JOURNAL OF ACOUSTIC EMISSION
VOLUME 23, 2005
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Copyright©2005 Acoustic Emission Group

Coden JACEDOCONTENTS
23-001
23-02523-03723-04723-06423-07223-081
23-091

EFFECTS OF NOISE ON LAMB-MODE ACOUSTIC-EMISSIONARRIVAL TIMES DETERMINED BY WAVELET TRANSFORM
M. A. HAMSTAD and A. O’GALLAGHER
1
ACOUSTIC EMISSION TECHNIQUE FOR DETECTING DAMAGE
AND MECHANISMS OF FRACTURE IN A KNITTED FABRIC
REINFORCED COMPOSITECARLOS R. RIOS, STEVE L. OGIN, CONSTANTINA LEKAKOU andK. H. LEONG
25
QUANTITATIVE DAMAGE ESTIMATION OF CONCRETE CORE
BASED ON AE RATE PROCESS ANALYSISWILLIAM PROSSER, ERIC MADARAS, GEORGE STUDOR, and
MICHAEL GORMAN
37
MOMENT TENSORS OF IN-PLANE WAVES ANALYZED BY
SIGMA-2D
MASAYASU OHTSU, KENTARO OHNO and MARVIN A. HAMSTAD
47
DEVELOPMENT OF AN OPTICAL MICRO AE SENSOR WITH AN
AUTOMATIC TUNING SYSTEMHIROSHI ASANUMA, HIRONOBU OHISHI, and HIROAKI NIITSUMA 64
DEVELOPMENT OF STABILIZED AND HIGH SENSITIVE OPTICALFIBER ACOUSTIC EMISSION SYSTEM AND ITS APPLICATIONHIDEO CHO, RYOUHEI ARAI and MIKIO TAKEMOTO
72
HIGH PRECISION GEOPHONE CALIBRATIONMASAHIRO KAMATA
81
DEVELOPMENT OF HEAT-RESISTANT OPTICAL FIBER AE SENSOR
PORNTHEP CHIVAVIBUL, HIROYUKI FUKUTOMI,
I-1

23-096
23-102

23-11323-11923-12923-13623-14223-15023-15623-16423-17323-181

23-189SHIN TAKAHASHI and YUICHI MACHIJIMA
91
DAMAGE DETECTION SYSTEM FOR STRUCTURES WITH
SMART AE SENSORSTAKAHITO YANASE and SEI IKEGAYA
96
HIERARCHICAL FRACTURE PROCESS IN BRITTLE ROCKS BY
MEANS OF HIGH-SPEED MONITORING OF AE HYPOCENTERXINGLIN LEI, OSAMU NISHIZAWA, ANDRE MOURA and
TAKASHI SATOH
102
MEASUREMENT OF HYDRAULICALLY ACTIVATED SUBSURFACEFRACTURE SYSTEM IN GEOTHERMAL RESERVOIR BY USING
ACOUSTIC EMISSION MULTIPLET-CLUSTERING ANALYSISHIROKAZU MORIYA, HIROAKI NIITSUMA and ROY BARIA
113
A MODELING METHOD ON FRACTAL DISTRIBUTION OF
CRACKS IN ROCKS USING AE MONITORINGYOSHINORI WATANABE, KEN-ICHI ITAKURA, KAZUHIKO SATO,YOSHIAKI FUJII, RAO BALUSU, HUA GUO and XUN LUO
119
INTERPRETATION OF RESERVOIR CREATION PROCESS AT
COOPER BASIN, AUSTRALIA BY ACOUSTIC EMISSIONYUSUKE KUMANO, HIROKAZU MORIYA, HIROSHI ASANUMA,NOBUKAZU SOMA, HIDESHI KAIEDA, KAZUHIKO TEZUKA,
DOONE WYBORN and HIROAKI NIITSUMA
129
MICROMECHANICS OF CORROSION CRACKING IN CONCRETE
BY AE-SIGMAFARID A. K. M. UDDIN and MASAYASU OHTSU136
EVALUATION OF PARAMETER DEPENDENCIES OF AE
ACCOMPANYING SLIDING ALONG A ROUGH SIMULATEDFRACTURE
KATSUMI NEMOTO, HIROKAZU MORIYA and HIROAKI NIITSUMA 142
AE CHARACTERIZATION OF THERMAL SHOCK CRACK GROWTHBEHAVIOR IN ALUMINA CERAMICS BY DISC-ON-ROD TESTHUICHI WAKAYAMA, SATOSHI KOBAYASHI andTOSHIYA WADA
150
FATIGUE DAMAGE PROGRESSION IN PLASTICS DURING CYCLICBALL INDENTATIONAKIO YONEZU, TAKAYASU HIRAKAWA, TAKESHI OGAWA
and MIKIO TAKEMOTO
156
EVALUATION OF FATIGUE DAMAGE FOR FRM WITH AE METHODMASANORI TAKUMA and NOBORU SHINKE
164
ACOUSTIC EMISSION BEHAVIOR OF FAILURE PROCESSES OF
GLASS-FIBER LAMINATES UNDER COMPLEX STATE OF LOADINGJERZY SCHMIDT, IRENEUSZ BARAN and KANJI ONO
173
ROLLING CONTACT FATIGUE DAMAGE OF WC-Co CERMETSPRAYED COATING AND ITS AE ANALYSISJUNICHI UCHIDA, TAKESHI OGAWA, MIKIO TAKEMOTOYOSHIFUMI KOBAYASHI and YOSHIO HARADA181
BORON EFFECTS ON AE EVENT RATE PEAKS DURING TENSILE
DEFORMATION OF Ni3Al INTERMETALLIC COMPOUND
K. YOSHIDA, Y. MASUI, T. NAGAMACHI and H. NISHINO
189
I-2

23-196

23-20623-21523-22423-23323-24323-24923-26023-272

23-27723-28523-29223-29923-310AE AND ELECTROCHEMICAL NOISE ANALYSIS FOR FRACTURESTUDY OF HARD SURFACE FILMAKIO YONEZU, HIDEO CHO, TAKESHI OGAWA andMIKIO TAKEMOTO
196
THE ORIGIN OF CONTINUOUS EMISSIONSKANJI ONO, HIDEO CHO and M. TAKUMA
206
PRECURSOR OF HYDROIGEN INDUCED GLASS LINING CHIPPINGBY AE MONITORINGKOHEI MURAKAMI and MIKIO TAKEMOTO
215
REAL-TIME EXECUTING SOURCE LOCATION SYSTEMAPPLICABLE TO ANISOTROPIC THIN STRUCTURESYU KUROKAWA, YOSHIHIRO MIZUTANI and MASAMI MAYUZUMI 224
INVESTIGATION ON AE SIGNAL/NOISE PROCESSING
IN CORROSION DAMAGE EVALUATION OF TANK BOTTOMZHENGWANG LI , SHIGENORI YUYAMA, MINORU YAMADA,KAZUYOSHI SEKINE, SHIGEO KITSUKAWA, HIROAKI MARUYAMA
and SHIGEO KONNO
233
EXAMINATION OF AE WAVE PROPAGATION ROUTESIN A SMALL MODEL TANKHIDEYUKI NAKAMURA, TAKAHIRO ARAKAWA,
MINORU YAMADA243
INTEGRITY EVALUATION OF GLASS-FIBER REINFORCED
PLASTIC VESSELS BY LAMB WAVE AE ANALYSISTAKASHI FUTATSUGI249
EVALUATION OF REINFORCEMENT IN DAMAGED RAILWAY
CONCRETE PIERS BY MEANS OF ACOUSTIC EMISSIONTOMOKI SHIOTANI, YASUHIRO NAKANISHI, KEISUKE IWAKI,
260
XIU LUO HIROSHI HAYA
WATER-LEAK EVALUATION OF EXISTING PIPELINEBY ACOUSTIC EMISSIONTETSUYA SUZUKI, YUKIFUMI IKEDA, YUICHI TOMODA and
MASAYASU OHTSU
272
ACOUSTIC EMISSION FOR FATIGUE DAMAGE DETECTION OF
STAINLESS STEEL BELLOWS
KOJI KAGAYAMA, AKIO YONEZU, HIDEO CHO,
TAKESHI OGAWA, and MIKIO TAKEMOTO
277
PLASTIC REGION BOLT TIGHTENING CONTROLLED BY
ACOUSTIC EMISSION MONITORINGTADASHI ONISHI, YOSHIHIRO MIZUTANI and MASAMI MAYUZUMI 285
ACOUSTIC EMISSION BEHAVIORS OF RECOVERY FOR Mg
ALLOY AT ROOM TEMPERATURE
Y. P. LI and M. ENOKI
292
AN ACOUSTIC EMISSION TEST SYSTEM FOR AIRLINE STEELOXYGEN CYLINDERS: SYSTEM DESIGN AND TEST PROGRAM
ALAN G. BEATTIE
299
DEVELOPMENT OF IN-SITU MONITORING SYSTEM FOR
SINTERING OF CERAMICS USING LASER AE TECHNIQUE
S. NISHINOIRI and M. ENOKI
310
I-3

23-318
23-331PATTERN RECOGNITION TECHNIQUES FOR ACOUSTIC EMISSION
BASED CONDITION ASSESSMENT OF UNFIRED PRESSURE VESSELSATHANASIOS ANASTASOPOULOS
318
THE ACOUSTIC EMISSION HALON 1301 FIRE EXTINGUISHER BOTTLETESTER: RESULTS OF TESTS ON 649 BOTTLESALAN G. BEATTIE and D. D. THORNTON
331

Contents23.pdf
AuIndex23.pdf
AUeAddress.pdf
AusNotes.pdf
JAE Index Folder*Contents of Volume 23 (2005)
Authors Index of Volume 23
Authors e-mail addresses
Policy/Author’s Notes/Meeting Calendar/
Subscription Information

I-1 - I-4
I-5
I-6
I-7 - I-9

Cumulative Indices of J. of Acoustic Emission, 1982 - 2005

* indicates the availability in CD-ROM only.
Photograph credit: CD-ROM label and Cover drawing from NASA, courtesy of Dr. William H.
Prosser.

I-4

Volume 23 (2005) AUTHORS INDEX
A. ANASTASOPOULOS
RYOUHEI ARAI
TAKAHIRO ARAKAWAHIROSHI ASANUMA

23-31823-07223-24323-064, 23-129

RAO BALUSU
IRENEUSZ BARANROY BARIA
ALAN G. BEATTIE

23-11923-17323-11323-299, 23-331

T. NAGAMACHIHIDEYUKI NAKAMURAYASUHIRO NAKANISHI
KATSUMI NEMOTOHIROAKI NIITSUMA
H. NISHINOS. NISHINOIRIOSAMU NISHIZAWA
A. O’GALLAGHERTAKESHI OGAWAPORNTHEP CHIVAVIBUL
23-091HIDEO CHO
23-072, 23-196, 23-206, 23-277
M. ENOKI
23-292, 23-310
YOSHIAKI FUJII
23-119HIROYUKI FUKUTOMI
23-091TAKASHI FUTATSUGI
23-249MICHAEL GORMANHUA GUO
M. A. HAMSTADYOSHIO HARADAHIROSHI HAYATAKAYASU HIRAKAWA

23-037
23-11923-001, 23-047
23-181
23-26023-156

YUKIFUMI IKEDASEI IKEGAYA
KEN-ICHI ITAKURAKEISUKE IWAKI

23-272
23-09623-11923-260

KOJI KAGAYAMAHIDESHI KAIEDAMASAHIRO KAMATASHIGEO KITSUKAWASATOSHI KOBAYASHIYOSHIFUMI KOBAYASHISHIGEO KONNOYUSUKE KUMANOYU KUROKAWA

23-27723-12923-081
23-23323-15023-181
23-23323-12923-224XINGLIN LEICONSTANTINA LEKAKOU
K. H. LEONGXIU LUO
XUN LUO
Y. P. LIZHENGWANG LI

23-10223-02523-02523-26023-11923-29223-23323-18923-24323-26023-142
23-064, 23-113,
23-129, 23-142
23-18923-31023-102STEVE L. OGINHIRONOBU OHISHIKENTARO OHNO
MASAYASU OHTSUTADASHI ONISHI
KANJI ONO

23-001
23-156, 23-196,
23-181, 23-277
23-02523-06423-04723-047, 23-136, 23-272
23-28523-173, 23-206

WILLIAM PROSSERCARLOS R. RIOS

23-03723-025

KAZUHIKO SATO
TAKASHI SATOHJERZY SCHMIDTKAZUYOSHI SEKINENOBORU SHINKETOMOKI SHIOTANINOBUKAZU SOMAGEORGE STUDORTETSUYA SUZUKI

23-11923-102
23-17323-23323-16423-26023-12923-03723-272SHIN TAKAHASHIMIKIO TAKEMOTO

23-09123-072, 23-156,
23-196, 23-181, 23-215, 23-277
MASANORI TAKUMA
23-164, 23-206
KAZUHIKO TEZUKA
23-129D. D. THORNTON
23-331YUICHI TOMODA
23-272

YUICHI MACHIJIMA
23-091ERIC MADARAS
23-037HIROAKI MARUYAMA
23-233Y. MASUI
23-189MASAMI MAYUZUMI
23-224, 23-285
YOSHIHIRO MIZUTANI
23-224, 23-285
HIROKAZU MORIYA 23-113, 23-129, 23-142
ANDRE MOURA
23-102KOHEI MURAKAMI
23-215JUNICHI UCHIDAFARID A. K. M. UDDIN

23-181
23-136TOSHIYA WADA
SHUICHI WAKAYAMAYOSHINORI WATANABEDOONE WYBORN

23-15023-15023-11923-129MINORU YAMADA
23-233, 23-243
TAKAHITO YANASE
23-096
AKIO YONEZU
23-156, 23-196, 23-277
K. YOSHIDA
23-189SHIGENORI YUYAMA
23-233

I-5

I-6

MEETING CALENDAR:
The 49th Meeting of AEWG
The next meeting of AEWG will be at University of California, Berkeley on June 19-21, 2006,
organized by Professor Frank Beall. The primer will be held on June 19, 2006 organized by Professor
Dave Dornfeld on Acoustic Emission Monitoring of Precision Machining. Registration forms, as well as
hotel information regarding this conference, can be found at:
http://www.cecs.pdx.edu/~nordsr/AEWG/aewg-49.htm
IAES18, The 18th International AE Symposium
The IAES-18 will be held in Tokyo on July 25-27, 2006. The venue will be Aoyama Gakuin University
at Sagamihara. It is sponsored by Japan Society for Non-Destructive Inspection. Professor Mikio
Takemoto is the organizer.
http://www.me.aoyama.ac.jp/IAES/
EWGAE2006
27th European Conference on Acoustic Emission Testing will be held September 20-22, 2006, in
Cardiff, Wales, UK. The meeting is sponsored by EWGAE and Cardiff University. Dr. Karen M.
Holford is the organizer. Meeting details can be seen at
http://www.ewgae.com/

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I-9

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28 February 2006.

EFFECTS OF NOISE ON LAMB-MODE ACOUSTIC-EMISSIONARRIVAL TIMES DETERMINED BY WAVELET TRANSFORM#∞
M. A. HAMSTAD1,2 and A. O’GALLAGHER1
National Institute of Standards and Technology, Materials Reliability Division (853)
325 Broadway, Boulder, CO 80305-3328 USA. 2) University of Denver, School of Engineeringand Computer Science, Department of Engineering, Denver, CO 80208, USA.
1)

Abstract
Precise acoustic emission (AE) signal arrival times of the fundamental Lamb modes can beobtained from the arrival time of the peak wavelet transform (WT) magnitude at a particular frequency of interest. Since these arrival times are not determined from a fixed threshold, they are
not affected by dispersion, attenuation and source amplitude. They also correspond to a single
group velocity and lead to source location results more accurate than those obtained by traditional AE location calculations based on threshold-based arrival times. In this research, noisefree finite-element modeled (FEM) AE signals were combined with experimental wideband
electronic noise to form noisy signals. Since the noise-free signal was available, the changes in
the WT-based arrival times from noise-free to noise-altered signals could be quantitatively
evaluated. Several signal-to-noise (S/N) ratios were examined in a statistical fashion for threeimportant types of AE sources at three propagation distances and at different radiation angles.
The arrival times determined by WTs were obtained for the two different frequency-mode combinations (A0 at 60 kHz and S0 at 522 kHz) that represent the most energetic portions of the signals in a large 4.7 mm thick aluminum plate. From the arrival times, statistical calculations oflinear source location were also studied to evaluate errors in location accuracy caused by noise.
Even at S/N ratios as low as 1 to 1, the location error was 2 % or less for a large majority of thecases. The errors in arrival time were also examined using two alternative time-frequency analysis approaches to obtain the arrival times. One algorithm (Choi-Williams) provided significantly
improved results for the noisy signals.
Keywords: Acoustic emission modeling, Finite element modeling, Source location, Wavelet
transform, Wideband acoustic emission.
#

Contribution of the U.S. National Institute of Standards and Technology; not subject to copyright in the United States. ∞Trade names are included only for complete scientific/technical description; endorsement is neither intended nor implied.
1. Introduction
Previous publications [1, 2] have demonstrated the use of wavelet transform (WT) results to
extract accurate acoustic emission (AE) signal arrival times in plate structures. This techniquedetermines arrival times that are independent of threshold, source amplitude, attenuation basedon geometric spreading, radiation direction and signal dispersion based on propagation distance.
Further, the arrival times correspond to a single group velocity (Lamb waves) of an energetic
mode-frequency combination in the signal. A wideband AE signal database for several burieddipole-type sources in a thin aluminum plate was used for these demonstrations. This database
was a subset of a broader database that was generated using a validated finite element modeling
J. Acoustic Emission, 23 (2005)

1© 2005 Acoustic Emission Groupcode [3-5]. The level of numerical noise in the AE signals obtained from the code is less by approximately three orders of magnitude than the typical out-of-plane displacement signal levels
produced by the code.
Since real AE signals normally have significant electronic noise that is superimposed on the
AE source-based signal, the purpose of the research reported here was to determine the effects ofelectronic (sensor/preamplifier-based) noise on the accuracy of WT-based arrival times. The finite element model results are ideal for such a study since the noise-free signals are available.
This situation is not the case with experimental AE signals. Further, with experimental signals
the exact location of the source is unknown, while for the finite-element signals the precise locations of the source and pseudo sensor(s) are known.
2. Finite Element Modeled AE Signal Database
The signal database used in the research reported here has been described in previous publications [1, 2, 6 and 7]. To provide a ready reference to key information, certain aspects are
summarized here. The signals were generated by finite-element modeling (FEM) in an aluminum plate (1 m x 1 m x 4.7 mm). The AE signals represent the out-of-plane top-surface displacement as recorded by a perfect flat-with-frequency point-contact sensor. The AE signals
were modeled for three in-plane propagation distances (60 mm, 120 mm and 180 mm from thesource epicenter) and seven in-plane radiation angles (0° [x-direction], 12°, 22.5°, 45°, 67.5°,
78°, and 90° [y-direction]). All the FEM signals were numerically processed with a 40 kHz(four-pole Butterworth) high-pass filter followed by resampling from 44.6 ns/point to 0.1
s/point. The AE signals were based on a 1.5 s source rise time, and they were examined out to150 s after the source operation time. This procedure avoids the plate edge reflections, whichappear well after the direct signals.
Table 1 provides pertinent information on the buried dipole-type AE sources used for a detailed examination of the effects of electronic sensor/preamplifier noise. Initially the study focused on data at a propagation distance of 180 mm and a radiation direction of 0˚. The six FEM
cases described in the table were selected based upon the desire to include AE signal cases withthe following features. First, an AE signal where its primary energy (as evaluated by a WT) wasin the low frequency region of the fundamental antisymmetric Lamb mode. Second, a casewhere the primary signal energy was in the high frequency portion of the fundamental symmetric
Lamb mode. Third, a case where the primary-signal energy was approximately equally distributed between these two portions of the fundamental modes. And fourth, additional cases so thateach of the three selected source types was represented at two source depths. The source types
selected were an in-plane dipole (aligned in the 0º direction), a microcrack initiation (with theprimary dipole in the 0º direction), and a shear (without a moment) about the y-axis with theshear directions at 45˚ to the x and z axes. Previous research [1, 7] had identified the primary WTsignal energy for this plate and these AE sources to be at the mode and key frequency combinations of A0 at 60 kHz and S0 at 522 kHz. Table 1 specifies the dominant mode and frequency
combination(s) for each case of source type and source depth. The table also provides the relevant average group velocities [1, 7] and the ratio of the WT peak magnitudes (for each case) of
the two mode and frequency combinations.

2

Table 1 Description of the source cases examined. [1, 7]
Case
number

Source (depth in mm)

9034
9004
2793
9002

Shear w/o moment (0.783)
Micro-crack initiation (1.41)
In-plane dipole (1.723)
Micro-crack initiation(2.037)
Shear w/o moment (2.037)
In-plane dipole (2.35)

9030
2791

WT-basedhigh intensitymode(s) andfreq., kHz
A0 at 60
A0 at 60
A0 at 60
A0 at 60
S0 at 522
S0 at 522
S0 at 522

Averagegroup
velocity,
mm/s
2.5
2.5
2.5
2.5
1.8
1.8
1.8

Ratio of noise-free
WT peak magnitudes(A0 at 60)/S0 at 522
and (reciprocal)
5.6 (0.18)
4.1 (0.24)
1.3 (0.77)
1 (1)
1 (1)
0.41 (2.3)
No A0 mode

3. Wavelet Transform Information
The WT results were obtained using the AGU-Vallen Wavelet freeware [8], with the key parameter settings being: maximum frequency = 700 kHz; frequency resolution = 3 kHz andwavelet size = 600 samples. The Wavelet Time Range Setting for the number of samples (i.e.,
points) was 1500. Thus, the signal was analyzed from the source operation time out to 150 s.
This allowed the full-direct-arrival signal to be transformed. The software automatically determines the arrival times of the peak WT magnitude at selected frequencies (for a 3-kHz wideband, starting at the selected frequencies of 60 and 522 kHz). The resolution of the arrival times
was taken at 0.1 s to correspond to the time resolution of the resampled FEM-based signals.
The correspondence of the determined arrival times with the fundamental Lamb modes was facilitated by the software option that superimposed the group velocity curves on the WT results.
4. Description of Noise Signals and Creation of Modeled AE Signals plus Noise
To make the study of the effect of electronic noise as realistic as possible, the noise signals
were obtained from a wideband high sensitivity conical sensor developed at NIST-Boulder [9,10]. The noise signals were recorded at a preamplifier gain of 55 dB with the sensor coupled
only to air and protected by soft foam from any airborne signals. A total of ten noise signals wereavailable. Each signal had been digitized by a 12-bit waveform recorder with a sampling intervalof 0.1 s per point, which corresponded to the resampled FEM signals. Each signal was about
16000 points in length, which resulted in the ten signals representing a total of about 16 ms ofnoise. The typical crest factor of the signals was 4.2. A typical time domain and Fast Fourier
Transform (FFT) from one of these ten signals is shown in Fig. 1. The slightly smoothed FFT
was calculated after the signals had been numerically bandpass filtered (six-pole Butterworth)from 40 kHz to 1.2 MHz. This filter was used to make all the noise signals more representativeof the frequency range of the FEM signals. After modifying the noise signal amplitudes (so they
were less than the FEM signal amplitudes) and changing the units to picometers, the signals were
examined to determine their consistency. First, the peak magnitudes for the 10 noise signals were
determined. The mean peak magnitude was found to be 0.63 pm with a dispersion of 10 % and arange of 0.54 to 0.73 pm. Thus, the different noise signals were considered to be relatively uniform in their peak signal magnitudes.
Figure 2 shows the WT result of about an 800 s portion of a typical noise signal. Figure 3
demonstrates typical plots of the noise WT magnitude versus time at each of the two key
3

0.6

Amplitude, pm

(a)

0

-0.6
0

0.3

0.6

0.9

1.2

1.5

Time, ms

Fig. 1 Time domain (a) and FFT (b) of a typical noise signal (after a gain of 55 dB) of 16384
points at 0.1 s/point.
frequencies. It is clear from this figure that the WT magnitude versus time varies over a wide
range for each of the two key frequencies. The figure also shows that the number of fluctuationsof the WT magnitudes increases with increasing frequency (likely a characteristic of the WT
used in this research). Further, the WT magnitude variations in Fig. 3 indicate that, when noise isadded to the FEM-generated AE signals, the WT peak magnitudes of the signal plus noise (S+N)
could experience noise-induced modifications. Since, for real-world AE signals, the amplitudesof the WTs of the underlying noise signal at the times of mode arrivals would be a random andunpredictable condition, a statistical study of noise effects on arrival times was necessary.
To form a suitable noise database for a statistical study, a total of 50 different noise segments, each nominally 160 s in length, was extracted (after the filtering and amplitude change,
as described above) from the modified database of ten noise signals. A S+N database was thenconstructed for the selected FEM cases (source type and depth) of interest. For each case thesame 50 noise signals were added to the FEM-based (noise-free) AE signal to form a database of4

Fig. 2 Wavelet transform of a 819.2 s portion of a typical noise signal. Color (maximum isred) or contrast shows WT intensity

Fig. 3 Magnitude of wavelet transformation (a) at 522 kHz and (b) at 60 kHz versus time for atypical 819.2 s noise signal.
50 S+N signals. Before adding the noise signals they were multiplied by a factor to obtain a certain S/N ratio. This S/N ratio was based on the peak amplitude of each noise-free FEM signal
and the mean peak amplitude (0.63 pm) representative of all the noise signals. The 50 S+N signals were called a “set” of S+N signals for a given FEM signal case. In order to be able to directly track the effects of different S/N ratios applied to the same FEM-based noise-free signals,
510

S/N 1 to 20

Displacement, pm-1010

S/N 1 to 1

0

-10
10

S/N 2 to 1

0

-10
10

S/N 4 to 1

0
-10
0

30

60

90

120

150

Time, sFig. 4 Typical examples of S+N signals for case 2793 at four different S/N ratios.
the same sequence of noise signals (multiplied by a different factor to create a different S/N ratio) was added respectively to the noise-free signal to form each “set” of S+N signals. WTs of
the S+N signals were then carried out for each case for the different S/N ratios, and the appropriate arrival times were obtained. Figure 4 shows examples of the S+N signals for case 2793 at
four different S/N ratios.
5. Effects of Electronic Noise on WT-Determined S+N Signal Arrival Times (at a propagation distance of 180 mm in the zero-degree radiation direction)
For each case (AE source type, source depth and S/N ratio), a total of 50 arrival times at themode and frequency combination(s) (shown in Table 1) was obtained from the WT results. The
50 arrival times represent the effect of the random variation in the noise. Table 2 shows a statistical characterization of the arrival times as a function of the S/N ratio for one source type anddepth (case 2793; see Fig. 4). The table provides the average arrival time, maximum time,minimum time and sample standard deviation (SD), as well as the noise-free arrival time. Even
6

Table 2 Statistical characterization of 50 arrival times for the in-plane dipole source(case 2793) as a function of the S/N ratio for 180 mm propagation distance and at 0˚ radiation direction.
S/N Ratio
1 to 2
1 to 1
2 to 14 to 1

Avg., s
81.2
77.4
77.9
78.2

Max., s
113.7
80.7
78.8
78.4

Min., s
73.6
73.8
75.6
77.7

SD, s
8.2
1.6
0.8
0.1

Noise-free time, s
78.2
78.2
78.2
78.2

Table 3 The average and standard deviation (50 samples) of the arrival times for all thecases in Table 1 for 180 mm propagation distance at 0˚ radiation direction.
Case number andfrequency, kHz9034 (60)9004 (60)2793 (60)9002 (60)9002 (522)9030 (522)2791 (522)Avg. arrival timeand SD, s with
S/N at 1 to 2
77.4 (2.2)
77.4 (1.9)
81.2 (8.2)
78.4 (13)87.7 (28)94.2 (15)92.6 (17)Avg. arrival timeand SD, s with
S/N at 1 to 1
77.5 (1.1)
77.6 (1.0)
77.4 (1.57)
77.5 (1.9)
98.1 (4.2)
98.1 (1.63)
98.7 (1.55)

Noise-free arrivaltime, s
77.9
78.0
78.2
78.2
98.2
98.0
98.0

at an S/N ratio of 1 to 1, the maximum and minimum times of 80.7 s and 73.8 s are not large
deviations from the noise-free value of 78.2 s. To justify the conclusion that these maximum
and minimum times are not large, a threshold-based determination of possible arrival times wasmade for the relevant noise-free signal. Since the threshold method depends on the actual
threshold used, a range of thresholds was used (equivalent to varying AE source signal amplitudes with a fixed threshold). The range of arrival times determined was from 32.9 s to 75.2
s. This range of 42.3 s is clearly much larger than the WT-based range of 6.3 s at the 1 to 1
S/N ratio. We note again that the WT method is independent of threshold. Thus the thresholdbased method would likely lead to significant location errors compared to the WT approach.
Table 3 shows the average and sample standard deviations (SDs) of the arrival times for all
the cases (listed in Table 1) at two S/N ratios (1 to 2 and 1 to 1). The results at two additional
S/N ratios (2 to 1 and 4 to 1) were nearly identical to the noise-free values as is illustrated for one
case in Table 2. Thus those results are not shown here. Compared to the noise-free arrival times,
the average values are quite close to the noise-free values, even at an S/N ratio of 1 to 2.
6. Discussion of Above Results
In subsequent discussions, the evaluation of the magnitude of noise-based arrival time errors
was based on comparisons of the sample SDs. The decision to use this measure arose from theobservation that smaller SDs indicated that the arrival times were tightly grouped around themean values. As Table 3 shows, the mean values at an S/N ratio of 1 to 1 were all very near thenoise-free arrival times. We assert that the smaller the SDs, the smaller the range of noiseinduced arrival time errors and the smaller the subsequent location errors.

7

Examination of the results in Table 3 leads to the interesting observation that the SDs are thelargest for case 9002 for each of the two key frequencies and for each S/N ratio. As can be seenin Table 1, the FEM-based noise-free signal for this case exhibited significant energy (in the WT
magnitude results) at both key frequency and mode combinations. This observation implies thatwhen the noise-free AE signal energy is significant in more than one mode, then errors in WTbased arrival times will be greater than when the signal energy is predominantly in a portion of asingle mode. This conclusion is consistent with the fact that when the FEM displacement signal
amplitude is determined by two modes, the WT peaks from those individual modes will besmaller relative to the WT magnitudes of the noise as compared to the case where the FEM signal is significantly dominated by a single frequency/mode. This conclusion is also consistent
with the fact that the S/N ratio was related to the time domain, while the WT-based results comefrom a time and frequency domain.
An important question relates as to whether the type of source changes the accuracy of WTbased arrival times when noisy signals are examined. If the information in the last column in Table 1 is considered in relation to the SDs of the arrival times (see Table 3) at an S/N ratio of 1 to
1, there is a correlation. In particular, the greater the dominance of the noise-free peak WT magnitude of the dominant frequency/mode combination, the smaller the SDs. The converse applies
the smaller the dominance. As with case 9002 above, this result is related to the fact that the S/Nratio is from the time domain, and the arrival times are from the frequency/time domain. Henceat a certain S/N ratio, strong single-mode dominance means that a greater proportion of the WT
magnitude comes from the source-based signal relative to the WT magnitude contribution fromthe noise. The WT contribution from the noise does not depend on the source type, while thatfrom the source-based signal can be from one or two modes. Thus, it is not the source type, but
the magnitude of the modal dominance in the displacement signal, that is significant. Previously
published work [2, 6] has shown that the modal dominance is directly related to the depth of thepoint source below the plate surface, and that the source type typically has only a minor effect on
the relative magnitudes of the modes generated.
When the frequency dependence of the results in Table 3 was examined at the S/N ratio of 1
to 2, it is found that the SDs were the largest for the cases where the 522 kHz/S0 mode dominates. This conclusion was not surprising considering the typical FFT spectrum of the noise
demonstrated in Fig. 2. The figure shows a region of higher noise near this frequency. To quantify the WT magnitude difference of noise between the two frequency bands (3 kHz wide) the 50
noise signals were studied. The WT peak magnitudes at both 60 and 522 kHz were obtained overa signal length of 150 s. The average (for 50 samples) WT peak values at each frequency wereobtained and their ratio was found. The average 522 kHz WT peak magnitude was found to be
1.6 times the average for the 60 kHz WT peak. This result implies that the peak regions of thenoise spectrum can be used to predict the frequencies where the noise will likely result in thelargest errors in WT-determined arrival times.
7. Effects of Radiation Angle (at a propagation distance of 180 mm)
To specifically examine the effects of radiation angle on the accuracy of WT-based arrival
times for signals with noise, it was decided to ignore changes in signal amplitudes as the radiation angle changed from zero to 90 degrees. Thus the S/N ratios (for this examination) were calculated based on the signal peak amplitude in the specific radiation direction under consideration. Hence, the arrival time errors at radiation angles other than zero could be directly compared8

at the same S/N ratio with the results for the zero-degree radiation direction. Only three cases
were chosen to examine the effects of radiation angle. This decision was based on the fact that in
the previous section all the trends were apparent within the results for cases 9002, 9004 and 9030
(selected from Table 1).
In review, case 9002 provided a case where both frequency and mode combinations weresignificant. And cases 9004 and 9030 provided examples where the 60 kHz/A0 and 522 kHz/S0
combinations respectively had a strong dominance. The S/N ratio considered was limited to 1 to
1 since in the above section the major trends were apparent at this ratio. In addition, to comparewith the above section results, the propagation distance was limited to 180 mm.
Table 4 The standard deviation (50 samples) of the arrival times at two different angles
for 180 mm propagation distance and an S/N ratio of 1 to 1.
Case numberand (frequency,kHz)SD, sat zerodeg.

Ratio: noisefree WT peakat indicatedfreq./alt. freq.
value at 0˚

SD,
s at
45˚

9004 (60)9002 (60)9002 (522)9030 (522)

0.99
1.9
4.2
1.6

4.1
1.01
0.99
2.3

-----1.3
12------

Ratio:
noise-free
WT peak at
indicated
freq./alt.
freq. value
at 45˚-----1.5
0.68------SD, sat 67.5˚

Ratio: noisefree WT peakat indicatedfreq./alt. freq.
value at 67.5˚

0.96----------1.7

33----------3.2

Table 4 shows the arrival time sample SD results at angles greater than 0˚ for 50 samples at
an S/N ratio of 1 to 1 along with the results at the 0˚ angle to allow a direct comparison. The results indicate that the SD at the larger angle is about the same as the value at the zero-degree radiation angle when a strong dominance of one mode and frequency combination is present. But
in case 9002, where both combinations had significant magnitudes, the SD results differed at thelarger angle compared to those at the 0˚ angle. In particular the arrival time errors had a higher
SD at 45˚ compared to the SD at 0˚ for the 522 kHz/S0 mode, while the arrival time errors had alower SD at 45˚ for the 60 kHz/A0 mode compared to the 0˚ direction. This observation is consistent with the discussion in the previous section about the effect of the level of dominance ofthe frequency/mode combinations. When the noise-free WT peak magnitude ratio of 60 kHz/A0
over 522 kHz/S0 was calculated, the ratio at 0˚ was 1, and at 45˚ it was 1.5 (see Table 4). Thus,
due to the source radiation characteristics, the 60 kHz/A0 mode content of the signal increases atthe larger radiation angle, while the 522 kHz/S0 mode content decreases. Hence the noise has asmaller effect on the arrival error results for the 60 kHz/A0 mode, and the converse applies forthe 522 kHz/S0 mode at the larger radiation angle. These results indicate that, as a general conclusion, the dependence (at a certain S/N ratio) of arrival time errors on radiation angle willagain depend on the level of dominance of the dominant frequency and mode combination as theradiation angle varies.
8. Effects of Propagation Distance (in the zero-degree radiation direction)
Since it is well known that the amplitude of AE signals falls off with increasing propagation
distance, the same philosophy used for the radiation angle study was used in the examination of9the effect of propagation distance. Namely, the noise levels were adjusted such that at eachpropagation distance the actual S/N ratio was the same. Hence, direct comparisons at the same
S/N ratio could be made at the different distances. For all of the cases in Table 1 the arrival times
at the 60 and 120 mm propagation distances (0˚ radiation direction) were determined from theWT-based technique at two S/N ratios: 1 to 2 and 1 to 1. As before, it is most insightful to makecomparisons using the SDs of the arrival times that were determined for the 50 trials for eachcase. Table 5 shows the SDs for an S/N ratio of 1 to 1 at these two distances along with the previously documented results from 180 mm (Table 3).
Table 5 The standard deviation (50 samples) of the arrival times at three different propagation distances for the 0˚ propagation direction and S/N of 1 to 1.
Case number andSD, s at 60 mm(frequency, kHz)and rank* ( )9034 (60)
1.5 (4)9004 (60)
1.6 (5)2793 (60)
3.0 (6)9002 (60)
5.8 (7)9002 (522)
1.3 (3)9030 (522)0.84 (1)2791 (522)0.97 (2)*Lowest S/N is rank (1)SD, s at 120 mm SD, s at 180 mm
and rank* ( )and rank* ( )1.0 (2)
1.1 (2)0.74 (1)0.99 (1)1.51 (5)1.57 (4)5.3 (7)
1.9 (6)
3.0 (6)
4.2 (7)1.45 (4)1.63 (5)
1.2 (3)
1.55 (3)Table 6 Ratios of noise-free WT peak magnitudes (dominant magnitude in the numerator) at
the two key frequencies as a function of propagation distance in 0˚ direction.
Case number

At 60 mm, dominant frequency(kHz) shown
9034
4.3
(60)
9004
3.1
(60)
2793
1.0 (522)9002
1.3 (522)9030
3.0 (522)
2791
N.A.*(522)* No A0 mode present

At 120 mm, dominant frequency(kHz) shown
5.0
(60)
3.4
(60)
1.2
(60)
1.1 (522)
2.5 (522)
N.A.*(522)

At 180 mm, dominant frequency (kHz)shown
5.6 (60)
4.1 (60)
1.3 (60)
1.0 (60)
2.4 (522)
N.A.*(522)

To facilitate the discussion of the effects of propagation distance on the SDs, the results inTable 5 at each distance have been numerically ranked from the best (1) with the lowest SD to
the worst (7) with the largest SD. In order to try to “break” ties, extra decimal places have beenshown in some cases. Clearly the order changes at the 1 to 1 S/N ratio as the propagation distance changes. Some general trends in Table 5 can be associated with the relative level of dominance of the modes. Table 6 shows how the ratios of the two WT peak magnitudes of the noisefree signals varied for the different source cases and propagation distances along the 0º direction.
When the 60 kHz/A0 mode dominates, the ratio is (60 kHz/A0 mode magnitude)/(522 kHz/S0
mode magnitude). For each case, the changes in the ratios only show the effect of the propagation distance. Table 6 clearly shows the relative peak WT magnitudes for the 522 kHz/S0 mode
fall off with increasing propagation distance (Cases 9002 and 9034). In contrast, Cases 9034 and9004 show the relative 60 kHz/A0 mode peak WT magnitudes increasing with distance. A proposed cause for this effect will be discussed in the next section.
10Based on the results in Table 6, we note that the ranking in Table 5 (at a certain propagation
distance) of the best to worst SD for each frequency and mode generally correlates with theamount of relative dominance of that frequency/mode regardless of the propagation distance. For
example at 60 mm, the best SD for case 9034 has a strong dominance for the 60 kHz/A0 mode
(4.3, see Table 6), and the worst SD for case 9002 is actually dominated by the 522 kHz/S0 mode
(1.3, see Table 6). Table 6 also provides a reason for the SD ranking (in Table 5) changing withthe propagation distance. The increase in 60 kHz/A0 mode dominance (see Table 6) with increasing distance correlates with the observation that at the larger propagation distances the 60
kHz/A0 mode generally has the best SDs, except for one case (we ignore case 9002 since bothmodes have about the same peak magnitude). The results for case (2791) rank as one of the bestat all the propagation distances due to the fact that the dominance of the 522 kHz/S0 mode is verystrong since there is no A0 mode for this midplane source.
A question arises, why are the 522 kHz/S0 mode results the best at the 60 mm distance eventhough there are two 60 kHz/A0 cases (9034 and 9004) with strong dominance? The answer tothis question is likely due to the “spreading in time” of the WT results at lower frequencies. The“spreading in time” terminology refers to the signal at a specific time contributing to the WT
magnitudes over a range in time about that specific time. This characteristic at lower frequencies
is illustrated in Fig. 5. In this figure the input signal to the WT is a Dirac delta function. Thus wesuggest the 60 kHz/A0 mode WT peak magnitude arrival time has larger errors since the WT
peak magnitude in that case reflects a wider time-range of the noise signal (see Fig. 5). In contrast, the 522 kHz/S0 mode WT magnitude does not have the same range of arrival time errors
because the WT peak magnitude at that frequency does not result from as wide a time range ofthe noise signal. At the longer propagation distances, the drop in the dominance of the 522
kHz/S0 mode is evidently sufficient to allow the higher magnitude noise in the region of 522 kHz(see Fig. 2) to increase the SDs. This results in the 60 kHz/A0 mode SDs being generally thelowest at the longer distances.
9. Relative Fall-off of the 522 kHz/S0 Mode with Increased Propagation Distance
The 522 kHz/S0 mode falls-off at a faster rate than the 60 kHz/A0 mode with increasing
propagation distance as is shown in Table 6. Since the finite element modeling results did notincorporate the available frequency-based attenuation with propagation distance, the source of
this frequency/mode fall-off is of interest. Since the frequency band is only 3 kHz wide in the
WT computation, there should not be much dispersion with increasing propagation distance. To
check this assumption, the profile of the WT magnitude (at each of the two frequencies) as afunction of time was plotted for one of the cases (case 9002, noise-free signal) at the three
propagation distances. The normalized results in Fig. 6 show some potential dispersion for the
522 kHz/S0 mode. For example, at 60 % of the peak magnitude of the 522 kHz profile (in thelargest magnitude peak region) the width of the profile increases by 50 % as the propagation
distance increases from 60 mm to 180 mm. On the other hand, the change of width over those
propagation distances at this level was not perceptible for the 60 kHz mode. It is suspected that
this fall-off can be attributed to the following observations. In the 522 kHz region of the S0mode, the group velocity curve makes nearly a “U-turn” (see Fig. 1 in reference 1) in the groupvelocity direction over a limited range of frequency change. Thus a range of frequencies in thisregion have about the same group velocity. This feature, in combination with the characteristic of
the WT described next, likely causes this attenuation.

11

Fig. 5 WT result with input signal of a Dirac delta function (amplitude in mV) as shown in theupper time domain. Vertical frequency scale of WT result (lower part of figure) from 0 to 1 MHz
and time scale of both plots 0 to 51.2 s. Color or contrast shows the WT magnitude.
Figure 7 shows that the WT used in this research has the characteristic of its magnitude“spreading in frequency” at higher frequencies. This “spreading in frequency” means that thesignal at a specific frequency contributes to the WT magnitudes over a range of frequencies
about the specific frequency. This effect does not appear in Fig. 7 at lower frequencies. The WT
shown in Fig. 7 was created from a time-domain signal that was made up of a sinusoidal signalthat has a change in frequency from 60 kHz to 522 kHz, as is shown in the top portion of the figure. The two sinusoidal portions of the time-domain signal had equal amplitudes, yet the WT result has significantly lower peak magnitude (only about 36 % of the 60 kHz value), with obvious“spreading in frequency” for the sinusoidal portion of the signal at the single frequency of 522
kHz. Thus it seems likely that the combination of the “spread in frequency” and the “U-turn” results in the “attenuation” of the 522 kHz/S0 WT peak magnitude with increasing propagation
distance. Thus the supposed dispersion-based “attenuation” is really related to the characteristicsof the WT used in this research in combination with the shape of the group velocity curves in aparticular region.
10. Location Errors versus S/N Ratio
The statistical properties discussed above do not directly address the important issue raisedby the potential for noise to alter WT-based signal arrival times. The central issue concerns theeffect of arrival time errors on the accuracy of the calculated AE source location. To address thisquestion, a decision was made to focus on linear location. This choice was based on a desire to12

Fig. 6(a) Profile of WT magnitude at 60 kHz versus time and (b) profile of WT magnitude at 522
kHz versus time, both for case 9004.
remove any dependence of the location error-analysis results on the particular computational
scheme used for two-dimensional location calculations. Figure 8 shows a schematic of the linear
location geometry and the notation used in this paper. Straightforward manipulation of the governing equations leads to the following:
d1 = 0.5 [d – cg (t2 - t1 )],d2 = 0.5 [d + cg (t2 – t1 )],d = d1 + d2,

(1)
(2)
(3)
13

Fig. 7 Signal and WT of the signal. Signal is 60 kHz sine wave followed by 522 kHz sine wave
of same amplitude. WT maximum magnitudes of 32,937 and 11,970 for 60 kHz, and 522 kHz
portions of WT.

Fig. 8 Geometry for linear location equations.
where cg is the appropriate group velocity, d is the sensor spacing, d1 is the distance from thefirst hit sensor to the source location, and d2 is the distance from the second hit sensor. The values t1 and t2 are the respective arrival times. To allow the analysis to focus directly on arrival
time errors due to the addition of noise, it was decided to consider geometry where the source
was equally spaced between the two sensors. With this approach, when two different S+N signals (associated with the same noise-free case) are used to calculate a location, the location error
will be directly due to noise-altered arrival times. When noise is present, equations (1) and (2)can be rewritten (with italics to denote the values when noise is present) as:
d1 = 0.5 [d - cg (± e)],d2 = 0.5 [d + cg (± e)],

(4)
(5)

where e = |t1 – t2| > 0 is the arrival time-difference error. On an absolute value basis the percent
location errors in d1 and d2 values are equal, and this error is given by
percent error = [(cg · e) / d] x 100 %.

(6)

To form databases of time differences for each case (source type, depth and S/N ratio) the 50
arrival times obtained from each “set” were used in the following fashion. The absolute value
differences of the first arrival time and each of the subsequent 49 values was determined.
14

Fig. 9 Fraction (%) of the 1225 calculated locations with an error of 2 % or less as a function of
the S/N ratio for all cases (source type and depth).

Fig. 10 Maximum location error (%) for the 1225 calculated locations for all cases as a functionof the S/N ratio (Note that the dip in the curves between S/N ratios of 1 to 2 is an artifact of theplotting routine).
15

Then the absolute value differences of the second arrival time and each of the subsequent 48 values were determined. This process was continued in a similar fashion so that a total of 1225 arrival time differences were obtained for each case. These differences correspond to the e values
in equation (6). By use of equation (6), the location errors were determined for each of the 1225
arrival time differences. Figure 9 shows as a function of the S/N ratio, the percentage of the1225 trials for each case when the location error was 2 % or less based upon the sensor spacing(360 mm) for a nominal propagation distance of 180 mm in the zero-degree direction. In addition, Fig. 10 illustrates the maximum location error found in the 1225 trials for each case as afunction of the S/N ratios.
The data illustrated in Fig. 9 demonstrates that even with an S/N ratio of 1 to 1, at least 73 %of the 1225 location calculations for each case resulted in a location error that was 2 % or less.
Further, Fig. 10 shows the maximum location error for the 1225 location calculations for eachcase was 13 % or less. To appreciate these results, it should be pointed out that a typical fixedthreshold AE system would not even trigger on any of these signals because the required low
threshold would trigger on multiple noise spikes. Hence, the technique used here could be ofvalue for an AE acquisition system that records continuously.
At a 2 to 1 S/N ratio, Fig. 9 shows that at least 99 % of the 1225 location calculations foreach case would have location errors of 2 % or less. Figure 10 also shows the maximum location
errors would be 2.4 % or less. With an S/N ratio of 2 to 1, a fixed-threshold AE system would
likely trigger on real AE signals instead of noise spikes. And for a source equidistant between
the sensors, the location errors would not be great. The reason is that the factors of geometricattenuation, dispersion, and variations in source amplitude would be the same for the signal atboth sensors. But for a fixed-threshold system, when the source is not equidistant from all the
sensors, the above three factors would typically result in relatively large location errors compared to the 2 % location error limit used in this research. On the other hand, the WT-based determination of arrival times would not be affected by the above three factors.
11. Discussion of Location Error Results at a 180-mm Propagation Distance
To better appreciate just how good the above location results are the following facts must be
considered. The noise level was taken at the average peak value of the original set of ten noise
signals. If it had been set at the peak value (0.73 pm) of the ten signals, then the S/N ratios of 1
to 1 and 2 to 1 would have been 0.86 to 1 and 1.73 to 1. Even these S/N ratios are conservativevalues from the view of a typical AE experimentalist, since the peak was that for only about 16
ms of noise signal data.
The analysis presented in equations (1) through (6) predicts that the error in the location
should depend linearly on the group velocity, cg. Clearly Figs. 9 and 10 do not support that conclusion. The lower group velocity of 1.8 mm/s for 522 kHz/S0 does not give the expected better
results. This lack of dependence on cg is likely due to the previously discussed high noise-signal
energy in the region of the 522 kHz frequency. This higher noise was evidently sufficient toovercome the expected dependence on cg.
For further discussion of the location results, it makes the most sense to focus on the fractionof the 1225 trials that resulted in location errors of 2 % or less. The reason for this focus is thatthese results represent the average results over many trials. In contrast, the results of the16

maximum location error represent only the worst case out of the 1225 trials for each source caseand S/N ratio.
With this focus, we can examine in more detail the cases that give the best and worst results
for the fraction of trials with location errors of 2 % or less. In each case it will be shown that thesame facts apply that explained the general trends of the ordering of the results by SDs of the arrival times for these cases. Hence these discussions will not be as detailed as they were in thediscussions of arrival times.
If the order of the best (highest fraction with small location errors) to worst results in Fig. 9(at 0˚, 180 mm and S/N ratios of 1 to 2 and 1 to 1) is examined, the best results are for the caseswith a strong dominance of the 60 kHz/A0 mode (9004 and 9034) trailed by the results for thestrong dominance of the 522 kHz/S0 mode (2791 and 9030). The reasons for the cases withstrong single mode dominance being best were already described in the discussion of arrival time
results. In addition this ordering of the results relates to the “attenuation”, as discussed previously, of the 522 kHz/S0 mode with propagation distance. This attenuation increases the dominance of the 60 kHz/A0 mode at a propagation distance of 180 mm. The worst location error results are for the cases (9002 and 2793) where both frequency/mode combinations are significant
in the noise-free signals. In these cases, as previously described, neither mode overpowers thenoise. The case (2791), which has a strong dominance of the 522 kHz/S0 mode, is not among thebest due to the extra noise in the vicinity of that frequency.
12. Location Error Effects of Propagation Distance and Radiation Angle
To examine the effect of propagation distance on source location errors, one must consider
Fig. 11. This figure shows the fraction of trials with errors in location less than or equal to 2 % at
the propagation distances of 60, 120, and 180 mm for S/N ratios of 1 to 1 and 2 to 1. The firstobservation is that the percentage of the 1225 trials with small location errors increases with increasing propagation distance. This result is not unexpected since the calculation of the location
error has the sensor spacing in the denominator. Thus, with similar SDs of the arrival times at thedifferent distances, the larger denominator will result in a smaller fractional error at the longer
distances.
The second observation is that the ranking of best to worst location accuracy changes withpropagation distance. These changes follow the same pattern that was observed for the dependence of the arrival time errors on propagation distance. The general explanation of this behavior
follows exactly the previous explanation given for the ranking in arrival time errors. These factors are the greater “attenuation” of the 522 kHz/S0 mode as compared to the 60 kHz/A0 modewith increasing propagation distance; the characteristic of the noise signal having increased noisein the vicinity of the 522 kHz frequency; and the “spreading in time” of the WT magnitudes at
low frequencies versus higher frequencies.
Table 7 shows the location errors for the three cases used for the study of the effects of theradiation direction on arrival times at the S/N ratio of 1 to 1. If the order of the best (highest percentage of trials with a location error of 2 % or less) to worst location errors are examined, thenthe results for cases 9004 and 9030 show little change with the change in radiation angle since
the primary frequency/mode combination has a strong dominance at both angles (see Table 7).
As before, however, for arrival time errors for case 9002, the rankings of location error accuracychange with the changes in the radiation angle. The change is due to the radiation pattern of the17

Fig. 11 Fraction of the 1225 calculated locations with an error of 2 % or less at S/N ratios of 1 to
1 and 2 to 1 for all cases (source type and depth) at propagation distances of (a) 60 mm, (b) 120
mm and (c) 180 mm.
18

two modes not being the same and both modes being significant in the signal. The result is that
9002 (60 kHz/A0) has a higher fraction with low location error at 45˚, since that mode becomes
more dominant at 45˚ versus 0˚. And the opposite is the case for 9002 (522 kHz/S0), since theeffect of the radiation pattern reduces the dominance of that mode at 45˚ versus 0˚.
Table 7 The percentage of the 1225 time differences with a location error  2 % at two
different radiation angles for 180 mm propagation distance and an S/N ratio of 1 to 1.
Case number
and(frequency,
kHz)

% of
trialswithlocation
error  2
% at 0˚.

Ratio: noisefree WT
peak atindicated
freq./alt.
freq. value atzero deg.

% of trials
with location error
 2 % at45˚9004 (60)9002 (60)9002 (522)9030 (522)

97
73
83
91

4.1
1.01
0.99
2.3

-----9162------

Ratio:
noise-free
WT peakat indicatedfreq./alt.
freq. value
at 0˚.
-------1.5
0.68------% of trials
with location error
 2 % at67.5˚97----------91

Ratio:
noise-free
WT peakat indicatedfreq./alt.
freq. value
at 67.5˚.
33----------3.2

13. Alternate Joint Time-Frequency Analysis Algorithms
Since time-frequency analysis results can be dependent on the particular algorithm used [seereference 11 for a discussion of various techniques], this research also examined the errors inmode arrival times determined when noise is present by the use of alternate (relative to the WT)
joint time-frequency analysis algorithms. We choose to use the Adaptive Spectrogram and the
Choi-Williams Distribution (spectrogram) in their LabVIEWTM implementations and compared
their results with those obtained from the WT. The joint time-frequency analysis (JTFA) algorithms provide the instantaneous power spectrum over time [12]. A number of these JTFA algorithms were available. After using these on some of the FEM-based signals, it was decided tofocus on two of them. These were the Easy Adaptive Spectrogram (ADS) and the Easy ChoiWilliams Distribution (CWD). The results from either of these can be displayed in a plot of frequency versus time with the colors representing the intensity of the signal energy at the time andfrequency values. Figure 12 shows for case 9002 that the JTFA results are qualitatively very
similar to the WT results.
In this research both the ADS and CWD results were calculated so as to maintain the 0.1 sresolution of the original time-domain signals. The frequency resolution could be selected based
only on powers of two applied over the range of zero to 5 MHz (based presumably on the Nyquist frequency for the time-domain signals). This led to the choice of a frequency resolution ofeither 1.22 kHz or 2.44 kHz. To be as consistent as possible with the WT nominal resolution of 3
kHz, the 2.44 kHz resolution was chosen. The ADS results were calculated with the “no-zoom”
implementation, the number of terms parameter set at two and the mode parameter selection of“chirplet”. In all other aspects the Easy ADS and CWD implementations were used with the default settings. Also one other selection was made, namely the intensity of the JTFA results was
multiplied by 1026 to be able to obtain intensity results with larger numbers. These larger numbers were more convenient to use.

19

(a)

(b)

(c)

Fig. 12 Spectrograms of WT (a), CWD (b) and ADS (c) for case 9002. Scaling of 0 to 700 kHz
vertical and 0 to 150 s horizontal. Note that non-linear amplitude (color or contrast) scales wereused in (b) and (c) to better show the two modes.
2014. Comparison of Results with the JTFA and the WT for Noise-free Signals
To create a comparison of the WT with the two selected JTFA spectrograms, the noise-freesignals were used for three key cases (9004, 9002 and 9030) in Table 1. The arrival times of thepeak magnitudes of the JTFA frequency bands nearest the 60 and 522 kHz values were determined for the dominant frequency/mode in each case for the 0˚ radiation direction and a propagation distance of 180 mm. The nominal frequency bands over which the peak spectrogram
magnitudes were determined were (a) WT at 60 to 63 kHz and 522 to 525 kHz and (b) ADS andCWD at 61 to 63.5 kHz and 522.5 to 524.9 kHz. Table 8 shows the results for the noise-free signals compared to the previously determined WT results. As can be seen from the table, the arrival time results are very similar. Thus the peak magnitudes of the key frequencies in the JTFA
spectrograms do in fact correspond to the Lamb mode arrivals as was seen for the WT. The tablealso shows the level of dominance of the primary frequency/mode as determined by the peakmagnitude ratios for each case. It is of interest to note that the levels of dominance were generally considerably higher for the CWD and ADS than for the WT.
Table 8 Comparison of noise-free arrival times and relative dominance of the modes forthe wavelet transform (WT), Choi-Williams (CWD) distribution and Adaptive (ADS)
spectrogram at a propagation distance of 180 mm and direction of 0˚.
Case number and(frequency,
kHz) of arrival time

WT
noisefree arrivaltime, s

Ratio:
CWD
noise-freenoiseWT domifree arnant peak
rival
/alt. modetime, svalue
9004 (60)78.0
4.1 (60)*
77.3
9002 (60)78.2
1 (60)*
77.2
9002 (522) 98.2
1 (60)*
100.0
9030 (522) 98.0
2.3 (522)*
99.8* Most dominant frequency in kHz

Ratio:
noise-free
CWD
dominant
peak /alt.
mode value
14 (60)*1.1 (522)*
1.1 (522)*
6.2 (522)*

ADS
noisefree arrivaltime, s
75.6
75.3
99.1
99.2

Ratio:
noise-free
ADS dominant peak
/alt. modevalue
13 (60)*1.3 (522)*
1.3 (522)*
7.8 (522)*

15. Comparison of Arrival Time Results with the JTFAs and the WT
To compare the different spectrogram approaches when noise was present, the three cases
from the previous section at a propagation distance of 180 mm and the zero-degree propagation
direction were used. As before, case 9004 was used since it has a strong dominance of the 60
kHz/A0 mode. Case 2791 was used since it has a strong dominance of the 522 kHz/S0 mode. Andcase 9002 was used since both of the above modes have a significant presence. As was done with
the WT, the arrival times were calculated for 50 S+N signals of each of the three cases. Table 9shows the average arrival times and sample SDs of the peak magnitude determined arrival times
for each of the cases at S/N ratios of 1 to 2 and 1 to 1. It should also be recalled that Table 8shows the level of dominance of the peak mode for each case of the noise-noise signals.
As before, the focus of the relative performance of the different approaches was based on theSDs. Clearly the CWD approach resulted in the lowest SDs followed by the WT and trailed by
the ADS. The better performance of the CWD was especially apparent for the S/N ratio of 1 to 2
for cases 9002 and 9030, where the SD with the CWD was substantially lower than that for the21

Table 9 Comparison of average arrival times and their SDs (from 50 samples) at S/N ratios of 1 to 2 and 1 to 1 for the Wavelet transform, Choi-Williams distribution and Adaptive spectrogram at a propagation distance of 180 mm and direction of 0˚.
Case num- WT 1 to 2,
ber and (fre- Average
quency*,and (SD),kHz)s

CWD 1 to2, Averageand (SD),s

ADS 1 to2, Averageand (SD),s

WT 1 to1, Average and(SD), s

CWD 1 to1, Averageand (SD),s

ADS 1 to1, Average and(SD), s

9004 (60) 77.4 (1.9)
77.4 (1.7)
74.1 (8.1)
77.6 (1)77.4 (0.72) 74 (6.1)
9002 (60) 78.4 (13)80.5 (8.8)
60.5 (36)77.5 (1.9) 77.5 (1.6)
74.9 (20)9002 (522) 87.7 (28)92.7 (18.7) 82 (37)98.1 (4.2) 100 (1.5)
95.5 (13)9030 (522) 94.2 (15)100.4 (9)84.7 (32)98.1 (1.6) 99.3 (1.4)
99 (1.2)* Most dominant frequency except for 9002 where both frequencies are significant.
WT. For example, for case 9002 for the 60 kHz/A0 mode the SD dropped from 13 s with the
WT to 8.8 s with the CWD, and for the 522 kH/S0 mode the drop was from 28 s to 19 s. The
SDs at the 1 to1 S/N ratio were still better with the CWD than with the WT, but the relative improvement was not as large.
16. Conclusions
These direct conclusions are based on the following key conditions: a large 4.7 mm thickaluminum plate; nominal wave propagation distances of 60, 120 and 180 mm; the use of a particular WT; experimental electronic noise from a particular wideband sensor/preamplifier; noisefree signals from finite element modeling that simulates perfect point contact sensors; and theparticular six cases of source types and source depths examined in detail.
•
•
•

•

•
•

Due to the variations in random-noise signals as a function of time, a statistical study ofnoise-induced errors was necessary.
At S/N ratios of 1 to 1 and 2 to 1 and a propagation distance of 180 mm in the zerodegree direction, the location error was 2 % or less for at least 73 % and 99 % respectively of the trials considering all six cases.
At the lower S/N ratios of 1 to 2 and 1 to 1 under the same conditions, the location andarrival time errors were greater if the combination of source type and source depth resulted in a signal with significant energy in portions of both fundamental modes. This result was in contrast to smaller errors in cases where the signal energy was more highly
concentrated in a portion of a single mode.
The order of ranking from smallest to largest errors in location and arrival times changed
with propagation distance due to (i) the higher propagation-distance “attenuation” of the
522 kHz/S0 mode relative to the 60 kHz/A0 mode, (ii) the higher noise amplitude in thevicinity of 522 kHz, and (iii) the “spreading in time” of the WT magnitude at lower frequencies.
The order of ranking of errors in arrival times and location also changed with radiation
direction changes when the relative WT intensity of the 60 kHz and 522 kHz modeschanged with the change of direction.
Use of the WT technique greatly enhanced the accuracy of arrival times for source location calculations, and also can provide reasonably accurate additional arrival times fromAE channels where hits would not even be recorded with threshold based systems. This
22

•
•

application requires the AE waveform system to record the signals from all the sensors ina local array subset when one sensor channel in that array results in a hit being triggered.
The errors in location with a fixed threshold are expected to be significantly greater, since
the measured arrival times at each hit from a single AE event will correspond to different
group velocities due to the factors of geometric attenuation, dispersion, and source amplitude. These factors do not alter the arrival times determined with the WT-based approach.

Alternate Spectrogram Techniques:
The comparison of the WT with the CWD and ADS alternative approaches revealed thatthe lowest SDs of the arrival times were obtained with the CWD. The improved arrival
time results with the CWD were substantial at an S/N ratio of 1 to 2. The poorest results
were obtained with the ADS method.
On the Extension of These Techniques to Other Cases
The technique of using the results of joint-time-frequency analysis (such as the WT or
CWD) to determine very accurate arrival times (of a certain energetic frequency of amode corresponding to a specific modal group velocity) in the presence of significant
background electronic noise is not expected to be limited to the list of key conditions
listed at the beginning of the Conclusion section. The potential main limitation of the approach is possibly for thick plates. In this case, potentially many Lamb modes may bepresent with significant energy such that either the amplitudes of individual modes making up the time domain are all small and/or the presence of so many energetic modes
leads to the extracted mode arrival times not being clearly associated with a particularmode.
References
1. Hamstad, M. A., K. S. Downs and A. O’Gallagher “Practical Aspects of Acoustic Emission
Source Location by a Wavelet Transform,” Journal of Acoustic Emission, 21, 2003, 70-94, A1A7.
2. Hamstad, M. A., A. O’Gallagher and J. Gary, “Examination of the Application of a Wavelet
Transform to Acoustic Emission Signals: Part 2. Source Location”, Journal of Acoustic Emission, 20, 2002, 62-81.
3. Gary, John and Marvin Hamstad, "On the Far-field Structure of Waves Generated by a Pencil
Break on a Thin Plate," Journal of Acoustic Emission, 12 (3-4), 1994, 157-170.
4. Prosser, W. H., M. A. Hamstad, J. Gary and A. O'Gallagher, "Reflections of AE Waves in Finite
Plates: Finite Element Modeling and Experimental Measurements," Journal of Acoustic Emission, 17, (1-2), 1999, 37-47.
5. Hamstad, M. A., A. O'Gallagher and J. Gary, "Modeling of Buried Acoustic Emission Monopoleand Dipole Sources With a Finite Element Technique," Journal of Acoustic Emission, 17 (3-4),
1999, 97-110.

23

6. Hamstad, M. A., A. O’Gallagher and J. Gary, “Examination of the Application of a Wavelet
Transform to Acoustic Emission Signals: Part 1. Source Identification”, Journal of Acoustic
Emission, 20, 2002, 39-61.
7. Downs, K. S., Hamstad, M. A., and A. O’Gallagher, “Wavelet Transform Signal Processing toDistinguish Different Acoustic Emission Sources,” Journal of Acoustic Emission, 21, 2003, 52-

69.
8. Vallen-Systeme GmbH, Münich, Germany, http://www.vallen.de/wavelet/index.html, 2001,
software version R2002.0703.9. Hamstad, M. A., and C. M. Fortunko, "Development of Practical Wideband High Fidelity Acoustic Emission Sensors," Nondestructive Evaluation of Aging
Bridges and Highways, Steve Chase, Editor, Proc. SPIE 2456, Published by SPIE – The International Society for Optical Engineering, Bellingham, WA, 1995, pp. 281-288.
10. Hamstad, M. A., "Improved Signal-to-Noise Wideband Acoustic/Ultrasonic Contact Displacement Sensors for Wood and Polymers," Wood and Fiber Science, 29 (3), 1997, 239-248.
11. Huang, Norden E., Zheng Shen, Steven R. Long, Manli C. Wu, Hsing H. Shih, Quanan Zhen,
Nai-Chyuan Yen, Chi Chao Tung and Henry H. Liu, “The Empirical Mode Decomposition andthe Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis.” Proc. R. Soc.
Lond. A, 454, 1998, 903-995.
12. LabVIEWTM Signal Processing Toolset, Version 7.0.1, National Instruments Corporation, Austin,
TX, 2004.

24

ACOUSTIC EMISSION TECHNIQUE FOR DETECTING DAMAGE AND
MECHANISMS OF FRACTURE IN A KNITTED FABRIC REINFORCEDCOMPOSITECARLOS R. RIOS1, STEVE L. OGIN2, CONSTANTINA LEKAKOU2 and K. H. LEONG3
1)
Unidad de Materiales, Centro de Investigación Científica de Yucatán, Mérida Yucatán, Mexico; 2) School of Engineering, University of Surrey, Guildford, Surrey GU2 5XH, UK;3)
Cooperative Research Centre for Advanced Composites Structures Ltd., Fishermans Bend,
VIC 3207, Australia. Currently PETRONAS Research, Malaysia.
Abstract
The relationship between fiber architecture and the damage accumulation sequence under
tensile loading has been investigated for a Milano weft-knitted reinforced composite oriented at a
range of angles to the loading direction (0° to 90°) using acoustic emission (AE) technique among
others. In order to observe progressive damage accumulation, model sandwich laminates were
fabricated using a single layer of the knitted fabric sandwiched between outer plies of unidirectional glass-reinforced epoxy resin. For comparison, commercial composites manufactured withthe same knitted fabric as a reinforcement and Derakane vinyl-ester resin as matrix were analyzed. These materials have been characterized for monotonic and cyclic tensile loading. Results
have been compared with those found earlier for a single layer and the sandwich model material
with epoxy resin as matrix. AE technique was demonstrated to be an excellent method for materials analysis especially in the second case where the matrix is not transparent. AE count rate wasused for damage detection and various failure mechanisms were observed in failed specimens,such as cracking at loop cross-over points, resin matrix cracking, fiber-bundle debonding andtensile fracture of fiber bundles.
Keywords: Composite materials, Textiles, Mechanical properties.
1. Introduction
Experimental results obtained on a model material and a composite manufactured by RTM
are presented in this paper. First, the structure of the knitted fabric is described, followed by the
results on a single knitted layer/epoxy composite using the 2x68 tex fabric. Second, after outlining the mechanical behavior of this material, and the difficulties encountered in analyzing damage propagation due to the premature failure of the material, results on a model sandwich laminate made up of the knitted fabric between the outer 0°-unidirectional glass fiber plies are discussed. These results include tests where the knitted fabric within the sandwich laminate is oriented at different angles to the loading direction. A detailed microstructural relationship between
fabric geometry and cracking behavior is discussed; the acoustic emission technique was used tosupport the investigation of crack initiation and accumulation, especially to understand the damage development along the sample in order to relate the damage behavior and failure to the textile architecture. These results together with tests on the cyclic behavior of the material enable
some conclusions to be drawn about the mechanical behavior of the knitted fabric layer during
damage accumulation. At the same time, an RTM composite manufactured with the same knitted
fabric as a reinforcement but using Derakane vinyl ester resin as matrix was analyzed in order to
J. Acoustic Emission, 23 (2005)25© 2005 Acoustic Emission Group

understand the similitude on damage development with the model material. The purpose of thislast material was the observation of damage behavior on a structural composite of high fiber volume fraction.
Many authors have commented on the development of damage during the tensile loading ofknitted-fiber composites. There has been general agreement that (a) for loading in the wale direction, the initiation of damage occurs due to fiber bundle/resin matrix interface failure at theneedle or sinker loops, with subsequent linking of this damage to form long cracks; and (b) for
loading in the course direction, that the damage initiates from the sides or legs of the loops [1- 3].
However, in most studies to date, it has only been possible to draw conclusions based on analysisof the final fracture surfaces of the composites. In this work, a knitted fabric reinforced composite layer has been fabricated as part of a sandwich laminate, in which the fabric layer is sandwiched between outer plies of unidirectionally reinforced epoxy resin. The resultant model
specimens have enabled the sequence of damage to be investigated directly during tensile loading.
2. Experimental Procedures
Milano weft-knitted fabric produced from E-glass yarns (2x68 tex) was used for manufacturing the sandwich panel using a wet impregnation technique. The knitted cloth was cut to sizeand fixed inside a steel frame using tape around its edges and the frame was placed in a filament
winder to wind unidirectional glass fibers around the cloth. The knitted cloth was fixed in theframe in which the wale direction of the cloth was at different angles to outer 0° plies (0°, 30°,45°, 60° or 90°). The matrix used for the sandwich panel was an epoxy resin (Astor Stag Epoxide
Resin, Astor Stag NMA curing agent and Ancamine K61B accelerator). The panel dimensions
were 250 mm x 250 mm and the unidirectional plies on each face of the sandwich panels had anominal thickness of 1 mm, with an overall panel thickness of about 3.1 mm. The overall fiber
volume fraction of the laminates was 0.29 although the fiber volume fraction within the central(knitted fabric) layer was much lower (about 0.13). In addition to the sandwich specimens, panels of knitted-fabric reinforced epoxy resin, without the reinforcing outer 0° plies were also
manufactured. RTM materials were produced by placing 5 layers of the Milano weft-knitted fabric in a mold, which was injected with Derakane vinyl ester resin resulting in a high fiber volume
fraction of 0.47.
Samples of 230-mm length and 20-mm width were cut from the panels for mechanical testing. Aluminum end tabs (50 mm long by 20 mm wide) were used for the tests and strain gauges
were bonded transversally and longitudinally near the center of the coupons. An Instron 1196
testing machine was used for the monotonic tests with a constant crosshead speed of 0.5
mm/min. Load/strain data were collected using a data-logger and acoustic emission (AE) eventswere recorded using an AECL 2100M system, which employs two guard transducers to reduce
external noise. The threshold for the AE equipment was established by performing preliminary
analysis in order to enable recording of the AE signals from the tests without spurious noise. The
processing unit was set to count the number of acoustic events in 0.1 s intervals. Photographs
were taken for two purposes: first to examine partially tested samples at different percentage ofstrain in order to identify the source of the cracks and to obtain the crack density, and second, toexamine the fracture surfaces to obtain an overall idea of the crack propagation and fracture surface contours. Optical microscopy was used to analyze and identify the cracking initiation sites
and to related them to the architecture of the knitted textile.

26

3. Results and Discussion
Figure 1 shows the architecture of the Milano 1x1 knitted fabric, which consists of the interlocking of loops of fiber bundles where two singles threads knitted in two separate sets of needles are held together by a row of 1x1 rib course. Note that this fabric consists of two main directions: course, the row of the loops in the width direction; and wale, the row of knit loops inthe longitudinal direction.

1
2
3

Wale

CourseFig. 1 Architecture of the Milano 1x1 knitted fabric.
Initially laminates containing only one knitted cloth embedded in epoxy resin were manufactured and tested at different angles in order to observe damage progression under tensileloading. However no damage was observed along the samples since samples failed catastrophically when the first signal of damage appeared. No pre-damage was produced. Wale direction
presented higher mechanical properties than the others due to a higher portion of fibers oriented
at 0°. Fiber volume fraction for single layer laminates was calculated to be 0.13±0.005.
Due to the sudden fracture of the single layer composite materials, sandwich laminates weremade by winding unidirectional glass fibers over the 2x68 tex knitted fabrics as described before.
Observation of damage accumulation was facilitated by the transparent nature of the composite,which allowed the plan view damage development to be observed during testing by in situ photography. For all sandwich laminates, the average fiber volume fraction was measured obtaining
a value of 0.29±0.002. Figures 2 and 5 display the strain-stress curve for the samples tested in the0° (wale), and 90° (course) direction, respectively. For wale direction, the stress-strain curve displays a region of pre-damage, which was registered by AE signal where visual damage is notdetected below 1% strain. After this strain value, a first “knee” is observed and a second discontinuity appears at about 1.5%, which is intimately related to the knitted architecture as explainson Fig. 4. AE signals give additional information with respect to the damage development. A
small number of AE events are observed between 0.80% and 0.95% strain. This is in advance ofthe development of the cracks and is related to crack initiation at crossover points in the knitted
fabric architecture. Next, in a range of 1.1% to 1.2% strain, the first significant cracks appear
producing many more AE events until total fracture of the sample.

27Model material in wale direction (0

o

)

400

Stress (MPa)

100

300
80

250
200

60

150

40

100
20

50
0
0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

AE event count rate

120

350

0
2.4

Strain (%)

Fig. 2 Stress vs. strain curve for sandwich composite tested in wale (0°) direction.
20 mm

Unloaded

4 mm

1.10%

1.15%

1.30%

1.60%

2.30%

Fig. 3 Crack development for the model sandwich composite tested in wale direction (0°). Applied strains are indicated
The cracking pattern shown in Fig. 3 is intimately connected with the fabric architecture.
Figure 4 gives an indication of the sequence of events. The first sign of damage develops at theloop crossover sites, represented by A, which is detected as micro-debonding at low AE signal.
Significant cracks (matrix cracking) appeared firstly in the plane where the float stitch of course
2 and 3 are together (represented by the dashed lines) possibly due to the strain magnification
caused by adjacent tows. These cracks have a spacing of 4 mm, which is the dimension of therepetitive unit in the knitted fabric. When these sites have been used, then at a higher strain
cracks develop where the rib holds threads 2 and 3 (represented by dotted lines), giving a final
crack spacing of 2 mm, which is the wale direction dimension of a single loop. This cracking
pattern is registered by AE signals in accord to the discontinuities in the stress-strain curve.
For sample tested in 90o (course) direction linearity can be appreciated up to 0.7% strain. In
this case visual and audible flaws are observed around 0.9% strain, and the first damage observed by AE is again in the form of micro-cracks at the loop crossover points at lower values.
Matrix cracking appeared at a strain of about 0.8% producing a small knee in the stress-strain
28

curve. During loading in this direction, branched-like cracks were observed to be produced perpendicular to the load direction.
A

Course 1 (rib)
4 mmCourse 2
Course 3

Fig. 4 Schematic diagram showing crack development for the model sandwich laminate with thefabric layer in the 0o direction (Wale).
Therefore, in both cases the AE information suggests that micro-debonding in the initiationsites (cross-over points) occurs due to stress magnification at lower levels below the knee point,
registered by AE signal. Later above the knee point, when visual damage is observed in the sample, interfacial debonding and resin matrix cracking take place as demonstrated by some researchers [4, 5]. Finally, fiber fracture arises toward the end of the tensile test.

400

80

350

70

300

60

250

50

200

40

150

30

100

20

50

10

0
0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

AE event count rate

Stress (MPa)Model material in course direction (90 o )02.4

Strain (%)

Fig. 5 Stress vs. strain curve for sandwich composite tested in course (90°) direction.
The cracking development sequence can be seen in Fig. 6. At low strains, cracking at thecrossover points is visible, again as black spots in the photographs. In this orientation, it is easy
for the developing matrix cracks to link up as they grow. Hence, the crack density increases
fairly uniformly. Figure 7 shows a schematic of crack formation. The crack initiation points are29

Unloaded

0.9%

1.00%

1.20%

1.60%

2.00%

Fig. 6 Crack development for the model sandwich composite tested in course direction (90°).
Applied strains are indicatedrepresented by A, where the yarns are joined together (crossover). All these sites coalesce topropagate the cracks along the sample perpendicularly to load direction by running on the sides
or legs of the loops.
A

2 mm

Fig. 7 Schematic diagram showing crack development for the model sandwich laminate with thefabric layer in the 0° direction (Course).
In Fig. 8 the crack density can be seen and compared. In wale direction crack initiation occurs at a strain of 0.85% (pre-damage) although a significant increase in crack density occurs
after a strain of 1.1%. The crack initiation strain of 0.9 % is in reasonable agreement with thestrain to failure of a single layer of knitted fabric reinforced resin tested alone, which was foundto be 0.87%. In Fig. 8 it is noticed that the crack density increases in a course direction sample
more rapidly than in samples tested at wale direction. This is due to the transversal branched-like
matrix cracks that grow almost randomly along the sample showing no steady section in the
curve.
In order to visualize the damage initiation detected by AE signals, data from crack density
was plotted against the AE event count rate. Thus, it is possible to observe in Fig. 9, for the sample tested on the wale direction, that the first AE signal appearing before the main cracking damage (just before 1% strain) suggesting that a lower values of strain a kind of micro-damage is30

detected (i.e. interfacial debonding). On the other hand, Fig. 10 confirm this statement since AE
signal is located before the initiation of significant cracks at 0.85% strain when the sample isloaded to the course direction.
Crack density for samples at course and wale directions
1.6
1.4

1/2s mm-1

1.2
1
0.8
0.6
0.4
0.2
0
0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

2.8

3

Strain (%)
CourseWale

Fig. 8 Crack density vs. strain for sandwich composites.

AE event count rate

80
70
60
50
40
30
20
10
0
0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
2.4

1/2s mm-1

Model material in wale direction (0 o )Crack density vs. AE event count

Strain (%)

Fig. 9 AE event count rate vs. crack density loaded in wale direction.
Typical stress-strain curves for samples with the wale and course direction of the fabric are
shown in Figs. 11 and 12 for a commercial material, made of 5 knitted layers by RTM process,which was tested in order to compare the behavior of a model material with a commercial composite. In this case, Derekane vinyl ester was used as matrix. In general, the stress-strain curves
appear non-linear from even very small strains. Overall, the curves show a remarkable similarity
to the stress-strain curves of ductile metals. Indeed, they even show a zero “work-hardening rate”
region above a strain of 1.7%, with failure at 2%. In these opaque RTM materials, it was impossible to determine the onset of cracking visually and the AE technique was used to monitor damage. AE activity is shown together with the stress-strain curves. AE activity begins at quite low
31Model material in course direction (90o)Crack density vs. AE event count rate

80

1.4
1.2

60

1

50

0.8

40
0.6

30

1/2s mm-1

AE event count rate

70

0.4

20

0.2

10
0

0
0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

Strain (%)

Fig. 10 AE event count rate vs. crack density loaded in course direction.
strains (approximately 0.2% to 0.4%). From strains of about 0.2% to 1%, the AE event countrises slowly, but a dramatic increase is observed from 1.0% strain. For this material, it was difficult to identify the different stages of damage as was demonstrated before. However, further microscopic analysis revealed that micro-debonding were as well presented in the crossover points
detected as pre-damage before significant matrix cracking and fiber fracture. Although matrix
cracking was difficult to observe, there were indications of such damage on the surface of thesample in the form of light-colored lines crossing the width of the coupon.

100

120

80

100
80

60

60
40

40

20

20

0

0
0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

AE event count rate

Stress (MPa)

RTM material in wale direction (0 o )2Strain (%)

Fig. 11 Stress-strain curve for 5 layers RTM material tested at 0o (Wale) direction.

32

100

120

80

100
80

60

60
40

40

20

20

0

0
0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

AE event count rate

Stress (MPa)

RTM material in course direction (90 o )2Strain (%)

Fig. 12 Stress-strain curve for 5 layers RTM material tested at 90o (Course) direction.
For these materials, identifying the limit of no significant damage was extremely difficult
even with the AE signal, since it increases gradually while the sample is loaded. Samples cut atdifferent angles from commercial RTM composite were tested in tensile loading by taking them
to a certain strain and, after reaching this point, they were unloaded and reloaded again to ahigher strain, and so on until fracture. The samples were tested at the same crosshead speed as in
the earlier work (0.5 mm/s) and stress, strain and AE data were recorded. Samples at 0° direction
were taken to 0.1% strain, unloaded, reloaded to 0.6%, unloaded, reloaded to 1.0%, unloaded,reloaded to 1.5% strain, unloaded and then reloaded to failure. Figure 13 shows the stress-strain
behavior for these cyclic tests, and Fig. 14 shows the stress-cumulative strain results. There are anumber of features to be noted, which were common to all the specimens cyclically loaded inthis way. Firstly, there is elastic behavior when the material is loaded to 0.1% and then unloaded. It is difficult to see this in Fig. 13 since the two lines superimpose. Although there iselasticity, the curves do not appear to be entirely linear even at this low strain. Secondly, loading
to 0.6% strain and unloading produces not only a small hysteresis loop, but also a residual strain(of 0.03%). It should be noted, for comparison, that a strain of 0.6% is above the strain, at which
the change in the Poisson’s ratio during the monotonic tests suggested as the strain for the onsetof matrix cracking. Thirdly, when reloading the sample to 1% strain, the hysteresis loop is‘closed’, in the sense that the stress passes through the previous peak stress value at a strain of
0.6% while the specimen is being loaded to a higher strain. Unloading from a strain of 1% produces a much larger residual strain (about 0.15%). Fourthly, when reloading the sample, now toa strain of 1.5%, the hysteresis loop is this time not ‘closed’. During this reloading, the stress at
1% strain is lower (by about 3 MPa) than the value on the previous cycle. Fifthly, when unloading and reloading to failure, the hysteresis loop is again not closed, and the stress reduction at
1.5% strain is now larger (about 4.5 MPa). Finally, for each successive reloading to higher
strains (above the initial strain of 0.1%) the hysteresis loops have a lower overall slope.
Figure 14 shows two acoustic phenomena, which are important in understanding the mechanism of internal failure. The first one is the Kaiser effect (Ke) in which it is necessary to reachthe same former stress value to go on whit the AE signal and it is observed at low strain values.

33

100

120

80

100
80

60

60
40

40

20

20

0

0
0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

AE event count rate

Stress (MPa)

Cyclic test of RTM material at 0 o

2Strain (%)
Fig. 13 Cyclic stress-strain curve for 5 layers RTM material at 0° (wale) direction.

Fig. 14 Cyclic stress-cumulative strain curve for 5 layers RTM material at 0° (wale) direction.
On the other hand, Felicity effect (Fe) is observed at higher strain when reloaded, and AE
signal is detected even when the last stress value has not been reached. At the same time, whenload is released AE signal is still detected. These effects suggest that at low strain values theknitted fabric reinforcement is receiving the load trough the matrix trying to behave elastically;
however at higher strains, the fibers are pulled out from the matrix sockets and when the load isreleased there is friction between the fiber and the matrix when trying to accommodate again in
the sockets. Therefore, it is possible to establish that at low strains where Kaiser effect is detected no damage is produced in the materials, and when micro-damage initiates (fiber/matrix
debonding), it originates low AE signal, which increases uniformly when matrix cracks are34

detected, at higher strain values then Felicity effect is sensed. Finally, load takes the maternal tofracture and fiber breakage is perceived by AE signal. For these materials, however, it is very
difficult to assure when the significant cracks initiate.
Of particular interest in this work is the relationship of the complex fiber architecture of theknitted fabric composite to the damage accumulation under load. For this reason AE technique
was used to find out the strain value where micro-damage initiates in the composites materials.
Obtaining the data, investigation was done at a macroscopic level by cutting and polishing several pieces in different planes in order to relate the damage initiation and propagation with thegeometry of the textile. The two principal material directions, wale and course, were investigatedsince they represent the two extremes in the orientation of the knitted fabric. Finally, analysis ofa course sample loaded to a strain of 0.7%, which is just below the onset of matrix cracking inthese specimens (which occurs at 0.8%) provided further evidence of early damage initiation in
the form of cracking at the loop crossover points (Fig. 15). This represent fiber/matrix debonding
interface detected by AE signal at low strain values (Fig. 5), which some researchers [4, 5] haveidentified as the first stage in the composite fracture mechanisms: interface debonding, matrix
cracking and finally fiber breakage.

Fig. 15 Evidence of crack initiation sites on crossover points.
4. Conclusions
The advantage of acoustic emission (AE) technique is shown as applied to composite materials in support of the analysis of damage development at lower strain to understand the cracking
process by identifying initiation sites, cracking progression and final fracture. By AE, various
failure mechanisms such as resin matrix cracking, fiber bundle debonding and tensile fracture offiber bundles were distinguished. Initial AE signal (event count rate) was attribute to the fiber
bundle debonding at the crossover initiation sites, followed by visual matrix cracking on thesample. Micro-cracks originating at the debonded fiber bundles propagated into the resin-richregions of the laminate and cracks coalesced until catastrophic fracture. Similar mechanism of
fracture were observed in both the model and the RTM materials that suggest the intimate effectof the knitted fabric architecture in the final mechanical behavior of the composite. The model
sandwich laminates enabled the damage behavior and failure mechanisms to be related to theorientation of the fabric. The observed pre-damage in the knitted fabric layer was identified for35the first time to be damage occurring at the loop cross-over points. The commercial RTM material was difficult to analyze due to the high fiber volume fraction, the tendency for the fabriclayers to be crushed out-of-plane during manufacture and the opaque nature of the Derekane
vinyl ester used as matrix. However, some features of the knitted fabric architecture were identified and related with the cracking damage. Finally, the influence of the knitted fabric architecture
in determining the damage development in the composites was also shown in the results.
Acknowledgements
The author wishes to thank to the Mexican Council for Science and Technology (CONACyT) that financed this research. In the same way, gratitude is expressed to Cooperative Research Centre for Advanced Composite Structures Ltd from Australia for providing the textiles
used in this work.
References
1. S. Ramakrishna, N.K. Cuong, H. Hamada. (1997), J. of Reinforced Plastics and Composites,
16 (10), 994-965
2. K.H. Leong, S. Ramakrishna, Z.M. Huang, G.A. Bilbo, (2000) Composites: Part A, 31, 197220
3. X. Ruan, T.W. Chou, (1998) J. of Composite Materials.32 (3), 199-221.
4. S. Ramakrishna, D. Hull (1994), Composites Science and Technology, 50, 237-247.
5. J. Bohse (2004), European Working Group on Acoustic Emission (EWGAE 2004), pp. 339348.

36

ACOUSTIC EMISSION DETECTION OF IMPACT DAMAGE ON SPACESHUTTLE STRUCTURESWILLIAM PROSSER1, ERIC MADARAS1, GEORGE STUDOR2,
and MICHAEL GORMAN3
1)
NASA Langley Research Center, MS 231, 3 E. Taylor St., Hampton, VA 23681, USA.
2)
NASA Johnson Space Center, Code ES2, 2101 NASA Road 1, Houston TX 77058, USA.
3)
Digital Wave Corporation, 11234A Caley Ave., Englewood, CO 80111, USA.
Abstract
The loss of the Space Shuttle Columbia as a result of impact damage from foam debris during ascent led NASA to develop and implement on-board impact detection technologies. AE
sensing, both with accelerometers and ultrasonic sensors, was utilized to monitor a wide varietyof impact conditions on Space Shuttle components ranging from insulating foam and ablator
materials, and ice at ascent velocities to simulated hypervelocity micrometeoroid and orbital debris impacts. Impact testing was performed on both reinforced carbon composite wing leadingedge materials as well as Shuttle tile materials on representative aluminum wing structures. Results of these impact tests are presented with a focus on the acoustic emission sensor responses tothese impact conditions. These tests demonstrated the potential for on-board Shuttle impact detection and provided a data base on which to analyze signals from sensors onboard the Shuttle
Discovery during the STS-114 Mission. On this flight, arrays of accelerometers mounted on thewing leading edge spar were monitored for potential impact damage. Preliminary results fromthis Shuttle Wing Leading Edge Impact Detection System (WLEIDS) are discussed.
Keywords: Impact detection, Space Shuttle, Thermal protection system, Reinforced carboncarbon
1. Introduction
Damage caused by impact of foam insulation shed from the external tank of the Space Shuttle shortly after launch was suspected as a leading candidate for the cause of the loss of the Space
Shuttle Columbia during reentry on February 1, 2003. As a result, an experimental test program
was initiated during the accident investigation to reproduce this impact event and determine theresulting damage to the thermal protective systems (TPS) on representative Shuttle wing structures. In addition to reproducing the impact and resulting damage that led to the accident, NASA
had the foresight to utilize these impact tests to develop and demonstrate acoustic sensor technology to detect impact damage on future Shuttle flights. Previous testing [1, 2] had alreadydemonstrated that such sensors might be used to detect and locate micrometeoroid and orbital
debris (MMOD) impact events on spacecraft. Although ascent debris damage was the focus of
the Columbia investigation, MMOD had also been identified as a significant potential danger toboth the Shuttle and the Space Station [3]. Both low frequency accelerometer and high frequency ultrasonic acoustic emission (AE) sensors were evaluated for this purpose during the accident investigation.
Testing during the investigation successfully validated the capability of these sensors for detecting major impact damage. However, additional testing was necessary to develop this sensing
J. Acoustic Emission, 23 (2005)37© 2005 Acoustic Emission Groupapproach for application to the remaining Shuttle fleet. These tests have included the determination of sensor response to a range of energies of foam impact events including those that are nearto and below the threshold of damage. Additionally, impact tests have been performed with anumber of other potential impact materials that can damage the Shuttle during ascent including
ice, ablator, and metal. Also, since it is desirable to have the impact sensing system not only detect ascent debris impacts, but also those of micrometeoroid and orbital debris (MMOD) during
orbit, testing has been performed to measure sensor response to hypervelocity impacts. In addition to impact testing on structural test articles, testing was performed on the Shuttle Endeavor to
study wave propagation effects and evaluate differences in structural configuration between Columbia test articles and the remaining Shuttle fleet. An overview of these test results is presented, along with preliminary results from sensors deployed on the Shuttle Discovery during theSTS-114 Mission.
2. Columbia Accident Investigation Foam Impact Testing
At the onset of the Columbia accident investigation, it was not known exactly where thefoam debris impacted the Shuttle wing. Video images showed that it struck on the lower surfaceof the left wing. However, the views and resolution available did not indicate whether it struck
the leading edge, which consists of reinforced carbon-carbon (RCC), or the lower wing surface,which has thermal protection consisting of tile. Thus, a variety of test specimens were fabricatedto investigate the damage caused by foam impacts on these structures. In addition, preliminary
testing to calibrate the foam impact gun performance as well as test instrumentation configuration was performed using aluminum plate targets. Accelerometers and acoustic emission sensors
were included on all of these tests and successfully detected the impacts in all cases.
As the investigation progressed, sensor data from Columbia and forensics of debris provided
indications that the damage had occurred on the leading edge, specifically on RCC panel 8. The
focus of the impact testing turned toward foam impacts on leading edge panels mounted on aleading edge support structure (LESS) as shown in Fig. 1. This test article consisted of a sectionof leading edge spar using the honeycomb structural configuration from Columbia, to whichleading edge panels 5-10 were attached. Because of the enormous expense and limited availability of RCC panels, initial testing was performed using fiberglass replicas of the leading edge
panels, with final testing performed on flight RCC panels. An array of 8 AE sensors (Digital
Wave Corp. model B225-5) was attached on the interior side of the spar. The bandwidth of thesetransducers was specified by the manufacturer to be 30 kHz to 300 kHz. However, responses
well below 10 kHz were measured. Initial testing with the sensors arrayed close to the impact
point demonstrated that signals of significant amplitude were produced and that these signals
propagated through the attach fittings into the spar. For later testing, the sensors were arrayedalong the length of the spar as shown in Fig. 1 to determine how well the signals propagatedalong the spar, and thus how remote the sensors could be located and detect the impact. As thefoam impacts and the attenuation of the complicated structure resulted in very low AE frequency
signal content, the AE data was acquired at a sampling frequency of only 500 kHz with a total of
32 K-points acquired for each sensor.
For the defining test of the investigation, a foam block weighing 758 g (1.67 lbs) was
launched at a velocity of 237 m/sec, striking panel 8 as indicated in Fig. 1. This impact produced
a significant hole in the RCC panel providing conclusive evidence for the Columbia Accident
Investigation Board in determining the cause of the accident [4]. The AE signals that were38

detected from this foam impact event are shown in Fig. 2. Only 6 dB of gain was applied to thesignals from the AE sensors. As would be expected, the largest signal, arriving earliest in time
was that from sensor 5, which was nearest the impact site. Quantitatively decreasing arrival times
and amplitudes of signals from sensors located further away from the impact point were observed. Although not noticeable in this figure as all signals are plotted on the same scale, signals
were detected all the way down to the location of sensor 1, suggesting that impact events can bedetected by sensors mounted several RCC panels away, a distance of more than 1 m. Examination of the arrival times for signals from sensors 7 and 8 showed that the impact site could be localized with respect to the upper and lower surface of the leading edge.

Lower Skin

RCC T-seals
RCC panels

109Direction of
Projectile
5
1

Forward864Upper Skin

Outboard

2

Leading
Edge Spar

8

5

7
3

6

7

Transition
Spar

LaRC UltrasonicSensors (8)Spar Fitting Locations

Fig. 1 Leading Edge Support Structure with RCC panels 5-10 and T-seals shown. The locationsof the AE sensors 1 through 8 are indicated with black stars.

Fig. 2 AE signals from foam impact on Shuttle RCC wing leading edge.
39

3. Return to Flight Testing
At the completion of the accident investigation, a number of questions remained regarding
the capability of acoustic impact sensing on the Shuttle. These included the detectability ofmuch smaller foam impacts including those near or below the threshold of damage, the characteristics of signals caused by other potential impact source materials including ice, ablator, andmetal at ascent velocities as well as hypervelocity impacts to simulate orbital impacts. These
effects needed to be assessed for impacts on both the leading edge as well as on the tile protected
lower wing surface including the main landing gear door. Another issue was that the construction of the wing spar on the remaining Shuttle fleet varied considerably from that of Columbia
and the effects of this difference on acoustic wave propagation had to be investigated. Thus, acomprehensive test program was initiated to address these questions. As it is impossible, as well
as expensive, to test all possible combinations of impact parameters, a simultaneous modeling
effort was initiated to develop capabilities to model impact events on Shuttle wing structures.
One key experimental piece of data required for these models was the measurement of the transfer function of the acoustic signals from the RCC leading edge to the spar where sensors are located. Additional experiments were preformed to acquire this critical data.
3.1 Launch Debris Impact Testing
Additional foam impact tests were preformed on RCC panels over a range of projectile sizes
and impact velocities. These impact tests were performed on different panels on the LESS test
article, as well as on the T-35 test article, which represented a more outboard section of the wing.
This test article allowed impact tests on panels 16 and 17 and further provided the opportunity toevaluate the effect of differing impact locations on measured signals. Signals from small projectiles and/or low impact velocities producing impact energies below the threshold of damage werestill readily detected. Variations in the signal amplitude correlated with the impact energy for agiven type of impact material. However, different impact materials such as foam and ice exhibited different amplitude- impact energy relationships. In addition to sensors on the spar, sensors
were also placed on the RCC panel of the T-35 test article to measure the transfer function response from the RCC panel to the spar. The frequency response plots in Fig. 3 show the significant loss in high frequency signal content that occurs as the signal propagates from the RCC tothe wing spar of Columbia construction. Preliminary testing on test articles with the wing spar
construction of the remaining Shuttle fleet suggests that this high frequency attenuation might
not be as severe.
Shot 2
Sensor 5 (RCC)
Amplitude (dB)

Amplitude (dB)-20

Shot 2
Sensor 8 (Wing Spar)

-20

-40
-60
-80

-40
-60
-80
-100

-100

-120

-120
0

20

40
60
Frequency (kHz)

80

100

0

20

40
60
Frequency (kHz)80

Fig. 3 Frequency content of foam impact signals for sensors on RCC panel and wing spar.
40

100

Foam impact tests were also performed on lower wing specimens representative of regions
on which the thermal protection material is tile. Specimens from this region of the wing also included a main landing gear door. Representative damage for a wing specimen impacted by foam
at approximately 290 m/sec is shown in Fig. 4 in which a hole formed by a tile that was broken
away by the impact can be observed. Signals were again readily detected by AE transducers for
all impact conditions studied. Although the signals were very complex due to the complicatednature of the source and the complex structural geometry of the tile and wing specimen, source
location could be determined using appropriate frequency filtering to selectively analyze theflexural mode of propagation.

Fig. 4 A wing acreage tile test article showing the resulting tile loss due to a foam impact.
Impact testing on RCC and tile specimens was also performed using other types of potential
launch debris. These materials included ice, ablator and metal. Again, the impact velocity andenergy was varied over a range from below the damage threshold to that causing substantial
damage. AE and accelerometer sensor data were obtained for all tests. Preliminary analysis
shows that all impacts were successfully detected with both accelerometers and AE sensors, andthat again there was a correlation between signal amplitude and energy of impact for a given impact material.
3.2 Hypervelocity Impact Testing
Hypervelocity impact tests were performed to simulate micrometeoroid and orbital debris(MMOD) damage that can occur once the Shuttle is in orbit. Initial tests were performed on flat
metal and fiberglass plates to develop a database to support modeling efforts as well as to determine appropriate instrumentation settings. Figure 5 shows typical damage resulting from two
hypervelocity impact events at 6.8 km/s in a fiberglass plate. The smaller impact was created by
a 2-mm diameter aluminum projectile while the larger was created by a 6-mm aluminum projectile, which fully penetrated the plate. Figure 6a shows the signals from a hypervelocity impact,
while for comparison, a lead break simulated AE signal near the impact site is shown in Fig. 6b.
Curiously, the flexural mode amplitude was generally smaller than the extensional mode, especially at the higher energy shots. This is interesting since low velocity impact usually produces alarge amplitude flexural mode due to the source motion perpendicular to the plate target. In thepresent case the attenuators played a role in filtering the low frequencies that generally confirm
the presence of a flexural wave. However, the source function for hypervelocity impact is quite abit different than ball drop at low velocity or a lead break. It is also interesting to note in41

comparing these signals that there was 64 dB of attenuation applied to the signals from the hypervelocity impact as compared to 47 dB of gain for the lead break signal. There is a tremendous amount of energy in the hypervelocity impacts. Figure 7 shows the raw signal amplitude,
after adjustment for the attenuation, from a series of hypervelocity impacts on a fiberglass plate
as a function of impact energy. As shown in this figure, the raw signal amplitude increases withcorresponding impact energy until it peaks at nearly 80 volts for an impact energy of nearly 100
J. In the fiberglass plates, for impacts exceeding 100 J, the projectile penetrates the plate and adecrease in AE signal amplitude was observed. However, for actual RCC leading edge specimens, a decrease in AE was not observed after impact penetration.

Fig. 5 Fiberglass panel showing damage from two hypervelocity impacts.

a) hypervelocity impactb) pencil-lead break
Fig. 6 AE signals produced by a) hypervelocity impact and b) pencil-lead break.
Propagation effects on AE signals from the impacted material through attachment mechanisms to likely sensor locations on the spar were also investigated. Initial testing for this consisted of multiple plates connected by threaded rods, followed by testing on a realistic Shuttle
wing spar test article. Again, because of the expense of RCC panels, testing included hypervelocity impacts on a number of fiberglass replicas of a leading edge panel, followed by shots onan actual RCC panel. These tests demonstrated that the much higher frequency hypervelocity
impact signals are much more heavily attenuated than was observed for the lower frequency
foam impact signals. Further analysis was performed to determine the transfer function from the
RCC to the spar where the sensors are located on the flight vehicle.
42

Fig. 7 AE signal amplitude versus impact energy.
3.3 Impact Hammer Testing
Impact hammer and pulsed pitch-catch ultrasonic measurements were made on the wing sparof the Shuttle Endeavor to investigate the effects on wave propagation due to differences in wingspar construction. As noted previously, the LESS and T-35 test articles represented the Columbia wing spar construction which is different from the remainder of the fleet. Transducers wereattached to the leading edge of the Shuttle’s wing, as indicated in Fig. 8. At various locations,ultrasonic signals between 10 to 150 kHz were introduced and recorded on the fixed transducers.
In addition, a series of low energy, instrumented hammer impacts (9.1, 27.2, 68.1, 113.5 kg) were
performed on the wing’s leading edge. Similar experiments were performed on the LESS and T35 test articles to develop a correlation between the different structures. Figure 9 shows theLangley Pitch-Catch Sensor and Impact Hammer Locations (7 locations - top)
Langley Acoustic Emission Sensors(8) – bottom only

1 X=835
2 X=891

Accessthrough
MLDG Wheel Well
3 X = 950
4 = Panel #1/2
5 = Panel #3/4
6 = Panel #5/6
7 = Panel #6/7
8 = Panel #7/8
9 = Panel #8/9
10 = Panel #9/10
11 = Panel #11/12
12 = Panel #12/13
13 = Panel #13/14
14 = Panel #14/15
15 = Panel #15/16
16 = Panel #16/17
16 = Panel #18/19

Fig. 8 Layout of transducer locations inside the Shuttle Endeavor’s wing.
43

LESS Ch. 4 (dB)Endeavor Ch. 4 (dB)120

Amplitude (dB)

100
80
60
40
20
0

50
100
Frequency (kHz)150

Fig. 9 Frequency response for 68.1-kg hammer impact on Shuttle Endeavor wing spar andLeading Edge Structural System (LESS) test article.
frequency response of AE sensors to a hammer impact on the Shuttle Endeavor wing spar as wellas on the LESS test article. Although the overall peak amplitudes of the time domain signal aresimilar, the frequency response shows that the peak amplitude is at a much lower frequency withmuch higher frequency attenuation for the LESS as compared to the Shuttle. These differences
are significant in that they indicate that higher frequency signal components may propagate from
impacts on the Shuttle to and along the spar. Such higher frequencies may enable improved signal to noise for detection as the background noise is expected to decrease with increasing frequencies. However, no database exists for measurements of the background noise for ultrasonicfrequencies on the Shuttle. A flight experiment to obtain this information is being planned toenable optimized sensor frequency selection for the next generation to the Shuttle impact sensing
system.
4. Shuttle Impact Detection Implementation
Although both accelerometers and ultrasonic AE sensors were demonstrated to be successful
at detecting impacts on Space Shuttle structures, accelerometers were chosen for the initial implementation of the WLEIDS because of the availability of existing flight qualified sensors andinstrumentation. Arrays of 66 accelerometers were deployed on each wing leading edge spar of
the Shuttle Discovery for the STS-114 Mission. The data from these sensors were recorded byarrays of 22 battery-powered data acquisition/wireless transmission units mounted in each wing
cavity. Each data acquisition unit recorded the output from three accelerometers as well as one
temperature sensor. The system recorded data from all sensors continuously during launch andascent to orbit, digitizing the signals at a sampling frequency of 20 kHz. Then, to conserve battery life, the system was switched into on-orbit monitoring mode during which smaller sets ofsensors were monitored to record the background noise level and any triggering MMOD impacts.
During this time, data was also transmitted wirelessly to a laptop computer in the crew compartment and then downlinked to Mission Control at the Johnson Space Center for analysis. Figure
10 shows the key components of the WLEIDS system.
44

Fig. 10 Key components of the Shuttle Wing Leading Edge Impact Detection System.
Because of limited data acquisition unit battery life and telemetry bandwidth, the complete
time history data from all sensors could not be transmitted to Mission Control for analysis during
the flight. Preprocessing routines in the data acquisition units calculated Grms (RMS value ofthe g-forces recorded by the sensors) values for the sensor units and then created summary filesof the largest Grms peaks. These summary files were then downlinked for preliminary analysis.
Peaks that occurred globally across the wing were discounted as impacts and most often correlated with mission specific events such as main engine ignition, solid rocket booster (SRB) ignition, maximum dynamic pressure, and tank and SRB separation. Local peaks were analyzed as
potential impacts by dowlinking and evaluating short intervals of the time history response formultiple sensors near a suspected impact location. Additionally, suspected impact events werecorrelated with other data sources such as video and radar recordings of the vehicle during
launch and ascent.
For the STS-114 Mission, the WLEIDS performed exceptionally well. All sensor data acquisition units successfully triggered at launch and data was recorded from all sensors. The summary files were successfully downlinked and led to the identification of only a small number ofprobable impact events. None of these probable impact events were of amplitude consistent withcritical damage to the RCC leading edge and in-flight inspection at the suspected impact locations using the Orbital Boom Sensing System did not reveal damage. The complete time history
data from all sensors was retrieved from the vehicle after the flight and is currently being analyzed. The focus of this post-flight analysis is to determine if any potential impacts were missed
during the analysis of the summary files during the Mission, and to develop and evaluate improved algorithms for impact signal identification during future flights.
45

5. Conclusion
AE sensors and accelerometers were used to monitor foam impact tests on Shuttle test articles as part of the Columbia accident investigation. These tests demonstrated that acoustic
sensing could be used to detect and locate impact events on the Shuttle wing leading edge. Follow-on testing has demonstrated this capability for a wide range of impact conditions on both theleading edge as well as the lower wing surface. These tests have included much smaller impact
energies at and below the threshold of damage, different impact materials, and hypervelocity impact conditions designed to simulate micrometeoroid and orbital debris damage. Additional
testing has analyzed the effects of different wing spar constructions on the propagation of impact
generated acoustic waves along the spar.
As a result of this testing, an initial impact sensing system was developed and successfully
deployed on the Shuttle Discovery on the STS-114 Mission. Accelerometers were used in thisWing Leading Edge Impact Detection System due to the availability of previously flightqualified sensors and wireless data acquisition units that could be easily integrated into the Shuttle wing spar. The system preformed as designed detecting only a small number of probable impact events that were of a magnitude small enough to have not caused damage. Post-flightanalysis of the complete data from the Mission is ongoing to develop improved impact detection
methodologies for future Shuttle flights.
References
1) W.H. Prosser, M.R. Gorman, and D.H. Humes, Journal of Acoustic Emission, 17(1-2),(1999), 29-36.
2) J. M. Nelson and B. M. Lempriere, "Space Station Integrated Wall Design and Penetration
Damage Control," Final Report for NASA Contract NAS8-36426, (1987).
3) GAO, "Space Program: Space Debris is a Potential Threat to Space Station and Shuttle,"
GAO Report # GAO/IMTEC-90-18. (1990).
4) Columbia Accident Investigation Board Report, (August, 2003).

46

MOMENT TENSORS OF IN-PLANE WAVES ANALYZED BY SIGMA-2D
1)

MASAYASU OHTSU1, KENTARO OHNO1 and MARVIN A. HAMSTAD2
Graduate School of Science and Technology, Kumamoto University, Kumamoto, Japan.
2)
Department of Engineering, University of Denver, Denver, CO 80208 USA.

Abstract
For quantitative waveform analysis of AE signals, SiGMA (Simplified Green’s functions for
Moment tensor Analysis) procedure has been developed. Kinematics on AE source, such as
crack location, crack type and crack orientation, can be determined from recorded AE waveforms. Since the SiGMA code was originally implemented for three-dimensional (3D) source
characterization, the applicability of SiGMA to AE signals detected in a plate-like sample wasstudied here. Employing a finite-element modeling code, theoretical AE waves of in-plane motion were computed. AE sources were modeled by assuming three types of moment tensors.
These tensor elements are recovered by two-dimensional SiGMA code (SiGMA-2D). Comparing results between assumed and recovered sources, the applicability of SiGMA-2D to in-plane
AE waves was confirmed.
As a practical application, AE waves due to water leakage from a pipe were analyzed. In
laboratory, water leakage from a slit-like defect was generated in a pipe model. It was found that
dominant cracking motions at the slit are associated with AE waves due to shear cracks, withorientations approximately parallel to the slit plane. For tensile cracks of AE sources, crackopening directions are perpendicular to the slit plane. These results show a great promise forclarifying AE generating mechanisms due to water leakage by SiGMA-2D analysis.
1. Introduction
Moment tensor analysis of acoustic emission (AE) waves can identify the kinematics of AE
sources, such as location, crack-type classification and crack orientation. Taking into accountonly the first motions of waveforms, an analytical procedure was implemented as the SiGMA(simplified Green’s functions for moment tensor analysis) procedure (Ohtsu, 1991). In threedimensional (3D) massive body of concrete, the applicability of SiGMA-3D has been confirmed(Ohtsu et al., 1998). In the case of plate-like samples, such as metals and composites, however,the SiGMA-3D procedure is difficult to use. This is because out-of-plane motions of AE waves
are basically taken into consideration in the SiGMA-3D analysis. For AE detection in a plate,
Lamb waves are predominantly observed as out-of-plane motions, but unfortunately they are notdue to the kinematics of AE sources.
Previously, we developed SiGMA-2D for a plate-like sample (Shigeishi and Ohtsu, 1999).
Taking into account in-plane motions of AE waves, crack kinematics of AE sources were determined in PMMA and mortar plates with a slit under compressive loading. To confirm the feasibility, results were compared visually with crack traces. One example of SiGMA-2D results in amortar plate is shown in Fig. 1. An arrow symbol shows a tensile crack with crack-opening orientation, and a cross symbol denotes a shear crack with two orientations of crack-motion andcrack-normal vectors. Reasonable agreement was observed between SiGMA results and
J. Acoustic Emission, 23 (2005)47© 2005 Acoustic Emission GroupFig. 1 Results of SiGMA-2D analysis in a mortar sample with a slit.
observed crack traces of the broken lines in Fig. 1. So far, the accuracy and applicability of
SiGMA-2D to other types have not yet been fully confirmed.
In order to synthesize AE waves in the plates, a dynamic finite-element modeling code has
been developed (Hamstad et al., 2001). Computing theoretical AE waves due to sources of different types, source identification was attempted by applying a wavelet transform (Hamstad etal., 2002).
In the work reported here, the accuracy of the SiGMA-2D code was examined with theoretical in-plane wave motions. By employing the finite-element modeling code with certain moment
tensor elements, theoretical AE waves with in-plane motions were synthesized. The moment
tensors were recovered by SiGMA-2D analysis of the waves. In addition, as one practical application, AE waves due to water leakage from a slit-like defect in a pipeline model were detected
and analyzed by SiGMA-2D.
2. SiGMA Analysis
2.1 Theory of Moment Tensor
As formulated in the generalized theory (Ohtsu and Ono, 1984), AE waves are elastic waves
generated by dynamic-crack (dislocation) motions inside a solid. By introducing moment tensor,
Mpq, the integral representation of the elastic wave uk(x,t) is represented,uk(x,t) = Gkp,q(x,y,t) Mpq*S(t).

(1)

Here, Gkp,q(x,y,t) are spatial derivatives of Green’s functions and S(t) represents the source kinetics (often called the source-time function). Inverse solutions of equation 1 are two-fold.
Source kinetics are determined from the source-time function S(t) by a deconvolution procedure.
Source kinematics are represented by the moment tensor, Mpq. In order to perform the deconvolution and to determine the moment tensor, the spatial derivatives of Green's functions or the
displacement fields of Green’s functions due to the equivalent force models are inherently48required. So far, numerical solutions of the displacement fields have been obtained by finite difference method (Enoki et al., 1986) and by FEM (Finite Element Method) (Hamstad et al.,1999). These solutions, however, need a vector processor for computation and are not readily
applicable to processing a large amount of AE waves. Consequently, based on the far-field termof the P-wave, a simplified procedure was developed (Ohtsu, 1991). This is suitable for a PCbased processor and is robust in computation. The procedure was implemented as the SiGMA(Simplified Green's functions for Moment tensor Analysis) code.
Mathematically, the moment tensor in equation 1 is defined by the tensor product of the elastic constants, the normal vector n to the crack surface and the crack-motion (dislocation or Burgers) vector l,
Mpq = Cpqkl lk nl V(2)

The elastic constants Cpqkl have a physical unit of [N/m2] and the crack volume V has a unit of[m3]. The moment tensor has the physical unit of a moment, [Nm]. This is the reason why thetensor Mpq was named the moment tensor. The moment tensor is a symmetric second-rank tensor and is comparable to the elastic stress in elasticity as,
 m11

[ M pq ] =  m12
m
 13

m12
m22
m23

m13 

m23  Vm33 

(3)

All elements of the moment tensor are illustrated in Fig. 2. In a similar manner to stress, diagonal elements represent normal components and off-diagonal elements are shown as tangential
or shear components.

Fig. 2 Elements of the moment tensor.
2.2 Equivalent Force Models
AE sources can be represented by equivalent force models, such as a monopole force, a dipole force and a couple force. Relations among crack (dislocation) models, equivalent force
models and moment tensors are straightforward. From equation 2, in an isotropic material we
have,
49

Mpq = (lk nk pq + lp nq + lq np).

(4)Here,  and  are Lamé constants.
In the case that a tensile crack occurs on a crack surface parallel to the x-y plane and opens in
the z-direction as shown in Fig. 3, the normal vector n = (0, 0, 1) and the crack vector l = (0, 0,1). Substituting these into equation 4, the moment tensor becomes,

 0
0 


[ M pq ] =  0 
0  V(5)
 0 0  + 2µ Only diagonal elements are obtained, which are shown in Fig. 3. Replacing these diagonal elements as dipole forces, three dipole-forces are illustrated in Fig. 3(c). This implies that combination of three dipoles is necessary and sufficient to model a tensile crack.

Fig. 3 (a) Tensile dislocation model, (b) related moment tensor elements, and (c) three-dipole
forces.
In Fig. 4, the case of a shear crack parallel to the x-y plane is shown with the normal vector n= (0, 0, 1). Shear motion occurs in the y-direction with the crack vector l = (0, 1, 0). Fromequation 4, we have,

0 0
[ M pq ] = 0 0
0 µ


0

µ  V0 (6)

As seen in Fig. 4(c), the double force couple model is comparable to off-diagonal elements of the
moment tensor in equation 6.
50

Fig. 4 (a) Shear dislocation model, (b) related moment tensor elements and (c) double-couple
forces.
2.3 SiGMA CodeTaking into account only P-wave motion of the far field (1/R term) of Green’s function in an
infinite space and considering the effect of reflection at the surface, the amplitude of the first
motion is derived from equation 1. The reflection coefficient Ref(t,r) is obtained as t is the direction of sensor sensitivity and r is the direction vector of distance R from the source to the observation point, and r = (r1, r2, r3). The time function is neglected in equation 1, and the amplitude of the first motion A(x) is represented,()

A x = Cs •

 m11

m12

 12
m
 13

m22

( ) • (r ,r ,r )  m

Ref t,r
R

1

2

3

m23

m13   r1  
m23   r2 
m33   r3 (7)where Cs is the calibration coefficient of the sensor sensitivity and material constants. Since themoment tensor is a symmetric tensor of 2nd rank, the number of independent elements is six.
These are represented in equation 7 as m11, m12, m13, m22, m23, and m33.
These can be determined from the observation of AE waves at more than six sensor locations. In SiGMA procedure, the two parameters, the arrival time (P1) and the amplitude of thefirst motion (P2), are visually determined from AE waveform as shown in Fig. 5. In the location
procedure, the source (crack) location y in equation 1 is determined from the arrival time differences ti between the observation xi and xi+1, solving equations,
Ri – Ri+1 = | xi – y | - | xi+1 – y | = vpti.

(8)Here vp is the velocity of P wave.
51

After solving equation 8, the reflection coefficient Ref(t,r), the distance R, and direction
vector r are readily obtained to solve equation 7. The amplitudes of the first motions P2 in Fig. 5
at more than 6 channels are substituted into equation 7, and all the elements of the moment tensor are determined. Since the SiGMA code requires only relative values of the moment tensor
elements, the relative coefficients Cs are sufficient.

Fig. 5 Detected AE waveform.

Fig. 6 Detection of AE waves of out-of-plane and in-plane motions.
2.4 Two-Dimensional Treatment
Special care must be taken for the case of a plate-like sample for two-dimensional (2-D)
problems. When cracks occur in a plate as shown in Fig. 6, wave motions detected are classified
into two types. One is in-plane motion, which can be detected at the edge of the plate. The otheris out-of-plane motion. AE waves are often detected as the out-of-plane motions even in the52

plate-like sample. In this case, the reflection coefficient in equation 7 is almost equal to zero,and thus the equation is neither well-posed nor solvable.
Consequently, only AE waves of the in-plane motion can be analyzed by SiGMA procedure.
Assuming that the z-components of vectors l and n are equal to zero, the moment tensor in equation 4 is represented by,0 
0  V lk nk   lk nk + 2l1n1 µ (l1n2 + l2 n1 )
[ M pq ] =  µ (l1n2 + l2 n1 )  lk nk + 2l2 n2

0
0


(9)

In the case that AE sensors are attached only on the edge of the plate, no motions are detected in
the out-of-plane direction. As a result, elements of the tensor in equation 9 could be estimatedexcept for the m33 element. Here, m11 + m22 = 2( + ) lk nk and,2( + ) = 2 + (1 – 2)/ = /.
Accordingly, the m33 component is readily determined from,
m33 = lk nk =  (m11 + m22)
where  is the Poisson’s ratio.

Fig. 7 Unified decomposition of eigenvalues of the moment tensor.

53

(10)

2.5 Unified Decomposition
In order to classify a crack into the tensile or shear type, a unified decomposition of the eigenvalues of the moment tensor was developed (Ohtsu, 1991). In general, crack motion on thecrack surface consists of slip motion (shear components) and crack-opening motion (tensile
components), as illustrated in Fig. 7. Thus, it is assumed that the eigenvalues of the moment tensor are the combination of those of a shear crack and those of a tensile crack, as the principal
axes are identical. Then, the eigenvalues are decomposed uniquely into those of a shear crack,
the deviatoric components of a tensile crack and the isotropic (hydrostatic mean) components ofa tensile crack. In Fig. 7, the ratio X represents the contribution of a shear crack. In that case,three eigenvalues of a shear crack become X, 0, –X. Setting the ratio of the maximum deviatoric
tensile component as Y and the isotropic tensile component as Z, three eigenvalues of a tensile
crack are denoted as Y + Z, –Y/2 + Z, and –Y/2 +Z. Eventually the decomposition leads to relations,
1.0 = X + Y + Z,
the intermediate eigenvalue/the maximum eigenvalue = 0 – Y/2 + Z,
the minimum eigenvalue/the maximum eigenvalue= –X – Y/2 + Z.

(11)

It should be pointed out that the ratio X becomes larger than 1.0 in the case that both the ratios Y
and Z are negative (Suaris and van Mier, 1995). The case happens only if the scalar product lknk
is negative, because the eigenvalues are determined from relative tensor components. Making
the scalar product positive and re-computing equation 11, the three ratios are reasonably determined. Hereinafter, the ratio X is called the shear ratio.
Classification of cracks was proposed elsewhere on the basis of angles between two vectors land n (Ouyang et al., 1992). But, the crack classification based on the angle is not reasonable,
because the relationship between the angle and the shear ratio X is nonlinear (Ohtsu, 1995).
In the present SiGMA code, AE sources with shear ratios less than 40%, are classified as tensile cracks. The sources with X > 60% are classified as shear cracks. In between 40% and 60%,
the cracks are referred to as mixed-mode.
From the eigenvalue analysis, three eigenvectors e1, e2, e3 are also obtained. Theoretically,
these are derived as,
e1 = l + n
e2 = l x n
e3 = l – n.

(12)

Here x denotes the vector product, and the vectors l and n are interchangeable. In the case of atensile crack, the vector l is parallel to the vector n. Thus, the vector e1 could give the directionof crack-opening, while the sum e1 + e3 and the difference e1 – e3 give the two vectors l and n
for a shear crack.
To locate AE sources, at least 5-channel system is necessary for 3-D analysis. Since 6channnel system is the minimum requirement for the moment tensor, 6-channel system is required for SiGMA-3D analysis. In the 2-D problem, a 3-channel system is applicable to determine the moment tensor elements, but generally a 4-channel system is necessary for 2-D location
analysis. Consequently, a 4-channel system is the minimum recommended for SiGMA-2D
analysis.
54

3. IN-PLANE THEORETICAL WAVEFORMS
3.1 Source Models
In order to study the accuracy of SiGMA-2D, in-plane theoretical waves are analyzed. These
were computed by employing the FEM-based AE signal database (Downs et al., 2003). The configuration of the detection is illustrated in Fig. 8. As a propagating medium, we selected aluminum with P-wave velocity of 6320 m/s and Poisson’s ratio of 0.34. The sources were located at
the coordinate origin at 1.41 mm depth. In-plane waves were calculated at five locations 180
mm from the source. The radiation angles were set to 12o, 22.5o, 45o, 67.5o and 78o. In-plane
displacements (waves) in both the x-direction and the y-direction were computed with 0.1-sincrement of 2000 words at each observation pointAt the origin, three moment tensors were considered as source models. One model consistsof three dipole forces, corresponding to the nucleation of a tensile crack. The crack surface isperpendicular to the x-axis and parallel to the y-axis. The second model was one dipole force
applied in the x-direction, and the third model was a shear crack with out-of-plane orientation.

Fig. 8 Five observation points for in-plane theoretical waveforms.
(1) Tensile crack
Configuration of the source is illustrated in Fig. 9. An associated dipole-force model is also
given. The moment tensor is represented by,

1
0
0 


[ M pq ] = 0 0.52
0 .
0
0
0.52 
(13)(2) One dipole-force model
The model is given in Fig. 10. The moment tensor is obtained as:

1 0 0 [ M pq ] = 0 0 0  .
0 0 0 



(14)

55

Fig. 9 Source model of in-plane tensile crack and corresponding moment tensor elements.

Fig. 10 Dipole-force model and corresponding moment tensor elements.
It is noted that no crack is equivalent to this model, because three dipole-forces are associatedwith a tensile crack and a shear crack is modeled by double couple-forces.
(3) Shear crack inclined 45o to x-axis.
A source model and the double-couple force model are illustrated in Fig. 11. The eigenvalues of the corresponding moment tensor are represented by, –1 0 0 [ M pq ] =  0 0 0  .
(15)
 0 0 1

56

Fig. 11 Source model of out-of-plane shear crack and corresponding moment tensor elements.
3.2 Results of SiGMA-2D Analysis
Examples of waveforms computed are shown in Fig. 12. Applying SiGMA-2D to the firstarrivals of these waves, moment tensor elements are recovered by equation 7. For the tensile
crack, tensor elements in equation 13 are recovered, 1–0.01 0 
[ M pq ] =  –0.01 0.25
0 
 0
0
0.43

Here, the tensor elements are normalized by the maximum value, and the element m33 is calculated by equation 10. It is noted that the elements m22 and m33 are recovered as smaller values
than those in equation 13.

(a) X-displacement at 45°; shear crack.
(b) Y-displacement at 45°; shear crack.
Fig. 12 Examples of theoretical waves by FEM analysis.
57

For the case of one dipole-force model in equation 14, results are obtained as, 1–0.02 0 
[ M pq ] =  –0.02 –0.06 0  .
 0
0
0 

Since the model does not correspond to a crack, the element m33 is not computed. Comparing
with equation 14, a good agreement is found.
In the case of the shear crack in equation 15, tensor elements are recovered as, –1 0.01 0 [ M pq ] = 0.01 0.26 0  .
 0
0
0 

Because this source corresponds to a shear crack, the scalar product lknk in equation 10 is equal
to zero. Accordingly, the element m33 is again not computed. As a result, agreement with equation 15 is not so clear that element m33 is not comparable to that in equation 15 and fairly large
error is observed in the element m22. This is because dominant motions are generated in the outof-plane direction due to shear motion of out-of-plane orientation, and no information from outof-plane motions was obtained.
These results imply that the elements of the moment tensors are recovered reasonably well by
SiGMA-2D in the case that such dominant source motions are generated in the in-plane direction
as one dipole-force model. For the general cases of cracking, crack types are classified correctly,
but the loss of information on out-of-plane motions can result in large errors.
4. LEAK DETECTION
4.1 Experiment
As one practical application, SiGMA-2D was applied to AE events due to water leakage. A
slit-like defect is modeled, through which water leakage occurs. A defect model of PMMA andAE sensor locations are shown in Fig. 13. The defect is a slit of 1 mm width and 5 mm length
located at the center of the 10-mm thick plate. AE events were detected by a 4-channel system,
which consists of AE sensors of 150 kHz resonance (R15, PAC) and the sampling frequency forrecording waveforms is 1 MHz.
The slit model was attached at the edge of a water chamber in a pipe model as shown in Fig.
14. By employing a large water chamber, 0.3 MPa pressure was constantly applied to the modelin laboratory. AE events due to leakage were detected. Detected AE waves for one event are
shown in Fig. 15. Although mostly continuous-type AE events were observed, in some cases thefirst arrivals could be determined as illustrated in the figure.
4.2 Analysis
During the leakage test, 109 AE events with detectable first arrivals were analyzed. The results are classified by the shear ratio X as plotted in Fig. 16. It is found that almost 40 % of theevents have the shear ratio of over 80 %. This implies that dominant source motions at the slitlike defect due to leakage are shear-type motions. This result might be associated with the fact58

Fig. 13 Slit model for a defect (dimensions in mm).

Fig. 14 Pipe model for water leakage (dimensions in mm).
that only large AE events were identified as the burst-type and readable for the analysis. That is,slip motions on the slit surface were mostly detected as AE events due to leakage.
Next, crack orientations were estimated. Typical events having the shear ratios smaller than
10% are plotted in Fig. 17. Because these are classified as the tensile cracks, crack-opening directions obtained are indicated. It is found the source opening directions are almost vertical to
the slit surface, suggesting that water flows due to leakage open the slit. Some events of the shear
ratios larger than 80 % are plotted in Fig. 18. It is clearly observed that orientations of the shear
events are parallel to the slit surface. This implies that the dominant source motions detected as
AE events are in-plane shear motions at the slit-like defect. This may result from dynamic water
outflow through the slit.
59

Fig. 15 Examples of detected AE waves.

Fig. 16 Results of SiGMA-2D analysis for water leakage from a slit-like defect.
5. CONCLUSION
The applicability of SiGAM-2D analysis to in-plane motions of AE waves was studied. Employing an FEM code, theoretical waves were computed by assuming moment tensor elements,
and the in-plane displacements were analyzed by SiGMA-2D. The accuracy of SiGMA-2D wasnumerically examined.
The elements of the source moment tensors are recovered reasonably well by SiGMA-2D insuch case that dominant source motions are generated in the in-plane direction as one dipoleforce model. For the general cases of cracking, loss of information on out-of-plane motions canresult in large errors, while crack types are classified correctly.
As a practical application, AE waves due to water leakage from a slit were detected andanalyzed. Dominant motions at the slit-like defect are found to be AE waves of shear type for60the large events, of which first arrival motions can be read. For the tensile events, crack-opening
directions were vertical to the slit surface, suggesting that water flows due to leakage open theslit. Orientations of the shear events are parallel to the slit surface. This implies that dominantmotions detected as AE events are in-plane shear motions at the slit-like defect, which may result
from dynamic water outflow through the slit. These results show promise for clarifying AE generating mechanisms due to leakage by the moment tensor analysis.
References
Downs, K. S., Hamstad, M. A. and O’Gallagher, A., (2003),”Wavelet Transform Signal Processing to Distinguish Different Acoustic Emission Sources,” Journal of AE, 21, 52-69.
Enoki, M., Kishi, T. and Kohara, S., (1986),"Determination of Micro-cracking Moment Tensorof Quasi-cleavage Facet by AE Source Characterization," Progress in Acoustic Emission III,
JSNDI, Tokyo, pp. 763-770.
Hamstad, M. A., O’Gallagher, A. and Gary, J., (1999), ”Modeling of Buried Monopole and Dipole Source of Acoustic Emission with a Finite Element Technique, Journal of AE, 17(3-4), 97110.
Hamstad, A. M., O’Gallagher, A. and Gary, J., (2001),”Effects of Lateral Plate Dimensions on
Acoustic Emission Signals from Dipole Sources,” Journal of AE, 19, 258-274.
Hamstad, A. M., O’Gallagher, A. and Gary, J., (2002),”A Wavelet Transform Applied to Acoustic Emission Signals: Part 1: Source Identification,” Journal of AE, 20, 39-61.
Ohtsu, M. and Ono, K., (1984), "A Generalized Theory of Acoustic Emission and Green's Functions in a Half Space," Journal of AE, 3(1), 124-133.
Ohtsu, M., (1991), ”Simplified Moment Tensor Analysis and Unified Decomposition of AE
Source: Application to in Situ Hydrofracturing Test,” J. Geophys. Res., 96(B4), 6211-6221.
Ohtsu, M., (1995), ”Acoustic Emission Theory for Moment Tensor Analysis,” Research in Nondestructive Evaluation, 6, 169-184.
Ohtsu, M., Okamoto, T. and Yuyama, S., (1998), ”Moment Tensor Analysis of Acoustic Emission for Cracking Mechanisms in Concrete,” ACI Structural Journal, 95(2), 87-95.
Ouyang, C., Landis, E. and Shah, S. P., (1992),”Damage Assessment in Concrete using Acoustic
Emission,” Nondestructive Testing of Concrete Elements and Structures, ASCE, pp. 13-24.
Shigeishi, M. and Ohtsu, M., (1999), ”Identification of AE Sources by using SiGMA-2D Moment Tensor Analysis,” Acoustic Emission: Standard and Technology Updated, ASTM STP
1353, pp. 175-188.
Suaris, W. and van Mier, J. G. M., (1995), ”Acoustic Emission Source Characterization in Concrete under Biaxial Loading,” Materials and Structures, No. 28, 1995, pp. 444-449.

61

Fig. 17 Crack orientations for tensile events.

62

Fig. 18 Crack orientations for shear events.

63

DEVELOPMENT OF AN OPTICAL MICRO AE SENSOR WITH AN
AUTOMATIC TUNING SYSTEMHIROSHI ASANUMA, HIRONOBU OHISHI, and HIROAKI NIITSUMA
Graduate of Environmental Studies, Tohoku University, Sendai 980-8579, Japan
Abstract
A prototype optical micro AE sensor, which has a mechanism for automatic tuning of the operating point, was fabricated using micro electromechanical systems (MEMS) technologies, andits performance was evaluated. The AE signal is detected by a interferometric pattern of returning lightwave from a Fabry-Perot interferometer (FPI) which consists of a half mirror at the edgeof a optical fiber and a full mirror on a moving mass. The mechanical part of the micro optical
AE sensor was designed to have a resonant frequency at 50 kHz. The linearity and sensitivity ofthis sensor is highly dependent on the initial gap length between the fiber and the moving mass
because of non-linearity in the interferometric pattern of the FPI. To control gap length, we fabricated electrodes on the cover glass plate. This allows the initial gap to be automatically tuned
by electrostatic force. Laboratory tests to evaluate performance of the AE sensor have demonstrated that the linearity, sensitivity and signal to noise ratio were improved by the automatictuning.
Keywords: AE sensor, MEMS, Optical sensor, Fabry-Perot interferometer, Automatic tuning
1. Introduction
Technologies related to micro-electromechanical systems (MEMS) have progressed remarkably over the last decade and commercial products, which use the MEMS technology, are
now widely available. Micro-sensors fabricated by the MEMS technology are one of the major
and promising application areas of this technology. Micro-mechanical sensors, micro-acoustic
and micro-seismic sensors, micro-medical sensors, and micro-optical sensors have been developed and their advantages over existing sensors have been demonstrated.
The authors have been developing microsensors mainly for geophysical/near-surface environmental measurement under the “Subsurface Microsensing Project” since 1993 [1]. Capacitive
accelerometer, fiber sensors, optical hydrophone and ultrasonic sensors are studied in the project,and some of the sensors have now become commercially available [2, 3].
One of the major research areas currently under investigation in the project is the field of optical acoustic/seismic/ultrasonic microsensors using the principles of the Fabry-Perot interferometer (FPI) [4, 5], because of the simplicity in the principle and the availability of MEMS technologies for fabrication. The FPI is a variation of interferometers, where a multiply reflected
lightwave at a gap of full and a half mirrors is outputted. The interferometric pattern (spectra) of
the output changes associated with gap length. We have employed this property by fabricating a
FPI which consists of a half-mirror at the fiber end and a full mirror on the moving
mass/diaphragm and have detected displacement of the moving mass/diaphragm. Although theprototypes of the optical microsensors with FPI worked successfully [4, 5], we have found that
J. Acoustic Emission, 23 (2005)

64© 2005 Acoustic Emission Groupthe precise control of the gap length presents a fabrication difficulty. In many cases, the authors
inserted an optical fiber with a half mirror to the silicon/glass structure at the final stage of thefabrication, and the gap length is defined manually at this stage. The typical gap length of the
FPI in the microsensors in our group is 20-40 m, and the variation in the gap length is not negligibly small for the commercial use of the sensors where a laser with a single wavelength should
be used to reduce the system cost.
Considering above mentioned problems, the authors had started to develop an ultrasonic/AE
sensor which can automatically control its operating point to have the best performance. The
concept, principles, fabrication process and results from laboratory tests are described in this paper.
2. Outline of the Autotuning Mechanism
The concept of the automatic tuning of the FPI sensor, which was employed in this study isshown in Fig. 1. The FPI consists of a half mirror at the end of the optical fiber and a full mirror
on a moving mass, which is suspended by thin silicon beams. The interferometric pattern of the
FPI changes with the reflectivity of the mirrors that is uniquely defined for each sensor, and thelength of the gap which changes by externally applied acceleration. The typical initial gap length
is around 20-40 m, and it is determined by the manual process of inserting the fiber into theglass/silicon/glass structure. In the micro-accelerometer with an automatic tuning mechanism,
an electrode is fabricated on a bottom glass plate, and an electrostatic force is generated by applying a voltage to it to change the length of the initial gap. The sensitivity and linearity of thesensor depends on the interferometric pattern of the FPI for a wavelength  and this system
automatically finds the optimum operating point by changing the gap length (see Fig. 2).

Fig. 1: A schematic view of the optical micro-accelerometer with an electrode to control the initial gap.
65

Fig. 2: Concept of changing the operating point of the FPI sensor.

Fig. 3: Block diagram of the sensing system.
A block diagram of the prototype system incorporating automatic tuning is shown in Fig. 3.
A wideband lightwave source (ASE: Amplified Spontaneous Emission) is used as an input of
the lightwave to the sensor. The interfered lightwave at the sensor is then transmitted to an optical narrow-band filter using the FBG (Fiber Bragg Grating) [6]. The intensity of the filtered
lightwave is converted to voltage at a photodiode (PD) and sent to both data display/acquisition
as an output and to a PC with system control software. To start the system, the PC sends a control signal to the DC-DC converter and the optical response is collected for different gap lengths.
The software finds the optimum operating point where the maximum sensitivity/linearity is obtained and then continuously applies a static voltage to hold this operating point.
66

3. Design and Fabrication of the Sensor
The mechanical part of this sensor is a resonant system in the first order. We have designed
the prototype to have a resonant frequency at 50 kHz. Considering the damping effect in thesensor, which is mainly influenced by air damping in the gap of glass/silicon, we can design asystem in the state of critical damping or over-damping for wideband performance. However,
we decided to design the system to have strong resonance, suppressing the damping factor, because there are some unknown points in the behavior of the air damping at higher frequency and
we preferred higher sensitivity rather than a flat frequency characteristic in this study.

Fig. 4: An external view of the sensor.
The external form of the sensor is shown in Fig. 4, where top, bottom and side views of thesensor are shown. The sensor has a “sandwich” structure of glass/silicon/glass and has a dimension of 5 mm x 5 mm x 2.2 mm. A moving mass with an aperture of 1.96 x 10-7 m2, is suspended
by 16 beams with a width of 20 m.
The MEMS process mainly consists of a combination of standard techniques including photolithography, etching, and sputtering. A part of the process of the silicon structure is shown in
Fig. 5. A technique of anodic bonding was used for the bonding of the silicon and glass parts.
Almost all the process, except for the sputtering of a half mirror on the end of the optical fiber,can be realized by conventional MEMS facilities. Because a number of microsensors can be fabricated on a single silicon wafer, which has a typical size of 4”/8” in the industry, cost reduction
and uniform performance of the sensors can be expected. A photo of the assembled sensor isshown in Fig. 6. The software for automatic tuning was developed using a commercial software
package (LabView).
4. Evaluation of System Performance
The interferometric pattern of the fabricated sensor at different applied voltages to the electrode for the control of the gap length is shown in Fig. 7. It is seen that the interferometric67

Fig. 5: A process chart of the silicon part.

Fig. 6: Fabricated sensor. Moving mass on silicon (left), sensors before dicing (center), and finished product (right).
pattern has moved approximately 6 nm after applying 200V to the electrode. This shift corresponds to a change in the gap length approximately 0.18 m. It is also seen that the interferometric pattern is distorted after applying 200V, suggesting that the silicon mass did not displace
uniformly relative to the electrode.
Because of difficulties in precisely evaluating the absolute sensitivity in the frequency rangeof several tens of kHz, we inputted a sinusoidal wave from a PZT transmitter to a thin (t = ~20mm) rock specimen and observed the output. Figure 8 shows the output from the optical sensor
and that from PZT receiver for an input of 50-kHz sinusoidal wave. In this experiment, theautomatic tuning was disabled as the objective was to prove that the optical sensor itself wasworking. It is seen from Fig. 8 that the transmitted signal is detected by both optical and68

Fig. 7: Interferometeric pattern for different voltage to the electrode.
reference detectors. We interpret the distortion that appeared in the trace to have resulted fromthe effects of transmission of the acoustic signal in the heterogeneous rock specimen and frompower instability of the optical source (ASE). Because our measurements recorded the sensitivity of the overall system, which is the sum of the sensor sensitivity and the sensitivity/gain of theprocessing circuit, it was not possible to determine the sensitivity of the sensor in isolation.
The waveform and power spectral density from the optical microsensor with and without the
automatic tuning for an input of 40 kHz sinusoidal wave propagated through rock specimen isshown in Fig. 9. It is seen that the detected signal is more stable after the automatic tuning. The
intensity of the spectra at 40 kHz is increased approximately 6 dB after the shift of the operating
point by the automatic tuning. The signal to noise ratio around 40 kHz is approximately 50 dB,
although higher contamination by noise, mainly from the optical source and photodiode, is seen
in lower frequency.
5. Conclusions
This paper describes the principles, design, fabrication and evaluation of a micro-optical
AE/ultrasonic sensor with a mechanism for automatic tuning. It has been demonstrated that the
prototype of the sensing system successfully worked at the optimum operating point where the
FPI had the best sensitivity and linearity.
The automatic tuning system brings additional merits to the improvement of sensitivity/linearity. The shift of operating point of the FPI allows the use of a laser working at a single
wavelength, bringing a further cost reduction of the system since a tunable laser is now not required. If a wideband optical source is used such as the ASE in this study, an array of sensors
could be realized because the interfered light wave could be multiplexed on one fiber.
There some points still to be investigated/improved in this system before commercialization.
One is the improvement of the dynamic range. A system to compensate for the noise in69troduced by instability of the ASE will effectively work to suppress noise under 10 kHz. The useof an optical amplifier in the input line or a low-noise OE will further improve SNR around theoperating frequency of the sensor. In the prototype we applied a maximum voltage of 200V, but
this was insufficient to move the mass in a wide range. Further optimization of the design of themechanical parts consisting of the moving mass, suspending beams and electrodes could improve the performance of the automatic tuning system.

Fig. 8: Outputs from optical and reference sensors for input of 50 kHz sinusoidal waves.
Acknowledgments
Most of the fabrication process in this study was done using facilities in the Venture Business Laboratory, Tohoku University. The authors also wish to acknowledge Prof. M. Esashi,
70

Fig. 9: Trace and power spectral density from the optical micro-sensor without/with the automatic tuning for an input of 40-kHz sinusoidal waves.
NiCHE, Tohoku University, Dr. M. Nishizawa, MEMS CORE, S. Takashima, formerly of
Graduate School of Environmental Studies (currently working in OKI Electronics), and Dr. P.
Chopra, ANU, Australia for their comments, suggestions and encouragements for this study.
References[1] H. Niitsuma: Ultrasonic Technology, 8 (2000), 4.
[2] H. Asanuma, M. Nishizawa, G. Suzuki, Y. Yoshida, H. Niitsuma and M. Esashi: Proc.
ASME InterPACK01, (2001)(CDROM)[3] S. Takashima, H. Asanuma and H. Niitsuma: “A water flowmeter using dual fiber Bragg
grating sensors and cross-correlation technique”, Sensors and Actuators A: Physical, 116,
66-74 (2004).
[4] M. Shiga, C. Suzuki, M. Yamada, M., Kitahara, M., and H. Niitsuma: Progress in Acoustic
Emission XI, (2002), JSNDI, Tokyo, pp. 152-159.
[5] H. Asanuma, S. Hashimoto, G. Suzuki, H. Niitsuma, and M. Esashi: SEG 2001 Expanded
Abstracts, (2001), 45-47.
[6] K. O. Hill, Y., Fujii, D. C. Johnson, and B. S. Kawasaki: Appl. Phys. Lett., 32, (1978), 647649.
71

DEVELOPMENT OF STABILIZED AND HIGH SENSITIVE OPTICAL FIBER ACOUSTIC EMISSION SYSTEM AND ITS APPLICATIONHIDEO CHO, RYOUHEI ARAI and MIKIO TAKEMOTO
Faculty of Mechanical Engineering, Aoyama Gakuin University,
5-10-1 Fuchinobe, Sagamihara, Kanagawa, 228-8558 JAPAN
Abstract
We developed a novel fiber-optic acoustic emission (AE) monitoring system and applied thisto gas-leak detection. The system is a Mach-Zehnder, homodyne-type optical fiber laserinterferometer with a phase compensation feedback circuit. The phase compensation was given
by a piezo-actuator, which can maintain the system at being quadurature condition. The
system was demonstrated to measure the in-plane motion of elastic waves. The system can also
detect Ao-mode Lamb waves generated by pulse YAG laser and cylindrical waves produced bylead breaking on a 2-inch steel pipe. We also detected continuous cylindrical waves from gas
leakage through a hole of 0.3-mm diameter in a 4-inch steel pipe. RMS voltage of the AE
signals, monitored by the sensor fiber wound around the pipe, was found to increase with gaspressure
Keywords: Optical AE sensor, Gas leak detection, Feedback control, Mach-Zehnder type interferometer
1. Introduction
Optical-fiber AE sensor is an attractive alternate AE sensor and can offer a number of advantages such as the long-range monitoring of large structures as pipelines, tanks and bridges.
This is possible due to the low optical loss. Fiber sensor can be set on the structure surface in arbitrary shapes due to its flexibility. AE system with fiber sensor is expected to be free from electro-magnetic noise, and can be used for high temperature structure under corrosive environment.
Utilization of optical fiber sensors in ignitable environment is most attractive. Low cost andlightweight are another advantage.
A number of optical sensors have been developed. Most of these were designed to detect the
low frequency vibration as a dynamic strain meter. Some optical sensors designed to detect ultrasonic waves have been reported. For instance, Pierce et al. [1] reported that the ultrasonicwaves generated by PZT transmitter in FRP could be detected by the optical interferometer using
a single-mode optical fiber. Tsuda et al. [2] developed a novel optical AE system utilizing a FBG(Fiber Bragg Grating) fiber. They measured large amplitude steady ultrasonic waves repeatedly
generated by a PZT transmitter, but could not detect transient elastic waves. Kageyama [3] developed an optical fiber system utilizing the Doppler effect, and reported that the sensitivity of
sensor is the same level as the resonant-type AE sensor. One of the authors [4] developed a homodyne Mach-Zehnder-type optical sensor and detected Lamb wave AE signals. Stability of thedeveloped system is, however, poor due to large drift by temperature changes.
Objective of this study is to develop a highly stable optical fiber AE sensor. We developed asystem with feedback control circuit with a PZT actuator and applied the system to detect thecylindrical wave AE signals from gas leak in pipe.
J. Acoustic Emission, 23 (2005)72© 2005 Acoustic Emission Group2. Optical Fiber AE Sensor System
A new optical AE system developed in house is schematically shown in Fig. 1. The system iscomposed of the sensing and feedback control section. The sensing section is a homodyne
Mach-Zehnder type interferometer.
We used a laser-diode (LD) (Mitsubishi
Electronic, FU-427SLD) with 1310 nm wavelength as a light source. Transmitted laser beamfrom the LD was split into two optical fibers with a 3-dB 1x2 coupler. One is the fiber as thesensor (object) and the other is the fiber for reference beam. We used a communication-grade
single-mode fiber covered with PVC. The part of the sensor fiber was attached on the sample
surface by an appropriate method. Output of the sensor fiber modulated by AE signals wasinterfered with the laser of the reference beam by coupling at another 2x2 coupler. Interferedlaser beam was split into two beams again and then detected separately by two photodiodes(Thorlabs, PDA400). Here the two lasers were at 180˚ out of phase. Then, two signals
were combined with a difference amplifier to improve the signal-to-noise ratio. The output of thedifference amplifier was digitized by a digital oscilloscope via a band-pass filter and fed to apersonal computer.

Fig. 1 Improved optical fiber AE monitoring system with a feedback control system using PZTactuator.
The system does not need any vibration control stage and optical components alignment
system, which are inevitable for an in-air laser interferometer, since the laser is transmitted in the
optical fiber from the LD to the photodiode detector. The system, however, tends to be sensitive
to low-frequency drifts by temperature change and environmental noise. We improved the system by adding an automatic low-frequency feedback control circuit. A phase difference between
the sensor and reference lasers was also needed to keep the system at quadurature condition. Thismeans that the phase difference between two lasers must be /2. For this part, we developed a
feedback system, which uses a PZT actuator as a phase shifter. The reference fiber was glued
firmly on the rectangular PZT actuator of 20 mm length x 3 mm width x 2 mm thickness (NECTOKEN, AE0203D08) so that the fiber is in the actuation direction. Feedback control wasachieved utilizing small changes of the reference fiber length by stretching and shortening the73

PZT actuator by an error signal. Error signals must correspond to the low frequency drifting.
Thus this signal was produced by integrating the output with an operational amplifier, to which
the low-frequency component, extracted by a low-pass filter with cut-off frequency of 400 Hz,
was fed. Figure 2 compares outputs of the system with and without a feedback control circuit.
Large oscillation of the system without feedback control was eliminated by adapting the feedback control circuit. The system with the feedback control can keep the system at quadurature
condition.

Fig. 2 Comparison of system outputs with (b) and without (a) a feedback control.

Fig. 3 Experimental setup for evaluating performance of the optical AE system.
3. Characteristic of Developed System
In order to study which vibration mode can be detected at what frequency by the improved
system, we detected laser-induced Lamb waves using the experimental setup of Fig. 3. We generated the Lamb waves on a thin Al plate of 0.5-mm thickness by a line focused Q-switched
YAG laser beam of 8-mm length. The Lamb waves were detected by the sensing fiber set on theplate 100 to 250 mm from the source. The sensing part of the fiber was glued by epoxy resin sothat it is parallel to the line laser. We also detected the out-of-plane displacement of Lamb waves
at the opposite side of sensing fiber by a commercial heterodyne-type laser interferometer.
Figure 4 compares the waveforms detected by the optical fiber system (a) and by the commercial interferometer (b). These waves were different in their frequency range. The optical74

Fig. 4 Detected waves by the developed system (a)and commercial laser interferometer (b).

Fig. 6 Overlapping of the first portion of Lambwaves in Fig. 4.

Fig. 5 Group velocities of Ao mode Lambwaves measured by the developed system andcommercial laser interferometer.

fiber system detected Lamb waves with low-frequency components since a 20 – 150 kHz bandpass filter was used. Both waves, however, clearly show the characteristics of Ao-mode (fundamental mode of anti-symmetric) Lamb waves. Shown in Fig. 5 are the overlaps of experimentalgroup velocity dispersion curves on the theoretical one. Group velocities by the optical fiber
system (open circles) agree well with that by the commercial interferometer and also theoreticalone. First portion of the waves in Fig. 4 was superposed in Fig. 6. It is noted that phase of the
waves detected by the developed system was in /2 out of that by the commercial interferometer.
This can be explained by the in-plane and out-of-plane displacement of the Ao-mode waves.
Figure 7 shows theoretical distribution of in-plane and out-of-plane displacement of the Aomode at 100 kHz across the plate. The out-of-plane displacement is positive or the same in phaseand constants over the thickness. In contrast, the phase of in-plane displacement at upper andlower surfaces was opposite or at /2 difference. This strongly implies that the developed fiber
system detects the in-plane motion of elastic waves.

75

Fig. 7 Comparison of in-plane and out-ofplane displacement of Lamb Ao modethrough plate wall at f = 100 kHz.

Fig. 8 A method for studying the directivity ofline optical fiber sensor’s sensitivity.

We next measured the directivity of sensor’s sensitivity using the setup of Fig. 8. Sensing fiber was glued on the Al plate so as its axis (longitudinal direction) be parallel to the line laser.
The line laser moved along a circle of 150 mm radius. We measured the Lamb waves as a function of angle . Here the angle  = 0 implies that the fiber be parallel to the line laser. It is also
noted that the line laser produces a plane Lamb wave, which propagates in the normal directionof the line laser. Figure 9 compares the waveforms at  = 0 (a) and  = 90˚ (b). The waves (a)agree well with the predicted waveform of Ao-mode Lamb waves. Contrary to this, the waves(b) are deformed and show low frequency components. Distribution of the maximum amplitudeat 30, 50 and 80 kHz, obtained by wavelet transform, was shown in Fig. 10. The amplitude wasnormalized by the maximum amplitude at each frequency. There observed no clear directivity ofthe sensor sensitivity. However, the waveform significantly changes depending on the angle.
This is supposed to be due to the integration of the waves along the axis of the sensor fiber.

Fig. 9 Comparison of Lamb waves detected by optical sensor fiber parallel (a) and perpendicular(b) to the source laser line.
4. Application to Detection of the Gas Leakage of Pipe
Detection of gas leak was attempted. Various sensing methods have been proposed [5] so far,but these cannot be applied to buried pipes. Fiber sensor can be a useful sensor to detect the gas
76

Fig. 10 Sensitivity distribution of line-shaped optical fiber sensor.

Fig. 11 Experimental setup for detecting transient cylindrical waves due to lead breakage on a 2inch pipe.

Fig. 12 Comparison of waves detected by the optical fiber system developed here (a) and thecommercial system using a piezoelectric AE sensor (b).
77

Fig. 13 Contour map of wavelet coefficients for the waves shown in Fig. 12(a).
leakage of long distance pipelines and buried pipes. We first detected the cylindrical waves of a1000 mm long 2-inch steel pipe (STPG 38, 2B x Sch. 80). As shown in Fig. 11, the cylindricalwaves were generated by breaking a pencil-lead and detected by the sensor fiber wound aroundthe pipe. The fiber was wound three turns at 300 or 500 mm from the source and fastened byrubber sheet and steel band. For comparison, a small PZT type AE sensor (PAC, PICO) was
mounted near the fiber. Figure 12 compares the waveforms detected by the developed system
and PZT sensor. The waveform by the developed system was much different from that by PZT
sensor due to their different frequency characteristic. The fiber system, however, detected thelower frequency component of the cylindrical waves. Figure 13 shows the wavelet contour mapof the waves of Fig. 12(a) and theoretical group-velocity dispersion of L(0,1) mode. Strong
wave components (dark part) coincide with the dispersion of L(0,1) mode in lower frequencies.
It was found that the breathing-type cylindrical waves can be effectively detected by the woundfibers due to the high sensitivity of the fiber to in-plane motion.
We next monitored the cylindrical wave AE signals from gas leak through the pipe wall using
the system shown in Fig. 14. Here argon gas from 0 to 0.6 MPa was leaked through a small holeof 0.3-mm diameter. Sensing fiber was wound one turn around the pipe at 1200 mm from thehole. Figures 15 shows the AE signals from the gas leak. Gas pressure above 0.2 MPa produced
continuous AE waves. Frequency of the AE increases with gas pressure. Root mean square voltages (RMS) of the waves are shown in Fig. 16 as a function of gas pressure. The RMS at 0.2
MPa was 2.7 mV and 4 times larger that at 0 MPa. Here the RMS at zero pressure (pressure 0)means the noise level. This demonstrates that the developed optical AE sensor can clearly detect
AE signals from gas leakage. The RMS also increased with gas pressure. Decrease of the RMS at
0.6 MPa appears to be due to the limited frequency band of the system (20-150 kHz).

78

Fig. 14 Experimental setup for detecting gas leakage through 0.3-mm diameter hole in a 4-inch
steel pipe.

Fig. 15 Detected AE waves due to gas leakage through 0.3-mm diameter hole on the 4-inch pipewall.
6. Conclusion
We developed an optical fiber AE system with a phase-compensation feedback control circuit
and applied the system to AE monitoring from gas leakage. Results are summarized below:(1) The feedback control circuit developed in house makes the system at quadrature conditionand provide with a high stability and robustness for low frequency drifting.
(2) We detected the laser-induced Lamb was by the developed system and found that the developed system sensitive to the in-plane motion of the elastic waves. The system can detect79

Fig. 16 Root mean square voltage of AE waves as a function of internal gas pressure.
the weak and transient AE produced by pencil-lead break(3) The system can detect weak and continuous type L-mode cylindrical wave AE signals fromthe 0.2 MPa gas leakage through the pipe wall. Both the RMS and frequency of cylindricalwaves increased with gas pressure.
Acknowledgment
This research was conducted as part of the High-Technology Research Center Program andthe Center of Excellence (COE) Program, funded by the Ministry of Education, Culture, Sports,
Science and Technology of Japan.
References[1] S. G. Pierce, W. R. Philp, A. Gachagan, A. McNab, G. Hayward and B. Culshaw: Applied
Optics, 35(25), 5191-5197, (1996).
[2] H. Tsuda: J. Japan. Soc. NDI, 52(10), 539-542, (2003). (in Japanese)[3] K. Kageyama, I. Ohsawa, M. Kanai, Y. Tanma, T. Nakanishi, M. Shinomura, K. Nagata and
F. Matsumura: Structural Health Monitoring and Intelligent Infrastructure, Z. S. Wu and M.
Abe (eds) pp. 285-290, (2003).
[4] T. Matsuo and M. Takemoto: Prog. Acoustic Emission XI, Tokushima, JSNDI, pp. 169-175(2002).
[5] For instance, Y. Morishita, H. Mochizuki, K. Watanabe, T. Nakamura, T. Nakajima and T.
Yamauchi: Journal of Nuclear Science and Technology, 32(3), 237-244, (1995).

80

HIGH PRECISION GEOPHONE CALIBRATIONMASAHIRO KAMATA
Schlumberger K.K., 2-2-1 Fuchinobe, Sagamihara, Kanagawa 229-0006 Japan
Abstract
Geophones are commonly used in seismic signal detection and are widely sold with response
parameters with specified tolerances. High precision seismic analysis can extract subtle information from seismic records, but requires knowledge of the in-situ response parameters of thesensors. The geophone response is specified at room temperature; however, geophone responsechanges with the orientation with respect to gravity, temperature and aging. The changes due totemperature and tilt may exceed the tolerance limits, so there is a need to perform in-situ calibration to know the exact response. In the impedance calibration method, the impedance of a geophone can be measured by injecting current. From the impedance, it is possible to describe all of
the response parameters except for the moving mass. The dynamic method is also known for
calibration of a vibration sensor relative to a reference sensor. The reference sensor can be fabricated with a pre-measured moving-coil mass so that all the geophone parameters can be determined. Alternatively the reference sensor can be calibrated by using a reciprocity method making use of three vibration sensors. We have developed unique geophones that output signals
proportional to acceleration. Complete calibration schemes have been established, and all thegeophones are fully calibrated during the manufacturing process. A method has been describedto re-evaluate the geophone’s moving mass to make in-situ calibration in the working environment with the impedance method.
Keywords: Geophone, Seismic sensors, Moving-coil accelerometer, Calibration, Impedance
method
1. Introduction
A geophone consists of a pair of moving coils suspended in a magnetic field by means of apair of springs as shown in Fig. 1. The spring is usually pre-stressed to compensate the natural
displacement of the coil due to gravitational force so that the coil is centered in the magnetic
field.
The output of a geophone is usually terminated by a shunt resistance R as shown in Fig. 2 toprovide external damping. The current flowing in the moving coil reduces coil motion. The
amount of shunt resistance is chosen so that the total damping factor is 70%.
The equation of motion for the moving coil relative to the magnetic flux for given external
displacement u may be written as;
d 2
d
d 2u
m 2 +c+ k = m 2  Bli
(1)
dt
dt
dt
where  : coil displacement; k: spring constant; m: moving mass of coil; c: friction coefficient; g:gravitational acceleration; B: magnetic flux density; l: length of coil wire; i: current; u: external
displacement.
J. Acoustic Emission, 23 (2005)81© 2005 Acoustic Emission GroupFig. 2 Damping control.
Fig. 1 Structure of a geophone.

 is the relative position of the moving coil inside the geophone. The first term in the left
hand side of equation (1) is the inertial force, the second term is the friction force proportional tothe velocity, and the last term is the spring force. The summation of the three forces balanceswith the force due to external displacement and the damping force caused by the electric current.
The electric signal generated by the moving coil is expressed in terms of magnetic flux density, length of coil wire and velocity of the coil as;de g = Bl(2)dt
For a given external displacement, u = a sin(t ) , the response may be expressed as;
e g = A sin(t   )(3)

The amplitude response and phase may be found from equations (1), (2) and (3) as;

A=a
S 0  0 
1   02

2tan ( ) =

and

2

 

 +  2
0
 

2

2


0

 
1  
 0(4, 5)

2

where

0 =

k
,
m

2

S0
,
 =0 +2(r + R )m 0

0 =

c
, and
2m 0

S 0 = Bl .

The geophone parameters are the natural frequency f0, the open circuit sensitivity S0, the open
circuit damping, the coil resistance r, and the moving mass m. The response of a geophone isdetermined with four geophone parameters and the shunt resistance R.
For  > 0, equation (3) may be approximated as e g = S 0 (a ) cos(t ) . The sensitivity is
proportional to the velocity (a) of the vibration at above the natural frequency. Figure 3 shows82Fig. 3 Geophone response.
the amplitude response eg/S0(a  ) and phase response of a geophone with 10 Hz natural frequency and 0.7 damping.
2. Geophone Response in Measurement Environment
Coil resistance is almost double at 200˚C and open circuit damping is reduced by 20% asshown in Figs. 4 and 5. Natural frequency f0, and the open circuit sensitivity S0 change by a few
percent each. (Figs. 6 and 7)
The geophone parameters are measured when the geophone is positioned vertically. In practice, geophones are planted by stamping into the ground by foot, and so are not necessarily vertical. In borehole seismic acquisition, a downhole tool with geophones is deployed in a boreholethat may not be vertical. Figures 8, 9 and 10 show measured results of a geophone under tilt.
There is a few percentage points change observed at ±30˚ tilt.

Fig. 5 Natural frequency change.

Fig. 4 Coil resistance change.

83Fig. 7 Sensitivity change.

Fig. 6 Open circuit damping change.

Fig. 9 Open circuit damping shift by tilt.

Fig. 8 Natural frequency shift by tilt.

Fig. 10 Open circuit sensitivity shift by tilt.
3. Geophone Accelerometer
We have developed a unique geophone that responds to acceleration between 1/10 x f0 and
10 x f0. A light moving coil is suspended in a strong magnetic flux density.
The imaginary short circuit of an operational amplifier applies a large damping current sothat the geophone responds to acceleration at near its natural frequency. See the schematic diagram in Fig. 11. For  = 0, equation (3) may be approximated asS(6)
e g = (a 2 ) 0 sin(t ) .
2 0
Equation (6) shows that a response is in proportion to acceleration. In general, equation (3)is rewritten as acceleration form (7);84Fig. 11 Imagenary short circuit.
Fig. 12 GAC response.

e g = ( a 2 )S0  
 0   0 
2

   
1  
 +  2 0    0 
2sin(t   ) .

(7)2

When the damping factor is large, the damping term in equation (7) becomes dominant andthe frequency range becomes wider. The natural frequency is chosen to be in the middle of theseismic band at about 20 Hz. For this natural frequency, the natural displacement of the coil due
to gravity is not large and the GAC design is omni-tiltable. (See Fig. 12.)The GAC was originally introduced for borehole seismic acquisition to obsolete the need fora gimbal-mount mechanism. A second generation GAC sensor is now bring used in seabed andland seismic recording.
4. Geophone Calibration Method
4.1 Impedance Method
A current runs into the moving coil that is suspended in a magnetic flux B. The force acting
to the moving coil is Bli, where B is the magnetic flux density, l is the effective wire length of themoving coil and i is the current. The equation of motion for the moving coil may be written as;

m

d 2
d
+c
+ k = Bli
2
dt
dt

(8)

Since the output signal is (see Fig. 13);e0 = e g + ri

(9)

85

Fig. 13 Impedance method.

Fig. 14. Geophone impedance circle.

Equation (8) may be rewritten in terms of output signal as;2
 d 2iS 0 did 2 e0
de0
di
2
2 


+ 2 0 0+  0 e0 =  r  2 + 2 0 0 +  0 i  
m dt
dt
dt
dt 2
 dt

(10)

The impedance may be found from the Laplace transformation of equation (10) as


j 
S
 0
Z ( j ) = r + 0
m 0    2 
1  
 + 2 j 0 
 0 
 0(11)

The real and imaginary parts of the impedance equation show a circle on a complex plane asshown in Fig. 14. At  = 0 and  = 0, the imaginary part is zero. At  = 0, the real part is the
DC resistance of the coil.
The absolute value of equation (11) yields an expression for the impedance circle as;22(12)
a 2 = {Re(Z ) c} + {Im(Z )}The impedance is calculated by the output signal e0 and the current injected in a geophone as;e(13)
Z = 0i
Measurement of impedance at two frequencies, 1 and 2 gives four equations by considering the real and imaginary parts of the impedance.

86

Re(Z 1 ) = r +Im(Z 1 ) = 
11  

 0

2

2

 
1
 +  2 0
0
 

2
S 0   1
1  
m 0
 0

 
1
1  
 0

2

2

 
1
 +  2 0
0
 

2

 
2
1  

 0

2

 
2
1  
 0

2

(15)

2

2

2

 
2
 +  2 0
0
 

2
S0    2
1  
m 0
 0

2

(14)

2

 
1


 0

2

S
2 0  0  2m 0   0

Re(Z 2 ) = r +Im(Z 2 ) =

2

2

S
2 0  0  1m 0   0

 
2


 0

2

 
2
 +  2 0
0
 

2

(16)

2

.

(17)

From equations (14-17), it is possible to find the four geophone parameters, r, 0, 0, and S0.
Inserting Z1 and Z2 into Equation (12) gives the center and radius of the impedance circle.
Im(Z 1 )  Im(Z 2 ) + Re(Z 1 )  Re(Z 2 )2{Re(Z 1 ) Re(Z 2 )}
2

c=

2

2

2

(18)

a = Im(Z 1 ) + {c  Re(Z 1 )}(19)

The DC resistance r is found to be;r =ca(20)

2

2

From impedance equations (14-17), 0, 0, and S0 may be found as,0 =( A   )  where A = Re(Z ) r • Im(Z )Re(Z ) r Im(Z )( A   )
1

2

2

1

2

1

1

1

2

2

(   ){Re ( Z )  r } =2  { Im ( Z )  r }
2

2
1

0

1

(22)

0

0

1

(21)

1

87

S0 =

( ){(   )} + 4  m Im Z1

2

2
1

0

2

(   )
2

0

2
1

2

0

2

0

2
1

(23)

1

4.2 Dynamic Method
In the dynamic calibration method, a geophone is mounted on a shaker with a reference sensor with a known response. The sensitivity may be evaluated ase(24)
S g = 0 Sr
er
where
Sg : sensitivity of a geophone to be calibrated
Sr : sensitivity of the reference geophone
e0 : output signal from the geophone to be calibrated
er: output signal from the reference geophone.
Calibrated accelerometers are commonly sold in the market; however, their calibration isusually to 1% accuracy. It is known that the temperature coefficient of a piezoelectric accelerometer is about 0.1%/˚C; however, the coefficient is not calibrated nor warranted by manufacturers. We do not know if this temperature coefficient can be applied to any calibrated accelerometers we can purchase. A five-degree temperature change can cause 0.5% error, and it is difficult to measure the temperature of the accelerometer, since the temperature of the housing may
not be the same as the temperature of the piezoelectric element.

Fig. 16 Reciprocity calibration.

Fig. 15 Dynamic method.
4.3 Reciprocity Calibration

An alternative to the dynamic method is the reciprocity method of calibration to determine
geophone sensitivities. In the reciprocity method, three geophones are mounted together asshown in Fig. 16. A current may be injected into a geophone to shake the entire assembly andthe output from the other geophones may be measured. Let eij denote a signal from geophone i
responding to signal input to shaker geophone j. Geophones G2 and G3 output signals are thene21 and e31 when shaking G1. Then by injecting signal into Geophone G2 we get e32 as the output
88

from G3. A geophone shakes the entire mass M along with three geophones. For the driving
signal e0 sin t , the force may be approximated as;
S je
(25)
F=
sin tZ j ( )Outputs, e21, e31, and e32 can be expressed in terms of the driving signal. The sensitivity Si ofeach geophone may be solved ase12 e13 M {Z 1 ( i )}e21 e0 Z 2 ( i )X i ( i )
2

S1 =

(26)

S2 =

e12 e23 M Z 2 ( i )e13 e0 X i ( i )

(27)

S3 =

e13e23 M Z 2 ( i )e12 e0 X i ( i )

(28)

where

( )

Xi  =

3(   ) + ( 2   )
2

i

2

2

i

(29)

2

i

It is then possible to calibrate the sensitivities of three geophones by knowing their impedances.
4.4 Moving Mass Determination and in-situ Calibration
The dynamic method requires a reference geophone for high precision calibration. The
moving coil mass of the reference geophone is measured during assembly. By knowing theamount of moving mass, the absolute sensitivity can be calibrated by the impedance method.
The reciprocity method can also be used to provide absolute sensitivity calibration. With the reference geophone, the absolute sensitivity of a geophone may be obtained by the dynamic
method.
In the impedance method, the amount of the moving mass, m was assumed, and the sensitivity was derived based on the assumed moving mass. If the absolute sensitivity is obtained by the
dynamic or reciprocity method, it is then possible to re-evaluate the moving mass. The absolute
moving mass, m0 may be found by using equations (23) and (24) as2
 Sg 
(30)
m0 = m  S0 
The moving mass is a constant that does not change with temperature or tilt of the geophone.
Once the moving mass is known, a geophone can be tested in-situ any time by the impedance
method by injecting current.
5. Conclusion
A high precision geophone calibration method has been established. The method integrates
the impedance and dynamic methods and determines the amount of moving mass. All the Geophone Accelerometers, GACs are fully calibrated during the manufacturing process to determine
DC resistance, natural frequency, open circuit damping, open circuit sensitivity, and moving
mass by impedance and dynamic methods. Since the moving mass does not change with89

temperature or tilt, it is also possible to make in-situ calibration in the working environment by
just injecting calibration signals. The moving mass does not change in temperature or in tilt. It
is also possible to make in-situ calibration at working environment by just injecting calibration
signals.
References
1) A. Obuchi, M. Kamata: “Moving Coil Accelerometer”, Japanese patent P3098045, H12,
Oct.102) A. Obuchi and T. Fujinawa: “Moving Coil Dynamic Accelerometer”, 53rd EAEG Meeting,May, 1991 (B021)
3) M. Kamata: “An Accelerometer for Seismic Signal Detection”, Proc. 6th Conference of Un-

derground and Civil Engineering Acoustic Emission, May 1999, Tohoku University, Sendai,
Japan, pp. 25-26.
4) Peter W. Rodgers, Aaron J. Martin, Michelle C. Robertson, Mark M. Hsu, and David B. Harris: "Signal Coil Calibration of E-M seismometers", Bulletin of the Seismological Society ofAmerica, 85, (3), 845-850, June 1995.
5) Mark Harrison, A.O. Sykers, and Paul G. Marcotte: “The Reciprocity Calibration of Piezoelectric Accelerometers”, Journal of Acoustical Society of America, 24, (4), July 1952.
6) Thomas B. Gabrielson: “Apparatus and Method for Calibration of Sensing Transducers”, U.S.
patent No. 5,644,067, July 16, 1996.

90

DEVELOPMENT OF HEAT-RESISTANT OPTICAL FIBER AE SENSOR
PORNTHEP CHIVAVIBUL1, HIROYUKI FUKUTOMI1, SHIN TAKAHASHI2 andYUICHI MACHIJIMA2
1)
Central Research Institute of Electric Power Industry (CRIEPI),
2-11-1 Iwado Kita, Komae, Tokyo 201-8511, Japan.
2)
LAZOC Inc., 7-3-1 Hongo, Bunkyo, Tokyo 113-8656, Japan.
Abstract
A heat-resistant optical fiber AE sensor with sustainable sensitivity and with no temperaturecompensation has been developed. This sensor has utilized the Fiber Doppler (FD) sensing
principle that detects frequency shifts of laser light transmitted through an optical fiber inproportion to strain velocity, which was discovered recently. Gold coating was applied to theoptical fiber to improve the temperature-limit of the conventional optical fiber. The sensitivity
was measured from room temperature to 600°C with the quasi-AE wave generated by PZT
sensor. A long-term heat exposure test at 600°C was also conducted for over 1000 hr. Bothexperiments showed that the sensor retained its sensitivity. The durable, low-cost and heatresistant sensor can invite a new scope to structural health monitoring in a variety of hightemperature conditions.
Keywords: Acoustic Emission sensor, Optical fiber, Heat resistance
1. Introduction
Acoustic emission (AE) has been extensively used in the petrochemical, nuclear, andaerospace industries. This technique provides a distinct advantage over other conventional
nondestructive testing techniques because it allows for the real time monitoring of in-service
structures. Generally, the PZT transducer is used as an AE sensor. There are a number ofcommercially available PZT sensors suitable for detecting AE in various applications. However,
it is difficult to directly apply these sensors at elevated temperatures because the Curie point of
the PZT is approximately 300°C. Although this problem can be solved by using waveguides, thequantitative AE analysis becomes difficult because of the change of waveforms at the
waveguide/structure connection.
Recently, AE sensors using optical fiber have been developed [1-5]. Optical fibers, ingeneral, have the following features: (1) small diameter and light weight, (2) flexibility, (3)immunity to electromagnetic interference, and (4) durability and corrosion resistance. The
optical fiber sensor developed by Kageyama et al. [3, 4], which is based on a new finding called
Fiber Doppler (FD) that the frequency of lightwave transmitted through a bent optical fiber isshifted by vibration or elastic wave at the bent region, has the following good characteristics: (1)wide bandwidth from 0.1 Hz to 3 MHz, (2) expandable dynamic range, and (3) high-sensitivity.
The main element of optical fiber is quartz that has heat resistance up to about 800°C. Kageyama
et al. succeeded in detecting elastic wave at 500°C with carbonized polyimide-coated opticalfiber sensor, but the life time was only about 2 hr [5]. This fiber broke when the temperature
increased to 600°C. In the present study, we have developed an optical fiber AE sensor based on
J. Acoustic Emission, 23 (2005)91© 2005 Acoustic Emission Groupgold-coated optical fiber for high temperature application. The characteristics of this sensor at
the elevated temperatures were examined.
2. Principle of the Optical Fiber Sensor
Laser light reflected from the surface of a moving object is shifted in frequency by an amount2v/0 , where v is the velocity of the surface and 0 is the wavelength of the laser radiation in air.
It is well known as the Laser-Doppler effect. A similar effect is observed in a lightwave
transmitted through a bent optical fiber. Frequency of a lightwave transmitted through a curved
optical fiber is shifted in response to vibration/motion of the curved region [5]. The phenomenon
can be explained as follows. Lightwave is transmitted in the optical fiber by repeated reflection
on the interface between core and cladding as schematically shown in Fig. 1. In the case ofstraight optical fiber as shown in Fig. 1(a), reflection angles at point A and B are the same, andDoppler shifts at point A and B cancel each other ( fD, A + fD, B = 0). In the case of a curved opticalfiber (Fig. 1(b)), the reflection angle, A , at point A is larger than the angle, B, at point B, anddifferent Doppler shifts are obtained ( fD, A + fD, B  0). As a result, the frequency is shifted along
the curved region, which vibrates or moves.
A lightwave in a curved optical fiber can be considered as a light beam as shown in Fig. 2.
The frequency shift, dfD, that occurs in an infinitesimal element, ds, is expressed by equation (1)according to the Doppler effect. The total frequency shift, fD, along the curved light beam can beobtained as equation (2) by integrating equation (1), where V, n and k are velocity vector, normal
vector, and curvature, respectively (See Fig. 2), and  is wavelength of the laser light in an
optical fiber.

V •n
d

1
fD =  kV • nds

df D = (1)(2)

Fig. 1 Doppler effect in an optical fiber.

Fig. 2 Light beam in a curved optical fiber.
Fig. 3 Optical fiber sensor.
92

2. Experiments
2.1 Optical Fiber Sensor System
Figure 3 shows the optical fiber sensor used in the present study. It consists of 5.5 loops offiber entirely bonded to a ceramic plate by using a gold paste. The dimensions of the ceramic
plate is 50 x 50 x 0.6 mm and optical fiber sensor has 30-mm diameter. The fiber (TOTOKUElectric) used in this study had no polyimide layer and was coated with gold with a thickness ofabout 20 m. Figure 4 shows an optical sensing system. Laser doppler velocimeter (Melectro,
V1002) based on the heterodyne interference technique is used. The He:Ne laser with a power of
1 mW is used as a light source. It is separated into two beams by a half mirror. One of the beams
is sent to the sensor, in which the frequency is changed from f0 to f0 ± fd by vibration or elastic
wave. The other beam is guided to the acousto-optic modulator and the frequency is shifted fromf0 to f0 + f M ( f M = 80 MHz). These beams are then combined again after passing a half mirror inorder to produce beating signals with a frequency of f M ± f d. The combined beam is sent to a
photodetector to transform light into electric current and then demodulated by a demodulator.

Fig. 4 System of the optical fiber sensor.
2.2 Experimental Procedures
In order to examine the potential of the developed optical fiber AE sensor, the quasi-AE
measurement was carried out at elevated temperatures. Figures 5 and 6 show the test piece andschematic diagram of the measurement system, respectively. The test piece was machined from a
SUS304 plate with two long legs for applying PZT sensors to generate and detect AE signals.
This test piece was cleaned and the optical fiber sensor was bonded with a ceramic cement(Three Bond, 3713B). An electric oven (Chino, MF-2000) was used to heat the test piece and thetemperature was measured by the thermocouple attached to the test piece. PZT sensors (PAC R15; frequency = 150 kHz) were attached to the end of the legs with silicone grease (Shin-Etsu
Chemical, HIVAC-G). One of the PZT sensors was driven by an electric pulse generator (PAC,
C-101-HV) to generate quasi-AE waves. The optical fiber sensor and the other PZT sensor
detected the generated quasi-AE signals. The signals detected by the optical fiber sensor were
filtered with an HPF of 10 Hz and an LPF of 250 kHz. The signals detected by PZT sensor were
amplified by 40 dB with a pre-amplifier (PAC, 1220A). Both signals were then recorded on adigital oscilloscope (Yokogawa Electric, DL-750) with 10 MHz sampling and 10 k length
setting. At least 20 signals were recorded in each test condition. Various maximum temperaturesof 200, 300, 400, 500 and 600°C were selected for measurement. The test piece was heated to the
maximum temperature and kept for 1 hr before measuring. A long-term heat exposure was also
made at 600°C up to 1000 hr.
93Fig. 5 Test piece.

Fig. 6 Measurement system.
3. Results and Discussion
Figure 7 shows signals detected by the optical fiber sensor and the PZT sensor at each test
temperature. The signals detected from the optical fiber sensor are similar for all test
temperatures while decrease in amplitude of signals detected by the PZT sensor with increasing
test temperature is clearly observed. The lower amplitudes of signals detected by the optical fiber
sensor compared to those detected by the PZT sensor is due to a small number of fiber loops.
Figure 8 shows the maximum amplitudes of signals detected by the optical fiber sensor as afunction of test temperature. Twenty signals from each test temperature are used for this plot.
The maximum amplitude increases from RT to 300°C and remains constant up to 400°C andthen slightly decreases up to 600°C. This behavior may arise from a change in structure ofceramic bonding or gold paste between sensor/test piece and sensor/ceramic plate due to the heateffect. However, it has not been identified yet. It clearly shows that the optical fiber sensor has
high heat-resistance. The previous investigation on the gold-coated conventional fiber (fiber withpolyimide layer) shows that the fiber broke at about 500°C and the AE signals generated fromthe fiber itself were detected between 300 and 400°C. This behavior is due to the sublimation ofpolyimide. Therefore, using non-polyimide optical fiber improves the heat resistance of thesensor. However, pure quartz optical fiber shows loss in flexibility. Figure 9 shows a result
obtained from long-term heat exposure at 600°C. Average amplitudes of waveforms are plotted
as a function of exposure time. A scattering in amplitude is observed. This behavior may arise
from the uncertainty due to attaching and detaching the pulser in each measurement. However,
we can say that the new optical fiber sensor maintains its sensitivity up to 1000 hr at 600°C.
94

Fig. 7 Detected quasi-AE waves.

Fig. 8 Maximum amplitude at eachtemperature.

Fig. 9 Maximum amplitude in the longterm exposure.

4. Conclusion
We have developed a new optical fiber sensor based on gold-coated optical fiber. Although
this new sensor shows lower sensitivity than that of a conventional PZT sensor, it can be used up
to 600°C. Its sensitivity does not change even in the long-term heat exposure up to 1000 hr at
600°C. The good heat-resistance of this sensor enlarges an application field of AE technique instructural health monitoring and research. However, an improvement of sensitivity is required.
References[1] I. Read, P. Foote and S. Murray: Meas. Sci. Technol. 13 (2002), N5.
[2] S. G. Pierce, W. R. Philip, A. Gachagan, A. McNab, G. Hayward and B. Culshaw: Appl. Opt.
35 (1996), 5191.
[3] K. Kageyama et al.: Japan patents 2001-193840 (2001).
[4] K. Kageyama et al.: Japan patents 2002-023091 (2002).
[5] K. Kageyama, H. Murayama, I. Ohsawa, M. Kanai, T. Motegi, K. Nagata, Y. Machijima and
F. Matsumura: Int. Workshop on Structural Health Monitoring 2003, (2003) Stanford, CA.
95

DAMAGE DETECTION SYSTEM FOR STRUCTURES
WITH SMART AE SENSORSTAKAHITO YANASE and SEI IKEGAYAJUST Co. Ltd., 2-4-1 Azamino-minami, Aoba, Yokohama 225-0012, Japan.
Abstract
We conducted experiments on reinforced concrete structures using a damage detection
system using Smart AE sensor. This system consists of a Smart AE sensor and a computer and issimpler than conventional AE systems. The Smart AE sensor contains an AE transducer, CPU,
memory and USB terminal on the substrate and its size is about 20 mm x 80 mm. The mainfeature of Smart AE sensors is to convert AE waves received by the AE transducer into digital
data, transmitting them to the personal computer. The personal computer receives the digital datafor damage index of the structure, and displays it for monitoring.
Keywords: Damage detection system, Smart AE sensor, Reinforced concrete structures
1. Introduction
In Japan, many concrete structures have been used for more than 30 years. The maintenanceand repair techniques for these existing structures are becoming significant. The maintenance isperformed at regular intervals by visual inspection and nondestructive inspection. In order toimprove the efficiency of the inspection, some damage detection systems for the structures weredeveloped and applied to real structures. Although the sensors currently used by such damage
detection systems are mainly optical fiber sensors, some sensors as listed below can also be used
for damage detection system. 1) Acceleration transducer, 2) Displacement transducer, 3) AE
sensor.
We choose AE sensor as the sensor of a damage detection system, because it has been often
applied to experiments in laboratory. In laboratory experiments, it was useful to evaluate
correctly the position of the damage using multiple AE sensors. However, such measurement
systems are complex and expensive, so we needed improvement to apply it to the damage
detection system. On real structures, it is often possible to estimate the area where damage by
external force occurs. Only one AE sensor is necessary to detect the damage around it, and thedamage detection system becomes simple.
We have developed a damage detection system, which consists of a personal computer and a
Smart AE sensor. A Smart AE sensor is attached to the area of expected damage. It is connected
to a PC by a USB cable. The Smart AE sensor can process the AE waves from an AE transducer,
convert them to digital data and transmit the data to the personal computer. The AE waveforms
are changed into the AE (or threshold-crossing) counts per unit time and this is used as thedamage index for the structure by the Smart AE sensor. Multiple AE sensors can be connected toa PC simultaneously. In this paper, we describe the damage detection system applied tostructural experiments of the concrete.

J. Acoustic Emission, 23 (2005)96© 2005 Acoustic Emission Group2. Outline of Damage Detection System
The single-channel damage detection system is shown in Fig. 1, and the detail of Smart AE
Sensor is shown in Fig. 2. A Smart AE sensor includes an AE transducer, CPU, memory and a
USB terminal on the substrate. Its size is approximately 20 mm x 80 mm.

Monitoring Software

Personal Computer

USB Cable

Smart AE Sensor
AE Transducer

Fig. 1 Damage detection system.

CPU

USB Terminal

Fig. 2 Detail of Smart AE sensor.

Functions of the Smart AE sensor are:1) The AE transducer receives AE wave generated from the damage.
2) The AE waves are changed into digital data as damage index or AE count rates.
3) The digital data is transferred to the personal computer (PC) by the USB cable.
Here, the damage index is the AE counts per second and PC always displays it. Since it is notnecessary to process AE waveform data, this damage detection system can display the damage inreal time. The amount of data obtained by this system is much smaller as compared with the
waveform data obtained by the conventional AE method and it is possible to connect four sets ofSmart AE sensors to one PC simultaneously.
In the second-generation damage detection system, shown in Fig. 3, it has larger memories
and operates for one month or more by a battery. We are going to use the next type of Smart AE
sensor. In the following experiment, we use this new type of Smart AE sensor.
3. Experiments
3.1 Specimens and Testing Method
We carried out two experiments with the Smart AE sensor on the specimen. One is on a prestressed concrete beam specimen (PS1) and the other on a reinforced concrete column specimen(RN47). The monitoring of damage occurrence was performed throughout experiment by the
new damage detection system.

97

USB Terminal

Memories

CP
Battery

Twisted Pair

Smart AE Sensor

Fig. 3 Next generation of Smart AE sensor.
The form and the arrangement of bars of the specimens are shown in Figs. 4 and 5. For the
pre-stressed concrete beam specimen (PS1), the size of rectangular section was 200 mm x 300
mm, and the length of the span was 3,000 mm. The PC strand was arranged in the position of 75
mm from the section bottom at the center of the span, in order that the edge tensile stress of theconcrete stays below 0.5 N/mm2. The specimen was supported by pin devices at both ends. The
load applied to two points, which were distant from a center every 250 mm. The Smart AE
sensor was attached to the specimen surface of the span at the central part with the adhesive.

Fig. 4 Outline of specimen (PS1).
For the reinforced concrete column specimen (RN47), the size of rectangular section was 275
mm x 350 mm, and the height of the column was 700 mm. See Fig. 5. There was less quantity ofthe transverse reinforcements than usual structure so that shear failure was expected. Gradualincrease in horizontal load was applied repeatedly to the specimen. Axial load was not applied.
The Smart AE sensor was attached on the specimen surface at the mid-height part with theadhesive.
3.2 Results for Pre-stressed Concrete Beam
The relationship between applied force (P) and displacement for PS1 specimen is shown in
Fig. 6. When applied force is 34 kN, the bending crack occurred in the center of span. The
yielding of main reinforcement and PC strand occurred at P=52 kN and 86 kN, respectively.
98

Fig. 5 Outline of specimen (RN47) and loading direction.
When compressive failure of concrete occurred at the upper surface, applied maximum force was
91 kN.
Applied Force (kN)
100
80

Yielding of PC Strand

60
Yielding of Main Reinforcement40

Bending Crack Occurred

20
0
0

10

20
30
40
50
Displacement (mm)60

Fig. 6 Relationship between applied force and displacement. PS1
The relationship between displacement and AE counts as damage index is shown in Fig. 7.
The horizontal axis shows the loading step (time). The displacement increased gradually with theloading. AE counts were first observed to rise, when bending crack occurred. AE counts wereobserved actively at the peak displacements. Fewer number of AE counts were observed below
the prior displacement, which is called Kaiser effect. Note very high AE counts during theunloading toward the end of experiment. This shows extensive concrete damage.
3.3 Results for Reinforced Concrete Column
The relationship between shear force (Q) and deflection angle (R) for RN47 specimen isshown in Fig. 8. The crack pattern after experiment is shown in Fig. 9. When shear force wasnear Q = 80 kN of first loading cycle, bending crack occurred at both ends of the specimen.
When shear force was near Q = 140 kN on the second loading cycle, shear cracks occurred.
When deflection angle was R = 15 x 10-3 rad., the shear force was 380 kN. The width of shear99

cracks, which occurred along the diagonal, became large after the maximum shear force, andshear failure occurred.
Displacement (mm)AE Count
60

60

50

50

40

40

AE Count30

30

Displacement

20

20

10

10

0

0

Loading Step

Shear Force(kN)

Fig. 7 The relationship between displacement and AE count.
400
300

RN47

200
100
0

-40

-30

-20

-10
-100 0
-200
-300

10
20
30
40
Deflection Angl
(1/1000rad.)-400

Fig. 8 Relationship between shear force and deflection.

Figure 9 Shear failure.

The deflection angle and AE counts vs. time of loading are shown in Fig. 10. The horizontal
axis represents the loading step. The deflection angle changed gradually upon loading andunloading. AE counts were first observed, when a shear crack appeared on the second loading
cycle. High AE counts were observed at the end of the second cycle, then increased intensively
near peak deflection angle during the fourth cycle. Few AE count was observed during fifthloading cycle, indicative of Kaiser effect. AE counts were observed even during the unloading
after the fourth loading cycle. This shows permanent damage of the concrete. Beyond the sixthloading, AE counts were almost always present.
4. Conclusion
We carried out experiments using a simple and low-cost damage detection system using the
Smart AE sensor.
1) Occurrence of damage of the structure can be detected with the damage detection system.
1002) The damage index transmitted from Smart AE sensor is observed as the concrete damage
progressed.
3) Kaiser effect was clearly observed, just as in the conventional AE methods.
Reference(1) S. Ikegaya and T. Yanase : Experimental Study on Health Monitoring for Structure Used by
Smart AE Sensor, Proceeding of National Conference on Acoustic Emission, 2003, JSNDI,
Tokyo, pp. 55-58.

60

Deflection Angle(1/1000rad.)
30

50

20

40

10

30

0

20

-10

10

-20

0

-30

AE Count

Loadin Step

Fig. 10 Relationship between displacement and AE counts (RN47 Specimen).

101

HIERARCHICAL FRACTURE PROCESS IN BRITTLE ROCKS BY
MEANS OF HIGH-SPEED MONITORING OF AE HYPOCENTERXINGLIN LEI*, OSAMU NISHIZAWA, ANDRE MOURA and TAKASHI SATOH
Geological Survey of Japan (GSJ), National Institute of Advanced Industrial Science andTechnology (AIST), Higashi 1-1-1, Tsukuba, Ibaraki, 305-8567, Japan; *Also at Institute ofGeology & Laboratory of Tectonophysics, China Seismological Administration, Beijing, China.
Abstract
Damage evolution and fault formation processes in brittle rocks under stress were studied
based on data collected with a high-speed recording system of acoustic emission (AE) waveforms. Experimental results show that the faulting process is hierarchical. Quasi-static nucleation of faults represents dynamic fractures of the asperities on the fault plane; likewise, a quasistatic nucleation process characterized by dynamic microcracking precedes the fracture of anasperity. The progressive fracturing of multiple and coupled asperities results in short-term precursory fluctuations in both the b-value and event rate of AE. For proposing more general models, three granites of different grain size distributions were used for fracture tests under triaxial
compression with two different loading rates of 27.5 and 2.5 MPa/min. The damage creation inthese intact brittle rocks shows a typical process characterized by three phases of microcracking
activity: named as primary, secondary, and nucleation phases. For applying the experimental
results to real problems, the damage evolution was quantitatively modeled from two approaches: 1) the corrosion-aided sub-critical crack growth theory for crack population withfractal size distribution; 2) renormalization-group theory applied to second-order phase transition with the time of the global failure assimilated to a critical time. The event rates and bvalues in the secondary and nucleation phases can be modeled well by the first model. Energy
release calculated from the event rates and magnitude can be modeled well by the second modelwith two domains corresponding to the secondary and nucleation phases. These results confirm
that 1) the larger the grain size, the stronger the crack-crack interaction; 2) the faster the loading
rate (stress-rate), the stronger the interaction; 3) the secondary domain, i.e., the nucleation
phase, shows normally longer interaction than the first domain, indicating the increase of interaction distance.
Keywords: Damage evolution, Rock fracture, Microcrack, Renormalization-group
1. Introduction
The study of damage evolution in rock and other industrial materials like cement understress is a subject of widespread interest, with relevance to both artificial applications such as
optimization of geothermal recovery, oil recovery, safe design of nuclear waste repositories,
rock bursts, and natural processes such as volcano eruptions and earthquakes. It is important topredict the time of catastrophic failure that corresponds to the lifetime of industrial material or
the occurrence of rock burst and earthquake. For this goal, challenges on modeling the damage
evolution have been made through both experimental and theoretical approaches.
In laboratory, damage evolution in stressed materials has been studied extensively by anumber of methods including 1) the direct observation of sample surface by scanning electron
J. Acoustic Emission, 23 (2005)

102© 2005 Acoustic Emission Groupmicroscopy [e.g., 1] or optical microscopy [e.g., 2] operated during or after a fracture test; and2) monitoring the hypocenter distribution of acoustic emission (AE) events caused by microcracking activity [e.g., 3, 4]), which is the so-called AE technique. AE technique is advancedin the analysis of the microcracking activity inside the sample volume and can be performed
under confining pressure, which is very important in the simulation of underground conditions.
The recently developed high-speed multi-channel waveform recording technology has madeit possible to monitor the hypocenters of AEs associated with spontaneously/unstably fracturing
processes in stressed samples with high precision. The system is capable of recording AE waveforms in 32 channels at sampling rates up to 5000 events per second, and has been successfully
used to study the final stage (quasi-static) of fracturing nucleation in intact brittle rocks. It has
been applied to the analysis of the fracture process of hornblende schist [5], granitic rocks [6],and mudstone containing quartz veins, which play a role as strong asperities [7]. Lei et al. [5]observed that once a shear fault is initiated or nucleated in fine-grained homogeneous rock, theshear fault grows quasi-statically with a process zone at the fault front. It is found that the process zone is governed by progressive triggering of tensile microcracking, in agreement with themicroscopic examination on a torque test revealed by Cox and Scholz [2].
More recently, it has been observed that the microcracking activity associated with thefracture of inhomogeneous fault consists of 3 stages or phases with a spatial hierarchical structure [8]. These long-term microcracking phases referred to as primary, secondary and nucleation can be identified from the changes in event rates, b-values, and AE hypocenter distribution.
It was suggested that the predictability of catastrophic fracture is strongly dependent on the heterogeneity on the potential fault plane. A homogeneously healed fault is notably unpredictable,
whereas a fault of non-homogeneous healing strength or asperity distribution undergoes a predictable fracturing process with a remarkably clear nucleation phase that can be observed as aprecursory anomaly of the b-values or other statistical parameters of AE activity. The appearance of a nucleation phase with rapidly decreasing b-values, a non-linearly increasing event
rates and spatio-temporal clustering can be considered to be a signal of the initiation of catastrophic fracture.
The above described experiments agree with the general knowledge that global failure
comes from spatially distributed micro-separations including microcracking, fiber rupture, interfacial debonding, etc., which is in contrast to the case of perfect crystal where global failure
occurs from a few or even from only one unstable crack. The multi-step character of arisingdamages, reflecting the discrete nature of the fracturing process, makes it possible to compare
the diffusion of damages in brittle materials with the diffusion on random lattices [9, 10]. This
analogy has been the starting idea for extensive numerical simulations applied to the latticemodels of fracture, which predicted the existence of scaling laws in the vicinity of fracture inbrittle materials [11]. The fracturing of stressed materials is considered then as analogous to a
critical phenomenon happening at a second-order phase transition in analogy to percolation
phenomenon [12, 13]. The moment of rupture is similar to a critical point, so that the fracturing
process can be described by a renormalization-group scheme [14]. In the vicinity of the criticalpoint of rupture, there exists a critical region [15, 16], where the variations of the free energy,
which is the energy release in the case of rock fracture, can be characterized by a power law oftime normalized by the critical time decorated by log-periodic oscillations [17]. Such oscillations, which are useful for prediction purpose, are related to complex exponents that appear inrenormalization-group solutions for critical phenomena [18].
103However, above model based on critical phenomena is somewhat mathematical. The physical bases behind the model parameters are still unclear. Hence, further studies are required forlinking mathematical models with physical mechanisms in order to elaborate reasonable prediction schemes. Another approach for modeling the damage evolution with some physicalbases has been proposed by considering the sub-critical crack growth under stress as the resultof stress-aided corrosion at the crack tip. Some models based on the experimental results of asingle macroscopic extensional crack have been proposed [e.g., 19, 20] and later extended to
crack population with fractal size distribution [21]. The models can fit not only energy release(here events rate) as the model from the renormalization-group solutions but also the b-value inthe magnitude-frequency relation. Hence these models could explain some precursory anomalies such as quiescence [22] and b-value decrease [23] associated with large earthquakes, although they are unable to take account for interaction phenomena.
In this paper, we will at first introduce the most recent experimental results associated withthe hierarchical fracturing process. Then we will model the damage evolution based on detailed
AE measurement by two different models come from firstly, the stress corrosion constitutive
laws and secondly, the renormalization-group scheme. We will discuss next on some possible
physics behind the model parameters and the relation between these models.
2. Experiment and Data Processing
It is normally observed that the AE rates may reach several thousands per second prior tothe catastrophic failure [e.g., 8]. For clarifying the damage evolution though the monitoring of
the spatio-temporal distribution of AE events, a high-speed waveform recording system has
been developed. The system has the following advantages.
i) It has a rapid waveform recording facility on 32 channels with a sampling rate up to 25
MHz and dynamic range of 12 bits.
ii) The mask time of the system is less than 200 s, thus it is possible to record AE waveforms without any important loss, particularly when the AE activity is very high - on the order of several thousands per second.
iii) The system has a 2-channel detector to capture the values of peak amplitudes with an effective dynamic range of 55 dB, which corresponds to a magnitude range of about 2.75.
Further, since the peak detector shares a common base-clock with the waveform recording
system, it is possible to obtain a complete set of temporal and spatial distribution of AE
events.
iv) The piezoelectric transducers (PZT) function not only as receivers of acoustic signals,
but also as acoustic sources for measuring the P- and S- velocity during experiment. Being a
receiver or transmitter is controlled by an automatic switching system.
v) It can record stress and strain data on 16 channels with 16-bit resolution and 100 kHz
sampling rate.
vi) AE hypocenters can be determined automatically by using the first arrival time data of
P-waves and measured P velocities. The location error of an AE hypocenter is generally lessthan 1~2 mm and 2~3 mm for fine-grained and coarse-grained rocks, respectively.
Test samples were normally shaped as cylinders of 125 mm (100 mm for some earlier tests)
in length and 50 mm in diameter. All samples were dried under normal room conditions for morethan one month and then compressed with constant stress rate including creep (sample was
loaded with constant stress rate to a stress level of ~95% fracture strength and then held constant)at room temperature. During the deformation confining pressure was kept constant at 40-60
104

MPa. Under these conditions, a rock sample normally fractures with a shear fault of ~30˚ withthe maximum stress axis. Table 1 is a list of rocks and their dominant grain sizes referred to inthis paper. These granitic rocks contain mainly quartz, K-feldspar, and biotite. Granitic Porphyry has a very low density of pre-existing microcracks due to recrystallization. Other rocks
contain relatively higher density of pre-existing microcracks. The size and spatial distributionof the pre-existing microcracks are controlled by the grain size distribution. Hence, from themicroscopic (from m to mm) point of view, the larger the grain size is, the more heterogeneous the rock.
Table 1 Dominant grain size in test samples.
Rock
Major grain size (mm)Westerly Granite,
WG<1-2

Oshima Granite,OG
1-5

Inada Granite,IG
1-10

Granitic Porphyry
1-10

A total of 32 PZT sensors (5 mm in diameter, resonant frequency of 2 MHz) were mounted
on the surface of each test sample to detect AE signals from microcracking events. A series of
6-8 cross-type strain gauges (12-16 channels) were mounted on the surface of the sample tomeasure local axial and circumferential strains. The local volumetric strain (v) was calculated
from the axial (a) and circumferential (c) strains according to the equation v = a + 2c.
The mechanics of rock deformation and crack growth can be inferred from AE statistics,
because the number of AE events is proportional to the number of growing cracks and AE amplitudes are proportional to the length of crack growth increments [23, 21, 25, 26]. The wellknown Gutenberg and Richter [27] relationship,(1)
log10 N = a  bM
where N is the number of events of magnitude M or greater, holds for not only earthquakes but
also AE events in laboratory [e.g., 28, 29]. In equation (1), a is a constant and b is the seismic
b-value. An estimate of the b-value in the Gutenberg-Richter relation can be obtained by using
either the least-squares method or the maximum likelihood method [30].
3. Hierarchical Fracturing Process
The final stage of the damage evolution prior to the catastrophic fracture is the most important subject because it is associated with the predictability based on either precursory phenomena or damage constitutive law. Some recent experimental results have improved our understanding on the fracture behavior leading to the catastrophic failure. For instance, the fracture ofa shear fault containing several unbroken asperities in a granitic porphyry is examined in detailby Lei et al. [24, 32]. A detailed description of the test sample, identification of asperities, andexperimental setup can be found from the cited references. Since this result has provided us
some physical bases for understanding the mathematical models presented in the following sections, it is convenient to briefly summarize the results here. As shown in Fig. 1(a), AEs causedby the fracture of individual asperities exhibit similar characteristics to the sequence for natural
earthquakes, including foreshock, mainshock, and aftershock events. Foreshocks, initiated at
the edge of the asperity, occur with an event rate that increases according to a power law of thetemporal distance to the mainshock, and with a decreasing b-value (from ~1.1 to ~0.5). One ora few mainshocks then initiate at the edge of the asperity or the front of the foreshocks. The aftershock period is characterized by a remarkable increase and subsequent gradual decrease in105the b-value and decreasing event rates obeying the modified Omori law, which has been wellestablished for earthquakes. The fracture of neighboring asperities is then initiated after themainshock of a particular asperity, presumably due to redistribution of the strain energy accumulated within an as perity, which is released by the mainshock, resulting in enhancement of
the stress concentration around the nearest neighboring intact asperities. The progressive fracturing of multiple, coupled asperities results in short-term precursory fluctuations in both the bvalue and event rate (Fig. 1(b)), which may prove useful in the prediction of catastrophic failure. For applying the experimental results to real problems, a constitutive model must be established firstly based on experimental data and then examined again and again based on measurable data from real applications.

Fig. 1 AE data associated with the fracture of an individual asperity (a) and coupled asperities(b) on the fracture plane. The b-values are calculated for sets of 100 (in a) or 500 (in b) eventswith a running step of 25/125 events by the maximum likelihood method. The standard error forthe estimated b-value is ~0.1b or 0.05b. Dashed line denotes the power law of temporal distancefrom the main shock.
106

3. Damage Model Based on Sub-Critical Crack Growth Theory
Under stress, sub-critical crack growth can occur as a result of stress-aided corrosion at thecrack tip though the details are still unclear. The quasi-static rupture velocity V is found experimentally to be related to the stress intensity factor K by Charles’ law [33]:V=dc= V0 ( K / K 0 ) ndt(2)where c is the half-crack length and n is referred to as the stress corrosion index that has a typical value in the range 20-60 for polycrystalline rocks [19]. In the classic Griffith crack loading
conditions, K = Yc q with q = 1/2, Y =  . The solution of the differential equation (2) depends on the loading history  (t). Under static loading, the crack length predicted from theabove equation of V and definition of stress intensity K has the form,c = c0 (1  t / t f ) 2 /( 2n )(3)where c0 is the initial crack length at an arbitrary initial time t = 0, and tf is the failure time [19].
For a time-varying stress, a similar equation is a good approximation if the stress-drop is small
compared to the ambient remote stress [34]. For stress-aided corrosion crack, Meredith and Atkinson [20] showed that the event rate N has also a non-linear relationship to the stress intensity
in the same manner as the above Charles’ power law as:'
N = N 0 (K / K0 )n(4)
where n’ is referred to as an ‘effective’ stress corrosion index and found to be equal to n withina few percent in brittle rocks [22].
The above model is somewhat based on the experimental results of microcracking during asingle extensional macroscopic crack growth. However, it can be modified for a crack population of fractal size distribution by introducing only an effective mean value for each parameter[21]. Put the definition of K with q = 1/2 into equation (2) then the event rate can be expressedaslog N =


 
2n'
t 
log1   + n ' log  + log N 0
2  n  t f 
0 

(5)

For examining the fracture model, three granites WG, OG and IG are selected for systematic fracture tests. As listed in Table 1, the major difference of these granites is their grain size
distribution. Two runs have been operated with constant stress rate at 27.5 MPa/m and 2.5
MPa/m, respectively. The confining pressure was kept constant at 50 MPa for all tests. Consistent with the previous study [8], the experimental data demonstrate that the damage creation, orin other words the microcracking activity, in these brittle rocks is characterized explicitly by
three typical long-term phases referred to as primary, secondary and nucleation, respectively(Fig. 2). The above theoretical model is applicable to the secondary and nucleation phases but
not to the behavior of the primary phase. Main features and fitting results are summarized asfollows:
Primary phase: The event rate is low and increases slightly with stress or time. The b-value
increases with increasing stress or time slightly from an initial value of 0.5~1.0 to 1.0~1.4. Thisinitial value and increment depends strongly on grain size and density of pre-existing microcracks. In the fine-grained WG, the primary phase has a small number of events, the b-value
is initially high and shows only a small increase (from ~1.3 to ~1.4). On the other hand, in the107

Fig. 2 The b-values and event rates of Westerly, Oshima, and Inada granite samples under constant stress-rate loading at 2.5 MPa/s. Dashed lines show the fitting results of equation (5).
Lower plots are zoom up views of the last 1000 seconds, corresponding to the secondary andnucleation phases. “P | S” and “S | N” mark the transitions from the primary to secondary andsecondary to nucleation, respectively.
coarse-grained IG, the primary phase has a larger number of events, the b-value is initially low
and shows a large increase (from 0.5 to 1.2). The medium-grained OG shows a primary phasesimilar to IG, but with a smaller event number, higher initial b-value, and smaller b-value increase. In an earlier work, it has been found that the coarse-grained granitic porphyry (GP)sample of a very low pre-existing microcrack density demonstrates a primary phase of a small
number of events [8]. Therefore, we conclude that the primary phase reflects the density andsize distribution of pre-existing microcracks, which are somehow controlled by grain size distribution. Since the major mechanism of microcracking in the primary phase is an initial opening (tensile) or rupturing (shear) of pre-existing microcracks rather than crack-growth, the theoretical model based on sub-critical crack-growth is presumably inapplicable.
Secondary phase: The secondary phase corresponds to the microcracking activity following
the primary phase. This phase shows increasing event rates and slightly decreasing b-values
with increasing stress or time. The event rate data can be fitted very well with the theoreticalmodel given that this phase is associated with the growth of pre-existing microcracks. For WG,
OG, and IG samples, event rate N can be represented well by equation (5) with parameters n’ =12 and n = 35. Interaction between cracks and linkage of neighbor cracks become higher andhigher as both the density and mean length increase together. That probably justifies that n’ =~0.3n.
Nucleation phase: The nucleation corresponds to the period where event rate rapidly increases and the b-value decreases to the global minimum of 0.5-0.8.
Experimental data show also short-term fluctuations on both the event rate and b-value on
the three long-term backgrounds. The amplitude of the fluctuations is function of the grain size108

in a positive relation. The larger the grain size, the larger the amplitude. The fluctuations
change both in amplitude and period following the fracture evolution, and therefore they may
be useful for failure prediction. However, this kind of fluctuation cannot be modeled from theabove fracture constitutive laws but fortunately recent models based on renormalization-grouptheory are able to describe this phenomenon.
4. Damage Model Based on Renormalization-Group Theory
In this section, we describe a scheme for modeling the damage evolution based on therenormalization-group theory. The key to this approach is to consider the stressed materials or
rocks as a thermodynamic system with the supplied energy coming from the applied externalload and, its transformation up to rupture, as a second-order phase transition. This transformation is highly irreversible and the system state is never in equilibrium. Micro-separations are thedominant irreversible processes, and the energy release, which has not been consumed by irreversible transformations such as crack advances and plastic deformation around the crack tip, isconverted into the decrease of the free energy of the system.
Concerning the catastrophic regime, we are currently far from establishing a behavior law
from the statistical approach on a purely thermodynamic point of view. This is because fracturing is both a non-linear and unstable process where the long-distance interactions play amajor role and the material properties are every time modified. However, based on the fact thatthe moment of rupture is similar to a critical point, the fracturing process can be described by arenormalization-group scheme [14]. In the vicinity of the critical point of rupture, there exists a
critical region, where the variations of free energy, here referred to as the energy release rate,
can be characterized by a power law of the normalized time x = (tc – t)/tc, where tc is the criticaltime, i.e., time of rupture, decorated by log-periodic oscillations [18]. For monophasic medium,the self-similar extrapolated form of the energy release far from the neighborhood of the criticalpoint is written as [35]E (x = (t c  t ) / t c )= cos(cx  sin g ( x) exp h( x)) exp(cx  cos g ( x) exp h( x)), x  [0,1] (6)E (x = 0 )where g(x) =  sin( ln x –  ), h(x) =  cos( ln x +  ), E(0) = Ec, , , c, parameters,  and 
the real and imaginary part of the complex critical exponent z =  + i  . There is no theoretical limit for  whereas  belongs to the interval [0, 1], as we are concerned with second-orderphase transitions. The value of  is correlated to both the heterogeneity and the disorder of thestructure of materials. For pure crystals, the damage is not diffuse and the rupture is unpredictable, so one has  > 0+. The value of  is somehow related with the interaction of events.
Longer interaction distance (both spatial and temporal) results in larger .
The scheme from single domain can be extended to multi-domain in order to take accountfor successive dominant damage mechanisms in different stages or phases as presented in theprevious section. Since the primary phase has a very low energy release rate, it is not considered here.
The cumulative energy release for above-mentioned experiments is shown in Fig. 3 together
with the corresponding 2-domain fits based on the least-squares method. We focus on the firstdomains where damage randomly occurs over the whole sample, which confirm that 1) the
higher the grain size, the longer the interaction, and the higher the  ; 2) the faster the loading109Fig. 3 Dimensionless cumulative energy release versus dimensionless time. a), b) and c) correspond to Inada, Oshima, Westerly granites, respectively. Upper and lower rows correspond to
the results of fast and slow loading conditions, respectively. The black circles and the red lines
represent the measurement data and the fits, respectively. Vertical dotted line indicates theboundary between the domains 1 and 2, respectively.
rate (stress-rate), the higher the , and therefore the stronger the interaction; 3) the secondary
domain, i.e., the nucleation phase, shows normally a higher  than the first domain indicating
the increase of interaction distance.
5. Conclusion
In our experiment, the nucleation of shear fault was found to correspond to the fracture ofcoupled asperities on the fault plane. The fracture of individual asperities has similar characteristics to the sequence of natural earthquakes, consisting of foreshocks, mainshock, and aftershocks. The progressive fracturing of multiple, coupled asperities during the nucleation of shear
faulting results in short-term precursory fluctuations in both the b-value and event rate. For
proposing more common models, three granites of different grain size distribution were used
for fracture test under triaxial compression at two different loading rates of 27.5 and 2.5
MPa/min, respectively. The damage creation in these intact brittle rocks showed a typical damage creation process characterized by three phases of microcracking activity named as: primary,secondary, and nucleation phases, respectively. The primary phase reflects the initial opening or
rupture of pre-existing microcracks, showing increases of event rate and b-value with increasing stress. The secondary phase corresponds to the sub-critical growth of the microcrack population, showing an increase of event rate and a decrease of b-value with increasing stress. The
nucleation phase corresponds to the initiation and accelerated growth of the final fracturing
along one or a few major fracture planes. In the nucleation phase, the b-value decreased rapidly
to the minimum value. Both the event rate and b-value in the secondary and nucleation phases
can be modeled very well by the model based on the sub-critical crack growth theory. Energy
release data calculated from the event rate and magnitude can be modeled quite
110well by the theoretical model considering materials failure as a second-order phase transition.
These results confirm that the higher the grain size, the longer the interaction, and the higher the. Experimental results also indicate that the faster the loading rate (stress-rate), the higher the, and therefore the stronger the interaction.
Our results indicate that the precursor-based predictability of the catastrophic failure
strongly depends on the pre-existing heterogeneity and loading condition. The three-phasemodel is meaningful for transforming the experimental results to real problems associated withrock bursts, volcanic eruptions as well as tectonic earthquakes.
Acknowledgments
This work was partially supported by the National Basic Research Program of China
2004CB418405 and the Joint Earthquake Science Foundation under Contract No.104146.
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112

MEASUREMENT OF HYDRAULICALLY ACTIVATED SUBSURFACEFRACTURE SYSTEM IN GEOTHERMAL RESERVOIR BY USING
ACOUSTIC EMISSION MULTIPLET-CLUSTERING ANALYSISHIROKAZU MORIYA1, HIROAKI NIITSUMA1 and ROY BARIA2
1)
Graduate School of Environmental Studies, Tohoku University, Sendai, 980-8579, Japan.
2)
GEIE, Soultz-sous-Forêts, Alsace, France.
Abstract
AE/Micro-seismic events can be induced by pressurizing a subsurface formation and thenused to identify hydraulically activated fracture and fluid flow direction in subsurface reservoir.
Recently, with the development of advanced mapping techniques using similar AE events,
faulting dynamics in the Earth’s crust have been studied based on such events, and internal
structures in the fault zone have been estimated from the precise source locations of those events.
In this paper, the pore-pressure of seismically activated fractures during hydraulic stimulation ofgeothermal reservoir at Soultz HDR field, France is estimated based on the high-resolution mapping method of induced AE events. The AE events with similar waveforms are analyzed, andthe seismically activated fracture planes are estimated using the precise source locations determined by the multiplet-clustering analysis. The tectonic stress measured by hydraulic fracturingmethod in borehole is introduced into the calculation of critical pore-pressure for frictional shear
slip of fractures according to Coulomb’s law. The estimated critical pore-pressure suggests thatthe fluid pressure in pre-existing fractures are higher near the permeable fracture zones in thewell, and declines as the fractures are away from the intersecting portions.
Keywords: AE multiplet, Fracture, Pore-pressure, Geothermal
1. Introduction
The measurement of permeable fracture is important in engineering fields, such as the extraction of geothermal energy through subsurface fractures. The Acoustic Emission (AE)method is useful in estimating the location of subsurface fractures and their orientations. Advanced mapping techniques for induced AE events are progressing in order to estimate the fracture system in the reservoir, where the clustering analysis, collapsing method, multipletclustering analysis, etc. had been developed [1-4]. The multiplet-clustering analysis is a method
to determine the relative locations of AE clusters consisted of multiplets and within the AE
events with similar waveforms [5]. A multiplet, which is a group of similar AE events, is considered to be the expression of stress release on the same subsurface fracture, and their source
locations of induced AE multiplet reflect the fracture plane behaving as a shear slip plane due toan increase of pore-pressure in the fracture. The multiplet-clustering analysis makes it possible
to estimate the fracture planes, which would cause shear slip, and the method has been applied toinduced AE events in geothermal reservoirs.
On the other hand, the measurement of pore-pressure distribution is important to understandthe fluid flow and the effect of hydraulic fracturing for enhancing the permeability in subsurface
fracture system. However, the direct measurement of pore-pressure is impossible for fractures
J. Acoustic Emission, 23 (2005)113© 2005 Acoustic Emission Groupaway from wells, although the spatial distribution and the gradient of pore-pressure are an indispensable indication of fluid flow around the well.
The orientation of subsurface fracture derived from the source locations of multiplet provides
us an estimate of stress condition on the fracture plane, if the principal stress directions and themagnitudes are known. According to Coulomb’s law, the shear slip associated with fluid injection is caused by the decrease of normal stress on fracture surface due to an increase of porepressure during fluid injection. If the shear slip is controlled by the Coulomb’s law, we can calculate the critical pore-pressure for shear slip by using the orientations of fractures, the directions
and magnitudes of principal stresses, which controls the dynamics of the fractures.
In this paper, we describe the method for estimating the critical pore-pressure distribution byusing the orientations of subsurface fractures derived form AE multiplets and the regional stressfield. The result of application to Soultz geothermal field is shown and the feasibility of the present method is discussed.
2. Induced AE Events at Soultz Field
The European HDR project at Soultz-sous-Forêts was founded by France, Germany and theEuropean Commission (EC) in 1987. Well GPK-1 is located at the Soultz HDR field site. The
well was drilled to 3,590 m depth (open hole below 2,850 m depth), and since 1987 it has beenused for a number of detailed experiments. A major hydraulic fracturing experiment was undertaken at GPK-1 in September 1993. For twenty days, about 45,000 m3 of fresh water was injected into granite ‘reservoir rock’ with 10 MPa maximum wellhead pressure, whilst the flowrate was increased to 50 l/s. Through the test, it was demonstrated that the fracture network inthe basement rock was well developed, with enhanced permeability and a substantial increase intransmissivity [6]. An inspection was undertaken of the borehole wall by BHTV (Borehole
Televiewer) and FMI (Formation Micro Imager) after completion of the hydraulic fracturing experiment, and stress measurements related to hydraulic fracturing were also performed [7].
Induced AE events were detected using three downhole 4-component detectors and one hydrophone, installed in wells #4550, #4616, #4601 and EPS1. The 4-componet detector consistsof accelerometers mounted in a housing and set in sand at the bottom of the borehole. The three
4-component seismic detectors and hydrophone were set at depths of 1,500 m, 1,420 m, 1,600 m
and 2,850 m, respectively. The downhole seismic detectors were installed in the same basement
rock, and high quality signals were readily detected, since the influence of surface waves could
be ignored and the transfer function from source to detectors is simple. The frequency band of
the acquisition system was from 10 Hz to 1 kHz, and the signals were digitized by 5 kHz sampling frequency [6].
Using these detectors, source locations for more than 10,000 events have been determined[6]. Figure 1 shows source locations for all of the analyzed events, with the location of 10,182
events having been plotted. As shown in this figure, the seismic cloud has approximate dimensions of 0.5 by 1.2 by 1.5 km, striking N30°W and dipping nearly vertically [2].
Multiplet-clustering analysis is a method for precise determination of AE event locations andis used to identify subsurface fractures and fracture networks. A multiplet is a group of microseismic events with very similar waveforms, despite different origin times, and is likely the expression of stress release on the same structure. The relative source locations of similar events
114

Fig. 1 Source locations of induced AE events, where the events determined by JHD method. The
cross section is the view toward N65˚E. Thick line denotes fracturing well GPK1.

(a)

(b)

Fig. 2 Source locations of multiplets determined by (a) JHD and (b) multiplet-clustering analysis.
Closed boxes along well describe the permeable fracture zones detected by well loggings. Thickline denotes fracturing well GPK1. The casing shoe is at the depth of 2,850 m.
can be determined with high resolution and accuracy by using the moving-window crossspectrum analysis technique. Deduced seismic clusters, called multiplet clusters, are indicativeof seismically activated structures, and the orientations of these structures can be estimated using
the seismic clusters even though the absolute locations of the multiplet clusters cannot
115themselves be determined. We have determined the relative locations of multiplet clusters aswell as the relative locations of similar events by using the multiplet-clustering analysis. Figure
2 shows the source locations of AE events before and after the multiplet-clustering analysis,
where about 1000 events identified as multiplets are analyzed.
3. Method for Calculation of Critical Pore-Pressure for Shear Slip
In principle, the pore-pressure for shear slip of a plane can be calculated if the orientation of
the plane and the stress field are known. In our case, the estimates of fracture orientations are
obtained from AE multiplets detected during fluid injection into subsurface reservoir. Then, we
calculate the critical pore-pressure of fractures by using the orientations of fractures and the regional stress field in the corresponding field. The procedures for the calculation of the criticalpore-pressure are described as follows.
In the Soultz field, the stress directions and magnitudes had been measured by hydraulicfracturing method in borehole [8]. According to Coulomb’s law of friction, the relationshipamong shear stress, , normal stress, N, the coefficient of friction, fS, and pore-pressure, PP,
within a fracture surfaces at the moment of shear slip (critical condition) due to an increase inpore-pressure are described as follow,
  f S ( N  PP ) = 0 .

(1)

The cohesion is ignored in the above formulation. The critical pore-pressure for shear slip PC canbe found as follow,

PC = PP  Ph=N 
 Ph .
µS(2)

Fig. 3 Stereographic projection (Schmidt net, lower hemisphere) of fracture planes estimatedusing multiplet and the calculated critical pore-pressure.
116

PC means the increase of fluid pressure to induced shear slip. When the orientation of a planeis given, the normal and shear stresses acting on the plane can be calculated. In our case, wehave estimates of fracture orientations and their depths from the source distribution of individual
multiplets, and the depth of fracture plane can be estimated from the center of gravity of source
locations of each multiplet. Figure 3 shows the stereographic projection of calculated PC and thepoles of fracture planes derived from the multiplets, where the coefficient of friction is 0.8. We
can see that the fracture planes are favorably oriented to principal stresses as the shear slip can beeasily caused. Figure 4 shows the calculated critical pore-pressure for each fracture plane andthe relationship to the depth, where the size of circle represents the value of critical porepressure. Zones in Fig. 4 describe the depth intervals of permeable fracture zones in fracturing
well GPK1, where the permeable fractures are confirmed using FMI and temperature loggings
[9].

(a)
(b)
Fig. 4 The calculated critical pore-pressure for each fracture plane and the relationship to thedepth, where the size of circle represents the value of critical pore-pressure.
The estimated critical pore-pressure for shear slip suggests that the fluid pressure in fractures
is higher near the fractured zones 1 and 2 around 2,900 m, and declines as the fractures are awayfrom the zones. The result in Fig. 4 implies that the fluid pressure was transmitted into fractures,and that the pore-pressure around zones 1 and 2 increased up to near maximum fluid pressure in
fracturing well. It is reported that the fractures in zone 1 and 2 are permeable under low flowrate [9]. Then, the fluid pressure easily penetrated to the fractures near the zone 1 and 2, and thefractures with variable orientations caused shear slips. This explanation would support the porepressure distribution in Fig. 4. On the other hand, the PC becomes below zero around 3,200 m.
The same result can be also obtained when the critical pore-pressure is calculated using the orientation of fractures detected by well logging. Then, the reason for lower value of P C would bethat the stress magnitudes are estimated lower than the actual values.

117

4. Conclusion
We have introduced AE multiplet to estimate the critical pore-pressure for shear slip of fractures during hydraulic stimulation of geothermal reservoir. Because the precise source locationsof multiplets can be determined using the similarity of the waveforms, the orientation of subsurface fracture planes can be also estimated from the source distribution. The stress field measured
by the hydraulic fracturing method in borehole has been introduced to calculate the stress condition on the estimated fracture planes. The critical pore-pressures for shear slip of fractures havebeen calculated by using the orientation of fractures and the stress field. The distribution of estimated pore-pressure has suggested that fluid pressure in fractures is larger around hydraulicallyalternated fracture zones, and declines as the fractures are distant from the permeable zones. The
estimation method based on induced micro-seismic multiplets is effective in evaluating the fluidpressure of subsurface fractures away from wells.
References
1) H. Niitsuma, M. Fehler, R. Jones, S. Wilson, J. Albright, A. Green, R. Baria, K. Hayashi, H.
Kaieda, K. Tezuka, A. Jupe, T. Wallroth, F. Cornet, H. Asanuma, H. Moriya, K. Nagano, W.
Phillips, J. Rutledge, L. House, A. Beauce, D. Alde, and R. Aster, Current status of seismic andborehole measurements for HDR/HWR development, Geothermics, 28 (1999), 475-490.
2) W. S. Phillips, Precise microearthquake locations and fluid flow in the geothermal reservoir at
Soultz-sous-Forêts, France. Bull. Seism. Soc. Am., 90 (2000), 212-228.
3) R. H. Jones and R. Stewart, A method for determining significant structures in a cloud ofearthquakes, J. Geophys. Res., 102 (1997), 8245-8254.
4) H. Moriya, K. Nagano, and H. Niitsuma, Precise source location of AE doublets by spectral
matrix analysis of triaxial hodogram, Geophysics, 59 (1994), 36-45.
5) H. Moriya, H. Niitsma and R. Baria,, Multiplet-clustering Analysis Reveals Structural Details
within Seismic Cloud at the Soultz Geothermal Field, France. Bull. Seismol. Soc. Am., 93(2003), 1606-1620.
6) R. Baria, J. Baumgärdner and A. Gérard, European HDR research programme at Soultz-sousForets (France) 1987-1996. Geothermics, 28 (1999), 655-669.
7) A. Getter and C. Dezayes, Fracture evaluation in GPK1 borehole by using FMI data, Field
Report, BRGM Orléans (1993).
8) G. Klee and F. Rummel, Hydraulic fracturing stress measurements in the geothermal borehole
GPK1, Soultz-sous-Forets. Report no. 04-93, MeSy, Bochum (1993).
9) K. F. Evans, The effect of the 1993 stimulations of well GPK1 at Soultz on the surroundingrock mass: Evidence for the existence of a connected network of permeable fractures. Proc.
World Geothermal Congress 2000, 6 (2000), pp. 3695-3670.

118

A MODELING METHOD ON FRACTAL DISTRIBUTION OF CRACKSIN ROCKS USING AE MONITORINGYOSHINORI WATANABE 1, KEN-ICHI ITAKURA1, KAZUHIKO SATO1,YOSHIAKI FUJII2, RAO BALUSU3, HUA GUO3 and XUN LUO3
1

Department of Computer Science and Systems Engineering, Muroran Institute ofTechnology, 27-1 Mizumoto, Muroran, Hokkaido, 050-8585, Japan;
2
Division of Structural and Geotechnical Engineering, Hokkaido University
Sapporo, Hokkaido, 060-8628, Japan; 3Exploration and Mining, CSIRO, Brisbane
Technology Court, Pullenvale, QLD 4069, Australia.
Abstract
For underground mining and construction, it is important to obtain a discontinuities’model of rocks for simulations to evaluate rock stability around the working space and fluid
flow in these rocks. We have developed a method using the fractal property of acoustic
emission (AE) source distribution to construct a feasible crack distribution model. We confirmed the usefulness of the model through laboratory experiments for granite specimens.
The experiments demonstrated that the AE source distribution was fractal and that the relationship between amplitude and frequency of AE was fractal. Crack distributions in thespecimen and on the surface were both fractal. Based on the results, 3-D crack distribution
models were constructed using AE monitoring data obtained. This model describes thecrack as a disk. The disk orientation was determined by a moment tensor analysis of AE
waveforms. In this study, we applied the modeling method to uniaxial and triaxial compression tests on specimens of coal measures rocks (sandstone, siltstone and coal). Results
showed that micro-cracking activity, which occurred prior to failure, differed depending on
the rock type. We obtained characteristic models for each rock type. This technique wasalso applied to a micro-seismic data set obtained during mining at an underground Australian coal mine. The crack distribution model of roof rock on a longwall mining panel
showed that most cracks were located in roof rock in front of the working face.
Keywords: Crack, Crack distribution model, Moment tensor, Fractal, Micro-seismicity,
Coal mine
1. Introduction
Existing crack distribution models for rock mass have been developed based on cracksystems’ stochastic and fractal characteristics [1–5]. These crack distribution models areuseful for simulation of structural stability and fluid flow, etc. To establish a much morefeasible model that represents the actual rock crack system, we developed a method using
source location and moment tensor analysis for acoustic emission (AE) data [6,7]. Thismethod describes the crack as a disk without thickness. The fractal dimension of sectionallines that appeared on the specimen surface was adjusted to coincide with the dimension of
lineaments that were extracted from an actual crack image of the same surface of the specimen by correcting the AE parameters. That is, the relative AE energies for respective eventswere altered iteratively until both fractal dimensions were matched. ThroughJ. Acoustic Emission, 23 (2005)

119© 2005 Acoustic Emission Groupapplication of this technique to laboratory experiments (uniaxial compression tests) for
granite specimens, we confirmed that the AE source distribution was fractal and that therelationship between amplitude and frequency of AE was also fractal. Crack distributions inthe specimen and on the surface were both fractal. Consequently, it became possible to construct a reasonable 3-D crack distribution model according to fractal properties of AE
source and amplitude-frequency distributions [6, 7].
As a next step, this modeling method was applied for the evaluation of an undergroundrock mass during stress change by coal excavation in an Australian coal mine. The rockmass comprised siltstone, sandstone and coal. A series of laboratory experiments was carried out for these coal measures rock samples before constructing a large-scale 3-D crack
distribution model of an underground coal mine.
2. Experimental Procedures
2.1 Rock Specimens and Experimental Conditions
For 17 pieces of coal measures rock specimens (11 sandstone, 2 silt stone and 4 coal
specimens), uniaxial and triaxial compression tests were conducted while changing the confining pressure and the strain rate. These core sample rocks were extracted from approximately 301 m to 334 m below the surface of an underground coal mine of Australia. Specimens were cut from the core samples and ground into a cylinder of 30-mm diameter and 60mm height. The confining pressure of triaxial compression test were set at 1, 4, 9 and 14
MPa for sandstone, 1 and 9 MPa for siltstone, 1, 4 and 9 MPa for coal specimens in addition
to the uniaxial compression tests. For the sandstone specimens, the strain rate was set at 2.5
x 10-6 /s, 5 x 10-6 /s and 25 x 10-6 /s. For other specimens, the strain rate was fixed at 5 x 106/s. However, the influence of the strain rate was not evident through the tests.
AmplifierMulti
Digitizer

4 mm
2 mm

CPU

plus

minus

Hard
Disk
Testing Envelope
Machine Processor

CountsEventsPC

1 mm

AD Converter

Fig. 1 AE monitoring system.

Z2T(1)4ED-LYXN(C-6)

Fig. 2 PZT element as an AE sensor.

2.2 Apparatus
Figure 1 shows an AE monitoring system for laboratory experiments. As an AE sensor,
10 pieces of PZT elements (shown in Fig. 2) were attached to the surface of the specimen.
After attaching sensors to the surface of the specimen, the specimen was covered by endpieces and oil shield plastics, and set into a pressure vessel. Setting the confining pressure120

as constant, Eight sensors were selected from the 10 elements while checking individual
sensitivity. AE waveforms from the 8 sensors were digitized with a sampling time of 100 ns
using a Multi-Digitizer (APC-5108; Autonics Corp.), and recorded on a hard disk of PC. All
signals were amplified 35 dB using preamplifiers (AE2A; Physical Acoustics Corp.). The
frequency band of the system was restricted using a 100–400 kHz bandpass filter of the preamplifier. The numbers of AE events and AE counts that were detected by the one of thesensors were also digitized and recorded on a hard disk of another PC. These events andcounts were regarded to represent the total amount of AE activity during the experiment.
3. Modeling Procedure
3.1 Disk Crack Model
This crack distribution model was constructed under the assumption that micro-cracking
in rock coincides with an occurrence of AE event in most brittle rocks under stress. According to that assumption, the micro-crack inside the rock with AE generation was described as a disk in this crack distribution model. To arrange the disk-type crack into a 3-D
rock space, the following must be known:
1) crack location (coordinate of the disk center),2) crack size (disk diameter),3) crack orientation (normal vector of the disk), and4) crack type (tensile strength, shear and other characteristics).
The crack location, i.e. the coordinate of the center of disk, was determined by AE source
location. In this model, the crack size is represented by the relative energy of an AE event.
The relative energy was calculated from the amplitude of the AE waveform considering thecorrection of propagation loss and directional sensitivity of the AE sensor. The crack orientation was determined from the result of moment tensor analysis of AE waveforms [8].
However, in the case of a shear crack, it is difficult to choose a shear plane from conjugate
directions. Therefore, in this modeling method, the orientation of the shear crack plane wasdetermined randomly. The crack type was determined by the tensile crack ratio obtained
from the principal value of the moment tensor and Poisson’s ratio. AE events that wereclassified as tensile cracks were those with a ratio of more than 60%. A ratio or less than
40% was regarded as a shear crack; others were inferred to be a combination type.
3.2 Compensation by Fractal Property of AE Amplitude-Frequency Distribution
If the amplitude-frequency distribution of AE are regarded as fractal, the number of AE
events with smaller size amplitude could occur during loading of laboratory experiments.
Figure 3 shows a typical amplitude-frequency distribution of AE. Assuming a fractal property based on the linearity of a higher amplitude region, many AE events are expected tohave occurred according to the difference between the observed number and the estimatedline. These events with small amplitude seemed not to be detected by the AE monitoring
system used for the experiments. In this modeling method, small AE events were added
into the model that was constructed using AE data analysis. The crack number and size
were estimated from the amplitude-frequency distribution. Locations, orientations and typesof compensation cracks were arranged randomly in the rock space with regard to the statistics of crack distributions obtained by AE data analysis.
121Fig. 3 Amplitude-frequency distribution.

Fig. 4 Confining pressure effect for peak stress.

Fig. 5 Confining pressure effect for numberof AE events.

4. Experimental Results
4.1 Effect of Confining Pressure
Figure 4 shows the relationship between confining pressure and peak stress of triaxial
compression tests for sandstone. This figure shows the confining pressure effect: the peakstress increases with increasing confining pressure. Figure 5 shows the relationship between
confining pressure and the number of AE events that were detected during load increase.
122

The number of AE events tends to decrease with increased confining pressure. The relative
energy of AE events also decreased concomitant with the increasing confining pressure.
Siltstone and coal also showed these tendencies.
4.2 AE Source Locations
Figures 6, 7 and 8 show typical results of AE source location for siltstone, sandstone
and coal. The confining pressure was 1 MPa for these specimens. These results were displayed three-dimensionally from different perspectives by means of virtual reality modelinglanguage (VRML). Pictures in the figures show specimens after loading tests. In Figs. 6 and7, most AE sources were located along the fracture planes. However, in the case of coal
specimen shown in Fig. 8, almost all AE events were characterized as concentrated into thecenter region of the specimen. Identical results were obtained from two other coal specimens in the low confining pressure of 1 MPa. Observation of the specimen showed thatthese concentrations of AE events reflected the coal specimens’ crush zone.

Fig. 6 AE source locations (siltstone).

Fig. 7 AE source locations (sandstone).

4.3 Crack Distribution Models of Specimens
Figure 9 shows typical results of crack distribution models for siltstone, sandstone andcoal specimens. In addition to the cracks determined by AE data analysis, small cracks were
included. The darkest disk indicates the shear crack. The brighter color disk shows the tensile crack; the brightest disk is for the combination type crack. Regarding siltstone andsandstone specimens, orientations of the tensile crack planes were almost parallel to theloading axis; the shear crack planes were perpendicular (vertical) to it. The percentages oftensile cracks and shear cracks for sandstone specimens were about 30–40% and 40–50%
respectively. A tendency for shear cracks to be slightly dominant for the sandstone specimens was shown by the coal specimens as well. However, no relationships were found between the percentage of tensile and shear cracks and the confining pressure.

123

Fig. 8 AE source locations (coal).

Siltstone

Sandstone

Coal

Fig. 9 Crack distribution models.
5. Crack Distribution Model During Mining of Underground Coal Mine
5.1 Micro-seismic Monitoring of Coal Mine
Micro-seismic monitoring was conducted in an underground coal mine of Australia using 20 triaxial velocity meters (geophone). Figure 10 shows the plan view of undergroundmining panel in the depth of about 285–335 m from the surface. Micro-seismicity was recorded during mining of longwall No. 4, which is 400 m wide and 600 m long. Geophones(GS-20DX; Oyo Geospace Corp.) were installed from the surface inside the four drill holes.
124

Fig. 10 Plan view of longwall mining panel.

Fig. 11 Vertical projection of geophone locations.
Figure 11 shows the vertical projection of the drill holes and the locations of triaxial geophones. During mining of longwall No. 4, 170,000 micro-seismic events were triggered.
5.2 Crack Distribution Model of Coal Mine
This report relates the results of three periods: the first period of 5–8, October 1999; thesecond period of 18–20, October 1999; and the third period of 26–28, October 1999. Figures 12, 13 and 14 show results of final crack distribution model that were constructed using micro-seismic data analysis. Figure 12 shows the crack distribution model during mining of the first period. It was found that most cracks were generated in the sandstone roof
rock; the crack locations were in front of the longwall working face. Figures 13 and 14125

Fig. 12 Crack distribution model of first mining period.

Fig. 13 Crack distribution model of the second mining period.
illustrate that the locations of cracks became closer to the mining area with advance ofworking face. In the third period, many shear type cracks were concentrated around drill
hole C. Subsequently, the geophones of this drill hole were broken. Through the mining
periods, it was clear that the shear-type cracks were dominant in the roof rock. This result
accorded with results of laboratory experiments using sandstone specimens.
6. Conclusions
This paper introduced a method to construct 3-D crack distribution model using AE
monitoring data. We described its application to coal measures rock specimens and to anunderground coal mine.
126

Fig. 14 Crack distribution model of the third mining period.
Uniaxial and triaxial compression tests were carried out for 17 pieces of siltstone, sandstone, and coal rock samples. The number of AE events decreased with the increase of confining pressure. Crack distribution models showed that shear type cracks were dominant forcoal and sandstone specimens.
For 170,000 AE waveforms recorded in the mine, AE source location and moment tensor were analyzed to construct a crack distribution model of the longwall panel. The AE activity and 3-D crack distribution model showed that the most cracks were generated in theroof rock of the longwall panel; furthermore, they were located in front of the working face.
The dominant crack type developed inside the roof rock was shear type, as in the sandstone
specimen, under the triaxial stress state.
The effectiveness of this modeling method, which considers the fractal properties of AE,
was demonstrated through a series of laboratory experiments and subsequent application to
micro-seismic data of field monitoring. As the next step, we plan to conduct a simulationthat uses this crack distribution model to evaluate gas flow in an underground coal mine.
References
1) Ivanova, V. et al.: Development of stochastic models for fracture systems, Rock Mechanics,
Daemen & Schults (eds.), Balkema, Rotterdam (1995), pp. 725–730.
2) Chiles, J.P.: Fractal and Geostatistical Methods for Modeling of a Fracture Network,
Mathematical Geology, 20, 6 (1988), 631–654
3) Kaneko, K. et al.: Evaluation of Rock Discontinuities – In-situ Measurement and Modeling–, Proc. Workshop on Characterization of Subsurface Cracks (1990), pp. 25–29. (in Japanese)

1274) Watanabe, M., Y. Nakayama and H. Takahashi: Fractal Characterization of Subsurface
Fracture Network, Proc. 2nd Workshop on Characterization of Subsurface Cracks (1992),
pp. 80–85. (in Japanese)5) Yamamoto, H. and K. Kojima: Modeling of Rock Fracture Systems Using the Concept ofFractal, Proc. 2nd Workshop on Characterization of Subsurface Cracks (1992), pp. 75–79.
(in Japanese)
6) Itakura, K. et al.: Modeling Method for Cracks in Rocks based on the Fractal Structure of
the AE Source Distribution, J. of MMIJ, 114, 12 (1998), 918–924. (in Japanese)
7) Watanabe, Y., K. Itakura and K. Sato: 3-D Crack Distribution Model Based on Crack Image
Analysis and Fractal of AE Sources from Stressed Rock Specimen, Proc. 6th Domestic
Conf. on Subsurface and Civil Engineering AE (1999), pp.100–105. (in Japanese)
8) Sato, K. and Fujii, Y.: Source Mechanism of a Large Scale Gas Outburst at the Sunagawa
Coal Mine in Japan, PAGEOPH, 129 (1989), 325–343.

128

INTERPRETATION OF RESERVOIR CREATION PROCESSAT COOPER BASIN, AUSTRALIA BY ACOUSTIC EMISSIONYUSUKE KUMANO1, HIROKAZU MORIYA1, HIROSHI ASANUMA1, NOBUKAZU
SOMA2, HIDESHI KAIEDA3, KAZUHIKO TEZUKA4,
DOONE WYBORN5 and HIROAKI NIITSUMA1
1)
Graduate School of Environmental Studies, Tohoku University, Sendai 980-8579, Japan;
2)
AIST, Tsukuba 305-8569, Japan; 3) CRIEPI, Abiko 270-1194, Japan; 4) JAPEX ResearchCenter, Chiba 261-0025, Japan; 5) Geodynamics Limited, Brisbane, Australia.
Abstract
Hydraulic stimulation of an engineered geothermal reservoir has been carried out at Cooper
Basin hot-dry-rock (HDR) field, Australia in 2003. AE monitoring network, which consists offour near surface and one downhole stations, detected approximately 32,000 triggers during thestimulation and approximately 12,000 of them have been located on-site by the single-event
determination method (SED). The location of AE showed a sub-horizontal structure that is
consistent with the state of tectonic stress and the orientation of existing fractures in thecorresponding field. We have estimated the composite focal mechanism, and it showed areverse fault with a low inclination to West. The plane of the nodal plane agrees well with thestructure by the locations of AE. We found that the plane from the focal mechanism is nearly
under the critical stress state for shear slip at Cooper Basin. The AE activity was higher thanthat at Soultz field, where EU and other organizations are carrying out an HDR project,suggesting that the stress state in Cooper Basin site is closer to critical.
Keywords: HDR, Hydraulic fracturing, Tectonic stress, Focal mechanism, Cooper Basin
1. Introduction
The analysis of AE from the hydraulic fracturing/stimulation of hot-dry-rock (HDR)
geothermal reservoir is an important method, which enables us to estimate the detailed structure
inside the reservoir and the process of reservoir creation. Researchers who worked inworldwide HDR projects in the area of AE/seismic monitoring collaborated to develop
advanced AE mapping methods, which has much higher resolution and reliability (superresolution mapping). The super-resolution mapping techniques have made the integrated interpretation of AE mapping, logging, hydraulic record in fracturing operation and geological information to understand reservoir physics [1]. The post-analysis of AE location and source
mechanism analysis using the super-resolution mapping techniques has been done in worldwide
HDR projects [2], and mechanism of the reservoir extension has been successfully interpreted
in many case.
An HDR project to develop a commercial geothermal power plant is underway at Cooper
Basin, Australia, managed by a public company, Geodynamics Limited. In this site, the temperature exceeding 250°C is expected inside the granitic basement at depths below approximately 4,000 m [3]. It has been reported that the maximum tectonic stress is sub-horizontal(East to West) in the central part of Australia (Fig. 1). Because the reservoir extends normal to
J. Acoustic Emission, 23 (2005)129© 2005 Acoustic Emission Groupthe minimum principal stress direction in theory, it was expected that hydraulic fracturing creates the reservoir extending horizontally.
The first hydraulic stimulation to create an HDR reservoir was carried out in November andDecember 2004 to demonstrate and evaluate the potential of geothermal energy in this site. The
Japanese team, consisting of the authors and people from Geodynamics Ltd., deployed an AE
monitoring network, and monitored AE during the stimulation. We detected 32,000 triggers
during injection of 20,000 m3 of liquid into the granitic basement over 3 weeks, and 11,724 AE
events were located on-site [4].
In this paper, we report the analysis of the induced AE events, where we located the AE
sources by the super-resolution mapping techniques as a post-analysis and investigated relationship among tectonic stress, focal mechanism, and AE source location. We also discuss the reservoir structures based on the analysis of AE events.
2. AE Monitoring during Hydraulic Fracturing
Figure 1 shows the tectonic stress state and the reservoir pressure as a function of depth, andthe maximum stress direction. The hydraulic fracturing was carried at the site, where the horizontal compressional stress is dominant. Therefore, it was expected that the nearly horizontal
reservoir was created, because the minimum principal stress direction is vertical. Figure 2 show
the history of pump operation during the fracturing.

a) Tectonic stress and reservoirb) Stress map around Cooper Basinpressure along depth
Fig. 1: Tectonic stress at Cooper Basin [3].
Figure 3 shows a plan view around the injection well “Habanero1” and AE monitoring stations deployed at Cooper Basin site. The injection well was drilled to a depth of 4,421 m (754m into granite). Several sub-horizontal over-pressured fractures were found in the granitic section in the well, and most of the existing fractures were plugged to stop lost circulation and inject the water into a targeted fracture at a depth of 4,254 m. This single fracture remained as an
initial dominant feed point into the formation. The monitoring network consists of one deep130

Fig. 2: Pumping data during the first hydraulic fracturing.

Fig. 3: Plan view of AE monitoring network.
downhole and four near surface stations. All the monitoring stations are deployed in sedimentary rock over granitic basement.
3. AE Source Location
Three-dimensional distribution of AE sources estimated by the single-event determination(SED) [5, 6] is shown in Fig. 4. We can see that the hydraulic stimulation brought a subhorizontal AE cloud that has lower inclination to West. The AE cloud has a thickness of approximately 300 m and horizontal extension of approximately 3 km North to South. Figure 5(a)shows the three-dimensional distribution of AE sources located by the collapsing method [7] aswell as a vertical cross section near the injection well. The collapsing method is one of the statistical optimization techniques of the AE to estimate structure of AE sources in large scale. The
plan view in Fig. 5(a) is similar to that of Fig. 4, although the thickness of the AE cloud after
collapsing is thinner than that by SED. The thickness of the AE cloud in Fig. 5(b) is 30 or 50
m, and the all-inclusive structure has low inclination to West. We can also see that the AE
cloud is composed of several large sub-parallel structures.
131

Fig. 4: 3-D AE source distribution by single event determination (SED).

a) 3-D distribution b) Cross sectional viewFig. 5: 3-D AE source distribution located by collapsing method.
132

4. Focal Mechanism
We observed the polarity at the onset of the P-wave of over 1,000 events with a magnitudelarger than M0.3. We have found that almost all the events showed similar polarity, implyingthat the focal mechanism of AE events was similar. Thus, we decided to estimate focal mechanism by the composite focal mechanism method [5]. Figure 6 shows distribution of polarity at
the onset of P-wave in the lower hemisphere of the stereographic projection. AE signals detected by stations at WA-1 and 2 showed dilatational motion, and those for WA-3, 4 andMcLeod-1 showed compressional motion. Figure 6 shows the estimated fault plane solution.
Since the observation network was sparse, the unique solution cannot be determined. Therefore, only one reliable nodal plane is drawn in Fig. 6. Another plane can be expected to have
low inclination, dipping to West. Referring the orientation of the entire AE cloud, the plane
shown in Fig. 6 is considered an auxiliary fracture plane and the actual plane is considered athrust fault with a lower inclination to West. The low inclination of the plane consists with allinclusive structure of the AE cloud.

     

Fig. 6: Distribution of first motion of P-wave on lower hemisphere stereographic projection.
5. Discussion
We have obtained a thin AE cloud with the thickness of 30-50 m by the collapsing method.
This result suggests that the AE cloud is a single-planer structure at many portions but in somepart the layered AE clouds are separated 200-300 m and they are connected with sub-vertical
cloud. According to the source locations by the collapsing method, we can interpret that single
fracture and the group of sub-parallel fractures were stimulated, although we cannot yet eliminate the possibility of “over-collapsing” because this method is based on statistical theory andnot directly related to the physical phenomena. On the other hand, the global orientation of the
AE cloud is consistent with the fault-plane solution estimated by the composite focal mechanism model.
We have drawn a Mohr’s stress circle of tectonic stress at the depth of 4,400 m (Fig. 7), assuming that the maximum horizontal stress is the first principal stress and the minimum is133vertical. This assumption is supported by the result of focal mechanism from AE. We alsoplotted a direction of a plane from the focal mechanism analysis (Fig. 7). From this figure, we
can find from the focal mechanism that the pre-existing subsurface fractures were under nearly
critical stress state for shear slip before the hydraulic fracturing.
We compared the characteristics of AE from hydraulic fracturing with the data collected at
Soultz HDR field in 2003. The hydraulic fracturing at Cooper Basin induced AE events andevents appeared at a distant point from the injection well from the initial stage of the stimulation. These characteristics of AE are consistent with the assumption that AE was induced at
fractures, which were favourably oriented to the principal stress and were under critical stressstate before the fluid injection.
The structure of AE source by the collapsing method is simple and we can find several large
sub-parallel structures. Results of focal mechanism analysis, discussion with Mohr’s circle andcomparison with Soultz data were consistent with the phenomena that limited fracture wasdominantly stimulated. AE multiplet analysis or clustering analysis must be done to confirm it,
because it has been proved that these methods are effectively used to estimate phenomenon of
AE sources [8].

Fig. 7: Mohr Stress Circle at the depth of 4400 m.
6. Conclusions
We analyzed AE during a hydraulic fracturing at Cooper Basin HDR site, Australia. The AE
cloud extended sub-horizontally with small inclination to west. The orientation of the AE cloudis consistent with the focal mechanism estimated by composite model as well as tectonic stressat the field. The stress analysis showed that the AE was induced at existing fracture that is under nearly critical stress state. These results can be interpreted by a reservoir creation process
where single or limited number of sub-parallel fracture with horizontal extension was mainly
stimulated in this field. The authors will try to obtain further interpretation of the reservoir
creation process through multiplet analysis.
134

Acknowledgements
The seismic monitoring/mapping at the Habanero-1 site by the Japanese team would nothave been realized without much help from Geodynamics especially from Dr. B. de Graaf. We
also highly appreciate all the contributors to the project from Japanese side. We would like toexpress our thanks to Dr. P. Chopra, ANU/Geodynamics, for his scientific discussion with somecomments and recommendations.
References
1) M. Fehler, A. Jupe and H. Asanuma, More Than Could: New techniques for characterizing
reservoir structure using induced seismicity, The Leading Edge, 3 (2001), 324-328.
2) K. F. Evans, H. Moriya, H. Niitsuma, R. H. Jones, W. S. Phillips, A. Genter, J. Sausse, R.
Baria, Microseismisity and permeability enhancement of hydro-geologic structures duringmassive fluid injections into granite at 3km depth at the Soultz HDR site, Geophys. J. Int.
(2004), (submitted).
3) Swenson, D, P. N. Chopra and D. Wyborn, Initial calculations of performance an Australian
Hot Dry Rock reservoir, Proc. WGC 2000, (2000), 3907-3912.
4) H. Asanuma, Y. Kumano, T. Izumi, N. Soma, H. Kaieda, K. Tezuka, D. Wyborn and H. Niitsuma, 2004. SEG 2004 Expanded Abstracts (in-press).
5) T. Lay and T. C. Wallance, Modern global seismology, Academic Press Inc. (1995).
6) K. Aki and P. G. Richard, Quantitative seismology, Theory and methods, W. H. Freeman,
New York (1980).
7) R. H. Jones and R. A. Stewart, A method for determining significant structures in a cloud ofearthquake, J. Geophys. Res., 102 (1997), 8245-8254.
8) H. Moriya and H. Niitsuma, Subsurface stress field determination using multiplets in downhole three-component microseismic measurement, Proc. 2nd North American Rock Mechanics Symposium, 2 (1996), pp. 853-858.

135

MICROMECHANICS OF CORROSION CRACKING IN CONCRETE BY
AE-SIGMAFARID A. K. M. UDDIN and MASAYASU OHTSU
Graduate School of Science and Technology, Kumamoto University, Kurokami,
Kumamoto 860-8555, Japan.
Abstract
Nucleation of micro-cracking due to corrosion is identified by AE-SiGMA, by which crack
kinematics of locations, types and orientations are quantitatively determined. These kinematicaloutcomes are obtained as three-dimensional (3-D) locations and vectors, and 3-D visualization isdeveloped by using VRML (Virtual Reality Modeling Language). Thus, the cracking modes areinvestigated for different crack patterns and the cracking mechanisms of 594 events were identified. It is confirmed that the mechanisms of micro-cracking due to corrosion of reinforcement arenearly equally of the mode-I, mixed-mode and mode-II failure. This is because cracks in concrete propagate in zigzag path, dynamic motions on micro-crack planes consist of the crackopening mode and the shear mode.
Keywords: AE-SiGMA, Corrosion cracking, Moment tensor, Virtual reality modeling language(VRML)
1. Introduction
Corrosion of reinforcing-steel bar (rebar) in concrete is a major cause of the deterioration in
reinforced concrete structures. For making decision on maintenance and repair works, the identification of cracking mechanisms due to corrosion is important. Expansion caused by corrosion
products generates micro-cracks. The mechanisms of micro-cracking can be investigated experimentally by acoustic emission (AE). AE-SiGMA (Simplified Green’s functions for Moment
tensor Analysis) was developed as a powerful technique for the moment tensor analysis [1].
Crack kinematics of locations, types and orientations are quantitatively determined. Because
these kinematical outcomes are obtained as three-dimensional (3-D) locations and vectors,
VRML (Virtual Reality Modeling Language) was implemented [2]. In this paper, mechanisms ofcorrosion cracking in concrete are studied by using 3-D visualization of AE-SiGMA results.
2. Experimental Procedures and SiGMA
To simulate crack patterns due to rebar corrosion in concrete, an expansion test was carriedout. Concrete was made up of mixture proportion as water (W):cement (C):sand (S):gravel (G) =0.5:1.0:2.41:2.95 by weight. The maximum size of aggregate was 20 mm. At the fresh state,slump value and air content were 7.0 cm and 5.0 %, respectively. Concrete specimens of dimensions 25 x 25 x 10 cm were tested. A circular hole of 3 cm diameter was created at the location ofreinforcement.

J. Acoustic Emission, 23 (2005)

136© 2005 Acoustic Emission GroupFig. 1 Arrangement of AE sensors and measuring system.
In the test, dolomite paste (water:dolomite = 1:2) was employed as an expansive agent andcast into a circular cavity of the specimen as shown in Fig. 1. Expansion pressure was measured
by using an embedded pressure gauge. AE events were detected by a six-channel system of AE
sensors (PAC, UT1000). Arrangement of AE sensors and the measuring system are also illustrated in Fig. 1. AE waves were amplified 50 dB with a preamplifier, and waveforms and events
exceeding the threshold level of 50 dB were recorded and analyzed by AE-TRA212 system(PAC), at 1-s sampling interval.

Tensile crack

Mixed-mode

Shear crack

Fig. 2 VRML models for tensile crack, mixed-mode, and shear crack.
In a previous paper [3], classification of cracks is denoted only by symbols. Crack orientations, however, are not so easily represented in a two-dimensional (2-D) projection. To improve
this aspect, VRML was introduced. Crack models of tensile, mixed-mode and shear cracks areshown in Fig. 2. Cracks are classified into tensile, mixed-mode and shear cracks from the shear
ratios. AE sources, of which the shear ratios are smaller than 40%, are classified as tensile
cracks. These greater than 60% are referred to as shear cracks. In between 40% and 60% of theshear ratios, AE sources are classified as mixed mode.

137

D1
D2

Sv

Fig. 3 Observed cracks (left) and visualized results (right) of AE-SiGMA in the specimen.
In the expansion test, peak pressure was measured as 17.1 MPa after about 18 hours elapsed.
Observed cracks on the specimen, and results of AE-SiGMA are shown in Fig. 3. Surface crack‘Sv’, diagonal cracks ‘D1’ and ‘D2’ are observed. According to the results of AE-SiGMA, tensile, mixed-mode and shear cracks are intensely observed near these three crack traces. During
the test, it was observed that surface cracks ‘Sv’ was first nucleated and propagated vertically tothe surface in the region of concrete cover. This is followed by the diagonal cracks ‘D1’ and ‘D2’,
running diagonally towards the inside.
Table 1 Results of AE-SiGMA.
Type of crack
Surface crack ‘Sv’
Diagonal crack ‘D1’
Diagonal crack ‘D2’
Total

Total Numberof AE events
102
197
295
594

Number of AE events classified
TensileMixed-mode
Shear
40 (6.7%)
33 (5.6%)
29 (4.9%)69 (11.6%)
66 (11.1%)
62 (10.4%)103 (17.4%)
100 (16.8%)
92 (15.5%)
212 (35.7%)
199 (33.5%)
183 (30.8%)

3. Identified Mechanisms Of Corrosion Cracking
By AE-SiGMA, the cracking mechanisms of 594 events were identified. Of these, 35.7 % of
the events are classified into tensile, 33.5% events into mixed-mode and 30.8% events into shear
cracks, as summarized in Table 1 and shown in Fig. 4. These AE events are classified as associated with crack patterns ‘Sv’, ‘D1’ and ‘D2’ in Table 1.
3.1 Surface Crack
Out of 594 events analyzed, 102 events were associated with the surface crack and 40 (6.7%)
events are classified into tensile mode, 33 (5.6%) events into mixed-mode and 29 (4.9%) events
into shear cracks. As seen in Fig. 3, the crack mechanisms of all 3 modes are mixed around the138surface crack. Relations between AE sources and expansion pressure are shown in Fig. 5. Nucleation of the surface crack is observed around the peak pressure as high AE activities. At thisstage, the tensile cracks are most numerous. Mixed-mode and shear cracks are slightly less activein the whole stage. This implies that cracking mechanisms of the surface crack are crack-opening
type, consisting of both tensile and shear motion due to zigzag crack extension.

Fig. 4 Expansion pressure and activities of classified cracks.

Fig. 5 Expansion pressure and activities of classified cracks for the surface crack ‘Sv’.
139

3.2 Diagonal Cracks
For the diagonal crack ‘D1’ and ‘D2’, 197 and 295 events in total were analyzed, respectively,
as shown in Table 1. 69 (11.6%) events are classified into tensile mode, 66 (11.1%) events into
mixed-mode and 62 (10.4%) events into shear cracks for the diagonal crack ‘D1’. For the diagonal crack ‘D2’, 103 (17.4%), 100 (16.8%) and 92 (15.5%), respectively, for the three modes. As
confirmed by VRML visualization of AE-SiGMA results in Fig. 3, these three modes were concentrated along the diagonal cracks. Results of AE activities and expansion pressure for the diagonal cracks ‘D1’ and ‘D2’ are shown in Figs. 6 and 7. All three modes of cracks became
equally active over the whole stages, but the tensile cracks were always the most numerous.

Fig. 6 Expansion pressure and activities of classified cracks for the diagonal crack ‘D1’.

Fig. 7 Expansion pressure and activities of classified cracks for the diagonal crack ‘D2’.
140

4. Conclusion
The traces of the surface crack and the diagonal cracks are identified by AE-SiGMA resultswith VRML. It is confirmed that AE sources are clustered close to the final failure surfaces. According to the results of AE-SiGMA, the number of AE events increases just before crack extension. Depending on the crack types, the contributions of mode I and mode II are varied duringcrack extension. These results are summarized, as follows:
1. Mechanisms for nucleating the surface crack and the diagonal cracks due to expansionof corrosive products are identified by AE-SiGMA results.
2. The surface crack starts to be initiated when the expansion pressure reaches the peak.
According to AE-SiGMA results, all three modes were nearly equally observed. However, mixed-mode and shear cracks were less active and tensile cracks mostly active.
Mixed-mode cracks were a little more active than shear cracks at the post-peak region.
Following the surface crack, the diagonal crack is prone to extension.
3. For the diagonal cracks, both tensile and mixed-mode cracks became equally active,
while shear-mode cracks were slightly less over the whole stage.
4. Mechanisms of corrosion cracking in concrete are of crack-opening failure, but thecracks follow zigzag paths, thus explaining nearly equal contributions of mixed-mode
and mode-II cracks. The results show a great promise to apply AE-SiGMA procedurefor inspection, identifying the mechanisms of corrosion cracking in concrete.
Acknowledgment
The authors wish to thank to Assoc. Prof. M. Shigeishi and Mr. Y. Tomoda, Mr. J. Shimasaki
of Kumamoto University, and Mr. T. Itoh of Kawada Industries Co., for their valuable assistancein carrying out experiments and analyses. A part of this research is financially supported by theforeign-researcher fellowship from the Japan Society for the Promotion of Science (JSPS).
References
1) M. Ohtsu, T. Okamoto, and S. Yuyama: Moment Tensor Analysis of Acoustic Emission forcracking Mechanics in Concrete, ACI Structural Journal, 95 2 (1998), 87.
2) M. Ohtsu and M. Shigeishi: Three-Dimensional Visualization of Moment Tensor Analysis
by SiGMA-AE, e-Journal of Nondestructive Testing, ISSN: 1435-4934, 7 9 (2002).
3) M. Ohtsu and S. Yoshimura: Analysis of Crack Propagation and Crack Initiation due to Corrosion of Reinforcement, Journal of Construction and Building Materials, 11 7-8 (1997) 437.

141

EVALUATION OF PARAMETER DEPENDENCIES OF AE ACCOMPANYING SLIDING ALONG A ROUGH SIMULATED FRACTURE
KATSUMI NEMOTO, HIROKAZU MORIYA and HIROAKI NIITSUMA
Graduate School of Environmental Studies, Tohoku University, 6-6-20 Aramaki-aza-aoba, Aoba,
Sendai, Miyagi 980-8579, Japan.
Abstract
Understanding shear slip behavior on fractures is indispensable for understanding earthquake
mechanisms. In this study, we have investigated the characteristics of AE accompanying sliding
along simulated fracture surfaces in an attempt to evaluate the dependencies of AE on sliding
velocity and stress applied on the fracture. Our sliding experiments using pre-fractured granite
specimens were conducted at room temperature and under a confining stress between 5 MPa and
20 MPa. Sliding velocity was kept constant during the experiments in the range of 1.15 m/s to1155 m/s. We evaluated the characteristics of AE by determining the m-value, which shows thestatistical characteristics of AE [1]. Evaluation of parameter dependencies of the m-values reveals that the m-values have little dependence on stress state on the fracture and that they havenegative dependence on sliding velocity. The small m-value indicates that AE events with larger
maximum amplitude occur. Therefore, our results imply that high sliding velocity has a tendency
to generate higher energy AE events during the sliding. This suggests that the characteristics ofbraking or slip of asperities on the fracture during the sliding were affected by sliding velocity
rather than stress on the fracture.
Keywords: Earthquake mechanisms, Laboratory sliding experiments, Asperity, Contact state
1. Introduction
Earthquakes, when it happens, will bring out enormous damage. Thus, it is important to understand the physical mechanisms and to feed back the understanding for prediction. Considering that earthquakes would be caused by dynamic failure or slip at the crustal faults, it is significant to clarify the physical mechanisms of the dynamic behavior on faults for understanding of
the mechanisms of earthquakes. On the crustal faults, fault surfaces have contacted each other
under compressive stress due to a tectonic stress. When shear displacement occurs on the fault,
micro-earthquakes would be caused by dynamic failure or slip of contact spots, which contact
actually on the faults associated with the shear displacement. Therefore, investigation of source
distributions and source parameters such as failure area, elastic energy released with microearthquakes by observing micro-earthquakes would provide the significant information of physical state on the faults, for example, contact state of the faults. On the other hands, AE events canbe observed during shear slip on an artificially prepared fracture in sliding experiments simulating a fault movement. We might understand the characteristics of micro-earthquakes by evaluating the characteristics of the AE observed during a sliding of the fracture in laboratory experiments as an analogue of micro-earthquakes on the crustal faults.
In this study, sliding experiments simulating fault movement using a granite specimen underconfining stress are conducted in order to evaluate the dependencies of AE characteristics onstress acting on a simulated fracture and sliding velocity.
J. Acoustic Emission, 23 (2005)

142© 2005 Acoustic Emission Group2. Sliding Experiments Using a Rock Specimen under Confining Pressure
2.1 Preparation of a Rock Specimen Containing a Simulated Fracture
Figure 1 shows the photograph of a rock specimen used in experiment. Rock type is Iidate
granite, of which grain size is approximately 1 mm. The size of the cylindrical specimen is 30
mm in diameter and 92 mm in axial length. The specimen has a precut surface split in half withan angle of 30˚ from the axial direction. This precut surface was roughened by hands with 16grit silicone-carbide abrasives, of which the mean particle size is 1 mm. A borehole of 3 mm indiameter was drilled along the axis in the lower half of the specimen to reach to the simulated
fracture. The specimen was saturated with water using a vacuum pump prior to the experimentand was used under the condition for the fracture being filled in water during the experiment.

Fig. 1 Rock specimen containing a simulated fracture and a borehole. The left figure is the upper
part, and the right is the lower part. A borehole in the lower part is indicated.
2.2 Experimental Procedure and Condition
All experiments were conducted under a constant confining pressure at room temperature.
Figure 2(a) shows a schematic illustration of our experimental system. Figure 2(b) shows an
overview of the apparatus during sliding experiments. The apparatus consists of a pressure vessel, a loading machine, and confining pressure generation system. The pressure vessel has a capability of maximum confining pressure of 25 MPa. The loading machine (Instron 8803) is ahydraulic servo-controlled uni-axial loading machine. The servo-control system of the loadingmachine allows accurate control of a ram position. Maximum loading capacity of the machine is
500 kN and a stiffness is 1.07 MN/mm. The confining pressure system consists of an accumulator, of which capacity is 10 l and a double-plunger pump. The accumulator was adopted in order to keep a confining pressure constant.
The specimen was jacketed with two rubber tubes and attached with upper and lower steel
end caps. It was placed on the bottom of the pressure vessel. After the fracture was filled with
distilled water, confining pressure was applied to the vessel by injecting water using a hand
143

Fig. 2 Experimental apparatus in this study; (a) Schematic illustration of experimental system(b) Overview of the apparatus during sliding experiments.
pump. The specimen was held for 30 min to stabilize the confining pressure. After that, we madean initial sliding by 0.5 mm on the fracture and held this condition for an hour in order to stabilize the surface contact state. After the holding, we made sliding via 10 to 11 mm in axial displacement until the jackets were broken by shear displacement on the fracture.
Two series of sliding experiments were conducted in order to investigate effects of normal
stress and that of slip velocity on AE activity during the sliding of fracture. To investigate effects
of normal stress, confining pressure was kept constant at 5, 10, 15 and 20 MPa, respectively, andsliding velocity was kept constant at 11.5 m/s. The normal stresses acting on a fracture duringsliding in each experiment were calculated as 7.5, 14.5, 21 and 28 MPa from the axial load andconfining pressure during experiments, respectively. In the experiments to investigate effects ofslip velocity, constant slip velocity was varied as 1.15, 11.5, 115 and 1155 m/s under constantconfining pressure of 10 MPa, or normal stress of 14.5 MPa. Pore pressure, which indicates apressure of water in the fracture aperture, was 0.1 MPa (atmospheric pressure) in all experiments.
We measured the axial load, confining pressure, and axial displacement as mechanical parameters indicating sliding behavior on the fracture. The axial load was measured with a loadcell placed on the top end of a loading piston of the vessel. The axial displacement was measured with a pressure-proof displacement transducer (LVDT) attached to the lower end cap.
These signals from transducers were digitized and recorded with a data recorder continuouslywith a sampling frequency of 200 Hz after applying a low-pass filter, with a cutoff frequency of
80 Hz. Normal stress and shear stress acting on the fracture were calculated from the axial loadand confining pressure recorded with the data recorder after the experiments. Sliding displacement along the fracture was calculated as a component along the slip fracture of the axial displacement measured by the LVDT.
144

AE events accompanying sliding were detected with a broadband-type piezoelectric AE sensor (Fuji-Ceramics Co., Type 1045S, frequency band of 100 kHz to 1 MHz) embedded in a cavity of the upper end cap. A 40-dB preamplifier and a main amplifier of 30 dB gain amplified thesignals from the AE sensor. The AE signals were digitized and recorded with a sampling frequency of 5 MHz after applying band-pass filter of 200 kHz to 1 MHz. The characteristic parameters of AE events such as triggering time and peak amplitude were recorded by AE data acquisition system (NF, 7600 module). We counted AE event rates per unit slip displacement by
regarding the number of triggers as the number of AE events. The mechanical parameters suchas axial load, axial displacement and confining pressure, and the AE event rates were recordedwith two different data acquisition systems, respectively. To synchronize the data from the twosystems, a common signal generated by a function generator was recorded by both systems during the experiments.
3. Results of Sliding Experiments
3.1 General Observation in Sliding Behavior and AE Characteristics on a Simulated Fracture
Figure 3 shows a representative result of our sliding experiment where the confining pressure
and the sliding velocity were kept constant as 15 MPa and 11.5 m/s, respectively. As shown in
Fig. 3(a), the normal stress and shear stress increased with an increase in slip displacement during the initial stage of sliding over approximately 0.5 mm. On the other hand, the normal stressand shear stress took almost stable value of 20 MPa and 10 MPa, respectively, after the initialstage. In Fig. 3(b), the friction coefficient µ during sliding shows the value of 0.45 ~ 0.5. Here, = /n, where  is shear stress and n is normal stress applied to the fracture calculated from theaxial load and the confining pressure observed during experiment. Friction coefficient  ranged
from 0.5 to 0.6 in all experiments, indicating that our experiments were appropriate.
AE event rates in unit slip shown in Fig. 3(c) decreased as the cumulative slip displacement
increased over several mm and stabilized finally. This phenomenon is known as the evolution
process on fracture surfaces [2]. The m-values were evaluated by using every 500 AE events.
An m-value represents the amplitude distribution of the AE events detected, indicating statisticalcharacteristics of AE accompanying the sliding of fracture. Figure 3(d) shows the m-values estimated. The m-values ranged from 1.5 to 2.5 and gradually increased with an increase in the
cumulative slip displacement. The gradual increase may reflect the evolution process as mentioned above, implying the fracture surfaces were varying with the increase in cumulative slip
displacement.
3.2 Effects of Confining Pressure on m-value
Figure 4(a) shows a relationship between the m-values estimated with every 500 AE eventsand confining pressure. Figure 4(b) shows the frequency distributions of the m-value. These
results imply that the confining pressure, and in turn, stresses acting on the fracture surfaces havelittle effect on the m-value. This independency of m-values on the stress condition on the fracture suggests that the AE characteristics are little affected by the normal stress acting on thefracture surfaces in the stress range used in this study.

145

Fig. 3 Representative results of sliding experiment shown as a function of slip displacement; (a)Normal stress and shear stress on a fracture calculated from axial load and confining pressure,(b) Friction coefficient, (c) AE event rates, (d) m-values estimated with every 500 AE events.
3.3 Effects of Sliding Velocity on m-value
Figure 5(a) shows the m-values shown as a function of sliding velocity. The m-values have
distributions on sliding velocity because the sliding velocities were calculated as an instantaneous velocity from sliding displacement. Figure 5(b) shows the frequency distributions of the mvalue. These figures suggest that the m-value decreases as the sliding velocity increases even146though a limited number of m-values could be estimated in the experiments indicated "D" and"E", in which the sliding velocity was 1155 m/s. This negative dependency of m-value on
sliding velocity implies that the contact state of fracture surfaces inferred from AE characteristics
changes with the sliding velocity.

Fig. 4 Confining pressure dependency of m-value; (a) Relationship between the m-values andconfining pressure, (b) Frequency distributions of m-values in each experiments. Alphabetsfrom A to F indicate frequency distributions of each experiment indicated with alphabets in (a).
4. Discussion
Our experiments showed that stresses on the fracture had almost no effect on the m-value in aseries of experiments where the normal stress were changed from 7.5 MPa to 28 MPa. It is reasonable that characteristics of AE accompanying sliding would reflect the contact state of the147

Fig. 5 Sliding velocity dependency of m-value; (a) relationship between the m-values and sliding velocity. (b) Frequency distributions of m-values in each experiment. Alphabets from A to F
indicate frequency distributions of each experiment indicated with alphabets in (a).
fracture surfaces because AE would be generated with slip or break of asperities on the surfaces,on which two rough surfaces contact and coalesce each other [3]. Dieterich and Kilgore [4] conducted direct observation of contact state on roughened surface, revealing that the distribution of
the real contact area did not significantly change with an increase in a static normal stress applied
on a fracture, although the real contact area itself increased with an increase in normal stress,
through the detailed experiments using quartz plates roughened with abrasive. The m-value indicates the amplitude distribution of AE events. The amplitude of AE events may depend on
contact state on the fracture surface during a sliding because AE would be generated by slip or
break of asperities contacting each other. Therefore, we could interpret the m-value as an148

indicator for the contact state of fracture. Based to this interpretation of the relationship between
the m-value and the fracture contact state, the insensitivity of the m-values to stresses acting on
the fracture observed in our experiments suggests that no significant changes in the distributionof contact area occurred in the range of 7.5 MPa to 28 MPa in normal stress and fracture surfaces, which we have prepared.
On the other hands, the negative dependency of m-value on sliding velocity indicates that the
number of AE events with large amplitude increased with the increase in sliding velocity, implying sliding velocity has large effect on AE characteristics. Considering that AE events would
occur as consequence of slips or breaks of small asperities, we could interpret the increase inamplitude of AE with the increase in sliding velocity as an increase in elastic energy released byslip or break of asperities.
In the experimental seismology, the investigation for the effect of sliding velocity on AE activity was carried out using a dry granite specimen under bi-axial compression condition at room
temperature in order to reveal mechanisms of earthquakes occurring on the plate boundary in a
subduction zone [5]. The results have also suggested the negative effect of sliding velocity on
the m-value, and the possibility of micromechanics on the asperity contacts. However, mechanisms of the negative effect on the m-value still remain as an open question.
5. Conclusion
The results of sliding experiments using a rock specimen containing an artificially preparedfracture under confining pressure condition ranging from 5 MPa to 20 MPa showed the insensitivity of m-value, which represents the characteristics of AE accompanying the sliding of a fracture, on normal stress acting on the fracture and also showed the negative dependency on sliding
velocity in the range of 1.15 m/s to 1155 m/s.
The mechanism of the insensitivity of normal stress on the m-value was interpreted as an insensitivity of the distribution of surface contact area on the normal stress shown by Dieterich andKilgore (1996). The mechanism of the negative dependency of sliding velocity was interpreted
as a consequence of an increase in elastic energy released by slip or break of asperities associatedwith the increase in sliding velocity. Therefore, it has been revealed that sliding velocity affects
the AE characteristics that may reflect contact state of fracture surfaces.
References
1) M. Ishimoto and K. Iida: Bulletin of the Earthquake Research Institute, Tokyo Imperial University (in Japanese), 17 (1939), 443-478.
2) P. Sammonds and M. Ohnaka: Geophys. Res. Lett. 5 5 (1998), 699-702.
3) F. B. Bowden and D. Tabor: The Friction and Lubrication of Solids, vol. 2, Clarendon Press,
Oxford (1964).
4) J. H. Dieterich and B. D. Kilgore: Tectonophysics, 256 (1-4) (1996), 219-239.
5) Y. Yabe: Geophys. Res. Lett. 10.1029/2001GL014369, 21 May 2002. (2002).

149

AE CHARACTERIZATION OF THERMAL SHOCK CRACK GROWTHBEHAVIOR IN ALUMINA CERAMICS BY DISC-ON-ROD TESTSHUICHI WAKAYAMA, SATOSHI KOBAYASHI and TOSHIYA WADA
Department of Mechanical Engineering, Tokyo Metropolitan University
1-1, Minami-Ohsawa, Hachioji, Tokyo 192-0397, Japan.
Abstract
Crack growth behavior from a pre-crack in alumina under thermal shock was evaluated byacoustic emission (AE) technique. In the Disc-on-Rod test used in the present study, the central
region of heated specimen was quenched by means of contacting with a cool copper rod. AE
signals during crack growth were detected by an AE sensor attached on the opposite end of thecopper rod. The temperature distribution on the specimen was measured by an infrared camera
and the crack growth behavior was observed by a digital video camera. In this study, microscopic crack growth behavior from the pre-crack was characterized by the Disc-on-Rod test system, and the onset of crack growth was detected by the AE measurement. Then, the criticalstress intensity factors for different pre-crack length were calculated and angles of crack deflection were investigated by FEM. Consequently, the crack growth behavior under thermal shock
was well understood by the AE technique.
Keywords: Alumina, Thermal shock fracture, Crack growth, Fracture mechanics, Stress intensity factor
1. Introduction
The application of ceramics at elevated temperature often involves rapid heating and coolingconditions leading to thermal stress that causes thermal shock fracture. Thermal stress field isresulted from the temperature distribution due to transient heat flux under temperature difference.
Under the transient thermal stress, the thermal shock fracture process consists of the microcrack
initiation and accumulation, the main-crack formation and the main-crack growth to final fracture. Furthermore, in the fracture process, it is considered that there is an interaction amongtemperature distribution, stress field and main crack. Therefore, it is important to characterizeboth the thermal stress field and the fracture process dynamically and simultaneously. However,in conventional studies, those requirements have not been satisfied. For example, the macroscopic crack growth process was investigated based on fracture mechanics, whereas the precise
evaluation of the micro-fracture process and thermal stress was still insufficient [1,2]. Thus, newexperimental technique for the investigation of thermal shock fracture behavior, Disc-on-Rod
test, has been developed by the authors [3-5]. In the Disc-on-Rod test, transient thermal stressfield can be calculated by FEM from the directly measured temperature distribution. In addition,
the micro-fracture process can be characterized by the AE analysis.
The objectives of this study are to develop this technique and to characterize crack growth
behavior of ceramics during thermal shock. The micro-fracture process was investigated by AE
analysis. Furthermore, the result of AE analysis was verified by the observation of crack growth
behavior by a digital video camera. Consequently, the crack growth behavior was characterizedbased on fracture mechanical approach.
J. Acoustic Emission, 23 (2005)

150© 2005 Acoustic Emission Group2. Experimental Procedures
2.1 Preparation of Specimens
The material used were alumina ceramics (ADS-11) offered by Toshiba Ceramics Co., Ltd.
Table 1 shows the material properties of ADS-11. Disc specimens with 20-mm diameter and
0.6-mm thickness were cut from sintered rods and both surfaces were polished with 1-µm diamond slurry. Specimens were dehydrated at 150 °C for 2h in vacuum to avoid the influence ofthe stress corrosion by the water.

Table 1 Material Properties of Alumina.

ADS-11[kg/m3]

Elasitic
Modulus[GPa]

4 Points
Bending
Strength[MPa]ThermalConductivity[W/mK]

3900

360

350

30.0

Grain
Size
[m]

Density6

Coefficientof Thermal
Expansionx 10-6 [/°C]
7.8

Fracture
Toughness[MPa·m1/2]
4.5

2.2 Introduction of Pre-crack
Figure 1 shows a pre-crack introduced into the specimen. An indentation was generated at
the center of the specimen using a Vickers indenter. Figure 2 shows schematic illustration ofprocedure to grow the pre-crack from the indentation. Only the edge of the specimen was supported, and the specimen was subjected to flexural loading by a metal rod of 4-mm diameter.
The pre-crack was propagated from the indentation. The pre-crack length was controlled bymonitoring AE signals detected by an AE sensor attached on the outer part of the specimen.
Thus, specimens with a long pre-crack (Type A), a short pre-crack (Type B) and without precrack (Type C) were prepared.

Fig. 1 A pre-crack introduced into
the specimen (Type A).

Fig. 2 Schematic illustration of procedure to
grow the pre-crack from the indentation.

2.3 Thermal Shock Test and Acoustic Emission Measurement
The apparatus of thermal shock test and AE measuring system are schematically shown in
Fig. 3. A thin disc specimen was heated uniformly to the required temperature by an infrared
lamp. Only the central part of disc was quenched by contacting with a copper rod. Initial151temperature was 480-530°C and the contact area of the rod was 8-mm diameter. Contact speed
was controlled by an air damper to restrain the generation of noise due to contacting. During athermal shock test, the temperature distribution on the disc surface was measured by a highspeed (30 frame/s) infrared camera (IRC). At the same time, the macroscopic crack growth behavior was observed by a digital video camera (DVC). Then, the transient thermal stress wascalculated from the measured temperature distribution using a commercial FEM code (ANSYS).
AE signals during thermal shock test were detected by an AE sensor attached on the bottom
end of the copper rod to prevent the heat damage to the sensor. The rod was used both as a coolant and a waveguide. The AE sensor with an integral pre-amplifier and the resonant frequencyof 180 kHz was used, and the initiation of microcrack can be detected with excellent sensitivity.
The total gain of the AE system was 75 dB (main amplifier, 20 dB; pre-amplifier with sensor; 55dB) and the threshold level was 40 dB, or 18 µV at the input of the pre-amplifier. AE signals
were fed to an AE analyzer, and sent to a personal computer and analyzed further.

(a) Thermal shock test apparatus.
(b) Photograph during thermal shock test.
Fig. 3 Apparatus of thermal shock test and AE measuring system.
3. Results and Discussion
3.1 AE Generation Behavior
Figures 4(a) and (b) show typical AE generation behavior in Type A (pre-crack half length of
2.3 mm) and Type B (pre-crack half length of 0.2 mm), respectively. In the AE generation behavior in Type A (Fig. 4(a)), it can be seen that both cumulative AE events and energy increased
remarkably at 0 s and high amplitude AE signals were detected during 0~0.28 s. Furthermore,
the onset of crack growth was observed at 0 s by DVC and the crack growth was also observed
during 0~0.28 s by DVC. Therefore, it was ascertained that remarkable increase in both cumulative AE events and energy corresponded to the onset of crack growth and high amplitude AE
signals corresponded to the crack growth. The former was similar to the result of previous study[3-5]. The fracture process in type A was initiated when the crack began to propagate at 0 s upon
contacting with the copper rod, followed by its gradual propagation at low speed for 0~0.28 s
and finally reaching the edge of the disc along radial direction at 0.28 s.
152

Fig. 4 AE generation behavior and crack growth process.

Fig. 5 Crack paths due to thermal shock fracture.
On the other hand, the AE generation behavior in Type B (Fig. 4(b)) shows that both cumulative AE events and energy increased suddenly at 0.9 s. Simultaneously, the fast crack growth
was observed by DVC. Therefore, the fracture process in type B began when the crack started topropagate at 0.9 s and reached the edge of the disc along radial direction within 0.03 s. Consequently, it was understood from the AE behavior that in type A the crack propagated in a stablemanner for a relatively long period (0.28 s), while in type B the crack propagated in an unstablemanner for a short period (0.03 s). The dependence of fracture behavior on the pre-crack length
showed good agreement with analytical prediction by Hasselman [6]. Thus, the AE techniqueshows that it can achieve the characterization of crack growth behavior and determination of theonset and stability of crack growth.
3.2 Macroscopic Fracture Behavior due to Thermal Shock
Figure 5(a), (b) and (c) show the photographs of fractured specimen of Type A, Type B andType C, respectively. The specimens of Type A were not completely separated, while the specimens of Type B were separated into 2~3 pieces and the Type C specimens were separated into
2~6 pieces. This implies that the number of crack branching decreased with an increase in the
pre-crack length. In addition, as mentioned above, it was recognized from the AE generation
behavior that the crack growth rate decreased with an increase in the pre-crack length. Hence, it
153

Fig. 6 FEM model of specimen with pre-crack.

Fig. 7 Tangential stress distribution.

was suggested that the longer pre-crack length was, the smaller the number of crack branching
and the crack growth rate were. It appears that this result was caused by the difference in strainenergy at the onset of crack growth.
3.3 Thermal Stress Field and Stress Intensity Factor
Since the ratio of the thickness to diameter was very small (= 0.03) and the pre-crack in TypeB penetrated along the thickness, FEM analysis was carried out assuming the 2-dimensional
problem. Figure 6 shows FEM model with the pre-crack bent at the indentation. The thermal
stress field at the onset of crack growth was computed from the corresponding temperature distribution, which was determined by the infrared camera measurement. Figure 7 shows the obtained tangential stress field in the polar coordinate, where the origin is the center of the specimen. In Fig. 7, the stress concentration is observed at both pre-crack tips. Strain energy release
rates were also calculated using virtual crack closure method and stress intensity factors were
determined.
Based on the maximum hoop stress criterion [7] and the minimum strain energy density criterion [8], the angles of crack deflection were examined. KI and KII obtained from this study were
substituted for equations proposed in both of these theories and the deflection angles were calTable 2 Stress intensity factors and angles of crack deflection.
Angles of Crack Deflection [°]Stress Intensity Factors [MPa•m1/2]

Type AKI

KII

Experiment

Minimum Strain
Energy Density
Criterion

Maximum Hoop
Stress Criterion

2.38

0.06

6.0

2.9

2.9

culated. Table 2 shows KI, KII and the calculated deflection angles. In this study, there was nodifference between the deflection angles determined from the two criteria, but those values
showed fairly good agreement with the experimental result.
4. Conclusions
Fracture behavior in alumina ceramic discs during thermal shock subjected to the Disc-onRod test was investigated by the AE technique. From the AE generation behavior and the observation of the crack growth behavior, the following conclusions were obtained.
1541) The crack growth behavior under thermal shock was understood from the AE generation behavior.
2) It was ascertained by DVC observation that remarkable increase in both cumulative AE eventsand energy corresponded to the onset of crack growth.
3) The angles of crack deflection determined by FEM from two types of the criteria showed
fairly good agreement with the experimental result.
Acknowledgment
The authors would like to thank Toshiba Ceramics Co., Ltd. for their offer of ceramic samples. The present study was partly supported by the Grant-in-Aid for Scientific Research(No.15260349) from the Ministry of Education, Culture, Sports, Science and Technology of Japan.
References
1) G. A. Schneider and G. Petzow: J. Am. Ceram. Soc., 74 (1991), 98.
2) Y. Mizutani at al.: J. Ceram. Soc. Japan, 103 (1995), 494.
3) S. Wakayama: Progress in Acoustic Emission IX (1998), p. III-73, AE Group, Los Angeles.
4) K. Nishino et al.: Progress in Acoustic Emission X (2000), p. 319, JSNDI, Tokyo.
5) S. Wakayama et al.: Proc. of 7th Japan International SAMPE, (2001), p. 585, Tokyo.
6) D. P. H. Hasselman: J. Am. Ceram. Soc., 52 (1969), 600.
7) F. Erdogan, G.C. Sih: J. Basic Eng., 85 (1963), 519.
8) G.C. Sih: Int. J. Frac, 10 (1974), 305.

155

FATIGUE DAMAGE PROGRESSION IN PLASTICS
DURING CYCLIC BALL INDENTATIONAKIO YONEZU, TAKAYASU HIRAKAWA, TAKESHI OGAWA
and MIKIO TAKEMOTO
Department of Mechanical Engineering, College of Science and Engineering,
Aoyama Gakuin University, Fuchinobe, Sagamihara, Kanagawa 229-8558, Japan.
Abstract
AE (acoustic emission) is utilized to monitor the progression of sub-surface damage in atransparent PVC (poly-vinyl-chloride) plate subjected to contact fatigue by cyclic ball indentation. We monitored AEs from sub-surface damage by four small AE sensors mounted on the fouredges of a square plate, and estimated both the source location and fracture mode. The AE analysis revealed that Mode-II lateral cracks initiated at cycle number, N = 8.9 x 104.AE sources ofcorresponding events were located in front of progressing sub-surface lateral cracks at the depthof 516 µm. We could not, however, monitor AE from Mode-I radial crack possibly due to small
crack opening. Finite element method (FEM) predicted lateral crack initiation at approximately
500 µm below the surface with the maximum shear stress.
Keywords: Cyclic ball indentation, Poly-Vinyl-Chloride plastics, Fatigue damage progression.
1. Introduction
Engineer Polymers or Plastics are widely used for contact components such as bearings andgears in industrial fields due to their excellent performance at low cost. Understanding themechanism of cyclic contact damage is important for practical applications [1, 2]. No study on
cyclic contact for plastics has been performed so far and fatigue fracture process has remained to
be understood [3, 4].
In the present study, cyclic ball indentation tests were performed on PVC (poly-vinylchloride) to study the mechanism of cyclic contact fatigue. The initiation and progression of
fatigue damage were monitored by AE (acoustic emission) technique. We studied the cyclic fatigue mechanism by AE analysis, observed the indentation damage and analyzed using finite
element method (FEM).
2. Materials and Experimental Procedures
We used transparent PVC as samples of 20-mm square with 5-mm thickness. Cyclic ball indentation tests were performed using an electro-hydraulic testing machine equipped with a ballindenter and two eddy current sensors (displacement resolution: 0.4 µm). We prepared four typesof indenter balls with diameters d = 5, 10, 15 and 20 mm. A software for controlling cyclic indentation test was developed in house. The system measures the relationship between indentation
force (F) and indentation depth (h) at high resolution under various testing conditions, controlling the maximum indentation force (Fmax) and the stress ratio (Fmin/Fmax), R = 0.05, 0.1 and 0.5.

J. Acoustic Emission, 23 (2005)

156© 2005 Acoustic Emission GroupFig. 1 AE monitoring system for cyclic ball indentation.

Fig. 2 (a) Experimental setup to measure the P wave velocity, (b) detected P wave and S wave.
In these tests, the maximum number of cycle was set as 106 cycles and cyclic frequency (f) as 5
Hz. All tests were performed at room temperature.
We monitored AE signals using four small AE sensors mounted on the side surfaces of a
specimen. As shown in Fig. 1, the AE system consists of resonant-type sensors (Type PICO,
PAC), 40-dB preamplifiers (9913, NF Circuit Block), A/D converter (Gage Applied Inc.) and apersonal computer. Output of channel-1 sensor was branched into another circuit for triggering
via a high-path filter. This eliminated low-frequency noise due to cyclic contact.
3. Results and Discussion
3.1 Determination of the Elastic Wave Velocity in Plate Specimen
We first studied the characteristics of AE waves using a laser system (Fig. 2). YAG laser
pulses were point-focused at the center of specimen surface as shown in (a). Out-of-plane displacement was measured by a laser interferometer on the back-plane. In order to detect the shear
157

(S-) wave as well the longitudinal (P-) wave, the measurement point was changed from the epicenter to 1t-off (t = the specimen thickness) epicenter. The timing of pulse laser irradiation wasmonitored by a photodiode and used as trigger signal. Outputs of the interferometer weredigitized by a two-channel digitizer (sampling interval 4 ns and sampling point 4096 points) andfed to a personal computer. Figure 2(b) shows a detected waveform at 1t-off epicenter. First pulse
at 3.28 µs was the P-wave traveled at velocity (Vp) of 2156 m/s. S-wave arrived at 6.96 µs traveled at velocity (Vs) of 1016 m/s. Young’s modulus (E) of PVC was calculated as 3.78 GPa, andPoisson’s ratio () as 0.36. These values were found to be similar to those determined by indentation and compressive loading test. The static tests gave, E = 3.55 GPa and  = 0.42 .
We next detected the elastic waves by a small AE sensor mounted on the side surface of thespecimen. Velocity of the first arrival wave was measured as 2194 m/s and coincided with thevelocity determined by laser system. Since the sheet velocity (C E) of symmetric (S0)-Lambmode of PVC was slower and measured as 1794 m/s, we need to estimate the source location
using the P-wave velocity.
3.2 Fatigue Damage Progression during Cyclic Ball Indentation Testing
We performed the cyclic ball indentation testing with the maximum indentation force, Fmax =981 N, stress ratio, R = 0.1, and indenter diameter, d = 10 mm. Figure 3 shows the variation of
the maximum and minimum indentation depth (hmax and hmin) with the number of cycles, N. This
figure also shows cumulative AE counts. We detected first AE at N = 8.9 x 104 cycles and AE
counts increased rapidly with cycle numbers. This test was terminated at N = 1.01 x 105 cycles
when total AE event counts reached 4097. Figure 4(a) shows the post-test micrograph of the indentation damage. White semi-elliptical cracks near the contact area (not clear due to transparency of PVC) in the photograph is sub-surface cracks induced by cyclic contact. A large cracknear the top and a smaller one at the bottom can be seen. Figure 4(b) shows a magnified view of(b) on the periphery of contact area. It shows a fine radial crack in the radial direction. Cross
section along the line A-A’ in Fig. 4(a) is shown in Fig. 5. We observed sub-surface cracks at thedepth of 516 µm. This is the white semi-elliptical lateral crack observed in Fig. 4(a).

Fig. 3 Indentation depth and cumulative AE counts as a function of indentation cycles.
158

Fig. 4 (a) Surface photograph of indentation damage, (b) magnified view of area marked (b).

Fig. 5 Cross sectional view of indentation damage (A-A’ line of Fig. 4(a)).

Fig. 6 AE source locations during cyclic ball indentation.
159

Fig. 7 Detected AE waveforms during cyclic ball indentation test (EC.3005, N=9.7 x 104 cycles).
We analyzed AE waveforms to study the progression of fatigue damage. Figure 6 showschange of AE source locations estimated for AE events detected between N = 8.9 x 104 and 10.1
x 104 cycles. It is noted that AE sources are located slightly ahead of the periphery of the whitecrack. Location of source moves away from the contact area with increasing N.
Next, we examined the fracture types based on the radiation pattern of longitudinal waves[5]. Figure 7 shows a set of AE waveforms. The polarity of the first arrival waves (marked by anarrow) from channel 1 and 3, mounted on the adjoining side surfaces, were negative while thosefrom channel 2 and 4 are positive. This polarity distribution indicates shear or Mode-II typefracture [5]. The first-peak amplitudes of channel 1 and channel 2 were much larger than those ofchannel 3 and channel 4. This implies that the sliding vector of the lateral crack is in the
direction connecting channels 1 and 2 (vertical in Fig. 1). This polarity distribution was observed
for all AE events during the test. It is concluded that the AEs were produced by Mode-II lateralcracks. In contrast, we could not detect any AEs from Mode-I radial cracks, even though some ofthem were clearly visible. This is possibly due to small crack volumes of the radial cracks.
3.3 Mechanism of Lateral Cracks
We examine next lateral crack mechanisms, based on the experimental data and FEM for different size balls. Figure 8 shows the depth of the lateral crack initiation site, h lateral as a functionof F. The hlateral were determined by cross sectional observation. Symbols with downward arrows indicate no lateral crack initiation. All lateral cracks developed at 550 µm below thesurface. This depth is independent of F.
160

Fig. 8 Depth at lateral crack initiation hlateral as a function of indentation force range F.

Fig. 9 F-h curve during the cyclic ball indentation test.
Figure 9 shows typical F vs. h curves of the specimen at pre-determined N values. Test condition is the same with that of Fig. 3. This revealed that the F vs. h curve differences between
loading (upper curve) and unloading (lower curve) decreased with increasing N. The area surrounded by two curves represents the stored deformation energy (Ed) in a single loading cycle[6]. The Ed decreased with increasing N, as shown in Fig. 10, and decreased to a low, constantvalue beyond N = 4.5 x 104 cycles. PVC produced permanent dent and deformed elastically aftera large number of cyclic contacts.
Two-dimensional stress distribution under the contact area in the transverse section was calculated to study the driving force and depth of lateral cracks using finite element method (FEM)
161

with MARC (K7-2) and MENTAT II (V3.2.0). Two-dimensional FEM model with 1588 elements and 1645 nodes for a permanent dent is shown in Fig. 11. Diameter d of the permanent
dent was given by the measured value after the test, and the permanent depth h was determinedfrom F-h curve in Fig. 11. Young’s modulus E = 3.78 GPa and Poisson’s ratio,  = 0.36 wereused. We calculated the stress distribution using elastic contact analysis with Fmax = 981 N and d= 10 mm.

Fig. 10 F-h curve during the cyclic ball indentation test.

Fig. 12 Shear stress rz with contours at Fmax
= 981 N with d = 10 mm diameter.

Fig. 11 FEM model for ball indentation testwith d = 10 mm diameter.

Figure 12 represents the contour map of shear stress rz at Fmax of 981 N during ball indentation test with d =10 mm diameter. Maximum rz,max of 94.3 MPa is generated at the depth of 512
162

µm from the surface. This depth agrees quite well with the experimental depth of 516 µm in Fig.
8. It indicates that the lateral crack is produced at the depth of maximum shear stress.
6. Conclusions
Utilizing AE technique, we studied damage progressing in PVC (poly-vinyl-chloride) plastics subjected to cyclic ball indentation test. Results are summarized below.
1.

In the test with maximum indentation force of 981 N and indenter diameter of 10 mm,
sub-surface lateral crack initiated and grew with cyclic ball indentations. This crack wasMode II fracture and produced AE signals. Sources of these events were located ahead ofgrowing lateral cracks. The lateral crack developed at 516 µm from the surface where themaximum shear stress is generated.

2.

The depth of lateral crack initiation was observed to be about 500 µm and independent ofindentation force, stress ratio and indenter diameter.

Acknowledgment
This work was performed as a part of the Center of Excellence (COE) Program, funded by
the Ministry of Education, Culture, Sports, Science and Technology.
References
1) e.g. A. Otsuka, K. Togo, H. Ishida, S-K Yoo, M. Shibata: J. Soc. Mat. Sci. Japan 41, 466,
(1992) 1136 (in Japanese).
2) J. Uchida, T. Ogawa, M. Takemoto, Y. Kobayashi and Y. Harada: J. Material testing research,
49-3 (2004) 140 (in Japanese).
3) T. Bushimata, H. Sakuta, K. Shinohara and T. Endo: J. Eng. Design. 36-11 (2001) 485 (inJapanese).
4) A. Makabe, K. Yahuso, T. Suzuki and H. Yaryo: J. Soc. Mat. Sci. Japan, 50-12 (2001) 1311
(in Japanese).
5) M. Ohtsu: J. Soc. Mat. Sci. Japan, 32-356 (1982) 52 (in Japanese).
6) J.L. Loubet, J.M. Georges and G. Meille: Am. Soc. Test. Mater, 889 (1985), 72.

163

EVALUATION OF FATIGUE DAMAGE FOR FRM WITH AE METHODMASANORI TAKUMA and NOBORU SHINKE
Faculty of Engineering, Kansai University, Yamate, Suita, Osaka 564-8680, Japan.
Abstract
Composite materials have the merit that its structure can be designed in accordance with thefunctional requirement and are widely used as structural members. However, fatigue
phenomenon cannot be avoided and mechanisms on the growth of fatigue damage arecomplicated. The fatigue damage becomes a serious problem in evaluating the reliability ofstructures. In this report, the parameters for evaluating the fatigue damage of fiber-reinforced
metals (FRM) with AE method are proposed. Detected AE signals during fatigue testing wereanalyzed with weighted-mean-frequency distribution and wavelet transform methods. As theresults, the possibility of evaluating the damage by the features of the frequency distribution
graphs and the fractal dimension that represented the complexity of the time fluctuation of
wavelet coefficient is shown.
Keywords: FRM, Fatigue damage, Weighted-mean-frequency distribution, Wavelet transform,
Fractal theory
1. Introduction
The technology of the composite materials progressed from the development of fiberreinforced plastics (FRP) to fiber-reinforced metals (FRM), by which the heat-resistance and thefatigue resistance, etc. were improved. The FRM materials are used as structural members underdifficult environment and the excellent results are reported. However, it is necessary to establish
the monitoring techniques, which can evaluate the fatigue damage. This is essential in order toavoid fatigue breakdown, which causes many of the accidents of the structures. In this study, theinformation processing techniques (i.e. the weighted-mean-frequency distribution, the wavelet
transform and the fractal theory) were applied for analyzing the AE signals (cf. Fig. 1). The
features of the frequency components for each fracture mode (i.e., the crack propagation of
matrix and the breaking of reinforcement material, etc.) were extracted. After that, the databasefor evaluating the fatigue damage was constructed by using the features. The damage and thefracture mechanism of FRM are evaluated by comparing the features in the database with thefeatures of the detected signals during the fatigue testing.
2. Analysis Methods of AE Signal
2.1 Weighted-Mean-Frequency Distribution (WMFD)
In the fatigue process of composite materials, extremely numerous AE events are detected
and the complicated multiple fracture modes are included in an event. Therefore, it is difficult tojudge which type the detected signal belongs to. For this reason, it is necessary to extract thefeatures of the frequency distribution on the basis of the FFT analysis result. In this study, theweighted-mean-frequency distribution (WMFD) method [1] was employed for extracting thefeatures of the distribution. The distribution graph by this method is obtained from the
J. Acoustic Emission, 23 (2005)

164© 2005 Acoustic Emission GroupFig. 1Conceptofthisstudy.
following process: (1) FFT analysis result is divided by the mesh and each mesh is weighted. (2)A frequency distribution graph, in which many analysis results were combined, is shown byadding the weight at every mesh. By applying this method, it becomes possible that the growthof the damage is grasped from the change in the features of the graph in each fatigue stage.
2.2 Wavelet Transform (WT)The frequency analysis results with FFT are effective for the identification and discriminationof the microscopic destruction factor, and FFT contributes to the solution of the fracture
mechanism [2]. However, it is not suitable for the analysis of the signals, whose the statisticalproperties change with time [3]. Therefore, another method that transforms a signal into a timefrequency domain is needed for analyzing the non-stationary or transient signals such as AE.
Wavelet transform (WT) is one of the methods for supplementing the shortcoming of FFT.
The WT of the signal f(x) is defined by:[WT ƒ](a,b) =   * (x)ƒ(x)dx

(1)



where * is the complex conjugate of the wavelet function  a,b(x). The function  a,b(x) is
defined by |a |-1/2  ((x – b)/a). The function  a,b(x) is the mother wavelet (e.g., Gabor wavelet)
with the scale parameter a and the shift parameter b, and provides a set of localized functions inboth frequency and time. The scale parameter a gives the width of window and consequently
frequency as the mother wavelet is expanded or compressed in time. The shift parameter b
determines the position of the window in the time, and thus defines that which part of the signal
f(x) is being analyzed. Therefore, it is possible to make the optional wavelet of the angularfrequency wo/a in time b by changing the both parameters, and the optional function isrepresented by putting the various optional wavelets together (cf. Fig. 2). The wavelet

165

Fig. 2 Wavelets in time-frequency plane.
coefficient increases, if  ((x – b)/a) resembles f(x), and the intensity is correspondent to theamplitude of f(x) [4]. Therefore, the coefficient shows the activity of the component with thefrequency o/a in time b. This becomes effective for the analysis of the AE signals, in whichsudden fluctuations and many phenomena are mixed, when the transform result is represented on
the plane of b and 1/a. However, the evaluation method of the result is not established yet.
Therefore, it is possible to contribute to the grasp of the fracture phenomena and the evaluationof fatigue damage if the features (i.e., the complexity of the time fluctuation waveform of the
wavelet coefficient in each frequency component) of the WT result can be evaluatedquantitatively. This is very important for grasping each fracture mode by the features of theanalysis result. Thus, the fractal dimension that is employed when the various shapes and thephenomena in the nature are quantitatively classified was applied for extracting the features of
WT result. This time, the dimension of time fluctuation waveform of wavelet coefficient in eachfrequency component was found with Box-counting method [5] (cf. Fig. 3).

Fig. 3 Fractal analysis in this study.
3. Specimens and Experimental Methods
Figure 4 shows the dimensions of the specimens and material details. The fatigue test was
carried out under the load control (i.e. the stress amplitude (a) 52 MPa, the loading frequency =2 Hz and the stress ratio R  0). The emitted signals from the specimen were detected by a
wideband AE sensor (NF 900S-WB) and they were recorded on two computers using Mistras
system (cf. Fig. 1). The following experiments and data analyses were carried out in order toelucidate the fracture mechanism and to evaluate the fatigue damage from the detected signals.
(a) Features of AE signals on the damage (i.e. the crack propagation of matrix and the fiber
breaking, etc.) were extracted from the two testing that were the fatigue test of aluminum alloy
and the tensile test of carbon fiber. The database for evaluating the damage was structured byusing the features.
166

(b) By comparing the features in the database with the detected AE signals during the fatigue
testing of FRM, the elucidation of fracture mechanism and the evaluation of fatigue damage arecarried out.

Fig. 4 Dimensions of specimen and material details.

Fig. 5 Frequency distributions in relationship between stress intensity factor range (K) andcrack propagation rate (da/dN) in fatigue process of matrix (i.e., aluminum).
4. Experimental Results and Discussion
4.1 Features of AE signals of matrix and fiber
Figure 5 shows the result of the fatigue test for the matrix (i.e., the aluminum alloy: A5052).
In this figure, the analysis results (i.e., the WMFD and WT) of the AE signals are superimposedon the relationship between the crack propagation rate da/dN and the stress intensity factor
167

Fig. 6 Typical frequency distribution of fiber breaking and fiber pullout.

Fig. 7 Typical fractal dimension m for each fracture mode.
168

range K. From this result, it is understood that the features of the frequency distribution of thesignals depend on the rate da/dN. In short, though the frequency components 100 to 150 kHz are
active when da/dN is slower than 4 x 10-7 mm/cycle, the components over 300 kHz are active
when da/dN is faster than 4 x 10-7 mm/cycle. Therefore, it is possible to discriminate thedifference of the rate da/dN. However, it is difficult that each fracture mode is discriminatedwith the WMFD when we considered Fig. 6 that shows the analysis results on the fiber pullout
and the fiber breaking because the frequency component of the fiber pullout is 100 to 150 kHz
and the component of the fiber breaking is near 300 kHz. Thus, the application of the fractal
dimension m of time fluctuation waveform of wavelet coefficient in each frequency was tried forextracting the features of each fracture mode. The results are shown in Fig. 7 and the following
can be considered from the figure.
Table1Frequencycomponentsoneachfracturemodeofspecimen.

(1) The fluctuations of the dimension m of each frequency for the fiber pullout and the fiber
breaking are comparatively smaller than the fluctuations of the crack propagation of the matrix.
(2) The dimension m around 100 kHz is high when the propagation rate da/dN of the matrix
is slow, and the dimension m of 250 to 300 kHz rapidly increases when the rate da/dN is fast.
(3) By using WT, it became possible to discriminate the differences of each fracture mode, inwhich it is difficult to discriminate the mode with the WMFD. One is the discrimination
between the fiber breaking and the fast crack propagation, and the other is the discrimination
between the fiber pullout and the slow crack propagation. The dimension m of 125 and 350 kHz
were noticed because the difference between two dimensions is large.
The obtained dimension m for discriminating each mode by the consideration of the above isshown in Table 1.
4.2 Relationship between Fracture Modes and Fatigue Damage
From the results (i.e. the AE event rates, the AE amplitude distribution and the crack
initiation point) of the fatigue test of FRM specimen, the fatigue phenomena were divided in 4
stages as shown in Fig. 8. These stages are the early stage (i.e. the range of many AE events
right after the test starts), the middle stage (i.e., the stable range of the event rates until crack
initiation point), the late stage (i.e., the crack propagation range) and the stage before fracture169

Fig. 8 Fatigue phenomena divided into 4 stages by AE counts and amplitude distribution.

Fig. 9 WMFD graphs and fractal dimension m on the time fluctuation of wavelet coefficient at
125 and 350 kHz in each fatigue stage.
(i.e., the range of many AE events again), respectively. The feature extraction in each stage wastried by considering the results of the WMFD graphs, the behavior of the fractal dimension m on
the time fluctuation of wavelet coefficient and Table 1.
170

Figure 9 shows the relationship between the analysis results (i.e. the WMFD graphs and the
behavior of the fractal dimension m) and the fatigue damage %. As the results, the following
became clear.

Fig. 10 An example on evaluation of fatigue damage.
Early stage: From the result of the WMFD graph, it is clear that the activities of thefrequency components 150, 250 and 300 kHz are high. Since the dimension m at 125 and 350
kHz shows 1.25 and 1.35, respectively, it appears that the fiber pullout and the fiber breaks aretaking place. It is also likely that many causes of the fatigue fracture intermingle in an AE signal
and many signals are emitted from the stress concentration zones of the defects inside thespecimen.
Middle stage: In addition to the activity of 300 kHz, the activities of 120 to 200 kHz are
strong. The fractal dimension m of 350 kHz is higher than that of 125 kHz. However, thedimension m of 125 kHz shows the increasing tendency, while the dimension m of 350 kHz
gradually decreased from 1.35. From these, it seems that the activity of the fiber breaks
gradually decreases, and the activity of the matrix damage increases. Therefore, even if thecrack is not found on the surface of the specimen, it is likely that the main fracture mode inside ischanged from the fiber breaks to the matrix damage.
Late stage: The dimension m comes to show the high value beyond 1.35 and the occurrence
ratio of 300 kHz increase. From this, it can be judged that the main factor in this stage is the
rapid crack propagation.
Before fracture: The main frequency component of the detected signals is near 100 kHz, andthe dimension m of 125 and 350 kHz is converged to 1.25 and 1.30, respectively. In this stage, itis the high possibility that the main factor of the fatigue phenomenon is not the crack propagationof matrix but the fiber pullout if the rate of crack propagation was high in the last stage. From
the above, it can be predicted that the fatigue process approaches the end when the dimension m
rapidly decreases.

171

4.3 Application to Evaluation of Fatigue Damage
In order to find a parameter that describes the relationship between the behavior of thedimension m and the fatigue damage, we defined the parameter F as follows;
F = m350kHz – m125kHz(2)

Here m 350kHz and m 125kHz is the fractal dimension of 350 and 125 kHz, respectively. Figure 10
shows an example of the evaluation on fatigue damage by the parameter F. From this result, it
can be seen that the parameter F appears to show different stages of the fatigue damage.
5. Conclusion
Composite materials have the merit in fulfilling its structural design requirement withcomparative ease. However, the fracture mechanism and the fatigue damage of the materials arecomplicated. Therefore, the establishment of the technique, which evaluates the reliability andthe safety of the structure materials, is desired. The following became clear from theexperiments for establishing the evaluation technique of the fatigue damage of FRM with AE
method.
(1) The WMFD is possible to grasp the features on the frequency distributions of the detected
AE signals from the fracture phenomena.
(2) Fractal property was recognized in the time fluctuation waveform of wavelet coefficientin each frequency component after the WT of the signals. The possibility of evaluating thefracture mode and the fatigue damage by the dimension m is shown.
(3) By the frequency analysis of AE signals, it is possible to relate matrix damage by thefracture of the reinforced fiber with the final fatigue fracture.
Acknowledgment
The authors are grateful for valuable assistance of Yoichiro Koyama of Kansai University.
This research was supported by a grant from Life Cycle Engineering Working Group in ORDIST(Organization for Research and Development of Innovative Science and Technology) of Kansai
University.
References
1) T. Asano and S. Somiya: Prepr. of Jpn. Soc. Mech. Eng., (1994), 74.
2) Y. Ichida, K. Kishi and Y. Hasuda: J. Jpn. Soc. Prec. Eng., 58(5) (1992), 841.
3) Q.Q. Ni, Y. Misada, K. Kurashiki and M. Iwamoto: Proc. National Conference of AE, (1997),
p. 173.
4) H. Inoue: J. Soc. Mat. Sci., 45 2(1996), 1353.
5) M. Kurose, M. Tsuda, T. Sasaki, Y. Hirose, Y. Yoshioka: J. Soc. Mat. Sci., 43(1) (1994),
1489.

172

ACOUSTIC EMISSION BEHAVIOR OF FAILURE PROCESSES OF
GLASS-FIBER LAMINATES UNDER COMPLEX STATE OF LOADINGJERZY SCHMIDT1, IRENEUSZ BARAN1 and KANJI ONO2
1)
Foundry Research Institute, Laboratory of Applied Research
30-418 Kraków, ul. Zakopianska 73, Poland.
2)
MSE Department, University of California, Los Angeles, CA 90095 USA.

Abstract
The strength of laminates made from glass fiber fabric with epoxy resin matrix was determined. The studies were carried out on two laminates with different resin types. The measurements were taken on pipe samples subjected to uniaxial and biaxial loading at three different temperatures. The process of crack formation was examined by acoustic emission (AE). For thispurpose, a multi-parameter analysis of AE signals was applied. It was observed that the type ofresin used for the laminate matrix had a significant effect on crack development, while it had noeffect on the laminate strength. The laminates were noted to have similar strength under both
uniaxial and biaxial state of stress. The application of AE enabled determination of the onset oflaminate failure. The analysis of the failure process used the pattern recognition classification
with Visual Class program. A classification tool was designed and constructed; it bore the features common to all the tests. Using this tool, the mechanisms of failure prevailing in the loading
process were determined along with their intensity and range within which they may have some
impact on the laminate failure process.
Keywords: Glass fiber fabric, Laminate composites, Failure processes
1. Introduction
The aim of this study is to determine the laminate strength under the conditions of uniaxial
and biaxial loading and to evaluate the effects of laminate matrix on the failure processes withacoustic emission (AE). It is well recognized that AE is valuable in evaluating failure of composite pipes [1], but AE experiments during biaxial loading of composites have been limited [2,3]requiring further research.
Since we need an analysis on the failure processes occurring during the loading of a sample,
we employed the AE technique. As the examined materials are used in civil aviation, we tested
the strength of the composite laminates at temperatures of 20°C, 54°C and 72°C, as commonly
applied in this technology. Analyzing the work of Owen and Griffith [4], confirming a significant
and negative impact of the edge effect on laminate strength, pipe-shaped samples were proposed
for use here. Laminates were composed of nine layers of Interglass fabric wetted with two types
of epoxy resin, viz. Ep53 and L285. The internal diameter of pipes was 50 mm, the wall thickness 2.5 mm, while their length was 300 mm, cut from longer pipes. The samples wetted with
J. Acoustic Emission, 23 (2005)

173© 2005 Acoustic Emission Groupresin were wound on mandrill and hardened at 20°C for 12 h and at 60°C for 8 h. Additionally,
the samples for tests at 72°C were hardened at 80°C for 8h.
For the measurement of acoustic emission a special device is required to eliminate the generation of acoustic signals from a sample holder. It is also indispensable to ensure a constant rate of
the load increment because of its possible effect on the composite strength. In view of these requirements, a special measuring system was designed utilizing a numerical control system operating in the Schenck PSB100 testing machine. A pressure generator installed on this machine enabled loading of samples with internal pressure at a constant rate of pressurization using a synthetic oil medium. The sample was placed in a thermostat filled with synthetic oil, providing
temperature control at an accuracy of ± 1°C. Pressure was controlled by two pressure gauges
made by Hottinger. Two types of clamps were used on the samples, thus producing two statesof stress. In the first case, the clamp was put on the sample ends and it produced in the walls of asample a biaxial state of stress with the hoop (circumferential)-to-axial stress ratio equal to1/2=2 (Mode II loading). In the second case the sample was free to move on its axis, thus producing zero axial stress 2=0 (Mode III loading)[3].
The values of acoustic emission parameters measured during loading were recorded by PAC
MISTRAS-2001 and Vallen AMSY-5 systems. The AE signals were detected by two wideband(WD) sensors made by PAC. The frequency range was 100-1000 kHz. The recorded AE signals
were next subjected to numerical analysis with Mistras software and exported to Visual Class
analysis. In the case of AMSY-5 system, the Visual AE and Visual Class programs were used.
2. Testing of Laminate Strength
Comparing the results of tests conducted under uniaxial (Mode III) and biaxial (Mode II)loading, one can observe little effect of the second component of the state of stress on the laminate strength (Fig. 1). These results confirm the difficulties in the choice of relevant criteria,
which could be used in evaluation of the laminate failure [5]. The difficulties in finding proper
criteria of the laminate failure behavior were also noted by Owen and Griffith [3]. Only subjective evaluation of the experimental results could enable their comparison with the computed results based on selected criteria of failure. For laminates examined in this study and positive components of the state of stress, the most sensitive seems to be the criterion of maximal stresses.
Results indicate insignificant effects of hardening temperature on the strength of both laminates, although laminates hardened at 80°C have slightly higher strength. The test temperature
generally decreases the strength of samples. The drop was highest for 80˚C-cured Ep53 matrix.
An exception is at 54˚C, L285 epoxy matrix retained the same strength. The effect of resin typeon the laminate strength is negligible under some conditions, and both resins give composites ofstrength within 20% at all temperatures.
The examined laminates are characterized AE event counts recorded during the loading. As
shown in the lower graphs of Fig. 1, L285 epoxy matrix produced 5 to 20 times higher counts
than the corresponding Ep53 matrix laminates. The differences are evident regardless of curing or
test temperature used and suggest that failure of these two laminates proceeds by different
174

Fig. 1. Statistical result of strength and AE events of pipe specimens for resins EP53 and L285.
Loading mode II (left figures) and mode III (right) are shown. Two cure temperatures (60 and
80°C) were used. Test temperatures were 20, 54 and 72°C as indicated.
mechanisms. Mode II of loading renders more AE events than Mode III, although the difference
is not drastic as one may expect from the change from uniaxial to biaxial loading. Figure 2 shows
this difference clearly; in L285 matrix laminates, AE event rates are nearly constant throughout
the loading and the cumulative plot shows a linear increase. In Ep53 matrix laminate, AE activities show distinct peak just before failure and the summed curve illustrates increased slope toward the end of loading.
The test temperature had some effects on the strength of the examined laminates. Cumulative
AE events similarly had relatively minor changes with both matrix resins in Mode II tests. For
the Ep53 resin under Mode III, however, AE events varied several times between two cure temperatures (60 and 80˚C) as indicated in Fig. 1. With increased test temperatures, differences in
AE event rates are also observed (see Fig. 3). AE event rates increased just before failure in Ep53
resin laminates regardless of test temperature (Fig. 2). This suggests in L285 a greater number ofmicrocracks form preceding the failure of sample and the greater activity due to debonding process of the matrix and glass fiber fabric, while Ep53 has most damage just prior to failure.
175

Fig. 2. AE activity and AE summation during loading (Mode II) for L285 and Ep53.

Fig. 3. AE event rates during loading (Mode II) for EP53 at different cure and test temperatures.
The AE signals recorded during the test were subjected to a multi-parameter analysis, computing the total of AE signals count, the total of AE events, mean time of AE signals duration,energy of AE events, and mean effective voltage.
176

The AE signals indicating the onset of failure appears at a low loading stage. In the case of
Ep53 resin laminate, cracks start appearing at ~20% of the failure pressure for mode Mode II
loading and at 30% failure pressure for Mode III loading. For laminate containing L285 resin, theonset of failure was recorded at still lower pressure, viz. ~15% failure pressure for mode Mode II
and ~20% failure pressure for mode Mode III.
3. ANALYSIS of the LAMINATE FAILURE PROCESS
The results obtained here indicate the possibility of using AE in practical evaluation of theresin and laminate failure process. In order to provide robust and efficient evaluation technique,
we developed a method of pattern recognition classification by using the Vallen Visual Class program. Because of the signal recording technique adopted by PAC Mistras system, which wasdifferent from AMSY-5 and used in the beginning to obtain AE data, some additional steps wereneeded to use the Visual Class program in a most effective way. At later stages, AE signals in theprocess of laminate failure were recorded via an AMSY-5 system.
Having noted certain similarities in the failure behavior of samples of each type of the laminate, it was considered to be both sufficient and representative to analyze using the reference
pattern for classification with samples tested at 20°C. Since it was necessary to provide highclarity of the recorded signals, we selected for analysis the measuring data covering the load range
from 10% to 95%. The classification proceeded according to the previously adopted work organization schedule as follow:
1. The analysis was conducted separately at the beginning, making classification for eachresin type and mode of loading,
2. Within the obtained group of classes, the classes with similar features were combined into
one whole set, thus obtaining one single class,
3. After accumulation of the source classes (groups of signals) a classifying tool with features common to the entire conducted studies was obtained.
Four classes (designated by the numbers from 1 to 4) prevailing in the process of laminate samples loading were thus obtained.

Fig. 4. Statistical results of separation of prototype classes.

177

The correct nature of the applied procedure has been proved in a graphic form as an image ofthe classification features division (Fig. 4). Classification thus developed was used in the analysis
of crack propagation during loading of the tested pipe-shaped laminate samples. The results ofanalysis expressed as a share of each class in the loading process of samples prepared from bothlaminates and tested by two modes of loading were plotted in the form of bar diagrams (Fig. 5).
The height of bars stands for a percent share of each class in the full process of sample loading.

Fig. 5. Percent share of separated class for Mode II and Mode III with two types of resin.
In recording of AE signals, the threshold level of 46 dB (re. 1 V/0 dB) for discrimination wasapplied. The reason was accumulation of too many signals generated below this level and causedby the laminate structure as well as a large volume of the laminate subjected to deformation. An
output has been restricted in the loading process analysis for the amount of class 2 events, which
occurs below the threshold of 46 dB.
Knowing the typical features of AE signals corresponding to each class enabled ascribing toeach class the prevailing mechanisms of failure active during sample loading. To this purpose
some published results of the experiments and simulation works made for the glass fabric reinforced resin-based composites were used [6,7]. In this case, the most important features of AE
signals are expressed by the values of signals amplitude, frequency, and intensity. Using theavailable information on a relationship between AE parameters and failure mechanism as well as
the data obtained in experimental analysis and in analysis of the classification effectiveness, theprevailing mechanisms corresponding to each class were determined.
Class 1
fiber breakage
Class 2matrix microcracking + propagation
Class 3fiber/matrix friction + fiber/matrix debonding
Class 4matrix friction + fiber/matrix debonding
Using the developed classification tool in practice, the zones of occurrence of all the classes
described by different selected AE parameters were determined in the tested samples during thelaminate stress increase (Fig. 6). The determination of such zones enables us to separate the178occurrence of individual mechanisms of failure in the process of sample destruction. The zones
determined for Mode II and III of an effect of the individual classes prove that the stress direction 2 can exert a small effect only on both the static strength of the laminate and the individual
mechanisms of its destruction. The differences in AE activity in the laminates based on Ep53
resin and L285 resin, observed for the whole population of the tests, were further supported bydifferent share of the classes, and hence by different share of the mechanisms in failure of thesamples.

Fig. 6. AE results for separated class: Duration vs. Amplitude. Class 1 to 4 are indicated.
The basic differences between the two types of laminates noted at the same level of stressduring loading result from different intensities of the internal friction behavior of the matrix andfrom the initiation and propagation of microcracks. In both laminates the process of failure starts
with microcracks in the matrix. For L285 resin this process is much more intensive. In parallel,from the very beginning of AE activity, the process of the resin debonding from the glass fiber
fabric is also developing at a high rate. In both resin laminates, the different AE activity is causedby different physical properties, and hence by different wettability and adhesion of each of theresins to the glass fibers. These effects cause some important differences in AE data set from theloading process of both laminates. Within the whole examined range of the laminate loading, individual damages and cracks in the fibers can be observed. For both laminates this process occurs at
a similar level.

1794. Conclusions
1. The type of resin used as a matrix for the laminate made from the glass fiber fabric has noeffect on the static strength of the laminate.
2. The biaxial state of stress with principal stresses ratio 1/  2= 2 has no effect on the strengthof the laminate made from the glass fiber fabric wetted with epoxy resin.
3. The process of laminate failure starts at a loading corresponding to roughly 15% of the failure
stress.
4. Under the condition of static loading at 20˚C, the rate of crack propagation is uniform for theL285 resin matrix, while it increases quite significantly for the Ep53 resin matrix at higher
stress levels just before failure. Ep53 composites showed the same characteristic at higher
test temperatures as well.
5. Laminates with the matrix made of L285 resin are characterized by a greater number of AE
events suggesting a greater number of microcracks preceding the failure of sample and thegreater activity due to debonding process of the matrix and glass fiber fabric.
6. Hardening at high temperatures raises the laminate strength only by a very small degree.
7. Applying in the analysis of laminate failure, AE method of the pattern recognition classification enables us to determine the ranges of occurrence of the individual mechanisms of failure
during loading of sample.
8. Existence of various mechanisms of crack formation may, depending on the resin type used
for the laminate matrix, have some effect on the long-term strength of laminate.
References
1. T.J. Fowler and R.S. Scarpellini, “Acoustic emission testing of FRP pipes”, Emerging High
Performance Structural Plastics Technology, A. Green, ed., ASCE, New York, 1982, pp. 5063.
2. L. Golaski and K. Ono, “Acoustic Emission Analysis of Laminate Failure Mechanisms with
Reference to Failure Criteria”, AECM 5, Proc. Fifth International Symposium on Acoustic
Emission From Composite Material, ASNT, Columbus, OH, 1995, pp. 170-178.
3. J. Schmidt, I. Baran, L. Golaski: “Failure Behaviour of Glass Fabric Laminates: Independence
on Resin Matrix and Temperature”, Integrity Reliability Failure, 19-22 July 1999, Porto,
pp.56-57.
4. M.J. Owen, J.R. Griffith, “Evaluation of biaxial stress failure surfaces for a glass fabric reinforced polyester under static and fatigue loading”, J. of Mat. Sci. 13. 1978. 1521-1537.
5. A. Neimitz: “The Review of the Failure Criteria for Composites”, in Proc. Joint FrenchPolish Seminar: Failure of Heterogeneous Materials. Paris 1994, Polytech. Swietokrzyskiej
1996, pp. 5-26.
6. S. Benmedakhene, M. Kenane, M.L. Benzeggagh: “Initiation and growth of delamination inglass/ epoxy composites subjected to static and dynamic loading by acoustic emission monitoring”, Comp. Sci. and Tech., 59. 1999. 201-208.
7. F. Meraghni, F. Desrumaux, M.L. Benzeggagh: “Implementation of a constitutive micromechanical model for damage analysis in glass matrix reinforced composite structures”,
Comp. Sci. and Tech., 62. 2002. 2087-2097.

180

ROLLING CONTACT FATIGUE DAMAGE OF
WC-Co CERMET SPRAYED COATING AND ITS AE ANALYSISJUNICHI UCHIDA1, TAKESHI OGAWA1, MIKIO TAKEMOTO1,YOSHIFUMI KOBAYASHI1 and YOSHIO HARADA2
1)
Department of Mechanical Engineering, Aoyama Gakuin University, Fuchinobe, Sagamihara229-8558, Japan. 2) Tocalo Co., Ltd., 4-13-4, Fukaekita, Higashinada, Kobe 658-0013, Japan.
Abstract
Rolling contact fatigue tests were performed for three kinds of WC-Co cermet-sprayed steels
in order to study the influence of spraying conditions on the rolling contact fatigue damage.
Testing machine for rolling contact fatigue with AE monitoring system was developed using athrust bearing. Displacements of the pulley were measured for real-time observation of the peeling, while acoustic emission (AE) method was utilized to detect both the peeling and flaking.
Two modes of damage were found in the WC-Co cermet-sprayed coatings, i.e. peeling and flaking. The former was initiated by micro-fracture around the WC particles near the surface due tothe contact force, and the latter by macro-crack initiation attributed to repeated shear stress by
the plane-sphere contact. The peeling damage initiated in the early stage of rolling contact fatigue test, and increased contact areas and thus reduced damage rate. Subsequently, flaking damage took place by Mode-II crack initiation developed by cyclic shear stress under contact stress
distribution.
Keywords: Rolling contact fatigue, Sprayed coating, WC-Co cermet, Peeling, Flaking
1. Introduction
WC-Co cermet coatings sprayed by high-velocity oxygen fuel (HVOF) method have been
widely used for wear-resistant applications since high-performance coatings are available [1]. It
was observed that cracks initiated in the coating progressed to the substrate boundary and led to
the final fracture of specimens, when the coating was harder than the substrate and the boundary
was well bonded [2]. This behavior is important for fatigue strength evaluation of sprayed components, because the coating cracks determine the fatigue strength of sprayed components [3].
Therefore, it is important to evaluate fatigue resistance of the sprayed coatings. We constructed acontact fatigue testing machine to evaluate the fatigue resistance of sprayed coating itself so thatthe maximum amplitude of cyclic shear stress is within the sprayed coating.
In the present study, rolling contact fatigue tests were performed for three kinds of WC-Co
cermet-sprayed steels in order to study the influence of spraying conditions on the rolling contact
fatigue damage. Detailed observation of fatigue damage and acoustic emission (AE) monitoring
were simultaneously conducted in order to analyze the influence of spraying conditions on thestrength and fracture mechanism of rolling contact fatigue.
2. Experimental Procedures
The substrate material is 12-mm-thick alloy steel, JIS-SCM440, oil-quenched from 870oC
after 1h heating and tempered at 560oC for 2h. The specimens were spray-coated WC-12%Co
J. Acoustic Emission, 23 (2005)

181© 2005 Acoustic Emission GroupTable 1 Parameter for spraying conditions, elastic properties andresidual stress of WC-12%Co sprayed materials.
Spraying
condition
A
B
C

Oxygen flow
rate
Nm3/h
30
40
50

Fuel flow ratel/h

Gas pressure
MPa

8
12
16

0.48
0.83
1.71

Eddy current
sensorPt

PulleyRotating
axis
Thrustbearing

Loadcell

Young’s
Modulus
Ec GPa157
197
217

Poisson’s ratio cResidual stressr MPa

0.15
0.14
0.24

-70 -35-347 -391-668 -465

Specimen
ch.1
ch.3
ch.2

High pass filter

ch.4 Pre-amp.

Additioncircuit
GAGE

Specimen
Pt

Ball

SpecimenholderFig. 2 AE monitoring system.

Fig. 1 Rolling contact fatigue testing machine.
by HVOF method using CJS system of OSU Co. with constant conditions of hydrogen gas supply: 4 Nm3/h, spraying distance: 350 mm, gun velocity: 500 mm/s, pitch: 5 mm and 8 passes.
Other spraying conditions are listed in Table 1, where the spraying conditions A, B and C areused for sprayed specimens A, B and C, respectively. Table 1 also presents the mechanical properties estimated by the inverse analysis of surface acoustic waves (SAW) and residual stress by
cutting method [4]. The sprayed thicknesses A, B and C were 390, 500 and 500 µm, respectively.
The sprayed surfaces were polished by diamond paste and the finished thicknesses were between
100 and 200 µm.
The rolling contact component is a steel ball made of high-carbon chromium steel, JIS-SUJ2,
with the diameter of 5.56 mm. It is a part of thrust bearing, No.51200, with the inner and outer
diameters of 10 and 26 mm, respectively, having 9 steel balls. Figure 1 shows the rolling contact
fatigue machine developed in house. The sprayed specimen was mounted on a holder supported
by a steel ball at the center. Thrust force, Pt, was applied to the bearing rotating on the specimen
at 400 rpm. Cyclic frequency of rolling contact by the 9 balls was then controlled as 30 Hz.
Maximum shear stress by the ball contact was confirmed to be 25 to 30 µm below the surface byfinite element analysis using a commercial code, MARC. The cyclic rolling contact made agroove on the coating surface due to fatigue damage. The depth of the groove was monitored indirectly in terms of the pulley displacement measured by eddy current sensor using our special
averaging software. The resolution of the measurement was about 1 µm.
Figure 2 shows a schematic illustration of AE monitoring system. Four small AE sensors(PAC, PICO) were mounted on the side surfaces of the specimen. AE signals were amplified by
40 dB, and acquired by a PC-based wave analyzer (Gage Applied Science). In order to detectdamage signals properly, AE signals of channels 1 and 2 were added and filtered out the lower
frequency component. The high-pass filter reduced the signal amplitude by 8.7 dB at 100 kHz
182

and by 1.8 dB at 1 MHz. Without the high-pass filter, no AE signal from fatigue damage wasobtained because of continuous acquisition of low frequency noise from cyclic contacts.
3. Results and Discussion
3.1 Observation of Rolling Contact Fatigue Damage
Variations of displacement of pulley, D, with the number of cycles, N, are presented in Figs.
3 and 4, which demonstrate influences of spraying conditions and contact force, respectively. For
all the cases, D increased rapidly just after starting the tests, followed by moderate increase in D.
Subsequently, D increased again except for specimen C at Pt =180 N for the cases in Figs. 3 and4. The changes in D tend to occur at larger values of N, when the Pt value is lower or the spraying pressure is higher, namely it is the largest for the specimen C. As will be discussed later, thethree stages of the changes in D resulted from damage mechanisms developed by rolling contact
fatigue.
After the tests shown in Fig. 3, the grooves were observed on the specimen surfaces by a surface roughness measuring instrument (Taylor Hobson, FORM TAYSURF). Figure 5 shows three
dimensional configurations together with the sections indicated by A-A’, B-B’ and C-C’ in theupper figures. The depth of the grooves can be determined by drawing inscribed circle in thesection profiles, and these agreed fairly well with the final displacement, D, in Fig. 3.

Fig. 3 Displacement change for different
sprayed conditions at Pt = 180 N.

Fig. 4 Displacement change at different P tvalues for specimen B.

The groove surfaces were observed by scanning electron microscope (SEM) and presented inFig. 6. The surface exhibits fine granular appearance observed as dark area, while the bright area
is flat and damaged much less than the dark area by rolling contact fatigue. The dark area isdominant in the specimen A, but it is reduced in specimens B and much reduced in specimen C.
The lower micrographs of Fig. 6 are magnified appearances of the upper micrographs. Figure6(d) shows a small hole, which might be a trace of removal of a small particle with several tensof µm. Figures 6 (e) and (f) demonstrated crack initiation on the groove surface in specimens B
and C.

183

Fig. 5 Three dimensional configurations and sections of grooves at Pt = 180 N.

Fig. 6 SEM micrographs of damaged surfaces at Pt = 180 N. (a) specimen A: N = 1.7106, (b)specimen B: N = 1.6106 (c) specimen C: N=1.5106. The magnified micrographs of specimens A, B and C, are shown in (d), (e) and (f), respectively.
After the surface observations, the specimens were cut and observed on the section of thegrooves. The SEM micrographs are presented in Fig. 7. Subsurface lateral cracks were observedin specimens B and C at the depth of about 30 µm from the surface. This depth coincides withthe location of maximum shear stress predicted by FEM analysis. On the other hand, crackingaround the particles and the particle removals can be found in specimen A.
3.2 AE analysis
Other specimens were prepared for AE analysis during the rolling contact fatigue tests at Pt =180 N. Figure 8 represents the simultaneously monitored displacement D and cumulative AE
event counts, NAE, with the number of rolling contact cycles, N. The variations of D are slightly
different from those in Fig. 3. However, general trends are the same, which support the repro184

Fig. 7 Sectional view of grooves at Pt=180N. (a) specimen A, (b) specimen B, (c) specimen C.
The magnified micrographs of specimens A, B and C, are shown in (d), (e) and (f), respectively.

Fig. 8 Displacement of pulley and cumulative AE event on Pt = 180N.

Fig. 9 Detected AE signals.
185

ducibility of these experiments. For the specimen A, AE signals were detected just after the beginning of the test, and then showed higher rates of increase in NAE after N = 5 x 105 cycles together with the second increase of D. For the specimen B, the first increase of D was obscure inthis experiment. The first AE signal was detected at N = 3.5 x 105 cycles, and the rate of increasein NAE became higher after N = 8 x 105 cycles. Subsequently, the second increase of D was foundafter N = 1 x 106 cycles. For the specimen C, the first AE signal was detected at N = 9.5 x 105
cycles, then NAE slowly increased, but the rate of increase was constant and the second increaseof D was not observed.
Examples of the AE signals are shown in Fig. 9. For the AE waves in Fig. 9(a), or type 1,
polarities of the first peaks were all positive, while in type 2 they were reversed between diagonal locations, i.e., ch. 1 vs. ch. 2 or ch. 3 vs. ch. 4, as shown in Fig. 9(b). These AE signals were
denoted as type 1 and 2, respectively. Most of the detected AE signals represented the characteristics of type 1. However, four signals of type 2 were found in the test for specimen B, and distinguished by open circles in Fig. 8(b).

Fig. 10 AE source locations at Pt = 180 N and the micrographs of the grooves.
In order to analyze the AE signals, traveling velocity of the longitudinal (P-) wave was determined for specimen B. Here, YAG laser pulses were radiated on the specimen surface and thefirst arrival waves were detected on the back surface using a laser interferometer. The P-wave
velocity, VP, was measured as 5929 m/s. Next, the AE sensor was mounted on the side surface ofthe specimen, and the YAG laser pulse was radiated at the distance of 20 mm from the sensor.
The traveling time was 3.35 µs, and resulted in the wave velocity of 5970 m/s, which agreed
with the VP. Therefore, the first peaks of the detected AE signals are the P-wave, and used for the
analysis of AE source location. The results are demonstrated in the upper illustrations of Fig. 10,
where the numbers of analyzed AEs were limited because of small amplitudes of the first peaks.
The circles in the figures indicate the rolling contact location, where the grooves are observed.
186

The analyzed locations are distributed near the grooves. The lower micrographs of Fig. 10 show
contact damages at the rectangular marks shown in the upper illustrations. Specimen A exhibits
uniform damage on the specimen surface, while the specimens B and C present removals oflarger particle. Those damage mechanisms are denoted as peeling and flaking, respectively.
AE source analysis was conducted by the simulation of AE wave pattern, using artificial AE
source. Figure 11 represents schematic illustrations of the artificial AE source excited by line focused pulse YAG laser and shear type PZT element for Mode I and II cracking, respectively.
Figure 11 presents corresponding AE waveforms with the AE sources. The polarities of the firstpeaks are the same between the diagonal AE sensors for Mode I cracking, while they are reversed for Mode II cracking. Therefore, type-2 AE signals shown in Fig. 9 reflect the subsurface
Mode-II crack initiation, which leads flaking damage on the specimen surface.

Fig. 11 Detected AE waves from simulated AE sources of mode I and mode II cracking.
3.3 Fracture Mechanism of Rolling Contact Fatigue
The above observation and AE analysis reveal the fracture mechanism of rolling contact fatigue of WC-Co cermet-sprayed materials as schematically illustrated in Fig. 12. When the rolling contact stress is applied, small cracks initiate around WC particles, which are peeled off fromthe specimen surface. This behavior corresponds to the initial increase in displacement D in Figs.
3 and 4 and produces the groove resulting in the increase in contact area and decrease in contact
stress. Therefore, the peeling damage becomes less significant with increasing D. In the subsequent rolling contacts, cyclic shear stress initiates the subsurface lateral crack as shown in Fig.
12(b) and leads to the flaking mechanism caused by Mode-I cracks. The AE analysis monitored
these cracking mechanisms and agrees with this sequence of events.
The spraying condition affected the strength of rolling contact fatigue. As shown in Figs. 3
and 8, D and NAE increased most rapidly for the specimen A, whose spraying pressure is the187

Fig. 12 Damage mechanisms of rolling contact fatigue.
lowest. For specimen A at P v = 180 N, the peeling damage developed so quickly that the lateralcrack did not grow large enough to develop flaking damage. In contrast, specimen C exhibits thehighest fatigue strength. For specimen C at Pv = 180 N, peeling damage was hardly seen, but thesubsurface lateral cracks developed and lead the flaking damage. The strength characteristics ofspecimen B was in between those of specimens A and C.
4. Conclusion
Rolling contact fatigue tests were performed for three kinds of WC-Co cermet-sprayed steels
in order to study the influence of spraying conditions on the rolling contact fatigue damage. Results are summarized as follows.
(1) Two modes of rolling contact fatigue damage were found in the WC-Co cermet-spray coatings. They are peeling and flaking.
(2) The strength of rolling contact fatigue is enhanced with increasing spraying pressure.
(3) The rolling contact damage developed more quickly with increasing contact stress.
References
1) K. Tani, H. Nakahira, J. Thermal Spray Technology, ASM International, 1 (1992), 333.
2) D.S. Platt, Master Thesis, Thayer School of Engineering Darthmoth College, Hanover, USA(1981).
3) T. Ogawa, K. Tokaji, T. Ejima, Y. Kobayashi and Y. Harada, Materials Science Research International, 4-1 (1998), 12.
4) A. Ishida, T. Ogawa, M. Takemoto, Y. Kobayashi and Y. Harada, J. Material Testing Research Association of Japan, 45-1 (2000), 289.
188

BORON EFFECTS ON AE EVENT RATE PEAKS DURING TENSILE
DEFORMATION OF Ni3Al INTERMETALLIC COMPOUND
K. YOSHIDA, Y. MASUI, T. NAGAMACHI and H. NISHINO
Faculty of Engineering, The University of Tokushima
2-1 Minami-josanji, Tokushima 770, JAPAN.
Abstract
Tensile testing of Ni3Al intermetallic-compound single crystals with no additive, 0.25 and0.50 at. % boron has been performed using acoustic emission (AE) method in order to examine
the interaction between boron atoms and mobile dislocations. Boron additives increase the yieldstrength of Ni3Al by 276 MPa/at. %. AE event rates have two peaks at yield point and in workhardening region. The peak event rates and the distance between these peaks increase with anincrease in boron contents. The latter results from the difficulty of the homogeneous deformation
due to active cross-slip because the stacking fault energy decreases with boron. The total AE
event counts near yield point increase with boron, implying that boron atoms act as interstitials in
Ni3Al and their frictional drag acts as AE sources. Furthermore, the inhomogeneous dislocation
motion not from the cross-slip may generate AE during the second peak.
Keywords: Intermetallic compound; Ni3Al; Tensile test; Boron additive; Yield strength
1. Introduction
We can make use of acoustic emission (AE) generated during deformation in materials to
study the dislocation motion, the interaction of dislocations with inclusions and precipitates, andthe dynamic behavior of micro-cracking. For example, the AE behavior during the yielding of
age-hardened copper and aluminum alloys has been investigated [1-5]. When dislocations bypass precipitates, the diameter and distribution of precipitates affect the AE behavior. When dislocations shear and pass through precipitates, the anti-phase boundary energy and the frictional
resistance to dislocation motion in precipitates mainly affect the AE behavior [5]. However, no
information is available on the AE behavior during deformation of precipitates themselves thatare often intermetallic compounds [6].
Intermetallic compounds have been considered as heat resisting alloys of the future. That ispartly because the yield strength of some intermetallic compounds increases with increasing
temperature in a certain high temperature range. Ni3Al is one of such intermetallic compounds,
but polycrystalline Ni3Al fails due to intergranular fracture because of the brittleness of grainboundary [7]. Additions of a small amount of boron [8] or some third elements [9], and unidirectional solidification [10] have been found to reduce the grain boundary brittleness. In the case ofboron doping, several studies from basic point of view have been conducted to understand thesolubility of boron atoms in Ni3Al, effect of boron additive on yield strength, lattice parameterchange and interaction between boron atoms and dislocations and deformation mechanism of
Ni3Al including boron additives [11-14]. In addition, several in situ straining experiments in atransmission electron microscope have been performed to determine dynamical dislocation
mechanism in stoichiometric Ni3Al [15-17]. However, there is no data on the AE behavior of
Ni3Al single crystal during tensile deformation.
J. Acoustic Emission, 23 (2005)

189© 2005 Acoustic Emission GroupIn this paper, the effect of boron additives on two peaks of AE event rates during tensile deformation has been investigated using Ni3Al intermetallic-compound single crystals. The interaction of mobile dislocations with boron atoms has been discussed analyzing the AE behaviors
obtained.
2. Experimental Procedures
Ni-24 at. % Al (~Ni3Al) with no additive, 0.25 and 0.50 at. % boron was prepared from purenickel (99.97 wt. %), pure aluminum (99.9 wt. %) and pure boron (99.9 wt. %) by a highfrequency induction-melting method under argon atmosphere at 1823 K. Melting and casting
were carried out twice to make ingots homogeneous. Single crystals were grown from such ingots by a modified-Bridgman technique. Tensile specimens with a gage size of 1 mm x 3 mm x
12 mm were spark-machined. All the specimens were annealed under argon atmosphere at 1473
K for 20 h and cooled in furnace. The surface layer was removed by mechanical polishing andelectro-polishing in a 10% sulfuric-acid and 90% ethyl-alcohol solution. Tensile test was performed using an Instron-type machine at a strain rate of 2.8 x 10-4 s-1. After the tensile test, thedislocation structures in the specimens were observed using a transmission electron microscope(TEM). Only Ni3Al X-ray diffraction peaks were observed for all the single crystals. All the tensile tests were performed to a strain of 0.25. After tensile tests, we could not find any macrocracks, and thus all specimens used were considered to be ductile.
Two-channel system (PAC: MISTRAS-2001) was used to obtain AE data that consists of rms(root-mean-square) voltage, cumulative event counts, event rates and peak amplitude. Two AE
transducers with 200-kHz resonance frequency were used, and the signals were amplified 60 dB
with a band-pass filter with the range of 0.1 to 1.2 MHz. Threshold level was 31.6 V at the input of preamplifier. AE data were recorded on a pen recorder and a microcomputer. Only AE
events generated between two transducers within time interval of 2 s were sent into the AE data
memory to detect the AE in the deformed part.
3. Results and Discussion
Figure 1 shows the stress-strain curves and AE activities (AE event rates and rms voltages)of Ni3Al single crystals with no additive, 0.25 and 0.50 at.% boron during tensile deformation.
The yield strength increases linearly with increasing boron contents as shown in Fig. 2. The flow
stress at the strain of 0.25 also increases with the increase of boron. The behavior of AE event
rates is similar to that of rms voltages. The AE event rates have two peaks during tensile deformation of Ni3Al single crystals regardless of boron contents. The first peak is located near theyield point and becomes more active with an increase of boron contents. The second peak exists
in the work-hardening region and its height decreases gradually with an increase of boron contents.
The deformation in all Ni3Al single crystal regardless of boron content is macroscopicallyuniform over the specimen gage section. The primary slip system is mainly active in Ni3Al intermetallic compound as in other fcc materials. Some cross slip systems are also found. That is
so different from the results obtained by the directionally solidified specimens [18].

190Fig. 1 Stress-strain curves, AE event rates (left) and rms voltages (right) of Ni3Al single crystals
during tensile deformation with (a) no additive, (b) 0.25 at. % B and (c) 0.50 at. % B.
The slope /C, where  is the change in yield strength and C is the change in concentration, indicates a very large strengthening effect of 276 MPa/at. % boron. This value is smaller
than that estimated by Baker et al. [14] who used coarse-grained (d = 0.5-2 mm) stoichiometric
Ni3Al, where grain boundary segregation of boron is expected. Therefore, this value is reasonable in Ni3Al single crystal. The same value in 0.25 was 497 MPa/at.% boron in the previous paper [19]. The work-hardening rate appears to become larger with increasing boron contents.

191

Fig. 2 Yield strength as a function of boron contentsFig. 3 Definition of Lüders strain and AE active strain from yield point.
The AE activity in the yielding region is mainly related to the yield point strains that are
known as Lüders strain and AE active strain from the yield point defined in Fig. 3. Figure 4
shows the relation between AE active strain from the yield point and Lüders strain. The AE activity increases with increasing the Lüders strain. This may imply that the source of the AE
means the more breakaway of pinned boron atoms from mobile dislocations.
We next consider the AE activity in the work-hardening region. Figure 5 shows the relation
between the boron concentration and the nominal strain at the second peak of the detected AE.
The nominal strain at the second peak of the AE decreases with an increase of boron content. If
the secondary AE activity depends on another inhomogeneous plastic deformation, the boron
contents clearly give the effect to hasten the inhomogeneous plastic deformation. Generally
speaking, the AE activity during plastic deformation decreases gradually after yielding or in thework-hardening region, unless there occurs micro-cracking in matrix, secondary phase precipitates, inclusions and on the interfaces. However, the AE here is re-activated to show the second
peak in the work-hardening region. It is interesting to find what the source of the AE is.
192

Fig. 5 Relation between the boron concentration and the strain at the second peak.

Fig. 4 Relation between AE active strain
from yield point and Lüders strain.

Amplitude distribution during tensile deformation has been examined to determine the sourceof AE. Figure 6 shows an example of amplitude distributions of AE in the specimen with 0.50 at.
% boron in stage 1 and stage 2. The stages mean the region of  = 0 to 0.05 and  = 0.05 to 0.25,
respectively. The amplitude in both stages has a peak between 50 dB and 60 dB. The amplitudein this region may be related to the plastic deformation or the inhomogeneous dislocation motions. Therefore, these AE activities that consisted of the first and second peaks of the AE event
rate are considered to contain the same source of AE during plastic deformation.

Fig. 6 Typical amplitude distributions of AE in the specimen with 0.50 at. % B in (a) stage 1 and(b) stage 2.
We next discuss the AE sources. Boron acts as interstitial in Ni3Al because a boron atom ismuch smaller than either Ni or Al atom. It is considered that plastic deformation is mainly performed due to anti-phase-boundary (APB)-coupled dislocations, called super-lattice dislocations,
within the range of boron contents used here. TEM photograph of super-lattice dislocations areshown in Fig. 7. [14] It is known that the APB energy is lowered due to the boron addition andthat the yield strength is enhanced as shown in Fig. 2 because of the increase in lattice parameter
due to the boron addition. It is possible that the increase in the yield strength promotes microscopically inhomogeneous plastic deformation. If so, the increase in the yield strength arises
193

from the frictional drag of boron atoms on mobile dislocations. This is one of the AE sources. In
this case, the amplitude may be comparatively small. It appears that the AE activities at the yield
point and in work-hardening region corresponds to the same dislocation source. The generationof cross slip appears to be consistent with that of the AE during work-hardening where the AE
event rates have shown the second peak. The cross slip is easy to operate and induce the homogeneous deformation in the specimen with no boron additive because of the large APB energy or
the narrow super-lattice dislocation spacing, as shown in Fig. 8. As a result, the uniform plastic
deformation continues and delays the secondary AE activity as shown earlier in Fig. 5. With increase of the boron additives, the inhomogeneous plastic deformation is hastened in the workhardening region because the super-lattice dislocation spacing is extended as shown in Fig. 8.
After the cross slip, the secondary AE activity occurs. Molenat et al. [16] considered this to be
due to the APB jumps so as to gain more strain in this region. The APB jumps occur when theleading super-partial is dissociated in a parallel (111) plane and may cause the AE second peak in
the work-hardening region. Unlocking is enhanced by the resolved shear stress acting on (100)plane.
The binding energy of a boron atom to an edge dislocation is considered to be large (~0.7 eV)
and is comparable to that calculated for carbon in iron (~1.0 eV) [14]. This magnitude indicates astrong interaction between a boron atom and an edge dislocation. In the case of this study, one
suggestion is that larger amplitude AE events can be detected when more Lüders strain occurs.
The more scattering of AE event counts at the yield point with increasing boron contents suggests that the boron produces an asymmetrical strain field, possibly by some of the boron atoms
occupying interstitial sites on the Ni3Al unit cell edge as mentioned by Baker et al. [14].
Super Lattice Dislocaition Spacing [nm]

200

150

100

50

0
0

0.2

0.4

0.6

B concentration [at. %]Fig. 7 Typical dislocation distribution of thespecimen with 0.50 at. % B after the strainof 0.225.

Fig. 8 Effect of boron content on superlattice dislocation spacing.

6. Conclusion
Acoustic emission measurement and tensile testing in boron-doped Ni3Al single crystals haveshown that:(1) Boron activates the AE activity.
194

(2) Boron has a large strengthening effect at yield point of 276 MPa/at.% boron.
(3) AE activities have two peaks that are at the yield point and in the work-hardening region.
(4) The first peak is due to a frictional drag of boron atoms on mobile dislocations, including theinteraction between boron atoms and mobile edge dislocations as shown in a solute atmospheremodel.
References[1] Masuda, J., J. Japan Inst. Met., 46 (1982) 620.
[2] Kishi, T. and Sakakibara, Y., J. Japan Inst. Light Met., 34 (1984) 472.
[3] Heiple, C. R., Carpenter, S. H. and Christiansen, S. S. Acta Met., 38 (1990) 611.
[4] Sakamaki, K., Yoshida, K., Takagi, H. and Yoshida, M. JSME Int. J., 33 (1990) 362.
[5] Yoshida, K., Pang, B., Zhu, A., Sakamaki, K. and Takagi, H., J. Japan Inst. Met., 60 (1996)
809.
[6] Zhu, A., Yoshida, K., Takagi, H. and Sakamaki, K., Intermetallics, 4 (1996) 483.
[7] Aoki, K. and Izumi, O., J. Japan Inst. Met., 41 (1977) 170.
[8] Aoki, K. and Izumi, O., J. Japan Inst. Met., 43 (1979) 190.
[9] Takasugi, T., Izumi, O. and Masahashi, N., Acta Met., 33 (1985) 1259.
[10] Mawari, T. and Hirano, T., Intermetallics, 3 (1995) 23.
[11] Liu, C. T., White, C. L., and Horton, J. A., Acta Met., 33 (1985) 213.
[12] Weihs, T. P., Zinoviev, V., Vien, D. V. and Schulson, E. M. , Acta Met., 35 (1987) 1109.
[13] Baker, I., Schulson, E. M. & Horton, J. A., Acta Met., 35 (1987) 1533.
[14] Baker, I., Huang, B. and Schulson, E. M., Acta Met., 36 (1988) 493.
[15] Horton, J. A., Baker, I. and Yoo, M. H., Phil. Mag. A63 (1991) 319.
[16] Molenat, G. and Caillard, D., Phil. Mag. A64 (1991) 1291.
[17] Molenat, G. and Caillard, D., Phil. Mag. A69 (1994) 939.
[18] Yoshida, K., Saito, T., Zhu, A., Takagi, H., and Sakamaki, K., Intermetallics, 6 (1998) 1.
[19] Yoshida, K., Horikawa, K. and Sakamaki, K., Progress in Acoustic Emission XI, 16 (2002),
JSNDI, p. 15.

195

AE AND ELECTROCHEMICAL NOISE ANALYSIS FOR FRACTURESTUDY OF HARD SURFACE FILMAKIO YONEZU, HIDEO CHO, TAKESHI OGAWA and MIKIO TAKEMOTO
Department of Mechanical Engineering, College of Science and Engineering,
Aoyama Gakuin University, 5-10-1, Fuchinobe, Sagamihara, Kanagawa, 229-8558, Japan.
Abstract
We propose a new hybrid technique of AE, corrosion potential and FEM for studying thefracture mechanism of hard surface films during micro-Vickers indentation. This method makes
it possible to determine the fracture strength of the film when hard film is deposited on soft substrate. Both the AE and corrosion potential fluctuation of PVD-TiN film deposited on AISI-304
steel in hydrochloric acid (1 mol/ l HCl + 0.01 mol KSCN) were simultaneously monitored during Vickers indentation test. We observed characteristic potential change in mV scale due to nonfaradic reaction during loading to 20 N. Timing of the potential fluctuations coincided well withstrong AE events. Both the AE and potential fluctuation were found to be produced by theMode-I fracture of the film bending during loading. The number of equidistance step-wise terrace or rippled cracks observed in pyramidal indentation agreed well with the number of potential fluctuation. It was found that potential fluctuation analysis, jointly utilized with AE analysis,
can reveal the fracture process of the film.
Keywords: Hard surface film, Indentation test, Electrochemical noise analysis, Finite element
method, Fracture strength
1. Introduction
Hard surface coatings are widely used to improve the functionality of the metals. Hard films
such as ceramic are brittle and often suffer damages as cracking and exfoliation. Evaluation of
fracture strength of the hard thin films is important for a practical application. Though various
test methods such as scratch and external loading have been utilized so far, property of the film
tends to change depending on the test method and mechanical characteristics of the substrate. It
is generally difficult to determine the intrinsic fracture strength of the film.
One of the authors once attempted tensile and compressive loading of the specimens with
CVD-TiN film deposited on steel substrate [1, 2]. They studied the crack dynamics of the filmusing quantitative source inversion technique. They also used finite-element method (FEM) to
determine the fracture strength of the film and residual stress [1], and revealed extremely large
compressive residual stresses. External tensile loading hardly generates crack in the film due tolarge compressive residual stresses. Ikeda et al. [3-5] reported that external loading of WC-Co
plate with CVD-diamond resulted in the unstable fracture of the substrate. This implies that theexternal loading method cannot be used for evaluating the intrinsic fracture strength of films.
We previously performed micro-indentation tests to TiN film deposited onto soft and ductile
stainless steel substrate AISI-304 [6, 7], and monitored AE to study the fracture mechanism of
the films. Fracture mode analysis [8, 9] revealed that a number of AEs by Mode-I fracture
were
J. Acoustic Emission, 23 (2005)

196© 2005 Acoustic Emission Groupproduced during loading. Indentation test of the hard film on the soft substrate makes the evaluation of the fracture strength of the film possible, since the film tends to suffer surface Mode-I
cracks due to tensile stresses induced by film bending. It was made unclear whether the AEs
were produced by Mode-I tensile crack or friction or cracking by compressive loading. In orderto determine the fracture strength of the film, we must clarify the film fracture process andmechanism during indentation testing.
Monitoring of corrosion potential fluctuation or electrochemical noise (EN) analysis has beensuccessfully utilized to study the initiation of localized corrosion and stress corrosion cracking(SCC) [10-12]. The potential fluctuation is caused by non-faradic anodic or cathodic local current [12]. If we use EN analysis, we can monitor the process of film cracking. Thus, we
simultaneously monitored EN and AE during indentation testing and analyzed the details of thecrack progression.
In this study, we first monitored AEs during indentation testing in air of TiN film deposited
onto WC-Co cermet and AISI-304 steel. Effect of hardness of the substrate on the damage characteristics of the film was studied. Next we utilized AE and EN analysis during indentation testin specially designed solution. We investigate the fracture behavior of the film. Finally, we estimated the fracture strength of TiN film using FEM [6, 7].
2. Materials and Experimental Procedures
The hard films tested are TiN films of a few m thick. The TiN films were deposited by PVD
method onto metastable austenitic stainless steel (AISI 304) and WC-Co cermet. Here, sample
with AISI-304 substrate has a middle layer of 0.5-µm thick titanium deposited with ion bombardment in order to improve the interfacial strength. These samples are designated as
TiN/Ti/AISI304 and TiN/WC-Co, and coated by hollow cathode discharge (HCD) and arc ion
discharge (AID), respectively. Film thickness and specimen size are shown in Table 1.

We performed micro-indentation test by an electromagnetic type servo-testing machine
equipped with a Vickers indenter and two eddy current sensors. This machine can measure theindentation force (F) and indentation depth (h) or F-h curve with high resolution. Maximum indentation force (Fmax), were optionally changed depending on the purpose. All tests in air were
performed with loading rate (dF/ds) of 5 mN/s and holding time (tv) of 10 s at room temperature.
We monitored AEs using four resonant-type small sensors (PICO: PAC). They are mounted
on the film surface to monitor the Lamb wave AEs. We also used a wideband frequency sensor(WD: PAC), which was mounted on the bottom surface of the specimen, as shown in Fig. 1. The
WD sensor signal was set on the trigger channel. Sensor outputs were amplified to 60 dB by preamplifiers (PAC), and digitized by an A/D converter (Gage Applied Inc.) and fed to a personal
computer. AEs are digitized at an interval of 40 ns with 2048 points. The threshold value forchannel 1 (trigger channel) was set at 25 µV.
197

Fig. 1 Experimental setup for AE system for indentation testing.

Fig. 2 F-h curve with cumulative AE count for TiN/Ti/AISI304 and TiN/WC-Co.
We also monitored the corrosion potential fluctuation induced by film fracture in a solution.
A 20-mm diameter plastic cell was glued on the specimen surface. This cell is connected to another cell with Ag/AgCl reference electrode (GS-5015C: TOA-DKK). The solution was hydrochloric acid (1 mol/ l HCl + 0.01 mol KSCN) [14], which can monitor the damage occurrence
sensitively. Output of the electrode was digitized by a digital voltmeter (RE6871E: ADVANTEST) at resolution of 1 µV and time interval of 76 ms. This indentation test in solution was performed with Fmax= 20 N, dF/ds = 1 mN/s and tv = 60 s at room temperature.
198

Fig. 3 Typical AE waveforms detected by AE small sensor during the indentation testing.
3. Results and Discussion
3.1 Analysis of micro-crack behavior during Vickers indentation test
Figure 2 shows the F-h curves and cumulative AE counts. The specimen TiN/Ti/AISI304
produced a number of AEs from small force, while TiN/WC-Co produced no AEs. Figure 3
shows the typical AE waveforms detected during loading of TiN/Ti/AISI304 specimen. It isnoted that polarities of first S0 mode Lamb waves are all positive as indicated by arrows, indicating Mode-I opening fracture type. Open triangles in Fig. 2 indicate the AEs from Mode-I
opening fracture types estimated by the fracture-type classification method [8, 9]. Most AEs detected are produced by Mode-I crack with opening vector in the direction parallel to the film surface. We detected only one AE from Mode-II crack at around 2.5 N.
Figure 4 shows the micrographs of indenter contact region. There observed a number of steps
in the pyramidal indentation of the specimen TiN/Ti/AISI304. These steps are film cracks,
known as the ripple cracks. This type of crack appeared to produce AEs during the loading process. In contrast, two small median cracks from the corners of indentation were observed for
TiN/WC-Co.

Fig. 4 Micrographs of contact region for TiN/Ti/AISI304 (a) and TiN/WC-Co (b), tested with
Fmax= 5 N using Vickers indenter.
199

Fig. 5 FEM analysis model for TiN/Ti/AISI304 and TiN/ WC-Co.

Fig. 6 x distribution for TiN/Ti/AISI304 (a) and TiN/WC-Co (b).
3.2 Stress analysis of thin films subjected to Vickers indentation test
In order to study the influence of the substrate on the film damage, we analyzed stress distribution by FEM as shown in Fig. 5. Here the film thickness was set as 4 m. The yield strength(ys) of the film is assumed to be 7 GPa. This value was estimated by three times the Vickers
hardness value [13]. Figure 6 shows stress x distribution in the direction parallel to the specimen surface for indentation depth h from 2 to 10 µm. Horizontal axis (r) designates the distancefrom the center of indentation. Open triangles on the curves indicate the contact boundary. Figure6(a) indicates that the x is compressive in the contact region, while it changes to tensile stressoutside the contact region. This stress distribution, with large tensile stress higher than the tensile
strength of the film, strongly indicates that crack is produced by the tensile stress induced bybending of the film. A number of Mode-I cracks, appeared as ripple crack, are produced outside
the contact point with progression of indentation. In contrast to this, only the compressive x is
induced for TiN/WC-Co, as shown in Fig. 6(b). The cermet substrate does not suffer plastic de200

formation. Thus, the crack morphology for TiN/WC-Co specimen is similar to that observed formonolithic ceramics.
3.3 Monitoring of micro-cracks using electrochemical noise analysis
Figure 7 shows the F-h curves and cumulative AE counts. 254 events were detected during
the loading and 17 events during the unloading. We analyzed the fracture mode of 60 events withlarge amplitude of So-Lamb wave. Timing of Mode-I type AEs, with all positive polarities, wereshown by open triangles near the F-h curve. The Mode-I AEs were detected from small loads
and produced at an almost constant rate with increasing load. This behavior is quite similar tothat found in Fig. 2 for TiN/Ti/AISI304.

Fig. 7 F-h curve with cumulative AE count for TiN/Ti/AISI304 in HCl solution.

Fig. 8 Variation of potential noise with cumulative AE count during indentation test.
201

Figure 8 shows overall changes of corrosion potential or electrochemical noise (EN) and cumulative AE count as a function of loading time. Loading history is also shown by the dashedline. The corrosion potential shifts from 300 mV vs. Ag/AgCl of TiN to active direction, with anumber of RD-type potential fluctuations (rapid drop to active potential and gradual recovery).
First drop was observed at a low load of F = 2.5 N, corresponding to the loading time of 380 s.
Shown in Fig. 9 is the potential fluctuation in the range from 303 to 296 mV. Note the position in the loading history in Fig. 8. RD type fluctuations with large and small amplitudes were
frequent. Figure 10(a) shows typical RD type fluctuation, which is caused by local anode current
flows due to the film fracture [12]. Figure 10(b) shows changes of cumulative counts of RD-type
fluctuations with the drop velocity of above 2 mV/s, AE event counts and indentation force withtime. Most ENs and AEs were detected during loading. We observed a good coincidence between the two, AE and EN. This strongly suggests that the ENs were produced by film cracks
occurring outside the contact region. FEM results (Fig. 5) suggested that ripple cracks were generated by the film bending, since large bending stress occurred outside the contact region.

Fig. 9 Potential fluctuation during the indentation testing.

Fig. 10 Typical RD-type EN detected (a) and cumulative RD-type EN and AE counts (b) during
the indentation testing.
202

Fig. 11 Examples of RD-type ENs with AE timing.
Figure 11 shows an example of RD-type ENs and the timing of AEs. It is noted that an RDtype EN was detected just or slightly after an AE timing. Similar behavior was observed over theentire potential range.
Figure 12 compares SEM photographs of the indentation induced in air (b) and in solution(a). A partial film delamination was observed near the edge of pyramidal indentation in solution,suggesting the film damage by solution diffused into the film cracks. We counted the number ofcracks on each pyramidal plane in Fig. 12(a). Figure 13 shows the number of cracks found in thecontact region as a function of distance from the center of indentation, r. Total number of cracks
counted was as 28 to 35 on one pyramidal plane. The total number of cracks reaches 128 andcoincides well with the cumulative RD-type ENs number of 112 shown in Fig. 10. Thus, we conclude that both AEs and RD-type ENs were produced by the film fracture of TiN.

Fig. 12 Micrographs of the contact region for TiN/Ti/AISI304 tested with F max= 20 N in HCl
solution (a) and in air (b).
4. Conclusion
In order to study the fracture mechanisms and strengths of PVD-TiN film, both acoustic
emission (AE) and electrochemical noise (EN) were monitored simultaneously during indenta203

Fig.13 Number of cracks as a function of the distance from the center of contact region.
tion tests. Effects of substrate hardness on the film fracture and the fracture process of TiN film
deposited on AISI-304 steel in 1 mol/l hydrochloric acid solution were analyzed in detail. Results are summarized below.
1.

2.

TiN film deposited on WC-Co cermet developed small median cracks at the corners of pyramidal indentation. TiN film on AISI-304 steel, however, sustained a number of film cracks(ripple cracks) but no median cracks. In the latter material, AEs from Mode-I film cracks
were detected from low indentation loading and increased with the load. FEM analysis predicted that Mode-I film cracks was produced by large tensile stresses due to the film bending.
Simultaneous monitoring of AEs and ENs for the TiN film on AISI-304 steel in the solutionof hydrochronic acid revealed that the number of EN coincides with that of AEs. Rapid shiftof the potential to active direction (RD-type EN) is induced by the Mode-I film fracture near
the indentation. EN analysis, jointly utilized with AE analysis, can reveal the fracture process of the film.

Acknowledgement
This work was performed as a part of the Center of Excellence (COE) Program, funded by
the Ministry of Education, Culture, Sports, Science and Technology.
References(1) S. Ogawa, T. Fukai, T. Futatugi and M. Takemoto: Surf. Tech., 46 (1995), 456 (in Japanese).
(2) S. Ogawa, H. Cho, K. Yamanaka and M. Takemoto: J. Soc. Mech. Eng. 63 (609) (1997),
1057 (in Japanese).
(3) R. Ikeda, M. Nakanishi, T. Ogawa, M. Takemoto: Journal of Material Testing Research, 47, 4(2002), 231 (in Japanese).
204

(4) R. Ikeda, Y. Hayashi, M. Takemoto: Prog. Acoustic Emission XI, (2002), JSNDI, Tokyo, p.
53.
(5) R. Ikeda, Y. Hayashi, M. Takemoto: J. Acoustic Emission, 21 (2003), 131.
(6) A. Yonezu, T. Ogawa and M. Takemoto: Proc. ICF11 (2005) 4209.pdf.
(7) A. Yonezu, T. Ogawa and M. Takemoto: J. Soc. Mat. Sci. Japan., 54, 11 (2005) 1030-1035.
(in Japanese).
(8) A. Yonezu, T. Ogawa and M. Takemoto: Prog. Acoustic Emission XI, (2002), JSNDI, Tokyo,
p. 86.
(9) R. Ikeda, M. Hayashi, A. Yonezu, T. Ogawa: Journal of Material Testing Research, 49, 3(2004), 157 (in Japanese).
(10) H. Inoue, K. Yamakawa, and N. Fukuda: J. Soc. Mat. Sci. Japan. 43 (1994), 1400 (in Japanese).
(11) H. Inoue, K. Yamakawa, T. Kikuchi and Y. Yoneda: Zairyo-to-Kankyo, 45, (1996) 717 (inJapanese).
(12) H. Inoue: Zairyo-to-Kankyo, 52, (2003) 544 (in Japanese).
(13) D. Tabor: The Hardness of Metals, Oxford University Press, (1951) 154.
(14) T. Fukai: Doctoral thesis, Osaka University (1995).
(15) S. Fujimoto and M. Takemoto: Zairyo-to-Kankyo, 53, (2004) 309 (in Japanese).

205THE ORIGIN OF CONTINUOUS EMISSIONSKANJI ONO1, HIDEO CHO2 and M. TAKUMA3
1)
Dept. of Materials Science and Engr., University of California, Los Angeles, CA 90095, USA.
2)
Department of Mechanical Engr., Aoyama Gakuin University, Sagamihara, Kanagawa, Japan.
3)
Department of Mechanical Engr., Kansai University, Yamate, Suita, Osaka, Japan.
Abstract
We have studied the random nature of continuous acoustic emission, typically observed during the plastic deformation of metallic materials. In particular, the yielding of variously treated
A533B HSLA steel samples was used as the emission source. Signal amplitude distribution was
found to be Gaussian. Some minor deviation can be also seen when burst emissions are superimposed. Time-series analysis techniques were applied to obtain chaos parameters. Signals proved
to be stochastic. When a laser excitation source is applied on tensile samples of common geometries, resultant simulated AE signals exhibit Gaussian distribution. The synthesis of these two
sets of observation points to the resolution of the origin of the continuous emissions, which has
been the subject of long running speculation.
Keywords: Continuous emissions, Gaussian distribution, Laser excitation, Time-series analysis,
Chaos
1. Introduction
We observe continuous-type acoustic emission (AE) signals during the plastic deformation of
metals and alloys, especially in the beginning stages [1,2]. Such continuous signals were found
to exhibit Gaussian random distribution [3], but little progress has occurred since that time. In
contrast, pulse-like AE signals, commonly known as burst emissions, are generated from distinct
elastic energy release processes, such as cracking and martensitic phase transformation [4].
These burst-type AE signals correspond to specific physical events and can be characterized with
Green’s functions using methods such as deconvolution analysis, moment tensor analysis andsimulation analysis when conditions are favorable. Strong research efforts have resulted in goodunderstanding of the origin of burst emissions from various materials (For recent review, seerefs. 5-7).
A large body of mathematical work exists dealing with stochastic and chaotic processes. Of
special interest here is the time-series analysis. Applying this methodology, Bukkapatnam et al.
[8] examined AE signals associated with flank wear and found them to emerge from a lowdimensional attractor of a chaotic process. They produced a compact set of signal features thatenabled them to generate neural network for wear estimation. Recently, Nishiura et al. [9]showed that continuous emissions during shear-type machining processes can be characterizedusing methods of time-series analysis. They were able to differentiate continuous-type AE signals from several distinct stages of tool wear in machining by determining maximum Lyapunov
exponents and correlation dimension. Signals from tensile, shear or mixed fracture processes
generated distinct shifts in these parameters. Their findings implied that the observed AE signals
also have their origin in chaotic non-linear dynamical systems. At present, the only information
we utilize in defining continuous emission signals is the root-mean-square (rms) voltage,J. Acoustic Emission, 23 (2005)

206© 2005 Acoustic Emission Groupaveraged over a certain duration. Our interest is to examine continuous-type AE signals and to
deduce other characteristic parameters if they exist.
Another objective of this work is to simulate continuous emission signals in order to identify
the origin of such emission. It is generally believed that continuous emissions are the consequence of many individual AE bursts occurring in random sequence. However, no definitive evidence has been obtained. Ono (unpublished work, 1978) attempted to generate random emission
signals via the superposition of random-amplitude pulses with randomly varied intervals. Twosets of random noise generator and AE equipment were used in the experiment. Each pulse wasnarrow-band filtered to represent typical burst emission (often referred to as a damped sinusoid).
Resultant signals contained regular features and randomizing effort was unsuccessful. The present work utilizes laser excitation of typical mechanical test samples and a non-contact,broadband detection method. Digitalized signals are conducive to the superposition procedures
and we attempt to artificially generate continuous emission signals.
2. Analysis of Continuous Emission Signals
We generated continuous emission signals by the plastic deformation of steel samples. These
are of round half-size ASTM E-8 tensile sample geometry. The gage diameter is 6.4 mm and theparallel section length is 32 mm. The grip section was threaded with 1/2”-20 thread and the total
length was 85 mm. Material was A533B steel, oil-quenched from 930˚C and tempered at 550˚C
for 2 hr. An AE sensor (either AET MAC375 or PAC WD) was placed on one end with resin
couplant. AE signals were recorded using a PAC Mistras board running under MI-TRA softwarewith external triggering of 100-ms interval. Digitization rate was 2 MHz and the record lengthwas 20 kw. Typical stress-time and rms-voltage-time curves are shown in Fig. 1. Here, the rmsvoltage data was manually obtained from digitized data using the statistical function of Excel.

Fig. 1 The stress-time and rms-voltage-time curves for an A533B tensile sample.
207

0.05
0.04
0.03
0.02
0.01
0
-0.01

1

47

93

139 185

231 277

323 369

415 461 507

163

217

553 599645 691 737

783 829875 921967 1013 1059 1105 1151 1197 1243 1289 1335 1381 1427 1473 1519 1565 1611 1657 1703 1749 1795 1841 1887 1933 1979 2025 2071

-0.02
-0.03
-0.04
-0.05

0.1
0.08
0.06
0.04
0.02
0
-0.02

1

28

55

82

109

136

190

244

271

298

325

352

379

406

433

460

487

514

541

568

595

622

649

676

703

730

757

784

811

838

865

892

919

946

973 100085

127 169 211 253 295 337 379 421 463 505 547 589 631 673 715 757 799 841 883 925 967 1009 1051

-0.04
-0.06
-0.08
-0.1

0.01

0.005

0
1

43

-0.005-0.01-0.015

Fig. 2 Representative signals. Top: E, Middle: G, Bottom: H
Figure 2 shows representative signals. Here, E is a continuous signal near the maximum signal level at yielding and H is the preamplifier noise, while G is a burst emission signal for comparison. Details of these and other signals examined are tabulated in Table 1. In the case of E,
one can see wave trains appearing repeatedly, although each high activity zone differs from allothers. Signal H shows more uniformity in peak distribution and no definitive wave train is discernible. As usual, the burst signal G indicates a rapid rise followed by slower decay in amplitude. Figure 3 gives wavelet transform result for a part of continuous emission E. While severalof the high coefficient value regions are separated by the round-trip time, these are
Table 1 Summary of signal characteristics. (Type: B=burst, C=continuous, MC = MAC375)
Signal Type rms V
sion

A
B
C
D
E
G
H

C
B
C
C
C
B
C

Data length Sensor Fractal dimension Lyapunov exp.

2.83 V
10.01
4.46
5.76
10.11
13.33
3.27

1 kw
1
20
20
20
1
2

MC
MC
WD
WD
WD
WD
WD

1.42
1.62
1.65
1.24
1.4
208

0.491
0.549
0.529
0.441
0.440

Correlation dimen-

0.0889-0.825-2.01-1.265-1.856

Fig. 3 Wavelet transform of continuous emission, E.
generally unrelated. When the cumulative amplitude distribution of signal H is plotted against
amplitude, it coincides with that of normal distribution, as shown in Fig. 4. Although signals E
and H look different, both signals follow the normal distribution. All other continuous emission
signals also have the same normal distribution characteristics.
Using sets of data of 1024-points, the fractal dimension of all the signals was calculated as afunction of frequency. Five curves for signals C to H are shown in Fig. 5. For the signals except
G, fractal dimensions were similar above 350 kHz and varied from 1.5 to 1.65. These signals
were obtained using a WD sensor that has sensitivity peaks in 100-300 kHz. This is reflected in
the changes observed for signals D and E, both of which have increased rms voltage levels overthe background noise. Here, the fractal dimensions increased from ~1.5 to almost 1.7. Signal C isonly slightly above the background and show the essentially same curve as that of signal H.
Burst signal G shows consistently lower fractal dimension (1.07 – 1.35) than the rest.
In the frequency range of 200-300 kHz, the average fractal dimension appears to increase
with the rms voltage of continuous emission signals. As listed in Table 1, it varies from 1.4, 1.45,
1.62 and 1.65. For the burst signal, it is markedly lower at 1.24. Since fractal dimension of 1.999
corresponds to Brownian motion and that of 2.0 to random white noise, the present result implies
that stronger continuous emission signals become less predictable.
20910000

1000

100

10
Diff Data
Cumul Data
ND
1
0.01

0.1

1

10

100

Fig. 4 Differential and cumulative distribution vs. amplitude (in µV) of signal H. ND = normal
distribution.

Fig. 5 Fractal dimension of the signals, C to H, as a function of frequency.
210

Parameters used in time-series analysis include correlation dimension and Lyapunov exponent. Figure 6 shows a plot of correlation index J(m) against embedding dimension m for signal
C. This plot indicates convergence above m=12 at J(m) = 10, or the correlation dimension to be
10. For signal D, correlation dimension was found to be 10.1. For signal E, no convergence resulted. When the value of correlation dimension is at this level, signals are best described as stochastic or random, rather than being chaotic. The maximum Lyapunov exponent and correlation
integral values were obtained for signals A – E and listed also in Table 1. While these vary depending on signals examined, allowing the signals to be differentiated, no consistent changes canbe recognized in the data.

Fig. 6 Correlation index J(m) vs. embedding dimension m for signal C.
It is concluded from these findings that the continuous emission signals examined exhibit
stochastic or random behavior. No new parameter that can characterize continuous emission signals in terms of chaotic behavior is identified. The average fractal dimension appears to correlatewith the rms voltage and further evaluation is warranted.
3. The Origin of Continuous Emission Signals
It is generally believed that continuous emission signals result from the superposition of numerous distinct AE events [1,2]. In the case of a high-strength aluminum alloy, the use of abroadband sensor resolved individual AE pulses in the post-yield region, where the fracture ofparticles is the source of the observed AE activities [10]. During the yielding of many metals andalloys, it is natural to attribute a slip step as the source of an AE event. When the slip step islarge and strain rate is high as in the case of dynamic strain aging, we have observed a spike ofAE activity. Accompanying a load drop of dynamic strain aging, Armentrout and Carpenter [11]reported a sudden increase in the amplitude of continuous emission, which was a single burst
AE. In usual yielding, no confirmation of individual slip step linking to a single AE event has
been reported.
Our attempt to simulate continuous emission signals starts from the excitation of representative tensile samples using energetic laser pulses. Laser pulses were from a YAG laser (Continuum; 1-3 mJ output), focused on the end of a tensile sample. Acoustic signal detection utilized211

a laser interferometer (Thalen Laser; SH-140) with bandwidth of 20 kHz to 2 MHz. Two roundbar steel samples (parallel gage section and hour-glass type) and one flat steel sample were used.
Signals were averaged 1000 times to improve the signal-to-noise ratio.

Fig. 7 Laser-excited signal on a round-bar tensile sample. Detected by non-contact method.

Fig. 8 Cumulative amplitude distribution of detected laser-induced signal and sum of 3 Gaussian
distribution (Two of the Gaussian distributions are shown.
Figure 7 shows one such detected signal on a round-bar sample. This exhibits typical characteristics of an AE burst; namely, a sharp rise, followed by a slow decay extending over 2 ms.
With a non-contact detection, this decay lasts beyond 10 ms. The wavelet coefficients show
broadband response as expected, but give sharp peaks at 80-150 kHz range and at 350-500 kHz
range. Some periodicity exists, but distinct reverberation peaks are absent. The cumulative amplitude distribution of this signal is shown in Fig. 8. The data is nearly log-linear and can be represented by the superposition of three sets of Gaussian distribution. The third one makes only212

a minor contribution, but keeps the distribution curve matched well below 10 counts. This is surprising to find as the sample had well machined ends and most length was round bar. Apparently,
the shoulder sections make wave reflections complex, randomizing the waves reaching the detection end. The other contributor is the point source nature of the laser excitation, unlike thecommonly used AE or ultrasonic sensor source.
A result of random summation of ten signals is shown in Fig. 9. Starting times were picked
arbitrarily and the amplitude was varied for each wave, which is identical to Fig. 7. Thus, theorigin of continuous AE signals with Gaussian distribution can be traced to the small source size
and the irregular reflections and refractions of sample geometry. This produces spreading waves
that are randomized through bouncing at the shoulders and enlarged grip regions.
4. Conclusion
Continuous AE signals were examined using the method of time-series analysis and found to
be stochastic. No useful parameter based of chaotic behavior was found. Using laser-excitedsource for AE bursts, the random superposition of such excited waves can produce randomized
signals that simulate continuous AE signals from plastic deformation. This confirms the longheld belief regarding the origin of continuous emissions.
4.00E-01
3.00E-01
2.00E-01
1.00E-01
0.00E+00
1

235 469 703 937 1171 1405 1639 1873 2107 2341 2575 2809 3043

-1.00E-01-2.00E-01-3.00E-01-4.00E-01

Fig. 9 Simulated continuous emission signal. Total signal duration = 1.6 ms. Ten signals of Fig. 7
were summed after selecting arbitrary starting time and scaling amplitude.

213

References
1. C. R. Heiple and S.H. Carpenter, “Acoustic Emission Produced by Deformation of Metals andAlloys - A Review: Part I”, J. Acoustic Emission, 6, 1987, 177-204.
2. C. R. Heiple and S.H. Carpenter, “Acoustic Emission Produced by Deformation of Metals andAlloys - A Review: Part II”, J. Acoustic Emission, 6, 1987, 215-238.
3. K. Ono, “Amplitude Distribution Analysis of Acoustic Emission Signals”, Materials Evaluation, 34, 1976, 177-184.
4. Kanji Ono, “New Goals for Acoustic Emission in Materials Research”, a chapter in “Acoustic
Emission – Beyond the Millennium,” eds. T. Kishi, M. Ohtsu and S. Yuyama, (2000), Elsevier,pp. 57-76.
5. M. Enoki, T. Kishi, “Acoustic Emission Source Characterization,” a chapter in “Acoustic
Emission – Beyond the Millennium,” eds. T. Kishi, M. Ohtsu and S. Yuyama, (2000), Elsevier,pp. 1-18.
6. M. Takemoto, H. Nishino and K. Ono, “Wavelet Transform-Application to AE Signal Analysis,” a chapter in “Acoustic Emission – Beyond the Millennium,” eds. T. Kishi, M. Ohtsu and S.
Yuyama, (2000), Elsevier, pp. 35-56.
7. M. Ohtsu, “Moment Tensor Analysis of AE and SiGMA Code,” a chapter in “Acoustic Emission – Beyond the Millennium,” eds. T. Kishi, M. Ohtsu and S. Yuyama, (2000), Elsevier, pp. 1934.
8. K. Bukkapatnam, S.R.T. Kumara and A. Lakhtakia, “Analysis of Acoustic Emission Signals
in Machining,” Trans. ASME, J. Manuf. Sci. and Engr., 121, 2000, 568-576.
9. T. Nishiura, N. Shinke, M. Takuma, and K. Akamatsu, “Evaluation of Tool Life in Shearing
Process – Application of AE Method,” Proc. 14th Acoustic Emission Conf., 2003, JSNDI, pp.
131-134.
10. C.M. Scala and S. McK. Cousland, Materials Science and Engr., 76, 1985, 83-88.
11. D. L. Armentrout and S. H. Carpenter, “An Investigation of Lüders Band Deformation and
the Associated Acoustic Emission in Al - 4.5% Mg Alloys”, J. Acoustic Emission, 15, 1997, 4352.

214

PRECURSOR OF HYDROIGEN INDUCED GLASS LINING CHIPPINGBY AE MONITORINGKOHEI MURAKAMI and MIKIO TAKEMOTO
Faculty of Science and Engineering, Aoyama Gakuin University, Fuchinobe, Sagamihara,
Kanagawa, 229-8558 Japan.
Abstract
Glass lining serves as corrosion-resistant barrier and is composed of two layers, i.e., a porous
ground coat and a dense top coat. Pores in the ground-coat serves for moderating the missmatches of mechanical and thermal properties of steel substrate and brittle glass layer, and also
for trapping the hydrogen gas induced via the substrate. Chipping is a sudden fly-off of the lining
layer and cannot be repaired on site. We monitored precursor of the chipping by AE and laser
surface-acoustic wave (LSAW) technique. We monitored AE signals before half of the time tocause final large scale chipping. The time of first AE coincides the time when hydrogen ion
reaches the ground coat. AE are supposed to be produced by the cracks connecting the pores in
the ground coat. LSAW technique was demonstrated to be useful in monitoring the precursor of
the chipping.
Keywords: Glass lining, Chipping, Hydrogen, Laser ultrasonic, Source dynamics, Waveform
matching
1. Introduction
Glass linings (GLs) are widely used for corrosion-resistant equipment in food and chemicalindustries, but tend to suffer various damages due to their brittleness. Among the damage ofGLs, hydrogen-induced damage, generally known as chipping, is a serious problem since no effective countermeasure exists [1]. Utilization of steels with a trace of titanium is recognized to beeffective for trapping the hydrogen gas in the steels, but is not the essential countermeasures.
Chipping sometimes occurs after a couple of days or after a few years of service. The worst
case is the chipping in the manufacturing locations. Previous research suggested that the chipping was induced by diffusible hydrogen, which was induced into the substrate steel from waterin the clay, milling water, and cleaning by acid solutions. No method to evaluate the susceptibility to chipping has been established [1-3]. Glass linings are generally composed of two layers;
porous ground coat near the steel substrate and dense cover coat. Pores in the ground coat aredesigned to moderate the stress mismatch between the steel substrate and GL, and to trap the hydrogen gas and hence contribute to reduce the chipping.
We attempted to study the precursor of chipping by AE and LSAW (Laser Surface-Acoustic
Wave) monitoring. AE was demonstrated to detect the micro-cracking of the porous ground coat
(GC hereafter) from early times, and the LSAW the change of acoustic properties associatedwith the micro-cracking of the GC.

J. Acoustic Emission, 23 (2005)

215© 2005 Acoustic Emission Group2. Specimen and Acoustic Properties of Glass Lining
Specimen is a corrosion-resistant GL. It is composed of 0.2-mm thick ground coat (GC) and
0.8-mm thick cover coat (CC). This GL was deposited on carbon steel (JIS SS400) of 3-mm
thickness, 61-mm width and 100-mm length. Figure 1 shows a typical cross-sectional structureof the GL. The GC contains a number of large spherical pores of 50 to 200 m diameter while
the CC isolated fine pores of 10 to 50 m. Porosity by a laser microscope was measured as 3.5 %in the CC and 48 % in the GC. Chemical composition and baking conditions are not reported.

Fig. 1 Transverse structure of glass lining. CC: cover coat, GC: ground coat.

Fig. 2 Waveform of LSAW measured on the glass lining.
216

Table 1 Acoustic and elastic properties.

First we measured the SAW velocities of the GL using a laser ultrasonic system [4, 5]. Figure 2(a) shows the waveforms of dispersive Rayleigh waves at a propagation distance of 12.12mm in the axial direction of the specimen. The Rayleigh velocity, determined by the zerocrossing time method, was from 3191 m/s to 3214 m/s. The velocities of SSCW (Surface skimming compressive wave) were measured as 6530 ± 40 m/s. Estimated acoustic and Young’s
modulus are shown in Table 1. Figure 2(b) shows the wavelet contour map of LSA waves. It
indicates that the velocity at higher frequencies above 4 MHz or in the surface layer less than 0.8
mm is non-dispersive, suggesting a homogenous structure of the GL. Late-arrival lower frequency components (surface depth over 0.8 mm) indicate that the GC layer has a lower velocity
than the steel substrate, possibly due to the large voids in the GC.
3. AE Monitoring of Chipping
Figure 3 shows AE monitoring method from the GL chipping during hydrogen charging. Hydrogen was supplied to the steel substrate in an area of 20 mm square by cathodically charging atcurrent density of 0.05 A/cm2 in a 0.5 kmol/m3 sulfuric acid solution with 4 kg/m3 CH4N2. Eightsmall AE sensors (PAC, Type PICO with center frequency of 0.45 MHz) were mounted on both
surfaces of the specimen. Sensor outputs were amplified 40 dB and digitized by an A/D converter. The AE system with 40 dB amplification did not detect any noise from hydrogen gas
bubbles. A plot of cumulative AE counts with charging time is shown in Fig. 4.
AE events increased from 38 ks (10.5 hr). A large-scale chipping, shown in Fig. 5, of about
30 mm x 50 mm (1200 mm2) suddenly occurred at 48.6 ks (13.5 hr). A number of fragmented
glass pieces from 0.5 mm2 to 200 mm2 were launched from the substrate with large sounds. We
observed at the right edge of the exfoliated GL a porous GC of 2 mm x 4 mm square. First arrival time of diffusible hydrogen at GL/substrate is calculated as 11.3 ks by using the hydrogen
diffusivity coefficient of 5.22 x 10-7 cm2/s in the steel. Thus the time lag of 11 ks between 11.3
ks and 22 ks, at which the first AE was detected is supposed to be the time during which the hydrogen gas pressure increases in the voids of the GC. In another word, this is the time during
which the voids serve as the gas container.
Figure 6 shows typical AE waveforms detected at 43407 and 48646 s. As AE signals weredetected as Lamb waves with weak So-component, the source locations were estimated using thefirst arrival sheet velocity of the So-wave. The sheet velocity of the substrate and GL is approximately the same since both the Young’s modulus and density of the substrate are approximately 3 times that of the GL. In spite of this fact, we observed some strange waveforms forchannel 1 and 2. For the event at 48646 s just before the final large chipping, the arrival of the217

Fig. 3 AE monitoring system for glass lining on the steel plate absorbing hydrogen.

Fig. 4 Cumulative AE count with hydrogen charging time for glass lining on the steel plate.
So-packet to the Ch.-1 and -2 sensors on the steel substrate were very weak, while the amplitudesof So-component from Ch.-1’ and -2’ sensors on the GL were much stronger.
Strange characteristics observed here may be due to the damage location in the GC. Strong
Lamb waves propagated along GL layer, of which GC was partially exfoliated from the substrate. Source location was performed for 40 events with sufficient amplitude on all channels
and results are shown in Fig. 5. Most sources were located in a small “fish-scale”-type chippingarea. Surface and transverse photos in this area show cracks connecting the voids. Crack openings are measured as 0.01 to 0.04 mm. These values are surprisingly large compared to those ofdelayed fracture of the high strength steels.
218

Fig. 5 Surface and transverse sketch and photographs of the glass lining suffered hydrogen induced large chipping
From these data, it is concluded that 1) voids in GC can hold the hydrogen gas pressure, 2)
fish-scale chipping is a typical delayed fracture and occurs when the hydrogen gas pressure
reaches a critical pressure, 3) AE signals are produced by the generation of micro–cracks connecting the voids in the GC. Thus, AE can be used to monitor the precursor of the large suddenchipping of the GL.

Fig. 6 Lamb-wave AE signals detected on glass lining and substrate before a large-scale chipping.

219

Fig. 7 Monitoring of laser-excited surface waves and AE during hydrogen charging.
4. AE Source Analysis
Another experiment, approximately the same as that in the previous section, was attempted.
In this test, damages monitoring by the LSAW system was simultaneously performed. As shownin Fig. 7, LSA waves were monitored on the surface of the GL. Figure 8 shows the cumulative
AE counts with the charging time. A large-scale final chipping of 2000 mm2 occurred at 60842 s(17 hr). AE counts rapidly increased at 20 ks just before the final chipping. Timing of both thefirst AE and final chipping agreed with those in Fig. 4. Twenty-seven events are located at theorigin of the chipping, as shown in Fig. 9.

Fig. 8 Cumulative AE counts with hydrogen charging to the steel with GL.
Fracture dynamics were estimated by the waveform matching of the first-arrival Socomponent. Detail of the waveform simulation can be found elsewhere [6]. The overall transfer
function was determined utilizing the pulse-laser breakdown of silicone grease in a slit as thesource. Source parameters of the break-down was first determined by the deconvolution of the220

Fig. 9 Chipped glass lining and source location of AE signals.
out-of-plane displacement of a large block measured by a laser interferometer with the theoretical Green’s function of the second kind [7]. Source parameters of chipping AE signals were estimated by matching the first So-mode waveform computed using the overall transfer function to
the detected waveform. Figure 10 compares experimental and computed waveforms of the Sopackets. Source parameters are shown in Fig. 11. The average crack volume 3.5x10-16 m3 appears to correspond to the volumes of the cracks connecting the voids in the GC.

Fig. 10 Lamb wave AE detected and simulated for a step-wise crack generation.
5. Monitoring of Lining Degradation by LSAWAs mentioned in the previous section, the low-frequency components of the dispersive
Rayleigh wave represent the characteristics of GC. Thus, we monitored periodically the SAWs
of the GL using the laser ultrasonic system. Figure 12 shows the change of the SAWs with time.
Slight waveform difference can be seen in the later times. Cross-correlation was determined andis shown in Fig. 13. It decreases with the charging time, but shows three discontinuous changes
at around 11 ks, 45 and 55 ks. Here, the 10 ks is the time when the hydrogen reaches the GC. At
around 50 ks as shown in Fig. 8, AE events increased rapidly. Cross-correlation of SAWs canpredict the minute changes in the GC when the SAW is measured at the same position.
221

Fig. 11 Source parameters of Mode-I microcracking.

Fig. 13 Change of cross-correlation factor of
LSAW with hydrogen charging time.
5. Conclusion

Fig. 12 Change of laser SAWs with hydrogencharging time.

AE and LSAW were monitored to study
the precursor of hydrogen-induced chipping of
the glass lining. Results obtained are summarized below:
1) Acoustic properties of Glass lining werestudied using an LSAW system. Both theYoung’s modulus and density of the covercoat are approximately 3 times smaller
than those of the steel substrate, but the
sheet velocity was found to be the same forthe dense cover coat and the substrate.
Group velocity dispersion of the Rayleighwaves represents the structural change inthe porous ground coat.
2) Large scale chipping of the cover coat, deposited on 3-mm thick carbon steel, occurred at 49 ks to 60 ks after the hydrogencharging to the substrate. AE signals areproduced by micro-cracking, connecting
the voids in the ground coat. These aredetected from less than half the time of the
final chipping. Hydrogen gas pressure in
the ground coat produces micro-cracking
when its pressure reaches the critical value.
3) Source parameters of micro-cracks were
estimated by the waveform matching of thefirst arrive So-mode, using the overall
transfer function experimentally determined. Breakdown of silicone grease in anarrow slit is useful in simulating theMode-I crack normal to surface in the thin222

ground coat. Crack volume and rise time were estimated as 3.5 x 10-16 m3 and less than 1 s,respectively.
4) LSAW measured on the lining showed slight changes reflecting the structural changes in the
ground coat. Cross-correlation factor of the SAWs showed discontinuous changes corresponding to those observed for AE count change.
References
1) C.G. Bergeron, J. Amer. Ceramic Soc. 36 (1953), 373.
2) Y. Nakazato, H. Kubiminato, N. Fukuda, T. Sonobe and H. Nagaishi, Kawasaki Seitetsu Gihoin Japanese, 7 (1977), 70.
3) I. Takahashi, A. Yasuda. K. Ito and N. Ohashi, Kawasaki Seitetsu Giho in Japanese, 5(1975), 53.
4) H. Cho, S. Ogawa, K. Yamanaka and M. Takemoto, JSME Int. Jr. Series A, 41 (1998) 439.
5) Y. Mizutani, M. Takemoto and K. Ono, J. Acoustic Emission, 16 (1998) S115.
6) H. Suzuki, M. Takemoto and K. Ono, J.Acoustic Emission, 14 (1995) 442.
7) H. Suzuki, M. Takemoto and K. Ono, Progress in AE, VII (1994), JSNDI, Tokyo, p. 457.

223

REAL-TIME EXECUTING SOURCE LOCATION SYSTEMAPPLICABLE TO ANISOTROPIC THIN STRUCTURESYU KUROKAWA, YOSHIHIRO MIZUTANI and MASAMI MAYUZUMIDepartment of Mechanical Sciences and Engineering, Tokyo Institute of Technology,
Ookayama, Meguro, Tokyo 152-8552, Japan.
Abstract
In this study, we developed a real-time executing source location system for anisotropic thin
structures such as CFRP (Carbon Fiber Reinforced Plastics) tanks. The system consists of AE
sensors, an A/D converter on a PC and a computer with proprietary software. We suggest twoalgorithms to realize the real-time executing source location. The first algorithm is a frequency
filtering algorithm utilizing a fast continuous wavelet transform. The second algorithm is afast source location calculation algorithm for unidirectional anisotropic structures. In the second
algorithm, the affine coordinate transform is used. The calculation time for each source locationis reduced to 1ess than 15 ms with reasonable accuracy.
1. Introduction
The fracture monitoring of CFRP (Carbon Fiber Reinforced Plastics) is important for assuring structural reliability. AE testing is one of the most effective methods for monitoring fracture. The AE source location can be estimated by considering the arrival times and the velocityof the AE signals. However, special techniques are required for conducting the source location
on anisotropic thin structures. The AE in thin plates, such as CFRP structures, propagates as
Lamb waves and its velocity depends on the frequency [1]. Due to its dispersive nature, it isdifficult to determine the arrival time from the AE waveform. Furthermore, in anisotropic
structures, the velocity of the AE has an orientation dependence due to the structural anisotropy.
The orientation dependence needs to be taken into account for precise source location to beachieved.
Several papers [2-7] dealing with the source location on anisotropic materials or thin platelike CFPR structures have been reported so far. For example, Promboon [2] and Blahacek [3]used neural networks for the source location. This method could be adopted without understanding the elastic wave propagation characteristics in the material, but the network needs
learning before the utilization. Kwon et al. [6] utilized wavelet and inverse wavelet transform
for locating pencil-lead breaks on aluminum and cross-ply CFRP plates. This method improves
the source location accuracy, but the anisotropy issue remains unresolved. In order to overcome
these problems, Yamada et al. [7] have used the wavelet transform as a frequency filter for determining the arrival time of AE signals. However, the wavelet transformation calculation required for this method is time consuming. Another problem is that the source location is estimated by scanning the virtual source. The method required an iterative calculation at a significant computational cost. For these reasons, the source location algorithm for anisotropic thin
structures had been found to be less than suitable for real-time execution. Therefore, we introduced two new algorithms for speeding up both the frequency filtering and the source location
calculation.
J. Acoustic Emission, 23 (2005)

224© 2005 Acoustic Emission GroupFigure 1 shows a schematic image of our final goal. The AE sensors are mounted on a CFRP
tank during a field test and staff monitors AE during the test. The source location is displayedon a computer screen simultaneously with the generation of AE. The two new algorithms proposed in this study are used in the analysis software and enable real-time source location.

Fig. 1 Schematic image of our final goal
2. Acceleration of the Source Location Calculation
In this study, we suggest two new algorithms for accelerating the source location calculation. One is a new frequency filtering algorithm using the fast continuous wavelet transform.
This algorithm enables the immediate extraction of the specific frequency component from theLamb wave AE. The other algorithm is a new source location algorithm utilizing an affine coordinate transform. This algorithm also shortened the calculation time for source location.
2.1 Fast Frequency Filtering Algorithm
In the source location calculation, the continuous wavelet transform is used as a frequency
filter. The general wavelet transform of real function f(t) is defined as

W (a, b ) =1t b

 f (t )  a
a
*



dt 

(1)

where a>0 and the superscript * denotes a complex conjugation. The analysis function for the
wavelet transform can be defined as in equation (2).

 a ,b (t ) =t b
  a  a 1

(2)

Its elements are generated by shifting and scaling a mother wavelet (t). The parameters aand b stand for the scale and shift of the mother wavelet. The calculation of equation (1) is time
consuming because the iterative calculation needs to be performed many times with different
values of the parameter b. On the other hand, equation (1) can be transformed into equation (3)utilizing Parseval’s theorem [8].

( ) 21  f (t ){ a ( a )} e d 

W a,b =





i b



(3)

where ˆ ( ) donates the Fourier transform of  ( ). Equation (3) can be calculated using the225following steps;
1) Calculate the Fourier transform of f (t ) utilizing an FFT.

2) Calculate ˆ (a ) using an FFT or other suitable technique.
3) Multiply fˆ (t ) by ˆ (a ). Calculate equation (3) using an Inverse FFT.

A schematic image of the methods stated above is shown in Fig. 2. The general wavelet
transform is represented by the convolution of an analytic signal with a mother wavelet functionin the time domain (see the left side of Fig. 2). This convolution is time consuming because an
iterative calculation is required. On the other hand, the fast wavelet transform used in this study
is shown in the right side of Fig. 2. As shown in this figure, the convolution of the two signals
is calculated in the frequency domain and the calculation cost is much lower than the general
wavelet transform. In this research, the computation time for filtering has been shortened 55
times, from 72 ms to 1.3 ms by this modification.

Fig. 2 Comparison of wavelet transform between the time domain and the frequency domain.
2.2 Source Location Algorithm using an Affine Coordinate Transform
The source location on anisotropic thin structures is complicated due to the velocity anisotropy of AE signals. For example, Yamada et al. [7] used the scanning of the virtual source
method, as shown in the left side of Fig. 3. The method is computationally intensive and is notsuitable for use with real-time source location systems. If velocity anisotropy does not exist, thesource location can be calculated immediately using simple simultaneous equations. In our new
procedure, the coordinate system is transformed by an affine transform in order to negate theeffect of the velocity anisotropy. The right side of Fig. 3 is a schematic representation of thismethod. The primary requirement is that velocity anisotropy can be approximated by an elliptical function. The new source location algorithm consists of the following steps;
1) Perform an affine coordinate transform using equations (4) and (5) to compensate forthe velocity anisotropy effect. The V 0 is the velocity of AE at  = 0°, V 90 is at  = 90°.
226

According to this process, the location of the sensors: (X1,Y1), (X2,Y2), (X3,Y3) aretransformed to (X1’,Y1’), (X2’,Y2’), (X3’,Y3’), respectively.
2) Calculate the source location on the transformed coordinate system using one of thestandard AE source location methods. The estimated source location is shown as (x’,y’)in Fig. 3.
3) Perform an inverse affine transform using equations (6) and (7) for the estimatedsource by Step 2 as (x’,y’) and the source location on the original coordinate system isdetermined as (x,y).
The calculation cost is drastically reduced using this algorithm compared to the scanningvirtual source method. In this research, the computation time for source location using thismodification has been shortened by three orders of magnitude, from 3.3 s to 3.2 ms.

(X , Y )T = C(X , Y )T

(4)

0 
1

C = 
 0 V0 V90 (5)(x, y )T = C 1 (x , y )T

(6)

0 
1

C 1 =  0 V90 V0 

(7)

3. Specimen and Velocity Anisotropy
For this study, we prepared a 820 mmL  300 mmW CFRP plate ([90°]7) with a 1-mmthickness aluminum liner. A schematic drawing of the specimen and the source location system
are shown in Fig. 4. The total thickness of the composite plate is 2 mm. The Y-axis in Fig. 4
corresponds to the fiber direction of the front CFRP layer. This specimen simulates the propellant tank wall of a future rocket being developed in Japan. We first investigated the orientation
dependence of the AE velocity using pulses produced by pencil-lead break on the front surface.
We measured the velocity anisotropy every 10˚ angle () at a particular frequency ( f ), and approximated it by an elliptical function, V(f, ); see Fig. 5. The measured and approximated orientation dependence of the 300-kHz components are shown in Fig. 5 as an example. We subsequently investigated the frequency dependence of the AE signals by utilizing a wavelet transformation by changing the filtering frequency. The frequency dependence of the AE velocity,which we used for source location, is shown in Fig. 6.
4. Source Location System and Experimental Results
The source location experiment used three AE sensors (PAC, type PICO; 4.0 mm diameter).
The sensors were mounted on the front surface (CFRP side) at the corners of an equilateral triangle, 200 mm on a side. The AE signals were artificially produced using pencil-lead breaks
and steel-ball drops. Outputs of the AE sensors were digitized using an A/D converter on a PC
(National Instruments, NI-5122; 14-bit resolution). The sampling frequency and points are 2.5
MHz and 2048 points, respectively. The digitized data is automatically analyzed in the computer and the results are displayed on a computer screen.
227

Y

Ch.3 (X3,Y3)

Velocity profile
V90
V?

0.1mm
Ch.3

(X1,Y1)(X3,Y3)

V0

Ch.2
X(X2,Y2)

Ch.1

Y

Affine coordinatetransformation

0.1mm

Y

Estimated source(x’,y’)

V? =const .

Ch.2
X(X1 ’,Y1 ’)(X2’,Y2 ’)Source location by traditional method

Ch.1

Ch.2
X
Ch.1 (X1,Y1)
Scanning virtual source(0.1mm pitch)

Ch.3 (X3’,Y3 ’)(X2,Y2)Inverse affine coordinatetransformation
Y

Equilateral triangle (200mm on a side)

Ch.3(X3,Y3)
Estimated source (x,y )Ch.1(X1,Y1)

Computation time: 3.3 seconds

Ch.2
X(X2,Y2)Computation time: 3.2 microseconds

Fig. 3 Comparison of source location methods.

Fig. 4 Schematic illustration of the specimen and the measurement of the orientation and frequency dependence of the AE velocity.
228Fig. 5 Orientation dependence of the AE velocities in the specimen.

Fig. 6 Frequency dependence of the AE velocity in the specimen.

Fig. 7 Schematic illustration of the source location system.
229

In this study, we conducted the source location using the following steps:
1) Detect the AE signals using three AE sensors.
2) Compare the maximum amplitude of three AE signals and find the weakest signal.
3) Calculate FFT and detect the maximum spectrum. 4)4) Search the spectrum from 320 kHz to 60 kHz and find frequency f that exceeds 4% of
the maximum spectrum first. The frequency “f” was used for the source location (Step 2 toStep 5 are shown in Fig. 8).
5) Using the frequency “f”, conduct the wavelet transform for each signal and determine
the AE arrival time from the first peak timing of wavelet coefficient.
6) Calculate the source location by the affine transformation algorithm using the arrival
time differences.
An example of detected AE signals produced by the pencil-lead break source and the steelball drop source at (x,y) = (100,120) [mm] in Fig. 7 are shown in the left side of Fig. 9 and Fig.
10, respectively. Dispersive AE signals propagating as Lamb waves (primary A-mode) wereobserved. It is difficult to determine the arrival times from the waveform. On the other hand, its
wavelet coefficients are shown in the right side of the same figures. The peaks were obtained
clearly, and the source location results were (x,y) = (100.6,119.2) and (100.5,121.0) [mm], respectively. The required time for source location is less than 15 ms for any AE sources.
Figure 11 indicates the source location results with pencil-lead break and steel-ball drop
sources. The source positions are indicated by squares and the estimated source positions bygray circles in the figure. The maximum error of 6.1 mm and average error of 1.8 mm wereobtained for the pencil-lead break source. For the steel-ball drop source, the average error was
3.9 mm and the maximum error was 12 mm. The large error is attributed to the problem of estimating the arrival time from the wavelet results, arising from the lower frequency contents ofsteel-ball sources.

Fig. 8 The method for determining the suitable frequency for source location.
5. Conclusion
We developed a real-time execution source location system applicable to anisotropic thin
structures. Two new algorithms for speeding up the source location are proposed. The first algorithm is a frequency-filtering algorithm, which uses a fast continuous wavelet transform. The
second algorithm is a fast source location calculation algorithm for unidirectional anisotropic230

Fig. 9 Example of detected AE signals and the wavelet coefficients produced by pencil-leadbreak source.

Fig. 10 Example of detected AE signals and the wavelet coefficients produced by steel-balldrop source.
structures. With both improvements, the online monitoring of anisotropic thin structures is now
possible. The validity of the developed system is examined using lined CFRP plates. The calculation time for each source location is less than 15 ms with a good location accuracy.
References
1.

M.R. Gorman and W.H. Prosser, “AE Source Orientation by Plate Wave Analysis”, J.
Acoustic Emission, 9, (1991) 283-288.

2.

Y. Promboon and T.J. Fowler, “Source location on fiber reinforced composites”, paper presented at 6-th Int. Symp. on AE from Composite Materials, San Antonio, Texas, 1998.

3.

M. Blahacek and Z. Prevorovsky, “Probabilistic definition of AE events”, European Conference on Acoustic Emission Testing, Prague, 2002, pp. I/63-I/68.
231

Fig. 11 Source location results for pencil-lead break and steel-ball drop sources.
4.

V. Venkatesh and J.R Houghton, “Neural Network Approach to Acoustic Emission Source
Location”, J. Acoustic Emission, 14 (2), (1996), 61-68.

5.

S.M. Ziola and M.R. Gorman, “Source Location in Thin Plates Using Cross-Correlation”,
J. Acoustic Soc. Am., 90 (5), (1991) 2551-2556.

6.

O.Y. Kwon and Y.C. Joo, Progress in Acoustic Emission VIII, (1998), JSNDI, Tokyo, pp. 917.

7.

H. Yamada, Y. Mizutani, H. Nishino, M. Takemoto and K. Ono, J. Acoustic Emission, 18(2000), 51-60.

8.

C.K. Chui, An Introduction to Wavelets, Academic Press, (1992), pp. 60-61.

232

INVESTIGATION ON AE SIGNAL/NOISE PROCESSING
IN CORROSION DAMAGE EVALUATION OF TANK BOTTOMZHENGWANG LI 1, SHIGENORI YUYAMA1, MINORU YAMADA2, KAZUYOSHI SEKINE3, SHIGEO KITSUKAWA4, HIROAKI MARUYAMA5 and SHIGEO KONNO6
1)
Nippon Physical Acoustics Ltd., 8F Okamoto LK Bldg., 2-17-10, Higashi, Shibuya-ku, Tokyo
150-0011, Japan; 2) National Research Institute of Fire and Disaster, Mitaka, Tokyo 181-8633,
Japan; 3) Yokohama National University, Yokohama, 240-8501, Japan; 4) High Pressure Instituteof Japan; Chiyoda, Tokyo 101-0025, Japan; 5) Japan Oil, Gas and Metals National Corp.; Bunkyo, Tokyo 112-0002, Japan; 6) Petroleum Assoc. of Japan, Chiyoda, Tokyo 100-0004, Japan
Abstract
Acoustic emission (AE) testing based on database consisting of test procedures and evaluation criteria has been widely used in Europe for the evaluation of floor conditions (corrosion
damage) in above-ground tanks. Since the test procedures were introduced to the Japanese industry in 1999, fundamental studies in laboratory and field applications have been made by several organizations. In performing AE field testing, various environmental noise caused by rain
and wind are unavoidable. This paper discusses a method of AE signal/noise processing in corrosion damage evaluation of tank bottom, based on the field tests conducted on more than 70
tanks in Japan.
Keywords: Tank bottom testing, Corrosion damage, Noise
1. Introduction
In recent years, much attention has been paid to the possibility of applying AE test to evaluate corrosion damage of tank bottom, as an alternative in-service monitoring method [1]. In
Europe, AE testing has been made for more than 1000 tanks per year as a cost-effective maintenance planning tool, based on the database [2] built by AE inspection of several thousands oil
storage tanks. This technique was introduced to the Japanese industry in 1999. Since then AE
field tests have been carried out on more than 70 tanks with different diameters by three researchgroups. Intensive works have been made of AE test procedures, data evaluation methods, and soforth [3].
This paper discusses about a method of AE signal/noise processing in corrosion damage
evaluation of tank bottom, based on the tests conducted in Japan. It summarizes test results fromthe research project of High Pressure Institute of Japan (HPI), sponsored by the National Oil
Corporation of Japan.
2. Case Studies - Noise in Tank
When applying AE testing to tank bottom evaluation, it is essential to discriminate valuable
AE signals from other noise. However, since AE signals from corrosion are usually very weak,
the AE testing system is set to very high sensitivity, resulting in data including possible noise.
Moreover, since oil storage tanks are in the outdoors, various environmental noise due to rain,wind and, in addition, EMI are unavoidable.
J. Acoustic Emission, 23 (2005)

233© 2005 Acoustic Emission GroupHere, case studies, based on examples of AE data showing typical environmental noise, aredemonstrated and a method of AE signal/noise processing is discussed.
2.1 Noise due to Wind
It has been demonstrated that the influence of wind generally appears at a wind speed ofgreater than 4 m/sec. Under the maximum wind speed of 10 m/sec, a source location result ofwind noise is shown in Fig. 1. Event location exhibits a radial pattern, spreading from the centerof the tank. Although it is possible to install a wind gauge and to record the output as an externalparameter of AE measurement in order to monitor wind speed, the data might be different between tank circumference and the place where the gauge is installed. The wind speed is quite
different around the tank, depending on the location where it is monitored. Because of this, it isoften very difficult to filter wind noise by post-test analysis. However, the noise resulting from asudden speed change can be filtered by post-test analysis, based on wind data monitored in realtime. Figure 2 shows an example of such wind noise. The periods when wind speed exceeded 6
m/sec are indicated by the boxes, which show a good correlation with very high AE activities.

Fig. 1 AE source location based on TANKPAC procedure for AE data collected under strong
wind (Crude oil tank, 82 m in diameter).
2.2 Noise due to RainAs shown in Fig. 3, noise due to rain usually produces AE signals with large amplitudes over
80 dB. Source locations resulted from rain noise are spread evenly all over the whole region of234

Fig. 2 History of AE hit rates (upper) and wind speed (lower), observed in a crude oil tank (83.3
m in diameter). High AE activities correspond to strong wind indicated by the boxes.
the tank bottom and clusters can be seen in some areas. Moreover, the noise does not stop until
rainwater on the tank roof flows out completely. Especially, in a case of a large tank with a diameter of 80 m, the influence may remain for more than 24 hours. An example of rain noise,measured 24 hours after the rain had stopped, is given in Fig. 4. Source location indicates linearly spread cluster, which can be attributed to flow of rainwater in a drainpipe.

Fig. 3 Source location (left) of AE data collected in a product tank (12 m in diameter) under rain.
Amplitude and hit rate history are also demonstrated.
2.3 Noise from Bird Movement on Tank RoofAs seen in Fig. 5, many signals were detected, showing high AE hit rates, for a period from
2600 seconds to 2700 seconds. Since several crows were seen to move on the tank roof during
this period, the sudden increase of AE signals was attributed to the movement of the birds. Although most of such noise can be filtered by post-test analysis, it is important to pay attention to
the situation around the tank to identify such noise in real time.
235

Fig. 4 Noise due to water flow in a drainpipe, observed 24 hours after rain stopped (Crude oiltank, 82 m in diameter).

Fig. 5 Noise due to movement of birds on the tank roof (Crude oil tank, 61 m in diameter).
236

Fig. 6 Source location of AE data including signals due to water drops from the tank roof (watertank, 17 m in diameter) .

Fig. 7 Source location of filtered AE data excluding signals due to water drops from the tank roof
(water tank, 17m in diameter).
237

2.4 Condensation Dripping in the Tank
Signals resulted from condensation dripping in the tank can be removed by using two rows of
sensor array. Figure 6 indicates a source location result including condensation-dripping noise.
842 AE sources are detected and some clusters are observed. After signals attributable to condensation-dripping noise were filtered, only 80 AE sources remained and clusters disappeared, as
seen in Fig. 7.
2.5 Pipe Vibration Noise due to Pump Operation
To perform AE testing properly, tanks must be set in quiet situation. Therefore, it is necessary to stop operation of pumps connected to the tank through pipes. Figure 8 shows amplitudeand hit rate history indicating noise from strong pipe vibration. It should be noted that even if
the threshold was set to 58 dB, many signals around 60 dB were detected, produced by pipe vibration. On the other hand, Fig. 9 gives results obtained under a threshold of 48 dB when pipe
vibration was weaker than that of Fig. 8. It should be noted that all noise may not have been filtered due to continuous signals from the vibration. Therefore, in order to avoid such noise, it isnecessary to stop pump operation near the tank during the test period.

Fig. 8 Amplitude (upper) and hit rate (lower) history of noise due to vibration of pipes (Corn rooftank, 4.8 m in diameter).
2.6 Periodic NoiseIn the case of a tank with dome roof, the inner pressure of the tank changes, depending on
outside temperature. Nitrogen gas is charged or discharged in order to maintain a constant pressure. As shown in Fig. 10, the injection of nitrogen gas generates periodic extraneous noise.
Note that nitrogen gas was charged at an interval of about 60 seconds and high noise continued
for about 18 seconds. Such periodic noise can be identified by installing a guard sensor to detectsignals beyond 90 dB in amplitude (Fig. 10) near the injection pipe of nitrogen gas. These noise
can be filtered by post-test analysis. However, it is recommended that AE test should be carriedout at a time when the atmospheric temperature is stable, so that the interval of gas injection becomes as long as possible.
238

Fig. 9 Amplitude (upper) and hit rate (lower) history for the data after having filtered noise due
to vibration of pipes by setting threshold at a high level (Corn roof tank, 11.6 m in diameter)

Fig. 10 Amplitude (upper) and hit rate (lower) histories, showing periodic noises due to pressure
control by nitrogen gas injection (Dome roof tank, 13.5m in diameter).
2.7 Unidentified Noise (I)When performing AE test, high AE activities are sometimes observed in a certain channel.
One-hour monitoring was conducted three times in a crude-oil tank for two days. As shown in
Fig. 11a to Fig. 11c, one of the three channels exhibits very high AE activities as compared toothers. Although the source of these signals is not clear, it is very likely that they should be attributed to unidentified noise.
Although one hour monitoring is usually enough to perform tank bottom evaluation, it is necessary to extend testing time in order to obtain meaningful data when high AE activities are observed due to unidentified noise.
239

Fig. 11a Amplitude (upper) and hit rate
(middle-upper) history of AE signals detected at other channels than CH-1 and thesame for CH-1 during the first test (Crude
oil tank, 82 m in diameter).

Fig. 11b Amplitude and hit rate history observed during the second test. High AE activities due to noise are observed at only
CH-18 (Crude oil tank, 82 m in diameter).

Fig. 11c Amplitude and hit rate histories observed during the third test. High AE activities due
to noises are observed at only CH 20 (Crude oil tank, 82 m in diameter).
2.8 Unidentified noise (II)Shown in Fig. 12 is discontinuous noise generated in more than two channels for certain periods. Although, many signals were detected for six periods (about 620 s to 680 s, 700 s to 1000 s,
1160 s to 1620 ss, 3860 s to 4030 s, 4180 s to 4230 s and 4380 s to 4503 s), the AE source that
produced them could not be identified. Meanwhile, these signals were different from those resulted from condensation dripping. Furthermore, as shown in Fig. 13, source location showed no
AE clusters in this case.
240

Fig. 12 Amplitude (upper) and hit rate (lower) history observed in a water tank (17 m in diameter). Strong noise detected during a certain period (1,160 -1,620 s) by multiple sensors.

Fig. 13 Source location of AE data detected during 1160-1620 s in the water tank (17 m in diameter). No cluster observed in the figure.
241

The AE signals due to corrosion do not show such AE behavior. Therefore the above highAE activities were considered to be unidentified noise and meaningful AE signals observed inother periods were used for the evaluation.
3. Conclusion
Since AE signals due to corrosion are usually very weak, AE system is set to very high sensitivity, which very often results in detection of useless noise. Therefore, discrimination of
meaningful signals from noise is essentially important in performing field test. Great care must
be taken during AE tests by experienced inspectors to avoid unnecessary noise in real time.
References
1) S. Yuyama: Inspection Engineering, 2 6 (1997), 58.
2) P. T. Cole, P. J. Van de Loo: Acoustic Emission-Beyond the Millennium, Elsevier (2000), p.
169.
3) S. Yuyama, M. Yamada and K. Sekine: Journal of High Pressure Institute of Japan, 40 4(2002), 176.

242

EXAMINATION OF AE WAVE PROPAGATION ROUTESIN A SMALL MODEL TANKHIDEYUKI NAKAMURA1, TAKAHIRO ARAKAWA2, MINORU YAMADA3
System Divisions, Ishikawajima Inspection & Instrumentation Co., 1720 Kamai, Azuma,
Inashiki, Ibaraki 300-0604, Japan. 2) R&D Divisions, Ishikawajima Inspection & Instrumentation Co., Yokohama 235-8501, Japan. 3) Fundamental Research Division, National ResearchInstitute of Fire and Disaster, Mitaka, Tokyo 181-8633, Japan.
1)

Abstract
This paper presents the results of a mock-up test executed to confirm accurate acoustic emission (AE) wave propagation routes. A 5-m diameter model tank was used and the propagation
characteristics of the AE waves generated on the tank bottom plate were measured with sensors
arranged at 100 mm intervals on the bottom plate and the wall plate in the test. It was confirmedthat Lamb waves reached the wall plate, and the Lamb waves converted into other modes in water.
Keywords: Oil storage tank, Corrosion, Evaluation, Propagation route
1. Introduction
In evaluating the corrosion status of oil-storage tank bottom plates, acoustic emission (AE)waves that propagate in liquid are measured because AE attenuation is smaller in liquid than insteel. Although some studies up to 2002 have reported experimentally that the corrosion AE
waves generated on the outside surface of the tank bottom plate propagate in water, no papers
have conducted a detailed analysis of the results. It is important to accurately understand AE
wave propagation routes. This is because locating the source of the AE wave generation requires
the processing to be executed at a sound velocity that has been set to match the AE wave propagation route.
This study is aimed at confirming accurate AE wave propagation routes by conducting precision measurements (with densely located measuring points) of the pseudo AE waves generated
on the tank bottom plate with a mock-up using a small model tank.
2. Experimental Procedures
2.1 Targets
This study was conducted under the following five test conditions (Points A, B and C areshown in Fig. 1).
a. AE waves generated on the center (point B) of the inside surface: Confirm the propagation
conditions of AE waves generated at point B on inside the tank bottom plate without large effects by detoured AE waves through the tank wall plate.
b. AE waves generated on the quarter-point of the radius (point A) of the inside surface of thetank bottom plate: Confirm the propagation conditions of the AE waves that are propagatedon the tank bottom and wall plates from point A on inside the tank bottom.
J. Acoustic Emission, 23 (2005)

243© 2005 Acoustic Emission Groupc. AE waves generated on the opposite quarter-point of the radius (point C) of the inside thetank bottom plate: Same as above in b.
d. AE waves generated in water 50 mm above point B: Confirm the propagation conditions of
AE waves in water. In water sources are expected to be affected less by steel plates.
e. AE waves generated at point B on the outside surface: Confirm the propagation conditions of
AE waves generated at point B on the outside surface of the tank bottom plate. This simulates the corrosion AE waves generated on the outside surface of the tank bottom plate.
2.2 Test Procedure
On a small, indoor model tank, we installed the pseudo-AE sources inside and outside of thetank, as shown in Fig. 1. The sources generated AE waves at each position. We measured AE
waves with 19 AE sensors attached to the wall and bottom plate of this tank (refer to Fig. 2). We
measured AE waves at 19 locations at a time. The measurement positions are on the intersectionof grids shown in Fig. 2. This test also used the waveguide shown in Fig. 3 in order to generate
AE waves in contact with the bottom plate (conditions a, b, c) and in water for the condition d.

Fig. 1 The view of the model tank and the positions A, B and C on the tank bottom plate.

On a wall plate, sensors (30 kHz) are attached ranging in height from 0 to 800 mm
with a 100 mm pitch over the range of 1/4
round.

For the tank bottom plate, sensors (30kHz) are mounted in one fourth of theranges of bottom plate with 100 mm pitch.

Fig. 2 Sensor mounting position and water surface height.

244

Fig. 3 Waveguide for in water AE source.
3. Results
A typical result of the AE wave propagation is shown in Fig. 4. The data are represented by athree dimensional graph, which corresponds to one fourth of the tank shown in Fig. 1 (obliquelines). The bar graph that projects from the bottom plate upward represents the intensity of the
AE waves measured on the bottom plate. The amplitude is color-coded in 10 dB range; i.e., 0 dB
or more in red, 0 to -10 dB in purple, etc. followed by yellow, blue and green. The bar graph thatprojects from the wall plate horizontally is the intensity of the AE waves measured on the wall
plate. In this figure, the red, blue and green solid lines indicate the furthest reach of propagatingwaves on the bottom plate and up the wall (green only in this example). These represent thetheoretical wave velocities (calculated values) of the AE waves. The slowest waves propagate
through the liquid (red circle), next is A0-mode (blue circle) and the fastest S0-mode (green circle) Lamb waves that propagate through the steel, respectively. The respective velocities were
calculated as 1480 m/s, 2400 m/s and 5460 m/s, respectively.
From the tip of the A0 and S0 waves, mode-converted waves are radiated into the liquid as“bow-waves”. The positions of these waves on the center-cross-section are marked by blue andgreen lines at angles, 2 and 1. As shown in Note 1 and 2 in Fig. 4, these are 31.7˚ and 15.2˚,
respectively.
3.1 AE Wave Propagation Results
The propagation results in each test are as follow:
a. AE waves at the center of tank bottom plate (B point)
Results of this test are given in Fig. 5, and we confirmed that AE waves spread concentrically.
AE waves with amplitude of –20 dB or less spread first, followed by stronger AE waves withthe amplitude of –20 dB or more. These AE waves are Lamb waves that propagate in steel because of their faster sound velocities than liquid propagating waves. The first waves propagated are S0-mode Lamb waves and the second, higher amplitude waves are A0-mode Lambwaves. The sensors attached to the wall plates observed AE waves radiated into water from thebottom plate. When the Lamb waves spread in steel while radiating P-waves into water, thefirst AE waves that propagate in water with the inclination defined by the ratio of velocities ofwater and Lamb waves. As noted above, these are 1 and 2.
b. AE waves from the quarter-point (A point)
We verified that AE waves of Lamb A0-mode propagated concentrically, centered at the quarter-point (A point), but S0-mode Lamb waves could not be measured. It appears that the tankframe support was directly under the AE generation point, and S0-mode waves were attenuatedby the rubber board inserted between the support and bottom plate.
c. AE waves at the second quarter-point (C point)
We verified that AE waves at C point of the tank bottom plate propagated concentrically to thewall plate near the AE source before they reached the other sidewall.
245

Fig. 4 AE data example.
d. AE waves in water above the center of tank bottom plate (B point)
This test was intended originally to measure only the water propagating waves. The results,
however, were similar to ‘a’ above in which AE waves were generated on the tank bottom
plate. It appears that AE waves first entered into steel through water, then propagated in steel.
This is because the distance between the AE source and the tank bottom plate was ~50 mm andwaves through water are much slower than the Lamb waves.
e. AE waves at the back surface of B point
In this test, AE waves generated at the back surface of the center tank bottom plate were detected. The results were similar to AE waves generated on the face shown in ‘a’ above.
4. Discussion
The test results given in Fig. 5 indicate that, although the first AE waves generated on thecenter of the bottom plate is small with its amplitude less than -20 dB, it reached the bottom platecircumference 600 s after the generation. Using the relationship between the propagation distance and the arrival time, the sound velocity of the first arrival wave was calculated as 4,167
m/s. While this sound velocity was close to that of the Lamb waves in S0-mode (5460 m/s), it isabout one-fourth slower. It is difficult to presume it to be the S0-mode Lamb wave. Since theLamb waves radiate mode-converted AE waves in water moving at the slow P-wave velocity(1480 m/s), as shown in Fig. 4, the first waves reaching the wall plate are likely to be the modeconverted P-waves. If the first arrival waves propagate in the bottom steel plate 2/3 of the time246

and the rest through water, the observed wave velocity is expected. In distance term, the waves
propagate almost 90% in steel and 10% in water.
Next, we examined the state of propagation of the AE waves indicating high maximum amplitude (hereafter the maximum AE waves), shown by orange bars in Figs. 4 and 5. This indicates a large amplitude of -20 dB or higher. The maximum AE waves reached the circumferenceof tank bottom plate in 1,300 s. The sound velocity of the maximum AE waves was calculated
to be 1,923 m/s. This is close to that of the A0-mode Lamb waves, but again it is slower, giving ahigh possibility that the maximum wave is mode-converted waves from the A0-mode Lambwaves.
We confirmed the propagation of the P-waves in water, shown in red in Figs. 4 and 5, which
expand spherically. In this test, many of the sensors that were installed on the wall plate measured the maximum value at times between 1,600 to 1,700 s when the waves propagating
through the water reached the wall plate.
In this study, we confirmed that, with a small tank of 5 m in diameter, the Lamb waves
propagate through the steel and reach the sensors without much attenuation. It was also foundthat the AE waves, which have undergone a mode conversion from the Lamb waves enteringfrom the bottom plate into the water, reached the wall plate. In actual tanks, the bottom platecontacts the ground, probably causing Lamb wave attenuation. In measuring AE waves in small
tanks, however, it is essential to take into account the effect of the Lamb waves.
5. Conclusion
This study clarified the details of the propagation of AE waves on the bottom plate and thewall plate, and the following findings were obtained:
1) It was confirmed that the AE waves generated on the bottom plate propagate in the liquid andreach the wall plate.
2) For a small tank (5 m in diameter), the attenuation of the Lamb waves propagating in thesteel is small, and the waves reach the wall plate with their signal intensities above thethreshold of 40 dBAE (–40 dB in the data of this test) as prescribed in corrosion evaluation by
the AE method. Therefore, it is important to evaluate corrosion by taking into account the effect of the waves propagating in water.
Based on the results of this test, it was confirmed that, in applying the evaluation technology fortank bottom plate corrosion to large actual tanks according to the AE method, the AE waves that
propagate in liquid with a little attenuation could be made the main measuring means. It was
confirmed that, in applying the AE measurement to small tanks, it is necessary to set measuringconditions that take into account the effect of the Lamb waves that propagate in the steel plate.
References
1) A. Proust, J.C. Lenain, S. Yuyama: Prog. in AE X, Tokyo, 2000, p. 147
2) P.T. Cole, Van De Loo: Acoustic Emission-Beyond the Millennium, Tokyo, 2000, p. 169
3) S. Yuyama, M. Yamada, K. Sekine: Proc. 2001 National Conf. on AE, Kokura, 2001, p. 37.
247

Fig. 5 AE waves generated at the tank bottom plate center (B point) surface.

248

INTEGRITY EVALUATION OF GLASS-FIBER REINFORCED PLASTICVESSELS BY LAMB WAVE AE ANALYSISTAKASHI FUTATSUGI
Ajinomoto Co. Inc., 1-1 Suzuki, Kawasaki, Kawasaki 210-8681, Japan.
Abstract
Integrity evaluation of glass-fiber-reinforced plastic (GFRP) vessels has become an important problem with the long-term aging of in-service vessels in corrosive environments. We proposed a method for estimating the integrity of aged GFRP vessels utilizing the sheet velocities
and developed a simple method for exciting and measuring the Lamb waves. Applying thismethod to GRRP vessels in service, the sheet velocity measured was found to correlate well tothe bending strength of the aged members. We successfully utilized it for the health monitoringof the vessels. Next we monitored AEs during the bending test of the aged members and determined the threshold tensile stress to cause internal damage. We also monitored AEs from the
manway located near the bottom of a vessel in service. AE activity was found to be closely correlated to the residual strength estimated by the sheet velocity.
Keywords: GFRP vessels，Integrity, Lamb waves，Sheet velocity, Residual strength
1. Introduction
Glass-fiber-reinforced plastic (GFRP) vessels have served well for over 30 years in various
chemical industries because of their excellent corrosion resistance and low price. GFRP vessels,
however, tend to lose their strength due to degradation, such as hydrolysis reaction, oxidization,
environmental stress cracking and so on. Although the GFRP vessels are designed to haveenough strength, with the safety margin of about 10, catastrophic disaster occurs if the degradation is severe. Figure 1 shows an example of failed FRP vessels in our company. We have several
hundreds of GFRP vessels, which have been used for more than 20 years. Hence, the integrity
evaluation of these GFRP vessels is becoming an important maintenance issue.

Fig. 1 An example of failed GFRP vessels, used as steam condensate tank. (left) overall view;(right) Cut-out GFRP wall part from around the failed side manway.
J. Acoustic Emission, 23 (2005)

249© 2005 Acoustic Emission GroupGFRP vessels have been examined by standards-based visual inspection [1]. However, thelifetime of GFRP vessels cannot be predicted by the visual inspection because sub-surface degradation tends to occur without remarkable change of the surface. In addition, deposits on theinside surface makes the visual inspection difficult.
Other inspection methods such as radiography or ultrasonic testing also have difficulty for
GFRP inspection because it is not easy to separate the defect-induced reflection waves fromthose from fibers, filler and initial defects. GFRP vessels generally have own specifications about
resins, thicknesses, service environment, laminated structures and fiber contents. Sometimes,
even the vessel owners do not have the details of the specification, especially about the laminatedstructures. In addition, GFRP vessels have many initial defects caused during the construction.
These make the prediction of residual strength and lifetime of GFRP vessels difficult.
The present author proposes to use the Lamb waves for estimating the residual strength of inservice GFRP vessels. We measured the sheet velocity of S0-mode excited by pencil-lead breaking on the vessel surface. The sheet velocity does not show any direct relationship with thestrength of the GFRP, but can estimate the strength of composite materials. Seale et al. [2] reported a correlation between the damages of composites with sheet velocities. Toyama et al. [3]studied effect of density of transverse crack and delamination length on the sheet velocities.
Hence, sheet velocity measurement has become an effective tool to predict damage of FRP materials. We measured the sheet velocities of many aged GFRP vessels in service and then studied
the relation between the sheet velocity and residual strength estimated by bending test. We used
AE technique to monitor the progression of internal damage of aged members of GFRP vessels
during the bending tests. Advanced AE analysis for fracture-mode identification was used. [4, 5]
Finally, we evaluated results of AE monitoring of two GFRP vessels in service. There observed agood correlation between the degradation predicted by sheet velocity and AE activities.
2. AE Monitoring and Sheet Velocity Measurement for the Aged Members
2.1 Test Specimens and Experimental Procedures
Test specimens were from the shell wall taken from a failed GFRP vessel. This vessel (Fig.
1) was used as steam condensate tank for 16 years. The structural layer of the vessel with the
thickness of ~14 mm is produced by hoop filament winding with isophthalate unsaturated polyester resin. The interior corrosion layer of ~1-mm thickness is constructed by Novolac vinyl esterwith chopped E-glass mats (450 g/m2). The vessel collapsed from cracks around the side manway. The cause of this accident is considered as the strength decrease by hydrolysis reaction ofresin and by environmental stress cracking. New specimens, whose lamination structure is thesame as the failed vessel, were prepared for comparison.
Bending strengths of these specimens b were measured by three point bending tests basedon JIS K7055. The sizes of bending test specimens are 30 mmw  245 mml  15 mmt. Outerspan distance of three point bending is set as 208 mm. AEs were monitored by four resonant sensors (PAC, type PICO, center frequency 450 kHz) mounted on the specimen surface. Here themember was set so as the tensile stress by bending is on the inner surface. This member setup isbased on the deformation pattern of GFRP vessel with internal fluid pressure.
For a vertical vessel with side manway, hoop stresses by liquid head concentrates around side
manway. Figure 2 shows the deformation pattern analyzed by FEM. The upper portion of the250

Fig. 2 Deformation of GFRP vessel with internal fluid pressure analyzed by FEM. (a) side viewand (b) top view.
manway bends to the inside by liquid head. The tensile stress acts on the inside of the shell wallaround the side manway and failure initiates from there. The failure of the actual vessel initiatedfrom around the side manway shown in Fig. 1(b). Therefore the bending load must be applied so
as to give the tensile stress on the inner surface of the member. Also the longitudinal directionsof bending specimens must be circumferential so as the hoop fibers bear tensile stresses. Directions of Lamb wave propagation for sheet velocity measurement should be in the circumferential
direction.
(b)

(a)

NotebookPC
V2

AmplifierReceivingtransducer(Pico X 4ch.)

Digitizer & PC
Gage CS12100

AmplifierMechanical pencil

Load

A/D
Resistance

Lead break

15mm

Receivingtransducer
Structural layer

Structural layer
110mm

Corrosion layer

Corrosion layer

V1

converter

Battery
FRP specimen / vessel
Aluminum foil

(Propagati on length)

208mm

L=130mmFig. 3 Experimental arrangements (a) Bending test with AE monitoring (b) Lamb wave measurement
AE sensors were mounted on the upper or outer surface of the shell wall, as shown in Fig.
3(a). Sensor outputs were amplified 40 dB by a preamplifier (NF Corp. 9913), digitized (Gage
CS-12100, 20 ns sampling interval), and fed to a personal computer. Figure 3(b) shows experimental setup for sheet velocity measurement. In order to measure the sheet velocities on site, we
excited Lamb waves by breaking a pencil-lead. Here the lead breaking triggered the digitizer by
a voltage change, which was applied between aluminum foil on the sample and the pencil lead
via a 50-ohm resistor. The Lamb wave was detected by a PICO sensor mounted at distance L =130 mm from the lead break. Both the voltage change and Lamb waves were recorded on digital
oscilloscope (Adtek system science, AXP-DS01, 20 ns sampling interval). Figure 4 shows anexample of detected Lamb waves. Sheet velocity was measured from the traveling time and distance.
251

Fig. 4 Detected Lamb waves at distance of 130 mm for a GFRP sample.

Fig. 5 Detected AE wave and its polarity distribution at bending test.
2.2 Test Result of Failed Vessel Samples
Figure 5 shows examples of AE waves detected during a bending test. These are typicalLamb waves AEs with weak S0-mode and trailing strong A0 mode. We classified the fracture
types by analyzing the polarity distribution of first arrival S0-waves. Two samples, i.e, (a) one
from the failed vessel wall and (b) a new sample, were bent under the same condition. Figure 6
shows the load-deflection curves and cumulative AE events for samples (a) and (b). Upper two
curves are the load-deflection curves and the lower two the cumulative AE counts. The new252

Fig. 6 Load-deflection curves and cumulative AE event counts.
specimen showed a pop-in at the load of 3.2 kN or ~50 % the maximum load. This pop-in is considered to be to due to the fiber fracture in anti-corrosion layer. AEs were detected from 2 kN,
and increased rapidly before the pop-in load. Above the pop-in stress, load increased almost linearly to 6 kN. For the failed vessel wall sample (a), the load-deflection curve showed many load
drops. At the load of 2.7 kN, the first and largest load drop was observed. This is the ultimate
bending load of the wall member. This value is less than a half that of the new specimen. Total
AE event counts from the failed vessel wall is less than a quarter of those from the new sample.
First AE was detected at the load approximately 50% of the maximum.
Figure 7 shows the separate plots of the cumulative AE counts from Mode-I and Mode-II
fractures classified from the polarity distribution of first arrival S0 waves. It is first noted that the
number of Mode-I AEs is three times larger than that from Mode-II AEs for the new specimen(b). In contrast, the Mode-II AEs are one-third larger than Mode-I AEs for the failed vessel wall
sample (a). It is also noted that Mode-I AEs from failed vessel wall is smaller than new specimen(b). Less Mode-I AEs from failed vessel wall is considered to be due to prior fiber breakage byenvironmental stress cracking. Fibers pull out due to weak interfacial strength between the fibers
and matrix in the failed vessel wall appears to be the reason for higher Mode-II signals.

Fig. 7 Cumulative AE numbers identified by fracture mode about fracture of corrosion layer.
Next we measured the sheet velocity (Ce) of the two samples. They were measured as 3.87
km/s for failed vessel specimen (a) and 4.87 km/s for new specimen (b). The velocity is decreased by 1 km/s by the degradation in the failed vessel wall specimen. Thus, the sheet velocity
is can be used for estimating the degradation. To confirm this on a larger database, we measured
the bending strength b and sheet velocities Ce for 23 samples taken from various aged vessels.
253

Every specimen has different thickness and laminar structures, but their detail is unknown. Figure 8 shows the plot of the bending strength b versus the sheet velocity Ce of these 23 samples.
Relationship between Ce and b can be represented by equation (1). Correlation coefficient R =0.86 was obtained.
b = 171Ce – 477(1)

The data points shown in an ellipse are the results from the failed vessel wall. The threshold of
sheet velocity to estimate the critical degradation condition appears to be at 4.00 km/s.

Fig. 8 Sheet velocities Ce vs. bending strengths b of aged GFRP specimens.

Fig. 9 Experimental arrangements for field AE monitoring.
3. Field Experiment for In-Service Vessels
From laboratory experiment presented above, Lamb-wave AE monitoring and sheet velocity
measurements are expected to provide the residual stress or integrity of anti-corrosion layer of
GFRP vessels. We monitored AEs of two GFRP vessels in service.
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3.1 Test Vessels and Experimental Procedures
Two vessels were evaluated by AE and sheet velocity measurements. Dimensions of thesevessels are almost identical. Capacity is 50 m3, with the inner diameters of 3000 mm, and 7900mm height. Maximum thickness of the wall is 10 mm and the maximum liquid level is 7000 mm.
Vessel A has been in service for 22 years. The stored liquid is a by-product of amino acid hydrochloride with pH = 0 at normal temperature. No agitator is installed in this vessel. During AE
monitoring testing, the level of liquid is kept at 50 %. AE had been monitored for 3.2 x 105 sec.
Vessel B has been in service for 2 years. The stored liquid is a by-product of amino acid hydrochloride with pH = 0 at 60˚C. An agitator with a diameter of 450 mm is installed from a side
nozzle near the bottom. The agitator speed is 350 rpm. To keep the inside temperature at 60˚C, asteam heat exchanger is installed from another side nozzle. During the AE monitoring, the levelof liquid is increased from 0% to 84 %. AE had been monitored for 4.9 x 105 sec.
Figure 9 shows the experimental setup for AE monitoring. AE monitoring system (Chiyoda
Corp. C-AEAS 4ch monitoring system with total gain 60 dB amplifier and 100 kHz high-passfilter) is used. Four resonant type AE sensors with center frequency of 200 kHz and diameter of
5 mm were mounted on the upper surface of the bottom manway. Thresholds of all channels are
set as 512 mV. To record AE waveforms, KEYENCE NR-500/HA-08 Wave Logger with 5 ssampling interval is connected to signal output terminal of C-AEAS. Threshold of this wave logger is set as 2 V. Attenuation was measured as 1.7 dB/cm. The four AE sensors were mounted at
the corners of a 250 mm  200 mm rectangle.
3.2 Experimental Results
Table 1 shows the results of sheet velocity measurements and AE monitoring for vessel A
and B. For vessel A, any degradation such as environmental stress cracking was not observed byvisual inspection, in spite of 22 years service. Measured sheet velocity 4.61 km/s is higher than
the threshold sheet velocity. No AE was monitored during approximately 4 days of measurement. For vessel B, overlap laminates between side plates and nozzles including the manway become white in color as shown in Fig. 10(a). In addition, pinholes were found over the whole anticorrosion layer. Their color becomes black due to the permeation of liquid as shown in Fig.
10(b). These defects appear to be produced from shoddy fabrication. These defects were repairedby grinding and laminating before AE monitoring. No significant defects inside the vessel areobserved at present. The sheet velocity of vessel B is measured as 3.46 km/s and is lower thanthat of the failed vessel. We detected a number of AE from this vessel.
Table 1 the results of sheet velocity measurements and AE monitoring for vessel A and B.

Vessel A
Vessel B

Stored liquor
Temperature

Agitator

Condition of
corrosion layer

Sheet velocity
Ce, km/s

Liquid level

AE

pH 0,
NormalpH 0, 60˚C

None

No degradation

4.61

None

500mm,350 rpm,

Whitening.
Pinholes(Already repaired)

3.46

50%
Constant
0~84%

100,199,743
EventsFigure 11 shows the change of liquid level (a), AE event rates (b) and AE energy rates (c) of
the events whose source location is in the sensors zone. As can be seen from (b), no AE was detected during feed pump operation since a high-pass filter was utilized.
255

(a)
(b)
Fig. 10 Inside defects on vessel B. (a) Overlapping of laminate around the side manway. (b) Pinholes with permeation of liquid into the corrosion layer.

Fig. 11 AE parameters for vessel B monitored by the AE parametric analyzer.
The agitator of vessel B starts when the liquid level is higher than 30 %. No AE events weredetected when the liquid level was less than 30%. AE events and energy increased after the256

Fig. 12 Typical AE waves for vessel B monitored. (a) Mode II signals at 48 % liquid level, (b)Mode I signals at 84 % liquid level.
257

Fig. 13 Cumulative AE event counts for vessel B identified by fracture mode.

Fig. 14 Level dependence of AE duration identified by fracture modes.
liquid level reached 30% or the agitator was started. AE events and energy, however, decreased
with an increase of the liquid level from 30 % to 84 %. As shown in Fig. 11(d) and (e), a large
number of AE events with long duration and rise time were monitored during from 48 % to 64%
liquid level. Some waveforms of Mode-I and Mode-II, detected at 48% and 84% liquid level
respectively, are shown in Fig. 12. Fracture mode was classified from the polarity distribution of
first arrival waves. Cumulative AEs from Mode-I and II are shown as a function of time and liquid level in Fig. 13. Waveforms, unfortunately, could not be recorded due to the capacity of thehard disk from 2.1 x 105 s to 2.4 x 105 s. Figure 14 shows effect of liquid level on the duration.
As shown in Figs. 13 and 14, a large number of Mode-II AEs with long duration were detected when the liquid level increased from 48 to 60 %. Mode-I AEs, however, increased after
liquid level reached 84 %. Duration of AEs above 84% level became short. Figure 15 shows relation between AE duration and maximum amplitude of Mode-I and -II AEs. Mode-I events arein the area surrounded by dashed curve, but so as most of Mode-II AEs. The details of these results are now under investigation. It is now apparent that AEs are produced from not only theoriginal defects but also agitation accelerated damages.
258

Fig. 15 Correlation between AE duration and AE maximum amplitude identified by fracturemode
4. Conclusion
The integrity of GFRP vessels was evaluated by means of AE monitoring and measurementof sheet velocities. AE monitoring during bending tests of aged and new GFRP plates and fromvessels in service are attempted. Results are summarized below.
(1) The sheet velocity is well correlated to the bending strength of aged members. It is successfully utilized for the diagnosis of the health condition of the GFRP vessels.
(2) AE from aged GFRP during bending test was much less than that from new sample andemitted more Mode-II signals than Mode-I.
(3) AE monitoring of new and aged vessel in service was performed. Old but undamaged vesselin service showed high sheet velocity and no AE was detected. Contrary to this, a new vessel
with agitator with visible defects, showed low sheet velocity and emitted a number of AE
signals. A relation between the sheet velocity and AE activity was observed. The details ofthese results are under further evaluation.
References
1) The Japan reinforced plastics society, FRPS C 002-1989: “Inspection guidelines for glassfiber reinforced plastic vessels” (In Japanese).
2) M. Seale, B. T. Smith, W.H. Prosser: Lamb wave assessment of fatigue and thermal damage
in composites, J. Acoust. Soc. Am, 103 (5), Pt. 1, 2416-2424, (1998).
3) N. Toyama, J. Noda, and T. Okabe: Quantitative damage detection in cross-ply laminates using Lamb wave method, Composite Science and Technology, 63 (10), 1473-1479, (2003).

4) T. Sato, M. Takemoto and K. Ono: Effect of fracture dynamics in thin plate on the waveform and radiation pattern of the Lamb waves, Jpn. J. Appl. Phys. 38 (1999, June) 31933200.
5) A. Yonezu, T. Ogawa, M. Takemoto: Fracture mechanism of toughened silicon Nitride studied
by AE waveform simulation, J. JSNDI, 49-12(2000, Dec.), 847-853.
259

EVALUATION OF REINFORCEMENT IN DAMAGED RAILWAY
CONCRETE PIERS BY MEANS OF ACOUSTIC EMISSIONTOMOKI SHIOTANI1, YASUHIRO NAKANISHI1, KEISUKE IWAKI1,XIU LUO2 and HIROSHI HAYA2
1)
Research Inst. of Technology, Tobishima Corp., 5472 Kimagase, Noda, Chiba 270-0222, Japan.
2)
Railway Technical Research Institute, Hikari, Kokubunji, Tokyo 185-0034, Japan.
Abstract
We focus on a particular case of concrete piers, in which the damaged state and repaired state
can both be investigated. In order to know the precise damage condition within the piers, boreholes are excavated, followed by internal wall observation with a borehole camera. Cracks
within the retrieved core samples are examined with impregnation of fluorescent epoxy. In AE
monitoring, active loads with train passage are utilized to induce AE signals. AE sources are
only extracted from the detected raw AE data set using 3D source location. On the basis of theextracted AE events only, the damage quantification is performed along with an analysis of AE
peak amplitude distribution. The AE monitoring is additionally performed in the structure after
repair works have been completed, and the repair work is evaluated with AE activity. Throughthe AE monitoring in comparison with several observation techniques, it is concluded that thedegree of damage can be quantified with the AE amplitude distribution reasonably well and theeffectiveness of repair works can be evaluated with the distribution.
Keywords: Railway structures, Damage quantification, Fluorescent epoxy impregnation, Borehole observation, Reinforcement/Repair, Seismic prospecting
1. Introduction
In Japan the number of civil structures of older than 50 years will rapidly increase from 2005
on, and the maintenance and renewal cost is expected to exceed that of new construction in 2022.
Determining the priority of structures to be maintained, for example, the damage assessment forthe structures is essential. Thus, the damage quantification of civil structures to assess the structural integrity is in strong demand. In general, crack density as well as crack widths have beentreated as damage indices so far; however, those cracks are conventionally evaluated visually andall information on cracks is based on the surface observation only. In evaluating damage degree
the method, which can show the inside condition of the structures, is highly sought. An AE testing seems to give a reasonable resolution for such demand since active cracks only generate AE
activity under in-service condition. Paying attention to the secondary AE activity, generated fromexisting cracks, the present authors have studied damage quantification in comparison with actual damage condition. In this paper, the AE testing is both applied for damaged and repaired
state of railway concrete piers. Combining a variety of observation results with the findings of
AE testing, damage degree as well as repair effect can be assessed.
2. Monitoring Site and Testing ProcedureThree railway concrete-piers, located in Chiba Prefecture, were examined in this study. Figure 1 shows the side view of the piers. The piers were made of plain concrete, have been used for
J. Acoustic Emission, 23 (2005)260© 2005 Acoustic Emission GroupFig. 1. Railway concrete piers to be tested.

Fig. 2. Crack condition of the pier (from left to right P2, P3 and P4).
over 70 years, and have sustained, due to long-term weathering as well as earthquakes, a variety
of damages. Degree of damage, severe (P3), intermediate (P4) and intact (P2), was each estimated by impact vibration tests [1, 2]. Figure 2 shows surface cracks observed. No visible cracks
were found in P2, but a serious crack in a horizontal direction, which corresponds to the construction joint of concrete, was found in both P3 and P4. These cracks are generated by a recent
earthquake. In P3 especially, a part of concrete was already separated from the stem of pier.
The monitoring protocol is shown in Fig. 3. Acoustic emission was carried out for threepiers: two damaged and one intact, followed by seismic prospecting. In order to quantify the internal damage and cracking condition of the pier, three boreholes in a lateral direction and one
borehole in a diagonal direction were made only in P3. This pier is employed as a representativeof the piers. A borehole CCD camera was subsequently inserted into the boreholes to record thecrack distribution precisely. In regard to the core retrieved, not only core observation but also
impregnation of fluorescent epoxy [3] was performed. With impregnation of fluorescent epoxy,crack width smaller than 1 µm and air voids can be revealed under ultraviolet light.
3. Repair Works
The damaged piers, P3 and P4, were repaired using an injector specifically designed for repair work (Konishi Co.) under a constant pressure of 0.05-0.10 MPa with a pair of elastomer
261

Fig. 3 Protocol of various testing methods.
band. The pressure varied depending on the amount of injection agent. In order to evaluate therepair agent, organic repair agent was used for P4 and inorganic agent with fine cement particles
was selected for P3. Before injecting with the repair agent, all visible surface cracks on the piers
were sealed with caulking material. Note that additional injection was made only for P3 using theborehole in a diagonal direction, where no pressure was given during the injection.
4. Damage Evaluation
Non-destructive techniques such as AE and seismic prospecting can provide rough locationsof damaged/cracked areas; however, the details of crack geometry cannot be deduced throughthose tests. In contrast, destructive techniques with core retrieved, such as visual and microscopic
crack observation, can give better insight in terms of crack distribution/geometry. In practice,
however, applications with those destructive techniques are limited only to a part of the structures tested. Furthermore even when the destructive techniques were implemented, the findings
are limited only to a partial condition of the structures. Thus, non-destructive techniques havebeen used as so-called global health monitoring, and the results of non-destructive techniques
would be subsequently verified by the destructive techniques, which are locally conducted.
Therefore, in this study, AE and seismic prospecting are performed in order to monitor the piers
globally, whereas a variety of observation is conducted to follow up the findings obtained fromthe non-destructive techniques.
4.1 Acoustic Emission
12 AE sensors of 60 kHz resonance were placed on the pier surface as shown in Fig. 4. AE
sensors were placed at four height levels in a set of three. The vertical spacing between AE sensors was 1 m for P2 and P3, and 0.5 m for P4 pier. AE monitoring was performed when passing
trains pass over the bridge and AE signals are induced by the live load due to train passage.
These were detected and amplified by 40 dB within the sensor. The AE signals over threshold of
40 dB (100 V at the amplifier input) were recorded for their AE parameters as well as AE
waveforms with an AE monitoring system (Mistras, Physical Acoustics Corp.). Besides AE activity, acceleration with accelerometers (ASW-2A, max acceleration 2g, Kyowa Dengyo Corp),
displacements with -shaped displacement meters (PI-2-50, Kyowa Electronic Instruments) andstrains with strain meters (PL-120, Kyowa Dengyo Corp) were each measured in the piers, where
the data was sampled at 1 kHz and stored in a data logger (DRA-101B, Tokyo Sokki Kenkyujo).

262

Fig. 4 Arrangement of AE sensors with other deformation-sensors. (left: P4 and right: P3)

Fig. 5 Arrangement of accelerometers for seismic prospecting in P3. (left: side view, right: planview)
4.2 Seismic Prospecting
Seismic prospecting was performed only in P3 for three sections as shown in Fig. 5. In eachsection, 12 piezoelectric accelerometers (SAF51, Fuji Ceramics Corp.), symmetrically arranged
were placed onto the pier surface spaced by 50 cm. In the seismic prospecting, elastic waves
were excited by impacting a steel ball of 5 cm in diameter. Elastic wave velocities in the section
were estimated by means of ray-tracing and simultaneous iteration methods.
4.3 Observation of Boreholes and Sampled Cores
In order to better evaluate the crack condition, four boreholes were excavated as shown in
Fig. 6. Three boreholes (No. 1-3) were made in the lateral direction and one (No. 4) in a diagonal
direction. In particular, No. 1 and No. 4 boreholes were set so as to penetrate cracks observed on
the surface. For the wall of boreholes excavated, optical images were recorded with BIPS (borehole image-processing system [4]). The BIPS enables us to obtain quantitative/qualitative results
by projected/unfolded image data, along with valuable color data and oriented and non-disturbed
borehole wall images. Besides the borehole image monitoring, cores retrieved were carefully observed visually to quantify the damage.
263

Fig. 6 Arrangement of boreholes in P3.
4.4 Impregnation of Fluorescent Epoxy
It is difficult to distinguish real cracks from all of the observed cracks, in the study of thedamage/cracks of concrete material with various observation techniques. This arises due to thelimitation of picture-resolution or disturbance of sampling and therefore it is essential to separate
real cracks from apparent cracks. This is possible by means of impregnation technique with fluorescent epoxy. In this study, the bulk cores retrieved were placed into an impregnation chamber
filled with fluorescent epoxy, under a reduced pressure (9.3 Pa) using a vacuum pump. By sectioning, cracks distributed within the core can be visualized under the ultraviolet light. Note that
sectioning was conducted after the impregnation and no additional cracks are introduced.
5. Results
5.1 Observation in Damaged Condition
As a typical case the developed borehole image of No. 4 borehole is shown in Fig. 7. At 1.4
m depth, a crack smaller than 0.5 mm can be seen. Here, the depth of 1.4 m corresponds to thatof the construction joint of concrete. Beside the observed crack and a void at 0.4 m depth, noother crack was found. Core samples retrieved at No. 4 borehole are also shown in the figure atright. A large number of ruptures, especially below 1.6 m depth, were obtained. Cracks corresponding to the depth of ruptures were not observed in the borehole image. These are likely to be
produced by the excavation.
In order to study unidentified damage from the above observations, further examination was
conducted by means of fluorescent epoxy impregnation. The core samples ranging from 1.1-1.6
m were subjected to the impregnation, and cuts were made at four locations representing 1-4 asshown in Fig. 7. Results are given in Fig. 8; one side of the cross-section at No. 1 and No. 4 areboth shown. Under ultraviolet light, No. 1 section includes voids of large area. At No. 4, which islocated close to the casting joint, a distinct macro-crack and branched cracks were detected.
These findings imply that even for the bulk core/un-ruptured core, large scale of cracks and large
area of voids existed, so that it is most likely that these critical cracks can readily generate AE
activity due to a live load.
264

Fig. 7 Developed borehole image (left) and core retrieved (right).
5.2 Acoustic Emission
In the damaged condition, AE sources in three-dimension are shown in Fig. 9, where the diameter of AE source reflects the average of peak amplitude of AE hits contributing to the AE
source/event. No significant AE sources were obtained in P2. In contrast to P2, substantial AE
sources both in P3 and P4 were found, not only along the existing joints but within the wholemonitoring area. These AE activities appear to agree well to the damage condition already evaluated from impact vibration tests and damage observation. Paying attention to the scale of AE
sources in P3 and P4, AE sources ranging from small to large average peak amplitude were observed in P3, while in P4 such differences in peak amplitude among AE sources were small.
The same 3D sources after repair are shown in Fig. 10. Comparing to the damaged condition(see Fig. 9), generally, no significant decrease of the scale of AE sources as well as the numberof AE sources could be found in repaired piers. In P3, however, not so many large scale sourceswere found in repaired condition. With regard to the average of peak amplitude, they were 39.24(39.82) and 38.29 (38.63) in P3 and P4, respectively, where the value in parentheses denotes theaverage peak amplitude in the damaged condition (Fig. 9). Thus, notable change/improvement
due to repair could not be obtained also from the average of peak amplitude.

265

Fig. 8 Sections after the impregnation at No. 1 (top left) and No. 4 (bottom left), andthe corresponding images under ultraviolet light. (No. 1: top right, No. 4: bottom right)
5.3 Seismic Prospecting
A typical distribution of elastic wave velocity on section II is shown in Figure 11 together
with 3D AE sources projected to the section II. Comparison is made between damaged and repaired condition, where it is noted that the repair agent was poured into the borehole No. 4; i.e.,
no repair was performed in the range higher than 2.0 m since no pressure was applied whenpouring. No improvement was observed at position higher than 2.0 m, which proves the accuracyof employed seismic prospecting. In the damaged condition, low velocity range of lower than3000 m/s can be seen along the casting joint; however, after the repair work the elastic wave velocity in such low range becomes higher, suggesting the improvement of mechanical property inthe pier. Effectiveness of repair work was thus verified with seismic prospecting. Note that therange of lower velocity could be found elsewhere except for the construction joint of concrete.
6. Discussion
6.1 Damage Condition Evaluated by Several Techniques
Except for P2, which was in the intact state, the predominantly evolved crack along the casting joint were found both in P3 and P4. These cracks could also be observed with borehole266

Fig. 9 AE sources in three-dimension.

Fig. 10 AE sources after repair. (left: P3, right: P4)
observations. AE sources show, however, not only along the joint but in the range above the joint
active AE sources were present. As shown in the core samples, many ruptures of core sample
were obtained, implying that the numerous cracks obtained by the boring were generated when
the pier was subjected to the torsion force due to the boring; i.e., the critical state of cracks as togrow macroscopic cracks with a slight stress had already existed within the pier. This fact was
further verified by the observation results with fluorescent epoxy impregnation. Thus thesefindings lead to a conclusion that critical state of cracks had already been distributed within the267pier, although these could not be evaluated with conventional observations like borehole camera
and unaided eyes, but could only be evaluated by AE activity.

Fig. 11 Elastic wave velocities estimated by seismic prospecting in P3. (left: before repair, right:
repaired)Fig. 12 Symmetric behavior of two -shaped displacement meters along with AE activity due totrain passage.
6.2 Quantification of Damage with AE Technique
In order to evaluate structural integrity, Calm ratio and RTRI have been proposed on the basisof train-induced AE activity, and they were successfully applied for damage evaluation of railway substructures [see 5]. When obtaining those two indices, structural behavior such as deformation is essentially incorporated with AE activity; i.e., a loading process as well as an unloading process should be defined when a train passes. As shown in Fig. 12, however, since the macroscopic crack had already existed in a horizontal direction along the casting joint and the piershowed rocking when a train passes. A variety of attempts to determine the processes were made,but no indices for the structures could be developed. AE amplitude distribution gives a unique268

Fig. 13 Amplitude distributions in P3 (left) and P4 (right). The average amplitude of a set of AE
hits contributing to an AE event is used to determine the distribution.
trend corresponding to the degree of damage and is used to quantify the damage. Cumulative
amplitude distributions are shown in Fig. 13. To quantify the distribution, a gradient of the curveis extracted on the basis of improved b-value analysis [6, 7]. Note when comparing a seismic bvalue, the improved b-value should be multiplied by a coefficient of 20.
As a typical result, the cumulative amplitude distribution in P3 is shown as in Fig. 13. In thestate of before repair, or damaged condition, more AE events of large amplitude are generated,while in the after repair state, more small-amplitude events are generated. Such a difference ofdistribution can be seen in improved b-value; i.e., 0.0817 in the damaged condition and 0.2508 in
the repaired condition. This difference is distinct enough to separate the two conditions. In Fig.
14, the Ib-values of the three piers are summarized in the left side along with the other resultsfrom I-site and K-site. In the present piers of P2, P3 and P4, damage recovery or repair effect canbe illustrated by the variation of Ib-value. Considering other results, the degree of damage can be
successfully classified with Ib-value;
serious damage
intermediate damage
intact or minor damage

Ib-value of 0 - 0.1,
Ib-value of 0.1 - 0.2, andIb-value of > 0.2.

Accordingly, damage quantification was successfully carried out using the improved b-value
analysis. The improved b-value analysis has already been applied to several railway structures
and was successful in quantifying the degree of damage.
7. Conclusions
Damaged railway concrete piers before and after repair were investigated with AE technique.
Details of damage condition were also studied with several observation techniques. Throughthose studies following findings are obtained:
1. AE activity monitoring reflects the actual damage of structures. Using improved b-value,
damage quantification is possible. However, more data with a variety of damage should be
systematically collected and evaluated.
269

2. It is difficult to quantify the damage only from visual observations and from borehole wall
observation. Critical damage assessment could only be done by means of fluorescent epoxy impregnation, but this is impractical in field.
3. Non-destructive techniques as AE and seismic prospecting showed reasonable results corresponding to the damage obtained from all observations. Seismic prospecting can givethe effectiveness of repair works as an increase of the wave velocity. AE results failed toshow substantial changes in terms of the number of AE events or the scale of amplitude.
However, improved b-value demonstrated the possibility to quantify the damage.

Fig. 14 Variations of improved b-value from various conditions of concrete piers.
References
1) Y. Kikuchi, A. Nishimura and M. Yamada, Investigation on the method of judging of integrity for the foundation of the bridge, The Proceeding of the 43rd JSCE Annual Meeting, Japan Society of Civil Engineering, pp. 250-251, 1988, in Japanese
2) A. Nishimura and H. Haya, Assessment of the structural integrity of bridge foundation by the
impact vibration test, Proceedings of GEO-COAST ’91, pp. 719-724, 1991.
3) T. Shiotani, J. Bisschop, J.G.M. van Mier, Drying Shrinkage Microcrack Measurements in
Cementitious Composites Using AE and FLM, Society of Experimental Mechanics, Proceedings of the 2002 SEM Annual Conference & Exposition on Experimental and Applied
Mechanics, Paper Number: 202 (CD-ROM), 2002.
270

4) T. Yasuda, S. Taniguchi, S. Kamewada and G. Okano, Crack evaluation system for analyzing
the condition of superannuated tunnel, Proceedings of the international symposium on Fracture and Jointed Rock Masses, pp249-255, 1992.
5) X. Luo, H. Haya, T. Inaba, T. Shiotani and Y. Nakanishi, Damage evaluation of railway
structures by using train-induced AE, Construction and Building Material, 18, 215-223,
2004.
6) T. Shiotani, K. Fujii, T. Aoki and K. Amou, Evaluation of progressive failure using AE
sources and improved b-value on slope model tests, Progress in AE VII, JSNDI, pp. 529-534,
1999.
7) T. Shiotani and Z. Li, S. Yuyama and M. Ohtsu, Application of the AE improved b-value toquantitative evaluation of fracture process in concrete-materials, Journal of Acoustic Emission, 19, 118-133, 2001.
8) T. Shiotani, Y. Nakanishi, X. Luo and H. Haya, Damage assessment in railway sub-structures
deteriorated using AE technique, 26th European Conference on Acoustic Emission Testing(EWGAE 2004), Vol. 1, p. 255.
9) T. Shiotani, X. Luo, H. Haya and M. Ohtsu, Damage quantification for concrete structures byimproved b-value analysis of AE, 11th International Conference on Fracture (ICF11), CDROM (2005).

271

WATER-LEAK EVALUATION OF EXISTING PIPELINEBY ACOUSTIC EMISSIONTETSUYA SUZUKI, YUKIFUMI IKEDA, YUICHI TOMODA and MASAYASU OHTSU
Kumamoto University, 2-39-1 Kurokami, Kumamoto, Japan.
Abstract
Degradation of existing pipeline systems often leads to water leakage. In most cases, nondestructive tests, such as a water leak detection method and a ground probing radar method areapplied to identify defects in pipeline. Although these methods are effective for surveying
limited sections, further technical improvement is necessary for wide-range and long-term
measurements for maintenance and management of the existing pipelines. In this study, an
existing pipeline system is examined, applying AE method. It is found that AE behaviors in thepipeline are dependent on the degree of damage or water leakage, and can be evaluated by AE
parameter analysis. AE activity in water-leak section is identified as tensile-type AE events. A
relation between AE rate and the water leak is analyzed by the rate process theory and allows
quantitative damage evaluation of water-leak in pipeline.
Keywords: Damaged pipeline, Water leak, AE parameter analysis, Rate-process theory
1. Introduction
In recent years, the maintenance for lifeline infrastructure has become a target of intensive
research. The necessity for damage monitoring is growing, because it is difficult to conductvisual observation in buried pipeline systems.
Deterioration of existing pipeline systems often results in overt-through water leaks (Fig. 1)
or inner pressure decrease. Conventionally, using a leak detection method or a leak survey
method, the leakage has been identified. Damage to the pipelines results from not only thedeterioration, but also water-hammer pressure. Wide-range and long-term measurements areneeded for maintenance. Though the conventional methods are effective for intensive survey
on a limited section, they are not effective for global and long-term measurements. In order todevelop a new monitoring technique, acoustic emission (AE) method is applied to on-sitemeasurement of pipelines. Based on a parameter analysis of AE results, water leaks areevaluated. AE activity is quantified by the rate process theory. Application of AE method tothe detection of leakage and the evaluation of damage in the existing pipeline is studied.
2. AE Monitoring of Water Leakage
In this study, AE sensors detected AE generated due to water leakage in a pipeline. Basedon AE characteristics, the degree of damage is evaluated. Compared to the conventional
methods, AE method is promising, because results are not affected by the degree ofdeterioration in an existing pipeline. AE monitoring was conducted at Kasanohara main
channel located in Kanoya, Kagoshima, Japan. The pipeline system was constructed 36 years
ago. Water leak accidents occurred 92 times (2.6 leaks per year on average) from 1967 to 2002.
70% of water leaks occurred at joint sections.
J. Acoustic Emission, 23 (2005)

272© 2005 Acoustic Emission GroupFig. 1 Water leakage in a joint section.

Fig. 2 Outline of AE Measurement on pipeline section.
A monitoring section of the length L = 1.1 km is illustrated in Fig. 2, consisting of a PC pipewith a diameter of 500 mm. Water leak was detected around a water-leak section in the figure.
It is confirmed that a water leak is about 1.14 m3/dcmkm, which is ten times as much as theamount of allowable water leak in Japan (0.10.15 m3/dcmkm) for a new PC pipeline [1].
Normal section in the figure corresponds to non-damaged line.
Three AE sensors were set on an exposed pipe at an existing manhole. Outline of themeasurement system is illustrated in Fig. 3. DISP-AE system (manufactured by PAC) was
employed. The duration of measurement was 30 minutes. AE hits were detected by using AE
sensor (resonance frequency: approx. 150 kHz). To count the number of AE hits, the thresholdlevel was set to 38 dB, with a total gain of 60 dB. AE measurement was conducted with a 3channel system.
3. AE Parameter Analysis
AE behavior of a pipeline with leakage was associated with inner water pressure. AE hits
were analyzed by using parameters of RA value (rise time/peak amplitude) and the average273

AE Measurement System

AE Sensor
Pre-AMP

Water flow

Pipe line

Concrete pit

Fig. 3 AE measurement system in pipeline.

(a) Water Leak Section(b) Normal Section
Fig. 4 Relations between AE hits and inner water pressure.
frequency (AE ring down counts/duration. [2] Inner water pressure was measured as 0.44 MPa
under normal operation. AE monitoring was conducted after a gate valve had been closed. The
inner water pressure dropped by 23% in 30 minutes, from 0.44 MPa to 0.34 MPa. Relations
between AE hits and inner water pressure are shown in Fig. 4 (a) and (b).
At the leaked section, AE activity became stables about ten minutes after the inner waterpressure started to decrease. It appears that high AE activity at the beginning is associated withwater leak, caused by a high inner water pressure (Fig. 4(a)). On the other hand, at the normal
section, AE activity is steady regardless of inner water pressure (Fig. 4(b)). Similar results
were reported elsewhere in buried pipeline. [3]AE generating behavior of the leaked section was compared with that of the normal sectionin terms of RA value and the average frequency. Results are shown in Fig. 5. Tensile-type AE
events are mostly observed at the leak section, whereas shear-type AE events are active at the274

A-FRQ(kHz)800

Normal Section
Water Leak Section

600

Tensile
Shear

400
200
0
0

100

200

300

400

500

RA Value(mmsec/V)

Fig. 5 Relations between A-FRQ and RA value.
normal section. It is expected that tensile-type AE events are generated by water leakage fromthe pipeline system as well as tension cracks of cover-coat mortar, while shear-type AE eventsare generated by the flow of water.
AE generating behavior observed in the existing pipeline system is closely related to inner
water pressure. When water leakage occurs under high inner-water pressure, a large amount oftensile-type AE events were observed. Thus, for the evaluation of leakage and damage ofexisting pipeline systems, AE parameter analysis is effective.
4. Quantitative Evaluation by AE Rate-Process Analysis
The rate process theory has been applied to quantify AE generating behavior. AE activity inthe existing pipeline with inner water pressure corresponds to the generation of micro-cracks
and water leakage in pipelines. AE activities seem to increase in accelerated manner withvariation in the inner water pressure. This process can be subject to stochastic process. (4)Equation 1 represents the number of AE hits dN with an increment of inner water pressure
from P to P+dP, where the total number of AE events is N and the rate of AE activity at the
inner water pressure level P is f(P):f (P)dP =

dN
N

(1)

The rates f(P) estimated in AE rate-process analysis are shown in Fig. 6. The inner waterpressure ratio, P/Pmax, refers to the ratio of the inner water pressure P to the maximum innerwater pressure Pmax. Results show the rate of AE activity under high inner water pressure ishigher than that of the normal section. In the normal section, the rate of AE activity f(P)remains always less than 0.04, without correlating to the decrease in the inner water pressure.
These results confirm that AE generating behavior in existing pipeline systems can bequantified by the rate process theory. Thus, it is possible to quantify the degree of damage inpipelines under different inner water pressures.

275

Fig. 6 AE rate-process analysis.

5. Conclusion
Leakage and damage of deteriorated pipeline are studied by AE method. AE activity isclosely related with water leakage or damage of pipeline, and is analyzed by AE parameter
analysis and the rate process theory. Thus, the degree of damage in existing pipeline isquantitatively evaluated. Conclusions are summarized, as follow:(1) AE generating behavior in pipeline with water leakage can be discriminated by applying AE
parameter analysis.
(2) The tensile-type AE hits are actively observed due to water leakage.
(3) Quantitative damage evaluation of pipeline with water leakage can be performed by AE
rate-process analysis.
References
1) N. Nawa, K. Sonoda, H. Iwata and T. Suzuki (2002), ”Survey and Evaluation Method for
Deteriorated Agricultural Pipeline Function”, JSIDRE, 70 (12) 31-36.
2) JSCM-IIIB-5706 (2003), ”Monitoring method for active cracks in concrete by acoustic
emission”.
3) S.J. Vahaviolos, R.K. Miller, D.J. Watts, V.V. Shemyakin and S.A. Strizkov (2001),”Detection and Location of Cracks and Leaks in Buried Pipelines using Acoustic
Emission”, J. Acoustic Emission, 19, 172-183.
4) T. Suzuki, H. Watanabe and M. Ohtsu (2002), ”Damage Evaluation in Concrete Using
Acoustic Emission Method”, Proc. The 6th Far-East Conference on Non-Destructive
Testing, JSNDI, Tokyo, pp. 111-116.
276

ACOUSTIC EMISSION FOR FATIGUE DAMAGE DETECTION OF
STAINLESS STEEL BELLOWS
KOJI KAGAYAMA, AKIO YONEZU, HIDEO CHO, TAKESHI OGAWA,
and MIKIO TAKEMOTO
Department of Mechanical Engineering, Aoyama Gakuin University,
Fuchinobe, Sagamihara, Kanagawa 229-8558, Japan
Abstract
We utilized acoustic emission technique to monitor crack progression of a formed 300 mm
long stainless steel bellows subjected to cyclic axial loading with vibration amplitude of 15mm. AEs from fatigue cracks in 0.12 mm thick bellow with 10 convolutions were monitored bythree resonant type AE sensors mounted on the flange weld to the bellow ends. The bellows
failed at 26770 cycles after producing three circumferential cracks near the upper jig and lower
flange. First AE was detected at 21782 cycles. This timing was 1500 cycles earlier than the first
circumferential crack near the upper jig observed by video camera. AE count increased after
24000 cycles at which large third circumferential cracks were produced near the lower flange.
Wavelet analysis of AE signals showed largest amplitude at approximately 180 kHz at which
the group velocity is measured as 3100 m/s. Three cracks in the axial direction were correctly
located using this group velocity.
Keywords: Bellows, Fatigue crack, Wavelet analysis
1. Introduction
Bellows is a convoluted shell consisting of a series of toroidal shells, usually connectedwith annular plates called sidewalls [1, 2]. It provides additional flexibility for shell structures
such as piping and heat exchanger. For these applications, fatigue fracture is important problem
because vibration is inevitable. The critical fatigue damage is a crack initiation which allows
fluid leakage. Therefore, fatigue crack initiation must be accurately monitored in the fatigue
critical application.
In this study we evaluated fatigue strength of the bellows using AE technique. Crack initiation and propagation were successfully monitored by cylindrical wave AE [3, 4]. Source location estimated by quantitative analysis of cylindrical wave AE agreed well with the fatigue
cracks.
2. Wave Propagation Characteristic and Source Location Method
Estimation of AE source location on the bellows is much more difficult due to complex
shape of the bellows. We first studied propagation characteristics of elastic wave in the axial
direction of the bellows. Shown in Fig. 1 is shape and size of the bellows tested. In the fatigue
test, we monitored AEs by four AE sensors mounted on the one-end flange. For the source location in axial direction, we first studied cylindrical wave of the bellows. We exited elastic
waves by lead breaking on the crown top of the bellows and monitored by two resonant-type
AE sensors (PAC, Type PICO, center frequency 450 kHz) mounted on the crown top in Fig. 1.
J. Acoustic Emission, 23 (2005)

277© 2005 Acoustic Emission GroupFig. 1 Experimental setup and shape and size of bellows [mm].

Fig. 2 Waveform (the upper) and wavelet coefficient of ch. 1 and ch. 2 at 180 kHz.
Distance of channel 1 and 2 sensors was changed from 5 convolutions (total zigzag propagation distance along 5 convolutions: 96.5 mm) to 10 convolutions (193 mm). Figure 2 shows
typical AE signals from ch. 1 and ch. 2 sensors and their time transient of wavelet coefficientsat 180 kHz. We utilized the first arrival component for the location analysis.
Group velocity of the first arrive wave was measured by the time differences of first peaks
and determinded as 3100 m/s. Figure 3 shows overlapping of theoritical velocity dispersion
curves of L (0, 1), F (1, 1), F (1, 2) and F (1, 3) of a 52.6-mm diameter pipe of 0.12-mm wall
thickness and the wavelet countour map of the wave measured for the bellows. Here thediameter 53 mm corresponds to an average diameter of inner and outer diameters of thebellows. The measured velocity of 3100 m/s at 180 kHz appears to coincide the group velocity
278

Fig. 3 Group velocity dispersion curves.

Fig.4 Setup for estimation for circumferential source location.
of F(1,1) mode. Source location in the axial direction was then estimated using the groupvelocity 3100 m/s and the equivanent diameter of 53 mm.
Next we attempted to determine the source location in the circumferential direction. Figure
4 shows an experimental setup for location determination. We excited elastic wave by breaking
a pencil lead at the crown top of several convolutions from the flange. Here the convolution
number l was changed from 1 to 10. AEs were monitored by three AE sensors (ch. 1 to ch. 3)
mounted on the flange at 90˚ interval. Figure 5 shows AE signals from convolution of 1, together with their time transient of wavelet coefficient at 180 kHz. The sensors on the flange
detects first packet of the wave with large enough amplitude for the source location. Then we
estimated the circumferential locations using the arrival time difference of the first 180 kHz
component and the group velocity of 3100 m/s. Figure 6 shows location results for l = 1~10.
There observed a fairly good agreement between the given and estimated sources.

279

Fig. 5 Detected AE signals (upper) and their amplitude variations of wavelet coefficient (lower)
at 180 kHz for l = 1.
3. Damage Detection on the Bellows
3.1 Experimental Setup
Experimental setup of fatigue test
was shown in Fig. 7. A 300 mm long
bellows was attached to jigs of fatigue
machine as shown in Fig. 8. Lower jig
was fixed and upper one was actuated at
1 cycle/s with a maximum displacementof 20 mm. Fatigue of upper 10 convolutions was, however, eliminated by attaching a special jig to give the lower ten
convolutions large vibration displacement.
AEs from fatigue cracks in lower 10
convolutions were monitored by PICO
sensors mounted on the lower 10-mm

Fig. 6 Estimated source location.

Fig. 7 Setup for AE monitoring during fatigue test of type-A (left), type-B (right).
280thick flange as shown in Fig. 8. The bellows was welded to the flange. We utilized
two-types of AE monitoring method shownin Fig. 7. One of four sensors (A) and fivesensors (B) was used as a trigger signal tostop the fatigue test. Thus, AE signals weremonitored by three (ch. 1, 2, 3) and four(ch. 1, 2, 3, 4) sensors in type-A and -B
system, respectively. Sensor outputs werepre-amplified 40 dB, digitized at 200 ns
interval, and fed to a computer. The trigger
sensor signals were digitalized at 1 µs interval. Type-A and -B systems were used
for fatigue tests with 25 mm and 30 mm
displacement, respectively.

Fig. 8 Figure of specimens with jig.

Fig. 9 Relationships between load Pmax and AE timing at number of cycle N.
3.2 Results of Fatigue Tests
Figure 9 shows that load, Pmax, changes with the number of cycles, N. Figure 9(a) represents
Pmax change and AE timing over entire cycles up to 38586, and Fig. 9(b) in expanded cycle
number from 37500 to 38586. This bellows sustained cracks at the root of convolution near the
lower flange between ch. 2 and ch. 3 sensors as shown in Fig. 10. Figure 9(b) shows that thefirst AE was detected at around 37630 cycles, 1000 cycles before the test finished at 38586 cycles. We detected 7 events before 38586 cycles. Figure 11 shows AE signals and their amplitude variations of wavelet coefficient at 180 kHz. We detected AEs with high S/N ratio andlarge S0 component that are adequate for source location. In Fig. 12, damage areas are indicatedby gray tone, and AE sources of 7 events were located close to the observed crack location.

Fig. 10 Observation of fatigue crack.
281

Fig. 11 Detected AE signals (upper) and their time transient of wavelet coefficient (lower) of
type-A experiment.

Fig. 12 Source location of type-A.
In the type-B test with 30 mm vivration displacement, we monitored crack initiation by one
video camera. Figure 13 presents load and cumulative AE counts with N. We monitored thefirst AE at 21782 cycles, but no load reduction at this moment. AE increased continuously
from 21782 to 2400 cycles and rapidly increased from 24000 cycles. Figure 14(a) shows thefirst circumferential fatigue crack near the upper jig at 22486 cycles. After this timing the load
slowly decreased. Fatigue crack progressed in the circumferential direction and second crack
started at around 22800 cycles when load decreased rapidly. This second crack could not be
captured by the CCD camera since the crack occurred on the opposite side. At 24000 cycles atwhich sudden load drop and large event rate were observed, third circumferential crack was
produced near the bottom flange, as shown in Fig. 14(b). Figure 15 shows the location result oftype-B test, with damage areas indicated by gray tone. Source locations estimated after 24000
cycles were designated by open triangles. Location results agreed well with observed crack
zone. Some of sources after 24000 cycles were located in upper gray tone. This is supposed tobe due to noises by contacts of crack surface.

282

Fig. 13 Relationship between load Pmax and AE count at number of cycle N.

Fig. 14 Picture of video monitoring at 22486 cycles (a) and 24000 cycles (b), circled point iscrack initiation.
4. Conclusion
We utilized AE technique to monitor the fatigue damage of formed bellows made of stainless steel. We monitored AE signals by sensors mounted on the weld flange, and utilized themfor detecting the initiation and location of fatigue cracks.
(1) Fatigue crack initiation of the bellows at vibration amplitude of 25 mm and 30 mm werecorrectly detected by AE at early time. AE technique was useful for detecting the fatigue
crack initiation of complicated components such as formed bellows with weld flange. The
flange can be successfully utilized for detecting the anti-symmetric-mode cylindrical AEs.
(2) Source location of fatigue cracks were successfully performed utilizing the first arrival
wave components. Locations of AE source, estimated by the group velocity of 3100 m/s at
180 kHz, agreed well with the fatigue cracks.

283

Fig. 15 Source location of type-B experiment.
References
1) C. Becht IV: International journal of pressure vessels and piping, 77 (2000), 843.
2) C. Becht IV: PVP, 388, Fracture, Design Analysis of pressure vessels, heat exchangers,
piping components, and fitness for service-1999 ASME (1999), p. 197.
3) H. Nishino, S. Takashina, F. Uchida, M. Takemoto and K. Ono: J. Jpn. Appl. Phys., 40(2001), 1.
4) Hideo Nishino: J. Jpn. Soc. for Non-Destructive Inspection, 52-12 (2003), 654.
5) T. Hayashi: J. Jpn. Soc. for Non-Destructive Inspection, 52-12 (2003), 662.

284

PLASTIC REGION BOLT TIGHTENING CONTROLLED BY ACOUSTICEMISSION MONITORINGTADASHI ONISHI, YOSHIHIRO MIZUTANI and MASAMI MAYUZUMI
2-12-1-I1-70, O-okayama, Meguro, Tokyo 152-8552, Japan.
Abstract
Troubles related to bolt relaxation and fatigue have occurred in many fields. In the automotive
industry, plastic-region tightening method is recognized as a good technique for preventing therelaxation and fatigue of bolts. However, this method requires a special torque wrench and involves a complicated procedure for stable tightening. Therefore, it is important that a new tightening method and a wrench that simplifies plastic region bolt tightening are developed. Two typesof tensile tests using a torque wrench and a tensile tester were conducted for two different
strength bolts. The Mahalanobis distance (MD) technique was utilized for discriminating acoustic
emission (AE) signals from noise signals. We found that we can monitor the yield point of a highstrength bolt during loading by AE monitoring combined with the MD technique. Here, we conducted a feasibility study for controlling plastic-region tightening by AE techniques.
Keywords: Plastic region tightening method, Mahalanobis distance
1. Introduction
There have been many problems related to bolt relaxation and fatigue in many fields. For
example, an accident involving a Nozomi express train in Japan from which a bolt fell off wascaused by bolt relaxation [1]. Reliable bolt tightening is important to prevent problems and tomaintain the efficiency of a machine. In the automotive industry, a plastic-region tightening
method is known and used as a good method for preventing the relaxation and fatigue of bolts.
There are three basic bolt-tightening methods for use at a work site; torque control, angle control
and torque gradient control. The torque control method using a torque wrench is the most common method, although it cannot be applied to plastic-region tightening because of the relationshipbetween the axial force and the torque of bolts is affected by a fluctuation of the friction coefficient of bearing surfaces. Both the angle control method and the torque gradient control method
can be used for plastic-region tightening, although a special torque wrench is required and complicated procedures are involved. Thus, the applicability of the tightening method is limited.
Therefore the development of a convenient plastic-region bolt tightening method is an important
industrial necessity.
Table 1 Bolt specifications

J. Acoustic Emission, 23 (2005)

285© 2005 Acoustic Emission GroupIt is well known that some steels generate acoustic emission (AE) during plastic deformation.
Therefore, there is a possibility to control plastic-region tightening by monitoring AE signals
during tightening. Here, a feasibility study of a plastic-region tightening control using AE techniques is conducted.
2. Bolt Specifications and Experimental Procedures
2.1 Bolt Specifications
Two types of different strength bolts were tested. The specifications are shown in Table 1.
Bolt-A, made of carbon steel, has a strength designation of 4.8; which, according to JIS B 10512000 [2], corresponds the tensile and yield strength of 420 MPa and 340 MPa, respectively. On
the other hand, bolt-B, made of SCM435, has a strength designation of 10.9, indicating the tensile
and yield strength of 1040 MPa and 940 MPa, respectively. In general, in the manufacturing
process for high-strength bolts, thermal treatment is carried out after cold forging. Therefore, athermal treatment was applied in the manufacturing process of bolt-B.
2.2 Experimental Procedures
The bolts were loaded in axial tension in two ways; using a torque wrench (experiment-1)
and a tensile tester (experiment-2). Figure 1 shows the experimental setup for experiment-1. The
bolt was set to a hydro-mechanical axial tension meter and an AE-sensor was mounted on the bolt
head. An axial load was applied by a torque wrench, and AE signals and axial tension weremonitored during the tightening. Detected AE signals were amplified 40 dB by a preamplifier anddigitized by an A/D converter. To monitor just those AE signals produced by the plastic deformation of the bolts, a tensile test is conducted. Figure 2 shows the experimental setup for experiment-2. The sensor position and monitoring conditions are the same as in experiment-1. The bolt
is loaded with axial tension in a constant stroke at 1 mm/min. Only bolt-A was tested in experiment-1; both bolt-A and -B were tested in experiment-2. Sampling conditions during the bolt
tightening tests (both experiment-1 and -2) are shown in Table 2.
Table 2 Sampling conditions during bolt tightening test.
Sampling frequency [MHz]Sampling points

Trigger level [V]Measuring range [V]

25

8,192

0.02

± 4.5

Fig. 2 Experimental setup for experiment-2.

Fig. 1 Experimental setup for experiment-1.
286

3. Experimental Results
3.1 Result for Experiment-1
In this test, bolt-A shown in Table 1 was loaded using a torque wrench and axial tension andcumulative AE counts were monitored during the tightening. Figure 3 shows the relationshipbetween cumulative AE counts and the axial tension on the bolt. Apparent electronic noise wasmanually excluded before analyzing the data. The yield load calculated for bolt-A is 19.7 kN,
although changes were not observed at this load. As the AE signals were observed from earlystage of the test, most of the AE signals detected seem to be friction noise.

Fig. 3 Relationship between cumulative AE counts and axial tension
3.2 Results for Experiment-2
To remove the friction effect and to investigate the characteristics of AE signals due to bolt
deformation, tensile tests of bolt-A and -B were conducted with a tensile tester. The results forbolt-A and -B are shown in Figs. 4 and 5, respectively. The relationship between the axial tension
and elongation is also shown in these figures. The yield load estimated from the elongation-axial
diagram (solid line in Fig. 4) for bolt-A is 23 kN. Although the authors expected that the cumulative AE counts increase simultaneously with the start of plastic deformation (23 kN), it rapidly
increased only from 26 kN. On the other hand, the cumulative AE counts for the bolt-B test rapidly increased at around the expected yield load (56 kN), which is estimated from the loadelongation diagram (Fig. 5).
Figures 6 and 7 show examples of detected AE signals. Signal-(A) was monitored during thetightening of bolt-A at 27.5 kN. Signal-(B) was monitored during the tightening of bolt-B at 63.5
kN. The durations of the two signals are quite different. Total cumulative AE counts are also different between bolt-A and bolt-B. These differences may be caused by the existence of the heat
treatment, because most of the AE may have already been produced during the manufacturing
processes and few AE is observed without heat treatment.
287

Fig. 4 Cumulative AE counts and elongation during a loading test for bolt-A. The yield point isindicated by an open circle.

Fig. 5 Cumulative AE counts and elongation during a loading test for bolt-B. The yield point isindicated by an open circle.

Fig. 7 AE signal observed during bolt-B
test at 63.5 kN.

Fig. 6 AE signal observed during bolt-A
test at 27.5 kN.
288

4. Discriminant Analysis
Many noise signals were monitored during the bolt loading tests; therefore, techniques that
discriminate AE signals from noise signals are required. In this study, the Mahalanobis distance(MD) approach was examined to attain this objective.
4.1 Definition and Calculation of the Mahalanobis Distance (MD)Formally, the MD is defined by equation (1),D 2 = ((x  x ) s )  R 1  ((x  x ) s ).

(1)

where x = [x1 x2  x p ] denotes a multivariate vector of the analyzed signal (AE and noise)parameters, x = [x1 x2  x p ] denotes the sample mean of the reference AE parameters,s = [s1 s2  s p ] denotes the sample variance of the reference data and R denotes the sample
correlation coefficient matrix of the reference data [3, 4].
Figure 8 explains the method to distinguish AE signals and noise signals using MD. Here,
two-dimensional representation of multi-dimensional space is shown. Group A is assumed to be
AE signal group, Group B noise group, and it is assumed that the data of each group have distribution as shown. When the new AE data is obtained, the Mahalanobis distance between the dataand each group (DA and DB) are calculated. When the DA is smaller than DB, a detected AE data
is estimated as an AE signal (belonging to Group A). If DA > DB, the detected signal is noise,belonging to Group B.
In this study, eleven AE parameters were used for the calculation of MD. The following parameters are used (see also Fig. 9);
1. Mean and variance of amplitude (2 parameters).
2. Peak magnitude
3. AE signal rise time
4. The count using the threshold of half of the peak magnitude
5. Mean and standard deviations of a power spectrum calculated for every section of theanalysis signal divided into three parts (6 parameters).

x 2 Group A

Peak magnitude

DA

Count

DB
Group B
x1

FFT 1

FFT 2

FFT 3

( x 1 ,  s1 )( x2 ,  s2 )( x 3 ,  s3 )Fig. 9 AE signal parameters used for calculating MD.

Fig. 8 Image of MD between groups A and B.
289

4.2 Cluster Analysis for Deciding on Reference AE Data
Cluster analysis seeks similar samples by comparing sample parameters (in this study, 11 parameters were used) and by forming a homogeneous group. We used the Ward method for calculating similarity [3]. By conducting cluster analysis, the dendrogram as shown in Fig. 10 isobtained. We decided reference AE signals used for MD calculation by this cluster analysis.
Reference GroupAE Signals
Electronic Noises
Friction Noises

Fig. 10 Example of a dendrogram determined by cluster analysis.
4.3 Results of the Discriminant Analysis
Figures 11 and 12 show the relationship between the inverse of the MD and the cumulative
count of AE for bolt-A and –B tests To clearly show the results, the MD was changed into an
inverse number. Twenty-two signals randomly selected from the AE group determined by cluster
analysis were used to make a reference group. The starting point for plastic deformation is indicated by an arrow in the figures. For bolt-B testing (see Fig. 12), the highest 1/MD value is larger
than 0.3 after the plastic deformation started. This shows the possibility that MD can be used fordiscriminating AE from noise. Furthermore, the result suggests that the plastic-region tighteningof bolts can be controlled by monitoring AE and utilizing the MD technique. On the other hand,for bolt-A testing (see Fig. 11), the value of 1/MD is small even after the plastic deformation
started. Considering the result together with that in Fig. 4, it appears difficult to control the plastic-region tightening of a bolt without undertaking thermal treatment in the manufacturing process by AE monitoring.
1

(A)

0.8

Plastic Region

0.6
0.4
0.2
0
0

20

40

60

80

Cumulative AE Counts
Fig. 12 Discrimination results

Fig. 11 Discrimination results290

Fig. 13 Cumulative AE counts selected by MD and elongation during loading tests for bolt-B.
Figure 13 shows the relationship between the cumulative AE counts and tensile elongation
during the loading test for bolt-B. The difference from Fig. 5 is that the noise data are separated
from the AE signals by an MD technique. Figure 13 shows that the starting point for plastic deformation can be clearly determined by monitoring AE signals. Although not the subject of thisreport, there is the possibility for hydrogen induced cracking to occur, especially in the case of ahigh-strength bolt when plastic-region tightening is conducted. Therefore, special caution is required if there is a possibility of hydrogen induced cracking in the conditions that the bolts areused.
5. Conclusions
A feasibility study was conducted to control plastic-region tightening of bolts using AE techniques. The results can be summarized as follows;
1) Two types of tensile tests using a torque wrench and a tensile tester were conducted for two
types of different strength bolts.
2) AE signals due to deformation were hardly observed for the low strength bolt since most of
the AE signals had already been produced during the manufacturing process.
3) Cumulative AE counts of the high strength bolt increased rapidly near the yield load. This
indicates the possibility of monitoring the initiation of plastic deformation by AE monitoring.
Although the noise signals were monitored with AE signals.
4) The MD technique is utilized for discriminating AE signals from noise.
5) The yield point of a high-strength bolt during tensile load testing can be monitored by AE
monitoring combined with MD technique.
References[1] T. Hattori, Failure analysis and prevention of mechanical, adhesive and welded joint, paperdelivered at Study group on the accident/failure analysis method of JSME (2002).
[2] JIS B 1083-1990 General rules for tightening of threaded fasteners.
[3] Y. Tanaka, K. Wakimoto: Methods of Multivariate Statistical Analysis, (Japan, 1983) pp.
101-136, 229-244.
[4] M. Miyagawa: Technology for Getting Quality; What the Taguchi Method Has Brought Us,(Japan, 2000), pp. 219-252.
291

ACOUSTIC EMISSION BEHAVIORS OF RECOVERY FOR Mg ALLOYAT ROOM TEMPERATURE
Y. P. LI and M. ENOKI
Department of Materials Engineering, The University of Tokyo, Hongo, Bunkyo, Tokyo, Japan.
Abstract
Traditionally, the recovery of materials was investigated by the evolution of microstructure,mechanical properties, electronic or magnetic properties. In present research, the recovery behavior of AZ31B alloy was investigated by acoustic emission (AE) at room temperature. Afterdeformation, the specimen was attached to an AE sensor with broad response frequency. Considerable AE signals were observed and numerous AE signals appeared at the initial stage of therecovery process. Theoretical analysis was conducted on the origin and mechanisms of AE. AE
signals appear to be from the dislocation annihilations driven by the internal stress of materials
after deformation. The results also show that the cumulative AE counts are in good agreementwith a non-linear relationship derived from the traditional recovery kinetics.
Keywords: AE count, Elastic energy, Recovery, Mg alloy
1. Introduction
Acoustic emission (AE) is the radiation of stress waves by materials, in which local dynamic
restructuring of the internal structure occurs with the release of elastic energy. During the second
half of the 20th century, AE from materials was studied extensively, especially in the AE fromdeformation and fracture of materials. More recently, the application of AE was extended greatly
in fields such as thermal cycling, in which dislocation motion and microstructure damage are
generally recognized to produce significant AE, martensitic transformation where the AE signal
was thought to accompany the rapid variation in the shape of deforming volume, solidification ofliquid, weld controlling, and fatigue, etc. Nearly all of the mechanisms of AE can be explainedby one of these two approaches: dislocation motion or elastic stress release by micro-fracture ofmaterials. In general, the model of AE source by dislocation motion is adequate in explaining
and predicting the AE behavior. Schaarwachter et al. [1] successfully applied dislocation modelof AE in the deformation of copper and its dilute alloys in explaining the relation between thedislocation motion and the behaviors of AE and later this results were applied by Moorthy et al.
in explaining the AE behaviors of solution-annealed AISI type-316 austenitic stainless steel [2]in detecting micro- and macro-yielding of the materials and predicting the AE response frequency by dislocation model. All of the above have shown that dislocation motion and AE areclosely related with each other. So AE is generated from the motion of dislocations, or we can
suppose that wherever there is dislocation motion, there will be AE from materials no matter
how weak the signal strength is. It is this supposition that paves the way for the present research.
The term of recovery denotes any modification of properties during annealing before the appearance of new strain-free recrystallized grains. Investigations performed on monocrystals have
proved that recovery takes places by means of annihilation of dislocations with opposite Burgers
vectors, tightening of dislocations loops, ordering of dislocation configurations, as well as bymeans of formation of subboundaries and their motions [3]. Recovery in polycrystals, apart from
J. Acoustic Emission, 23 (2005)

292© 2005 Acoustic Emission Groupintra-grain mechanism, can also take place by means of annihilation of dislocation, points defects
on grain boundaries. From the above, we can conclude that recovery process is a source of dislocation motion and it is reasonable to expect that the recovery process should be accompaniedwith a release of elastic energy or acoustic emission. Traditionally, recovery behavior is characterized by the mechanical properties of materials such as strength, hardness, ductility or by theobservation of microscopic mechanisms in dense dislocation tangles. The observation of microscopic mechanisms by electron microscopy is difficult for the large density of dislocations involved (between 1014 and 51015/mm2) and their heterogeneous distribution, which makes theirenergy density and their individual (cross-slip, climb, solute drag) or collective behavior difficult
to handle either analytically using simple elementary dislocation models or by computer simulation. More recently, there is a growing interest in the characterization of microstructure by magnetic non-destructive techniques [4]. Structure sensitive magnetic macroscopic properties include
coercive field, permeability, residual induction and power loss derived from a hysteresis loop.
Recovery behavior of many materials can be evaluated properly by this method. However, likethe method of mechanical properties, the detection of magnetic properties of materials is not so
convenient for it has to be quenched each time after heat treatment in getting an instantaneous
value of the property. That is to say, it is difficult to get continuous and synchronized behavior ofrecovery by this method. Furthermore, it is a time consuming and expensive process especially insome alloy containing noble metals because each datum shown in the recovery dynamic curveusually needs a specimen to be tested.
There has beenmuch research about the recovery of Mg alloy [5, 6]. However, only few
studies were specially conducted in the recovery of Mg alloy at room temperature. Koike et al.
[7] reported that recovery can take place at room temperature by investigating the Kikuchi line
and the deformation curve of the AZ31 Mg alloy. They analyzed the active role of dynamic recovery at room temperature to the deformation of Mg alloy. In fact, other researchers such as
Miller in 1991 [8] and Conrad in 1957 [9] also proposed that the recovery of Mg alloy can takeplace at room temperature. The AE from phase transformation has been observed in many areas
such as martensitic transformation, solidification of liquid, and so on. Many researches haveshown that AE method is a powerful method in investigating the dynamitic behavior of phasetransformation [10, 11]. However, until now, no research of AE was performed upon diffusioncontrolled (thermally activated) structural transformations – recovery and recrystallization. So it
is the aim of this research to get the continuous and synchronized AE signal for a better and moreconvenient material evaluation method in understanding the recovery behavior of AZ31B alloyat room temperature.
2. Experimental Procedures
The materials system chosen was extruded bar of standard AZ31B Mg alloy from Osaka Fuji
Magnesium Alloy Co.. Cylindrical samples with 15 mm in diameter and 15 mm in height werepreliminarily subjected to annealing at 450oC for 1h. The samples were deformed to different
strain levels at a deformation rate of 1.5  10-2 mm/min. As soon as deformation was finished,
the specimen was attached to an AE sensor. AE sensor used in present study was low noise type
(M304A, Fuji Ceramics, Japan). AE signals were measured by DISP (PAC, USA) with athreshold of 40 dB and high-pass filter of 50 kHz effectively reducing the noise from environment. The RMS voltage of AE signal was also recorded by the AE discriminator system with again of 60 dB as shown in Fig. 1.

293

Fig. 2 RMS voltage of AE from recovery andnoise level.

Fig. 1 AE system for evaluating the recovery
behavior of AZ31B alloy.
3. Results

Figure 2 shows the RMS voltages of AE signals during the recovery stage of AZ31B alloy atstrain of 9.3% along with environmental noise level. Both the AE signals and noise were continuous waves. The RMS value of recovery decreased to the noise level eventually, which indicates that this AE system is effective in recording the AE signal from recovery.
(b)

(a)

Fig. 3 (a) Typical AE waveform, (b) the frequency spectrum (FFT).
It is interesting to note that all of the AE waveforms are similar as shown in Fig. 3. They areof low level and some kind of continuous and the peak frequency is about 550 kHz. AE from recovery is similar to that from deformation process in Mg alloy [12]. With increasing recovery
time, the amplitude or count of AE signal decreased gradually as shown in Fig. 4.
Figure 5 shows the cumulative AE event as a function of recovery time. It can be seen thatmost of the AE event occurred at the beginning of the recovery process where a steep slope in
the initial section (<2000s) exists. Later, the cumulative AE event increased slowly and reached ahorizontal stage during the final recovery stage (>4000s). It is found that no systematic relationship between the strain of AZ31B alloy and the overall AE events during the entire recovery
process exists. Probably this is due to the difference in the time of specimen transferfrom thecompression jig to AE sensor since the initial AE event rate is so high that a little difference of
294

(a)

(b)

(c)

(d)

Fig. 4 AEsignals during recovery at room temperature at strain of (a) 9.3%, (b) 8.7%, (c) 7.3%,
and (d) 5.3%.

Fig. 5 Cumulative curve of AE event vs. the recovery time.
time can resulted in a large discrepancy in the overall AE events. We will discuss it later in detail.
295

4. Discussion

4.1 Theoretical Analysis of AE from Recovery
Only limited research has focused on the recovery of materials at room temperature [7, 8],even though recovery at room temperature is critical in affecting the deformation properties ofmaterials. Some research focused on the evolution of yield stress during recovery stage [13, 14].
Friedel model [15] predicts a logarithmic decrease of the yield stress and assumes that appliedstress is equal and opposite to the internal stress of the dislocation structure. If one assumes further that the relaxation rate of this structure, and that of the internal stress, occurs by thermally
activated mechanism according todσ i / dt = − K exp(−U (σ i ) / kT )(1)

where K is a constant, and that the activation energy decreases with stress U = U 0 − υσ i , υ being
the activation volume of the elementary recovery events, then a logarithmic time decay of theyield stress isσ = σ i = σ t =0 − (kT / υ ) ln(1 + t / t 0 )and the plastic relaxation strain rate from recovery is(2)ρb 2ν DUσνexp(− 0 ) sinh( i )
(3)
M
kT
kT
where ν D is the Debye frequency, M the Taylor factor, and the U0 and ν the parameters to be
decided.
ε p =

From above equations we can think the recovery process to be a deformation process, inwhich the force is applied opposite to the direction of deformation and decreases with increasing
time, resulting into a plastic relaxation of materials. If we assume that the internal stress relaxation corresponds to dislocation annihilation and reorganization, and therefore to plastic relaxation
strain, the AE activities from recovery process can also be thought as a result of dislocation motions and annihilations applied by the internal stress.
During the recovery stage, it is also assumed that the recovery is controlled mainly by thedisappearance of the dislocations with opposite Burgers vectors (although many phenomena occurred in this process such as tightening of dislocations loops, ordering of dislocation configurations), and the disappearance of the dislocation will give rise to micro-slip in the slip plane and at
the same time a certain strain energy will be released which results in AE signal (some of thestrain energy will be released as a form of heat). During the AE generation process, it can bethought that the elastic wave was produced just as the origin of earthquake waves [16].
4.2 AE Behaviors and Recovery Kinetics
Theoretical considerations indicate that some properties of the materials are proportional to
the square root of dislocation density during recovery process, e.g., the flow stress (σ – σy) [14].
Take Ry as the fraction of recovery at a certain temperature, then
Ry = (σ – σy)/ (σ – σ0) ∝ k ρ

(4)

296

where (σ – σy) is the decrease of flow stress in recovery, and (σ – σ0) the overall decrease of flow
stress after recovery. Then a general relation is satisfied to a specific property of materials at a
fixed temperature [17] as follows:
Ry = a + b ln (t)(5)

where a and b are constants for the specific property at each given temperature at a fixed prestrain. Figure 6 shows the relative cumulative AE counts curve of both the linear and logarithmic
relationship with time. The relative cumulative AE counts increase with recovery time. In thisfigure we find a similar trend of AE at different pre-strain levels indicating the same mechanismof AE during recovery process.
Assuming the AE event counts proportional to the number of annihilated dislocation, the cumulative AE event (Ni) have the same relationship with dislocation density as event counts, andthe relative cumulative event counts, Ni/N has the following relation with dislocation:
N i / N ∝ K (ρ − ρ r )(6)
where ρ is the dislocation density just after the deformation process, ρ r is the dislocation density
during recovery. From equations 4, 5 and 6, we can get
2Ni= A ⎡⎣ ln t ⎤⎦ + B ln(t)+ C(7)N
Here A, B, C are constants. The exact meaning of these constants needs further research. The
non-linear fit results of relative cumulative AE counts are shown in Fig. 7 at strains of 9.3% and5.3%, respectively. It can be seen that the prediction of theory is in good agreement with the experimental data.

()

(a)

(b)

Fig. 6 Relative cumulative AE counts curve during recovery process in(a) a linear and (b) a logarithmic time scale.
5. Conclusions
1. AE signals were successfully detected in the recovery of AZ31B alloy at room temperature
with a high pass filter of 50 kHz.
2. Cumulative AE events depend on the recovery time. Most of the AE events were recorded at
the initial stage of recovery (<2000s), and AE event rate decreases gradually with the increase of recovery time.
297

3. The cumulative AE counts were assumed to be proportional to the dislocation density that
decreases during recovery. A non-linear equation describes the relation between cumulative
AE counts and the recovery time.

Fig. 7 Non-linear fit of the relative cumulative AE counts of Mg alloy
with strain of 9.3% and 5.3%.
References
1)
2)
3)
4)
5)
6)
7)
8)
9)
10)
11)
12)
13)
14)
15)
16)
17)

W. Schaarwächter, H. Ebener, Acta metal. Mater. 38 (1990), 195.
V. Moorthy, T. Jayakumar and Baldev Raj, International J. of Pressure Vessels and Piping,64 (1995), 161.
T. Hasegawa and U. F. Kocks, Acta metal. 27 (1979), 1705.
A. Martinez-de-Guerenu, F. Arizti, et al., Acta Materialia, 52 (2004), 3657.
C. J. Beevers, Acta Metal., 11 (1963), 1029.
J. V. Sharp, A. Mitchell, Acta Metal., 13 (1963), 965.
J. Koike, T. Kobayashi, Acta Materialia, 51 (2003), 2055.
W.K. Miller. Metall Trans A, 22A (1991), 873.
H. Conrad, W.D. Robertson, J. Metals, 1957, 503.
F. J. Perez-Reche, E. Vives, J. Phys. IV France, 112 (2003), 597.
J. Baram, J. Avissar, Scripta Metallurgica, 14 (1980), 1013.
K. Ono, Acoustic Emission-Beyond the Millennium, 2000, 57.
M. Verdier, Y. Brechet, Acta Mater. 47 (1999), 127.
E. Nes, Acta Metall. Mater., 43 (1995), 2189.
J. Friedel, Dislocation. Pergamon Press, Oxford, 1964.
K. Aki, P. G. Richards, Quantitative Seismology, theory and methods, Volume 1, 1980.
A. Martinez-de-Guerenu, F. Arizti, Acta Materialia, 52 (2004), 3667.
298

AN ACOUSTIC EMISSION TEST SYSTEM FOR AIRLINE STEELOXYGEN CYLINDERS: SYSTEM DESIGN AND TEST PROGRAM
ALAN G. BEATTIE
Physical Acoustics Corporation, Princeton Junction, NJ 08550-5303, USA.
Abstract
An acoustic emission (AE) system, designed to perform pressure tests on steel airline oxygen
cylinders, has been developed and tested. This system is similar in operation to the previously
developed Halon Bottle AE Tester. The Halon tester source location algorithm was modified tolocate events on the surface of cylinders with hemispherical end caps instead of on spheres. The
proof load on the cylinder in this system is applied by gas pressurization instead of heating ahermetically sealed container as was done in the Halon System. A prototype of the AE Oxygen
Cylinder Tester was built and used to test a random set of 72, 115 cubic-foot (3.26 m3) steel oxygen cylinders in a commercial oxygen bottle test facility. The bottles belonged to Federal Express (FedEx) and had been sent to the facility for their scheduled triennial hydrostatic tests.
This testing program indicated that the cylinder test system works well and has adequate sensitivity to detect growing flaws in the cylinders. However, in contrast to Halon bottle tests, there
was absolutely no indications of flaw growth in any of the cylinders at pressures up to 30 %above their normal fill pressure. The results suggest that the required 3-year cycle for pressure
testing these bottles is excessive and that there is no reason to arbitrarily limit their service life to
24 years.
1. Introduction
Commercial airliners carry steel cylinders containing pressurized oxygen gas. This gas isused for emergency respiration of the passengers and crew in the event in the failure of the cabinpressurization at high altitudes. Many modern airliners have substituted short-term sources ofchemically produced oxygen for the passengers but all carry oxygen cylinders for the crew.
United States Department of Transportation regulations required an inspection of the pressure
boundary of these cylinders every three years. The only currently accepted test is the hydrostatic
test, where the cylinder is pressurized with water to twice its working pressure. The volumetric
expansion of the bottle is measured and the bottle is then depressurized and the volume measured
again. Any increase in bottle volume in the second zero pressure measurement over the first is
attributed to inelastic expansion. Too high a value of inelastic expansion will result the cylinder
failing the test.
Practically, steel airline oxygen cylinders are removed from service for several reasons.
These are: obvious physical damage to the cylinder or its threaded connection for the valve, visual determination of excessive corrosion, inside or outside the bottle, and exceeding an arbitrary
life span, currently set at 24 years. Failure of the hydrostatic test is extremely rare. Most apparent failures due to excess inelastic expansion have been determined to have been caused by operator errors during the test. The author has anecdotal knowledge of one cylinder, which blew up
during pressurization. The failure of the hydrostatic test to find defective cylinders is not surprising. The test was designed over sixty years ago specifically to detect cylinders, which had
experienced significant wall thinning due to excess corrosion. Better process control by the
J. Acoustic Emission, 23 (2005)

299© 2005 Acoustic Emission Groupmanufacturers and better maintenance procedures by the airlines eliminated this problem many
years ago.
This development program started with the goal of testing oxygen cylinders in situ in the airplane by over-pressurizing the cylinders with breathing oxygen. Real-time AE monitoring was tobe performed during the pressurization. Three problems arose. First, to pressurize the cylinders
in the plane with breathing oxygen requires a portable oxygen supply and oxygen booster pump.
Second, access to the valve on the mounted oxygen bottles was sometimes difficult and the distribution plumbing in multiple bottle installations was not always easy to work with. Finally,
there was little enthusiasm for introducing 15.9 to 18.6 MPa* oxygen gas into an airplane. Toomany materials become highly flammable when subjected to pure oxygen at these pressures. A
revised program was designed to test the system and develop failure criteria by testing a set of
200 oxygen cylinders in an airline bottle shop. This program was not implemented, partly from amisunderstanding over the requirements of the test procedure but primarily because of an increasing workload in the bottle shop. An agreement was finally reached with a commercial
testing facility to test with AE cylinders sent to them by FedEx for their routine triennial inspection in return for an extra fee for each cylinder involved. In reaching this agreement, the decision was made to pressurize with nitrogen gas instead of oxygen since the bottles were received
empty by the facility. This paper will cover the development of the AE oxygen cylinder test
system and the testing of 72 oxygen cylinders just before they were subjected to a hydrostatic
test. The testing program was ended when the AE system developed electronic problems. The
test results from both this set of bottles and a dozen preliminary experiments did not find a single
locatable AE event at pressures exceeding the 12.8-MPa fill pressure in any of the 84 bottles.
(* 1000 psi gas pressure = 6.9 MPa)
2. System Design
The oxygen cylinder test system is based upon the previously developed AE Halon Bottle
Test System [1]. The Halon bottle system uses a six-channel Physical Acoustics Corporation(PAC) Mistras system to monitor the spherical Halon bottles. This monitoring is performed as
their internal gas pressure is raised by heating them to 62.8°C. The system locates AE sources
using a nonlinear least-squares solution of an over-determined set of analytic equations in a
spherical coordinate system. As each source is located, it is checked to determine whether it is amember of a cluster of AE events and each cluster, as it develops, is checked for its rate of
growth as a function of temperature (pressure). There were three necessary changes to adapt thissystem for use with oxygen cylinders. First, the location algorithm had to be changed to calculate the shortest distance between any two points on a cylinder with spherical end caps. Second,a gas handling system had to be substituted for the industrial oven. Third, a new fixture to position and hold the sensors on the cylinder had to be designed. The whole system is shown in Fig.
1.
3. Location Program
The calculation of the shortest path length between any two points on a cylinder with spherical end caps is not a trivial problem. The coordinate system that was decided upon is a modified
spherical coordinate system where each point is specified by the angles theta and phi and a radial
vector and is constrained to lie either on the surface of the cylinder or on one of the hemispherical end caps. The cylinder surface is defined by the radius (cylinder and hemispheres), and thelength of the cylindrical portion of the surface. The approach taken is to divide the path into up
300

Fig. 1 Oxygen cylinder test system.
to a maximum of three separate pieces: the section on the top hemisphere, the section on the cylindrical surface and the section on the bottom hemisphere. The minimum distance on each surface is easily calculated. These paths are great circles on the hemispherical surfaces and astraight line on the unrolled cylindrical surface. The ends of the lines are joined at the junctionof spherical and cylindrical surfaces. The shortest distance on the surface is defined when thephi derivatives of the great circles on the hemispheres and the spiral line on the cylinder are
equal at the points of intersection. The resulting equation to be solved for the points of intersection is analytically complex (involving both the angle and trigonometric functions of the angle),
so a numerical routine was written to solve for the coordinates of such points of intersection.
The distances calculated between two specified points on the surface are then used by the same
nonlinear least-squares fitting routine used in the Halon Bottle tester. The same clustering routine
used in the Halon Bottle tester is also used to determine the severity of the located emission.
Extensive testing with lead breaks on the surface of oxygen cylinders showed that the locationand clustering programs worked well and that the actual deformation of the end caps of the oxygen cylinders from perfect hemispheres had only minor effects on the location accuracy. Whilethe calculation of the source location can involve a very large number of steps, the program ran
sufficiently fast on a 160-MHz computer to keep up with moderate data rates. With the increases
in CPU speeds during the last few years, computation speed is no longer a problem for this typeof program.
4. Gas Handling System
The proof load for the oxygen cylinders is provided by pressurized gas instead of the heatingof a sealed sphere, used in the Halon Bottle system. The original intention was to pressurize the301cylinders using aviation-grade oxygen so that the cylinders would neither have to be emptied nor
cleaned. This would still be a possibility if the system were installed in a hanger where cylinders
were removed from the planes. However, the results of this series of tests along with extensive
conversations with bottle shop personnel indicate that the major problem with oxygen cylinders
is wear on the valve parts and not deterioration of the cylinder itself. Therefore if the valve is to
be inspected, the bottle must be emptied and a cheaper and safer pressurizing medium than oxygen gas can be used.
The system was designed to use pressurized gas, originally introduced into the cylinder
through the valve. The use of the standard valve for the cylinders presents some problems due toits incorporation of two sintered metal filters. These filters give some resistance to high gas
flows. However, the main problem with these sintered filters is that they are acoustic noise generators during high gas flows. Thus, the fill rate had to be relatively slow to keep the noise level
down. Even when using a valve without filters, there is a large amount of acoustic noise during
the initial pressurization to about 3.5 MPa for pressurization rates between 0.7-1 MPa/min. This
flow noise proved useful as an additional verification that all sensors were working satisfactorily
during the test and did not produce locatable events.
The gas handling system, shown in Fig. 2, consists of a needle valve to control the input flow
rate, two solenoid valves, a check valve to prevent cylinder gas from flowing into the gas source,
and a pressure gauge. The needle valve is used to control the pressurization rate. One solenoid
valve controls the input gas and the other is a dump valve which both reduces the pressure at theend of the test and acts as a safety valve to rapidly reduce the pressure in the cylinder if the system should detect a growing flaw in the cylinder. The computer constantly monitors the gaspressure and controls both solenoid valves. The dump solenoid valve can also be operated
manually.

Fig. 2 Gas control system.
If the standard oxygen cylinder valve is used, the pressure gauge is separated from the cylinder itself by the two sintered filters when the valve is open. The actual pressure drop across theentire valve assembly when gas is flowing at the desired rate is less than 35 kPa, and this drop
moves toward zero as the pressure inside the cylinder approaches the test pressure. The use of
dry nitrogen gas as the pressure medium allows the use of a valve without the sintered filters andremoves any detectable pressure drop across the valve assembly.
302

5. Sensor Fixture
The sensor fixture both positions the sensors on the oxygen cylinder and holds them inspring-loaded contact with the cylinder. Many possible sensor configurations were tried. The
configuration which gave the best results with the non-linear least squares location program consisted of two rows of sensors around the circumference of the cylinder portion just adjacent tothe cylinder-hemisphere junction. The sensors are spaced at 120° intervals. Studies showedslightly better location when both rows had the same phi angles instead of one row displaced by
60° from the other. Using the same phi positions for both rows made for a much simpler portable fixture so this lay out was used in the fixture, shown in Fig. 3. The PAC Nano-30 sensors
are contained in magnetic hold-downs, which are mounted on about 260° arcs of spring steel.

Fig. 4 Sensor holder.
Fig. 3 Sensor mounting fixture.
The arcs are attached to a stainless steel bar at the correct spacing for the cylinders under test
(this design requires a different fixture for each size of oxygen cylinder. It was dictated by theoriginal requirement for the capability of testing the cylinders while mounted in the aircraft).
The six PAC 110-5015 preamplifiers are mounted on the end of the stainless bar. They have again of 40 dB and a band pass of 250 to 500 kHz. The 300-kHz center frequency of the sensorpreamplifier combination was designed to be relatively insensitive to the gas flow noise.
The magnetic sensor hold-downs are diagramed in Fig. 4. The magnet has a diameter andheight of 22.2 mm with a holding force of 40 N. This was a compromise between relatively easy
handling of the fixture and sufficient force to have a strong spring for adequate coupling. A
holding force of 90 N would have been more satisfactory from a coupling point of view. However, trying to change position of the fixture or detach all six magnets at once would have beenquite difficult. A nylon sleeve glued inside the magnet and a compression spring inside thesleeve completed the assembly. The sensor is slipped inside the nylon sleeve, compressing the303

spring and a retaining clip placed under the coaxial cable. The hard rubber disk glued onto thesensor is about one mm thick and aids in the acoustic coupling to the cylinder. A thin film ofsilicone grease is also used as the spring and magnet are not strong enough to insure effective dry
coupling.
6. Autosensor Test
A successful AE test is a negative test. One looks for the absence of AE in a good test specimen. For an automatic AE test, it is essential that the system is known to be working. In thisprogram, the first step after the operator starts the test is to perform an Auto Sensor Test (AST).
The program will not start the pressurization until this test is passed. The AST test consists ofeach sensor being excited with twenty sequential electrical pulses. These pulses generate strong
acoustic signals in the specimen, which are detected by each of the other sensors. The detectedsignals have their AE parameters at each detecting sensor averaged over the twenty pulses. The
oxygen bottle test program uses only the signal peak amplitude, which is measured in decibels,
to determine whether the AST test is passed. The averaged peak amplitudes received by all the
sensors from each pulsed sensor are themselves averaged (six sensors times five transmitted signals equal 30) and then the five signals received by each individual sensor are averaged. To passthe AST test, the average from each individual sensor has to be within ±4 dB of the overall average. This states that the average sensor peak amplitude has to be within ±60 % of the overall average. This is a fairly stringent requirement when differing path lengths, coupling efficiencies
and minor variations in sensor calibrations are considered. The Mistras system has a dynamic
range of over 80 dB and many AE tests show signal strength variations of over 60 dB. Extensive
testing with pencil-lead breaks on the surface of these cylinders have shown that the location accuracy with a system, which passes this AST test, will locate the signal source with an accuracyof about ±4 mm.
7. Total AE System
A block diagram of the system is shown in Fig. 5. The AE system is a standard six-channel
PAC Mistras system. The computer and three Mistras cards are mounted in an industrial case.
An Iomega pressure readout feeds the computer and electric signals from a Mistras card operate
two solid state relays which control the solenoid valves. In accordance with the original design
specifications, the relays, Mistras cards and pressure readout are connected to the solenoid
valves, preamplifiers and pressure gauge through a 15-m umbilical cord. This cord consists of
six UG-316 coaxial cables and wiring to carry the 115V ac to the solenoids as well as the excitation and output voltages of the pressure gauge. If the system had been used inside an aircraft, a
15-m flexible pressure hose would also have been included in this umbilical cord. The preamplifiers are powered by the Mistras cards through the signal coaxial cables.
8. Pressurization System:
Arrangements were made with AV-OX Co. in Louisville KY to operate the Oxygen Bottle
Tester on one hundred 115 cubic-foot oxygen cylinders owned by FedEx, which were sent to
AV-OX for their normal triennial hydrostatic test. The cylinders are delivered to the company
empty so there was no compelling reason to use oxygen as the pressurizing medium. Therefore
dry nitrogen gas was used instead. This decreased the cost of the tests and increased their safety.
A booster pump and containment cage was installed at AV-OX. The booster pump allowed theuse of most of the gas in the commercial nitrogen cylinders. A valve without sintered304

Fig. 5 A block diagram of the test system.
filters was installed in each cylinder under test and the pressurized gas fed in through it. Thisvalve was fully open during the testing. All gas flow adjustments were made with the needle
valve in the gas control fixture. This needle valve proved more sensitive than anticipated soonce set, it was left alone. At the end of the test, the cylinder was bled down to atmosphericpressure. No attempt was made to recycle the nitrogen gas. Gas pressure was monitored by a
Heise gauge on the supply line and the pressure gauge in the gas handling fixture. In operation,
the supply pressure was set to 17.9 MPa to insure a steady pressurization rate.
9. System Operation
The duties of the test operator were to install the nitrogen valve in the test cylinder, place thecylinder in the safety cage and connect the gas line from the gas handling fixture. The sensor
fixture was then placed on the cylinder and all sensors checked to insure that they were properly
seated. To produce the best coupling of the sensor to the cylinder, a light coating of silicone
grease was applied to the rubber disk on the sensor face. This would not have been necessary
with stronger magnets and coupling springs. Finally the operator enters the cylinder serial number into the computer on the table shown in Fig. 6. The entries in italics were the only information entered by the operator.
After checking to make sure the pressurization system is working and all the valves are in thecorrect position, the operator starts the computer program. The first operation is the AST test. If
it is not passed, the computer asks the operator to check the sensors and try again. It should be305noted that an AST test failure will be caused by any type of operational failure in the system aswell as by poor acoustic coupling of the sensors. Once the AST test is passed, the computer
opens the solenoid fill valve.
1
2
3
4
5
6
7
8
9

AirlinePart Number
Cylinder Size
Final Pressure (psi)Cylinder Serial #Hold Pressure (psi)Hold Time (sec)
FAA Center
Test Facility

Federal Express
ABC-01234
E – 115 ft 3
1600
09333
2400
120
QX3D80SL
AV-OX

Fig. 6 Computer entry table.
During the pressurization, the initial gas flow produces a large amount of low amplitude AE.
This flow-produced emission usually falls below the detectable threshold of 30 dB by the timethe internal pressure of the cylinder reaches 2.8 MPa. This creates a moderately large data file
but has the advantage of showing that AE system was actually working during the fill. Oncestarted, the system is constantly searching for locatable emission sources. However despite
many hundreds of detected emissions during the initial fill stage in each of the tested cylinders,the arrival sequences of gas-flow-produced AE signals have never produced a located emission
source.
The AE test continues until the internal pressure of the cylinder reaches 16.56 MPa (2400
psi). Then the computer closes the fill valve. AE monitoring is continued for another two minutes. The program then evaluates the entire test and decides whether the cylinder has passed or
failed the test. The operator is asked to print out the report and the program then opens the dump
valve until the internal pressure falls below the normal operating pressure of 12.8 MPa (1850
psi). At this point, the test is over and the operator can either manually open the dump valve todrain the remaining gas or shut the cylinder valve, disconnect the cylinder from the sensor andgas control fixtures remove it from the protective cage and drain the remaining gas from thecylinder in another location.

Fig. 7 First and second pressurization of a corroded cylinder. Located AE events vs. pressure.
306

10. Experimental Results
Seventy-one FedEx cylinders were tested as they arrived at AV-OX for their triennial hydrostatic tests. One cylinder from another airline, which had been mistreated (probably exposed tosalt water) during the removal and shipping process, was added to this test set. This cylinder had
visually identifiable corrosion spots on the inside surface and was scheduled to be returned to thecylinder manufacturer to have its inside surface reworked. It was the closest thing to a bad cylinder, which was available for testing. The prototype system was operated primarily by one employee of AV-OX. The testing was on a sporadic basis, being carried out whenever FedEx cylinders arrived for their scheduled hydrostatic test. The only cylinder, which was deliberately selected for testing, was the corroded one.

Fig. 8 Locations of AE sources on the corroded
cylinder. First pressurization.

Fig. 9 Peak amplitude distribution oflocated AE at the first hit sensor.

At the beginning of the pressurization all of the cylinders produced low-level AE. This emission decreased in amplitude to below the 30 dB threshold below 4.2 MPa. All sensors were excited by this flow noise. However these randomly occurring signals never generated a sensor hit
sequence with delta times which would locate on the surface of the cylinder. Two bottles
showed a small gas leak in the piping connection to the valve at ~16 MPa. Again no locatableemission was produced by these leakage signals. Very few AE hits were seen above the pressures where the flow noise ended.
The final result of this testing was that none of the FedEx bottles produced any locatableemission at any pressure. During set up of the system, several pencil-lead breaks were done at
the cylinders at 16.6 MPa (2400 psi) and all were located by the system. The results for the corroded cylinder were quite different. Locatable emission started occurring at 1.5 MPa (220 psi).
The arbitrary safety limit written into the program was reached at 3.3 MPa (470 psi) and the program terminated the test automatically. In order to determine how badly the corrosion affected
this cylinder, the pressure was released and the test was restarted. Locatable emission returned at
4.5 MPa (620 psi) and continued at a lower level until the last locatable emission occurred near 7
307

MPa. This cylinder was then quiet up to the maximum pressure of 16.6 MPa (2400 psi). Figure
7 shows the located emissions plotted against pressure for the first and second pressurization ofthis cylinder. Figure 8 shows the location of emission sources on one view of the cylinder. Visual observation of the interior of the cylinder had shown most of the corrosion to be near the top,in agreement with Fig. 8.
11. Discussion:
The locatable emission in the test of the corroded cylinder is attributable to corrosion particles fracturing or flaking off as the metal wall of the cylinder elastically expanded with increasing pressure. Once all of the particles which could be affected by the expansion of the cylinder
had been affected, emission ceased. It is apparent that the corroded spots had no measurable effect on the strength of the cylinder. The test of this cylinder is important as it demonstrated thatsystem is capable of detecting and locating AE produced by a cylinder. Figure 9 shows the distribution of peak amplitudes of the first hit sensor for these located events. The large majority
have peak amplitudes which lie between 32 and 40 dB. It should be noted that 40 dB corresponds to peak amplitude out of the sensor of 100 V. Therefore the system is locating well on
signals with peak amplitudes between 30 and 100 V. For a comparison, a pencil-lead break on
one of these bottles generates a signal with a peak amplitude of approximately 70 dB or 3000
V.
It is assumed that these cylinders are normally filled to a pressure of 12.8 MPa at 21°C. It isfurther assumed that these bottles will not exceed a temperature of 43.5°C in normal usage. The
test pressure of 16.6 MPa is then 21% greater than the 13.7 MPa, which one of these bottles
might reach at 43.5°C. It can be concluded from these tests that there was no microscopic crack
initiation or growth in these cylinders at a pressure of 20% above the maximum pressure that thecylinder could have experienced in normal operation.
During the preliminary experiments on the development of the AE Oxygen Cylinder Tester,
7 cylinders, which had been retired from service because they had exceeded the DOT mandated
life span of 24 years, were hydrostatically pressurized to 18.6 MPa. No emission was seen
which could be attributed to the cylinder itself. One cylinder then had a depression etched in its
side approximately 6.4 mm wide by 51 mm long by 2.5 mm deep. This cylinder was hydrostatically pressurized to 26.2 MPa. Again, no emission was detected from the cylinder. These preliminary tests support the conclusion that no evidence of any flaw growth was seen in these cylinders as well as the conclusion that these cylinders are quite conservatively designed.
12. Conclusions
1. The AE Oxygen Cylinder Tester will detect and locate AE signals from flaw growth inairline steel oxygen cylinders. The level of detection appears more than adequate to detect flaw growth long before there is any danger of rupture. A reproof test by an AE
tester would give a greater margin of safety than a hydrostatic test.
2: The original premise of having the cylinders tested in situ in the airplane is impracticaland probably not cost effective.

308

3. There was no indication at all of detectable flaws in any of the cylinders tested. Normal
handling procedures in the airline industry appear adequate to prevent damage to these
cylinders.
4. The three year inspection cycle for these cylinders does not appear necessary. Visual inspection of the cylinders when the valve is serviced should be more than sufficient to insure safety of the cylinders.
5. The arbitrary 24 year service life for these cylinders does not appear justified. A wellmaintained cylinder should have an indefinite life span.
Acknowledgement
The author would like to thank Kamran Ghaemmaghami of Federal Express for his counsel,
encouragement and aide during the course of this program. Richard W. Anderson of the ATA
has helped keep the program going despite setbacks. Paula and Ed Wilden were instrumental insetting up and conducting the testing program at AV-OX Inc. and David Croan of AV-OX did
most of the actual testing.
The development of the Oxygen Bottle Tester was funded by the Air Transport Association.
AV-OX Company provided space, materials and labor and were recompensed in part by Federal
Express, which provided an extra fee to them for each cylinder tested.
Reference
1. Alan G. Beattie “An Acoustic Emission Tester for Aircraft Halon-1301 Fire Extinguisher
Bottles,” Journal of Acoustic Emission”, 15 (1-4) 1997, 63-68.

309

DEVELOPMENT OF IN-SITU MONITORING SYSTEMFOR SINTERING OF CERAMICS USING LASER AE TECHNIQUE
S. NISHINOIRI and M. ENOKI
Department of Materials Engineering, The University of Tokyo,
7-3-1, Hongo, Bunkyo, Tokyo 113-8656, Japan.
Abstract
Acoustic emission (AE) technique is useful for in-situ detection of micro-fracture in materials. However, a piezoelectric sensor has a limitation in application at elevated temperatures because PZT has Curie point. We have investigated a non-contact AE technique using laser interferometer as a sensor. Cracking in firing of structural ceramics presents serious fabrication problems. Cracks form in the heating, sintering or cooling step of firing, and their initiation isstrongly affected by shape, density, particle size and firing pattern. It is important to investigate
when cracks occur for the optimization of firing condition. In this study, we firstly developed a
non-contact in-situ monitoring system for firing of ceramics. We tried to monitor the cracking in
firing of alumina (Al2O3) with the maximum temperature of 1600°C. A sample was heated inelectrical furnace and probe beam was irradiated on surface of SiC disc inserted between thesample and the furnace through small opening. Developed monitoring system could successfully
detect cracking in firing. This system can be applied to AE measurement of samples above
1500°C. It was demonstrated that laser AE system could detect internal cracks, which did notreach to surface of sample in firing process. This means that integrity of sintered ceramics may
be ensured based on non-contact AE measurement.
Keywords: In-process monitoring, Laser AE technique, Firing, Ceramics, Alumina
1. Introduction
In recent years, the production of large advanced engineering ceramic parts has been needed
for industries. It is very important to inhibit the generation of surface or internal cracks during
firing of these ceramic components. These cracks are caused by several factors: one is a thermal
stress due to uneven temperature distribution of a sample during heating or cooling. The other isthe difference in shrinkage rates between the surface and interior of a sample during sintering.
Therefore, it is difficult to optimize the firing of complicated ceramic parts from empirical findings. Our co-workers have investigated the strength and toughness of partially sintered alumina
during firing [1]. It was reported that the strength and deformation behavior of alumina change
dynamically during firing. Thus, it is difficult to predict cracking during firing. There are somereferences about the simulation for shrinkage of alumina during firing [2]. As actual cracking
condition is complicated, in-situ detection of micro-cracks is useful in evaluating the failure ofceramics.
Acoustic emission (AE) technique is useful for in-situ detection of micro-fracture in materials. However, piezoelectric sensor has a limitation in application at elevated temperature because
PZT has Curie point. Many studies have detected micro-fracture at elevated temperatures using
waveguide and cooling of specimen near sensor for such experiments. However, the use ofwaveguide causes distortion of propagating waveforms and quantitative analysis of AE using
J. Acoustic Emission, 23 (2005)

310© 2005 Acoustic Emission Groupsuch modulated waveforms is difficult. In addition, contact of sensor may damage brittle or soft
samples. Therefore direct detection technique applicable even at elevated temperatures is needed.
We have investigated a non-contact AE technique using laser interferometer as a sensor [3,4]. Surface vibration can be detected as frequency shift of laser beam based on Doppler effect oflight. The intensity of beat signal detected using photo diode is converted into velocity bymodulator. Laser AE technique has some advantages: such as absolute measurement, applicability for measurement under severe environment and measurement at small areas. We applied thistechnique to monitoring of various failure processes and in-process monitoring of fabrication [57].
The first objective of this study is to develop a non-contact in-situ monitoring system for firing of ceramics. Then we monitor the cracking in firing of alumina with the maximum temperature of 1600°C, and investigate the cracking process of alumina during firing based on detected
AE signals with single-channel inverse analysis of waveforms.

Fig. 1 Experimental setup for in-process monitoring of firing of ceramics using laser AE technique: (a) a side-detection system, (b) a bottom-detection system.
2. Development of Measuring System
2.1 Configuration of Measuring System
Figure 1(a) illustrates a side detection type system. Alumina compact was fired in a box-type
electric furnace (KBF524N, Koyo Thermo System Corp.). Temperature control and measurement of the furnace were carried out using B-type thermocouples. Probe beam is irradiated to theside surface of a sample through a small opening (15-mm diameter) on a side of the furnace. AE
events during the firing were detected using a heterodyne-type interferometer (AT-0022, Graphtec Corp.) as a non-contact AE sensor. Low-noise demodulator (AT-3600S, Graphtec Corp.) wasused for the measurement of out-of-plane surface velocity with 1 mm/s/V sensitivity. In order to311

reduce the noise level, output signals were filtered with a high-pass filter (HPF) of 500 Hz andlow-pass filter (LPF) of 200 kHz. Detected AE waveforms were recorded by AE analyzer(DCM-140, JT-Toshi Corp.) with the velocity threshold of 0.15 mm/s. Filtered signals were also
fed to a discriminator and then averaged signal (root-mean-square value (RMS) of the surface
velocities) were recorded using a data logger (NR-1000, Keyence Corp.) with temperature inside
the furnace.
We also developed bottom detection type system as shown in Fig. 1(b). Alumina compact
was sintered in an elevator type electric furnace (NHV-1515D-SP, Motoyama Corp.), placed on
an alumina block and on the bottom plane of the furnace. Probe beam is irradiated to the bottom
surface of sample through an opening of 15-mm diameter. Other AE measurements and measuring conditions are identical to the side-detection system.
2.2 Evaluation of AE Detectability
Figure 2(a) shows the change in noise with temperature measured on the mirror-polished SiC
plate using the side-detection system. Because of convection in the firing chamber and opening,
noise increased with temperature. Periodic variation due to mechanical or electric noise was also
observed. In addition, large deformation due to sintering may cause the defocusing of probe
beam.

Fig. 2 RMS of surface velocity on measuring plane as a function of temperature in the furnace,(a) side-detection type, (b) bottom-detection type.
Figure 2(b) shows the change in noise with temperature measured on SiC plate using thebottom-detection system. This result is different from the RMS value of the side-detection system. There is no increase of noise even at elevated temperatures, and the periodic variation innoise disappeared. This means that the convection in the furnace does not affect AE measurement. In addition, defocusing of probe beam due to deformation during firing may be suppressed. Thus it was expected that directly detected AE signals at elevated temperatures with alower threshold of recording was possible.
2.3 Final Setup for In-process Monitoring of Firing
There was still a serious problem in direct detection on alumina surface with the bottomdetection system. That is the warping of a sintered sample. The cause of warping was local
312temperature distribution of the sample. We carried out several additional improvements of thesystem to reduce the temperature variation around the opening. SiC plate of 5-mm thickness wasinserted between the sample and furnace, and the size of opening in contact to the SiC plate was
reduced. From these improvements, the warping of a sample was successfully inhibited. Therefore, in-situ noncontact AE measurement in firing was conducted with this improved bottomdetection system.
3. AE Measurement during Firing
3.1 Preparation of Specimens and Firing Conditions
Two types of alumina, fine grain powders and mixed grain powders, were prepared as raw
materials. The purity of each powder was 99.9 % and 99.5 %, respectively. Green compacts wereformed by uniaxial press and cold isostatic press (CIP). All compacts were heated up to 500°C toremove binder. Then rectangular samples of 35 x 35 x 20 mm were cut out. Surface of samples
were polished to surface roughness Ra of 0.8 m. Unless otherwise noted, tested samples aresmooth. In some samples a center notch of depth of 15 mm by a blade of thickness of 0.1 mm
was machined.
Samples were fired in the elevator-type electric furnace. Maximum temperature was 1600°C.
Heating rates were 800 K/h or 1600 K/h. After holding at 1600°C, samples were air cooled untilroom temperature. Holding time was 0, 30, 60 or 120 min.
3.2 Estimation of Microcrack Size
Single-channel inverse analysis was applied to estimate the microcrack size using a deconvolution method. In this study, the breaking of a pencil lead was used as the simulated AE signal
source. With assumption of mode-I type penny-shaped cracking, the Green’s function of the media was calculated from the simulated AE signal by the response waveform detected at the epicenter of the media and converted numerically to a dipole from the monopole source. The crack
radius, a, was estimated as follows [3]:1

 3(1  2 ) D0  3
(1)
a = 
2 16(1   ) 0
where D0 is the first peak of source function, 0 is the strength for microfracture and  is the
Poisson’s ratio, respectively. Properties used in calculation were that of fully-sintered alumina( = 0.23, 0 = 340 MPa).

4. Results
3.1 Observation of Fracture
Figure 3(a) shows a fractured smooth sample after firing. Observation shows that cracks were
initiated at the center of the sample and then propagated in a radial pattern toward edges. Figure3(b) shows a fractured notched sample after firing. Cracks propagated to the bottom surface ofthe sample from the notch tip.

313

Fig. 3 Observation of fractured samples after firing; (a) a smooth sample, (b) a notched sample.

Fig. 4 Temperature history and crack radii as a function of temperature and time. The mixedgrain sample was fired with heating rate of 1600 K/h, holding time of 120 min.

Fig. 5 Cumulative AE events as a function of generation temperature with different th; (a) resultsof mixed-grain samples with heating rate of 1600 K/h, (b) results of fine-grain samples withheating rate of 800 K/h.
314

4.2 AE Behavior
Figure 4 shows a typical AE behavior during firing. AE events were detected only in brittle
region (below 900°C) during cooling. Within tested firing conditions, no AE signal was detectedduring heating or sintering period of firing.
Figure 5 shows the change in cumulative AE events as a function of cooling temperature (AEgeneration temperature) for various holding time, th, at the maximum temperature. Generation
temperature of the first AE event increased with the increase in holding time regardless of heating rate. Cumulative AE events also increased with the increase in holding time. Although AE
signals were detected in samples fired with heating rate of 800 K/h and holding time of over 60
min, no surface crack was observed. Some of these samples cracked a few days later.

Fig. 6 Crack radius as a function of generation temperature of samples with different grain,
heating rate, holding time and notch type; (a) mixed grain, 1600 K/h, 30 min, both notched ( )and smooth (triangles), (b) fine grain, 1600 K/h, 120 min, smooth, (c) mixed grain, 800 K/h, 120
min, smooth.

°Fig. 7 Crack radius as a function of generation time. Firing conditions of the samples were sameas indicated in Fig. 6(a-c).
Figure 6 shows the relation between generation temperatures of AE signals and crack radii
estimated by the inverse analysis of AE. The generation temperature of the first AE of notchedsample was higher than that of smooth one, shown in Fig. 6(a). No AE signal was detected in thenotched sample after generation of the microcrack radius of over 1 mm, while some AE events315

were detected in the smooth sample. Smooth samples with 120-min holding time in Fig. 6(b) and(c) also demonstrated a similar behavior, and many AE events with relatively small microcracks
were detected compared with the case of 30 min. Most frequent crack size in Fig. 6(b) and (c)was about 250 and 300 µm, respectively. No visible surface crack was found in the mixed-grain,smooth sample (Fig. 6(c)) after firing, while the other samples were cracked after firing. Figure 7
shows the relation between crack generation time and crack radii. Microcracks with generation
time of 1.5-5.5 µs were found, and larger cracks had relatively longer generation time.
5. Discussion
5.1 AE Detectability during Firing
As shown in Fig. 4, AE signals during cooling were successfully detected with the developed
in-process monitoring system. Although the temperature of measuring plane of SiC plate reachedapproximately 1500°C during firing, it was demonstrated that AE signals could be detected evenat elevated temperature using non-contact laser AE technique. As shown in Fig. 2(b), the change
in noise level at the measuring plane through firing is small. If microcracks generate during
heating or sintering period, they will be detected as well as microcracks during cooling period.
As shown in Figs. 6 and 7, microcracks with radius of 150-1500 µm and generation time of 1.55.5 µs could be detected using the measuring conditions for long-term stable recording. Because
observed surface cracks after firing were of the mm-order, the initiation and growth of microcracks seems to be detectable using this technique within tested firing conditions and geometryof the sample. Applicability for larger samples remains to be solved. As described above, AE
signals were also detected when no surface crack was observed on samples fired with lower
heating rates and longer holding times. This means that internal microcracks during firing can bedetected using this technique.
5.2 Firing Conditions and Cracking Behavior
Because of stress concentration at the notch tip, the notched samples will be easily cracked.
Therefore generation temperature of the first AE of the notched sample (shown by circles) was
higher than that of the smooth sample (triangles), as shown in Fig. 7(a). In addition, only a few
AE events were detected and no AE event was detected after the generation of larger microcracks in the case of notched sample. The difference in crack path of samples as shown in Fig.
3 can be described based on the difference in these AE behaviors. Several microcracks generated
near the notch in the notched sample and then a main crack from the notch propagated throughthe specimen. Microcracks initiate in the center of specimen in the smooth samples and a crack
gradually propagates toward the surface. Visible cracks on the surface may not be found in somesintering conditions because a crack does not reach the surface. Samples show a brittle behavior
below the first AE event temperature because sintering of alumina occurs in ductile region above
1400°C. All samples, especially notched samples, fractured in brittle manner. This means thatinternal temperature distribution of samples is the dominant factor of cracking within tested firing conditions and geometry.
As the progress of densification during holding affects cracking behavior, AE behavior canbe used to understand the relation between densification and fracture process. Shrinkage after
firing increased with the increase in holding time, especially in the early stage of holding. As
shown in Fig. 5, cumulative AE events and generation temperature of the first AE increased with
the increase in holding time. The increase of densification causes the loss of stress relaxation in316

relatively low densification range. However, the specimens were not fractured in the samples
fired with heating rate of 800 K/h and holding time over 60 min, although AE signals were detected. Many AE signals were observed in the sample of Fig. 6(b) where a crack from the center
reached the surface. A relatively small number of AE signals were found in the sample of Fig.
6(c) where no visible crack was observed on the specimen surface. AE signals corresponding to a
crack size of around 250-300 µm were detected around 750°C in both samples. The further densification increases the fracture resistance and arrests the propagation of cracks to the surface.
This laser AE technique is expected to find practical applications in detecting internal cracks,
which cannot be observed in the surface.
6. Summary(1) In-situ monitoring system of firing of ceramics using a laser AE technique was developed
and cracking in firing was successfully detected. This system can be applied to AE measurement of samples above 1500°C.
(2) In the case of heating rate of 800 and 1600 K/h, micro-cracks due to non-uniform temperature distribution in a sample occurred during cooling period where the sample showed brittle
behavior.
(3) As the results of the inverse analysis of AE waveforms, it was demonstrated that cracking
with radius of 150-1500 µm and generation time of 1.5-5.5 µs occurred in firing. Most frequent crack size was 250-300 µm.
(4) It was demonstrated that the laser AE system can detect internal cracks, which did not reachthe surface of the sample in firing process. This means that the integrity of sintered ceramics
may be ensured based on non-contact AE measurement.
Acknowledgment
This work is supported in part by a Grant for 21st Century COE Program 'Human-Friendly
Materials based on Chemistry" from the Ministry of Education, Culture, Sports, Science, andTechnology of Japan.
References
1) S. Nambu and M. Enoki: Key Engineering Materials, submitted.
2) N. Miyata, T. Shiogai, C. Yamagishi and Y. Matsuo: J. Ceram. Soc. Jpn., 106 (1998), 665.
3) M. Enoki, M. Watanabe, P. Chivavibul and T. Kishi: Sci. Technol. Adv. Mater., 1 (2000), 157.
4) M. Watanabe, M. Enoki and T. Kishi: Mater. Sci. Eng. A., 359 (2003), 368.
5) S. Nishinoiri, M. Enoki and K.Tomita: Sci. Technol. Adv. Mater. 4 (2003), 623.
6) S. Nishinoiri, M. Enoki, T. Mochizuki and H. Asanuma: Mater. Trans., 45 (2004), 257.
7) M. Enoki, S. Nishinoiri, T. Seki and A. Kishimoto: Key Engineering Materials, 270-273(2004), 485-490.

317

PATTERN RECOGNITION TECHNIQUES FOR
ACOUSTIC EMISSION BASED CONDITION ASSESSMENT OFUNFIRED PRESSURE VESSELSATHANASIOS ANASTASOPOULOS
Envirocoustics S.A., El. Venizelou 7 & Delfon, 144 52 Athens, Greece.
Abstract
The basic principles of supervised and unsupervised pattern recognition (PR) techniques forclassification of acoustic emission (AE) data are presented herein together with successful applications of the techniques, for AE condition-assessment of unfired pressure vessels. The paper
discuss the basic principles of supervised classification, consisting of a learning process, where
representative AE data are used as examples to train a classifier for subsequent classification andautomatic evaluation of unknown AE data. It also discusses the use of unsupervised PR, as amultidimensional sorting technique aiming to identify and separate noise-related AE (EMI, friction, mechanical impacts, flow noise) from legitimate AE. Strategies for validating the unsupervised PR outputs, as a mean to define the required training examples for supervised pattern recognitions, are investigated. Case studies from AE testing and PR analysis applied for the evaluation of pressurized vessels and reactors are presented. The advantages and limitations of the entire methodology are discussed in relation with conventional evaluation techniques. The feasibility of real time classification is considered.
Keywords: Pattern recognition, Structural integrity, Health monitoring
1. Introduction
Acoustic emission (AE) testing of unfired pressure vessels is usually performed during controlled stimulation [1] where mechanical or thermal stresses give rise to AE signals from active
discontinuities. Most of the times, empirical techniques are used for noise identification and filtering, while evaluation of genuine emissions from filtered data is performed by means of codesand standards and in association with knowledge based systems [2, 3]. None of the codes andstandards discusses in depth filtering process and in most cases the quality of AE data and noise
filtering strongly depends on operator experience. Pattern recognition (PR) techniques are presented as an alternative and/or complementary AE data processing technique, to the well established knowledge based systems, aiming to help operators in noise identification and/or filtering
as well as assisting the overall AE based evaluation of unfired pressure vessels.
The use of pattern recognition techniques for the analysis of AE data has been proposed thepast decade in order to overcome limitations of conventional analysis techniques using single AEfeature, such as amplitude, or using two-dimensional correlation plots. The initial applications of
PR were limited to laboratory AE tests and research applications. Since then, the use of PR for
AE data analysis in industrial applications has increased drastically [4-8]. The present paper outlines the basic principles of both unsupervised and supervised pattern recognition for AE data
analysis and reports on some successful application examples of the technique, for AE conditionassessment of unfired pressure vessels. Case studies from AE testing and pattern recognition
J. Acoustic Emission, 23 (2005)

318© 2005 Acoustic Emission Groupanalysis applied for the evaluation of AE data from pressurized vessel [9, 10] and reactors arepresented.
2. Acoustic Emission Patterns & Pattern Recognition Implementations
Pattern Recognition (PR) [11, 12] is the process, by which similarities between objects arerecognized and weighted so as to distinguish alike objects. Recognition is a basic attribute ofliving organisms and mainly of human beings. Our everyday life involves the recognition ofsounds, voices, faces, and any kind of objects around us. This is a complicated task based on
sensory information acquisition (eyes, ears, etc), information processing and memory recall andfinally comparisons, classification and recognition. Pattern recognition, as a branch of Artificial
Intelligence, aims to develop theories and techniques, particularly suited for computer implementation, in order to transfer some of the human's intelligence on a machine. Speech analysis
and recognition and ultrasonic signals classifications, for medical or NDT needs, are typical applications closely related with those of Pattern Recognition of AE signals.
The first, and very crucial, step in any pattern recognition application is the collection of theappropriate and good quality data to describe the objects. Any object can be described using a
number of its characteristics (features). If the features used are not descriptive of the specificobject, then, no matter how elaborate the applied pattern recognition scheme is, the result will bepoor.
For most practical application, a pattern is a vector X in a feature space. By this process offeature measurement and representation, we can think of an object or event abstractly as a point
in an n-dimensional feature space as shown in Fig. 1. The collection of all the vectors (AE Hitsor data points in general) forms the Pattern Matrix. Thus the Pattern Matrix contains the whole
data set and is easily viewed in a data table format. The concept of the Pattern Matrix is shownin Fig. 2 where each row represents one AE Hit (record, pattern vector) composed of extracted
features from the corresponding waveform.
 ArrivalTime Param1  Param2  RiseTime  Counts  Amplitude vi = 
Duration  Energy 


.




.


.


 featuren 

Fig. 1: Typical AE signal. Some of the features extracted are shown. The Features Vector foreach AE hit (record) is the representation of that hit as a vector in n-dimensional space.
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Feature 1
(Rise
Time)

Feature 2(Amp dB)

Feature 3(counts)….

Feature k(MARSE)

R1

89

83

79

…

157

R2

93

87

92

…

994

R3

249

97

19

…

…

…

76

52

11

Waveform 

…

Rn

...

155

…

…

2

Fig. 2: Pattern matrix of a typical set of AE data. Data No. = n, Feature No. = k.
Upon features extraction, the next step is pre-processing, i.e. to process the data in a usable
form to compare with known data. Pre-processing includes additional features calculation (e.g.
as a combination of extracted features), features selection (e.g. removal of confusing features orhighly correlated features) and features transformation. Features normalization, is an importantpart of classifier design and pattern recognition in general. Normalization aims to scale features
to the same range. Other transforms, such as zero mean or unit variance are also commonly usedduring normalization phase. Simple normalization actions (e.g. scaling) stretches or shrinks theaxes in feature space (e.g. amplitude and counts will both range from zero to one instead of 40
dB to 100 dB and 1 to 65000, respectively) and might distort their geometrical shape (e.g. aspherical clusters of data points might get stretched into ellipsoid and vice versa). Within theframework of pre-processing, principal component projections might be used as well.
The next step in developing pattern recognition process for AE application, is the intercomparison of the collected data to sort unknown objects in groups by similarity, or the comparison against a data base trying to match the object(s) with known. For the needs of applying pattern recognition during AE testing of unfired pressure vessels, pattern recognition is treated as
pure classification process - assigning an input to a category called class - where:
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•
•

AE hits (Patterns) in the same class exhibit similar properties, and they are SIMILAR.
AE hits (Patterns) in different classes, exhibit different properties, and they are NOTSIMILAR, they are DISSIMILAR.

Classification can be performed by two main methodologies:• Unsupervised Pattern Recognition is the process by which objects are classified in general groups according to their similarity. This process does not require any previous
knowledge or database. Objects are classified into groups by comparing their features anddeciding upon their similarity. In such cases, the number of classes/categories must be estimated as well as a meaningful grouping of the data for further use as a training set during the classifier design.
The nature of AE (non-stationary signals, irreversible phenomena, such as crack propagation) implies the absence of a priori knowledge about recognition problem and "Unsupervised Pattern Recognition" techniques are usually employed [5-6, 12, 15].
•

Supervised Pattern Recognition, which involves a learning process and where each new(unknown) set of data is processed and classified to one of the previously known and predefined groups by comparing its features to a database or using rules derived from thelearning process. In this case, the Classifier design is a process of “Learning from Examples” and is called Supervised Pattern Recognition.
Applying Supervised Pattern Recognition in AE implies previous knowledge about the
number of classes as well as a set of known examples from different AE sources (called
also training set) to be used in the classifier design. Different Supervised Algorithms
might be used, depending on the complexity of the problem as well as the required speed
performance of the classifier. Among the different classifiers the simple minimum distance classifier, the linear classifier and the complex Neural Networks have been used forthe classification of AE data [12-15].

Classification is a very elaborate step as the measure of similarity has to be chosen, the significance of each feature must be taken into account and ways to measure the effectiveness ofclassification have to be defined.
The case of Supervised PR, is relatively easier compared with Unsupervised, since it assumes
that a priori knowledge exists in the form of representative AE hits from each signal class(training set). In this case, three general types of classifiers, able to realize a wide range of decision surfaces, are considered [12-14] named k-Nearest Neighbor Classifier (k-NNC), Linear
Classifier and Back Propagation (BP) Neural Network.
• The first algorithm k-NNC, base its decision on direct distance measurements. The
Euclidean distance Dij of a test pattern X from the jth training pattern of the ith class serves
as a measure of similarity between patterns (where X(i) , i = 1,...,N, denotes a set of N
training samples distributed in c classes, C1,...,CC). The k-NNC algorithm classifies theunknown pattern vector X by assigning it to the class label most frequently occurring
among the k nearest samples.
• In the second class of algorithms, multi-class Linear Classifiers, “c” linear discriminantfunctions, one for each class, are defined. The unknown pattern X is classified to the classscoring the highest value among the c discriminant functions [11-14]. The weights involved in the linear discriminant functions are estimated during the training process using
previously known examples.
321•Finally, the BP Neural Network or artificial neural system (ANS) is characterized by its
multilayer Perceptron topology where connection weights and processing element biases
are modified using the generalized delta rule. The BP learning (or otherwise encoding)
process is an iterative one and thus needs to be repeated until a satisfactory output is attained. Upon training completion, BP classification (recall) is done simply by introducing
the new input vector to the ANS's input layer and computing the values towards the
ANS's output layer. A complete description of the “plain” BP model used here is given
by Simpson [16].

Concerning Unsupervised PR, data partition is performed by means of cluster analysis, whichaims to separate the AE hits (pattern vectors) in a limited number of clusters characterized
mainly by their mean pattern vector. Different clustering algorithms have been proposed to deal
with the needs of various AE data structures and signatures [5, 12, 15]:a) Cluster Seeking/Wish,b) Max-Min distance,c) K-Means/Forgy,d) Isodata.
All the above mentioned algorithms use Euclidean distance as a measure of dissimilarity between pattern classes. The first two algorithms are heuristic and are based on the selection of arepresentative distance threshold by which the bound of each class is defined in the multidimensional space. The remaining two algorithms aim to minimize the square error for a given numberof clusters based on an iterative procedure. From the different clustering algorithms, Wish, Forgyand Isodata implement heuristic procedures for creating new clusters and/or deleting small ones.
All the above algorithms clustering as well as conventional Unsupervised Neural Net algorithms [15] are influenced by the initial cluster selection and require multiple runs for the selection of the optimum clustering. Such instabilities are avoided by the proposed methodology [5,12], which is based on the interactive coupling of Max-Min distance and Forgy algorithm. The
modified Max-Min Distance algorithm implemented uses two starting clusters, which are selected as the point furthest to the mass center and the point furthest to the previous one. A newcluster center is created if : Dmi >Tm Dav, where Dmi is the maximum of the minimum distances
between each pattern/AE hit to the existing cluster centers, Dav is the average between clusters
distance and Tm a user specified parameter in the range [0,1]. The algorithm identifies cluster
regions, which are farthest apart and therefore is particularly useful either for extreme noise condition identification or for the first approximation of initial clusters centers to be refined by
Forgy algorithm. Forgy algorithm is an iterative procedure aiming to minimize the sum ofsquared error for a given number of classes. In the present study, the algorithm starts from theinitial cluster centers previously established by Max-Min Distance algorithm. Nearest neighborclassification is applied to reclassify all the patterns followed by update of the cluster centers by
calculating the mean vector of the resulting classes. The above procedure is repeated until convergence. In addition to that, heuristic procedures are implemented for controlling the number ofclasses:a. A new cluster is created if Davi - Dmi < Tf Dmi , where Davi denotes the average distance ofpattern i to the existing cluster centers, Dmi is the distance of pattern i to the nearest cluster
centre and Tf a user specified parameter in the range (0,1),
b. An existing cluster is omitted if it is sufficiently small and composing with less than Nminpatterns. This is the only way to decrease the number of clusters.
Further details on implementation of the clustering algorithms as well as on the estimation of thenumber of classes and the selection of optimum clustering the can be found in references [5, 12].
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3. Limitations, Errors and Classifier Validation
What makes pattern recognition problems difficult is that there can be a large degree of variability of inputs that belong in the same class, relative to the differences between patterns in different classes; i.e. data is not really separable. Prior to deciding on the efficiency of PR methodology and validating the results, the following items should be clarified: “What does a classreally mean?”, “Which clustering is valid?” and furthermore, “How many classes do we expect?”
From the AE point of view one may expect the number of classes to be equal to the active failure
mechanisms plus the detected noise sources. On the other hand, two failure mechanisms may be
indistinguishable if they result in similar AE signatures, or one failure mechanism might result in
two clusters. From the PR point of view, valid is any clustering identifying the structure of thedata, while optimum might be considered the one maximizing the separability between classes.
From the engineering point of view, valid is the methodology, which results in partitions that
represent the physical phenomena. In any case, further validation should be performed by detailed evaluation of the respective cumulative hits of each class and comparison with conventional NDT methods and/or stress analysis.
For the needs of supervised PR analysis, the results are validated by means of overall classifier error or within-class error as revealed from the trial of the classification process in previously known examples. Universal classifier design rules do not exist and the entire PR methodology should be seen as an iterative procedure where each step; i.e., features selection, preprocessing, definition of training and test set, algorithm selection, is repeated and the overall performance is evaluated. In other words, a trial-and-error investigation should be scheduled.
Causes of errors include inadequate or correlated features, complicated class boundaries or insufficient training set. In the first case, if the features do not contain the information needed to separate the classes, a different feature set should be investigated. For example it is a common practice to start the classifier design using well-known features such as hit amplitude, duration andenergy. However, in case of poor results, alternative features from the frequency domain, such as
frequency centroid or partial powers, might be investigated. The classifier design is complicatedand its performance degrades in case two features are highly correlated, as it is often the case for
AE counts and durations. On the other hand, the piece-wise linear decision surface produced by
the linear classifier may not be sufficient for some problems necessitating the use of neural network, which in principle performs better in complex problems. However, when using neuralnetworks, the size and the quality of training data will play an important role on the classifier design.
For the needs of unsupervised PR analysis of AE data (or for the definition of training set
during unsupervised PR analysis), multiple runs of clustering algorithms are performed and theresults are ranked in terms of separability and compactness of the resulting classes by means of
numerical indices based on the within-class scatter matrix and the overall scatter matrix. Since
unsupervised PR partitions are not uniquely defined resulting in multiple solutions [5], extensive
validity studies should be scheduled.
Understanding that pattern recognition is not a panacea, the users should realize that the algorithms and respective software [15] offers all the necessary tools for evaluating the complexity ofthe problem and proceed with classifier design. However, measurement set-up and data quality
are always the key issues.

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4. AE Classifier Design - Demonstration Case Study
Three resonant frequency sensors (PAC-R15I, 150 kHz) were mounted on a thick metallic
plate in a triangular pattern (320 mm X 540 mm). Four channels PAC-DiSP board was used forreal-time data acquisition and NOESIS [15] PR software for the analysis and PR studies. Simulated AE signals produced by mechanical pencil-lead breaks (0.3 mm, 2H) at various positions
on the plate. Mechanical friction simulated signals produced by sliding a small metal piece
across the surface of the plate. Finally, electromagnetic interference (EMI) signals were generated by unplugging the sensor cable during acquisition.

Fig. 3: Simulated AE used for Supervised PR example.
The time-amplitude plot of Fig. 3 presents the sequence of experimentation. Since the experiment was performed in a controlled way and representative AE hits – examples – from eachsignal class are available, the associated pattern matrix can be used for Supervised PR. In orderto demonstrate a classifier design, the available data divided in two parts (at random) in training
and testing set, are used to train and test the classifier. The various supervised algorithms available have both advantages and disadvantages [14].
For the demonstration needs of the present study a Back Propagation Neural Network wastrained. The network topology is presented in Fig. 4. As can be seen the input layer contained ten
nodes, representing the dimension of the pattern vectors, i.e., the 10 features used (Rise Time,Counts to Peak, Counts, MARSE, Duration, Amplitude, Absolute Energy, Frequency Centroid
and Peak Frequency), the hidden layer contained three nodes, the same with the output layer. The
three nodes of the output layer represent the three different classes of interest, i.e., Simulated AE,
Friction and EMI. The network successfully trained, resulting 1.8% overall error, which represent 1 misclassified point (from AE class to friction class). The error is due to pure definition of
the training set. More specifically the two low amplitude hits at time 22.5 sec (Fig. 3), were labeled and used in both training and test sets as simulated AE, although it represents a type of324

friction at the time just before the pencil break. Deleting those two hits from the training and test
set, a 100% recognition (0% error) was achieved.
Further optimization of classifier performance, aiming to improve classification speed without sacrificing accuracy, can be performed by reducing the size of pattern vector, i.e. removing
features. A step-by-step approach of reducing the size of the pattern vector by one (using 9 features), repeating training and evaluating the overall and within-class error might be adopted. In
any case, classifier stability should be evaluated by evaluating the classifier performance using
different training-test sets.

Fig. 4: Back Propagation Neural Network topology.

Fig. 5: Results of unsupervised PR applied on data of Fig. 3.
325

Fig. 6: 3D Scatter plot of unsupervised PR results.
Presuming that a priori knowledge about the data of Fig. 3 is unavailable, Unsupervised PR
techniques are used in order to demonstrate the classification sequence. Following the unsupervised PR methodology, discussed in previous paragraph and presented in depth elsewhere [5,12], the interactive coupling of Max-Min distance and Forgy algorithms resulted in the five different classes, as presented in Fig. 5. Close observation of the resulting partition indicates that all
the data from pencil breaks were grouped together (named class 3 – red circles in this case) resulting in 100% recognition. Similarly the EMI data recorded from times greater than 50 sec,
were all grouped together (named class 1 – green boxes in this case) resulting in 100% recognition. However, the AE data, due to friction – between 37 sec and 47 sec, were grouped in threedifferent classes (named 0, 2 and 4 in Fig. 5). Close investigation of the 3D scatter plot of Fig. 6
indicates the wide scatter of friction-like AE hits when viewed in the rise time vs. peak frequency plane. In other words, grouping of these data in 3 different classes reflects hidden correlations, not expected from the pure AE point of view. It is worth noting that different results
should be expected if a subset of the 10 features was used (for example using only Counts, Amplitude and Duration).
Validating the results is a complicated task and requires a great deal of intuition andexperimentation. However, validity studies concerning the result of clustering algorithms arenecessary in order to increase our confidence on the estimated number of clusters and to decide
for the best partition of the data.
Once a desired clustering scheme has been achieved, the resulting partitioning can be used toautomate the classification process on other data sets of similar-in-nature AE testing. This is bydesign a classifier, and we then have supervised PR.
326

5. Pressure Spheres–AE/PR Case Study
SPR was applied [10] on data acquired during hydrostatic pressure tests of three 19.7-m diameter, spherical pressure vessels. The tests were conducted per MONPAC procedure to a
maximum test pressure of 16.5 bar. 49 PAC R15I sensors were mounted at the locations shownin Fig. 7. From the three hydrostatic pressure tests, AE data were acquired for two loading cycles. Additional data were collected; such as simulated AE sources at various locations aroundthe vessels as well as impact noise data. Various noise sources appeared during each test (slidingof the fire-line supports on the vessels and the mechanical noise from bolts in new blinds andmanholes). These AE data were mixed to provide a data set including all representative datatypes found in these tests.

Fig. 7: Located AE events from the classifier output of the simulated and AE
classes for sphere simulated AE sources data.
The data set created contained 3 classes:a) simulated sources and real AE,b) debris noise,c) impact and mechanical noise.
A k-NNC was trained to separate the data. A random half of the data was used to train the classifier and the rest were used to test the classifier. The testing classification error was 2.95%(87/2951 hits). It is worth noting, that statistical analysis showed that even in 7D space (7 features), it would not be mathematically efficient to separate the various types of data due to data327

concentration at low feature values. To improve this, all features were converted by logarithmic
functions and projected based on principal component covariance analysis.
Further analysis using spherical location techniques confirmed classifier performance as forexample for sphere B, where debris noise existed while performing pencil breaks (simulatedAE), the classifier recognized in the data all three classes. The data classified as Simulated AE
gave good spherical location with 16% first hits, and 6.7% spherical events (required 3 hits foreach spherical event) concentrated in the bottom section of the sphere where the simulated
sources were introduced (see Fig. 7). Spherical location on the data classified as Debris Noiseprovided 65.9% first hits but only 1.5% spherical events. Most of the first hits were observed inchannels from the top section of the sphere where the debris problem was encountered. Further
application of the classifier to the actual test data showed similar results to the traditional analysis techniques for spheres C and D. For the case of sphere B it was the only method that provided
meaningful results as attempts to analyze the data via traditional techniques failed to produce
good results due to the complexity of the problem.

(a)

(b)
Fig. 8: a) Cumulative AE hits from three different hit categories Temperature variation superimposed. b) Principal components projection of reactor data.
328

6. Reactor Cool-Down – AE/PR Case Study
Eighteen PAC-R15I AE sensors were mounted on waveguides, on a triangular set-up, on areactor vessel (O.D. = 3.3 m, L = 5.7 m, thickness 42 mm). AE was monitored during partial
cooling down from 260oC to 170oC. AE data were originally analyzed by means of standard
MONPAC practices. The results of conventional analysis using AE signatures are presented inFig. 8a together with temperature variation (decreasing as a function of time – grey line). Amongthe three cumulative hits lines plotted in Fig. 8a, genuine AE (red line) can be seen well correlated with temperature variation while the remaining two categories not. The green line, activefrom the beginning of the test, corresponds to friction and mechanical noise, while the light blueline of cumulative hits, corresponds to flow noise at the end of the cool down, as confirmed by
the operation department of the plant. The AE data were further analyzed by means of supervised
PR using NOESIS [15] software.
During preprocessing stage, rise angle, initiation and reverberation frequencies were calculated [5, 12] and used as part of the pattern vector. Principal component projection (scatter plotof the 1st and 2nd principal components is presented in Fig. 8b) performed before the training of
the k-NNC classifier. The overall error (misclassified hits over the total number of hits) was
bellow 5%. However, an increase of within-class error, up to 19%, was encountered in somecases, depending on the training and test set used. In most of the cases of increased within-classerror, flow noise signals were recognized as genuine AE. Confusion found also between friction
and AE where friction and mechanical noise signals were classified as AE. Closer investigationon the classifier performance and the misclassified hits, indicated that the confusion was due tothe secondary hits and reverberated signals, since the first hit (as revealed by zonal analysis)
were classified successfully). Therefore, first hit analysis was applied as a preprocessing step. In
this way, only the first hits were presented to the classifier resulting in overall error of less than
4% and within-class error of less than 7.5%. Overall the results show that supervised PR can beapplied effectively for the classification of AE data during cool-down of thick-wall vessels.
7. Discussion
Pattern recognition techniques have proven as a fast and effective tool for AE data analysis
from pressure vessels and reactors. Care, though, must be taken to understand the limitations of
the technique so as to use it properly. Pre-processing should be considered as an important stepin classifier design as well as the use of AE hits versus AE events (first hit only).
A generalized classifier applicable in all types of vessels cannot be designed. A number ofreasons (e.g., sensors spacing or the use of waveguides, a variety of noise sources for test type,
different source mechanisms etc.) limit such generalization. However, once a large databasefrom similar tests is established, and once PR results are validated, the technique can be used forreal time interpretation of AE data. Optimizing the speed performance of the classifier should beconsidered when attempting to use PR for real time data classification.
References
1) ASTM E569-02, “Standard Practice for Acoustic Emission Monitoring of Structures During Controlled Stimulation”
2) ASME, Section V, Article 12, “Acoustic Emission Examination of Metallic Vessels During
Pressure Testing”, 1988.
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3) Journal of Acoustic Emission, 8 (3) July-September 1989, 1-33, “Selection of 5 papers on
MONPAC technology and applications”.
4) R. W. Chan et al., J. of Acoustic Emission, 4 (4), 1985, 115-123.
5) A. Anastasopoulos, T. Philippidis, J. of Acoustic Emission, 13 (1/2), 1995, 11-21.
6) T. Philippidis, V. Nikolaidis, A. Anastasopoulos, NDT&E, 31 (5), 1998, 329-340.
7) D. Kouroussis et al., J. of Acoustic Emission, 18, 2000, 217-223.
8) A. Anastasopoulos et al., J. of Acoustic Emission, 18, 2000, 224-230.
9) A. Tsimogiannis et al., NDT.net, Sept. 2002, 7 (09), www.ndt.net/v07n09.htm
10) A. A. Anastasopoulos, A. N. Tsimogiannis, D. A. Kouroussis, Proc. EWGAE 2002, Vol. I,
pp. 29.
11) J. T. Tou, R. C. Gonzalez, Pattern Recognition Principles, 1974, Addison-Wesley,
Reading, Massachusetts.
12) A. Anastasopoulos, PhD Thesis, University of Patras, 1995.
13) L. Schilemans et al., Advanced Composite Materials, 1993, 3 (1), 57-91.
14) A. Anastasopoulos, et al., Neural Computing & Applications, 1999, 8, 53-61.
15) NOESIS V 4.1, Overview & Reference Manuals, Envirocoustics S.A., 2004.
16) Simpson, P. K. Artificial Neural Systems, Pergamon Press (1990).

330

THE ACOUSTIC EMISSION HALON 1301 FIRE EXTINGUISHERBOTTLE TESTER: RESULTS OF TESTS ON 649 BOTTLES
A. G. BEATTIE1 and D. D. THORNTON2
Physical Acoustics Corporation, Princeton Junction, NJ 08550-5303, USA.
2)
American Airlines Maintenance and Engineering Center, Tulsa, OK 74158-2809, USA.
1)

Abstract:
The first commercial model of the acoustic emission Halon Bottle Tester has been in operation at the American Airlines Maintenance Facility, Tulsa OK for about two years. A data set of649 bottle tests was reanalyzed with a FORTRAN program including all recent tester programmodifications. Thirty-four bottles, 5.2%, failed the test. The analysis showed no reasons tomodify the pass-fail criteria given in DOT-E 11850. Metallographic studies of some of the failed
American bottles showed that the detected acoustic emission attributed to corrosion came fromcorroded regions on the interior of the bottles. The cause of the corrosion appears to be the deterioration of Halon 1301 in the presence of small amounts of water.
1. Introduction:
An acoustic emission (AE) tester for airline Halon 1301 (CBrF3) fire extinguisher bottles wasdeveloped at Sandia National Laboratory under the sponsorship of the Federal Aviation Administration (FAA) and the Air Transport Association (ATA). After extensive testing at two bottle
manufacturers’ testing and repair facilities, an exemption to allow its use on commercial aviation
bottles was given to the ATA by the Department of Transportation. Physical Acoustics Corporation (PAC) produced a commercial version of the tester and sold the first machine to American
Airlines, which had it installed in its Tulsa Maintenance Facility. This machine has been in operation for about two years and, despite several unexpected problems characteristic of new technology, has tested almost 500 Halon bottles to date. The original pass–fail criteria built into thetester and the DOT Exemption were based upon a data set of 208 bottle tests. To this original
data set, the tests of about 475 bottles by American Airlines were added. This combined data set
was then examined and slightly culled to arrive at a data set covering 649 tests.
The Halon Bottle Tester writes a data file for each test containing the raw data before any
analysis by the AE system. All of these files are stored on compact disks. This allows post-test
analysis using any desired methodology. During the commercialization of the test system, a series of minor modifications to the original Halon Bottle test program have been made. To assurethat all data was treated equally, an analysis program was written in FORTRAN, which included
a series of small changes incorporated in recent commercial version of the tester program. This
FORTRAN program was used to analyze all data sets. Because of this accumulation of minor
program changes during the commercialization of the tester, the classification of three of thebottles, whose test results placed them right at the pass-fail boundary, changed, one from pass to
fail and two from fail to pass. Such a discrepancy can be expected any time arbitrary criteria areused. The original criteria were set at a level where it was expected that all bad bottles would
fail and a few good bottles would be rejected. All indications are that these expectations havebeen met.
J. Acoustic Emission, 23 (2005)

331© 2005 Acoustic Emission Group2. Test System
The AE Halon Bottle test system has been described in some detail [1]. It is based on a PAC
six-channel Mistras AE system. The program is unique to the Halon tester. It controls the loading, collects and analyzes the AE data and evaluates the results, giving a pass or fail grade to thebottle at the end of the test. The test load is applied to the bottle by heating it in an industrial
oven up to a surface temperature of 65.6°C. This temperature produces a pressure, which isroughly twice the fill pressure and is more than 140% of the pressure at a maximum expected
operating temperature of 43.5°C. After the oven heaters and fan are shut off, the bottles are held
for five minutes to allow the temperature of the contained Halon to reach equilibrium with thebottle surface. The final temperature of the Halon is between 64o and 65°C. At the end of thetest, the system prints a report grading the bottle and giving the locations with respect to the sensor locations of the suspect regions if the bottle has failed the test.
All AE signals during the heating cycle and hold period are detected down to a threshold of
25 dB (18 V from the sensor). The AE system records the signal arrival time (to an accuracy of
0.25 s) and several signal parameters for each AE signal detected by a sensor. Each sensor can
detect up to about 500 signals per second, depending upon the signal length. The waveform ofeach emission is also recorded. The computer program then looks for groupings in time of thedetected signals. All signals detected within a time period equal to 120% of the time that it takes
for a flexural-mode AE signal to travel half way around the sphere are grouped into an event data
set. If the data set meets certain criteria, such as each sensor only being excited once and at least
four of the six sensors being excited, a nonlinear least-squares fitting routine attempts to calculate the location of the signal source on the sphere. The use of an over-determined data set(mathematically only three sensors have to be hit for exact location) results in the calculation of
the most probable location for that set of data. The accuracy of the location is dependent upon
many factors. The two which have the most influence on when the system triggers for each arriving AE signal are the initial amplitude and the physical conditions in the bottle wall along theshortest paths to each sensor. Doubler plates, tack welded on the external surface containing attachment fixtures and ports for gas fill or exhaust will distort the waveform of AE signals passing under or near them. This can result in the system triggering on different points of the waveform at different sensors. The program uses both the over-determined data set and other features
such as weighting the data points based upon signal characteristics to overcome some of these
problems but there is always some ambiguity in the location accuracy. On a test specimen without attached surface features, such as a gas cylinder, the location accuracy can routinely be ±1.6mm, but on Halon Bottles the location accuracy is partially dependent upon the relationship between the position of the source and that of the surface features. The accuracy usually falls between ±6.4 and ±18 mm. The location routine succeeds in finding a location on the surface ofthe sphere for between 70 to 90% of the signal groupings. The main reason for failing to locate asource on the surface is triggering at quite different places on the waveform at the different sensors, resulting in either totally wrong arrival times or the determination of arrival times at different sensors from different acoustic modes.
The analysis program is not based on the individual AE signals but on the located AE events.
A basic assumption is that AE signals produced by the same growing flaw will have their sources
all located in the same region on a specimen. Therefore, one should search for a spatial clusteringof the emissions. In this program, as each event is located, it is checked to determine whether it
is a member of a cluster of events. A cluster is defined in the program as two or more events located within a circle on the surface of the sphere, whose radius is 15° of arc (0.26 times the332radius of the sphere). This definition of a cluster removes the influence of sphere size from theanalysis. The program tracks how many events are included in each cluster and the temperature,at which each event occurred. The slope of the curve for the number of events as a function oftemperature is monitored for each cluster. The pass-fail criteria are based upon the number of
events contained in a cluster and the behavior of the slope of the events vs. temperature curve forthe cluster.
3. Rejection Criteria
Acoustic emission can be generated by a wide variety of mechanisms. For aircraft Halonbottles, the two most likely mechanisms are crack growth and corrosion. These two mechanisms
show somewhat different patterns in the generation of the emission. In crack growth a crack tip
advances in discrete jumps when the local stress field exceeds the level necessary for crack advance. These jumps are small, usually involving one or two metal grains to start with but they
produce detectable bursts of AE. In stable crack growth, each jump decreases the local stressfield enough so that the crack arrests until a further increase in the applied load brings the local
stress field back to a level sufficient to produce another crack jump. This behavior appears as anapparent steady AE event rate as a function of increasing load. As the crack grows, it will reach apoint where each jump not only decreases the local stress field but also decreases the strength of
the surrounding material. At this point, the jump rate and thus the emission rate starts to increasein an exponential manner. This condition is unstable crack growth and will result in fracture of
the specimen if the loading is not reduced or stopped. If the increase in the external load is slow,
the exponential increase in the AE rate can usually be detected long before failure occurs. Thusan AE rate, which is constant with load increase, is indicative of stable crack growth while anincreasing rate with load increase is a sign of unstable crack propagation.
The term corrosion, as one might expect, covers a large variety of conditions. For steel bottles there are at least two different types of corrosion. In alloy steels such as AISI4130, small
patches of corrosion can form on the interior surface of the bottles. When the bottle wall isstrained by pressure loading, the corrosion product can either fracture or pop off of the surface.
Either process produces large bursts of emission. A characteristic of this type of corrosion is ahigh initial emission rate, which decreases as an increasing percentage of the corrosion products
are affected by the strain. After a certain load level is reached, all emission will cease. This typeof corrosion is usually not seen in stainless steels. However, these steels can be etched by anacid attack. If the acid attacks the grains preferentially, either with respect to composition or orientation, one will end up with a rough surface and possible pitting as the acid follows either thegrain surface boundary or the grain itself down into the base metal. The application of a strain tosuch a surface may cause many small fractures at weakened grain boundaries or dissolved pits inthe surface. These fractures produce detectable AE. Again, the probability of fracture of any defect will depend on the local stress field. The difference between controlled crack growth and theetching corrosion is the larger number of potential fracture sites produced by the corrosion. The
emission rate from this type of corrosion will be proportional to the strain and thus the applied
load. This should produce a roughly constant emission rate for a constant loading rate. Anotherpossibility for this surface corrosion is that there may be coalescence of the small surface cracks,
which can lead to unstable crack growth.
This discussion predicts three types of characteristic emission behavior for three different
sources of the emission. Small patches of surface corrosion produce an emission rate, which
may be high initially but falls off with an increasing load. Such surface corrosion has little effect333

on the strength of a bottle. Etching corrosion and controlled crack growth produce an emission
rate linearly proportional to the loading rate. Small amounts of either are relatively innocuous tothe bottle. Unstable crack growth will produce an emission rate, which can increase exponentially with respect to the loading rate. Such a crack will inevitably lead to failure of the bottle if
the load is not reduced or removed. However, the AE rate usually predicts imminent fracture far
enough in advance to enable a test to be stopped before catastrophic failure occurs.
By locating the source of each burst of emission, the growth rate of the individual flaws canbe estimated. The decreases in the local stress field with crack advance will often restrict activity to a single crack in a local region. The amplitude of each emission is related to the volume of
the growing crack. However, by the time an AE signal reaches a sensor and is detected, the exact waveform is determined by the characteristics of both its source and the details of the path it
travels to the sensor. A study of the characteristics of all waveforms detected by sensors from asingle event can give information about the event but this is not a process easily automated, especially for a wide variety of waveforms. Therefore, the information that is used in the gradingof a Halon Bottle in this tester is the existence of located events; whether they form local clusters
with other events; what is the number of events contained in a cluster and what is the event rate
as a function of load for the cluster. By focusing only on located events, the effect of the finedetails of the waveforms at different sensors is minimized and external noise becomes a nuisanceinstead of a severely limiting factor.
Two concepts are used in setting the failure criteria for the Halon Bottle Tester. First: thesize of a flaw is roughly proportional to the number of emission bursts produced. Second: theseverity of a flaw is proportional to the rate of increase of the number emission bursts in thecluster with increase in load. A simple method is used to determine when the rate of bursts versus load is increasing as a function of load. The number of cluster events occurring above 54°C
is divided by the number of events occurring between 43° and 54°C. If this rate ratio reaches 3.0
or higher, it indicates that the burst rate is undergoing an exponential increase and that uncontrolled crack growth is occurring. By applying these concepts to the original set data set of 208
tests, a failure criterion for growing cracks was set. It is that a cluster of AE events having 18 or
more members and a rate ratio of 3.0 or higher has failed the test. The failure criterion for controlled crack growth and corrosion is based only on the number of located events in the cluster.
Any cluster containing 35 or more members irrespective of the value of the rate ratio fails thetest. It must be stressed that these criteria are arbitrary and are based upon the experimental results from 208 bottle tests by the prototype system. They were in no way meant to apply to any
other type of test or AE system. Any major change in the Halon Bottle Tester or its software
would require a new set of pass-fail criteria.
4. Data Sets
Two separate data sets were combined for this study. The first is the original data obtained
during the development of the system and used in setting the pass-fail criteria quoted in the Department of Transportation exemption DOT E-11850. The second set is data obtained between
February 2002 and October 2003 from the commercial Halon Bottle system at the American
Airlines Bottle Shop, Tulsa OK. The original data set was obtained at the Halon Bottle repair
facilities of Walter Kidde Aerospace in Wilson NC and Pacific Scientific Company in Duarte
CA. These sets were combined and the data then culled in the following manner. First: a test
had to either complete the full temperature cycle, 66°C plus a five minute hold, or had to continue until an automatic safety provision stopped the test due to excess emission in a cluster.
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Second: any test, which did not detect a single emission was rejected. There were only eightsuch tests, most in the original data set. Despite an Automatic Sensor Test, which had to bepassed before any bottle test could start, the absence of any AE raised a question as to whether
the system was working correctly. Third: only data from steel bottles was included. Both of thesets contained data from some titanium bottles. However, recent testing at American has raisedquestions about the results of tests on titanium bottles. Therefore, only data from bottles made ofvarious steels was included.
Most Halon bottles are spheres but there are three models of elliptical Halon bottles. They
consist of two hemispheres with a short cylindrical section centered between them. It had been
determined that the elliptical bottles with a 38-mm-wide center section could be tested successfully in the spherical fixture with the spherical program. Nineteen of these tests are included in
the data set (three of them failed the test). However, 12 data sets from bottles, which had 76-mm
and 127-mm cylindrical sections, were excluded because they required a different fixture andprogram to analyze them. The sizes of the Halon spheres included in the data set ranged from170 to 356 mm in diameter. The elliptical bottles had a diameter of 356 mm. Most of the bottles
were made of Nitronic stainless steel (21-6-9). Some of the older bottles were made of Almar,
some of 4130 steel and a few bottles of unknown stainless steel. No significant differences in thetest results were seen between these materials.
No AE testing has been done on new Halon Bottles. All tested bottles had either been removed from an aircraft or had been warehoused as a spare part for many years. No attempt wasmade to determine the age of these bottles. The majority of the bottles is currently owned by either FedEx or American Airlines but bottles, which were or are owned by other airlines, were
included in the data set. Most of the bottles were manufactured by either Walter Kidde or Pacific
Scientific but some of the older bottles were made by companies which have been sold or are nolonger in business.
Table 1 Distribution of located events per bottle.
Number of locatedevents per bottle0
1 - 10
11 – 20
21 – 30
31 – 40
41 +Number of bottles
160
345
45
31
11
57

Percent of total bottles
24.7
53.2
6.9
4.8
1.7
8.8

5. Analysis
The failure criteria are based only on the located events. Table 1 shows a rough distributionof the number of events per bottle without considering their location. Almost one fourth of thebottles had no locatable events and half the bottles had between one and ten locatable events.
After 30 events per bottle, the distribution drops off to along tail with an upper end at 588 events
for a bottle. The number of located events per bottle could be used as a simple pass-fail criterion
but with the locations of the sources available, better criteria can be developed.

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Fig. 1 Location of sources on Bottle HTAL0875.

Fig. 2 Number of located events as a function of temperature for HTAI 9828.
When the distribution of the sources over the surface of the bottles is examined, the idea ofgrouping these sources in clusters becomes obvious. Figure 1 shows a location graph of thesources from a test. For simplicity of display, the surface of the sphere has been distorted to thatof a cylinder with the poles of the sphere at the top and bottom of the cylinder. This will show
reasonably accurate relationships for the center band around the equator with extreme distortion
only near the poles. The sensor positions are shown and numbered with #1 being at the northpole (arbitrarily defined on the sphere) and #6 at the south-pole. This bottle had 15 emission
sources contained in the cluster defined by the circle with the rest of the 63 located sources scattered over much of the bottle. Figure 2 shows a running sum of all located events as a function336

Fig. 3 Distribution of located events on bottle HTAH1254

Fig. 4 Number of located events vs. temperature for two clusters seen on bottle HTAH1254.
of temperature. This curve shows the behavior for mild corrosion on the inside of the bottle. As
the strain in the bottle wall increases, the number of corrosion products left unaffected on thewall surface decreases and emission rate approaches zero. This bottle appears to have random
corrosion with only a small amount of localization. The conclusion is that this amount of corrosion has only a minor effect on the integrity of the bottle.
In Fig. 3, a bottle is shown with 217 located emissions. This bottle has almost all of theemissions located in two clusters, one containing 85 events and the other, 102. It appears that the337

bottle has two obvious problems. The question here is not whether these clusters are serious or
not but, rather, is the bottle in imminent danger of failing during the test. Figure 4 shows a graphof the running sum of events in each cluster as a function of bottle temperature. Both clusters
show a constant rate of emission as a function of temperature. There is a slight indication of arate increase for cluster “a” above 63°C (145 F), but it is not definitive. The best scenario is thatthese both show controlled crack growth. There is no question that they appear serious but there
is also no indication impending failure. Under the pass-fail criteria currently in place, the systemdeclared them as corrosion and failed the bottle.
The next bottle test to be presented is show in Fig. 5. This bottle did not just fail the test, it
ruptured a burst disk inside the oven at a temperature of 65°C (one degree before the computer
would have turned off the fan and heater). The result was spectacular although the resulting
damage was rather easily repaired. Two tight clusters are seen in the figure. There is no question that these are the exit ports containing the burst disks.

Fig. 5 Located events on bottle HTAI6427

Fig. 6 Number of events vs. temperature for two clusters on bottle HTAI6427.
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The spacing between the clusters on the graph is about an inch less than the actual spacing on
the bottle. A discrepancy could be expected as the events occurred on the top of the ports andthe acoustic waves had to travel about two inches down the ports before they reached the surfaceof the sphere. Figure 6 shows the number of events as a function of temperature for the twoclusters. The shows that the burst disk on “b” was the disk, which fractured. The last emission
from “a” occurred about one minute and 0.8°C before the last event seen on “b”. However, fromthese curves, it is apparent that both disks were well on their way to fracture. The curves indicate that the transition to uncontrolled crack growth occurred at about 56°C (130 F) for “a” and58°C (137 F) for “b”. This is in contrast to Fig. 3 where the first indication of an upturn in therate was at 63°C. These curves also illustrate that the growth of each crack depends strictly on
local conditions. With several cracks growing on a structure, it is impossible to predict which
one will result in failure of the structure.

Fig. 7 Located events on bottle HTAI9828.
The data from this unexpected failure made several important points. First the significanceof the increasing slope of the AE versus load curves is quite real. Second, the choice of thenumber of events in a serious cluster is certainly realistic and is of the correct order of magnitude. Third, the safety criteria for shutting down the system before a failure occurs needs to bereviewed and revised.
The question of whether corrosion was producing some of the emission or not, was resolved
when American [2] sectioned and examined the inside of several of the bottles, which had failed
the corrosion criteria. All of the sectioned bottles showed corrosion stains at the apparent low
point, during storage, of the spheres. These stains were between 51 and 63.5 mm diameter circles. Micrographs showed corrosion and wall thickness measurements revealed thinning up to
25% of the wall thickness. Figure 7 shows the emission locations seen during the test of bottle
HTAI9828, one of the sectioned bottles. The emission rates, seen in Fig. 8, suggested unstablecrack growth for cluster “a” and stable crack growth for cluster “b”. A micrograph of a stained
region on the interior wall is shown in Fig. 9. The etching of the surface is clearly visible as aretool marks that remain on the original portions of the surface.

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Fig. 8 Number of events as a function of temperature for clusters on bottle HTAI9828.

Fig. 9 Corrosion on inside of bottle HTAI9828. Magnification 31x.
Thirty-four of the bottles in the data set of 649 bottles failed the AE test. Eighteen, 2.8%,
failed the test based on the unstable crack growth criterion and 16, 2.5%, failed the test based
upon the corrosion or stable crack growth criterion. Of the 16, 11 had curves characteristic ofstable crack growth, some with hints of transition to unstable crack growth near the end of thetest and five were more characteristic of corrosion with the emission rate slowing down near theend of the test. One of the failures in the original data from Walter Kidde was due to a replacedpressure switch protective housing, which was mounted with screws instead of being welded.
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This test is included as the system definitely failed the bottle although there was probably nothing wrong with the actual structure of the bottle
6. Discussion
The pass-fail criteria established for the prototype Halon Bottle Tester have held up remarkably well for this expanded data set. Changing the required number of events in a cluster necessary to fail the test due to unstable crack growth would have only minor effect on the number ofbottles failed. Decreasing the number from 18 to 15 would have failed one more bottle while increasing it to 30 would have passed four more bottles. However, two of those bottles had identified flaws. The definition of the critical number for the ratio test of 3.0 is supported by catastrophic failure of Bottle HTAI6247. The final ratios of the two clusters at failure were 10.8 forthe burst disk that appears to have survived and 4.63 for the burst disk that failed. Lowering theratio to 2.5 would have failed two more bottles while raising it to 4.0 would come close to missing the unstable crack, which caused the failure of HTAI6247. The best compromise is approximately the original pass-fail ratio and there appears little reason to change it.
When the original pass-fail criteria were being developed, the primary problem was thought
to be unstable crack growth. Then several bottles appeared that had clusters containing large
numbers of locatable events but a constant emission rate as the pressure increased so anothermechanism was suspected. Experience had shown that a corroded piece of metal, when strained,
produced emission, whose rate decreased as the finite supply of corrosion products was used up.
When several bottles showed clusters with either steady or decreasing emission rates as the temperature increased, it was recognized that while failure of the bottle was not imminent, the larger
clusters did indicate a problem with the bottles. Corrosion was thought to be the most likely
cause and thus it was labeled. This was confirmed by corrosion stains with accompanying wall
thinning, found when American sectioned some of the bottles, which had failed the corrosion
criteria. However, it is not clear that many of the bottles contain brittle corrosion products in thestained areas. Only 5 bottles show the decreasing emission rate characteristic of fracturing corrosion byproducts. Most bottles that failed the corrosion criteria had a constant emission rate and a
few even had a noticeable rate increase above 60°C. Some of the micrographs show pitting in
the corroded area but few flakes of corrosion byproducts are seen. What is postulated as amechanism for generating the AE is the formation of microcracks associated with the pits. As
the load is increased, not only do more fractures occur but some propagate into the surface.
Some of these microcracks may coalesce into larger cracks and eventually start to show unstablecrack growth. This scenario would tend to produce the emission curves seen in these bottles.
This explanation leaves one problem. Halon 1301 is supposed to be stable. However, in thepresence of water, fluorocarbons can react very slowly with water. Products of such a reactionfor Halon 1301 (CBrF3) will include hydrogen fluoride HF and hydrogen bromide HBr. Waterand Halon 1301 are not miscible with Halon being the denser liquid (the density is 1.57). Any
free water would float on top of the liquid Halon. Most but not all of the corrosion stains in theexamined bottles appear to be positioned at the bottom of the bottle as it is mounted in the aircraft. HF and HBr appear to be able to dissolve in Halon and sink to the bottom, where they produce the observed puddle shaped corrosion stains. These puddles are between 38 and 64 mm indiameter. The sizes of these puddles agree with the sizes of the AE clusters shown in Figs. 3 and7. Both micrographs and wall thickness measurements show that these stains are corrosion puddles. A wall thinning of 27% has been measured at the low point of one of the puddles.
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The sectioning studies of the failed bottles show that the corrosion puddles found by AE may
have a significant effect on the integrity of the Halon bottles and justifies the inclusion of thecorrosion criteria in the original Halon bottle test program. Examination of the cluster curves for
all 30 bottles with 20 or more events per cluster and rate ratios less than 3.0 shows that 6 havecorrosion-type curves and the rest have emission rates, which are relatively constant and independent of the temperature. Sixteen failed the current criteria with 2 bottles having between 35
and 40 events in the cluster. Five more bottles had between 30 and 34 events. None of these 5
showed any indication of unstable crack growth at the higher temperatures. Changing the current
corrosion criterion to 40 events per cluster would have passed two more bottles, while the criterion of 30 or more events would have failed five more bottles. The bottles that failed the corrosion criteria averaged 64 ± 20 events in the largest cluster. Because of the reported wall thinningin several of the sectioned bottles, the corrosion criteria should not be raised very far and there
seems no compelling reason to lower it. Therefore, it is recommended that neither failure criteria
should be changed from the current values.
7. Conclusions
The original pass-fail criteria for the AE Halon Bottle Tester were based upon 208 bottle
tests. The addition of another 475 bottle tests from the American Airlines Halon Bottle Tester in
its Tulsa Bottle shop gave no reason to change these criteria. The combined data set of 649 bottles included 34 bottles, which failed the criteria. Metallographic studies of some of the failed
bottles at American showed that the AE tester detected the corrosion on the interior of the bottle.
This corrosion included significant wall thinning. The cause of the corrosion is thought to be thedecomposition of Halon 1301 in the presence of water. The most likely decomposition products
are HF and HBr.
Acknowledgements
The authors would like to thank Kamran Ghaemmaghami of Federal Express for support andaide throughout the Halon Bottle tester program. Fred Munkholm and Randy Attebury in theAmerican Airlines bottle shop were unwavering in their support of the tester despite a series of“teething” problems and Tom Forsberg both performed and interpreted many of the metallographic examinations. We would also like to thank Ric Anderson of the Air Transport Association and Dr. Mark Toughiry of the DOT for their interest and support.
Reference
1. A.G. Beattie, “An Acoustic Emission Test for Aircraft Halon 1301 Fire Extinguisher Bottles”
DOT/FAA/AR-97/9, Apr 1998. Journal of Acoustic Emission, 15, 1997, 63-68.

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