Journal of Acoustic Emission, Volume 27, 2009VIEWING AN ARTICLE IN CD-ROM
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PDF Files: Contents are listed below. Individual papers have file names of 27-xxx.pdf, with xxx as theirfirst page number.

Contents
27-001 MONITORING THE CIVIL INFRASTRUCTURE WITH ACOUSTIC EMISSION:BRIDGE CASE STUDIES
1-10
D. ROBERT HAY, JOSE A. CAVACO and VASILE MUSTAFA
27-011 ACOUSTIC EMISSION TESTING OF A DIFFICULT-TO-REACH STEEL BRIDGEDETAILDAVID E. KOSNIK
11-17
27-018 ACOUSTIC EMISSION AS A MONITORING METHOD IN PRESTRESSEDCONCRETE BRIDGES HEALTH CONDITION EVALUATION
MAŁGORZATA KALICKA
18-26
27-027 ACOUSTIC EMISSION LEAK DETECTION OF LIQUID FILLEDBURIED PIPELINEATHANASIOS ANASTASOPOULOS, DIMITRIOS KOUROUSISand KONSTANTINOS BOLLAS

27-39

27-040 ACOUSTIC EMISSION MONITORING AND FATIGUE LIFE
PREDICTION IN AXIALLY LOADED NOTCHED STEEL SPECIMENSFADY F. BARSOUM, JAMIL SULEMAN, ANDREJ KORCAK and ERIC V. K. HILL

40-63

27-064 AE ANALYSIS ON BLADE CUTTING PRESSURE ADJUSTMENT IN DYNAMIC
CUTTING OF PAPERBOARDDARULIHSAN A. HAMID, SHIGERU NAGASAWA, YASUSHI FUKUZAWA,YUUKI KOMIYAMA and AKIRA HINE
64-76
27-077 DAMAGE ONSET AND GROWTH IN CARBON-CARBON COMPOSITEMONITORED BY ACOUSTIC EMISSION TECHNIQUE
ARIE BUSSIBA, ROMANA PIAT, MOSHE KUPIEC, RAMI CARMI, IGAL ALON
and THOMAS BÖHLKE

77-88

27-089 FUNDAMENTAL STUDY ON INTEGRITY EVALUATION METHOD FOR COPVSBY MEANS OF ACOUSTIC EMISSION TESTINGYOSHIHIRO MIZUTANI, SOTA SUGIMOTO, RYOSUKE MATSUZAKI
and AKIRA TODOROKI
89-97
I-1

	
  
27-098 ACOUSTIC EMISSION FROM IMPACTS OF RIGID BODIESTATIANA B. PETERSEN

98-11327-114 SOME OBSERVATIONS ON RAYLEIGH WAVES AND ACOUSTIC EMISSION IN
THICK STEEL PLATES
M. A. HAMSTAD
114-136
27-137 FRACTURE BEHAVIOR IN BONE CHARACTERIZED BY AE WAVELETANALYSISSHUICHI WAKAYAMA, KEISUKE MOGI and TETSUYA SUEMUNE
137-143
27-144 ABOUT PLASTIC INSTABILITIES IN IRON AND POWER SPECTRUM OF
ACOUSTIC EMISSION
ALEXEY LAZAREV and ALEXEI VINOGRADOV
144-156
27-157 ACOUSTIC AND ELECTROMAGNETIC EMISSION FROM CRACK
CREATED IN ROCK SAMPLE UNDER DEFORMATIONYASUHIKO MORI, YOSHIHIKO OBATA and JOSEF SIKULA

157-166

27-167 IDENTIFICATION OF AE MULTIPLETS IN THE TIME AND FREQUENCYDOMAINSHIROSHI ASANUMA, YUSUKE KUMANO, HIROAKI NIITSUMA, DOONE WYBORN
and ULRICH SCANZ
167-175
27-176 CRACK GROWTH MONITORING WITH HIERARCHICAL CLUSTERING OF AE
N. F. INCE, CHU-SHU KAO, M. KAVEH, A. TEWFIK and J. F. LABUZ
176-185
27-186 ACOUSTIC EMISSION FOR CHARACTERIZING BEHAVIOR OF COMPOSITE
CONCRETE ELEMENTS UNDER FLEXURESHOHEI MOMOKI, HWAKIAN CHAI, DIMITRIOS G. AGGELIS, AKINOBU HIRAMA
and TOMOKI SHIOTANI
186-19327-194 DISTINCT ELEMENT ANALYSIS FOR ROCK FAILURE CONSIDERING AE
EVENTS GENERATED BY THE SLIP AT CRACK SURFACESHIROYUKI SHIMIZU, SUMIHIKO MURATA and TSUYOSHI ISHIDA
194-21127-212 ELECTROMAGNETIC METHOD OF ELASTIC WAVE EXCITATION FOR
CALIBRATION OF ACOUSTIC EMISSION SENSORS AND APPARATUS
SERGEY LAZAREV, ALEXANDER MOZGOVOI, ALEXEI VINOGRADOV, ALEXEY LAZAREV
and ANDREY SHVEDOV
212-223
27-224 MONITORING OF PIPE CLOGGING BY MUSSELS UTILIZING AN OPTICALFIBER AE SYSTEM
TAKUMA MATSUO, YUTA MIZUNO and HIDEO CHO
224-232
27-233 CORROSION DETECTION BY FIBER OPTIC AE SENSORYUICHI MACHIJIMA, MASAHIRO AZEMOTO, TOYOKAZU TADA
I-2

	
  
and HISAKAZU MORI

233-240

27-241 EFFECT OF SHOT PEENING ON THE DELAYED FRACTURE USING THE
ALMEN STRIP AND AE TECHNIQUEMIKIO TAKEMOTO, MOTOAKI NAKAMURA, SEIJI MASANO and SHUICHI UENO 241-253
27-254 CONTRIBUTION OF ACOUSTIC EMISSION TO EVALUATE CABLE STRESSCORROSION CRACKING IN SIMULATED CONCRETE PORE SOLUTION
S. RAMADAN, L. GAILLET, C. TESSIER and H. IDRISSI
254-262
27-263 FLEXURAL FAILURE BEHAVIOR OF RC BEAMS WITH REBAR CORROSION
AND DAMAGE EVALUATION BY ACOUSTIC EMMISSIONNOBUHIRO OKUDE, MINORU KUNIEDA, TOMOKI SHIOTANIand HIKARU NAKAMURA
263-271
27-272 ACOUSTIC EMISSION METHOD FOR SOLVING PROBLEMS IN DOUBLEBOTTOM STORAGE TANKSMAREK NOWAK, IRENEUSZ BARAN, JERZY SCHMIDT and KANJI ONO
272-280
27-281 STUDY OF IDENTIFICATION AND REMOVAL METHOD FOR DROP NOISE IN
AE MEASUREMENT OF TANKSHIDEYUKI NAKAMURA, TAKAHIRO ARAKAWA, HIRAKU KAWASAKI, KAZUYOSHISEKINE and NAOYA KASAI
281-29027-291 A GENERIC TECHNIQUE FOR ACOUSTIC EMISSION SOURCE LOCATIONJONATHAN J. SCHOLEY, PAUL D. WILCOX, MICHAEL R. WISNOM,MIKE I. FRISWELL, MARTYN PAVIER and MOHAMMAD R ALIHA
291-298
27-299 ACOUSTIC EMISSION TESTING – DEFINING A NEW STANDARD OF
ACOUSTIC EMISSION TESTING FOR PRESSURE VESSELS
Part 1: Quantitative and comparative performance analysis of zonal location andtriangulation methods
JOHANN CATTY

299-313

Contents27
AUindex27
AusNotesContents of Volume 27 (2009)
I-1 – I-3
Authors Index of Volume 27
I-4
Policy/Author’s Notes/Meeting Calendar/DVD/Subscription Information
I-5 – I-7
IAES19JCAE Kishinoue Award Acceptace Speech by T.F. Drouillard
I-8 – I-10
AE Literature AE conference proceedings in China, 2001-2006: Gongtian Shen
I-11 – I-16
Cover photographs See 27-001 by Hay et al. for details.
JAE Index Folder*

Cumulative Indices of J. of Acoustic Emission, 1982 – 2009
Contents1-27
Contents Volumes 1-27
Authors Index1-27 Authors Index Volumes 1-27

* indicates the availability in CD-ROM only. Indices are also available for download fromwww.aewg.org.
I-3

AUTHORS INDEX, Volume 27, 2009
DIMITRIOS G. AGGELIS,
27-186
MOHAMMAD R ALIHA
27-291
IGAL ALON
27-077ATHANASIOS ANASTASOPOULOS, 27-027
TAKAHIRO ARAKAWA,
27-281
HIROSHI ASANUMA,
27-167MASAHIRO AZEMOTO,
27-233
IRENEUSZ BARAN,
27-272FADY F. BARSOUM,
27-040
THOMAS BÖHLKE
27-077KONSTANTINOS BOLLAS 27-027
ARIE BUSSIBA,
27-077RAMI CARMI,
27-077JOHANN CATTY
27-29927-001JOSE A. CAVACOHWAKIAN CHAI,
27-186HIDEO CHO
27-224
MIKE I. FRISWELL,
27-291
YASUSHI FUKUZAWA,
27-064
L. GAILLET,
27-254DARULIHSAN A. HAMID,
27-064M. A. HAMSTAD
27-114
D. ROBERT HAY,
27-001ERIC V. K. HILL
27-040AKIRA HINE
27-064
AKINOBU HIRAMA
27-186
H. IDRISSI
27-254N. F. INCE,
27-176TSUYOSHI ISHIDA
27-194MAŁGORZATA KALICKA 27-018
CHU-SHU KAO,
27-176NAOYA KASAI
27-281
M. KAVEH,
27-176HIRAKU KAWASAKI,
27-281YUUKI KOMIYAMA
27-064
ANDREJ KORCAK
27-040DAVID E. KOSNIK,
27-011
DIMITRIOS KOUROUSIS
27-027YUSUKE KUMANO,
27-167MINORU KUNIEDA,
27-263MOSHE KUPIEC,
27-077
J. F. LABUZ
27-176
ALEXEY LAZAREV
27-144, 27-212
SERGEY LAZAREV,
27-212
YUICHI MACHIJIMA,
27-233
SEIJI MASANO
27-241

TAKUMA MATSUO,RYOSUKE MATSUZAKIYUTA MIZUNOYOSHIHIRO MIZUTANI,KEISUKE MOGISHOHEI MOMOKI,HISAKAZU MORIYASUHIKO MORI,ALEXANDER MOZGOVOI,SUMIHIKO MURATA
VASILE MUSTAFASHIGERU NAGASAWA,
HIDEYUKI NAKAMURA,
HIKARU NAKAMURA
MOTOAKI NAKAMURA,
HIROAKI NIITSUMA,MAREK NOWAK,YOSHIHIKO OBATANOBUHIRO OKUDE,
KANJI ONOMARTYN PAVIERTATIANA B. PETERSEN
ROMANA PIAT,
S. RAMADAN,ULRICH SCANZJERZY SCHMIDTJONATHAN J. SCHOLEY,
KAZUYOSHI SEKINE
HIROYUKI SHIMIZU,TOMOKI SHIOTANIANDREY SHVEDOVJOSEF SIKULATETSUYA SUEMUNESOTA SUGIMOTO,JAMIL SULEMAN,TOYOKAZU TADA
MIKIO TAKEMOTO,
C. TESSIER
A. TEWFIKAKIRA TODOROKISHUICHI UENOALEXEI VINOGRADOVSHUICHI WAKAYAMA,PAUL D. WILCOX,MICHAEL R. WISNOM,DOONE WYBORN
I-4

27-22427-08927-22427-08927-13727-18627-23327-15727-21227-19427-00127-06427-28127-26327-24127-16727-27227-15727-26327-27227-291
27-09827-07727-25427-16727-27227-29127-281
27-19427-186, 27-263
27-21227-15727-13727-08927-04027-23327-24127-25427-17627-08927-24127-144, 27-212
27-13727-29127-29127-167

JOURNAL OF ACOUSTIC EMISSION
Editor: Kanji Ono
Associate Editors: A. G. Beattie, T. F. Drouillard, M. Ohtsu and W. H. Prosser
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 andsummaries 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 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.

policies and other aspects. The Board presently includes thefollowing members:
A. Anastasopoulos
F.C. Beall
J. Bohse
P. ColeL. Golaski
M.A. Hamstad
R. HayK.M. Holford
O.Y. Kwon
J.C. Lenain
G. Manthei
P. Mazal
C.R.L. Murthy
A.A. Pollock
F. Rauscher
T. Shiotani
P. Tschliesnig
H. Vallen
M. Wevers
B.R.A. Wood
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 2121H, Engr. V,
University of California, Los Angeles, California 90095-1595(USA). tel. 310-825-5233. FAX 310-206-7353. e-mail:aegroup7@gmail.com or ono@ucla.edu
5. Subscription

2. Endorsement
Acoustic Emission Working Group (AEWG), European
Working Group on Acoustic Emission (EWGAE), have endorsed
the publication of Journal of Acoustic Emission.
3. Governing Body
The Editor and Associate Editors will implement the editorial
policies described above. The Editorial Board will advise the editors on any major change. The Editor, Professor Kanji Ono, has
the general responsibility for all the matters. Associate Editors
assist the review processes as lead reviewers. The members of the
Editorial Board are selected for their knowledge and experienceon AE and will advise and assist the editors on the publication

ISSN 0730-0050 Copyright©2009 Acoustic Emission Group(Greece)(USA)(Germany)(UK)(Poland)(USA)(Canada)(UK)(Korea)(France)(Germany)(Czech Republic)(India)(USA)(Austria)(Japan)(Austria)(Germany)(Belgium)(Australia)

Subscription should be sent to Acoustic Emission Group.
Annual rate for 2010 is US $111.00 including CD-ROM delivery,
by priority mail in the U.S. and by air for Canada and elsewhere.
For additional print copy, add $40-49. Overseas orders must be
paid in US currencies with a check drawn on a US bank. PayPal
payment accepted. Inquire for individual (with institutional order)
and bookseller discounts. See also page I-7.
6. Advertisement
No advertisement will be accepted, but announcements forbooks, training courses and future meetings on AE will be
included without charge.

I-5

CODEN: JACEDO All rights reserved.

Notes for Contributors
1. Generalat the end of the paper in alphabetical and chronologicalorder. Journal references should be arranged as below.
Titles for journal or book articles are helpful for readers,
but may be omitted.

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
Rm. 2121H, Engr. V, MSE Dept.
University of California
420 Westwood Plaza,
Los Angeles, California 90095-1595 USA
FAX (1) 818-990-1686
e-mail: ono@ucla.edu

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.

Authors of any AE related publications are encouraged tosend a copy to:
Mr. T.F. Drouillard, Associate Editor - JAE
11791 Spruce Canyon Circle
Golden, Colorado 80403 USA

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.

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.

Illustrations and tables should be planned to fit a single
page width (165 mm or 6.5"). For the reviewing processes,
these need not be of high quality, but submit glossy prints
or equivalent electronic files with the final manuscript.
Lines and letters should be legible.
4. Review

The language of the Journal is English. All papers should
be written concisely and clearly.

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.

2. Page Charges
No page charge is levied. The contents of CD-ROM will be
supplied to the authors free of charge via Internet site.

5. Electronic Media
3. Manuscript for Review
This Journal will be primarily distributed electronically by
CD-ROM. In order to expedite processing and minimize
errors, the authors are requested to submit electronic filesof the paper. We can read Macintosh and IBM PC formats.
On the INTERNET, you can send an MS Word file and theseparate figure files to "aegroup7@gmail.com".

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 articles. Except for shortcommunications, descriptive heading should be used todivide the paper into its component parts. Use theInternational System of Units (SI).

6. Color Illustration

References to published literature should be quoted in the
text citing authors and the year of publication orconsecutive numbers. These are to be grouped together

We can process color illustration needed to enhance thetechnical content of an article. With the new format,
authors are encouraged to use them.

I-6

MEETING CALENDAR:EWGAE29
EWGAE-2010: 29th European Conference on Acoustic Emission Testing will be held September 8-10,
2010, hosted by TÜV Austria Services GmbH at Wirtschaftskammer Wien, Vienna, Austria. Peter
Tscheliesnig is the organizer. Abstract deadline is past but more information is available at
http://www.2010.ewgae.eu/

IAES20The 20th International Acoustic Emission Symposium (sponsored by Ad Hoc Committee on Acoustic
Emission of JSNDI) will be held November 16-19, 2010, hosted by Kumamoto University at Ark Hotel
Kumamoto, Kumamoto, Japan. City of Kumamoto is located at the middle of Kyushu Island (thesouthern island of Japan). By air, it takes 90 minutes from Tokyo. Masayasu Ohtsu is the organizer.
Abstract deadline is June 30, 2010 and more information is available at
http://iaes20-kumamoto.com/index.html/

JAE DVD: Vol. 1-24 (1982-2006)
A DVD contains all the articles from the past 25 years of Journal of Acoustic Emission. Cost is $200
plus shipping of $8. Send an order to Acoustic Emission Group (address below).

2010 SUBSCRIPTION RATES
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For inquiry through Internet, use the following address:
Editor-Publisher
Publication Date of This Issue (Volume 27):

I-7

aegroup7@gmail.com
Kanji Ono Tel. (818) 849-9190
19 April 2010.

19th International Acoustic Emission Symposium
Fukui Institute for Fundamental Chemistry, Kyoto University, Kyoto, Japan, Dec. 10, 2008

JCAE Kishinoue Awards and AEWG Awards
Inaugural Kishinoue Awards of the Japanese Committee on Acoustic Emission were presented toProfessor Teruo Kishi and Mr. Thomas F. Drouillard for their outstanding contributions to thefield of acoustic emission. AEWG Gold Medal Award and AEWG Student Paper Award werealso given to, respectively, Professor Masayasu Ohtsu and Mr. Kentaro Ohno.

From L to R, K. Ohno, T.F. Drouillard, T. Kishi and M. Ohtsu.

Acceptance Speech by Thomas F. Drouillard
Thank you very much!
It is indeed a great honor to have been
considered, and even a greater honor to havebeen selected to receive the Japanese Committeeon Acoustic Emission’s Inaugural KishinoueAward*. I consider this one of the crowninghighlights in my career of over a half century in
the field of Nondestructive Testing: 17-yrs. in
Ultrasonics, and 41-yrs. in Acoustic Emission.
I am very impressed with the most appropriatename you have chosen to call this award, theKishinoue Award. As many of you know, theTom Drouillard accepts Kishinoue Award fromtwo things I have devoted most of my efforts
JCAE Chair Prof. Enoki.
towards have been my bibliography work andresearching and writing the history of AE. In the process of searching to find all the early publishedliterature on AE, I would first read any document I thought might be on the subject, then check each_______________* The spelling Kishinoue is currently used by the Japanese and appears on the award; in the text the spelling
Kishinouye is used which is in agreement with all original references to and publications by Kishinouye.
I-8reference in that document to see if it pertained in any way to AE, which often led to some earlier
publication on the subject. Those that looked promising, I would order a copy through my library. Those
documents that were published in a language other than English, I would have translated into English.
In 1990, I wrote an article entitled “Anecdotal History of Acoustic Emission from Wood” for publicationin the Journal of Acoustic Emission. During my search for material, a number of references led me towhat I believe was the first article ever written on a study of acoustic emission from wood by Prof.
Fuyuhiko Kishinouye. The article was published in 1934 in the Japanese seismological journal Jishin,with an English version published in 1937 in the Bulletin of the Earthquake Research Institute, Tokyo
Imperial University. The reference was found in an article by Prof. Kiyoo Mogi, who was at the
Earthquake Research Institute, University of Tokyo. He stated, “The elastic shocks caused by the fractureof solid materials were measured by F. Kishinouye for the purpose of investigation of earthquakes. He(Kishinouye) described the process of shock occurrences in wood specimens under flexural stress.”
Prof. Kishinouye had designed and performed a series of Gedanken, or thought experiments to amplify
and record the many rapid, inaudible vibrations and cracking sounds produced by the fracture of wood, inorder to study the fracture of the earth’s crust as the cause of earthquakes, and solve the problem of timedistribution of earthquakes and, in particular, the Ito earthquake swarm that had occurred between
February and June of 1930.
The apparatus for the experiment consisted of a phonograph pickup using a steel needle inserted into thetension side of a wooden board to which a bending stress was applied to cause fracture. Kishinouyestated: “When the board cracked, electric current was generated in the coil of the pick-up, the current
being amplified with an amplifier, which formed a part of the Haeno radio-seismograph. The current wasrecorded with an oscillograph on cinematographic films.....As bending proceeded, cracking sounds were
heard, while the oscillograms recorded many inaudible vibrations. The process of fracture varied with thevelocity of bending, the kind of wood, the grain of the board, and the moisture content of the wooden
board.....When the materials were the same and velocity of bending equal, the board broke with nearly
equal deformation....When deformation proceeded slowly, occasionally loud creaking sounds were heard
owing to rupture of the grains, and low silent vibrations were found on the oscillogram.....It was foundthat the process of fracture is affected considerably by the condition of the experimented material and theparticular way in which the force acts.”
It is unfortunate that the pioneering instrumented AE experiment Prof. Kishinouye conceived andperformed was published in a journal in a field of technology outside the realm of AE scientists andengineers. However, the significant accomplishment we do credit Prof. Kishinouye with, is that theoscillograms he made were the first acoustic emission waveforms ever recorded.
But, the birth of traditional acoustic emission would then have to wait until 1950 for the research work ofJoseph Kaiser in Germany, and publication of his doctoral dissertation. In 1954 a copy of Kaiser’s
dissertation was translated for Brad Schofield in the U.S., who repeated and expanded on Kaiser’s
experiments, which he published in a series of reports entitled “Acoustic Emission Under Applied
Stress.” These publications precipitated research in a number of laboratories throughout the U.S. By the
1960s, we find AE research being performed in many countries throughout the world.
Thus we moved into what I call the Golden Age of AE with the formation of the first three working
groups: the Acoustic Emission Working Group in 1967, the Japanese Committee on Acoustic Emission in
1969, and the European Working Group on Acoustic Emission in 1972.
I will elaborate on just one of these working groups, the one here in Japan. It all started here in Kyoto in
the summer of 1969, at the International Institute of Welding Conference where the term Acoustic
Emission was mentioned in several of the papers and in some of the discussions. This piqued the interestof a number of the attendees. Later that year, in response to the interest generated from the IIW
I-9

Conference, along with that from then Assistant Prof. Kanji Ono’s lecture on materials education andresearch, and some discussion on AE during his visit to Fuji Steel in the spring of 1969, Dr. Eiji Isono of
Fuji Steel and Prof. Morio Onoe of the Institute of Industrial Science, University of Tokyo, organized theJapanese Committee on Acoustic Emission, under the sponsorship of the High Pressure Institute of Japan,
and in co-operation with the Japanese Society for Non-Destructive Inspection. JCAE was composed ofapproximately 40 members, most of whom came from steel makers, plant fabricators and universities.
In July of 1972, the first biennial International AE Symposium was held in Tokyo. Some ofthe sessions were in Japanese, and some in English. The proceedings consisted of 2 volumes:
a Japanese volume with 11 papers, and an English volume with 6 papers. The number of Japaneseparticipants was 130, reflecting the increasing interest in AE here in Japan. The following summer, July
1973, Dr. Onoe and eight of his colleagues visited a number of laboratories in the U.S., concluding theirstudy mission by attending the 11th meeting of the AEWG in Richland, Washington.
As you can see, international communication and technical exchange was well underway. JCAE
scheduled their Second AE Symposium to be held in Tokyo the next year. Starting with this symposium,
English was adopted as the official language for all presentations and published proceedings.
Starting with the 5th symposium, the name was changed to International Acoustic Emission Symposium –IAES. Then, starting with the 6th symposium, the title of the proceedings was changed to Progress in
Acoustic Emission. The proceedings of these biennial symposia have indeed become an ongoing
documentation of the progress in AE throughout the world and comprise a major component of thepermanent world literature on acoustic emission.
It is through the working groups, their local meetings, and the international meetings that AE technology
has, and continues to progress. The working groups bring together people with expertise in all of thebasic sciences that form the foundation of AE technology. They provide forums for the exchange of ideas
and information. And through the dedicated research of hundreds of scientists and engineers throughout
the world, AE has become a highly developed, mature technology and recognized NDT method. AE has
become an international fraternity of these scientists, engineers, and technicians, creating camaraderie andworking relationships on an international scale. The fact that the Japanese, European, and U.S. working
groups have existed for nearly half a century attests to the importance of AE.
I urge each and everyone of you to continue to take an active roll by presenting papers, participating inopen discussions, asking questions, posing problems, and sharing reports of your work. And remember, it
is important to have input from all areas of AE activity: research, development, and application.
I consider myself very privileged to have spent the better part of my career in such an exciting field oftechnology, to have personally known and worked with so many people from all over the world, andwatched our technology develop from the grass roots to an important and unique NDT method.
In closing, I want to congratulate the Organizing Committee for the excellent job they have done inputting together another fine symposium in the tradition of the past 36 years.
Again, thank you very much for this most distinguished award.
Thomas F. Drouillard

I-10

AE LITERATURE
Chinese Acoustic Emission Conferences, 2001 - 2006
Compiled by Gongtian Shen
The Paper List of Proceedings for 9th Chinese Acoustic Emission Conference
August 19-24, 2001, Chengdu, China
1. Up-to-date Acoustic Emission Technique and Problems
Geng Rongsheng (Beijing Aeronautical Technology Research Centre)
2. Development on Applications and Standardizations of Acoustic Emission Technique in Aerospace
Industry
Jin Zhougeng (Aerospace Research Institute of Materials & Processing Technology)
3. AE Testing System for Acoustic Emission
Yang Mingjian,Yang Xiyi (Shenyang Computer Technology Research & Design institute)
4. Waveform Acoustic Emission Technology
Liu Shifeng,Wang Yong (Tsinghua University)
5. Recognizing the Availability of Acoustic Emission Signals on a Method
Li Wei, Dai Guang (Daqing Petroleum Institute)
6. Technique of Multi-sensor Data Fusion on Acoustic Emission Source Recognition
Li Guanghai (Guangzhou Institute of Boiler & Pressure Vessel Inspection)
7. Recognizing Method Study of the Acoustic Emission Signals Availability
Li Wei, Zhang Ying (Daqing Petroleum Institute)
8. Experience Of Calibration in AE Detection
Li Huaping, Li Yongjian (Lanzhou Petroleum Machinery Research Institute)
9. A Discussion of Uncertainty of Measurement about Acoustic Emission Source Location
Yao Li, Lai Deming (China Air-dynamical Research and Development Center)
10. Noise Rejection for Modal Acoustic Emission
Zhang Ying (Capgold Development Inc)
11. AE Detection and Evaluation On Multilayer Binding High Pressure Vessels
Dai Guang, Li Wei (Daqing Petroleum Institute)
12. The Example of Acoustic Emission Test in Titanium Alloys Pressure Vessels
Wang Jian (Aerospace Research Institute of Materials & Processing Technology)
13. Online Acoustic Emission Testing on In-service C4 Spherical Vessel
Jiang Shiliang, Dong Zhiyong (Machinery Research Institute Of Tianjin Petrochemical
Corporation)
14. On-line Acoustic Inspection and Integrity Assessment of Vertical Metal Storage Tanks
Xu Yanting, Dai Guang (Daqing Petroleum Institute)
15. A Experimental Study on Engine Piston-liner Wear Fault Diagnosis by Acoustic Emission
Technology
Yang Zhancai, Zhang Laibin (University of Petroleum)
16. Research on AE Inspection for Stay Ring Machining and Spiral Case of Water Wheel
I-11

Wang Yong, Liu Shifeng (Tsinghua University)
17. Acoustic Emission Monitoring of the Slope Stability of the Permanent Rock for the Three
Georges Project
Chen Cuimei, Yu Guojing (Wuhan Safety and Environmental Protection Research Institute)
18. Application of AE Technology on State Monitoring and Fault Diagnosing of Power Plant
Equipment
Zhang Aiping (Northeast China Institute of Electric Power Engineering)
19. Analysis on Acoustic Emission Signals Features of Gas and Coal Outburst
Su Xian (Chongqing Branch, CCMRI)
20. Acoustic Emission Testing of the Shell of Electromagnetic Valve
Xu Guozhen, Chen Yiwei (The 801st Institute of CASC)
21. The Problems of Material Testing Machine in the Measurement of Rock Stress by AE Method
Ding Yuanchen, Wang Xihai (Institute of Geology, Chinese Academy of Science)
22. Micromechanism of Improving the Interface Properties of SiCf /Al Composite by Fiber
Modification with Double and Gradient Coating
Zhu Zuming, Guo Yanfeng (Institute of Metal, Chinese Academy Of Sciences)
23. Application of Acoustic Emission in Metallic Compounds Research
Kong Fangong Ma Guiying (Shanghai Jiaotong University)
24. Study on Relationship between Microstructure and Fatigue Life of Wire Rope
Guo Yenfeng, Zhu Zuming (Institute of Metal, Chinese Academy of Sciences)
25. The Monitoring of Acoustic Emission during the Pull Testing of A3 Steel Samples with Notches
Liu Guoguang, Cheng Qingchan (East China University of Science And Technology)
26. Frequency Distinguish of Acoustic Emission Signals for a Fiber/Al Composite
Liu Zhejun (Aerospace Research Institute of Materials & Processing Technology)
The Paper List of Proceedings for 10th Chinese Acoustic Emission Conference
July 30 to August 3, 2004, Daqing, China
1. Acoustic Emission Progress in China
Shen Gongtian,Dai Guang,Liu Shifeng(Chinese Committee on AE)
2. Application of Acoustic Emission for Aviation Industry-current State Difficulties and Approaches
Geng Rongsheng (Beijing Aeronautical Technology Research Centre)
3. A Dynamic Detection Technology and Its Study Progress for Pressure Vessels in Service
Dai Guang, Li Wei (Daqing Petroleum Institute)
4. The Research Progressing in Acoustic Emission Instrument
Liu Shifeng, Li Guanghai (Tsinghua University)
5. Recent and Ongoing Development of AE in Europe
Hartmut Vallen (Vallen-System GmbH)6. Development on Applications of Acoustic Emission Technique in China Aerospace Industry
Liu Zhejun (Aerospace Research Institute of Materials & Processing Technology)
7. The Progress of Acoustic Emission Technique Application to Pressure Vessel Test
Shen Gongtian, Li Bangxian (China Special Equipment Inspection and Research Center)
8. Suggestions on Great Enhancement of Acoustic Emission Applications in China
Xu Yanting, Ding Shoubao (Special Device and Boiler & Pressure Vessel Inspection Center ofZhejiang Province)
9. An Overview of the Development of Acoustic Emission Signal Analysis Technique
I-12

Li Guanhai, Liu Shifeng (Tsinghua University)
10. Study and Applications of Acoustic Emission Technology in Canada
Wang Xiaowei, Xu Yanting (Special Device and Boiler & Pressure Vessel Inspection Center ofZhejiang Province)
11. Application in Location of Acoustic Emission Using Neural Network Technique
Li Dongsheng, He Lin (Harbin Institute of Technology)
12. An Application of Multi-scale Wavelet Decomposition Analysis on Leak Location of Pipelines
Meng Tao, Wu Bin (Beijing University of Technology)
13. The Acoustic Emission Source Location Technique in Pipeline Based on Waveform and Modal
Analysis
Jiao Jingpin, Wu Bin (Beijing University of Technology)
14. Experimental Research of Corrosion Damage on Above-ground Atmospheric Vertical Storage
Tank
Long Feifei, Li Wei (Daqing Petroleum Institute)
15. Study on Acoustic Emission Pipeline Leak Signal with the Propagation Distance
Meng Tao, Liu Jiangqiang (Beijing University of Technology)
16. Application of Neural Network in Acoustic Emission Test
Yang Nengjun, Wang Hangong (The Second Artillery Engineering College)
17. A Study on the Attenuation of Acoustic Emission
Yue Yalin, Li Shenghua (China Oil Science Research Center)
18. Using Measured AE Signal Loss for Setting AE Parameters
Jans Forker (Vallen-system GmbH)
19. Acoustic Emission of the Demolish Test of Model P350V50 High Pressure Steel Gas Cylinder
Yao Li, Lai Deming (China Aerodynamic Research and Develop Center)
20. Application and Research of Acoustic Emission Testing on In-service Pressure Vessel
Shao Feng, Jiang Jun (Nanjing Center of Boiler and Pressure Vessel Inspection)
21. Acoustic Emission Sources of Hydrogen Tank Test
Wang Yong, Li Bangxian (China Special Equipment Inspection and Research Center)
22. Detection and Safety Evaluation on Hydrogenation Refine Reactor
Yang Zhijun, Liu Yanlei (Daqing Petroleum Institute)
23. The Practice of Acoustic Emission Testing on 39.2MPa Gas Cylinder
Zhao Yuanyuan, Chen Yiwei (Shaihai Aerospace Technology Institute)
24. Tank Bottom Location-Example of Location Uncertainty
Hartmut Vallen (Vallen-system GmbH)
25. On-line Detection and Estimation of Large-scale Above Ground Storage Tanks by Acoustic
Emission
Tao Yuanhong, Guan Weihe (Hebei General Machinney Research Institute)
26. Study on Possibility of Monitoring Cracking for Manual Welding by Acoustic Emission
Zhang Wei, Leng Jianxing (China Ship Scientific Research Center)
27. Monitoring the Turning Machine for Train on Port Using Acoustic Emission
Liu Zhejun, Chen Guicai (Aerospace Research Institute of Materials & Processing Technology)
28. Acoustic Emission Inspection for Wheel Band Fitting Test
Tao Yuanhong, Li Jian (Hefei General Machinery Research Institute)
29. Acoustic Experiment Study of Estimating the Inner Leakage Rate of Gas Valves
ZhangYing, Zhao Junru (Daqing Petroleum Institute)
30. Experimental Study on Detecting the Working Load on In-service Metallic Bolt by Employing
I-13

Barkhausen-effectJi Hongguang, Sha Haifei (Beijing University of Science and Technology)
31. The Application of Acoustic Emission Technique in Detecting Metal Corrosion
Wang Hangong, Zhong Jianqiang (Second Artillery Engineering College)
32. Wavelet Analysis of Acoustic Emission Signal of Metal Corrosion Contaminated by Noise
Li Wei, Li Baoyu (Daqing Petroleum Institute)
33. The Events Influence on AE Signal’s Amplitude in Metal Deformation and Fracture
Wang Hangong, Li Zhi (The Second Artillery Engineering College)
34. The Acoustic Emission Criterion of Corrosion Fatigue Crack Propagation for LY12CZ and7075-T6 Aluminum Alloys
Chang Hong, Han Enhou (Institute Of Metal Research, Chinese Academy of Science)
35. The Acoustic Emission Monitoring of Yield Process for A3 Steel Plate Samples During the Pull
Testing
Liu Guoguang, Cheng Qingchan (East China University of Science and Technology)
36. Numerical Simulation of the Quasi-brittle Material Acoustic Emission Character
Tai Shibin, Wang Shuhong (Northeastern University)
37. The Preliminary Study on Acoustic Emission Phenomena of PBX during Thermal ShockGao Dengpan, Tian Yong (The Institute of Chemical Materials, CAEP)

The Paper List of Proceedings for 11th Chinese Acoustic Emission Conference
July 28 to August 1, 2006, Hangzhou, China
1. Acoustic Emission Based Mechanics Solution to Micro-fracture Localization and Its
Clinical SignificanceGang Qi (The University Of Memphis)
2. Advanced Inspection/Monitoring Technique Used to Evaluate the Integrity of Large
Vertical Storage Tanks
Yanting Xu, Fujun Liu (Zhejiang Special Equipment Inspection Center)
3. Acoustic Emission Technique for Titanium Alloys Pressure Vessels
Liu Zhejun, Wu Song (Aerospace Research Institute of Materials & Processing
Technology)
4. Discussion of Interior Testing Technique about Pressure Equipment
Yao Li (China Air-dynamic Research and Development Center)
5. The Present Situation and Development of Detection of Partial Discharges in Power
Transformers using Acoustic Emission Technology
Hu Ping, Lin Jiedong (Guangdong Power Testing and Research Institute)
6. Application and Design Method of a Late-model PVDF Acoustic Emission Sensor
Wu Bin, Wang Qingfeng (Beijing University of Technology)
7. Structure Health Diagnosis Technique Based on OPCM Sensors and Gabor Wavelet
Gu Jian, Wang Xinwei (Nanjing University of Aeronautics and Astronautics)
8. Analysis of Acoustic Emission Signal Based on Independent Component Analysis
Li Wei, Dai Guang (Daqing Petroleum Institute)
9. The Technique of Noise Rejection for AE Signal Based on Correlation Analysis
Tang Taoshan, Yang Nengjun (The Second Artillery Engineering College)
10. Application of AE Technique onto Fatigue Test of a Full-scale Aircraft Body- AE
I-14

Monitoring during Landing Gear Controlling Test
Geng Rongsheng, Jing Peng (Beijing Aeronautical Technology Research Center)
11. Application of AE Technique onto Fatigue Test of a Full-scale Aircraft Body- AE
Monitoring during Aircraft Body Test under Flight Loading Conditions
Geng Rongsheng, Jing Peng (Beijing Aeronautical Technology Research Center)
12. Application of Acoustic Emission to Condition Monitoring of Half-axis of Horizontal
Tail of an Aircraft
Wang Jan-xin, Wu Keqin (PLA Univ. of Science and Technology)
13. The Application of Acoustic Emission Technique in Crack Monitor during Anti-torque
Bracket Fatigue Test of a Certain Helicopter
Xia Guo-wang, Peng Jiangshu (China Helicopter Research and Development Institute)
14. Two Different Applications of AE Technology on Detecting the Leakage on Storage
Tank Floor
Yanting Xu, Yadong Wang (Zhejiang Special Equipment Inspection Center)
15. Applications of Acoustic Emission Technology on Vertical Tank Bottoms Detection and
Evaluation
Xiaolian Guo, Fujun Liu (Zhejiang Special Equipment Inspection Center)
16. Application of PAC-TankpacTM Technique in Tianjin Petrochemical Corporation
Jiang Shiliang, Li Zhengwang (Tianjin Petrochemical Corporation Machinery ResearchInstitute)
17. Acoustic Emission Detection of Power Transformers in 500kV Zengcheng Substation
Lin Jiedong, Hu Ping (Guangdong Power Testing and Research Institute)
18. Acoustic Emission Testing and Assessment of Hydrogenation Refine Reactor
Tao Yuanhong, Guan Weihe (Hefei General Machinery Research Institute)
19. Acoustic Emission Inspection and Evaluation On-line of Diesel Oil Hydrogenation
ReactorJiang Jun, Liang Hua (Nangjing Boiler and Pressure Vessel Inspection Institute)
20. Application of Acoustic Emission Technique on a Supercharger System
Yadong Wang, Yanting Xu (Zhejiang Special Equipment Inspection Center)
21. Acoustic Emission Testing of 2300m3 Nitrogen Sphere of 15MnVNR SteelCha Keyong, Mao Gengxin (Shanghai Baosteel Industry Inspection Corp.)
22. The Detection of 8000 Cubic Meters Spherical Tanks by Acoustic Emission
Shao Feng (Nanjing Boiler and Pressure Vessel Inspection Institute)
23. The Acoustic Emission Testing Of High Pressure Spherical Tanks
Yao Li, Lai Deming (China Air-dynamic Research and Development Center)
24. The Acoustic Emission Detection Application to Well-control Device Safety Evaluation
Deng Yonggang, Liu Niannian (Technique Inspection Center, SPA)
25. Application of Acoustic Emission Inspection in an Single Ram-bop
Zhu XiangJun (Technology Test Center of Sichuan Petroleum Administration)
26. Research on Fatigue Life of Casting TI46AL8NB Alloy by Acoustic Emission
G.B. Rao, X.L. Wang (Institute of Metal, Chinese Academy of Science)
27. Acoustic Emission Study of Polarization Corrosion of LY12CZ Aluminum Alloy
Hong Chang, En Houhan (Institute of Metal, Chinese Academy of Science)
28. Study on Acoustic Emission Characteristic of LF3 Aluminum Corrosion Process
Yang Nengjun, Wang Hangong (Second Artillery Engineering College)
29. The Acoustic Emission Monitoring for Aluminum and Stainless Steel Pressure Vessel
I-15

Hydraulic Pressure Testing
Tang Zhiwen, Sun Wei (The second Artillery Corps of P.L.A)
30. AE Monitoring of Three-point-bending Fracture Process for Duralumin LY12CZ
Gao Chengqiang, Wang Hangong (The Second Artillery Engineering College)
31. Study on Matching Technique of Acoustic Emission Testing on Pipeline
Long Feifei, Dai Guang (Daqing Petroleum Institute)
32. Research on Corrosion Damage of LF3 Aluminum Alloy Based on Character Parameter’s
Pattern Recognition of Acoustic Emission Signal
Xianhai Long, Hangong Wang (The Second Artillery Engineer Institute)
33. Study on Monitoring Cracks Generated during Fatigue Test of Titanium Alloys by
Acoustic Emission
Yue Yalin, Li Shenghua (China Ship Scientific Research Center)
34. Research on Mechanical Performance of Composite Cylinders by Acoustic Emission
Technology
Sun Bingjun, Chen Jinghua (Institute of Nuclear Physical and Chemical Engineering)
35. Characteristic Analysis of Acoustic Emission Signals from the Flaw of Wood-Plastic
Composites
Yin Dongmeng, Liu Yunfei (Nanjing Forest University)
36. Study on the Characteristics of Acoustic Emission Signal of PE/Peself-reinforced Composites
Zhang Tonghua, Peng Yongchao (Donghua University)
37. Study on the AE Characteristics of the Cracking Process in Pre-stressed Concrete Beams
Gu Jianzu, Luo Ying (Jiangsu University)
38. Study on Acoustic Emission Characteristics of Tensile Fracture for Particleboard
Xu Hui, Zhou Youping (Nanjing Forestry University)
39. The study on the Influence of Thermal Shock on the Mechanics Performance of PBX by
Acoustic Emission
Gao Dengpan, Tian Yong (The Institute of Chemical Materials, CAEP)
40. The Inspection and Computing of the Valves Inner Leakage Rate in the Estate of BarrageZhangYing, DaiGuang (Daqing Petroleum Institute)
41. Acoustic Emission Detection and Location for Hypervelocity Impacts
Liu Wugang, Sun Fei (Beijing Institute of Structure and Environment Engineering)
42. Application of Volume Comparison Method included Domain Differentiation Method to
Acoustic Emission Monitoring
Huang Zhenha (Shanghai Baosteel Industry Inspection Corp)
Note: Names in the listing has last name first as is customary in China.

I-16

MONITORING THE CIVIL INFRASTRUCTURE WITH ACOUSTICEMISSION: BRIDGE CASE STUDIES
D. ROBERT HAY1, JOSE A. CAVACO2 and VASILE MUSTAFA1
1)
TISEC Inc., 2755 Pitfield Boulevard, Montreal, QC, H4S 1T2 Canada;
2)
Canadian National Railways, Walker Operations Bldg, Floor 2,
10229 127 Avenue, Edmonton, AB, T5E 0B9 Canada
Abstract
Acoustic emission (AE) is one of an evolving array of inspection methods being applied tobridge inspection. The authors have used it as one of an ensemble of methods for bridge
condition assessment and to prescribe maintenance follow-up actions with major financial
implications. Over twenty years of AE application in risk-informed approach to maintenancemanagement, in which AE has been applied to over 400 bridges out of an ensemble of over 1000
bridges in the authors’ bridge maintenance experience, has clearly established a role for AE. This
experience has also provided a combination of experimental and theoretical input for enhanced
interpretation consistent with established codes and standards. Positioning of AE in therisk-informed maintenance management context, its application in bridge inspection andinterpretation through the recommendations it provides and case-study examples are described.
Keywords: Ageing, Damage quantification, Infrastructure, Maintenance management, Structural
integrity
Inspection-Based Bridge Maintenance
The inspection component of contemporary bridge-maintenance policies and programs extends beyond visual inspection to include a wide range of monitoring methods. These range from
inspection that detects and assesses fatigue crack growth, through methods that provide characterization over span lengths, to methods that monitor the overall bridge for displacement and settling. The combination of these diverse inspection and monitoring methods permits not only thedetection of fault conditions but also assists in diagnosing the cause of the condition and in recommending follow-up maintenance actions.
Infrastructure maintenance-management programs for load-bearing structures ensure theirsafe, reliable and economic operation without undue risk to public health and safety. The infrastructure must be fit for service with a probability of satisfactory performance according to established performance functions under both specific service and extreme operating and environmental conditions for the duration of its design life. Accordingly, the total ownership cost of abridge or any other infrastructure component throughout its lifecycle is affected by both the typesof maintenance policies selected and the implementation schedule for these policies as well as
the risk associated with unplanned or unscheduled repairs arising from contingencies such as accidental damage.
The benefits of both risk-based and risk-informed approaches used extensively in the nuclear
and aerospace industries are starting to be appreciated in the civil infrastructure. Quantitative risk
assessment (QRA) is the structured and systematic examination of possible hazards associatedwith a structure, facility or process. In the risk-based approach, decision-making is solely basedon the numerical results of a risk assessment. It places a heavy reliance on risk assessment results
J. Acoustic Emission, 27 (2009)1© 2009 Acoustic Emission Groupthan may be currently impracticable for bridge maintenance management due to uncertainties inprobabilistic input. The risk-informed approach lies between the risk-based and purely deterministic approaches. Risk-informed input complements the traditional deterministic approach andcan be used to reduce unnecessary conservatism in purely deterministic approaches and to identify areas with insufficient conservatism in deterministic analysis. The details of the issue underconsideration will determine where the risk-informed decision falls within this spectrum.
The risk-informed approach uses quantitative or qualitative probabilistic risk assessment andrisk insights and complements them with bridge engineering knowledge, operating experience,engineering judgment and a broad range of resources to mitigate risk. The ensemble of inspection methods available for bridge monitoring includes, but is not limited to, those in Table 1.
Acoustic emission (AE) is a significant resource with a specific role to play in the spectrum ofrisk-informed bridge maintenance resources both in its own right and as a complement to othermonitoring technologies. Acoustic emission detects and locates propagating defects and quantifies their severity. Also, using supplementary information on load and strain, the AE is correlated
to the condition, under which the damage occurs. Therefore, as in risk based inspection, AE isused to identify components and areas of a large structure suspected of having active defects,
which then can allow cost-effective NDT and further analysis by fracture mechanics to determine
the severity of defect from the structural integrity point of view. At this point, the decision to repair, strengthen or replace the damaged component is taken or to re-monitor structure at a latertime with a certain schedule or to monitor continuously. This strategy allows early detection ofactive defects and aids the development of cost-effective priority-based maintenance for complementary NDT depending on the actual damage and its significance for the safety of the structure.
Table 1 Bridge monitoring technologies.
StrainResistanceFiber Optic
Bragg
Long gage
BrillouinVibration
Acoustic Emission
Satellite Imaging
Inclination
Alignment
Settlement
Temperature

Candidate Sites for Acoustic Emission Monitoring
Inputs into selecting candidate sites for AE monitoring from bridge engineering knowledge,
operating experience and engineering judgment include the formal categorization of fracture-critical locations, experience with a specific types of bridges, for which a bridge engineer
has responsibility, and bridge-specific experience including inspection and in-service bridge history. A combination of these is generally used to select the sites. Typically, bridge members
susceptible to fatigue crack initiation and eventual failure due to fracture are those that receive
stress ranges above the threshold stress range for a designated fatigue-detail category, as
identified by AREMA-established fatigue-detail categories (A to E’) [1].
Long bridge members that have internal or load-path redundancy and good fabrication details
are least susceptible to fatigue cracking. Shorter members such as floor-system components2

including stringers, floor beams (including the connections) and truss hangers that receive
considerably more stress cycles and are subjected to other, usually not designed for out-of-planestresses, should be investigated more closely. Truss eye-bars, due to a low fatigue-detail category
deserve special attention. Also, any bridge members that have been subjected to collision, section
loss due to corrosion, fire or any other damage could be subjected to accelerated loss of fatigue
life.
Beside drawings of design bridge details, records of past maintenance performed on thebridge are valuable in establishing any fatigue-prone details that may have been inherently built
into the structure or introduced afterward. These include tension members that may be subject tostress concentrations, such as sharply coped or welded members, unusual or suspect repairs or
those with loosely attached connectors (rivets or bolts). The type of steel used in the fabricationof the bridge is useful to know in establishing the criticality of existing fatigue cracks that may
have already initiated.
The history of loading (typical train configurations, car weights and magnitude of annual
tonnage) is valuable information for calculating stress ranges and the corresponding number ofcycles and determining any member that may have reached its useful and safe remaining fatigue
life and progressed to the crack initiation phase. For bridges with two-track loading, theincidence of the tracks being occupied at the same time is also useful for a more accurate fatigue
life assessment.
Representative AE Monitoring Locations
Maintenance history complemented by extensive bridge inspection has identified thefollowing bridge structure locations where AE monitoring has been applied most extensively:
Hanger connections
Link pin connection
Copes and stringers
Stiffener to weld connection.
These locations are shown in Fig. 1. Other areas where AE has been applied include
intermittent welds on cover plates, riveted connections in high-stress zones, cracks in restraint
connectors where the web is rigidly fixed to the web of another girder and collision damage.
Figure 2 shows a complex hanger eye-bar and link-pin location and an instance of impact
damage monitored using AE.
AE Monitoring Procedure
Resonant AE transducers are used in linear or planar arrays to detect the presence and the location of defects and to monitor their activity under normal service loads. In the case of railroad
bridges that are normally subject to high loads relative to their design loads, the loading is provided by normal rail traffic. For highway bridges, normal traffic can be used. In addition, proof
loads with a loaded truck have been used to apply static and a variety of dynamic loads. Also,
traffic management including stopping traffic and allowing it to accumulate, allowing various
lanes to proceed and similar controls over the traffic during testing provides load management. In
certain areas such as remote communities where special vehicles, such as logging trucks, are3

active, management of the passage of these vehicles provides for loading management as does
the case where other large trucks such as concrete mixers and similar vehicles are present. The
sensors are positioned to optimize the detection of the signals coming from the area of interest.
An example is shown in Fig. 3.

Fig. 1. Areas of extensive application of AE on bridges.

Fig. 2. Areas monitored to detect and assess load-induced and impact damage.
Areas of interest, such as long welds, can be monitored using linear location. While the AE
data, the severity and location, are valuable inputs to condition assessment, correlations to othersensed data significantly enhance the value of the AE data for maintenance decision-making.
Two complementary sensing streams that can provide useful correlations are strain and temperature. In Fig. 4, the results of testing an active crack in a floor-beam web at its connection to amain girder show the location of the source of emission, its correlation to strain and representative AE waveform.

4Fig. 3. A sensor array for monitoring an eye bar head at the pin connection.
Standard AE guidelines [2] for AE testing under controlled stimulation can be adapted to thistype of bridge inspection. In Fig. 5, AE activity is defined by the relationship between a stimulusthat is conventionally a controlled monotonically increasing load and an activity metric, such as
AE counts. Under normal bridge load traffic, the stimulus is the number of fatigue cycles introduced by the passing traffic. The correlations between strain and AE in Fig. 4 provide a basis forassessing activity using the fatigue cycles as the stimulus.
AE activity and intensity are measured to generate an AE source index shown in Fig. 6. Thisis further complemented with information from other non-destructive testing information to define the Fatigue Assessment Index that, in turn, provides the series of recommendations in Fig. 7
for follow-up action based on the Fatigue Assessment Index. Correlations between AE and laboratory studies of crack growth rates on bridge members that have been removed for repair andfrom data available in the literature provide a basis for calculation of crack growth rates.

5

Fig. 4 Data obtained from an active crack in a floor beam web at its connection to a main girder.

Fig. 5. ASTM 569 Controlled stimulation AE activity classification.

Fig. 6. Acoustic emission source ranking matrix.

6

Fig. 7. Maintenance follow-up recommendations.
Case Studies
AE monitoring with strain correlation was applied to a 376-m long open deck bridge carrying
two tangent tracks on separate spans located in central Canada. The 20 north track spans of riveted construction were built in 1910 and designed to American Railway Engineering Association
specifications of 1908. The 20 south track spans each ranging in length from 16.7 m to 22.6 m
were designed according to Canadian Standards Association (CSA)-1950 and CN-1972 specifications and were of welded girder construction fabricated by various contractors during the period from 1963 to 1974.

Fig. 8. Representative cracks in the case study.
Presently, the south track receives roughly 33 million gross tons of annual traffic, (476,000
freight cars and 16,000 locomotives), which is approximately a 115% increase since its construction. Most loaded cars have a gross weight of 120 t but the bridge has recently been subjected to
an increasing number of 130 t cars. Fatigue cracks were first detected on these spans approxi7

mately 10 years ago at the bottom of the welded end stiffener to web connections. Figure 8
shows a typical fatigue-prone location at the bottom connection of a vertical end stiffener weldedto the interior girder web. These cracks were not considered threatening as they initiated at thebearing areas and drilling the crack tips seemed to contain them in the same vicinity.
However, as time progressed, this same type of crack was now being detected at the bottom
of intermediate stiffeners where they connect to the transverse brace frames at the middle of thespans. This disturbing situation quickly initiated procedures to monitor the growth of thesecracks by CN inspectors at increasing frequencies. AE monitoring supplemented visual inspection with quantitative data on crack activity. The replacement of these spans, estimated at approximately $10 million CDN, appeared to be the inevitable recourse. To avoid a possible premature replacement, an intensive effort was undertaken to research available methods of safely
extending the useful life of the south track spans. Operating speeds were reduced and AE monitoring assessed the crack activity levels of critical areas. This AE information was essential forthe development of manageable risk strategies required to maintain existing and projected levelsof safe train operations to date over the bridge.
Another case study of a 97.5-m long open deck bridge located in Western Canada consistedof one 46-m deck truss, and three deck plate girders measuring 21.8 m, 15 m and 11.7 m inlength, two of which are suspended and connected to stringers projecting beyond the end floor
beams at each end of the deck truss. All spans were of riveted construction built in 1913 and designed to Dominion Government Class Heavy Specifications of 1908.
The bridge traffic serviced a new intermodal terminal that led to a dramatic increase in total
annual tonnage over this bridge. Last year, the bridge received approximately 20 million grosstons of annual traffic – an increase of over 80% from just 2 years ago. The majority of this traffic
is comprised of articulated double-stack container cars, which can have axle loads up to 35 tons.
The link-pin connection between the webs of the top stringer and the suspended deck plate
girder consisted of a rectangular plate on each side of the main member webs connected by top
and bottom pins (see attached photo). The photo shows a typical fatigue-prone location on thelink plate at the center of the pin hole.
This connection detail is subjected to considerable out-of-plane bending stresses caused bylateral sway from wind and train motion, which were not considered in the design. In addition
the connection is also susceptible to high impact stresses from rail joints and out-of-round andflat rolling-stock wheels. A crack initiating behind the pin nut would not be detected by visualinspection and could propagate quickly and without warning before detected by a visual inspection. Crack propagation in this area could lead to a catastrophic failure before detection by thenext scheduled inspection. Signs of distress include unusual sway, wear in the pin connection andrust stains on the surface area of the link-pin connection.
The potential for catastrophic failure at this location was assessed and in light of increased
and more frequent heavy axle traffic, measures to mitigate the possibility of any failure wastaken beyond the normal inspection procedures in place. Monitoring of these locations by TISEC
Inc. using AE and the results enabled the development of a suitable risk management strategy forthis location including the projected timely scheduling for the retrofit of the link-pin connection
to include a redundant load system.
8

Fig. 9 Fatigue-prone location on the link plate at thecenter of the pin hole.

Fig. 10 Hanger connection.

A third example is a 1050-m long bridge in Western Canada consisting of 107 spans of various types ranging from through trusses to timber pile trestles. The spans in question are five
48.5-m riveted through trusses built in 1904 by the Dominion Bridge Company and designed to
Dominion Government Specifications of the early 1900’s. The traffic includes cars from four
railroads accumulating an annual gross tonnage of approximately 54 million tons (610,000 carsand 20,000 locomotives). The spans were originally designed for a Cooper’s E40 loading andwere rehabilitated in the mid-1990’s to include a new/strengthened floor system and truss members to accommodate heavier axle cars weighing up to 130 tons having a rating of up to E65.
Related in-service observations include the fact that truss hangers have been known to be theweak link members of through truss spans (several failures of these members have been noted in
the railroad industry). These members are particularly susceptible to fatigue distress due to theadditional action caused by the floor beams subjecting the hangers to bending stresses along withthe designed axial stresses. Bending stresses attributed to floor beam connections were typicallynot accounted for in the design of hangers. With the introduction of heavier car loading thesemembers have been subject to stress ranges that exceed the threshold stress range for the rivetedhanger connection detail. Coupled with increased cyclic loading from more frequent car loading,the fatigue life of the hanger connection at this location was substantially reduced. Figure 10
shows the top hanger connection where cracks have been known to initiate leading to a failure
scenario.
9

As this bridge is a vital link for various railroad loadings destined to overseas markets frominternational ports, disruption of this traffic due to bridge component failure could not be tolerated. Hanger failure at this location could potentially result in a derailment situation serious
enough to put the bridge out of service for many weeks and cause millions of dollars in damage.
As the top hanger connections are difficult to inspect and identify initiating cracks, on-going inspection-based maintenance was implemented by TISEC at the hangers at this location. Resultsof the monitoring substantiated a need for the retrofit of the hangers and provided the Bridges
and Structures Department vital information required to schedule and complete the retrofit within
the time frame provided by establishing a manageable level of risk.
Summary
Multiple data streams of bridge monitoring data, AE and strain provide a basis to assess thecondition of a bridge in terms of the fatigue-related defects, to quantify the effect of the condition
on structural integrity and to generate recommendations for follow-up maintenance actions.
Acknowledgements
The authors wish to recognize the contributions of their respective companies in supporting
the development and application of AE monitoring over the last 20 years and bringing it to its
important role in risk-informed inspection-based maintenance. They also recognize the supportof Precarn Inc. for development of much of the intelligent systems technology for data interpretation.
References
1. AREMA Manual for Railway Engineering, Article 1.3.13i, (2004), American Railway Engineering and Maintenance-of-Way Association, Washington, DC.
2. ASTM E569-07 Standard Practice for Acoustic Emission Monitoring of Structures During
Controlled Simulation, ASTM International, West Conshohocken, PA, www.astm.org, 2002.

10

ACOUSTIC EMISSION TESTING OF A DIFFICULT-TO-REACH STEELBRIDGE DETAILDAVID E. KOSNIKInfrastructure Technology Institute / Department of Civil & Environmental Engineering
Northwestern University, Evanston, Illinois 60208, USA
Abstract
Discovery of a 130 mm (5 in) long full-depth crack in a fracture-critical member of a large
steel through truss bridge led to the deployment of acoustic emission (AE) testing in concert withother, more traditional, non-destructive evaluation methods. While AE continues to gain acceptance as a method for evaluating civil structures, the application of AE testing to steel bridge details that are fully exposed to the elements and difficult to reach presents some special challenges; as such, AE work typically has been contingent on favorable field conditions. In thisstudy, a custom weatherproof enclosure and robust communication and control methods were
deployed to obtain useful AE data in this environment. First-hit channel analysis, planar location,and spatial/temporal clustering analysis were used to determine if the crack was actively growing. The AE results were validated by corroborating results from ultrasonic testing and radiography.
Introduction
The John F. Kennedy Memorial Bridge, a large cantilever through truss bridge opened in
1963, carries Interstate 65 across the Ohio River between Louisville, Kentucky andJeffersonville, Indiana. According to a count by the Kentucky Transportation Cabinet (2003), thebridge carries over 120,000 vehicles per day. Inspections revealed a 130 mm (5 in) long fulldepth transverse crack in the horizontal web in a tension region of the top chord on the east truss,
the site indicated in Fig. 1a. A partial-depth saw cut and an irregularly shaped hole of unclear
origin are present along the web-flange weld, and a 25 mm (1 in) diameter stop hole is present at
the end of the crack, as shown in Fig. 1b. The crack is in a fracture-critical member, meaning thatfracture of the member would likely cause partial or complete failure of the bridge. Acoustic
emission (AE) monitoring was employed in conjunction with other non-destructive evaluation
techniques, including ultrasonic testing and radiography, to help detect and characterize any indications that the crack might jump the stop hole or propagate into the vertical flange of themember.
Previous AE Work on Steel Bridge Details
Acoustic emission testing is well established as a technique for locating and characterizing
cracks and other defects in a variety of engineering materials. Though the application of AE testing of details on in-service steel bridges dates as least as far back as the 1970s (Holford andLark, 2005), it continues to present some special challenges. From a data acquisition perspective,
the most vexing problems stem from the very noisy environment on highway bridges, where“noise” takes the form of both spurious electrical signals and real physical phenomena that may
obscure events of interest. The former may stem from radio-frequency interference due to capacitive coupling between the AE transducers and the bridge itself, which serves as a large antenna, or from signals picked up in the cables between the AE transducers and data acquisition
system. The most common examples of real phenomena causing spurious AE events on a steel
bridge are fretting at bolted or riveted connections and other noises associated with live traffic.
J. Acoustic Emission, 27 (2009)11© 2009 Acoustic Emission Group

(a)

(b)
Fig. 1. I-65 Kennedy Bridge. (a) Overall view of Kennedy Bridge showing crack location. (b)130 mm (5 in) long transverse crack, stop hole, partial-depth saw cut, and an irregularly-shapedhole in horizontal web of top chord
As it is rarely practical to completely shut down a major highway bridge for testing, most AE
applications will take place with uncontrolled traffic loads on the bridge.
Most steel bridge members do have a characteristic that simplifies AE testing, particularly
source location: AE signal wavelengths in steel at frequencies of interest are on the order of
25 mm. Consequently, most steel bridge members may be analyzed as thin plates where Lambwave conditions are predominant (Prine, 1985). This condition greatly simplifies testing, particularly in light of the complicated geometry of many steel bridge details.
In the 1980s, Hopwood and Prine (1987) deployed AE equipment originally developed for
welding process monitoring on a number of steel highway bridges. They employed a flaw detection model described by Prine (1985), which discriminates between AE events from a growing
flaw and background noise (e.g., fretting of bolted or riveted connections) based on the assumption that a growing flaw will produce AE events at a high rate and from a very specific location.
That is, a group of events must occur within a certain time interval and be located within a given
12

radius of each other in order to pass the filter. A later study on a movable bridge (Prine, 1994)
showed good corroboration between AE and ultrasonic testing for detection of cracks. Prine’s
method forms the basis for analysis of data in this paper.
More recently, AE testing of steel elements in bridges and other large civil structures has
been shown to be quite useful for failure analysis (Prine, 2001), noise localization (Prine, 2004),
fatigue crack monitoring (McKeefry and Shield, 1999), and retrofit evaluation (Kosnik and Marron, 2007).
A Note on “Crack Activity”
In AE literature, AE hits generated by internal material mechanisms (e.g., microstructuralchanges inherent in crack development) are often designated as primary AE, while hits fromother sources, particularly fretting along existing crack faces, are called secondary AE (Holford
and Lark, 2005). Much work has been done to distinguish these two types of events so thatanalysis may focus exclusively on the primary AE associated with crack growth, and this is indeed helpful in a variety of situations. However, the experience of the author’s group has beenthat AE hits from both primary and secondary sources can be instructive, particularly in characterization of a known defect.
For example, if a crack is actively growing, primary AE will emanate from the crack tip, but
it is likely that the local state of stress along the crack will result in fretting along the crack faces
or crushing of debris inside the crack, and secondary AE will occur at the same time. By contrast, if a crack has extinguished itself, there will be no primary AE from the crack tip, and theamount of secondary AE likely will be reduced as well, since the local stresses have been relieved. Thus, provided that appropriate transducer geometry and guard channels are employed torestrict data to the detail in question, it is sufficient to consider both primary and secondary AE
in aggregate as crack activity for the purpose of characterizing cracks and other defects in steel
highway bridge details.
AE Testing in a Challenging Environment
As illustrated in the preceding section, AE testing of steel bridge elements can be quite usefulin a variety of engineering situation. This utility comes with an important caveat, however: AE
monitoring of bridges generally has been limited to short-term tests contingent on either fairweather or availability of some shelter on site, e.g., a bridge tender’s shack or the inside of a box
girder; furthermore, AE testing generally has been practical only on elements where access isrelatively easy. Experience has shown that these favorable conditions are rarely encountered in
the field. In the case of the Kennedy Bridge, the upper chord is completely exposed to the elements, requires a lift bucket — available only during limited lane closures — for access, andprovides no electrical connection.
To meet this challenge, the customized weatherproof enclosure shown in Fig. 2a was developed to protect the AE hardware and connect it to a rugged laptop computer and a battery-backed
uninterruptible power supply (UPS). This enclosure may be clamped to the bridge near an area ofinterest, as shown in Fig. 2b, making long, noise-prone instrument cable runs unnecessary. To
reduce electrical noise and spurious AE hits from the enclosure itself, the enclosure was mounted
on rubber feet and placed well outside the AE arrays so any events would be rejected by the AE
processing filters. An umbilical consisting of extension cords and Category 5e Ethernet cable,
available at home improvement stores, connected the enclosure to a gasoline-powered generator
13

Fig. 2. Weatherproof enclosure for AE testing of difficult-to-reach details on steel bridges.
(a) Interior of enclosure showing AE unit, rugged
laptop, and battery-backed UPS.

(b) AE enclosure deployed on the top
chord of the Kennedy Bridge.

and the operator’s laptop computer on the bridge deck. The operator used remote access software
to control the AE acquisition software running on the rugged laptop in the enclosure.
Test Procedures and Results
Two test configurations were employed. The first configuration, a planar array on the verticalflange of the cracked member, was used to detect indications that the crack might have propagated beyond the partial-depth saw cut into the vertical flange. The second configuration, a planar array on the horizontal web around the stop hole, was used to detect indications that the crack
might have jumped the stop hole. Both test configurations used a Vallen Systeme AMSY-5 AE
system with Vallen VS150-RIC 150 kHz-resonant piezoelectric transducers with integrated preamplifiers. A 40-dB recording threshold was used. Pencil-lead breaks were performed before
each test. Auto-calibrations were performed before and after each test to show that the array had
not been disturbed during the test.
For both the horizontal web and vertical flange tests, a transducer was installed directly on
the area where crack activity was suspected, and four additional transducers operating in combination guard/normal mode were installed in a rectangular array with the “crack” transducer at thecenter. These combination-mode transducers were used for both planar location and filtering via
first-hit channel (FHC) analysis; FHC filtering was particularly important to intercept noise froma bolted connection near the crack. Finally, a guard transducer was deployed on the member
element not being tested at that time (i.e., on the vertical flange while the five-sensor array wason the horizontal web, and vice versa) to intercept noise from that element. The transducer arraysfor the vertical flange and horizontal web tests are shown in Figs. 3a and 3b, respectively. Three
distinct techniques were employed for analysis of the acquired AE data: first-hit channel analysis, planar location, and spatial/temporal clustering.

14

Fig. 3. AE transducer arrays. (a) Array on vertical flange. (b) Array on horizontal web.
Vertical Flange Test
The two test runs on the vertical flange yielded low hit rates of 0 and 10 hits/min, respectively. Consequently, there was no indication that the web crack was propagating beyond the partial-depth saw cut into the vertical flange. Due to the low hit count and complicated geometry ofthe detail, location and clustering analyses were not practical.
Horizontal Web Test
For the horizontal web test, the “crack” transducer was placed near the stop hole opposite thecrack. FHC analysis showed considerable AE activity around the stop hole; the two test runs
yielded hit rates of 206 and 377 hits/min, respectively. However, the bulk of these events had an
amplitude less than 45 dB, and are believed to be caused by fretting of the existing crack sides
rather than crack propagation. Planar location analysis yielded locations for many AE hits for thehorizontal web test. Due to the complicated geometry of the detail, not all hits yielded a location;
those hits that could be located are shown superimposed on a photograph of the array in Fig. 4.

Fig. 4. Planar location results on horizontal web superimposed on a photograph of the transducer
array. Individual located events are shown as red squares.
15

Spatial/temporal cluster analysis provided particular insight into the behavior of the horizontal web. This filter, originally developed for welding process monitoring (Prine, 1985), requires aminimum of three AE events within a 25 mm radius and one-second time interval. A tight groupof clusters was observed at the point highlighted with a green circle in Fig. 5, indicating a probable defect at that point. Subsequent radiography confirmed the presence of this defect, which isbelieved to be a slag inclusion (Marron and Kosnik, 2008).

Fig. 5. Spatial/temporal clusters on horizontal web. Individual located events are shown as redboxes, while the one cluster that passed the spatial/temporal filter is circled in green.
Conclusions
The AE data revealed no indication that the crack had propagated into the vertical flange in
the area of interest. However, considerable AE activity was measured in the horizontal web.
These events were generally of low amplitude, which suggests that they originate from frettingof the existing crack faces. There were no AE indications that the crack had jumped the stop
hole. Spatial/temporal AE cluster analysis did show indications of a defect in the horizontal web.
The presence of this defect, which is believed to be a slag inclusion, was later confirmed by radiography.
These measurements were made possible by special techniques for AE testing and monitoring of large civil structures. A weatherproof enclosure, which could be installed at the area ofinterest from a lift bucket during a brief lane closure and then powered and controlled via a simple umbilical, was developed to eliminate long lead cables and exposure of AE equipment to theelements. This approach also facilitates longer-duration tests, allowing AE events to be recorded
under a wider variety of traffic and other environmental conditions. The flexibility and robustness of this method promise to make AE testing of large civil structures, especially fracturecritical bridges, easier and more widely available.
16References
Holford, K. and Lark, R. (2005). Acoustic emission testing of bridges. In Fu, G., editor, Inspection and Monitoring Techniques for Bridges and Civil Structures. CRC Press.
Hopwood, T. and Prine, D. (1987). Acoustic emission monitoring of in-service bridges. Technical Report UKTRP-87-22, Kentucky Transportation Research Program, University of Kentucky,
Lexington, Kentucky.
Kentucky Transportation Cabinet (2003). Traffic station counts: Jefferson County, Kentucky.
Division of Planning traffic station map.
Kosnik, D. and Marron, D. (2007). Acoustic emission evaluation of retrofits on the I-80 Bryte
Bend Bridge, Sacramento, California. In Ono, K., editor, Advances in Acoustic Emission: Proceedings of the Sixth International Conference on Acoustic Emission. Acoustic Emission Working Group, Colorado, USA, and Acoustic Emission Group, Encino, California, USA.
Marron, D. and Kosnik, D. (2008). Acoustic emission monitoring of a top chord on the John F.
Kennedy Memorial Bridge over the Ohio River, Louisville, Kentucky. Technical report, Infrastructure Technology Institute, Northwestern University, Evanston, Illinois.
McKeefry, J. and Shield, C. (1999). Acoustic emission monitoring of fatigue cracks in steel
bridge girders. Technical Report MN/RC–1999–36, University of Minnesota, Department ofCivil Engineering, Minneapolis, Minnesota.
Prine, D. (1985). Structural fatigue damage detection with the GARD Acoustic Emission Weld
Monitor (AEWM). Technical report, Chamberlain Manufacturing Corp., GARD Division, Niles,
Illinois.
Prine, D. (1994). Acoustic emission monitoring of the trunnion shafts on Oregon DOT
Bridge #1377A, the I-5 Columbia River Bridge east lift span, Portland, Oregon. Technical report,
Infrastructure Technology Institute, Northwestern University, Evanston, Illinois.
Prine, D. (2001). Acoustic emission monitoring of the Hoan Bridge. Presented at AEWG-44,
Montreal, Quebec.
Prine, D. (2004). Localization of noise sources in large structures using AE. In Proceedings,
EWGAE 2004. 26th Meeting of European Working Group on Acoustic Emission, Vol. 1, pp. 247254, DGfZP, Berlin.

17

ACOUSTIC EMISSION AS A MONITORING METHOD INPRESTRESSED CONCRETE BRIDGES HEALTH CONDITIONEVALUATION
MAŁGORZATA KALICKA
Institute of Structural Engineering, ETH Zurich, 8093 Zurich, Switzerland
Abstract
A system, based on acoustic emission (AE) monitoring technique, has been studied at Kielce
University of Technology for several years. A reference-signals database has been prepared basedon laboratory tests on samples, beams and full-scale girders. The developed reference data has
been verified during field monitoring of prestressed and post-tensioned concrete bridges. Somephenomena, which are usually very difficult or even impossible to discover by the traditional
methods, were detected by this system.
Keywords: Destructive processes (DP) analysis, Prestressed concrete bridges, Structural healthmonitoring.
Introduction
Prestressed concrete bridges, which are nowadays in service, were designed to work safely
for an average of 50 years. A large number of structures are reaching this age. Throughout 50
years, the traffic has enlarged significantly, both in quantity of vehicles and transferred goods.
The actual loads mostly exceeded the design loads applied at the time of construction. To assure
both serviceability and safety of structures, inspections are undertaken periodically. However, theassessment relies mainly on visual observations. Material degradation evaluation and nondestructive techniques (NDT) cover simply a limited volume of a structure. Moreover, thecondition of a structure is estimated only at the time of an inspection. The history of damage
initiation and development is unknown. In most cases, loading conditions as well as
environmental factors are also unidentified. As a result, the assessment relies mainly on theinspector’s own experience and subjective decision-making. Road management institutions
responsible for infrastructural networks had come to the conclusion that the traditional methods
for bridge inspections are insufficient and new assessment methods and damage/deterioration
evaluation criteria should be established. Following this challenge in the last decade, Europeanand American engineering grants [1-9] have supported programs related to structural healthmonitoring, safety of infrastructure, increase of service life, reduction of maintenance costs andsmart structural monitoring. These programs resulted in a great number of comments andconclusions. Here are some main remarks regarding structural monitoring. Most important aim is
to improve already existing structural monitoring methods and to develop new innovative
structural monitoring systems to prevent unexpected structural failures/collapses. To have abetter understanding of existing condition, the measurement of the loading variations and its
influence on the structural reliability should be controlled globally as well as locally. Moreover,measurement of loading should be carried out together with registering the environmental
conditions. To control the initiation and the development of damages and deteriorations, longterm (at least 1-2 weeks) and continuous monitoring should be conducted. For the collected
monitoring results, a central database management should be created. Prognosis of the structuralreliability concerning the decrease of strength of material is the final aim for a complete model ofstructural health monitoring system.
J. Acoustic Emission, 27 (2009)18© 2009 Acoustic Emission GroupUnfortunately, there are neither a simple way nor suggested guidelines recommending
monitoring/diagnosis procedures, which should be applied for fulfilling these expectations.
Therefore, a bridge monitoring system has been studied at Kielce University of Technology(KUT), which seems to meet some of the required expectations for assessment. In contrary to
traditional inspections, which are based on evaluation of detected defects/deteriorations, thesystem developed at KUT relies on localization and classification of active destructive processes(DP), which lead to failure. Assessment of DP also allows taking into account influences on
structural degradation due to load changes in damaged zones, which are the intensity of traffic,
and other external factors; i.e., environmental conditions, like temperature, humidity,
freezing/defrosting, floods or foundation subsidence. The traditional estimation of remaining
loading capacity considers only design and/or proof static load, which does not corresponddirectly to reality, where dynamic loads are more crucial.
As a main monitoring technique at KUT, the acoustic emission (AE) analysis is used. AE
analysis appeared to be suitable due to its ability to detect active DP, the possibility ofmonitoring under regular traffic, easy on-site installation and most importantly because AE
allows long-term global monitoring of a considered structure.
Acoustic Emission Signal Analysis
During laboratory tests on samples and full-scale girders, 8 different AE signal classes havebeen identified representing 8 different DP, in which an individual damage mechanism isdominant. Each one of the classes represents a different level of damage severity. AE signals
produced by DP are clustered into classes distinguished by an advanced pattern recognition
analysis. For this purpose, the software NOESIS Professional ver. 4 was chosen. In point
diagrams of AE signals the different colors and shapes represent each class corresponding to thelevel of damage severity as it is shown in Table 1. Among the clusters SC-1 and SC-2
(represented in graphs by pink circle and red square), contain signals of low energy, signal
strength, AE counts and duration. These appear to come from microcracking and from friction in
concrete aggregates, respectively. SC-3 (blue diamond) belongs to AE signals of moderated
duration and signal strength. They appeared when tiny cracks initiated in the girder (frequently
before they become visible). These signals lead to the appearance of readily visible cracks. Highenergy AE signals in clusters SC-4 (black triangle), SC-5 (violet triangle) and SC-6 (green spots)
are much stronger than the low energy clusters (SC-1, SC-2 and SC-3). Long duration and highsignal strength characterized them. Signals of clusters SC-4, SC-5, SC-6 were observed with thedevelopment of severe deterioration processes [10]. Classes SC-7 and SC-8 appeared mostly
when plastic deformation and fracture of wires took place. There is a high possibility that thesignals in clusters SC-4 and SC-8 might come from the process of interaction between different
DP from the same or neighboring zone. The classified AE signals are considered as the reference
database; i.e., identified dominant DP, for undertaken monitoring of bridges. Details concerning
the processing and AE signal classification have been presented in a journal [10] and conference
papers [11, 12].
The development of the AE reference signals database required a number of laboratory tests
on concrete samples, sample concrete beams and full-scale girders to be performed. One of theexperiments was undertaken on a full-scale 26.5-m-long prestressed concrete girder. The girder
was loaded in four-point bending in five cycles up to failure. AE monitoring was carried out
together with measurements of force, displacement and crack width, respectively (see Fig. 1).
19

Table 1: Severity codes and colors corresponding to AE signal clusters.
Signal class no

No. 4

No. 0

No. 1

No. 3

No. 5

No. 2

No. 6

No. 7

Severity code

SC-1

SC-2

SC-3

SC-4

SC-5

SC-6

SC-7

SC-8

Severity color

Pink

Red

Blue

Black

Purpule

Green

Grey

Lt. Grey

Class symbol

For AE monitoring, a zonal location was applied. As the girder was divided into 12 zones i.e.
one AE sensor per zone, the 100% extension covers 12 zones correspondingly. The developmentof the destructive processes was studied in accordance with the increase of load anddisplacement. During the first two cycles; i.e., 0-232 kN and 0-498 kN, visible damages did notappear. First visible cracks initiated during Cycle III, i.e., 0-662 kN. AE analysis was carried out
only during Cycle IV, i.e., 0-996 kN, to discover the development of DPs in an already damaged
girder. AE results from Cycle IV have been analyzed in four loading ranges with respect toCycle I – III. The presented results are classified according to the previously developed reference
database (see Table 1). The extension of each cluster, which corresponds to damages/deteriorations severity, has been estimated and compared with respect to the load level (see Table 2). Thisaction allowed the evaluation of the load level, i.e., up to 232 kN, under which the alreadydamaged girder during loading Cycle III could still safely operate. The load 232 kN was chosen
as the safe load level due to appearance only of clusters SC-1 to SC-3, which are the low severity
classes.

Fig. 1. Load-displacement diagram for loading cycles III-V.

20

Table 2: Total damage extension of each signal class corresponding to the Loading Cycle IV.
EXTENSION
A (0%)B (< 5%)0-232 kN (cycle IV)

LOADING CYCLES
0-498 kN (cycle IV)
0-662 kN (cycle IV)

0-996 kN (cycle IV)

C (5% - 20%)
D (20% - 50%)
E (> 50%)

AE signals corresponding to DP appear periodically even under continuous load increase as it
is shown in Fig. 2, where all diagrams are showing AE activity in the same zone under different
loading ranges. It has also been noticed that the signals may be unevenly distributed between theselected zones alongside the element under observation, i.e., “quiet” and “hot spot” areas exist.

Fig. 2. AE activities of one selected girder zone during four following loading steps.
Acoustic Emission in Field Monitoring
The KUT system has been implemented by the Regional Road Management in Poland. Morethan 50 prestressed concrete bridges have been monitored during past several years. Some of themonitored structures required minor or major refurbishment/rebuild works. Some otherstructures required restriction of loading. In some cases, the monitoring had to be repeatedannually to control the development of DP and to recognize the critical events of the structural
condition. Most of the monitoring has been carried out under regular traffic; some also during21

controlled transports of overloaded vehicles. Analyses of AE signals during proof loading andfield monitoring have revealed many unknown phenomena concerning the initiation anddevelopment of DP, the character of which is not continuous. Representative examples aredescribed below.
One of the achievements of the AE monitoring was detecting the activation of DP caused by
the external sources. The monitored structure was a prestressed concrete viaduct over a railway
line. The AE sensors were placed alongside the pile-capping beam, which spread over five piles.
Monitoring of an individual pile-capping beam was carried out for a significant period of time,
which was ~4 hrs. During the monitoring, signals belonging to the high levels of severity classes
appeared (see Fig. 3), but only at the time when trains passed underneath the structure.
The highest AE activity was registered only by one of the sensors placed over one of the pillars,
where at the time of monitoring no visible signs of damage were discovered. Further
investigation revealed that the activation sources of the high AE signals appearance were thedamaged rail tracks. The passing train caused vibration of the damaged rail tracks, whichtransferred to the pile-capping beam and caused an activation of already existing interior
damages. The registered signals were classified as a high intensity class according to the KUT
reference database, which indicates a crucial structural condition. Multiple recurrence of thislevel of DP would cause a heavy damage or even failure of this element. Early discovery of thisdefect allowed avoiding the failure of the pile-capping beam, which would result in highrebuild/replacing costs. It should be noted that the vehicles crossing the viaduct at the time ofmonitoring did not activate any high-energy AE signals.

Fig. 3. AE activities of the pile-capping beam in viaduct during train passage.

22

Some structures, even when visibly heavily damaged, are permitted for service under loading
restrictions. Such limitations are usually based on engineers’ experience and their subjective
judgment. When it comes to decision-making, it is very important to reduce uncertainties tominimum and if possible to avoid them. To investigate the condition of a 40-year-old damaged
post-tensioned concrete viaduct, which was initially designed for 30 tons, KUT carried out AE
monitoring. Due to the high level of developed damages, the loads were limited to 10 tons. As aconsequence of the continuous heavy traffic, the applied load limit appeared to be exceeded.
Therefore, the AE monitoring was performed immediately after reducing the load limit and 18
months later (Fig. 4) to verify the development of DP. The blue points in Fig. 4 (right), i.e., SC-3
clusters, demonstrate the progress of DP. The final decision contained strengthening of the
viaduct and a regular monthly AE monitoring.

Fig. 4. AE activity of the same zone in a post-tensioned concrete bridge. (left): right after
execution of the load restriction, (right): 18 months later.

Fig. 5. AE activity of a limited zone of the prestressed concrete bridge. (left): under regulartraffic, (right): during crossing of an oversized vehicle.
DP might also be initiated or activated when the service restrictions are not followed, i.e.,oversize heavy trucks cross a bridge. The moment of DP activation could have been discovered
only by the AE technique. The AE signals, shown in Fig. 5, present the activation instance of DP
during the crossing of an overloaded vehicle. The same defects did not appear to be active underregular traffic. Early discovery of strong DP activity could prevent possible failures followed bymajor repairs.
23

The research has shown that DP depends not only on the level of loading but also on the
speed, number, frequency and type of vehicles crossing a structure. Together with loading, theenvironment has an influence on the structural reliability. Both of these factors, i.e.. loading andenvironmental factors are time variable processes. This is why bridges are required to bemonitored continuously during a significant period of time (at least 1-2 weeks) to distinguish thereal character and severity of DPs.
Discussion
The presented DPs are not always leading directly to a collapse. DPs decrease the loadcarrying capacity, change the distribution of stresses and initiate different destructive
mechanisms, which could lead to a critical event. These phenomena were discovered during theproof loading up to failure of prestressed concrete girders. Concrete cracking and plastic
deformations of tendons had led to the increase of stresses in the compression zone, whichcaused concrete crushing and girder collapse. The process had lasted round 30 sec and did notreveal in high-energy AE signals, but only in minor severity signals belonging to the cluster SC3. Only the final collapse produced high-energy AE signals belonging to the high severity
clusters [11].
In general, two different main groups of DP can be recognized: short-duration high-energy
AE signals, i.e., cracking or wire breaking and long-duration low-energy AE signals, i.e.,
corrosion or micro-cracking, which belong to the clusters SC-1 and SC-2. It should also be notedthat AE signals originating from single DP overlap and could be recognized as a new process, or
low-energy AE signals could be overridden by others with high-energy AE signals. Therefore,
some of the processes, especially low energetic processes, cannot be discovered by AE
monitoring alone.
The presented cases demonstrate the advantages of DP evaluation by AE technique. In theperformed field tests, the damages were not visible; this made them very difficult or even
impossible to be detected by the traditional visual inspections. It seems that the AE-based healthmonitoring system meets the high expectations of bridge users and management. However, there
are some important technical issues, which need to be considered. First of all, the procedurerequires long-term continuous monitoring. For this purpose, suitable AE monitoring equipment isrequired, which is capable of registering the data without human presence. Therefore, the systemshould be resistant to variation of weather conditions and should also be safely installed on a
structure with respect to external impacts and vandalism. Main developers of AE equipment areapproaching this topic in the field of concrete bridge monitoring. The other issue concerns datatransfer and analysis. Data processing and evaluation together with decision-making concerning
further operation should be performed instantaneously. Therefore, for fast decision-making theanalysis of the whole structure should be reduced to a small quantity of registered data. Only
with a limited data, it is possible to perform an on-line and continuous signal analysis.
Conclusion
The KUT AE-based system has been used to monitor bridges for several years. Based on themonitoring results, it is concluded that the KUT AE-based system meets the requirements of an
innovative monitoring system, which have been established by European research during the past
few years. This system is not based on the assessment of detected defects like in the traditional
inspections, but on the development of destructive process analysis and classification; i.e.,
24identification processes leading to failure/collapse. It allows estimating the level of serviceability
and structural safety and is also capable of detecting unknown internal destructive processes,
which could not be identified by other inspection methods. The proposed classification ofdestructive processes is also compatible with the standard European bridge inspection reports;
i.e., damage/deterioration severity and extension. This classification allows the quantitative
assessment of damage/deterioration development under regular traffic. Moreover, the monitoring
can be performed for a significant period of time without any traffic interruption.
This procedure can be used as an automatic warning system against structural or structuralelement failure/collapse. The on-site decision-making requires minimal human intervention, so it
may be considered as an objective procedure.
All of the results should be further studied and extended. Unfortunately, like all in situ tests
this AE-based system requires a significant quantity of time and large quantity of funding. For
this reason, international cooperation is highly recommended. At the moment, studies on
structural health monitoring for prestressed concrete bridges are being prepared at the ETH
Zurich in Switzerland. The system covers the analysis of severity of damage/deterioration
processes together with the observation of the external parameters like load, environment factors
as well as unexpected impacts; i.e., explosions close to a structure, or ground movement. Future
research is focused on continuous bridge monitoring that should cover all seasons of year.
Acknowledgements
The author would like to thank Professor Leszek Gołaski from Kielce University ofTechnology in Poland for guiding me as a member of his team, for his great support and fruitful
discussion. She would also like to thank Professor Thomas Vogel from ETH Zurich in
Switzerland for supporting the further development of the topic of structural health monitoring,for profitable discussion and comments.
Presented field tests were carried out within the program of road network inspections by the
Regional Road Management in Kielce, Poland.
References
1) COST Action 509, “Corrosion and protection of metals in contact with concrete”, 1991-1996.
2) BRIME Bridge Management in Europe, European Commission DG VII 4th Framework
Program, Transport Research Laboratory, Crowthorne, Berkshire, UK, 1998-1999, Final report
D14, 2002.
3) COST Action 521, “Corrosion of steel in reinforced concrete structures”, Final report,
Directorate-General for Research 2003.
4) FP5 LIFECON, “Life cycle management of concrete infrastructure”, Deliverable, TechnicalResearch Center of Finland 2001-2003, Final Report 2004.
5) FP5 REHABCON, “Strategy for maintenance and rehabilitation in concrete structures”, 20012004, Project Report 2005.
6) COST Action 345, “Procedures required for assessing highway structures”, 1999-2003,
http://cost345.zag.si/final_reports.htm, 2007.
7) FP6 SUSTAINABLE BRIDGES, “Assessment for future traffic demands and longer lives”,
2003-2007, Editors: J. Bien, L. Elfgren, J. Olofsson, DWE Wrocław, Poland, 2007.
25

8) BRITE-EURAM III SMART STRUCTURES, “Integrated monitoring system for durability
assessment of concrete structures”, 1998-2002, Project report, September 2002.
9) Project “Health Monitoring of Bridge Structures and Components using Smart Structure
Technology”, 2003-2004, Wisconsin Highway Research Program, Center for Transportation for
Research and Education, Iowa State University, Final report, vol. 1.
10) L. Gołaski, G. Świt, M. Kalicka and Kanji Ono, Journal of Acoustic Emission, 24, 2006,
187-195.
11) M. Kalicka, “Defect Development and Failure Evaluation in Prestressed Concrete Girder by
Acoustic Emission”, AEWG-50, ICAE-6, Lake Tahoe, 2007.
12) M. Kalicka, “Health Assessment of Prestressed Girder by Deterioration Processes
Evaluation”, International Association for Bridge and Structural Engineering (IABSE)
conference, Helsinki 2008.

26

ACOUSTIC EMISSION LEAK DETECTION OF LIQUID FILLEDBURIED PIPELINEATHANASIOS ANASTASOPOULOS, DIMITRIOS KOUROUSISand KONSTANTINOS BOLLAS
Envirocoustics ABEE, El. Venizelou 7 & Delfon, 14452 Metamorphosis, Athens, Greece
Abstract
Several limitations and difficulties exist in the inspection and maintenance of underground
pipelines that cannot use pigs (pipeline inspection gauges). Leaking is unavoidable in such buried pipelines and poses serious problem to the environment as well as the pipeline owners. Pipeline leakages are usually apparent either when the pressure is dropping for no other obvious reason or when valuable product is lost. However, even in the best-case scenario, where the operators can isolate specific pipeline sections suspected to leak, it is often the case that the operators
cannot reliably locate the exact position of the leak so as to take corrective measures. Acoustic
emission (AE) is an excellent tool for detecting and locating leaks in buried pipelines. Access tothe pipeline is required only locally for mounting AE sensors. Pipeline is pressurized and AE
tested in 600-to-1000-m-long sections at a time. The AE sensors detect the turbulent flow at theleak orifice, and with the use of digital AE systems and specialized software, the position of theleak is provided. The present paper deals with the technical description and the physics of the AE
leak detection technique, presents the advantages, limitations and requirements of the method,
describes the necessary functions of AE equipment for performing such a task, and, finally, reports on several case-studies of successful leak detection and location of buried pipelines. The
case studies cover both new and in-service buried pipelines of different sizes.
Keywords: Pipeline inspection, Leak test, Loss control, Pipeline integrity
Introduction
The undesirable fluid losses due to leaks constitute one of the bigger problems in industrial
installations, refineries, power stations and, in general, anywhere there are moving or stored liquids or gases, with occasionally enormous, environmental and economic repercussions. Nondestructive leak testing deals with the leaking of liquids or gases in pressurized or evacuated components or systems as a result of pressure differential.
Acoustic emission (AE) is widely used for locating such leaks [1-4]. The turbulence causedby the flow of a pressurized fluid through an orifice produces energy waves of both sonic andultrasonic frequencies. Figure 1 presents some physical features related to and affecting the leakage flow. Pollock and Hsu [2] provided a basic understanding of the leak mechanism and AE
testing. Miller and others [3] conducted laboratory tests and experiments to evaluate existing leakdetection and location methods. Standards such as ASTM or ASME describe the method for detecting and locating the steady-state source of gas and liquid leaking out of a pressurized system[4, 5].
It is a common understanding in all the above works that AE can be produced by the highly
unstable turbulent pressure field at the orifice, and the condition of detection is that the Reynolds
number Re > 1000 at the orifice, so as to ensure turbulent flow. The corresponding AE signals
generated are of a “continuous” nature. Additional sources that may produce AE in the occasion
J. Acoustic Emission, 27 (2009)27© 2009 Acoustic Emission Group

U
d
l
v
P
Patm
Re

Turbulent Flow Condition: Re > 1000

= Mean fluid velocity through orifice= Mean orifice diameter= Orifice length= Kinematic viscosity of fluid= Pressure inside the pipeline= Atmospheric Pressure= Reynolds number

Fig. 1. Leaking flow features.
of a leak are local crack/orifice growth, cavitation due to local sub-pressure at the orifice, temporary entrapments and impacts of solid particles at the orifice, soil movements, or even externalsources such as impacts etc., which are mainly “burst” type sources. The generated AE waves
from such sources propagate through the fluid or through the pipeline itself. Acoustic emission
sensors operating between 20 and 100 kHz are mounted on the pipeline, monitoring both continuous and burst type emissions through simultaneous monitoring of time-driven data (thresholdindependent sampling) and hit-driven data (threshold dependant). In addition to that, acquisitionof AE waveforms or waveform streaming is often used.
Simplistic estimation of the leak location can be made by measuring the amplitude variationsof continuous signal at various positions along the pipe. Based on signal attenuation (known ormeasured independently on the pipe itself) and signal amplitude reduction with the distance fromthe source (leak), as measured at various positions, an amplitude variation ratio is recorded.
Based on this ratio the distance to the source can be roughly calculated. However, a more effective and accurate method to locate a leak on a buried pipeline is linear location. Two (2) AE sensors placed on either side of the leak are required for this method. If an AE event occurs at an“x” distance from the first sensor, then x = (L - VΔt)/2, where “L” is the known distance between
the two sensors “V” is the (known or measured) AE wave velocity and Δt the time difference of
the wave arrival on the two sensors measured by the acquisition system [6]. Finally, postprocessing of streamed waveforms (continuous long waveforms) might be used to enhance both
detectability and location accuracy.
Requirements, Advantages and Limitations
Pipeline surface access holes are excavated at pre-defined sensors distances (typically every
100 m) along the pipeline, in order to expose a small part of the pipe (a small exposed surface
about 15x15 cm2 on the top part of the pipeline is required). Any protective sleeve, insulation or
fiberglass coating has to be removed for sensor mounting. The section of the pipeline that will betested has to be isolated (in order to apply static pressure) and without any medium flow (toavoid the associated noise).
For testing, pressure in the tested section is increased and kept stable. Although a single
channel leak detection portable instrument might be used to acquire the average AE signal levelof the pipe at the exposed points and identify the area that is suspected for the leak, a multichannel system is needed for reliable source location. Therefore, multiple AE sensors are placed on
the exposed points along the suspected pipeline section and a multi-channel AE leak detection
system is used to acquire the leak signals. Special software is used to acquire the signals, to28

evaluate and to calculate the linear location of the associated leak-type sources. Once detected,
the location of the leak can be calculated within a few minutes [6]. The use of a fixed array of
sensors and monitoring during pressurization and/or decay gives the best available detection
sensitivity, since very small changes of the AE signal in time may be detected (by the use of averaging and/or advanced post-processing) when compared with, for example, periodic measurements using a portable instrument where the detector is repeatedly re-mounted.
Successful detection of leaks with AE depends upon the distance of the leak from the AE
sensors, the attenuation characteristics of the pipe material (thickness, material, etc.) and the typeof fluid (gas, liquid) inside the pipe. It also depends upon the surrounding environment (air, soil)
and the condition (Reynolds number) at the leak orifice, which, in turn, depends on flow rate,
differential pressure, orifice size and type of fluid. Condition for detection is the existence of turbulence at the leak orifice, ensured by adequate differential pressure. In case of a two-phaseflow, the detectability is enhanced. In general, higher the Re number (i.e., higher pressure differential), more detectable the leak is.
Leak detection can be performed in various types of pipelines with AE, including main pipelines, firewater pipes, aerial, river, road or railway bed crossings, pipes of pumping and compressor stations, gas distributing stations and pipelines inside refineries and industries.
Depending on test needs and required sensitivity, local access on the pipe’s surface about
every 60 to 200 meters or even higher, is required for sensor mounting and measurements. Adequate pressurization is necessary, depending on test type and requirements, usually 7-8 bars andhigher, while the pipeline is isolated, i.e., without fluid flow (in order to avoid additional noise).
A leak detection test may be performed during controlled pressurization with water (e.g., hydro test) or with the regular product of the pipeline. Apart from testing pipelines suspected toleak, periodic testing or even permanent installations are possible for critical pipeline sections,even without indications of a leak. Provided above test conditions are met (local access, pressurization etc.), any buried pipeline can be tested in its entirety, even areas that are not possible to betested with other NDT techniques. In the vast majority of cases, leaks can be located with good
accuracy, fast and efficiently.
Case Studies
Test Case 1: New pipeline leak detection in a 4.3-km, 16.5”-diameter, buried pipeline
During hydro test of a new pipeline at 80 bar, pressure was constantly dropping and theowner estimated a leak rate of about 120 l/hr. There was absolutely no visible indications of leakposition and the leak could be anywhere within the 4.3 km of the pipeline section length. Trials
to identify and locate the leak using audible frequencies and/or cross correlation of pressure signals failed.
Twenty-nine (29) small pits were excavated for mounting the AE sensors, every 125 m. Initial measurements of the Average Signal Level (ASL) were made during pressurization at 8.5 bar
using a portable AE device (PAC 5120). The ASL results (Fig. 2) narrowed down the potential
leak location to a length of 375 m (at points 1 to 4). Further AE testing in the said section during
pressurization, with multi-channel AE system (PAC Mistras-2001) using linear location locatedthe leak. After local excavation at the point indicated by AE the leak location was confirmed.
29

Directly measured leak rate was found 80 l/hr at 20.0 bars pipeline internal pressure. Total test
duration was 4 days.

Fig. 2. Average Signal Level (ASL) measurements across the pipeline.
Test Case 2: Pipeline leak detection in 400-m, 12”-diameter, buried pipeline.
Indication of a leak appeared as a pressure drop during pigging inspection. During subsequent hydro test of the pipe, pressure was falling from 12 bar to 3 bar in 1 hour. Since there wereabsolutely no visible indications of leak position, it was decided to apply AE in order to find theleak location.
Initial ASL measurements were executed using portable AE device (PAC 5110) at parts ofthe pipe that were already exposed during trials to locate the leak based on inspectors expectations and past history, while pressure was kept constant at about 9 bar. These initial measurements narrowed down the potential leak location to a length of about 110 m, out of which 70 m
were covered by concrete. Only two positions were exposed (owner opened holes and cleared theinsulation) and further AE testing was performed in the said section during pressurization, using
4 AE sensors and a multichannel AE system (PAC 16-channel PCI-DiSP4 System).
Figure 3 shows an example of a leak signal arriving at the 4 sensors. The acquired waveforms clearly exhibit the attenuation of the signal, apparent as signal amplitude drop (note difference in y-axis scales). According to the amplitude vs. time graph (Fig. 3 bottom) the signal arrived at first on channel 3, meaning that the source is closer to channel 3. The arrival times onchannels 1 and 4 are about the same, meaning that the source has about the same distance fromchannels 1 and 4 or, in other words, the source is very close to the middle between the 2 channels.
Figure 4 shows the ASL measurements on each channel and location graphs indicating thesuspected location, based on data acquired for a period of 240 sec. The system gave an indicationof a possible leak point (at about 15m from sensor 3, under the inaccessible concrete area).

30

Further analysis was made on-site using different location setups. The same location appeared also during post-processing when only channels 1 and 4 (having a distance of about 110m) were used for locating the AE source. The pipeline was exposed at the advised location and a
7-mm hole was found as the cause of the leak. Total test duration was less than 1 day.

Fig. 3. Waveforms (Top part), and amplitudes vs. arrival times of a leak AE signal on 4 different
channels.
Test Case 3: Pipeline leak detection in a 1.5-km, 4”-diameter pipeline.
When pressurized to 34 bar the pressure was dropping to 0 bar in an average rate of 2 bar/hr,
which suggested a small, active leak. For the test, the pipe had been filled with water and thepressure was kept nearly constant at approx. 9 bar. A portable AE device (PAC 5110) provided
initial information (ASL measurements) for the existence of a leak in the pipe. After this indication the investigation was focused on a road crossing, about 92 m of the pipe.
A desktop, multi-channel Acoustic Emission system (MISTRAS 2001) was used to find theactual position of the leak. Real-time linear location indicated a possible leak at approx. 4.6m
from the position of sensor no.3. Figure 5 presents the location graphs that provided indications
about the leak position. A 3m length of the pipe was exposed at the location suggested by AE
and a small dripping leak was found.
Test Case 4: Pipeline Leak detection in a 100-m, 5”-diameter pipeline.
The pipe was reported to lose pressure from 30 bar down to 3 bar after 10 minutes whenpressurized. For the AE test, the pipe had been filled with water and the pressure was kept nearly
constant at approx. 25 bar. A desktop system (PAC 24ch.-DiSP) was used to find the exact position of the leak. Real-time linear location indicated a possible leak located near sensor No. 3.
Figure 6 shows the ASL measurements (top graph) and the linear location (two bottom graphs)
during the AE acquisition where both manual and automatic threshold adjustments (“smart
threshold”) were employed. Note that the linear location graph based on the number of hits (2nd31

Fig. 4. ASL vs. channel (top) and linear location indicating the leak point based on number ofhits, energy and counts (bottom) of the acquired signals, after only 240 seconds of acquisition.

Fig. 5. ASL measurements indicated the suspected leaking area between channels 2 and 3. AE
system graphs located the exact leak position, after 27 minutes of acquisition.
32

graph) shows higher activity between channels 2 and 3, while the linear location graph based on
the energy of the signals shows higher activity between channels 3 and 4. The part of the pipe
between channels 2 and 3 was buried at shallow depth (about 40 cm) while the part between
channels 3 and 4 was buried deeper in the ground (about 1.5 m). This probably results in significant attenuation difference between the two sections. This fact and the variability of thresholdcombined with the high ASL values that indicate a strong source may have resulted in the linear
location not being exact.
Initially, a 5-m length part of the pipe was exposed starting from channel 3 to channel 2. ASL
was getting lower when measuring towards channel 2 along the pipe. Thus, it was decided to expose the part indicated by the energy graph and a big leak was found (Fig. 7).

Fig. 6. ASL measurements (top) indicating the suspected area. AE acquisition linear locationeach one showing different locations near channel 3, after 25 minutes of acquisition.

Fig. 7. Picture of the leak found by AE.
33

Test Case 5: Complex network pipes leak detection in a 100-0m, 4”-diameter pipeline.
Complex network of pipes, transferring product from refinery to neighbour tank farms andfilling stations was suspected for leak. The numerous branches of the lines connected to the mainline made the detection difficult. Therefore, the primary aim of this test was to identify the suspect line and narrow the area of inspection prior to detailed investigation.
When pressurized to approximately 25 bar, the pipe was reported to lose pressure (down to 0bar) after 5-6 hours. Five (5) excavated areas provided access to the pipeline surface every 100
m. Together with the 6 pre-existing access points of the pipe, ASLs at totally 11 points were initially measured using portable AE equipment (PAC 5110) while the pressure was kept nearly
constant at approx. 25 bar. The measurements provided a rough indication (see Fig. 8(b)). The
test was focused at the area near point No. 7 and one more point was excavated providing additional access (point No. 12). The suspected leak area was narrowed down between points 11, 7
and 12 (see Fig. 8(a)). One more hole was opened near point No. 7 and a T-joint of the pipe was
exposed (see Fig. 9). The high ASL measurements at the T-joint confirmed once more the initial
indication of the leak.

(a)
34

(b)
Fig. 8. (a) Aerial photo of the complex pipe network and measuring points. (b) ASL vs. channel
measurements gives the first indication of the suspected leaking area (near channel 7).

Fig. 9. Picture of the T-joint (top left), with an enlarged picture of the small leak found (right).
Schematic of the complex topology of the pipe near the leak point and sensor positions at thesuspected area (bottom left), with sensor 2 on the T-joint.
The distance between the T-joint and point No. 7 was 4 m and the pipe was crossing a wall.
Although it was obvious that the leak was a few meters next to this area, even after opening theholes there was no visual evidence of any leak (e.g., smell, sludge or humid ground). Therefore,
AE sensors were placed at the points showing on Fig. 9 for further detailed investigation. A
35

Fig. 10. Real-time linear location graph based on signal energies.
desktop system (PAC-DiSP 24 ch.) was used to locate the actual position of the leak. According
to the system (Fig. 10) the leak was located between sensors 1 and 2 (between point No. 7 andthe T-joint). The area was excavated and extended right below the wall where a small leak wasfound.
Test Case 6: Leak detection on a newly built 1000-m long, 80-cm-diameter water pipeline crossing a small river.
Pipe was part of a newly built 20-km water pipeline, which was built along a bank of a small
river. At some points, the pipeline was crossing the river underwater. Hydro tests were performed on each km of the pipeline. The pressure inside the suspected part could not be raised
more than 8.5 bar giving the first leak indication. Pressure decreased from 8.5 bar to 5.5 bar
within 30 minutes.
The constructor injected green paint inside the pipe in order to locate the leak without success. As a result AE monitoring was applied. Totally 13 positions were excavated at distances
ranging between 70 to 100 m, as shown in Fig. 11. The exposed areas of the pipe were tested using portable AE equipment. Points 1 and 13 were the blinded edges of the pipe where two water
pumps were connected to increase pressure inside the pipe.

Fig. 11. Pipeline drawing with sensor positions.
The pipe had been filled with water and the pressure was increased slowly. During pressure
increase, AE ASL measurements were performed across the pipe. The first indication appeared
at approximately 8.3 bar, where point 5 showed 12-dB ASL for the first time. Point 4 was excavated at this stage (not exposed from the beginning of the test) after inspector’s suggestion and
the ASL measurements showed 39 dB (Fig. 12), indicating potential leak between positions 3
and 5.
In order to locate the leak, three PAC R3I resonant sensors were coupled at positions 3, 4 and
5 and AE monitoring performed using digital multichannel AE system (PAC DiSP). A locationgroup was setup and the linear location graphs indicated the suspected area at about 9 m fromposition 4, between positions 3 and 4 (Fig. 13). Although for this particular case further off-line
36Fig. 12. ASL measurements at 8.3 bar.

Fig. 13. Location graphs as recorded by the multichannel system at 8.3 bar.
post-processing was unnecessary, advanced processing applied as a mean to validate the methodology and to increase our confidence on the real-time location results.
The customer started to expose the upper part of the pipe from point 4 to point 3 for about 11
meters. At this point, the green paint injected by the customer in the past, appeared together withwater from the river inside the excavated area (Fig. 14), making the follow-up activities difficult.
Since the confirmation of the leak was not an easy task, it was decided to use again the portable
instrument as a mean to narrow the search and identify the leak area close to sensor 4.
ASL measurements using PAC-5110 leak detector were performed along the 11-m exposed
pipe, but all measurements showed the same ASL (about 39 to 42 dB) and nothing was indicating the exact position of the leak. In this case, it was decided to use the portable leak detector
PAC-5131 (VPAC). The instrument show the highest ASL (42 dB) at the indicated point (9 mfrom point 4), while the ASL values left and right of this point were lower (18-39 dB).
37

Fig. 14. Picture of exposed pipe overflown with river water.
Due to the high amount of soil above the suspected point and due to the existence of thesmall river next to it, water was covering the excavated areas of the pipe, while the wet soil rendered the process dangerous to proceed to full recovering of the suspected point. The constructor
decided to stop excavation until drying the area. Test completed within one day.
Discussion and Conclusions
The use of AE for pipeline leak detection and leak location has been presented. An important
requirement for executing the test is that the pipe or the suspect section can be isolated and pressurized to at least a minimum pressure, which starts from as low as 4 to 9 bar, while the desired
minimum pressure is above 10 bar. Based on experience, excavations and measurements are performed using low-frequency resonant sensors at about every 100 m. In case that there is no indication of the leak, either with the use of portable or multi-channel AE system, new areas are excavated at smaller distances and new measurements are performed. Real-time linear location during acquisition provides most of the times a precise leak position within a few minutes, without
any further analysis and the leak is confirmed immediately.
The AE signal attenuation appears to be higher on small diameter pipes (4-6”). In that casesensors have to be placed on smaller distances. In addition, on small diameter pipes, the AE location graphs are broader, giving location indications over a long part of the pipe (can be up to 7m) instead of just one point. Noise sources, like truck passing over the buried pipeline, bangs
coming from pumping station or refinery installations, ground movements at the sensors due tothe opened holes, sand dropping on the pipe due to wind, etc., may usually appear and have to be
filtered. In case that the pipe passes through different types of ground or depths, signal attenuation changes and might complicate source location.

38

Today’s modern AE systems offer increased dynamic range (e.g., using 18-bit resolution)and low noise together with the option of waveform streaming (e.g., PCI-2-based systems of
PAC). The waveform streaming enables the recording of continuous waveforms of the AE activity independently of threshold adjustment at high sensitivity and offers enhanced evaluationand location capabilities to the operator. In case of small pipe lengths, the test can be fully performed with the sole use of a 2-channel portable instrument, such as PAC’s Pocket AE, that can
provide ASL and location indications. Furthermore, advanced processing using special patternrecognition software [7] might be used in order to discriminate noise from leak signals and toprovide the leak position reliably and/or to automate the evaluation process, especially in thecase of remote pipeline monitoring.
Given the successful application of AE for the leak detection of liquid-filled pipelines, remote pipeline monitoring is feasible and can be implemented specially for local, continuous
monitoring of known areas of concern in underground pipelines. Modern AE systems, solar
powered with wireless Internet connections, are well suited for remote monitoring and control ofpipelines.
References
1) ASM Metals Handbook, Vol. 17, Nondestructive Evaluation and Quality Control, p. 113, p.
132, Leak Testing, Revised by G. L. Anderson, ASM International, Ohio, 1989.
2) A.A. Pollock and S.-Y.S. Hsu, Leak Detection Using Acoustic Emission, Journal of Acoustic
Emission, 1 (4), 1982, 237-243.
3) Miller, R.K., et al., The development of acoustic emission for leak detection and location inliquid-filled, buried pipelines, Acoustic Emission: Standards and Technology Update, ASTM
STP 1353, Vahaviolos, S.J., Ed., American Society for Testing and Materials, 1998.
4) ASTM E1211-02, Standard Practice for Leak Detection and Location Using SurfaceMounted Acoustic Emission Sensors, 2002.
5) ASME Section V, Article 10, Leak testing, Appendix X, Ultrasonic leak detector test.
6) Envirocoustics document FT-DE-1.5.2E(2), “Buried Pipe Leak Detection and Location”Method Description.
7) NOESIS Ver. 5.2, Envirocoustics, user manual.

39

ACOUSTIC EMISSION MONITORING AND FATIGUE LIFE
PREDICTION IN AXIALLY LOADED NOTCHED STEEL SPECIMENSFADY F. BARSOUM, JAMIL SULEMAN, ANDREJ KORCAK and ERIC V. K. HILL
Multidisciplinary NDE Group, Embry-Riddle Aeronautical University,
Daytona Beach, FL 32114
Abstract
This research applies the nondestructive evaluation (NDE) methodology used in aviation tomonitor structural steel in the form of axially loaded notched specimens for fatigue-life prediction. It applies acoustic emission (AE) nondestructive testing (NDT) to monitor the developmentof fatigue-crack growth and employs a Kohonen self-organizing map (SOM) artificial neuralnetwork (ANN) to identify the failure mechanisms in A572-G50 steel. In addition, a backpropagation neural network (BPNN) was utilized to perform fatigue-life prediction from the first quarter (0-25%) of the experimental fatigue life or cyclic life data. A second BPNN was created forprediction based on the third quarter (50-75%) of fatigue-life data. Testing of axially loaded
notched specimens was completed and experimental results were used to generate the characteristic alternating stress versus fatigue life (S-N) curves. These results were compared to those
calculated from linear elastic fracture mechanics (LEFM) using the damage tolerance analysis
software Air Force Growth (AFGROW). AE data generated from fatigue-crack growth werealso processed using a Kohonen SOM neural network to categorically identify the failure modesof plastic deformation plus plane-strain and plane-stress fracture. Fatigue-prediction analysis
focused on developing a BPNN for high-cycle fatigue (HCF) prediction from AE amplitude histogram distributions. This network provided prediction results within ±20% for first quarter data
and ±12% for third quarter data predictions, respectively, which demonstrated the feasibility ofmaking fatigue-life predictions in steel structures from AE data.
Keywords: A572-G50 steel, Artificial neural networks (ANN), Backpropagation neural network(BPNN), Fatigue life prediction, Kohonen self-organizing map (SOM), Neural networks
1. Introduction
1.1 AE Methodology in Fatigue Analysis
Acoustic emission is the phenomena, in which transient elastic waves are generated by the
rapid release of energy from localized sources within a material as it undergoes deformation [1].
One special advantage of this method over other NDT methods is that AE captures the dynamic
process related to structural degradation. Since plastic deformation and fatigue-crack growth are
two principal sources of AE signals in isotropic materials, this approach is highly desirable forfatigue assessment. The AE signals produced are quantified by five basic parameters, as depicted in Fig. 1. These AE signal parameters are amplitude (A), duration (D), rise-time (RT),counts (C), and mean area under the rectified signal envelope (MARSE), more commonly known
as energy (E). Other parameters, such as average frequency (counts/duration), are combinationsof basic AE parameters; these are also useful in many cases. Cumulative counts and cumulative
absolute energy are two parameters that are used to develop plots that correlate to the fatiguecrack growth process with time. Background work in fatigue-life prediction using artificial neural networks (ANNs) has been accomplished by several researchers [2 - 5].
J. Acoustic Emission, 27 (2009)40© 2009 Acoustic Emission GroupFig. 1: Primary AE parameters [6].
1.2 Artificial Neural Networks for Failure Mode Classification
Artificial neural networks are mathematical algorithms that function similar to the humanbrain, which, if trained properly, can conclusively identify complex patterns in nonlinear data
space. These networks are comprised of artificial neurons, or processing elements (PEs), thatform the building blocks of the system. Structurally, the network consists of an input layer, inwhich each neuron or PE represents a specific AE input data parameter. Based on the classification, defined by the user, the output can be in the form of a binary or a normalized x-y coordinate
layer. The output layer is linked to the input layer through weight functions, which can bethought of as coefficients in an equation. These weights are adjusted during the training phase of
the network to provide the appropriate output.
One particular ANN employed for pattern recognition is Kohonen self-organizing map(SOM), which is capable of classifying AE failure mechanism data, based on the principle ofcompetitive “soft learning”. Soft learning allows not only the winning neuron weight to be updated, but also the weights of the adjacent neurons that are linked through a neighborhood function, to optimally exhibit the input space. Due to this learning style, relationships existing in the
input space (data) are maintained in the output. Overall, the objective of this network is to modelan unknown distribution of a multidimensional input by a topological map of lower dimensionality.
Figure 2 shows a Kohonen SOM neural network as a fully connected, two layer network.
The two layers are defined as the input layer and the Kohonen classifying output layer. In Fig. 2,
there are “D” AE inputs – in this case, duration, counts, amplitude – which can be classified into
up to “k” categories within the 2-D Kohonen output layer. The two layers are connected byweighted connections that continually change until the network is fully trained to best classify
the input data into clusters of “like” data at the output [7].
1.3 Artificial Neural Networks for Failure Prediction
The backpropagation neural network (BPNN) is another form of ANN that is widely used in
predictive learning and in cases, where a substantial amount of data is available with complex
relationships. Architecturally, this network consists of at least three layers: input, hidden, and41

output layers. In some cases, an additional hidden layer can also be added to act as a data classifier, which is necessary when the data are nonlinearly separable.
Figure 3 shows an artificial neuron or processing element (PE) with a hyperbolic tangent(tanh) activation function. The artificial neuron has several inputs, xn, each of which is multiplied by a weight coefficient, wn. These weighted inputs are then summed up in the PE and theresult processed (squashed into the range -1 to +1) by the activation function before being senton as the output of the artificial neuron. Collections of these artificial neurons are what make up
the architecture of neural networks.

Fig. 2: Kohonen SOM neural network structure [7].

Fig. 3: Artificial neuron and hyperbolic tangent activation function.
42

Fig. 4: Backpropagation neural network (BPNN) architecture.
As shown in Fig. 4, the BPNN utilized herein is fed an AE amplitude distribution into the
input layer (seen in blue). This is linked to the hidden layer through the adjustable normalized (0≤ w ≤ 1) weights. The neurons in the hidden layer sum the weighted inputs then multiply theoutput by the nonlinear sigmoidal (s-shaped) activation function, in this case a hyperbolic tangent (tanh) function, which squashes the output into the range [-1, 1]. The number of neurons
used in the hidden layer typically follows a (2n + 1) rule, where n represents the number of failure mechanisms identified (prior to employing the BPNN) using a Kohonen SOM [8]. Finally,
the single output neuron yields the prediction results: cycles to failure. Additional neurons calledbias neurons, which have a fixed input of +1, are used to speed up the training of the network.
Unlike the Kohonen SOM neural network that functions on “soft learning”, the BPNN operates on error correction learning [8, 9]. This means that the true output (fatigue life to failure forthis research) of the network must be known prior to applying any inputs for predictions. The
network starts by setting the weighted connections between the various layers to random values
between [0, 1]. Next, the amplitude-distribution data are input to the network. The network output is then compared to the true output to determine the error. This error is then used in a delta
rule to make corrections to the hidden layer weights. Further delta-rule weight corrections are
backpropagated until the input layer is reached. At this point, all the weights linking the neurons
between the layers of the network have been updated. Reapplying the amplitude-distribution
data at the input, this process is repeated until the RMS output error preselected by the user isattained. The amplitude distributions seen in Table 3 can serve as an example of the training or
learning data, which is input into the first layer along with the known fatigue lives. The training
algorithm then compares the predicted value to the actual and calculates the percent error between the two. If this error is less than the user specified RMS error, then the training is stopped.
The user specified RMS value serves to control how tightly the networks trains. In other words,
43

if the value is set at 5%, the network will train until the RMS error for all the training files is lessthan 5%. The reason why it is often not desired to train to very small errors is that the network
will then predict poorly on files that are even slightly different from the training files. Hence, anintermediate value is normally chosen -- not too small, not too large – that provides optimal predictions.
2. Experimentation
2.1 Specimens
For this research, 32 specimens of A572-G50 steel having three different notch lengths were
fatigue tested. 20 specimens with a 3.81-mm deep V-notch were fatigued at six discrete stresslevels for prediction purposes. The specimens were marked with a black transverse line 25.4mm from each end as shown in Fig. 5. These lines served for proper alignment of the clamping
grips that held the specimen in the MTS fatigue machine. Additionally, two lines denoting thecenterline location of the AE transducers were also drawn at 101.4 mm and 228.6 mm, measured
from the top (left-hand end) of the specimen.

Fig. 5: Typical test specimen detail [10].
2.2 Noise Analysis and Front End Filters
To ascertain the acoustic nature of the MTS fatigue machine and the ambient environment,
several noise tests were performed with the two transducers mounted on a specimen. The MTS
machine was then switched on with both the mean cyclic load and the span setting at zero. This
meant that the load was applied just to grip the test coupons with no tensile load applied along
the length of the specimen. Readings from PAC Pocket AE instrument were recorded for half anhour, and three graphs were plotted to illustrate the noise behavior. The sources of noise present
were the hydraulic pump located less than 1 m from the specimen plus the hydraulic valves andthe actuator piston. The hydraulic grips used to hold the specimen contributed rubbing noise.
The reason why there are many AE sources in these tests is because the system (see Fig. 10) is
coupled together, meaning all connections are steel to steel, thus providing for acoustic transmission. Another source of noise is electromagnetic interference from the Pocket AE charger interface. This is a long duration signal, as it is a relatively continuous noise signal, which sometimes
44

does not drop below the set threshold (as seen in Fig. 1) and therefore leads to long duration signals.
The first plot of the ambient noise data was an average frequency histogram that shows theaverage frequency limits of the noise spectrum (Fig. 6). It is apparent from this plot that thenoise spans over a diverse range of average frequencies; however, the bulk of it falls between theranges of 1 kHz to 30 kHz. Additionally, there is high average frequency noise, which occurs at
discrete values. For simplicity, the average frequency histogram is here limited to 275 kHz.

Fig. 6: Average frequency histogram for a typical noise test.
The second essential ambient noise plot was duration vs. counts. This is a key graph because
it illustrates the length of the noise signal hits. Noise signals can have short or long durations,and they thereby establish the average frequency limits based on the counts present in each hit.
In Fig. 7 the noise signature is overlapped with a typical AE test for a bar specimen in fatigue,
showing that the principal portion of the noise lies outside the bounds of the real AE data generated from crack propagation. Notice that limited noise is still present within the crack propagations AE data; this is acceptable, however, since this data forms a very small portion of the overall data set.
The final plot of value in the noise analysis was the amplitude histogram of Fig. 8. Thisgraph illustrates the ranges of amplitudes that are present in the noise signals. Since it was determined in Fig. 7 that noise fell into two primary average frequency ranges, Fig. 8 shows themeasure of noise present in each of these average frequency limits. It is observed that higher
noise content is present at lower average frequencies. In summary, the noise signals consist of
amplitudes between the threshold values of 30 and 34 dB.

45

Fig. 7: Duration versus counts point plot with noise overlap.
Based on the limits defined by these plots, front-end filters were set up on each transducer
before relevant AE data were recorded during fatigue testing. The Pocket AE hardware allows
up to two filters on each of the two channels. For the fatigue tests performed in this research,
two filters were applied on each channel. The first filter eliminated all signals of average frequencies up to 25 kHz (Fig. 6), and the second filter removed all hits of counts less than 10 (Fig.
7). Although these filters were highly effective, the Pocket AE still recorded hits with durations
up to 10 ms. These are the constant noise signals as discussed previously. These hits were later
removed manually from individual files, as this data subset was obviously noise.
2.3 Pocket AE Data Acquisition Set-Up
The Pocket AE data acquisition system requires several parameters to be set properly before
testing. The two transducers used and seen in Fig. 10 are PAC Type R15, 150 kHz resonanttransducers with operation range of 50-200 kHz. The key settings that were adjusted from thedefault values are listed in Table 1. The selection of a 30-dB amplitude threshold was based on
previous research on isotropic materials. A value below this threshold adds unnecessary noise to
the analysis, and a value above this setting is likely to eliminate valuable fatigue data, which isalso undesirable. The value of maximum duration was chosen arbitrarily as 1,000 µs in order toensure that all reasonable duration signals were captured. This high upper limit allows for continuous noise of long duration to be recorded by the Pocket AE. This data subset consists of signal hits from continuous rubbing or machine hydraulics. The remaining parameters of peakdefinition time (PDT), hit definition time (HDT), and hit lock-out time (HLT) are called waveform parameters, and are important to separate noise from fatigue data. Proper setting of the
PDT guarantees the correct recognition of signal peak for rise time measurements. A proper46

Fig. 8: Amplitude histogram for a typical noise test.
value of the HDT parameter ensures that a single signal hit is recorded as only one hit. Essentially, this value determines the end of a signal hit. The HLT closes out the measurement process
and stores the hit waveform quantification parameters (amplitude, counts, duration, rise time, andenergy) in the data acquisition buffer. The values of HDT and HLT parameters are selected inconjunction with pencil-lead break tests from various locations on the specimen. These values
are crucial, since incorrect selections will result in multiple hit data, where two AE hits merge
and become one due to insufficient time setting of the HDT and HLT parameters. This preventsthe first hit from being fully stored and closed out before the second hit arrives and is recorded.
The AE parameters for multiple hit data will obviously be different than these for single hit data.
Table 1: Pocket AE setup parameters.
Standard Setup

Timing Parameters

Timing Parameters

Amplitude Threshold Ch1 = 30 dB

Max Duration = 10 ms

Hit Definition Time (HDT) = 400 µs

Amplitude Threshold Ch2 = 30 dB

Peak Definition Time (PDT) = 200 µs

Hit Lockout Time (HLT) = 900 µs

47

Fig. 9: Pocket AE waveform parameters [11].
2.4 Test Procedure
After the specimens were marked as described, and the transducers were attached using hotmelt glue as a couplant, the specimens were loaded using an MTS hydraulic testing machine asshown in Fig. 10. The upper grip was first activated and the specimen was aligned 25 mm fromthe top end before closing. Next, the bottom grip head was moved using the MTS 407 controller
until it was aligned with the lower grip marker. The bottom grip was then closed to secure thespecimen. The vertical alignment of the specimen was verified using a bubble level. Next, the
MTS controller was set to a sinusoidal load function with 1 Hz frequency. The two transducers
were connected to the Pocket AE data acquisition system, which was activated simultaneously at
the initiation of the test to acquire data.

Fig. 10: Typical notched specimen in MTS machine.
3. Results
3.1 Experimental Fatigue Life Results
AFGROW is a widely recognized open-source, damage-tolerance analysis program designed
to model crack initiation, growth, and prediction of fatigue life for isotropic materials based onlinear elastic fracture mechanics (LEFM). Initially, AFGROW calculations were performed toascertain the fatigue life; however, there was a high difference (30%) between AFGROW andthe experimental tests due to the fact that AFGROW calculations are based on LEFM, and thepresence of a 3.81-mm notch generates a stress concentration, which exceeds the yield strengthof A572 steel. These high stresses at the crack tip initiate substantial plastic zones as shown in
Fig. 11. AFGROW fails to account for the plastic deformation effects, which retards the crack
growth and thus under-predicts the total life of the specimens.
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Table 2 shows the tests for the 3.81-mm notch. Here it can be observed that, as the cycles tofailure increase, the coefficient of variation or variability of the data increases as well. This becomes significant when considering the accuracy of any fatigue life prediction, be it AFGROWor BPNN. An example of this is the two specimens at the 172 MPa loading, where the coefficient of variation is 12.5%. This variation can be noted in Table 2 and in Fig. 12.

Fig. 11: Crack tip stress distribution [12].

Fig. 12: S-N curve for 3.81-mm notched specimens.
3.2 Acoustic Emission Graphs Capturing Failure Mechanisms and Fatigue Behavior
From the AE point of view, the fundamental plot that suggests the existence of failure
mechanisms is the amplitude histogram, Fig. 13. This graph typically comprises several overlapping humps with each hump indicative of a failure mechanism. This is useful as different failure mechanisms or AE sources have different amplitude ranges. For example the histogram in
Fig. 13 could be showing the mechanism 1 as plastic deformation while mechanism 2 is plane
49

Table 2: Experimental fatigue life for 3.81-mm notch specimen.

strain and mechanism 3 is the plane stress. It is possible to detect variability in these amplitude
distributions due to the stochastic nature of material composition. However, if the experimental
conditions and front-end filter settings are kept the same, then the histograms should display
similar characteristics. It is imperative to observe any variance in the amplitude distributions,since the accuracy of the BPNN predictions depends upon the selection of the training files.
Thus, maximum variability should be provided to the network for training in order to attain thebest prediction accuracy, meaning that the BPNN needs to be trained on both the highest andlowest fatigue-life values plus a few in between.
The fatigue process is best described using AE data by the plot of cumulative energy versus
cycles to failure as shown in Fig. 14. The cumulative energy initially shows an increase due tocrack nucleation or initiation (Region I), followed by steady state growth (Region II), and thenfinally an exponential increase progressing to failure, known as critically active AE (Region III).

Fig. 13: Amplitude histogram with possible mechanisms.
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Fig. 14: Cumulative energy versus counts.

Fig. 15: Failure mode variation with specimen thickness [13 and 14].

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Fig. 16: Failed specimen showing failure surfaces.
3.3 Kohonen SOM Failure Mode Classifications of Fatigue Test Data
The AE data obtained from fatigue testing were separated into three classifications using theKohonen SOM neural network: plastic deformation, plane strain fracture (Mode-I tensile), andplane stress fracture (Mode-III tearing). This classification was based on the AE parameters ofduration and amplitude in the SOM network. The decision to classify the AE data into three
categories was based on a familiarity with the material failure surface under testing and the geometry of the specimens. Figure 15 shows the appearance of the failure surface and its variation
for a single-edge crack under Mode-I fracture, similar to this research, with changes in thickness.
From this figure, it can be discerned that multiple failure modes can exist based on specimen
thickness and composition. Therefore, it becomes extremely important to observe the failuresurface of each specimen to recognize the failure character and upon this identification the Kohonen SOM neural network can readily be used to separate the data into the appropriate failure
mechanism clusters. It is important to note that while in terms of the fracture surface area thetested specimens, an example of which is shown in Fig. 16, had a plane-strain area (triangularregion) slightly smaller than plane-stress area. This however is misleading because as the crack
propagates, most of the time is spent on the plane-strain fracture, and the plane-stress crack surface appears very rapidly, typically within the last few minutes of the test.
Figure 17 is the SOM classification of the amplitude histogram data. Here, the classificationshows two major failure mechanisms (the plastic deformation and plane-strain fatigue) alongwith an overlapping less prevalent third (plane-stress) mechanism. In a continuous tensile test,
where the load is constantly increased, the plane-stress events would have higher amplitude as aresult of increasing load. However, in the case of constant amplitude fatigue testing, while highstresses are produced as a result of area reduction, which lead to higher emission rate rather thanhigher amplitude as the stresses built up are relieved during unloading.
Figure 18 from PAC shows the approximate amplitude ranges of different AE sources. The
classification in Fig. 17 shows that plastic deformation, which is caused by dislocations, as having an amplitude range of about 33 to 50 dB, which roughly agrees with the PAC document. The
52

crack jumps have a higher amplitude range, while no distinction is made in the PAC document
between plane-strain and plane-stress cracking. The cracking in the PAC document also roughly
agrees with the SOM classification above.

Fig. 17: SOM classification of amplitude histogram.

Fig. 18: Approximate amplitude ranges of various AE sources (adopted from a PAC document).
Figure 19 is the classification of the duration versus counts plot. Three clearly distinct dataclusters are evident. Here plastic deformation is classified as a data set with low counts andsmall durations (orange cluster). Plane-strain data, occurring due to Mode-I tensile crack opening, are represented as a longer signal with higher counts (blue cluster). The plane-stress data
have the largest scatter (light blue cluster), highest duration and counts, which are consistent with53

the multiple paths associated with Mode-III tearing. Also note that the data-cluster boundaries
are nonlinear with some overlap, both of which indicate that the data classification is not dominated by a single AE parameter and is therefore most likely correct.

Fig. 19: Duration versus counts SOM classification.
Figure 20 is the quintessential plot that compares the energy level among the three failuremodes of plastic deformation, plane-strain fracture, and plane-stress fracture. Here it is observedthat plastic deformation occurs throughout the fatigue process but at lower energy (amplitude)levels than plane-strain and plane-stress fracture, which occur immediately following.
3.4 BPNN for Fatigue Life Prediction
When it comes to fatigue failure, the primary concern is the assessment of residual fatigue
life before a catastrophic failure. In this context, the benefit of the BPNN is that it can be trained
to predict a desired solution, in this case fatigue life, based on the characteristics of the AE datafrom early cycle crack growth. This section details the results from two BPNN networks, bothtrained for a 3.81-mm notch length, but encapsulating a range of stress levels to predict fatigue
life based on AE amplitude distributions.
3.4.1 BPNN for Fatigue Life Prediction
To get acceptable results, it is essential to train the network with the greatest amount of variability in the output; this variability in fatigue lives can be seen in Table 2. For example, at an
applied stress of 230 MPa, the network would be best trained using the highest (10,969 cycles)
and lowest (9,771 cycles) fatigue lives resulting from that stress. As such, any future prediction
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between these two fatigue life values should have a reasonably low error value. Also, for a typical BPNN analysis, a good rule of thumb is that 60% of the AE data are used to generate a training file, and the remaining 40% are used to produce a testing file.

Fig. 20: Absolute energy SOM classification in real time. 3.81-mm notch, Stress at 55% of YS.
The training files depicted in Table 3 display the specimen ID and the corresponding amplitude distributions starting from 30 dB up to 100 dB, the latter being the upper limit displayed by
the Pocket AE data acquisition system. Note that for the training file, the experimental fatigue
life value is added as the final input (marked in bold). This is because the training file has toprovide the true fatigue life as a target value for the BPNN to compute the error. On the other
hand, the testing files (Table 4) include the amplitude distributions only: the fatigue-life value isleft blank, since that is the output, which is being predicted by the BPNN.
Upon compiling the training and testing files, the distributions were input to the network.
Table 5 begins with the number of hidden layer neurons. The remainder of the values listed, arethe optimized training parameters determined for the network. Seven hidden layer neurons were
initially used in accordance with the (2n + 1) rule [9] and the fact that there are three mechanisms
prevalent in the AE data, per Kohonen SOM classification. Determining the final trained parameters for the BPNN requires extensive trial and error by the user. This process involves running the network and comparing the output error after each parameter change in the initial settings, and then comparing the output results with the true values to determine the error in order tobackpropagate the adjusted weights. Table 6 displays the optimized prediction results using thetrained settings of Table 5. It can be observed that the BPNN has managed to achieve a worstcase error of 19.4% [10].
3.4.2 BPNN #1 for HCF Prediction (25% Fatigue Life Data)
The first neural network shown herein was trained to concentrate on capturing high-cycle fatigue (HCF) specimens having fatigue lives of over 10,000 cycles. Thus, out of the 20 files thatwere generated originally, nine files were not considered since they represented fatigue lives below the HCF threshold of 10,000 cycles. This further added to the complexity since the total
55

number of data sets was now reduced by almost 50% for the BPNN analysis. In this case, eight
files were used for training, and three files were used for testing. It should be noted that the variability is still maintained at 10,000 cycles, and that the testing files were chosen to capture theentire range of life spectrum. As before, the first task was to determine the variability present in
the amplitude histograms. Figure 21 displays the amplitude distributions of all 11 specimens
used for this analysis. Based on this figure, the training files and testing files for this network are
given by Tables 3 and 4.
Table 3: 25% HCF BPNN training file.

Table 4: 25% HCF BPNN testing file.

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Table 5: 25% HCF BPNN testing file.

Table 6: 25% HCF BPNN results.

Fig. 21: 25% HCF training file amplitude distribution.
The first network was trained on only the data from the initial 25% of the fatigue life asshown in Fig. 22. This is extremely useful in real life applications where it is desirable to predict
fatigue life long before the specimen reaches failure. Table 5 shows the default and fully trained
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network parameters for this network. The results from this network are provided in Table 6.
These results show that even with only 25% of the data, the BPNN can be trained to predict below a 20% worst-case error.
3.4.3 BPNN #2 for HCF Prediction (Third quarter of Fatigue Life Data)
The second network that was trained using the 50% to 75% AE data; this corresponds to thethird quarter of the specimens’ life. While it is more desirable to predict the fatigue life as earlyas possible, oftentimes this is not possible either as the specimen has already been service or because prediction is desired after maintenance crews have discovered a crack and it is desired to
predict remaining life at that point.
As seen in Table 7 the parameters for the BPNN are different, as the data itself is comprisedof more cracking signals than the early life data which includes the plastic deformation processes
involved in crack initiation. This increase in actual fatigue crack data available for training
yielded a prediction error improvement from 20% down to 12%.

Fig. 22: Cumulative counts vs. cycles. Range of data used for 25% prediction.
3.4 Discussion of Results
Larger notch lengths result in higher stress concentrations, which led to substantial plastic
zones that extend beyond local yielding; this also restricts the applicability of LEFM. This aspectof the fatigue process will be further investigated in future research using elastic-plastic fracture
mechanics (EPFM) in which the larger plastic zones and their effects are taken into consideration.

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Fig. 23: Range of data used for 50%to 75% prediction.
Table 7: 50% to 75% HCF BPNN training parameters.

Table 8: 50% to 75% HCF BPNN results.

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The AE graph of cumulative energy versus cycles to failure illustrates a linear increase in AE
activity during the crack propagation phase of the fatigue spectrum. This is followed by a rapid
increase in the total counts as the crack becomes critically active and the data acquisition system
experiences data avalanche near failure.
The Kohonen SOM, being an unsupervised network, classifies the AE data per the user’s
comprehension and familiarity with the problem. Here the AE fatigue data were separated into
the three failure modes of plastic deformation, plane strain fracture, and plane stress fracture.
This was evident by the amplitude histogram, Fig. 17, where two dominant humps represented
failure mechanisms with plane stress being an indeterminate subset without a well-defined distribution. The absolute energy versus cycles-to-failure plots (Fig. 20) showed the presence of thelower energy mode (plastic deformation) prevalent throughout the crack development process
followed immediately by the high energy mode of plane strain (Mode-I tensile) and plane stress(Mode-III tearing) fracture. However, by observing the fatigue process and fracture surfaces ofthe specimens at failure, severe in-plane bending and plastic deformation were observed justprior to final failure at which point plane stress or shear lips were also developing simultaneously. There was some mid range noise misclassified as one of these failure mechanisms as it isclearly present in the noise test seen in Fig. 7.
The backpropagation network for predicting the fatigue life of A572-G50 steel under fivedifferent loading conditions was trained and had a 30% error. While at first blush this appeared
to be substantial, given the variance in the fatigue life from under 5,000 to above 24,000 cycles,
the results matched reasonably well. As such, attention was focused on only the 3.81-mm notch
samples, which met the diverse fatigue life spectrum criteria. The worst-case prediction results
for the 3.81-mm notch HCF based on first 25% data were under ±20% and based on third quarterthe error was reduced to ±12%.
The noise, which has been filtered out, would have had a negative impact on the networks
ability to predict accurately; however, depending on if the noise is constant during all the tests,
then the network can be trained to essentially ignore the noise. On the other hand, if the noisechanges from specimen to specimen and overlaps the failure mechanism, then the noise first has
to be classified for example by using SOM network and then taken out so that the network only
trains on clean data. This step was unnecessary in data presented herein as the filters eliminatedmost of the noise signals, and the small amount of noise that remained had no significance to the
BPNN network. In future tests, linear source location will be utilized as an additional method offiltering.
The one downfall of the BPNN networks presented herein is that if a network is trained wellon certain data, it will perform poorly on data, which is dissimilar. Thus, a test was attempted touse the network, which was trained based on the 50-75% (steady crack growth) data to test or
predict on the 0-25% (strain hardening plus crack initiation) data (Table 4). This, however,yielded a worse case error of almost 50% largely due to the fact that the failure mechanisms in
the two regions are significantly different: fatigue cracking is more prevalent in later part of thetest, whereas strain hardening dominates the early part of life. The next step in the research is toisolate the plastic deformation signal using a SOM and predict on these data only, as that would
reduce the data variance.

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4. Conclusions and Future Work
Acoustic emission graphs of cumulative absolute energy versus fatigue cycles captured thefatigue process exceptionally well and may be considered as characteristic curves to view fatigue
crack growth behavior with time. These types of principal graphs can be used to see the severityof the AE activity associated with the crack growth process. In this way AE monitoring can behighly effective in determining the extent of damage in the structure.
Herein the Kohonen SOM neural network classified AE data into the three primary categories of plastic deformation, plane strain, and plane stress fractures. It was observed that the plastic deformation failure mode occurred continually throughout the fatigue crack development
process, and that once the plastic zones became strain hardened due to mechanical work, theplane strain and plane stress fracture modes dominated. This research provided a deeper understanding of the fatigue process and failure mode identification in structural steel members in tension.
One objective of this research was to create a backpropagation neural network (BPNN) for
fatigue life prediction in structural steel. From the results discussed above, it can be concluded
that a BPNN, if trained properly, can accurately predict fatigue life provided sufficient test data
are available to perform the training phase. From the large complex structural fatigue prediction
viewpoint, it would be appropriate to develop several BPNN networks based on the AE data acquired from the regions of stress concentrations as well as from the AE activity resulting due todifferent temperatures. In this way the environmental effects and their contributions could be
included in predicting residual life. It is fitting to conclude at this point in the development that
one neural network may not be able to solve the entire fatigue problem of an operational structure, but rather several neural networks working in conjunction might offer a sound prediction of
fatigue life with a high confidence level. Despite the challenges mentioned above, an attempt
will be made to design a BPNN that can capture simultaneously various initial notch lengths aswell as various applied loads.
In order to validate the applicability of the NDE approach described in this paper -- to monitor and evaluate typical structural steel bridge members in bending -- fourteen notched A572G50 steel I-beams (S4x7.7) in flexure were tested under fatigue loading [15] subsequent to thisresearch. These I-beams were thought to be good representatives of bridge stringers subjected totransverse cyclic loads from moving traffic. AE data were recorded for high cycle fatigue(HCF); that is, the beam would undergo more than 10,000 load cycles before a fatigue failure
occurred [16]. These data were processed using a Kohonen SOM neural network to identify
failure mechanisms, then a back-propagation neural network was be used to predict fatigue lives.
This resulted in even better results: a +13.4% worst-case error for the 0-25% data and a +4.5%
worst case error for the 50-75% data. This was due primarily to the fact that the I-beams wereless highly loaded than the tensile specimens, and therefore, the BPNN was able to predict on amuch larger number of AE hits and the data contained less noise.
Acknowledgments
This work was supported in part by Florida Center for Advanced Aero-Propulsion (FCAAP)
and Embry-Riddle Aeronautical University (ERAU) College of Engineering Research Grants.
Specimens used for experimental work were contributed by the ERAU Mechanical and Civil
61

Engineering Department. The authors are also grateful for the support of Mr. William Russo of
the ERAU-College of Engineering for preparing the specimens and test setups for all the experimental work presented in this paper. In addition, the authors would like to extend special thanks
to Mr. Michael Potash for his unending support and guidance pertaining to use of the MTS machine.
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[13] Anderson, T. L., Fracture mechanics fundamentals and applications, Boca Raton: CRC
Press, 1995.
[14] Stephens, R.I., Fatemi, A., Stephens, R.R., and Fuchs, H.O., Metal Fatigue in Engineering.
2nd Ed. New York: Wiley, 2001.

62

[15] Suleman, J., Korcak, A., Barsoum, F.F., and Hill, E.v.K, “Fatigue Life Prediction in Steelusing Back-propagation Neural Networks based on Acoustic Emissions”, presented at The
Acoustic Emission Working Group 52nd Meeting (AEWG-52), October 2009.
[16] Collins, J. A. Failure of Materials in Mechanical Design Analysis, Prediction, Prevention.
New York: Wiley, 1993.

63

AE ANALYSIS ON BLADE CUTTING PRESSURE ADJUSTMENT IN
DYNAMIC CUTTING OF PAPERBOARDDARULIHSAN A. HAMID1, SHIGERU NAGASAWA1, YASUSHI FUKUZAWA1,
YUUKI KOMIYAMA1 and AKIRA HINE2
1)
Department of Mechanical Engineering, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata 940-2188, Japan; 2) Katayama Steel Rule Die Inc., 3-7 Higashigoken,
Shinjuku, Tokyo 162-0813, Japan
Abstract
A method for cutting-blade pressure adjustment during dynamic cutting of paperboard is introduced in a combination of resin bridgeless die and high-carbon center-bevel blade. In thismethod, the blade bottom is embedded in a dead-end bridgeless die slot where unbalance of
blade cutting will be compensated from the sinking of the blade bottom. This paper reports on
the relationship between the acoustic emission (AE) and blade-cutting pressure balance in acrank machine under quasi-static reciprocal motion. Two kinds of blade bottom condition withbottom thickness of 0.71 mm were experimented and their results on force difference and AE
signals during cutting were compared. Through this research, the proposed measurement technique was found reliable in detecting the characteristics of blade-pressure adjustment, and thecorrelation between the AE signal amplitude, the frequency spectrum and the blade shimming
condition.
Keywords: Paperboard cutting, Pressure balance, Tool collision, Blade crushing, Self-shimming
Introduction
Problems that occur on the cutting tip of a blade during paperboard cutting inevitably affect
the quality of a wedged sheet [1, 2]. One of them is an early damage of the cutting-tip profile,resulting in differences of blade height. In order to prevent this problem, several methods ofpressure-misalignment reduction on the cutting blade were considered and empirically trialed.
Grebe [3] considered a double-structure blade with a hardened cutting tip and a soft bottom tip.
The basic principle of this blade is the soft bottom tip will plastically deform more readily thanthe cutting tip at a certain exerted cutting forces. Namely, the soft bottom tip plays the role of arelief element in order to avoid an excessive crushing of the cutting tip.
For examining the deformation behavior of the cutting tip of blades, Darulihsan et al. hasstudied the fundamental mechanics of a 16°-center-bevel bottom blade subjected to a pushingload [4, 5]. For the supplemental use of cushion sheets, a mild resin sheet or a rubber underlay
was inserted behind the counter plate or the cutting-die board [6]. Figure 1 illustrates a conceptual adjusting method by using a shimming sheet on a blade bottom. Nagae et al. studied the effect of sinking characteristics of a mild resin die board for compensating the eccentricity of the
cutting tip [7, 8]. This was named as the bridgeless die. In this type of die board, the cutting-die
board and the shimming sheet were combined in a block unit and hence no bridge was requiredfor imbedding the cutting blade. In that study, a certain depth of dead-end slot was made in theresin plate where a cutting blade was inserted. When a certain value of force was applied to the
cutting tip of a blade, the blade bottom sunk into the resin plate. The sinking of blade bottom resulted in the cutting-tip force balance but this sinking speed is totally dependent
J. Acoustic Emission, 27 (2009)64© 2009 Acoustic Emission Group

on the resin plate hardness or its yielding stress and the blade-bottom-tip thickness. From thosestudies, the characteristics of shimming mechanics in terms of cutting force balance along a
blade line were revealed.

Fig. 1 Shimming method using plastic shimming sheet.
Regarding the nondestructive diagnosis of blade-tip crushing, Sadamoto et al. proposed thepossibility of estimating the crushed state on the blade tip by using audible sounds radiated by
the cutting collision [9], while Nagasawa et al. and Suzuki et al. discussed the advanced diagnosis of the transition state of blade crushing using acoustic emission [10, 11]. From those studies,
the correlation between the AE signals of tool collision energy and the average thickness of blade
tip was revealed. However, there were no studies conducted on a correlation between the cutting-force balance along a blade line and the cutting acoustics.
In this paper, cutting AE signals detected by an AE sensor and force differences were used
for investigating this phenomenon under two conditions: (1) a blade insertion into a metal die
without any shimming sheet in the blade bottom, and (2) a blade insertion into a resin (Unilate)die board, which is of the bridgeless type. Furthermore, the effect of shim-tape insertion was investigated for reconfirming the dependency of AE signal on the force difference, while the upperand lower half-die cutters were diagnosed by using a standard test in order to discuss about thefrequency spectrum.
Experimental Method
In the converting process used in the paper conversion industry, a reciprocal motion is used
for cutting off a paperboard. The mechanic of an actual paperboard cutting using a crank motion
is shown in Fig. 2. A cutting blade is fixed on the upper holder and reciprocally moves with acertain speed. The cutting force is measured by using four load cells mounted underneath thelower base table. Figure 3 illustrates a quarter-stroke motion of a cutting blade of the crank machine. At the bottom-dead position, the blade tip is slightly crushed. In this situation, the unevenness of the blade height causes an eccentric tip crushing and it is needed to detect such stateof pressure unbalance using a non-destructive inspection method. In this case, the blade is moved
in the upward/downward direction resulting from the rotation of the crank mechanism, which hasa speed NC rpm and the length of an eccentric arm e = 25 mm. The blade tip continues to penetrate the paperboard until it reaches the bottom-dead point (Fig. 3(b, c)) where the paperboard isseparated before returning to its initial position (Fig. 3(d)). The crushing of blade tip occurs at
the bottom-dead point during paperboard separation and this is where the severe crushing andeccentric cutting pressure of blade tip are reduced using the shimming technique.
65

Fig. 2 Experimental apparatus (crank type machine).

Fig. 3 Mechanic of paperboard cutting on the crank motion.
Figure 4 shows (a) a photograph of the crank machine cutting area, (b) the blade metal holder
and (c) composition of a blade and machine base table. Before starting the experiment, the blade
specimen was initially inserted in a blade holder (jig) and gripped by two parallel steel bars. A
resin sheet was also placed at one side of the blade for the purpose of additional gripping (Fig. 4(c)). Two hardened counter plates (SUS630) with average hardness of 550VHN were placed on
both the cutting and bottom tip sides. The crank-shaft rotation speed of the press machine was set
to NC = 5 rpm.
Figure 5 shows the schematics of a bridgeless die board and an embedded blade. Instead ofusing the metal holder, we used a new die-board system, which is called the self-levelingbridgeless die. This die is composed of a resin die board, which has a dead-end slot for fixing the
cutting blade and also adjusting the unevenness of blade height. The hardness pa of Unilate resin
was estimated by using a steel ball of 11-mm diameter subjected to 600 N as pa= 169.4 MPa [8].
66Fig. 4 Schematics of cutting load and AE signal measurement at cutting zone.
The (total) cutting-line force f (kN・m-1) and the AE signal amplitude a (mV) were measured
with respect to the elapsed time t. Here, f is the sum of forces measured by the four load cells(f = f1 3 f2 3 f3 3 f4). f has the maximum value at the bottom-dead point, and this is named
as the maximum idle line force fimax =5?. When a certain value of the force from a cutting
blade is applied on the counter plate, the four load cells will balance the four generated eccentric forces. This is because unbalance always exists at the initial stage on the composition of the machine table and the upper blade holder. From the four force values generated
at the four load cells, f#, f%, f&, f', the force difference Δf is defined as the largest difference
among the four force values. When the crank angle θ = 0 (at the bottom-dead point), the Δfis denoted as Δfimax.
The cutting blade was straight and had the specification as follows: the tip angle α = 42J,
the length of blade L = 40 mm, the blade thickness b = 0.72 mm, the height of blade H = 23.6mm, the hardness of tip = 600VHN. The contact condition between the blade tips and thecounter plates was considered as non-lubricant.
From our pre-stage inspection, the AE signal measurement was carried out at the rangeof fimax = 70S90 kN・m-1 while actual works were empirically processed with fimax = 60S150
kN・m-1. A white-coated paperboard was used as the work material, which had the nominal basic
weight of 350 g・m-2, the thickness of 0.45 mm, the in-plane breaking strain εB = 1.85% and thein-plane tensile strength σB = 33 MPa in the MD (machine direction). The paperboard has a long67

rectangular shape with the width of 100 mm. The cutting was carried out across the MD up to n
= 10 times for each board (ambient conditions: relative humidity, 42%, temperature, 297K).

Fig. 5 Bridgeless die, resin die board with dead-end slot.
The AE transducer used was resonant type of 200 kHz, and was set behind the counter plate(distance from the cutting-edge end, La = 25 mm) by a cyanoacrylate adhesive. AE signals wererecorded with a sampling time of 44 µs (9 kHz). The waveforms of AE signals were detected in adigital oscilloscope.
Results and Discussion
Comparison of bridgeless die with normal die
Figure 6(a) is an example of measured reaction forces for each of the load cells. A force different Δfimax was evaluated from these results at the bottom-dead position. Figure 6(b) shows theforce different Δfimax at the bottom-dead point when the maximum line force fimax was varied. Incase of n = 5 and for fimax/fC0 < 0.7, the relationship between Δfimax/fC0 and fimax/fC0 was linearly
approximated for the non-arranged (normal) die type (without shimming) and for the bridgelessdie type. Those results were expressed with Eqs. (1) and (2), respectively. Here, fC0 is the criticalyielding line force of cutting tip at the hazard layer, defined as 1.15σY(core)w1b = 234.8 kN・m-1[5].

Δfimax/fC0 = 0.0064 + 0.042 fimax/fC0(w/o shimming)(1)

Δfimax/fC0 =(bridgeless)(2)

0.050 fimax/fC0

As the value of Eq. (1) is larger than that of Eq. (2) for fimax/fC0 < 0.7, it was found that thebridgeless die type was superior for reducing the cutting line force difference, compared with the
non-arranged die type. Needless to say, since the bridgeless die board has the time dependencywith respect to the sinking of blade [7, 8], the relationship between the load balance and the cutting times must be further investigated. In this situation, we discuss with the AE signals observedat the cutting point in those two die board systems.
68(a) Line force during idle cutting for each load cell.

(b) Force difference.
Fig. 6 Relationship between maximum (total) line force and force difference.
Typical AE signals detected and its corresponding cutting line force f are shown in Fig. 7.
Here, the cutting blade was embedded in the resin die board (Unilate) with the thickness of 8 mm
and with the groove depth of 6 mm. An AE signal was detected near the collision region between
the cutting edge tip and the counter plate. Since the blade tip thickness tC is varied with respect tothe cutting times n, the cutting line force difference Δfimax and AE signal amplitude were evaluated at n = 5, 10.
69

Fig. 7 Line force and AE signal with bridgeless die.
In this work, we discuss the AE signal occurred at loading conditions with fimax/fC0 = 0.3~0.4.
Namely, the primary aspect is to investigate the correlation between the load balance and the AE
signal. Figure 8 illustrates a cutting resistance model of paperboard by using a reciprocal motion.
Through all the experiment, it was found that the breaking-down process behaved as the residualload response (2) or (3) as shown in Fig. 8 when the pressure of blade tip was not even in thelongitudinal direction due to the eccentric crushing of the blade tip. If the crushing of blade tip is
not even, the paperboard is not properly cut off at one time along the longitudinal direction of theblade.

Fig. 8 Cutting resistance model.
70(a) Non-arranged die type, fimax/fC0 = 0.38.

(b) Bridgeless die type, fimax/fC0 = 0.31.
Fig. 9 AE signal waveform occurred at collision of tools (n = 10).
Figure 9(a) shows an AE signal waveform, which occurred upon the collision of the bladewith the counter plate in case of non-arranged (normal) die type, while Fig. 9(b) shows thesame in case of bridgeless die type. The peak amplitude of a detected signal, A, is related to(f C22 – fCR2)0.5,
which is a function of the magnitude of fC2 [12]. Here, the square of A corresponds to the released elastic energy of cutting line force fC2. According to Fukuzawa et al. [11, 13], the relation of fC2 and A and the relation of tC and A can be expressed as follow:
A/Ao = Cw (tC/t)
fC2 = kfC2A (A/Ao) + fC20A

(3)
(4)
71

Here, A0 is the referenced level, such as the pencil-lead break test. The coefficients Cw, kfC2A
and fC20A are experimentally decided for the specified measuring condition. From Eqs. (3) and(4), if the crushed thickness tC of blade tip is stable and uniform along the longitudinal direction of the blade, the magnitude of fC2 and the peak amplitude of AE signal are expected toremain the same. The peak amplitude of AE signal A was 3 times higher for the bridgeless die
than that for the non-arranged die. The force drop of fC2 − fCR with Fig. 9(a) was supposed tobe less than that with Fig. 9(b). Namely, the bridgeless die seemed to be adjusted much morethan the non-arranged die.
Figure 10 shows the frequency spectra, which were derived from the AE signals of Fig. 9.
The sample size was 2048 and the sampling time was 0.11 ms. The frequency spectra calculated with FFT show that the main components were at 10, 30, 50, 70, 120, 195, 510, and1252 Hz. Comparing the non-arranged die type with the bridgeless die type, the following
features were detected. (i) The spectrum of AE signal at frequencies less than 70 Hz was almost invariant; (ii) The spectrum at frequencies above 1500 Hz also seem to be almost identical; (iii) The intermediate frequency spectrum, from 70 Hz to 1500 Hz, remarkably varied.
This difference arose from the range of N-1 (Fig. 9(a)) and B-1 (Fig. 9(b)); (iv) The peak amplitude of AE signal on the bridgeless die type was larger than that on the non-arranged (normal) die type. From Figs. 9 and 10, the possibility for detecting the cutting line force balance along the longitudinal direction of blade line was confirmed.

Fig. 10 Frequency spectra of AE signals (beneath the counter plate).
Diagnosis of upper half, lower half die cutter system by the pencil-lead break test
In order to discuss the frequencies observed at the counter plate when the cutting blade
collided, the pencil-lead break test [15] was applied to both the upper and lower half experiment system. Figures 11(a-c) show a setup of AE sensor and a target position for the upper
half system, while Fig. 11(d) shows a target position for the lower half system. A pencil lead,which was used for this test, had the hardness of 2H, the diameter of 0.5 mm and the protruded length of 3 mm. Figure 12(a) shows an example of frequency spectrum of AE signal
measured at Channel 1 (in the lateral direction of cutting blade) for the normal die and thebridgeless die. Several zones of peak frequency, at 800~1000 Hz and at 2000 Hz were detected in both die systems. A peak of 582 Hz was detected in the bridgeless die, which was
supposed to correspond to the occurrence of a peak at 510 Hz in Fig. 10.
72

Fig. 11 Sensor position and impact position.
Regarding the measurement at Channel 2, the signal level was quite low and the difference
between the normal die and the bridgeless die could not be confirmed up to 5 kHz. Figure12(b) shows an example of frequency spectrum of AE signal measured at Channel 3 (by the
AE transducer embedded behind the counter plate), where no large spectrum peak could beseen up to 4 kHz. When the sampling time was changed for monitoring the low frequency
zone, we could detect spectrum peaks at 20, 50, 85, 110, 150 Hz in the low frequency zone.
From the past experiment of this die cutting system [14], we knew that the natural frequencies of paperboard cutting were roughly 150 and 190 Hz, measured by the strain gauge
method. This seemed to be caused by the spring motion of supporting mechanism with four
load cells, which deform with a large cutting force. It appears that the pencil-lead break impact did not sufficiently excite the large mass of the lower table. Since we could detect thefrequencies of 120 and 195 Hz in case of the bridgeless die (Fig. 10), these spectrum peaks
correspond to the lower table motion caused by the spring effect of the four load cells.
Correlation between force different and maximum amplitude of AE signal
From the comparison of Figs. 9(a) and (b), we find that the peak amplitude A of AE signal
generated by the collision of blade tip with the counter plate varies with the force different
Δfimax. That is, the unevenness of blade tip pressure reduces the peak amplitude A. In order toverify this characteristic of force difference, a shimming of lower counter plate was carriedout. Figure 13 shows the schematic of cutting test apparatus.

73

(a) Pencil break response at Channel 1 of upper half system.

(b) Pencil break response at Channel 3 of lower half system.
Fig. 12 Frequency spectrum of AE signal (Channels 1 and 3).

Fig. 13 Schematic of cutting apparatus.
74Fig. 14 Relationship between Δfimax and A.
A metal shim of 10 µm thickness was inserted beneath the lower counter plate and was set
up from the front side at a distance of s = 0~65 mm. The pressure distribution on the blade tip
was varied with s. The cutting test was carried out 10 times by setting the distance of s = 0, 5,… 65 mm with the maximum line force at the bottom-dead position chosen as 48 kN・m-1 andthe rotary speed of crank shaft of 5 rpm. The cutting blade was a round-edge knife, with the
tip radius r = 20 µm.
Figure 14 shows the relationship between the force difference Δfimax and the AE peak amplitude A. Although the relationship between the position s of the shim and the force difference was not always linear, there was an optimum position of s for decreasing the force difference. From Fig. 14, we can confirm that the peak amplitude A tends to decrease with theforce difference. The residual line force fCR tends to increase with the force difference. Thus,
we find a negative correlation between the force difference Δfimax and the peak amplitude A of
the AE signal.
Conclusions
In order to clarify the AE behavior of the cutting blade collision, by using a non-arranged
die board (normal die without shimming) and a bridgeless (resin) die board, a white-coatedpaperboard of 0.45-mm thickness was experimentally cut off. AE analysis techniques wereapplied to the cutting process. The summary of this work is as follow:(1) The bridgeless type (resin board) is superior in reducing the line force difference among
the four load cells when the crank type cutting machine is used.
(2) The peak amplitude of AE signal tends to be large when the line force difference among
the four load cells is smaller.
(3) When the line force difference among the four load cells is increased, the spectrum of AE
signal on the intermediate frequencies tend to decrease.
(4) From the above points (2) and (3), the possibility for detecting the line force eccentricity isshown.
75

Acknowledgement
This work was supported by Nagaoka University of Technology, Research and Development Center Project of Grant Number 16R.
References[1] Hesse, F. & Tenzer, H.J.: Grundlargen der Papier-verarbeitung, VEB Verlag fur Buch und
Bibliothekswesen, (1963), pp. 58-60, Leipzig (in German).
[2] Inaba, Y.: Flatbed Diecutting and Maintenance of Diecutter (DSJ ’98 Seminar text),
CARTON BOX, 17 (200), (1998), 17-20 (in Japanese).
[3] Grebe, W.: Bandstahlstanzwerkzeng mit mindestens einem eine Schneide aufweisenden
bandformigen Stanzmesser, Patent of Federal Republic of Germany, P31-35-980.9 (1981), (inGerman).
[4] Abdul Hamid, D., Nagasawa, S., Fukuzawa, Y., Kojima, K. & Hine, A.: Mechanical Characteristics of Center Bevelled Double Structure Blade, Materials Transactions, 49(5), (2008),
1199-1201.
[5] Abdul Hamid, D., Nagasawa, S., Fukuzawa, Y., Kojima, K. & Hine, A.: Pressure Balance
Characteristic of a Double Structure Blade under Quasi-Statically Reciprocal Loading Condition, Journal of Solid Mechanics and Materials Engineering, 2(10) (2008), 1253-1264.
[6] Nagasawa, S., Murayama, M., Fukuzawa, Y. & Sadamoto, A.: Mechanics of Die Cuttingfor Paperboard Materials Processing (Introduction to Fundamental Mechanics), Kameda Book
Service (2004), pp. 115-119 (in Japanese).
[7] Nagae, S., Nagasawa, S., Fukuzawa, Y., Katayama, I., Yoshizawa, A. & Furumi, T.: Sinking characteristics of blade height position into resin die board (1st report), Proc. of Japan
Soc. Technol. Plast. Spring Conf., Tokyo (2002), pp. 171-172 (In Japanese).
[8] Nagae, S., Nagasawa, S., Fukuzawa, Y., Katayama, I., Yoshizawa, A. & Furumi, T.: Sinking characteristics of blade on holding base plate (2nd report), Proc. of Japan Soc. Technol.
Plast. Autumn Joint Conf., Hamamatsu (2002), pp. 319-320 (In Japanese).
[9] Sadamoto, A., Yamaguchi, T., Nagasawa, S., Fukuzawa, Y., Yamaguchi, D. & Katayama,
I.: Analysis of sound radiated by paperboard die cutting, Audio Engineering Society Proceedings of 21st Int. Conference, St.Petersburg, June (2002), pp.136-139.
[10] Nagasawa, S., Fukuzawa, Y., Suzuki, S., Katayama, I. & Sadamoto, A.: Acoustical Estimation of Die Cutting Process, Proceedings of International Conference on Advanced Manufacture, Nov. 28-Dec. 2 (2005), Taipei, ROC, pp.1085-1091.
[11] Suzuki, S., Fukuzawa, Y., Nagasawa, S., Katayama, I. & Iijima, H.: Acoustic Emission
Characteristics on Variation of Cutter Tip Thickness during Cutter Indentation on Paperboard,
J. of the Japan Soc. Technol. Plast., 46(538), (2005), pp.1061-1065 (In Japanese).
[12] Nagae, S., Nagasawa, S., Fukuzawa, Y., Hine, A. & Katayama, I.: Effects of Quenched
Hardness on Cutting Resistance and Crushing of Blade Tip Indented on Paperboard, J. of theJapan Soc. Technol. Plast., 45(524), (2004), pp.742-746 (In Japanese).
[13] Fukuzawa, Y., Nagasawa, S., Suzuki, S., Katayama, I. and Sadamoto, A.: Analysis acoustic emission and sound during the paperboard of cutting process, J. Mat. Proc. Technol. ,192-193, (2007), pp. 134-138.
[14] Nagasawa, S., Sudo, A., Fukuzawa, Y., Sugita, A. & Katayama, I.: Dynamic Characteristics of Blade Cutting Force during Pushing Shear on Paperboard, J. of the Japan Soc. Technol.
Plast., 47(543), (2006), pp.309-313 (In Japanese).
[15] Higo, Y., Inaba, H.: Convenient AE sensor calibration method, in: National Conferenceon Acoustic Emission, 7, (1989), pp. 185-192.

76

DAMAGE ONSET AND GROWTH IN CARBON-CARBON COMPOSITEMONITORED BY ACOUSTIC EMISSION TECHNIQUE
ARIE BUSSIBA1, ROMANA PIAT2, MOSHE KUPIEC1, RAMI CARMI1, IGAL ALON1
and THOMAS BÖHLKE2
1)
Nuclear Research Centre Negev, P.O. Box 9001, Beer-Sheva, Israel;
2)
Institute of Engineering Mechanics, University of Karlsruhe, Postfach 6980
76128 Karlsruhe, Germany
Abstract
Carbon-carbon (C/C) composites with different densities (1.8, 1.35, 0.8 g/cm3), produced bychemical vapor infiltration (CVI) were tested mechanically under quasi-static loading in bending
mode of uniform and notched beams. Acoustic emission (AE) technique was used to track themechanical threshold parameters as well as to characterize the damage build-up profile to fracture. In both states of stress (uniform and tri-axial), threshold values detected by AE activity indicated the damage onset. The sensitivity of the AE method to the density changes was apparent
by variations of the threshold values. Decreasing the density from 1.8 to 0.8 g/cm3 decreases thethresholds values (σth, KIth) from 25 to 2 MPa and from 0.8 to 0.1 MPa·m1/2, respectively. Threestages in damage evolution to fracture were observed: Stage I, with no AE activity, Stage II,
gradual/linear growth in AE counts up to an abrupt jump and Stage III with exponential increasein AE counts. Similarity in profile and threshold value were found between the cumulative AE
counts vs. strain data and the crack density vs. strain predicted by micro mechanical model, indicating the importance of using AE in monitoring the damage evolution in composites with regard
to structural integrity aspect. Wave analysis using fast Fourier transform (FFT) and short-timefast Fourier transform (ST-FFT) points out four possible failure micro-mechanisms: multilayer
cracking, breaking of fiber bundles, interfacial matrix de-bonding and micro-crack growth.
Breaking of fiber bundles was found to be the major damage mechanism for the low density C/Ccomposite.
Keywords: C/C composites, Threshold parameters, Damage accumulation, Structural integrity,
FFT.
Introduction
Carbon-fiber reinforced carbon-matrix composites, known also as carbon-carbon (C/C) composites, have densities in the range of 1.6-2.0 g/cm3. Their unique physical properties, such as low coefficient of thermal expansion, high thermal conductivity and high thermal shock resistance, combinedwith high strength at high temperatures (3000°C) and of increasing strength with temperature, makethem good candidates as structural materials for aerospace and defense applications [1]. Enhancing
and integrating such composites in load-bearing engineering components require special attention in
the assessment of structural integrity and life prediction procedures [2]. The complexity of the fracture process of both initiation and evolution stages of multiple flaws in different modes (matrix
cracking, interfacial de-bonding, fiber fracture, delamination and more) makes this task of reliable
failure prediction more difficult than for metallic materials. For metals under quasi-static and cyclicloading, threshold parameters, such as threshold stress intensity factor in fatigue and in stress corrosion cracking, ΔKth and KISCC, respectively, are crucial in order to increase the performance

J. Acoustic Emission, 27 (2009)77© 2009 Acoustic Emission Groupreliability of structure in service. These factors are well defined and the experimental proceduresin acquiring them are documented and given in various standards. In contrast, it is very difficult
to define such parameters in composites due to the complicated structure and the variation in the
lay-ups of composite materials and the inherent defects due to the fabrication process.
The current study adopted the AE technique to evaluate threshold parameters during loadingof C/C composites under various states of stress (uniform and multi-axial in the presence ofnotch) [3]. In order to examine the sensitivity of the AE method to define useful and reliable mechanical properties, three C/C composites with different densities were designed (symbolized
range of porosity from high to low) and the mechanical response was monitored simultaneously
by AE. In addition, AE data was used to characterize the damage accumulation profile to fracture
and to specify the possible active micro-failure modes by using waveform-processing analyses,
FFT and ST-FFT [4]. Finally, an attempt to compare between a micro-mechanical model developed for some composites with AE experimental data is suggested and discussed.
Materials Production and Experimental Procedures
In general C/C composites are produced by isothermal, isobaric CVI. This process consists ofinfiltrating carbon from hydrocarbon gas and depositing on carbon fibers in a hermetically closed
high temperature reactor. In the current work the CVI process was performed at 1095°C with total pressure of 30 kPa. The carbon was deposited in the form of a pyrolytic carbon matrix aroundthe fibers resulting in a layered morphology [5, 6]. For the infiltration, carbon fiber felts made ofrandomly oriented chopped carbon fibers were used as performs. The fiber volume content was
about 12% (of the total volume) and the diameter of the fiber was of the order of 10 µm. The
felts were infiltrated over different times to obtain matrices with different densities; 120 h resulted in high density (HD) of 1.8 g/cm3, 45 h with medium density (MD) of 1.35 g/cm3 and 24 hwith low density (LD) of 0.85 g/cm3. The infiltration was carried out in a vertical gap reactor using pure methane as precursor gas. More details of the infiltration procedure are given elsewhere[7, 8]. Typical microstructure of the HD composite is shown in Fig. 1(a-b). The layered structurearound the fibers was revealed after fracture (Fig. 1c) using scanning electron microscopy. Moreabout material characterization including uses of selected area electron diffraction technique toobtain orientation angle as a function of the distance to the fiber surface are reported elsewhere[9].
Flexural tests were conducted on bars with dimensions of 5 x 7 x 55 mm using three-point
bending (3PB) and four point bending (4PB-inner span of 20 mm) set-ups, with outer span of 40
mm. The deflection at the outer surface of the specimen was measured by a sensitive extensometer (0.05 mm/mm full scale). Quasi-static loading with velocity of 0.5 mm/min was carried out
using a computerized machine with a load cell of 10 kN. The flexural properties, modulus, stressand strain at fracture were calculated according to the standard expressions for bending beam
[10].
Fracture toughness (KIC) was determined by loading of notched bar in 3PB set-up, at test velocity of 1 mm/min. Specimen dimensions were 4 x 5 x 55 mm and notch depth was 2 mm. The
V-notch with radius of 30 µm has been treated as a sharp crack. Clip-on gauge was attached at
the front of the notch measuring the crack opening displacement (COD). KIC was calculated according to the standard expression for stress intensity factor [10].

78

(a)

(b)

(c)

50µm

10µm

Fig. 1. (a-b) Polarized light microscopy micrographs showing microstructure of the HD C/C composite, (c) the layered morphology around the fibers as revealed after fracture.
AE technique was used to detect the very early damage during loading in 3PB of the notched
and un-notched specimens of the three composites of different density. The damage initiation
monitored by AE, was quantified by means of mechanical properties obtained at various tests
that were noted as threshold parameters. AE activity was tracked continuously to characterize thedamage accumulation profile up to fracture. The AE data, such as counts, amplitude, duration,
peak frequency and more, was analyzed in order to detect possible sequence of fracture events.
This was also based on waveforms analyses by applying FFT, ST-FFT and Wavelet functions inorder to elucidate possible active failure mechanisms (fibers fracture, matrix and interface cracking, micro-crack growth). More details on the AE set-up, sensors, amplification and thresholdvalue are given elsewhere [11].
The fracture modes classification using scanning electron microscopy for the HD and MD
were already reported at previous works [11, 12] and here attention is given to the LD composite.
Experimental Results
Mechanical properties
Figure 2 depicts the dependency of the flexural properties on the composite density. As
shown, almost linear behavior was observed with sharp influence of the modulus and fracture

Fig. 2. Flexural modulus, fracture stress and strain vs. density.
79Fig. 3. Fracture toughness and elastic stress intensity factor vs. density.
stress and moderate one of the strain as the density decreases. Figure 3 shows the effect of thedensity on the fracture resistance properties. As for the uniform properties, the fracture toughness(KIC) drops off significantly as the density decreases. The same trend was noticed for the linear
elastic stress intensity factor (KIlin.), which indicates on macroscopic deviation from linearity in
the K–COD curve. This deviation indicates an intensification of the AE count rates resulting
from a transition in damage accumulation mechanism as will be discussed later.
Figures 4(a) and 4(b) illustrate the AE activity with flexural stress in terms of AE count rates as afunction of the flexural strain for the high and low density composites, respectively. Although themechanical response is almost linear, the AE count rates intensified as the stress increases to fracture.
The threshold strain (eth) and the corresponding stress (σth) are noted in each composite and represent
the early damage progress monitored by AE. As expected, the threshold values decrease as the composite density decreases. Actually, for the LD composite, nearly no threshold is observed, namely AE
occurred from the start of loading. Figure 4(c) shows a significant change in both values with moderate change in the composite density. This trend points out that minor variation in the composite density, as a result from manufacturing process, will alter considerably the threshold values detected by
AE.

Fig. 4. Flexural stress and AE counts rates vs. flexural strain in different composite densities: (a) HD(left). (b) LD (right).
80

Fig. 4(c). The dependency of the threshold stress and strain vs. C/C composite density.
AE response in tri-axial stress mode
Figure 5(a) and 5(b) display the AE activity with the stress intensity factor in terms of counts rate
as a function of the COD with the composite density. In contrast to the gradual increase in the AE
counts rate in the former case, here the rate is almost constant at the linear portion of the curve andbeing intensified as slow crack growth occurs. The irregularities at this stage as manifested by crack
extension, arrest and re-occurrence in alternately form are being reflected by peaks in AE counts(dashed arrows in Fig. 5a). This type of AE profile pointes out on the damage accumulation mechanism up to the localized crack growth. Again, the onset of the damage in the presence of notch ismarked by the threshold COD (CODth) and the related stress intensity factor (KIth). Fig. 5c emphasizes the great influence of the density on the threshold values.

Fig. 5. Stress intensity factor and AE counts rate vs. COD in different composite densities: (a) MD,(b) LD.
Damage accumulation profile
The damage accumulation profile in terms of AE is demonstrated in Fig. 6(a-b) in the formof cumulative AE counts for the uniform and notched specimens, respectively. While almost linear mechanical response is well depicted in case of flexural mode, an exponential type of contour
is obtained for the AE response. Actually, there is an intersection point where the damage behavior is being changed from linear to exponential shape. This type of behavior was also observed
81Fig. 5(c). The dependency of the threshold stress intensity and COD vs. density.
for the other composite densities. For the notched one this type of response is characterized up to
the linear portion of the curve (denoted by KIlin) and the deviation is due to incremental crack
growth. Thus, the damage evolution by AE data in such composites follows the exponentialfunction of the strain or COD. As will be discussed later, this type of AE damage agrees with theexponential curve of the crack density with the strain for some composites. Beyond the potentialof the AE to detect threshold values, some data on the transition in damage accumulation and on
the entire damage to fracture is being detected and displayed in Fig. 6(c). As shown, more than
one order of magnitude in the total AE counts at fracture is monitored for the HD as compared to
the LD composite, while for the transition point two orders of magnitude are found. The mentioned trends indicate clearly that a moderate decrease in density affects dramatically the damage
evolution and quantity for this composite subjected to quasi-static loading.

Fig. 6. Damage accumulation profile for; (a) flexural mode, (b) stress gradient.
82

Fig. 6 (c) cumulative AE counts at fracture and at intersection point vs. composite density.
Discussion
The AE findings in both stress modes (uniform and localized) indicate clearly the existenceof threshold values, below which no damage is being initiated. Actually, three stages of damage
evolution can be noticed from the AE extended curve profile shown in Fig. 7(a). The first stage
ends at a threshold strain (or alternatively by the threshold COD in the case of a notched specimen) as observed in other composite materials [13, 14]. Here, no AE activity is detected at thisrange of loading, characterized by a reversible mechanical behavior. The second stage is almost
linear, ending by a significant jump in the cumulative AE counts. This phenomenon indicatesdamage intensification, followed by the third stage with exponential-type behavior up to final
fracture. This sudden jump is the result of coalescence of micro-defects or cracks to form significant local damage. Similar type of damage accumulation behavior was reported for glass-fiber
epoxy as shown in Fig. 7(b). As noticed, the threshold parameters for the C/C composites are notaccompanied by a measurable change in the stiffness property, whereas in PVC/glass-fiber composite, the threshold is followed by a deviation from linearity reflected by stiffness decrease withfurther loading; cf. Fig. 7(c). In case of an agreement in threshold behavior in both mechanicaland AE responses, as shown in Fig. 7(c), the AE is a supporting tool. In the current tested C/Ccomposites where no mechanical changes are noticeable especially for the HD and MD composites, the AE is the only means of tracking threshold mechanical value, which characterizes damage onset as well as damage evolution profile. This behavior is also emphasized in flexural testof Si3N4+SiC [15], where the onset of damage monitored via AE activity is denoted by σAE (Fig.
7(d)), a stress which is well below the elastic limit, σe. Moreover, it mentioned that the allowablecalculated stresses σmax in all the components made by this ceramic should not exceed 60 MPa, avalue which is close to the σAE as shown in Fig. 7(d). Hence, use of threshold stress defined by
AE in structural integrity consideration is being used more practically.
The current research shows clearly the sensitivity of the AE technique in determining thethreshold values with the variation of the C/C composite densities. Here, the change of the density
was considerable from one to another and the AE response was changed accordingly. However,more refined work by English [13] shows that modified polyester by adding 6-phr elastomer in thesystem of PVC/glass-fiber composite, nearly doubled the threshold strain for the onset of matrix
cracking, from 0.5 to 0.9%. However, this change in the AE response is not accompanied by a83

significant variation of the standards mechanical properties namely, stress and strain at fracture. Thisknowledge is important in composite structures where no damage is allowed, especially in ones
where no mechanical behavior is being altered. Therefore, by getting curves with different processing parameters as shown here (Figs. 4(c), 5(c), 6(c)) for different time of infiltration and withvarying stress modes and loadings (quasi-static, cyclic, constant), will gain more reliable performance and more insight on the characteristics of structural integrity of composites being usedin critical load bearing components.
(a)

(b)

(c)

(d)

Si3N4+SiC

Fig. 7. The three stages in damage accumulation for; (a) C/C MD composite, (b) Glass fiber composite, (c) threshold strain and deviation from linearity in stress-strain curve for PVC/glass-fiber
composite, (d) the onset of acoustic emission activity denoted by σAE in flexural test of
Si3N4+SiC.
The validity of the non-destructive evaluation techniques in assessing microstructures, mechanical properties, deformation, damage initiation and growth is not doubtful. However, tremendous efforts are being devoted in developing damage models for composite materials under quasi-static [1618] and fatigue [19-21] loading, usually based on micro-mechanics. Such models predict changes inthe stiffness of composite materials due to cracking [22] and others evaluate damage evolution due tochanges in the initial material symmetry caused by damage. For example, Talreja [16] suggested a
micro-mechanical model for inter-laminar cracking in composite laminates, and determined the damage parameter for a bilinear stress–strain curve in terms of crack density in an exponential behavior84

as shown schematically in Fig. 8(a). This prediction was found experimentally [23] (Fig. 8(b)) as
well as in the current study for the LD composite (Fig. 8(c)) with almost bi-linear curve. As shown,the micro-mechanical model predict also a threshold strain ( ) as also detected in the experimental
works.
(a)

(c)

(b)

Fig. 8. Comparison between the predicted damage profile and threshold parameter to the experimental ones for various composite materials; (a) Talreja micro-mechanical model [16], (b) Kistnerwork [23], (c) current work.

Fig. 9. (a) Stress and peak frequency density vs. flexural strain, (b) Amplitude, duration and stressintensity factor vs. crack opening displacement.
Finally, some insight views on the active micro-mechanisms failure are presented in Fig. 9(a-b)
resulted from AE wave analysis. The peak frequencies (PFs) density and the stress versus flexural
strain are shown in Fig. 9(a). Four main PFs are observed up to fracture with different level of density. The dominant one appears at 330 kHz and its density increases as the strain increases, the second one appears at 130 kHz and its density increases moderately as approaching to the final fracture,
the third one appears at 190 kHz where it is almost unchanged in the density up to fracture and thefinal one begins to be dominant at 550 kHz at the later stage of loading. In case of notched specimen,
amplitude, duration and K are displayed versus COD (Fig. 9(b)). Following Siron et al. [24] approach based on AE waveform parameter analysis and microscopic observations, the early stage ofloading is characterized by low duration values associated with low/medium amplitudes. Further on,
as approaching to the K transition from elastic to inelastic mechanical behavior (see dashed arrow),
medium duration values and amplitudes are observed. The inelastic region without microscopic crack
growth is depicted by medium duration and high amplitude. Finally, macroscopic crack growth isaccompanied by high values of duration and amplitude. Applying FFT analysis of an AE wave taken
85

close to fracture (not shown), shows the PFs in the frequency domain (Fig. 10(a) and in time domainusing ST-FFT function (Fig. 10(b)) and in three-dimensional display using wavelet function. From
the above results one can assume that four dominant micro-mechanisms controlled/involved thefracture process of LD composite with correlation also to Siron [24] approach. At the early stageof loading, low and medium frequencies are dominant and maybe related to cracking of the multi-layered matrix and interface matrix de-bonding, respectively (Figs. 11(a,b)). With further
loading, the dominant frequency is related to fiber failure (Fig. 11(c)) and the high frequency
maybe is associated with propagation of micro-cracks. The occurrence sequence of the mentioned micro-failure mechanisms is shown in Figs. 9(b-c) whereas the multilayered cracking is
the first to be active, followed by matrix debonding, micro-cracks and finally bundle fibers failure. Ni et al. [4] presented a similar investigation on carbon-fiber-reinforced carbon composites.
However, additional data using artificial intelligence techniques such as partitional clustering
analysis or unsupervised pattern recognition for classification of AE events may discriminate between various failure micro-mechanisms [25], and give more comprehensive view on the sequence of events during facture process of C/C composites.
(a)

(b)

Fig. 10. AE waves analyses in LD composite: (a), (b) FFT with frequency domain.

Fig. 10(c). ST-FFT with frequency and time domains data; 3-D display of wavelet analysis.
86

(a)

(b)

(c)

(d)

200µm
Fig. 11. Micro-failures mechanisms; (a) multilayered cracking, (b) interfacial matrix de-bonding, (c)bundles fiber failure , (d) micro-crack growth.
In summary, the current research emphasizes the power of AE method in detecting the very earlydamage in C/C composite materials, which can be quantified by means of threshold value both inuniform and in intensified stress field. This finding is important from structural integrity viewpoint
where conventional methods failed to detect and evaluate damage initiation. In addition, the AE datacharacterizes the damage accumulation profile up to fracture and points out the transition in damage
progress. Finally, by using enhanced AE wave analyses as shown briefly here, we can evaluate thesequence of events during fracture process up to the final fracture of such C/C composites.
Acknowledgements
The authors thank S. Lichtenberg, O. Deutschmann for the sample synthesis and R. Piat
gratefully acknowledges the financial support of the German Research Foundation (DFG, Centerof Excellence 551 in Research on "Carbon from the gas phase: elementary reactions, structuresand materials") and to B. Rezink of using Fig. 2a.
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R. Piat, T. Böhlke, I. Tsukrov, B. Reznik, O. Deutschmann and A. Bussiba, The Annual World Conference on Carbon, Biarritz, France, 14-19 June (2009).
9) ASTM Standard D790-95a: (1996), 155.
10) ASTM E399-90 (Re-approved): (2005), 462.
11) A. Bussiba, M. Kupiec, S. Ifergane, R. Piat and T. Bohlke: Comp. Sci. and Tech., 68(2008), 1144.
12) A. Bussiba, M. Kupiec, R. Piat, T. Bohlke: Carbon, 46 (2008), 618-630.
13) L. K. English: Materials Engineering, TR 103-75-6 (1987).
14) M. Bourchak, I. R. Farrow, I. P. Bond, C. W. Rowlandand and F. Menan: International
Journal Fatigue, 29 (3) (2007), 457–470.
15) G.A. Gogotsi: Ceramics International, 35, (2009), 1109-1114.
16) R. Talreja: Proc. of the 7th Int. Conf. on Fracture, Houston, TX (1989), pp. 2191–2199.
17) Z. Hashin: J. Appl. Mech., 54 (4) (1985), 121.
18) K. H. Leong and J. E. King: Joint FEFG/ICF Int. Conf. of Fracture Eng. Mat. and Structures, Singapore, (1991), 251-256
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20) N. V. Akshantala and R. A. Talreja: Mech. Mater., 29(7) (1998), 123.
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88

FUNDAMENTAL STUDY ON INTEGRITY EVALUATION METHOD FOR
COPVS BY MEANS OF ACOUSTIC EMISSION TESTINGYOSHIHIRO MIZUTANI, SOTA SUGIMOTO, RYOSUKE MATSUZAKI
and AKIRA TODOROKITokyo Institute of Technology, Department of Mechanical Sciences and Engineering,
2-12-1-I1-70, Ookayama, Meguro, Tokyo 152-8552, Japan
Abstract
It is important to evaluate the integrity of composite overwrapped pressure vessels (COPVs)
used for space applications. In this study, applicability of acoustic emission (AE) monitoring to
the integrity evaluation of COPV materials was evaluated by using coupon-level specimens. It
was found that by evaluating emissions during load-hold and relationship between AE signal
peak amplitude and duration, damage occurrences during the test can be monitored. We also
found that Kaiser effect and Felicity effect can be used for evaluating previously induced damages. Detectable minimum damage size for previously induced damage by AE method may besame or less than those by ultrasonic testing.
Keywords: COPVs, CFRP, Impact damage, Integrity evaluation
Introduction
Composite overwrapped pressure vessels (COPVs) are widely used for space applications
such as accumulators for space satellites and propellant tanks for rockets. It is known that
COPVs are likely to suffer complicated internal damages by being dropped, by rough handling,or by impacts of dropped tools. Even when such internal damages occurred, only invisible small
damages are caused on the impact surface in many cases. Therefore, it is difficult to find thesedamages from outer surface by visual testing (VT). Since internal damages may reduce strengthof the vessels, a reliable nondestructive testing (NDT) for the damages is desired. Several researchers [1-3] evaluated the applicability of various NDT methods for COPVs, although the optimal inspection technique is not yet found.
Acoustic emission (AE) method is one of the candidates for the inspection method of COPVs.
Several AE standards, such as ASME section V, article 11 [4], which can be used for evaluating
integrity of COPVs are available. Several papers [5 - 10] related to this problem appeared from1970’s, but further studies are needed for AE method to become a major NDT method for
COPVs. For example, key-parameters to selecting the inspection method, like the minimum detectable damage size, are not yet defined with AE.
In this study, in order to verify the applicability of acoustic emission (AE) method for inspecting damages of COPV materials, coupon-level CFRP specimens with impact damages ofvarious size are prepared. AE signals from previously induced damages (impact damages) and
newly induced damages (or progression of previously induced damages) are monitored during
cyclic-load testing of the specimens. Suitable AE parameters for integrity evaluation of CFRPs
are investigated. Detectable minimum damage sizes are also discussed.
Specimen and Impact Test
A large-size plate of CFRP [0o/45o/90o/-45o]10 of 5.3 mm thickness was prepared by laminating pre-preg sheets. PAN-based carbon fibers of TR350 from Mitsubishi Rayon Co., Ltd. and
J. Acoustic Emission, 27 (2009)89© 2009 Acoustic Emission Groupepoxy resin (hardening temperature: 150℃) are used for the sheets. Rectangular specimens with150 mmL 100 mmW were cut with the fiber directions (0o) on the top surface along the longitudinal direction.

Fig. 1 The picture of drop-weight test machine.
Figure 1 shows a drop-weight test machine used for the impact test, which satisfied SACMA
SRM 2R-94 standard. In this test, however, a hemispherical tip of 12.7-mm diameter was usedbased on ISO14623. The impactor (impact rod) was dropped along guide rails to the center of a
specimen. The weight of the impactor was 1.57 kg. Edges of the specimen were clamped all
around by steel flanges. Impact energy was 3, 7 or 10 J, controlled by the height of the impactor,
Figure 2(a) shows an impacted specimen surface loaded at 10 J. Although it is the result of applying the largest energy (10 J) in this test series, surface damage is not easily locatable by visual
testing. Figure 2(b) shows the cross-sectional profile of impacted surface along the dotted line in
Fig. 2(a) by a surface profilometer. Maximum depth of the damage is less than 150 µm. The
characteristics of shallow damage make it difficult to find. Damage will become less clear if the
tip of impactor is blunter.

Fig. 2 (a) Surface observation results. (b) cross-sectional shape of impacted surface along withdotted line in (a) for the specimen impacted at 10 J.
Figure 3 shows C-scan images of an impacted CFRP by ultrasonic immersion testing (sensor
frequency: 5 MHz, scanning pitch: 0.3 mm). Circular shape internal damages are revealed for the
specimen impacted at 10 J. Small damage is observed for the specimen impacted at 7 J and no90

Fig. 3 UT C-scan images of internal damages in impacted CFRP plates.

Fig. 4 Specimen and sensor arrangement.
damage is observed for the specimen at 3 J. Considering that the surfaces of real COPVs arerougher than the test pieces and inspection of COPVs is conducted manually, detectable minimum damage size by ultrasonic testing (UT) in field may be that caused by 10 J-impact (Damage
caused by 7 J-impact may be difficult to detect by field test.).
Experimental Setup and Method
After the impact test, both sides of the specimen were cut lengthwise into a 30-mm wide strip
with the impacted position at center. Aluminum tabs were bonded to the end of each specimen
for gripping. As shown in Fig. 4, an AE sensor of 150-kHz resonant frequency (Physical Acoustic Corp. (PAC): Type R15) was mounted on the surface of the specimen at the center along with
two guard sensors (PAC: Type Pico, 500 kHz resonant frequency) (Fig. 4). Noise events generated outside of the specimen gage section were removed by these guard sensors. Outputs of the
AE sensors were amplified 40 dB and digitized at an interval of 250 ns with 4096 points, and fed
to a computer. AE analysis was conducted using VisualAE from Vallen Systeme GmbH.
91

Fig. 5 Loading sequence for the tensile test.

Fig. 6 Examples of newly induced damages during the test.
Figure 5 shows the loading sequence of the experiment. Note that the loading/unloading rateis constant ( 0.3 mm/min) for all cycles. At the load of 15, 30, 45, 60, 75 and 90 kN, crosshead
was stopped and load was held. The load-hold period of each cycle was constant (3 minutes) for
all cycles. Four specimens (sound specimen, specimen impacted at 3 J, 7 J and 10 J) were testedby this loading sequence.
Result and Discussion
During the tensile testing of the sound specimen and the specimen impacted at 3 J, new observable damages became visible on the surface at 82 and 72 kN (Fig. 6). On the other hand, no
new observable damage was found for the specimens impacted at 7 and 10 J. Observable damages were not found for the latter specimens, although new invisible small damages or impact
damage extension may have occurred above around 70 kN (Ultimate tensile load for these
specimens is about 100 kN).
Figures 7 and 8 show AE signal peak amplitude and cumulative AE hits against elapsed time.
The load history is overlapped with these graphs. During the load-holds at less than 60 kN, AE
92

Fig. 7 AE signal peak amplitude and load against elapsed time.
activities were low. Hence, it is assumed that no macro damage or large damage progression occurred at these stages. On the other hand, when the load-hold value reaches at 75 or 90 kN, highAE activities were observed. These correspond to macro damage as shown in Fig. 6 and internal
damage progression at these stages. The results show possibility to monitor damage occurrences(newly induced damages or exteision of previously induced damages) during the pressurization
test of COPVs by using these parameters. It is also noted that several AE signals were detectedduring the unload process for damaged specimens. This result coincides with that of Downs andHamstad [9], who focused on AE signals during the unload process (They defined Shelby ratio,
which is related to AE signals during the unload process, and used it for damage evaluations).
93Fig. 8 Cumulative AE hits and load against elapsed time.
These unload AE signals are useful for evaluating the previously induced damages (such as impact damages). We will discuss these AE signals again along with the Felicity effect.
Figure 9 shows relationship between AE signal peak amplitude and duration. High amplitude
and long duration emissions are detected with both the sound specimen and the specimen impacted at 3 J, from which observable large damages occurred. Therefore, it can be said that thisrelationship can be used for monitoring occurrences of large damages during the pressurization
test of COPVs. A similar trend of emission is detected for the specimen impacted at 10 J, while94Fig. 9 Relationship between AE signal peak amplitude and duration.
observable damages were not found. Invisible new damages or impact damage extension might
have occured in the specimen. On the other hand, no such emission is detected for 7J-impacted
specimen. The size of new damage or damage extension might be small compared to those forother specimens.
Next, we examined the feasibility of AE method for detecting previously induced damages.
Kaiser effect is the phenomenon that no AE activity is observed until the load reaches the level ofthe previous maximum load. When incomplete Kaiser effect is shown under cyclic loading, i.e. a
considerable amount of AE is detected before the previous maximum load, the phenomenon iscalled Felicity effect. These effects have been used for evaluating integrity of composite vessels
and piping. Figure 10 shows relationship between cumulative AE hits and load history. In Fig.
10(a), all AE data was used to draw the graph. In Fig. 10(b), only AE signals above 45 dB wereused. If perfect Kaiser effect is established (i.e., no AE signals are observed during unloading,holding and loading below the previous maximum load), cumulative AE hits should increase95

Fig. 10 Relationship between cumulative AE hits and load: (a) AE signals with peak amplitude
above 40 dB, (b) above 45 dB.
96monotonically with load. As mentioned previously, new damage is expected occur above 70 kN.
Since these new damages are considered to influence the evaluation of previously induced damages, the data above 70 kN were ignored (hatched part in the graph). Perfect Kaiser effect wasobserved when sound specimen was tested (surrounded by a dotted line in Fig. 10). On the other
hand, when damaged specimens were tested, Felicity effect is observed (indicated by arrows).
The Felicity effect is mainly caused by AE signals during unloading process as shown in Fig. 7.
It is also noted that Felicity effect seems less influenced by threshold values (40 or 45 dB) in thisrange. These results indicate that AE method is a useful NDT method for evaluating previously
induced small damage. Although database is limited, it appears that minimum detectable damage
size by AE method is same or less than that by UT method.
Conclusion
In this study, we examined the applicability of AE method to the integrity evaluation of
COPVs. We first prepared coupon level specimens and conducted impact test at various impact
energy. We confirmed that impact damages were difficult to detect by visual testing. Furthermore,
it was estimated that detectable minimum damage size by ultrasonic testing in field requires 10
J-impact for this test piece. AE monitoring was conducted during cyclic load testing of thespecimens with various damage sizes. It was found that emissions during load-hold and relationship between AE signal peak amplitude and duration can be used for monitoring newly induced damages or progression of previously induced damages. On the other hand, Kaiser effectand Felicity effect can be used for evaluating previously induced damages. The results showed apossibility that detectable minimum damage size for previously induced damage by AE method
is the same as or less than that by UT method.
Acknowledgement
A part of this research was supported by Japan Aerospace Exploration Agency. AE analysis
was conducted by using demonstration version of VisualAE from Vallen-Systeme GmbH. We
used impact test machine made by Prof. Y. Shimamura of Shizuoka University.
Reference
1. N. Greene et al.: Proc. of 9th Joint FAA/DoD/NASA Conference on Aging Aircraft, March 6-9,
2006, Hyatt Regency, Atlanta, GA, USA.
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2006, Hyatt Regency, Atlanta, GA, USA.
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The Nondestructive Testing Information Analysis Center, 2002.
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Montreal, Canada, pp. 44-49.
8. C. Wilkerson: NASA Technical Memorandum 108520, 1996.
9. K.S. Downs and M.A. Hamstad: J. of Comp. Mat., 32 (3), 1998, 258-306.
10. Y. Mizutani, Y. Morino and T. Takahashi, Proc. 44th AIAA/ASME/ASCE/AHS Structures,
Structural Dynamics, and Materials Conference, April 7-10, 2003, Norfolk, Virginia, USA.
97

ACOUSTIC EMISSION FROM IMPACTS OF RIGID BODIESTATIANA B. PETERSEN
DIAPAC Ltd., 1-st Pechotny per. 6, Moscow 123182, Russia
Abstract
The characteristics of stress waves accompanying the collisions of rigid bodies are investigated. It is shown that high-frequency transducer of acoustic emission apparatus transforms initial impact perturbation into two separate signals, arriving with delay equal to impact duration.
AE signals are generated at the moments corresponding to discontinuities of the derivative of
surface displacement function of impacting bodies; i.e., at the initial moment of loading and thefinal moment of contact. It is shown experimentally that different Lamb modes recorded in the
far-field zone of the impact source contain double signals arriving with the same delay as thesignals in the near-field zone. The relationship between the AE waveform and the impact parameters determined in the study enables one to estimate physical characteristics of impact, suchas surface displacement, contact time and impact force. Practical significance of these findings
for evaluation of structural integrity is discussed.
Keywords: Impact waveform, Contact duration, Double AE signals, Wave dispersion
Introduction
Stress waves excited by impacts of rigid bodies are studied in different applications of acoustic nondestructive testing. In the practice of AE testing the impacts are considered mechanicalinterferences, which have to be filtered from “useful” signals related to fracture process. However, since an impact itself presents a danger to the structural integrity, the relationship between
the mechanical characteristics and AE parameters of impacts may be used for estimation of the
impact hazard. The latter consideration, particularly, served as a basis for designing a loose-partmonitoring nondestructive method, which is used widely in nuclear reactor industry; see [1].
To study the mechanical parameters of colliding bodies and stress distribution in the contact
zone the analysis known as Hertz theory of impact are generally applied. The theory was basedon assumption of absolutely elastic collision. For spheroidal surfaces, the force-deformation relation needed to estimate the duration of impact and the maximum indentation was obtained using
Hertz calculations. Johnson [2] and Goldsmith [3] covered the theory in detail.
However, most impacts are not fully elastic. Impact energy loss may incorporate different
forms of dissipation such as viscoelastic work performed on the materials of the impacting bodies, plastic deformation of contact surfaces and emission of stress wave in the bodies. To analyze
an inelastic stage of impact loading a rigid-perfectly-plastic material model is commonly used. It
assumes that the elastic deformation is small enough to be negligible and the material flows plastically. For sphere-sphere contact, Johnson [2] shows that, under those assumptions, yield is initiated when the mean contact pressure is 1.1Y and the flow becomes fully plastic at about 3 Y,
where Y is the yield stress. He gives the ranges of initial velocities of colliding bodies for preliminary estimation of impact regime.

J. Acoustic Emission, 27 (2009)98© 2009 Acoustic Emission GroupA phenomenon of stress wave generation occurring at impact is an important aspect of a dynamic contact mechanics focused in many studies. The wave approach was applied by Goldsmith to many problems [3] and also covered by Zukas et al. [4]. Tsai [2] got a theoretical solution of wave motion in elastic half-space by combining Hertz theory of impact with a Lambwave theory. The results were obtained on assumption that stress wave effects account for asmall fraction of impact energy and do not influence the local deformation significantly.
Proctor and Breckenridge [5] conducted AE analyses of elastic sphere collisions with a thickplate. They showed that when a Green’s transfer function and an impulse response of receiving
transducer are known, a dynamic force of acoustic source may be obtained using a transducer
response function. Their numerical results agree with theoretical calculations.
Ono et al. [6] studied the impact damage of CFRP plates using AE monitoring and surface
evaluation. The force-indentation was obtained experimentally. Authors distinguished twoclasses of AE signals: impact related and matrix fracture related. Their study demonstrated thecapabilities of AE method for diagnostics of impact failure processes in composite materials.
The idea of this paper was to determine the most informative and persistent AE waveform
parameters of impact and to attempt evaluating the impact hazard. To this end, an investigationof collisions of different bodies on thick metallic plates at various distances from the source location was conducted and numerical analysis of normal impacts of sphere was carried out. The influence of the frequency range of the receiving equipment on the output signals calculated atmodeling was studied.
Impact properties.
Derived on the basis of the Hertz law, a solution for the maximum indentation hm and contact
time T of elastic impact of a sphere on a smooth surface of rigid massive plate is given by Landau and Lifshitz [7]:
,

(1)

where

.

(2)

Here, hm is maximum displacement of the bodies; i.e., total of deformation of both surfaces, v0 is
a sphere velocity at a moment of collision, R is a sphere radius, m is a sphere mass,
andare Young’s modulus and Poisson’s ratio for the plate (or sphere), respectively. The formula was obtained on the assumption that m << M, where M is a plate mass.
A force-indentation relationship for sphere-to-sphere contact was given by Johnson in a
power form [2]:F = k hm3/2,(3)99

where F is normal force pressing the solids together and k is a constant depending on the sphereradius and elastic properties of the sphere materials.
Landau [7] and Johnson [2] show that the latter formula is also valid for any 3D nonconformal contact of solids, under the condition that the contact area must remain small compared to the bodies’ dimensions. Since these requirements are met for sphere to plate contact, we
may apply this relationship to force estimation.
Deresiewicz [8] first derived a temporal dependence of surface displacement at the top pointof a sphere in a form of a half-sine function, describing the dependence with high precision:(4)
Frequency range of impact perturbation may be estimated in a standard way given by
Harkevich [9] on assumption that the main impact energy lies in the range between zero and thefrequency value, at which the spectrum Sw of the displacement function vanishes for the firsttime. Fourier transform of a half-sine is calculated by the formula:(5a).
This function gives a first zero at ωT 3
= π , from which we obtain the desired frequency range:
2 2

3
(5b)
2T
As expected, the frequency range and the contact time are related inversely, meaning the shorter
is a contact time; i.e., the less is a mass and the higher is a velocity of a body, the broader is an
impact spectrum.
Δf =However, the physical variable h(t), is not a stress wave itself. According to Aki and Richards [10] waves are generated at moments corresponding to discontinuities of the perturbation
function. The lower is the order and the higher is the value of the discontinuity, the higher is theamplitude of the wave front; i.e., the amplitude of AE signal. While the surface deformation h(t)is a continuous function, its first derivative, which is a displacement velocity has two ordinary
discontinuities at the initial and the final moments of the impact contact time. Besides, the highfrequency tract is known to be more sensitive to derivative of the function, than to the function
itself 1. It means that impact should produce two high frequency wave fronts, arriving with delay
equal to the contact period.
Difference of function derivative limits in the points of discontinuity gives the velocity jumpvalue (discontinuity value) in a form of!v = ±" hm T

(6a)

If a complex spectrum of function f(t) is S(ω), then a spectrum of derivative of this function is j ω S( ω).
This implies that while the signal response at low frequencies is determined mainly by the function itself,
f(t), at high frequencies the influence of the term related to derivative of the function becomes predominant.
1100

where plus sign refers to the first point and minus sign to the second one. Hence, the following
relation between the jump value and the signal amplitude:(6b)Fig. 1. Modeling of a high-frequency impact response a) system pulse characteristic. b) half-sine
input function, h(t). c) the convolution of a) and b). d) the resulting high-frequency output signal.
Output AE Signals Analysis
To analyze the AE waveforms from impacts, a numerical modeling of collisions of a metal
sphere (R = 11 mm) dropped upon a thick aluminum alloy plate from the height of 300 mm witha zero initial velocity was carried out. High-frequency Butterworth digital filter and PAC R50I
transducer response were used in the modeling.
An input source function, h(t), given in Fig. 1b was calculated using Equations (1-4) in aform of half-sine. A response of the plate - transducer - apparatus system on Hsu-Nielsen pencillead break, presented in Fig. 1a, is assumed to be a pulse characteristic of the system, psys(t).
A convolution of the pulse characteristic of the system and the input source function, given in Fig. 1c demonstrates the presence of low frequency components,
101

Fig. 2. Modeling of a high-frequency AE crack movement response. (a) The scheme of a slow
crack movement function; (b) a modeled output waveform consisting of three signals, which areseparated by time delays corresponding to the break points of the function. (с) The scheme of a
rapid crack movement function; (d) a modeled output waveform consisting of overlapping signals; (e, f) the dependence of output signal amplitude on a value of the function derivative jump.
which should be removed from the final high-frequency output signal. To this end, the convolution, s(t), was filtered by a high-pass 5-th order Butterworth filter with a cutoff frequency of 50
102

kHz. The result of filtration is shown in Fig. 1d, where one can easily distinguish two separate
signals coming with a delay equal to the impact duration.
Our analysis shows that a pair of signals occurs when the cutoff frequency of a high-pass filter has the same order as the highest frequency of impact, determined from Eq. (5b). If the impact duration and the sensor decay constant, τ, satisfy the condition of T >> τ, the signals do not
overlap and are separated in a time domain by a delay equal to impact duration.
Basically the same considerations may be used in the analysis of arbitrary AE source, e.g.,cracking. Let’s assume the crack movement function is determined by a broken line function likethat given in Fig. 2a. Then a deformation/stress jump will occur at three break points, which are
the beginning of crack extension, maximum and crack propagation arrest. The output signals
modeled by the convolution of input function and a high-frequency AE transducer response(with range from 50 kHz to 200 kHz) are shown in Fig. 2b. Each of three signals presented inthis figure occurs in the corresponding breakpoint of the crack movement function. They are well
distinguished because the intervals between the discontinuity points exceed the decay period. On
the contrary, signals in Fig. 2d overlap and are not separated. Finally, Figs. 2(e, f) illustrate thedependence of an output signal amplitude on the value of the derivative jump (velocity jump),
which in turn is determined by the line slopes.
Experimental Setup and Results
The experimental part of the study included three types of experiments, which are normal
collisions of metallic spheres dropped upon an upper surface of thick duralumin plate; grazing
collisions of spheres against a vertical steel plate; and impacts of a metal screwdriver.
Recording system used in the study was a PAC 4-channel DiSP AE system, with R50I and
R15I sensors, produced by Physical Acoustic Corp., USA. Signals were filtered by analog bandpass filter of 10 - 1000 kHz, digitized at a sampling rate of 2 MHz, and recorded as 2048-point
waveforms.
Normal impacts of metal spheres
The collisions of steel spheres dropped upon the plate surface from three different heights (H= 0.1 m, 0.2 m and 0.3 m) with a zero initial velocity served as AE sources. Recoil heights wereregistered for impact energy loss analysis. Four different sizes of spheres were used, with radius
ranging from 2.5 mm to 11 mm. Experimental setup consisted of a horizontal aluminum alloy
plate having size 300 x 495 x 95 mm3, DiSP system and R50I AE transducer mounted on thesame surface with the sphere impact location at the distance of 20 mm from the source.
The data given in Table 1 were obtained at a height of falling H = 0.3 m and include bothgeometrical and mechanical parameters of the spheres and the corresponding characteristics ofimpacts, such as contact time, maximum impact surface displacement and highest frequency calculated in accordance with Eqs. (1-3).
The initial velocities of the spheres at impacts calculated as v =√2gH were equal to 2.42 m/s,
where g is acceleration of gravity. Note that the same velocity value is given by Eq. (6a).

103

Fig. 3. (a) Hsu-Nielsen pencil-lead break related waveform; (b-e) AE signals registered from impacts of different size spheres (with the radius of 2.5, 3.5, 8 and 11 mm) dropped on the aluminum plate. Arrow #1 points to the first waveform peak related to impact loading; arrow #2
points to the secondary peak related to unloading of the plate.

104

Table 1 Mechanical parameters of dropping spheres and impacts.
Sphereradius,
R, mm
11
8
3.5
2.5

Sphere
mass,
m, g
43.49
16.73
1.40
0.51

Maximum
indentation,hm (calc), mm
0.069
0.050
0.022
0.016

Impactduration, T(calc), µs
84
61.1
26.7
19.1

Impactfrequency, F(calc), kHz
17.9
24.5
56
78.5

Impactduration,<T> (exp), µs
82.8
61.4
28.4
21.7

Standard
deviation, σ,(exp), µs
1.03
0.4
0.9
0.85

A restitution coefficient, e, which is a measure of an energy loss during impact was estimatedas a quotient e =vf/vi, where vi and vf are velocities before and after impact, respectively. The
coefficient changed from 0.61 for the large sphere (R = 11 mm) to 0.7 for the small one (R = 2.5mm) indicating the presence of energy dissipation during the impact experiments. Dissipation
mechanisms observed are both plastic deformation of the plate and wave emission, which is confirmed by the presence of shallow indentations at the surface of the plate remaining after the collisions and a stress wave emission during impacts.
The examples of typical impact waveforms recorded for each sphere size are given in Figs.
3(b – e). For more comprehensive analysis a Hsu-Nielsen pencil-lead break response, which isconsidered as an impulse response of the whole system (plate – transducer - AE apparatus) at a
distance of 20 mm from the source is plotted in Fig. 3a.
It follows from Fig. 3a that if a short step-pulse is imposed at t = 0 µs, a peak signal amplitude appears at a moment of t ~ 7.4 µs (arrow #1). Similar patterns may be seen in Figs. 3(b - e),
where first peaks occur at the same moments from the beginning of the waveforms.

Fig. 4. Amplitude – Velocity dependence obtained experimentally for three sphere sizes each
dropped from three different heights: 0.1 m, 0.2 m and 0.3 m.
Values of first peak amplitudes measured in experiments amounted to ~ 2 mV (86 dB) for all
spheres. The independence of AE amplitudes from the sphere size may serve as experimental
validation of the statement (see Eq. 6b) that at high frequencies the first peak amplitude corresponds to the initial loading velocity. The latter in all experiments was determined by the same105

dropping height of 0.3 m and was equal to ~ 2.42 m/s. This conclusion is also confirmed by thecorrelation between the velocities obtained for three different heights of dropping spheres andthe corresponding loading related amplitudes of the AE waveforms. Amplitude (A)-Velocity (V)relationship shown in Fig. 4 is fitted by a linear function A = 1.1V - 0.38 with the correlation coefficient Rc= 0.85. Note that the obtained coefficients of the linear function are valid only for the
given plate.
In addition to loading related peaks, secondary peaks marked by arrow #2 may be also observed in Figs. 3(b - e). These peaks relate to unloading of the plate and are separated from theloading-related peaks by the delays equal to impact duration. The delay values obtained in morethan 10 impacts for every sphere size agree well with the calculated durations of impacts; average durations measured as signal delays and their standard deviations are given in the last columns of the Table 1.
The measured amplitudes of the secondary peaks exceed those of the first peaks and risefrom 2.8 mV (89 dB) for the small sphere to 8 mV (98 dB) for the large one, implying the unloading displacement velocity exceeds the initial loading velocity. Seeming contradiction between the decrease of recoil velocity and the increase of unloading velocity estimated by AE
amplitudes may be explained by the fact that velocity is a vector quantity, depending on impact
regime. Thus, at the beginning of loading the impact contact is determined by a normal impulse
component only; at the stage of plastic deformation tangential components occur as well. Achange in velocity component orientation leads to the reduction of the algebraic value of the velocity, and hence to the decrease of recoil height. The ratio of tangential to normal impulse components was first introduced by Brach [11] for treating oblique impact problems.
Taking into account that the impacts observed are not fully elastic, we may infer that thetemporal indentation dependence here can be described by a symmetrical half-sine only during
the elastic stage of loading, with the parameters of the function calculated from equations (1-4).
Since the unloading velocity determined by a second peak amplitude, 1.5-3 times higher than atinitial stage, the penetration maximum should shift to the right on the time axis, and due to thematerial hardening its value should be less than that calculated for a case of elastic impact, hm.
This reveals some perspectives of AE verification of impact models and estimation of impact
hazard. In particular, the above conclusions based on AE data agree with calculations of temporal dependence of the contact area carried out by Johnson [2] for modeling of a purely viscousmaterial behavior under the action of a sinusoidal varying force.
Also, the obtained relationships between the AE features and the mechanical parameters allow one to estimate quantitatively the loading and the unloading velocities, impact duration andmaximum penetration (estimated for elastic impact). A velocity ratio obtained as Aunld/Ald, where
Aunld and Ald are the unloading/loading related amplitudes of impact waveform, respectively, may
serve as an acoustic measure of energy loss. The higher is the velocity ratio, the greater is theviscoelastic work performed on the materials of the impacting bodies, and therefore the larger isa size of a plastic zone occurring below the contact surface.
Besides, the value of Ald, corresponding to impact velocity, may be used together with thenon-dimensional parameter (ρV2/Yd) obtained by Johnson for preliminary estimate of impact regime. Here Yd is dynamic yield strength. The following table from [2] gives the impact regimes
as a function of this parameter and initial velocity:106RegimeElastic
Fully plastic
Limits of shallow indentation

ρV /Yd (MPa)<10-6~ 10-3~ 10-1
2

Approximate velocity (m/s)
<0.1
~5
~ 100

According to this table and taking into consideration that the initial velocities of the spheres wereabout 2.42 m/sec, we can conclude that in all cases an elasto-plastic regime of impacts was observed.
It is important that regardless of the fact that the collisions were not fully elastic, the obtained
experimental values of impact durations show a good agreement with the theory. Similar results
were obtained by Gugan [12], who demonstrated that the measurements on a croquet-ball contact
time are well described by Hertz theory, even when about 40% of the kinetic energy is lost on
collision.
Finally, note that the emission of stress waves occurs during the entire period of impact duration and over the whole contact area, which in our case is a circle of a variable radius. More specifically, these factors should be considered for the calculation of the displacement function.
However, when a displacement velocity is measured by a high-frequency sensor, the output AE
signal is determined mainly by the strongest discontinuity of the function, while the contributionof the other components may be neglected. The strongest displacement discontinuity here occurs
at the top point of a dropping sphere at the two characteristic moments of contact. This is the reason why the observed delays between the pairs of loading-unloading related signals correlate
well with the impact durations calculated using equation (2).
Analysis of impact waveforms in the presence of wave dispersion.
Investigation of tangential impact waveforms was conducted on a vertical steel plate having asize of 2000 x 1000 х 100 mm3. Steel sphere (R = 8 mm) fixed on a 0.3-m long thread was
dropped with a zero initial velocity upon the plate side, and two R15I sensors were mounted on
the same side of the plate at distances of 20 mm and 920 mm from impact source. Waveform recording in both channels was triggered by the first arriving signal.
Estimation of a wavelength at a frequency of 150 kHz and shear wave velocity of 3.2 km/s
gives a value of 21 mm, which means that the neighboring sensor is located in the near-fieldzone, while the remote one – in the far-field zone. The wavelength and the plate thickness (100mm) proportion indicate a presence of acoustic dispersion thus complicating interpretation ofwave pattern. To simplify the waveform analysis we first used a pencil-lead break as an acoustic
source, producing a step displacement source function. AE signals from this source recorded by
the neighboring and the remote channels are shown in Figs. 5a and 5b, correspondingly. The firstsignal looks rather compact because all harmonics are in phase near the source. On the contrary,
the second signal occurs to be compound and diffused due to the dispersion. To interpret the obtained wave pattern we used a classic spectral approach and a group velocity concept. Such approach is possible due to the spatial spectral decomposition of initial perturbation occurring in adispersive medium. Under these conditions a group velocity, which is a propagation velocity ofneighboring harmonics envelope, may be used for analysis of signal envelope peaks.
This analysis allows us to interpret waveforms, to analyze arrival times corresponding to different peaks, and to calculate peak delays, peak velocities and wave propagation distance. Algorithm of the procedure can be easily realized and embedded into the standard software.
107

a

b

Fig. 5. AE signals from Hsu-Nielsen source. (a) at a distance of 20 mm from sensor; (b) at a distance of 920 mm from sensor.
Thus, the first step of data processing includes calculation of signal envelope, determinationof peak arrival times and peak widths, i.e., peak segments and carrier spectrums of these segments. Next, group velocities, corresponding to the dominant frequency of the carrier spectrum
are calculated. The results of the processing are shown in Fig. 6, where our considerations arelimited to four main waveform peaks marked by the corresponding numerals. Carrier segment
belonging to the first peak of the envelope and the corresponding spectrum with a resonance at
130 kHz are given in Figs. 6a and 6b, respectively.
Dispersion analysis (made using a special PAC software PLOTRLQ) allows us to conclude
that the first envelope maximum is formed mainly by symmetric (S) and anti-symmetric (A) 0th-order Lamb modes: Al0, Ss0, As0 (here small l and s mean longitudinal and transverse modes,
correspondingly). As follows from Fig. 7, at 130 kHz, these modes converge to the point of 3.13
km/s, which determines the group velocity of the first waveform peak. The obtained value shows
good agreement with the velocity calculated from delta-T, Δt =287 µs, and the propagation distance, ΔL= 900 mm: c = ΔL/Δt = 3.136 km/s.
The second peak formation may be interpreted similarly. For the dominant carrier frequency
equal to 114 kHz, modes come together at the velocity of 2.56 km/s; see Fig. 8. As follows fromthis figure the second peak is formed mainly by the first-order Lamb modes: Ss1, As1, Sl1. The
point where the modes converge is circled. The obtained velocity value agrees with the one
computed from delta-T from delta-T, Δt = 352 µs, and the propagation distance, L= 900 mm: c =L/Δt = 2.556 km/s. The parameters of the other waveform peaks, obtained in the similar way, arethe following: the third maximum has a dominant frequency of 99 kHz, propagating at the velocity of 2.31 km/s; the forth at the frequency of 72 kHz, propagating at the velocity of 1.62 km/s.

108

a

b

Fig. 6. AE signal at the remote sensor: (a) signal envelope with the carrier of the first peak; (b)Spectrum of the first peak carrier.

Fig. 7. Zeroth-order Lamb modes forming the first peak of AE signal envelope at a distance of
920 mm from the source. The point where the modes converge is circled.
The advantage of the considered classic time-spectrum approach is that it provides a convenient physically based tool of AE data processing and gives a physical interpretation to wave pattern obtained at AE testing. Besides, it may be applied for estimation of wave propagation distance to improve source location results. The location formula, using characteristics of a single
waveform is the following:109

Fig. 8. First-order Lamb modes forming the second peak of AE signal envelope at a distance of
920 mm from the source. The point where the modes converge is circled.
.

(7)

Here R is a distance from a sensor to a source, ci, where i =1,2, …, velocity of i-th peak of the
waveform envelope; Δt is the delay between two peaks. Note that the distance may be calculatedusing different combinations of several peaks. Also note that for a given method of distance estimation, precise determination of wave arrival does not play any role, which is important in acase of low signal-to-noise ratio.
Following the same way of AE wave pattern interpretation we can analyze a tangent impactof a steel sphere against the vertical plate. As in previous case, the sensors were mounted at a
distance of 20 mm and 920 mm from the source. Impact waveforms shown in Fig. 9a reveal two
distinct signals coming at a delay of 54 µs. Though the contact time was not calculated, the characteristic features of the waveforms obtained affirm that the delay corresponds to the contact
time, which was 7 µs less than at normal impact. A group of double signal pairs recorded by theremote channel is shown in Fig. 9b. Signal numbering corresponds to that obtained at the pencillead break, while stroke denotes the repeating disturbance. Double signal delays exactly correspond to the delay obtained at the near sensor, which confirms the presence of double disturbances in different Lamb modes. The absence of peaks denoted as 2 and 2’ relates to the overlapping of 1’-2 and 2’-3 disturbances.

110

a

b

Fig. 9. AE waveforms from impact of 8-mm steel sphere recorded in near-field and far-fieldzones by R15I sensors. (a) Repeated signals registered by R15I sensor mounted on the surface ofthick steel plate near the source location. (b) Series of repeated signals registered by R15I sensor
at the distance of 920 mm from the impact source.

abFig. 10. AE waveforms from impact of metal screwdriver recorded in near and far zones by R15I
sensors. (a) Repeated signals registered by R15I sensor mounted on the surface of thick steel
plate near the source location. (b) Series of repeated signals registered by R15I sensor at the distance of 920 mm from the impact source.
111

Finally, a wave pattern obtained from the impact of an 80-mm length metal screwdriver on
the vertical steel plate was analyzed. Typical impact waveforms captured by near-field (20 mm)
and remote-field (920 mm) sensors are shown in Figs. 10a and 10b, correspondingly. At interpretation of Fig. 10a, it is easy to suppose that the repeating signal, denoted as 1’ relates to reverberation in long hammering object itself. However, such signal should arrive at a delay of ~50µs, while the observed delay is equal to 298 µs. It means that like in previous cases, the only
suitable explanation to double signal effect is that the second signal relates to the release of theplate after the impact and that the delay value corresponds to the impact duration. Figure 10b
illustrates the presence of double signals in different Lamb modes recorded at the remote channel. From Figs. 9 and 10, it can be seen that the loading and the unloading amplitudes in bothexperiments were practically the same. It implies that the impacts of steel bodies against the steel
plate were fully elastic.
Conclusions
1. High-frequency AE channel transforms input impact function into two separate signals related first, to loading and second, to unloading of bodies at impact. Signal delay correlates
well with the impact duration.
2. A pair of signals is obtained when the lower cutoff frequency of the transmission channel isof the same order of the upper frequency of the impact.
3. Use of double signal effect allows one to estimate the contact time at collision of rigid bodies
regardless of the elastic properties and geometry of the bodies, impact direction, etc.
4. An AE amplitude from the impact correlates with the jump in the velocity of the loading
function, but not with the function itself. Together with delay-based measurements of impact
duration, the A-V relationship allows one to estimate the maximum indentation and the impact force. The ratio of peak amplitudes provides a preliminary evaluation of an impact regime, and therefore, the danger of impact damage.
5. The presence of double signals from impacts is observed in different Lamb modes. Signal
delays in far-field zone are equal to that measured in near-field zone.
6. Finally, the theoretical solution for impacts of metal spheres against thick plates allow considering such impacts as calibrated acoustic sources with known parameters along with apencil-lead break, which is a widely used simulator of AE.
References
1. K. Fujita and M. Tanaka. Shock and vibration analysis on the impact of metal parts for PWR
diagnosis. Progress in Nuclear Energy, 1982, pp. 531-540.
2. K.L. Johnson, Contact Mechanics, Cambridge University Press, 1987, 452 p.
3. W. Goldsmith, Impact. E. Arnold Ltd., London, 1960.
4. Jonas A. Zukas, T. Nicholas, H.F. Swift, L.B. Greszczuk and D.R. Curran, Impact Dynamics,
Krieger Publishing Company, Malabar, FL, 1992.
5. T.M. Proctor, Jr. and F.R. Breckenridge. Source Force Waveforms: the use of a calibrated
transducer in obtaining an accurate waveform of a source. Journal of Acoustic Emission, 10,(3/4), 1991, 43-48.
6. K. Ono, Y. Mizutani and M. Takemoto. Analysis of acoustic emission from impact and fracture of CFRP laminates. Journal of Acoustic Emission, 25, 2007, 179-186.
7. L. D. Landau and E. M. Lifshitz, Course of Theoretical Physics, Vol. 7, Theory of Elasticity,
Pergamon, Oxford, 1959.
8. H. Deresiewicz. A note on Hertz’s theory of impact. Acta Mech., 6, 1968, 110–112.
112

9. А.А. Harkevich, Spectrums and analysis. Moscow, Nauka, 1962, 236 p (in Russian).
10. K. Aki and P. G. Richards, Quantitative seismology, theory and methods, W.H. Freeman, San
Francisco, 1980.
11. Raymond M. Brach, Mechanical Impact Dynamics: Rigid Body Collisions, John Wiley &Sons, New York, 1991.
12. D. Gugan. Inelastic collision and the Hertz theory of impact. American Journal of Physics,
68, (10), 2000, 920-924.

113

SOME OBSERVATIONS ON RAYLEIGH WAVES AND ACOUSTICEMISSION IN THICK STEEL PLATES#∞
M. A. HAMSTAD
National Institute of Standards and Technology, Materials Reliability Division (853), 325
Broadway, Boulder, CO 80305-3328 and University of Denver, School of Engineering andComputer Science, Department of Mechanical and Materials Engineering, Denver, CO 80208
Abstract
Rayleigh or surface waves in acoustic emission (AE) applications were examined for nominal 25-mm thick steel plates. Pencil-lead breaks (PLBs) were introduced on the top and bottom
surfaces as well as on the plate edge. The plate had large transverse dimensions to minimize edge
reflections arriving during the arrival of the direct waves. An AE data sensor was placed on the
top surface at both 254 mm and 381 mm from the PLB point or the epicenter of the PLB point.
Also a trigger sensor was placed close to the PLB point. The signals were analyzed in the time
domain and the frequency/time domain with a wavelet transform. For most of the experiments,
the two data sensors had a small aperture (about 3.5 mm) and a high resonant frequency (about
500 kHz). These sensors effectively emphasized a Rayleigh wave relative to Lamb modes. In
addition, finite-element modeling (FEM) was used to examine the presence or absence ofRayleigh waves generated by dipole point sources buried at different depths below the plate top
surface. The resulting out-of-plane displacement signals were analyzed in a fashion similar to theexperimental signals for propagation distances of up to 1016 mm for out-of-plane dipole sources.
Rayleigh waves were generated in the experiments for all three locations of PLBs. In the case of
the bottom surface PLBs, the Rayleigh wave propagated to the plate edge, up the edge and thenalong the plate top surface to the sensors. Due to the time delay from the propagation up the edge
to the plate surface, Rayleigh waves from edge PLBs resulted in a strong signal that interferedwith a straightforward analysis of the intense frequency/time regions of the Lamb modes fromthese source positions. The FEM results for the out-of-plane dipoles showed that the surface outof-plane displacement amplitude of the Rayleigh wave decayed relative to the Lamb mode amplitudes as the depth of the source below the surface “pseudo” sensors increased. A Rayleighwave was not observed for sources deeper than about 23 % of the plate thickness. In contrast, fora case of an in-plane buried dipole, an out-of-plane Rayleigh wave was not observed in the FEM
results for a source depth of only 5 % of the plate thickness.
Keywords: Pencil-lead break, Rayleigh waves, Source depth, Thick plate, Wavelet transform
Introduction
One of the key roles of acoustic emission (AE) technology in plate-like structures is to locate
in two dimensions the source that generated the waves. To obtain accurate locations requires thedetermination of arrival times of a part of the signals that corresponds to a known velocity. In thecase of thin plates, typically only the fundamental Lamb modes are present with significant#

Contribution of the U.S. National Institute of Standards and Technology; not subject to copyright in the UnitedStates∞Trade and company names are included only for complete scientific/technical description; endorsement is neither
intended nor implied.
J. Acoustic Emission, 27 (2009)114© 2009 Acoustic Emission Groupamplitudes in the AE signals. In thick plates, additional higher-order Lamb modes significantly
contribute to the signals. Previously for thin plates, a series of publications demonstrated bothwith finite-element modeling (FEM) results and experimental results (with an AE sensor that had
relatively uniform response over its frequency range) that monopole pencil-lead breaks (PLBs)
on the edge of the plate created waves that most closely resembled those from buried dipole-typesources [1, 2]. The reason for this result was that PLBs on the plate top or bottom surface createdwaves that exaggerated the amplitude of the A0 mode relative to its amplitude from buried-dipole
AE sources. Additional publications showed, for a thin plate, that accurate group velocity arrival
times could be obtained by use of time/frequency analysis of the signals obtained from real AE
sensors excited by waves from edge PLBs [3, 4].
The research presented here was originally meant to follow up previous publications thatprovided results from FEM of AE source operation and subsequent wave propagation in a steel
plate of 25.4 mm thick [5, 6]. The follow-up was intended to obtain experimental group-velocity
arrival times from the signals from different types of real AE sensors excited by waves from
PLBs on the edge of a nominal 25-mm thick steel plate. With some of the sensor types, the initial
analysis of these signals demonstrated the presence of a Rayleigh wave, whose arrival time did
not correspond to the time calculated by the distance (parallel to the plate surface) from the plateedge divided by the velocity of a Rayleigh wave in steel. Instead the Rayleigh-wave arrival time
(as will be demonstrated later) corresponded to the distance from the PLB position up the plateedge to the plate top surface plus the distance along the top surface from the edge to the sensor.
Since the original FEM study [5, 6] showed an identifiable Rayleigh wave only when a buried
out-of-plane dipole source was near the surface, on which the “pseudo” sensors (out-of-plane
displacement at a point from the FEM results) were located, the potential to use PLBs on theedge of a thick plate at different distances below the plate surface to simulate real AE from buried sources presented complications. In addition, the absence of Rayleigh waves from AE
sources not near the plate surface raised questions relative to the common practice in AE fieldtests of using surface PLBs to obtain information on expected signal frequencies and propagation
velocities. With these two issues in mind, the goal of the research was revised. Thus, the researchpresented here is the result of a more general examination of Rayleigh waves from both surface
PLBs with real AE sensors and finite element modeling of buried AE sources.
Previous literature on the subject of Rayleigh waves typically concerned the generation ofRayleigh waves by laser pulses. Some examples of these studies involved experiments and others
involved both experiments and corresponding theory [7, 8, 9]. Since the source rise times inthese studies were on the order of 10 ns to 15 ns, their applicability to PLBs with typical risetimes on the order of 1400 ns to 2500 ns [10] is uncertain due to the large differences in therange of wavelengths that would be present in the Rayleigh waves. Further, the frequencies observed in the signals from real AE sources indicate that their source rise times are in the sameranges at those for PLBs. Thus, an extended study relative to Rayleigh waves generated by PLBs
seemed to be in order, and in addition a study that examined in detail the effects of the depth of
an AE source on the potential generation of a surface Rayleigh wave.
Experiments with PLBs on the Bottom and Top Surface of a Thick Steel Plate
A thick steel plate with dimensions of 1320 mm x 780 mm x 24.4 mm (the physical plate had
to be resurfaced so its thickness was 1 mm less than the previously completed FEM results) was
instrumented on its top surface with resonant sensors (resonant at about 500 kHz) of small aperture (about 3.5 mm). These sensors were chosen due to their small aperture and their high115

resonance frequency, which would emphasize the amplitude of Rayleigh waves versus their response to Lamb modes. The sensors were placed on the top surface with their centers at 356 mm
and 483 mm from the long edge of the plate. They were coupled with vacuum grease and wereconnected to preamplifiers of 40 dB gain without filtering. After passing through a custom built
decoupling circuit (to remove the dc voltage to the preamplifiers), the signals were high-pass filtered at 50 kHz by passive four-pole Butterworth filters. The resulting signals were digitized at
an interval of 0.1 µs/point with 12-bit vertical resolution. PLBs (at least three at each position toassure that a representative result was used for the subsequent analysis) were carried out as illustrated in Fig. 1 with 0.3 mm nominal diameter lead (2H, about 3 mm long) on both the top andbottom surfaces at a location 102 mm from the long edge of the plate. In addition, a third sensor
was coupled on the plate top surface close to the PLB points to provide a first-arrival trigger signal that would correspond to wave transmission at the bulk longitudinal velocity from the PLB
position to this sensor. The signal from this sensor was digitized (with pre-trigger data) simultaneously with those from the other two sensors.
a) Analysis of Top Surface PLBs
By use of a bulk longitudinal wave velocity for steel of 5940 m/s [11] along with the distanceof the direct path from the PLB point to the center of the trigger sensor, the propagation timefrom the PLB position to the trigger sensor was calculated. By use of this time increment alongwith the first arrival time in the trigger sensor signal, the zero times of the small aperture sensor
signals were adjusted to correspond to the time of the PLB. In addition, wavelet transform (WT)
results were calculated for these signals. Appropriate Lamb-mode group velocity curves [5] weresuperimposed on the WT results by use of the propagation distance parallel to the plate surface
from the PLB point to the centers of these sensors. The parameters used in the WT [12] for all
the results in this paper were a frequency resolution of 3 kHz (frequency band) and a wavelet
size of 600 samples.
Figures 2 and 3, respectively, show the time domain signals and the WT results of the signals
from the two small-aperture sensors. In the WTs of Fig. 3, the arrival times at the peak magnitude for the high frequency (really the frequency band) intense portion of the signals are shown(using the most intense frequency at the furthest propagation distance) for both distances. It is
important to point out that for frequencies above about 320 kHz (for the current plate thickness)the group velocities of the S0 and A0 modes are asymptotic to the Rayleigh velocity. Propagation

Fig. 1. Steel plate (24.4 mm thick) showing locations of top-surface mounted sensors and PLB
points. All dimensions are in millimeters.
116

Fig. 2. Signals from small aperture sensors for PLB on top surface of steel plate. Propagation distances from the PLB point to the sensors were (a) 254 mm and (b) 381 mm.
velocities were calculated for the direct path along the top surface by use of the indicated arrival
times (see Fig. 3) at the intense (high amplitude of WT coefficients) frequency of 498 kHz. In
addition, the velocity of this signal portion between the two sensors was calculated. The results
are shown in Table 1 along with a published velocity for Rayleigh waves in steel [11]. The tablealso shows the percentage differences of the experimental results as compared to the publishedvalues. These results demonstrate that the calculated experimental velocities correspond to theRayleigh velocity. In addition, the WT figures show that the arrival times correspond to the partsof the group velocity curves that are asymptotic to the Rayleigh velocity. Finally, as will bepointed out later in the analysis of the bottom surface PLBs, sensors on the surface opposite the
PLB did not exhibit a high frequency signal of significant amplitude that was asymptotic to theA0 and S0 Lamb modes. Hence, because the Lamb modes did not contribute significantly to thesignal amplitude, the appropriate wave regions in Figs. 2 and 3 are labeled as Rayleigh waves
and indentified with arrows. As a final observation, Figs. 2 and 3 show reflections from the edgenearest the PLB point. These reflections arrived (based on the WT results) at about 156 µs and198 µs, respectively, for the sensors at 356 mm and 483 mm. Using the distance from the PLB
point to the near plate edge and then from the edge back to the sensors, one can verify that these
arrivals correspond to waves propagating at the Rayleigh velocity.
Table 1 Experimental and published [11] Rayleigh velocity for top surface PLB.
Distance description

Velocity [mm/µs]

PLB to 1st sensor
PLB to 2nd sensor
First to second sensor

2.92
2.94
2.98

Published velocity[mm/µs]2.98

117

Difference [%]2
1.3
0Fig. 3. Wavelet transforms of the signals shown in Fig. 2. Parts (a) and (b) correspond to thoseparts in Fig. 2. Initial portion of the time scale not shown to focus on the arrival of the Rayleighwave at the most intense portion of the signals.
b) Analysis of Bottom Surface PLBs
By use of a procedure similar to that applied to the top surface PLB data, the zero times ofthe signals from the small aperture sensors were adjusted to correspond to the PLB time. Figures
4 and 5, respectively, show the time-domain signals and the WT results of the signals. As before,group-velocity curves for the Lamb modes were superimposed on the WT results by use of the
distances from the epicenter of the PLB point (on the bottom surface at 102 mm from the edge)
to the two sensors. This propagation distance was used, because the guided Lamb modes are created by the various reflections from the top and bottom surfaces of the plate. In both the time
domains and WT figures, a portion of the signal with high-frequency intensity (beyond the highfrequency portion of the group velocity curves in Fig. 5) is identified with arrows. This portionof the signals is well beyond the slowest high-frequency region of the group velocity curves thathave significant amplitude. The arrival times (at the WT peak magnitude in the region of the arrows) shown on the WT results in Fig. 5 at a frequency of 510 kHz (from the most intense118

Fig. 4. Signals from small aperture sensors for PLB on bottom surface of steel plate. Propagation
distances from the epicenter of the PLB point to the sensors were (a) 254 mm and (b) 381 mm.

Fig. 5. Wavelet transforms of the signals shown in Fig. 4. Parts (a) and (b) correspond to thoseparts in Fig. 4. Initial portion of the time scale not shown to focus on the arrival of the Rayleighwave at the most intense portion of the signals. Multiple modes are shown to establish where
they appear in time.
119frequency in that region at the further propagation distance for both distances) were used to calculate velocity. The velocities were calculated by dividing the indicated arrival times into a
propagation distance, computed from the distance from the PLB to the near long plate edge plus
the thickness of the plate (up the edge) plus the distance from the edge to the appropriate sensors.
These velocities are shown in Table 2. The table also includes a published [11] velocity forRayleigh waves in steel and the percentage difference of the experimental velocities compared tothe published value. Based on the closeness of the experimental velocity to the published one andthe observed frequency range, it was concluded that this portion of the signals was indeed aRayleigh wave that traveled around two 90º corners to reach the sensors. This result is not surprising since the fact that Rayleigh waves can travel around corners has been observed in the pastwith laser-generated signals [7]. It is also apparent in the WT results that there is not a high frequency signal of significant intensity present in the high-frequency region where the A0 Lambmode is asymptotic to the Rayleigh wave velocity. Thus a Rayleigh wave (in Fig. 5) corresponding to the distance from the epicenter of the PLB to the sensors was not present, as was the casefor the top-surface PLB, as shown in Fig. 3. As an interesting aside, one can see in Figs. 4b and5b the arrival at about 165 µs (using the WT result) of a high frequency signal portion that corresponds with the “reversal” in time of the A3 mode.
Table 2 Experimental and published [11] Rayleigh velocity for bottom surface PLB.
Distance description
PLB to 1st sensor; two 90° bends
PLB to 2nd sensor; two 90° bends
First to second sensor

Velocity[mm/µs]
2.96
2.94
2.90

Published velocity[mm/µs]2.98

Difference
[%]
0.7
1.3
2.7

Rayleigh Wave Amplitude Versus Depth of Source – Out-of-plane Source
Previously published FEM results for thick steel plates [5, 6] showed that a recognizable
Rayleigh wave was not present for buried out-of-plane dipole sources that were not near the surface where the “pseudo” AE sensor was located. Thus, the use of signals from the above described out-of-plane PLBs to guide the setup of AE monitoring and/or analysis parameters may
not be fully appropriate relative to real AE from sources at different depths. Hence, a more detailed FEM study was conducted of the Rayleigh wave out-of-plane displacement amplitude as afunction of out-of-plane sources with more closely spaced source depths (below the surface). The
examination of depth-of-source effects on the presence of significant Rayleigh waves is relevant,because the AE technique is sensitive to sources throughout the thickness of a plate, and in particular, to sources on the bottom surface, which in field testing may not be accessible to placement of sensors. Further, previous FEM results have shown that similar Lamb modes in plates
are excited by both in-plane and out-of-plane sources as a function of source depth [2].
The modeling was performed by use of a previously validated finite-element approach [13,14] with an axisymmetric code for out-of-plane buried dipoles located at various depths below asurface. The out-of-plane displacement was obtained at various distances from the epicenter ofthe sources. In addition, to enhance the analysis of the modeling results, the out-of-plane displacement on the bottom surface was obtained at the same distances. The axisymmetric code wasable to provide results for a thick plate with large propagation distances for sources of short risetime by use of a domain large enough to prevent reflections from the domain edge arriving

120

during the duration of the direct waves. The axisymmetric code could accomplish these modelingconditions without excessive computer run times. The steel plate domain had a thickness of 25.4
mm. In addition, a source rise time of 1.5 µs was used with a maximum wave propagation distance of 1016 mm from the source epicenter to the “pseudo” sensor. The input properties for the
FEM calculation were based on the bulk velocities and density for steel (longitudinal velocity =5940 m/s, shear velocity = 3220 m/s and density = 7.8 kg/m3) [11]. The 1016 mm propagation
distance resulted in a large amount of dispersion for the Lamb waves, while the Rayleigh wave
was not subject to dispersion. Hence, at this distance the amplitude of a Rayleigh wave would
potentially not be reinforced to the same degree by the presence of Lamb waves with similar velocities as compared to the situation when only short propagations distances were present. The
short rise time was used since previous work [14] had shown that shorter rise times were needed
to generate a reasonable Rayleigh wave. The FEM was carried out with a domain of 2540 mm, acell size of 0.125 mm and a time step of 18.9 ns. The out-of-plane dipole source was composedof a single cell (without a body force) along with a one-cell monopole above and below eachwith a body force equivalent to 1 N. The resulting displacement results were resampled to 0.1µs/point to correspond to typical waveform recording of AE signals.

Fig. 6. Out-of-plane displacement versus time after a propagation distance of 1016 mm for dipole
sources at two depths below the top surface of a steel plate 25.4 mm thick. Parts (a) and (b) arefor a source at a depth of 1.93 mm in contrast to (c) and (d) at a source depth of 7.78 mm.
A typical set of out-of-plane displacement versus time results is shown in Fig. 6 for sources
centered at 1.93 mm (parts (a) and (b)) and 7.78 mm (parts (c) and (d)) below the top surface ofthe plate. Note that the femtometer scale for the displacement does not imply that AE sensors candetect such signals. The results in this section and the later section dealing with FEM signals are
not dependent on this scale. Figure 6 shows both the top and bottom surface displacements at a
propagation distance of 1016 mm. In the top surface result (part (a)), an arrow points out the121

Fig. 7. WTs of the top surface signal (a) and the bottom surface signal (b) respectively of Fig. 6.
Initial portion of the time scale not shown to provide better visual resolution. Note the insets ofthe same WT results to remove the “blocking” effect of the group velocity curves.
Rayleigh wave (verified by the analysis below). Figure 7 shows, for the source at a depth of 1.93
mm, the WT results of the displacement signals corresponding to Fig. 6 parts (a) and (b), respectively, along with superimposed group-velocity curves. In addition from the result in Fig. 7 (a),the arrival time at the maximum WT magnitude of the most intense portion of the WT results
was found to be 341.8 µs at a frequency of 510 kHz. When the velocity of this portion of the
wave was calculated by use of the propagation distance of 1016 mm (parallel to the top surfaceof the plate), a value of 2.97 mm/µs was obtained, which is within 0.3 % of a published value[11] of 2.98 mm/µs. This result, along with the associated high-frequency region, indicates that
this portion of the signal is a Rayleigh wave that traveled at the Rayleigh wave velocity. In thebottom-surface time domain (Fig. 6(b)) and corresponding WT result (Fig. 7(b)), there is no evidence of a relevant portion of a Rayleigh wave. To more clearly show this fact, Fig. 8 shows atime-expanded view of the out-of-plane displacement versus time for both surfaces. Clearly,
there is no distinct Rayleigh wave present in the bottom surface signal that corresponds to that
present on the top surface. Because in the far field, the top and bottom surface displacement of
Lamb waves are essentially the same except for a phase reversal (for one set of modes), the absence of this arrival on the bottom surface further demonstrates that the top surface arrival at theRayleigh velocity is in fact a Rayleigh wave and not a part of the Lamb modes. Figures 6(c) and(d) for the source at a depth of 7.78 mm show no evidence in the time domain of a Rayleighwave, and the WTs (not shown) showed no evidence of a Rayleigh wave.
122Fig. 8. Expanded time scale view of the region of the Rayleigh wave for the top surface (a) andthe bottom surface (b) results shown in Fig. 6. Source at a depth of 1.93 mm.
In order to examine an approach to provide a best estimate of the amplitude of the Rayleighwave versus source depth, the bottom surface out-of-plane displacements at a propagation distance of 1016 mm were subtracted from the top surface displacements at the same distance for an
out-of-plane source at a depth of 5.79 mm. The “subtraction” signal and corresponding WT result with superimposed symmetric group-velocity curves are shown in Fig. 9. For comparison,Fig. 10 shows the “sum” of the top and bottom surface signals and the corresponding WT resultwith superimposed antisymmetric group-velocity curves. When the WT results in these two figures are compared, it is clear from Figs. 9 and 10 that the “subtraction” of the signals removes
the antisymmetric modes, and the “sum” removes the symmetric modes. It is also clear that theA0 anti-symmetric mode could contribute to the amplitude of the Rayleigh wave in the time domain if the “sum” were used (see arrow in the WT result in Fig. 10). This potential contribution
could arise from the portion of the A0 mode that has a velocity only a small amount greater thanthat of the Rayleigh wave. In contrast, Fig. 9 illustrates that most of the signal intensity in thehigher frequency portion of the S0 mode has a velocity slower than that of the Rayleigh wave.
Hence, the “subtraction” of the signals was used to obtain a best estimate of the peak amplitudeof the Rayleigh wave. This choice seems to provide a best estimate of the Rayleigh wave amplitude, because the “sum” peak amplitudes were all larger. This fact indicated there was more reinforcement (as described above) of the Rayleigh wave amplitude by the A0 mode in the case of
the “sum.” Hence, the “subtraction” approach was applied to the time-domain signals obtained
from the FEM results for the different source depths.
The Rayleigh wave peak amplitudes (absolute values) and their arrival times determinedfrom the “subtraction” time domain signals are shown in Table 3 as a function of the source
depth for a propagation distance of 1016 mm. This table also shows the WT-determined frequency of the most intense high-frequency portion of the Rayleigh wave and the peak signal amplitudes (absolute values) of the original top and bottom surface out-of-plane displacement signals. It must be pointed out that the “Rayleigh amplitude” value in this table for the depth of 7.78mm was taken from the peak signal amplitude in the same time region of the Rayleigh peak123

Fig. 9. Displacement signal resulting from the bottom surface signal subtracted from the top surface signal to eliminate the anti-symmetric modes. Source at 5.79 mm below the top surface andpropagation distance 1016 mm. Inset of same WT result to remove the “blocking” effect ofgroup velocity curves.
Table 3 Amplitudes of Rayleigh wave and top and bottom surface peak amplitudes versus depthof source.
Depth
[mm]

Frequency atTime atRayleigh
Top surface
Bottom surface
Rayleigh WT Rayleigh peak amplitude peak amplitude peak amplitude
peak [kHz]amplitude
[fm]
[fm]
[fm]
[µs]
0.311
339
343.4
111
73
62
0.809
648
342.1
111
122
48
1.31
573
342.0
201
195
65
1.93
495
342.0
237
219
82
2.18
468
341.9
238
217
87
2.81
420
342.1
223
199
93
3.8
378
342.7
179
152
82
5.79
330
343.6
104
73
69
7.78
-------71*
67
58* No Rayleigh wave; amplitude at the typical arrival time of Rayleigh wave.
124

Fig. 10. Displacement signal resulting from the bottom surface signal added to the top surface
signal to eliminate the symmetric modes. Source at 5.79 mm below the top surface and propagation distance 1016 mm. Inset of same WT result to remove the “blocking” effect of group velocity curves.
observed for the sources that were not as deep. This approach was used because no distinct
Rayleigh wave was observed in the signals at this source depth or at greater depths. As can beobserved in Table 3, the Rayleigh wave amplitude decreased for sources very near the surface.
This decrease may be associated with the reduction in the constraint of the top monopole when
the dipole is very close to the surface. The results for all three displacement amplitudes in Table
3 are plotted in Fig. 11 as a function of the source depth. Clearly the top-surface peak amplitude
was dominated by the Rayleigh wave amplitude when the buried source was close to the surface.
Since the top-surface peak amplitude includes the net effect of the Rayleigh wave’s interaction
with the Lamb modes, it is not clear how to explain why these amplitudes are a little less than thebest estimate of the Rayleigh wave amplitudes. Also, as the depth of the source increased, allthree amplitudes approach each other. Finally, the frequency of the peak intensity of theRayleigh wave was above the approximate value of 320 kHz, where the S0 and A0 modes are asymptotic to the Rayleigh velocity. An equation has been suggested [9] to provide an approximate
frequency, f, below which for a given plate thickness, t, Rayleigh waves cannot propagate. Using
this equation [f = 8 cT/(tπ)] (where cT is the bulk shear velocity), f was calculated to be 323 kHz.
We note that all the frequencies at the Rayleigh wave peak are above this value. Also in Table 3,
except for the source nearest the surface, the WT-determined frequency (at the WT determined125Fig. 11. Rayleigh wave peak amplitude and peak amplitudes of the top and bottom surface outof-plane displacement signals versus depth of buried dipole AE source at a propagation distanceof 1016 mm for 25.4 mm thick steel.
peak intensity) at the Rayleigh wave decreased as the depth of the source increased. This behavior of the high-frequency components (of the Rayleigh wave) exhibiting greater prominence forsources nearer the surface was previously observed in an analytical treatment of buried monopole pulses [15]. From the results in this table, an average arrival time was calculated to be342.5 µs. This arrival time translates to a velocity of 2.97 mm/µs for the 1016 mm propagation
distance. This velocity is almost identical to the Rayleigh wave velocity already used in this paper.
Rayleigh Wave Amplitude and Depth of Source – In-plane Buried Source
An additional question relative to the presence of a detectable out-of-plane Rayleigh wave
concerns the case of an in-plane buried dipole AE source. To model this case with FEM, it wasnecessary to use a three-dimensional code. Due to the huge increase required for the computing
resources, a smaller domain was used and only one source depth was run. Thus, the maximum
propagation distance was limited to 381 mm so as to eliminate significant edge reflections fromthe smaller domain. Also a larger rise time of 2.3 µs was used for the 25.4-mm thick steel plate.
Hence, the cell size was 0.5 mm and the time step was 75.5 ns. Since the rise time was slower(compared to the out-of-plane results above), to provide a meaningful comparison, both in-planeand out-of-plane dipole results were calculated with the 3-D code. As before, these dipoles werecomposed of single cells with body forces on either side (or above and below for the out-of-planesource) of a cell without a body force. The FEM results for the out-of-plane displacement versus
time were first digitally high-pass filtered at 40 kHz with a four-pole Butterworth filter and thenre-sampled to 0.1 µs/point. The top surface displacement results for the two different dipoles
centered at 1.25 mm below the top surface are shown in Fig. 12 for the 381-mm propagation distance. These displacements are for the waves propagated in the direction of the forces of the inplane dipole. It is worthwhile to note that for the in-plane dipole, the FEM results showed thatthe amplitude of the waves decreases in other in-plane directions to a minimum of nearly twoorders of magnitude less in peak amplitude in the direction 90º from the dipole direction (a result126

Fig. 12. Top surface out-of-plane displacement signals after propagation of 381 mm. Obtainedusing 3D code for out-of-plane dipole (a) and in-plane dipole (b). The sources were each centered at 1.25 mm below the top surface of a 25.4 mm thick steel plate.
of the source radiation pattern [16]). In Fig. 13 the corresponding WT results are shown with thefundamental Lamb modes superimposed. Examination of the WT results for the out-of-plane dipole in Fig. 13 part (a) shows a high-frequency intense region with an arrival where the fundamental group velocities are asymptotic to the Rayleigh velocity. The arrival time as shown in thisfigure at the peak magnitude of the frequency of greatest intensity of 411 kHz was at 130.2 µs.
The corresponding velocity for the 381-mm propagation distance is 2.93 mm/µs, which is only
1.8 % less than the Rayleigh velocity. Hence, this part of the signal was determined to represent
a Rayleigh wave, and the corresponding high-frequency peak at this arrival time was designatedas a Rayleigh wave in the time domain of part (a) of Fig. 12.
In contrast, the WT results for the in-plane dipole shown in part (b) of Fig. 13 show no intense high-frequency region corresponding to the arrival of a Rayleigh wave. In fact the mostintense WT region at the 411 kHz frequency is at about 160 µs. We note in Fig. 13(b) that themost intense arrivals from 335 kHz to at least 600 kHz are all at about this same time. These arrivals correspond to a region of higher intensity from higher Lamb modes (not shown) that also
appears in the out-of-plane WT result (Fig. 13(a)). Likewise the high-frequency Rayleigh-wave
arrival is missing in the time domain of part (b) of Fig. 12. These Rayleigh-wave conclusions
were reinforced by the absence of high-frequency Rayleigh wave intensity on the bottom surface
out-of-plane displacement results (not shown) that showed only the Lamb waves. Thus, for aburied in-plane dipole source at a depth (below the surface with the sensors) of only about 5 % of
the total plate thickness, there was no evidence of the presence of a Rayleigh wave. In contrast,
an out-of-plane dipole at the same depth does create a Rayleigh wave on the adjacent surface, as
was shown earlier. Because the radiation pattern from an in-plane dipole generates very little
127

Fig. 13. Wavelet transform results for the corresponding signals in Fig. 12.
displacement in the out-of-plane direction [16], it might be expected that the in-plane dipole excited no significant Rayleigh wave. In addition, because the relative amplitude of the out-ofplane wave displacement is greater than that of the in-plane displacement for a Rayleigh wave[11], the lack of a Rayleigh wave for the in-plane dipole source may not be too surprising.
Rayleigh Waves from Edge PLBs
The original intended goal of this research was to examine whether key information on theeffect of the depth of buried AE sources on the signals monitored with real AE sensors could beobtained by use of PLBs on the edge of a thick plate at various distances below the plate top surface (where the sensors were mounted). As was pointed out earlier, this approach was suggestedby the positive results with such a strategy for thin plates [3, 4]. But as demonstrated in the previous sections of this paper, the intended goal was complicated by the presence of a Rayleighwave with relatively large amplitude that moves up the plate edge (from the PLB point) and thenacross the plate surface to the sensors.
The complication is best illustrated by the results from a PLB on the plate edge well below
the top surface. For this experiment the small-aperture sensors shown in Fig. 1 were moved sothat they were at 254 mm and 381 mm from the plate edge. In addition, the trigger sensor was
moved for these experiments to a similar position relative to the PLB position. Then, the PLB
was done on the edge at a distance of 22 mm down from the top surface of the plate. For the two128

Fig. 14. Signals from small aperture sensors on the plate top surface. PLB on plate edge at 22.2
mm below the plate top surface. Propagation distances of (a) 254 mm and (b) 381 mm.

Fig. 15. WTs of the respective signals (a) and (b) in Fig. 14 showing the delayed arrival of theRayleigh wave (at Max 1 in both (a) and (b)) compared to the position in time of the A0 and S0
modes at frequencies above about 320 kHz.
129

propagation distances, Figs. 14 and 15, respectively, show the time domains and their WTs at thetwo propagation distances. The Lamb-mode group-velocity curves were superimposed in the
WT results based on the top surface distance from the edge to the sensors (for the same reason as
already indicated for the bottom-surface PLBs). The signals for these figures were zeroed to the
PLB time in the same fashion as explained earlier in this paper. Propagation velocities were calculated by use of the distance up the edge (22 mm) plus the distance along the top surface fromthe edge along with the arrival times at the high frequencies shown in Fig. 15. The values of 2.90mm/µs at the closest sensor and 2.95 mm/µs at the farther sensor were close to the Rayleigh velocity (less than 3 % below). This result, along with the fact that the frequencies present in thisportion of the signals were sufficiently high to be associated with Rayleigh waves, led to theconclusion that this portion of the signals was indeed a Rayleigh wave. This Rayleigh wave (indicated by arrows in Fig. 14 and by “Max 1” in Fig. 15) is a dominant feature of the signals obtained from the sensors, but, as shown in the WT results with superimposed group velocity
curves, it does not arrive at the time expected within the Lamb-mode group velocities. Hence, thepresence of this Rayleigh wave complicates the determination of the intense mode/frequency
combinations in the Lamb modes. Similar difficulties were present for PLBs on the edge at otherdistances below the plate top surface.
Upper Frequency Range of Rayleigh Waves from PLBs
The data from an experiment with an edge PLB at the depth of 3 mm below the top surface
with a wideband (nearly flat with frequency [17, 18]) conical-type sensor (called FHWA withappropriate preamplifier and 50 kHz high-pass passive filter) demonstrated the high frequency
content present in the PLB-generated Rayleigh wave. Note that at the Rayleigh wave velocity,
the 3-mm distance is equivalent to a propagation time of about 1 µs. Figure 16 shows this interesting result. In this case, a series of time-domain waveforms (not adjusted in time such that zerotime corresponded to the time of the PLB) for a 381-mm propagation distance are shown for
high-pass filtered signals (six-pole digital Butterworth) ranging from 50 kHz to 2 MHz. Clearly,
even though the Rayleigh wave amplitudes diminished as the high-pass frequency increased, theRayleigh wave signal extended to relatively high frequencies of up to 2 MHz. This result indicates that, depending on their frequency response, some AE sensors might be expected to be insensitive to a Rayleigh wave. The results of an experiment to test this hypothesis are presented in
the next paragraph.
Detection of Rayleigh Waves by Different Sensor Types
Using the same edge PLB source at a depth of 3 mm and a propagation distance of 381 mm
with a high-pass passive filter at 50 kHz (after the preamplifiers), a total of six differently designed AE sensors (with appropriate preamplifiers; internally with either no filter or a 5-kHz
high-pass filter) were used to obtain results. Table 4 illustrates some of the characteristics ofthese sensors. Figure 17 shows the time-domain waveforms (again not adjusted to the PLB time)of the signals along with a classification of the sensors and a qualitative measure of the ability to
distinguish by eye (in Fig. 17) the presence of a Rayleigh wave (ordered from the least to maximum ability to observe the presence of a Rayleigh wave; the rankings were called “not present”,“present” and “distinct”). The ability to see the potential Rayleigh wave was facilitated by thefact that a common trigger sensor was used and some of the sensor signals exhibited very distinct
Rayleigh waves. To attempt to enhance the potential for detection of a Rayleigh wave in the time
domain, the signals were digitally filtered (four-pole Butterworth) with a high-pass frequency of
500 kHz. The resulting signals (not shown) did not materially enhance the ability to pick out a130

Fig. 16. Filtered signal showing high frequency content in Rayleigh wave at a propagation distance of 381 mm with signal from FHWA sensor. Right column shows the WTs of the left column signals along with the arrival times of the most intense signal peak corresponding to theRayleigh wave.
Rayleigh wave in the cases where it could not be seen in Fig. 17. The best approach to determine
whether a Rayleigh wave was present in the signals from the different sensors was by use of the
WT. The results from the WT applied to the 50 kHz high-pass data are shown in Fig. 18. In thisfigure arrows point to the evidence of the presence of a Rayleigh wave. Close examination of
Figs. 17 and 18 shows that the Res #3 sensor (a 150 kHz resonant frequency) signal exhibited noevidence of a Rayleigh wave. Res #1 also lacked a very clear response to the Rayleigh wave. It
131

is worthwhile to note that even though a resonant sensor that did not respond to a Rayleigh wave
would remove the problem caused by the Rayleigh wave that propagates up the edge of the plate,
it would not be very useful. The reason is that such sensors do not fully characterize the effect ofthe distance below the surface on the waves generated by edge PLBs over the range of frequencies in the Lamb modes.
Table 4 Sensor characteristics
Sensor
names
FHWA
WB #1
WB #2
Res #1Res #2*Res #3

Expected response
character
Wideband
Wideband
WidebandResonant
Resonant
Resonant

Freq. of peak response[kHz]
Not applicableNot applicableNot applicable
125
500
150

Approximate aperture[mm]
1.6
Proprietary
6
15
3.5
15

* The small aperture sensor used in the first part of this research.

Fig. 17. Waveforms from different sensors showing the existence (at different levels of distinctiveness) or non-existence of evidence of Rayleigh wave from PLB at a propagation distance of
381 mm, 50 kHz high-pass data.
132

Fig. 18. WTs of the first 200 µs of the time domains in Fig. 17 demonstrating the presence of aRayleigh wave and its relative intensity.
Discussion of Detection of Rayleigh Waves in AE Monitoring
There are two primary questions that are relevant to the potential detection of Rayleighwaves in AE monitoring of plates. First, is a surface Rayleigh wave present on the surface wherea sensor is mounted? Second, will the particular type of sensor respond to a Rayleigh wave?
With regard to the first question, the preceding parts of this paper have considered two situations:(a) either a source on the surface (includes surface edges or bottom surface, if a surface-tosurface path to the sensor mounting surface is present); or (b) a buried out-of-plane dipole sourcethat is located not far below the sensor mounting surface. An additional case (not addressed inthis research), involves a part-through crack that is open to the plate surface on which the sensors
are mounted. In this situation a Rayleigh wave can be generated on the crack faces by crack
growth or pop-in, as shown in experimental work [19]. This Rayleigh wave can propagate on thecrack faces to the plate surface. In this situation as well as for the edge source location, the133

Fig. 19. Approximate frequency below which a Rayleigh wave cannot propagate versus thickness of steel plate. Governing equation taken from reference number [9]. Wavelength values
shown corresponding to frequency and Rayleigh velocity.
possibility for a part of the Rayleigh wave to continue to propagate on the plate surface will depend on whether the plate is thick enough to support a Rayleigh wave according to the equation
previously introduced. A plot of this equation demonstrating frequency versus plate thickness isshown in Fig. 19 along with a second vertical axis that provides the wavelengths of Rayleighwaves corresponding to the frequency scale. This figure illustrates the approximate frequency fora steel plate, below which a Rayleigh wave cannot propagate as a function of the plate thickness.
In addition, the figure points out the conditions (frequency and wavelength) for a nominal 25 mm
thick steel plate. In addition, it should be noted that when a PLB is done on a plate edge, the“thickness” relative to that in Fig. 19 is typically very large. Hence, a Rayleigh wave will often
be generated, but when the wave reaches the plate surface then its ability to continue to propagate will depend on the plate “thickness.” Finally, in all the cases that could lead to a Rayleighwave, the rise time of the source must be short enough to excite frequencies above those indicated in Fig. 19. Based on FEM results [20], an estimate of the approximate high frequency froma source can be calculated from the reciprocal of the source rise time.
With regard to the second question, the sensor must have sufficient frequency response to thefrequencies present in the Rayleigh wave. A key aspect of the required high-frequency response
is a sensor with an aperture small enough that the Rayleigh wave (which travels on the plate surface under the sensor) wavelengths are greater than the sensor aperture size. The last column inTable 4 indicates a potential reason why signals from the sensors designated Res #1 and Res #3
exhibited no Rayleigh wave (or had almost no sensitivity to a Rayleigh wave) as discussed in thesection dealing with the different sensors. For these sensors, the signal at the lowest Rayleighwave wavelengths would “average out” over the sensor face. It is worth noting that Fig. 19
clearly shows that the sensor aperture size must be quite small for thin plates.
134Summary
There are three summary observations for thick plates (on the order of 25 mm in thickness)that can be made based on the results presented here. First, plate-surface sensor signals from
PLBs on a plate edge at various distances below the top surface of a thick plate are difficult tointerpret (relative to the Lamb-wave frequency/mode combinations that are excited), because astrong Rayleigh wave is generated that arrives at the sensors within the range of arrivals of theLamb modes. Thus, the effects of the depth of buried dipole sources on the determination of intense Lamb mode/frequency combinations cannot be easily determined by use of such PLBs.
This conclusion is contrary to that determined for the waves generated by PLBs on the edge of athin aluminum plate (4.7 mm thickness).
Second, some resonant sensors have poor or no evident response to a Rayleigh wave. Thesesensors do not present the Rayleigh wave interpretation problem, but because of their limited
bandwidth of sensitivity they are not useful to characterize the full response of the Lamb modesin the waves generated by edge PLBs at various edge depths.
Third, if PLBs on thick plates are done on the surface where the sensors are mounted to provide indications of the potential character of real AE, the signals may be dominated by Rayleighwaves unless the sensor does not respond to Rayleigh waves. Thus, the extraction of informationfrom such PLB generated signals must be treated with care relative to the potential influence of aRayleigh wave. This result is particularly true when subsequent experiments will generate real
AE from buried in-plane sources, where Rayleigh waves are not generated even for sources just asmall distance below the adjacent surface. It is also true for out-of-plane buried sources unlessthey are within about 23 % of the 25-mm plate thickness from the adjacent surface where thesensors are mounted.
References
1. Hamstad, M. A., “Contrasts between the Acoustic Emission Signals Generated by Monopole
versus Dipole Sources,” Advanced Materials Research, 13-14, 2006, 61 – 67.
2. Hamstad, M. A., “Acoustic Emission Signals Generated By Monopole (Pencil Lead Break)Versus Dipole Sources: Finite Element Modeling And Experiments,” J. Acoustic Emission,
25, 2007, 92 – 106.
3. Hamstad, M. A., “On Determination of Accurate Lamb-Mode Group-Velocity Arrival Times
with Different Types of Acoustic Emission Sensors,” Advances in Acoustic Emission – 2007,
Proceedings of Seventh International Conference on Acoustic Emission, Edited by Kanji
Ono, 2008, pp. 178 – 183.
4. Hamstad, M. A., “Comparison of Wavelet Transform and Choi-Williams Distribution to Determine Group Velocities For Different Acoustic Emission Sensors,” J. Acoustic Emission,
26, 2008, 40 – 59.
5. Hamstad, M. A., “Lamb Modal Regions with Significant AE Energy in a Thick Steel Plate”,
Advances in Acoustic Emission – 2007, Proceedings of The Sixth International Conferenceon Acoustic Emission, Edited by Kanji Ono, 2007, pp. 247 – 252.
6. Hamstad, M. A., “Acoustic Emission Source Location in a Thick Steel Plate by Lamb
Modes,” J. Acoustic Emission, 25, 2007, 194 – 214.
7. Cooper, Jeremy A., Ross Crosbie, Richard J. Dewhurst, Andrew D. W. McKie and Stuart B.
Palmer, “Surface Acoustic Wave Interactions with Cracks and Slots: A Noncontacting Study
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Using Lasers,” IEEE Trans. on Ultrasonics, Ferroelectrics, and Frequency Control, UFFC33, (5), 1986, 462 – 470.
8. Scala, C. M. and P. A. Doyle, “Time-and Frequency-domain Characteristics of Lasergenerated Ultrasonic Surface Waves,” J. Acoust. Soc. Am., 85, (4), 1989, 1569 – 1576.
9. Bushell, A. C., C. Edwards and S. B. Palmer, “Laser-generated Surface Waves on Plates ofVarying Thickness,” British J. of NDT, 33, (4), 1990, 177 – 182.
10. Breckenridge, F. R., T. M. Proctor, N. N. Hsu, S. E. Fick and D. G. Eitzen, “Transient
Sources for Acoustic Emission,” Progress in Acoustic Emission V, The Japanese Society for
NDT, 1990, pp. 20 – 37.
11. Kolsky, H., Stress Waves in Solids, Dover Publications, New York, 1963.
12. Vallen, J., AGU-Vallen Wavelet transform software, version R2009.0525, Vallen-Systeme
GmbH, Icking, Germany, 2009.
13. Hamstad, M. A., A. O’Gallagher and J. Gary, “Modeling of Buried Acoustic Emission Monopole and Dipole Sources With a Finite Element Technique,” J. Acoustic Emission, 17,(3/4), 1999, 97 – 110.
14. Hamstad, M.A., J. Gary, A. O’Gallagher, “Far-field Acoustic Emission Waves by ThreeDimensional Finite Element Modeling of Pencil Breaks on a Thick Plate,” J. of Acoustic
Emission, 14 (2), 1996, 103 – 114.
15. Pekeris, Chaim L. and Hanna Lifson, “Motion of the Surface of a Uniform Half-space Produced by a Buried Pulse,” J. Acoust. Soc. Am., 29, (11), 1957, 1233 – 1238.
16. Scruby, C. B., “Quantitative Acoustic Emission Techniques,” Research Techniques in Nondestructive Testing, Chap. 4, Vol. 8, ed. R. S. Sharpe, Academic Press Inc., London, 1985,
pp. 141 – 210.
17. Hamstad, M.A., “Improved Signal-to-Noise Wideband Acoustic/Ultrasonic Contact Displacement Sensors for Wood and Polymers,” Wood and Fiber Science, 29 (3), 1997, 239 –248.
18. Hamstad, M.A. and C.M. Fortunko, “Development of Practical Wideband High Fidelity
Acoustic Emission Sensors,” Nondestructive Evaluation of Aging Bridges and Highways,
Steve Chase, Editor, Proc. SPIE 2456, 1995, pp. 281 – 288.
19. Thaulow, C. and W. Burget, “The Emission of Rayleigh Waves from Brittle Fracture Initiation, and the Possible Effect of the Reflected Waves on Crack Arrest,” Fatigue & Fracture ofEngineering Materials & Structures, 13, 4, 1990, 327 – 346.
20. Hamstad, M.A., Unpublished results, National Institute of Standards and Technology, Boulder, CO, July 2009.

136

FRACTURE BEHAVIOR IN BONE CHARACTERIZEDBY AE WAVELET ANALYSISSHUICHI WAKAYAMA, KEISUKE MOGI and TETSUYA SUEMUNE
Department of Mechanical Engineering, Tokyo Metropolitan University
1-1 Minami-Ohsawa, Hachioji, Tokyo 192-0397, Japan
Abstract
Fatigue tests of bovine cortical bone were carried out. Compressive stress was appliedalong longitudinal axis of bones and fracture surfaces were parallel to the loading direction.
Damage accumulation during tests was monitored by the measurements of acoustic emission(AE) signals and ultrasonic wave velocity. For the static compression test, specimens fractured
catastrophically and the most of AE signals were detected close to final fracture. On the other
hand, AE events increased and wave velocity decreased gradually during fatigue fracture of bone.
A majority of AE signals were detected during unloading and they formed characteristic ‘AEbands’. AE wavelet analysis demonstrated that the peak frequencies of unloading AE, as well
as loading AE, were equivalent to the resonant frequency along the specimen thickness. Finally,
it is strongly suggested that microcrack extension due to wedging effect of debris took place
during unloading in the fatigue process of cortical bone.
Keywords: Fatigue, Cortical bone, Microdamage accumulation, AE wavelet analysis, Wavevelocity
Introduction
Bone is the primary structural material and has the role of supporting load in a human body.
Bone can be classified into two types of structure. One is cancellous bone with sponge-likestructure, and the other is dense cortical bone that consists of hydroxyapatite matrix and collagenfibers aligned along the long axis and have the microstructure similar to unidirectionally reinforced composites [1, 2]. During the life, damages were initiated and accumulated in bones,
which result in the degradation of bone. When bone is subjected to excessive cyclic loading, fatigue fracture will take place similar to fatigue in metals [3]. The microfracture process must be
clarified for the early diagnosis of fatigue fracture of bone. Previously, various studies on fracture behavior of human or bovine bone under cyclic loading were carried out; since the bovinebone has the mechanical properties similar to human bone, it is frequently used for the investigation on mechanical behavior of bone, as in this study. The damage accumulation or mechanicalproperty degradation of bone under cyclic loading was characterized by means of the measurement of stiffness or wave velocity during fatigue fracture [4 - 6]. However, few AE study has
been reported and microscopic understanding of fatigue fracture processes of bone has been
inadequate.
In the present study, compressive fatigue tests of bovine cortical bone were carried out. The
purpose of this study is to understand the mechanism of bone fatigue fracture for the development of detecting technique of fatigue damage in bone. Damage accumulation during fatigue
fracture of bone was characterized by AE monitoring during fatigue test. The longitudinal wave
velocity was also measured and compared with AE generation behavior. In particular, the natureof AE sources was investigated by AE wavelet analysis. Finally, the degradation process of boneunder cyclic loading was discussed.
J. Acoustic Emission, 27 (2009)137© 2009 Acoustic Emission Group(a) Schematic representation of dyaphisis. (b) Optical micrograph of cross section.
Fig. 1. Cross section of bovine cortical bone (Plexiform bone) and geometry of dyaphisis.
Experimental Procedures
Bone Specimens
Bovine cortical bone was used in this study. The microstructure of the bovine cortical bone isclassified into two tissues, plexiform and haversian bones. Here, the plexiform bone was investigated. Figure 1 shows the schematic structure of diaphysis and microstructure of bovine corticalbone (plexiform bone). Diaphysis consists of a tubular cortical bone and the interior marrow, asshown in Fig. 1(a). The radial, tangential and longitudinal directions are x1, x2 and x3, respectively, as indicated. An optical micrograph of typical cross-section, perpendicular to longitudinalaxis, of the plexiform bone is shown in Fig. 1(b), which shows that lamellae are aligned along
longitudinal direction, resulting in the orthogonal elastic properties. A number of lacunae werealso observed among lamellae. These act as flaws under loading.
Specimens of 5 mm x 5 mm x 12.5 mm were cut from the diaphysis of bovine femoral bone.
The specimens were kept frozen at –20°C, cut under flowing water and kept wet during the mechanical tests. The mechanical load was applied along longitudinal direction (x3) as in the body.
Static Compression Tests
Static compression tests of bovine cortical bone (plexiform bone) were carried out in air atroom temperature. Uniaxial compressive load was applied along the longitudinal (x3) axis ofbone specimen under constant crosshead speed of 0.1 mm/min. Testing system of static compression test is shown in Fig. 2, schematically. During the tests, longitudinal strain was measured
using a strain gage attached directly on the specimen. Microfracture process of bone under compressive stress was evaluated by AE technique. A wideband AE sensor (NF; AE-900M) was attached on the specimen. Detected AE signals were amplified by a preamplifier (Gain; 60 dB)
through the bandpass filter with a range of 100 kHz to 1200 kHz. The threshold level was 43 dB(= 141 µV at the input terminal of the a preamplifier). Amplified AE signals and strain were thenrecorded by an AE analyzer (PAC; Mistras), while load and strain were recorded by another PC.
Cyclic Compression Tests
In order to investigate the fatigue fracture behavior of bone, cyclic compression tests were
performed in air at room temperature. The bone specimen was subjected to sinusoidal loading at
3 Hz along the longitudinal (x3) axis of the bone. Stress ratio (minimum stress divided by maximum stress) was 0.05. For the convenience, compressive stress is taken as positive in this study.
138

Fig. 2. Schematic diagram of static compression test system.

Fig. 3. Schematic diagram of cyclic compression test system.
The variation of mechanical property of bone is large, because bone is not industrial but natural
material. Therefore, the stress amplitude (a half of the sum of maximum and minimum stresses),
σa, was normalized by the initial Young’s modulus, E*, of each specimen, which was determinedfrom the linear part of the stress-strain relationship at the first cycle.
The damage accumulation in bone under cyclic loading was monitored by AE measurement.
AE measurement system for the cyclic compression tests is shown in Fig. 3(a). The system wasalmost same as the static compression test (Fig. 2), but a resonant type AE sensor (PAC; Pico,resonant frequency = 400 kHz), which is more sensitive than the broadband sensor, was additionally used since the AE signal level during fatigue fracture was expected to be lower thanstatic compressive fracture. Threshold level was 45 dB (= 178 µV), which was higher than static
compression test because of higher mechanical noise of a fatigue testing apparatus.
In order to examine the degradation in mechanical properties of bone, Young’s modulus andlongitudinal wave velocity were also measured at every 300 – 400 cycles. Figure 3(b) shows themeasurement system of wave velocity schematically. Two AE sensors were used as a transmitterand a receiver. Rectangular wave (frequency: 1 MHz, amplitude: 10 V) was transmitted along theradial (x1) axis and wave velocity was determined as the specimen thickness divided by transit
time.
Results and Discussions
Static Compression Tests
Figure 4 shows a typical result of stress-strain relationship and the behavior of cumulative
AE events and energy for the static compression test. The stress-strain curve is almost linear except for the slight deviation from linearity at the final stage. Most of AE events and energy was139

detected close to the final fracture. These results suggest the catastrophic fracture without damage accumulation during static compression tests. Average strength, fracture strain and Young’s
modulus were 139.2 MPa, 0.0061 and 24.1 GPa, respectively.
After the static compression tests, most of the specimens were fractured along the x3 (longitudinal)-x2 (tangential) plane, i.e., fracture surfaces were parallel to loading direction. It is thensuggested that the final fracture of bone under longitudinal compression derived from the nucleation and propagation of microcracks, which might be induced by lacunae, among lamellae shownin Fig. 1(b).

Fig. 4. Stress-strain relationship and AE behavior during static compression test.

Fig. 5. Distribution of AE events during compressive fatigue test [σa / E* = 0.0024].
Cyclic Compression Tests
A number of AE signals were detected during compressive fatigue test of bovine corticalbone. Figure 5 shows the distribution of AE signals detected during the fatigue test at σa /E* =0.0024 (E* = 26.7 GPa). AE events detected during loading and unloading were discriminatedand plotted in the figure. The figure indicates the normalized stress and cycle when each AE signal was detected. It can be seen that the majority of AE events were detected during unloading140

and AE events during loading were detected around peak stress. It is also observed that most ofloading AE events was detected at the initial and final stages. It is worth noting that several characteristic ‘AE bands’ consisting of many AE signals are found in the figure. Those ‘AE bands’
can possibly correspond to specific features of fatigue fracture.

Fig. 6. AE generation behavior and ultrasonic wave velocity during compressive fatigue test
[σa/E* = 0.0024].
Cumulative number and energy of AE events detected during loading and unloading areshown in Fig. 6(a) and (b), respectively. In the figure, cumulative AE events and energy arenormalized by each maximum and the rapid increase at final fracture for loading AE is omitted.
For the loading AE (Fig. 6(a)), most of AE events and energy were detected just before the final
fracture. On the other hand, cumulative energy of unloading AE increases gradually until ~200
cycles followed by nearly a steady state and rapid increases at final fracture.
The longitudinal wave velocity measured during fatigue tests is also plotted in Fig. 6. The
wave velocity decreased gradually, which suggest that the strength of bone decreased during fatigue test due to microdamage accumulation [6]. Comparing with generation behaviors of loading and unloading AE, it is important that decreasing behavior of wave velocity has a tendency
similar to generation behavior of unloading AE. It appears that gradual decrease in strength during fatigue resulted from the microfracture during unloading.

141

(a) Loading AE (Peak load).
(b) Unloading AE (Low load).
Fig. 7. Waveforms and wavelet patterns of loading and unloading AE.
AE Wavelet Analysis
AE events detected during loading and unloading correspond to crack opening and closure,respectively. The nature of the AE sources was investigated using AE wavelet analysis. Figure 7
shows the wavelet patterns of loading and unloading AE signals detected during fatigue fractureof bovine cortical bone. Figure 7(a) shows a typical result for the loading AE detected at aroundpeak load. A peak WT coefficient is recognized at ~350 kHz. Since the frequency value is comparable to the resonant frequency of longitudinal wave along thickness (x1-axis) direction, thesource of the AE generated a strong signal in that thickness direction. Considering the radiation
pattern of AE, the source is suggested as a longitudinal crack parallel to applied compressive
load. The WT-coefficient pattern of unloading AE detected at a low stress level, shown in Fig.
7(b), is quite similar to that of loading AE. Therefore, it is strongly suggested that the unloading
AE was emitted from microscopic crack propagation at the longitudinal crack tip due to thewedging effect of debris. Thus, it can be suggested that the damage during crack closure is thedominant factor of strength degradation by compressive cyclic loading.
Conclusions
The microfracture process in bovine cortical bone under cyclic loading was investigated inthis paper. Microdamage, such as microcracking, was monitored by AE measurement. The
wavelet transform analysis of AE signals detected during loading and unloading yields the essential information of fatigue fracture. The following conclusions were obtained.
1. For the static compression test of cortical bone, few damage accumulation was detected.
2. AE signals detected during loading and unloading can be discriminated. The majority of AE
signals were detected during unloading, which parallels the decrease in strength during fatigue as suggested by the observed decrease in ultrasonic wave velocity.
3. Unloading AE exhibits ‘AE bands’, which is corresponding to the behavior of individual
microcracks. AE wavelet transform analysis suggested that unloading AE indicates the crack
propagation parallel to compressive loading.
142

References[1] Hirai T., Katayama T., Inoue N. and Yamamoto H.: Trans. JSME (Ser. A), 58 (551), 1992,
34.
[2] Niimoto M., Akahori T., Kim J., Tajima H. and Kodama T.: Trans. JSME (Ser. A), 69 (687),
2003, 121.
[3] Mori S.: J. Soc. Biomechanisms Japan, 21 (2), 1997, 81.
[4] Caler W. E. and Carter D. R.: J. Biomechanics, 22 (6), 1989, 625.
[5] Pattin C. A., Caler W. E. and Carter D. R.: J. Biomechanics, 29 (1), 1994, 69.
[6] Lee S. C., Coan B. S. and Bouxsein M. L.: Bone, 21 (1), 1997, 119.

143

ABOUT PLASTIC INSTABILITIES IN IRON AND POWER SPECTRUM
OF ACOUSTIC EMISSION
ALEXEY LAZAREV and ALEXEI VINOGRADOV
Department of Intelligent Materials Engineering, Osaka City University, Osaka 558-8585, Japan
Abstract
Plastic instabilities are investigated in iron with different purity by means of acoustic emission (AE) power spectral analysis. Special attention is paid to AE accompanying the yield drop
followed by Lüders-band propagation and macroscopic strain localization associated with necking followed by crack nucleation and propagation. Both the Lüders instability and necking belong to the same generic type strain-softening instability although the former refers to the socalled propagative instability while the latter is static. In both cases, significant shift of the power
spectral density towards low frequencies is observed and discussed.
Keywords: Strain localization, Lüders bands, Impurity
Introduction
Various kinds of instabilities of plastic deformation and strain localization appear in focus ofpractically any research related to fracture since the synergy between the plastic deformation andfracture has become evident for many years. Acoustic emission (AE) has long been recognized
to be capable of monitoring the local structural rearrangements associated with both plastic deformation and fracture. Many aspects of AE phenomenon, which occurs during uniform deformation of metals and alloys, have been fairly well understood in terms of dislocation kinetics anddislocation interaction with impurities. On the other hand, AE accompanying the processes ofstrain localization under loading has not been fully understood and rationalized from either phenomenological or microscopic viewpoint. Problems with AE interpretation during strain localization are associated not solely with the AE technology itself, but also with the generic complexityof the multi-scale processes underlying localization of plastic flow. Beside the academic interest
related to strain localization phenomenon, its practical aspect cannot be overvalued. Since plasticstrain localization always precedes crack nucleation, the identification of the onset of localization
process can be important for non-destructive routines. The present paper aims to shed some lighton the AE behavior during strain localization in ductile materials such as iron. A variety of ways
how the plastic instabilities manifest themselves under loading is broad. At first glance, theproblem seems to be quite intricate being dependent on a type of instability, which in turn depends on numerous internal and external variables in a specific way. However, we believe thatgeneral tendencies in the AE behavior due to strain localization still can be revealed and classified at least in a phenomenological manner with little reference to underlining fine microscopic
dislocation mechanisms in a way similar to non-linear thermodynamic formulations or mechanistic constitutive approach, etc. Anyway, before any generalization can be made, available experimental results should be reviewed and the experimental database should be broadened.
A simple classification of plastic instabilities associated with the stress-strain behavior has
been proposed by Estrin (1988) [1], (see also [2, 3]) by linear stability analysis of a generic constitutive relation between the flow stress σ, the plastic strain ε and the plastic strain rate . In adifferential form this relation is expressed as(1)
J. Acoustic Emission, 27 (2009)144© 2009 Acoustic Emission Groupwhereandstand for the strain-hardening coefficient and thestrain rate sensitivity, respectively. Apparently, this simplistic constitutive formulation does notinclude any internal microstructural variables, but rather lumps various contributions into thephenomenological material characteristics, h and S, which are easily measured experimentallyand are representative of the materials mechanical response. It is not difficult to show that expressing a local strain perturbation in a conventional exponential form
with initial perturbation
through the equality

and growth parameter λ, the stability criterion can be formulated(2).
Obviously, a positive λ corresponds to instability, i.e., to susceptibility to strain localizations. In
this case, either of the two following conditions is fulfilled(3)
These two kinds of instability are commonly referred to as h- and S-type instabilities in the literature [3]. Instability of the first kind prevails locally when strain-hardening capability is exhausted through dislocation-accumulation mechanisms and/or strain softening. The most known
example of this h-type of unstable mechanical behavior is necking, which is attributable to themacroscopic instability related to a whole specimen. Another vivid example of the same generictype of instability can be given with regard to the yield-drop phenomenon followed by Lüdersand Lüders-like-band propagation in impure or irradiated metals, Fig. 1. The second S-type instability is clearly associated with negative strain-rate sensitivity, giving rise to discontinuous
yielding known as the Portevin-Le Châtelier effect. The S-type instability has received a great
attention in AE community, resulting in many publications (see reviews [4, 5]). Although someauthors reveal an ample similarity between AE due to Lüders-band propagation and Portevin-Le
Châtelier bands [6-8], and although a certain similarity does exist in the underlying dislocation
mechanisms associated with dislocation avalanches (particularly at the onset of the yield drop),
according to the classification (3), they belong to different classes: strain (or h-) instability for
the Lüders band and strain-rate (or S-) instability for the Portevin-Le Châtelier effect.
In the present paper we will confine ourselves to the h-type instabilities only, with a focus on
the Lüders-band propagation. A need to understand the AE behavior in line with underlining dislocation interactions with metallurgical and structural parameters of materials initiated us to investigate the common AE behavior in simple relatively pure materials rather than in complex
alloys. We therefore have chosen iron with different impurity content and different grain sizes
for the present study. It has long been understood that the yield drop appears in bcc metals in association of impurities and grain size. To be more specific, in very pure iron the yield drop is notobserved while it becomes pronounced if a few tens ppm interstitials (mainly C, N, O) are present [2, 9-12]. Furthermore, the yield drop and Lüders strain is larger in fine grain materials (fewtens µm grain size) than in coarse grain ones; in polycrystals with very coarse grains, the Lüders
bands cease to appear at all. Some results obtained on mild steel will also be reported for thesake of comparison. Iron was chosen also for the reason that AE has been relatively rarely studied in this material because of the low level of acoustic emissions emerging in pure iron during
plastic deformation (see, for example, a comprehensive review by Heiple and Carpenter [5]).

145To our best knowledge, no attempts to investigate the evolution of the AE power spectral density
in iron have been performed so far. In line with our former studies of AE in pure copper and its
alloys [13, 14], we therefore employ a broadband AE acquisition scheme for further Fourier
spectral analysis.

Fig. 1. AE rms voltage behavior during plastic deformation of a plain carbon (0.07wt% C) steel;= 5 x 10-4 s-1. The optical micrographs (a)-(c) illustrate the Lüders-band front, separating thedeformed and non-deformed regions at the time corresponding to the respective arrows on AE
diagram (in-situ observation by a Keyence VHX-8000 digital microscope, an arrow on (b) shows
the direction of front propagation).

Fig. 2. Optical micrograph of the electrolytically polished and then etched surface of 3N-Fe sample prior to mechanical testing.
146

Experimental
The samples for mechanical testing were cut by spark erosion from sheets of iron with nominal purity of 99.5% (3N) and 99.99% (4N), supplied by Nilaco Corp. (Japan). Plain carbon steel
with 0.07wt% C is used for illustrative purposes and comparison. The sample dimensions were
10 × 5 × 2 mm3 in the gauge section. All samples were annealed in vacuum at 500˚C for 2h toachieve a uniform structure. Figure 2 shows a micrograph for 3N-Fe with a mean grain size of 50µm. The samples were then mechanically and electrolytically polished to a mirror finish. The
grain size was estimated after etching by intercept method. The samples were tested undermonotonic tensile loading with nominal strain rates of (1, 2, 4, 8) × 10-3 s-1. An extensometer was
not used in the present study because the AE level in iron is quite low so that we tried to minimize any possible influence from external sources. For this reason the stress-strain curves areshown in terms on nominal strain calculated from the crosshead velocity.
A broadband AE sensor AE-900S-WB (NF Electronics, Japan) with a bandwidth of 1001000 kHz was securely mounted on a lateral surface of the specimen below a gauge part with arubber band. Vacuum oil was used as coupling media. The signal from the transducer output was
amplified 60 dB with a 2/4/6 preamplifier (PAC, USA) and then transferred through a main amplifier-filter FA-010 (Microsensors AE, Russia) to either a PC-controlled digital AE-recording
system AS-1 (Microsensors AE, Russia) or the PCI-2 AE acquisition board (PAC, USA). AS-1
was operating at 6.25 MHz sampling rate with 12 bits amplitude resolution and 8 ksamples recorded per single realization. PCI-2 was used due to its streaming capability for continuous data
recording at 2 MHz sampling frequency and 18 bits amplitude resolution. The frequency band
was chosen at FA-010 from 50 to 1200 kHz and the total gain was set at 90 dB. The AS1 ADC
was triggered by threshold crossing. The threshold was set slightly higher than the laboratory
noise.
The details of data processing have been described elsewhere [13]. AE amplitude (peak voltage) and root-mean-square voltage (corrected for the background electrical noise) were calculated in time domain for every waveform record. The power spectral density (PSD) function G(f)was calculated after the standard FFT followed by smoothing with a rectangular spectral windowof 40 kHz width. The average spectral density of the pre-recorded noise was subtracted before
calculating the spectral parameters. The AE energy (power) per record was calculated as the integral of PSD over the whole frequency band. To characterize the frequency distribution in the
power spectral density, a median frequency, fm, was calculated via conventional numerical procedures to obtain.
Results
A typical AE pattern in plastically deformed 3N iron polycrystal is shown in Fig. 3. Comparing Figs. 1 and 3, the following common features are worth noticing.
(i) AE commences shortly before the upper yield point and(ii) AE reaches its sharp maximum at the yield drop.
(iii) During Lüders-band propagation (lower yield point) the AE level (energy) is lower thanthat at the upper yield point. The pronounced feature of this stage is that the AE PSD has shiftednotably to a low frequency region, which is reflected by lowering of the median frequency value.
147

Fig. 3. AE behavior during plastic deformation of 3N (99.95%) iron;= 1 x 10-3 s-1.

The latter is clearly visible despite obvious scattering of experimental data. The AE energy E andfm value remain nearly constant on average during this stage although the fluctuations of AE parameters are also quite evident reflecting the fluctuating character of the Lüders-band propagation.
(iv) The onset of parabolic hardening is accompanied by the rise of AE energy as is typicallyobserved in fcc metals [4, 5-7, 13, 14]. Shortly after yielding, the uniform deformation withstrain hardening begins and then the AE energy (or rms voltage) gradually reduces to a very low,
hardly distinguishable from noise, level while the median frequency is monotonically increasing
on average with the flow stress. This has been commonly observed in a wide range of fcc metals[13, 14].
(v) The uniform deformation ends when, according to the h-type instability criterion (3), theincreasing true stress becomes equal to the reducing hardening coefficient (the strain rate sensitivity is, of course, positive). This point corresponds roughly to the ultimate tensile strength.
Around this point a certain increase in the AE energy is noticed again and the low frequency
components prevail again in the frequency distribution, i.e., the power spectral density shifts towards low frequency domain and the average fm value reduces accordingly.
(vi) However, the evolution of AE during necking does not occur monotonically and the AEenergy peak is formed around the point of instability and the flow becomes “quasi-static” again.
(vii) The last point of instability in the AE pattern is apparently associated with ductile microcrack initiation and propagation at largest strains before fracture. Overall, the oscillatory behavior of AE spectral parameters reflects sequential transitions from one metastable state to another, passing through a number of points of instability.
Together with the discrete (burst) nature of AE phenomenon (see Fig. 4 for illustration), thisemphasizes a strong intermittency of plastic deformation, which occurs on different structuralscales. The characteristic scale lengths do not appear to be strictly defined, but rather evolving,which is reflected by gradually changing AE parameters in the course of deformation of astrongly non-equilibrium dissipative system.
148

Fig. 4. Typical AE waveforms and their Fourier power spectral density corresponding to different stages of plastic deformation of 3N-iron: (a) pseudo-elastic loading stage near the upper yield
point, (b) upper yield point, (c) Lüders-band propagation stage, (d) uniform deformation stageprior to the ultimate tensile strength.
149

Fig. 5. Fragments of AE continuously streaming data during the Lüders-front propagation at different time scales for 3N-Fe at strain rate of 2 × 10-2 s-1. (PCI-2, sampling rate 2 Ms/s).
Figure 4 illustrates typical AE waveforms and their Fourier power spectral densities corresponding to different stages of plastic deformation of 3N-purity iron. At the given strain rate =1 × 10-3 s-1, AE amplitude is fairly low when compared to the noise level. Most of the signals
150

detected just before the upper yield point (a) and right at the yield point (b) are of burst type.
Comparing spectra (a) and (b), one can notice the shift of the PSD to low frequency domain.
During the Lüders-front propagation, a mixture of low amplitude bursts, Fig. 4(c), and continuous noise-like signals has been observed with dominant low frequency components. As deformation proceeds in the parabolic hardening stage (uniform deformation region), the amplitude andrms voltage of the signals reduce progressively and significance of high frequency content in the
PSD increases, Fig. 4(d).

Fig. 6. Evolution of the normalized AE PSD during the Lüders plateau for 3N-Fe at strain rate of
2 × 10-2 s-1. The dominance of the low frequency components is evident and the highly fluctuating character of the AE spectra is seen.
It is particularly instructive to have a closer look at the Lüders plateau. This is possible if weincrease the strain rate to 2 × 10-2 s-1 and record the AE data by the PCI-2 board enabling continuous data streaming. The results are illustrated in Figs. 5-7. Figure 5 shows the typical fragments of AE records during the Lüders-front propagation at different time scales. Although the
AE still appears as a series of the transient signals (Fig. 5(d)), the activity of sources is so highthat the transients overlap and the AE time-series manifests itself as a continuous fluctuating
noise-like signal at coarse time scales. The significance or, to be more precise, the dominance oflow frequency components becomes evident from Fig. 6 where the AE PSD is plotted as a function of time. It is worth noting that the AE spectrum fluctuates considerably during front propagation, which is associated with the intermittency of the dislocation avalanche production in differently oriented grains. The grains have a notable distribution of sizes at the propagating front ofa localized deformation, which becomes zig-zag shape in a scale of grain size. The claim that the
AE spectrum shifts towards low frequencies when strain localization occurs due to increasing
spatial and temporal correlation in the ensembles of the emitting defects is particularly obvious
in terms of the median frequency reduction as seen in Fig. 7(b). Interestingly, the sharp fall of the
fm value can be noticed slightly before the upper yield point, i.e., the tendency to strain localization and the processes of localized stress accumulation commence prior to the apparent stress151drop, avalanche dislocation release and the start of the Lüders-front motion. This result appears
in line with early findings by Rosenberg [15], who has reported the initiation of the Lüders bandby pre-yield relaxation tests in pure iron. This result also illuminates the high potential capacityof AE for the detection of early stages of strain localization; usually AE can detect the signaturesof strain localization earlier than those can be found by any mechanical measurement, (for avivid example see [16] where the extreme sensitivity of AE spectrum for the formation of persistent slip bands has been demonstrated for copper single crystals).

Fig. 7. A close-up view of the AE rms voltage and median frequency during the Lüders-band
propagation for 3N-Fe at strain rate of 2 × 10-2 s-1. The fluctuating AE rms voltage behavior andthe sharp drop of the median frequency are worth noting.
Turning back to the features of the Lüders-band propagation, we notice that in most experiments we observed a single band propagating from the lower grip to the upper one as shown in
Fig. 1 (monitoring was performed in-situ by a Keyence digital microscope VHX-8000 with along-focus lens). It has been demonstrated by simultaneous AE measurements and optical
speckle-interferometry that the motion of the localized AE sources at the band front can betraced by means of linear location technique [17]. The phenomenological Lüders-band behavior
is well documented for a large number of materials at different temperatures, strain rates andgrain sizes [2, 6-12] and it is not surprising that the Lüders strain, stress drop at yield and Lüdersplateau stress increase slowly (logarithmically) with the strain rate, Fig. 8(a). The behavior of152the AE energy in iron upon Lüders-band propagation is also in fair conformity with available
literature data [6, 8] (experimental data are still scarce, however). While the first AE peak at theyield point does increase notably with , the AE energy tends to decrease with increasing nominal machine strain rate.

Fig. 8. (a) Dependence of Lüders strain, stress and yield drop on the strain rate, (b) strain ratedependence of average AE parameters during Lüders type instability; AE1 = AE peak energy,
EL = AE energy during Lüders deformation, fmL = fm during Lüders deformation (3N-purity Fe,
50 µm grain size).
153

Let us notice that compared to the results shown in Fig. 1 for low carbon steel and Fig. 3 for
3N Fe with the grain size of 50 µm, the samples of the same purity but having a greater grain
size (of 150 µm) show considerably less pronounced yielding and considerably lower AE. In thespecimens with the grain size over 500 µm, neither yield phenomenon nor associated AE wasobserved and the overall AE level was quite low. This fact emphasizes the significance of thegrain size for Lüders-band nucleation as will be discussed below.
Discussion
A consistent explanation for the occurrence of the stress drop and associated Lüders phenomenon was proposed by Cottrell (1953) [9] (see also the details in [2, 10]) in terms of locking
effect, which is furnished by relatively mobile interstitials, e.g., C and N, in ferritic alloys. Theenergy of dislocation-impurity interaction in a bcc lattice is rather high (of ~0.5 eV) bringing
about a significant dislocation anchoring at low temperatures, provided that the interstitials concentration is larger than 10 ppm. Thus, the unlocking stress is substantially larger than the propagation stress. This causes abrupt dislocation multiplication at the yield point. If the time it takes
the solutes to recapture a dislocation is longer that the time it takes for a Lüders band to traverse
the entire specimen gauge length, no repetitive stress drop is observed. The band propagates atconstant stress maintained by the constant dislocation density, which is balanced dynamically byinterplaying processes of dislocation multiplication and the band front and their storage in theband tail. It has long been recognized undoubtedly that the magnitude of the first AE peak corresponding to the stress drop at yield is sensitive to heat treatment, previous mechanical working,concentration of impurities responsible for dislocation pinning and other factors influencing theextent and strength of the dislocation locking. Although dislocation breaking away from pinningpoints at the onset of plastic deformation of fcc metals is unlikely to be a major mechanism responsible for the AE peak observed [14], this mechanism undoubtedly governs the stress drop atyield of bcc metals, particularly in Fe: indeed, in higher purity Fe (99.99%) the stress dome atyield was smaller and the associated AE peak was by a factor of 1.5-2 smaller than that shown in
Fig. 3 for 99.95% Fe (let us notice that same tendencies in the Lüders band and AE behavior are
observed in 4N Fe as those in 3N Fe with only quantitative differences, which will be discussed
elsewhere).
The appearance of AE before yield is consistent with the original ideal by Cottrell [9] that
dislocation motion can occur before the upper yield point in a microstrain region, but the upper
yield point is not reached until these dislocation avalanches, or dynamic pile-ups (see also [10]),
become strong enough to be able to cross the grain boundaries. In parallel to Cottrell, Hahn [11]has suggested that the yield point phenomenon can be viewed as an abrupt dislocation multiplication process. As Estrin and Kubin [2] have stressed, the significance of multiplication models
is that they allow identifying new parameters influencing the yield behavior: a difference instrain rate or dislocation velocity between the upper yield point (where the velocities are large)
and low yield point (where they have reached dynamic equilibrium values) results in stress difference proportional to the strain rate sensitivity S of the flow stress. Starting from essentially thesame premise Hähner [18] has proposed a most elaborated quantitative phenomenological dislocation dynamical model of the reaction-diffusion type to describe the spatio-temporal dynamics
of Lüders-band propagation, viewed as solitary wave. Caceres and Rodriguez [6] have employed
the dynamic pile-up concepts to account for the observed AE behavior in Al-Mg and Cu-Zn alloys in a rather qualitative manner.

154

The most intriguing issue, which has not received a consistent explanation yet, is apparently
the reduction of the AE energy (rms or amplitude) with the strain rate during strain localization
on a plateau region. The opposite behavior is expected from the strongly experimentally supported linear relation between the imposed strain rate and AE energy (power) during uniform
deformation of a wide range of pure metals [5]. It is worth noting, that the local strain rate in the
Lüders band can be by two orders of magnitude greater than the external strain rate. The AE
level, however, was found reduced. Two possible explanations can be proposed for that fromeither the experimental settings or from the standpoint of physical metallurgy and the dislocation
behavior during the yield pint. On one hand, our observations highlighted significant shift of thepower spectral density to low frequency domain, Figs. 5-7. In the region of 20-50 kHz either an
AE sensor has limited sensitivity or the acquisition system frequency band may be selected inadequately with too high high-pass filter cut-off frequency (say 50 kHz as in our case) in attempts to minimize the laboratory noise. On the other hand, it is well known that the yield drop
ratio Δσ/σL increases as strain rate increases [19, 20] which could be due to reduction in the density of unlocked dislocation, i.e. potential AE sources. Besides, from a bulk amount of experimental results obtained on various pure fcc metals, the present authors are aware of AE relation
to the rate , at which dislocation density changes (accumulation or annihilation) rather than tothe dislocation density ρ itself.
With regard to AE during macroscopic necking, one should notice that many essential AEfeatures appear to seemingly resemble those during Lüders phenomenon; e.g. at high imposed
deformation rates, recorded AE is relatively low. Besides, it seems obvious that the increasing
values of AE energy during the onset of necking cannot be attributed simply to the rising local
strain rate in the reducing cross-section of the specimen. If it would be so, the spectrum shift to ahigh frequency domain (increasing fm) is expected from a large experimental database obtainedby the same authors on various fcc metals and alloys (e.g. [16]) as well as from theoretical expectation of AE produced by a collection of temporarily and spatially correlated sources by Braginskii [21]. The picture is however opposite – the AE spectrum apparently shifts towards lower
frequencies. This indicates that the plastic instability at necking manifests itself as a sort of local
material softening, which is accompanied by increasing local dislocation activity (multiplicationand motion). This, in turn, stimulates local dislocation storage and hardening processes as well.
The latter inhibits dislocation motion in a way similar to that during uniform elongation so thatthe only accommodation mechanism possible after such an extreme hardening would be a crack
initiation.
References
1) Y. Estrin; Solid State Phenomena, 3&4 (1988) 417.
2) Y. Estrin and L.P. Kubin; in Continuum Models for Materials with Microstructure, ed. H.-B.
Mühlhaus (1995) John Wiley & Sons, p. 395.
3) Y. Brechet and F. Louchet; Solid State Phenomena, 3&4 (1988) 347.
4) K. Ono: in Fundamentals of Acoustic Emission, ed. K. Ono, Proc. of Joint Meeting of
Acoust. Soc. of America and Japan, Honolulu, USA, Nov. 27-Dec. 1 (1978) p. 167.
5) C.R. Heiple and S.H. Carpenter, J. Acoust. Emission, 6 (1987) 177.
6) C. H. Caceres and A. H. Rodriguez, Acta Metall. 35 (1987) 2851-2864.
7) F. A. Chmelik, A. Ziegenbein, H. Neuhauser, P. Lukac, Mat. Sci. Eng. A324 (2002) 200-207.
8) M. M. Krishtal and D. L. Merson, Fizika Metallov i Metallovedenie 81 (1996) 156.
9) A.H. Cottrell; Trans. Met. Soc. AIME, 212 (1953) 192.
10) J. Friedel; Dislocations, Pergamon Press, 1964.
15511) G.T. Hahn; Acta Metalurica 10 (1962) 727.
12) A.S. Keh, Y. Nakada and W.C. Leslie; in Dislocation Dynamics, ed. by A.R. Rosenfield,
G.T. Hahn, A.L. Bement and R.J. Jaffee, (1968) McGraw Hill, pp. 381-408.
13) A. Vinogradov, D. L. Merson, V. Patlan, S. Hashimoto; Mat. Sci. Eng. A341 (2003) 57-73.
14) D. Merson, M. Nadtochy, V. Patlan, A. Vinogradov, K. Kitagawa, Mat. Sci. Eng, A234-236(1997) 587.
15) R. Rosenberg, Acta Metallurica, 13 (1965) 561.
16) A. Vinogradov, V. Patlan, S. Hashimoto, Phil. Mag. A, 81 (2001) 1427.
17) T. Murav’ev and L. Zuev, Technical Physics, 53 (2008) 1094.
18) P. Hähner; Appl. Phys, A58 (1994) 41.
19) R.J. Arsenault, Acta Metallurgica, 11 (1963) 1111.
20) H. Fujita and S. Miyazaki, Acta Metallurgica, 26 (1978) 1273.
21) A.P. Braginskii; Theoretical and applied aspects of AE analysis of defect dynamics in solids,
Institute of Physics of Metals Ukrainian Academy of Science, 18.86 (1986) 30p.

156

ACOUSTIC AND ELECTROMAGNETIC EMISSION FROM CRACK
CREATED IN ROCK SAMPLE UNDER DEFORMATION
1)YASUHIKO MORI1, YOSHIHIKO OBATA1 and JOSEF SIKULA2
College of Industrial Technology, Nihon University, Izumi 1-2-1, Narashino, Chiba 275-8575,
Japan; 2) Czech Noise Research Laboratory, Brno University of Technology
Technicka 8, CZ-616 00 Brno, Czech Republic

Abstract
In order to characterize the generations of electromagnetic emission (EME) and cracks created inside the materials in detail, we measured EME under monotonously increasing and repeated compressive loading of the granite sample. In the loading tests, AE signals were simultaneously measured with the EME signals, so that the generation of EME could be directly compared with the entire fracture process of the rock sample estimated by AE. In the uniaxial compressive tests, the sample was loaded at different displacement speeds from 0.02 to 0.5 mm/min.
In comparison with AE, the number of EME events discriminated is lower, because of a lower
signal-to-noise ratio of EME channel. The relationship between signal amplitudes of EME andAE suggests the existence of the correlation between AE and EME. The EME signals with thelarger amplitude are associated with the AE signals of larger amplitude. Dispersion observed in
the EME vs. AE signal amplitude plots is related to crack orientation with respect to EME electrodes. This paper also demonstrates that the simultaneous measurement of AE and EME
would be useful for estimating the rock in-situ stress, as an example.
Keywords: NDT, Electromagnetic emission (EME), Cracks, Granite
Introduction
It has been known that the changes in geo-electric potential and the anomalous radiation ofgeo-electromagnetic waves were observed before major earthquakes. These phenomena also
have been observed in laboratory experiments on rock samples, and it was found that micro- andmacro-cracking processes are often accompanied by acoustic and electromagnetic emission[1-7].
Generation of cracks in solids is accompanied by the generation of electric charges and themechanical vibrations. Mechanical vibrations generate acoustic emission (AE) signal. The
crack surfaces are electrically charged due to the loss of chemical bonds. Electric charges at thefaces of the newly created cracks constitute an electric dipole or quadrupole system. They aresources of electromagnetic field and measurable quantity of this phenomenon is called electromagnetic emission (EME). EME signal can be detected by capacitive electrodes placed on thesample surfaces of low electrical conductivity. In this case the electric field vs. time is detected. A coil can detect the magnetic field of good electrical conductors.
Experimentally we have observed two different type of EME signals like damped harmonic
motion with: (i) exponentially time-dependent transient value (Fig. 1(a)) and (ii) constant average value (Fig. 1(b)). We suppose that the first one is generated by electrical dipole structureand second one is generated by an electrical quadrupole [8].

J. Acoustic Emission, 27 (2009)157© 2009 Acoustic Emission Group(a) Signal generated by dipole.
(b) Signal generated by quadrupole.
Fig. 1 Two type of EME signal waveforms seen in the compression test of granite sample [8].
We also found that the voltage on the measuring capacitor is directly proportional to theelectric charge distribution. The recorded electric signal is superposition of crack walls“self”-vibration given by the crack geometry and vibration due to an ultrasonic wave given bysample dimensions. The EME signal precedes the AE response and the time delay corresponds
to the distance between AE sensor and crack position due to the difference of propagation velocities of sound and electromagnetic field in the sample.
In order to characterize the generations of EME and cracks created inside the materials in
detail, the measurements of EME were conducted under the monotonously increasing compressive loading test and the repeated compressive loading test of the granite sample. In the loading
tests, AE signals were simultaneously measured with the EME signals, so that the generation of
EME could be directly compared with the entire fracture process of the rock sample estimated by
AE. This paper also demonstrates that the simultaneous measurement of AE and EME would
be useful for estimating the rock in-situ stress, as an example.
EME under Uniaxial Compression
A rectangular block sample of Inada Granite with dimensions of 10 mm x 10 mm square andheight of 30 mm was deformed under the uniaxial compression stress. Schematic sample assembly is shown in Fig. 2. Details of the loading set-up are described in [3].
AE transducer with a frequency range of 200 to 700 kHz was mounted on the sample surface,
as shown in Fig. 2(b), to detect the AE signals during the loading test. EME signals from the rocksample were detected as the electric potential change appearing between two electrodes A-A.
The electrodes were formed by painting a conductive paste on the sample surfaces, as shown in
Fig. 2(b). Both the EME and AE signals were amplified by 40-dB preamplifiers and led into
the input channels of the AE system used. When an AE signal was detected at AE transducer,
the AE and EME signals were digitized and stored on the memory in the AE system. The
threshold level of 60 dB was used for AE event detection. To eliminate interference arisingfrom ambient noise, the sample and the preamplifiers were electromagnetically shielded by using
a thick aluminum alloy chamber.
In the uniaxial tests, the sample was loaded at different displacement speeds ranged from0.02 to 0.5 mm/min, so that the effects of displacement speed on the generation behaviors of
EME and AE were examined.
158

Fig. 2 Sample assembly: (a) setup for uniaxial loading and (b) arrangement of AE transducer,
electrodes for EME detection, and strain gages.

Fig. 3 A set of simultaneous measurement of EME and AE signals recorded in the compression
test conducted on Inada Granite.
The background noise of EME signal channel is larger than that of the AE channel, since theelectrodes for EME detection forms a high-impedance circuit with more than 10-time higher
equivalent noise resistance than that of corresponding AE transducer. This result in difficulty of
discriminating the small amplitude EME signals expected. In Fig. 3, three strong AE signals
are clearly recognized. As the onset of AE signal indicated by an arrow on the EME waveform
matches the EME signal onset, it can be concluded that this EME signal is associated with AE.
In this manner, even small amplitude EME signals, such as the signal generated near 100 µs in
Fig. 3, are detectable. In the present experiments, most of EME signals recorded were classified
into the type of signal generated by electrical quadrupole structure, of which waveform wasshown in Fig. 1(b).
159

160

(b) displacement speed = 0.06
mm/min.

(c) displacement speed = 0.5mm/min.

Fig. 4 Results of simultaneous measurements of AE and EME signals during monotonously increasing compression testsof the granite samples conducted at three different displacement speeds of 0.02, 0.06 and 0.5 mm/min.

(a) displacement speed = 0.02
mm/min.

We have observed that the EME signal is generated several µs before AE generation. Thisdelay in AE is expected as EME signal propagates with the velocity of electromagnetic waves
while AE signal propagates with the velocity of mechanical waves. Origin of EME signal corresponds to the time of crack creation. This effect was used to improve crack localization [3].
The results of simultaneous measurements of AE and EME signals during monotonously increasing compression tests of the granite samples conducted at three different displacement
speeds of 0.02, 0.06 and 0.5 mm/min. are shown in Figs. 4(a), (b) and (c), respectively. In Fig.
4, applied load (P) and dilatant strain of the rock sample (εv), cumulative AE event count (NAE)
and EME event count (NEME), and amplitude of AE event signal (VAEp) and EME event signal(VEMEp) are plotted as a function of elapsed time of the loading (t).
Generation of AE starts at a level of dilatant strain of the sample, where the dilatant strain
deviates from the linear trend, and increases until the sample failure takes place. Generation of
EME events starts after AE generation and increases with an increase of the applied stress. It isalso observed that the generation of active EME events is peculiar to the stressing stage, at which
the volume of sample changes from the contraction due to compression to the dilatancy developed in a direction vertical to the sample axis stressed. This result suggests that the EME signals were emitted from the micro-crack created in the tensile direction normal to the appliedload.
In comparison to AE, the number of EME events discriminated is lower, because of a lower
signal-to-noise ratio of EME channel. It can be said that the displacement speed has no effecton the generation behavior of AE and EME during the stressing. However, there is a differencein the number of total AE event counts among three displacement speeds. Though the total
event counts measured in the tests conducted at the displacement speeds of 0.02 and 0.06
mm/min show almost the same value of 20,000, while only one fifth of the value, i.e., 4000
counts were measured in the test at the displacement speed of 0.5 mm/min. The reason for thedifference in event counts observed are explained as follows: The waveforms shown in Fig. 3
were the simultaneously recorded AE and EME signal waveforms detected in the test conducted
at a displacement speed of 0.5 mm/min. By the visual observation three strong AE event signals are recognized in a period of 500 µs. In this case, a dead time for AE detection was set at 1
ms on the AE system used. Therefore, the AE system was triggered by the first hit signal generated at about 100 µs, and counted the signals as one event, even though three events were visually recognized. In general, it is expected that when the material is stressed, the time interval of
micro-crack creation in the material becomes short with increasing deformation rate of thestressing. Thus, when the frequency of the occurrence of micro-cracks exceeds the AE deadtime, as seen in Fig. 3, the AE event counts measured by the AE system show a smaller value
than actual.
Next, the plots of the signal amplitude of EME (VEMEp) and AE (VAEp) as a function of theelapsed time of loading, are shown in Fig. 4. These clearly demonstrate that the EME and AE
events having the larger signal amplitude are generated with increasing time: i.e., with increaseof the dilatant strain of the sample. This nature is irrespective to the displacement speeds
tested.

161

Fig. 5 Relationship between EME (VEMEp) and AE (VAEp) signal amplitude.
The EME signals detected in the loading tests, without exception, were accompanied with thegeneration of AE signals. Thus, the relationship between the signal amplitudes of EME and AE
was analyzed for the experimental results shown in Fig. 4. The results, which are summarized
in Fig. 5, strongly suggest the existence of the positive correlation between AE and EME in thesignal amplitude. That is, the EME signals with the larger amplitude associate with the AE
signals of larger amplitude. The signal amplitude of EME would correspond to the amount of
the electric charges re-distributed on the newly created crack surfaces. It is expected that theamount of electric charges would depend on the size of the crack surfaces created. Dispersion
observed on the relationship in Fig. 5 is related to crack orientation with respect to EME electrodes.
162

EME under Repeated Loading
Rectangular block samples of Inada Granite, of which dimensions were 20 mm x 20 mm x 80
mm and 10 mm x 10 mm x 40 mm, were stressed in repeated uniaxial compression load. The
sample was pre-loaded up to a certain value and then unloaded. It is then loaded to a larger value
and then unloaded again. This loading-unloading procedure was repeated until the sample rupture took place. Each of repeated loading-unloading procedures was conducted at a constant displacement speed of 0.5 mm/min. The sample assembly for the repeated loading test was basically the same as that shown in Fig. 2.
In the repeated loading test, the EME signals were amplified by 20 dB using 3S Sedlak PA 21
ultra-low-nose preamplifier. The internal noise level of PA21 preamplifier was 3 nV/√Hz in thefrequency range of 500 Hz – 10 MHz. The output signals of PA21 were further amplified by 40
dB with a PAC 1220A preamplifier. AE signals were amplified by 40 dB with a PAC 1220A
preamplifier. In addition, the sample assembly and the preamplifiers were electromagneticallyshielded. The threshold levels were 70 dB for EME signal and 60 dB for AE signal.
The result of simultaneous measurements of EME and AE signals during the repeated loading test conducted on the block sample of 20 mm x 20 mm x 80 mm is shown in Fig. 6. The
loading history for the repeated loading test is shown as the stress curve, σ. After an initial stressof 12.5 MPa, which corresponds to approximately 10 % of compression strength of the rocktested, was applied, the maximum stress level of successive loading was increased by 12.5 MPa
steps until the sample failure. In Fig. 6, EME and AE events are represented by the cumulative
event counts measured for each stressing step.

Fig. 6 History of the repeated stressing test and the result of simultaneous measurement of EME
and AE signals conducted on Inada Granite sample of 20 mm x 20 mm x 80 mm.

163

Fig. 7 Expanded stressing steps #3 and #8 in the test procedure shown in Fig. 6.
The EME signals are detected during every stressing step, with the exception of step #1. The
EME signals are generated only in the stage where the applied stress increases and reaches at the
maximum stress level for each step, whereas the generation of AE signals is observed not only inthe stress increasing stage but also in the unloading stage. For example, the details of the stressing steps #3 and #8 are shown in Fig. 7. In the figures, the arrowhead indicates the stress corresponding to the maximum pre-stress for each step. In Fig. 7, open symbol plotted on the stresscurve denotes the onset stress of active AE event generation during the stress increasing stage.
Similarly, the solid symbol on the stress curve denotes the onset stress of EME signal. Theseonset stresses of AE and EME are referred as σAE and σEME, respectively.

Fig. 8 Plots of estimated stresses σEME and σAE as a function of the pre-stress σpre. The stresses
plotted are normalized by the strength of the rock sample tested σb.
The values of the σAE and σEME for each repeated stressing step as shown in Fig. 6 and also
for the experimental results obtained in the test conducted on the block sample of 10 mm x 10mm x 40 mm were evaluated. In Fig. 8, the σAE and σEME obtained are plotted as a function of the
pre-stress level σpre. In the figure, these stresses are normalized by the compression strength of
the rock tested, σb. The Kaiser effect of AE is well recognized in the range of the pre-stress below 22 % of the σb, giving the underestimation of pre-stress. Specifically, the amount of the underestimation is 10 % to 25 % of the actual pre-stress. This underestimation would be caused by164

the fact that the AE due to the friction between the fracture surfaces induced during the previous
loading process is measured in the early stage of each successive stressing step. However, it
should be noted that in general, Kaiser effect shall be discussed within the region of elastic orelasto-plastic deformation. In the case of the sample tested, it is expected that the corresponding stress range is 30 % of the maximum strength.
On the other hand, the onset stress level of EME signal, σEME, estimates the pre-stress levelwithin the deviation of 10 %, over a wide range of the pre-stress up to 90 % of the compression
strength of the sample tested. This result clearly suggests that the emission of EME signals is associated with the creation and/or extension of micro-cracks, and is non-reversible phenomena,
similar to the Kaiser effect of AE. Therefore, it can be concluded that the EME signal measurement can estimate more accurately the current stress level, to which the rock samples have been
subjected, than that of AE activity.
Summary
Simultaneous measurement of EME and AE during the monotonously increasing compressive loading test and the repeated loading test has been conducted on granite samples to characterize the EME generation in detail.
1. EME signal is accompanied with the generation of AE signal. In comparison with AE,
the number of EME events discriminated is lower, because of a lower signal-to-noise ratioof EME channel.
2. EME and AE signals are correlated and their amplitudes increase just before the sample
rupture.
3. The relationship between signal amplitudes of EME and AE suggests the existence of thecorrelation between AE and EME. The EME signals with the larger amplitude areassociated with the AE signals of larger amplitude. Dispersion is related to crackorientation with respect to EME electrodes.
4. The emission of EME signals is associated with the creation and/or extension of micro-cracks, and is non-reversible phenomena, similar to the Kaiser effect of AE.
5. EME signal is detected several µs before AE generation. This delay in AE is expected as
EME signal propagates with the velocity of electromagnetic waves while AE signal
propagates with the velocity of mechanical waves. Thus, the origin of EME signal corresponds to the time of crack creation, and this effect can be used to improve crack localization by AE.
6. EME signal measurement is applicable to estimate the current stress level, to which therock samples have been subjected. The greatest advantage is that the current stress canbe directly estimated without any post signal analysis. In addition, the detection of EMEfrom rock sample under stressing is very simple and easy, and a usual AE system can beused to record and analyze the EME signals.
Acknowledgement
This work was partially supported by the Grant Agency of the Czech Republic under Grants
106/07/1393 and project VZ MSM 0021630503.

165

References
1) J. Šikula, B. Koktavy, P. Vašina and T. Lokajíček, Detection of Crack Position by AE andEME Effects in Solids, Proc. of Acoustic Emission Conf., Boulder, CO, USA, 1997.
2) T. Ogawa, K. Oike and T. Miura: Electromagnetic radiation from rocks, J. Geophys. Res., 90,
6245-6249, 1985.
3) P. Sedlak, J. Sikula, T. Lokajicek, Y. Mori, Acoustic and electromagnetic emission as a toolfor crack localization, Meas. Sci. Technol. 19, 2008. doi:10.1088/0957-0233/19/4/045701.
4) I. Yamada, K. Masuda, H. Mizutani: Electromagnetic and acoustic emission associated withrock fracture, Phys. Earth. Planet Int., 57, 157-168, 1989.
5) T. Lokajíček and J. Šikula, Acoustic emission and electromagnetic effects in rocks, Progressin Acoustic Emission VIII, 1996, JSNDI, pp. 311-314.
6) Y. Mori, K. Sato, Y. Obata and K. Mogi, Acoustic emission and electric potential changes ofrock samples under cyclic loading, Progress in Acoustic Emission IX, 1998, AEWG, II-1.
7) V. Hadjicontis and C. Mavromatou, Transient electric signals prior to rock failure under
uniaxial compression, Geophys. Res. Lett., 21, 1994, 1687-1690.
8) Josef Sikula, Yasuhiko Mori, Tomas Lokajicek, Pavel Koktavy, Jiri Majzner and Petr Sedlak,
Crack creation kinetics characterization by electromagnetic and acoustic emission, Proc. 28th
European Conf. AE Testing, Krakow, Poland, September 2008, 118-123.

166

IDENTIFICATION OF AE MULTIPLETS IN THE TIME AND FREQUENCY DOMAINSHIROSHI ASANUMA, YUSUKE KUMANO, HIROAKI NIITSUMA,
DOONE WYBORN and ULRICH SCANZ
Graduate School of Environmental Studies, Tohoku University
Aramaki Aza Aoba 6-6-20, Sendai, 980-8579, Japan
Abstract
Previous studies by the authors have revealed that AE multiplets, which are groups of eventswith closely similar waveforms, can be used effectively to precisely delineate structures inside
artificially stimulated geothermal, oil, and gas reservoirs and to determine their response to thestimulation. The similarity of the waveforms among the collected AE events, which are evaluatedat some fixed frequency, can be represented by a product of the transfer functions of the source,
the earth, and the receiver/recorder. Meanwhile, the low-pass characteristics of the earth transfer
function appear more strongly in the coda, where reflected, refracted, mode-converted, and scattered waves arrive randomly at the receiver. This feature of the time and frequency characteristics of the AE signals led to the idea of identifying multiplets in the time and frequency domains.
This paper presents results from multiplet identification in the time and frequency domains byusing data sets collected at engineered geothermal development sites at Cooper Basin, Australia,
and Basel, Switzerland, demonstrating that AE multiplets from different physical phenomena can
be clustered by their identification in the time and frequency domains.
Keywords: Induced AE, AE multiplet, Coherence, HDR/HFR, Stimulation
Introduction
It is widely accepted that monitoring AE from induced shear slip of existing fractures (AEmonitoring, passive seismic monitoring, and microseismic monitoring) is one of the few methods
to monitor the dynamic response of a reservoir during the hydraulic stimulation that is applied toincrease the permeability and productivity of a fracture system. This is because AE monitoring
can provide information about a reservoir at locations as far as several kilometers from boreholes
and at depths of several kilometers. In the development of deeper reservoirs where seismic
waves artificially generated at the surface may be highly attenuated and for which little borehole
instrumentation is available because of the high temperature and pressure, AE monitoring shows
its advantages over conventional geophysical techniques.
A group of AE events with highly similar waveforms despite having different origin times
and magnitudes can be identified by observing the traces of AE events. These groups of events
are referred to as “AE multiplets” and comprise events that are related to the same shear slip of afracture or neighboring sub-parallel fractures [1]. Analysis of AE multiplets is typically carriedout in the following manner: (a) the collected AE events are clustered into groups of multiplets(multiplet clusters) by either manual observation or cross-correlation in the time or frequency
domain; (b) the relative times of arrival among the multiplets are determined with high resolution
and reliability by a correlation-based technique; (c) the hypocenters of the multiplet clusters are
re-located by relative mapping techniques, which have better reliability than standard absolute
mapping techniques; and (d) the physics behind the AE multiplets is interpreted by techniques
J. Acoustic Emission, 27 (2009)167© 2009 Acoustic Emission Groupsuch as evaluation of the hypocentral structure, estimation of the source mechanism, and comparison with hydraulic records.
The similarity of the waveforms among the events in a multiplet is dependent on the source
function, the earth transfer function (the transfer function on the raypath is a Green’s function),and the receiver/recorder function. Because all of these functions are frequency dependent, thefrequency characteristics of the detected AE events are determined by the product of these threefunctions, and, hence, the similarity of the AE events also has a frequency dependence. However,the frequency dependence of the similarity among multiplets has not been investigated adequately in previous studies [2, 3]. Therefore, it is expected that additional information about themultiplets can be obtained by the identification of multiplets in the time and frequency domains.
We have investigated the frequency dependence of multiplet clustering using data sets collected during hydraulic stimulations at Basel, Switzerland, in 2006 [4] and Cooper Basin, Australia, in 2003 [5]. In this paper, we present results of multiplet clustering in the time and frequency domains and discuss the physics related to the clustered multiplets
Time and Frequency Characteristics of Multiples
Because of close similarity of their traces, it has been widely accepted that the multiplets in acluster have the same source mechanism in terms of the orientation of fracture and direction ofslip. Moreover, from a seismological point of view, an AE signal should have frequency characteristics related to the source size [5], even though the AE events have an identical source
mechanism. This is because the frequency characteristics of the source signal S( f ), which has alow-pass characteristic that is normally represented by a corner frequency, are mainly determinedby the size of the ruptured area and the rupture speed. A conceptual model of the “source radius”
commonly used by seismologists employs an equivalent circular rupture zone to the actuallyruptured zone. In the case of AE from hydraulic stimulation in the basement crystalline rock, thecorner frequency of detectable AE is expected to be in a range of 10~200 Hz, depending on thesize of the pre-existing fracture system and the sensitivity of the AE detectors. On the raypath,the effects of reflection, refraction, diffraction, mode-conversion and scattering make the signal
arriving at the detector extremely complex. The attenuation of rock has a low-pass characteristic
in the frequency range of AE monitoring (~10 kHz), and the scattering effect, which appears
more strongly with time in the attenuating phase of the signal (coda), also has a low-pass characteristic [5]. Hence, the effect on the raypath is of a time-variant low-pass characteristic T(f, t).
Typically, the sensors for subsurface AE monitoring are designed to have a flat response in therange of the monitoring frequency. However, the shell of the AE sonde and its coupling to theground or borehole may have resonance characteristics. Surface units such as the amplifier, conditioner, and recorder sometimes have low-pass characteristics to eliminate noise or aliasing. The
overall frequency characteristic of the sensor, sonde, and surface units can be represented by R(t).
Therefore, the frequency characteristic O(f, t) of the recorded AE signal can be written as.

(1)

It is clear from equation (1) that AE signals from the same fracture with an identical source
mechanism can have different frequency characteristics that correlate with the source function
S( f ) if the variation in frequency characteristic of the earth transfer function is negligible and the168

sensor/sonde/surface units have flat frequency characteristics. This suggests that in some cases
identification of multiplets in a fixed frequency or time domain might be insufficient for interpreting the behavior of a fracture system. Meanwhile, the source function cannot appear in therecorded AE if the earth transfer function or sensor/sonde/surface unit function have cut-off frequencies much smaller than that of the source function.
Data Analysis and Discussion
Two data sets from the hydraulic stimulation of HDR/HFR (hot dry rock/hot fractured rock)geothermal reservoirs were used to investigate the coherency of AE events in the time and frequency domains. One data set was collected at Basel, Switzerland, in 2007, where AE eventsfrom a reservoir at a depth of around 3800~4600 m were detected by 7 downhole stations at
depths between 60~4422 m [4]. The other data set was collected at Cooper Basin, Australia, in
2003, where AE events occurring at a depth of around 4400 m were detected by 4 near-surface
stations (88~114 m) and 1 downhole station in sedimentary rock (1793 m) [5].

Fig. 1. Waveforms of AE for different magnitudes and power spectra of AE signals collected
at Cooper Basin.
Examples of AE waveforms with different magnitudes and power spectra from Cooper Basinand Basel are shown in Figs. 1 and 2, respectively. The AE waveforms from Cooper Basin are ofclose similarity in spite of variations in local magnitude, and the shapes of the power spectra are
not appreciably different. For the data set from Basel, however, the similarity among AE events
is highly dependent on moment magnitude, with relatively greater similarity among events with
169

Fig. 2. Waveforms of AE for different magnitudes and power spectra of AE signals collected atBasel.
similar magnitudes. The power spectra clearly show a corner frequency, which for these data iscorrelated to the magnitude. The differences come mainly from the frequency characteristics ofthe earth transfer function T(f, t). In the case of Cooper Basin, the sediment around the detector
was soft and dry sandstone in a desert, which attenuated the higher frequencies and limited theavailable information about the source function. The AE sondes were deployed into a relatively
hard sedimentary formation in Basel, thus corner frequencies could be observed. These results
demonstrate that the identification of multiplets using source function information is difficult for
AE events that propagate through a formation with high attenuation.
The coherency between a pair of AE events as a function of the corner frequencies of each
event is shown in Fig. 3. The time window for the Fourier transformation and the frequency
range, over which coherency is evaluated, are different in Figs. 3(a) ~ (d). It can be seen that the
number of coherent pairs is larger at lower frequencies (Figs. 3(a) and (b)) than at higher frequencies (Figs. 3(c) and (d)). This is because the effect of the corner frequency does not appear
at lower frequencies. It can also be seen that the number of coherent pairs decreases if the coherency is evaluated with a longer time window that includes the coda because of thetime-variant transfer function of the earth T(f, t); see Figs. 3(b) and 3(d).

170

Fig. 3. Coherency between a pair of AE events with different corner frequency and window
length for data collected at Basel. Coherence is high for red and low for blue.
The hypocentral location of the AE multiplets in the Basel data set identified at low and highfrequencies is determined by double difference (DD), one of the high-resolution relative mapping
techniques [7]. The relative time of arrival was obtained by manually aligning the first peak of
the traces. The absolute location of each multiplet cluster was fixed at the center of gravity of the
hypocenters determined by joint hypocenter determination (JHD) [8], which is a conventional
absolute location technique. Vertical projections of the distributions of the hypocenters areshown in Figs. 4 and 5, where the circle size indicates the estimated source radius of the multiplets, and grey dots show the hypocenters of uncorrelated (single) events. Figures 4 and 5 show
the hypocenter distributions of multiplets identified at low and high frequencies, respectively.
Multiplets identified at lower frequency show large sub-vertical seismic structures with sizes up
to 400 m and heterogeneous source radii (10~100 m), while the multiplets
171

Fig. 4. Hypocenter distribution of multiplets identified in low frequency (1-60 Hz).
identified at higher frequency show smaller sub-vertical seismic structures of less than 200 m,
and their source radii are more homogeneous.
We have evaluated spatio-temporal distribution of the multiplet events. At first, multiplets are
identified at low frequency, and each cluster was sub-clustered at high frequency. Examples of
hypocenter location, source radii and time of occurrence are show in Figs. 6 and 7. In the rightbottom figures the color of the source radius correlates to the order of the origin time of each
event. The multiplets were not sub-clustered and source radii of the events overlap for the multiplet clusters near the feed point for the data set shown in Fig. 6. However, multiplets distantfrom the feed point (Fig. 7) were sub-clustered at high frequency, and showed extension of the
hypocenters to the far-field and less overlap. Aseismic zones within were also seen.
By summarizing these observations, we can conclude the following:(a) If we can find corner frequencies and they are related to the magnitude, AE multiplets can be
identified in the time and frequency domains. Multiplet clustering at frequencies lower thanthe corner frequencies identifies multiplets with a similar source mechanism independently
from the size of the ruptured area. Multiplets clustering in a frequency range that is of thesame order as the corner frequency identifies multiplets from rupture area with similar size.
172

These multiplets may be related to a “repeating slip” of some part of the fracture or a “gradual rupture” of an asperity. By using a longer time window for the Fourier transformation,
multiplets with neighboring hypocenters can be identified because of the filter characteristicsof the earth transfer function.
(b) If there is no significant relationship between the power spectra and magnitude, the identified
multiplets are independent of the size of the ruptured area. The response by the internal
structure of a fracture to the stimulation cannot be obtained because information on
sub-clusters of the AE multiplets is not available.

Fig. 5. Hypocenter distribution of multiplets identified at high frequency (40-100 Hz).
Conclusions
We demonstrated that the similarity of AE multiplets originating from the stimulation of areservoir is dependent on time and frequency. The identification of multiplets at frequencies
lower than the corner frequencies of all events yields information about the single/sub-parallel
fracture, on which slips with identical direction occurred. Meanwhile, information about repeating slips on a particular part of the fracture or the gradual rupture of an asperity can be obtained
from multiplets identified at higher frequencies. We have also shown that this criterion for identification of multiplets becomes more restricted if we use a longer time window because of theeffect of the time-variant earth transfer function.
173

Identifying multiplets as described in this paper is possible only when the higher frequency
component of the AE signal is not attenuated during propagation from source to detector and thesurface unit has a wideband nature. It should be noted that the energies of AE events with highcorner frequencies are relatively smaller than those of events with lower corner frequencies because of the difference in released energy at the source. Downhole AE monitoring has advantages over surface monitoring for time-frequency coherence evaluation of multiplets because of
its higher signal to noise ratio and its wideband nature that avoids soft-surface-layer attenuationand ground noise.

Fig. 6. Hypocenter location and source radius of a cluster of multiplets (repeating slip case).
Acknowledgments
The provision of the microseismic data sets collected at Basel, Switzerland, and Cooper Basin, Australia, and the approval to publish these post-processing results by Geothermal Exploeres
Ltd., Geopower Basel AG and Geodynamics, Ltd., is greatly appreciated. We also thank themembers of the MTC Project, especially Dr. Prame Chopra, Earthinsite, for their comments andencouragement for this study. This study was supported in part by MEXT (Ministry of Education,
Culture, Sports, Science and Technology), Japan and JOGMEC (Japan Oil, Gas and Metals National Corporation).

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Fig. 7. Hypocenter location and source radius of a cluster of multiplets (extending rupture case).
References[1] H. Moriya, H. Niitsuma: Progress in Acoustic Emission VII, JSNDI, pp. 481-486, (1994).
[2] S. Michelet, M. N. Toksoz: J. Geophysical Research, 112, B07315, doi:10.1029/
2006JB00442 (2007).
[3] W. S. Phillips: Bul. Seismological Society of America, 90, 212-228 (2000).
[4] H. Asanuma, Y. Kumano, A. Hotta, H. Niitsuma, U. Schanz, M. Haring: Trans. Geothermal
Resources Council, 31, 265-270, (2007).
[5] H. Asanuma, Y. Kumano, T. Izumi, N. Soma, H. Kaieda, Y. Aoyagi, K. Tezuka, D. Wyborn,
H. Niitsuma: Trans. Geothermal Resources Council, 28, 191-195, (2004).
[6] K. Aki, P. G. Richards: Quantitative Seismology, W. H. Freeman, Inc. (1980).
[7] F. Waldhauser, W. L. Ellsworth: Bul. Seismological Society of Am., 90, 1353-1368, (2000).
[8] C. Frohlich: Computational Geoscience, 5, 387-389 (1979).

175

CRACK GROWTH MONITORING WITH HIERARCHICAL
CLUSTERING OF AE
1)

N. F. INCE1, CHU-SHU KAO2, M. KAVEH1, A. TEWFIK1 and J. F. LABUZ2
Department of Electrical and Computer Engineering, 2) Department of Civil Engineering,
University of Minnesota, Minneapolis, MN, USA

Abstract
This paper presents a sequence of signal processing and hierarchical clustering techniques
utilized to process signals with low signal-to-noise ratio (SNR) measured by multiple AE sensors. Noise and other extraneous events present major challenges for the detection and monitoring of AE signals generated by the inception of microcracks and their growth. Characteristics of
AE waveforms released during controlled mode-I fracture are explored, and these characteristics
are used for clustering AE and locating the fracture. With hierarchical clustering and signal “denoising” techniques, it is possible to locate the position of the crack plane with high accuracy
using AE signals of poor quality, wherein the spatial distribution of clusters is indicative of crack
propagation.
Keywords: Crack propagation, Signal processing, Hierarchical clustering, Denoising.
Introduction
Broadband acoustic emission (AE) events are widely used for characterizing damage, particularly as generated from the initiation and propagation of a fracture [1 - 3]. The waveform
history of an AE signal is frequently used to investigate the health of a structure [2]. For example, the cumulative count of AE from different sensors has been used as a marker for damage
quantification of structures under overload conditions.
Besides investigation of waveform history, as a more advanced application, AE events can beused to locate the damage. The spatial distribution of AE events estimated with techniques basedon time of arrival can be used for determining position of a fracture. However, the use of AE
events for locating damage is often obscured by noise and spurious events, which may cause amisinterpretation of the data. Even in controlled laboratory settings, it is difficult to account for
all the sources of noise. Therefore, an AE system that automatically “learns” crucial patterns
from data may provide clues for distinguishing between real events and extraneous signals, thus
improving the spatial accuracy of localized AE and reduce false alarms. Consequently, the separation of AE events from noise is an important challenge.
Other investigators have employed signal processing techniques to improve the interpretationof AE data. A subspace approach based on principal component analysis and a self-organizing
map was proposed to discriminate AE events due to crack propagation from noise of various origins [3]. In particular, their proposed algorithm consisted of two steps. In the first step, a combination of principal component analysis and differential time estimates were used to separate thenoise from actual AE. A self-organizing map from a neural network was used to cluster thenoise and AE signals into separate neurons. The algorithm was verified with two sets of data,
and a correct classification ratio over 95% was achieved.

J. Acoustic Emission, 27 (2009)176© 2009 Acoustic Emission GroupAccordingly, semi-supervised or unsupervised algorithms [3] are quite important in real life
scenarios for distinguishing between signals of interest and noise. In this scheme, continuously
stored events can be evaluated by their similarities in signal characteristics and spatial origin.
For example, cluster identification plays a critical role in petroleum geomechanics, by computing
cross-correlation of AE waveforms in both time and frequency domains, in order to precisely
trace the structures activated during oil or gas stimulation [4]. Therefore, clustering methods
may be used as an effective tool for the exploration and discrimination of AE events, whereminimum or no prior knowledge is available.
In this paper, a sequence of signal processing and clustering techniques is presented, which
can be used to locate damage in the form of a propagating fracture. Specifically, this work focuses on adaptive autoregressive modeling to reduce certain imperfections from the AE signals,
and on the implementation of hierarchical clustering from the cross correlation across different
events. The feasibility of the proposed techniques in determining the location of a fracture ispresented by examining AE events recorded by eight sensors attached to a structure where microcracks have localized and crack propagation is occurring.
The experiments and the AE data sets recorded from two specimens during controlled crack
propagation trials are described. Next, the signal preprocessing techniques used for enhancing
the measured AE signals in the presence of noise and data acquisition imperfections are presented. This is followed by a description of a novel clustering technique to group the AE events.
Finally, the spatial distribution of localized events is related to the results of the experiments interms of crack monitoring.
Fracture Testing
Mode-I fracture tests using three-point bend specimens were conducted in a closed-loop,
servo-hydraulic load frame with crack-mouth-opening displacement (CMOD) as the feedback
signal, which provided the opportunity to monitor crack propagation during unstable growth(Fig. 1). The beams were composed of a brittle material called St. Cloud (Charcoal) granite,which produces significant AE before and during crack propagation. The rock has a density of
2.70 Mg/m3, P- and S- wave velocities of 5.4 and 3.2 km/s, Young’s modulus of 68 GPa, Poisson’s ratio of 0.23, and tensile strength of 13 – 14 MPa. Test 1, as a reference test, was conducted on a specimen with dimensions of 220 × 73 × 32 mm and a 4-mm notch cut in the bottom
surface of the beam to induce AE events within a known region; i.e., the fracture initiated andpropagated from the tip of the notch. Test 2 was performed on a specimen with a similar size
(220 × 71.5 × 32 mm), but this beam was not notched; the boundary was smooth and the locationof the eventual fracture was unknown, although the loading configuration promoted crack development at the center of the beam. The distance between the supports for both beams was thesame, 169 mm, and both beam were loaded at a CMOD rate of 5×10-2 mm/s.
Eight AE sensors were coupled to the front and back surfaces of the beams using a cyanoacrylate adhesive. The sensors surrounded a region approximately 50 mm in diameter adjacent to the lower-center region of beam (Fig. 1). The AE data were collected with high-speed,
CAMAC-based data acquisition equipment, consisting of four two-channel modular transient
recorders (LeCroy model 6840) with 8-bit analog-to-digital converter (ADC) resolution and asampling rate of 20 MHz. The data acquisition system was interfaced with eight piezoelectrictransducers (Physical Acoustics model S9225), and eight preamplifiers with bandpass filtersfrom 0.1 to 1.2 MHz and 40 dB gain were used for conditioning the raw AE signals. The
177

frequency response of these transducers ranged from 0.1 to 1 MHz, with a diameter of approximately 3 mm. All channels were triggered when the signal amplitude exceeded a certain threshold on the first sensor. This sensor is referred to as the “anchor” sensor. The recording window
was 100 µs, with a 50-µs pre-trigger. In each test, a total of approximately 1600 events were recorded, with 62% and 45% of the events for Test 1 and Test 2, respectively, being located within
an error of 5 mm using standard techniques [5].

Fig. 1. Schematic diagram of the fracture test setup with a granite specimen.
Signal Enhancement
The AE data acquisition system was set up for targeting the P-wave arrivals. Due to highamplifier gain, a number of AE time histories in both tests were “saturated” (Fig. 2), and these
events were uniformly distributed during the tests. Although the signals were saturated, the Pwave amplitudes remained in the linear range of the measurement system. Because the energy
released was greater for the saturated events (the amplitudes was larger), the signal-to-noise ratio(SNR) of the P-waves of saturated AE events was 5.2 dB (Test 1) and 7.12 dB (Test 2) higher
than the unsaturated AE events, indicating that the saturated events have P-wave amplitudes
around two times larger than the unsaturated cases. Consequently, the arrival time can be determined with greater accuracy for the saturated signals, which means that the AE location havesmaller error for the saturated events.
In order to extract information from signals with low SNR, the unsaturated events, a clustering procedure was used. Basically, the procedure captures those AE events that are correlated
and then averages the time histories to improve the SNR. Approximately 82% (Test 1, notched)and 48% (Test 2, unnotched) of the total events recorded by the anchor sensor were selected forfurther analysis, as these were unsaturated signals. Within this group, several events contained
spikes (Fig. 3), which probably originated from ADC sign errors. To remove these spikes, theprediction error of an adaptive autoregressive (AAR) model was employed. The autoregressivemodel is a stochastic signal modeling technique, which represents the current sample as aweighted linear combination of past samples and a white noise error [6]. In the adaptive version,
the model parameters (weights) are time-varying, which can be adjusted to the non-stationary
characteristics of the signal:
178

(1)
Indeed, the AE events are also non-stationary, and the AAR model is expected to adapt to thetime-varying signal characteristics. In this study, each AE was assumed as an AAR model oforder p = 2. The model parameters ap,n were estimated with the energy normalized least meansquares algorithm [7]:(2)where λ is a regularization constant for those values where the local energy of the signal is closeor equal to zero. A learning rate of µ = 0.005 and a regularization parameter of λ = 1 was usedin this study. The model parameters and prediction error e[n] were computed sample by sample
on normalized AE events, where the sample mean was removed (i.e. zero mean) and then normalized to the sample standard deviation (i.e., unit variance). When the absolute value of theprediction error is larger than a pre-defined threshold (8), related sample points were replacedwith the predicted values of the AAR model. Two representative corrupted AE events and related prediction errors and corrected signals are given in Fig. 3. In some cases, where several
consecutive spikes occurred, the AAR-based method failed to enhance the signal. These eventswere associated with large final prediction error values. A simple search algorithm was developed to find and remove those events with maximum absolute error values greater than eight.

Fig. 2. Time histories of (a) unsaturated and (b) saturated events. The P-wave amplitude of thesaturated events was much higher, but still remained in the linear range of the system.
Clustering of AE Events
Once the locations are computed from the time-of-arrival (TOA) information, the damage
features (e.g. distributed or localized microcracking) are inspected visually by projecting the AE
locations on a 2D surface [8]. The TOA is typically calculated by comparing the signal amplitude to a predefined threshold, where the earliest arrival is associated with the P-wave, as shownin Fig. 2. This type of method may produce misleading TOA information if the signal is noisy.
Moreover, the spurious events recorded by the system may be included during the inspection of
spatial distribution of cracks, and this may cause misinterpretation of the damage features.
Therefore, before applying the amplitude threshold for TOA estimation, correlated events generated from microcracking were grouped and averaged to increase the SNR. A hierarchical clustering approach that uses the cross-correlation function computed between different events wasapplied.
179

Fig. 3. AAR model of corrupted AE events. Top row: corrupted signals. Middle row: prediction
error. Bottom row: corrected signals.
As a first step, the normalized cross-correlation function
was computed for only 256
shifts between any two events on the signals x[n] and y[n] acquired at the anchor sensors:,(3)

Then, a correlation matrix was constructed that keeps the maximum value of the absolute crosscorrelation function between all event pairs. This correlation matrix was used to build a hierarchical cluster dendrogram [2]. The correlation matrices of the two datasets of the fracture tests
are shown in Fig. 4.

Fig. 4. Between event correlation matrices of data sets (a) Test 1 and (b) Test 2.

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Fig. 5. Overlap plot of AE events from eight channels from two different clusters.
The average linkage method was employed to construct the dendrogram, and then it was cut
at level 0.2 in order to cluster those events that have average cross correlations equal or larger
than 0.8. At this level, 212 and 82 clusters were obtained with two or more members for the dataof Test 1 (notched beam) and Test 2 (unnotched beam), respectively. Two examples of the overlap plot of clusters with different numbers of AE events are shown in Fig. 5, where the averagecorrelation values are 0.875 and 0.810, respectively.

Fig. 6. AE averages of two particular clusters for eight channels. The envelope of each signal(in red) was plotted and the threshold (dashed line) was used to detect the TOA.
This step was followed by computing the averages of each cluster. Indeed, due to averaging,
the random components in the data were suppressed, where repetitive components remained thesame. Therefore, the average acoustics computed from each cluster have higher SNR than individual AE events (Fig. 6). In this scheme, averaging will cancel out the uncorrelated noise in thesignal and the repetitive components will remain the same, which improves the SNR of the signals in their amplitude with a factor of, where N is the number of correlated events in a181

cluster. In order to improve the SNR by a factor of more than two, clusters with at least fivemembers were used. Furthermore, the reliability increases since it may be difficult to observe
repeated recording by chance in real life situations.
In order to locate the “source” of the clustered AE waveforms (actually several sources ofsimilar character), the envelope of the signal was computed with a Hilbert transform (Fig. 6).
The TOA is then computed on the envelope of the signal if it exceeds a threshold of 0.5. Aftercalculating the arrival information on each sensor location, a location algorithm [5] was used toestimate the 3D position of the “source.”
Results
Figure 7a shows the estimated positions of all the unsaturated AE events, regardless of thelocation error. For the notched beam in Test 1, the estimated AE locations clustered around thenotch area and extended along the Y-axis. For the unnotched beam in Test 2, however, the AE
locations were more scattered on one side of the centerline (mid-span), and it is more difficult to
pinpoint the fracture location. To verify the AE locations, upon the completion of each test, thefront (Z = 32 mm) and back (Z = 0) surfaces of the specimens were scanned with 1200 dpi resolution and 24 bit color. After enhancing the contrast level, the crack trajectories were carefully
mapped onto the image (Fig. 8).
Using hierarchical clustering on both specimens, the clustered events (Fig. 7b) and the larger
clusters with at least five members (Fig. 7c) accurately find the fracture position. Most of theclustered events in Test 1 are localized around the notch tip, with a vertical extension expected in
the mode I fracture test. Similarly, in Test 2, although the individual events were dispersed on
the 2D projection plane, the hierarchical clustering analysis extracted the fracture location from acloud of AE events (Figs. 7b, 7c) and clearly indicated the fracture path. In Fig. 9, the positionof AE clusters in Test 2 (Fig. 7c) was overlapped by the mapped crack trajectory, indicating that
the AE clusters with more than five events were highly correlated with the actual fracture.
Plots of event indices of the largest clusters with decreasing number of members are shownin Fig. 10. It is observed that the clusters were formed from consecutive or closely spaced eventsin time. Note that the AE recorded in both tests were mostly after reaching peak load (failure).
For the notched beam (Test 1), larger clusters tended to occur well after failure, where the peakload was reached at the time of event number 42. This observation is reasonable, since the notch
promoted the fracture earlier in loading, and the well-localized fracture created more space- andtime- correlated events. For the unnotched beam (Test 2), the trend was not obvious due to(a) the cluster analysis being performed on only 48% of the events (i.e. unsaturated eventsonly) with low SNR, and(b) the number of events in each cluster was fewer.
If AE with higher SNR were used, a similar result would be expected from the hierarchical clustering analysis. Note that none of the clusters span the entire duration of the experiments, whichis reasonable for a mode-I fracture reaching a critical opening with a traction-free region, where
no AE is expected.

182

Test 1

Test 2(a) Individual Events(b) All Clusters(c) Clusters of five events or more

Fig. 7. Locations of (a) individual unsaturated events; (b) all clusters; (c) clusters of five members or more.
Conclusions
The use of low signal-to-noise (SNR) ratio events for damage detection was investigated byintroducing various approaches for clustering AE signals. Specifically, adaptive autoregressivemodeling was used to remove spikes from the recorded waveforms as they cause poor estimationof correlations between events and the P-wave arrivals using a simple threshold. This was followed by the calculation of the cross-correlation between events to build clusters of AE. By
computing the averages of each cluster, the SNR of the signals was improved by a factor of thesquare root of the number of correlated events. In particular, this step suppressed the random
fluctuations in the baseline segment before the P-wave arrival. As a result, the time of arrival was
detected with higher accuracies with the low-amplitude AE signals. This was accomplished bycomputing the envelopes of averaged events in each cluster and then comparing its amplitude toa predefined threshold. It was observed that the clustered events can accurately locate the position of a fracture, even with AE of low SNR. As the cluster size increases the spatial location ofthem were highly correlated with the location and propagation direction of the cracks. Further183

more, a well-localized fracture exhibits both space- and time- correlated events. These results
suggest that the proposed signal processing algorithm can be used to build a damage detection
system that has low false positive rates and can work with higher accuracies in real life applications where the SNR of AE are low.

Fig. 8. Crack trajectories of Test 1 (left) and Test 2 (right) projected on the X-Y plane.

Fig. 9. Crack trajectories and locations of clusters in Test 2 (no notch beam).
Acknowledgements
Partial support was provided by the National Science Foundation, Grant CMMI-0825454.
References
1. C. Grosse, S.D. Glaser & M. Krüger, (2006). Proc. European Conf. on Non-Destructive Testing (ECNDT), DGZfP BB-103-CD, Berlin, Tu.1.7.3, 1-8.
184

Fig. 10. The distribution of event indices of each cluster for both datasets.
2. L. Golaski, P. Gebski & K. Ono, (2002). Journal of Acoust. Emission, 20, 83-98.
3. V. Emamian, M. Kaveh, A.H. Tewfik, Z. Shi, L. J. Jacobs & J. Jarzynski, (2003). EURASIP
Journal on Applied Signal Processing, 2003(3), 276-286.
4. H. Asanuma, Y. Kumano, H. Niitsuma, D. Wyborn & U. Scanz, (2008). Proceedings of the
19th International Acoustic Emission Symposium, Dec. 9-12, Kyoto, Japan, pp. 385-390.
5. N. Iverson, C.-S. Kao & J. F. Labuz, (2007). Journal of Acoustic Emission, 25, 364-372.
6. M.H. Hayes, (2006). Statistical Digital Signal Processing and Modeling, New York, NY:
John Wiley & Sons, pp. 408-412.
7. P.S.R. Diniz, (1997). Adaptive Filtering Algorithms and Practical Implementation, Kluwer
Academic.
8. S. Kim, S. Pakzad, D. Culler, J. Demmel, G. Fenves, S. Glaser & M. Turon, (2007). Proceedings of the 6th International Conference on Information Processing in Sensor Networks(IPSN '07), Cambridge, MA, ACM Press, pp. 254-263.

185

ACOUSTIC EMISSION FOR CHARACTERIZING BEHAVIOR OFCOMPOSITE CONCRETE ELEMENTS UNDER FLEXURESHOHEI MOMOKI1, HWAKIAN CHAI1, DIMITRIOS G. AGGELIS2,
AKINOBU HIRAMA1 AND TOMOKI SHIOTANI3
1)
Research Institute of Technology, Tobishima Corp., 5472 Kimagase, Noda, Chiba 270-0222,
Japan; 2) Materials Science and Technology Department, University of Ioannina, 45110 Ioannina,
Greece; 3) Department of Urban Management, Graduate School of Engineering,
Kyoto University, Katsura, Nishikyo, Kyoto 615-8540, Japan
Abstract
Acoustic emission (AE) measurement was used to assess the behavior of concrete beams
subjected to flexural loading. Plain concrete specimens and those with vinyl fiber-reinforced
mortar layer as composite were prepared. Discussions are based on utilizing various AE parameters for investigating the fracture behavior of composite specimens from those of plain concrete
specimens, as well as assessing locations of cracking and occurrence of debonding at interface
between concrete and fiber-reinforced mortar layer. Part of the assessment was justified by visualinspection carried out during the loading tests. Results in general indicated that in terms of fracture development, the composite specimens behaved similarly to the plain concrete specimen,with higher flexural strength and possibly higher shear resistance as suggested by AE parameters.
Besides, there was no significant interfacial debonding observed throughout the flexural tests,
inferring that the bond stayed intact during the test, such that the specimens were able to sustain
stress sufficiently as a composite element. The AE parameters were found useful in distinguishing between the flexure and shear fracture modes and thus can be utilized as indicators to predict
fracture behavior of concrete structures in health monitoring process.
Keywords: Concrete, Fiber-reinforced mortar, Flexural test, Fracture behavior
Introduction
Acoustic emissions (AE) are stress waves produced by mechanical activities, such as dislocations, cracking and other irreversible changes in a stressed material. Typical AE sources are deformation processes caused by crack development, in which elastic energy is released andpropagates through the structure in the form of stress waves that can be recorded as transient AE
signals by piezoelectric sensors attached to the structure. The source of the signal, which is related to the cracking, is known as an AE event. The fracture behavior and its location in themeasured structure can be evaluated when the relevant group of AE events and other characteristics of the recorded signals are properly interpreted. In AE techniques, the detected energy is released from the interior of measured structure rather than from external sources. Furthermore,
AE technique has the capability of detecting dynamic processes associated with the integrity lossof the measure structure [1]. In other words, the major advantage of AE measurement is its feature that enables monitoring of fracturing process of the measured structure during an entire
loading history, so that the initiation or changes in the mode of fracture can be determined.
Due to its importance as a widely used construction material, extensive research activities
have been carried out to study the fracture and failure behaviors of concrete material. To achieve
this purpose, one of the approaches is the application of AE technique in small-scaled laboratory
J. Acoustic Emission, 27 (2009)186© 2009 Acoustic Emission Grouploading tests of concrete elements [2 - 5]. For failure assessment and maintenance of full-scaled,actual reinforced concrete and prestressed-concrete structures, successful applications of AE
technique to account for various cases have also been reported [6 - 8].
In this paper, AE technique was applied to monitor the behavior of fiber-reinforced composite concrete elements subjected to four-point bending. Specimens to be investigated are composed of plain concrete and vinyl-fiber reinforced mortar layers with rebars at the tension side.
As for the control specimen, plain concrete only with rebars was also prepared. Four-point bending tests were carried out to assess not only the strength but also the fracture behavior of thespecimen, by interpretation of AE signals acquired from the testing. Besides, visual inspections
were also conducted at specific intervals to investigate the surface condition and interfacialdebonding to validate the results of AE measurement.
Experimental
Two sets of beam specimens were prepared for the study: (1) plain normal concrete beams;and (2) composite specimens composed of normal concrete layer and vinyl-fiber-reinforced
mortar layer as the top and bottom half of specimen, respectively. Each specimen has a dimension of 150 mm x 150 mm x 530 mm, with two D13 SD295 (JIS G 3112) rebars embedded as
tensile reinforcements. Concrete was designed to achieve a 28-day compressive strength of 40
MPa. Moist curing was adopted after concrete casting. The vinyl-fiber-reinforced mortar layer
was overlaid by the shotcrete method, at 14 days of concrete age. The surface concrete wasroughened by means of water blasting before the shotcrete. Curing of specimens was continued
up to 28 days of age, before four-point bending tests in compliance with JSCE-G 552 were carried out. At the time of bending test, the average compressive strengths for concrete and vinyl-fiber-reinforced mortar were 47.4 MPa and 57.2 MPa, respectively.
For AE measurements, twelve PAC R6 AE sensors with resonant frequency of 60 kHz wereattached, as illustrated in Fig. 1. The signals were pre-amplified 40 dB and recorded by a PAC
DiSP 16-channel data acquisition system. A threshold of 40 dBAE was selected to ensure highsignal-to-noise ratio. Figure 2 shows a snapshot of the test.

Fig. 1: Loading configuration and sensor arrangement.
187

Fig. 2: Test set-up.

Fig. 3: Examples of load-displacement curves and AE cumulative events. a) Overall. b) Initial.
Results
Mechanical behavior
In Fig. 3, typical results of load vs. mid-span vertical displacement for both types of specimens are shown. The composite specimen exhibited a higher maximum load than that of theplain concrete specimen (129.8 MPa compared to 109.1 MPa). It is found from the area under
the curve that the flexural toughness of the composite specimen is higher than that of the plain
concrete specimen. Cumulative AE events of the specimens are given in the same figure. In thecase of the plain concrete specimen, the number of AE events started to increase rapidly from theearly stage (after approximately 38 MPa, or 35% of the maximum load), while in the composite
specimen, major event outburst was recognized at a later stage of loading (after approximately
65 MPa, or 50% of the maximum load). Additionally, the total number of AE events was substantially smaller in this case (2255 compared to 5539 events of the plain concrete specimen).
These strongly suggest that the incorporation of fiber-reinforced mortar layer has effectively enhanced the mechanical performance of concrete beam in such a way that early cracking was restrained by vinyl fibers, subsequently leading to the reduction in energy released due to intensive
fracture.
188

Fig. 4: Typical cracking of specimen under flexure. (a) Plain concrete. (b) Composite.
Typical fracture conditions of the specimens as observed through tests are shown in Fig. 4.
Both type of specimens exhibited a similar way of cracking: propagation of vertical cracks near
the mid-span due to flexure before diagonal cracks started to develop in the shear span. The shear
cracks became dominant at later stages of loading. Both types of specimens were ruptured as aresult of development of critical shear cracks at one side that extended from the loading point at
the top to the support at the bottom of specimen before joining each other to form discontinuity.
It is also worth mentioning that in all the composite specimens, no significant fracture along theinterface between concrete and fiber-reinforced mortar layers was observed. This infers that thebond between concrete and fiber-reinforced mortar layers remained intact throughout the loading
tests and premature interfacial debonding was not a concern that affects strength performance ofthe composite specimens.

Fig. 5: Ib-value vs. displacement. (a) Plain concrete. (b) Composite.
189

AE parameters
In general, it is reasonable that the fracture scale becomes larger with approaching the maximum load. The occurrence of large-scale fracture always gives rise to AE events of larger amplitude. However, at the same time, the accumulated damage also increases, leading to higher
attenuation rate of the material and delaying the propagation of AE waves. These make the studyof the amplitude by itself misleading. Thus, the amplitudes are studied by their cumulative distributions, using the improved b-value (Ib-value) analysis. This index takes the number of thelatest events and their amplitude range into calculation. According to the cumulative amplitude
distribution, the Ib-value changed following the progress of fracture. From the results, it is confirmed that as fracture becomes more intense, the percentage of the strong events increases relative to the weak ones in the total population of AE events. Therefore, the absolute gradient of this
distribution or Ib-value will exhibit a small magnitude, together with an abrupt drop.
Examples of Ib-values calculated during the whole loading process until the yield stage forboth types of specimens are depicted in Fig. 5. At the initiations of flexure cracking and critical
shear fracture that lead to ultimate failure, significant drops in Ib-value can be identified. During
the former, Ib-value dropped to less than 0.06 at 35% of the load, while in the latter, it dropped toless than 0.04 at the maximum load. These values can become the thresholds that are utilized to
predict the fracture behavior of concrete elements. Also, Ib-value ranging from 0.04 to 0.06 canbe considered as a range where an intermediate, mixed fracture mode of flexure and shear
emerged. As shown in Fig. 5(a), Ib-value for the plain concrete specimens fluctuated around 0.05
between the initiation of flexure and the ultimate shear fracture. Furthermore, the value becameless than 0.04 occasionally during the loading process. In contrast, in the composite specimen(Fig. 5(b)), the Ib-value was higher than 0.05 most of the time throughout the loading. The results suggests that the plain concrete specimen experienced a mixed mode of fracture from theearly loading stage to failure, while it was highly possible that the composite specimen experienced flexure mode for a considerably longer period. By using Ib-value analysis, it becomes
clear that the fiber-reinforced mortar has effectively enhanced the strength performance of concrete beam by restraining extensive damage with reinforcing fibers from the early stage of loading.
Another noteworthy detail is that elastic waves originating from the shear events exhibited
generally higher amplitude than the flexure. This is supported by AE parameters known as AE
counts and energy [9]. In Fig. 6, the results of AE counts and energy are presented. Similar to thefinding of Ib-value analysis, both AE counts and energy can also be used to distinguish between
shear and flexure fracture mode because both changes significantly. To be specific, AE counts
rose to more than 80 at 35% of the load during flexure cracking. Subsequently, AE counts rose to
more than 160 at the maximum load, when critical shear occurred. On the other hand, AE energyrose to more than 100 at 35% (flexure) and 200 at the maximum load (shear), respectively. Additionally, both parameters have lower values for the composite specimens, suggesting higher shear
resistance. Table 1 summarizes the relevant AE parameters and their corresponding values to theTable 1: Useful indicators for predicting fracture behavior.
FlexureMix mode

Shear

Ib-valueLess than 0.06

0.06 - 0.04

Less than 0.04

AE countMore than 80

80 - 160

More than 160

AE energy

More than 100

100 - 200

More than 200

190

(a) Plain concrete.

(b) Composite.
Fig. 6: AE count and energy vs. displacement.
various fracture modes. The AE parameters can serve as useful indicators to predict fracture behavior of large-scaled concrete structures in health monitoring, when proper modifications or
configurations are made to account for different cases. In fact, similar types of indicators, which
are derived from various AE parameters, have already been used for the monitoring the deformation of rock slopes [9].
Locations of AE events
Examples of the 2D-AE event location for the following specific load stages are depicted inFig. 7. In the same figure, the observed cracks after ultimate failure is also illustrated. The events
are represented by circles, with the center and area of each circle representing the exact locationof source and amplitude of energy released in a proportional manner, respectively. Figure 7(a)concerns the plain concrete specimen during loading from 35% to 57% of the maximum load. A
great number of events are evident in the vicinity of the shear cracks on one side of the specimen.
The smaller crack was also found on the other side, accompanying numerous AE events. It isnoted that the development of cracks could hinder the acquisition of all AE signals. Severe scattering imposes wave attenuation and resulted in delay of the acquisition of individual signals,
191

and therefore for severely damaged material the accuracy of source location is expected to decrease.
In the composite specimen, as given in Fig. 7(b), during loading from 70% to 90% of the
maximum load, the accumulation of events were noticed around the edges of the shear cracks at
the top, as well as near the tips of smaller cracks (flexural cracks) that propagated vertically toward the top surface. There was no significant event suggesting debonding occurred at the interface between concrete and the fiber-reinforced mortar layer. This is consistent with the visualobservations in confirming that the bond between concrete and fiber-reinforced mortar layers
was adequate up to the ultimate failure.

Fig. 7: Location of AE events and actual pattern of cracks. (a) Plain concrete. (b) Composite.
Conclusions
Acoustic emission technique was used to assess the fracture behavior of composite concrete
specimens. It is found that the composite specimens with vinyl-fiber-reinforced mortar layer exhibited higher strength than that of the plain concrete specimens. Study of various AE parameters
enabled us to distinguish between flexure and shear fracture modes that occurred during theloading. In both specimen types, the maximum failure was accompanied by macroscopic diagonal shear cracks.
Incorporation of vinyl fibers has effectively restrained the shear mode of fracture from its
occurrence even from the early loading stage, delaying the initiation of critical fracture. AE event
locations coincided well to a great extent with the observed crack locations. Throughout the tests,
no debonding was observed in the composite specimens.
References
1) C. Ouyang, E. Landis and S. P. Shah: J. Eng. Mech. 117 (11), (1991), 2682-2690.
2) E. Landis: Const. Build. Mater. 13, (1999), 65-72.
1923) T. Shiotani, M. Shigeishi and M. Ohtsu: Const. Build. Mater. 13, (1999), 73-78.
4) B. Schechinger and T. Vogel: Const. Build. Mater. 21(3)(2007), 483-490.
5) B. Chen and J. Yu: Cem. Con. Res. 34, (2004), 391-397.
6) T. Shiotani, Y. Nakanishi, X. Luo and H. Haya: J. Acoust. Emiss. 22, (2004), 39-48.
7) S. Colombo, M. C. Forde, I. G. Maine, B. Hill and J. Halliday: Proc. Struc. Faults Repr.
(2003).
8) T. Shiotani, Y. Nakanishi, K. Iwaki, X. Luo and H. Haya: J. Acoust. Emiss. 23, (2005),
260-271.
9) T. Shiotani, M. Ohtsu and K. Ikeda: Const. Build. Mater. 15, (2001), 235-246.

193

DISTINCT ELEMENT ANALYSIS FOR ROCK FAILURECONSIDERING AE EVENTS GENERATED BY THE SLIP AT CRACKSURFACESHIROYUKI SHIMIZU, SUMIHIKO MURATA and TSUYOSHI ISHIDA
Dept. of Civil and Earth Resources Engineering, Kyoto University
Katsura, Nishikyo, Kyoto, 615-8540, Japan
Abstract
As the fundamental research of rock fracture, we have simulated the uniaxial compression
test of rock using the distinct element method (DEM) and discussed the influence of the slip at
crack surface to a relative number of AE events. Simulation results agree well with the AE activities observed in an actual experiment and provide new findings to resolve the disagreement; theconventional theories and microscopic observations suggest that tensile cracks cause AE events,
whereas an abundance of shear AE events is observed in experiments. Our simulation results indicate that the energy released from a tensile microcrack is very small and is most likely buried
in noise compared with that from a shear crack, which should be observed predominantly, due tomuch smaller tensile strength compared to compressive strength. Further, AE is mainly generated
from new tensile microcracks when the stress level is low, while the main sources of AE shift tothe slip at the existing crack surface as the macroscopic failure approaches. That is, the burst of
AE events during the formation of macroscopic fracture is from the slip occurrence at the existing crack surface. The results indicate that DEM is an effective numerical analysis technique forstudying the dynamics of microcracking in brittle materials like rock.
Keywords: Distinct element method (DEM), Uniaxial compression, Crack, Slip, Rock1. Introduction
Microcracking in rock is a very important issue in rock engineering, because macroscopic
behaviors, such as fracture and failure, are strongly controlled by the generation and interactionof microcracks [1]. Actual rock specimen contains many pre-existing flaws such as pores, microcracks and grain boundaries. The fracture process of rock is complicated and sometimes
shows probabilistic aspects because such microstructures in a rock specimen cause the heterogeneous transmission, orientation and magnitude of microscopic forces and moments.
In order to understand the mechanism of microcracking in brittle rock samples, a considerable amount of experiment has been conducted by various methods in the past few decades.
Among them, one approach is monitoring acoustic emission (AE) events caused by microcracking activity. By using the recently developed high-speed, multichannel waveform recording device, we can record many waveforms of AE events associated with fracture process in a stressedrock specimens with high resolution. Thus, the measurement of the AE is an effective technique
for studying the dynamics of microcracks [2-5].
However, even at present, it is still difficult to record the waveform of all AE events generated in an experiment due to the limitation of storage capacity and recording speed of a measuring device, and the influence of noise. In particular, when the catastrophic fracture is formed in arock specimen, there is a burst of AE events in a short time. Therefore, sufficient AE waveformJ. Acoustic Emission, 27 (2009)194© 2009 Acoustic Emission Groupdata cannot be recorded by most experimental systems. In addition, though the generation of new
cracks and propagation of existing cracks seems to be the dominant mechanisms of AE events,
slipping at the crack surface should also cause AE events. However, it is difficult to distinguishAE events caused by slip at pre-existing crack surfaces from those by new cracks.
Hazzard et al. [6, 7] have reported the distinct element method (DEM) modeling for AE activity. They presented a technique to simulate AE behavior in brittle rock under uniaxial compression using the commercially available DEM code (particle flow code: PFC) by considering
the kinetic energy released when the bonds break. The DEM can represent grain-scale microstructural features directly by considering each grain in actual rock as a DEM particle. The grainscale discontinuities in the DEM model induce complex macroscopic behaviors without complicated constitutive laws [8, 9]. However, one of inaccuracies with the AE produced by their PFC
model is the narrow range in observed magnitudes and consequently low b-values. According totheir results, the magnitude of smallest AE events produced by their model is about an orderlarger than the corresponding actual AE monitoring. One possible solution for this problem is tosomehow consider re-activation of cracks such that seismicity could occur on the contacts wherebonds had already broken [6, 7].
Therefore, we have newly programmed our own DEM code that can model the AE events
generated by the slip at pre-existing crack surfaces, and have simulated the uniaxial compression
test of rock by using our DEM model. The mechanical behavior in a brittle rock including not
only generation of microcracks but also slip occurrence at existing crack surfaces can be discussed in detail. The simulation results are compared with the fracture process deduced from thelaboratory AE measurements conducted by previous researchers in order to discuss the process,in which microcracks are induced inside a rock and result in a macroscopic fracture.
2. Simulation methodology
2.1 Formulation of mechanics of bonded particles
In this study, two-dimensional distinct element method (2D-DEM) was employed. The DEM
for granular materials was originally developed by Cundall and Strack [8]. In this section, only asummary of formulation for the mechanical behavior of bonded particles will be given.
In 2D-DEM, the intact rock is modeled as a dense packing of small rigid circular particles.
Neighboring particles are bonded together at their contact points with a set of three kinds ofsprings as shown in Fig. 1 and interact with each other. The increments of normal force , thetangential force , and the moment can be calculated from the relative motion of the bonded
particles, and are given as
(1)
(2)
(3)
and are the stiffness of normal, shear, and rotational springs, respectively;,
where, ,andare normal and shear displacements and rotation of particles; and are the radiiof the bonded particles. A bond between the particles is presented schematically as a
195

(a) Normal spring.
(b) Shear spring.
(c) Rotational spring.
Fig. 1 Three kinds of springs between two bonded particles.

Fig. 2 Bonded particles model.
gray rectangle in Fig. 2, where, L and D are the bond length and the bond diameter, respectively.
D is obtained from harmonic mean of the radius of two particles. L and D are given by(4)(5)
The stiffness of the normal and rotational springs, kn and kθ are calculated using beam theory,and the stiffness of shear springs ks is calculated by multiplying the stiffness of the normal springkn and stiffness ratio α [9]. Thus, the stiffness of the springs is given by the following equations:(6)k s = α ⋅ kn

(7)

(8)

196

where A and I are the area and moment of inertia of the bonds, and Ep is the Young’s modulus ofparticle and bonds. The moment of inertia I depends on the shape of the cross-section, and rectangular cross-section is assumed in this study.
The normal stress σ and shear stress τ acting on the cross-section of the bond are calculatedusing the following equations. The stress and the strain are positive in compression.
(9)(10)
2.2 Microcrack generation and slip occurrence
When σ exceeds the strength of normal spring σc or τ exceeds the strength of shear spring τc,then the bond breaks and three springs are removed from the model altogether. Each bond breakage represents generated microcracks. A microcrack is generated at the contact point between
two particles, and the direction of it is perpendicular to the line joining the two centers.
(Bond break criterion 1)| σ | ≥ σc and σ < 0 (tensile stress)(Bond break criterion 2)| τ | ≥ τc
In the parallel-bond model developed by Potyondy and Cundall [9], the moment acting on
the parallel-bond (which is expressed as elastic beam) contributes the normal stress acting on theparticles. This means that the bond breakage is judged by the maximum tensile stress acting on
the cross section of the assumed elastic beam. On the other hand, in this study, since the spring is
introduced to restrict the rotation of the particles and used only to calculate the moment acting on
the particles, the normal stress calculated by equation (9) does not include the moment of theelastic beam. This means that the bond breakage in our model is judged by the average normal
stress acting on the cross section of the assumed elastic beam. This is the difference in themechanism of particle bondage between the parallel-bond model proposed by Potyondy andCundall and our model presented in this paper.
When the unbonded particles or particles with bond breakage are in contact with each other,
springs and dashpots are introduced into the contact points in both normal and tangential directions, and compressive normal force and tangential (frictional) force act at the contact
points. The no-tension constraint condition should be satisfied for the springs in the normal direction. If the frictional force exceeds the critical value, the slip occurs at the contact
points between the particles and the frictional force will be replaced. According to the Coulomb's frictional law, the critical value
is calculated by the following equation.
(11)
where

is a coefficient of friction.

2.3 Correlation with AE
In actual AE measurement, the AE hypocenter can be calculated by the arrival time of the Pwave first motion and focal mechanisms of AE events are determined from the spatial distribution of P-wave first-motion polarities [10]. For tensile AE, all sensors detect the P-wave first motion as compression wave. On the other hand, for shear AE, both compressional and dilatational197

P-wave first motions are detected. This suggests that the mode of cracking (tensile or shear) depends on the stress state at the crack generation because the polarity of the P-wave first motion
will depend on the stress state. Therefore, in our previous works [11, 12], the crack modes in the
DEM simulation are classified by shear-tensile stress ratio |τ/σ| regardless of broken spring type(normal spring or shear spring) as follows.
(Crack classification criterion 1)|τ/σ| ≤ 1 and σ < 0 (tensile stress)
Tensile Crack(Crack classification criterion 2)|τ/σ| > 1 and σ < 0 (tensile stress)
Shear Crack(Crack classification criterion 3)
σ > 0 (compressive stress)
Shear CrackIn this research, in addition to the shear AE and the tensile AE, we have introduced the classification and the failure criterion for the slip AE in our own code by expanding conventional concept of the DEM [13]. When the frictional force acting at the contact points exceeds the criticalvalue, the slip occurs as mentioned in previous section. It is thought that such a slip occurring at
the crack surface should also generate AE events. Thus, the slip at crack surfaces is added to thebond breakage as a possible mechanism of AE event occurrence. Consequently, AE events in the
DEM simulation are classified by their source mechanisms as follows.
-

Generation of new tensile cracks
Generation of new shear cracks
Slip occurrence at the crack surfaceTensile AE
Shear AE
Slip AE

When a new microcrack is generated, the strain energy stored in both normal and shear
calculated using following equation
springs at the contact point is released. The strain energy
is assumed to be the energy corresponding to the magnitude of tensile and shear AE event.
(12)
On the other hand, when a slip occurs, frictional force will be replaced by the critical value
calculated by the equation (11). During this process, the strain energy stored in springs at thecontact point is partly released. The released strain energy Eslip is given by(13)
where Ekbef ore and Ekaf ter are the strain energy calculated by equation (12) at the time step before
and after slip occurrence, respectively. The released strain energy Eslip is assumed to be the energy corresponding to the magnitude of slip AE.
3. Rock Specimen Model and the Loading Condition for the Simulation
As shown in Fig. 3, the rock model, which was 10 cm in width and 20 cm in height, was used
to simulate the uniaxial compression test. The rock model is expressed by the assembly of particles bonded to each other. The particle radius was chosen to have a uniform distribution between
maximum radius and minimum radius. The number of particles was 9319. The particles were
irregularly arranged in positions by using a random number.
The platen under the rock model was fixed and the upper loading platen was moved downward slowly to reproduce the uniaxial compression test. At this time, frictional force was acting
between the rock model and the platens.
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Fig. 3 Loading condition for the simulation of uniaxial compression tests. The monitored particles for the axial and radial strain were located slightly inside from the edge of the rock model.
The distance between two measuring points is 90% of the rock model width or height.
The axial stress applied to the rock model during the uniaxial compression test was calculated from total force acting on the upper loading platen from particles and model width. The
strain is calculated by displacements of the monitored particles. As shown in Fig. 3, four monitored particles for the axial and radial strain were located slightly inside from the edge of therock model. The distance between two measuring points is 90% of the rock model width orheight. Axial strain ε1 and radial strain ε2 can be calculated using the following equations.

where superscript 0 and t means initial and measuring time, respectively. Plane strain condition isassumed to calculate elastic macroscopic parameters, and Young’s modulus and Poisson’s ratio
were calculated according to the ISRM (International Society for Rock Mechanics) Suggested
Method [14, 15]. For proper simulation using DEM, appropriate microscopic parameters are required. Therefore, preliminary simulations of the uniaxial compression test and the Brazilian test
were repeated beforehand, and the microscopic parameters should be adjusted to represent a certain macroscopic mechanical properties. In this study, macroscopic mechanical properties ofKurokamijima granite are used to calibrate the microscopic parameters. The microscopic parameters and calibration results are shown in Table 1.

199

Table 1. Rock model properties and calibration results.

Fig. 4 Stress-strain curves.
200

4. Simulation Results
4.1 Stress-strain curves
Figure 4 shows the stress-strain curves obtained from the DEM simulation. Though actual
deformation is three-dimensional, this simulation is two-dimensional, and the strain in the direction of depth is not considered. Therefore, the volumetric strain εv in this simulation is defined byusing the axial strain ε1 and lateral strain ε2, as below. The stress and the strain are positive incompression.
(16)
Figure 5 shows the relation between the axial stress and the number of AE events. The solidline in Fig. 5 shows the evolution of the axial stress. The open, closed and hatched bar diagramsin the figure express the number of tensile AE, shear AE and slip AE, respectively. As shown in
Fig. 5, the number of total AE event increases gradually as the axial stress increases. This result
agrees well the typical tendency observed in actual rock fracture under compression [16].

Fig. 5 Transition of the number of cracks and slip with the evolution of the axial stress.
Figure 6 shows the close-up view of the dotted rectangle in Fig. 5 to clarify the activities ofshear and tensile AE. The solid line in Fig. 6 shows evolution of the volumetric strain. The
volumetric strain increases (volume of the model decreases) constantly in the initial stage of theloading, and gradually changes into nonlinear behavior as the axial stress increases. It is known
that the dilatancy in an actual rock is caused by the growth and opening of microcracks. When ashear crack is generated and slip occurs at the existing crack surface, the tensile cracks develop
from both ends of the shear crack with large opening of tensile cracks [17, 18]. Then, the volumeof the model increases, and the dilatancy occurs. As shown in Fig. 6, the volumetric strain curve
begins to change when the generation of shear AE begins, and decreases (volume of the modelincreases) with an increase in shear AE and slip AE. This result indicates that occurrence of the201

Fig. 6 Close-up view of the transition of the number of shear and tensile AE (dotted rectangle inFig. 5) with the evolution of the volume strain.

Fig. 7 Spatial distribution of the tensile and the shear AE events generated in each phase. Tensile
and shear cracks are expressed as closed and open circles, respectively. The diameters of eachcircle correspond to their respective magnitudes of energy. (a) Phase I [Step 1-190 (×104)]. (b)Phase II [Step 190-320 (×104)]. (c) Phase III [Step 320-360 (×104)]202

dilatancy observed in an actual uniaxial compression test can be appropriately reproduced by the
DEM simulation.
4.2 Transition of the number of AE events and AE source mechanism
As shown in Figs. 5 and 6, the rock fracture process under uniaxial compression can be divided into three phases (Phase I, II and III) according to the AE activities [5]. Figure 7(a), (b) and(c) show the spatial distribution of the tensile and the shear AE events in each phase, respectively.
The tensile and shear AE are classified and expressed as closed and open circles, respectively.
The diameters of each circle correspond to respective magnitude of tensile and shear AE obtained by equation (12). On the other hand, Fig. 8(a), (b) and (c) show the spatial distribution of
the slip AE in each phase, respectively. The diameters of the circle correspond to respective
magnitude of slip AE obtained by equation (13). The AE activities in each phase are described asfollows.

Fig. 8 Spatial distribution of the slip AE events generated in each phase. The diameters of eachcircle correspond to their respective magnitudes of energy. (a) Phase I [Step 1-190 (×104)]. (b)Phase II [Step 190-320 (×104)]. (c) Phase III [Step 320-360 (×104)]
In Phase I, tensile AE initiated at a stress level about 35% of the uniaxial strength. As the axial stress increases, the number of AE events increases gradually and shear AE also initiated. As
shown in the bar diagram in Figs. 5 and 6, dominant mechanism of the AE events in low stresslevel was new tensile microcrack generation.
As shown in Fig. 7(a), the energy of AE events generated in Phase I was very small. Although these AE events were widely distributed over the whole model, the density of AE events
decreased from the center toward the loaded ends of the rock model. On the other hand, no slip
AE was generated in Phase I as shown in Fig. 8(a). After the tensile crack generation, these203tensile cracks opened immediately due to the tensile stress acting perpendicular to the loading
axis. Thus, the surface of open crack never touched mutually, and slip did not occur.
In Phase II, in addition to the tensile and shear AE, the slip AE began to be generated. As theaxial stress increases, the number of slip AE increased further. This result suggests that thedominant mechanism of the AE occurrence changes from new crack generation to slip occurrence. As shown in Fig. 6, burst of microcracking observed temporarily in this phase, and thenumber of microcracks decreased substantially after each burst of AE. By comparing the AE
magnitudes and location shown in Fig. 7(b) and Fig. 8(b), it is found that a few shear AE events
that release comparatively large energy were generated in this phase. The slip AE events weregenerated at the same positions where the strong shear AE occurred.
In Phase III, the number of AE events increased rapidly. A macroscopic fracture was formed
in a very short time, and the model resulted in collapse. The macroscopic fracture grew toward
upper left and right from the center of the model. At this stage, 95% of AE events were due to theslip occurrence. As shown in Fig. 7(c), the shear and tensile AE concentrated near the center ofthe model and they progressed to both the upper left and upper right of the model along the macroscopic fracture. These AE events were the shear AE and released large energy compared withother AE. Moreover, the slip AE events that released large energy were also generated along themacroscopic fracture path as shown in Fig. 8(c).
4.3 b-value
The b-value is defined as the log-linear slope of the frequency–magnitude distribution of AE[19, 20]. It represents the scaling of magnitude distribution of AE, and is a measure of the relative numbers of small and large AE, which are signatures of localized failures in materials understress. A high b-value arises due to relatively large number of small AE events comparing to thenumber of AE events that have relatively large amplitude. A low b-value arises in the contrarycase. The b-value is calculated by the Gutenberg–Richter relationship [21], which is widely usedin seismology. The equation is as follows.
(17)
where M is the magnitude of AE event, n is the number of AE events of magnitude M or greater,a is a constant and b is the seismic b-value.
In this simulation, the magnitude M of an AE event is calculated using equation (18) as logarithm of the energy obtained by equations (12) and (13), and the b-value was calculated by the
maximum likelihood method using equation (19) [22, 23].
(18)
(19)
where Mm is the minimum magnitude of AE event.
In this simulation, AE events with extremely small magnitude can be observed. However, suchsmall AE events are hardly observed in an actual AE experiment due to the influence of noise.
For this reason, AE events having magnitude of less than Mm = ‒3.2 were excluded from the calculation of the b-value in this study.
204

Fig. 9. Energy-frequency distributions and temporal variations in b-value.
Figure 9 shows the relation between the magnitude of AE events and cumulative AE eventswith corresponding b-value in each phase. In Phase I, b-value is relatively high at 1.46, since all
AE events generated are small. In Phase III, b-value decreased to 0.68 as the axial stress increased. This result agrees well with the trend of actual AE measurements conducted by Lei et al.
[4, 5, 24].
The strain energy given by equation (12) or (13) is released from the model when a bondbreaks or a slip occurs. This produces a force imbalance, and subsequent stress redistribution induces an AE event. Therefore, logarithm of the energy given by equation (18) does not directly
express the magnitude of AE event. However, as shown in Fig. 9, the relation between the magnitudes calculated from equation (18) and the number of AE events appropriately represents thetendency of actual AE. This finding suggests that the strain energy given by equation (12) or (13)is at least qualitatively valid as a value that corresponds to the magnitude of AE. Moreover, several researchers pointed out that the b-value depends on the heterogeneity of rock [4, 5, 19, 20].
Therefore, the DEM simulations with various heterogeneous rock models are effective to discuss
the influence of the heterogeneity on the fracturing process of rock that is difficult to examine inexperiment.
5. Discussion
5.1 Generation of tensile AE at lower stress level
During Phase I, tensile AE events were dominant and widely distributed over the wholemodel. This result is in agreement with experiment that shows the major mechanism of the AE
events at lower stress level being the tensile cracks associated with the initial rupture of preexisting flaws [4, 5]. This indicates that the DEM can successfully represent the grain-scale microstructures such as pores, microcracks and grain boundaries directly by considering each grain
as a DEM particle.
Figures 10(a-c) show the distribution of maximum principal stress, the minimum principal
stress, and the maximum shear stress in the model at time step, 71 × 104. The stress is positive in205compression. The arrows in Fig. 10(a) and (b) indicate the direction of maximum and minimum
principal stress, respectively. As shown in the figure, the stress distribution in the rock model is
non-uniform. This is because the stresses that act between particles are evaluated by using theradii of the particles. The radius and position of a particle are generated by random numbers,
while the microscopic parameters, such as Young's modulus and strength of the spring, are constant. Therefore, the transmission of force becomes irregular, and the stress distribution in therock models is heterogeneous.
As shown in Fig. 10(b), there are some regions where a relatively large tensile stress exists.
The tensile AE events were predominantly generated in such regions because the tensile strengthof the spring that connects between particles is small compared with the shear strength as shownin Table 1. Thus, the tensile microcracks are widely distributed in the rock model. However, thenumber of macro-cracks is few in Phase I as the tensile microcracks do not influence each otherand did not grow further. Figure 10(b) also indicates that the tensile stress at the loaded ends ofthe rock model is lower than elsewhere. According to this stress distribution, the density of AE
events decreases from the center toward the loaded ends. This is due to the frictional restraints
between the rock model and the loading platen interfaces [25].

Fig. 10 Stress distribution at time step 71 × 104. Cracks initiate at this time step. (a) Maximum
principal stress, (b) Minimum principal stress, (c) Maximum shear stress.
5.2 AE clustering
Phase II produced a few shear AE events that released comparatively large energy. Additionally, slip AE events were generated at the same position as shear events, previously shown in Fig.
7(b) and Fig. 8(b). Figure 11 shows the cumulative distribution of all AE events (tensile, shear
and slip AE) in Phase II. The size of each symbol corresponds to the number of the overlapping
AE events. We find that occurrence of AE events became active at several points of the model inthis phase. Such concentration of AE events is usually called “clustering”.
The total number of microcracks increases, intensifying the interaction between microcracks
in Phase II. Once the interaction becomes strong enough within a certain region, enhancing the
local stress concentration, new microcracks are generated one after another in the same region
and an AE cluster is formed [2, 26]. Such a concentration of microcrack generation relieves local
stress. When stress has been sufficiently relieved in the region, a new microcrack stops forming.
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After the AE cluster formation, microcracking activity migrates to other clustering regions, andmany small AE clusters are formed [2]. Thus, many small AE clusters form in Phase II, with attendant reduction in the number of microcracking after each clustering as shown in Fig. 6.

Fig. 11 Spatial distribution of AE events(tensile, shear and slip AE) in Phase II. The
size of each symbol corresponds to thenumber of the overlapping AE events.

Fig. 12 Propagation processes of the macroscopic fracture in four periods of Phase III. Small
microcracks are ignored. (a) Step 339 × 104, (b) Step 340 × 104, (c) Step 341 × 104, (d) Step 342
× 104.
5.3 Formation of the catastrophic fracture
In Phase III, a catastrophic fracture was formed and the model resulted in collapse within avery short time. Figure 12 shows the propagation processes of the macroscopic fracture in four
periods of Phase III preceding the collapse. The solid lines express the fracture, which is represented by the connection of large opened microcracks. To clarify the macroscopic fracture, small
207dispersed microcracks are ignored in these figures. Figure 12(a) shows microcracks initially concentrated near the center of the model in the region surrounded by the dotted ellipse. Most of the
microcracks are tensile cracks, and stably propagated in the direction of loading axis.
At the next time step (340 × 104), shown in Fig. 12(b), microcracks were repeatedly connected by sliding and the fracture grew rapidly toward top left (see arrow). Next, at time step 341
× 104, fracture also grew toward top right from the center (arrow in Fig. 12(c)). Most of the microcracks generated in these two time steps were shear cracks, and these cracks released large
energy, as shown in Fig. 7(c). Such concentration of shear cracks is called “shear band”. This
result suggests that the formation of shear bands is guided by the development of a process zone
where the tensile microcracks have coalesced into dominant shear cracks in this phase [2, 5].
Finally, as shown in Fig. 12(d), a large wedge-shaped block is separated from the rock modelby the formation of shear bands. The wedge-shaped block moves downward by the loading in the
direction as shown by an open arrow, and many tensile fractures propagate toward the bottom of
the rock model in the region surrounded by the dotted circle in Fig. 12(d).
Numerous slips occurred at the existing crack surfaces due to the impact from the formationof shear band, and many slip AE events occurred. Moreover, strong slip AE events were generated at the wedge-shaped block in the rock model (cf. Fig. 8(c)). This suggests that the burst of
AE events when the rock model collapsed was governed by the slip of pre-existing cracks.
Numerous microcracks developed in Phase III. The interaction among the microcracks isstrong and the local stress concentration is very intense compared with the previous two phases.
Therefore, Phase III is unstable and once a catastrophic fracture initiated at one location, microcracks joined catastrophically until completely collapse [2, 4, 5]. This process is similar to the
AE clustering process in Phase II, but the stress level is lower and the interaction among microcracks is less. Therefore, each AE cluster cannot sufficiently grow.
Since many strong AE events occurs during the formation of shear bands, weaker AE waves
may be hidden, making it is difficult to locate the sources of all AE correctly in experiment. On
the other hand, the forming processes of the cluster and the shear band are difficult to evaluate inexperiment, but the DEM reveals details of such processes.
5.4 Comparison of the energy
The conventional theories suggest that tensile cracks cause AE events because the number ofaccumulated AE events is positively related to the amount of the dilatancy, and the tensile
strength of rock is obviously small compared with compressive strength [27]. The microscopic
observations also revealed that many tensile cracks exist in the rock specimen under uniaxial
compression, and the shear crack is few [28], indicating the dominant mechanism of AE to betensile crack. Figure 13(a) expresses the spatial distribution of all the cracks generated during
this simulation. Many tensile cracks were generated. 72% of all the cracks generated in Phases I
and II were tensile. This is in accord with the conventional theories and microscopic observations. In AE experiment, many of observed AE events originated in the generation of shear
cracks [2, 29, 30]. Thus, there is an inconsistency between the conventional theory and the AE
results. This simulation resolves the inconsistency by considering the energy of AE as discussed
below.

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Fig. 13 Spatial distribution of all the cracks obtained during this simulation. (a) A tensile crack isshown with a closed circle of same size, (b) a shear crack is expressed with an open circle. Its
diameter indicates energy magnitude.
Figure 13(b) expresses the spatial distribution of the shear cracks, and the diameters of thecircles correspond to their respective magnitudes of energy. It turns out that the energy released
from a tensile crack is small compared with that of a shear crack. Theoretically, the same result
has been predicted [31, 32]. The present simulation results are consistent with the theory.
Although a large number of the tensile cracks are generated in the simulation, the energy released from the tensile cracks is small because the tensile strength of rock is low. Such weak AE
is easily buried in noise and hard to detect in experiment. Lei at al. [2] recorded several thousands AE events with waveforms and more than 50% of the recorded events were located appropriately. However, only 10% among the located events have clear P-wave first motions and reliable focal mechanism solutions can be obtained from their radiation pattern. It is difficult tomake clear assignments of the focal mechanisms for other events since some of polarities of thefirst motions cannot be determined due to their vague first motions. In AE experiment, energetic
shear AE events that can be recorded with clear waveforms are observed predominantly.
6. Conclusion
We simulated the uniaxial compression test of rock using a self-programmed DEM code considering AE events generated by the slip at crack surfaces. The findings are as follow:
1.

The volumetric strain increases constantly in the first stage of the loading, and graduallychanges into nonlinear behavior as the axial stress increases. The volumetric strain curve began to change when the generation of shear AE begins. Our research indicates that occur209

2.

3.
4.
5.

6.

7.

rence of the dilatancy observed in an actual uniaxial compression test can be appropriately
reproduced by the DEM simulation.
Since extremely energetic AE events occur during the formation of shear bands, weaker AE
waves of tensile microcracks may be hidden. Therefore, it is difficult to locate all AE
sources correctly in experiment. The formation of cluster and shear band is difficult to follow in experiment, but can be evaluated in detail using the DEM simulation.
Initially, dominant mechanism of AE events under low stress level in a uniaxial compression
test was tensile microcracking. As the axial stress increases, the dominant mechanism of AE
changed to the slip occurrence at the existing crack surface.
The burst of AE events when the rock model resulted in collapse was governed by the slip
occurrence at the existing crack surface.
The simulation result indicates that the rock fracturing process proceeds in three phases. In
Phase I, tensile microcracks are dominant. The microstructures of rock such as pores, microcracks and grain boundaries govern this process. In Phase II, the number of cracks increases and the interaction between the cracks becomes stronger. This induces the coalescence of neighboring microcracks and results in clustering of microcracks. In Phase III, oncea catastrophic fracture initiated at one location, it grows rapidly within a very short time. The
catastrophic fracturing is guided by the development of a process zone encompassing tensile
cracks.
The b-value at the beginning of loading (Phase I) is high because all AE events generated inthis phase were weak. The b-value decreases as the axial stress increases, and becomes thelowest at the collapse stage of Phase III. This result agrees with the tendency of actual AE
experiment. Since the b-value depends on the heterogeneity of the rock, the DEM simulations are effective to examine the influence of the heterogeneity on the fracturing process ofthe various heterogeneous rocks. This is difficult to do in experiment.
The conventional theories and the microscopic observations suggest that tensile cracks cause
AE events. Many tensile cracks are generated during the rock fracturing under uniaxial
compression, but the energy released from the tensile microcracks is small because the tensile strength of rock is small. The weak AE is easily buried in noise and may be missed inexperiment. In AE experiment, many AE originated from the generation of shear cracks.
This inconsistency can be resolved by considering the energy of AE and shear AE with large
energy is dominantly observed.

The results of our simulation can explain time-space distribution of AE activity in the courseof a uniaxial compression test, and agree well with the fracturing process deduced from previous
AE measurements in laboratory. This indicates that DEM is an effective numerical analysis technique for studying the dynamics of microcracking in brittle materials like rock.
References
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ELECTROMAGNETIC METHOD OF ELASTIC WAVE EXCITATIONFOR CALIBRATION OF ACOUSTIC EMISSION SENSORSAND APPARATUS
SERGEY LAZAREV1, ALEXANDER MOZGOVOI2, ALEXEI VINOGRADOV3,
ALEXEY LAZAREV3 and ANDREY SHVEDOV1
1)
Microsensors AE, Ltd., Sarov 607190, Russia; 2) Institute of Experimental Physics, RussianFederal Nuclear Center, Sarov 607190, Russia; 3) Osaka City University, Osaka 558-8585, Japan
Abstract
We propose a new sensor testing technique, which may be a good candidate for the absolute sensor calibration, for the routine laboratory and in-field sensor response checking and forselecting the sensors with similar responses for AE source location problems. We suggest utilizing the energy of the high power electromagnetic field in a coaxial transfer line with a specificgeometry to excite a mechanical wave at the surface of a conducting media. Theoretical modeling approach is discussed and the experimental validation of the proposed method is presented.
Advantages of this method are as follows: i) the extremely high stability of the elastic wave at
the epicentral point where the AE sensor is attached; ii) the field of mechanical displacements
produced by the magnetic field pressure is computable as a function of time with an aid of finite element method; iii) dimensions are small, installation is easy and convenient both for theacademic laboratory experiments and for the everyday NDT practice; iv) low cost and simple
maintenance.
Keywords: AE sensor, Absolute calibration, Electro-magnetic excitation
AE Calibration - Background
Various methods of excitation of acoustic pulses in solids are known for absolute calibrationof acoustic emission (AE) transducers [1]. The sensor response on the external influence is usually represented as a convolution integral in time domain:(1)
where f(t) is an external exciting function such as the velocity of surface displacement, u(t) is thesensor output voltage and G is the characteristic sensor function (Green function). Fourier transform of eq. (1) yields:(2)
The purpose of any calibration technique is finding the Green function G(t) or its Fouriertransform Gω(ω). It is apparent that for this purpose one needs to know the exciting function andthe sensor response. Following eqs. (1) and (2), the closer the exciting function to the δ-function
the easier the absolute sensor calibration, i.e., fω = 1 and Gω = uω. Thus, the AE sensor response
is directly determined from its response to the influence from a short surface displacement. According to ASTM Standard E1106-86 (2002) used for the primary calibration of AE sensors inworldwide AE practice, the rise time of the of the step function source should not exceed 0.1 µs,
which is obtained during glass-capillary breaking on a massive transfer block. Since most commercially available AE transducers have a frequency band from tens kHz to a few MHz, it is
J. Acoustic Emission, 27 (2009)212© 2009 Acoustic Emission Groupobvious that the calibrating AE source with 0.1 µs duration can be approximated by a δ-function
indeed with an affordable accuracy.
Short calibrating pulses of elastic waves on a surface of a test block can be produced, for example, by 0.5-mm pencil-lead break (ASTM E976) [2] or glass-capillary break (ASTM E110686) [3]. The major problem, which most researchers and engineers experience with this type ofstep source, is a poor reproducibility of amplitude and spectral characteristics of exciting pulse.
This must be controlled every time by, for example, independent “ideal” reference sensor of acapacitive type or by a laser interferometer if the absolute sensor calibration is the primary goal.
This makes it virtually impossible to use this method for practical and laboratory calibration of
the whole AE system involving an actual object under control, AE sensor coupled with the surface, analog electronics and digital recording circuits.
The use of short laser-pulse irradiation for excitation of thermo-elastic waves on the surface
of a test block for AE transducer calibration is popular [4]. During the influence of the laser irradiation the surface heating occurs and thermo-elastic stresses arise leading to excitation of elastic
waves with a rather short rise time. The duration of the elastic pulse decay depends largely on thelinear thermal expansion of a solid. Of course, in most materials the duration of the elastic pulse
decay may considerably, by orders of magnitude, exceed the duration of the laser pulse. This inevitably reduces the accuracy of sensor calibration. Besides, the effectiveness of the energy utilization in this way is quite low due to considerable losses on every stage of multi-step process ofenergy conversion into laser irradiation followed by the thermal energy of the radiated solid andthen to the elastic energy of the acoustic wave. Thirdly, the appropriate laser equipment is costly
and spacious. Furthermore, the laser equipment is very sensitive to external conditions and mechanical vibration, which limits its applicability to academic laboratories primarily. In otherwords it is practically impossible to use laser excitation for everyday practice of non-destructive
integrity testing and health monitoring of large scale facilities and machinery.
Many other methods, such as those using white-noise calibration signal produced by a gas jet[5], generation of elastic waves in solids by a broadband piezoelectric transducer [6], and reciprocity techniques [7], do not use a pulsed influence of the test object. Therefore, they represent agroup of alternative calibrating measures. Since our proposed method is based in the excitationof the conducting surface by a magnetic force during a short time, we shall not review othermethods here leaving the references above for reader’s attention.
Proposed Electromagentic Method of AE Sensor/System Calibration - Theoretical ModelIn the present work, we introduce a novel approach [8] towards absolute calibration of AE
transducers and a whole AE setup by creating an impulse of mechanical surface displacementwith the characteristic duration of the elastic pulse acceptable in the standard AE calibration
techniques, i.e., not greater than 100 ns.
The idea of our current approach is to utilize the energy of powerful magnetic field to generate the mechanical wave at the surface of a conducting media as illustrated schematically in Fig.
1. When the electromagnetic wave produced by an electric pulse generator leaves the transfer
line and enters the conducting surface of the load (head), the electric field component E, which isparallel to the surface, creates the electric currents with the density

213

Fig. 1. Schematics of AE sensor calibration using surface displacements caused by magnetic
field pressure.
(3)
where σ is the specific conductivity of the material of the head. Due to the magnetic componentof the fieldinteracting with electric current the Ampere force is created as(4)
where F is absolute value of the force acting on the unit area of the surface and oriented perpendicular to vectors j and H, i.e., normal to the surface, µ is the relative permeability and µ0 is themagnetic permeability of free space. The pressure of the wave is, therefore,(5)
where х is the axis along which the force is acting andforce F:is the average per period T of the wave
(6)

214

Fig. 2. Geometry and schematic illustration of
the model used for calculation of elastic displacements created by magnetic field pressure
at the plane z = 0.
The pressure pulse of the electromagnetic wave creates a pulsed elastic (acoustic) wave in thegiven conducting head, which can be used for sensor testing and calibration, provided the duration of the pulse is short enough and the amplitude of the surface displacement at epicenter ishigh enough. .
To estimate the feasibility of the proposed scheme let us make some elementary estimates
first before the full scale 3D-modeling will be implemented to evaluate the required electriccharacteristics of pulse generator and select the materials for the transfer line and the head. Let
us consider a problem in the coaxial symmetric geometry shown in Fig. 2. The high-voltage
RCL circuit is shorted by a low ohmic load, which is designed as a coaxial transfer line connecting with a metallic head. The testing sensor is supposed be mounted to the head at plane z = 0.
Suppose the capacitor is pre-charged to a few kV and then discharges quickly through the LR
circuit to produce a short, 10-40 ns, electric pulse of 1-10 kA passing through the conducting cylinder. Consider two conducting coaxial cylinders of the length l and radius r1 and r2, respectively, as in the geometry shown in Fig. 2. The inductance of such a system can be written as:(7)
When the electric current I flows through the circuit, the energy W of the magnetic field is:(8)
Assume(plane geometry approximation). Hence, taking µ = 1 we obtain:(9)

The magnitude of the repealing force F between the conductors is given by definition as. Taking the surface areaand pressure
we obtain:
215

(10)
If c is the velocity of sound and τ is the duration of the acoustic pulse, the wavelength is.
2
Thus, the elastic energy density WE = P /2E (where E is the Young’s modulus of the material)
is expressed as:
emitted from the area
(11)
Neglecting attenuation, the density of elastic energy in a wave emitted into a semi-sphere will
decrease with the distance r from the source as:(12)
Taking for estimates µ0 = 4π × 10-7 H/m, E = 2 x 1011 Pa (typical for steel), I = 1 kA, r1 = 0.15
mm we estimate the pressure value P ≈ 6 bar, and the elastic strain ε ≈ 6 × 10-6. If the current
pulse duration is 40 ns and the velocity of sound с = 5 × 106 mm/s, λ = 0.2 mm, the displacementin the elastic wave is estimated as δ = ε λ ≈ 10 Å, which suffices for most practical needs of AE
sensor calibration. This elementary estimate suggests that an excitation of elastic waves in the
body of the conducting solid is feasible with a short pulse from the strong magnetic field.
To model the behavior of the elastic wave quantitatively, the system of governing equations
including Maxwell equations for electric and magnetic vectors E and H, respectively, heat transfer equation accounting for the Joule heating, and the Lamé equation for the mechanical displacement in the isotropic approximation with account for linear thermal expansion and Lorentz
force takes a form:(13)

Here, G is the shear modulus, K is the bulk modulus, ν is the Poisson ratio, εij are the strain tensor components, Ur, Uz are the displacement vector components, k is the heat conductivity coefficient, is the specific heat capacity, ρ is the electrical resistance, αT is the linear thermal expansion coefficient and is the Lamé constant. Using Rayleigh damping, which is commonlyused to provide a source of energy dissipation in analyses of structures responding to dynamic
loads and having in cylindrical coordinates "r# ϕ# z$% H = {0, Hϕ, 0}, Е = {Er, 0, Ez} andU = {Ur, 0, Uz} from the symmetry, equations (9) were solved with obvious initial conditions:
216

(13)
The boundary conditions for temperature, components of electric and magnetic fields and their
gradients were set depending on the desired geometry of the coaxial load as follows.
The boundary conditions for

:

(14)

The boundary conditions for T:(15)

The boundary conditions for on all coaxial surfaces:(16)

Here σr, σϕ and σz are normal components of the stress tensor. Due to the symmetry, the boundary conditions on z-axis take a form
(17)
Apparently, the system of equations (9) should be completed with the following ordinary electric
equations for the external circuit:(18)

with initial conditions U(0) = Uo, I(0) = 0.
The problem was solved numerically by finite element method using the second-order Lagrange polynomial as local approximating functions. Taking copper as a coaxial material the
results of calculations are shown in Fig. 3. Maximum electric current has reached the amplitude217

of 10.46 kA while the duration of the current pulse at base was of 52 ns. Excited longitudinal ultrasonic wave reached the surface of the head at the epicenter (r = 0, z = 0) at t = 708 ns with thedisplacement amplitude of 102.68Å. The obtained elastic wave pulse has the duration of 44 ns atbase. The temperature at the surface of the central coaxial line has reached 356.48K. The ultimate displacement in radial direction in the coaxial line was found to be Ur = 536Å, and the
maximum radial displacement at the point (r = 0, z = z0) was equal to Uz = 615Å. The calculated
shape of the propagating wave front is illustrated in Fig. 1 and in more detail is Fig. 4.

Fig. 3. Vc : Voltage drop at capacitor (5.0±0.285 kV), I : total electric current in the circuit(10.462±0.607 kA), T : maximum temperature in the external surface of the central coaxial wire
(356.48 ~ 300 K), U(0,0): amplitude of the elastic surface displacement at point r = 0, z = 0(102.68±2.7 Å).

Fig. 4. Shapes of the elastic waves emitted
from the tip of the shorted coaxial line andpropagating towards the external surface: results of numerical modeling.
218

Calculations have shown that in order to create a short 40-50 ns ultrasound pulse with amplitude of 100Å in copper sample, one has to ensure a point source of acoustic excitation at (r = 0,z = z0). For a given geometry of a coaxial line this was achieved by reduction of the central coaxial wire radius to 0.1 mm. Three different geometries of a coaxial line were considered for modeling as illustrated in Fig. 5. As an example, the calculated displacement at the sensor location(z = 0) excited by a high-power electric pulse flowing through the coaxial geometry shown in Fig.
5B with different values of the radius of curvature r0 of the central coaxial wire are shown in Fig.
6. One can see that reducing r0 gives rise to a sharper main acoustic response: the amplitude of
the pulse increases while its width reduces. Eventually, the variant B had been shown inappropriate to meet the point source requirements and was ruled out. The variants A and C show similar performance and we have chosen the variant C for technological reasons.

Fig. 5. Coaxial geometries used in modeling.

Fig. 6. Calculated surface displacements at the sensor location (z = 0) excited by a high-powerelectric pulse flowing through the coaxial geometry shown in Fig. 5B with different values of theradius of curvature of the central coaxial wire.
219

Proposed Method of AE Sensor/System Calibration - Implementation
Since the elementary estimates and the 3D modeling have convincingly shown the feasibility
to propose a new method for AE sensor calibration, the prototype device has been designed at
Microsensors AE, Ltd. (Russia) as shown in Fig. 7. A high voltage unit, discharge capacitor andloading coaxial line are assembled together inside the metallic cylindrical body.

Fig. 7. A view of the AE calibrating head and the control unit MSAE-UCA-01.

Fig. 8. Surface displacement as a function of time measured by a high-speed interferometer on
the contact surface of the calibrator MSAE-UCA-01.
The data on surface mechanical displacement occurring as a result of capacitor discharge
were obtained by means of a high-speed laser interferometer and stored as a reference, giving theestimate of the exciting (source) function f(t), eqs. (1) and (2). The interferometric measurements
showed that the duration of the mechanical displacement exciting the elastic waves on the contact surface does not exceed 100 ns indeed while the displacement amplitude can possibly reach10 Å in a good agreement with calculations. For practical purposes the smaller exciting pulse
220Fig. 9. Example of an AE sensor calibration report generated by the CalibrTM software.
amplitude can be used for example as shown in Fig. 8 with the amplitude of 40 pm. The slowly
varying vibrations associated with the present design of the calibrating head have low frequency
part that neither causes any notable excitation of the sensor nor causes any essential error inevaluation of the sensor response. The high-voltage capacitor discharge can be externally triggered by a standard TTL signal or manually or internally with 10 Hz frequency of pulse repetition. The calibrating sensor is placed on top of the head (the coaxial surface). The signal from thesensor is amplified with a wideband amplifier and transferred to a digital oscilloscope, as shownin Fig. 1 or to an analog-to-digital converter in a PC. The sensor response is obtained using
MSEA-CalibrTM software generating report automatically, using the stored reference interfer221

ometric data, and applying Fourier transform and smoothing procedures (the moving average
procedure with 20 kHz window is implemented in the current version). Excellent reproducibilityof the exciting force and mechanical displacement at the point of calibration has been observedwith the variance of less than 1% in a series of 100 measurements. The report sheet is illustrated
in Fig. 9 and examples of calibration results for the NF Electronics sensor AE-900S-WB and the
MSAE-1300WB wide-band sensor are shown in Fig. 10.

Fig. 10. Examples of calibration curves obtained for two types of wide-band AE sensors, NF
AE900S-WB and MSAE-1300WB.
Conclusions
The proposed method meets high metrological requirements for primary calibration, testing
selection of sets of AE sensors with similar response or for calibration of a whole AE setup.
Testing procedure agree with the ASTM E1106-86 (2002) and E976-00 (2000) standards forprimary calibration of AE sensors and reproducibility of AE sensor response. The method is featured by excellent reproducibility of exciting surface sources of elastic waves.
222

Hence, the distinct features and advantages of this method can be summarized as follows:i) The stability of the elastic wave at the epicentral point where the AE sensor is to be attached has been proven extremely high. Therefore, the pulse response function of the sensor or
the whole experimental setup can be obtained in-situ, provided the surface displacement asfunction of time due to the magnetic pressure force is known;ii) The mechanical displacement field produced by the magnetic field pressure is computable with an aid of finite element method and is in agreement with experiments;iii) Low dimensions, weight and power consumption, easy installation operation and maintenance.
Overall, all these features make the proposed method convenient and useful for both theacademic laboratory experiments and for everyday NDT practice in field. Furthermore, thepurposed technique can be potentially expanded for in-situ sound velocity measurements, which
is promising for enhanced accuracy of the AE source location. The quantitative comparison of
the proposed method with other ASTM approved methods of primary sensor calibration has yet
to be done.
References
1) L. Goujon, and J. C. Baboux: Measurement Science & Technology, 14, (2003) 903.
2) ASTM Standard E1106-86 (2002).
3) ASTM Standard E976-00 (2000).
4) C.B. Scruby, H.N.G. Wadley: Mater. Eval., 39 (1981) 1250.
5) S.L. McBride and T.S. Hutchison: Canadian Journal of Physics, 54 (1976) 1824.
6) H. Hatano and E. Mori: J. Acoust. Soc. Am., 64 (1978) S154.
7) T.J. Esward, P.D. Theobald, S.P. Dowson and R.C. Preston: NPL Report CMAM 82, UK(2002) 74 p.
8) A. Vinogradov, S. Lazarev, A. Mozgovoi, A. Shvedov, V. Gornostai: Method and Device for
Acoustic Emission Sensor Absolute Calibration, Patent Russian Federation N2006129419(2006).

223

MONITORING OF PIPE CLOGGING BY MUSSELSUTILIZING AN OPTICAL FIBER AE SYSTEM
TAKUMA MATSUO, YUTA MIZUNO and HIDEO CHO
Faculty of Science and Engineering, Aoyama Gakuin University,
5-10-1 Fuchinobe, Sagamihara, Kanagawa, 228-8558 Japan
Abstract
Clogging of pipes caused by bivalves such as mussels is a serious problem preventing safe
operation of plants. Effective early detection of mussel clogging was studied using an optical fiber AE system. This system was developed to detect minimum flow velocities when AE signals
are generated from mussels. First, a sheet-type optical fiber sensor was developed for the detection of cylinder-wave AE signals from mussels. The sensor was used by winding it around a pipe.
The frequency response of 13 kHz to 27 kHz from the developed sensor depended on its width.
AE signals from living mussels attached on the inside surface of PMMA pipe were monitored
next. The flow velocity when the first AE signal was detected increased depending on the shellfish size. AE signals were produced by mussels that were more than 11 mm long. AE signals
from mussel colony were than monitored. The flow velocity, when the first AE signal was detected, was also dependent on shell size. However, the flow velocity was lower than that of thesingle mussel test and mussels that were less than 5 mm produced AE signals. Additionally, theflow velocity decreased linearly with the shell length of colony members. We identified theminimum mussel size for AE detection for a given flow velocity.
Keywords: Optical fiber sensor, Cylinder wave, Pipe clogging, Biofouling, Mussel
Introduction
Biofouling pest caused by small freshwater mussels, Limnoperna Fortunei, is a serious problem in Japan [1-5]. They clog small diameter pipes for water quality monitoring and in water
heat exchangers. Although a strainer is installed at the inlet of pipes to prevent clogging, themussels enter the pipes as larvae (plankton) where their size is approximately 100 µm; they growand attach themselves to the inner surface of the pipes [6]. It is known that chlorination can effectively control the growth of larvae. However, the use of chlorine is regulated (restricted) infresh water supply facilities. Hence, the pipes can only be cleaned by dismantling the pipe network. This is a time-consuming process and needs considerable manpower. Although detectionof mussels attached in a pipe and finding the location is important to decrease the cleaning costs,
early detection (to detect small mussel) is difficult by present techniques.
The acoustic emission (AE) technique, in which an optical fiber is used as an AE sensor, is apotential tool for monitoring the condition of water supply facilities; the optical fibers are intrinsically safe, lightweight, and flexible [7, 8]. In our previous study, we found that AE could be
produced by the collision of mussels with the pipe wall or among each other [9, 10]. However,
the relationship between shell size and flow velocity of water when AE signals are detected(hereafter called “minimum flow velocity”) is still unclear. In the present study, first, a sheet-type
sensor for effective detection of the cylinder wave AE signals was designed. Next, AE signals
produced by native mussels were monitored and the relationship between the shell size and theminimum flow velocity was studied. Here, we could not use live Limnoperna Fortunei, becauseof Japanese regulations [11]; therefore, we used “Mytilus Galloprovincialis”.
J. Acoustic Emission, 27 (2009)224© 2009 Acoustic Emission GroupDeveloped Sensor Configuration
Relationship between sensor width and frequency characteristics of the sensor
The frequency spectra of the AE produced by mussel attached to the inner surface of a pipe
showed narrowband characteristics [10]; we attempted to design a sensor that could detect AE
signals effectively and could be handled easily. The sensor was designed in order to detect cylinder waves having a specific frequency.
Using the experimental setup shown in Fig. 1, we studied the output of the optical fiber sensor for a cylinder wave that was generated by a PZT transmitter as a function of sensor width.
The sensor fiber was wound on the outer surface of a steel pipe having the diameter of 34 mm,
thickness of 3.5 mm and length of 2000 mm. The pipe was filled with water. Cylinder waves
were excited by a PZT transmitter (PAC, R3) mounted at a distance of 1000 mm from the opticalfiber. The input signal of the transmitter was a continuous sine wave with peak-to-peak amplitude of 10 V, and its frequency was changed from 10 kHz to 30 kHz. The sensor width was alsochanged from 34 mm to 67 mm.

Fig. 1 Experimental setup for monitoring cylinder AE wave utilizing optical fiber AE sensor
wound on the pipe surface.

Fig. 2 Amplitude profile as a function of frequency of generated signal.
225

Figure 2 shows the change in the peak-to-peak amplitude of the output of the optical fiber
sensor as a function of the frequency of the generated wave when W = 40 and 67 mm. Higher
peaks were observed at 13–18 kHz and 22–30 kHz when the values of W are 67 mm and 40 mm,
respectively. The values of the half wavelength of the cylinder wave in the F(1,1) mode at peakfrequencies of 13 kHz and 23 kHz were 62 mm and 38 mm, respectively, and they corresponded
to the width of the sensor. By matching the sensor width to the half wavelength of the AE to be
detected, we can selectively detect the AE produced by a mussel.

Fig. 3 The sheet-type optical fiber sensor.
Sheet-type optical fiber sensor
Initially, an optical fiber sensor was wound around a pipe in order to monitor the cylinder
wave; however, it is difficult to wind a long optical fiber sensor. Sheet-type sensors are easy tohandle. Figure 3 shows a sheet-type optical-fiber sensor. The optical fiber was wound and fixedusing polyimide films. Sheet-type sensors are commercially available [12]. With these sensors,
however, the sensor length and width were not taken into consideration.

Fig. 4 Half wavelength of cylinder wave as a function of frequency.
226

In this study, the sensor width was determined to be the half wavelength at the matched frequency. The sensor length L corresponded to half the circumference of the pipe. Figure 4 showsthat the relationship between the half wavelength of cylinder wave and the frequency. In this test,
we designed a 47-mm wide and 55-mm long sensor. The matched frequency of the sensor was 18
kHz in the F(1,1) mode.
Figure 5 shows a comparison of the waveforms and their frequency spectra detected by the
sheet-type sensor and the directly wound optical-fiber sensor (fiber width: 6 mm) on the surface
of a pipe. The experimental setup was the same as that shown in Fig. 1; however, the input signal
was a single-cycle sine wave of 18 kHz with peak-to-peak amplitude of 10 V. The amplitude of
the signal detected by the sheet-type sensor was higher than that of the signal detected by the directly wound sensor. The peak frequency was observed to be near 45 kHz in both frequency
spectra. However, in the case of the sheet-type sensor, the strong peak was observed near 18 kHz.
This is because the sheet-type sensor effectively detected the F(1,1)-mode of 18 kHz.

Fig. 5 Waveforms (upper graphs) and their frequency spectra (lower graphs) detected by the
sheet-type sensor (left) and directly wound sensor (right).
AE from Mussels Attached Inside PMMA Pipe
Experiential setup
We monitored the AE signals produced by a mussel or mussel colony attached to a pipe andcorrelated the shell size of the mussels with the minimum flow velocity. Figure 6 shows the experimental setup used for AE monitoring. We used a transparent 3-mm-thick PMMA pipe withan outer diameter of 30 mm in order to observe the motion of the mussels inside the pipe. The
mussels were allowed to stay in the pipe for 7 days and they grew their threads and strongly attached themselves to the pipe wall. The shell size ranged from 2 mm to 25 mm. The flow velocity was varied from 0.7 m/s to 2.0 m/s.
227

Fig. 6 Experimental setup for monitoring AE generated by mussels.
Figure 7 shows photographs of a mussel (Mytilus Galloprovincialis). Some threads are visible. Cylinder-wave AE signals were monitored using the sheet-type sensor attached to the pipewith adhesive tape at a distance of 300 mm from the shellfish. The sensor width was 13 mm,
which was sufficient to detect the cylinder wave of the F(1,1) mode of 15 kHz.

Fig. 7 Photographs of Mytilus Galloprovincialis (left) and its statement in the pipe (right)
AE detected from a shellfish
We first monitored the AE signals produced by a single mussel. We conducted 22 tests withmussels of different sizes. During the test, 7 mussels produced AE signals and attached themselves to the wall. The other 7 mussels did not produce AE signals. Another mussel could not
attach itself to the pipe and drifted out. Vibration of the shells was observed at a flow velocity of
1.0 m/s; however, no AE signal was observed. The amplitude of vibration increased with theflow velocity. The AE signals were generated at 1.2 m/s. Figure 8 shows examples of AE waveforms and their power spectra generated by mussels with a length of 25 mm detected at the flow
velocity of 1.9 m/s. Most AE waveforms were similar to them with a peak frequency of 10–20
kHz. The minimum flow velocity decreased with the shell length.
Figure 9 shows the relationship between the shell length and the flow velocity measured
when the first AE signal was observed. The open circle () indicates shellfish that generated AE
signals, and the diamond () indicates mussel that generated no AE signals. The symbol near
the mark denotes the angle of attachment with respect to the water flow. Type-A mussel attach in228

Fig. 8 Typical AE waveforms and their power spectra excited by collision between shellfish andpipe wall.
a direction parallel and type-B mussel attach perpendicularly to the flow. No AE was generated
by type-A mussel regardless of size. Type-A attached mussel vibrated weakly because they did
not disturb the water flow. The generation of AE signals by type-B mussel depended on the sizeof the mussel, and mussel whose length was greater than 11 mm generated AE since type-B
mussels disturbed the water flow strongly. They vibrated and collided with the wall. The collisions produced AE signals. The solid line in Fig. 9 shows the boundary between AE generationand no generation. The mussel on the right side of the line generated AE signals and those on theleft side did not. The length of an 11-mm shell corresponds to a blockage ratio of approximately
0.11. We could not detect AE at a blockage ratio of less than 0.11, which is the limiting value fordetecting AE at the flow velocity of 1.9 m/s. Larger type-B mussels required a lower flow velocity for AE generation; 1 m/s for 25 mm size.

Fig. 9 Relationship between shell length and the minimum flow velocity.
229

The shell length of the mussels attached to the pipe could be roughly estimated from the limiting flow velocity. It is known that the length of Limnoperna Fortunei increases by approximately 15 mm per year [1]. This implies that we can determine the start of pipe blockage 8–9months after the mussel attaches itself to it and grows to 11 mm size.
Monitoring of AE produced by a colony of mussels
We next monitored the AE produced by a colony of mussels in the pipe. The shellfish attached themselves to the pipe through their threads. The numbers of individual members in thecolony varied from 2 to 30; 19 colonies were tested. Figure 10 shows the relationship between
the number of mussels and the flow velocities when the first AE signal was detected. Each data
point indicates the result for a colony. The dashed line in Fig. 10 shows the limiting velocity for
AE generation in the case of a single mussel. All colonies produced AE signals and AE signals
were detected at a flow velocity lower than that with a single mussel. This is because AE signals
were produced by the collisions of mussels in addition to AE by shells colliding with the walls.

Fig. 10 Relationship between blockage ratio and the minimum flow velocity.
Figure 11 shows the relationship between colony member shell size and the minimum flow
velocity for AE detection. The flow velocity changed from 0.2 m/s to 1.2 m/s. Number of members was set to 5. A linear relation shows that flow velocity is inversely proportional to the shelllength of colony members. Member of colony in the pipe was generally of similar size because
they attached at the same time and grew in the same environment. Thus, the size of mussels thatcan be detected were decided by the flow velocity of water flow in the pipe.
Conclusions
Using an optical fiber AE system, we detected the clogging in a pipe caused by mussels. We
first designed the sensor for detecting the cylinder-wave AE signals. We then monitored AE signals produced by the mussels and studied the relationship between the minimum flow velocity
and the shell size or number of mussels in the colony.
230

Fig. 11 Relationship between shell size of colony member and the minimum flow velocity.
The results are summarized below:
1)
A sheet-type optical fiber sensor was developed. By matching the sensor width to half
wavelength of the AE signal, we could detect a selected frequency component. The sensor
successfully detected the specific frequency of AE signals in comparison with the directly
wound optical fiber sensor.
2)
AE caused by mussels was monitored. There was a relationship between the AE generated by a mussel and the minimum flow velocity for AE detection. The flow velocity decreased with shell length. The AE was detected for a shell length greater than 11 mm and anangle of attachment such that the mussels are attached approximately perpendicular to theflow.
3)
AE caused by colony of mussels was monitored. The AE signals were generated atlower flow velocity than those for a single mussel and the minimum flow velocity was inversely proportional to the shell length of colony members.
Acknowledgement
We would like to thank Dr. Katsuyama (Japan NUS Co., Ltd.) and Dr. Kado (JGC Corporation) for their valuable advice.
References[1] A. Statz, J. Finlay, J. Dalsin, M. Callow, J.A. Callow and P B. Messersmith, Algal antifoulingand fouling-release properties of metal surface coated with a polymer inspired by marine mussels, Biofouling, 22(6), (2006), 391-399.
[2] B. Gama, R.C. Pereina, A.R. Soares, V.L. Teixeira and Y.Y. Valentin, Is the Mussel Test agood Indicator of Antifoulong Activity? A Comparison Between Laboratory and Field Assays,
Biofouling, 19, (2003), 161-169.
231

[3] M. Bruijs, H. Polman and H. Jenner, Optimising cooling seawater antifouling strategy byadopting and environmentally friendly/BAT technology, 14th Int. Congress on Marine Corrosion and Fouling, 30B-2-4 (2008)(CD-ROM).
[4] K. Ito, Expansion of the invasive freshwater mussel, Limnoperna Fortunei (Dunker, 1857)(Mytilidae) in Japan, NIAES International Symposium 2007, (2007)[5] D. Boltovskoy and D.H. Vataldo, Population Dynamics of Limnoperna fortunei, an Invasive
Fouling Mollusc, in the Lower Parana River (Argentina), Biofouling, 14(3), (1999), 255-263.
[6] Y. Magasa, Y. Matsui, Y. Goto and A. Yuasa, Invasion of the non-indigenous nuisance mussel,
Limnoperna Fortunei, into water supply facilities in Japan, J. Water Supply, 50-3, (2001),
113-124.
[7] H. Cho, R. Arai, and M. Takemoto, Development of stabilized and high sensitive optical fiber
acoustic emission system and its application, J. Acoustic Emission, 23, (2005), 72–80.
[8] T. Matsuo, N. Yokoi, H. Cho and M. Takemoto, Michelson-Type optical fiber laser interferometer for cylinder wave monitoring, Materials Transactions, 48, (2007), 1208–1214.
[9] T. Matsuo, H. Cho, T. Ogawa and M. Takemoto, Optical fiber AE sensor for blockage monitoring of water pipes of power generation plant, Proceedings of The Sixth International Conference on Acoustic Emission, (2007), pp. 40–45.
[10] T. Matsuo, H. Cho, M. Kado and I. Katsuyama, Monitoring of blockages by shellfishes in apipe utilizing an optical fiber acoustic emission system, 14th International Congress on Marine
Corrosion and Fouling, (2008), 30B-2-2 (2008) (CD-ROM).
[11] The Invasive Alien Species Act, Ministry of the Environment, Government of Japan, 2004.
[12] T. Mori, M. Nakajima, K. Iwano, M. Tanaka, S. Kikuyama and Y. Machijima, Application of
the fiber optical oscillation sensor to AE measurement at the rock compression test, 11th Congress of the International Society for Rock Mechanics, (2007), pp. 1101–1104.

232

CORROSION DETECTION BY FIBER OPTIC AE SENSORYUICHI MACHIJIMA1, MASAHIRO AZEMOTO1, TOYOKAZU TADA2
and HISAKAZU MORI2
1)
Lazoc Inc., Hongo 3-40-9, Bunkyo, Tokyo 113-0033, Japan; 2) Process & Production
Technology Center, Sumitomo Chemical Co. & Ltd., Sobiraki 5-1, Niihama, Ehime, Japan
Abstract
CUI (corrosion under insulation) of the piping at industrial plants gathers more attention thanever. Currently, plant owners need to shut down their operation, scaffold, disassemble insulation,
carry out non-destructive test and reassemble insulation of extensive piping installation.
On-stream inspection (OSI), or on-line monitoring is a key to improve economics. To evaluate
CUI without plant shutdown, we have carried out a preliminary research on detecting AE produced by corrosion. Fiber optic AE sensor is explosion proof, and is suitable for applications inpetrochemical plants. Evaluation testing was successful, and one sensor can detect corrosion 3.9m away. We report experimental results and subsequent field test, using fiber optic AE sensor.
Keywords: CUI (corrosion under insulation), OSI (on-stream inspection), Fiber optic AE sensor
Introduction
Our study attempts to detect and evaluate outer piping corrosion under insulation material.
Such corrosion is accelerated by humidity and correlates to temperature. To ensure that whole
piping is corrosion-free or within corrosion-allowable for safe operation, plant-owners currently
have to shutdown the operation periodically for precise inspection using methods like ultrasonicthickness gage. This requires scaffolding to elevated piping level (in a typical Japanese petrochemical plant, the majority of piping runs at 7-8 m high), disassembling and reinstalling insulation. To mitigate this work, our research focuses on utilizing AE methods as an OSI tool and on
enabling to screen/choose where among the long distance of piping to be precisely inspected.
Another point of research is the utilization of fiber optic sensor. Fiber optic sensor is explosion proof due to its non-electric principle. Most location of a petrochemical plant requires explosion proof devices. Short-time AE monitoring does not need to be explosion proof, but assuming continual monitoring at a corrosion-critical or inflammable gas areas, sensors need be
explosion proof.
This paper reports on the outline of experiment, which used piping mock-up. Corrosion was
accelerated by sodium chloride (NaCl). Fiber optic AE sensor was installed from 0.3 m to 3.9 m
away from corroded region and obtained AE signals.
Fiber Optic AE Sensor - Principle
When an object vibrates in elastic manner, the attached fiber optic element on the object surface elongates and shortens simultaneously. Light-wave frequency fo is modulated by suchchanges in length of fiber optic element, because the number of light waves in that vibrating region is constant at a moment. This is called “laser Doppler effect”, given as “fo – fD”. The frequency modulation fD is proportional to the changing velocity of fiber optical length. Doppler
J. Acoustic Emission, 27 (2009)233© 2009 Acoustic Emission Groupeffect is described as equation (1), with fD as modulated frequency, λ as light wavelength, anddL/dt as velocity [1].
1 dL
(1)fD = −
λ dt
The frequency modulation fD is detected using Mach-Zender/heterodyne interferometry as
shown in Fig. 1. The laser with frequency fo is emitted and divided with a half-mirror (HM) into
the sensing optical path and detecting optical path. At the detecting optical path, frequency f M(80 MHz) is added by AOM (acousto-optical modulator) to create the frequency “f o + f M”. Thisis combined with f o + f D from the detecting optical path, again using an HM. The difference infrequency given as “f M + f d” is converted into the voltage output using a detector. Hereinafter,
we call this sensor “fiber optical Doppler” sensor or “FOD” sensor.
Sensor

Detector

Fig. 1 Optical interferometry by Mach-Zender/heterodyne system.
Fiber Optic AE Sensor - Calibration Data
For this experiment, a 65-m long, multi-layered FOD sensor was employed as shown in Fig.
2 and Table 1. This sensor was originally developed for field micro-seismic monitoring, and embedded into a borehole near an excavated tunnel. The sensor was targeted for sensing below
200-kHz frequency. To reconfirm acceptance to corrosion monitor, we have calibrated the frequency response of FOD sensor.

Table 1 FOD sensor specification.
Fiber optic

Polyimide coatedFiber length (m)65Height (mm)6.00

Inner diameter (mm）8.00

Outer diameter (mm)22.00

Fig. 2 FOD sensor element.
Calibration was in accordance with NDIS 2109-1991, particularly for longitudinal wave detection. System and transmitting signal specifications are shown in Fig. 3. Three PZT sensors
were employed to calibrate transmitting PZT sensor initially and then replaced a receiving PZT
234

sensor by an FOD sensor. Due to the 400-mm thickness of steel cube, the elimination of reflectedwave (80 µs delayed at the shortest distance) was carefully examined. Accordingly, frequency
response from 60 kHz to 300 kHz was calibrated at the same number of transmitted waves (5waves). Calibrated frequency response is shown in Fig. 4 (Below 50 kHz, data is for reference
only, as the number of transmitted waves is only 3). Results for a 70-kHz-resonant PZT (40-dB
amplified) sensor was also shown. The 65-m long/multi-layered FOD sensor has superior response to PZT sensor from 70 kHz to 140 kHz. This was adequate for corrosion monitor [2].

Fig. 3 System and transmitted signal data for FOD calibration.

Bold : Fiber optic
Dotted : PZT

Fig. 4 Frequency response data.
235

Experimental Setup
Piping mock-up is shown in Fig. 5. It is made of a carbon steel, 5-m long, outer diameter
60.5 mm, thickness 3.9 mm (STPG-370-50A-sch.40) and has inner flow of silicone oil for heating by a circulation pump. Artificial corrosion area was located at 1 m from the right edge (Fig.
5) and was accelerated by NaCl solution and cyclic heating (maximum ~80ºC). FOD sensors
were located at 300, 2000, 3000 mm away on the pipe, and 3900 mm away both on the pipe andon a welded flange. Each FOD sensor was installed on the pipe via a U-shape bolt, while directly
attached on welded flange with a C-clamp, as shown in Fig. 6.

Fig. 5 Experimental mock-up.

on-pipeon flange
Fig. 6 Sensor installation.
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Corrosion AE Monitoring
AE monitoring was implemented 3 times with approximately 2-month interval. First data
was obtained 1 month after the start of continual NaCl dripping. Until then, corrosion spread on
the surface, but no peeling was observed visually. At the third monitoring, corrosion progressed
aggressively, and peeling crack was confirmed even visually. Figure 7 shows corrosion at thefirst and third monitoring.

Fig. 7 Corrosion of pipe (left: first monitoring, right: third monitoring).
Detected AE Waveform and Data Analysis
AE was detected by FOD sensors successfully even at 3.9 m away from the corroded region,
with enough SNR (signal-to-noise ratio) margin. From those data, we made a sample analysis, 1)
AE activity and corrosion status, 2) AE difference by sensor location between on-pipe andon-welded-flange, and 3) AE frequency-amplitude histogram.
Figure 8 shows a sample of AE waves and FFT data from the first experiment. It was obtained by an FOD sensor 300 mm away from the corroded region.
Figure 9 shows AE hits per 30 min during the second and third monitoring at the same location (FOD at 3.9 m away). Corrosion was clearly severe during the third monitoring, as confirmed by visual observation. Figure 10 shows AE hits per 30 min, describing whether AE data
differs by sensor location between on-pipe and on-welded-flange, both being at 3.9 m away fromthe corroded area. Data indicates the welded flange location was slightly lower in detected AE
hits than the on-pipe sensor at the same distance. The attenuation of AE on the flange location
was not serious, giving us more flexibility to install an FOD sensor on piping structures.
Figure 11 shows the correlation of AE peak frequency and peak amplitude by sensor location.
As monitoring duration is different from each other, the density of AE hits is indicated qualitatively. However, it is clear that 60-70 kHz peak frequency is more prominent at any location, andlarger amplitude events have lower frequency.

237

Fig. 8 AE waveforms and their FFT data.

Fig. 9 AE hits by corrosion progress detected by FOD sensor at 3.9 m.
Field Test
Following in-house experiment, we have conducted field test on an insulated reactor vessel atthe owner’s plant (3.5-m outer diameter, 25.5-m height). The vessel was partially corroded due toingress of rain water to the extent of 0.3 to 7 mm depth, as confirmed by visual test. Four FOD
sensors were installed at 90º interval at the same height near corroded region. Figure 12 is a water-proof FOD sensor, which was bonded to the vessel by epoxy resin. Figures 13 and 14 show
AE hits per 30 min before and after de-rusting work. Some AE hits still remained after thede-rusting work, which seemed to be from internal liquid flow, because this vessel was being238

operated at the normal condition. Separation of operation noise is under study, installing the applicable equipments on site.

Fig. 10 AE hits by sensor location at 3.9 m.

Fig. 11 Peak frequency – amplitude correlation.

Fig. 12 Water-proof FOD sensor for field-use.
239

Fig. 13 AE hits at corroded region.

Fig. 14 AE hits after repair work.
Conclusions
We have successfully detected and evaluated AE signals, caused by corrosion progression
using fiber optic AE sensor both in laboratory and at plant. Assuming a pipe is roughly 10-m
length, one sensor can cover the pipe in order to screen CUI presence. Fiber optic AE sensor isnaturally explosion proof and this is especially advantageous in petrochemical plants.
References
1) K. Kageyama，H. Murayama，Ｋ. Uzawa，I. Ohsawa，M. Kanai，Y. Akematsu，K. Nagata
and T. Ogawa: Doppler effect in flexible and expandable light waveguide and developmentof new fiber-optic vibration/acoustic sensor ， J. of Lightwave Technology, 24, 2006
1768-1775．
2) High Pressure Institute of Japan: Recommended Practice for Acoustic Emission of Corrosion
Damage in Bottom Plate of Oil Storage Tanks, HPIS G 110 TR 2005.
240

EFFECT OF SHOT PEENING ON THE DELAYED FRACTURE
USING THE ALMEN STRIP AND AE TECHNIQUEMIKIO TAKEMOTO, MOTOAKI NAKAMURA, SEIJI MASANO and SHUICHI UENO
Kanmeta Engineering Co. Ltd., Nakano Higashi 2-3-54, Tondabayashi, Osaka 584-0022, Japan
Abstract
This research aims to study the effect of shot peening on the delayed fracture using the Almen strips and AE technique. The Almen strip is a thin spring-steel coupon for measuring thepeening intensity from its arc height. We used the conventional delayed-fracture test of
three-point bent strips in two types of charging solutions with and without poison (thiourea) andstep-wise strain increase (SSI) method. AE technique was successfully utilized to determine thethreshold strain to induce the subsurface micro-cracks in the strips. Proposed test method wasfound to be valid for high strength steels (>1 GPa) with trapped hydrogen, and provides us with
important information on hydrogen.
Keywords: Shot peening, Alemen strip, Step-wise strain increase (SSI), Delayed fracture,
Trapped hydrogen
Introduction
Delayed fracture of ferritic steels occurs at static and dynamic tensile stresses exceeding thethreshold stress. The threshold stress is reported to decrease with tensile strength higher than 1.1
GPa. Countermeasures against the delayed fracture are to reduce the strength of the steels and toprevent hydrogen diffusion into the steel. Steels with Rockwell hardness lower than C22 does
not suffer the delayed fracture even if the steels absorb hydrogen. Coating of the steels by ceramics and polymer is another effective countermeasure. Thermal spraying of titanium oxide has
been demonstrated to be an effective countermeasure against the sulfide stress cracking.
Shot peening has been considered to be an effective countermeasure, but very few experimental data were reported. Shot peening, however, appears to possess both negative and positiveeffects. Compressive residual stresses in peened layer can be beneficial for preventing the delayed fracture. Negative effects are supposed to be due to defects such as vacancies and dislocations, which act as the trap site of hydrogen. Disrupted grain boundary induced by strong
shot peening, however, has positive effect in reducing the initiation sites of micro-cracks along
grain boundary. Work hardening of the surface layer by shot peening is negative due to higher
hardness.
Watanabe et al. [1] reported that the time to fracture at higher applied stresses is increased byshot peening, possibly by the trapping of diffusible hydrogen, while the threshold stress is notchanged by the shot peening [1]. The present authors believe that the threshold stress is much
more important than the fracture times. Extension of fracture time by shot peening is not usefulby itself in process plants. We focus our attention to the threshold strain or stress to cause the delayed fracture of peened or non-peened steels.
We studied effects of shot peening on the delayed fracture using the Almen strips (ASE J442).
Here, the Almen strips are thin spring-steel coupons, which are used to measure the peening intensity from the arc height. The strip is 0.6%-carbon steel (SAE1070, JIS G4801), and quenched
J. Acoustic Emission, 27 (2009)241© 2009 Acoustic Emission Groupand tempered to the strength level of 1400-1500 MPa. We used a three-point bend method forconventional delayed fracture test (abbreviated as the DFT). Fracture times of bent strips aremonitored in two charging solutions with and without poison (thiourea). The former is called as
an aggressive solution and the latter a less-aggressive solution. As it takes extremely long times
for determining the threshold strain to cause the delayed fracture by the conventional DFT, weused acoustic emission (AE) technique to determine the threshold strain quickly, where the hydrogen pre-charged strips were step-wise bent in air within one hour. This method, named as
step-wise strain increase (SSI) method, was found to be useful to determine the threshold strain
and supply us with important information on the trapped and diffusible hydrogen.
In this report, we discuss whether shot peening is effective to prevent the delayed fracture ofhigh-strength steel. We tested as-received Almen strip (1480 MPa), additionally tempered strip(1100 MPa) and as-cast maraging steel strips (1000 MPa ) using both the DFT and SSI methods.
Specimen and Test Method
Size of the Almen strips (strip-A specified in the SAE J4542) is 76 mm long, 19 mm wide
and 1.4 mm thick. Strips are oil-quench from 650ºC and water cooled from 425ºC tempering.
Rockwell hardness of the strip is C45 and estimated tensile strength is 1480 MPa. Thus, thestrips show extremely high sensitivity to the delayed fracture due to their tempered martensite
structure. We also used additionally tempered Almen-A strips (named as 2-ST strips), whichwere oil cooled from 575ºC tempering. The hardness and tensile strength of the 2-ST strip are
Hv = 360 and 1110 MPa, respectively. As we can not reduce the strength level of the Almen strip
lower than 1110 MPa, we also utilized as-cast maraging steel strip with tensile strength of 1000
MPa.

Fig. 1 Step-wise strain increase (SSI) and three-point bend delayed fracture test (DFT) with AE
technique.
242

Figure 1 shows the SSI and three-point bend DFT method with AE sensors. Arc height of the
strips, bent by fastening the bolt, was measured by the Almen gage. We observed a good agreement between the surface strains measured by strain gage and calculated one from the arc height(curvature) of the bent strips.
Table 1 Six types of shot peening

Fig. 2 Hardness and residual stress distribution of peened Almen strip-A.
Hydrogen was charged to the bent strips by cathodic charging at current density of 600µA/cm2 using an electrolyte of pH = 4 (1 N-H3BO4 + 0.033 M-KCl) solution with and without
thiourea (0.001 M) in a 16-mm diameter glass cell attached on the convex surface. Here thethiourea acts as poison and remarkably increases the diffusion of atomic hydrogen into the strips.
Hydrogen content in the steel by the electrolyte without poison is fourteen times smaller thanthat by the electrolyte with poison. Charging method of Fig. 1 allows both the in-diffusion of hydrogen into strips from the convex surface and the out-diffusion from the concave surface. Thiscan simulate actual component exposed to internal corrosive fluid.
AE events were monitored by two small sensors (PAC Type-PICO) mounted on the concaveside. Outputs of the sensors are amplified 40 dB and digitized by an A/D converter (Alazer). AE
monitoring during hydrogen charge aimed to detect both the sub-surface and internal cracks.
243

Shot peening was performed by direct compressed-air (0.5-0.55 MPa) method using partiallystabilized zirconia (PSZ) and glass beads (GB) of 1-mm or 0.6-mm diameter. Here, the shots(particles) were accelerated by compressed dry air and impinged to strips tightly fixed on a thickplate. Peening conditions are shown in Table 1. Here the arc height (AH) is measured for the
Almen strip-A and shown such as “xx mmA” according to SAE J4542. Shot peening by PSZ ofhigh density (4900 kg/m3) and high fracture toughness (KIc = 12 MPa m1/2) can produce a smooth,clean and hardened layer of 100 to 200 µm thickness with the maximum compressive residual
stress equivalent to tensile strength of the strips, as shown in Fig. 2. It is noted that the first crackby the DFT occurs at higher hydrostatic pressure field below the surface (sub-surface crack) andcannot be detected by visual inspection. Contrary to the PSZ peenig, glass beads were easily
broken during the peening and embedded in the strips. The embedded pieces sometimes act as
defects and notches.
Results and Discussion
We first present test results of Almen strip, two-step tempered Almen strip and as-castmaraging steel strip in aggressive solution. Next, we discuss the result of as-received strip inless aggressive solution.

Fig. 3 Time to surface crack curves of Almen strip-A in an aggressive electrolyte with poison.
Delayed fracture in aggressive environment1) As-received and peened Almen strip-A (1480 MPa)
Figure 3 shows the delayed fracture curves of as-received (non-peened) and shot-peened
strips-A by conventional DFT in aggressive solution. Time to cracking on the surface at higher
applied strains is shortened by the shot peening. The shot peening by glass beads (method-4)
shortened the time, possibly due to the notch effect. Threshold strain was unaffected by the shot
peening, but was low at 0.22%. This fact implies that the shot peening does not give any beneficial effect to such a system of extremely high strength steel and aggressive environment. Hydrogen content in the non-peened strip-A was measured as 28-30 ppm. Residual compressive244stresses do not give any positive effect to prevent or reduce the delayed fracture. In Fig. 3, we
showed three threshold strains, determined by the SSI method, near the vertical axis. The threestrains are measured as 0.22% and coincided well the threshold strains determined by the conventional DFT.

Fig. 4 Relation between step-wise strain increase and cumulative AE counts for non-peened Almen strip-A. The strip was pre-charged in aggressive solution.

Fig. 5 Relation between step-wise strain increase and cumulative AE counts for the Almen
strip-A peened by method-5.
We next introduce how the threshold strains are determined by the SSI method. Two examples of the SSI results for non-peened and peened strips are shown in Figs. 4 and 5, respectively.
The strips were first hydrogen charged for 6 hrs or more at no strain and then bent step-wise in245

air. AE signals were monitored during strain holding time for 1 to 10 min. The SSI test was
finished within 15 to 60 min, depending on the initial strain. For the non-peened strip-A of Fig. 4,
we observed a rapid increase of AE signals at applied strain of 0.22%. We also heard strong
audible sound (sound emission) at approximately 3 min after the first emission of AE. At thetiming of the strong sound emission, we observed a surface crack. Thus, the AE generation justafter the applied strain of 0.22% indicates the initiation of subsurface micro-crack, and soundemission the surface open crack. In Fig. 5 for peened strip (by method-5), we clearly observed
two-step rapid increases of AE events, i.e., the first at 0.21% and the second at 0.218%. We heard
weak sound emission during the first rapid increase of AE, and strong sound emission during thesecond AE increase. The latter sound emission is produced by an open surface crack. Such clear
two- or three-step emission is characteristic features of peened strips, possibly due to the arrest of
subsurface micro-cracks by hardened layer with large residual compressive stresses.

Fig. 6 Surface and transverse photos of the peened Almen strip-A after the SSI-test.
Figure 6 shows surface and transverse photos of another peened strip (method-3), which wasinterrupted at the end of the first step-wise increase of AE. We observed no surface crack, but
found buckling trace on the concave surface at this moment. In the transverse section of thisspecimen (right photo), we observed an open surface crack in addition to branched internalcracks. The surface crack was supposed to be produced by an additional loading during cutting
and molding, and opened due to the release of compressive residual stresses. Problems in constant-strain type DFT is the difficulty of detecting internal micro-cracks, which initiate at highhydrostatic pressure field below the surface. AE makes it possible to detect the generation ofsuch hidden micro-cracks.
246

Fig. 7 Examples of Lamb waveforms produced during SSI test of peened Almen strip-A.
We detected three types of Lamb-wave AE signals during SSI test of peened strip-A, buthave inadequate to correlate the wave and fracture types. Two examples are shown in Fig. 7.
Type-A is Lamb waves with high-frequency component. Type-B waves with low-frequency
component were often detected at the timing of the sound emission or emergence of surface
crack. The sound emission can be used to monitor the opening of surface crack but the AE canaccurately monitor the initiation of hidden subsurface micro-cracks.

Fig. 8 Time to crack curves of two-step tempered Almen strip-A in aggressive solution.
2472) Two-Step Tempered Almen Strip (1100 MPa)
Fracture curves of 2-ST strips in aggressive solution are shown in Fig. 8. There, again, observed no beneficial effect of shot peening on the delayed fracture. Surface cracks were detected
at similar times in both peened and unpeened strips, as shown by solid lines. In Fig. 8, the timeof the first AE detection or the initiation of sub-surface crack is shown by symbol ◊ with a dottedline. The time shown is approximately half of the time for surface crack detection of the peenedor unpeened strips.
We studied how AE technique can monitor the progression of subsurface micro-cracks tosurface crack during hydrogen charging. Figure 9 shows an example of cumulative AE counts
with charge time for the non-peened 2-ST strip at applied strain of 0.5%. Event counts increased
exponentially after the first AE at 2.4 hr and showed a plateau from 4 to 5 hr. It is noted that theinvisible internal damage occurs at surprisingly fast time. We could not hear audible sound fromthis strips of 1140 MPa strength.

Fig. 9 Change of cumulative AE counts of two-step tempered Almen strip-A at applied strain of
0.5% during delayed fracture test.

Fig. 10 Relation between step-wise strain increase and cumulative AE counts for the two-step
tempered Almen strip peened by method-3. The strip was charged in aggressive solution.
248

Figure 10 shows results of an SSI test. Threshold strain (0.295%) of the peened strips(method-3) determined by the SSI method agrees well with that (0.29%) of Fig. 9. We interrupted the strain increase at 0.34% and studied internal cracks. As shown in Fig. 11, we observed several branched buckling traces on the concave surface. These traces were found to be
produced by several internal cracks, which did not reached the convex surface, as shown in thelower figure. Subsurface cracks tended to progress to concave surface rather to the convex surface, where large compressive residual stress existed. Residual compressive stresses prevented
the progression of internal cracks, but could not prevent their initiation in aggressive solution
where extremely high hydrogen was supplied.

Fig. 11 Buckling traces and internal cracks produced by SSI of peened two-step tempered Almen
strip.
3) Maraging steel strips (1000 MPa)
Effect of shot peening on the crack curves of maraging steel strips is shown in Fig. 12.
Threshold strain of peened strips increased from 0.29% (un-peened) to 0.41% for the strips
peened by method-2. For these strips with 1000 MPa strength, we first observed positive effectof shot peening. High resistance of QT-treated maraging steel to the delayed fracture is wellknown, but we improved the crack resistance of as-cast maraging steel by shot peening. Positiveeffect is considered to arise from both the low strength level and low concentration of diffusiblehydrogen in this steel. Low hydrogen concentration is possibly due to slow in-diffusion of hydrogen in hardened layer with large compressive residual stresses. Indeed, hydrogen concentration of strip charged for 20 hrs, measured by Glycerol method, is approximately 15 ppm and halfthat in peened Almen strip.
Data for three types of strips with different strength levels implies that the peening effect isstrongly affected by hydrogen concentration and strength level, and positive effect can be expected for low strength material in less-aggressive environment. In Fig. 12, near the left vertical249

Fig. 12 Time to crack curves of maraging steel strips in aggressive solution.
axis threshold strains of four kinds of strips are shown. These were determined by the SSI
method, and agree well with those determined by time-consuming DFT.
Figure 13 shows SSI result for the maraging steel strip peened by method-2 (0.6 mm A).
Strain to cause a rapid increase of AE is measured as 0.4%. Feature of SSI test for these strips isthat we did not observe multi-step increases of AE events as seen for Almen strips (Figs. 5 and
10).

Fig. 13 Relation between step-wise strain increase and cumulative AE counts for the maraging
steel strip peened by method-2. The strip is pre-charged in aggressive solution.
4) Almen strips in less aggressive solution
DFT result for Almen-A strip (1480 MPa) in less-aggressive solution is shown in Fig. 14.
Threshold strain (0.75%) of the strips peened by method-6 is higher than that (0.55%) of250

as-received strip. It is noted that the coverage by peening method-6 is as low as 40%; nevertheless, we observed positive effect. It is our future project to study what kind of peening, strong or
weak, is most effective for delayed fracture.

Fig. 14 Time to crack curves of peened and un-peened Almen-A strips in less aggressive solution.
The fracture curve for the peened strips showed discontinuous yielding behavior. This ispresumably caused by the higher applied strains. The applied strains for the peened strips are
more than 1%, above the yield strain of the strips. True stress of the bent strip at yield strain isreported to decrease slightly at the end of the Lüders yielding [2]. Thus, the longer crack time atstrain of 1.1% is apparently due to lower stress. Whether this reduced the actual driving force for
gliding dislocations and diffusible hydrogen is unknown at present. This peculiar phenomenon isconsidered to be not from the weak peening, but needs future study.
Hydrogen concentration in the peened strip-A, charged for 40 hrs in less aggressive solution,is measured as 2 ppm. This concentration is 14 times smaller than that (28 ppm) in aggressive
solution. It is also noted that the threshold strains of un-peened and peend strips by the conventional DFT agree quiet well those determined by the SSI tests.
Validity of the SSI Method
The SSI method takes 15 to 60 min for determining the threshold strain. Both the rest time ofthe strips from hydrogen charging to the SSI test and the step holding time can be utilized to
study the effect of trapped and diffusible hydrogen on the delayed fracture. Diffusible hydrogen
diffuses out from strips during the rest time and step-holding time.
Figure 15 compares the critical arc heights determined by the SSI and DFT. Left four strips
were tested within 10 min after the hydrogen charging and right three strips were tested after
several rest conditions shown. Threshold strains of strips, which were rested for 50 and 24 hrs at2510˚C, are slightly higher than those determined by the DFT. This suggests that the diffusible hydrogen easily out-diffuse from the strips, but is essentially needed for crack initiation. Both thetrapped and diffusible hydrogen are needed for sub-surface crack initiation. The SSI method does
not need any sophisticated tensile machine but can save time and cost of the testing significantly.
Quantitative data between the rest time, threshold strain and hydrogen concentration need further
elaboration and is also future research project.

Fig. 15 Comparison of threshold strains determined by delayed fracture tests (DFT) and SSI
method.
Conclusion
Effect of shot peening on the delayed fracture of QT-treated spring steel (Almen strip) andmaraging steel were studied by both the conventional delayed fracture test (DFT) and step-wisestrain increase (SSI) method assisted by AE technique.
Results are summarized in Fig. 16 and below.
1) Shot peening shows no beneficial effect on the delayed fracture when high strength strips(1480∼1100 MPa) are hydrogen-charged in aggressive solution with poison. Sub-surface
cracks tended to generate at the bottom of hardened layer and propagate into the concave side.
Surface crack was first detected by visual inspection when the internal cracks propagatedthrough the peened layer with compressive residual stresses. This time is much longer than
the time for sub-surface crack initiation.
2) Positive effect of peening, i.e., increasing the threshold strain, were observed when i) maraging steel strip (1000 MPa) was hydrogen charged in aggressive solution and ii) high strength252

Almen strips were charged in less aggressive solution. Effect of peening on the delayed fracture significantly changes depending on both the strength and hydrogen concentration in the
strips.
3) The SSI method, assisted by AE technique, can determine the threshold strain correctly when
it is used with short rest time after the hydrogen charging. This method can save test time
significantly and be utilized for studying the effect of trapped and diffusible hydrogen on the
delayed fracture.

Fig. 16 Effect of shot peening on the delayed fracture.
References[1] Y. Watanabe, N. Hasegawa and M. Inoue: J. Soc. Materials Science, 41 (1992), 933.
[2] H. Asahi, M. Ueno: Evaluation of Cracking Susceptibility of Steels in Wet H2S Environment,
Symp. Committee on Stress Corrosion of Steel, Iron and Steel Institute of Japan, (1991) p. 11.

253

CONTRIBUTION OF ACOUSTIC EMISSION TO EVALUATE CABLE STRESS CORROSION CRACKING IN SIMULATED CONCRETE PORE SOLUTION
S. RAMADAN1, L. GAILLET2, C. TESSIER2 and H. IDRISSI1
Laboratoire MATEIS Equipe RI2S, INSA-Lyon, Bât L. de Vinci, 21 avenue J. Capelle, 69621
Villeurbanne Cedex, France; 2) LCPC, Division MACOA, Centre de Nantes, BP 4129, 44341
Bouguenais Cedex, France

1)

Abstract
Failure of high-strength steel cables of prestressed-concrete structures (PCS) is becoming aserious problem, as the deterioration of structures progresses due to corrosion induced in severe
environment as chloride attack. This attack is dangerous due to the fact that these cables are tensioned at 80% UTS in concrete, so a stress-corrosion cracking (SCC) mechanism can be developed in this condition causing steel brittle failure without any external warning. The applicability of acoustic emission (AE) technique for evaluation and detection of SCC and localized corrosion of steel cables in simulated concrete-pore solution (0.01M of NaOH at high alkalinity) contaminated by chloride ions (0.1M) is studied. Tests performed in laboratory show that the cracking process can be practically monitored by AE, as well as wires failure under constant load deformation. A novel analysis of AE parameters using the principal component analysis (PCA) is
used to discriminate localized corrosion from SCC. Kmean is used first as unsupervised method,
and to validate the clustering k-nearest neighbor is used as supervised method. Among this study,
the AE monitoring of cable corrosion was proven to be useful under severe load condition.
Keywords: Cable steel corrosion, Stress corrosion cracking, Simulated concrete pore solution
Introduction
In recent years, the deterioration of concrete structures such as bridges caused by the corrosion of pre-stressing cables has been a significant problem. Proper techniques for the inspectionof damaged structures are important to make rational decision regarding rehabilitation, repair or
replacement. Thus, the development of non-destructive techniques (NDT) to evaluate the degradation by corrosion of pre-stressing cables in long-term service has been one of the most important issues for effective maintenance programs.
Several conventional NDT techniques such as electromagnetic testing have been applied tolocate and determine corrosion severity. However, these techniques are ineffective because prestressing tendons are buried deeply in the concrete or put in a plastic duct. Some works havebeen published on the application of in situ monitoring techniques of the corrosion process but
few concerning non-destructive control methods [1- 5].
However, knowledge about corrosion mechanism and the detection of localized corrosion
and SCC of cables in concrete is insufficient. The reason is probably because the characteristicsof these types of corrosion detected by non-destructive evaluation (NDE) techniques are still
unclear. Consequently, this paper presents a study on determining the type of corrosion cracking
by acoustic emission (AE). An acoustic emission is defined as the transient elastic wave generated by the rapid release of energy from a localized source or sources within the material. The
elastic energy propagates as a stress wave (AE event) in the structure and is detected by one or
J. Acoustic Emission, 27 (2009)254© 2009 Acoustic Emission Groupmore AE transducers. AE events may be generated by moving dislocations, crack growth andpropagation, plastic deformation, etc. AE differs from other NDT methods in two key respects.
First, the signal has its origin in the material itself and is not introduced from an external source.
Second, AE detects movements or strain, whereas most other methods detect existing geometricdiscontinuities or breaks. One of the main objectives of AE is to discriminate among different
sources of damage; thereby attributing each emission to a particular source type or failure mode.
The parameters analysis of an AE event evaluates and correlates AE features such as amplitude,
counts, energy, peak frequency, etc. The classification of these parameters drives the investigator
toward the correlation of the AE with the source. In the present study AE was used to monitor
SCC of high strength steel tendons in chloride medium.
High-strength steel cable (T13-7) was selected as the material in this work. Corrosive solution contaminated by 0.1M of chloride ions was used and to accelerate corrosion, a pit potentialof 0.6 V/SCE was applied. Correlations of analyzed AE parameters were used to describe thecorrosion characteristics and their mechanism. An unsupervised clustering analysis of selected
AE parameters (counts, amplitude, duration, and rise time) was used after removing AE signals
related to steel failure.
Two well-separated clusters, A and B, which correspond to two types of signals due to localized corrosion propagation and sub-critical growth of the cracks were discriminated. To validate
this classification a supervised method was used. With the help of AE analysis and fractographic
observations it was possible to evaluate a crack propagation rate of 10-8 m/s.
Experimental Method
Material description
Eutectoid cold-drawn steel wires (4.2 mm in diameter) were used. This steel has a fully oriented fine pearlitic microstructure, which is fairly different from usual reinforcement steel bars
(Fig. 1). The steel wires after drawing were treated for a few seconds at 400°C for stress relief.
Due to the cold-drawing process, wires have the ultimate tensile strength (UTS) higher than 1800
MPa, and a fracture elongation lower than 5% (Table 1).

Fig. 1. AFM images of steel microstructures showing pearlite oriented to cold-drawn axis, andpearlite perpendicular to cold drawn axis.

255

Table 1. Chemical composition of major elements and steel mechanical properties.

AE monitoring system and experimental results
Acoustic emission instrumentation consisted of an acquisition card (MISTRAS) developedby Euro Physical Acoustics (EPA). Two wide-band WD piezoelectric transducers (namely, S1and S2), which have a frequency range from 100 to 1000 kHz, were employed. The electricalsignal from each transducer was pre-amplified using EPA 1220A preamplifier with a gain of 60
dB. The sampling frequency for data acquisition was 5 MHz. In order to eliminate external noise,
two frequency filters were also used: a low-pass filter with a cut-off frequency at 20 kHz and ahigh-pass filter with a cut-off frequency at 1200 kHz. The threshold level was fixed at 26 dB. S1and S2 were positioned respectively at 64 cm and 49.5 cm from the corrosion cell (Fig. 2a). For abetter separation of AE activity generated during localized corrosion and SCC stages on steel
surfaces the principal component analysis coupled to clustering algorithm were used.

Fig. 2. Experimental device using during this study (a); Hit-time correlation and current densitytime correlation during SCC test (b).
The principal component analysis (PCA) is a mathematical algorithm used to reduce the dimensionality of a data set for compression, pattern recognition and data interpretation. The algorithm projects, by a linear transformation, a p-dimensional data vector X into a new q-dimensional data vector Z, containing what is referred to as the data’s principal components.
256

Given the datawith, and the new data vector
where Z1 is the linear combination of the original Xj (j =1, …, p) withmaximal variance; Z2 is linear combination, which explains most of the remaining variance andso on. If the p-coordinates are a linear combination of q < p variables, the first q principal components will completely characterize the data and the remaining (p – q) components will be zero.
In addition, Kmean clustering algorithm is used for partitioning N AE data points into K classes. In®
this study, Noesis software developed by Euro Physical Acoustics was used for AE data clustering.
Corrosion processes of cables in chloride medium (0.1M of Cl-) was monitored by AE. Strand
was exposed to a constant load, which was equal to 80% of its previously determined UTS. Steelfracture occurred at 61 h of the test (Fig. 2b).
The analysis of AE data shows a low number of hits from the beginning till 59 h of the test,
since localized corrosion occurs throughout the surface of the material. Passive-layer breakdown
at the beginning of the corrosion process and metal dissolution into the solution do not generate a
detectable energetic AE activity [6-13]. Indeed, localized Corrosion is an electrochemical process with cathodic and anodic half-cell reactions. At high alkalinity, pH 12, the anodic reaction(1) leads to the formation of iron cations, according to:(1)
This reaction is balanced by the cathodic reduction of oxygen, which produces hydroxyl anions
according to reaction (2).
(2)
The products of both reactions combine together and in a last stage they produce a stable filmthat passivates the reinforcing steel. The stability of this film depends essentially on the oxygen
availability that controls reaction (2) and on the pH of the interstitial solution in the interface
steel/solution. The passive film on iron steel surface is thermodynamically stable in the alkaline
environment even when chloride ions are present. In this situation, corrosion tends to be localized and chloride-induced corrosion initiation follows the model of pitting corrosion. It is a twostage process in which pit nucleation is followed by pit growth. Pit nucleation is accompanied bya local fall in pH and increase in the chloride content at the pit nucleation site. The local fall in
pH renders the passive film locally unstable and the presence of chloride ions promotes the dissolution of iron and stabilizes the local fall in pH. These steps of localized corrosion are not sufficiently energetic to be detected by AE.
The number of AE hits increased after 58 h when the SCC occurs on metal surface till the
final failure of steel specimen. The question now is how to well discriminate localized corrosion
from SCC? To answer this, the principal component analysis (PCA) is applied using NOESIS
software after removing AE signals related to steel failure.
For a better separation of AE signals released during corrosion stages (localized corrosion
and SCC) on steel surfaces, Kmean clustering algorithm is used. Step I in the classification analysis consists of suppressing of AE parameters that have no physical significance (e.g., threshold,channel number...). In step II the most relevant AE parameters are identified by the determination of the correlation coefficient between parameters. In Step III, the classification algorithms
are applied on non-correlated AE parameters (e.g. amplitude, duration, counts, and rise time)257

and to segment signals into several populations (Fig. 3). Step IV assesses and optimizes cluster
validity based on minimization of Davies-Bouldin Rij criterion.

Fig. 3. Example of single link clustering of AE features according to NOESIS software.
Kmean clustering is used as unsupervised method for data analysis. Figure 4(a) shows the projection of correlation matrix obtained by PCA, first principal components (PC1) versus zerothprincipal components (PC0). This plot clearly shows the presence of two well-separated clusters, A and B, which correspond to 2 types of signals due to localized corrosion propagation and
SCC. The class A presents low amplitude, low AE counts, short duration (see Fig. 4(b) to 4(d))
and a central peak frequency around 130 kHz (Fig. 5). This is in accordance with several studies
reported in the literature [8-11]. Indeed, the breakdown of passive film formed on steel surface athigh alkaline pH due to chloride attack in severe load condition and the propagation of localized
corrosion, due to local acidification caused by concentrated chloride ions (0.1M) leading to Fe2+
release, does not generate sufficiently energetic AE waves.
The class B presents higher amplitude, long duration and a large signal power spectrum(<400 kHz). Indeed, the typical steps in energy relaxation during SCC crack propagation are
generally much larger than that during localized corrosion. Consequently, AE events generated
by SCC have higher amplitude. To validate this classification, a test set using k-nearest neighbor(supervised method) is applied on selected AE parameters. In the k-nearest neighbor (kNN) algorithm, the classification of a new sample is determined by the class of its k nearest neighbors.
This method gives less than 2% error and confirms the existence of two well separated classes.
Table 2 shows the feature-discriminated statistics of AE parameters. Based on AE monitoring(Fig. 2b) and microscopic observations (Fig. 6) a mean value of crack propagation Vp = da/dt of
10-8 m/s can be estimated. Here, dt is calculated taking into account the propagation time (tf – ti)determined by AE monitoring and da is estimated from fractured steel surface equal to 3.2 mm(see Fig. 6). This crack propagation value is faster than the theoretical value obtained for thissteel in air (2.3 x 10-13 m/s). This difference confirms the effect of corrosion on the deteriorationof mechanical properties of the studied material.
258

Fig. 4. Projection of AE data: (a) PC1 and PC0 axes; (b) Correlation of amplitude versus AE
counts; (c) Rise time versus amplitude and (d) 3D plot of AE counts, rise time and duration.
Table 2. Feature discriminated statistics for PCA analysis for class A and B.
AE parameters

Wilk’sRij

Tou

Amplitude
CountsDuration
Rise time

0.96470.9816
0.99780.9997

0.031
0.088
0.279
0.372

49.67520.666
7.009
4.780

According to SCC theory based on surface mobility, developed by Galvele, the crack velocity, Vp will given by:(3)
where Vp is the crack velocity in ms-1, Ds the surface self-diffusion coefficient in m²s-1, L thediffusion distance of the adatoms or vacancies in m; σ the elastic surface stress at the tip of thecrack in Nm-2, a the atom size in m; k the Boltzmann constant in JK-1; and T the temperature in
K. The measurement of surface self-diffusion coefficients, in the presence of electrolyte, has
proved to be very difficult, and no Ds values for metals in the presence of chloride medium are259

found in the literature. To find a very crude estimate of Ds, the following equation, based in thework of Gjostein and Rhead can be used:(4)

R is the gas constant (R=1.987 cal mol-1K-1), and Tm the melting point of the surface adsorbed
impurity in K (magnetite). The theoretical value of Vp is 2.3 x 10-13 m/s. Vp value obtained inchloride medium at high alkalinity of pH = 12 is between 10-7 and 10-8 ms-1 according to ourprevious studies. SCC cracking generates a significant AE activity in comparison with the localized corrosion, since the most dominate cracks process is transgranular (TG) (Fig. 7).

Fig. 5. Corresponding FFT of three types of signals recorded during the test.
260

Table 2. Feature-discriminated statistics for PCA analysis for class A and B.
AE parameters

Wilk’sRij

Amplitude
CountsDuration
Rise time

0.96470.9816
0.99780.9997

0.031
0.088
0.279
0.372

Fig. 6. Microscopic observation of fractured steel surface: (a) transverse section, (b) crack opening.

Fig. 7. Microscopic observations of fractured steel surface showing a ductile failure and transgranular (TG) cracks.
Conclusions
In laboratory, AE technique is powerful for the study of localized corrosion and stress corrosion mechanisms of prestressing strand in the presence of chloride ions at high alkaline pH. The
unsupervised clustering analysis of AE data allows the discrimination of corrosion mechanisms,
that is, localized corrosion and cracks growth due to stress corrosion.
261

The classification of clusters was made using k-nearest neighbor. Four AE discriminating
feature were found, e.g., amplitude, rise time, AE counts and duration of collected signals. The
analysis of power spectra of collected events show the presence of one peak frequency in thecase of localized corrosion, a large frequency band in the case of SCC (< 400 kHz) and in thecase of steel failure it is lower than 800 kHz. By means of AE monitoring and SEM it was possible to estimate a crack propagation rate of 10-8 m/s.
Acknowledgment
This study was carried out at MATEIS-RI2S laboratory in collaboration with the French Public Works Research Laboratory-Nantes. Authors are grateful for French Electricity (EDF) andthe National Research Agency (ANR) for their financial support.
References
1
2
3

Kovac C. Leban M. Legat A.: Electrochemica Acta, 52, 2007, 7607.
Proverbio E. Longo P.: Corrosion Science, 49, 2007, 2421.
Yuyama S. Yokoyama K. Niitani K. Ohtsu M., T. Uomoto: Construction Building Materials,
21, 2007, 491-500.
4 Colombo S. Main IG. Forde MC.: Journal of Material in Civil Engineering, 15, 2003, 280286.
5 Zejli H. Laksimi A. Tessier C. Gaillet L. Benmedakhene S.: Advanced Material Research,
13-14, 2006, 345-351.
6 Assouli B. Simescu F. Debicki G. Idrissi H.: NDT & E International, 38, 2005, 682-670.
7 Idrissi H. Limam A.: NDT & E International, 36, 2003, 563-572.
8 Fregonese M. Idrissi H. Mazille H. Renaud L. Cetre Y.: Corrosion Science, 43, 2001, 627639.
9 Ramadan S. Gaillet L. Tessier C. Idrissi H.: Applied Surface Science, 254, 2008, 2255-2262.
10 Ramadan S. Idrissi H.: Desalination, 219 (1-3), 2008, 358-366.
11 Didier-Laurent S. Idrissi H. Roue L.: Journal of Power Sources, 179, 2008, 412-416.
12 Ramadan S. Gaillet L. Tessier C. Idrissi H.: Measurement Science and Technology, 19, 2008,
115702 (9 pp).
13 Idrissi H. Ramadan S. Maghnouj J. Boulif R.: Progress in Organic Coatings, 63, 2008, 382388.

262

FLEXURAL FAILURE BEHAVIOR OF RC BEAMS WITH REBAR CORROSION AND DAMAGE EVALUATION BY ACOUSTIC EMMISSIONNOBUHIRO OKUDE1, MINORU KUNIEDA2, TOMOKI SHIOTANI3
and HIKARU NAKAMURA2
1)
Tokai Technology Center, Inokoshi, Meito, Nagoya 465-0021, Japan; 2) Dept. of Civil Engineering, Nagoya University, Furo, Chikusa, Nagoya 464-8603, Japan;
3)
Dept. of Urban Management, Kyoto University, Katsura, Nishikyo, Kyoto 615-8540, Japan
Abstract
It is important to interpret and evaluate the fracture mechanism of deteriorated concrete
structures with rebar corrosion. Monitoring by means of acoustic emission (AE) technique basedon the clarified fracture process of the deteriorated concrete is also useful for maintenancescheme. This paper presents an experimental investigation on the flexural failure behavior of RC
beams having different weight loss of 0, 5, 10 and 30% due to corrosion. AE was also monitored
during the loading tests of the deteriorated concrete beams. As corrosion levels represented byweight loss increased, load carrying capacity of the beams decreased dramatically. In the case of
the severest damage having weight loss of 30%, the rebar was eventually broken at the final
stage. The mechanical behavior was attributed to the decrease of cross section of rebar and deterioration of bond properties (debonding) between concrete and rebar with corrosion. The deterioration of bond properties also provided the decrease of number of cracks within the beams.
Regarding observation of AE activity, the number of AE events in the beams increased with increasing the corrosion levels, especially as it became more intense at the initial loading level before yielding of rebar. The bond deterioration in the beams with corrosion might occur aroundthe rebar at an early loading stage. The AE sensors attached directly onto the rebar detected more
AE signals with lower frequency than that of concrete. It was concluded that such detected AE
signals with lower frequency appear to be generated by debonding behavior due to corrosion.
Keywords: Reinforced concrete (RC), Corrosion, Bond deterioration
Introduction
Corrosion of rebar is one of the serious deterioration phenomena in concrete structures, affecting their safety. Past research works and specifications classify the deterioration processes
due to corrosion as dormant stage, initiation stage, accelerated stage and deterioration stage. For
maintenance scheme, it is important to identify the deterioration process more specifically. Previous research on AE applications to corrosion of reinforced concrete aimed at evaluating thecorrosion process of rebar, including cracking of concrete and corrosion of rebar itself [1-3].
However, there was few investigation on AE activity of corroded RC members subjected to external loading.
In this study, loading tests were conducted to clarify two main objectives: One is to interpret
fracture processes of RC beams with a rebar corrosion; the other is to characterize a detected AE
signal in RC beams with a rebar corrosion during loading tests, and also to identify the bondcharacteristics of the RC beams through AE technique.

J. Acoustic Emission, 27 (2009)263© 2009 Acoustic Emission GroupSpecimens
Table 1 gives the specified mix proportions of concrete used in the tests. The cement, fineaggregate, and coarse aggregate were ordinary Portland cement with a density of 3.15 g/cm3,
river sand with a density of 2.55 g/cm3, and river gravel (maximum size: 15 mm) with a densityof 2.57 g/cm3, respectively. The chemical admixture was an air entraining and water reducing
agent. The slump and air content of the mixed concrete were 7.5 cm and 2.2%, respectively.
Table 1. Mix proportions of concrete.

Beam specimens measuring 140 x 80 x 1460 mm were used for bending tests, as shown in
Fig. 1. A deformed rebar with a nominal diameter of 13 mm and nominal yielding strength of
345 MPa was placed in the specimen. All stirrups were wrapped with polyvinyl tape to prevent
corrosion of stirrups themselves. One specimen was fabricated for each series. After being demolded at the age of 1 day, specimens were covered with wetting-cloth, and cured in a thermostatic room at 20°C for 28 days. Compressive strength of the concrete at the age of 28 days was
22.7 MPa.

Fig. 1. Specimen configuration.

Fig. 2. Setup of accelerated corrosion tests.
264

Table 2. Weight loss of rebar after corrosion tests.

Accelerated Corrosion Tests and Results
In order to induce rebar corrosion, an accelerated corrosion test was carried out for the beamspecimens, as shown in Fig. 2. The specimens were placed on a copper plate in a chamber filledwith NaCl solution (concentration: 3%), and current of 0.6 A (0.907 mA/cm2) was applied to rebar in each specimen. Table 2 tabulates the investigated corrosion levels represented by different
weight loss of rebar. The weight loss was controlled by total applied current. Here, we used therelationship between expected weight loss and total applied current proposed by Tamori et al. [4].
Good correlation between weight loss and total applied current was observed in this test, asshown in Fig. 3.

Fig. 3. Amount of rust after corrosion tests.

Fig. 4. Crack width.
265

Figure 4 shows the longitudinal crack width measured at bottom surface that is close to arebar. Basically, crack width became wider with increasing of corrosion levels that represented
by weight loss. In severe corrosion case (10 % and 30 % cases), crack width along a specimen
axis was not so constant that it indicates that corrosion was occurred locally. Figure 5 indicatesthe diameter of rebar measured in longitudinal direction at an interval of 50 mm. Nominal diameter of the rebar used in this test was 13 mm. Significant decrease of rebar diameter was observed locally, especially in the case of severe corrosion (30%).

Fig. 5. Diameter rebar measured in longitudinal direction.
Loading Tests
Test Setup
Four-point bending tests were conducted with a constant moment length of 280 mm and total
span length of 1260 mm, as shown in Fig. 1. In this test, load and displacement at loading
points were measured by a load cell with a capacity of 100 kN (sensitivity: 33 N) and LVDT witha capacity of 50 mm (sensitivity: 0.01 mm), respectively.
Test Results
Figure 6(a) shows the measured load-displacement curves in all series. There was no significant difference in 0% and 3% cases. In other cases, however, the yielding and maximum loads
became lower with increasing corrosion levels (weight loss). Especially in the case of 30%, rebar
was finally broken. Figure 6(b) indicates the load-displacement curves up to displacement of 5mm. First cracking load of each case was similar to each other, but the tension stiffening zone,
which was equal to debonding region of rebar and concrete, was slightly different. As shown in
30% case, the corrosion of rebar gave significantly lower bond properties.
Figure 7 represents the crack patterns after the loading tests. In the case of no corrosion, morethan five cracks were observed. Decrease in the number of cracks, which normally represents
lower bond property, was observed in only 30% case. In addition, the location of broken rebar
agreed quite well with the wider crack width that was also similar to the minimum part of rebar
in diameter, as shown in Figs. 4 and 5.
266

(a) Global(b) Close up (only skeleton curves)

Fig. 6. Load-displacement curves for each specimen.

Fig. 7. Crack patterns after loading tests.
AE Measurement
Test Setup
AE signals were detected by 150-kHz resonant sensors, and amplified 34 dB with integrated
pre-amplifiers. The signals exceeding the threshold level of 54 dB were recorded by 16-channel
AE monitoring system (AMSY-5, Vallen Systeme). The detected AE signals were characterized
by several parametric features and by recorded waveforms. Four sensors were positioned on theconcrete surface of the specimen, as shown in Fig. 8. Although ten sensors were attached to thespecimen basically, it seems that several sensors frequently detected ambient noise. So only thedata of four sensors, that included less noise, was used in this study. In addition, two AE sensors
were attached to the rebar, as shown in Fig. 8.
267

Fig. 8. Location of AE sensors.

Fig. 10. AE hits detected by sensors on rebar.

Fig. 9. AE hits detected by sensors on concretesurface.

Test Results
AE hits corresponding to the number of the detected AE signals were used first for theevaluation. Figure 9 shows the AE hits at interval of 1 mm in displacement, which were detectedby the sensors attached to the concrete surface. In all cases, the number of AE hits during displacement of 1 to 4 mm was most active among loading steps. As described in theload-displacement curves in Fig. 6, yielding of rebar occur at displacement of 4 to 5 mm. Regarding the no corrosion case, the number of AE hits during displacement of 1 to 3 mm was mostactive among loading steps. As described in the load-displacement curves, first crack occurred at
displacement of about 0.5 mm, and the number of cracks increased gradually. Each crack propagated to the direction of specimen depth. This fracture mechanism agrees well with the trend of268

AE hits. By comparison among corrosion cases, the number of AE hits increased with increasing
corrosion levels as well, except for 30% case. It seems that corroded rebar induced many AE
signals due to debonding behavior of rebar, in addition to the cracking behavior. In the 30% case,the number of cracks in concrete decreased and it caused less AE activity to the specimen.
Figure 10 shows the AE hits detected by AE sensors attached to rebar directly. This figure
shows clearly that the number of AE hits increased with increasing corrosion levels. Regarding
the number of AE hits up to displacement of 3 mm, a remarkable increase of AE hits was observed in 30% case, because debonding of corroded rebar appears to have started at an earlyloading stage. Correspondingly, the number of AE hits has a strong correlation with corrosion
levels. It was also evident that the technique of using the AE sensors attached to a rebar was
useful to recognize fracture mechanism of RC beams with corrosion as well as to evaluate corrosion level.

Fig. 11. Frequency of AE signals detected by
sensors on concrete surface.

Fig. 12. Frequency of AE signals detected by
sensors on rebar.

Figure 11 shows the peak frequency obtained from averaging all of derived peak frequencies
though fast Fourier transform (FFT) of detected AE waveforms by sensor attached to the concrete surface. The AE signals from concrete surface showed no significant difference in the peakfrequency at each step in this experiment. The peak frequency appears to be constant around 150
kHz that corresponds to the resonant frequency of AE sensors used. Figure 12 shows the peakfrequency in the case of rebar-mounted sensor, obtained in the same manner as Fig. 11. This figure shows clearly that the peak frequency decreased with increasing corrosion levels. Different
crack patterns and difference of restraining condition of rebar by surrounding concrete corresponding to the corrosion level appeared to result in this behavior. It seems that AE signals withlower frequency characterized fracture at interface between concrete and corroded rebar. It was
found that AE signals detected by AE sensors on rebar provided sensitive results to detect failure
behavior with different corrosion levels.

269

Fig. 13. Raw data of peak frequency through FFT (white dots: concrete attached AE sensor andblack dot: rebar attached AE sensor). (a) No corrosion, (b) 30% corrosion.
Raw data of peak frequency through FFT are shown in Fig. 13. Two extreme cases of no
corrosion and 30% corrosion level are demonstrated as in Fig. 13(a) and (b), respectively. No
definite trends of peak frequencies of AE hits correlating to corrosion levels could be found forthe concrete-surface AE sensor (see white dots); however, a decrease of the peak frequency from
more than 150 kHz to about 100 kHz was obtained from rebar-mounted AE sensor (see black270

dots). It seems that the ratio of AE events of low frequency to those of high frequency quantitatively evaluate the rebar corrosion level.
Conclusions
In this paper, the experimental investigation on the flexural failure behavior of RC beams
having the different weight loss of 0%, 5%, 10% and 30% due to corrosion was conducted, andacoustic emission (AE) was monitored during the loading tests. Following conclusions were obtained;
1) With an increase of corrosion levels represented by weight loss, the load carrying capacity of
RC beams was decreased. In the 30% case, the rebar was broken at the final stage. The mechanical behavior significantly depends on the deterioration of bond properties between concrete and rebar with corrosion, especially in 10% and 30% cases. The deterioration of bondproperties also decreased the number of cracks within the beams.
2) Based on the AE activity, the number of AE events in the beams increased with increasing
the corrosion levels, especially at the initial loading level before the yielding of rebar. The AE
sensor directly attached on a rebar detected more AE signals with lower frequency than thatof the sensors on concrete surface. It was concluded that the detected AE signals with lower
frequency might be generated due to debonding process of corrosion. It seems that the ratioof AE events of low frequency to those of high frequency quantitatively evaluates the rebar
corrosion level.
Acknowledgement
The loading tests for the specimen with rebar corrosion were conducted as an activity of
JSCE 331 subcommittee (Chair: Prof. Shimomura). The authors would like to thank their usefulactivities and helpful discussions. The authors wish to thank Mr. Keisuke Kawamura, graduate
student of Nagoya University, for his help in experiments.
References
1) Z. Li, F. Li, A. Zdunek, E. Landis and S. P. Shah: ACI Material Journal, 95 (1) (1998), 68-76.
2) M. Ing, S. Austin and R. Lyons: Cement and Concrete Research, 35 (2005), 284-295.
3) M. Ohtsu and Y. Tomoda: ACI Material Journal, 105 (2) (2008), 194-199.
4) K. Tamori, K. Maruyama, M. Odagawa and C. Hashimoto: Proc. of the Japan Concrete Institute, 10 (2) (1988), 505-510. (in Japanese).

271

ACOUSTIC EMISSION METHOD FOR SOLVING PROBLEMS IN DOUBLE-BOTTOM STORAGE TANKSMAREK NOWAK1, IRENEUSZ BARAN1, JERZY SCHMIDT1 and KANJI ONO2
1)
Laboratory of Applied Research, Cracow University of Technology, Krakow, Poland
2)
Department of Materials Science and Engr., University of California, Los Angeles, USA
Abstract
The paper describes the examples of AE method in industry for detecting and locating leaks
in constructions of double bottom storage tanks. The tests were made on new and modernizedtanks in various test conditions and utilized different ways of defect location. It presents also
results of laboratory tests to evaluate the possibility of detecting corrosion processes in this inaccessible space of double bottom.
Keywords: Double-bottom storage tank, Leakage, Location algorithm
Introduction
Leakage problems in industrial containers and tanks have been serious issues over many
years [1]. Acoustic emission (AE) method has been applied in evaluation of tank bottoms withmuch success, especially finding the location of leakage [2-5]. There are some newer problems.
Currently in Poland and in other countries, the codes and regulations require that all new storage
tanks must have double bottoms and working tanks have to be modernized by adding the second
bottom. The second bottom should reduce the risk of environmental pollution compared to when
the inside bottom undergoes damage in old single-bottom construction. There are many different
double-bottom constructions and for assurance of free (empty) space occurrence between thebottoms, one of the materials is ribbed or there is structural mesh between bottoms. An example
of a double-bottom storage tank is shown on Fig. 1.
Different methods of monitoring the space between two bottoms exist. In some cases thespace is monitored by sensors detecting hydrocarbon in that space or by using sensors, whichmonitor the reduction of vacuum pressure. A small change of pressure is monitored. Both new aswell as modernized tanks have the leakage problem in the space between double bottom and thecorrosion problem within this space. The leakage is very often disclosed during the test before
operation. Double-bottom construction can prevent a sudden leak of the medium, but it is hard tolocate the leakage without the disassembly of the inside bottom once the leakage in outer bottom
occurs. Acoustic emission (AE) method is effective for the location of a leakage in double bottom constructions. The other problem is the corrosion in this space. This problem was brought to
us by one of our clients. At present, AE method has not resolved this and we made a series oflaboratory tests to explore the possibility of corrosion detection. Some results are given.
Leakage Location in Double-Bottom Construction
The leakage in double-bottom construction is detected on different stages of tank construction or on operation stages. In case the leakage occurs on the inside bottom, different methods
can be used to locate the defects.

J. Acoustic Emission, 27 (2009)272© 2009 Acoustic Emission GroupFig. 1. The scheme of double-bottom storage tank.
The leakage in the outer bottom requires the partial removal of the inside bottom. Therefore,
it is essential to precisely locate the defect. As an enabling method for the leakage location, theacoustic emission method was used. In some cases, such defects are disclosed during the acceptance tests when the tank is full of water. Standard tank-bottom monitoring procedures are usedin such cases. Sometimes such a tank is empty and we can set up AE monitoring on the insidebottom plate. The main issue is to narrow the potential leakage area in order to minimize cuttingof the inside bottom for repair.
We discuss examples referring to both of these cases. All the tests were done with the use ofVallen AMSY-5 AE system and sensors with resonance frequency at 30 kHz.
a) The location of the leakage in tanks filled with water
The first two cases refer to new tanks, one of them 9900-m3 capacity and 24.8-m diameter,
and the second 10000-m3 capacity and 29-m diameter. In both cases the decreases of vacuum
pressure were small, and for the 24.8-m diameter tank additionally the leakage depended on
weather conditions. The builder of the tanks carried out a number of different tests, but they did
not obtain satisfactory results. Next, AE method was used for the first time to locate the leakage
in double-bottom storage tanks. The tests were done with water inside the tanks.
The first test was done with the low level of medium in a tank and with the environment conditions that caused the insignificant decrease of vacuum. Therefore, it was impossible to locate
the position of the defect with AE method as well. Next test on this tank was done in a lower
noise condition. The observed decrease of vacuum during the test performed according to prescribed procedures was higher, and it was possible to use a lower threshold level because of low
noise. This permitted to record AE signals coming from the bottom of the tank.
The sensors were placed on the wall of the tank and were arranged the same as a standard
test of tank bottom. The standard algorithms contained in Vallen software for the location of AE
sources on tank bottom were used. This software provided the source locations and parametricand frequency analysis could be done. On this basis, the position of a potential leakage was chosen in the outer bottom of the tank. Figure 2 shows AE source locations at the tank bottom with273

an indication of leakage position at (–5.5 m, –9.5 m). The reason of the leakage was a very small
defect in a weld of the outer bottom.

Fig. 2. AE source location at the tank bottom and indicated position of leak.
The second case examined 29-m diameter tank, and the standard algorithms for source location were used and a parametric analysis of AE signals was performed. During the first test series, there were unfavorable environmental conditions and it was impossible to locate AE sourcesof the leakage. In case of the small leakage occurrence in outside bottom of the tank, the energy
value of recorded AE signals is low. Therefore, for the detection and location of the leakage inthe bottom, very good test conditions with low interference are necessary.
The next test series was performed in better weather conditions. This test series permitted us
to locate several AE sources at the storage tank bottom. The raw source location results are presented in Fig. 3. Here, green, blue and red circles indicate clusters of 5, 10 or 20 or higher AE
events. That is, four red circles represented the more active areas; one at the center and two werenear the wall.
For the analysis to choose the leakage positions in tank bottom, the experience from the test
presented earlier in this article was used. Figure 4 presents the duration versus amplitude of thelocated AE signals before and after filtering. Here, we applied a combination of filtering, including location distance to first hit, rise time of signals, first hit amplitude of events. The effect offiltering can be seen in Fig. 5 where four AE sources are located as potential positions of theleakage. Two of them at 5 and 10 o’clock positions near the wall were at the same positions as in274

Fig. 3. The other two at 2 and 7 o’clock position near the wall emerged anew, while the center
cluster in Fig. 3 disappeared.

Fig. 3. The location of AE sources on the tank bottom before filtering. Blue, green and red circles represent clusters of >5, >10 and >20 AE events.

Fig. 4. Duration vs. amplitude of located AE signals before and after filtering.
The result was presented to the builder of the tank and the defects were verified by cutting
the inside bottom and by careful inspection. In the places indicated by AE testing, welding defects were found in welds of the outer bottom, which could be the reason of the leakage.
275

Fig. 5. Located AE sources at the tank bottom after filtering.
b) The location of the leakage in tanks without medium
The next storage tank having the capacity of 10000 m3 was built 30 years ago and it wasmodernized after the evaluation of bottom condition. The new inside bottom was made but during the pressure test of the space between the bottoms the rapid loss of vacuum was found. It
indicated the appearance of a large defect but the inspection of an internal bottom did not disclose the leakage.
The AE sensors for test were arranged on the inside bottom. The very large activity of AE
signals was recorded after obtaining a vacuum target value. The amplitude of signals achieved 88
dB with 46-dB preamplifier. The use of planar location algorithm indicated two high activity
sources near sensors no. 9 and 10 as presented on Fig. 6.
The analysis of signal intensity, their amplitudes as well as signal rms value on each sensor
and the use of algorithm for linear location indicate an occurrence of high activity sources incompletely different place. It shows the linear location below (Fig. 7), the intensity of recorded
signals as well as rms value for chosen sensors (Fig. 8).
An incorrect location as well as very small intensity of the recorded signals on channels 12
and 13 at the beginning of the test resulted from overflow of these test channels, which is visible
on graphs of RMS value. Therefore the analysis of intensity and RMS value was used in this casefor the aim of locating the leakage at the outer bottom. The mechanical damage of a material
during mounting work was the cause of an occurrence of the leakage at the indicated position by
the AE method.

276

Fig. 6. Located AE sources with the use of planar location algorithm.

Fig. 7. Linear location for sensors near the tank wall.
277

Fig. 8. Intensity of recorded signals and their RMS value for chosen sensors.

Fig. 9. The source location after the modification of sensors layout.
The last case concerns the tank of 3500-m3 capacity. As in the previous tank it was modernized by building the new inside bottom. Loss of vacuum during the test was considerable but lessthan in the previous case. The AE test with sensors on inside bottom was also made. Therefore,
after the first test indicating the zone with the leakage, the layout of sensors was changed by concentrating on the chosen zone. It indicated the source location near sensor no.18 with the planar
location setup as presented on Fig. 9. The analysis of RMS parameter and hit intensity on chosensensors presented on Fig. 10 confirmed the leakage near sensor No.18. In order to obtain a moreexact location of the leakage in a leakage occurrence zone, extra sensors were added to limit thesize necessary to remove the inside bottom. In this case, recorded signal parameters provided abetter result.
Because the condition of an old bottom was inaccurately evaluated, this bottom material had
a bigger number of holes in different places. The holes were filled with dirt over the years andthey opened during the following tests; therefore one test was insufficient. Every time the use of
AE method permitted for the precise location of leakage.
278

Fig. 10. The intensity of recorded signals and their RMS value for chosen sensors.

Fig. 11. The location of AE signals on samples a) with medium between plates, b) for local contact of plates.
Corrosion Detection in the Between-Bottom Space of Double-Bottom Storage Tanks
In order to explore the detection of corrosion in the space between double bottoms, a seriesof laboratory tests were conducted. Some example results are given.
The corrosion detection by AE method requires the acoustic coupling enabling the propagation of AE signals from source to a sensor mounted on a wall. The quality of acoustic couplingon metal bottoms of the tank has the essential influence on the possibility the corrosion signal
detection.
The following variants of contact were assumed:a) the contact between the metal plates on the whole surface with a corrosive medium between them,b) the local contact of metal plates near the source of corrosion,c) the direct contact between the metal plates on the whole surface.
For these variants of contact of metal plates the tests were conducted on especially preparedsamples of metal plates simulating the construction of the double bottom. The laboratory test in279

the first variant of plate contact gives good results for detecting corrosion sources from outsidebottom. The example of AE signal location for such contact was presented on Fig. 11. For thecase of local contact of metal plates (variant b), the signals are located also locally, and theirquantity mainly depends on the intensity of the process.
In case of a direct contact between the metal plates (variant d), the essential influence on an
acoustic coupling was the surface condition and the strength of contact. According to the tests
performed, the large dispersion of recorded signals was noted depending on various conditions.
For the most unfavorable case, an acoustic coupling was inadequate to detect the corrosion.
Conclusions
The use of AE method made it possible to effectively locate the leakage in outer bottom of adouble-bottom storage tank, both in a tank filled with medium and in an empty tank with thepossibility of coming into the tank.
1. For the test with a filled tank with a small loss of vacuum, the standard location algorithms allowed to locate the leakage but required the ideal test conditions with low noise.
2. For an effective location of the leakage in the test with an empty tank and a very large
vacuum loss rate, the AE signal parametric analysis was required.
3. In case of a slower vacuum loss rate it was effective to use both the planar location algorithm and the location based on parameters of the recorded signals.
The laboratory tests performed indicated the possibility of detecting corrosion occurred in thespace between double bottoms. However, the specific conditions had to occur. Additionally it
requires the verification on real objects.
References
1. Jackson C.N., Scherlock C.N., Moore P.O., eds. Leak Testing, Nondestructive Testing Handbook, vol. 1, ASNT, (1998).
2. Nowak M., Baran I., Schmidt J.: Application of AE Technique for Detection of Leakages in
Double-Bottom Structure of Above-Ground Storage Tank, 48th AEWG, Houston, TX, USA(2005).
3. Morofuji K., Tsui N., Yamada M., Maie A., Yuyama S., Li Z.W.: Quantitative study of acoustic emission due to leaks from water tanks, J. of AE, 21, (2003) 213-222.
4. Eckert E.G., Fierro M.R., Maresca J.W. Jr.: The Acoustic Noise Environment Associated with
Leak Detection in Above-ground Storage Tanks, Materials Evaluation, 52, (1994) 954-958.
5. Lackner G., Tscheliesnig P.: Acoustic Emission Testing (AT) on Flat Bottomed Storage
Tanks: How to Condense Acquired Data to a Reliable Statement Regarding Floor Condition, J.
of AE, 20, (2002) 179-187.

280

STUDY OF IDENTIFICATION AND REMOVAL METHODFOR DROP NOISE IN AE MEASUREMENT OF TANKSHIDEYUKI NAKAMURA1, TAKAHIRO ARAKAWA2, HIRAKU KAWASAKI1,
KAZUYOSHI SEKINE3 and NAOYA KASAI4
1)
Inspection Technology Dept., Inspection Division, 2) Research & Development Center, IHI
Inspection & Instrumentation Co., Ltd., Fukuura 2-6-17, Kanazawa, Yokohama 236-0004,
Japan; 3) Center for Risk Management and Safety Sciences, 4) Dept. of Risk Management andEnvironment Sciences, Graduate School of Environment and Information Sciences, Yokohama
National University, Tokiwadai 79-5, Hodogaya, Yokohama 240-8501, Japan
Abstract
Recently, the corrosion evaluation technology of tank floors by acoustic emission (AE)method has been put to practice in Japan. However, the corrosion evaluation with the AE method
requires the judgments of the influence of various noises, and this factor decreases the accuracyof this evaluation technology. In this research, we studied the discrimination and removal
methods of noise due to condensate dropping on the surface of the stored medium. We identified
noise sources with three-dimensional source location (3D source location), verified a feature of
the waveform of the drop noise, and confirmed the effects of some removal methods for the drop
noise.
Keywords: Oil storage tank, Corrosion evaluation, Drop noise, Guard sensor
Introduction
In the AE measurement for the corrosion evaluation of tank bottoms, it is known that thenoise is generated by condensate dropping on the surface of the stored medium in the tank with
fixed roof [1]. Therefore, the AE measuring method provided by HPIS [2] uses sensors that are
installed in double rows on the wall plate. The sensors in the upper row are used as guard sensors to remove the drop noise. The effect of the noise removal greatly influences the evaluation
result in the corrosion evaluation of the tank floors, because the corrosion condition is judged
based on the numbers of AE signals. However, in the past research, the effect of the removal ofthe drop noise was not verified adequately on the basis of waveform data that were acquired on
the actual tanks. In this research, we evaluated the occurrence of the noise by analyzing acquired
data on an actual tank, and studied the effectiveness of the noise removal methods.
Experimental Procedures
The outline of the tank used in the examination is shown in Fig. 1. The diameter of this tankis 15.5 m, height is 12.2 m, the material is SS400, the bottom plate is 9 mm in thickness, and theroof is of the corn-roof type. In the sensor arrangement, eight AE sensors (30-kHz resonancetype) were arranged in double rows (height: 1 m and 1.5 m), with four sensors installed at intervals of 90° for each row and the azimuth of the sensor arranged: ch5 and ch9 at 0°, ch6 and ch10
at 90°, ch7 and ch11 at 180°, ch8 and ch12 at 270°. In addition, to confirm the influence of thenoise that originated in the wind, the anemometer was placed at the windiest position of the tanksurroundings. The operation of the tank was stopped 8 hr or more before the AE measurement
begins, and the oil level in the tank was 5.4-m high. AE testing was conducted for 1 hr with the
J. Acoustic Emission, 27 (2009)281© 2009 Acoustic Emission Grouptank at rest. The possibility of corrosion is very low for this tank because the bottom plate wasexchanged in recent past, and the minimum thickness of the bottom plate was 9.1 mm (design
thickness: 9.0 mm) in the post-test inspection.
Result of Measurement
Measurement condition
When wind velocity exceeds 4 m/s during AE measurement for the corrosion evaluation of
the tank bottom plate, noise is generated by the tank and by movement of stored medium. The
present measurement is immune from the wind noise, because of calm meteorological conditions
with the maximum wind velocity of 3.0 m/s, averaging at 0.5 m/s, as shown in Fig. 2.

Fig. 1 Equipment placement.

Fig. 2 Wind velocity during AE measurement.

Result of measurement
The data measured for 1 hr with sensors (ch5-ch8) installed on the lower row of the tankwall is shown in Fig. 3, showing a high AE activity level of 8187 (hits/channel). In Fig. 4, many
AE clusters exist on two circles. If corrosion is evaluated by this AE test results, it wrongly suggests that the corrosion activity in the tank bottom plate be very high. However, this evaluation
obviously conflicts with the result of the post-test inspection.

Fig. 3 Histories of peak amplitude (dB) and number of AE hits.
282

Fig. 4 2D Location result with ch5-ch8.

Fig. 5 3D Location result with ch5-ch12.
On the other hand, we tried the confirmation of the cause of AE by 3D location of the AE
sources with sensors on the wall plate in double rows. Many of detected signals have concentrated on the height near 5.4 m or the oil level in the tank as shown in Fig. 5. This result showsthat most of the detected signals was noise generated on the oil surface. It is also confirmed
through the design drawing of the roof that there were the ring-like parts as shown in Fig. 4 on
the inside of the roof.
Consequently, it was concluded that the noise was generated by condensate dropping fromthe rings on the inside of the roof, and falling to the surface of the stored medium. The wave283

form observation of AE located at the oil level showed features in Fig. 6. These are from the inside and outside circles on Fig. 5. These waveforms differ greatly from the AE waveform ofcorrosion shown in Fig. 7 [3], and show a wavy feature of the drop noise.

Fig. 6 Waveform of located AE on the outer and inner circles.

Fig. 7 Typical waveform of AE generated by corrosion.
Noise removal with the guard sensors
First, we studied the effect of the removal method with the guard sensor. This was an existing method and some sensors are installed in double rows on the tank wall, with the upper row
acting as the guard sensors. In commercial AE systems, all signals measured within a pre-set
time after the first hit are considered to belong to the same event. If the first signal for the eventis received with the sensor in the upper row, it can be judged that the AE signal comes from theupper part of the tank. Therefore, this method is effective in removing the drop noise generated
on the oil surface above the sensors.
284

Fig. 8 Noise removal result with the guard sensors.
In this paper, we distinguish AE “Event (Ev)” and an AE event, whose location is known; thelatter is called “Located Event (LEv)”. The result of executing the noise removal with the guard
sensors described above is shown in Fig. 8. The upper figure shows the time history of AE hit
rates after noise removal, and the lower figure shows a 2D location result with the four sensors
in the lower row. In this noise processing, it was found that 91% of AE hits was removed, while
95% of the located events was removed. Thus, the noise removal was effective. However, thevalues of 739 hits ch-1 and 179 LEv in Fig. 8 seem to be still too high from a sound tank without
corrosion.
From waveform observation of the located events that remained after removing the noise, it
was found that 46% of the located events had a feature of the drop noise shown in Fig. 9. The
waveforms of these signals have changed compared with the drop noise shown in Fig. 6 and theaverage peak amplitude was lowered to 45 dB. It appears that these signals are drop noise thatwere reflected from the bottom and wall plate. It is not possible to remove the reflected drop
noise by the noise removal method with the guard sensor. Thus, these AE signals may be judged
to come from the bottom plate of the tank, and to be due to corrosion.
The noise removal with the guard sensors is an effective technique that can remove about
90% of the drop noise. However, it is also certain that the noise cannot be removed completely.

285

Fig. 9 Waveform and amplitude of signals that remained after removing noise.

Fig. 10 Height distribution of events in AE test of a sound tank.
286

Identification and removal for drop noise by the 3D location
We studied the noise removal by 3D location because it was difficult for the noise removalwith the guard sensors to remove the drop noise that includes the reflection waves from the tankbottom plate or the wall plate. This method obtains the AE source location in 3D with the sensor
arranged in double rows, and identifies the noise by the height of the AE source.
Figure 10 shows the height distribution of located events (LEv). Numerous AE signals weregenerated around the height of 5.4 m that corresponds to the oil level in the tank. In addition, thelocated number of events (LEv) is very low at 0-m (the tank bottom plate) height. The distribution of the located events (LEv) under the bottom plate shows the existence of the drop noisethat was reflected from the bottom plate or the wall plate. In 3D locations, the events shown tobe higher than 1 m above the sensors have the possibility of being noise from the upper side.
The events located under the bottom plate were miscalculated by the influence of the reflection
wave.

Fig. 11 3D location result after removing noise.
Here, we tried to keep only the signals located within the range of ±1 m of the bottom plateas the valid signals with a comparatively small influence of the drop noise. The signals that werelocated outside the range were removed. The 3D location result of the AE source after the noise
removal processing is shown in Fig. 11. A time history of AE hits and 2D location result obtained from the data after the noise removal processing is shown in Fig. 12.
After the noise removal processing, the number of AE hits for each channel decreased to 69
hits ch-1 and the number of located events has decreased to 103 LEv. These results show that
99% of the AE hits and 97% of the located events were removed. We were able to obtain thedata that is indicative of a sound tank where the risk of corrosion was very low.
Verification of the 3D Location Noise Removal Method
Experimental procedures
In noise removal, it is important to remove the noise completely and to leave the signals forevaluating corrosion. We applied the 3D location noise removal method to a tank that corroded
severely to verify the validity of this method.
287

Fig. 12 Noise removal result with 3D locations.

Fig. 13 Equipment placement.
The tank used for the verification is 6.3 m in diameter, 4.5 m in height, the bottom plate
thickness of 6.0 mm, and 140 kl in capacity as shown in Fig. 13. This tank was removed fromthe production line and left on the soil for seven years. In the AE measurement, the tank was
filled with water to the height of 2.8 m.
288

It was confirmed that a minimum thickness reached 3.9 mm (35% of the design thickness)by the post-test inspection following AE testing. In the AE measurement, the sensor arrangement
was almost the same as Fig. 1, but six sensors (30-kHz resonance type) were placed in double
rows (height: 1 m and 2 m) at 120° apart.
Result of measurement
AE was measured for 1 hr with 3 sensors (ch1-ch3) installed in the lower row of the tankwall, as shown in Fig. 13. The data is given in Fig. 14. This measurement included little noisecaused by the wind, because the maximum wind velocity was 3.2 m/s and the average wind velocity was 1.0 m/s. The data is showing a high AE activity level, because cumulative hits per 32
seconds exceed 400 (Fig. 14(c)). This value shows that the corrosion risk is very high in theevaluation by HPIS [2]. The waveform of located AE on the corroded tank bottom plate showedfeature in Fig. 15. The waveform differs from the drop noise shown in Fig. 6. Figure 16 shows
the height distribution at AE sources obtained from the test data of 1 hr by 3D location. In thisfigure, it is shown that many AE events were obviously generated in the height near 0 m corresponding to the bottom plate. This result strongly suggests the corrosion of the bottom plate.
Therefore, it was confirmed that this method is suitable for evaluating the corrosion of the bottom plate and removing the drop noise.

Fig. 14 AE data from the corroded tank. (a) Wind velocity vs. time, (b) Amplitude vs. time, (c)AE hits per 32 sec. vs. time, (d) Location result.
Conclusion

The expectation of the AE measurement has risen as a global diagnostic technique of corrosion for the tank bottom plate. It is important to develop a proper method of removing the noise
to answer this expectation. In this research, we examined a method of removing the drop noise289

due to condensate dropping from inside roof on the surface of the stored medium. Using a new
method with 3D location, 99% of the drop noise was removed, while only 90% of the drop noise
was removed using an existing method with guard sensors. We confirmed that the new 3D location noise removal method was extremely effective with AE sensors arranged in double rows on
the wall.

Fig. 15 Typical waveform of located AE on the corroded tank bottom.

Fig. 16 Height distribution of events in AE test of corroded tank.
References
1) S. Yuyama, M. Yamada, K. Sekine, S. Kitsukawa, “HPIS Recommended Practice for Acoustic Emission Evaluation of Corrosion Damages in Bottom Plate of Above Ground Tanks”,
Progress in Acoustic Emission XIII, (2006), pp. 397-404.
2) High Pressure Institute of Japan, “Recommended Practice for Acoustic Emission Evaluationof Corrosion Damages in Bottom Plate of Oil Storage Tanks, HPIS (G 110 TR)”, May 2005.
3) H. Nakamura, T. Arakawa, T. Fukuda, “Examination of AE wave generation mechanism withcorrosion” in Proceedings of JSNDI Fall Conference (2004), pp. 119-121.
4) A. Proust, J.C. Lenain, S. Yuyama, Progress in Acoustic Emission X (2000), pp. 147-152.
5) P.T. Cole, P. Van De Loo, Acoustic Emission - Beyond the Millennium (2000), pp. 169-178.
290

A GENERIC TECHNIQUE FOR ACOUSTIC EMISSIONSOURCE LOCATIONJONATHAN J. SCHOLEY1,2, PAUL D. WILCOX2, MICHAEL R. WISNOM1,
MIKE I. FRISWELL1, MARTYN PAVIER2 and MOHAMMAD R ALIHA3
1)
Department of Aerospace Engineering, University of Bristol, Bristol, BS8 1TR, UK;
2)
Department of Mechanical Engineering, University of Bristol, Bristol, BS8 1TR, UK;
3)
Fatigue and Fracture Laboratory, Department of Mechanical Engineering, Iran University of
Science and Technology, Narmak, 16846, Tehran, Iran
Abstract
Acoustic emission (AE) source location is an essential part of any quantitative AE test as itprovides information about damage mechanisms and allows spatial separation so that signals
from unwanted sources can be eliminated. In this paper, an AE source location technique described as the best-matched point search method is presented. The application of the bestmatched point search method is demonstrated in two source location experiments: one on a large
anisotropic carbon-fibre composite (CFC) plate and one on a thick oolitic limestone disc. In thelarge composite plate test, source location is achieved using the S0 mode, which displays a complicated group velocity pattern. In the oolitic limestone experiment, three-dimensional source
location is demonstrated. The best-matched point search method successfully determines the location of AE sources in both tests. Errors in source location are attributed to the extraction ofdelta-t times from the AE signals.
Keywords: Source location, Best-matched point search method, Carbon-fibre composite, Limestone
Introduction
Acoustic emission (AE) source location is an essential part of any quantitative AE test. In
addition to confirming the spatial origin of AE signals, AE source location can be used to selectively eliminate AE signals from unwanted acoustic sources and provide useful information
about the development of damage mechanisms [1]. Further, in quantitative source characterisation experiments, AE source location is used to determine the distance between the acoustic
source and AE sensors, which is subsequently used to remove propagation effects from measured
signals [1 - 4]. The majority of reported AE source location techniques involve two independentstages which can be considered separately: the measurement of arrival times from received
waveforms and the use of these arrival times to determine the origin of the acoustic source. The
work in this paper is concerned with the latter of these two stages.
An AE source location technique described as the best-matched point search method is presented. The best-matched point search method is an approach, which builds on ideas mentioned
by Tobias [5]. The technique was first introduced in a recent conference paper [3] and is expanded upon in this paper to demonstrate its application to three-dimensional solids. The technique was originally developed to determine the origin of acoustic events in plates with complex
angular group-velocity patterns and an example of its application to a cross-ply carbon-fibre
composite (CFC) plate is given. AE source location for plates with circular or elliptical group
J. Acoustic Emission, 27 (2009)291© 2009 Acoustic Emission Groupvelocity patterns, found in composite plates with quasi-isotropic or uni-directional lay-ups respectively, can be determined analytically using algorithms proposed by Tobias [5], Paget et al.
[6] and Kurokawa et al. [7]. Some composite plates, frequently used in laboratory-based source
characterisation experiments, contain lay-ups, which lead to complicated angular group-velocity
patterns [8, 9]. Analytical two-dimensional source location in plates with complicated groupvelocity patterns is exceptionally challenging and as a result, a generic technique for determining
the location of acoustic sources on plates with any angular group-velocity pattern is highly desirable. Although some success in this area has already been achieved using iterative convergenceschemes [4, 10, 11], the authors are unaware of any literature, which describes source location
using group-velocity patterns as complicated as the S0 mode in cross-ply composite plates.
The best-match point search method is a generic source-location technique and as a second
example, the technique is used to determine the three-dimensional source location of HsuNielsen pencil-lead breaks (PLBs) in an oolitic limestone disc. The results of the AE source location testing on the oolitic limestone disc demonstrate the versatility of the technique. The practical application of the best-matched point search method and suggestions for how the technique
can be applied in specimens with more complicated geometries are discussed.
The Best-Matched Point Search Method
The best-matched point search method is a simple numerical approach for determining AE
source location. The method is broken down into two stages: point generation and point matching. In the point generation stage, the specimen geometry is represented by an array of points
with spatial location vectors r. The theoretical time, ti, taken for an elastic wave to propagate
from a point, r, to the ith sensor is given by:(1)
where si is the spatial location vector of the ith sensor and vgr is the group velocity of the elastic
wave, which is a function of the propagation direction between the sensor and point.
The unit vector, e, which describes the propagation direction between the ith sensor and apoint is given by:(2)
The difference in arrival time between two sensors (known as a delta-t time, Δt) is then calculated for each point in the array. With the exception of certain ambiguous points, discussed
later in this section, each point has a unique combination of delta-t values, which corresponds toa location on the plate. The array of delta-t values only needs to be compiled once for any given
specimen/sensor configuration:(3)
where i and j denote sensor locations.
In the point matching stage, the delta-t array is searched for the best match to the experimentally measured delta-t values. The estimated position of the source, r’, is given by:(4)292

where ∆texp are the experimentally measured delta-t values. The summation is applied for every
independent combination of delta-t values, N, which for a number of sensors, S, is given by:(5)
It should be noted that certain combinations of delta-t times are ambiguous and in these situations, a source location is not unique [3, 5]. If two points on the specimen are separated by alarge distance and have similar delta-t values, then an error in the source location can occur.
Unique combinations of delta-t values can be obtained by adding sensors to the specimen andincreasing the number of independent delta-t values. The topic of ambiguous delta-t values is
described by Tobias [5] with visual examples of the problem presented by Scholey et al. [3].
Application of the Best-Matched Point Search Method to Two-Dimensional Plates
To demonstrate the source location capability of the best-matched point search method on
anisotropic plates, a source location test was conducted on a large, cross-ply CFC plate. The
plate was constructed from uni-directional SE84HT prepreg with a lay-up [(0, 90)6]s. The plate
was 3.6-mm thick and had in-plane dimensions of 1166 x 924 mm. Experimental measurementsof the S0 group velocity were made on the plate. The measurements were made in different directions relative to the surface ply, between 0o and 90o at 10o intervals. A 2-cycle Hanningwindowed toneburst with a centre frequency of 150 kHz was used to pulse a transducer at thecentre of the plate. A second transducer, located 294 mm away, was placed at different angularlocations and the arrival time of the S0 signal used to calculate the group velocity in that direction. The delay in the equipment was measured and accounted for in the group velocity calculation. Figure 1 shows the experimental points measured. It should be noted that measurements
were only taken between 0o and 90o and that the points between 90o and 360o are only shown forcompleteness. It can be seen that the angular group-velocity pattern of the S0 mode is neither circular nor elliptical and therefore source location on this plate cannot be solved analytically using
the methods reported in the literature [5 - 7].

Fig. 1: S0-mode group velocity in different directions on a SE84HT [(0, 90)6]s plate.
293

Three AE sensors were mounted on the plate at locations (450, 350), (750, 350) and (450,
650) with units in mm. The AE sensors were manufactured from cylindrical pz-27 piezoelectricelements, with a diameter of 3 mm and a height of 3 mm. The sensors were attached to the plateusing commercial superglue. A 0.5-mm Hsu-Nielsen pencil-lead break (PLB) was used as anacoustic source at 13 different locations on the plate. Due to the high attenuation of ultrasonicwaves in the CFC plate at 150 kHz and the low excitability of the S0 mode, the locations of the
PLB sources were chosen to ensure that each sensor could measure the S0-mode signals. The received signals were amplified using Physical Acoustic Corporation (PAC) 2/4/6 amplifiers andwere received on a LeCroy 6030 Waverunner digital oscilloscope.
To obtain delta-t values, the signals were frequency-filtered with a raised cosine window centred on 150 kHz and a bandwidth of 300 kHz. The filtered signals were enveloped and the arrival
time determined using a threshold amplitude just above the noise level. Delta-t values were calculated using Eq. (3). Figure 2(a) shows the estimated source location for the 13 different points,
calculated using the mean group velocity of 5.35 mm·µs-1 (i.e., the average at all angles). With
the exception of the points near the centre of the sensor array, where the propagation directionsof all ray-paths from the source to the sensors is similar, the source location is quite poor. Figure2(b) shows the estimated source location using the measured group-velocity pattern with a point
array resolution of 2 mm. It can be seen that the estimated source locations are in good agreement with the actual source locations. Only one point, far from the centre of the sensor arrayprovides any substantial error. It should be noted that the errors in the extraction of the arrival
times from measured waveforms are automatically incorporated in these plots.

(a)

(b)

Fig. 2: Source location on the SEHT84 CP CFC plate. (a) average group velocity, (b) S0 groupvelocity profile (Sensor locations ‘o’, true PLB locations ‘•’, estimated PLB locations ‘x’).
Application of the Best-Matched Point Search Method to Three-Dimensional Solids
The best-matched point search method is a generic source location technique, which can beapplied to different types of structure. The ability of the technique to locate acoustic sources inplates with complex group-velocity patterns was demonstrated in the previous section. In thissection, the technique is used to determine the source location of PLBs in a thick oolitic lime294

stone disc. Oolitic limestone is a soft, homogenous rock, which is a composed of calcite. The
rock, widely found in the UK, is porous and beige in color.
Elastic wave propagation in the limestone disc at ultrasonic frequencies is assumed to be
dominated by bulk waves. The oolitic limestone disc is assumed to be isotropic and as a result,the propagation velocity is equal in all directions. The velocity of a longitudinal wave propagating through the oolitic limestone was determined experimentally using ASTM standard D284508 [12]. A cylindrical oolitic limestone specimen, diameter 47 mm and length 100 mm, was usedin the velocity test. A 2-cycle Hanning-windowed tone-burst with a centre frequency of 250 kHz,generated by an Agilent 33220A Arbitrary Waveform Generator, was used to pulse a piezoelectric transducer. The transducer was manufactured from a PCM51 cylindrical element with a diameter of 3 mm and a height of 3 mm. The transducer was mounted at the centre of the face on
one end of the specimen. At the opposite end of the specimen, a second PCM51 transducer wasused to receive the elastic wave energy. The received signal was amplified using a PAC 2/4/6
amplifier and recorded using a LeCroy 6030 Waverunner digital oscilloscope, which also captured the output signal from the waveform generator. Due to the high attenuation of elastic wave
energy in the oolitic limestone material at 250 kHz, the signal-to-noise ratio was improved byaveraging the received signal 10,000 times.
The time taken for the wave to propagate along the length of the specimen was taken to be
the difference in arrival times of the output signal from the waveform generator and the arrival of
the signal from the propagated wave. A system delay of 1.1 µs was determined and accounted
for in the calculation. The group velocity was estimated as 3.34 mm·µs-1. ASTM Standard
D2845-08 [12] presents a crude method for measuring the velocity of elastic waves since material attenuation and energy spreading lead to changes in the waveform shape and amplitude. In
the absence of a phase delay technique [13], an improvement in the propagation time estimation
time was sought by enveloping the signals used in the calculation. The application of the signal
envelopes gave a new propagation time and an estimated group velocity of 3.06 mm·µs-1. The
measured velocity is in the range reported for limestone material [14].
The source location test was conducted on an oolitic limestone disc. The disc was 30-mm
thick and had a diameter of 100 mm. A flat slit, width 30 mm, passed through the entire thickness of the disc. The purpose of the slit was to act as a crack initiator in an unrelated series oftests. Four AE sensors were mounted on the side of the disc on the mid-plane at regular intervals.
The AE sensors were manufactured from cylindrical pz-27 piezoelectric elements, with a diameter of 3 mm and a height of 3 mm. The sensors were attached to the plate using commercial
superglue. A 0.3-mm Hsu-Nielsen pencil-lead break (PLB) was used as an acoustic source at 17
different locations on the upper surface of the disc. The signals were amplified using PAC 2/4/6
amplifiers set at 40 dB gain and were captured on a PAC PCI-2 AE system. The arrival times ofthe signals were obtained by using a threshold crossing technique applied to the enveloped raw
signals. The threshold amplitude was set a few dB about the ambient noise level. Delta-t values
were then calculated using Eq. (2). A three-dimensional array of points with a resolution of 1
mm was established and was searched in the best-matched point search method. Since the experiment is symmetrical about the mid-plane of the disc, only one half of the disc is consideredin the point array.
Figure 3 shows the estimated source location for the 17 different points, calculated using abulk wave velocity of 3.06 mmµs-1. At each PLB location (marked ‘•’), the estimated throughthickness location of the source is also given; the range is from 0 mm (the mid-plane of the disc)295

up to 15 mm (the upper surface where the PLBs were applied). It can be seen that for all events,
the lateral location of the PLB source as estimated by the best-point search method is good. In
most cases, the best-point search technique also successfully identifies the through-thickness location of the source. However, there are large errors in the estimated through-thickness locationof the PLB source at five locations, with errors in through-thickness location of up to 15 mm.

Fig. 3: Source location results for the oolitic limestone disc experiment (Sensor locations ‘o’,true PLB locations ‘•’, estimated PLB locations ‘x’, numeric subscripts describe estimatedthrough-thickness location).
Discussion
The best-matched point search technique relies on delta-t values obtained from experimental
AE signals. In this work, arrival times were measured from enveloped RF signals using a threshold crossing technique. Threshold crossing techniques are frequently used in AE testing to determine the arrival of signals, but errors can exist in the measured arrival times, which lead toerrors in delta-t times and estimated location. A second source of error is the elastic wave velocity used to calculate the theoretical propagation times in the point array. In the absence ofknown material properties, the elastic wave velocity must be determined experimentally. In thiswork, the elastic wave velocity was determined experimentally for both specimens.
The sensitivity of the reported source location with respect to changes in delta-t value canvary and is dependent both on the position of the acoustic source and the configuration of thesensors [3]. In regions “outside” the sensor array, the location resolution decreases rapidly and as
a result, a small change in a delta-t value leads to a large change in the estimated source location.
The reduction in source location resolution exaggerates any errors in the assumed velocity and/or
experimental delta-t values. The effects of poor location resolution are seen in both tests. In the296

CFC plate test, larger errors occur away from the centre of the sensor array. In the limestone disclocation test, all of the sensors were mounted on the mid-plane of the specimen and as a result,the specimen had a poor location resolution in the through-thickness direction. Consequently, thesource location in the plane parallel to the mid-plane of the disc was excellent but the estimatedthrough-thickness location of some events was poor.
The best-matched point search method is a simple, generic technique, which can be applied
to many practical structures. The technique relies on the generation of a point array, which forsome geometries could be difficult to generate. In this work, the point arrays used for both tests
were generated in Matlab and had a regular spacing in all spatial dimensions. An example of aregularly spaced point array is shown in Fig. 4(a). More sophisticated arrays can be used. For
example, Fig. 4(b) shows an array where the spacing of the points varies on the specimen. Suchan array could be used in a test where the acoustic source is expected to occur in the centre of thespecimen, allowing improved resolution where desired without increasing the total number ofpoints that need to be searched. Further, the powerful meshing algorithms in finite element software could be used to generate the co-ordinates for the point arrays in more complex geometries"(a) Regular spacing.

(b) Variable spacing.
Fig. 4: Point arrays.

Conclusions
The best-matched point search method has been used to determine the source location of
PLBs on an anisotropic cross-ply CFC plate and in a thick oolitic limestone disc. Source locationon the anisotropic plate was determined using the S0-mode signals, which have a complicatedgroup-velocity pattern. Source location on the oolitic limestone disc gave both in-plane andthrough-thickness source location. Errors in both tests have been attributed to errors in the capture of delta-t times from the AE signals, errors in the assumed elastic wave velocity and the position of the source relative to the sensors. The applicability of non-regular point arrays has been
discussed and it has been noted that the approach may be extended to more complicated geometries using the powerful meshing algorithms in finite element software.

297

Acknowledgements
This work was supported by the UK Engineering and Physical Sciences Research Council(EPSRC) through the UK Research Centre in NDE (RCNDE) and by Airbus, Rolls-Royce andNexia Solutions. The authors are grateful to Mike Lowe of Imperial College for the loan of thelarge cross-ply CFC plate.
References[1] Scholey J.J.: PhD Thesis, University of Bristol, UK, 2008.
[2] Scholey J.J., Wilcox P.D., Lee C.K., Friswell M.I., Wisnom M.R.: Proceedings of the 27th
European Conference on Acoustic Emission Testing, September 2006, Cardiff, UK, pp. 325332.
[3] Scholey J.J., Wilcox P.D., Lee C.K., Friswell M.I., Wisnom M.R.: Proceedings of the 28th
European Conference on Acoustic Emission Testing, September 2008, Cracow, Poland, pp.
268-273.
[4] Kinjo T., Suzuki H., Takemoto M., Ono K.: Japanese Journal of Applied Physics, 36, 1997,
3281-3286.
[5] Tobias A.: Non-Destructive Testing, 9, 1976, 9-12.
[6] Paget C.A., Atherton K., O’Brien E.: Proceedings of the 4th International Workshop on
Structural Health Monitoring, 2003, Stanford, CA, pp. 363-370.
[7] Kurokawa Y., Mitzutani Y., Mayuzumi M.: Journal of Acoustic Emission, 23, 2005, 224232.
[8] Neau G., Lowe M.J.S., Deschamps M.: Quantitative Non-Destructive Evaluation, 21, 2002,
1062-1069.
[9] Neau G.: PhD Thesis, Imperial College London, UK, 2003.
[10] Toyama N., Koo J.H., Oishi R.: Journal of Materials Science Letters, 20, 2001, 1823-1825[11] Yamada H., Mizutani Y., Nishino H., Takemoto M., Ono K.: Journal of Acoustic Emission,
18, 2000, 51-60.
[12] ASTM Standard D 2845-08 – Standard Test Method for Laboratory Determination ofPulse Velocities and Ultrasonic Constants of Rock, 2008.
[13] Sachse W., Pao Y.H.: Journal of Applied Physics, 49 (8), 1978, 4320-4327.
[14] Hardy H. R.: Acoustic Emission/Microseismic Activity: Volume 1: Principles, Techniques
and Geotechnical Applications, Balkema Publisher, Netherland, 2003.

298

ACOUSTIC EMISSION TESTING – DEFINING A NEW STANDARD OF
ACOUSTIC EMISSION TESTING FOR PRESSURE VESSELS
Part 1: Quantitative and comparative performance analysis of zonal location andtriangulation methods
JOHANN CATTY
CETIM, 52 Avenue Félix Louat, 60304 Senlis Cedex, France
Abstract
For decades now, acoustic emission (AE) testing of pressure vessels has been used in France,
Europe and the rest of the world. There are several regulatory rules, codes and standards worldwide, which define the application rules of this method. Since 2004, France has officiallyadopted a Best Practices Guideline [1] used as a reference for customers and service providers toapply this technology to various pressure vessels. According to the Guideline, like several other
European (European standards) or American (ASME) codes, AE testing can be applied based on
two techniques (zonal location method and planar source location by triangulation method).
However, no comparative study of their performance, thus enabling their assessment, has been
carried out. By means of simple simulation calculations, this study highlights the significant
differences in performance between these two techniques. The effects of other fundamental parameters, for example, acquisition threshold, are also quantified with respect to AE usage. This
study may also be used as a basis for defining a new AE testing standard specifically andquantitatively defining the expected performance of a given configuration. Today, the CETIM
may apply this new testing methodology based on significant feedback enabling a greater
reproducibility and sensitivity of AE testing.
Introduction
Acoustic emission is especially useful in testing of pressure vessels. Indeed, AE enables
global and rapid testing of large structures, significantly reducing maintenance time and shutdown of facilities. Methods have changed over the last decades, moving from very traditionaland diversified methods to more standardized ones. However, some tests are still currently performed according to procedures, which have more to do with the service provider's "reputation"rather than on a proven technique. The authorities responsible for the safety of facilities inFrance requested "uniformization" of AE testing methods for pressure vessels: this led to thecreation of the Best Practices Guideline (Guide des Bonnes Pratiques – GBP [1]), which has
been officially adopted since 2004 and is used as reference for customers and service providers
for application of this technology to various pressure vessels.
Several regulatory rules, codes and standards in other parts of the world define the generalapplication rules of this technique via European standards or American ASME Boiler Codes.
These rules, like GBP, authorize AE testing according to two techniques (zonal location and planar source location by triangulation or planar method, in short). However, no comparative studyof their performance, thus enabling their assessment, has been carried out. Lack of quantitative
comparison leads to subjective assessment of performance concerning the techniques and themost cost effective, reputed "basic" solution is often selected. We have no objective answers to
the following questions:
What is the real detection capacity of an AE source using these two methods?
What is the coverage ratio of the tested structure?
J. Acoustic Emission, 27 (2009)299© 2009 Acoustic Emission GroupHow can the testing level of two different structures be compared?
By means of simple simulation calculations, this study highlights the significant differences in
performance between these two techniques. The effects of other fundamental parameters in theuse of AE, for example, acquisition threshold, are also quantified.
A. Analysis of Performance for the Zonal Location and Triangulation Methods
A.1. Definition of the case studied – context
The performance for both techniques used will be compared based on real cases, dealt in accordance with the recommendations from the Best Practices Guideline (GBP) used as regulationin France. It should be noted that several other European or American rules are not much different from the GBP and lead to similar testing configurations.
This analysis uses a specific application case. It represents several pressure vessels as regards attenuation values: it is a spherical storage tank with a 35-mm-thick wall; this wall ispainted and coated with thermal insulating material. The AE wave attenuation curve (the frequency of the AE transducers is near 200 kHz) obtained from the Hsu-Nielsen source is shown in
Fig. 1.

Fig. 1: Attenuation curve obtained on 35-mm thick, unalloyed steel, painted and covered withthermal insulating material.
Using the GBP recommendations as basis, the maximum allowed distances between sensors forthis case are:- for zonal location, the maximum authorized distance between sensors is 1.5 times [Distance at the assessment threshold = 50 dBAE maximum]; that is, in this case, approximately 1.5 x 4 = 6 m.
- for planar location case, the maximum authorized distance between sensors of a single
mesh, in the case of a maximum acquisition of 50 dBAE, is equal to the distance to the acquisition threshold + 6 dB; that is approximately 2.5 m.
A very different number of sensors would therefore be needed for the two testing configurations:a 10-m diameter spherical tank would require approximately 50 to 60 sensors in planar location
against approximately 20 sensors for zonal location.

300

What is the detection performance of each of these configurations and how can this performance be quantified?
A.2. Performance analysis – Calculation of source-sensor distances
In order to assess the two testing configurations, the detectability performance in each case isdetermined:- for zonal location, by calculating the distance separating each point of the structure fromthe closest sensor,- for planar location, by calculating the distance separating each point of the structure fromthe last sensor used for calculating the location (the 3rd sensor reached is used for thesecalculations).

Fig. 2(a) Mapping representing the distance to the closest sensor, zonal testing configuration(sensor position in red; maximum distance between sensors = 6.0 m).

Fig. 2(b). Mapping representing the distance to the closest 3rd sensor, zonal testing configuration(maximum distance between sensors = 6.0 m).
301

Fig. 2(c). Mapping representing the distance to the closest sensor, planar testing configuration (inred, sensor position; maximum distance between sensors = 2.5 m).

Fig. 2(d). Mapping representing the distance to the closest 3rd sensor, planar testing configuration(in red, sensor position; maximum distance between sensors = 2.5 m).
In conclusion, these various mappings show:- For zonal location configuration, (distance between sensors = 6.0 m), the point of thestructure that is hardest to detect with this method is situated at 3.4 m. For the same configuration, planar location requires distances to the 3rd sensor reached between 3.4 m and5.8 m.
- For planar location configuration (distance between sensors = 2.5 m), the structure pointthat is hardest to detect with this method is situated at 1.4 m. For the same configuration,
planar location requires distances to the 3rd sensor reached ranging between 1.45 m and2.45 m.

302

A.3. Performance analysis – Calculation of minimum detectable amplitudes
We will later assess what is the minimum amplitude of a detectable source for each structure
point for both location methods in order to better interpret these results and the performance differences that exist between these two testing configurations. The acquisition threshold must be
taken into account in these calculations, as it defines the minimum measurable amplitude. In thiscase, the most unfavorable case authorized by the Best Practices Guideline is used; that is to say,a 50 dBAE acquisition threshold. Figures 3(a) to 3(d) show these results:

Fig. 3(a). Mapping representing the minimum amplitude that can be detected by zonal location
method using zonal location configuration (sensor position in red; maximum distance between
sensors = 6.0 m).

Fig. 3(b). Mapping representing the minimum amplitude that can be detected by planar location
method, using zonal testing configuration (maximum distance between sensors = 6.0 m).

303

Fig. 3(c). Mapping representing the minimum amplitude that can be detected by zonal location
method using planar testing configuration (maximum distance between sensors = 2.5 m).

Fig. 3(d). Mapping representing the minimum amplitude that can be detected by planar location
method, using planar testing configuration (maximum distance between sensors = 2.5 m).
Much information can be drawn from these mappings:- Zonal testing configuration (6.0 m distance between sensors) enables detection of any AE
source with equivalent source amplitude to that of a Hsu-Nielsen source (approximately
100 dBAE initially – 98 dBAE used in the modeling).
- Zonal testing configuration only enables restricted application of planar location method
for this type of source (see Fig. 3(b)). More specifically, planar location method is only
possible on 34 % of the surface (this surface corresponds to that, for which the amplitude
calculated is less than 98 dBAE).
304

-

Planar testing configuration (2.5 m distance between sensors) obviously enables detectionof any AE source with equivalent source amplitude to that of a Hsu-Nielsen source withthe zonal location method.
- Planar testing configuration also enables application of the planar location method on theentire surface (100 %, against 34 % for zonal testing configuration).
When only these considerations are taken into account, the performance differences are therefore
relatively low. Indeed, the only difference between both configurations would simply be a loss of
66 % of the planar location surface. However, two significant parameters are not considered inthis initial comparison: all real AE sources do not necessarily generate as much energy as a Hsu
Nielsen source; furthermore, the measuring error, that it to say, assessment of its amplitude, carried out on the source is not quantified.
A.4. Performance analysis – Consideration of variable amplitude acoustic emission sources
Only the detectability of an AE source equivalent to a Hsu-Nielsen source (0.5 mm – 2H)
was considered in the previous calculations. It can be assumed that detectable AE sources in areal structure do not necessarily give off as much energy as a Hsu-Nielsen source. The detectability of a source that is X dB less than a Hsu-Nielsen source was therefore calculated for various
source amplitude values. This detectability was moreover quantified in terms of detection ratio(for zonal location), and location ratio (for planar location).
Table 1 shows the performance for both types of testing configurations on detectability(zonal) and planar location. Analysis of this table enables views for given amplitude of the detection and location capability of both testing configurations. The following elements can also bereiterated:o Amplitude source that is 2 dB less than the reference source:
For the zonal testing configuration, we observe:o A 99 % detection capabilityo A 0 % location capability
Whereas in the planar testing configuration, this same source can be:o 100 % detected ando located at 100 %o For an amplitude source that is 10 dB less than the reference source:
For the zonal testing configuration, we observe:o A 31 % detection capabilityo A 0 % location capability.
Whereas in the planar testing configuration, this same source can be:o 100 % detected ando located at 15 %.
The performance differences for both AE testing methods, which may be carried out in compliance with current rules, can therefore be quantified and qualified by this analysis. By highlighting performance differences, it becomes obvious that AE testing carried out with zonal location
method is fundamentally different from testing with the planar location method in terms of sensitivity and information quality.
Thus, when we consider for example that AE sources from the tested structure are included
in an amplitude distribution centered on 85 dBAE (amplitudes ranging between 55 and 115 dBAE),
for the zonal testing configuration we will note that:o 34.6 % of the sources can be detectedo 10.6 % of the sources can be located,
305

Table 1: Performance differences between zonal and planar testing configurations depending on
the amplitude of the acoustic emission source. The mappings in white represent the regions thatcan be detected or located, and those in black, the regions that cannot be detected or located.
Zonal Testing Configuration
Zonal Location
Planar Location
Amplitude Rate ofof source detection(dBAE)(%)

Mapping

Rate oflocation(%)

Mapping

Planar Testing Configuration
Zonal Location
Planar Location
Rate ofdetection(%)

Mapping

Rate oflocation(%)

98 (HsuNielsen
source)

100

34

100

100

97

100

6

100

100

96

99

0

100

100

95

93

0

100

100

93

72

0

100

100

90

47

0

100

73

88

31

0

100

15

85

11

0

65

0

80

4

0

23

0

75

2

0

11

0

70

5

0

65

2

0

306

MappingWhereas in the planar testing configuration:o 59.0 % of the sources can be detected
o 35.6 % of the sources can be located.
A.5. Performance analysis – Assessment of error on the observed amplitude
Detecting an AE source is the first step. However, what is the relevance of the information
gathered by the operator? How does the operator view the intensity (or amplitude) of this source?
We are here again going to rely on the modeling previously carried out to answer these two questions so as to obtain an estimation of these two factors.
The case of an amplitude distribution of AE sources centered on 85 dBAE will therefore be
used. We will assess the detection accuracy by calculating both factors:
o The total percentage of "good" assessment. For example, a source measured at 75 dBAE
whereas its initial amplitude is 95 dBAE will give an error of 21.1 %. The total percentage
is the mean calculated on a given surface for a given amplitude distribution.
o The mean error expressed in dB: This criterion is the mean of errors, expressed in dB, between the measured amplitude and the initial amplitude. Two values will be differentiated: the first incorporating errors on all AE sources, the second only incorporating errors
on the detected sources.
The following results are therefore obtained:
When, for example, we consider that the AE sources generated by the tested structure are comprised of an amplitude distribution centered on 85 dBAE (amplitudes ranging between 55 and115 dBAE) for the zonal testing configuration, we note:o An overall detection accuracy of 20.9 %o An overall amplitude measurement error of 64.7 dBAE.
o An amplitude measurement error on the AE sources detected of 28.6 dBAE.
In a planar testing configuration, we obtain:o An overall detection accuracy of 39.3 %.
o An overall amplitude measurement error of 48.9 dBAE.
o An amplitude measurement error on the AE sources detected of 26.1 dBAE.
The difference between both configurations is clearly shown and can therefore be quantified using these different criteria.
Performance of both testing configurations for the case studied may be summarized as follows:o Zonal testing configuration:- 65.4 % of the sources are not detected- 34.6 % of the sources are detected, with a mean amplitude measurement error of 28.6
dBAE
- 10.6 % of the sources can be located (included in the detected 34.6 %)o Planar testing configuration:- 41.0 % of the sources are not detected- 59.0 % of the sources are detected, with a mean amplitude measurement error of 26.1
dBAE
- 35.6 % of the sources can be located (included in the detected 59.0 %).

307

Fig. 4(a). Summary of performance for the zonal testing configuration.

Fig. 4(b). Summary of performance for the planar testing configuration.

A.6. Performance analysis – Consideration of information provided by planar location, enabling
the correction of the measured amplitude
The current AE testing practices do not fully use information provided by planar location:indeed, the rules defining acceptability criteria, for example GBP in France, are restricted to giving the criteria based on the measured amplitude, which, as was shown in the previous section,
involves a 26 dBAE error, on average (for the case studied).
When the information provided by planar location is used, that is to say, the specific positionof the AE source, the attenuation amplitude measured can be corrected and the real amplitude of
the source can be estimated. In this case, any AE source located will be measured without error(or less error). What are the overall performance gains of both testing configurations? By using
the calculations carried out in Sec. A.4 (amplitude distribution centered on 85 dBAE, amplitudes
ranging between 55 and 115 dBAE), we therefore obtain:
For zonal testing configuration:o An overall detection accuracy of 25 %.
o An overall amplitude measurement error of 60.5 dBAE.
o An amplitude measurement error on the AE sources detected of 26.5 dB.
For planar testing configuration:o An overall detection accuracy of 51.1 %.
o An overall amplitude measurement error of 37.6 dBAE.
o An amplitude measurement error on the AE sources detected of 14.7 dB.
It can be noted that using the information connected with location makes it possible to reduce
the amplitude measurement error on the detected AE sources:- Zonal testing configuration: from 28.6 dBAE to 26.5 dBAE
- Planar testing configuration: from 26.1 dBAE to 14.7 dBAE
308

We can see that full use of the planar testing configuration makes it possible to obtain an information quality that is twice better than the zonal testing configuration with a lower measuring
error.
B. Influence of Testing Parameters on Detection Performance
B.1. Influence of the acquisition threshold level
The acquisition threshold is a fundamental parameter influencing the results of an AE test.
Indeed, this value sets the minimum detectable amplitude. The rules for determining this value
are defined in the existing codes and standards and are based on the following two factors:- The acquisition threshold value must be X dB above (for example 6 dB) the backgroundnoise, so that signals considered as non-representative are not recorded,- The acquisition threshold value must be less than the "reference" amplitude value used to
calculate the activity criteria for example.
Compliance with these two rules, in most cases, implies a certain freedom in choosing the acquisition threshold. Indeed, the most frequently encountered background noise conditions may makeit possible to work with acquisition threshold levels less than the maximum authorized level.
How does this impact detection of acoustic emission sources?
The effect of lowering the acquisition threshold will be quantified using the same case as thatstudied in previous sections (where calculations were carried out with the maximum authorized
threshold, that is to say 50 dBAE). Figures 5a and 5b summarize these results: it can be noted thatthe "performance" gain obtained by lowering the threshold from 50 to 40 dBAE is significant as itenables:o For the zonal testing configuration,- the undetected source ratio decreased from 65.4 % to 33.2 %.
- the located source ratio to be increased from 10.6 % to 35 %.
o For the planar testing configuration,- the undetected source ratio decreased from 41 % to 15.1 %.
- the located source ratio increased from 35.6 % to 70.6 %.

Fig. 5. Summary of performances depending on the acquisition threshold used. (a) the zonal testing configuration. (b). the planar testing configuration.
309

B.2. Influence of the meshing type used
The meshing used in the tested structure is determined by several factors including the attenuation curve and the presence of specific structural elements. Some constraints (access forexample) may also prevent installation of the sensors in specific areas of the structure. The operator may have to use triangular, rectangular or other meshing depending on these elements. In
order to evaluate the performance of the control, the influence of the meshing geometry on thequality of detection and location must be assessed and quantified. Here, the differences between
triangular and rectangular meshing for a given case will be highlighted. Figures 6(a) and 6(b)show the mapping differences (minimum detectable amplitude): note that the topography is different. Whereas in the case of a triangular mesh the most "critical" regions are those close to sensors, they are found in the middle of segments between sensors for rectangular meshing.

Fig. 6(a). Mapping representing the minimum amplitude that can be detected by planar location
method with planar testing configuration, triangular mesh (sensor position in red; maximum distance between sensors = 2.5 m).

Fig. 6(b). Mapping representing the minimum amplitude that can be detected by planar location,
planar testing configuration, rectangular mesh (maximum distance between sensors = 2.5 m).
310

B.3. Influence of the minimum number of sensors used in the location calculations
The meshing used in the tested structure (that is to say sensor coordinates) is not the only
input data necessary for the location to be calculated. The number of sensors taken into accountin the calculation is also an important factor influencing the result obtained as regards position
and accuracy. As a rule, location algorithms (planar location) use three sensors or more by default when the information on the fourth or nth sensor is available. The operator is able to filter
calculations and can select only the calculation, which used four sensors in order to obtain better
location accuracy for example.
Increasing from three to four sensors minimum in the location calculation conditions significantly changes the results. The differences for a triangular meshing between location using atleast 3 sensors and that using at least 4 sensors will be shown here. Figure 7 in comparison to
Fig. 6a shows the mapping differences (minimum detectable amplitudes): note that the topography is different as well as the values of the minimum detectable amplitude (difference recordedin this case from 2 to 3 dBAE). Using an additional sensor in the location calculation causes a lossof location capability.

Fig. 7. Mapping representing the minimum amplitude that can be detected by planar location
method using planar testing configuration, triangular mesh using at least 4 sensors (maximum
distance between sensors = 2.5 m).
C. Defining a New AE Testing Assessment Methodology
C.1. Definition of an acoustic emission testing assessment methodology
The study carried out in the previous sections illustrates that current AE testing practices
defined and authorized by the various regulatory rules, codes and standards whether in France orelsewhere, may result in extremely varied performance levels. As a simple example, let us compare AE testing carried out in zonal configuration and with a 50 dBAE acquisition threshold withtesting using planar location configuration with a 40 dBAE threshold (amplitude distribution of
the sources centered on 85 dBAE with amplitudes ranging between 55 and 115 dBAE):
311

o Zonal testing configuration, threshold = 50 dBAE:- 65.4 % of the sources are not detected- 34.6 % of the sources are detected- 10.6 % of the sources are located (included in the 34.6 % detected)
o Planar testing configuration, threshold = 40 dBAE:- 15.1 % of the sources are not detected- 84.9 % of the sources are detected- 70.6 % of the sources are located (included in the 84.9 % detected)
How can these significant sensitivity differences be taken into account, given that there is no
possible comparison between these two testing methods!
Based on the approach developed and described in this article, we propose that any AE testing should be "assessed" in terms of location performance expressed using simple and quantitative indicators. This performance calculation will be the same as that described in Section A.5;
that is, it will involve calculating the detection and location percentage for a population of AE
sources, for example with amplitude centered on 85 dBAE (ranging between 55 and 115 dBAE).
As in the previous example, it can be proven using both simple criteria that the first configuration (Zonal testing configuration, threshold = 50 dBAE) is 5 to 7 times less efficient than the second testing configuration (Planar testing configuration, threshold = 40 dBAE).
This assessment would enable:- Firstly, quantitative comparison of AE testing performance. Instructing parties, users of
this technique as well as organization using testing results may take into account the levelof testing quality and also request a minimum level of requirements, more specifically set
by these criteria. Depending on the criticality of the tested vessel, a minimum level of requirements could be required.
- Classification criteria to be adapted depending on the performance levels of the adopted
testing configuration. Indeed, to date, no rule, standard or code defines classification rules
incorporating the testing "coverage ratio".
C.2. Changes and perspectives
The calculations performed in this study highlight that AE testing currently conducted is notadequately controlled and is therefore not used to its full potential. There is significant room for
improvement for it to be more relevant and more accurate in its diagnosis. This study moreovershows that the following factors should be used today:- Amplitude correction: it was shown in the specific case developed, that use of amplitude
correction enables the mean error as regards amplitude measurements of the detectedsources to be decreased by half from 26.1 to 14.7 dBAE. Furthermore, this amplitude correction reinforces the benefit of implementing and using the planar testing configuration
to its full potential.
- Knowledge of the less monitored or located areas: this should enable the testing configuration to be better adapted to the structure. A "critical" region may be optimally tested byinstalling sensors so that they are located in the optimum meshing area.
Conclusions
Acoustic emission is a unique, high potential testing technique as it enables fast and global
testing of large structures thus allowing operators to reduce the shutdown times for their facilities. All regulatory rules, codes and standards, which define the general application rules for this312

technique such as GBP in France, authorize use of AE according to two methods (zonal locationand planar location by triangulation). However, no comparative study of their performance, thus
enabling their assessment, has been carried out.
From the study carried out using modeling calculations, we are able to determine that the performance differences between these two authorized techniques are significant and may reach acoefficient of 5 to 7, without being considered when analyzing the information gathered; that is,
the results of the test. Furthermore, without this quantitative comparison, the "a minimal" solution is often preferred by instructing parties as it is less costly and nevertheless recognized.
We propose that any AE test should be assessed on the basis of the approach developed inthis study in terms of location performance by expressing this assessment through quantitative
criteria such as detection and location percentage of a defined population of AE sources. These
criteria, that is, the testing coverage ratio may be taken into account in analyzing recorded information to obtain a more relevant diagnosis.
Finally, the results of this study show that use of a planar testing configuration must be preferred given that it allows the measuring error levels to be significantly decreased. The impact of
the error levels on the testing result is reduced, while enabling calculation of the source amplitude.
Backed by its experience, CETIM may now use these assessment tools and carry out wellcontrolled AE testing. Nevertheless, the professional guides, standards and codes should change
so as to allow the industry to take advantage of the real potential of acoustic emission.
Reference[1] Guide to good practice for AE testing of pressure equipment, 1st Edition, May 2004. AFIAP(French Association of Pressure Equipment Engineers). Edited by SADAVE. ISBN 2-90631982-1

313

CUMULATIVE CONTENTS
J. of Acoustic Emission, Volumes 1 - 27, 1982 - 2009
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 Steel

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

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

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 REVIEW

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

R. J. Landy and K. Ono
Alan G. Beattie

P. G. Bentley and A. Plevin
Kiyoo Mogi

T. F. Drouillard

1

H. R. Hardy, Jr.

K. OnoRoger Hill

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

Page 149

Elastic Waves in Solids

Roger Hill

Number 3
Page 151

Testing Fiber Composites with Acouatic Emission Monitoring

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

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

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

Thomas F. Drouillard
2

Ronald B. Melton

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

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

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

Page 143
Page 143
Page 146
Page iPage ii

Conference and Symposia
The 25th MEETING OF AEWG
AEWG Awards
AEWG XXV
Editorial
S.L. McBrideThomas F. Drouillard
A.G. Beattie

3

P. Jax and B. Richter

M.A. Hamstad

A.G. Beattie

A.G. Beattie

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

A Note from the Editor
Number 3
Page 151

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 1Page 11

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
Determination of the Source of Acoustic Emission Generated during the Deformation of Magnesium
4Mark 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

AE Literature Thomas F. Drouillard
Conferences and Symposia
EWGAE-13
5

Page 173
Page 174
Page 117
Page 157
Page 175
Number 4
Page 176

5th Colloquium on AE D. Schumann and W. MorgnerBook Review
Kanji Ono
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

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
6

C. B. Scruby

Page iPage ii

AE Events of Interest
ASME Boiler and Pressure Vessel Code/Activities at AEWG-27

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
7

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

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
8

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

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
9

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

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 Al2O3 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
10

S329
S330
Number 4
Page 71

Advanced AE Instrumentation Concepts With Real Time Source Identification Through Correlation Plotsand Soft Ware Filtering, S. J. Vahaviolos and John M. Carlyle
Authors Index
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

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

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
11

Page iPage 6
Page 14
Page 59
Page 60
Number 2
Page 61

Appreciation K. Ono / European Perspective Roger Hill
Available Books on AE
EWGAE Provisional Form
Montreal Conference Information / Cover Photo
AE Training Courses
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

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 iFrank C. Beall

C. 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
12

Thomas M. Proctor, Jr.

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
Page 185
Page 186
Page S69
Page 155
Page 143
Page 171
Page 185
Page 151
Page 161
Page iPage ii

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
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

A. E. Lord, Jr.
R. TetiA. G. Beattie and C. W. Pretzel

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

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
13

J.M. Berthelot and J.L. RobertPage 83
Number 2
Page 85

Progress in Acoustic Emission III
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

Number 3
Page 137

Application of Acoustic Emission to the Field of Concrete Engineering

Masayasu Ohtsu

J.C. Spanner, Sr.

Brian R.A. Wood

J. R. Mitchell

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
Page 215

C. R. Heiple

The Application of Acoustic Emission Techniques in High-Temperature Oxidation Studies A. S. Khanna,
B. B. Jha and Baldev Raj
Acoustic Emission Produced by Deformation of Metals and Alloys - A Review: Part II
and S.H. Carpenter
14

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

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

Chung Chang and C. T. Sun

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

15

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

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

M.V.M.S. Rao

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
16

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

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
17

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

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

18

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

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
19

S231

"Tool Monitoring by Acoustic Emission", J. Roget, P. Souquet, M. Deschamps and N. Gsib

S236
Vessels","MONPAC - An Acoustic Emission based System for Evaluating the Structural Integrity of Metal
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
Rao

"Characterization of Failure Modes in Glass Fibre Reinforced Plastic Composites", M. N. Raghavendra
and C. R. L. Murthy

S288"Spectrum Analysis of Acoustic Emission Signals from Carbon-Glass Hybrid Composites", M. R.
Madhava and H. N. Sudheendra

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
20

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

Page 61
Page 61
Page 34
Page 20, 30, 40
Page 50, 50, 62

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)
21

Phillip T. Cole

Number 4
Page 65

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

Page 69
Page 72
Page 73

AE Literature
A Comprehensive Guide to the Literature on Acoustic Emission from Composites,
Supplement II Thomas F. Drouillard
Conferences and Symposia
Abstracts of 33rd Acoustic Emission Work Group Meeting
ASNT Spring Conference
1st International Conf. on AE in Manufacturing, AECM-4
22

Page 8Page 31
Page ii

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

Page 189

The Effect of Moisture Content and Ring Angle on the Propagation of Acoustic
Signals in Wood Stephen L. Quarles
23

Kanji Ono and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
24

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
Source Thomas 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

Pages 61-65

Maximum Curvature Method: A Technique to Estimate Kaiser-Effect Load from Acoustic Emission Data25

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

26

INDEX to Journal of Acoustic Emission, 1993-2002
Volume 11, 1993
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 PE16
T. 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
Pages 85-94

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
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
27

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
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

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
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-528

Volume 12, 1994
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

AE Literature
Pages 177-198

Trends of Recent Acoustic Emission Literature
29

Kanji Ono

D. Geisse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
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

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
30

Pages S54-59

Characterization of Thermal Cracking by Acoustic Emission Time Series Analysis
Koji Nagano, Katsuhiro Sugawara, Ken-Ichi Itakura and Kazuhiko Sato

Page 30
Pages 42-44
Page S60
Page S07

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
Pages 87-96
Pages 97-100

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
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
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
31

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
Pages 97-102
Pages 103-114
Pages 115-118
Pages 119-126

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
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 a32

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
Page 1-18
Page 19-32

Page 33-42

Page 43-52
Page 53-61
Page 63-68
Page 69-78
Page 79-87

Wideband and Narrowband Acoustic Emission Waveforms from Extraneous Sources
during Fatigue of Steel Samples
M.A. Hamstad and J.D. McColskey
Fracture-Mode Classification Using Wavelet-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

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
33

Page S70-S79
Page S80-S88
Page S90-S94

Page S95-S102

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
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

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
34

Page S115
Page S125
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
Page S317
Page S324
Page S333

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
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
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
35

Page S343

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 KrietschPage i-iii
Page iii
Page iv
Page v
Page vi-viii
Page viii
Page I-1
Page I-2

Preface
Conference Events
Theme Discussion
In Memoriam
Contents
Cover Photograph
Authors index
Selected e-mail address of authors

Volume 17, 1999
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
Page 83-93

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
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

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
36

Brian R. A. Wood, Robert W. Harris and Elizabeth L. Porter
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

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
Selected papers from “EWGAE 2000, 24th European Conference on Acoustic Emission Testing”, publishedby CETIM, Senlis, France
Page 1Page 8Page 15
Page 21

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 andthe structure of sedimentary rocks B. Zogala, and R. Dubiel
Acoustic emission/Acousto-Ultrasonic data fusion for damage evaluation in concrete
37Page 29
Page 34
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Page 51
Page 61

Page 68
Page 75
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Page 189

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 ofcomposite 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
emissionP. 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 smallpressure vessels F. Rauscher
Monitoring of weld's defects evolution submit to static and dynamic loading thanks tothe acoustic 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 H.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
38

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Page 307

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 resistancewelding (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
M. Blahacek, Z. Prevorovsky, and J. Krofta
Dynamics and damage assessment in impacted cross-ply CFRP plate utilizing thewavaform 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)

39

VOLUME 19, 2001
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 ACOUSTIC EMISSIONANALYSIS
Kenji Nagashima, Hideo Nishino, Mikio Takemoto and Kanji Ono
11
MONITORING OF MICRO-CRACKING DURING HEATING OF HYDROGEN DOPED GERMANIUM 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 GUIDED WAVE
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 EMISSION METHOD
Chiaki Kato and Kiyoshi Kiuchi
53
ACOUSTIC EMISSION MONITORING OF CHLORIDE STRESS CORROSION CRACKING OFAUSTENITIC STAINLESS STEEL
Shinya Fujimoto, Mikio Takemoto and Kanji Ono

63

CYLINDER WAVE ANALYSIS FOR AE SOURCE LOCATION AND FRACTURE DYNAMICS OF STRESSCORROSION CRACKING OF BRASS TUBE
Fukutoshi Uchida, Hideo Nishino, Mikio Takemoto and Kanji Ono
75
DETECTION OF PRE-MARTENSITIC TRANSFORMATION PHENOMENA IN AUSTENITIC STAINLESSSTEELS USING AN ACOUSTIC EMISSION TECHNIQUE
T. Inamura, S. Nagano, M. Shimojo, K. Takashima and Y. Higo
85
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
40

134

DAMAGE DIAGNOSIS OF CONCRETE-PILES BY MACHINERY-INDUCED ACOUSTIC EMISSION T.
Shiotani, S. Miwa, Y. Ichimura and M. Ohtsu
142
ACOUSTIC EMISSION MONITORING OF CLOSELY SPACED EXCAVATIONS IN AN UNDERGROUNDREPOSITORY 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 ACOUSTICEMISSION
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
DEVELOPMENT OF ABNORMALITY DETECTION TECHNOLOGY FOR ELECTRIC GENERATIONSTEAM 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 FROM DIPOLE
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 UNDER BIAXIAL
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

41

i
iii
v
vi
ix

VOLUME 20, 2002
Contents
20-00120-01620-02520-03920-06220-083

DAMAGE ESTIMATION OF CONCRETE BY AE RATE PROCESS ANALYSIS
Masayasu Ohtsu, Makoto Ichinose and Hiroshi Watanabe

1

EXTRACTION OF HISTORIES OF DAMAGE MICRO-MECHANISMS IN
UNIDIRECTIONAL COMPOSITES BY TRANSIENT-PARAMETRIC ANALYSIS
Jie Qian and Yuris Dzenis

16

ACOUSTIC EMISSION SOURCES BY ATOMISTIC SIMULATIONS
M. Landa, J. Cerv, A. Machová and Z. Rosecky

25

A WAVELET TRANSFORM APPLIED TO ACOUSTIC EMISSION
SIGNALS: PART 1: SOURCE IDENTIFICATION
M. A. Hamstad, A. O’Gallagher and J. Gary

39

A WAVELET TRANSFORM APPLIED TO ACOUSTIC EMISSION
SIGNALS: PART 2: SOURCE LOCATION
M. A. Hamstad, A. O’Gallagher and J. Gary62

DIAGNOSTICS OF REINFORCED CONCRETE BRIDGESBY ACOUSTIC EMISSION
Leszek Golaski, Pawel Gebski and Kanji Ono

83

20-099ANALYSIS AND IDENTIFICATION OF ACOUSTIC EMISSION FROM DAMAGE AND
INTERNAL FRETTING IN ADVANCED COMPOSITES UNDER FATIGUE
Yuris Dzenis and Jie Qian
99

20-108

ACOUSTIC EMISSION FROM MAGNESIUM-BASED ALLOYS
AND METAL MATRIX COMPOSITES
Frantisek Chmelík, Florian Moll, Jens Kiehn, Kristian Mathis, Pavel Lukác,
Karl-Ulrich Kainer and Terence G. Langdon

10820-129TRAINING AND CERTIFICATION ON THE FIELD OF ACOUSTIC EMISSIONTESTING (AT) IN ACCORDANCE WITH THE EUROPEAN STANDARDISATION(EN 473)
P.
Tscheliesnig
129

20-134

THRESHOLD COUNTING IN WAVELET DOMAIN
Milan Chlada and Zdenek Prevorovsky

134

20-145DAMAGE DIAGNOSIS TECHNIQUE FOR BRICK STRUCTURES USING ACOUSTICEMISSION
Takuo Shinomiya, Yasuhiro Nakanishi,
Hiroyuki Morishima and Tomoki Shiotani
145

20-153

EVALUATION OF DRYING SHRINKAGE MICROCRACKING IN CEMENTITIOUS
MATERIALS USING ACOUSTIC EMISSION
Tomoki Shiotani, Jan Bisschop and J. G. M. Van Mier
153

42

20-163

20-172

TRANSFORMATION PROCESSES IN SHAPE MEMORY ALLOYS BASED ON
MONITORING ACOUSTIC EMISSION ACTIVITY
Michal Landa, Václav Novák, Michal Blahácek and Petr Sittner

163

CHARACTERIZATION OF STRESS CORROSION CRACKING OF
CuZn-ALLOYS BY ACOUSTIC EMISSION TESTING
U.-D. Hünicke, M. Schulz, R. Budzier and J. Eberlein

172

20-179ACOUSTIC EMISSION TESTING ON FLAT-BOTTOMED STORAGE TANKS: HOW
TO CONDENSE ACQUIRED DATA TO A RELIABLE STATEMENT REGARDING
FLOOR CONDITION
G. Lackner and P. Tscheliesnig
179

20-188

ACOUSTIC EMISSION MEASUREMENTS ON SHELL STRUCTURES WITH
DIRECTLY ATTACHED PIEZO-CERAMIC
Franz Rauscher and Mulu Bayray

188

AE MONITORING OF CRYOGENIC PROPELLANT TANK
Yoshihiro Mizutani, Takayuki Shimoda, Jianmei He, Yoshiki Morino
and Souichi Mizutani

194

NON-DESTRUCTIVE TESTING FOR CORROSION MONITORINGIN CHEMICAL PLANTS
M. Winkelmans and M. Wevers

206

20-19420-20620-218AE TECHNOLOGY AS A KEY ELEMENT OF THE OPERATION SAFETY SYSTEM AT
REFINERY
B. S. Kabanov, V. P. Gomera, V. L. Sokolov, A. A. Okhotnikov and V. P. Fedorov
218

20-229STRUCTURAL INTEGRITY EVALUATION OF WIND TURBINE BLADES USINGPATTERN RECOGNITION ANALYSIS ON ACOUSTIC EMISSION DATA
A. A. Anastassopoulos, D. A. Kouroussis, V. N. Nikolaidis, A. Proust, A. G. Dutton, M. J.
Blanch, L. E. Jones, P. Vionis, D. J. Lekou, D. R. V. Van Delft, P. A. Joosse, T. P.
Philippidis, T. Kossivas and G. Fernando229

20-238LOW ALLOY STEEL METAL DUSTING: DETAILED ANALYSISBY MEANS OF ACOUSTIC EMISSION
P. J. Van De Loo, A. Wolfert,
R. Schelling, H. J. Schoorlemmer and T. M. Kooistra

238

20-248RECONSTRUCTION METHOD OF DYNAMIC FRACTURE PROCESS INSIDE THE
MATERIAL WITH THE AID OF ACOUSTIC EMISSION
Yasuhiko Mori, Yoshihiko Obata and Takateru Umeda
248

20-257

NON-DESTRUCTIVE EVALUATION OF BRAZED JOINTS BY MEANS OF ACOUSTICEMISSION
H. Traxler, W. Arnold, W. Knabl and P. Rödhammer257

20-265DAMAGE MODE IDENTIFICATION AND ANALYSIS OF COATED GAS TURBINE
MATERIALS USING A NON-DESTRUCTIVE EVALUATION TECHNIQUE Y.
Vougiouklakis, P. Hähner, V. Kostopoulos and S. Peteves265

20-274

ACOUSTIC EMISSION DURING STRUCTURE CHANGES IN SEMI-CRYSTALLINE
POLYMERS
J. Krofta, Z. Prevorovsky, M. Blahácek and M. Raab
43274

20-285

ACOUSTIC EMISSION CHARACTERISTICS OF SURFACE FRICTION IN BIOMEDICAL APPLICATION
D. Prevorovsky, Z. Prevorovsky, J. Asserin, and D. Varchon285

20-292

ACOUSTIC EMISSION DURING MONOTONIC AND CYCLIC DEFORMATION OF A
BRITTLE LIMESTONE
A. Lavrov, M. Wevers and A. Vervoort
292

20-300LEAK DETECTION BY ACOUSTIC EMISSION USING SUBSPACE METHODS
Amani Raad, Fan Zhang and Ménad Sidahmed

300

Contents20.pdf

Contents of Volume 20 (2002)

AuIndex20.pdf
AusNotes.pdf

Authors Index of Volume 20
Policy/Author’s Notes/Authors e-mail/Subscription Information

I-1 - I-3
I-4
I-5 - I-7

Ewgae02.pdf

EWGAE 2002, 25-th European Acoustic Emission Conference

10 p.

APPENDICES (Available on CD-ROM only)
Prosser.ppt
PAC Folder
Vallen Folder
AGU-Vallen

Presentation at AEWG45 by W. Prosser (NASA Langley)
Presentation at AEWG45 by T. Tamutas (Physical Acoustics Corp.)
Presentation at AEWG45 by H. Vallen (Vallen Systeme)
Wavelet Transform Freeware and Introduction by M. Hamstad

44

Volume 21, 2003
21-00121-01421-02921-05221-070
21-A01
21-095

21-11221-12021-13121-14221-14921-157
21-16621-17621-187

Identifying Acoustic Emission Sources In Aging Bridge Steel
Takao Kobayashi And Donald A. Shockey

1

Analysis Of Source Location Algorithms, Part I: Overview And Non-Iterative Methods
Maochen Ge

14

Analysis Of Source Location Algorithms, Part Ii: Iterative Methods
Maochen Ge

29

Wavelet Transform Signal Processing To Distinguish Different Acoustic Emission
SourcesK. S. Downs, M. A. Hamstad And A. O’Gallagher

52

Practical Aspects Of Acoustic Emission Source Location By A Wavelet Transform
M. A. Hamstad, K. S. Downs And A. O’Gallagher

70

Appendices: Practical Aspects Of Acoustic Emission Source Location By A Wavelet Transform
M. A. Hamstad, K. S. Downs And A. O’Gallagher

A1

Acoustic Emission Monitoring Of A High Pressure Test Of A Steel Reactor
Containment Vessel Model
A. G. Beattie

95

Micro-Cracking And Breakdown Of Kaiser Effect In Ultra High Strength Steels
Hideo Cho, Kenzo Fukaura And Kanji Ono

112

Acoustic Emission From The Fracture Of Atmospheric Rust
M. Takemoto, T. Sogabe, K. Matsuura And K. Ono

120

Acoustic Property Of Cvd-Diamond Film And Acoustic Emission Analysis
For Integrity Evaluation
R. Ikeda, Y. Hayashi And M. Takemoto

131

Evaluation Of Coated Film By Laser-Based Ae-Ut Technique
M. Enoki And T. Kusu

142

Acoustic Emission From Micro-Fracture Processes Of Bio-Ceramics In
Simulated Body Environment
Shuichi Wakayama, Teppei Kawakami, Satoshi Kobayashi,
Mamoru Aizawa And Akira Nozue

149

Corrosion Monitoring In Reinforced Concrete By Acoustic Emission
Masayasu Ohtsu And Yuichi Tomoda

157

Evaluation Of Bond Behavior Of Reinforcing Bars In Concrete Structures
By Acoustic Emission
K. Iwaki, O. Makishima, H. Tanaka, T. Shiotani And K. Ozawa

166

Development Of A Novel Optical Fiber Sensor For Ae Detection In Composites
Isamu Ohsawa, Kazuro Kageyama, Yukiya Tsuchida And Makoto Kanai

176

Acoustic Emission Evaluation Of Corrosion Damages In Buried Pipes Of Refinery
S. Yuyama And T. Nishida

187

45

21-197

21-20621-21321-22321-230New Concept Of Ae Standard: Jis Z 2342-2002 “Method For Acoustic Emission
Testing Of Pressure Vessels During Pressure Tests And Classification Of Test Results”
Y. Mori, M. Shiwa, M. Nakano And K. Iwai

197

Acoustic Emission Caused By Environmental Embrittlement Of An Al-Mg-Si Alloy
Keitaro Horikawa, Kenichi Yoshida, A. Ohmori And Kiyoshi Sakamaki

206

Quantitative Study Of Acoustic Emission Due To Leaks From Water Tanks
K. Morofuji, N. Tsui, M. Yamada, A. Maie, S. Yuyama And Z. W. Li

213

Effect Of Pinhole Shape With Divergent Exit On Ae Characteristics During Gas Leak
K. Yoshida, Y. Akematsu, K. Sakamaki And K. Horikawa

223

Operation Monitoring Of Roll Cover By Acoustic Emission
Juha Miettinen And Pekka Salmenperä

230

Contents21.Pdf
Auindex21.Pdf
Ausnotes.Pdf

Contents Of Volume 21 (2003)
Authors Index Of Volume 21
Policy/Author’s Notes/Meeting Calendar/Subscription Information

Iaes16.Pdf

Iaes16, 16-th International Acoustic Emission Symposium

I-1 - I-3
I-4
I-5 - I-7

Appendices (Available On Cd-Rom Only)
AGU-Vallen

Wavelet Transform Freeware And Introduction By M. Hamstad

Volume 22, 2004
22-S0122-001
22-A01*22-02222-03022-039The Kaiser-Effect And Its Scientific BackgroundHans Maria Tensi

S1

Modal-Based Identification Of Acoustic Emission Sources In The Presence Of Electronic Noise
M. A. Hamstad And A. O’gallagher

1

Appendix A: Details Of The Application Of The Source Identification Scheme
M. A. Hamstad And A. O’gallagher

A1

Experience With Acoustic Emission Monitoring Of New Vessels During Initial Proof Test
Phillip Cole And Stephen Gautrey

22

Quantitative Damage Estimation Of Concrete Core Based On Ae Rate Process Analysis
Masayasu Ohtsu And Tetsuya Suzuki

30

Damage Assessment In Deteriorated Railway Sub-Structures Using Ae Technique
Tomoki Shiotani, Yasuhiro Nakanishi, Xiu Luo And Hiroshi Haya3922-049

Defect Detection By Acoustic Emission Examination Of Metallic Pressure Vessels
Franz Rauscher

49

22-059Evaluation Of Acoustic Emission Signals During Monitoring Of Thick-Wall Vessels Operating At
Elevated Temperatures.
Athanasios Anastasopoulos And Apostolos Tsimogiannis
59

22-071

Acoustic Emission Technique And Potential Difference Method For Detecting The Different
Stages Of Crack Propagation In Carbon And Stainless Steels
C. Ennaceur, A. Laksimi, C. Hervé, M. Mediouni And M. Cherfaoui
4671

22-077

Detection Of Incipient Cavitation And Best Efficiency Point In A 2.2mw Centrifugal Pump Using
Acoustic Emission
L. Alfayez And D. Mba
7722-083

Investigation Of Fracture Processes Using Moment Tensor Inversion Technique
F. Finck, C. U. Grosse And H.-W. Reinhardt

83

AE Kaiser Effect And Electromagnetic Emission In The Deformation Of Rock Sample
Yasuhiko Mori, Yoshihiko Obata, Jan Pavelka, Josef Sikula And Thomas Lokajicek

91

Acoustic Emissions From Transpiring Plants– New Results And Conclusions
Ralf Laschimke, Maria Burger And Hartmut Vallen

102

Acoustic Detection Of Cavitation Events In Water Conducting Elements Of Norway Spruce
Sapwood
Sabine Rosner110

AE Monitoring From Cvd-Diamond Film Subjected To Micro-Indentation And Pulse Laser
Spallation
R. Ikeda, H. Cho, M. Takemoto And Kanji Ono

119

Composites From Piezoelectric Fibers As Sensors And Emitters For Acoustic Applications
Andreas J. Brunner, Michel Barbezat, Peter Flüeler And Christian Huber

127

Processing Of Ae Signals In Dispersive Media
Michal Blahacek, Zdenek Prevorovsky, And Michal Landa

138

Basic Principles Of Acoustic Emission Tomography
Frank Schubert

147

Acoustic Emission Behavior Of Martensitic Transformation During Deformation Of Cu-Al-Ni
Shape-Memory Alloy Single Crystals
Kenichi Yoshida, Kotaro Hanabusa And Takuo Nagamachi

159

22-09122-10222-11022-11922-12722-13822-14722-15922-166

Acoustic Emission Monitoring Of Concrete Hinge Joint Models
K. M. Holford, R. Pullin And R. J. Lark
166

22-173Characterization Of Acoustic Emission Sources In A Rock Salt Specimen Under Triaxial Load
Gerd Manthei

173

Testing Of Diamond-Like Carbon Coatings Under Slip-Rolling Friction Monitored By Acoustic
Emission
Manuel Löhr

190

Field Testing Of Flat Bottomed Storage Tanks With Acoustic Emission – A Review On The
Gained Experience
Gerald Lackner And Peter Tscheliesnig

201

Acoustic Emission Examination Of Polymer-Matrix Composites
Jürgen Bohse

208

Acoustic Emission From Rust In Stress Corrosion Cracking
Hideo Cho And Mikio Takemoto

224

22-19022-20122-20822-22422-236

Acoustic Emission Measurement System For The Orthopedic Diagnostics Of The Human Femur
And Knee Joint
R.P. Franke, P. Dörner, H.-J. Schwalbe And B. Ziegler236
4722-243
22-25322-26422-274Rods And Tubes As Ae Waveguides
Kanji Ono And Hideo Cho

243

Acoustic Emission For On-Line Monitoring Of Damage In Various Application Fields
Martine Wevers, Gert Van Dijck, Wendy Desadeleer, MarkWinkelmans And Koen Van Den Abeele

253

The Effect Of Waveguide Material And Shape On Acoustic Emission Transmission
Characteristics - Part 1: Traditional Features
Joanna Sikorska And Jie Pan

264

The Effect Of Waveguide Material And Shape On Ae Transmission Characteristics - Part 2:
Frequency And Joint-Time-Frequency Characteristics
Joanna Sikorska And Jie Pan

274

Contents22.Pdf
Contents Of Volume 22 (2004)
Auindex22.Pdf
Authors Index Of Volume 22
Ausnotes.Pdf
Policy/Author’s Notes/Meeting Calendar/Subscription Information
Berlin2004.Pdf
Activities At 2004 Ewgae Meeting;
Inmemorium.Pdf Dick Blackburn (T.F. Drouillard)Tensi.Pdf
Professor H.M. Tensi
Ewgae Folder*2004 And 2006 Ewgae Meetings, 2004 Program, 2006 Local Information

Jae Index Folder*

Cumulative Indices Of J. Of Acoustic Emission, 1982 - 2004

I-1 - I-4
I-5
I-6 - I-8
I-9
I-10
I-12

* Indicates The Availability In CD-Rom Only.

Volume 23, 2005
23-001

23-02523-03723-04723-06423-07223-081
23-091

Effects Of Noise On Lamb-Mode Acoustic-Emission
Arrival Times Determined By Wavelet Transform
M. A. Hamstad And A. O’gallagher

1

Acoustic Emission Technique For Detecting Damage
And Mechanisms Of Fracture In A Knitted Fabric Reinforced Composite
Carlos R. Rios, Steve L. Ogin, Constantina Lekakou And
K. H. Leong

25

Quantitative Damage Estimation Of Concrete Core
Based On Ae Rate Process Analysis
William Prosser, Eric Madaras, George Studor, And
Michael Gorman

37

Moment Tensors Of In-Plane Waves Analyzed By
Sigma-2D
Masayasu Ohtsu, Kentaro Ohno And Marvin A. Hamstad

47

Development Of An Optical Micro AE Sensor With An Automatic Tuning System
Hiroshi Asanuma, Hironobu Ohishi, And Hiroaki Niitsuma

64

Development Of Stabilized And High Sensitive OpticalFiber Acoustic Emission System And Its Application
Hideo Cho, Ryouhei Arai And Mikio Takemoto

72

High Precision Geophone Calibration
Masahiro Kamata

81

Development Of Heat-Resistant Optical Fiber Ae Sensor
48

23-096

23-102

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

23-189Pornthep Chivavibul, Hiroyuki Fukutomi,
Shin Takahashi And Yuichi Machijima

91

Damage Detection System For Structures With
Smart Ae Sensors
Takahito Yanase And Sei Ikegaya

96

Hierarchical Fracture Process In Brittle Rocks By
Means Of High-Speed Monitoring Of Ae Hypocenter
Xinglin Lei, Osamu Nishizawa, Andre Moura And
Takashi Satoh102

Measurement Of Hydraulically Activated Subsurface Fracture System In Geothermal Reservoir
By Using Acoustic Emission Multiplet-Clustering Analysis
Hirokazu Moriya, Hiroaki Niitsuma And Roy Baria

113

A Modeling Method On Fractal Distribution Of
Cracks In Rocks Using Ae Monitoring
Yoshinori Watanabe, Ken-Ichi Itakura, Kazuhiko Sato,
Yoshiaki Fujii, Rao Balusu, Hua Guo And Xun Luo

119

Interpretation Of Reservoir Creation Process At
Cooper Basin, Australia By Acoustic Emission
Yusuke Kumano, Hirokazu Moriya, Hiroshi Asanuma, Nobukazu Soma, Hideshi Kaieda,
Kazuhiko Tezuka,
Doone Wyborn And Hiroaki Niitsuma

129

Micromechanics Of Corrosion Cracking In Concrete
By Ae-Sigma
Farid A. K. M. Uddin And Masayasu Ohtsu

136

Evaluation Of Parameter Dependencies Of Ae Accompanying Sliding Along A Rough
Simulated Fracture
Katsumi Nemoto, Hirokazu Moriya And Hiroaki Niitsuma

142

Ae Characterization Of Thermal Shock Crack Growth Behavior In Alumina Ceramics By DiscOn-Rod Test
Huichi Wakayama, Satoshi Kobayashi And Toshiya Wada

150

Fatigue Damage Progression In Plastics During Cyclic Ball Indentation
Akio Yonezu, Takayasu Hirakawa, Takeshi Ogawa
And Mikio Takemoto

156

Evaluation Of Fatigue Damage For Frm With Ae Method
Masanori Takuma And Noboru Shinke164

Acoustic Emission Behavior Of Failure Processes Of
Glass-Fiber Laminates Under Complex State Of Loading
Jerzy Schmidt, Ireneusz Baran And Kanji Ono

173

Rolling Contact Fatigue Damage Of WC-Co Cermet
Sprayed Coating And Its Ae Analysis
Junichi Uchida, Takeshi Ogawa, Mikio Takemoto
Yoshifumi Kobayashi And Yoshio Harada

181

Boron Effects On Ae Event Rate Peaks During Tensile
Deformation Of Ni3al Intermetallic Compound
K. Yoshida, Y. Masui, T. Nagamachi And H. Nishino

189

49

23-196

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

23-27723-28523-29223-29923-310Ae And Electrochemical Noise Analysis For Fracture
Study Of Hard Surface Film
Akio Yonezu, Hideo Cho, Takeshi Ogawa And Mikio Takemoto

196

The Origin Of Continuous Emissions
Kanji Ono, Hideo Cho And M. Takuma

206

Precursor Of Hydroigen Induced Glass Lining Chipping
By Ae Monitoring
Kohei Murakami And Mikio Takemoto

215

Real-Time Executing Source Location System
Applicable To Anisotropic Thin Structures
Yu Kurokawa, Yoshihiro Mizutani And Masami Mayuzumi

224

Investigation On Ae Signal/Noise Processing
In Corrosion Damage Evaluation Of Tank Bottom
Zhengwang Li , Shigenori Yuyama, Minoru Yamada,Kazuyoshi Sekine, Shigeo Kitsukawa, Hiroaki Maruyama
And Shigeo Konno

233

Examination of AE Wave Propagation Routes
In A Small Model Tank
Hideyuki Nakamura, Takahiro Arakawa, Minoru Yamada

243

Integrity Evaluation Of Glass-Fiber Reinforced
Plastic Vessels By Lamb Wave Ae Analysis
Takashi Futatsugi

249

Evaluation Of Reinforcement In Damaged Railway
Concrete Piers By Means Of Acoustic Emission
Tomoki Shiotani, Yasuhiro Nakanishi, Keisuke Iwaki,
Xiu Luo Hiroshi Haya

260

Water-Leak Evaluation Of Existing Pipeline
By Acoustic Emission
Tetsuya Suzuki, Yukifumi Ikeda, Yuichi Tomoda And Masayasu Ohtsu

272

Acoustic Emission For Fatigue Damage Detection Of
Stainless Steel Bellows
Koji Kagayama, Akio Yonezu, Hideo Cho,
Takeshi Ogawa, And Mikio Takemoto

277

Plastic Region Bolt Tightening Controlled By
Acoustic Emission Monitoring
Tadashi Onishi, Yoshihiro Mizutani And Masami Mayuzumi

285

Acoustic Emission Behaviors Of Recovery For Mg
Alloy At Room Temperature
Y. P. Li And M. Enoki

292

An Acoustic Emission Test System For Airline Steel
Oxygen Cylinders: System Design And Test Program
Alan G. Beattie

299

Development Of In-Situ Monitoring System For
Sintering Of Ceramics Using Laser Ae Technique
S. Nishinoiri And M. Enoki

310

50

23-318

Pattern Recognition Techniques For Acoustic Emission Based Condition Assessment Of Unfired
Pressure Vessels
Athanasios Anastasopoulos
318

23-331The Acoustic Emission Halon 1301 Fire Extinguisher Bottle Tester: Results Of Tests On 649
Bottles
Alan G. Beattie And D. D. Thornton

Contents23.Pdf
Contents Of Volume 23 (2005)
Auindex23.Pdf
Authors Index Of Volume 23
Aueaddress.Pdf Authors E-Mail Addresses
I-6
Ausnotes.Pdf
Policy/Author’s Notes/Meeting Calendar/
Subscription InformationI-7 - I-9
Jae Index Folder*

I-1 - I-4
I-5

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

* Indicates The Availability In CD-Rom Only.
Volume 24, 2006
24-001

A Variable Velocity Approach To Locate Fatigue-Induced Microcracks Occurred In Structures With
Multiple Material Layers
Jihui Li And Gang Qi
124-012

Lamb-Wave Acoustic Emission For Condition Monitoring Of Tank Bottom Plates
Mikio Takemoto, Hideo Cho And Hiroaki Suzuki

12

Wavelet Transform Analysis Of Experimental Ae Waveforms On Steel Pressure Vessel
Mulu Bayray And Franz Rauscher

22

Acoustic Emission Pattern Recognition Analysis Applied To The Over-Strained Pipes In A
Polyethylene Reactor
Ireneusz Baran, Marek Nowak And Kanji Ono

44

24-02224-04424-052

Acoustic Emission Evaluation Systems Of Tool Life Forshearing Of Piano And Stainless Steel Wires
Masanori Takuma, Noboru Shinke, Takako Nishiura And Kensuke Akamatu
52

24-067

Optical Fiber System For Ae Monitoring Of High Temperature Damage Of Stainless Steel Tubing
Tomoharu Hayano, Takuma Matsuo, Hideo Cho And Mikio Takemoto
67

24-076 Development Of Measurement System Using Optical Fiber Ae Sensors For Actual Piping
Satoshi Nishinoiri, Pornthep Chivavibul, Hiroyuki
Fukutomi And Takashi Ogata
24-08476

Utilization Of Cascade Optical Fiber Ae System For Source Location Of Lamb Waves Through A
Cross-Ply Cfrp Plate
Takuma Matsuo, Hideo Cho And Mikio Takemoto84

24-097 Elastic Waves From Fast Heavy-Ion Irradiation On Solids
Tadashi Kambara, Yasuyuki Kanai, Takao M. Kojima, Yoichi
Nakai, Akira Yoneda, Yasunori Yamazaki And Kensuke Kageyama
9724-10424-111

Acoustic Emission Rate Behavior Of Laminated Wood Specimens Under Tensile Loading
Andreas J. Brunner, Martin T. Howald And Peter Niemz
Ae Measurements For Superconducting Devices
51

104

331

Kazuaki Arai, Katsuyuki Kaiho, Hiroshi Yamaguchi,
Hirofumi Yamasaki, Akira Ninomiya, Takeshi Ishigohka,
Katsutoshi Takano, Hideo Nakajima And Kiyoshi Okuno

11124-119

Acoustic Emission Of Sensitized 304 Stainless Steel With Simultaneous Hydrogen Charging
119
S. H. Carpenter, Kanji Ono And D. Armentrout

24-127

Ae And Corrosion Potential Fluctuation (Cpf) For Environmental Assisted Fracture
Koji Kagayama, Takeshi Ogawa, Akio Yonezu, Hideo Cho And
Mikio Takemoto

127

Damage Evaluation By Frequency Analysis Of Continuous Recorded Ae Waveform
Kaita Ito And Manabu Enoki

139

Frequency Filtering Algorithms Of Plate Wave Ae For Source Location
Yu Kurokawa, Yoshihiro Mizutani And Masami Mayuzumi

14524-13924-14524-153

Evaluation Of Two Types Of Martensitic Transformation In Cu-Al-Ni Shape Memory Alloy Single
Crystal By Acoustic Emission Waveform Analysis
Takeshi Yasuda, Daiki Tani, Hideo Nishino And Kenichi Yoshida153

24-161

Fatigue Fracture Dynamics Of High Strength Steel Studied By Acoustic Emission Technique
Akio Yonezu, Takeshi Ogawa And Mikio Takemoto
161

24-173

Quantitative Detection Of Microcracks In Bioceramics By Acoustic Emission Source
Characterization
Shuichi Wakayama, Takehiko Jibiki And Junji Ikeda

173

24-179Determination Of Wave Attenuation In Rock Salt In The Frequency Range 1 - 100 Khz Using
Located Acoustic Emission Events
Gerd Manthei, Jürgen Eisenblätter And Thomas Spies
179

24-187

Acoustic Emission Behavior Of Prestressed Concrete Girder During Proof Loading
Leszek Gołaski, Grzegorz Swit, Małgorzata Kalicka
And Kanji Ono

187

Multiplet Analysis For Estimation Of Structures Inside An Ae Cloud Associated With A
Compression Test Of A Salt Rock Specimen
Hirokazu Moriya, Gerd Manthei, Hiroaki Niitsuma And
Jürgen Eisenblätter

196

24-19624-205

Damage Diagnosis Of Railway Concrete Structures By Means Of One-Dimensional Ae Sources
Tomoki Shiotani, Xiu Luo And Hiroshi Haya
205

24-215Charactaristics Of Damage And Fracture Process Of Solid Oxide Fuel Cells Under Simulated
Operating Conditions By Using Ae Method
Kazuhisa Sato, Toshiyuki Hashida, Hiroo Yugami,
Keiji Yashiro, Tatsuya Kawada And Junichiro Mizusaki
215

24-222

Acoustic Emission Detection Of Damage Evolution In Short-Fiber Composites
Jerzy Schmidt, Ireneusz Baran, Marek Nowak And Kanji Ono

222

Ae Monitoring Of Microdamage During Proof Test Of Bioceramics For Artificial Joints
Shuichi Wakayama, Chikako Ikeda And Junji Ikeda

228

24-22824-234

Small Diameter Waveguide For Wideband Acoustic Emission
M. A. Hamstad
52

234

Contents24.pdf
AUindex24.pdf
AusNotes.pdf

Contents of Volume 24 (2006)I-1, -2, -3Authors Index of Volume 24 I-4, -5
Policy/Author’s Notes/Meeting Calendar/Subscription Information

JAE Index Folder*

I-6 - I-8

Cumulative Indices of J. of Acoustic Emission, 1982 - 2006

* indicates the availability in CD-ROM only.

Volume 25, 2007
25-001STRUCTURAL INTEGRITY EVALUATION USING ACOUSTIC EMISSION
KANJI ONO

25-021

ACOUSTIC EMISSION TECHNIQUES STANDARDIZED FOR CONCRETESTRUCTURES
MASAYASU OHTSU, TOSHIRO ISODA and YUICHI TOMODA

25-033

ACOUSTIC EMISSION MONITORING OF REINFORCED CONCRETE
FRAME DURING SEISMIC LOADING
A. ANASTASOPOULOS, S. BOUSIAS and T. TOUTOUNTZAKIS

25-042

ACOUSTIC EMISSION LEAK TESTING OF PIPES FOR PRESSURIZEDGAS USING ACTIVE FIBER COMPOSITE ELEMENTS AS SENSORSANDREAS J. BRUNNER and MICHEL BARBEZAT

25-051

ACOUSTIC EMISSION TECHNIQUE APPLIED FOR MONITORING AND
INSPECTION OF CEMENTITIOUS STRUCTURES ENCAPSULATINGALUMINIUM
L. M. SPASOVA, M. I. OJOVAN and C. R. SCALES

25-069EVALUATION OF REPAIR EFFECT FOR DETERIORATED CONCRETE
PIERS OF INTAKE DAM USING AE ACTIVITYTOMOKI SHIOTANI and DIMITRIOS G. AGGELIS

25-080

ACOUSTIC EMISSION MONITORING OF FLEXURALLY LOADEDARAMID/EPOXY COMPOSITES BY EMBEDDED PVDF SENSORSCLAUDIO CANEVA, IGOR MARIA DE ROSA and FABRIZIO SARASINI

25-092

ACOUSTIC EMISSION SIGNALS GENERATED BY MONOPOLE (PENCILLEAD BREAK) VERSUS DIPOLE SOURCES: FINITE ELEMENT MODELING AND
EXPERIMENTS
M. A. HAMSTAD

25-107

HIGH-TEMPERATURE ACOUSTIC EMISSION SENSING USINGALUMINUM NITRIDE SENSORHIROAKI NOMA, TATSUO TABARU, MORITO AKIYAMA, NORIKOMIYOSHI, TOMOHARU HAYANO and HIDEO CHO

25-115DAMPING, NOISE, AND IN-PLANE RESPONSE OF MEMS ACOUSTICEMISSION SENSORSAMELIA P. WRIGHT, WEI WU, IRVING J. OPPENHEIM and DAVID W.
GREVE

25-124IMMERSION-TYPE QUADRIDIRECTIONAL OPTICAL FIBERAE SENSOR FOR LIQUID-BORNE AE
TAKUMA MATSUO, HIDEO CHO, TAKESHI OGAWA and MIKIOTAKEMOTO
53

25-132

A SIMPLE METHOD TO COMPARE THE SENSITIVITY OF DIFFERENTAE SENSORS FOR TANK FLOOR TESTING
HARTMUT VALLEN, JOCHEN VALLEN and JENS FORKER

25-140DAMAGE IN CARBON FIBRE COMPOSITES: THE DISCRIMINATION OF
ACOUSTIC EMISSION SIGNALS USING FREQUENCYMARK EATON, KAREN HOLFORD, CAROL FEATHERSTON and RHYS
PULLIN

25-149CHARACTERISTICS OFACOUSTIC EMISSIONS FROM DEHYDRATINGWOOD RELATED TO SHRINKAGE PROCESSESSABINE ROSNER

25-157CHARACTERIZATION OF TITANIUM HYDRIDES USING A HYBRIDTECHNIQUE OF AE AND FEM DURING INDENTATION TESTYOSHIHIRO TANIYAMA, HIDEO CHO, MIKIO TAKEMOTO and GEN NAKAYAMA

25-166ANALYSIS OF THE HYDROGEN DEGRADATION OF LOW-ALLOYSTEEL BY ACOUSTIC EMISSIONKRYSTIAN PARADOWSKI, WOJCIECH SPYCHALSKI, KRYSTYNALUBLINSKA and KRZYSZTOF J. KURZYDLOWSKI

25-172

HYDROGEN RELATED BRITTLE CRACKING OF METASTABLE TYPE304 STAINLESS STEELHIDEO CHO and MIKIO TAKEMOTO

25-179ANALYSIS OF ACOUSTIC EMISSION FROM IMPACT AND FRACTURE
OF CFRP LAMINATESKANJI ONO, YOSHIHIRO MIZUTANI AND MIKIO TAKEMOTO

25-187NEURAL NETWORK BURST PRESSURE PREDICTION IN COMPOSITEOVERWRAPPED PRESSURE VESSELSERIC v. K. HILL, SETH-ANDREW T. DION, JUSTIN O. KARL, NICHOLAS
S. SPIVEY and JAMES L. WALKER II

25-194

ACOUSTIC EMISSION SOURCE LOCATION IN A THICK STEEL PLATEBY LAMB MODES
M. A. HAMSTAD

25-215NOVEL ACOUSTIC EMISSION SOURCE LOCATION
RHYS PULLIN, MATTHEW BAXTER, MARK EATON, KAREN HOLFORD and
SAM EVANS

25-224

ACOUSTIC EMISSION SOURCE LOCATION ON AN ARBITRARYSURFACE BY GEODESIC CURVE EVOLUTION
G. PRASANNA, M. R. BHAT and C. R. L. MURTHY

25-231PROBABILITY OF DETECTION FOR ACOUSTIC EMISSIONADRIAN POLLOCK

25-238PLASTIC-REGION TIGHTENING OF BOLTS CONTROLLED BY
ACOUSTIC EMISSION METHODYOSHIHIRO MIZUTANI, TADASHI ONISHI and MASAMI MAYUZUMI

25-247

REAL-TIME DENOISING OF AE SIGNALS BY SHORT TIME FOURIER
TRANSFORM AND WAVELET TRANSFORM
54

KAITA ITO and MANABU ENOKI
25-252

MONITORING THE EVOLUTION OF INDIVIDUAL AE SOURCES INCYCLICALLY LOADED FRP COMPOSITESRUNAR UNNTHORSSON,
THOMAS P. RUNARSSON AND MAGNUS T. JONSSON

25-260ON USING AE-HIT PATTERNS FOR MONITORING CYCLICALLYLOADED CFRPRUNAR UNNTHORSSON, THOMAS P. RUNARSSON
and MAGNUS T. JONSSON

25-267

AE MONITORING OF SOIL CORROSION OF BURIED PIPEHIDEO CHO and MIKIO TAKEMOTO

25-276

THIRTY YEARS EXPERIENCE OF INDUSTRIAL APPLICATIONS OF
ACOUSTIC EMISSION TESTING AT TÜV AUSTRIAPETER TSCHELIESNIG

25-286

ACOUSTIC EMISSION TESTING OF SEAM-WELDED HIGH ENERGY PIPINGSYSTEMS IN FOSSIL POWER PLANTSJOHN M. RODGERS

25-294

ACOUSTIC EMISSION AND X-RAY TOMOGRAPHY IMAGING OF SHEAR
FRACTURE FORMATION IN CONCRETE
TATYANA KATSAGA and R. PAUL YOUNG

25-308

GLOBAL MONITORING OF CONCRETE BRIDGE USING ACOUSTICEMISSION
T. SHIOTANI, D. G. AGGELIS and O. MAKISHIMA

25-316

DEMAND ON FLEXURAL TENSION STEEL REINFORCEMENTANCHORAGE ZONES IN FULL-SCALE BRIDGE BENT CAPSQUANTIFIED BY MEANS OF ACOUSTIC EMISSION
THOMAS SCHUMACHER, CHRISTOPHER HIGGINS, STEVEN GLASER
and CHRISTIAN GROSSE

25-324DAMAGE EVALUATION OF POST-TENSIONED CONCRETE VIADUCTBY AE DURING PROOF LOADINGEDOARDO PROVERBIO,
GIUSEPPE CAMPANELLA and VINCENZO VENTURI

25-331EARLY FAULT DETECTION AT GEAR UNITS BY ACOUSTIC EMISSION
AND WAVELET ANALYSISCHRISTIAN SCHEER, WILFRIED REIMCHE
and FRIEDRICH-WILHELM BACH

25-341APPLICATION OF ACOUSTIC EMISSION IN MONITORING OF FAILURE
IN SLIDE BEARINGSIRENEUSZ BARAN, MAREK NOWAK and WOJCIECH DARSKI

25-348

MAPPING OF WHEEL FLANGE RUBBING ON RAIL USING AE:
LABORATORY TEST
N. A. THAKKAR, R. L. REUBEN and J. A. STEEL

25-355DAMAGE ASSESSMENT OF GEARBOX OPERATING IN HIGH NOISYENVIRONMENT USING WAVEFORM STREAMING APPROACH
DIDEM OZEVIN, JASON DONG, VALERY GODINEZ and MARK CARLOS

25-364

CLUSTERING ANALYSIS OF AE IN ROCK
N. IVERSON, C-S. KAO and J.F. LABUZ
55

25-373

IMPACT IMAGING METHOD TO MAP DAMAGE IN CONCRETE BRIDGEDECK SLABSSTEPHEN D. BUTT, VIDYADHAR LIMAYE and JOHN P. NEWHOOK

Contents25
AUindex25
AusNotes
In Memoriam
EWGAE27

Contents of Volume 25 (2007)I-1 – I-4
Authors Index of Volume 25 I-5 – I-7
Policy/Author’s Notes/Meeting Calendar/Subscription Information
Professor Reginald Hardy, Jr. I-11
Note on expanded contributions from Cardiff Conference.

JAE Index Folder*

I-8 – I-10
I-12

Cumulative Indices of J. of Acoustic Emission, 1982 - 2007

* indicates the availability in CD-ROM only.
Cover photograph is from 25-294 by TATYANA KATSAGA and R. PAUL YOUNG.

Volume 26 (2008)
26-001

Acoustic Emission Investigation of Coating Fracture and Delamination in
Hybrid Carbon Fiber Reinforced Plastic Structures
Markus G. R. Sause, Daniel Schultheiß and Siegfried Horn

1-13

26-014

Acoustic Emissions Related to the Dehydration Stress Behavior of Green Norway Spruce Wood
Sabine Rosner, Bo Karlsson, Johannes Konnerth and Christian Hansmann
14-22

26-023

Investigation of the Z-Direction Strength Properties of Paper by Use of Acoustic Emission Monitoring
S. Norgren, P. A. Gradin, S. Nyström and M. Gullikson
23-31

26-032

Assessment of Stress Corrosion Cracking in Prestressing Strands Using AE Technique
Marianne Perrin, Laurent Gaillet, Christian Tessier and Hassane Idrissi

32-39

Comparison of Wavelet Transform and Choi-Williams Distribution to Determine Group
Velocities for Different Acoustic Emission Sensors
M. A. Hamstad

40-59

A Comparison of AE Sensor Calibration Methods
Jiri Keprt and Petr Benes

60-71

Experimental Transfer Functions of Acoustic Emission Sensors
Kanji Ono, Hideo Cho and Takuma Matsuo

72-90

Couplants and Their Influence on AE Sensor Sensitivity
Pete Theobald, Bajram Zeqiri and Janine Avison

91-97

26-04026-06026-072
26-091
26-098Laboratory Experiments for Assessing the Detectability of Specific Defects by Acoustic Emission
TestingFranz Rauscher
98-10826-109Integrity Evaluation of COPVs by Means of Acoustic Emission Testing
Yoshihiro Mizutani, Kouki Saiga, Hideyuki Nakamura, Nobuhito Takizawa, Takahiro Arakawa
and Akira Todoroki
109-11926-120

Structural Integrity Evaluation of CNG Composite Cylinders by Acoustic Emission Monitoring
Olivier Skawinski, Patrice Hulot, Christophe Binétruy and Christian Rasche
120-13126-132

Automated Method for Statistical Processing of AE Testing Data
56

V. A. Barat and A. L. Alyakritskiy

132-141

26-142

Termites Detection via Spectral Kurtosis and Wavelet De-Noising of Acoustic Emission Signals
Juan-José G. De La Rosa, Antolino Gallego, Rosa Piotrkowski, Enrique Castro andAntonio Moreno-Muñoz
142-151

26-152

Acousto-Ultrasonic Signal Analysis for Damage Detection in GFRP Adhesive Joints
Andreas J. Brunner and Giovanni P. Terrasi

152-159

26-160

Bending Fracture Behavior of 3D-Woven SiC/SiC Composites with Transpiration Cooling
Structure Characterized by AE Wavelet Analysis
Toshimitsu Hayashi and Shuichi Wakayama
160-171

26-172

Acoustic Emission Monitoring of Bridge Structures in the Field and Laboratory
Rhys Pullin, Karen M. Holford, Robert J. Lark and Mark J. Eaton

172-181

26-182

Arrival Time Detection in Thin Multilayer Plates on the Basis of Akaike Information Criterion
Petr Sedlak, Yuichiro Hirose, Manabu Enoki and Josef Sikula
182-188

26-189Some Possibilities of AE Signal Treatment at Contact Damage Tests of Materials and Bearings
Pavel Mazal, Filip Hort, Martin Drab and Tomas Slunecko
189-198

26-199Laser Cutting and Acoustic Emission SignalsTomaž Kek and Janez Grum199-207

Online Monitoring of Hot Die Forging Processes Using Acoustic Emission (Part I)
Islam El-Galy and Bernd-Arno Behrens

208-219Natural Fiber Composites Monitored by Acoustic Emission
Igor Maria De Rosa, Carlo Santulli and Fabrizio Sarasini220-228

26-20826-22026-229

Acoustic Emission Feature for Early Failure Warning of CFRP Composites Subjected to Cyclic Fatigue
Runar Unnthorsson, Thomas P. Runarsson and Magnus T. Jonsson229-239

26-240Identification of Damage Initiation and Development in Textile Composite Materials Using Acoustic
Emission
D.S. Ivanov, S.V. Lomov, I. Verpoest and M. Wevers
240-246

26-247

Damage Identification in Corroded Galvanized and Duplex Coatings Using Wavelet Power and Entropy
Rosa Piotrkowski, Antolino Gallego and Enrique Castro
247-261

26-262

AE Entropy for the Condition Monitoring of CFRP Subjected to Cyclic Fatigue
Runar Unnthorsson, Thomas P. Runarsson and Magnus T. Jonsson262-269

26-270Experimental Simulation and Dynamic Behavior of the AE due to Martensitic Transformation
Using Shear Wave Transmission Sensor
Takeshi Yasuda, Shinya Kondo, Hideo Nishino and Kenichi Yoshida270-278

26-279Implementation of Acoustic Emission Method to the Conventional NDT Structure in Oil Refinery
V.P. Gomera, V.L. Sokolov and V.P. Fedorov
279-289

26-290An Experimental Analysis of Frequency Emission and Noise Diagnosis of Commercial Aircrafton Approach
S. Khardi
290-31026-311

New Developments of Software for A-line Family AE Systems
Sergey Elizarov, Аnton Bukatin, Мikhail Rostovtsev and Denis Terentyev

26-317

Application of Acoustic Emission in Optimizing the Design of New Generation castings of57

311-317

High-Voltage Electric Devices
Jan Płowiec, Wojciech L. Spychalski, Huber Matysiak and Jakub Michalski
Contents26
AUindex26
AusNotesContents of Volume 26 (2008)
Authors Index of Volume 26
Policy/Author’s Notes/Meeting Calendar/DVD/Subscription Information

318-325
I-1 – I-3
I-4
I-5 – I-7

JAE Index Folder*
Cumulative Indices of J. of Acoustic Emission, 1982 - 2008
PhD Thesis of Runar Unnthorsson, University of Iceland** indicates the availability in CD-ROM only.
Cover illustration is from 26-160 by Toshimitsu Hayashi and Shuichi Wakayama.
This figure shows the WT diagram, AE signal, projected WT-frequency curve and FFT spectrum. While WT
identifies the first major frequency at 0.56 MHz and the second major frequency at 0.32 MHz, FFT shows the 1st
and 2nd major frequencies of ~0.3 MHz as these frequency components have long duration. This clearly shows that
FFT analysis failed to detect the highest characteristic frequency at 0.56 MHz.
In using FFT, it is essential to be cognizant of this shortcoming.

Volume 27, 200927-001 MONITORING THE CIVIL INFRASTRUCTURE WITH ACOUSTIC EMISSION:BRIDGE CASE STUDIES
D. ROBERT HAY, JOSE A. CAVACO and VASILE MUSTAFA
1-10
27-011 ACOUSTIC EMISSION TESTING OF A DIFFICULT-TO-REACH STEEL BRIDGEDETAILDAVID E. KOSNIK
11-17
27-018 ACOUSTIC EMISSION AS A MONITORING METHOD IN PRESTRESSEDCONCRETE BRIDGES HEALTH CONDITION EVALUATION
MAŁGORZATA KALICKA
18-26
27-027 ACOUSTIC EMISSION LEAK DETECTION OF LIQUID FILLEDBURIED PIPELINEATHANASIOS ANASTASOPOULOS, DIMITRIOS KOUROUSISand KONSTANTINOS BOLLAS
27-040 ACOUSTIC EMISSION MONITORING AND FATIGUE LIFE
PREDICTION IN AXIALLY LOADED NOTCHED STEEL SPECIMENSFADY F. BARSOUM, JAMIL SULEMAN, ANDREJ KORCAK and ERIC V. K. HILL

27-39

40-63

27-064 AE ANALYSIS ON BLADE CUTTING PRESSURE ADJUSTMENT IN DYNAMIC
CUTTING OF PAPERBOARDDARULIHSAN A. HAMID, SHIGERU NAGASAWA, YASUSHI FUKUZAWA,YUUKI KOMIYAMA and AKIRA HINE
64-76
58

27-077 DAMAGE ONSET AND GROWTH IN CARBON-CARBON COMPOSITEMONITORED BY ACOUSTIC EMISSION TECHNIQUE
ARIE BUSSIBA, ROMANA PIAT, MOSHE KUPIEC, RAMI CARMI, IGAL ALON
and THOMAS BÖHLKE

77-88

27-089 FUNDAMENTAL STUDY ON INTEGRITY EVALUATION METHOD FOR COPVSBY MEANS OF ACOUSTIC EMISSION TESTINGYOSHIHIRO MIZUTANI, SOTA SUGIMOTO, RYOSUKE MATSUZAKI
and AKIRA TODOROKI
89-97
27-098 ACOUSTIC EMISSION FROM IMPACTS OF RIGID BODIESTATIANA B. PETERSEN

98-11327-114 SOME OBSERVATIONS ON RAYLEIGH WAVES AND ACOUSTIC EMISSION IN
THICK STEEL PLATES
M. A. HAMSTAD
114-136
27-137 FRACTURE BEHAVIOR IN BONE CHARACTERIZED BY AE WAVELETANALYSISSHUICHI WAKAYAMA, KEISUKE MOGI and TETSUYA SUEMUNE
137-143
27-144 ABOUT PLASTIC INSTABILITIES IN IRON AND POWER SPECTRUM OF
ACOUSTIC EMISSION
ALEXEY LAZAREV and ALEXEI VINOGRADOV
144-156
27-157 ACOUSTIC AND ELECTROMAGNETIC EMISSION FROM CRACK
CREATED IN ROCK SAMPLE UNDER DEFORMATIONYASUHIKO MORI, YOSHIHIKO OBATA and JOSEF SIKULA

157-166

27-167 IDENTIFICATION OF AE MULTIPLETS IN THE TIME AND FREQUENCYDOMAINSHIROSHI ASANUMA, YUSUKE KUMANO, HIROAKI NIITSUMA, DOONE WYBORN
and ULRICH SCANZ
167-175
27-176 CRACK GROWTH MONITORING WITH HIERARCHICAL CLUSTERING OF AE
N. F. INCE, CHU-SHU KAO, M. KAVEH, A. TEWFIK and J. F. LABUZ
176-185
27-186 ACOUSTIC EMISSION FOR CHARACTERIZING BEHAVIOR OF COMPOSITE
CONCRETE ELEMENTS UNDER FLEXURESHOHEI MOMOKI, HWAKIAN CHAI, DIMITRIOS G. AGGELIS, AKINOBU HIRAMA
and TOMOKI SHIOTANI
186-19327-194 DISTINCT ELEMENT ANALYSIS FOR ROCK FAILURE CONSIDERING AE
EVENTS GENERATED BY THE SLIP AT CRACK SURFACESHIROYUKI SHIMIZU, SUMIHIKO MURATA and TSUYOSHI ISHIDA
194-21159

27-212 ELECTROMAGNETIC METHOD OF ELASTIC WAVE EXCITATION FOR
CALIBRATION OF ACOUSTIC EMISSION SENSORS AND APPARATUS
SERGEY LAZAREV, ALEXANDER MOZGOVOI, ALEXEI VINOGRADOV, ALEXEY LAZAREV
and ANDREY SHVEDOV
212-223
27-224 MONITORING OF PIPE CLOGGING BY MUSSELS UTILIZING AN OPTICALFIBER AE SYSTEM
TAKUMA MATSUO, YUTA MIZUNO and HIDEO CHO
224-232
27-233 CORROSION DETECTION BY FIBER OPTIC AE SENSORYUICHI MACHIJIMA, MASAHIRO AZEMOTO, TOYOKAZU TADA
and HISAKAZU MORI

233-240

27-241 EFFECT OF SHOT PEENING ON THE DELAYED FRACTURE USING THE
ALMEN STRIP AND AE TECHNIQUEMIKIO TAKEMOTO, MOTOAKI NAKAMURA, SEIJI MASANO and SHUICHI UENO 241-253
27-254 CONTRIBUTION OF ACOUSTIC EMISSION TO EVALUATE CABLE STRESSCORROSION CRACKING IN SIMULATED CONCRETE PORE SOLUTION
S. RAMADAN, L. GAILLET, C. TESSIER and H. IDRISSI
254-262
27-263 FLEXURAL FAILURE BEHAVIOR OF RC BEAMS WITH REBAR CORROSION
AND DAMAGE EVALUATION BY ACOUSTIC EMMISSIONNOBUHIRO OKUDE, MINORU KUNIEDA, TOMOKI SHIOTANIand HIKARU NAKAMURA
263-271
27-272 ACOUSTIC EMISSION METHOD FOR SOLVING PROBLEMS IN DOUBLEBOTTOM STORAGE TANKSMAREK NOWAK, IRENEUSZ BARAN, JERZY SCHMIDT and KANJI ONO
272-280
27-281 STUDY OF IDENTIFICATION AND REMOVAL METHOD FOR DROP NOISE IN
AE MEASUREMENT OF TANKSHIDEYUKI NAKAMURA, TAKAHIRO ARAKAWA, HIRAKU KAWASAKI, KAZUYOSHISEKINE and NAOYA KASAI
281-29027-291 A GENERIC TECHNIQUE FOR ACOUSTIC EMISSION SOURCE LOCATIONJONATHAN J. SCHOLEY, PAUL D. WILCOX, MICHAEL R. WISNOM,MIKE I. FRISWELL, MARTYN PAVIER and MOHAMMAD R ALIHA
291-298
27-299 ACOUSTIC EMISSION TESTING – DEFINING A NEW STANDARD OF
ACOUSTIC EMISSION TESTING FOR PRESSURE VESSELS
Part 1: Quantitative and comparative performance analysis of zonal location andtriangulation methods
JOHANN CATTY
Contents27
AUindex27
AusNotes

299-313

Contents of Volume 27 (2009)
I-1 – I-3
Authors Index of Volume 27
I-4
Policy/Author’s Notes/Meeting Calendar/DVD/Subscription Information
I-5 – I-7
60

IAES19
JCAE Kishinoue Award Acceptace Speech by T.F. Drouillard
AE Literature AE conference proceedings in China, 2001-2006: Gongtian Shen
Cover photographs See 27-001 by Hay et al. for details.
JAE Index Folder*

I-8 – I-10
I-11 – I-16

Cumulative Indices of J. of Acoustic Emission, 1982 – 2009
Contents1-27
Contents Volumes 1-27
Authors Index1-27 Authors Index Volumes 1-27

* indicates the availability in CD-ROM only. Indices are also available for download fromwww.aewg.org.

61

Authors Index, Volumes 1-27 (1982-2009)
Satoshi Abe
A. R. Acharya
C. Howard Adams
E. Aernoudt
B. D. Agarwal
Dimitrios G. Aggelis,
Mamoru Aizawa
T. Aizawa
Chandra Ajay
Kensuke Akamatu
Y. Akematsu
M. Akiyama
Morito Akiyama,
L. Alfayez
R.S. Algera
S. S. Ali
MOHAMMAD R ALIHA
Bernhard Allemann
A.F. Almeida
IGAL ALON
A. L. Alyakritskiy
Masashi Amaya,
J. F. R. Ambler
G. AmirA. A. Anastassopoulos,
Naoto Ando
E. Andres,
M. Annamalai
K.-I. Aoki
K. AokiKazuaki Arai
Ryouhei Arai
Takahiro Arakawa
D. Armentrout
Baxter H. Armstrong,
J. H. Armstrong
M. Arrington
Ikuo Asano
M. Asano,
Hiroshi Asanuma
J. Asquith,
S. K. Athithan
Y.H.J. Au,
B. Audenard

19-045
8-S88
1-165
4-S186
8-S29725-069, 25-308, 27-186
21-149
6-85
4-S17424-05221-22316-S150
25-10722-077
2-69
4-S42, 4-107
27-291
14-11915-S107, 15-S108
27-07726-132
13-S35
5-S16
2-64
13-011, 18-021, 18-217, 18-224, 20-229, 22-059, 23-318,
25-033, 27-027
9-209
18-155
8-S8
8-S131
8-S14524-11123-07223-243, 26-109, 27-281
10-97, 10-103, 15-43, 16-S10, 24-119
8-S250, 12-117
4-S135
4-S165
4-S24019-13423-064, 23-129, 27-167
18-211
4-S26
18-196
1-148
1

J. Aviassar
Janine Avison
J. Awerbuch
MASAHIRO AZEMOTO,

1-179
26-091
8-S301
27-233

Friedrich-Wilhelm Bach,
Baldev Raj

25-331
4-S102, 6-209, 7-S1, 8-S126, 8-S140, 8-S149,
11-43
3-182
23-11919-2098-S217, 9-142
1-179
17-S37, 23-173, 24-044, 24-222, 25-341, 27-272
8-S140, 8-S149
26-13222-127, 25-042
23-11318-87
2-69
27-040
18-61
8-S75
12-1494-S22425-21518-131, 19-241, 20-188
24-022
4-S244, 4-19, 5-71, 6-151, 8-S311, 9-197, 9-215,
10-83, 12-i (1/2), 12-55
1-21, 1-300, 2-67, 2-69, 2-95, 2-143, 3-224, 3-239, 4-65,
5-53, 5-172, 15-63, 15-S111, 21-095, 23-299, 23-331
7-59
26-208
12-45
4-71, 9-271
5-1
15-S117
26-0601-35, 7-59
11-85
15-S117
4-S178, 4-S300, 6-43, 11-11
12-27
8-S2094-71
25-224
4-S102, 8-S149, 11-43
5-S69

G. R. Baldwin
Rao Balusu
R.H. Bannister
V. Bansal
J. Baram
Ireneusz Baran,
P. Barat
V. A. Barat
Michel Barbezat,
Roy Baria
A. Barron,
J.A. Baron
FADY F. BARSOUM,
M.W. Barsoum,
H. Barthelemy
B. L. Baskin,
M. Nabil Bassim
Matthew Baxter,
M. Bayray,
Mulu Bayray
Frank C. Beall,
Alan G. Beattie
M. J. Beesley
Bernd-Arno Behrens
S. Béland,
R. M. BelchamberDavid A. BellS. H. Benabdallah
Petr Benes
P. G. Bentley
Avraham Berkovits
C. C. Berndt
J. M. Berthelot,
Yves H. Berthelot,
M. M. Besen
D. Betteridge
M. R. Bhat,
D. K. Bhattacharya,Frank S. Biancaniello2

Thomas Bidlingmaier
Jacek M. Biernacki,
T. Kevin Bierney
Christophe Binétruy
B. Birolo
Jan Bisschop
Michal Blahácek
P.R. Blackburn
M. J. Blanch
Richard W. Blank
J. A. Blessing
Thomas Blum
Andrew R. Blystra
T. Boczar
THOMAS BÖHLKE
P. Böhm
Jurgen Bohse
Yasuyuki Bokoi
KONSTANTINOS BOLLAS
R. J. Boness
R.W. Bosch,S. F. Botten
S. Bousias,
P. Bowen,
S. J. Bowles
P. Bowman
Marcelle Brachet
John Brandon
Franklin R. Breckenridge
J. C. Briggs
W. D. Brosey
Nancy Brown
C. Brun,A.J. BrunnerAndreas J. Brunner,
G. Budenkov
R. Budzier
C. Buelens,Аnton Bukatin
E. Bulatova
O. Burenko
Maria Burger
ARIE BUSSIBA,
Stephen D. Butt,
D. J. Buttle
G. Buzzacchi

14-S47
12-55
4-S321
26-1204-S25520-153
18-279, 20-163, 20-274, 22-138
7-49
20-2299-181
8-S236, 8-1
7-179
10-S4917-S727-077
9-29
15-S108, 16-S233, 16-S343, 19-001, 22-208
16-S19627-027
8-S192, 15-S117
18-2938-S33025-03313-S0810-49
7-225, 8-S4
2-159
17-49
1-87, 3-59, 10-43
8-S2097-31, 8-S280, 9-75, 9-84
10-71
18-15515-S108,22-127, 24-104, 25-042
17-S13, 17-S51
20-172
18-34
26-311
17-S13
7-31
22-10227-07725-3737-211, 8-S158, 8-S201, 9-243, 9-255
2-11

CARP Aerospace/Advanced Composites Subcommittee
Giuseppe Campanella,25-324
3

11-C1

Bruce Campbell
Claudio Caneva,
Nicholas J. Carino
Mark Carlos
John M. Carlyle
RAMI CARMI,
Victor Caron
Steve H. Carpenter,

Damian Carter
M. Cartoceti
Dick W. Caster
Enrique Castro
JOHANN CATTYJOSE A. CAVACO
M. Cerny
J. Cerv
HWAKIAN CHAI,
Roger W.Y. Chan
C. Chang
Chung Chang
C. Chapelier
T. Chelladurai
D. Chellman
Chung-Mei Chen
Jihua Chen
M.C.Cheresh
M. Cherfaoui
Michael J. Chica
A. Chichibu
Chang-Sheng Chien,
Y. S. Chin
Pornthep Chivavibul
Robert Chivers
T. Chow
Y. T. Chow
Min-Hwa Chung,T. Chelladurai,
M. Cherfaoui
Akiyoshi Chichibu,
Milan Chlada
Frantisek Chmelík
Hideo Cho
N. S. Choi
A-A. R. Choudhury

3-81
25-080
5-S24
15-S104, 15-S107, 15-S109, 18-189, 18-248, 18-272, 25-355
4-S32927-077
4-S259, 4-115
1-251, 2-191, 3-11, 3-81, 4-S119, 4-S135, 5-77, 6-115,
6-136, 6-177, 6-215, 7-9, 7-161, 8-S135, 8S184, 8-125, 9-1, 10-97, 10-103, 11-5, 12-141, 13-S01,
15-43, 16-S10, 24-119
17-49
2-11
9-197
26-142, 26-247
18-205, 27-299
27-001
17-S2020-02527-186
3-118, 4-S259, 4-115, 8-S12
4-S62
7-21
5-S52
8-S88
4-S2637-161
19-001
2-289
5-S66, 22-071
9-197
8-107
15-S118
11-71
23-091, 24-076
10-1238-S166
4-71
13-1
12-111
11-1, 18-144
11-S47, 12-S1
17-S57, 20-134
17-S29, 20-108
16-S115, 21-112, 22-119, 22-224, 22-243, 23-072, 23-196,
23-206, 23-277, 24-012, 24-067, 24-084, 24-127, 24-161,
25-107, 25-124, 25-157, 25-172, 25-267, 26-072, 27-224
16-S324
16-S125
4

S. Y. Chuang
S. S. Christiansen
F. Cipri
M.A. Clark
T. N. Claytor
Roger B. Clough
D. M. Chuck
P. ColePhillip T. ColeC. E. Coleman
J. Collins
M. P. Collins
Peter J. Conlisk
A. W. CookDavid B. Cook
R. Daniel Costley, Jr.,R. A. Coyle
J. Crha
H.-A. Crostack
Timothy G. Crowther
M. E. A. Cudby

4-S137
4-S116, 5-85, 8-S184
4-S25515-S107
4-S69
5-S69
8-S25815-S109
8-S239, 8-31, 18-61, 18-180, 18-232, 19-191, 22-022
5-S16
4-S1349-271
8-1
8-S101
17-83
12-27
6-249
17-S45
9-29
10-71
4-71

C. E. D'Attellis
V. Dal Re
Wojciech Darski,
B. Dattaguru
A.W. Davies,
Jack F. Dawson
V. G. Ruiz de Argandoña
Igor Maria De Rosa,
Juan-José G. De La Rosa
S. De Bondt,
I. De Iorio
P. De Meester
C. De Michelis
L. M. Suárez del Río
L. Delaey
H. A. L. Dempsey
O. Derakhshan
J. Derenne,
A. Deruyttere
Wendy Desadeleer,
M. Diez
Seth-Andrew T. Dion,John J. Ditri,
Nitin Dhond
G. Dionoro
Jason W.P. Dong
Jason Dong,

10-13
4-S255, 4-S270, 5-39
25-341
6-19
18-232
10-117
10-S3525-080, 26-220
26-142
11-95
3-158
4-S186, 8-S272, 13-79, 15-S105, 18-34
2-11
10-S35
11-95
4-S46
8-S22318-29911-95
22-2534-S29625-187
12-23
4-S30
2-281
16-S1
25-3555

P. Dörner,David A. Dornfeld
Henrique L. M. dos Reis,
R.D. Douglas,
Karyn S. Downs,
Martin Drab
M. W. Drew
T.F. Drouillard,
Q. Duan
Qingru Duan
R. Dubiel,
J. C. Duke
H.L. Dunegan
J. Dunning
C. Divaker Durairaj
F. Dusek
A. G. Dutton
C. DuytscheJ. Dvoracek,
B.C. Dykes
Yuris A. Dzenis
Mark Eaton,
J. Eberlein
Davis M. Egle
Gernot Eilers
J. Eisenblätter
Islam El-Galy
T. El-Raghy,Sergey Elizarov
R.K. Elsley
T. ElyD. C. Emmony
C. Ennaceur,
Manabu Enoki,22-2363-242, 4-S123, 4-S228, 7-103, 7-111, 8-S227,
6-29, 6-37, 6-157
see Reis18-96
13-31, 13-45, 13-56, 14-S61, 15-S109, 16-iv, 16-S333,
21-052, 21-070, 21-A01
26-1896-239
1-45, 1-81, 1-121, 1-195, 1-271, 2-129, 2-221, 2-292, 3-46,
3-90, 3-164, 3-212, 4-41, 5-103, 9-45, 9-155, 9-215,
11-53, 12-71, 12-79, 13-42, 14-1, 16-v
8-S97
16-S243
18-15
8-S1798-S71, 15-53, 15-S106, 16-v
4-S22
17-15
10-1
20-229
3-176
18-81
4-S35
15-S112, 20-016, 20-099

R. M. Esbert
V. A. Eshwar
Sam Evans,
W. T. Evans

25-140, 25-215, 26-172
20-1722-ii (3), 3-104, 4-S30, 4-S46, 6-205
19-100
15-S119, 16-S85, 19-100, 19-153, 24-179, 24-196
26-20818-61
26-311
2-47
16-S101-263
22-071
4-S195, 8-S154, 13-S29, 15-S90, 15-S120, 16-S269,
21-142, 23-292, 23-310, 24-139, 25-247, 26-182
10-S35
8-S217, 9-142
25-21511-71

A. Fahr,S. D. Falls
Daining Fang
Ahmed M. Farahat

8-S314, 12-39, 15-S122
8-S166
11-85
11-S37
6

J.-P. Favre
Carol Featherston,
V. P. Fedorov
G. Fernando
F. Ferrer,
Steven E. FickAndrzej Figiel
Chantal Filion
F. Finck,
P. Finkel,
R.D. Finlayson,
J. F. Finn
P. FleischmannPeter Flüeler
T. Flynn
Jens Forker,
D. S. Forsyth
R. Fougeres
Timothy J. Fowler
H. Frackiewicz,
R.P. Franke,
Michael J. Friedel
M.A. Friesel,
MIKE I. FRISWELL,
L. Froyen,
Yoshiaki Fujii
Taisaku Fujioka
Tetsuro Fujiwara
T. Fuketa,
Hiroyuki Fukutomi
YASUSHI FUKUZAWA,
Roy D. Fultineer, Jr.
Masami Fushitani,
Takashi Futatsugi

9-97
25-14020-218, 26-279
20-22918-155
4-S311
4-S182
16-S186
22-083
18-61
18-61, 18-189, 18-272
8-S79
3-176, 5-S42, 9-91, 19-229
22-127
10-S5918-258, 25-132
15-S122
5-S42, 9-91
8-S236, 8-1
12-14922-236
10-S77
3-11, 3-239, 7-119, 10-117, 18-61, 18-189
27-291
11-95
23-11915-S31
10-S63, 11-S65, 15-S40, 15-S113
11-21
23-091, 24-076
27-06415-S103
4-S240, 9-209, 13-S42
23-249Laurent Gaillet
M. Gakumazawa
Antolino Gallego
Thomas Gandy
D. S. GardinerJohn Gary,

26-032, 27-254
16-S150
26-142, 26-247
4-S166
4-S19912-157, 14-103, 15-S115, 16-S251, 17-37, 17-97, 19-258,
20-039, 20-062
7-111
14-119
7-S13
18-180, 19-191, 22-022
8-S27715-S105, 8-S32, 21-014, 21-029

Michael F. Gasick
Ludwig Gauckler
B. K. Gaur
Stephen N. Gautrey
A. Gavens
Maochen Ge

7

P. Gebski
D. Geisse,
B. Georgali,
János Geréb
S. Ghaffari
S. Ghia
Al Ghorbanpoor
J.D. Gill,
Steven D. Glaser
T. G. GlennA. M. G. Glennie
M. W. Godfrey
Valery Godinez,
L. Golaski
Douglas E. Goldsack
Edward Goliti
V.P. Gomera,
Javier Gomez,
Carlos M. Valdes-Gonzalez,
M. Gori,Michael R. Gorman
G.L. Goswami
J. Goudiakas,
Per A Gradin,
Igor Grabec
L. J. Graham
A. T. GreenJ. E. GreenP. Gregson,
David W. Greve,
Arthur T. Grodotzke
Boguslaw Gronowski
D.J. Gross
S. Gross,
C. U. Grosse
Christian Grosse,
C. M. Grossi
Janez Grum
N. Gsib
P.-Y. GuF. J. Guild
T. J. Gulley
M. Gullikson
Dawei Guo
Hua GuoYanfeng Guo
B.C. Gupta

17-S37, 19-285, 20-083
12-171
18-21
10-19
8-S301
4-S86
4-S307
18-96, 18-211
10-S1, 25-316
1-81
4-S1701-263,
4-103, 18-272, 25-355
1-95, 4-S182, 17-S37, 19-285, 20-083, 24-187
16-S186
5-7
18-111, 20-218, 26-279
13-S21
12-117
18-167
8-51, 9-131, 9-283, 13-S01, 14-i (3/4), 17-29, 23-037
4-S98, 4-S251, 7-S40, 7-S43
18-15513-97, 26-023
8-S20, 8-S205, 11-79
2-47
4-124, 8-S306
1-191, 2-289
18-23925-115
1-29
4-S82, 5-25
8-25
18-23914-S74, 22-083
25-316
10-S3526-199
5-S60
8-S188
1-244
4-S17026-02314-S19, 16-S222
23-11916-S317
8-S217, 9-142

Robert A. Haack

9-181
8

Y. Haddad
M. E. Hager
H. Thomas Hahn
P. Hähner
Shigenori Hamada
Takashi Hamada
DARULIHSAN A. HAMID,
A. Hampe
M. A. Hamstad

L. Hanacek
Kotaro Hanabusa
Mineyuki Hanano
L. D. Hall,
V. Hänel
J. J. Hanley
Christian Hansmann
Yoshio Harada
H.R. Hardy, Jr.
Robert W. Harris
W. F. Hartman
J. Harvey
H. Nayeb-Hashemi,
Toshiyuki Hashida,
F. P. Hassani
Hiroaki Hata
Hajime Hatano
D. Robert Hay
J. R. Hay
Hiroshi Haya
Tomoharu Hayano
F. Havlícek
M. W. Hawman
Takefumi Hayashi
Toshimitsu Hayashi
Yoshie Hayashi,
Y. Hayashi
Yasuhisa Hayashi
Jianmei He
R. Hecker
C. R. Heiple
D. P. Henkel,
Edmund G. Henneke II

8-S314
8-S42
5-15
20-265
15-S404-S32527-0649-103
2-57, 2-i(3), 5-95, 5-110, 5-123, 6-93, 9-75, 9-84,
11-33, 12-157, 13-31, 13-45, 13-56, 14-103, 14-S61,
15-1, 15-S108, 15-S109, 15-S115, 16-S222, 16-S251,
16-S333, 17-37, 17-97, 19-258, 20-039, 20-062, 21-052,
21-070, 21-A01, 22-001, 22-A01, 23-001, 23-047,
24-234, 25-092, 25-194, 26-040, 27-114
8-S84
22-159
11-S7519-20914-115
11-27
26-01423-181
3-242, 4-S19, 8-S32, 8-S42, 8-S262, 8-65,
10-61, 15-S105, 15-S118, 16-S277
6-239, 8-S14, 8-S66, 10-S29, 10-S59, 15-S113, 17-121
1-144, 4-S64, 5-31
4-S220
12-1
13-S68, 24-215
8-99, 10-61
7-173, 10-S63
15-S115
3-118, 4-S259, 4-115, 8-S12, 9-9, 27-001
8-S12
22-039, 23-260, 24-205
24-067, 25-107
17-S45
4-S131
8-35
26-16019-03521-1317-185, 14-69, 15-S108
20-194
4-S78
1-221, 1-251, 4-S116, 5-85, 6-177, 6-215, 8-S184,
8-125, 9-1, 10-97, 10-103
8-S318
8-S277, 14-53
9

B. Herrmann,
C. Hervé,
Stefan Heusermann
S. Hewerdine
Hartmut Heyse
H. HickKazuo Hiekata
Christopher Higgins,
Y. HigoHiroshi Hikosaka
Eric v. K. Hill,
Roger Hill
AKIRA HINE
Takayasu Hirakawa
AKINOBU HIRAMA
Yuichiro Hirose
Y. Hisamatsu
Koji Hisamatsu
S.V. Hoa
A. B. M. Hoff
Karen Holford
T. J. Holroyd
Kyoji Homma
Michel Hone
K1-Jung Hong,
Theodore Hopwood II
K. Horikawa,
Keitaro Horikawa
Siegfried Horn
P. M. Horrigan
Filip Hort
J.R. Houghton
Martin T. Howald
O. Hoyer
Nelson N. Hsu,
S.-Y. S. Hsu
J. Hu
Christian Huber
J. H. Huh
Derek Hull
Patrice Hulot
Donald H. Humes
Wolfgang Hundt
U.-D. Hünicke
David A. Hutchins
David V. Hutton
H. Hutton

18-167
18-125, 22-071
16-S85
8-S238, 8-21
5-45
10-67
19-04525-316
8-S24, 16-S150, 16-S196, 19-085
10-S1325-187, 27-040
1-73, 1-149, 1-294, 5-51, 5-i(1), 10-124, 16-S125
27-06423-15627-18626-182
2-19, 2-71
11-S1
7-145, 9-37, 11-65
4-S16517-49, 18-232, 22-166, 25-140, 25-215, 26-172
4-S132, 7-193, 8-S219
10-35
4-S259, 4-115
13-S61
4-S30419-02221-206, 21-223
26-001
8-S79
26-1898-S28, 8-S223, 14-61
24-1049-103
4-S311, 5-S24, 5-S28, 5-S29, 13-23
1-183, 1-237, 2-169
16-S150
22-127
15-S80
1-95
26-120
17-29
14-11920-172
5-S29, 5-S34, 8-S38, 8-S166
8-41
3-239, 4-S74, 4-S138, 6-167

Hatsuo Ichikawa

4-S32510

Y. Ichimura
Makoto Ichinose
Hassane Idrissi,
Chikako Ikeda
Junji Ikeda
R. Ikeda
Yukifumi Ikeda
Sei Ikegaya
H. Imaeda
Takuichi Imanaka
Hidehiro Inaba,
Takako Inaba
T. Inamura,
Ichiro Inasaki
N. F. INCE,
Tomoaki Inoue
Yoshiki Inoue
Akichika Ishibashi
T. Ishida
TSUYOSHI ISHIDA
Takeshi Ishigohka
K. Ishihara
Hisashi Ishitani
C. Ishiyama
Toshiro Isoda,
Ken-Ichi Itakura,
Kaita Ito
D.S. Ivanov
V.I. Ivanov,
N. Iverson,
K. Iwai
K. IwakiKeisuke Iwaki
Y. Iwata
S. Iyer,Takeshi Izuta,

19-14220-001
18-299, 18-307, 26-032, 27-254
24-22824-173, 24-228
21-131, 22-119
23-272
23-096
4-S294
4-S38
8-S24, 19-196
10-S9019-085
7-179
27-1762-1
11-S89
11-S65, 15-S40, 15-S113
10-S4227-19424-111
6-13
4-S32516-S150, 16-S196
25-021
13-S54, 13-S75, 19-109, 23-119
24-139, 25-247
26-24018-14425-36421-197
21-16623-260
16-S142
18-18913-S42

Laurence J. Jacobs,
Jay B. JamesP. JaxT. Jayakumar
J. S. Jeng
Hee-Don Jeong,
B. B. Jha
S. K. Jha
Kyung-Young Jhang
C.Y. Jian,
C. G. Jiao
Takehiko Jibiki

12-27
4-S119, 5-77
2-29, 8-S53
4-S102, 7-S1, 8-S126, 8-S140, 8-S149, 11-43
8-S268
13-S616-209, 8-S149
7-S43
16-S261
13-S68
8-S10524-17311

K. JoJ. Johkaji
C. H. Johnson
Malcolm J. S. Johnston,
W.D. Jolly
H. Jonas
L. E. Jones
R. H. Jones
R. K. Jones
Magnus T. Jonsson,
Young-Chan Joo
P. A. Joosse,
G. JothinathanMusa K. Jouaneh
S. J. Jung,

8-107, 10-S55
8-S1
4-S111, 4-S263
12-117
4-103
4-S78
20-2293-239
7-119, 8-S223
25-252, 25-260, 26-229, 26-262
15-S1, 16-S212
20-2294-S207
10-83
8-S326

B.S. Kabanov,Koji Kagayama
Kazuro Kageyama,Kensuke Kageyama
Hideshi Kaieda
Katsuyuki Kaiho
Karl-Ulrich Kainer
Koji Kaino
K. Kajiyama
T. Kakimi
Y. Kakino
Małgorzata Kalicka
S. Kallara
P. Kalyanasundaram
T. Kamada,
M. Kamata
Masahiro Kamata
Tadashi KambaraPeter Kamlot
Makoto Kanai
Yasuyuki Kanai
Yasuhiro Kanemoto
Elijah Kannatey-Asibu, Jr.
Shigeto Kano
C-S. Kao,
CHU-SHU KAO,
Justin O. Karl,
Bo Karlsson
NAOYA KASAI
M. KatChiaki Kato
Tatyana Katsaga,
K. Katsuyama

18-111, 20-218
23-277, 24-127
13-S89, 19-045, 21-176
24-09723-12924-11120-108
5-61, 9-277
19-022
2-19
3-108
24-187, 27-018
8-S28
8-S140, 8-S149
19-1348-107
23-081
24-097
16-S8521-17624-097
15-S40
15-S118
6-109, 6-145
25-36427-17625-18726-01427-281
8-99
19-05325-294
11-S1912

Kunihisa Katsuyama
Harold E. Kautz,
M. KAVEH,
Tatsuya Kawada
Yoshiaki Kawaguchi,
Yutaka Kawai,
Teppei Kawakami
Kenji Kawamoto
HIRAKU KAWASAKI,
Tomaž Kek
James R. Kennedy
Jiri Keprt
Shahla Keyvan
A. Wahab Khair
A. S. Khan,
A. S. Khanna
S. Khardi
E. W. Khokhlova,
Jens Kiehn
M. T. Kiernan
S. Kihara,
Tadashi Kikuchi
Cindi Kilkenny
Byoung-Geuk Kim
Byung-Nam Kim
Dal-Jung Kim
K. H. Kim
K. Y. Kim
Kyung-Woong Kim,
Sang-Hyo Kim
Isao Kimpara,
H. Kimura
Ron King
Tetsuo Kinjo
N. Kinoshita
Teruo Kishi
Fuyuhiko Kishinouye
Takahiro Kishishita
N. N. Kishore
P. Kisnomo
Tatsuo Kita
K. Kitadate
K. Kitano
Shigeo Kitsukawa
Kiyoshi Kiuchi
R. A. Kline
W. Knabl

11-S275-144, 12-65
27-17624-21512-127
7-167
21-1499-109
27-281
26-1994-S90
26-06010-91, 14-97, 15-79
4-S1, 8-S326, 15-S105, 16-S53
8-S246
6-209, 8-S103
26-29012-14920-108
8-S176
18-68
11-S47
16-S186
15-S120
12-S24, 15-S90, 16-S269
16-S261
4-S282
4-S62, 8-S170
13-1
15-S11
13-S89, 19-045
4-S294
10-91
15-19, 14-69
10-S42
1-1, 2-19, 2-71, 4-S191, 4-S195, 4-S278, 4-S282,
4-S325, 6-85, 8-S131, 8-S154, 12-127, 13-S29,
15-S90, 15-S120, 16-S269
9-177, 9-180
10-S55, 11-S47
8-S297
15-33
10-S908-S131
10-S4223-23319-0534-S42, 4-107, 6-205, 8-S246
20-25713

Markku Knuuttila
E. Kobayashi
H. Kobayashi
Satoshi Kobayashi
Takao Kobayashi
Yoshifumi Kobayashi
Y. Kobayashi
Miroslav Koberna
R. M. Koerner
Tsuguaki Koga
Takao Koide,
Masami Koike,
Takao M. Kojima
B. Koktavy,
J. G. Kolaxis
V. Kolovos,
YUUKI KOMIYAMA
Hidemichi Komura
Shinya Kondo
Johannes Konnerth
Shigeo Konno
Ja-Ho Koo
T. M. Kooistra
Atsushi Korenaga,
ANDREJ KORCAK
M. Korenska,
Leszek Korusiewicz
I. Kosiková
DAVID E. KOSNIK,
T. Kossivas
V. Kostopoulos
A. Kotolomov
D. A. Kouroussis,
DIMITRIOS KOUROUSISTorsten Krietsch
R. Krishnamurthy
Vladimir Krivobodrov,
J. Krofta,
J. Królikowski,
D.A. Kronemeijer,
Joseph Krynicki
Takafumi Kubo,
I. Kukman
P. G. Kulkarni,
Yusuke Kumano
J. Sampath Kumar
M. Kumosa
M. Kunieda
MINORU KUNIEDA,

19-1623-130, 4-93
8-S14521-149, 23-150
21-001
23-181
8-S1
11-61
1-220, 2-187, 2-195, 4-S11, 4-31
2-1
13-S47
19-20224-097
17-S100
17-69
18-21727-06419-19626-27026-01423-23315-S90, 16-S269
20-23819-19627-040
18-29
2-272
17-S100
27-011
20-22920-265
17-S51
18-217, 18-224, 20-229
27-027
16-S233, 16-S343
4-S26, 8-S88
13-87
18-279, 20-274
12-14918-17415-S116
13-S42
8-S205
7-S13
23-129, 27-167
7-135
1-95, 16-S10
19-13427-26314

MOSHE KUPIEC,
D. S. Kupperman
Yu Kurokawa
Krzysztof J. Kurzydlowski
Yasufumi Kusano,F. M. Kustas
T. Kusu
May Man Kwan
Oh-Yang Kwon
H. Kwun,J.F. Labuz,
G. Lackner,
J.-C. Laizet
A. Laksimi,
V. Lalitha
Michal Landa
E. Landis
R. J. Landy
Terence G. Langdon
F. Langella
Stanislaw Lasocki
Robert J. Lark
Ralf Laschimke,
A. Lavrov
ALEXEY LAZAREVSERGEY LAZAREV,
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
Xinglin Lei
Jack Leifer
Constantina Lekakou
D. J. Lekou
A. M. Leksowskij,
A. Lemascon
Richard L. Lemaster,

27-077
4-S69
23-224, 24-145
25-166
13-S75
10-97
21-1423-144, 3-190
4-S106, 9-123, 9-227, 9-237, 13-1, 13-S83, 15-S1,
15-S19, 16-S212
11-27
25-364, 27-176
18-167, 20-179, 22-201
9-97
18-125, 22-071
4-S26
17-S57, 20-025, 20-163, 22-138
10-S97, 15-S104
1-7
20-108
3-158
4-S7
22-166, 26-172
22-10220-29227-144, 27-212
27-212
10-S18, 11-19, 16-S186
3-204, 4-S22, 8-S322, 12-117
15-S122
13-S61, 15-S80
13-S8315-S80
15-S117
15-S11
15-S1915-S120
15-S11
16-S261
18-14423-102
4-S12723-02520-22912-149
5-S66
4-S228, 6-157, 7-103, 7-111, 8-135, 9-17, 9-203,
10-83, 12-55
15

J. C. Lenain,
K. H. Leong
Armand F. Lewis
Luidmila Lezvinsky
Steinar Lfvaas
Bang xian Li
Jihui Li
L. Li
Y. P. Li
Zhengwang (Z. W.) Li
Z. W. Li
Steven Y. Liang
T. Lilley
S. Lim,Jae-Kyoo Lim
R. Lima
A. Limam,Vidyadhar Limaye,
C. K. LinDan Lindahl
J. Liöka
S. LiuShifeng Liu
T. LiuX1-qiang Liu
Y.-H. LiuDavid A. Lockner
Manuel Löhr,
T. Lokajícek
Thomas Lokajicek
S.V. Lomov
M. I. López Pumarega
Arthur E. Lord, Jr.
Luis Lorenzo
E. Lowenhar
M.G. Lozev
Guozhi Lu
Krystyna Lublinska,
P. Lukác
K. Lundgren
Xiu Luo
Xun Luo
D. Lupascu

18-161
23-025
4-S127
16-S35
4-S161
16-S243
24-001
7-145, 9-37
23-29221-213, 23-233
19-118
6-29, 6-37
4-71
19-13416-S309
15-S11718-30725-37315-S117
19-162
17-S108
8-S97
15-S124
15-S117
15-S123
9-9
14-S8822-19017-S100
22-091
26-24017-61
2-187, 2-195, 3-107, 4-S11, 4-31, 5-152
5-15
15-S109
15-S104
4-S20325-16617-S29, 20-108
5-S29
22-039, 23-260, 24-205
23-11917-S78

Yukuan Ma
J. D. MacPhail
Yuichi Machijima
A. Machová
J. W. Maclachlan

4-S58
8-S4
23-091, 27-233
20-0251-223, 1-229, 2-39, 2-179, 3-1, 4-S151
16

Eric Madaras
M. R. Madhava
Morihiko Maeda,
S. Maharshak
A. Maie
Ian Main,
M. A. Majeed,
Z. J. Majewska,
Zofia Majewska
S.A. Majewski,
Arup Maji
O. Makishima
Ajit Mal
M. Manoharan
Ll. Mañosa
Gerd Manthei
Theodore J. Mapes
J. Maram
H. Marcak,
P. A. March
K. Marsh
G. G. Martin
Hiroaki Maruyama
SEIJI MASANO
A. Maslouhi,
Y. MasuiS. Mata
O. Matal
Kristian Mathis
Takuma Matsuo
K. Matsuura
Kimitoshi Matsuyama
RYOSUKE MATSUZAKI
Huber Matysiak
S. C. Maxwell
Masami Mayuzumi
P. Mazal
H. Mazille,
D. MbaStuart L. McBride
J. F. McCardle
J.D. McColskey
John W. McElroy
D. Michael McFarland
K. I. McRae
M. Mediouni
Ronald B. Melton
P. G. Meredith,23-037
8-S28819-202
2-64
21-213
13-S21
4-S147, 8-S16, 12-107
12-S7
16-S105, 18-1
12-S7
15-S116
21-166, 25-308
14-S19, 16-S222
4-S207
5-S49
15-S119, 16-S85, 19-100, 22-173, 24-179, 24-196
1-29
4-S134
12-S7
8-S22315-S104
4-S14223-23327-241
8-S292, 12-45, 15-S122, 16-S299
23-18917-23
17-S6520-10824-067, 24-084, 25-124, 26-072, 27-224
21-12011-S65, 15-S40
27-08926-317
8-S38
23-224, 23-285, 24-145, 25-238
17-S2, 17-S20, 17-S70, 18-75, 26-189
18-299, 19-229
19-209, 22-077
1-223, 1-229, 2-39, 2-179, 3-1, 4-S151, 4-S220, 7-225,
8-S4, 8-S192
10-49
15-1, 15-S108, 15-S111, 15-S119
4-S77
5-67
7-225
22-071
1-266
12-S12
17

Philip Meredith,Jakub Michalski
K. Michihiro
W. Mielke
Juha Miettinen
Hamish D. S. Miller
M. E. Miller
R. (K.) Miller
O. Minemura
J. R. Mitchell
J. Mitchell
A. Mittelman
S. Miwa,
Makoto Miwa,
Kouitsu Miyachika,
Kazuya Miyano
Hiroyuki Miyatake
Noriko Miyoshi,
YUTA MIZUNO
Junichiro Mizusaki
Souichi Mizutani
Yoshihiro Mizutani
Kenji Mochizuki
Mark B. Moffatt
K. Mogi
KEISUKE MOGIJames Mohr
Waldemar Molinski
Florian Moll
M. Momayez
SHOHEI MOMOKI,
Keiichi Monma
M. Montoto
Antonio Moreno-Muñoz
Bryan C. Morgan
L. Morgan
Winfred Morgner
HISAKAZU MORI
Y. Mori
Yoshiki Morino
Hiroyuki Morishima
Hirokazu Moriya
D. G. Morris
K. Morofuji
Zofia Mortimer
W. J. Moś cicki,
H. G. Moslé

13-S2126-317
10-S63
9-103
21-230
5-S1
4-S22
8-S241, 8-25, 15-S104, 15-S107, 15-S108, 18-61,
18-189, 18-272
16-S75
6-135
15-S109
3-41, 6-73
19-142
13-S42
13-S47
10-S9010-S1325-10727-22424-21520-19416-S115, 18-51, 18-286, 19-035, 20-194, 23-224, 23-285,
24-145, 25-179, 25-238, 26-109, 27-089
8-35
1-29
1-37, 8-113, 16-S45
27-137
5-162
10-107
20-108
10-61
27-18615-S113
10-S3526-142
15-69, 15-S103
16-S125
3-172, 5-45, 6-133, 8-S70
27-2338-S131, 16-S45, 20-248, 21-197, 22-091, 27-157
20-19420-14523-113, 23-129, 23-142, 24-196
7-95
21-21316-S105, 18-1
12-S7
8-S31718

F. Mostert
Andre Moura
ALEXANDER MOZGOVOI,
F. MudryAmiya K. Mukherjee
M. C. Mumwam
Kohei Murakami
Yuji Murakami
SUMIHIKO MURATA
K. Murayama
Boris Muravin
Gregory Muravin
S. A. F. Murrell,
C. R. L. Murthy,
VASILE MUSTAFA
T. M. Mustaleski

18-18923-10227-212
6-85
5-162
16-S6523-215
10-S9027-194
16-S75
16-S35
16-S35
12-S12
4-S30, 4-S147, 6-19, 7-S18, 8-S16, 8-S122,
8-S284, 12-107, 25-224
27-001
4-S247

S. Naemura
T. Nagamachi
Takuo Nagamachi
Koji Nagano,
S. Nagano,
Jyothi Nagaraj
SHIGERU NAGASAWA,
Kenji Nagashima,
Yasuaki Nagata
M. M. Nagl,
G. Jayachandran Nair
P. K. K. Nair
Hidehumi Naito,
Yoichi Nakai
Hideo Nakajima
Hideyuki Nakamura
M. Nakamura
MOTOAKI NAKAMURA,
Syouzou Nakamura
Yasuhiro Nakanishi
Hiroyasu Nakasa
Eisaku Nakashima,
M. Nakano
Noritaka Nakaso,
Gen Nakayama,
B. C. Nakra
H. Nayeb-Hashemi,
O. Nedzvetskaya
Priscilla P. Nelson
Katsumi Nemoto
Y.S. Neo,

10-S5523-18922-15911-S1, 13-S54, 13-S75
19-085
14-97
27-06419-011
4-S191
11-71
8-S266
4-S98
12-S2424-09724-111
23-243, 26-109, 27-281
10-S55, 8-107
27-241
10-S1320-145, 22-039, 23-260
16-S2519-202
21-197
13-23
25-157
9-25
12-1, 14-85, 15-33
17-S13, 17-S51
10-S1
23-14218-96
19

K. P. Nerz
John P. Newhook
Eng T. Ng
K. D. Nicklas
Jackson A. Nickerson
A. NielsenPeter Niemz
Hiroaki Niitsuma
Akira Ninomiya
T. Nishida
S. Nishikawa
Koichi Nishimoto
Shigeto Nishimoto
Hideo Nishino
M. Nishino
Satoshi Nishinoiri
V. N. Nikolaidis,
Minoru Nishida,
H. Nishino,
Takako Nishiura
Osamu Nishizawa
Masami Noguchi
M. Noguchi
C. Nojiri
Hiroaki Noma,
Richard Nordstrom
S. Norgren
Václav Novák
Marek Nowak
A. Nozue
Akira Nozue
J. Nuffer
Staffan Nyström,
Y. Obata
S. I. Ochiai
Satoshi Oda,
A. O’Gallagher
Takashi Ogata
Takeshi Ogawa
Steve L. Ogin
S. Ogino
Eu Seok Oh
Yoshitugu Ohigashi
Takanori Ohira
Hironobu Ohishi
A. Ohmori

5-S56
25-373
19-275
4-S247
6-37
8-S57
24-1047-201, 11-i(4), 11-S1, 23-064, 23-113, 23-129, 23-142,
24-196, 27-167
24-111
21-187
10-S55
4-S23619-202
23-189, 24-153, 26-270
8-S13123-310, 24-076
17-69, 20-229
12-S1818-51, 18-102, 18-286, 19-011, 19-035, 19-075
24-052
11-S27, 23-102
4-S236
11-21
16-S150
25-10715-S108, 16-S204
26-02320-16324-044, 24-222, 25-341, 27-272
1-1
21-14917-S78
13-97, 26-023
8-S145, 16-S45, 20-248, 22-091, 27-157
2-289
13-S47
14-103, 15-S115, 16-S251, 17-37, 17-97, 19-258, 20-039,
20-062, 21-052, 21-070, 21-A01, 22-001, 22-A01, 23-001
24-07623-156, 23-196, 23-181, 23-277, 24-127, 24-161, 25-124
23-025
5-61, 9-277
16-S277
10-25
4-S27423-06421-20620

Tatsuma Ohnishi
Tadashi Onishi,
Kentaro Ohno
Isamu Ohsawa,
Shiro Ohta
M. Ohtsu

M. OnoeIrving J. Oppenheim,
Hisanori Otsuka
P. Ouellette
C. Ouyang
H. Oyaizu
K. Ozawa
Didem Ozevin,

19-10925-23823-047
13-S89, 19-045
15-S401-103, 2-151, 2-247, 3-27, 3-59, 3-69, 4-S38, 4-S50,
4-S316, 5-124, 6-43, 6-79, 6-99, 7-167,
8-S162, 8-S242, 8-93, 10-i (1/2), 11-i(4), 11-S37,
11-S57, 11-S65, 11-S89, 13-S14, 15-S31, 15-S40, 15-S50,
15-S60, 15-S70 16-S65, 16-S95, 18-S1, 18-S7, 19-118,
19-142, 19-184, 20-001, 21-157, 22-030, 23-047, 23-136,
23-272, 25-021
4-S28225-051
13-S14
16-S75
4-S240
18-111, 20-218
27-263
11-21
17-15
1-7, 1-67, 1-69, 1-141, 1-145, 1-146, 1-183, 1-211, 1-213,
1-214, 2-169, 2-247, 2-ii(1/2), 3-19, 3-27, 3-59, 3-69,
3-130, 3-144, 3-174, 3-190, 3-233, 3-ii(4), 4-S50,
4-S106, 4-S111, 4-S263, 4-S316, 4-61, 4-93, 5-124,
5-i(1), 5-ii(4), 6-1, 6-43, 6-84, 8-S268,
9-109, 9-123, 9-177, 9-227, 9-270, 11-117, 12-177,
14-35, 14-69, 14-S19, 15-19, 15-S95, 15-S105, 15-S108,
16-S115, 16-S134, 16-S289, 17-S37, 18-51, 18-102, 18-286,
19-011, 19-035, 19-063, 19-075, 19-091, 19-285, 20-083,
21-112, 21-120, 22-119, 22-243, 23-173, 23-206, 24-044,
24-119, 24-187, 24-222, 25-001, 25-179, 26-072, 27-272
5-i(4)
25-115
10-S139-37, 11-65
10-S97
8-S1
21-16625-355

Jie Pan
Yih-Hsing Pao
Krystian Paradowski,
Philip Park
Young-Jin Park
H. B. Patel
S. C. Pathak
J. Pavelka

22-264, 22-274
4-S27425-16615-S11
15-S11
8-S12, 8-S101
4-S32, 4-S147, 8-S122
17-S100, 22-091

H. Ohyama
M. I. Ojovan,
Takahisa Okamoto,
T. Okamoto
Takeshi Okano
A.A. Okhotnikov,
NOBUHIRO OKUDE,
S. Okumura
L. Okushima
Kanji Ono,

21

MARTYN PAVIER
J. Pazdera,
L. Pazdera,
Martin Peacock
L. H. Pearson
Peter Pellionisz
R. Pensec,
L. V. Perez
Marianne Perrin
D. T. Peters,
TATIANA B. PETERSEN
S. Peteves
J. Petras,
L. Petras,
J. Petrasek
Christian Pfleiderer,
T. P. Philippidis,
ROMANA PIAT,
Rodney G. Pickard
C. Picornell
Aleksander Pilarski,
S. Pilecki,
J. Pininska,
Rosa Piotrkowski
A. Plevin
Jan Płowiec
T. Pocheco
Stefan Poliszko
M. D. Pollard,
Adrian A. Pollock
Elizabeth L. Porter
O. Prabhakar
Arun Prakash
A. Prateepasen,
C. W. Pretzel
D. Prevorovsky
Zdenek Prevorovsky
David W. Prine
Thomas M. Proctor, Jr.
William H. Prosser
A. ProustJose Pujol
R. Pullin
M. I. López Pumarega
K. K. Purushothaman,

27-291
18-81
18-29
4-S166, 8-S240, 8-11
4-S19910-19
18-125
10-13
26-032
8-S4
27-09820-265
18-75
18-81
17-S8312-141
13-11, 17-69, 20-229
27-077
15-79
5-S49
12-23
10-1, 12-149
18-8
17-61, 26-142, 26-247
1-35
26-317
14-85
10-107
8-S4
1-237. 1-303, 9-140, 10-122, 15-S107, 15-S108, 25-231
17-121
4-S207
9-142, 8-S217
18-196
5-172
20-285
17-S57, 18-279, 20-134, 20-274, 20-285, 22-138
4-S304, 15-S106
1-173, 5-134, 7-41, 10-43
9-283, 14-S1, 15-S106, 15-S115, 17-29, 17-37, 23-037
17-S83, 18-161, 19-229, 20-229
17-11122-166, 26-172
17-61
12-111Gang Qi
Jie Qian
Stephen L. Quarles

17-111, 19-275, 24-001
20-016, 20-099
8-134, 9-17, 9-189
22

M. Raab
Amani RaadJan Raczkowski
Andre Raharinaivo
Baldev Raj,
P. RajD. S. Rajan
P. K. Rajan
S. RAMADAN,
Jerzy Ranachowski
A.K. Rao
M. V. M. S. Rao
S. P. Mallikarjun Rao
M. N. Raghavendra Rao
Christian Rasche
Franz Rauscher
Henrique L. M. dos Reis,
F. Rehsteiner
Wilfried Reimche,
H. W. Reinhardt
R.L. Reuben,
W. G. Reuter
L.E. Rewerts
Marie-Christine Reymond
M. W. Richey
J. Richter
B. Richtor
G. L. Rigby
Carlos R. Rios
L. Rippert,
Steffen Ritter
J. L. Robert
R.A. Roberts
N. RochatJ. RödelJohn M. Rodgers
P. Rödhammer
P. Rodriguez
L. M. Rogers
J. Roget
K. Rokugo
I. Roman
Igor Maria De Rosa,
A. P. G. Rose
Joseph L. Rose,Z. Rosecky

20-27420-30010-107
2-159
4-S102, 6-209, 7-S1, 8-S126, 8-S140, 8-S149,
11-43
4-S98
8-S297
8-S28
27-2544-S82, 5-25
4-S147, 6-19
7-S29, 8-S262
7-135, 11-101
8-S28426-12017-S92, 18-118, 20-188, 22-049, 24-022, 26-098
4-S232, 5-67, 5-144, 9-197, 11-107, 12-15, 17-83
14-11925-331
14-S74, 22-083
18-96, 18-211, 25-348
4-S26915-S1072-159, 4-S296, 5-S63
6-257
17-S702-29
10-S2223-025
18-41
14-S47
4-S300, 6-43, 11-11
15-S107
9-91
17-S78
1-114, 4-S155, 4-1, 25-286
20-257
4-S102, 8-S140, 8-S149, 11-43
2-319
4-85, 5-S60, 5-S66, 8-S231, 8-34
19-1342-64, 3-19, 3-41, 3-130, 4-S106, 4-S111, 6-73, 8S109, 8-47, 10-31
25-080
1-213
12-23
20-02523

Sabine Rosner
J. N. Rossettos,
Мikhail Rostovtsev
R. Rothea
D. Rouby
C. Rowland,
C. RoyP. R. Roy
Gottfried A. Rubin
D. Rubio
Thomas P. Runarsson,
J. E. Ruzzante

22-110, 25-149, 26-014
12-1
26-311
19-2293-176, 9-117
18-87, 18-239
8-S292, 12-45
7-S40, 4-S251
10-S18, 11-19
10-13
25-252, 25-260, 26-229, 26-262
10-13, 17-61

H. Saadaoui,
Wolfgang Sachse
S. Sagat
Kouki Saiga
Masahiro Saito,
Naoya Saito
Koji Sakai
Norio Sakaino
K. Sakamaki
Kiyoshi Sakamaki
N. Sakata
Yasunori Sakata
Tatsuro Sakimoto
Takumi Sakuma
C. SalaH. M. Sallam
Pekka SalmenperäPeter Sammonds,
A. Sampath
R. SamuelR. N. Sands
A. S. Sankaranarayanan,
N. Saniei
Mary Sansalone
Carlo Santulli
Fabrizio Sarasini,
Hiroaki Sasaki
Soji Sasaki
S. SaseL. Hanumantha Sastry
Akihiro Sato,
Ichiya Sato
Isamu Sato
Kazuhiko Sato,
Kazuhisa Sato
K. Sato12-45
4-S62, 8-S20, 8-S170, 11-79
5-S16
26-10913-S6815-19, 16-S289
10-S104
15-S50
16-S134, 18-68
21-206, 21-223
16-S75
15-S70
7-167
16-S196
2-11
14-85
21-23013-S21
5-S12
7-S35
4-31
12-111
15-33
5-S24
26-22025-080
16-S25
2-1
17-15
11-101
19-2022-1, 7-173, 8-S213
8-35
13-S54, 13-S75, 19-109, 23-119
24-215
16-S4524

Keiichi Sato,
Kouichi Sato
Takashi SatohMarkus G. R. Sause
S. G. Savanur
M.W. Scaife,
C. M. Scala
C. R. Scales,
ULRICH SCANZ
G. Schauritsch,
Christian Scheer,
R. Schelling
C. Schepacz
Jerzy Schmidt
JONATHAN J. SCHOLEY,
H.J. Schoorlemmer,
Frank Schubert
M. Schulz
Daniel SchultheißK. Schumacher
Thomas Schumacher,
D. Schumann
H.-J. Schwalbe
J. S. Schwartzberg
Ian G. Scott
C. B. Scruby
Petr Sedlak
C. Seguf
Kazuyoshi Sekine
G. P. Sendeckyj
Hirofumi Sentoku
U. Senturk
M. SetoMasahiro Seto,
B. K. ShahS. P. ShahR. Douglas Sharp
V.V. Shemyakin,G. Shen,Gongtian Shen
H.W. Shen
Ping Shen
Nanling Shi
K. Shibata
M. Shibata
Mitsuhiro Shigeishi,
Hisatoshi Shimada
HIROYUKI SHIMIZU,
Shigeo Shimizu,

4-S240, 8-S213, 9-209, 13-S42
7-173
23-10226-001
7-S18
18-2112-261, 6-249, 10-49
25-05127-167
18-13825-331
20-2382-267
23-173, 24-222, 27-272
27-291
18-180, 20-238
22-147
20-17226-001
9-103
25-316
3-172
22-236
10-97
4-S142
3-182, 4-9, 7-81, 7-211, 8-S158, 8-S201, 9-243, 9-255
26-182
5-S49
23-233, 27-281
11-33
16-S1915-S117
11-S1911-S27
7-S13
10-S973-118, 4-S259, 4-115
19-172
8-S97
16-S243
15-S105, 18-189
15-S123
16-S317
8-S1453-144, 3-190, 4-93,
11-S57, 13-S14, 15-S50, 15-S60, 16-S65
10-S104
27-19419-19625

Tatsuya Shimizu,
Takayuki Shimoda
J. Sikula
T. Simo
Masayuki Shimojo
Noboru Shinke
Takuo Shinomiya
Tomoki Shiotani
M. Shiwa
Donald A. Shockey
ANDREY SHVEDOV
Ménad Sidahmed
Khalid J. Siddiqui
J. Siedlaczek
Joanna Sikorska
Josef Sikula
F. Simacek
A. K. Singh
A. K. Sinha
N. K. Sinha
Petr Sittner
Olivier Skawinski
C. Sklarczyk
A. Skraber,
Jerzy Skubis
A. Slimani
Daniel R. Smith Jr.
J. H. Smith
M. Sobczyk
T. Sogabe
V.L. Sokolov,
Amir Soltani
Nobukazu Soma
Jun-Hee Song
M. SorelM. Ben Souda
P. Souquet
J.C. Spanner, Sr.
L. M. Spasova,
Th. Spies
Nicholas S. Spivey,
Wojciech Spychalski,
M. K. Sridhar
K. V. Srincivasan,
T.S. Sriranga
K. A. Stacey
J.A. Steel,

13-S6820-19417-S100
17-S6516-S196, 19-085
10-25, 16-S134 , 23-164, 24-052
20-145
15-S50, 16-S95, 18-248, 19-118, 19-142, 20-145, 20-153.
21-166, 22-039, 23-260, 24-205, 25-069, 25-308, 27-186,
27-263
4-S195, 21-197,
21-00127-21220-3003-118, 9-9
10-1
22-264, 22-274
22-091, 26-182, 27-157
10-67
4-S174
7-S13
4-S29020-16326-120
8-S93
18-1442-261, 4-S82, 5-25
5-S42
4-S135, 7-9
8-S79
8-S19221-120
18-111, 20-218, 26-279
4-S17
23-12916-S309
2-261
11-11
4-85, 5-S60, 8-S231
6-121
25-051
15-S119, 19-100, 19-153, 24-179
25-18725-166, 26-317
4-S174
8-S8
7-S35
7-81
18-96, 18-211, 25-348
26

Wolfgang Stengel
D. Stöver
H. Strauven,
V. Streicher
V. A. Strelchenko
V. A. Strizhalo
S.A. Strizkov
C. E. Stuart,
George Studor
V. Suba
S. V. Subba Rao
Iyer Subramaniam
H. N. Sudheendra
TETSUYA SUEMUNE
C. Y. Suen
Katsuhiro Sugawara,
SOTA SUGIMOTO,JAMIL SULEMAN,
J. Summerscales
C. T. Sun
X. SunJ. Q. Sun
M. J. Sundaresan
M. Surgeon
Hiroaki Suzuki
Ippei Suzuki
M. Suzuki
Masahiko Suzuki
Tetsuya Suzuki
Toshio Suzuki,
Toshitaka Suzuki
P. Svadbik,
V. Svoboda
Terry L. Swanson
Grzegorz Swit

4-S312
4-S78
13-79
8-S53
8-S3348-S33419-172
12-S1223-037
17-S208-S8
4-S1748-S28827-137
9-9
13-S5427-08927-0404-S170
7-21
8-S262
8-S1148-S27715-S105, 18-34
11-117, 14-35, 14-69, 15-19, 15-S108, 16-S178, 16-S289,
24-012
7-179
9-277
5-61
22-030, 23-272
13-S892-1
18-29
17-S2, 17-S83
8-1, 8-S236
24-187

Tatsuo Tabaru,
TOYOKAZU TADA
A.N. Tafuri
V.L. Tahiri
F. Taioli,
H. Takagi
Hideaki Takahashi,
K. Takahashi
Shin Takahashi
Katsutoshi Takano
Atsushi Takashima,
Kazuki Takashima,

25-10727-23315-S104
16-S299
8-S42
16-S1426-261, 7-1, 13-S68
16-S324
23-09124-111
19-10912-S18, 13-S08, 16-S150, 19-085
27

S. Takashina,
Nobuo Takeda,
Mikio Takemoto

Hajime Takeuchi
K. Takigawa
Masanori Takuma
S. Talebi
Okiharu Tamura
H. Tanaka
M. Tanaka
T. TanakaToshiyuki Tanaka
S. Tanary,
N. Tandon
Daiki Tani
Yoshihiro Taniyama,
O.B. Tarutin
Masayuki Tateno,
A. Taylor,
M. A. Taylor
C. M. Teller
Hans Maria Tensi
H.B. Teoh
Satoshi Teramura
Giovanni P. Terrasi
Christian Tessier
R. Teti
Lawrence W. Teufel
A. TEWFIK
Kazuhiko Tezuka
N. A. Thakkar,
Christian Thaulow
Heinrich Theiretzbacher
W. Thelen
Pete Theobald
Richard E. Thill
P.M. Thompson
D. D. ThorntonPeter G. Thwaite
R. M. Tian
B. Tirbonod
Anil Tiwari
G. P. Tiwari

18-10212-1277-185, 11-117, 14-35, 14-69, 15-19, 15-S108, 16-S115,
16-S178, 16-S289, 18-51, 18-102, 18-286, 19-011, 19-035,
19-063, 19-075, 21-120, 21-131, 22-119,
21-120, 21-131, 22-119, 22-224, 23-072, 23-156,
23-196, 23-181, 23-215, 23-277, 24-012, 24-067, 24-084,
24-127, 24-161, 25-124, 25-157, 25-172, 25-179, 25-267
27-241
9-209
8-S21316-S134, 23-164, 23-206, 24-052
8-S25416-S178
21-166
10-S55
8-S213
7-173
8-S314, 12-39
9-25, 17-23
24-15325-15715-S120
11-S75
18-2398-S322
11-27
22-S01
3-19, 3-130, 6-1
6-261, 7-1
26-15226-032, 27-254
3-158, 5-156
15-S124
27-17623-12925-348
4-S211
4-S157
14-11526-091
10-S77
6-93
23-33114-vi (3/4)
4-S94
8-S84
14-53
4-S102, 7-S43
28

R.G. Tobin
Akira Todoroki
S. Tomecka-Suchoń ,Yuichi Tomoda
H. Tonda,
F. Tonolini
G. Tonolini
V. Torra
T. Toutountzakis,
D.T. Tran
H. Traxler
D. Tsamtsakis,
P. Tscheliesnig
Ming-Kai Tse
A. Tsimogiannis,
Yukiya Tsuchida
N. Tsui
Nobuyuki Tsuji
Yusuke Tsukahara,
Koichi Tsukiyama
F. R. Tuler

8-25
26-109, 27-089
12-S7
15-S31, 21-157, 23-272, 25-021
13-S08
8-S62
4-S86
5-S49
25-033
8-25
20-257
13-79
4-S157, 17-S108, 18-138, 18-167, 20-129, 20-179, 22-201,
25-276
4-S127, 8-S188, 8-S209
18-21, 18-224, 22-059
21-17621-21315-S6013-23
6-261, 7-1
8-S79

F. Uchida,
Junichi Uchida
Atsushi Uchiyama,
Farid A. K. M. Uddin
SHUICHI UENO
Toshiyuki Uenoya,
Takateru Umeda
Runar Unnthorsson,
Taketo Uomoto
T. I. Urbancic
E. Uria,

18-102, 19-075
23-181
13-S4223-13627-241
13-S95, 15-S112
20-24825-252, 25-260, 26-229, 26-262
6-137
8-S254
13-79

S.J. Vahaviolos
S. Vajpayee
Carlos M. Valdes-Gonzalez,
H. Vallen,
J. Vallen,
P.J. Van De Loo,
D. R. V. Van Delft
Koen Van Den Abeele
Gert Van Dijck,
S. Van Huffel,
J. G. M. Van Mier
R. Van Nieuwenhove,
Donald W. Vannoy
D. Varchon

3-i(1), 4-S329, 15-S109, 18-189, 18-248, 18-272, 19-172
5-S12
12-117
18-167, 18-258, 18-265, 22-102, 25-132
18-265, 25-132
18-174, 20-238
20-22922-25322-253
18-41
20-15318-2934-S30720-28529

Alex Vary,
F. A. Veer
Vasisht Venkatesh
Vincenzo Venturi,
E. Verbrugghe
I. Verpoest
A. Vervoort
R. Vijayaraghavan,
G. Villa
M. R. Viner
A. Vinogradov
A. Yu. Vinogradov
P. Vionis
R. Visweswaren
L.E. Vlasov,Aaron C. Voegele
J. von Stebut,
Y. Vougiouklakis

8-S175, 12-i (1/2), 12-71, 12-79, 14-53
8-S118
14-61
25-32411-1
4-S186, 8-S272, 26-240
20-292
7-S13
4-S86
8-S192
17-1, 27-144, 27-212
16-S158
18-217, 20-229
4-S207
18-150
17-83
18-25820-265

Toshiya Wada
Adrian P. Wade
Haydn N.G. Wadley
N. Wakabayashi
Shuichi Wakayama,

23-150
10-71
5-S69
10-S42
4-S278, 12-S24, 16-S170, 21-149, 23-150, 24-173, 24-228,
26-160, 27-137
12-S1
12-S1
7-31
25-187
8-S97
19-02214-i, 14-v, 14-vi (3/4), 14-S47
11-107
8-S93
15-S104
3-41
20-001
13-S42
3-59
2-1
23-11918-232
4-S17, 8-S258
15-S104, 19-172
4-S54, 5-S40
18-29
4-S132, 8-S197
11-S75
14-S74

Teruyuki Waki,
Akimasa Waku,
D. A. Waldrop
James L. Walker II,
Y. Wan,Y. Wang,
Alexander Wanner
Kris A. Warmann
E. Waschkies,
G. Washer
M. Watad
Hiroshi Watanabe
Naoaki Watanabe,
T. Watanabe
Takashi Watanabe
Yoshinori Watanabe
J.R. Watson,
Robert J. Watters
D.J. Watts
Richard L. Weaver
Z. Weber,J. R. Webster
Qiang Wei
B. Weiler

30

M. Wevers,J. D. Wood
D. G. M. Wood
G. Wormser
Amelia P. Wright,
Bobby Wright
Min WuWei Wu,Doone Wyborn

4-S186, 8-S272, 13-79, 15-S105, 18-34, 18-41, 20-206,
20-292, 22-253, 26-240
1-263
1-147, 4-S247, 5-148, 6-257, 7-31, 7-95, 8-S280,
9-75, 9-84, 10-113
27-291
10-S22
10-67
1-244
2-191
ICAE Banquet
20-206, 22-253
27-291
10-67
3-239
20-2383-51
6-125, 6-239, 8-S14, 8-S66, 10-S29, 10-S59, 15-S113,
17-121
8-S318
4-71
5-S52
25-115
4-S166
11-5
25-11523-129, 27-167

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

8-S246
4-S215
3-81

H. Yamada,K. Yamada
M. Yamada
Minoru Yamada
Yoshiaki Yamade,
Hiroshi Yamaguchi
Katsuya Yamaguchi
Kusuo Yamaguchi
T. Yamaguchi
Koji Yamamoto
Hirofumi Yamasaki
Akihiko Yamashita,
Akio Yamashita
T. Yamauchi
Yasunori Yamazaki
Tinghu Yan
W. Yan

18-51
6-13
21-21323-233, 23-243
12-12724-111
9-209
4-S191, 4-S286, 4-S325, 8-S1
8-S14519-19624-111
13-S894-S286
11-21
24-097
17-49
15-S109

R. G. WhiteJ. W. Whittaker
PAUL D. WILCOX,
B. J. S. Wilkins
H. Willer
A. J. Willis
P. E. Wilson
Leo Windecker
M. Winkelmans
MICHAEL R. WISNOM,
E. Winter
S. M. Wolf
A. Wolfert
Jörg Wolters
Brian R. A. Wood

31

M. Yanagibashi
Masa-aki Yanaka,
Takahito Yanase
J. M. Yang
Xuanhui Yang
C. K. Yao
Takeshi Yasuda
A. Yasuo
Daisuke Yasuoka
J.J. Yezzi, Jr.
Akira Yoneda
Takao Yoneyama
Akio Yonezu
Dong-Jin Yoon,
Kenichi YoshidaToshikatsu Yoshiara
Sumio Yoshikawa
Tatsuhiko Yoshimura
Hiroshi Yoshino,
H. Yoshioka
Takeo Yoshioka,
R.D. Young
R. P. Young
R. Paul Young
Y. Youssef,
Qing Huan Yu
Hiroo Yugami
Syuro Yuji,
Hironobu Yuki
Kunihiro Yuno
Shigenori Yuyama,
J. Zaloudek
F. Zeides
Bajram Zeqiri
Kornelija Zgonc,
B. Q. Zhang
Fan Zhang
Zhizhen Zheng
X. Q. Zhu
Z. ZhuZu-Ming Zhu
Zuming Zhu
B. Ziegler
J. Zietek,
Steve Ziola
B. Zogala,8-S213
13-23
23-096
8-S26815-S123
4-S94, 8-S105
24-153, 26-270
4-S294
15-S60
15-S104
24-0972-1, 7-173, 8-S213
23-156, 23-196, 23-277, 24-127, 24-161
9-237, 13-S83, 15-S11
16-S142, 18-68, 19-022, 21-206, 21-223, 22-159,
23-189, 24-153, 26-270
19-202
8-113
6-109, 6-145
12-S1
10-S6319-196
6-93
8-S38, 8-S166, 8-S254
5-S29, 5-S34, 25-294
12-39
8-41
24-215
7-167
10-35
11-S892-19, 2-71, 4-S38, 13-S14, 15-S107, 16-S75, 18-248,
19-118, 19-184, 21-187, 21-213, 23-233
17-S65
8-S109, 8-47, 10-31
26-091
11-79
4-S94, 8-S49, 8-S105, 8-S114
18-144, 20-300
15-S123
4-S215
8-S135
6-115
16-S31722-236
12-S7
8-51, 14-S12
18-15
32

Daihua Zou
Ryszard Zuchowski
J. Zuidema

5-S1
2-272
8-S11833