68
Page 69-78
Page 79-87

Wideband and Narrowband Acoustic Emission Waveforms from
Extraneous Sources during Fatigue of Steel Samples
M.A. Hamstad and J.D. McColskey
Fracture-Mode Classification Using Wavelet-Transfonned AE
Signals from a Composite Tetsuo Kinjo, Hiroaki Suzuki, Naoya Saito,
Mikio Takemoto and Kanji Ono
Nondestructive Evaluation of fiberglass-Reinforced Plastic
Subjected to Localized Heat Damage Using Acoustic Emission
H. Nayeb-Hashemi, P. Kisnomo and N. Saniei
An Investigation of LUders Band Defonnation and the Associated Acoustic
Emission in Al - 4.5% Mg Alloys
D. I.. Annentrout and S. H. Carpenter
Modal Analysis of Acoustic Emission Signals
H.L. Dunegan
An Acoustic Emission Tester for Aircraft HaJon-1301 Fire-Extinguisher Bottles
Alan G. Beattie
AE Detection of Cracking in Pipe Socket Welds
Bryan C. Morgan
Feature Extraction of Metal Impact Acoustic Signals For Pattern
Classification by Neural Networks
Shahla Keyvan and Rodney G. Pickard

Page 5-i
Conferences and Symposia
Page 51-S94
Fourth Far-Eastern Conference on NOT
Page 595-5102 Congreso Regional de Ensayos No Destructivos y Estructurales
Page 5103-S110 Program and Abstracts of the 40th AEWG Meeting
Page SIII-SI24 Abstracts of Oral Briefs, 14th International Acoustic Emission

Symposium & 5th Acoustic Emission World Meeting
Page SI25-S126 Progam of the 6th AECM Symposium
Page 62
Page 68
Page 1-1

Meeting Calendar
Cover PhotographIndex to Volume 15

Authors Index
A.F. Almeida
D. L Annentrout
Alan G. Beattie
S. H. Benabdallah
C. C. Berndt
J. Bohse
R. J. Boness

S107.5108
43
63. Sill

S1I7
S117

5108
5117

AJ. Brunner
M. Carlos
S. H. Carpenler
Chang-Sheng Chien.
M.A. Clark
P. ColeP. De Meester
1-1

S108
5104.SI07.5109
43

S1t8
S107
S109
5105

K.5. Downs
5109
H.L. Dunegan
53, 5106Y. A. Dzenis
5112
J. Eisenblatter
5119
Manabu Enoki
590,5120
A. Fahr
5122
D. S. Forsyth
5122
531
Taisaku Fujioka
Tetsuro Fujiwara
S40,SI13
Roy D. Fultineer, Jr.
5103
J. Gary
5115
Maochen Ge
5 105
5higenori Hamada
540
1,5108,5109,5115
M.A. Hamsrad
H.R. Hardy, Jr.
S105,5118
5113
Robert W. Harris
Hajime Hatano
5115
Y. Hayashi
5108
J. H. Huh
580
Akichika Ishibashi
540,5113
Young-Chan Joo
51
Yasuhiro Kanemoto
540
Elijah Kannatey-Asibu, Jr. 5118
79
Shahla Keyvan
A.W. Khair
5105
5120
Byoung-Geuk Kim
590
Byung-Nam Kim
Sang-Hyo Kim
511
Tetsuo Kinjo
19
Teruo Kishi
590,S120
P. Kisnomo
33
la-Ho Koo
590
Joseph Krynicki
S116
Oh-Yang Kwon
51,519E. Landis
5104
R. D. LeblancS122
P. Y. Lee
5117
Seung-Hwan Lee
519
Sekyung Lee
5120
SIl
Sang-Ho Lee
Seung-Seok Lee
511
K. A.Lee
580
C.S.Lee580
R. Lima
5117
C. K. Lin
Sl17
T. Liu5117
Xi-qiang Liu
5123
Shifeng Liu
5124
E. Lowenhar
SI09
M.G. Lozev
SI04
Arup Maji
5116
Gerd Manthei
5119
K. Marsh
S104

A.Maslouhi
Kimitoshi Matsuyama
1.0. McColskey
R. (K.) Miller
J. Mitchell
Keiichi Monma
Bryan C. Morgan
H. Nayeb-Hashemi
R. Nordstrom
A. O'GalJagher
ShiroOhra
Masayasu Ohtsu
Kanji Ono
Philip Park
Young-Jin Park
Rodney G. Pickard
David W. Prine
A.A. Pollock
William H. Prosser
L.E. Rewerts
R.A. Roberts
Naoya 5aito
Norio 5akaino
Yasunori 5akata
N.5aniei
U.5enturk
Ping Shen
H.W.ShenMitsuhiro Shigeishi
Tomoki Shiotani
Th. Spies
M. Surgeon
Hiroaki Suzuki
A.N. Tafuri
Mikio Takemoto
O.B.Tarutin
Lawrence W. Teufel
Yuichi Tomoda
Nobuyuki Tsuji
Toshiyuki Uenoya
5J. Vahaviolos
G. Washer
DJ. Watts
M. Weyers
Brian R. A. Wood
W.Yan
Xuanhui Yang
Daisuke Yasuoka
J.J. Yezzi, Jr.
Dong-lin Yoon
5. Yuyama
Zhizhen Zheng

1-2

5122
540
I, S108.5111,5119
5104,5107,5108
5109
5113
69,5103
33
5108
5115
S40
531.540,550,560.570
19,595,5105.5108
511
511
79
5106
5107,5108
5106.5115
5107
5107
19

SSO
570
33
5117
5123
5105
550$60
5S0
5119
5105
19,51085104
19. S108
5120
5124
531
560

5112
5109
5104
5104
5105
5113
5109
5123
560

5104
511
5101
SI23

Notes for Contributors
I. Geneml
The Journal will publish contributions from all parts of theworld and manuscripts for publication should be submitted
to the Editor. Send to:Professor Kanji Ono. Editor - JAE
6531 Boelter Hall. MSE Dept.
University of California
Los Angeles. California 90095-1595 USA
FAX (I) 818-990-1686
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
submitted 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.t. Dunegan. D.O. Harris and C.A. 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, cds. 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.

Illustrations and tables should be planned to fit a single
column width (87 mm or 3.3") or a double width (178 mm
or 7"). For the reviewing processes. these need not be ofhigh quality. but submit glossy prints or equivalent with thefinal manuscript. Lines and letters should be legible.

An abstract of about 200 words is needed for Research andApplications articles. while it should be shorter than 100
words for other articles.

4. Review

The language of the Journal is English. All papers should
be written concisely and clearly.
2. Page Charges
No page charge is levied. Fifty copies of off-prints will be
supplied to the authors free of charge.

3. Manuscript for Review
Manuscripts for review need only to be typed legibly;
preferably. double-spaced on only one side of the page withwide margins and submitted in duplicate.
The title should be brief. Except for short communications,descriptive heading should be used to divide the paper into
its component parts. Use the International System of Units(SI).

All manuscripts will be judged by qualified reviewer(s).
Each paper is reviewed by one of the editors and may besent for review by members of the Editorial Board. The
Board member may seek another independent review. In
case of disputes, the author may request other reviewers.
S. Electronic MediaIn order to minimize typographical error. the authors areencouraged to submit a floppy disk copy of the lext. We
can read Macintosh and IBM PC formats. Those who canconnect to INTERNET can send the text portion to"ono@ucla.edu". This will greatly shorten the time ofcommunication.

6. Color Photogmph
We can print color photographs needed 10 enhance thetechnical content of an article. Because of the cost. theaUlhor is asked to pay $350 per page.

Contents
Wide band and Narrowband Acoustic Emission Waveforms from
Extraneous Sources during Fatigue of Steel Samples

Page 1-18

M.A. Hamstad and J.D. McCol...key
Fracture-Mode Classification Using Wavclet-Transformed AE
Signals from a Compositc 7'etsllo K;'ljO, Hiroaki Suzuki, Naoya Saito, Mikio

Takemoto ami Kmlji 0'10"-

Page 33-42-.

Page 43-52

Page 53·61
Page 63-6R
rage 69·7R
Page 79-87

Nondestructive Evaluation of Fiberglass-Reinforced Plastic
Subjected to Localized Heal Damage Using Acoustic Emission
H. Na)'eb-Hashemi, P. Ki:lf/omo and N. Salliei
An Investigalion of LUdcrs Band Deformation and the Associaled Acoustic
Emission in AI - 4.5% Mg Alloys
D. L Annell11Vut gild S. f-I. Carpemer
Modal Analysis of Acoustic Emission Signals
H.L. DI/negall
An Acoustic Emission Tester for Aircraft Halon-130 I Fire-Extinguisher Bottles
Ahm G. Beal1ie'
AE Detection of Cracking in Pipe Socket Welds
Bryall C. Morga1J
Feature EXlraction of MClallmpact Acoustic Signals For Pattern
Classification by Ncural Networks

Shahla Keyvan and Rodney G. Pickard
Page 5-i
Page 5 1-$94
Page 595·$102
Page 5103-5110
Page5111-5124
PageS125-S126
Page 62
Page 6R
P:lge I-I

Conferences and Symposia
Fourth Far-Eastern Conference on NOTCongreso Regional de Ensayos No Destructivos y Estructuralcs
Program and Abstracts of the 40th AEWG Meeting
Abstracts of Oral Briefs, 14th International Acoustic Emission
Symposium & 5th Acoustic Emission World Meeting•
Progam of the 6th AECM Symposium
Meeting Calendar and Recent Books on AE
Cover PhotographIndex to Volume 15