JOURNAL OF ACOUSTIC EMISSION
VOLUME 19, 2001
You need a software that can read a pdf-file. Two folders are provided for Windows 95/98/ME/2000 and
for Macintosh. Each contains Adobe Acrobat Reader® Installer (ACRD5ENU.EXE or Reader Installer).
Install one first before going further.
Adobe Acrobat®, which can be purchased from Adobe: www.adobe.com, or from other suppliers, will
enable you to utilize "Search" function using an INDEX file provided (in the Index folder). This allows
you to perform full-text search (Launch Acrobat and choose Search button in the toolbar. See Acrobat
Search.pdf for detail. Go also to “Using the Search command” in the Acrobat online help.)
Contents are listed below and in 19Conts.pdf. Authors Index, selected authors' e-mail, AE Literature,and meeting calendar are also available in the 19Conts file. Individual papers have file names of 19xxx.pdf, with xxx as their first page number.
Online Text: The pdf files of Volume 19 will become available online (but with security code) soon.
Look for them at www.e-jae.com.
ISSN 0730-0050

Copyright©2001 Acoustic Emission Group

Coden JACEDO

VIEWING AN ARTICLE
A. Use Adobe Acrobat Reader® (or Acrobat®) and open an individual file, 19-xxx.pdf.
B. Open READ-ME.pdf file, go to CONTENTS and browse the titles and authors, use a HAND tool of
Acrobat and click on the file you want to open.

CONTENTS
ACOUSTIC EMISSION EXAMINATION OF MODE I, MODE II AND MIXED-MODE I/II
INTERLAMINAR FRACTURE OF UNIDIRECTIONAL FIBER-REINFORCED POLYMERSJürgen Bohse and Jihua Chen
19-001
FRACTURE DYNAMICS IN NOTCHED PMMA PLATES BY LAMB WAVE ACOUSTICEMISSION ANALYSIS
Kenji Nagashima, Hideo Nishino, Mikio Takemoto and Kanji Ono
19-011
MONITORING OF MICRO-CRACKING DURING HEATING OF HYDROGEN DOPEDGERMANIUM AND SILICON SINGLE CRYSTALS BY ACOUSTIC EMISSION METHOD
K. Yoshida, Y. Wang, K. Horikawa, K. Sakamaki and K. Kajiyama
19-022aDAMAGE DETECTION IN A FIBER-REINFORCED CYLINDER (FISHING ROD) BY GUIDEDWAVE ACOUSTIC EMISSION ANALYSIS
Yoshie Hayashi, Yoshihiro Mizutani, Hideo Nishino, Mikio Takemoto and Kanji Ono
19-035
ACOUSTIC EMISSION CHARACTERIZATION AND NUMERICAL SIMULATION OF INTERNALDAMAGE PROGRESSION IN CFRP MULTI-DIRECTIONAL SYMMETRIC LAMINATESIsamu Ohsawa, Isao Kimpara, Kazuro Kageyama, Satoshi Abe, and Kazuo Hiekata
19-045SCC MONITORING OF ZIRCONIUM IN BOILING NITRIC ACID BY ACOUSTIC EMISSIONMETHOD
Chiaki Kato and Kiyoshi Kiuchi
19-053
ACOUSTIC EMISSION MONITORING OF CHLORIDE STRESS CORROSION CRACKING OFAUSTENITIC STAINLESS STEEL
Shinya Fujimoto, Mikio Takemoto and Kanji Ono
19-063CYLINDER WAVE ANALYSIS FOR AE SOURCE LOCATION AND FRACTURE DYNAMICS OF
STRESS CORROSION CRACKING OF BRASS TUBE
Fukutoshi Uchida, Hideo Nishino, Mikio Takemoto and Kanji Ono
19-075
DETECTION OF PRE-MARTENSITIC TRANSFORMATION PHENOMENA IN AUSTENITICSTAINLESS STEELS USING AN ACOUSTIC EMISSION TECHNIQUE
T. Inamura, S. Nagano, M. Shimojo, K. Takashima and Y. Higo
19-085
ACOUSTIC EMISSION ANALYSIS OF CARBIDE CRACKING IN TOOL STEELS
Kenzo Fukaura and Kanji Ono

19-091

SOURCE PARAMETERS OF ACOUSTIC EMISSION EVENTS IN SALT ROCK
Gerd Manthei, Jürgen Eisenblätter, Thomas Spies and Gernot Eilers

19-100

GEOMETRICAL COMPLEXITY OF ROCK INCLUSION AND ITS INFLUENCE ON ACOUSTICEMISSION ACTIVITY
Ken-Ichi Itakura, Atsushi Takashima, Tatsuma Ohnishi and Kazuhiko Sato19-109APPLICATION OF AE IMPROVED b-VALUE TO QUANTITATIVE EVALUATION OF
FRACTURE PROCESS IN CONCRETE MATERIALS
T. Shiotani, S. Yuyama, Z. W. Li and M. Ohtsu
19-118EVALUATION OF FRACTURE PROCESS IN CONCRETE JOINT BY ACOUSTIC EMISSION
T. Kamada, M. Asano, S. Lim, M. Kunieda and K. Rokugo
19-134b

DAMAGE DIAGNOSIS OF CONCRETE-PILES BY MACHINERY-INDUCED ACOUSTICEMISSION T. Shiotani, S. Miwa, Y. Ichimura and M. Ohtsu
19-142
ACOUSTIC EMISSION MONITORING OF CLOSELY SPACED EXCAVATIONS IN AN
UNDERGROUND REPOSITORY Thomas Spies and Jürgen Eisenblätter
19-153
ACOUSTIC EMISSION MONITORING OF THE JAS 39 GRIPEN COMBAT AIRCRAFTDan Lindahl and Markku Knuuttila
19-162
DETECTION AND LOCATION OF CRACKS AND LEAKS IN BURIED PIPELINES USING
ACOUSTIC EMISSION
S.J. Vahaviolos, R.K. Miller, D.J. Watts, V.V. Shemyakin, and S.A. Strizkov
19-172RECOMMENDED PRACTICE FOR IN SITU MONITORING OF CONCRETE STRUCTURES BY
ACOUSTIC EMISSION Masayasu Ohtsu and Shigenori Yuyama
19-184
ACOUSTIC EMISSION FROM ACTIVE CORROSION UNDER THE INSULATION OF A
SULPHUR TANK Phillip T. Cole and Stephen N. Gautrey
19-191A NEW SYSTEM FOR MACHINERY DIAGNOSIS USING AE AND VIBRATION SIGNALS
Atsushi Korenaga, Shigeo Shimizu, Takeo Yoshioka, Hidehiro Inaba, Hidemichi Komura and Koji
Yamamoto
19-196
DEVELOPMENT OF ABNORMALITY DETECTION TECHNOLOGY FOR ELECTRIC
GENERATION STEAM TURBINES
Akihiro Sato, Eisaku Nakashima, Masami Koike, Morihiko
Maeda, Toshikatsu Yoshiara and Shigeto Nishimoto
19-202
ACOUSTIC EMISSION SIGNAL CLASSIFICATION IN CONDITION MONITORING
USING THE KOLMOGOROV-SMIRNOV STATISTIC
L. D. Hall, D. Mba and R.H. Bannister19-209CHARACTERIZATION BY AE TECHNIQUE OF EMISSIVE PHENOMENA DURING STRESSCORROSION CRACKING OF STAINLESS STEELS
A. Proust, H. Mazille, P. Fleischmann and R. Rothea
19-229INVESTIGATION OF ACOUSTIC EMISSION WAVEFORMS ON A PRESSURE VESSEL
Mulu Bayray
19-241c

EFFECTS OF LATERAL PLATE DIMENSIONS ON ACOUSTIC EMISSION SIGNALS FROMDIPOLE SOURCES M. A. Hamstad, A. O'gallagher and J. Gary
19-258A WAVELET-BASED AMPLITUDE THRESHOLDING TECHNIQUE
FOR AE DATA COMPRESSION Gang Qi and Eng T. Ng

19-275

ACOUSTIC EMISSION MONITORING OF FATIGUE OF GLASS-FIBER WOUND PIPES UNDERBIAXIAL LOADING
Pawel Gebski, Leszek Golaski and Kanji Ono,
19-285

AE LITERATURE (CARP, Texas DOT, Wavelet, Indices, Meetings)
EWGAE 2002 (Prague, Czech Rep.)

IAES-16 (Tokushima, Japan)

JAE Cumulative Index for vol. 1-10, 1982-1992
JAE Cumulative Index for vol. 11-18, 1993-2000

2002 SUBSCRIPTION RATE
Base rate (CD-ROM) for one year
Add Postage of
U.S. - Priority rate
All others - Air mail rate
CD-ROM + printed copy, surface mail: add
New for 2002
Print copy only
surface$ 96.00$ 8.00$ 8.00$ 40.00$134.00

VISA and Mastercard orders accepted. Payment must be in U.S. dollars drawn on a U.S. bank. Banktransfer accepted. (Account No. 080-03416470) at
California Bank and Trust, San Fernando Valley Office16140 Ventura Blvd,
Encino, CA 91436 USA
One volume of four issues per year through vol. 17. Index included in the last issue. Back issues
available at $50 per volume for Vols. 1-3, $90 per volume for Vols. 4-8, $96 per volume for Vols. 9-18.
(CD-ROM for Vol. 18). US surface postage $8 per volume. For Canada and overseas surface delivery,
add $11 and for air mail, add $30 per volume (shipping charges will be adjusted for multi-volume
order).
All orders should be sent to (or Fax 1-818-990-1686): Note our new address.
Acoustic Emission GroupPMB 409, 4924 Balboa Blvd.,
Encino, CA 91316 USA
For inquiry through Internet, use the following address:
Editor-Publisher
Publication Date of This Issue (Volume 19):d

ono@ucla.edu
Kanji Ono Tel. (310) 825-5233
30 November 2001.

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

ISSN 0730-0050

Copyright©2001 Acoustic Emission Grouptors on any major change. The Editor, Professor Kanji Ono, has
the general responsibility for all the matters. Associate Editors
assist the review processes as lead reviewers. Mr. T.F. Drouillard
is responsible for the AE Literature section. The members of the
Editorial Board are selected for their knowledge and experience on
AE and will advise and assist the editors on the publication
policies and other aspects. The Board presently includes thefollowing members:
P. Fleischmann
L. Golaski
M.A. Hamstad
H.R. Hardy, Jr.
T. Kishi
O.Y. Kwon
J.C. Lenain
S.L. McBride
C.R.L. Murthy
A.A. Pollock
I. RomanC. Scala
C. ScrubyM. Wevers
B.R.A. Wood

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

4. Publication
Journal of Acoustic Emission is published annually in CDROM by Acoustic Emission Group, PMB 409, 4924 Balboa Blvd,
Encino, CA 91316. It may also be reached at 6531 Boelter Hall,
University of California, Los Angeles, California 90095-1595(USA). tel. 310-825-5233. FAX 818-990-1686. e-mail:ono@ucla.edu
5. Subscription
Subscription should be sent to Acoustic Emission Group.
Annual rate for 2001 and 2002 is US $104.00 including CD-ROM
delivery, by priority mail in the U.S. and by air for Canada andelsewhere. For additional print copy, add $32. Overseas orders
must be paid in US currencies with a check drawn on a US bank.
VISA/MC accepted. Inquire for individual (with institutional
order) and bookseller discounts.
6. Advertisement
No advertisement will be accepted, but announcements forbooks, training courses and future meetings on AE will be
included without charge.

- CODEN: JACEDO-

All rights reserved.

JOURNAL OF ACOUSTIC EMISSION, VOLUME 19, 2001
CONTENTS
ACOUSTIC EMISSION EXAMINATION OF MODE I, MODE II AND MIXED-MODE I/II
INTERLAMINAR FRACTURE OF UNIDIRECTIONAL FIBER-REINFORCED POLYMERSJürgen Bohse and Jihua Chen
1
FRACTURE DYNAMICS IN NOTCHED PMMA PLATES BY LAMB WAVE ACOUSTICEMISSION ANALYSIS
Kenji Nagashima, Hideo Nishino, Mikio Takemoto and Kanji Ono

11

MONITORING OF MICRO-CRACKING DURING HEATING OF HYDROGEN DOPEDGERMANIUM AND SILICON SINGLE CRYSTALS BY ACOUSTIC EMISSION METHOD
K. Yoshida, Y. Wang, K. Horikawa, K. Sakamaki and K. Kajiyama
22
DAMAGE DETECTION IN A FIBER-REINFORCED CYLINDER (FISHING ROD) BY GUIDEDWAVE ACOUSTIC EMISSION ANALYSIS
Yoshie Hayashi, Yoshihiro Mizutani, Hideo Nishino, Mikio Takemoto and Kanji Ono
35
ACOUSTIC EMISSION CHARACTERIZATION AND NUMERICAL SIMULATION OF INTERNALDAMAGE PROGRESSION IN CFRP MULTI-DIRECTIONAL SYMMETRIC LAMINATESIsamu Ohsawa, Isao Kimpara, Kazuro Kageyama, Satoshi Abe, and Kazuo Hiekata
45
SCC MONITORING OF ZIRCONIUM IN BOILING NITRIC ACID BY ACOUSTIC EMISSIONMETHOD
Chiaki Kato and Kiyoshi Kiuchi
53
ACOUSTIC EMISSION MONITORING OF CHLORIDE STRESS CORROSION CRACKING OFAUSTENITIC STAINLESS STEEL
63
Shinya Fujimoto, Mikio Takemoto and Kanji Ono
CYLINDER WAVE ANALYSIS FOR AE SOURCE LOCATION AND FRACTURE DYNAMICS OF
STRESS CORROSION CRACKING OF BRASS TUBE
Fukutoshi Uchida, Hideo Nishino, Mikio Takemoto and Kanji Ono
75
DETECTION OF PRE-MARTENSITIC TRANSFORMATION PHENOMENA IN AUSTENITICSTAINLESS STEELS USING AN ACOUSTIC EMISSION TECHNIQUE
T. Inamura, S. Nagano, M. Shimojo, K. Takashima and Y. Higo
85i

ACOUSTIC EMISSION ANALYSIS OF CARBIDE CRACKING IN TOOL STEELS
Kenzo Fukaura and Kanji Ono

91

SOURCE PARAMETERS OF ACOUSTIC EMISSION EVENTS IN SALT ROCK
Gerd Manthei, Jürgen Eisenblätter, Thomas Spies and Gernot Eilers

100

GEOMETRICAL COMPLEXITY OF ROCK INCLUSION AND ITS INFLUENCE ON ACOUSTICEMISSION ACTIVITY
Ken-Ichi Itakura, Atsushi Takashima, Tatsuma Ohnishi and Kazuhiko Sato109
APPLICATION OF AE IMPROVED b-VALUE TO QUANTITATIVE EVALUATION OF
FRACTURE PROCESS IN CONCRETE MATERIALS
T. Shiotani, S. Yuyama, Z. W. Li and M. Ohtsu

118

EVALUATION OF FRACTURE PROCESS IN CONCRETE JOINT BY ACOUSTIC EMISSION
T. Kamada, M. Asano, S. Lim, M. Kunieda and K. Rokugo
134
DAMAGE DIAGNOSIS OF CONCRETE-PILES BY MACHINERY-INDUCED ACOUSTICEMISSION T. Shiotani, S. Miwa, Y. Ichimura and M. Ohtsu

142

ACOUSTIC EMISSION MONITORING OF CLOSELY SPACED EXCAVATIONS IN AN
UNDERGROUND REPOSITORY Thomas Spies and Jürgen Eisenblätter

153

ACOUSTIC EMISSION MONITORING OF THE JAS 39 GRIPEN COMBAT AIRCRAFTDan Lindahl and Markku Knuuttila

162

DETECTION AND LOCATION OF CRACKS AND LEAKS IN BURIED PIPELINES USING
ACOUSTIC EMISSION
S.J. Vahaviolos, R.K. Miller, D.J. Watts, V.V. Shemyakin, and S.A. Strizkov
172
RECOMMENDED PRACTICE FOR IN SITU MONITORING OF CONCRETE STRUCTURES BY
ACOUSTIC EMISSION Masayasu Ohtsu and Shigenori Yuyama
184
ACOUSTIC EMISSION FROM ACTIVE CORROSION UNDER THE INSULATION OF A
SULPHUR TANK Phillip T. Cole and Stephen N. Gautrey

191

A NEW SYSTEM FOR MACHINERY DIAGNOSIS USING AE AND VIBRATION SIGNALS
Atsushi Korenaga, Shigeo Shimizu, Takeo Yoshioka, Hidehiro Inaba, Hidemichi Komura and Koji
Yamamoto
196

iiDEVELOPMENT OF ABNORMALITY DETECTION TECHNOLOGY FOR ELECTRIC
GENERATION STEAM TURBINES
Akihiro Sato, Eisaku Nakashima, Masami Koike, Morihiko
Maeda, Toshikatsu Yoshiara and Shigeto Nishimoto
202
ACOUSTIC EMISSION SIGNAL CLASSIFICATION IN CONDITION MONITORING
USING THE KOLMOGOROV-SMIRNOV STATISTIC
L. D. Hall, D. Mba and R.H. Bannister

209

CHARACTERIZATION BY AE TECHNIQUE OF EMISSIVE PHENOMENA DURING STRESSCORROSION CRACKING OF STAINLESS STEELS
A. Proust, H. Mazille, P. Fleischmann and R. Rothea
229
INVESTIGATION OF ACOUSTIC EMISSION WAVEFORMS ON A PRESSURE VESSEL
Mulu Bayray

241

EFFECTS OF LATERAL PLATE DIMENSIONS ON ACOUSTIC EMISSION SIGNALS FROMDIPOLE SOURCES M. A. Hamstad, A. O'gallagher and J. Gary
258
A WAVELET-BASED AMPLITUDE THRESHOLDING TECHNIQUE
FOR AE DATA COMPRESSION
Gang Qi and Eng T. Ng

275

ACOUSTIC EMISSION MONITORING OF FATIGUE OF GLASS-FIBER WOUND PIPES UNDERBIAXIAL LOADING
Pawel Gebski, Leszek Golaski and Kanji Ono,
285
Contents of Volume 19
Authors Index
e-Mail Addresses of Selected Authors
AE Literature (CARP/TEXAS DOT; AGU-Vallen Wavelet; JAE Indices)
Meeting Calendar

i
iii
v
vi
ix

AUTHORS INDEX, Volume 19
Satoshi Abe,
M. Asano,
R.H. Bannister
Mulu Bayray
Jürgen Bohse
Jihua Chen
Phillip T. Cole
Gernot Eilers

Jürgen Eisenblätter,
P. Fleischmann
Shinya Fujimoto,
Kenzo Fukaura
J. Gary
Stephen N. Gautrey
Pawel Gebski
Leszek Golaski

45
134
209
241
1
1
191
100
iii

100, 153
229
63
91
258
191
285
285

L. D. Hall,
M. A. Hamstad,Yoshie Hayashi,Kazuo Hiekata
Y. HigoK. Horikawa,Y. Ichimura
Hidehiro Inaba,
T. Inamura,
Ken-Ichi Itakura,
Kazuro Kageyama,
K. Kajiyama
T. Kamada,
Chiaki Kato
Isao Kimpara,
Kiyoshi Kiuchi
Markku Knuuttila
Masami Koike,
Hidemichi Komura
Atsushi Korenaga,
M. Kunieda
Z. W. Li
S. Lim,Dan Lindahl
Morihiko Maeda,
Gerd Manthei,H. Mazille,
D. MbaR.K. Miller,
S. Miwa,
Yoshihiro Mizutani,
S. Nagano,Kenji Nagashima,
Eisaku Nakashima,
Eng T. Ng
Shigeto Nishimoto
Hideo Nishino,
A. O'gallagher
Tatsuma Ohnishi
Isamu Ohsawa,
Masayasu Ohtsu

Kanji Ono11, 35, 63, 75, 91, 285
A. Proust,229
Gang Qi
275
K. Rokugo
134
R. Rothea
229
K. Sakamaki
22
Akihiro Sato,
202
Kazuhiko Sato
109
V.V. Shemyakin,
172
Shigeo Shimizu,
196
M. Shimojo,
85
T. Shiotani,
118, 142
Thomas Spies
100, 153
S.A. Strizkov
172
Atsushi Takashima,
109
K. Takashima85
Mikio Takemoto
11, 35, 63, 75
Fukutoshi Uchida,
75
S.J. Vahaviolos,
172
Y. Wang,
22
D.J. Watts,
172
Koji Yamamoto
196
Toshikatsu Yoshiara
202
K. Yoshida,
22
Takeo Yoshioka,
196
Shigenori Yuyama
118, 184

209
258
35
45
85
22
142
196
85
109
45
22
134
53
45
53
162
202
196
196
134
118
134
162
202
100
229
209
172
142
35
85
11
202
275
202
11, 35, 75
258
109
45
118, 142, 184iv

e-MAIL ADDRESSES OF SELECTED AUTHORS
Mulu Bayray <mbayray@iaa1.iaa.tuwien.ac.at>
Juergen Bohse <Juergen.Bohse@bam.de>Phil Cole <philcoleuk@aol.com>
Leszek Golaski <bmtlg@tu.kielce.pl>
Marvin Hamstad <hamstad@boulder.nist.gov>, <mhamstad@du.edu>
Yakichi Higo <yhigo@pi.titech.ac.jp>
Ken-ichi Itakura <itakura@epsilon2.csse.muroran-it.ac.jp>
T. Kamada <kamada@cc.gifu-u.ac.jp>
Chiaki Kato <multi@popsvr.tokai.jaeri.go.jp>
Atsushi Korenaga <korenaga@mel.go.jp>
Dan Lindahl <dan.lindahl@csm.se>
M. Maeda <maeda@hihakaikensa.co.jp>
Gerd Manthei <gmugmrh@t-online.de>David Mba <D.Mba@cranfield.ac.uk>
Isamu Ohsawa<ohsawa@ygg.naoe.t.u-tokyo.ac.jp>
Masayasu Ohtsu <ohtsu@gpo.kumamoto-u.ac.jp>
Kanji Ono <ono@ucla.edu>
Alan Proust <epa.mkg@wanadoo.fr>
Gary Qi <gangqi@memphis.edu>
Tomoki, Shiotani <shiotani@mx1.alpha-web.ne.jp>, <t.shiotani@citg.tudelft.nl>
Thomas Spies <thomas.spies@bgr.de>
Mikio Takemoto <takemoto@me.aoyama.ac.jp>
Sotirios J. Vahaviolos <sotirios@pacndt.com>
Kenichi Yoshida <yoshida@me.tokushima-u.ac.jp>
Shigenori Yuyama <aeyuyama@sh0.po.iijnet.or.jp>v

AE LITERATURETWO SECURE PDF FILES: CARP and TEXAS DOT
The following two documents were provided by the courtesy of Professor Tim Fowler,
University of Texas. Both documents are working papers at this time and should be treated as “tobe copyrighted” materials. That is, these are not in the public domain. Since the CARP is
dormant at present, the CARP document publication is not imminent, while Texas DOT is
expected to complete its work soon. However, both will be valuable to AE workers and areincluded here with security code. If interested, please send request for the security codes to the
Editor at ono@ucla.edu and the best possible accommodation will be attempted.
CARP, RECOMMENDED PRACTICE FOR ACOUSTIC EMISSION EVALUATION OF
FIBER REINFORCED PLASTIC (FRP) TANKS AND PRESSURE VESSELS, Draft I, October
1999. (CARP.PDF)
TEXAS DEPARTMENT OF TRANSPORTATION, PROCEDURE FOR ACOUSTICEMISSION MONITORING OF PRESTRESSED CONCRETE GIRDERS, Draft B, May 2001.
(TEXAS.PDF)

AGU-VALLEN WAVELET
A free software, AGU-Vallen Wavelet is online and can be downloaded as Zip-file. A password
is required to unpack the file, which can be requested from the same site.
http://www.vallen.de/wavelet
Text file of the site follows:
AGU-Vallen Wavelet
AGU-Vallen Wavelet is a tool which allows to calculate and display the wavelet transformon individual waveforms. AGU-Vallen wavelet is free!! See below how to request it.
AGU-Vallen Wavelet has been developed in collaboration between Vallen-Systeme GmbHand Aoyama Gakuin University (AGU), Tokyo, Japan. The AGU group has pioneered in the
research of wavelet analysis in the field of Acoustic Emission and Vallen has actively generated
software tools.

viAbout Wavelet Transformation
A wavelet transform (WT) of an AE signal provides information which wave modes carrysignificant energy and where in a mode the energy as a function of frequency and time is to befound. Thereby a WT can communicate very useful information. In the following diagram you
see an example for a waveform (top) and the corresponding wavelet transform (below). The
superposed curves show the result of a dispersion curve calculation (by Vallen Dispersion). The
wave modes are identified by arrows. [See figures at Vallen site. Ed.]
Contributors
AGU-Vallen Wavelet has been developed in collaboration between Vallen-Systeme GmbHand with Aoyama Gakuin University (AGU), Tokyo, Japan. The AGU group has pioneered in
the research of wavelet analysis in the field of Acoustic Emission and Vallen has actively
generated software tools. The following is a list of persons, who have significantly contributed to
the AGU-Vallen Wavelet program:
Prof. Mikio Takemoto, Aoyama Gakuin University (AGU), Tokyo, Japan
Head of Materials Science Laboratory, Aoyama Gakuin University (AGU). Prof. Takemoto
has guided the development of the AGU-wavelet from the first stage. Together with Prof.
Ono (UCLA), Prof. Takemoto ignited the initial spark to start the development of AGUVallen Wavelet project.
Prof. Kanji Ono, University of California (UCLA), Los Angeles, USA
Prof. Ono has participated in wavelet research at AGU and facilitated a contact between
AGU and Vallen. Prof. Ono is author of many AE and wavelet papers published or presented
at international conferences. See also the literature references at the end of this page.
Prof. Yasuhisa Hayashi*, Shizuoka University, Shizuoka, Japan
Dr. Hideo Cho*, Tohoku University, Sendai, Japan
Dr. Hiroaki Suzuki*, Chiyoda Co., Kawasaki, Japan
Tetsuo Kinjo**, Ishikawajima-Harima Heavy Industries Co. Ltd.
* Former assistants of Materials Science Laboratory in AGU.
** Graduate of Graduate School, AGU.
Prof. Hayashi, Dr. Suzuki and Mr. Kinjo created the initial AGU-wavelet software for
Acoustic Emission analysis. Prof. Hayashi and Dr. Cho demonstrated the usage of wavelet
transform for determining the group velocity of elastic waves.
Dr. Yoshihiro Mizutani, Aoyama Gakuin University (AGU), Tokyo, Japan
Assistant, Experimental Solid Mechanics Laboratory, AGU. Graduate of Materials Science
Laboratory in AGU.Dr. Mizutani completely upgraded the original AGU wavelet algorithm
for arbitrary frequency analysis. His algorithm is implemented in the AGU-Vallen Wavelet
program with kind permission of Dr. Mizutani and Prof. Takemoto.

viiProf. Marvin A. Hamstad, National Institute of Standards and Technology (NIST), Boulder, Co
USA and University of Denver, Co. USA
Prof. Hamstad is the very first scientific user of AGU-Vallen Wavelet. He accompanied thedevelopment of the program and contributed data samples created by finite element
modelling (FEM) and an overview presentation about the use and value of wavelet
transformation. This presentation together with FEM samples is included in the distributionof AGU-Vallen Wavelet with the kind permission of Prof. Hamstad and can be downloaded
below.
Presentation of Professor Hamstad
An Illustrated Overview of the Use and Value of a Wavelet Transformation to Acoustic Emission
Technology (hamstad.pdf: ca. 661 KB)
References
Takemoto M., Nishino H., Ono K., "Wavelet Transform - Applications to AE Signal Analysis",
Acoustic Emission - Beyond the Millennium, Elsevier (2000), pp. 35-56.
Suzuki H., Kinjo T., Hayashi Y., Takemoto M., Ono K., Appendix by Hayashi Y., "Wavelet
Transform of Acoustic Emission Signals", Journal of Acoustic Emission, Vol. 14, No. 2(1996, April-June), pp. 69-84.
Hayashi Y., Ogawa S., Cho H., Takemoto M., "Non-contact estimation of thickness and elastic
properties of metallic foils by wavelet transform of laser-generated waves", NDT and E
International, Vol. 32, No. 1 (1999, January) pp. 21-27 (1999).
Yamada H., Mizutani Y., Nishino H., Takemoto M. Ono K., "Lamb Wave Source Location of
Impact on Anisotropic Plates", Journal of Acoustic Emission, Vol. 18, (2000, December), pp.
51-60.

INDICES OF J. OF ACOUSTIC EMISSION, 1982-2000
Two pdf files are included with this CD-ROM. These are
JAE1-10.pdf (for 1982-1992) and JAE11-18.pdf (for 1993-2000)
and list the contents of Journal of Acoustic Emission from the beginning. These are indexed
together with Volume 19 so that subject and author search can be made.

viii

MEETING CALENDAR
EWGAE 2002: 25-th European Conference on Acoustic Emission Testing
September 11 - 13, 2002, Prague, Czech Republic
EWGAE 2002 will be organised by European Working Group on Acoustic Emission, Czech
Society for Non-destructive Testing (Acoustic Emission Working Group), Czech Technical
University in Prague, Brno University of Technology.
October 31, 2001
November 2001
May 31, 2002
August 2002

• submitting paper abstract (200-300 words)• notification of acceptance, second call for papers• full papers, preliminary payment• announcement of the final programme

Further information on web site: http://www.cndt.cz: E-mail: cndt@cndt.cz
See also EWGAE.pdf.

The 16-th International Acoustic Emission Symposium (IAES-16)
November 12 – 15, 2002: The University of Tokushima, Tokushima, JAPAN
Prospective authors are requested to submit title, author’s names, affiliation and address by fax
or e-mail with an abstract of 100-200 words with key words. The official language of thesymposium and the proceedings will be English. IAES-16 will be organized by the Japan Soc.
For Non-Destructive Inspection, T. Kishi and Y. Higo, co-chairs. Please note dates changed froman earlier announcement.
April 30, 2002
Deadline for arrival of abstract.
June 30, 2002
Notification of acceptance and instruction for authors.
August 31, 2002 Deadline for full papers. Registration and remittance of fee.
September 30, 2002 Second announcement and program.
Symposium secretary and e-Mail Address:
Prof. Kenichi YOSHIDA
E-mail: yoshida@me.tokushima-u.ac.jp: See also IAES-16.pdf.

The 45-th Meeting of AEWG
Details to be announced. To be held in 2002 at Northwestern University, Evanston, IL.
Send inquiry to David Prine (dprine@northwestern.edu).

ix

JOURNAL OF ACOUSTIC EMISSION, VOLUME 19
Two folders are provided for Windows 95/98/ME/2000 and for Macintosh. Each contains Adobe
Acrobat Reader® Installer (ACRD4ENU.EXE or Reader Installer). If you don't have a softwarethat can read a pdf-file, install one first. Adobe Acrobat®, which can be purchased from Adobe:
www.adobe.com, will enable you to utilize "Search" function using an INDEX file provided (in the
Index folder). This allows you to perform full-text search (Launch Acrobat and choose Edit >Search > Quary.)
Contents are listed in 19Conts.pdf. Authors Index, selected authors' e-mail, AE Literature, andmeeting calendar are also available in the 19Conts file.
Online Text: The pdf files of Volume 19 will become available online (but with security code)soon. Look for them at www.e-jae.com.
ISSN 0730-0050

Copyright©2001 Acoustic Emission Group

Coden JACEDO

2002 SUBSCRIPTION RATE
Base rate (CD-ROM) for one year
Add Postage of
U.S. - Priority rate
All others - Air mail rate
CD-ROM + printed copy, surface mail: add$ 96.00$ 8.00$ 8.00$ 40.00

New for 2002

$134.00

Print copy only

surfaceVISA and Mastercard orders accepted. Payment must be in U.S. dollars drawn on a U.S. bank.
Bank transfer accepted. (Account No. 080-03416470) at
California Bank and Trust
San Fernando Valley Office16140 Ventura Blvd,
Encino, CA 91436 USA
One volume of four issues per year through vol. 17. Index included in the last issue. Back issues
available at $50 per volume for Vols. 1-3, $90 per volume for Vols. 4-8, $96 per volume for Vols.
9-18. (CD-ROM for Vol. 18). US surface postage $8 per volume. For Canada and overseas surface
delivery, add $11 and for air mail, add $30 per volume (shipping charges will be adjusted for multivolume order).
All orders should be sent to (or Fax 1-818-990-1686): Note our new address.
Acoustic Emission GroupPMB 409, 4924 Balboa Blvd.
Encino, CA 91316 USA
For inquiry through Internet, use the following address:
Editor-Publisher
Publication Date of This Issue (Volume 19):x

ono@ucla.edu
Kanji Ono Tel. (310) 825-5233
30 November 2001.

Notes for Contributors
1. General
The Journal will publish contributions from all parts of theworld and manuscripts for publication should be submitted
to the Editor. Send to:Professor Kanji Ono, Editor - JAE
6531 Boelter Hall, MSE Dept.
University of California
Los Angeles, California 90095-1595 USA
FAX (1) 818-990-1686
e-mail: ono@ucla.edu
Authors of any AE related publications are encouraged tosend a copy for inclusion in the AE Literature section to:
Mr. T.F. Drouillard, Associate Editor - JAE
11791 Spruce Canyon Circle
Golden, Colorado 80403 USA
Only papers not previously published will be accepted.
Authors must agree to transfer the copyright to the Journal
and not to publish elsewhere, the same paper submitted toand accepted by the Journal. A paper is acceptable if it is arevision of a governmental or organizational report, or if it
is based on a paper published with limited distribution.
Compilation of annotated data files will be accepted forinclusion in CD-ROM distribution, so that others can sharein their analysis for research as well as training. ASCII or
widely accepted data formats will be required.
The language of the Journal is English. All papers should
be written concisely and clearly.
2. Page Charges
No page charge is levied. One copy of CD-ROM will be
supplied to the authors free of charge.
3. Manuscript for Review
Manuscripts for review need only to be typed legibly;
preferably, double-spaced on only one side of the page withwide margins. The title should be brief. An abstract of 100200 words is needed for Research and Applications articles.
Except for short communications, descriptive heading
should be used to divide the paper into its component parts.
Use the International System of Units (SI).
References to published literature should be quoted in the
text citing authors and the year of publication. These are to

be grouped together at the end of the paper in alphabeticaland chronological order. Journal references should bearranged as below. Titles for journal or book articles arehelpful for readers, but may be omitted.
H.L. Dunegan, D.O. Harris and C.A. Tatro (1968), Eng.
Fract. Mech., 1, 105-122.
Y. Krampfner, A. Kawamoto, K. Ono and A.T. Green(1975), "Acoustic Emission Characteristics of Cu Alloys
under Low-Cycle Fatigue Conditions," NASA CR-134766,
University of California, Los Angeles and Acoustic Emission Tech. Corp., Sacramento, April.
A.E. Lord, Jr. (1975), Physical Acoustics: Principles andMethods, vol. 11, eds. W. P. Mason and R. N. Thurston,Academic Press, New York, pp. 289-353.
Abbreviations of journal titles should follow those used in
the ASM Metals Abstracts. In every case, authors' initials,
appropriate volume and page numbers should be included.
The title of the cited journal reference is optional.
Illustrations and tables should be planned to fit a single
page width (6.5"). For the reviewing processes, these neednot be of high quality, but submit glossy prints orequivalent electronic files with the final manuscript. Lines
and letters should be legible.
4. Review
All manuscripts will be judged by qualified reviewer(s).
Each paper is reviewed by one of the editors and may besent for review by members of the Editorial Board. The
Board member may seek another independent review. In
case of disputes, the author may request other reviewers.
5. Electronic MediaThis Journal will be primarily distributed electronically by
CD-ROM. In order to expedite processing and minimize
errors, the authors are requested to submit electronic files
or floppy disk copy of the paper. We can read Macintosh
and IBM PC formats. On the INTERNET, you can send an
MS Word file or the text portion to "ono@ucla.edu".
6. Color Illustration
We can process color illustration needed to enhance thetechnical content of an article. With the new format,
authors are encouraged to use them.

ACOUSTIC EMISSION EXAMINATION OF MODE I, MODE II AND MIXED-MODE
I/II INTERLAMINAR FRACTURE OF UNIDIRECTIONAL FIBER-REINFORCED
POLYMERSJÜRGEN BOHSE and JIHUA CHEN *BAM, Federal Institute for Materials Research and Testing,
Unter den Eichen 87, D-12200 Berlin, Germany*
Technical University of Berlin, Institute of Nonmetallic Materials
Englische Strasse 20, D-10587 Berlin, Germany
ABSTRACTThe interlaminar fracture behavior of unidirectional fiber-reinforced GF/Epoxy and
GF/Polyamide-12 laminates is evaluated by mode I, mode II and mixed-mode I/II fracture
mechanics tests combined with acoustic emission (AE) monitoring. New fracture criteria of the
micro-crack initiation point (INITlocAE) and the start of macroscopic delamination (INIPROPlocAE) propagating along the whole crack front are defined by means of time-dependent
linear location of AE sources. Furthermore, by AE examination, the length l D of the damage
zone around the delamination tip is determined. From the analysis of the frequency power
spectra of individual AE signals, matrix cracking and fiber/matrix debonding are identified as
micro-failure mechanisms and the energy contribution of these mechanisms in specific fracture
stages is calculated. The damage zone geometry is calculated by finite element (FE) modeling of
the elastic strain-energy distribution around the delamination tip using the critical strain-energy
Wc . The critical strain-energy was determined experimentally with three-point bending tests bymeans of AE monitoring. Results indicate that the micro-failure mechanisms are induced by thedelamination process itself as well as by compression of the flexure-loaded cantilevers. The
height of the damage zone is approximated by 2rD ≈ l D /10.
1. INTRODUCTION
According to the standard procedure [1], crack initiation is characterized by the onset ofvisually recognizable delamination growth on the edge of the specimen (VIS-point) or by theonset of non-linearity on the load-displacement curve (NL-point). However, the visual
determination of the initiation point and the NL-point is operator-dependent. Sometimes it isdifficult to find out the NL-point in the load-displacement curve. Besides, the NL-point is notwell defined from the fracture mechanics point of view. Another problem arises from friction
between the cantilever surfaces under mode II and mixed-mode I/II loading conditions, whichlead to an overestimation of the delamination toughness by use of standard methods. A
theoretical calculation of the fracture toughness on the basis of micro-mechanical dissipation
mechanisms requires information on which failure mechanisms are activated and on the size ofthe damage zone [2, 3]. The AE monitoring is a powerful tool for evaluation and interpretation of
fracture stages by characterizing (a) the failure on a microscopic scale, (b) the mechanisms
involved and (c) the size of the damage zone. In this article, AE results from different
interlaminar fracture modes are discussed and a micro-mechanical interpretation of the damage
zone is presented.

J. Acoustic Emission, 19 (2001)

1© 2001 Acoustic Emission Group2. EXPERIMENTAL
Specimens and Materials
Double Cantilever Beam (DCB: mode I), End Notched Flexure (ENF: mode II) and MixedMode Bending (MMB: mixed-mode I/II) fracture mechanics tests on specimens with 120 mm
total length, 20 mm width, about 4 mm total thickness and initial delamination length a0 were
performed to measure the critical energy release rate under mode I, mode II and mixed-mode I/II
crack loading conditions. A load-introduction with a crosshead speed of 2 mm/min (DCB) and
0.5 mm/min (ENF and MMB) was used. Starter cracks were produced by double layers of Alfoil insert with 20 µm total thickness. The delamination growth was observed visually with an
optical microscope on one edge of the specimen as well as by the located AE sources.
Unidirectional continuous reinforced glass-fiber/epoxy (GF/Epoxy) laminates (42% fiber
volume; 20-µm fiber diameter) were fabricated by using a hot press and wet impregnation
process. Glass-fiber/polyamide-12 (GF/PA-12) laminates (34% fiber volume; 20-µm fiber
diameter) were manufactured by special dry powder impregnation of tapes and following hot
press process [4].
Delamination Modes and AE Sensor Position
Figure 1 characterizes the loading conditions and the positions of the AE sensors at theexamined delamination modes. The vertical arrows show the loading points and directions, a isthe actual delamination length, Y is the thickness direction and X is the length direction of thespecimen, which also indicates the direction of crack growth and the position of located AE
events.
DCB test

ENF test
Sensor 1

Sensor 1

Sensor 2

Sensor 2

MMB test
Sensor 1a

Sensor 2

Y

Fig. 1 Loading and sensor positions
at different delamination modes.

x

AE Instrumentation
A two-channel MISTRAS 2001 system (Physical Acoustics Corp.) was used for thedetermination of AE events and AE sources in a linear-location setup during delamination tests.
AE signals were monitored by two wideband transducer (PAC WD) with a sensor spacing of 75mm, preamplifiers (PAC 1220A) with 40 dB gain and bandpass filters (20 to 1200kHz), fixed
thresholds of 40 dBAE and a transient waveform recording with a sampling frequency of 4 MHz.
2

3. RESULTS
Delamination Behavior and AE Characteristics

Load (N) and Energy

Position X (mm)

The AE examination characterizes microscopic and macroscopic aspects of the delamination.
Figure 2a shows the detected AE signals from a DCB fracture test. The characterization of thedamage zone length l D is realized by time-dependent linear location of AE sources around thetip of the delamination (Fig. 2b and Fig. 3). The crack speed has been quantified from the slope.

(a)Time (s)(b)

Time (s)DCB test (a0 = 40 mm)

Position X (mm)Position X (mm)Fig.2 (a) Delamination load (line) and AE signal energy (rel. units, points) and (b) timedependent position and size of the damage zone determined by location of AE sources withamplitudes at the sensor ≥ 60 dBAE under mode I loading of GF/PA-12.

Time (s)Time (s)(a) ENF test (a0 = 25 mm)(b) MMB test (GI/GII = 4/1; a0 = 40 mm)

Fig.3 Time-dependent position and size of the damage zones (AE events with amplitudes at thesensor ≥ 60 dBAE) under (a) mode II and (b) mixed-mode I/II loading of GF/PA-12.
3

The analysis of AE signal parameter combined with a linear location of AE sources gives ahigher reliability that recorded signals are emitted really from the delamination process zone.
First AE events located near the delamination tip (X = a0) characterize the start of micro-crack
initiation (INITlocAE). AE location plots also show the movement of the damage zone. This canbe used for the determination of the initiation of macroscopic delamination (INI-PROPlocAE)
propagating along the whole crack front before the maximum load is reached. Fracture criteria
derived from location of AE sources reflect an improved definition of the stages of delamination
initiation and propagation than the visual criterion and the NL-point.
Calculation of the Interlaminar Fracture Toughness
If criteria derived from the AE event location are applied, the critical energy release rate
values, GIC, GIIC and GI/IIC given in Table 1 are calculated. These results are compared with GC
values from NL-points and stable delamination growth (PROP) according to the standard
methods [1].
Table 1: Interlaminar fracture toughness calculated from different fracture stages at mode I,
mode II and mixed-mode I/II loading conditions.
Composites -a0

B

INITlocAE *NL ***

Toughness

mm

mm

J/m2

J/m2

J/m2

J/m2

- GIC

40

20

28±4

195±20388±70379±53

GF/PA 12 - GIC

40201028±843454±161

4801±365

5213±493- GIIC2520160±361700

1512±62-- GI/IIC (4/1)

4020450±201862

2031±276-- GI/IIC (1/1)

40

20

510

1228

1251

-

GF/EP

INI-PROPlocAE ** PROP ****Calculated with the Corrected Beam Theory from load-displacement point where first AE sources withamplitudes at the sensor ≥ 60 dBAE are located near the delamination tip using a0 as delamination length.
** Calculated with the Corrected Beam Theory from that point where located AE sources (amplitudes at thesensor ≥ 60 dBAE) indicate a propagation onset of the complete delamination tip using a0 as delamination
length.
*** Average values calculated with the Corrected Beam Theory using a0 as delamination length for NL-point andactual delamination length for PROP values.

The initiation values GC(INITlocAE) calculated from AE results agree with the theoretically
expected tendency: GIC > GI/IIC > GIIC. In contrast to NL-points or PROP values according tostandard methods, the AE evaluation is based on the delamination initiation on a microscopic
level where the friction between the cantilever surfaces is low and hence, the fracture toughness
results are not falsified by the friction energies. Besides, it is not possible to determine a
GC(PROP) by the standard methods under mode II and mixed-mode I/II in consequence of theflexure load introduced in front of the delamination tip. However, the GC(INI-PROPlocAE) values
based on the AE analysis is able to characterize the beginning of the macroscopic crack
propagation under these delamination modes. Table 1 shows that the GC(NL) values are lower
than the GC(INI-PROPlocAE) values. A reason for this phenomenon is damages around thedelamination crack surface and in the compression zones of the flexure-loaded cantilevers (X <4

a0 in Fig. 2b and Fig. 3b), which yields a non-linear load-displacement curve without
delamination propagation. Additional damage is detected at the upper load point (X ≈ 62.5 mm)
during ENF tests (Fig. 3a).

Fig. 4. Averaged AE power spectra of matrix cracking (top) and fiber breakage (bottom) insingle fiber CF/Epoxy.
5

Identification of AE Source Mechanisms
A micro-mechanical fracture model was developed to approximate the contribution of matrix
and interface mechanisms to the interlaminar fracture energy. A correlation of the AE energy
rate with the delamination energy release rate was found [2, 3].
By means of model specimens, we stimulated different AE source mechanisms.
Characteristic relaxation frequencies in the ranges between 100 to 350 kHz (matrix cracking) and
350 to 700 kHz (fiber break) are good candidates for distinguishing the mechanisms by a power
spectrum analysis of the AE waveforms (Fig. 4).
For the assignment of AE waves to single failure mechanisms a 70%-power criterion wasapplied. It is assumed that matrix cracks have at least 70% of the signal power in the 100 to 350
kHz and fiber break in the 350 to 700 kHz frequency interval. All signals in-between are
expected to be released from fiber/matrix debonding processes. The procedure of selection andclassification of AE signals is described elsewhere [5]. AE signals from micro-cracks at theinitiation of delamination process belongs to matrix cracking (Fig. 5). Fiber/matrix debonding is
detected if the whole delamination front propagates.
250

Total

DCB: Mode I

MatrixCracking

2

Cumulative Power (V ms)

200

150

100

50

Fibre/Matrix
Interface Debonding

0
100

200

300

400

500

600

700

800

900

Time (s)

(a)

12

2

Cumulative Power (V ms)

Total

ENF: Mode II

10

8

MatrixCracking

64

Fibre/Matrix
Interface Debonding

2

0
100

200

300

400

500

Time (s)

6

600

700

800

900

(b)

120

2

Cumulative Power (V ms)

100

MMB: Mixed Mode I/II
(GI/GII=4/1)

Total

80

MatrixCracking

60

40

Fibre/Matrix
Interface Debonding
20

0
200

400

600

800

1000

1200

Time (s)

1400

(c)

Fig. 5 Accumulated AE power from specific micro-failure mechanisms in GF/PA-12 under
different delamination modes. (a) DCB; (b) ENF; (c) MMB.
The results in Fig. 5 show that fracture modes with components of shear failure (mode II andmixed-mode I/II) reduce the AE power absolutely but cause relatively higher interfacial energies.
The power from fiber breakage is negligible in all cases.
Micro-Mechanical Interpretation of the Damage Zone Geometry
From the stress intensity concept of linear-elastic fracture mechanics, equation (1) calculates
the size 2rD of damage zones as:
G Ic Ec2rD(i ) = 2 β(1)(σ c(int) ) 2
with mode I delamination energy G Ic , Young's modulus E c of the composite and a material
specific factor ß . The critical stress σ c(int) for initiation of fiber/matrix debonding depends on
the interface adhesion and has been quantified by AE examination [3].
By FE modeling the elastic strain-energy distribution around the delamination tip was
calculated (Fig. 6). In Fig. 6 (only one cantilever is shown), two density maxima are observed.
One is near the crack surface around the delamination tip and the other one is in the opposite
compressive zone of the flexure-loaded cantilever.
From three-point bending tests combined with AE measurements (Fig. 7) the critical strainenergy density Wc for initiation of damages by equation (2) was determined:

Wc =

σ c2
2E f

(2)

with flexure modulus E f and critical stress σ c in the tensile zone of flexure-loaded specimens
where AE signals with defined amplitudes are detected.
7

Fig. 6 Results of FE calculation of the elastic strain-energy density distribution for stable mode I
delamination propagation of GF/PA-12.
This bending test simulates the damage behavior resulting from the flexure of cantilever
during the DCB fracture test. The critical strain-energy density for damage initiation results fromthe critical stress in the tensile zone of the deflected specimen where AE signals with defined
amplitude threshold are detected. Normally, the damage starts where the load is introduced andthe strain in the tensile zone reaches a maximum. Damages occur within areas where thecalculated strain-energy density is higher than the critical value Wc . By this value mode I
fracture damage zones around the delamination tip and in the compression areas of thecantilevers were estimated (Fig. 8).
The height of the damage zone is approximated by 2rD = hD ≈ l D / 10 from the contour of thedelamination tip zone. As above discussed, matrix cracking and fiber/matrix debonding
processes define the geometry of the damage zones.
ACKNOWLEDGEMENT
The authors wish to thank Dr. Linan Qiao for the FE modeling of the strain-energy density.

8

P
S2

Time (s)S1

Position X (mm)Fig. 7 Time-dependent location of AE sources during 3-point bending tests.
REFERENCES
1. Peter Davis, ed., (1993), „Protocols for Interlaminar Fracture Testing of Composites“, ESIS,
September, Marine Materials Laboratory, IFREMER, Centre de Brest, BP 70, 29280 Plouzane,France.
2. J. Bohse, T. Krietsch, J.H. Chen and A.J. Brunner, (2000), „Acoustic emission analysis andmicro-mechanical interpretation of mode I fracture toughness tests on composite materials“,
3. J.G. Williams & A. Pavan, eds., Fracture of Polymers, Composites and Adhesives, ESIS
Publication 27, Elsevier Science Ltd, pp. 15-26.
4. J. Bohse and T. Krietsch, (1998), „Interpretation of fracture toughness in unidirectional glassfibre/polypropylene composites by acoustic emission analysis of damage mechanisms“, Progressin Acoustic Emission IX, Big Island, Hawaii, USA. August 9-14, pp. V55-V62.
9

P
2,0

Compressive Damage Zone

1,6
1,2

Position Y (mm)

0,8
0,4

Delamination
Damage Zone

Delamination Crack

0,0
-0,4
-0,8
-1,2
-1,6

Compressive Damage Zone

-2,0
0

10

P

20

30

40

Position X (mm)Fig. 8 Damage zones in GF/PA 12 at stable mode I delamination propagation.
( Wc = 0,38 N/mm²; AE amplitudes ≥ 46 dBAE)
5. J. H. Chen, E. Schulz, J. Bohse and G. Hinrichsen, (1999), „Effect of fibre content on theinterlaminar fracture toughness of unidirectional glass-fibre/polyamide composite“, Composites
Part A, 30A, 747-755.
6. T. Krietsch and J. Bohse, (1998), „Selection of acoustic emissions and classification ofdamage mechanisms in fibre composite materials“, Progress in Acoustic Emission IX, Big
Island, Hawaii, USA. August 9-14, pp. IV30-IV39.

10

FRACTURE DYNAMICS IN NOTCHED PMMA PLATES BY LAMB WAVE
ACOUSTIC EMISSION ANALYSIS
KENJI NAGASHIMA, HIDEO NISHINO, MIKIO TAKEMOTO and KANJI ONO*
Faculty of Science and Engr., Aoyama Gakuin University, Setagaya, Tokyo 157-8572, JAPAN* Dept. Materials Science and Engr., UCLA, Los Angeles, CA 90095-1595, USA
ABSTRACT
We studied the dynamics of unstable fracture and crazing in PMMA plates by using thesource simulation analysis of Lamb wave AE. Speed of unstable crack propagation estimated by
the waveform simulation agreed well with those measured by the surface film method (SFM) for
cracks below 3 mm. Crack volume and source rise time of an unstable crack jump were 4.3 x
10-10 m3 and 16 µs for 3 mm long crack. For monitoring AE from crazing, other AE systems withhigher amplification were used. Craze expansion volume of mechanical craze under a constantextension test is estimated as 1.5 to 4.6 x 10-16 m3, and 1000 times smaller than those of unstablecrack jump. Expansion volume and source rise time of environmental craze by acetone are
estimated as 1.9 to 4.5 x 10-16 m3 and 0.21 to 0.35 µs. Crazes in stressed PMMA plates induced
by 50 vol.% methanol have the expansion volume of 7 x 10-17 to 2.3 x 10-16 m3 and rise time of
0.3 to 0.6 µs. The present results represent the first determination of craze microdynamics.
1. INTRODUCTION
Fracture dynamics of polymethyl-methacrylate (PMMA) have been extensively studied byusing laser holography and shear-wave modulation methods [1]. Crack velocity is reported to be
independent of molecular weight, specimen shape and test temperatures (-50 to 40˚ C) andloading speed. Takahashi [2] reported that the crack propagation speed abruptly changes from a
few tenths m/s to several hundred m/s. Real time monitoring of crack velocity by AE monitoringis generally difficult due to fast alternation of small metastable cracking to large-scale unstablecracking. The main goal of our research is to quantify the propagation velocity of unstablecracks by AE. The crack velocity estimated by the waveform simulation of Lamb So-packet wascompared with those measured by the surface-film method (SFM) [3]. For this purpose, we useda special specimen with a crack-arresting hole in front of single notch. The crack propagation
distance was controlled by the holes.
Amorphous thermoplastics like PMMA tend to suffer crazing prior to unstable cracking. AE
monitoring of craze generation is particularly important for the integrity evaluation of plastic
containers [4-6]. AE has been correlated to cracking and to splitting of spherulites [7,8].
However, no clear evidence of AE from crazing of PMMA has been reported. AE due to crazing
is weak, and could not be monitored simultaneously by an AE system suitable for crack
detection. With higher amplification, we succeeded in monitoring weak AE from crazing andanalyzed it with the source simulation analysis method of detected Lamb waves. We examinedboth mechanical and environmental crazing.

J. Acoustic Emission, 19 (2001)

11© 2001 Acoustic Emission Group2. EXPERIMETAL METHOD
Figure 1 shows a specimen and AE monitoring method. A PMMA plate (3 mm thick, 40 mm
wide and 160 mm long) with a single notch and a through-wall hole was loaded in tension. The
hole was machined at 1, 3 or 8 mm from the notch tip and stops the crack propagation. On thesurface, aluminum film was vapor-deposited over 10-mm width on one side of the specimen as a
crack gage. Resistance change caused by the film fracture indicating crack propagation wasmeasured by using Wheatstone bridge (WS) circuit. The output of the WS bridge was digitizedby digitizer A. We used two independent AE monitoring systems. One consists of two resonanttype AE sensors (channel 1 and 2, PAC Type PICO) and an A/D converter (digitizer B). Thissystem is called System B and has no preamplifier. It was used to monitor strong AE signals
from unstable cracks. System C uses two PICO sensors, 40-dB preamplifiers and digitizer C.
System C monitors weak AE signals from crazing. Digitizer A operates at 1-µs sampling intervalwith 4096 points at 10-bit resolution. Digitizers B and C work at 50-ns interval with 1024 points
at 10 bit. All AE signals and resistance changes were synchronized by digitizer A and fed to awork station.
3. RESULTS AND DISCUSSION
Unstable Crack
We first show experimental results of 3-mm long unstable crack. Figure 2(A) shows the loadvs. displacement curve with AE timing. We recorded five weak AE events (event count or EC 1
to 5) by System C prior to a pop-in at 0.4 kN, and a strong AE signal (EC 6) recorded by System
B. A sudden resistance change was also observed upon the pop-in (see (B)). Resistance increased
with a rise time of 77 µs and decreased afterward due to closing of the fractured aluminum film.
Crack propagation velocity is estimated from the resistance change as 125 m/s. AE waveforms
shown in Fig. 2(C) to (E) represent typical Lamb wave features, with first arrival P-wave andtrailing So- and Ao-mode components. Note that the amplitude of EC 1 - 5 are as low as 5 µV (atpreamplifier input), while that of EC 6 by an unstable crack reaches 1 V without amplification.
The So- and Ao-components of EC 6 are also stretched in time, compared to weak events,
probably due to slow source rise time.
We next estimated the source dynamics of these waves by the waveform simulation analysisof P- and So-components. As shown in Fig. 3, we used a PZT element to determine the overall
transfer function of the system (Fig. 3(c)). The displacement of the element was directly
measured by a heterodyne laser interferometer. The procedure used the time-domain
deconvolution of the detected wave (a) with the source function (b), which was simplified fromthe measured. The crack dynamics (crack opening volume change, approximated by a sine4 stepwise function with effective rise time ∆Tr*) were estimated by iteration so that the first portionof computed waveform best matches the detected one. Shown in Fig. 4 are the examples ofwaveform simulation for EC 2 and 6. Source parameters for EC 2 were estimated as ∆Tr* = 0.21
µs and ∆V = 4.8 x 10-15 m3, and those for EC 6, 13.3 µs and 4.3 x 10-10 m3. Both the rise time andcrack volume of EC 2 are far smaller than those of EC 6. Other weak AE signals monitored for 1
and 8 mm long crack specimens showed ∆Tr* of 0.2 - 0.35 µs and ∆V of 1.5 - 4.8 x 10-15 m3.
Smaller expansion volume, generally 104-105 times smaller than that of EC 6, suggests thatcrazing produced these weak AE events. Small cracks are also possible sources, but these appear
unlikely as will be discussed later.
12

Fig. 1 Experimental setup of tensile testing. WS indicates Wheatstone bridge.

13

Fig. 2 (A) Load vs. displacement curve with AE timing indicated by arrowhead. (B) Crack gage
output. (C-E) AE signals for event count (E.C.) 4 to 6. Start of wave components P, So, Aoshown.
Table 1 Summary of fracture dynamics results.

14

Fig. 3 Simulation of Lamb waves from Mode-I fracture using a PZT element and back-up mass.
Table 1 compares ∆Tr* estimated by the waveform simulation method and SFM for large
crack jump from 1 to 8 mm. ∆Tr* (and the propagation velocity of the unstable crack) agreed
fairly well with SFM for shorter cracks below 3 mm, but showed a large difference for 8-mm
crack. This arises from the following: Fractographic observation revealed multiple cracks for 8mm crack. As AE system detected only one AE signal during the pop-in while the SFM
measured the resistance change of the entire jump.
AE from Environmental Crazing under Constant Extension Rate Test
In order to identify whether the weak AE monitored before the unstable crack jump are fromcrazing or micro-cracks, we recorded AE from environmental crazing by using different AE
systems. Figure 5 shows the setup for craze monitoring. Specimen is the same as that of Fig. 1
without the surface film and crack-arresting hole. We monitored the radiation pattern of Soamplitudes by using 7 PICO sensors mounted on a semi-circle of 30-mm radius. Outputs of thesensors were amplified by 40 dB and fed to a digitizer/work station. We also monitored changes
in the transparency of PMMA by using a laser detection system. A He-Ne laser beam passing at
the crack tip was continuously monitored by a photo-diode.
15

Fig. 4 Comparison of detected AE and simulated waveforms with less oscillations. Left: EC 2;
right: EC 6. Note that initial 6 to 12 µs of the waveforms match well.
Figure 6 shows the load–displacement curve with AE timing. A small amount of acetone
was injected into the crack tip at load of 0.4, 0.55 and 0.61 kN. At about 1 s after the first acetone
injection at 0.4 kN, the photodiode detected a sudden decrease in the transmitted light intensity
over a period of 4.7 µs. We visually observed radially spreading lines, and color change at thenotch tip. Since no crack was observed until the second acetone injection, AE events monitored
at loads below 0.55 kN are believed to be produced by environmental crazing. Upon the second
acetone injection at 0.55 kN, fine cracks with small opening were also observed as clear whitelines. Figure 7 shows the surface photograph around the notch tip after the test. This damage
pattern is quite different from single unstable crack jump observed during mechanical loading inair. Twenty-two weak AE events were monitored below 0.7 kN, but no unstable crack was
visible.
Both the kinetics and kinematics of craze were analyzed by the waveform simulation andradiation pattern analysis. Craze normal n and opening vector b were determined by the method
proposed by Sato et al. [9]. Figure 8 show the profile of first peak amplitude of So-mode and two
typical waveforms (EC 1 and 4). Radiation profile of EC 1, observed at 0.3 kN before anyacetone injection, suggests crack-opening (Mode-I) type fracture with n and b in the loading (Y-)
direction. The craze volume is estimated as 4.5 x 10-16 m3 and ∆Tr* is estimated as 0.33 µs. This
opening volume is smaller than any we have observed. ∆V and ∆Tr* of EC 4 are calculated as
1.9 x 10-15 m3 and 0.33 µs, respectively. The EC 4 results, observed upon the first acetone
injection, suggest a slant craze with b in the direction of 40˚ to the loading axis. This kinematics
can explain the crazes spreading in radial directions. Among the first 13 events, 80 percent of theevents showed Mode-I type opening. AE from crazing appears to be produced by the rapid
formation of fibril in the direction of maximum principal stress. Relatively large amplitude of the
So-mode wave in Fig. 8, in spite of extremely small source volume, appears to be due to fastsource rise time. These craze events are surprisingly fast and their ∆Tr* values are comparable
to carbon- or glass-fiber fracture.

16

Fig. 5 Experimental setup of AE monitoring during acetone-induced crazing under tensileloading.
AE from Environmental Crazing under Constant Strain Test
Using a new AE system composed of a PICO sensor, a 60-dB pre-amplification by PAC 1220A
amplifier and a PAC Mistras board, we monitored AE from environmental craze in a PMMA
plate under three point bending (initial fiber stress of 25 MPa). This test is a stress-relaxation
type, and gives a moderate stress condition compared to constant extension rate test. Acetone
was also changed to a less aggressive aqueous solution of 50% methanol by volume. Upon thespraying of the solution, we observed 5 events over a 63 ms interval. These waves are only
several times the noise level as shown in Fig. 9. Waveform simulation was attempted for somewaves after removing the base line fluctuation and noise by using the epsilon–filtering andcosine-taper windowing. We obtained small values of ∆V = 7 x 10-17 to 2.3 x 10-16 m3 and fast17

Fig. 6 Tensile load vs. displacement. Arrows indicate the timing of AE due to acetone-induced
crazing.

Fig. 7 Photograph of crazing near the slit tip following the test.
18

(a) EC 1(b) EC 4
Fig. 8 Radiation patterns observed for EC 1 (top) and EC 4 (bottom). Note that the crack opening
vector for EC 4 appears to incline 40˚ from the Y- (vertical) axis.
19

Fig. 9 Detected AE waveforms due to crazing. Five events (event count 1 to 5) occurred over a
63-ms interval.
rise times of ∆Tr* = 0.3 to 0.6 µs. The crack volumes and source rise times are comparable tothose obtained in the previous section. We observed needle-shaped crazes of 2 to 5 mm long
(Fig. 10(a)) in the specimen. Some surface fibril in a grown craze (Fig. 10(b)) appears to bebroken, but the most fibrils retain small voids among them (cf. Fig. 10(b, c)). No open crack was
observed in the present test and the observed AE signals are concluded to be from the craze
formation.
4. CONCLUSIONS
Utilizing both Lamb waveform simulation analysis and surface film method, we studied the
dynamics of unstable fracture and crazing in PMMA plates with single-edge notch and crackarresting hole. Crack propagation velocity (125 - 158 m/s) estimated by the Lamb waveform
simulation agreed well with those measured by the surface film method for shorter cracks.
20

Fig. 10 Optical micrograph (a) and SEM photos (b,c) of surface crazing due to the spraying of
methanol solution.
We also monitored weak AE signals from mechanical and environmental crazing using
separate AE systems. AE signals from crazing were also analyzed by Lamb waveform
simulation. Expansion volumes of mechanical craze under constant extension test are estimatedas 1.5 to 4.6 x 10-16 m3, which are 105 times smaller than those of unstable cracking (4.3 x 10-10
m3 for 3-mm crack). The source rise times are fast and in the range of 0.21 to 0.35 µs. Similar
expansion volumes and source rise times of environmental crazes by acetone and by 50 vol.%methanol are observed.
This study established that crazing in PMMA occurs with short rise times, and produces
detectable AE. However, AE amplitude is small because of their small volume expansion.

REFERENCES[1] S. Suzuki, H, Honma and R. Kusaka, J. Mech. Phys. Solids, 36 (1998) 631.
[2] K. Takahashi, K. Matsushige and Y. Sakurada, J. Mater. Sci., 19 (1984) 4026.
[3] T. Ogawa and S. Suresh, Eng. Fracture Mech., 39 (1991) 629.
[4] C. Ishiyama, T. Asai, M. Shimojo and Y. Higo, Proc. 7th Int. Fatigue Congress, 3 (1999)
1766.
[5] C. Ishiyama, T. Sakuma, Y. Bokoi, M. Shimojo and Y. Higo, Proc. 2nd ESIS TC4 Conf.
Polymers and Composite, (1999) 142.
[6] M. Kobayashi, T. Asai, Y. Bokoi, C. Ishiyama, M. Shimojo, Y. Higo, Proc. 2nd ESIS TC4
Conf. Polymers and Composite, (1999) 144.
[7] M. Gakumazawa, M. Akiyama, C. Ishiyama, J. Hu, K. Takashima, Y. Higo and C. Nojiri, J.
Acoustic Emission, 16, (1998) S150.
[8] C. Ishiyama, T. Sakuma, Y. Bokoi, M. Shimojo and Y. Higo, J. Acoustic Emission, 16,(1998) S196.
[9] T. Sato, M. Takemoto and K. Ono, Jpn. J. Appl. Phys., 38 (1999) 3193.

21

MONITORING OF MICRO-CRACKING DURING HEATING OF HYDROGEN DOPEDGERMANIUM AND SILICON SINGLE CRYSTALS BY ACOUSTIC EMISSIONMETHOD
K. YOSHIDA, Y. WANG, K. HORIKAWA, K. SAKAMAKI AND K. KAJIYAMA*
Faculty of Engineering, The University of Tokushima, Tokushima 770-8506, Japan.
* Ion Engineering Research Institute Corporation, Hirakata-shi, Osaka 573-0128, Japan.
ABSTRACT
Acoustic emission activity was measured at constant heating speeds in hydrogen-doped
germanium and silicon single crystals. Ion-implanted hydrogen in these crystal lattices arechanged into hydrogen molecules, piled up near 1 µm depth from the surfaces and cause themicro-cracking due to hydrogen-induced embrittlement. It is apparent that the acoustic emission(AE) could be detected during this process in the semiconductor materials. As a result, it ispossible to determine the temperature of the hydrogen-induced cracking. AE is effective tomonitor the smart cutting in these materials.
1. INTRODUCTION
Recently, the development of silicon on insulator (SOI) wafers has been active for their low
electric power consumption, low voltage operation and large-scale integration of high density.
Although some manufacture methods have been already suggested for the SOI wafer with the
sub-micron thick single crystal thin layers stacked on each insulator layer, the practical use of
SOI is not accomplished now. There is a smart cutting method using ion implantation and is oneof the most effective methods so far. Oxygen and hydrogen are considered as doping ions. In theoxygen-doping method, difficult problems still exist because of a large amount of oxygen needed
and higher annealing temperature over 1300˚C (1). On the other hand, the hydrogen-doping
method is more useful with less difficulty (2). The method is to separate single crystals of submicron thickness from a semiconductor substrate such as silicon and germanium using thehydrogen embrittlement that fortunately brings about by hydrogen molecules along defects
induced by ion implantation after annealing (3). Now there does not seem to be any effective
method to measure in-situ the generation of micro-cracks due to the hydrogen induced
embrittlement.
Several papers have reported that acoustic emission (AE) signals generated during
heating/cooling process can be applied to the material evaluation, such as the determination ofglass transition temperature for dental porcelain (4), the polymorphic transformation temperature
in Au-Cu and Pd-Cu alloys and the activation energy of the transformation (5, 6), and thegeneration of micro-crack in the investment mould for dental casting (7, 8).
Objectives in this paper are to detect hydrogen-induced micro-cracking during heating inhydrogen ion-implanted silicon and germanium single crystals by acoustic emission method andto reveal the temperature and conditions for the micro-cracking to initiate and grow.

J. Acoustic Emission, 19 (2001)

22© 2001 Acoustic Emission Group2. EXPERIMENTAL PROCEDURES
Materials used were a p-type silicon wafer with (100) surface orientation (0.25-mm
thickness) and a p-type germanium wafer with (100) surface orientation (0.40-mm thickness).
After the formation of oxide film on the surface of the materials with thickness of 100 nm,
hydrogen ion-implantation in 40 x 40 mm2 silicon and germanium wafers were performed at 100
keV with the dose of 5 x 1016 and 1 x 1017 ions/cm2, respectively. The size of specimen for AE
testing was 10 x 10 mm2. Ion implanted specimens were annealed at constant heating rates to
700˚C in vacuum and nitrogen gas atmosphere in an electric furnace for acoustic emission
measurement as shown in Fig. 1. Acoustic emission generated in these specimens was detectedusing a 2-channel AE monitoring system of which block diagram is shown in Fig. 2. Detected
AE signals were transmitted to the AE system through a waveguide that was made of an
austenitic stainless steel rod with diameter of 4 mm and length of 330 mm, transformed into
electric signals by an AE transducer with resonant frequency of 200 kHz. They were amplified
by constant gain of 100 dB through a band-pass filter of 0.1 to 1.0 MHz and stored by acomputer as AE event and amplitude above the threshold level of 58 dB that corresponded to 25µV at input voltage of the preamplifier.
The composition of the gases discharged during heating was analyzed by a thermal
desorption spectroscopy (TDS) device. Fracture appearances in these specimens were
determined by cross-sectional SEM micrographs after the heating test.

Fig. 1 System of suspending the specimen for detecting AE signals in an electric furnace.

23

Fig. 2 Block diagram for AE measurements.

Fig. 3 AE behavior during heating at 10˚C/min in vacuum in the H+ implanted Si single crystal.
(a) AE event rate.

24

Fig. 3 AE behavior during heating at 10˚C/min in vacuum in the H+ implanted Si single crystal.
(b) AE amplitude
3. RESULTS AND DISCUSSION
Detection of Hydrogen Induced Micro-cracking in Silicon Wafers
Figure 3 and 4 show AE behaviors in hydrogen implanted silicon single crystal wafers fromroom temperature to 700˚C at heating rate of 10˚C/min in vacuum and nitrogen gas atmosphere,
respectively. The AE event rate in vacuum indicates two peaks in Fig. 3(a). The detected
amplitude of AE is low as shown in Fig. 3(b). On the other hand, the event rate in nitrogen gas
atmosphere increases in the range of 600 to 700˚C and indicates a peak at 650˚C, as shown in
Fig. 4(a). The dominant medium amplitude in this temperature range is detected as shown in Fig.
4(b). Total AE event counts in vacuum are smaller than in nitrogen gas atmosphere, but the AE
event rate in both atmospheres increases in the range of 600 to 700˚C. It is suggested thatstructural changes occur in the specimen, while AE sources depress the activity because of more
escape of hydrogen gas through the surface in vacuum.
Figure 5 shows the dependence of the heating temperature at 10˚C/min on the desorbedhydrogen gas intensity in argon gas atmosphere. The intensity indicates two peaks at 450˚C and650˚C. Acoustic emission data that is consistent with the result is shown in Fig. 6. The cause ofthis behavior has been considered to be the structural change of composite defects formed by thehydrogen ion-implantation in the silicon single crystal (9). Accordingly, Si-Si bonds are cut off
by the hydrogen ion implantation with high energy. Implanted hydrogen ions are attracted to theseparate bonds. These hydrogen ions become saturated with progress of the implantation time.
Then hydrogen and silicon atoms constitute a tetrahedral structure that denotes SiH4. Hydrogenions are precipitated at 405˚C during heating process.
25

Fig. 4 AE behavior during heating at 10˚C/min in nitrogen gas in the H+ implanted Si single
crystal. (a) AE event rate.

Fig. 4 AE behavior during heating at 10˚C/min in nitrogen gas in the H+ implanted Si single
crystal. (b) AE amplitude.
26

Fig. 5 H2 desorption curve in Si single crystal wafer.

Fig. 6 AE event rate corresponding to Fig. 5.

27

The structure of defects changes into relatively unstable triangle structures that denote SiH3
or Si2H6. Excess hydrogen ions react to form hydrogen gas. The hydrogen gas is released out of
the specimen and shows the peak of intensity. With the temperature increased further, Si-H
bonds are cut again at 650˚C. Similarly the hydrogen gas shows second peak of intensity. As aresult, it is suggested that at 450˚C micro-cracks may be initiated partially in the specimen and at
650 ˚C they are extended all over the specimen.

Fig. 7 A typical cross-sectional SEM micrograph in H+ implanted silicon wafer.
A typical example of cross sectional SEM photograph in the silicon wafer is shown in Fig. 7.
A uniformly fractured layer at the depth of 0.85 µm from the surface can be observed, indicating
the evidence of hydrogen-induced embrittlement. The depth of the layer was almost similar to
0.87 µm in the previous paper by Kajiyama et al. (10).
Detection of Hydrogen-Induced Micro-cracking in Germanium Wafers
Figure 8 shows AE behavior in hydrogen-implanted germanium single crystal wafers fromroom temperature to 700˚C at three heating rates of 10, 5 and 2.5˚C/min in nitrogen gas
atmosphere. In 10˚C/min heating, the AE event rate indicates two peaks at 330 and 390˚C, anddecreases gradually with increase of the temperature as shown in Fig. 8(a) and (b). The detected
amplitude of the AE event is very high at 330 and 380˚C, and changes into the extremely low
level with increase of the temperature. This trend is identical in all heating rates. Thesetemperatures are consistent with those in two peaks of the AE event rate. In 5˚C/min heating, theAE event rate increases steeply near 290˚C, indicates a remarkable peak at 310˚C, and decreases
steeply and gradually with increase of the temperature as shown in Fig. 8(c) and (d). The
detected amplitude of the AE event is very high near 300˚C. In 2.5˚C/min heating, the AE event
rate also increases steeply near 280˚C, indicates a remarkable peak at 300 ˚C, and decreases
similarly in the case of 5˚C/min as shown in Fig. 8(e) and (f). The amplitude also is very highnear 280˚C. As for the gases desorbed during heating, the intensity of the hydrogen gas is much
more active than that of other gases and has a peak value at 280 ˚C as shown in Fig. 9. After thatit decreases steeply but continues to increase gradually to 520˚C, and finally decreases gradually28

to null at 600˚C. The fact is well correlated with the behavior of the AE event rate. However, thepeak temperature of the desorbed hydrogen intensity is lower by 30 ˚C from that of the AE event
rate. It appears that the hydrogen induced micro-cracking takes a time lag to the hydrogen
desorption, possibly meaning a delayed fracture.
A typical example of cross sectional SEM photograph at heating rate of 10˚C/min in the
germanium wafer is shown in Fig. 10. A uniformly fractured layer at the depth of 1.0 µm fromthe surface can be also observed. Irregularity near the macro-crack of the germanium wafer ismuch more obvious than in the silicon wafer because of lower fracture strength in germanium. It
is suggested that the fact may be related to the more active AE behaviors such as the event rate
and the amplitude in the germanium wafer.
When these specimens were reheated under the same experimental condition, the AE eventswere almost absent because of no micro-cracking occurring again in these specimens. Therefore,
it is apparent that the hydrogen-induced cracking ceased after only one heating/cooling process.
The temperature of the peak value in the AE event rate increases gradually with increase of
the heating rate as shown in Fig. 11. On the contrary, the AE total event counts decrease fairly
drastically as shown in Fig. 12. The generation of the hydrogen-induced embrittlement seems toinclude in part some activated processes.
4. CONCLUSIONS
Acoustic emission monitoring during heating process in the hydrogen implanted silicon andgermanium wafers has been carried out to reveal the generation and growth conditions of microcracking by hydrogen-induced embrittlement. Results obtained are as follows.
1. The cracking by hydrogen-induced embrittlement was confirmed in the hydrogen implanted
silicon and germanium wafers.
1. The temperature to complete the micro-cracking should be more than 600 ˚C in the siliconwafer, while it was in the range of 280 to 350 ˚C in the germanium wafer. It shifted to ahigher temperature with an increase of the heating rate.
3. The fractured layer surface in the silicon wafer was uniform and linear. Its depth from thesurface was 0.85 µm.
4. The fractured layer surface in the germanium wafer was uniform, but less linear. Its depth
from the surface was 1.0 µm.
5. Acoustic emission method is considered to be a useful means to do the in-situ monitoring in
the hydrogen implanted silicon and germanium wafers.
REFERENCES
1. K. Kajiyama: Appl. Surf. Sci., 85 (1995), 259.
2. N. M. Johnson, F. A. Ponce, R. A. Street and R. J. Nemanich: Phys. Rev. B35 (1987), 4166.
3. T. Hara, Y. Kakizaki, S. Oshima, T. Kitamura, K. Kajiyama, T. Yoneda, K. Sekine, and M.
Inoue: Electrochem. Soc. Proc. 97-23 (1997), 33.
4. K. Asaoka, K. Yoshida and K. Sakamaki: J. Mat. Sci., 27 (1992), 3118.
5. K. Yoshida, K. Sakamaki, K. Asaoka and M. Yamanaka: Proc. 9th National Conf. AE, Japan,
9 (1993), 277.
6. K. Yoshida, K. Asaoka, M. Yamanaka and K. Sakamaki: J. Japan Inst. Metal, 59 (1994), 989.
7. K. Kim, K. Asaoka and K. Yoshida: Dental Materials Journal, 17 (1998), 11.
29

(a, b) AE event rate and amplitude: 10˚C/min.
Fig. 8 AE behavior during heating at three rates in nitrogen gas in the H+ implanted Ge single
crystal.

30

(c, d) AE event rate and amplitude: 5˚C/min.
Fig. 8 AE behavior during heating at three rates in nitrogen gas in the H+ implanted Ge single
crystal.

31(e, f) AE event rate and amplitude: 2.5˚C/min.
Fig. 8 AE behavior during heating at three rates in nitrogen gas in the H+ implanted Ge single
crystal.

32

Fig. 9 Desorption curves of various gases in a H+ implanted Ge single crystal wafer.

Fig. 10 A typical cross sectional SEM micrograph of hydrogen implanted Ge wafer (10˚C/min).

8. K. Asaoka, K. Yoshida, K. Hamada and M. Kon: Dentistry in Japan, 35 (1999), 89.
9. M. F. Beaufort, H. Garem and J. Lepinoux: Phil. Mag. A, 69 (1994), 881.
10. K. Kajiyama, T. Yoneda, M. Inoue and T. Hara: Technical Report of IEICE, SDM 96-161(1996-12), 31.

33

Fig. 11 Effect of heating rate on the temperature for the maximum AE event rate.

Fig. 12 Effect of heating rate on total AE event counts.

34

DAMAGE DETECTION IN A FIBER-REINFORCED CYLINDER (FISHING ROD) BY
GUIDED WAVE ACOUSTIC EMISSION ANALYSISYOSHIE HAYASHI, YOSHIHIRO MIZUTANI, HIDEO NISHINO, MIKIO TAKEMOTO
and KANJI ONO*
Faculty of Science and Engr., Aoyama Gakuin University, Setagaya, Tokyo 157-8572, Japan* Dept. of Materials Science and Engineering, UCLA, Los Angeles, CA 90095-1595, U.S.A.
ABSTRACT
We propose an AE source location method in a fiber-reinforced composite cylinder on thebasis of guided waves. The composite has a complex lay-up; a fishing rod composed of glass
fiber mat in the inner surface, 5 layers of unidirectional carbon fiber and a spiral carbon fiber inthe outer surface. We first quantified the velocity dispersion of L(0,1) and F(1,1) mode cylinderwaves using a pulse laser and an AE sensor. Artificial AE sources were next excited by a point
YAG laser and detected by two sets of three AE sensors attached at 120o step on the surface.
Waveform similarity and arrival time difference of F(1,1) mode at 225 kHz were utilized forlocating the circumferential zone (split thrice into 120o zone) and axial zone (length direction in
7 mm step), respectively. Most artificial sources were identified into a correct zone. Utilizing thelocation algorithm developed, the initiation and progression of local damage during
axial–compression of the rod were studied. The source locations estimated agreed with thelocation of fiber delamination revealed by post-test dye-penetrant examination.
1. INTRODUCTION
Fiber-reinforced composite cylinders and cylindrical containers are widely used as sport ing
goods, corrosion resistant pipes, cryogenic vessels and so on. These composites have complex
lay-ups of reinforcement, and possess excellent mechanical characteristics. Drawback lies in thebuckling and impact damage. Damage detection by AE monitoring is valuable for the integrity
evaluation of newly developed equipment. However, the source location of AE in hollow
cylinders is difficult compared to those of bulk-mode and Lamb-wave AE, since signals in acylinder propagate as multi-mode guided waves (or cylinder waves). Complex nature of cylinderwaves is complicated by anisotropic reinforcement. We recently developed new source location
methods of Lamb waves in anisotropic CFRPs [1] and of the cylinder waves in metallic pipes[2]. For the Lamb-wave AE, we utilized group velocities at a selected frequency obtained by the
wavelet transform of Ao-mode waves. The source location was accurately identified within theerror of the wall thickness. A source location method of cylinder waves is also given in this
symposium [3].
In this paper, we introduce a new and simplified source location method of cylinder wave
AE in a fiber reinforced composite. It is applied successfully to the source location of local
damage during axial compression of the composite.
2. TEST SPECIMEN AND SOURCE LOCATION
Specimen is a fishing rod (5 m long, 14.6 mm diameter at grip end, for fresh-water fishing),
reinforced with a mix of 96 mass% carbon fiber and 4% glass fiber. As shown in Fig. 1, it is
J. Acoustic Emission, 19 (2001)

35© 2001 Acoustic Emission Groupcomposed of glass-fiber mat in the inner surface, 5 layers of unidirectional carbon fiber andspiral carbon fibers in the outer surface. Young’s modulus: E and Poisson’s ratio: ν in the axial
direction, measured for as-received rod with spiral carbon fibers, are 30 GPa and 0.24,
respectively. Those for the rod without spiral carbon fibers are 10 GPa and 0.26, respectively.

Fig. 1 Ply lay-up and a photo of a pipe section.

Fig. 2 Experimental setup. (a)Lamb wave AE signal produced by pulse YAG laser. (b) The
contour map of wavelet coefficients and group wave dispersion curves for cylinder waves.
(c) The wavelet coefficient at 225 kHz vs. arrival time.
36

Due to the difficulty in measuring the anisotropic elastic properties by mechanical testing,
we first quantified the characteristics of cylinder waves propagating in the rod. As shown in Fig.
2, we excited cylinder-waves by directing a point-focused YAG laser pulse on the rod surface.
The arriving waves were monitored by an AE sensor (PAC Type PICO). Detected waves, Fig.
2(a), show characteristic features of cylinder waves, with small amplitude L(0,1) mode arriving
at around 20 µs, followed by large amplitude F(1,1) mode. Group velocity dispersions,constructed from the contour map of wavelet coefficients and time-of-flight method, are shownin Fig. 2(b). Theoretical group velocity dispersions of the L(0,1) and F(1,1) modes, computed byusing E=20 GPa and ν=0.25, are superposed in the figure. These elastic constants are average oftwo values measured for the rod with and without outer spiral fiber. Due to strong anisotropic
properties of the rod, ridges (higher wavelet coefficients) of the contour map do not completely
coincide with the computed group velocity dispersions. Therefore, we measured the groupvelocity at selected frequency and utilized them for source location. Figure 2(c) is an example oftime transient of wavelet coefficient at 225 kHz. The main peak at ~80 µs corresponds to thearrival of large amplitude wave.
3. SOURCE LOCATION ALGORITHM AND ACCURACY
We first measured the axial distribution of group velocities as a function of frequency. As
shown in Fig. 3, cylinder wave AE signal was excited by a laser pulse on the rod surface, andmonitored by two sets of three PICO sensors attached at 120o step on the surface 110 mm apart.
The origin of the axial (z-) coordinate was at the left sensors and angle θ was measured fromch.1 sensor. AE signals were excited at (z mm, 0o) and monitored by ch.-1 sensor at (0 mm, 0o).
Figure 4 shows the distribution of group velocities at frequencies from 150 to 350 kHz. Variationof group velocity is smallest at 225 kHz. Large amplitude of F(1,1) mode at 225 kHz is also
useful for the source location (see Fig. 2(b)).

Fig. 3 Setup for cylinder wave generation and monitoring system with the cylindrical coordinate.

37

Fig. 4 Changes in the axial group velocities of cylinder waves at different frequencies.
Source location was performed in three steps. First, a rough z-location of the source was
identified by using the average arrival time of 225-kHz component at two sets of the sensors.
Next, a correct circumferential zone is identified by using similarity coefficient gij amongwaveforms detected [4]. The coefficient gij represents the similarity of dominant F(1,1) mode.
An example is given with AE data in Fig. 5. The waves shown in the middle column are excitedat position (40 mm, 180o) and monitored by six sensors (ch.1 to 6). Corresponding wavelet
coefficients at 225 kHz are shown in the right column. We identify the circumferential zone bycomparing the similarity coefficient gij of the three waves monitored by a set of 3 sensors furthestfrom the source location. Here, it is the right set. For this case, gij was highest between ch.5 and6, and the source was identified to the zone from 120 to 240˚.
For the axial source location, or the third and final step, we select two AE sensors closest to
the previously estimated circumferential zone. These sensors are ch.2 or ch.3 in the left set, andch.5 or 6 in the right set. From the arrival times of 225 kHz component to these sensors, thesource location is identified into one of axial zones by using a linear location algorithm. Here, we
set the axial zone width of 7 mm since the group velocity slightly varies with z (Fig. 4). The
axial position was located as z = 31 mm, from the arrival time 56.4 µs and 29.2 µs to ch.5 andch.2, respectively. Location error from the exact position (40 mm) was 9 mm.
Location accuracy was examined by using a point-focused YAG laser. Result is shown in
Fig. 6. The top figure shows the located zone (dark mesh) for sources placed along sensorconnecting lines (indicated by ). Eight of ten sources were correctly located. The bottom figure
gives the located zones for the sources midway between two sensor-connecting lines (or at ∆θ =60˚). Here, four sources were misplaced to another zone near-by. Incorrect location was due tomisreading of the first arrival time of weak 225kHz component. The present location algorithm
seems to have a drawback of misplacing the axial zone when the circumferential zone isincorrectly located.

39

Fig. 5 Finding the source location using similarity coefficients, gij.

39

Fig. 6 Estimated source zones and true source positions (). Top: sources on the sensorconnecting lines. Bottom: sources between two sensor-connecting lines.
4. DAMAGE LOCATION DURING COMPRESSION
Using the test method shown in Fig. 7, we monitored AE during axial-compression of a 210mm long composite rod. Both ends of the rod were protected by polyester resin skirts frombuckling. Figure 8 represents the load vs. displacement. The first AE event was monitored ataround 0.7 mm displacement. The amplitude of 12 events (indicated by black arrows) was too
weak. At 1.3 mm displacement, a large pop-in was observed and accompanying AE exceeded themonitoring range. Among 27 events monitored below 5 kN, the source location analysis of only
10 events, indicated by white arrows, were possible.

40

Fig. 7 Setup for axial compression of composite cylinder.
Figure 9 shows AE event count (EC) 16 observed at 1.14 mm displacement. The arrival
times are similar for ch.2-6 waves, and the high gij values between ch.2-3 and ch. 4-5 are at 0.33.
This led to the location at (49-56mm,120-240o). Using the arrival time of 42 and 45.6 µs to ch.2
and 5, and 50.2 and 52.6 µs to ch.3 and ch.6, the source was located near the rod center, i.e., z =49-56 mm.
Progression of source locations is shown with event numbers in Fig. 10. Here, the darkzones at (50-70 mm, 180o) designate that several sources were located there, suggesting theconcentrated damage initiation. These locations agreed with the axial fiber-delamination revealedby the post-test dye-penetrant examination (Fig. 11). The fiber delamination, as a precursor ofbuckling, occurred at z= 50 to 70 mm and θ=180o.

41

Fig. 8 Load-displacement curve with timing for AE signals indicated by arrows.
5. CONCLUSION
We propose a new and simplified source location method of cylinder-wave AE from a fiberreinforced composite cylinder. Utility of the developed method was demonstrated for a fishingrod with complex lay-up. Results are summarized below:
1) Wavelet transform makes it possible to determine the group velocity at a selected frequency.
We used the axial group velocity of 1350 m/s at 225 kHz for the composite rod.
2) We devised a source location method using the AE signals monitored by two sets of three AE
sensors attached on the surface. Source location was conducted in three steps. First, a roughz-location of the source was obtained, then a circumferential zone was identified by using
the similarity coefficients of three AE signals (F(1,1)-mode waveform) away from thesource. Finally, an axial zone was determined by using the axial velocity at 225 kHz. Most
artificial sources (YAG laser ablation) are correctly identified.
3) Sources of AE signals monitored during axial-compression of a 200-mm long composite
cylinder were located to zones, in which the fiber-delamination was revealed by the post-test
dye-penetrant examination.
REFERENCES[1] H. Yamada, Y. Mizutani, H. Nishino, M. Takemoto and K. Ono, “Source Location on
Anisotropic Plates Using Lamb Waves”, 42nd AEWG Meeting, June 15-16, Princeton, USA,(1999).
[2] H. Nishino, F. Uchida, S. Takashina, M. Takemoto and K. Ono, “A new method of source
location in Pipes using cylindrical guided waves”, 24th EWGAE 2000-European Working
Group on Acoustic Emission, May 24-26, France, (2000).
[3] F. Uchida, H. Nishino, M. Takemoto and K. Ono, “Cylinder Wave Analysis for AE Source
Location and Fracture Dynamics of Stress Corrosion Cracking of Brass Tube”, paper at 15th
International Acoustic Emission Symposium, September 11-14, Tokyo, Japan, (2000).
[4] William R.Uttal, “Real-Time Computers”, Harper & Row Publishers, (1967), p.185.
42

Fig. 9 Cylinder waves (EC 16) monitored during compression (middle), their similarity
coefficients (left) and wavelet coefficients at 225 kHz vs. time (right).

43

Fig.10 Estimated source location of AE events.
80

20

40

180o

Fig.11 Surface photograph of the rod after the dye-penetrant examination.

44

ACOUSTIC EMISSION CHARACTERIZATION AND NUMERICALSIMULATION OF INTERNAL DAMAGE PROGRESSION
IN CFRP MULTI-DIRECTIONAL SYMMETRIC LAMINATESISAMU OHSAWA, ISAO KIMPARA, KAZURO KAGEYAMA, SATOSHI ABE,
and KAZUO HIEKATA*
Department of Environmental and Ocean Engineering, The University of Tokyo, Bunkyo, Tokyo
113-8656, Japan, *SWG, IBM Japan Ltd., Yamato, Kanagawa 242-8502, Japan
ABSTRACT
Acoustic emission characteristics and internal damage progression of multi-directionallyreinforced carbon/epoxy symmetric laminates were investigated by applying tensile loads oncoupon specimens, which were composed of 0-, 45-, and 90-degree layer. The initiation oftransverse lamina crack in 90- and 45-degree layers and the onset of edge delamination in theinterlaminar region were monitored by acoustic emission. The monitored results were comparedwith the results of numerical simulation based on our proposed theory of internal damage
progression in multi-directional laminates. The internal cracks were observed by microscopy,
and the interlaminar delamination was detected by using ultrasonic C-scan technique. Predicted
stress of crack initiation by the proposed theory agreed well with critical stress observed byacoustic emission.
1. INTRODUCTION
It is well known that two kinds of damage, namely transverse lamina crack and interlaminardelamination occur in case of tensile loading of a multi-directional laminate [1], which iscomposed of extremely anisotropic uni-directional CFRP, at an early stage before ultimate
failure. Therefore, the prediction of reduction in stiffness of a laminate due to damage isimportant in the application of laminated composites to real structures. This damage progressionis also likely to influence the ultimate failure strength.
To clarify the damage mechanism of laminates, a large number of damage models have been
proposed and analytical simulations have been performed for relatively simple laminatedstructures such as cross-ply laminates [2] . However, many problems still remain to be resolved,
and it appears that no damage prediction method has been established yet. The initial damage
occurs in stable progression in such laminated composites. If the damage phenomenon exhibits
stable progression, then it is possible to set the allowable stress for composite laminate design at
a higher value. However, for setting the allowable design stress at initial damage, it is essential tothoroughly understand the mechanism of damage progression in laminated composites to be usedin structures. This requires the establishment of a damage prediction method especially for
general-purpose multi-directional laminated composites.
In the present study, AE techniques are used for studying the damage progression of crossply laminates. The correlation between crack initiation in 90-degree layer and AE characteristics
under tension are discussed first. Next, damage due to transverse lamina cracks is modeled for
multi-directional laminates, a theory for internal damage progression is proposed and theinfluence of stacking sequence on the damage behavior is analyzed by numerical simulation.
J. Acoustic Emission, 19 (2001)45© 2001 Acoustic Emission GroupDamage progression during tensile tests is monitored using acoustic emission (AE) techniques
and the correlation between predicted stress of crack initiation by the proposed theory and AE
behavior is discussed.
2. MATERIALS AND SPECIMENS
Cross-ply laminates and multi-directional symmetric laminates were molded using
TR340H150 prepreg sheets (0.3 mm/ply and 0.6 mm/ply). Table 1 shows the laminates used fortensile tests. The tensile test specimens were cut from laminated plates using a diamond wheel.
The coupons were 300 mm long and 20 mm wide. The specimen edges were polished bywaterproof emery paper of #1000 for microscopic inspection of transverse cracks. During thetensile tests, #1000 sand paper followed by #600 sand paper were wrapped around the end of thespecimens instead of bonding end-tabs. This measure prevented unnecessary AE generation fromthe grip area, and proved to be a useful and easy technique for AE measurements during thetensile tests.
3. AE INSTRUMENTATION
AE signals were detected by two 150-kHz resonant-type piezoelectric AE transducers(PAC.R-15), which were attached 150 mm apart at the mid-length of a specimen. A standard
pencil lead (JSNDI-006, dia. 0.5 mm, HB) recommended by the Japan Society for NonDestructive Inspection (JSNDI), was used to confirm adequacy of contact between the AE sensor
and the specimen, and also for checking the sensitivity of the AE sensor. The AE instrumentationconsisted of Physical Acoustic Corporation (PAC) MISTRAS-2001, filtered through a 20-dB
amplifier set for the frequency range of 100 kHz to 300 kHz. The threshold level of the AE
system was 1.7 mV at the sensor. The dead time was set at 300 µs.
Table 1 Cross-ply and multi-directional laminates for tensile specimen.

4. RESULTS AND DISCUSSION
Tensile specimens were loaded at the crosshead speed of 0.5 mm/min. Mean strain wasmeasured using an extensometer with a gage length of 100 mm to exclude strain due to local
damage.
Cross ply symmetric laminates
The strength of the 90-degree layer is extremely small compared to that of a 0-degree layer.
The stress in the 90-degree layer is in a tensile state because of the influence of thermal residual
stress. It is well known that transverse lamina cracks easily occur in 90-degree lamina when a46

tensile load is applied on the specimen. To correlate cracks in the 90-degree lamina with AE
activity, tensile load was increased progressively in steps of 2 kN, the specimen was removed
from the testing machine at each step, and the number of cracks on the specimen edge wasobserved.
Figure 1 shows point plots of AE amplitude vs. time, energy vs. time and tensile load vs.
time. Until the maximum load of about 14 kN, most of the data can be clearly classified into AE
groups with high amplitude of more than 80 dB, and into regions of no AE activity in theamplitude range of 60 to 80 dB.

Pmax:14 kN

εmax : 8,400µ)

4th loading step

Pmax:18 kN((

εmax : 10,900µ)

6th loading step

EnergyEnergyAmplitude

Amplitude

80dB

80
60

60dB

Fig. 1 Diagram of AE output of [02/904/02] cross-ply laminates.
The high amplitude AE corresponds to transverse lamina cracks in the 90-degree lamina [3], andshows a trend similar to that of the number of cracks observed on the specimen edge after
removal of the specimen from the testing machine. The plot of load vs. time increased linearlywith practically no discontinuities even when AE with high amplitude occurred.
It is concluded, therefore, that the influence of cracks in the 90-degree layer on the reduction
in stiffness is extremely small for cross-ply specimens. At loads greater than 14 kN, AE activity
began to occur in the range of 60-80 dB, while the number of AE data over 80 dB decreased
gradually. Cracks with high AE energy are considered to have occurred initially until a load of 14
kN, while damage modes at low energy started subsequently.
Multi-directional symmetric laminates
In these experiments, load was applied until 90% of the failure strain, the specimen wasremoved from the testing machine after unloading, and crack density on the specimen edge wasmeasured. Figure 2 shows the point plots of load vs. time, energy vs. time and AE amplitude vs.
time for a monotonic static tensile test. The amplitude distribution increased rapidly from about
650 s indicating AE behavior totally different compared to the AE behavior in cross-ply
laminates in spite of similar AE measurement conditions, probably because of the inclusion ofdifferent damage modes.
47

After the rapid increase of AE, the plot of load vs. time became discontinuous, indicating thatthe reduction in stiffness was strongly influenced by the damage. AE activity also showed a peak
and included high amplitudes after the rapid increase. The results evidently indicate a transition
from the occurrence of cracks requiring high energy to damage phenomenon at low energy suchas interlaminar delamination.
Pmax:45 kN

EnergyAmplitude

Fig. 2 Diagram of AE output of [454/-454/04/904]s laminates.
5. NUMERICAL SIMULATION AND COMPARISON WITH EXPERIMENT
Prediction of cracks in transverse lamina and internal damage progression
The theory proposed here attempts to predict the occurrence of transverse lamina cracks andthe progression of damage in multi-directional laminates using the energy release rate. The
prediction of damage is expressed schematically in Fig. 3. An overview of the proposed theory
is as follows. An imaginary damage progression of ∆c (crack density) is considered for eachlamina. The reduction in stiffness, which is the change in potential energy of laminate, indicatedby the area with oblique lines in the figure, is assumed to be equal to the energy expended forgenerating a crack. The stress in each lamina at damage progression is calculated using theenergy balance described above. Assuming that damage progresses in each lamina, in which this48

stress becomes a minimum, the stress-strain relationship is obtained by repetitive calculations of∆c.
The degradation in tensile stiffness and shear stiffness is obtained by calculating the stressfield and the strain field numerically after dividing the symmetrical part in the layer damaged bytransverse lamina cracks into elements as shown in Fig. 4.

Fig. 3 Schematic illustration of damage estimation using the energy criterion.

Fig. 4 Schematic illustration of degradation model of tensile and shear modulus.
Comparison with tensile tests
Figures 5 and 6 indicate the comparison of results of numerical simulation and results of tests
on multi-directional symmetric laminates. The thermal residual stress was calculated by using
the coefficient of thermal expansion and the theory of laminated plate, and then correcting thethermal residual stress of the 90-degree layer. The black arrow in the figure indicates the49Predicted
Experimental
Crack density of 90 °°layer

600

1.6
1.4
1.2

Cracked

1.0

400

AE
initiate

200

Exp.

0.8

(90° layer)

0.6
0.4
0.2

0

0

4,000

8,000

12,00016,000

Crack Density ( / mm )Stress ( MPa )800

0Strain ( µ m )Fig. 5 Comparison of numerical simulation and AE results of [452/-452/02/902]s laminates.

800

Predicted
Experimental
Crack density of 90 ° layer
Crack density of -45 ° layer

600

1.5

CrackedExp.
(-45° layer)

400
AE
200
0

2.0

Exp.
(90° layer)

initiate
0

4,000

12,0008,000

16,0001.0

0.5

Crack Density ( / mm )Stress ( MPa )

1,000

0
20,000Strain ( µ m )Fig. 6 Comparison of numerical simulation and AE results of [452/02/-452/902]s laminates.
initiation of damage due to crack (crack density equal to 0.1/mm) by simulation, and the grayarrow indicates the point, at which AE starts increasing rapidly.
50

The results of two kinds of laminates showed that good correspondence for the stress atinitiation of damage. However, experimental data of crack density (given in Figs. 5 and 6 as •)showed low values compared to simulation results. This is because only transverse lamina cracks
were considered in the simulation, whereas in the tests, other damage modes such as interlaminardelamination and filament breakage are likely to have occurred after transverse lamina cracks
were saturated.
Figure 7 indicates the reduction in stiffness under tension. Experimental values were
extremely low compared to the simulation results. Again, this is attributed to the influence ofother damage modes, such as interlaminar delamination. Figure 8 shows the C-scan image of a
specimen after test. It can be confirmed from this figure that interlaminar delamination
originating at the edge of the test specimen extends to the center of the specimen.

Normalized Stiffness

1.05

CrackedPredicted

1.00

AE initiate

0.95
0.90

Experimental

0.85
0.80
5,000

7,000

9,000

11,00013,000Strain ( µ m )Fig. 7 Comparison of stiffness reduction of numerical simulation and experimental results of[452/02/-452/902]s laminates.

200 mmGage length :100 mm
Fig. 8 C-scan image of [454/-454/04/904]s laminates including interlaminar delamination.

51

6. CONCLUSIONS
Acoustic emission technique was used during tests on cross-ply and multi-directionallaminates, and the behavior of transverse lamina cracking mainly in 90-degree lamina wasmonitored. It was confirmed that the two kinds of laminate tested showed entirely different AE
characteristics. Based on the proposed theory for multi-directional laminates, good correlation
between predicted stress at crack initiation and AE initiation stress caused by cracking wasobserved. However, the actual damage included other damage modes such as interlaminardelamination and extension of crack to the adjacent layer. Accordingly, a new simulation
approach using an advanced model that considers the damage modes mentioned above isconsidered to be essential.
REFERENCES
1. T.K. O’Brien, M. Rigamonti and C. Zanotti, ” Tension Fatigue Analysis and Life Prediction
for Composite Laminates,” NASA Technical Memorandum 100549; Avscom Technical
Memorandum 88-B-015 (1988).
2. K. Tohgo, K. Akizuki and Y. Sugiyama, ”Ply Cracking Damage Theory and Damage Behavior
in CFRP Cross-ply Laminates,” Proceedings A of JSME, 64 (621), (1998), 30-37. (in Japanese)
3. I. Ohsawa, I. Kimpara, K. Kagayama and T. Suzuki, ”Acoustical Analysis of Transverse
Lamina Cracking in CFRP Laminates under Tensile Loading,” Proceedings of 4th International
Symposium on Acoustic Emission from Composite Materials, (1992), pp. 55-64.

52

SCC MONITORING OF ZIRCONIUM IN BOILING NITRIC ACID BY ACOUSTICEMISSION METHODCHIAKI KATO and KIYOSHI KIUCHI
Japan Atomic Energy Research Institute, Department of Nuclear Energy System,
Tokai, Ibaraki 319-1195, Japan
ABSTRACT
Zirconium has been used as structural materials for heavily corrosive devices in recent
commercial reprocessing plants. Although it has excellent corrosion resistance against nitric acidsolutions, the susceptibility to stress-corrosion cracking (SCC) increases with increasing theconcentration of nitric acid and temperature. We attempted to monitor the initiation andpropagation of the trans-granular type SCC by measuring acoustic emission (AE). AE signals
were monitored during the slow strain-rate tensile test (SSRT) with notched specimens. Two AE
sensors were mounted for reducing boiling and mechanical noise. The SSRT was pursued in aboiling nitric acid with 3 to 12 in normality, comparing with the reference tests in silicone oil atthe same temperature. The stress level for detecting the AE signals due to TGSCC wasdecreased from near the tensile strength to 0.2%-proof strength with increasing the concentrationof nitric acid. The practical possibility of AE method for monitoring the SCC was establishedfrom a good correlation between the AE signals and the fraction of surface area fractured by
SCC on the SSRT.
1. INTRODUCTION
Sufficient corrosion resistance against boiling nitric acid solutions including oxidizer ions of
TRU and FP elements is required for the materials used in commercial reprocessing plants forspent oxide fuels (1). Austenitic stainless steels with ultra-low carbon grade such as types 304L
and 310Nb had been developed for this purpose. However, these materials have suffered frominter-granular attacks due to trans-passive corrosion mainly at heat transfer surfaces with highoxidation potential. In the recent design, the application of these steels is limited to devices
operated at low boiling point of less than 70˚C at low pressures for inhibiting the trans-passive
corrosion (2). On the other hand, zirconium has been used as one of the alternative materials
applied for devices operated in the normal pressure like a dissolver and Pu evaporator. This
metal without trans-passive behavior shows excellent corrosion resistance in heavily oxidizing
nitric acid solutions. However, it shows high susceptibility to transgranular-type stress corrosion
cracking (TGSCC) in nitric acid solution (3). Our previous study has shown that thesusceptibility to SCC depends strongly on the concentration of nitric acid and the
crystallographic texture of zirconium, because it is easy to propagate along the closed packed
J. Acoustic Emission, 19 (2001)

53© 2001 Acoustic Emission Groupplanes like (0001) of hcp crystal structure (4). The SCC mechanism is interpreted as the quasicleavage type cracking controlled by the film rupture mechanism. The appropriate monitoring
method for the SCC of devices made of zirconium would be required to confirm the reliability
during the operation.
In this study, the practical applicability of AE measurement for monitoring the SCC ofzirconium was examined using the slow strain-rate tensile test (SSRT) in boiling nitric acidsolutions.
2. EXPERIMENTAL PROCEDURES(1) Test Specimens
Test specimens were prepared from zirconium metal plates with the chemical composition of
ASTM 702 pure grade as shown in Table 1. This material was refined by vacuum arc remelting(VAR), hot rolled to 8 mm in thickness and annealed in the alpha-phase stable temperature. The
mechanical properties of this material are shown in Table 2. In the present test, round notched
specimens have been employed for confirming AE signals at the cracking position. The shapeand dimension of this specimen are shown in Fig. 1. The susceptibility to SCC of the notched
specimen in nitric acid solutions was confirmed in another report (5). The notch was machinedparallel to the hot rolling direction, which is easy to propagate SCC. The AE sensor was directly
contacted to specimens to obtain high sensitivity. Two AE sensors were attached for getting AE
signals at two different locations. Type A with the notch position set on the equal distance fromeach sensor was used in oil and in boiling nitric acids with 8 and 12 in normality. However, thenoise level reduction was not adequate in this setting. Therefore, type B with the notch position
set on difference distance from each sensor was used in boiling nitric acids with 3 and 6 innormality. The noise level was markedly suppressed due to the time lag of AE signals input
between each sensor.
(2) AE Measurement
A block diagram for the AE measurement used in the present study is shown in Fig. 2. The
MISTRAS-2001 system made by the Physical Acoustics Corporation (PAC) was used. The
outline for AE measurement was described in Table 3. The resonance-type AE sensor having thecenter frequency at 150 kHz (PAC, R15) was used. The AE parameters, time, rise time,
amplitude, counts to peek, duration, total counts and AE waveform, were measured for each AE
event. The sampling rate of AE waveform is 4 MHz. AE signals obtained from the sensor were
amplified at 80 dB and filtered to pass signals with the frequency range from 10 to 400 kHz.
This system consisted of a pre-amplifier and main amplifier with band-pass filter. The thresholdof AE signals was set at 10-20 mV. The front-end filter was used in order to reduce theelectrical-noise-like impulse. In addition, the location of objective AE source was clarified bycomparing the time lag of AE signals at each position for eliminating noises caused from bubbles
or mechanical contact. The difference-time filter (delta-T) of input signal from each sensor was
54

Table 1 The chemical composition of zirconium plate. (wt.%)

Table 2 The mechanical properties of zirconium plate.

Table 3 The setting condition of AE
measurements.
AE Sensor type
Amp-gainPreamp-gain
Wave Sampling rate
High pass filter
Low pass filter
Threshold
Peak definition timeHit definition tumeHit lockout time
Front end filter
Delta-T filter

R15
150kHz resonance
40 dB
40 dB
4 MHz
10 kHz
400 kHz
40 ~ 45 dB
300 µs
600 µs1000 µs
> 3 counts
<10 µs (12,8N HNO3,OIL)>2,<7 µs (6,3 HNO3)

set within 10 µs for type-A specimens. The same AE event would be monitored at 42 mm awayfrom the notch center by assuming the velocity of AE waves of 4200 m/s. However, the setting
condition was unable to reduce the noise due to boiling bubbles. Therefore, the setting conditionof delta-T filter was changed to 2 to 7 µs for type B specimens.
(3) Slow Strain-Rate Tensile Test (SSRT)The susceptibility to SCC of zirconium depends on the concentration of nitric acid asreported in the previous study. The present experiment examined SSRT in boiling nitric acids
with 3 to 12 in normality. The reference data was obtained by the same SSRT in chemically
non-active silicone oil at 115˚C (6). The crosshead speed on the SSRT was controlled at 1.67 x
10-6 mm/s, which correspond to the appropriate strain rate for detecting the susceptibility to film55

15 mmM6
AE sensor

φ8120 mm107.5 mmmeasure area

φ3

90°

0.25r
measure area

the hot rolling direction
107.5 mm

95 mm

AE sensor
M6

15 mm

Type A(12,8N HNO 3,OIL)

Type B(6,3N HNO3)

Fig. 1 The notched specimen used for the SSRT.

rupture-type TGSCC. The test cell consists of the see-through type double glass wall with theevaporated metal film heater and hard Teflon flanges. The internal glass diameter is 70 mm. The
test solution was used without renewal during the SSRT, because the solubility of zirconium inboiling nitric-acid solutions is lower than several ppm. After the SSRT, the morphology of thefracture surface was examined in detail by a scanning electron microscope (SEM) for
fractographic analyses.

56

Load cell
Condense tube
PreAmp.
PAC 1220A

Test specimen
Glass cell withthe film heater

Thermocouple
TeflonAE analyzer
PAC
MISTRAS 2001
AEDSP-32/16

PreAmp.
PAC 1220A

AE Sensor
Load

Fig. 2 The schematic diagram of the AE measurement system.

3. RESULTS AND DISCUSSION
Figure 3 shows the stress-time (strain) curves obtained by SSRT in boiling nitric acid and in
silicone oil at 115˚C, respectively. Figure 4 shows the histograms of AE hits obtained in boiling
nitric acids and silicone plotted against time. Compared with silicone oil, the time to failure is
reduced with increasing nitric-acid concentration as expected from the previous SCC reports (6).
The reduction rate of rupture time in nitric acids to that in silicone oil were 19%, 21%, 22% and
25% in 3N, 6N, 8N and 12N HNO3, respectively. It shows the sensitivity to SCC increases withincreasing the concentration of nitric acid. In the present analysis condition, the AE event due tothe SCC was only obtained in nitric acid solutions, because that due to the plastic deformation
was not detected. The background noise on the event of AE signals was detected in a wide rangeof stress–strain curves, even if the low stress region in 8N and 12N HNO3. It was expected tocome from noise caused by the formation and annihilation of boiling bubbles. The difference in
the initial AE counting rate between 8N and 12N HNO3 would be dependent on the boilingcondition as the background noise level was different.
In 12N HNO3, the rising time of AE hits is nearly 35 hrs and the stress level reaches 215
MPa. In 8N HNO3, the rising time of AE hits is nearly 52 hrs and the stress level is 305 MPa. In
6N HNO3 there are two points of rising AE hits. The first rising time of AE hits is nearly 57 hrs
57

Fig. 3 The stress-time(strain) curves of SSRT.
and the stress level is 293 MPa. In 3N HNO3, the rising time of AE hits is extended to nearly
109 hours and the stress level becomes 420 MPa. This stress level is comparable the ultimate
tensile stress of zirconium at 373 K (see Table 2). The rising time is clearly shortened byincreasing the concentration of nitric acid. The stress level of AE event increase due to SCC in
12N HNO3 was decreased to 0.2%-proof stress level of zirconium at 373 K. In 6N HNO3, thesecond peak is arising at close to the tensile stress. This AE hits behavior is similar to that in 3N
HNO3. The present observation shows that the difference of the AE generation mechanisms
depends on the concentration of nitric acids.
The optical photographs of the fracture surface after SSRT are shown in Fig. 5. The surface
morphology fractured in silicone oil showed the metallic luster and ductile fracture. On the other
hand, fracture surfaces in boiling nitric acids showed dark color and quasi-cleavage-type fracture
along circumference of notch groove. The area ratio of the quasi-cleavage-type fracture increase
with increasing the nitric acid concentration, as 38%, 38%, 65% and 80% in 3N, 6N, 8N and
12N respectively. It depends on the susceptibility to SCC. SEM photographs of the fracturesurfaces of SSRT specimens are shown in Fig. 6. On specimens fractured in boiling nitric acids,
the ductile fracture is only observed in the center of the fracture surface due to mechanicaltearing at the final stage on tensile tests. Figure 7 summarizes the relationship between thesusceptibility to SCC, the area ratio of the quasi-cleavage-type fracture and the nitric acidconcentration. The fracture surface area by SCC was coincident with the generation rate of AE
signals. It is clear that the susceptibility to SCC increases with increasing the concentration of
nitric acids to more than 6 in normality.
The waveforms of AE signals were analyzed in order to discriminate the AE signals due to
SCC from noise due to bubbles. Figure 8 shows typical AE waveforms and power spectra
originated from the bubble and SCC, respectively. The amplitude value of the two conditions is58

Fig. 4 The histograms of AE hits rate of SSRT.
59

Fig. 5 Optical photographs and the area ratio of quasi-cleavage fracture after SSRT.

Fig. 6 SEM photographs of the fracture surfaces after SSRT.
not clearly different. Therefore, it is difficult to separate the AE signal due to the SCC from thenoise by amplitude discrimination. However, the AE waveform due to the bubbles obtained by
two sensors located at two different positions did not show any similarity. On the other hand, theAE waveforms by SCC detected at two sensors coincide with each other. In addition, the risetime of AE parameters showed the difference between the bubble and SCC. Accordingly, it
appears possible to separate the AE signal due to SCC from the noise by analyzing the similarity
in waveforms measured at different locations.
4. CONCLUSION
The applicability of AE monitoring method for detecting the transgranular-type SCC ofzirconium in boiling nitric acid solutions was examined and following results were obtained.
(1) The AE method is one of the most promising methods for clarifying the crack initiation and60

Fig. 7 The relationship between the susceptibility to SCC and the nitric acid concentration.
propagation of SCC of zirconium.
(2) Both the AE hits and the susceptibility to SCC increased with increasing the concentrationof nitric acid. The stress level for generating the AE event due to the SCC was decreased to
the 0.2%-proof stress in boiling 12N HNO3.
(3) The AE signals derived from the SCC can be separated from the noise of bubbles, byanalyzing the waveforms obtained from a pair of sensors at different positions. A similar
waveform was only obtained with the AE signals due to the SCC.
REFERENCES(1) K. Kiuchi; J. Atomic Energy Society of Japan, 31 (1989), 229.
(2) K. Kiuchi, N. Yamanouchi, M. Kikuchi and Y. Kuriki; Proc. RECOD’91, Vol. 2, (1991), p.
549(3) J.A. Beavers, J.C. Griess and W.K. Boyd; Corrosion, 36 (1981), 292.
(4) P. Fauvet and G. Pinard Legry; EUROCORR’92, Espoo, Finland.
(5) C. Kato, Y. Takagi, K. Kiuchi; Proc. International Symp. on Plant Aging and Life Predictionsof Corrodible Structures, 1995, Japan, p. 883.
(6) K. Kiuchi, M. Hayashi, H. Hayakawa, M. Sakairi and M. Kikuchi; Proc. RECOD’94, Vol. 3,(1994).

61

5

3

0

2

-20

1

-40

0

40

Ch2 / mVPower / mV

4

20

4

20

3

0

2

-20

1

-40

Power / mV

CH1 / mV

40

0

0

100

200

300 400 500 0

100Time / µs

200

300

400

500

Frq / kHz(a) the void boiling
5
40

3
0

2
-20

1

Power / mV

CH1 / mV

4
20

-40

0
40

3
0

2
-20

1

Power / mV

Ch2 / mV

4
20

-40
0

100

200

300

400

500 0

Time / µs

0
100

200

300

400

500

Frq / kHz(b) SCC
Fig. 8 The typical AE wave pattern and power spectra derived from the bubbling voidand SCC respectively.

62

ACOUSTIC EMISSION MONITORING OF CHLORIDE STRESS CORROSIONCRACKING OF AUSTENITIC STAINLESS STEELSHINYA FUJIMOTO, MIKIO TAKEMOTO and KANJI ONO*
Faculty of Science and Engr., Aoyama Gakuin University, Setagaya, Tokyo 157-8572, JAPAN* Dept. of Materials Science and Engineering, UCLA, Los Angeles, CA 90095-1595, USA.
ABSTRACTWe analyzed the fracture dynamics of chloride-SCC of austenitic stainless steel AISI 304 by
AE source simulation method. Using the varied mechanical and thermal loading systems in 3538 mass % MgCl2 solution at 95 to 98˚C, we produced transgranular (TG) and intergranular (IG)type SCC. We monitored no AE from the transgranular (TG) type SCC even by using an AE
monitoring system with 80 dB amplification, but did monitor a large amount of AE by using 40
dB amplification system, from intergranular (IG) type SCC. Source dynamics estimated byunique waveform simulation of Lamb So-packet revealed grain-size fractures with fast source
rise time of 0.17 to 1.05 µs. Though the mechanism of SCC is not well-understood, it isconfirmed that IG-SCC does emit detectable AE signals.
1. INTRODUCTION
Stress corrosion cracking (SCC) of austenitic stainless steel by chloride ions is a continued
problem in chemical and nuclear power plants. SCC is believed to be caused by the anodic
dissolution of metal along active-paths such as slip lines, strain-induced martensite andchromium-depleted zone along grain boundaries, and is called APC (active-path corrosion) type
SCC. Chloride-SCC of austenitic stainless steels, typical APC-SCC, shows two types of fracture
morphology, transgranular (TG) and intergranular (IG), depending on the environmental(temperature, concentration), loading (mechanical and thermal) conditions as well as the
microstructure (sensitized or not). In 1970s and 80s, extensive AE research [1,2,3] of TG-type
SCC of AISI304 steel in a boiling MgCl2 solution (B.P.:146˚C, test solution specified by JIS
G0576(79) and NACE G36(78)) had been attempted, and led to a conclusion that the TG APCSCC did not emit AE. The use of highly concentrated MgCl2 solution (42 % MgCl2) with pH
less than 1.6 was criticized since it could not simulate the IG-type SCC experienced in heatexchanger tubing. At present, 30 or 35 mass % MgCl2 solution with pH of about 3.5 at nonboiling temperatures are widely used as test solutions. In these solutions, IG-type SCC occurs
readily by changing the temperature and loading condition as well as the sensitizing treatment.
The present author [4] reported that the heat-flux and bi-axial stress state promotes IG-SCC in
low concentration MgCl2 solutions, as in the case of SCC in heat exchanger tubes. SCC of
Inconel 600 and that of low carbon 304L and 316L steels in high temperature light water, typical
IG-SCC, are serious problems in nuclear power plant. Whether such IG-SCC produces AE or not
is an important and critical factor for SCC detection by AE monitoring and is the thrust of ourstudy; in particular, whether the APC-SCC produces AE, and if so, by which fracture
mechanism.
We reported previously [5] that the IG-SCC of sensitized AISI 304 steel by polythionic acidat room temperature produces numerous Lamb-wave AE signals with sufficient amplitude fordetailed analysis. Fracture dynamics were determined by using an experimental transfer function
J. Acoustic Emission, 19 (2001)

63© 2001 Acoustic Emission Groupfor So-mode Lamb wave. The effective source rise times estimated were in the range of 0.28 to
0.77 µs. This result indicated that the IG-SCC progresses discontinuously, with fast grainboundary fractures.

Fig. 1 Experimental setup for Type A SCC test.
In this paper, we report our recent results of AE monitoring from TG- and IG-SCC ofAISI304 steel in 35 and 38 mass % MgCl2 solution under various loading conditions. We
observed no AE from TG-SCC, but detected frequent AE signals from IG-SCC. Fracture
dynamics of IG-SCC were obtained by a unique waveform simulation method and comparedwith those obtained for IG-SCC due to polythionic acid.
2. TEST METHODS AND AE MONITORING
In order to stimulate the TG- and IG-SCC of AISI304 steel, we used two test methods. Figure
1 shows Type-A test, where a thin plate (0.8 mm thick) of AISI 304 stainless steel was loaded intension in a 35 mass % MgCl2 solution at 95˚C. The specimens were heated at 1050˚C for 30
min and quenched in water or furnace-cooled, The furnace-cooled specimens are slightly
sensitized and more susceptible to IG-SCC than the water-quenched ones. Load was applied tothe specimen using a compressed spring with through-wall double-sided slits of 10-mm depth.
This method is a decreasing-stress test since the applied load decreases with SCC progression.
Four AE sensors were mounted on both surfaces away from the corrosion cell. Three sensors(#A-C, JT M204) with a pre-amplification of 40 dB and one sensor (#1, PAC PICO) without64

were used. Outputs of the sensors were further amplified by 40 dB, and digitized by a fast A/Dconverter at 50 ns interval with 1024 points at 10-bit resolution. We used highly sensitive M204
sensors since we detected no AE in preliminary SCC tests. Possible noise due to 80-dB total
amplification was monitored by the same type sensor (#D) mounted on a stress-free plate
immersed in the same solution. Change of corrosion potential was simultaneously measured via areference electrode of Ag/AgCl.

Fig. 2 Experimental setup for Type B SCC test.
Figure 2 shows Type-B test method, which can produce IG-SCC. Sheet specimen of 0.8mm-thick 304 stainless steel was loaded in tension by a cantilever loading system with constantdead weight (increasing-stress type), and heated by an infrared lamp from the lower surface. In
order to localize the SCC initiation, a shallow (0.5-mm depth) circular dimple of 4 mm diameter
was machined at the center of the specimen. A 30-mm-diameter alumina tube was attached as acorrosion cell on the specimen with an O-ring. This test, using 38 mass % MgCl2 solution at
98˚C, produces typical IG-SCC due to the biaxial thermal stress state and applied stress whensensitized specimens are used. Four PICO sensors, mounted separately at 50 and 55 mm fromthe cell center monitor Lamb-wave AE signals. Corrosion potential was monitored by the same
system as shown in Fig. 1.
3. TEST RESULTS AND FRACTURE DYNAMICSOF SCC
Trasngulanular SCC by Type-A Test (decreasing-stress type)
We repeated SCC tests of both non-sensitized and sensitized specimens, but could not detect
any AE in spite of SCC occurrence. When a test was interpreted, all cracks that propagated 4 mmlong were found to be TG-type cracking even for sensitized specimens. A few AE signals could
be observed occasionally only at the final stage of SCC. Figure 3 show the source location of65

three such AE signals (E.C. 1, 2 and 3) at ligament of two main SCCs from opposite slits. SCCs
started at slit corners after 20 hr at 118 MPa loading, but did not emit any AE until 50 hr at
which two main SCCs propagated in parallel. AE signals were detected when SCCs connect toeach other. Interesting to note is that cracks near the slit corner (a) are TG-type while that at theligament (b) is IG-type. IG-SCC is caused by higher biaxial tensile stresses at branched crack
tips.

Fig. 3 The source location of three AE signals (EC.1-3). Crack morphology at (a) and (b) are
shown below. SCC conditions: 115 MPa in 35% MgCl2 at 95˚C. 10 µm marker shown.
Figure 4 show three AE signals (EC1-3) at around 51 hr. No noise was detected by sensor #D(M204). AE signals show typical features of the Lamb wave, i.e., first arriving weak So-mode
and trailing strong Ao-mode.
We examined whether AE is produced by SCC or by mechanical fracture of 304 stainlesssteel, since these AE signals are monitored near the final fracture. The fracture dynamics of three
events were studied by conducting the So-mode waveform simulation. The overall transfer
function, including the sensor, medium and AE monitoring system, was determined by themethod shown in the top of Fig. 5. Lamb-wave AE signals were excited by a compression-mode
PZT element, glued to a sheet specimen (304 SS) and a steel block by epoxy, and were detectedby M204 and PICO sensors. Here, the out-of-plane displacement of PZT element was monitored
by a laser interferometer and converted to crack volume as shown in Fig. 5(b). The over-alltransfer function, Fig. 5(c), for the So-Lamb wave due to the Mode-I crack opening was obtainedby the time-domain Gauss-Zeidel deconvolution of detected wave (a) with source wave (b).
66

Fig. 4 Lamb wave detected by four sensors during Type-A SCC test.
Crack opening of SCC was determined by matching the simulated waveform of the So-packet tothe detected ones. Detail of this method can be found elsewhere [8].
In Fig. 6, simulated So-packet is overlapped on the detected waveform. The effective source
rise times are estimated as 0.7 to 0.8 µs. These times are slightly larger than those (0.3 to 0.77µs) of IG-SCC by polythionic acid (pH = 2.0) of heavily sensitized 304 steel [9]. Crack size,
estimated by assuming the crack opening of 1 µm, coincides with the grain size (about 30 µm).
This indicates that the AE events observed were produced by discontinuous fast fracture alongindividual grain boundary.
IG-SCC by Type-B Test (increasing-stress type)
We first measured the attenuation of Lamb waves in a water-loaded thin plate, since theamplitude of Lamb-wave AE was generally low. Using the system in Fig. 7, we monitored Lambwave AE, excited by ablation of focused YAG pulse irradiated at mid-thickness distal plane, forplate with and without corrosion cell. The left PICO sensor was used to monitor the intensity ofablation. This setup simulates the wave propagation in Type-B test. Amplitudes of the So-packet
did not change with and without the cell, while those of Ao-mode was slightly decreased withthe cell in place. We also measured the wave attenuation for the plate coated by silicone rubber
over 10 to 40 mm long. So amplitude was unchanged, but Ao amplitude significantly decreased
by rubber coating. These indicate that weak So-mode signals are not due to the energy leaking
into solution, but due to the small surface crack.
67

Fig. 5 Experimental setup for simulating Mode-I fracture by 1 MHz PZT-element and procedureof determining the over-all transfer function.

Fig. 6 Overlapping of simulated S0-packed of Lamb wave on the waves monitored during Type A(decreasing stress) SCC test.
Using Type-B test, we observed 80 events during a test lasting 100 ks (28 hr), as shown in
Fig. 8. AE counts are relatively small, since our AE system has a long dead time for storing thedigitized data. Many more AE counts, almost 5 times, can be expected by using a fastacquisition system when AE occurs at a high rate [6]. SCC propagated for 17 mm long in both
directions, as shown in Fig. 9. Most AE sources are located close to the main SCC. Event count(EC) number is shown in the location. Here, the source location was identified by using thefrequency independent sheet velocity (first arrival time) of the So-Lamb wave, and some events
68Fig. 7 Laser-induced Lamb waves monitored by #2 PICO for plate with and without corrosion cell.

Fig. 8 Cumulative AE event counts and elongation change during Type B (increasing stress) SCC test.
with weak So-amplitude were omitted. Fractographic observation (Fig. 10) shows typical IG
cracking with many secondary (or sub-) cracks along grain boundaries. Grain boundary fracturewith smooth surface are locally observed. This feature is similar to that in IG-fracture producedby polythionic acid and fluoride SCC of sensitized AISI 304 steel [5,7].
69

Fig. 9 Source location of Lamb wave monitored by Type B SCC test.

Fig. 10 Intergranular SCC by Type B (increasing-stress) SCC test with numerous secondary IGcracks. A marker for 10 µm is shown at bottom right.

70

Table 1 summarizes the results of selected SCC tests among 28 runs, including those by
Type-A test. No AE was detected for TG-SCC but a large number of AE events for IG-SCC areevident. Because most chloride-SCC in process plants are associated with IG-cracking, AE canbe a useful monitoring method of SCC. Disputable conclusion on AE by APC-SCC has long
precluded the application of AE to SCC monitoring, but the present work demonstrates beyond
doubt that AE can monitor the IG- SCC. Next we studied the fracture dynamics of IG-SCC by
the same method used in the previous section. Shown in Fig. 11 are the examples of AE events
monitored. So-waves are generally weak, indicating grain size fracture near the surface. Analysis
was applied to AE signals (11 events) with sufficient amplitude. Typical waveform simulation
results are shown in Fig. 12. Source parameters are shown in Fig. 13. Estimated source rise timevaried widely from 0.17 to 1.0 µs, and the crack volume was estimated in the range from 2x10-16
to 1.4 x10-15 m3.
Source mechanisms of these SCC are not well understood at present. No AE produced from
TG-SCC suggests that SCC silently advances by the anodic dissolution of metal along active
paths in the grain. AEs from IG-SCC may be due to the hydrogen-assisted cracking or protoninduced grain boundary de-cohesion. The fact that IG-SCC is caused by neutral fluoride ions [7]suggests the grain boundary de-cohesion by specific anions. More research, using AE systemwith fast data acquisition, on SCC under various environmental conditions of sensitized steel isneeded.
4. CONCLUSIONS
We monitored and analyzed Lamb–wave AEs from chloride-SCC of austenitic stainlesssteel (AISI 304) by using two SCC test methods. Results are summarized below:
1) SCC of sensitized or non-sensitized AISI304 steels in 35% MgCl2 at 98˚C under decreasingstress tests produced transgranular SCC. No AE signal was detected for this TG-SCC.
2) Dead loading (increasing-stress type) SCC test of sensitized steel under heat flux, in 38 mass
% MgCl2 at 95˚C, produced IG-SCC. Many AE signals were monitored during the initiationand propagation of IG-SCC.
3 ) Source simulation of So-Lamb AE events, monitored during IG-SCC, revealed fast,
discontinuous fracture with rise time of 0.17 to 1.0 µs over an individual grain.
4) AE can be used for the detection of IG-SCC initiation and progression.
REFERENCES[1] H. Okada, K. Yukawa and H. Tamura, Application of Acoustic Emission Technique to the
Study of Stress Corrosion Cracking in Distinguishing Between Active Path Corrosion andHydrogen Embrittlement, Corrosion, 30 (1974) 253.
[2] S. Yuyama, Detection and Analysis of Crevice Corrosion-SCC Process by the Use of AE
Technique, J. Iron and Steel of Japan, 68 (1982) 2019.
[3] S. Yuyama, Fundamental Aspects of Acoustic Emission Applications to the Problems Caused
by Corrosion, ASTM STP 908 (1986) p. 4.
[4] M. Takemoto and J. Nagata, Trans. J. Soc. Chemical Engineers, 12-6, (1986) 681-687 and
675-680.
[5] M. Takemoto, O. Tamura and H. Suzuki, AE Study of Stress Corrosion Cracking Mechanismof Stainless Foil Using Quantitative Lamb Wave Analysis and Video Images, Progress in AE IX,(1998) AEWG, Hawaii, pp. 178-185.
71

Table 1 Test result of various chloride SCC test.

[6] T. Sato, H. Nishino, M. Takemoto and K. Ono, Dynam ics of Delayed Fracture of HighTension Steel Plate by the Lamb Wave Analysis (in Japanese), Paper presented at The Annual
meeting of KSMDI (2000, May) Tokyo.
[7] M. Takemoto, External Stress Corrosion Cracking (ESCC) of Austenitic Stainless Steel,Materials Performance, 24-6, (1985) 26-32.
[8] T. Sato, M. Takemoto, K. Ono, Effect of Fracture Dynamics in Thin Plate on the Waveformand Radiation Pattern of the Lamb wave, Japan. J. Appl. Phys., 39 (1999), 3193-3200.
[9] M. Takemoto, O. Tamura, A Fast Cleavage Fracture in the Polythionic SCC of Type 304
Stainless Foil Revealed by the Lamb Wave Analyses and Direct Observation, J. Japan Soc. ofCorrosion Engineering, 46 (1997) 662-668.

72

Fig. 11 Lamb wave detected by PICO sensor during Type B (increasing-stress) SCC test.

Fig. 12 Overlapping of simulated S0-packet on the lamb waves detected during Type B SCC test.

73

Fig. 13 Source parameters of Mode-I fracture during Type B SCC test.

74

CYLINDER WAVE ANALYSIS FOR AE SOURCE LOCATION AND FRACTURE
DYNAMICS OF STRESS CORROSION CRACKING OF BRASS TUBE
FUKUTOSHI UCHIDA, HIDEO NISHINO, MIKIO TAKEMOTO
and KANJI ONO*
Faculty of Science and Engr., Aoyama Gakuin University, Setagaya, Tokyo 157-8572, JAPAN*Dept. Materials Science and Engineering, UCLA, Los Angeles, CA 90095, U.S.A.
ABSTRACT
Based on the quantitative modal analysis of cylinder waves, we developed new source
location and fracture dynamics analysis methods. Source location along the tube axis was
identified by using the arrival time differences of L- and F-modes at a selected frequency.
Wavelet transform was utilized for effective determination of wave mode and frequency to be
used. Source locations were identified within maximum error of 6% for propagating distancefrom 100 mm to 3000 mm for 5 mm diameter aluminum tube. Next we analyzed AE signals
from ammonia-SCC of 5-mm brass tube exposed to Mattsson’s solution. Source locations were
determined, by using L(0,1) and F(2,1) modes, and the results agreed with actual position of
SCC. Fracture dynamics of SCC, estimated by the L(0,1) mode waveform simulation, indicatedan association of fast grain boundary fracture in ammonia-SCC of brass.
1. INTRODUCTION
Quantitative analysis of cylinder-wave acoustic emission (AE) is important for monitoring
the local damage such as stress corrosion cracking (SCC), fretting, denting and pitting corrosionin heat exchanger tube and buried pipeline. AE signals in a hollow cylinder are propagated as
multi-mode cylinder (cylindrical) waves. Quantitative analysis of cylinder wave poses muchdifficulty due to complex circumferential modes. Theoretical background of the cylinder wave
was first provided by Gazis [1], who showed three modes; longitudinal (L-), flexural (F-) andtorsional (T-) modes. Fitch [2] generated L- and F-modes by using annular transducers andverified the validity of Gazis's theory. Alleyne and Cawley [3] utilized certain modes of thecylinder wave in nondestructive testing application for chemical plant piping. They reported thatthe cylinder waves can propagate several tens of meters in a 76 mm steel pipe. Rose et al [4]analyzed attenuation of cylinder wave due to energy leakage into liquid. We recently proposed a
new source location method in small size tube (5 mm diameter and 1 mm thickness aluminumpipe) by using the group velocity difference between L(0,1) and F(1,1) modes [5].
In this paper, we examined AE signals produced as cylinder waves by ammonia-SCC ofbrass tube. Source location and fracture dynamics were analyzed by using modal analysis andwaveform simulation method, respectively. First, we briefly introduce quantitative analysis of
the cylinder waves and the source location method of AE signals monitored by a single AEsensor. Source location accuracy was studied for laser generated AE signals in 5 mm diameteraluminum tubes. Both the location and fracture dynamics were analyzed for SCC-produced AE

J. Acoustic Emission, 19 (2001)75© 2001 Acoustic Emission Groupsignal. The source dynamics were estimated by unique waveform simulation of L(0,1) mode
cylinder wave monitored by a single AE sensor.
2. SOURCE LOCATION OF CYLINDER WAVE AE
We computed the group velocity dispersion of cylinder waves based on the characteristic
equation of a 6 x 6 matrix form [1]. Vibration mode of the cylinder wave is classified into three;L(0,m), F(n,m) and T(n,m) with vibration mode parameters (n,m) in the circumferential directionn and radial (thickness) direction m. However, we do not discuss the T-modes in this paper,
because their vibration is limited in-plane and can not be monitored by a conventional AE sensor
or a laser interferometer. L-mode is axially symmetric mode (n=0) and exists as L(0,m). Figure
1 shows group velocity dispersions of L- and F-modes for an aluminum pipe of diameter: d = 5mm and thickness: t = 1 mm or t/d = 1/5. Velocity dispersions of A0- and S0-mode Lamb waves
for 1 mm thick aluminum plate are also shown. It is noted that the shape of the dispersion curves
is identical for constant t/d. Therefore, the horizontal axis is normalized by the product of
frequency (f in kHz) and wall thickness (t in mm). This behavior is the same as that of Lambwave dispersion curve.

Fig. 1 Group velocity dispersion of aluminum tube with diameter 5 mm and thickness 1 mm,
t/d = 1/5.

76

We determined the source location of AE signal monitored by a single AE sensor mounted
on tube end, by using the velocity difference of the two wave modes at a selected frequency.
Though different wave modes and frequency can be used depending on both the tube dimension
and acoustic properties, we here show the method with the use of the L(0,1) and F(1,1) modes.
As shown in Fig.1, there exists a large group velocity difference at frequency fm (180 kHz for 5
mm aluminum tube). Utilizing the group velocities VL(0,1) of L(0,1) mode and VF(1,1) of F(1,1)
mode, the source location along the z axis (z being the axial distance from the end sensor) is
given by equation (1),z = {(VL(0,1) VF(1,1))/(VL(0,1) – VF(1,1))} ∆t.

(1)∆t designates the arrival time difference between L(0,1) and F(1,1) modes at the frequency fm.
This is obtained from the wavelet contour map of a detected signal.

Fig. 2 Setup for cylinder-wave AE monitoring.
Experimental group velocity dispersion derived from wavelet contour maps of the detectedsignal guide us in selecting the wave mode and frequency. For a larger diameter tube (16 mm),
we used the group velocities of L(0,2) mode and F(1,2), as reported elsewhere [5]. For 5 mmdiameter brass tube, as will be discussed later, we use group velocities of L(0,1) and F(2,1)
modes at fm = 190 kHz. Location accuracy of the proposed method was studied using artificial
sources. As shown in Fig. 2, we excited cylinder-wave by ablation due to pulse-YAG laser beamon the distal plane and monitored by a small resonant type AE sensor (PAC, PICO, nominal
center frequency 450 kHz) mounted at 100, 200, 300, 400, 500, 1000, 2000 and 3000 mm fromthe tube end. Figure 3 show the waveform of a cylinder- wave signal (a) for 5 mm tube
(t/d=1/5). The group velocity dispersion curves are given in (b), derived from wavelet contour
map. Group velocity in the vertical axis was calculated from wavelet peak at each frequency by
the time-of-flight method, and shown in logarithmic scale. Theoretical velocity dispersion curvesof L(0,1), L(0,2), F(1,1) and F(1,2) are shown in (b) by solid lines. For this tube, we used arrival
time difference of L(0,1) and F(1,1) at 180 kHz. Time transient of wavelet coefficient at 180
kHz is shown in (c) as a function of z. The first wavelet peak corresponds to the L(0,1) mode andand the second peak the F(1,1). Using the time difference ∆t between the two peaks andtheoretical group velocities of VL(0,1) (4749m/s) and VF(1,1) (2490m/s) at 180 kHz, location isdetermined as shown in Table 1.
77

Fig. 3 Waveform (a), group velocity dispersion (b) and time-transient of wavelet coefficient at
180kHz (c).
Table 1 Error of source location.

Large error for z =100 mm is due to the difficulty in separating the L(0,1) and F(1,1) modes.
Source location errors for z =200 to 500 mm were less than 3.4 % and 5.9 % for z=1000 to 3000
mm. These errors are small enough for the source location of AE signal. Source location ispossible for longer distances as long as the cylinder wave is detected.
3. ANALYSIS OF AE SIGNALS PRODUCED BY STRESS CORROSION CRACKING
Stress corrosion cracking (SCC) of brass by ammonia is an old and still continuing problem
in cartridge cases, heat exchangers, condensers and automobile radiators. Ammonia is produced
from ammunition (powder smoke) and decomposition of microorganism. Radiator tubes ofsanitary car suffers ammonia–SCC twice every year when it is not periodically cleaned.
Mattsson’s solution (NH4OH+(NH4)2SO4+ CuSO4, pH = 7.2, specified in NACE G37-73)
stimulates the ammonia-SCC of brass at ambient temperature. This SCC occurs at extremely
low tensile stresses below 40 MPa, and has been known to be an active path corrosion (APC)
78

(APC) type SCC [6 - 8]. It has not been ascertained whether AE is produced by ammonia–SCC,
but we detected numerous AE signals.

Fig. 4 Setup for AE monitoring during ammonia-SCC of brass tube.

Fig. 5 Cumulative AE events detected during ammonia-SCC test..
Figure.4 shows experimental setup for AE detection from ammonia-SCC. Mattsson’s
solution was brought in contact to the outer surface of 5 mm diameter and 1 mm thick brass tube

79

using a cotton wick of 20 mm width. A PICO sensor was mounted on one end at 200 mm fromthe center of the wick. Output of the AE sensor was amplified by 40 dB and digitized at 40 ns
sampling interval with 4096 points at 8 bit resolution, and fed to a work station. Figure 5 shows
cumulative AE counts. Open cracks and many fine cracks were observed underneath the cotton
wick, at position z = 200-210 mm (Fig. 6). We detected 113 events during 46-ks (13-hr) test at
90 MPa. Most of them are weak signals and only 22 events with sufficient L(0,1) amplitude,
monitored at later times, were subjected to the source location analysis.

Fig. 6 IG-SCC of brass exposed to Mattsson’s solution.(a)surface crack by optical and (b) fracture surface
by SEM.
Figure 7 shows two AE signals (Event Count 66 and 70) and their time transient of wavelet
coefficient at 190 kHz. Waveform shows the characteristic features of cylinder waves, with firstarriving weak L-mode and trailing strong F-modes. Difficulty in cylinder wave AE monitoringis a large amplitude difference between L- and F-modes. We must detect entire waves of L- andF-modes over longer time period for source location using the slow sampling interval. Thisrequirement is, however, as discussed later, contradictory to that for waveform simulation.
Group velocity dispersions of these two waves are shown in Fig. 8 with theoretical dispersion
curves of L(0,1) and F(2,1) modes. Gray scale is shown in logarithmic scale to cover the entire
intensity range. L(0,1) mode at low frequency is weak but F(2,1) mode is strong, indicating
weak generation efficiency of F(1,1) mode. The contour map indicates the usage of L(0,1) and80

Fig. 7 Waveform (the upper) and wavelet coefficient at 190 kHz of AE signals, produced by ammoniaSCC of brass tube.

Fig. 8 Group velocity dispersion of cylinder wave AE produced by ammonia-SCC of brass tube.
81

F(2,1) mode velocities at the frequency of 190 kHz for the source location. The group velocity ofL(0,1) and F(2,1) modes were calculated as 3935 m/s and 1320 m/s, respectively. Estimatedsource locations were distributed at 195 ±6 mm from the sensor position and very close to theactual position of SCC. Ammonia-SCC was intergranular (IG) type per SEM observation (seeFig. 6(b)). This indicates that the ammonia-SCC, typical APC-IG-SCC, does produce acoustic
emission, as in the case of APC-IG-SCC of austenitic stainless steel by chloride ions [9].
Next we estimated the fracture dynamics of SCC by waveform simulation of first arriving
L(0,1) mode wave. Here, the waveform simulation must be done to the L(0,1) mode, whichshows uniform out-of-plane displacement (or velocity component of displacement) over theentire surface of the tube. We determined the overall transfer function for L(0,1) mode
experimentally by using the same method for Lamb wave. As shown in Fig. 9, we excited L(0,1)
mode wave by an expansion of small compression mode PZT element (1 x 1 x 2 mm). Out-ofplane displacement of the element was measured by a laser interferometer and converted to thevolume opening; see Fig. 9(b). The overall transfer function, Fig. 9(c), was obtained by thetime-domain Gauss-Zeidel deconvolution of the wave (a) with (b). The source parameters;
effective rise time ∆Tr* and crack volume ∆V, with its time dependence approximated by a 4th
power sine step-wise function, were obtained by waveform matching of the first portion of theL(0,1) mode. Detected (EC 79) and simulated waveforms are compared in Fig. 10. Here, thethird and fourth peaks are matched. ∆Tr* and ∆V are estimated as 0.32 µs and 2.7x 10-15 m3,
respectively.

Fig. 9 Experimental setup for monitoring cylinder wave due to Mode-I fracture and procedure for preparing
the over-all transfer function for the cylinder wave.

82

Fig. 10 Comparison of waveform detected (A) and simulated (B).
We must detect the first weak waveform of L(0,1) for the source dynamic analysis using
short sampling interval and large amplification. This requirement is contradictory to those forsource location (long sampling interval and low amplification). Usage of two AE systems
satisfies these requirements and is needed for further research.
4. CONCLUSION
Based on the quantitative modal analysis of cylinder wave, we proposed a new source
location method of AE signal monitored by a single AE sensor. We analyzed the AE signal
produced by ammonia-SCC of brass tube. Results are summarized below;
831) Source location in the axial direction was obtained by using the arrival time difference
between the L- and F-modes, and the theoretical group velocities at selected frequency. Wavemodes and frequency are chosen using the wavelet transform of AE signal detected.
Location accuracy of artificial sources for 5 mm diameter pipe was less than 5.9 % for 100 to3000 mm distance.
2) The source location method developed was applied to AE signals produced by ammoniaSCC of 5 mm diameter brass tube. Estimated AE location agreed well with the location of
SCC. Source dynamics were estimated by the waveform simulation of the first arriving
L(0,1) mode, and suggested grain boundary fractures with fast source rise time.
3) Due to large amplitude difference of L- and F-mode and different sampling conditions, two
independent AE systems, one with long sampling interval and low amplification and otherwith fast sampling interval at large amplification, are needed for both analyses.
REFERENCES[1] D. C. Gazis, “Three-Dimensional Invenstigation of the Propagation of Waves in Hollow
Circular Cylinders,” J. Acoust. Soc. Am. 31, 568-573 (1959).
[2] A. H. Fitch, ”Observation of Elastic-Pulse Propagation in Axially Symmetric and Nonaxially
Symmetric Longitudinal Modes of Hollow Cylinders,” J. Acoust. Soc. Am. 35, 706-708 (1963).
[3] D. N. Alleyne and P. Cawley, “The Excitation of Lamb Waves in Pipes Using Dry-Coupled
Piezoelectric Transducers,” J. Nondestr. Eval., 15, 11-20 (1996).
[4] J. L. Rose, J. J. Ditri, A. Pilarski, K. Rajana and F. Carr, “A Guided Wave Inspection
Technique for Nuclear Steam Generator Tubing,” NDT &E Int., 27, 307-310 (1994).
[5] H. Nishino, F. Uchida, S. Takashina, M. Takemoto and K. Ono, “A New Method of AE
Source Location in Pipes Using Cylindrical Guided Waves,” Proc. of EWGAE 2000.
[6] H.W. Pickering and P.J. Byrne, “Stress Corrosion of α−Brass in an Acid Sulfate Solution,”Corrosion, 29, 325 (1973).
[7] R.P.M. Procter and F.N. Stevens, “The Formation of Cuprous Oxide Films on α−Brass Stress– Corrosion Fracture Surfaces,” Corrosion. Sci., 15, 349 (1975).
[8] N.W. Polan, J.M. Popplewell and M.J. Pryor, “Mechanically Assisted Dezincification of CuZn alloys during Stress Corrosion in Ammoniacal Solutions,” J. Electrochem. Soc., 126, 1299
(1979).
[9] S. Fujimoto, M. Takemoto and K. Ono, “AE Monitoring of Chloride Stress Corrosion
Cracking of Austenitic Stainless Steel,” Progress in AE X, Proc. 15th IAES, JSNDI, Tokyo, 2000,
pp. 129-134.

84

DETECTION OF PRE-MARTENSITIC TRANSFORMATION PHENOMENA IN
AUSTENITIC STAINLESS STEELS USING AN ACOUSTIC EMISSION TECHNIQUE
T. INAMURA, S. NAGANO, M. SHIMOJO, K. TAKASHIMA and Y. HIGO
Precision and Intelligence Lab., Tokyo Institute of Technology, Yokohama 226-8503, Japan
ABSTRACT
Acoustic emission (AE) measurements were performed on a 304 austenitic stainless steel
during cooling to investigate pre-martensitic transformation phenomena of α’-martensitic
transformation. Low amplitude AE signals were observed sporadically in the temperature range
from 225 to 209 K, above Msb (=208 K) (M sb is the temperature, at which burst-like
transformation starts, in this study). However, no visible martensites were observed above 212
K in optical microscopy observation. In contrast, low amplitude AE signals were measured onlyin the temperature range from 212 K to Msb temperature in the specimen which had been cooled
down to 212 K before the AE measurement. In this temperature range, the formation of fine α’martensite particles was confirmed in our previous study. Therefore, the source of low
amplitude AE is supposed to be the formation of fine α’-martensites. AE technique is apromising method to investigate the kinetics of the pre-martensitic transformation phenomenon
in this alloy.
1. INTRODUCTION
Martensitic transformation is a diffusionless transformation, which is achieved bycooperative and shear-like movements of atoms, and by a nucleation and growth [1]. It has been
well known that burst-like transformation of austenite (f.c.c.) into α’-martensite (b.c.c.) is causedby simple cooling or plastic deformation in austenitic stainless steels. The formation of α’martensite is accompanied by a large volume dilatation due to the Bain distortion. In addition,
the transformation propagates at a speed of shear waves in the material and the formation of alath or a plate α’-martensite is completed within about 10-8~10-7 seconds [2]. Therefore, acoustic
emission (AE) waves are generated during the transformation. AE waves include information of
dynamic properties of martensitic transformation. AE technique is, thus, one of the mostpromising methods to examine dynamic properties of such an extremely fast phenomenon, andhas been used to determine Msb (Msb is defined as the temperature at which spontaneous burstlike martensitic transformation occurs, in this study) and to investigate kinetics or dynamics ofthe transformation [2-4]. In these previous studies, it has been reported that relatively low
amplitude AE signals were observed sporadically before the burst-like martensitic transformation
during cooling in Fe-Cr-Ni [2], Fe-Pt [3], Ti-Ni-Fe [4] alloys. These low amplitude AE signals
have been considered to be related to the pre-transformation phenomena and/or the nucleation
process of burst-like martensitic transformation [3]. The pre-transformation phenomena areconsidered to be essential to understand the nucleation mechanisms of martensitic
transformation, however the nucleation process of α’-martensite has not been investigatedexperimentally yet. In our previous study, low amplitude AE signals were also detectedsporadically just above the Msb, and are deduced to be related to a pre-martensitic transformation
phenomenon and/or the nucleation of the burst-like α’-martensitic transformation in a 304
austenitic stainless steel [5]. However, the source of the low amplitude AE signals have not yet
J. Acoustic Emission, 19 (2001)85© 2001 Acoustic Emission Groupbeen clarified either in this alloy. The objective of the present study is to investigate the source of
low amplitude AE signals which are observed above Msb in a 304 austenitic stainless steel.
2. EXPERIMENTAL PROCEDURE
The material used in this study was a hot-rolled 304-type austenitic stainless steel (Fe-18Cr8Ni) rod, which is commercially available. Two pieces of disc-shaped specimens, withdimensions of 15 mm in diameter and 2 mm in thickness, were cut by a wheel cutter. The
specimens were electropolished to remove the deformed layer which may be introduced during
cutting, and then electro-etched using an oxalic acid solution. Microscopic observation was
performed using a scanning laser microscope with resolution of less than 1 µm. One of the diskshaped specimens was used without any pre-cryogenic treatment before the AE measurement. A
pre-cryogenic treatment was performed on the other specimen at 212 K to observe the change inthe frequency of the occurrence of low amplitude AE above this temperature (As mentioned in
below, the temperature of 212 K is above Msb of this specimen). Hereafter, these specimens are
termed “non-treated specimen” and “pre-cooled specimen”, respectively.
AE measurements were carried out during cooling. A disc-shaped specimen was attached tothe bottom of the wave-guide using vacuum grease. The wave-guide, which was made of an
austenitic stainless steel, had been immersed in liquid nitrogen for 6 hours to avoid martensitic
transformation during the measurements. The specimen was then cooled down from room
temperature (~ 298 K) to 200 K with a cooling rate of approximately 1 K/min. The temperatureof the specimen surface was measured using a Cu-Cu constantan thermocouple which was spotwelded onto the specimen surface. The AE transducer used was a piezoelectric (PZT) 140 kHzresonant type. The detected AE signals were amplified by a pre-amplifier (NF-9917) and a mainamplifier (NF-AE922) with a total amplification of 80 dB, and analyzed by an AE analyzer (NFAE932). The threshold level was set at 100 µV at the output of the transducer. Root meansquared (RMS) voltage of AE signals and total AE event counts were plotted as a function of
specimen surface temperature.

Fig. 1: Scanning laser microscope image of the specimen before the AE measurement.
86

Fig. 2: (a) AE-RMS voltage during cooling of 304-type stainless steel (non-treated); (b) Total
AE event count and event-rate of 2(a) as a function of temperature;

Fig. 2: (c) α-martensite plates observed in the non-treated specimen after the AE measurement(inside the ellipse, along 2 o’clock direction).
3. RESULTS AND DISCUSSION
Figure 1 shows the initial microstructure of the specimen. The specimen is fully austenite andno deformation-induced martensites are observed. Figure 2(a) shows AE-RMS voltage of the
non-treated specimen during cooling and Fig. 2(b) shows the AE event rate and the total AE
87

event counts as a function of temperature. No AE signals were detected above 226 K duringcooling. Burst AE-signals were started to be detected at 208 K and some plate-like α’martensites were observed on the surface of the specimen after this AE measurement as shown in
Fig. 2(c). According to Takashima et al. [6], the burst-like formation of α’-martensites generates
burst-like AE-signals. Thus, the Msb of the non-treated specimen was determined to be 208 K.
Low amplitude AE signals were detected sporadically in the temperature range of 226~209 K.
The total event counts increased suddenly in the temperature range of 220~212 K and the event
rate was extremely increased in this temperature range. Figure 3 shows scanning laser
microscope image of the typical microstructure of the specimen which was cooled down to 212
K (pre-cooled specimen) before the AE measurement. No martensite was observed in thisspecimen. Therefore, it is considered that the low amplitude AE signals observed above Msb were
not due to the formation of α’- and ε-martensite plates which are visible under the microscopeused. This result corresponds to the results obtained by other researchers that no martensites
were observed above Msb despite low amplitude AE signals were sporadically detected above
Msb [2-4]. Therefore, a kind of fast phenomena, which generates AE, is deduced to occur in thetemperature range from 226 K to 209 K, especially in the temperature range from 220 to 212 K.

Fig. 3: Microstructure of the specimen after cooled down to 212K (pre-cooled specimen).
Figure 4 shows AE-RMS voltage and event rate and total AE event observed during the AE
measurement of the pre-cooled specimen. Hereafter, the temperature range from 226 to 212 K istermed “region I” and the temperature range from 212 K to Msb is termed “region II” , in both
Figs. 2(b) and 4(b). As mentioned above, “pre-cooled specimen” had been cooled down to 212 K
and then warmed up to room temperature in the laboratory air before the AE measurement. The
Msb of the pre-cooled specimen was determined to be 207 K (see Fig. 4(a)). The Msb of the precooled specimen and the non-treated specimen seem to be approximately the same temperature.
Sporadic low-amplitude AE signals were observed below 226 K also in the AE measurement of
the pre-cooled specimen. However, the temperature range in which low amplitude AE wasobserved frequently was different from that observed in the non-treated specimen. In non-treatedspecimen, an increase in total AE event counts was mainly observed in region I as shown in Fig.
88

(a)

AE-RMS Voltage [mV]2(b). In contrast, such an increase in total AE event counts was not observed in region I in the
pre-cooled specimen. In the pre-cooled specimen, total AE event counts started to increase
mainly in region II, i.e., below the pre-cooling temperature. This result indicates that thegeneration of low amplitude AE which occurs above 212 K had been completed by the precooling at 212 K before the AE measurement. The generation of AE is caused by the release ofelastic strain energy in the shorter duration time. In addition, it is considered that α’-martensitic
transformation is the only phenomenon which occurs during simple cooling and generates AE.
Thus, the generation of low amplitude AE is considered to be related to α ’-martensitic
transformation.
50
40
30
20
10
0
205

210

215

220

225

230

Total Event
Event Rate

30

150

20

100

10

50

0

0
205

210

215

220

225

230

Total AE Event [counts](b)

Event rate [counts / K]

Temperature / K

Temperature [K]Fig. 4: (a) AE-RMS voltage during cooling of the pre-cooled specimen; (b) Total event counts
and event-rate of (a) as a function of temperature in pre-cooled specimen.
In our previous study [5], the formation of fine α’-martensite particles, the diameter of whichwas 30~100 nm, were confirmed by transmission electron microscopy (TEM) in the 304-type
stainless steel which had been cooled down to just above M sb. In general, the growth of α’martensites needs driving force to deform matrix in order to absorb the Bain distortion. Thisdriving force is provided by the free energy difference between the parent phase (f.c.c.) andmartensite (b.c.c.) phase. It is considered that these micro-sized α’-martensites cannot grow into
“large martensites (as shown in Fig. 2(c))”, which cause plastic deformation of matrix, because
the specimen used in the previous study had not been cooled down below Msb (The driving force
for the growth of “large martensite” had not been provided above Msb). In addition, Bogers and89

Burgers showed that two intersecting {111}<112> shears transform f.c.c. lattice into b.c.c. lattice[7]. According to this, it is reasonable to consider that intersections of partial dislocations inaustenite have a b.c.c.-like stacking and to be a preferential nucleation site of α’-martensite. The
atoms in the b.c.c.-like stacking region should move to b.c.c. positions with relatively lower
energy barrier which can be overcome by cooling to a certain temperature above the M sb. It isthus suggested that the generation of low amplitude AE observed above Msb in this study is dueto the formation of micro-sized α’-martensite particles at intersections of partial dislocations.
The micro-sized α’-martensitie particles are deduced to be formed in the temperature range from226 to 209 K, especially in the temperature range of 220 to 212 K, according to the results
obtained in this study. Some of the b.c.c.-like stacking regions and the atoms around the regionsin the specimens are supposed to complete the transformation into b.c.c.-stacking above 212 K.
This should be the reason for that the number of the observed low amplitude AE above 212 K
was smaller in the pre-cooled specimen than that observed in the non-treated specimen. These
results strongly suggest that AE technique is a useful method to investigate also the pretransformation phenomenon, and/or the kinetics of the nucleation of burst-like α’-martensitic
transformation in this alloy system.
4. CONCLUSIONSThe Msb of the material used was determined to be 208 K and low amplitude AE signals weredetected frequently in the temperature range of from 226 K to 212 K. No visible martensites
were formed above 208K using a scanning laser microscope. While the generation of low
amplitude AE was observed in the temperature range from 212 K to Msb in the specimen which
had been cooled down to 212 K before the AE measurement. Then, the generation of low
amplitude AE which occurs above 212 K had been completed by the pre-cooling at 212 K before
the AE measurement. The formation of fine α’-martensite particles was confirmed in thespecimen which had been cooled down to just above M sb using TEM in our previous study.
Thus, the source of the low amplitude AE is deduced to be due to the formation of micro-sized
α’-martensite particles at intersections of partial dislocations in this alloy system. AE technique
is found to be a promising method to clarify the kinetics of the pre-martensitic transformation
phenomenon of burst-like α’-martensitic transformation.
REFERENCES[1] J. W. Christian, G. B. Olson and M. Cohen, J. de Physique IV, 5, (1995), C8-3-C8-10.
[2] K. Takashima, Y. Higo and S. Nunomura, Phil. Mag. 49, (1984), 231-241.
[3] H. Ohtsuka, K. Takashima and G. B. Olson, Mat. Res. Soc. Symp. Proc. 459, (1997), 407412.
[4] K. Takashima and M. Nishida, J. de Physique IV 5, (1995), C8-735-C8-740.
[5] T. Inamura, M. Shimojo, K. Takashima and Y. Higo, Proc. of 1999 National Conference on
Acoustic Emission, (1999), JSNDI, Tokyo, pp. 103-106 (in Japanese).
[6] K. Takashima, Y. Higo and S. Nunomura, Scripta. Metall. 14, (1980), 489-491.
[7] A. J. Bogers and W. G. Burgers, Acta Metall. 12, (1964), 255-261.

90

ACOUSTIC EMISSION ANALYSIS OF CARBIDE CRACKING IN TOOL STEELSKENZO FUKAURA and KANJI ONO*
Himeji Institute of Technology, Shosha, Himeji 671-2201, Japan*University of California, Los Angeles, California 90095-1595, USA
ABSTRACT
Fracture of hardened tool steels is expected from large carbides, although the initial state of
fracture has been difficult to evaluate. We employed acoustic emission analysis during the tensile
testing of two tool steels in order to determine the progression of internal damage. Steels used are
1.4C-11Cr-0.8Mo-0.23V (JIS SKD11) and 0.8C-8.1Cr-1.9Mo-0.52V (modified SKD11). Bothwere quenched and tempered to obtain the maximum secondary hardening. For the modified steel,a lower hardness temper was also used. In SKD11 steel, AE signals from carbide cracking started
at about half the fracture stress and AE-event counts reached ~10,000 at the fracture stress. In
modified-SKD11 steel, AE signals from carbide cracking started at 70-90% of the fracture stress(20% higher than that of SKD11 for similar tempering) and AE-event counts were only several
hundreds near the fracture stress. (These were less than 50 at stress corresponding to the fracture
stress of SKD11.) The average amplitude of pre-fracture AE signals was also reduced by 6 dB in
the modified steel. These changes reflect the reduction in the amount and size of primary carbide
particles. When the carbide size becomes twice larger, the stress for carbide cracking is reduced to
one-half. It is thus critical to refine the primary carbide in alloy design.
1. INTRODUCTION
Hardened tool steels are an important class of steels and are used as cold-forming dies,
punches, etc. under increasingly severe conditions [1,2]. For such applications, improvements of
their low-cycle fatigue strength are the critical task for ferrous metallurgists [3-6]. In hardened toolsteels, both primary and secondary carbides contribute to their high hardness and superior wear
resistance [7]. The carbide fracture and decohesion of carbide/matrix interface have significant
effects on the deformation and fracture behavior of these steels, as suggested by fractographic
evidence. One of the authors has investigated a series of tool steels and identified the primary
carbide fracture as the likely cause of their brittle fracture by careful analysis of fractured carbides
and by the use of Stroh theory of fracture initiation [8]. However, the fracture initiation stage isdifficult to examine using conventional metallurgical characterization techniques. Thus, acoustic
emission (AE) is used in this work to determine the condition of fracture initiation and subsequent
progression in standard and modified tool steels. Carbide cracking can be identified
unambiguously with AE and crack-induced waveforms correlated to the AE source function via
experimentally determined Green’s function.
J. Acoustic Emission, 19 (2001)

91© 2001 Acoustic Emission Group2. MATERIALS AND EXPERIMENTAL PROCEDURES
A conventional tool steel (1.4C-11Cr-0.8Mo-0.23V, JIS SKD11) and its modification are usedin this study. The modification reduced Cr and C content and increased Mo and V, resulting in the
optimized composition of 0.8C-8.1Cr-1.9Mo-0.52V (modified SKD11). The chemicalcomposition of the steels used is given in Table 1. After hot rolling, both steels were homogenizeannealed and air-quenched from 1303 K following a carbide-spheroidizing treatment. Tempering
was between 473 and 833 K. Vickers hardness after tempering is given in Fig. 1. In the asquenched state, SKD11 has a higher hardness, but the modified steel has a slightly higher peakhardness after secondary hardening at higher tempering temperatures. Test specimens weretempered to achieve the peak hardness (tempering at 773 K for SKD11 and 793 K for modified
SKD11). For the modified steel, tempering at 473 K was also used. In order to stabilize themicrostructures, double tempering was used in all the specimens.
Table 1. The chemical composition of the steels used.
JIS SKD11
modified SKD11

1.41C; 11.10Cr; 0.80Mo; 0.23V; 0.24Si; 0.41Mn; 0.023P; 0.001S
0.80C; 8.10Cr; 1.90Mo; 0.52V; 0.88Si; 0.39Mn; 0.022P; 0.001S

The shape of specimens is hour-glass type. The specimen has the reduced section of 5 mm
minimum diameter with the radius of 30 mm. This geometry was selected so that it can be also
used for fatigue testing. The stress concentration factor is 1.406. The grip parts are threaded(M18). It has shoulder flat on both side of the reduced section. Tensile tests utilized a floor-model
Instron and the cross-head speed was 0.254 mm/min. Fracture surface observation utilized ascanning electron microscope (JEOL 5200).
Two small AE sensors were coupled to machined shoulders on both sides of the reduced
section. The sensors are resonant type with the center frequency of 375 kHz (AET MAC-375).
The distance between the sensors was 45 mm. Two 40-dB pre-amplifiers (PAC 1220A) and two 2channel MISTRAS systems (on 2 separate PCs) were used. One system used MI-LOC and theother MI-TRA software. Most of AE data to be presented here is from the MI-TRA system, as
synchronized triggering did not function properly in MI-LOC system. The threshold level was 40
dB in reference to 1 µV as the pre-amplifier input as 0 dB. Only those AE signals from thereduced section were analyzed by limiting the arrival-time difference to 10 µs via the use ofgroup-replay function in MI-TRA. The rms voltages from one of the channels were recorded withload on the parametric channels and by using an HP-3400A and a two-pen recorder.

92

Fig. 1 Changes in Vickers hardness as a function of tempering temperature.

Fig. 2 Tensile and AE test results for SKD11 steel, quenched and tempered at 773 K.
93

Fig. 3 Tensile and AE test results for modified SKD11 steel, quenched and tempered at 473 K.

Fig. 4 Fracture surfaces of quenched and tempered steel. Left: SKD11; Right: Modified SKD11.
White marker indicates 10 µm.
3. RESULTS AND DISCUSSION
Tensile and AE test results for SKD11 steel are shown in Fig. 2. Both rms voltage plot andcumulative AE event counts are given. (Note that the event count data includes only those fromthe reduced gage section, whereas the rms voltage plot shows large spikes that come from the grip
sections. These spikes should be ignored.) The load-displacement curve is essentially linear and no94

plastic yielding was recognized. AE events started at 1.01 GPa and the event rates continually
increased until reaching the fracture stress of 2.41 GPa. The total AE events at fracture was~10000. The rms voltage plot showed a slight increase from the background level starting as theAE event count curve began to rise. However, the increase was small (<0.7 mV). The waveform
examination indicates that no continuous AE signals existed and the increase in the rms voltage
was due to numerous burst emissions at close intervals. Typical AE signals observed from thegage section have a sharp initial rise with relatively short decay. The waveform was essentiallyunchanged throughout the test. Its frequency spectrum closely corresponds to that of the AE
sensor used.
For the modified steel tempered at 473 K, tensile and AE test results are given in Fig. 3. The
load-displacement curve is again linear and no plastic yielding was observed. AE events started at
1.84 GPa and the event rates continually increased until reaching the maximum stress of 2.74 GPa,
where the test was stopped. The total AE events decreased substantially to 250. The rms voltage
plot showed a slight increase from the background level near the end of the test, but waveforms
showed no indication of continuous emission. Again, spikes are spurious noise and should be
ignored. AE signal waveforms were similar to those in SKD11. For the modified steel tempered at
793 K, AE events started at 2.06 GPa. Similar AE behavior was observed otherwise.
When conventional tensile test specimens with uniform gage section are used, the yieldstrength can be determined and the values are listed in Table 2. These values are slightly different
from the present experiment due to different test geometry. However, the AE start stress increases
significantly by the alloy modification. In SKD11, AE start stress was 60 % of the yield strength,
while that for the modified steel was comparable to or higher than the yield strength.
Microstructural test result is given in Table 3. In the modified steel, the mean diameter of the
primary carbide (M7C3) was reduced by a factor of two and the maximum size was also reduced to
2/3. The volume percentage decreased from 7.5% to 2.2%. Effects of the modification were minor
in prior austenite grain size and retained austenite content. Thus, the change in the AE start stressis believed to be a consequence of the size and volume reduction of the primary carbide. In
particular, it appears that large carbides are responsible for the AE signals observed. Even though
the maximum size was decreased by only a third, the reduction in overall volume and the shift tosmaller average size contribute to a large drop in carbides that generate AE signals.
In Fig. 4, two fractographs are shown, indicating cracked carbides of 10+µm size. Naturally,
SKD11 has more of larger carbides. Since the minimum cross sectional area is ~20 mm2, theviewed area is approximately 1/10,000. Since either side of the final fracture surface is expected to
be also the sites of carbide cracking, only a fraction of the cracked carbides are detected as AE.
Thus, it is estimated that an AE signal is due to those carbides greater than 10 µm in diameter.
Note that carbides that contribute to AE are those fracturing parallel to the fracture surface. The
95

fracture also produces secondary cracks normal to the main crack plane, but the displacement
due to such secondary cracks is less effective in exciting the AE sensor.
The amplitude of AE signals was examined and the trend is shown in Fig. 5 against the load.
Generally, the amplitude is low at less than 55 dB. Further, the amplitude decreased slightly as
the load level increased and that the average decreased from SKD11 steel to modified steel at
473 K temper (by ~2 dB) to modified steel at 793 K temper (by ~6 dB). The lower amplitude in
the modified steel is consistent with the reduced carbide size, but the effect of tempering in themodified steels is not clear at present.
Table 2. Mechanical properties and AE start stress.
______________________________________________________________________________
Yield strength
Tensile strength
AE start stress
SKD11 (773K temper)
1.66
2.19
1.01 GPa
Modified SKD (473K)
1.58
2.04
1.84 GPa
Modified SKD (793K)
2.04
2.62
2.06 GPa
Table 3. Primary carbide size of the steels.
______________________________________________________________________________
Mean dia. Max. dia.
Volume Austenite GS Retained austenite
(µm)
(µm)
(µm)
SKD11
5.3
21.5
7.5%
5.3
7% at 773K
Modified
SKD
2.5
14.4
2.2%
6.9
4% at 793K______________________________________________________________________________

Fig. 5 AE event amplitude vs. load for the three different steels.
96Fig. 6 (a) Experimental transfer function for a crack in tensile specimen with a resonant sensor(AET MAC375) mounted on a shoulder flat. (b) Bell-shaped source function. (c) Measured AE
signal during a tensile test. (d) Simulated signal. (e) Comparison of measured and simulated
signals.
Experimental determination of Green’s function is possible when a force impulse can beapplied and the resultant displacement can be measured with a wide-band sensor of known
response. Usual impulse sources use the fracture of glass capillary or pencil lead, but these aremono-pole sources. A new laser source was recently devised. Masaki et al. [8] demonstrated thevalidity of using the break-down of silicone oil in a confined space by a laser beam as the source97

of impulse. This source has a short rise time of 0.07 µs and is a dipole source. This method has
opened a path for getting an experimental transfer function for geometry that is impossible tocalculate a theoretical Green’s function. We used this approach for a tensile specimen with hourglass-shape reduced section. Here, a shallow notch was made at the smallest diameter and a laser
source is excited inside the notch. A resonant sensor (AET MAC375) was mounted on a shoulder
flat. The waveform obtained contains the transfer functions of the specimen and the sensor andcan be compared to AE waveforms recorded from specimens of the same geometry (albeit withgrip arrangement less notch).
An AE signal from a carbide crack is shown in Fig. 6c. This is a time-expanded initial
portion of a typical signal. The transfer function for this geometry and sensor combination is
given as Fig. 6a, which resulted from laser excitation and has the main peak at ~400 kHz,extending to 1.5 MHz. The convolution of a bell-shaped source function with 1 µs rise time
(Fig. 6b) with the transfer function produces a simulated waveform (Fig. 6d). For the first two
oscillations, the waveforms (c and d) match closely as shown in Fig. 6e. (The overall frequency
range appears to peak at 200 kHz in the AE signal, especially when the entire signal of ~0.5 ms
is used. This is probably due to the presence of grips.) Thus, the crack opening mode of brittle
fracture generated the AE, and cracking process is fast (~1 µs). This result is consistent withmany other studies of brittle fracture. The use of relatively low-frequency sensors, however, may
be limiting the detection of faster events and future experiment using higher speed
instrumentation is desirable to verify this aspect.
4. CONCLUSION(1) This study demonstrates the utility of AE method for detecting the initiation of carbide
cracking, which is critical in alloy development work for improved fracture resistance. The
minimum size of the carbide is approximately 10 µm.
(2) Laser-induced impulse method simulating the crack-opening mode of fracture in hour-glass
tensile sample has verified the nature of AE sources in high-strength steels to be a high-speed(~1 µs) phenomenon. An experimental system transfer function is also determined.
ACKNOWLEDGEMENT
The authors are grateful for valuable assistance of Messers. R. Masaki and Y. Mizutani of
Aoyama Gakuin University (AGU) in conducting the laser experiment. One of the authors (KO)gratefully acknowledges the hospitality of Prof. M. Takemoto during his sabbatical leave at
AGU.

98

REFERENCES
1. F.K. Kirk, Metal Tech., 49 (1982) 198.
2. S. Inoue, Tetsu-to-Hagane, 73 (1987) 1461.
3. Y. Murakami, S. Kodera and S. Konuma, Trans. Japan Soc. Mechanical Eng., A54 (1987)
688.
4. M. Knoerr, K. Lange and T. Altan, J. Mater. Processing Tech. 46 (1994) 57.
5. T. Abe and K. Kanazawa, J. Soc. Mater. Sci. Japan, 45 (1996) 9.
6. J. Yoshida, M. Katsumata and Y. Yamazaki, Tetsu-to-Hagane, 84 (1998) 79.
7. A.M. El-Rakayby and B. Mills, J. Mater. Sci., 23 (1968) 4340.
8. K. Fukaura, H. Sunada, Y. Yokoyama, K. Teramoto, D. Yokoi, N. Tsujii, Tetus-to-Hagane, 84(1998), 72-77.
9. R. Masaki, M. Takemoto and K. Ono, Nondestructive Characterization of Material IX, AIP
Conference Proceeding 497, ed. R.E. Green, Jr. (1999) pp. 3-8.

99

SOURCE PARAMETERS OF ACOUSTIC EMISSION EVENTS IN SALT ROCKGERD MANTHEI, JÜRGEN EISENBLÄTTER, THOMAS SPIES*,
and GERNOT EILERS§
GMuG Gesellschaft für Materialprüfung und Geophysik, D-61239 Ober-Mörlen, Germany,*Federal Institute for Geosciences and Natural Resources, D-30655 Hannover, Germany,§
Federal Office for Radiation Protection (BfS), D-38201 Salzgitter, Germany
ABSTRACTLong term acoustic emission (AE) measurements have been carried out in a salt mine innorthwestern Germany with the aim to investigate the micro- and macro-fracturing processes in aregion of a high degree of excavation. The AE events were detected by a network of 24 boreholesensors. In order to get information about the source mechanisms, the moment tensor method has
been applied to strong events, which were localized with good accuracy using more than 29
compressional and shear wave onsets. The selected events originated from a pillar in the vicinityof large rooms. The mechanism of each acoustic emission event has been calculated considering
the first motion of at least eight clearly discernible P-wave onsets. In order to get a better fit ofcalculated and measured amplitudes, specific correction factors for the sensors have been
applied. The moment tensor analysis indicates that a mixed mode of shear and tensile cracking
occurs. For most investigated events, the P and T axes show similar orientations. Additional
geomechanical data such as deformation measurements and crack opening measurements
confirm the obtained results of the moment tensor analysis.
1. INTRODUCTION
Since 1995, a segment of a salt mine in northwestern Germany has been monitored by a
network of 24 acoustic emission (AE) sensors [1]. The network covers a rock volume of about
150 m x 150 m x 100 m in an underground repository for low-level nuclear waste. The aim of the
AE measurements is to investigate the micro- and macrofracturing processes, which areimportant for the evaluation of the stability of cavities and the hydraulic integrity of the rock,
especially in the case of the underground disposal of hazardous waste in salt rock. The AE events
are automatically located using the compressional (P) and shear (S) wave onsets. The maximum
amplitudes of the signals and the location of the events are used to calculate a measure ofstrength of the source analogue to the magnitude in seismology.
In this paper, the measured radiation pattern of AE sources has been used to determine thefracture type and the orientation of the fracture planes utilizing the moment tensor method. Thismethod requires a good sensor coverage of the focal sphere in all directions. In the present case,the spatial distribution of the AE sensor network was designed for source location in a large
region and not for determination of source parameters. Therefore, in most areas of the monitored
region the coverage of the focal sphere by the sensor network was only poor. We therefore
selected AE events from a region in the center of the network where the best coverage was
supposed.

J. Acoustic Emission, 19 (2001)

100© 2001 Acoustic Emission Group2. EXPERIMENTAL SETUP
24 borehole sensors equipped with piezoelectric transducers are installed in 3 to 20 m deep
boreholes. Figure 1 shows the configuration of the network in a plan view on three levels. The
central unit of the measuring system consists of a transient recorder monitoring the data streamof the sensors (frequency range of 1 to 100 kHz), a personal computer controlling all actions ofdata acquisition and in-situ evaluation, and a modem for telecommunications. Two types ofborehole sensors with different coupling have been used. Five sensors (Type W) were pressedpneumatically against the wall of boreholes of diameter 100 mm and further 19 sensors (Type S)
were pressed by means of springs against the smooth face of boreholes with smaller

Fig. 1 Configuration of the network of sensors at three levels of a salt mine in horizontal
projection one upon another. The distance between the crosses is 20 m. The upper level, theintermediate level, and the lower level are indicated by continuous lines, by dotted, and bydashed lines, respectively. Rectangular frame indicates the locations of the investigated events.
101

diameter 46 mm. Both types of borehole sensors were characterized in situ with respect to Pwave sensitivity as a function of incidence angle. Fig. 2 shows that the radiation patterns of both
sensor types are similar at small angles up to 50°. The half width (-6 dB) is in the range of 80° to100°. At greater angles of incidence the sensitivity shows more variations and decreases by 20dB and 30 dB, respectively. Note that there is a polarity change for incidence angles greater than
90° (i.e. wave passes the sensor from behind). Type S is approximately 20 dB more sensitive forthese angles compared to Type W.

Fig. 2 P-wave sensitivity of the borehole sensors Type W (left) and Type S (right) as a functionof the incidence angle.
3. APPLICATION OF THE MOMENT TENSOR METHOD
The moment tensor method has been applied to 68 events with strong signal amplitude,
which were recorded in a period of 19 months. The events occurred in a pillar at the upper level(continuous lines in Fig. 1) in the vicinity of Sensors No. 9 and 10 (marked by a rectangularframe in Fig. 1). The dimension of this area is 22 m x 13 m. In vertical direction the sources lie
in a small area of 5 m width, 7 m above a large room at the intermediate level. The signals of theselected events were band-pass filtered in a frequency range from 2 to 25 kHz. These frequencies
correspond to wave lengths of the P wave between 2.25 m and 0.18 m. In Fig. 3, aligned P-wave
onsets of an AE event are plotted. To the right hand side of the signal traces, the peak-to-peakamplitude in volt and its polarity, the distance between sensor and source in meter, and the angle
of incidence to the sensor normal vector are denoted. The data were treated in the following
steps:• Calculation of the theoretical P-wave onsets utilizing the source location and P-wave
velocity.
• Plotting of the signals in a small time window of 400 µs around the theoretical onsets.
• Manual picking of the exact signal onsets.
• Multiplication of the data in the time window by a Hanning window function.
102

•

Storing of the most important event parameters like event number, peak-to-peak amplitude,polarity, onsets, and the unfiltered signal in the small time window of the P-wave onset.

Figure 3 shows that the polarities of the P waves detected at Sensors No. 1, 5, 8 and 11 arereversed in comparison to the others. In these cases the sensors were behind the source(incidence angle greater than 90°). In the case of not clearly discernible P-wave onsets, thesignals have been dropped. This happened in the shown example at Sensors No. 7, 12, 13 to 16,
18, 19, 22, and 23, where the travel paths were very long or the wave radiation was disturbed(''shaded'') by galleries. Note that the polarities of all P-wave onsets are positive after changing
the polarities for incidence angles greater than 90°. Maybe, this fact indicates a tensile sourcecomponent. The radiation pattern of a pure tensile source shows the same polarity in all
directions (as for an explosion source type as well).

Fig. 3 Aligned P-wave onsets of an AE event located in the middle of the salt rock pillar close toa large room at the upper level.
The measured signal amplitudes are influenced by spherical divergence which is proportional
to r (r is the source-receiver distance) in the far field radiation pattern, by the radiation patternof the borehole sensor (compare Fig. 2), by wave attenuation, and by coupling effects of theborehole sensors. The wave attenuation and the coupling effects of the sensor were investigatedexperimentally. The maximum amplitudes A and the distances r are used to determine the wave
attenuation according to the relation
A ∝ [exp (–α r)]/ r ,-1103

where α is the attenuation coefficient. In a semi-logarithmic plot of A⋅r versus r for all sensors alinear relationship is obtained as expected and a straight line is fitted to the data. The slope of theline corresponds to the attenuation coefficient, systematic deviations from the fit at specificsensor positions correspond to coupling or site effects which can occur in the vicinity of thesensor location. By averaging over many events located in the whole monitored area a very low
attenuation coefficient at the test site of 1.7 dB/10 m is obtained. The coupling coefficient variedin the range between -10.5 dB (Channel No. 6) and 9 dB (Channel No. 7).
Under consideration of the above-mentioned amplitude effects the results of the inversion
calculation for one AE event is displayed in the upper part of Fig. 4. This figure shows thecalculated radiation pattern (left hand side) in a projection to the lower hemisphere. The
locations of the calculated P and T axes are indicated by black and white triangles, respectively.
Projected rays are plotted as circles. Black circles indicate compressional first motion. The
channel numbers are also shown. It can be seen that the polarity of the P-wave first motion isalways positive.

Fig. 4: P-wave radiation patterns and projected rays of an AE event projected on the lower
hemisphere (equal area projection, north is on top). Black/dotted area (white) indicatescompressional (dilatational) first motion. The upper and lower parts show the results of theinversion calculations without and with specific sensor correction factors, respectively.
104

We decomposed the momentum tensor into isotropic and deviatoric source components. The
isotropic part is described by the trace of the moment tensor. It is quantifying a volume change inthe source. The percentage of the isotropic part is defined by the ratio of the squared trace andthe sum of squared eigenvalues ei of the moment tensor multiplied by 100 after reference [2].
The purely deviatoric part, which is calculated by subtracting a third of the trace from eacheigenvalue of the moment tensor, is further decomposed into a double couple (DC) and acompensated linear vector dipole (CLVD) with the same principal axis orientation. Here,
DC% = [1 − 2(|e3 − tr|/|e1 − tr|)]⋅100%,|e1 − tr| ≥ |e2 − tr| ≥ |e3 − tr| is assumed, and tr = (e1 + e2 + e3 )/3 [3].
In the present case, the decomposition leads to a mixed mode mechanism with 59% DC
component (corresponding to 41% CLVD component) and 9% isotropic component. The nodal
lines indicate the two possible orientations of the crack plane. Note, that a double couple has two
possible fault planes which are perpendicular to each other, the so called normal fault andauxiliary fault plane which cannot be distinguished from the focal mechanism. Additional
information is necessary to find the actual fault plane, as e.g. knowledge about the stresssituation in the region near the source location. In our example one possible crack plane isvertical and has a strike of 37°, measured clockwise from north.
4. RESULTS
In order to investigate the influence of single sensors on the inversion result, a jackknife test
has been applied. In this test, one sensor after the other is systematically excluded from the input
data. Every possible permutation was tested. The right hand side of Fig. 4 shows the resulting
radiation patterns. These do not strongly vary apart from the case in which sensor No. 20 has
been excluded. In this case, the locations of the P and T axes indicated by crosses and circles,
respectively, differ from all other inversion runs. This indicates that the solutions are not wellconstrained and strongly influenced by amplitude at a single sensor (like No. 20 in the above
example). Therefore, additional specific corrections for the sensors were introduced to constrain
the solution and to obtain a better fit between calculated and measured data. The additionalcorrection factors were determined by averaging the ratio of measured and calculated amplitudesof each sensor for all 68 events. This leads to relative high values of correction factors up to 4.5
for shaded sensors (No. 4 and No. 5) and to very low correction factors down to 0.15 for sensors,which are in a favourable position for most source locations. The lower part of Fig. 4 shows theresult of the inversion calculation after application of the correction factors to the input data. In
comparison to the results of the previous calculations, the P-wave radiation patterns and thelocations of the P and T axes do not strongly vary. The DC component increases up to 71%. The
jackknife test indicates that the solution is well constrained now and not influenced by themeasured amplitudes at single sensors any more. Now, sensor No. 20 makes no exception.
Figure 5 displays the results of the moment tensor calculations of all 68 selected events. The
left-hand side gives the spatial distribution of the estimated P-wave radiation patterns projectedon the lower hemisphere (equal area projection) in a plan view (north on top). On the right-handside of Fig. 5, the contours of the P and T axes distributions are plotted. In spite of the fact thatthe sources are distributed in an extended area of about 22 m in Y-direction and 13 m in Xdirection, it can be seen that all events have similar radiation patterns as well as orientations of
the crack planes. The distributions of T and P axes show peaks in the middle and in north or
south directions, respectively. This means, the T axis is vertical and the P axis horizontal in north105

direction. Figure 6 shows the probability density of the DC and isotropic components. The
distributions are normalized, so that the integral over full range of possible source components is
unity. Most of events show a mixed mode of shear and tensile cracking. The majority of the DC
components range between 20 % and 80% with a maximum at 60%. The isotropic components
show a maximum below 5%. Nevertheless, most of the events have a significant isotropic

Fig. 5 Radiation pattern for all evaluated events in an equal area projection on the lower
hemisphere (left). Distribution of the P and T axes (right).

Fig. 6: Probability density of the double-couple component (DC) and the isotropic component(ISO).
106

component. Nine events were identified as ''pure'' shear cracks (DC component greater than 90%
and isotropic component less then 5%). Pure tensile sources were not obtained. A pure tensile
crack has 100% CLVD component and approximately 75% isotropic component.
5. DISCUSSION OF RESULTS AND CONCLUSIONS
All investigated events originated from a small region at the contour of a large room. In thisregion, deviatoric stresses occur, which lead to creep of salt and in longer-time consequence tomicrocracking and permeability increase. In the present case, the stresses were further enhancedby the fact that the stopes of two large rooms at the intermediate and lower level are situatedbelow the area under investigation (cf. Fig. 1, location of Sensor No. 10).
The moment tensor analysis pointed out that mixed mode of shear and tensile cracking
occurs. Under the consideration that the motion of both modes happened on the same
microfracture plane, Fig. 7 displays the model of the source mechanism schematically. It shows ahatched area where the events originated and the location of the large rooms in a vertical crosssection together with the directions of the P and T axes. The arrows parallel to the crack plane
represent the shear motion and those perpendicular to the crack plane represent the tensile
motion. The vertical T axis could be explained as a consequence of tensile stress release in thearea of the large room at the intermediate level (Room 2 North at Level 2a in Fig. 7). In the wallof the north drift at the upper level, a large horizontal crack has been observed. The opening of
this crack is monitored by displacement measurements, which show opening rates of 0.15mm/annum (or mm/year) in vertical direction. The above-mentioned source model is inaccordance with these in-situ displacement measurements.
We cannot fully explain the influence of the sensor arrangement and the coupling of thesensors. The fact that the data fit becomes better after applying specific correction factors raises
some questions and needs to be further discussed. This uncertainty may be explained by the puresensor coverage in vertical direction. In order to raise more confidence in these results, a checkwith an alternative method like the relative moment tensor method [4] would be helpful, where
the sensor coupling effects play no role.
REFERENCES
1. T. Spies, J. Eisenblätter, Micro- and Macrocracking at the Boundary of Rock Salt andAnhydrite, Proc. International Society for Rock Mechanics, Paris, Eds. G. Vouille, P. Berest.
A. A. Balkema, Rotterdam, 1999, pp. 1071-1074.
2. P. G. Silver, T. H. Jordan, Optimal estimation of the scalar seismic moment, Geophys. J. R.
Astr. Soc. 70, 1982, 755-787.
3. M. L. Jost, R. B. Herrmann, A students guide to a review of moment tensors, Seis. Res.
Letters, 60, 1989, 37-57.
4 . T. Dahm, G. Manthei, J. Eisenblätter, Relative Moment Tensors of Thermally Induced
Microcracks in Salt Rock, Tectonophysics 289, 1998, 61-74.

107

Fig. 7 Location of the crack planes where shear and tensile motion take place together with thearea of AE events (hatched) and the location of the rooms.

108

GEOMETRICAL COMPLEXITY OF ROCK INCLUSION
AND ITS INFLUENCE ON ACOUSTIC EMISSION ACTIVITY
KEN-ICHI ITAKURA, ATSUSHI TAKASHIMA, TATSUMA OHNISHI
and KAZUHIKO SATO
Faculty of Engineering, Muroran Institute of Technology, Muroran 050-8585, Japan
ABSTRACT
Underground rock mass and rock slopes consist of various types of rocks with different
physical properties and geometry. In such a rock-engineering site, unexpected failure tends tooccur when rocks containing inclusions are affected by stress change. Depending on thegeometry and the physical properties of the inclusion in matrix rock, stress concentration induces
local fractures with acoustic emission (AE), and catastrophic failure can occur. To monitor an
unstable rock area using AE techniques, therefore, it is important to know the relationshipbetween the features of the inclusion rock and AE activity.
In this study, using cylindrical rock specimens consisting of andesite and tuff, Brazilian anduniaxial compression tests were carried out to determine the relationship between AE source
locations and the complexity of inclusion geometry appearing on the surfaces of sliced sectionsof the specimens. It was found that more AE sources were present in sliced sections with ahigher complexity index value of inclusion. It was thought that local stress concentration
occurred in a section with a complex structure and that more micro-fracturing was therefore
generated with AE.
1. INTRODUCTION
Underground rock mass and rock slopes consist of various types of rocks with different
physical properties and geometry. In such a site, the volume encompassing with a discontinuity
plane, such as a pore, a crack, or a lump of rock within the matrix, can be regarded as aninclusion. If stress change is exerted on rock mass with inclusions, complex and unexpected
fractures might occur due to the influence of the difference in physical properties and thegeometry of the inclusion. Such rock fractures can cause a disaster in a mining area or civilengineering field.
Figure 1 shows differences in AE activities of rock specimens subjected to uniaxial
compression stress. The specimen shown in Fig. 1(a) includes andesite particles in a tuff matrix,
and the specimen shown in Fig. 1(b) consists mainly of andesite with a tuff layer in the bottom
area. In the case of the latter specimen, high AE activity is maintained for a long period from alow stress stage despite the fact that the experimental conditions were almost the same as thosefor the specimen shown in Fig. 1(a). Generally, in a specimen consisting of only andesite, it is
known that AE activity becomes higher just before the maximum stress, though Fig. 1(b) showsa different AE activity pattern. This AE activity pattern is thought to be caused by crushing of
the tuff layer at a low stress stage, which induces unstable failure of the andesite. In otherwords, it seems that the physical properties and the geometry of the inclusion affect the microfracturing activity and main failure of the specimen.
J. Acoustic Emission, 19 (2001)

109© 2001 Acoustic Emission GroupFig. 1 Differences in rock structure and AE activities during uniaxial compression tests.
(a) Specimen consisting of andesite inclusions within a tuff matrix. (b) Specimen consisting of atuff layer within an andesite matrix.
In this study, laboratory experiments using specimens consisted of two different kinds ofrock were conducted to determine the relationship between complexity of the boundary plane ofa rock and AE activity around the plane. In the experiments, Brazilian tests and uniaxial
compression tests were carried out on specimens that had been collected at the rock slope at
Toyohama, Hokkaido. The activity of this rock slope has been monitored to try to predict largescale rock falls. It is expected that the results of laboratory experiments, i.e., the determinationof the relationship of the complexity of inclusion geometry and AE (or micro-fracturing)
activity, can be applied to field monitoring techniques to determine the optimal allocation of AE
sensors for effective monitoring. This paper describes the results of Brazilian tests and therelationship of the complexity of a boundary plane of rocks and AE source locations.
2. ROCK SPECIMEN AND EXPERIMENTAL PROCEDURERock specimen and its inside structure
Specimens of tuff breccia rock consisting of tuff and andesite were prepared from drill cores
sampled at the rock slope of Toyohama, Hokkaido. The size of the specimen used for theBrazilian test was 49.3 mm in diameter and 51.2 mm in height. Figure 2(a) shows the
110

(a)

Andesite

X

(b)

Y

Tuff
Fig. 2 Rock specimen used for the Brazilian test. (a) Photo image. (b) 3-D image reconstructedfrom X-ray CT data.

Fig. 3 Tuff region extracted from a 3-D image.
appearance of the specimen. The dark parts of the specimen shown in Fig. 2(a) are andesite,which has micro pores, and the other parts are tuff.
Before loading, sectional images of the specimens were obtained by X-ray computerized
tomography (CT). Twenty-two images were taken parallel to the bottom base of the cylindrical
specimen for the Brazilian test. Figure 2(b) shows a 3-D image that was reconstructed by using
22 CT data of the specimen. The whitish area shows a low-density region, i.e., a tuff region and111

Fig. 4 Arrangement of AE sensors for a specimen.
pores in the andesite. The densities of andesite and tuff are ~2.48 g/cm3 and 1.56-1.78 g/cm3,
respectively. Figure 3 shows the tuff region extracted from the 3-D image of Fig. 2(b).
Complex changes in the boundary plane of tuff and andesite parts along the z-axis can be seen.
Experimental procedure
To detect the AE waveforms, six small-sized PZT elements were affixed on the surface ofthe specimen, as shown in Fig. 4. The shape of the PZT was a frustum of circular cone with an
upper base of 1 mm in diameter, lower base of 4 mm in diameter, and height of 5 mm. The
resonant frequency of this PZT was 320 kHz. The apparatuses used in the experiments were thesame as those used previously [1, 2]. The AE waveform detected by PZT sensors is amplified
up to 85 dB using a preamplifier and signal conditioner, and recorded on the hard disk of a PC
with a sampling time of 100 ns. AE source locations were determined by the conventional leastsquares method, and a P-wave velocity structure was assumed by an ellipsoid model with threevelocity axes of 1289.7, 700.3 and 1084.2 m/s [3, 4]. In the Brazilian test, strain rate wascontrolled in the range of 10-5/s using an MTS servo controlled testing machine. The loading
direction is along the y-axis of Figs. 2 and 3.
3. EXPERIMENTAL RESULTS AND DISCUSSION
AE source location
AE source locations were determined for 8.4% (334 events) of the total number of recorded
AE events. The magnitude of these AE events is relatively high, and the location error by theleast-squares method is less than 5 mm. Figure 5 shows the location distribution projected onto
three-axes directions of the specimen. Solid dots show the coordinates of the AE source. It canbe seen that almost all of the AE sources are distributed along the loading plane (y-axis
direction), which is the eventual fracture plane. However, there are a few AE sources around thearea of loading points. This is thought to be because the waveforms generated around the areasof loading points attenuated on their way to more distantly located sensors, so that the source
location is not determined by the lack of waveforms.

112

Fig. 5 AE source distribution projected into three-axes directions.

Fig. 6 Typical AE source locations and stress concentration in sections of a specimen.
113

Figure 6 shows typical X-ray CT sections at z=3 mm, 26 mm and 47 mm, and the results of
FEM analysis of the corresponding sections. In the X-ray CT images (upper three circles), solid
dots show the AE source locations determined within ±5 mm from the CT image sections, andthe area with white spots of high contrast is andesite. In the lower three circles, the boundary
between tuff and andesite regions is indicated by a line. Considering the differences in thephysical properties of tuff and andesite regions, Young’s moduli of tuff and andesite were
estimated to be 3.2 GPa and 37 GPa, respectively, for elastic 2-D FEM analysis under the planestrain conditions. The dark area in this figure shows a higher shear stress region at a load of 102
N. To emphasize the contrast and stress concentration, the gray scale levels have been changed
for each section. Figure 6 shows that the stress concentration occurred in the area near theloading axis and where the geometry of the boundary was complex. The AE source is locatednot only along the loading axis but also in the area of stress concentration. That is, the results of
FEM analysis and AE source location suggest that microfracturing and AE generation are activein the area of stress concentration, where the boundary of rocks is complex.
Geometry of inclusion and its complexity
To explain the complexity of a boundary line of rocks in a certain section, fractal dimension
and some indices used in texture analysis were introduced. The fractal dimension of a boundaryline was calculated by the box counting method [5]. A typical index used for representing thecomplexity of a binary image is Cl based on the length of the boundary line [6]:Cl = L/(2R+1(2R – 1)),
where L is the total boundary length along the white or black binary pixels, and 2R shows thenumber of columns or rows for the square binary image of 2R x 2R.
Table 1 shows the values of the complexity index Cl and fractal dimension of the boundary
curves of three typical sections. These results show that Cl and fractal dimension accuratelyreflect the complexity of the boundary curve of each section.
Table 1 Complexity index for representing boundary lines of rocks.
Location of Section(mm)

z=3

z=26

z=47

Fractal Dimension

1.15

1.18

1.13

Complexity Index Cl

0.008977

0.012745

0.007475xy

AE and complexity index of inclusion
The number of AE events was compared to the complexity index for the boundary of rocks
in an X-ray CT image of each section. The thin cylinder of 11.3 mm in height was assumed tocontain the number of AE events in the region at each sections of the CT images, as shown in
Fig. 7. Figure 8 shows the variations in the complexity index Cl and total number of AE events
inside each disk region along the z-axis. This figure shows that the region with a higher value of
114

Fig. 7 Thin cylinder to count the number of AE events in the vicinity of a section of CT image.

Fig. 8 Variation in the complexity index value Cl and number of AE events along the z-axis ofa specimen.
complexity index almost corresponds to the region, in which more AE events were generated.
Furthermore, the variation patterns of the two parameters along the z-axis were shown to be
similar. Figure 9 shows the correlation between the number of AE events and complexity index
Cl. Similar correlation was found for the fractal dimension and for other indices representing thecomplexity of an image. Also, the total relative AE energy inside the perimeter cylinder shows asimilar correlation to the complexity indices.
Consequently, it was found that the more complex the boundary geometry of rocks was, themore AE events were generated in that area. These micro-fracturing activities with AE were
thought to be caused by the stress concentration.
4. CONCLUDING REMARKSRock specimens of tuff breccia used in this study consisted of tuff and andesite with acomplex geometrical structure. To examine the region of AE generation, Brazilian tests wereconducted on the specimens. X-ray CT images were also taken before the loading tests tounderstand the complexity of the boundary geometry of rocks, and FEM analysis was carried out
to confirm the region of concentrated stress inside each specimen. The experimental and115

Fig. 9 Relationship between number of AE events and complexity index value Cl.
analytical results are as follows.
1) From X-ray CT images representing density distribution, the geometry of the boundary
between tuff and andesite was determined. The boundary lines changed according to the sectionof the specimen.
2) Fractal dimension and indices used in texture analysis were found to be useful for evaluating
the complexity of the boundary geometry.
3) Although most of the AE sources were located along the eventual fracture plane, some ofthem were located along the boundary of rocks near the loading axis. These AE sources wereaffected by the complexity of the boundary curve.
4) The results of 2-D FEM analysis considering the physical properties of rocks showed thatstress concentration occurred in the region where the boundary geometry of rocks was complex.
5) It was found that the variation in the number of AE sources located in the vicinity of a section
along the z-axis was correlated with the variation in the complexity index for boundary
geometry. That is, stress concentration occurred at the region of complex boundary geometry,
and then microfracturing activities were generated in the same region with AE activity.
ACKNOWLEDGMENTS
The authors would like to thank Hokkaido Engineering Consultants Co., Ltd., and Dr. T.
Goto and Dr. K. Kodama of Muroran Institute of Technology for their kind assistance. Dr. K.
Sugawara and Dr. Y. Obara of Kumamoto University cooperated with us to obtain the X-ray CT
images of specimens. This work was supported by a grant-in aid for scientific research (No.
10650912) from the Ministry of Education, Japan.

116

REFERENCES
1. K. Itakura, Y. Watanabe, K. Sato, T. Goto, J. Kodama and Y. Yoshida: "Modeling Method for
Cracks in Rocks based on the Fractal Structure of the AE Source Distribution", J. of the MMIJ,
114(12), (1998), 918-924 (in Japanese).
2. K. Itakura, K. Sato, K. Nagano and Y. Kusano: "Fractals on Acoustic Emission during
Hydraulic Fracturing," J. of Acoustic Emission, 13(3/4), (1995), S75-S82.
3. K. Itakura, K. Sato and A. Ogasawara: "The Observation of Microcracking Cluster in Stressed
Rock by AE and Seismic Tomography Techniques," Progress in Acoustic Emission, V, (1990),
JSNDI. Tokyo, pp. 422-429.
4. K. Itakura, K. Sato and A. Ogasawara: " Monitoring of AE Clustering Activity Prior to Main
Faulting of Stressed Rock by Acoustic Tomography Technique," Proc. 5th Conf. on AE/MA in
Geologic Structures and Materials, (1995), pp. 1-17.
5. J. Feder: Fractals, Plenum Press, (1988).
6. R. Taniguchi and E. Kawaguchi: "On the Relation of Binary Image Complexity to GrayScaled Image Thresholding", Trans. Institute of Electronics, Information and Communication
Engineers, J70-D(1), (1987), 164-173 (in Japanese).

117

APPLICATION OF AE IMPROVED b-VALUE TO QUANTITATIVE EVALUATION OF
FRACTURE PROCESS IN CONCRETE MATERIALS
T. SHIOTANI, S. YUYAMA*, Z. W. LI* and M. OHTSU§Technological Research Institute, Tobishima Corporation, Sekiyado, Chiba 270-0222, Japan* Nippon Physical Acoustics Ltd., Shibuya, Tokyo 150-0011, Japan§ Graduate School of Science and Technology, Kumamoto University, Kumamoto 860, Japan
ABSTRACTIn the present study, fracture processes are investigated quantitatively in four different kindsof concrete specimens by applying improved b-value (Ib-value) analysis. It is observed that thevariation of the Ib-value corresponds well to the fracture process of specimens during eachloading stage, independent of dimension, shape and reinforcement of the specimen. The resultsof the Ib-value analysis were in good agreement with those obtained from the moment tensor
analysis. Even when the moment tensor analysis may be inapplicable, the fracture process of thespecimen can be evaluated by applying the Ib-value analysis. It is concluded that the Ib-value isa very useful parameter for quantitative evaluation of the fracture process in concrete materials.
1. INTRODUCTION
In recent years, it has been recognized that concrete structures deteriorate under various
service environments. Especially, during the 1995 Great Hanshin Earthquake, many reinforced
concrete (RC) structures were severely damaged due to strong ground motions. It is strongly
desirable to establish non-destructive inspection methods, which can evaluate the deterioration of
concrete structures quantitatively from the early stage of fracture. Since acoustic emission (AE)is effective in monitoring the initiation of cracks in concrete materials and structures, its
applicability has been extensively studied in both materials (Yuyama et al., 1995a) and structures(Yuyama et al., 1994; Yuyama et al., 1995b; Murakami et al., 1993; Minemura et al., 1998).
To analyze AE data acquired, parameter analysis is widely and frequently employed. Among
AE parameters, in particular, the AE peak-amplitude is considered a closely related parameter tothe magnitude of fracture. Many researchers have studied b-value and m-value determined froma negative gradient of the peak-amplitude distribution. Recently, in order to apply the b-value
analysis for fracture evaluation in slope failure, the method of calculating the b-value has been
modified by incorporating statistical values of amplitude distribution (Shiotani et al., 1994;
Shiotani and Ohtsu, 1999). This is now referred to as improved b-value (Ib-value) analysis. In
this paper, fracture processes in four different kinds of concrete specimens are evaluated byapplying the new analysis. Comparison is made for the results obtained from both the Ib-value
analysis and the moment tensor analysis (Ohtsu et al., 1998).
2. ANALYSIS
2.1 Improved b-value
Because AE peak amplitude is associated with the magnitude of fracture, the b-value that isdefined as a slope of the amplitude distribution is known as an effective index related to thestates of the fracture (Mogi 1962; Sholz 1968). The b-value was originally defined in
J. Acoustic Emission, 19 (2001)

118© 2001 Acoustic Emission Groupseismology. In AE applications of b-value, however, there were some problems to be resolved.
First, even though both earthquake and AE magnitudes are defined in logarithm, seismic
activities are distributed from 5 to 8 in earthquake-magnitude, and AE activities in peak
amplitude range from 30 dB to 100 dB. Second, seismic b-value is obtained by using seismic
data in a limited period and in confined range. However, the b-value in AE applications isconventionally obtained by using accumulated number of AE amplitude and from whole of thespecimen. Third, a maximum likelihood method, provided that M - Mz has an exponential
distribution, is applied in obtaining seismic b-value, where M is a seismic magnitude and Mz is aminimum magnitude. In the maximum likelihood method, the b-value is given by,

b=

log e
.
M − Mz(1)

It is noted in equation (1) that, when calculating the b-value with the maximum likelihood
method, minimum magnitude should be previously determined. Figure 1 shows AE amplitude
distributions resulting from a trap door test in a sand mass. The trap door test can provide several
numbers of fractures by dropping the bottom of experimental box in the sand mass. In the trap
door test, the condition of passive earth pressure in the geotechnical materials can be
successively reproduced. In Fig. 1, the accumulated number of AE events integrated frominfinity to zero (see a blue colored line) and frequency of AE events (see a red colored line) are
drawn. The blue colored line shows that a linear approximation can be applied only in a narrow
range from 43 to 50 dB, although AE events distributed from 30 to 59 dB. Moreover, it ispossibly expected that the amplitude distributions can be shifted horizontally due to theconditions of AE sensor response, types of couplant and so forth. Hence, the maximum
likelihood method using the minimum amplitude would be inapplicable for the AE applications.

Fig. 1 AE amplitude distribution resulted from a trap door test.
119Fig. 2 3D amplitude distributions from a trap door test.
The value of AE peak amplitude is thus varied with each monitoring. In AE applications, amethod for determining the amplitude range independent of the range of amplitude is necessary
in order to obtain the b-value quantitatively. In an improved b-value (Ib-value) analysis being
proposed, the range of AE amplitude is determined based on such statistical values as the mean µand standard deviation σ. Figure 2 shows three-dimensional amplitude distributions withelapsed time resulting from the trap door test. Each distribution is drawn by 100 data points ofAE events, with green colored arrows showing the mean in each distribution and yellow coloredarrows showing the standard deviation. It is found that the distributions between the two arrows
can be approximately represented by a single straight line. When the amplitude range is thus
obtained, the upper amplitude w2 and lower w1 are formulated as µ + α1σ and µ – α2σ,
respectively. Setting the accumulated amplitude over w1 and w2, as N(w1) and N(w2), Ib-value(improved b-value) is given by,Ib =log10 N ( w1 ) − log10 N ( w2 )(α1 + α 2 )σ,(2)where α1 and α2 are constants.

120

Fig. 3 The b-value resulted from a trap door test by means of accumulating AE data.
The other modification in the Ib-value analysis over the conventional b-value is the numberof AE event analyzed. In determining a b-value, the number of the peak amplitudes as denoted β
in equation (3) should be set for the calculation. Figure 3 shows b-value that resulted from thetrap door test by means of accumulating AE data. In this accumulation, for example, the b-value
at t is calculated by using derived AE data from time zero to t. In Fig. 3, the b-value up to thefinal failure is intentionally repeated three times. It resulted from Fig. 3, and three different
variations are observed, and b-value approaches 0.12 with elapsed time. This suggests that the bvalue is highly dependent on a population parameter. This non-quantitative behavior would bealso obtained for the b-value analysis of AE data within a fixed test period. For instance, AE
activities would become higher with approaching the final failure. The population parameters
would also become larger with elapsed time. Thus, b-value with the fixed time is also difficult toapply in AE applications. Figure 4 shows the b-value by means of constant number of 50 data
points from the same data set as given in Fig. 3. One characteristic variation is similarly observed
through the repetition. This implies that the constant number of population is so effective for
performing the b-value analysis. To further improve the calculation of the b-value, the number of
AE data points is formulated by,∞∫ n ( a ) da = β ,(3)0where n(a) is the number of AE hits (or events) at da and β is the total number of AE hits (orevents) data subjected to the Ib-value analysis.
Figure 5 shows the correlation coefficients r with respect to the population (a sample numberof AE amplitude data) where the data are approximated by the Gutenberg-Richter formula asgiven in equation (4), and the same data set as used in Fig. 2. Any number over 50 is considered121Fig. 4 The b-value by means of constant number of 50 data from the same data as Fig. 3.

Correlation coefficient: r

1.0

0.9

0.8

0.7

0.6

0.5
0

40

80

120

160

200

Sam ple num ber of am plitude data

Fig. 5 Correlation coefficients approximated by Gutenberg-Richter’s formula in a trap door test.
appropriate as β. In consideration of the problem in population parameters mentioned earlier, βfrom 50 to 100 would be an appropriate range to calculate the Ib-value. (Shiotani et al., 1994).
122

Correlation coefficient1
0.9
Concrete beam with FRP
sheet re inforcement

0.8

Concrete beam without
FRP reinforcement
Four point ben ding test
for RC beam
L-shaped rigid frame ofreinforced concrete

0.7
0.6
0.5
0

20

40

60

80

100

120

Sample number of amplitude data
Fig. 6 Correlation coefficients approximated by Gutenberg-Richter’s formula in various concrete
specimens.
These results were obtained in geotechnical materials. Appropriate numbers of β in concretematerials are discussed in the following section.
2.2 Sampling number of amplitude in concrete materials for Ib-value analysis
In concrete materials, correlation coefficients with increasing the population is examined todetermine the proper number of sampling data necessary for calculating µ and σ. Figure 6 shows
the correlation coefficients in different specimens as noted, approximated by the GutenbergRichter formula of equation 4. These correlation coefficients are obtained when an
approximation by the Gutenberg-Richter formula is performed.

log N ( M ) = A − bM ,(4)Here, N is the total number of earthquake-frequency at over M and A is an empirical constant. In
the AE applications, peak amplitude (dB) is substituted for M and b is the b-value. The peak
amplitude is defined in dB, multiplying the log-amplitude by 20. When a comparison between
the seismic b-value and Ib-value is made, the Ib-value should be multiplied by 20.
It is shown from Fig. 6 that, in all the different specimens, the relations between thelogarithmic total number of AE events and the AE amplitude give a reasonable straight line
when the number of sampling data becomes larger than 50. Hence, in this study for concretematerials, AE amplitude data of 50 samples (β = 50) are used to obtain the Ib-value.

1232.3 Moment tensor analysis
Moment tensor analysis is a method developed for quantitative AE waveform analysis basedon the measurement of very first P-wave arrivals. The procedure can give quantitative
information on three-dimensional location of cracks, crack types, crack orientation and directionof crack motion. A computer code for simplified moment tensor analysis is named SiGMA(Ohtsu et al., 1991). In the SiGMA procedure, digital records of AE waveforms are analyzed, andmore than six waveforms for each AE source are necessary. After experiment, each AE
waveforms corresponding to one AE event are displayed on the CRT screen. Two parameters, thearrival time (P1) and the amplitude of the first motion (P2), are read on the screen. Threedimensional AE sources are located from the arrival time differences (P1). Then, moment tensor
components are determined based on the amplitudes of the first motions (P2). Finally, eigenvalue
analysis is performed, and the classification of crack types and the determination of crackorientation are made. In this study, results of the moment tensor analysis are compared withthose obtained from the Ib-value analysis.
3. SPECIMENS AND TEST PROCEDURES

A four-point flexure test of reinforced concrete beams was conducted as shown in Fig. 7.
Cyclic loading was applied. Locations of six AE sensors (S1-S6) are also given in the figure. A
sketch of an L-shaped RC rigid frame and arrangement of six AE sensors are illustrated in Fig. 8.
Tensile force was applied (see the two arrows) to expand the corner of the L-shape model.
Center-notched concrete beams with and without FRP sheet reinforcement were subjected tothree-point flexure load. Figure 9 gives a sketch of the specimen. The coordinate system andsensor locations are also shown in the figure. In the FRP reinforced specimen, one ply of CFRP
sheet was bonded on the bottom corresponding to the tensile zone.

S6
S3

S4

S1

S2

150

50

125

30

S5

150

Deformed Bar, φ19

200

125

50

550 mmFig. 7 Reinforced concrete specimen in flexural test and AE sensor locations.
4. RESULTS AND DISCUSSION

4.1 Cyclic flexure test of reinforced concrete beams
The loading history for the cyclic flexure test of a reinforced concrete beam specimen isshown in the top of Fig. 10. Results of the Ib-value analysis for the Ch-5 sensor data are also
124

100

200

100
D19
1095

z

S3

S4

977

S5
x

S2

D16

S6

S1
D16
120
234

D13

y

D16

D16
170

1074

170

500

1414 mmAE sensor

Fig. 8 Cyclic loading test of “L” shaped rigid frame.
Load

2.3

z

FRP sheet

d
o

x

150

60

z
o

500

y

600
75

710 mmx-y-z: AE sensor coordination
d=100mm for specimen without reinforced FRP sheet
d=10mm for specimen with reinforced FRP sheet
Ch
1
2
3
4
5
6

Without CFRP sheet
x
y
z
0
55
0
200
20
0
0
20
150
200
55
150
50
75
130
150
0
130

With CFRP sheet
x
y
z
180 8
0
30
75
10
0
0
140
110 38150
160 75
120
110 0
30

Fig. 9 Details of three-point flexure tests for concrete specimen with a center notch.
given in Fig. 9. This sensor was placed below the center of the specimen in the tensile zone. The
dotted vertical lines represent the time of the maximum load of each stage. The maximum load
was 49 kN in both stage I and stage II. Subsequently, it was increased in steps to the final load of
120 kN.
125

150
I

Load (kN)

120

II

III

IV

V

120 kN
Maximum load

90

49 kN

60
30
0
0

500

1000

1500

2000

2500

Elapsed time (second)lb-value (50sample)

0.25
I

0.20

II

III

IV

V

0.15
0.10
0.05

Ch-5

0.00
0

500

1000

1500

2000

Elapsed time (second)Fig. 10 Results of cyclic flexure test in reinforced concrete beam.

126

2500

300
lb-value
duration

0.20

200

0.15
0.10

100

duration (usec)

Ib-value of 50 data

0.25

0.05
0.00

0

1050

1070

1090

1110

1130

1150

Elpsed time (sec)Fig. 11 Results of cyclic flexure test in reinforced concrete beam.
In stage I, the Ib-value started at ~0.14 and varied to 0.06 to 0.19 as the load approached the
maximum. This sudden variation of drop and rise in the Ib-value appears to coincide theinitiation of microscopic fracture; i.e., the generation of mortar cracks. The decrease in the Ibvalue represents the addition of high amplitude events due to cracking, while the subsequent
drop implies the frictional AE of numerous high amplitude signals. In stage II, the Ib-value wasstable; it remained at 0.085-0.09 and decreased suddenly at the maximum load to 0.076. (Thisimplies that cracks opened at the nominally the same load; presumably, the load slightly
exceeded the previous maximum.) Thus, the Ib-value exhibits the same characteristic as that ofKaiser effect. In stage III, the Ib-value starts to increase at the previous maximum load of stage II(due to opening of crack face), and then reaches below 0.05. The Ib-value smaller than 0.05
implies the onset of macroscopic fractures, because the specimen has experienced functional
yield stress, indicated by the reduced slope of the load-time curve. In stage IV, the Ib-value starts
to decrease at the load much lower than the previous maximum load of stage III. Considering thecharacteristic of the Kaiser effect, this represents the severely damaged state in the specimen, in
which shear type of cracks predominantly occurred within the specimen. In stage V, the Ib-value
begins to increase from the load 50% of the previous maximum load. The Ib-value subsequently
increases to over 0.15 when cracks between mortar and reinforcement were possibly grown, andabruptly decreases near the peak load. While the load was kept close to the maximum, the Ibvalue decreases gradually, representing continual crack development. Final unloading increases
the Ib-value close to 0.3 reflective of closure of crack face from unloading. Figure 11 shows anenlarged view of the Ib-value in stage III. Every AE event is plotted with its event duration in the
figure. The process of increase in Ib-value corresponds to the prevailing generation of AE eventswith short event duration (see the cluster of 0 - 25 µs), while in the process of decrease it reaches
the bottom of 0.04, and AE events with long event duration are predominantly observed (see the
127

80.0
I II IIIIV V VI

VII VIII

Load (kN)

60.0
40.0
20.0
0.0
0

500

1000

1500

2000

2500

Elapsed time (second)lb-value (50sample)

0.30
I II III

0.24

IV V VI

VII VIII

0.18
0.12
0.06
0.00
0

500

1000

1500

2000

2500

Elapsed time (second)Fig. 12 Results of cyclic loading test in L-shape rigid frame.
cluster around 200 µs). Fracture phenomenon with short event duration means the microscopic
fracture of the tensile type, and that with long duration implies the generation of shear type offracture or frictional slip along the crack interface. Interpretation of the Ib-value behavior is thus
feasible.
In the moment tensor analysis, AE events obtained are 8 (I), 2 (II), 32 (III), 25(IV) and 9 (V)through the test. The parentheses denote the stages. AE events become the largest at stage III,
which conforms the consideration of the Ib-value. In stage V, only 9 events are obtained from themoment tensor analysis, although the Ib-value changed between 0.09 and 0.16. In order toperform the moment tensor analysis, sets of waveforms consisting of at least six readable
128

waveforms are required. In the latter period of fracture, since there exist many cracks generated
in the specimen, it is very difficult to obtain sets of waveforms with six valid waveforms. Thus,
the moment tensor analysis may not be applicable to the latter period of fracture wheremacroscopic cracks of shear type are dominant. Unlike the moment tensor analysis, the Ib-value
analysis could evaluate the progress of fracture even in a heavily damaged phase. This is because
the Ib-value analysis only requires amplitude information independently of the number of AE
events.
4.2 L-shaped rigid frame
Load was applied to the L-shaped rigid frame in the step-wise manner. The loading levels are
divided into eight stages as shown in Fig. 12. The maximum loads are reached at times indicatedby the dotted vertical lines. The Ib-value analysis for the Ch-1 sensor data, giving representative
behavior, is shown in the figure.
The Ib-value cannot be determined in stages I and II due to low AE event totals. It stays low
in stage III, then slightly drops and increases near the peak of stage III. The Ib-values of ~0.15
are obtained at the end of stage IV, V and VI. In these stages, however, Ib-value reduction to 0.05
is not observed. The Ib-value smaller than 0.05 implies the onset of macroscopic fractures, where
the specimen has experienced functional yield stress. Thus, the specimen reaches the yield point
only past the stage VI, although the load-time curve shows significant non-linearity. In stage VII,because it is obviously recognized that the Ib-value grows up to 0.20 and then falls to the bottomof 0.03, the specimen has possibly been subjected to the functional yield load. In stage VIII, at
the load smaller than the previous maximum load, the specimen has reached the final failure. It
suggests that the specimen has already experienced the functional yield stress during theprevious stage VII. Thus, it appears that progress of fracture in the previous stage VII, evaluatedfrom Ib-value analysis, is reasonable.
Meanwhile, among a total of 1024 sets of waveform data, only 19 sets could be subjected tothe moment tensor analysis. This is because the size of specimen is so large and with a quite
complicated shape. No comparison was feasible between the Ib-value and moment tensor
analysis.
4.3 Center-notched concrete beams with and without FRP sheet reinforcement
In center-notched concrete beam specimens without FRP reinforcement, brittle nature offracture is expected. Figure 13 shows results of the three-point flexure test of such a sample. The
top shows the loading history and the bottom shows the Ib-value for Ch-1 AE data against
elapsed time. In the top figure, results of the moment tensor analysis are also shown, wherearrows denote cracks of tensile type and cross symbols exhibit those of shear type. Bothdirections of the symbols correspond to crack orientations. The results of the moment tensor
analysis could be classified into two stages.
In Fig. 13, the Ib-value starts to increase at around 32 s (80% of the maximum load) andreaches 0.14. The Ib-value gradually decreases to 0.07 at the maximum load and beyond. The Ibvalue successively decreases below 0.05 during strain softening. The increase may be caused by
nonlinear deformation of the specimen, while the decrease may be due to catastrophic failure. On
the other hand, the fracture process of the specimen could be divided into two stages by the
moment tensor analysis. The first stage is determined to be until the maximum load, where the
129

3.50

Load (kN)

2.80
2.10

z

z

1.40

x

x

0.70
Stage I

Stage II

0.00
0

0

10
10

lb-value (50sample)

0.16

20
30
40
20
30
40
Elapsed time (second)

50
50

60
60

Elapsed time (second)
Stage I

Stage II

0.12
0.08

Ch-1

0.04
0.00
0

10

20

30

40

50

60

Elapsed time (second)Fig. 13 Results of center notched concrete beam without a FRP sheet reinforcement.
cracks occurred only at a narrow area near the notch tip. After the maximum load, as the second
stage began, cracks grew rapidly upward and the specimen failed right away. Thus, the results of
Ib-value analysis were in good agreement with those obtained from the moment tensor analysis.
Figure 14 shows results of load (top) and the Ib-value (bottom) in the concrete beam
reinforced with a CFRP sheet. Figure 15 shows results of moment tensor analysis. Also in the
figure, a photo corresponding to fracture stages is given.
130

12.00B

Load (kN)

10.00

A

8.00
6.00
4.00
2.00
0.00

Staga I

0

0

60

120

60

0.20

180

Staga III

240

300

120
240 300
Elapsed180
time (second)

360

360

Elapsed time (second)

Staga Ilb-value (50sample)

Staga II

Staga II

Staga III

0.16
0.12
0.08
0.04
0.00
0

60

120

180

240

300

360

Elapsed time (second)Fig. 14 Results of center notched concrete beam with a CFRP sheet reinforcement.
The fracture process is divided into three stages by the moment tensor analysis. Each stage isdenoted as in Fig. 14. In the first stage corresponding to the loading level before point A, cracks
are observed only within the area close to the notch tip as shown in Fig. 15(a). This is the same
behavior as in the first stage of the specimen without reinforcement. Therefore, the loading point
A can be regarded as the functional maximum allowable load in this type of specimen. The
second stage is determined between loading point A and loading point B, where the main cracks
ahead of the notch tip grew rapidly as shown in Fig. 15(b). Also in this stage, a diagonal crack
131

a)

b)

1st stagec)

2nd stage

3rd stage

Fig. 15 Results of center notched concrete beam with a CFRP sheet reinforcement.
initiates from the bottom of the specimen toward the notch tip and a bottom crack propagatesnear the interface between the CFRP sheet and the concrete. After the loading point B or thethird stage, cracks are only observed in the bottom of the specimen, continuing to propagate
towards the supports. Thus, it is obvious that, during the second stage, the main crack had
already reached the upper surface of the specimen.
From Fig. 14 (bottom), it is seen that the Ib-value starts to decrease at about 80% of loading
point A in the first stage. This is just the same behavior as that from the specimen without
reinforcement. After loading point A, during the second stage, the Ib-value increases anddecreases repeatedly over a large range (0.03 to 0.17). Furthermore, the Ib-value is observed toreach below 0.05 many times in this stage. This implies that the similar magnitude of large
fracture is consecutively generated in the specimen during the stage. As the load approaches thepoint B, the Ib-value changes most drastically and reaches as low as 0.02. It is thought that thisresulted from the rapid propagation of the main crack up to the upper surface of the specimen.
Consequently, the coincidence between the results of the Ib-value analysis and the moment
tensor analysis is further confirmed.
5. CONCLUSIONS

Fracture processes in four types of concrete specimens were evaluated by the Ib-value
analysis. Comparing the results with those obtained from the moment tensor analysis, thefollowings are concluded:
1. The Ib-value analysis can evaluate the fracture process of concrete specimens withdifferent dimensions, shapes and reinforcement arrangements. Thus, the Ib-value is a usefulfeature to characterize fracture process in concrete structures.
2. Fracture process can be reasonably evaluated by the Ib-value analysis even when the
moment tensor analysis is inapplicable due to specimen size and shape.
132

REFERENCES

O. Minemura, N. Sakata, S. Yuyama, T. Okamato and K. Maruyama (1998), "Acoustic Emission
Evaluation of an Arch Dam during Construction Cooling and Grouting," Construction and
Building Materials, 12, 385-392.
K. Mogi (1962), “Magnitude Frequency Relation for Elastic Shocks Accompanying Fractures of
Various Materials and Some Related Problems in Earthquakes,” Bulletin of Earthquake ResearchInstitute, 40, 831-853.
Y. Murakami, S. Yuyama, T. Shimizu, H. Kouyama and M. Matsushima (1993), "Deformation
Behavior and AE Characteristics for Anchorage Pulling Tests on the Foundations of Power
Transmission Pylons (Part II, Moment Tensor Analysis)," Proc. the 9th National AE Conference,
JSNDI, pp. 143-150 (in Japanese).
M. Ohtsu, T. Okamoto and S. Yuyama (1998), "Moment Tensor Analysis of Acoustic Emission
for Cracking Mechanisms in Concrete," ACI Structural J., 95(2), 87-95.
M. Ohtsu, M. Shigeishi, S. Yuyama and T. Okamoto (1991), “SiGMA Procedure for AE Moment
Tensor Analysis,” J. JSNDI, 42(10), 570-575.
T. Shiotani, K. Fujii, T. Aoki and K. Amou (1994), "Evaluation of Progressive Failure Using AE
Sources and Improved b-value on Slope Model Test," Progress in Acoustic Emission VII, eds. T.
Kishi, Y. Mori and M. Enoki, JSNDI, Tokyo, pp. 529-534.
T. Shiotani and M. Ohtsu (1999), "Prediction of Slope Failure Based on AE Activity," Acoustic
Emission: Standards and Technology Update, ASTM STP 1353, ed. S. J. Vahaviolos, ASTM, pp.
156-172.
H. Sholz (1968), “The Frequency-Magnitude Relation of Microfracturing in Rock and Its
Relation to Earthquakes,” Bulletin of Seismological Society of America, 58(1), 399-415.
S. Yuyama, T. Okamoto and S. Nagataki (1994), "Acoustic Emission Evaluation of Structure
Integrity in Repaired Reinforced Concrete Beams," Materials Evaluation, 52, 86-90.
S. Yuyama, T. Okamoto, M. Shigeishi and M. Ohtsu (1995a), "Quantitative Evaluation and
Visualization of Cracking Process in Reinforced Concrete by a Moment Tensor Analysis of
Acoustic Emission," Materials Evaluation, 53, 751-756.
S. Yuyama, T. Okamoto, M. Shigeishi and M. Ohtsu (1995b), "Acoustic Emission Generated inCorners of Reinforced Concrete Rigid Frame under Cyclic Loading," Materials Evaluation, 53,
409-412.

133

EVALUATION OF FRACTURE PROCESS IN CONCRETE JOINT BY ACOUSTICEMISSION
T. KAMADA, M. ASANO, S. LIM, M. KUNIEDA and K. ROKUGO
Department of Civil Engineering, Gifu University, Yanagido 1-1, Gifu 501-1193, Japan
ABSTRACT
Fracture process in concrete joint between old and new concrete was investigated thoroughAE procedures. Both bending and shear tests were conducted at each specimen with concrete
joint. The joint surfaces were treated with different surface preparation methods. AE
measurements were performed during tests and the AE features were examined. The resultspresented that the AE characteristics had strong correlation with surface roughness at joint. Thismethod would be an effective tool for the comparison among different concrete joints withsimilar strength.
1. INTRODUCTION
Mechanical behavior of a construction joint, which has interface between old and new
concrete, strongly affects the performance of a concrete structure. Bonding that is improved bysurface treatments is one of the important performances for construction joints. Some indicesand testing methods have been proposed for evaluating the bond property. Bond strength
calculated by the maximum load has been only an index at final failure, and it has not been usual
to evaluate a softening region in the fracture behavior. Fracture mechanics parameters, such as
tension softening diagram and fracture energy, are effective indices to evaluate the bond property
including the softening region [1]. These indices, however, are estimated by using
comprehensive behavior such as load-displacement relationship in the tests, and cannot monitor
the fracture process.
Acoustic emission (AE) technique, which is a method to receive elastic waves due to microcracks in concrete, is useful to evaluate fracture processes. Especially, locations of cracks, which
are changing step by step, can be also estimated by this technique. The AE hit, which is one ofthe parameters in AE technique, has been applied to evaluation of the fracture process of jointed
specimens under bending tests [2] or direct tensile tests [3]. In this study, bending tests andshear tests for jointed specimens were carried out. The fracture process of jointed specimens wasinvestigated by using not only the AE hit but also the characteristics of received waveform. The
relationship between AE characteristics and bond properties due to different surface treatments
was studied.
2. EXPERIMENTAL PROCEDURES
Bending test
Specimens The size of the jointed specimen, that had a joint at the center of the specimen, was
10x20x80 cm (width x depth x length). The mixture proportions of old and new concrete areshown in Table 1. Cement used was the high early strength Portland cement (specific gravity:
3.12), and the maximum size of coarse aggregate was 15mm. Compressive strengths of the newand the old concrete at the age of 21 and 28 days are shown in Table 2. In order to prevent a
sudden crack propagation, a steel bar (D13) was embedded at compressive side (2 cm from the
J. Acoustic Emission, 19 (2001)

134© 2001 Acoustic Emission Groupupper side) in the beam specimen, as shown in Fig. 1.

Fig. 1 Bending test setup.

Fig. 3 Shear test setup.

Fig. 2 AE sensor location.
Three types of surface roughness, namely a non-treatment surface after removing aformwork, wire-brushed surface, and washed-out surface (washed-out depth: 2mm and 6mm),
were chosen as the old concrete surfaces. For comparison, specimens without joints were also135

made. Three specimens in each series were made in this test. Four-point bending tests (span: 60cm) were carried out as illustrated in Fig. 1. The results of strength tests were shown in Table. 2.
AE measurement The system manufactured by Physical Acoustics Corporation was used for themeasurement of AE. AE signals detected by 150 kHz resonant sensors were amplified with the
pre-amplifier by 40 dB, and were also amplified with the main-amplifier by 40 dB, so that the
total gain was 80 dB. For the bending tests, the threshold value was 45 dB. The detected AE
signals were analyzed and recorded in terms of waveform parameters. As shown in Fig. 2, six
sensors were attached on the front (four sensors) and back (two sensors) of the specimen.
Rubber pads and teflon sheets were inserted in order to eliminate the noise due to contact of thespecimens with the loading apparatus.
Shear test
Specimens As shown in Fig. 3, compressive load was applied to the rectangular specimens witha joint, of which angle was 45 or 60 degrees. The size of specimens was 10x10x40 cm (width x
depth x length). The mixture proportions of old and new concrete are shown in Table 1.
Compressive strengths of the new and the old concrete at the age of 103 and 106 days are shownin Table 2.
Two types of rough surfaces (washed-out depth: 2 mm and 4 mm), which are treated byusing washed-out method, were chosen as the old concrete surfaces. In addition, specimens
without joints were also made. Casting direction was taken at right angle with the longitudinal
axis of the specimen. Test series for the slope-angle and surface roughness of the old concrete
are shown in Table 3. Three specimens were made in each series.
AE measurement Four sensors were attached on the surface of a specimen, as shown in Fig. 4.
The procedure for AE measurement was the same in the bending test.

136

2

Table 3 Test series.
SeriesSlope angle
α (degrees)

Surfacepreparation

A0

-

-

A64

60

Washed-out 4 mm

A62

60

Washed-out 2 mm

A44

45

Washed-out 4 mm

A42

45

Washed-out 2 mm

Joint

12
12

40

12
2
10

Unit cm

Fig. 4 AE sensor location.

Fig. 5 Load and AE activity under bending tests.

137

3. RESULTS AND DISCUSSIONS
Bending test
AE hits Figure 5 shows the relationship between applied load and AE hits in the bending test.
The maximum load of the specimen without joint was the highest in these specimens. For the
specimens with the joint, the maximum loads of the specimens, in which old concrete surfaces
were washed-out (6 mm and 2 mm) or wire-brushed, were higher than that of the specimen with
non-treatment surface. Especially, the AE signals in the specimen without joint and the washedout specimen were hardly detected up to the maximum load. However, they suddenly increasedon reaching the maximum load. On the other hand, in the case of the specimens with wirebrushed and non-treatment surface, AE activities, which are thought to indicate occurrence of
micro or macro crack, could be found before the maximum load. Although the maximum loads
of wire-brushed specimens were as high as those of the washed-out specimens, it seems thatdebonding at the joint occurred in the low load level. These micro or macro cracks in low load
levels might change the transport characteristics for water or chloride ion, which affect a
corrosion of steel reinforcing bar or esthetics of concrete structures. Therefore, it can beconcluded that AE parameters are effective indices for a quantitative evaluation of these cracks.

Fig. 7 Amplitude distribution (Region 1,Group 2).

Fig. 8 Amplitude distribution (Region 2,Group 2).

Fig. 6 Amplitude distribution(Group 1).

Maximum amplitude
In this study, the test results wereclassified into two groups from the viewpoint of the AE
hits. One was Group 1 that consisted of the jointless andwashed-out (2mm and 6mm) specimens. The other wasGroup 2 that consisted of the non-treatment surface andthe wire-brushed specimens. In the investigation for Group
1, the amplitude distribution obtained from the test-start tohalf of the total measurement time, was adopted becauseof removing the AE due to the compression failure or the138

Fig. 9 Maximum loads in shearing tests.
debonding between the concrete and the steel bar. The amplitude distributions for the Group 1
are shown in Fig. 6. The ratio of the AE hits with lower amplitude (45~50 dB) to the total AE
hits in the jointless specimens was larger than that in the washed-out specimens (2 mm and 6mm). Although AE behavior of the jointless specimens that evaluated by AE hits was similar tothat of washed-out specimens, the failure process that was revealed by the amplitude
characteristics was different from that of washed-out specimens [4]. It seems that the energy due
to crack generations at the joint is larger than that of cracks in plain concrete. The influence of
washed-out depth on the AE behavior is not clear in this experiment.
In the investigation for Group 2, the amplitude distributions in the Regions 1 and 2 (which
are defined in Fig. 5) are shown in Fig. 7 and 8, respectively. For the non-treatment surface
specimens, the amount of AE signals with lower amplitude (45 ~ 50 dB) in the Region 2 became
larger than that in Region 1. For the wire-brushed specimens, the difference of amplitude
distributions between the Regions 1 and 2, however, was not clear. In the case of the wirebrushed specimens, the ratio of the AE hits whose amplitude were over 50dB to the total AE hits
was relatively larger than that of non-treatment specimens. Because the consumed energy for the
failure of the non-treatment surface specimens was lower than that of the wire-brushedspecimens, the magnitude of the AE sources of the non-treatment surface specimens became
smaller than that of the wire-brushed specimens. These results indicate that the failure easily
progresses afterwards, when the failure of the non-treatment surface specimens is once
generated. It was clarified that not only the AE hits but also the amplitude distributions wereeffective indices for the evaluation of failure process.
Shear test
Comparison with maximum loads
Figure 9 shows maximum loads under shear test. The
effect of slope-angle or washed-out depth on the maximum loads was not observed in these tests.
Evaluation using maximum amplitude distribution
Figure 10 shows the result of AE source
location analysis in each specimen. The detected AE sources were located around the joint, andthe failure at the joints was confirmed through this analysis. In the experiment, a crack along thejoint can be observed in the specimens with the joint.
Figure 11 shows the amplitude distribution of each specimen. These results were obtainedby using the data, whose locations were clarified through the AE source location analysis.
139

Fig. 10 Results of AE source location analysis.

Fig. 11 Amplitude distribution of five test specimens. AE hit ratio given in %.
140

The shape of amplitude distributions of Series A44 and A42 were similar to that of Series
A0 that was compressive failure type. Therefore, it seems that the failure of Series A44 and A42
can be also classified into the compressive failure type. For Series A64 and A62, the amplitudes
at the maximum AE hits ratios in the amplitude distributions were smaller than those of Series
A44 or A42. In the case of the Series A62, the amplitudes at the maximum AE hits ratios in theamplitude distribution was smaller than that of Series A64, and the ratio of AE hits whose
amplitudes were over 60 dB was definitely smaller than that of Series A64. The failure of Series
A62 depends on slips at interfaces under shear stress, which was clarified by referring to theprevious study [5]. In the shear tests for jointed specimens, it was revealed that the roughness ofold concrete surface improved the resistance against a large deformation [6]. The results of AE
measurement could be also effective to evaluate the resistant properties at joint under the shear
stress.
4. CONCLUSIONSThe conclusions were obtained as follows;(1) In the bending tests of the specimens with a construction joint, AE activity was affected by
the difference of surface roughness of old concrete.
(2) For the evaluation by using the amplitude distribution, the shape of amplitude distribution
was affected by surface roughness. The amplitude distribution, which was affected byfracture process, was more effective index than the maximum load.
(3) In the shear tests, the relationship between washed-out depth and maximum loads was notaffected by the slope-angle.
(4) For the evaluation by using the amplitude distribution, the amplitude distribution in case ofsmaller slope angle of 45 degrees, the shape of amplitude distribution was similar to that ofjointless specimens. The amplitude distribution in the case of the larger slope-angle of 60
degrees was, however, affected by the washed-out depth of old concrete.
(5) The failure of Series A62 depends on slips at interfaces under shear stress as compared to theSeries A64.
REFERENCES
1. N. Kurihara, M. Kunieda, Y. Uchida and K. Rokugo, “Bond Property of Concrete Joint andSize Effect,” Journal of Materials, Concrete Structures and Pavements, JSCE, No. 613/V-42,(1999), 309-318.
2. I. Adachi and S. Sakoda, “Acoustic Emission in the Construction Joints of New and Old
Concrete,” The Proceedings of the 10th National AE conference, (1995), JSNDI, pp. 179-184.
3. K. Miyano, T. Kita, Y. Murakami and T. Kyogoku, “Evaluation of Joint Properties of AntiWashout Concrete by Acoustic Emission,” Proceedings of Japan Concrete Institute, 12(2),(1990), 359-364.
4. A. A. Pollock, “Acoustic Emission Amplitudes” Nondestructive Testing, 6(5), (1973), 264269.
5. T. Kamada, M. Iwanami and S. Nagataki, “Acoustic Emission Classification of Cracks in
Concrete,” Tokyo Institute of Technology, Department of Civil Engineering Technical Report,
No. 55, (1997), 1-12.
6. T. Makitani, K. Katori and S. Hayashi, “An Evaluation of Surface Roughness of Pre-Cast
Panel and Experiments on Ability of Shear Transfer through Construction Joint,” Proceedingsof Japan Concrete Institute, 17(2), (1995), 171-176.

141

DAMAGE DIAGNOSIS OF CONCRETE-PILES BY MACHINERY-INDUCEDACOUSTIC EMISSION
T. SHIOTANI, S. MIWA, Y. ICHIMURA* and M. OHTSU§
Research Institute of Technology, Tobishima Corp., Sekiyado, Chiba 270-0222, Japan*Public Works Research Institute, Ministry of Land, Infrastructure and Transport, Tsukuba,
Ibaraki, Japan; §Graduate School of Sci. and Tech., Kumamoto University, Kumamoto, Japan
ABSTRACT
After recent strong earthquakes in Japan, it has become important to investigate thesoundness of structures in the disaster areas. Investigation of the above-ground structures iseasily conducted by visual observation, whereas that of substructure, especially pile-foundations
installed in deep bearing strata, presents substantial difficulty. In order to investigate thestructural integrity of concrete-piles, a procedure to evaluate the damage conditions of theconcrete-piles by utilizing an AE technique has been studied. In this paper, the procedure isapplied to actual cast-in-place concrete-piles connected with a footing. To generate AE eventsfrom the existing cracks, heavy-machinery is utilized. AE activity is monitored both directly andindirectly in the pile. One-dimensional source location is performed to identify locations of thedamaged zone and existing cracks. Comparing the results from the AE technique with those ofborehole camera, core samples retrieved and wave velocity sounding, it is concluded that the AE
technique provides useful information, which could not be obtained otherwise. Thus, theapplicability of the proposed AE technique by inducing heavy machinery is confirmed in
evaluating the structural integrity of damaged concrete-piles.
1. INTRODUCTION
In pile-foundations, the degree of damage has been discussed based on the existence andwidth of crack, although the relation between these is still unsolved. Even in techniques forinvestigating cracks, however, standard techniques have not yet emerged. So far, there exist a
few methods to investigate the crack conditions of the piles. The pile integrity test (PIT)(Middendorp and Reiding, 1988), which is a sort of impact-echo test, has been widely applied(Tsukada and Ichimura, 1998). Originally PIT have been developed as a test for quality controlof piles after completion to check the sufficiency of design lengths. Instrumentation of PIT is
modified with low frequency characteristics less than 1 kHz to apply to longer piles. As for thefrequency of 1 kHz, it has not be clarified for its effectiveness in evaluating pile-damage.
Consequently, the applicability of PIT is not yet confirmed in assuring the soundness ofconcrete-piles. Acoustic emission (AE) is known as elastic waves related directly to fracture. As
for the fracture process of concrete-piles, AE signals due to both crack initiation and crack
growth could be observed (Ohtsu, 1995). In damaged concrete-piles, it is also expected that
secondary AE signals due to fretting at existing crack surfaces would be released (Shiotani et al.,1999a). Paying attention to the secondary AE signals, therefore, we have proposed a procedure
to evaluate the damage and structural integrity of concrete-piles (Shiotani et al. 1997).
In the present paper, the AE technique is applied to actual cast-in-place concrete piles
connected with a footing. The piles have already been subjected to substantial lateral loads andare expected to be heavily damaged (Ishii et al. 1994). To induce AE generation from the
J. Acoustic Emission, 19 (2001)

142© 2001 Acoustic Emission Groupexisting cracks, heavy-machinery is utilized. A load is first applied from a side of the footing.
Secondly, the heavy-machinery is placed on the footing and dynamic loads are driven into thepiles. AE sensors attached on the pile and in a monitoring well were used to detect AE waves
and these were examined primarily with respect to their arrival times. One-dimensional source
location is performed to identify locations of the damaged zone. Finally, results of the damage
locations identified by the AE technique are compared with those by borehole camera
observation, core samples retrieved and wave velocities
2. EXPERIMENT
2.1. Site of a Pile Tested and AE Monitoring
An in-situ test was carried out at the reclaimed land of Kishiwada City in Osaka Prefecture.
The land was reclaimed from the seabed of 13 m depth in 1992, and composed of sandy gravelof 5 to 10 in SPT N-value, with underlying alternation of cohesive soil and sandy soil. A footingof 9.0 m x 9.0 m x 2.3 m was made on the land on pile foundation, consisting of nine piles. A
damage investigation is conducted for the pile No. 9 (1.2 m dia. and 28.9 m length), located at
one corner of the footing as shown in Fig. 1. AE waves generated are directly monitored by AE
sensors placed on the internal wall of a borehole P9 drilled in the pile. AE activity is also
monitored by AE sensors placed in a monitoring well B1, which is excavated in the ground. The
center-to-center distance between the pile and well is 1.9 m.

Footing
1.5 m

No.9
Borehole P9, φ116 mm
Well B1, φ116 mm
PVC pipe (VU 100)

1.9 m

1.5 m

Pile (φ 1.2 m, l 28.9 m)

B2
B3

Fig. 1 Plan view of a pile foundation tested.
2.2. AE Monitoring Depth and Methods of AE Sensor Setup
Figure 2 shows an array of AE sensors and zones of AE monitoring. The depth of concern is11.5 m from the surface of the footing. In the case of direct AE monitoring on the borehole wall,
five AE sensors of 60 kHz resonance are arranged in a thin plate of 3 m long, where AE sensor isequally spaced at 50 cm. Thus, a set of AE sensors covers the range of 2 m. The array is placedin step of 1.5 m with 0.5 m overlap down to 11.0 m in stages (zone 1 to 7). For the zone 8 (the
143

16

6th zone

4th zone

AE sensors

Shifting method for directly
AE monitoring within pile18
20

4 X 500 mm = 2,000 mm

14Overlap: 150 cm

Cast in place piles

12

8th zone

Depth (m)10

7th zone8

5th zone6

2nd zone

4

Overlaps: 50 cm

3rd zone2

1st zone0

AE sensor(60 kHz resonance)

AE sensor(60 kHz resonance)
114 mm22

PVC pipe(VU100)24
1,200 mm

26

Water

28

Fig. 2 Array of AE sensors and zones of AE monitoring.
bottom stage), the array is placed between 9.5 m and 11.5 m with an overlap of 1.5 m with thezone 7 immediately above. The borehole monitoring was limited, but covered the depth thought
to be the location where pile is severely damaged.
To move and set the sensor array to the design depth, a special device consisting of rubberballs with shifter levers was developed as shown in Fig. 3. The rubber balls can be expanded andcontracted by air pressure. The array can move in the borehole when the rubber is expanded anda pile attachment is directed downward with spring tension. To set the sensor at a proper
location, the rubber is contracted and the pile attachment is directed to the pile with spring
compression. Thus, the device can be firmly placed against the interior wall by the constantpressure due to the spring.
In the case of indirect AE monitoring, the monitoring well was dug and a polyvinyl-chloride(PVC) pipe was buried. Sensor arrangements and the resonant frequency of AE sensors are thesame as those of direct monitoring, although the sensors are fixed in a fiberglass-reinforced bar.
They are set in design depths as to keep spaces between the sensors and the wall. The indirect
144

PilesPile attachment
Shifter lever

Rubber ballSpring: moving position: fixing position
Fig. 3 A mechanical device to move and set AE array in borehole.
AE monitoring is performed by filling the remaining space with water. By adapting thisprocedure, AE waves detected are successively propagated through ground, PVC pipes and water
up to AE sensors. It is confirmed that this sensor arrangement combining PVC pipes and water
has twice the sensitivity of the conventional type of steel-waveguide (Shiotani et al, 1999b).
2.3. AE Instrumentation
Ten AE sensors of 60 kHz resonance (PAC) are employed for detecting AE signals, which
are amplified by 40 dB in a sensor-integrated preamplifier. The signals of amplitude over 45 dB
are processed by a 10-channel MISTRAS AE system (PAC) under the condition of 4 kwords inwaveform length and 10 ms dead time.
2.4. Loading Procedure by Heavy-Machinery
To induce AE activity from existing cracks of the pile, a hydraulic excavator of 0.59 m3 inbucket capacity and 11.8 tons in operating weight is used. A loading procedure by the hydraulicexcavator is demonstrated in Fig. 4. Such static loads as in lateral, in diagonal and inperpendicular directions are first applied by putting the shovel of the hydraulic excavator on theside of the footing. Next, the hydraulic excavator is moved on the footing, applying dynamic
loads into the piles by arm’s expansion and contraction with up-and-down movement.
3. RESULTS AND DISCUSSION
3.1. Observation of Borehole by a CCD Camera and Core Samples Retrieved
To observe the damage state, a charged coupled device (CCD) camera is inserted into theborehole drilled in the pile. Cumulative numbers of cracks observed every 0.5 m are shown by145

W (f)

N (g)

S (i)

E (h)
P (d)

(e)

D (c)

L (b)

(a) BGN monitoring(b) Lateral load(c) Diagonal load(d) Perpendicular load(e) Placing machinery on footing(f) Arm's expanding with up-and-down movements to the west(g) ditto to the north(h) ditto to the east(i) ditto to the south(j) removal from footing

Fig. 4 A loading procedure by hydraulic excavator.
broken lines in Fig. 5(a). Also given in Fig. 5(b) are pictures of core samples retrieved. It isestimated from Fig. 5(a) that the damage around the pile head is particularly serious. Cracks arealso found around 5.0 m, 7.0 m and 10.0 m. Comparable locations of cracks are observed fromthe core samples and the camera observation. Specifically, in the footing, only two cracks of 2.2
m and 2.3 m corresponding to the pile-head are observed from the camera. From the coresamples, however, further numbers of ruptures are obtained above the pile-head. The rupture of
the core samples within the footing is believed to be generated when the boring is performed. By
considering the fact that there are no similar ruptures due to boring below the footing, it suggeststhat critical microcracks, which can not be observed by the CCD camera, have already been
generated in the footing due to the lateral loads applied prior to this investigation. When the146Number of cracks observed

0

0

1

2

3

4

0.0
0.6 m

Footing

1.9
2.7

2

3.5

Depth (m)

3.9
4.8

4
6

from core

5.5
6.3
7.1

from camera

7.9
8.6

8
9.2

10.1

10

10.7
11.6

12

Fig. 5 Condition of cracks in the pile tested. (a) Accumulated Number of cracks with depth,(b) Core samples retrieved.
torsion by the boring was given, coalescence and progress of existing cracks were introduced
into the footing. Because the accuracy of crack widths measured by the image processing systemwith the CCD camera is 0.25 mm, crack widths evaluated by the system are limited to larger than
0.25 mm. Incidentally, it is reported that the width of surface cracks is critically associated withassessment of the deterioration in reinforced concrete structures (Yuyama et al, 1994). According
to their report, the crack widths ranging from 0.12 mm to 0.20 mm correspond to the damage
degree of “light” or “medium”, and crack widths over 0.5 mm correspond to “severe” indeterioration process. Thus, it suggests that the image processing system with the CCD camera
adapted may not have sufficient capacity as to evaluate structural integrity of the piles.
3.2. AE Activity
Figure 6 shows AE sources computed by one-dimensional source location algorithm duringloading procedure. To examine the integrity of the footing, only the depth from the surface to 2.0m, zone 1, is evaluated. AE sources are indicated by circles where the magnitude of diameterreflects the number of AE (ringdown) count. AE sources with open circles denote results
obtained by the direct AE monitoring, and solid circles show those by the indirect monitoring. It
is found from the figure that AE sources are not observed in the period (a) of background noisemonitoring. In the period from (b) to (e), static loads are applied to the footing, and AE sources
are observed only inside of the footing (down to 2.3 m deep). However, the magnitudes ofsources are relatively small. A large number of AE sources of greater magnitude are produced at
the onset of dynamic loads due to arm’s movement to the north in the period (g), although there
are not many emissions during the previous load application (f). The north direction iscompatible to that of lateral load applied prior to the investigation. AE sources are also observed
under the condition of unloading (i), when the hydraulic excavator is removed. These emissions
during unloading are thought to be produced from friction along the pre-existing cracks. Thisimplies that there obviously exist macroscopic shear cracks in the pile. AE activity during
unloading is thus expected to be a damage index in concrete structures (Shiotani el al., 1999a;147

T ime (sec)
0

200

400

600

800

1000

1200

1400

0.0

Depth (m)

1.0

2.0

Pile

3.0

PVC pipe
4.0

5.0

a

b

c

d

e

f

g

h

i

j

AE monitoring range:0.0 - 2.0m(a) BGN, (b) Lateral load, (c) Diagonal load, (d) Perpendicular load, (e) Placing machinery on footing, (f) Arm Õsexpanding with up-and-down movement to the west, ( g) ditto to the north, (h) ditto to the east, (i) Removal fromfooting, (j) BGN

Fig. 6 One-dimensional AE sources during loading procedure in zone 1.
NDIS 2421, 2000). AE sources during dynamic loading may contain mechanical noise signals
generated on the footing surface due to the motion of the hydraulic excavator. However, AE
events within the footing are actively generated, and thus damage is reasonably evaluated. The
degree of damage is further verified by results of core samples retrieved.
Figure 7 shows cumulative AE parameters at every 5-cm depth. Results of accumulated AE
events by direct AE monitoring, AE counts by direct AE monitoring, the averaged AE count bydirect AE monitoring, and the averaged AE counts by indirect AE monitoring are given. From
Figs. 7 (a) and (b), it is found that AE activities are intensely observed at the locations of 1.0 m
to 1.5m and around 2.0 m. Intensive AE activities are also observed from 3.0 m to 3.5 m, andaround 5.0 m, although the cumulative number is relatively small. It becomes clearer byexpressing the average of counts as shown in Fig. 7 (c). The averaged counts is given by,accumulated ringdown counts divided by accumulated AE events at every 5-cm depth. Largecounts of AE events are remarkably generated in areas from 3.0 m to 3.5 m, 4.2 m to 5.0 m and6.3 m to 7.0 m except for the footing. According to a result of indirect AE monitoring in Fig. 7(d), the averaged counts are large around 1.8 m, 3.2 m and 4.8 m, although arrival time of AE
waves are out of the allowable time difference between the wave velocity and the pile-AE sensor
arrangement. These AE activities are supposed to be generated from ground deformation, whichis strongly related to coalescence and progress of cracks in the piles due to dynamic loads.
Therefore in direct AE monitoring, the monitoring well should be set in range where grounddeformation due to pile-behavior with external loads could be reached.

148

Fig. 7 Cumulative AE parameters at every 5-cm depth.
149

Velocity (m/sec)
2500
0

3000

3500

4000

4500

5000

Depth (m)

2

4

6

8

Average velocity
3751m/sec
10

Fig. 8 Distribution of wave velocities in the pile.
A wave-velocity sounding is performed by using AE sensors placed in the pile. Elastic waves
are artificially generated by hammering the surface of the footing with a steel hammer. Figure 8
shows the distribution of wave velocities in the pile, which is computed from the arrival times at
the AE sensors. In the figure, the wave velocity between 1.0 m and 1.5 m, for example, is plottedat depth of 1.25 m. In general, it is well known in cast-in-place concrete piles that a zone where
the lower wave velocity is obtained in the piles, the lower strength and density are expected. Thisis because the wave velocity is strongly related to the mechanical properties of concretematerials. Thus, the zones of the pile showing extremely low velocities are thought to be
identical to those of damaged pile. It is found from Fig. 8 that portions of lower wave velocity
range from 1.5 m to 2.0 m, 3.0 m to 4.0 m, 4.5 m to 5.5 m, and 6.0 m to 6.5m. These locations
are judged to be damaged and in remarkable agreement with the damaged locations evaluated by
AE activity.
All estimated locations of damage by several techniques are summarized in Fig. 9. The
following findings are derived.
1) Damages below the pile head are observed by a CCD camera and in core samples.
2) From results of AE sources and wave-velocity sounding, existence of critical cracks
within the footing is expected, although they are not possibly observed by the CCD camera.
From observation of core samples retrieved, it is found that there possibly exist the criticalcracks within the footing.
3) Damage is estimated at 3.0-m depth by AE sources. This location agrees remarkably
with that by the wave-velocity sounding, although the CCD camera is unable to observe it.
4) Damage around 5.0-m depth is estimated by all techniques.
5) Damages ranging from 6.5 m to 7.0 m are observed from AE sources. These locations
approximately correspond to those of the CCD camera and the wave-velocity sounding.
150

1
2

Footing

0

5
6
7
8
9

AE monitoring depth (11.5 m)

4Cast-in-place pile φ: 1.1 m

Depth (m)3(c) by indirect AE(d) by elastic wave velocities(b) by direct AE

10Core samples

11

Borehole camera

12

0

1

2

3

4

Number of cracks observed

Fig. 9 Locations of damage evaluated by several techniques.
4. CONCLUSIONS
It is found that applications of static and dynamic loads by heavy-machinery is practical togenerate AE events from damaged and cracked zones of concrete-piles. Results are concluded asfollow:
1) For the diagnosis of damaged concrete-piles, it is necessary to develop the image
processing system by the CCD camera with more than 0.1 mm resolution.
2) Because AE events within the footing are actively generated due to dynamic load, thedamage of a footing could be estimated. This is confirmed by the results of core samples
retrieved. It is promising to apply the AE technique to actual civil structures that potentiallyexperienced earthquake damage.
3) Identification of damaged locations can be examined from the intensity of AE sources,
along with the average of AE (ringdown) counts.
4) Zones of slow wave velocity remarkably conform to those estimated as damaged by thepresence of AE sources.
As a result, it is apparent that the AE technique is effective to damage evaluation of concretepiles. The result of the AE technique provides useful information, which could not be evaluatedby the other observation. Although the damage of the footing evaluated by AE sources are not151

identical to actual cracks observed by the CCD camera, there exists the zone of slow wave
velocity and the ruptures of the core samples retrieved. Because the footing is firmly connectedwith reinforcing steel bars to the piles, the damaged zones estimated would be reasonably
identified. Thus, the applicability of the proposed AE technique by vibration-inducing heavymachinery is confirmed.
ACKNOWLEDGEMENTS
The present study has been achieved jointly with Public Works Research Institute of Ministryof Land, Infrastructure and Transport, Public Works Research Center, Hanshin Expressway
Public Corporation, and 12 private companies. The authors would like to express their gratitude
to the institutions and all those who were involved in this joint research.
REFERENCES
Y. Ishii, Y. Maekawa, N. Murata, K. Yonemura and H. Mizoguchi (1994), “Large Lateral
Displacement Experimental Studies of Grouped Cast-in-Place Piles,” Proceedings of the 29th
Japan National Conference on Geotechnical Engineering, The Japanese Geotechnical Society,pp. 1603-1606. (in Japanese)
P. Middendorp and F. J. Reiding (1988), “Determination of Discontinuities in Piles by TNO
Integrity Testing and Signal Matching Techniques,” Proceedings of the 3rd International
Conference on the Application of Stress-Wave Theory to Piles, pp. 33-43.
NDIS (Standard of the Japanese Society for Non-Destructive Inspection) 2421 (2000),“Recommended Practice for in Situ Monitoring of Concrete Structures by Acoustic Emission,”
The Japanese Society for Non-Destructive Inspection, 24 pages.
M. Ohtsu (1995), “The History and Development of Acoustic Emission in ConcreteEngineering,” Concrete Library of JSCE No. 25.
T. Shiotani, N. Sakaino, M. Ohtsu and M. Shigeishi (1997), “Damage Diagnosis of Concrete
Piles after Earthquakes by Acoustic Emission,” Proceedings of Forth Far East Conference on
Nondestructive Testing, Korean Soc. Nondest. Testing, pp. 579-588.
T. Shiotani, M. Shigeishi and M. Ohtsu (1999a), “Acoustic Emission Characteristics ofConcrete-Piles,” Construction and Buildings Materials, 13, 73-85.
T. Shiotani and M. Ohtsu (1999b), “Prediction of Slope Failure Based on AE Activity,” Acoustic
Emission: Standards and Technology Update, ASTM STP 1353, ed. S. J. Vahaviolos, pp.156172.
Y. Tsukada and Y. Ichimura (1998), “Application of Integrity Tests for Quality Control of CastIn-Situ Piles,” Journal of Geotechnical Engineering, JSCE, No.603/ III-44, 139-146. (inJapanese)
S. Yuyama, T. Okamoto, M. Shigeishi and M. Ohtsu (1994), “Quantitative Evaluation and
Visualization of Cracking Process in Reinforced Concrete by Moment Tensor Analysis of
Acoustic Emission,” Materials Evaluation, 53, 751-756.

152

ACOUSTIC EMISSION MONITORING OF CLOSELY SPACED EXCAVATIONS IN
AN UNDERGROUND REPOSITORY
THOMAS SPIES and JÜRGEN EISENBLÄTTER*
Federal Institute for Geosciences and Natural Resources (BGR), D-30655 Hanover, Germany* Gesellschaft für Materialprüfung und Geophysik mbH, D-61239 Ober-Mörlen, Germany
ABSTRACT
Acoustic emission (AE) measurements were carried out as a contribution for the evaluationof the hydraulic integrity around and the stability of cavities in salt rock. The locations of AE
events and their magnitudes were determined using a network of 24 sensors. High AE activity
regarding the rates of events was found between the closely spaced rooms. These concentrationsof locations appear in form of shear bands. The exact location of a small drift connecting two
rooms in the level of their roofs could be reconstructed by the AE activity around its contour.
1. INTRODUCTION
In the field of underground disposal of radioactive waste, the evaluation of the hydraulicintegrity of the rock and the stability of the cavities is an important task. Microcracking changes
the microstructure of the rock so that permeability might increase. Macrofracturing can lead tostability problems. Results of laboratory experiments and geomechanical model calculations areused to derive conclusions on integrity and stability. In any case, in-situ measurements have tobe performed to test these results and conclusions especially in complex situations regarding thegeometry of cavities, the geological structure or the material behavior of the rock [1].
Acoustic emission (AE) measurements in the rock are the only method to detect microcracking directly by registration of high frequency seismic or acoustic waves radiated duringcrack generation. The present AE measurements were performed in rock salt, which is afavorable rock for radioactive waste disposal underground as it is capable of creep deformation
without occurrence of fracture in a wide range of the conditions of state. Nevertheless, inpresence of high deviatoric stresses microcracks are generated. Dilatancy of rock salt - theirreversible volume increase caused by microcracking - has been well investigated withlaboratory experiments [2] providing the stress conditions at the onset of dilatancy (dilatancy
boundary). At stress conditions above the dilatancy boundary, the progressive generation of
microcracks results in an increase of permeability and in the occurrence of creep failure as longterm consequences.
AE measurements have been successfully applied to monitor dilatancy around cavities(excavation damaged zone) as well as micro- and macrofracturing at prominent geologicalboundaries in the rock [3], [4]. In this paper results of monitoring in a mine segment with closely
spaced cavities are presented where high stress concentrations occur between the cavities.

J. Acoustic Emission, 19 (2001)

153©2001Acoustic Emission GroupFig. 1. Principle of acoustic emission measurements in the rock.
2. MEASURING PRINCIPLE AND ON-SITE EVALUATION OF AE EVENTS
Figure 1 displays the principle of AE measurements in a vertical cross section. The piezoelectric
sensors are installed in boreholes drilled from the cavities. 24 sensors have been connected to thesystem used. The seismic signals are recorded in the frequency range from 1 to 100 kHz. Using thisrange high sensitivity and location accuracy is achieved compared to microseismic investigations in
the frequency range of 0.01 to 1 kHz. Thus cracks with linear dimensions of some millimeters tocentimeters can be detected.
The locations of AE events are determined on site by inversion of the travel times of P-waves
and S-waves, which are extracted from the signals. This is done in different steps. After preprocessing of the signals by filtering and smoothing operations, in the first step, the P-wave onsets
are automatically picked using high-quality signals only, i.e. signals with high signal-to-noise ratio.
From these P-wave onsets a first estimation of the source coordinates is made using an iterative
least-squares procedure. If the residual error is larger than 0.8 m, the onsets with the largest residues
are stepwise eliminated and a new solution is calculated until the residual error falls below the said
limit.
In order to be able to locate sources outside of the sensor network, we also consider the S-wave
onsets. Therefore, we automatically pick the S-wave onsets in the time interval expected due to theresult of the location using P-wave onsets only. In the second step of source location, we use thefound S-wave onsets together with the remained P-wave onsets. P-wave and S-wave onsets withlarge residues are again eliminated until the residual error falls below 0.8 m. Locations are
considered valid only if at least ten onsets (P-wave or S-wave) remain.
In spite of the complex geometric situation with AE sensors in the vicinity of closely spaced
excavations and resultant masking of direct wave propagation paths, we obtain a location accuracyof about one meter in distance up to 50 m around the sensor network, using the described procedure.
154

A measure of strength analogue to the magnitude in seismology is determined on site as well.
The maximum amplitudes A of the 24 sensor signals and the distances r of the AE source from thesensors are used for this purpose. We define the mean amplitude at a reference distance of 50 mfrom the source as the AE magnitude of the source. This magnitude is specified in dB with the usualreference voltage of 1 µV prior to amplification.
In the case of body wave propagation, we may presume the following relationship between
amplitude A and distance r:
A ∝ [exp(–αr)] /r,
where α is the wave attenuation coefficient covering the effects of intrinsic absorption andscattering. In a semi-logarithmic function of the product A⋅r versus r of all sensors a linear
relationship is obtained as expected and a straight line is fitted to the data. The value of this straightline at the reference distance of 50 m is determined and regarded as the magnitude of the AE event.
An example for the determination of magnitude is displayed in Fig. 2. The amplitudes are specified
in dB, which is a logarithmic measure: 20⋅log (A/A0) with normalization amplitude A0. The distancer as a factor of the product A⋅r is normalized as well using the reference distance. The crosses in Fig.
2 denote the maximum signal amplitudes of the single sensors whereas the rectangles denote theamplitudes after sensor corrections. The corrections for the single sensor are obtained byminimization of the deviations of its signal amplitudes from the mean trend of all sensors when alarge number of AE events are considered. Thus the scatter of the amplitude data is reduced slightly.
In the example of Fig. 2 the magnitude is determined to be 60 dB. It has to be emphasized that thisprocedure results in relative magnitude, which cannot be compared to the values of seismology.

Fig. 2. Determination of magnitude of an AE event defined as the amplitude value at thereference distance of 50 m using the maximum signal amplitudes at the single sensors. In thisexample a magnitude of 60 dB is obtained.
3. DESCRIPTION OF OPERATIONAL SITE
The salt mine of Morsleben located between the towns of Braunschweig and Magdeburg innorthern Germany is used as a repository for low level radioactive waste since 1978. It is a salt
deposit with folded thick strata of rock salt in succession with strata of anhydrite, potash and salt
155

Fig. 3. Plan view of the monitored region with the projection of three levels plotted one uponanother: upper level in dashed lines (Rooms 9n and 9s), intermediate level in solid lines (Drift S),
lower level in dotted lines (Rooms 2 and 3). The AE sensors in the boreholes are denoted by theblack dots. The profile lines of the vertical cross sections S1 and S3 through the cavities areindicated (see Figs. 4 and 5).
clay [5]. The waste is disposed of in large rooms mainly in rock salt. In specific segments of themine, geomechanical investigations are performed to characterize the rock behavior by strain andstress measurements, hydraulic measurements and AE measurements.
Figure 3 shows a mine segment in a plan view (horizontal projection) where cavities withclose spacing were mined in rock salt about 70 years ago. The orientation of the y-axis is south
to north and of the x-axis west to east. In this plan view, three different levels are displayed one
upon another in a rather complex arrangement: Rooms 9n and 9s at the upper level in dashedlines, the drift S at the intermediate level in solid lines and Rooms 2 and 3 at the lower level indotted lines. To clarify this complex figure, the vertical cross-section (vertical projection in Figs.
4 and 5) along the profile line S1 shown in Fig. 3 can be used. In Figs. 4 and 5, the large Room
9n at the upper level, the drift S at the intermediate level and the large Room 2 at the lower levelare displayed using the same signatures as in Fig. 3. The vertical section of profile line S3
156

Fig. 4. Cavities in the vertical cross section S1 and locations of AE with magnitudes M > 0 dB as
dots (data of one month, 17,241 events).
exhibits a similar arrangement for the southern vertical row of cavities in Fig 3. The average
depth level of the cavities displayed is 500 m. In the drift of the intermediate level, fissures in thewalls and floor heave are observed. The opening of the fissures is controlled by strain
measurements, which result in opening rates of 0.2 mm/annum (mm/year). To monitor thissituation, an AE network has been installed. The boreholes in Fig. 3 are 3 to 30 m long. The
locations of the sensors are denoted by the black dots. The deeper boreholes are equipped with
two sensors. All boreholes were drilled from the drift as the large rooms are not accessible withdrilling equipment. So only AE events in the region between the large rooms can be located.
From the geomechanical point of view, this is no disadvantage as the stress concentrations are
expected between the large rooms.
4. RESULTS
As an example for the obtained AE locations, Figs. 4 and 5 show data of a spatial intervalwith a thickness of 20 m in the direction perpendicular to profile S1. The data of this interval is
projected into the vertical cross section S1. Figure 4 contains all events regardless of magnitude
whereas in Fig. 5 only larger events with magnitudes M > 40 dB are included. Some of the
microcracks seem to be located inside the openings. At one hand, this can be explained byvariations of the geometry of the rooms perpendicular to the profile line S1. In this 20-m
horizontal interval the level of the floor of Room 9n is varying as indicated by the two lines in
Figs. 4 and 5. Furthermore Room 2 has a more irregular shape of the roof compared to the157

Fig. 5. Cavities in the vertical cross section S1 and locations of AE with magnitudes M > 40 dB
as dots (data of one month, 3320 events).
information provided by standard mine survey. The crosses in Figs. 4 and 5 denote the locationsof sensors in the horizontal interval.
The locations in Figs. 4 and 5 show dense accumulations in the shape of several narrow
bands between the rooms. Outside of these shear bands no significant AE activity can beobserved. In the bands intensive microcracking takes place indicating a high geomechanical load.
Comparable results are obtained by geomechanical model calculations for such arrangements ofcavities. Deviatoric stresses and deformations are found to be high in the region between the
openings and they also show the band-like spatial distribution. The locations of events with M >0 dB (Fig. 4) and M > 40 dB (Fig. 5) indicate that there are no or only few stronger events close
to the openings. This may be explained by a high degree of damage close to the contours so thatthe storage of elastic energy is limited and the radiated seismic energy of crack generation islower compared to the less damaged rock.
Figure 6 is a plan view, showing AE locations in a 2-m thick vertical interval between theroofs of Rooms 2 and 3 and the floor of Drift S. Here, only the contours of the drift at theintermediate level in solid lines and Rooms 2 and 3 at the lower level in dotted lines are given.

158

Fig. 6. Plan view of intermediate level (Drift S) and lower level (Rooms 2 and 3). The AE
locations of a 2-m thick depth interval between the floor of the intermediate level and the roof ofthe lower level are displayed (data of one year, 72,902 events). Two boreholes were drilled fromthe drift to verify the existence of the bow shaped small drift. Strain measurements at fissures in
the walls are marked by small circles.
The contours have been revised in comparison with the results of standard mine survey (see Fig.
3) to be consistent with the observed spatial distribution of AE. Again, it can be seen that the AE
activity is limited to the rock directly above the roof of the lower rooms. The AE locations also
map smaller loaded elements of the arrangement of cavities indicating the achieved accuracy oflocation of less than 1 m. For instance, a roughly cylindrical cavity with a vertical axis and with159

a diameter of 2 m can be recognized as a ring of AE (see notation in Fig. 6). This cavity is avertical connection from the floor of drift S down to the roof of Room 3. Furthermore specificactivity outside the cavities which were documented in the mine’s maps, in the shape of a bow isobserved connecting the corners of Rooms 2 and 3. From detailed analysis of the locations, it
was concluded that there could be another small drift causing this unexpected activity. As thelower rooms are not accessible to look for such a feature this had to be proofed by two boreholes(see notation RB973 and RB974 in Fig. 6). The boreholes reached the proposed drift in thepredicted locations within the accuracy of better than 1 m.

Fig. 7. Cumulative frequency distribution of magnitudes of AE events from a sub-volume of themonitored region (data of one year, about 145,000 events).
As an example for the frequency distributions of AE magnitudes, Fig. 7 displays data of theregion below Drift S and the roof of Room 2. The locations of the cavities and the AE can befound in Figs. 3 and 4. The cumulative frequency distribution in Fig. 7 has a lower bound value
at about 30 dB, which marks the sensitivity of the AE network for weak events. The largest
recorded magnitudes are around 80 dB. Regarding the slope of the curve and the largest valuesof magnitude, this distribution is similar to results of other AE investigations characterizing
dilatancy around cavities in rock salt [3]. At the operational site no significant change of themagnitude distributions within the monitoring period of more than two years could be observed.

160

5. SUMMARY AND CONCLUSIONS
AE measurements were carried out to monitor microcrack generation between closely spaced
cavities in an underground repository for low level radioactive waste. The locations of AE eventsand their magnitudes were determined. Regarding the rates of events high AE activity is foundbetween the large rooms. The concentrations of microcracks in form of shear bands are inagreement with the results of geomechanical calculations. The exact location of a small drift
connecting two rooms in the level of their roof could be reconstructed by the AE activity aroundits contour.
From the obtained results on the microcrack generation, it can be concluded that permeability
has increased between the large rooms and it will further increase due to continuing
microcracking. The AE activity regarding the rates of AE events, their locations and theirmagnitudes are not varying strongly during the monitoring period of more than two years. So it
is concluded that no change of the geomechanical situation took place within this period and nosudden change will take place in the near future. This is also confirmed by the results ofgeomechanical model calculations. On the other hand, it is concluded that the roofs of Rooms 2
and 3 have to be supported to preserve their stability permanently, e.g. by backfilling of therooms.
REFERENCES
1. N. Diekmann, H. Schnier and T. Spies, “Geotechnical measurements for safety assessments
in different development phases of radioactive waste repositories in salt,“ Proc. of
GeoEng2000, Melbourne, (2000), Paper 0788.
2. U. Hunsche and A. Hampel, “Rock salt – the mechanical properties of the host rock material
for a radioactive waste repository,” Engineering Geology 52, (1999), 271 – 291.
3. T. Spies and J. Eisenblätter, ”Crack detection in salt rock and implications for thegeomechanical situation“, Proc. 3rd Intl. Conference on Mechanics of Jointed and Faulted
Rock, Vienna, ed. Rossmanith, H., (1998), Balkema, Rotterdam, pp. 405 - 410.
4. T. Spies and J. Eisenblätter, ”Micro- and macrofracturing at the boundary of rock salt andanhydrite“, Proc. 9th Intl. Congress on Rock Mechanics (ISRM), Paris, eds. Vouille, G., andBerest, P., (1999), Balkema, Rotterdam, pp. 1071 – 1074.
5. J. Behlau and G. Mingerzahn “Geological and tectonic investigations in the former
Morsleben salt mine (Germany) as basis for the safety assessment of a radioactive waste
repository,“ EUG 10, Strasbourg, J. Conf. Abst. 4: 518, Cambridge, (1999), ISBN 13620886, and Special issue, Engineering Geology, ‘Geosciences and Nuclear Waste Disposal’,in press.

161

ACOUSTIC EMISSION MONITORING OF THE JAS 39 GRIPEN COMBATAIRCRAFTDAN LINDAHL and MARKKU KNUUTTILA
CSM Materialteknik AB, Box 1340, 581 13 Linköping, Sweden
ABSTRACTJAS 39 Gripen is a lightweight multirole combat aircraft partly manufactured from carbonfibre reinforced plastic (CFRP). With an aim to verify the strength of the airframe, an extensive
test programme has been carried out at Saab Aerospace in Linköping, Sweden. This paper
presents some results and experiences from two of these tests, done on two separate, almost
complete airframes, which were monitored by acoustic emission (AE). The AE equipment used
is a 72-channel system.
The first test was the full-scale static structural test. AE was used to monitor the wings and thevertical stabiliser, which are primary manufactured from CFRP. The main purpose of the AE
monitoring was to obtain information on sub-critical damage, which might occur during loadingof the wings and vertical stabiliser. With the AE system we were able to detect and localise
damage at an early stage and to prevent unexpected failure occurring during testing at moderateload levels. Localised areas of damage pointed out by AE were confirmed by traditional nondestructive testing (NDT). The AE monitoring did not cause any false alarms and no additional
damage was detected by other NDT techniques.
The second test was the full-scale fatigue test. Only metal parts of the airframe such as thethree main wing attachment bulkheads, the side panels of the fuselage and some joints weremonitored by AE. The main reason for using AE monitoring this time was to lower the risk thatan undetected fatigue crack would grow critical causing failure or damages hard to repair.
During testing of the originally planned 16,000 flight hours, which equals four life times, nodamage have been detected in the monitored parts neither with AE nor traditional NDT. Cracks
induced by a testing tool in an adjacent area were on the other hand successfully detected by AE,
which clearly illustrates its potential to detect damage.
1. INTRODUCTION
JAS 39 Gripen, the world’s first lightweight multirole combat aircraft, is the result of a joint
development program by the Swedish aircraft industry. The designation JAS is a Swedish
abbreviation for Fighter, Attack and Reconnaissance. Each JAS 39 aircraft will perform all three
missions. As of September 1999, 75 JAS 39 Gripen aircraft are in service within the Swedish
airforce and a total of 204 aircraft have been ordered.
With an aim to verify the strength of the airframe as demanded by the Swedish customer
FMV (Swedish Defence Material Administration), an extensive test programme has been carriedout from simple specimens and minor components to joints and complex structures. The testingprocedures agreed on by Saab and FMV basically follows the specifications issued by the US
Ministry of Defence, MIL-SPEC. As the crowning achievement, two full-scale tests were done
J. Acoustic Emission, 19 (2001)

162© 2001 Acoustic Emission Group

on two separate, almost complete JAS 39 Gripen airframes. The first test, the full-scale static
structural test, was carried out in 1992-93. In Fig. 1, the test rig used to load the airframe isshown. At this overarching test a total of 35 different load cases were tested gradually to higher
load levels, starting at 100 % of limit load. The second test, the full-scale fatigue test, was carriedout in 1993-99. At this test a flight spectra built upon 56 different missions was used repeatedly
to symbolise four lives of 4000 flight hours each.

Fig. 1. The full-scale static structural test.
During these two full-scale tests acoustic emission (AE) has been used to monitor parts of theairframe. This paper presents some results and experiences from the AE monitoring of these two
tests.
The full-scale fatigue testing was extended beyond the 16,000 flight hours already achieved in
1999 so that export versions of the aircraft were also tested. AE was also used during this testing
and the results from the AE monitoring may be reported elsewhere.
2. AE MONITORING AT THE FULL-SCALE STATIC STRUCTURAL TEST
Objective
During the testing AE was used to monitor the wings and vertical stabiliser. The wings are
CFRP skins with aluminium ribs and fittings and U-channel CFRP spars bolted together. The
vertical stabiliser is made of CFRP skin and spars with aluminium honeycomb and aluminium
163fittings. The main purpose of the AE monitoring was to obtain information on sub-criticaldamage, which might occur during loading of the wings and vertical stabiliser. A second
objective was to prevent unexpected failure occurring prematurely during testing at moderateload levels.

Load
[%]

Pressurisation of the fuel tanks etc.

4 minutes

20

Time
Fig. 2. Loading sequence used at the full-scale static structural test. Percent of limit load vs. time.
Equipment
A 72 channel Physical Acoustics Corporation SPARTAN-AT system and R15I integral preamplifier AE sensors were used at the test. The AE sensors, 30 on each wing and 12 on thevertical stabiliser, were attached in a triangular pattern to enable planar location of damage. The
average distance between the sensors was approximately 0.5 m.
Testing Procedures and AE Criteria
The airframe was tested using a loading sequence such as shown in Fig. 2. In order to avoid
rig noise from slack take-up the AE monitoring was not started until 20% of limit load. The load
was then increased in three stages with a 4-min. hold at each test load. Any pressurisation of thefuel tanks, canopy, intakes etc. was carried out as a separate load stage after 4 min. at the
maximum load. The load was then reduced and a second loading to the maximum test load took
place.
For the real-time evaluation, PAL (Physical Acoustics Limited) had worked out a criteria for
issuing a warning. During the testing we paid special attention to the amount of AE hits during a
10 to 20% load increase. Continuing AE activity during the last 2 min. of a 4-min. hold was alsoof particular interest as it indicates creep in the structure. We also looked for local areas withconcentrated AE or high energy events. Finally we checked the Felicity ratio; i.e. the load, atwhich AE recommences during the second loading divided by the previous maximum load. In
CFRP a low Felicity ratio indicates that the material is in poor condition or close to failure. [1]Results from the TestingMore than 100 real-time AE tests were performed successfully. With the AE system we wereable to detect and localise damage at an early stage and to prevent unexpected failure occurring
during testing at moderate load levels. Below four cases are discussed that illustrates the use of
the AE monitoring at the full-scale static structural test.
Case 1. Incipient deformation of honeycomb core:At this particular load case, representing a yaw, large amount of AE was detected at a sensor
on the leading edge of the vertical stabiliser. The target load at the test was 180 % of limit load164

but high AE activity already during loading to 160 % followed by a slow decay in activity during
the hold resulted in a recommendation to stop the test and unload the airframe. This was when
AE was new to the Structural Testing Crew and the people at the Stress Office so they trusted
only readings from strain gauges and load cells. Additionally, the AE monitoring was notallowed to interfere with the testing schedule. So when all readings except the AE monitoring
showed nothing alarming the testing was continued. During the loading to 170 % of limit load aneven higher AE activity was observed and the PAL criteria for issuing a warning was wellexceeded. At this point the structural testing people agreed to make a longer hold at 170 % to see
how the AE activity developed. After approximately five minutes at constant load the AE
activity suddenly increased dramatically and a few minutes later we decided to stop the test andunload the airframe. The AE activity versus time and load during the test is shown in Fig. 3.
At a visual inspection afterwards one could clearly see that the CFRP skin underneath one of
the loading pads was cambered inwards. The high AE activity during the test was mainly caused
by deformation of the honeycomb core underneath the same loading pad. Due to the AE
monitoring the test was stopped in time to avoid major damage. If the testing had continued to
180 % of limit load, the leading edge of the vertical stabiliser had most likely been wrecked.
Now it was possible to replace the loading pad by a larger one and continue according to theoriginal testing schedule.

Fig. 3. AE activity versus time and load. The increase of AE hits at approximately 1400 s marks
the onset of the honeycomb core deformation.
165

Case 2. Oblique loading at one of the loading pads on the vertical stabiliser:
At this particular load case, representing a supersonic roll, concentrated AE was located at aloading pad on the central part of the vertical stabiliser. The AE activity was first observed aftera test to 180 % of limit load and the zone underneath the specific loading pad were examined byultrasonics. The ultrasonic test, however, could not confirm any damage in the area.
Furthermore, the load was not very high at this attachment point and damage of the honeycombcore due to high pressure was not very likely to occur according to people at Stress Office.
During further testing at higher load levels a continuously increasing amount of concentrated
AE was observed in the area. A recommendation to replace the specific loading pad by a larger
one was issued after a 220 % test. The loading pad was, however, not replaced and during testing
to 230 % of limit load the AE activity increased tremendously and did not drop during the hold.
After this test, a second inspection by ultrasonics was carried out and the damage was thenconfirmed. The NDT inspection pointed out that the damage was caused by oblique loading as
showed in Fig. 4. This oblique loading gave rise to local damage of both the CFRP skin and thealuminium honey-comb core as a consequence of high local stresses already at low loads on thehydraulic jack.

Fig. 4. Oblique loading of the vertical stabiliser resulting in high local stresses.
Case 3. AE activity due to an uneven load distribution of the airframe:
In this particular test exceptionally high AE activity was observed at load levels which had
previously been exceeded using the same load case. At that earlier test the airframe was loaded
to 200 % of limit load with no indications of damage. This time the target load was 220 % withholds at 200-, 210- and 220 %. Because this load case had already been tested to 200 % we
expected insignificant AE activity during the first load step. The AE activity, however, was very
high during the load increase and we decided to make a hold at 190 %. During the hold the actualload on each hydraulic jack was checked and compared to the set point adjustments. No
discrepancy was found but the load was decreased to 80 % in order to find the reason for the AEactivity. A visual inspection of the linkage was performed. It turned out that parts of the linkage
for some unknown reason had hooked on to one another causing an uneven load distribution of166

the airframe. The linkage was easily adjusted and the test was later carried out to 220 % without
any problem.
Case 4. Concentrated AE caused by high contact pressure during unloading of the wing:
In this particular case the actual test was already completed and the airframe was being
unloaded when we noticed some strange AE activity from a local area on the left wing. The
unloading was stopped immediately and a visual inspection showed that one of the loading pads
had slipped over its holder as shown in Fig. 5. This resulted in a high local contact pressureduring the unloading hence the AE activity. The loading pad was put back in place and theairframe could be unloaded without substantial damage.
Besides the four cases discussed above significant AE was only observed at very high loads
where minor damage is expected and at the final testing of the vertical stabiliser where theintention was to test until failure. At this final testing the same load case was used as discussed incase 2 and a lot of AE early at the test clearly pointed out the previously accumulated damages.
This time we were not supposed to stop the test but a tremendous increase in AE activity about
40 seconds before the final failure made it obvious that the vertical stabiliser was definitely
going to fail.

Fig. 5. Scenario causing local AE due to high contact pressure during unloading of the wing.
Summary
The AE monitoring has well fulfilled its purpose, i.e. to detect sub-critical damage and toprevent a total failure.
167• The AE monitoring made it possible to detect and locate damage at a very early stage.
• The AE monitoring did not cause any false alarms.
• No damage of interest was missed by AE and later detected by other NDT-technique.
Because of its high ability to detect sub-critical damage in real time, AE have later been usedduring static tests of several structure parts such as the canard and the outboard wing elevon.
These measurements have also been carried out with great success.
3. AE MONITORING AT THE FULL-SCALE FATIGUE TEST
Objective
The main reason for using AE monitoring during the full-scale fatigue testing was to lower
the risk that an undetected fatigue crack would grow critical causing failure or damages hard to
repair. For such complex testing there is always a risk of unexpected damages in areas difficult
to inspect with ordinary NDT, or worse, in areas not inspected at all. Because AE, in opposite tomost traditional NDT technique, is a real time global monitoring technique, the chances to detectsuch unexpected damage is increased and at the same time a better prediction of the occasion of
the initiation is given. The main purpose of the AE monitoring therefor was to detect damage, ifany, early during the testing and guide ordinary NDT inspection to interesting areas. A second
objective has been to build up our knowledge and proficiency aiming to future inspections ofageing aircraft both on the ground and during flights.
Monitored Parts
Only metal parts of the airframe such as the three main wing attachment bulkheads, the sidepanels of the fuselage and some joints were monitored during the testing. The wings and verticalstabiliser, which were monitored during the static testing, have not been an object of AE
monitoring since they are not considered sensitive to fatigue.
Equipment
The 72-channel AE system that was used at the full-scale static structural test has also beenused during the full-scale fatigue testing with some modifications. First, mini-30S sensors inconjunction with 1220A preamps equipped with 200-400 kHz bandpass filters have been used
for most parts instead of the R15I sensors. The mini-30S sensor has a higher resonant frequency,
300 kHz compared to 150 kHz for the R15I, and is more suitable to reject loading machine noise,
which can be a problem when monitoring metal parts. Second, the SPARTAN AT was upgradedso that each channel could be inhibited individually depending on the actual load in the partmonitored by that channel. For this purpose we used a strain gauge at each monitored partconnected to a 9100A Voltage Time Gate and an Inhibit Select Box. Finally, a custom-madesoftware was developed by PAC that allowed linking of the fatigue test control system to the AE
system so that the AE data could be correlated precisely to a specific flight number and flightsegment. The software has a built in feature called Noise Mask which allows real time filtering of
events based on location and signal features. According to PAC this facility was needed to rejectmost of the noise so that we would not get drowned in data.
Evaluating Procedures
In order to reduce the amount of data only those parts of the load spectra having the highest
probability for crack growth have been monitored, i.e. at high loads. In spite of this we havereceived a lot of noise from both the airframe and the test rig. The PAC software was supposed168

to filter most of this noise on-line without a risk of filtering interesting AE caused by real crack
growth. This was the reason why the noise mask facility was developed. PAC, however, could
not find any AE parameter to separate a growing crack from external noise and fretting whentrying to apply the noise mask on AE-data obtained from test samples subjected to controlled
crack growth. We have therefor been forced to collect all AE data at high loads. This large
amount of data has been analysed as follows: First all the AE-data are divided into blocks of 200
flight hours originating from recurrent loading sequences. Out of each of these blocks a reduced
amount of AE-data are picked from a large number of specially selected loading sequences thatare considered as particularly interested from a crack propagation point of view. This reduced
amount of AE-data is then plotted in a number of location graphs (MS EXCEL 3D-Graphs)
which are compared to location graphs originating from corresponding loading sequences fromearlier testing. If increased AE-activity is detected locally, further analysis of this additional AE
is done manually from the signal features as well as time and place of origin. If the AE by thismeans can not be rejected as external noise or fretting, the area of interest is examined bytraditional NDT. In areas where we do not see any distinct signs of additional AE during thetesting, the AE in fact present is considered as the natural level of background noise and nofurther analysis is performed.
Results from the Testing
During the testing to 16,000 flight hours, no damage has been detected in the monitored parts
neither with AE nor traditional NDT. Locally increased AE activity has been observed at someoccasions but often the AE has been rejected as slack take-up or buckling in adjacent structure. A
few times, however, we have had some indications of fretting in rivet- and screw-joints where
crack propagation in combination with fretting could not be excluded. These areas have later
been examined by traditional NDT but no cracks have been found in any of the cases mentioned
above.
Damage has on the other hand been successfully detected by AE at two holes through the wallof the air-channel where a sealing end is attached via two pegs. These holes only exist as a test
adaptation in this test aircraft. The spots are actually outside the monitored structure but near to
the aft bulkhead, which has been monitored by AE, Fig. 6. The sealing end itself is a part of thetesting equipment needed to allow pressurisation of the air-channel (not shown in Fig. 6). At thetwo holes we noticed a substantial increase in AE activity during the testing from 11,000 to
12,000 flight hours. From the signal features we made the judgement that most of this activity
was caused by slack take-up and that a minor part could have been caused by crack propagation
or fretting. Our observations were reported and cracks were later indeed confirmed by a visualinspection at 12,000 flight hours. After the damages have been repaired the AE activity got backto a normal level during the continued testing, Fig. 7. Even though a testing tool in this caseinduced the damages the potential to detect damage with AE is clearly illustrated.
Summary
On the whole, the AE monitoring at the full-scale fatigue test has performed well. Inadequate
hardware/software tools for filtering most of the noise from the test rig and the airframe itself
have made it difficult and time consuming to evaluate the data from the AE monitoring. On theother hand, the only proven damage within or close to the monitored structure has in fact been
detected by AE. This has strengthened our confidence in finding cracks by AE, if any, during thetesting beyond 16,000 flight hours.

169

Fig. 6. Damage at one of the two holes through the wall of the air-channel.

Fig. 7. Increased AE activity due to the damage incurred during the testing from 11,000 to
12,000 flight hours.
4. DISCUSSION
The way we have been evaluating the AE data have a few disadvantages. First, because thestress wave from an active crack must reach at least two sensors in order to be located, thesensors have to be closer together. To cover all appointed areas we therefore needed 102 AE
sensors instead of the 72 sensors planned from the beginning. Because we only had a 72-channel
AE system at our disposal some of the sensors were divided in two groups and each group170

was monitored half the time. This was done by switching between the two groups every 200
flight hours of testing. Second, because the AE data had to be sorted and compared withcorresponding blocks of sequences from previous testing, the AE monitoring could no longer beconsidered as ”real time” in the strict sense of the word. The probability, however, that a crack
would initiate and grow critical in this short period of time was regarded as insignificant. Naturalvariations in the amount of background noise was then of greater concern as this makes it moredifficult to detect real damage at an early stage. Finally, it has been a very time consuming wayof evaluating the AE data even though most of the data processing has been made automatically.
Despite the disadvantages mentioned above we believe this has been the safest and mostefficient way of evaluating the AE data. A great amount of the noise could probably have been
rejected during acquisition but far from all the noise could have been filtered on-line without
running the risk to also filter interesting AE from real damage. Hence, it has been necessary tolimit the detailed analysis to local areas with increased AE activity.
Even though we feel a great confidence in our way of evaluating the AE data from the fullscale fatigue test, no guaranties can be given that all cracks have been detected. High acoustic
attenuation in local areas may have caused growing cracks to remain undetected. Our chances todetect active cracks are also influenced by which of the loading sequences we have chosen tomonitor. Even if a crack has in fact been physically detected we may still have missed it during
the evaluation. In spite of all this, we consider the likelihood that we have missed ”significant”
damage to be small, which was fortified by laboratory tests on smaller but similar structure. Our
judgement is therefore that the AE monitoring have fulfilled its purpose and that the monitored
parts are still at 16,000 flight hours without any damage. The AE monitoring has, however, been
a lot more complicated than first expected.
5. CONCLUSIONS
AE has shown to be a very useful tool for finding cracks and sub-critical damage early during
full-scale structural testing of military aircraft. During the static tests of CFRP structure, AE isoften the only reliable NDT method for detecting significant damage and for preventing
unexpected failure. AE monitoring of metal structure exposed to fatigue demands an experienced
operator to sort out significant information out of AE data mainly consisting of mechanicallyinduced noise. In both cases AE should be considered as complementary to traditional NDT thatlowers the risk of unexpected damages during the testing. Many times, however, the AE
monitoring may justify a reduced number of time-consuming inspections with traditional NDT.
REFERENCES
1 . Nondestructive Testing Handbook, Acoustic Emission Testing, 2nd edition, Volume 5,
ASNT, Columbus, OH, 1987, p. 18.

171

DETECTION AND LOCATION OF CRACKS AND LEAKS IN BURIED PIPELINES
USING ACOUSTIC EMISSION
S. J. VAHAVIOLOS, R. K. MILLER, D. J. WATTS*, V. V. SHEMYAKIN§,
and S. A. STRIZKOV§
Physical Acoustics Corp., Princeton Junction, NJ 08550-5303 USA; * New Jersey Institute ofTechnology, Newark, NJ 07102-1982 USA; § PAC Russia, Moscow, Russia;
ABSTRACT
Studies in both Russia and the United States indicate that buried pipeline testing with acoustic emission is feasible. Whether performing defect detection or leak detection, it has beenshown that the ideal sensor is a resonant device directly coupled to the outer surface of the pipeand operating somewhere between 10 kHz and 40 kHz. Source location for all applications begins with traditional linear location but requires special algorithms in order to achieve the highest
accuracy and to avoid false calls. This is illustrated for the case of near simultaneous emissions
from defects. Also shown are leak detection cases where an adaptable threshold is required tosequence hits for proper event definition. Field studies have proven that testing can be performed
online, if required, with the test procedures tied to operating conditions. Off line static testing is
still shown to be the best testing environment.
1. INTRODUCTION
Studies performed in the United States and Russia demonstrated the use of acoustic emission(AE) for testing buried pipes. The work presented in this paper is a review of these efforts, whichhave led to the development of new hardware, software, procedures and analysis tools for detecting both cracks and leaks in buried pipelines.
The efforts in the United States (1,2) focused on the development of AE for leak detection
and location in liquid-filled, buried pipelines. This effort addressed the major objective of annual testing as defined by the U.S. Environmental Protection Agency (EPA) with the additional
objective of maximum sensor spacing as defined by the U.S. Army and Navy. This paper will
present the highlights of this study in terms of the equipment and sensors developed to improvedetection sensitivity as well as the tuned linear location technique, which was presented as themost attractive method for performing leak location.
The efforts in Russia (3) have focused on the development of the AE testing technique to be
used in pre-service periods to avoid incipient accidents when a pipeline is excavated for visualinspection and re-coating. The major defects, which are detected by AE method in this case, areflaws in butt welds. For this application, linear location is also a very useful technique to detect
the flaws. In order to extract the true data from a great number of recorded AE hits, a special
correlation technique has been developed and will be discussed.
2. DETECTION AND LOCATION OF CRACKS IN BURIED PIPELINES USING AE
The total length of pipelines for gas, oil and product transportation in Russia is several hundred thousand kilometers. Numerous pipelines are buried with many lying in swamps (which are
J. Acoustic Emission, 19 (2001)

172© 2001 Acoustic Emission Groupnearly frozen all year long) and located in northern Siberia. The most familiar method of inservice pipeline inspection is internal diagnostics by means of special inspection "pigs". A lot ofpipelines cannot be inspected with “pigs” because the pipe has a specific diameter or curvature,
which does not allow the pig to pass through the pipe. In addition, many crack-like flaws go undetected (for reasons we will not discuss) after pig inspection. It is well known that AE testinghas been successfully used on vessels and tanks. It can be effectively used on pipelines as well.
AE testing can be conducted during different periods of the pipeline’s life: when the pipeline
is in operation; when the pipeline is being lowered after field fabrication (to define its plasticity
during the lifting); during hydro-test and before major repair; for integrity checking; when segments of the pipe are being lifted from the ground for isolation change. In the last case, the pipe
may fail due to a flaw that was deformed during the lifting.
One of the main requirements specified by our customers was the demand to carry out AE
testing when their pipeline was in operation. This means that the load cannot exceed the working pressure during the AE test. The only way to stimulate flaws was to quickly raise the pressure, hold it and then let it reduce to the operational value. When the operational pressure is ashigh as possible, the sequence of steps is the opposite: the pressure is reduced, held and thenraised back to the operational value. It turned out that the AE testing results were almost thesame for both procedures. Flaw stimulation (for generating AE signals) is accomplished with thehelp of the pressure wave which spreads along the pipeline when the pressure changes as a resultof a valve opening or closing or by changing the operational regime of the pump. The two pipeline load schemes (Load/Hold/Unload or Unload/Hold/Load) are shown in Fig. 1. The correspondent total AE in one channel of AE system is shown on Fig. 2.

Fig. 1 Loading scheme.
The structure defects that can be defined by AE are cracks, lack of penetration, undercuts,
inclusions, edge displacements in butt welding, corrosion, laminations, leaks and ice plugs, but
the most frequently found defects are flaws in butt welds. The most important parameters of the173

Fig. 2 Total AE during loading.
test are the distance between the sensors and the frequency band. Typically we use LDS-1 sensors with a distance between sensors of about 100 m and a frequency band from 10 kHz to 100
kHz. A lot of experimental measurements of attenuation and wave propagation in pipes with different diameters, wall thickness (empty and filled with water) were carried out. Several measurements were carried out on real buried pipelines with oil and gas. A few examples of the results are shown in Fig. 3 to Fig. 8.

Fig. 3 The distance between source and sensor is 2 m. Empty pipe.
The figures illustrate that the wave speed used for event location for gas filled pipe is 3.3
m/ms, and for liquid filled pipes is about 1.5 m/ms (speed in the water). The attenuation graphs
show that in both cases, attenuation of the highest amplitudes modes is about 0.6 dB/m. All themeasurement of the waveforms and amplitude attenuation were made using the Hsu-Nielsen
pencil-lead break (PLB) method. Figure 9 shows the comparison of the waveforms captured by
the same type of sensors for PLB’s and the case where the source of the wave was a real crack.

174

Fig. 4 The distance between source and sensor is 20 m. Empty pipe.
Attenuation of 2 wave modes in empty pipe. Sensor R6.

10 0
90

Amplitude, m

80
70
60
50
40

V=5.4 m/ms

30

V=3.3 m/ms

20
10
0
0

5

10

15

20

25

Distence, mFig. 5 Attenuation curves for two types of the wave in empty pipe.

Fig. 6 The distance between source and sensor is 2 m. Pipe filled with water.

Fig. 7 The distance between source and sensor is 21 m. Pipe filled with water.
175

Attenuation of 4 wave modes in the pipe filled with water. Sensor R6.
80

Amplitude, dB

70
60
V=5.3 m/ms

50

V=3.3 m/ms

40

V=2.2 m/ms
30

V=1.5 m/ms

20
10
0
0

5

10

15

20

25

Distance, mFig. 8 Attenuation curves for several modes of wave in pipe filled with water.

Fig. 9 Signals from real and artificial AE sources in pipe filled with water. The distance between
sensor and source is 9 m.
176

Fig. 10 The principal of correlation location.
When the distance between the sensors is about 100 m, a big problem arises. The period oftime, which is equivalent to this distance (if we use wave velocity 1.2 or 1.5 m/ms) is about 80
ms. If we take into account that the loading time is several minutes and the AE activity is very
high, a mixing of hits on the sensors takes place. It means that the location of real AE sources
can be false. The location in this case is the only way to define the flaw. That is why the problemof mixing the hits becomes very serious.
To solve this problem, special programs (that locate sources by mean of cross correlation of
the hits from neighbor sensors) were developed. Figure 10 illustrates the concept of correlation
location. The program works with files that were made by Malachite AS-12, SPARTAN 2000,
MISTRAS and DiSP systems. The number of properly defined flaws has grown significantly
since we started to use this program.
The procedure of AE testing is as follows. The team moves in a car along the pipeline withexcavated pits in order to test a segment which is about 500 m long. Six sensors are attached andsensitivity calibration is carried out. Attaching more sensors would require longer cable lines
thus requiring more setup time and exposing the test to electromagnetic interference (EMI). After 10 minutes of AE data acquisition at the operational pressure, an order for pressure change is
given through walkie-talkie and data acquisition continues as the pressure changes. The test is
terminated after a second sensitivity calibration is performed. Then the team moves to theneighboring 500-m segment. When the team is well organized and do not encounter any delays,
they can test about 2 km of pipeline per day.

177

3. DETECTION AND LOCATION OF LEAKS IN BURIED PIPELINES USING AE
The interest in evaluating AE was based on previous studies by the U.S. EPA, on sensitivity
requirements found in federal regulations, and the needs of various agencies. Sensitivity is defined by the U.S. EPA as reliable detection of 0.1 gallon/hour (1 gph ≈ 1 ml/s) leaks during annual testing. A sensor spacing objective of 150 m was added for this program based on an understanding of conventional access to buried pipelines such as manholes, hydrants, inspection
pits and valves pits.
Detection of a 0.014 gph leak was demonstrated with AE at the SERDP Test Pipeline Facility(STPF) located at the U.S. EPA in Edison, New Jersey. This demonstration was performed using a gas enhancement technique developed under SERDP funding. This technique mixes aninert gas with the liquid contents of the pipe to generate two phase flow through the leak. This
small leak was accurately located (see Fig. 11) to within 0.3 m of its actual location, using sensors spaced at 7.5 m. Off-the-shelf equipment and sensors were used and a modification to aconventional location technique, tuned linear location, was introduced to improve the location
accuracy. Without the gas enhancement and the improved location technique, detection of theleak would not have been possible.

Fig. 11 Leak location on the EPA Test Loop for a 0.014 gph leak. The high frequency setup wasused with 25 foot sensor spacing on a 2 inch diameter pipe.
Large sensor spacing and detection of multiple leaks were demonstrated on a High Temperature Hot Water (HTHW) systems, operated by the U.S. Army and located in Fort Drum.
Multiple leaks were detected and located (see Fig. 12) during a single test using 271 m sensor
spacing, while testing this system during normal operation. Confirmation of the leak locations in178

2

Events vs. X Position

1

Fig. 12 Location peaks from 874 foot run
containing three expansion loops and problem areas corresponding to the sections ofmelted snow.

RMS vs leak orientation and pressure( 4" standard)
20 psi

RMS,µ

V

RTI

7000
6000

40 psi5000

60 psi

4000
3000
2000
1000
0

upwardside

downward

Fig. 13 Leak rate as a function of sensor spacing for various sizedleaks.
this double-wall piping was made possible by melted snow and comparison with the known history of the HTHW system.
The combined objective of detecting 0.1-gph leaks with 150-m sensor spacing remains as agoal for future studies. On the Navy Test Loop (simulating a 30.5-cm diameter, aviation fuelhydrant systems) at the STPF, we were able to demonstrate leak detection sensitivity and accurate leak location for 1.0-gph leaks using 21-m sensor spacing and 20-gph leaks using 61-m sensor spacing (see Fig. 13) during normal hydrostatic testing.
179

Fig. 14 Results for tuned linear location for 17.4 gph leak using 111-m sensor spacing on the
Army Test Loop.

Gas enhancement techniques were also used on the Navy Test Loop in order to extend thesensor spacing limits. The results showed that the gas enhancement technique cannot take fulladvantage of the water-borne signal and should be restricted to cases where very small leaks are
suspected and the pipe can be accessed every 15.3 m (maximum) for sensor placement.
The Navy Test Loop was also used for the demonstration/validation task at the end of the
program. A test procedure was developed and refined for use on several blind tests. Based on
the success of the validation effort, this procedure has been recommended for use in the proposedtransition program. It was also found that the same procedures could be used on the double-walldesign (of interest to the U.S. Army), as long as we could access the inner carrier pipe with oursensors. Coatings were of prime interest because they are typically part of the outer cathodic
protection. Coal tar, one of the most typical coatings, was found not to attenuate the signal as
anticipated. Coupling of the sensors to the outside surface of the pipe did not disturb this protective layer.
Elbows and tees were investigated for comparison with straight pipe. It was found that they
can create problems leading to false leak locations. It was also determined that these problems
can be overcome using elbow and tee as locations for mounting sensors (see Fig. 14).
Leak orientation and the effects of soil loading were studied in both the laboratory environment and at the STPF facility. The results show that vertically oriented leaks (up or down) produce the highest AE signals and therefore are more likely to be detected and located (see Fig.
15). We also determined that without the flow restriction, caused by soil loading, there would be180

0.040"dia PAC Plug#4
0.010"dia PAC Plug #5

300

Distance, feet

250
200
150
100
50

0.1

1

10

0
100

Leakage rate, gallons/hour

Fig. 15 Leak signal as a function of leak orientation and pressure
for a 100-mm diameter pipe.
no leak signal to detect regardless of leak orientation and for the leak rates of interest. At the
STPF, we determined that 0.9 m or more of backfill is required to produce the maximum AE
signal at the leak.
Through laboratory studies, the work at the STPF and at Fort Drum, we were able to evaluate
the effects of piping diameters in the range of 51 mm up to 410 mm. Using the test procedures
developed in this program, we were able to characterize dispersion effects and determine the appropriate leak location method and characteristic wave propagation velocity.
Four different methods of performing leak location were studied. Of these four, the tuned
linear location technique developed (see Fig. 16), provided a method that worked for all cases
studied and yielded excellent location accuracy. The signal difference method, which was modified for this program, provided an additional means of leak location as well as a means for verifying the results of the tuned linear location technique.
Concerns about sensors with high sensitivity at low frequencies and immunity to environmental noise, were present when this program was initiated. Several sensors were delivered under SERDP funding that addressed these concerns and demonstrated excellent performance forleak detection. PAC’s pipeline leak sensor (PLS-1) was integrated into a 38 mm by 1.8 m longsearch tube for work at the STPF. The performance of this device for leak detection and location
was excellent but its diameter was deemed too big for practical use. This resulted in the development of a low profile sensor, designated as the low profile EPLS-1 (see Fig. 17), with a diameter of 19 mm and a peak frequency response of 15 kHz. The performance of the low profile
EPLS-1 was evaluated during the demonstration/validation phase of this program and determined to be better than the PLS-1.

181

Fig. 16 Tuned linear location results for 100-foot sensor spacing and a 1.2 gph leak at the
40-foot position.

The equipment that was used was available off-the-shelf and required little modification. A
PAC MISTRAS 2001 instrument was utilized in all phases of this study with only one modification. That modification extended its performance from 10 kHz down to 1 kHz allowing theevaluation of all frequency bands of interest with a single instrument. In addition, the tuned linear location technique introduced in this program, was implemented in real time using an “adaptive threshold” method called, Smart Threshold.
4. CONCLUSIONS
Studies in both Russia and the United States indicate that pipeline testing with acoustic emission is feasible. In both countries, whether performing defect detection or leak detection, theideal sensor is a resonant device operating somewhere between 10 kHz and 40 kHz. Source location for both applications required special algorithms in order to achieve the highest accuracy
and to avoid false calls when near simultaneous events are generated.
REFERENCES
1. Miller, R.K., Pollock, A.A., Finkel, P.E., Watts, D.J., Carlyle, J.M., Tafuri, A.N., Yezzi, J.J.,“The Development of Acoustic Emission for Leak Detection and Location in Liquid Buried

182

Pipelines,” Acoustic Emission: Standards and Technology Update, ASTM STP 1353, ed. S.J.
Vahaviolos, American Society for Testing and Materials, 1998, pp. 67-78.
2. Miller, R.K., Pollock, A.A., Watts, D.J., Carlyle, J.M., Tafuri, A.N., Yezzi, J.J., “A reference
standard for the development of acoustic emission pipeline leak detection techniques,” NDT&E
International, 32(1), 1999, pp. 1-8.
3. Samoilov, B.V., “In-line inspection, repairs used on ageing Russian product lines,” Oil & Gas
Journal, International Petroleum News & Technology, Nov. 1998, pp. 42-46.

Fig. 17 The low profile EPLS-1 as it is coupled to the buried pipeline through an access tube.

183

ACOUSTIC EMISSION FROM ACTIVE CORROSION UNDER THE INSULATION
OF A SULPHUR TANKPHILLIP T. COLE and STEPHEN N. GAUTREY
Physical Acoustics Limited, Over, Cambridge, CB4 5QE, UK
ABSTRACTIron, in the presence of water, combines with atmospheric oxygen to form a hydrated ironoxide, commonly called rust. This simple definition describes the one chemical reaction, which
probably costs industry more in losses and maintenance costs than any other. This brief paper
describes an extreme case of corrosion on a sulphur tank and the acoustic emission that resulted
from this.
1. INTRODUCTION
The detection of corrosion activity by acoustic emission (AE) has largely focussed on
phenomena such as stress-corrosion cracking, corrosion fatigue, and other “esoteric” forms ofcorrosion. For some reason, listening to plain old rusting has never been regarded as interesting,possibly because rust is very visible and usually easy to detect. Nevertheless, there are situations
where remote detection can be useful, as in the case of under-insulation corrosion, and this paper
describes one such case in detail. The basic principles and fundamentals of the use of AE,
including metallurgical details, for a range of corrosion monitoring applications is described inreference 1.
2. DETECTION PRINCIPLE
Most acoustic emission applications concern emission resulting from yielding and crack
growth as materials and structures are locally over-stressed. These stress-waves are detected bysurface-mounted piezoelectric sensors, and the resulting electrical signals conditioned and analysedusing an appropriate method.
The present authors consider the source of the AE detected from significant rusting of steel tobe primarily the result of fracture and de-bonding of the scale, while many researchers mentionhydrogen evolution and other mechanisms, which are usually very minor sources and low
amplitude signals. These are unlikely to be detected in the field. As metal corrodes away the
resulting scale has a volume of approximately twelve times that of the original metal, this meansthat as the scale forms it is forced to repeatedly fracture and de-bond. This results in significant
AE, providing a means of detection, and monitoring for the rate of corrosion.
3. SULPHUR TANK TESTThis carbon steel tank was 23-metres circumference and 7-metres high, fully insulated, andoperating at up to 120˚C. The owner was concerned about the condition of the tank wall under the
J. Acoustic Emission, 19 (2001)

191© 2001 Acoustic Emission Groupinsulation, and had decided to conduct an AE test. Small holes were cut in the insulation for sensor
mounting, and the sensors attached using magnetic clamps and high temperature acoustic couplant.
Normal practice with AE testing is to calibrate the sensor sensitivity in-situ, then checkbackground noise levels prior to commencing the test, which normally consists of raising the levelin a tank or the pressure in a pressure vessel. In this case, however, there was such a high level ofAE activity due to active corrosion under the insulation that normal integrity test procedures
became irrelevant. The vessel had “failed” all the evaluation criteria without changing the stress. In
the words of the operator, “on a scale of A to E, (A is “good”, E is “bad”), this vessel rates as a
K”.
After reducing the product level substantially, subsequent inspection involved removal of all
the insulation. The vessel was quickly de-commissioned and scrapped, the rust and insulation
being the main component of the structure.
The AE data detected from the tank over a one hour period is shown in Figs. 1-5. In reference,
the amplitude of a Hsu-Nielsen source, the European standard “field” calibration, which consistsof the fracture of a 2H 0.5-mm pencil lead, is approximately 90dB on the amplitude scale, andconsidered a large signal by AE standards. Figure 1 shows hundreds of signals exceeding even thislevel. Expanding the time scale to look more closely at a 20-s period shows the pattern of activity
more clearly, as shown in Fig. 2.

Fig. 1 AE data detected from the tank over a one-hour period.
192

Analysing the distribution of AE activity as a function of height up the tank wall, having
obtained source locations first by 3D analysis, gave the main sources as just above the bottom
knuckle and about half way up the tank coinciding with an insulation support ring. General AE
activity decreased with height. This result, which showed excellent correlation with corrosion
damage, is probably a function of water-retention under the insulation. See Fig. 3:

Fig. 2 Expanded AE data detected from the tank over a one hour period.

Fig. 3 AE data of corrosion damage.
193

The top plot of Fig. 3 shows signal amplitudes as a function of height, and the largest signals
came from the most active areas. The lower plot shows number of events as a function of height inmetres. The rate of activity was variable over a range of about five to one in energy terms, and two
to one in hit rate. Figure 4 shows these together with amplitude as a function of time:

Fig. 4 AE energy and hit rates with amplitude as a function of time.
In terms of AE signal shape, there was nothing particularly special about the emissions. They
were “classic” acoustic emissions, fast rise-time relative to duration, (10x), counts/duration related
to sensor frequency, except for small numbers “corrupted” by overlapping events, and reflections,
together with multiple transmission paths (through fluid and in structure). The planar location ofsources on the unwrapped wall, with top and bottom reversed for clarity, is shown in Fig. 5:
4. CONCLUSIONS
Active rusting of carbon steel results in acoustic emission. In extreme cases, copious numbersof high amplitude AE signals are observed, which may be located by their relative time arrival.
194

REFERENCE
1. S. Yuyama, Fundamental Aspects of Acoustic Emission Applications to the Problems Caused
by Corrosion, ASTM STP 908 (1986) p. 4.

Fig. 5 The planar location of sources on the unwrapped wall.

195

A NEW SYSTEM FOR MACHINERY DIAGNOSIS USING AE AND VIBRATION
SIGNALS
ATSUSHI KORENAGA, SHIGEO SHIMIZUa, TAKEO YOSHIOKA, HIDEHIROINABAb, HIDEMICHI KOMURAc and KOJI YAMAMOTOd
Mechanical Engineering Laboratory, MITI, Tsukuba, Ibaraki, 305-8564 JAPAN,a
Meiji University, Tama, Kawasaki, Kanagawa, 214-8571 JAPAN,b
Fuji Ceramics Corporation, Fujinomiya, Shizuoka, 418-0111 JAPAN,
c
RION Co., Ltd., Kokubunji, Tokyo, 185-8533 JAPAN,d
NF Corporation, Kouhoku, Yokohama, Kanagawa, 223-8508 JAPANABSTRACT
Diagnostic monitoring of machinery usually involves detecting separately acoustic emission(AE) and vibration. We have developed a new diagnostic system that detects simultaneously AE
and vibration from machinery and provides useful information for diagnosis in order to improve
the reliability of machinery diagnostic technique. This diagnostic system comprised a sensor, ameasurement unit, a computer and analysis software. The sensor, which is made by an advanced
sensor fusion technique and is called a multi-sensor, can detect a signal that contains bothingredients of AE and vibration. The measurement unit and the software decomposed the signal
into the AE and vibration and converted them into diagnostic information.
1. INTRODUCTION
Maintenance of industrial machinery usually involves time-based maintenance rather thancondition-based maintenance. The result is not only the futility in having to replace machineelements that can be used for a longer time, but also the disruption in having to do periodic
checks. Now, condition-based maintenance is being introduced into the industry; for example,
vibration-based condition monitoring is often done for rotating machinery. However, vibration
techniques cannot predict failure even if the detection of failure is possible, and the detection of
failure is also difficult in low-speed rotating machines. AE-based diagnostic monitoring systems
also have been developed for early prediction of failure (1). Such systems separately analyze AE
and vibration, but no system that simultaneously analyzes AE and vibration by using only one
sensor has yet been reported.
In 1998, the Research Group on Multiple Sensing Techniques for Machinery Diagnosis(MSD Research Group) was established by 29 organizations that include a bearing manufacturer,a bearing user, a diagnosis equipment manufacturer, a university, and a public institute. The
purpose of this research group is to establish higher reliability diagnostic systems used tomonitor bearings by analyzing multiple information, such as AE and vibration. The first
prototype of a system designed by the group consists of a multi-sensor, a measurement unit, acomputer and analysis software. The development process involves members of the researchgroup using and evaluating the prototype. After evaluation, the system will be modified and usedin practice in the future.
This paper describes the development of the multi-sensor, outlines the diagnostic system thatsimultaneously analyzes AE and vibration signals, and describes rolling fatigue tests done to
J. Acoustic Emission, 19 (2001)

196© 2001 Acoustic Emission Groupevaluate the performance of the system. The multi-sensor acts as a vibration sensor and AE
sensor. The diagnostic system simultaneously analyzes vibration and AE, and outputs the result
as a digital file in a computer.
2. MULTI-SENSOR
Description
The appearance and sectional view of the multi-sensor are shown in Fig. 1. The detecting
faceplate is made of alumina, same as that of ordinary AE sensors. The detection element of apiezoelectric acceleration sensor is bonded to the detection faceplate. The electrode plate is held
between two piezoelectric ceramics, and the signal from this plate passes through the connector
to the measurement unit (described in the next section). Load mass above the second
piezoelectric ceramic is the damper material. The two piezoelectric ceramics and dampermaterial are ring-shaped, whose outer diameter is 9 mm, inner diameter is 3.6 mm, and thickness
is 2 mm. In our experiments to optimize the multi-sensor (discussed later in this paper), we used
damper material of various thickness (1.6, 2.6, and 5.6 mm). A cover is screwed on over thedamper material at sufficient torque because it is easy to be transformed. The multi-sensor has astainless steel case that is 20 mm in diameter and 30 mm high. Future versions of the multisensor will include a preamplifier in the upper space.

Fig. 1 The multi-sensor.
Principle of Detection
The multi-sensor has both a piezoelectric acceleration sensor and damper material of thewide-band type AE sensor as a load mass. To miniaturize the sensor, high-density tungsten alloy
whose density is 17.1 Mg/m3 is used as the load mass of the acceleration sensor. While thedamper material of the wide-band type AE sensor acts as the load mass of the acceleration
sensor, this sensor also operates as an acceleration sensor because the damper material functions
as a load mass in the low-frequency range. Furthermore, the damper material initially functionsin the high-frequency range. Thus, this sensor operates as a compound sensor for both vibration
and AE.
Evaluation of the Acceleration Sensor
Frequency characteristics were measured by a comparative calibration method in order toevaluate characteristics as an acceleration sensor component of the multi-sensor. Figure 2 shows197

Fig. 2 Block diagram of the calibration apparatus.
a block diagram of the calibration apparatus. The multi-sensor was fixed on a pre-calibrated
reference sensor, which was fixed on the excitation equipment. Vibration from the excitationequipment was applied to both sensors simultaneously, and each output was analyzed by using
fast Fourier transform (FFT), and the sensitivity of the multi-sensor was obtained by comparing
the output of the multi-sensor with that of the reference sensor. The units of the sensitivity are in
m/s2, and the sensitivity at 100 Hz is shown as 0 dB in frequency characteristic data. The
resonant frequency of the reference sensor is about 100 kHz.
Evaluation of the AE Sensor
Characteristics of the AE sensor component of the multi-sensor were evaluated by measuring
the longitudinal wave sensitivity of the multi-sensor with a secondary calibration method of thereciprocity method. The multi-sensor and the reference sensor were installed on opposite
surfaces of a calibration block (0.6 m in diameter, 0.36-m thickness). The sensitivity wascalculated from both the transmitting sensitivity of the reference sensor and the output of themulti-sensor. The units of the sensitivity are m/s, and 0 dB corresponds to 1 V/m/s.
Evaluation Results
Table 1 shows the sensor characteristics and Fig. 3 shows the frequency characteristics forthe different thickness of damper material.
Table 1 The sensor characteristics.

The sensitivity of the acceleration sensor improved and the resonant frequency decreased
when the thickness of the damper material was increased (Table 1). The reason is that resonanceoriginates from the spring-mass system of the damper material and piezoelectric ceramics.
198

Fig. 3 Frequency characteristics of sensors.
However, the resonant frequency for the 2.6-mm-thick damper material was slightly lower thanthat for the 5.6-mm-thick damper material due to the junction condition of the damper material
and piezoelectric ceramic. When a thinner damper material was used, the sensor could be used at
a higher frequency. But the sensor using thinner damper material is difficult to use as an AE
199

sensor, because the sensitivity of the sensor decreases and the resonant frequency shifts into the
AE region. Furthermore, when a thinner damper material was used (1.6 mm or less), the wideband characteristics of the sensor deteriorated because the damping of the AE wave decreased.
Based on these evaluation results, the optimum thickness of the damper material is 1.6 mm
because the sensor can then be used at 50 kHz as an acceleration sensor and at 110~660 kHz asan AE sensor.
3. DIAGNOSTIC SYSTEM
The diagnostic system (Fig. 4) consists of a multi-sensor, a measurement unit, a computer,and analysis software.

Fig. 4 Composition of the new diagnostic system.
Measurement Unit
The measurement unit contains a high-pass filter (20, 50, 100 kHz), low pass filter (100,200, 500 kHz), amplifier (0~60dB), and signal processor. The measurement unit receives asignal from the multi-sensor, and then processes it by dividing it into AE and vibration, both ofwhich are then processed into enveloped waveforms assuming a time constant of 1ms. The
output signals are AE signal envelope, vibration signal envelope and vibration acceleration wave.
These waveforms are transferred to the computer for storage and analysis.
Computer and Analysis Software
The computer receives processed signals from the measurement unit through an A/D
converter and analyzes the signal by using the software we developed. The software runs on a
DOS/V, Pentium 133MHz computer. The analysis software counts AE events, calculates basic
statistics (i.e., Si = ∑xii, i = 1~6), and does the FFT analysis for the AE and vibration signals. The
sampling time is 100µs. The software creates a digital data file and outputs the count of AE
events and the calculation result to a file at 1 minute intervals.
4. EXPERIMENT
As one of the experiments to evaluate the performance of the diagnostic system, rolling200

fatigue test of bearing has been carrying out. The test bearing simulated a thrust ball bearing#51205. To accelerate the fatigue test by increasing the stress in the contact area, three balls wererolled on the outer raceway without a groove shoulder. The hertzian maximum stress induced in
the outer raceway was 5.9GPa. To confirm the performance of the multi-sensor, AE andvibration signals were measured by conventional sensors which are on commercial base and by
the multi-sensor.

Fig. 5 Trend of AE events and basic statistics.
Figure 5 shows the trend of AE events, basic statistics S2 of AE enveloped waveform and S1of vibration acceleration waveform. This test was stopped at 15799 min because of the spallingon the outer raceway. It seemed to generate the spalling at 15794 min, because the S1 ofvibration acceleration increased. Before that, AE Events and S2 of AE enveloped waveform
increased by originating from the propagation of the crack.
5. SUMMARY
We have proposed a predictive system with high reliability of the bearing diagnosis bymonitoring multiple information of AE and vibration, and produced an experimental system. The
17 of the 29 organizations that belong to the MSD Research Group are making thedemonstrational experiments using this system. These experiments will provide insight into theeffectiveness of this system. When successfully implemented, the combination of the multisensor and the monitoring of multiple information will significantly improve the maintenance ofindustrial machinery.
REFERENCE
1. T. Yoshioka and H. Mano, “Relationship between Acoustic Emission Source Position andSpalling Position in Radial Rolling Bearings” Tribology for Energy Conservation, Elsevier
Science B.V., (1998), p. 413.
201

DEVELOPMENT OF ABNORMALITY DETECTION TECHNOLOGY FOR
ELECTRIC GENERATION STEAM TURBINESAKIHIRO SATO, EISAKU NAKASHIMA, MASAMI KOIKE, MORIHIKO MAEDA*,TOSHIKATSU YOSHIARA* and SHIGETO NISHIMOTO*
Research Laboratory, Kyushu Electric Power Co., Minami, Fukuoka 815-8520 Japan*Research Institute of Safety, Non-Destructive Inspection Co., Nishi, Osaka 550-0014 Japan
ABSTRACTAt thermal power plants that have been in service for more than 5,000 hours, steam-oxidized
scales peel off inside the pipes of the boiler super-heater and flow into the steam turbine, causing
erosion damage to the blades and nozzles. In order to avoid such damage, the nozzles are coatedby boron and the particle levels are controlled in the condenser. Japanese electric powercompanies are facing an amendment of the Electric Utility Industry Law, which requires
voluntary maintenance practices for longer intervals between periodic repairs and for lifeextension of the existing plants. Therefore, a new technique is urgently needed to detect erosion
damage at an early stage for effective maintenance. Acoustic emission (AE) has been monitoredcontinuously for two years at a main turbine of Ainoura thermal power station. It wasdemonstrated that damage of the turbine nozzle can be detected by AE at an early stage. This
paper reports on the procedures and evaluation criteria to detect erosion damage in the turbine.
1. INTRODUCTION
The collision of oxide scale in a steam flow causes the erosion of steam turbine blades, which
may affect stable operation of the power generation plant by unexpected shut-down of steam
turbines. It is necessary to detect the progress of damage of the turbine blades due to erosion as
early as possible. However, the monitoring technique for the erosion of turbine blades inoperation has not been established. Acoustic emission (AE) techniques have been regarded as
useful for on-line monitoring of abnormal phenomena of industrial equipment. Thus, we appliedan AE technique to detect the erosion of turbine blades at a thermal power plant.
At first, we obtained the characteristics of simulated AE at the collision of oxide scale withthe steam turbine blades [1]. Furthermore, taking into consideration of AE measurement
conditions, it was concluded that the AE technique enables us to detect erosion of the steam
turbine blade. In this study, we examined AE from the erosion of steam turbine blades at an
actual operating power plant.
2. AE MEASUREMENT DEVICE AND MEASUREMENT CONDITIONSAE Measurement System
We designed a measurement system, which allows stable long-term evaluation of AE signals
originated from a turbine. The block diagram of the system is shown in Fig. 1. The locations of
the AE sensors and cables are shown in Fig. 2. AE sensors (wideband; AE-1544WA), areinstalled near each turbine bearing using glue and magnetic holders. Evaluation of AE from theturbines was conducted, mainly in sensors I, III, and V, while sensors II, IV and VI were pairedwith sensors I, III and V, respectively, and used to evaluate the origin of AE signals.
J. Acoustic Emission, 19 (2001)

202© 2001 Acoustic Emission GroupBearing I sensor
Bearing II sensor
Bearing III sensor
Bearing IV sensor

Bearing V sensor
Bearing VI sensor

Local processor

GP-IB

Local processor

ComputerWave memory

Turbine information

MO diskComputer

MO disk

Data recorder

Signal discriminator(special order)

Off-lineLP

MO diskComputer
WM

Data recorderDT

A/D converter*Number of occurrences, occurrence rate
*Amplitude and distribution*Energy and distribution*Frequency tendency*Correlation with turbine information*Other AE parameters*Detected waveform*Frequency*Envelope waveform*Average amplitude*Turbine information

Waveform analysis software

Fig. 1 Measurement system block diagram.

Bearing I

Bearing II
Bearing III

Bearing IV
Bearing V

Measurement device[JIS vertical standard rack x 2 units]
420(W) x 500(D) x 2,000(H)Turbine information
Power source(100V 30A 60Hz)

Isolation amplifierFig. 2 AE sensor installation location and measurement device installation.

203

Bearing VI

AE signals detected at each sensor were analyzed, extracting waveform parameters using a
local processor, and the data were stored in the MO disk of the computer. The detected
waveforms themselves were first recorded in the wave memory, and transferred to the MO diskof the computer, and recorded. The envelope waveforms were recorded on DAT tape of the data
recorder. Detailed analysis on the AE parameters stored in the MO disk and the detected
waveforms were made using a computer. The envelope waveforms recorded on the DAT tapewere analyzed after A/D conversion.
Measurement Conditions
It has been confirmed through laboratory tests and other means that, at the time of turbine
damage, we observed AE signals with amplitudes of 100 dB or more (in reference to 10 µV at 0dB) and frequency components with the first moment (or the center of frequency spectrum) of
200 kHz or higher. However, the amplitude of an AE signal can be high depending on the type ofa fracture source. Therefore, we used wideband sensors, which can withstand a wide range oftemperature and vibration. Other conditions are shown in Table 1.
Figure 3 shows the wave propagation study being conducted by inputting simulated AE
signals using an impulse-force hammer into the high-pressure (HP) and intermediate-pressure(IP) turbine blades. Signal level was attenuated by 20 dB. This measurement found that the AE
generated inside the turbine can be evaluated by the sensors placed outside the turbine. The
threshold value for each sensor installed at each bearing was set in advance. Signals thatexceeded the respective threshold values were collected as AE signals.
Table 1 Measurement conditions.
Items

Conditions

HPF

200 kHz

LPF

1 MHz

Amplification
factorThresholdvaluesAE sensors

LOGARITHMIC
77 dB – 97 dB
Wide-band typeAE-1544WA

Fig. 3 Wave propagation measurement withsimulated AE signals.
Signal Analysis Parameters
As a method for evaluating turbine damage, it is necessary to examine the change in the
waveforms and frequency components of AE. In conducting the evaluation, the elements below
are particularly important:
1. waveform (detected waveform)
2. waveform (waveform envelope)
3. frequency (analysis of detected waveform frequency)
4. interval of AE generation
5. amplitude
6. number of AE events2043. TEST RESULTS
Outline of the Tests
AE measurement was conducted on a 500-MW supercritical steam power unit during daily
start-ups and shut-downs. Note that the plant had been in service for over 120,000 hours. The
general information on the facilities is shown in Table 2. The operation status of the test unit
was as follows:• Period of measurement: November 29, 1996 to October 9, 1998
• Operation hours during AE monitoring: 6,483 hours• Number of start-ups and shut-downs: 57 (cold start 15)
Prior to testing, the following two points were checked to confirm the absence of abnormalities:
1. Electro-magnetic waves were to have no effect on the electric generation equipment, nor on
existing vibration sensors
2. Conditions of sensor coupling and cables.
Separation of Background Noise
Background noise from the steam in-flow and that from the high-speed revolutions during
operation were separated according to threshold values set for each bearing. The number of AE
hits and the AE amplitude (energy) detected from the collision of particles and damage on thefacilities were continuously monitored. Figure 4 shows the background noise and AE signals
with high amplitudes.
Table 2 Unit 2 Facilities.
Commissioning

October 1976

Accumulated operation hours

126,662 hours(as of September 29, 1996)
500 MW

Fuel

Heavy oil, crude oil

TurbineSpecifications

BoilerOutputModel

Mitsubishi through CE
Combined circulation boiler
Radiative reheat divided
furnaceMax. evaporation

1,720 T/H

Model

Toshiba tandem-compound 3
casing 4 flow
Exhaust air reheat condenser
type

Steam pressure

246 kg/cm2

Steam temperature

Main steam

811K

Reheated
steam

811K

AE Generated from the HP and IP Rotors (Cold Start)
It was found that the location of AE generation could be identified from the difference inpropagation speed (time difference) between AE sensors installed on the 1st and 2nd bearings.
Particles in the steam flow move from the HP first stage nozzle (center in Fig. 5), through the HP
turbine, then, after being reheated in the boiler, head towards the LP turbine from the center partof Fig. 5 through the IP turbine.
205

Fig. 4 Background noise and AE signals

Fig. 5 Locations of AE generation.
206

Figure 5 shows the AE amplitude generated by the collision of solid particles in the HP and
IP rotors (between the 1st and 2nd bearings) during a cold start. AE is concentrated on the HPside due to its high steam energy level. It also shows that the locations of AE amplitude agrees
well with the locations of the rotor blades.
AE Wave during Normal Operation
Figure 6 shows AE generation for one typical month (August 1997) which includes daily
start-ups and shutdowns during the measurement period.
• The detected AE signals show the highest value of 90 dB, immediately after start-up; thefirst moment of 250 kHz or more was found in the range of 80-90 dB.
• There was no problem in turbine operation during this period as shown by the value on theexisting shaft vibration meter. It is 15-25 µm, which is significantly lower than the control
value of 50 µm.

Fig. 6 AE wave during normal operation (August 1997).

Fig. 7 AE wave at signs of turbine abnormality (October 1998)
AE Wave at Signs of Turbine Abnormality
Figure 7 shows AE generation immediately after a cold start (October 1998) and immediately
after a restart. The axis of ordinates represents the amplitude and first moment; the axis of theabscissas represents the passage of time.
• AE signals with an amplitude of 80 dB or more are observed during and after the speed-up
between turbine revolutions of 800-3,600 rpm. Especially upon a load increase from theparallel mode to 250 MW, the highest amplitudes of 120 dB or more are detected. First
moment at this time varied in excess of 300 kHz.
207

• The 120 dB amplitude detected at this time should correspond to the damage which islarger than 30-100 times amplitude of 80-90dB, seen during the normal cold start.
• After this large amplitude is detected, the occurrence of unstable turbine vibration wasobserved at a load range of 400 – 500MW. The values measured by the existing shaft
vibration meter registered 45-60 µm, exceeding the control value of 50 µm.
• At the time of the overhaul (October, 1998), cracks and damage, which are considered to becaused by the collision of particles, were observed on the turbine nozzle blades as shown in
Fig. 8.

Fig. 8 HP first-stage turbine nozzle damaged by solid particles.
4. DISCUSSION
After examination of AE propagation characteristics on actual turbines, a system capable oflong-term stable detection of AE was developed. This technology utilizes the AE sensors
installed near the bearings for the detection of AE signals due to turbine damage.
• It detected AE waves from an actual turbine on a continuous basis for two years.
• Analyses and evaluation of collected data revealed that an examination of the amplitude
variations of the waveform and first moment is effective to detect turbine damage.
• It was confirmed that high amplitude AE signals are observed prior to the onset of unstablevibration caused by turbine nozzle damage. Therefore, it is possible to detect turbine failurein the early stages by monitoring AE.
5. CONCLUSION
AE behavior was studied during the operation of a main steam turbine. The results suggestthat AE is a promising technique for effective diagnosis to detect turbine failure in its earlystages and found to be effective for practical maintenance program. In the future, further study
will be made to develop a reliable AE system to evaluate turbine conditions in real time.
REFERENCE
1. M. Maeda, T. Yoshiara, T. Imanaka, S. Nishimoto, A. Sato, E. Nakashima, and M. Koike,
Progress in AE X, eds. T. Kishi et al., JSNDI, Tokyo (2000), pp. 395-398.
208

ACOUSTIC EMISSION SIGNAL CLASSIFICATION IN CONDITION MONITORING
USING THE KOLMOGOROV-SMIRNOV STATISTIC
L. D. HALL, D. MBA and R.H. BANNISTER
School of Mechanical Engineering, Cranfield University, Bedfordshire, UK
ABSTRACT
Acoustic emission (AE) measurement at the bearings of rotating machinery has become auseful tool for diagnosing incipient fault conditions. In particular, AE can be used to detect
unwanted intermittent or partial rubbing between a rotating central shaft and surrounding
stationary components. This is a particular problem encountered in gas turbines used for power
generation. For successful fault diagnosis, it is important to adopt AE signal analysis techniques
capable of distinguishing between various types of rub mechanisms. It is also useful to develop
techniques for inferring information such as the severity of rubbing or the type of seal material
making contact on the shaft.
It is proposed that modelling the cumulative distribution function of rub-induced AE signals
with respect to appropriate theoretical distributions, and quantifying the goodness of fit with the
Kolmogorov-Smirnov (KS) statistic, offer a suitable signal feature for diagnosis. This paper
demonstrates the successful use of the KS feature for discriminating different classes of shaftseal rubbing. A hierarchical cluster algorithm was employed for grouping extracted KS values.
AE rub signals were simulated with various metallic seals and measured at the journal bearingsof a test rig rotating at approximately 1500 rev/min. Also, the KS classification results weredirectly compared with more established AE feature vectors.
1. INTRODUCTION
Measurement of high frequency acoustic emissions (AE) has become a viable technique inthe condition monitoring of many types of rotating machinery (McFadden and Smith, 1983;
Sato, 1990; Holroyd and Randall, 1992; Mba and Bannister, 1999; Choudhury and Tandon,
2000;). Due to its superior sensitivity over conventional low frequency vibration analysis, AE
measurements can provide an earlier indication of incipient faults such as fictional rubbing. In
addition, time-synchronous measurement from more than one AE transducer often allows thelocation of rubbing to be estimated.
However, in real operational machinery it is often only practical to take AE measurementsfrom non-rotating members, at or on the bearing housing. Consequently, AE signals originatingfrom the rotating shaft will incur significant perturbation across the transmission path to an AE
receiver attached at the bearing housing. This can be related to inhomogenities and scatterers
within the structure, reflections at acoustic boundaries, interference and attenuation effects acrossthe bearing interfaces. Moreover, the AE signal will be further coloured by the characteristicfrequency response of the AE transducer itself. In light of these factors, interpretation of the AE
signals is not trivial and often departs from the classic AE signal model (Mitrakovic and Grabec,
1985; Venkatesan, 1996).

J. Acoustic Emission, 19 (2001)

209© 2001 Acoustic Emission GroupIn recent years various signal processing and pattern recognition techniques have beensuccessfully applied to AE signals for diagnosing the severity and location of defects in various
types of rotating machinery. Notably artificial neural networks (ANN) (Li, 1989) and clustering(Ono and Huang, 1994) have been adopted for AE signal classification. Regardless of theclassification engine employed, it is invariably identification of the key resolving features or
descriptors within the AE signal that is paramount for successful classification. Generally, thesize of the feature vector chosen depends upon the specific application and recognition
requirements. Previous studies (Chan, 1985) employed relatively large feature vectors for the AE
signal classification problem. However, for discriminating between different classes of rub
signatures, it was considered useful to define a feature vector with a minimum number ofparameters (often assessed by a quality index). This can be related to the stability of theclassification result in a clustering algorithm and is justified by ‘the curse of dimensionality’(Bishop, 1999) within an ANN approach.
Typical features extracted from AE signatures in condition monitoring include peak or totalenergy, standard deviation, median, AE counts, RMS voltage and duration. However, these are
all related to absolute energy levels of the measured waveform or rely upon pre-set amplitude
thresholds. As such, the quantities exhibit considerable variability from one bearing
measurement to the next and are thus extremely dependent upon factors such as backgroundnoise, in addition to AE transducer positioning and coupling. Consequently, it is believed thatsuch features are not ideal for AE waveform classification, especially in cases where several
measurement positions are required.
Alternative features more related to the amplitude statistics of the measured AE waveforms
and independent of absolute energy levels have also been considered. Notably, the fourth
statistical moment known as kurtosis and the ratio of peak to RMS voltage known as crest factor
have been applied for condition monitoring in rotating machinery. However, laboratory tests
conducted as part of this research have indicated both of these quantities to be unsuitable forclassification.
Spectral analysis techniques, such as the FFT have found numerous applications in acoustic
signal classification (Chan et al., 1985; Liangand and Dornfeld, 1987). Although some successhas been reported in using certain spectral information within specified bands for AE
classification, it is not generally believed to provide robust classification results. This is
primarily because of the intrinsic broadband nature of measured AE activity and the frequency
characteristics of the measurement system. Alternatively, transformations that do not involve a
total averaging of time information such as wavelet transforms, spectrograms or the WignerVille distributions could yield more suitable AE signal features.
In contrast, it has been shown that modelling an AE signal as an autoregressive stochastic
process, as described by Melton (1982) and later Mba (1999), can provide good AE classification
results. However, the use of AR coefficients as signal features approximating the shape of thesignal has some disadvantages. Primarily, it is always necessary to determine the number of AR
coefficients necessary to adequately represent each AE signal. Although numerous algorithms
exist for determining the model order, it should be noted that the classification results could besensitive to the AR model order. Secondly, it was evident from this study that AE signal
classification using AR model coefficients was severely impaired when the measured AE signals
was modulated by small levels of background acoustic noise.
210

In light of this discussion, it is postulated that a robust AE signal feature based upon
amplitude statistics and independent of absolute energy levels or pre-defined thresholds, and lesseffected by pre-signal processing, can be a useful addition to AE signal classification. This paper
proposes that the standard Kolmogorov-Smirnov statistic can provide such an AE waveformfeature parameter for classifying different types of rubbing in rotating machinery. To
demonstrate this, classification results are presented from rub experiments conducted on ajournal bearing test rig that rotates at 1500 rev/min (rpm). In addition, classification performanceof this technique was tested by modulating the measured AE signals with background noise
taken from bearings of an operational 550 MW turbine unit.
2. ACOUSTIC EMISSION AND RUBBING IN ROTATING MACHINERY
The AE approach to condition monitoring in slow-speed rotating machinery is wellestablished (Choudhury and Tandon, 2000). Reasons for this include the obvious unsuitability of
conventional vibration analysis at very low rev/min, the relatively low levels of backgroundnoise in such plant and the possibility of direct measurement upon the shaft. In contrast,
application of AE to faster rotating machinery (i.e. >1000 rpm) has been less researched. Thiscan be attributed to the proven success of vibration analysis and potentially higher levels of
background noise. However, Sato (1990) reported that AE measurement can provide a valuable
complementary tool for diagnosing rubbing in fast rotating plant such as turbine generators. For
this investigation, signatures were measured at frequency bands greater than 100 kHz,overcoming mechanical background noise whilst increasing the probability of direct rub
detection.
Fundamentally, a light frictional rub between the central shaft and surrounding stationary
components, such as the seals within a turbine, will cause microscopic perturbation and atransient release of broadband strain energy referred to as stress waves (SW). Althoughoriginating from a different mechanical process, this wave motion is in practice extremely
similar to the wave energy that propagates from microscopic cracks within solid structures,
known as acoustic emissions (AE). Hence rubbing, although strictly a pseudo-AE source, isinvariably associated with this term.
A number of reasons can be identified for the onset on light rubbing in a rotating plant. These
include thermal effects, foundation movement, component movement, rotor unbalance ormisalignment. Regardless of the exact relationship between cause and effect, the existence of
rubbing is unwanted as it can often develop into more significant mechanical distress. Two maincategories of light rubbing can be identified. Primarily partial rubbing constitutes distinct orintermittent rub events occurring instantaneously within the period of the shaft rotation.
Secondly continuous rubbing involves more sustained contact between shaft and surrounding
components. Although the mechanisms by which rub phenomena escalate are complex, it issuggested that for machinery of higher rotational speeds, a high concentration of partial rub
events can lead to more sustained rubbing, which in turn can induce more serious vibration via
mechanisms such as thermal bending of the shaft. Consequently, it is considered in this paperthat an AE system capable of diagnosing individual partial rub events might promote the early
diagnosis of impending mechanical distress.
Unlike the AE waveforms measured from continuous rubbing, partial rub events induce timeresolved AE waveforms. Propagation of such discrete rub signals departs from the elementary211theory of plane wavefronts and they are considered to approximate discrete accumulation ofpoint source transients that can propagate via both the metallic volume and surface. The latter ofthese modes is considered to be predominant in propagation along the shaft and is known as
Rayleigh surface waves. This elliptical wave motion is slower than both longitudinal andtransverse modes and penetrates to only a few wavelengths (Pollard, 1977).
To detect rubbing using AE, it is generally advantageous to minimise both the physicaldistance and number of interfaces between the location of rubbing and the AE receiver. This isbecause significant acoustic attenuation will occur, especially at higher frequencies (>100 kHz),due to frequency-dependent absorption, geometric spreading losses and reflection at interfaces.
In some slow rotating cases, it is possible to place transducers directly on the rotating shaft, (Mbaand Bannister, 1999). However in many types of operational rotating machinery, such as inturbine-generators, it is only practical to make measurements remotely at the bearing housing. In
such cases, AE signals produced by rubbing on the shaft will incur considerable attenuation as
the signatures propagate along the shaft surface to the bearing, across an oil film and into thebearing housing. This attenuation issue is considered to be the limiting factor for AE in many
examples of large-scale machinery.
Both commercially available resonant and wideband piezoelectric ceramic transducers areprimarily used to measure the AE response from mechanical rub events on account of their
sensitivity. Although resonant devices possess some advantages in terms of sensitivity and cost,
it is considered that broadband ceramic devices are generally more suited to partial rub signal
classification and are used in this paper. The primary reason for this is the superior fidelity
achieved by broadband transducers allowing the motion of the impinging acoustic wave to be
reproduced as a voltage signal more accurately and less influenced by the transducer response.
Finally, AE techniques have significantly benefited from the exponential improvement in signal
acquisition and computing power.
3. THEORY
Figure 1 shows an example AE signal measured at the bearing housing of the test rig whilst
rotating at ~1500 rpm. This signature was a result of simulating a partial rub on the shaft with asteel seal fixture. Clearly, the AE burst shows some resemblance to the shape of a classic AE
waveform produced by crack propagation or a Hsu-Nielson source, although it is not possible toidentify individual extensional and flexural wave modes as can be often obtained by the Modal
AE approach in thin plates. A notable feature of this rub waveform is the initial onset of acoustic
energy through the succession of initial high energy peaks prior to the exponential decay.
Therefore, it might be inferred that useful signal features might include kurtosis or crest factor as
they describe the extreme values in the amplitude distribution. However, investigation ofnumerous rub signatures has shown considerable inconsistency in these parameters under stable
experimental conditions. Consequently, they are not considered suitable parameters forclassifying rubbing.

212

0.15

Amplitude (/volts)

0.1

0.05

0

−0.05

−0.1

−0.15

0

0.001

0.002

0.003

0.004
0.005
0.006
Time (/seconds)

0.007

0.008

0.009

0.01

Fig. 1 A shaft-seal rub induced AE signal measured at the journal bearing of a test rig.
Alternatively, it is conceived that the Kolmogorov-Smirnov (KS) test statistic (Press et. al.,1993) might provide a more appropriate feature vector for classifying AE rub signals. In essence,
this standard goodness-of-fit test quantifies the difference between the amplitude statistics of themeasured AE signal and a specified theoretical distribution function model. It can be defined inequation (1) as the maximum absolute difference (D) between the empirical cumulative
distribution function SN2(x) and a hypothesised theoretical distribution function SN1(x).

D = max SN 1( x) − SN 2( x)(1)−∞< x <∞As SN1(x) and SN2(x) are non-decreasing and SN2(x) is considered to be constant between thedefined values of amplitude x, as illustrated in Fig. 2, the maximum deviation between the two
curves will occur at one of the defined observation points x1,x2,x3…xn. It should also be notedthat the distribution of D, in the null hypothesis, can be calculated to give the significance of any
observed non-zero value of D. Moreover the statistic is invariant under reparametrisation of x.
For example, the same significance exists under x as for log (x).
The basic algorithm devised for this paper is summarized in Fig. 3 and was implemented
using MATLAB V5 and incorporated functions from the Statistics Toolbox. Primarily, each
digital AE signal was decimated by a factor of 5 to reduce the KS computation time. However, it
should be noted that this decimation in AE information was not expected to degradeclassification results because of the high initial sampling rate used in the analogue-to-digitalconverter (ADC) (4 MHz). Following this stage, theoretical Gaussian distribution models foreach of the AE signals were derived, assuming the null hypothesis that each signal belongs tothis candidate family of distributions. These theoretical cumulative distribution function (CDF)
curves were derived directly by the method of moments as opposed to a maximum likelihood
approach. That is to say, the mean µ and variance σ2 of each input signal was determined and themodel estimate of the CDF evaluated using the two parameter equation for the Gaussian
distribution family given in equation (2).

213

CDF(x)1

Sn2(x)D

Sn1(x)

x
0

x1 x2

x3

x4 …

xn

Fig. 2 D is the greatest distance between an empirical distribution Sn2(x) and the theoretical
distribution model Sn1(x).

Fig. 3 A schematic for the AE classification algorithm.
214

f (x | µ,σ ) =

1
2π σ

∫

− (t − µ )2

x

2σ 2

e

dt

−∞

(2)

In addition to the Gaussian fit, each AE signal was modelled using theoretical Rayleigh andexponential distributions (D’Augostino and Stevens, 1986). as defined in equations (3) and (4),respectively. Therefore, it was possible to evaluate three D values for each of the AE signals,
which in effect constituted three independent measures of amplitude statistics for an AE signal as
illustrated in Fig. 4. These values provided the feature vector input used for classification.

t
e
0 b2

f ( x | b) = ∫x −t 2  2b2 



dt

(3)

where f(x|b) is Rayleigh CDF with parameter b derived from the mean or variance.

4 −π 2mean =b2

1

 π 2or var iance = b  
2
t

x

−
1 −µ
f ( x | µ ) = ∫ e dt ≡ 1 − e µ
0 µ
x

(4)

where f(x|µ) is the exponential CDF with parameter equal to the standard deviation (µ).
CDF(x)3

Sn2(x)
2

1

xFig. 4 Modelling the CDF of an AE signal with respect to three distribution families.
As shown in Fig. 3, rub signal classification involved hierarchical clustering (Chan, 1985;
Murthy, 1987; Bently, 1988; Anastassopoulos and Philippidis, 1995), although it is believed that
other approaches could equally demonstrate the use of the KS descriptor. Clustering describes
the action of partitioning a set of objects into natural groups so that the profile of the objects in215

the same cluster are very similar and the profiles of objects in different clusters are quitedistinct. It is proposed that AE signatures from the same rub category (e.g. partial steel rubbing,partial brass rubbing, etc…) could be clustered in well-defined groups. This involves sequentialstages of:
I Similarity, in which the Euclidean distance between every pair of KS signal vector wasdetermined within the similarity matrix and
II Binary Linkage; in which a series of binary clusters of increasing size are made using theinformation in the similarity matrix, starting with the closest two signal objects, until all
the objects are linked together in a hierarchical tree.
This is represented graphically within this paper using an agglomerative dendrogram plot, inwhich individual signals are labelled on the x-axis whilst the distance between the centroids ofclusters are shown on the y-axis. The classification procedure described was used to assess
whether KS features associated with AE signatures was sufficiently robust to cluster known rubclasses into the correct natural groups. It should be noted that hierarchical clustering can besusceptible to undesirable early combinations involving class outliers and this can lead tospurious results.
4. EXPERIMENTAL
Pentium PC

40/60dB
Pre-Amp

Wide band
Transducer

Anti-Alias
FilterDigitalSignal
Processor

16-bit ADC

TMS320C40

Memory

ISA bus

Post-Signal
and Data
Processing

MonitorFig. 5 A schematic of the AE measurement system.
The AE signal measurement system employed for this study is shown schematically in Fig. 5.
The broadband piezoelectric transducer was a commercially built WD device from PAC with ameasurement bandwidth of 100 kHz-1 MHz. This ceramic sensing element was differentiallyconnected to a 40-dB gain pre-amplifier, in order to effect an immediate improvement in themeasured signal level and to reduce electromagnetic noise through common mode rejection.
Moreover the separate pre-amplifier incorporated a plug-in analogue high-pass filter to suppress
low frequency acoustic noise components and exhibited better temperature performance than
could be achieved using an integral pre-amplifier. The signal output from the pre-amplifier wasconnected (i.e. via BNC/coaxial cable) directly to a commercial data acquisition card thatoccupies one of the ISA slots within a Pentium host PC. This AEDSP card also from PAC
provided up to an 8 MHz sampling rate and incorporates 16-bit precision ADCs giving a
dynamic range of more than 85 dB. Moreover, an extended local memory allowed the sequential
recording of signals containing up to 256,000 samples. This corresponded to the continuous
measurement over more than 0.06 s at a sampling rate of 4 MHz. Prior to the ADC, the card
employs anti-aliasing filters that can be controlled (i.e. the band-pass altered) directly insoftware.

216

Journal
Bearing

JournalBearing

Rub-Simulator

Drive Motor
1

2

3

4

5

≤ 3000rpm

A-A

1219mm1

AQUISITIONSYSTEM

2

Waveguide
Transducer

A-A
Fig. 6 (a) The journal bearing test rig,

Fig. 6(b) The rub simulation mechanism.

Figure 6(a) depicts the rotating test rig. As shown, the shaft is supported by two journalbearings. A wave-guide made contact with the inner bearing housing and the receiving AE
transducer was attached to the other end, ensuring a direct transmission path. Throughout theexperimentation high temperature acoustic couplant was applied to ensure a good acoustic
contact between transducer and the wave-guide and between wave-guide and the bearing
housing. The rub simulation mechanism is positioned between the third and forth of five steel
217

discs shrink-fitted on to the central shaft that rotates at up to ~3000 rpm. Figure 6(b) illustrates
how partial rubs are simulated using this device. Essentially, a non-concentric dummy shaft
fixture is attached to the rotor so as to rub against a supported seal fixture on every shaft rotation.
The seal fixtures employed were initially machined in mild steel or brass. The reaction pressure1
exerted by the seal upon the incident shaft was set by masses applied to the rub fixture as shownin Fig. 6(b). These masses applied a force of approximately 140 N throughout the measurements.
5

RESULTS AND DISCUSSION

Using the aforementioned system, AE signals were recorded from different types of partial
shaft-seal rubbing. Primarily burst signals from the partial rubbing of steel and brass seal fixtures
were taken. Secondly, partial rub signals were taken from three steel seal fixtures exhibiting
different states of wear. This section presents results pertaining to the use of the KS statistic toclassify the defined rub classes by employing hierarchical clustering. By comparing the results
to alternative AE feature extraction methods, the potential of the KS statistic is demonstrated.
a. Seal Material Classification

22mm

16mm

Fig. 7 Steel and brass seal fixtures.
In condition monitoring of rotating machinery, a technique that predicts the types of material
rubbing on the shaft is potentially useful. Specifically, it might infer whether rubbing on the shaft
is significant and should be acted upon or could provide information pertaining to the location of
rubbing. To illustrate the use of the KS statistic in discriminating between rubbing from different
materials, partial rub AE signatures were measured from the test rig using the identically shaped
brass and steel seal fixtures shown in Fig. 7. In each case, fifteen AE signatures are recorded
under the same experimental conditions, i.e., the shaft period and the load applied to the rub
simulation mechanism remained constant. Examples of the AE signals produced from steel andbrass seal rubbing are shown in Fig. 8.
Table 1 lists mean and standard deviation values for extracted AE features for the example
steel and brass signals, see Appendix for full table. The KS values were obtained using theGaussian, Rayleigh and exponential distribution families. Gaussian KS values for each of thesignals multiplied by two were also shown and it should be noted that these values are notsignificantly different to the Gaussian KS values obtained from the original signals. Although1
Calculation of the normal reaction pressure applied by the seal upon the rotating shaft is not trivial. It depends upon:- the incident
forces of the rotating shaft, the weight distribution of the entire upper section of the rub simulator (the moving components), the contact
area of the seal-fixture and the supporting screws and the coefficient of friction between the vertical motion guidance posts and theupper section.

218

many established AE signal features were considered, only the RMS, median, kurtosis andautoregressive (AR) coefficients are represented in Table 1 as they proved more significant rub
indicators.
Table 1 KS values for brass and seal rubbing.
KSGauss

KSRayl

KSExp

KSGauss
×2

RMS

Med

Kurt

AR (1)

AR (2)

AR (3)

AR (4)AR (5)AR (6)/MATERIAL

Steel Mean

0.106

0.616

0.825

0.106

0.008

0.016

10.948-1.999

1.234

0.059

-0.123-0.030-0.070

Steel STD
Brass Mean
Brass STD

0.012

0.008

0.017

0.011

0.003

0.003

1.101

0.014

0.025

0.020

0.007

0.017

0.024

0.053

0.580

0.881

0.054

0.006

0.014

7.849

-2.006

1.186

0.117

-0.100-0.066-0.068

0.008

0.004

0.009

0.008

0.002

0.001

0.617

0.017

0.040

0.028

0.010

0.016

0.031

SIGNALPrimarily, use of the KS values derived from the Gaussian, Rayleigh and exponential models
for all thirty signals are considered as the input vector to the clustering algorithm, i.e., threevalues per vector per signature. The achieved clustering results are depicted using thedendrogram plot in Fig. 9. As shown, the x-axis represents the steel fixture rub signals by labels
1 to 15 and the signals produced using the brass fixture as signals 16 to 30. Moreover, the y-axis
shows the Euclidean distances between the input KS values. It is evident that the two main
clusters do discriminate between the brass and steel rub classes. Moreover, ignoring signal-6 andsignal-12 corresponding to steel rubbing, the separation is relatively pronounced.
It should be noted that the algorithm used to determine KS values involves the generation ofa random reference signal and hence produces slight changes in the result for a given AE signal
over repeated calculation. However, this variability was not seen to prevent adequate separation
between brass and steel signals over many independent experiments. Moreover, it was apparent
that using a signal feature vector that constitutes repeated KS evaluations for a specified
reference distribution could improve the clustering results. Figure 10 shows the clustering
Steel seal fixture

0.15

Amplitude (/volts)

0.1

0.05

0

−0.05

−0.1

−0.15

0.5

1

1.5

2

2.5
3
Time (/sample no)

3.5

4

4.5

5
4

x 10

Fig. 8 (a) Examples of the AE signals produced using steel seal fixture.

219

Brass seal fixture

0.15

0.1

Amplitude (/volts)

0.05

0

−0.05

−0.1

−0.15

0.5

1

1.5

2

2.5
3
Time (/sample no)

3.5

4

4.5

5
4

x 10

Fig. 8 (b) Examples of the AE signals produced using brass seal fixture.

Fig. 9 Gaussian KS discrimination, single-linkage (nearest neighbour).
achieved using a feature vector consisting of four independent KS evaluations assuming theGaussian model. In this plot, the Ward’s method of clustering is employed in preference to thesingle-linkage (i.e., nearest neighbor). This is a procedure in which the similarity used to join
two clusters is calculated as the sum of squares between the two clusters summed over all
variables. It is effective in increasing the ‘within cluster’ homogeneity and prevents chaining.
220

Fig. 10 Gaussian KS discrimination, Ward’s method.
In contrast to the robust KS results, clustering of the thirty partial rub signals using
established AE signal features such as peak energy, total energy, RMS, median, standard
deviation, counts and peaks within an FFT revealed poor separation between brass and steel
signals. The most appropriate of such features was the signal kurtosis. As indicated in Table 1,
this energy independent measure of signal spikiness generally yielded larger values for steel
rubbing signals than from brass rubbing. However, repeated tests revealed a number of spurious
clustering results. This was attributed to the large spread of kurtosis values obtained across all of
the AE signals measured.
Although the KS technique outperformed these established signal features dependent uponenergy or extreme values within the amplitude distribution, it should be noted that application of
autoregressive coefficients as input AE signal features also produced effective discrimination
between brass and steel rubbing. This technique (Melton, 1982) involves modelling each AE
signal as an Nth order linear stochastic process and using the subsequent AR coefficients within
the clustering algorithm to represent the signal ‘fine-scale’ shape to within a certain accepted
prediction error. The optimum order (N) for the AR model is often the lowest order at which theminimum mean-squared error becomes stationary and can be determined via the application of
the final prediction error (FPE) and Akaike’s information criterion (AIC) (Kay and Maple, 1981;
Makhoul, 1975). However, it was considered that the success of subsequent AE signal clustering
was extremely sensitive to the number of AR coefficients employed, as the optimum order varied
for different classes of rub signal. In addition to this drawback, it became clear that the AR
approach to partial rub classification breaks down when the measured rub signals are modulated
by background acoustic noise.
221To demonstrate the effect that background noise can have upon rub classification, acoustic
noise from the bearings of an operational 500-MW turbine unit was superimposed upon the brassand steel rub signals. In this test, the same levels of turbine noise were added to the thirty rub
signals and the same band-pass filter was applied. Figure 11 shows hierarchical clustering results
achieved from fifteen noisy brass signals and fifteen noisy steel signals using a 20th order AR
coefficients as the signal descriptors. This dendrogram plot clearly demonstrates that the AR
approach becomes inappropriate for brass and steel classification when even low levels of
background noise are present. It is interesting to note that this lack of class resolution was also
observed when attempts were made to remove background noise using an appropriately designed
band-pass filter. In contrast, it became clear that KS-based rub classification is less affected by
the addition of real noise, see Fig. 12. As shown, AE signal classification using KS descriptors ismore suitable than that based upon the more established AR technique.

Fig. 11 AR(20) discrimination with low levels of real noise measured from a 500MW turbine.
b. Seal Wear Discrimination
It is proposed that some diagnosis of the ‘wear state’ of rubbing seals might be inferred fromthe measured AE data. Three mild steel V-groove seals at different stages of wear were prepared.
These are shown in Fig. 13 and are referred to as unworn, mid-wear and worn.
Applying each of these fixtures to the partial rub simulator in turn, AE signatures wererecorded at the bearings. Figure 14 shows examples of the signatures obtained for the three wear222

Fig. 12 KS discrimination with low levels of real noise measured from a 500MW turbine.

22
mm

16 mm

Fig. 13 The AE signatures from steel V-Groove.
states at the bearing-1. By inspection, it is obvious that the unworn signal appears to be more
complex2 than the other two wear state signals. Specifically, it is clear that two smaller bursts
appear in the tail of the unworn AE waveform. However, visually identifying the difference
between the mid-wear and worn state is more difficult, especially as the peak amplitudes of all
three are very similar.

2This difference between the AE produced from ‘sharp’ surfaces rubbing and more planar contact is related
to known asperity contact effects and is generally observed in low-speed rotating machines.
223

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1

0.4
0.3

0
-0.1

0.1
Amplitude (/v)

Amplitude (/v)

Amplitude (/v)

0.2

0
-0.1

0
-0.1

-0.2

-0.2

-0.2

-0.3

-0.3

-0.3

-0.4

0

1

2

3

4
5
6
Time (/sample Number)

7

8

9

10
4

x 10

-0.4
0

1

2

3

4
5
6
7
Time (/sample Number)

8

9

10
4

x 10

-0.4

0

1

2

3

4
5
6
7
Time (/sample Number)

8

9

10
x 10

4

(a)
(b)
(c)
Fig. 14 Example AE signals from a V-Grooved steel seals exhibiting. (a) No wear, (b) Mid-wear,(c) Extreme wear.
To illustrate an automatic classification approach for these wear states, fifteen example
signals were taken; five from each of the wear states. Initially, the AR model approach toclustering was applied. As in the previous section, inspection of the residual error plot, using the
Akaike’s Information Criterion, suggests that an AR order of 8 was sufficient to represent thesignature shape. To test the applicability of the AR approach, a plot of the cluster achieved bymeasuring Euclidean distances between centroid values of AR coefficients associated with eachsignature is shown in Fig. 15. No clear pattern in the cluster was observed and it was deduced
that the AR classification approach is not suitable for determination of the wear state of therubbing seal.
To investigate using the KS statistic for distinguishing between the unworn, mid-wear andworn partial rubbing states, the same fifteen signals were used. The Gaussian distribution wasused as the reference within the KS algorithm and the cluster result can be seen in Fig. 16. It isclear from this that reasonably good wear classification has been achieved. Primarily, the two
major clusters discriminate between the unworn AE signals and the other two types. This can bejustified in that the second and third smaller bursts within the unworn signals cause significant
deviation from Gaussian amplitude statistics. Secondly, it is encouraging to observe that theGaussian KS statistic is sensitive enough to separate mid-wear and very worn rubbing signals
within the broader cluster group.
6. CONCLUSIONSThis paper introduces the use of the Kolmogorov-Smirnov (KS) test statistic as a usefulsignal descriptor in AE analysis. The KS results presented via hierarchical dendrograms indicate
the potential of this statistic in classification of partial rubbing on shafts of fast rotatingmachinery. Moreover, the success of KS classification has been shown when the measured AE
rub signals were modulated by background noise from a real operational 500-MW turbine.
It should be noted that the KS statistic tends to be more sensitive around the medium value(i.e., where P(x) = 0.5 within the CDF) and less sensitive at the extreme ends of the distribution,
where P(x) approach either 0 or 1. This is because the KS value does not have a probability
distribution independent of amplitude (x). Therefore, it is postulated that while the KS statistic is224

Fig. 15 Clustering achieved using 8 AR coefficients for unworn (signals 1-5), mid-wear (signals6-10) and very worn (signals 11-15) shaft-seal rubbings.
good at finding shifts in the probability distribution, especially changes in medium value, it is notalways so good at finding spreads, which more strongly affect the tails of the probability
distribution. Consequently, it is concluded that the KS statistic is robust and not too sensitive tovariability in amplitude outliers as is the case with the kurtosis statistic. Thus the KS statistic
provides a suitably stable feature for broadband AE signals that characterises the entire
amplitude statistics.
Implementation of this KS method for AE signal classification requires choosing theappropriate theoretical distribution families for reference. For the partial rub induced AE signals
measured in this paper, the Gaussian distribution appeared to provide the closest fit, as indicatedby the KS values given in Table 1. It is generally considered that the classification performanceis increased by using reference distributions that are not rejected within the null hypothesis byexhibiting KS values that exceed the 5% significance level (Press, 1993). However, it can not be
concluded from the results presented that the Gaussian distribution is the best amplitude statistics
model for partial rub induced AE signals and other possible candidates include the log-normal(Lopez Pumarega et al., 1999) and t-distributions. Regardless of the distributions used for a
specific classification task, it should be noted that determining the best set of reference
distributions is the central consideration for the KS technique.
Interpretation of individual AE signal waveforms from partial rubbing is considered to bedifficult. However, it is suggested that the general suitability of the Gaussian distribution and useof the KS statistic might infer more detailed information related to physical rub mechanisms. It is225

Fig. 16 Clustering achieved using the KS-statistic for unworn (signals 1-5), mid-wear (signals 610) and very worn (signals 11-15) shaft-seal rubbings.
postulated that the Central Limit Theorem, as used for modelling ambient noise in underwater
acoustics, might be adopted in AE condition monitoring. This could state that a rub induced AE
waveform, which is formed by the addition of a number of temporally close but independent
asperity contacts, has a probability density function, which approaches the Gaussian distribution,
as the number of contributors or AE sources increases. Consequently, it is proposed that absoluteKS values, assuming a Gaussian fit, might directly classify quantities such as rub severity.
The KS statistic essentially quantifies the difference between any two distributions.
Therefore, in addition to diagnosing rubbing in fast rotating machinery, it is believed that thetechnique might find application in other condition monitoring applications. For example, the KS
statistic could be useful for diagnosing tool wear or for detecting and classifying incipient faults
in low-speed rolling-element bearings. In these cases, the KS value might indicate fault severityby quantifying the deviation of a measured AE signal from a known healthy-state signature.
Moreover, alternative goodness of fit statistics could be applied if required for specific condition
monitoring task. For instance, one way of increasing the statistic resolution out on the tails is toreplace the KS statistic with a stabilised or weighted statistic such as the Anderson-Darling
statistic D*:
Sn1( x) − Sn 2( x)D* = max(5)−∞ < x <∞P ( x)[1 − P ( x)]
Finally, it should be noted that these classification results were achieved using a high-fidelity
wideband AE transducer. However, it is realised that the KS technique could break down using
resonant devices due to low fidelity.
226

REFERENCES
A. Anastassopoulos and T.P. Philippidis (1995), “Clustering Methodology for the Evaluation of
Acoustic Emission from Composites”, Journal of Acoustic Emission, 13(1-2), 11-22.
P.G. Bentley and M. J. Beesley (1988), “Acoustic emission measurement on PWR weld material
with inserted defects using advanced instrumentation”, Journal of Acoustic Emission, 7(2), 59.
C.M. Bishop (1999), Neural Networks for Pattern Recognition, Oxford University Press. ISBN:0198538642.
Roger W.Y. Chan, D. Robert Hay, Victor Caron, Michel Hone and R. Douglas Sharp (1985),“Classification of Acoustic Emission Signals Generated During Welding”, Journal of Acoustic
Emission, 4(4) 115.
A. Choudhury and N. Tandon (2000), “Application of Acoustic Emission techniques for thedetection of defects in rolling element bearings”, Tribology International, 33(1), 39-45.
R.B. D’Augostino and M.A. Stevens, Eds. (1986). Goodness-of-Fit Techniques, Marcel Dekker;
ISBN: 0824774876
T. Holroyd and N. Randall (1992), “The Use of Acoustic Emission for Machine Condition
Monitoring”, British Journal of Non-Destructive Testing, 35(2), 75.
S.M. Kay, and S.L. Marple, Jr. (1981), “Spectrum analysis - A modern perspective”,
Proceedings of the IEEE, 69(11), 1380-1419.
Li (1989), “On-line bearing localized defect detection by pattern recognition analysis”, ASME
Trans., J. Eng. Ind, 111, 331.
S. Liang and D. Dornfeld (1987), ”Punch stretching Process Monitoring using Acoustic
Emission Signal Analysis-Part-1/2”, Journal of Acoustic Emission, 6(1), 29.
M.I. Lopez Pumarega, R. Piotrkowski and J.E. Ruzzante (1999),” Discussion of the log-normal
distribution of amplitude in Acoustic emission signals”, Journal of Acoustic Emission, 17(1-2),
61-67.
J. Makhoul, (1975), “Linear prediction: A tutorial review”, Proc. of the IEEE, 63(4), 561-580.
D. Mba, and R. H. Bannister (1999), “Condition monitoring of low-speed rotating machinery
using stress waves: Part 1 and Part 2”, Proc. Inst. Mech. Engrs., 213, Part E, 153-185.
R. McBride, J.S. Barton, W.K.D. Borthwick and J.D.C. Jones (1993), “Fibre optic interferometry
for acoustic emission sensing in machine tool wear monitoring”, Meas. Sci. Technol., 4, 1122–
1128.
P.D. McFadden and J.D. Smith, (1983), “Acoustic Emission transducers for vibration monitoringof bearings at low speeds’’, Report CUED/C-Mech. TR29, University of Cambridge, Cambridge.
R. Melton (1982), “Classification of NDE waveforms with autoregressive models”, Journal of
Acoustic Emission, 1(4), 266.
C.R.L. Murthy, B. Dattaguru and A.K. Rao (1987), “Application of pattern recognition concepts
to acoustic emission signal analysis”, Journal of Acoustic Emission, 6(1), 19.
K. Ono and Q. Huang (1994), “Pattern recognition analysis of acoustic emission signals”,
Progress in Acoustic Emission, VII, Japanese Society for NDI, Tokyo, pp. 69-78
H.F. Pollard (1977), Sound Waves in Solids - Applied Physics Series, Pion Limited. ASIN:
0850860539
W.H. Press, S.A. Teulosky, B.P. Vetterling and W.T. Flannery (1993), Numerical Recipes in C,
ISBN: 0521431085.
I. Sato (1990), “Rotating machinery diagnosis with acoustic emission techniques”, Electrical
Engr. Japan, 110(2), 115-127
W. Venkatesan (1996), “Detection and Modeling of Acoustic Emissions for Fault Diagnostics”,
IEEE, ISBN 0-8186-7576-4/96.
227

APPENDIXSIGNAL

KS-

KS-

KS-

KS-

Gauss

Rayl

Exp

Gauss

RMS

Med

Kurt

AR

AR

AR

AR

AR

AR

AR

AR

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

×2
Steel-1

0.108

0.618

0.839

0.103

0.007

0.013

10.176-2.002

1.251

0.041

-0.126-0.011-0.094

0.081

0.052

Steel-2

0.114

0.622

0.846

0.113

0.005

0.011

10.690-1.989

1.219

0.066

-0.124-0.010-0.104

0.077

0.062

Steel-3

0.103

0.609

0.832

0.100

0.006

0.015

11.159-2.012

1.244

0.070

-0.132-0.040-0.054

0.043

0.067

Steel-4

0.097

0.613

0.833

0.108

0.012

0.016

10.644-2.007

1.259

0.022

-0.106-0.005-0.104

0.064

0.068

Steel-5

0.130

0.629

0.828

0.124

0.010

0.012

12.443-2.001

1.236

0.058

-0.120-0.047-0.047

0.047

0.062

Steel-6

0.083

0.605

0.849

0.089

0.009

0.014

9.3301-1.973

1.180

0.099

-0.130-0.039-0.055

0.048

0.065

Steel-7

0.121

0.621

0.834

0.126

0.004

0.012

13.603-2.012

1.248

0.065

-0.126-0.058-0.035

0.044

0.061

Steel-8

0.098

0.615

0.834

0.097

0.008

0.016

10.462-2.010

1.263

0.032

-0.117-0.027-0.068

0.052

0.065

Steel-9

0.103

0.614

0.836

0.104

0.006

0.014

11.377-2.020

1.271

0.041

-0.119-0.048-0.042

0.039

0.066

Steel-10

0.107

0.617

0.819

0.107

0.010

0.017

11.894-2.003

1.233

0.068

-0.121-0.035-0.068

0.051

0.067

Steel-11

0.104

0.618

0.821

0.102

0.010

0.014

10.951-2.005

1.245

0.051

-0.130-0.011-0.074

0.040

0.073

Steel-12

0.088

0.598

0.806

0.086

0.005

0.024

9.953

-1.975

1.198

0.066

-0.114-0.016-0.107

0.092

0.052

Steel-13

0.110

0.624

0.796

0.111

0.006

0.018

10.109-1.987

1.208

0.079

-0.119-0.048-0.053

0.052

0.062

Steel-14

0.107

0.616

0.794

0.107

0.006

0.019

9.958

-1.998

1.221

0.080

-0.134-0.039-0.057

0.056

0.058

Steel-15

0.119

0.621

0.805

0.113

0.012

0.018

11.467-1.995

1.237

0.049

-0.121-0.016-0.091

0.076

0.056

Brass-16

0.048

0.579

0.893

0.047

0.007

0.014

7.536

-2.003

1.163

0.141

-0.101-0.071-0.079

0.055

0.065

Brass-17

0.052

0.583

0.877

0.059

0.006

0.013

8.056

-2.015

1.209

0.097

-0.098-0.070-0.059

0.045

0.063

Brass-18

0.063

0.582

0.872

0.066

0.006

0.015

8.793

-1.984

1.143

0.126

-0.084-0.029-0.143

0.089

0.058

Brass-19

0.043

0.576

0.893

0.042

0.006

0.014

7.090

-2.028

1.236

0.082

-0.095-0.087-0.023

0.009

0.078

Brass-20

0.054

0.570

0.882

0.059

0.006

0.014

7.494

-1.979

1.118

0.163

-0.090-0.068-0.089

0.049

0.073

Brass-21

0.042

0.576

0.896

0.042

0.001

0.014

6.662

-2.010

1.192

0.121

-0.109-0.069-0.060

0.042

0.067

Brass-22

0.053

0.578

0.873

0.048

0.007

0.016

7.880

-1.991

1.155

0.142

-0.110-0.062-0.070

0.045

0.067

Brass-23

0.063

0.583

0.867

0.062

0.007

0.014

8.362

-1.998

1.170

0.133

-0.106-0.079-0.041

0.022

0.075

Brass-24

0.059

0.577

0.873

0.055

0.007

0.015

8.032

-2.017

1.218

0.079

-0.080-0.075-0.058

0.036

0.069

Brass-25

0.053

0.581

0.874

0.050

0.006

0.015

7.236

-2.041

1.264

0.073

-0.118-0.056-0.058

0.045

0.060

Brass-26

0.061

0.580

0.882

0.056

0.007

0.014

8.643

-2.015

1.206

0.103

-0.101-0.066-0.065

0.046

0.063

Brass-27

0.064

0.587

0.882

0.065

0.009

0.013

8.608

-1.996

1.162

0.141

-0.103-0.088-0.036

0.022

0.075

Brass-28

0.042

0.578

0.886

0.044

0.005

0.014

7.448

-2.017

1.217

0.095

-0.109-0.051-0.074

0.049

0.064

Brass-29

0.058

0.584

0.872

0.064

0.005

0.014

8.196

-2.006

1.188

0.110

-0.091-0.080-0.046

0.024

0.075

Brass-30

0.044

0.580

0.888

0.051

0.006

0.014

7.700

-1.986

1.143

0.143

-0.099-0.043-0.120

0.078

0.061

228

CHARACTERIZATION BY AE TECHNIQUE OF EMISSIVE PHENOMENA DURING
STRESS CORROSION CRACKING OF STAINLESS STEELS
A. PROUST*, H. MAZILLE, P. FLEISCHMANN, and R. ROTHEA+
Institut National des Sciences Appliquées, 69621 Villeurbanne, France*Now with Euro Physical Acoustic, 94373 Sucy en Brie, France+ Now with Rhodia, 69200 Vénissieux, France
ABSTRACTThe aim of this study is to investigate the propagation of stress corrosion cracking (SCC)
with acoustic emission (AE) technique in order to better understand the cracking mechanism anddevelop a technique for monitoring SCC on industrial structures in service. The
material/environment system is austenitic, ferritic and duplex stainless steels in a 44-wt.%
aqueous MgCl2 solution at 145°C. Slow strain-rate tensile tests are used to obtain well-controlled
SCC conditions. Tensile tests are performed in a scanning electron microscope to correlate local
deformation mechanisms with recorded AE activity. All possible AE sources are identified andcharacterized in order to discriminate the signals caused by SCC, allowing a good correlation
between AE signals and SCC damages. The optimization of the experimental setup and of a
relevant frequency parameter confirms a discontinuous mechanism of cracking including a highvelocity cleavage step.
1. INTRODUCTION
Stress corrosion cracking (SCC) is an important damaging phenomenon in the oil, chemical,
aerospace and nuclear industry. Unpredicted failures occur in service, which have economic,
safety, health and environmental consequences. Austenitic stainless steel in chloride environmentis one of the major alloy/environment system susceptible to SCC. In the last two decades, duplex
and ferritic stainless steels have also been developed for service in chloride media, but they are
not fully immune to SCC either. Therefore, it is worthwhile to study and compare the different
grades of stainless steels.
Acoustic emission (AE) is the only non-destructive evaluation technique that allows thedetection and localization of propagating cracks. Its great sensitivity is sufficient to detect stresscorrosion cracks [1-7]. It can evolve into a non-intrusive method for in situ continuous
monitoring of SCC process on critical industrial structures. AE technique can also shed light into
the mechanisms of SCC.
The aim of this study is to assess the possibility of detecting and characterizing AE signals
produced by SCC on three different types of stainless steels in hot chloride environment duringslow strain-rate tests.
2. EXPERIMENTAL
Experiments were performed on austenitic, ferritic and duplex stainless steels, chemicalcompositions of which are shown in Table 1. Two different experimental setups are used [8].
J. Acoustic Emission, 19 (2001)

229© 2001 Acoustic Emission Groupa) Tensile tests either in hot chloride environment (SCC) or in inert environment (hot siliconeoil) Samples are specially designed to minimize the distance between the sensors and thegauge area. The cylindrical tensile geometry of the sample with inner holes allows one to fit AE
sensors at each side of the gauge area (5-mm long and 3-mm diameter) and also into the grips(Fig. 1). These samples were previously solution-annealed in argon atmosphere and waterquenched in order to eliminate residual stresses and compositional heterogeneity. The corrosion
cell is a glass cylinder fitted with two PTFE caps at each side, with exits for samples, thecondenser, the temperature probe and the reference electrode. A resistance heater is woundaround the glass cylinder. The environments are MgCl2 solution for SCC tests and silicone oil asa reference medium for purely mechanical straining. The slow strain-rate experiments are carriedout with a 20 kN Roël+Korthaus prototype machine, which is computer controlled. The strain ismeasured with a displacement transducer between the two grips. After mounting the sample in
the grips, the AE instrumentation is tested with simulated AE signals. The cell is filled with
MgCl2 aqueous solution, which is prepared overnight to eliminate dissolved gases and tominimize bubbling during the test.

Fig. 1. Experimental setup for SCC tests.
230b) Test implemented in a scanning electron microscope (SEM)
A special tensile machine has been developed in order to observe the sample surface during
plastic deformation. AE sensors fixed on the sample surface allow to measure AE activity during
a tensile test. The aim of these experiments is to identify the film rupture mechanism andcorrelate them with simultaneous AE measurements. A flat tensile sample of 1-mm thickness isused. In this case, the sample is not heat treated in order to keep it as flat as possible. Afterdiamond polishing, a fine grid (2.5 x 2.5 µm) of gold is deposited on the sample surface. This
grid acts actually as an optical extensometer [9]. An interference network, due to interaction of
the grid and the electron beam, can be observed at low magnifications. The deformation of the
network allows the detection and localization of a strain appearance. Then, at high magnification,these areas are identified and characterized in terms of micro-deformation.
Table 1: Composition of three types of stainless steels.
StainlesssteelsCompositions (% weight)
C

Mn

P

S

Si

Cr

Ni

Mo

N

Heat
treatment(°C)

Austenitic
Duplex

0.015
0.022

1.45
1.61

0.018
0.027

0.003
0.003

0.4
0.38

17.6
21.92

14.29
5.36

2.41
3.08

0.036
0.110

30min 1050
30min 1050

Ferritic

0.005

0.109

0.004

0.007

0.166

25.86

3.764

3.99

0.014

10min 1035

Acoustic Emission Instrumentation
The same AE acquisition system is used for all types of experiments. The two-channel AE
instrumentation consists of sensors, preamplifier, filter and amplifiers (Bruel & Kjaër), and atransient recorder (Tektronix 2430A). The sensors are 150-kHz resonant PZT disks either
laboratory made or commercial ones (PAC nano 30). The two channels are digitized at 10-MHz
sampling rate. The acquisition system is completely computer controlled. The acquisition
threshold of the oscilloscope is set up just above the background noise. The waveforms aretransferred and stored on a hard disk as soon as detected, whereas all the test parameters are
periodically sampled. The parameters measured by the analog-to-digital converters are stress,
strain and free corrosion potential for SCC tests. AE signals are analyzed and processed after thetest completion to obtain usual characteristic AE parameters: amplitude, rise time, energy andlocation. The frequency components are determined only with the first part of the waveform,
which is more characteristic of the AE source and not of the sensor characteristics. A Blackmanweighting window is used to analyze only the very beginning of the transient. Then, thefrequency parameter corresponds to the center of mass in frequency of the frequency density
spectrum.
3. RESULTS AND DISCUSSION
Before analyzing the SCC process by AE, several types of tests were performed to identify
and characterize the different possible sources of AE signals, related to the experimental
conditions but not due to cracking propagation.
1) Electromagnetic interference and mechanical noise
AE sensors are designed to minimize their sensitivity to electromagnetic noise, butnevertheless few parasitic transients can be detected. Those parasitic transients with highfrequency content (≥ 1 MHz) cannot be totally eliminated by analog filtering but they can be231

Amplitude (µV)

Time (µs)

Number of hits

Fig. 2. Waveform relative to bubbles evolution in MgCl2 solution.

kHz

Fig. 3. Frequency parameter distribution (kHz) for boiling of MgCl2 solution.
easily rejected after test completion by signal processing. No signals are detected during thecrosshead motion of the tensile machine, but direct impact to the experimental device has to be
avoided.

Amplitude (µV)2) Physico-chemical effects due to environmenta) Boiling and crystallization
Bubble evolution (Figs. 2 and 3) in MgCl2 induces AE signals with low frequency
distribution (between 50 and 100 kHz). MgCl2 crystallization produces high amplitude hits in afrequency range around 200-300 kHz (Figs. 4 and 5). The activity of these two types of AE
sources can be avoided by an appropriate medium concentration and an accurate temperature
control of the corrosive environment.

Time (µs)

Fig. 4. Waveform relative to MgCl2 crystallization.
232

Number of hits

kHz

Amplitude (µV)

Fig. 5. Frequency parameter distribution (kHz) for crystallization of MgCl2.

Time (µs)

Amplitude (µV)

Fig. 6. Waveform detected during hydrogen evolution.

Time (µs)

Fig. 7. Waveform detected during pitting corrosion.
b) Electrochemical effects
Electrochemical polarization tests are performed in order to analyze both pitting and/orhydrogen evolution from an AE standpoint. These two phenomena may occur during SCC tests.
Hydrogen evolution is a source of low frequency (50 kHz) AE signals, as shown on Fig. 6. This
source cannot be avoided because hydrogen evolution always occurs in our experimental
conditions. Signals detected during pitting obtained by anodic polarization or after a long-time
immersion have a low frequency content (≤ 150 kHz) and long rise times as reported on Figs. 7
and 8. At the free corrosion potential, which is the usual experimental condition for SCC tests,
pitting phenomenon can be strongly reduced by an appropriate filling up of the corrosion cell inorder to avoid differential aerated zone on the tested samples. Whatever the stainless steel grade
233

Number of hitskHz

Hit amplitude(µV)

Fig. 8. Frequency parameter distribution (kHz) for signals obtained during pitting corrosion.

Hit rate(h/sec)Strain (%)

Hit rate
Load
Hit Amplitude

Hit amplitude(µV)

Fig. 9. Slow strain rate test on austenitic stainless steel.

Hit rate(h/sec)Hit rate
Load
Hit Amplitude

Strain (%)

Fig. 10. Slow strain rate test on ferritic stainless steel.
tested, all the previous data are somewhat the same.
3) Acoustic emission sources related to plastic deformation
Tensile tests in inert environment have been conducted on the three types of stainless steels
at different strain rates. Figures 9, 10 and 11 are relative to the three steels for the same strain
rate of 3 x 10-3s-1.
For austenitic and duplex stainless steels, the main AE activity appears around the yield
point. However, for ferritic stainless steel, early high amplitude AE signals occur from 20% up to234

Hit amplitude(µV)

Hit rate(h/sec)Hit rate
Load
Hit Amplitude

Strain (%)

Frequency parameter (kHz)Fig. 11. Slow strain rate test on duplex stainless steel.

Load
(kN)

Strain (%)

Fig. 12. Frequency parameter evolution versus strain.
100% of the yield point. AE signals are mainly detected in a frequency range from 150 kHz to
300 kHz with a shift toward high frequencies as deformation increases (Fig. 12). For the three
types of stainless steel, from the ultimate tensile stress up to the final rupture, no or very few AE
signals are detected. The final rupture provides some high amplitude signals in the same
frequency range as in the yield stress area. With the same acquisition threshold, more hits aredetected on ferritic than on duplex stainless steel. In comparison, austenitic stainless steel is far
less emissive. This classification is the same whatever the strain rate [5].
With the tensile machine implemented in the SEM, the sample surface can be observed
during plastic deformation. The aim of this set of experiment is to clearly identify local
deformation mechanisms, responsible of the passive film rupture and correlate them with AE.
Two tensile tests are performed on each type of stainless steels. The first test is carried out withcrack-free samples in order to study general plastic deformation. The second is performed withpre-cracked samples (through a preliminary SCC test in MgCl2) in order to observe the strain
evolution at the crack tip. In all cases, AE from pre-cracked samples appears at the very
beginning of the loading because of the Kaiser effect (load exceeding the load used for precracking).
For austenitic stainless steel, the slip-band formation is well correlated with the beginning of
the first AE signals. Nevertheless, the overall activity on the crack-free samples is quite low
because the plastic deformation is very homogeneous. For pre-cracked sample, the AE activity ishigher due to localized strain at the crack tip (Fig. 13). AE signals are related to the cooperative235

Fig. 13. First appearance of slip band at the crack tip (grid 2.5 x 2.5 µm).

Fig. 14 (a) Microtwinning of ferritic stainless steel. (b) Glide process at grain boundary andmicrotwinning opening. (grid 2.5 x 2.5 µm)
dislocation motion, such as the breakaway of pinned dislocations from obstacles [10, 11]. If thedislocation density increases or if a second slip system is activated, the free glide distance isreduced corresponding to higher frequency component and lower amplitude of AE signals. For
ferritic stainless steel, micro-twinning occurs at very low stresses up to the yield point (Fig. 14a).
This phenomenon is an important source of high-energy AE signals. After the yield point, neither
twinning nor high-energy AE signal is observed. The plastic strain is mainly produced by theglide process at the grain boundary with a lower AE activity. Micro-twinning and glide processes
are able to break down the passive film and to induce SCC (Fig. 14b).
For duplex stainless steel, some AE bursts are detected at the beginning of loading but notwinning can be observed in the ferritic phase because of the small size of each phase in the
duplex stainless steel. On the pre-cracked sample, the crack tip appeared to be located atinterphase boundaries. The cracks were mainly transgranular, but at each interphase they236

Fig. 15. Fractograph of rupture surface of ferritic stainless steel after SCC.
6700 Hits
Amplitude (µV)

Load
(kN)

Time (h)Fig. 16. AE Evolution during SCC test for a duplex stainless steel (strain rate = 3.3 x 10-6 s-1.
preferentially followed the interphase boundaries before continuing transgranular propagation. In
any case, localized strain at the interphase boundary can be large enough to produce film rupture,thus explaining why duplex steels are quite susceptible to SCC at high level of applied stress.
4) Acoustic emission sources related to SCC
For the three types of stainless steels, a strain rate of 3 x 10-7 s-1 is sufficiently low to induce
total brittle rupture by SCC and even with a strain rate of 3 x 10-6 s-1, the final brittle rupture
surface is around 90% (Fig. 15). SCC-propagation process produces a large number of AE
signals (up to a few thousands). As shown in Fig. 16, the temporal distribution of AE iscompletely different from what is observed in an inert environment. AE is detected in237Events

Events location (mm)Fig. 17. Correlation between located events and actual cracks position along the sample.
experimental conditions with magnesium chloride, while very few AE signals have been foundin silicone oil.
The AE sources location is in good agreement with the multi-branching and secondary
cracking observed on optical and SEM micrographs (Fig. 17). Therefore, for the three types ofstainless steels, the AE sources are clearly related to the SCC process. Figure 18, whichsummarizes the data obtained with the three types of steels, shows that the total number of hits
recorded during each SCC test decreases when the strain decreases. In other words, we observe a
good correlation between the AE activity and the total surface rupture.
Table 2: Characteristics of AE signals detected during SCC processes.

SCC process
Cooperative
dislocation
motionTwinning
Pitting/crevice

Amplitude(dB)

Energy(µV_s)

Rise Time(µs)

0.01-0.3
0.01-0.2

Frequency content(with resonanttransducer-150 kHz)
150-600
100-400

30-70
30-45

30-70
30-45

0.01-50.01-0.2

150-600
50-200

1-30
50-70

1-30
1-30

Table 2 summarizes AE parameters, which can be processed from recorded signals during
SCC test in our experimental conditions. From a practical point of view, it is worthwhile to note238

Fig. 18. Number of detected AE hits versus strain rate for three types of stainless steel during
SCC tests at 145°C.
that AE hits related to pitting or crevice process are easily identified because of their long risetime [12]. The twinning process can also be easily recognized, considering the high AE energy
released in this case. AE hits from SCC events have frequency components much higher thancooperative dislocations motion process. That confirms that a high-velocity cleavage
phenomenon is involved during the crack propagation [13].
4. CONCLUSION
It is demonstrated that AE related to SCC can be detected during slow strain-rate experimentswith three different types of stainless steels (austenitic, ferritic and duplex) in concentrated
magnesium chloride solution at 145°C.
1. Several complementary experiments were necessary to perfect and optimize the AE
instrumentation and testing configuration in order to detect and analyze all other possible AE
sources (such as pitting or crevice propagation, twinning and co-operative dislocations motion)that can be also active during on SCC tests.
2. Pattern recognition of AE signals due to SCC events allows the monitoring of the SCC process
for tested stainless steels whatever the strain rate. In particular, the analysis of appropriate
frequency parameters indicates valid information on the discontinuous steps of propagation
during SCC in chloride environment, even with a resonant sensor. Moreover, the high-frequency
components of the SCC signals indicate that at least one high-velocity step is involved during thecrack propagation with local brittle behavior of the stainless steels.
239

ACKNOWLEDGMENT
This study has been carried on the financial support of CEE (European Brite contract N°RI
1B-0306 C, Proposal P-2032)
REFERENCES[1] E.N. PUGH, (1985), Corrosion, 41(9) 517.
[2] D.G. EITZEN, and H.N.G. WADLEY (1984), NBS J. Res. 89, 75.
[3] S. YUYAMA, T. KISHI, and Y. HISAMATSU, (1983), J. Acoustic Emission 2(1/2), 71.
[4] R.H. JONES, M.A. FRIESEL and R. PATHANIA (1991), Corrosion, 47(2), 105-115.
[5] A. PROUST, Etude par la technique d'EA de la corrosion sous contrainte de trois nuancesd'acier inoxydable en milieu chloruré chaud. Thèse 92 ISAL 0081, INSA de Lyon (1992)[6] R. ROTHEA, Etude par la technique d'EA de la corrosion sous contrainte des aciers
inoxydables austénitiques en milieu chloruré chaud. Thèse 90 ISAL 0024, INSA DE Lyon
(1990)[7] T. CASSAGNE, D. CARON, J. DARET, A. PROUST, H. MAZILLE, G. TURLUER, and D.
BOULANGER. (1999), Ninth Intl. Conf. on Environmental Degradation of Materials in Nuclear
Power Systems-Water Reactors. Newport Beach, CA.
[8] A. PROUST, P. FLEISCHMANN, H. MAZILLE and R. ROTHÉA (1992), Intl. Conf.
Corrosion-Deformation-Interactions, Fontainebleau, pp. 829 - 839[9] A. MOCELLIN, G. THOLLET, A. PROUST, P. FLEISCHMANN, H. MAZILLE, and R.
FOUGÈRES (1992), Proc. X European Congress on Electron Microscopy, EUREM92, Grenade[10] C.R. HEIPLE, and S.H. CARPENTER (1987) J. of Acoustic Emission, 6(3), 177.
[11] D. ROUBY, P. FLEISCHMANN, and C. DUVERNIER (1983) Phil. Mag. A, 47(5), 671.
[12] M. FREGONESE, H. IDRISSI, H. MAZILLE L. RENAUD and Y. CETRE. (submitted toCorrosion Science August 1999).
[13] A. PASKIN, K. SIERADSKY, D.K. SOM, and G.J. DIENES (1983), Acta Met. 31, 12531265

240

INVESTIGATION OF ACOUSTIC EMISSION WAVEFORMS ON A PRESSURE
VESSELMULU BAYRAYInstitute of Pressure Vessels and Plant Technology, Vienna University of Technology
Gusshausstr. 30, A – 1040 Vienna, Austria
ABSTRACTIn this paper, acoustic emission waveforms due to simulated sources and during pressure
tests on a pressure vessel will be presented. Lamb wave theory has been utilised to predict the
propagation of the extensional mode S0 and the flexural mode A0. Electrical pulse was used assimulated AE source to investigate dispersion of the S0 and A0 modes. Experimentally measured
dispersion plot has been found to be in close agreement with that predicted by Lamb theory.
Pencil-lead breaks were used to simulate in-plane and out-of-plane type of sources. Window
Fourier Transform was applied to analyse the waveforms. Results of simulated tests were
employed to investigate AE signals during pressure tests of the vessel. It was found that some inplane type AE waveforms have been recorded during each pressure test.
1. INTRODUCTION
One of the recent developments in acoustic emission (AE) testing is the use of modal
analysis technique. The knowledge of the modes of the elastic waves due to an AE source has
paramount importance in the analysis of AE data. This paper will present results of aninvestigation of AE waveforms on a pressure vessel. The aim of the paper is to show theobservation of AE waveforms due to simulated sources and during pressure tests of a vessel.
In the next section, the theory of elastic waves, specifically the extensional mode S0 and theflexural mode A0 will be briefly discussed. Some literature on experimental work andcomparison with theoretical predictions will then be given. In the following section, theWindowed Fourier Transform (WFT) method of analysing waveforms to identify the two modes
will be presented, followed by the investigation of AE waveforms due to simulated AE sources.
The last section deals with the investigation of AE waveforms during pressure tests. Finally,some concluding remarks will be given.
2. THEORY OF ELASTIC WAVES
Lamb waves
Lamb waves refer to elastic perturbations propagating in a solid plate with free boundaries,
for which displacement occur both in the direction of wave propagation and perpendicular to theplane of the plate. Results of Lamb wave discussion from Royer [2000] will be utilised here.
Assuming a plane harmonic Lamb wave propagating in a plate of thickness 2h in the positive x1
direction, as shown in Fig. 1a, there are two possible wave modes. Those are the symmetricalLamb wave (also called extensional wave) or S-mode and the anti-symmetrical Lamb wave (alsocalled flexural wave) or A-mode, shown in Fig. 1b. The respective mode displacements can beobtained by considering the boundary condition σ 13 = 0 and σ 33 = 0 at the faces x3 = ±h. The
phase and group velocities can then be determined from numerical solution of the characteristic
equations.
J. Acoustic Emission, 19 (2001)

241© 2001 Acoustic Emission GroupSymmetricAnti-symmetricx1

Mid-planea)b)Fig. 1 a) Definition of axes and b) the two possible wave modes.
The Lamb-Rayleigh equation for an isotropic material, which relates the frequency ω of awave with its wave number k, is given by:ω4
2 2 1 − p tan( ph + α ) 
4 = 4k q
1− q tan( ph + α ) VTwith α = 0 or π/2,
(1)where α = 0 for symmetrical mode and α = π/2 for anti-symmetrical mode, and
ω2
ω2
2
2
2
q = 2 − k and p = 2 − k 2
VLVTEquation 1 is commonly plotted as dispersion curve, phase velocity Vϕ = ω/k and group velocity
Vg = dω/dk as a function of the product (frequency x plate thickness). Refer [Royer, 2000] forplot of the dispersion curve including higher order modes. In this paper, only part of thedispersion plot for the zero order modes will be considered. Figure 2 shows the dispersion curve,for carbon steel material (E = 209 GPa, ν = 0.3 and ρ = 7850 kg/m3), with frequency x plate
thickness ranging 0.35 – 2.45 MHz x mm. This is the range of interest appropriate for plate
thickness 3 – 6 mm and frequency 100 – 400 kHz. In this range, it could be noted that, S0 is
highly dispersive while A0 is slightly dispersive.
6000

Group Velocity (m/s)

S0
4000

A0

2000

0
0 ,0 0

0 ,5 0

1 ,0 0

1 ,5 0

2 ,0 0F re q u e n c y x T h ic k n e s s (M H z x m m )Fig. 2 Dispersion of S0 and A0 wave modes.

242

2 ,5 0

3 ,0 0Figure 2 shows that the low frequency component of S0 arrives earlier than its high frequency
component. Also, the high frequency component of A0 arrives earlier than its lower frequency
component. It is expected that AE waves would have the propagation characteristics represented
on the figure. This will be the basis on which the AE waveforms will be analysed.
Wave theory and AE signals
Plate wave theory (for the case h/λ << 1) was used in Gorman [1991] to discuss the AE
phenomena in aluminium and composite plates. Pencil Lead Break (PLB) was used as source ofAE and the resulting waves were detected with a broadband ultrasonic transducer. Comparisonswere made between the velocity predicted by the theory and experimental measurements. It was
found that the symmetrical mode velocity was in close agreement with the theory while theflexural mode velocity was in discrepancy with the theory.
Another work that considered plate wave theory with experimental measurements wasreported in [Dunegan, 1997]. In this work the specimen were steel bars with different thickness.
In-plane and out-of-plane PLB were used as AE source and broadband transducer was used todetect the waves. The measured velocities were found to be in good agreement compared withtheoretical solutions described in the form of dispersion curves. It was also shown that therelative amplitude of the extensional mode was the highest in the mid-plane of the bars.
Theoretical discussion on Mindlin’s plate theory was reported in Huang [1998]. A detailed
discussion and derivation of both the extensional and flexural motions for a finite plate were
presented. A comparison of the theoretical AE waveform with experimentally recorded signal
was made. The experiment was conducted on aluminium plates using PLB as AE source andbroadband transducer to detect the signal. The experimental waveform was in good agreementwith predicted waveform. The experimental AE waveform clearly showed the two wave modes.
The frequency in the S0 mode increased as the time arrival increased, while in the A0 mode it
decreased as the arrival time increased.
3. WINDOWED FOURIER TRANSFORM (WFT)
Definitions
Fourier transform (FT) is often used in AE data analysis to extract the frequency spectral
information. FT generates a frequency domain data out of a time domain data. Generally, FT isdefined as:S(ω)=∫ s(t)exp(iωt)dt,(2)

where S(ω) is the FT of a time signal s(t), ω is the frequency and exp(iωt) = cos(ωt) + i sin(ωt)
is the basis function.
Windowed Fourier Transform also known as Short Time Fourier Transform (STFT), is amathematical tool by which a signal is analysed considering a fixed width window centred at a
certain time, τ. The window is shifted at intervals throughout the whole signal data. Fourier
Transform is carried out at every interval to obtain an estimate on the frequency-timedependence. WFT is well known in signal processing literature [Carin, 1997]. It was alsomentioned in [Suzuki et al., 1996] while discussing wavelet transform of AE signals.

243Considering a window function W(t – τ), centred at time τ, the WFT is defined as:
WFT(ω,τ) =∫ s(t) W(t-τ) exp(iωt)dt(3)

WFT(ω,τ) yields spectral density as a function of frequency, at time τ. The window function isshifted in time over the whole signal producing frequency spectrum of the signal as a function oftime. A commonly used window function is the Gaussian function, given by:W(t-τ) = exp[µ-(t-τ)]2 / (2π)1/2σ2(4)where µ (mean) and σ (standard deviation) are constants of the Gaussian normal distribution.
Results of the WFT calculation can be plotted in three-dimensional contours of PowerSpectral Density (PSD) versus Frequency versus Time. (as will be shown in Figs. 11 –12.)
WFT in identifying arrival of flexural wave mode
Consider a signal having wave characteristics shown previously in Fig. 2. Its frequency-timedependence would reveal the arrival of the flexural wave mode. The arrival time could be
estimated as the time, at which the frequency reaches maximum and starts to decrease. Thisestimated time of arrival of A0 mode after a trigger by S0 mode will be designated by tA0.
A simple algorithm, which is employed to extract tA0 out of the WFT data, will be utilised in
section 4 and 5. In this algorithm, instead of analysing the whole frequency spectrum at eachinterval of time, the centre of gravity of the spectrum (Fcg) is evaluated and considered as arepresentative frequency. When the value of Fcg is plotted with time, a maximum is expected tooccur. The time at which a maximum Fcg has been reached, gives an estimate of tA0. The
extensional mode amplitude (AS0) could then be estimated as the maximum amplitude data
before time tA0. Similarly, the flexural mode amplitude (AA0) could be estimated as the
maximum amplitude after time tA0.
4. INVESTIGATION OF WAVEFORMS DUE TO SIMULATED AE SOURCES
Test vessel and AE instrumentation
Pencil lead break (PLB) and electrical pulse input (PI) were used as simulated AE sources.
The tests were carried out in two phases. The first phase was conducted prior to failure of thevessel and it was only possible to simulate out-of-plane perturbation, using PLB and PI. The tests
were conducted when the vessel was empty (air medium) and filled with water. In the second
phase, the vessel was cut out partly to access the edge of the vessel and simulate in-plane
perturbation.
A sketch of a section of the vessel and instrumentation is shown in Fig. 3. The vessel has anouter diameter of 600 mm and a thickness of 5 mm. The AE instrumentation consists of a pulser(Panametrics V103), a trigger sensor (SE900) and AE sensors (VS150 and SE375) arranged asshown in Fig. 3. All transducers were coupled to the vessel with silicone grease. The signals
from the trigger and sensors were pre-amplified by 40 dB and a 100-kHz built-in filter was used.
Vallen Systeme AMS3 was used to acquire and analyse the signals. Other acquisition parameters
employed were a threshold of 34 dB and a rearm time of 3.2 ms.
244

Pre-amp

Acquisition

PLB, PIOn surface

S
Pre-ampS
P

T
Pre-amp

PLB onedge

Analysis
SignalGenerator

Fig. 3 Section of the test vessel and AE instrumentation. Sensors were also arranged along thecircumference. P: pulser; T: trigger; S: sensors.
4.1 Experimental procedure
Phase I Narrow-band pulse input tests
In order to study the dispersion of the two modes, narrow-band pulses 100-400 kHz atintervals of 10 kHz were transmitted via the ultrasonic transducer (Panametrics V103) on to thesurface of the vessel. A signal generator was used to produce the pulses. The amplitude of thepulses were 120 dB (referenced to 1 µV) for empty vessel tests and 126 dB for the full vessel
tests. A trigger (SE900) at about 100 mm and a sensor (VS150 or SE375) at about 600 mm fromA0

S0a) PI source with constant frequency

S0A0b) PLB source
Fig. 4 Arrival of S0 and A0 modes.
245the pulser were used to record the AE signal. Investigating the transient record of the AE signal
the velocities of the respective modes were determined from the arrival times. Figure 4a shows asingle transient record with the arrival times labelled for the PI source.
Phase II Pencil-lead break tests
In-plane and out-of-plane sources were simulated by breaking pencil leads (0.3 mm, 2H) on
the edge and on the surface of the vessel respectively. A trigger (SE900) at about 150 mm and asensor (VS150 or SE375) at about 400 and 600 mm from the PLB were used to record the AE
signal. Out-of-plane PLB source was made on the inside (z = –2.5 mm) as well as the outside (z= 2.5 mm) of the vessel. In-plane PLB source was made at three different positions on the edgeof the vessel. The three positions were at about 1.25 mm above the inside surface (z = –1.25mm), at mid plane (z = 0) and at about 1.25 mm below the outside surface (z = 1.25 mm). The
transient records were then investigated for the extensional and flexural modes. The arrival timeof the low frequency and high frequency component of each mode was identified. Figure 4b
shows one of such records.
The two modes were identified, as mentioned earlier, from the velocity of arrival.
Extensional mode arrives earlier than the flexural mode. A second characteristic used to identify
the two modes was the arrival of the low and high frequency components. For the extensionalmode, its low frequency component arrives earlier than its high frequency component. For the
flexural mode, its high frequency component arrives earlier than its low frequency component.
This is clearly observed in Fig. 4b.
Another simple test was carried out to confirm the identification of the two modes. Two
sensors were mounted one outside and the other inside of the vessel at equal distance from PLB
source. Referring Fig. 1, for the extensional mode, the two sensors would record signals that arein phase. However, for the flexural mode the two sensors would record signals that are out ofphase. Figure 5 shows waveforms of such a test, confirming the identification of the two modes.

Fig. 5 PLB at z = –1.25 mm, Channel 2 VS150 sensor on outside surface and Channel 3 VS150
sensor on inside surface, both at 400 mm from source.
4.2 Experimental Results and Discussion
Dispersion
The tests described in Phase I (see section 4.1) were used to study the dispersion of the wave
modes. Transient record of hits at selected frequencies, (100 – 400 kHz, at 10 kHz interval), were
evaluated and the arrival time of the modes was read. The velocities were calculated using the
distance between the trigger and sensor and the respective arrival time. Figure 6 shows the plotof the velocity versus frequency. Theoretical predictions discussed in the first section are
included in the figure.
246

6000

a
Group Velocity (m/s)

5000
S0 Theory
A0 Theory
S0 VS150
S0 SE375
A0 VS150
A0 SE375

4000
3000
2000
1000
0
0,00

0,50

1,00

1,50

2,00

2,50

3,00

FrequencyxThichkness (MHz.mm)

6000bGroup Velocity (m/s)

5000
S0 Theory
A0 Theory
S0 VS150
S0 SE375
A0 VS150
A0 SE375

4000
3000
2000
1000
0
0,00

0,50

1,00

1,50

2,00

2,50

3,00

FrequencyxThickness (MHz.mm)

Fig. 6 Dispersion plot. a) Empty vessel. b) Vessel with water.
The measured velocities of the two modes agree with theoretical predictions. The average
error was about 7%.
IP and OOP simulation
In order to simulate a crack growing from inside of the vessel to the outside surface, PLB at
five different positions on the edge of the vessel were made. Figure 7 shows representative
waveforms at three different positions. The amplitude ratio AS0/AA0 is the highest when PLB was
made at the mid-plane. The ratio is the lowest when PLB was made at the inside as well as theoutside surface. The amplitude ratio decreases as the PLB position is shifted away from the midplane towards the inside and outside surfaces.

247

(a)

(b)

(c)
Fig. 7 Waveforms, SE375 sensor at 400 mm from source. a) PLB at z = –2.5 mm,b) PLB at z = –1.25 mm, c) PLB at mid plane.

248

Table 1 shows a summary of results obtained on waveforms from PLB simulated sources.
Theoretically predicted values for tA0 at distance of 400 and 600 mm are, tA0 = 53.3 µs and tA0 =80.0 µs, respectively. Those values are for extensional velocity of 5000 m/s and flexural velocityof 3000 m/s, which are valid for the frequency range of 100-300 kHz. The percentage error
between the estimation from the WFT and predicted value is included in Table 1. The percentage
error was high in some of the cases. It was possible to identify the type of the PLB sources by
examining the amplitude ratio AS0/AA0, specially, for the OOP and IP at mid plane.
Table 1 Results of WFT for waveforms from PLB source.
Sensors : model VS150
TRA
PLB
Source-Sensor
tA0
AS0/AA0
position Z
Distance(µs) % error
(mm)
(mm)
1726
-2.5
400
44.8
15.9
0.077
1727
-2.5
600
57.6
28.0
0.076
1731
-1.25
400
50.4
5.4
0.480
1732
-1.25
600
69.6
13.0
0.553
1737
0
400
53.6
0.5
1.349
1738
0
600
71.2
11.0
1.426
Sensors : model SE375
TRA
PLB
Source-Sensor
AS0/AA0tA0
position Z
Distance(µs) %error
(mm)
(mm)
1906
-2.5
400
50.4
5.4
0.298
1907
-2.5
600
53.6
33.0
0.243
1909
-1.25
400
35.2
33.9
0.478
1910
-1.25
600
74.4
7.0
0.642
1915
0
400
> 2.0
1916
0
600
> 1.2
5. INVESTIGATION OF WAVEFORMS DURING PRESSURE TESTS
Set-up and Instrumentation
The test vessel, with outer diameter of 600 mm, overall length of 2000 mm and thickness of
5 mm, was made of steel. The vessel was manufactured with a 1600 mm long cylinderical shellwith a longitudinal weld, and two toro-spherical heads. The vessel rests on two pairs of feet, at a
distance of 1300 mm from each other. A check hole, with elliptical shape of 200 mm by 150 mm
size, is situated at the centre of the shell. A hydraulic system was utilised to pump water into thevessel providing controlled internal pressure.
Five AE sensors (one SE375 and four VS150) and three strain gauges were mounted on thevessel. The AE sensors, pressure sensor and strain gauges were connected to the Vallen Systeme
AE data acquisition instrumentation. The test vessel and part of the set-up are shown in Fig. 8.
The general AE instrumentation was similar to the tests conducted previously and reported in
Rauscher [1999]. Similar acquisition parameters to that used during the simulated tests were
employed.

249

Test Procedure
Cyclic pressurisation and intermittent pressure tests were conducted to initiate crack growthon the vessel. According to a finite element analysis conducted previously, the expected failure
region would be around the check hole. The cyclic pressurisation was conducted in the range 2
bar - Pc, at frequency of 0.1 Hz. Specification of a single pressure test is shown in Fig. 9.
Pressure tests were carried out intermittently every few number of cycles. Different loadintensities were employed, namely type A, B, and C. Table 2 shows these three types of loading.

Fig. 8 Test vessel and set-up of sensors.
PressureHold
10 minutes

1.5Pc
1.1Pc
Pc

Table 2 Load levels (in bar).
Type
A
B
C

Hold
10 minutes
2 bar

Pc
11
16
22

1.1 Pc1218
24

1.5 Pc
16.5
24
31

Time

Fig. 9 Pressure test specification.
After conducting 5 pressure tests and 8000 cycles of type A, 4 pressure tests and 23360
cycles of type B, 1 pressure test and 10000 cycles of type C, without failure, it was decided to
introduce a notch to accelerate the initiation and growth of crack. The notch was a saw cut madeon inside of the check hole. The vessel was further loaded for 3 pressure tests and 10350 cycles
of type B until leakage. The vessel failed during cyclic pressurisation, when a crack initiated at
the root of the notch grew to the outer surface of the vessel.

250

5.1 Experimental Result and Discussion
Noise Filtering
Possible sources of noise identified during the test were:• Electrical interference (EMI) due to a sudden starting and stopping of an oil pump used in thehydraulic system. The hits from the EMI noise were recorded with less than 1 µs difference
in arrival time.
• Mechanical vibration from the oil pump mentioned above was also transferred via the vessel
support from the ground.
• Noise due to friction and slight movement of a rubber gasket used to seal the check hole.
Also some movement of the inlet pipe due to variation of pressure created noise. Noise dueto these two cases was significant especially during cyclic pressurisation.
The following filters were utilised to minimise noise in analysing AE data:
1. Amplitude filter: Hits with amplitudes less than 40 dB were filtered out. Such low amplitude
hits are likely not to be properly located.
2. Arrival time filter: Hits due to EMI noise were filtered using arrival time filter. Hits due to
EMI noise were recorded as almost arriving at the same time on all channels. Therefore,
events with arrival time difference between first and third hit less than 5 µs (dt13 < 5 µs)
were filtered out. Only those events with dt13 ≥ 5 µs were considered for further analysis.
Since the arrangement of the sensors could not possibly acquire an event arising on the vessel
with dt13 < 5 µs.
3. Location filter: To avoid some hits due to mechanical noise and other hits located from thearea outside the region of interest a location filter was used. The expected area of crack
growth, where a notch was introduced, was located in the region: -100 ≤ X ≤ 100 and 900 ≤Y ≤ 1100 mm. Events located inside this region were considered for further analysis.
Table 3 below shows the AE events acquired during the three pressure tests. The first, second
and third pressure tests were conducted 800, 7000, and 9000 cycles, respectively, after
introducing the notch. The table shows the number of events before and after utilising the threefilters outlined above.
Table 3 Number of events before and after filtering.
Pressure Test
First
SecondThird

Total no of
located events
87
71
24

No of events after
filters 1, 2 and 3
19
22
7% total
21.8
31.0
24.2

The distribution of the amplitude of the first hits after filtering is summarised in Table 4. The
amplitude of most of the hits (> 70%) were between 50 – 70 dB. Most of the events occurred
during pressure increase from the 18 bar hold until the end of 24 bar hold. Few events (about14%) occurred during pressure increase until 18 bar and after the end of 24 bar hold.

251

Table 4 Distribution of amplitude of first hits.
A (dB)
40 – 45
45 – 50
50 – 55
55 – 60
60 – 65
65 – 70
70 – 75
75 – 80>80
Total

First Pressure
Test
1
3
2
3
6
3
1
19

Second Pressure Third Pressure
Test
Test
1
2
3
2
6
5
1
2
2
3
1
1
22
7

5.2 Fracture Surface Investigation
Part of the vessel (BM-01) was cut out, to investigate the crack growth and its fracturesurface. Figure 10a shows a picture of the cut out during magnetic particles inspection of thecrack. The picture shows the crack length as seen on the reinforcement of the check hole. The
crack initiated at the root of the notch and the final length of the crack was about 10.5 mm.

Fig. 10 a) A view of the crack on BM-01 vessel. Scale shown is in cm unit. b) Sketch of thefracture surface (not to scale).

252

A piece was cut out for further microscopic analysis. The fracture surface was a typicalelliptical crack profile. Figure 10b shows a sketch of the fracture surface as seen under amicroscope describing the different stages of the crack growth. The first pressure test occurred atearly stage of the crack growth. The last two pressure tests are clearly observed on the fracturesurface. The explanations of the different regions of Fig. 10b are given below.
Region 0: This region is the saw cut notch on the reinforcement of the check hole.
Region 1: A small area distinctly seen close to the root of the notch. This region is crack growthof the first 800 cycles and a crack blunting mark of the first pressure test.
Region 2: In this region the crack front is about 5.5 mm from the root of the notch. Areas 2a and2b are fracture surfaces but at different planes. Area 2c is void due to incomplete
penetration of the outside and inside welds. A crack blunting at the second pressure test isclearly seen. This region represents the crack growth during 800 – 7000 cycles.
Region 3: In this region the crack grew for further 3 mm. A crack blunting at the third pressure
test is clearly seen. This region represents the crack growth during 7000 – 9000 cycles.
Region 4: In this region the crack grew for another 2 mm. The crack front reached the outside of
the surface of the vessel, causing leakage. This region represents the crack growth during
9000 – 10350 cycles.
5.3 Waveform Analysis
The previous section explained the fracture surface and the different stages of the crack
growth. During the pressure tests crack blunting was clearly observed. It is expected that thecrack blunting would give rise to acoustic emission. Since this phenomenon occurs at the planeof the joint between the shell and the reinforcement, it is expected to produce waves similar to IP
type of sources. However, the source of the AE could be from one end of the elliptical mark onto
the other end along the fracture surface. Hence, the amplitude of the extensional mode would be
expected to vary depending on the position of the source.
The AE events of the three pressure test were examined using the WFT discussed in previous
section. Results of some selected waveforms are shown in Tables 5, 6 and 7. All the waveforms
were hits recorded on sensor 2 (VS150), which was located about 400 mm from the crack. After
the WFT calculations the waveforms were visually examined for any corrections overlooked by
the relatively simple WFT algorithm. Those adjusted are marked on the comment column on thetables. The Transient record index (TRAI) is modified to include the prefix p1t, p2t and p3t to
identify the first, second and third pressure tests.
Some of the events will be further discussed below. In addition to the WFT results of hits on
sensor 2, the waveforms of sensor 1 (SE375) will also be analysed. Sensor 1 was located at about
100 mm from the crack. This distance is very short to look in to arrival time of the flexuralmode. But the frequency content of the waveform on sensor 1 will provide additional
information on the source type. Table 8 shows summary of six selected events. Figures 11 and 12
are waveforms of event 1 and 6, respectively.

253

Table 5 WFT results of events during First
Pressure Test. (Hits on sensor 2)

Table 6 WFT results of events during
Second Pressure Test. (Hits on sensor 2)

TRAI
P1t20645
P1t20679
P1t20701

tA0 (µs)
50.4
36.0
20.0

AS0/AA0
0.096
0.134
0.454

TRAI
p2t54
p2t100p2t114tA0 (µs)
45.6
45.0
20.0

AS0/AA0
0.191
0.094
0.300

P1t20706
P1t20757
P1t20790
P1t20860

26.4
50.0
53.6
50.0

0.184
0.280
0.204
1.259

p2t195p2t233p2t305p2t340

45.6
39.2
37.6
20.0

0.400
0.612
0.076
0.300

P1t20871
P1t20906
P1t20981

18.4
100.0
50.4

0.287
0.290
0.913

p2t357

50.0

0.500

p2t363

30.0

0.100

Comment

Adjustment
was needed

Adjustment
was needed

Comment

Adjustment
was needed

Adjustment
was needed
Adjustment
was needed

Table 7 WFT results of events during Third Pressure Test.(Hits on sensor 2)
TRAI
p3t26
p3t38
p3t53
p3t56
p3t85
p3t105tA0 (µ
µs)
58.4
20.0
45.0
50.0
32.0
20.0

AS0/AA0
0.703
0.445
0.300
0.670
0.700
0.087

Comment

Adjustment was needed

Table 8 Summary of events.
Event

ChannelTRAI

Frequency

1

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

p1t20678
p1t20679
p2t98
p2t100p3t104p3t105
p1t20858
p1t20860
p2t231
p2t233p3t54
p3t56

Low (200-275 kHz)

2
3
4
5
6

AS0/AA0

Figurenumber11

0.134
Low (200-300 kHz)
0.094
Low (150-200 kHz)
0.087
High (350-400 kHz)
1.259
High (250-325 kHz)
0.612
High (200-325)

12
0.670

In summary, events 1, 2 and 3 have relatively low frequency content on sensor 1 and the ratio
AS0/AA0 , on sensor 2, is low. These events might have been produced from AE source near thesurface of the vessel. Events 4, 5, and 6 have relatively higher frequency content on sensor 1 andthe ratio AS0/AA0, on sensor 2, is close to unity or higher. Such events might have a source close
to mid plane of the vessel. The above waveform analysis was limited to sensor 1 and sensor 2
only. Sensor 3 (model VS150) was close to the AE source. Only the frequency information of
Sensor 1 was considered, as it was also very close to the AE source. The location of sensor 2 wasalso not ideal with respect to the notch. The reinforcement of the check hole had shading effecton the waveform arriving at sensor 2. With a change in the sensor arrangement better results
would be expected.
254

Amplitude (mV)
Frequency (kHz)Peak (mV)

Amplitude (mV)
Frequency (kHz)Peak (mV)AA0
AS0

F cg (kHz)F cg (kHz)tA0Time (µs)

Time (µs)

Fig. 11 Event 1, a) showing low frequency on sensor 1 and b) AS0/AA0 = 0.134 on sensor 2.
6. CONCLUSION
Dispersion characteristics of the two modes were experimentally measured and comparedwith theoretical prediction. The measured group velocities of the extensional and flexural mode
were in good agreement with the predictions.
It was possible to observe the IP and OOP simulated sources and their respective signals. IP
sources generate a strong extensional wave mode but a weak flexural wave mode. Especially,
when IP perturbation was made at mid plane the amplitude of the extensional mode was higher
than that of the flexural mode. OOP sources generate a weak extensional mode but a strong
flexural mode.
WFT was utilised as a tool in identifying extensional and flexural modes. WFT was used toestimate the arrival of the flexural wave and the amplitude ratio AS0/AA0. Results of the WFT on
waveforms from PLB sources were good enough to differentiate between OOP and IP sources.
The percent error in estimating the arrival time as compared to that predicted in theory was highin some of the cases. However, the accuracy could be improved by employing better
mathematical algorithms.
WFT was utilised to identify the possible source of AE (OOP and IP) during pressure tests.
Some events were identified during the three pressure tests as an IP source employing the WFT.

255

Amplitude (mV)

Amplitude (mV)

Frequency (kHz)Frequency (kHz)F cg (kHz)F cg (kHz)Peak (mV)Peak (mV)

AA0
AS0

tA0

Time (µs)

Time (µs)

Fig. 12 Event 6, a) showing high frequency on sensor 1 and b) AS0/AA0 = 0.67 on sensor 2.
Fracture surface investigation also showed crack blunting during the pressure tests. It is impliedthat the AE events were due to the crack blunting phenomenon.
Further work in developing the WFT as a tool in identifying the two modes is recommended.
This could be in the direction of making automated analysis that could be utilised online.
ρ
ν
λ
S0
A0
Fcg
tA0

NOTATION
x1, x3Co-ordinate axes
σ 13, σ 33
Normal stresshHalf plate vessel thicknessωFrequencykWave number
Transverse wave velocity
VT
VL
Longitudinal wave velocity
VϕPhase velocity
VgGroup velocity
E
Elastic modulus

AS0
AA0

256

Density
Poisson’s ratio
Wave length
Extensional wave mode (0th order)
Flexural wave mode (0th order)
Frequency centre of gravity
Arrival time of A0 after trigger by
S0
Amplitude of S0
Amplitude of A0

REFERENCES
Carin, L., Felsen, L.B., Kralj, D.R., Oh, H.S., Lee, W.C., and Pillai, S.U., (1997), “WaveOriented Signal Processing of Dispersive Time-Domain Scattering Data”, IEEE Transactions
on Antennas and Propagation, 45 (4), 592-600.
Dunegan, H.L., (1997), “Modal Analysis of Acoustic Emission Signals,” Journal of Acoustic
Emission, 15 (1/4), 53.
Gorman, M.R., (1991), “Plate Wave Acoustic Emission,” Journal of Acoustical Society ofAmerica, 90, (1), 358.
Huang, W., (1998), “Application of Mindlin Plate Theory to Analysis of Acoustic Emission
Waveforms in Finite Plates,” Review of Progress in Quantitative Non-destructive Evaluation,
Vol. 17, p. 493.
Rauscher, F., (1999), “ Acoustic emission of vessels with partially penetrated longitudinalseams,” Proceeding of the EWGAE conference AE’99, Brno, Czech Republic.
Royer, D., and Dieulesaint, E., translated by Morgan, D.P., (2000), ”Elastic Waves in Solids I,
Free and Guided Propagation,” Springer, Berlin, pp. 311-321.
Suzuki, H., Kinjo, T., Hayashi, Y., Takemoto, M., and Ono, K., (1996), “Wavelet Transform of
Acoustic Emission Signals”, Journal of Acoustic Emission, 14(2), 69-84.

257

EFFECTS OF LATERAL PLATE DIMENSIONS ON ACOUSTIC EMISSION SIGNALSFROM DIPOLE SOURCES
M. A. HAMSTAD*, A. O'GALLAGHER and J. GARYNational Institute of Standards and Technology, Boulder, CO 80305* Department of Engineering, University of Denver, Denver, CO 80108
ABSTRACTWith a validated three-dimensional finite element code, the out-of-plane displacements
corresponding to model sources of acoustic emission (AE) were calculated in aluminum platesamples of two different lateral dimensions. Both samples were 4.7 mm thick. The lateraldimensions were 480 mm by 25.4 mm, which represented a laboratory-size coupon, versus 1000
mm by 1000 mm, which represented a larger field-size sample. The displacement signals were
calculated for positions of the receiver on the top plate surface at several different distances (inthe far-field) from the source’s epicenter. The signals were predicted for the same propagation
distances of source-to-receiver in both the large and small samples. Models of both point-likesensors as well as sensors with a large aperture were used. The signals were filtered with either a
40-kHz high-pass filter or a 100-to-300-kHz bandpass filter. The AE sources were modeled aseither a point single dipole (both in-plane and out-of-plane) or a point multi-dipole located at
different depths within the plate thickness. Analysis of the simulated AE signals shows that thesuperposition of edge reflections on the arrivals of the direct signal significantly distorts andamplifies AE signals in the laboratory-size coupon relative to a larger field-sized sample. This
results in significantly larger AE signal features such as amplitude, duration, and energy in thelaboratory-sized sample. Edge reflections also distort the frequency spectrum of signals in thesmall sample.
1. INTRODUCTION
Since acoustic emission (AE) technology is widely used in both laboratory settings and fieldtesting of large structures, a broad range of dimensions of test samples is encountered. Thus,
some sample dimensions are conveniently described in millimeters and others in meters. A
natural question arises; what effects do the sample dimensions have on the measured AE signals?
In particular, how do these changes in signal affect the identification of the source of the detected
AE? Further, what are the effects of specimen size on the detectability of AE and thedetermination of the significance of detected AE? The research described herein used a recently
developed and validated dynamic-finite-element modeling method for wave propagation(Hamstad et al., 1999; Prosser et al., 1999) to examine the effects on predicted AE signals ofvarying the lateral dimensions of plate specimens. For the two different specimen dimensions
considered, the same sensor positions relative to the source (i.e., propagation distances) weremodeled. Due to the ability to include reflections from all sample boundaries (including theedges of the plate), the finite-element method can fully model cases that analytical AE modelingapproaches have been unable to address.
Contribution of the U.S. National Institute of Standards and Technology; not subject to copyrightin the US.

J. Acoustic Emission, 19 (2001)

258© 2001 Acoustic Emission GroupAn earlier study (Gorman, 1998) used experimental techniques to study the effects of lateralplate dimensions. However, the experimental source was an out-of-plane surface-applied pencillead break, which does not accurately represent an internal AE crack source. The current study
modeled buried dipole point sources, which more closely represent real AE sources than leadbreak surface monopole sources. Further, the previous study did not compare the AE signals at
exactly the same distances from the source to the sensor in the small and large plates. Thus,
quantitative comparisons with parameters such as peak signal amplitude were not possible. The
current study does allow such comparisons. The current study also examines specimen
dimension effects on frequency spectra. Both wideband point-type sensors as well as sensors oflarger aperture with narrowband sensitivity are modeled.
2. FINITE ELEMENT AND SOURCE CONDITIONS
An extensive database was created by calculating AE signals for three different types ofburied sources and several different source depths in the plate samples. In particular, the buriedsources were (i) an in-plane single dipole aligned along the direction of propagation to thesensors; (ii) an out-of-plane single dipole; and (iii) a multi-dipole with three dipoles that
represent initiation of a microcrack source (Scruby, 1985). For the latter source, the strongest
dipole was again in-plane and aligned along the direction of propagation to the sensors. The
dipole forces (body forces) were all 1 N except for the two smaller dipoles in the microcrack
case, which were 0.52 N (based on the elastic constants of aluminum). Each dipole was made up
of a "central" cell having no body force along with single cells on each side of the "central" cellhaving the body forces. This approach follows that used in a buried-source finite-element
validation study (Hamstad et al., 1999). The source body forces had a "cosine bell" temporaldependence with a rise time of 1.5 µs. All the calculations were based on a uniform threedimensional cell size of 0.313 mm (except as noted below). The material constants for the 4.7mm-thick aluminum plate samples were: a bulk longitudinal velocity of 6320 m/s, a shear
velocity of 3100 m/s, and a density of 2700 kg/m3. The modeled sensor outputs were the out-ofplane top-surface displacement from a single nodal point for point-type sensors, and from theaverage of all the nodal points in a circle of 13-mm diameter in the case of a finite aperture
sensor. The parameters for the finite-element code were selected in accordance with theconditions previously (Hamstad et al., 1999) determined to be necessary to model point sources
with a particular rise time. In one case, convergence was checked by proving the modeled AE
signals did not change for a calculation with a cell size decreased by 20 %. The conditions
necessary for convergence (Hamstad et al., 1999) resulted in a time step of 44.62 ns for thecomputations, which were carried out for a total of 200 µs after the start of the operation of thedipole sources. For all the waveform figures in this paper, the zero time is the start of operationof the dipole source.
3. LATERAL PLATE GEOMETRY AND SOURCE/SENSOR LOCATIONS
The choice of geometry for the small coupon specimen was taken from a previous
experimental study (McBride and Hong, 1992), in which a sample 480 mm by 25.4 mm wasused to study the AE generated from fatigue crack growth in specimens of 7000-series aluminumalloys 4.7 mm thick. Lateral dimensions of 1000 mm by 1000 mm (4.7 mm thick) were selected259

to represent a field-test configuration that does not have nearby sample edges. These lateraldimensions were sufficiently large such that reflections from the sample edges did notsuperimpose on directly arriving signals. A total of seven point-type sensor positions wereselected. Two coincided with the sensor positions used in the referenced original experimental
study. For the finite-element simulation, these two positions were modeled to obtain
displacements representing both point-type and finite-aperture sensors. Figure 1 is a drawing of
the small specimen superimposed on the large sample, identifying the various sensor positions
along with the source position. The locations for the finite-aperture sensors used in thereferenced crack-propagation study are at locations 2 and 5 (see Fig. 1) at 120 mm and 60 mm
from the plane that contained the source. The source position (below sensor 4) is located in thesame cross-sectional plane, in which the crack was propagated in the referenced experimental
study. For most of the finite element (FEM) cases, the source was located halfway across thewidth of the small sample (see Fig. 1). A few computations were made with the source in thesame plane, but not symmetrically located with respect to the long sides of the small sample.
1000
500
Source below
sensor 4

Sensornumbers500
1 2 3 4 5 6 7
1000
90

30

25.4

Fig. 1 Finite-element specimens. Dashed lines show the small sample superimposed on thelarge sample. Sensor numbers show location of sensors, and the source is below sensor 4. All
dimensions in millimeters with sensors 60 mm apart.
4. FEM RESULTS AND DISCUSSION
The FEM signals include frequency content from zero hertz up to the highest frequencies
generated by the source (based on calculated spectra, a little above the reciprocal of the rise time,hence about 700 kHz to 1 MHz in the current FEM modeling). To more closely approximate
signals acquired with real AE sensors, the FEM signals were filtered with a recursive four-pole
Butterworth filter. The selected filter ranges were: a bandpass of 100 to 300 kHz for the large
260aperture sensor to represent conventional resonant-sensor AE technology, and 40-kHz high-pass
for the point-type sensors to represent wideband nearly flat with frequency waveform-based AE
technology. In some cases, the point-type sensor signals were also filtered with the 100-to-300kHz filter.

(a)

35

Small sample

Displacement, pm

0

-35
35

Large sample

0

-35

0

25

50

75

100

125

34

38

Time, µs(b)

Displacement, pm

5
Small

0
Large

-5

18

22

26
30
Time, µs

Fig. 2. Comparison of out-of-plane displacement signals at sensor 2 located at 120 mm from thein-plane dipole source centered at a depth of 0.783 mm below the top surface of the plate.
(a) a view of longer duration. (b) a view of expanded scale of the early part of the signals fromthe point-type sensors.
261

A. Signal Amplitude
Figure 2 compares the FEM-generated signals at sensor 2 for the large and small platesamples. These signals at 120 mm from the source were modeled for the point-type sensor witha single in-plane dipole source (dipole aligned along the axis of propagation to the sensor) with
its center located at 0.783 mm below the plate top surface. Figure 2(a) shows the signals withthe same vertical displacement scale, and Fig. 2(b) shows the initial portion of both signals withan expanded displacement scale. To provide results with a more complex source, Fig. 3 displays
similar views for a microcrack initiation source (three dipoles, major dipole directed along theline of sensors) located at 1.723 mm below the top surface of the plate. These two figuresdemonstrate the fact that the signals in the laboratory sample have amplitude significantly larger
than those in the large sample without nearby edges. The actual peak amplitude increases areabout 11.1 dB for the single dipole and about 12.1 dB for the microcrack stimulation. Further, as
Figs. 2(b) and 3(b) demonstrate, edge-reflection-based interference and distortion of the signal in
the small sample begins only a few microseconds after the direct arrival of the signal. A more
extensive examination of the signal database as a function of varying sources, depths andpropagation distances indicates that the increase in amplitude of the peak signal relative to thepeak amplitude without edge reflections is typically greater at sensors further from the source.
As summarized in Table 1, this is consistent for both point-contact sensor signals and finiteaperture sensor signals, and also for both the bandpass of 100 to 300 kHz and the 40-kHz highpass frequency range. Table 1 also indicates that peak amplitude increases with the point-type
sensor were not as large when a bandpass filter of 100 to 300 kHz was used.
Table 1 Observed decibel ranges of increase in peak signal amplitude in the small specimen for
the range of source types and depths
At 60 mm(dB)

At 120mm(dB)(i) Point-type sensor
40 kHz H. P. filter
100-300 kHz filter

7.4 to 8
4.3 to 5.0

9.5 to 10.9
6.5 to 7.8(ii) 13-mm aperture sensor,
100-300 kHz filter

3.8 to 7.7

6.0 to 9.9

Edge reflections that can superimpose on the direct signals can come not only from the sideedges of the small coupon, but also from the ends of the sample. Figure 4 demonstrates this
aspect. In this figure, the signals are shown for the point-type sensors with a bandpass of 100 to
300 kHz, located at 60, 120, and 180 mm from the source. The source was an in-plane dipole
located at a depth of 0.47 mm below the top surface. The end-edge reflections at the 60-mm and120-mm sensors are clearly distinguishable from the earlier-arriving part of the signal that iscomposed of the direct signal and the side-edge reflections. The presence of end-edge
reflections modifies the signal propagating from the other direction in a different fashion, as thesensor is positioned more closely to the specimen’s edge. In such cases, the part of the end
reflection of largest amplitude and the major amplitude portion of the signal coming from theother direction interfere with each other. In such cases, the amplification of amplitude may befurther enhanced. We note that these sensor positions were not used in the original referenced
experimental study. Also the fact that at the sensor positions of 60 and 120 mm the majoramplitude part of the end-edge reflections are more distinct from the major-amplitude part of the
262

(a)
15

Small specimen

Displacement, pm

0

-15
15

Large specimen

0

-15
0

25

50
75
Time, µs

100

125

32

36

(b)
3.5
Displacement, pm

Small

0.0
Large

-3.5

20

24

28

Time, µs
Fig. 3. Same views as in Fig. 2, but for a microcrack initiation source centered 1.723 mm below
the top surface of the plate.
signal from the other direction means that these reflections could easily be mistaken as comingfrom a different AE source event.
Although attenuation due to material-based absorption of the wave is not included in thesemodel calculations, significant decreases in signal amplitudes are observed with increasing
propagation distance where no edge reflections are present. The origins of these attenuation
effects are both geometric spreading and signal dispersion. This decrease in peak amplitude forthe modeled signals versus propagation distance in the large plate is shown in Fig. 5. This figure263

Small specimen, not to scale
5
6

7

Source25

Sensor 5

Displacement, pm

0
-25
25

Sensor 6

0-25
25

Sensor 7

0
-25

0

40

80

120

160

200

Time, µs
Fig. 4. Out-of-plane displacements at point-type sensors with 100 to 300 kHz bandpass locatedat 60, 120, and 180 mm from the in-plane dipole source located 0.47 mm below the top surfaceof the small sample. End-edge reflections from specimen right end shown by connected arrows.
also shows the theoretical prediction for geometric spreading (1/√r, where r is the propagation
distance from the source). The source for the modeled signals in Fig. 5 was an in-plane dipole
located at the mid-plane in the aluminum plate 4.7-mm thick. Additional sensor positions wereadded for the computation of the data for Fig. 5. The data shown are for the out-of-plane
displacement after a 40 kHz high-pass filter for the point-type sensors. It seems clear that the
1/√r curve is valid out to 20 mm, but at farther distances dispersion begins to dominate.
In the case of the small coupon sample, the signal attenuation from the two effects ofgeometric spreading and dispersion is typically partially overcome by the signal amplificationfrom edge reflections. Due to these reflections, a much larger share of the 360-degree radiationof source energy reaches the sensors. We have not calculated any coupon cases wheredestructive interference between the direct signal and the reflected signals resulted in a decrease264

Peak amplitude, dB

0
-10
-20

1/√
√r

-30
-40

0

80
120 160200
40
Propagation distance, mm

Fig. 5. Calculated loss of AE signal peak amplitude of out-of-plane displacement for a pointtype sensor for a mid-plane dipole source with an in-plane orientation. The signal from the large
specimen was filtered with a 40 kHz high-pass filter.
in peak signal amplitude. This fact is consistent with the many different reflections that transfer
a larger fraction of the original source energy to the sensor in the small coupon. The largest
increases in signal (see Table 1) were observed in the case of the 40-kHz high-pass signals withthe point-type sensor. One possible explanation for this observation is that a larger fraction of
the reflected signals contribute with the point sensor since a wider frequency window allows
more of the modes in the reflected signals to contribute to the displacement at the sensor. Also,from Table 1 it is clear that the increases in signal varied considerably as the type and depth ofsource changed. Further, this table indicates a wider range of signal increases for the largeaperture sensor. This result is likely due to the wider range of reflection paths that can create
displacements under the sensor of 13-mm diameter.
The question arises as to whether the observed amount of signal increase is particularly large
due to (a) the symmetry of the source, and (b) the sensors being exactly one-half way across thesmall sample. To examine this question, some finite-element calculations were made with thein-plane dipole source at two non-symmetrical positions. These positions, at 6.13 mm and 3.3mm from the sample side, are shown in Fig. 6 along with the symmetric position at 12.7 mmfrom the sample side. Figure 6 also compares the signals for both the symmetric source position
and the two different non-symmetric positions. These results were for the point sensor at two
distances (60 mm and 180 mm) from the source. Additionally, Fig. 6 includes values of theincrease in peak amplitude relative to the same large-sample case. The in-plane dipole source forthese calculations was 2.035 mm below the top surface, and the signal was filtered with a 40-kHz
high-pass filter. The results shown in this figure indicate that the increases in signal amplitude,
while still significant, typically are not as large for the non-symmetric source positions. Table 2
summarizes these results along with similar results for the large-aperture sensors. The range ofincreases for the large-aperture sensors is typically smaller compared to the point-type sensors.

2653.3 mm•••(a) @ 60 mm

24+ 8 dB-2424(b) @ 180 mm

15+ 12.7 dB

Source @12.7 mmfrom side 0(center)0pm

12.7 mm

6.13

Sensor 4

Amplitude,

12.7 mm-15
15@ 6.4 mm

+ 0.83 dB

0

0

-24
24

-15
15

+ 5.1 dB

+ 7.7 dB

+ 10.1 dB@ 3.3 mm
0

0

-24

-15
0

25

50
75
Time, µs

100

125

25

60

95

130 165
Time, µs200

Fig. 6. Examples of point sensor out-of-plane displacement (40-kHz high-pass filter) when thesource is located at three different transverse positions (12.7, 6.13, and 3.3 mm from small
sample edge as shown, not to scale) in the source plane. Results shown for sensors nominally at
60 mm (sensor 3) and 180 mm (sensor 1) from the source plane with the source centered 2.035
mm below the plate’s top surface (at the mid-plane).
The wide range of values as a function of source position and sensor position again results fromthe many differences in the reflections as these two variables change.
The effects of reflections have some practical implications for both AE field tests and AE
laboratory tests. Probably most important is the fact that some AE sources detected in smalllaboratory coupons will not be detected in large specimens. Without the signal amplificationfrom edge reflections, some sources will be hidden by the background noise (electronic noisefrom the preamplifier) in large samples. Thus, even with the sensors at the same distances266

Table 2 Increase (dB) in peak signal amplitude as a function of position of source relative to thesmall specimen edge(i) Point-type sensor (40-kHz H.P. filter)(a) At 60 mm(b) At 120 mm

Symmetric
at 12.7 mm
812.7(ii) Large-aperture sensor (100-300 kHz)(a) At 60 mm(b) At 120 mm

910.3

at 6.13 mm
0.83
7.7
0.1
1.5

at 3.31 mm
5.1
10.1
3.8
4

relative to the source as in a small laboratory coupon, the database of detected signals will besmaller for large samples (same material and thickness) than for a coupon. A second important
aspect relates to the use of peak amplitudes for source identification in a laboratory-size sample.
As Fig. 6 shows, moving the sensor from 60 to 180 mm from the source typically results in a lossof signal amplitude (note vertical scale goes from ±24 pm to ±15 pm). An even larger effect isthe potential change in peak amplitude as the source is moved laterally across a single crosssectional source plane (from Fig. 6, 0.83 to 8 dB at 60 mm and 7.7 to 12.7 dB at 180 mm). In
addition to the dependence of peak amplitude on lateral source positions, the peak amplitude alsochanges with the depth of the source. Figure 7 demonstrates these changes in the small specimen
at 120 mm from the source. The point-type sensor results (40 kHz high pass at sensor 2) shownin this figure are for an in-plane dipole at depths of 0.47 mm and 2.35 mm (mid-plane). The
peak amplitude difference for the same source at these two different depths is about 9.3 dB. The
equivalent large-aperture sensor data (100 to 300 kHz bandpass) produces a difference of about a
8.1 dB in the small specimen. For the large sample, the peak amplitude changed by about 8.9 dB
with the point sensor, and by about 8.4 dB for the large-aperture sensor under the same
conditions.
B. Spectra of Signals
Numerous users of AE have attempted to use the frequency spectrum of an AE signal for the
purpose of source identification. The premise for such studies is that different source types will
have different frequency spectra. We note that some researchers have recognized the need to use
wideband sensors of small aperture and relatively flat with frequency response, in such
experiments. Typically, experiments analyzing frequency spectra have been performed in thelaboratory with relatively small samples, which inherently have nearby edges. In some cases, theAE researchers have recognized that edge reflections could cause a problem. This recognition
leads to the technique of calculating the spectrum of only some arbitrarily selected early portionof the measured signal. The database developed by the FEM modeling provides for a systematicinvestigation of the effects on spectra of reflections from nearby specimen edges in coupon
specimens.
To develop key insight into the effects of edge reflections on spectra, we will first examinesome results obtained from FEM calculations with the large sample. Figure 8 shows the out-ofplane displacement (40 kHz high-pass) for point sensors located at 180 and 60 mm from an inplane dipole located at the mid-plane. The reflection from the left edge of the sample is shownby an arrow for both propagation distances. These reflections follow the expected pattern of

267

40

At 0.47 mm

20

Displacement, pm

0
-20
-40
20

At 2.35 mm

10
0
-10
-20

0

40

80
120
Time, µs160200Fig. 7. Out-of-plane displacement in small sample at 120 mm for the same in-plane dipole
source located at two different depths (0.47 mm and 2.35 mm below the top surface).
arriving first at the sensor farthest from the source, since it is nearer the left edge of the large
sample. If the whole signal shown in Fig. 8(a) is used to calculate the Fast Fourier Transform(FFT), the result is shown in Fig. 9(a). If the signal in Fig. 8(a) is truncated at 145 µs (i.e. before
the reflection arrival) and the FFT is calculated the result is shown in Fig. 9(b). As a part of the
FFT calculation, both signals were extended with zeros so as to have the same number of points.
By comparing Figs. 9(a) and 9(b) it is clear that the spectrum shown in Fig. 9(a) has been
distorted due to the presence of the small reflected signal. Further, the distorted region of thefrequency spectrum is the frequency range contained in the reflection signal. This fact wasverified by calculating the FFT of the reflection signal and showing that it was nearly identical to
the FFT of the first part of the direct signal. If we examine the spectrum for the same sourcecase (0 to 145 µs signal) but in the small specimen, the presence of substantial distortion isshown in Fig. 10(a). Notice also that the peak amplitude of the spectrum in the small specimen
is more than 16 dB higher than that in the large sample (Fig. 9(b)).
It is interesting to note that, if the result in Fig. 10(a) is substantially smoothed, then as
shown in Fig. 10(b) a spectrum more similar to that of the large specimen (Fig. 9(b)) can beobtained. The amount of smoothing necessary was determined by trial and error. If Fig. 9(b)had not been available then the appropriate smoothing (in this case, over 150 kHz portions orover 30 digital points) could not have been easily determined.

268

3.5

Displacement, pm

(a)
0.0

-3.5
9

(b)

0

-9

0

40

80

120

160

200

Time, µs
Fig. 8. Out-of-plane displacement (with point sensors and 40-kHz high-pass filter) at (a) 180 mm
and (b) 60 mm from the source in the large sample. Edge reflection indicated by arrows. Sourceis an in-plane dipole located at the mid-plane.

269

80
(a)

60

Amplitude, fm

40
20
0
80
(b)

60
40
20
0

0

0.2

0.4

0.6

0.8

1

Frequency, MHz
Fig. 9. FFT spectra of the signal shown in Fig. 8(a). Fig. 9(a) is for the whole time domain out to200 µs (including the edge reflection of the highest-velocity part of the fundamental symmetricmode), and Fig. 9(b) is for the same time-domain signal truncated at 145 µs (prior to thereflection).
Distortion of the AE signals and their FFT due to edge reflections has several implications
for the materials researcher who is attempting to use AE techniques to identify different damage
mechanisms. Most obvious is that the use of a large sample that does not result in early edge
reflections would significantly enhance the determination of the basic source characteristics ofAE signals as determined in the far-field. This size sample has been used to monitor growth of
fatigue cracks (Hamstad and McColskey, 1999). Further, the approach of using only a small
portion of the beginning of the AE signal to calculate the FFT in small specimens does notprovide useful information for identification of AE sources. The reason for this is two-fold.
First, truncating the time-domain signal may eliminate source information present in the slowervelocity (direct-path) components of the signal. Second, even the early-arriving parts of thesignal have experienced distortions from early edge reflections. These effects are illustrated inFig. 11. Starting from the top of Fig. 11(a), a series of five truncated signals is shown for a small
specimen with an in-plane dipole source located at 1.723 mm below the top surface. The pointtype sensor data (40 kHz high-pass) are for a sensor located at 60 mm from the source epicenter.
The durations of the non-zero portion of the signals are 10, 20, 30, 110, and 165 µs. The bottom
signal in this figure is the large-specimen equivalent case truncated at 175 µs total duration.

270

600
(a)

Amplitude, fm

400
200
0
300

(b)

200
100
0

0

0.2

0.4

0.6

0.8

1

Frequency, MHz
Fig. 10. FFT spectra of a 145-µs portion of the out-of-plane displacement at 180 mm for an inplane dipole source located at the mid-plane of a small sample. (a): the spectrum withoutsmoothing; (b): a smoothed version of (a). Note that the source type and location (relative to thesensor) is the same as shown in Fig. 9 for the large sample.
Figure 11(b) shows in the same order the calculated FFTs of the signals. We note that prior to
calculating the FFTs all the signals were extended with zeros to the same number of points.
Clearly the FFTs of the early part of the signal in the small sample fail to include the essence ofthe real spectrum as shown at the bottom of Fig. 11(b). And in fact a longer truncated signal(e.g. 110 µs of non-zero signal) provides a more correct (but still distorted) result. The reason forthis fact, as indicated above, is that the slower-velocity parts of the signal define significant
aspects of the true signal FFT. We conclude that it is not surprising that AE source identificationby the use of spectra has not been a broadly successful approach since most researchersattempting to do this have used small samples.
C. Concluding Remarks
The user of AE technology who does not understand the effects of nearby specimen edges
can make incorrect conclusions. On one hand, a field practitioner testing a large sample without
nearby edges may not detect damage-indicating AE events. These same events may have been
easily detected in small laboratory coupons due to edge reflections that amplify the direct signal.
Further, if events are detected in the field case, the field practitioner may underestimate theirsignificance. The reason is that the events will not have the amplitude, energy and durationlevels associated with the same original source amplitude in the results for laboratory coupons.
On the other hand, the materials scientist attempting to use AE techniques to identify certain
271

(a)

Displacement, pm

6
0
-6
25
0
-25
35
0
-35
35
0
-35
35
0
-35
12
0
-12
0

35

70

105

140

175

Time, µs
Fig. 11. (a) Out-of-plane displacement signals (truncated at different lengths) at 60 mm from thesource.
types of damage in a small laboratory coupon may become frustrated with the lack ofconsistency in source identification using features such as the hit peak amplitude, energy,
duration, and frequency content. In addition, the materials scientist may find that the distorted
frequency spectra make it difficult or impossible to distinguish one type of source from another.
Finally, we believe the development of AE technology could experience a much larger payoff in
the practical application of the technology to large samples if associated laboratory research wereregularly carried out on larger samples.

272

(b)
90

Amplitude, fm

0
180
0
380
0
600
0
600
0
280
0

0

0.2

0.4
0.6
0.8
Frequency, MHz

1

Fig. 11(b): FFTs for the signals in Fig. 11(a). In each figure, the first five plots (starting from thetop) are for a small specimen, and the bottom plot is for the equivalent large specimen. The
source was an in-plane dipole located at 1.723 mm below the plate’s top surface in the small andlarge specimens, and the signals were for a point-type sensor (40-kHz high-pass).

273

REFERENCES
M. R. Gorman (1998), "Some connections between AE testing of large structures and small
samples," Nondestr. Test. Eval., 14, 89-104.
M. A. Hamstad, A. O'Gallagher, and J. Gary (1999), "Modeling of buried monopole and dipole
sources of acoustic emission with a finite element technique," J. Acoust. Emission, 17(3/4), 97110.
M. A. Hamstad and J. D. McColskey (1999), "Detectability of slow crack growth in bridge steelsby acoustic emission," Materials Evaluation, 57(11), 1165-1174.
S. L. McBride and Y. Hong (1992), "Acoustic emission from crack growth in 7050 aluminumand 7075 aluminum as a function of temperature and heat treatement," Progress in Acoustic
Emission VI, edited by T. Kishi, K. Takahashi, and M. Ohtsu, The Japanese Society for NDI,
Tokyo, pp. 521-528.
W. H. Prosser, M. A. Hamstad, J. Gary, and A. O'Gallagher (1999), "Reflections of AE waves infinite plates: finite element modeling and experimental measurements," J. Acoust. Emission,17(1/2), 37-47.
C.B. Scruby (1985), "Quantitative acoustic emission techniques," Chapter 4 in Non-destructive
Testing, Vol. 8, Academic Press Inc., London, pp. 141-210.

274

A WAVELET-BASED AMPLITUDE THRESHOLDING TECHNIQUE
FOR AE DATA COMPRESSIONGANG QI and ENG T. NG
Department of Mechanical Engineering, The University of Memphis
Memphis, TN 38152-6576
ABSTRACT
Data compression is one of the major functions of the wavelet analysis. This paper addresses
an application of the discrete wavelet analysis of digital compression of the acoustic emission(AE) signals. An amplitude thresholding algorithm was developed especially for the AE signals.
To demonstrate the proposed algorithm, a simulated transient periodical signal and actual AE
signal were used to compare the proposed and three existing signal compression algorithms. In
the case of the simulated signal, the existing maximum absolute threshold (MAT) algorithm
provided the highest compression ratio and maintained a low error. However, in the case of AE
signal, all three existing algorithms failed to provide reasonable results. The proposed algorithm
provided the highest compression ratio of 6.5 with a relatively low error. The results of thisstudy indicated that the wavelet technique is an efficient tool to use in compressing the AE
signals.
1. INTRODUCTION
Engineering data compression draws more and more attention to developing sophisticatedinstruments. Traditional data compression techniques (the Fourier transformation based
techniques) are being challenged by the recently developed wavelet-based technique. Since thelate-80s, numerous studies of wavelet analysis have been reported in the area of imaging process.
Recently, researchers studied the wavelet-based signal processing method to compress various
engineering testing data. Santoso [1] presented an algorithm to compress power quality
disturbance data. Staszewski [2] and Tanaka [3] utilized the wavelet transformation to compress
vibration data. In their studies, vibration data were decomposed into various wavelet levels fordenoising. The denoised data were then compressed. The results showed that the compression
ratio was between 5 and 10. They concluded that the wavelet-based signal processing method isa potential tool to compress digital test data. However, the above-mentioned methods failed tocompress AE data. The reason for the failure may be due to the specific nature and the size oftesting data volume of the AE signal. It is the intention of this paper to develop a new algorithm
by using the discrete wavelet transformation method to compress AE data. The algorithm wasthen tested numerically using both the simulated transient data and actual AE signals.
2. DIGITAL DATA COMPRESSION ALGORITHMS
Recently, the wavelet-based signal processing techniques have shown superior advantages
over the traditional data compression methods that were based on the Fourier transformation
techniques [4]. In this section, a brief review of the wavelet-based signal processing techniqueand existing data compression methods are provided, and a new data compression algorithm
based on the discrete wavelet techniques is proposed.

J. Acoustic Emission, 19 (2001)

275© 2001 Acoustic Emission GroupAlgorithms for Digital Data Compression
Wavelet is a mathematical transformation method. One-dimensional wavelet analysis
transforms a time-series signal into a two-dimensional representation of the signal in both timeand scale (frequency) domains. Since the wavelet scale varies with the frequency components inthe signal, wavelet decomposition is also called multi-resolution analysis technique. A
pyramidal decomposition algorithm was developed by Mallat in the mid-90s [5]. In Mallat’s
pyramidal algorithm, the signal decomposition is completed by two operators h and g on l2
space:

where H and G are called low- and high-pass filters, respectively, and h(n) and g(n) are thecoefficients of the corresponding filter. These two equations defined iterative procedures for the
wavelet decomposition. At each decomposition level, the components with low frequency aredecomposed into low and high components. For instance, cAi(n) is a smoothed approximation
coefficient set processed by filter h(n), whereas the filter g(n) processes the detail coefficients,
cDi(n). After the completion of each decomposition, the length of level components is half ofthe previous one, and this procedure is known as downsampling. The inverse procedure is called__signal reconstruction. The reconstruction is completed by a pair of reconstruction filters ( H , G )and the corresponding coefficients of the filters (h˜ , g˜ ) are given as follows:

In the actual calculations, the high-pass filter G only supplies a certain number of zeros tocomplete the upsampling process. The non-lossy signal reconstruction is given by:where k is the level of wavelet decomposition, and S(t) is theoretically the original signal. On
the other hand, this signal can also be reconstructed when certain high frequency components are
discarded:

Practically, the above equation establishes the ground for signal compression. There exists a
number of wavelet-based data compression methods applicable to engineering testing data.
These methods are reviewed in the following:276a) Maximum Absolute Threshold (MAT) [6]This method used a simple absolute value as a threshold to limit the amount of unnecessary
information in transient periodical signals. An absolute value, δab, is a predetermined empiricalthreshold to discard those data that are below this value. This threshold, in general, can be set
according to the following relationship:

where cDk are the wavelet decomposition detail coefficients at the associated scale of k, and θ isan adjustable parameter.
b) Unbiased Risk Estimate (URE) [1,2,6]
This method adapts thresholding selection using the principle of Stein's unbiased risk
estimate. The threshold algorithm for this method is given by:where n is the length of the given signal, and α, m, and j are constants [6].
c) Square-Root of Log Length (SRLL) [1, 6]This method establishes a threshold value using the square root of logarithm length of thegiven signal. The threshold value is given by:d) Tail-Maximum Cut (TMC)
The algorithms listed above are frequently used for common signals such as vibration
signals. However, because of the uniqueness of the AE signal, these algorithms are notnecessarily suitable for the compression of AE signals. AE is known as a sonic wave due to theformation of new free surfaces under loading. The sound source information is often associatedwith various noises such as electrical, servo-valves hydraulic, mechanical fretting, and various
other unknown noises. These acoustic sources form a systematic sound pattern. To be able tocompress AE signals, it is required to distinguish and identify each of those individual sources.
In general, the magnitude of the wavelet transformation coefficients of the AE signal associatedwith fracture and crack information are considerably larger than the noise in AE signals [7, 8],
especially when the signals were decomposed into different wavelet levels [8]. This factpromotes the idea of using the wavelet transformation to compress the AE data. The
compression procedure is employed in the wavelet transformation domain by retaining thosewavelet transformation coefficients associated with failure information and disregarding all othertransformation coefficients. The reverse of the above procedure was utilized to reconstruct thecompressed AE data. In the actual practice, the compressed data was first decoded by theentropy decoder, and was returned to real values through a reverse quantizer. Then, the restored
data was achieved through the inverse wavelet transformation. Because the AE datacompression process discards over 90% of the total wavelet coefficients, a biased reconstruction
is inevitable. However, because most of those discarded wavelet coefficients are associated withvarious noises, the reconstructed signal can still very well represent the original one with only aminimum loss. As a result, the restoration quality of the AE data is relatively high.
277

Considering the physical features of AE signals, the authors proposed the following algorithm:

where index is the maximum amplitude of the detail coefficients at the associated scale k. In
other words, it corresponds to the AE rise time (more details are provided in the following
section). The length(cDk) is the total length for the detail coefficients at the same scale k.
)
To evaluate the quality of the reconstructed signal S (t ) , the normalized mean-square error)(MSE) between the original S(t) and restored signal S (t ) is:

The compression is measured by the ratio of the original file size to the compressed file size andgiven as below:

3. NUMERICAL EXPERIMENTS AND RESULTS
To test and compare the existing compression algorithms, simulated transient signals and real
acoustic emission signals were explored. In this paper, the one- and three-level wavelet
decompositions were performed on the simulated data and actual AE signals. The fourth order
Daubechies discrete wavelet [5] was used.
Experiment with the Simulated Transient Periodical Signal
To demonstrate a new data compression method, the existing SRLL, MAT, and URE
thresholding methods were applied to a simulated transient periodical signal. This signal wasapplied to a first order wavelet decomposition. In Fig.1, the top curve is the original signal witha length of 500. The middle and bottom curves show the first level of approximation and detailcoefficients, respectively. When the MAT, URE, and SRLL methods were applied, thecorresponding numbers of nonzero coefficients were reduced to 2, 1, and 148 as shown in Fig.2.
The total corresponding sample points of the first level compression were 256, 258, and 550.
The file sizes of the original signal were reduced from 4400 bytes to 2064, 2048, and 4400 bytes
apiece. Because the compression ratio of the SRLL method was less than one, it means that theSRLL thresholding method was not suitable for this type of signal and was not discussed. The
normalized MSE between the original and reconstructed signals were computed for the MAT
and URE methods.
When the third level wavelet decomposition was applied, the total numbers of nonzero
elements were reduced dramatically to 82 and 72 for the MAT and URE methods, respectively,
shown in Fig. 3. The relationships between the compression ratio and wavelet levels andcorresponding errors were shown in Fig. 4. The results were summarized in Table 1 as well. In
this table, it can be seen that the URE algorithm provides a slightly higher compression ratio thanthat of the MAT algorithm. But, the MAT algorithm introduces significantly less error relative278

to the URE algorithm. It may be concluded that the MAT thresholding method is a better
candidate to compress this type of transient periodical signals. However, when the MAT, URE,
and SRLL methods were applied to the AE signals, all of them failed to provide a reasonable
result compared with the new method proposed by the authors as demonstrated in the following
section.
Table 1. The CR and Normalized MSE of Simulated Signals for Various Thresholding Methods.
First level wavelet decomposition
Thresholding
MethodCR
Normalized MSE
SRLL
0.91
----MAT
1.96
0.0003URE
1.94
0.0005----: yielded a CR that was less than 1.

Third level wavelet decomposition
CR
Normalized MSE------6.11
0.00086.96
0.0017

Fig. 1. First level wavelet decomposition of simulated signal.
Experiment with real AE Signal
Standard AE calibration signals (using pencil lead break) were generated to demonstrate theproposed method. A pencil lead break signal was recorded as shown in the top view of Fig. 5.
Similarly, the one-level wavelet decomposition for the AE signal was performed. The original
signal had 1024 sample points. The approximation and detail coefficients on the first scale had
512 sample data each as shown in the middle and bottom views. The SRLL algorithm again
failed to provide a positive compression ratio and was not considered. When the MAT, URE,

279

Fig. 2. The detail wavelet coefficients after thresholding of one level decomposition of simulated
signal.

Fig. 3. The third level detail wavelet coefficients after thresholding of simulated signal.
280

and TMC algorithms were applied to the corresponding detail coefficients, cD1, the numbers of)nonzero elements in cD1 became 51, 305, and 0, respectively as shown in Fig. 6. The filtering
procedures were completed by the simple thresholding determined in the MAT and URE
algorithms. The TMC algorithm is to set a threshold related to the rise time and account for
additional 20% (or more) for the second lobe. After the second lobe, any additional coefficients
will discarded. For example, the maximum value of the first level detail coefficient is achievedat around the index of 60 in Fig. 5. In this particular case, all the detail coefficients werediscarded according to the TMC algorithm. The total corresponding compressed data samples
were 617, 1125, and 515, which means that the actual storage spaces were 4936, 9000, and 4120
bytes comparing to the original one that required 8192 bytes.

Fig. 4. The compression ratio and the normalized mean squared error of simulated signal using
traditional method (MAT, URE, and SRLL).
When the third level wavelet decomposition was performed, the nonzero detail coefficients,)
cD3 , from MAT and TMC algorithms were 141 and 11, respectively. URE failed to provide a
positive compression ratio at this level and was discarded. The compressed data for the MAT
and TMC algorithms were 416 and 156 sample points. The relationships between thecompression ratio and wavelet levels and corresponding errors were shown in Fig.7. The results
were summarized in Table 2 as well. In this table, it can be seen that the TMC algorithm
provides the highest compression ratio and maintains a relative low error. Figure 8 shows thecomparison of original and reconstructed AE signals from MAT and TMC algorithms. It canseen that there is almost no visible difference between the original and the reconstructed signal
from TMC algorithm.

281

Fig. 5. First level wavelet decomposition of AE signal of one level decomposition.
Table 2. The CR and Normalized MSE of AE Signals for Various Thresholding Methods.
Thresholding
First level wavelet decomposition
MethodCR
Normalized MSE
MAT1.6592~10-4 to 10-3URE
0.9102~10-4 to 10-3
TMC
1.9883~10-4 to 10-3----: yielded an CR that was less than 1.

Third level wavelet decomposition
CR
Normalized MSE
2.46150.3959------6.5641
0.2994

4. CONCLUSIONS
The wavelet-based AE signal compression using the MAT, URE, SRLL, and TMC
thresholding methods were described in this paper. In the case of the simulated transientperiodical signal, the MAT algorithm provided a compression ratio as high as 7 with thenormalized MSE in the order of 10-4 to 10-3. While, the SRLL failed to compress the simulated
signal. In the case of the real AE signal, only the TMC algorithm proposed by the authors
provided a compression ratio as high as 6.5 and maintained the lowest error of 0.6 for three-levelcompression. It was also demonstrated that the wavelet-based AE data compression technique isa highly efficient method to significantly reduce storage space.
282

Fig. 6. The detail wavelet coefficients after thresholding of AE signal.

Fig. 7. The compression ratio and the normalized mean squared error of AE signal using
traditional and wavelet-based methods. SRLL
283

REFERENCES[1] Santoso, S., Powers, E. J., and Grady , W. M., (1997), “Power Quality Disturbance Data
Compression Using Wavelet Transform Methods,” IEEE Transactions on Power Delivery, 3,1250-1256.
[2] Stasewski, W. J., (1998), “Wavelets Based Compression and Feature Selection for Vibration
Analysis,” Journal of Sound and Vibration, 5, 735-760.
[3] Tanaka, M. and Sakawa, M., (1998), “Application of Wavelet Transform to Compression of
Mechanical Vibration Data,”, Electronics and Communications in Japan, 1: 1379-1387.
[4] Wickerhauser, M. V. (1994), “Adapted Wavelet Analysis from Theory to Software
Algorithms,” A.K. Peters, Wellesley, MA.
[5] Mallat, S.G. (1994), “A Theory for Multi-Resolution Signal Decomposition: The Wavelet
Representation”, IEEE Pattern Anal. and Machine Intell. 11(7): 674-693,.
[6] Misiti, M., Misiti, Y., Openheim, G., and Poggi, J.-M., (1996), Wavelet Toolbox, The
Mathworks, Inc., Matick, MA.
[7] Fang, D. and Berkovits, A. 1995 “Fatigue Design Model Based on Damage Mechanism
Revealed by Acoustic Emission Measurements.” Journal of Engineering Materials andTechnology, 117: 200.
[8] Qi, G., (2000) “Wavelet-based AE Characterization of Composite Materials,” NDT & E
International, 33(3): 133-144.

Amplitude

1000
(a)
0
-1000

0

200

400

600

800

1000

1200

800

1000

1200

800

1000

1200

Amplitude

1000
(b)
0
-1000

0

200

400

600

Amplitude

1000
(c)
0
-1000

0

200

400

600
Sample points

Fig. 8. Original and reconstructed AE signals: (a) original signal; (b) reconstructed signal from
MAT algorithm and (c) reconstructed signal from TMC algorithm.
284

ACOUSTIC EMISSION MONITORING OF FATIGUE OF GLASS-FIBER WOUNDPIPES UNDER BIAXIAL LOADINGPAWEL GEBSKI, LESZEK GOLASKI and KANJI ONO*,
Department of Civil Engineering, Kielce University of Technology, Kielce, Poland*Department of Materials Science and Engineering, University of California,
Los Angeles, California, USA
ABSTRACT
Damage accumulation during combined fatigue loading has been monitored by acoustic
emission (AE) technique in filament wound pipes. The tests were made on epoxy resin-E-glassfiber filament wound pipes with winding angle φ = ±55˚.. In order to simulate the service
conditions more fully, the pipes were subjected to two loading modes: M II and M III.
Depending on the loading modes fatigue processes were controlled either by transverse or shear
stress component in reference to fiber direction. During fatigue the acoustic emission wasrecorded. The damage evolution with fatigue cycles for each loading mode was estimated on thebasis on acoustic emission parameter analysis. Emphasis has been placed on changes in AE
activity for different fatigue loading ranges during fatigue progression. AE signals after filtrationand multi-parameter analysis show that not only the beginning of failure but also the pre-failurechanges in angle-ply pipes can be estimated. A procedure of AE signal analysis that warns about
incipient failure before any serious damage takes place in the composite/laminate structures, hasbeen suggested. This procedure is valid for both applied loading modes in spite of the differencesin failure modes. The approaching fatigue failure is detected by changes in selected AE
parameters, when samples exceeded 70-90% of fatigue life. Using this procedure, when multichannel AE equipment and zonal localization are applied, the time and location of fatigue failure
in large laminated structures can be predicted experimentally.
1. INTRODUCTION
Acoustic emission (AE) technique has been applied with success for testing of composites
and composite laminates. Advantages of this technique are volume inspection, real time
monitoring, damage accumulation assessment and possibility of damage localization. AE is used
for first damage detection, strength prediction, damage history estimation and quality control ofcomposite materials and structures [1-6]. Most studies concern statically loaded coupons andcomposite structures. Fatigue testing of composites by AE methods started in early seventies [79]. These and further results [10-14] indicated that there exists qualitative relationship between
AE activity and damage evolution. AE procedures usually are used for fatigue damage initiation
detection, damage progression and accumulation. However, the inadequacy of AE method in
warning against fatigue failure of composite and composite laminate structures -is evident as
reviewed by Hamstad [15]. This problem is important for composite users because the scatter inreal service time for the same loading conditions may differ as much as ten or more. Moreoverwarning against failure together with failure localization makes it possible to repair thecomposite under consideration.
In this paper the AE behavior during fatigue of laminate tubular specimens was analyzed. In
particular an attempt was made to exploit AE results for the monitoring of imminent fatigue
J. Acoustic Emission, 19 (2001)

285© 2001 Acoustic Emission Groupcollapse. To relate the tests with the service conditions more fully fatigue loading was performedin two different loading modes leading to different failure mechanisms.
2. EXPERIMENTAL
The tests were made on E-glass-fiber filament wound pipes with the winding angle Φ = ±550.
Electrotechnical Institute – Wroclaw, Poland, fabricated the pipes from epoxy resin EPIDIAN 53
mixed with Z -1 catalyst and PPG JFG E C15 120-glass fiber. The nominal fiber volume fraction
was Vv = 0.5. All tests were made on tubular specimens with dimensions: length 300 mm,
internal diameter 42 mm and wall thickness 1.5 mm. The pipes were subjected to variableinternal pressure giving rise to biaxial fatigue loading with frequency 1 or 2 Hz, stress ratio R =0.1 and different pressure amplitude. The loading of samples were performed using a modified
Schenck computer-controlled pressurization system.
In laminates the fatigue processes depend only indirectly on external loads, which usually areapplied along symmetry axis of a laminate. In reality, fatigue damage is controlled by stresscomponents in laminae of the laminate oriented with regard to fiber direction. They are normal
stress along fiber σ ||, transverse to fiber stress σ ⊥ and shear stress τ# along fiber. Because thestrength of the laminae in fiber direction is relatively high, it can be expected that incipient
fatigue cracking depends mainly on shear and transverse stress components.
To analyze fatigue processes under conditions where either transverse stress or shear stress isdominant in fatigue damage development, two loading modes were applied. They were Mode 2(M II) giving rise to high transverse to shear stress ratio and Mode 3 (M III), where the shear totransverse stress ratio was high. In M II, both ends of a pipe sample were closed and the ratio ofhoop stress σH to axial stress σA was equal to two while in M III both ends of pipe can slide on
seals so that the axial stress was equal to zero. No reinforcing mandrels at pipe ends were appliedin M III loading.
Damage evolutions during fatigue loading was investigated using acoustic emission (AE)technique. For AE signals acquisition, a two-channel PAC MISTRAS 2001 instrumentation
together with wideband sensors (PAC WD) was used. Channel No. 1 was furnished with 20 kHz
high-pass filter while in channel No. 2 the high-pass filter cut-off was 100 kHz. The gain of preamplifiers was 40 dB while 40-dB threshold level was applied for all the tests. During loadingclassical AE parameters were recorded. They are:
1. event rate / cumulative events
2. count rate / cumulative counts
3. duration / cumulative duration
4. rise time / cumulative rise time
5. energy / cumulative energy
6. AE signal amplitude.
To select appropriate load amplitude the fatigue tests were preceded by failure analysis ofpipes under smooth pressurization. These tests were performed on two sets of pipes withdifferent matrix resin. They were epoxy matrix and polyester matrix while lamina sequences andfibers arrangements were the same in both sets. The pipes were loaded in axial tension andinternally pressurized in M II and M III. To estimate failure evolution AE parameters listed
above were analyzed. In addition, waveforms of AE signals were recorded and analyzed with
fast Fourier transform. Comparison of results for the two sets of pipes demonstrated that,286

although the failure strength was close to each other, the differences in AE behavior werepronounced. The classical AE parameters depended on loading modes, filtration and sensor
position. Especially AE behavior was influenced by matrix resin confirming earlier result [16].
Among the considered AE parameters, those less dependent on testing conditions appeared to beevent summation. Variability of AE parameters makes it difficult to develop a general AE failure
criterion for both sets of pipes. However, changes in AE parameter qualitatively indicated theincipience of damage processes and damage accumulation or damage evolution. Details
concerning pipes behavior during smooth pressurization are given elsewhere [17].
The waveform analysis, which was performed during quasi-static loading, revealed the same
waveforms for all of tested pipes independently of matrix resin and loading mode. Three types ofwaveforms denoted as A, B and C prevailed. The matrix resin and loading mode changed only
the proportion between them. When pipes were loaded in tension additional waveforms
appeared, which were not observed during internal pressurization [17]. The observed changes inwaveforms, which took place during smooth pressurization and tension testing of pipes,
suggested that waveforms of AE signals cannot be used to characterize damage accumulation or
to warn against failure. Therefore during fatigue tests emphasis was put on the classical AE
parameters analysis.
In this study, two types of variable loading were performed during fatigue tests. Firstly,
continuous fatigue loading with constant load amplitude, and secondly, fatigue loading in blocks
containing 1000 cycles each separated by constant load-hold periods at maximum stress for 300
seconds each. Pipes were loaded up to failure.
3. RESULTS
3.1 Fatigue in Mode III (Hoop loading; No axial loading)To estimate the strength of pipes under static loading, which is necessary for fatigue load
selection, the pipes were smoothly internally pressurized in mode M III up to failure. Duringloading AE signals were recorded for damage evolution evaluation. A typical AE event rate andAE duration vs. pressure (the latter in point graph) are shown in Figs. 1a and 1b, respectively.
Increase in AE activity took place at internal pressure in the vicinity of 100 bars, indicating
intensification of failure processes. This pipe burst at pressure of 201 bars. For fatigue tests in M
III internal pressure of 100 bars was taken as the maximum load. Pressurization with thispressure is leading to hoop stress equal to 140 MPa while the axial stress is 0. Such hoop stress,in accordance to Puck theory [18,19], produced shear stress along fibers of 50 MPa in laminae.
This stress was below the shear strength of a single lamina, which was approximately equal to 62
MPa. However, it was high enough to exceed the stress of 37 MPa, at which departure from thelinearity on shear stress - strain curve took place. The transverse to fiber component was only
about 4 MPa; i.e., well below the transverse strength of lamina. For this reason, when specimens
are loaded in M III, incipient fatigue damage by shear is expected.
Most of fatigue tests in M III were performed at pressure range pmax = 100 bars and pmin = 10
bars. Some pipes were loaded at lower pressure ranges. The applied load and number of cycles
up to failure are given in Table I.
All pipes, which were fatigued in M III, showed failure by burst. Failure was not preceded byswelling or buckling. The origin of fatigue crack is situated at a fiber interlacing point. Next,
287

Fig. 1. AE events rate and energy point plots vs. pressure during smooth pressurization of GRP
pipe in M III.
cracks spread in two directions. These directions are parallel to fiber directions in internal layerof pipe. The pipe after fatigue failure is shown in Fig. 2a (on the left side). When pipes weresmoothly pressurized in M III, failure took place by delamination and fiber matrix decoupling.
This failure was not accompanied by intensive fiber breakage, which was observed in pipes after
fatigue tests. For comparison a typical damaged pipe surface after smooth pressurization isshown in Fig. 2b on the right side.
Table I Fatigue strength data of pipes loaded in M III
SampleNo
MIII/001

NumberFailureof cycles

by

Failure at 201 bars after smooth pressurization

Burst

Pressure in bars
max

min

MIII/002

100

10

4 981

Burst

MIII/003

100

10

8 452

Burst

MIII/004

100

10

6 990

Burst

MIII/005

100

10

6 134

Burst

MIII/006

100

10

5 301

Burst

MIII/007

80

8

53 000No damage

MIII/00890

9

15 075

Burst

MIII/009

90

9

105 623

Burst

The micrograph of fatigue fracture surfaces, which is demonstrated in Fig. 3, revealed that in
the first lamina (internal) and third one the cracking took place along fibers by shear while in thesecond and fourth (external) laminae failure was accompanying by intensive fiber breakage andfiber pull out. In the vicinity of the crack, in external lamina, the resin was crumbled. During288

Fig. 2. Failure pipes as a result of (left) fatigue in M III, (right) smooth pressurization in M III.

Fig. 3. Fatigue fracture surface of pipe loaded in M III.
samples washing and preparation for microscopic examination, crumbs of resin were removed
and naked fibers can be seen as it is demonstrated in Fig. 4.
The test procedure evaluation criteria, which were used in this paper, are based on acoustic
emission technique. However, most of AE evaluation criteria, assessing the integrity ofcomposite structures, do not consider the components loaded by variable loading and fatigue
damages. The most frequently used criteria for smoothly loaded composite structures are thetotal number of acoustic emission counts and number of large amplitude hits. Applicability of289

Fig. 4. Micrograph of fatigue area on pipe surface loaded in M III.
these criteria for composite damage evaluation under fatigue loading conditions has been
analyzed in this study.
The summation of classical AE parameters, which were recorded during fatigue loading ofpipes in Mode III, is given in Table II. The only AE parameter, which does not depend on
filtration, is AE event summation. As it was indicated elsewhere [17], AE events do not dependon sensor position on these pipes as well. For this reason AE summation in all analysis is taken
as the basic AE parameter, while the other parameters were taken as complementary ones. It
should be noticed that in some samples (for example sample No 004 and 005) number of cycles
up to failure is higher than number of AE events. This means that there is a number fatigue
cycles, during which no AE signals above 40 dB threshold were produced.
Table II AE parameters recorded during fatigue of pipes loaded in M III
Sam
ple

Cycles to
failureNo

20 kHz high-pass filter
AE

AE

AE

EventsCounts3

100 kHz high-pass filter

AE risetime

AE
Energy82000

1,6*104

2,7*103

Duration

AE

AE

AE

EventsCounts7800

4,0*103

AE risetime

AE
Energy43000

0,9*104

0,5*103

Duration

002

4 981

7800

3,8*10003

8 452

15 600440*103

6 500

118*104

114*10315 600310*103

3 600

92*10442*103

004

6 990

3 200

100*103

3 000

64*10434*103

3 200

72*103

1 500

34*10412*103

005

6 134

5 200

130*103

2 200

48*10435*1035 200

70*103

1 050

25*10412*103

006

5 301

5 400

120*103

4 800

50*10440*1035.400

75*1033 800

50*10421*103007

53 00048 000980*103

38.000220*104

82*10348 000420*10335 000200*104

74*103008

15 07512 200200*103

13 40024*10461*10312 20098*10313 00020*10420*103009

105 623

94 0001120*103

88 00080*104220*103

94 000560*103

84 00075*10436*103290

Fig. 5. Point diagrams of AE signal amplitude (in dB) vs. pressure (in bars) for GRP pipes
fatigue-loaded in M III: (a: left) sample 005, (b: right) sample 006.
The results demonstrated that there is no relationship between the number of cycles to failureand number of AE events. This behavior can be expected because during fatigue loading oflaminated composites two categories of AE sources are active. The first one is randomly
distributed in structure and started from the beginning of variable loading. This category ofsources is attributed to cracking processes and friction of cracked surface in all volume ofsample. Another type of fatigue-related AE sources starts to become active after a number offatigue cycles and is localized in comparatively small volume of sample. AE sensors record
signals generated by both groups of AE sources. Thus, the AE signals, which are collectedduring variable loading, depend both on general damages, which took place in all volume of thesample and on localized processes leading to fatigue failure. The proportion between the two
types of signals may vary from sample to sample. For this reason total summation of classicalAE parameters could not be used as a measure of residual fatigue life.
For the evaluation of using large amplitude signals as an indicator of fatigue damage
verification, AE amplitudes were recorded as a function of pressure within fatigue cycle up to
failure. Typical AE signal amplitude point diagrams plotted against pressure range of fatigue
cycles for two GRP pipes loaded in M III are given in Fig. 5. The distribution of large amplitude
hits depends on load value; in Fig. 5a, signals over 75 dB and in Fig. 5b, signals over 70 dB arefound above 75 bar. The maximum amplitude value can change from sample to sample. The
differences may be as much as 20 dB. In all samples the signals with the highest amplitudes took
place at or near the maximum loads. However, high amplitude (>60 dB) signals were observed at
the lowest load of the fatigue cycles as well. As shown in Fig. 5, these are abundant over the
lower half of the loading cycles. In most of the samples, the AE activity remains constant until
70-85% of the maximum load, followed by an increase. General shape of these diagrams did notdepend on the number of fatigue cycles and is nearly identical when AE data from only a limited
number of total fatigue cycles are taken into account. Nevertheless it should be noticed that AE
parameters varied from sample to sample, both in the intensity and maximum values, even forsamples, which exhibited similar numbers of cycles to fatigue failure. In some samples, there
was a slight reduction in AE amplitude at mid-range of applied pressure.
291

Fig. 6. AE events summation as a function of number of fatigue cycles for GRP pipe loaded in M
III (sample 003). a) loading ranges 0 – 50% of fatigue load cycle (top), b) loading ranges 50 –100% of fatigue load cycle (bottom).
Because it is unlikely that cracking occurs at low load levels, a large number of highamplitude signals were produced by friction. This assumption is in accordance with data
presented by Awerbuch et al. [10]. Undoubtedly, when laminate composites are loaded byvariable loading in M III, which produces shear cracking along fibers, significant part of AE is
produced by friction. These data demonstrate that criteria based on high values of AE amplitude
parameter should be used with great caution for warning against fatigue failure of composite
structures.
292

Fig. 7. AE energy summation as a function of number of fatigue cycles for GRP pipe loaded in
M III (sample 003). a) loading ranges 0 – 50% of fatigue load cycle (left), b) loading ranges 50 –100% of fatigue load cycle (right).
More promising are the criteria based on changes in AE parameter with number of fatigue
cycles. The load cycle was divided into ten load range (LR). The AE parameters, which wereanalyzed, are summations of events, counts, duration, rise time and energy. The data for analyses
were taken from both channels; that is channel with 20 kHz high-pass filter and 100 kHz highpass filter. However, for fatigue loading in M III, filtration has little influence on the shape of AE
parameter diagrams. It was expected that the sensitivity of AE parameter variation with fatigue
processes improves, when the recorded parameters are subjected to different types of filtration;
namely, low frequency signals from friction and high frequency signals from cracking. In thepresent case, it appears that both types of signals may originate primarily from friction. While
cracking may contribute, the dominant AE is from friction. As discussed earlier in conjunction
with Table II, the summations of events had little effect of filtration, implying that separate
cracking signals were not detected. This is most likely the result of the laminate stacking
sequence; that is, when a shear crack develops, the fibers in the next lamina are subjected toshear loading. The bending load may not be high enough for immediate fiber failure, but fatigue
loading produces resin crumbling as observed in Figs. 3 and 4. Fibers are subsequently fractured
under low loads, giving rise to only low level AE signals. Thus, the fiber failure in shear does notproduce high level AE signals as normally found under tensile loading of fibers.
Typical AE event summation vs. No of cycles graphs, for different LR of fatigue load cycle,
are shown in Fig. 6a and 6b while the same diagrams concerning AE energy summation are
given in Fig. 7a and 7b. As expected, these plots depend on LR and number of cycles. AE event
summations decrease with lower LR value. The only exception is for the lowest LR, where event
summation is greater than that for the next higher LR. The shape of the other AE parameters(number of counts, duration, rise time) vs. number of fatigue cycle plots, for the same sample,
are generally similar; however, they can differ in details. The differences concern sudden
increases (jumps) in AE parameters. Mostly the jumps are observed in some of LR, while in theother no or little changes in AE parameters could be seen. It is interesting to notice thatfrequently a jump in one AE parameter was not accompanied by a jump in another AE
parameter. The origin of these sudden increases of AE parameters in random places of fatigue
processes is unknown; however, they underline the intermittent nature of fatigue processes incomposite laminates.

293

Fig. 8. AE duration summation for increasing and decreasing part of fatigue cycle as a functionof number of fatigue cycles for pipe loaded in M III (sample No 004). a) loading range 0 –20%
of fatigue load cycle (top): b) loading range 80 – 100% of fatigue load cycle (bottom).
In addition the AE parameter changes with number of fatigue cycles were separately
recorded for increasing and decreasing parts of LR. It was noticed that while the parametervalues recorded during both parts of loading cycles are the same when the data concern thewhole fatigue test of sample, differences took place when AE activity is recorded during selected
LR. When higher LRs are considered, parameter summation during load decreasing part ishigher in comparison with that which is recorded during load increasing part, while for lower
LRs data recorded during load increasing part are higher. Examples are given in Figs. 8a and 8b,
where event summation curves during increasing and decreasing parts of loading for 0-20 % and80-100% of load are presented.
294

In all the pipes fatigue tested in M III, the oncoming fatigue failure is anticipated by anincrease in AE parameter values with the number of fatigue cycles. This increase is observed
during the last 7 to 15 percent of fatigue life. In close observation, changes can be noted startingat 50-60% of fatigue life. The general shape of these curves is identical to the classical AE
behavior under fatigue of metallic materials or structures.
These changes did not occur in all AE parameters at the same degree. The most pronounced
changes are in energy and duration while the weakest was in event summation. On the other
hand, the changes in event summation with fatigue cycle are the most regular. When one
parameter would be taken into consideration only (for example energy), jumps in diagrams cancomplicate the diagram interpretation and the fatigue failure prediction could be misleading.
Usually the most distinct changes are observed when AE parameters are recorded during low
part of LR; however; this cannot be taken as a rule. Therefore, for warning against fatigue
failure, more than one AE parameter and more than one LR of fatigue load cycle should beanalyzed. Results from this study suggest that increases in AE events, energy and/or duration
diagrams vs. time or number of cycles jointly, for upper and lower 20 % of LR are the mostuseful in warning against fatigue failure. When these two loading ranges of fatigue load cycle areconsidered both effects of cracking processes (at high loads, giving rise to increased friction AE)
and of existing fatigue cracks (at low loads, from friction AE as well) are used for fatigue
collapse prediction.
The AE signals, which are produced by processes leading to fatigue collapse, are “hidden” bygeneral AE activity, which took place in all volume of the test piece through the friction
following local shearing failure. Many factors, for example components of laminate, laminae
configuration and quality and loading conditions, affected this activity. Therefore AE parameterchanges with number of fatigue cycles may differ from sample to sample even when they are
made from the same material and were underwent to the same loading conditions. These
differences can be seen when compare the demonstrated data for sample No 003 (Figs. 6 and 7)
with data for sample No 004 (Fig. 9). The number of fatigue cycles to their failure is close toeach other while the differences between AE parameters are pronounced. However, for both ofthem, increases in AE parameters provided the warning against incipient fatigue failure.

Fig. 9. AE parameters summations as a function of fatigue cycles for different load ranges for
GRP pipe loaded in M III (sample 004). a) event summation (left), b) energy summation (right).

295

Fatigue failure prediction may also be possible by AE signal amplitude vs. the number offatigue cycles plots. In this type of diagrams, both the increase in AE activity (events rate) andincrease in AE parameter values can be seen. The incipient fatigue failure is well indicated when
the selected AE parameter is recorded during high or low LRs. An example is shown in Figs. 10aand 10b, where the AE amplitude vs. number of cycles for the upper and lower 20 % of fatigue

Fig. 10. AE signal amplitudes point graph event and energy summation as a function of fatigue
cycles for pipe loaded in M III (sample 004) for loading ranges equal: a) 0 – 20% of fatigue load
range, b) 80 – 100 % load range, c) 0 – 20 % load range, decreasing part of cycle only.
296

loading cycle for sample No. 004, are presented. Results are more distinct when the parameters
are recorded during decreasing part of lower 20% of fatigue load cycle as it is shown in Fig. 10c.
These again illustrate the role of friction AE in revealing impending failure due to the presenceof fatigue damage.
The above procedures need continuous structure monitoring over a long period of time.
However, in engineering practice it is important to predict the fatigue collapse when loading
history is unknown and, in addition, except fatigue, damages introduced by stress corrosion,
impact or wear may lower fatigue and service resistance. When monitoring includes the final
fatigue cycles only, the recorded changes in AE parameters may be insufficient for fatigue
collapse prediction. In this situation, an alternate procedure is needed. In this study, fatigue
loading of selected samples was periodically interrupted and during each pause the loading washeld constant for 300 seconds. AE event summation and energy summation recorded for the first
and the last load holds are presented in Fig. 11. These diagrams show AE activities during few
cycles before pause, during pause and few cycles after pause. The spots in these diagrams
indicate the beginning and the end of pauses. During the first pause some stability in AE
parameters can be observed and an increase in AE parameters was insignificant, but, in the lastone, AE activity is as strong as during fatigue loading before and after. These results indicatedthat, in fatigue-damaged areas, further damage progression as a result of creep was observed.
This phenomenon suggests that in the final part of fatigue life, fatigue damage is serious enoughto produce creep damage and fatigue-creep interaction, influencing the durability of laminate

Fig. 11. AE event and energy summation during constant loading holds for pipe loaded in M III(sample No 005 failure after 6990 cycles). a) first pause after 1000 fatigue cycles, b) last pause
after 6000 fatigue cycles.

297

under variable loading conditions. The comparison of AE activity during fatigue loading before
pause and AE activity during pause provide the data, which may serve for warning against
failure.
3.2 Fatigue in Mode II (Internal pressure loading; σH/σA = 2)
Mode II (M II) loaded pipes initiate laminae cracking as a result of stress component
transverse to fiber. This is in contrast to pipes loaded in M III, where the cracking initiation iscontrolled by shear along fibers. The transverse strength of lamina in tested pipes iscomparatively low (14 MPa) and the beginning of cracking, estimated on the basis of laminate
theory, is expected at internal pressure in the vicinity of 30 bars. For winding angle ±550 and M
II loading, after a relatively small amount of transverse cracking, the load is transferred entirely
to the fibers.
To select the internal pressure range for further fatigue tests, the pipes were smoothly
pressurized in M II. No protection against weepage was applied. During the pressurization, AE
activity was recorded. The AE activity evolution is demonstrated in Fig. 12, where AE event rateand duration point graphs are presented. The loading was stopped at 232 bars. At this pressuretransverse cracking together with cracks, which intersect boundaries between laminae, produced
complete weepage path and intensive leakage made impossible further pressure increase. AE
activity started at pressures in the vicinity of 40 bars, indicating the beginning of failure
processes. At pressure exceeding 100 bars, AE intensity decreased leading to local minimum at
the pressure of 140 bars. When rubber sleeve protected against leakage, these pipes failed byburst at pressure in the vicinity of 420 bars.

Fig. 12. AE events rate and energy plots vs. pressure (in bars) during smooth pressurization of
GRP pipe loaded in M II.
The fatigue tests in M II were performed for maximum pressures in the range from 100 to
140 bars and stress ratio R = 0.1. For these pressure ranges, shear stress component, along fiberin laminae was low and has no influence on crack formation. The maximum of variable pressure,298

applied during fatigue in M II, corresponds to 30 % of the burst pressure. It was 40 % higher incomparison with the maximum fatigue pressure in M III. The numbers of cycle up to failure forpipes loaded in M II are given in Table III. As can be seen, fatigue life for both loading modes
and maximum applied pressure were close to each other. Nevertheless, the failure mechanisms
changed with the loading modes.
Table III Fatigue strength data of tested pipes loaded in M II
SampleNo

Pressure in bars
max
minNumberof cycles

Failureby

MII/010212

Smoothly pressurized

Leakage

MII/011
MII/012232
140

Smoothly pressurized
14854

Leakage
Leakage

MII/013

140

14

1 000

Leakage

MII/014

140

14

9 853

Leakage

MII/015
MII/016

120
120

12
12

47 02889 231

Leakage
Leakage

MII/017100

10

121 789

Leakage

MII/018100

10150 000

No leakage

The failure of all pipes loaded in M II occurred by intensive leakage, which exceeded thecapacity of applied hydraulic system, making impossible further fatigue loading. Samples loadedin M II exhibited higher scatter in number of cycles to failure than the pipes loaded in M III. For
example, the number of cycles to failure varied by a factor of ten between samples 012, 013 and014. This is because in our M II fatigue tests, failure is not a typical one, where fiber breaking
and perforation of pipes wall take place. Instead, the present “fatigue failure” in M II occurred by“opening transverse cracking” of the matrix and through thickness slots that have enough width
for liquid to flow out. The cross-sections of pipe wall after fatigue in M II are shown in Fig. 13.
During fatigue cycles in M II the width of transverse cracks are changed periodically allowing
the penetration of oil into the laminate (see Fig. 13b). This process facilitated the development of
weepage paths. However, after the tests and unloading no visible damage on pipe surfaces could
be seen.

a. Magnification x50;

b. Magnification x500.

Fig. 13. Cross-section of pipe wall after fatigue failure in M II loading.
299

Fig. 14. AE signal amplitude point diagram as a function of pressure for after fatigue in M II.
Complexity of fatigue processes during M II is reflected by AE activity evolution with loadchanges within the fatigue load cycles and number of cycles. The AE event amplitudes in M II
fatigue generally were higher in comparison with the event amplitudes recorded during fatigue in
M III. Compare Fig. 5 and Fig. 14. Under M II loading, numerous hits with 80+ dB amplitude
were observed. High amplitude events were generated at the maximum or minimum pressure of
fatigue cycles (high and low LR). At high pressure ranges, very high amplitude signals occurredat 120-140 bars, but 80+ dB signals started at about 100 bars or higher, as shown in Fig. 14. The
high amplitude hits were generated at different parts of fatigue cycles, as demonstrated in Figs.
15 and 16. Here, event summation, energy summation and energy point graphs are plottedagainst the number of cycles for the increasing and decreasing parts of fatigue loading. The
events with amplitudes in the range from 80 to 100 dB appeared during the first 20% of loading
cycles only. That is, most strong events occurred at the beginning and between 800 and 2100
cycles. However, there are differences in number, energy and duration depending on whether thesignals were recorded during the increasing or decreasing part of a load cycle. High amplitude
AE signals first appeared during the initial loading cycles in their increasing part. Although somehigh amplitude signals also occurred during the decreasing part of cycles, their energy wassignificantly lower. It is likely that during increasing parts of a load cycle the AE events were300

Fig. 15. AE high amplitude events (in the range 80 to 100 dB), energy summation and energy
graphs vs. number of cycles for pipes loaded in M II fatigue. Increasing part of loading cycle.
generated by transverse cracking processes while those produced during decreasing parts of aload cycle were attributed to the friction of crack surfaces. Subsequently, low activities of strong
signals were observed until 800 fatigue cycles have passed. From 800 to 2100 cycles, highamplitude hits appeared again in abundance (up to the second loading pause). Beyond this,almost no hits with amplitude exceeding 80 dB were recorded. This is shown in the energy plots
as well. The data demonstrates that serious damage took place during the first cycles of fatigue
and during a limited cycle interval, as indicated by very high amplitude signals.
For the pipes fatigue-loaded in M II, the filtration of AE signals significantly affected theduration and energy of AE events. When pipes were fatigued in M III, in contrast, the filtration
did not influence recorded AE parameters. The influence of filtration on AE parameters isdemonstrated in the duration vs. number of cycles plots (see Fig. 17 and Fig. 18). Beyond about
10 % of fatigue life, AE signals, which are recorded by the channel with 20 kHz high-pass cutoff, started to divide themselves into two groups with different duration. One group had duration
below 40,000 µs and the other above 100,000 µs. Duration of the second group increased withnumber of cycles and, as shown in Fig. 17, after 40% of fatigue life, it increased to the range
from 200,000 to 250,000 µs. At the same time, the duration of AE signals recorded by thechannel with 100 kHz high-pass filter was mostly below 10,000 µs and did not exceed 50,000 µs(see Fig. 18). Filtration removed the low frequency components with extended signal length. It is301

Fig.16. AE high amplitude events (in the range 80 to 100 dB), energy summation and energy
graphs vs. number of fatigue cycles for pipes loaded in M II and decreasing part of loading cycle.
noticed that the long-duration AE signals appear in a narrow LR from 20 to 50 % (Fig. 19),
exhibiting the energy values of more than 500. In comparison, AE signals during LR up to 20%
had energy values less than 30, while those near the maximum load (LR: 80-100%) were moreconsistent and of intermediate strength. Amplitudes of these signals were mainly contained302

Fig. 17. AE duration vs. number of fatigue cycles (time) recorded by 20 kHz HP channel for pipe
loaded in M II

Fig. 18. AE duration vs. number of fatigue cycles (time) recorded by 100 kHz channel
for pipe loaded in M II.
303

Fig. 19. AE energy vs. number of cycles for different load ranges of fatigue load cycle recordedby 20 kHz HP channel for pipe loaded in M II.
304

Fig. 20. AE energy point graphs vs. number of cycles for two amplitude ranges. Unfilteredchannel for pipe loaded in M II. a) amplitude range 60-65dB (top), b) 70-80dB (bottom).
within the range from 60 to 65 dB (Fig. 20a), although some had higher amplitudes to 80 dB(Fig. 20b).
It should be noticed that when fatigue cycles were interrupted with pauses, during which aconstant load was maintained, AE signal duration increased during first cycles after each pause.
This was especially evident in 100-kHz-filtered channels (see Figs.17 and 18).
When leakage was not protected in M II fatigue, no fiber breakage was observed andtherefore no AE signals from fiber cracking or friction of fractured fibers can be expected.
Possible sources of AE activity in tested pipes loaded in M II fatigue loading are transverse resin
cracking and fluid migration inside composite during loading. This is because M II loading305

Fig. 21. AE event summation as a function of number of fatigue cycles recorded by unfilteredchannel for pipe loaded in M II. a) load ranges 0 – 50% of fatigue load cycle (top), b) load ranges
50 – 100% of fatigue load cycle (bottom).
produces no fiber rotation and only “opening type” of transverse cracking in laminae takes place.
AE signals by friction are also expected when cracking between laminae occurs.

306

Fig. 22. AE energy summation as a function of number of fatigue cycles recorded by 20 kHz HP
channel for pipe loaded in M II. a) load ranges 0 – 20% of fatigue load cycle, b) load ranges 20– 50% of fatigue load cycle.
307

Fig. 22. AE energy summation as a function of number of fatigue cycles recorded by 20 kHz HP
channel for pipe loaded in M II. c) load ranges 50 – 100% of fatigue load cycle.
The observed AE signals are dominated by very long duration (50-250 ms) signals, especiallyfrom 20 kHz-filtered channels. These appear to be produced by one type of AE source, which are
related to oil penetration into transverse resin cracks and interlaminar cracks upon loading. Thisis consistent with microstructural findings of Fig. 13. Resin cracking certainly generates AE, but
is unlikely to have signal duration more than several ms. Here, we observe 50-250 ms duration
signals that cannot be attributed to cracking. It is possible that such cracking AE contributed tothose signals observed in the first few hundred cycles (see Fig. 19, 80-100% LR).
We are interested in predicting incipient fatigue failure, although in M II loading only
produces massive weepage. Toward this objective, AE parameter summations as a function of
fatigue cycles have been analyzed. The data were recorded for different LR of fatigue load cycle
separately. Changes in event summation with number of cycles are presented in Fig. 21. This
figure reveals that AE events summation for several LRs of fatigue load cycle increased at theend of fatigue life. Specifically, the summation curves started to rise (or showed a knee) at 65 to
77% of the fatigue life for 10, 20, 40, 90 and 100% LRs. This trend was most clearly exhibitedfor 100%LR curve, but the two lowest LRs also indicated distinct knee behavior. Morepronounced changes are observed in energy summation for the same three LRs, as shown in Fig.
22. Increase in AE activity took place after 75% of fatigue life. This data is from the 20-kHzhigh-pass channel. Most of AE energy is attributed to very-long-duration AE activity, which
occurs in narrow LR of 20-40 % of fatigue load cycle. However, in this LR, no change in energy
plots is observed. This feature implies that these AE events are related to oil penetration toward
interlaminar cracks, rather than weepage through transverse resin cracks. The latter contributes tooil leakage that defines fatigue failure in this loading mode and is closely related to AE emitted
during the low and high LRs. The incipient fatigue failure is better indicated by changes in AE
event summation and energy summation vs. number of fatigue cycles plots when AE activity
was recorded by 100-kHz-high-pass filtered channel. These plots are presented in Fig. 23 and308

Fig. 23. AE event summation as a function of number of fatigue cycles recorded by 100 kHz HP
filtered channel for pipe loaded in M II. a) load ranges 0 – 40% of fatigue load cycle, b) loads
ranges 40 – 100% of fatigue load cycle.
309

Fig. 24. AE energy summation as a function of number of fatigue cycles recorded by 100 kHz
HP filtered channels for pipe loaded in M II. a) load ranges 0 – 30% of fatigue load cycle, b)load ranges 30 – 100% of fatigue load cycle.
310

Fig. 24, respectively. The changes in AE activity, which can serve as warning against fatigue
failure, are found in low LRs of fatigue load cycle (Fig. 24a). Detectable changes are absent
when data are recorded during higher LRs (Fig. 24b) or for all of the fatigue cycle. This finding
again emphasizes the importance of proper filtration in fatigue damage monitoring by AE.
The changes in AE activity in selected LR of fatigue load cycle are useful in assessing theincipient failure. In M II loaded fatigue, the loss of pipe strength is not so serious because nofiber breakage occurs. This is confirmed by low AE activity, when constant load was maintained
during a pause in fatigue loading.
The above methodology for incipient fatigue failure of laminated composites was developedon the basis of experiments performed on tubular specimens and laboratory generated data. The
sample dimensions were significantly smaller than typical dimensions of engineering structures.
In large heterogeneous structures, laboratory data application to full-size structure testing should
be applied with caution [15,21]. Large dimensions, complicated geometry and signal attenuation
can significantly change the waveforms. However, this testing procedure should be valid for
local failure of larger structures.
4. DISCUSSION
AE activity has been monitored during fatigue of filament wound pipes. The analysis of AE
signals aims at field-testing development for warning against fatigue failure. We studied twoloading modes. In the first fatigue mode, (M III), shear along fibers in lamina was dominant. The
fatigue failure occurred by shear fracture at the fiber-matrix boundaries. This leads to secondary
failure of fiber breakage (in bending on the next lamina), resin crumbling and, finally, wall
perforation. In the second fatigue mode, (M II), fatigue process was controlled by the transverseto-fiber stress component. Because no protection against fluid leakage was applied in M II,
fatigue induced matrix cracking led to intensive leakage on all the surface of pipe, defining the
failure point. In M II, no massive fiber breakage and wall perforation was observed.
A large scatter in AE parameters allowed no quantitative relationship between AE parametersand fatigue durability. The scatter for fatigue cycles to failure was also substantial, even when
the loading conditions were the same and tested pieces were cut off from the same tube.
High amplitude hits appeared weakly dependent on number of fatigue cycles both in M III
and M II fatigue modes. Observed high amplitude hits both at low and high stresses of fatigue
load cycle indicate that not only fracture processes produce high amplitude hits. These results
demonstrate that high amplitude hit criterion cannot be applied or should be applied with caution
in fatigue testing of composites.
In this study, we anticipated that waveforms recorded by broadband sensors depend on AE
sources. Careful examination showed waveforms or their fast Fourier transforms are insensitive
to loading modes and signal origin [17]. Beside the usual reasons, it is likely that AE signals are
dominated by a single source for each loading mode; that is, friction for M III and oil penetrationin M II. Therefore, waveform analysis cannot identify the damage modes of composite laminate
structures.
Fatigue damage, however, is revealed by an increase in AE parameters that were recorded forselected LR of fatigue load cycle. The analysis indicated that for pipes loaded in M III, AE
parameter increasing with number of fatigue cycles took place both at the high and low LRs. For311

pipes loaded in M II, AE parameters increased with fatigue cycles only at low LRs. In bothmodes, AE parameters are indicative of secondary AE sources, rather than the primary sources(shear cracking or fiber bend fracture in M III and transverse resin cracking in M II). It is also
important to define the “failure” in warning against incipient failure since this varies dependingon the loading mode applied.
The results of these studies demonstrated that many factors strongly affect acoustic emission
activity from GRP laminates. Serious scatter in AE parameters between samples with the sameloading conditions is observed even when test pieces were cut off from the same tube. The
complementary tests performed on similar pipes and similar failure envelope with different
matrix resin indicated different AE behavior. In addition the waveform of AE signals when they
travel through laminate is altered, making it difficult for AE source identification. All thesevariability in AE parameters complicate the development of general fatigue failure criteria on thebasis of critical value of AE parameters. Damage accumulation as a result of fatigue in laminates
and laminated structures can be characterized by changes in AE activity with the number ofcycles (or time). However, the useful information about incipient fatigue failure is “hidden” in anarrow cyclic loading range. Also different filtration of AE signals should be explored. The
performed experiments demonstrated that the most sensitive to fatigue damage is AE activity, ina narrow LR at bottom and at top of fatigue load cycles. In combination with zonal localizationof AE sources, the present analysis scheme allows the prediction of incipient fatigue failure insamples and in laminated structures. Further studies are necessary with other types of composite
laminates and more complicated loading modes.
5. CONCLUSIONS
Damage accumulation during combined fatigue loading has been monitored by acoustic
emission (AE) technique in filament wound pipes. The tests made on epoxy resin-E-glass-fiber
filament wound pipes subjected to two loading modes: M II and M III. Fatigue processes were
controlled either by transverse (M II) or shear (M III) stress component in reference to fiber
direction. The damage evolution with fatigue cycles for each loading mode was estimated on thebasis on acoustic emission parameter analysis. Based on changes in AE activity for different
fatigue loading ranges during fatigue progression, incipient failure can be predicted. This
procedure is valid for both applied loading modes in spite of the differences in failure modes.
The approaching fatigue failure is detected by changes in selected AE parameters, when samples
exceeded 70-90% of fatigue life. It was also found that high amplitude hit criterion andwaveform analysis were ineffective, as secondary AE sources were predominantly detected.
ACKNOWLEGMENT
This research was supported by the U.S.-Poland Maria Sklodowska-Curie Joint Fund II,
administered by the US Department of Energy. Foundry Research Institute, Krakow, Poland,
provided technical support. The authors are grateful for all the financial and technical assistance.
REFERENCES
1. Williams J.H., Lee S.S., Promising Quantitative Nondestructive Evaluation Techniques for
Composite Materials, Materials Evaluation 43, 1985, pp. 561-565.

312

2. Jeng J.S., Ono K., Yang J.M., Fracture Mechanism Studies of Carbon/PMR-15 Composites by
Acoustic Emission, Journal of Acoustic Emission 8, 1989, pp.151-165.
3. McNally D.J., Inspection of Composites rocket Motor Cases Using Acoustic Emission,Materials Evaluation 43, 1985, pp. 728-732.
4. Golaski L., Kumosa M., Hull D., Acoustic Emission Testing of Filament-Wound Pipes underRepeated Loading, Journal of Acoustic Emission 1, 1982, pp. 95-113.
5. Recommended Practice for Acoustic Emission Testing of Fiberglass Reinforced Plastic Resin(RP) Tanks/Vessels. Reinforced Plastics/Composites Institute, the Society of the Plastic Industry,
Inc New York, 1982. Revised and Republished, 1987.
6. Fowler T.J., Blessing J.A., Strauser F.E., Intensity Analysis, AECM-4 Fourth International
Symposium on Acoustic Emission From Composite Material, Seattle, ASNT, 1992, pp. 237-242.
7. Williams R.S., Reifsnider K.L., Investigation of acoustic emission during fatigue loading ofcomposite specimens, Journal of Composite Materials, 8, 1974, 340-355
8. Hamstad M.A., Chiao T.T., Acoustic emission from stress rupture and fatigue of an organic
fiber composite, Composite Reliability, ASTM STP 480 American Society for Testing andMaterials, 1975, pp.191-201
9. Williams R.S., Reifsnider K.L., Real-time Nondestructive evaluation of composite materials
during fatigue loading, Materials Evaluation, 35, 1977, pp.50-54
10. Awerbuch J., Gorman M.R., Madhukar M., Monitoring damage accumulation in filament
wound graphite/epoxy laminate coupons during fatigue loading through acoustic emission, First
Int. Symp. On Acoustic Emission from Reinforced Composites, SPI, New York, 1983, Session 2,
pp. 1-30.
11. Eckles W.E., Awerbuch J., Monitoring acoustic emission in cross-ply graphite/epoxy
laminates during fatigue loading, Journal of Reinforced Plastics and Composites, 7, 1988,
pp.265-283
12. Roy C., Ghorba M., Monitoring progression of mode II delamination during fatigue loading
through acoustic emission in laminated glass fiber composite, Polymer Composites 9, 1988, pp.
345-351
13. Awerbuch J., Ghaffari S., Analysis and characterization of the acoustic emission associatedwith matrix splitting during fatigue loading through acoustic emission in notched unidirectionalgraphite/epoxy composite, Journal of Reinforced Plastics and Composites, 7, 1988, pp.245-264
14. Henriksson A., Thesken J.C., Acoustic emission and residual strength in fatigue loaded
notched carbon/epoxy composites, Fifth Int. Symp. On Acoustic Emission from Composite
Materials, Sundsvall, Sweden, ASNT 1995, pp. 81-90
15. Hamstad M.A., An examination of acoustic emission evaluation criteria for aerospace typefiber/polymer composites, AECM-4 Fourth International Symposium on Acoustic Emission
From Composite Material, Seattle, USA, ASNT, 1992, pp. 436-449.
16. Schmidt J., Baran I., Golaski L., Failure behavior of gass fabric laminates in dependence on
resin matrix and temperature, First Int. Conf. “Integrity Reliability, Failure, University of Porto,
Portugal, 19-22 July, 1999, pp.56 –57.
17. Golaski L., Gebski P., Baran I., Ono Kanji, Waveform analysis of acoustic emission during
pressurization of glass-fiber composite pipes, Journal of Acoustic Emission, 17, 1999, pp. S37S44
18. Puck A., Zur Beanspruchungund Verformung von GFK - Mehrschichtenverbund Bauelementen, Kunstoffe, 57, 1967, pp. 284-293, 573 - 582 and 965 - 973.
19. Puck A., Schneider W., On failure mechanisms and failure criteria of filament-wound glassfibre/resin composites, Plastics and Polymers, 59, 1969, pp.33 – 37.
20. Perreux D., Joseph E., Effect of frequency on the fatigue performance of filament-woundpipes under biaxial loading, Composites Science and Technology, 57, 1997, pp. 353-364.
313

21. Fowler T.J., Revisions to the CARP recommended practice for tanks and vessels. AECM-5
Fifth International Symposium on Acoustic Emission From Composite Material, Sundsvall,
Sveden, ASNT, 1995, pp. 263-271.
22. Leszek Golaski, Pawel Gebski, “Diagnostyka mostu kablobetonowego metod_ emisji
akustycznej”, accepted for presentation during conference Diagnostyka i badania mostów, Opole
czerwiec 2001 (in Polish).

314

EWGAE 2002
25-th European Conference on Acoustic Emission TestingFirst Announcement

September 11 - 13, 2002, Prague, Czech Republic
CONFERENCE WILL BE ORGANISED BY.
•••

European Working Group on Acoustic Emission
Czech Society for Non-destructive Testing (Acoustic Emission Working Group)
Czech Technical University in Prague
Bmo University of Technology

Prague, a city with over a thousand-year history, was established and developed at theintersection of strategic routes connecting key centres on the European continent. Its leading
position and favourable climate led to the successful development of trade, arts and crafts,
scientific research and knowledge. The past centuries have left their mark here primarily in art andarchitecture. Through the course of time, Prague has gained a reputation as a major European
centre of culture, education and humanism. In recent years, Prague has become a top tourist
destination and has been recognised by UNESCO as a gem of the world's cultural heritage. The
unique magic of our city is down to the outstanding quality of its architectural sights and to aharmonious relationship between the various historical styles. It is this harmony that helps to create
an environment of mutual understanding, tolerance, respect and partnership.
EWGAE 2002: European Working Group on Acoustic Emission
The European Working Group on Acoustic Emission (EWGAE) was founded in 1971 to exchange
information with particular emphasis on scientific and engineering developments of Acoustic
Emission
Organizing Committee invites all companies to active participation through corporate sponsorships.
Corporate sponsors are sought to host evening reception
Language: English will be the only official language of the conference .
Call for papers:
Authors are invited to contribute papers on the areas of conference topics (in both oral and poster
session)Deadlines:
October 31, 2001
November 2001
May 31, 2002
August 2002
September 11-13, 2002

• pre-registration, submitting paper abstract (200-300 words)• notification of acceptance, second call for papers• full papers, preliminary payment• announcement of the final programme• Conference

Further information on web site:
http://www.cndt.cz
E-mail: cndt@cndt.cz

Conference Topics:
AE utilisation in material engineering
AE signal detection
AE signal processing
Localisation of defects
Practical applications of AE technique
AE in civil engineering and geology
AE in transport engineering
Standardisation and certification in the area of AE
Developments in AE instruments and sensors
Software
OthersAccompanying Actions:Get-together party
Sight-seeing tour
Lady's programme
Scientific Committee:
Mohammed Cherfaoui
Philip ColeDavid Dornfeld
Tomasz Boczar
Len Rogers
Joachim Sell
Josef SikulaPeter Tscheliesnig
Martine Wevers
Jan Zizka

FranceUK
USA
PolandUK
Germany
Czech Republic
Austria
Belgium
Czech Republic

National Organising Committee:
Vaclav Svoboda
Petr Benes
Jifi Blaha
Pavel Mazal
Jaroslava Polednova

Chairman

SecretaryFIRST CALL FOR PAPERS

The 16th International
Acoustic Emission Symposium (IAES-16)
November 12 – 15, 2002
The University of Tokushima, Tokushima, JAPAN
Topics to be discussed at the symposium (IAES-16) will cover all the areas of acoustic emission
testing, including: Materials Research, Structures, Manufacturing, Diagnostics,
Instrumentation, Code and Standards, Applications to Medical Field, Geo-structures, Civil
Engineering and so on.

Call for Papers
Prospective authors are requested to submit title, author’s names, affiliation and address by fax or email with an abstract of 100-200 words with key words. The deadline for submission of abstract isApril 30, 2002. The official language of the symposium and the proceedings will be English.

Time Table
April 30, 2002
Deadline for arrival of abstract.
June 30, 2002
Notification of acceptance and instruction for authors.
August 31, 2002 Deadline for full papers. Registration and remittance of registration fee.
September 30, 2002 Second announcement and program.

Note
Papers will be included in the symposium proceedings only if the authors have definitely registeredand made a remittance before August 31, 2002.

Venue
The symposium will be held at the University of Tokushima to which it takes 2 hours and a half by
limousine bus from the Kansai International Airport near Osaka. Tokushima is a small city andfamous for the “ Awa dance”, ethnic dance, the Naruto tidal vortex, the indigo dying and so on. We
have some sightseeing programs and make all participant and companion welcome. Information on
accommodation in Tokushima will be available from Symposium-Secretariat.

Symposium Organization(1) Organizing Committee
Co-chairperson : T. Kishi (National Institute for Materials Science)
Y. Higo (Tokyo Institute of Technology)(2) Secretary Committee
Chairperson : K. Yoshida (The University of Tokushima)

Symposium-secretariat and Mailing Address :Prof. Kenichi YOSHIDA
Department of Mechanical Engineering, The University of Tokushima
2-1 Minamijosanjimacho, Tokushima 770-8506, JAPAN
Phone: +81-88-656-7358 Fax: +81-88-656-9082
E-mail: yoshida@me.tokushima-u.ac.jp

CUMULATIVE INDEX
J. of Acoustic Emission, Volumes 1 - 10, 1982 - 92
Authors Index
Authors with four or more contributions
Baldev RajFrank C. Beall
A. G. Beattie
W. D. Brosey
D. J. Buttle
S. H. Carpenter
Roger W.Y. ChanDavid A. Dornfeld
Thomas F. Drouillard
Davis M. Egle
Mark A. Friesel
G.L. Goswami
M. A. Hamstad
H. R. Hardy, Jr.
R. W. Harris
D. Robert Hay
C. R. Heiple
Roger Hill
Nelson N. Hsu
David A. Hutchins
P. H. Hutton
T. Jayakumar
Teruo Kishi
R. A. Kline
R. M. Koerner
Oh-Yang Kwon
Richard Lemaster
Arthur E. Lord, Jr.
J. W. Maclachlan
Stuart L. McBride
Winfred Morgner
C. R. L. Murthy
Masayasu Ohtsu
Kanji Ono

Adrian A. Pollock
Thomas M. Proctor, Jr.
Henrique L.M. dos Reis
J. RogetI. RomanC. B. Scruby
J. W. Whittaker
B. R. A. Wood
Kusuo Yamaguchi
B. Q. Zhang

IV-S102, VI-209, VII-S1, VIII-S126, VIII-S140, VIII-S149
IV-S244, IV-19, V-71, VI-151, VIII-S311, IX-197, IX-215, X-83
I-21, I-300, II-67, II-69, II-95, II-143, III-224, III-239, IV-65, V-53, V-172
VII-31, VIII-S280, IX-75, IX-84
VII-211, VIII-S158, VIII-S201, IX-243, IX-255
I-251, II-191, III-11, III-81, IV-S119, IV-S135, V-77, VI-115, VI-136, VI-177, VI-215, VII-9,
VII-161, VIII-S135, VIII-S184, VIII-125, IX-1, X-97, X-103
III-118, IV-S259, IV-115, VIII-S12
III-242, IV-S123, IV-S228, VII-103, VII-111, VIII-S227, VI-29, VI-37, VI-157
I-45, I-81, I-121, I-195, I-271, II-129, II-221, II-292, III-46, III-90, III-164, III-212, IV-41, V-103,
IX-45, IX-155, IX-215
II-ii (3), III-104, IV-S30, IV-S46, VI-205
III-11, III-239, VII-119, X-117
IV-S98, IV-S251, VII-S40, VII-S43
II-57, II-i(3), V-95, V-110, V-123, VI-93, IX-75, IX-84
III-242, IV-S19, VIII-S32, VIII-S42, VIII-S262, VIII-65, X-61
VI-239, VIII-S14, VIII-S66, X-S29, X-S59
III-118, IV-S259, IV-115, VIII-S12, IX-9
I-221, I-251, IV-S116, V-85, VI-177, VI-215, VIII-S184, VIII-125, IX-1, X-97, X-103
I-73, I-149, I-294, V-51, V-i(1), X-124
IV-S311, V-S24, V-S28, V-S29
V-S29, V-S34, VIII-S38, VIII-S166
III-239, IV-S74, IV-S138, VI-167
IV-S102, VII-S1, VIII-S126, VIII-S140, VIII-S149
I-1, II-19, II-71, IV-S191, IV-S195, IV-S278, IV-S282, IV-S325VI-85, VIII-S131, VIII-S154
IV-S42, IV-107, VI-205, VIII-S246
I-220, II-187, II-195, IV-S11, IV-31
IV-S106, IX-123, IX-227, IX-237
IV-S228, VI-157, VII-103, VII-111, VIII-135, IX-17, IX-203, X-83
II-187, II-195, III-107, IV-S11, IV-31, V-152
I-223, I-229, II-39, II-179, III-1, IV-S151
I-223, I-229, II-39, II-179, III-1, IV-S151, IV-S220, VII-225, VIII-S4, VIII-S192
III-172, V-45, VI-133, VIII-S70
IV-S30, IV-S147, VI-19, VII-S18, VIII-S16, VIII-S122, VIII-S284
I-103, II-151, II-247, III-27, III-59, III-69, IV-S38, IV-S50, IV-S316, V-124, VI-43, VI-79, VI-99,
VII-167, VIII-S162, VIII-S242, VIII-93, X-i (1/2)
I-7, I-67, I-69, I-141, I-145, I-146, I-183, I-211, I-213, I-214, II-169, II-247, II-ii(No. 1/2), III-19,
III-27, III-59, III-69, III-130, III-144, III-174, III-190, III-233, III-ii(4), IV-S50, IV-S106,
IV-S111, IV-S263, IV-S316, IV-61, IV-93, V-124, V-i(1), V-ii(4), VI-1, VI-43, VI-84, VIII-S268,
IX-109, IX-123, IX-177, IX-227, IX-270
I-237. I-303, IX-140, X-122
I-173, V-134, VII-41, X-43
IV-S232, V-67, V-144, IX-197
IV-85, V-S60, V-S66, VIII-S231, VIII-34
II-64, III-19, III-41, III-130, IV-S106, IV-S111, VI-73, VIII-S109, VIII-47, X-31
III-182, IV-9, VII-81, VII-211, VIII-S158, VIII-S201, IX-243, IX-255
I-147, IV-S247, V-148, VI-257, VII-31, VII-95, VIII-S280, IX-75, IX-84, X-113
VI-125, VI-239, VIII-S14, VIII-S66, X-S29, X-S59
IV-S191, IV-S286, IV-S325, VIII-S1
IV-S94, VIII-S49, VIII-S105, VIII-S114

A. R. Acharya
C. Howard Adams
E. Aernoudt
B. D. Agarwal
T. Aizawa
Chandra Ajay
R.S. Algera
S. S. Ali
J. F. R. Ambler
G. Amir
Naoto Ando
M. Annamalai
K.-I. Aoki
K. AokiD. L. Armentrout
J. H. Armstrong
B. H. Armstrong
M. Arrington
Ikuo Asano
S. K. Athithan
B. Audenard
J. Aviassar
J. Awerbuch

VIII-S88
I-165
IV-S186
VIII-S297
VI-85
IV-S174
II-69
IV-S42, IV-107
V-S16
II-64
IX-209
VIII-S8
VIII-S131
VIII-S145
X-97, X-103
IV-S135
VIII-S250
IV-S165
IV-S240
IV-S26
I-148
I-179
VIII-S301

Baldev Raj
see above listing
G. R. Baldwin
III-182
V. Bansal
VIII-S217, IX-142
J. Baram
I-179
P. Barat
VIII-S140, VIII-S149
J.A. Baron
II-69
H. Barthelemy
VIII-S75
M. Nabil Bassim
IV-S224Frank C. Beall
see above listing
A. G. Beattie
see above listing
M. J. Beesley
VII-59R. M. Belchamber
IV-71, IX-271
David A. Bell
V-1
P. G. Bentley
I-35, VII-59
J.M. Berthelot
IV-S178, IV-S300, VI-43
M. M. Besen
VIII-S209
D. Betteridge
IV-71
D. K. Bhattacharya
IV-S102, VIII-S149
Frank S. BiancanielloV-S69
T. Kevin Bierney
IV-S321
B. Birolo
IV-S255
P.R. BlackburnVII-49Richard W. Blank
IX-181J. A. Blessing
VIII-S236, VIII-1
Thomas Blum
VII-179
Andrew R. Blystra
X-S49
P. Böhm
IX-29
R. J. Boness
VIII-S192
S. F. Botten
VIII-S330
S. J. Bowles
X-49
P. Bowman
VII-225, VIII-S4
Marcelle Brachet
II-159Franklin R. Breckenridge I-87, III-59, X-43
J. C. Briggs
VIII-S209
W. D. Brosey
see above listing
Nancy Brown
X-71
O. Burenko
VII-31D. J. Buttle
see above listing

G. Buzzacchi

II-11

Bruce Campbell
Nicholas J. Carino
John M. Carlyle
Victor Caron
S. H. Carpenter
M. Cartoceti
Dick W. Caster
Roger W.Y. Chan
C. Chang
Chung Chang
C. Chapelier
T. Chelladurai
D. Chellman
Robert Chivers
Chung-Mei Chen
M.C.Cheresh
M. Cherfaoui
Michael J. Chica
A. Chichibu
T. Chow
Y. T. Chow
S. S. Christiansen
S. Y. Chuang
D. M. Chuck
F. CipriT. N. Claytor
Roger B. Clough
Phillip T. ColeC. E. Coleman
J. Collins
M. P. Collins
Peter J. Conlisk
A. W. Cook
R. A. Coyle
H.-A. Crostack
Timothy G. Crowther
M. E. A. Cudby

III-81
V-S24
IV-S329
IV-S259, IV-115
see above listing
II-11
IX-197see above listing
IV-S62
VII-21
V-S52
VIII-S88
IV-S263
X-123
VII-161II-289
V-S66
IX-197
VIII-107
VIII-S166
IV-71
IV-S116, V-85, VIII-S184
IV-S137
VIII-S258
IV-S255
IV-S69
V-S69
VIII-S239, VIII-31
V-S16
IV-S134
IX-271
VIII-1
VIII-S101
VI-249
IX-29
X-71
IV-71

C. E. D'Attellis
X-13
V. Dal Re
IV-S255, IV-S270, V-39
B. Dattaguru
VI-19
Jack F. Dawson
X-117
V. G. Ruiz de Argandoña X-S35
L. M. Suárez del Río
X-S35
C. De Michelis
II-11
M. Diez
IV-S296
I. De Iorio
III-158
P. De Meester
IV-S186, VIII-S272
H. A. L. Dempsey
IV-S46
O. Derakhshan
VIII-S223
Nitin Dhond
IV-S30G. Dionoro
II-281David A. Dornfield
see above listing
Henrique L. M. dos Reis see Reis
M. W. Drew
VI-239Thomas F. Drouillard
see above listing
Q. Duan
VIII-S97
J. C. Duke
VIII-S179

IIH. L. Dunegan
J. Dunning
F. Dusek
C. Duytsche
B.C. Dykes

VIII-S71
IV-S22
X-1
III-176
IV-S35

Davis M. Egle
R.K. Elsley
D. C. Emmony
M. Enoki,
R. M. Esbert
V. A. Eshwar

see above listing
II-47
I-263
IV-S195, VIII-S154
X-S35
VIII-S217, IX-142

A. Fahr,S. D. Falls
J.-P. Favre
Steven E. FickAndrzej Figiel
J. F. Finn
P. Fleischmann
T. Flynn
R. Fougeres
Timothy J. Fowler
Michael J. Friedel
Mark A. Friesel
T. Fujiwara
Masami Fushitani

VIII-S314
VIII-S166
IX-97
IV-S311
IV-S182
VIII-S79
III-176, V-S42, IX-91
X-S59
V-S42, IX-91
VIII-S236, VIII-1
X-S77
see above listing
X-S63
IV-S240, IX-209

Thomas Gandy
D. S. Gardiner
B. K. Gaur
A. Gavens
M. Ge
János Geréb
Michael F. Gasick
S. Ghaffari
S. Ghia
Al Ghorbanpoor
Steven D. Glaser
T. G. GlennA. M. G. Glennie
M. W. Godfrey
V. Godinez
Leszek Golaski
Edward Goliti
Michael R. Gorman
G.L. Goswami
I. Grabec
L. J. Graham
A. T. GreenJ. E. Green
Arthur T. Grodotzke
Boguslaw Gronowski
D.J. Gross
C. M. Grossi
N. Gsib
P.-Y. GuF. J. Guild
T. J. Gulley
B.C. Gupta

IV-S166
IV-S199
VII-S13
VIII-S277
VIII-S32
X-19
VII-111
VIII-S301
IV-S86
IV-S307
X-S1
I-81
IV-S170
I-263,
IV-103
I-95, IV-S182
V-7
VIII-51, IX-131, IX-283
see above listing
VIII-S20, VIII-S205
II-47
IV-124, VIII-S306
I-191, II-289
I-29
IV-S82, V-25
VIII-25
X-S35
V-S60
VIII-S188
I-244
IV-S170
VIII-S217, IX-142

Robert A. Haack

IX-181III

Y. Haddad
M. E. Hager
H. Thomas Hahn
A. Hampe
M. A. Hamstad
Takashi Hamada
L. Hanacek
H. R. Hardy, Jr.
R. W. Harris
W. F. Hartman
J. Harvey
F. P. Hassani
Hiroaki Hata
M. W. Hawman
D. Robert Hay
J. R. Hay
Takefumi Hayashi
Yasuhisa Hayashi ,R. Hecker
C. R. Heiple
D. P. Henkel,
E. G. Henneke, II
S. Hewerdine
Hartmut Heyse
H. Hick
Y. HigoHiroshi Hikosaka
Roger Hill
Y. Hisamatsu
S.V. Hoa
A. B. M. Hoff
T. J. Holroyd
Kyoji Homma
Michel Hone
Theodore Hopwood II
P. M. Horrigan
J. R. Houghton
O. Hoyer
S.-Y. S. Hsu
Nelson N. Hsu
Derek HullDavid A. Hutchins
David V. Hutton
P. H. Hutton

VIII-S314
VIII-S42
V-15
IX-103see above listing
IV-S325
VIII-S84
see above listing
see above listingI-144, IV-S64, V-31
IV-S220
VIII-99, X-61
VII-173, X-S63
IV-S131
see above listing
VIII-S12
VIII-35
VII-185
IV-S78see above listing
VIII-S318
VIII-S277
VIII-S238, VIII-21
V-45
X-67
VIII-S24
X-S13
see above listing
II-19, II-71
VII-145, IX-37
IV-S165
IV-S132, VII-193, VIII-S219
X-35
IV-S259, IV-115
IV-S304
VIII-S79
VIII-S28, VIII-S223
IX-103
I-183, I-237, II-169
see above listingI-95
see above listing
VIII-41
see above listing

Hatsuo Ichikawa
H. Imaeda
Takuichi Imanaka
H. Inaba
Takako Inaba
Ichiro Inasaki
Tomoaki Inoue
T. Ishida
K. Ishihara
Hisashi Ishitani

IV-S325
IV-S294
IV-S38
VIII-S24
X-S90
VII-179
II-1
X-S42
VI-13
IV-S325

Jay B. JamesP. Jax,T. Jayakumar
J. S. Jeng

IV-S119, V-77
II-29, VIII-S53
see above listing
VIII-S268

B. B. Jha
S. K. Jha
C. G. Jiao
K. Jo
C. H. Johnson
J. Johkaji
W.D. Jolly
H. Jonas
R. H. Jones
R. K. Jones
G. JothinathanMusa K. Jouaneh
S. J. Jung,

VI-209, VIII-S149
VII-S43
VIII-S105
VIII-107, X-S55
IV-S111, IV-S263
VIII-S1
IV-103
IV-S78
III-239
VII-119, VIII-S223
IV-S207
X-83
VIII-S326

Koji Kaino
T. Kakimi
S. Kallara
P. Kalyanasundaram
M. KamataY. Kakino
Shigeto Kano
M. KatHarold E. Kautz
Yutaka Kawai,Kenji Kawamoto
James R. Kennedy
Shahla Keyvan
A. Wahab Khair
A. S. Khan,
A. S. Khanna
M. T. Kiernan
K. H. Kim
K. Y. Kim
H. Kimura
Ron King
N. Kinoshita
Teruo Kishi
Fuyuhiko Kishinouye
T. Kishishita
N. N. Kishore
Tatsuo Kita
K. Kitadate
K. Kitano
R. A. Kline
E. Kobayashi
H. Kobayashi
Y. Kobayashi
R. M. Koerner
Tsuguaki Koga
Leszek Korusiewicz
R. Krishnamurthy
I. Kukman
P. G. Kulkarni,
J. Sampath Kumar
Maciej Kumosa
D. S. Kupperman
F. M. Kustas
May Man Kwan
Oh-Yang Kwon

V-61, IX-277
II-19
VIII-S28
VIII-S140, VIII-S149
VIII-107
III-108
VI-109, VI-145
VIII-99
V-144
VII-167
IX-109
IV-S90
X-91
IV-S1, VIII-S326
VIII-S246
VI-209, VIII-S103
VIII-S176
IV-S282
IV-S62, VIII-S170
IV-S294
X-91
X-S42
see above listing
IX-177, IX-180
X-S55
VIII-S297
X-S90
VIII-S131
X-S42
see above listing
III-130, IV-93
VIII-S145
VIII-S1
see above listing
II-1
II-272
IV-S26, VIII-S88
VIII-S205
VII-S13VII-135
I-95
IV-S69
X-97
III-144, III-190
see above listing

J.-C. Laizet
V. Lalitha

IX-97
IV-S26IV

E. Landis
R. J. Landy
F. Langella
Stanislaw Lasocki
Marcel F. LeachJames D. Leaird
Jack Leifer
A. Lemascon
Richard LemasterArmand F. Lewis
L. Li
Steven Y. Liang
T. Lilley
S. LiuY.-H. Liu
Arthur E. Lord, Jr.
Luis Lorenzo
Steinar Lφvaas
Guozhi Lu
K. Lundgren

X-S97
I-7
III-158
IV-S7
X-S18
III-204, IV-S22, VIII-S322
IV-S127
V-S66
see above listing
IV-S127
VII-145, IX-37
VI-29, VI-37
IV-71
VIII-S97
IX-9
see above listing
V-15
IV-S161
IV-S203
V-S29

Yukuan Ma
J. W. Maclachlan
M. R. Madhava
M. A. Majeed
M. Manoharan
Ll. Mañosa
Theodore J. Mapes
J. Maram
P. A. March
G. G. Martin
S. Maharshak
A. Maslouhi
S. C. Maxwell
Stuart L. McBride
J. F. McCardle
John W. McElroy
D. Michael McFarland
J. D. MacPhail
K. I. McRae
Ronald B. Melton
K. Michihiro
W. Mielke
R. K. Miller
Hamish D. S. Miller
M. E. Miller
J. R. Mitchell
A. Mittelman
Kazuya Miyano
Hiroyuki Miyatake
Kenji Mochizuki
Mark B. Moffatt
Kiyoo Mogi
James Mohr
Waldemar Molinski
M. Momayez
M. Montoto
Winfred Morgner
Y. Mori,
D. G. Morris

IV-S58see above listing
VIII-S288
IV-S147, VIII-S16
IV-S207
V-S49
I-29
IV-S134
VIII-S223
IV-S142
II-64
VIII-S292
VIII-S38
see above listing
X-49
IV-S77
V-67
VIII-S4
VII-225
I-266
X-S63
IX-103
VIII-S241, VIII-25
V-S1
IV-S22
VI-135
III-41, VI-73
X-S90
X-S13
VIII-35
I-29
I-37, VIII-113
V-162
X-107
X-61
X-S35
see above listing
VIII-S131
VII-95

H. G. Moslé
Amiya K. Mukherjee
Yuji Murakami
C. R. L. Murthy
F. MudryT. M. Mustaleski

VIII-S317
V-162
X-S90
see above listing
VI-85
IV-S247

S. Naemura
Yasuaki Nagata
G. Jayachandran Nair
P. K. K. Nair
M. Nakamura
Syouzou Nakamura
B. C. Nakra
Priscilla P. Nelson
K. P. Nerz
K. D. Nicklas
Jackson A. Nickerson
A. Nielsen
Hiroaki Niitsuma
S. Nishikawa
Koichi Nishimoto
M. Nishino
Masami Noguchi

X-S55
IV-S191
VIII-S266
IV-S98
X-S55, VIII-107
X-S13
IX-25
X-S1
V-S56
IV-S247
VI-37
VIII-S57
VII-201
X-S55
IV-S236
VIII-S131
IV-S236

Y. Obata,
S. I. Ochiai
S. Ogino
Yoshitugu Ohigashi
Takanori Ohira
Masayasu Ohtsu
H. Ohyama
Takeshi Okano
Kanji Ono
M. Onoe
Hisanori Otsuka
P. Ouellette
C. Ouyang
H. Oyaizu

VIII-S145
II-289
V-61, IX-277
X-25
IV-S274
see above listing
IV-S282
IV-S240
see above listingV-i(4)
X-S13
IX-37
X-S97
VIII-S1

Yih-Hsing Pao
H. B. Patel
S. C. Pathak
Martin Peacock
L. H. Pearson
Peter Pellionisz
L. V. Perez
D. T. Peters,
C. Picornell
S. Pilecki
A. PlevinStefan Poliszko
M. D. Pollard,
Adrian A. Pollock
O. Prabhakar
Arun Prakash
C. W. Pretzel
David W. Prine
Thomas M. Proctor, Jr.
William H. Prosser

IV-S274
VIII-S12, VIII-S101
IV-S32, IV-S147, VIII-S122
IV-S166, VIII-S240, VIII-11
IV-S199
X-19
X-13
VIII-S4
V-S49
X-1
I-35
X-107
VIII-S4
see above listing
IV-S207
IX-142, VIII-S217
V-172
IV-S304
see above listing
IX-283

Stephen L. Quarles

VIII-134, IX-17, IX-189

Jan Raczkowski
X-107
Andre Raharinaivo
II-159B. Rajsee Baldev Raj
P. Raj
IV-S98D. S. Rajan
VIII-S297
P. K. Rajan
VIII-S28
Jerzy Ranachowski
IV-S82, V-25
A.K. Rao
IV-S147, VI-19
M. V. M. S. Rao
VII-S29, VIII-S262
S.P. Mallikarjun Rao
VII-135
M. N. Raghavendra Rao VIII-S284
Henrique L. M. dos Reis see above listing
W. G. Reuter
IV-S269
Marie-Christine Reymond II-159, IV-S296, V-S63
M. W. Richey
VI-257
B. Richtor
II-29
G. L. Rigby
X-S22
J.L. Robert
IV-S300, VI-43
N. Rochat
IX-91
John M. Rodgers
I-114, IV-S155, IV-1
P. Rodriguez
IV-S102, VIII-S140, VIII-S149
L. M. Rogers
II-319
J. Rogetsee above listing
I. Romansee above listing
A. P. G. Rose
I-213
D. RoubyIII-176, IX-117
C. RoyVIII-S292
P. R. Roy
VII-S40, IV-S251
Gottfried A. Rubin
X-S18
D. Rubio
X-13
J. E. Ruzzante
X-13
Wolfgang Sachse
S. SagatKoji Sakai
Tatsuro Sakimoto
C. SalaA. Sampath
R. SamuelR. N. Sands
Mary Sansalone
Soji Sasaki
Ichiya Sato
Isamu Sato
Keiichi Sato
Kouichi Sato
S. G. Savanur
C. M. Scala
C. Schepacz
K. Schumacher
D. Schumann
J. S. Schwartzberg
Ian G. Scott
C. B. Scruby
C. Seguf
B. K. ShahS. P. ShahR. Douglas Sharp

V

IV-S62, VIII-S20, VIII-S170
V-S16
X-S104VII-167
II-11
V-S12
VII-S35
IV-31
V-S24
II-1
II-1, VII-173, VIII-S213
VIII-35
IV-S240, VIII-S213, IX-209
VII-173
VII-S18
II-261, VI-249, X-49
II-267
IX-103
III-172
X-97
IV-S142see above listing
V-S49
VII-S13
X-S97
III-118, IV-S259, IV-115

G. Shen,
K. Shibata
M. Shibata
Hisatoshi Shimada
Noboru Shinke
M. Shiwa
A. K. Sinha
J. Siedlaczek
Khalid J. Siddiqui
F. Simacek
A. K. SinghN. K. Sinha
C. Sklarczyk
Jerzy Skubis
A. Slimani
Daniel R. Smith Jr.
J. H. Smith
M. Sobczyk
Amir Soltani
M. Sorel
P. Souquet
J.C. Spanner, Sr.
M. K. Sridhar
K. V. Srincivasan,
T.S. Sriranga
K. A. Stacey
Wolfgang Stengel
D. Stöver
V. Streicher
V. A. Strelchenko
V. A. Strizhalo
S. V. Subba Rao
Iyer Subramaniam
H. N. Sudheendra
C. Y. SuenJ. Summerscales
C. T. Sun
X. SunJ. Q. Sun
M. J. Sundaresan
Ippei Suzuki
M. Suzuki
Masahiko Suzuki
Toshitaka Suzuki
Terry L. Swanson

VIII-S97
VIII-S145
III-144, III-190, IV-93,
X-S104
X-25
IV-S195
VII-S13
X-1
III-118, IX-9
X-67
IV-S174
IV-S290
VIII-S93
II-261, IV-S82, V-25
V-S42
IV-S135, VII-9
VIII-S79
VIII-S192
IV-S17II-261
IV-85, V-S60, VIII-S231
VI-121
IV-S174
VIII-S8
VII-S35
VII-81
IV-S312
IV-S78
VIII-S53
VIII-S334
VIII-S334
VIII-S8
IV-S174
VIII-S288
IX-9
IV-S170
VII-21
VIII-S262
VIII-S114
VIII-S277
VII-179
IX-277
V-61
II-1
VIII-1, VIII-S236

F. Taioli,
Hideaki Takahashi
Mikio Takemoto
Hajime Takeuchi
K. Takigawa
S. Talebi
M. Tanaka
T. TanakaToshiyuki Tanaka
S. Tanary
N. Tandon
M. A. Taylor
H.B. Teoh
Satoshi Teramura
R. Teti
Christian Thaulow

VIII-S42
VI-261, VII-1
VII-185
IX-209
VIII-S213
VIII-S254
X-S55
VIII-S213
VII-173
VIII-S314
IX-25
VIII-S322
III-19, III-130, VI-1
VI-261, VII-1
III-158, V-156
IV-S211VI

Heinrich Theiretzbacher
Richard E. Thill
P.M. Thompson
R. M. Tian
B. Tirbonod
G. P. Tiwari
R.G. Tobin
F. Tonolini
G. Tonolini
V. Torra
D.T. Tran
Peter Tscheliesnig
Ming-Kai Tse
Koichi Tsukiyama
F. R. Tuler

IV-S157
X-S77
VI-93
IV-S94
VIII-S84
IV-S102, VII-S43
VIII-25
VIII-S62
IV-S86
V-S49
VIII-25
IV-S157
IV-S127, VIII-S188, VIII-S209
VI-261, VII-1
VIII-S79

Taketo Uomoto
T. I. Urbancic

VI-137
VIII-S254

S. J. Vahaviolos
S. Vajpayee
Donald W. Vannoy
A. Vary
F. A. Veer
I. Verpoest
R. Vijayaraghavan,
G. Villa
M. R. Viner
R. Visweswaren

III-i(1), IV-S329
V-S12
IV-S307
VIII-S175
VIII-S118
IV-S186, VIII-S272
VII-S13
IV-S86
VIII-S192
IV-S207

Adrian P. Wade
Haydn N.G. Wadley
N. Wakabayashi
S. Wakayama
D. A. Waldrop
Y. Wan,E. Waschkies,
M. WatadT. Watanabe
Takashi Watanabe
Robert J. Watters
Richard L. Weaver
J. R. Webster
M. Wevers
R. G. WhiteJ. W. Whittaker
B. J. S. Wilkins
H. Willer
A. J. Willis
P. E. Wilson
Leo Windecker
E. Winter
S. M. Wolf
Jörg Wolters
B. R. A. Wood
J. D. Wood
D. G. M. Wood
G. Wormser
Bobby Wright

X-71
V-S69
X-S42
IV-S278
VII-31
VIII-S97
VIII-S93
III-41
III-59II-1
IV-S17, VIII-S258
IV-S54, V-S40
IV-S132, VIII-S197
IV-S186, VIII-S272
I-263
see above listing
X-S22
X-67
I-244
II-191ICAE Banquet
X-67
III-239
III-51
see above listing
VIII-S318
IV-71
V-S52
IV-S166

Y. Xiang,
J. Z. Xiao
Yue-Huang Xu

VIII-S246
IV-S215
III-81

K. YamadaKatsuya Yamaguchi
Kusuo Yamaguchi
T. Yamaguchi
Akio Yamashita
M. Yanagibashi
J. M. Yang
C. K. Yao
A. Yasuo
Takao Yoneyama
Dong-Jin Yoon
Sumio Yoshikawa
Tatsuhiko Yoshimura
H. Yoshioka

VI-13
IX-209see above listing
VIII-S145
IV-S286
VIII-S213
VIII-S268
IV-S94, VIII-S105
IV-S294
II-1, VII-173, VIII-S213
IX-237
VIII-113
VI-109, VI-145
X-S63

R.D. Young
R. P. Young
R. Paul Young
Qing Huan Yu
Syuro Yuji,
Hironobu Yuki
Shigenori Yuyama

VI-93
VIII-S38, VIII-S166, VIII-S254
V-S29, V-S34
VIII-41
VII-167
X-35
II-19, II-71, IV-S38

F. Zeides
B. Q. Zhang
X. Q. Zhu
Z. ZhuZu-Ming Zhu
Steven M. Ziola
Daihua Zou
Ryszard Zuchowski
J. Zuidema

VIII-S109, VIII-47, X-31
see above listing
IV-S215
VIII-S135
VI-115
VIII-51
V-S1
II-272
VIII-S118

Contents
Volume 1 (1982)
Number 1
Page 1An Acoustic Emission Study of the Intergranular Cracking of AISI 4340 SteelA. Nozue and T. Kishi

Page 7

Acoustic Emission Behavior of a Low Alloy SteelR. J. Landy and K. Ono

Page 21

An Acoustic Measurement of Boiling Instabilities in a Solar ReceiverPage 29

A Broadband Acoustic Emission Transducer
Mark B. Moffatt, Theodore J. Mapes and Arthur T. Grodotzke

Page 35

A Simple Tape Recording System for Acoustic Emission

Page 37

Earthquakes as Acoustic Emission - 1980 Izu Peninsula Earthquake ln Particular

Page 45

AE Literature

Page 67
Page 68

Conferences and Symposia
The Fifth International Acoustic Emission Symposium K. Ono
Third Conference on AE/Microseismic Activity in Geologic Structures and Materials

Page 69
Page 72

The Tenth European Working Group on Acoustic Emission
Future Meetings

Number 2
Page 73

Reciprocity and Other Acoustic Emission Transducer Calibration Techniques

Page 81

Production Acoustic Emission Testing of Braze Joint

Page 87

Acoustic Emission Transducer Calibration by Means of the Seismic Surface Pulse

Page 95

Acoustic Emission Testing of Filament-Wound Pipes under Repeated Loading
Leszek Golaski, Maciej Kumosa and Derek Hull

Alan G. Beattie

P. G. Bentley and A. Plevin
Kiyoo Mogi

T. F. Drouillard

VII

H. R. Hardy, Jr.

K. OnoRoger Hill

T. F. Drouillard and T. G. Glenn
F. R. Breckenridge

Page 103

Source Mechanism and Waveform Analysis of Acoustic Emission in Concrete

Masayasu Ohtsu

Page 114

Acoustic Emission in Aircraft Structural Integrity and Maintenance Programs

J. M. Rodgers

Page 121

Page 149

AE Literature
T. F. Drouillard
Conferences and Symposia
The 23rd Meeting of AEWG
K. OnoThe 24th Meeting of AEWG
W. F. Hartman and J.W. Whittaker
AEWG AWARDS
K. OnoThird National Conference on Acoustic Emission
K. OnoCARP - Sixth Meeting
J. Mitchell
The XIth EWGAE Meeting
B. Audenard
BOOK REVIEWElastic Waves in Solids
Roger Hill

Number 3
Page 151

Testing Fiber Composites with Acouatic Emission Monitoring

Page 141
Page 144
Page 145
Page 146
Page 147
Page 148

Marvin A. Hamstad

Page 165

On the SPI/CARP Recommended Practice for Acoustic Emission Testing of Fiberglass Tanks and Vessels
C. Howard Adams

Page 173

Some Details on the NBS Conical Transducer

Page 179

Low Temperature Behavior of Liquid Couplants used in Acoustic Emission Experiments
J. Baram and J. Avissar

Page 183

Acoustic Emission Behavior of Nickel during Tensile Deformation

Page 191

Instrumented Impact Testing of Structural Fiber-Reinforced Plastic Sheet Materials and theSimultaneous AE Measurements
S. I. Ochiai, K. Q. Lew and J. E. Green

Page 193

On the Sensitivity of the Acoustic Barkhausen/Magnetomechanical Acouatic Emission Effect
A. E. Lord, Jr.

Page 195

AE Literature
Thomas F. Drouillard
Conferences and Symposia
1982 ASNT Spring Conference
K. OnoFifth Internatl Conf. on NDE in the Nuclear Industry
K. OnoThe Institution of Metallurgists Meeting on AE
A. P. G. Rose
Review of Progress in Quantitative NDE
K. OnoThe 24th Meeting of AEWG
K. OnoThe Sixth International Acoustic Emission Symposium
Symposium on Structural Faults: Inspection and Repair
First International Sympoaium on AE from Reinforced Composites
Sixth Internatl Conf. on NDE in the Nuclear Industry
BOOK REVIEW
AE in Geotechnical Engineering PracticeRobert M. Koerner
Acoustic Emission
Clinton Heiple
New Products and Services
AE Events of Interest / Letter from the Editor

Page 211
Page 211
Page 211
Page 213
Page 214
Page 215
Page 219
Page 220
Page 220
Page 220
Page 221

Number 4
Page 223

Thomas M. Proctor, Jr.

S.-Y. S. Hsu and K. Ono

In-Flight Acoustic Emission Monitoring of a Wing Attachment Component
S. L. McBride and J. W. Maclachlan

Page 229

Effect of Crack PreAence on In-Flight Airframe Noises in a Wing Attachment Component
S. L. McBride and J. W. Maclachlan

Page 237

Leak Detection Using Acoustic Emission

Page 244

Acoustic Emission from Glass/Polyester Composites; Effect of Fibre Orientation
F. J. Guild, B. Harris and A. J. Willis

A. A. Pollock and S.-Y. S. Hsu

VIIIPage 251

Changes in Acoustic Emission Peaks in Precipitaion Strengthened Alloya with Heat Treatment
C. R. Heiple and S. H. Carpenter

Page 263

A Miniature Optical Acoustic Emission Transducer
D. C. Emmony, M. W. Godrrey and R. G. White

Page 266

Classification of NDE Waveforms with Autoregressive Models

Page 271

AE Literature
Thomas F. Drouillard
Conferences and Symposia
The l1th Meeting of the European Working Group on Acoustic Emission Roger Hill
ASTM Subcommittee E 7.04 on Acouatic Emiaaion Alan G. Beattie
Meetings of Japanese Committee on Acoustic Emission
The 10th World Conf. on Non-Destructive Testing
Conference on Periodic Inspection of Pressurized Components
1983 ASNT Spring Conference
Symposium; Nondeatructive Methods for Material Property Determination
First International Symposium on AE from Reinforced Composites
Review of Progress in Quantitative NDE
25th Meeting of Acoustic Emission Working Group
BOOK REVIEW
Progress in Acoustic Emission Adrian A. Pollock
ANNOUNCEMENTS
AE Events of Intereat / Letter from the Editor
Index to Volume 1

Page 294
Page 300
Page 300
Page 301
Page 302
Page 302
Page 302
Page 303
Page 303
Page 303
Page 303
Page I-1

Ronald B. Melton

Volume 2 (1983)
Number 1/2
Page 1

Rotating Machinery Diagnosis With Acoustic Emission Techniques Ichiya Sato, Takao Yoneyama,
Soji Sasaki, Toshitaka Suzuki, Tomoaki Inoue, Tsuguaki Koga and Takashi Watanabe

Page 11

In-Field Experience in Condition Monitoring of Rotating Machinery by Demodulated Resonance Analysis
G. Buzzacchi, M. Cartoceti, C. De Michelis and C. Sala

Page 19

Acoustic Emission from Environmental Cracking of a High Strength Titanium Alloy
S. Yuyama, T. Kishi, Y. Hisamatsu and T. Kakimi

Page 29

Detection of Corrosion Fatigue by Acoustic Emission

Page 39

Effect of Overaging on Acoustic Emission Behaviour of 7075-T651 Aluminum During Crack Growth
S.L. McBride and J.W. Maclachlan

Page 47

AE Source Identification by Frequency Spectral Analysis for an Aircraft Monitoring Application
L. J. Graham and R. K. Elsley

Page 57

A User's Perspoctive of Small Computer-Based Acoustic Emission Equipment

Page 64

Magnetomechanical Acoustic Emission: A Non-Destructive Characterization Technique of Precipitation
Hardened Steels I. Roman, S. Maharshak and G. Amir

Page 67

Acoustic Emission Couplants I: The ASTM Survey

Page 69

Acoustic Emission Couplants II: Coupling Efficiencies of Assorted Materials
R. S. Algera and C. C. Feng

Page 71

AE Analysis During Corrosion, Stress Corrosion Cracking and Corrosion Fatigue Processes
S. Yuyama, T. Kishi and Y. Hisamatsu

Page 96

Acoustic Emission, Principles and Instrumentation

Page 129

AE Literature

Thomas F. Drouillard
IX

P. Jax and B. Richter

M.A. Hamstad

A.G. Beattie

A.G. Beattie

A. G. Beattie, J. A. Baron,

Page 143
Page 143
Page 146
Page iPage ii
Number 3
Page 151

Conference and Symposia
The 25th MEETING OF AEWG
A.G. Beattie
AEWG Awards
AEWG XXV
Editorial
S.L. McBride
A Note from the Editor
Kanji Ono
Acoustic Emission Source Kinematics Based on the Moving Dislocation Theory
Masayasu Ohtsu

Page 159

Characterization of Concreto Damages by Acoustic Emission Analysis
Marie-Christine Reymond and Andre Raharinaivo

Page 169

Effects of Solute Distribution on Acoustic Emission Behavior of Solid Solution Alloys
S.-Y. S. Hsu and Kanji Ono

Page 179

Acoustic Emission During Fatigue Crack Growth in 7075-T6 Aluminum at 20°C and 120°C
S.L. McBride and J.W. Maclachlan

Page 187

An Acoustic Pressurementer to Determine In-Situ Soil Properties
Arthur E. Lord, Jr., and Robert M. KoernerPage 191

An Acoustic Emission Data Acquisition and Analysis System Using an Apple II Microcomputer
P.E. Wilson and S.H. Carpenter

Page 195

Acoustic Emissions in Geological Materials

Page 221
Page 239
Page iPage ii

AE Literature
Thomas F. Drouillard
Conferences and Symposia
AE Event of Interest
M.A. Hamstad
Editorial
Davis M. Egle/ERRATUM

Number 4
Page 247

Resonance Analysis of Piezoelectric Transducer Elements

Page 261

Acoustic Emission Evaluation of Metal-Elastomer Junctions

Page 267

Partial Discharge Detection in Bushings by an Acoustic Emission Method

Page 272

Acoustic Emission as a Measure of Material Damage under Thermal Cycling
Ryszard Zuchowski and Leszek Korusiewicz

Page 275

Acoustic Emission Sensors

Page 281

Mechanical Properties and Acoustic Emission in Laser Welded HSLA Steel

Page 289

Acoustic Emission Detection of Crack Initiation during Dynamic Fracture Testing of High Strength
Materials
S. I. Ochiai, M. C. Cheresh and J. E. Green

Page 292
Page 319
Page 319
Page 327

AE Literature
T.F. Drouillard
Conferences and Symposia
12th EWGAE Meeting
L.M. Rogers
Cover Photos

Page I-1

Index to Volume 2

Arthur E. Lord, Jr. and Robert M. Koerner

M. Ohtsu and K. Ono
M. Sorel and C. Schepacz
J. Skubis

C.M. Scala
G. Dionoro and R. Teti

Volume 3 (1984)
Number 1
Page 1

Acoustic Emission Due to Crack Growth, Crack Face Rubbing and Structural Noise in the CC-130
Hercules Aircraft S. L. Mcbride and J. W. Maclachlan
X

Page 11

Determination of the Source of Acoustic Emission Generated during the Deformation of MagnesiumMark Friesel and Steve H. Carpenter

Page 19

Thermal Restoration of Burst Emissions in A533B SteelI. Roman, H. B. Teoh and Kanji Ono

Page 27

A Generalized Theory of Acoustic Emission and Green's Functions in a Half Space
Masayasu Ohtsu and Kanji Ono

Page 41

Acoustic Emission Charachterization of the Mechanical Strength of Sintered SAE-316 Stainless SteelI. Roman, M. Watad and A. Mittelman

Page 46
Page I
Page ii

AE Literature Thomas F. Drouillard
Editorial S. Vahaviolos
Meeting Calendar

Number 2
Page 51

Description of Compound Parameters of Particle-Filled Thermoplastic Materials by Acoustic
Emission Techniques Jorg Wolters

Page 59

A New Method of Acoustic Emission Transducer Calibration
Masayasu Ohtsu and Kanji Ono With Appendix By F.R. Breckenridge and T. Watanabe

Page 69

Pattern Recognition Analysis of Magneto-Mechanical Acoustic Emission Signals
Masayasu Ohtsu and Kanji Ono

Page 81

An Investigation of the Acoustic Emission Generated during The Deformation of Carbon Steel Fabricatedby Powder Metallurgy Techniques Yue-Huang Xu, Steve H. Carpenter and Bruce Campbell

Page 90
Page 100
Page 100
Page 104
Page 104
Page 105
Page 106
Page 107
Page iPage ii
Page ii

AE Literature Thomas F. Drouillard
Conferences and Symposia
AEWG-26 (Abstracts)
AEWG-27/AEWG AWARDSAEWG Chairman's Letter Davis M. Egle
CARP-8/JCAE/ASNT Methods Committee
Other Conferences
Book Review Arthur E. Lord, Jr.
AE Events Of Interest
2nd Internat'l AE Conf/EWGAE-13/7th AE Symp.
Ultrasonics 85/ASME Symposium

Number 3
Page 108

Monitoring of Metal Cutting and Grinding Processes by Acoustic Emission

Y. Kakino

Page 118Classification of Acoustic Emission Signals from Deformation Mechanisms in Aluminum Alloys
D. Robert Hay, Roger W.Y. Chan, Douglas Sharp and Khalid J. Siddiqui

Page 130

Effects of Interfacial Segregation on Acoustic Emission Behavior of A533B Steel
H.B. Teoh, Kanji Ono, E. Kobayashi and I. Roman

Page 144

Magnetomechanical Acoustic Emission of Ferromagnetic Materials at Low Magnetization Levels(Type I Behavior) May Man Kwan, Kanji Ono and M. Shibata

Page 158

Technical Note
Acoustic Emission during Nickel Electroplating of Copper
I. De Iorio, F. Langella and R. Teti

Page 164
Page 172
Page 172
Page 173
Page 174

AE Literature
Thomas F. Drouillard
Conferences and Symposia
EWGAE-13
5th Colloquium on AE D. Schumann and W. MorgnerBook Review
Kanji Ono
XI

Page 117
Page 157
Page 175
Number 4
Page 176

Meeting Calendar
Available Books on AE
Cover PhotographMeasurement, Detection and Analysis of Longitudinal and Transverse AE Waves Emitted Near A Crack
C. Duytsche, P. Fleischmann and D. Rouby

Page 182

Three-Dimensional Crack Location by Acoustic Emission
C. B. Scruby and G. R. Baldwin

Page 190

Magnetomechanical Acoustic Emission of Ferromagnetic Materials At High Magnetization Levels(Type II Behavior) May Man Kwan, Kanji Ono and M. Shibata

Page 204

Technical Note
A Report on the Pulsed Acoustic Emission Technique Applied to Masonry

Page 212
Page 224
Page 224
Page 226
Page 233
Page 239
Page 242
Page 243
Page 181
Page 189
Page iPage ii
Page ii

AE Literature
Thomas F. Drouillard
Conferences and Symposia
AEWG-27 A. G. Beattie
EWGAE-13 (Abstracts)
7th Int'l Ae Symposium Kanji Ono
ASTM A. G. Beattie/PROC. AE Workshop R. H. Jones et al.
ASME Symposium D. A. Dornfeld/4th Conf on AE/MA H. R. Hardy, Jr.
Cover Photograph/Announcements
Meeting Calendar
Available Books On AE
Ae Events Of Interest
Editorial
Kanji Ono
Thomas/Hochwald Award for Dr. Tim Fowler

Page I-1

Index to Volume 3James D. Leaird

Volume 4 (1985)
Number 1
Page 1

Acoustic Emission Monitoring of Flaw Growth in A Graphite-Epoxy Experimental Wing Segment
John Rodgers

Page 9

Acoustic Emission Measurments Using Point-Contact Transducers

Page 19

Relationship of Acoustic Emission to Internal Bond Strength of Wood-Based Composite Panel
Materials
F. C. Beall

Page 31

Laboratory Leak Detection in Gas and Liquid Storage Tanks Using Continuous Wave Acoustic Emission
A. E. Lord, Jr., R. M. Koerner and R. N. Sands

Page 41
Page 61
Page 61
Page 65
Page 66
Page 67
Page 68
Page 40
Page 69
Page 70
Page iPage ii

AE Literature Thomas F. Drouillard
Conferences And Symposia
AEWG-27 (Abstracts)
Kanji Ono
ASTM E-7.04
Alan G. Beattie
EWGAE-14 (Provisional Programme)
WCNDT-11/ Other Conferences
AE in Brazil
Call for Papers (2nd Int'l Symp AE from RP/8th Int'l AE Symp)
EWGAE-14
2nd Internat'l AE Conf
AE Events of Interest
ASME Boiler and Pressure Vessel Code/Activities at AEWG-27

XII

C. B. Scruby

Numbers 2 and 3
Pages S1-S332 Proceedings of the Second International Conference on Acoustic Emission, Oct. 28 - Nov. 1, 1985
S1
Characterizing Fracture Types in Rock/Coal Subjected to Quasi-Static Indentation Using Acoustic
Emission Technique, A. Wahab Khair
S7

Applications of Statistical Inference to Improve an Evaluation of Rockburst Danger in Underground Coal
Mines, Stanislaw Lasocki

S11

Field Determination of Prestress (Existing Stress) in Soil and Rock Masses Using Acoustic Emission,
A. E. Lord, Jr. and R. M. Koerner

S17

Directional Acoustic Emission Activity in Response to Borehole Deformation in Rock Masses,
Robert J. Watters and Amir Soltani

S19

Acoustic Emission during Dissolution of Salt, H. Reginald Hardy, Jr.

S21

Kaiser Experiment in sawcut Rock, J. D. Leaird, J. Dunning and M.E. Miller

S26

Acoustic Emission Technique for Solid Propellant Burn Rate Control, V. Lalitha, S. K. Athithan and V. N.
Krishnamurthy

S30

AE Montioring of Jet Engine Breech Chambers, Nitin Dhond and Davis M. Egle

S32

Acoustic Emission Studies for Detection and Monitoring Incipient Cracks in a Simulated Aero Engine
Mount under Fatigue, S. C. Pathak and C. R. L. Murthy

S35

Post-Test Selective Screening of Acoustic Emission Data - How Helpful Is It?, B.C. Dykes

S38

Comparison between Experimentally Detected Surface Motions Due to a Disbonding and Simulated
Waveforms, Shigenori Yuyama, Takuichi Imanaka and Masayasu Ohtsu

S42

Attenuation and Dispersion in AE Waveforms: a Comparison of Theory and Experiment, R. A. Kline and
S. S. Ali

S46

The Effects of Transducers on the Decay of a Diffuse Energy Field, H. A. L. Dempsey and Davis M. Egle

S50

The Generalized Theory and Source Representations of Acoustic Emission, Masayasu Ohtsu and Kanji
Ono

S54

Experimental Studies of Diffuse Waves for Source Charcterization, Richard L. Weaver

S58

Effect on Flaw Location by the Wave Shape of Acoustic Emission Propogating in a Limited Medium,
Yukuan Ma

S62

Applications of Quantitative AE Method; Dynamic Fracture, Materials and Transducer Characterization,
Wolfgang Sachse and K. Y. Kim

S64

A Case for Acoustic Emission Surveilence of Operating Reactors, William F. Hartman

S69

Characterization of Acoustic Emission Signals Generated by Water Flow Through Intergranular Stress
Corrosion Cracks, T. N. Claytor and D. S. Kupperman

S74

On-Line Acoustic Emission Monitoring of Nuclear Reactor Systems - Status and Future, P. H. Hutton

S77

Acoustic Leak Detection in Nuclear Power Plants, John W. McElroy

S78

On the Application of Acoustic Emission Analysis to Evaluate the Integrity of Protective Oxide Coatings,
H. Jonas, D. Stover and R. Hecker

S82

Utilization of Acoustic Emission for Detection, Measurement and Location of Partial Discharges, Jerzy
Skubis, Jerzy Ranachowski and Boguslaw Gronowski
XIII

S86

Acoustic Emission Examination of Power Plant Components, G. Tonolini, G. Villa and S. Ghia

S90

Acoustic Emission from Aluminum Alloy 6061 Strengthened by Whisker and Particulate Silicon Carbide,
James R. Kennedy

S94

Acoustic Emission during Phase Transformation in Alloys GCr15 and Fe-32Ni, B. Q. Zhang, C. K. Yao
and R. M. Tian

S98

Acoustic Emission Studies of Structural Relaxation in a Metallic Glass , G. L. Goswami, P. K. K. Nair, P.
Raj and G. P. Tiwari

S102

Effects of Secondary Phases on Acoustic Emission in 316 Stainless Steel and a Nimonic Alloy PE-16
during Tensile Deformation and Fracture, Baldev Raj, T. Jayakumar, D. K. Bhattacharya and P. Rodriguez

S106

Burst-Type Behavior of Structural Steels, O. Y. Kwon, I. Roman and Kanji Ono

S111

Acoustic Emission Behavior of an Advanced Aluminum Alloy, I. Roman, Kanji Ono and C. H. Johnson

S116

Acoustic Emission Produced by the Deformation of Uranium, C. R. Heiple and S. S. Christian

S119

An Investigation of the Acoustic Emission Generated during the Deformation and Fracture of
Molybdenum, J. B. James and S. H. Carpenter

S123

Manufacturing Process Monitoring and Analysis using Acoustic Emission, David A. Dornfeld

S127

Acoustic Emission of Flexible Disk Magnetic Media Systems, Ming-Kai Tse and Armand F. Lewis

S131

Acoustic Emission Monitoring of Drilling, M. W. Hawman

S132

Vibro-Acoustic Emission - A Conventional Means of Inspection using AE Technology, J. R. Webster and
T. J. Holroyd

S134

Weld Penetration Monitoring Using Acoustic Emission, J. Maram and J. Collins

S135

Using Acoustic Emission Measurements to Establish the Quality of Bonding of Fine Wires to Microchips,
S. H. Carpenter, D. R. Smith and J. H. Armstrong

S137

Real-Time Aircraft Structural Monitoring by Acoustic Emission, S. Y. Chuang

S138

Aircraft Structure Surveillance in-Flight Using Acoustic Emission, P. H. Hutton

S142

In-Flight AE Monitoring, G. G. Martin and I. G. Scott

S147

In-Flight Monitoring for Incipient Cracks in An Aero Engine Mount: An Approach Through Pattern
Recognition, C. R. L. Murthy, M. A. Majeed, S. C. Pathak and A. K. Rao

S151

Acoustic Emission Monitoring of Aircraft Structures, S. L. McBride and J. W. Maclachlan

S155

Real-Time Acoustic Emission Monitoring Requirements For Cold Proof Testing of the USAF F-111
Aircraft, J. M. Rodgers

S157

Leakage Test by Acoustic Emission Testing (AET) on Flat Bottom Tanks, Peter Tscheliesnig and Heinrich
Theiretzbacher

S161

Acoustic Emission of Offshore Structures; Attenuation - Noise - Crack Monitoring, Steinar Lφvaas

S165

Acoustic Emission Monitoring of a Node in An Off-Shore Platform, A. B. M. Hoff and M. Arrington

S166

Acoustic Emission Monitoring of a Bellows and Wye Section on a Fluidized Catalytic Cracking Unit,
Thomas Gandy, Martin Peacock and Bobby Wright

XIV

S170

Acoustic Emission Modulus Determination and Source Location in Unidirectional Fibre Reinforced
Polymer Composites, A. M. G. Glennie, T. J. Gulley and J. Summerscales

S174

Acoustic Emission Monitoring of Iosipescu Shear Test On Glass Fibre-Epoxy Composites, M. K. Sridhar,
Iyer Subramaniam, Chandra Ajay and A. K. Singh

S178

Fracture Mechanisms Characterization in Discontinuous Fibre Composites using Acoustic Emission
Amplitudes, J-M. Berthelot

S182

Testing Stress Corrosion of Glass Reinforced Plastic With Acoustic Emission Monitoring, Leszek Golaski
and Andrzej Figiel

S186

Analysis of Fatigue Damage in CFR Epoxy Composites by Means of Acoustic Emission: Setting up a
Damage Accumulation Theory, M. Wevers, I. Verpoest, E. Aernoudt and P. De Meester

S191

Characteristics of Acoustic Emission Generated from GFRP during Tensile Test, Kusuo Yamaguchi,
Hirotada Oyaizu, Yasuaki Nagata and Teruo Kishi

S195

Acoustic Emission during Load-Holding and Unload-Reload in Fiberglass-Epoxy Composites, M. Shiwa,
M. Enoki and T. Kishi

S199

Acoustic Emission Monitoring of Composite Damage Occurring under Static and Impact Loading,
D. S. Gardiner and L. H. Pearson

S203

Fatigue Crack Closure Study, Guozhi Lu

S207

Amplitude Distribution Analysis of Acoustic Emission during Fatigue Testing of Steels Used in Offshore
Structures, R. Visweswaren, M. Manoharan, G. Jothinathan and O. Prabhakar

S211

AE Monitoring of Corrosion Fatigue Growth; Secondary AE Sources, Christian Thaulow

S215

Slow Strain Rate Stress Corrosion Cracking of Compact Tension Specimen and Measurement with
Acoustic Emission, X. Q. Zhu and J. Z. Xiao

S220

Temperature Dependence of Inclusion-Fracture-Related Acoustic Emissions in 7075-T651 Aluminum,
S. L. McBride and J. Harvey

S224

Asssessment of Fatigue Damage with Acoustic Emission, M. Nabil Bassim

S228

Monitoring the Wood Cutting Process with Acoustic Emission, Richard L. Lemaster and David A.
Dornfeld

S232

Acoustic Emission Characterization of Wood Fiber Hardboard, Henrique L. M. dos Reis

S236

Detection of Western Hemlock Wood in Very Early Stages of Decay using Acoustic Emissions, Masami
Noguchi and Koichi Nishimoto

S240

Application of AE to Mechanical Testing of Wood, Keiichi Sato, Takeshi Okano, Ikuo Asano and Masami
Fushitani

S244

Effect of Moisture Conditioning on AE from Particleboard, Frank C. Beall

S247

In-Process Acoustic Emission Monitoring of Laser Welds, J. W. Whittaker, T. M. Mustaleski and K. D.
NicklasS251

Monitoring of Thin Welds by Acoustic Emission, G. L. Goswami and P. R. Roy

S255

Defect Detection in Stainless Steel Uranus 45 Tig Welded Joints by Acoustic Emission, Vincenzo Dal Re,
B. Birolo and F. Cipri

S259

Classification of Acoustic Emission Signals Generatied during Welding, Roger W. Y. Chan, D. RobertHay, Khalid J. Siddiqui and Douglas R. Sharp
XV

S263

Acoustic Emission Behavior of Metal Matrix Composites, C. Johnson, Kanji Ono and D. Chellman

S269

Detection of Crack Initiation and Propagation Using Acoustic Emission, W. G. Reuter

S270

Fracture Toughness Measurement of a Nicrmov Steel by Acoustic Emission, Vincenzo Dal Re

S274

Microcrack Initiation and Acoustic Emission of A533B Steel in Fracture Toughness Tests, Takanori Ohiraand Yih-Hsing Pao

S278

Quantitative Evaluation of Microcrackings during Fracture Toughness Testing of Al 2O3 by AE Source
Characterization, S. Wakayama and T. Kishi

S282

Three Dimensional Location and Quantitative Evaluation of Cracking Size in Ti Alloy by Acoustic
Emission Source Characterization, T. Kishi, H. Ohyama and K. H. Kim

S286

The Features and the Mechanism of AE Generation from Fatigue Cracks of SUS304 Piping Components,
Kusuo Yamaguchi, Hirotada Oyaizu and Akio Yamashita

S290

Acoustic Emission Study in Arctic Sea Ice in a Field Laboratory, N. K. Sinha

S294

Acoustic Emission Monitoring of the Main Shaft in the Hydroelectric Power Plant, H. Imaeda, H. Kimura
and A. Yasuo

S296

Acoustic Emission Applied to Reinforced Concrete Wall, M. C. Reymond and M. Diez

S300

Damage Process Characterization in Concrete by Acoustic Emission, J-M. Berthelot and J-L. Robert

S304

Detection of Fatigue Cracks in Highway Bridges with Acoustic Emission, David W. Prine and Theodore
Hopwood II

S307

Laboratory Acoustic Emission Investigation of Full Size ASTM A-588 Bridge Beams, Al Ghorbanpoor andDonald W. Vannoy

S311

Acoustic Emission Made Audible by Time Dilation of Digitally-Recorded AE Signals, Nelson N. Hsu andSteven E. Fick

S312

Magnetoelastic Resonance Spectroscopy, Wolfgang Stengel

S316

Discrimination of Fracture Mechanisms via Pattern Recognition Analysis of AE Signals during Fracture
Testing, Kanji Ono and Masayasu Ohtsu

S321

Some Design Concepts for an Accurate, High Speed AE Signal Acquisition Module, T. Kevin Bierney

S325

Advanced Acoustic Emission Monitoring System by Distributed Processing Waveform Microdata and the
System Configuration, Kusuo Yamaguchi, Takashi Hamada, Hatsuo Ichikawa, Hirotada Oyaizu, Teruo
Kishi and Hisashi Ishitani

S329

Advanced AE Instrumentation Concepts With Real Time Source Identification Through Correlation Plotsand Soft Ware Filtering, S. J. Vahaviolos and John M. Carlyle
Authors Index

S330
Number 4
Page 71

Evaluation of Pattern Recognition Analysis of Acoustic Emission from Stressed Polymers andComposites
R. M. Belchamber, D. Betteridge, Y. T. Chow, T. Lilley, M. E. A. Cudby and D. G. M. Wood

Page 85

The Use of Acoustic Emission to Measure the Ductility of Hardened Surface Layers
J. Roget and D.P. Souquet

Page 93

The Detection of Longitudinal Rail Force via Magnetomechanical Acoustic Emission
M. Shibata, E. Kobayashi and K. Ono
XVI

Page 103

Application of Acoustic Emission on Railroad Car Cushioning Devices
V. Godinez and W.D. Jolly

Page 107

Methods of Calculating Attenuation and Dispersion Effects on Acoustic Emission Signals
R. A. Kline and S. S. Ali

Page 115Classification of Acoustic Emission Signals Generated during Welding
Roger W.Y. Chan, D. Robert Hay, Victor Caron, Michel Hone and R. Douglas Sharp

Page 124
Page 124
Page 124
Page 125
Page S333

Conferences and Symposia
The 2nd International Conference on AE/28th AEWG Meeting
29th AEWG Meeting/AE Primer/The 8th International AE Symposium
AE Training Course Announced
Extended Abstracts from The Second International Conference on AE

Page I - 1

Index to Volume 4

Volume 5 (1986)
Number 1
Page 1

Nondestructive Monitoring of Installed Refractories by Acoustic Emission

David A. Bell

Page 7

Improving the Reliability of Critical Parts by Acoustic Emission Surveillance
during Proof Testing
Edward Goliti

Page 15On the Applicability of Amplitude Distribution Analysis to the Fracture
Process of Composite Materials
Luis Lorenzo and H. Thomas Hahn

Page 25

Detection, Measurements and Location of Partial Discharges in High Power Transformers using Acoustic
Emission Method
Jerzy Skubis, Jerzy Ranachowski and Boguslaw Gronowski

Page 31

Is it Time for Acoustic Emission Surveillance of Operating Nuclear Reactors?

W. F. Hartman

Page 39

Fracture Toughness Measurement of a NiCrMoV Steel by Acoustic Emission

V. Dal Re

Page 45

Uncommon Cries of Cast Iron Elucidated by Acoustic Emission Analysis
Winfred Morgner and Hartmut Heyse

Page 51
Page 53
Page 54
Page ii
Page 50
Page iPage 6
Page 14
Page 59
Page 60

The XVth and XIVth EWGAE Meetings Roger Hill
ASTM E7.04 Meeting A. G. Beattie / French AE Codes / E. German AE Colloquium
The Second International Conference on AE from Reinforced Plastics
29th AEWG Meeting / AE Primer
Meeting Calendar
Appreciation K. Ono / European Perspective Roger Hill
Available Books on AE
EWGAE Provisional Form
Montreal Conference Information / Cover Photo
AE Training Courses

Number 2
Page 61

Prediction of Lumber Checking during Drying by Means of Acoustic Emission TechniqueShigeru Ogino, Koji Kaino and Masahiko Suzuki

Page 67

On the Acousto-Ultrasonic Characterization of Wood Fiber Hardboard
Henrique L. M. dos Reis and D. Michael McFarland

Page 71

Effect of Moisture Conditioning on Acoustic Emission from Particleboard

Page 77

Acoustic Emission During the Deformation and Fracture of Molybdenum at Low Temperatures
Jay B. James and Steve H. Carpenter
XVII

Frank C. BeallPage 85

Acoustic Emission Produced by the Deformation of Uranium

Page 95

A Discussion of the Basic Understanding of the Felicity Effect in Fiber Composites

Page 103
Page 110Page 111

AE Literature - ConcreteThomas F. Drouillard
Book Review
M. A. Hamstad
The 29th Meeting of Acoustic Emission Working GroupThe Second International Symposium on Acoustic Emission from Reinforced Composites
XVth Meeting of The European Working Group of Acoustic Emission (EWGAE)
The 8th Int'l AE Symposium / The 30th Meeting of AEWG
Acousto-Ultrasonics: Theory and Application
Meeting Calendar
The Second CARP Symposium
Cover Photogragh
AE Events of Interest

Page 66
Page 94
Page 102
Page iC. R. Heiple and S. S. Christiansen
M. A. Hamstad

Number 3
Page S1Page S42
Page 113

Page 114
Page 116
Page 122
Page 123
Number 4
Page 124

A SPECIAL ISSUE ON AEWG AND EWGAE MEETINGS - EXTENDED ABSTRACTSThe 29th Meeting of Acoustic Emission Working GroupXVth Meeting of The European Working Group of Acoustic Emission (EWGAE)
The 29th Meeting of Acoustic Emission Working Group (AEWG)
XVth Meeting of The European Working Group of Acoustic Emission (EWGAE)
The 8th International AE Symposium/ The 30th Meeting of AEWG
Acousto-Ultrasonics: Theory and Application
Program of The 29th Meeting of Acoustic Emission Working Group
Abstracts of The 29th Meeting of Acoustic Emission Working Group Page 121
Program of XVth Meeting of The EWGAE
Program of The 8th International AE Symposium
The 2nd International Symposium on AE from Reinforced Composites M. A. Hamstad
The Generalized Theory and Source Representations of Acoustic Emission
Masayasu Ohtsu and Kanji Ono

Page 134

More Recent Improvements on the NBS Conical Transducer

Page 144

Nondestructive Evaluation of Adhesive Bond Strength Using the Stress Wave
Factor TechniqueHenrique L. M. dos Reis and Harold E. Kautz

Page 148

A Note on the Prediction of Fatigue Life of Metal Structures by Use of the
Felicity EffectJ. W. Whittaker

Page 152

Vibro-Acoustic Emission in Plastic Bars

Page 156

Acoustic Emission Testing of Glass Fiber Reinforced Plastic Components

Page 162

Correlation of Acoustic Emission to Microstructural Sources
James Mohr and Amiya K. Mukherjee

Page 172

Acoustic Emission Characterization of Pinch Welds

Page 184

XVth EWGAE Meeting/ 8th Int'l AE Symp.
DGM Symp. on AE/ 16th Symp. on NDE/ Prog. QNDE/ AE in Sci. & Tech.
30th AEWG Meeting/ Acousto-Ultrasonics/ XVIth EWGAE Meeting
9th Int'l AE Symp./ 3rd Int'l AE Conf./ 3rd Int'l Symp. on AE - Composites
Instruction for AEWG-EWGAE Extended Abstracts
Abstract; XVth EWGAE Meeting
Meeting Calendar
Call for Papers - 30th AEWG Meeting and AEWG Short Course
Registration Forms for 30th AEWG Meeting and AEWG Short Course
Call for Papers/ Announcements/ AE Training Courses

Page 185
Page 186
Page S69
Page 155
Page 143
Page 171
Page 185

Thomas M. Proctor, Jr.

A. E. Lord, Jr.

XVIII

R. TetiA. G. Beattie and C. W. Pretzel

Page 151
Page 161
Page iPage ii

Available Books on AE
Progress in Acoustic Emission III
AE Events of Interest
Acoustic Emission of a Kouros
Kanji Ono

Page I - 1

Index to Volume 5

Volume 6 (1987)
Number 1
Page 1

Fracture-Induced Acoustic Emission during Slow Bend Tests of A533B Steel H.B. Teoh and Kanji Ono

Page 13

Real-Time Monitoring of Multi-Pass Welding by Acoustic Emission K. Ishihara and K. Yamada

Page 19

Application of Pattern Recognition Concepts to Acoustic Emission Signals Analysis
C.R.L. Murthy, B. Dattaguru and A.K. Rao

Page 29

Punch Stretching Process Monitoring Using Acoustic Emission Signal Analysis--Part 1:
Basic Characteristics Steven Y. Liang and David A. Dornfeld

Page 37

Punch Stretching Process Monitoring Using Acoustic Emission Signal Analysis - Part 2:
Application of Frequency Domain Deconvolution
Steven Y. Liang, David A. Dornfeld and Jackson A. Nickerson

Page 43

Modeling Concrete Damage by Acoustic Emission

Page 61

Pattern Recognition Analysis of Acoustic Emission from Unidirectional Carbon Fiber-Epoxy
Composites by using Autoregressive Modeling
Masayasu Ohtsu and Kanji Ono

Page 73

A New Approach to the Use of Acoustic Emission Peak Amplitude Distribution as a Tool of
Characterizing Failure Mechanisms in Composite Materials
A. Mittelman and I. Roman

Page 84
Page 79
Page 80
Page 81
Page 83
Page 83

Waveform Digitizers Kanji Ono
30th AEWG Meeting/8th Int'l AE Symp.
M. Ohtsu
Int'l Conf. of NDE with AE Technology
Letter to Editor/An Erratum/ AE Training Courses
31st AEWG Meeting and AEWG Short Course
Progress in Acoustic Emission III

Number 2
Page 85

J.M. Berthelot and J.L. Robert

Acoustic Emission Wave Characterization: A Numerical Simulation of the Experiments on Cracked
and Uncracked Specimens T. Aizawa, T. Kishi and F. Mudry

Page 93

Flaw Growth in Alumina Studied by Acoustic Emission
M.A. Hamstad, P.M. Thompson and R.D. Young

Page 99

Acoustic Emission Characteristics in Concrete and Diagnostic Applications

Page 109

Measurement of the Maximum Applied Loads to Automobile Components by Acoustic Emission
Technique Tatsuhiko Yoshimura and Shigeto Kano

Page 115

Effects of Heat Treatment on the Acoustic Emission Generated During the Deformation of 7075-T651
Aluminum Alloy Zu-Ming Zhu and S.H. Carpenter

Page 121

An Overview of Acoustic Emission Codes and Standards

Page 125

Acoustic Emission Applied to Pressure Vessels

Page 133
Page 133
Page 135
Page 136

Third Symposium on AE W. Morgner
Fourth European Conf. on NDT and 16th EWGAE
AE Codes/Standards Activity within ASME and ASNT
Prof. S.H. Carpenter, 1987 University Lecturer
XIX

J.C. Spanner, Sr.

Brian R.A. Wood

J. R. Mitchell

Masayasu Ohtsu

Number 3
Page 137

Application of Acoustic Emission to the Field of Concrete Engineering

Taketo Uomoto

Page 145

A Method of Rapidly Estimating the Fatigue Limits by Acoustic Emission
Tatsuhiko Yoshimura and Shigeto Kano

Page 151

Preliminary Investigation of Acoustic Emission from Wood During Pyrolysis and Combustion
Frank C. Beall

Page 157

Preliminary Investigation of the Feasibility of Using Acousto-Ultrasonics to Measure Defects in Lumber
R.L. Lemaster and D.A. Dornfeld

Page 167

Development and Application of Acoustic Emission Methods in the United States - A Status Review
P.H. Hutton

Page 177

Acoustic Emission Produced by Deformation of Metals and Alloys - A Review: Part I
and S.H. Carpenter

Page 205
Page 205
Page 206
Page 166
Page 156
Page 176
Page 208

30th Meeting of AEWG D.M. Egle and R.A. Kline
A Brief Note on the Kaiser and Felicity Effects
R.A. Kline and D.M. Egle
The Third Int'l Workshop on Composite Materials / The Sixth (Japanese) Conference on AE
31st Meeting of AEWG
Available Books on AE
Cover Photograph
AE Training Courses/Announcements

Number 4
Page 209

C. R. Heiple

The Application of Acoustic Emission Techniques in High-Temperature Oxidation Studies A. S. Khanna,
B. B. Jha and Baldev Raj

Page 215

Acoustic Emission Produced by Deformation of Metals and Alloys - A Review: Part II
and S.H. Carpenter

C. R. Heiple

Page 239

Acoustic Emission During Deformation and Crack Initiation of Pipeline Steels M. W. Drew, B. R. A.
Wood and R. W. Harris

Page 249

Acoustic Emission Waveform Analysis to Identify Fatigue Crack Propagation in a Mirage Aircraft
C. M. Scala and R. A. Coyle

Page 257

The Detection of Hydrogen-Assisted-Crack Formation in U - 0.8% Ti Alloy Electron-Beam Weldments
J. W. Whittaker and M. W. Richey

Page 261

The Detection of the Fracture of Autoclaved Aerated Concrete during Autoclave Curing Process by
Acoustic Emission Satoshi Teramura, Koichi Tsukiyama and Hideaki Takahashi

Page 267
Page 273
Page 238
Page 256

31st Meeting of AEWG - Abstracts
Meetings on AE and related topics
9th Int'l AE Symp/World Meeting on AE
Cover Photograph

Page 273

INDEX to Volume 6

Volume 7 (1988)
Number 1
Page 1

Evaluation of Fracture Toughness of Autoclaved Lightweight Concrete by Means of Acoustic
Emission Technique, Satoshi Teramura, Koichi Tsukiyama and Hideaki Takahashi

Page 9Identification of Crack Propagation Modes in 304 Stainless Steel by Analysis of Their
Acoustic Emission Signatures Daniel R. Smith Jr. and Steve H. Carpenter

Page 21

A New Sensor for Quantitative Acoustic Emission Measurement
XX

Chung Chang and C. T. Sun

Page 31

Acoustic Emission Propagation and Source Location in Small, Spherical Composite Test Specimens
J. W. Whittaker, W. D. Brosey, O. Burenko and D. A. Waldrop

Page 41

A High Fidelity Piezoelectric Tangential Displacement Transducer for Acoustic Emission
Thomas M. Proctor, Jr.

Page 49

Acoustic Emission from Fatigue Cracks in Chrome-Molybdenum Steel Cylinders
P.R. BlackburnPage 57
Page 20
Page 40A
Page 48
Page 30

31st Meeting of AEWG
Meeting Schedule
World Meeting on AE
Instructions to Authors - World Meeting on AE
Books Available

Number 2
Page 59

Acoustic Emission Measurements on PWR Weld Material with Inserted Defects using Advanced
Instrumentation P. G. Bentley and M. J. Beesley

Page 81

Acoustic Emission Measurements on PWR Weld Material with Inserted Defects
C. B. Scruby and K. A. Stacey

Page 95

Characterization of Acoustic Emission from Thermally-Cycled Lithium Hydride
J. W. Whittaker and D. G. Morris

Page 103

Apparatus for Coupling an Acoustic Emission Transducer to a Rotating Circular Saw
Richard L. Lemaster and David A. Dornfeld

Page 111

Measurement of Density Profiles in Wood Composites Using Acoustic Emission
Richard L. Lemaster, Michael F. Gasick and David A. Dornfeld

Page 119

Acoustic Emission during Intergranular Stress Corrosion Cracking of Iron
M. A. Friesel and R. H. Jones

Page 129
Page 129
Page 80
Page 94
Page 102

XVIIth Meeting of EWGAE - Program
The 9th International Symposium on AE - Program
Meeting Schedule
World Meeting on AE
Books Available/Training Courses

Number 3
Page 135

Acoustic Emission from Porous Films of Aluminum Oxide during the Application of Electrical
Voltage J. Sampath Kumar and S.P. Mallikarjun Rao

Page 139
Page 140
Page 141

Third Domestic Conf. on Subsurface Acoustic Emission (Sendai, Japan) - Program
Acoustic Emission (UK) - Abstracts
Seminar on Acoustic Emission (India) - Abstracts

Page S1

Extended Abstracts of Presentations at AEWG(I) Seminar on ACOUSTIC EMISSION
Acoustic Emission during Tensile Deformation and Fracture in Austenitic Stainless Steels
Baldev Raj and T. Jayakumar

Page S13

Detection of Breakaway Oxidation of Zircaloy-2 by Acoustic Emission Technique
B. K. Gaur, A. K. Sinha, B. K. Shah, P. G. Kulkarni and R. Vijayaraghavan

Page S18

Magnetomechanical Acoustic Emission Behaviour of Some Structural Steels
S. G. Savanur and C. R. L. Murthy

Page S29

A Study of Acoustic Emission Activity in Granites during Stress Cycling Experiments

Page S35

Acoustic Emission Studies on Adhesive Potted Inserts of Honeycomb Sandwich Panels
T.S. Sriranga and R. Samuel
XXI

M.V.M.S. Rao

Page S40

Location of Weld Defects by Acoustic Emission

Page S43

Thermally Induced Relaxation Behaviour in a Metallic Glass
G. L. Goswami, S. K. Jha and G. P. Tiwari

Page S47
Page S48

Meeting Schedule/Training Courses
Announcements

Number 4
Page 145

G.L. Goswami and P.R. Roy

Acoustic Emission During Quasi-Static Loading/Hold /Unloading in Notched Reinforced
Fiber Composite Materials S. V. Hoa and L. Li

Page 161

The Acoustic Emission Generated during the Plastic Deformation of High Purity Zinc
S. H. Carpenter and Chung-Mei Chen

Page 167

Evaluation of Concrete Structure Deterioration via AE Observation of Core Tests
Masayasu Ohtsu, Tatsuro Sakimoto, Yutaka Kawai and Syuro Yuji

Page 173

Diagnosis of Rotating Slides in Rotary Compressors using Acoustic Emission Technique
Ichiya Sato, Takao Yoneyama, Kouichi Sato, Toshiyuki Tanaka and Hiroaki Hata

Page 179

AE-Monitoring Systems for the Detection of Single-Point and Multipoint Cutting Tool Failures
Thomas Blum, Ippei Suzuki and Ichiro Inasaki

Page 185

Direct Measurement of Water Hammer Pressure by AE Source Wave Analysis
Mikio Takemoto and Yasuhisa Hayashi

Page 193

Acoustic Emission from an Industrial Applications Viewpoint

Page 201

Downhole AE Measurement Technique and Its Application to Geothermal Fields

Page 211

Acoustic Emission Source Location in Fiber Reinforced Plastic Composites
D. J. Buttle and C. B. Scruby

Page 225

The Acoustic Emission Source Mechanism for Fatigue Crack Propagation in 7075 Aluminum
S. L. McBride, P. Bowman and K. I. McRae

Page 231
Page 231
Page 192
Page 200
Page 210
Page 224

Conferences and Symposia
Third International Symposium on AE from Composite Materials/EWGAE Meeting
Meeting Schedule
Books Available
Progress in AE IV, Proc. The 9th International AE Symposium
The World Meeting on AE/ASNT Recognition/Codes and Standards Committee

Page I-1

Index to Volume 7

Trevor J. Holroyd
Hiroaki Niitsuma

Volume 8 (1989)
Numbers 1 and 2
Pages S1-S338 Proceedings of the World Meeting on Acoustic Emission, March 20-23, 1989
S1
"Acoustic Emission Technology using Multi-parameter Analysis of Waveform and the Applications to
Fracture Modes and Growth Recognition in Composites", K. Yamaguchi, H. Oyaizu, J. Johkaji and Y.
Kobayashi
S4"Acoustic Emission Detection of Crack Presence and Crack Advance During Flight", S. L. McBride, M. D.
Pollard, J. D. MacPhail, P. S. Bowman and D. T. Peters

S8"Time-frequency domain (3-D) Analysis of AE Signals using Simple Instrumentation Techniques", S. V.
Subba Rao, K. V. Srincivasan and M. Annamalai

S12"Improving Acoustic Emission Crack/Leak Detection in Pressurized Piping by Pattern Recognition
Techniques", R. W. Y. Chan, D. R. Hay, J. R. Hay and H. B. Patel
XXII

S16

"An Efficient Unsupervised Pattern Recognition Procedure for Acoustic Emission Signal Analysis", M. A.
Majeed and C. R. L. Murthy

S20"Solving AE Problems by a Neural Network", I. Grabec and W. Sachse

S24"The General Problems of AE Sensors", Y. Higo and H. Inaba

S28"Acoustic Emission Transducer Modelling Using System Identification Techniques", S. Kallara, P. K.
Rajan and J. R. Houghton

S32"Design of 3-Dimensional AE/MS Transducer Arrays", M. Ge and H. R. Hardy

S38"Simultaneous Velocity Tomography and Source Location of Synthetic Acoustic Emission Data", S. C.
Maxwell, R. P. Young, and D. A. Hutchins

S42"Use of Mechanical Waveguides and Acoustic Antennae in Geotechnical AE/MS Studies", H. R. Hardy,
Jr., F. Taioli and M. E. Hager

S49"Linear Location of AE Simulated Sources on Steel Pipelines with Waveguides", B. Q. Zhang

S53"AE Application and Recent Results on Nuclear Components", P. Jax and V. Streicher

S57"Acoustic Emission from Steel Structures", A. Nielsen

S62"AE Role in the Diagnosis and Prognosis of Defects in Industrial Plant Steel Components", F. Tonolini

S66"Structural Integrity Evaluation Using Acoustic Emission Techniques", B. R. A. Wood and R. W. Harris

S70"On-line Application of Acoustic Emission Analysis", W. Morgner

S71"AE - For when you absolutely cannot afford a Failure of a Critical Pressure Boundary", H. L. Dunegan

S75"Periodic Inspection of Compressed Gas Cylinders and Transport Vessels by Using Acoustic Emission
Testing", H. Barthelemy

S79"Locating Fatigue Cracks by Acoustic Emission Testing", P. M. Horrigan, J. F. Finn, F. R. Tuler and J. H.
Smith

S84

"A New Acoustic Emission Measurement System and its Application to the Local Monitoring of a Crack in
a Pressure Vessel", B. Tirbonod and L. Hanacek

S88"An Approach for the Integrity Assessment of M250 Maraging Steel Pressurized Systems", T. Chelladurai,
R. Krishnamurthy and A. R. Acharya

S93"Detectability of Defects in Reactor Pressure Components by Location and Interpretation of AE-Sources",
C. Sklarczyk and E. Waschkies,

S97"Application of Acoustic Emission Technique during In-service Pressure Vessel Inspection", S. Liu, G.
Shen, Y. Wan and Q. Duan

S101

"Acoustic Emission Leak Monitoring in Pressurized Piping", H. B. Patel and A. W. Cook

S103

"Application of Acoustic Emission Technique in the High-temperature Oxidation Studies - A Review", A.
S. Khanna

S105"Acoustic Emission during Phase Transformation in Cr12MoV Steel and Fe-33Ni-4Ti-10Co Alloy", B. Q.
Zhang, C. K. Yao and C. G. Jiao

S109

"Effect of Pre-exposure to Water on the Acoustic Emission Behavior of 2091-T3 Al-Li Alloy", F. M.
Zeides and I. Roman

S114"Acoustic Emission from Weld-seam of 16MnR Steel during Stress Corrosion", B. Q. Zhang and J. Q. Sun
XXIII

S118

"The Influence of Crack Front Geometry on Acoustic Emission During Fatigue of Al2024-T351", F. A.
Veer and J. Zuidema

S122"Acoustic Emission Monitoring of Incipient Crack Propagation and its Growth Rate in EN-24 Steel under
Fatigue", S. C. Pathak and C. R. L. Murthy

S126"Acoustic Emission during Tensile Deformation and Fracture in Austenitic Alloys", B. Raj and T.
Jayakumar

S131"Relationship between Acoustic Emission and Flaw Size in Si3N4 Ceramics", Y. Mori, M. Nishino, K.-I.
Aoki, T. Kishi and K. Kitadate

S135"A Comparison of the Acoustic Emission Generated from the Fracture and Decohesion of Graphite
Nodules with Theoretical Predictions", S. H. Carpenter and Z. Zhu

S140"Influence of Grain Size on Frequency Spectra of AE Signal Generated during Tensile Deformation in an
AISI Type 316 Stainless Steel", B. Raj, P. Kalyanasundaram, T. Jayakumar, P. Barat and P. Rodriguez

S145"Evaluation of Fatigue Crack Growth Rate of Carburized Gear by Acoustic Emission Technique", Y.
Obata, H. Kobayashi, K. Aoki, T. Yamaguchi and K. Shibata

S149"Acoustic Emission Investigations in Poland", J. Ranachowski, Poland "Comparative Studies on Acoustic
Emission Generated During Lüder's Deformation in Mild Steel and Portevin-Le Chatelier Effect inAustenitic Stainless Steel", B. Raj, T. Jayakumar, P. Kalyanasundaram, P. Barat, B. B. Jha, D. K.
Bhattacharya, P. Rodriguez

S154"Development and Future Aspects in AE Source Characterization", M. Enoki and T. Kishi

S158"AE Source Modelling-Comparison of Inversion and Forward Techniques", D. J. Buttle and C. B. Scruby

S162"Source Inversion of Acoustic Emission for the Determination of Crack Kinematics and Kinetics", M.
Ohtsu

S166

"Acoustic Emission Analysis and Ultrasonic Velocity Imaging in the Study of Rock Failure", S. D. Falls, T.
Chow, R. P. Young and D. A. Hutchins

S170"Thin-Film Acoustics: Line and Point Sources Generation and the Testing of thin Films", K. Y. Kim and
W. Sachse

S175"Acousto-Ultrasonics: An Update", A. Vary

S179

"Theoretical Basis of Acousto-Ultrasonics", M. T. Kiernan and J. C. Duke

S184

"Fracture of Boron Particles in 2219 Aluminum as a Known Acoustic Emission Source", C. R. Heiple, S.
H. Carpenter and S. S. Christiansen

S188"Surface Analysis by Tribo-acoustic Emission", M.-K. Tse and P.-Y. Gu

S192"Acoustic Emission Measurements of Rubbing Surfaces", S. L. McBride, R. J. Boness, M. Sobczyk and M.
R. Viner

S197"Vibro-Acoustic Emission Rubbing Sources", J. R. Webster

S201"Acoustic Characterisation of Small Particle Impact", D. J. Buttle and C. B. Scruby

S205"Estimation of Impact Force between Rough Surfaces by Means of Acoustic Emission", I. Kukman and I.
Grabec

S209"Acoustic Emission Monitoring of Magnetic Hard Disks during Accelerated Wear Testing", J. C. Briggs,
M. M. Besen and M.-K. Tse

XXIV

S213

"Applications of Acoustic Emission Techniques for Diagnosis of Large Rotating Machinery and Mass
Products", I. Sato, T. Yoneyama, K. Sato, T. Tanaka, M. Yanagibashi and K. Takigawa

S217"Quality Inspection of Rolling Element Bearings using Acoustic Emission Technique", V. Bansal, A.
Prakash, V. A. Eshwar and B. C. Gupta

S219"Stress Wave Sensing - Affordable AE for Industry", T. J. Holroyd

S223"Cavitation Monitoring of Hydroturbines with True-RMS Acoustic Emission Measurement", O.
Derakhshan, J. R. Houghton, R. K. Jones and P. A. March

S227"Monitoring of the Cutting Process by Means of AE Sensor", D. A. Dornfeld

S231"Tool Monitoring by Acoustic Emission", J. Roget, P. Souquet, M. Deschamps and N. Gsib

S236

"MONPAC - An Acoustic Emission based System for Evaluating the Structural Integrity of Metal Vessels",
T. J. Fowler, J. A. Blessing and T. L. Swanson

S238"ICI's Perspective on Acoustic Emission Monitoring", S. Hewerdine

S239"MONPAC - Condition Monitoring for Static Plant - Case Histories", P. T. Cole

S240

"Acoustic Emission Monitoring of a Large Pressure Vessel during a Pneumatic Re-Qualification Test", M.
Peacock

S241"Summary of Experiences with MONPAC Testing by MQS/Dunegan Testing Group", R. K. Miller

S242"Microseismics and Geotechnical Applications", M. Ohtsu

S246"Acoustic Emission Phenomena in Geological Media", R. A. Kline, A. S. Khan, Y. Xiang, J.
Shlyapobersky and F. Irani

S250"Acoustic Emission/Microseismic Activity at very low Strain Levels", B. H. Armstrong

S254"Focal Mechanism Determinations for a Sequence of Mining-Induced Seismic Events Recorded at a Hard
Rock Mine", T. I. Urbancic, S. Talebi and R. P. Young,

S258"Effects of Porosity on Acoustic Emission Signatures", R. J. Watters and D. M. Chuck

S262"Acoustic Emission Monitoring and Analysis Procedures Utilized during Deformation Studies on Geologic
Materials", X. Sun, H. R. Hardy, Jr. and M. V. M. S. Rao

S266"PDP 11/34 Based Microseismic Monitoring System for Kolar Gold Fields, India", G. Jayachandran Nair

S268

"Fracture Mechanism Studies of Carbon/PMR-15 Composites by Acoustic Emission", J. S. Jeng, K. Ono
and J. M. Yang

S272

"Assessment of Fatigue Damage in Carbon Fibre Reinforced Epoxy Laminates with the Energy
Discrimating Acoustic Emission Method", M. Wevers, I. Verpoest, P. De Meester and E. Aernoudt,

S276"NDE Procedure for Predicting the Fatigue Life of Composite Structural Members", M. J. Sundaresan, E.
G. HennekeII and A. Gavens

S280"Detection of Impact Damage in Composite Structures by Use of Thermally-activated Acoustic Emission",J. W. Whittaker and W. D. Brosey

S284"Characterization of Failure Modes in Glass Fibre Reinforced Plastic Composites", M. N. Raghavendra Rao
and C. R. L. Murthy

S288"Spectrum Analysis of Acoustic Emission Signals from Carbon-Glass Hybrid Composites", M. R. Madhava
and H. N. Sudheendra
XXV

S292

"Analysis of AE Signals in Time and Frequency Domains Coupled to Pattern Recognition to Identify
Fracture Mechanisms in CFRP", A. Maslouhi and C. Roy

S297"Some New Results in the Damage Identification in Kevlar-Epoxy Composites", D. S. Rajan, N. N.
Kishore and B. D. Agarwal

S301"Monitoring Initiation and Growth of Matrix Splitting in a Uni-directional Graphite/Epoxy Composite",
S. Ghaffari and J. Awerbuch

S306"Correlation of Internal Bond Strength of Particleboard with Acousto-Ultrasonics", A. T. Green

S311"Acoustic Emission during Contact Drying of Southern Pine Veneer", F. C. Beall

S314"Nondestructive Evaluation of Adhesively Bonded Joints using Acousto-Ultrasonics", S. Tanary, A. Fahr
and Y. Haddad

S317"AE Research on Adhesion in Composite Materials", H. G. Moslé

S318"Acoustic Emission Testing of Flexed Concrete Beams Reinforced with Bonded Surface Plates", D. P.
Henkel and J. D. Wood

S322

"Acoustic Emission Investigation into some Concrete Construction Problems", J. D. Leaird and M. A.
Taylor

S326"Correlation of AE and Fracture Intensity during Impact Indentation of Coal Block", S. J. Jung and A. W.
Khair

S330

"Acoustic Emission Monitoring of Pressure Vessel Preventing Catastrophic Failure", S. F. Botten

S334

Limiting State Prediction from AE Signals for Large-size Structures under Static, Cyclic and Thermocyclic
Loads, V.A. Strizhalo and V.A. Strelchenko

S337

Authors Index

Number 3
Page 1The MONPAC System

Timothy J. Fowler, James A. Blessing, Peter J. Conlisk and Terry L. Swanson

Page 11

Acoustic Emission Monitoring of a Large Pressure Vessel during a Pneumatic Re-qualification Test
M. J. Peacock

Page 21

ICI's Perspective on Acoustic Emission Monitoring S. Hewerdine

Page 25

A Summary of Experiences with MONPAC™ Testing by the MQS/Dunegan Testing Group
R.K. Miller, R.G. Tobin, D.J. Gross and D.T. Tran

Page 31MONPAC - Condition Monitoring for Static Plant - Case Histories

Page 35

Detection of an Impulse Force in Head-Disk Media Contact using Small Piezoelectric Transducer
Kenji Mochizuki, Isamu Sato and Takefumi Hayashi

Page 41

Investigations of Sensor Placement for Monitoring Acoustic Emission in Machining
David V. Hutton and Qing Huan Yu

Page 47

Use of Plane-Strain Compression for the Diagnosis of Acoustic Emission Source during Plastic
Deformation F. Zeides and I. Roman

Page 51

Transverse Cracking and Longitudinal Splitting in Graphite/Epoxy Tensile Coupons as Determined byAcoustic Emission Steven M. Ziola and Michael R. Gorman

Page 8Page 9Cover Photographs
Meeting Calendar -- 33rd AEWG Meeting, 10th IAES, Kumamoto Workshops
XXVI

Phillip T. ColePage 61
Page 61
Page 34
Page 20, 30, 40
Page 50, 50, 62
Number 4
Page 65

Third International Symposium on AE from Composite Materials (AECM-3)
EWGAE Meeting/Future Meetings/Errata
The Status of EWGAE Panel on Standards J. Roget /Codes and Standards Committee
Abstracts of Third International Symposium on AE from Composite Materials (AECM-3)
Selected Abstracts of AECM-3 (continued)
A Review of International Research Relative to the Geotechnical Field Application of Acoustic Emission/
Microseismic Techniques H. Reginald Hardy, Jr.

Page 93

A Review of Acoustic Emission in Civil Engineering with Emphasis on Concrete
Masayasu Ohtsu

Page 99

Application of Acoustic Emission for the Evaluation of Microseismic Source Location Techniques
M. Kat and F. P. HassaniPage 107

Acoustic Emission Characteristics of Unstable Slopesand M. Kamata

Page 113

Experimental Studies on the Effect of Stress History on Acoustic Emission Activity -- A Possibility for
Estimation of Rock Stress
Sumio Yoshikawa and Kiyoo Mogi

Page 125

Acoustic Emission from Phase Transformations in Au - 47.5 at. % Cd
C. R. Heiple and S. H. Carpenter

Page 135

The Effect of Same-side and Through-Thickness Transmission Modes on Signal Propagation in Wood
Richard L. Lemaster and Stephen L. Quarles

Page 92
Page 143
Page 200
Page 210

Meeting Schedule
Abstracts of Third International Symposium on AE from Composite Materials
EWGAE Meeting/AEWG -33 Books Available
Progress in Acoustic Emission IV, Proc. The 9th International AE Symposium

Page I-1

Index to Volume 8

A. Chichibu, K. Jo, M. Nakamura, T. Goto

Volume 9 (1990)
Number 1
Page 1

Origin of Acoustic Emission Produced during Deformation of Beryllium
C. R. Heiple and S. H. Carpenter

Page 9Optimal Waveform Feature Selection Using a Pseudo-Similarity Method
K. J. Siddiqui, Y.-H. Liu, D. R. Hay and C. Y. Suen

Page 17

The Effect of Same-Side and Through-Thickness Transmission Modes on Signal Propagationin Wood Richard L. Lemaster and Stephen L. Quarles

Page 25

Defect Detection in Rolling Element Bearings by Acoustic Emission Method
N. Tandon and B. C. Nakra

Page 29

Monitoring of a Pressure Vessel (ZB2) by means of Acoustic Emission
H.-A. Crostack and P. Böhm

Page 37

A Procedure for Acceptance Testing of FRP Balsa Wood Core Pressure Vessels
P. Ouellette, S.V. Hoa and L. Li

Page 45

AE Literature
A Comprehensive Guide to the Literature on Acoustic Emission from Composites,
Supplement II Thomas F. Drouillard

XXVII

Page 69
Page 72
Page 73
Page 8Page 31
Page ii

Conferences and Symposia
Abstracts of 33rd Acoustic Emission Work Group Meeting
ASNT Spring Conference
1st International Conf. on AE in Manufacturing, AECM-4
Available Books on AE
Cover PhotographMeeting Calendar

Number 2
Page 75

Composites
Felicity Ratio Behavior of Pneumatically and Hydraulically Loaded Spherical Composite Test Specimens
J. W. Whittaker, W. D. Brosey and M. A. Hamstad

Page 84

Correlation of Felicity Ratio and Strength Behavior of Impact-Damaged Spherical Composite Test
Specimens J. W. Whittaker, W. D. Brosey and M. A. Hamstad

Page 91

Delayed Acoustic Emission: A Rheological Approach
P. Fleischmann

Page 97

Acoustic Emission Analysis of the Accumulation of Cracks in CFRP Cross-ply Laminates under Tensile
Loading J.-P. Favre and J.-C. Laizet

Page 103

Analysis of Acoustic Emission Events from Single Fiber Pullout Experiments
W. Mielke, A. Hampe, O. Hoyer and K. Schumacher

Page 109

Digital Signal Analysis of Acoustic Emission from Carbon Fiber/ Epoxy Composites
Kenji Kawamoto

Page 117

Acoustic Emission: A Micro-Investigation Technique for Interface Mechanisms in Fiber Composites
D. Rouby

Page 123

Acoustic Emission Characterization of the Deformation and Fracture of an SiC-Reinforced, Aluminum
Matrix Composite Oh-Yang Kwon and Kanji Ono

Page 131

Burst Prediction by Acoustic Emission in Filament-Wound Pressure Vessels
Michael R. Gorman

Page 140

Critical AE Problems for the Researcher

Page 142

Quality Inspection of Rolling Element Bearing using Acoustic Emission Technique
Vibha Bansal, B.C. Gupta, Arun Prakash and V.A. Eshwar

Page 147
Page 152
Page 153
Page 154
Page 122
Page 130
Page 154

N. Rochat, R. Fougeres andAdrian A. Pollock

Conferences and Symposia
XIX Meeting of EWGAE, 10th International Acoustic Emission Symposium
1st Symp. Evaluation of Adv. Materials by AE
Intl Joint Meeting at Kumamoto
Korean Working Group on Acoustic Emission (KWGAE), AE Training Courses
Meeting Schedule
34th AEWG Meeting
Cover Photograph

Number 3
Page 155

Wood
Frank C. Beall, Topical Editor
Anecdotal History of Acoustic Emission from Wood

Thomas F. Drouillard

Page 177

An Experiment on the Progression of Fracture (A Preliminary Report)
Fuyuhiko Kishinouye (Translated by Kanji Ono)

Page 181

Acoustic Emission from Drought-Stressed Red Pine (Pinus resinosa)
Robert A. Haack and Richard W. Blank

XXVIII

Kanji Ono andPage 189

The Effect of Moisture Content and Ring Angle on the Propagation of Acoustic
Signals in Wood Stephen L. Quarles

Page 197

Nondestructive Evaluation of Adhesive Bond Strength of Finger Joints in Structural
Lumber Using the Acousto-Ultrasonic Approach Henrique L. M. dos Reis,
Frank C. Beall, Michael J. Chica and Dick W. Caster

Page 203

Determining the Abrasiveness to Tools of Wood-Based Composites
with Acoustic Emission Richard L. Lemaster

Page 209
Page 215

Lumber Stress Grading utilizing the Acoustic Emission Technique Keiichi Sato,
Hajime Takeuchi, Katsuya Yamaguchi, Naoto Ando and Masami Fushitani
AE Literature - Wood Thomas F. Drouillard and Frank C. Beall

196, 214, 223
Page 226
Page 188
Page 180

Conferences and Symposia
10th International Acoustic Emission Symposium (abstracts)
Intl Joint Meeting at Kumamoto/ KWGAE/ 34th AEWG Meeting, AE Training Courses
Meeting Schedule
Fuyuhiko Kishinouye (biography) / Cover Photograph

Number 4
Page 227

Detection of Irradiation Effects on Reactor Vessel Steels by Magneto-Acoustic Emission
Oh-Yang Kwon and Kanji Ono

Page 237

New Algorithm for Acoustic Emission Source Location in Cylindrical Structures
Dong-Jin Yoon, Young H. Kim and Oh-Yang Kwon

Page 243

Characterization of Fatigue of Aluminum Alloys by Acoustic Emission, Part I Identification of Source Mechanism D. J. Buttle and C. B. Scruby

Page 255

Characterization of Fatigue of Aluminum Alloys by Acoustic Emission, Part II Discrimination Between Primary and Other Emissions D. J. Buttle and C. B. Scruby

Page 271

Acoustic Emission Source Location Using Simplex Optimization
M. P. Collins and R. M. Belchamber

Page 277

Acoustic Emission during Lumber Drying

Page 283

AE Source Orientation by Plate Wave Analysis
and William H. Prosser

Page 289
Page 270
Page 276A

Conferences and Symposia
10th International AE Symp.
Cover Photograph AE Inspection of Monorail Trains
Meeting Calendar

I-i

Index to Volume 9

S. Ogino, K. Kaino and M. Suzuki
Michael R. Gorman

Volume 10 (1991/92)
Number 1/2
Page iCurrent Research and Future Trend of AE Applications to Civil Engineering and Geological Technology
Masayasu Ohtsu, Topical Editor

S1-S12

Variety of Acoustic Emission Waveforms Produced by Discrete Crack Growth in Rock
Steven D. Glaser and Priscilla P. Nelson

S13-S17

Estimation of Maximum Stress in Old Railway Riveted I-Girder Bridges using Acoustic Emission Signals
Hisanori Otsuka, Hiroshi Hikosaka, Hiroyuki Miyatake and Syouzou Nakamura

S18-S21

Another Look at Booming Sand

Marcel F. Leach and Gottfried A. Rubin

XXIXS22-S28

Expected Acoustic Emission from Around a Shaft in Intact Rock

S29-S34

Acoustic Emission Monitoring during Microseismic Activity Caused by Mine Subsidence
Brian R. A. Wood and Robert W. Harris

S35-S41

Acoustic Emission/Microseismic Activity Monitoring of Salt Crystallization for Stone Conservation
M. Montoto, R.M. Esbert, L.M. Suárez del Río, V.G. Ruiz de Argandoña and C.M. Grossi

S42-S48

Acoustic Emission Monitoring during In-situ Heater Test of Granite
T. Ishida, K. Kitano, N. Kinoshita and N. Wakabayashi

S49-S54

Using Acoustic Emission Testing in Seepage Investigations

S55-S58

Acoustic Emission of Penetration Experiments to Judge Soil Condition
S. Naemura, M. Tanaka, S. Nishikawa, M. Nakamura, K. Jo and T. Kishishita

S59-S62

A Laboratory Investigation of AE from Coal

S63-S76

Determination of the Initial Stresses on Rock Mass using Acoustic Emission Method
K. Michihiro, K. Hata, H. Yoshioka and T. Fujiwara

S77-S89

U.S. Bureau of Mines Research on the Kaiser Effect for Determining Stress in Rock
Michael J. Friedel and Richard E. Thill

S90-S96

Evaluation of Joint Properties of Anti-washout Underwater Concrete by Acoustic Emission Measurement
Kazuya Miyano, Tatsuo Kita, Yuji Murakami and Takako Inaba

S97-S103

Automated Determination of First P-Wave Arrival and Acoustic Emission Source Location
E. Landis, C. Ouyang and S. P. ShahS104-S109

Application of Acoustic Emission Techniques in the Evaluation of Frost Damage in Mortar
Hisatoshi Shimada and Koji Sakai

Number 3/4
Pages 1-11

B.J.S. Wilkins and G.L. Rigby

Andrew R. Blystra

R. W. Harris, B. R. A. Wood and T. Flynn

Acoustic Emission of the 45HNMFA Structural Steel during Low-Cycle Fatigue
J. Siedlaczek, S. Pilecki and F. Dusek

Pages 13-17

Parameter Estimation in Acoustic Emission Signals
C. E. D'Attellis, L. V. Perez, D. Rubio and J. E. Ruzzante

Pages 19-23

Acoustic Emission Technique at Proof Tests of Nuclear Pressure Vessels in Hungary
Peter Pellionisz and János Geréb

Pages 25-29

Effects of Wave Velocity Change on Magnetomechanical AE in Sintered Iron
Noboru Shinke and Yoshitugu Ohigashi

Pages 31-33

Origin of Acoustic Emission in Naturally Aged Aluminum-Lithium Alloys
F. Zeides and I. Roman

Pages 35-41

Analysis of Artificial Acoustic Emission Waveforms Using a Neural Network
Hironobu Yuki and Kyoji Homma

Pages 43-48

Source Force Waveforms: The Use of a Calibrated Transducer in Obtaining an Accurate Waveform of a
SourceThomas M. Proctor, Jr. and Franklin R. Breckenridge

Pages 49-60

Acoustic Emission Monitoring of a Fatigue Test of an F/A-18 Bulkhead
C. M. Scala, J. F. McCardle and S. J. Bowles

XXX

Pages 61-65

Maximum Curvature Method: A Technique to Estimate Kaiser-Effect Load from Acoustic Emission Data
M. Momayez, F. P. Hassani and H. R. Hardy, Jr.

Pages 67-70

Acoustic Emission Measurements on Bridges

Pages 71-82

Acoustic Emission Monitoring of the Sensitivity of Chemicals to Impact
Timothy G. Crowther, Adrian P. Wade and Nancy Brown

Pages 83-89

Study of Acoustic Emission Generation in Sliding Motion
Musa K. Jouaneh, Richard Lemaster and Frank C. Beall

Pages 91-95

The Role of Acoustic Monitoring as a Diagnostic Tools in Nuclear Reactors
Shahla Keyvan and Ron King

Pages 97-101

Acoustic Emission Produced by Sliding Friction and its Relationship to AE from Machining
S. H. Carpenter, C. R. Heiple, D. L. Armentrout, F. M. Kustas and J. S. Schwartzberg

Pages 103-106

Comments on the Origin of Acoustic Emission in Fatigue Testing of Aluminum Alloys
C. R. Heiple, S. H. Carpenter and D. L. Armentrout

Pages 107-111

Acoustic Emission of Wood during Swelling in Water
Stefan Poliszko, Waldemar Molinski and Jan Raczkowski

Pages 113-116

Characterization of the ASL Parameter

Pages 117-121

Acoustic Emission from Bubbles in a Water Column,

Pages 122-124

In Memoriam, Dr. Raymond W. B. Stephens (1902-1990)

12, 18, 24, 30, 34
42, 66, 90, 96, 102
Page I-i

H. Hick, H. Willer, E. Winter and F. Simacek

J. W. Whittaker
Mark A. Friesel and Jack F. Dawson

Conferences and Symposia
Progress in Acoustic Emission VI, Proceedings of The 11th International AE Symposium
36th Meeting of Acoustic Emission Working GroupIndex to Volume 10

XXXI

INDEX to Journal of Acoustic Emission, 1993-2000
Volume 11, 1993
Contents
Number 1
Pages 1-4

Pages 5-10
Pages 11-18
Pages 19-20
Pages 21-26
Pages 27-32
Pages 33-41
Pages 43-51

Acoustic Emission Inspection of Defects under Coating in Nozzles of
Vessels - Fatigue Study on a Coated Steel Sample
E. Verbrugghe and M. Cherfaoui
Acoustic Emisslon Generated from Silicon Particles during Deformationof Al-Si Alloys
Min Wu and Steve H. Carpenter
Frequency Analysis of Acoustic Emission Signals in Concrete
J.-M. Berthelot, M. Ben Souda and J. L. Robert
Acoustic Emission of Booming Sand Analyzed in the Laboratory
Marcel F. Leach and Gottfried A. Rubin
Comparison of AE Source Location Methods in Paper Sheets under
TensionT. Fuketa, S. Okumura, M. Noguchi and T. Yamauchi
Performance of a Noncontact Magnetostrictive AE Sensor on a Steel Rod
H. Kwun, J. J. Hanley and C. M. Teller
Acoustic Emission Technology for Smart Structures
M. A. Hamstad and G. P. Sendeckyj
Influence of MC-Type Carbides on Acoustic Emission Generated duringTensile Deformation in a Nimonic Alloy PE16T. Jayakumar, Baldev
Raj, D. K. Bhattacharya, P. Rodriguez and O. Prabhakar

Conferences and Symposia
Pages 42, 52

Second International Conference on Acousto-Ultrasonics, Review of Progress in
Quantitative Nondestructive Evaluation, Future Meetings

Pages 53-59
Pages 60

AE Literature

T. F. Drouillard
Available Books on AE

Number 2
Pages 61-63
Pages 65-70
Pages 71-78
Pages 79-84

Broadband Acoustic Emission Sensor with a Conical Active Element in Practice
Miroslav Koberna
Acoustic Emission Signal Trends during High Cycle Fatigue of FRP/Balsa Wood Core Vessels
P. Ouellette and S.V. Hoa
Analysis of the Acoustic Emission Generated by the Failure of Oxide Scales and Brittle
Lacquer Layers M. M. Nagl, Y. S. Chin and W. T. Evans
Solution of a Simple Inverse Source Characterization Problem using Associative Recall

Pages 85-94

Kornelija Zgonc, Igor Grabec and Wolfgang Sachse
Acoustic Emission during Fatigue of a Nickel Base Superalloy
Daining Fang and Avraham Berkovits

Pages I - XXXI Cumulative Index, J. of Acoustic Emission, Volumes 1 - 10, 1982 - 92
Pages I - VII
Author Index
Pages VII-XXXI Contents, Volumes 1 - 10
Page 64
Page XXXI

Future Meetings on AE
Cover Photograph
Available Books on AE

Number 3
Page C1-C24
Page 95-99
Page 101-106
Page 107-115
Page 117-128

Guidance for Development of AE Applications on Composites
CARP Aerospace/Advanced Composites Subcommittee
Finite Element Simulation of Acoustic Emission due to Fiber Failure in aSingle Fiber Composite S. De Bondt, L. Froyen, L. Delaey and A. Deruyttere
Acoustic Emission from Aluminum during Hydrostatic Extrusion
L. Hanumantha Sastry and S. P. Mallikarjun Rao
Nondestructive Evaluation of Damage in Steel-Belted Radial Tires using
Acousto-Ultrasonics Henrique L. M. dos Reis and Kris A. Warmann
A Study of Fracture Dynamics in a Model Composite by Acoustic Emission
Signal Processing Hiroaki Suzuki, Mikio Takemoto and Kanji Ono

Page 116
Page 129

Conferences and Symposia

Page 100
Page 115
Page 130
Page 130

Available Books on AE
Cover Photograph
Early Days of AEWG Allen T. Green
Internet Address List, Erratum

The 37th Meeting of Acoustic Emission Working Group

Number 4
Fifth National Conference on Subsurface and Civil Engineering
Acoustic Emission, Japan
Page iPage iPage ii
Page S1-S18

Preface
Hiroaki Niitsuma, Topical Co-Editor
Research Activities on Acoustic Emission in Civil Engineering in Japan
Masayasu Ohtsu, Topical Co-Editor
Papers Presented at the Conference
Analysis of Acoustic Emission from Hydraulically Induced Tensile Fracture of RockHiroaki Niitsuma, Koji Nagano and Koji Hisamatsu

Page S19-S26
Page S27-S36
Page S37-S46
Page S47-S56
Page S57-S63
Page S65-S73
Page S75-S88
Page S89-S98

Page S56
Page S64
Page S74
Page S98

Acoustic Emission Activities during the Injection of High Pressure Water into Coal Measures
M. Seto and K. Katsuyama
The Variation of Hypocenter Distribution of AE Events in Coal under Triaxial Compression
Masahiro Seto, Osamu Nishizawa and Kunihisa Katsuyama
Assessment of Concrete Deterioration using Plastic Analysis and Acoustic Emission Technique
Ahmed M. Farahat and Masayasu Ohtsu
Principal Components Analysis of AE Waveform Parameters for Investigating an Instability ofGeotechnical Structures
Akiyoshi Chichibu, Tadashi Kikuchi and Takahiro Kishishita
Observation of Mixed-Mode Fracture Mechanism by SiGMA-2D
Mitsuhiro Shigeishi and Masayasu Ohtsu
Field Application of Acoustic Emission for the Diagnosis of Structural Deterioration of Concrete
K. Matsuyama, T. Fujiwara, A. Ishibashi and M. Ohtsu
An Evaluation of Subsurface Fracture Extension Using AE Measurement in Hydraulic Fracturingof a Geothermal Well Masayuki Tateno, Mineyuki Hanano and Qiang Wei
Assessment of Concrete Deterioration by Acoustic Emission Rate Analysis
Masayasu Ohtsu, Kunihiro Yuno and Yoshiki Inoue
Cover Photograph
Papers Presented at the Conference (continued)
Available Books on AE
AECM-5

Volume 12, 1994
Contents
Number 1/2

Acousto-Ultrasonics

Pages i-ii
Pages 1-14

Foreword Frank C. Beall and Alex Vary, Topical Co-Editors
Nondestructive Evaluation of Adhesively Bonded Joints by AcoustoUltrasonic Technique and Acoustic Emission H. Nayeb-Hashemi and J. N. Rossettos
Acousto-Ultrasonic Nondestructive Evaluation of Porosity in PolymerComposite Structures of Complex Geometry Henrique L. M. dos Reis
Wave Mechanics in Acousto-Ultrasonic Nondestructive Evaluation
Joseph L. Rose, John J. Ditri and Aleksander Pilarski
Laser-based Techniques to Resolve Mode Propagation of Lamb Waves
in Plates R. Daniel Costley, Jr., Yves H. Berthelot and Laurence J. Jacobs
Adhesive Bond Evaluation Using Acousto-Ultrasonics and Pattern
Recognition Analysis
A. Fahr, Y. Youssef and S. Tanary
Acousto-Ultrasonic Signal Classification to Evaluate High
Temperature Degradation in Composites A. Maslouhi, H. Saadaoui, S. Béland and C. Roy
The Use of Acousto-Ultrasonics to Detect Biodeterioration in Utility Poles
Frank C. Beall, Jacek M. Biernacki and Richard L. Lemaster
Determination of Plate Wave Velocities and Diffuse Field Decay Rates
with Broadband Acousto-Ultrasonic Signals Harold E. Kautz

Pages 15-21
Pages 23-26
Pages 27-38
Pages 39-44
Pages 45-54
Pages 55-64
Pages 65-70

AE Literature
Pages 71-78
Pages 79-103

Acousto-Ultrasonic Reflections Thomas F. Drouillard and Alex Vary
Acousto-Ultrasonics Thomas F. Drouillard and Alex Vary

Conferences and Symposia
Page 22
Pages 104-106
Page 106

37th Meeting of AEWG
12th International AE Symposium
Cover Photograph

Number 3/4
Pages 107-110
Pages 111-115
Pages 117-126

Pages 127-140
Pages 141-148
Pages 149-155
Pages 157-170

Pages 171-176

A Double Exponential Model for AE Signals M. A. Majeed and C. R. L. Murthy
Acoustic Emission Response of Centre Cracked M250 Maraging Steel Welded Specimens
T. Chelladurai, A. S. Sankaranarayanan and K. K. Purushothaman
Low Strain Level Acoustic Emission due to Seismic Waves and Tidal/Thermoelastic
Strains Observed at the San Francisco Presidio Baxter H. Armstrong, Carlos M.
Valdes-Gonzalez, Malcolm J. S. Johnston and James D. Leaird
Fracture Analysis of Mullite Ceramics using Acoustic Emission Technique
Yoshiaki Yamade, Yoshiaki Kawaguchi, Nobuo Takeda and Teruo Kishi
Acoustic Emission from AISI 4340 Steel as a Function of Strength
Steve H. Carpenter and Christian Pfleiderer
Acoustic Emission in Laser Bending of Steel Sheets H. Frackiewicz, J. Królikowski,
S. Pilecki, A. M. Leksowskij, B. L. Baskin and E. W. Khokhlova
On the Far-field Structure of Waves Generated by a Pencil Lead Break on a Thin PlateJohn Gary and Marvin A. Hamstad
Improving the Coupling Reproducibility of Piezoelectric Transducers

D. Geisse

AE Literature
Pages 177-198

Trends of Recent Acoustic Emission Literature

Kanji Ono

12th International Acoustic Emission Symposium
Pages S1-S6
Pages S7-S11

Pages S12-S17
Pages S18-S23
Pages S24-S28

Acoustic Emission Monitoring for Shield Tunneling Akiyoshi Chichibu, Akimasa
Waku, Teruyuki Waki and Hiroshi Yoshino
Acoustic Emission of Coal Induced by Gas and Water Flow, Gas Sorption or Stress
Z. J. Majewska, S.A. Majewski, H. Marcak, W. J. Moś cicki, S. Tomecka-Suchoń
and J. Zie tek
Acoustic Emission Study of Anisotropic Stress Memory in Rock Subjected to Cyclic
Polyaxial Loading C. E. Stuart, P. G. Meredith and S. A. F. Murrell
Characterization of Acoustic Emission Signals During Phase Transformations in a
TiNiFe Shape Memory Alloy Kazuki Takashima and Minoru Nishida
Simulation of AE Generation Behavior during Fracture of Alumina Ceramics
Byung-Nam Kim, Hidehumi Naito and Shuichi Wakayama

Conferences and Symposia
Page 116
Page 156
Pages 199-200
Page 200

Future Meetings of AE
38th Meeting of AEWG
5th International Symposium on Acoustic Emission from Composite Materials
On the CoverPages I-i, I-ii

Index to Volume 12

Volume 13, 1995
Contents
Numbers 1/2
Pages 1-10
Pages 11-22
Pages 23-29
Pages 31-41

Early Detection of Damages in Journal Bearings by Acoustic Emission Monitoring
Dong-Jin Yoon, Oh-Yang Kwon, Min-Hwa Chung and Kyung-Woong Kim
Clustering Methodology for the Evaluation of Acoustic Emission from Composites
A. A. Anastassopoulos and T. P. Philippidis
Investigation of AE Signals Emitted from an SiOx Layer Deposited on a PET Film
Masa-aki Yanaka, Noritaka Nakaso, Yusuke Tsukahara and Nelson N. Hsu
On Characterization and Location of Acoustic Emission Sources in Real Size Composite
Structures -A Waveform Study M. A. Hamstad and K. S. Downs

Conferences and Symposia: 12th International Acoustic Emission Symposium
Pages S01-07
Pages S08-13
Pages S14-20
Pages S21-28

Pages S29-34
Pages S35-41
Pages S42-46

Pages S47-53
Pages S54-59

Page 30
Pages 42-44
Page S60
Page S07

A Waveform Investigation of the Acoustic Emission Generated during the Deformation andCracking of 7075 Aluminum Steve H. Carpenter and Michael R. Gorman
Acoustic Emission and Damage Evolution in an SiC Fiber Reinforced Ti Alloy Composite
K. Takashima, H. Tonda and P. Bowen
Cracking Process Evaluation in Reinforced Concrete by Moment Tensor Analysis of Acoustic
Emission Shigenori Yuyama, Takahisa Okamoto, Mitsuhiro Shigeishi and Masayasu Ohtsu
The Interaction between Pore Fluid Pressure Changes and Crack Damage Evolution in Rocks
And Subsurface Rock Structures Modeled from Acoustic Emission Data
Peter Sammonds, Philip Meredith, Javier Gomez and Ian Main
Acoustic Emission Analysis of TiAl Intermetallics Manabu Enoki and Teruo Kishi
Recent Applications of Acoustic Emission Testing for Plant Equipment
Masashi Amaya
Effects of Soil Acidity on Acoustic Emission Properties of Sugi (Cryptomeria Japonica) Seedling
Keiichi Sato, Atsushi Uchiyama, Takeshi Izuta, Makoto Miwa, Naoaki Watanabe, Takafumi Kubo
and Masami Fushitani
Acoustic Emission of Bending Fatigue Process of Spur Gear Teeth
Kouitsu Miyachika, Satoshi Oda and Takao Koide
Characterization of Thermal Cracking by Acoustic Emission Time Series Analysis
Koji Nagano, Katsuhiro Sugawara, Ken-Ichi Itakura and Kazuhiko Sato
Future Meetings on Acoustic Emission
Historic Files (AEWG Meetings, AEWG Awards), T.F. Drouillard / Call for papers
Available Books on Acoustic Emission
Cover Photograph

Numbers 3/4
Composites
Pages 45-55

Pages 56-66
Pages 67-77
Pages 79-86

Correlation of Acoustic Emission Felicity Ratios and Hold-Based Rate Moments with Burst
Strengths of Spherical Graphite/Epoxy Pressure Vessels,
Karyn S. Downs and Marvin A. Hamstad
Correlation of Regions of Acoustic Emission Activity with Burst Locations for Spherical
Graphite/Epoxy Pressure Vessels, Karyn S. Downs and Marvin A. Hamstad
A Study of Acoustic Emission-Rate Behavior in Glass Fiber-Reinforced Plastics,
A.J. Brunner, R. Nordstrom and P. Flüeler
The Deterioration of Foamglas® under Compression Studied with the Acoustic Emission

Pages 87-96
Pages 97-100

Technique, M. Wevers, D. Tsamtsakis, P. De Meester, E. Uria and H. Strauven
Localization of Acoustic Emission in the Fracture of Fiber Composites,
Vladimir Krivobodrov
Investigation of Damage Development in Paper Using Acoustic Emission Monotoring,
Per A Gradin and Staffan Nyström

Conferences and Symposia: 12th International AE Symposium, Sapporo, Japan
Pages S61-S67

Acoustic Emission Behavior during Plastic Deformation of 8090 Al-Li Alloy,
Ki-Jung Hong, Hee-Don Jeong and Chong Soo Lee
Pages S68-S74 Development Of Thermal Shock and Fatigue Tests of Ceramic Coatings for Gas Turbine
Blades by AE Technique,
C.Y. Jian, Tatsuya Shimizu, Toshiyuki Hashida, Hideaki Takahashi and Masahiro Saito
Pages S75-S82 Fractals on Acoustic Emission during Hydraulic Fracturing
Ken-Ichi Itakura, Kazuhiko Sato, Koji Nagano and Yasufumi Kusano
Pages S83-S88 Acoustic Emission during Tensile Loading of Low Velocity Impact-Damaged CFRP
Laminates, Oh-Yang Kwon, Joon-Hyun Lee and Dong-Jin Yoon
Pages S89-S94 AE Characterization of Compressive Residual Strength of Impact-Damaged CFRP Laminates,
Isamu Ohsawa, Isao Kimpara, Kazuro Kageyama, Toshio Suzuki and Akihiko Yamashita
Pages S95-S102 Acoustic Emission Analysis on Interfacial Fracture of Laminated Fabric Polymer Matrix
Composites, Toshiyuki Uenoya
Page 78
Page 100
Page S74
Pages I-i

Future Meetings on Acoustic Emission
Cover Photograph and AECM-5
Professor Hideaki Takahashi (1940-1995)
Index to Journal of Acoustic Emission, Volume 13

Volume 14, 1996
Contents
Number 1
Pages 1-34
Pages 35-50

A History of Acoustic Emission Thomas F. Drouillard
The Fracture Dynamics in a Dissipative Glass-Fiber/ Epoxy Model Composite with AE
Source Simulation Analysis Hiroaki Suzuki, Mikio Takemoto and Kanji Ono

Conferences and Symposia
Pages 51-52
Page 52

22nd European Conference on AE
Cover Photograph

Number 2
Pages 53-59

Pages 61-68
Pages 69-84
Pages 85-95

Modeling of Stress-Strain Response of Unidirectional and Cross-Ply SiC/CAS-II Ceramic
Composites by Acousto-Ultrasonic Parameters
Anil Tiwari, Edmund G. Henneke II and Alex Vary
Neural Network Approach to Acoustic Emission Source Location
Vasisht Venkatesh and J.R. Houghton
Wavelet Transform of Acoustic Emission Signals Hiroaki Suzuki, Tetsuo Kinjo, Yasuhisa
Hayashi, Mikio Takemoto and Kanji Ono with Appendix by Yasuhisa Hayashi
Acoustic Emission in a Nextel 440 Fiber Reinforced 6061 Al Composite
T. Pocheco, H. Nayeb-Hashemi and H. M. Sallam

Pages 97-102
Pages 103-114
Pages 115-118
Pages 119-126

Pattern Recognition of Acoustic Signatures Using ART2-A Neural Network
Shahla Keyvan and Jyothi Nagaraj
Far-field Acoustic Emission Waves by Three-Dimensional Finite Element Modeling of
Pencil-Lead Breaks on a Thick Plate M. A. Hamstad, J. Gary and A. O'Gallagher
Acoustic Emission Testing of Bolted Connections under Tensile Stress
V. Hänel and W. Thelen
A Method to Determine the Sensor Transfer Function and its Deconvolution from Acoustic
Emission Signals
Bernhard Allemann, Ludwig Gauckler, Wolfgang Hundt and F. Rehsteiner

Conferences and Symposia
Page 60
Page 96
Page 127-128

39th Meeting of the Acoustic Emission Working Group and Primer
40th Meeting of the Acoustic Emission Working Group and Primer
13th International AE Symposium (IAES-13)

Number 3/4
Proceedings of International Workshop at Schloss Ringberg
Pages i-iv
Page vPages vi-viii

Materials Research with Advanced Acoustic Emission Techniques
Alexander Wanner and Michael R. Gorman, Topical Co-Editors
Friedrich Förster and Erich Scheil, Two Pioneers of Acoustic Emission
Alexander Wanner
Acoustic Investigation of Martensite Needle Formation by Fritz Förster and Erich Scheil,
Translated by Peter G. Thwaite and Alexander WannerPages S1-S11
Pages S12-S18
Pages S19-S46

Advanced AE Techniques in Composite Materials Research William H. Prosser
Digital Signal Processing of Modal Acoustic Emission Signals Steve Ziola
Wave Theory of Acoustic Emission in Composite Laminates
Dawei Guo, Ajit Mal and Kanji Ono
Pages S47-S60 Fiber Fragmentation and Acoustic Emission
Alexander Wanner, Thomas Bidlingmaier and Steffen Ritter
Pages S61-S73 Wave Propagation Effects Relative to AE Source Distinction of Wideband AE Signals froma Composite Pressure Vessel Karyn S. Downs and Marvin A. Hamstad
Pages S74-S87 Relative Moment Tensor Inversion Applied to Concrete Fracture Tests
C. U. Grosse, B. Weiler and H. W. Reinhardt
Pages S88-S101 Brittle Fracture as an Analog to Earthquakes: Can Acoustic Emission Be Used to Develop a
Viable Prediction Strategy? David A. Lockner
Pages S102-S105 Abstracts of Talks Presented
Conferences and Symposia
Page S106
6th International Symp. on Acoustic Emission from Reinforced Composites
Page S107
14th International Acoustic Emission Symposium and 5th Acoustic Emission World Meeting
Pages S108-S109 40th Meeting of the Acoustic Emission Working Group and Primer
Page S110
Available Books on AE
Pages I-i - I-ii

Index to Vol. 14

Volume 15, 1997
Contents
Page 1-18

Wideband and Narrowband Acoustic Emission Waveforms from Extraneous Sources

Page 19-32
Page 33-42
Page 43-52
Page 53-61
Page 63-68
Page 69-78
Page 79-87

during Fatigue of Steel Samples
M.A. Hamstad and J.D. McColskey
Fracture-Mode Classification Using Wavelet-Transformed AE Signals from a Composite
Tetsuo Kinjo, Hiroaki Suzuki, Naoya Saito, Mikio Takemoto and Kanji Ono
Nondestructive Evaluation of Fiberglass-Reinforced Plastic Subjected to Localized Heat
Damage Using Acoustic Emission
H. Nayeb-Hashemi, P. Kisnomo and N. Saniei
An Investigation of Lüders Band Deformation and the Associated Acoustic
Emission in Al - 4.5% Mg Alloys D. L. Armentrout and S. H. Carpenter
Modal Analysis of Acoustic Emission Signals
H.L. Dunegan
An Acoustic Emission Tester for Aircraft Halon-1301 Fire-Extinguisher Bottles
Alan G. Beattie
AE Detection of Cracking in Pipe Socket Welds
Bryan C. Morgan
Feature Extraction of Metal Impact Acoustic Signals For Pattern Classification by Neural
Networks
Shahla Keyvan and Rodney G. Pickard

Conferences and Symposia

Page S1-S10
Page S11-S18

Page S19-S30
Page S31-S39
Page S40-S49

Page S50-S59
Page S60-S69
Page S70-S79
Page S80-S88
Page S90-S94

Page S95-S102

Fourth Far East Conference On NDT (FENDT '97)
October 8-11, 1997, Cheju-Do, Korea, sponsored by Korean Society of Nondestructive Testing.
Source Location in Highly Dispersive Media by Wavelet Transform. of AE Signals
Oh-Yang Kwon and Young-Chan Joo
Acoustic Emission Monitoring of the Fatigue Crack Activity in Steel Bridge Members
Dong-Jin Yoon, Seung-Seok Lee, Philip Park, Sang-Hyo Kim, Sang-Ho Lee andYoung-Jin Park
Acousto-Ultrasonic Evaluation of Adhesively Bonded CFRP-Aluminum Joints
Seung-Hwan Lee and Oh-Yang Kwon
Estimation of Initial Damage in Concrete By Acoustic Emission
Masayasu Ohtsu, Yuichi Tomoda and Taisaku Fujioka
Application of AE to Evaluate Deterioration of Port and Harbor Structures
Kimitoshi Matsuyama, Akichika Ishibashi, Tetsuro Fujiwara, Yasuhiro Kanemoto, Shiro Ohta,
Shigenori Hamada and Masayasu Ohtsu
Acoustic Emission Diagnosis of Concrete-Piles Damaged By Earthquakes
Tomoki Shiotani, Norio Sakaino, Masayasu Ohtsu and Mitsuhiro Shigeishi
Observation of Damage Process in RC Beams under Cyclic Bending by Acoustic Emission
Mitsuhiro Shigeishi, Masayasu Ohtsu, Nobuyuki Tsuji and Daisuke Yasuoka
Spectral Response and Acoustic Emission of Reinforced Concrete Members under Fatigue
Bending Yasunori Sakata and Masayasu Ohtsu
Acoustic Emission Behavior during Tensile Deformation of Welded Steel Joints
J. H. Huh, K. A. Lee and C. S. Lee
Acoustic Emission Signal Analysis in C/C Composites
Ja-Ho Koo, Byung-Nam Kim, Manabu Enoki and Teruo Kishi
Congreso Regional de Ensayos No Destructivos y Estructurales
Oct. 27-30, 1997, Mendoza, Argentina, sponsored by Comisión Nacional de Energía Atómica
and Universidad Tecnologica Nacional.
Recent Development in Acoustic Emission Kanji OnoPage S103-S110 40th AEWG Meeting, Program and Abstracts
Page S111-S124 14th International Acoustic Emission Symposium & 5th Acoustic Emission World
Meeting, Abstracts of Oral Briefs
Page S125-S126 Program of the 6th AECM Symposium
Page 62
Page 68
Page I-1

Meeting Calendar
Cover PhotographIndex to Volume 15

Volume 16, 1998
Contents
Number 1-4
Page S1Page S10
Page S19
Page S25
Page S35

Page S45
Page S53
Page S65
Page S75
Page S85

Page S95
Page S105
Page S115
Page S125

Improvements Of Grinding/Dressing Monitoring Using Acoustic Emission
Jason W.P. Dong
An Investigation Of Brittle Failure In Composite Materials Used For High Voltage
Insulators
D. Armentrout, T. Ely, S. Carpenter, and M. Kumosa
AE in Tooth Surface Failure Process of Spur Gears
Hirofumi Sentoku
Long-Term Continuous Monitoring Of Structural Integrity Of Steel Storage Tanks
Hiroyasu Nakasa and Hiroaki Sasaki
Using Of Non-Stationary Thermal Fields and Thermal Stresses As A Method For
Evaluating The Danger Of Damage Development In Chemical and Refinery Equipment
Boris Muravin, Luidmila Lezvinsky, Gregory Muravin
Acoustic Emission and Electric Potential Changes of Rock Sample under Cyclic Loading
Y. Mori, K. Sato, Y. Obata and K. Mogi
Correlations Of AE Signatures To Mechanical and Petrologic Properties Of Four Types Of
Rocks A. Wahab Khair
Damage Mechanics and Fracture Mechanics Of Concrete By SiGMA
M. Ohtsu, M. Shigeishi and M. C. Mumwam
Acoustic Emission Applications To An Arch Dam Under Construction
S. Yuyama, T. Okamoto, O. Minemura, N. Sakata, K. Murayama
Acoustic Emission Measurements During Hydraulic Fracturing Tests In A Salt Mine Using A
Special Borehole Probe
Gerd Manthei, Jürgen Eisenblätter, Peter Kamlot and Stefan Heusermann
Evaluation Of Progressive Slope-Failure By Acoustic Emission
Tomoki Shiotani and Masayasu Ohtsu
Fractal Description Of Acoustic Emission Produced In Systems: Coal-Gas and Coal-Water
Zofia Majewska and Zofia Mortimer
Characterization Of The Lamb Waves Produced By Local Impact Fracture In Thin Brittle
PlatesYoshihiro Mizutani, Hideo Cho , Mikio Takemoto and Kanji Ono
Acoustic Emission and Magnetic Flux Leakage Associated With Magnetisation Of Cracked

Page S134
Page S142

Page S150
Page S158
Page S170
Page S178

Page S186
Page S196

Page S204
Page S212
Page S222
Page S233
Page S243
Page S251
Page S261
Page S269
Page S277
Page S289
Page S299
Page S309

and Uncracked Ferromagnetic Materials
R. Hill, A-A. R. Choudhury and L. Morgan
Wavelet Transform Of Magnetomechanical Acoustic Emission Under Elastic Tensile Stress
With Displacement Sensor Masanori Takuma, Noboru Shinke, and Kanji Ono
Effect Of Boron Addition On Acoustic Emission Behavior During Tensile Deformation Of
Ni3Al Intermetallic Compound Single Crystals
K. Yoshida, Y. Iwata, H. Takagi and K. Sakamaki
Acoustic Emission Characteristics During Deformation Of Polyether Ether Ketone (PEEK)
M. Gakumazawa, M. Akiyama, C. Ishiyama, J. Hu, K. Takashima, Y. Higo and C. Nojiri
Principals Of Statistical and Spectral Analysis of Acoustic Emission and Their Application
To Plastic Deformation of Metallic Glasses A. Yu. Vinogradov
Microfracture Process In Ceramics Under Thermal Shock Fracture Characterized By
Acoustic Emission Shuichi Wakayama
AE Study Of Stress Corrosion Cracking Mechanism Of Stainless Foil Using Quantitative
Lamb Wave Analysis and Video Images
Mikio Takemoto, Okiharu Tamura and Hiroaki Suzuki
A Study Of The Acoustic Emission From Musical Sand and Silica Gel
Marcel F. Leach, Douglas E. Goldsack, Cindi Kilkenny and Chantal Filion
Effects Of Humidity On Acoustic Emission Characteristics During Environmental Stress
Cracking In Polymethyl Methacrilate (PMMA) Chiemi Ishiyama, Takumi Sakuma,
Yasuyuki Bokoi, Masayuki Shimojo and Yakichi Higo
Optimizing AE Location Accuracy: A Measurement Approach
Richard Nordstrom
Source Location in Plates by Using Wavelet Transform of AE Signals
Oh-Yang Kwon and Young-Chan Joo
Waveform Analysis Of Acoustic Emission Signals
Ajit Mal and Dawei Guo and Marvin Hamstad
Selection Of Acoustic Emissions and Classification Of Damage Mechanisms In Fiber
Composite Materials
Torsten Krietsch and Jurgen Bohse
Grey Correlation Analysis Method Of Acoustic Emission Signals For Pressure Vessels
Gongtian Shen, Qingru Duan and Bangxian Li
On Wideband Acoustic Emission Displacement Signals As a Function Of Source RiseTime and Plate Thickness M. A. Hamstad, J. Gary and A. O'Gallagher
2-D AE Source Localization On The Material With Unknown Propagation Velocity Of AE
Wave Kyung-Young Jhang, Weon-Heum Lee, Dal-Jung Kim
Three Dimensional Acoustic Emission Signal Analysis In C/C Composites With
Anisotropic Structure Ja-Ho Koo, Manabu Enoki and Teruo Kishi and Byung-Nam Kim
Calibration Of Low-Frequency Acoustic Emission Transducers
H. Reginald Hardy, Jr. and Euiseok Oh (The Pennsylvania State University)
Advanced AE Signal Classification For Studying The Progression Of Fracture Modes In
Loaded UD-GFRP Naoya Saito, Hiroaki Suzuki, Mikio Takemoto and Kanji Ono
Fatigue Monitoring of Heat Exposed Carbon Fiber/Epoxy By Means Acoustic Emission
and Acousto-Ultrasonic A. Maslouhi and V.L. Tahiri
Thermal Shock Evaluation Of Functionally Graded Ceramic/Metal Composites By AE
Jae-Kyoo Lim and Jun-Hee Song

Page S317

Page S324
Page S333

Page S343

Page i-iii
Page iii
Page iv
Page v
Page vi-viii
Page viii
Page I-1
Page I-2

Effect Of Surface Modification Of SiC Fiber On Acoustic Emission Behaviors andInterface Strength Of SiCf/Al Composite
Zuming Zhu, Yanfeng Guo, Nanling Shi
Characterization Of Fracture Process In Short-Fiber-Reinforced Plastics By Acoustic
Emission
K. Takahashi and N. S. Choi
Effects of Foam Thermal Insulation and Previous Thermal Exposure on the Acoustic
Emission Recorded From Graphite/Epoxy Pressure Vessels With and Without Impact
DamageK. S. Downs and M. A. Hamstad
Interpretation Of Fracture Toughness In Unidirectional Glass-Fiber/Polypropylene
Composites By Acoustic Emission Analysis Of Damage Mechanisms
Jurgen Bohse and Torsten KrietschPreface
Conference Events
Theme Discussion
In Memoriam
Contents
Cover Photograph
Authors index
Selected e-mail address of authors

Volume 17, 1999
Contents
Numbers 1/2
Page iPage ii
Page 1-13
Page 15-21
Page 23-27
Page 29-36
Page 37-47
Page 49-59
Page 61-67
Page 69-81

Announcement: New Format for Journal of Acoustic Emission
Founding members of GLEA, Grupo Latinoamericano de Emisión Acústica (Coverphotograph); Color plate for Figure 8 on page 9.
Classification of Acoustic Emissions in Metallic Glasses
A. Vinogradov
Acoustic Emission Characteristics of Soil and Sand in Response to Simulated Root
GrowthC. Divaker Durairaj, L. Okushima and S. Sase
Detection of Defects in Gears by Acoustic Emission Measurements
N. Tandon and S. Mata
Acoustic Emission Signals in Thin Plates Produced by Impact Damage
William H. Prosser, Michael R. Gorman and Donald H. Humes
Reflections of AE Waves in Finite Plates: Finite Element Modeling and Experimental
Measurements
W. H. Prosser, M. A. Hamstad, J. Gary and A. O’Gallagher
Classification of Acoustic Emission Signatures Using a Self-organization Neural
Network
Tinghu Yan, Karen Holford, Damian Carter and John Brandon
Discussion of the Log-Normal Distribution of Amplitude in Acoustic Emission Signals
M. I. López Pumarega, R. Piotrkowski and J. E. Ruzzante
Unsupervised Pattern Recognition Techniques for the Prediction of Composite Failure
T. P. Philippidis, V. N. Nikolaidis and J. G. Kolaxis

Page 83-93

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

Conferences and Symposia
Page 14
Page 22-95
Page 96

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

Numbers 3/4
Page 97
Page 111
Page 121

Page S1Page S2
Page S7
Page S13
Page S20
Page S29
Page S37
Page S45
Page S51
Page S57
Page S65
Page S70
Page S78
Page S83
Page S92
Page S100
Page S108

Modeling of Buried Monopole and Dipole Sources of Acoustic Emission with a Finite Element
Technique M. A. Hamstad, A. O'Gallagher and J. Gary
Numerical Assessment of the Quality of AE Source Locations Gang Qi and Jose Pujol
Structural Integrity and Remnant Life Evaluation Using Acoustic Emission Techniques
Brian R. A. Wood, Robert W. Harris and Elizabeth L. Porter

Selected Papers from International Conference Acoustic Emission ’99
Report on International Conference Acoustic Emission ’99 Pavel Mazal and Václav Svoboda
Identification of fundamental forms of partial discharges based on the results of frequency
analysis of their acoustic emission T. Boczar
Technical possibilities of the non-contact acoustic emission method at testing hollow articles
integrity
G. Budenkov, O. Nedzvetskaya, E. Bulatova
Method of AE and possibilities of corrosion degradation detection
M. Cerny, P. Mazal, V. Suba
Application of acoustic emission in metal physics and materials science
F. Chmelík, P. Lukác
Waveform analysis of acoustic emission during pressurization of glass-fiber composite pipes
L. Golaski, P. Gebski, I. Baran, Kanji Ono
Acoustic emission monitoring during solidification processes
F. Havlícek, J. Crha
Radiation of acoustic emission waves during stress corrosion cracking of the metal
A. Kotolomov, G. Budenkov, O. Nedzvetskaya
NDE of phase transformations in Cu based shape memory alloys by ultrasonic techniques
M. Landa, M. Chlada, Z. Prevorovsky
VVER steam generators and acoustic emission
O. Matal, J. Zaloudek, T. Simo
Application of acoustic emission technique on fatigue testing machine
Rumul P. Mazal, J. Richter
Acoustic emission and state of fatigue of ferroelectric Pb(ZrxTi1-x)O3 ceramics
J. Nuffer, D. Lupascu, J. Rödel
Application of AE method at pressure tests of boiler header
V. Svoboda, J. Petrasek, A. Proust
Acoustic emissions of vessels with partially penetrated longitudinal seams F. Rauscher
Electromagnetic emission from polycrystalline solids
J. Sikula, B. Koktavy, I. Kosiková,
J. Pavelka, T. Lokajícek
The testing of LPG vessels with acoustic emission examination
P. Tscheliesnig, J. Liöka

Conferences and Symposia
Page S116
Page S6

24th EWGAE Meeting (EWGAE 2000)/ The 43rd Meeting of Acoustic Emission Working
Group/ 15th International AE Symposium (IAES-15)/ Short courses
Next EWGAE Meeting / Cover Photograph

Page I-i

Index to Volume 17

Volume 18, 2000
Contents
Selected papers from “EWGAE 2000, 24th European Conference on Acoustic Emission Testing”, publishedby CETIM, Senlis, France
Page 1Page 8Page 15
Page 21
Page 29
Page 34
Page 41
Page 51
Page 61

Page 68
Page 75
Page 81
Page 87
Page 96
Page 102
Page 111
Page 118

Studies of the non-linear dynamics of acoustic emission generated in rocks
Z. Majewska and Z. Mortimer
Acoustic emission as result of tensile and shearing processes in stable and unstablefracturing of rocks
J. Pininska
Relation between acoustic emission signals sequences induced by thermal loading and thestructure of sedimentary rocks B. Zogala, and R. Dubiel
Acoustic emission/Acousto-Ultrasonic data fusion for damage evaluation in concrete
A. Tsimogiannis, B. Georgali, and A. Anastassopulos (Envirocoustics S.A. - Greece)
Concrete Crossbeam Diagnostic by acoustic emission method Z. Weber, P. Svadbik,
M. Korenska and L. Pazdera
Waveform based analysis techniques for the reliable acoustic emission testing of composite
structures
M. Surgeon, C. Buelens, M. Wevers, and P. De Meester
Optical fibres for in situ monitoring the damage development in composites and therelation with acoustic emission measurements M. Wevers, L. Rippert, and S. Van Huffel
Lamb-wave source location of impact on anisotropic plates
H. Yamada, Y Mizutani, H Nishino, M. Takemoto and K. Ono
Characterisation of the damage and fracture mechanisms in Ti3SiC2 using acoustic emission
P. Finkel, R.K. Miller, M.A. Friesel, R.D. Finlayson, P.T. Cole, M.W. Barsoum and
T. El-Raghy
Evaluation of martensitic transformation dynamics of Cu-Al-Ni shape memory alloy single
crystals by acoustic emission method
K. Yoshida, S. Kihara, and K. Sakamaki
The identification of basic fatigue parameters on electroresonance pulsator with help of
acoustic emission technology P. Mazal, and J. Petras
The phase of contact damage and its description by help of acoustic emission
J. Dvoracek, J. Pazdera, and L. Petras (Brno University of Technology - Czech Republic)
Acoustic emission monitoring of delayed hydride cracking in Zirconium
A. Barron and C. Rowland
Examination of plate valve behaviour in a small reciprocating compressor using acoustic
emission
J.D. Gill, R.D. Douglas, Y.S. Neo, R.L. Reuben and J.A. SteelA new method of acoustic emission source location in Pipes using cylindrical guided waves
H. Nishino, F. Uchida, S. Takashina , M. Takemoto and K. Ono
Acoustic emission method for pressure vessel diagnostics at a refinery
B.S. Kabanov,
V.P. Gomera, V.L. Sokolov, and A.A. Okhotnikov
Optimisation of acquisition parameters for acoustic emission measurements on small

Page 125
Page 131
Page 138
Page 144
Page 150
Page 155
Page 161
Page 167
Page 174
Page 180
Page 189
Page 196
Page 205
Page 211
Page 217
Page 224
Page 232
Page 239
Page 248
Page 258
Page 265
Page 272
Page 279

pressure vessels F. Rauscher
Monitoring of weld's defects evolution submit to static and dynamic loading thanks to theacoustic emission method C. Hervé, R. Pensec, and A. Laksimi,
Acoustic emission due to cyclic pressurisation of vessels with partially penetrated
longitudinal seams M. Bayray
Inspection of LPG vessels with AE examination
P. Tscheliesnig, and G. Schauritsch
Inspection of pressure vessels used in refrigeration and air conditioning systems
A. Skraber, F. Zhang, M. Cherfaoui, and L. Legin
The new Russian standards in the field of acoustic emission
V.I. Ivanov, and L.E. Vlasov
Using acoustic emission to monitor metal dusting
F. Ferrer, E. Andres, J. Goudiakas, and C. Brun (Elf Atochem - France)
Use of acoustic emission to detect localised corrosion philosophy of industrial use,
illustrated with real examples A. Proust, and J. C. Lenain
Inspection of flat bottomed storage tanks by acoustical methods. Classification of corrosion
related signals P. Tscheliesnig, G. Lackner, M. Gori, H. Vallen and B. Herrmann
Screening of tank bottom corrosion with a single point AE detector: AE-Simple
P.J. Van De Loo, and D.A. Kronemeijer
Case histories from ten years of testing storage tank floors using acoustic emission
S.N. Gautrey, P.T. Cole, and S.J. Schoorlemmer,
Acoustic emission detection of damage in reinforced concrete conduit H.W. Shen,
S. Iyer, M.A. Friesel, F. Mostert, R.D. Finlayson, R.K. Miller, M.F. Carlos and S. Vahaviolos,
Comparison of artificial acoustic emission sources as calibration sources for tool wear
monitoring in single-point machining A. Prateepasen, Y.H.J. Au and B.E. Jones
Development of an equipment to monitoring and control the quality of resistance welding(CRAFT Project) J. Catty,
A study of small HSDI Diesel engine fuel injection equipment faults using acoustic
emission J.D. Gill, R.L. Reuben, J.A. Steel, M.W. Scaife and J. Asquith
Unsupervised pattern recognition of acoustic emission from full scale testing of a windturbine blade D. Kouroussis, A. Anastassopoulos, P. Vionis and V. Kolovos
Acoustic emission proof testing of insulated aerial man lift devices
A. Anastassopoulos, A. Tsimogianis and D. Kourousis
BOXMAP - Non-invasive detection of cracks in steel box girders
J.R. Watson, K.M. Holford, A.W. Davies and P.T. Cole,
Monitoring failure mechanisms in CFRP orthopaedic implants during fatigue testing
A. Taylor, S. Gross, C. Rowland and P. Gregson
Continuous monitoring of rock failure by a remote AE system T. Shiotani, S. Yuyama,
M. Carlos and S. Vahaviolos
New AE signal conditioner for industrial use H. Vallen, J. Forker and J. von Stebut,
New software tools for the AE-practitioner
H. Vallen and J. Vallen,
Improved source location methods for pressure vessels V. Godinez, S. Vahaviolos, R.D.
Finlayson, R.K. Miller, and M.F. Carlos
Neural network localization of noisy AE events in dispersive media

Page 286

Page 293
Page 299
Page 307

M. Blahacek, Z. Prevorovsky, and J. Krofta
Dynamics and damage assessment in impacted cross-ply CFRP plate utilizing the wavaform
simulation of Lamb wave acoustic emission
Y. Mizutani, H. Nishino, M. Takemoto and K. Ono
Acoustic emission detection during stress corrosion cracking at elevated pressure andtemperature R. Van Nieuwenhove, and R.W. Bosch
Detection of pitting corrosion of aluminiurn alloys by acoustic emission technique
H. Idrissi, J. Derenne, and H. Mazille
Reliability of acoustic emission technique to assess corrosion of reinforced concrete
H. Idrissi, and A. Limam

AEWG43 Presentation
Page S1Page S7

Theoretical Treatment of AE in Massive Solid….M. Ohtsu
Diagnosis of Concrete Structures by AE….M. Ohtsu

EWGAE 2000 (EWGAE.pdf)
Page i – v
SOMMAIRE -- Content of EWGAE 2000 Proceedings
Page vi
Page vii

AVANT-PROPOS
Mohammed CHERFAOUI, Christel RIGAULT
Présentation – EWGAE
Authors Index (18Auindx.pdf)
Contents of Volume 18, 2000 (18Conts.pdf)

Page I-1 – I-2
Page I-3 – I-6

e-mail Addresses of Authors (e-mail.pdf)
EWGAE 2000 Participant List (Senlis.pdf)

Authors Index

M. Akiyama
Bernhard Allemann
A.F. Almeida
Masashi Amaya,
A. A. Anastassopoulos,
E. Andres,
D. Armentrout
Baxter H. Armstrong,
J. Asquith,
Y.H.J. Au,
I. Baran,
A. Barron,

XVI-S150
XIV-119
XV-S107, XV-S108
XIII-S35
XIII-11, XVIII-21, XVIII-217, XVIII-224
XVIII-155
XV-43, XVI-S10
XII-117
XVIII-211
XVIII-196
XVII-S37
XVIII-87

M.W. Barsoum,
B. L. Baskin,
M. Bayray,Frank C. Beall,
Alan G. Beattie
S. Béland,
S. H. Benabdallah
Avraham Berkovits
C. C. Berndt
J.-M. Berthelot,
Yves H. Berthelot,
D. K. Bhattacharya,
Thomas Bidlingmaier
Jacek M. Biernacki,
M. Blahacek,
T. Boczar
Jurgen Bohse
Yasuyuki Bokoi
R. J. Boness
R.W. Bosch,P. Bowen,
John Brandon
C. Brun,A.J. Brunner
G. Budenkov
C. Buelens,
E. Bulatova

XVIII-61
XII-149
XVIII-131
XII-i (1/2), XII-55
XV-63, XV-S111
XII-45
XV-S117
XI-85
XV-S117
XI-11
XII-27
XI-43
XIV-S47
XII-55
XVIII-279
XVII-S7
XV-S108, XVI-S233, XVI-S343
XVI-S196
XV-S117
XVIII-293
XIII-S08
XVII-49
XVIII-155
XV-S108
XVII-S13, XVII-S51
XVIII-34
XVII-S13

CARP Aerospace/Advanced Composites Subcommittee
XI-C1
M. Carlos
XV-S104, XV-S107, XV-S109, XVIII-189, XVIII-248, XVIII-272
Steve H. Carpenter,
XI-5, XII-141, XIII-S01, XV-43, XVI-S10
Damian Carter
XVII-49
J. Catty,
XVIII-205
Chang-Sheng Chien,
XV-S118
Y. S. Chin
XI-71
Min-Hwa Chung,
XIII-1
M.A. Clark
XV-S107
P. ColeXV-S109
P.T. Cole,XVIII-61, XVIII-180, XVIII-232
David B. Cook
XVII-83
M. Cerny
XVII-S20
T. Chelladurai,
XII-111
M. Cherfaoui
XI-1, XVIII-144
Akiyoshi Chichibu,
XI-S47, XII-S1
M. Chlada
XVII-S57
F. Chmelík
XVII-S29
Hideo Cho
XVI-S115
N. S. Choi
XVI-S324
A-A. R. Choudhury
XVI-S125
R. Daniel Costley, Jr.,
XII-2716

J. Crha
A.W. Davies,
S. De Bondt,
P. De Meester
L. Delaey
J. Derenne,
A. Deruyttere
John J. Ditri,
Jason W.P. Dong
Henrique L. M. dos Reis,
R.D. Douglas,
Karyn S. Downs,
T.F. Drouillard,
Qingru Duan
R. Dubiel,
H.L. Dunegan
C. Divaker Durairaj
J. Dvoracek,
Y. A. Dzenis

XVII-S45
XVIII-232
XI-95
XIII-79, XV-S105, XVIII-34
XI-95
XVIII-299
XI-95
XII-23XVI-S1
XII-15
XVIII-96
XIII-31, XIII-45, XIII-56, XIV-S61, XV-S109, XVI-iv, XVI-S333
XI-53, XII-71, XII-79, XIII-42, XIV-1, XVI-v
XVI-S243
XVIII-15
XV-53, XV-S106, XVI-v
XVII-15
XVIII-81
XV-S112

J. Eisenblätter
T. El-Raghy,
T. ElyManabu Enoki,
W. T. Evans

XV-S119, XVI-S85
XVIII-61
XVI-S10
XIII-S29, XV-S90, XV-S120, XVI-S269
XI-71

A. Fahr,
Daining Fang
Ahmed M. Farahat
F. Ferrer,
Chantal Filion
P. Finkel,
R.D. Finlayson,
J. Forker,
D. S. Forsyth
H. Frackiewicz,
M.A. Friesel,
L. Froyen,
Taisaku Fujioka
Tetsuro Fujiwara
T. Fuketa,
Roy D. Fultineer, Jr.
Masami Fushitani,
M. Gakumazawa
John Gary,
Ludwig Gauckler
S.N. Gautrey,

XII-39, XV-S122
XI-85
XI-S37
XVIII-155
XVI-S186
XVIII-61
XVIII-61, XVIII-189, XVIII-272
XVIII-258
XV-S122
XII-149
XVIII-61, XVIII-189
XI-95
XV-S31XI-S65, XV-S40, XV-S113
XI-21
XV-S103
XIII-S42XVI-S150
XII-157, XIV-103, XV-S115, XVI-S251, XVII-37, XVII-97
XIV-119
XVIII-18017

Maochen Ge
P. Gebski
D. Geisse,
B. Georgali,
J.D. Gill,
V. Godinez,
V.P. Gomera,
M. Gori,
L. Golaski
Douglas E. Goldsack
Javier Gomez,
Carlos M. Valdes-Gonzalez,
Michael R. Gorman
J. Goudiakas,
Per A Gradin,
Igor Grabec
P. Gregson,
S. Gross,
C. U. Grosse
Dawei Guo
Yanfeng Guo

XV-S105
XVII-S37
XII-171
XVIII-21
XVIII-96, XVIII-211
XVIII-272
XVIII-111
XVIII-167
XVII-S37
XVI-S186
XIII-S21
XII-117
XIII-S01, XIV-i (3/4), XVII-29
XVIII-155
XIII-97
XI-79
XVIII-239
XVIII-239
XIV-S74
XIV-S19, XVI-S222
XVI-S317

Shigenori Hamada
XV-S40M. A. Hamstad
XI-33, XII-157, XIII-31, XIII-45, XIII-56, XIV-103, XIV-S61, XV-1,
XV-S108, XV-S109, XV-S115, XVI-S222, XVI-S251, XVI-S333, XVII-37, XVII-97
Mineyuki Hanano
XI-S75
V. Hänel
XIV-115
J. J. Hanley
XI-27
H.R. Hardy, Jr.
XV-S105, XV-S118, XVI-S277
Robert W. Harris
XV-S113, XVII-121
B. Herrmann,
XVIII-167
H. Nayeb-Hashemi,
XII-1
Toshiyuki Hashida,
XIII-S68
Hajime Hatano
XV-S115
F. Havlícek
XVII-S45
Yasuhisa Hayashi
XIV-69, XV-S108
Edmund G. Henneke II
XIV-53
C. Hervé,
XVIII-125
Stefan Heusermann
XVI-S85
Y. HigoXVI-S150, XVI-S196
R. Hill
XVI-S125
Koji Hisamatsu
XI-S1
S.V. Hoa
XI-65
Karen Holford
XVII-49, XVIII-232
Ki-Jung Hong,
J.R. Houghton
Nelson N. Hsu,
J. Hu

XIII-S61
XIV-61
XIII-23XVI-S150
18

J. H. Huh
Donald H. Humes
Wolfgang Hundt

XV-S80
XVII-29
XIV-119

Yoshiki Inoue
H. Idrissi,
Akichika Ishibashi
C. Ishiyama
Ken-Ichi Itakura,
V.I. Ivanov,Y. Iwata
S. Iyer,Takeshi Izuta,

XI-S89XVIII-299, XVIII-307
XI-S65, XV-S40, XV-S113
XVI-S150, XVI-S196
XIII-S54, XIII-S75
XVIII-144
XVI-S142
XVIII-189
XIII-S42

Laurence J. Jacobs,
T. Jayakumar,
Hee-Don Jeong,
Kyung-Young Jhang
C.Y. Jian,
Malcolm J. S. Johnston,
B.E. Jones,
Young-Chan Joo
B.S. Kabanov,
Kazuro Kageyama,Peter Kamlot
Yasuhiro Kanemoto
Elijah Kannatey-Asibu, Jr.
K. Katsuyama
Kunihisa Katsuyama
Harold E. Kautz,
Yoshiaki Kawaguchi,
Shahla Keyvan
A. Wahab Khair
E. W. Khokhlova,
S. Kihara, ...........................................................
Tadashi Kikuchi
Cindi Kilkenny
Byoung-Geuk Kim
Byung-Nam Kim
Dal-Jung Kim
Kyung-Woong Kim,
Sang-Hyo Kim
Isao Kimpara,
Tetsuo Kinjo
Teruo Kishi
Takahiro Kishishita
P. Kisnomo
Miroslav Koberna
Takao Koide,

XII-27
XI-43
XIII-S61
XVI-S261
XIII-S68
XII-117
XVIII-196
XV-S1, XVI-S212
XVIII-111
XIII-S89
XVI-S85
XV-S40
XV-S118
XI-S19XI-S27
XII-65
XII-127
XIV-97, XV-79
XV-S105, XVI-S53
XII-149
XVIII-68
XI-S47XVI-S186
XV-S120
XII-S24, XV-S90, XVI-S269
XVI-S261
XIII-1
XV-S11
XIII-S89
XV-19, XIV-69
XII-127, XIII-S29, XV-S90, XV-S120, XVI-S269
XI-S47
XV-33
XI-61
XIII-S47
19

B. Koktavy,
J. G. Kolaxis
V. Kolovos,
Ja-Ho Koo
M. Korenska,
I. Kosiková
A. Kotolomov
D. Kouroussis,
Torsten Krietsch
Vladimir Krivobodrov,
J. Krofta,
J. Królikowski,
D.A. Kronemeijer,
Joseph Krynicki
Takafumi Kubo,
M. KumosaYasufumi Kusano,
Oh-Yang Kwon
H. Kwun,XVII-S100
XVII-69
XVIII-217
XV-S90, XVI-S269
XVIII-29
XVII-S100
XVII-S51
XVIII-217, XVIII-224
XVI-S233, XVI-S343
XIII-87
XVIII-279
XII-149
XVIII-174
XV-S116
XIII-S42XVI-S10
XIII-S75
XIII-1, XIII-S83, XV-S1, XV-S19, XVI-S212
XI-27

G. Lackner,
A. Laksimi,
M. Landa
E. Landis
Marcel F. LeachJames D. Leaird,
R. D. LeblancChong Soo Lee,
Joon-Hyun Lee,K. A. LeeP. Y. Lee
Sang-Ho Lee
Seung-Hwan Lee
Sekyung Lee
Seung-Seok Lee
Weon-Heum Lee
L. Legin
A. M. Leksowskij,
Richard L. Lemaster,J. C. Lenain,
Luidmila Lezvinsky
Bangxian Li
Jae-Kyoo Lim
R. Lima
A. Limam,
C. K. Lin
J. Liöka
Shifeng Liu
T. LiuXi-qiang Liu

XVIII-167
XVIII-125
XVII-S57
XV-S104
XI-19, XVI-S186
XII-117
XV-S122
XIII-S61, XV-S80
XIII-S83
XV-S80
XV-S117
XV-S11
XV-S19
XV-S120
XV-S11XVI-S261
XVIII-144
XII-149
XII-55
XVIII-161
XVI-S35XVI-S243XVI-S309
XV-S117
XVIII-307
XV-S117
XVII-S108
XV-S124
XV-S117
XV-S123
20

David A. Lockner
T. Lokajícek
M. I. López Pumarega
E. Lowenhar
M.G. Lozev
P. Lukác
D. Lupascu

XIV-S88
XVII-S100
XVII-61
XV-S109
XV-S104
XVII-S29
XVII-S78

Ian Main,
M. A. Majeed,
Z. J. Majewska,
Zofia Majewska
S.A. Majewski,
Arup Maji
Ajit Mal
Gerd Manthei
H. Marcak,
K. Marsh
A. Maslouhi,
S. Mata
O. Matal
Kimitoshi Matsuyama
P. Mazal
J.D. McColskey
P. G. Meredith,Philip Meredith,R. (K.) Miller
O. Minemura
J. Mitchell
Makoto Miwa,
Kouitsu Miyachika,
Yoshihiro Mizutani
K. Mogi
Keiichi Monma
Bryan C. Morgan
L. Morgan
Y. Mori
Zofia Mortimer
W. J. Moś cicki,
F. Mostert,
M. C. Mumwam
K. Murayama
Boris Muravin
Gregory Muravin
S. A. F. Murrell,
C. R. L. Murthy,

XIII-S21
XII-107
XII-S7XVI-S105, XVIII-1
XII-S7
XV-S116
XIV-S19, XVI-S222
XV-S119, XVI-S85
XII-S7
XV-S104
XII-45, XV-S122, XVI-S299
XVII-23
XVII-S65
XI-S65, XV-S40
XVII-S2, XVII-S20, XVII-S70, XVIII-75
XV-1, XV-S108, XV-S111, XV-S119
XII-S12
XIII-S21
XV-S104, XV-S107, XV-S108, XVIII-61, XVIII-189, XVIII-272
XVI-S75
XV-S109
XIII-S42
XIII-S47
XVI-S115, XVIII-51, XVIII-286
XVI-S45
XV-S113
XV-69, XV-S103
XVI-S125XVI-S45XVI-S105, XVIII-1
XII-S7
XVIII-189
XVI-S65
XVI-S75
XVI-S35XVI-S35
XII-S12
XII-107

Koji Nagano,
Jyothi Nagaraj
M. M. Nagl,
Hidehumi Naito,
Hiroyasu Nakasa

XI-S1, XIII-S54, XIII-S75
XIV-97
XI-71
XII-S24XVI-S25
21

Noritaka Nakaso,
H. Nayeb-Hashemi,
O. Nedzvetskaya
Y.S. Neo,
Hiroaki Niitsuma,
V. N. Nikolaidis
Minoru Nishida,
H. Nishino,
Osamu Nishizawa
M. Noguchi
C. Nojiri
Richard Nordstrom
J. Nuffer
Staffan Nyström,

XIII-23
XII-1, XIV-85, XV-33
XVII-S13, XVII-S51
XVIII-96
XI-i(4), XI-S1
XVII-69
XII-S18
XVIII-51, XVIII-102, XVIII-286
XI-S27XI-21
XVI-S150
XV-S108, XVI-S204
XVII-S78
XIII-97

Y. Obata
Satoshi Oda,
A. O’Gallagher
Eu Seok Oh
Isamu Ohsawa,
Shiro Ohta
M. Ohtsu

XVI-S45
XIII-S47
XIV-103, XV-S115, XVI-S251, XVII-37, XVII-97
XVI-S277
XIII-S89
XV-S40
XI-i(4), XI-S37, XI-S57, XI-S65, XI-S89, XIII-S14, XV-S31, XV-S40, XV-S50,
XV-S60, XV-S70 XVI-S65, XVI-S95, XVIII-S1, XVIII-S7
Takahisa Okamoto,
XIII-S14
T. Okamoto
XVI-S75
A.A. Okhotnikov,
XVIII-111
S. Okumura
XI-21
L. Okushima
XVII-15
Kanji Ono,
XI-117, XII-177, XIV-35, XIV-69, XIV-S19, XV-19, XV-S95, XV-S105, XV-S108,
XVI-S115, XVI-S134, XVI-S289, XVII-S37, XVIII-51, XVIII-102, XVIII-286
P. Ouellette
XI-65
Philip Park
Young-Jin ParkJ. Pavelka
J. Pazdera,
L. Pazdera,
R. Pensec,
J. Petras,
L. Petras,
J. Petrasek
Christian Pfleiderer,
T. P. Philippidis,
Rodney G. Pickard
Aleksander Pilarski,
S. Pilecki,
J. Pininska,
R. Piotrkowski
T. Pocheco
A.A. Pollock

XV-S11
XV-S11
XVII-S100
XVIII-81
XVIII-29
XVIII-125
XVIII-75
XVIII-81
XVII-S83
XII-141
XIII-11, XVII-69
XV-79
XII-23
XII-149
XVIII-8
XVII-61
XIV-85
XV-S107, XV-S108
22

Elizabeth L. Porter
A. Prateepasen,
Z. Prevorovsky
David W. Prine
William H. Prosser
A. ProustJose Pujol
M. I. López Pumarega
K. K. Purushothaman,

XVII-121
XVIII-196
XVII-S57, XVIII-279
XV-S106
XIV-S1, XV-S106, XV-S115, XVII-29, XVII-37
XVII-S83, XVIII-161
XVII-111
XVII-61
XII-111Gang Qi

XVII-111

Baldev Raj,
S. P. Mallikarjun Rao
F. Rauscher
Henrique L. M. dos Reis,
F. Rehsteiner
H. W. Reinhardt
R.L. Reuben,
L.E. Rewerts
J. Richter
L. Rippert,
Steffen Ritter
J. L. Robert
R.A. Roberts
J. RödelP. Rodriguez
Joseph L. Rose,J. N. Rossettos,
C. Rowland,
C. Roy,Gottfried A. Rubin
J. E. Ruzzante

XI-43
XI-101
XVII-S92, XVIII-118
XI-107, XII-15, XVII-83
XIV-119
XIV-S74
XVIII-96, XVIII-211
XV-S107
XVII-S70
XVIII-41
XIV-S47
XI-11
XV-S107
XVII-S78
XI-43
XII-23
XII-1
XVIII-87, XVIII-239
XII-45
XI-19
XVII-61

H. Saadaoui,
Wolfgang Sachse
Masahiro Saito,
Naoya Saito
Norio Sakaino
K. Sakamaki
N. Sakata
Yasunori Sakata
Takumi Sakuma
H. M. Sallam
Peter Sammonds,
A. S. Sankaranarayanan,
N. Saniei
Hiroaki Sasaki
S. SaseL. Hanumantha Sastry

XII-45
XI-79
XIII-S68
XV-19, XVI-S289
XV-S50XVI-S134, XVIII-68
XVI-S75
XV-S70XVI-S196
XIV-85
XIII-S21
XII-111
XV-33
XVI-S25
XVII-15
XI-10123

Kazuhiko Sato,
K. SatoKeiichi Sato,
M.W. Scaife,
G. Schauritsch,
S.J. Schoorlemmer,
G. P. Sendeckyj
Hirofumi Sentoku
U. Senturk
M. SetoMasahiro Seto,
Gongtian Shen
H.W. Shen
Ping Shen
Nanling Shi
Mitsuhiro Shigeishi,
Tatsuya Shimizu,
J. Sikula
T. Simo
Masayuki Shimojo
Noboru Shinke
Tomoki Shiotani
A. Skraber,
V.L. Sokolov,
Jun-Hee SongM. Ben Souda
Th. Spies
J.A. Steel,H. Strauven,
C. E. Stuart,
V. Suba
Katsuhiro Sugawara,
M. Surgeon
Hiroaki Suzuki
Toshio Suzuki,
P. Svadbik,
V. Svoboda

XIII-S54, XIII-S75
XVI-S45
XIII-S42
XVIII-211
XVIII-138
XVIII-180
XI-33
XVI-S19
XV-S117
XI-S19XI-S27XVI-S243
XV-S105, XVIII-189
XV-S123XVI-S317
XI-S57, XIII-S14, XV-S50, XV-S60, XVI-S65
XIII-S68
XVII-S100
XVII-S65
XVI-S196
XVI-S134
XV-S50, XVI-S95, XVIII-248
XVIII-144
XVIII-111
XVI-S309
XI-11
XV-S119
XVIII-96, XVIII-211
XIII-79
XII-S12
XVII-S20
XIII-S54
XV-S105, XVIII-34
XI-117, XIV-35, XIV-69, XV-19, XV-S108, XVI-S178, XVI-S289
XIII-S89
XVIII-29
XVII-S2, XVII-S83

A.N. Tafuri
V.L. Tahiri
H. Takagi
Hideaki Takahashi,
K. Takahashi
Kazuki Takashima,
S. Takashina,
Nobuo Takeda,
Mikio Takemoto

XV-S104
XVI-S299
XVI-S142
XIII-S68
XVI-S324
XII-S18, XIII-S08, XVI-S150
XVIII-102
XII-127
XI-117, XIV-35, XIV-69, XV-19, XV-S108, XVI-S115, XVI-S178, XVI-S289
XVIII-51, XVIII-102, XVIII-286
XVI-S134
XVI-S178

Masanori Takuma
Okiharu Tamura

24

S. Tanary,
N. Tandon
O.B.Tarutin
Masayuki Tateno,
A. Taylor,
C. M. Teller
Lawrence W. Teufel
W. ThelenPeter G. Thwaite
Anil Tiwari
S. Tomecka-Suchoń ,Yuichi Tomoda
H. Tonda,
D. Tsamtsakis,
P. Tscheliesnig
A. Tsimogiannis,
Nobuyuki Tsuji
Yusuke Tsukahara,

XII-39
XVII-23
XV-S120
XI-S75
XVIII-239
XI-27
XV-S124
XIV-115
XIV-vi (3/4)
XIV-53
XII-S7
XV-S31
XIII-S08
XIII-79
XVII-S108, XVIII-138, XVIII-167
XVIII-21, XVIII-224
XV-S60
XIII-23

F. Uchida,
Atsushi Uchiyama,
Toshiyuki Uenoya,
E. Uria,

XVIII-102
XIII-S42
XIII-S95, XV-S112
XIII-79

S.J. Vahaviolos
Carlos M. Valdes-Gonzalez,
H. Vallen,
J. Vallen,
P.J. Van De Loo,
S. Van Huffel,
R. Van Nieuwenhove,
Alex Vary,
Vasisht Venkatesh
E. Verbrugghe
A. Vinogradov
A. Yu. Vinogradov
P. Vionis,
L.E. Vlasov,Aaron C. Voegele
J. von Stebut,

XV-S109, XVIII-189, XVIII-248, XVIII-272
XII-117
XVIII-167, XVIII-258, XVIII-265
XVIII-265
XVIII-174
XVIII-41
XVIII-293
XII-i (1/2), XII-71, XII-79, XIV-53
XIV-61XI-1
XVII-1XVI-S158
XVIII-217
XVIII-150
XVII-83
XVIII-258

Shuichi Wakayama,
Teruyuki Waki,
Akimasa Waku,
Alexander Wanner
Kris A. Warmann
G. Washer
Naoaki Watanabe,
J.R. Watson,

XII-S24, XVI-S170
XII-S1XII-S1
XIV-i, XIV-v, XIV-vi (3/4), XIV-S47
XI-107
XV-S104
XIII-S42
XVIII-232
25

D.J. Watts
Z. Weber,
Qiang Wei
B. Weiler
M. Wevers,
Brian R. A. Wood
Min WuXV-S104
XVIII-29
XI-S75
XIV-S74
XIII-79, XV-S105, XVIII-34, XVIII-41
XV-S113, XVII-121
XI-5

H. Yamada,
Yoshiaki Yamade,Akihiko Yamashita,
T. Yamauchi
Tinghu Yan
W. YanMasa-aki Yanaka,
Xuanhui Yang
Daisuke Yasuoka
J.J. Yezzi, Jr.
Dong-Jin Yoon,
K. Yoshida
Hiroshi Yoshino,
Y. Youssef,
Kunihiro Yuno
Shigenori Yuyama,

XVIII-51
XII-127
XIII-S89
XI-21
XVII-49
XV-S109
XIII-23
XV-S123
XV-S60
XV-S104
XIII-S83, XV-S11
XVI-S142, XVIII-68
XII-S1
XII-39XI-S89XIII-S14, XV-S107, XVI-S75, XVIII-248

J. Zaloudek
Kornelija Zgonc,
F. Zhang,
Zhizhen Zheng
Zuming Zhu
J. Zie tek,
Steve Ziola
B. Zogala,

XVII-S65
XI-79
XVIII-144
XV-S123XVI-S317
XII-S7
XIV-S12
XVIII-1526