Journal of Acoustic Emission, Volume 28, 2010
VIEWING AN ARTICLE IN CD-ROM
You need a software that can read a pdf-file, such as Adobe Acrobat Reader®. Install one first before
going further. You can download the latest version from www.adobe.com. Use of Adobe Acrobat® (notprovided) allows one to utilize the Search function. This gives the power to search for a name or a wordin the entire volume and is highly recommended.
PDF Files: Contents are listed below. Individual papers have file names of 28-xxx.pdf, with xxx as theirfirst page number.

CONTENTS
28-001 ESTIMATION OF CRUSTAL STRUCTURE IN HORONOBE AREA, HOKKAIDO,JAPAN, USING MULTIPLET-CLUSTERING ANALYSISHIROKAZU MORIYA, KOICHI ASAMORI, ITARU KITAMURA, HIKARU HOTTA,HIDEFUMI OHARA and TADAFUMI NIIZATO
1-10
28-011 ACOUSTIC EMISSION TESTING - DEFINING A NEW STANDARD OF ACOUSTICEMISSION TESTING FOR PRESSURE VESSELS
Part 2: Performance analysis of different configurations of real case testing andrecommendations for developing a new guide for the application of acoustic emission
JOHANN CATTY
11-31
28-032 ACOUSTIC EMISSION MONITORING OF STEEL-FIBER REINFORCED
CONCRETE BEAMS UNDER BENDING
DIMITRIOS G. AGGELIS, DIMITRA SOULIOTI, NEKTARIA M. BARKOULA, ALKIVIADIS
S. PAIPETIS, THEODORE E. MATIKAS and TOMOKI SHIOTANI
32-40
28-041 ON LAMB MODES AS A FUNCTION OF ACOUSTIC EMISSION SOURCE RISE
TIME
M. A. HAMSTAD
41-58
28-059 WAVEFORM ANALYSIS OF ACOUSTIC EMISSION MONITORING OF TENSILETESTS ON WELDED WOOD-JOINTSANDREAS J. BRUNNER, THOMAS TANNERT and TILL VALLÉE
59-67
28-068 USE OF ACOUSTO-ULTRASONIC TECHNIQUESTO DETERMINE PROPERTIESOF REMANUFACTURED PARTICLEBOARDS MADE SOLELY FROM RECYCLEDPARTICLESSUMIRE KAWAMOTO
68-75
28-076 ACOUSTIC EMISSION ACTIVITY OF SPRUCE SAPWOOD BECOMES WEAKER
AFTER EACH DEHYDRATION-REWETTING CYCLESABINE ROSNER and SUMIRE KAWAMOTO
76-84

I-1

	
  
28-085 ACOUSTIC EMISSION SOURCE LOCATION IN PLATE-LIKE STRUCTURES
USING A CLOSELY ARRANGED TRIANGULAR SENSOR ARRAYDIRK ALJETS , ALEX CHONG , STEVE WILCOX and KAREN HOLFORD
85-98
28-099 NEURAL NETWORK AE SOURCE LOCATION APART FROM STRUCTURE SIZEAND MATERIALMILAN CHLADA, ZDENEK PREVOROVSKY and MICHAL BLAHACEK
99-10828-109 INTELLIGENT AE SIGNAL FILTERING METHODSVERA BARAT, YRIJ BORODIN and ALEXEY KUZMIN

109-11928-120 DISCRIMINATION OF ACOUSTIC EMISSION HITS FROM DYNAMIC TESTS OF
A REINFORCED CONCRETE SLAB
ENRIQUE CASTRO, ROSA PIOTRKOWSKI, ANTOLINO GALLEGO and
AMADEO BENAVENT CLIMENT
120-128
28-129 USE OF CLUSTER ANALYSIS OF ACOUSTIC EMISSION SIGNALS IN
EVALUATING DAMAGE SEVERITY IN CONCRETE STRUCTURES
L. CALABRESE, G. CAMPANELLA and E. PROVERBIO
129-141
28-142 SIMULATION OF LAMB WAVE EXCITATION FOR DIFFERENT ELASTIC
PROPERTIES AND ACOUSTIC EMISSION SOURCE GEOMETRIESMARKUS G. R. SAUSE and SIEGFRIED HORN
142-154
28-155 ACOUSTIC EMISSION EVENT IDENTIFICATION WITH SIMILAR TRANSFERFUNCTIONSFRANZ RAUSCHER
155-162
28-163 ANALYSIS OF FRACTURE RESISTANCE OF TOOL STEELS BY MEANS OF
ACOUSTIC EMISSIONEVA MARTINEZ-GONZALEZ, INGRID PICAS, DANIEL CASELLAS and JORDI ROMEU
163-169
28-170 COMPARISON OF ACOUSTIC EMISSION SIGNAL AND X-RAY DIFFRACTION
AT INITIAL STAGES OF FATIGUE DAMAGE
FRANTISEK VLASIC, PAVEL MAZAL and FILIP HORT
170-178
28-179 AE SIGNALS DURING LASER CUTTING OF DIFFERENT STEEL SHEETTHICKNESSES
TOMAŽ KEK and JANEZ GRUM
179-187
28-188 ACOUSTIC EMISSION ANALYSIS AND THERMO-HYGRO-MECHANICALMODEL FOR CONCRETE EXPOSED TO FIRE
CHRISTIAN GROSSE, JOŠKO OŽBOLT, RONALD RICHTER
and GORAN PERIŠKIĆ
188-203I-2

	
  28-204 AE-SiGMA ANALYSIS IN BRAZILIAN TEST AND ACCELERATED CORROSIONTEST OF CONCRETE
MASAYASU OHTSU and YUMA KAWASAKI
204-21428-215 ACOUSTIC EMISSION INSPECTION OF RAIL WHEELSKONSTANTINOS BOLLAS, DIMITRIOS PAPASALOUROS, DIMITRIOS KOUROUSIS andATHANASIOS ANASTASOPOULOS
215-228
28-229 E07.04 – OVERVIEW OF CURRENT AND DEVELOPING ASTM ACOUSTICEMISSION (AE) STANDARDSMARK F. CARLOS
229-­‐233
28-234 USE OF AE METHOD FOR DETECTION OF STEEL LAMINATION IN THE
INDUSTRIAL PRESSURE VESSEL
V.P. GOMERA, V.L. SOKOLOV, V.P. FEDOROV, A.A. OKHOTNIKOV
and M.S. SAYKOVA
234-245
28-246 COMPARISON OF ACOUSTIC EMISSION PRODUCED DURING BENDING OF
VARIOUS OXIDE CERAMIC AND SHORT FIBER OXIDE CERAMIC MATRIXCOMPOSITES
S.A. PAPARGYRI-MPENI, D.A. PAPARGYRIS, X. SPILIOTIS and A.D. PAPARGYRIS
246-275
28-256 NEW CHARACTERIZATION METHODS OF AE SENSORSKANJI ONO, HIDEO CHO and TAKUMA MATSUO

Contents28
AUindex28
AusNotes

256-277

Contents of Volume 28 (2010)
Authors Index of Volume 28
Policy/Author’s Notes/Meeting Calendar/DVD/
Subscription Information
JCAE Kishinoue Awards

I-1 – I-3
I-4
I-5 – I-7
I-7
I-8

AE Literature
AELit28
Book on AE by Markus Sause
12th AE conference proceeding in China, 2009: Gongtian Shen

I-9 – I-11

IAES20Cover illustration

See 28-142 by Sause and Horn for details.

JAE Index Folder*

Cumulative Indices of J. of Acoustic Emission, 1982 – 2010
Contents1-28
Contents Volumes 1-28
Authors Index1-28 Authors Index Volumes 1-28

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

I-3

	
  

AUTHORS INDEX, Volume 28, 2010
DIMITRIOS G. AGGELIS
28-03228-085DIRK ALJETS
ATHANASIOS ANASTASOPOULOS 28-215
KOICHI ASAMORI
28-001VERA BARATNEKTARIA M. BARKOULA
AMADEO BENAVENT CLIMENTMICHAL BLAHACEKKONSTANTINOS BOLLASYRIJ BORODIN
ANDREAS J. BRUNNER

28-10928-03228-12028-09928-21528-10928-059L. CALABRESE
G. CAMPANELLAMARK F. CARLOSDANIEL CASELLASENRIQUE CASTROJOHANN CATTYMILAN CHLADA
HIDEO CHO
ALEX CHONG

28-12928-12928-22928-16328-12028-011
28-09928-25628-085

V.P. FEDOROVANTOLINO GALLEGO
V.P. GOMERACHRISTIAN GROSSE
JANEZ GRUM

28-23428-12028-23428-18828-179M. A. HAMSTADKAREN HOLFORD
SIEGFRIED HORNFILIP HORTHIKARU HOTTA

28-04128-08528-14228-17028-001SUMIRE KAWAMOTOYUMA KAWASAKITOMAŽ KEKITARU KITAMURADIMITRIOS KOUROUSISALEXEY KUZMIN

28-068, 28-076
28-20428-17928-00128-21528-109EVA MARTINEZ-GONZALEZ
THEODORE E. MATIKAS
TAKUMA MATSUOPAVEL MAZALHIROKAZU MORIYA

28-16328-03228-25628-17028-001

I-4

TADAFUMI NIIZATOHIDEFUMI OHARA
MASAYASU OHTSU
A.A. OKHOTNIKOVKANJI ONOJOŠKO OŽBOLT

28-00128-00128-20428-23428-25628-188ALKIVIADIS S. PAIPETISDIMITRIOS PAPASALOUROS
S.A. PAPARGYRI-MPENI
A.D. PAPARGYRIS
D.A. PAPARGYRISGORAN PERIŠKIĆINGRID PICASROSA PIOTRKOWSKIZDENEK PREVOROVSKY
E. PROVERBIO

28-03228-21528-24628-24628-24628-18828-16328-12028-09928-129FRANZ RAUSCHERRONALD RICHTERJORDI ROMEUSABINE ROSNER

28-15528-18828-16328-076MARKUS G. R. SAUSE
M.S. SAYKOVATOMOKI SHIOTANI
V.L. SOKOLOVDIMITRA SOULIOTI
X. SPILIOTIS

28-14228-23428-03228-23428-03228-246

THOMAS TANNERTTILL VALLÉE
FRANTISEK VLASICSTEVE WILCOX

28-05928-05928-17028-085

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©2010 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 2011 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. Journal references should bearranged as below. Titles for journal or book articles arehelpful 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 by e-mail or to:Professor Kanji Ono, Editor - JAE
Rm. 2121H, Engr. V, MSE Dept.
University of California
420 Westwood Plaza,
Los Angeles, California 90095-1595 USA
e-mail: kanjiono1@gmail.com

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:
Acoustic Emission Collection
Grainger Engineering Library
University of Illinois at Urbana-Champaign.
c/o: Mary C. Schlembach, Engineering Physics &Astronomy Librarian
154 Grainger Engineering Library Information Center
1301 West Springfield Ave. Urbana, IL 61801
Email: schlemba@illinois.edu

A.E. Lord, Jr. (1975), Physical Acoustics: Principles andMethods, vol. 11, eds. W. P. Mason and R. N. Thurston,Academic Press, New York, pp. 289-353.
Abbreviations of journal titles should follow those used in
the ASM Metals Abstracts. In every case, authors' initials,
appropriate volume and page numbers should be included.
The title of the cited journal reference is optional.
Illustrations and tables should be planned to fit a single
page width (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.

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.

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. On the INTERNET, you can send an MS
Word file and the separate figure files (in JPEG or otherformats) 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
With the new format, authors are encouraged to use them.

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

I-6

MEETING CALENDAR:
World Conference on Acoustic Emission - 2011 Beijing
WCAE-2011 is the first international event on AE organized in China. It is organized by the Chinese
Society for Non-destructive Testing (ChSNDT) and endorsed by Chinese Mechanical Engineering Society. WCAE-2011 is undertaken by Technical Committee on Acoustic Emission of ChSNDT. The primary objective of the WCAE 2011 is to exchange research and application information on AE, with particular emphasis on scientific and technical development and cooperation between China and the world.
The official language of this event is English. WCAE-2011 is refereed conference and the papers will bepeer-reviewed. Abstract deadline is May 15, 2011. Gongtian Shen, Chairman; www.wcae2011.org
Mr. Zhanwen Wu, WCAE-2011 Secretariat; E-mail: wcae2011@163.com, wcae2011@vip.csei.org.cn

EWGAE30/ICAE7EWGAE-2012: 30th European Conference on Acoustic Emission Testing will be held September 12-15,
2012, at Granada, Spain. This doubles as the 7th International Conference on Acoustic Emission with thesupport of the AEWG (Acoustic Emission Working Group), JCAE (Japanese Committee on Acoustic
Emission) and GLEA (Grupo Latinoamericano de Emisión Acústica). It is hosted by the University ofGranada and Antolino Gallego is the organizer. More information is available at
http://www.ewgae.es/en/conference-area/welcome-letters.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).

2011 SUBSCRIPTION RATES
Base rate (CD-ROM) for one year
$111.00
CD-ROM + printed copy, US priority mail:$151.00
CD-ROM + printed copy, non-US priority mail:$160.00
Print copy only with priority shipping (US)$140.00
Print copy only with priority/air shipping (non-US)$149.00
Payment must be in U.S. dollars drawn on a U.S. bank. Bank transfer accepted at
California Bank and Trust, San Fernando Valley Office(Account No. 080-03416470)
16130 Ventura Blvd, Encino, CA 91436 USA(Swift code: CALBUS66)PAYPAL payment is accepted. Inquire via e-mail.
Back issues available in CD only; Inquiry and all orders should be sent to (Fax no longer available):
Acoustic Emission GroupPMB 409, 4924 Balboa Blvd. Encino, CA 91316 USA
For inquiry through Internet, use the following address:
Editor-Publisher
Publication Date of This Issue (Volume 28):

I-7

aegroup7@gmail.com
Kanji Ono Tel. (818) 849-9190
30 March 2011.

20th International Acoustic Emission Symposium
hosted by Kumamoto University, held in Kumamoto, Japan, on November 16-19, 2010

JCAE Kishinoue Awards
Kishinoue Awards of the Japanese Committee on Acoustic Emission were presented toProfessors Gerd Manthei and Kanji Ono for their outstanding contributions to the field of
acoustic emission on Nov. 18, 2010 at the 20th IAES at Kumamoto. Two student paper
awards were accepted by their mentors, Profs. S. Wakayama and A. Vinogradov.

From L to R: Gerd Manthei, Kanji Ono, S. Wakayama and A. Vinogradov.

I-8

AE LITERATURE
Book on AE
M. G. R. Sause, Identification of failure mechanisms in hybrid materials utilizing patternrecognition techniques applied to acoustic emission signals. ISBN: 978-3-86664-889-0,
mbv-Verlag, Berlin, 305 pg. (2010).
This new book is a PhD thesis of Markus Sause and can be purchased via Amazon.de. The".de" does matter, since Amazon.com is not able to find the book. Since most of theinternational community may struggle to find the book on a german webpage, the author
offers to handle the book order. Send an email and transfer funds (59€ + shipping).
Dr. Markus Sause
Institute for Physics
Augsburg University
Universitätsstr. 1
D-86159 Augsburg, Germany
markus.sause@physik.uni-augsburg.de

Chinese Acoustic Emission Conference, 2009
Proceedings of 12th National AE Conference of China, July 31-August 3, 2009,
Nanjing, China
Compiled by Gongtian Shen
Technical Review
The Development of Acoustic Emission Testing Technologies in China
DAI Guang，SHEN Gong Tian（1）Progress and Status of Research on Acoustic Emission Sensors Calibration
HE Longbiao，YANG Ping，LI Guanghai（11）Influences of the Microstructure on the Acoustic Emission of the Metallic Materials
Han Zhiyuan，LUO Hongyun（18）Research on Applicability of Acoustic Emission Technology in Complex Environment
BAI Lu，ZHANG Hui，LIU Yantao et al.（25）
Applications of Acoustic Emission Technology on Composite Materials Damage Detection
GONG Lihai，YANG Nengjun（30）Application and Development of Acoustic Emission Technology
JIANG Alan，ZHAO Yinghua，ZHANG Liwei（37）Instrument & Signal Processing
Development of Leakage Location Testing Instrument For Underground Gas Pipeline
SHEN Gong Tian，LIU Shifeng，WANG Wei（47）Development of the AE System Based on the Special USB2.0 Acquisition Card
LIN Dongxia，HU Bin，WANG Xudong et al.（54）The Design of A Novel Fiber Optic Acoustic Sensor System
XU Zhihong，Fahard Ansari（64）Pattern Recognition of Acoustic Emission Signals from the Flaws and Damages in WPC and DSP Realization
YUAN Zhe, LIU Yunfei, YIN Dongmeng et al.（72）
Acoustic Emission Monitoring System Based Matlab
LIU Lei, PAN Yongdong,LIU Wuxiang et al.（79）Applications of Uitrasionic Detection
I-9

Technology on Geophysical Test About Natural Gas Hydrate
YE Yuguang, HU GaoWei, ZHANG Jian et al.（83）
Numerical Simulation of Acoustic Emission Signal Produced by Hypervelocity Impact on Aluminum Plate
LIU Wugang, HUANG Zehuan, LIU Yantao et al.（88）
The Research of Time Delay Estimation Algorithm
LIU Weidong, ZHANG Shen, XUN Guangshuai et al.（93）A Method of Comparing Sensitivity of Sensor Using Bottom Plate Testing
DUAN Qingshu, MA Juan, ZHU qin et al（99）Research on Simulated Source in the Acoustic Emission Sensor Calibration
HUANG Shan, LI Da zi, He Longbiao（107）Theoretical Analysis and Experimental Investigation on Sound Source Location for Thick-wall Pressure Vessel
GUO Xiaolian, XU Yanting, LIU Fujun, et al（114）
Study on pattern recognition of Crack Acoustic Emission Using Neural Network in Fatigue Testing of Fighter
Aircraft
GAO Xiaxia, LIU Jian Jun, Wang Wei（123）
Acoustic Emission Signal Analysis Based on HHT For GFRP
LI Wei, JIANG Peng, DING Lei et al.（130）
Applications of Morphology Filter in Acoustic Emission Signal Processing For Rolling Bearing Faults
HAO Rujiang MA Bingyu（136）
Applications of Correlation Analysis in Locating Defect By Acoustic Emission Testing
ZHAO Meiyun, LI Li, LI Xiujuan（144）
Applications of Structure
Acoustic Emission Based Integrated NDT in Full Scale Aircraft Fatigue test
GENG Rongsheng（150）
Investigation of Acoustic Emission Technique (AET) Application on Bridge Cranes Testing
SHEN Gong Tian, WU Zhan Wen（158）
Research on Acoustic Emission Signal of Active Defect in Crane Beam
LI li, CHEN XianQian, ZHAO Meiyun, et al.（166）
Acoustic Emission Testing of Crane Hanger in Power Plant
CHEN XianQian, LI li, DENG Daijun, et al.（171）
Study on Acoustic Emission Testing for Atmospheric Storage Tanks
LI Guanghai, SHEN Gongtian, YAN He（177）
Dependability of Acoustic Emission Inspection of Vertical Storage Tanks
XU Yanting, WANG Yadong, XU Jiele, et al.（184）Preliminary Exploration About Acoustic Emission Position of Influencing Factors in Tank Bottom Testing
TIAN Yatuan, JIANG Shiliang（195）
On-line Detection and Safety Assessment of Dangerous Chemical Bottom of Tanks by Acoustic Emission
FANG Jiangtao, GUO Chao, XIE Tao（201）
Application of Acoustic Emission to Integral Panel Fatigue test
LI Zhong, HUANG Huabin, SHI Lei, et al.（207）
Research on Acoustic Emission Signal Regularity of Fatigue Damage For Birdge structure
KONG De Lian, WANG Wen You, XU Feng Jin（211）
Acoustic Emission Monitoring of Birdge Cable Rope in Fatigue Test
LUO Wen Hai, XU Feng Jin（217）
Research on Acoustic Emission Signal Feature of Fatigue Damage For Birdge structure
YANG Jian Feng, WANG Wen You, KONG De Lian, et al.（225）
Research on Acoustic Emission Sources Orientation of Jacket Offshore Platforms
SUN Haijiao, LIN Zhe, Zhao Deyou（231）
Acoustic Emission Testing of Backbone in Large Cable Car
WU Zhan Wen, SHEN Gong Tian WANG Yong, et al.（239）
A Brief Analysis on the Three Parts in the Acoustic Emission Monitoring of the Stability of Large Intrusion —An
Inspiration from the Acoustic Emission monitoring of the Stability of Permanent Navigation Lock’s Side SlopeHUO Zhen , CHEN Cui Mei（244）
Acoustic Emission On-line Monitoring of Bead Weld Layer Crack For Hydrogenation Reaction Chamber
JIANG jun, YI Rong（251）I-10

Acoustic Emission On-line Testing of Ammonia Reactor
LIU Jian Jun, Liu Shi Feng（269）
Application of Acoustic Emission Testing to Spherical Tank
LI Jianrong, YAO Jianping, DENG Zurong（274）
Structure integrity evaluation and safe valuations of multi-layer high-pressure tanks according to TOFD and AE
technology
SHENG Shui Ping, YU Bing, Wang Bing（279）
Research and Application the Method of On-line Acoustic Emission Periodical Test for Adsorption Tower
ZHANG Ying, DAI Guang, LiWei（284）
The Application of Acoustic Emission Technology on Coke Tower Inspection
ZHANG Zhongzheng,Liang Hua, Gong Jianming, et al.（291）
Large-sized Vessel Detecting Realized By Connecting AE Units
Duan Qingru, Ma Juan, Zhu Qin, et al.（296）
Full-life Evaluation of LPG Storage Tank Using Acoustic Emission Testing
MAO Guojun, SHEN Jianmin, Sun Lei, et al.（301）
Acoustic Emission Testing of Near Surface Crack Flaw on Spheric Tank
LI Hong Gang, YE Wei Wen, PENG GO Ping, et al.（306）
Acoustic Emission Testing Technique For Composite Pressure Vessels
LIU Zhejun, GE Li, WU Song, et al.（309）
Research on Acoustic Emission Inspection of Gas Cylinders For the On-Board Storage of Compressed Natural Gas
As a Fuel For Automotive Vehicles
LIANG Hu, JIANG Jun, YI Rong, et al.（318）Research on Acoustic Emission Testing Technique of Cylinders For Tube Trailer
WANG Yong, ZHENG Hui, LI Guang Hai, et al.（326）Research on On-line Inspection of Gas Cylinders For the On-Board Storage of Compressed Natural Gas As a Fuel
For Automotive Vehicles During the Fatigue Test
ZHANG Yao Feng, YI Rong, Jiang Jun , et al.（335）
Research on Acoustic Emission Location Test For Gas Cylinders
LI Li Fei, BO Ke, LI Bang Xian, et al.（341）
The Acoustic Emission Testing About the Primary Entering Air Pipeline of Large Structure
YAO Li（347）
Research on Spectral Characteristics of Leakage Acoustic Emission Signals For Gas Pipeline
QIN Xianyong, SHEN Gongtian, ZHANG Zheng, et al.（352）
Identification and Analysis of Acoustic Emission Signal on Pressure Pipeline Leakage
HAO Yongmei，WANG Kaiquan，ZHANG Changshun（360）
Acoustic Emission Source Assessment of Defective In-service Pipeline
SHI Ji Qing（366）Experimental Study on Leakage of Simulation Natural Gas Pipeline By Acoustic Emission Technique
ZHOU Ning, CHEN Li,WANG Xiao-Yu, et al.（371）
Inspection and Repairing of the High-pressure Accumulator
LI Jian Rong, YAO Jian Ping (375）
Application of Acoustic Emission Testing to Vessel For the 20m3Ammonia Storage
ZHENG Yuan Wei, LIU Jian Li（380）Application Status of Acoustic Emission For Bop TestingZHU XiangJun, LI Ping（384）Material Characterization
Experimental Investigation on Feature of Acoustic Emission For 16MnR_0Cr18Ni9Ti Composite Armor Plate in the
Damage and Fracture
LIANG Zhi Gang, WU Yan Hong , DING Li Wei, et al. （390）The Early Warning of Acoustic Emission Technique in Figure Test
FANG Jing, WU Hui Yong（397）Tensile Damage Analysis of Composite Unidirectional Samples Based on Acoustic Emission Inspection
HAN Kunfeng, YANG Nengjun, LONG Xianhai（401）
Acoustic Emission Real-time Monitoring of Laser Melting process For Nickel-base Alloy
LIU Zhe Jun, GE Li, YUAN Jin Ping（406）I-11

Elementary Experimental Analysis on Evaluation of the Penetration Depth of Electron Beam Weld By Acoustic
Emission Testing
SUN Chaoming, TANG Guangping, TANG Xing（414）Acoustic Emission Characteristic of Hr-2 Steel Weld joint during tension
TANG Xing, ZHANG Wei Bin, XU Lai, et al.（420）Supported AE Analysis By FEM Waveform Simulation For Proportion of Super-elastic Martensitic Transformation
Takeshi YASUDA, Baojun PANG, Hideo NISHINO, et al.（425）
Random Damage Statistics and Their Modeling Using Maximum Entropy Principle
Gang Qi, Steve F. WAYNE（432）Parameter Analysis of Acoustic Emission Signals About Magnesium Alloy Tensile Test
LIANG Zhigang, WU Yanhong, JIANG Xu-xin, et al.（439）
The AE Signature Analysis of PBX-9003 Satchel Column in the Course of Single Axle Secondary Compression
XU Lai, ZAHNG Wei Bin, Tang Xing, et al.（446）Research on Acoustic Emission of Fibre Reinforced Concrete Materials Containing Fatigue Damage
XU Feng Jing（450）
Assessment of the Damage in Carbon Fiber Reinforced Concrete By acoustic Emission and Resistance
Measurement Methods
ZHAO Jiao, XU Zhihong（456）
AE Characteristic Experiment Research of Process of Whole Stress-strain of Coal and Sandstone
YANG Yongjie,, ZHANG Yangqiang, LI Guibing, et al.（460）
Experimental Study on Shear Behavior and AE properties of Concrete Corroded By Acidic Solution
ZHANG Liwei, ZHAO Yinghua, JIANG Alan, et al.（465）Study on Acoustic Emission Characteristic of Common Defect in the process of welding
SUN Guohao, BAI Mingqing, ZHAO Jiuguo, et al.（470）Brazilian test study on the Acoustic Emission Characteristic of PBX-159
ZHANG Wei-bin, TIAN Yong, TANG Xing, et al.（477）

I-12

ESTIMATION OF CRUSTAL STRUCTURE IN HORONOBE AREA,
HOKKAIDO, JAPAN, USING MULTIPLET-CLUSTERING ANALYSISHIROKAZU MORIYA1, KOICHI ASAMORI2*, ITARU KITAMURA3, HIKARU
HOTTA3, HIDEFUMI OHARA2** and TADAFUMI NIIZATO2
1)
Graduate School of Environmental Studies, Tohoku University, Sendai 980-8579, Japan;
2)
Horonobe Underground Research Unit, Japan Atomic Energy Agency, 432-2 Hokushin, Horonobe, Hokkaido, 098-3224, Japan; * Now at Tono Geoscientific Research Unit, Japan Atomic
Energy Agency, Toki, Gifu, 509-5102, Japan; 3) Construction Project Consultants, Inc., 3-231 Takadanobaba, Tokyo, 169-0075, Japan; ** Now at Kumagai Gumi Co., Ltd., Tsukudo 2-1,
Shinjuku, Tokyo 162-8557, Japan
Abstract
Hypocenter locations of shallow earthquakes in Horonobe area, northern Hokkaido, Japan,
which is near the convergent boundary between Okhotsk and Amuria plates, were determined toreveal the subsurface structure and the mechanisms of earthquake occurrence. The absolutesource locations of 211 earthquakes, which occurred in the period from December 2002 to September 2005, were determined; then, those earthquakes with similar waveforms were identified,
and the source locations of 26 multiplet groups were relocated by using cross-spectrum and clustering analyses. The relocated hypocenters allowed two seismically active areas to be identified,at 10–20 km and 25–30 km depth. The earthquake locations indicate structures trending nearlyN-S direction, and the structures causing repeated stick-slips at asperities, thus generating similarearthquakes. The relationships between magnitude and the distance to the next event, and between magnitude and the time interval of event occurrences were investigated. The relationships
between them suggest that earthquake swarms would be induced by continuous strain accumulation at asperities along faults and its subsequent release as a result of plate tectonic movements.
A cutoff line could be also seen in the relationship between magnitude and the distance to thenext event, suggesting that the distance between the source locations depends on the event magnitude. These results have given us the knowledge that the asperities on the delineated faults intermittently release strain energy as similar earthquakes with magnitudes of less than about 3.0.
Keywords: Subsurface measurement, Multiplet-clustering analysis, Repeating similar earthquakes
Introduction
The eastern edge of the Japan Sea approximately coincides with the convergent boundary between Okhotsk plate and Amuria plates [1], and earthquakes are probably associated with stressaccumulation and its release by the fault system in the crust owing to the movement of the plates.
GPS and microearthquake data suggest that the plate boundary lies under the land surface innorthern Hokkaido, although it is difficult to trace the plate boundary. The Hokkaido Nansei-Oki
earthquake (M7.8), which occurred inside the mobile belt, suggests a complicated fault structure,in which the fault planes incline to the east in its northern part and to the west in its southern part.
Therefore, it is reasonable to suppose the existence of multiple boundaries in a wide fault zone
[e.g., 2]. On the other hand, Horonobe area, northern Hokkaido, is in an area that a recently installed seismic network has shown to be characterized by microearthquake swarms [3]. Although
the location accuracy in depth is not still confident, the estimated source depth of the earthquakes
is usually shallower than 25 km, and the earthquakes are considered to occur along hidden faults
in a stress-concentrated zone. Knowledge of the structures causing these earthquake swarms andunderstanding of their mechanisms are expected to provide information on the stability of the
J. Acoustic Emission, 28 (2010)1© 2010 Acoustic Emission Groupcrust and the relationships between plate tectonics and earthquakes in this region. Similar earthquake events and high-resolution mapping techniques can be used to reveal these structures andearthquake mechanisms [4 - 12]. In this study, similar earthquakes in Horonobe area were relocated using a multiplet-clustering analysis and the subsurface structures and the mechanisms ofearthquake swarms were evaluated.
Earthquake Data Collection
The earthquake events studied in this research were collected by the Hi-net system of the National Research Institute for Earth Science and Disaster Prevention (NIED) and the observatoriesof the Japan Atomic Energy Agency (JAEA) from 20 December 2002 to 30 September 2005 (Fig.
1). Most of the events had magnitudes of less than 3.0. Among the total of 4217 events locatedby Hi-net, we searched for those events whose P- and S-wave arrival times were detected atmore than four observatories in the JAEA system. A total of 221 events were selected for analysis in this study. Similar earthquakes were identified within the selected earthquakes for application of multiplet-clustering analysis (MCA) (Figs. 2 and 3) [13]. Our aim was to investigate
whether seismic clusters and structures could be identified after improvement of the location

Fig. 1. Locations of observatories around Horonobe, showing both those of JAEA and Hi-net.
The dotted box denotes the area of the source locations shown in Fig. 3.
2

Fig. 2. Example of the waveforms of a multiplet.
accuracy. The vertical component signal at the JAEA downhole station, –97 m below sea level,
was used to identify similar waveforms, because the P-wave waveforms at this observation sitewere clearer than those at other observation sites. The waveforms of all events were inspected byhuman eyes, and finally, 188 similar events were classified into 26 groups.
Relocation of Similar Earthquakes Using Multiplet-Clustering Analysis
First, the absolute source locations of the selected similar earthquakes were determined using
the hypocenter determination software, Hypomh, based on a three-layered velocity structure
shown in Table 1, where the focal mechanism was also estimated [3, 14, 15]. Figure 4 shows theabsolute source location determined by Hypomh. The source locations were widely dispersed
because of location errors of more than 1 km, and no structures could be identified. After determination of the absolute source locations, MCA was applied to relocate the source locations [8,13]. The MCA is a method for precise determination of microseismic event locations and is used
to identify subsurface fractures and fracture networks. A multiplet is a group of microseismic
events with very similar waveforms, despite different origin time, and is likely the expression ofstress release on the same structure.
3

Fig. 3. Cross spectrum analysis, and estimated averaged coherence and delay.
Table 1. Velocity structure for estimation of source location [3].

The relative source locations of similar events can be determined with high resolution andaccuracy by using the moving-window cross-spectrum analysis technique. Deduced seismic clusters, called multiplet clusters, are indicative of seismically activated structures, and the orientations of these structures can be estimated using the seismic clusters even though the absolute locations of the multiplet clusters cannot themselves be determined.
The time-window length for the cross-spectrum estimation was 1 s and that for the fast Fourier transform was 2.56 s, where the frequency resolution was 0.039 Hz. The frequency range
for the time-delay estimation was 5–15 Hz, and the mean value of the time delays estimated for
10 different moving time windows was used to estimate differential times.

4

Fig. 4. Source locations of analyzed earthquakes, where the locations are determined by “Hypomh”.
After the relocation of the events in each group, typical events were selected from each group,and the similarity of the waveforms was evaluated within the selected representative events.
Events with similar waveforms, but with insufficient similarity to have been classified into thesame group by the initial classification, were selected, and a clustering analysis was applied todetermine the relative positions of the clusters.
The clustering analysis method is used to estimate the relative location of multiplet clusters
by detecting phase differences between similar stacked events. The representative events wereselected from each group, and the cross-spectrum analysis was applied to estimate the relative
travel time delays. After the determination of relative source location of representative events,
the other events in each group were moved to the new locations following the representative
events. Through the MCA, the precise relative source location of similar events can be determined within each multiplet group and also determine the relative positions among the multiplet
clusters. The frequency band for the cross-spectrum analysis was the same to that in estimationof relative source locations. Figure 5 shows the source locations after the MCA and the fault
plane solutions assuming double-couple source mechanism and estimated using the P-wave first
motions at stations.
5Fig. 5. Source locations of analyzed earthquakes, where the cross denotes the locations by “Hypomh” and the closed circle and the hexagon denote the source locations after relocation.
Discussion
According to the estimated source locations, the depths are less than 30 km (Fig. 5), and two
seismically active areas (areas A and B) were identified. The seismic sources in area A were located at around 30 km depth, and those in area B were shallower, at 10–25 km depth.
Tamura et al. (2003) [3] imply the existence of earthquakes deeper than 20 km. However, we
think that the absolute source location, especially the depth, should not be discussed in Fig. 5,
because the station correction is not considered and the coverage of seismic network is notenough to determine the confident velocity structure for the event locations. We doubted the long
period events for the reason of deep events. We have investigated the source locations of long
period events, which are observed since 2000 and recorded on catalogue in this area, and con6

firmed that the analyzed events in this study are different from the long period events. On theother hand, the result of MCA is confident because the source location is the relative locations,and the influence of velocity structure in determination of source locations is smaller. The average of RMS in the relative locations is from 0.16 ms to 0.09 ms, and which corresponds to 0.8 km
and 0.45 km in distance if the wave velocity is assumed to be 5 km/s. Therefore, we do not discuss the absolute location of the sources and the structures.

Fig. 6. Plot of the distance to the next earthquake as a function of the magnitude of the previous
event for each consecutive pair of similar earthquakes.
Because similar events were observed, there must exist unstable structures with asperities
causing repeated stick-slips and continuous stress loading enforcing the repeated slips. The nodal
planes (Fig. 5) suggest steeply inclined structures oriented NNW to NNE, and the P-axes are oriented nearly NNW-SSE and NNE-SSW. These results indicate that compressive stresses oriented nearly E-W are acting on faults with steep inclinations. Considering that Horonobe area isat the convergent boundary between the Okhotsk plate and Amuria plates, we infer that the district is under continuous stress loading in a nearly West to East direction, and strain energy accumulates on the fault planes as a result of the loading. This interpretation is supported by
movements of the land surface detected by GPS. Therefore, it is reasonable to infer that thegroups of similar earthquakes are expressions of the release of strain energy at faults oriented
nearly N-S, and that the faults are subjected to compressive stress in an E-W direction.
The relationship between the magnitude of each event and the distance to next event (Fig. 6)
shows a cutoff magnitude (shown by a broken line), although the range of magnitude is limited(0 to 3.0). The distance to the next source location of similar earthquakes is more than several
hundreds of meters, and the distance between event locations is larger than the expected source7radius of the earthquakes, suggesting that the distance between source locations depends on theevent magnitudes and that the source areas of similar earthquakes do not overlap. It is remarkable that a similar phenomenon has been observed along faults in California, where multiplets
have been observed and streaks of earthquakes identified [6]. In the case of creeping faults, lockand release at asperities generate repeated earthquakes. Our results suggest that asperities releasestrain energy, and no more shear slip is induced at the same asperities. This means that strain energy accumulates at several asperities distributed along the fault because of the regional stressloading, and that the strain energy is released at the different asperities one after the other.
Figure 7 shows the relationship between magnitude and the time interval between the occurrence of similar events for the earthquakes in areas A and B. In area A, the maximum magnitude
was nearly 2.0, and it did not vary in relation to time intervals of more than 10 hours. On theother hand, in area B, the maximum magnitude increased with the time interval up to magnitude
3.0.
These results suggest that the maximum values of the strain energy release rate and magnitude are limited in area A, where the size of asperities and the frictional strength of asperities arelimited. On the other hand, the asperities in area B seem to have different sizes or different frictional strength, and the range of released energy is extensive in comparison to that of the asperities in area A. This difference between the two areas could not be definitively explained, but wethink that it relates to the size and strength of asperities and also to conditions around the faults,
such as temperature and rock properties changing with depth. These results give us the key
knowledge that the asperities on the delineated faults intermittently release strain energy as similar earthquakes, and that the occurrence of similar earthquake, such as relationship between
magnitude and time interval, is different depending on the seismic clusters.

Fig. 7. Magnitude and time interval of event occurrence for each consecutive pairs of similarearthquakes. (a) Area A, (b) Area B.
8

SummaryIn this paper, similar earthquakes in Horonobe area were relocated by multiplet-clustering
analysis to reveal the mechanisms of earthquake swarms in this district. The seismic clusters revealed after the relocation allowed two seismically active areas to be identified, at 10–20 km andaround 25–30 km depth. The relationships between magnitude and the distance to the next event,and magnitude and the time interval of event occurrences were investigated. A cutoff line wasseen in the relationship between magnitude and distance to next event, suggesting that the distance between the source locations depends on the event magnitude and that the source areas ofsimilar earthquakes do not overlap. The relationships between them have suggested that earthquake swarms would be induced by continuous strain accumulation along faults and its subsequent release as a result of plate tectonic movements. These results have given us the key
knowledge that the asperities on the delineated faults intermittently release strain energy as similar earthquakes, and that the features of similar earthquake occurrence, such as relationship between magnitude and time interval are different depending on the seismic clusters.
Acknowledgement
The earthquake waveform data was downloaded from the web site of Hi-net owned by National Research Institute for Earth Science and Disaster Pretension (NIED), Japan, and the information on the source location was used from the database of Japan Meteorological Agency(JMA). The author (TM) thanks NIED and JMA for providing important waveform and source
location data.
References
1) G. F. Sella, T. H. Dixon and A. Mao, REVEL: A model for Recent plate velocities from space
geodesy, J. Geophys. Res., 107 (B4)(2002), 10.1029/2000JB000033.
2) M. Kasahara, Chikyu Monthly, 20 (1998), 16-21. (in Japanese)
3) S. Tamura, M. Kasahara and T. Moriya, The micro-seismicity and crustal structure in thenorthern part of Hokkaido, inferred from temporal observation, J. Seism. Soc. Japan, 55(2003), 185-193. (in Japanese)
4) G. Poupinet, W. L. Ellsworth and J. Fréchet, Monitoring velocity variations in crust using
earthquake doublets: an application to the Calaveras fault, California, J. Geophys. Res., 89(1984), 5719–5731.
5) H. Moriya, K. Nagano and H. Niitsuma, Precise source location of AE doublets by spectral
matrix analysis of triaxial hodogram, Geophysics, 59 (1994), 36–45.
6) D. Schaff, G. C. Beroza and B. E. Shaw, Postseismic response of repeating aftershock, Geophys. Res. Lett., 25 (1998), 4549–4552.
7) A. M. Rubin, G. Dominique and J. Got, Streaks of microseismicities along creeping faults,
Nature, 400 (1999), 635–641.
8) W. S. Phillips, Precise microearthquake locations and fluid flow in the geothermal reservoir at
Soultz-sous-Forêts, France, Bull. Seism. Soc. Am., 90 (2000), 1, 212–228.
99) F. Waldhauser and W. L. Ellsworth, A double-difference earthquake location algorithm:
Method and application to the North Hayward fault, California, Bull. Seism. Soc. Am., 90(2000), 6, 1353–1368.
10) C. A. Rowe, R.C. Aster, B. Borchers and C.J. Young, An automatic, adaptive algorithm forrefining phase picks in large seismic data sets, Bull. Seism. Soc. Am., 92 (2002), 1660-1674.
11) T. Matsuzawa, N. Uchida, T. Igarashi, T. Okada and A. Hasegawa, Repeating earthquakes
and quasi-static slip on the plate boundary east off northern Honshu, Japan, Earth Planets
Space, 56 (2004), 803–811.
12) N. Uchida, A. Hasegawa, T. Matsuzawa and T. Igarashi, Pre- and post-seismic slow slip on
the plate boundary off Sanriku, NE Japan associated with three interplate earthquakes as estimated from small repeating earthquake data, Tectonophysics, 385 (2004), 1–15.
13) H. Moriya, H. Niitsuma and R. Baria, Multiplet-clustering analysis reveals structural details
within the seismic cloud at the Soultz geothermal field, Bull. Seism. Soc. Am., 93 (2003),
1606–1620.
14) N. Hirata and M. Matsu’ra, Maximum-likelihood estimation of hypocenter with origin timeeliminated using nonlinear investigation technique, Phys. Earth Planet. Inter., 47 (1987), 5061.
15) P. Reasenberg and D. Oppenheimer, U.S. Geol. Surv. Open File Rep., 109 (1989), 85-0739.

10

ACOUSTIC EMISSION TESTING - DEFINING A NEW STANDARD OF
ACOUSTIC EMISSION TESTING FOR PRESSURE VESSELS
Part 2: Performance analysis of different configurations of real case testing and recommendations for developing a new guide for the application of acoustic emission
JOHANN CATTY
CETIM, 52 Avenue Félix Louat, 60304 Senlis Cedex, France
Abstract
Several regulatory rules, such as the Best Practices Guideline (GBP) [1], exist for acoustic
emission (AE) testing of pressure vessels in France and the rest of the world and allow AE testing based on two techniques (zonal location and planar source location methods). However, theanalysis criteria of data recorded during the testing lack adequate basis. This work highlights
inconsistencies in analysis methods defined in these guides or codes based on modelling calculations in Part 1 of this study [2], while this paper shows, from a real case (AE testing of a 2000 m3
spherical storage tank), that the results of an AE test cannot be reproducible due to a lack ofstrictness in the application rules of AE. Thus, depending on the testing configuration used, someemissive defects can be detected or missed. This study may also be used as a basis for defining a
new AE testing standard specifically and quantitatively defining a methodology of analysis basedon a different approach from those used currently. 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, enabling global andrapid testing of large structures, significantly reducing maintenance time and shutdown of facilities. Methods have followed several regulatory rules, codes or standards that have been created,defining the general application rules of this technique. AE testing can be applied according to
two techniques (zonal location and planar source location by triangulation or planar method, inshort), without any major impact on the analysis criteria of data recorded during the testing. Indeed, the criteria are primarily derived from the zonal testing method, which has limitation interms of accuracy of analysis, limited to very basic criteria of counting signals collected by thesensors.
In Part 1 of this study [2], the author highlighted, from simulation calculations, that there
were significant differences in performance between these two techniques (up to a ratio 5 to 7).
The effect of the acquisition threshold has been also quantified. Using planar location method,
the use of the amplitude correction has been evaluated, and considered as a good tool for increasing the accuracy of the result. The study presented in this article, based on a real industrial casetreated by CETIM, aims to highlight the differences in results of testing applied on the samestructure with different configurations, all consistent with current rules. We will answer to thesequestions:
What is the actual performance of the two testing techniques?
Which level of accuracy can we affect, or, what uncertainty should we associate to these criteria?
What is the effect of operating conditions on the result of the testing?
J. Acoustic Emission, 28 (2010)11© 2010 Acoustic Emission GroupA. Analysis of Testing and Data Processing Methodology: BPG - Real industrial case
A.1 Definition of the case studied – context
The performance for both techniques used will be compared in accordance with the recommendations from GBP (Best Practices Guideline) as regulated in France. This analysis uses a
specific application case; a spherical storage tank with a 45-mm thick unalloyed steel wall, isexternally painted. The AE wave attenuation curve is shown in Fig. 1.

Fig. 1: Attenuation curve obtained using the Hsu-Nielsen source on 45-mm thick, unalloyedsteel, painted. The frequency of the AE transducers is near 200 kHz.
Based on the GBP recommendations, the maximum allowed distances between sensors forthis case are:
• For zonal location, the maximum authorized distance between sensors is 1.5 times [Distanceat the assessment threshold = 50 dBAE maximum]; or approximately 1.5 x 6 = 9 m.
• For planar location, the maximum authorized distance between sensors of a single mesh(maximum acquisition threshold of 50 dBAE), is equal to the distance to the acquisition threshold + 6 dB; approximately 6 m.
We analyzed real AE data of pressurization testing of the spherical tank, first using the planar
configuration, and second by the zonal configuration. Table 1 gives the following main characteristics:
Table 1: Main characteristics of the two configurations of testing.
Configuration

Number of Sensors

Zonal Location
Planar Location

22
77

Maximum Distance
between sensors
About 8.6 m
About 5.2 m

A.2 Location tests results from Hsu-Nielsen source
A.2.1 Test analysis: In order to assess the two testing configurations, it is interesting to analyze
the performance in terms of the detectability of Hsu-Nielsen sources (standard AE source). Thesesources are generated prior to the pressure test on the singularities of the structure. These sources
are generated on the vertical and circular welds (location tests 1 and 3), and on the welds of the
supports (location test 2). For each case, we calculate the number of detected events (zonal location), and the number of localized events (planar location). We note that the real number ofgenerated sources (pencil lead breaks) is very close to the number of localized events in planar
testing configuration. Results are shown in Figs. 3-5.
12

In the case of location test 2, the difference of performance is significant between the twotesting configurations. Only 2 events are located with the zonal testing configuration while 210
events are located with the planar testing configuration. This is because the presence of obstacles
to the propagation of the AE waves (support of the tank) disrupts the detection of events by sensors close enough in theory. Therefore, only two events were collected by 3 or more sensors.
However, the zonal testing configuration meets the minimum requirements for detection, since it
receives each Hsu-Nielsen source by at least one sensor (zonal location).

abFig. 2a: Overview of planar configuration; 2b: zonal configuration.

abFig. 3a: Result of location test 1, planar test configuration; 3b: zonal test configuration.
Results of these tests are summarized in Table 2. Each entry indicates the number of detected
or located events. The threshold level used are 50 dB or 65 dB. These tests show vast differencesin terms of detection and level of information between the two test configurations. From thesetests conducted on a real case, the zonal test configuration will be able to locate only 18% of the
Hsu-Nielsen AE sources on average, against 100% for the planar testing configuration.
13

We also observe that in the case of planar configuration, the number of detected events exceeds the number of generated sources. This can be caused either by the detection of pencil-leadbreak rebounds, or through contacts caused by the operator during testing.
It is, however, important to remember that these two test configurations are in accordancewith the rules defined in the ‘Best Practices Guideline’ (GBP).

abFig. 4a: Result of location test 2, planar test configuration; 4b: zonal test configuration.

abFig. 5a: Result of location test 3, planar test configuration. 5b: zonal test configuration.
A.2.2 Comparisons with simulation calculations: Referring to simulation calculations developed
in Part 1 [2], we find:• A location rate of 43% of the Hsu-Nielsen AE sources in the zonal test configuration,• A location rate of 100% in the case of planar test configuration.
14

Table 2: Number of detected and located events according to test configurations for 3 tests.
Configuration

Location Test
1
Location Test
2
Location Test
3

Zonal Configuration
Number of Detected
Events, A>=50 dBAE

Zonal Configuration
Number of LocatedEvents, A>=65 dBAE

Planar Configuration
Number of Detected
Events, A>=50 dBAE

Planar Configuration
Number of LocatedEvents, A>=65 dBAE

250

85

360

210

245

2

350

210

375

50

500

350

To illustrate these calculations, we observe in Fig. 6 the minimum locatable amplitudes inzonal test configuration (with the distance between sensors of ~8.6 m). When these amplitude
values are less than 98 dBAE (approximate magnitude of a Hsu-Nielsen source), this means thatthe Hsu-Nielsen source is locatable. When this value is greater than 98 dBAE, this means that the
Hsu-Nielsen sources are no longer locatable.
We notice that the Hsu-Nielsen sources are locatable at the center of each mesh, and for an
ideal configuration where no obstacle disturbs the propagation.

Fig. 6: Simulation of minimum locatable amplitudes, zonal test configuration.
A.2.3 Conclusions:
1. These location tests carried out on a real structure show that the two test configurations produce different level of detection performance. Although the zonal test configuration is able todetect any Hsu-Nielsen source, it was able to locate only 18% of sources against 100% for theplanar testing configuration. Nevertheless, these two configurations are in accordance with therules defined in GBP.
2. There is a good correlation between simulation and tests. Indeed, the simulation, which predicted a location rate of around 40% for zonal test configuration does not take into account any
obstacles to the propagation of AE waves. This value of 40% is one actually measured on the test
location test 1, the only one for which no obstacle does not disturb the propagation.
15

A.3 Results of AE monitoring during pressurization of the spherical tank
The AE test requires pressurization, and its schedule is shown in Fig. 7. The analysis criteria
come from GBP. First, the zonal location analysis is performed for the two test configurations. In
the second step, we analyze the located AE activity.

Fig. 7: Pressure cycle applied to the spherical tank.
A.3.1 Zonal location analysis: The analysis criteria used in this section are based on GBP [1] andare:• AE events of amplitude greater than or equal to 50 dBAE (N1S). This criterion is calculatedon the whole duration of the test,• AE events of amplitude greater than or equal to 65 dBAE (N2S). This criterion is calculatedon the whole duration of the test,• AE recorded during the constant pressure stages. We consider the AE events of amplitude
greater than or equal to 50 dBAE (N3S). This criterion is calculated from 2 minutes after the
beginning of the plateau,• Changes in activity and intensity during the test. The activity (number of AE events) andintensity (energy) are evaluated throughout the test.
• Felicity ratio calculated on the second stage of pressure rise.
Tables 3a (zonal test configuration) and 3b (planar test configuration) include the values of analysis criteria for all areas (an area is the part of the structure covered by a sensor).
•••

Several observations can be drawn from these two tests:
The total number of recorded events (≥50 dBAE) is about 1300 for zonal test configuration
against about 2500 for the planar test configuration, or about twice.
We can translate that into saying that half of the information disappears from this type ofanalysis using a zonal test configuration: It is obvious that the higher the number of sensors,
the better the coverage level of the structure. Therefore, we will have more chance to perceive AE events.
If we analyze these results further, we note that some areas classified as category 2 with theplanar test configuration disappear from this category with the zonal test configuration: For
example, the area covered by the sensor c2 (Table 3b), which reported 292 events over 50
dBAE is classified in category 2.

16

•
•

•

With zonal test configuration, we can assume that some of this activity is 'recovered' by thesensor c1, perhaps by the sensors c17 and c19, but much of this information is lost (moreover, none of the sensors, c1, c17 and c19 are classified in category 2);
The classification criteria defined in the GBP are almost identical for both cases. For criteria
N1S and N2S, values boundary between Level 1 and Level 2 are higher in the case of thezonal location. It is hardly justifiable, since two opposing effects should be taken into account:- The average distance from a source to the sensor is higher in the case of the zonal configuration (then, the attenuation is higher, therefore less probability to record signal withamplitude exceeding 50 dBAE)- However, the areas being more extensive in this case, the number of events is potentiallyhigher for each zone.
The positions of the sensors used in the case of a zonal test configuration more strongly influence the result: In the case studied, if the sensors 2 and 4 were kept in place of the sensors
c1 and c3, the sensor c2 area would have been classified in category 2 or more. Chance has agreater impact in the case of a zonal test configuration.

A.3.2 Comparison with simulation calculations: The simulation calculations developed in Part 1[2], assuming an amplitude distribution of AE sources between 55 and 115 dBAE, showed that
34.6% of sources were detected in the case of a zonal test configuration, against 59% (ratio 1.71)
for a planar testing configuration. In the real case studied, we find this order of difference between the two configurations (1300 vs. 2500 events; ratio of 1.92). This shows that the hypothesis used in the simulation calculations are quite close to reality.
Table 3a: Classification table, according to GBP, in the case of the zonal test configuration.

17

Table 3b: Classification table, according to GBP, in the case of planar test configuration.

18

Table 3b (continued)

A.3.2 Planar location analysis: The planar location analysis is based on the description of theevents that have been located. Only concentrations of localized events (clusters) are taken into
account. Concentration thresholds are defined in the GBP [1]. The results of location analysis
obtained in the two test configurations are illustrated in Figs. 8a to 8d below. First, we observethat the number of localized events is significantly different with the two configurations:Fig. 8a: Upper hemisphere, zonal test configuration.

19

Fig. 8b: Lower hemisphere, zonal test configuration.

Fig. 8c: Upper hemisphere, planar test configuration.
•

On the lower hemisphere, 37 events for the zonal test configuration vs. 139 with the planar
test configuration,• On the upper hemisphere, 45 events for the zonal test configuration vs 144 with the planar
test configuration.
Thus, less than 30% of events located with the planar test configuration are observed with azonal test configuration. On the other hand, if we observe carefully the concentration areas (clusters) identified in the 2 cases, we note:
20

•
•

On the lower hemisphere, 1 cluster for the zonal test configuration vs. 3 with the planar test
configuration; the cluster near c2 (12 events), classified in category 2 with the planar test
configuration, does not appear with the zonal test configuration.
On the upper hemisphere, 1 cluster for the zonal test configuration vs. 2 with the planar test
configuration.

Fig. 8d: Lower hemisphere, planar test configuration.
A.3.3 Conclusions: If we make a synthesis of both analyses (zonal and planar), we reach the conclusions summarized in Table 4:
Table 4: Summary of analysis performed in both test configurations.
Configuration

Zonal test configuration

Areas classified as
Category 2
Clusters classified
as Category 2

c35, c39, c41, c65 andc77

Planar test
configuration
c2, c21, c25, c36,
c40, c41, c65 and c77-

1 cluster (near c2)

If the differences in this table seem minor, the consequences in terms of further investigations on them are very different: In fact, for any area or cluster in category 2 or more, further investigations are recommended. Therefore, considering the differences in distances between sensors for both test configurations, it would be necessary to conduct these investigations on:
5 areas of 58 m2 or 290 m2 for the zonal test configuration
8 areas of 21 m2 or 168 m2 for the planar test configuration (in fact, the surface would belower, since the sensors are closer to each other near the poles of the spherical tank).
On the other hand, it is important to note that the information from these two tests is not identical: For example, areas c2 and c21, classified in category 2 with the planar test configuration(including a cluster classified in category 2, near c2) will not undergo any further investigation
after the AE inspection carried out with the zonal test configuration.

•
•

21

A.4 Effect of acquisition threshold on the results of AE monitoring
Based on the case discussed above, we adopt, in this section, an acquisition threshold of 40
dBAE (instead of 50 dBAE in the previous sections). The test configuration is the one that uses 77
sensors (planar test configuration). Anxious to achieve the testing of this spherical tank in thebest detection conditions, CETIM had adopted this acquisition threshold value, allowing a better
detection without being disturbed by environmental conditions. The analysis follows the same
pattern as in the previous section, under GBP requirements: First, zonal location analysis, andsecond, analysis of the located AE activity.
A.4.1 Zonal location analysis: The zonal location analysis is identical to that conducted in A.3.1
for the planar test configuration. Indeed, the zonal location analysis, as defined in the GBP, takes
into account only the events of magnitude greater than or equal to 50 dBAE. The conclusions of
the analysis are identical to those in A.3.1. However, we note that approximately 11,500 eventswere recorded at acquisition threshold of 40 dBAE, vs. about 2500 for a threshold of 50 dBAE.
A.4.2 Planar location analysis: The analysis in planar location provides different results fromthose described in A.3.2, because the location capability is enhanced by the lower acquisition
threshold used. Figures 9a and 9b illustrate the located AE activity on both hemispheres. In thiscase, the number of located events is:• On the lower hemisphere, 460 vs. 139 AE events observed with a threshold of 50 dBAE, and37 with the zonal test configuration,• On the upper hemisphere, 440 vs 144 events observed with a threshold of 50 dBAE, and 45
with the zonal test configuration.
Therefore, lowering the threshold from 50 to 40 dBAE will have multiplied by 3 the number oflocated AE events; In this configuration, we get about 10 times more located events than zonal
test configuration.
The information obtained is more extensive with a threshold of 40 dBAE: 24 clusters of morethan 5 events are identified, vs. 5 with a threshold of 50 dBAE. The information obtained with ahigher threshold is included at a lower acquisition threshold, but this information is amplified.
For example, the cluster located near c2, which does not appear with the zonal test configuration,
is identified as having 12 events with the planar testing configuration (50 dBAE threshold), andbecomes a region with 55 events by lowering the acquisition threshold to 40 dBAE.
It is important to note that using a lower acquisition threshold allows more explicit information: For example, the area of sensor c21, classified in category 2, did not have any cluster identified with a threshold of 50 dBAE. Lowering the threshold has highlighted a cluster in this area.
Another example: a located high activity area (15 events) appears between sensors c16, c27 andc28. This active region was not detected with a threshold of 50 dBAE, and it did not belong to acategory-2 area.
A.4.3 Conclusions: The acquisition threshold is a fundamental parameter of an AE test. The example of the real case shows that lowering the threshold from 50 to 40 dBAE amplifies the information picked up by a factor of three. A volume of information three times larger allows greater
accuracy in diagnosis, and therefore a smaller area of investigation for possible additional testing. The effect of the acquisition threshold is not taken into account in GBP, which imposes a'maximum' acquisition threshold, not to exceed. The zonal analysis is still limited to AE events
22

Fig. 9a: Upper hemisphere, planar test configuration (Acq. Thr. = 40 dBAE).

Fig. 9b: Lower hemisphere, planar test configuration (Acq. Thr. = 40 dBAE).
of magnitude exceeding 50 dBAE, and analysis of located AE events has the same criteria, regardless of the threshold used for acquisition.
A.4.4 What is the interest of increasing the sensitivity of a testing? Additional checks (penetrant
testing, magnetic particle testing, ultrasonic testing) were made following the AE results (realized with the planar test configuration, with a 40 dBAE acquisition threshold), on a majority ofthe emissive regions. It was found that 8 of the tested areas (clusters) showed significant indications (acceptable for the most part according to codes, unacceptable for the others). None of23

these regions would have been the subject of further investigations following the AE test carriedout with the zonal test configuration (22 sensors). Only one of these regions would have been
investigated following the AE test with the planar test configuration (77 sensors), with an acquisition threshold of 50 dBAE.
This example shows that an application guide such as GBP in France, or other guides substantially equivalent in other countries do not allow reproducibility of AE testing, authorizing
very different test configurations, whose differences are not taken into account in the analysis
and interpretation of data recorded. So, being in accordance with such a guide does not guarantee
the relevance of the result.
A.4.5 Comparisons with simulation calculations The simulation calculations developed in Part 1[2], assuming a distribution amplitude of AE sources between 55 and 115 dBAE, showed that theeffect of lowering the acquisition threshold from 50 to 40 dBAE for a planar test configuration,
reduces the rate of non-detected sources by 3, and multiplies by 2 the rate of located AE sources.
In the cases studied, we observed that the number of AE events (= detected sources) increased
from 2500 to 11500, a ratio of about 4.6. The number of located AE sources has been multipliedby 3. Even if the results do not completely fit together, this shows that the assumptions used in
the simulation calculations are however quite close to reality.
B. Pathways of Progress in the AE Test Implementation - Recommendations for Developing a new Guide for AE application
B.1 The necessity of a planar testing configuration
The analysis developed from numerical simulations or from real example (see preceding section 'A') shows that the first parameter influencing the testing is the configuration; that is to say,the number and arrangement of the sensors coupled to the structure. On this point, too muchflexibility is allowed under the current guideline because only ‘minimum’ rules are imposed. On
the other hand, both techniques (planar location mode and zonal location mode) are allowed,
with almost no impact on the methods and analytical criteria, which are the same in both cases.
In order to make guideline meaningful, we should consider two new rules. The first ruleneeded is to reduce drastically the choice in the configuration of testing. That is to say, the distances between sensors have to be justified by demonstrating and overcoming the impact of theconfiguration adopted on the 'coverage level' of the structure. Given the large difference in performance between the zonal and planar test configurations, it seems reasonable to recommend, if
not impose, the second rule of using the planar test configuration. By the addition of these tworules, AE tests will be more reliable, precise and reproducible.
B.2 Reference source
The term 'planar test configuration', which means that we are capable of carrying a planar
location, is not sufficient to establish a strict framework. Indeed, the ability of a test configuration to locate is evaluated for a given source type, characterized by power or amplitude. Thus,
the Hsu-Nielsen source (0.5 mm-2H) is now taken as a reference.
The comparison of data from simulation (see Part 1 [2]) with tests on real structures (unalloyed steel structure) tends to show that a distribution amplitude source centered on 85 dBAE isfairly close to experimental data. This shows that taking as reference the Hsu-Nielsen source (0.524mm-2H), amplitude source between 95 and 100 dBAE is a minimum requirement of 'good supervision' of a trial. A source of lower energy, whose amplitude is closer to 85 dBAE could be taken
as new reference. The integration of these requirements in a practice guide then will involve,
from the end-users to justify its test configuration, firstly by providing the attenuation curve
taken into account, and secondly by demonstrating, either by simulation or by experiment, thatthe tested structure is completely covered. The advantage of simulation is to highlight and identify the less-well monitored area, and to adopt compensatory measures if necessary.
B.3 Simulation: A tool for evaluating a test configuration
The simulation also enable an evaluation of ‘performance’ of a given test configuration by
calculating the percentage of detection and location of a reference population of AE sources(e.g., amplitude centered on 85 dBAE, between 55 and 115 dBAE). Simple and synthetics criteria,such as the rate of detected sources, the rate of located sources, the overall error of amplitude
measurement could be calculated. By the use of mapping (see e.g., Figs. 11a and b), it allows
visualizing the sensitivity level of a testing. This assessment enables:• To compare quantitatively the performance of AE tests. Customers, users of this technology,
and organizations using the results of a testing on one hand can take into account the quality
level of a testing, and also ask, following the criticality of the tested device, a minimum requirement, more precisely determined by these criteria.
• To adjust classification criteria to the performance level of the test configuration adopted.
Indeed, currently, no guide, standard or code does define the classification rule incorporating
the 'coverage' testing.
B.4 The advantage of the amplitude correction
The calculations developed in Part 1 [2] showed that taking into account the informationfrom the location calculation, correcting the measured amplitude to reach the amplitude at thesource allows appreciable gain in terms of accuracy of information: In fact, any located source ismeasured, in theory, without any error. In the case studied [2], for a planar test configuration,
the average error of amplitude measurement on the detected AE sources would decrease from
26.1 to 14.7 dBAE (amplitude distribution of sources centered on 85 dBAE). This implies againthat the test configuration should allow planar location as complete as possible, prohibiting theuse of a zonal test configuration.
If we consider the case studied in Section A, we can observe (Figs. 10a and 10b) that thecontribution of the amplitude correction modifies the information: For location tests with HsuNielsen sources (Fig. 10a), the amplitudes are measured between 50 and 90 dBAE (average valueof about 70 dBAE). After correction, the amplitudes are between 80 and 107 dBAE (average valueof 94 dBAE). We should find in theory the origin amplitude of a Hsu-Nielsen source, which isapproximately 100 dBAE. The amplitude correction can result in an average value close to thetheory, and concentrates all the sources in the range of 27 dB. Without correction, the amplitudes
are spread over an interval of 40 dB, centered at 70 dBAE, which is 30 dB below the real value.
If we analyze the AE test results of this spherical tank during the pressure test (Fig. 10b), theamplitude correction changes the perception that we may have observed on the emitting regions:
For example, the region 'D' has a maximum amplitude of 76.1 dBAE without amplitude correction, and the region 'K' 75.7 dBAE. After amplitude correction, the region 'D' has a maximum
amplitude of 91.5 dBAE, and the region 'K' 104.1 dBAE. Their ranking (in terms of maximum
25

Fig. 10a: Tests of location from Hsu-Nielsen source.

Fig. 10b: (bottom) Results of the pressure test. Comparison of measured amplitudes and corrected amplitudes (planar test configuration).
amplitude) is reversed. The information is much more relevant, without demanding excessive
effort of analysis, in the case where the testing is implemented for a planar location.
26

In order to summarize the study developed in this article, and to show the great disparity inresults of AE testing allowed by the existing guides, such as GBP in France, we can take the caseof two active regions:Region K:- In the case of zonal test configuration, it is absolutely not detected and no inspection required after test.
- In the case of planar test configuration, with an acquisition threshold of 50 dBAE, it belongs
to category 2 (area of the sensor c40), and a cluster of 6 events appears (category 1).
- In the case of planar test configuration, with an acquisition threshold of 40 dBAE, it belongs
to category 2 (area of the sensor c40), and it is considered as an emissive region (cluster of
17 events), classified in category 2 (with a ranking 4 / 11 of the emissive regions).
- In the case of planar test configuration, with an acquisition threshold of 40 dBAE, and theuse of amplitude correction, it becomes the 2nd most emissive region.
This region, corresponding to a section of a vertical weld, has been inspected by ultrasoundafter the AE test. This revealed the presence of 3 internal indications, unacceptable under theapplicable standards.
Region D:- In the case of zonal test configuration, it is absolutely not detected and no inspection required after test.
- In the case of planar test configuration, with an acquisition threshold of 50 dBAE, it is absolutely not detected and no inspection required after test.
- In the case of planar test configuration, with an acquisition threshold of 40 dBAE, it belongs
to an emissive region (cluster of 5 events), classified in category 1 (with a ranking of 3 / 11 of
the emissive regions).
- In the case of planar test configuration, an acquisition threshold of 40 dBAE, and the use of
amplitude correction, its ranking changes from 3 / 11 to 7 / 11.
This region, corresponding to a section of a vertical weld, has been inspected by ultrasoundafter the AE test. This revealed the presence of 5 indications acceptable under the applicablestandards.
These conclusions demonstrate first the benefit of using a planar test configuration, and secondly the contribution of applying the amplitude correction in the diagnosis.
B.5 A correction factor: the rate of local coverage
Although the planar test configuration allows to locate AE sources at least equivalent to a
Hsu-Nielsen source, it is however important to be aware that the coverage of a structure is nothomogeneous. The simulations developed at CETIM by Catty and Pinto using the CASTOR®
software (for the calculation of pressure vessels), as represented in Figs. 11a and 11b illustrate
this inhomogeneity. In Fig. 11a, we can observe that in the zonal test configuration, the distances(distance from the sensor reaching the 3rd) are between 3.3 m and 9 m. Figure 11b (planar test
configuration) shows that these values fall between 1.4 m and 4.4 m.

27

Fig. 11a: Coverage mapping in the case of zonal test configuration with 22 sensors.

Fig. 11b: Coverage mapping in the case of planar test configuration with 77 sensors.
28This kind of picture, once interpreted, can prove that a Hsu-Nielsen source can be locatedanywhere with the planar test configuration, and on the contrary only on a limited part of thespherical tank (represented in blue in Fig. 11a) in the case of the zonal test configuration. We
also note that the presence of nozzles (represented by a hole in Fig. 11b) can be taken into account. However, the modelling of the disturbance of acoustic waves caused by such obstacles
must be refined.
By recognizing this situation, inherent to the AE technique, we could act on the implementation of AE testing:- Avoid placing the 'critical' regions in the less-well monitored areas of the vessel.
- Refine the diagnosis, for example, by adding an analysis parameter that would ‘evaluate’
the level of AE activity observed in a region compared to the minimum detectable. Thatis, a cluster with an average amplitude of 90 dBAE observed in an area where the minimum detectable amplitude is 60 dBAE has necessarily been well detected and localized. If
the same cluster is located in an area whose minimum detectable amplitude is 90 dBAE, it
will be necessary to consider it differently.
B.6 Conclusions: Towards an application guide based on planar location
The AE test aims to detect emissive phenomena in a structure under examination. Then, thecentral concern of an application guide should be the detectability of a potential active source.
The current guides are, however, constructed from an 'instrumentation' approach. The recommendations for implementing this technique and analysis criteria do not take into account (oronly partly) what we must detect, but what one is capable of recording. For example, defining acriterion for counting the number of events exceeding a defined amplitude (N1S, N2S in GBP)
does not provide relevant and reproducible information from one test to another, if we do nottake into account, for example, the surface covered by a sensor. It would be necessary to rebuild
a process from what is sought, meaning to ask the question: How can I be sure of detecting asource, with an amplitude at the origin of X dBAE, taking into account its position on the structure? For instance, the implementation guide should establish for each testing, the following
phases:• Phase 1: From the geometry of the structure and attenuation of acoustic waves, defining agrid plan, justified by calculation or modelling. The proposed mesh must meet specific criteria: Overall coverage rate, local minimum and maximum coverage rate, taking care to
specify the source of reference taken for the calculation of these criteria. After this phase, themesh being defined in terms of geometry, coverage surfaces of each sensor are known.
• Phase 2: Defining criteria for zonal analysis related to the covered area and attenuation. This
step consists in adjusting the criteria for zonal analysis to the specific case studied. We
should remember that the adjustment of analysis criteria should allow treating each case thesame way.
• Phase 3 (instrumentation of the structure, preliminary verification, pressure test, post-test
verification): The different phases are substantially identical to those currently practiced.
However, special attention should be paid to the verification of the location. It is especially
important to check the conditions of localization in the most penalizing regions of the structure, which are not necessarily located close to sensors (see Sections A.2 and B.5). This step
must also verify that the process of amplitude correction permits to find the real source amplitudes (allowing a margin of error).
• Phase 4 (zonal analysis): The analysis is globally identical to the one currently used. However, some of the criteria are poorly defined, such as, for example, estimating the evolution29

of the activity or the intensity, or Felicity ratio. This new version of the Guide (see Part 3
under preparation) will lay down rules that will make the calculation of these parameters independent of the operator. On the other hand, more accurate analysis criteria will be defined,
replacing the only 3 classes actually used.
• Phase 5 (analysis of the located activity): This step necessarily incorporates the amplitude
correction on the observed events. Specific criteria for this analysis will be integrated so that
it is not confined to a single count of events, which is currently not identical from one testing
to another, since it is related to the distance between sensors. Thus, criteria of density ofevents per square meter, average or maximum will be calculated to give a more physicalsense to this analysis.
The implementation of these changes should provide more reproducibility of AE testing, and amore relevant diagnosis.
Conclusions
Acoustic emission is a unique method of testing, with great potential because it allows rapid
diagnosis of large structures in a minimum time, enabling operators to minimize downtime of
their facilities. All regulatory rules, codes and standards, which define the general application
rules for this technique, authorize use of AE according to two methods (zonal location and planar
location by triangulation). However, no comparative study of their performance, thus enabling
their assessment, has been carried out.
The study described here from a real case shows that the differences of performance foundbetween these two techniques are substantial, and therefore have a major impact on the results ofa testing. Moreover, it shows that an application guide such as GBP does not implement reproducibility of AE testing, allowing too diverse test configurations. Moreover their differences are
not taken into account in the analysis and interpretation of data recorded. So, being consistent
with such a guide does not guarantee the relevance of the result.
Many ways of improvement exist, some of which are described in this article:• Reducing drastically the choice in the test configuration, such as the distances between sensors, and better controlling the impact of the configuration adopted on the 'coverage level' of
the structure,• Evaluating the performance of a given test configuration using simple and synthetic criteria,such as the rate of detected sources, the rate of located sources, the overall error of measurement of amplitude, etc.
• Systematically applying amplitude correction to make a more relevant diagnosis.
• Taking into account the local rate of coverage in order to monitor most critical areas optimally.
• Adapting the analysis criteria to the test configuration, making these criteria closer to thestructure.
Finally, the results of this study show that using a test configuration for planar location methodshould be preferred. By increasing sensitivity and allowing the calculation of the real amplitudeof an active source, it can greatly minimize the error levels on the measure, and thus the test results.

30

Backed by its experience, CETIM now plans to use these assessment tools and carry out
well-controlled AE testing. Nevertheless, the professional guides, standards and codes shouldchange 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, 2nd Edition, June 2009. AFIAP(French Association of Pressure Equipment Engineers). Edited by SADAVE.
[2] J. Catty, “Acoustic Emission Testing – Defining a new standard of acoustic emission testingfor pressure vessels Part 1: Quantitative and comparative performance analysis of zonal locationand triangulation methods”, Journal of Acoustic Emission, 27 (2009) 299-313.

31

ACOUSTIC EMISSION MONITORING OF STEEL-FIBER REINFORCED
CONCRETE BEAMS UNDER BENDING
DIMITRIOS G. AGGELIS 1, DIMITRA SOULIOTI 1, NEKTARIA M. BARKOULA 1,
ALKIVIADIS S. PAIPETIS 1, THEODORE E. MATIKAS 1 and TOMOKI SHIOTANI 2
1

2

Dept. of Materials Science and Engineering, University of Ioannina, Ioannina 45110, Greece;
Dept. of Urban Management, Graduate School of Engineering, Kyoto University, C1-2-236, KyotoDaigaku-Katsura, Nishikyo, Kyoto 615-8540, Japan

Abstract
The present paper describes acoustic emission (AE) behavior of concrete under four-point
bending. Different contents of steel fiber were included to investigate their influence on the loadbearing capacity, as well as on the fracture mechanisms. The main crack was accompanied bymany minor cracks indicating that the fibers increased the width of the fracture process zone.
The total AE activity was directly proportional to the fiber content. The subsequent study of the
AE waveform characteristics revealed that the increase in the fiber content resulted in a shift
from the tensile to the shear mode of failure due to improvement of the weak tensile properties ofconcrete.
Keywords: Bending, concrete, fracture mode, steel fibers.
Introduction
The application of fiber reinforcement in cementitious materials continues to expand. Fibers
restrain the breakage of the brittle matrix and enhance its weak tensile properties (Stahli and vanMier, 2007). As the content of the fibers increases, the possibility that the crack growth will behindered through an arrest mechanism also increases. As a result, the material toughness is also
increased (Mobasher et al., 1990, Sivakuram and Sathanam, 2007). An unreinforced concrete
member fails catastrophically at the maximum load. Fibers mainly improve its post-peak behavior, while in many cases the maximum load is also significantly increased (Fischer and Li, 2007,
Washer et al. 2004).
To clarify the mechanisms for this behavior, acoustic emission (AE) monitoring has been
carried out during fracture tests. There are numerous applications of the AE technique for damage characterization of concrete structures (Shiotani and Aggelis, 2007, Shiotani et al. 2007,
2001, Aggelis et al. 2007, Triantafyllou and Papanikolaou, 2006, Golaski et al., 2006, Shah andWeiss 2006). Study of the AE behavior can lead to the characterization and quantification of thedamage level via the use of AE descriptors and thus provides an early warning prior to the macroscopically observed fracture. Further study of the transient waveforms provides information
about the fracture process. The source of the AE activity is closely connected to the mode offracture (Shigeishi and Ohtsu, 1999). Nucleation of shear cracks follows tensile crack nucleation.
Therefore, the determination of the dominant mode provides an early warning prior catastrophic
failure. However, the application of AE to such materials entails certain difficulties. Thesemainly concern the accurate interpretation of the results due to the different individual processes
that contribute to the fracture of concrete. Fracture occurs between different interfaces; cement
paste and sand, mortar and aggregates, concrete and fibers. Additionally, failure includes aggregate crushing and fiber rupture (Kumar and Gupta, 1996). The fracture process of fiber reinforced concrete can be divided in three distinct stages. The first is the “micro-cracking stage”.
J. Acoustic Emission, 28 (2010)32© 2010 Acoustic Emission GroupThe second stage is the growth of micro-cracks and development of macro-cracks up to a saturation level. The final stage includes the rapid expansion of the macro-cracks and the material’s
macroscopic fracture (Wu et al. 2000, 2001, Kim and Weiss, 2003, Weiler et al. 1996).
In the present study, steel-fiber reinforced concrete beams were subjected to four-point bending with simultaneous recording of their AE behavior. An increase of the maximum load andtoughness with fiber content was observed, as expected. The improvement of the weak tensile
behavior of concrete with the addition of fibers was mirrored in the recorded AE activity, which
exhibited events with more “shear” character. Finally, the number of hits correlated well with themeasured toughness properties of the reinforced concrete.
Experimental Part
The specimens were square beams with dimensions 100x100x400 mm. The mix was typicalfor shotcrete applications. The water-to-cement ratio was 0.5 by mass and the aggregates-tocement ratio was 3.6. The maximum aggregate size was 10 mm. The steel fibers were of wavy
shape with diameter 0.75 mm and length 25 mm. Three different volume contents were used,
namely 0.5%, 1% and 1.5%. For reference purposes, a mixture of plain concrete was also produced. For each of the four mixtures three specimens were tested in bending with concurrent AE
monitoring. The four-point bending experiments for the toughness determination were performed
according to the ASTM C1609/C1609M-05 standard. The bottom and top spans were 300 mm
and 100 mm, respectively, as shown in Fig. 1. The displacement rate was 0.08 mm/min. Moredetails concerning the mechanical loading procedure can be found in (Soulioti and Matikas,
2008).
For the AE monitoring, two AE sensors resonant at 150 kHz (R15, Physical Acoustics Corp.,
PAC) were attached to the bottom side of the beams at a 50-mm distance from either side of the
mid-span (see Fig. 1). The signals were amplified 40 dB and those exceeding 40 dBAE (in reference to 1 µV input) were recorded in a two-channel monitoring system (PCI-2, PAC). The sampling rate was 5 MHz.

100 mmAE sensors
Deflection
measurement
systemConcrete specimen

100 mm
300 mmFig. 1. Experimental setup.
Mechanical Results
Typical load-deflection curves of fiber-reinforced concrete specimens are depicted in Fig. 2.
The behavior was typically linear up to the maximum load for all specimens. As expected, the33specimens reinforced with 1.5% of fibers exhibited higher maximum load. However, the maindifference was observed in the post-peak behavior. The plain concrete failed catastrophically in
two parts without absorbing any energy after the maximum load was reached. On the other hand,fiber-reinforced concrete specimens, exhibited a sudden drop of load after the initial fracture, but
continued to absorb energy while their load was gradually decreased until the end of the test. As
can be seen in Table 1, although the increase of fiber content did not necessarily increase thepeak load of the material it certainly enhanced its flexural toughness as manifested by the area
under the load-deflection curves of Fig. 2 (see also Table 1).

1.5%

1%
0.5%
0%

Fig. 2. Load vs. deflection curves for specimens with different fiber contents.
Table 1. Mechanical properties for different fiber contents
Fiber Content
Maximum load
Flexural toughness,(%)(kN)
T100,2* (J)
0
14.9
0.5
13.2
7.0
1
15.8
15.3
1.5
19.9
17.3
*The subscripts denote the specimen thickness (100 mm) and the maximum center deflection (2 mm) according to ASTM C 1609/C 1609M-05.

In Fig. 3(a), the main crack of a plain concrete specimen after failure is shown. The specimen
failed in tension with the crack initiating from the bottom surface (bottom of Fig. 3(a)), which
was under tensile load and propagating towards the top splitting the specimen into two parts. On
the other hand, fiber-reinforced specimens did not break into two parts even after final failure.
As can be seen in Fig. 3(b) the main crack was accompanied by smaller cracks. This phenomenon demonstrates the main reason for including fibers into concrete. The fracture energy is distributed in a larger volume leading to a reduction of the stress intensity. This leads to higher
maximum loads of the fiber-reinforced concrete and higher absorbance of fracture energy, which
was demonstrated by the increased area under the load-deflection curves of Fig. 2. As was obvious even by visual inspection the inclusion of fibers increased the width of the fracture processzone (FPZ). The same effect is also reported in the literature for large aggregates, which had asimilar effect on the FPZ (Mihashi et al., 1991). As the crack propagated to the top side, it split34

to different smaller cracks expanding the width of the energy absorbing zone. This bifurcationand changes of direction will be discussed along with the AE data in the next sections.

Fig. 3. (a) Cracks obtained for plain concrete, (b) concrete with 1.5% fibers.
Acoustic Emission Results
Total AE hit activity
Figures 4(a) and (b) show the history of cumulative AE hits along with the loading for typicalcases of 1.5% fiber concrete and plain concrete, respectively. In the case of the specimen withfiber, at approximately 70% of the maximum load, a small increase of the AE hits was recorded
and the hit rate reached a peak at the macroscopic fracture. This was demonstrated by the verticalincrease of the hit line at the moment of peak load. The total number of hits was typically 3000 4000 (see Fig. 4(a)). However, in the case of the plain concrete specimens (Fig. 4(b)), a notably
smaller number of hits were recorded. From this figure it is also obvious that immediately after
the peak load the experiment was terminated due to the complete fracture of the plain specimen.
In Fig. 4(c) the total AE activity is shown for the different fiber contents. Each point is theaverage of the total number of hits of the three specimens of each category. The relation between
the number of hits and the fiber content is close to linear. The increase of AE hits was attributed
to the development of more cracks with increasing fiber content leading to a considerably larger
fracture surface area. This trend of AE-hit increase corresponded to the fiber pull-out incidents,
which naturally depended on the number of fibers involved in the failure zone. At the moment of
the macro-crack formation (main fracture), the maximum rate of AE-hit generation was exhibited. In Fig. 4(d) the maximum hit rate (at the moment of main fracture) is shown for the different types of material. As can be seen, the fibers contributed significantly to the fracture process,even during the critical crack growth stage through the numerous pullout events (approximately
100 hits/s). On the other hand, plain concrete exhibited only 40 hits/s during the catastrophic
fracture.
An interesting correlation was noticed between the number of cumulative AE hits and thetoughness of the materials, as depicted in Fig. 5. As the content of fibers increased, so did the35toughness and the AE activity. As a result, with proper study, the AE activity can be used as ameasure of fracture toughness.

Fig. 4. Load and AE history for specimen with (a) 1.5% fibers, (b) 0% fibers, (c) total AE activity vs. fiber content, (d) maximum AE hit rate vs. fiber content.

Fig. 5. Toughness vs. AE activity for different fiber contents.
Fracture mode
The shape of the AE waveforms is reported to be characteristic of the fracture mode. Shear
events are characterized by longer rise time and higher amplitude than tensile events (Shiotani et36

al., 2001). This is examined by the AE “Grade”, which is defined as the ratio of waveform amplitude to the rise time. It has been shown that the higher the grade, the more tensile is the natureof the fracture events (Shiotani et al., 2001).
In the present case, the cracking process started at the bottom surface for all specimens due tothe tensile stress. This implied that the initial active failure mode was tensile. In the case of theplain concrete, this failure mode was the dominant mode and led to the catastrophic failure of thespecimens. However, the presence of the reinforcement changed the mode of failure of the composite. This was manifested by the creation and propagation of multiple cracks and hence alarger zone of influence together with the decrease of the grade of the AE waveforms. The gradechange may be either expressed as a mean value for each fiber configuration or as a dynamic
phenomenon throughout the loading process.
In Fig. 6(a), the average grade (dB/µs) is depicted for different fiber contents. As can be seen,
the plain concrete specimens exhibited the highest grade from the total of the AE hits recorded
throughout the experiments. The inclusion of fibers, even at just 0.5% by volume led to the considerable decrease of the grade, which was associated with the change of the failure mode fromtensile to shear. Further increase of fibers had a small influence on the grade since a plateau wasreached.
(a)(b)

Fig. 6. (a) Average grade vs. fiber content, (b) moving average of grade for the three specimens
with 1.5% fibers.
Figure 6(b) shows the change of grade vs. time for all three tests in the case of 1.5% fiber
content. The curves correspond to the moving average of grade for 50 consecutive hits in orderto reduce scatter. It is obvious that as the fracture proceeds, the grade gradually decreased until
the end of the experiment.
Similar analysis can be conducted using the average frequency (RILEM, 2008), for which recent results showed that it drops suddenly after a strong fracture incident. This is illustrated inFig. 7 for two specimens with 1% fibers. It is evident that at the moment the main fracture incident occurs, the average frequency of the signal drops by approximately 200 kHz (marked byarrows). Note that the symbols represent the average frequency of each hit while the black solidline stands for the moving average of the recent 40 hits. It should be highlighted that the data ofFig. 7 were captured using broadband sensors with a sensitivity range between 50 and 800 kHz
37

(Pico, PAC). The dynamic change of average frequency should be connected to the failure mode.
Before the main fracture, matrix cracking is the most dominant mechanism. After the main
fracture, fiber pull-out is added as an active fracture mechanism. The first mode resembles themode I tensile crack while the latter resembles mode II (shear). This large frequency drop can be
captured more adequately in this case than the case of resonant sensors at 150 kHz. However, thebroadband sensors record only about one third of the hits a resonant sensor records in a similar
experiment. Thus they are not suitable for location purposes since their sensitivity in terms ofamplitude response is limited; on the other hand they prove to be more sensitive to the dynamic
nature of fracture when they are located near the fracture zone.

Fig. 7. Time history of load and average frequency for specimens with 1% fibers.
From the above it is shown that the nature of events is moving from tensile to shear with the
propagation of damage. Although the cracks were initiated by tensile loads, gradually shear
stresses dominated the failure process. This has also recently been reported for vinyl-fiber concrete (Aggelis et al. 2009). Similar behavior has been observed in corrosion cracking of concrete
where crack initiation was due to the tensile mode, while as the crack length increased the shear
mode became more active (Farid Uddin et al., 2004). As seen in Table 1, the increase of fiber
content, which activated the shear mode was connected to the increased fracture toughness offiber concrete. Therefore, the specimens did not break with a brittle vertical crack but the crack
bifurcated to different smaller cracks on different directions, spreading the fracture energy towider volume. It is mentioned that use of more sensors and application of inversion analysis suchas Moment Tensor (Ohtsu et al., 1998) will enable the characterization of different clusters ofevents according to their mode type, their location in the volume of the specimen and consequently the actual width of the FPZ (Grosse and Finck, 2006).
Conclusions
In the present study the influence of steel fibers in concrete under bending is discussed. Macroscopically, increased fiber content resulted in the increase of the maximum load and the fracture toughness of the material. At the same time, the number of AE events was proportional to
the fiber content and the measured toughness. Analysis of the “AE grade” parameter showed thatthe tensile mode of fracture was dominant for plain concrete, changing to shear mode as the fiber
content increased. This demonstrated the definite reinforcing effect of the fibers against the weaktensile nature of concrete. As the AE activity implied, the mode of fracture changed during theexperiment from tensile (initial stage) to shear (final fracture). This was macroscopically mani38

fested by the crack bifurcation and deflection from parallel to perpendicular direction relatively
to the loading axis and the concurrent increase of the zone of influence with increasing fiber content. The average frequency of AE hits is proved to be another parameter sensitive to the damage
status especially when broadband sensors are employed.
The identification of the fracture mode, which is feasible via the acoustic emission technique,is of primary importance as it can lead to more suitable design of the reinforcement in order towithstand the specific stresses. At the same time classification of cracks can be employed forearly warning prior to macroscopic failure.
The next step is the use of different types of fibers. This would reveal the best configurationin terms of volume content, fiber shape and material for the enhancement of the structural component. Furthermore, source location of the AE events via the use of more sensors can determine
the actual expansion of the fracture process zone with increased fiber content.
Acknowledgement
The fibers were supplied by ETAL S.A., Greece.
References
D.G. Aggelis, T. Shiotani, S. Momoki, M. Terazawa (2007) Advances in Acoustic Emission –2007, (ed. K. Ono), pp. 390-395.
D.G. Aggelis, T. Shiotani, S. Momoki, A. Hirama (2009), “Acoustic emission and ultrasound fordamage characterization of concrete elements”, ACI Materials Journal 106(6). 509-514.
A.K.M. Farid Uddin, K. Numata, J. Shimasaki, M. Shigeishi, M. Ohtsu (2004), Construction and
Building Materials, 18, 181-188.
G. Fischer, V.C. Li (2007), Engineering Fracture Mechanics 74, 258-272.
L. Golaski, G. Swit, M. Kalicka, K. Ono (2006), Journal of Acoustic Emission 24, 187-196.
C.U. Grosse, F. Finck (2006), Cement and Concrete Composites 28, 330-336.
B. Kim, W.J. Weiss (2003), Cement and Concrete Research 33, 207-214.
A. Kumar, A.P. Gupta (1996), Experimental Mechanics 36(3), 258-261.
H. Mihashi, N. Nomura, S. Niiseki (1991), Cement and Concrete Research 21, 737-744.
B. Mobasher, H. Stang and S.P. Shah (1990), Cement and Concrete Research 20, 665-676.
M. Ohtsu, T. Okamoto, S. Yuyama (1998), ACI Structural J. 95(2), 87-95.
RILEM TC212-ACD (2008) Acoustic emission and related NDE techniques for crack detection
and damage evaluation in concrete. Recommendation 3.
H. R. Shah, J. Weiss (2006), Materials and Structures 39(9), 887-899.
M. Shigeishi, M. Ohtsu (1999), Acoustic Emission: Standards and Technology Update, ASTM
STP 1353, Vahaviolos SJ, ed. American Society for Testing and Materials, pp. 175-188.
T. Shiotani, M. Ohtsu and K. Ikeda (2001), Construction and Building Materials 15(5-6), 235246.
T. Shiotani, D.G. Aggelis, O. Makishima (2007), Journal of Acoustic Emission 25, 308-315.
39

T. Shiotani, D.G. Aggelis (2007) Journal of Acoustic Emission 25, 69-79.
A. Sivakuram, M. Sathanam (2007), Cement and Concrete Composites 29, 603-608.
D. Soulioti, T.E. Matikas (2008), Proc. of the 1st Conference on Structural Materials and Components, 21-23 May, Athens, Greece, vol. C, 1287-1298, (In Greek)
P. Stahli, J.G.M. van Mier (2007), Engineering Fracture Mechanics 74, 223-242.
T.C. Triantafillou and C.G. Papanikolaou (2006), Materials and Structures 39(1), 93-103.
G. Washer, P. Fuchs, B.A. Graybeal, J.L. Hartman (2004): IEEE Transactions on Ultrasonics,
Ferroelectrics, and Frequency Control 51(2), 193-201.
B. Weiler, C.U. Grosse, H.W. Reinhardt (1996), Proceedings of the 22nd European Conferenceon Acoustic Emission Testing, Aberdeen, Scotland, pp. 119-124.
K. Wu, B. Chen, W. Yao (2000), Cement and Concrete Research 30, 1495-1500.
K. Wu, B. Chen, W. Yao (2001), Cement and Concrete Research 31, 919-923.

40

ON LAMB MODES AS A FUNCTION OF ACOUSTIC EMISSIONSOURCE RISE TIME#∞
M. A. HAMSTAD
National Institute of Standards and Technology, Materials Reliability Division (853),
325 Broadway, Boulder, CO 80305-3328 and University of Denver, Department of Mechanicaland Materials Engineering, Denver, CO 80208
Abstract
A study was carried out to examine Lamb-wave modal content as a function of the acoustic
emission (AE) source rise-time. The study used a validated finite-element code to model thesource operation and subsequent wave propagation up to a distance of 480 mm in a 4.7-mm thickaluminum plate with large transverse dimensions. To obtain the large propagation distances with
sufficient transverse dimensions so that plate edge reflections did not arrive during the significant part of the direct AE signal, an axisymmetric code was used. The buried dipole AE sourceswere located at three different depths below the top surface of the plate, where the pseudo-AE
sensors were located. These sensors provided the out-of-plane displacement as a function oftime. The rise-times for the different finite-element runs varied from 0.5 µs to 15 µs. The resulting data were high-pass filtered at 40 kHz and re-sampled with a time step of 0.1 µs. The intense
portions of the Lamb modes were determined by use of a Choi-Williams transform (CWT) for
the range of source rise-times, three different source depths and the signal propagation distances.
Higher Lamb modes were observed in the CWT results for the shortest rise-times, but fundamental mode frequencies still dominated for all the rise-time cases and different source depths. Thus,
only the fundamental modes need to be considered in the determination of accurate signal arrival
times.
Keywords: AE source rise-times, Choi-Williams transform, Lamb modes, thin plate
Introduction
In the analysis of burst-type acoustic emission (AE) signals obtained with wideband sensors(nearly flat frequency response), the use of intense Lamb mode/frequency combinations has produced an improvement in the accuracy of source location calculations [1-3]. This result comesfrom the determination of signal arrival times that are independent of a voltage threshold and that
correspond to the same group velocity at each sensor in an array.
A previous paper [4] used finite-element modeling to study the effects of different sourcerise-times on the peak amplitudes and frequency content of the far-field AE signals in a plate.
The results of that study demonstrated a large drop in the peak amplitude as the source rise-timeincreased. The purpose of this paper is to extend the previous examination to the modal content
of the out-of-plane AE signals that result from sources with different rise-times. Finite-element
modeling (FEM) offers several key advantages: i) generation of AE signals from buried selfequilibrated sources at different depths (below the top surface) in a plate, ii) generation of AE
signals with specific source rise-times; iii) obtaining the exact out-of-plane displacement versus______#

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 isneither intended nor implied.
J. Acoustic Emission, 28 (2010)41© 2010 Acoustic Emission Grouptime from a pseudo sensor (a perfect, wideband, point-contact sensor with no resonances), iv)
obtaining the sensor results at different known propagation distances from the source and v) use
of a specimen that did not produce reflections from the specimen edges arriving at the sensors
during the major portion of the direct arrival of the AE displacement waves from the source.
Relevant Details on the Specimen Domain and Finite-element Modeling
A circular aluminum plate, 4.7-mm thick with a radius of nearly 1000 mm, served as the
sample domain. The large in-plane dimension allowed propagation distances of up to 480 mm
such that the direct arrival of the significant portion of the AE waves was not altered by reflections from the plate edge. A total of nine selected source rise-times varied from 0.5 µs to 15 µs.
The basis for the selection of this range in rise-times was previously presented [4]. Because the0.5-µs rise-time generated frequencies well above the frequency sensitivity range of even special
AE sensors, shorter rise-times were not examined. The sources in the continuous mesh domain
were dipoles (self-equilibrating forces of two oppositely directed single-cell monopoles with one
cell between them) by use of the “equivalent body force” concept for displacement discontinuities [5]. The forces (each monopole of the dipole had a force of 1 N) were applied with a “cosinebell” temporal time dependence T(t) given by0T(t) = (0.5 – 0.5 cos [ t / ] )1

for t < 0,
for 0 tfor t > ,

, and

(1)

where was the source rise-time. The same very small cell size (54 µm) and time step (7.7 ns)
was used for all the FEM runs. These values were chosen to meet the requirements of the validated finite-element code [6] for the shortest rise-time source. An axisymmetric version of thecode was used to enable carrying out the modeling in such a large domain with the very small
cells and time steps with reasonable computing resources. The finite-element code was run for asmall period of time beyond 300 µs from the start of the operation of the AE source. The dipole
sources were oriented out of plane, with the center of the dipole located at one of three different
depths below the top surface of the plate where the sensors were located. The depths were 2.35mm (mid-plane), 1.32 mm (mid-depth) and 0.24 mm (near the plate top surface). The entire AE
signals were numerically processed with a 40 kHz (eight-pole) high-pass Butterworth filter, followed by resampling from the original time step to 0.1 µs per point (comparable to the digitization rates often used in AE measurement systems). The aluminum material properties used for
the FEM calculations were bulk longitudinal and shear velocities of 6320 m/s and 3100 m/s, respectively, and a density of 2.7 kg/m3.
Determination of Frequency/Time Content
Choi-Williams transform (CWT) results were used to enhance the identification of the AE
signal Lamb modes, to indicate highly excited frequency regions within the modes, and to obtain
the CWT magnitudes (coefficients) in these regions. The CWT results were obtained by use of
the AGU-Vallen Wavelet freeware [7] with the key parameter settings being maximum frequency up to 2000 kHz, frequency resolution = 2.4 kHz, 112 terms in the damping summation,
and an exponential damping parameter of 20. The time-domain setting of the number of samples
was up to about 3000 for the furthest propagation distance. In the color CWT figures, red indicates the highest-intensity region of the CWT coefficients. By use of the known propagation distances, the appropriate group velocity curves [8] were superimposed on the CWT results to identify the Lamb modes. In some cases, the relevant group-velocity curves for the figures in this42Fig. 1. Comparison of CWT results for in-plane versus out-of-plane source orientation. Scalingof 0 to 1000 kHz for vertical axis and 0 to 150 µs for horizontal axis.
paper are shown at the bottom of the CWT results, so they do not hide the intense regions inthese results.
Out-of-plane versus In-plane Source Orientation
Based on the CWT results from the three-dimensional FEM calculations shown in Fig. 1, theuse of an out-of-plane dipole source orientation provides frequency/time results similar to thosefrom in-plane dipoles for sources at different depths. The results in Fig. 1, for the same material
and plate thickness, were for a source rise-time of 1.5 µs and a propagation distance of 180 mm.
Again, these CWT results were calculated from 40-kHz high-passed, out-of-plane displacement
data. Even with the use of a smaller domain and a longer rise-time (allowing a larger cell sizeand longer time steps), considerable computer resources were required to make the threedimensional runs. The results described above indicated that the out-of-plane sources could be
used in this study, resulting in a significant reduction in required computer resources.
Modal Content versus Source Rise Time – Mid-plane Source Location
Figures 2 and 3 show the CWT results (first columns) as well as the out-of-plane displacement signals (second column of Fig. 2; zero time corresponds to the start of the dipole source
function) and their frequency domains (second column of Fig. 3) for a selected set of rise43

Fig. 2. CWT (first column) and AE out-of-plane displacement signal (second column) for a selection of rise-times for the source at the mid-plane and at a propagation distance of 480 mm.
Note: only the group velocity curves are present at the bottom plot of the first column.
times for the signals at a propagation distance of 480 mm. Because the FEM code did not apply
material attenuation, the displacement results at a propagation distance of 480 mm were used exclusively in these figures for all the rise-time cases. This distance provided the best separation of
the dispersive modes. Note that the CWT results are duplicated in both figures so that these44

results can be directly compared by the reader to the AE signals and the frequency-domain results without making the figures so small that they lose their illustrative role. Also, prior to thecalculation of the fast Fourier transform (FFT) results, the waveforms were shortened and terminated at a zero to remove any effects from the first edge reflection that occurs at about 280 µs forthis propagation distance. (Note that the FFTs were calculated with a rectangular window after
each terminated signal was extended with zeros to obtain a total of 4096 points). This reflection

Fig. 3. CWT (first column) and corresponding spectra (second column) for a selection of risetimes for the source at the midplane and at a propagation distance of 480 mm. See note in Fig. 2
caption.
45

can be observed most clearly for the longer rise-times. Except for the 15-µs rise-time CWT result, the group-velocity curves were not superimposed on the results. Instead, these curves areshown at the bottom of the CWT results. For the 15-µs rise-time CWT, the S0 curve was superimposed to show that the intense arrival appears to be both late and extended in time. To examine this situation, the CWT was calculated for this rise-time and propagation distance without
applying any frequency filter. In this case, the S0 mode peak intensity was at 41 kHz (9% late intime, based on the thin-plate extensional velocity). For the 40-kHz high-pass data, the S0 mode
peak intensity was at 61 kHz (25 % late in time). Thus, the 40-kHz filter contributes to the latearrival. In addition, since the period of the signal in this low frequency range is relatively long, asshown in Fig. 2, it is possible that this influences the CWT calculation and also contributes to thelate and extended arrival in the CWT result.
A number of observations for the mid-plane source depth can be made. First, for the 0.5 µs
rise-time, a portion of the higher-frequency part of the S1 mode can be seen the CWT result (seearrow in Figs. 2 and 3). However, relative to the CWT magnitude of the most intense portion of
the S0 mode at a frequency of 539 kHz, its peak magnitude, which occurs at a frequency of 1430
kHz, is only 18 % of that S0 peak magnitude. At rise-times of 0.75 µs and longer, the S1 mode is
no longer clearly visible. Second, at a rise-time of 3 µs and greater, the most intense portion of
the CWT moves to the initial arrival portion of the S0 mode as compared to a CWT peak arrival
later in the signal for shorter rise-times. Third, as can be observed from the second column ofFig. 2, for all the rise-time cases, the time of arrival of the peak CWT intensity corresponds tothe highest or nearly highest-amplitude portion of the signal. Further, in Fig. 2 (second column),
this high-signal-amplitude arrival time shifts from late in the signal to early as the rise-time increases. Fourth, the most intense region of the CWT corresponds to the highest or near-highest
amplitude frequency region of the spectra results, as shown in the second column of Fig. 3. Finally, it is worth noting that for the 3-µs rise-time, the second most intense portion of the S0mode, with a peak at a frequency of 630 kHz, is directly in a region where the S0 and S1 groupvelocity curves first intersect.
The above discussion demonstrates that determining signal arrival times at the most intense
portions of the CWT corresponds to both the signal amplitudes and signal frequencies that areamong the largest in the signals. These are the regions of the AE signals where the best signal-tonoise ratio (based on the electronic preamplifier noise) would be present.
Modal Content versus Source Rise Time – Near Top Surface Source Location
Figures 4 and 5 show the CWT results (first columns) as well as the out-of-plane displacement signals versus time (second column of Fig. 4) and the frequency domains (second columnof Fig. 5) for a selected set of rise-times for the signals at a propagation distance of 480 mm. The
same comments relative to the duplication of the CWT in the figures and the calculation of the
FFTs apply to these figures as were made relative to Figs. 2 and 3. Clearly, for this source depth
and all the rise-times, there is a strong dominance of the lower-frequency regions of the A0
mode. As the rise-time decreases, higher-frequency portions of the A0 mode are apparent in the
CWT, and as the rise-time increases, the most intense portion of this mode gradually decreases infrequency with a significant change in the arrival time of the peak CWT intensity due to theshape of the A0 group velocity curve. This trend of arrival times is observed in the time domainsof Fig. 4. Specifically, the change in the most intense frequency of the A0 mode in the CWT result goes from 66 kHz for the 0.5-µs rise-time to 51 kHz at the 15-µs rise-time, while the respective change in the arrival time of the peak intensity changes from about 200 µs to about 230 µs.
46Over the same range of rise-times, the peak magnitude of the spectra hardly changes from about
50 kHz. At these lower frequencies there is likely some effect of the 40-kHz high-pass filter. As
expected, the frequency content (see Fig. 5) in the spectra above 400 kHz gradually fades as therise-time increases.

Fig. 4. CWT (first column) and corresponding out-of-plane displacement AE signal (second column) for a selection of rise-times for the source near the top surface and at a propagation distance of 480 mm. See note in Fig. 2 caption.
47

Fig. 5. CWT (first column) and corresponding spectra (second column) for a selection of risetimes for the source near the top surface and at a propagation distance of 480 mm. See note in
Fig. 2 caption.
Modal Content versus Source Rise Time – Mid-depth Source Location
Figures 6 and 7 show the CWT results (first columns) as well as the out-of-plane displacement versus time (second column of Fig. 6) and frequency domains (second column of Fig. 7)
48

Fig. 6. CWT (first column) and corresponding out-of-plane displacement AE signal (second column) for a selection of rise-times for the source at the mid-depth and at a propagation distance of
480 mm. See note in Fig. 2 caption.
for a selected set of rise-times for the signals at a propagation distance of 480 mm. The samecomments relative to the duplication of the CWT in both figures and the calculation of the FFT
apply to these figures as were made relative to Figs. 2 and 3. Again at the two shortest rise-times,
there is evidence in the CWT results of a higher mode (A1, higher-frequency regions of the fundamental modes and an intense region due to mode intersections (second intersection of the S049

and A1 modes). As was the case with the mid-plane source at 0.5-µs rise-time, a higherfrequency region at 542 kHz of the S0 mode is still the most intense. Compared to the magnitudeof the S0 mode at that frequency, the magnitude of the CWT coefficients at the intersection of theS0 and A1 modes at 837 kHz is 93 %, and the magnitude of the A1 mode at 993 kHz is 61 %. The

Fig. 7. CWT (first column) and corresponding spectra (second column) for a selection of risetimes for the source at the mid-depth and at a propagation distance of 480 mm. See note in Fig. 2
caption.
50

A0 mode also is visible (for this rise-time) in the low frequency region with a comparative magnitude of 35 % at its most intense point at 63 kHz.
Clearly, for this source depth, both the axisymmetric and anti-symmetric modes are apparent.
For all the rise-times, the spectra show a low-frequency peak that is the largest in magnitude at
the longer rise-times, and it nearly has the largest magnitude at the shorter rise-times. In contrast
to the mid-plane source, at the longer rise-times the largest-magnitude region of the CWT of the
AE signal is in the low-frequency region of the A0 mode rather than the low-frequency region of
the S0 mode at an earlier arrival time for the mid-plane source. At the rise-times of 2.3 µs or
longer, the AE signal peak amplitude corresponds to the most intense portion of the A0 mode in
the CWT. It is interesting that the spectra for the two shortest rise-times show a high magnitudein a low-frequency region with a peak at approximately 50 kHz, but this frequency is less apparent in the CWT and the AE signal. This situation is likely to be the result of the continued presence in time of the A0 mode in the CWT for these two rise-times (see arrows to A0 in Figs. 6 and
7).
Modal Content Versus Propagation Distance
Figure 8 illustrates how the CWT results change as a function of selected propagation distances from 480 mm to 60 mm for two source depths for the sources with a rise-time of 0.5 µs.
The first column is for a mid-depth source, and the second column is for a mid-plane source. The
cases for the shortest rise-time were selected, since they excite the largest number of modes. In
addition, the near top source was not selected for illustration, because only the A0 mode strongly
dominated all the rise-time cases at all the propagation distances.
Examination of the first column of Fig. 8 shows that as the propagation distance decreases
for the mid-depth source, similar dominant excited modal features can be identified down to a
propagation distance of 180 mm. At 60 mm, the modal results are not clear in this figure. To
clarify the CWT results in the first column of Fig. 8 for the shortest propagation distances, Fig. 9shows in the first column the CWT results for the mid-depth source at distances of 120 mm and
60 mm. In addition, the same results are shown in the second column for the mid-plane source
where the time axis is only from zero to 80 µs in both columns. The first column of Fig. 9 showsthat the closeness in time of the multiple modes that are excited results in distortion of the CWT
results above about 600 kHz for both the 120 mm and 60 mm distances. To determine whether
intense arrivals below this frequency corresponded to the correct Lamb wave group velocity, thearrival times of the CWT peak magnitudes were determined at 564 kHz for all the propagation
distances. This frequency was that of the peak CWT magnitude for the S0 mode at a propagation
distance of 180 mm. A plot (not shown) of propagation distance versus these arrival times had aslope of 1.85 mm/µs, with all the data points on the straight line including those at 120 mm and
60 mm. This velocity is within 1.2 % of the associated Lamb-wave S0 mode group velocity atthat frequency. Thus, in spite of the distortion in the CWT results, the correct mode arrival times
can be obtained at the most intense mode/frequency combination below 600 kHz.
If the second columns of Figs. 8 and 9 are examined, the same dominant mode and frequency
combinations for the S0 and S1 modes are apparent at each propagation distance from 480 mm
down to 60 mm. Thus, when multiple frequency modes are not sufficiently excited over the same
frequency range (as was the case in the first column of Fig. 9), the CWT results are not as distorted at the shorter propagation distances. Again, as a check on the application of the use of the
CWT magnitude peaks at certain frequencies to obtain arrival times, the arrival times at the peak51

magnitude of the CWT for the S1 mode at 1430 kHz were determined for all the distances. The
same type of plot for this data had a slope of 2.20 mm/µs, which differed by less than 1 % fromthe value obtained from the Lamb-mode group-velocity curve for the S1 mode at that frequency.
Thus, even this relatively small intensity mode arrival (for example about 17 % of the CWT peakS0 mode arrival at the propagation distance of 360 mm) provided accurate arrival times.

Fig. 8. CWT results versus propagation distance for 0.5-µs rise-time. First column for mid-depthsource and second column for mid-plane source depth.
52

Fig. 9. CWT results versus distance at the two shortest propagation distances. First column for
mid-depth source and second column for mid-plane source depth.

Fig. 10. CWT results at 480 mm for the indicated two rise-times and three source depths.

53

Modal Content to Possibly Indentify Sources with Different Rise Times
The changes in the intense modal regions as the source depth changes imply that, unless thesources with distinctly different rise-times all originate at the same depth, the modal pattern ofintense mode/frequency combinations cannot easily be used to distinguish the different risetimes. To demonstrate this directly, Fig. 10 shows the CWT results at 480 mm for sources at thethree different depths with two different rise-times of 1.5 µs and 4 µs (rise-times differ by a factor of 2.7). In this figure, it is clear that if the sources were at the same depth, it would bestraightforward to distinguish the different rise-time sources by the differences in modal intensity
at different frequency points in the modes excited. For example, in the mid-plane source case, the4-µs rise-time excites a lower frequency portion of the S0 mode, which is not sufficiently excitedin the 1.5-µs rise-time case. In the least straightforward case for the near top source, the calculation of the ratio of the CWT peak magnitudes (for the A0 mode) at the frequency of 60 kHz divided by that at 300 kHz would serve to distinguish the two different rise-time sources. In the
CWT example in the figure, this ratio for the 1.5-µs source is 4.7, and for the 4-µs source it is150. However, if the source depths varied, a much more complicated approach would be needed,
and to verify such a proposed approach, a more extensive database of possible source depths
would be required.
To examine another aspect of the dependence of the modal content on the source rise-time,
the CWT magnitudes of the intense mode/frequency combinations were examined as a functionof the source rise-time and the source depth. The normalized results are shown in Fig. 11 for a
propagation distance of 480 mm. The selected intense combinations for each depth were as follows: i) mid-depth – S0 at 537 kHz, A0 at 63 kHz and S0 intersection with A1 at 837 kHz, ii) near
top surface – A0 at 66 kHz and iii) mid-plane – S0 at 539 kHz. These combinations were selectedfrom the CWT results for the 0.5 µs rise-time cases shown in Figs. 2 through 7. Figure 11 clearlyshows that the three high-frequency mode/frequency combinations experience a rapid fall-off ofthe CWT magnitude as the rise-time increases. The most rapid falloff is the mid-depth source
CWT magnitude at 837 kHz followed closely by the mid-plane source result at 539 kHz and the
mid-depth result at 537 kHz. It should be noted that these two similar frequency values are those
provided by the software, but they clearly are from the same mode/frequency region. These results with rise-time increases are expected, because longer rise-times do not sufficiently excite
these higher frequencies. It should be noted that the modal arrivals of the three higher
mode/frequency combinations could not be identified in the CWT results for rise-times greaterthan about two microseconds. The falloff in the CWT magnitude with increasing rise-time of thetwo lower-frequency combinations was nearly identical, because they involved the same mode,
A0, for the two different source depths. The slower fall-off with increasing rise-time for thesecases is expected due to the relatively low frequency of this A0 modal region. The potential ability to use these results to identify sources with different rise-times must be combined with theresults of CWT-determined mode attenuation with propagation distance.
Figure 12 demonstrates how typical intense mode/frequency combinations attenuate withpropagation distance for the 0.5-µs rise-time sources at two different depths. In this figure, theattenuation of the A0 mode at 66 kHz for a near top source and the S0 mode at 539 kHz for a
mid-plane source are compared to the amplitude-based geometric attenuation of a wave spreading in-plane. The higher-frequency combination has slightly more attenuation than the lowerfrequency combination, but both decay significantly more than the geometric attenuation alone.
Thus, the CWT magnitudes of the intense mode/frequency arrivals experience an additionalsource of attenuation beyond the expected geometric attenuation. Kerber et al. [9] recently exam
54

ined Lamb wave modal attenuation with propagation distance by comparing the results from achirplet transform (CT) to those from a short-time Fourier transform (STFT). The CT allowed
them to use an additional degree of freedom “to adjust the window function to the group delay ofthe signal.” The CT-based algorithm thus allowed them to use the known group-velocity curves.
With experimental data for an aluminum plate one millimeter thick, they found significantly
smaller mode attenuation with propagation distance with the CT-based algorithm as compared to
the STFT results. In their work, the source of the out-of-plane displacement signals was a laser
ablation on the aluminum plate surface (out-of-plane surface source). A point-based laser measurement system was used to obtain the displacement signals. They also showed further reductions in the attenuation with both the CT and STFT results when synthetic signals were used.
Due to the differences in plate thickness and a lack of specific data in this reference, a directcomparison could not be made with the current results for the CWT. However, the results in reference 9 indicate that the added attenuation beyond geometric attenuation is at least in part artificial, as it depends on the signal-processing approach.

Fig. 11. Normalized CWT magnitudes of the intense mode/frequency combinations for the threesource depths (at a propagation distance of 480 mm) as a function of the source rise-time.
It is interesting to note that the added attenuation relative to geometric attenuation for thesemode/frequency combinations is about 12 to 15 dB for these two cases. This result contrasts withthe peak amplitude attenuation of about 3 to 5 dB more than geometric attenuation [4]. To further illustrate this aspect, Fig. 12 also includes the attenuation of the peak AE signal amplitudewith propagation distance for a mid-plane source with a rise-time of 0.5 µs. It is clear that thepeak amplitude does not attenuate as rapidly with propagation distance as the intensemode/frequency combinations. It is worth noting, that the peak signal amplitude attenuation with
distance is approximately the same for the different source depths and source rise-times [4]. Fur
55

ther, the “wavy” character of the peak amplitude attenuation with distance is due to differences(at different propagation distances) in the superposition of the multiple modes excited by theshort rise-time. It was demonstrated previously [4] that the “wavy” character decreases when thesource rise-time is longer when fewer modes are generated. Also, when the CWT magnitude of asingle mode/frequency combination is considered, the attenuation with propagation distance issmooth, as shown in Fig. 12. Thus, unless the propagation distance to the sensors is approximately the same, the attenuation of the CWT magnitude of the intense mode/frequency combinations creates an additional complication to the use of modal information for the identification ofsources with different rise-times.

Fig. 12. Normalized CWT attenuation of intense mode/frequency combinations and normalized
peak amplitude for mid-plane source, 0.5 µs rise-time sources versus geometric attenuation.
Comments on Comparisons of Modal Content versus Source Depth
In the previous publication [4] which specifically analyzed the frequency spectra dependence
on rise-time and source depth, it was pointed out that for rise-times less than 2.3 µs, the middepth spectra could be roughly viewed as a superposition of the peak-frequency regions from the
mid-plane and near top spectra results at the same rise-times. Examination of Figs. 2, 4 and 6
demonstrates that such a statement cannot be made relative to the CWT results. For example, inthe case of a 0.5-µs rise-time, the primary modal content for the source near the top surface is theA0 mode, and for the mid-plane source it is the S0 mode with a small magnitude of the S1 mode.
The mid-depth primary modal content is the S0 mode, the A1 mode, the intersection point ofthese two modes and a small intensity of the A0 mode. Clearly, this does not correspond to thesuperposition of the primary modal content of the mid-plane and near top depths at this rise-time.
At other rise-times, similar inconsistencies were present.
56

Conclusions
Applicable for Choi-Williams transform (CWT) results for a 4.7-mm thick aluminum platebased on rise-times from 0.5 µs to 15 µs and propagation distances from 60 mm to 480 mm:• For sources at the mid-plane and mid-depth, as the rise-time decreases, higher Lamb modes
become visible in the CWT results.
• In contrast, for sources near the top surface, as the rise-time decreases, the CWT results are
dominated by the A0 Lamb mode to such an extent that higher Lamb modes are not visible.
• For all the source depths, rise-times and propagation distances, the intense portions of the
CWT results are dominated by different frequency portions of only the A0 and S0 fundamental Lamb modes. Thus, only the fundamental modes need to be considered in the determination of accurate signal arrival times which would lead to accurate AE source locations.
• In an experimental situation, to monitor the intense portions of the fundamental modes forthe rise-times and source depths considered here would require a sensor response that isnearly flat with frequency (ASTM E1106) from about 40 kHz to about 600 kHz.
• As a function of propagation distance, similar frequency regions of the fundamental modes
dominate for a given rise-time and the source depths of mid-plane and near the top.
• For the mid-depth source at the shortest propagation distances (60 mm and 120 mm) andshortest rise-time (0.5 µs), the excitation of both symmetric and anti-symmetric modes results in some distortion of the CWT results at higher frequencies due to the close arrivals intime of the modes. But, by focusing on the lower-frequency intense portions of the fundamental modes, accurate arrival times can be obtained that correspond to the theoreticalLamb-wave group velocities.
• The CWT magnitudes of higher-frequency intense mode/frequency combinations falloff rapidly as the source rise-time increases, in contrast to a slower falloff for the lower-frequency
intense mode/frequency combinations for this change of rise-times.
• The rate of attenuation with increasing propagation distance of the CWT magnitudes of theintense mode/frequency combinations is greater than that of the peak signal amplitude.
• In AE applications, the above conclusions are applicable relative to the potential to detect AE
signals, to obtain accurate source locations and to approaches that use the modal content of
AE signals to identify AE source types.
Acknowledgement
The finite-element calculations by Dr. John Gary (retired NIST, Boulder, CO, USA) aregratefully acknowledged.
References
1. Hamstad, M.A., K.S. Downs and A. O’Gallagher, “Practical Aspects of Acoustic Emission
Source Location by a Wavelet Transform,” 21, 2003, 70-94.
2. Hamstad, M.A., A. O’Gallagher and J. Gary, “Examination of the Application of a Wavelet
Transform to Acoustic Emission Signals: Part 2. Source Location”, Journal of Acoustic
Emission, 20, 2002, 62-81.
3. 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.
57

4. Hamstad, M.A., “Frequencies and Amplitudes of AE Signals in a Plate as a Function of
Source Rise-time,” Proceeding of the 29th European Conference on Acoustic Emission Testing, Vienna, Austria, 2010.
5. Burridge, R. and L. Knopoff, “Body force equivalents for seismic dislocations,” Bulletin
Seismic Society of America, 54, 1964, 1875-1914.
6. Hamstad, M.A., A. O'Gallagher and J. Gary, "Modeling of buried acoustic emission monopole and dipole sources with a finite-element technique," Journal of Acoustic Emission,17(3-4), 1999, 97-110.
7. Vallen, J., “AGU-Vallen Wavelet transform software version R2009.1215,” Vallen-Systeme
GmbH, Münich, Germany, 2009, Available at http://www.vallen.de/wavelet/index.html.
8. Vallen, J., “Dispersion software version R2009.1215,” Vallen-Systeme GmbH, Münich, Germany, 2009, Available at http://www.vallen.de/wavelet/index.html.
9. Kerber, F., H. Sprenger, M. Niethammer, K. Luangvilai and L.J. Jacobs, “Attenuation analysis of Lamb waves using the chirplet transform,” EURASIP Journal on Advances in Signal
Processing, Vol. 2010, Article ID 375171, 6 pages.

58

WAVEFORM ANALYSIS OF ACOUSTIC EMISSION MONITORING OFTENSILE TESTS ON WELDED WOOD-JOINTSANDREAS J. BRUNNER 1, THOMAS TANNERT 2 and TILL VALLÉE 3
1
Empa, Swiss Federal Laboratories for Materials Science and Technology, Laboratory of Mechanical Systems Engineering, Überlandstrasse 129, CH-8600 Dübendorf, Switzerland, 2 Bern
University of Applied Sciences, Timber and Composite Construction, Solothurnstrasse 102, CH2500 Biel, Switzerland, 3 Ecole d’ingénieurs at d’architectes de Fribourg, Boulevard de Pérolles
80, CH-1705 Fribourg, Switzerland
Abstract
Wood welding is a relatively recent procedure for preparing joints between pieces of woodthat shows promising potential for avoiding some of the problems related to conventional adhesive jointing of wood. As a first step towards more extensive characterization of their damage
behavior and of the failure mechanisms, tensile tests on welded wood joints have been monitoredwith acoustic emission (AE). This contribution attempts a more detailed analysis of the AE signals from stair step-load tests on welded wood joints.
Keywords: Welded wood, tensile test, AE waveform analysis, failure mechanisms
Introduction
To connect load bearing timber structures, practitioners have at their disposal a series ofmethods; a first fraction of them rely on mechanical fasteners, a second type achieves loadtransmission by means of direct compressive contact between timber members, and a third increasingly considered option is adhesively bonding. It is less known that welding of wood also
allows for load bearing connections of timber elements.
Wood-to-wood connections by means of welding are an innovative process, which holds
high potential for development. To achieve joints by means of welding, the wooden parts arepressed against each other and a rapid vibration heats up and melts the material at the interface
within few seconds. Once the motion stops, and after cooling down, a solid bond is formed [1].
Bonds are completed in less than a minute, and no further preparation of the surfaces is required.
First reports related to welding of wood date back to Sutthoff et al. [2], welding of wood basedon Linear Vibration Welding (LVW) greatly improves homogeneity and resistance of the resulting bonds [3]. Two sets of parameters proved to have an influence on the bond strength: firstly,parameters related to the wood [4-5], and secondly parameters related to the LVW device [6].
Since welding of wood is an alternative to adhesive bonding, it is also of interest to compare
the mechanical properties and the AE behavior, i.e., the damage accumulation and failure withthat of adhesively bonded joints made from the same type of wood [7]. The AE behavior (AEactivity, AE intensity, Felicity-ratio, and linear AE source location) under stair step loading forboth types of joints is compared and discussed in [8].
The objectives of this paper are (i) to compare AE waveforms recorded with the two types of
sensors used in the experiments on welded wood joints, and (ii) to look for trends in the frequency content of the AE signals with increasing intensity (e.g., AE signal amplitudes observedwith increasing loads).
J. Acoustic Emission, 28 (2010)59© 2010 Acoustic Emission GroupMaterial and Experimental
In order to investigate the strength of wood welded joints, a series of single-lap welded timber joints was produced in which the sole varied parameter was the overlap length, L. The timber
species used was spruce (Picea abies) cut from high quality almost defect-free boards. The material was conditioned to 12% moisture content prior to manufacturing of the specimens, and thenagain stored in constant climate before and after welding and until testing.
A total of 25 wood welded single lap joints were manufactured by welding two timber boards(700 mm long, 60 mm wide and 15 mm thick) by means of a Branson M-DT24L linear vibration-welding machine; Fig.1 details a specimen. Subsequently, a grove up to the wood weld was
cut in each of the now connected boards, the distance between the two groves defining the overlap length. The overlap length was varied from 100 mm to 400 mm in steps of 100 mm. Each of
the welded lap joints was manufactured and subsequently tested five times. Five additional specimens with an overlap length of 100 mm were produced to study the AE behavior.

Fig. 1: Schematic geometry of single lap joint welded specimens (not to scale).
Tensile tests with AE monitoring were performed on a servo-hydraulic test machine (type Instron 1251) with hydraulic grips (clamping pressure 30 bar) at 3 mm/min (first specimen) and 0.3
mm/min (all other), respectively. Specimens were stored and tested under laboratory conditionsof +23°C and 50% relative humidity (differing from the standard conditions +22°C and 65%
relative humidity for wood) after reducing the total thickness from 30 mm (Fig. 1) to 25 mm.
Aluminum distance bars were used to prevent extensive compression of the specimens in thegrips. A load cell with 200 kN (specimens 1 and 2) and 50 kN range (specimens 3-5) was used.
Acoustic Emission (AE) monitoring of the tensile tests was performed with commercial AE
equipment (type AMSY-5 from Vallen Systeme GmbH) with a total of eight AE sensors (fortype SE45-H, and four type SE150-M). Two sensors (SE150-M) were used as guard sensors on
the hydraulic grips, the other six were mounted on the specimen with spring clamps using silicone-free vacuum grease as couplant. Threshold was set at 40 dBAE and 50 dBAE, for specimens
mounted near the top and bottom grip, respectively and at 55 dBAE for the guard sensor on thebottom grip. Both, AE signal parameters and full AE waveforms were recorded, with 5 MHz
sampling. Machine load and crosshead displacement was recorded simultaneously with sampling
every 100 ms. Data analysis was performed with equipment specific software (VisualAE® and60

VisualTR® from Vallen Systeme GmbH). Specifically, the Fast Fourier Transform (FFT) feature
extractor routine (VisualTR®) has been extensively used in the AE waveform analysis.
Results and Discussion
Mechanical Properties
The mechanical properties are reported in detail in [8] and only a few essential points are repeated here. All wood welded single lap joints tested with AE monitoring exhibited almost perfectly linear-elastic load-displacement behavior, and failed in a brittle and sudden manner. Failure load and displacement at failure show a fairly linear correlation for all specimens. One
specimen was tested quasistatically with AE monitoring (displacement control at 3 mm/min).
The remaining four specimens were then tested under quasistatic tensile loading with stepwise
increase and unloading under displacement control (0.3 mm/min). The specimen tested quasistatically to failure yielded a failure load of 6.7 kN and a crosshead displacement at failure of
0.28 mm; those tested stepwise an average of 5.3 kN (coefficient of variation ± 2.4 kN or 38.1%)
with an average crosshead displacement at failure of 0.24 mm (specimen deformation was notdirectly measured). A closer post-failure observation indicates that the welding process did notalways yield in perfectly welded surface, see Fig. 2.

Fig. 2: Failure surfaces of two welded wood specimens tested with AE monitoring (left) and of
two others including one specimen without separation of the two joint faces (right), on which thesensor positions are marked with pencil; note the light areas indicating poorly welded surfaces
among the dark surface features.
The failure surfaces of the specimens after tensile failure showed a pattern of dark brown andlight yellow color. Qualitatively, specimens 1 and 2 (Fig. 2, left) show a larger amount of lightyellow color. This seems to correlate with lower failure loads (6.7 kN, but at higher strain rate,and 3.0 kN, respectively) than the other specimens. It can be noted that on the edges, the fracturesurfaces indicate sufficient welding (dark brown areas). Visual inspection before testing hence
did not reveal the difference in welding quality.
AE Signal Parameter Analysis
AE activities, AE intensity as a function of load, and linear as well as planar AE signal
source location have also been discussed in detail in [8]. Again, the major conclusions are summarized here. AE activity and AE intensity both are increasing with increasing level of loads.
Under stepwise loading, the joints show a clear Felicity effect with decreasing Felicity-ratio for61increasing load steps. Values of the Felicity-ratio around or below 0.90 are indicative of criticaldamage. Even though AE activity and AE intensity (e.g., measured by the AE signal amplitude)
drop when holding the load constant, a surge in AE activity and AE intensity is observed upon
unloading from each load level (even if the Felicity-ratio is still >1). This could indicate damage
that occurred during loading, but also damage existing in the specimens from the welding process, such as, e.g., incomplete bonding evident as light yellow surface parts (Fig. 2).
Linear AE signal source location plots projected on the narrow (thickness) side of the specimens yield AE source location clusters (especially when filtered for events with amplitudes ≥60dBAE), isolated in time roughly in the area of the weld or nearby. The times of occurrence correlate well with load changes, i.e., the AE activity and AE intensity surges. This highlights theweld zone as a weak area with significant damage accumulation. Damage starts quite early in thetests, i.e., at relatively low loads. This leads to failure by separation of the two adherends in aplane inside the weld-zone. The mechanisms contributing to failure can, however, not be identified with this type of analysis.
AE Waveform Analysis
Analysis of the recorded AE waveforms will be explored for providing more information on
the events and mechanisms leading to failure. As a first step, Fast Fourier Transforms (FFT) calculated from the recorded AE waveforms are compared for both types of sensor and for different
load levels. In a second step, two features of the FFT are compiled (using the FFT Feature Extractor routine provided in the Transient. Recording data analysis package of Vallen SystemeGmbH). This routine determines the frequency at which the peak amplitude in the FFT occurs, aswell as that of the center of gravity of the FFT spectrum. These features are compared for thedifferent sensors types and for the different stages of the tests.
From a visual inspection of the FFT calculated from the AE waveforms, there is a slight
trend for a higher share of AE signals with relatively low frequency content, i.e., essentially below 100 kHz and 150 kHz, for the sensor type SE45-H and SE150-M, respectively (Fig. 3). It
has to be noted that a high-pass frequency filter of 30 kHz has been used in all tests, and that a
Hamming window was applied for calculating the FFT.
Visual inspection, therefore, roughly yields two classes of AE waveforms, one with relatively
low frequency content and one with higher frequency contributions (above about 100 kHz and
150 kHz for the two sensor types, respectively). This hints at different AE signal source mechanisms, since signal attenuation in the specimens is relatively small and the effect is observed forsensors at comparable locations (near the top of the specimens).
As noted above, a software routine (FFT Feature Extractor, VisualTR® from Vallen SystemeGmbH) has yielded two features from the FFT of the recorded AE waveforms, namely two frequencies, i.e., that at which the maximum amplitude in the power spectrum and that at which thecenter of gravity of the FFT occur. Visual inspection of FFT calculated with different cut-off
windows do, in some cases, show significant differences (e.g., between rectangular or Hamming), notably resulting in different values for the frequency at which the maximum amplitudeof the FFT occurs. Further, in order to reduce the influence of noise at higher frequencies (above
250 to 300 kHz), a cutoff of 5% of the maximum amplitude has been applied. Calculation of the
FFT features without a cut-off resulted in a significantly larger spread in the center-of-gravity
frequencies, especially for low amplitude AE signals. Figure 5 shows plots of the FFT features
for both types of sensors used in the test.
62

595

581

2658

2628

6531

6455

Fig. 3: Fast Fourier transforms (with Hamming window) of AE signals recorded in the early (below 3 kN), middle (below 7 kN) and late stage (below 9 kN) of a tensile test with stair step load
pattern, (Left) Sensor type SE-45H, (Right) Sensor type SE-150M, showing relatively low frequency content (essentially below 150 kHz and 200 kHz, respectively).
From the graphs in Fig. 5 it is clear that there are at least two distinct classes of AE signalsthat occur during the load tests to failure. This distinction is evident for both types of sensors,even though the separation between the two clouds is smaller for the sensor type SE-45H thanfor the resonant sensor type SE-150M. Clearly, there is a larger spread in the frequency at thecenter-of-gravity of the FFT for the AE signals recorded with the resonant sensor (type SE150M). For sensor type SE-45H, the plot of frequency at maximum amplitude of the FFT may
63

even indicate a separation into three classes of signals, one ranging from about 30-50 kHz, one
from about 70 to 85 kHz, and third from about 90 to 110 kHz.

585

594

3146

2876

6319

6415

Fig. 4: Fast Fourier transforms (with Hamming window) of AE signals recorded in the early (below 3 kN), middle (below 7 kN) and late stage (below 9 kN) of a tensile test with stair step load
pattern, (Left) Sensor type SE-45H, (Right) Sensor type SE-150M, showing higher frequency
content above 150 kHz and 200 kHz, respectively (bottom).
The data sets from both sensor types further indicate that higher amplitude AE signals (above
about 60 dBAE) tend to have a higher frequency at which the maximum amplitude in the FFT
occurs. With respect to the frequency at which the center-of-gravity of the FFT occurs (data not64

shown), the separation into two signal classes is not observed. Figure 6 shows a further analysisof the frequency at which the maximum amplitude of the FFT occurs. This time, the data areplotted as a function of time and the corresponding load signal is shown as well. It can be notedthat the separation into two classes is observed almost from the start of the loading (except for
the first load step, for which very few signals are recorded). If this separation is interpreted interms of different AE signal source mechanisms, they are active throughout the test while load isapplied (except for the Felicity-effect upon reloading). A closer inspection of the distributions
shows a trend for signals with a higher frequency at maximum FFT amplitude to occur mainly
during load increase (at least for the first few load steps), with fewer signals observed for increasing hold times at constant loads than those with lower frequencies. With increasing load
steps, this difference becomes less obvious and at the last load level before failure, both types ofsignals are observed even during unloading.

Fig. 5: Features of fast Fourier transforms of AE signals recorded in a tensile test with stair step
load pattern, (Left) Sensor type SE-45H, (Right) Sensor type SE-150M, (top) frequency of centerof gravity versus that of maximum amplitude, (bottom) frequency of maximum amplitude versus
AE signal amplitude.
Even with clear evidence for at least two, possibly three classes of AE waveforms that are recorded during the tests, a direct identification of the responsible AE signal source mechanisms isstill not possible. Since mechanical properties of the wood from the welded zone (e.g., stiffness,hardness) are not available it is, however, not clear whether and possibly how much AE signal
source mechanisms or AE signal propagation are affected by a possible variation in mechanicalproperties. The relatively low frequency content of one class of AE signals tentatively hints at
65

friction or friction-like effects. This would also be consistent with the effective shear load that is
induced in the welding zone by the tensile load applied to the specimens. It is possible that thepoorly welded areas and the observed roughness of the failure surfaces (see Fig. 2) provide asource of these friction-like signals. The AE signals with higher frequency content then quite
likely reflect other damage mechanisms. The absence of AE signals with high amplitudes or highsignal energy throughout the test (except at failure) would be consistent with defect formation ona “small” scale, excluding, e.g., large, sudden disbonding or delamination.

(a)

(b)
Fig. 6: Frequency of maximum amplitude of fast Fourier transforms of AE signals recorded in atensile test with stair step load pattern versus time. (a) Sensor type SE-45H, (b) Sensor type SE150M.
66

Conclusions
AE monitoring of load test on welded wood joints has indicated that AE activity and hencedamage occurs mainly in the weld zone (around the center plane of the specimens). AE waveform analysis has provided evidence for two or three distinct classes of AE signals that occur
throughout the test. AE signals with higher frequency content do appear more frequently duringloading and less during hold at constant displacement (i.e., slight load decay). The AE signal
amplitudes (mostly below 70 dBAE) and the AE signal energies (mostly below 1x105 e.u. fromtime-integration of the squared voltage signal) hint at damage occurring at micro-or meso-scale,slowly accumulating to larger defects resulting in final failure. Friction or friction-like effects
may also play a role, since there are a significant number of signals with “low” frequency content. Visual inspection of the failure surfaces after the test further shows that there is a certain
percentage of unbonded or poorly bonded weld area. The size of that roughly correlates with the
failure loads (low loads for larger unbonded area). This may provide a possible source of friction-like signals with low frequency content.
Acknowledgment
The assistance of Mr. D. Völki for test setup and data acquisition at Empa, and of Dr. Thomas
Thenickl (Vallen Systeme GmbH) for support with the data analysis is gratefully acknowledged.
References
1. Ganne-Chédeville C, Properzi M, Leban JM, Pizzi A, Pichelin F.: Journal of Adhesion Science and Technology, 22 (7), 2008, 761–773.
2. Sutthoff B, Franz U, Hentschel H, Schaaf A (in German) Patentschrift (Patent) DE 196 20 273
C2, 1996, Deutsches Patent- und Markenamt (German Patent and Trade Mark Office).
3. Gfeller B, Zanetti M, Properzi M, Pizzi A, Pichelin F, Lehmann M, Delmotte L.: Journal of
Adhesion Science and Technology, 17 (11), 2003, 1573–1589.
4. Properzi M., Leban J.M., Pizzi A., Wieland S., Pichelin F., Lehmann M.: Holzforschung 59(1), 2005, 23-27.
5. Stamm B, Natterer J, Navi P.: Holz als Roh- und Werkstoff, 63 (5), 2005, 313–320.
6. Ganne-Chédeville C, Duchanois G, Pizzi A, Leban JM, Pichelin F.: Journal of Adhesion Science and Technology, 22 (12), 2008, 1209-1221.
7. Brunner A.J., Terrasi G.P., Vallée T., Tannert Th.: Proceedings 22nd International Symposium
Swiss Bonding, May 11-13, 2009, Rapperswil, Switzerland.
8. Brunner A.J., Tannert Th., Vallée T., Proceedings 29th European Conference on Acoustic
Emission, European Working Group (EWGAE), September 8-10, 2010, Vienna, Austria, paper No. 4, 7 p. (2010).

67

USE OF ACOUSTO-ULTRASONIC TECHNIQUESTO DETERMINE
PROPERTIES OF REMANUFACTURED PARTICLEBOARDS MADE
SOLELY FROM RECYCLED PARTICLESSUMIRE KAWAMOTO
Forestry and Forest Products Research Institute, Ibaraki 305-8687, Japan
Abstract
Properties of particleboard manufactured entirely from recycled particleboard were determined and a method for processing three-layered particleboard from all-recycled particles was
described. Dynamic MOE (modulus of elasticity) before and after re-manufacturing was tested
by a longitudinal stress wave technique and compared with other stress wave techniques. Nondestructive AU (acousto-ultrasonic) techniques were used to evaluate static, dynamic, and dimensional properties of re-manufactured boards. The results showed a large decrease in mechanicaland physical properties for remanufactured boards, particularly for those made from high-density
boards. AU parameters corresponded with density in the volume between the surfaces of transducers. Correlation between static MOE and dynamic MOE was improved by using the AU calculated density. Water absorption and thickness swell corresponded with the AU calculated density.
Keywords: Particleboard, AU (acousto-ultrasonics), recycle, remanufacture, dynamic MOE(modulus of elasticity), MOR (modulus of rapture), density, water absorption
Introduction
For the sake of the environment, raw materials for manufacturing wood-based materials
could be derived from unprocessed forest products such as wood material from thinning operations [1, 2], industrial waste from veneer manufacture or sawmills, and slabs produced by industrial operations on dry wood. In addition to such wet and dry wood wastes, fibrous agricultural
lignocellulosic natural resources such as flax or kenaf can be used as raw materials [3, 4]. Generally, wood-based composites manufactured from recycled materials use a combination of recycled and unused new materials. It is necessary to add new fiber, particles, or strands to compensate for the decrease in strength resulting from the reuse of materials. Although new products
made from waste wood have been developed in many projects [1, 5], only a few previous works
focused solely on recycling particleboard [6, 7].
The objectives of this study are (1) to provide fundamental data regarding the decrease inmechanical properties resulting from the use of recycled materials, (2) to compare dynamic MOE
values of longitudinal stress wave techniques, and (3) to evaluate the mechanical and physicalproperties of particleboard using acousto-ultrasonic (AU) technique. A long-term goal of thisstudy includes developing better methods for adding new constituents to increase the strength ofa recycled structural composite panel.
This study shows an example of using AU techniques to determine the decreased propertiesof remanufactured particleboard compared to the original three-layered industrial particleboard.
Particleboard is a general term for a panel manufactured from lignocellulosic materials (usuallywood), primarily in the form of discrete pieces or particles, as distinguished from fibers, combined with synthetic resin or other suitable binder. The particles are bonded together under heatand pressure in a hot press by a process, in which the entire interparticle bond is created by the
J. Acoustic Emission, 28 (2010)68© 2010 Acoustic Emission Groupadded binder [8]. Particleboard manufacture is a valuable process for material recycling woody
waste material that might otherwise be landfilled or burned.
This study shows an example of using AU techniques to determine the decreased propertiesof remanufactured particleboard compared to the original three-layered industrial particleboard.
Particleboard is a general term for a panel manufactured from lignocellulosic materials (usuallywood), primarily in the form of discrete pieces or particles, as distinguished from fibers, combined with synthetic resin or other suitable binder. The particles are bonded together under heatand pressure in a hot press by a process, in which the entire interparticle bond is created by theadded binder [10]. Particleboard manufacture is a valuable process for material recycling ofwoody waste material that might otherwise be landfilled or burned.
Properties of particleboard before and after re-manufacturing were compared nondestructively by both an AU method and a conventional stress wave technique. The acousto-ultrasonictechnique, a combination of acoustic emission (AE) signal analysis with an ultrasonic characterization method [9], was used to detect and assess damage conditions and variation in mechanical properties of a test material [10]. Vary used AU evaluation to determine the mechanicalproperties of composite materials [11]. In regard to wood composite research using AU technique, Beall et al. obtained a patent for bond strength measurement of composite panel products
using rotary transducers [12]. Green [13] and Rodgers et al. [14] reported that AU parameters
corresponded with IB (internal strength) [13, 14]. These reports described the correlation of AU
to IB and were reviewed by Beall in 1993 [15]. Reis investigated AU behavior concerning thedimensional stability of medium-density fiberboard (MDF) [16]. Unlike solid wood, few studies
have used AU technique to evaluate wood composites, probably because of the difficulty ofmeasuring properties because of attenuation of AU waves traveling through wood-based composites [17].
This study provides fundamental data on a processing technique and on the mechanicalproperties of boards made with recycled particles from industrial recycled waste wood. Some
new AU techniques are suggested for evaluating the mechanical and dimensional properties ofparticleboard.
Experimental
Original Materials
Raw materials were commercial three-layered particleboard (Tokyo Board Co. Ltd.). Dimensions were 0.9 m in width, 1.8 m in length, and 20 mm in thickness. Boards were manufactured
from industrial waste wood such as plywood or slabs that were discarded as a waste in the same
company. Species used in the original particleboard were identified as western hemlock (Tsuga
heterophylla), Douglas-fir (Pseudotsuga menziesii), and a mixture of tropical species. Threeboards, 150 mm in width, were sawn from the edge of the commercial particleboards forremanufacture into three 3-layered particleboards. These three original boards were selectedfrom 50 boards, based on dynamic MOE (modulus of elasticity) values that were measured nondestructively. The dynamic MOE was calculated by a longitudinal stress wave technique using a
PVDF (polyvinylidene fluoride) transducer [18]. Density of original boards was 0.675, 0.754,
and 0.806 (g/cm3), and dynamic MOE tested by Metrigard equipment was 2.98, 4.09, and 4.60
GPa, respectively.

69

Remanufacturing Method
The original particleboards were cut into 50 by 50 mm square pieces on a table saw, thenhammer milled. Particles were classified by size using the screen after drying to 3% moisture
content. Particles were then separated using a 16-mesh screen. For the core layer, 16-mesh-on
were used. For the surface layers, particles of 16-mesh-off were used. Weight loss from size reduction process by table saw was 12% to 13% and by hammer mill 3% to 4%, respectively.
Phenolic resin binder (10% of total weight of mat) was sprayed in a rotating drum blender.
Forming (spreading particles in 30- by 30-cm square mat former) was conducted by hand. The
weight ratio of the surface layer and core layer was 1.5:7:1.5. Planned densities and thickness
were the same as values of the original particleboards (0.675, 0.754, and 0.806 (g/cm3). Hotpressing temperature was 180°C. Pressing time was 20 min. Boards were trimmed to 280 by 280
mm and stored in a conditioning room at 20°C and 65% relative humidity, before and during theproperty test. For the control, a single-layered particleboard was manufactured using industry
sawdust. The species was identified as a mixture of yellow pine and spruce (636 kg/m3).
Testing Properties of Remanufactured Particleboard
Properties of the remanufactured particleboard were measured both by dynamic and static
methods. Figure 1 shows locations of each specimen for property tests, which included dynamic
and static MOE and modulus of rapture (MOR), density, internal bond strength (IB), thickness
swelling (TS), and water absorption (WA). The test was basically performed in accordance with
JIS A 5908 [19]. To avoid the effect of water repellant added to original materials, specimens cut
from control board were used.
AU Measurements
AU equipment included a pulse generator, transmitting and receiving transducer, and AE
equipment. Resonant 1.5-MHz, 20-mm diameter and 150-kHz, 9-mm diameter AE transducers
were used. The pulse generator generated a square wave, which excited a transmitting transducer.
The output signal was amplified by 40 dB. The maximum amplitude (up to 50-µs from start ofsignal) and time differential, observed by oscilloscope, were taken as AU parameters.
For determination of density and dimensional properties of whole specimen using AU technique, 25- by 25-mm specimens were used so that a face of the 1.5-MHz transducer covered
most of the surface area of the specimens. AU waves that propagated through the thickness of
specimens were observed. Preliminary tests confirmed that AU parameters detected in using a
specimen having conventional test size (50 by 50 mm) did not correspond to actual density anddimensional properties. For measurement of AU transmission time traveling in the through longitudinal direction, 150-kHz AE transducers, 9-mm diameter, were used.
For determination of dynamic MOE by AU transmission time, an impact source was a sudden stress release generated by pencil-lead breaks (PLB) at the edge of the specimen. The lead
length was always about 10 mm. This stress wave signal was very similar to the AE signal andwas thus called the artificial AE. Transmission of the longitudinal direction was observed. AU
transmission time (the time it took for the signal to travel between the two 150 kHz transducers),
was observed by using an oscilloscope. For this AU technique, refer to author’s original report
[20].

70

Fig. 1. Geometry for testing properties. Measurement for: D, density; IB, internal bond strength,
TS-D, thickness swell and water absorption; MOE, dynamic and static modulus of elasticity; andMOR, modulus of rapture.
Determination of Dynamic MOE by Longitudinal Stress Wave Technique1) Stress Wave Technique by Transmission TimeStress wave transmission time was measured two different ways (AE and PVDF transducers).
With AE transducers, an impact was induced by the sudden break of lead of a mechanical pencil.
That is, this artificial AE wave method used a mechanical pencil as an AU source and is included
as an AU technique. Using PVDF transducers, an impact was induced by tapping an end of eachspecimen.
2) Stress Wave Technique by Resonant Frequency of Longitudinal Vibration
Tapping the end of each specimen generated a stress wave. The signal detected by a PVDF
transducer was processed through an FFT (fast Fourier transform) analyzer, and the resonant
frequency of the first resonance mode was observed.
Table 1. Physical and mechanical properties of original and remanufactured particleboards.
Specimen
NumberＩ
II
III
ControlDensity (kg/m3)
Original1
Remanufactured2

675
754
806

640
656
677
664

Dynamic MOE (GPa)
Original3 Remanufactured(a)4(b) 5 (c) 5

2.98
4.09
4.60

1.59
1.46
1.95
1.96

1.62
1.45
1.90
1.89

1.25
1.35
1.44
1.52

Static
MOE5
(GPa)

1.36
1.25
1.48
1.84

MOR5
(MPa)

7.68
6.09
6.98
11.2

IB6
(MPa)

0.39
0.22
0.56
0.27

Notes: 1 Specimen dimensions: 150 mm x 20 mm x 1.8 m. 2 Specimen dimensions: 280 mm x 280 mm x 20 mm.
3Specimen dimensions: 150 mm x 20 mm x 1.8 m. Longitudinal AU technique using PVDF transducer [19]. 4 PLB on 280
mm x 280 mm remanufactured particleboards before MOR specimens were cut. 5 Specimen dimensions: 345 mm x 50
mm x 20 mm. (cf. Fig. 2). 6 Specimen dimensions: 50 mm x 50 mm x 20 mm. (cf. Fig. 2).
71

Result and Discussions
Decrease of Properties of Remanufactured Particleboard
Table 1 shows the properties of the original and remanufactured boards. The original particleboard with a highest density (0.806 g/cm3) showed a larger decrease of properties than boards
made from the lower densities. Property decrease was the largest for the particleboard (III) that
had the largest density and dynamic MOE in original boards. The remanufactured boards wereintended to have the same density as the original boards, however, their density decreased to 94,
87, and 84% (Table 1, density). This is because larger density raw materials are subjected tolarger deformation by pressure and heat during the processing.
Decrease of dynamic MOE obtained by resonant frequency of longitudinal vibration was
40%-65%. Compared to representative values of similar samples from the company that provided original specimens, Static MOE of remanufactured board decreased to 53% to 40%. MOR
decreased to 44% to 26%. MOR values showed larger decrease than MOE.
Comparison of Stress Wave Techniques
Generally dynamic MOE values obtained by vibration of resonant frequency are smaller than
dynamic MOE values obtained by stress wave transmission time propagating in solid wood.
However, in the case of particleboard, that relationship is reversed. Comparing the stress wave
technique by using PVDF, dynamic MOE values by stress wave transmission time (Table1, (c))
were smaller than values by longitudinal vibration technique calculated by resonant frequency(Table1, (b)). This is probably because the transmission time would be extended by attenuationof waves reaching the further transducer. Attenuation of stress waves in particleboard is muchlarger than in solid wood. Observing wave signals by oscilloscope, larger attenuation caused difficulty identifying starting points of signal waves. If it attenuated below the noise level, the actual initiation point would be missed. Several cycles of wave would be hidden under the noiselevel. Thus the initiation point would be extended and longer wave transmission time was observed in particleboard.
The stress wave method by artificial AE (Table 1, (a)), showed larger dynamic MOE values
than the static MOE values, which was the same as the trend for solid wood and unlike using
PVDA transducers. The sudden break of mechanical pencil lead generated a sharp gradient at thestart of artificial AE waves. Compared to output signal by PVDF, the artificial AE signal waseasy to identify at the starting point.
Comparing three techniques (a), (b) and (c) in Table 1, dynamic MOE values obtained byresonant frequency (b) corresponded with static MOE the best. MOE values (a) obtained by artificial AE transmission time also showed the same trend and corresponded with static MOE as
much as the conventional method (b). Advantage of the artificial AE transmission method is thatthe measurement is simple. Specimens don’t need to be cut, and can be measured just using amechanical pencil without using large equipment to induce a pulse.
AU and IB Strength
AU amplitudes and AU transmission time of AU signals that propagated through the thickness of IB specimens were observed at five locations on IB specimens. Unlike other properties,
AU result did not correlated with IB strength. This was because AU values corresponded withdensity in the volume between the surfaces of transducers (20 mm in diameter), whereas IBstrength values were measured in specimens, 50 by 50 (mm). This result also indicates that AU
72

measurement should be taken with considerable attention to ensure using the right AE transducers. Beall [15] and Green [13, 14] reported high correlation of IB strength and AU amplitude ofsignal propagating through the thickness of IB specimens. In these studies, they used transducers
having large diameters that covered almost all the areas of the surface of IB specimens.
AU and Density
AU amplitude and transmission time through thickness of specimens correlated with density.
Regression coefficient between AU transmission time and density was higher (Fig. 2, r = 0.99)
than the one obtained by AU amplitude (r = 0.94). This indicates that the AU technique to determine density can be used to estimate property values that correlate with density. In this study,
it was used to determine dynamic MOE and the dimensional properties (Fig. 3). The benefit ofthis method is that specimens don’t require cutting because this AU technique uses AU values inpropagation through the thickness of specimens and can be done on a large specimen.

Fig. 2. AU transmission time versus density for particleboard (control). AU Density was determined from AU transmission time measured in propagation through thickness.
AU and Dimensional Properties
Water absorption and Thickness swelling (TS) correlated with density (r = 0.97 for 16.5 h, r
= 0.96 for 6.4 h). Water absorption corresponded more than TS. Water absorption was calculated
from density determined with AU transmission time propagating through thickness (Fig. 2). The
regression coefficient was the highest in the relationship between calculated density and water
absorption after 16.5 h (Fig. 3). Although only five specimens were used in this work, it shows
the possibility for using AU techniques to estimate dimensional pensile proprieties.
Conclusions(1) Mechanical and physical properties of remanufactured board decreased more with original
particleboard having larger density. This indicates that it is important to use lower density material as a raw material.
(2) The decrease in MOR, caused by the manufacturing, was larger than in static MOE.
(3) The decrease in dynamic MOE obtained by resonant frequency of longitudinal vibration,measured by PVDF transducer, was 40 - 75 %.
73

Fig. 3. Calculated density versus water absorption for particleboard (control). ■: 16.5 h, ●: 6.5h.
(4) Dynamic MOE using PVDF transducer obtained by stress wave transmission time wassmaller than the one by frequency of longitudinal vibration.
(5) Dynamic MOE obtained by artificial AE wave signals induced by breaking the lead of amechanical pencil was similar to stress wave MOE values calculated by longitudinal vibration
using PVDF transducers.
(6) AU behaviors highly corresponded with density in the volume between the surfaces of thetransducers. Properties involved with density can be estimated by AU techniques. Scanning of
AU signals that propagated through the thickness of particleboards can provide density value
nondestructively, thus properties that correspond with density can be estimated by this AU
techniques.
(7) For the specimens that did not contain water repellent, water absorption correlated withcalculated density determined by AU techniques.
Acknowledgments
This research project at the USDA Forest Products Laboratory, Madison, Wisconsin, wasfunded by former Science Technology Agency, Government of Japan. The authors wish to thankMr. Saito, Tokyo Board Co. Ltd. for providing commercial particleboard, Mr. Yasuto Chiba,
Japan Forestry and Forest Products Research Institute, retired, for conducting the internal bondstrength test, Dr. Regis Miller, (retired), USDA Forest Service, Forest Products Laboratory, for
identifying species of particles. First author thanks James H. Muehl (retired), R. Sam Williams(retired), and Robert J. Ross, Forest Products Laboratory, for providing laboratory space at theForest Products Laboratory.

74

References
1. T. Shibusawa, S. Kawamoto, H. Korai, and T. Fujii. US Patent No. 5814170. 1998.
2. H. Spelter, R. Wang, and P. Ince. FPL-GTR-92. Madison, WI: US Department ofAgriculture, Forest Service, Forest Products Laboratory. May 1996, 17 p.
3. R.M. Rowell. Composites from Agri-Based Resources. In: R. Falk, ed. Proceeding No. 7286.
Madison, WI: Forest Products Society, 1996, 217-222.
4. S. Kawai, Mokuzai Gakkaishi. 43(8) 1997, 617-622. (in Japanese).
5. Forest Products Laboratory. FPL-GTR-110. Madison, WI: US Department of Agriculture,
Forest Service, Forest Products Laboratory. May, 2000, 31 p.
6. T. Shibusawa, Y. Hatano, S. Kawamoto, M. Zhang, Y. Okuno and Y. Chiba. 50th Annual
Meeting of Japan Wood Research Society, 2000, 286. (in Japanese)
7. Y. Tominaga, N. Kobayashi, M. Ohmi, Mokuzai Kogyo (Wood Industry). 54 (10) 1999, 473477.
8. ASTM D 1554-2005, Standard Terminology Relating to Wood-Base Fiber and Particle Panel
Materials, Annual Book of ASTM Standards. Philadelphia, PA: American Society for testingand Materials. 2005.
9. ASTM E1316-10a. Terminology for Nondestructive Examinations, Annual Book of ASTM
Standards. Philadelphia, PA: American Society for testing and Materials. 2009.
10. ASTM E1495-2007. Standard Guide for Acousto-Ultrasonic Assessment of Composites,
Laminates, and Bonded Joints, Annual Book of ASTM Standards. Philadelphia, PA:
American Society for testing and Materials. 2007.
11. A. Vary, NASA Technical Memorandum 79180. 1979.
12. D.M. Shearer, R.C. Beetham and F.C. Beall, Bond Strength measurement of composite panel
products. Patent No. 4750368. 1998, 7p.
13. A.T. Green, Journal of Acoustic Emission. 8(1/2). 1989, S306 S310.
14. J.M. Rodgers, A. T. Green and S.W. Borup, Materials Evaluation, 5 1991, 556-571.
15. F.C. Beall, Second International Conference on Acousto-Ultrasonics. Atlanta. The American
Society for Nondestructive Testing, Inc. 1993, 153-161.
16. H.L. Reis and D. M. McFarland, Journal of Acoustic Emission. 5(2). 1986, 67-70.
17. S. Kawamoto and R.S. Williams, General Technical Report. FPL-GTR-134. Madison, WI:
US Department of Agriculture, Forest Service, Forest Products Laboratory. May 2002, 16 p.
18. R.J. Ross and R.F. Pellerin, General Technical Report. FPL-GTR-70. Madison, WI: US
Department of Agriculture, Forest Service, Forest Products Laboratory. May, 1994, 40 p.
19. JIS A 5908. Particleboard, Japanese Industrial Standard. Tokyo, Japan. 1994.
20. S. Okumura, S. Kawamoto and M. Toyoda and M. Noguchi. Bulletin of the Kyoto University
Forest. 60, 1988, 299-309. (in Japanese)75

ACOUSTIC EMISSION ACTIVITY OF SPRUCE SAPWOOD BECOMESWEAKER AFTER EACH DEHYDRATION-REWETTING CYCLESABINE ROSNER 1 and SUMIRE KAWAMOTO 2
1
Institute of Botany, Department of Integrative Biology, University of Natural Resources andApplied Life Sciences, BOKU Vienna, Gregor Mendel-Str. 33, A-1180 Vienna, Austria;
2
Forestry and Forest Products Research Institute; Tsukuba, Ibaraki 305-8687 Japan
Abstract
Acoustic emission (AE) during dehydration at ambient temperature was compared between
fully saturated fresh Norway spruce (Picea abies (L.) Karst.) sapwood and sapwood exposed totwo dehydration-rewetting cycles in order to get information about the differences in dehydrationstress. AE testing was performed within a frequency range of 35-100 kHz (R6) and 100-1000
kHz (WD) at ambient conditions. AE activity became lower and AE energies weaker after eachdehydration-rewetting cycle. During the first dehydration run, highest mean AE energies weredetected at the beginning of dehydration. In rewetted wood, highest mean AE energies were detected towards the end of dehydration. AE of rewetted wood was also characterized by a higher
percent of AE with frequencies >70 kHz and 100-175 kHz, as detected by R6 and WD transducers, respectively. It is concluded that fresh never-dried sapwood is more prone to dehydrationstresses than pre-dried sapwood. Differences in AE number and AE features appear to be due tomicro-mechanical failure that decreased after each dehydration-rewetting run. Dehydration stressseems also to decrease because the membranes of bordered pits, which act as a valve to avoid thebreakage of the water columns inside the conduits, become weakened after each dehydrationrewetting run.
Keywords: Acoustic emissions, wood drying, dehydration stress
Introduction
Dehydration stress can lead to a loss in mechanical strength in re-wetted conifer sapwood due
to structural changes of the cell walls and micro cracks [1, 2]. Sapwood is the wetter outer part ofa tree trunk, where water is transported from the roots up to the crown within a network of small
conduits. The transport from conduit to conduit is achieved through small valves termed bordered pits (Fig. 1). Moderate drought stress of trees leads to a reversible closure of the pit membranes, in order to avoid the breakage of the water column in the adjacent conduit [3]. Highnegative pressures that develop during dehydration of isolated sapwood can however induce aweakening or rupture of the membranes of the pits [4] or lead to irreversible pit closure [5]. The
dryer inner part of a tree trunk is termed heartwood. Conifer heartwood does not transport water
anymore because the pits are permanently closed (aspirated) [6].
Cell wall shrinkage starts when the moisture content drops below fiber saturation; that iswhen the conduit contains no longer free water but the cell walls are fully saturated with liquid[7]. Dimensional changes can however be observed long before most of the conduits reach fiber
saturation [8, 9]. Similar dimensional changes can be observed as diurnal stem diameter changes
in living trees out in the field [e.g. 10]. Due to its low free moisture content, heartwood does notcontribute to these diameter changes [8]. In sapwood, negative hydrostatic pressures acting perpendicular to the conduit walls try to draw the walls inwards. When the negative pressure be
J. Acoustic Emission, 28 (2010)76© 2010 Acoustic Emission Group

comes too high the water column will break, which leads to a sudden stress release. This sudden
stress release induces AE with the highest amplitudes in the range of 100-300 kHz [9, 11].

Fig. 1. Four isolated water-conducting elements (tracheids) of Norway spruce earlywood. Arrows indicate bordered pits. The reference bar represents 1 mm.
Drying checks develop because fiber saturation is reached far earlier in the drier shell than in
the wetter core of a dehydrating specimen and because of the anisotropy of wood concerning cellwall shrinkage [2, 7]. Internal checking can, however, be induced as well by the negative pressure of free water [12, 13].
AE testing is a useful tool for optimizing lumber drying conditions. The analysis of the burst
rates, the amplitude or energy distribution of AE signals has been successfully used to pinpoint
lumber checking [14 - 16]. The bulk of AE (>15 kHz) during lumber drying is induced by thebreakage of the water columns inside the conduits after the sudden release of negative pressures[15]. Crack formation during dehydration is indicated by high AE burst rates and by AE signals
with very high amplitudes or energies [12, 16, 17].
The aim of this study was to compare AE during dehydration at ambient temperature between fresh Norway spruce sapwood and sapwood exposed to two rewetting cycles. Unlike previous work on this topic [5], AE testing was performed within an operating frequency range of
35-100 kHz and 100-1000 kHz. It was, therefore, possible to analyze the average frequency of
the AE, which potentially gives additional information about the dehydration stress of fresh andpre-dried sapwood.
Material and Methods
Wood specimens came from a 50 year-old healthy Norway spruce (Picea abies (L.) Karst.)
harvested in Prinzersdorf (Lower Austria). Wood bole segments, 20 cm in length, were taken
immediately after felling at 3 – 4 m height from the ground.
Outer sapwood specimens with a transverse surface of about 0.9 x 0.9 cm were isolated by
splitting the wood along the grain with a chisel. Tangential and radial faces of the beams wereplaned on a sliding microtome. Transverse sample ends were re-cut using a razor blade. During
preparation steps the wood specimens were kept wet. Fresh wood specimens were then soaked in
distilled water under partial vacuum for 24 h to refill empty conduits [18] and afterwards stored
at 4 °C in degassed water containing 0.01 vol. % Micropur (Katadyn Products Inc., Switzerland)
to prevent microbial growth. The final shape of the specimens was 0.6 cm tangential, 0.6 cm
radial, and 10.0 cm longitudinal. Fresh and pre-dried wood specimens were dehydrated for 24 h
at ambient climatic conditions (25 °C, 30 % r.h.) to the equilibrium moisture content of 9.5 %.
AE (acoustic emission) was monitored with the AMSY-5 High-Speed AE system (Vallen
Systeme GMbH, Munich, Germany). Preamplifiers (40 dB) were used in connection with R6 and77

WD transducers with an operating frequency range between 35-100 kHz and 100-1000 kHz,
respectively (Physical Acoustics Corporation Princeton Jct, PA, USA). Data were recorded witha detection threshold of 40 dB (R6) and 35 dB (WD) (0 dB = 1 µV input). AE features assessed
were the peak amplitude, the absolute energy and the average frequency.
AE transducers were positioned on the tangential face of fully saturated standard beams using an acrylic resin clamp (Fig. 2). Silicone paste (Wacker, Burghausen, Germany) was used as acoupling agent. The specimen was positioned on a sample holder fixed upon a compression
spring, which was used to minimize the decrease in contact pressure due to wood shrinkage during the dehydration processes [9]. Contact pressure was monitored by a load cell (DMS, Type8416-5500, range 0 - 500 N; Burster, Gernsbach, Germany) positioned between the AE transducer and the screw of the acrylic resin clamp (Fig. 2). Contact pressure between transducer andwood was set to 30 N [14] and the clamp assembly was kept so deep in water that the wood wastotally covered till the applied pressure reached a constant value, which was achieved after lessthan 20 min. Thereafter, superficial water was quickly removed from the assembly and recordingof AE and coupling pressure was started.
After the cessation of all AE activity, which was achieved after about 10 hours, specimens
were re-soaked at 4 °C in distilled water containing 0.01 vol. % Micropur under partial vacuum.
Specimens were re-soaked till they reached their former saturated weight, which took >96 h.
Thereafter, the second AE testing run took place. Before the third AE testing run was started,wood specimens were again re-soaked till the former saturated weight was reached.

Fig. 2. Acrylic resin clamp assemblage for AE testing.
78

Results and Discussion
Acoustic activity monitored with the R6 transducer (35-100 kHz)The total number of AE became increasingly lower with each dehydration cycle. Dehydration of a once rewetted specimen produced 55.9 % and dehydration of a twice rewetted specimen
33.6 % of the AE of fresh sapwood. After the second rewetting cycle, a very low AE rate at theonset of dehydration was detected (Fig. 3). With each rewetting-dehydration cycle, the signals
became also weaker. AE energy had much higher mean values in the fresh than in the two predried specimens (Fig. 3). Additionally, the time course of the mean AE energy values differed
between treatments. Whereas highest mean energies were measured in fresh sapwood at the beginning of dehydration, in pre-dried sapwood highest energies were measured towards the end of
dehydration (Fig. 3). Attenuation decreases with increasing moisture loss [14]. Accordingly, theFig. 3. Time courses of AE rate/0.1 h clustered in amplitude and frequency steps and time courseof AE Energy (eu = aJ) of fresh, pre-dried, and two times pre-tried sapwood detected with the R6transducer (35-100 kHz). The red line in the AE Energy plot represents the mean AE Energy/0.1h.
79amount of signals >60 dB became higher towards the end of dehydration in pre-dried wood. In
fresh wood, however, highest amounts of signals >60 dB were found at the onset of dehydration[9]. All treatments were characterized by very weak signals in the last AE peak. The frequency
composition was quite different in fresh and pre-dried sapwood; fresh sapwood showed a higher
amount of signals <70 kHz during the first AE peaks, whereas pre-dried wood only during thesecond AE peak. The third AE peak showed a very high amount of AE >70 kHz in all three
specimens.

Fig. 4. Time courses of AE rate/0.1 h clustered in amplitude and frequency steps and time courseof AE Energy (eu = aJ) of fresh, pre-dried, and two times pre-tried sapwood detected with the
WD transducer (100-1000 kHz). The red line in the AE Energy plot represents the mean AE Energy/0.1h.
Acoustic activity monitored with the WD transducer (100-1000 kHz)AE detected with the WD transducer showed a similar amplitude and energy distribution as
detected with the R6 transducer. With each rewetting cycle the signals became weaker and highest mean energies were measured at the onset of dehydration in fresh, whereas towards the endof dehydration in pre-dried sapwood (Fig. 4). Rewetted sapwood showed no first AE peak,which was characterized in fresh wood by a very high amount of signals >60 dB. The amplitude80

distribution of pre-dried wood resembled the amplitude distribution of the last two AE peaks offresh wood, which had a very high amount of AE <50 dB. Pre-dried wood lacked also single
high energy AE during the first two hours of dehydration, at moderate moisture loss. The firstand second AE peak of fresh sapwood had highest amounts of signals between 100 and 175 kHz,
whereas the amount of AE within this frequency range was neither higher in once rewetted nor intwice rewetted sapwood during the whole dehydration cycle (Fig. 4). The lowest amount of 100175 kHz AE was detected after the second rewetting-dehydration cycle. The last AE peak offresh dehydrating wood was characterized by a high amount of high frequency AE, as it was
typical for pre-dried sapwood throughout the whole dehydration period.
Possible reasons for lower AE activity in pre-dried sapwood
During the first dehydration run, irreversible damage must have occurred, which caused no
AE during the second and third dehydration run. Events during the first run were characterizedby higher AE peak amplitudes and a higher percentage of AE with lower frequency. A lower
number of AE detected during the following dehydration runs indicated that fresh sapwood wasmore prone to dehydration stress than pre-dried wood [19]. Differences in AE rate, peak amplitude and AE energy were quite small between the second and third dehydration run [19, 20].
Recently, we [5] reported higher dimensional changes at moderate moisture loss in fresh than in
pre-dried wood. It is supposed that dimensional changes of fresh wood during early dehydration
stages are merely induced by the negative pressure of free water inside the capillaries [9]. Differences in AE and sensitivity to dimensional changes induced by negative pressure could havetheir causes in pit functioning and mechanical failure.

Fig. 5. Transverse stained (Toluidine blue) sections of fresh (A) and pre-dried (B) Norway
spruce sapwood. Arrows indicate bordered pits, which are open in fresh and permanently closed
in pre-dried earlywood. Reference bars represent 40 µm [5].
AE generated during dehydration of conifer sapwood generally exceeds AE generated during
heartwood dehydration [20]. In spruce sapwood, bordered pits of the first formed earlywood cellrows become irreversibly closed when sapwood is dried at ambient conditions (Fig. 5). The hydraulic permeability at full saturation is therefore much lower in pre-dried than in fresh sapwood[5]. If pit closure was a rapid process, it could be an AE source in fresh wood. Permanently
closed pits in pre-dried wood will then produce no AE during the following dehydration cycles.
81Pre-dried sapwood resembles heartwood, where conduits lost their function of water conductancedue to permanent pit membrane closure [6]. Heartwood does not contribute to the dimensionalchanges induced by negative pressures [8] because the conduits contain no or only a low amountof free water. However, even if conduits would refill through the cell walls, pit closure might beadvantageous in order to avoid internal checking in living trees by reducing the transmission ofnegative pressure from conduit to conduit. Reference [13] supposed that sapwood within-ring
internal checking is caused by deformation due to negative pressure and not by differential cellwall shrinkage, because checking occurred already at an early stage of kiln drying. Internal sapwood checks caused by negative pressure may also occur in living trees during periods of summer drought, and are a severe problem of short rotation forests [21].
Some pit membranes can also be ripped out of their sealing position due to high negative water pressures [4], which might be an AE source in fresh wood but not in pre-dried sapwood.
Drought stress leads as well to a phenomenon termed “cavitation fatigue”. Cavitation fatigue is
induced by the rupture or loosening of the cellulose mesh of inter-conduit pit membranes, which
results in a weakened response of the pits to drought stress [3]. Pre-dried spruce sapwood is,therefore, more vulnerable to the breakage of the water columns than fresh wood, which meansthat less negative pressure is necessary in pre-dried than in fresh wood to result in the breakageof the water columns [5]. As a consequence, pre-died rewetted wood is less prone to deformation
under negative pressure, because the stress is more easily released due to the breakage of thewater columns (cavitation fatigue).
Fresh wood emitted much stronger signals than pre-dried wood. The sudden breakage of thewater column in a conduit of the same size should give AE with lower frequencies and higher
peak amplitudes when it occurs at more negative pressure, because more elastic energy (moredeformation) is stored before the breakage occurs. Minimizing any kind of dimensional changes
is advantageous for heartwood concerning crack formation in stems of living trees.
High AE burst rates or AE with high peak amplitudes indicate mechanical failure in freshwood at moderate water losses [12, 15 - 17]. Single high energy AE measured during the first
dehydration run suggests that micro-cracks occurred in the sapwood specimens. In fresh sapwood, highest peak amplitudes were measured during the first hours of dehydration, whereas in
pre-dried sapwood during the last hours. Moreover, in some pre-dried specimens, the first AE
peak was totally missing. This might indicate that some AE arose from irreversible damage induced by negative water pressures because it was detected already at an early stage of dehydration [13]. By means of X-ray CT scanning (resolution 4.5 µm) no mechanical failure on a set ofcomparable Norway spruce specimens could be detected [5]. Micro-cracks in dried specimens
are, however, sometimes hard to detect, because they close again after dehydration [2]. For detection of matrix-cracks in the conduit cell walls, higher resolution techniques were required,such as scanning electron microscopy [1]. Changes in the frequency composition might be due tomicro-structural changes in the cell walls, which influenced wave propagation. Towards the endof dehydration, a high amount of high frequency AE was detected in fresh and pre-dried specimens. Fresh specimens showed, however, a much higher amount of lower frequency signals at
the onset of dehydration than pre-dried specimens (Figs. 3 & 4). At this stage of dehydration, we
detected a higher amount of signals <70 kHz by means of the resonant R6 transducer and ahigher amount of AE <175 kHz by means of the broad-band WD transducer. The average frequency composition can give relevant information about critical dehydration stages, whichshould be topic of further studies.
82

Conclusions
AE testing results suggest that fresh sapwood is more prone to dehydration stresses than predried sapwood. The dehydration stress became lower after the first dehydration cycle; thereafter
it showed only a slight decrease. Differences in AE might be due to changes in pit functioning
and mechanical failure. Permanently closed pits, pit membrane rupture and micro-cracks in thecell walls, which developed during a previous dehydration cycle, will produce no AE during thenext dehydration cycle. Additionally, the weakening of the pit membranes might lower dehydration stress because the breakage of the water columns occurs at less negative pressures. Therefore, a higher number of AE with higher AE energies and lower average frequencies were measured in fresh than in pre-dried wood. If it is assumed that pre-dried sapwood resembles heartwood, changes in pit functioning could be advantageous for avoiding internal checking in living
trees by reducing dehydration stress.
Our results can also be relevant for analyzing checking processes during industrial lumber
drying, especially when wood boards contain a high amount of sapwood. Critical stages of drying could be pinpointed by the analyzing the frequency composition and the AE energy. Extremely high energy AE events could be used in future studies on source location of lumber
checking processes.
Acknowledgements
This study was financed by the Austrian Science Fund FWF (Project V146-B16). Peter
Kritsch is thanked for providing Norway spruce sample material.
References
1. Müller U., Joscak T., Teischinger A.: Holz als Roh- und Werkstoff 61, 2004, 439-443.
2. Sakagami H., Tsuda K., Matsumura J., Oda K. IAWA J. 30, 2009, 179-187.
3. Hacke U.G., Stiller V., Sperry J.S., Pitterman J., McCulloh K.A.: Plant Physiol. 125, 2001,
779-786.
4. Domec J.-C., Lachenbruch B., Meinzer F.C.: Amer. J. Bot. 93, 2006, 1588-1600.
5. Rosner S., Konnerth J., Plank B., Salaberger D., Hansmann C.: Trees 24, 2010, 931-940.
6. Hansmann C., Gindl W., Wimmer R., Teischinger A.: Wood Research 47, 2002, 1-16.
7. Skaar C.: Wood-Water Relations, 1988, Springer-Verlag, Berlin, Germany.
8. Irvine J., Grace J.: Planta 202, 1997, 455-461.
9. Rosner S., Karlsson B., Konnerth J., Hansmann C.: Tree Physiol. 29, 2009, 1419-1431.
10. Offenthaler I., Hietz P., Richter H.: Trees 15, 2001, 215-221.
11. Tyree M.T., Zimmermann M.H.: Xylem Structure and the Ascent of Sap, 2002, Springer,
Berlin.
12. Quarles S.L.: Wood Fiber Sci. 24, 1992, 2-12.
13. Booker R.E.: Proceedings of the 4th IUFRO Wood Drying Conference: Improving Wood
Drying Technology, 1994, Forest Research Institute, Rotura, NZ, pp 133-140.
83

14. Beall F.C.: Wood Sci. Techn., 36, 2002, 197-212.
15. Kawamoto S., Williams R.S.: Gen Techn Rep FPL-GTR-134, 2002, U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, Madison, WI, pp 1-16.
16. Beall F.C., Breiner T.A., Wang J.: For. Prod. J. 55, 2005, 167-174.
17. Niemz P., Emmler R., Pridöhl E., Fröhlich J., Lühmann A.: Holz als Roh- und Werkstoff 52,
1994:162-168.
18. Hietz P., Rosner S., Sorz J., Mayr S.: Ann. For. Sci. 65, 2008, 502p7.
19. Okumura S., Kiyotaki T., Noguchi M.: Bull. Kyoto University Forests. 59, 1987,283-291.
20. Kuroiwa M., Okumura S., Fujii Y.: Bull. Kyoto University Forests. 68, 1996, 151-160.
21. Grabner M., Cherubini P., Rozenberg P., Hannrup B.: Scand. J. For. Res. 21, 2006, 151-157.

84

ACOUSTIC EMISSION SOURCE LOCATION IN PLATE-LIKE
STRUCTURES USING A CLOSELY ARRANGED TRIANGULARSENSOR ARRAY1

DIRK ALJETS 1, ALEX CHONG 1, STEVE WILCOX 1 and KAREN HOLFORD 2
University of Glamorgan, Faculty of Advanced Technology, Department of Engineering,
Pontypridd, Wales, UK CF37 1DL; 2 Cardiff University, Cardiff School of Engineering,
Cardiff, UK CF24 3AA

Abstract
In order to identify the location of acoustic emission (AE) sources in large plate-like structures it typically requires the use of at least three widely spaced sensors. The distance between
these sensors is defined by, for example, expected AE intensity and attenuation of the signals.
This work presents a novel configuration of the three sensors, which are installed in a closely
arranged triangular array with the sensors just a few centimetres apart. The algorithm locates AE
sources by determining the direction from which the wave is approaching the array using thetime of arrival and the distance the wave has travelled using the wave mode separation. Tests
were conducted on a composite (CFRP) plate with anisotropic lay-up. In this work it is shownthat the technique is able to accurately identify the source location. The technique is particularly
suitable for Non-Destructive Testing (NDT) and Structural Health Monitoring (SHM) applications where the close positioning of the sensors allows the array to be installed in one housing tosimplify mounting and wiring. It is expected that this sensor arrangement could reduce the number of sensors needed to monitor large plate-like structures compared to more conventional AE
source location methods.
Keywords: Source location, Wave mode analysis, sensor array, composite material
Introduction
Source location of acoustic emission events has become an important tool for NonDestructive Testing (NDT) and Structural Health Monitoring (SHM) research and field applications. Localising the exact origin of an AE wave in a structure can help determine the source
type, for example, crack propagation from a drill hole or rivet, impact damage or even just friction between different parts of the structure. This information can be used to evaluate the severityof the damage for the structure and can also help understand the damage mechanism and propagation.
Many different source location methods have been developed in the past with a variety of
applications and accuracies. The simplest method is the “first hit” method or zone location where
the sensor with the first arrival or the highest amplitude of the wave is said to be the closest to
the source [1]. This is adequate when the sensor spacing or the area to be monitored is small or
the likely damage initiation point is known (for instance a bolt or notch). Other source location
methods use the time of arrival of the AE wave at the different sensors. This method is often referred to as Time of Arrival (TOA) [2]. Two sensors can be used for linear source location between the sensors. At least three sensors are necessary to pinpoint a source on a plate. The sourcemust be located inside or near the sensor array to give an adequate result and the wave velocity
must be known.
J. Acoustic Emission, 28 (2010)85© 2010 Acoustic Emission GroupRecent papers are increasingly using the modal nature of AE waves for source location purposes [3 - 5]. Acoustic emission in thin structures propagates in two fundamental wave packages.
The first, usually faster wave package is a compressional wave where the material oscillates inthe same direction as the wave propagates. The wave mode is referred to as symmetrical mode
(S0 mode). The velocity of this mode depends on the material properties (tensile stiffness), which
can result in different propagation velocities in different direction in anisotropic materials [6].
The second, usually much slower propagating mode is a flexural wave where the material oscillates perpendicular to the wave propagation. This mode is referred to as anti-symmetrical mode
(A0 mode) and is mainly caused by shear stress inside the material [7]. This mode is dispersive,
depends on the thickness of the structure (flexural stiffness) and is not affected by the fibre orientation [6]. The amplitude ratio between both modes can vary depending on the source orientationin the specimen and the amount of shear stress and tensile stress affecting the material [8]. Sinceboth modes travel with different velocities the wave packages separate during propagation andthe distance between the origin of the wave and the sensor can be calculated when both velocities
are known. Using this method source location is not limited to the area enclosed within an arrayof sensors. Other authors use the modal analysis to determine the exact wave velocity dependingon which mode triggered the AE system [9].
Sachse [10] patented a source location method with a small array of four sensors. Thismethod monitors the area exterior to the array by determining the distance and direction to thesource without prior system calibration and group velocity measurements.
Another method proposed in the literature where the source location is not calculated but thearrival time of an event at different sensors is compared with an arrival time map. This map has
to be generated for the area to be investigated beforehand using artificial AE [2, 5].
Proposed Source Location Method
The proposed source location method in this paper uses modal analysis to evaluate the originof the AE event with respect to the centre of the sensor array. The sensor arrangement is a close
triangular setup with a sensor spacing of just 45 mm. The algorithm in the method first determines the direction from which the wave travels to the array and secondly, the distance between
sensor array and source using the wave mode separation.
Experimental setup
Artificial AE was generated using pencil-lead fractures (Hsu-Neilsen source or H-N source)
on a large carbon fibre reinforced epoxy composite plate (CFRP). The plate was manufactured
by Carbon-Composite Technology with a carbon fibre twill weave, an approximate fibre content
of 60% and fibre alignments in 0° and 90°. The dimensions of the plate were 1300 x 900 x 2 mm
(1.17 m2 area). Bubble wrap was placed between the composite plate and the table to reducewave dispersion effects into the table. Three AE sensors (Micro-80S, Physical Acoustics Corporation, 100-1200 kHz, sensor diameter: 10 mm) were installed in the centre of the plate in a triangular arrangement with a sensor to sensor distance of 45 mm (centre point to centre point) respectively. The signal was pre-amplified by a Vallen Systeme AEP4 (40 dB) and recorded by aGage Octopus CompuScope CS-8280 data acquisition card (12 bits, 5MS/s, 100 MHz bandwidth) driven by LabView. The recording was threshold triggered, where a pre-trigger setting of
the data acquisition card allowed the capturing of data prior the triggering event to ensure thatthe whole event was recorded. Afterwards the raw data was digitally filtered by a band-pass filterof 100-1000 kHz and processed using LabView and Scilab. The plate was divided into a 15 x 23
86

grid starting 100 mm from the four edges of the plate respectively to avoid signal corruption byreflections. The grid spacing was 50 mm in horizontal and vertical directions. Three pencil-leadbreaks were performed on every grid point (a total of 1035 events) and the arrival time of the S0
mode and the A0 mode was determined for all events and for all three sensor signals respectively.

Fig. 1: Test rig.
Wave mode analysis
An algorithm was written in LabView to automate the arrival time measurement of both
wave modes for every event. First of all, the Gabor wavelet transform (WT) was calculated togenerate a time–frequency representation of the signal. Fig. 2a) shows an AE signal generated bya pencil-lead break. This event was located in a distance of about 540 mm from the sensor array.
Both wave modes are clearly visible in the Gabor WT, as shown in Fig. 2b). Since the wave velocity is dependent on material, wave mode and frequency [7], a narrow frequency band waschosen to measure the mode arrival time for both modes respectively. For the automated wave
mode detection a frequency was chosen where both modes were present, but the earlier arriving
S0 mode had a much smaller amplitude in comparison to the A0 mode. In the test setup used here,
the frequency of 100 kHz was best suited and an example of the WT coefficient of the signal isshown in Fig. 2c). A threshold was set just above the noise level to detect the beginning of theevent and a peak detection tool in LabView was used to find the first peak after the first threshold crossing. The peak detection should lower the influence of the actual threshold setting.
Since the S0 mode is the faster propagation wave, the first peak is said to be the S0 mode. A second threshold is set to 90% of the maximum amplitude of each event respectively and again a87

peak detection tool is used to find the first peak after the crossing. This second peak is said to be
the A0 mode.

Fig. 2: H-N source 540mm away from sensor: a) sensor signal, b) Gabor WT, c) Gabor WT coefficient at 100 kHz.
In comparison, Fig. 3 shows the sensor signal and the Gabor transform of a pencil-lead fracture event generated in close approximation to the sensor. The same wave feature triggered boththe S0 and A0 threshold. In general, both modes are generated at the same time and separate dueto their velocity difference. In this example travelled distance of the wave was not sufficient enough to develop a noticeable separation and hence the wave mode separation is 0 µs.
88Fig. 3: H-N source close to sensor: a) sensor signal, b) Gabor WT, c) Gabor WT coefficient at
100 kHz.
Misreading of the mode arrival times were identified when one result differs significantly
from the result of neighbouring grid points or from the result of the other two sensor signals of
the same event. These events were then manually inspected. A total of 47 of the 3105 AE signals(1035 events x 3 sensors) had to be reassessed. This is an error of 1.51%. In most cases just oneof the three sensor signals were corrupted.
This automated wave mode detection was used for H-N sources, which are quite repeatable.
However the wave mode amplitude ratio varies significantly for real AE data dependent on the89source orientation and failure mechanism [8]. This will affect the accuracy of this method for“real” AE testing. Also noisy environmental could make the detection of a weak S0 mode at low
frequencies difficult. In that case it is recommended to measure this mode at a higher frequency
where it is more pronounced.
Evaluating direction from which the wave approaches the array
In the first step to locate an AE source the direction from which the wave travels to the sensor array has to be determined. In anisotropic materials, such as CFRP, this has to be undertaken
before calculating the distance because the S0 wave velocity is dependent on the fibre orientation. The A0 mode is chosen to determine the direction since its velocity is not dependent on thefibre orientation.
[µs]

Fig. 4: A0 mode arrival time differences (ΔtA0) for all three sensor pairs in µs.
Figure 4 shows the arrival time differences of the A0 mode (ΔtA0) for all three sensor pairs at
every grid point. Values between grid points were interpolated. The maximum absolute difference in the arrival time of the A0 mode (ΔTmax) between two sensors would be measured whenthe AE source is in line with the sensor pair (dark red and dark blue colour shades). This time iscalculated using the distance between the sensor and the wave velocity. When the source is located perpendicular to a strait line through the sensors the wave would arrive at exactly the sametime at both sensors (green colour shades), thus ΔtA0 is zero.

Fig. 5: Theoretical ΔtA0 hyperbola.
90

The theoretical ΔtA0 hyperbola example is shown in Fig. 5 to clarify the ΔtA0 pattern found in
Fig. 4. The direction in which the hyperbola opens is dependent on which sensor picks up thesignal first. The opening angle is dependent on the time delay between both sensors. Each hyperbola represents the position of all points with the same ΔtA0. Strictly speaking, the algorithm presented in the following paragraph calculates the angle of the asymptotes of the hyperbola (dashedlines).

Fig. 6: Angle of arrival calculation.
The angle of arrival referenced to the centre point of one sensor pair can be calculated using
the inverse cosine function (Arcus-Cosine) (Fig. 6). However this leads to two possible results:
One angle between 0° and 180° (denoted as α) and the other one is the angle α mirrored about
the 0°-180° axis (360°– α). This ambiguity can be removed by introducing the third sensor to thearray. The First Hit method is used to break down the search area in three segments (Fig. 7). The
sensor at which the AE wave arrived first gives the indication in which segment the source is located. The angle is calculated with the smallest absolute arrival time difference of all three sensor
pairs in the array. Thereby the two results of the Arcus-Cosine function are relatively far apart(First result: between 30° and 150°; second result between 210° and 330° with respect to the sensor pair). Only one result can be within the area found by the first hit method.

Fig. 7: First hit method.
This result is relative to the sensors pair chosen for the calculation. Dependent on the orientation of this sensor pair, the angle needs to be rotated to coincide with the global coordinates. For
example: the coordinate system for the array should be 0° pointing to the right and 90° pointing
upwards, the angle α for sensor pair 1-2 is α – 60°, for sensor pair 1-3 α = α – 120° and sensor
pair 2-3 coincide already with the global coordinate system.
91

Evaluating Sensor to Source DistanceThe distance between each sensor and the AE source can be calculated by the formula [11]:If the velocities of both modes (VA0 and VS0) are known and the arrival time of both modes isbA0 and bS0, the distance between sensor and source is L.
If the material is isotropic or quasi-isotropic, the waves propagate with the same velocity inall directions. However in anisotropic materials the wave velocity depends on the material properties and its orientation. In the case of CFRP, the wave speed of the compression wave mode(S0) varies depending on the fibre orientation. This problem must be addressed for an accurate
source location.
[µs]

Fig. 8: Wave mode separation in µs at sensor 1.
The composite plate used in this experiment had a fibre lay-up in 0° and 90°. The S0 mode
has a velocity of 5.80 mm/µs in fibre direction and 4.80 mm/µs in 45° direction. The A0 mode
had a constant velocity of 1.68 mm/µs in all directions. Hence the modes are slightly faster separating in fibre direction compared to 45° (and 135°) direction. The wave mode separation for thewhole plate is shown in Fig. 8. The separation gradually increases with distance from the sensor.
The effect of the fibres is barely visible in this plot but had noticeable effect on the source location result if not considered.
92

a)

b)

Fig. 9: Wave mode velocity dependent on angle.
The wave propagation pattern in the plate is illustrated in Fig. 9. This pattern can be represented sufficiently by the cosine function with an offset and an amplitude dependent on the wave
velocities of both modes. The actual wave velocity (VS0) can be calculated as a function of theangle of arrival (α) using the following formula:

The maximum S0 velocity (VSmax) is in the 0° and 90° direction, VSmin is diagonal to the fibreorientation (±45°). If the propagation pattern is elliptical in unidirectional composites, the factorof 4α needs to be replaced by a factor of 2α.

Fig. 10: Sensor-to-Source distance.
The source-sensor distance was identified for all sensors and then the average of all three results was calculated to get a result, which is relative to the array centre point. This causes an error for sources inside or close to the array but decreases rapidly with distance (Error <1.7 mm at
a radius 100 mm). This error is tolerated since it is very small in comparison to the error causedby mode arrival measurements. For example sources inside of the array are too close to the sensors to detect a separation of modes and hence the calculated distance is zero. The Standard
Deviation of wave mode separation measurements for H-N sources at the same location was
about 4µs which is correspondent to an error of ±10 mm.
93Source Location Block Diagram
Figure 11 illustrates the source location algorithm. It gives a stepwise guideline to reproduce
the algorithm but does not include the actual programming code.

Fig. 11: Block diagram of source location algorithm.
94

The input is the A0 mode arrival and mode separation at all sensors. Furthermore the S0 and
A0 wave velocities are needed and the maximum theoretical time delay of the A0 mode arrival atdifferent sensors (ΔTmax), which is a function of the sensor-to-sensor distance and the wave velocity.
The algorithm could also be enhanced to issue warnings when an error in the measured mode
arrival is likely. This could be, for example, when the time delay of the mode arrival from one
sensor to another is larger than the theoretical maximum time delay (ΔTA0 > ΔTMAX) or when
there is a significant deviation between the measured mode separation at different sensors.
Results
The result of the source location of each event can either be expressed as polar coordinates(distance, angle) or as Cartesian coordinates (x, y) relative to the array centre point. Figure 12
shows the source location result of one AE event for each grid point respectively (randomly selected). “x” represents the actual source location and “Δ” the calculated location. Each calculated
location is connected to its real counterpart by a line.
It can be seen that this method works quite accurate on close range, but its accuracy decreases with distance. The Standard Deviation in a radius of 350 mm from the array was 13 mm,
whilst for the whole plate was 33 mm.
It also can be seen that the radial distance between array and source is usually determined
quite accurately but the angle is more prone to errors. This is probably due to errors when measuring the exact arrival times especially of highly attenuated signals. The Standard Deviation of
the angle was 4.3° in a radius of 350 mm and 6.0° for the whole plate. The Standard Deviation of
the distance was 7 mm and 11mm respectively.
Benefits and Limitations of the Proposed Source Location Method
The biggest challenge for this method is to get the exact mode arrival times. There are situations when one mode is very weak and hardly detectable. This source location technique only
works when both modes are detected in the same event. Tests with real damage had shown thatusually a large number of events are emitted long before the specimen finally fails and even if
not all events can be used for source location, areas with high AE activity can still be located.
The range of this array is limited by the actual attenuation in the material. Theoretically theaccuracy of the direction decreases with distance but the main error is thought to be associatedwith the uncertainty in detecting the mode arrival times.
The main benefit of a closely arranged sensor array is the possibility to install all sensors in
one housing. This would simplify mounting and wiring up of the AE system. Furthermore it issuitable for wireless application where the three sensors, the power supply and the transmitter
could be installed as one unit.
Since the sensors are installed very close to each other, the signal of an event should lookfairly similar at all sensors. This makes it easier to identify exactly the same wave feature in all
three sensor signals. In a wide spread sensor array, the signal changes quite significantly due to95

attenuation and dispersion. As a result wave features could alter, be hidden or completely disappear in the different sensor signals. This could lead to measuring errors.

Fig. 12: Source location results (x = actual location; Δ = calculated location; ● = sensor).

Fig. 13: Comparison of covered area of TOA (left) and local triangular sensor array (right)
method.
96

Another advantage of this source location method is the potential for reducing the sensor
number needed to monitor a large area. A propagating damage releases energy in form of motion, heat and acoustic emission. The energy of the acoustic wave is proportional to the overall
energy released during the event [12]; hence is related to the damage size and propagation speed.
It also depends on other different factors such as the material involved. The AE wave propagatesfrom the source and attenuates due to wave spreading, reflections and friction in the material.
The maximum distance between sensors depends on the minimum damage size to be detected
and the intrinsic attenuation characteristics in the material. The circles in Fig. 13 represent thisminimum distance. The left figure represents a conventional Time of Arrival (TOA) source location method. Source location is only possible in the red area in the middle where all threecircles are overlapping. Acoustic emission will also be detected in the other areas, but the exact
position cannot be evaluated. The sensor array presented here covers a bigger area as long as
both wave modes can be detected.
Conclusion
A new source location method has been introduced which uses a closely arranged sensor array. The location of AE sources is determined by a combination of time of flight and modal analysis. Its benefits and limitations have been highlighted as well.
References
1.

K. Ono, "Acoustic Emission," in Handbook of acoustics, M.J. Crocker, Ed. New York:
Wiley-Interscience Publication, 1998, ch. 53, p. 643.

2.

M.G. Baxter, R. Pullin, K.M. Holford, and S.L. Evans, "Delta T source location for acoustic emission," Mechanical Systems and Signal Processing, 21, 1512-1520, 2007.

3.

M. Surgeon and M. Wevers, "One sensor linear location of acoustic emission events using
plate wave theories," Materials Science and Engineering, A265, 254-261, 1999.

4.

K.M. Holford and D.C. Carter, "Acoustic Emission source location," Key Engineering Materials, 167-168, 162-171, 1999.

5.

J.J. Scholey, P.D. Wilcox, M.R. Wisnom, and M.I. Friswell, "Two dimentional source location techniques for large composite plates," in European Conference on Acoustic Emission Testing, Krakow, 2008, pp. 160-165.

6.

A.A. Pollock, "Classical Wave Theory in Practical AE Testing," in 8th International
Acoustic Emission Symposium, Tokyo, 1986, pp. 708-721.

7.

J. Rose, Ultrasonic waves in solid media. Cambridge: Cambridge University Press, 1999.

8.

W.H. Prosser, "Applications of advanced, waveform based AE techniques for testing
Composite Materials," in SPIE Conference on Nondestructive Evaluation Techniques forAging Infrastructure and Manufacturing, Scottsdale, 1996, pp. 146-153.

97

9.

J.J. Scholey, P.D. Wilcox, M.R. Wisnom and M.I. Friswell "Quantitative experimantal
measurements of matrix cracking and delamination using acoustic emission," Composites:
Part A, 41, 612-623, 2010.

10. W.H. Sachse, "Acoustic Emission source location on plate-like structures using a small array of transducers," 4592034, May 27, 1986.
11. J. Jion, B. Wu, and C. He, "Acoustic emission source location methods using mode andfrequency analysis," Structural Control and Health Monitoring, 15, 642-651, 2008.
12. ASTM, "Standard Definition of terms relating to Acoustic Emission," ANSI/ASTM E61977, 1977.

98

NEURAL NETWORK AE SOURCE LOCATIONAPART FROM STRUCTURE SIZE AND MATERIALMILAN CHLADA, ZDENEK PREVOROVSKY and MICHAL BLAHACEK
Institute of Thermomechanics AS CR, v. v. i., Dolejskova 1402/5, 182 00 Prague 8,
Czech Republic
Abstract
AE localization procedures using artificial neural networks (ANN) represent extremely effective alternative to classical triangulation methods. Nevertheless, their application always requires
full-scale, time consuming ANN training on each specific structure. Disadvantage of particularly
trained ANN algorithm is in its non-transferability to any other object. A new ANN-based AE
source location approach is proposed in this paper to overcome such limitation. The method replaces standard arrival time differences at the ANN inputs by so called signal arrival time profiles, independent on material and scale changes. The ANN training can be also performed theoretically on geometrical models (i.e. without any experimental errors) and learned ANN is thenapplied on real structures of different dimensions and materials. Such approach enables considerable extension of ANN application possibilities. The use of new AE source location method is
illustrated on experimental data obtained during aircraft structure part testing.
Keywords: AE source location, artificial neural networks, arrival time profiles.
Introduction
Acoustic emission (AE) method is widely applied in non-destructive testing of various technical structures. After the signal detection, accurate location of AE source is the primary goal ofthe defect analysis and the basic requirement for further damage mechanism characterization. AE
source coordinates are mostly determined by common triangulation algorithm based on arrival
time differences of AE signals recorded by several transducers [1]. The algorithm computes thepolar coordinates (r, α) of AE sources using analytical formulas. Time differences Δt of AE signal arrival times to different transducers along with elastic wave velocity are necessary input data
for triangulation source location algorithm (see Fig. 1).
Analytical formulas are known for isotropic plates. However, there are many practical situations, in which the triangulation algorithm fails, especially if the more complex structure istested. Procedures based on ANN are used in such cases as an alternative approach to triangulation algorithm. Contrary to the classical methods, the ANN-based location procedures have two
important advantages: they are suitable for AE source location in highly anisotropic media, andelastic wave velocity is not necessary input parameter of the algorithms [1 - 3].
The basic ANN-based algorithm uses time differences as input parameters similarly to common triangulation algorithm. However, the correct arrival time (signal onset) determination is the
crucial factor for the algorithms using time differences and, therefore, the methods can give erroneous results, especially in cases of high noise background level. To solve the problem, certain
modification of ANN-based method for AE source location was proposed [4]. The method uses
common AE signal parameters, such as RMS, rise time, maximum signal amplitude, etc, which
represent redundant input data set for ANN location algorithm. The algorithm is flexible, because it enables selection of the optimal parameters set in each specific situation. Nevertheless,
J. Acoustic Emission, 28 (2010)99© 2010 Acoustic Emission Groupthe sensitivity analysis of trained neural networks has extracted RMS parameter as the most significant signal feature suitable for AE source location, which corresponds with the basic featuresof dispersive elastic-wave propagation. The information about the source location, which is hidden in energy parameters, can be better extracted when we use certain modifications of RMS
signal parameter, as it was shown and successfully tested in [4].
Contrary to the computational power of ANN's, their application possibility is strongly restricted due to several reasons. The first problem is collecting sufficiently extensive training andtesting data sets, which may be excessively time-consuming, expensive or even impossible inmany practical cases. The next important reason is the non-portability of particular trained network to any other situation. ANN should be learned and applied in exactly the same task only,
i.e. the similar computation in the same input-output parameter space is required. Hence, theabove-mentioned method using RMS signal parameter for AE source localization need not represent suitable solution.
To solve both above problems of ANN-based AE source localization, a new approach was
proposed. This innovative localization method uses new way of signal arrival time parameterization (called signal arrival time profiles), independent on the material and scale changes. Under
some non-restrictive conditions, such approach employs the ANN training on numerical model
data (i.e., without experimental errors) and allows the application of learned ANN on real structures with diverse scales and materials.

Fig. 1. Demonstration scheme of AE signal measurement (2D model).
Definition of Arrival Time Profile (ATP)The new way of signal arrival time treatment is inspired by the preliminary expert analysis of
AE source location. The whole monitored structure is separated into several zones according tosignal first arrival to each sensor (see Fig. 2). In this way, the AE source can be roughly localizedusing the information which sensor is the nearest one. To describe the signal detection chronology more precisely, so‑called arrival time profiles are proposed.
100

Fig. 2. Illustrative splitting of a complex structure by first AE signal arrival to each sensor Si.
Let's suppose hypothetical configuration of several AE sensors S1, ... ,SN placed on planarmaterial (in a case of four sensors see Fig. 1), detecting elastic waves emitted by AE sources invarious locations. Signal propagation time from the source to sensor i is denoted Ti (arrival time
period). Then, the arrival time profile (ATP) is a vector of numbers pi defined as follows:(1)
No AE analyzer measures that arrival time periods Ti before the precise AE source location is
performed. Only the AE signal arrival times ti are available. Nevertheless, if we assume ts as thetime of AE source initiation, it is easy to revise the original formula, while Ti = ti - ts :(2)

Fig. 3. Arrival time information processing
101Computation of arrival time profiles is similar to general data standardization. In a first step,
the mean value of arrival times is subtracted from each ti. Such data is then normalized by themean of its absolute values. Figure 3 illustrates described arrival time information processing.
Independence of ATP on Wave Velocity and Scale Changes
Let us denote di the distance between AE source and sensor i, and v the elastic wave velocity.
By substitution of relation Ti = di /v to (1) we obtain:(3)
Arrival time profiles can be also calculated using the distances di, which enables the learningof neural networks with numerically computed data from the distances measured on proportionalmodel of considered structure (i.e. without experimental errors) and, afterwards, testing by processed real arrival times. The elastic wave velocity is cancelled out in eq. 3, so the arrival time
profiles are independent on elastic wave velocity. Analogical cancellation of any common multiple of distances di proves the independence to structure scale changes.
Theoretical Aspects
The potentialities of newly proposed parameterization using arrival time profiles were at firstexamined numerically. Theoretical case of 2D plate of any isotropic material and scale was taken
into account. Due to the complex topology of ATP parametric space, it was necessary to findsensor configurations warranting numerical stability of the method. Possible division by small
numbers in eq. 3 may generate rapidly changing data that neural network cannot learn. Typically,
this occurs in regions where the distances of AE sources to all considered sensors are similar.

Fig. 4. Numerical testing of sensor configurations.

102

Three different configurations of four sensors (black dots) are shown in Fig. 4. To computethe training data, the rectangular domain from 5 to 150 and from 5 to 60 units of the scale wastaken into account for x- and y-coordinates, respectively. Equidistantly spaced points having 2.5
units of the scale between each other cover the whole area. For all such virtual AE sources, thedistances to considered sensors were computed and, consequently, eq. 3 was applied. Before
neural network learning, data standardization is realized. The two-hidden-layer back-propagation
networks (see Fig. 5) were trained to estimate the original coordinates of AE source using corresponding arrival time profile. Finally, all training points (their arrival time profiles) were projected by neural network onto the original area (see crosslets in Fig. 4).

Fig. 5. Localization ANN scheme.
Many theoretical experiments proved that for ATP defined by eq. 1, it is needed to configure
sensors and select the training data so as to avoid learning with signal source locations having
nearly the same distances to all sensors. The problem is well illustrated by the configuration 3 inFig. 4. Nevertheless, it can be solved by another definition of ATP, where the mean of absolutevalues of centered arrival times in denominator is substituted by normalization period T representing the time interval, which AE signal needs to propagate between two appropriately selected
points. However, compared to original definition, such method demands additional measurementof normalization period on real structure, which can be a source of additional experimental errors.
Regarding the comparison of the new ATP-based algorithm with the ANN approach exploiting the time differences, both methods give similar results in accordance with the network learning speed, input sensitivity and final mean-square error (MSE) of network outputs. Possible little
variances of outputs are caused rather by different versions of trained networks, than by arrival
time parameterization method. However, the parametric space of time profiles provides many
advantages. Beyond the mentioned portability to another scale and material, it is more robustwith regard to input errors. Due to the subtraction of arrival time mean value, the error of particular signal edge determination is consequently spread out to all inputs.
It is well known that 2-D location of AE sources by triangulation algorithm using three transducers is ambiguous [5]. In Fig. 6 the two triangle configurations (rectangular and equiangular)of transducers are drawn. Areas of ambiguity are marked out for both configurations. Any AE
source in dark grey area has the same time-differences corresponding to source in light grey areaand vice versa. The problem is usually solved by exploitation of four transducers, while two different triplets of transducers are used in triangulation algorithm. However, theoretically it is still103not a correct solution, because the area of ambiguity is only reduced to insular points. Nevertheless, the probability of such AE source location is close to zero in the real world.

Fig. 6. Ambiguity of 2-D AE source location using 3 AE transducers.
Now, if we take into account the theoretical ambiguity "behind the three sensors" in Fig. 6, it
is possible to interpret the erroneous accumulation of crosslets in analogous problematic areas inFig. 4. As in the case of time differences, if one arrival time profile corresponds to different AE
source locations, neural network cannot interpret such relation, since it is possible only for functional dependencies. However, the problem can be solved using more dimensional time profiles,
i.e., by additional suitably placed sensors. The higher errors in some subsets of training area (e.g.
near the sensors) are caused by closeness of points in time profiles parametric space.
Practical Results
To demonstrate the new AE source location method in practice, an experiment on a small aircraft part has been performed. The task was to localize AE sources on a steering actuator bracket(SAB), which is a part of the aircraft nose landing gear. AE signals were monitored and recorded
by DAKEL XEDO AE system. For signal onset assessment, an "expert edge detection"
algorithm of the elastic wave arrival was applied [6]. Although the method is relatively precise,its accuracy is much lower than the approximation error of neural network, especially for longer
distances from AE source to sensor. Therefore, the two training areas of ANN were considered(left and right part of critical zone of SAB) instead of the “global” one.
The scheme in Fig. 7 shows the configuration of four transducers in case of the left trainingzone. The learning points were selected to equidistantly cover the zone among the sensors
S2, S3, S4 and S9. To compute the arrival time profiles, eq. 3 was applied using the distances
evaluated by the algorithm finding the shortest ways in raster (digital) picture of the structure.
The length of the real elastic wave path through the material is approximated by the shortest polygonal line, while the connecting line segments between the nodes must come through the pixels representing the body. The third dimension of the structure could be neglected because of thesmall thickness of SAB.
Similarly to numerical testing of the method, neural network with two hidden layers having
39 and 17 neurons was trained by virtual AE sources corresponding to points equidistantly
spaced 5 pixels between each other (see Fig. 7), covering the whole training area. Distances of104

all such virtual AE sources to four sensors considered were computed, and eq. 3 was applied.
During the learning process, weights and biases of the network were iteratively adjusted by fast
resilient back-propagation training algorithm and generalization-improving regularization toachieve sufficiently low MSE (less than 0.001). For illustration of approximation error, all training points were projected by neural network back onto the SAB (see Fig. 8).

Fig. 7. Configuration of AE sensors and training points for ANN.

Fig. 8. ANN projection of training points.
105

Trained neural networks were finally tested with real experimental data. Three sets of 25 AE
signals recorded during pencil-lead break (PLB) tests were obtained. The original locations of
PLB tests correspond to cross points of the dashed lines in Fig. 9. The arrival times of each groupof 25 signals were determined by expert edge detection algorithm and eq. 2 was used to computethe arrival time profiles as ANN inputs. The outputs of the network were coordinates of estimated PLB test locations illustrated as crosslets in Fig. 9.

Fig. 9. ANN PLB tests localization results.
While analyzing the localization results, the inaccuracies of experimental measurement must
be considered. These are mostly caused by the signal onset determination errors. Due to dispersion and reflections, the shape of signal envelope undergoes substantial changes with distance
between the source and sensor. Hence, it is possible to expect higher signal edge detection error
due to signal dissimilarity for sources near single sensor and simultaneously far from other transducers. Such situation is evident in PLB test location #3 (see Fig. 9), where the localization error
is worse than in wider structure areas. For location #3, the PLB tests were done not too close to
sensors. Next undesirable effect is that the centers of crosslet groups are not placed at original
PLB test locations. This can be interpreted by different magnitude of the signal edge detection
error in each of four channels, which can result in the shift of arrival time profiles. However, theresults for locations #1 and #2 can be rated as very good, since the error is comparable with theaperture of sensors (9 mm) and better results are not expected. We can say that the majorityof location errors is caused by experimental measurement inaccuracies, not by the low arrival
time profile robustness, or neural network approximation variance.
After testing of complex methodology for AE sources with known localization, let us introduce the results measured during the laboratory fatigue tests of SAB. The bracket was loaded
under stress cycles (R = 0, frequency 1 Hz) on Instron-Schenck 100-kN uniaxial loading machine to the maximal load level of 43 kN causing the maximum stress of about of 870 MPa in thecritical points. AE was monitored during the loading cycles by DAKEL XEDO AE system. All
106

detected AE signals were stored, and after certain loading periods, the detailed AE source location analysis was performed so as to detect crack initiation. Most AE events arose around themounting holes of the bracket. Figure 10 shows the typical localization results of sources in theleft part of SAB. Most of the AE sources originated from the clearly visible damages on the interior surface of the hole caused by the friction with mounting shank (shown in insert of Fig. 10).

Fig. 10. Results of real AE source localization. Insert shows damages inside the hole, producing
most of AE.
Conclusions
A new approach to the AE source location is introduced in this paper. Described algorithm isbased on neural networks exploiting newly defined signal arrival time profiles (ATP) as inputs.
This innovative way of AE signal arrival time relativism facilitates considerable extension of
ANN applications in practice. Processing of the real experimental data measured on a small aircraft part confirmed advantages of such approach. Main aspects of the method can be summarized as follow:• The new method is inspired by the preliminary expert analysis of AE source location exploiting the signal detection chronology. To utilize such information more precisely, so-called arrival time profiles are introduced.
• Arrival time profiles enable numerical generation of sufficient ANN training data sets with
optimal precision and no measurement errors. Afterwards, trained networks can be successfully tested on data coming from real experiment.
• Arrival time profiles are independent on elastic wave velocity and structure scale changes.
Hence, the trained neural networks are applicable to all proportionally identical structuresof any isotropic material.

107

•
•
•
•
•

Theoretical experiments showed that it is needed to configure sensors and select the training
data so as to avoid learning with signal source locations having nearly the same distances toall sensors.
The time profiles parametric space is featured by robustness regarding the input errors. The
subtraction of arrival time mean distributes one particular signal edge determination error to
all inputs, which reduces the global input error.
The new AE source location approach based on neural network with arrival time profiles wasdemonstrated by experiments on a small aircraft part of complicated shape.
Resulting real localization error is influenced mostly by experimental measurement inaccuracies, i.e. it is not caused by neural network approximation variance.
It has been proved that the arrival time profiles enable portability, robustness and easy applicability of ANN-based algorithm for AE source location.

Acknowledgements
The work was supported by the Grant Agency of the Czech Republic under project no.
104/10/1430 and by the Czech Ministry of Trade and Industry under project no. FR-TI1/274.
References
1. M Blahacek, 'Acoustic Emission Source Location Using Artificial Neural Networks', PhD.
Thesis, Institute of Thermomechanics AS CR, Prague 2000 (in Czech).
2. M Blahacek and Z Prevorovsky, 'Probabilistic Definition of AE Events', Proc. 25th European
conference on Acoustic Emission Testing, (1): Prague 2002, pp. 63-68.
3. I Grabec and W Sachse, 'Synergetics of Measurements, Prediction and Control', SpringerVerlag, Berlin, 1995.
4. M Blahacek, M Chlada and Z Prevorovsky, 'Acoustic Emission Source Location Based on
Signal Features', Proc. 27th European Conference on Acoustic Emission Testing, Cardiff, 2022 September 2006, ed. by R. Pullin, K. M. Holford, S. L. Evans, J. M. Dulieu-Barton, Trans
Tech Publications, pp. 77-82.
5. M Blahacek, 'Time Differences Uncertainty Influence on Acoustic Emission Source Location
Accuracy', Proc. 4th workshop NDT in progress, Prague, 2007, ISBN 978-80-214-3505-6, pp.
15-22.
6. M Chlada, 'Expert AE Signal Arrival Detection', Proc. 4th Workshop NDT in Progress, Prague, 2007, pp. 81-88.

108

INTELLIGENT AE SIGNAL FILTERING METHODSVERA BARAT, YRIJ BORODIN and ALEXEY KUZMINInterunis Ltd., bld. 24 corp 3-4, Myasnitskaya str., Moscow, 101000, Russia
Abstract
One problem of the acoustic emission (AE) testing is a high level of noise affecting the diagnosis results. Electric noise, electromagnetic interference, background acoustic noise, and rubbing noise are far from the full list of noise present during measurements. With the high level ofnoise, the operator has to increase the recording threshold of the AE impulses through reducing
the testing sensitivity at the risk of missing a dangerous defect. Lack of the data filtering can result in an incorrect location and erroneous definition of the danger level of AE source. To improve the noise immunity of the AE system, the data filtering algorithms are to be used and examined in this paper.
Keywords: AE data filtering, AE signal processing, wavelet analysis
Introduction
The noise recorded during the acoustic emission (AE) testing is highly varied. Noise can becaused by various physical reasons, such as sensor noise, imperfection of a measuring path, andtechnological noise of the testing object. The noise can be essentially different in the signal
waveform. They can be stochastic and deterministic, stationary and non-stationary, broadband
and narrow-band. To solve effectively the problem of filtering of the AE testing data, it is possible to suggest one method of noise classification – according to the filtering complexity. In Table
1 the noise of different types arising during the AE testing are divided into three groups. The signals from the first group have a low filtering complexity, the signals from the second group havean average filtering complexity, while filtering of the third group noise is a complex nontrivial
problem.
The types of noise that can be successfully removed by means of traditional filtering methods
are impulse and harmonic noise belonging to the first group, as well as the low-frequency andhigh-frequency noise lying outside the informative range of frequencies. Such noise can be easily
removed by means of the frequency or median filtering.
Now, only one type of noise is related to the second group – a stationary white noise. The filtering complexity lies in the fact that both the white noise and the impulse signal are broadband
processes. This complicates the separation by means of the traditional frequency filtering. To
detect AE impulses against the background of the stationary white noise, we utilize the algorithmof filtering based on a discrete wavelet-transform [1, 2].
The noise, which is similar to the AE signals by both the shape and the spectrum, is related tothe third group. These are generally technological noise of the test object, such as rubbing, vibration, various hydrodynamic noise, etc. For filtering noise of this type, it is recommended to record long, from 5 to 30 minutes, waveform of AE signals. Increase in data recording time allows
to make the total time of process observation exceed the stationary interval of noise. By means oflong observation we can observe the short impulse’s quasi-deterministic components corresponding to the AE impulses on the background of stationary noise being present continuously.
J. Acoustic Emission, 28 (2010)109© 2010 Acoustic Emission GroupTable 1. Type of noise signal and filtering methods.
Type of noise signal

Cause of appearance

Filtering methods

Filtering
complexity
low

1

Spikes (short impulses), Electric andharmonic noise,electromagnetic noisehigh-frequency and lowfrequency noise

FIR filters, IIR filters, median filter

2

White stationary noise

AES noise, noise ofelectronic components

Wavelet-filtering

average3

Impulse,Non-stationary noise

Rubbing noise, hydrodynamic disturbances,and cavitation

Analysis of long
realizations of AE
signalshighFiltering of Harmonic and Impulse Noise
The traditional frequency filtering is applied when the signal and noise lie in the different
frequency ranges. Uses of the low-frequency, high-frequency, band-pass and band-rejection filters allows for allocating the ranges of frequencies relevant to the AE diagnostic signal.
On the basis of digital filtering it is possible to perform, if need be, integration and differentiation of the signals. The possibility to define an arbitrary shape of the filter transfer characteristic allows for carrying out a deconvolution operation or inverse filtering that results in compensation of distortions contributed into the signal by the measuring path.

Fig. 1. Some types of noise signals. a) electromagnetic noise; b) electric noise; c) narrow-band
harmonic noise.
The impulse noise is widely found, such as various electric and electromagnetic noise (Fig.
1a, b), arising in case of problems with grounding or violation of principles of electromagnetic
compatibility.
Application of the frequency filtering for removing the impulse noise does not result in an
acceptable solution, because the noise-like impulse signals have wide spectrum overlapped with
spectrum of AE signal. The median filtering is applied for suppressing the impulse noise. A onedimensional median filter is a sliding window including an odd quantity of signal readings. The
110

central reading is replaced with the median of all readings in the window. When selecting thefiltering window that exceeds duration of impulse components of noise, the impulse will be suppressed, while energy of the AE signal will decrease insignificantly.
The median filtering can also help in filtering of narrow-band amplitude-modulated noise(Fig. 1c); in this case the signal spectrum undergoes the median filtering. The spectrum of suchsignal has a well-defined single peak, which is removed during the median filtering of the signal
spectrum. Energy of the narrow-band quasi-harmonic noise decreases by hundreds of times during the spectrum median filtering, while energy of the AE signal, which has no defined single
peaks in the spectrum, varies insignificantly.
Additive White Noise Filtering
After allocating the signal in the desired frequency range and excluding the impulse noise,the problem of exclusion of an additive white noise from AE signals remains undetermined. For
filtering of additive random noise in the system, it is recommended to use the algorithm forwavelet-filtering.
The wavelet-thresholding algorithm is performed on the basis of a discrete wavelettransformation [3]. The AE signal s(n) is represented as a wavelet-decomposition, as a set of detailing and approximation coefficients, Dm,k and Am',k, which correspond to different scale values.
s(n) =

Am',k ϕm',k(n) +Dm,k ψm,k(n),(1)

where ϕ(n) is basis function of sequence of orthogonal embedded subspaces, while ψ(n) is its
orthogonal complement.
Representation as a wavelet-decomposition is natural for a multicomponent AE signal, which
is a superposition of different wave modes. Characteristics of separate modes of the wave packet
are described by the detailing and approximation coefficients.

Fig. 2. Wavelet thresholding algorithm.
Figure 2 shows the algorithm diagram. Upon calculation of the wavelet-decomposition, athreshold restriction of the detailing coefficients is performed followed by a signal reverse recovery [4]. There are two main methods of thresholding; that is, soft and hard thresholding. Under the hard thresholding the coefficients below the threshold values are set to zero, while thesoft thresholding, which is recommended herein, suggests linear approximation of the subthreshold values.
One of key questions is the correct selection of a threshold. The criterion of Stein's unbiased
risk estimation (SURE) is the most effective and widespread [2, 5]. Assume that the informative
AE signal is observed against the background of the additive noise. When using the Stein criterion, it is expected that the coefficients resulting from the wavelet-transformation of signals arein conformity with the model,
111

,

(2)

where ri,j are empirical coefficients of the wavelet transformation of noisy signal, di,j are true coefficients, free from noise, value σj characterizes a noise dispersion, while ξi,j corresponds to thenormally distributed noise with zero mean and single dispersion.
As a result of the wavelet-filtering, on the basis of ri,j evaluation of the true values of wavelet-coefficients,
is obtained, and the creation rule is described by equation (3), whereis an operator of thresholding of empirical coefficients of wavelet-transformation witha threshold value τj.
(3)
(4)
(5)
For selecting the optimum threshold value, the risk function Rij given by equation (4) is
minimized. For soft thresholding minimization of equation (4) results in equation (5), in conformity with which the threshold value is adaptively selected from the range of τi,j, with provision
for values of empirical coefficients of the wavelet-transformation and the noise dispersion estimation.
Application of the threshold algorithm is equivalent to an adaptive smoothing. When readingsof the detailing coefficients are put to zero, which correspond to the signal high-frequency components, the signal low-frequency filtering is carried out. On the other hand, only the readings,
whose fraction of the high-frequency components is relatively small, below the threshold, are put
to zero; in this case no distortion of the signal fragments, in which the high-frequency component
prevails, occurs. Thus, a local selective removal of the high-frequency noise components takes
place.
Figure 3 shows an example of the wavelet-filtering of AE signal. The stationary white noise isremoved practically completely; the ratio of signal energy and noise for the smaller amplitude
impulse is increased fivefold.
Filtering of Noise Similar in Waveform to Acoustic Emission Signals
The noise similar in waveform to AE signals are of particular complexity for filtering. These
are the noise generated by various mechanical reasons - impact, impact of foreign objects, andprecipitations (such as rain and hail). The noise of this kind also occurs at heating a testing object and at various hydrodynamic phenomena, for example, resulting from cavitation.
Such noise neither in waveform, nor in spectrum, does not differ from AE signals. Thus, neither of the above-mentioned methods is suitable for their filtering. The fundamental difference ofthe noise process and the plastic deformation process is a distinct regularity of generation of impulses characterizing one or another process. Times of recording of impulses relevant to thenoise action are spread quasi-uniformly (as in case of impact or precipitations) or in accordancewith the Poisson's law (in case of cavitation or heating effect). Impulses characterizing an active
source of AE (defect) follow the complicated distribution law, of which parameters vary as112

defect develops. The defect impulses are recorded, as a rule, infrequently, and times of their recording bring a disharmony in the form of a certain trend to the distribution law of the background noise impulses.
To research statistical regularities of various processes, it is recommended to increase the supervision time in such a way that the observation length exceeds the stationary interval of noise
signal.

Fig. 3 Result of wavelet-filtering: a) initial AE signal; b) AE signal after wavelet-filtering; c)wavelet decomposition of initial signal; d) wavelet coefficient after thresholding reconstructed tothe initial length

Fig. 4 Signals characterizing a) single rubbing event; b) rubbing process of 4-second duration.
113

Figure 4 shows signals received under simulation of rubbing. In Fig. 4a one can see a signal
characterizing a single rubbing event. This signal is of the impulse and non-stationary type. Figure 4b shows a plot of the 4 second-long signal, being a continuous stationary process, in which
a periodic component is distinctly present. According to this example it is possible to make aconclusion that various scales of process supervision create various representations of it, thelonger is the time of process supervision, the more definitely we can define its properties.
Wavelet-filtering of long durations
Record of long-waveform AE signals creates additional capabilities for algorithms of filtering. For example, the wavelet-filtering can more effectively be performed for the long duration.
At the greater value of supervision time the dispersion of stochastic noise, whose value governs
the threshold value, is determined more precisely. Further, for the long supervision time the procedure of adaptive wavelet-thresholding can be realized. This procedure assumes representationof the analyzed signal as a set of different-length segments, the length and quantity of segmentation intervals are selected with provision for the signal shape in such a way that the maximum
ratio of signal energy and noise after filtering is provided.

Fig. 5 Wavelet-filtering of long-duration AE signal. a) initial signal; b) filtering result.
Optimum filtering of long-waveform AE signals
Effective optimum and adaptive methods of filtering [6] can be applied only in the case ofrecord of the long-waveform AE signals. Synthesis of optimum filters supposes application ofpriory information, both on the useful AE signals, and on the noise. When employing a traditional threshold procedure for the detection of AE impulses, the limited supervision time does
not allow for estimating the statistical parameters required. On the contrary, when recording forlong durations, the probability distribution, spectral, correlation and cross-correlation characteristics required for optimum and adaptive filtering can be estimated.
The simplest and widespread method of optimum filtering is a Wiener filter. The Wiener filter is an optimum filter for the detection of the useful signal, which contains in the initial AE signal along with noise. Priory information on spectral density of signal (or noise) is required for its
use. As a criterion of its optimization used is the mean-square deviation of signal at the filter
output from the specified waveform of signal (or noise).
With the use of this filter, it is supposed that the noise has an additive character, given inequation (6). The filter coefficients w are calculated in compliance with the optimization criterion on the basis of equation (7), where Rff and Rnn are autocorrelation matrices of the AE signal
and the noise signal, respectively.

114

(6)
(7)
(8)
The filter frequency characteristic is set by equation (8), where Pff and Pnn are the power spectraof f(n) and noise(n). Figures 6 to 8 give the results of Wiener filter application. Figure 6 shows
the initial signals corresponding to the AE impulse and rubbing noise. Both signals are received
in laboratory conditions as a result of simulation. Its duration is approximately 0.5 second. By
applying numerical amplification of the noise signal, we can achieve various values of signal-tonoise ratio (SNR). Figures 7 and 8 show the result of the Wiener filter application for the SNR
ratio 0.5 and 0.9, respectively. After filtering we can detect with confidence the AE impulse
against the noise background, and SNR has increased approximately tenfold.

Fig. 6 a) AE impulse, b) friction noise; received under simulation.

Fig. 7 a) AE impulse against the rubbing noise background SNR<0.9; b) result of the Wiener filter application SNR>10.

Fig. 8 a) AE impulse against the rubbing noise background SNR<0.5; b) result of the Wiener filter application SNR>5.
115

Signal compression
To reduce the data volume, it is recommended to operate not the signal itself, but its envelope. Such replacement does not result in diminution of algorithm accuracy, since the signal
spectrum, in this case, is not informative. The envelope characterizes a low-frequency portion of
the signal, thus it can be effectively compressed through wavelet-transformation. The data compression ratio depends on the contraction ratio of frequency range. If under detection of the signal envelope the value of upper frequency of spectrum is decreased by 28 (or 256) times, the envelope duration can be reduced in comparison with the initial signal length by 28 times without
significant losses of information.
Noisy AE signal is shown in Fig. 9. The noise source is friction, which is artificially modeledwith help of abrasive paper. Besides noise we can observe impulse AE signal generated by HsuNielsen source. Figure 9b shows the signals envelope compressed thousand times.

Fig. 9. Noisy AE signal and its envelope.
Principle components method for noise filtration
To identify processes with a different nature of periodicity, it is possible to employ one of
methods of time series analysis – method of principal components. The method of principal
components allows for dividing the AE signal represented as the time series, into several elements (components) – periodic components, chance variations and a trend. In the problem of interpretation of AE signals, the periodic components characterize, as a rule, deterministic noise,chance variations – random noise, and the trend characterizes the AE source activity.
The method of principal components is a method of data multi-dimensional analysis, but for
the time series analysis, the scientists from the St.-Petersburg State University designed its special modification – singular spectrum analysis (or Caterpillar method) [7-10]. This method suggests a transformation of the univariate time series (Caterpillar), Xi, to the set of time series, si,
which represent a great number of fragments of the original signal (Caterpillar links), cut with asliding time window, as given in equation (9). The time window duration shall be selected so thatall processes (the noise processes and AE process) are able to become apparent.
Xi = (si-1,…,si+L-2), where L is window length(9)

When the parameters are selected correctly as a result of application of the method of principal components, the signal is divided into several components, which characterize the various
processes generated by various sources, both the noise sources and the AE sources.
To divide the time series into components, the covariance matrix V is calculated by (10), andits eigenvalues and eigenvectors are calculated by (11); under expansion of the covariance matrix
in terms of eigencomponents, a set of principal components of time implementation is obtained.
116V = (1/k) XT X

(10)

T

V=PLP ,

(11)

where P is the orthogonal matrix of eigenvectors. The eigenvectors by themselves are impulse
components of the filters yi, as given in (12), detecting various components of the process.
X P = Y = (y1, y2,...,yM)(12)

In particular, among the principal components one may select a trend, slowly varying components, periodic and impulse components. Expansion of the AE signal into principal components is informative, because it allows for getting an insight of the processes occurring in thetesting object. Thus, for example, the presence of an increasing trend in the signal can give evidence of a progressive defect, while the periodic component characterizes noise

Fig. 10. Filtered AE signal without friction noise.
Figure 10 shows an example of successful application of the method of principal components
for the filtering of AE signal (see Fig. 9a). As a result of signal decomposition, equation (13), it
turns out that the periodic noise and the useful impulse signal comply with various components
of decomposition. The signal shown in Fig. 9 is restored without considering the periodic noise
components.

Fig. 11 Long-waveform AE signals corresponding to a) corrosion process; b) rain and hail noise;c) sum of a) and b) processes.
A more interesting example is shown in Fig. 11. Figure 11a shows the AE signal characterizing development of a corrosion defect, while the process shown in Fig. 11b is noise received as aresult of simulation of precipitations effect (rain, hail). Figure 11c gives the sum of these processes. Figure 12 shows the decomposition of the signal presented in Fig. 11c into the main components. As a result of grouping of the correlated components, we have deduced 9 groups of thecomponents of different types. The first 5 groups showed no indication of the corrosion signals.
Next three groups exhibited the presence of the corrosion signals, but still inadequate for clear
117

identification. The highest-order components allow for restoring the useful signal characterizing
a corrosion attack, as separately shown in Fig. 13. The strongest 5 AE signals are clearly recovered from Fig. 11c.

Fig. 12 Main component decomposition for the detection of useful corrosion signal. Top left is
the lowest order and the bottom right is the highest order components.

Fig. 13 The highest order component for the detection of useful corrosion signal, recovered fromnoisy signals (Fig. 11c). Enlargement of the bottom right in Fig. 12.
118

In the near future, Interunis Ltd. plans to release new generation of A-Line family AE systems. The main advantage of new systems will be the non-threshold principle of AE signal detection and adaptive noise filtration, whose principles are stated in this article.
Conclusion
The paper is concerned with various methods of filtering of the AE data. The algorithm of
impulse noise filtering by means of the median filter is described. The efficiency of wavelet
thresholding for removal of white stochastic noise is shown. Also the method of noise removal isdescribed by means of the time series analysis using the method of principal components andoptimal filtration algorithm.
Acknowledgement
Authors would like to acknowledge and extend their heartfelt gratitude to Professor Kanji
Ono for his encouragement and valuable remarks.
References
1. Daubechies, Ingrid, Ten Lectures on Wavelets, SIAM, Philadelphia, 1992.
2. Donoho, D.L., De-Noising by Soft-Thresholding. IEEE Transaction on Information Theory.
41: 613-627, 1995.
3. Mallat S., A Wavelet Tour of Signal Processing, 2nd ed., Academic Press, 1999, 673 p.
4. Ajvazyan S.A., Buhshtaber V.M., Enjukov I.S., Meshalkin L.D., Applied Statistics in 3 parts,
1989. (in Russian).
5. Stein, C.M. "Estimation of the Mean of a Multivariate Normal Distribution". The Annals of
Statistics 9 (6): 1135–1151, 1981.
6. Vaseghi, S.V., Advanced Digital Signal Processing and Noise Reduction, John Wiley & Sons
Ltd, 2000.
7. Golyandina, N., Usevich, K., Principal Components of Time Series: the Caterpillar Method,
1997 (in Russian).
8. Golyandina, N., Nekrutkin, V., Zhigljavsky, A., Analysis of Time Series Structure. SSA and
Related Techniques, Chapman and Hall/CRC, 2001, 320 p.
9. Special Issue on "Theory and Practice in Singular Spectrum Analysis of Time Series", Statistics and Its Interface, 3 (3), 2010.
10. Golyandina N. and Osipov, E., “The “Caterpillar”-SSA method for analysis of time series
with missing values”. Journal of Statistical Planning and Inference, 137 (8), 2642-2653,
2007.

119

DISCRIMINATION OF ACOUSTIC EMISSION HITS FROM DYNAMICTESTS OF A REINFORCED CONCRETE SLAB
ENRIQUE CASTRO 1, ROSA PIOTRKOWSKI 2, ANTOLINO GALLEGO 3 and
AMADEO BENAVENT CLIMENT 4
1
Politechnic University of Cartagena, Department of Applied Physics, Cartagena, Spain,2
National University of San Martín, ECyT, Buenos Aires, Argentina,
3
University of Granada, Department of Applied Physics, Granada, Spain,
4
University of Granada, Department of Mechanics of Structures, Granada, Spain
Abstract
In the evaluation with acoustic emission (AE) of the damage and state of a concrete structure
under a dynamic force, the most relevant information come from the AE hits produced by cracking processes. But, unfortunately, there are other sources of AE, which are unrelated with thedamage state of the specimen, as the closing of cracks, friction between different elements, ornoise from the testing equipment. The discrimination between AE hits from cracking processes
and the other sources is useful in order to have an accurate evaluation. In this paper the classification of AE hits with several signal-processing techniques is investigated. Several dynamic tests
were carried out with a reinforced concrete slab attached to a 3-m square MTS shaking table, andduring these experiments the AE hits were recorded. The specimen represents, at the 1/3 scale, aflat slab supported on four box-type steel columns, and it was submitted to a simulation of theCampano-Lucano earthquake recorded at Calitri (Italy). After the test, the AE transients wereextracted and classified according to several signal parameters. The autocorrelation, wavelet
power, kurtosis, RMS in different parts of the signal and approximate entropy were calculated
for each signal. A comparison and evaluation of the different classifications according to eachparameter is presented.
Keywords: concrete structures, transient classification, dynamic test.
Introduction
One considerable source of damage in reinforced concrete (RC) structures when they are located in earthquake-prone areas is cyclic loading induced by ground acceleration during seismic
events. These structures are commonly designed to sustain, essentially, two levels of seismic
action: low-to-moderate intensity earthquakes (level I), and strong earthquakes (level II). Underlevel I earthquakes, the RC structure must remain basically elastic. In moderate or high seismicity regions, an RC structure can experience several tens of low-to-moderate intensity earthquakes
during its lifetime. When an RC structure experiences a high number of low and moderate intensity earthquakes there is a concrete degradation of the RC structure that can even produces theslip between reinforcing steel and concrete, which is an important damage for this kind of structures [1]. As visual inspection is not possible in most cases and does not allow knowing in whichstate the internal part of the structure is, it would be very useful to have a non-destructive technique, which can evaluate the state of the RC structure during its lifetime. The AE technique isvery suitable of this purpose because it renders the possibility of recording information of thecracking process during an earthquake, even when it is very low [2].
In order to investigate the health monitoring of an RC structure submitted to low-to-moderateintensity earthquakes using AE, a prototype structure consisting of an RC slab supported on four
J. Acoustic Emission, 28 (2010)120© 2010 Acoustic Emission Groupbox-type steel columns was placed on a shake table. This structure underwent several simulations of the Campano-Lucano earthquake recorded at Calitri (Italy) and the generated AE hits
were recorded by eight AE low-frequency sensors (type VS30 set in the range 20-100 kHz) distributed near the most critical parts of the structure.
As in a dynamic test like this there are many sources of noise, a lot of precautions were taken
in order to prevent spurious hits. So rubber layers and teflon films were inserted between any
surfaces whose contact could generate friction noise, and four guard sensors were placed near thebottom end of the columns to prevent noise generated by the contact between the base plate of
the columns and the shake table.
In spite of all the preventive measures adopted to avoid undesired noise, during the analysisof the results it was observed that the AE sensors registered spurious signals coming from friction or electromagnetic noise. As this noise could disturb the AE analysis and could also appear
in a real situation when a building is monitored, it was necessary to investigate the way of filtering out the hits, which are not coming from concrete cracking processes. The investigation thathas been carried out in order to classify the AE hits is presented in this paper. Different parameters calculated from the AE transients have been tested in order to know their capacity of filtering out the hits coming from friction or noise. These parameters were: the autocorrelation of thesignal, its wavelet power, the kurtosis, its approximate entropy and the root mean square (RMS)of different parts of the signal.
The objective of this paper is to obtain a filter using signal parameters of the AE hits. The description of the experiment and the obtaining and analysis of the data can be found in [3]. Nevertheless, the experiment is also briefly presented here for clarity.
Experiment
A prototype structure consisting of a RC slab supported on four box-type steel columns was
designed according to Spanish codes. The prototype structure has one story 2.8 m in height and4.8×4.8 m2 in plan. It is assumed to be located in the moderate-seismicity Mediterranean area.
Accordingly, from the prototype structure, the corresponding test model was derived by applying
the following scaling factors for geometry, the acceleration and the stress, respectively: λl = 1/2,
λa = 1 and λσ = 1. Figure 1 shows the geometry and reinforcing details of the test model. The slab
measures 125 mm in depth and it is reinforced with two steel meshes, one on the top made with
6mm diameter bars spaced 100 mm, and another on the bottom consisting of 10 mm diameterbars spaced 75 mm. The slab was reinforced at the corners by shearheads consisting of steel Ushapes 60 mm in depth in order to prevent punching shear failure. The average yield stress fs of
the reinforcing steel was 467 MPa, and the average concrete strength, fc = 23.5 MPa.
The test model was placed on the uniaxial MTS 3×3 m2 shake table as indicated in Fig. 2. To
satisfy the similitude requirements between the prototype and the test model, additional steel
blocks were attached on the top of the RC slab (total mass m = 7,390 kg). The shake table movements were patterned after the Calitri 1980 NS earthquake (Campano-Lucano, Italy) with thetime scale compressed by a factor of λl = (1/2)0.5 = 0.707. Two series of seismic simulations wereconducted using the Calitri 1980 NS accelerogram scaled to different amplitudes.

121Fig. 1. Test model: (a) elevation; (b) plan (bottom view).
The Vallen Systeme ASMY-5 was used to measure AE during the tests. Eight low-frequency
AE sensors (type VS30 set in the range 20-100 kHz) were placed on the test model as shown in
Fig. 1b. The threshold detection of the AE sensors was set at 45 dB. To prevent undesired noise
generated by the contact between the base plate of the columns and the shake table, four guard
sensors were placed near the bottom end of the columns as indicated in Fig. 1a (one sensor ateach column). Moreover, in order to remove or reduce the sources of spurious friction noise,rubber layers and teflon films were placed between the added steel blocks and the slab. Teflon
films were also inserted between any metallic surfaces whose contact could generate spurious
friction noise, such as screws, steel plates for fixing the accelerometers to the slab, cables, etc.
122Fig. 2: Experimental set-up.
Noise Problem
All the preventive measures were not enough to avoid the presence of hits coming from noise
sources. Three principal noise sources were indentified:a) The friction between different elements of the experimental structure.
b) The closing of cracksc) Electromagnetic noise
The noise source a) was reduced by the rubber layers and teflon films, but it was impossible tohave it completely eliminated due to the fast movements produced during the experiment. The
noise sources b) and c) could not be suppressed in any way, the first one because the same
specimen has to be used during different seismic simulations, and the second one because it was
produced by the shake table, at which the test model were attached.
During a cracking process a high amount of elastic energy is released fast and it propagatesas elastic waves with high amplitude at its beginning and then decaying exponentially. So thetemporal signal of the AE hits is non-stationary. In this article we will name them as “crackinghits”. However, in signals coming from friction or electromagnetic noise the amplitude must be
approximately constant over all the duration of the hit, as the energy is released continuously
during a longer period of time. So, the temporal signal of these AE hits is typically stationary.
We will call them as “noisy hits”. In Fig. 3 two examples of these kinds of AE hits from our experiment are shown.
The objective of the paper is to find a way of filtering out the noisy hits. To do this, a first attempt was made noticing that hits with long duration and low amplitude were noisy hits. So thehits that do not meet the following two conditions were not taken into account:- The duration in µs must be less than 3000 µs + 80 µs/dB*(Amplitude (in dB)-45 dB).
- The amplitude must be higher than 60 dB.
It was found, however, that this filtering was not enough as many noisy hits meet the conditions
and pass the filter, so it seems that a filter based on the classical AE parameters could not work
123

properly. Thus, it was necessary to find another filtering condition to eliminate the remaining
noisy hits based on signal processing of the hits waveform.

Fig. 3. Examples of a cracking hit (left) and noisy hit (right).
Filtering Parameters
In order to build a proper filter, it is necessary to look for a signal parameter, which always
takes different values for cracking and noisy hits. If this ideal parameter is found, setting theright threshold will allow us to filter out all the noisy hits. To find this ideal parameter, several
signal parameters were investigated and their discrimination capacity tested. These signal parameters were:• The autocorrelation [4]. As the cracking hits are non-stationary and have the maximum amplitude at the beginning of the hit, their autocorrelation must decay faster than the autocorrelation of the noisy hits, which are stationary. We measure this difference by calculating the
distance between the two symmetric points of the autocorrelation whose value is 66% of the
maximum value. We call this parameter “Autocorrelation Width”• The wavelet power. In previous investigations in other applications of AE, it was found thatthe density of wavelet power (DWP) in different frequency bands can be used to determinate
the source of AE hits. See [5] for more details. In this paper, the quotient of the DWP in theband [40-100] kHz divided by the DWP in the band [10-40] kHz is used as a signal parameter for the discrimination of AE hits. We call it “DWP quotient”.
• The kurtosis. The kurtosis [6] is a measure of the number of extreme values that are in a signal or in a distribution. A higher kurtosis implies that more of the variance is the result of extreme values, so their frequency is higher than in a signal with lower kurtosis.
• The approximate entropy. The approximate entropy (ApEn) is a measure of the complexityof a signal [7]. A higher value of ApEn means a higher degree of complexity. As the relevant
part of the transient signal of the cracking hits is concentrated at the beginning of the hit, they
are less complex and disordered than the noisy hits.
124•The root mean square (RMS) [6]. As the cracking hits are non-stationary, the amplitude distribution is concentrated at the beginning of the hit, and the RMS of the amplitude in the firstpart of the signal is higher than the RMS in the final part. The quotient of the RMS in theband [0-400 µs] divided by the RMS in the band [400-1300 µs] is used for discriminating
signal parameter and we named it as “RMS rate”.

Results
All the considered parameters have very different values for pure cracking and noisy hits, as
can be seen in Table 1 for the hits shown in Fig. 3, so it seems that all of them must filter the
noisy hits and allow the cracking hits to pass. However, the problem is that the system understudy is very complex and dispersive, so the waveform can change very much in the path fromthe source to the sensors. It shows that the noisy and cracking hits can have similar values of theparameters.
Table 1. Values of the filtering parameters for the hits shown in Fig. 3.

Cracking hit
Noisy hit

Autocorrelation
Width
12
114

DWP quotient
2.16
0.056

KurtosisApEn

RMS rate

16.87
2.18

0.73790.8392

3.59551.2001For these reasons, it is necessary to test the filtering capacity of each parameter. We havevisually classified a set of 200 AE hits in cracking or noisy hits and the values of the filteringparameters have been calculated. In Fig. 4 these values can be seen for the cracking and noisy
hits. The ideal situation would be to have two sets or clusters of cracking and noisy hits perfectly
separated, but, unfortunately, the two sets are mixed for all of the filtering parameters, although
not in the same degree. In the case of the Autocorrelation Width and the Approximate Entropy
the cracking and noisy hits have similar values. With the DWP quotient, the kurtosis and the
RMS rate, the noisy hits have in general a lower value than the cracking hits, so the separation
between the two groups and the filtering of the noisy hits is possible. Some cracking hits have a
low value of these parameters and are mixed with the noisy hits, and some noisy hits have a highvalue of the parameters and are mixed with the cracking hits. So, any separation of the twogroups of hits based on the value of the filtering parameters will have some hits wrongly classified because they have a parameter value more similar to the other group.
The automatic classifications of the hits using the filtering parameters is made by taking athreshold and place the hits with a parameter value higher than the threshold in a group and thehits with the parameter value lower in the other group. The value of the threshold is very important to achieve a correct discrimination between the cracking and noisy hits and its influence inthe filtering process has to be checked in order to choose the best possible threshold for eachfiltering parameter. So that, a study of how adequate the filtering process is for each parameterand different thresholds has been carried out.
The results can be seen in Fig. 5, where the rate of cracking and noisy hits that passed the filter for each parameter depending on the threshold is represented. It can be seen that it is not possible to make a perfect filter, because when all the cracking hits pass the filter there is a high125Fig. 4. Values of the filtering parameters for the cracking and noisy hits: a) Autocorrelation
Width. b) Kurtosis. c) Approximate Entropy. d) DWP Quotient. e) RMS Rate.

Fig. 5. Percentage of cracking and noisy hits that passed the filter depending on the threshold foreach parameter.
126percentage of noisy hits that pass the filter too. When all the noisy hits are eliminated, almost allof the cracking hits are eliminated too. In general, the percentage of cracking and noisy hits thatpasses the filter increase and decrease together with the variation of the threshold, although the
behavior is different for each parameter. In the case of the Autocorrelation Width, the percentage
of noisy hits that pass the filter is higher than the percentage of cracking hits for most of the values of the threshold. The other parameters have a better performance because the percentage ofcracking hits is always higher than the noisy ones, but, as it have been said before, neither ofthem is perfect.
At this point, it is necessary to choose the parameter and threshold that we are going to use.
With Fig. 5 it is possible to choose the parameter and the threshold that can be used depending of
our needs. If it is necessary to have all the cracking hits and it does not matter if a certain percentage of noisy hits is not filtered, then a low threshold of the kurtosis or the DWP quotient canbe used. In our case, it is desirable to have the highest percentage of cracking hits together withthe lower percentage of noisy hits, so we have chosen to use the parameter and threshold with the
maximum difference between the percentage of cracking and noisy hits that pass the filter. With
this choice we make sure that most of the hits that pass the filter are cracking hits.
Table 2. Percentage of cracking and noisy hits that passed the filter when the best threshold isused, their difference and best threshold for each parameter.
Parameter
Cracking hits
that pass thefilter (%)
Noisy hits thatpass the filter(%)
Maximum
Difference:
Threshold:

Autocorrelation
Width
91.01 %

DWP
quotient
76.40 %

Kurtosis74.16 %

Approximate
Entropy
59.55 %

RMS
rate
79.78 %

84.68 %

18.92 %

14.41 %

33.33 %

17.12 %

6.33 %

57.48 %

59.75 %

26.22 %

62.66 %

58.1

0.49

4.5

0.72

1.69

In Table 2 the maximum difference between the percentage of cracking and noisy hits thatpass the filter for each filtering parameter can be seen. Using this criterion, the best filtering parameters are RMS rate, Kurtosis and DWP quotient. As the maximum difference is achieved byusing RMS rate, it has been used to filter the AE hits in [3]. The AE hits that have been used toobtain the filtering results are a subset of the AE hits, which have been recorded in the experiment, so it is expected that very similar percentages of cracking and noisy hits passed the filter
when we applied it to the whole set of AE hits. By this reason, after the filter we must have about
a 79.89 % of cracking hits and about 17.12 % of noisy hits. In the AE hits, which are visuallyclassified we have a 55.5 % of noisy hits and a 44.5 % of cracking hits, so we have a little more
noisy hits than cracking hits, but the difference is very low. It is expected to have similar rates in
the whole set of recorded AE hits, so we have that after the filtering process we have 3.83 times
more cracking hits than noisy hits. This means a very good improvement over the initial situation.
The quality of the filter can be checked in the analysis of the results. After the filtering process the AE hits are analyzed in [3], where it can be seen that the cumulative AE energy is related127with the hysteretic strain energy. In [3] it is shown that the results are consistent with the experiment and the hysteretic strain energy, so we concluded that the filtering process is adequate forthis kind of experiment.
Conclusions
We have investigated the way of filtering noisy AE hits, which come from friction or electromagnetic noise. To do so, several signal parameters have been tested in order to find which
one provides very different values for the noisy and cracking hits. It was been found that thecomplexity of the specimen and the dispersion properties of the material make it difficult toseparate the noisy and cracking hits because sometimes parameters have similar values.
In order to find which parameter is the most appropriate the percentage of cracking and noisy
hits that pass the filter for each parameter were obtained considering a wide interval of thresholdvalues. It was found that RMS rate, Kurtosis and DWP quotient are the most effective parameters because they have the highest difference between the rate of cracking and noisy hits that pass
the filter.
This filtering process makes the analysis of the AE results clearer and more precise, and allows us to carry out conclusions with more confidence as we are sure that most of the AE hits
that we are analyzing came from a cracking process and not from a noise source. The results of
the AE analysis are shown in [3].
References
1. Yuyama S., Li Z.W., Yoshizawa M., Tomokiyo T., Uomoto T.: Evaluation of fatigue damage
in reinforced concrete slab by acoustic emission. NDT & E International, 34, 2001, 381-387.
2. Benavent-Climent A., Castro E. Gallego, A.: AE Monitoring for Damage Assessment of RC
Exterior Beam-Column Subassemblages Subjected to Cyclic Loading. Structural Health
Monitoring, 8, 2009, 175-189.
3. Gallego A., Benavent-Climent A., Vico J.M., Infantes C., Castro E.: Health monitoring of
reinforced concrete slabs during seismic tests using acoustic emission. 29th European Conference on Acoustic Emission Testing, Vienna, 2010, Austria.
4. Shiavi, Richard: Introduction to Applied Statistical Signal Analysis. Academic Press, 2007.
5. Piotrkowski R., Castro E., Gallego A.: Wavelet power, entropy and bispectrum applied to AE
signals for damage identification and evaluation of corroded galvanized steel. Mechanical
Systems and Signal Processing, 23 (2), 2009, 432-445.
6. Lutes L.D., Sarkani S.: Random Vibrations. Butterworth-Heinemann, 2004.
7. Pincus S.M.: Approximate Entropy as a measure of a system complexity. Proceedings of the
National Academy of Sciences of United States of America, 88, 1991, 2297-2301.

128

USE OF CLUSTER ANALYSIS OF ACOUSTIC EMISSION SIGNALS IN
EVALUATING DAMAGE SEVERITY IN CONCRETE STRUCTURES
L. CALABRESE, G. CAMPANELLA and E. PROVERBIO
University of Messina, Dept. Industrial Chemistry and Material Engineering, Messina, Italy
Abstract
AE technique gained increasing interest in the last decade in the field of civil engineering as
a monitoring methodology and as an assessment tool for safety and reliability evaluation ofreinforced concrete structures, historic and masonry buildings. There are several establishedstatistical methods (Z, RA, b and Ib value), which can be used in analyzing AE data to evaluate
damage status of a structure subjected to a particular loading condition. Artificial neuralnetworks (ANN) have recently been applied as a tool to reduce data redundancy and to optimize
feature set of AE signals. Cluster analysis was generally used to separate a set of parameters into
several classes reflecting dataset internal structure. In this paper such analytical procedure wasapplied in evaluating acoustic emission data obtained during 4-point bending tests on concretebeams under cycling and constant load condition and at increasing loads. Two kinds of
unsupervised clustering methods were used: the principal component analysis (PCA) and theself-organized map (Kohonen map). Combining both methods, it has been possible to quantify
the damage severity and to identify the evolution of the damage itself during the test.
Keywords: Civil engineering, concrete, damage analysis, cluster analysis, principal component
analysis, Kohonen map, SOM
Introduction
The correlation between the propagation of acoustic waves due to cracking phenomena inside
a concrete structure and the seismic wave propagation during an earthquake activity is a well known concept. Based on these similarities, starting from seismology studies, some statisticaltechniques to evaluate the “health condition” of concrete structure in a particular load condition
have been developed. The b value analysis, from Gutenberg and Richter formula (Gutenberg andRichter, 1954), originally outlines the relation between the magnitude of earthquake with thenumber of events connected to them. In an AE structure monitoring test, this method and its
improved evolution (Ib value) becomes a powerful tool for the identification of fracture modes.
This kind of investigation does not require the identification of sources location (Kurz et al.,2006).
Seismicity rate changes have been used in great number of studies as a significant tool inorder to explain the stress distribution in a specific area of the Earth’s crust. Some studies on
precursors of past earthquakes suggest that particular space-time seismicity patterns, including
the phenomenon of precursory quiescence, can be related to the seismotectonics process that leadto earthquakes. The quiescence hypothesis, as formulated by Wiss and Haberman (1988),
postulates that the quiet volume overlaps the main shock source volume. The seismic quiescence
hypothesis assumes that some main shocks are preceded by seismic quiescence that is by asignificant decrease of the mean seismicity rate (number of events of magnitude exceeding agiven threshold, per unit time), as compared to the preceding background rate in the same crustal
volume. This consideration has been transferred to cracking phenomena in concrete structurecomparing the AE rate within a temporal window with a background rate (Proverbio et al. 2009).
J. Acoustic Emission, 28 (2010)129© 2010 Acoustic Emission GroupAs far as fracture mode evaluation is concerned, different approach can be used, the mostcommon one being the combination of two particular AE parameters (RA value and averagefrequency) (Shiotani et al., 2001a). Trying to define a common rule to identify the different
damage mechanisms, particular AE descriptors like amplitude or duration can indeed be used.
Sometimes, the analysis of a single parameter, or a combination of a few of them is sufficient;but sometimes this procedure does not provide immediately clear and coherent results. As aconsequence, a discrimination of the AE signatures based only on one parameter is debatable,
and a multi-variate technique appears an essential instrument for analyzing data not immediately
linked to each others (Rippengill et al., 2003; Johnson, 2002).
When the type of damage mechanisms is known in advance the supervised patternrecognition is used as in the K-nearest neighbors method (K-NN method) (Godin et al., 2003;
Hattori and Takahashi, 1999). The term unsupervised pattern recognition is on the other handused to describe the complete methodology consisting of procedures for descriptors selection,cluster analysis and cluster validity, when no information on attended clusters are available. A
popular clustering method is the k-means algorithm (Likas et al., 2003, Ng, 2000). The
dimension reduction of large data set can be instead obtained by means of the principal
component analysis (PCA), which is a classical method of multivariate statistics (Jolliffe, 2002).
PCA involves a mathematical procedure that transforms a number of possibly correlated
variables into a smaller number of uncorrelated variables called principal components. The firstprincipal component accounts for as much of the variability in the data as possible, and eachsucceeding component accounts for as much of the remaining variability as possible.
Neural network procedures also have been successfully adopted to separate numericallydifferent classes of data, among other the Kohonen's self-organizing map (SOM) (Kohonen,1990). The self-organizing map is a sheet-like artificial network, the cells of which become
specifically tuned to various input signal patterns or classes of patterns through an unsupervised
learning process. The main characteristic of the Kohonen algorithm is its ability to develop
feature maps corresponding to the distributions of vectors in the input set and to organize suchmaps in a topologically coherent manner (Godin et al., 2004; Huguet et al., 2002). Thus, given a
set of data, the Kohonen algorithm would be expected to organize the output layer as a map, on
which similar shapes are detected by clusters of neurons close to each other. The combination of
AE multi-parameter analysis and neural networks, in the form of a Kohonen's SOM, wassuccessfully employed to discriminate signals originating from different types of damage (deOliveira and Marques, 2008).
In the following sections the application of different analytical procedures applied to AE datacollected during concrete beam loading test will be described. Univariate analysis procedures
like Ib, Z and RA values were adopted to identify damage occurred during the different loading
tests. Furthermore, multivariate analysis based on clustering procedures like PCA and SOM werealso applied. Aim of this work was to develop a methodology investigation procedure based on
the use of different unsupervised algorithms that could allow not only to handle a great amountof data to obtain a generic information about the structure monitored, but also, in synergy withother classical methodologies, to allow us to extract specific consideration about the structural
integrity of the tested structure.
Univariate Analysis Methodologies

130Ib and Z value
Starting from the original Gutenberg and Richter formula (Gutenberg and Richter, 1954), it
could be possible to distinguish the different fracture condition by calculating the slope (b value)of the function:(1)log N = a – b(A dB /20)

where N is the number of AE events with an amplitude higher than AdB. The b-value analysis of
AE is in general applied to groups of subsequent events, a group of 300 events was used in thiswork. While testing materials undergoing brittle failure, the b-value is found to range from 1.5 to
2.5 in the initial stages (Rao, 2005), it then decreases with increase in stress to attain values about
1.00 and less, showing temporal fluctuations as the impending failure approaches in the material.
A high b-value arises due to a large number of small AE hits (or events) representing new crack
formation and slow crack growth, whereas a low b-value indicates faster or unstable crack
growth accompanied by relatively high amplitude AE in large numbers (Colombo et al., 2003).
By implementing the formula in order to avoid the problem to define amplitude range and thenumber of AE data to obtain the b-value, an improved b-value (Ib-value) was proposed by
Shiotani (Shiotani et al., 2001b);
Ib =log10N(w1 ) − log10N(w 2 )(a1 + a2 )σ(2)where N(w1) is the accumulated number of AE events, in which the amplitude is more thanµ – α1σ, and N(w2) is the accumulated number of AE events, in which the amplitude is more thanµ + α2σ, σ is the standard deviation of the magnitude distribution of one group of events, µ is themean value of the magnitude distribution of the same group of events, α1 and α2 are constants.
A parameter to describe changes in seismicity activity is the seismic quiescence. A test
commonly used to compare seismicity rate changes is the Z-value test for a difference between
two means. The Z value measures the significance of the difference between the mean seismicity
rates µ1 and µ2 within two time intervals (Monterosso, 2003).
Z=µ1 − µ2(3)

σ 12 σ 22+n1 n2

here σ and n are, respectively, the standard deviation of the rate and the number of events in thegroup. In particular, the long term average (LTA) is the Z value resulting from the comparison of
the rate within a window, µWin and the background rate, µAll defined as the overall mean rate in
the volume. Negative Z values indicate rate increases while a positive Z corresponds to rate
decrease. In this work, groups of events detected in a temporal window of 2000 seconds during
each load condition were considered.
RA values analysis
During structure damage, it is well known that the cracking fracture mode evolves frommode I type (nucleation and growth of tensile crack) to mode II type (shear propagation of crack)with progression of fracture. RA value and average frequency (AF) are classical parameters toclassify active cracks (Japan Construction Material Standards, 2003). RA value is defined as theratio between rise time and the peak amplitude, while the AF of a waveform is calculated as aratio between the AE counts and duration. Small RA values and the large average frequencies are131attended under flexural load, where tensile cracking was prominent, while under shear loading,large RA values and lower average frequency can be observed.
Multivariate Analysis: Clustering Algorithms
Principal components analysis (PCA)
PCA is a mathematical algorithm used to reduce the dimensionality of a data set forcompression, 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’ (Johnson, 2002). Given thedata Xi = (x1i, x2i,..., xpi) with i = 1,...,N, the new data vector is Zi = (z1i, z2i,...., zqi) where z1 is thelinear combination of the original xj (j = 1,....,p) with maximal variance, z2 is the linear
combination, which explains most of the remaining variance and so on, i.e. the new data vector,
referred to as the principal component, is a linear combination of the original data and is both
uncorrelated and orthogonal to all other principal components. The first principal component
accounts for the maximum variance in the data, the second principal component accounts for the
maximum of the remaining variance and so on. This method is based on achieving a specified
cumulative percentage of total variance extracted by successive factors. The purpose was toensure practical significance for the derived factors by ensuring that they explain at least a
specified amount of variance. In our case 67% of total variance was evaluated and this led torealize PCA plots by using only three principal components.
Self-organizing neural networks
The self-organizing map (SOM) is one of the most prominent artificial neural networkmodels adhering to the unsupervised learning paradigm (Kohonen, 1990). The model consists ofa number of neural processing elements, i.e. units. Each of the units i is assigned an ndimensional weight vector mi, mi Є Rm. The training process of self-organizing map may bedescribed in terms of input pattern presentation and weight vector adaptation. Each training
iteration t starts with the random selection of one input pattern x(t). This input pattern ispresented to the self-organizing map and each unit determines its activation. Euclidean distance
between the weight vector and the input pattern was used to calculate a unit’s activation. In thisparticular case, the unit with the lowest activation is referred to as the winner, c, of the training
iteration, as given in equation 4;(4)

c: mc (t) = mini x(t) – mi (t) .

Subsequently, the weight vector of the winner as well as the weight vectors of selected units
in the vicinity of the winner are adapted. This adaptation is implemented as a gradual reductionof the difference between corresponding components of the input pattern and the weight vector,
as shown in equation 5;
mi(t+1) = mi(t) +α(t)*hci(t)*[x(t) – mi(t)](5)

Geometrically speaking, the weight vectors of the adapted units are moved slightly toward
the input pattern (Fig. 1). The amount of weight vector movement is guided by a so-called
learning rate, α, decreasing with time. The number of units that are affected by adaptation as wellas the strength of adaptation is determined by a so-called neighborhood function, hci. This
number of units decreases with time. Typically, the neighborhood function is a unimodal
function, which is symmetric around the location of the winner and monotonically decreasing
with increasing distance from the winner.
132

Fig. 1. Scheme of SOM operation.
A Gaussian is used to model the neighborhood function. At the beginning of training a wide
area of output space is subject to adaptation. The special width of units affected by adaptation is
reduced gradually during the training process. Such a strategy allows the formation of large
clusters at the beginning and a fine-grained input discrimination towards the end of training
process. The results of the training process are the so-called U-matrix. Additional information
can be obtained plotting the hit histogram U-matrix. This map shows the projection of data
samples into map. The data are calculated by finding the BMU (best matching unit) of each data
sample from the map, and increasing a counter in the map unit each time it is the BMU. The
colors are related to a specific level of a variable and hexagon size is related to the numbers of
AE hits related to that cluster point.
Experimental
Concrete beams, 500x140x140 mm, made with 15 MPa flexural strength ordinary Portland
cement, have been tested in a four-point bending test configuration (Fig. 2). AE signals wererecorded by ten-channel Vallen AMSY-5 equipment. A total of 8 piezoelectric transducers (4 foreach lateral side), VS30-V type, with a flat response between 23-80 kHz were used. Thresholdvalue after pencil-lead break calibration was set at 40 dB: that allowed to eliminate thebackground noise and to record only the emissions due to cracking of the concrete. Load tests
including 4 main loading cycles were performed with increasing loads. Every cycle, moreover,
was set up by 3 different load conditions (Table 1 and Fig. 3).
Every main loading test starts with a 10-min. constant rate load condition and 10-min.
constant rate unloading condition for two reasons: first our aim was to pre-crack the samples
before applying a long time load, and the second aim was to evaluate the differences in energetic
release during unloading step from the stored energy during loading. Every sub-cycle of thealternate loading step was at a frequency of 0.1 Hz. Constant load condition at the maximum
load had duration of about 300 minutes. Each AE signals was described by 12 parameters,
including Amplitude, Counts, Duration, Rise-time and Energy that were calculated by the133acquisition system, while Historic Index, Severity, RA value and Average Frequency were
derived from standard parameters. Time, sensor number and “load cycle” were also here
considered as variables. To allow data comparison with different scale, logarithmic values wereused instead of their natural values.

Fig. 2- Four point bending test set-up
Results
Ib and Z values vs. test number (i.e. loading history) is reported in Fig. 4. It is possible toobserve that values were characterized by a floating trend. Before the 8th load test, the curve has
low values. A crescent trend can be identified in each group of test characterized by same
maximum load.
134

Table. 1. Load cycle sequences.
Test no Cycle no

1

2

3

4

Maximum load
(kN)

Load condition

Duration (min)1

15

Slow loading and slow unloading

21

2

15

Alternating loading

340

3

15

Constant loading

320

4

18

Slow loading and slow unloading

21

5

18

Alternating loading

390

6

18

Constant loading

320

7

19

Slow loading and slow unloading

21

8

19

Alternating loading

370

9

19

Constant loading

230

10

20

Slow loading and slow unloading

21

11

20

Alternating loading

410

12

20

Constant loading

253

Fig. 3. Load cycle sequences.
As expressed in literature, low Ib values correspond to the macrocrack opening. Once thegenerated macrocracks opened up, most of the energy has been released. It implies the creationof many weak events leading to an increase of Ib value. In our results, during cycling loading
tests low values of Ib are obtained, as a consequence of the activation of macrocracks. In theconstant load tests, the Ib value increases due to reduced energy released for crack propagation.
It could be related with microcracking events. The constant load conditions (3rd and 6th load
tests) influence the progression of Ib value more than the alternating loading ones (2nd and 5thload tests). With the 8th load test (alternating load test), it is possible to observe the dominant
generation of new microcracks (evidenced by relevant increase of Ib) that propagate and become
macrocracks during the constant load condition (9th load test). This behavior clarify that test
sequence at 19 kN (Test No. 3) critically compromised the mechanical integrity of the concrete
structure.
135

Fig. 4. Ib and Z values vs typology load test.
The Z and Ib statistics show a similar pattern. A significantly high Z-value was found at the
8th load test. Paraphrasing the seismologic terminology, this particular load condition could beconsidered like the beginning of a quiescent period preceding a severe damage condition of thestructure. However, it should be pointed out that Z trend does not show efficiently the local
maxima and minima, evidenced instead in the Ib pattern. This suggests that the Ib analysis is
more sensitive to local stress variations, while the pattern of Z can be useful in understanding the
global behavior of the investigated structure

Fig. 5. Cracking changing from tensile type of fracture to shear type during load step cycles.
136

Figure 5 shows relationship between RA value and average frequency of concrete specimen
under all loadings. The data are clustered with a centroid representative of all data for all sensors
and then classified for load cycle typology. Analyzing the figure, it’s possible to observe twodifferent behaviors: before the 8th load test, the plotted data are distributed in the area where theaverage frequencies are medium/high and RA values are low. In particular, at increasing cycle,
the magnitude of AF parameter increases. This region is representative of mode I crack behavior.
In the 9th load test an inversion point can be found; in fact at very high load cycles, the AE events
are observed in the region where the average frequencies are low. This region is typicallyconnected with mode II crack behavior. Thus, 8th and 9th load tests represent the transition fromtensile type to shear type of fracture mode.

Fig. 6. Variable distribution on components; dotted circles refer to main variable groups; dottedarrow indicates trend of data clusters against time.
These results confirm that “traditional methods” can be useful to investigate the damage
evolution of the structure, even if information is sometimes incomplete and difficult to interpret.
The support by “unconventional methods”, specifically applied to multivariable systems (such as
PCA and SOM) can be considered a valid tool for a better comprehension of the damage
phenomena. In Fig. 6 the PCA plot is shown. In the graph, the vertical axis represents theunrotated factor II (component 2), and the horizontal axis represents the unrotated factor I(component 1). The axes are labeled with 0 at the origin and extend outward to ±0.5. The
numbers on the axes represent the factor loadings. In this graph, all the variables are labeled.
From visual inspection, it shows the presence of 2 main groups of variables: Test, Load andTime (variables that we can define generically as time dependent) are grouped together and137

factor loading is fairly high. The variables strictly connected to the wave properties (Rise-time,
Duration, Counts and Energy) generates a second group high on the unrotated first factor.
Moreover, analyzing the particular data distribution, we can consider them as organized in threeclusters strictly connected with time dependent variables trend. This indicates the degree ofcontribution of these variables as data discriminating factors. Irregular are the distribution ofother variables like RA, Historical Index and AF. Severity is equally correlated to time and wave
dependent variables, as shown by its high factor loading in all principal components andtherefore included in the following SOM analysis. Analyzing the variables distribution on first
and third component graph, Amplitude loads high on the unrotated third factor. A little moredifficult is the comprehension of groups analyzing the distribution on first and second
component graph. These plots are not included in this work because the variable classificationand their clustering are less interesting for the interpretation of experimental data.
For better comprehension of PCA results, factor loadings grater than ± 0.3 are considered tomeet the minimal level of significance; loadings greater than ± 0.4 are considered practicallysignificant. Comparing PCA analysis results with correlation matrix of variables, Load and Testvariables can be removed, because these correlated with Time variable. Rise-time, Counts andEnergy can be removed because of their equivalence to Duration. Using theses residual variables
and the ungrouped ones, the SOM algorithm was adopted.

abcFig. 7 a) U-matrix resulting from the application of the SOM; b) Hit histogram U-matrix for the
Load variable; c) Hits U-matrix considering the cycling load conditions as variables.
In Fig. 7a, the U-matrix map related to SOM analysis is reported. This map shows distancesfrom maps units and their nearest neighborhood, evaluated by Euclidean method. High values ofU-matrix map (red and yellow pixels) mean large distances between neighboring map units.
Elements belonging to the same cluster are therefore identified by uniform areas of low value
138

(blue pixels). In this specific case, we identified 3 main depression areas associated with theircluster areas.
In Fig. 7b, the hit histogram U-matrix related to the load variable is shown. Cluster 1 ismainly related to 19 kN load condition (blue hexagons) and as discussed before, this region
represents the condition, in which weak events increase Ib value, as a consequence of microcrackdevelopment, where Z value revealed a quiescence period. Cluster 2 is related to 15 kN load (redhexagons) and 18 kN (green hexagons) conditions. This area was connected to nucleation andgrowth of macrocracks (mode I fracture behavior). Cluster 3 is related to 20 kN load condition(yellow hexagons) and as reported before, related to existing crack propagation in shearing mode
(mode II or III fracture behavior).
In Fig. 7c, the hits U-matrix related to the specified test conditions is represented. The 19 kN
alternating load condition, identified as 8th load cycle, outlines the transition from tensile type toshear type of fracture mode. Through the Hits U-matrix, it was possible to localize, inside themap, hits connected to this particular load condition and to compare these positions with the hits
representative of two other alternating load conditions; that is, the hits connected to 18kN
alternating load condition (5th load cycle) and 20 kN alternating load condition (11th load cycle).
As can be seen, the 8th load cycle (red hexagons) is mainly present in Cluster 1 region and we
can identify this area as quiescent seismic region; the 5th load cycle (blue hexagons), is present in
Cluster 2 region (mode I cracks area) while the 11th load cycle (green hexagons) is mainlypresent in Cluster 3 region (mode II cracks area).

Fig. 8. Variable maps resulted from application of Kohonen self-organizing map algorithm.
It is interesting to compare U-matrix with topological maps of single variables (Fig. 8). The
cluster regions observed in U matrix are quite similar to Time Map, outlining the strong weightof this variable compared with others. Similar considerations came out in PCA analysis abouttime dependent variables. Sensor Map is characterized by homogeneous distribution of results.
This correspondence confirms the high coherence in data acquisition by each sensor during all139

loading tests. Furthermore, analyzing Severity Map, the highest severity conditions wereconnected to Cluster 3 region. In this region we identified hits connected to 20 kN load condition
and, referring to Z value results in this area exceeding quiescence condition, the structural
integrity of specimen starts to be compromised. Minimum values correspond to data inside
Cluster 1 region (intermediate test loading condition).
Conclusions
Clustering algorithms were used and compared with traditional procedures for damage
evaluation (Ib, Z and RA values). Principal components analysis allowed us to correlate clusters
to groups of variables and to identify related variables. In such a way a reduction of original data
matrix was obtained. The Kohonen’s self-organizing map (SOM) algorithm was adopted to
identify clusters and relate them to the AE patterns. Although the use of these new
methodologies appears promising as a practical tool of evaluation of many different parameters
in a restricted number of graphs, it requires the development of validation procedure to optimize
a correct interpretation of great amounts of data, even requiring integration with classical wellestablished procedures.
References
Brock G., Pihur V., Datta S. Datta S. (2008), “clValid: an R package for cluster validation”, J
Stat Softw, 25 (4), 1-22.
Colombo S., Main I.G., Forde M.C.J. (2003), “Assessing damage of reinforced concrete beamusing “b-value” analysis of acoustic emission signals”, J. Mater. Civil Eng. 15, 280-286.
Cox T.F. (2005) An Introduction to Multivariate Data Analysis, Hodder Arnold Publication,
London.
de Oliveira R., Marques A.T., (2008), “Health monitoring of FRP using acoustic emission andartificial neural networks”, Comput Struct, 86 (3-5), 367-373.
Duch W., (1997) “Neural minimal distance methods”, in Proc. 3rd Conference on Neural
Networks and Their Applications , Kule, Poland, Oct. 1997, p. 183–188.
Emamian V., Kaveh M., Tewfik A., Shi Z., Jacobs L.J., Jarzynski J., (2003) “Robust clusteringof acoustic emission signals using neural networks and signal subspace projections”, EURASIP J
Applied Signal Processing, 3, 276-286.
Godin N., Huguet S., Gaertner R., Salmon L., (2004), “Clustering of Acoustic Emission Signals
Collected during Tensile Tests on Unidirectional Glass/Polyester Composite Using Supervisedand Unsupervised Classifiers” NDT&E Intl, 37, 253-264.
Golaski L., Gebski P., Ono K. (2002), “Diagnostics of Reinforced Concrete Bridges by Acoustic
Emission”, Journal of Acoustic Emission, 20, 83-98.
Gutenberg B., Richter F., (1954), Seismicity of the Earth and Associated Phenomena, Princeton
University, Princeton.
Hair J.F., Anderson R.E., Tatham R.L., Black W.C. (1995), Multivariate Data Analysis,
Prentice-Hall, Upper Saddle River, NJ.
Hattori K. and Takahashi M., (1999), “A new nearest-neighbor rule in the pattern classification
problem”, Pattern Recogn, 32 (3), 425-432.
140

Huguet S., Godin N., Gaertner R., Salmon L., Villard D., (2002), “Use of acoustic emission to
identify damage modes in glass fibre reinforced polyester”, Compos Sci Technol, 62 (10), 14331444.
Japan Construction Material Standards (2003), “Monitoring Method for Active Cracks in
Concrete by Acoustic Emission”, JCMS-IIIB5706 The Federation of Construction Material
Industries; Tokyo, Japan.
Johnson M., (2002), “Waveform Based Clustering and Classification of AE Transients in
Composite Laminates Using Principal Component Analysis”, NDT&E Int, 35, 367-376.
Jolliffe I.T., (2002), Principal Component Analysis, 2nd ed., Springer, NY.
Kohonen T. (1990), "The Self-organizing Map." Proceedings of IEEE, 78 (9), 1464-1480.
Kurz J.H., Finck F., Grosse C.U., Reinhardt H.W. (2006) “Stress Drop and Stress Redistribution
in Concrete Quantified Over Time by the b-value Analysis”, Structural Health Monitoring, 5; 6981.
Likas A., Vlassis N., Verbeek J.J., (2003), “The global k-means clustering algorithm” Pattern
Recogn, 36 (2), 451-461.
Monterosso Juarez A.D., (2003), “Statistical Seismology Studies in Central America, b-value,
seismic hazard and seismic quiescence”, Comprehensive Summaries of Uppsala Dissertations
from the Faculty of Science and Technology, Acta Universitatis Upsaliensis, Uppsala, 897, 1-27.
Ng M.K., (2000), “A note on constrained k-means algorithms” Pattern Recogn, 33 (3), 515-519.
Proverbio E., Venturi V., Campanella G. (2009) “Damage assessment in post-tensioned concrete
viaduct by b- and Ib-value analysis of AE signal”, in Proc. of NDTCE’09 Non-Destructive
Testing in Civil Engineering ,Nantes, France, June 30th -July 3rd.
Rao M.V.M.S., Prasanna Lakshmi K.J., (2005) “Analysis of b-value and improved b-value of
acoustic emissions accompanying rock fracture”, Curr. Sci. India, 89, 1577-1582.
Rippengill S., Worden K., Holford K.M., Pullin R., (2003), “Automatic Classification of
Acoustic Emission Patterns”, Strain, 39 (1), 31-41.
Shiotani T., Ohtsu M., Ikeda K., (2001a) “Detection and evaluation of AE waves due to rockdeformation”, Constr. Build.Mater, 15, 235-246.
Shiotani, T., Yuyuma, S., Li, Z. and Ohtsu, M. (2001b) “Application of AE improved b-value toquantitative evaluation of fracture process in concrete materials” Journal of Acoustic Emission,
18, 118-133.
Wyss M., Habermann R. E. (1988) “ Precursory seismic quiescence”; Pageoph, 126, 319-332.

141

SIMULATION OF LAMB WAVE EXCITATION FOR DIFFERENT ELASTIC PROPERTIES AND ACOUSTIC EMISSION SOURCE GEOMETRIESMARKUS G. R. SAUSE and SIEGFRIED HORN

University of Augsburg, Institute for Physics, Experimentalphysik II,
D-86135 Augsburg, Germany
Abstract
In the present study the influence of microscopic elastic properties and the geometry of theacoustic emission (AE) source are investigated by finite element simulation. We investigate theformation process of Lamb waves in isotropic and anisotropic plate specimen by simulation offive different AE source configurations. As reference a microscopically and macroscopicallyhomogeneous, isotropic aluminum specimen with point force couples is used. For fiberreinforced materials, the elastic properties are typically anisotropic. A comparison is made between the Lamb wave formation of the anisotropic, homogeneous model specimen and those ofan anisotropic, microscopically inhomogeneous model. It is demonstrated, that the microscopic
elastic properties of the AE source have significant influence on the excitation of distinct Lambwave modes. This can be used to distinguish between failure mechanisms like fiber breakage orresin fracture in fiber reinforced materials.
1. Introduction
Analytical descriptions of AE sources due to crack formation and propagation and the subsequent propagation of ultrasonic signals are well established in literature [Ohtsu1984, Giordano1999, Lysak1996]. Based on the generalized theory of AE, the microscopic crack surface
displacement can be linked to the macroscopic displacement at the surface of the solid by suitable Green’s functions [Ohtsu1986]. While the Green’s functions for partially infinite media areeasy to obtain, the situation grows more difficult, when dealing with complex geometries of finite extent. In addition, another major difficulty is the application of Green’s functions to inhomogeneous, anisotropic media. Although such cases can be investigated by analytical descriptions [Green1995, Green1998], they are typically only suited for the specific geometry and material under investigation.
In recent years various authors have applied the finite element method (FEM) to the simulation of AE formation and AE signal propagation for the case of plate specimens [Dietzhausen1998, Prosser1999, Hamstad2002]. The AE source was typically modeled as point force
couple. Due to the increase in computational capacities such simulations are becoming an important part to improve the understanding of AE signal formation and propagation.
The finite element method allows an intuitive approach to subdivide modeling and simulationof AE signals. Utilizing a multi-scale approach the modeling of AE can be split into three distinct steps:• Modeling of source mechanism• Modeling of signal propagation• Modeling of signal detection
The first step addresses the description of the AE source. Typically buried dipole sources realized as point force couples are used. This requires knowledge of the source radiation direction
J. Acoustic Emission, 28 (2010)142© 2010 Acoustic Emission Groupand the associated magnitudes of the modeled source mechanism. In addition, various sourcetime functions are employed in literature, which model the source kinematics [Ohtsu1986, Giordano1999, Hamstad1999]. In a previous publication, we presented a new source model to simulate the microscopic failure in carbon-fiber reinforced plastics (CFRP). This source model takes
into account the finite extent of the crack and the microscopic geometry and elastic properties of
the AE source [Sause2010]. Thus in the following the term “source model” refers to the finite
dimensions of the crack model and the geometry and elastic properties in the vicinity of the crackmodel.
Subsequent to signal excitation, the elastic wave propagates into the solid and is reflected at
the respective boundaries of the geometry. In plate-like geometries with thickness d the signal
propagation is analytically described by Lamb waves for wavelengths λ < 3d [Lamb1917]. In
finite element simulations the propagation of such signals is modeled based on time dependent
solutions of differential equations of the equilibrium states [Castaings2004, Nieuwenhuis2005,
Prosser1999, Hamstad1999, Sause2010]. As pointed out for isotropic media by Hamstad et al.,different source radiation directions can excite different ratios of symmetric and antisymmetric
Lamb wave modes [Hamstad2002]. This in turn can lead to different frequency compositions of
the detected AE signals, as the propagation of the zero-order symmetric (S0) mode occurs at
higher frequency than the zero-order antisymmetric (A0) mode [Hamstad2002]. Due to the microscopically inhomogeneous nature of fiber-reinforced composites, strong differences in thesource radiation direction are expected depending on the failure mechanism. In a previous publication it was demonstrated, that such differences of the source radiation direction and of the microscopic elastic properties cause distinct differences in the excited Lamb wave modes, which
compare well to experimental data recorded during failure of CFRP specimens [Sause2010,
Sause2010b]. In addition, influences of the displacement magnitude and the source excitation
time were investigated [Sause2010].
Signal propagation in the previously used specimen types suffered drastically from boundaryreflections [Sause2010, Sause2010b], since interference of signals with their boundary reflections makes identification of distinct Lamb wave modes more difficult. Therefore, this approachis extended to larger specimen geometries. In addition, the case of isotropic media is treated as areference.
Finally, the importance of simulation of the detection process of AE signals cannot be overemphasized. While in [Sause2010] an abstract representation of a typical broadband piezoelectric
sensor was used, this approach was extended to a full-scale simulation in [Sause2010b]. However, since the present study will focus on basic investigation of the influence of the source microstructure the detection process will be limited to the simulation of surface displacements atdistinct positions at the model specimen surface.
2. Description of Model
The present simulations were performed for rectangular plate specimens with 200 mm x
400 mm edge length and 1.4 mm thickness. Utilizing symmetric boundary conditions at the yzand xz-plane only one quarter of the total plate volume was modeled as shown in Fig. 1. The AE
source is located at the medial plane at the center of the plate specimen (x,y,z) = (0,0,0) mm. Allelastic properties describing the propagation medium and the source model are summarized inTable 1. The respective AE signals are obtained from the surface displacements at the positions
marked in Fig. 1 (red crosses) under an angle .
143

Fig. 1: Schematic drawing of plate model including dimensions and symmetry planes.
Table 1: Summary of elastic properties of the materials used within the simulation.
Aluminum6063-T83

CFRP
T800/913

Resin HexPly
913

Carbon Fiber
T800S

Density[kg/m³]

2700

1550

1230

1810

Poisson-Ratio

0.33

-

0.35

0.20

Elastic
coefficients[GPa]

E = 69.0

C11 = 154.0
C12 = 3.7
C13 = 3.7
C22 = 9.5
C23 = 5.2
C33 = 9.5
C44 = 2.5
C55 = 4.2
C66 = 4.2

E = 3.39

E = 294.0

A quarter-volume representation of the various microscopic source models is shown in Fig.
2-a-f. Five different configurations of source models were used in the following:(A) Isotropic aluminum plate and homogeneous source(B) Anisotropic CFRP plate and homogeneous source(C) Anisotropic CFRP plate and inhomogeneous source (resin properties)(D) Anisotropic CFRP plate and inhomogeneous source (carbon fiber properties)(E) Point force couple
The first configuration is shown in Fig. 2-a. The crack is modeled as a three-axes cross,which is cut out of the homogeneous medium around the source. Each of the three axes of thecross is composed of a bar of 30-µm length, and 10 µm x 10 µm cross-section, respectively. The
remaining domains visible in Fig. 2-a are not relevant for this case and will be explained below.
For the present configuration, all domains were assigned elastic properties of aluminum. The
second configuration is also represented by Fig. 2-a, although all domains were now assigned
144homogeneous, anisotropic elastic properties of T800/913. The third configuration is shown in
Fig. 2-b. Here the microscopic elastic properties of a cube with edge length rcube = 200 µm were
those of the HexPly 913 resin as given in Table 1. The elastic properties of the macroscopic plate
were those of T800/913. While the dimensions of the crack model and the remaining domains
were all comparable to the configurations considered before, now a smooth transition between
microscopic and macroscopic elastic properties is required. This is achieved utilizing a cube with
2rcube = 400 µm edge length which encloses the resin cube. This so-called perfectly matchedlayer (PML) gradually changes the elastic properties of the enclosed medium Cij, 0 to those of thesurrounding medium Cij, 1 as a function of distance r as follows:(1)

The material density was gradually changed accordingly. This smooth transition is exemplarily
visualized in Fig. 2-d in a pseudo-color diagram. The fourth source model configuration differsfrom the third configuration only by a different assignment of elastic properties of the domain
marked in red in Fig. 2-c. In this case the cuboid with edge length of 85 µm and 10 µm x 10 µm
cross-section has elastic properties of T800S carbon fibers instead of HexPly913 resin. The respective transition to the macroscopic properties of the T800/913 CFRP plate is realized by arespective PML as shown in Fig. 2-e. For the purpose of comparison, point force couples asshown in Fig. 2-f were also simulated. In the present configuration, the total distance of the twopoints acting as buried dipole source was 300 µm with 1 N force magnitude.
In order to excite an AE signal, in the following a linear source-time function was used.
Since the influence of various source radiation directions was already addressed in [Sause2010],
in the present study only in-plane crack surface displacements dx as marked in Fig. 2-b were considered. Following the previous publications, a linear source-time function was used to deflect
the respective crack surface by a magnitude d0,x within an excitation time Te,x [Sause2010,
Sause2010b]:(2)

After the excitation time Te,x, the boundaries of the source model are free of constraints. The
subscript x marks the respective direction of crack surface deflection. It is worth to note that due
to the symmetry conditions of the model the crack surface displacement is also symmetrical withrespect to the yz-plane. For better comparison of the obtained results, the magnitude d0,x and excitation time Te,x was kept constant for all models at d0,x = 100 nm and Te,x = 100 ns.
Since the present approach introduces a multi-scale problem, the resolution of the finite element mesh was gradually refined from 2.0 mm down to 0.01 mm when approaching the model
source. The higher mesh resolution close to the source is necessary to resolve the geometric details of the source model, while the coarse macroscopic resolution is still sufficient to describe
signal propagation within the plate specimen up to frequencies of 1 MHz. Since exact symmetry
and quality of arbitrarily generated 3D tetrahedral meshes is typically difficult to achieve [Freitag1997, Parthasarathy1994], the source model could not be meshed symmetrically with respect
to the xy-plane.
145

Consequently, there is a break of symmetry with respect to the xy-plane, which can cause
differences in the calculation of signal propagation along the positive and negative direction of
the z-axis, respectively. As a result antisymmetric Lamb wave modes can occur. The sensitivityof the Lamb wave formation based on this effect cannot be overemphasized. Within reasonable
steps of refinement of the mesh density around the source model the excitation of antisymmetric
Lamb waves could not be prevented. Based on physical considerations such microscopic asymmetry within the material is always expected and therefore the present configuration is meant toreflect realistic conditions better than perfect symmetric mesh conditions. To ensure comparability of the results of different source model configurations, in the following all simulations were
performed using an identical mesh.
The temporal resolution was chosen to be 10 ns for < 1 µs, which is required to resolve theexcitation process. For 1 µs ≤ ≤ 50 µs the temporal resolution was decreased to 100 ns, which
is sufficient to temporally resolve the signal propagation process.

Fig. 2: Quarter-volume representation of AE source model configurations. (a) Homogeneouscase. (b) model of matrix cracking. (c) model of fiber breakage. (d, e) Pseudo-color diagram ofmaterial density visualizes transition of elastic properties utilizing perfectly matched layer and (f)model of point force couple.
146

3. Results
The results of the simulations are presented below for the five different source model configurations. To visualize the modal composition of the AE signals the Choi-Williams transformation is calculated using the software package AGU-Vallen Wavelet [Choi1989, Vallen2010]. The
results are compared to analytical solutions of dispersion curves calculated for the respective
propagation medium and distance [Vallen2010, Zeyde2010].
3.1 Homogeneous, isotropic medium
The Lamb wave propagation excited by source model configuration A is shown in Fig. 3 atsix distinct times. The in-plane displacement located at the medial plane of the plate excites astrong S0 Lamb wave mode with preferential orientation along the x-axis. As visible in Fig. 3, for
35 µs
50 µs minor reflections occur at the boundary in y-direction. But for times
40 µs
no boundary reflections arrive at the designated detection positions.

Fig. 3: Propagation of S0 Lamb wave mode in aluminum plate at six distinct times.
As already mentioned in the introduction section, point force couples are often used in literature to model AE sources. Following the approach of Hamstad et al. [Hamstad1999] a respective
aluminum plate with dimensions as given in Fig. 1 was modeled, using an in-plane point force
couple. The resulting CWD-diagram of the z-displacement of the surface at (x,y,z) =(50,0,0.7) mm is shown in Fig. 4-a. For comparison the CWD-diagram of the signal at the respective position using the source model configuration A is shown in Fig. 4-b. In addition both
CWD-diagrams show dispersion curves for the S0 and A0 Lamb wave modes calculated for the
propagation distance of 50 mm. For aluminum with elastic properties as given in Table 1, the147

longitudinal and transversal wave velocities were calculated as vL = 6153 m/s and vT = 3100 m/s,
respectively.
Clearly, the signals of both simulations are dominated by the contribution of the S0 Lambwave mode as seen in Fig. 3. In direct comparison the source model configuration of finite extent
shows in addition an excitation of the A0 Lamb wave mode. This contribution is hardly visible in
the CWD-diagram in Fig. 4, since the magnitude of the coefficients is dominated by the intensityof the S0 Lamb wave mode. Instead, the minor contribution of the A0 Lamb wave mode is observed better as low frequency oscillation of the signal in time domain after t = 13 µs.

Fig. 4: Comparison of surface displacement signals at (x,y,z) = (50,0,0.7) mm for point force
couple model (a) and model of finite extent (b) with superimposed dispersion curves.
Since the source model configuration A uses an axially oriented surface displacement, a dependency on the orientation of the source relative to the sensor can be expected. As depicted inFig. 1 the surface displacement signals were detected at three distinct points, reflecting threedifferent source-sensor angles of θ = 0°, 45° and 90°, respectively, between the crack surface
normal (x-axis) and the investigated propagation direction.
The CWD-diagrams in Fig. 5 show the respective simulated signals for the source model
configuration A with additionally calculated dispersion curves. Clearly, the modal compositionof the detected Lamb waves depends on the angle. While for the source-sensor angle 0° the S0
mode dominates, the S0 mode contribution decreases for 45° and vanishes for 90°. In contrast,
the contribution of the A0 Lamb wave mode is almost constant for all three angles, althoughbarely visible in the color-range of Fig. 5-a. Due to the orientation of the in-plane crack surface
displacement, this preferential spatial distribution of the S0 mode intensity is expected [Eaton2008].
3.2 Homogeneous, anisotropic medium
The Lamb wave propagation excited by source model configuration B is visible in Fig. 6.
Generally, the propagation of Lamb waves in anisotropic media like CFRP is more complex thanfor isotropic media. Due to the elastic anisotropy, the propagation of distinct Lamb wave modes
is strictly asymmetric. Due to the high elastic modulus in x-direction (154 GPa) the propagation
velocity of the S0 Lamb wave mode along the x-axis is very high and thus the reflection at the xedge of the specimen interferes at the observation position (x,y,z) = (50,0,0.7) mm for35 µs.
148

Fig. 5: Influence of source-sensor angle for source model configuration A: (a) at (x,y,z) =(50,0,0.7) mm, (b) (x,y,z) = (35,35,0.7) mm, (c) (x,y,z) = (0,50,0.7) mm.
However, the other observation positions marked in Fig. 6 show no interference with incident
reflections for the investigated time
50 µs.
In analogy to the case of isotropic media considered before, a comparison is made between
signals excited by point force couples in an anisotropic plate and those of source model
configuration B. The resulting CWD-diagrams of the z-displacement signal of the surface at(x,y,z) = (50,0,0.7) mm are shown in Fig. 7-a and 7-b. In addition, dispersion curves of the S0
and A0 Lamb wave modes are shown. Both were calculated for propagation in x-direction basedon the anisotropic elastic properties given in Table 1 using the software package of R. Zeyde[Zeyde2010]. As visible in Fig. 7, both signals are clearly dominated by the S0 Lamb wavemode.
3.3 Inhomogeneous, anisotropic medium
After introducing an anisotropic propagation medium and AE sources of finite dimensions insource models A and B, the source model configurations C and D introduce microscopic inhomogeneous elastic properties. These are used to model the microscopic presence of resin andcarbon fibers as shown in Fig. 2-b and 2-c. A comparison of CWD-diagrams of both source
model configurations is shown in Fig. 8 for each source-sensor angle investigated. In addition,
the dispersion curves of the fundamental Lamb wave modes are shown in each CWD-diagram.
149

These were calculated independently for each source-sensor angle and the respective propagation
distance using the software package of Zeyde [Zeyde2010].

Fig. 6: Propagation of several Lamb wave modes in CFRP plate at six distinct times.

Fig. 7: Comparison of surface displacement signals at (x,y,z) = (50,0,0.7) mm for point force
couple model (a) and model of finite extent (b) with superimposed dispersion curves.
For source model configuration C (model of matrix cracking), the signals are dominated by
the A0 mode for all source-sensor angles (see Fig. 8a-c). Only minor contributions of the S0
Lamb wave modes are observed. These are barely visible in the CWD-diagrams, but are easier to150

Fig. 8: Influence of source-sensor angle for source model configuration C (a) at (x,y,z) =(50,0,0.7) mm, (b) (x,y,z) = (35,35,0.7) mm, (c) (x,y,z) = (0,50,0.7) mm and (d – f) for source
model configuration D, respectively.
identify in the time domain at the beginning of the signals, in particular for observation position(x,y,z) = (50,0,0.7).
151Different CWD-diagrams are observed for source model configuration D (model of fiber
breakage) at the respective observation positions. Here a strong contribution of the S0 Lambwave mode is found for propagation direction parallel to the crack surface normal (see Fig. 8-d).
With increasing source-sensor angle, the contribution of the S0 mode decreases, since it is directed mostly along the x-axis as seen in Fig. 6. Thus the dominant contribution for (x,y,z) =(35,35,0.7) mm and (x,y,z) = (0,50,0.7) mm originates from the A0 mode.
It is worth pointing out that the same source excitation time was used in the current simulations for both source model configurations. Based on the transversal sound velocity, the source
excitation time of fiber breakage is expected to be shorter than for resin fracture. As already presented in [Sause2010] this results in a stronger contribution of the intensity of the S0 mode forthe case of fiber breakage and, consequently, emphasizes the characteristic difference between
the associated source mechanisms further.
4. Conclusions
For isotropic media the results of conventional point force couples and of the source modelof finite extent as introduced in [Sause2010] agree reasonably well. For the source model of finite extent a weak additional contribution of the A0 Lamb wave mode is found, which is attributed to the influence of asymmetry of the used tetrahedral mesh on a microscopic scale. Although the asymmetry was introduced accidentally, it is expected to reflect real experimental
conditions better than symmetric meshes, since minor deviations from perfect symmetry in theform of voids and flaws will always be present. Due to the fact that the dimensions of these inhomogeneities are within the range of the wavelengths occurring close to the source they willstrongly affect the symmetry of the source radiation patterns.
Independent of the chosen mesh, for the investigated source model configurations in anisotropic media a strong influence of the microscopic elastic properties on the modal composition of
the excited Lamb waves was found. In this context it was demonstrated, that just the change of
local elastic properties from those of resin to those of carbon fiber cause a different excitation
ratio of symmetric and antisymmetric Lamb wave modes.
In all specimens a strong dependency of the signal propagation on the orientation between
the axis normal to crack surface displacement and the source-sensor axis was found. For isotropic media the orientation of the in-plane source causes a preferential orientation of the S0
mode propagation along the direction of the crack surface normal. A superposition of the calculated fundamental Lamb wave modes shows good agreement to simulated signals.
Since for anisotropic media the dispersive propagation of Lamb waves depends on the direction, different dispersion curves were calculated for the investigated propagation angles of 0°,
45° and 90°. Similar to the isotropic propagation medium, the S0 mode propagates with preferential orientation along the direction of the crack surface normal. For the current model this direction coincidences with the fiber axes and thus results in a fast propagation of the S0 mode. For
the purpose of source identification procedures, this dependency of Lamb wave propagation on
the source-sensor angle introduces additional difficulties. For a valid identification of the microscopic source mechanism this effect has to be taken into account.

152

For further investigations of the effect of the source microstructure the geometry of the presented source model should be extended. In order to model typical failure mechanisms in CFRP,
the influence of the vicinity of the source should be investigated in more detail. In particular, amore realistic crack geometry should be considered. The influence of inhomogeneities, like additional fibers and voids in the surrounding of the source should be investigated. In addition, theinfluence of fiber-matrix interfacial strength is not taken into account in the current model. In
future attempts, this interaction should be treated accordingly.
For signals detected at distances below 50 mm, geometric spreading is the dominating contribution to attenuation. To interpret signals at longer propagation distances additional contributions to Lamb wave attenuation should be taken into account by modeling of dispersive media.
In summary, the presented simulations demonstrate that the microscopic conditions close to
the source influence the excitation of distinct Lamb wave modes significantly. For experimental
attempts regarding source identification procedures, such influences should be taken into account, since they can dominate the signal characteristics and thus superimpose effects of dispersive signal propagation.
Acknowledgments
We like to thank Professor M. Hamstad for valuable suggestions regarding simulation of
acoustic emission.
References[Castaings, 2004] M. Castaings, C. Bacon, B. Hosten and M.V. Predoi, “Finite element predictions for the dynamic response of thermo-viscoelastic material structures”, Journal of the Acoustic Society of America, 115 (3), 1125.1133 (2004).
[Choi, 1989] H.-I. Choi and W. Williams, “Improved Time-Frequency Representation of Multicomponent Signals Using Exponential Kernels”, IEEE Transactions on Acoustics, Speech andSignal Processing, 37 (6), 862-872 (1989).
[Dietzhausen, 1998] H. Dietzhausen, M. Dong and S. Schmauder, “Numerical simulation of
acoustic emission in fiber reinforced polymers”, Computational Materials Science, 13, 23-30(1998).
[Eaton, 2008] M.J. Eaton, R. Oullin, K.M. Holford and C.A. Featherston, “AE wave propagation and novel source location in composite plates”, 28th European Conference on AE Testing,Krakow, Poland (2008)[Freitag, 1997] L.A. Freitag and C. Ollivier-Gooch, “Tetrahedral mesh improvement using
swapping and smoothing”, Numerical Methods in Engineering, 40 (21), 3979-4002 (1997)[Giordano, 1999] M. Giordano, L. Condelli and L. Nicolais, “Acoustic emission wave propagation in a viscoelastic plate”, Composites Science and Technology, 59, 1735-1743 (1999).
[Green, 1995] E. R. Green, “Acoustic emission sources in a cross-ply laminated plate”, Composites Engineering, 5, 1453-1469 (1995).
[Green, 1998] E.R. Green, “Acoustic Emission in Composite Laminates”, Journal of Nondestructive Evaluation, 17 (3), 117-127 (1998).
153

[Hamstad, 2002] M.A. Hamstad, A. O’Gallagher and J. Gary, “A Wavelet Transform Applied To
Acoustic Emission Signals: Part 1: Source Identification”, Journal of Acoustic Emission, 20, 3961 (2002).
[Hamstad, 1999] M.A. Hamstad, A. O’Gallagher and J. Gary, "Modeling of Buried Acoustic
Emission Monopole and Dipole Sources With a Finite Element Technique", Journal of Acoustic
Emission, 17 (3-4), 97-110 (1999).
[Lamb, 1917] H. Lamb. “On Waves in an Elastic Plate”, Proceedings of the Royal Society of
London Series A, Containing Papers of a Mathematical and Physical Character, 93, 114-128(1917).
[Lysak, 1996] M. Lysak. “Development of the theory of acoustic emission by propagating cracks
in terms of fracture mechanics”, Engineering Fracture Mechanics, 55 (3), 443-452 (1996).
[Nieuwenhuis, 2005] J.H. Nieuwenhuis, J. Neumann, D.W. Greve and I.J. Oppenheim, “Generation and detection of guided waves using PZT wafer transducers”, IEEE Transactions Ultrasonics, Ferroelectrics and Frequency Control, 52, 2103-2111 (2005).
[Ohtsu, 1984] M. Ohtsu and K. Ono, “A generalized theory of acoustic emission and Green's
function in a half space”, Journal of Acoustic Emission, 3, 27-40 (1984).
[Ohtsu, 1986] M. Ohtsu and K. Ono, “The generalized theory and source representation of
acoustic emission”, Journal of Acoustic Emission, 5, 124-133 (1986).
[Parthasarathy, 1994] V.N. Parthasarathy, C.M. Graichen and A.F. Hathaway, “A comparison oftetrahedron quality measures”, Finite Elements in Analysis and Design, 15 (3), 255-261 (1994).
[Prosser, 1999] W.H. Prosser, M.A. Hamstad, J. Gary and A. O’Gallagher, “Finite Element andPlate Theory Modeling of Acoustic Emission Waveforms”, Journal of Nondestructive Evaluation, 18 (3), 83-90 (1999).
[Sause, 2010] M.G.R. Sause and S. Horn, “Simulation of acoustic emission in planar carbon fiber reinforced plastic specimens”, Journal of Nondestructive Evaluation, 29 (2), 123-142 (2010).
[Sause, 2010b] Markus G.R. Sause and Siegfried Horn, “Influence of Specimen Geometry on
Acoustic Emission Signals in Fiber Reinforced Composites: FEM-Simulations and Experiments”, Conference Proceedings: 29th European Conference on Acoustic Emission Testing, Vienna, Austria (2010).
[Vallen, 2010] Vallen Systeme GmbH (Munich, Germany), Aoyama Gakuin University (Tokyo,
Japan). AGU-Vallen Wavelet (2010).
[Zeyde, 2010] R. Zeyde, “Notes on orthotropic Lamb waves”, Technion - Israel Institute ofTechnology, http://mercurial.intuxication.org/hg/elasticsim/ (2010).

154

ACOUSTIC EMISSION EVENT IDENTIFICATION WITH SIMILAR
TRANSFER FUNCTIONSFRANZ RAUSCHER
Vienna University of Technology, Institute for Engineering Design and Logistics
Engineering, Getreidemarkt 9, 1060 Vienna, Austria
Abstract
A special similarity measurement for acoustic emission (AE) events based on sampled signals from multiple channels was developed. Data from pairs of channels is processed in a waythat events with different source functions, but the same source mechanism and transfer functions show large correlation. The evaluated X22-correlation can be used for clustering and subclustering. Additionally, a double time difference is evaluated, which can be used for double
difference location [1] also called relative location [2]. An additional application of this similarity measurement is the comparison of measured signals to the ones calculated by simulations.
Keywords: Cross-correlation, clustering, relative location, double difference location
Introduction
Many methods exist for the evaluation of AE data [3], and clustering of events is an important task. Techniques applied in seismology [2, 4] are also interesting for the evaluation of AEdata, but one has to consider that dispersive plate waves are important in AE. We consider here asimilarity measurement based on sampled signal data, which can be used for clustering, but also
for relative location processing, as used in seismology.
Mathematical Model
The model (Fig. 1) consists of a body with a number of AE (or seismic) sensors (SENj)
mounted at fixed positions. Transient AE signals from micro-fracture or other natural sources(Ei) are acquired by the AE equipment with sensors, preamplifiers and filters.
The wave initiation, wave propagation, and the measurement of the surface movement aremathematically described as follows:(1)source function for the event Eitransfer function from the source function si(t) to the sensor SENj
transfer function of sensor (SENj), amplifiers, filters and acquisition equipment
signal function at sensor SENj from event Ei*

convolution operator.

The model is based on the following assumptions:• Acquired signal data aij can be separated in a way that every aij is caused by one single
event. This means that the time difference between events Ei must be large enough thatsignals from different events do not overlap.
J. Acoustic Emission, 28 (2010)155© 2010 Acoustic Emission GroupFig. 1: Basic model for wave transfer.
••

The signals aij acquired at different sensors SENj caused by one event Ei can be grouped.
The signals aij acquired at different sensors SENj caused by one event Ei have a common.
source function•

The relation of the source function
to the acquired signals can be described by linear differential equation and can, therefore, be described by convolution with transfer).
functions (Events with similar transfer functions
A pair of events (E1 and E2) and the measured signals from these events at two sensors (SEN1and SEN2) are investigated (Fig. 2).
We search for a check to find out whether or not the transfer functions to the considered sensors (u1j and u2j) are (almost) the same for the considered two events - The source functions may
be different. Such a similarity is expected if the source orientation and mechanism of both events
are the same, and the events are located close to one another (). To allow for local
shifts dx12, differences in the time delays
andshould not destroy the similarity:(2)(3)
The time differenceis the difference of the delays in the transfer functions from thetwo source functions to the first sensor andis the same difference to the second sensor.
In AE measurements only the acquired signals aij, not the source functions, are available forchecking similarity. Using the signals from one sensor only does not allow to “cancel” the source
function. Ratios of the acquired signals at one channel to the one at another channel appear to bepromising, because in a fraction the parts, which are identical for the numerator and the denominator are cancelled. The problem with fractions is that here instead of division, deconvolution
operation have to be used, which is impractical. Therefore, another approach, based on convolutions of signal functions, is chosen as follows:156

Fig. 2: Two similar events.
(4)
In this equation, * is convolution and (t – dt1-2, 1-2). at the right side means that the function
resulting from convolution (a12 * a21) is delayed by a time dt1-2, 1-2. Equation 4 is fulfilled if Eq. 2,
Eq. 3 and a few simple conditions are fulfilled. To show these conditions, the model for the wave
transfer given in Eq. 1 is inserted in Eq. 4:(5)(6)
Rearranging Eq. 5 leads to Eq. 6, where we can see the following:• The convolution (s1 * s2) appears on both sides so that the similarity even holds for different source functions s1 and s2. The important condition is that the signals acquired fromone event at different sensors have a common source function.
• The transfer functions of sensor and acquisition equipment arise also on both sides. If thesame sensors and equipment are used for both events, sensor and equipment transfer
functions of the two channels may be different without destroying similarity in Eq. 4.
Another case, which is not covered by the notation used here, may be useful when measured
signal data has to be compared to simulated ones: If channels 1 and 2 have the same transfer
function e1 = e2 (equivalent sensors, filters, and equipments), different transfer functions could be
used for event 1 and for event 2. The data a11 and a12 (event 1) could be a measured measurement
signal and a21 and a22 (event 2) a calculated surface movement.
Quantification of similarity (X22-correlation)
When applying Eq. 4, the left and the right hand sides are never exactly equal. Therefore, aquantitative measurement of similarity is necessary. In this investigation the largest success wasreached by taking the maximum of a normalized cross-correlation function. Therefore, the sampled signals aijk (k indicating sampled signals) are used, and the left and the right sides of Eq. 4
were evaluated:
(7)
(8)

157

The normalized cross-correlation function is evaluated in the following way:(9)

For the normalisation in Eq. 9, it is assumed that the mean values of the convolution functions c2211k and c1221k are zero. The set K of integers, for which the sums are evaluated, has toinclude the beginning of the transient signal. If the whole time window with nonzero signal
caused by the corresponding event is acquired and usable, Eqs. 7 to 9 can easily be evaluated in
the frequency space. If this is not the case (which was the case for the data used in this paper),for each specific Δk, the set K must be the same for the numerator as for the dominator. For incomplete signals, consistent evaluation of the equations was done in time space. This time consuming option was chosen here, being aware that further development is necessary to reduce
calculation time.
The similarity is now quantified as the maximum of this normalized cross-correlation function:
(10)
(11)
Because of the crosswise convolution of the signals from two events and two channels, thisis called X22-correlation. Additionally, the time shift
type of similarity measurement
for the maximum the cross-correlation function
difference, which corresponds to the double time

, is evaluated. This double time difference can be used for double difference

location calculations [1], also called relative location [2].
Simple cross-correlation between measured signals
A simpler way of comparing the signals from different events is to build the maximum of thenormalized cross-correlation function (Eqs. 9-11) directly for the signals acquired at one channelj (a1j and a2j) instead of applying it on the convolutions c2211k and c1221k. In this way, one gets asimilarity measurement for each channel (instead of each channel combination) and differential
travel times for earthquake relocation are sometimes evaluated [5]. The maxima of these normalized cross-correlation functions are called simple correlation
and used for comparison inthis paper.
Applying the Similarity Measurement on Different Artificial Sources
For testing the introduced similarity measurements, artificial sources with equal direction of
stimulation force were applied on a simple rectangular steel plate (Fig. 3a). Four resonant AEsensors (Vallen VS150) with a resonant frequency of 150 kHz were attached on the plate andconnected to a Vallen AMSY5 equipment. For the first two artificial source types (see Table 1),
a VS150 sensor was electrically stimulated by two different signal forms. Additionally, pencillead breaks (PLB) of 0.3 mm and hardness 2H were used directly and via a center punch (Fig.
3b).
158

a)
b)
Fig. 3: a) Arrangement for simple tests; steel plate with thickness of 6 mm; b) Pencil lead break
via centre punch.
Table 1: Tested event types.
Description
PU1 Piezoelectric transducer VS150 with broadband voltage pulse (Vallen internal pulser)
PU2 Piezoelectric trans. VS150 with 110 kHz narrow-band voltage pulse (function generator - sine-function windowed by a sine-function with 10 oscillations)
PL1 0.3-mm pencil-lead break, hardness H2 on surface
PLK 0.3-mm pencil-lead break, hardness H2 via a centre punchFirst, all the artificial sources were applied at point P1 (Fig. 3a). The signals were acquiredwith a sample frequency of 5 MHz, and 2056 samples were used (Fig. 4). Selected X22correlations and simple cross-correlations are shown in (Table 2). Here the X22-correlations() are given for all sensor combinations. At the left side mean values () are given.
In the line below the evaluated double time differences (listed. For comparison simple cross-correlations (corresponding to) are), are given for each sensor.

In the case of the pulsar, a common source function for each event exists, so perfect X22correlation is shown for different electrical sources at the same pulsar. Also large X22correlation was found for the other combinations of source types. The X22-correlation of PU1
vs. PL1 at sensor combination 1-3 shows a double time difference, which is invalid, indicating that a check of the evaluated time differences is necessary. The simple cross-correlations() are small in these cases because events with different source functions are compared.
The mean values of these correlations are summarized in Table 3. Also correlations of thesame type of source (repeated experiments) are given. Additionally, one source 70 mm from P1
is included. The simple cross-correlation shows only large values (>0.9) if source location andsource type are the same. For different source types or larger distances, the values are between
0.2 and 0.5 (0.59). In the case of X22-correlation, large values are seen also if the source functions are different. Sources with larger distances (different transfer function) from one anotherhave X22-correlations from 0.6 to 0.83.
159

Fig. 4: Signals (voltage in [mV] vs. time in [µs]) of tested event types at sensor 2.
Dependence of the Similarity Measurement on the Distance between Sources
For relative location processing based on the evaluated double time differences, it is important to know the behavior of the X22-correlation if small distances arise between the sources.
Therefore, some PLB were performed in different distances from P1 (direction to sensor 4 – Fig.
3a). In Table 4, the X22-correlations between sensors 2 and 4 were listed, showing high values
and appropriate double time differences up to a distance of 5 mm. This corresponds to a double
difference in the propagation distances of 10 mm.
Table 2: X22-correlation for different combinations of selected event types at point P1.

PU1
vs.
PU2
PU1
vs.
PL1
PL1
vs.
PLK

1-2
0.999
[µs]

[µs]

[µs]

0

Sensor combination k-l
1-3
1-4
2-3
2-4
3-4
0.997 0.997 0.9995 0.9993 0.9992
0

0

0.2

0.2

Mean
value
0.999

0

SEN1: 0.30; SEN2: 0.24; SEN3: 0.13; SEN4: 0.18

0.21

0.98

0.94

0.97

0.82

0.86

0.97

0.92

0

-6.4

0.6

0.8

0.8

-0.2

SEN1: 0.74; SEN2: 0.65; SEN3: 0.61; SEN4: 0.68

0.67

0.98

0.97

0.95

0.95

0.98

0.97

0.97

-0.2

-0.4

-0.4

-0.6

-0.6

0.2

SEN1: 0.64; SEN2: 0.60; SEN3: 0.56; SEN4: 0.54

160

0.59

Symbolmean

Table 3: Summery of mean values X22-correlations and simple cross-correlations for different
event types at point P1 and 70 mm from this point.
PU1
PU1

PU2

PL1

PLK

PL1 – 70mm

0.999
0.21

0.92
0.67
0.99
0.52
0.99; 0.97
0.88; 0.96

0.95
0.44
0.995
0.51
0.97
0.59
0.995; 0.998
0.96; 0.992

0.64
0.34
0.83
0.43
0.58
0.45
0.69
0.38
0.98
0.95

PU2
PL1
PLK
PL1 – 70mm

Table 4: X22-correlation for OP1 to different event types at different distances.
Window 1
[µs]

2 mm
0.99

5 mm
0.92

10 mm
0.76

15 mm
0.73

-2.0

-4.2

35

-5.2

Applying Relative Location with PLB (double difference location)
Pencil lead breaks were performed on the plate to form the letters “AE” (Figs. 3a and 5a).
For the measured signals, X22-corrrelations and the corresponding double time differences were
evaluated. Based on this data, a simple least-square fitting algorithm, with the center of the cluster as input value, was used for the evaluation of relative location. When using the signals fromone event at each point no recognizable pattern was received. Extending the set by a second
event at each point leads to a recognizable pattern (Fig. 5b).

a)b)Fig. 5: Relative location of pencil lead breaks; a) pattern of pencil lead breaks; b) results from
relative location processing161

Conclusions
A similarity measurement called X22-correlation, which is able to group source according tothere transfer function, was developed. With artificial sources, it could be shown that events atthe same location and with the same direction of stimulation force, but with different source
functions, show large X22-correlation. This X22-correlation can be used for clustering and subclustering. The evaluated double time differences were used for double difference (relative) location processing.
At present, it is not known which type of source mechanism show this type of similarity. For
some applications, development is necessary to increase the maximum local distance between
events for recognizing similarity. When large numbers of events have to be processed, development for decreasing computer load is necessary.
References
1. Waldhauser F., Ellsworth W. L.: A Double-Difference Earthquake Location Algorithm:
Method and Application to the Northern Hayward Fault, California. Bulletin of the Seismological Society of America, 90 (6), (2000) 1353–1368.
2. Young R. P., Collins D. S., Reyes-Montes J.M., Baker C.: Quantification and interpretation ofseismicity. Int. J. of Rock Mechanics & Mining Sciences 41 (2004) 1317-1327.
3. Ono K.: Structural Integrity Evaluation Using Acoustic Emission. J. Acoustic Emission, 25,(2007) 1-20.
4. Brückl E., Mertl S.: Seismic Monitoring of Deep-Seated Mass Movements. Vortrag: Interpraevent, Niigata, Japan; 25.09.2006 - 29.09.2006; in: Disaster Mitigation of Debris Flows,
Slope Failures and Landslides, Universal Academic Press, Inc. Tokyo, Japan, (2006), S. 571 580.
5. Menke W.: Using waveform similarity to constrain earthquake locations. Bulletin of the Seismological Society of America, 89 (6), (1999) 1143–1146.

162

ANALYSIS OF FRACTURE RESISTANCE OF TOOL STEELS BY
MEANS OF ACOUSTIC EMISSIONEVA MARTINEZ-GONZALEZ 1, INGRID PICAS 2, DANIEL CASELLAS 2,3
and JORDI ROMEU 1
1
Laboratory of Mechanical and Acoustical Engineering (LEAM), Universitat Politècnica deCatalunya (UPC), EUETIB c/ Urgell 187, 08036 Barcelona, Spain; 2 Department of Material
Technology, CTM-Technological Centre, Av. Bases de Manresa 1, 08242 Manresa, Spain;3
Department of Materials Science and Engineering Metallurgy, Universitata Poltècnica de Catalunya (UPC), EPSEM, Avda Bases de Manresa 61, 08242 Manresa, Spain
Abstract
The automotive manufacturers currently use advanced high-strength steels (AHSS) to produce lightweight vehicles with increased safety performance. While showing excellent strengthto-weight ratios, AHSS have several limitations due to the high loads required in cold forming
and cutting processes, which lead to accelerated wear and premature fracture of tools. Thus, newtool materials with improved mechanical behavior ought to be developed with regard to the tool
failure mechanisms. The aim of this work is to shed light on the fracture mechanisms acting intools (i.e. crack nucleation and propagation) applying the acoustic emission (AE) technique.
Bending tests using two different tool steels were monitored in order to establish a relationshipbetween AE signals and fracture events.
Keywords: Carbides, microstructure, advanced high-strength steels, AHSS
Introduction
The utilization of advanced high-strength steels (AHSS) in structural automotive components
has been broadened in the past few years to satisfy the stronger regulation towards low CO2
emissions and high safety performance of vehicles. The high fracture strength of AHSS (from
600 to around 1500 MPa) enables to manufacture parts with high crash-resistance and lightweight. Conversely, the high yield stress of AHSS is a main inconvenience during cold forming
and cutting processes, since the high loads, which are required cause accelerated wear and premature fracture of tools [1]. Thus, the development of new tool materials combining high wear
resistance and fracture toughness is required to fully exploit the potential of AHSS. In thisframework, proper tool-steel microstructural design is needed, taking the influence of the micro
constituents on the fracture event into account. The size, shape and distribution of the primary
alloy carbides embedded in the matrix, as well as their chemical composition and mechanicalproperties, are being studied to optimize the mechanical response of tool steels [2].
A detailed examination of conventional AHSS forming tools (i.e. tools employed in cutting,drawing, stamping, bending and profile rolling) has permitted to identify that fatigue is one of
the most common mechanisms involved in tools failure. Fatigue initiation sites are usually related to the presence of broken primary carbides, from which cracks start to propagate under theapplied stress fields [3, 4]. Thus, an increase of carbides mechanical properties is expected toraise fracture and fatigue resistances of tool steels. This is why Casellas et al. [2] used nanoindentation techniques to determine the mechanical properties (i.e., hardness, Young’s modulus
and fracture toughness) of primary carbides embedded in several tool steels. However, as shownby Picas et al. [3], the fracture strength of carbides not only depends on their mechanical properJ. Acoustic Emission, 28 (2010)163© 2010 Acoustic Emission Groupties, but also on their shape, arrangement and presence of internal defects. Accurate data on the
primary carbides fracture strength in tool steels will bring valuable knowledge to light, concerning the global mechanical response of tools.
The obtained results were promising, showing the applicability of AE to the characterizationof fracture mechanisms in tool steels. It is well known that AE can be used as a non-destructive
method for testing material degradation. AE is based on real-time detection of elastic waves,generated in a volume of stressed material as a result of the micro energy emissions induced indifferent internal dynamic micro mechanisms (i.e. plastic deformation, movements in edge dislocations, sliding, grain rotation, micro fractures of inclusions, appearance or propagation of cracking, etc.). The first work showing the application of AE in tool steels was carried out by Fukaura
and Ono [5]. They used AE to determine the stress level at which carbides start to break. Fukaura
and Ono showed the relationship between the characteristic AE wave parameters and material
damage evolution. However, as these authors indicated, the use of relatively low-frequency sensors (375 kHz) might have had introduced some limitations that could have been avoided by theapplication of higher resonance frequency detectors. That is why in the present work, 700 kHz
sensors are employed. Yamada and Wakayama [6] related the signal types registered in a bending test to their predominant frequency, and correlated continuous low-frequency signals to plastic deformation events, while sudden high-frequency signals to fracture events.
Aimed at better understanding the role of the microstructural constituents (primary carbides
and metallic matrix) in the fracture mechanisms of tool steels, this work is focused on the application of AE in monotonic bending tests to determine the stress levels, at which carbides start tobreak.
Experimental procedure
A. Specimens
Two commercially available tool steels were selected: DIN 1.2379 (equivalent to AISI D2)
and a tool steel named as UNIVERSAL (developed by ROVALMA S.A.). The chemical composition of both can be found in [2]. The DIN 1.2379 tool steel shows a ledeburitic microstructurewith primary carbides formed during solidification by the eutectic reaction ⇒ γ + M7C3 [7]. In
UNIVERSAL steel two types of primary carbides are present, one type is M7C3 but shows higher
vanadium and tungsten content than those found in DIN 1.2379, and the second type is vanadium-rich MC and shows increased hardness and toughness values compared to the other types.
Properties of these carbides can be found in reference 2. Carbides in DIN 1.2379 have elongatedshapes and are dispersed in the matrix forming bands along the forging direction. In the meanwhile, primary carbides of UNIVERSAL steel are more equiaxed and homogeneously distributedin the matrix [3].
Prismatic specimens with dimensions 50 x 8 x 6 mm were extracted from forged billets, withthe longitudinal axis parallel to the forging direction. The obtained samples were heat treated by
quenching in oil and tempering up to a hardness of 60 - 61 HRC (heat treatment schedule can befound in [2]).
B. Monitored AE-bending test.
A three-point bending test was used to evaluate the mechanical behavior of specimens. The
surface exposed to tensile stress was ground and polished, and the edges were rounded in order164

to avoid stress concentration effects. Tests were carried out in a universal testing machine in air
at room temperature and the load rate was fixed at 1 mm/s.
Two small magnetic AE sensors were attached at both ends of the specimens in order to detect the AE signals. The sensors had a fixed resonance frequency of 700 kHz (VS700D, Vallen
System Gmbh). Two pre-amplifiers with a 34-dB gain of the same brand were used (AEP4).
Signals were recorded and analyzed using the Vallen Systeme Gmbh AMSY5 analyzer. During
the measurements, digital filters of 95-850 kHz were applied.

Fig. 1: Diagram of the experimental setup.

C. Test procedure.
For each type of tool steel, several bending tests were first carried out to the breakage of thespecimen in order to define the typical AE signal pattern in the fully run test. Later, the tests
were paused upon significant changes of the AE signals. However, a maximum load of 70 % of
the material fracture strength was applied so as to prevent the sensors from being damaged as aconsequence of the sudden specimen break. DIN 1.2379 and UNIVERSAL characteristic fracture strength values were determined by Picas et al. and can be found in [3]. At each halt, thesurface subjected to tensile loading was examined by means of optical and confocal microscopy(OM and CM) to identify the potential sources in the microstructure responsible for the detectedchanges in AE signals.
Results and Discussion
The results obtained in the fully run tests are displayed in Figs. 2 and 3 for DIN 1.2379 and
UNIVERSAL respectively. They show the correlation between the applied stress and the AE
signal intensity as a function of time. Both DIN 1.2379 and UNIVERSAL steels show a similar
pattern with regard to the captured AE signals. Three different zones can be distinguished:
First zone
During the first stage of the test, AE signals are almost absent. The reason is that the material
is under elastic deformation and neither the carbides nor the matrix show fracture or plastic deformation. In other words, once the noise is filtered out, the absence of AE signals can be associated with material linear behavior. Nonetheless, the detection of a few isolated AE signals could
presumably be caused by the fracture of some carbides or a material chip off due to polishing;even though no certainty can be given at this point.

165

Fig. 2: Bending test results for steel DIN 1.239. Blue line: stress vs. time; red points: AE signal
amplitude vs. time.

Fig. 3: Bending test results for steel UNIVERSAL. Blue line: stress vs. time; red points: AE signal amplitude vs. time.
Second zone
In this zone appear the first AE signals. In the DIN 1.2379 they start at approximately 11 s
and in the UNIVERSAL at 27 s, which correspond to a stress level of 640 MPa and 1700 MPa,
respectively. The signals increase gradually in intensity and abruptly in number. During thisstage, several broken carbides can be discerned in the steel microstructures. As shown in Figs. 4
and 5 for DIN 1.2379 and UNIVERSAL, respectively, the amount of broken carbides increases
considerably with the rising stress. In some cases a small area of plastic deformation appears.
Third zone
The amounts of detected signals, as well as the cumulative energy, considerably increase.
The intensity does not increase but, in contrast, the average value shows a slight decrease. At
these stress levels, a high number of carbides are found to be fractured, the plastically deformed
areas have spread and deepened and even small cracks are observed in the matrix, as shown in
Fig. 6. At this point, the test has been stopped to prevent the sensors from damage due to thesudden specimen breakdown. As shown by Picas et al. [3], the number of broken carbides andmatrix cracks is expected to keep growing up to coalesce the ones with each others, leading to
the final fracture.
166

Fig. 4: Example of broken carbides observed by means of CM in the tensile stressed surface of
DIN 1.2379 specimens.

Fig. 5: Example of broken carbides observed by means of CM in the tensile stressed surface of
UNIVERSAL specimens.
In this study, the stress level at which carbides are found to start breaking in DIN 1.2379 is
640 MPa. This is around 20 % of the fracture strength determined for this material [3]. In thecase of UNIVERSAL, the signals that point out breakage of carbides appears from 1700 MPa,
which corresponds to approximately 45 % of the fracture strength of this material [3]. Such results can be explained by the lower fracture toughness of primary carbides in DIN 1.2379, as has
been previously evaluated by Casellas et al. [2], and the elongated shape and arrangement in167

bands (in which local stresses can be higher) of carbides in DIN 1.2379, as has been stated by
Picas et al [3].
	
  

	
  	
  	
  	
  	
  	
  	
  	
  

Fig. 6: Broken carbides, plastically deformed areas and small cracks in the matrix can be identified by means of CM in the tensile stressed surface of specimens: a) DIN 1.2379 at 2100 MPa
and b) UNIVERSAL at 2800 MPa.
This investigation is currently run in order to study other AE parameters that could bringvaluable information regarding the physical phenomena involved in the fracture event. The
analysis of waveforms to identify the sources, using their frequencies, is of special interest to
develop an efficient diagnostic system. Some of the results presented here show agreement withthose obtained by Yamada and Wakayama with Ti(C,N)-based cermets [6]. Basically, two typesof AE signals can be identified: a high-frequency burst-type signal and a lower-frequency continuous type. The first type could be attributed to the carbide micro-cracking processes while thecontinuous type could be related to plastic deformation in the matrix. Although this theory is
consistent with the results obtained in this study, additional research is required to ascertain these
correlations.
Conclusions
According to the experimental results obtained by means of coupling AE to bending tests,
and the microstructural observation of two different tool steels, the following conclusions can be
drawn:
1. AE is an appropriate experimental tool to detect carbide cracking in tool steels microstructure.
2. An experimental relationship has been established between the fracture of carbides and theobtained AE signal. The stress levels, at which carbides were broken, were determined by AE
and experimentally confirmed by microstructural inspection.
3. The tool steel with the highest broken carbide content induces the highest quantity of AE signals with more temporal dispersion.
Acknowledgments
This work has been supported partially by Department of Education and Science (Spanish
Government) by means of the program of helps to Projects of Scientific Investigation and Tech-

168

nological Development, 2007-2010, project DPI2007-66688-C02-01, and by the ACC10 under
the grant CT09-1-0006.
References
1. Advanced High Strength Steel (AHSS) Application Guidelines Version 3. International Iron
& Steel Institute. Committee On Automotive Applications, 2006.
2. Casellas, D., Caro, J., Molas, S., Valls, I., Prado, J.M., “Fracture toughness of carbides in toolsteels evaluated by nanoindentation”, Acta Materialia, 55, 4277, 2007.
3. Picas, I., Cuadrado, N., Casellas, D., Goez, A., Llanes, L., “Microestructural effects on thefatigue crack nucleation in cold work tool steels”, Procedia Engineering, 2, 1777, 2010.
4. Berns, H., Broeckmann, C., “Fracture of Hot Formed Ledeburitic Tool Steels”, Engineering
Fracture Mechanics, 58, 311, 1997.
5. Fukaura, K., Ono, K., “Acoustic emission analysis of carbide cracking in tool steels”, J.
Acoustic Emission, 19, 91, 2001.
6. Yamada, K., Wakayama, S., “AE monitoring of microdamage during flexural fracture of cermets”, Proceedings EURO PM2009- Hardmetals & Cermets, 2009, pp. 247-252.
7. Metals Handbook – Metallography, Structures and Phase Diagrams, ASM International, Vol.
8, p. 402, 1978.

169

COMPARISON OF ACOUSTIC EMISSION SIGNAL AND X-RAY DIFFRACTION AT INITIAL STAGES OF FATIGUE DAMAGE
FRANTISEK VLASIC, PAVEL MAZAL and FILIP HORT
Brno University of Technology, FME, Technicka 2, CZ 616 69 Brno, Czech Republic
Abstract
This contribution treats the evaluation of degradation processes appearing during the cyclicloading of the samples from Al alloys EN AW-2017/T4 and EN AW-6082/T6. Two NDT methods have been applied in this case, namely the acoustic emission (AE) and the X-ray diffractionused to detect the microstructural changes in the material during the fatigue damages as well as
to report the microcracks appearing afterwards. These measurements have been completed with
reports of changes to the loading (resonance) frequency coming from fatigue loading equipment.
Moreover, the article describes the records of AE signal coming from a newly developed AE
analyser. The main advantage of this analyser (compared with current AE measuring systems) is
continuous sampling and full recording of the entire AE signals. The AE detects the changes in
the material during the damage accumulation. It is a problem to identify such changes. The structure monitoring by means of X-ray diffraction assists in identifying the AE sources. The results
imply a correlation between the AE signal changes and the changes of so-called mosaic blocks of
crystal structure detected by means of X-ray diffraction.
Keywords: Fatigue; aluminium alloys; fatigue crack; damage; X-ray diffraction
Introduction
The fatigue damage is divided into several stages. It is the dislocation structure that develops
changes by cyclic hardening/softening at the beginning of the cyclic loading. During the nextstage, no material changes appear visually, but new processes proceed by continuous changes
inside the structure. This stage is usually designated as the damage accumulation. At the end of
this stage, the damage becomes localized at predisposed sites. Microcracks appear and some ofthem start to join neighboring ones producing short cracks and finally the main crack forms.
In fatigue damage research, the biggest attention is given to the description of crack initiation. It is, however, quite important to follow the material property evolution immediately preceding the appearance of the fatigue crack. The material property changes take place on the microstructural level and the current investigation methods identify them only with great difficulty.
Cyclic property degradation process of aluminum alloys is quite different from that of iron alloys. The main difference is in identifying fatigue threshold. For example, fatigue damage of
AlMg alloys proceeds even at low loading amplitudes, while fatigue threshold of ferrous materials can typically be defined. That is why a conventional fatigue limit is defined for ferrous materials, which is on the level of 108 loading cycles at least [1].
An Al alloy EN AW-2017/T4 has been used as an experimental material as test specimens.
Similar to the materials tested previously (i.e., EN AW-6082, EN AW-7075), it has structuralanisotropy due to the extruding technology [2, 3] (see Fig. 1). Chemical composition of the alloys is shown in Table 1.
J. Acoustic Emission, 28 (2010)170© 2010 Acoustic Emission GroupFig. 1 Structural inhomogeneity (orientation effects) of EN AW-2017/T4 alloy.
Table 1 Chemical composition of used alloys EN AW-2017 and 6082 (wt %).
material
element
EN AW-2017/T4

Si

Fe

Cu

Mn

Mg

Cr

Zn

Ti

Al

0.8

0.7

2.5

0.2

0.25

0.1

0.25

-

rest

EN AW-6082/T6

1.01

0.17

0.067

0.66

0.84

0.16

0.030

0.032

rest

The test specimens for fatigue tests are shown with their geometry and dimensions in Fig. 2.
These have directions LS, LT (in the extrusion direction) and TS and TL (transversal directions),
see Fig. 1. Shallow notches are used for localization of the crack starting point.

Fig. 2 Specimen geometry for the fatigue tests.
The fatigue tests have been conducted using an electro-resonance RUMUL Cracktronic
8204/160 machine working on the principle of electromagnetic resonance and loading the specimen with four point bending. The resonance frequency of the specimens was 70~90 Hz with the(bending) tensile stress of 210 MPa (strength limit = 340 Mpa).
Two piezoelectric AE sensors made by DAKEL (type MIDI, see Fig. 3 on right) have beenused for AE detection during fatigue cycling process. One has been placed near the notch bymeans of mechanical fixing lug and the other one has been glued to the specimen front. The signal coming from AE sensor is amplified by a preamplifier and sent to the measuring systems
DAKEL-XEDO and DAKEL-IPL.
Methods and Test Operating Conditions
The test procedures have been divided into 3 phases. The first one concerned the test specimens in all directions up to the fracture without AE measurements. These specimens served to171monitor the changes in structure (as produced by cyclic loading) using X-ray diffraction. The
second phase detected the AE signals throughout the whole fatigue test to the fracture of thespecimen. The system DAKEL-XEDO has been used for measurements. The main aim here wasto measure the AE signal activity during the cyclic loading when the microstructural changes andthe microcracks occur. The last phase has again measured the AE signals; however, this time weused a measuring system DAKEL-IPL with fully continuous signal recording. All measurements
have been completed with noticed resonance frequency changes of the tested sample comingfrom the fatigue load apparatus RUMUL Cracktronic.
The microstructural changes were monitored by means of X-ray diffraction at an interval of5000 cycles for EN AW-2017 and 10,000 cycles for EN AW-6082 between each measurement[4, 5].

Fig. 3 Basic parts of the Cracktronic 8204/160 machine.
Overview of Experimental Results
Figures 4 and 5 show examples of AE activity entries, completed with the diagram of loading
frequencies for both materials. Particular entries show the cyclic softening (or hardening) stage,rise and development of the first microcracks and the course of main crack propagation. The plotof the loading frequency (see Figs. 4a, 5a) indicates some changes in the period of damage accumulation but it is not very conclusive.
The creation and propagation of fatigue crack is registered unambiguously. The higher AE
activity confirms this result (see Fig. 4b, for example, between 22nd and 32nd minute). It is, however, evident that the AE signal changes also in the period between these basic phases. Thechanges of AE signal before the final period gives the chance to see the beginning rise of the firstmicrocracks and other changes of microstructure. The record of AE signal indicates well in Fig.
4b the particularly period before the course of main crack propagation (35th to 45th minute). The
period of main crack propagation we can see well in Fig. 5b (from 55th minute).
The change of AE activity is also observed before crack initiation (for example in Fig. 4b at 6
and 18 minute of loading). Classical techniques of optical observation of surface do not provide
any obvious reason for this phenomenon. On the basis of analogy with other observations, it ispossible to expect that the reason of AE activity can be for example the accumulation of energy
sufficient for loosening of dislocations captured in structural obstacles. The reason of increased
AE activity during the so-called stable mode can be also the changes of material substructure –172emphasizing of sub-seeds, mutual rotation of suitable areas in frame of individual seeds of material, etc. can appear [4, 5].

a)b)Fig. 4 a) Changes of loading frequency during a fatigue test of EN AW-6082/T6 alloy (symmetric cycle, σ a = 210 MPa, Nf = 315,500 cycles), b) AE counts rate (in log scale – multi-color),
RMS (blue), cumulative AE events (black) during a fatigue loading test of the same sample.
The changes of basic parameters of AE events (i.e., peak amplitude and rise time) during thefatigue test of aluminum alloy EN AW-2017/T6 are shown in Fig. 6.
Results of AE Signal Measured by DAKEL – IPL Analyzer
The AE signal activity has been measured during the fatigue tests of the Al alloys with analyser DAKEL-IPL. The same sensors used for DAKEL-XEDO measuring system are used. The
examples of results of AE signal measurement in the LS direction during the fatigue test under
173

a)

b)
Fig. 5 a) Plot of loading frequency changes, b) AE counts rate, RMS, cumulative AE events foralloy EN AW-2017/T6 (symmetric cycle, σa = 210 MPa, Nf = 442,000 cycles).
the bending tension 210 MPa are plotted in Fig. 7. Here, time changes of AE signal frequency inthe interval 75-400 kHz (Fig. 7b) and 460-750 kHz (Fig. 7c) are compared – these are horizontal
cuts by the 3D plot from Fig. 7a. From the record of lower frequencies (Fig. 7b), it is possible toidentify creation and propagation of fatigue crack.
Changes of structure in the period before the creation of this crack are, however, not wellvisible. On the record of higher frequencies in Fig. 7c, we can see an evident change of the character of AE signal source in the period between 6th and 15th minute of loading. We suppose thatin this period the above-mentioned changes of microstructure occur. It is difficult to reliably
identify the essence of these changes from AE signal and therefore it is necessary to use additional NDT procedures (at least at this stage of research).

174

Fig. 6 Changes of basic parameters of AE events (peak amplitude and rise time) during the fatigue test of EN AW-2017/T6 (symmetric, σa = 210 MPa, Nf = 442,000 cycles).

Fig. 7 Records of the amplitude changes on selected frequencies in time of Al alloy EN AW6082/T6 (horizontal cut of the 3D spectral map), b) 75 – 400 kHz, c) 460 – 750 kHz.
175

Figure 8 shows the changes of frequency spectra (left) and the scan of frequency domain at 5
test times chosen. From the sensor on the sample notch (see Fig. 8 right), it is possible to see thefrequency maxima moved in broader dissipation area, namely from 130 up to 400 kHz. We canalso notice the signal power exists in the higher frequency domains (650 kHz) mostly toward thetest end.

Fig. 8 Continuously sampled AE signal obtained by new analyzer DAKEL-IPL (AE sensor MIDI
– at the notch (channel 1, 10 dB).
Overview of X-ray Diffraction Analysis
X-ray diffraction analysis of loaded sample structure (Fig. 9) is based on the knowledge thatin case of loading of wrought materials the redistribution of deformation strengthening occurs,
which is caused by re-arrangement of dislocations.

a)b)Fig. 9 X-ray diffraction analysis: a) test specimen in the initial state - azimuth profile of the(200) Al diffraction line, b) results of the same test specimen after 60,000 loading cycles [4].
By X-ray examination of the aluminum alloy EN AW-6082/T6 (results of EN AW-2017/T6
are not at disposition at this paper deadline), we have found that its microstructure, as characterized by the proportion R of the large coherently scattering regions (CSR´s) (i.e., mosaic blocks or176structural cells) greater than 10 µm, does change much under repeated stress [4]. In the first stageof cyclic loading, the CSR´s due to the introduced plastic strain disintegrate. As a result, theboundary surface of the cell aggregate extends, and, consequently, its energy finally increases
enough to initiate its coarsening. The energy necessary to activate the growth of the larger blocks
at the expense of the smaller ones is supplied by further cycling. During this growth, paracrystalline distortions emerge, which gradually accumulate in the interior of the widening cells, raising
their volume energy. After next fatigue loading, the energy increases to such degree that theblocks begin to disintegrate anew. Due to this disintegration, the paracrystalline distortions relax
and the alloy energy drops [7, 8].

Fig. 10 Record of real change of x-ray K factor vs. time of fatigue loading for EN AW-6082/T6
alloy [6]In a cyclically loaded Al alloy we found correlation between the increased AE activity andthe growth of CSR’s (so-called mosaic blocks of the crystal structure) independently reflecting
X-rays (see Fig. 12) [7, 8]. Cyclic deformation, unlike unidirectional deformation, does not
spread uniformly through a grain but tends instead to concentrate cycle after cycle in preferredzones. These dislocation sinks, which eventually turn into microcracks, reduce the grain into adisoriented cell structure. This is readily proved by the so-called „diffraction imaging“ or „grain
by grain“ X-ray diffraction technique [7, 8]. The interior of a cell, being relatively free of dislocations, scatters the x-rays coherently giving rise to a diffraction spot.
From Fig. 10 it is seen that the cellular structure changes during the cyclic loading considerably. Figure 10 also shows the records of the X-ray “K coefficient” changes, which were measured during fatigue tests of four samples made from alloy EN AW-6082 (with various orientation
relative to forming direction) with same level of cyclic loading.
This K coefficient is the width of the peak of azimuth profile of diffraction line (200) of thealloy matrix at the half height of this peak; this coefficient represents the amount of disorientation of mosaic block structure. From Fig. 10 are evident the cyclical changes of mosaic blocks,
which appear at all the mentioned samples – on the given level of loading approximately in thearea 60 and 110~120 × 103 loading cycles. These changes of microstructure can be the cause for
local amplification of AE signal in the stage of damage accumulation.
177

Conclusion
The AE signal measurement during the fatigue loading of Al alloys and monitoring the structural changes in the material by means of the X-ray diffraction topography have been conducted.
These show the application of these non-destructive methods on research of the fatigue processes
in the material greatly contribute to basic knowledge. The AE methods identified the changes in
the material during the fatigue load, namely the process of accumulation of damages and the startof microcrack propagation. Detailed information of AE parameters (rise time, maximum amplitude, etc.) was obtained in this research. Unfortunately, the correlation between these parameters
cannot yet describe the changes occurring inside the material or process of damage accumulation. X-ray diffraction measurements of the evolution in tested material structure under cyclicload were made, showing changes (growth and shrinkage) in the disorientation of mosaic blocks
during the cyclic loading. The resonance frequency observances acquired by loading instrument
Cracktonic appeared also to be useful parameter on the fatigue degradation of the material.
AE brings advantageous information on macroscopic volumes of structurally heterogeneousmaterial under cyclic loading. Combining the observations of AE and X-ray diffraction enables
to extend our knowledge of fatigue processes.
Works presented in this paper have been carried out in the framework of the project ofMSMT CR nr. 1M2560471601 „Ekocenter of applied research of nonferrous metals“ - workplace Brno University of Technology, FME.
References[1] Michna S. et al.: Aluminium materials and technologies from A to Z, Adin s.r.o., Presov 2007,
Slovakia, ISBN 987-80-89244-18-8.
[2] Cerny I., Ocenasek V., Hnilica F.: Problems of fatigue crack growth in strongly anisotropic
Al-alloys, Materials Science Forum, 251-252, 2003, 61-72.
[3] Garratt M.D., Bray G.H., Koss D.A.: Influence of texture on fatigue crack growth behavior.
In: Proc. of Materials Solution Conf., Indianapolis, ASM International, 2001, pp. 151-159.
[4] Fiala J., Mazal P., Kolega M.: Cycle induced microstructural changes. In: Int. Conf. NDE for
Safety, 2007, Prague, CNDT, pp. 73-80, ISBN 978-80214-3506-3.
[5] Mazal P., Pazdera L., Fiala J.: Contribution to identification of cyclic damage development of
AlMg alloy. In: NDE for Safety, 2007, Prague, CNDT, pp. 169-174, ISBN 978-80214-3506-3.
[6] Mazal P., Vlasic F.: Applications of selected NDT procedures for more detailed identificationof crack initiation stage at fatigue tests of aluminium alloys. In Fatigue design 2009. Senlis,
France, CETIM Senlis. 2009. pp. 68 - 75. ISBN 978-2-85400-908-8.
[7] Fewster P.J., Andrew N.L.: Reciprocal space mapping and ultrahigh resolution diffraction ofpolycrystalline materials. In: Defect and microstructure analysis by diffraction, Snyder R.L.,
Fiala J., Bunge H.J. (eds.), pp. 346-364, Oxford University Press, New York, 1999.
[8] Fiala J., Kolega M.: Application of two-dimensional detectors in x-ray diffraction materials
structure analysis, Particle and Particle Systems Characterization 22, 2005, 397-400.
[9] Mathis K., Chmelik F., TrojanovA Z., Lukac P., Lendvai J.: Investigation of some magnesium alloys by use of the acoustic emission technique, Materials Science and Engineering A387389 (2004), 331–335.
178

AE SIGNALS DURING LASER CUTTING OF DIFFERENT STEELSHEET THICKNESSES
TOMAŽ KEK and JANEZ GRUM
University of Ljubljana, Faculty of Mechanical Engineering, Askerceva 6,1000 Ljubljana, Slovenia
Abstract
Laser cutting is an established industrial method for cutting of various steels in the production process. However, each laser cut poses a question of quality estimation during and immediately after the laser cutting process. The capturing and evaluating of acoustic emission (AE) signals reveals a great potential for the laser cut quality estimation. This paper presents the resultsof the AE bursts measurement after the laser cutting of steel sheets of various thicknesses using
oxygen or nitrogen as cutting gas. The method of the laser cut quality estimation described here
was applied to the austenitic stainless steel, mild steel, and structural steel. The laser cut quality,
which is related to the size of the dross and the waviness of the cut surface can be successfully
forecast based on the captured AE signals.
Keywords: Burst acoustic emission, laser cutting, PZT sensor, dross, unalloyed steel, austenitic
stainless steel
Introduction
The price of the equipment and operation costs are considerable when it comes to laser systems, but the higher cost is well justified by exceedingly faster treatment processes as well as
good dimensional accuracy and repeatability. Laser cutting quality depends mainly on the selection of the process parameters that were established with trial and error method. Changes in the
beam quality, gas flow and surface of materials can lead to reduced laser cut quality. In order toeliminate these weaknesses, the monitoring and adequate adjustments of the treatment need to beensured.
Laser cut quality is defined primarily by the condition of the laser cut surface and the presence of the re-solidified material occurring as dross at the lower cut edge. The condition of thelaser cut surface is defined to a great extent by the occurrence of striations. There are a numberof interpretations of mechanisms of striation formation. The three more important models arepresented below.
(1) Schuöcker attributed the periodic striation formation to a pulsation of the melt layer on
cutting front. The fluctuations in the laser beam energy absorption and reactive gas flow causes
thickness and temperature oscillations of the melt layer (Schuöcker 1987). The fluctuations in thelaser beam energy absorption can be influenced by the periodic nature of the laser beam or thechanges in the material’s absorptivity. The changes in the heat generated by the oxidation reaction can be the result of an oscillation in the cutting gas flow due to the turbulences in the laser
cut.
(2) The second model describing striation formations on the laser cut surfaces was introduced
by Arata, Miyamoto and Maruo and is generally more established. They defined the laser cutting
mechanism as ignition and extinction of oxidation reaction (Arata et al. 1979). The model isconvincing in explaining the formation of striations on the laser cut surface. However, certain
researchers consider it as a quality model with lacking mathematical definition (Di Pietro and
J. Acoustic Emission, 28 (2010)179© 2010 Acoustic Emission GroupYao 1995). Despite this incomplete definition of the cutting process, it nevertheless offers themost widely adopted definition of striation formation occurring on the laser cut surfaces during
the cutting processes performed on structural steels.
(3) Arata´s model was upgraded by Iverson and Powell by describing more accurately thecyclic laser cutting oxidation processes (Iverson et al. 1994). The speed of the oxidation reaction
depends on oxygen diffusion in the cutting front, which changes with time. At the beginning theoxidation velocity is high, but it rapidly reduces as the thickness of the oxide layer increases.
This means that the oxide layer thickens intensely in the initial phase and then gradually slower.
If the oxide layer is sufficiently thick, the oxygen flow can blow it off the cutting front surface
and a new oxidation cycle will begin. The most important contribution by Iverson and Powell is
an explanation of the termination of the oxidation process during laser cutting.
The waviness of the laser cut surface depends on the laser cutting parameters. Striving for amore cost-efficient laser cutting by accelerating the cutting speed increases the waviness of thelaser cut surface and even causes deterioration in striation pattern, mainly at the lower cut edge.
This in turn reduces laser cut quality. Accelerating the cutting speed also leads to an accumulation of the re-solidified material at the lower cut edge. A lower energy input at the interaction
zone caused by increased cutting speed results in lower temperature and increased viscosity ofthe melt. The portion of the oxidized melt also decreases. When the melt flows out from the cutting front, droplets form at the lower cut edge. When the forces due to gas flow cannot exceed
the adhesion forces of the melt to the sheet surface, the melt’s leftover solidifies in the form of
droplets at the lower cut edge, i.e. dross will form. The solidification of the molten material persisting at the lower edge and the cracking of the oxide layer produce AE bursts in the continuous
signal.
With the AE signals captured during and immediately after the termination of laser cutting
process, the deterioration of the laser cut surface and occurrence of dross formation at the lower
cut edge can be detected. It has been determined that captured AE signals offers good information for controlling the laser cutting process in order to ensure high laser cut quality.
Experimental Procedure
The laser cutting of steel sheets was performed using a CO2 laser system with a TEM00 laser
beam power distribution. The steels used in the process were unalloyed deep-drawing steel DC04
of 1.5-mm thickness and austenitic stainless deep-drawing steel X5CrNi18-10 of 1.5-mm thickness. Both types are commonly used in the production of automotive body parts. Oxygen wasused as cutting gas. In addition to the thinner steel sheets, structural steel St37 of 8-mm thickness
and austenitic stainless steel X5CrNi18-10 of 6-mm thickness were laser cut, using oxygen andnitrogen, respectively.
During the laser cutting process, the steel sheets were positioned on a soft rubber support toeliminate noise. For the detection of AE, a contact resonant PZT sensor was used. The sensor
measures ultrasonic waves in a frequency range between 100 and 450 kHz. The sensor was connected to an AE measuring device, via a pre-amplifier.
Laser Cutting of Unalloyed Steel with Different Thicknesses
During the laser cutting process, a turbulent flow of the cutting gas produces continuous AE
signals, in which changes of the signal amplitude and frequency can be detected (Kek et al.
180

2009). Laser cut quality depending on the laser cutting parameters can be determined from boththe continuous signals and AE bursts after the termination of laser cutting. Figure 1 shows theinfluence of the position of the laser-beam focal point regarding the sheet’s surface on the AE
signals captured during laser cutting. Due to the bent steel sheet and the straight movement of thelaser head the laser-beam focal point moves away from its ideal position, which reduces the energy density in the cutting front leads to poorer cut quality and dross formation. The solidification of the molten material persisting at the lower cut edge and the cracking of the oxide layer
produce AE bursts in the continuous signal and an increase of the amplitude value in the continuous signal.

Fig. 1. Influence of the position of laser-beam focal point during laser cutting on the AE signals.
DC04, δ = 1.5 mm, P = 430 W, O2, v = 1500 mm/min.
Figure 2 shows the changes in laser cut quality that occur with the changing of the cutting
speed with constant power. A pronounced dross growth can be detected at the lower cut edge
with deviation from ideal parameters of the laser cutting. There is no formation of secondary
striations on the laser cut surface in the cutting of the unalloyed steel sheet DC04 (δ = 1.5 mm).
Only a change in striation form at the lower cut edge can be seen with greater cutting speeds.
The reduced laser cut quality in the cutting of thinner steel sheets is attributed mainly to drossformation at the lower edge.
Striations can be divided to primary and secondary. Primary striations are generally detectedin the laser cutting of thin low-carbon steel sheets using oxygen. In the cutting of steel sheets
thicker than 2 mm, primary striations occur only in the upper part of the cut, while in the lower
part usually more random pattern of secondary striations is formed. As a rule, it can be said thatthe lower cut edge is more closely linked to the forces causing the ejection of the melt due toshear and the pressure gradient, which are connected to the gas flow (Iverson et al. 1994, Shariff
et al. 1999). Therefore, the highest quality of the lower surface can be obtained when the local
maximum of the gas mass flow rate has been reached, which is connected with a turbulence ofgas stream flowing to the cutting front.
181

v[mm/min]

P = 430 W

2500

2000

1500

Fig. 2. a) Images of dross at lower cut edge at
cutting of DC04 with different cutting speeds (δ= 1.5 mm, O2).
b) laser cut surface at v = 1000 mm/min andc) laser cut surface at v = 2500 mm/min.

1000

(a)

0.5 mm

0.5 mmv = 2500 mm/min(bad quality cut)(c)v [mm/min]

2000

2100

2200
2300

Increased surface roughness

1900

More intense striation pattern on a lower
part of the laser cut

v = 1000 mm/min(good quality cut)(b)

Fig. 3. Laser cut surfaces of the structural steel St37 for different laser cutting parameters.
182

Figure 3 shows the influence of different cutting speeds on the quality of the laser cut surface
in the structural steel sheets St37. This figure also shows characteristic pattern of primary andsecondary striations. Degradation of the secondary striation pattern at the lower part of laser cut,
and a greater surface waviness, are important indicators of laser cut quality. In this case, reduced
laser cut quality is not to be attributed to greater dross formation.
On the basis of the captured AE signals, laser cut quality level can successfully be determined in instances of dross formations at the lower cut edge and changes (degradation) of striation patterns.
When cutting is stopped, AE in the form of bursts with appertaining signal duration can be
captured. The signal duration is a time interval between the first and last transition of the absolute signal voltage value across the amplitude threshold set, i.e. 0.1 mV (40 dB). Figure 4 shows
the relation between AE bursts and laser cut quality after termination of laser cutting of theDC04 steel sheet (δ = 1.5 mm). The distance of the exponential trend line from the origin of thecoordinate system indicates laser cut quality. In comparison, Figure 5 shows AE bursts obtainedin the considerably thicker structural steel St37 (δ = 8 mm). A distinct Fe2O3 oxide layer can benoticed on the mild steel and structural steel laser cut surfaces. Different coefficient of thermal
expansion and the resultant shear stresses between the base material and the oxide layer cause
the oxide layer to crack and peel during cooling, which provokes intensive burst AE. The formation of dross or intensive striation pattern on the surface of the laser cut results in an increased
area of the oxide layer, which is reflected in the measured AE. Thicker steel sheets therefore
reveal a greater number of AE bursts. Although no significant dross formation occurs at thelower cut edge during the cutting of the thicker structural steel sheet, its quality can neverthelessbe evaluated.

Fig. 4. Amplitude distribution after termination of laser cutting of DC04 steel sheet, using oxygen.
Laser Cutting of Austenitic Stainless Steel Sheet with Different Thicknesses and Different
Cutting Gas Jets
The laser cuts obtained with austenitic stainless steel differ from those with unalloyed steels.
With stainless steels a laser-cut surface does not show pronounced striations. Surface roughness183

Fig. 5. Amplitude distribution after termination of laser cutting of St37 steel sheet, using oxygen.
of a laser cut on stainless steels results primarily from rapid solidification of a thin film of themelt flowing out during cutting. The laser-cut surface is covered by the oxide layer consisting ofa mixture of iron (Fe2O3) and chromium oxides (Cr2O3). The portions of the iron and chromiumoxides in the oxide layer are approximately equal, which indicates higher affinity of chromium tooxygen atoms in comparison to iron atoms (Powell 1993). During laser cutting the hot melt in
the cutting front is exposed to the oxygen jet. The iron and chromium atoms enter an oxidation
reaction. Greater affinity of chromium results in a higher chromium content at the outer layer ofthe melt flowing out and lower chromium content below the oxide layer. Below the oxide layer
there is a quickly solidified layer of the unoxidized substrate. This results in a re-solidified melt
holding to the substrate and the presence of dross at the lower cut edge (Fig. 6). Larger amountsof the solidified oxides and of the substrate at the cut surface and in the form of dross at thelower cut edge in comparison with unalloyed steel were confirmed also by the results of theanalysis of the AE bursts after the termination of cutting.
High-quality cuts can be produced by using pressure inert-gas jet rather than the more usual
oxygen jet. The quality is superior to laser-oxygen cutting, but production costs are up to threetimes higher. Cutting mechanism is melt shearing. The resulting cut surface on austenitic stainless steel is unoxidized. The absence of oxidation means that the cut edge will have the same
corrosion resistance as the bulk material. Also the cut edges may be welded without any postcutting preparation. Greater thicknesses of stainless steel can be cut by high-pressure inert-gas
cutting than are possible by oxygen cutting, but speeds are very low. Figure 7 shows the laser-cut
surfaces obtained in the cutting of the austenitic stainless steel X5CrNi18-10 of 6-mm thickness,using nitrogen. Although the nitrogen is not completely inert gas, this is also used in place ofinert gas. The reason nitrogen was chosen over the completely inert argon is the cost.

184

v[mm/min]
2500

2000

1500

1000

a)

b)

v
[mm/min]
2750

2800

2850

2900

Bigger amount of dross at the lower laser cut edge

Fig. 6. a) Images of dross at lower cut edge by cutting with different cutting speeds. b) Laser-cut
surface at v = 2000 mm/min. (X5CrNi18-10, δ = 1.5 mm, P = 430 W, O2).

Fig. 7. Austenitic stainless steel laser surfaces for different process parameters.
(X5CrNi18-10, δ = 6 mm, P = 2.4 kW, N2).
Figure 8 shows the amplitude distributions of AE burst signals in a period of 30 s after thetermination of laser cutting of the flat X5CrNi18-10 steel sheet. The results shown refer to cutting with a power P = 430 W and with various cutting speeds. Similarly as with DC04 steel, an
185

exponential trend line can be fitted to the amplitude distributions. In cutting with different cutting speeds but with the same power, i.e. 430 W, different qualities of the cut are obtained. The
latter are indicated also by the appertaining reference images of dross at the lower cut edge.
Similarly as with unalloyed steel, poorer cut quality will show in a larger number of bursts after
the termination of cutting. A reduced cutting speed with the power chosen permits a higher energy input into the cutting front. This results in scarcer occurrence of dross and a cut of higher
quality, which is confirmed by a displacement of the exponential trend line towards the origin of
the coordinate system in the diagram of the amplitude distribution of AE signals.

Fig. 8. Amplitude distribution after termination of laser cutting of the austenitic stainless steel
sheet X5CrNi18-10, δ = 1.5 mm, using oxygen.

Fig. 9. Amplitude distribution after termination of laser cutting of the austenitic stainless steel
sheet X5CrNi18-10, δ = 6 mm, using nitrogen.
186

Greater differences in the captured AE signals are found when laser cutting of austenitic
stainless steels is performed with nitrogen (Fig. 9). Using nitrogen as cutting gas significantly
reduces the oxidation of the laser cut surface. The whirling of the gas at the lower part of the
cutting edge induces the oxidation of a smaller section of the heated material, which results in
AE bursts even in excellent cuts. In case of dross formation, the surface of the oxide layer increases and causes the exponential trend line to move away from the origin of the coordinate
system. Comparing Figs. 8 and 9, a marked difference can be noted in the measured AE bursts
between the cutting processes using oxygen and nitrogen, even with four times greater thicknesses.
Conclusions
The following conclusions can be drawn from the analysis of the AE signals captured duringlaser cutting process:- The AE signals captured during the laser cutting process and immediately after its termination allow the estimation of laser cut quality defined by the state of the laser cut surface andthe occurrence of dross at the lower cut edge.
- The measurements confirm that this method of laser cut quality determination is applicable tounalloyed and alloy steels with different thicknesses.
- The method of the laser cut quality estimation was performed for laser cutting using oxygenand nitrogen as cutting gases.
- The laser cut quality estimation requires a prior system calibration for various types of materials and cutting gases.
The new AE monitoring technique described in this paper was developed at the Faculty of
Mechanical Engineering, University of Ljubljana, and proves to be a promising method of thelaser cutting production monitoring system.
References
Arata Y., Maruo H., Miyamoto I., Takeuchi S. (1979), Dynamic Behavior in Laser Cutting ofMild Steel; Transactions of Japanese Welding Research Institute, 8 (2), 15 - 26.
Di Pietro P., Yao Y.L. (1995), A New Technique to Characterize and Predict Laser Cut Striations; Internationa Journal of Mach. Tools Manufacturing, 35 (7), 993 - 1002.
Iverson A., Powell J., Kamalu J., Magnusson C. (1994), The oxidation dynamics of laser cuttingof mild steel and the generation of striations on the cut edge; Journal of Materials Processing
Technology, 40, 359-374.
Kek T., Grum J. (2009), AE Signals as Laser Cutting Quality Indicators; Insight 51 (3), 124 128.
Powell J. (1993), CO2 Laser Cutting; Springer-Verlag, London, pp.16 - 22.
Schuöcker D. (1987), The Physical Mechanism and Theory of Laser Cutting; The Industrial Laser Annual Handbook, Penn Well Books, Tulsa, Oklahoma, pp. 65-79.
Shariff S. M., Sundararajan G., Joshi S. V. (1999), Parametric Influence on Cut Quality Attributes and Generation of Processing Maps for Laser Cutting; Journal of Laser Applications, 11 (2),54 - 63.

187

ACOUSTIC EMISSION ANALYSIS AND THERMO-HYGROMECHANICAL MODEL FOR CONCRETE EXPOSED TO FIRE
CHRISTIAN GROSSE1, JOŠKO OŽBOLT2, RONALD RICHTER1and GORAN PERIŠKIĆ2
1
Technische Universität München, Non-destructive Testing Laboratory, cbm, Baumbachstr. 7,81245 Munich, Germany; 2 Universität Stuttgart, Institut für Werkstoffe im Bauwesen, Pfaffenwaldring 4, 70569 Stuttgart, Germany
Abstract
High performance concrete (HPC) is often used in tunneling, for columns of high raise buildings and similar structures, which require high compressive strength. However, when exposed tohigh temperature (fire) there is a strong degradation of mechanical properties of HPC and it
shows unfavorable behavior i.e., explosive spalling of concrete cover. In the present experiments
the behavior of normal strength concrete that was exposed to fire is monitored by acoustic emission (AE) technique. By the use of the AE technique damage processes in concrete can be observed during the entire fire history and therefore the detection of initiation of explosive spalling
can also be detected. The method enables the location and characterization of the micro-cracking
before failure. The paper describes the proposed concept and preliminary results of fire experiments on concrete specimens made of normal strength concrete. To support experimental results
it is also important to have a numerical model, which is able to realistically predict behavior of
concrete at high temperatures. Therefore, in the paper is also briefly discussed fully coupled
thermo-hygro-mechanical model for concrete that is implemented into a 3D finite element code.
The application of the model is illustrated on one numerical example, which demonstrates thatthe pore pressure in combination with thermally induced stresses can lead to explosive spallingof concrete cover.
Keywords: Concrete, high temperature, micro-cracking, spalling, numerical analysis
Introduction
When temperature increases for a couple of hundred of degrees Celsius, behavior of concretechanges significantly. The concrete mechanical properties, such as strength, Young’s modulus
and fracture energy, are at high temperatures rather different from the concrete at normal temperature. At high temperatures, large temperature gradients subject concrete structures to temperature-induced stresses, which cause damage. Furthermore, creep and relaxation of concretethat is due to high temperature play also an important role. One of the reasons for the complexityof the behavior of concrete at high temperatures is due to the fact that concrete contains water,which at high temperature changes its state of aggregation and can generate significant porepressure. Furthermore, the microstructure of concrete is extremely complex and at high temperatures there are chemical changes, which significantly influence overall properties of concrete.
Moreover, at high temperatures the aggregate can change its structure or it can lose its weight
through the emission of CO2, as in calcium-based stones. Consequently, the behavior of concrete
at high temperatures is strongly dependent on concrete type.
Although the behavior of concrete at high temperatures is in the literature well documented(Thelandersson 1983, Khoury et al. 1985, Schneider 1986, Bažant and Kaplan 1996, Zhang and
Bićanić 2002, Khoury 2006, Zieml et al. 2006) further experimental and theoretical studies are
J. Acoustic Emission, 28 (2010)188© 2010 Acoustic Emission Groupneeded to clarify the interaction between hygro-thermal and mechanical properties; for instance,
explosive type of failure due to spalling of concrete cover, which is typical for high performance
concrete (HPC). The main problem in the experimental investigations is that such experiments
are rather demanding, i.e. one has to perform loading and measurement at extremely high temperatures. Furthermore, such experiments can be carried out only on relatively small structures.
To better understand the behavior of concrete at high temperatures and to support the experiments, numerical analysis can be useful. However, one needs models, which can realistically
predict behavior of concrete at high temperatures. To obtain realistic predictions the calibrationof the models is needed. This can be done using acoustic emission and other non-destructive
testing techniques. In the present paper, an experimental setup is proposed in order to observe theinitial micro-cracking process and possibly explosive spalling during fire loading of concrete.
The measured data will be in future compared with numerical results in order to improve themodel. Acoustic emission (AE) technique is a well-established method for the detection of damage in concrete structures that can be used to verify and improve numerical models. In the paper
are shown some preliminary experimental results obtained by using AE technique on concrete
specimens exposed to high temperatures. Moreover, a thermo-hygro-mechanical model for concrete is discussed, which should be in future verified and improved based on the experimental
results on HPC using AE technique.
Setup and Equipment
For the preliminary fire tests one of the controlled furnaces at the material testing institute,
University of Stuttgart (MPA), is used (see Fig. 1). The furnace is equipped with test specimen at
two sides as well as at the top center of the oven as shown in Fig. 2. In the figure are marked thepositions of the AE sensors. The burner cannot be seen because it is covered by the concrete
specimen that have a size of 540 x 220 x 480 mm (side wall) and 800 x 630 x 220 mm (top wall),respectively. In the experiments the normal strength concrete was used with uniaxial compressive strength of approximately 30 MPa.

Fig. 1: Furnace stands at the MPA University of Stuttgart.
189

For the preliminary tests, a set of 24 different sensors in total (see Fig. 3a) was used including resonant, multi-resonant and broadband transducers (Grosse et al. 2003; Grosse and Ohtsu
2008; Richter 2009). After pre-amplification AE signals were recorded using three multi-channeltransient recorders. In parallel to the AE data the temperature in the furnace as well as on different positions of the surface was recorded. Additionally the specimens were monitored using an
infrared video camera observing the specimen from the outside and with a camcorder inspecting
the inner furnace chamber (Fig. 3b).

Fig. 2: 3D model of the experimental setup including the positions of the AE sensors.
(a)

(b)

Fig. 3: (a) Some AE sensors and one of the transient recorders and (b) view into the furnacechamber.

190

Measurements
Concerning AE recording, the experimental setup is critical due to the noise emitted from thefurnace. Accordingly, the number of noise signals was unusually high. However, several hundred
events were recorded showing a good signal-to-noise ratio enabling localization of events. An
example of such signals showing an event recorded with eight channels is presented in Fig. 4.

Fig. 4: Acoustic emission event recorded by an eight-channel transient recorded. The verticallines mark the signals' onset at each channel.
The AE hit rate is an equivalent for the number of recorded AE signals crossing the trigger
level previously set. A comparison of the hit rate with the temperature in the furnace is displayedin Fig. 5, which gives a first overview of the deteriorations. According to AE signals, the cracking process (damage) starts at 500°C and the number of AE hits develop with a high slope up toa temperature of approximately 750°C. After reaching this temperature the hit rate gradient isgetting lower. In this temperature range strong degradation of hydration products usually occur
in concrete (Schneider 1986).
191

Fig. 5: Hit rate and temperature evolution inside the furnace chamber.

Fig. 6: 3D localization of the AE epicenters and projection to the planes.
The AE signals were classified in a way that only events recorded by six or more channels
with localization accuracy better than 20 mm were recorded. An example of the localized eventsof one of the experiments is shown in Fig. 6. Most of the source locations are detected in thecenter of the slab that is the region of the highest temperature gradient. The accuracy of the AE
locations given by the deviations in x and z direction is shown in Fig. 7.
192

Fig. 7: Projection to the x/z plane and error bars of localization.
A reliable localization is based on the knowledge of the compression wave velocity that is
usually determined prior to the experiment. However, the temperature gradients in the slabs during the experiment caused velocity anomalies prohibiting the use of uniform velocity values.
Therefore, infrared thermography (Fig. 8) will be used in further experiments to overcome thisproblem. Moreover, the fracturing of the material at higher temperatures leads to further ambiguities due to micro-cracks that change the travel path of the waves between source and receiver.
A clustered AE event analysis using events clustered in a timely manner will be performed infuture experiments to eliminate this effect. Travel time tomography methods will enable for abetter determination of the velocity model.

Fig. 8: Infrared thermography showing the temperature gradient in a slab during the experiment.
193

The present experiments were conducted on normal strength concrete. Due to the fact that
normal strength concrete is rather porous and has relatively high permeability, no explosive
spalling was observed in the experiments. Most of damage detected by AE is due to the degradation of concrete properties at high temperatures and thermally induced stresses in concrete
specimen. However, in future experiments the technique presented here will be used to detectdamage processes in HPC before explosive spalling that takes place 4 to 10 minutes after thestart of heating (Ozbolt et al. 2008).
Thermo-hygro-mechanical Model
The present phenomenological model for concrete is a thermo-hygro-mechanical one. It is
formulated in the framework of continuum mechanics under the assumption of validity of irreversible thermodynamic. The following unknowns control the response of the model: temperature, pore pressure (moisture), stresses and strains. In the numerical model, temperature, moisture and pore pressure are coupled with stresses and strains, i.e. thermo-hygral part of the model
depends on damage of concrete. Moreover, the relevant macroscopic mechanical properties ofconcrete (Young’s modulus, tensile strength, compressive strength and fracture energy) are temperature dependent.
Coupled Heat and Moisture Transfer in Concrete
The general approach for the solution of the problem of coupled heat and mass transfer in a
porous solid, such as concrete, is well known within the framework of irreversible thermodynamic. However, there are a number of complex details; therefore, for the practical application
the problem must be simplified (Bažant and Thonguthai 1978).
After introducing simplifications and assuming for a moment that the moisture flux (J) andheat flux (q) in concrete are independent of the stress and strain, the following is valid (Bažant
and Thonguthai 1978):(1)(2)
where p = pore pressure, T = temperature, b = heat conductivity, ap/g = permeability, which is in
the present model taken as a function of temperature according to the proposal of Bažant andThonguthai (1978), with g = gravity constant.
The governing equation for mass conservation is written in Eq. (3), where w = water content,t = time and wd = total mass of water released into the pore by dehydration. In the present model,
dehydration is not accounted for. The balance of heat is written in Eq. (4), where C = mass density and isobaric heat capacity of concrete, Ca = heat sorption of free water and Cw = heat capacity of water, which is in the present model neglected.
(3)(4)
Boundary conditions at concrete surface can be defined as:
194

(5)
(6)
where αw = surface emissivity of water, αG = surface emissivity of heat, T0 and p0 are temperature and pore pressure at concrete surface and TE and pE are temperature and pore pressure ofenvironment.
The main difficulty in the above equations is the determination of the material properties.
Assuming for a moment no stress-dependency, the constitutive laws for p, w and T follow simplified suggestions proposed by Bažant and Thonguthai (1978). To describe the state of porewater in concrete, one has to distinguish between three different states: (i) non-saturated concrete, (ii) saturated concrete and (iii) transition from non-saturated to saturated concrete. For
non-saturated concrete, the pore pressure is mainly influenced by the total amount of free water
w and by the saturation water content ws, which is a function of temperature. In the present
model, a semi-empirical expression proposed by Bažant and Thonguthai (1978) is adopted.
For saturated concrete one can theoretically calculate p for given w and T by using steam tables, accounting for the porosity as the given volume available to pore water. This would, however, already for relative low temperatures yield to extremely high pore pressure, which is notrealistic. Therefore, one has to take into account the increase of the available pore space (porosity), which is mostly due to the decrease of the adsorbed water portion; i.e. the release of somewater molecules, which were chemically bound at room temperature (Bažant and Thonguthai1978). In the presented model, the pore pressure for saturated concrete is mainly controlled byporosity, which is defined as a function of temperature. Following the weight loss experiments
by Harmathy and Allen (1973), it is assumed that the amount of mass loss of concrete at certain
temperature equals to the mass of evaporated water. Assuming constant water weight density, it
is possible to calculate the change of water content in the available volume.
Except for an extremely slow change in pore pressure, the transition from non-saturated tosaturated concrete can be abrupt. For most practical situations the transition is most likely
smooth. Furthermore, an abrupt transition would cause numerical difficulties. Therefore, for relative humidity between 0.96 and 1.04, linear increase of free water content is assumed (Bažant
and Thonguthai 1978).
Thermo-hygro-mechanical Coupling
To account for the influence of temperature on the strain development in concrete, the totalstrain tensor ε for stressed concrete exposed to high temperature is decomposed as (Khoury et al.
1985, Nielsen et al. 2002):(7)
where ε m = mechanical strain tensor, ε ft = free thermal strain tensor, ε tm = thermo-mechanicalstrain tensor and ε c are strains due to the temperature dependent creep of concrete. For more details about the mentioned strain components, see Ožbolt et al. (2008).
The mechanical strain tensor
that comes into the 3D constitutive law for concrete (microplane model) is calculated as=. The mechanical strains are than used to
calculate the effective stresses increments(stress in solid phase of concrete matrix) and195

macroscopic stresses increments
2005, 2008):

from the microplane constitutive law (Ožbolt et al. 2001,(8)

where D = tangent material stiffness tensor obtained from the microplane model,= incrementof the mechanical strain tensor and= increment of pore pressure, which is calculated from the, with = increment of pore pressure. Note that
increment of volumetric pore pressure
according to definition pore pressure p is negative.
In general, the mechanical strain component can be decomposed into elastic, plastic anddamage part. The source of mechanical strain is external load, thermal strain induced stress andpore pressure in concrete. The parameters of the microplane model are modified such that themacroscopic response of the model fits temperature dependent mechanical properties of concrete(Ožbolt et al. 2005). To account for finite strains the co-rotational stress tensor together with
Gree-Lagrange strain tensor (Ožbolt et al. 2008) are used in the formulation of microplanemodel. The finite strain formulation is needed in order to investigate the influence of geometricalinstabilities of concrete layer (buckling) on the explosive type of spalling of concrete cover.
Free thermal strain is stress independent and it is experimentally obtained by measurements
on a load-free specimen. The total free thermal strain consists of temperature dilatation andshrinkage of concrete, which can be experimentally isolated (Khoury 2006). In the present
model, the temperature dependent shrinkage is a part of the free thermal strain.
The thermo-mechanical strain is stress and temperature dependent. It appears only during
first heating and not during subsequent cooling and heating cycles (Khoury et al. 1985, Khoury2006). This strain is irrecoverable and leads to severe tensile stresses in concrete structures during cooling. Temperature dependent creep strain is of the same nature as the thermo-mechanicalstrain except that it is partly recoverable. In an experiment it is not possible to isolate this strain.
For low temperature rate, which is normal case in the experiments, this strain component compared to the thermo-mechanical strain is small. Therefore, temperature dependent creep strain isneglected in the present model.
(a)

(b)

Fig. 9. Porosity (a) and normalized permeability (b) as function of crack width.

196

The thermo-hygral processes are coupled with mechanical properties of concrete. It is known
that permeability and porosity of concrete are relevant parameters that control transport processes in concrete. On the other hand, both porosity and permeability are strongly influenced bydamage, i.e. for higher level of damage, porosity and permeability increase. To account for this,permeability and porosity of concrete are assumed to be strain dependent. Following suggestions
from the literature (Wang et al. 1997, Aldea et al. 2000), the relations plotted in Figs. 9 are
adopted in the present model. Damage (crack opening) is calculated using temperature dependent
microplane model (Ožbolt et al. 2008).
Besides the constitutive law in the mechanical part of the model the equilibrium must be fullfield:(9)
where V represents volume forces (gravity loads and pore pressure).
Numerical Analysis
The strong finite element (FE) formulation described above is first rewritten into a weak FE
form and then implemented into a 3D FE code. The FE analysis is incremental and based on the
direct integration method. To assure stability of the time integration, a backward difference
method is used. Since the controlling parameters are coupled, the linear differential equations 3
and 4 have to be solved iteratively. In solving the non-mechanical part of the model, the mechanical properties of concrete (damage) are assumed to be constant. Similarly, in solving equilibrium equation, non-mechanical properties of concrete (temperature and pore pressure) are
assumed to be constant. For more detail see Ožbolt et al. (2001, 2005, 2008).
Numerical Example – Explosive spalling of concrete cover
The proposed thermo-hygro-mechanical model for concrete is employed to investigate explosive spalling of concrete cover. In the numerical example a concrete slab of infinite length islocally heated at the free surface (see Fig. 10a). The height of the segment of the slab is 150 mm
and the considered width of the slab is 480 mm (only symmetric part is analyzed). Varied areconcrete strength, porosity, permeability and moisture content. Moreover, the role of geometricalnonlinearity (instability) for the problem of explosive spalling is studied as well. To assure meshobjective results, in the mechanical part of the model the crack band method (Ožbolt et al. 2005)
is used. In the analysis eight-node solid elements are used (see Fig. 10b) assuming plane strain
condition. Except at the free concrete surface, all boundaries of the specimen are restrained in all
three directions. Note that the analysis is static, i.e. structural inertia forces are not accounted for.
The linear increase of air temperature in time is applied with a temperature gradient of 80°C/min.
Pore pressure at the surface is taken to be 1.0 kN/m2. The analysis is performed for the time period of 12 minutes (duration of heating). The initial thermo-hygro-mechanical properties of concrete are summarized in Table 1.
The concrete properties used in the present example of explosive spalling of concrete cover
were: initial permeability a0 = 10-12 m/s, humidity RH0 = 75%, initial temperature of concrete
T0 = 20°C, initial porosity n0 = 0.10. The remaining parameters were the same as listed in Table 1. The failure mode, which is typical for explosive spalling, is shown in Fig. 11. Figure 11ashows the initiation of spalling. The dark zone (maximal mechanical principal strain) shows localization of damage. After initiation, complete spalling takes place very quickly. The corre197

sponding failure mode is shown in Fig. 11b. To understand the failure mechanism, the distribution of macroscopic stresses parallel (σy) and perpendicular (σx) to the free surface, at initiation
and after spalling, are shown in Fig. 12. From Fig. 12a it can be seen that because of equilibrium
reason the stresses in direction perpendicular to the free surface are almost zero. On the other
hand, stresses parallel to the free surface are relatively high compressive stresses, which after
spalling reduce almost to zero. It turns out that these stresses are not high enough to cause buckling of concrete cover. Namely, the companion numerical analysis, in which pore pressure was
not considered, predicts almost the same macroscopic stresses but no spalling. In absence of lateral stresses, which are generated by pore pressure, there is no spalling. Therefore, it can be concluded that in the present example the main driving force for initiation of spalling is pore pressure and not strain induced stresses.
(a)

(b)

Fig. 10: Geometry of the heated concrete slab and the corresponding FE model.
Table 1 Properties of concrete used in the FE analysis.
Young’s modulus E [MPa]Poisson’s ratio νTensile strength ft [MPa]Uniaxial comp. strength fc [MPa]Fracture energy GF [N/mm]
Conductivity b [J/(msK)]
Heat capacity C [J/(kgK)]

30000
0.18
2.0
30.0
0.1
1.67
900

Mass density ρ [kg/m3]
Water/cement ratio
Saturation water content [kg/m3]
Parameter αaParameter αnSurface emissivity of water αw
Surface emissivity of heat αG

2400
0.5
100
0.0
0.375
max.
max.

Figure 13a shows the time evolution of pore pressure in the element, in which spalling is initiated. The relative high pore pressure predicted by the model is supported by a simple engineering model according to which the critical pore pressure, i.e. pore pressure that corresponds to thetensile strength of bulk material (concrete), reads: pcrit = ft (1-n)/n. For initial porosity n = 0.1,
pore pressure at initiation of spalling is pcrit = 9ft. After accounting for the reduction of tensile
strength due to high temperature, tensile strength of concrete is in the range of 1.0 to 1.5 MPa.
For this strength the critical pore pressure is in the range of 9.0 to 13.5 MPa, which is in good
agreement with the outcome of the present model.
In the present study, pore pressure, and therefore initialization of spalling, is mainly controlled by permeability of concrete. To illustrate this, the evolution of pore pressure for the initial198

permeability, which is 10 times larger than the original one, is also shown in Fig. 13a. Comparedto the original case, the maximal pore pressure is reduced by the factor of approximately 5 andtherefore no spalling takes place.
(a)

(b)

Fig. 11: a) Localization of damage (max. mechanical principal strains) at initialization of spallingand b) crack pattern due to spalling.
(a)

(b)

Fig. 12: Distribution of stresses caused by deformation of concrete before and after spalling: a)in direction perpendicular to concrete surface (σx) and b) in direction parallel to concrete surface(σy).
Figures 13a, b also show the evolution of volumetric stress, temperature, humidity and saturation pressure. In Figs. 13c, d, the results are plotted for the analyses without geometrical nonlinearity. It can be seen that the evolution of relevant quantities in the critical section (initialization element) is similar as shown in Figs. 13a, b (geometrical nonlinearity), except that in theanalysis with geometrical nonlinearity spalling initiates earlier and closer to the concrete surface.
This clearly indicates that geometrical nonlinearity increases the risk for explosive spalling.
Typical distribution of pore pressure, volumetric stresses, moisture and temperature as afunction of the depth is shown in Fig. 14. The results are plotted for time steps before and after199

spalling (dotted lines). It can be seen that the maximal moisture (over saturation) is localized in
front of maximal pore pressure. The highest pore pressure and volumetric stresses are generated
at crack initiation. After crack propagation (spalling) pore pressure and volumetric stresses
abruptly decrease. The pore pressure decreases from approximately 20 MPa at initiation of spalling to 4 MPa at spalling. The volumetric stress drops from 8 MPa to 1 MPa.
(a)

(b)

(c)

(d)

Fig. 13: Time evolution of: a) pore pressure, volumetric stress and saturated pore pressure andb) temperature and humidity in the finite element in which spalling initiates for the case withgeometrical non-linearity; c) and d) the same as a) and b) but without geometrical non-linearity(with initial parameters – a0 = 10-12 m/s, RH0 = 75%, T0 = 20°C and n0 = 0.1).
Although the present results are not directly compared with predictions of similar models,
based on the literature review (Gawin et al. 1999, 2006, Tenchev et al. 2001), it should be mentioned that the majority of models principally predict similar results. The largest discrepancy inthe predictions of various models is due to the prediction of pore pressure. For the same or similar material properties and boundary conditions as used in the present example, the predictedmaximal pore pressure in the literature varies from 1 MPa to 10 MPa (Gawin et al. 1999, 2006,
Tenchev et al. 2001). Therefore, the results of the present model must be considered as a preliminary, i.e. further work is needed to verify the model and to come up with results, which canbring more light in the mechanisms that govern explosive spalling of concrete cover. This is especially the case for the loading with higher heating rates where besides pore pressure the200compressive stresses parallel to free concrete surface together with geometrical nonlinearity may
have influence on explosive spalling.
(a)

(b)

(c)

(d)

Fig. 14: Distribution of: a) pore pressure, b) volumetric stresses, c) temperature and d) humidity,
before and after spalling, measured from the concrete surface (with initial parameters – a0 =10–12 m/s, RH0 = 75%, T0 = 20°C and n0 = 0.1).
Conclusions and Outlook
The preliminary experiments presented demonstrated that AE method can successfully be
used to investigate deterioration of concrete under fire load. Signal-based AE techniques can
produce detailed 3D picture of the fracture process during fire loading. Problems related to thismethod of the analysis are due to the large temperature gradient and the micro-cracking phenomena that both cause ambiguities of the velocity model. In future these problems will be handled
and a series of experiments testing different concrete mixes will be conducted. The main goal of
future research is to better understand explosive spalling of concrete in order to improve performance of the HPC under fire load. Moreover, future experiments based on the AE method
will be used to verify and improve the thermo-hygro-mechanical model of concrete presented
here.
201

Acknowledgements
The authors are grateful for the collaboration with unit 51320 "Fire resistance" of the Materialprüfungsanstalt Universität Stuttgart (MPA) permitting the use of the furnace stand and conducting the described experiments. Further the advice of Dr. Jürgen Frick, Dr. Markus Krüger
and Mr. Gerhard Bahr is acknowledged. The research project started January 2010 and is sponsored by the Deutsche Forschungsgemeinschaft DFG via grants OZ 10/8-1 and GR1664/7-1.
References
1. Aldea, C.M. Ghandehari, M. Shah, S.P. Karr, A. Estimation of Water Flow through Cracked
Concrete under Load. ACI Materials Journal, 97, 567-575, 2000.
2. Bažant, Z.P. and Kaplan, M.F. (1996), Concrete at High Temperatures: Material Properties
and Mathematical Models, Harlow, Longman.
3. Bažant, Z.P. and Thonguthai W. (1978). Pore pressure and drying concrete at high temperature, ASCE, J. of Eng. Mech., 104, 1059-1079.
4. Gawin, D., Majorana, C.E. and Schrefler, B.A. (1999). Numerical analysis of hygro-thermal
behavior and damage of concrete at high temperature, Mechanics of cohesive frictional materials, 4, 37-74.
5. Gawin, D., Pesavento, F. and Schrefler B.A. (2006). Towards prediction of the thermal spalling risk through a multi phase porous media model of concrete, Comput. Methods Appl.
Mech. Eng., 195, 5707-5729.
6. Grosse C., Reinhardt H.-W., Finck F. (2003). Signal-based acoustic emission techniques incivil engineering. J. of Mat. in Civ. Eng. 15 (3), 274-279.
7. Grosse, C. and Ohtsu M. (2008). Acoustic Emission Testing in Engineering – Basics andApplications. Springer publ., Heidelberg, ISBN: 978-3-540-69895-1, 415 p.
8. Harmathy, T.Z. and Allen, L.W. (1973). Thermal properties of selected masonry unit concretes, J. of Amer. Inst., 70, 132-144.
9. Khoury, G.A., Grainger, B.N. and Sullivan, P.J.E. (1985). Transient thermal strain of concrete: literature review, conditions within specimens and behavior of individual constituents,
Mag. of Conc. Res., 37 (132), 131-144.
10. Khoury, G.A. (2006). Strain of heated concrete during two thermal cycles – Parts 1,2 and 3,
Mag. of Conc. Res., 58 (6), 367-385, 58 (6), 387-400 and 58 (7), 421-435.
11. Nielsen, C.V., Pearce, C.J. and Bićanić, N. (2002). Theoretical model of high temperatureeffects on uniaxial concrete member under elastic restraint, Mag. of Conc. Res., 54 (4), 239249.
12. Ožbolt, J., Li, Y.-J. and Kožar, I. (2001). Microplane model for concrete with relaxed kinematic constraint, International Journal of Solids and Structures, 38, 2683-2711.
13. Ožbolt, J., Kožar, I., Eligehausen, R. und Periškić, G. (2005). Instationäres 3D Thermomechanisches Modell für Beton. Beton- und Stahlbetonbau, 100 (2005), Heft 1, 39-51.
14. Ožbolt, J., Periškić, G. Reinhardt, H.W. and Eligehausen, R. (2008). Numerical analysis ofspalling of concrete cover at high temperature, Comp. & Concrete, 5 (4), 279-294.

202

15. Richter, R. (2009). Einsatz der Schallemissionsanalyse zur Detektion des Riss- und Abplatzungs verhaltens von Beton unter Brandeinwirkung, Diplomarbeit, Materialprüfungsanstalt Universität Stuttgart.
16. Schneider, U. (1986). Properties of Materials at High Temperatures, Concrete, 2nd. Edition,
RILEM Technical Committee 44-PHT, Technical University of Kassel, Kassel.
17. Tenchev, R.T., Li, L.Y. and Purkiss, J.A. (2001). Finite element analysis of coupled heat andmoisture transfer in concrete subjected to fire, Numerical Heat Transfer, 39, 685-710.
18. Thelandersson, S. (1983). On the multiaxial behaviour of concrete exposed to high temperature, Nucl. Eng. and Design, 75 (2), 271-282.
19. Wang, K., Jansen, D.C. and Shah, P.S. (1997). Permeability study of cracked concrete”,
Cem. and Conc. Res., 27 (3), 381-393.
20. Zhang, B. and Bićanić, N. (2002). Residual fracture toughness of normal- and high-strength
gravel concrete after heating to 600°C, ACI Mat. J., 99 (3), 217-226.
21. Zieml, M., Leithner, D., Lackner, R. and Mang, H.A. (2006). How do polypropylene fibers
improve the spalling behavior of in-situ concrete, Cem. and Conc. Res., 36 (5), 929-942.

203

AE-SiGMA ANALYSIS IN BRAZILIAN TEST AND ACCELERATEDCORROSION TEST OF CONCRETE
MASAYASU OHTSU and YUMA KAWASAKI
Graduate School of Science and Technology, Kumamoto University
2-39-1 Kurokami, Kumamoto 860-8555, Japan
Abstract
The moment tensor analysis of AE waveforms has been developed and is available for identifying crack kinematics of a location, a crack-type and a crack orientation in a material as the
SiGMA (Simplified Green’s functions for Moment tensor Analysis) analysis. Mechanisms ofcracking can be visually and quantitatively studied at the meso-scale in concrete. Since the tensile strength of concrete is normally evaluated by the Brazilian test, mechanisms of macro-scaletensile failure in concrete are examined as the cracking process at the meso-scale using the
SiGMA analysis. Evolution of the fracture process zone under the combination of tensile andcompressive stresses is discussed. In the corrosion process of reinforced concrete, high AE activities are observed twice during the corrosion process at the onset of corrosion in rebar and thenucleation of cracking in concrete. The SiGMA analysis is applied to an accelerated corrosion
test of a reinforced concrete beam. Kinematics of corrosion-induced cracks in concrete is identified and applicability to early warning of corrosion damage in reinforced concrete structures isdiscussed.
Keywords: AE-SiGMA analysis, concrete, Brazilian test, corrosion-induced cracking in reinforced concrete
Introduction
AE source mechanisms in engineering materials can be kinematically identified by applying
the moment tensor analysis of AE signal-based methods (Grosse and Ohtsu, 2008). One powerful technique for the moment tensor analysis was developed as SiGMA (Simplified Green’s
functions for Moment tensor Analysis) analysis, by which crack kinematics of locations, crack
types and orientations are quantitatively determined (Ohtsu, 1991). The SiGMA analysis was
successfully applied to fracture tests of reinforced concrete specimens to visualize the cracking
mechanisms (Ohtsu, et al., 1998; Ohno and Ohtsu, 2007). Because the SiGMA analysis is
closely associated with AE source modeling, a relation with such force models as a dipole force
and a couple force is discussed. In addition, another relationship with linear fracture mechanics
in concrete is briefly summarized.
As applications of the SiGMA analysis, results of two experimental studies are discussed.
In the first application, the fracture process of the Brazilian test was observed. In concrete, thetensile strength of concrete is normally evaluated by this test, where a cylindrical specimen iscompressed in the diametral direction, and macro-scale tensile failure is observed. A relation
between the generation of macro-scale tensile cracks and the accumulation of meso-scale cracks
is studied. In the second application, continuous AE measurement is conducted to monitor the
corrosion process in reinforced concrete specimens. During cyclic wet-dry tests, kinematics ofcorrosion-induced cracks in concrete is identified, and an early warning for the corrosion damage
in concrete is discussed.
J. Acoustic Emission, 28 (2010)204© 2010 Acoustic Emission GroupAE-SiGMA Analysis
Although the generalized theory of AE was published (Ohtsu and Ono, 1984), AE source fora tensile crack is still mistakenly modeled by a single dipole force. In principle, AE source canbe modeled by the dislocations, which are not referred to as crystalline motions, but discontinuities of displacements in a material (Eshelby, 1973). As is well known, the moment tensor isdefined,M pq = ∫ S C pqjk nk b j (y,t)dS = C pqjk nk l j ∫ S b(y,t)dS = C pqjk nk l j ΔV .

(1)

Here, vector l is the unit vector of crack motion and ΔV is the crack volume, corresponding to theintegration of crack motion b(y,t) over the crack surface S. Since nkbj corresponds to the eigenstrain in micromechanics (Mura, 1982), the moment tensor as the product of the elastic constants
with strain is equivalent to stress as the second-rank tensor. It is noted that the physical unit is
the moment as the elastic constants [N/m2] times the crack volume [m3]. From Eq. 1, it is derived that AE source for a tensile crack is modeled by three normal components of the stresses.
These components are equivalent to three dipole forces, which are decomposed into the compensated-linear vector dipoles (CLVD) defined by Knopoff and Randall (1970) and the hydrostatic components. In the case of a shear crack, AE source for a shear crack is modeled byshear stresses, which are equivalent to double-couple forces in the equilibrium state.
In order to classify crack type, the eigenvalue analysis of the moment tensor was developed(Ohtsu, 1991). The eigenvalues of the moment tensor are to be composed of the combination of
the shear crack and the tensile crack. The decomposition leads to the shear component (X), thedeviatoric tensile component (Y), and the hydrostatic component (Z) of the tensile crack. The
last two components correspond to CLVD and the equivalent stresses to the crack volume ΔV in
Eq. 1. In the present SiGMA code, AE source with the shear ratio X > 60 % is classified as theshear crack, one with X < 40 % as a tensile crack and one with 40 % < X < 60 % as amixed-mode crack. The crack-motion vector l and the normal vector n in Eq. 1 are determinedfrom three eigenvectors. Results are visually displayed, by using a graphic software (LightWave 3D, NewTek). Three models of these cracks are illustrated in Fig. 1. Cracks are classified into three types of shear, mixed-mode and tensile, and their crack planes normal to vectors n
are illustrated by circles and directions of crack motions l are shown by arrows.

Fig. 1 Models for tensile crack, mixed-mode crack and shear crack.
It is worth noting a relationship with the modes in linear fracture mechanics. Although thecrack classification of the tensile crack and the shear crack in the SiGMA analysis was referred
to as identical to mode I and mode II in fiber-reinforced concrete (Carpinteri et al., 2007), the205treatment is not rational. Rigorously speaking, the crack classification by the moment tensor isbased on crack displacements (motions) on the crack surface at the meso-scale. In contrast, themodes in fracture mechanics represent crack propagation from the crack-tip at the macro-scale incomposite materials. In case that crack orientation θ is taken into account for crack propagation,
the modes can be calculated from the criterion on the crack extension (Erdogan and Sih, 1963),

K I sin θ + K II (3cosθ − 1) = 0 , and

(2)

θ
θ 3
cos (K I cos 2 − K II sin θ ) = K IC .
(3)
2
2 2
Here KI and KII are the stress intensity factors of mode I and mode II, respectively. KIC is thecritical stress intensity factor of mode I. Referring to the propagation angle θ as the angle between consecutive AE sources determined from normal vectors n, the extension of tensile cracks
due to volumetric expansion in concrete was studied, applying Eqs. 2 and 3 (Ohstu and Uddin,2008). It was found that the extension of the tensile cracks at the macro-scale is predominantly
governed by mode I failure, even though all the types of tensile, mixed-mode and shear cracks
were observed at the meso-scale in the SiGMA analysis.
In the SiGMA analysis, the determination of the two parameters of the arrival time and theamplitude of the first motion is essential and previously carried out by visual and hand-picking
methods. To improve these enormous efforts for the analysis, we have currently developed an
automated detection method based on AIC (Akaike Information Criteria) (Ohno and Ohtsu,
2010).
Brazilian Tests(1) Tensile strength of concrete
The Brazilian test is also known as a split-tensile test for the tensile strength of concrete.
Later, a direct tensile test was alternatively applied, because the test is conducted under compression. Currently, it is found that the difference between the strengths determined by theBrazilian test and those by the direct-tensile test is within 10 %. As a result, the Brazilian test isusually applied to estimate the tensile strength of concrete.
As is well-known in elasto-statics, stresses at the center of a disk of diameter d and unit
thickness under compressive load P in the diametral direction is known (Timoshenko and Goodier, 1970) as,
and,where σxx is the tensile stress in the horizontal direction and σyy is the compressive stress in thevertical direction. σxx is the maximum tensile stress, and stresses near loading plates are evenmore compressive than σyy in the cross-section. This implies that a tensile crack at the macroscale could start from the center of the disk, but experimental observations suggest that the crack
starts from the areas near the loading plates.
Recently, fracture mechanics has been applied to the tensile test of concrete (Akita, 1998).
It is realized that the fracture process zone is nucleated prior to final failure in the direct tensile
test. So, observation of the fracture process in the Brazilian test could provide a new insight forcracking mechanisms at the meso-scale in concrete.
206

(2) Experiments
Cylindrical specimens of 150 mm diameter and 100 mm height were made. The compressive strength of concrete at 28 days under the standard curing was 37.2 MPa and the velocity of
P wave was 4410 m/s. AE measurement was performed by employing AEWin SAMOS (PAC).
Eight sensors of 150 kHz resonance (R15I-AST) were attached to the specimen. The frequency
range was 10 kHz to 2 MHz and total amplification was 60 dB gain. Three specimens were
tested to confirm the reproducibility of results.
Prior to the test, the sensor array was determined by a simulation analysis. As shown in Fig.
2, AE sources are assumed at lattice points with 5 mm grid at five cross-sections, C1 to C5, at 5mm apart. The velocity of P wave was set to 4000 m/s and arrival times were estimated with 1µs sampling. It was found that computational errors of the source locations were the minimum
within 1.6 mm in the case of the sensor array shown in Fig. 3 and their coordinates given in Table 1.
Table 1 Optimal sensor array determined.
Sensor coordinates
Channel 1
Channel 2
Channel 3
Channel 4
Channel 5
Channel 6
Channel 7
Channel 8

x (m)
0.065
0.065
-0.065-0.065-0.0400.040
0.040-0.040

y (m)
0.025
0.075
0.075
0.025
0.000
0.000
0.100
0.100

z (m)
0.113
0.038
0.113
0.038
0.120
0.030
0.120
0.030

Fig. 2 Source locations assumed in a simulation analysis.
(3) Results and discussion
Because all results of the SiGMA analysis are similar among three specimens, those of two
specimens A and B are shown in Figs. 4 and 5. At the beginning (1st) stage, AE sources are207

Fig. 3 Cylindrical specimen and AE sensor array for Brazilian test (unit: mm).

Fig. 4 Results of SiGMA analysis in the Brazilian test (Specimen A).
observed only near two loading plates in the diametral direction. Although a few events are
identified, dominant cracking-modes are tensile and mixed mode. This suggests that tensile
cracks in the meso-scale are generated due to contact between the loading plates and the cylindrical specimen under compressive loading in the diagonal direction. In all three specimens, it
was demonstrated that AE sources were observed near the loading plates at the 1st stage.
At the 2nd stage, AE sources of all kinds are distributed near the final tensile failure-plane.
In the elevation view, AE sources are concentrated near the failure plane at the macro-scale,
while they are distributed widely at the cross-section in the side view. It is noted that no visual208

Fig. 5 Results of SiGMA analysis in the Brazilian test (Specimen B).
cracks were found at this stage in the test. In total, all events are plotted and compared with themacro-scale tensile cracks finally observed at the surface. Agreement between AE clusters andthe final failure surfaces is reasonable. At the macro-scale, tensile-type cracks are observed,
while all kinds of tensile, mixed-mode and shear cracks are identified at the meso-scale. These
results suggest that the fracture process zone is nucleated around the diagonal cross-section, andthese meso-scale cracks are coalesced to create macro-scale tensile cracks. This fact is in good
agreement with detailed observation in the direct tensile test (Akita, 1998). Thus, in the both
Brazilian test and direct-tensile test, the fracture process zone is, in advance, nucleated aroundthe final failure surface. Coalescing process of meso-scale cracks identified by the SiGMA
analysis results in the fact that the tensile strengths estimated by both tests become comparable.
Accelerated Corrosion Test of Reinforced Concrete(1) Corrosion process
In concrete structures, reinforcing steel-bars (rebars) normally do not corrode because of apassive film nucleated on the surface of rebar in concrete of high pH. Corrosion of embedded
rebars arises when a reinforced concrete structure is located in marine or salt environment.
When the chloride concentration at rebar exceeds the critical value (Peterson, 1992), a passive
film on the surface of rebar is destroyed and the corrosion is started. The electrochemical reaction continues with a supply of oxygen and water. Due to the expansion of corrosion products,
corrosion-induced cracks are generated in concrete. In order to avoid these harmful cracks, anearly warning by nondestructive evaluation (NDE) is desirable. It was reported that by applying
AE technique, micro-cracks were readily detected (Yoon et al., 2000).
According to a phenomenological model of reinforcement corrosion in marine environments(Melchers and Li, 2006), a typical corrosion loss is illustrated as shown in Fig. 6. At stage 1,
209

Fig. 6 Typical corrosion loss for steel in seawater immersion (Melchers and Li, 2006).
the corrosion is initiated. The rate of the corrosion process is controlled by the rate of transportof oxygen. As the corrosion products build up on the corroding surface of rebar, the flow ofoxygen is eventually inhibited and the rate of the corrosion loss decreases at stage 2. The corrosion process involves further corrosion loss as stages 3 and 4 due to anaerobic corrosion.
Thus, two-step corrosion losses are modeled. As physical phenomena, the periods of the onsetof corrosion in reinforcement and that of the nucleation of cracking in concrete are to be identified for inspection. So far, these stages are normally defined by the chloride threshold (Nygaard and Geiker, 2005). This is because NDE techniques presently available are marginallysuccessful for identifying the two periods.
Recently, we have demonstrated that high AE activities are observed twice during the corrosion process (Ohtsu and Tomoda, 2008). It is found that a curve of total AE hits (counts) is inremarkable agreement with the curve shown in Fig. 6. Thus, in reinforced concrete, the firsthigh AE activity at stage 1 reasonably corresponds to the onset of corrosion in reinforcement.
During stages 3 and 4, corrosion-induced cracks in concrete could be generated due to expansionof corrosion products in reinforced concrete, and the second high AE activity is observed.
Based on these findings, continuous AE measurement was conducted to monitor the corrosion process in a reinforced concrete specimen in laboratory. During cyclic wet-dry tests, the
SiGMA analysis is applied, and kinematics of corrosion-induced cracks in concrete are investigated.
(2) Experiment
For the accelerated corrosion test, reinforced concrete specimens of dimensions 100 mm × 75
mm × 400 mm were made. One deformed rebar of 13-mm diameter was embedded with 20
mm cover-thickness from concrete surface. Configuration of the specimen is illustrated in Fig.
7. The rebar was coated by epoxy except for a target area. NaCl solution was employed as
mixed-water. After the standard curing, chloride content was measured and found to be 0.175
kg/m3 in concrete volume. The compressive strength of concrete at 28 days of the standard
curing was 43.9 MPa, and the velocity of P wave was 4330 m/s.
To simulate the corrosion process in a typical seawater environment, a cyclic wet-dry test
was carried out. The specimens were submerged into 3%-NaCl solution up to the height of the210rebar in the container for a week, and subsequently taken out of the solution to dry under ambient
temperature for another week. In one specimen, AE measurement was continuously conducted,
by using AE measurement system (DiSP, PAC) and six AE sensors (R15, PAC). The sensor
array is also shown in Fig. 7. These six sensors were arranged so that the target area was reasonably covered, because the simulation analysis was difficult in reinforced concrete. The frequency range of the measurement was also10 kHz to 2 MHz and total amplification was 60 dB
gain. For event counting, the dead-time was set to 2 ms and the threshold level was set to 40
dBAE.

Fig. 7 Reinforced concrete specimen and AE sensor array for corrosion test.

Fig. 8 Distribution of ferrous ions on rebar surface.
(3) Results and discussion
During the cyclic wet-dry test, a numerical simulation predicted that chloride concentration
at rebar reached the lower-bound threshold for corrosion of 0.3 kg/m3 after 28 days. At approximately 70 days elapsed, the chloride concentration at rebar became higher than 1.2 kg/m3,
which is known as the nominal corrosion-trigger level of chloride concentration. Accordingly,
at 28 days elapsed and 70 days elapsed, rebars were removed from the specimens and inspectedby scanning electron microscopy (SEM).
Figure 8 shows SEM photographs; At left, no corrosion is identified after 28 days from thecross-sectional view, although some exfoliation of the oxide film is observed at the surface. At
28 days, only the surface of rebar is slightly corroded due to penetration of chloride ions. At 70
211

days (shown at right), rust and loss of the oxide film are clearly observed. The corrosion started
to penetrate inside rebar, nucleating corrosion-induced cracks in concrete due to expansion of
corrosion products. Thus, the growth of corrosion products is confirmed after 70 days.
In the SiGMA analysis, AE event definition time (EDT) is set to 100 µs. EDT is applied torecognize AE waves occurring within the specified time from the first-hit wave and to classify
them as part of the current event. Results of the SiGMA analysis at stage 1 and at stage 2 areshown in Fig. 9. At the stage 1 (at 28 days elapsed), only 6 AE events are determined. These
events are located mostly near the top of the specimen, as it is realized that these events are notdirectly related to the onset of corrosion. This is because only large AE sources can be analyzed in the SiGMA analysis. Some shrinkage cracks might be responsible for these events inan early age of concrete.

Fig. 9 Results of SiGMA analysis in reinforced concrete beam.
At stage 2 of 70 days elapsed, 49 AE events are analyzed by the SiGMA analysis. These
events are located, surrounding the rebar, especially at the left portion of the specimen. Because expansion of corrosion products is suggested and generation of corrosion-induced cracks
in concrete is expected, these results suggest that the final crack could progress mostly at the left
portion of the specimen. In addition, some cracks (AE sources) are located from the rebar
212

toward the bottom of the specimen, of which AE sources are mostly classified into tensile cracks
and mixed-mode cracks. These results clearly suggest that corrosion-induced cracks extend
outward from the rebar. Shear cracks were dominantly observed in the later part of the stage.
Thus, the mechanisms in the corrosion process are indicated as follow: tensile and mixed-mode
cracks start to be first generated around the rebar. Next, coalescing and connecting of thesecracks lead to the nucleation of shear cracks. This finding is useful for early warning of corrosion damage in reinforced concrete structures.
Conclusions
The SiGMA analysis is applied to the Brazilian test of concrete cylinders, and corrosion-induced cracks in a reinforced concrete beam during the cyclic wet-dry test. The following conclusions are derived.
(1) A relation between macro-scale tensile failure and nucleation of AE sources in themeso-scale is clarified in the Brazilian test for the tensile strength of concrete. In themacro-scale, tensile-type cracks are only observed, while all kinds of tensile, mixed-mode andshear cracks are identified at the meso-scale. During propagation of tensile cracks at themacro-scale, other types of AE sources of mixed-mode and shear cracks were actively identified.
Thus, nucleation of the fracture process zone is confirmed around the final failure surface. Thisis a reason why the tensile strengths estimated by the Brazilian test are comparable to those by
the direct-tensile test, although stress distributions are quite different.
(2) It is found that high AE activities are observed twice during the corrosion process, whichcorrespond to the onset of corrosion in rebar and the nucleation of cracking in concrete due toexpansion of corrosion products. In relation to the 2nd AE activity, the generating mechanismsof corrosion-induced cracks are studied in reinforced concrete. Concerning the mechanisms in
the corrosion process, it can be summarized that tensile and mixed-mode cracks start to be generated around the rebar, and coalescing and connecting these cracks, shear cracks are nucleatedat the meso-scale. This implies that the SiGMA analysis is useful for early warning of corrosion damage in reinforced concrete structures.
Acknowledgement
The research conducted was supported by Kumamoto University Global COE (Center of Excellence) Program: Global Initiative Center for Pulsed Power Engineering. To perform experiments and analyses, the assistance of technical associate, Dr. Yuichi Tomoda was valuable.
The authors wish to deeply thank the program and his support.
References
Akita, H., Koide, H. and Tomon, M. (1998), ”Uniaxial Tensile Test of Unnotched Specimens
under Correcting Flexure,” Fracture Mechanics of Concrete Structures, Aedificatio Publishers,
Freiburg, Vol. I, 367- 377.
Carpinteri, A., Lacidogna, G. and Manuello, A. (2007), ”An Experimental Study on Retrofitted
Fiber-Reinforced Concrete Beams using AE,” Fracture Mechanics of Concrete and Concrete
Structures, Taylor & Francis, London, Vol. 2, 1061-1068.
Erdogan, F. and Sih, G. C. (1963), ”On the Crack Extension in Plates under Plane Loading andTransverse Shear,” J. Basic Eng., 12, 519-527.
213

Eshelby, J. D. (1973), ”Dislocation Theory for Geophysical Applications,” Phil. Trans. R. Soc.
London, A274, 331-338.
Grosse, C. U. and Ohtsu, M. (2008), Acoustic Emission Testing, Springer-Verlag, Berlin.
Knopoff, L. and Randall, M. J. (1970), ”The Compensated Liner-Vector Dipole,” J. Geoohys.
Res., 75(26), 4957-4963.
Melchers, R. E. and Li, C. Q., (2006) Phenomenological Modeling of Reinforcement Corrosionin Marine Environments, ACI Materials Journal, 103(1), 25-32.
Mura, T. (1982), Micromechanics of Defects in Solids, Martinus Nijhoff Pub. The Hague.
Nygaard, P. V. and Geiker, M. R. (2005), ”A Method for Measuring the Chloride Threshold
Level required to initiate Reinforcement Corrosion in Concrete,” Materials and Structures, 38,489-494.
Ohno, K. and Ohtsu, M. (2007), ”Cracking Mechanisms of Diagonal-Shear Failure monitored
band identified by AE-SiGMA Analysis,” Fracture Mechanics of Concrete and Concrete Structures, Taylor & Francis, London, Vol. 2, 991-998.
Ohno, K. and Ohtsu, M. (2010), ”Crcak Classification in Concrete based on AE,” Construction
and Building Materials, 24(12), 2339-2346.
Ohtsu, M. and Ono, K. (1984), ”A Generalized Theory of AE and Green’s Functions in a Half
Space,” Journal of AE, 3(1), 124-133.
Ohtsu, M. (1991), ”Simplified Moment Tensor Analysis and Unified Decomposition of AE
Source,” J. Geophys. Res., 96(B4), 6211-6221.
Ohtsu M, Okamoto T, Yuyama S. (1998), ”Moment Tensor Analysis of AE for Cracking
Mechanisms in Concrete,” ACI Structural Journal, 95(2), 87-95.
Ohtsu, M. and Tomoda, Y. (2008) Phenomenological Model of Corrosion Process in Reinforced
Concrete identified by AE, ACI Materials Journal, 105 (2), 194-199.
Ohtsu, M. and Uddin, F. A. K. M. (2008),”Mechanisms of Corrosion-Induced Cracks in Concrete at Meso- and Macro-Scales,” Journal of ACT, JCI, 6(3), 419-429.
Timoshenko, S. P. and Goodier, J. N. (1970), Theory of Elasticity, McGraw-Hill Book Company,
New York.
Peterson K. (1992),”Corrosion Threshold Value and Corrosion Rate in Reinforced Concrete,”
Swedish Cement and Concrete Research Institute, CIB report 2.
Sause, M. and Horn, S. (2010), ”Influence of Specimen Geometry on AE Signals in Fiber Reinforced Composite: FEM Simulations and Experiments,” Proc. The 29th European Conf. on AE
Testing, [CD-ROM].
Yoon D. J., Weiss W. J. and Shah S. P. (2000), ”Assessing Damage in Corroded Reinforced
Concrete using AE,” J. Engineering Mechanics, 126(3), 273-283.

214

ACOUSTIC EMISSION INSPECTION OF RAIL WHEELSKONSTANTINOS BOLLAS, DIMITRIOS PAPASALOUROS,
DIMITRIOS KOUROUSIS and ATHANASIOS ANASTASOPOULOS
Envirocoustics SA, El. Venizelou 7 & Delfon, 14452 Metamorphosis, Athens, Greece
Abstract
The ever-increasing demand for safer and faster surface transportation such as railway imposes many challenges for the inspection of critical components such as train axes and wheels.
These components, mostly during usage, are subjected to highly complex dynamic loads and active defects may result in catastrophic failures, possibly with human casualties. Within thescopes of an R&D project, aiming to develop novel methodologies and techniques for the inspection of wheel sets, extensive acoustic emission (AE) measurements have been performed on
various trains and trams. AE sensors were mounted on the rail in order to diagnose wheel problems by monitoring the AE transferred through the rail in real-time and while the vehicles weremoving. The purpose of the trials was to investigate the usage of AE for on-line detection of defects on wheels such as flats, bearing failures and possibly significant cracks, and to establish
optimum setup parameters in this respect. The present paper presents the raw data and evaluation results from AE experiments on train and tram wheels, (both healthy ones and wheels having known defects). During measurements different AE sensors were placed on the side of therails while the railcars or trams were passing at different speeds. The effect of sensor frequencies
and placement were investigated. Multiple AE datasets, i.e., Time Driven Data (TDD), Hit
Driven Data (HDD) as well as long (>10 sec.) waveforms were acquired simultaneously. Data
analysis involved traditional AE features, source location and digital signal processing of acquired waveforms. Initial results presented highlight the different AE behavior for defective andnon-defective wheels, and indicate clearly the potential of AE as diagnostic tool. Furthermore,
results show that the availability of acquired long, continuous waveforms significantly enhanced
analysis capabilities, when combined with advanced AE DSP software and pattern recognition
analysis.
Keywords: Train wheels inspection, rail axes monitoring, long waveforms
Introduction
Inspection of rail wheels poses inherent difficulties due to accessibility issues, when thewheels are mounted on the train, but also due to the complex geometry of the wheels, their shapeand their attachments. As a result, current inspection methodologies are mainly based on dismounting the wheels and inspecting them off-vehicle, or, in the best case, on an immobile train,either on a periodic or on a need basis (e.g., see [1]), by means of localized NDT (MT, PT, UT,
EC, VT etc.). Still, however, failures occur, that occasionally lead to catastrophic accidents. The
need to identify wheel flaws at early stages and in a more efficient way, reducing maintenancecosts, has steered research efforts towards on-line wheel inspection techniques, such as vibration
and AE. The aim of such techniques is to be able to identify flawed train wheels, while the train
is in-service (moving) and the “screen out” of bad wheels for further inspection and maintenance, in a fast, effective way.
In this respect, AE [2] method has been identified as a very promising tool. Acoustic emission uses high-frequency, passive piezoelectric transducers, which are mounted on the side of the
J. Acoustic Emission, 28 (2010)215© 2010 Acoustic Emission Grouprails, in order to detect the stress waves generated by the various possible flaws of the wheel, forinstance, flats, or other circularity defects, which are impacting on the rail while the wheel isturning, or crack opening, or even faulty bearings from the axes. Since other AE sources are present, even in a healthy train (engine noise, frictional noise, banging noise, etc.) the technique, at afirst stage, usually involves acquiring the AE “signature” of a vehicle with healthy, “reference”
wheels, and comparing this to the one of a vehicle with defective wheels. The method offers significant advantages compared to other passive methods, mainly due to the high sensitivity it offers, as well as the fact that the AE transducers operate within high-frequency range (301000bkHz), eliminating to a large extent, mechanical noise. Once calibrated and standardized,
the method may be employed very effectively, for instance by mounting sensors on all rails of a
depot and “listening” to the trains or trams as they exit for their shift. Each wheel is “inspected”
as it passes from the point of the rail where the AE sensor is located, thus, within a single day, allwheels in usage may be inspected.
Acoustic emission has already been applied for the detection of rail track faults at laboratorylevel [3, 4]. However, only a limited number of field experiments have been carried out [5, 6].
The present research has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement no 218674 of SAFERAIL project [7].
The SAFERAIL consortium seeks to minimize wheel set failures by developing and successfully
implementing a novel on-line system for the inspection of wheels and axles of moving trains, anda combined ultrasonic-electromagnetic system for faster and more reliable inspection of the quality of new and old wheel sets during their production and maintenance. The present work studies
the application of AE for the on-line inspection of rail wheels, investigating various hardware
parameters such as sensor frequency, test parameters such as type and velocity of vehicles, aswell as data acquisition and analysis strategies. Results show the ability of the technique to detect
wheel flaws, while the methodology is still under optimization in terms of resources used, andanalysis time.

Fig. 1 – 56 tons ALLAN railcar with distances between wheels.
216

Instrumentation
All presented data has been acquired using systems based on the Physical Acoustics Corp.
PCI-2 AE boards [8], which have the capability of acquiring AE data with long (>10 s) waveform streaming directly to hard drive. Four different types of AE sensors have been investigated:
Physical Acoustics’ R3i (30 kHz resonant), R15 (150 kHz resonant), R30 (300 kHz resonant)and R50 (500 kHz resonant). Data was acquired by AE-Win real-time data acquisition software[9], while advanced data analysis has been performed using NOESIS, a specialized AE data
analysis and pattern recognition software [10].
For the scopes of this work three different data sets of field trials are presented, which wereacquired at three different sites, two involving trains and one involving trams. AE equipment has
simultaneously acquired TDD, HDD and waveform streams (WFS). The importance of the availability of each individual data set and its significance in data interpretation are clearly demonstrated. Additionally, advanced and versatile software tools were utilized in order to fully analyze multiple aspects of the acquired data.
Case Study - AE Monitoring of Train Wheelsets in EMEF Depot, Oporto, Portugal
Experimental Setup and Wave Propagation Study
A 56-tons ALLAN railcar (Fig. 1) was used for the AE measurements and was provided by
EMEF. On all experimental setups the sensors were placed on the external rear side of the rail,using silicon grease and supported by magnetic clamp, as shown in Fig. 2a. Pencil-lead breaks(Hsu-Nielsen sources) were performed on the rail track for attenuation and wave velocity estimation (Fig. 2b).

(a)(b)
Fig. 2 – a) Sensor placement, b) Hsu-Nielsen source positions for wave propagation study.
Sensors were installed in ways that could lead to location detection of possible wheel defects
during train passing along the rail. On the 3&1 setup, three sensors were placed on one rail on a
distance equal the half of the wheel perimeter and a fourth sensor was placed on the other rail
opposite to sensor No. 1 position (Fig. 3). The same setup was maintained for all sensor types,
e.g. 3&1 setup for R15s, the same 3&1 setup for R30s etc. in order to be to able to compare directly the sensitivity differences of each sensor type.
In order to be able to have location evidence of possible wheel defect, different location settings were investigated, using the calculated wave velocities and placing the three (3) sensors at a217

time within known distances from each other. For the specific setup the distance between sensors
was 1.8 m. Pencil-lead breaks (PLB) were performed on distances approximately 0.6 m, 1.2 m,
1.8 m and 2.4 m from sensor No. 1. Figure 4 shows the results of a location check. Y-axis shows
the sum of AE events caused by the PLB while X-axis shows the sensor position using the first
sensor as reference. It is evident from Fig. 4 that the specific location settings and/or setup are
adequate to locate four distinct pencil breaks in a 3.6 m span with high accuracy. Therefore, any
possible wheel surface defects are expected to yield similar results.

Fig. 3 – 3 & 1 Sensor setup (3 on one side and 1 on the opposite side).

Fig. 4 - Location check graph showing the real position of the PLB and the position as located by
the AE system.
Figure 5 shows the arrival times and amplitudes of a PLB signal performed at 0.6 m fromsensor No. 1, as acquired from three sensors located on the same rail track at 1.8-m distance fromeach other. The corresponding waveform as received for the sensor No. 1 at reference position isshown in Fig. 6, while Fig. 7 shows at sensor No. 2 at 1.8-m distance from sensor No. 1 position
and 1.2 m distance from the PLB source. From the sensor No. 3 at 3.6-m distance from sensor
No. 1 and 1.8-m distance from the PLB source, waveform in Fig.8 was observed. Note sensor
numbers correspond to channel numbers in Fig. 5, which shows 7 dB attenuation over 0.6-m distance from No. 1 to No. 2 and 13 dB over 2.4 m from No. 1 to No. 3.

218

Fig. 5 - Time arrivals and amplitudes of a PLB source at 0.6 m from sensor No. 1 position.

Fig. 6 – Signal amplitude in V vs. time from sensor No. 1.

Fig. 7 – Signal amplitude in V vs time from sensor No. 2.

Fig. 8 - Signal amplitude in V vs time from sensor No. 3.
AE Monitoring of Train Wheelsets
AE acquisition measurements were performed during train passes at the area where the sensors were installed. Train passes were made with different speeds from 5 to 40 km/h as reportedby the train driver and in different directions (to left or to right). In all cases the train speed was
kept constant. Three parameters were selected as most probable either to produce and/or change
the AE activity during the test runs. These are the actual speed of the train, the geometry of therailcar and the actual surface condition of the wheels.
219

AE data was analyzed using special pattern recognition software (NOESIS v.5.5). AE features like signal amplitude, duration, energy, average signal level, etc. of acquired AE signals
were compared using the different resulting data sets from:
Same Railcars - Different Railcar Speeds,
Different Railcars - Similar Railcar Speeds.
Railcar before Defect Generation – Same Railcar after Defect Generation
All comparisons were performed using data from same train direction.
Specifically for the testing of the surface condition parameter, flat surfaces were generated(Fig. 9) on the defect-free wheels by applying the emergency brakes multiple times. The AE after each emergency brake was acquired enabling the comparison of the damage accumulation indifferent states of the flat surface development. An example of raw waveform streams of 10-s
duration after seven emergency brakes is shown in Fig. 10. Here, 3 & 1 setup was used, and theside with three sensors shows slightly lower amplitude. Note increasing signal attenuation inchannel 1, 2 and 3. Since the surface of each wheel during emergency braking is accumulating
damage in the form of either newly created flat surfaces or enlargement of the existing ones,
each wheel can be characterized by a “defect status” directly dependent on the number of emergency brakes. The defect status is shown in Fig. 11 as AE events location graphs, representativeof AE signals density for 40-km/h railcar travel.

Fig. 9 - Flat defect on a wheel surface created by emergency breaking of the train. Courtesy of
EMEF.
The same data analysis was applied on location capabilities of hit-driven AE data (HDD) forall datasets. In cases where location was applied, more events appeared during defective railcar
movement. As can be observed in Fig. 12, at low (5 km/h) and medium (20 km/h) speed thenumber of located AE events is higher for the defective railcar. In contrast, at higher speed (40km/h) less AE events can be located.

220

Fig. 10 –Waveform stream of 10-s duration with 3&1 setup using 4 R50 sensors after sevenemergency brakes. Amplitude full-scale is 10.2V.
Case Study - AE Monitoring of Tram Wheelsets, Antwerp, Belgium
Experimental Setup
AE acquisition was performed on four different trams of different types (see an example on
Fig. 13a) and defect status (two with defective wheel sets and two with non-defective ones). AE
was acquired during 22 tram passes at varying speeds, in both directions. Four different types of
AE sensors were used (PAC R3i, R30, R15, R50). Due to the difficulty of accessing the rear partof the rail, no location groups could be setup and no magnetic clamps could be used for placing
the sensors. All sensors were attached on the rail using special adhesive grease (see Fig. 13b).
Depending on the different resonant frequencies of the sensors, measurements using various lowand high-pass frequency filters were performed.
221

Fig. 11 - AE events location graphs of AE signals density for 40km/h railcar speed with different
defects statuses.

Fig. 12 - AE events location graphs of AE signals density for different railcar speeds of 5, 20 and
40 km/h, before any emergency brake (clear wheelsets) and after 7 emergency breaks (defective
wheelsets).
222

R30
R50

R15
R3i

Fig. 13 – (a) Photo of a PCC vehicle that was used for the AE measurements, (b) sensor position.
Various types of analyses have been performed. These include comparison between data ofdefective and non-defective railcars, repeatability check of data acquired with same conditions(same railcar and railcar speed, same railcar direction, etc.), attempt to separate signals of different wheels by long-duration waveform analysis, filtering effectiveness check on acquired data.
However, the analysis of this particular set of data (tram measurements) focused mainly on longduration waveform processing with advanced DSP methods.
Prior to experimental measurements, signal attenuation was calculated across the rail. Placing the sensors on a steady point on the rail and using three different AE sources (Hsu-Nielsen,
and small/large metallic spherical impactors) across the rail, attenuation measurements were performed and graphs were created (Fig. 14).

Fig. 14 - Attenuation graph showing signal amplitude (in dB) vs. distance from sensor, for four
different types of sensors (R3i, R15, R30, R50), (a) using a Hsu-Nielsen source (PLB), (b) usinga small metallic spherical impactor.
223

Case Study - AE Monitoring of Tram Wheelsets
Analysis of long-duration waveforms either by visual observation or using DSP tools revealed the potential that continuous AE data offers. Observation of the overall signal acquired
during passing of a wheel above the sensor position allows the determination of the effective
monitoring time, i.e. the period of time during which the train creates detectable AE signals,
thus, allowing data collection period determination for future tests (Fig. 15). Focusing on the
waveform allows the user to observe peaks in the voltage signal, which are generated when eachaxis passes right on top of the sensor. Furthermore, the existence of flats may also be demonstrated as voltage peaks, thus, zooming on such, and performing frequency analysis on specificsections, it is possible to observe the frequency contents of specific sources, enhancing signal
interpretation. Finally, RMS or ASL processing, which basically offer a “smoother” or averaged
representation of the signal, allows the user to conveniently perform time-based calculations,such as observe the time difference of the axes and bogies as they pass from each sensor and allow macroscopic observation of signal level differences, which may be periodic, indicating transient/banging AE from flats, or continuous, indicating possible bearing problems.

Fig. 15 - AE waveform (top) and RMS graphs (bottom) acquired by a PAC R50 sensor during
passes of a railcar (tram) with 3 bogies (6 axes) and a flat on the first axis.
AE acquisition measurements were performed during 22 tram passes at the sensor installation
point (see e.g., Fig. 16). Trams were passing with different speeds from 15 to 40 km/h, as reported by the train drivers, and different directions (back or front). In all cases the tram speed
was kept constant, while passing on the sensor position. Data was analyzed using patternrecognition software (NOESIS v.5.5). AE features like signal amplitude, duration, energy, average signal level, etc. of acquired AE signals were compared using the different data sets from:
Same Trams - Different Trams Speeds,
Different Trams - Similar Trams Speeds.
Tram before Defect Generation – Same Tram after Defect Generation
It was expected that tram passes with same characteristics (same tram, same speed, same direction) would result in similar signals and waveforms.
Following the above measurements and preliminary analysis of the acquired data, observations made on the conditions of the experiments produced recommendations below:
224

(1) Location groups have to be setup by placing AE sensors on known distances along the rail.
The reporting of vehicle speed has to be done exactly. Signals acquired with similar but notequal speeds may result in inconsistent results.
(2) As noted on the last experimentation in Oporto Portugal, the defects have to be created on thewheels surface by controlled damage and their number, size and position on each wheel has to be
known on-site. They also have to be characterized as “accepted” or “not accepted” according tointernational or local (train company) standards. Their position on each wheel has to be known,in order to be able to calculate exactly the position of the contact point between the flat defectand the rail, for location purposes.
(3) A trigger, in combination with known train speed, has to be used on the AE system, in order
to be able to synchronize the train position with the acquired data.

(a)

(b)
Fig. 16 - AE waveforms acquired by (a) R15 sensor and (b) R30 sensor during passing of thesame defective tram at 40 km/h. Display duration: 4 s.

Fig. 17 - Sensor setup on the right rail track.
AE Monitoring of Train Wheel sets at VTG Site in Long Marston UK
Previous results have demonstrated the potential of location analysis for feature-based AE
data, as well as long-duration AE waveform analysis. It is concluded that geometric wheel
225

anomalies appear in the time domain as specific peaks of the voltage signal. In order to fine-tune
the detection capabilities of AE to be used for the real time monitoring a 4&2 (4 sensors on 1
track an 2 on the opposite) setup was used.
Two R30a sensors and two R15i sensors were mounted on the same track at 1/3 distancefrom the tracks ends (Fig. 17). Similarly on the opposite track two R15i were mounted in orderto acquire any AE transients that may propagate through various wave paths. The use highfrequency sensors are deemed necessary in a noisy environment. The acquired signals are lessprone to active mechanical noise sources (engine, etc) that are usually found at lower frequenciesof the spectrum and contaminate or even mask useful AE data. The results presented here arebased on raw data acquired with high-density TDD while full analysis is ongoing. Using TDD
with a very high sampling rate, a near-continuous acquisition of time-based AE Features can beachieved (ASL, RMS, Absolute Energy, etc.). In order to correlate the AE activity generated
with the active defects, two different flat surfaces that spanned 2.5 cm and 5 cm on the wheels
circumference were generated with an angle grinder (Fig. 18).

(a)

(b)
Fig. 18 - (a) 2.5-cm flat surface on wheel, (b) 5-cm flat surface on wheel.

The train’s configuration was a locomotive and two attached wagons with identical dimensions. The first wagon after the locomotive was used as a reference wagon, since its wheel sets
had no defects. AE activity was acquired during forward (left-to-right) and backwards (right-toleft) movement. Different data sets were acquired for different speed levels for both directions.
As shown in Fig. 19, both defects are clearly depicted as periodic spikes in the ASL vs. time
graphs. Such spikes are completely missing from the reference (healthy) wagon. Additionally,time measurements between the edges of the spikes have shown that these occur at a frequency
coinciding with the wheel rotation frequency.
Conclusions
Work on AE monitoring of train wheelsets on moving trains and trams using AE sensors
mounted on the rails has verified the potential of the method in detecting geometric defects, suchas flat surfaces on wheel circumference. To achieve optimum results, multi-dimensional analysis
has been performed, combining TDD, HDD and long-duration waveform streams acquired simultaneously. Combined analysis with specialized software is required to reveal the full potential of all available data in the time and frequency domain. Placing more than one AE sensor on
the rail track and setting up a location group can show existence of flats on lower railcar speeds,
but for exact location of the defect further work is required.
226

Fig. 19 - (a) Reference train movement, (b) 2.5-cm flat defect surface, (c) 5-cm flat defect surface. All top graphs: Channel 5 ASL vs. time, bottom: Channel 6 ASL vs. time.
227

Acknowledgment
The research leading to these results has received funding from the European Community's
Seventh Framework Programme (FP7/2007-2013) under grant agreement no 218674,
SAFERAIL project (http://www.saferail.net/). The authors would like to thank all Project Partners and gratefully acknowledge the contribution of Vlaamse Vervoersmaatschappij De Lijn,
Belgium, VTG Rail, UK and EMEF SA, Portugal for providing access and resources for thetests.
References
1. Norme Européenne CEN/TC 256, prEN 15313:2007: Railway applications — In-service
wheelset operation requirements and in-service and off-vehicle wheelset maintenance.
2. ASTM Standard E1316, 2005, “Standard Terminology for Nondestructive Examinations”,
ASTM International.
3. Bruzelius K., Mba D., “An initial investigation on the potential applicability of Acoustic
Emission to rail track fault detection”, NDT&E International 37 (2004) 507–516.
4. Thakkar N.A., Steel J.A., Reuben R.L., “Rail–wheel interaction monitoring using Acoustic
Emission: A laboratory study of normal rolling signals with natural rail defects”, Mechanical
Systems and Signal Processing, 24 (2010) 256–266.
5. Thakkar N.A., Steel J.A., Reuben R.L., Knabe G., Dixon D., Shanks R.L., “Monitoring of
Rail-Wheel Interaction Using Acoustic Emission (AE)”, Advanced Materials Research Vols. 1314 (2006) 161-168.
6. Private Communication with MISTRAS GROUP SA – EPA, 27 Rue Magellan ZAC desPortes de Sucy, F 94470 Sucy-en-Brie, France.
7. “SAFERAIL - Development of Novel Inspection Systems for Railway Wheelsets”, byAnastassopoulos, A., Barros, P., Bey, F., Blakeley, B., Boynard, C. Braz, R, Courinha, J., Davis,
C., Day, N., Dias, D., De Donder, E., Kerkyras, S., Kerkyras, Y., Laeremans, M., Liaptsis, D.,
Lugg, M., Lugg, P., Nicholson, P.I., Nobre, T., Papaelias, M., Pinto, M., Roberts, C., Vanhonacker, T., Vermeulen, F., Presented at CM 2010 and MFPT 2010, The Seventh International
Conference on Condition Monitoring and Machinery Failure Prevention Technologies, 22-24
June 2010, Ettington Chase, Stratford-upon-Avon, England.
8. PAC’s site: http://www.pacndt.com/index.aspx?go=products&focus=/multichannel/pci2.htm
9. Physical Acoustics Corporation, AEwinTM Software, Installation, Operation and User’s Reference Manual, rev. 2. Princeton Junction, New Jersey, USA.
10. NOESIS Ver. 5.2, Envirocoustics, User’s Manual.

228E07.04 – OVERVIEW OF CURRENT AND DEVELOPING ASTM ACOUSTIC EMISSION (AE) STANDARDSMARK F. CARLOS
Chairman, ASTM E07.04 AE Sub-Committee andPhysical Acoustics Corporation, 195 Clarksville Rd., Princeton Jct., NJ 08550-5303
Introduction:
The ASTM International, E07.04, Acoustic Emission Subcommittee, is responsible for theformulation, development (writing), standardization and maintenance of test methods, terminology, practices and guides, related to acoustic emission (AE). Currently, there are about 50 members in the subcommittee. E07.04 is a Subcommittee under the larger ASTM E07 “Committee
on Nondestructive Testing”, a large organization of over 450 worldwide members, who are dedicated to developing and writing worldwide NDT consensus standards in Radiography, Digital
Radiography, Penetrant Testing, Ultrasonic Testing, Electromagnetic Testing, Leak Testing,NDT Agencies, Emerging NDT Methods and of course, Acoustic Emission. All NDT standard
documents developed and maintained by the E07 Committee are published in Volume 03.03
Nondestructive Testing, Annual Book of Standards. Currently there are 24 existing AE related
ASTM standards, with three more currently in development.
Four different types of standard documents are developed by the group, each one requiring a
specific document format and content in order to qualify. These documents can be broken down
into the following standard types.
• Test Method: A definitive procedure, which produces a test result. (e.g., material property measurement).
• Standard Practice: A definitive set of instructions for performing one or more specificoperations that does not produce a test result.
• Guide: A compendium of information or series of options that does not recommend a
specific course of action. A tutorial.
• Terminology: A document comprising definitions of terms; explanations of symbols,
abbreviations or acronyms.
The goal of this article is to provide an overview of the existing AE standard documents,
their use and importance in industry, an overview of new standard documents in process, and acall for support and participation in the development of new and relevant standard documents
related to AE examination.
AE Documents Under Jurisdiction in E07.04:
Below is a list of the existing AE standard document numbers and titles that can be found in
Volume 03.03 Nondestructive Testing, Annual Book of Standards:
E569, “Standard Practice for Acoustic Emission Monitoring of Structures During Controlled
Stimulation”
E650, “Standard Guide for Mounting Piezoelectric Acoustic Emission Sensors”
E749, “Standard Practice for Acoustic Emission Monitoring During Continuous Welding”
J. Acoustic Emission, 28 (2010)229© 2010 Acoustic Emission GroupE750, “Standard Practice for Characterizing Acoustic Emission Instrumentation”
E751, “Standard Practice for Acoustic Emission Monitoring During Resistance Spot-Welding”
E976, “Standard Guide for Determining the Reproducibility of Acoustic Emission Sensors Response”
E1067, “Standard Practice for AE Examination of Fiberglass Reinforced Plastic Resin (FRP)”
E1106, “Standard Method for Primary Calibration of Acoustic Emission Sensors”
E1118, “Standard Practice for Acoustic Emission Examination of Reinforced Thermosetting
Resin Pipe (RTRP) ”
E1139, “Standard Practice for Continuous Monitoring of Acoustic Emission from Metal Pressure
Boundaries”
E1211, “Standard Practice for Leak Detection and Location Using Surface-Mounted Acoustic
Emission Sensors”
E1419, “Standard Test Method for Examination of Seamless, Gas-Filled, Pressure Vessels Using
Acoustic Emission”
E1495, “Standard Guide for Acousto-Ultrasonic Assessment of Composites, Laminates, andBonded Joints”
E1736, “Standard Practice for Acousto-Ultrasonic Assessment of Filament-Wound Pressure
Vessels”
E1781, “Standard Practice for Secondary Calibration of Acoustic Emission Sensors”
E1888, “Standard Test Method for Acoustic Emission Examination of Pressurized Containers
Made of Fiberglass Reinforced Plastic with Balsa Wood Cores”
E1930, “Standard Test Method for Examination of Liquid Filled Atmospheric and Low Pressure
Metal Storage Tanks Using Acoustic Emission”
E1932, “Standard Guide for Acoustic Emission Examination of Small parts”
E2075, “Standard Practice for Verifying the Consistency of AE-Sensor Response Using an
Acrylic Rod”
E2076, “Standard Test Method for Examination of Fiberglass Reinforced Plastic Fan Blades Using Acoustic Emission”
E2191, “Standard Test Method for Examination of Gas-Filled Filament-Wound Composite Pressure Vessels Using Acoustic Emission”
E2374, “Standard Guide for Acoustic Emission System Performance Verification”
E2478, “Standard Practice for Determining Damage-Based Design Criteria for Fiberglass Reinforced Plastics (FRP) materials, Using Acoustic Emission”
E2598, “Standard Practice for Acoustic Emission Examination of Cast Iron Yankee and SteamHeated Paper Dryers”
In comparison to the number of standard documents in other test methods in Volume 03.03,
Acoustic Emission standards have a respectable showing, indicating the interest in the technology and the continuing involvement and contributions of the active membership. A comparison
table showing the number of standards being maintained by each of the main Test Methods isshown below.
230

Test Method# of Active Standards

Radiography Standards

59

Ultrasonic Standards

29

Acoustic Emission

24

Electromagnetic Standards

20

Liquid Penetrant Standards

14

The 24 AE documents published can be broken out in various ways, the most important of
which would be to classify the types of documents from the user perspective as discussed below:
1. Six documents are related to sensor primary and secondary calibration, sensor and systemcharacterization, and verification,
2. Two are tutorial documents, one associated with attaching sensors to the test article, andthe other describing how to conduct a typical AE examination.
3. 14 documents are associated with actual AE examinations procedures.
4. Two documents are associated with acousto-ultrasonic applications.
Sensor and system related standards: Of the 6 documents related to sensors and systems, they
include the following;• E1106 describes the primary AE sensor calibration system located at NIST, in Washington DC.
• E1781 describes a secondary AE sensor calibration standard, which parallels the NIST
system for sensor developers and users. These two standards are recognized worldwide
as the de facto sensor calibration standard.
• For users who need a practical method of verifying the sensor response, we have E976,
which also is a worldwide de facto standard and describes various techniques of exciting
an AE sensor and measuring its response in all types of situations. The Pencil-Lead
Break source is described here.
• E2075 describes a method of measuring consistency of the AE sensor response utilizing
an acrylic rod.
• In regards to AE systems, E750 describes various measurements that can and should bemade to characterize an AE system (on the bench).
• E2374 describes a method of verifying an AE system before, during and after an AE examination.
Tutorial documents: There are two excellent tutorial documents on the use of AE.
• E650 describes a method for mounting piezoelectric AE sensors for maximum performance for different applications.
• E1932 is a step-by-step guide in preparing for, carrying out, analyzing and reporting on a
typical AE examination on smaller parts. Small parts are emphasized in this standard
guide in order to minimize complications often associated with the examination of large
structures.

231

AE specific examination documents: There are 14 specific application documents, which describe examinations on specific types of structural elements or components. These include thefollowing:• E569 describes the monitoring of structures, such as pressure vessels and pipelines,
which can be stressed by mechanical or thermal means, such as raising the internal pressure or temperature.
• E749 describes AE examination procedures during continuous welding.
• E751 describes AE examination procedures during resistance spot welding.
• E1067 describes AE examination or monitoring of fiberglass-reinforced plastic (FRP)
tanks/vessels under pressure or vacuum to determine structural integrity.
• E1118 describes AE examination or monitoring of reinforced thermosetting resin pipe
(RTRP) to determine structural integrity. It is applicable to lined or unlined pipe, fittings,
joints and piping systems.
• E1139 describes techniques for continuous AE monitoring from metal pressure boundaries in industrial systems during operation. Examples include pressure vessels, piping
and other system components, which serve to contain system pressure. Pressure boundaries other than metal, such as composites are specifically not covered by this document.
• E1211 describes the method of detecting and locating the steady state source of gas andliquid leaking from pressurized systems.
• E1419 describes AE examination of seamless pressure vessels (tubes) of the type used
for distribution or storage of industrial gases. This document is used worldwide and isbased on a DOT inspection exemption.
• E1888 describes AE examination techniques of pressurized containers made of fiberglass
reinforced plastic (FRP) with balsa wood cores. Containers of this type are commonlyused on tank trailers for the transport of hazardous chemicals.
• E1930 describes guidelines for AE examination of new and in-service above-groundstorage tanks of the type used for storage of liquids.
• E2076 describes a method for AE examination of fiberglass reinforced plastic (FRP) fan
blades of the type used in industrial cooling towers and heat exchangers.
• E2191 describes guidelines for AE examination of filament-wound composite pressure
vessels; for example, the type used for fuel tanks in vehicles, which use natural gas.
• E2478 describes how to use AE in the design process of testing of composites samples(specimens) in order to evaluate different designs for damage tolerance.
• E2598 This practice provides guidelines for carrying out acoustic emission (AE) examinations of Yankee and steam heated paper dryers (SHPD) of the type to make tissue, paper, and paperboard products.
Acousto-ultrasonics standards: Of the 24 existing documents, two relate to acousto-ultrasonics(A-U). Acousto-ultrasonic instruments utilize existing AE instruments but instead of passively
listening for generation of stress waves, artificial means are used to pulse or excite the structure,
while standard AE techniques are used for detection and processing of the artificial, known signals. Of the two documents,• E1495 is a general tutorial guide to A-U, in which the rationale and basic technology of
the A-U technique is fully discussed. Also, E1495 provides guidelines for NDE of flaws
and physical characteristics that influence the mechanical properties and relative strengthof composite structures; for example, filament-wound pressure vessels, adhesive bonds
and inter-laminar and fiber/matrix bonds in man-made composites and natural composites(such as wood).
232

•E1736 describes an A-U procedure for the assessment of filament-wound pressure vessels.

Current AE Work Items in Process
Currently there are three standards in progress as discussed below.
• WK658, “Standard Test Method for Acoustic Emission Examination of Seam-WeldedHigh Energy Piping”, provides guidelines for on-line AE monitoring of seam-weldedhigh energy steam piping, of the type found in fossil power plants, for detection of flaws.
The AE measurements are used to detect, locate and classify emission sources undernormal operational conditions. The primary intent of the examination is to identify thepresence and relative severity of high-temperature creep damage to seam welds and isbased on an EPRI-funded test method that was developed for the energy industry.
• WK19889: “Standard Practice for AE Examination of Plate-like and Flat Panel Composite Structures Used in Aerospace Applications”. This standard practice in development is based on testing of aerospace composite panels as part of a qualification process.
This procedure is being developed in conjunction with the aerospace industry, which requires NDT standards for testing and verifying aerospace composites. There will be
more documents coming out for the aerospace composites industry as we try to develop
inspection standards to assure safety and structural integrity, for this relatively new material technology in that marketplace.
•

WK12759: “Standard Guide for Preparing an AE Examination Plan for Plate-like andFlat Panel Aerospace composite Structures” is a companion document to WK19889, andprovides guidance into developing a test plan for various composite materials and geometries.

Other documents are in an early formulation stage and we are always interested in receiving
new documents and suggestions. Each AE community member is involved in research and developing new AE test methodology for new applications should consider authoring and submitting documents to ASTM for publishing.
Summary
ASTM E07.04 AE Sub-Committee currently maintains twenty-four AE-based standard
documents, with three additional standards in development. Our standards are used worldwide
and some are actually de facto worldwide standards. These days, standard documents and examination procedures are important to the growth and relevance of the AE method (and other
NDT techniques, also). E07.04 is a small, close-knit group, working hard to publish meaningful
AE standard documents. We welcome new members interested in assisting in the development of
new AE standards. We are also interested in new techniques and are open to inputs, suggestions
and assistance from all.
Anyone interested in becoming an ASTM - AE Subcommittee member is urged to go to the
ASTM webpage at www.astm.org or to contact the present author, current E07.04 Chairman, at
mfcarlos@pacndt.com.

233

USE OF AE METHOD FOR DETECTION OF STEEL LAMINATION IN
THE INDUSTRIAL PRESSURE VESSEL
V.P. GOMERA1, V.L. SOKOLOV1, V.P. FEDOROV1, A.A. OKHOTNIKOV1and M.S. SAYKOVA2
1
Kirishinefteorgsintez, 187110, Kirishi, Russia,
2
Central Boilers and Turbine Institute (CKTI), Saint-Petersburg, Russia
Abstract
This paper discusses results of two AE tests performed of the pressure vessel within a 5-year
period. The AE testing capabilities in detecting the lamination defects are demonstrated. This
experience also shows the capabilities of AE technique for condition monitoring of large metallic
industrial facility with structural defects evolving with time. This approach allows us to define
the main trends in defect development process and to make a decision about what kind of inspection should be performed while facility is under operation or intermittently stopped.
Keywords: pressure vessel, lamination, blistering, complex parameter
Introduction
The object under observation is a pressure vessel (Fig. 1), where the mixture of hydrocarbons
and hydrofluoric acid is separated. Because of the contents and internal operating conditions under active stresses, the diffusion of atomic hydrogen occurs in areas of tensile stress concentration. The accumulation of atomic and consequently molecular hydrogen induces the formation ofinternal cavities with the pressure reaching several hundred atmospheres. This is one of thecauses of vessel structural failure. The hydrogen induced cracking (HIC) or blistering usuallytakes the form of cavities (called blisters when surfaces expand locally). Sometimes blistering
takes the form of thin stepped cracks inside a sheet in parallel to the rolling plane. The major factor that determines a tendency of steel to HIC is the presence of atomic hydrogen collectors
within the steel matrix. The blistering forms in steels contaminated with sulfide inclusions or insteels that have the banded ferrite-pearlite matrix.
The previous paper [1] presented some results of the first AE testing of the same separator
vessel after seven years of operation. Details of the inner structure and chemical locations are
given there. Consideration was also given to characteristics of the steel (A516 Grade 70) used in
manufacturing of the vessel including results of its metallographic and chemical analysis. It is
the mild steel with banded ferrite-pearlite matrix minimally contaminated with non-metallic inclusions. Microstructures of the steel, shown in Fig. 2, were taken from the shell of anothervessel (the reactor), which was in the process chain immediately before the separator vessel. This
reactor was removed and replaced with a new reactor, because of the laminations that occurred at
depths of 11.5-12 mm and 6-7 mm in the wall thickness of 18 mm. These laminations were
found after 7 years of operation. Both of the vessels were manufactured together from the same
steel stock and were working with similar chemicals. However the reactor was working inheavier operating conditions in comparison to the separator (for example, operation pressure of
the reactor was twice). Band structures in Fig. 2 generally lead to discontinuous HIC formations.

J. Acoustic Emission, 28 (2010)234© 2010 Acoustic Emission GroupFig. 1: Vessel for separating the mixture of hydrocarbons and HF acid (V = 243 m3, ID = 4,200
mm, t = 23 mm).

Fig. 2: Metallographic results of vessel material:(a) specimen No. 1 (x500), (b) specimen No. 2 (x1000).

Fig. 3: Photo of the inside surface of the steel of pipe-manhole with disclosed cavity.
235

Figure 3 shows the part of the inner surface of the pipe-manhole of the removed reactor vessel. The small area of surface shows the cavity (it is in red due to dye-penetrant inspection). The
lamination was located at 6-mm depth. This lamination was found while planar location AE results were analyzed in the area of the manhole on the vessel head (Fig. 4). Unfortunately, we did
not make additional UT-inspection in other parts of the reactor shell after the vessel was removed, but we cut several specimens from the shell for steel structure analysis.

Fig. 4: Results of planar location for the part of the reactor shell near the manhole.
Sudakov et al. [2] shows that the principal operational load (internal pressure) usually does
not lead to the development of lamination because the stress intensity factor does not exceed thecritical value for lamination of any size. The stress intensity factor reaches its maximum with thedefinite length of lamination and then with increase of this length it decreases. The internal pres236

sure, however, can induce the growth of laminations closer to areas of high stress concentration –around welding seams and holes in the shoulder ring. The probability of growth increases during
operation in transient modes, for instance, during startup (or pneumatic tests).
According to the data from non-destructive inspection, continuous lamination detected by UT
inspection actually is the aggregate of closely located relatively small defects [2]. Our practicalexperience of AE testing of vessels with laminations confirms this [3]. Normally, we detect theindications of lamination at an early stage of the process of large blister buildup in a form of aggregate of multiple AE events clustered in the area where blister subsequently appears. Usuallythese events have relatively low energy characteristics. Afterwards, when a blister is formed(Fig. 5), the number of detected AE events in these areas significantly decreases. For the mostpart, high-energy events are occasionally detected and localized on blister borders. Apparently
they are linked to such rare processes like blister border movement or formation of long cracks at
merging of individual blisters. The most serious is a defect in the form of a crack, opening a blister from inside of a vessel (Figs. 6 and 7).
Generally, we detect the presence of blisters with AE during the reduction of vessel pressure.
However, we have also detected clearly AE events from blisters of confirmed presence duringrising pressure, not just when the blisters join nearby blisters under stress concentration. For example, we found an early stage of blisters in a column in 1999 and AE retest in 2010 showed thegroup of blisters has evolved substantially. This unit was withdrawn from service because of thisAE and subsequent UT inspection. We recorded AE waveforms and are in the process of data
analysis and will be compared to destructive test data.
In our practice, we have many examples of test vessels with large blisters, located far fromeach other. Typically, AE does not respond to these defects. Hence, the opportunity to detect thebundle with the AE is strongly dependent on the type of distribution of metallurgical defects inthe metal.

Fig. 5: Evolution of lamination to blister for the mild carbon steel with banded ferritic-pearlitic
structure having minimal non-metallic inclusions.
237

Fig. 6: The defect opening the blister in the form of crack on the inner side of the vessel.

Fig. 7: Transverse cut of the vessels shell in zone of crack opening the blister.
Results and Discussion
The forecasting of HIC development is considered to be a challenging task. According to thedata of ТNK-BP (Fig. 8) [4], the number of failures due to HIC sharply increases with the extension of operating life beyond 10 years.

Fig. 8: Statistics of equipment failure from HIC (data of TNK-BP).
The second AE inspection of the separator vessel was performed after five years from thefirst inspection (after 12 years of the vessel operation). The major differences in procedures ofthese two inspections are the following:238

1) The number of channels increased from 15 to 27; during the second inspection probes
were installed at the same 15 positions they had previously been attached during the first inspection, and 12 additional probes were installed at new positions (Fig. 9);
2) The schedule of vessel pressure increase was faster and nearly linear as shown in Fig. 10.
Figure 9 shows results of planar location for the pressure increase interval P = 0.15 → 0.6
MPa that is imposed on vessel structural parts forming potential areas of stress concentration(welds and holes). The amplitude filtering is used for AE events. It is possible to note the overallhigh AE activity and existence of several clustering areas of high amplitude AE events.

Fig. 9: Results of planar location of AE sources on shell reamer (inner side view) for the 2nd test
(P = 0.15 → 0.6 MPa, V = 3.30 m/ms). ▲ – positions of sensors during both tests, ▼- positionsof sensors during the 2nd test only.

Fig. 10: The loading versus time curve during two pressurization tests.
239

Figure 11 shows the result of superposition of two location diagrams and comparison of position of AE activity areas (without amplitude filtering). Areas with the highest AE activity are
pictured as outlines on Fig. 12. It can be seen that the highest AE activity areas during the second
inspection are typically located in areas with relatively low density of AE sources during the firstinspection. Conversely, high AE activity areas from the first inspection during the second one
showed lower number of AE sources. Predominant are the adjacent areas with partial overlapping at edges.

Fig. 11: Results of combination of locations graphics for two tests: ▼- AE sources of 1st test (P =0.13→ 0.5 MPa), ♦- AE sources of 2nd test (P = 0.15→ 0.6 MPa).
These results enable us to suppose that during the 5-year period activity areas from the firstinspection have undergone stress relaxation with disappearance of local stress concentrators
around small sulfide inclusions. Aggregation of small defects has taken place and significant size
laminations were formed. These laminations have more regular structure than the accumulationof multiple small discontinuities [3]. It defines the response to applied load from the viewpoint of
AE testing results. The process of lamination build-up was registered during the second AE inspection at the new vessel areas.
Figure 12 also shows areas with no activity recorded either during the first or second inspection. In one instance, these are the areas where lamination processes ended prior to the first inspection. In the other instance, these are the areas where there are no conditions for developmentof structural defects under loads active during operation. The order of degradation of different
areas of the steel vessel through time is defined by the distribution of products with various corrosive activities within the space of vessel and by the location of expanded areas of stressbuildup [1].
240Fig. 12: The contours of zones of the highest AE activity for two tests:- 1st test (P = 0.13→0.5 MPa),- 2nd test, after 5 year (P = 0.15→0.6 MPa).

Figure 13 presents the result of simulation of interaction of laminations located at various
depths within the middle third of sheet. It is defined that these laminations may join together as aresult of breakthrough of long cracks between their borders, for instance, during a startup or any
other sharp pressure increase.

Fig. 13: The result of lamination’s interaction simulation, located at the different depth in the
middle one third of the sheet.
241

The analysis of condition monitoring data for this separator vessel allowed us to make an assumption that the development of similar cracks could be registered during the AE inspection.
The search for such objects required the application of complex parameter – the combination ofseveral AE signal characteristics (duration, amplitude, and rise time) and their relations witheach other taken with weighted coefficients. Preliminary consideration was given to forms of
distribution of basic characteristics of AE events that fell into location (Fig. 14). The presence of“heavy tails” in distributions makes possible isolation of AE events that are potentiallyconnected with the specific type of structural alterations. The parameter was deducted withconsideration to specific type of defect, i.e. very long cracks.
The AE testing system employed for the inspection is a flexible data processing tool, and themathematical expression for complex parameter was completely generated with a help of processors included in the system software. In our opinion, one of the basic principles of operation ofthis system is in priority of expert decision before formal computer decision-making technologies. Such a feature of the system is specifically useful in abnormal situations of data analysis,
when there are not enough regular criteria available for assessment of results. An expert can select necessary functions from a wide range of data processing and presentation means and create
arbitrary data analysis routines including his/her own local criteria tuned to any particular application.

Fig. 14: Distributions for characteristics of AE events used in complex parameter in the 2nd test
data analysis. The "heavy tails" in distributions allocated.
Figure 15 shows the diagram similar to the segment given in Fig. 11. This diagram has theadded filtering of AE sources using the K5.2 parameter as defined below.
K5.2 = 1.8 log(Duration) + log(A_Lin) + 2 (RiseTime/Duration)
242

Here, A_Lin is linear amplitude. This enables us to visually pinpoint several groups of sources
forming bands or lines that cross new and connect old lamination areas. The significant numberof individual sources with the high value of K5.2 is located between laminations of various ages.

Fig. 15: The combination of locations graphics for shells fragment:▼- AE sources of 1st test, ♦AE sources of 2nd test, ■ - AE sources of 2nd test with high value of complex parameter, K5.2.
These findings compare favorably to the preceding AE inspections of vessels with laminations and provide solution to the lamination problem obtained during simulation study. The
feature of this vessel is abnormally large areas of the AE activity. Previously, the AE activity
appeared on different stages of the lamination development and we observed only on local
sections of the vessel shells. The complex parameter K5.2 turned out to be the helpful characteristic for the localization of position of such critical defects as lamination with linking cracks.
This significantly reduced the scope of additional NDT techniques needed for the determinationof exact defect geometry.
Now, the vessel is back in operation. In 2010, two years after the second AE test, two zonesof the vessel shell (areas enclosed in red rectangles; see Fig. 16) were tested with ultrasonicthickens gage during a brief stoppage of operation. The size of the areas was 700 x 920 mm and660 x 580 mm. A number of areas had high AE activities, but the particular areas were chosen as
sample areas because of their accessibility.
The design thickness of the vessel wall is 23 mm and these areas had the thickness of 22 to
22.5 mm when tested in 2008. Thickness scanning (with 2 mm pitch) showed that wall thickness
was reduced to 16.7 to 19.6 mm range over the whole scanned areas. More than 98 % of measurements were in the range of 18.1-18.2 mm; i.e., smooth UT result. The depth of the potential
243lamination did not coincide with the usual location of the laminations – in the central part of the
sheet. The steel plate typically contains a large number of pearlitic bands (as shown in Fig. 2)
located at different depth levels of the plate profile. It can be assumed that any of the bands (andsulfide inclusion stringers also seen in Fig. 2 a) could be collector for hydrogen. In addition, such"smooth" UT results are not typical for the wall thickness measurements of industrial vessels,
which had operated for a long time (16 years). However, these results are typical for laminations.
This is likely to confirm the hypothesis that the groups of hydrogen-induced blisters are present
in the metal shell over a large area. These are located at 5 mm from the inside surface. Anotherpossible cause is erosion-induced thickness loss, but this is unlikely because of location and also
the lack of such loss in the first 14 years of operation.

Fig. 16: The positions on the shell of two zones of ultrasonic scanning of wall thickness.
Summary and Conclusions
This paper shows acoustic emission as the optimal inspection method from the time-saving
standpoint for obtaining information about the condition of large vessels. After two AE
inspections with the total duration of several hours, we obtained the assessment of the quality ofsteel for almost the whole area of shoulder ring; made correct suggestion about the main type of
defects (blistering), compiled the layout of defect distribution along the shoulder ring; localized
the most serious defects and also determined the main trends in development of the defectsystem. In fact, the time of AE testing is actually shorter because it was performed during the
mandatory periodical pneumatic tests. The pneumatic test time constitutes a significant part of
the whole AE testing procedure.
Specific features of large lamination behavior will probably allow operating this vessel for
additional time. Our recommendations, however, point to optimal decision in replacement of thisvessel with a new one considering its current condition and the nature of products circulating244

inside. The new vessel shall be fitted with permanent AE monitoring system. In an event of periodic AE condition monitoring, it is recommended to perform the first test in 3-4 years after the
beginning of operation and a follow-up test every 2-3 years. Prior to replacement of the vessel itis necessary to inspect the steel at the boundaries of lamination defined by using AE testing.
Additional results include the data obtained during AE testing that helped reveal technological factors initiating the steel degradation process and provided grounds for checking out the initial quality of steel used in manufacturing of the vessel and other process equipment of this facility. The steel microstructure examination has defined that the used steel properties in combination with process conditions can be the source of problems during the operation of some of thesefacilities. This became the reason for the increase of periodic NDT of all these facilities.
References[1] Kabanov B., Gomera V., Sokolov V., Fedorov V., Okhotnikov A., Use of AE method abilities for petrochemical equipment inspection, In: Proceedings of the 26th European conference on
Acoustic Emission Testing, Berlin, 2004, Germany, Vol. I, pp. 131-138.
[2] Sudakov A., Danyushevsky I., Saikova M., Accounting of defects during expert appraisal ofindustrial safety and operating life of boilers and pipelines: Berg Kollegium, 2 (65), 2010, pp.
20-22.
[3] Kabanov B., Gomera V., Sokolov V., Fedorov V., Okhotnikov A., AE Testing of Refinery
Structures, In: Proceedings of the 27th European Conference on Acoustic Emission Testing, Cardiff, 2006, Wales, UK, pp. 133-138.
[4] Makarenko V., Gorbunov S., Ogorodnikov V., Bakeev R., Shevtsov V., Causes of failure ofequipment at gas processing plants of Western Siberia: Mechanical Engineering, #8, 2007, pp.
45-48.

245

COMPARISON OF ACOUSTIC EMISSION PRODUCED DURING
BENDING OF VARIOUS OXIDE CERAMIC AND SHORT FIBER OXIDECERAMIC MATRIX COMPOSITES
S.A. PAPARGYRI-MPENI, D.A. PAPARGYRIS, X. SPILIOTIS
and A.D. PAPARGYRISTechnological Educational Institute, Larissa, General Department of Applied Science,
Materials Technology Laboratory, Nea Ktiria, 41110 Larissa, Greece
Abstract
AE can be used in characterization of oxide ceramic and ceramic matrix fiber composites. In
oxide systems the AE activity was influenced by chemical composition, sintering temperatureand structure development. AE activity increased from kaolin to pottery mixture as a result of thedeveloping a stronger structure with incorporation of a second phase in the matrix, while decreased in kaolin-fly ash composites due to weakening of the structure and formation of cracks.
Kaolin ceramics showed a clear Kaiser effect, while pottery mixture, kaolin-fly ash and brickclay composites were more or less acoustically active during the second loading. Short fiber ceramic matrix composites indicated that fiber composites present higher AE activity since, matrix
sub-critical events, fracture of the fibers, fiber-debonding or fiber pull-out from the matrix may
produce a comparatively large amount of AE events. Increase of sintering temperature produced
composites with higher AE activity for similar reasons to that of powder ceramics. Increase of
the volume fraction of fibers increased the AE activity. In the examined fiber ceramic systems
Kaiser effect was observed in all cases irrespective of the kind of the fibers, a fact that indicatesthat the sub-critical activity during loading is not a reversible process. AE examination is a valuable tool giving useful information about the strength, structure and the sintering techniques usedduring manufacturing of ceramic systems.
Keywords: Oxide ceramics, oxide fibers, graphite fibers, short fiber composites
1. Introduction
Acoustic emission (AE) is a potential testing method to monitor the integrity of materials,
mechanisms of fracture, and other processes. It is nowadays applied in almost all areas of scienceand technology of materials, e.g. [1, 2]. The monitoring of AE events from ceramic specimens,
which are stressed can be divided into two stages. In the first stage the damage (any active discontinuity) inside the specimen produces an AE event, which propagates through the material
and arrives at a transducer. In the second stage, the event is converted into an equivalent electronic signal and finally detected as an AE count. It is generally assumed that in coarse structured
poly-phase ceramics where there are mismatch stresses between grains, the minimum stress, atwhich AE activity starts, coincides with the onset of grain size fracture. During repeated loading/unloading of most of ceramic materials, Kaiser effect is observed. This means that during thesecond and next loadings the AE is zero or close to the background level till the stress reaches
the largest previously reached stress level. Above that level the AE activity is increased dramatically.
In the present work, which is based in a paper presented at the EWGAE 2010 [3], the AE behavior of a number of oxide ceramics and short fiber composites is compared and discussed.
J. Acoustic Emission, 28 (2010)246© 2010 Acoustic Emission Group2. Experimental
The chemical composition and other characteristics of the materials compared in the present
work are given in Tables 1 and 2.
Table 1. Chemical composition of ceramic materials.
Chemical compound
weight %
SiO2
Al2O3
Fe2O3
TiO2
CaO
MgO
K2 O
Na2O
L.O.I.

Mineral phases

Remblend
Pottery
Chinaclay
mixtureECC International
48.2
59.7
37.1
25.5
1.0
1.5
0.05
1.6
0.07
3.1
0.3
0.2
2.0
1.9
0.1
0.1
12.1
9.5
83% kaolinite
39% kaolinite
13% mica
28.6% lime feldspar
2% feldspar
14% quartz
2% other minerals 11% potash feldspar
0.8% soda feldspar
6.7% misc. oxides& organic matter

Brick
clay

Fly
Ash

50.6
20.1
6.7
1.2
3.8
3.4
3.2
2.1
9.9

34.4
19.6
4.1
0.5
32.2
1.9
0.7
0.4
6.0

Table 2. Characteristics of fibers.
Characteristics
Classification temp. oC
True density, g/cm3
Chemmical comp.
Al2O3
SiO2
Al2O3+SiO2
Other
Crystallinity ratio
α-Al2O3
mulliteFiber diameter, µm
Strength, MPa
E, GPaColor

Mulliteshort fibers
Denka B-80
1650
3.3

Alpha-alumina
short fibers
Denka B-95
1600
3.5

80
20
99.7
0.3

95
5
99.7
0.3

5≥
50≤
2-5
1670
167
white

30-65
5≥
2-5

white
247

Delta-alumina Graphite PAN
short fibers
short fibers
Saffil RF
Grafil XAS> 1000
3.3
1.79
96-97
3-4

graphite

3.4 (1-10)
2000
300
white

4480
227
black

A large number of specimens (75 x 20 x10 mm, length x width x height) was produced forevery formulation by slip casting. Mechanical testing was performed using an Instron 1195 machine in compression. The specimens were placed in the three-point bend loading rig with a spanof 50 mm. In order to permit sensitive AE monitoring the supports of the bend rig (10-mm steelbars) were covered with neoprene rubber to reduce the possibility of recording noise from themachine and the metal ceramic contact points. The fractured surfaces were examined in a JEOL
T-330 scanning electron microscope with resolution of 4.3 nm at 25 kV accelerating voltage.
Charging was avoided by application of a gold layer of 200-400 A, under low-pressure argongas, using a ‘cool’ Edwards S150B Sputter Coater. A Philips X-ray diffractometer with a
PW1820/00 vertical goniometer and a PW1710 microprocessor-based control and measuringsystem were used for XRD analysis. AE analysis was performed using a low noise MRP-01
MARANDY preamplifier with 60-dB gain and processing of the amplified signals was done by a
MARANDY/MR 1004 amplitude analysis system, which stores the digital AE data as a functionof time together with A-D converted load signals. The system provides information about thepeak amplitude of each event (the peak amplitude is given relative to 10 mV). All AE activity
was recorded during three-point bending. Schematic representation of the three-point bend testing rig and the AE system is given in Fig. 1. Details on specimen preparation and other experimental details are given elsewhere [4-6].
3. Results and Discussion
Selected SEM and AE curves from the examined systems sintered at various temperatures
are presented in Figs. 2 to 8.

Fig. 1 Schematic representation of the three-point bending rig and the Marandy AE system [4].
a. Oxide ceramics
Kaolin, a characteristic oxide ceramic, is an acoustically quiet material of low strength in the
non-sintered condition. For comparison, specimens of soda-lime glass with similar dimensions
were tested in the same rig. The results (Fig. 5) showed that kaolin specimens break with almost
no AE. This is probably due to the absence of any connecting phase since it is composed of kaolin aggregates. Similar behavior was observed in the glass specimens (Fig. 5a). After sintering,
they become acoustically active even at very low stresses during flexure testing (Fig. 6a). The
acoustic activity increases with increasing sintering temperature probably as a result of structuredevelopment during sintering (Fig. 2a); kaolin changes from the weak point-bonded structure to248

a well-formed glass-mullite structure as sintering temperature is increased from 1000oC to
1300oC. The latter structures are stronger and produce more AE events during testing since more
sub-critical activity (micro-cracking) occurs before fracture. The AE activity starts at low
stresses, virtually from the very beginning of load application. In most of the examined specimens a load application of a few N (2-3% of the ultimate fracture load) was enough for constant
AE. The event rate (count/s) is extremely low (less than 5) during loading of specimens sinteredat 1000oC and is increased to around 10 at fracture, while it is around 60 in specimens sintered at
1300oC and is increased to around 300 count/s near fracture [4].

(a)

(b)

Fig. 2 SEM from (a) Kaolin and (b) Pottery mixture sintered at 1300oC and 1100oC, respectively.

(a)

(b)
Fig. 3 SEM micrographs from brick-clays sintered at (a) 900oC and (b) 1100oC.

(a)(b)
Fig. 4 SEM from (a) Kaolin+8.55 vol.% Grafil, (b) Kaolin+9 vol.% mullite-fiber composites sintered at 1000oC.
249

(a)
(b)
Fig. 5 (a) AE from glass specimens and (b) from kaolin before sintering.

(a)(b)
Fig. 6 (a) AE and (b) Kaiser effect from kaolin specimens sintered at 1300oC, during 3-point
bending.

(a)(b)
Fig. 7 (a) AE cumulative events versus load showing Kaiser effect from a pottery mixture specimen sintered at 1100oC, (b) AE from a kaolin-fly ash 5 wt.% ceramic sintered at 1000oC
The Kaiser effect (Fig. 6b) was observed in all kaolin specimens during recording of cumulative events versus load in repeated loadings and is especially clear in those ceramics with highAE activity. The first loading is sufficient to cause all the subcritical damage in the specimen up
to that load. Further damage does not happen until the first load is exceeded. There is absolutelack of any AE activity during the second loading till the same load level, after which AE activity started again till fracture. Although it is not absolutely correct to allocate to each AE event a
subcritical event, since the number of AE events collected through the transducer is influenced
by many factors, it is reasonable to accept that high AE counts indicates numerous subcriticalevents.
Pottery mixture specimens generally produced the same behavior as kaolin specimens. Structure (Fig. 2b) and strength development increases the sub-critical sites for AE activity, which are"activated" during mechanical testing. Pottery mixture specimens sintered at 900oC present the250

same kind of AE cumulative events, rate, distribution and linear relation between load and deflection as the kaolin specimens sintered at 1000oC. As the sintering temperature increases from
900 to 1300oC, AE activity becomes more intense in a similar manner to that of kaolin. Pottery
mixture specimens show the Kaiser effect (Fig. 7a) in a similar manner to that of kaolin.
SEM examination of brick-clays sintered at various temperatures (900-1100oC) showed thatduring sintering a glass structure is gradually developed (Fig. 3) [5]. Brick-clay specimens sintered at the same temperatures as pottery mixture generally showed the same behavior and theKaiser effect, but are more acoustically active (giving higher cumulative events) and have higher
event rates. As an example, comparison of AE activity between pottery and brick-clay sintered at
1100oC shows almost four times higher cumulative AE events in the case of brick-clay specimen. During cooling of the sintered brick-clay, radial tensile stresses are produced forming
cracks due to difference in contraction between sand particles and glassy matrix. The applicationof external stresses produces an increase of the total stress field (enlargement of the processzone) resulting in additional cracking and consequently additional AE events.
AE examination of 5 wt.% and 10 wt.% fly ash-kaolin composites (sintered at 1000oC)
showed low AE activity (Fig. 7b) during mechanical testing. This is lower than that of pure kaolin, since fly ash is a fine grain constituent (most has a grain size less than 45 µm). When fly ash
is added to kaolin, such composites possess a cracked structure, reducing their strength and fracture toughness and making the structure weaker and looser. This is why Kaiser effect is less defined with large noise background, as has been reported in rocks [7, 8]. In Fig. 7b, the slope of
AE activity curve starts to increase when the load reaches the maximum load during the firstloading.
b. Fiber oxide ceramic matrix composites
SEM micrographs from (a) a kaolin+9 vol.% mullite fiber and (b) kaolin+8.55 vol.% Grafil
composites sintered at 1000oC are shown in Fig. 4. Generally, increased sintering temperature
produces composites with higher AE activity for similar reasons to that of powder ceramics. Increase of the volume fraction of fibers in the kaolin matrix also increases the AE activity. In purekaolin, a low AE event rate increases just before fracture (Fig. 6a), which is a common characteristic of clay ceramic materials. In composite ceramics, while the event rate is low at the beginning, later there is a comparatively high AE event rate at loading at about half of the fractureload. The AE event rate eventually drops before fracture. This behavior could be attributed to thepresence of fibers, the breakage of which produces a high AE event rate before the final fracture.
SEM examination of oxide-fiber composites (Fig. 4b) shows that the main fracture path isthrough the matrix and fiber with no indication of fiber pull-out or fiber-debonding. Increase offiber volume fraction produces a large increase of AE activity. As an example, AE counts underidentical conditions from pure kaolin (Fig. 6a) with a kaolin-matrix composite having 9.2% B-95
α-Al2O3 fiber (Fig. 8a) were compared. The comparison shows a five-fold increase, from about
2000 of 8000 events, which means that about 80% of the composite AE activity is due to fibers
and only 20% to matrix sub-critical activity. Further measurements showed that increase of α Al2O3 fiber content to 14% gives an increase to about 26000 AE events (not illustrated), showingthat about 90% of the AE activity is due to fiber content. It could be assumed that this increase isa result of numerous sub-critical events due to fiber fracture during the application of load.
In almost all the cases, the way that AE events are distributed is similar to that of kaolin
specimens. Another general observation is that the event rate in composites under the testing251

conditions used in this work is about 1000 events/sec, and is almost constant irrespective of sintering temperature, fiber content and chemical composition of fibers.
Finally, Kaiser effect was observed in all ceramic composites irrespective of the kind of thefibers (Figs. 8a-9). No AE activity was observed during unloading, a fact that indicates that the
sub-critical activity during loading is an irreversible process. Another observation was the difference of AE event rates between the first and second loadings. During the first loading a highevent rate (up to 800 counts/s) was recorded in all cases with sudden peaks of high activity, during the second loading the event rate curve is smooth with a rate of almost nil up to the load of
the first loading.

(a)

(b)
Fig. 8. Kaiser effect in (a) 9.2 vol.% Denka B-95 α-alumina-fiber composite sintered at 1300oC,(b) 14 vol.% Denka B-80 mullite-fiber composite sintered at 1300oC.
252

Fig. 9. Kaiser effect in kaolin+8.5% Grafil fiber composite sintered at 1300oC.

Fig. 10. SEM micrographs from graphite fiber kaolin composites sintered at 1200oC.
The load-deflection-AE event rate curves of ceramic composites with oxide fiber and graphite fiber show clear differences and similarities, which may be connected to interfaces and fracture mechanism of these composites. The oxide fiber composites have a load-deflection curve
similar to that of powder ceramics with an almost linear increase of load with deflection up to
fracture, since there is a strong interface between matrix and fibers (e.g., Fig. 4b). The fracture
proceeds through matrix and fibers. The graphite fiber composites have a curve similar to that ofunidirectional composites, where we can detect at first a matrix cracking region, then a region ofload transfer to fibers and that of fiber pull-out and finally a fracture region, probably as a resultof weak fiber-matrix interface giving fiber pull-out (Figs. 4a and 10).
Comparison of results from Saffil and B-95 Denka α-Al2O3 fiber-composites, sintered at temperatures above 1200oC, shows the effect on AE activity, of fibers having the same chemicalcomposition but different fiber diameter. It must be noted that the Saffil fibers before sintering
have a δ structure, which is transformed to α-Al2O3 after sintering above 1200oC and the maindifference that remains after sintering is the shape and fiber-diameter of the fibers. Comparisonof AE from a 9.2 vol.% B-95 α -Al2O3 -kaolin composite with that of 9.7% Saffil fiber-kaolin
253composite specimen, both sintered at 1300oC, shows about the same AE event rate. It also showsthat the B-95 fiber-composites produce a little more cumulative AE events and higher event rate,a fact which justifies the hypothesis that fiber content is directly connected to AE events produced during 3-point bend testing. The difference in fiber diameters (which results in different
number of fibers per volume) also affects the AE behavior of the composites. Due to higher
range of lengths and diameters, the number of B-95 α-Al2O3 fibers is higher compared to that ofSaffil fiber composites with the same fiber content and so the number of AE events is higher.
Comparison of results from B-95 alumina and B-80 mullite-fiber composites showed that
there is great difference (probably due to the nature, morphology, diameter distribution etc. of
the fibers) in the AE behavior, as shown in Figs. 8a and b. Figure 8a exhibits a high AE activity
with about 10000 counts with the Al2O3 fiber-composite sintered at 1300oC. On the other hand,
low AE activity (only about 5% of that of B-95 fiber composite) was measured in the B-80mullite fiber-kaolin composite (Fig. 8b). The comparatively low AE activity of the B-80 mullite
composite (which was observed also at other sintering temperatures as well) is probably due tothe fact that B-80 Denka-mullite fibers have a high content of non-fibrous materials confirmed
by SEM (appearing as large "shots" in the composite structure), which cannot produce as much
AE events at the equivalent amount of fibers.
The kaolin-graphite fiber composites with Grafil fibers belong to a totally different class ofceramic composites with a different fracture mechanism. SEM examination (Fig. 4a) shows there
is a weak fiber-matrix interface and the fracture occurs after matrix cracking and fiber pull-out(Fig. 10), which indicates that any AE activity during loading may be due matrix cracking andfiber pull-out.
This fracture mechanism implies a high number of AE events since every matrix fiber pullout could give at least one AE event. However, it must be noted that the graphite fibers have analmost constant diameter of 6.8 µm, which is about double of that of Saffil fibers. This meansthat Saffil-kaolin composites will contain about twice as much fibers as the Grafil-kaolin composites for the same volume content and consequently it could be proposed that Grafil-composites
with double fiber volume fraction could have about the same AE activity to that of Saffil-kaolin
composites with half fiber volume fraction. Comparison of AE activity from a 9.7 vol.% Saffil
kaolin composite and a 16.5% Grafil-kaolin composite (which have roughly the same number offibers), both sintered at 1300oC, shows roughly the same number of events, which means that theabove hypothesis could be considered valid.
4. Conclusions
AE activity was increased from kaolin to pottery mixture as a result of developing a stronger
structure with incorporation of a second phase in the matrix, while decreased in fly ash composites due to weakening of the structure and formation of cracks. In kaolin ceramics, there was a
distinct Kaiser effect, while pottery mixture and the kaolin–fly ash and brick clay composites hadhigh background AE activity during the second loading. Fiber-ceramic-matrix composites indicated that fiber composites present higher AE activity since the matrix sub-critical events, fracture of the fibers, fiber-debonding or fiber pull-out from the matrix may produce a comparatively
large amount of AE events. Increase of sintering temperature produced composites with higher
AE activity for similar reasons to that of powder ceramics. Increase of the volume fraction offibers increased the AE activity.
254

Kaiser effect was observed in all the examined systems indicating "memorization" of the applied load due to the fact that the sub-critical activity during loading is an irreversible process.
The second loading AE was almost zero or close to the background level. The low AE activity
during the second loading found in kaolin-fly ash and B-80 mullite-fiber composites could be
attributed to the presence of large fly ash particles, non-fibrous "shots", etc.
AE examination under loading can give useful information about the strength, structure, sintering techniques used during manufacturing and the "history" of ceramic systems.
References
1. S. Momon, M. Moevus, N. Godin, M. R’Mili, P. Reynaud, G. Fantozzi, G. Fayolle, Composites A: Appl. Sci. and Manuf., 41 (7), (2010), 913-918.
2. Tao Fu, Y. Liu, Q. Li, J. Leng, Optics and Lasers in Eng, 47 (10), (2009), 1056-1062
3. A.D. Papargyris et al, Proc. 29th EWGAE, 2010, paper 66, Vienna.
4. A.D. Papargyris and S.A. Papargyri, J. Applied Clay Science, 18, (2001), 191-204.
5. A.D. Papargyris, et al., J. Constr. & Build. Mat., 15, (2001), 361-369.
6. A.D. Papargyris, 4th Euro Ceramics, 12, (1995), 131-138.
7. A. Lavrov, Int. J. of Rock Mech. & Mining Sci., 40 (2), (2003), 151-171.
8. G. G. Zaretskii-Feoktistov and G. N. Tanov, Strength of Materials, 17 (5), (1985) 696-701.

255

	
  

NEW CHARACTERIZATION METHODS OF AE SENSORS
1KANJI ONO 1, HIDEO CHO 2 and TAKUMA MATSUO 2
University of California, Los Angeles, Los Angeles, CA 90095 USA;
2
Aoyama Gakuin University, Fuchinobe, Chuo, Sagamihara, Japan

Abstract
This paper reports on experiment on characterizing AE sensors commonly available and utilized. Many AE applications need to detect plate waves, but no calibration scheme for thesewaves is available. We utilized bar waves with a long metal bar and plate waves using a large
aluminum plate. The waves are excited using a transmitter driven by a fast step-down pulse ofhigh voltage. Displacements of the bar are characterized with a laser interferometer. Displacement signals lasting over several hundred µs are generated, and the uniformity across the widthis adequate. For the plate, ultrasonic transducers are used as reference to obtain power spectra of
AE sensors. Frequency from 20 kHz to 1 MHz showed substantial variations in intensity, but thechanges are smooth enough for characterization purpose. Using FFT, the power spectra of AE
sensors were obtained. Deconvolution procedures were tried, but currently suffer from noise.
The bar-wave based approach of sensor evaluation better simulates many AE applications and its
further development is warranted.
Keywords: Bar waves, plate waves, sensor characterization, laser interferometer
1. Introduction
In any acoustic emission (AE) work, AE sensors play an important role. It is essential that weunderstand how these are excited and produce outputs. The transfer function describes its inputoutput relationship. When we set out to determine the transfer function of piezoelectric AE sensors using pulse laser input and measuring the displacement response, we anticipated obtaining
results in a straightforward fashion. As we reported previously [1, 2], it was anything but simple.
Piezoelectric AE sensors have complex behavior and we needed to account for the types of input
waves, even among the normal incident waves detected at the epicenter on the opposite face of aplate. With a point-like pulse laser source, spherical waves reach the receiving sensor and its response depended on the thickness of the plate, which corresponds to the radius of the sphericalwaves. A broadband ultrasonic transmitter can generate plane (flat wave front) waves within thearea facing the transmitter in near-field region. Strictly, these waves should be called quasi-planewaves as diffraction effects produce non-uniform intensity distribution within the near-field region. In this case, most AE sensors with disc-shape piezoelectric elements and piezoelectric discs
themselves responded as dictated by the thickness resonance. This offers experimental confirmation of the reverse effect of piezoelectric disc vibration, to which a short transient electrical pulse
was applied. This result agreed with theory. Sato and Yoshida [3] analyzed the transient behavior
using the equivalent circuit method and predicted alternate polarity pulse with period, T = t/v (t =disc thickness and v = wave velocity). This was observed in our experiment. However, no radial
resonance was excited in our experiment [1, 2]. Since typical AE sensors are designed using radial resonance mode, the characterization with plane waves has no practical value despite providing substantive physical insight to sensor behavior.
In this work, we first report sensor responses to plate- and bar-wave excitation. This is expected to lead to useful characterization methods applicable to practical AE work. This is
J. Acoustic Emission, 28 (2010)256© 2010 Acoustic Emission Group	
  

	
  
followed by evaluation of sources of out-of-plane displacement using an ultrasonic transmitter.
This is used to examine sensor responses to plane wave input, but buffer plate thickness has unexpected effect that needs to be studied further, making the use of transfer function approacheven more restrictive than previously reported [1]. Lastly, we report additional findings that raise
serious questions on AE sensor calibration methods as currently practiced.

	
  Fig. 1 Displacement output of FC500 excited by a pulse and its power spectral density.
2. Plate-Wave ExperimentSensor characterization with plate waves utilized a large aluminum plate, 1230 x 1150 x 12.7mm (6061-T6 Al). An ultrasonic transducer (AET FC500; 19-mm element diameter, 2.25 MHz
nominal resonance) was driven by high-voltage step-down pulses (385 V step maximum, 0.040.08 µs rise time). Surface displacement waveform on the face of FC500 was measured using alaser interferometer and is shown in Fig. 1. The initial fast rise and trailing tail over 10 µs resembles a one-sided exponential pulse, which has inverse frequency-squared (f –2) power spectral
density (PSD). The PSD shown in Fig. 1 follows f –2, illustrated by the slope of –2. The trailingpart is due to the radial response of the transmitter and has center frequency of 25 kHs. The peak
displacement in Fig. 1 corresponds to 31 nm (100 mV interferometer output = 1 nm).
This transducer was mounted to one edge of the plate at the middle position. After traveling
400 mm, surface motion was detected using another ultrasonic transducer (Aerotech, 5 MHz,
6.4-mm diameter) using 60-dB PAC preamplifier (1220A, 20-1000 kHz filter). The output isshown in Fig. 2a, with FFT power spectrum in Fig. 2b. The spectrum indicates relatively flat (±6dB) response over 20 kHz to 1.0 MHz. Since the displacement at the sensing position is expected
to have f –2-PSD as will be discussed later, the nearly flat PSD response appears to arise from thevelocity sensitivity of the receiver. When one differentiates a harmonic displacement signal, D(t)= A sin(ωt), resultant dD(t)/dt contains the proportionality with ω = 2πf and its power density has
f 2-term [4]. Therefore, the sensor response becomes flat in frequency.
The waveform may contain reflected waves beyond 250 µs after the initial arrival (at 210 µs
in the figure), but these are buried in noise due to heavily damped nature of this receiver anddamping materials (plumber’s putty) placed on the plate surface beyond 450-mm distance. At
400 mm from the transmitter, 25-mm off-center position reduced the intensity 1 dB, while thecorresponding reduction was 3 dB at 100 mm. Thus, the larger distance was chosen even though
the effect of reflected waves is less at the shorter distance. Without damping, reflected waves
were usually visible; flexural waves appear to attenuate only ~10 dB/m at >100 kHz and ~4
257

	
  

	
  
dB/m at 30-60 kHz. Damping adequately reduced higher frequency components, but was ineffective at lower frequency below 100 kHz.
	
  

	
  
Fig. 2 a) Surface motion detected by Aerotech, 5-MHz transducer using 60-dB PAC preamplifier (1220A, 20-1200 kHz filter). b) FFT spectrum in dB scale.
	  By placing an AE sensor on the aluminum plate, its response to arriving waves can be determined. Figure 3 shows the power spectral density of four sensors (in dB scale) against frequency.
The sensitivity of these sensors is to be compared directly to the reference (Aerotech) sensor. No
spectrum subtraction is performed. R15 sensor indicates the peak sensitivity at 160 kHz and subsidiary peaks at 250-450 kHz. This is similar to PAC’s calibration curve with the sweepfrequency face-to-face method. FC500 sensor has peaks at 140, 310 and 625 kHz and dips at 225
and 485 kHz. This sensor had a flat response in face-to-face calibration, but the dips appear toresult from wave cancellation, as discussed by Beattie [4]. Sensing element was 19-mm diameterso flexural waves can provide this effect. For Pico and WD sensors, the response curves differ
markedly from factory calibration, which showed smoother curves. It is possible to subtract thecorresponding spectrum of the reference Aerotech sensor. However, the nearly flat PSDresponse curve allowed omitting this step. These spectra (Figs. 2-4) were obtained using AEWin
software.
Effects of reflected waves were minor after adding damping layer on one surface. However,at frequency below 100 kHz, these remained clearly visible as shown in Fig. 4 for AET AC30L.
This sensor has strong responses at 30-60 kHz. Initial weak waves arrived at 62 µs from the excitation pulse, indicating the longitudinal wave velocity of 6.2 mm/µs. This is followed by So
Lamb waves at 74 µs and other modes. At 225 µs in Fig. 4, reflected waves started to arrive.
Both extensional (225-300 µs portion) and flexural (beyond 300 µs) waves were attenuated only
6-7 dB, but the high frequency components are no longer prominent.
258

	
  

	
  
It is important to note that radial responses of these AE sensors are excited with the present
plate-wave excitation method. When a sensor is excited using plane-wave input [1] or using fastrise-time spot laser excitation, initial sensor response is due to reverberating waves between thefront and back faces. In the case of AC30L, radial component at 170 kHz was strong initially,masking the low-frequency sensitivity obtained by mass-loading effect.
	
  

	
  
Fig. 3 FFT results of AE sensors. a) PAC R15. b) AET FC500, c) PAC Pico, and d) PAC WD.
	
  

	
  
Fig. 4 Plate-wave responses of AE sensor: waveform and FFT for AET AC30L.
259

	
  

	
  
The results shown above indicate that the present plate-wave calibration setup provides realistic basis for calibrating AE sensors for plate-wave detection applications. We still need to getabsolute displacement or velocity responses at the calibration position in order to give the procedure the solid basis. One issue is the size of the plate needed. Although the width of the plate
may be cut in half, the length is still insufficient to eliminate low-frequency reflection. However,as long as the complete wave train can be characterized, the entire wave can be used for calibration. Even the plate size used here is large for its handling, two to three times longer plates would
present a difficulty.
Another aspect of plate wave calibration is the orientation dependence. Some sensors, likeshear sensors (DECI SH225), have high sensitivity direction. In the case of SH225, it responds towaves propagating parallel to the shear sensing direction 7 dB higher than to the normal direction. The spectral response was centered at 225 kHz for the parallel direction, but additional subsidiary peaks were present for the normal direction.
3. Bar-Wave Experiment
We next experimented using long aluminum bars as wave propagation media. In order toavoid reflected waves from returning to the calibration position, a longer propagation medium isrequired. However, it is impractical to use wide and long plates. Hayashi and Tanaka analyzed
guided waves in rectangular bars using semi-analytical finite element method [5]. They showed
more numerous modes propagating in a flat Al bar (5 x 100 mm), but some modes are similar toplate waves of the same thickness. They also reported that modes giving higher surface displacements (on the broad bar face) were those propagating at 3.5 mm/µs or higher and having
extensional modes.
Here, we used 6061Al bars with 6.4 x 25.4-mm cross section and 3.66-m long, either straightor bent into a coil with large radii and a 700-mm long flat section. Shorter bars were also used intrials. One end was polished and AET FC500 was attached to this end. Again, step-pulse excitation was applied. Displacement signals were detected using an interferometer as shown in Fig. 5.
At 300 mm from transmitter, signal intensity decreased 3 dB at 10 mm off-center. At 640 mm,
the difference between the center and near edge positions was 1 dB and this distance was selected. Signals lasted well over 600 µ s, indicating the presence of numerous slow-moving
modes. The main motions corresponded to initial So and stronger flexural modes. Reflection canbe ignored as it returns only after 1 ms or more and attenuated fully with plumber’s putty.

	
  Fig. 5 Surface displacement waveforms at 300 and 640 mm from transmitter. Edge position is
100 mm off-center.
260

	
  

	
  
Power spectrum density of the displacement signal shown in Fig. 6 has many peaks and valleys. The strongest peak appeared at 25 kHz and next strongest was at 330±20 kHz. Generally,
the envelope of PSD has a decreasing trend, similar to the inverse frequency squared, found in
the input PSD (Fig. 1). From 25-kHz peak to 1 MHz, PSD decreased about 30 dB. (Note herethat dB scale for power has the multiplier of 10.) Wavelet transform of displacement signal isshown in Fig. 7. Effects of dispersion are visible, especially in the spreading of signal powerover a longer period. Initially, we determined the transfer functions of AE sensors placed on the
bar at 600 mm using deconvolution. However, signal-to-noise ratio was less than 10 dB and results were noisy. We need a stronger pulser to increase the amplitude of surface motion.

Fig. 6 Power spectrum of the main part of displacement
signal at 600 mm.

Fig. 7 Wavelet transform of displacement signal at 500 mm.

Fig. 8 Effect of smoothing filter on displacement signals at 600 mm (280-µs segment).
We then examined effects of propagation distance systematically. Because of 10-20 dB dips
observed in the spectra, a smoothing filter was applied, as given below;
2fi = ∑ [ fi + n ] / ( n + 1)
n = −2where fi is the ith component of power spectral density. This reduced large dips, yet preserved
most peaks and valleys observed. Effect of smoothing filter on the power spectral density of displacement signals at 600 mm can be seen in Fig. 8. All subsequent spectra are treated with thisfilter. Increasing n value to 3 improved the smoothness slightly, but visibly affected peak height.
The smoothed PSD were used in a partial deconvolution scheme (ignoring phase data) to obtain
sensor PSD responses by the frequency-domain divisions; i.e., by subtracting logarithmic valuesof displacement PSD from the corresponding signal PSD values of AE sensor under test.
261

	
  

	
  
Figure 9 show smoothed power spectra of the main part of displacement signals at 100 mm to
600 mm. Left figure is for the distance of 100 to 300 mm over 0-500 kHz and right figure is forthe distance of 400 to 600 mm over 0-1 MHz, respectively. The delay of signal arrival was uncompensated and resulted in some loss of 330-380 kHz components for 600-mm result (signal
used was the segment from 110 to 300 µs). Otherwise, the overall trend of inverse frequencysquared is carried over from 100 mm to 600 mm data, although it is obvious that oscillations arelarger at shorter distances of 100 and 200 mm. In all distances, peaks exist at 25 kHz and around
300 kHz. As noted elsewhere, the 25-kHz peak comes from the radial resonance of FC500.

Fig. 9 Power spectral density of the main part of displacement signals at 100 to 300 mm (left);
Same for the data taken at 400 to 600 mm (right).

Fig. 10 AE sensor output due to displacement signal on the aluminum bar. R15, 600 mm.
As the first step, we placed a PAC R15 sensor at the positions where displacement signals
were recorded; i.e., at 100 to 600 mm from the transmitting FC500, driven by step-down pulses.
An example waveform taken at 600 mm is shown in Fig. 10, which displays output signal from100 to 250 µs. Here, R15 output was directly fed to a digital oscilloscope at 1 M-ohm input impedance and digitized at 20-ns interval, as was the case for the interferometer output. The waveform shows So-arrival at 110 µs, followed by Ao-mode at 170 µs. The latter appears about 10%
faster than expected in an Al plate of the same thickness, while the So-arrival corresponds to 5.45mm/µs speed in agreement with the So-mode in a plate. The sensor response was obtained by thefrequency-domain division of the sensor power spectral density by displacement power spectral
density using the subtraction of respective logarithms at each frequency. Results were converted
to dB scale by multiplying 10 as these are power spectra. Figure 11 plots the obtained spectra foran R15 sensor, where six graphs are given representing the data at various distances. The distance values in mm are noted with each figure.
262

	
  

	
  

Fig. 11 Power spectral density (PSD) representing the sensitivity of an R15 sensor. Vertical scale
is in dB relative to the displacement spectra. Horizontal axis: 0 – 1 MHz.
The variation of the sensitivity spectra of the R15 is remarkably small; All six show the main
peak frequency at 159 kHz and similar peak height (27±1 dB). A few secondary peaks also appear at same frequencies. The main peak seems to be excited by the So-mode waves at 130-160µs, while Ao-mode (170-250 µs) contribute to slightly higher frequency (~200 kHz) output. A
peak at ~580 kHz seems to exhibits large variation in amplitude and this may be related to aspectral scatter in the displacement spectra. This near independence on the propagation distanceon the bar indicates that even though the displacement spectra show large changes as a functionof distance, the spectral division method can be utilized to obtain the sensor spectrum.
Comparing Fig. 11 to the plate calibration result in Fig. 3, one notes the similar main peak
position, but secondary peaks have slightly different frequencies and varying relative peakheights. A peak does seem to exist near 600 kHz, as observed in Fig. 11. Since the reference
spectrum used in the plate method needs further verification, we have to wait for assessing correlation of the two methods.
The use of a shorter propagation distance, like 300 mm, is advantageous as one can use ashorter overall length. A comparison between spectra taken at 300 and 600 mm is favorable in
263

	
  

	
  
using the shorter bar. However, the structure of peaks with 600-mm distance appears more natural and is expected to be more representative of the true spectrum. Use of bent bar shape reduces
the need to carry a long bar and improves the convenience factor.
The sensor calibration curves from the manufacturers are not useful in our discussion here as
almost all of them are obtained by a face-to-face method. The waves we use are plate or bar
waves propagating some distance from a source. Still, the peak resonance frequency of R15
matches what was found in Fig. 11. Since the reference data we utilize is displacement measurement, the sensitivity has the basic dimension of sensor output voltage per displacement or
V/m. However, both are expressed in power spectral density and results are given nondimensionally in relative dB values. Here, it is noted that 10-nm displacement is detected as 1.0
V interferometer output. Thus, 0 dB is nominally 1-V sensor output per 10-nm displacement.
Further study is needed to verify the absolute calibration scale in our case because the sensor response must be integrated over the sensing surface, whereas the displacement was taken at apoint.

Fig. 12 The power spectral density of six broadband sensors. a) V101; b) V103; c) V112; d)
B1080; e) Pinducer; f) WD. See text for details on sensors. PSD in dB vs. frequency (0-1 MHz).
264

	
  

	
  
Bar-Wave Calibration of AE Sensors
Using the method described in the previous section, we determined sensor responses from anumber of sensors from various manufacturers. These include broadband types, such as
Panametrics V101, V103 and V112; Digital Wave B1080; Valpey-Fisher Pinducer (VP-1093);
PAC WD and S9208. Miniature sensors include: PAC S9220, Pico, HD50 and conventional
resonant sensors include: Dunegan S140B, AET AC30L, MAC175L and AC375L; PAC R3 andR6. Results are given in the following figures and appropriate discussion will be provided. Most
data was taken at 600 mm propagation distance. When two curves are show, blue curve wastaken at 300 mm, while red curve was at 600 mm. We anticipate the 600-mm data is the morereliable spectrum. However, these all show that 300-mm data is quite good approximation for the
600-mm data and may be substituted for field use, for example.
Figure 12 shows the power density spectra of six broadband sensors. First three are welldamped ultrasonic transducers (UT). Some of them, like V103 has been widely used in concreteand geotechnical applications [6, 7]. B1080 has an integral FET and shows higher sensitivity.
Pinducer has perhaps the smallest aperture size (of 1.3 mm diameter) among AE sensors with thehighest sensor-element frequency (10 MHz), but requires care to mount it. PAC WD is a popularbroadband sensor based on multiple sensor elements.
These six sensors have broadband response, but can hardly be called flat frequency response.
In detecting propagating bar waves, it is inevitable that geometrical cancellation occurs, explaining some of the dips in the observed spectra. Figure 12 a) and b) show peaked nature moststrongly. These Panametrics UT sensors are difficult to even classify as broadband with a seriesof distinct frequency peaks. V112 (Fig. 12 c) may be called broadband with the sensitivity range

Fig. 13 The power spectral density of a displacement and three small sensors. a) S9208; b)
S9220; c) HD50; d) Pico. PSD in dB vs. frequency (0-1 MHz).
265

	
  

	
  
of only ±5 dB albeit with many peaks and valleys. B1080 is similar to V112, but sensitivity
variation is greater. Among the six, Pinducer (Fig. 12 e) is the smoothest, but it also suffers fromnon-flatness of the spectrum. WD (Fig. 12 f) was designed to have multiple resonances and gives
good sensitivity at the designated frequencies even without integral FET.
Figure 13 a) shows the sensitivity of a displacement sensor (S9208). When used in the barwave detection, this has two distinct peaks and becomes similar to any common resonant sensors. Three other sensors have small sizes. In particular, Pico (Fig. 13 d) has been used often as abroadband detector, even though manufacturer calibration showed a broad peak at ~500 kHz.
The present result indicates its response from 150 to 900 kHz is closer to flatness than any other
we tested. S9220 does have good high frequency response in the bar-wave condition, but not tothe extent shown by the manufacturer calibration.

Fig. 14 The power density spectra of six conventional AE sensors. a) AC30L; b) R3; c) R6; d)S140B; e) MAC175L; f) AC375L. See text for further identification of sensors. PSD in dB vs.
frequency (0-1 MHz).
266

	
  

	
  
Figure 14 shows the spectra of six resonant sensors, commonly used in AE testing. They allshow good sensitivity at the peak frequencies. (Except for R3 and R6, these are vintage sensors,
all 20-30 years old. Additionally, AC30L was slightly lower as it had been subjected to shockwave testing.) Again, these show quite different characteristics in comparison to the manufacturer’s face-to-face calibration curves, as expected. It may be noted that even though some of thesensors have similar element sizes, expected geometrical cancellations do not manifest themselves. We may need to reexamine this aspect more carefully.
When R15, S140B and MAC175 are compared, one finds a large variation in spectral response in terms of the sharpness of the main peak and the location and height of secondary
peaks. All of them have piezoelectric ceramic disks around 12 mm in diameter and 6 mm thickand the differences arise from backing and case design. One may think of them as a group ofnearly identical sensors, but each of them provides unique characteristics.
The low-frequency sensors (AC30L, R3 and R6) appear to rely on mass-loading effect to obtain the low-frequency resonance. In order to monitor low-frequency AE signals, Fig. 14 clearly
demonstrates the need of low-pass filtering the natural resonance response of the sensing piezoelectric element, as manufacturers often omit the high frequency responses of these sensors.
From the results described here, the bar-wave calibration method provides sensor calibration
for common measurement needs. It does require an access to a laser interferometer, but once thebent bar setup with a transmitter is characterized it can be taken to field since it is transportable.
Additional work is needed for getting averaged displacement history (or velocity) covering an
area of typical AE sensor faces. Comparison with the results using a velocity-sensitive interferometer should shed light on the nature of sensor responses.
4. Related Experiments on Sensor Characterization
Out-of-plane sources
In characterizing AE sensors using normally incident waves, we reported previously a useful
setup of using a UT sensor as a transmitter, coupled to a plate [1]. This complements anothersource using a YAG laser as a point-like source [8]. The latter was also used in NPL study [9]and generates spherical waves at the surface where a sensor-under-test is placed. Our setup witha UT transmitter produces waves closer to planar wavefront due to a larger size of the source.
We typically utilize 19-mm diameter FC500 sensor (AET). In the previous study, we used Al
plates of two thickness, 25 mm and 100 mm. We added 12.5 mm Al and 11.7-mm and 48-mm
thick PMMA plates here and obtained the out-of-plane displacement at the epicenter position.
The pulser was the same as before, supplying 280 V step-down pulses (40 ns rise time).
Displacement waveforms obtained on the face of FC500 (shown in Fig. 1, but expanded theinitial part here) and those after passing various thickness plates are shown in Figs. 15 and 16.
The steepest part of the waveforms remains essentially unchanged, but the trailing part varies
systematically with increasing thickness of buffer plate. As can be seen in both figures, the displacement starts to decrease more rapidly as the thickness of the buffer plate becomes larger.
This sharpens the initial broad pulse that lasted over 10 µs into a narrower pulse. With 100-mm
Al plate, the unipolar initial pulse has an extrapolated base width of 0.6 µs [1], while the use of
48-mm PMMA plate produced a pulse width of 2 µs at base. At medium thickness, Figs. 15 b, cand 16 b show double-peak shapes, although the second peaks are broader than the first peaks.
Higher frequency components are dictated by the initial fast-rising part of the displacement and
267

	
  

	
  

Fig. 15 Displacement waveforms from FC500 transmitter directly or with Al buffer plate. a) Direct, b) with 12.5 mm Al plate, c) with 25 mm Al plate, d) with 100 mm Al plate.

Fig. 16 Displacement waveforms from FC500 transmitter directly or with PMMA buffer plate. a)
Direct, b) with 11.7 mm PMMA plate, c) with 48 mm Al plate, d) same over longer period.
268

	
  

	
  
produce useful out-of-plane calibration sources at different thickness. Larger thickness reduces
the displacement amplitude, especially for Al plates but less so for PMMA, but it acts to separate
the radial component of 25 kHz that propagates at shear wave speed. With 48-mm PMMA, theshear waves arrive at 36.5 (=65–28.5) µs while the main out-of-plane displacement step arrives
at 18 (=46.5–28.5) µs.
By placing a sensor-under-test at the epicenter position, we can get the sensor response. As
reported previously [1, 8], the transfer function of a sensor can be determined or the frequency
response obtained using the same procedure described in the previous two sections of this paper.
Actually, this is partly identical to one of the procedures described in ASTM E-976-10 standard,
except E976-10 does not utilize direct optical measurements. By adding the use of modern optical instrumentation, the standard E-976-10 procedures acquire the physical foundation. This isurgently recommended.
Buffer plate effects
Because the steep initial parts of the displacement pulses observed after passing buffer platesof varying properties and thickness remain basically unchanged, sensor responses at varying
thickness should be similar. However, this was not the case for the reason to be discussed in thissection. First, we report changes in the responding waveforms produced by sensors as the thickness of buffer plates increases.

Fig. 17 Output waveforms from a Pinducer. a) face-to-face to FC500 transmitter, b) after passing
12.5-mm PMMA buffer plate and c) after passing 51-mm PMMA.
The first example is given in Fig. 17, which shows the output waveforms from a Pinducer
with a 1.3-mm aperture size; a) face-to-face to FC500 transmitter, b) after passing 12.5-mm
PMMA buffer plate and c) after passing 51-mm PMMA. The Pinducer was directly connected toa digitizer input (1 M-ohms impedance). The response to direct acoustic coupling to FC500 was
a negative-going unipolar pulse, as shown in Fig. 17 a). As the thickness of the buffer plate increases, the Pinducer output changes to bipolar, as can be seen in Fig. 17 b) and c). The amplitude ratio of the second (positive) to first (negative) peaks was 0.58 or 0.6. It is noted that theshape of the first pulse is similar to the direct pulse even after passing PMMA plates; only thesecond peak height grew with increasing plate thickness. This behavior was observed with different combination of plate material (Al, PMMA and steel) and thickness (up to 154 mm wasused).
Next two examples are shown in Fig. 18, where V103 sensor was used with Al and steel
plates. We used this sensor because Manthei used a capillary break to show its apparent velocity
response behavior [7]. In both, the left-most curve is for face-to-face to FC500 transmitter.
269

	
  

	
  
Figure 18 a) shows the changes due to increasing Al plate thickness (data for 13.1 to 46 mm
shown). The amplitude ratio of the second-to-first peaks increased from near zero to 0.34 and0.93 and, at 154-mm thickness, it was 0.87. Similar trend was found with steel plate (25-mm
thickness, Fig. 18 b) and the amplitude ratio was 0.78. The amplitude ratio of the peaks grows
linearly at first and tends to saturate at 0.6, 0.8 and 0.9 for PMMA, steel and Al plates. The saturation is reached at about 25 mm for steel and Al, and for PMMA at 40~50 mm. Again, the peak
shape remains almost identical independent of the thickness. In case of Al plates, the first peakwidth is slightly reduced when the pulse became bipolar; i.e., after passing 25 mm of Al. In allcases, the bipolar pulse is not the result of differentiation of the initial unipolar pulse because thepulse width did not decrease substantially. A slight decrease of pulse width (20-30%) can be seenin Figs. 17 and 18. This implies that we cannot use the bipolar pulse as an indication of the velocity response of a sensor.

(a)

(b)
Fig. 18. a) Output waveforms from V103 sensor. Face-to-face to FC500 transmitter, after passing
13.1, 25.1 and 46.2-mm Al buffer plates. b) Same after passing 25.0-mm steel.
In order to examine whether the medium has an effect on the observed shape change, weused immersion test method with water as transferring medium. The results for Pinducer are
given in Fig. 19. The face-to-face data is similar to Fig. 17 a) with the peak amplitude of –0.4 V.
Figures 19 a) and b) correspond to water paths of 9.0 and 20.0 mm. Both show the bipolar naturein contrast to the unipolar nature of direct contact data (Fig. 17 a). Thus, the presence or absenceof shear waves is not a factor in the shape change behavior. Immersion test results for larger diameter UT sensors with water path of 51 mm are shown in Fig. 20. For V103 (12.7-mm diameter), the second peak height now is larger than the first (Fig. 20 a), while the amplitude ratio for
FC 500 (19-mm diameter) is almost unity (0.98). Thus, the unipolar to bipolar transition in pulse
shape is observed regardless of receiver aperture size. Note that in the immersion tests of threedifferent sensors, the rise time was unchanged as was the case of contact testing.
270

	
  

	
  

Fig. 19. a) Immersion tests for Pinducer. Water path of 9 mm. b) Same as a), but with 20-mm
water path. FC500 transmitter was used.

Fig. 20. a) Immersion test for V103 receiver. Water path of 51 mm. b) Same for FC500 receiver,
51-mm water path. FC500 transmitter was used.
It was noted that a similar waveform change results from band-pass filtering of a unipolar
pulse, as shown in Fig. 21. Here, a signal from V112 was generated by FC500 transmitter, faceto-face (Fig. 21 a) and was fed to a PAC 2/4/6 preamplifier (20-1200 kHz filter, gain at 20 dB).
The output is shown in Fig. 21 b), and has the bipolar nature. Because of the lower input impedance of 2/4/6, actual amplification is 3.15x, much less than the nominal value of 10x, and the polarity was reversed. Obviously, this indicates that cutting off high and low frequency components
produces a similar effect on waveforms, adding the trailing half cycle to a unipolar pulse (amplitude ratio = 0.46) and increasing the pulse width slightly. Naturally, the similarity of pulse shapechange is in form only and based on an unrelated physical phenomenon.

Fig. 21. a) Output from V112 receiver into high impedance input; FC500 transmitter. b) Output
in a) passed through PAC 2/4/6 preamplifier at 20 dB gain with 20-1200 kHz filter.
271

	
  

	
  
Next, we examined the PSD of full- and half-cycle sinusoidal pulses, as shown in Fig. 22.
The two pulses compared have 1-V peak amplitude with periods of 2 µs and 1 µs, respectively(Fig. 22 a); 4 µs-portion shown, 10 ns interval, 8192 points). Their FFT are shown in Fig. 22 b).
The comparison yields the decreasing low-frequency components of the full-cycle pulse, withsimilar high-frequency responses. The PSD value decreased 10 dB going from 400 to 25 kHz.
Thus, the pulse shape transition reflects the loss of low-frequency components, or from high-passfiltering in electrical circuits. Obviously, this arises from the presence of dc component in the
unipolar half-cycle pulse.

Fig. 22 a) Full- and half-cycle sinusoidal pulses, 1 Vp. b) PSD of the two pulses.
It has become apparent that the buffer plates exert similar influence on the frequency spectraof the displacement pulse generated by an UT transmitter. Up to now, the similarity of the initial
fast-varying segments was assumed to provide the similarity in PSD. From the above result, suchan assumption is untenable. Figure 23 illustrates the PSD of six displacement waveforms, given
in Figs. 15 and 16. We used 160 µs segment for this calculation (20 ns interval, 8192 points) andthe same spectral smoothing filter was applied as before.

Fig. 23 a) PSD of displacement signals with Al plates, shown in Fig. 15. 160 µs long segment. b)
Same for signals with PMMA plates, shown in Fig. 16. PSD for FC500 direct and with 100-mm
Al plate shown in both for comparison.
PSD results show clearly higher attenuation of the low-frequency components. In the case of
Al buffer plates, PSD difference at 1 MHz is 17 dB for 100-mm plate and the difference becomes
28 dB at 200 kHz and 32 dB at 20 kHz, respectively. Similarly, for 48-mm PMMA plate, PSD
difference at 1 MHz is 2 dB and the difference becomes 10 dB at 200 kHz and 12 dB at 20 kHz,
respectively. Such differences in PSD are adequate to produce the observed changes in received
waveforms from unipolar to bipolar pulse shape. Consequently, the unipolar responses of AE
sensors, such as Pinducer and V103, can be concluded to be due to their displacement responses.
The apparent velocity response of V103, as reported by Manthei [7], arises from the differential
272

	
  

	
  
attenuation of the low-frequency components in the buffer plate, and it is not from the inherent
sensor behavior.
The observed differential attenuation of the low-frequency components in buffer plates cannot be attributed to the material absorption because such attenuation is higher at higher frequency. This effect is instead due to the directivity of the waves generated by the transmitter. It
is well known that a circular piston radiator (or a piezoelectric disk transducer) has the angle ofbeam spread, θ = sin-1 (λ/D), with λ = wavelength and D = disk diameter [10]. Here, the transmitter (FC500) has D = 19 mm. In Al, θ = 22º at 1 MHz, but λ ~ D when the frequency becomes
300 kHz, implying θ = 90º. The equation for the beam spread cannot apply at such large angles,
but the beam tends to become omni-directional. The beam spread is less in PMMA and water,
but the trend of larger θ is unchanged. In PMMA, wave velocity is 2.67 µs/mm and λ ~ D at 140
kHz.
The present finding points to another problem in AE sensor calibration guidelines per ASTM
E-976-10. In particular, arbitrary choice of buffer plate thickness must be narrowed and the presumed flatness of transmitter PSD must be replaced by the measured PSD using an independent
instrumentation, such as optical interferometers.
Radial mode excitation
It has been well known that the radial resonance of piezoelectric disks provided the foundation of most widely used AE sensors, such as S140B, AC175, R15 and more recently VS150. In
Sec. 3, we showed one of them, R15, responds with the peak frequency of 159 kHz with barwave excitation. In a previous study [1], we showed that the same sensor does not exhibit thisradial resonance until 10+ µs after the initial longitudinal wave arrival (as shown in Fig. 24 c).
Initial response of R15 is due to the thickness reverberation, in agreement with theory [1, 3]. In
contrast, when this sensor is excited face-to-face with a broadband transmitter (FC500, Fig. 1), it
responds immediately from the beginning at the radial resonance frequency (see Fig. 24 a). This
excitation of the radial mode comes from the radial vibration of the transmitter, FC500. The radial motion measured on FC500 under pulse excitation is shown in Fig. 25, where part (a) gives
details of setup using an interferometer. A small metal block is glued to the face just outside thepiezoelectric element and the measuring laser beam was focused at 1-mm height. Figure 25 b)shows the waveform of the radial (or shear) motion, which has 20.4 kHz resonance frequency(the first cycle was at 36 kHz). This radial motion excites the radial resonance of a disk transducer and generates the main peak of sensors such as R15. When R15 is coupled to a Pinducer,
the thickness response is detected clearly (see Fig. 24 b), but the 159-kHz component (6.3 µs period) is difficult to discern.
Using S140B, the same face-to-face result was found, showing the radial resonance (Fig. 26a). When S140B was immersed in a water tank and was separated by 51 mm from FC500 transmitter, only the thickness resonance response is clearly visible. Water effectively eliminates theshear motion generated at the transmitter.
This observation on radial motion also has practical significance in sensor calibration. It is
normal for a sensor manufacturer to provide a calibration curve obtained by the face-to-face calibration method as described in ASTM E-976-10. This standard specifies a transmitter of 2.25-5
MHz UT sensor of 1.25-cm or larger diameter with face-to-face arrangement or through a metal
buffer plate or block. Such a condition does excite sensor resonances and provides a measure of
AE sensor sensitivity. However, the lack of specifics of this procedure contributes to the varia273

	
  

	
  
tions in measuring the sensor response and it is highly doubtful that AE sensor sensitivity canrefer to normal incidence acoustic waves or to any physically measurable standards. As shown in
Fig. 15, displacement waveforms depend on the buffer plate thickness (also PSD, see Fig. 23).

Fig. 24. a) R15 face-to-face with FC500 transmitter. b) R15 face-to-face with Pinducer. c) R15
excited by FC500 transmitter via 100-mm Al buffer plate [same as Fig. 16 of ref. 1].

Fig. 25. a) Measurement setup of FC500 radial displacement using an interferometer. b) Displacement waveform, excited by a step-down pulse.
These can be used to convert ASTM E-976-10 procedures into absolute measurement standard, not just relative guidelines. Since E-976-10 does not specify the metal plate thickness,moreover, the arrivals of longitudinal and shear wave components can be separated differently.
In Fig. 16 d), for example, the shear components arrive 30 µs after the longitudinal waves for 48mm PMMA and for the case of 100-mm Al plate, the delay is also about 30 µs (not shown in
Fig. 15 d). This gives different responses compared to the face-to-face arrangement, where radial
274

	
  

	
  
transmitter motion arrives instantaneously. Conversely, the use of face-to-face calibration excites
the radial resonance of the sensor-under-test. Thus, the method no longer can refer to the “soundpressure” as has been assumed commonly. Therefore, both of these issues further necessitate therevision of E-976-10 procedures in addition to the problems raised earlier.

Fig. 26. a) S140B face-to-face with FC500 transmitter. b) Immersion test of S140B with 51-mm
water path.
Reciprocity Breakdown
Reciprocity AE sensor calibration method has been claimed to be “absolute” [11–13] and had
recent follow-up studies [14, 15]. However, the fundamental assumption of sensor reciprocity
has never been tested or proven experimentally.
We showed previously [1] and here that normal incidence mechanical pulse input to sensors
based on piezoelectric disks results in a series of electrical pulses with the interval equaling thetravel time through the thickness. See Fig. 24 c) for an example in the case of R15 sensor or Fig.
26 b) for S140B. This is in accord with theory [3]. Radial resonance builds up only after several
oscillations [1]. Figure 11 also shows that the same sensor, R15, is most sensitive at 159-kHz
radial resonance when excited by bar waves.

Fig. 27 Displacement response of R15 sensor to a fast-rise step pulse, initial part of the same
waveform, and PSD of displacement waveform.
When an electrical pulse is imposed on a disc-based sensor, it responds immediately via radial resonance and thickness resonance effects are secondary. Figure 27 shows the displacement
response of R15 sensor to a fast-rise step electrical pulse; the initial seven cycles average 146
kHz and PSD at 150 kHz is 24 dB above the thickness resonance frequency of 350 kHz. Thesefindings clearly show the absence of reciprocity.
The same conclusion is reached when we examined the behavior of PAC WD sensor, which
has three (disc and rings) sensing elements. Figures 28 a) and b) illustrate the displacement
275

	
  

	
  
response of a WD sensor to electrical pulse of a fast-rise step and its PSD. This was measured bya laser interferometer. The low-frequency (30-kHz) oscillations are the strongest when WD waselectrically driven. When a plane-wave mechanical pulse excited the WD sensor at normal incidence [1], its electrical output had much higher frequency contents as shown in Fig. 28 c). No
reciprocity is observed in this case as well.

Fig. 28 a) Displacement response of a WD sensor to a fast-rise step electrical pulse, b) PSD ofwaveform in a), c) WD sensor electrical output due to a displacement pulse input. [1]The results given here demonstrate the invalidity of reciprocity calibration for AE sensors[13] because such methods disregard the nature and condition of incident waves as well as thewaves transmitted from the sensors under test. We have previously shown that sensor responses
are sensitive to the nature of incident waves, so that current standards of sensor calibration basedon the NIST procedure (such as ASTM E-1106-10) also need reevaluation. The methods proposed in this work using bar and plate as the propagating media are promising and worth conducting further development.
5. Conclusions
We have conducted a series of experiment for AE sensor characterization aided by laser interferometry and conclude that;(1) Plate-wave and bar-wave calibration schemes work well in characterizing AE sensor underrealistic plate-wave conditions, which simulate practical AE applications. AE sensor behavior
was obtained excluding effects of reflected waves in the bar-wave setup, which is the most promising because of its direct traceability to displacement measurements and of its portability.
276

	
  

	
  
(2) Out-of-plane sources using a UT sensor as a transmitter with or without a buffer plate arecharacterized. Systematic thickness effect on detected sensor output is noted and is attributed to
the frequency-dependent directivity of transmitted ultrasonic beams. Shear motion also plays asignificant role in exciting AE sensor resonances of radial mode.
(3) Break-down of reciprocity principle is demonstrated for AE sensors, showing that the socalled reciprocity sensor calibration method lacks foundation.
(4) Sensor calibration guide, ASTM E976-10, should be revised because of demonstrated variability of PSD as a function of buffer block thickness. Radial motion of transmitter also must be
accounted for in the revised procedure.
Acknowledgment
One of the authors acknowledges fruitful discussion with Dr. A.G. Beattie.
References[1] Ono, K., Cho, H. and Matsuo, T., J. Acoustic Emission, 26, 72-90 (2008).
[2] Ono, K., Cho, H. and Matsuo, T., “Bar-Wave Calibration of Practical AE Sensors”, Proc.
EWGAE 29, Vienna, (2010) p. 426.
[3] Sato M. and Yoshida, Y., J. Acoustical Soc. Japan, 53 (11), 857-863, (1997).
[4] Beattie, A.G., J. Acoustic Emission, 2 (1/2), 95-128 (1983).
[5] Hayashi, T., Tanaka, T., Proc. Japan Soc. Mech. Engr., 72 (717), 743-748 (2006).
[6] Katsaga, T. and Young, R.P., J. Acoustic Emission, 25, 294-307 (2007).
[7] Manthei, G., Bull. Seism. Soc. America, 95 (5), (2005) 1674-1700; “Characterization of
Broadband Acoustic Emission Sensors”, Proc. EWGAE 29, Vienna, (2010) p. 435.
[8] Ono, K., Cho, H. and Matsuo, T., Proc. EWGAE 28, Krakow (2008), p. 25.
[9] Theobald, P., and Pocklington, R., Proc. EWGAE 29, Vienna, (2010), p. 406.
[10] Krautkrämer, J. and H., Ultrasonic Testing of Materials, Springer, (1969) p. 57.
[11] Hatano, H. and Mori, E. J. Acoustical Soc. America, 59 (2), 344-349 (1976).
[12] Hatano, H, Chaya, T., Watanabe, S. and Jinbo, K., IEEE Trans. Ultrasonics, Ferroelectrics,
and Frequency Control, 45, 1221-28 (1998).
[13] "Absolute calibration of acoustic emission transducers by a reciprocity technique," NDIS
2109, Japanese Society for Nondestructive Inspection, Tokyo, Japan, 1991.
[14] Herve, C., Maillard, S., Zhang, F. Jaubert, L., Catty J. and Cherfaoui, M., Proc. EWGAE-28,
Krakow 2008, pp. 1-11.
[15] Keprt J. and Benes, P., Proc. EWGAE 28, Krakow 2008, pp. 19-24.

277

	
  

CUMULATIVE CONTENTS
J. of Acoustic Emission, Volumes 1 - 28, 1982 - 2010
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
Page 41
Page 51
Page 61

Page 68
Page 75
Page 81
Page 87
Page 96
Page 102
Page 111
Page 118
Page 125
Page 131
Page 138
Page 144
Page 150
Page 155
Page 161
Page 167
Page 174
Page 180
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

Page 196
Page 205
Page 211
Page 217
Page 224
Page 232
Page 239
Page 248
Page 258
Page 265
Page 272
Page 279
Page 286
Page 293
Page 299
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

	
  

Volume 28, 2010
28-001 ESTIMATION OF CRUSTAL STRUCTURE IN HORONOBE AREA, HOKKAIDO,JAPAN, USING MULTIPLET-CLUSTERING ANALYSISHIROKAZU MORIYA, KOICHI ASAMORI, ITARU KITAMURA, HIKARU HOTTA,HIDEFUMI OHARA and TADAFUMI NIIZATO
1-10
28-011 ACOUSTIC EMISSION TESTING - DEFINING A NEW STANDARD OF ACOUSTICEMISSION TESTING FOR PRESSURE VESSELS
Part 2: Performance analysis of different configurations of real case testing andrecommendations for developing a new guide for the application of acoustic emission
JOHANN CATTY
11-31
28-032 ACOUSTIC EMISSION MONITORING OF STEEL-FIBER REINFORCED
CONCRETE BEAMS UNDER BENDING
DIMITRIOS G. AGGELIS, DIMITRA SOULIOTI, NEKTARIA M. BARKOULA, ALKIVIADIS
S. PAIPETIS, THEODORE E. MATIKAS and TOMOKI SHIOTANI
32-40
28-041 ON LAMB MODES AS A FUNCTION OF ACOUSTIC EMISSION SOURCE RISE
TIME
M. A. HAMSTAD
41-58
28-059 WAVEFORM ANALYSIS OF ACOUSTIC EMISSION MONITORING OF TENSILETESTS ON WELDED WOOD-JOINTSANDREAS J. BRUNNER, THOMAS TANNERT and TILL VALLÉE
59-67
28-068 USE OF ACOUSTO-ULTRASONIC TECHNIQUESTO DETERMINE PROPERTIESOF REMANUFACTURED PARTICLEBOARDS MADE SOLELY FROM RECYCLEDPARTICLESSUMIRE KAWAMOTO
68-75
28-076 ACOUSTIC EMISSION ACTIVITY OF SPRUCE SAPWOOD BECOMES WEAKER
AFTER EACH DEHYDRATION-REWETTING CYCLE
76-84
SABINE ROSNER and SUMIRE KAWAMOTO

61

	
  
28-085 ACOUSTIC EMISSION SOURCE LOCATION IN PLATE-LIKE STRUCTURES
USING A CLOSELY ARRANGED TRIANGULAR SENSOR ARRAYDIRK ALJETS , ALEX CHONG , STEVE WILCOX and KAREN HOLFORD
85-98
28-099 NEURAL NETWORK AE SOURCE LOCATION APART FROM STRUCTURE SIZEAND MATERIALMILAN CHLADA, ZDENEK PREVOROVSKY and MICHAL BLAHACEK
99-10828-109 INTELLIGENT AE SIGNAL FILTERING METHODSVERA BARAT, YRIJ BORODIN and ALEXEY KUZMIN

109-11928-120 DISCRIMINATION OF ACOUSTIC EMISSION HITS FROM DYNAMIC TESTS OF
A REINFORCED CONCRETE SLAB
ENRIQUE CASTRO, ROSA PIOTRKOWSKI, ANTOLINO GALLEGO and
AMADEO BENAVENT CLIMENT
120-128
28-129 USE OF CLUSTER ANALYSIS OF ACOUSTIC EMISSION SIGNALS IN
EVALUATING DAMAGE SEVERITY IN CONCRETE STRUCTURES
L. CALABRESE, G. CAMPANELLA and E. PROVERBIO
129-141
28-142 SIMULATION OF LAMB WAVE EXCITATION FOR DIFFERENT ELASTIC
PROPERTIES AND ACOUSTIC EMISSION SOURCE GEOMETRIESMARKUS G. R. SAUSE and SIEGFRIED HORN
142-154
28-155 ACOUSTIC EMISSION EVENT IDENTIFICATION WITH SIMILAR TRANSFERFUNCTIONSFRANZ RAUSCHER
155-162
28-163 ANALYSIS OF FRACTURE RESISTANCE OF TOOL STEELS BY MEANS OF
ACOUSTIC EMISSIONEVA MARTINEZ-GONZALEZ, INGRID PICAS, DANIEL CASELLAS and JORDI ROMEU
163-169
28-170 COMPARISON OF ACOUSTIC EMISSION SIGNAL AND X-RAY DIFFRACTION
AT INITIAL STAGES OF FATIGUE DAMAGE
FRANTISEK VLASIC, PAVEL MAZAL and FILIP HORT
170-178
28-179 AE SIGNALS DURING LASER CUTTING OF DIFFERENT STEEL SHEETTHICKNESSES
TOMAŽ KEK and JANEZ GRUM
179-187
28-188 ACOUSTIC EMISSION ANALYSIS AND THERMO-HYGRO-MECHANICALMODEL FOR CONCRETE EXPOSED TO FIRE
CHRISTIAN GROSSE, JOŠKO OŽBOLT, RONALD RICHTER
and GORAN PERIŠKIĆ
188-20362	
  28-204 AE-SiGMA ANALYSIS IN BRAZILIAN TEST AND ACCELERATED CORROSIONTEST OF CONCRETE
MASAYASU OHTSU and YUMA KAWASAKI
204-21428-215 ACOUSTIC EMISSION INSPECTION OF RAIL WHEELSKONSTANTINOS BOLLAS, DIMITRIOS PAPASALOUROS, DIMITRIOS KOUROUSIS andATHANASIOS ANASTASOPOULOS
215-228
28-229 E07.04 – OVERVIEW OF CURRENT AND DEVELOPING ASTM ACOUSTICEMISSION (AE) STANDARDSMARK F. CARLOS
229-­‐233
28-234 USE OF AE METHOD FOR DETECTION OF STEEL LAMINATION IN THE
INDUSTRIAL PRESSURE VESSEL
V.P. GOMERA, V.L. SOKOLOV, V.P. FEDOROV, A.A. OKHOTNIKOV
and M.S. SAYKOVA
234-245
28-246 COMPARISON OF ACOUSTIC EMISSION PRODUCED DURING BENDING OF
VARIOUS OXIDE CERAMIC AND SHORT FIBER OXIDE CERAMIC MATRIXCOMPOSITES
S.A. PAPARGYRI-MPENI, D.A. PAPARGYRIS, X. SPILIOTIS and A.D. PAPARGYRIS
246-275
28-256 NEW CHARACTERIZATION METHODS OF AE SENSORSKANJI ONO, HIDEO CHO and TAKUMA MATSUO

Contents28
AUindex28
AusNotes

256-277

Contents of Volume 28 (2010)
Authors Index of Volume 28
Policy/Author’s Notes/Meeting Calendar/DVD/
Subscription Information
JCAE Kishinoue Awards

I-1 – I-3
I-4
I-5 – I-7
I-7
I-8

AE Literature
AELit28
Book on AE by Markus Sause
12th AE conference proceeding in China, 2009: Gongtian Shen

I-9 – I-11

IAES20Cover illustration

See 28-142 by Sause and Horn for details.

JAE Index Folder*

Cumulative Indices of J. of Acoustic Emission, 1982 – 2010
Contents1-28
Contents Volumes 1-28
Authors Index1-28 Authors Index Volumes 1-28

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

63

Authors Index, Volumes 1-28 (1982-2010)
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
Dirk ALJETS
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
Koichi Asamori
Ikuo Asano
M. Asano,
Hiroshi Asanuma
J. Asquith,
S. K. Athithan

19-045
8-S88
1-165
4-S186
8-S29725-069, 25-308, 27-186, 28-032
21-149
6-85
4-S17424-05221-22316-S150
25-10722-077
2-69
4-S42, 4-107
27-29128-085
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, 28-215
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-S16528-001
4-S24019-13423-064, 23-129, 27-167
18-211
4-S26
1

Y.H.J. Au,
B. Audenard
J. Aviassar
Janine Avison
J. Awerbuch
Masahiro Azemoto,18-196
1-148
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
26-132, 28-109
22-127, 25-042
23-11328-032
2-69
18-87
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-S11728-12026-0601-35, 7-59
11-85
15-S117
4-S178, 4-S300, 6-43, 11-11
12-27
8-S209G. R. Baldwin
Rao Balusu
R.H. Bannister
V. Bansal
J. Baram
Ireneusz Baran,
P. Barat
V. A. Barat
Michel Barbezat,
Roy Baria
Nektaria M. Barkoula
J.A. Baron
A. Barron,
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
Amadeo Benavent ClimentPetr Benes
P. G. Bentley
Avraham Berkovits
C. C. Berndt
J. M. Berthelot,
Yves H. Berthelot,
M. M. Besen

2

D. Betteridge
M. R. Bhat,
D. K. Bhattacharya,Frank S. BiancanielloThomas 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
Yrij Borodin
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,Andreas J. Brunner,
G. Budenkov
R. Budzier
C. Buelens,Аnton Bukatin
E. Bulatova
O. Burenko
Maria Burger
Arie Bussiba,
Stephen D. Butt,
D. J. Buttle

4-71
25-224
4-S102, 8-S149, 11-43
5-S69
14-S47
12-55
4-S321
26-1204-S25520-153
18-279, 20-163, 20-274, 22-138, 28-099
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, 28-215
8-S192, 15-S117
28-10918-2938-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, 28-059
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
3

G. Buzzacchi

2-11

L. Calabrese
28-129CARP Aerospace/Advanced Composites Subcommittee
11-C1
Giuseppe Campanella,25-324, 28-129
Bruce Campbell
3-81
Claudio Caneva,
25-080Nicholas J. Carino
5-S24
Mark Carlos
15-S104, 15-S107, 15-S109, 18-189, 18-248, 18-272, 25-355,
28-229John M. Carlyle
4-S329Rami Carmi,
27-077
Victor Caron
4-S259, 4-115
Steve H. Carpenter,
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
Damian Carter
17-49
M. Cartoceti
2-11
Daniel Casellas
28-163
Dick W. Caster
9-197
Enrique Castro
26-142, 26-247, 28-120
Johann Catty
18-205, 27-299, 28-011
Jose A. Cavaco
27-001
M. Cerny
17-S20J. Cerv
20-025
Hwakian Chai,
27-186
Roger W.Y. Chan
3-118, 4-S259, 4-115, 8-S12
C. Chang
4-S62
Chung Chang
7-21
C. Chapelier
5-S52
T. Chelladurai
8-S88, 12-111
D. Chellman
4-S263
Chung-Mei Chen
7-161
Jihua Chen
19-001
M.C.Cheresh
2-289
M. Cherfaoui
5-S66, 22-071, 11-1, 18-144
Michael J. Chica
9-197
A. Chichibu
8-107
Chang-Sheng Chien,
15-S118
Y. S. Chin
11-71
Pornthep Chivavibul
23-091, 24-076
Robert Chivers
10-123
Akiyoshi Chichibu,
11-S47, 12-S1
Milan Chlada
17-S57, 20-134, 28-099
Frantisek Chmelík
17-S29, 20-108
Hideo Cho
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,
4

N. S. Choi
Alex Chong
A-A. R. Choudhury
T. Chow
Y. T. Chow
Min-Hwa Chung,
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

28-25616-S32428-08516-S125
8-S166
4-71
13-1
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,

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
22-2535

M. Diez
Seth-Andrew T. Dion,John J. Ditri,
Nitin Dhond
G. Dionoro
Jason W.P. Dong
Jason Dong,
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,
R. M. Esbert
V. A. Eshwar

4-S29625-187
12-23
4-S30
2-281
16-S1
25-35522-2363-242, 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
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
6

Sam Evans,
W. T. Evans

25-21511-71

A. Fahr,S. D. Falls
Daining Fang
Ahmed M. Farahat
J.-P. Favre
Carol Featherston,
V. P. Fedorov

8-S314, 12-39, 15-S122
8-S166
11-85
11-S37
9-97
25-14020-218, 26-279, 28-234

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
Kenzo Fukaura
T. Fuketa,
Hiroyuki Fukutomi
Yasushi Fukuzawa,
Roy D. Fultineer, Jr.
Masami Fushitani,
Takashi Futatsugi

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
19-091, 21-112
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. Gardiner26-032, 27-254
16-S150
26-142, 26-247, 28-120
4-S166
4-S199
7

John Gary,
Michael F. Gasick
Ludwig Gauckler
B. K. Gaur
Stephen N. Gautrey
A. Gavens
Maochen Ge
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,
Christian U. Grosse
C. M. Grossi
Janez Grum
N. Gsib
P.-Y. GuF. J. Guild
T. J. Gulley12-157, 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
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, 28-234
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, 28-188
10-S3526-199, 28-179
5-S60
8-S188
1-244
4-S1708

M. Gullikson
Dawei Guo
Hua GuoYanfeng Guo
B.C. Gupta

26-02314-S19, 16-S222
23-11916-S317
8-S217, 9-142

Robert A. Haack
Y. Haddad
M. E. Hager
H. Thomas Hahn
P. Hähner
Shigenori Hamada
Takashi Hamada
Darulihsan A. Hamid,
A. Hampe
M. A. Hamstad

9-181
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, 28-041
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
26-16019-03521-131

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

9Yasuhisa Hayashi
Jianmei He
R. Hecker
C. R. Heiple
D. P. Henkel,
Edmund G. Henneke II
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
Hikaru Hotta
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

7-185, 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
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, 28-085
4-S132, 7-193, 8-S219
10-35
4-S259, 4-115
13-S61
4-S30419-02221-206, 21-223
26-001, 28-142
8-S79
26-189, 28-170
28-001
8-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
26-12010

Donald H. Humes
Wolfgang Hundt
U.-D. Hünicke
David A. Hutchins
David V. Hutton
H. Hutton

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
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 IshidaTakeshi 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,

4-S32519-14220-001
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

12-27
4-S119, 5-77
2-29, 8-S53
4-S102, 7-S1, 8-S126, 8-S140, 8-S149, 11-43
11J. S. Jeng
Hee-Don Jeong,
B. B. Jha
S. K. Jha
Kyung-Young Jhang
C.Y. Jian,
C. G. Jiao
Takehiko Jibiki
K. Jo
J. 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-S268
13-S616-209, 8-S149
7-S43
16-S261
13-S68
8-S10524-173
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,

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
25-36412

Chu-Shu Kao,
Justin O. Karl,
Bo Karlsson
Naoya Kasai
M. KatChiaki Kato
Tatyana Katsaga,
K. Katsuyama
Kunihisa Katsuyama
Harold E. Kautz,
M. Kaveh,
Tatsuya Kawada
Yoshiaki Kawaguchi,
Yutaka Kawai,
Teppei Kawakami
Kenji Kawamoto
Sumire Kawamoto
Hiraku Kawasaki,
Yuma KawasakiTomaž 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

27-17625-18726-01427-281
8-99
19-05325-294
11-S1911-S275-144, 12-65
27-17624-21512-127
7-167
21-1499-109
28-068, 28-076
27-28128-20426-199, 28-179
4-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
13

Takahiro Kishishita
N. N. Kishore
P. Kisnomo
Tatsuo Kita
K. Kitadate
Itaru Kitamura
K. Kitano
Shigeo Kitsukawa
Kiyoshi Kiuchi
R. A. Kline
W. Knabl
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 Kourousis
Torsten Krietsch
R. Krishnamurthy
Vladimir Krivobodrov,
J. Krofta,

10-S55, 11-S47
8-S297
15-33
10-S908-S13128-001
10-S4223-23319-0534-S42, 4-107, 6-205, 8-S246
20-257
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, 28-215
16-S233, 16-S343
4-S26, 8-S88
13-87
18-279, 20-274
14

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,
Moshe Kupiec,
D. S. Kupperman
Yu Kurokawa
Krzysztof J. Kurzydlowski
Yasufumi Kusano,F. M. Kustas
T. Kusu
Alexey Kuzmin
May Man Kwan
Oh-Yang Kwon
H. Kwun,12-14918-17415-S116
13-S42
8-S205
7-S13
23-129, 27-167
7-135
1-95, 16-S10
19-13427-26327-077
4-S69
23-224, 24-145
25-166
13-S75
10-97
21-14228-1093-144, 3-190
4-S106, 9-123, 9-227, 9-237, 13-1, 13-S83, 15-S1,
15-S19, 16-S212
11-27

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 Lazarev
Sergey 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

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

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,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

15-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
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-S20316

Krystyna Lublinska,
P. Lukác
K. Lundgren
Xiu Luo
Xun Luo
D. Lupascu

25-16617-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
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
Eva Martinez-Gonzalez
Hiroaki Maruyama
Seiji MasanoA. Maslouhi,
Y. MasuiS. Mata
O. Matal
Kristian Mathis
Theodore E. Matikas
Takuma Matsuo
K. Matsuura
Kimitoshi Matsuyama
Ryosuke Matsuzaki
Huber Matysiak
S. C. Maxwell

4-S58
8-S4
23-091, 27-233
20-0251-223, 1-229, 2-39, 2-179, 3-1, 4-S151
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-S14228-16323-23327-241
8-S292, 12-45, 15-S122, 16-S299
23-18917-23
17-S6520-10828-03224-067, 24-084, 25-124, 26-072, 27-224, 28-256
21-12011-S65, 15-S40
27-08926-317
8-S38
17

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,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 Mogi
James Mohr
Waldemar Molinski
Florian Moll
M. Momayez
Shohei Momoki,
Keiichi Monma
M. Montoto

23-224, 23-285, 24-145, 25-238
17-S2, 17-S20, 17-S70, 18-75, 26-189, 28-170
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
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-S3518

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
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

26-142
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, 28-001
7-95
21-21316-S105, 18-1
12-S7
8-S317
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

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
19

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,K. P. Nerz
John P. Newhook
Eng T. Ng
K. D. Nicklas
Jackson A. Nickerson
A. NielsenPeter Niemz
Hiroaki Niitsuma
Tadafumi Niizato
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,

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
23-142
18-96
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
28-00124-111
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
20

Y. Obata
S. I. Ochiai
Satoshi Oda,
A. O’Gallagher
Takashi Ogata
Takeshi Ogawa
Steve L. Ogin
S. Ogino
Eu Seok Oh
Hidefumi Ohara
Yoshitugu Ohigashi
Takanori Ohira
Hironobu Ohishi
A. OhmoriTatsuma Ohnishi
Tadashi Onishi,
Kentaro Ohno
Isamu Ohsawa,
Shiro Ohta
M. Ohtsu

H. Ohyama
M. I. Ojovan,
Takahisa Okamoto,
T. Okamoto
Takeshi Okano
A.A. Okhotnikov,
Nobuhiro Okude,
S. Okumura
L. Okushima
Kanji Ono,

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
28-001
10-25
4-S27423-06421-20619-10925-23823-047
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, 28-204
4-S28225-051
13-S14
16-S75
4-S240
18-111, 20-218, 28-234
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,
28-25621

M. Onoe
Irving J. Oppenheim,
Hisanori Otsuka
P. Ouellette
C. Ouyang
H. Oyaizu
K. Ozawa
Joško Ožbolt
28-188Didem Ozevin,5-i(4)
25-115
10-S139-37, 11-65
10-S97
8-S1
21-166

Alkiviadis S. Paipetis
Jie Pan
Yih-Hsing Pao
Dimitrios Papasalouros
S.A. Papargyri-Mpeni
A.D. Papargyris
D.A. Papargyris
Krystian Paradowski,
Philip Park
Young-Jin Park
H. B. Patel
S. C. Pathak
J. Pavelka
Martyn Pavier
J. Pazdera,
L. Pazdera,
Martin Peacock
L. H. Pearson
Peter Pellionisz
R. Pensec,
L. V. Perez
Goran Periškić
Marianne Perrin
D. T. Peters,
Tatiana B. Petersen
S. Peteves
J. Petras,
L. Petras,
J. Petrasek
Christian Pfleiderer,
T. P. Philippidis,
Romana Piat,
Ingrid Picas
Rodney G. Pickard
C. Picornell
Aleksander Pilarski,
S. Pilecki,
J. Pininska,
Rosa Piotrkowski

28-03222-264, 22-274
4-S27428-21528-24628-24628-24625-16615-S11
15-S11
8-S12, 8-S101
4-S32, 4-S147, 8-S122
17-S100, 22-091
27-291
18-81
18-29
4-S166, 8-S240, 8-11
4-S19910-19
18-125
10-13
28-18826-032
8-S4
27-09820-265
18-75
18-81
17-S8312-141
13-11, 17-69, 20-229
27-07728-163
15-79
5-S49
12-23
10-1, 12-149
18-8
17-61, 26-142, 26-247, 28-120

25-35522

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. ProustE. Proverbio
Jose Pujol
R. Pullin
M. I. López Pumarega
K. K. Purushothaman,

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, 28-099
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
28-12917-11122-166, 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

M. RaabAmani RaadJan Raczkowski
Andre Raharinaivo
Baldev Raj,

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, 28-155
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

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,

23

W. G. Reuter
L.E. Rewerts
Marie-Christine Reymond
M. W. Richey
J. Richter
Ronald 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. RomanJordi Romeu
Igor Maria De Rosa,
A. P. G. Rose
Joseph L. Rose,Z. Rosecky
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

4-S26915-S1072-159, 4-S296, 5-S63
6-257
17-S7028-1882-29
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
28-16325-080
1-213
12-23
20-02522-110, 25-149, 26-014, 28-076
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

12-45
4-S62, 8-S20, 8-S170, 11-79
5-S16
26-10913-S6815-19, 16-S289
10-S104
15-S5024

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. SatoKeiichi Sato,
Kouichi Sato
Takashi SatohMarkus G. R. Sause
S. G. Savanur
M.S. Saykova
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

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-S45
4-S240, 8-S213, 9-209, 13-S42
7-173
23-10226-001, 28-142
7-S18
28-23418-2112-261, 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

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,
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

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
27-19419-196
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, 28-032
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-S17426

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
Dimitra Soulioti
P. Souquet
J.C. Spanner, Sr.
L. M. Spasova,
Th. Spies
X. Spiliotis
Nicholas S. Spivey,
Wojciech Spychalski,
M. K. Sridhar
K. V. Srincivasan,
T.S. Sriranga
K. A. Stacey
J.A. Steel,
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,

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, 28-234
4-S17
23-12916-S309
2-261
11-11
28-0324-85, 5-S60, 8-S231
6-121
25-051
15-S119, 19-100, 19-153, 24-179
28-24625-18725-166, 26-317
4-S174
8-S8
7-S35
7-81
18-96, 18-211, 25-348
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-S5427-08927-04027

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
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,
S. Takashina,
Nobuo Takeda,
Mikio Takemoto

Hajime Takeuchi
K. Takigawa
Masanori Takuma
S. Talebi
Okiharu Tamura
H. Tanaka
M. Tanaka
T. TanakaToshiyuki Tanaka

4-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
24-18725-10727-23315-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
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-S2137-173
28

S. Tanary,
N. Tandon
Daiki Tani
Yoshihiro Taniyama,
Thomas Tannert
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
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

8-S314, 12-39
9-25, 17-23
24-15325-15728-05915-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
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
21-17629

N. TsuiNobuyuki Tsuji
Yusuke Tsukahara,
Koichi Tsukiyama
F. R. Tuler

21-21315-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,
Till Vallée
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
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
Frantisek Vlasic
L.E. Vlasov,3-i(1), 4-S329, 15-S109, 18-189, 18-248, 18-272, 19-172
5-S12
12-11728-05918-167, 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-S30720-285
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-S20728-170
18-15030

Aaron C. Voegele
J. von Stebut,
Y. Vougiouklakis

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
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
27-29128-085
10-S22
10-67
1-244
2-191
Icae Banquet
20-206, 22-253
27-291
10-67

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
M. Wevers,
R. G. WhiteJ. W. Whittaker
Paul D. Wilcox,
Steve Wilcox
B. J. S. Wilkins
H. Willer
A. J. Willis
P. E. Wilson
Leo Windecker
M. Winkelmans
Michael R. Wisnom,
E. Winter

31

S. M. Wolf
A. Wolfert
Jörg Wolters
Brian R. A. Wood
J. D. Wood
D. G. M. Wood
G. Wormser
Amelia P. Wright,
Bobby Wright
Min WuWei Wu,Doone Wyborn

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. YanM. 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,

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
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
32

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,
Daihua Zou
Ryszard Zuchowski
J. Zuidema

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
5-S1
2-272
8-S11833