JOURNAL OF ACOUSTIC EMISSION
VOLUME 22, 2004
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ISSN 0730-0050

Copyright©2004 Acoustic Emission Group

Coden JACEDOCONTENTS
22-S01

THE KAISER-EFFECT AND ITS SCIENTIFIC BACKGROUND
HANS MARIA TENSI

22-001

S1

MODAL-BASED IDENTIFICATION OF ACOUSTIC EMISSION SOURCES
IN THE PRESENCE OF ELECTRONIC NOISE
M. A. HAMSTAD and A. O’GALLAGHER

22-A01*

Appendix A: DETAILS OF THE APPLICATION OF THE SOURCEIDENTIFICATION SCHEME
M. A. HAMSTAD and A. O’GALLAGHER

22-02222-04922

QUANTITATIVE DAMAGE ESTIMATION OF CONCRETE CORE BASEDON AE RATE PROCESS ANALYSIS
MASAYASU OHTSU and TETSUYA SUZUKI

22-039A1

EXPERIENCE WITH ACOUSTIC EMISSION MONITORING OF NEWVESSELS DURING INITIAL PROOF TESTPHILLIP COLE and STEPHEN GAUTREY

22-0301

30

DAMAGE ASSESSMENT IN DETERIORATED RAILWAY SUBSTRUCTURES USING AE TECHNIQUETOMOKI SHIOTANI, YASUHIRO NAKANISHI, XIU LUO and HIROSHI HAYA
39
DEFECT DETECTION BY ACOUSTIC EMISSION EXAMINATION OF
METALLIC PRESSURE VESSELS
I-1

FRANZ RAUSCHER
22-05949

EVALUATION OF ACOUSTIC EMISSION SIGNALS DURING
MONITORING OF THICK-WALL VESSELS OPERATING AT ELEVATEDTEMPERATURES.
ATHANASIOS ANASTASOPOULOS and APOSTOLOS TSIMOGIANNIS 59

22-07122-077

ACOUSTIC EMISSION TECHNIQUE AND POTENTIAL DIFFERENCEMETHOD FOR DETECTING THE DIFFERENT STAGES OF CRACKPROPAGATION IN CARBON AND STAINLESS STEELS
C. ENNACEUR, A. LAKSIMI, C. HERVÉ, M. MEDIOUNI and M. CHERFAOUI71
DETECTION OF INCIPIENT CAVITATION AND BEST EFFICIENCYPOINT IN A 2.2MW CENTRIFUGAL PUMP USING ACOUSTIC EMISSION
L. ALFAYEZ and D. MBA

22-08377

INVESTIGATION OF FRACTURE PROCESSES USING MOMENT TENSOR
INVERSION TECHNIQUE
F. FINCK, C. U. GROSSE and H.-W. REINHARDT

22-09183

AE KAISER EFFECT AND ELECTROMAGNETIC EMISSION IN THE
DEFORMATION OF ROCK SAMPLEYASUHIKO MORI, YOSHIHIKO OBATA, JAN PAVELKA, JOSEF SIKULA and
THOMAS LOKAJICEK91

22-102

ACOUSTIC EMISSIONS FROM TRANSPIRING PLANTS– NEW RESULTS
AND CONCLUSIONSRALF LASCHIMKE, MARIA BURGER and HARTMUT VALLEN

22-110

ACOUSTIC DETECTION OF CAVITATION EVENTS IN WATERCONDUCTING ELEMENTS OF NORWAY SPRUCE SAPWOOD
SABINE ROSNER

22-119110

AE MONITORING FROM CVD-DIAMOND FILM SUBJECTED TO MICROINDENTATION AND PULSE LASER SPALLATION
R. IKEDA, H. CHO, M. TAKEMOTO and KANJI ONO

22-127

102

119

COMPOSITES FROM PIEZOELECTRIC FIBERS AS SENSORS AND
EMITTERS FOR ACOUSTIC APPLICATIONSANDREAS J. BRUNNER, MICHEL BARBEZAT, PETER FLÜELER andCHRISTIAN HUBER
127

22-138PROCESSING OF AE SIGNALS IN DISPERSIVE MEDIAMICHAL BLAHACEK, ZDENEK PREVOROVSKY, and MICHAL LANDA 138

22-147BASIC PRINCIPLES OF ACOUSTIC EMISSION TOMOGRAPHYFRANK SCHUBERT

22-159147

ACOUSTIC EMISSION BEHAVIOR OF MARTENSITIC

I-2

TRANSFORMATION DURING DEFORMATION OF Cu-Al-Ni SHAPEMEMORY ALLOY SINGLE CRYSTALSKENICHI YOSHIDA, KOTARO HANABUSA and TAKUO NAGAMACHI 159
22-166

ACOUSTIC EMISSION MONITORING OF CONCRETE HINGE JOINTMODELSK. M. HOLFORD, R. PULLIN and R. J. LARK

22-173

CHARACTERIZATION OF ACOUSTIC EMISSION SOURCES IN A ROCKSALT SPECIMEN UNDER TRIAXIAL LOADGERD MANTHEI

22-190173

TESTING OF DIAMOND-LIKE CARBON COATINGS UNDER SLIPROLLING FRICTION MONITORED BY ACOUSTIC EMISSIONMANUEL LÖHR

22-201190

FIELD TESTING OF FLAT BOTTOMED STORAGE TANKS WITH
ACOUSTIC EMISSION – A REVIEW ON THE GAINED EXPERIENCEGERALD LACKNER and PETER TSCHELIESNIG

22-208208

ACOUSTIC EMISSION FROM RUST IN STRESS CORROSION CRACKINGHIDEO CHO and MIKIO TAKEMOTO

22-236236

RODS AND TUBES AS AE WAVEGUIDES
KANJI ONO and HIDEO CHO

22-253224

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. ZIEGLER

22-243201

ACOUSTIC EMISSION EXAMINATION OF POLYMER-MATRIXCOMPOSITESJÜRGEN BOHSE

22-224166

243

ACOUSTIC EMISSION FOR ON-LINE MONITORING OF DAMAGE IN
VARIOUS APPLICATION FIELDSMARTINE WEVERS, GERT VAN DIJCK, WENDY DESADELEER, MARK
WINKELMANS and KOEN VAN DEN ABEELE
253

22-264

THE EFFECT OF WAVEGUIDE MATERIAL AND SHAPE ON ACOUSTICEMISSION TRANSMISSION CHARACTERISTICS - PART 1:
TRADITIONAL FEATURESJOANNA SIKORSKA and JIE PAN

22-274264

THE EFFECT OF WAVEGUIDE MATERIAL AND SHAPE ON AE
TRANSMISSION CHARACTERISTICS - PART 2: FREQUENCY AND
JOINT-TIME-FREQUENCY CHARACTERISTICSJOANNA SIKORSKA and JIE PAN

I-3

274

Contents22.pdf
AuIndex22.pdf
AusNotes.pdf
Berlin2004.pdf
InMemorium.pdf
Tensi.pdf

Contents of Volume 22 (2004)
Authors Index of Volume 22
Policy/Author’s Notes/Meeting Calendar/Subscription Information
Activities at 2004 EWGAE Meeting;
Dick Blackburn (T.F. Drouillard)Professor H.M. Tensi

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

EWGAE Folder*2004 and 2006 EWGAE Meetings, 2004 Program, 2006 Local information

JAE Index Folder*

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

* indicates the availability in CD-ROM only.

Photograph credit: CD-ROM label, EWGAE photos by Hartmut Vallen; Cover photos from
H.M. Tensi article; EWGAE photo by Vaclav Svoboda; Dick Blackburn photo: JAE file photo.
I-4

AUTHORS INDEX OF VOLUME 22 (2004)
L. ALFAYEZ
22-077
A. ANASTASOPOULOS
22-059MICHEL BARBEZAT,
22-127MICHAL BLAHACEK,
22-138JÜRGEN BOHSE
22-208
ANDREAS J. BRUNNER, 22-127
MARIA BURGER
22-102
M. CHERFAOUI
22-071
H. CHO,
22-119, 22-224, 22-243
PHILLIP COLE
22-022WENDY DESADELEER, 22-253
P. DÖRNER,
22-236
T.F. DROUILLARD
22-I10C. ENNACEUR,
22-071
F. FINCK,
22-083
PETER FLÜELER
22-127
R.P. FRANKE,
22-236
STEPHEN GAUTREY
22-022
C. U. GROSSE
22-083M. A. HAMSTAD
22-001, 22-A01
KOTARO HANABUSA
22-159HIROSHI HAYA
22-039
C. HERVÉ,
22-071K. M. HOLFORD,
22-166CHRISTIAN HUBER
22-127
R. IKEDA,
22-119GERALD LACKNER
22-201
A. LAKSIMI,
22-071MICHAL LANDA
22-138R. J. LARK
22-166
RALF LASCHIMKE,
22-102
MANUEL LÖHR,
22-190
THOMAS LOKAJICEK
22-091XIU LUO
22-039
GERD MANTHEI
22-173
D. MBA22-077
M. MEDIOUNI
22-071YASUHIKO MORI,
22-091TAKUO NAGAMACHI
22-159YASUHIRO NAKANISHI, 22-039
YOSHIHIKO OBATA,
22-091
A. O’GALLAGHER
22-001, 22-A01
F MASAYASU OHTSU
22-030
KANJI ONO
22-119, 22-243

JIE PAN22-264, 22-274
JAN PAVELKA,
22-091ZDENEK PREVOROVSKY, 22-138
R. PULLIN
22-166RANZ RAUSCHER
22-049
H.-W. REINHARDT
22-083SABINE ROSNER
22-110FRANK SCHUBERT
22-147
H.-J. SCHWALBE
22-236TOMOKI SHIOTANI,
22-039JOANNA SIKORSKA
22-264, 22-274
JOSEF SIKULA
22-091TETSUYA SUZUKI
22-030M. TAKEMOTO
22-119, 22-224
HANS MARIA TENSI
22-S01
PETER TSCHELIESNIG
22-201
A. TSIMOGIANNIS
22-059HARTMUT VALLEN
22-102
KOEN VAN DEN ABEELE 22-253
GERT VAN DIJCK,
22-253MARTINE WEVERS,
22-253
MARK WINKELMANS
22-253
KENICHI YOSHIDA,
22-159B. ZIEGLER
22-236

I-5

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
responsible for the AE Literature section. The members of the
Editorial Board are selected for their knowledge and experience on
AE and will advise and assist the editors on the publication policies
and other aspects. The Board presently includes the following
members:

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 atmeetings, technical notes, communications and sum-maries ofreports. Current news of interest to the acoustic emis-sioncommunities, announcements of future conferences and working
group meetings and new products will also be included.
Journal of Acoustic Emission includes the following classes ofsubject 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 advances inthe engineering applications of acoustic emission. Material will be
subject to reviews for adequate description of procedures,substantial database and objective interpretation.
C. Technical Notes and Communications: These allow publications of short items of current interest, new or improved experimental techniques and procedures, discussion of published articles
and relevant applications.
D. AE Literature section will collect the titles and abstracts ofpapers published elsewhere and those presented at meetings andconferences. Reports and programs of conferences and symposia
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E. AE Program and Data Files: Original program files and data
files that can be read by others and analyzed will be distributed in
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Reviews, Tutorial Articles and Special Contributions willaddress the subjects of general interest. Nontechnical part will coverbook reviews, significant personal and technical accomplishments,
current news and new products.

A. A. Anastassopoulos
F.C. Beall
J. Bohse
P. ColeL. Golaski
M.A. Hamstad
H.R. Hardy, Jr.
K. M. Holford
O.Y. Kwon
J.C. Lenain
P. Mazal
S.L. McBride
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I. RomanTomoki Shiotani
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4. Publication

Journal of Acoustic Emission is published annually in CDROM by Acoustic Emission Group, PMB 409, 4924 Balboa Blvd,
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2. Endorsement

5. Subscription

Acoustic Emission Working Group (AEWG), European
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Subscription should be sent to Acoustic Emission Group.
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3. Governing Body
The Editor and Associate Editors will implement the editorial
policies described above. The Editorial Board will advise the edi
tors on any major change. The Editor, Professor Kanji Ono, has thegeneral responsibility for all the matters. Associate Editors assist thereview processes as lead reviewers. Mr. Thomas F. Drouillard is
ISSN 0730-0050 Copyright©2004 Acoustic Emission Group(Greece)(USA)(Germany)(UK)(Poland)
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No advertisement will be accepted, but announcements forbooks, training courses and future meetings on AE will be included
without charge.
I-6

CODEN: JACEDO

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Notes for Contributors
be grouped together at the end of the paper in alphabeticaland chronological order. Journal references should bearranged as below. Titles for journal or book articles arehelpful for readers, but may be omitted.

1. General
The Journal will publish contributions from all parts of theworld and manuscripts for publication should be submitted
to the Editor. Send to:

H.L. Dunegan, D.O. Harris and C.A. Tatro (1968), Eng.
Fract. Mech., 1, 105-122.

Professor Kanji Ono, Editor - JAE
6531 Boelter Hall, MSE Dept.
University of California
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FAX (1) 818-990-1686
e-mail: ono@ucla.edu

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 for inclusion in the AE Literature section to:
Mr. T.F. Drouillard, Associate Editor - JAE
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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
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MEETING CALENDAR:
The 48th Meeting of AEWG
The next meeting of AEWG will be at Holidays Inn, NASA, Houston, TX on May 9-12, 2005. Send
inquiry to Napoleon Douglas at napoleon.douglas@stress.com. The primer will be held on May 9, 2005.
Also see AEWG48.pdf in this CD-ROM. Registration forms, as well as other information regarding thisconference, can be found at: http://www.stress.com/aewg48.php/

E-GLEA 4 Meeting 2005

This 4th Meeting of Latin American Acoustic Emission Group (GLEA), with the collaboration of the
University of Messina, Sicily, Italy, will be held at Taormina, Sicily, Italy, October 16-19, 2005. Send
an inquiry or abstract to GLEA at glea@cnea.ar. Also contact G. Sacca: gsacca@unime.it. See theattachment of GLEA4.pdf in this CD-ROM.

EWGAE2006
27th European Conference on Acoustic Emission Testing will be held from 20th to 22nd of September
2006, in Cardiff, Wales, UK. The meeting is sponsored by EWGAE and Cardiff University. Details will
be announced later, but the local information can be found in the PowerPoint file in this CD-ROM under
EWGAE folder. Dr. Karen M. Holford will be the organizer.

IAES18, The 18th International AE Symposium
The IAES-18 will be held in Tokyo in the mid-July. The venue will be Aoyama Gakuin University near
Shibuya, Tokyo. It is sponsored by Japan Society for Non-Destructive Inspection. Professor Mikio
Takemoto will be the organizer.

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For inquiry through Internet, use the following address:
Editor-Publisher
Publication Date of This Issue (Volume 22):
I-8

ono@ucla.edu
Kanji Ono Tel. (310) 825-5233
24 January 2005.

Activities at EWGAE 2004

EWGAE2004 participants.

Dr. V. Svoboda opening the meeting;

Mr. H. Vallen with Banquet speaker, Prof. Tensi.

Late dinner after an evening city tour of Berlin.
I-9

In Memorium

PHILIP RICHARD (DICK) BLACKBURN(1937-2003)

It is with deep sorrow that we learned of the passing of one of our esteemed colleagues.
Dick Blackburn passed away in his home in Buffalo, New York, Sunday, December 7,
2003, of diabetes-related causes. He was 66 years old. Dick is survived by his threedaughters, Melanie Hoffman of Bethesda, MD, Elizabeth Dziersk of Winnetka, IL, and
Amy Gable of Aliso Viejo, CA; his former wife, Martha Rumage Blackburn of
Albuquerque, NM; his mother, Margareta Blackburn of Rosemont, PA; a sister, Judith
Hawkins of Mount Holly, NJ; and six grandchildren.
Dick Blackburn was born January 4, 1937 in Philadelphia, PA. He received his B.S.
degree in aeronautical engineering in 1959 from Rensselaer Polytechnic Institute inTroy, NY.
After spending over a decade at various aircraft and aerospace companies, Dick joined
the engineering staff at Union Carbide Corp., first in Indianapolis, IN, then in Tarrytown,
NY. In 1976 he moved to Tonawanda, NY with the Linde Division of Union Carbide,
which later became Praxair Corp. He spent over 25 years in the development of plant
operations in the industrial gas business. Dick implemented AE testing of gas cylinders
and of trailer tubes in lieu of hydrostatic testing. After retiring from Praxair in 1995, hebecame a consultant in acoustic emission.

I - 10After becoming involved in acoustic emission testing, Dick began attending AEWG
meetings, his first being the 21st at Ithaca, NY in 1980. He became a member of the
AEWG at the 26th in 1984. At the 37th meeting in Sacramento in 1994, Dick received
the AEWG Achievement Award "...in recognition of his pioneering work in thedevelopment, application, and acceptance of an acoustic emission technique to test
compressed gas tubes, his leadership in writing ASTM Standard E 1419-91, Standard
Test Method for Examination of Seamless, Gas Filled, Pressure Vessels Using Acoustic
Emission, and his efforts in getting this concept accepted by the Compressed Gas
Association and other technical bodies throughout the world." At the following meetingin 1995, he received the AEWG Fellow Award. In 1997, Dick was elected an officer ofthe AEWG, serving as Secretary-Treasurer of the 40th and 41st meetings, ViceChairman of the 42nd and 43rd meetings, and Chairman of the 44th and 45th meetings.
He was Past-Chairman when he passed away. Dick was a member of the American
Society for Testing and Materials (ASTM), serving as Secretary of Subcommittee E7.04
on Acoustic Emission Testing; and a member of the American Society for
Nondestructive Testing (ASNT). Dick was a certified ASNT Level III in Acoustic
Emission. During his career he published a number of technical papers on acoustic
emission testing and received four patents.
Dick had a unique sense of humor and a very direct, no-nonsense approach to businessmatters. According to his daughter Amy, these were characteristics that carried over toall other areas of his life. Dick was a lifelong aficionado of jazz, especially Be-bop,hence the pony tail. He loved good food and a good cigar. He was a voracious reader ofcontemporary American literature and had a deep appreciation for art. Dick will besincerely missed by his family, friends, and colleagues.
Thomas F. Drouillard

I - 11

Professor Hans Maria Tensi
Professor Hans Maria Tensi retired from his extraordinariat at the Technical University
Munich (TUM) in 2000. He was born in Bavaria, Germany in 1932. For his doctoral
work, he studied acoustic emission during crystallization of metals and their mechanicalloading. From 1971 up to his retiring he held the position as head of FATUM in the
Faculty of Scientific Engineering, Institute of Metallurgy TUM. One of his interests was
the fundamental of solidification of metals and alloys. Therefore he made crystallization
experiments under micro-g-conditions on-board US- and USSR- spacecrafts to study
the mass transport in the vicinity of solidification fronts. Using these results he was able
to develop high strength materials - the latest success being the development ofmaterial and parts for AIRBUS planes. His other great interest was the scientificbackground of quenching heated metallic parts and the control of their distortion. Other
work involved diffusion bonding with superplastic forming and human implants. These
resulted in about 140 journal articles, three books and numerous presentations andlectures. He is a member of ASM International and TMS, Deutsche Gesellschaft fürMaterialkunde (DGM), Verein Deutscher Eisenhüttenleute (VdEH). Professor Tensi is a
Fellow of ASM and received many awards of international scientific associations. Up tohis accident in 2000, in the frame of his consulting office, he was scientific adviser ofindustrial companies. (HMT)

Professor Tensi (left) at the installation as Fellow of ASM International. c. 1998
I-12

THE KAISER-EFFECT AND ITS SCIENTIFIC BACKGROUND
HANS MARIA TENSI
Metallurgie und Metallkunde, Technische Universität München, Munich, Germany
PrefaceThis paper is given on the occasion of honoring Joseph Kaiser with a festive event at the 26th
European Conference on Acoustic Emission Testing in Berlin, September 2004 and the dedication of a commemorative plaque at the Technical University Munich, the birthplace of the acoustic emission technology, by the Acoustic Emission Working Group.
Outline of This Paper
With this presentation three goals shall be achieved:• Firstly important facts from the works of Joseph Kaiser will be presented, which lead tothe discovery of the "Kaiser-Effect". I also want to emphasize the severe technical andorganizational problems in Germany during 1945 - 1950.
• Secondly I want to define exactly the phenomenon of the "Kaiser-Effect". This effect has
already been studied in the sixties and seventies of the last century and I shall refer to experimental results from that time.
• Thirdly I will describe the solid-state background of the acoustic emission (AE) during
mechanical loading of metals and alloys. Additionally this analysis is correlated withother sources of an AE. Results of newer research are mostly presented schematically.
This paper is not intended to be a summary and aggregation of the literature from this area. The
few references are fundamental research papers directly covering this topic.
1. Work of Joseph Kaiser
Immediately after World War II in 1945, Dipl.-Ing. (Univ.) Joseph Kaiser visited the chair of
the mechanics at the Technical University Munich (TUM), Prof. Dr. phil. Ludwig Föppl. He
asked Prof. Föppl whether he could do research on the sounds which metals issue upon mechanical stressing.
The so-called "tin-cry" had already been known in the Middle Ages. Tin-casters had manually cambered tin plates and listened to the "tin-cry". Thus they could estimate the material quality of those plates given to them for melting. The sound revealed whether the plate contained
many impurities, for example, Pb and Zn - disallowed even then - which would reduce the brilliance of the new castings.
In 1945, two points made the project very questionable: could the technical effort for themeasurements be done in post-war Germany and are there at all other technically interesting
metals which would issue sounds? One has to imagine the situation at that time: about 80% of
Munich and the Technical University in its center were destroyed. Figure 1 gives an impressionof the TUM in 1945. Of course, the circumstances for such an innovative research were prettybad!

J. Acoustic Emission, 22 (2004)

S1© 2004 Acoustic Emission GroupFig. 1 Technical University of Munich (TUM) destroyed during the World War II. 1945/46.
front: The ‘Auditorium Maximum’; rear: institutes and laboratories; in front of all one of thefamous two ‘Rosselenker’ (leader of a horse). Source: Münchener Stadtmuseum, Archiv für
Fotographien.
Nevertheless, Prof. Föppl had fundamental scientific interest in researching the backgroundof technical materials' behavior under mechanical stress. Such research had been done worldwide at that time. Only little was known on the processes in the crystal lattice upon deformation.
Hence, Prof. Föppl gave his approval and Kaiser could start with his work. First we ought to pay
attention to Kaiser’s doctoral thesis, which was finished in early 1950. Figure 2 shows the coverof this work.
At first Kaiser had to build his devices from fragments of obsolete military equipment. He
built piezo-crystal microphones from quartz- and Rochelle-salt-crystals, all electronic devices(e.g. a dc-amplifier with a maximum amplification of 106) and as displays for the acoustic signals an oscilloscope with an old Braun tube (CRT).
In about 1947, his first experimental setup was completed (Fig. 3). This setup was continually improved (especially when better-suited parts had become available). The figure shows an
old pendulum tensile testing device, which could only be manually operated because of the
background noise. The specimen had the tightly fitted microphone at its top (Fig. 4), the signals
were transferred via a pre-amplifier (VV) and a main-amplifier (HV) to the oscilloscope.
There was a hard and nearly unsolvable problem remaining: how to record and sample thesignals permanently? The institute had a cine camera with a continuous film transport (with the
maximum of 30 m/s). When switching off the time sweep in the oscilloscope, this could serve asa signal sampling and registration device in principle.

S2

Fig. 2 Cover page of Kaiser’s doctoral thesis from February 15th 1950; Library of the Technische Hochschule München ‘Diss.10/1320.'

S3

Fig. 3 Photograph of the first experimental set-up to measure the Acoustic Emission (AE) withpendulum tensile machine, amplifier and oscilloscope from 1949; J. Kaiser ‘Diss.10/1320'

S4

Fig. 4 Experimental set-up of tensile probe’s gripping with the piezo-microphone in the tensile
machine; J. Kaiser ‘Diss.10/1320'

S5

Unfortunately the camera could only record for about 30 seconds, which was too
little in comparison to the length of a tensile
test. Also because of the infernal noise produced by the camera, such registration wasout of the question.
At least Kaiser could use this signal registration method for checking his selfdesigned piezo-microphones. To give an
impression of the effort and hardship of analyzing the registered high-frequency signals,
at that time all photographs from signals
shown in the CRT had to be analyzed by ameasuring stick!
Finally, a 16mm-cine camera acted as asufficient makeshift device to record the AEof complete tensile tests. Its mechanicaltransport mechanism did not produce loudnoise. The oscilloscope’s time sweep was
activated during the experiments.
Figure 5 shows the example of a successful experiment. Kaiser told that typicallymany experiments had to be done until allcomponents of the setup were simultaneously (!) working correctly: When testing
the specimen from soft carbon steel, theload-elongation-curve shows the linear
Hooke’s law area and at its end a distinctive
yield strength. After that there is a monotonous increase in load with discontinuities of
the 'Portevin-le-Chatelier effect’ (also 'dynamic strain aging').
For some selected points of this curve,
we show how each is represented in threepictures of the camera, recording the displayof the CRT. This means the sampling isdone within a time period of 3 x 1/25 s approximating a point in time. Those pictures
were analyzed for 'jumps’, 'amplitude’ and'frequency’ of the signals with a measuring
stick.

Fig. 5 Stress-strain diagram of a tensile
sample of soft carbon steel with triple photographs of the screen of the oscilloscope(with time sweep), assigned to points of thestress-strain function; J. Kaiser, ‘Diss. 10
1320'.
S6

Fig. 6 Analysis of Fig. 5; J. Kaiser ‘Diss.10/1320'
The result of this tedious analysis is shown in Fig. 6 (all evaluated characteristics were plotted against the load or stress): The amplitude function shows an oscillation with three maxima,
the frequency (in Hz) only has a maximum at about 7.5 kN. The position of the highest frequency matches the position of the distinctive yield strength. Unfortunately due to the significant
size reduction of the original picture, this cannot be satisfactorily recognized here.
Another problem was the low resonance frequencies of the microphones, which were evendecreased by the mechanical fitting to the specimen! Hence eight years later a qualification of
the AE was done by a comparative energy measurement (where the acoustic energy is assumed
to be proportional to frequency x amplitude).
In his dissertation Kaiser examined different metals and alloys and even organic materials,
like wood.
The most important consequence of Kaiser's dissertation can be found on page 27, line 7 to
10. Here he describes the effect when a specimen having previously been loaded to 500 N was
loaded again over this previous maximum load. The original words from Kaiser:»Bei einer nun folgenden Wiederbelastung traten nur vereinzelt Sprünge auf, bis die ursprüngliche Belastung von 50 kg wieder erreicht war und sofort war die Wirkung der Effekte in ihrer ursprünglichen Heftigkeit wieder zu erkennen.«S7

By a new reloading [of the tensile specimen] only few jumps occurred [in the photograph of the
CRT] until the loading reached the former highest level of 50 kg and immediately the impact of theeffects [acoustic emission AE] could be observed with the former vehemence.

And in the summary of his dissertation on page 37, second paragraph, line 1 to 4:»Ein wichtiges Ergebnis der angewandten Versuchsmethode ist die Tatsache, daß nachträglich, ohne den Prüfling zu zerstören, sich Aussagen machen lassen über die Höhe derhöchsten Belastung, die das Material bereits ausgehalten hat. Also die Kenntnis der Beanspruchung, die auf dem Material war und nicht nur augenblicklich ist.«
One important result of this testing method developed is the fact that the ex-post statements can bemade about the maximum of load, which the material has endured before, without destroying theprobe. Also the knowledge of the stress that had laid on the material and not the stress, which exists at the moment.

Those statements were the basis of the great success of his AE-measurements in the area of
non-destructive material tests and are the core of the 'Kaiser-Effect’. For that reason Dr. Joseph
Kaiser got a patent on his method for registering the AE upon mechanical loading of materials,
Patent Nr. 852 771 -Kl.42 k Gr.34 01". Figure 7 (taken from his patent) describes how the absence of AE was exemplified by a tensile test, done by Kaiser.

Fig. 7 The effect to determine the previous load on a probe by measuring the AE, first describedin Dr. Joseph Kaiser’s doctoral thesis 1950; J. Kaiser’s Patent ‘Nr. 852 771 - Kl.42 k Gr.3401'.
Those results were further circulated by talks at the departments of mechanics and of metallurgy (now institute of materials) of the TUM and on international conferences and by publications. Figure 8 shows Dr.-Ing. Joseph Kaiser at the department of metallurgy of Prof. Dr. Heinz
Borchers.
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Fig. 8 Dr. Joseph Kaiser giving a lecture at
the Technical University of Munich (TUM),
Institut für Metallurgie und Metallkunde,about acoustic emission during tensile testing; circa 1955; Prof. Borchers to the left of
Dr. Kaiser; private photo of the Institute (R.
Meier).

During the very few days of the formal hand-over (in 1957), caused by his severe illness, Dr.
Kaiser told me about a spectacular success of his method: In the United States a big pressuretank had burst although the pressure gage had shown that the specified maximum load had not at
all been reached. Kaiser took tensile specimens from different areas of the burst wall of the pressure tank. Apparently he took enough specimens and also at the relevant positions to do a stringent analysis. By his AE-analyses of these specimens he could prove that indeed the tank wall
had not been overstrained at any time. But the specimens from the support area were definitely
overstrained! The tank had unnecessarily been welded to the support structure, which could later
be verified. The strain peaks - originating from welding - were the cause of the catastrophe!
Dr. Joseph Kaiser passed away in March 1958.
2. AE Experiments with Mechanical Loading of Metals and Introduction of the Term
'KAISER EFFECT'
When in 1957 I was offered to do further research on those - at that time - unusual effects, I
made some changes in the equipment: First of all the old pendulum tensile test machine was replaced by a hydraulic machine with a big oil accumulator tank. Using a specially designed valve
system, the oil pressure could be applied to a floating working piston, such that its stroke remained constant for different velocities and for loads up to 10 kN along the elongations to be
expected. Of course during the experiments the pumps were switched off.
Since there were many specimens made of different alloys and different kinds of heat treatment the gripping was changed to also allow flat specimens. At first I abandoned Kaiser's aim ofgathering a lot of experimental parameters (like graphical sampling of jumps, frequency and amplitude) and simply measured the AE with an electronic counter for frequency and amplitude.

S9

Fig. 9 Stress-strain diagram and AE-strain diagram of a soft-annealed steel sample with 0.15
wt% carbon; H. M. Tensi’s doctoral thesis 1960.
All tensile tests revealed similar diagrams as can be seen in Fig. 9 for a soft-annealed steel
specimen with 0.15%C, which had been grinded extremely scale-free. In close conformance toKaiser, the AE maximum is in the elongation area of the distinctive yield strength.
The little AE in the Hooke’s law area is rather to be explained by faults in the experimental
process (like imprecise gripping, the floating working piston or some deformed specimen). After
passing the distinctive yield strength, the AE falls exponentially in the area of strain-hardening
and a potential 'Portevin-le-Chatelier effect’, with following reduction of the cross sectional area.
Finally, there is an extreme peak in the AE at the fracture point, which comes mostly from themachine.

Fig. 10 The Kaiser Effect, schematic from Fig. 9; H. M. Tensi’s doctoral thesis 1960.
S10

Figure 10 shows schematically the AE and the stress as functions against the elongation for atensile test specimen, which has already been loaded up to point “Z”. Clearly the sudden AE
increase upon passing the previous load can be recognized.
With those kinds of diagrams (also of specimens with multiple step-like loads) since about
1961 I established the so-called Kaiser-Effect and disseminate it in publications and lectures atinternational conferences to honor Kaiser's pioneering work, with the support of my doctoral
advisor, Prof. Dr. Heinz Borchers.
For illustration, Fig. 11a and 11b show the areas to use the Kaiser-Effect as shaded for functions plotted against stress or elongation.

(a)(b)
Fig. 11a and b Areas in the scales of stress and of elongation for using the Kaiser Effect.
3. Reasons for AE during Mechanical Loading of Metals
3.1 Dislocation reactions and 'up-hill-diffusion’To understand the background of AE, different metals and alloys with different kinds of heat
treatment were analyzed with tensile tests. At that time, the confirmed knowledge had been that
the AE intensity has its maximum for an occurrence of a distinctive yield strength, i.e., during
the extremely inhomogeneous plastic deformation.
Hence different materials with different heat treatment and significant differences in the yieldstrength were compared with respect to their AE (Figs. 12a to d). As one can clearly recognize,
the maximum of the AE in width and height decreases with the decrease of the yield elongation:from carbon steel (Fig. 12a) over differently heat-treated aluminum alloys (Figs. 12b and c) topure aluminum (Fig. 12d). Pure aluminum has - after being high-temperature-annealed withabrupt quenching - not only the least strength but high ductility, but also the least AE.
Those and other metallurgical research revealed that the AE is strongly influenced by inhomogeneous slip of the so-called slip dislocation. Dislocations are considered to be the only medium for plastic deformation in metallic crystals. This can be illustrated with a simplified model(see Figs. 13a and b): When applying a force the dislocations slip on so-called slip planes within
the crystal. They distort the crystal lattice in their environment with fields of compression above,
fields of tension below the slip-plane. In Fig. 13a the dislocations slip exactly for one lattice disS11

Fig. 12a to 12d Differences in yield strength elongation and AE of different materials and heat
treatments to show the connection of yield strength and AE; H. M. Tensi’s doctoral thesis 1960.
tance 'b' when a load is applied. Homogeneously distributed impurities will not disturb their
movement. Integrating those multiple dislocation reactions, the material will deform very steadily.
Now consider a heat treatment which allows a so-called ‘up-hill diffusion’ of the impurities.
They will diffuse to the lower side of the dislocation, where there is a local field of stress (Fig.
13b). The impurities are now distributed heterogeneously near the dislocation. This causes a release of tension and the dislocations are blocked in their mobility. The local amount of shear
stress increases, until the blocked dislocation is torn off of its so-called “Cottrell cloud” and it
jumps over several lattice distances (n x 'b'). This causes minuscule concussions in the metal,
which combine to the AE.
Those models were later confirmed by specific solid-state analyses (e.g. with an electron microscope).
S12

a)

b)

Fig. 13a and b Reaction of dislocation on a slip plane of a metal with impurity atoms. a) The
impurity atoms are distributed homogeneously; the dislocations are not blocked. b) The impurity
atoms create clusters at the dislocations and block them; by increasing stress the dislocation suddenly jumps out of the Cottrell cloud.

Fig. 14 AE in dependence of the length of the yield strength elongation (or the increasing ‘uphill diffusion’) showing the influence on the AE by blocked dislocations. H. M. Tensi’s doctoral
thesis 1960.
To reinforce that theory about the AE, tensile tests were done with the same material but withvarious distinctive yield elongation. Those differences in the elongation for the yield strength
were produced by heat treatment with materials with interstitial but also with substitutional impurity atoms.
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Flat tensile test specimens from a technical AlMg3-alloy were at first heat-treated uniformly
such that all impurity atoms (also the Mg-atoms) were homogeneously distributed within the
crystal. Then several groups of specimens were differently heat-treated to produce different levels of “up-hill-diffusion”.
As had been expected from the theory (see Fig. 14), with little deviation the AE in specimens
with zero elongation of the yield strength and no “up-hill-diffusion” (i.e. an ideal distribution of
impurity atoms) increased monotonously to the specimens with the highest level of “up-hilldiffusion” (and blocked dislocation).
3.2 Stress AE caused by other events in materials and constructions
In practice there are very seldom those simple materials we had during research where thetheory can be applied without modification. When you want to apply the AE measurement, it isimportant to know the history of the material: its production as well as all its treatment whenconstructing the final technical product. Also one has to consider all imaginable stress conditionsof the part in operation; e.g. corrosion with or without mechanical load.
Besides the dislocation reactions described above, Figs. 15a and b sketch some also important microstructure properties, which affect the triggering of the AE. At position 1 there is a socalled micro-cavity (note that there are no macro-cavities, since we assume that the material isfault-free in a technical sense). The micro-cavity is caused by the solidification and normallycannot be prevented. Even when the casting material is post-processed by heat and/or mechanicaltreatment, removal of the micro-cavities is impossible without leaving other faults.
Such cavities are characterized by extremely sharp notches. Already for a very small mechanical load the local stress accumulates near this notch because of the so-called 'notch-stress’(after H. Neuber). Dislocation processes (as described above) start very early in those areas, evenwhen the part is still for most of its volume in its elastic deformation phase (where the Hooke’s
law applies).
Additionally those notches tear open and the surface of cavity gets larger (Fig. 15b). Whenthe load of the part (or specimen) vanishes, those exposed surface fold up. When a reload is applied those planes are separated again. This causes rubbing noises with additional AE, eventhough the previous load has not yet been exceeded.
load

without any external tensile load
1
3

1
3

2
2

4

4

a)
b)

load

Fig. 15a and b Metallurgical irregularities in the structure of technical materials contributing to
the AE. 1: micro-cavities; 2: internal cracks and fissures; 3: inclusions e.g. metallic and nonmetallic phases; 4: external cracks. a) without external load; b) with load applied.
S14

This example makes clear what kind of sources the AE has: normal dislocation reactions, reinforced dislocation reactions caused by the 'notch-stress’ and repeated tearing open of notches
followed by folding up of the cavity flanks: the Kaiser-Effect blurs for reloading.
At position 2 in Figs. 15a and b there is an internal crack: It can be so minuscule that it is invisible even for ultrasonic. But it will cause the same effects causing AE as the cavity. At position 3 in Figs. 15a and b there is an inter-metallic phase having a needle or plate shape from its
formation or from hot rolling. Inter-metallic phases (IP) typically have no ductility, and thiscauses their extreme brittleness. They are mechanically comparable to a crack in the grid (seeabove). Additionally they have a very weak connection to the interface of the lattice such thatthey behave like a crack. All mechanisms of AE triggering described above can be applied here.
The refractory IPs may break and cause cracks or additional sharp edges, which multiply thoseeffects.
At position 4 there is an external crack. This may be caused by leaking dislocations (socalled extrusions) often in combination with corrosion. This is the dangerous 'stress-corrosion’,
where crack growth is exponential. The tensile test for such a specimen also shows only ablurred step in the AE-function and thus only an indistinct Kaiser-Effect. A simplistic interpretation of the Kaiser-Effect is not appropriate here, because when you ignore the stress-corrosion
before you would expect a distinctive Kaiser-Effect. Hence whenever you find this behavior of
AE it may be an indication of stress-corrosion!
The theory presented above has been proven correct in many situations from practice.
4. Summary
In this paper the cornerstones of the work of Dr. Joseph Kaiser were described as far as necessary to understand the Kaiser-Effect.
I have mostly reverted to original presentations of Kaiser. This should also emphasize thenearly unimaginably hard circumstances of his research in post-war Germany. Newer measurements have revealed dislocation reactions as the cause of the AE and proposed the term “KaiserEffect” to honor his pioneering work. Additionally fundamental and other sources of AE havebeen described which can be found in real technical materials.
Literature
1) J. Kaiser: ‘Untersuchung über das Auftreten von Geräuschen beim Zugversuch’, Dr.-Ing.
Dissertation, Fakultät für Maschinenwesen und Elektrotechnik der Technischen Universität
München (TUM); 15.2.1950.
2) J. Kaiser: Patentschrift der Deutschen Patentamtes Nr. 852771, Klasse 42 k, Gruppe 3401
vom 20.10.1952 - ‘Materialprüfverfahren’.
3)

J. Kaiser: Arch. Eisenhüttenwesen 24 (1953) 43.

4)

J. Kaiser: Forsch. Ing. Wesen (1957) 38.

5)

J. Kaiser: Bsp. angewandter Forschung 1957, Fraunhofer-Gesellschaft.
S15

6)

H. Borchers and J. Kaiser: Z. Metallknde. 49 (1958) 2.

7) H. M. Tensi: Piezoelektrische Impulsmessung zur Untersuchung von Vorgängen in Metallen
bei Phasenänderungen und bei mechanischer Beanspruchung, Dr.-Ing. Dissertation, Fakultät für
Maschinenwesen und Elektrotechnik der Technischen Universität München (TUM); 5.1961.
8)

H. Borchers and H. M. Tensi: Z. Metallknde. 51 (1960) 212.

9) A. H. Cottrell: Dislocations and Plastic Flow in Crystals, Oxford at the Clarendon Press,
Amen House, London E.C.4, 1958, 223 p.
10) C. Kittel: Einführung in die Festkörperphysik, R. Oldenbourg Verlag München and Wien,
1969, 744 p.
11) T. F. Drouillard: Acoustic Emission, a Bibliography with Abstracts, IFI/Plenum New YorkWashington-London, 1979, ISBN 0-306-65179-3, 787 p.

S16

MODAL-BASED IDENTIFICATION OF ACOUSTIC EMISSIONSOURCES IN THE PRESENCE OF ELECTRONIC NOISE #∞M. A. HAMSTAD1,2 and A. O’GALLAGHER1
National Institute of Standards and Technology, Materials Reliability Division (853), 325
Broadway, Boulder, CO 80305-3328 USA; 2 Department of Engineering, University of Denver,
Denver, CO 80208 USA.
1

Abstract
Based on the results of an earlier study, a specific scheme was demonstrated to identify acoustic
emission (AE) source types using ratios of Lamb-wave modal amplitudes from two radiation directions in an aluminum plate 4.7 mm thick. The modal amplitudes were obtained from certain
peak magnitudes of wavelet transforms (WT) of the AE signals at a propagation distance of 180
mm. The peak magnitudes were taken from energetic frequency-mode combinations. These
combinations were the fundamental modes A0 at 60 kHz, S0 at 270 kHz and S0 at 522 kHz. The
AE signal database was obtained using a validated finite element model (FEM) for three different source types, each located at six or seven depths in the plate, which had large lateral dimensions. The technique of source identification was demonstrated using ratios of the WTdetermined modal magnitudes of the signals at a radiation angle of 45˚ divided by those from a
0˚ angle. Then, the effect on source identification of adding electronic noise (sensor/preamplifier) to the FEM signals was studied. Due to the random nature of the experimental wideband noise, a statistical study was necessary. At each signal-to-noise (S/N) ratio, 50 cases were
examined for certain depths of each source type. The percentage of correct source identifications
was determined as a function of the S/N ratio. To obtain a high percentage of correct identifications, a S/N ratio of at least six to one was found to be necessary.
Keywords: acoustic emission; acoustic emission modeling; AE; electronic noise; finite-element
modeling; modal AE; source identification; wavelet transform; wideband acoustic emission.
#

Contribution of the U.S. National Institute of Standards and Technology; not subject to copyright in the United States; ∞ Trade names are included for information only; endorsement is neither intended nor implied.
1. Introduction
In a previous publication [1], a technique to identify acoustic emission (AE) sources in aplate sample was demonstrated. The technique was based on the use of ratios of peak magnitudes
at certain energetic frequency-mode combinations obtained from wavelet transforms (WT) of the
AE signals. The WT-based peak magnitudes correspond to the magnitude of specific Lambmodes at energetic frequencies. The ratios were formed from the wavelet transforms of the AE
signals present at various radiation angles from the AE source. The approach was developed andvalidated using far-field (fully developed Lamb modes) AE signals, which were generated byfinite-element modeling (FEM) in a plate of large lateral dimensions. Since the forwardmodeling FEM technique starts with fully defined AE sources, the signal processing results
could be related to different source types in an unambiguous fashion. This signal-processing approach presumes a known source location relative to the location of the pseudo-AE sensors. Currently the technique has been demonstrated for a uniform propagation distance to each sensor. In
J. Acoustic Emission, 22 (2004)1© 2004 Acoustic Emission Groupthe future we plan to extend the technique to cases where the propagation distance to the sensors
varies.
The purpose of the study presented here was to examine the impact of electronic noise on theaccuracy of the source-identification approach. The authors were unable to locate any previous
references of such studies. Since in real AE applications the signals that are measured result fromthe superposition of the source-based displacement waves and the electronic noise of the sensorpreamplifier combination, the usefulness of the approach will depend on its sensitivity to the signal-to-noise (S/N) ratio. In the previous publication [1], the numerical noise in the FEMgenerated AE signals had no real effect since it was about three orders of magnitude below thesignal amplitudes.
2. The FEM-based AE Signal Database and Signal Processing
The previous publication [1] along with its references should be consulted for details on the
database and signal processing. Briefly, the FEM signals (out-of-plane displacements of the top
surface of the plate) from buried dipole sources were generated in an aluminum plate (1 m x 1 m
x 4.7 mm). The AE signals in the database were obtained at a propagation distance of 180 mm
for seven in-plane radiation angles (0°, 12°, 22.5°, 45°, 67.5°, 78°, and 90°). Three AE source
types composed the database subset: (a) a single in-plane dipole in 0° direction (x-axis, the presumed applied stress direction); (b) a microcrack initiation with major axis in 0° direction; and,(c) a shear about the in-plane y-axis with the shear directions at 45° to the x-axis with no net
moment. The depths (from the top of the plate surface) to the modeled sources were 2.35, 2.037,
1.723, 1.41, 1.097, and 0.783 mm for all three source types. Additionally, the in-plane dipole had
one more source depth of 0.47 mm.
All of the FEM-calculated AE signals were processed in the following fashion. Prior to performing the wavelet transform, all the FEM-calculated signals were numerically filtered with a
40 kHz four-pole Butterworth high-pass filter. Each filtered FEM signal had a WT performed[with the parameters listed in Ref. 1] upon it using an AGU-Vallen software program [see Ref.
2]. The resulting output for each WT consisted of numerical values for the WT magnitude as afunction of both time (source operation time zero to 150 µs with 0.1 µs increments) and frequency (0 to 700 kHz in 3 kHz wide increments). This output can be viewed numerically in a
spreadsheet format or in a more qualitative, graphic format where various colors are used to indicate the WT magnitude on a frequency-vs.-time plot. For each WT, several features were extracted and recorded for energetic regions (largest WT magnitudes) of the WT results. First theoverall absolute peak WT magnitude was recorded. Then, the predominant mode (A0 or S0) was
determined and recorded at the previously documented [1, 3] three key frequencies of 60, 270
and 522 kHz by use of superimposed group-velocity curves. Additionally, the peak WT magnitude (modal magnitude) at each frequency was recorded. These features, as extracted at thesethree key energetic frequencies, provided the raw data for the source identification scheme.
3. Terminology and Related Discussion
The frequency having the greatest peak WT magnitude as a function of time was defined as
the “primary” frequency for each WT; in a similar manner, the other two frequencies examined
were ranked with respect to their peak WT magnitudes (in descending order) as “secondary” and“tertiary.” It was observed that when the primary frequency occurred at 60 kHz, the mode that
corresponded to the arrival time of the peak WT magnitude was always A0, whereas when the2primary frequency occurred at 270 kHz or 522 kHz, its mode was always S0. These observations did not hold for the modes that were associated with the secondary- and tertiary-frequency
WT peaks, but the modes were always either A0 or S0.
4. Summary of Single-Mode WT Ratio Approach for Source Identification
In the previous publication [1], the radiation pattern of the peak WT magnitudes (as normalized by the zero-degree direction value) for a fixed frequency and mode was found to be almost
totally independent of the depth of the source. Also for particular key frequencies and modes, it
was determined that the radiation pattern changes as a function of the AE source type. Thus,
among the three source types studied (in-plane dipole, microcrack initiation, and 45° shear without a moment) and for the six or seven depths considered, it was observed that the source type
could be uniquely identified by certain simple ratios of peak WT magnitudes at two selected radiation angles by use of the primary frequency-mode combination, and in some cases additionally by use of the secondary frequency-mode information. Figures 1 through 4 (shown with new
figure numbers here), from the previous work [1], show the WT peak magnitude ratios for eachcombination of a given frequency, source type and mode as a function of the two radiation directions for each ratio. In subsequent discussion in this paper, the term “angle ratios” is used as
shorthand to refer to the ratios of the WT peak magnitudes in two radiation directions.

Fig. 1 Angle ratios of peak WT magnitudes for A0 mode at 60 kHz at various radiation angles for
all three source types for 180 mm propagation distance
5. Electronic Noise Effects on Source Identification by Single-Mode WT Magnitude Ratios
for Various Radiation Angles
The successful application [1] of WT results to distinguish different AE source types depends
on WT peak magnitudes (modal magnitudes) at the three key frequencies of 60, 270 and 522
kHz in a 4.7 mm thick aluminum plate. Thus, the primary focus in this section is WT peak magnitudes rather than AE signal magnitudes. Since the distinctions [1] between the source types
depend on different “angle ratios” within a single mode at a time, the focus was on single-mode
WT peak values as a function of the factors of source type, source depth and radiation angle.
Figures 5, 6 and 7 show the pertinent WT-determined modal magnitudes versus the source depth
for each of the three source types at a 0˚-radiation angle and a 180 mm propagation distance. For
example, when the sources are located near the mid-plane these figures quantitatively show when3

Fig. 2 Angle ratios of peak WT magnitudes for S0 mode at 522 kHz at various radiation angles
for all three source types for 180 mm propagation distance.

Fig. 3 Angle ratios of peak WT magnitudes for A0 mode at 270 kHz at various radiation angles
for all three source types for 180 mm propagation distance.

Fig. 4 Angle ratios of peak WT magnitudes for S0 mode at 270 kHz at various radiation angles
for all three source types for 180 mm propagation distance.
4Fig. 5 Modal magnitudes for 0ºdirection, in-plane dipole, 180 mm distance.

Fig. 6 Modal magnitudes for 0ºdirection, microcrack, 180 mm distance.

Fig. 7 Modal magnitudes for 0ºdirection, shear, 180 mm distance.
the WT magnitudes for A0 at 60 kHz and A0 at 270 kHz become small or not identifiable. Thus,
if electronic noise were present, for sources near the mid-plane, the source distinctions shown in
Figs. 1 and 3 might not be present due to the potential for noise to significantly alter the magni5

tudes of the WT peaks. On the other hand, Figs. 5, 6 and 7 quantitatively show that, for sources
located nearer the surface, the WT peak magnitudes for S0 at 522 kHz and S0 at 270 kHz become
small or not identifiable. Thus for similar reasons, the source distinctions shown in Figs. 2 and 4
may not be useable when electronic noise is present for near surface sources.
Figures 5 through 7 show that, for the three source types, the primary frequency is either 60
or 522 kHz. In certain cases two frequency-mode combinations were required to uniquely identify a source type [1]. Thus, from the point of view of maximizing S/N ratios, it is clear from
Figs. 5, 6 and 7 that when a second combination is required, the 270 kHz modes should be usedas the lower-amplitude second mode. For example, with an in-plane dipole (IPD) source at a
depth of 0.783 mm, A0 at 270 kHz might be expected to provide a better S/N ratio than S0 at 522
kHz.

Fig. 8 Normalized peak WT magnitudes for A0 mode (averaged for multiple depths) for all threesource types for 180 mm propagation distance.
To examine the final factor affecting the WT magnitude as related to S/N ratio concerns, thefour frequency/mode combinations (in Figs. 1 through 4) were examined as a function of radiation angle. Figure 8 shows the normalized WT peak magnitudes (averaged for the multiple
depths shown in the figure) versus the radiation angle at the 180 mm propagation distance for the
relevant frequencies (60 and 270 kHz) of the A0 mode. Also, Fig. 9 shows the normalized andaveraged WT peak magnitudes at 180 mm for the relevant frequencies (270 and 522 kHz) of theS0 mode. The WT peak magnitudes were normalized by the zero-degree magnitudes. In general,these two figures show that the largest-magnitude decrease with increasing radiation angles takes
place for the in-plane dipole source. The total decrease for this source type is about 98 % for allof the frequency/mode combinations. In addition S0 at 522 kHz for the microcrack initiation
source also experiences a large decrease. For all three source types, most of the magnitude decrease occurs from 22.5 to 67.5 degrees at all four frequency/mode combinations.

6Fig. 9 Normalized peak WT magnitudes for S0 mode (averaged for multiple depths) for all threesource types for 180 mm propagation distance.
6. Discussion of Radiation Angle Choices to form the Source Identification Ratios
The WT modal peak magnitude losses (Figs. 8 and 9) with increasing (0˚ to 90˚) radiation
angle are possibly the most important aspect to consider when electronic noise is present. But, it
is not just the loss with increasing angle that is a potential problem. The ability to distinguish thesource types by the “angle ratios” as illustrated in Figs. 1 through 4 is critical. Since the presenceof noise will have the least effect on the zero-degree direction peak WT magnitudes (which werealways the largest), one of the angles selected for the WT magnitude ratio should be the zerodegree direction. With this choice, it is likely that only the second angle required to form the ratio will be most strongly affected by electronic noise. This approach is more desirable than having both WT peak magnitudes that are used to calculate the ratio being strongly affected. Thus,
the key choice is the second angle, which is greater than zero degrees.
Straightforward analysis demonstrated that with the current radiation angles the best choicefor the second angle would be either 45˚ or 67.5˚. If 45˚ or 67.5˚ is chosen as the second angle,
then there are some significant ratio distinctions between source types that are potentially resistant to small noise-induced “angle ratio” errors. For example, using the 45˚/0˚ “angle ratio” from
A0 at 60 kHz, a difference of (Fig. 1) about 32% (percent based on the no-noise ratio and theclosest alternate source type) is present between the in-plane dipole and either the microcrack or
shear sources. At this same “angle ratio”, the S0 at 522 kHz ratio exhibits (Fig. 2) a difference ofabout 34% between the shear source and either an in-plane dipole or a microcrack initiation
source. For the alternate “angle ratio” at 67.5˚/0˚, the equivalent results are about 190% for thein-plane source as compared to the other two possible source-type choices for A0 at 60 kHz, andabout 74% between a shear source and either the in-plane dipole or the microcrack source for S0
at 522 kHz. These results suggest that a choice of 67.5˚ as the second angle would tolerate
greater noise-induced ratio errors without resulting in incorrect source-type determinations.

7

Table 1 For mode and frequency combinations: (a) 45/0 degree average “angle ratios” and[ranges]; (b) 67.5/0 degree average “angle ratios” and [ranges]; (c) source depths (mm) averaged
to obtain the values in part (a); (d) source depths (mm) averaged to obtain the values in part (b).
(a)
Source typeIn-plane dipole
45 Deg. Shear
Microcrack(b)Source typeIn-plane dipole
45 Deg. Shear
Microcrack(c)Source typeIn-plane dipole
45 Deg. Shear
Microcrack(d)Source typeIn-plane dipole
45 Deg. Shear
Microcrack

A0/60 kHz
A0/270 kHz
S0/270 kHz
S0/522 kHz
0.50 [0.50-0.50] 0.50 [0.50-0.50] 0.50 [0.50-0.50] 0.49 [0.48-0.49]
0.66 [0.66-0.67] 0.64 [0.62-0.65] 0.69 [0.69-0.70] 0.74 [0.74-0.75]
0.68 [0.67-0.68] 0.71 [0.70-0.73] 0.64 [0.64-0.64] 0.46 [0.44-0.47]
A0/60 kHz
A0/270 kHz
S0/270 kHz
S0/522 kHz
0.15 [0.15-0.15] 0.15 [0.15-0.15] 0.15 [0.15-0.15] 0.15 [0.15-0.15]
0.43 [0.43-0.43] 0.38 [0.37-0.40] 0.48 [0.48-0.48] 0.59 [0.58-0.60]
0.45 [0.45-0.45] 0.51 [0.48-0.54] 0.38 [0.38-0.39] 0.09 [0.08-0.10]
A0/60 kHz2.037-0.47
2.037-0.783
2.037-0.783

A0/270 kHz
1.41-0.47
1.41-0.783
1.723-0.783

S0/270 kHz
2.35-1.097
2.35-1.41
2.35-1.723

S0/522 kHz
2.35-0.783
2.35-0.783
2.35-1.41

A0/60 kHz2.037-0.47
2.037-0.783
2.037-0.783

A0/270 kHz
1.41-0.47
1.41-0.783
1.723-0.783

S0/270 kHz
2.35-1.097
2.35-1.41
2.35-1.723

S0/522 kHz
2.35-0.783
2.35-1.097
2.35-1.723

7. Development of a Quantitative Source Identification Scheme for Noise-free Signals
To continue this evaluation of the effect of electronic noise a quantitative “scheme” to analyze and identify all the noise-free signals in the three-source-type database was developed. This
“scheme” was then used for two purposes. The first purpose was to determine which “angle ratio” (45˚/0˚ or 67.5˚/0˚) best identified the noise-free signal sources. Then, the noise-free ratio
values for the best “angle ratio” were used (in a later section) to determine when significant identification errors occurred when the signal plus noise data were analyzed with the same “scheme.”
To assist in the development of this “scheme”, Table 1 was created. This table for the noise-freesignals gives the average “angle ratio” values as well as their ranges for the 45˚/0˚ (a) and67.5˚/0˚ (b) angles. In addition the table provides [(c) and (d)] the applicable depths over which
the “angle ratios” were averaged. After development, the “scheme” was used to analyze eachpair (from the two radiation angles) of AE signals as if their source type was unknown. Briefly,
the “scheme” was to first find the primary frequency/mode for the zero-degree radiation direction. Then the WT peak magnitude in this direction was divided into the WT peak magnitude of
the same frequency/mode combination from the second selected radiation direction (either 45˚ or
67.5˚) to form a so-called “primary” “angle ratio”. This calculated ratio was then compared withthe values in table 1 (which more accurately portrays the information in Figs. 1 and 2) to obtain
input as to the source type. If the primary “angle ratio” was such that the source type was clearly
defined, then the source type was recorded (labeled as “conclusion from table”). If the type was
not clearly defined, the possible source types were recorded (“conclusion from table”). The question about whether or not a source type distinction was clearly defined was8based on the percentage difference between the “angle ratios” given in Table 1 [(a) and (b)] forthe different source types at a particular frequency/mode combination. If the difference between
the average “angle ratios” for different source types (based on the smaller of the two ratios) was
less than 8%, then the source type was assumed not to be clearly defined. Thus with the 45˚/0˚“angle ratios”, each frequency/mode combination distinguishes only one source type from theother two, except for A0 at 270 kHz, where all three can be distinguished. In contrast the 67.5˚/0˚“angle ratios” distinguish all three source types for all of the frequency/mode combinations except for A0 at 60 kHz. The arbitrary choice of 8% as the minimum value to distinguish between
source types was based upon an examination of the percentage differences between the average“angle ratios” as given in table’s 1(a) and 1(b). The lowest percentage differences between the
average “angle ratios” of different source types were 3, 4.7, 6.5, 7.8 and 10.9%. Since the presence of noise would alter the “angle ratio” values, it was decided to use a minimum of an 8% difference to distinguish two different source types even though the noise-free “angle ratios” could
successfully distinguish source types with smaller percentage differences.
In cases where the primary angle ratio did not uniquely define the source type, the WT peakmagnitude of the secondary frequency/mode combination was determined in the zero-degree radiation direction. This magnitude was then divided into the WT peak magnitude of the same frequency/mode combination determined in the second radiation direction (either 45˚ or 67.5˚) toform a “secondary” angle ratio. This secondary angle ratio was then used with Table 1 (whichcorresponds to Figs. 1 through 4) to make the final determination of the source type. The resultsof the analysis by the “scheme” and a detailed example of the process of source-type determination are given in Appendix A and its associated tables A-1 and A-2. After examination of the results there (described in Appendix A), it was decided to focus on the effects of noise on source
identification using the 45˚/0˚ angle ratio for the remainder of this research. The primary reason
for this choice was due to the fact that the source identification “scheme” was not totally successful when the 67.5˚/0˚ angle ratios were used.

Fig. 10 Typical wideband experimental electronic noise (a) and FFT spectrum (b) showing thecharacteristics of the sensor/preamplifier system.
8. Description of Noise Signals
To make the study of the effect of electronic noise as realistic as possible, the noise signals
were obtained from a wideband high-sensitivity sensor developed at NIST-Boulder [4, 5]. The
noise signals were recorded with the sensor coupled only to air and protected by soft foam fromany airborne signals. A total of ten noise signals were available. Each signal had been digitizedby a 12-bit waveform recorder with a sampling interval of 0.1 µs/point. Each signal was about
16000 points in length, which resulted in the ten signals representing a total of about 16 ms of9noise. A typical time domain and fast Fourier transform (FFT) from one of these ten signals isshown in Fig. 10. This slightly smoothed FFT was calculated after the signals had been numerically bandpass-filtered (six-pole Butterworth) from 40 kHz to 1.2 MHz. This filter (applied to all
the noise signals) was used to make the noise signals more representative of the frequency rangeof the FEM-modeled signals. After modifying the signal amplitudes (so they were less than the
FEM signal amplitudes) and changing the units to picometers, the noise signals were examined
to determine their consistency. First, the peak magnitudes for the 10 noise signals were determined. The mean peak magnitude was found to be 0.63 pm with a dispersion (mean value divided by the standard deviation) of 10% and a range of 0.54 to 0.73 pm. Thus the noise signals
were considered to be relatively uniform in their peak signal magnitudes.

Fig. 11 WT magnitudes versus time showing the random changes of the electronic noise (8192
points) at the three key frequencies.
Before carrying out WTs on signal-plus-noise waveforms, some exploratory WTs were performed on the electronic noise signals. Since the WT software was limited to 8192 points, eachnoise signal was split into two equal parts. This resulted in 20 sub-signals. The WT computations
used the same parameters as for the FEM-based signals except that the number of points was increased to 8192 to match the length of the 20 sub-signals. Then plots of WT magnitude versus
time at each of the three key frequencies were obtained. Figure 11 shows results for one subsignal. It is clear from this figure that the WT magnitude versus time varies over a wide range foreach of the three key frequencies. Comparisons of these results with those from the other subsignals showed differing variations of WT magnitudes with time. Also, the number of fluctuations of the WT magnitudes increases with increasing frequency (likely a characteristic of theWT used in this research). Further, the WT magnitude variations in Fig. 11 imply that whennoise is added to the FEM-generated AE signals, the WT peak magnitudes of the signal plus
noise (S+N) could experience noise-induced modification. It might also be expected that in aparticular S+N case the noise-induced changes in WT peak magnitudes would likely depend on
the noise characteristics near the times where the noise-free AE signals have peak WT magnitudes (at the key frequencies) that represent the arrivals of the fundamental Lamb modes. Since,10

for real-world AE signals, the amplitudes and frequencies of the underlying noise signal at thetimes of mode arrivals would be a random and unpredictable condition, it was concluded that astatistical study of noise effects would be necessary.
9. Creation of Modeled AE Signals with Noise
To form a suitable noise database for a statistical study, the modified database of ten noise
signals was divided into a total of 100 different segments, each nominally 160 µs in length. The
S+N database was then constructed for particular cases (source type and depth) of interest. One
noise signal was added to the FEM-based (noise-free) AE signal from a radiation angle of zero
degrees, and another was added to the signal for the 45˚ radiation angle. Thus a pair of noisemodified signals was obtained. Before adding the noise signals they were multiplied by a factor
to obtain a certain S/N ratio. The “overall” S/N ratio was based on the peak amplitude of thenoise-free FEM signal divided by the result of the same factor times the mean peak amplitude(0.63 pm, given above) representative of all the noise signals. The overall S/N ratio was also
based on the noise-free AE signal’s peak amplitude in the zero-degree radiation direction. Thus,
since the same factor was used to prepare each noise signal for addition to the 45-degree FEMbased AE signal, the real S/N ratio in that direction was actually less. The decrease in the S/Nratio in this direction depends on the loss of peak AE signal amplitude with radiation direction
for the particular source type and source depth. This loss of noise-free signal-peak amplitude as afunction of the radiation angle and the source depth is shown in Figs. 12, 13 and 14. The loss is
considerable for the in-plane dipole and more moderate for the other two source types (smallest
for the shear source). This strategy of using different noise signals for the zero- and 45-degree
FEM-based signals corresponds to real AE testing, where there is no direct relationship between
the electronic noise at one sensor versus another. Thus, pairs (0˚ and 45˚) of S+N signals wereprepared using the same noise-free FEM signals and different noise signals. A total of 50 pairs
was created for each overall S/N ratio that was studied for the chosen source types and depths.
These 50 signals were called a “set” of S+N signals for a given case. In order to be able to directly track the effects of different “overall” S/N ratios applied to the same pair (zero and 45 degrees, FEM based for a given case) of noise-free signals, the same sequence of noise pairs (multiplied by a different factor to create a different “overall” S/N ratio) was added respectively to
the 0˚ and 45˚ pairs to form a new set of S+N signals. In summary, 100 noise segments werecombined with the same 0˚ and 45˚ (FEM, noise-free signals) to form the S+N signal database
set at each overall S/N ratio.

Fig. 12 Peak noise-free signal amplitude versus radiation angle at 180 mm propagation distancefor in-plane dipole source.
11

Fig. 13 Peak noise-free signal amplitude versus radiation angle at 180 mm propagation distancefor microcrack initiation source

Fig. 14 Peak noise-free signal amplitude versus radiation angle for balanced shear source at 45
degrees about the y-axis and at 180 mm propagation distance
The “effective” S/N ratio for source-type distinctions is really determined by the WT peakmagnitude representing a mode arrival at that frequency compared to the WT magnitudes of thenoise signal around the time of the mode arrival. But since the noise WTs have considerable
variation, it is not possible to compute these effective S/N ratios. It is possible to point out whenthe AE noise-free WT peak magnitude will be the smallest. On the average, when the AE noisefree WT peak magnitude is the smallest, the effective S/N will be the smallest. Thus, the poorest
effective S/N ratio will be for the 45˚ radiation direction when it is necessary to use a secondary
frequency to complete the source identification scheme. The noise-free WT peak magnitudes inthis situation are essentially twice reduced in magnitude compared to the 0˚ primary-frequency
peak WT magnitude. The first reduction is that due to the use of the secondary-frequency WTpeak magnitudes, and the second reduction is due to the use of peak WTs from the 45˚ signal toform the secondary ratio. These reductions are shown by the “reduction factor” in tables A-1. If
only a primary angle ratio was necessary, then as shown in the last column (reduction factor) oftable A-1, the loss of peak WT magnitude was not as great. In all cases it is expected that the effective S/N ratio (primary or if it is required for unique source identification, the secondary) for
the 45˚ direction will most often control the accuracy of the source identification results.
1210. Lowest S/N ratios for High-Probability Correct Source Identification of the Three
Source Types
The cases (source type and depth) most likely to require the largest overall S/N ratio to correctly identify most of the AE S+N signals are those where an angle ratio based on the secondary
frequency is necessary, so the research was focused on cases of that type. These cases can be
identified in table A-1 as those with the largest reduction factor shown in the last column of thetable. Since for certain source types and depths only a 45˚/0˚ primary angle ratio was required,some of those cases were examined as well. The selection of particular cases also covered a variety of the combinations of modes and frequencies that appear in table A-1. The final selections
for full statistical analysis were as follows: (i) shear at 0.783 mm depth with primary angle ratio
from A0 at 60 kHz, secondary angle ratio from A0 at 270 kHz, and a reduction factor of 5; (ii) inplane dipole at 1.723 mm depth with primary angle ratio from A0 at 60 kHz, no required secondary angle ratio, and a reduction factor of 2; (iii) microcrack initiation at 1.41 mm depth withprimary angle ratio from A0 at 60 kHz, secondary angle ratio from A0 at 270 kHz, and a reduction factor of 4.7; (iv) in-plane dipole at 2.35 mm depth with primary angle ratio from S0 at 522
kHz, secondary angle ratio from S0 at 270 kHz, and a reduction factor of 5.1; and (v) shear at
2.037 mm depth with primary angle ratio from S0 at 522 kHz, no required secondary angle ratio,
and a reduction factor of 1.4.
The first analysis of the set of data for a particular case of source type and source depth isdescribed here in detail. We believe the detailed description is necessary to provide the reader
with a fuller understanding of the complications caused by changes in modal amplitudes due tonoise. For subsequent cases, the focus will be on the statistical results of correct source identification for the 50 different pairs of S+N signals making up the set at a particular overall S/N ratio.
For the first analysis, a shear source at a depth of 0.783 mm [(ii) above] was selected. As can beseen from table A-1(b) the identification of this source requires use of both a primary and secondary angle ratio.

Fig. 15 Typical signals for zero degree radiation direction at various overall S/N ratios for balanced shear source at 180 mm propagation distance and a depth of 0.783 mm.
13

Fig. 16 Typical signals for 45- degree radiation direction at the same various overall S/N ratios
for balanced shear source at 180 mm propagation distance and a depth of 0.783 mm.
Figures 15 and 16 show some typical S+N waveforms at different S/N ratios for this case.
Figure 15, for the 0˚ direction, indicates even at an S/N ratio of 4 to 1 that the first portion of the
AE signal can be seen. At higher S/N ratios the early portion (extensional mode) of the AE signal
can be seen even more clearly. The lower “actual” S/N ratio in the 45˚ direction is demonstrated
in Fig. 16 at the same overall S/N ratios. The actual S/N ratios are less here, since, for example,
at an overall S/N ratio of 2 to 1, the actual S/N ratio in this direction is 1.3 to 1.
Before turning to statistical results for the above shear source (at particular overall S/N ratios), it is instructive to look at the typical plots of WT magnitudes versus time (from which thepeak magnitudes are extracted) that are required to identify this source at a particular S/N ratio. It
is most insightful to examine the WT magnitudes versus time separately for the noise-free AE
signals and the noise signals. As table A-1(c) shows, there are actually four noise-free WTcurves (from the 0˚ and 45˚ propagation directions, with WTs at both 60 and 270 kHz) to consider along with the two noise-only curves used to build the S+N signals. Thus Fig. 17(a) shows(top) the WT magnitudes versus time from the 0˚ direction noise-free signal for the primary frequency (A0 at 60 kHz). This figure also shows (bottom) with the same WT-magnitude scale a
WT magnitude versus time at this frequency from a typical noise signal. Similarly part (b) showsat the primary frequency the WT magnitude versus time for the 45˚ direction noise-free signal
and a different typical noise signal. The amplitude of the noise signals for these figures was
based on an overall S/N ratio of 2 to 1 for the 0˚ direction. In a similar fashion, Fig. 18 shows thesame type of results for the secondary frequency (A0 at 270 kHz), where the two noise signals
were the same ones used for Fig. 17. Since the WT magnitude scales are the same for both thenoise-free signal and noise in each case, these figures directly show the potential for greater impact of noise on the WT-based secondary angle ratios when they are necessary for source identification. Clearly, based on WT magnitude results for the secondary frequency, the noise-free signals are not as dominant relative to the WTs of the typical noise signals as the primary frequency
WT magnitude results are. This statement is particularly true for the secondary frequency at the45˚ radiation angle. It should be noted that the WTs of S+N signals are not the linear sum of theWT of the noise-free signal plus the WT of the noise.
14

Fig. 17 WT magnitude versus time for primary frequency (60 kHz/A0) at (a) zero-degree radiation angle and (b) 45-degree radiation angle. Top is for the noise-free shear AE signal at 0.783
mm depth and 180 mm propagation distance. Bottom is for typical noise added to the AE signal
for an overall S/N ratio of 2 to 1.

Fig. 18 WT magnitude versus time for secondary frequency (270 kHz/A0) at (a) zero-degree radiation angle and (b) 45-degree radiation angle. Top is for the noise-free shear AE signal at 0.783
mm dept and 180 mm propagation distance. Bottom is for typical noise added to the AE signal
for an overall S/N ratio of 2 to 1.
To generate the statistical results for this shear case, the primary and secondary angle ratios
were determined for all 50 pairs of S+N signals in each set at different overall S/N ratios. Figure
19 shows the percentage of correct source-type determinations as a function of the overall S/Nratio (defined by the 0˚ direction) for the shear case. This figure shows the percentage of correct
identification choices based on the primary WT peak-magnitude angle ratio, the corresponding
results based on the secondary WT peak-magnitude angle ratio, and the total percentage of correct identifications (when neither the primary nor the secondary angle ratio resulted in an identification error). In these and subsequent similar results, the decision about whether a source determination was in error was based on the arbitrary assumption that incorrect results would beobtained if the calculated angle ratio for the S+N data was more than ±10% from the average nonoise value for the correct source type. The choice of ±10% is fairly conservative in that the15

Fig. 19 Percent of trials out of 50 with correct source identification for balanced shear at 45 degrees about the y axis and 0.783 mm depth.
typical differences in table A-1 between correct or incorrect source choices are on average about
32 %. But a close examination of all the percentage differences in table A-1 shows that there are
four cases where a 10% difference in the right direction would likely result in the selection of anincorrect source type. These cases, along with the ratio percentage difference to the next nearest
and incorrect source type, are: (i) balanced shear at a depth of 0.783 mm with a +9.2% difference
for the secondary-angle ratio, and (ii) microcrack initiation at depths of 1.41, 1.097 and 0.783
mm with respective secondary angle-ratio differences of -11%, -9.9% and -11%. In these cases,
the choice ±10% is not conservative in the direction towards the next nearest source type. An
allowed difference of about ±5% would be conservative in these cases. In spite of this observation, the decision was made to use ±10% so that the allowed percentage variation used as the basis for judging correct source identification would always be the same for this initial study. Thus,in some cases, the ±10% overestimates the number of errors in source identification, while inother cases, it underestimates the number of errors. A future study could develop a more complicated approach to potentially deal with this issue. It is clear from Fig. 19 that the total percentage
of errors is dominated by errors in the secondary ratio at S/N ratios where the correct identification is made about 50 % or more of the time.
We next describe how this ±10% process was implemented for the shear case to obtain the
results in Fig. 19. The primary frequency/mode was A0 at 60 kHz. Table 1(a) gives the no-noise
average angle ratio for this shear source type at 0.66. Thus, this primary angle ratio eliminates
the in-plane dipole (average no-noise angle ratio of 0.50) source. But the source could still beeither a microcrack (average no-noise angle ratio of 0.68) or a shear (average no-noise angle ratio of 0.66). With the adopted criteria that ratio errors must be ±10% or less, the ratio when noiseis present must be between 0.59 and 0.73 to result in a correct source-type decision. This criterion was used, even though in the current case an unlimited range of angle ratios above 0.66
would not result in incorrect source-type decision. Thus, when a signal with noise had a primary
angle ratio within the above range (0.50 to 0.73) the correct decision was that the source was either a microcrack or shear source.
Since the primary angle ratio did not uniquely identify the source type in this detailed case, asecondary ratio was required for unique identification. The secondary angle ratio, in this case,comes from A0 at 270 kHz [see table A-1(c)]. Table 1(a) shows that a no-noise average ratio of
16

Fig. 20 Percent of trials out of 50 with total correct source identification versus S/N ratio for allcases studied.
0.64 distinguishes a shear source from a microcrack source (no-noise ratio of 0.71). To be correct(±10 %), the angle ratio when noise is present must be between 0.58 through 0.70. It should be
noted that at this point the use of ±10 % is problematic for this case, since 0.70 is too close to the
no-noise value of 0.71 for the incorrect microcrack source. This potential problem is ignored inthis work as was discussed above.
Figure 20 shows the percentage of total correct identifications versus the overall S/N ratio for
all the cases (a source type and depth) studied in detail. Again, for Fig. 20, an incorrect result occurs if either or both the primary and/or secondary angle ratios gave an incorrect source type (aratio more than ±10% from the no-noise average ratio of the correct source type).
11. Discussion of Results on Percentage of Correct Source Identifications in the Presence ofElectronic Noise
Figure 20 shows in general that a relatively high overall S/N ratio is required to obtain proper
source identification approximately 90% of the time. But this figure also shows some variation in
the minimum S/N ratio required to reach the approximately 90% correct result. There seems to
be at least two factors that lead to this variation. First, changes in the amount of decrease in thepeak WT magnitudes at 45˚ compared to the 0˚ magnitudes as a function of source type anddepth. This change, as was discussed earlier, is greatest for the in-plane dipole source and ismore moderate for the other two sources types. Second, when a secondary angle ratio is required
to obtain unique source identification, there is the additional drop in the secondary WT peakmagnitudes compared to the primary WT peak magnitudes.
We have seen that many factors enter into a decision as to what overall S/N ratio is necessary
obtain the correct source type approximately 90% of the time. Because of this a simple conclusion of the required S/N ratio cannot be made. In a multi-source environment, where all three ofthe source types examined in this study are present, the typical choice would be to assume that an
overall S/N ratio of about 6 to 1 would be required to identify the correct source type approximate 90% of the time. But as Fig. 20 shows the in-plane dipole case at a depth of 2.35 mm re-17

quires a S/N ratio of more than 8 to 1 to obtain about a 90% rate of correct source identification.
In spite of extensive efforts, a reason for this difference could not be determined.

Fig. 21 WT peak magnitudes for the 50 samples at a S/N ratio of 4 to 1 for balanced shear source
at a depth of 0.783 mm: (a) 60 kHz and zero-degree direction; (b) 60 kHz and 45-degree direction; (c) 270 kHz and zero-degree direction; (d) 270 kHz and 45-degree direction.
For signals with smaller overall S/N ratios, the source identification procedure could still be
carried out. In such cases, the identified source could be flagged as questionable. Alternatively,
for experiments where a group of sensors are all located in an array about AE sources that all
originate from the same location such as the tip of a crack, more than one sensor’s data could be
used at the two angles (zero and 45 degrees). For example, if two sensors were located at eachangle (a total of four sensors is not unlikely in an experimental setup) then several primary andsecondary angle ratios could be formed to determine the likely source type. The four possible
ratios are for data at the angles of 451/01, 451/02, 452/01 and 452/02, where the subscripts 1 and 2
refer to sensors 1 and 2 at each radiation angle. Thus potentially four different determinations of
18

the source type could be made. Due to the random variations in the noise, it is possible that thesource type that occurs most frequently is the actual source type. An additional study would benecessary to predict the probability of determining the correct source type by the use of such
data.
Table 2 Statistical results of WT peak magnitudes for S+N shear case at 0.783 mm depth compared to no-noise values (from data shown in Fig. 21).
Statistical results for 50 trials of
S+N at S/N ratio of 4 to 1
Frequency No-noise Mean WT Difference Dispersion(kHz) and WT peak peak mag- no-noise of the mean
to mean WT peak
propagation magnitude nitude
WT peaks magnitude
direction(%)(%)(deg.)
60 at 01722741722620.01
1.9
60 at 451147271142980.4
3.3
270 at 0
53006
53877
2
5.3
270 at 45
34273
34114
0.4
7.5
Further analysis of the results generated in the examination of the S+N sets of data confirms
some of the expectations that have been discussed earlier. Figure 21(a) shows the typical scatterin the magnitude of the WT peak for the 0˚ radiation direction at 60 kHz for the shear case at a
depth of 0.783 mm. This scatter was determined from a data set with the overall S/N ratio at 4 to
1. Statistical analysis of the WT peak values resulted in a mean value of 172300 with a dispersion of 1.9%. The mean value is within 0.01 % of the no-noise value that is shown in Fig. 21(a)as a straight line. In Figs. 21(b) to 21(d) similar results are shown for the other WT magnitudes
necessary for unique source identification in this case. These figures show that the WT peakmagnitudes scatter in a random fashion both above and below the no-noise value. Table 2 shows
the mean values and their dispersions for all four figures [WT peak magnitudes of the 60 kHz
primary frequency at 0˚ and 45˚ (radiation angles) and the same results for the 270 kHz secondary frequency, which was necessary for unique source identification]. The results in the tableshow that the dispersion increases from a minimum for the primary frequency at the 0˚ radiation
angle to the expected maximum at the secondary frequency at the 45˚ radiation angle. The dispersion values in this table also indicate that the noise-based errors in the primary and secondary
angle ratios depend primarily on the errors in the WT peak magnitudes in the 45˚ directions,since the 45˚ dispersion value is always greater than the 0˚ direction results at the same frequency. And further, the table shows that the secondary angle ratios will typically control theaccuracy of source identification since these dispersions are higher. These results are consistent
with those shown in Figs. 17 through 21.
Ideally, it may be possible that a filtering technique could be developed that would improve
S/N ratios and thus allow correct sources types to be determined more often at lower S/N ratios.
The development of such a filtering scheme is not an easy task. The primary reason is because
electronic noise also inhabits the three key frequency bands where the energetic modes of the AE
noise-free signal are present. Thus, it may be difficult to selectively reduce the electronic noise
without significantly changing the important parts of the AE signal that will be used to identify
the source.
19

In the application of this research study to real experimental data, a question needs to be answered. Given a S/N ratio determined from the ratio of the peak of the S+N to the typical electronic noise, is this ratio close to the overall S/N ratio used in this study where the AE sourcebased signal was available without noise? Take for example a typical set (microcrack initiation at
a depth of 1.41 mm) of 50 S+N waveforms used in this study at an overall S/N ratio of 6 to 1
(where likely the correct source type is determined about 90% of the time). Examination showedthat for this example the range of the S+N to N ratios was from about 5.8 to 1 to 6.6 to 1. Also,
the mean ratio was 6.2 to 1 with a dispersion of 3%. Thus, at overall S/N ratios where correctsource identification is likely about 90% of the time, the overall S/N ratio used in this study is
not much different from the S+N to N ratio that can be measured in experimental cases.
12. Conclusions
The following conclusions apply to the results for the 4.7 mm thick aluminum plate and theexperimental wideband electronic noise. For other materials and/or plate thicknesses, appropriate changes in key frequencies and associated modes will be necessary. These changes could
likely be predicted using modeled acoustic emission (AE) signals.
• A total of three different factors were found to result in attenuation of the AE signals andtheir wavelet transform (WT) magnitudes needed for the source identification scheme. These are:(1) loss as the radiation angle increases from 0˚ to 90˚; (2) decrease in amplitude of the A0 modeat depths near the plate midplane and of the S0 mode near the plate surface; and (3) need for botha primary and secondary (which is based on lower-amplitude portions of the source-based signal)“angle ratio” for complete source identification in some cases (a case is a source type and depth).
• After considering the above attenuation factors, it was concluded that the effects of electronicnoise on the accuracy of source identification could best be examined by considering a particularratio of WT peak magnitudes at two angles. The ratio used was for the 45˚/0˚ angles.
• Due to time-based random variations in the WTs of the electronic noise signals, it was necessary to do a statistical study of the effects of noise on correct source identification.
• To assure that the correct AE source type could be correctly identified approximately 90% of
the time (from among the three source types under study in this report) typically an overall S/Nratio of at least 6 to 1 is required. This result implies that all source identification approaches (notjust the one used on this research) that depend on Lamb-wave modal magnitudes will requirelarge S/N ratios to be successful. The results also indicate that the modal magnitudes that areused in an identification approach should be those from the large modal magnitudes (e.g., using0˚ radiation angles where possible) since these magnitudes will be less subject to noise-induced
errors.
• The current database of S+N signals is ideal to use both to develop and check the accuracy ofvarious noise-reduction approaches, since the original noise-free signal is available.
Acknowledgement
This work was partially supported by NASA Langley Research Center. We also acknowledge the original development of the finite element code by John Gary (retired) of NIST, Boulder, Colorado. We wish to express our gratitude to Prof. M. Takemoto, who released the sourcecode of the wavelet transform that his group had developed; this wavelet transform source code
was utilized in the creation of the AGU-Vallen Wavelet software. We also thank Dr. Y. Mizutani
and Mr. Jochen Vallen for making the AGU-Vallen Wavelet software into a highly usable form.
The contributions of all the above people have significantly advanced the field of acoustic emission.
20

References
1. M.A. Hamstad, K.S. Downs, and A.O’Gallagher, “Wavelet Transform Signal Processing toDistinguish Different Acoustic Emission Sources,” Journal of Acoustic Emission, 2003, 21, 5269.
2. Vallen-Systeme GmbH, Münich, Germany, http://www.vallen.de/wavelet/index.html, 2001,
software version R2002.0703.
3. M.A. Hamstad, K.S. Downs and A. O’Gallagher, “Practical Aspects of Acoustic Emission
Source Location by a Wavelet Transform,” Journal of Acoustic Emission, 2003, 21, 70-94, A1A7.
4. M.A. Hamstad and C.M. Fortunko, "Development of Practical Wideband High Fidelity
Acoustic Emission Sensors," Nondestructive Evaluation of Aging Bridges and Highways, Steve
Chase, Editor, Proc. SPIE 2456, 1995, pp. 281-288.
5. M.A. Hamstad, "Improved Signal-to-Noise Wideband Acoustic/Ultrasonic Contact Displacement Sensors for Wood and Polymers," Wood and Fiber Science, 29 (3), 1997, pp. 239248.
For Appendix, see 22-A01.

21

Table A-1(a) Source identification process using ratios of WT peak magnitude ratio of 45˚/0˚ for in-plane dipole source
Primary Table to Conclusion Percentage Secondary Secondary Table to Conclusion
consult from table difference to mode/freq. angle ratio consult from tableangle
next nearest
ratio
source type(kHz)(mm) (kHz/mode)
Either inplane dipole
orIn-plane
In-planemicrocrack
51
270/S0dipole 2.35
522/S0
0.49
2(a)
0.5
2(a)
dipoleEither inplane dipole
orIn-plane
In-planemicrocrack
dipole 2.037
dipole5160/A0522/S0
0.49
2(a)
0.5
2(a)
In-plane
In-plane
dipole 1.723
60/A0
0.5
2(a)
dipole
32
NA
NA
NA
NA

Source Depth Primary
type
freq.

Percentage Finaldifference to conclusionnext nearest
source type28

In-plane
dipoleNA

In-plane
dipoleIn-plane
dipole36

Reduction
factor =(Highest
0˚)/(lowest
45˚ peak)

5.1

3.3
2

In-plane
dipole 1.41

60/A0

0.5

2(a)

In-plane
dipole

32

NA

NA

NA

NA

NA

In-plane
dipole2In-plane
dipole 1.097

60/A0

0.5

2(a)

In-plane
dipole

32

NA

NA

NA

NA

NA

In-plane
dipole

2

32

NA

NA

NA

NA

NA

In-plane
dipole

2

32

NA

NA

NA

NA

NA

In-plane
dipole2In-plane
dipole 0.783

60/A0

0.5

2(a)

In-plane
dipoleIn-plane
dipole 0.47

60/A0

0.5

2(a)

In-plane
dipoleA2

Table A-1(b) Source identification process using ratios of WT peak magnitude ratio of 45˚/0˚ for shear at 45˚ about y-axis source
Primary Table to Conclusion Percentage Secondary Secondary Table to Conclusion
consult from table difference mode/freq. angle ratio consult from tableangle
to next
ratio
nearestsource type (kHz)(mm) (kHz/mode)

Source Depth Primary
type
freq.

Shear
Shear
Shear

2.35
2.037
1.723

522/S0522/S0522/S0

0.74
0.74
0.74

2(a)
2(a)
2(a)

Shear

1.41

60/A0

0.66

2(a)

Shear
Shear
Shear
Shear ormicrocrack

Shear

1.097

60/A0

0.67

2(a)

Shear ormicrocrack

Shear

0.783

60/A0

0.67

2(a)

Shear ormicrocrack

Reduction
Percentage Finaldifference conclusion factor =(Highest 0˚
to nextpeak)/(lowest
nearest45˚ peak)source type

34
34
34

NA
NA
NA

NA
NA
NA

NA
NA
NA

NA
NA
NA

NA
NA
NA

Shear
Shear
Shear

1.4
1.4
1.4

24

522/S0

0.74

2(a)

Shear

38

Shear

2.2

25

522/S0

0.75

2(a)

Shear

39

Shear

3.8

25

270/A0

0.65

2(a)

Shear

9.2

Shear

5

A3

Table A-1(c)Source
type

Depth

(mm)

Micro
-crackMicro-crackMicro-crackMicro-crackMicro-crackMicro-crackSource identification process using ratios of WT peak magnitude ratio of 45˚/0˚ for microcrack initiation source
Primaryfreq.
(kHz/mode)

Primary
angle
ratio

Table toconsult

2.35

522/S0

0.47

2(a)

2.037

60/A0

0.68

2(a)

1.723

60/A0

0.68

2(a)

1.41

60/A0

0.68

2(a)

1.097

60/A0

0.71

2(a)

0.783

60/A0

0.68

2(a)

Conclusionfrom table

In-plane
dipole or
microcrack
Shear ormicrocrack
Shear ormicrocrack
Shear ormicrocrack
Shear ormicrocrack
Shear ormicrocrack

Secondary
angle
ratio

Table toconsult

Conclusionfrom table

Percent.
Difference tonextnearestsourcetype

270/S00.642(a)Micro-crack

2227

522/S00.462(a)Micro-crack

6127

522/S00.462(a)Micro-crack

61

27

270/A00.722(a)Micro-crack

1130

270/A00.712(a)Micro-crack

9.9

27

270/A00.722(a)Micro-crack

11

Percent.
difference
to next
nearestsourcetype

Secondarymode/freq.

57

(kHz)

A4

Final
conclusion

Microcrack
Microcrack
Microcrack
Microcrack
Microcrack
Microcrack

Reduction
factor =(Highest
0˚)/(lowest
45˚ peak)

2.7
2.2
4.9
4.7
4.4
4.1

Table A-2(a) Source identification process using ratios of WT peak magnitude ratio of 67.5˚/0˚ for in-plane dipole source
Sourcetype

Depth

Primary
freq.

(mm)

Primary
angle
ratio

Table toconsult

Conclusionfrom table(kHz/mode)
InplanedipoleInplanedipoleInplanedipoleInplanedipoleInplanedipoleInplanedipoleInplanedipolePercent.
diff. tonext
nearestsourcetype

Second.
mode/freq.

Secondary
angle
ratio

Table toconsult

Conclusionfrom table

Percent.
diff. tonext
nearestsource
type

(kHz)

Final
conclusion

Reduction
factor =(Highest
0˚)/(lowest45˚ peak)

2.35

522/S00.152(b)In-plane
dipole

40

NA

NA

NA

NA

NA

In-plane
dipole6.6

2.037

522/S00.152(b)In-plane
dipole

40

NA

NA

NA

NA

NA

In-plane
dipole

6.6

1.723

60/A0

0.15

2(b)

In-plane
dipole

187

NA

NA

NA

NA

NA

In-plane
dipole

6.8

1.41

60/A0

0.15

2(b)

In-plane
dipole

187

NA

NA

NA

NA

NA

In-plane
dipole

6.8

1.097

60/A0

0.15

2(b)

In-plane
dipole

187

NA

NA

NA

NA

NA

In-plane
dipole

6.8

0.783

60/A0

0.15

2(b)

In-plane
dipole

187

NA

NA

NA

NA

NA

In-plane
dipole

6.8

0.47

60/A0

0.15

2(b)

In-plane
dipole

187

NA

NA

NA

NA

NA

In-plane
dipole

6.8

A5

Table A-2(b) Source identification process using ratios of WT peak magnitude ratio of 67.5˚/0˚ for shear at 45˚ about y-axis source
Source Depth
typePrimaryfreq.

Primary Table Conclusionfrom table
angle ratio toconsult(mm)(kHz/mode)

Percent.
diff. tonext
nearestsourcetype

Secondary Secondarymode/freq. angle ratio

Table to Conclusion Percentage Finalconsult from table difference to conclusionnext nearest
source type

(kHz)

Reduction
factor =(Highest
0˚)/(lowest
45˚ peak)

Shear

2.35

522/S0

0.58

2(b)

Shear

74

NA

NA

NA

NA

NA

Shear

1.7

Shear
Shear

2.037
1.723

522/S0522/S0

0.58
0.58

2(b)
2(b)

74
74

NA
NA

NA
NA

NA
NA

NA
NA

NA
NA

Shear
Shear

1.7
1.7

Shear

1.41

60/A0

0.43

2(b)

65

522/S0

0.59

2(b)

Shear

85

Shear

2.8

Shear

1.097

60/A0

0.43

2(b)

65

522/S0

0.6

2(b)

Shear

85

Shear

4.8

Shear

0.783

60/A0

0.43

2(b)

Shear
Shear
Shear ormicrocrack
Shear ormicrocrack
Shear ormicrocrack

65

270/A0

0.4

2(b)

Shear

28

Shear

8.2

A6

Table A-2(c) Source identification process using ratios of WT peak magnitude ratio of 67.5˚/0˚ for microcrack initiation source
Primary Table to Conclusion Percentage Secondary Secondary Table to Conclusion
consult from table difference mode/freq. angle ratio consult from tableangle
to next
ratio
nearest(kHz)(mm)
source type
(kHz/mode)

Source type Depth Primary
freq.

Percentage Finaldifference conclusion
to next
nearestsource type

Reduction
factor =(Highest 0˚)/(lowest 45˚
peak)Micro-crack 2.35

522/S0Zero
differsfrom67.5˚

Micro-crack 2.037

60/A0

0.45

2(b)

Shear ormicrocrack

67

Zero differsfrom 67.5˚

NA

2(b)

Not
determinedNA

Not
determined

NA

Micro-crack 1.723

60/A0

0.45

2(b)

Shear ormicrocrack

67

Zero differsfrom 67.5˚

NA

2(b)

Not
determinedNA

Not
determined

NA

Micro-crack 1.41

60/A0

0.45

2(b)

Shear ormicrocrack

67

270/A00.522(b)Micro-crack

27

Micro-crack

6.5

Micro-crack 1.097

60/A0

0.45

2(b)

Shear ormicrocrack

67

270/A00.52(b)Micro-crack24

Micro-crack

6.2

Micro-crack 0.783

60/A0

0.45

2(b)

Shear ormicrocrack

67

270/A00.482(b)Micro-crack

21

Micro-crack

5.9

2(b)

Not
determined

NA

NA

NA

2(b)

NA

NA

Not
determined

NA

A7

EXPERIENCE WITH ACOUSTIC EMISSION MONITORING OF
NEW VESSELS DURING INITIAL PROOF TESTPHILLIP COLE and STEPHEN GAUTREY
Physical Acoustics Limited, Cambridge, U.K.
Abstract
This paper looks retrospectively at experiences with the acoustic emission (AE) monitoringof new vessels during initial proof test and discusses the implications for applying the proposed
European Standard for AE monitoring of proof tests. Case histories discussed in detail are a newreactor and a storage sphere, both intended for refinery service. Comparison between first andsecond loading and the practical implications for running tests according to the code are discussed.
Keywords: Pressure vessels, proof test, reactor, storage sphere
1. Background
The initial proof test of equipment serves two purposes, the first is to mechanically stress relieve the vessel by loading well above the intended service stress, typically x1.45, this yields
high stress areas and blunts crack tips, retarding subsequent fatigue crack initiation, and ensuringthat local microstructure is operating well below yield. For vessels that do not have post-weldheat treatment, typically those <30 mm thick, this is very important as the local welding residual
stress will be up to yield in many areas. The second purpose is to demonstrate that the vessel
does not leak under pressure.
The design codes specify inspection requirements prior to the test to ensure significant defects are not present, but they do not specify inspection following the proof test. This means thatany defects, which grow during the proof test will be put into service with the vessel.
Acoustic emission monitoring during the first loading at initial proof test can help to identify
growing defects or problems with material and heat treatment but interpretation is complicatedby the emission that occurs as a result of normal stress relief, so care must be exercised. ASME
defines criteria for AE monitoring of proof tests, and in the event that these are exceeded during
the first application of load, as is always the case for vessels that have not been heat treated, allows a second cycle to be applied. If the second cycle is “quiet” then any defects present are considered stable and the vessel “passes” the test. It may however still have large but “stable” defects present, so careful analysis of the initial loading to try and identify these is important if you
wish to minimise the defects that find their way into subsequent service.
2. Vertical Reactor Shop Hydrotest
Material:
Size:
Thickness:
Design pressure:A516-70
2.74 m diameter, 13.9 m high (tested horizontally)
25 mm
25.2 bar (403 psi), at 94ºC

J. Acoustic Emission, 22 (2004)22© 2004 Acoustic Emission GroupHydrotest pressure:Intended service:
Special features:

38.75 bar (620 psi) at ambient, ~10ºC (January 1993)
Refinery, hydrogen.
Catalyst loading man-way in top head, ~half diameter of vessel.

Seventeen integral pre-amplifier sensors were used to monitor this vessel, from the signal attenuation measurement (see Fig. 1) this was far more than required according to ASME, however, planar location was used to aid interpretation of the emission, particularly useful for the
first loading of a new vessel, and this requires many more sensors as each emission must reach
three sensors to be located. Planar location highlights specific areas, comparison of relativeemission density from welds over the entire vessel gives an indication of which areas might be
growing defects or have material problems, allowing follow-up NDT to check these out efficiently.

Fig. 1 Attenuation of the Hsu-Nielsen source on vessel.
The first pressurisation initially appeared quite normal, with emission starting from low loads
as expected as a result of stress relief (Fig. 2 shows the history of hits, energy and amplitude forthe entire vessel, the vertical black line is the start of the final hold period), it became clear that alarge amount of emission was from a single weld, the one around the catalyst loading man-way,
sensor 13 was adjacent to this weld where it was close to the top head circumferential weld. Thisemission was so pronounced that the weld drew itself out on the location plot (Fig. 3a, 3b), thefirst cluster appeared at a small % of test pressure, and had a total of >200 events by the end of
the first loading, (Fig. 4) by which time clusters had appeared around that entire weld circumference. At the time this test was carried out there was no official CEN location code, had the test
stop criteria from the code been applied the test would have been repeatedly stopped for NDT ofthese and other cluster locations (>26 in total). Emission continued to the end of the final holdperiod, predominantly from the top man-way area.
The second loading was totally quiet, until at ~70% of the previous maximum pressure emission suddenly started, once again predominantly from the top man-way weld near sensor 13 (Fig.
5).

23

Fig. 2 History of AE hits, energy and amplitudes for the first loading (vertical black line is startof final hold period).

Fig. 3a Top head of vessel showing large
catalyst loading man-way.

Fig. 3b Location of emissions around top
man-way weld during first loading.

MONPAC™ analysis of the first pressurisation produced grade “E” for the sensors aroundthe man-way area, this activity reduced to “C” on the second loading resulting in a recommendation for further evaluation and possible follow-up NDT.

24

Fig. 4 Cluster history from small section of the man-way weld.

Fig. 5 History of emission during second loading (vertical black line is start of final hold period).
25

After the test, extensive NDT was carried out to the man-way weld area, both from insideand outside, no reportable indications were found. Since 90% of the emission from the vessel
had come from the one weld there were still questions and an investigation into the manufacturing process ensued. The outcome of this was the discovery that a much lower weld pre-heat had
been used on the man-way weld than on any other weld on the vessel, resulting in higher residual
stresses, clearly not fully relieved after even two cycles to proof pressure. The reason given forthe lower pre-heat was that from a design standpoint the man-way had been treated as a pipe, allowable under the codes, despite this clearly being the most highly stressed part of the vessel.
The end-users concern was the high risk of hydrogen damage to the area once the vessel went
into service, and the risk of cracks developing from the still present high local stresses frommanufacturing.
It is interesting that despite emissions with source amplitudes well in excess of 80 dB (usually associated with cracking) no reportable indications were found by NDT. One possibility isthat micro-cracking was distributed around the weld and there was as a result no individual “bigcrack” for NDT to find. Localised inspection may not be sensitive to micro-cracking distributed
around a large area; however this can still be a threat to integrity.

Fig. 6 Activity from first proof test and repeat loading of a 21-m sphere.
3. Storage Sphere Initial Hydrotest
Diameter:Legs:

21.3 m10
26

Material:
Thickness:
Design pressure:First Hydro pressure:
Subsequent hydro pressure:

BS5500 carbon steel~18 mm
4.83 bar
6.79 bar
6.1 barForty-six sensors were used to monitor this vessel, due to the thin wall (~one wavelength) theattenuation was relatively high, since energy rapidly transferred into the liquid. This made planar
location of smaller signals impractical as the test threshold would have needed to be so low thatchannels would be continuously active from emission resulting from normal stress relief, the alternative was many hundreds of sensors, again impractical and too costly.
The first loading produced high levels of activity from all areas of the sphere, (Fig. 6) suggesting extensive yielding, and high amplitudes indicated possible micro-cracking, activity exceeded the evaluation criteria many times over.
Zone intensity location analysis of the initial loading produced nothing less than a “C” grade,with eighteen “D” zones, and five “E” zones (Fig. 7). Planar location was carried out but the located data represents only 3.7% of the total emission, and did not identify active clusters (Fig. 8).

Fig. 7 First load zone intensity analysis.
The second loading was relatively inactive; however there was still one “D” grade near the
top head pressure relief valve stack, (Figs. 9, 10), follow-up NDT found no indications.
27

Fig. 8 Planar location, typical view.

Fig. 9 Second load zone intensity analysis.
A few years after putting the sphere into service it was taken out for inspection, and MPI
carried out internally, thousands of HAZ cracks were found, present in most of the welds. These
were ground out together with one crack that was quite deep, approaching 50% through wall.
Metallurgical analysis showed all the cracks to be of manufacturing origin, with no evidence of28

in-service growth. The implication, since the vessel was inspected prior to its initial hydro-test,
was that the cracks opened up during the first loading.

Fig. 10 Second load planar location.
4. Discussion
It is clear that AE gives useful information during initial proof testing; there are cautions
when using the method, however:• Emission resulting from crack growth during the first loading can be difficult to identify
amongst “normal” emission from expected stress relief. Intensity analysis identifies the higher
levels of activity, and where it is practical and cost effective to use planar location this can beenormously useful to identify “hot spots”.
• The second loading is relatively easy to interpret, no emission means no propagating defects
or continuing stress relief, this should be the case.
• “Abnormal” levels of emission from stress relief may be the result of inadequate weld preheat or other manufacturing problems that leave high residual stresses in the welds, not necessarily defects that can be found by NDT. It is also common to see abnormal levels of activity fromstress relieved vessels, where the manufacturer has welded attachments after the stress relief has
been carried out!!!
• Perhaps the most controversial issue is vessels that “pass” on the second loading, what thismeans is that any defects present are stable under these stress conditions, it does not mean thevessel is defect free, defects may have grown or yielded during the first proof and become stable.
The question is whether you want even these defects to go in to service, if not then inspection
after the hydro-test is necessary if there is any significant emission, even on the first loading.
• Where “test stop” criteria are used for the initial loading, it is important to realise and planfor the eventuality that multiple clusters may mean multiple test interruptions for NDT, especially if the activity is the result of excessive weld stress relief. The practicality of this may often
be questionable.
29

QUANTITATIVE DAMAGE ESTIMATION OF CONCRETE CORE BASEDON AE RATE PROCESS ANALYSIS
MASAYASU OHTSU and TETSUYA SUZUKI
Kumamoto University, Kumamoto 860-8555, Japan
2
Nippon Suiko Consultants Co., Kumamoto 862-0936, Japan
1

Abstract
Damage of concrete is quantitatively estimated by applying acoustic emission (AE) measurement and damage mechanics. Concrete samples of controlled damage due to accelerated carbonation are examined to confirm the applicability of damage estimation in the uniaxial compression test. AE activities under compression is analyzed, on the basis of the rate process theory. Using Loland's model in damage mechanics, a relationship between stress and strain ismodeled. The database on a correlation between AE rate and the damage parameter is updated,
and relative damages of concrete samples are quantified. Results are in remarkable agreementwith actual damages of controlled samples. Applying the database, relative damages of coresamples taken from a buried pipeline are estimated.
1. Introduction
As widely recognized, concrete structures are no longer maintenance-free. Diagnostic inspections of the structures in service are in great demand. For detailed inspections, core samples
are often drilled out and then both chemical and physical properties are examined. Concerning
mechanical properties, the compressive strength and the modulus of elasticity (Young’s modulus)
are normally determined by conducting a uniaxial compression test. The strengths are compared,
if possible, with those of the specification. Otherwise, there is no qualified procedure to estimate
the deterioration of concrete. Consequently, it is desirable to estimate the damage of concrete
quantitatively, not relying only on the strength.
To inspect existing concrete structures, acoustic emission (AE) techniques deserve to draw
recognition. This is because crack nucleation and extension are readily detected and monitored.
AE techniques have been investigated in concrete engineering for more than four decades (1).
Achievements of AE research are being applied to practical use (2, 3) and are standardized as acode (4). A feasibility of this standard has been experimentally confirmed by testing reinforced
concrete beams damaged under cyclic loading (5).
As another application, measurement of AE activity in a uniaxial compression test was proposed (6). To model AE generating behavior under compression, the rate process theory was introduced. It is demonstrated that AE rate estimated is closely associated with the presence of microcracks in concrete (7).
In the present study, AE measurement is conducted in a uniaxial compression test. Concrete
samples chemically damaged were prepared by an accelerated carbonation test. AE activity under compression is analyzed as the rate process, and the damage parameter is evaluated by using
Loland's model in damage mechanics. Correlating AE rate with the damage parameter, a database is updated, which has been constructed as applicable to a limited number of core samples
J. Acoustic Emission, 22 (2004)30© 2004 Acoustic Emission Grouptaken from existing structures (8). Relative damages of cores taken from an existing aqueduct ofconcrete are estimated.
2. AE Analysis and Damage Mechanics
2.1 AE rate process analysis
AE behavior of a concrete sample under uniaxial compression is associated with the generation of microcracks. These cracks tend to be gradually accumulated until final failure. The
number of AE events increases in accordance with the accumulation of microcracks, which accelerates toward failure. Since this process could be referred to as stochastic, the rate process
theory was introduced (7).
The following equation of the rate process is derived to formulate the number of AE events,
dN, due to the increment of stress from V to V+ dV,
dN/N = f(V) dV,(1)

where N is the total number of AE events and ƒ(V) is the probability function of AE at stresslevel V(%). Then, a function f(V) is introduced,f(V) = a/V + b,(2)

where a and b are empirical constants. Here, the value ‘a’ is called the rate, and reflects AE activity at a designated stress level. The probability varies, in particular, at low stress level, depending on whether the rate ‘a’ is positive or negative. When the rate ‘a’ is positive, the probability of AE activity is high at a low stress level. This implies that concrete could be damaged.
If the rate is negative, the probability is low at a low stress level, and then concrete is referred toas sound.
Substituting Eq. (2) into Eq. (1), a relationship between total number of AE events N andstress level V is obtained as,
N = CV a exp(bV ) .
(3)
where C is the integration constant.
2.2 Loland’s model
Damage parameter Ω in continuum damage mechanics is defined as a relative change in themodulus of elasticity, as follows,Ω = 1 – E/E*,(4)where E is the modulus of elasticity and E* is the modulus of the concrete which is assumed to
be intact and undamaged. Loland (9) assumed that a relationship between damage parameterΩ and strain ε under compression is represented,Ω = Ω0 + A0 ελ,(5)where Ω0 is the initial damage at the onset of compression test, and A0 and λ are empirical constants. From Eqs. (4) and (5), the following equation is derived,and

σ = ( E0 − E * A0ε λ )ε ,(6)

E 0 = E * (1 − Ω 0 ) .

(7)

31

2.3 Damage estimation
To estimate the initial damage Ω0 in Eq. (7), it is essential to obtain the Young’s modulus of
intact concrete E*. Yet, it is not feasible to determine E* of concrete in an existing structure. To
estimate E* from AE measurement, the relation between total number of AE events and stresslevel in Eq. (3) is correlated with Loland’s model.
In the uniaxial compression test, a relation between stress and strain is obtained as shown in
Fig. 1(a). Young’s modulus varies from initial E0 to final Ec. The former is a tangential modulus
and the latter is a secant modulus. Corresponding to the stress-strain relation, the damage Ω increases from Ω0 to Ωc as shown in Fig. 1(b).

Fig. 1 (a) Young’s moduli E0 and Ec and (b) damage evolution.
In a previous study (10), it was found that a correlation between the increase in the damage(Ωc – Ω0) and the rate ‘a’ is the highest. According to Loland’s model, the increase in the damage corresponds to the decrease in Young’s modulus (E0 – Ec), as follows;

(

)

(

)

(

E0 − Ec = E * 1 − Ω0 − E * 1 − Ωc = E * Ωc − Ω0

)

(10)

Thus, a linear correlation between loge(E0− Ec) and the rate ‘a‘ value is proposed as,

(

)

(

)

log e E0 − Ec = log e  E * Ωc − Ω0  = Da + c.
(11)
It is assumed that E0 = E* when a = 0. This allows us to estimate Young's modulus of intact concrete E* from,(12)
E* = Ec + exp(c).
3. Experiment

3.1 Accelerated carbonation test
Cylindrical samples of 10 cm in diameter and 20 cm in height were made. Concerning mixture proportion, 1 m3 concrete consist of 182 kg water, 331 kg cement, 746 kg sand and 1204 kg
gravel, and the water-to-cement ratio = 55%. The maximum size of gravel is 20 mm. At thestate of fresh concrete, the slum value was 7.9 cm and air content adjusted by admixture was
6.3%. The compressive strength of concrete was 39.4 MPa after 28-day moisture cure.
After curing in the standard condition, cylindrical samples were stored in a reservoir to which
10%-CO2 gas was continuously supplied. This accelerated the deterioration of the concrete samples. Three samples of controlled damage were prepared after 2 weeks, 4 weeks and 6 weeks ofcarbon-dioxide gas exposure.
32

3.2 Core samples
Cylindrical samples of 5 cm in diameter and 10 cm in height were taken from a thrust blockof an agricultural aqueduct (buried pipeline) as shown in Photo 1. In Kasanohara district, Kagoshima prefecture, Japan, this pipeline was constructed in 1967 and repaired in 1979. The degreeof carbonation was measured by spraying 1% phenolphthalein solution to the samples from concrete constructed in 1967. Heavy carbonation was observed in these cores. The concrete pipeline had been buried at the depth of 3 m in Ando volcanic soil originated from Mt. Sakurajima.
Table 1 shows soil properties, which imply that sulfate was detected and acid environment wasminor as pH = 6.7.

Photo 1 A thrust block of an agricultural aqueduct.
Table 1 Soil properties
Soil

TextureAndo soil

LiC

ParticleDensity(g/cm3)
2.424

pH (H2O2)

Sulfate6.7

Detected

Water Content(%)
84

3.3 Uniaxial compression test
All cylindrical samples were tested in compression. A set-up for the uniaxial compression
test is shown in Fig. 2. Silicone grease was pasted on the top and bottom of the sample, and a
Teflon sheet was inserted to reduce AE events generated by friction. MISTRAS-AE system(PAC) was employed as AE measuring device. AE sensor was of wideband type (UT-1000;
resonance frequency: approx. 1 MHz). The frequency range was from 60 kHz to 1 MHz. To
count the number of AE hits, the threshold level was set to 45 dB (0 dB = 1 µV input) with a 40
dB gain in a pre-amplifier and 20 dB gain in a main amplifier. For AE event counting, the deadtime was set at 2 ms. It should be noted that AE measurement was conducted with two channels
and the axial and lateral strains were measured as well. The averaged values of two-channel
measurement were analyzed.
33

Fig. 2 Set-up for the uniaxial compression test and AE measurement system.
4. Results and Discussion

4.1 Young’s modulus
A stress-strain curve measured was first analyzed by Loland’s model. To determine the initial damages Ω0 in Eq. (7), averaged Young’s moduli E0 of three sample right after 28-day moisture cure was referred to as E* in the compression test. Here, Young’s modulus E0 was quantitatively determined as the initial tangent of the stress-strain curve, which is approximated as a hyperbolic function as,
σ = a1ε + a 2 ε 2 .
(13)
where a1 and a2 are empirical constants. From the stress-strain relation in Eq. (13), Young’s
modulus E0 is determined as a tangent modulus: dσ/dε at ε = 0,
a1 = E 0
As indicated in Fig. 1(a), two moduli of elasticity, E0 and Ec, were determined from the test. The
rate process analysis was conducted at stress level in the range from 30％ to 80％. This is because AE events occurring at initial loading below 30% strength are due to contact with the loading plate and at an accelerated stage above 80% have little to do with the damage.
Concerning core samples, Young’s moduli E0 and Ec were also determined. Table 2 shows
mechanical properties of all the samples. Carbonation ratio was estimated as the ratio of carbonation depth to total depth. f’c is the compressive strength, and ED is dynamic Young’s
modulus. As seen in the table, initial Young’s modulus E0 varies from 8.7 to 34.0 GPa, while theunconfined compression strength varies from 8.0 to 20.4 MPa.
4.2 Estimation of relative damage
In data analysis, Young's modulus of intact concrete E* was estimated by Eq. (12). The relative damage of concrete was determined as the ratio E0/E*, which is the ratio of the initial tangent Young’s modulus to intact Young’s modulus estimated.
To determine E* from the relationship in Eq. (11), a large number of data are desirable.
However, the number of concrete cores available is limited in existing structures. Thus, a database, which could allow even a single concrete core to be evaluated, is constructed as shown in
Fig. 3.
34

No.
1
2
3
4
5
6
7
8
9
10

Table 2 Mechanical properties
Construction
Carbonation
f’c
E0
Ec
ratio (%)
(MPa)
(GPa)
(GPa)
1979
13.4
20.4
26.2
18.2
1979
4.7
19.0
34.0
13.1
1979
6.8
9.9
22.9
11.9
1979
62.9
8.0
10.4
4.4
1967
100.0
18.0
19.9
10.0
1967
100.0
13.8
18.4
7.8
1967
16.3
15.7
18.6
8.2
1967
100.0
9.7
18.4
12.2
1967
100.0
8.7
8.7
4.2
1967
100.0
9.6
16.1
6.8
f’c: Compressive strength, ED: Dynamic Young’s modulus

ED
(GPa)
29.8
36.7
34.0
21.7
24.7
26.7
25.6
16.6
15.0
22.1

Fig. 3 Relations between loge(E0-Ec) and the ratio ‘a’.
The samples included in the database shown in Fig. 3 are all tested in our previous studies,including the data of the present tests. In total, 343 points are plotted. By using this database,
Young's modulus of the normal concrete E* and then the relative damage of specimens can be
estimated.
Damages due to carbonation were estimated as the ratio of initial tangent Young’s modulus,
En, after carbonation periods of n weeks, to that of 28-day cure only, E0. These were comparedwith E0/E*. Results are given in Fig. 4. A remarkable agreement is observed. These results confirm that the relative damage can be estimated quantitatively by AE rate process analysis.
35

Fig. 4 Relative damages estimated Eo/E* and actual damages.

Fig. 5 Relations between relative damages and carbonation depths.
In order to investigate an applicability of the relative damage as a damage index, the data on
E0/E* of the carbonation tests are compared with the depths of carbonation. As seen in Fig. 5, astrong correlation between the relative damages and the depths of carbonation is observed. Thisimplies that AE rate process analysis could give us quantitative information on the damage evendue to a chemical effect.
It is also demonstrated that the relative damage based on Young’s modulus of intact concrete
E* is practically determined by using the database. The database is applicable to evaluate therelative damage even when an inadequate number of specimens are available from the existing
structures.
36

Fig. 6 Relative damage E0/E* (bar graph) and the compressive strengths (circle) in actual
structures.
Relative damages of ten core samples in Table 2 were then determined and are shown in Fig.
6. Relative damage E0/E* varies from 0.48 to 1.25. Damages estimated in nine samples are below 1.0. All except core No. 2 sustained damages, some of them substantially. In Fig. 6, the line
connecting open circles denotes the compressive strengths determined. Relative damage andcompressive strength correlate well. Thus, by using the relative damages from AE measurement,
the compressive strengths can be quantitatively estimated.
The damages of concrete are quantified in comparison with the specified values at the time of
construction. In the case of old concrete, however, these data are often unavailable. In addition,
because the strength of concrete normally increases due to chemical reaction after construction,
the degree of damage cannot be estimated from current strength values alone. Consequently, therelative damage is useful for quantifying the degree of damage, estimating the intact modulus at
the current state by AE.
5. Conclusions

In uniaxial compression tests of concrete, the relation between the AE activity and concrete
damage is analyzed, based on the rate process theory and damage mechanics. The degrees ofdamage in concrete samples are quantitatively estimated, even when the initial physical properties of concrete structure at the time of construction are unknown. Conclusions are summarized
below.
1) AE behavior in concrete is dependent on the damage, and can be approximated by applying
the rate process analysis. Loland's damage mechanics model can approximate the relation
between stress and strain, and the applicability of the damage parameter in the model is confirmed.
2) Based on the correlation between the decrease of Young's modulus and the rate ‘a’, Young's
modulus of intact concrete is successfully evaluated. Using the ratio of Young's moduli,
E0/E*, the relative damage of concrete can be estimated.
373) Using the database, it is demonstrated that Young's modulus of intact concrete can be estimated from a small number of core samples taken from existing structures. Because thestrength of concrete normally increases due to chemical reaction after construction, the degree of damage cannot be estimated from current strength values alone. The relative damage
through AE measurement is useful for quantifying the degree of damage.
References

1) Ohtsu, M. (1989). A Review of AE in Civil Engineering with Emphasis on Concrete, Journalof Acoustic Emission, 8 (4), 93-98.
2) Ohtsu, M. (2000). Diagnosis of Reinforced Concrete Structures by AE, Concrete Journal, 38(7), 10-16.
3) Colombo, S., Forde, M., Das, O. and Halliday, J. (2001). AE Experiments on Concrete Beams:
General Overview and Research in Progress on Bridges, Proc. 9th Int. Conf. on Structural Faults& Repair, CD-ROM (Day 2).
4) NDIS 2421, Recommended Practice for Ins-Situ Monitoring of Concrete Structures by AE,
Japanese Society for Nondestructive Inspection, 2000.
5) Ohtsu, M., Uchida, M., Okamoto, T. and Yuyama, S. (2002). Damage Assessment of Reinforced Concrete Beams qualified by AE, ACI Structural Journal, 99 (4), 411-417.
6) Ohtsu, M. (1987). Acoustic Emission Characteristics in Concrete and Diagnostic Applications,
Journal of Acoustic Emission, 6, (2), 99-108.
7) Ishibashi, A, Hidaka, E., Farahat, A. M. and Ohtsu, M. (1995). Deterioration Evaluation by AE
in Concrete Samples, Proc. 6th Int. Conf. Structural Faults and Repair, Vol. 2, pp. 69-74.
8) Suzuki T., Watanabe, H. and Ohtsu, M. (2002). Damage Evaluation in concrete Using Acoustic Emission Method, 6th Far-East Conference on Non-Destructive Testing, pp. 111-116.
9) Loland, K.E. (1989). Continuous Damage Model for Load-Response Estimation of Concrete,
Cement and Concrete Research, 10, 395-402.
10) Ohtsu, M. and Watanabe, H. (2001). Quantitative Damage Estimation of Concrete by Acoustic Emission, Construction and Building Materials, 15, (5-6), 217-224.

38

DAMAGE ASSESSMENT IN DETERIORATED RAILWAY
SUB-STRUCTURES USING AE TECHNIQUETOMOKI SHIOTANI1, YASUHIRO NAKANISHI1, XIU LUO2 and HIROSHI HAYA2
1
Research Institute of Technology, Tobishima Corporation,
5472 Kimagase, Noda, Chiba 270-0222, Japan.
2
Railway Technical Research Institute, Kokubunji, Tokyo 185-0034, Japan.
Abstract
Due to earthquakes, differential settlement and long-term weathering, civil structures havesustained deterioration. In super-structures located above ground, except for seriously damaged
structures, evaluation of moderately or intermediately damaged structures is difficult. In
sub-structures the damage would not be readily assessed even for severely damaged structures.
Acoustic emission (AE) testing has a potential to evaluate the damages not only for super-structures but sub-structures since it is a monitoring technique to detect fracture-associatedelastic waves. In the present paper, damage indices using AE activity are discussed with realdamaged concrete piers. Railway traffic is utilized to induce the AE activity. Through in-situ
monitoring, AE monitoring promises to quantify the damages in the railway structures.
Keywords: Calm ratio, damage assessments, railway structures, RTRI, improved b-value
1. Introduction
Currently, strong efforts to create standards for AE monitoring in concrete engineering arereflected in the recommended practices for in-situ monitoring of concrete structures [1]. Since
the standard was based on the laboratory studies [2], while AE applications are practical, there
are some issues to be resolved. In the present paper, the applicability of AE technique will be
discussed with two railway sub-structures, and a plain concrete pier and an RC pier, both deteriorated. As indices for the degree of damage, RTRI and Calm ratio as well as improved b-value
are studied in comparison with actual damage observed.

Fig. 1 Representation of AE activity with damage progress.
J. Acoustic Emission, 22 (2004)39© 2004 Acoustic Emission GroupFig. 2 Peak amplitude distribution.
2. Characteristic AE Activity with Damage Evolution
Under incremental cyclic loads, AE activity in concrete can be represented with damage progress as shown in Fig. 1. Provided that damage levels are classified into four different levels: intact; almost intact; slightly damaged; and heavily damaged, AE activity corresponding to eachdamage level is demonstrated as in the chart. In the second cyclic load, AE activity would start
to be observed at the load level of the maximum prior load. This phenomenon is referred to as
Kaiser effect. In the subsequent third cyclic load, in which the material shows slightly damaged
condition, the onset of AE appearance would be at a lower load level than previously applied.
Decrease of effective areas against external load or accumulation of microcracks within materials
appears to play significant roles for the observed behavior. In order to express the relation of
AE activity to the stress level experienced, such damage-indices as Felicity ratio [3], CBI ratio[4], and Load ratio [1] have already been proposed. It is also important to pay attention to the AE
activity during unloading processes [5]. With the evolution of damage, not only the AE activity
during uploading, but that during unloading becomes more intense. Accumulation of shear-type
cracks seems to produce this phenomenon. The ratio of the cumulative number of AE events
during uploading to that during unloading, is referred to as Calm ratio [1]Damage indices using AE parameters
The previously mentioned ratios may be difficult to apply for in-situ monitoring since themaximum stress to which the materials have been subjected is not readily available. Thus, wehave proposed an RTRI ratio [6]. The RTRI ratio is defined by the following procedure: the onsetof AE activity is estimated on the basis of whichever measured parameters such as stress or load,strain or deformation; the RTRI ratio is defined as the ratio of the parameter at the onset of the
AE activity to the maximum value (or peak value) during an inspection period, instead of the
maximum stress which the structure has experienced.
In addition to those indices shown, AE peak amplitudes are closely related to the scale offracture, namely the degree of damage. Thus, the level of peak amplitude becomes larger withthe progress of fracture. However, it seems difficult only to pay attention to the peak amplitude.
40

As fracture develops, an apparent mechanical property of the structure changes as well, so thatthe condition of medium through which AE signals travel worsens; i.e., a high attenuation reduces even high AE peak amplitude at the source to low amplitude levels at the sensor. Accordingly, in the present paper, the peak amplitudes are studied through their distributions, not by
the absolute amplitude values. Toward this end, the method for calculating the improved
b-value (or Ib-value) has been used incorporating the statistical nature of amplitude distribution[7, 8]. See Fig. 2.

Fig. 3 Arrangement of AE sensors together with 3D positions of AE sources.
3. In-situ Applications
3.1 A concrete pier damaged due to differential settlement (C-pier)In-situ AE monitoring was performed in a concrete (C-) pier (height x length x width = 5.5 x
12.3 x 2.6 m) of a railway bridge. The monitoring site was located in a suburb of Tokyo, andabout 420 train passages a day loaded the bridge repeatedly. The pier was made of plain concrete,
and more than 70 years has passed since the construction. During the extended service, the pierdeteriorated due to differential settlement, and resulting macroscopic shear cracks were found.
The crack observed was previously penetrated, and repaired traces were found along the crack.
Crack motions were measured by using π-shaped displacement meters. The AE sensors array
along the penetrated crack is shown in Fig. 3 as well as AE sources located. 12 AE sensors withbuilt-in preamplifier (R6I, Physical Acoustics Corp.) were firmly fixed onto the pier surface withsprings. AE events induced by train passage in service were subsequently amplified by 40 dB at
preamplifiers and fed to AE monitoring system (Mistras, Physical Acoustics Corp.) Both AE
parameters and waveforms were recorded with the system.

41

Fig. 4 Crack traces observed in RC-pier, showing north, east and south side from left to right.

Fig. 5 AE sensor array.

.
Fig. 6 3D AE sources in RC-pier. (a) AE after 10 train passages; (b) After 4 passages.
423.2 A reinforced concrete pier damaged by an earthquake (RC-pier)AE monitoring was conducted on an RC pier of a railway rigid-frame bridge, on which bullet
trains pass several times an hour. Due to a large earthquake (Mw = 7.0) in 2003, super-structuresof most piers were seriously damaged. In the pier subjected to monitoring, however, criticalcracks were not found above ground but serious damages were readily found in the lower part
embedded into ground. The cracks observed below ground are shown in Fig. 4. In the northside, for example, a crack of 0.7 mm width was found just above the footing and a diagonal
crack, namely a shear-type crack, was observed at 1 m above the footing. These cracks are principally distributed up to 1.5 m above the footing. To know the AE activity not only for seriously
damaged but for intermediately damaged state, two arrays of AE sensors, A-array and B-array,
were employed as shown in Fig. 5. A-array corresponds to the seriously damaged area while
B-array shows the intermediately damaged area. In each array, 12 AE sensors of 60-kHz resonance surround the pier. The same AE monitoring system and monitoring condition as those in
the C-pier (Sec. 3.1) was used.
4. Results
4.1 AE sources locatedFigure 3 shows the results of 3D source locations in case of the C-pier. The AE sources were
induced by train passage in service. The sources are drawn with open circles. Solid circles show
the source positions that have the peak frequency of more than 20 kHz. The raw AE sources
spread over the monitoring area irrespective of the crack location, but the sources of over 20 kHz
were narrowly located along the pre-existing crack. Presumably, the AE sources with the peakfrequency of over 20 kHz are closely related to the AE events generated due to the friction alongpre-existing crack interfaces. Details of the discussion can be found in reference [9].
Figure 6 shows 3D source locations in case of the RC-pier (Sec. 3.2), where all the AE
sources are projected to the north side and the scale of the circle reflects the average peak amplitude of all hits contributing to the AE source/event. Here, we focused only on the level of AE
sources because of different population parameters of train passages. A-array shows many AE
sources generated actively around the observed cracks, and the levels of AE sources distributed
widely, namely from small to large amplitude. In B-array, the high-level AE sources found in
A-array were absent, but almost the same amplitude level of AE sources were present.

Fig. 7 AE and displacement activities.
43

Fig. 8 AE and displacement activities with a passage of bullet train.
4.2 AE activity due to train passage
To determine the RTRI and Calm ratios, it is essential to obtain AE activity as a function of
external parameter such as displacement. Figure 7 shows the case of the C-pier, where cumulative AE activity and behavior of π-displacement meter, installed on the observed crack, are plotted against elapsed time. To evaluate only those AE events attributable to the secondary AE activity, a 20-kHz high-pass filter was applied to the raw AE data set. In the plot, both activities offiltered AE hit and AE energy are shown. Even after filtering, different trends were observed forthe onset of AE activity and the time when they suddenly increased. The maximum value of
π-displacement meter, which is essential to obtain the RTRI, was readily identified with the plot
in the C-pier (at 17:26:17 in Fig. 7).
In the RC-pier, the AE activity and displacement behavior are given in Fig. 8. The displacement showed cyclic behavior when a bullet train passes. Each peak corresponds to an individualcar of the bullet train, suggesting the difficulty in obtaining both Calm ratio and RTRI. In thispaper, the peak displacement (P) for the RC-pier was determined using the envelope of the cyclic
variation (details can be seen in Discussion).
4.3 RTRI and Calm ratio
Based on the detected AE activity and the measurement of the π-displacement meter, the degree of C-pier damage is quantified using the RTRI and Calm ratios (see Fig. 9). The AE activityof raw data is denoted with open circles (hit) and open triangles (energy), whereas that extractedwith the high-pass filtering is exhibited by solid circles (hit) and solid triangles (energy). Dottedlines in the chart show reported criteria with regard to damage levels [2]. It is noted that raw AE
activity is mainly caused by the train movement but filtered data presents the emissions inducedby the crack movement [9]. The RTRI shows smaller value than 0.2 in the filtered AE activity. In
seriously damaged structures, AE events within the material started to be generated even with aslight movement of the structure, namely from smaller displacements. A different trend can befound in the Calm ratio. Smaller Calm ratios were both obtained in the raw and filtered AE activity, whereas larger ratios were additionally observed in the filtered AE hits, suggesting that the
number of AE hit during the loading process almost equals the number of AE hit during theunloading process in the filtered activity (about 0.8 in Calm ratio). It should be noted, however,44

Fig. 10 Calm ratio and RTRI in RC-pier,where ‘Event’ stands a located source.

Fig. 9 Calm ratio and RTRI in C-pier,showing H: heavily damage, I: intermediately damage and M: moderately damage.

that the filtered AE energy showed smaller value than 0.2 in contrast to the filtered AE hits. Thisimplies that the AE hits during the unloading process featured very low energy values in comparison to those during the loading process. Thus, in the C-pier, it can be concluded that RTRI ispotentially useful for damage evaluation. In contrast, the Calm ratio depends strongly on the AE
parameter used and its application must await further study.
Figure 10 shows a similar plot of Calm and RTRI ratios for the RC-pier, in the same manneras in Fig. 9. The solid symbol denotes the case of A-array and the open symbol represents thecase of B-array. Distributions of filtered AE hits and events show different trends. B-arrayshowed almost 0.5 for the RTRI. However, RTRI for A-array distributed in two regions; one
smaller than 0.4 and the other larger than 0.6. In the Calm ratio, B-array shows values smaller
than 0.3, whereas A-array values are widely distributed from 0.1 to 0.7. In the RC-pier, damage
quantification was thus difficult with either Calm ratio or RTRI.
4.4 Improved b-value
Figure 11 shows the amplitude distribution for the C-pier. A line shows the differential distribution and a cross symbol shows the cumulative distribution. In the plot, the result of Ib-value
analysis is indicated by the broken line as well, where a negative gradient of the line denotes the
Ib-value of about 0.05. In the C-pier, since a shear-type macroscopic crack was observed, itshould be recognized that the Ib-value corresponding to the critical damage is about 0.05.
The AE peak amplitude distributions of the RC-pier are exhibited in Fig. 12. Open circles(B-array) and open squares (A-array) show differential distributions and corresponding solid
symbols show the cumulative distributions. In the figure, the averages of peak amplitudes in botharrays from all derived AE events are also indicated. Here, A-array corresponds to seriously
damaged state while B-array shows the intermediately damaged state. When the average amplitude is compared, the average peak amplitude in B-array is larger than that for A-array. Although45Fig. 11 Amplitude distributions in C-pier.

Fig. 12 Amplitude distribution in RC-pier.
this may suggest that AE events generated from the intermediately damaged state had larger amplitude than seriously damaged state, this is incorrect. While, in general, the larger the scale offracture becomes, the larger the AE peak amplitude is, the use of the average peak amplitude isinadequate and misleading. In the differential distribution in B-array, one sees that AE eventsare distributed narrowly around 45 dB, and few AE events have amplitude larger than 50 dB. In
46

A-array, AE events are distributed widely from low to high (from around 40 dB to 55 dB). The
difference of the range in the distribution produced the anomaly. This is another reason for theuse of the Ib-value analysis. Solid plots show that the gradient in A-array (Ib = 0.06) is smaller
than that in B-array (Ib = 0.10), implying that the improved b-value becomes smaller with damage evolution. This agrees with past observations [10]. Considering the Ib-value of 0.05 was obtained from the C-pier, this corresponds to the criterion for the seriously damaged condition ofthese concrete structures.
5. Discussion
5.1 Damage quantification with RTRI and Calm ratio
As shown in Figs. 7 and 8, both AE activity and external parameters such as displacement
should be measured to obtain the RTRI and Calm ratio. In Fig. 8, however, considerably varied
behavior with up and down movement was observed with passing bullet trains. Even with conventional trains passing, the same manner of cyclic behavior was still confirmed (see Fig. 7).
This suggests that when searching the corresponding value at the onset of AE activity, for example, it should be carefully treated during unloading processes. Such attention also should be paideven during uploading processes: the displacement value corresponding to the onset of AE activity showed smaller value than previously during a series of loading process, namely ‘one’ train
passage. Furthermore, it is also important to decide the maximum value during the loading process, particularly for obtaining Calm ratio. In Fig. 9, Calm ratios were obtained on the basis of Fig.
8 using the following cumulative AE activity: AE events were accumulated between U and P for
the loading process, while for the unloading process, those between D and T were accumulated.
This is a finally employed idea to decide AE activity both during loading and unloading processes; i.e., accumulation of AE events during the uploading was tried first between U and P*.
However, many cases showed no AE events in such condition at all. This suggests that it is crucially important to decide the term both of loading (up-loading) and unloading (down-loading)
processes in obtaining the Calm ratio.
Not only for Calm ratio (see Figs. 9 and 10) but also RTRI (see Fig. 10), damage quantification depending on the damage level, was difficult to find, and therefore further studies should beneeded to clarify the applicability of those damage indices for actually damaged structures aswell as considering the load/unload definition mentioned above.
5.2 Damage evaluation using improved b-value
Improved b-values were ~0.05 in seriously damaged structures (see Fig. 11 and A-array inFig. 12), while in intermediately damaged, Ib-value of about 0.1 was obtained. As noted, it is difficult to identify the damage degree with peak amplitude only (more details of discussion can befound in [11]), and the analysis of improved b-value does not need such external parameters as
displacement. This leads to a conclusion that Ib-values can indicate damage condition reasonably
well although more applications with a variety of damage levels should be collected systematically.
6. Conclusions
Main findings obtained through the study can be summarized as follows:
1) RTRI and Calm ratio, obtained from AE activity with reference to structural deformation,
were strongly dependent both on the definition of loading processes, namely loading and47

unloading, and an AE parameter employed. Thus, much more applications with a variety offracture types and levels should be needed to clarify the applicability as a damage index.
2) It was difficult to associate damage levels with the average AE peak amplitude. The peak
amplitude distribution showed characteristic trends depending on the damage levels. In particular, improved b-value, indicative of the gradient of the distribution, is a promising parameter providing the degree of damage quantitatively.
References
1) The Japanese Society for Non-Destructive Inspection: Recommended practice for in situ
monitoring of concrete structures by acoustic emission, NDIS2421, p. 24, 2000.
2) Ohtsu, M., M. Uchida, T. Okamoto and S. Yuyama, Damage assessment of reinforced concrete beams quantified by acoustic emission, ACI Structural Journal, 99(4), 411-417, 2002.
3) Fowler, T.J., Experience with acoustic emission monitoring of chemical process industry vessels, Progress in Acoustic Emission III, JSNDI, pp. 150-162, 1986.
4) Yuyama, S., T. Okamoto, M. Shigeishi, M. Ohtsu and T. Kishi, A proposed standard forevaluating structural integrity of reinforced concrete beams by AE, Acoustic Emission: Standards and Technology Update, ASTM STP1353, pp. 25-40, 1998.
5) Shiotani, T., M. Shigeishi and M. Ohtsu, Acoustic emission characteristics of concrete-piles,
Construction and Buildings Materials, 13, 73-85, 1999.
6) Luo, X., H. Haya, T. Inaba, T. Shiotani and Y. Nakanishi, Experimental study on evaluationof breakage in foundations using train-induced acoustic emission, Proc. Structural Engineering
World Congress 2002, Paper No. T9-1-e-3, 2002.
7) Shiotani, T., K. Fujii, T. Aoki and K. Amou, Evaluation of progressive failure using AE
sources and improved b-value on slope model test, Progress in Acoustic Emission VII, JSNDI,
Tokyo, pp. 529-534, 1994.
8) Shiotani, T. and M. Ohtsu, Prediction of slope failure based on AE activity, Acoustic Emission: Standards and Technology Update, ASTM STP 1353, pp. 156-172, 1999.
9) Shiotani, T., Y. Nakanishi, X. Luo, H. Haya and T. Inaba, Evaluation of structural integrity inrailway structures using train-induced acoustic emission, Structural Faults & Repair 2003
(CD-ROM), Engineering Technics Press, 2003.
10) Shiotani, T., Z. Li, S. Yuyama and M. Ohtsu, Application of the AE improved b-value toquantitative evaluation of fracture process in concrete-materials, Journal of Acoustic Emission,
19, 118-133, 2001.
11) Shiotani, T., X. Luo, H. Haya and M. Ohtsu, Damage quantification for concrete structuresby improved b-value analysis of AE, 11th International Conference on Fracture, 2005, submitted.

48

DEFECT DETECTION BY ACOUSTIC EMISSION EXAMINATION OF
METALLIC PRESSURE VESSELSFRANZ RAUSCHER
Vienna University of Technology, Vienna, Austria
Abstract
There are only a few publication which give hints to the questions, when acoustic emission
examination should be applied and which defects can be detected? This paper addresses flaws,
cracks, and crack-like defects in pressure vessels made of ductile steel. Mechanisms which enable defect detection are identified, and the characteristics of these mechanisms are described.
Based on the identified mechanisms and their characteristics, examples of conditions which leadto good or poor ability of defect detection are given. This could be the basis for a quantitative
grading system for defect detection by acoustic emission examination. Such a scheme could befurther developed for the evaluation of probabilities of detection (POD), which are necessary forquantifying the benefit of acoustic emission examinations in reliability analyses and risk based
inspections.
1. Introduction
There are many standards dealing with acoustic emission (AE) examination of metallic pressure vessels, e.g. [1, 2]. In these standards, only fuzzy answers are given to the question, which
defects can be detected by acoustic emission examination. The problem that plastic deformationand crack growth in ductile pressure vessel steels may be only a weak AE source [3 - 6] leads tofrequent discussions about the effectiveness of AE examination.
This paper concentrates on flaws, cracks, and crack-like defects in metallic pressure vessels.
Because usually high ductility is required for pressure vessel steels, such defects in ductile steels(elongation after fracture ≥14% and Charpy-V-notch impact energy not less than 27 J) are considered here. Of course, material embrittlement in combination with the considered defects ispossible.
To reduce the complexity of the problem, mechanisms which may activate AE sources during AE examination are classified according to the following criteria:•••

Within one group of mechanisms the AE characteristics should be similar.
Based on the known operating conditions of a vessel, it should be possible to decide
whether or not a mechanism is relevant for the considered vessel.
The expected total AE is the sum of the AE from the different mechanisms.

One of the most important parameters is the loading at which the AE occurs. Because of the
Kaiser effect, it has to be distinguished between first and subsequent loading. The loading at thedefect is increased if the pressure is increased but also if the defect size is increased. As single
loading parameter, which reflects both influences, the rate of the pressure to the burst pressure isused. The burst pressure is pressure when a pressure vessel with the considered defect fails by
burst.
J. Acoustic Emission, 22 (2004)

49©2004 Acoustic Emission GroupFusion line

Inclusions
Crack tipCrackbranchingPoorly bonded
Brittle filler

Surface layers

Brittle layera)b)Fig. 1 Weld flaw; a) initial flaw after welding; b) flaw after operating period.

Fig. 2 Example of bad bonding at weld with incomplete penetration after welding.
2. Description of Flaws, Cracks, and Crack-like Defects
To show the appearance of defects, typical properties of weld defects are shown in Fig. 1. A
flaw near to the fusion line is sketched. There may be slag or a brittle surface layer in the flaw.
The flaw may end in a zone of poorly bonded material. Other smaller inclusions may be present.
After a period of operation, at one side the connection of the flaw may be open to the surface ofthe vessel due to crack propagation. At the opposite side of the flaw, the poorly bonded material
may be fractured, und crack initiation may have taken place. Due to deformation, brittle fillers(slag) and surface layers are broken. Also smaller brittle inclusions may be broken. If the flaw isopen to the surface, the operating medium may penetrate into the flaw. Corrosion or oxide layers
may be built at the crack or flaw surface. A combination of such flaws and cracks with material
embrittlement is probable. Typically, only some of the properties described above arise in a real
weld defect. Figure 2 shows an incompletely penetrated weld as an example of a defect. A poorbonding zone can clearly be seen at the tip of the notch.
For situations similar to the ones described above, the following source mechanisms for AE
activities are identified. Most of the described mechanisms can also arise in flaws and cracks at
the base material.
2.1 Plastic deformation of ductile material
For the zone where the plastic deformation occurs, material with good quality (comparable tomaterial of usual tensile test samples) is considered. Material deficiencies, e.g. embrittlement and50

inclusions, are dealt with separately. Local plastic deformation of ductile pressure vessel steels
may be a very weak AE source (see [4, 5]).
For typical ductile pressure-vessel steels, only low amplitude signals are expected. At thefirst loading the AE activity typically increases (Fig. 3) until general yielding; afterwards theactivity decreases. In the case of subsequent loading or loading of cold-worked material, the activity is minimal.

Pressure

StrainStrain [%]Pressure [bar]Fig. 3 AE activity due to plastic deformation of defect free material depending on the load level.

Amplitude [dB]σTime [s]Fig. 4 AE during plastic deformation of a notch in a pressure vessel.
To obtain information on the AE due to the local plastic deformation, which is expected during the loading of a crack or crack-like defect, the following experiment was performed: An old
51vessel, with a diameter of 800 mm and a wall thickness of 15 mm, was pressurized to the test
pressure twice. During the second pressurization, the AE activity was low due to Kaiser effect.
Afterwards, a notch was ground into the longitudinal direction of the cylindrical shell. The shell
was made of St41KT, which is a carbon pressure-vessel steel with a carbon content of 0.17%C
according to an old Austrian standard. At the middle the width of the notch was extended, and astrain gauge was applied.
The AE was measure with five sensors (VS150M, Vallen Systeme, Germany), the nearest
one at a distance of 100 mm. A pencil-lead break (0.5 mm, 2H) at the notch results in amplitudes
over 85 dB at the nearest sensor. Figure 4 shows the pressure, the measured strain at the notch,
and the AE during the first and second pressurization after introducing the notch. A number ofsignals, most of them with small amplitudes, were recorded. Only a few AE events could be registered at sensors at larger distances. The test was repeated with a notch in the weld material resulting in even less AE activity. This may be caused by the smaller carbon content and, therefore, smaller cementite content of the weld material.
This example shows the possibly small AE activity due to the local plastic deformation. Of
course this was measured in a simple low-carbon pressure-vessel steel and may be different inother materials.

Burst

Crack
initiation

Acoustic emission
activity

2.2 Crack initiation or crack propagation in ductile material
For the material in the ligament, good quality (comparable to material of usual fracture mechanics test samples) is considered, and material deficiencies are dealt with separately. Crack
initiation and crack growth in ductile pressure-vessel steel is dominated by the mechanism ofvoid coalescence, which is a weak AE source [4, 6, 14]. Acoustic emission may be expectedfrom brittle micro-fracture events, which may take place, depending on the material quality andstress state, more or less frequently.

First
pressurization

Subsequent
pressurization
1 Pressure
burst pressure

Fig. 5 Activity due to stable crack growth in ductile materials depending on the load level.
Stable crack growth in ductile materials takes place at relatively large loadings, after crack
blunting and crack initiation [7 - 9]. Therefore, AE from this mechanism can only be expected at
the first loading at relatively high ratios of the loading to the limit load (Fig. 5). At subsequent52

loading stable crack growth may only be expected if the loading approaches to the maximum
value of the previous loading. If the pressure of the actual pressure test is not larger than the oneof a previous pressure test, a larger ratio of the pressure to the limit pressure can only be reached
if the defect had grown during the operating period.

Fig. 6 AE during stable crack growth in a pressure test.
To obtain information on the AE activity during crack extension the following example isgiven: A longitudinal notch was introduced into the cylindrical shell (diameter 250 mm, wall
thickness of 7 mm, material similar to 34CrMo4) of a small non-welded vessel, which had already a pressure test during fabrication. Afterwards the vessel was cycled until crack initiation,
decreasing the estimated burst pressure below the test pressure. In a pressure test performed afterwards, which approached the burst pressure, the AE shown in Fig. 6 was measured. The micrograph from the crack tip (Fig. 6) shows clearly that stable crack growth took place at thispressure test.
In the case of this ductile steel (elongation after fracture >14%) with relatively high strength(0.1% proof strength of about 900 MPa) a small number of AE events took place at pressuresrelatively near the burst pressure. In this case the AE amplitudes were relatively large, whichindicates brittle micro-fracture at some instances. For comparison, a Hsu-Nielson source (2H, 0.5mm) at the defect location resulted in amplitudes of about 78 dB. In the case of carbon steelswith low strength and large ductility, it is possible to find stable crack growth with lower rates ofbrittle micro-fracture events, resulting in a smaller AE activity, [14].
2.3 Fracture, decohesion or friction of fabrication-caused brittle fillers or layers, and opening of
poorly bonded zones
In the case of fracture, or decohesion of fabrication-caused brittle fillers or layers, and opening of poorly bonded zones, high amplitude AE signals can be expected, but friction may cause
low amplitude signals. Fracture and decohesion of these defects take place at relatively low
loadings (Fig. 7). Such inclusions and layers fracture only once, and AE is expected mainly during the first loading, and for relatively small ratios of the loading to the limit load. Due to the low
loading required for fracture, or decohesion of such defects, small defects can be detected.

53

Firstpressurization

Burst

Acoustic emission
activity

After the fracture, such inclusion and layers may split into parts, which may be wedged together. During subsequent loading AE from friction at the contact surfaces of these parts can be
expected. This activity will start with low amplitude signals at low loadings - signals which arecaused by the usual friction of the defects surfaces. Because these fracture surfaces may bewedged together, burst signals due to release of wedged surfaces (Fig. 10) can be expected when
the crack is opened. These burst signals may have larger amplitudes and this AE activity is increased, if the crack opening is increased. This AE activity stops when the crack is opened in a
way that the fracture surfaces are not wedged together anymore.

Low amplitude
signals (friction)
Subsequent
pressurization
1 Pressure
burst pressure

Fig. 7 AE activity due to fabrication caused brittle fillers, layers, and poorly bonded zones asfunction of the load level.

Fig. 8 AE at first pressurization of a longitudinal weld with incomplete penetration.
As an example, the AE activity during the first pressurization test of 21 small vessels (diameter 350 mm, wall thickness 3.5 mm) with incompletely penetrated longitudinal seam was investigated in [10]. The distribution of the AE events versus amplitude and pressure (Fig. 8) shows thefollowing: There is a maximum of the large amplitude signals at about half the burst pressure.
54

Near the burst pressure the rate of high amplitude signals is small. For the small and medium
amplitude signals one maximum exists at about half the burst pressure and one at large pressures.
If it is assumed that most AE activity, mainly the signals with large amplitude, during the firstloading of an incomplete penetrated weld results from the fracture of slag inclusions and theopening of poorly bonded material, the observed distribution indicates clearly that these AE
events mainly take place at lower loads. A maximum in the AE activity at loads far blow the
burst pressure is indicated.
2.4 Fracture, decohesion, or friction of inclusions in the ligament
The difference between brittle fillers at the flaw itself and smaller inclusions is mainly thesize. Brittle fillers are at the flaw itself, while inclusion may be present in the highly stressed
ligament. Due to fracture of the material between inclusions larger flaws may be built. The intensity of the AE sources depends mainly on the size and strength of the inclusion. Larger sizeand strength of inclusions lead to larger amplitudes. The number of events correlates with thenumber of inclusions. If the density of inclusions is constant, the number of AE events is proportional to the volume of the (plastically) deformed material.
The loading and/or deformation, at which inclusions fracture, depend mainly on the strengthand size of the inclusions. Inclusion with low strength may fracture before plastic deformation
starts. The largest rate of AE events due to such events can be expected at the beginning of plastic deformation. Therefore a distribution of the AE signals over the loading similar to the one forplastic deformation is expected (Fig. 3).
2.5 Fracture, decohesion, or friction of layers and fillers built due to operation
Layers and fillers produced during the operation period are different to the ones produced
during the fabrication process. Layers produced due to operation are not present in the pressure
test after fabrication, because they are produced during the operation period. If the crack or flaw
is open to the inside of the vessel, the operating medium can penetrate into the flaw or crack; if it
is open to the outside surface, the ambient air penetrates into it. At flaws or cracks, which are notopen to the surface, such layers or fillers are not to be expected. High amplitude signals can be
expected from fracture and decohesion of such layers, while friction may cause low amplitude
signals.
These layers are produced during operation and will be stress-free at the loading at which
they are produced. Below the loading at which they are produced, they will be mainly in compression, resulting mainly in AE due to friction (Fig. 9). For loadings larger than the one at
which these layers or fillers are produced, fracture and decohesion of these layers and fillers may
cause burst signals with large amplitudes. Subsequent loading will cause mainly AE due to friction. Such layers and fillers may fracture due to changes in the operating load, and, therefore,
large variations of the operation pressure may decrease the AE in a pressure test.
2.6 Material embrittlement in ligament
Material embrittlement in the ligament will lead to an enhanced rate of brittle micro-fracture
events at the crack front and at the ligament. High amplitude signals can be expected long before
burst [11] especially if hydrogen embrittlement is included [12, 13].
If the concept of linear fracture mechanic applies also at the micro-scale, fracture at the micro-scale occurs if K = Kc. Following this concept, micro-fracture occurs mainly during the first55

BurstMax. operationpressure

Pressure for building
the layers

Acoustic emission
activityFirstpressurization

Low amplitude
signals (friction)

Subsequent
pressurization
1 Pressure
burst pressure

Fig. 9 Activity due to operation caused brittle fillers and layers as function of the load level.
loading. In subsequent loadings considerable AE can only be expected, if something haschanged, e.g. further embrittlement, crack growth, etc.
2.7 Friction at crack surface
The friction at the crack surfaces without brittle layers or fillers is considered, e.g. fatigue
cracks without any corrosion or other layers. Low amplitude events may be expected at low
loadings if crack surfaces lose contact. Larger AE amplitudes are expected when wedged connections (due to larger irregularities of the fracture surfaces) are released.

Fig. 10 Release of surfaces which are wedged together.
Typical small amplitude events from friction at the fracture surface will occur at low loading.
At higher loadings, when the crack opens, further AE events with larger amplitudes from therelease of wedged connections (Fig. 10) may be expected. Such AE events occur during the firstas well as in subsequent loadings. Due to the decreased plastic deformation in subsequent loadings, the AE activity due to friction may also be decreased.
56

3. Conclusions
From the different mechanisms for the detection of cracks and crack-like defects in ductile
material by AE examination, it is obvious that details of the defect are important for the detectability. Some examples for good detectable defects are:
• Flaws or poorly bonded regions caused by the welding process: They result in large AE activity during the first pressurization test after the fabrication. It has to be noted that also
very small flaws and inclusions may result in considerable AE activity.
• Cracks or crack-like defects with layers and fillers produced due to the operational environment, e.g. corrosion layers.
• Cracks and crack-like defects which have their crack-tip in material with insufficient ductility• Cracks with irregular fracture surfaces and multiple branches, leading to crack surfaces
which are wedged together.
Conditions which lead to a low probability of detection of cracks and crack-like defects inductile material:• Ductile material with low probability of brittle micro-fracture in the ligament, e.g. low
strength steel, base material• Non-corrosive environment, or in corrosive environment which does not lead to corrosion
layers or material embrittlement, e.g. acid corrosion.
• Plane fracture surface and low surface roughness of the fracture surface, e.g. high cycle fatigue cracks, or cracks which started from a single sharp notch.
References[1]

prEN 15584:2002(E), Non-destructive testing – Acoustic emission –Examination of metallic pressure equipment during proof testing – Planar location. CEN European Committee
for Standardization 2002.

[2]

ASTM E1930-02 Standard Test Method for Examination of Liquid Filled Atmospheric and
Low Pressure Metal Storage Tanks Using ACOUSTIC EMISSION[3]

Rauscher F.: Pressure Test and Acoustic Emission Examination. Habilitationsschrift,
Technische Universität Wien, 2004.

[4]

Wadley H. N. G., Simmons J. A.: Microscopic Origins of Acoustic Emission. In: Nondestructive testing Handbook Volume Five Acoustic Emission Testing. American Society for
Nondestructive Testing (1987).

[5]

Bassim M. N.: Macroscopic Origins of Acoustic Emission. In: Nondestructive testing
Handbook, Volume Five, Acoustic Emission Testing. American Society for Nondestructive
Testing (1987).

[6]

Palmer I. G.: Acoustic Emission Measurements on Reactor Pressure Vessel Steel. Materials Science and Engineering, 11 (1973) 227-236.

[7]

Schwalbe K. H.: Bruchmechanik metallischer Werkstoffe. Carl Hanser Verlag Mün-chen,
Wien 1980.

[8]

Blumauer H., Pusch G.: Technische Bruchmechanik. Deutscher Verlag Grundstoffindustrie, Leipzig Stuttgart 1993.
57

[9]

Larsson L. H. (Ed): Basic Principles of Fracture Mechanics and an Overview of ASFM6.
In: The Assessment of Cracked Components by Facture Mechanics. EGF Publication 4,
Proc. the 6th Advanced Seminar on Fracture Mechanics (ASFM6), Joint Research Centre,
Ispra, Italy 28 Sep.–2 Oct. 1987; Mechanical Engineering Publications Ltd, London 1989.

[10] Rauscher F.: Acoustic Emissions of Vessels with Partially Penetrated Longitudinal seams.
Journal of Acoustic Emission, 17 (3-4) 1999, 92-99.
[11] Long X., Cai G., Svensson L. E.: Investigation of fracture and determination of fracture
toughness of modified 9Cr-1Mo steel weld metals using AE technique. Materials Scienceand Engineering A270 (1999) 260-266.
[12] Jax P., Gillot R., Spöhrer H.-J.: Innendruck Schwellversuche an einem Behälter in der
Zwischengröße (ZB 1). VGB Kraftwerkstechnik 63, Heft 11, November 1983.
[13] Jax P.: Application and Results of Acoustic Emission During the ZB1 Vessel Testing Program. The 3rd German-Japanese Joint Seminar on Research of Structural Strength and NDE
Problems in Nuclear Engineering, Stuttgart August 1985.
[14] Kobayashi T., Shockey D. A.: Identifying Acoustic Emission Sources in Aging Bridge
Steel. J. Acoustic Emission, 21 (2003) 1-13.

58

EVALUATION OF ACOUSTIC EMISSION SIGNALS DURING
MONITORING OF THICK-WALL VESSELS OPERATINGAT ELEVATED TEMPERATURES.
ATHANASIOS ANASTASOPOULOS and APOSTOLOS TSIMOGIANNIS
Envirocoustics S.A., El. Venizelou 7 and Delfon, 144 52 Athens, GREECE,
Abstract
Acoustic emission (AE) testing of thick-wall vessels operating at elevated temperatures isdiscussed and pattern recognition methodologies for AE data evaluation are presented. Two different types of testing procedures are addressed: Cool-down monitoring and semi-continuous
periodic monitoring. In both types of tests, temperature variation is the driving force of AE as
opposed to traditional AE testing where controlled pressure variation is used as AE stimulus.
Representative examples of reactors cool-down testing as well as in-process vessel monitoring
are given. AE activity as a function of temperature and pressure variation is discussed. In addition to the real-time limited criteria application and/or complimentary source location analysis,cluster analysis and principal components projection is applied as a post-processing tool for signatures evaluation, multidimensional sorting, noise discrimination, characterizing defects and/or
damage. Supervised pattern recognition is also used for data classification in repetitive criticaltests, leading to an objective quantitative comparison between repeated tests. Results show thatdamage sustained by the equipment can be described by plotting the cumulative energy of AE
from critical signal classes against temperature or by evaluating located sources from the respective critical signal classes. Overall, the proposed methodology can reduce the complexity of AE
tests in many cases leading to higher efficiency. The possibility for real time signals classification during permanent AE installations and continuous monitoring is discussed.
1. Introduction
Detecting defects in pressure vessels with acoustic emission (AE) testing is most commonlyused in conjunction with a hydro-test or during an on-line over-pressurization. Thousands ofsuch AE tests were performed since mid-1980s based on ASME [1] code and MONPAC [2] system. In addition to that, on-stream [3, 4] AE inspection is often used for continuous or semi continuous AE monitoring.
In contrast with pressure vessels operating at or near ambient temperatures, thick-wall vessels operating at high temperatures experience different stress states and damage mechanisms
while in operation. In pressure vessels operating at ambient temperature, AE is produced due tostressing by the application of internal pressure. In high-temperature vessels the most significant
parameter and the driving force of AE sources is the temperature variation. More specifically, therate of temperature variation is the most significant factor in the production of AE from the structure as significant thermal gradients can be experienced through the thickness of the material
leading to thermal stressing.
On-stream AE testing of thick-wall vessels operating at high temperatures is often based onsemi-continuous AE monitoring. It is applied either periodically (as short-time continuous monitoring at time intervals, appropriate for the conditions of the vessel) or during specific conJ. Acoustic Emission, 22 (2004)59© 2004 Acoustic Emission Groupditions e.g. start-up or cool-down, introducing highest stresses. The recorded AE is more complex to analyze for this kind of tests, compared to AE analysis during controlled pressurization.
At these conditions the processes/contents of the vessel can produce some AE activity whichmust be distinguished from material related emission. Thus, more advanced data treatment techniques are needed. The comparative nature of such tests also renders the automation and standardization of data treatment (filtering, noise elimination, evaluation, criticality etc) an importantpart of the test. Here, pattern recognition offers particular advantages over traditional data analysis. In the present paper, advanced techniques for data analysis and evaluation are presented andautomated data processing will be investigated using commercially available software packages.
2. Cool-Down Monitoring
Cool-down monitoring is typically performed on high-energy piping or reactors as they aretaken out of service and cooled down for turn-around maintenance and inspection. As the internal temperature drops (the internal pressure is held at the operating level), a thermal gradient isestablished through the vessel wall. The effect of temperature and pressure variation during thecool-down process is demonstrated in Fig. 1. It is worth noting that significant pressure variation
does not affect AE activity. The higher the cooling rate, the higher the thermal gradient. The
thermal gradient gives rise to thermal strains that add to the existing hoop and longitudinalstrains. The ideal cool-down test is one where, for a given internal pressure, the thermal gradientstays within limits that correspond to 110% and 150% of normal operating load. However, deliberate increase in stressing by increasing cooling rate is not recommended as this may actuallycause defects.

(a)

(b)

Fig. 1 Typical response of a reactor vessel during cool-down and related AE activity.
(a) temperature change, (b) significant pressure variation for the same period.
Case study from another reactor is presented in Figs. 2, 3 and 4, showing source locations
and cumulative AE activity, during cool down. Eighteen PAC-R15I AE sensors were mounted
on waveguides on a triangular set-up (3 x 4 attached on the shell and 2 x 3 on the heads of a reactor with O.D. = 3.3 m, L = 5.7 m, thickness 42mm).
The vessel was monitored with AE for short periods during normal operation in order to assess background noise. Actual monitoring started before the cool down. Acoustic emission data
evaluated during partial cooling down from 260oC to 170oC (as the reactor was not cooled down
to ambient temperature - such thermal loading due to partial cool down is often performed for theneeds of catalyst regeneration). AE data were originally analyzed by means of standard MON60

PACTM practices where traditional filtering, zonal location and zonal intensity plots (ZIP Grading) were applied.
The results of conventional analysis based on Swanson filtering and further filtering using
AE signatures are presented in Fig. 2 together with temperature variation (decreasing as a function of time – grey line). Among the three cumulative hits lines plotted in Fig. 2, genuine AE(red line) can be seen well correlated with temperature variation while the remaining two categories un-correlated. The green line, active from the beginning of the test, corresponds to friction
and mechanical noise as identified mainly by Swanson filter. The light-blue line of cumulative
hits, corresponds to flow noise at the end of the cool down, and this was confirmed by the operations department of the plant.

Fig. 2 Cumulative AE hits (top graph) and cumulative AE energy (bottom graph) from three different classes (genuine AE, friction-mechanical noise and flow noise). Temperature variation
superimposed (grey line).
The traditional analysis was further enhanced by means of cylindrical location. Location stability was investigated in depth as a means to validate and further characterize sources. Within
this framework, a parametric study performed producing location maps using traditional algorithms based on 3 hits per event as well as advanced algorithms based on 4 and 5 hits per event.
Typical cylindrical location results are presented in Fig. 3, together with structural details of thevessel (welds position and nozzles). Further source characterization performed by means of cluster analysis is shown in Fig. 4.

61

Fig. 3 Cylindrical location results. Nozzles and welds position superimposed. Note concentrated source at the intersection of longitudinal and circumferential weld. (view on theright). Dual circumferential lines indicate weld and reinforcing ring.

Fig. 4 Cluster analysis applied for source characterization. Cluster color as a function of totalcluster energy, point color as a function of hits concentration. Structural details (nozzles,
welds, reinforcing ring) superimposed on graph.
62

In order to investigate the feasibility of automated analysis by means of pattern recognition,
the AE data were further analyzed using supervised pattern recognition (SPR) algorithms of
NOESIS [6] software. During pre-processing stage, additional features were calculated as a
combination of measured signal features. Rise angle, initiation and reverberation frequencies
were calculated [5] and used as part of the pattern vector.

Fig. 5: Principal components projection of reactor data (AE, friction-mechanicalnoise and flow noise).
Principal component projection (typical scatter plot of the 1st and 2nd principal components ispresented in Fig. 5) performed before the training of k-Nearest-Neighbor Classifier (k-NNC).
Euclidean distance was used as measure of similarity, while the value of “k” was set to 3.
The data in hand were split at random to form training and test set. The data composing thetraining set, were used as representative examples to train the classifier, while the data composing the test set were used to test the classifier and measure the classification error. The classifier
stability (variation of classification error) investigated throughout parametric study where multiple training/test sets produced using the random split utility of NOESIS software. In addition, theeffect of training-set size on the classification results was investigated by training the classifier
using a training set each comprising 20%, 30% and 50% of the data. The overall error (misclassified hits over the total number of hits) was below 5% in all trials. However, an increase withinclass error of up to 19% was encountered in some trials, depending on the training and test set
used.
In most of the cases of increase-within-class error, flow noise signals were recognized as
genuine AE. Confusion found also between friction and AE where friction and mechanical noise
signals were classified as AE. Closer investigation on the classifier performance and the misclassified hits indicated that the confusion was due to the secondary hits and reverberated signals,
since the first hit (as revealed by zonal analysis) were classified successfully.
In order to enhance further the classifier performance, two different approaches were followed. In the first one, first-hit analysis was applied as a pre-processing step. In this way only the63first hits were presented to the classifier resulting in overall error of less than 4% and withinclass error of less than 7.5%. As an alternative a two-step classification process is establishedwhere at first AE and friction data were discriminated from flow noise and during the second
step AE data were discriminated from friction and mechanical noise. The classifier performance
improved in both cases by more than 50%. Overall the results show that supervised pattern recognition can be applied effectively for the classification of AE data during cool down of thickwall vessels.
3. Semi-Continuous Periodic Monitoring
A series of autoclave vessels, serially connected one after the other, are used in a production
process. A bauxite-soda mixture passes through steam-heated autoclaves operating at elevatedtemperatures and under high pressure. The nature of the contents and operational parameters
produce a number of problems with not a single reliable solution in the condition evaluation andrepair of such vessels as reported by the operators. Caustic stress corrosion or caustic embritlement is a common type of failure mechanism encountered in such operating environments [7, 8].

Fig. 6: Autoclave views, sensors locations andzones grades during hydro test, 1st loading(purple=C, light blue=B, green=A).
Autoclaves are inspected and repaired in a cyclic manner (one after the other). This requires
the shut down of the entire process and re-piping the entire line of the reaction process. It was
decided to attempt on-line evaluation with AE to determine the possibility of damage evaluationand the level at which various repair methods perform. One autoclave was removed from service
and repaired at a number of welds and other areas. Main defect mechanisms were cracks andcaustic stress corrosion cracking. Various repairs were attempted by using stainless-steel lining,
stainless-steel electrode overlay and traditional crack repairs (remove and fill).
Upon completion of the hot-work repairs, the autoclave was monitored by AE during hydrotest. At this point AE data were recorded for reference. Data were recorded and evaluated bystandard techniques (MONPAC procedure for AE monitoring during hydro-test). The AE activity of the 1st cycle produced some moderate MONPAC grades (grade C), primarily in the repair
64areas (zones near the lower manhole and the top of the autoclave as presented in Fig. 6). In addition significant events were located in these areas. Some emissions and source locations werealso found on the lower and upper circumferential welds.
Due to the hot-work repairs, though, data from the 1st cycle include significant activity fromlocal yielding (not necessarily structurally significant defects), thus 2nd pressurization cycle was
performed. During the 2nd cycle AE activity was significantly lower indicating that no structurally significant sources are present for the test loads. The reduced activity produced only low
MONPAC grades (grade A). Shortly after the vessel went on-line, semi-continuous AE monitoring was scheduled every four to six months, aiming to record data during normal operation inorder to assess the conditions of the vessel and repair effectiveness. Variation of the operating
parameters for one of the monitoring periods is presented in Fig. 7. Conventional AE data analysis (using traditional filtering and location techniques) was originally followed.

Fig. 7 Typical range of operating parameters for the autoclave being monitored.
In an attempt to automate evaluation of the semi-real time periodic monitoring, pattern recognition schemes were investigated. Within this framework, real and artificial AE data were used
to produce a representative set which contained the main signal categories of interest, which are:1) AE from cracking or other intense sources,
2) AE from low energy sources such as corrosion, erosion and others,3) Mechanical type of noise such as impacts, and
4) Process noise.
The representative set of AE data (to be used for training and testing), initially composed of 6
AE features (rise time, counts to peak, AE counts, MARSE, duration and amplitude). The initialfeatures selection was based on correlation analysis and visual examination of 2D and 3D correlation scatter plots, as the one shown in Fig. 8. The dimensionality of the features space was further reduced using the auto pre-processing wizard and principal component covariance pro65

Fig. 8 3D signature plot of training/testing set.
jections of NOESIS 4.0 AE Pattern Recognition software [6]. Typical projection of the original
data to their principal components axes is shown in Fig. 9.
A k-Nearest-Neighbour (k-NNC) Supervised Pattern Recognition (SPR) classifier was thentrained using the representative data. The training used a random part of the data set to train andthe rest to test the classifier. The stability of the classifier was investigated using different training-test sets. The overall (all classes) test error was in any case below 5%. The best performancewas 2.4% error while an average performance from all different trials resulted in 3.56% error.
The algorithm was further tested on data acquired during normal process at a later date and artificial Hsu-Nielsen sources. The trained classifier successfully recognized the different data categories (see Fig. 10).
In order to facilitate AE evaluation and correlation of the AE sources with the physical phenomena, the classification results shown in Fig. 10 are presented in the original AE feature space,using scatter plot of AE counts vs. Amplitude, as opposed to the principal components projectionof Fig. 9. Note that overlapping between signal classes is an artifact of projecting multidimensional space to the two-dimensional space of AE counts-amplitude.
The classifier was then used to investigate data from subsequent AE tests performed during
normal operation, to assess the existence of damage mechanisms in the pre-defined categories.
The test covered two short periods of monitoring (6-8 hours) over a two-day period. The classifier showed that the Mechanical Noise and AE from crack-like sources were almost non-existent(<0.1%). A large portion of the signals was classified as AE from low energy sources (40% of
the data) and the rest as process noise (see Fig. 11).

66

Fig. 9: Representative AE data set - Covariance projection.

Fig. 10: New test data set. The trained classifier successfully recognized the different data categories.
67Fig. 11: Classifier application to On-Line data acquisition.
Classifier validity was further investigated by means of location analysis from each class of
AE data from the previously mentioned set of data. The location plot of Fig. 12 shows that theProcess-Noise class does not produce any location on the vessel. On the contrary, the LowEnergy AE class results in significant and concentrated location at the repairs areas. This first setof data were also analyzed and evaluated by traditional AE methods (graphs, filtering etc.),
which produced a similar result to the SPR classifier. This is an indication of the classifier being
able to produce the desired data separation for in-process monitoring.

Fig. 12 Location from the second monitoring during normal operation: Process class (left) andthe low energy AE (right).
Application to another monitoring period (third period) during acquisition also provided
similar information (Fig. 13). The low energy AE was at 47% of the total data and the Mechani68

Fig. 13 Location from the third monitoring during normal operation: Process class (left) and thelow energy AE (right).
cal Noise and Crack-like AE were practically non-existent in the data (0.2 and 0.3%, respectively). Process noise was again the dominant class in the data.
AE evaluation and correlation with the conditions that these results appear are being investigated, considering that the location of AE data from genuine AE classes is the first indication ofa damage mechanism. The change in AE behavior (compare Figs. 12 and 13) of the vessel between the two tests during normal operation depends on a number of parameters that were observed during the tests. More specifically, the operational parameters in the first test had a significant temperature drop at the end of the monitoring period (last 3 hours) when a large numberof the data were recorded. The temperature variations in the third test were not so significant andthus any sources are not expected to be as active. However, as the mixture inside the vessel is notknown precisely and follow-up results by means of metallographic or other analysis are not yet
available correlation with damage mechanisms and classifier validity can only be performed
based on experience. Classifier validation will be further investigated in future tests.
4. Conclusions
Thick-wall vessels operated at elevated temperatures were evaluated by means of traditional
AE practices as well as pattern recognition techniques applied for the analysis of recorded AE
data. The rate of temperature variation is the most significant factor in the production of AE fromthe structure since significant thermal gradients develop through the thickness of the material
leading to thermal stressing. Source location is a valuable tool for both source characterizationand SPR classifier validation.
The success and validation of the SPR classifiers have been proven so far in these pilot tests.
Classifier stability and within-class error should be considered as important factors in addition tothe overall classification error. Within-class error assessment will indicate whether the classification scheme might lead to false alarms (whenever noise is classified as genuine AE) or
69

whether the classification scheme might lead to over-filtering of AE data. The use of first hit asopposed to all hits is found to improve further the classifier performance. However such type ofclassifier complicates the subsequent location analysis and source identification.
Overall, pattern recognition analysis techniques are suitable AE data analysis from both cooldown and semi-continuous monitoring tests. Their use and refinement will continue in order toprovide a solid, universally applicable method for AE data evaluation for thick-wall vessels operating at elevated temperatures.
Finally it is worth noting that real time classification investigated during autoclave monitoring using NOESIS Live software [6]. The real-time classification showed that processing speedof modern AE systems and PCs renders real-time supervised pattern recognition feasible.
References
1. ASME, Section V, Article 12, “Acoustic Emission Examination of Metallic Vessels during
Pressure Testing”, 1988.
2. T. J. Fowler, J. A. Blessing, P. J. Conlisk and T. L. Swanson, “The MONPAC System”,
Journal of Acoustic Emission, 8 (3), 1989, 1-8.
3. P. T. Cole and S. N. Cautrey, “On Stream monitoring of process plant”, in Emerging Technologies in NDT, A.A. Balkema – ISBN 90 5809 645 9, 2004, pp. 163-166.
4. M. F. Carlos et all, “Advanced Acoustic Emission for on-stream inspection of petrochemical
vessels”, in Emerging Technologies in NDT, A.A. Balkema – ISBN 90 5809 645 9, 2004, pp.
167-172.
5. A. A. Anastasopoulos, T. P. Philippidis, “Clustering Methodologies for the evaluation of AE
from Composites“, J. of Acoustic Emission, 13 (1/2), 1995, 11-21.
6. NOESIS, “Users Manual”, Envirocoustics S.A. 2003.
7. API Recommended Practice 571, “Damage Mechanisms Affecting Fixed Equipment in the
Refining Industry”, Dec. 2003.
8. G. Roach, G. Rubenis, P. Mastin and R. Byrne, “Caustic Embittlement in the Bayer Industry”, Proceedings of the 6th International Alumina Quality Workshop, 2002, pp. 204-211.

70

ACOUSTIC EMISSION TECHNIQUE AND POTENTIAL DIFFERENCEMETHOD FOR DETECTING THE DIFFERENT STAGES OF CRACKPROPAGATION IN CARBON AND STAINLESS STEELS
C. ENNACEUR1, A. LAKSIMI1, C. HERVÉ2, M. MEDIOUNI2 and M. CHERFAOUI2Laboratoire Roberval unité Mixte 6066 CNRS, UTC, BP 20529, 60205 Compiègne, France.
2
Département contrôles et essais industriels, CETIM, BP 80067, 60304 Senlis, France.

1

Abstract
This paper reports on pressure-vessel steels to establish the relationship between the growthof ductile cracks and acoustic emission (AE) produced during the different steps of crack propagation in laboratory specimens (plastic deformation, crack initiation and propagation). To determine the efficiency of the AE method to detect the evolution of the cracks in carbon and stainlesssteels we have used a simultaneous monitoring of electric resistance and acoustic emission, alongwith acoustic parameters, potential difference, and SEM fractograph. This study shows that AE
characteristics of crack propagation depend on the category of steels and the different zones of
propagation, as the base metals, the welds, and the heat affected zone (HAZ).
Keywords: Pressure-vessel steel; acoustic emission; potential difference; crack propagation.
1. Introduction
At present, pressure vessels are subject to extensive non-destructive testing, especially acoustic emission (AE) method. This method is one of the few techniques having the potential for realtime structural integrity evaluation. However, before the AE inspection of pressure vessels becomes fully practical, it is essential to understand the AE behavior of crack propagation in laboratory specimens.
This paper studies the relationship between the growth of ductile cracks in pressure-vessel
steels and AE produced during crack extension in compact tension (CT) specimens. It is shownthat it is possible to detect the different stages of the damage in laboratory specimens of pressure
vessel materials.
2. Experimental Procedure
2.1 Materials and specimens
The work was carried out on two kinds of pressure-vessel steels: AISI 304L stainless steel and
P265GH carbon-manganese steel. In the present study, we examined three kinds of CT specimens, designated as specimen A, B and C. Specimen A was not welded, whereas specimens B
and C were welded. The specimens were fatigue pre-cracked, and this crack was introduced into
different zone of CT specimens. The crack was in the base metal in specimen A. In specimen B,
the crack ran in the heat-affected zone (HAZ) next to the weld seam, while the crack was in theweld seam in specimen C (Fig. 1).
2.2 AE testing and electric method
Acoustic emission signals were detected by two kinds of piezoelectric sensors, a wideband
sensor (100 to 900 kHz) and sensors with a resonance frequency of 180 kHz. The sensors were
J. Acoustic Emission, 22 (2004)71© 2004 Acoustic Emission Groupplaced near the tip of the fatigue pre-crack. The acquired AE signals were amplified by 40 dB
preamplifiers. The threshold selected was 40 dB (0 dB = 1 µV input), which was well above machine noise level.
SPECIMEN A

SPECIMEN B

SPECIMEN C

Pre-crack in basemetal

Pre-crack in HAZ

Pre-crack in weld
seam

TYPES OF SPECIMENSPOSITION OF THE
CRACK

Fig. 1 Different kinds of specimens used during the tensile tests.
During the tests, electrical method has been adopted together with the acoustic emission. To
measure the potential difference, a direct current from source is passed trough the CT specimen.
Potential contacts are placed at points 1, 2, 3 and 4. Point 1 is located near the end of the fatigue
pre-crack and it’s coupled with point 2, which is located on the opposite side of the specimen.
Point 4 is on the same side as point 1 and it is coupled with point 3. This procedure was used anddetailed by Ennaceur et al. [1].
3. Results and Discussion
3.1 Mechanical and electrical results
The recording of the mechanical and electrical results allows us to distinguish the different
stages of rupture as shown in Fig. 2. This shows the load required to initiate and propagate thecrack, and the voltage curve measured during the CT test against the displacement of the specimens C of P265GH steel. The analysis of mechanical and electrical results of the different kindsof specimens (A, B and C) and different steels (304L and P265GH) provided us with the chronological classification of different stages of damage in CT specimens.
3.2 AE response during the CT test
The correlation between the results of electrical method and AE techniques allows to differentiate every mechanism intervening during the crack propagation. The different sources of AE
during crack propagation in 304L stainless steel and P265GH steel are:






Plastic deformation: slip of dislocation in front of the crack tip;
Micro-crack: creation of micro-voids in the plastic zone;
Stable crack propagation: micro-voids coalescence;
Unstable crack propagation.

3.2.1 Plastic deformation in front of the crack tip
The slip, which is the source of AE during plastic deformation, produces low amplitude continuous emission. Others have shown that there is very little AE generated during the tensile deformation of 304 stainless steel (James et al., [2], Heiple and Carpenter, [3] and Scruby et al.
72

Fig. 2 Voltage is plotted along with the load versus the displacement during the CT test of
specimen C of P265GH steel.
[4]). These results were confirmed by performing tensile tests on the 304L stainless steel used inthis investigation. The data indicated that there is very little AE generated by the slip in our 304L
stainless steel as well. For example, the maximum amplitude recorded during this stage for the A
specimen is 51 dB.
Contrary to the 304L steel, the plastic deformation in CT specimens (B and C) on P265GH ischaracterized by an important acoustic emission. The analyses of the signals recorded during thisdeformation prove the presence of two sources of this AE:

Plasticity 1 is the first source of AE. It presents the first motion of dislocation that produces a low amplitude continuous emission (64 dB).
Plasticity 2 is characterized by an important acoustic emission, which is significant in a
number of events and amplitude (90 dB in the case of the welding). This emission,caused by the energy liberated by the movement of dislocations, is emitted by peaks ofsignals (Fig. 3). These peaks of AE activity correspond to co-operative movements of
dislocations. Several studies concerning carbon steel confirm this result. Among these researchers, one quotes Ono et al. [5] who worked specifically on carbon steels and concluded that the acoustic activity is very significant during the plastic deformation.

In all specimens (in 304L steel and P265GH steel), along the plastic deformation, the mostimportant AE was produced during the deformation in the weld seam. That means that the signature of this deformation depends of the heterogeneity and the ductility of the weld metals. The
heterogeneity makes the movement of dislocations more difficult and more emissive because of
the presence of a significant number of inclusions. The ductility reduces the periods of plastic
instability, which constitute the principal sources of AE.
73

Fig. 3 Plasticity1 and 2 during plastic deformation in the weld seam of CT specimens on
P265GH.
One can conclude that in these CT specimens acoustic emission is a suitable means of detecting the initiation (plastic deformation) of crack growth.
3.2.2 Micro-crack in the plastic zone
The source of the AE during this stage is the fracture and /or decohesion of the inclusions because they are generally very brittle, so they should be fractured very quickly. The results of thetwo steels show that the AE signature of the micro-crack mechanisms depends of the microstructure of the propagation zone (Table 1). Table 1 shows the correlation between the micrographs of
specimens A, B and C of 304L and the AE recorded during their micro-cracking.
Table1. Scanning electron micrographs of fracture surface in specimen A, B and C of 304L steel.
Base metal

HAZ

Weld seam

Big and shallow
dimples

Big and deep dimples

Small and deep dimples

3

1

2

As shown in this fractography, the fracture surfaces of this material consist of dimples, which
are the symbol of ductile rupture. Careful scanning electron microscope (SEM) examinations of
the crack surface indicated that the dimensions of the dimples depend of the zone of propagation
744. Comparison of the AE Signature of the Two Steels
The AE signature of the plasticity of the two steel is different. The plasticity of steel P265GH
is more emissive and more energetic than 304L steel. Contrary to carbon steel, the plasticity ofthe stainless steel starts with a very little AE activity and increases progressively with the crack
propagation. This difference is due to the nature of the plasticity which is a homogeneous deformation for 304L steel and a heterogeneous plastic deformation for carbon steel P265GH.
The number of AE events recorded during the tensile test of the CT specimens on carbon
steel is more important than that recorded for 304L steel, which is true in the weld or the HAZ.
Moreover, the most important AE parameters were recorded during the cracking of specimens oncarbon steel. This phenomenon is due to the fact that 304L steel is much more ductile than carbon steel. The stainless steel amplifies the phenomena of plastic deformation and the resistanceof the CT specimens. It has toughness superior to that of P265GH steel. This increase in toughness is accompanied by a greater discretion with regard to AE, because the periods of plastic
instability, which constitutes the principal sources of AE, are absent.
5. Conclusion
This investigation has shown the effectiveness of making AE measurements along with detailed metallographic and electrical methods in determining the sources of AE during crack
propagation in CT specimens on pressure-vessel steels (304L and P265GH) and in comparison
with the AE signature of the two steels.
The feasibility of using AE techniques to detect the different stages of ductile cracks propagation in laboratory specimen of 304L steel and P265GH steel has been shown. The identification of the AE signature of the different sources of the damage and the correlation with the micrographs was based on the AE parameters like amplitude and energy.
The results of this study are on the scale of laboratory specimens; their extension to the structures
would be interesting to detect and locate the zone of the damage propagation and evaluate theirseverity in the pressure vessel in order to avoid their catastrophic ruptures.
References[1] C. Ennaceur, S. Benmedakhene, A. Laksimi, C. Hervé, M. Mediouni, M. Cherfaoui, Study ofmechanical behaviour of 304L steel using acoustic emission technique and the method of potential difference, EWGAE 2002, 25th European Conference on Acoustic Emission Testing, Prague,
September 2002.
[2] J.B. James, S.H. Carpenter, M.A. Hamstad, Proceedings of Seventh International Acoustic
Emission Symposium, JSINDI, Tokyo, 1984, pp. 33-41.
[3] C.R. Heiple, S.H. Carpenter, J. Acoustic Emission, 6, 177-204, 1987; 6, 215-238, 1987.
[4] C.B. Scuby, H.N.G. Wadley, K. Rusbridge, D. Stockham-Jones, Met. Sci, 15, 599-608.
[5] K. Ono, G. Huang and H. Hatano, Acoustic emission characteristics of structural steels, 8èmeConférence Internationale sur les Essais Non Destructifs, Cannes, France, 1976.

76

DETECTION OF INCIPIENT CAVITATION AND BEST EFFICIENCYPOINT IN A 2.2MW CENTRIFUGAL PUMP USING
ACOUSTIC EMISSION
L. ALFAYEZ1 and D. MBA2
Consulting Service Department, Saudi Aramco, 31311 Dhahran. SA.
2
School of Mechanical Engineering, Cranfield University, Cranfield, Beds. MK43 0AL UK.
1

Abstract
Pumps play a significant role in industrial plants and need continuous monitoring to minimize loss of production. This paper investigates the application of acoustic emission (AE) for
detecting incipient cavitation. This paper also presents results from an on-going program to ascertain the applicability of the AE technique for determining the best efficiency point (BEP) of
an operating system. A large-scale pump rig (2.2MW) formed the basis of this investigation onwhich a series of NPSH and performance tests were undertaken. Results obtained demonstrate
the successful use of the AE technique for detecting incipient cavitation. Furthermore, it isshown that the AE technique can clearly identify the BEP for a system employing pumps.
1. Introduction
Pumps are generally used in most of our domestic and industrial applications. Every pump
manufacturer supplies characteristic curves for their equipment illustrating pump performance
under given conditions. These curves demonstrate the inter-relationship between discharge capacities, pump head, power and operating efficiency. The ideal operating point for a pump is
known as the best efficiency point, or BEP. This is the point where pump capacity and headpressure combine to provide the maximum efficiency performance from the pump. If the pump
operates too far to the left or right of the BEP, not only may its efficiency be compromised, but it
can also be subjected to increased wear, reducing operational life. In specifying pump requirements the purchaser would attempt to simulate the actual operating conditions that the pump will
experience. This is always an approximation and as such the most plants do not operate at themost efficient point.
Typically the pump manufacturer will undertake performance and NPSH (Net Positive Suction Head) tests on supplied pumps, the significance of the latter is to determine the 3% drop inhead at which serious cavitations will occur. The Net Positive Suction Head - NPSH - can be approximated as the difference between the Suction Head and the Liquids Vapor Head. The concept of NPSH was developed for purpose of comparing inlet condition of the system with theinlet requirement of the pump. It must be noted that cavitations will have started to develop before the 3% drop in head. Cavitation causes a loss of pump efficiency and degradation of the mechanical integrity of the pump. It is generally accepted that the critical pressure for inception ofcavitation is not constant and varies with operation fluid physical properties and the surface
roughness of the hydraulic equipment.
Application of the high frequency acoustic emission (AE) technique in condition monitoringof rotating machinery has been growing over recent years [1-5]. Typical frequencies associatedwith AE activity range from 20 kHz to 1 MHz. None of the currently employed diagnostic techJ. Acoustic Emission, 22 (2004)77© 2004 Acoustic Emission Groupniques can provide early indication of cavitation. The most commonly used method is to observe
the drop in head. Whilst other techniques such as vibration analysis and hydrophone observations
for pump fault diagnosis are well established, the application of AE to this field is still in its infancy. In addition, there are a limited number of publications on the application of AE to pump
health and cavitation monitoring. Derakhshan et al. [6] investigated the cavitation bubble collapse as a source of AE and commented that the high amplitude pressure pulse associated withbubble collapse generates AE. The experimental tests reported involved placing a test specimen
in a tank of water. The specimen was subject to a jet stream of water within which bubbles wereartificially introduced. AE transducers were placed on the specimen and the water tank external
casing to offer insight into AE attenuation characteristics. It is interesting to note that Derkhshan
observed increasing AE r.m.s. voltage levels with increased pressure of flow and cavitation.
However, with the AE sensor mounted on the wall casing the reverse was observed, decreasing
AE r.m.s. levels with increasing pressure and cavitation. This was attributed to a visible bubble
cloud that increased with pressure. It was commented that this cloud attenuated the AE signature
prior to reaching the transducer on the casing wall.
Neill et al. [7, 8] assessed the possibility of early cavitation detection with AE and noted thatthe collapse of cavitation bubbles was an impulsive event of the type that could generate AE. It
was observed that when the pump was under cavitation the AE operational background levels
dropped in comparison to non-cavitating conditions. It is worth stating that prior to, and during
cavitation, vibration measurements showed no significant change. In conclusion Neill stated thatloss in NPSH before the 3% drop-off criterion was detectable with AE. Hutton [9] investigated
the feasibility of detecting AE in the presence of hydraulic noise. It was noted that artificiallyseeded AE bursts were detected above background operational noise for turbulent flow, with andwithout cavitations. Furthermore, Hutton noted that the presence of cavitations in the system increased the operational AE noise levels by a factor of 50. In addition, cavitation was found to
generate a significant increase in noise level up to about 800 kHz.
This paper presents results from an on-going program to ascertain the applicability of the AE
technique for determining the best efficiency point (BEP) of an operating system, and the opportunities offered by the AE technique for detection of incipient cavitations. The results presented
were centered on a 2.2MW centrifugal pump.
2. Sensors and Acquisition Systems
The AE sensors used for this experiment were broadband type sensors with a relatively flat
response in the region between 100 kHz to 1 MHz (Model: WD, Physical Acoustics Corporation). AE sensors were located at five deferent positions; at a distance 1.8 m from the pump suction flange; 30 cm from the suction flange, on the pump casing in the vicinity of the impeller
suction eye; on the outboard bearing housing and 30 cm from pump discharge flange. The output
signal from the AE sensors was pre-amplified at 40 dB. Continuous AE r.m.s. voltage values
were calculated in real time by the analog-to-digital converter (ADC) controlling software. This
software employed a hardware accelerator to perform calculations in real time. The hardware
accelerator takes each value from the ADC and squares it. These results are added into an accumulator for a programmable time interval set by the user, 100 ms in this instance. The accumulator is cleared at the start of the time interval, and the accumulator value will only be stored at theend of the time interval. The r.m.s. voltage is then calculated by taking the square root of the sumof the accumulated squared ADC readings. The time interval for acquisition was also set at 100
ms.
78

3. Experimental Setup and Results
Two tests were undertaken; a pump performance test and an NPSH test. The pump employed
was a 2.2MW David Brown Pump (Model DB34-D) with a maximum capacity of 2210 m3/h at
an efficiency of 85.5%. These tests were undertaken using a closed loop arrangement with a vacuum facility in accordance with BS 5316. Acoustic emission sensors were located at positions
already described. Figure 1 shows the test pump. It must be noted that best endeavors were undertaken to reduce the amount of time required to reach the required flow rate during tests.

Fig. 1 2.2MW centrifugal pump which underwent perfomance and NPSH tests (insert: locationof AE sensor on the pump casing in the vicinity of the impeller suction eye.)Figure 2 details the performance characteristics for seven test points (flow rates) with the
BEP at 1760 m3/h. The performance test were undertaken twice to ensure repeatability. Observations of AE r.m.s. activity during the performance test are displayed in Fig. 3. Three separate
NPSH were conducted at deferent flow rates 1686, 2192 and 1150, respectively. A result of the
NPSH test at flow rate 1686 m3/h is shown in Fig. 4, while Fig. 5 highlights the AE r.m.s. activity during the NPSH test.
600
550

Deferential Head (M)

500
450
400
350
300
250
200
150
100
0

556

940

1155

1370

1674

Flow Rate (m3/h)

Fig. 2 Performance test result.
79

1910

2192

3

556 m /hr

0 flow

940

3

1370

1155

1674 m /hr

1910

2192

1.2

1

Impeller eye position

Predicted BEP

AE in r.m.s (v)

0.8

0.6

0.5 m from suction flange

0.4

0.5 m from discharge flange
0.2

2 m from suction flange

0
0

300

600

900

1200

1500

1800

2100

2400

2700

Time (sec)Fig. 3 AE r.m.s. observations during performance test.

Fig. 4 Differential head vs. NPSH at flow rate 1686 m3/ hr.
4. Discussion
During the performance test, AE activity from the pump casing was found to have the largest
magnitude, providing the best indicator for correlating AE activity to pump performance, see Fig.
3. Based on these results the minimum AE r.m.s. value was obtained for a flow rate of 1830
m3/hr. At this flow rate there was the minimum AE activity generated from the turbulent flow
and associated interaction with boundaries. Either side of this flow rate resulted in continuously
80Fig. 5 AE r.m.s. activity during NPSH testing.
increasing AE r.m.s activity with increased flow rates. The predicated efficiency point waschecked with the manufacturer performance test and was found to be accurate within 70 m3/hr or
4%. The difference is attributed to experimentation and approximations made in estimating the
BEP.
During the NPSH tests an increase in AE (0.6 to 0.77 V) was observed at the start of cavitation, see Fig. 5. Relatively constant AE r.m.s. levels followed this until an NPSH of 21 m when arapid decrease in AE r.m.s. levels was noted. With further reductions in NPSH the AE r.m.s. levels rose again to the original levels prior to the sudden drop in AE r.m.s. To understand the AE
signal behavior, it is essential to understand the cavitation sequence. Once the suction pressure
starts to decrease, vortexes start to occur at the impeller-blade tips. With further reduction inpressure these vortexes take the form of traveling bubbles in the liquid. These bubbles are initially created in lower pressure area on the suction surface of the blades. Eventually the bubbles
move to higher-pressure areas where they collapse. With even further reduction in the suction
pressure, the bubbles combine into larger cavities. These cavities are usually formed on the impeller blade suction surface [10].
It is postulated that at the start of the NPSH test the increase in AE r.m.s. levels was attributed to the onset of cavitation. At an NPSH value of 23 m the sudden drop in AE r.m.s. was attributed to the attenuation caused by bubble clouds. The loss in AE intensity due to the presenceof cavitation was noted by Neill [7, 8] and Derakhshan [6]. This is not surprising if cognizanceis taken on the findings of Derakhshan [6] and Hutton [9] where it was noted that where directcontact between the cavitating liquid and the housing structure (pipe), increases in AE activity
correlated directly with increasing cavitation. In the case of the AE sensor mounted on the impeller casing, direct constant is not made with the impeller itself but rather the casing, and again81as noted by Derakhshan et al. [6] this arrangement causes a decrease in AE activity with increasing cavitation. At an NPSH of 19 m, the AE r.m.s. values increased again and are maintained at
the original r.m.s. levels attributable to incipient cavitation. At this point the 3% drop had beenreached. Interestingly observations of AE r.m.s. levels for Channels 2 and 3 showed continuous
increase in levels with decreasing NPSH.
5. Conclusions
The results from AE analysis have shown a relation between the amplitude of AE and therate of cavitation. An increase in the r.m.s. value was observed long before having a significant
sign of cavitation. At high NPSH value where incipient cavitation is known to occur, significant
increase in AE was noticed with decreasing the pump suction pressure. From experimental results of several NPSH at different flow rates, it was validated that the best results were obtainedat flow nearer to the best efficiency point or BEP. AE was also found to be a practical technique
to detect the BEP of the pump/system. Comparing this practical technique with the currently
available ones will validate the feasibility of using this technique in field of condition monitoring.
The sensor position was found to have a significant influence on the measured AE signal.
Sensor installed on pump casing in the vicinity of the impeller suction eye was found to detect
the most observable signal.
References
1. Mba, D. and Bannister, R.H. (1999). Condition monitoring of low-speed rotating machinery
using stress waves: Part 1 and Part 2. Proc Inst Mech Engrs. 213(3), Part E, 153-185.
2. Morhain, A, Mba, D, Bearing defect diagnosis and acoustic emission, Journal of Engineering
Tribology, I Mech E, 217 (4), Part J, 257-272, 2003.
3. Mba, D. (2002). Applicability of acoustic emissions to monitoring the mechanical integrityof bolted structures in low speed rotating machinery: case study. NDT and E International.
35(5), 293-300.
4. Mba, D., Cooke, A., Roby, D., Hewitt, G., Detection of shaft-seal rubbing in large-scale
power generation turbines with Acoustic Emissions; Case study. Journal of Power and Energy - Part A, I Mech E, 218 (2), Part A, 71-82, 2004.
5. Toutountzakis, T. and Mba, D. (2003). Observation of Acoustic Emission Activity during
Gear Defect Diagnosis. NDT and E International. 36(7), 471-477.
6. Ourang Derakhshan, J. Richard Houghton, R. Keith Jones (1989). Cavitation Monitoring ofHydroturbines with RMS Acoustic Emission Measurements. World Meeting on Acoustic
Emission, pp. 305-315, March 1989.
7. Neill, G.D., Reuben, R.L., Sandford, P.M., (1997). Detection of Incipient cavitation in
Pumps Using Acoustic Emission. Journal of Process Mechanical Engineering [H.W. Wilson- AST]. 1997. 211 (E4) 267.
8. Neil, G.D., et al. Detection of Incipient cavitation in Pumps Using Acoustic Emission. In proceedings of COMADEM 96. Sheffield University, 1996. pp. 391-401.
9. Hutton, P.H., (1969). Detecting Acoustic Emission in Presence of Hydraulic Noise. NonDestructive Testing, May 1969.
10. Brennen, C. (1994). Type of Impeller Cavitation. Hydrodynamics of Pumps, Oxford University Press, 1994, pp. 84-88.

82

INVESTIGATION OF FRACTURE PROCESSES USING MOMENTTENSOR INVERSION TECHNIQUE
F. FINCK, C. U. GROSSE and H.-W. REINHARDT
Institute of Construction Materials, University of Stuttgart, Pfaffenwaldring 470550 Stuttgart, Germany
Abstract
The development and progress of fractures is accompanied by the radiation of acoustic waves
due to microcracking. In the transient waveforms of acoustic emissions a huge number of information about fracture processes and the state of the material is inherent. Signal-based analysis of
acoustic emissions allows, amongst other studies, for a fracture-mechanics based investigation of
failure. From the spatial distribution of seismic energy, an inversion on a system of equivalent
forces – the so-called seismic moment tensor – is performed. The inversion is simplified by theassumption of a point source and the omission of near-field effects, implying that the source volume is small compared to the distance between source and receiver. With the help of the moment
tensor, arbitrary and complex source mechanisms can be described. For an interpretation of theresults, the moment tensor is decomposed into simple fracture modes on the basis of an eigenvalue analysis. Furthermore, the appropriate radiation pattern of particle motion of the moment
tensor is plotted on a sphere enclosing the source, revealing a 3D-visualization of the stress field.
These results and additional data from further investigations can be combined in a comprehensive representation of the failure. Basic principles of moment tensor inversion and some examples of experimental data and their analysis are presented.
Keywords: moment tensor, decomposition of moment tensor
1. Introduction
The investigation of failure mechanisms is an important task in the field of concrete technology. With the help of signal-based acoustic emission (AE) technique damages in concrete, suchas concrete matrix cracking or debonding of reinforcement, can be registered and localized. Furthermore, an analysis of the wave field emitted during microcracking allows for a fracture mechanics based interpretation of failure. With this background, the concrete composition and thestructural design can be optimized during the period of development and planning, and after
completion, the quality and the state of the structure can be evaluated and monitored.
The concept of the moment tensor as a physical representation of an earthquake was invented
in the early 1970’s [e.g. Gilbert, 1970] for a detailed analysis of fracture processes and the role ofnon-double couple mechanisms. The moment tensor as a mathematical description of equivalent
forces and moments in a point source, is very well suited to investigate source processes with abetter resolution. Using an eigenvalue analysis, the source mechanism is decomposed into basicmodels of fracture mechanics [Jost, Herrmann, 1989].
A complete inversion on the seismic moment tensor implies a broad knowledge of the source
time function, the size of the source, the Green’s functions of the medium and the displacement
at the point of observation [Aki, Richards, 2002; Udias, 1999]. Since the mathematical description of this inverse problem is extremely complex and the transfer functions of various compoJ. Acoustic Emission, 22 (2004)83© 2004 Acoustic Emission Groupnents within the travel path of seismic energy are underdetermined, simplifications are performed to calculate moment tensors of seismic sources. The acoustic waves are assumed to beobserved in the far-field of a time invariant point source. The onset of the analyzed wave phases
must be well determined with a reasonable signal to noise ratio.
In these studies, a hybrid moment tensor inversion approach introduced by Andersen [2001]
and a relative inversion method by Dahm [1993] were used. Results of a splitting test of a concrete cube are shown. Tensile stress generated a mode I failure along a well-defined damage
zone. In another experiment – the pull out of a ribbed steel bar out of a concrete cube – failure isaccompanied by mode II failure.
2. Theoretical Principles
In the following, the principles of the concept of the moment tensor are summarized briefly.
A detailed derivation of the displacement field of a general elastodynamic source within a volume can be found in the literature (e. g. Aki, Richards [2002], Ben-Menahem, Singh [1981] and
Jost, Herrmann [1989]). To simplify the forward problem, the damage zone is assumed to besmall compared to the distance between source and receiver and the source time function is approximated by a step function. Such a point source can be represented by the physical model ofthe moment tensor M. In the common form the displacement d at sensor position x at time t canthen be calculated from the convolution of the Green’s functions G and the moment tensor M(1)d(x, t ) = G * M .
The Green’s functions are representing all propagation effects of the medium on elastic waves.

M is represented by the scalars Mkj with the unit of a moment of force. Additional external
forces are neglected. This symmetric 3x3-tensor is containing six independent elements:
 M xx
M kj =  M yx
 M zx


M xy
M yy
M zy

M xz 

M yz 
M zz 
.

(2)

The physical meaning of the moment tensor elements is illustrated in Fig. 1. The componentsof the moment tensor can be thought of dipoles (see the joints) oriented in the three spatial dimensions (columns of Mkj) on which ends forces (see the arrows) act in the three spatial dimensions (rows of Mkj). On the diagonal, the axes of the joints are parallel to the forces. For the otherelements, the forces lead to a torque around the axis perpendicular to the plane containing theforce and the dipole [Aki, Richards, 2002].

Fig. 1 The elements of the moment tensor as
dipoles in the spatial dimensions and theequivalent forces, which lead to moments offorce [after Aki, Richards, 2002].
84

The symmetry of the tensor yields a total zero moment of forces. This leads to the model ofthe double couple. In Fig. 2 the double couple concept is illustrated for the two highlighted elements Myz and Mzy.

Fig. 2 The p-wave radiation pattern of a pure shear event with the corresponding pair of adouble couple. The symmetry of the tensor yields a zero total torsion.
The two elements Myz and Mzy build a double couple, which leads to a pure shear failure
when the stress in the material exceeds a critical mechanic threshold. Dependent on the global
stress regime or the constitution of the material, a crack will occur in the xy-plane or the xzplane. These two planes are called the nodal planes. From the radiation pattern alone, it cannot
be resolved if the stress was released by a relative slip of the upper part to the right or a relative
slip of the right part upwards. To solve this ambiguity, more information about the stress regime
or an analysis of other acoustic events is necessary. Along the nodal planes the amplitudes arezero, parallel to the T-axis (Tension) the amplitudes are positive (compressional) and along the
P-axis (Pressure) the amplitudes are negative (dilatational). This nomenclature appears confusing
at a first glance. It originates from different points of view, the source with its particle motion
following the stress regime (P- and T-axis and vectors in Fig. 2), and the inverse corresponding
amplitudes at a receiver.
Various methods for an inversion on the moment tensor are in use. Absolute inversion can be
performed on single events, but require detailed knowledge of the Green's functions. If the structure can be assumed to be homogeneous, an inversion using simplified Green's functions can beapplied [Ohtsu, Ono, 1988, Ohtsu et al. 1991]. Relative methods with or without a reference
mechanism are applied on clusters of events where the Green's functions can be neglected by theassumption of common ray paths [Dahm, 1993]. Therefore, the size of a cluster has to be in theorder of the investigated wavelength and has to be short enough to make sure, that the Green’s
functions are constant within that period. The effects of alternating Green’s functions with theprogress of damage are discussed in Kurz et al., [2004].
Problems occur when the signal to noise ratio is poor or the events within one cluster have
similar mechanisms, which is often the case in acoustic emission analysis. Andersen [2001] introduced a new approach, the hybrid moment tensor inversion, as a combination of the previous
methods, where the effects of noise, low quality data, site effects, etc. are minimized and thesolutions become more robust. The method is based on an iterative weighting scheme using themedian of the distribution of residuals (for a particular geophone site, channel and wave phase),
calculated using all events in the cluster.
With the introduction of the double couple we already began a mechanical interpretation of
the moment tensor. In Fig. 3 some basic source models are introduced and correlated with theestimated radiation pattern of seismic p-waves. The upper part contains a collection of seismic
sources and failure mechanisms, which could play a major role in material sciences. The coordi85

nate systems on the left help to connect these models with the radiation pattern images in thelower part of Fig. 3, where the respective columns belong to the same models above. The illustration of the radiation pattern as a projection into the Schmidt net [Fowler, 1990] gives all important information of the 3D-spatial distribution of seismic energy on a fictive focal spherearound the source point in a view from above (indicated by a circle). The normalized amplitudes
are plotted in a color scale from red (+1) to blue (-1). The white areas denote the zones with zeroamplitude (i. e., nodal planes for the double couple).

Fig. 3 Correlation between models of failure mechanisms (top) and the estimated radiation
patterns of these events in a spatial projection with view from above (same column, respectively) [Finck et al., 2003].
The simplest model is a pure isotropic source with constant energy in all directions, comparable to an ideal explosion (positive amplitudes) or implosion (negative amplitudes). Uniaxial
tension (mode I), displayed in the second model from left, leads to an opening crack. Here, themean particle motion is parallel to the tension axis. In the plane perpendicular to this axis somesmall energy might be radiated with positive or negative amplitudes, depending on the elastic
properties of the medium. A change of volume will be remaining. A similar mechanism is the socalled compensated linear vector dipole (CLVD), shown in the middle. Here, the change of volume is compensated by the particle motion in the plane parallel to the largest stress. The CLVD
was suggested as a possible deep earthquake mechanism due to mineralogical processes[Knopoff, Randall, 1970]. A second pure deviatoric mechanism can be formulated by a double
couple. Shear is the corresponding failure mechanism. Mode II and mode III can de distinguished considering the direction of the crack growth relative to the particle motion. For the first,
the directions are parallel, for the latter, perpendicular to each other. In reality, the stress field
and inhomogeneities in the material lead to a superposition of these basic mechanisms.
The radiation of seismic energy can also be projected on a 3D focal sphere to be implemented in 3D visualizations of the specimen. This is performed by assuming isotropic and homogeneous Green’s functions. The simplified Green’s functions yield a constant factor, which isneglected, since the amplitudes are normalized to maximum amplitude equal to 1. Then the Pwave amplitudes can be calculated after Pujol, Herrmann [1990]:
d P ( x) =R
(γ i M ijγ j )
R
.
86

(3)

Geometrical spreading is realized by the factor 1/R. The second term accounts for the convolution of the moment tensor and the direction cosine γ.
The basis of a physical interpretation of the real and symmetric moment tensor is a transformation into the system of its principal axes by an eigenvalue analysis [Jost, Herrmann, 1989].
With an eigenvalue analysis, the moment tensor can be decomposed into an isotropic and a deviatoric component. Furthermore, the deviatoric portion can be separated into a double couple
and a CLVD component:
M = M ISO + M DC + M CLVD .

(4)

To measure the magnitudes of the mechanisms involved, percentage decompositions can be calculated [e.g. Andersen, 2001]
% ISO

=

100tr(M )3tr(M ) + ∑ mi*i =1=

m3* (1 − 2 F )(100 − % ISO)
m3* (1 − 2 F ) + 2m3* F%CLVD =

2m3* F(100 − % ISO)
m3* (1 − 2 F ) + 2m3* F

% DC

(5)

,

where the mi are the eigenvalues of M withm3* ≥ m2* ≥ m1*(6)

and F = - m1* / m3*.
The model of the CLVD is not undisputable, since a Poisson’s ratio of 0.5 would be requiredfor its realization, even under an idealized stress regime. In construction material sciences, the
CLVD alone has no great significance as a mechanism of failure. In combination with a mayor
positive isotropic component it can be seen as an opening crack (mode I).
3. Mode I Failure in a Splitting Test
To generate tensile stress within a well defined zone, compressive load was applied on a concrete cube (edge length 200 mm) over two parallel steel edges (see Fig. 4, left side) [Finck et al.,2003]. The load in the test device was controlled over the crack opening, which was observedwith two displacement sensors, one on the front, the other on the backside of the specimen. Eight
acoustic emission sensors registered the microcracking, which occurred during the growth of atensile crack. The localization of these events yielded the run of the crack through the specimen,
starting on the back to the front side. This correlated well with the visible damage progress, aswell as with the crack opening data. Subsequent to the experiment, the specimen was ground in a
stepwise manner, to digitize the run of the crack through the various sections. Thereby, the topography of the crack could be evaluated. The results of the localization of the acoustic emissions (black dots) and the inner crack surface (reticule) are plotted in Fig. 4, right side. Additionally, the radiation patterns of two selected events are visualized.
87

Fig. 4 Left: Setup of the splitting test. Right: The topography of the crack surface and the radiation patterns of selected events from the two clusters. The radiation patterns were evaluatedfrom the moment tensor solutions inverted with the hybrid method. Both radiation patterns reveal positive (red) amplitudes parallel to the mean tensile stress and some negative (blue) amplitude parallel to the z-axis due to compressive stress. [Finck et al., 2003].
The hybrid moment tensor inversion for these selected events revealed solutions, which very
well approve the presumption of mode 1 failure. The principle tensile stress is oriented parallel tothe y-axis, where the highest positive amplitudes occurred in the radiation patterns.
4. Mode II Failure in a Pull-out Test
The bond behavior of steel reinforcement in a concrete matrix was investigated with signalbased AE technique in a pull-out test. A steel bar (15.6 mm ø) with five ribs (see Fig. 5, left) was
pulled out of a concrete cube (edge length 200 mm). In the zone, where the ribs provided bond
between the concrete matrix and the steel reinforcement, numerous acoustic emissions were localized with the POLARAE (Program for Onset Detection, Localization and Amplitude Readout
for Acoustic Emission) software [Rosenbusch, 2003] (dots in the view from above, Fig. 5, rightside). Moment tensors were calculated for some selected events using the relative inversion approach by Dahm [1993]. The solutions of these inversions yielded stable results with nearly
100 % double couple mechanisms, and for each event one of the nodal planes was oriented tangential to the boundary the steel bar. The radiation patterns of the moment tensors indicate the88relative downwards motion (blue, negative amplitudes) of the bar relative to the concrete block(red, positive amplitudes).

Fig. 5 Left: Setup of the pull-out test. Right: Localization of acoustic emissions, which occurred
during the pull-out of a ribbed steel bar out of a concrete cube. The solution of the moment tensor inversion illustrates the pull-out of the bar, with a downward motion relative to the concrete
cube [Finck et al., 2002].
5. Conclusions
On the basis of a signal-based analysis of acoustic emissions, valuable information is obtained about failure processes. An accurate localization of microcracks yields the temporal andspatial distribution of damages. The concept of moment tensor inversion is well-suited for a fracture mechanics based interpretation of failure in engineering structures. Relative and hybrid moment tensor inversion methods were applied on different data sets and plausible solutions wereobtained.
In a splitting test the evolution of a tensile crack was observed from the localization of theevents and the moment tensors. In a pull-out test, the failure of bond and the slip of a steel bar inconcrete was investigated in detail.
A 3-dimensional visualization of the moment tensors was developed to give a descriptive
presentation of the results. This visualization method can easily be implemented into the illustration of the results of other investigations.
Understanding of the waveforms and development of standard routines during data processing allows for the automation of the evaluation of fracture processes. Some of these routines are
implemented in the new POLARAE (Program for Onset Detection, Localization and Amplitude
Readout for Acoustic Emission) software developed at the IWB.
The experiences gained in digital signal processing and signal-based AE analysis is the basis
for future applications in the field of construction health monitoring for example.

89

Acknowledgements
These studies were performed within the collaborative research center SFB 381, supportedby the Deutsche Forschungsgemeinschaft (DFG).
References
K. Aki, P. G. Richards: Quantitative Seismology, 2nd edition. University Science Books, Sausalito, CA, 2002.
L. M. Andersen: A relative moment tensor inversion technique applied to seismicity induced bymining. Ph.D. thesis, University of Witwatersrand, Johannesburg, South Africa, 2001.
A. Ben-Menahem, S. J. Singh: Seismic Waves and Sources. Springer-Verlag, New York, 1981.
T. Dahm: Relativmethoden zur Bestimmung der Abstrahlcharakteristik von seismischen Quellen.
Ph.D. thesis, Universität Karlsruhe, 1993.
F. Finck, M. Motz, C. U. Grosse, H.-W. Reinhardt, B. Kröplin: Integrated Interpretation and
Visualization of a Pull-Out Test using Finite Element Modeling and Quantitative Acoustic Emission Analysis. Published online: http://www.ndt.net/article/v07n09/09/09.htm, 2002.
F. Finck, M. Yamanouchi, H.-W. Reinhardt, C. U. Grosse: Evaluation of mode I failure of concrete in a splitting test using acoustic emission technique. International J. of Fracture, 124, 139152, 2003.
C. M. R. Fowler: The Solid Earth – An Introduction to Global Geophysics. Cambridge University Press, New York, 1990.
M. L. Jost, R. B. Herrmann: A students guide to and review of moment tensors. Seism. Res. Letters, 60, 37-57, 1989.
L. Knopoff, M. J. Randall: The Compensated Linear Vector Dipole: A Possible Mechanism for
Deep Earthquakes. J. of Geophysical Research, 75 (26), 4957-4963, 1970.
J. H. Kurz, F. Finck, C. U. Grosse, H.-W. Reinhardt: Similarity matrices as a new feature foracoustic emission analysis of concrete. EWGAE 2004, Proceedings, Berlin, 2004.
M. Ohtsu, K. Ono: The generalized theory and source representations of acoustic emission. Journal of Acoustic Emission, 5 (4), 124-133, 1988.
M. Ohtsu, M. Shigeishi, H. Iwase, W. Koyanagi: Determination of crack location, type and orientation in concrete structures by acoustic emission. Magazine of Concrete Research, 43 (155),
127-134, 1991.
J. Pujol, R. B. Herrmann: A student's guide to point sources in homogeneous media.
Seismological Research Letters, 61 (3-4), 209-214, 1990.
N. Rosenbusch: Entwicklung eines Programms zur Bestimmung von Ersteinsätzen und
Amplituden sowie zur 3D-Lokalisierung von Schalemissionen. Diplomarbeit, Institut fürWerkstoffe im Bauwesen, Universität Stuttgart, 2003.
A. Udias: Principles of Seismology. Cambridge University Press, Cambridge, 1999.
90ACOUSTIC EMISSIONS FROM TRANSPIRING PLANTS– NEW RESULTS AND CONCLUSIONSRALF LASCHIMKE1, MARIA BURGER1 and HARTMUT VALLEN2
1
FH Sigmaringen-Albstadt, Schlosshalde 1, 72479 Strassberg, Germany,
2
Vallen Systeme GmbH, 82057 Icking, Germany
Abstract
Using Ulmus glabra as an example we revealed that acoustic emissions (AEs) from plants do
not necessarily occur in conjunction with water stress. Various signal waveforms show that AE
may possibly be generated by still unknown hydraulic events, more complex than cavitation.
Using a special microscopic technique we established the presence of minute gas bubbles attached firmly to the xylem vessel walls of the water-conducting conduits. The micro-bubbles
accumulate especially to the poorly wettable lignin domains of the vessel pits. Our conclusion isthat minute gas bubbles at the xylem vessel walls are a normal feature of actively waterconducting conduits. By means of physical model systems we ascertained that by virtue of thecohesion of water the entire system of wall adherent bubbles acts as a force-transmitting mediumthat creates the xylem tension. During transpiration the wall adherent gas bubbles expand, andduring refilling they contract. Since the bubble system always reacts intermittently this creates
AE both during transpiration and refilling. Our hypothesis is that under a gradient of the wall
attraction forces of the bubble system the water column moves in a similar fashion as under agradient of actual negative pressure. The outward appearance of xylem tension caused by the
bubble system is deceptively similar to that caused by real negative pressure. Our investigations
have resulted in an alternative concept of water transport in plants.
Keywords: Acoustic emissions, signal-waveform, xylem transport, xylem tension, negative
pressure, embolism repair, cohesion theory.
1. Investigations
Since the first report in 1966 about audible acoustic emissions (AAE) from plants being under water stress the acoustic signals have been considered as convincing evidence of disruptionsin the water column caused by negative pressure as it is claimed in the cohesion theory (1).
However, when in 1983 (2) ultrasonic acoustic emissions (UAE) were introduced into the investigation it became apparent that the density of signals was then shown to be extremely high. Provided that even the UAE signals are attributable to local disruptions of the water column, an uninterrupted flow of water is only conceivable if the embolism defects are continuously being
repaired. However, a generally accepted hypothesis is missing still today in order to explain theembolism repair. Therefore, the cohesion theory has been questioned since the beginning one
hundred years ago and the debate still continues today (3).
Until recently, most reports on AE were focused on plants being definitely under water stressor wilting (4). In many investigations the intensity of water stress has been assessed by the number and the amplitude of the AE hits versus time. In our investigation we re-examine the customary opinion that water stress is a pre-condition of AE from plants. In order to extract more information about the mechanism of AE signal generation we concentrate our investigation on thefrequency pattern of the emissions and the waveform of the individual signals emitted during
J. Acoustic Emission, 22 (2004)102© 2004 Acoustic Emission Groupnormal transpiration without water stress. Our AE data interpretation is supported by microscopic observations and newly developed physical model systems.
2. Acoustic Emission Detection
For UAE measurements we selected a 4-year old, potted Ulmus glabra which had vigoroustranspiration activity. We chose this angiosperm species of tree because its ring-porous xylem
facilitates microscopic examination of the vessel walls, which constitutes part of our investigation. In order to record transpiration loss whilst simultaneously monitoring AE data we fitted theflowerpot and the elm tree with AE sensors linked to a computerized electronic scale (Fig. 1).
Prior to monitoring the AE data the elm was watered well, so that no water stress could develop
in the plant during a period of 5 or 6 days. Temperature was maintained in the laboratory at 22 ±1.5 C° and relative humidity at 60 ± 3%. Thus, transpiration depended primarily on the intensityof light during the rhythm of day and night. Our experiments were carried out in August 1998.
Figure 2 shows the plot “transpiration loss versus time” during an 80-hour period. The start ofthe measurement was at 18:00 (6:00 p.m.) To improve the assessment we calculated the derivation curve, called “transpiration intensity versus time”. This curve reveals minimum transpiration intensity at night of 15 % of the maximum intensity during daylight hours.

Fig. 1 Experimental set-up. A young Ulmus glabra tree with AE sensors (above left) is
placed on a computerized scale.
We detected the AE signals emitted by an elm leaf by means of a piezoelectric sensor SE-45
produced by Duncan Engineering Consultants, USA. Normally, AE signals are taken from stems,
twigs or cut leaves. The advantage of detecting AE from a living leaf is that the signal generation
occurs in the immediate area of the site of transpiration. Although we applied a mild medical
contact gel, the mesophyll, in the contact area became necrotic very soon after contact was made.
This was prevented once the contact area was coated with a chemically inert silicone releaseagent.
The AE sensor attached to the upper side of the leaf exerted no pressure on the leaf. So, transpiration was practically unaffected at the reverse side of the leaf where the majority of the stomata are located. Close to this measuring sensor we placed another one, which had no contact
with the leaf. With the help of this second sensor we could eliminate disturbing noise from the
AE data.
103

16

1,2

Transpiration Loss [g]

1

12
0,8

10
8

0,6

6

0,4

4
0,2

2
0
18:00 0:00

6:00 12:00 18:00 0:00

6:00 12:00 18:00 0:00

Tranpiration Intensity [g/h]140
6:00 12:00 18:00 0:00

Time [h]Transpiration Loss

Transpiration Intensity

Fig. 2 Transpiration loss and transpiration intensity of the examined young Ulmus glabra tree.
900

1,2
1

700
600

0,8

500

0,6

400
300

0,4

200

0,2

100
0
18:00

0:00

6:00

12:00 18:00

0:00

6:00

12:00 18:00

0:00

6:00

12:00 18:00

Transpiration Intensity [g/h]Hits (cumulative)

800

0
0:00

Time [h]Hits (cumulative)

Transpiration Intensity

Fig. 3 Transpiration intensity and cumulative AE activity of the examined Ulmus glabra tree.
1,2

Hits (cumulative)

0,8

150

0,6
100

0,4

50

0,2

0
18:00 0:00
20-37kHz

6:00 12:00 18:00 0:00
45-90kHz

6:00 12:00 18:00 0:00
Time [h]90-105kHz

0
6:00 12:00 18:00 0:00
Transpiration Intensity

Fig. 4 Transpiration intensity and cumulative AE activity of selected dominant frequencies.
104

Transpiration Intensity [g/h]1200In order to extract maximum information from the AE we employed a monitoring systemequipped with transient recorders. The Vallen Systeme AMSY-4 suited our purposes very well.
In parallel to normal feature extraction, the complete waveform can be saved in a transient recorder module. The data can be analyzed by means of special Vallen software. Data compiled
during a period of 77 hours is shown in Fig. 3. Intense signals occur during both maximum andminimum transpiration intensity. Interestingly, high AE activity developed even during the night.
The most important results of this work were obtained from the frequency pattern and the
waveform of the signals. The review of stored signals revealed accumulation of signals with amaximum magnitude of the spectrum between 20 and 37 kHz (signal type A), 37 and 54 kHz(signal type B), and between 88 and 105 kHz (signal type C). Figure 4 shows the AE activity forthese three frequency ranges. Signals of type A and type B are emitted in turn, during intensetranspiration and reduced transpiration, respectively. Signals of type C are increasingly emitted
between declining transpiration in the evening and increasing transpiration in the morning. The
significant change of the signal-types indicates that the mechanism of AE generation during intense transpiration differs from that during reduced transpiration and refilling, respectively. In
addition to the frequency pattern we analyzed the waveform of a number of signals. Figure 5
gives the most illuminating variations of the waveform. We established quickly that there areevanescent signals and longer, modulated signals. This indicates that similar events occur both inisolation and in a coherent sequence.
In our investigation we provide proof that AE from plants do not necessarily occur in conjunction with water stress. Apparently, it is not possible, with our current level of knowledge, todescribe properly the complex AE activity of plants. Therefore, we attempt to search for othersources of AE from the water conducting system of vascular plants.

Fig. 5 Typical variations of the waveform of the signals. Top left: time signal. Top right: result of
the fast Fourier transforms. Bottom: Wavelet diagram. The magnitude of the spectrum is encoded in rainbow colors. Violet shows the lowest magnitude and red the highest.
4. Microscopic Examinations
In textbooks the wetting ability of the xylem vessel walls is considered to be perfect. However, in a previous investigation we proved that the wetting behavior of the vessel walls is a more
complex one caused by inhomogeneous distribution of perfectly wettable cellulose and poorly
wettable lignin (5).

105

It is known that lignin can be detected by its yellow-green auto-fluorescence when excited byblue light (6). But in a normal epi-fluorescence microscope the intensity of the lignin fluorescence is low and in general the source of the fluorescence cannot be localized sharply due to thestray light and the depth of focus is too low. This disadvantage is overcome by using confocal
laser scanning microscopy (CLSM).

10µm
10µm
10µm
Fig. 6 Xylem vessel walls of Ulmus glabra. Left: Scanning electron micrograph. Center: Confocal laser scanning micrograph in the fluorescence mode. The concentrations of lignin appear
brightly. Right: accumulation of gas bubbles at the vessel pits.

10 µm
10 µm
Fig. 7 Gas accumulations at the scalariform
vessel pits of Vitis vinfera.

10 µm
Fig. 8 Gas pads at the xylem
vessel walls of Ulmus glabra.

Figure 6 shows CLSM fluorescence images of the vessel walls of Ulmus glabra. In order toreveal the implication of the lignification pattern and the wetting ability of the vessel walls wehave carried out special microscopic examinations. From our potted elm a branch was bent down
into a container of water and a sample of about 3 cm in length was cut off. On this sample a longitudinal cut was made and then the sample, still immersed, was transferred under the microscope equipped with a lens for water immersion (high resolution objective lens “PLABO”;
106160x/1.25W, Leitz Wetzlar, Germany). The whole procedure took only a few minutes. As it isvisible in Fig. 6, the vessel wall shows bubble-shaped gas accumulations at the pits. Under certain conditions of illumination some of the bubbles show intense light reflection, which is convincing proof of the existence of gas/water interfaces. We are aware that the gas bubbles, which
we have first described in 1989 (5), are not maintained in the original shape as in the intact vessel tubes in vivo. By opening up of the vessel tubes there resulted a drop in tension in the xylem
resulting in a contraction of the bubbles. However, new bubbles may scarcely develop during
sample preparation. We ascertained the genuine nature of the wall adherent bubbles not only for
Ulmus glabra but also for Quercus robur, Acer pseudoplatanus, Betula alba, Populus nigra,
Salix alba and Vitis vinifera. We observed repeatedly that the accumulation of expelled gas takes
place at the lignin domains of the vessel pits.
Figure 7 shows the formation of gas bubbles at the scalariform vessel walls of Vitis vinifera.
The formation of gas pads due to the coalescence of single bubbles is shown in the example of
Ulmus glabra in Fig. 8. In this it becomes visible that each flat gas pad integrates numerous single bubbles.

1 mm
Fig. 9 Physical model system. Gas pads adherent at the holes of an aluminum plate. The depthof the holes is 0.05 mm. Left: Birds eye view. Right: Angle of viewing 45°.
5. Physical Model Systems
We studied the properties of wall adherent gas pads by means of several physical model systems. Compared with the geometrical dimensions of the bubble systems in the xylem vesseltubes our model systems are larger by two orders of magnitude. However, the advantage of thesemacro models is that they reveal clearly the peculiar surface curvature of the wall adherent gas
pads.
Figure 9 shows wall adherent gas pads in several phases of coalescence. Affected by thebuoyancy, each bubble tries to detach from the solid structure. However, since the energy needed
to detach cannot be obtained, the bubble aggregates cannot achieve their spherical equilibrium
shape, so they remain in non-spherical metastable configurations. It is obvious that at the undulated rims of the gas pads tension develops. The rims act like elastic bonds. Some of the humpshaped gas pads integrate up to 35 single bubbles. Of course, at the xylem vessel walls due to the107dense pattern of the very minute pits many more bubbles are able to coalesce, so that sizeable gas
pads develop (Fig. 8). We observed under the microscope that the bubbles in our model systemintermittently coalesce quite violently. It is our hypothesis that in plants it is the transpirationinduced intermitting alteration of the wall adherent bubble system that creates AE signals.
Figure 10 shows a glass capillary perforated by two rows of minute lateral holes. The holes
were made by means of a laser. A limited length of the water column in the capillary is balanced
since the lateral concave menisci create a resultant force against gravity. Our conclusion is that inplants the countless number of wall adherent gas bubbles and gas pads restrain the water column
against gravity.

Fig. 10 Left: Glass capillary with lateral holes. Hole diameter 0.15 mm. Center: A water column of 23.5 mm length is balanced in the capillary by the forces of the lateral menisci. Right:
Forces acting on the water column in the capillary.
6. Discussion
In our opinion the AE from plants do not necessarily occur in conjunction with water stress.
The frequency pattern and the waveform in the various signals show that AE may possibly begenerated by still unknown hydraulic events, more complex than cavitation. We provided evidence of accumulation of gas bubbles at the vessel walls of the water conducting conduits of
Ulmus glabra and some other angiosperm tree species. Encouraged by these findings we conclude that free gas in the active conduits does not represent a disturbing factor in long-distancewater transport. Clearly, the persistent presence of gas spaces in the active conduits can never bereconciled with negative pressure in terms of the cohesion theory. It belongs to the basic conceptof this theory to consider the transpiration stream to be forced merely by the surface tension of
the transpiring menisci in the leaves. We take a distinctive view. In our opinion the water column
is forced over the whole length of the xylem conduits. Our experiments with physical model systems give rise to the assumption that in plants the whole water column is balanced by a wall adherent micro-bubble system extending in the xylem conduits from the roots to the leaves. According to the principle of buoyancy the system water/gas moves its centre of gravity into thelowest possible position. Since the wall adherent bubbles are coupled both with the liquid and thewall, tension develops when the bubbles get distorted due to the weight of the clinging watercolumn. Thus, we re-define the xylem tension as being a distortion-tension of a wall adherent108bubble system rather than a gradient of negative pressure in terms of physics. We consider theoutward appearance of xylem tension induced by the wall adherent bubble system to be deceptively similar to negative pressure. In these premises we also explain the famous pressure chamber test applied to the determination of the water potential of plants. Seen from our point of viewthe chamber-pressure exerted to compensate the xylem tension represents mainly the pressure inorder to compress the wall adherent bubble system. The stronger the tension of the bubble system the higher the chamber pressure needed to alter the reluctant bubble system. So the readingsof the pressure chamber test are in correlation with the state of the bubble system. We consider
the surface energy of the bubble system to be part of the water potential.
Our transport model might be described as an energy-storage model. Without influence of
external forces the system “bubble layer/water column” shifts due to an exchange of internalenergy into the minimum of free energy. Transpiration-induced loss of water deflects the systemfrom the minimum of free energy. Since the entire bubble system works as force-transmitting
medium, the deflection propagates slowly in basal direction to the roots. (In the cohesion theory,
it is assumed that a change of negative pressure propagates with the velocity of sound throughthe whole plant). (8). The propagation of the energy-deflection is accompanied by a rearrangement of the wall adherent bubble system. This rearrangement undoubtedly creates AE. Whentranspiration declines (at night) then the deflected system returns to the energy minimum by uptake of water from the roots. According to the laws of thermodynamics, the return to zero is notexactly the reversal of the initial energy deflection. This might be the reason why the frequency
pattern of AE is different during the transpiration phase from that in the refilling phase. We assume the long-distance water transport in plants to be a non-metabolic physico-chemical mechanism. However, we do not doubt that the installation and the maintenance of the wall adherent
bubble systems in the xylem is the work of metabolic forces.
References
1. Milburn, J. A. and Johnson, R.P.C. (1966): The conduction of sap. Detection of vibrations
produced by sap cavitation in Rhicinus xylem. Planta, 69, 43-52.
2. Tyree, M.T. and Dixon, M.A. (1983): Cavitation events in Thuja occidentalis? Ultrasonicacoustic emissions from the sapwood can be measured. Plant Physiol. 72, 1094-1099.
3. Holbrook, N.M. and Zwieniecki (1999): Embolism repair and Xylem tension: Do we need amiracle? Plant Physiol. 120, 7-10.
4. Spinarova, S. and Hendrics, L. (2003): Factors influencing acoustic emission profiles of cut
roses. Poster, Tagung der Deutschen Gartenb. Ges, 2003.
5. Laschimke, R. (1989): Investigation of the wetting behaviour of natural lignin a contribution
to the cohesion theory of water transport in plants. Thermochimica Acta 151, 35-56.
6. Laschimke, R. (1991): Localisation of lignin in wood by fluorescence laser scanning microscopy. Mitteilungen der Bundesforschungsanstalt für Forst- und Holzwirtschaft 168, 243-248.
7. Cruiziat, P. and Richter, H. (2002): The cohesion theory at work. Essay 4.2 In: Taiz, L. andZeiger, E.: Plant Physiology, 2002, Sinauer Associated, Sunderland MA USA.

109

ACOUSTIC DETECTION OF CAVITATION EVENTS IN WATER CONDUCTING ELEMENTS OF NORWAY SPRUCE SAPWOOD
SABINE ROSNER
University of Natural Resources and Applied Life Sciences - BOKU Vienna,
Gregor Mendel Str. 33, 1180 Vienna, Austria
Abstract
Two different methods to assess the hydraulic vulnerability of juvenile and mature Norway
spruce (Picea abies (L.) Karst.) sapwood were compared. Vulnerability curves were constructedby using the percent loss of conductivity versus pressure application (classical method) and byusing cumulative acoustic emission (AE) hits versus the relative loss of water. Acoustic emissions were detected in the high-frequency range of 100 kHz to 1 MHz during drying the wood
samples. Concerning the classical method, juvenile wood is less sensitive to dehydration thanmature wood. The two methods gave similar results for the hydraulic vulnerability of juvenile
wood. In juvenile wood 50% cumulative AEs were recorded after 52.5% relative water loss,
whereas in mature wood after 71.6% relative water loss. AE recording may provide additional
information about other physiological wood functions such as water storage, but more investigations are needed to establish these relationships firmly.
Keywords: acoustic emission method, conifer sapwood, hydraulic conductivity, hydraulicsafety, Picea abies.
1. Introduction
Wood fulfills many of the functions required for survival of a tree, including water and nutrient transport, mechanical support, and storage of water, carbohydrates and secondary compounds. Variability in anatomical structure and composition of wood within a stem includes
within-ring differences known as early wood and late wood, the radial variations resulting fromcambial maturation and sapwood ageing, and the differences associated with different heights(Gartner 1995). The greatest overall cause of wood variation is the presence of juvenile woodand its relative proportion to mature wood. Juvenile wood is directly related to the age of thecambium. Thus, regardless of tree age, a juvenile zone occurs at the top of the tree as well as atits base. It is characterized by shorter cells, larger microfibril angles, more compression wood,
different specific gravity and higher lignin content (Zobel and van Buijtenen, 1989). Juvenile
wood is not much appreciated by the wood industry because of its poor mechanical properties
and its shrinkage behavior, but for tree survival it is of utmost importance due to its high hydraulic safety compared to mature wood (Domec and Gartner, 2001).
The liquid water in the tracheids is under tension, caused by transpiration from the needles.
The negative pressures in the conduits require high mechanical strength of the cell walls in order
to avoid implosion, and hydraulic safety against breakage of the water column. Such a breakageof the liquid water columns inside the conduits is defined as cavitation. It is followed by embolization, the diffusion of dissolved air into the near vacuum thus created. Drought stress-induced
cavitation occurs when an air bubble is pulled into the lumen of a water-filled conducting element via the pores of the pit membranes from an adjacent conduit already embolized (air-filled)
J. Acoustic Emission, 22 (2004)110© 2004 Acoustic Emission Groupor from intercellular spaces filled with air. Liquid water is thus replaced by water vapor and laterby air. Consequently, the conduit does not conduct water any longer and the hydraulic conductivity of the plant is reduced, which leads to impairment of water supply of the transpiring needles(Tyree and Zimmermann, 2002).
Vulnerability to cavitation can be determined by the construction of vulnerability curves. A
vulnerability curve (VC) is a plot of the extent of embolism versus the pressure potential in thesolution transported in the conduits that induced the embolism or versus the percent loss of watercontent. From VCs the specific water potential or water content thresholds for the onset of cavitation are determined. Methods to induce embolism are dehydration in air, centrifugal force,compressed air applied in a pressure collar and air injection. According to the “air seeding” hypothesis, the “positive pressure needed to blow air through the largest water-filled pores shouldbe the same in magnitude but opposite in sign to that needed to cause embolism (during drought
stress)” (Tyree and Sperry, 1989). The classic method to quantify the extent of embolism is thedetermination of the native hydraulic conductivity in relation to the hydraulic conductivity after
artificially refilling all embolized tracheids. Cavitation events can also be detected directly byrecording acoustic emission (AE) in the high-frequency range of 100 kHz to 2 MHz (Tyree andDixon, 1983, Jackson and Grace, 1996, Kikuta et al., 2003). AE signals are supposed to be induced by elastic oscillations of conduit cell walls following the cavitation event, because thepressure rises rapidly in the tracheid lumen as liquid water at negative pressure is replaced bywater vapor very near vacuum pressure.
This study was a first attempt to find differences in the cavitation behavior of mature and juvenile wood of Norway spruce by detection of acoustic emissions. We also compared the acoustic method to other approaches for constructing VCs.
2. Materials and Methods
2.1 Plant material
Samples of wood produced by cambium of different age were obtained in the following way:a.) Wood boles (20 cm length) from the base of freshly felled 40-year-old Norway spruce trees(Picea abies (L.) Karst.) were debarked and brought to the laboratory. Outer sapwood samples with a transverse surface of about 1.2 x 1.2 cm were isolated by splitting the wood along
the grain with a chisel. Beams with a cross section of 0.8 x 0.8 cm and a length of 13 cm
were machined from the samples, with the wood rays parallel to the edges. Tangential andradial faces of the beams were planed on a sliding microtome. During all these steps wood
samples were kept wet. The final standard shape of the samples was 0.65 x 0.65 x 10.0 cm.
b.) Four-year-old Norway spruce trees were debarked in the laboratory, and wood samples of
10 cm length (Ø 0.5 – 0.9 cm) were prepared.
All samples were soaked in distilled water under vacuum for at least 48 h to refill embolized
tracheids.
2.2 VCs based on percent loss of conductivity (PLC) and applied pressure
Specific conductivity (ks) describes the permeability of a wood segment following Darcy´s
Law and is defined as:
ks = Q · l · As-1 · ∆P-1 [m2s-1MPa-1],

[1]

111

where Q is the volume flow rate [m3s-1], l is the length of the segment [m], As is the sapwood
cross-sectional area [m2], and ∆P is the pressure difference between the two ends of the segment[MPa]. Calculated conductivity data were corrected to 20°C to account for changes in fluid viscosity. The modified Sperry Apparatus designed by Mayr (2002) was used to measure the volume flow rate. Conductivity measurements were carried out under a hydraulic pressure head of
0.008 MPa with distilled, filtered (0.22 µm), and degassed water containing 0.005 vol. % Micropur (Katadyn Products Inc.) to prevent microbial growth (Sperry et al., 1988). VCs were obtained with methods described by Spicer and Gartner (1998) and Domec and Gartner (2001).
These methods involved measuring the PLC on wood samples taken directly from the trunk andsaturated in water. After determination of the conductivity at full saturation (ks(i)), a pressure (P)was applied to the lateral sides of the samples, while the transverse ends protruded from a double-ended pressure chamber (PMS Instruments Co., Corvallis, Oregon), to induce cavitation.
After pressure treatment samples were wrapped in parafilm for about 30 min to permit diffusion of air into cavitated tracheids. Then hydraulic conductivity was measured again (ks(P)). Initially, the pressure chamber was pressurized to 0.5 MPa, and the pressure was subsequently increased after each conductivity measurement in steps of 0.5 – 1.0 MPa till more than 95% PLC
was reached. PLC at a given pressure was calculated using the following equation:
PLC = (ks(i) - ks(P))/ ks(i) · 100 [%][2]

Hydraulic VCs were fitted by the least square method based on a sigmoidal function:
PLC [%] = 100/(1 + exp (a(P - b)),[3]

where a indicates the slope of the linear part of the curve and b is the potential at which 50 PLC
occurred.
2.3 VCs based on AE rate and relative water loss
Monitoring of AEs was achieved with the µDiSP Digital AE system from PhysicalAcoustics Corporation. Two PCI-2 cards in a portable, 4-channel notebook-based chassis allowed simultaneous measurement of 4 samples. Preamplifiers (40 dB) were used in connection
with resonant 150 kHz R15C sensors over a standard frequency range of 50 – 200 kHz. Dataacquisition was performed with a detection threshold of 31 dB (0 dB = 1 µV input), a frequency
range between 100 kHz and 1 MHz (Sandford and Grace, 1985), and a rate of 5 Msamples/s.
Parameters recorded were cumulative AE hits and the rate of AE hits versus time.
AE sensors were positioned on fully saturated wood samples using an acrylic resin clamp
construction (Fig. 1). Continuous water loss was quantified by placing a sensor plus wood sample on a balance (resolution 10-4 g, Mettler). Sample weight was recorded automatically every
minute. Hydraulic VCs were fitted by the least square method based on a sigmoidal function:
Cumulative AEs [%] = 100/(1 + exp (a(RWL - b)),[4]

where a indicates the slope of the linear part of the curve and b is the relative water loss at which
50 % cumulative AEs occurred. The relative water loss (RWL) was calculated as:
RWL (%) = (1- (actual fresh weight – dry weight)/(saturated weight - dry weight))·100[5]
Dry weight was obtained by drying wood samples at 103°C to constant weight. Correlations
were accepted as significant if P was ≤ 0.05. * implies P ≤ 0.05, ** P ≤ 0.01 and *** P ≤ 0.001.
112

Fig. 1 AE monitoring equipment; insert (top left) shows a hit signal of a cavitation event.
3. Results
In juvenile wood 50% conductivity loss (a = 1.03; b = -3.65; R=0.98***, equation 3) required 3.7 MPa air pressure, whereas in mature wood only 2.2 MPa were needed to achieve thesame effect (a = 3.79; b = -2.16; R=0.98***, equation 3) (Fig 2a). In juvenile wood 50% cumulative AEs were recorded after 52.5% relative water loss (R=0.99***), whereas in mature woodafter 71.6% relative water loss (R=0.98***) (Figs. 2b and 3).
A model vulnerability curve was calculated for mature wood, based on the strong relationship of the water content with the applied pressure and with the cumulative AE (%) (cubic regression fit model R=0.98*** and R=0.99***, respectively) (Fig. 2c). VCs based on AE% versus pressure and VCs based on PLC versus pressure lead to similar results for 50 PLC (3.77 MPa
and 3.65 MPa) in juvenile wood, whereas 50 PLC calculated for adult wood was 5.22 MPa compared to 2.16 MPa, respectively. However, in the range between 2 and 3 MPa mature wood
seemed to be more vulnerable to cavitation as indicated by the steeper slope of the regression
line.
The total number of AE hits was higher in juvenile wood compared to mature wood, indicating a higher number of tracheids per volume (Fig. 3). Maximum hit rate was reached later inmature wood than in juvenile wood. In many samples, especially in mature wood, the hit rate
showed a double peak.
113

Fig. 2 (a) Vulnerability curves based on the loss of conductivity versus applied positive pressure,light lines represent juvenile, dark lines mature wood; (b) Vulnerability curves based on cumulative AE hits (%) versus relative water loss during dehydration. (c) Model vulnerability curve formature wood based on the correlation between relative water loss and applied pressure as well as
AE rate (dark line), and measured VC of a juvenile twig system (a = 1.71; b = -3.77 MPa: lightline). Water potential (MPa) was measured with a pressure chamber (Soil Moisture Equipment
Corp.).
4. Discussion
Monitoring hydraulic vulnerability using VCs based on the PLC versus applied pressure led
to results similar to those described in the literature for juvenile wood (Cochard, 1992, Lu et al.,1996). Air pressure of 3.7 MPa resulted in 50% conductivity loss in juvenile wood, whereas inmature wood only 2.2 MPa were needed to achieve the same effect, indicating that mature woodof Norway spruce is much more vulnerable to dehydration (Fig. 2a). Domec and Gartner (2001)
were the first to investigate hydraulic vulnerability of mature trunk wood segments and also reported higher hydraulic safety values in juvenile wood compared to mature wood of Douglas fir(Pseudotsuga menziesii). The high (less negative) values of “b” (equation 3) for mature wood
samples indicate wide pit membrane pores of the tracheids and the high numbers of “a“ (equation 3) suggest a narrow range in maximum pore sizes across tracheids (Pammenter and Vander
Willigen, 1998). In mature wood, conductivity loss occured over a narrower range of appliedbalance pressures than in juvenile wood (Fig. 2a).

114

Fig. 3 Time courses (seconds) of the cumulative AE hits (absolute numbers and % values) (left),
and the AE rate/second (right) during the dehydration of a juvenile (above) and a mature sapwood sample (below). Bold lines represent the relative water content (actual water content related to the water content at full saturation (%)) during the dehydration process.
Results obtained by recording AE hits versus weight loss were difficult to interpret. In juvenile wood 50% cumulative AE hits corresponded to 52.5% relative water loss, whereas in maturewood to 71.6% relative water loss (Fig. 2b). In mature wood, the bulk of AE hits occurred after
50% relative water loss. One may suggest that mature wood is less vulnerable to cavitation thanjuvenile wood. However, concerning hydraulic conductivity, emissions detected after 50% relative water loss cannot be of any physiological relevance for tree survival: a relative water loss of
only 36.6 % leads to 50% loss of hydraulic conductivity in mature wood (R=0.98***, data notshown).
The characteristic slopes of the AE hit rate versus relative water loss may reflect that thephysiological and mechanical functions of wood differ with cambial age. After an exponential
increase of AE hit rate during dehydration of juvenile wood, the hit rate decreased slowly over a
relatively long period of time. The hit rate of mature wood was very low at the beginning, increased rapidly till it reached a peak of about 17 hits/s and decreased quickly afterwards till asecond peak was reached (Fig. 3). In mature wood, AE hits emitted at a low rate at the beginningshould be the most important ones for conductivity loss. Vulnerability of a tracheid to embolism
is a direct function of its diameter (Lo Gullo and Salleo, 1991, Jackson and Grace, 1996). Lumen
diameter and tracheid length of Norway spruce wood increase with cambial age (Brändström,
2001). The cavitation of a small number of very large (and thus very vulnerable) tracheids may
have a dramatic effect on conductivity, since flow rate is proportional to the 4th power of the radius of a capillary. Mature wood of Norway spruce contains much more free water than juvenile
wood because it consists of tracheids with wider lumina, contains less compression wood and isthus less dense. We calculated 220% water content per unit dry weight in mature, and about
100% in juvenile wood. Wood at the base of the trunk is known to have higher water storage
115

capacities than juvenile wood from the top of conifer trees (Domec and Gartner, 2001). AE hits
recorded after 50 PCL may represent cavitations in tracheids that are not primarily needed forhydraulic conductance but for other functions, such as storage of water. The rapid increase of AE
signals caused by small water losses in juvenile wood may represent the cavitation events incompression wood. Compression wood typically occurs in juvenile trunk wood and branch woodand is very sensitive to cavitation; its main function is to achieve mechanical safety (Spicer andGartner, 1998, Mayr and Cochard, 2003).
The successive increase of AE signals in mature wood after 70% relative water loss (Fig. 3),
when PLC has already reached values of 95%, may be produced by mechanical failure during
shrinking processes. The cell shrinkage causes intracellular and intercellular micro-cracks. Highsignal rates, including a second peak, were still measured around the “fiber saturation point”,
when the slope of the relative water content became almost linear. Similar results have been reported from lumber drying experiments (Ogino et al., 1986, Beall, 2002, Beall et al., 2003), although they were performed at higher temperatures and sometimes on heartwood only. Niemz etal. (1994) and Cunderlik et al. (1996) could distinguish two phases during Norway spruce lumber
drying by analyzing AE burst rates. The first peak in the AE hit rate was interpreted as checking
caused by surface tension stress, the second peak (below the fiber saturation point) as checking
caused by tensile stresses inside the samples. Booker et al. (2001) suppose that in radiata pine
(Pinus radiata) sapwood within-ring internal checking is caused by water tension and not bydifferential shrinkage, because checking occurred before the fiber saturation point was reached.
Kawamoto and Willliams (2002) report that most AE waves detected by 150 kHz transducers
during drying are associated with moisture movement, and that it is difficult to distinguish AEs
caused by checking from those associated with movement of free water (capillary action).
Analysis of the spectra of signals emitted during drying may help to gain more knowledge about
these relationships.
5. Conclusions
The AE method is useful for monitoring vulnerability to dehydration in young Norway
spruce stems or twigs. For mature wood samples additional measurements are needed, such as
the measurement of the hydraulic conductivity in combination with the pressure applied or the
relative water loss. Concerning VCs based on PLC and applied pressure, juvenile wood is lesssensitive to dehydration than mature wood. For mature wood, AE recording can provide additional information about properties such as the water storage potential of the trunk wood, butmany more investigations are needed to establish these relationships firmly.
Acknowledgements
This study was financed by the Austrian Science Fund FWF (Project P16275-B06). Hanno
Richter and Silvia B. Kikuta are thanked for critical reading of the manuscript and linguistic corrections. The author also thanks Joachim Sell from PAC Europe for technical advice.
References
J. Brändström (2001) “Micro- and ultrastructural aspects of Norway spruce tracheids: a review,”
IAWA Journal, 22, 333-353.

116

F.C. Beall (2002) “Ultrasonic Methods to monitor and control lumber drying,”4th COST E15
Workshop “Methods for Improving Drying Quality of Wood”, 30-01. May 2002, Santiago deCompostela, Spain.
F.C. Beall, T.A. Breiner and J. Wang (2003) “Control of lumber drying using ultrasonic methods,” Proceedings of the 8th International IUFRO Wood Drying Conference – 2003, pp. 391-395.
R. Booker, T.N. Haslett and J.A. Sole (2001) “Acoustic emission study of within-ring internal
checking in radiata pine-wood NDT 2000,” The e-Journal of Nondestructive Testing, NDT.net,
6(3), 1-8.
H. Cochard (1992) “Vulnerability of several conifers to air embolism,” Tree Physiology, 11, 7383.
I. Cunderlik, W. Molinski and J. Raczkowski (1996) “The monitoring of drying cracks in thetension and opposite wood by acoustic emissions and scanning electron microscopy methods,”
Holzforschung, 50, 258-262.
J.-C. Domec and B.L. Gartner (2001), “Cavitation and water storage in bole segments of matureand young Douglas-fir trees,” Trees, 15, 204-214.
B.L. Gartner (1995) “Patterns of xylem variation within a tree and their hydraulic and mechanical consequences,” Plant Stems: Physiology and Functional Morphology,” B.L. Gartner, ed.,Academic Press New York, pp. 125-149.
G.E. Jackson and J. Grace (1996) “Field measurements of xylem cavitation: are acoustic emissions useful?,” Journal of Experimental Botany, 47, 1643-1650.
S. Kawamoto and R.S. Williams (2002) “Acoustic emission and acousto-ultrasonic techniques
for wood and wood-based composites A Review,” Gen. Techn. Rep. FPL-GTR-134. U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. Madison, WI, 16 pp.
S.B. Kikuta, P. Hietz and H. Richter (2003) “Vulnerability curves from conifer sapwood sections
exposed over solutions with known water potentials,” Journal of Experimental Botany, 54, 21492155.
M.A. Lo Gullo and S. Salleo (1991) “Three different methods for measuring xylem cavitationand embolism: a comparison,” Annals of Botany, 67, 417-424.
P. Lu, P. Biron, A. Granier and H. Cochard (1996) “Water relations of adult Norway spruce(Picea abies (L.) Karst.) under soil drought in the Vosges mountains: whole-tree hydraulic conductance, xylem embolism and water loss regulation,” Ann. Sci. For., 53, 113-121.
S. Mayr (2002) “A modified Sperry-Apparatus for measuring embolism in the xylem of trees,”
Berichte nat.-med. Verein Innsbruck, 89, 99-110.
S. Mayr, and H. Cochard (2003) “A new method for vulnerability analysis of small xylem areas
reveals that compression wood of Norway spruce has lower hydraulic safety than opposite
wood,” Plant, Cell and Environment, 26, 1365-1371.
117

P. Niemz, R. Emmler, E. Pridöhl, J. Fröhlich and A. Lühmann (1994) “Vergleichende Untersuchungen zur Anwendung von piezoelektrischen und Schallemissionssignalen bei der Trocknung von Holz,” olz als Roh- und Werkstoff, 52, 162-168.
S. Ogino, K. Kaino, and M. Suzuki (1986) “Prediction of lumber checking during drying bymeans of acoustic emission technique,” Journal of Acoustic Emission, 5, 61-65.
N.W. Pammenter, and C. Vander Willigen (1998) “A mathematical and statistical analysis of thecurves illustrating vulnerability of xylem to cavitation,” Tree Physiology, 18, 589-593.
A.P. Sandford and J. Grace (1985) “The measurement and interpretation of ultrasound fromwoody stems,” Journal of Experimental Botany, 36, 298-311.
J.S. Sperry, J.R. Donnelly and M.T. Tyree (1988) “A method for measuring hydraulic conductivity and embolism in xylem,” Plant, Cell and Environment, 11, 35-40.
R. Spicer and B.L. Gartner (1998) “Hydraulic properties of Douglas-fir (Pseudotsuga menziesii)
branches and branch halves with references to compression wood,” Tree Physiology, 18, 777784.
M.T Tyree and M.A. Dixon (1983) “Cavitation events in Thuja occidentalis L.?,” Plant Physiology, 72, 1094-1099.
M.T. Tyree and J.S Sperry (1989) “Vulnerability of xylem to cavitation and embolism,” Annual
Review of Plant Physiology and Plant Molecular Biology, 40, 19–38.
M.T. Tyree and M.H. Zimmermann (2002) Xylem structure and the ascent of sap, 2nd edition.,
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B.J. Zobel and J.P. van Buijtenen (1989) Wood variation: Its causes and control, Springer, Berlin, Heidelberg, New York.

118

AE MONITORING FROM CVD-DIAMOND FILM SUBJECTED TO
MICRO-INDENTATION AND PULSE LASER SPALLATION
1R. IKEDA1, H. CHO2, M. TAKEMOTO2 and KANJI ONO3
Asahi Diamond Industrial Co., Ltd, Ichihara, Chiba, Japan; 2 Aoyama Gakuin University,
Sagamihara, Kanagawa, Japan; 3 UCLA, Los Angeles, CA USA

Abstract
We monitored AE signals during micro-indentation and pulse laser spallation test of CVD
diamond film deposited on sintered SiC, and classified the fracture type by Lamb wave AE
analysis. In the indentation test with a Rockwell sphere indenter, we observed Hertzian ringcracks and simultaneously detected AE signals from Mode-I fracture. Crack types were classified from polarity distribution of the first arrival So-mode Lamb waves. Fracture strength of the
diamond film was estimated as 7.1 GPa by FEM analysis, using the critical indentation force,indentation depth and Hertzian crack diameter. In the laser spallation test, strong pulse expansion wave was produced by the pulse laser ablation of the confined silicone grease on the opposite surface of the diamond film and was utilized to cause Mode-I delamination of diamond film.
Critical tensile interfacial stress to cause the spallation was estimated as 222 MPa. We attempted
to detect the spallation initiation by monitoring the in-plane motion of So-mode Lamb wave AE
using broadband pinducer mounted on the edge planes. Waveform change due to the Mode-I
spallation was detected as high-frequency AE.
Keywords: Diamond film, indentation test, Hertzian ring crack, fracture strength, laser spallation, interfacial strength
1. Introduction
CVD-diamond-coated tools are widely used for machining graphite, ceramics and Al-Si alloys. Wear performance of diamond film as a cutting tool has been demonstrated to be excellent.
However, decohesion (fracture) of the film during machining has remained a problem. This is
more serious than the antifriction problem of the film. In order to study the fracture mechanism,
fracture strength of the film and film/substrate interface must be correctly evaluated.
Indentation fracture test using spherical indenter gives us much useful information about micro-fracture. When this test is applied to hard materials, Hertzian ring crack is caused by the surface tensile stress, as given by the Hertz theory [1]. Fracture strength of ceramic coating has been
evaluated using the threshold load to cause the first ring crack and FEM analysis [2,3]. Here, accurate determination of crack initiation load is critical. We monitored progression of microcracks during loading using AE system. We classified fracture types by analyzing the polarity
distribution of the first arrival So-mode Lamb wave. This AE system is different from the conventional AE system utilized for indentation [4, 5] and scratch test [6, 7].
We developed a pulse laser spallation method to measure the absolute adhesion strength of
hard coatings. It utilizes strong shock waves produced by pulse laser breakdown of confinedgrease or liquid. For the measurement of adhesive strength, scratching and indentation methods
have been adopted so far [8 - 10], but could not measure the absolute adhesion strength. The
strength estimated by these conventional methods changes depending on the testing conditions,such as the surface roughness, shape and stiffness of the indenter. Furthermore these methods
J. Acoustic Emission, 22 (2004)

119© 2004 Acoustic Emission Groupcan hardly be applied to diamond film. Contrary to this, the laser spallation technique [11] canestimate the Mode-I adhesion strength when both the out-of-plane displacement of expansion
wave and critical laser energy to cause micro-spallation are correctly measured.
We first conducted an indentation test to CVD-diamond film to estimate the fracture strengthof the film, and next estimated the interfacial adhesion strength of the film utilizing the pulse laser spallation technique. AE was successfully utilized for damage detection and fracture modeclassification.
2. Experimental
2.1 Indentation test
We prepared polycrystalline diamond films with grain size of 10 to 20 µm deposited on sintered SiC substrate by hot-filament CVD method of 3% methane and 97% hydrogen gas mixture.
The films were polished using diamond wheel before the indentation and spallation tests of the
diamond films. A diamond film of 35-µm thickness on a 20 x 20 x 5 mm SiC substrate was submitted to the indentation test. For the spallation tests, a diamond film of 78-µm thickness deposited on a 35 x 35 x 5 mm SiC substrate was used.
Experimental setup for the indentation test is shown in Fig. 1. We used a diamond brale indenter with spherical tip (radius: 0.2 mm and angle: 120 degree). Maximum indentation load,
Fmax, were selected at 98, 196, 294, 392 and 490 N. Loading rate and holding time are controlled
at 0.49 N/s and 10 s, respectively. Four small AE sensors (PAC, PICO, center frequency: 500
kHz) were mounted on the four edge surfaces of the substrate. Sensor outputs were amplified by
60 dB using a pre-amplifier (NF9913, NF Circuit Co.), digitized by an 8-channel fast A/D converter (Gage CompuScope, CS12100) and fed to a personal computer. Sampling interval andsampling points were set as 50 ns and 2048, respectively.

Fig. 1 Experimental setup for the indentation test and AE monitoring.
The AE system monitors the Lamb (or plate) wave AE. We classified fracture modes orcrack types by analyzing the polarity distribution or radiation pattern of So-mode Lamb wave.
Two types of crack, the Mode-I fracture with a crack opening vector parallel to the surface corresponding to the ring crack, and the Mode-II fracture or lateral crack at the film/substrate interface, were examined. When all polarities of the first arriving So-mode waves are positive,120

this is produced by a Mode-I crack. When two opposite polarities are found for the initial AE
waveforms among 4-channels of AE signals, this is induced by a Mode-II crack. Detail of polarity distribution analysis can be found elsewhere [12].
2.2 Laser spallation test
Experimental setup for the laser spallation test is shown in Fig. 2. A high-energy pulse from a
Q-switched Nd:YAG laser (New Wave Research, Tempest300, maximum energy: 300 mJ, pulse
duration time: 3-5 ns, wavelength: 1064 nm) was irradiated on the uncoated surface of the substrate, opposite the diamond film under test. Silicone grease containing fine MoS2 particles waspainted on the surface as an energy-absorbing layer. Thickness of the layer was controlled precisely using a thickness gage to 20 µm and confined rigidly by a 6-mm-thick silica plate with
anti-reflection coating. Impact wave was excited in the substrate by the laser breakdown of thegrease. The beam diameter was controlled to be 1 mm by changing the defocusing distance. Laser energy was changed from 0.7 to 75 mJ. A heterodyne-type laser interferometer (He-Ne laser,
532 nm) was used to measure the out-of-plane displacement of the stress wave at the epicenter ofthe source on the diamond film. In order to detect the spallation initiation by AE, a small broadband sensor (Valpey Fisher, pinducer) was glued on a side surface of the substrate. Sensor tosource distance was kept as 14.5 mm. Leaked laser light was used as a trigger signal for the laserinterferometer and AE system. Interferometer and AE signals were digitized and stored in ahigh-resolution digital oscilloscope at sampling interval of 0.4 ns with sampling points of 8192.
Output of the AE sensor (pinducer) was directly fed to the digital oscilloscope.

Fig. 2 Experimental setup for laser spallation and AE monitoring.
3. Results and Discussion
3.1 Indentation test
We observed Hertzian ring crack generation during the indentation test. The number of ringcracks increased with maximum indentation load Fmax and expanded outward into area aroundthe first ring crack. Figure 3 shows the indentations produced by the test of Fmax: 98 N, and theindentation force F vs. indentation depth h with AE timing. We detected frequent AE signals
from the Mode-II fracture (open triangles). These AE signals were, however, considered to be
the contact noise. Only one Mode-I AE, from the ring crack was detected as indicated by solid
triangle at 39.1 N. The number of the Mode-I AE signals increased with Fmax. Here we focused
our attentions on the load to cause the first ring crack in order to evaluate the tensile strength ofthe diamond film. Table 1 shows the load at which the first Mode-I AE was detected. The load
was almost the same, except that (127.4 N) for Fmax= 196 N. AE signals from the Mode-I ring121

crack at the load of 39.1 N for Fmax= 98 N are shown in Fig. 4. Polarities of first So mode waves
were all positive, and indicate the initiation of Mode-I Hertzian crack.

Fig. 3 Laser microscopic image of indentation (a) and indentation curve with AE timings (b), forthe test of Fmax = 98 N.

Fig. 4 AE waves from the Mode-I Hertz crack during the indentation test of Fmax = 98 N.

Next we calculated the film stress at the load of 39.1 N by FEM. Figure 5 (left) shows theaxi-symmetric model for the FEM. We calculated the film stress for the indentation depth of 2.7µm at 39.1 N. Elastic properties of the same CVD-diamond measured by laser ultrasonic technique [13] were used. Figure 5 (right) shows the distribution of radial stress. The maximum tensile stress is computed as 7.1 GPa to the contact radius of 23 µm. This radius is consistent withthe measured radius in Fig. 3. Thus the tensile fracture strength of the CVD-diamond film wasestimated to be 7.1 GPa.
3.2 Laser spallation test
Preliminary test showed that the diamond film suffers spallation at laser energy of around 46
mJ. Figure 6 shows microscopic images of spallated area. Spallation occurred above laser energyof 46.1 mJ. We observed very small delamination of 0.3 mm diameter at 46.1 mJ, though not
clear in the photo. Delaminated circle expanded with laser energy. In order to estimate the adhesion strength correctly, we must determine the delamination initiation. Owing to the transparency
122

Fig. 5 Analysis model for FEM (left) and calculated stress distribution (right).

Fig. 6 Optical microscopic images of spallation.
of the polished diamond film, we can detect the spallation by eye inspection when a finite size
delamination occurs, however, much accurate detection technique is needed for non-transparent
and rough surface films.
We are now using two methods. One is waveform analysis of bulk waves detected by the interferometer. Figure 7 compares the outputs of laser interferometer for the case of film spallation(b) and no-spallation (a). Very clear waveform differences exist at the arrival of the first Pwaves. Wave oscillation of 28-33 ns period in (a) is due to the multi-reflection of the P-wave(1400 m/s) in the confined grease layer (20 µm thick). This oscillation disappears when spallation occurs. This is because the film spallation was induced by the first P-wave arrival and thesubsequent P-waves cannot be transmitted to the delaminated film.
We next estimated the critical interfacial stress to cause spallation. We detected out-of-plane
displacement of the stress wave at the epicenter on the substrate (without CVD diamond film).
Figure 8 shows the waveforms as a function of laser power from 4.8 to 46.1 mJ. At low energies
from 4.8 to 15 mJ, the compressive wave or down-shooting wave is the major component. The

123

expansion component appears above 35.5 mJ and increases with laser energy. Tensile stress ofthe expansion component is given by Eq. (1) [14].

(a) without spallation, showing oscillation; (b) with spallation, no oscillation.
Fig. 7 Bulk waves detected on the film by laser interferometer at the epicenter of oil breakdown.

Fig. 8 Change of first arrival bulk wave from oil breakdown, detected for SiC substrate without
the film.

σ = − ρV l∂u( t )∂t(1)

where ρ is the density of the medium and Vl denotes the velocity of the longitudinal wave. ρ and
Vl of sintered SiC are measured as 3150 kg/m3 and 11.80 km/s, respectively. The partial differentiation of displacement with respect to time defines the particle velocity of the longitudinal wave.
Since the thickness of the diamond film is negligibly small, the same calculation procedures
were applied to the diamond-film spallation experiments. Figure 9 shows the stresses calculated
for compression (filled circle) and expansion (open circle) waves. Here compressive stress was
designated by negative values. Tensile stress increased with increasing the laser energy. Data
scattering appears to be due to the difficulty in determination of the particle velocities. Three
data points represent the case of spallation. From this, the interfacial adhesive strength of the
diamond film is estimated as 222 MPa.
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Fig. 9 Interfacial stresses generated by impulse waves due to the oil-breakdown.
In order to determine the spallation initiation accurately, we utilized AE monitoring. We previously detected the impact–induced cracks in the PMMA hit by flying objects [15]. For suchcase, weak AE by fracture is hidden by strong impact induced AE and could not be detected by
the AE sensor mounted on the plate surface. However, it is feasible to detect the fracture-induced
weak AE by monitoring the in-plane motion of the So-Lamb waves using the sensors mounted
on the edge plane of the target [15]. We used this technique and detected Lamb-wave AE fromthe spallation using a broadband (1 to 25 MHz) pinducer mounted on the edge (see Fig. 2).

Fig. 10 Detected Lamb-wave AE signals (top row) and their Fourier transform (bottom row) as afunction of laser energy. Two left waves: non-spallation; Two right waves: spallation.
Figure 10 (top row) shows detected Lamb-wave AE signals as a function of laser energy. In
this sample, the diamond film started to spall at 69 mJ, but it did not spall at 67 mJ. Two left
waves represent non-spallation cases, while the right two waves are for the spallation. The corresponding frequency spectra of the waves are given in the bottom row. Significant signal
125

power exists at 2 – 6 MHz. When no spallation occurs, signal power at 13 – 17 MHz is strong.
The peak height at 16 MHz is comparable to that at 2.5 MHz for 37 mJ pulse. When the spallation occurs, the high frequency peak diminishes rapidly. At both 69 and 75 mJ tests, 14-MHz
peak decreases almost to the noise level. Large reduction of the ratio of signal power at 13 – 17
MHz range to that of 2 – 6 MHz is a good indicator of the presence of spallation.
4. Conclusion
We utilized AE for detecting and classifying the film fractures during Rockwell microindentation and laser spallation test of CVD-diamond films deposited on SiC plate. Results are
summarized below:
1) Mode-I Hertzian ring crack during Rockwell indentation was detected by advanced AE system utilizing four AE sensors around the indenter. Critical indentation load, indentation depth
and ring crack diameter to cause the first Hertz crack were determined and submitted to FEM
analysis. Tensile strength of the diamond film was estimated to be 7.1 GPa.
2) Laser spallation technique can determine the absolute interfacial strength of CVD-diamondfilm on SiC substrate by measuring the fast out-of-plane displacement of the expansion stresswave. Adhesion strength of the test sample was estimated as 222 MPa.
3) In order to detect the spallation initiation, we monitored in-plane motion of the So-mode
Lamb wave by broadband small AE sensor (pinducer) mounted on the distal planes. The sensor detected the AE from spallation through the loss of high-frequency (13-17 MHz) components.
Acknowledgement
This research was conducted as part of the High-Technology Research Center Program andthe Center of Excellence (COE) Program, funded by the Ministry of Education, Culture, Sports,
Science and Technology of Japan.
References[1] H.E. Powell and F.W. Preston, J. Amer. Cerami. Soc., 28, 6, (1945) 145.
[2] H. Kagawa, M. Ichikawa, T. Takamatsu and H. Kuwano, Trans. J. Soc. Mech. Eng. A60, 570(1994), 396.
[3] K. Hayashi and Y. Fang, Trans. J. Soc. Mech. Eng. A60, 617 (1998), 30.
[4] M. Shiwa, E.R. Weppelmann, A. Bendeli, M.V. Swain, D. Munz and T. Kishi, Sur. Coat.
Technol., 68/69 (1994), 598.
[5] Y. Tsukamoto, H. Kuroda, A. Sato and H. Yamaguchi, Thin Solid Films, 213 (1992), 220.
[6] P.A. Steinmann and H.E. Hintermann, J. Vac. Sci. Technol., A3 (6) (1985), 2394.
[7] C. Julia-Schmutz and H.E. Hintermann, Sur. Coat. Technol., 48 (1991) 1.
[8] P.J. Burnett and D.S. Rickerby: Thin Solid Films 157 (1998) 233.
[9] C. Julia-Schmutz and H.E. Hintermann: Sur & Coat. Tech. 48 (1991) 1.
[10] P. C. Jindal, D. T. Quinto and G. J. Wolfe: Thin Solid Films 154 (1987) 361.
[11] R. Ikeda, S. Tasaka, H. Cho and M. Takemoto, Jpn. J. Appl. Phys, to be published.
[12] A. Yonezu, T. Ogawa and M. Takemoto, Progress in Acoustic Emission XI (2002) 86-93.
[13] R. Ikeda, Y. Hayashi and M. Takemoto, J. Acoustic Emission 21 (2003) 131.
[14] V. Gupta, J. Yuan and D. Martinez, Surf. Eng. Struc. Ceram. 76 (1993) 305.
[15] H. Tanabe, Y. Mizutani and M. Takemoto, J. Jpn. Soc. Non destructive Inspection, 53 (4)(2004), 230.
126

COMPOSITES FROM PIEZOELECTRIC FIBERS AS SENSORS AND
EMITTERS FOR ACOUSTIC APPLICATIONS*ANDREAS J. BRUNNER, MICHEL BARBEZAT, PETER FLÜELER
and CHRISTIAN HUBER
Polymers/Composites Laboratory, EMPA, Materials Science and Technology,Überlandstrasse 129, CH-8600 Dübendorf, Switzerland
Abstract
Active Fiber Composites (AFC) manufactured at EMPA with commercially available piezoelectric ceramic fibers have been investigated for their potential use as sensors and emitters invarious applications. The AFC elements have been evaluated with methods derived from thecharacterization of ultrasonic transducers and acoustic emission (AE) sensors. Electrical impedance measurements as a function of frequency yielded indications of the frequency ranges of theAFC. Their planar design and lower mass, combined with their intrinsic conformability makeAFC elements seem well suited for integrity monitoring of composite structures with AE,
acousto-ultrasonics, or low-frequency ultrasonic methods.
Keywords: active fiber composites, acoustic emission, sensor characterization, acoustoultrasonic, ultrasonic
1. Introduction
Active Fiber Composite (AFC) elements initially developed by Bent and Hagood [1] consistof piezoelectric lead-zirconate-titanate (PZT) fibers, aligned uniaxially between two foils, eachwith two sets of electrodes arranged as interdigitated electrical contacts. Analogous to thin PZTpatches, they function as actuators and sensors [2, 3]. The PZT-fibers of the AFC elements arepolarized along the fiber length (piezoelectric strain constant d33), i.e. contracting and extending
in length, respectively, if a voltage is applied to the electrodes. Conventional planar piezoelectrictransducers for AE or acousto-ultrasonic applications with which the AFC elements are compared are usually polarized in the direction normal to their plane (piezoelectric strain constantd31). The absolute value of d33 is about twice that of d31 [3]. The comparison between AFC andconventional PZT-transducers is based on established methods for the characterization of piezoelectric ultrasonic transducers or for the verification of AE sensors.
2. Experimental
The types of AFC elements manufactured at EMPA and considered in the present paper havean active area of 20 mm (width), a total width of 40 mm, fiber lengths (equal to length of activearea) between about 8 and 45 mm, an electrode width of 200 µm, and electrode spacing between
400 and 1600 µm (Fig. 1). The fibers are made from PZT and commercially available, the elec_____* Based on presentations at the Meeting of the German, Austrian and Swiss Societies for Nondestructive Testing, Salzburg (Austria), May 17, 2004, and at the 26th European Conference on
Acoustic Emission Testing, Berlin (Germany), European Working Group on Acoustic Emission(EWGAE), September 16, 2004.
J. Acoustic Emission, 22 (2004)127© 2004 Acoustic Emission Grouptrodes are screen-printed on Kapton foil with a process developed at EMPA. The thickness of the
AFC elements is around 300 µm. Basic manufacturing and processing details of the AFC aredescribed in [3].

Fig. 1 Photograph of Active Fiber Composite (AFC) element manufactured at the authors laboratory. The length of the piezoelectric fibers is about 31 mm, electrode spacing is 0.9 mm andelectrode width about 0.2 mm. The piezoelectric fibers are contacted with one set of interdigitated electrodes on both, top and bottom sides.
Electrical impedance measurements were performed on an impedance analyzer (HP 4194A)
in the frequency range from 100 Hz to 2000 kHz. The devices were connected through a test
fixture (HP 16074D) and short wires. A total of 400 data points were recorded over the frequency range up to 2000 kHz, and the data converted to a text-file for further analysis.
A commercial AE sensor tester (VST, Vallen Systeme GmbH) consisting of a frequency
generator (Agilent 33120A), ultrasonic transducers (Panametrics V-101 or V-103), AE equipment (Vallen AMSY-4) with preamplifier (Vallen AEP4) and dedicated software was used toverify the resonant sensor response characteristics (Dunegan Engineering Corporation Inc.,
DECI, SE150-M or Physical Acoustics Corp, PAC, R-15). Devices were tested in the frequency
range between 5 kHz and 500 kHz, scan step size as 2 kHz, and the signals band-pass filtered
between 3 kHz and 3000 kHz, and recorded with an offset of –135 dB, –155 dB, or –175 dB.
Various types of coupling to the ultrasonic transducer were used: face-to-face, face-to-back (device mounted on an Aluminum plate), face-to-wedge (device mounted on a polymer-wedge
made from polymethyl-methacrylate, PMMA), with an inclination of the wedge of 40° with respect to the horizontal base). The PMMA-wedge was used to enhance the in-plane component of
the excitation, the longitudinal wave speed of PMMA (2760 m/s ± 25 m/s) yielding a limiting
angle for total reflection around 40°.
Hsu-Nielsen sources, i.e., pencil-lead breaks (0.5-mm diameter, hardness rating HB) wereused to simulate AE signals at various distances from the AFC element on a PMMA plate to

128

up

generate polar plots of the axial sensitivity characteristic at fixed distances, or to generate attenuation curves over certain distance ranges.
Alternatively, so-called auto-calibration pulses generated by the AE equipment (Vallen
AMS-3), used to record all AE data except those from the sensor tester with amplitude and duration dials set to maximum and minimum (typically yielding 60 V peak-to-peak and about 1 µs
duration), respectively, were used as stimulus for AFC elements and AE sensors (DECI SE150M or SE1000-H flat response displacement sensor with small aperture). All simulated AE signals
were recorded by either AFC elements or AE sensors connected to an AE equipment (AMS-3),
typically with 34 dB pre-amplifier gain and around 40 dBAE detection threshold (1 µV = 0 dBAE).
Full waveform signals were recorded for some of these tests yielding frequency spectra via FastFourier-Transformation (FFT) implemented in the transient waveform analysis package (Vallen
VisualTR).
Silicone-free, temperature resistant vacuum grease was used as coupling agent in all interfaces between transducers, test plates, AE sensors, and AFC elements. Contact pressure was either supplied by a special fixture (spring-loaded plates between which transducer and AE sensor
or AFC were put), dead weights (AE sensors on test plates), or scotch tape (AFC elements on
test plates).
For all measurements, one set of the interdigitated electrodes on each side of the AFC wasconnected to mass (ground) of the preamplifier or impedance analyzer, while the other provided
the signal level. Typical AE instrument (AMS-3) settings were a threshold of 40 dBAE or 31
dBAE, preamplifier gain of 34 dB, and a rearm-time of 3.28 ms. Signals were band-pass filteredeither between 30 kHz and 1000 kHz or between 95 kHz and 1000 kHz.
3. Results
3.1 Impedance measurements
Figure 2 shows the electrical impedance determined from the applied voltage and current in
the form of (a) the absolute value |Z|, and (b) the phase angle, both as a function of frequency for
AE sensor and AFC element. Typically, the absolute value is rapidly decreasing with increasing
frequency (note the logarithmic scale), except for certain frequencies. The phase angle plot
clearly indicates these frequencies. Figure 3 shows the impedance phase angle as a function of
frequency for a range of AFC elements with different lengths. Depending on the fiber length of
the AFC elements, the main peak of the phase angle versus frequency lies in the range between
about 50 and 280 kHz. The typical width of these peaks is a few tens of kHz.
3.2 Sensor tester measurements
Figure 4 shows the sensitivity curves obtained with the commercial AE sensor tester (VST)for AE sensors and AFC, again as a function of frequency between 5 kHz and 500 kHz (transducer V-101). AE sensor and AFC were tested both, coupled directly to the transducer (AFCsupported on the back with an aluminum plate), and coupled to the backside of an Aluminum
plate sitting on top of the transducer. Figure 5 shows the measurements for AE sensor (SE150M) and AFC when mounted on the slope of PMMA wedge with 40° inclination with its basal
plane coupled to the transducer in order to enhance the in-plane component of the excitation.
Both, face-to-face and face-to-back, the AFC element is clearly less sensitive than the commercial AE sensors. However, when mounted in the PMMA-wedge, the respective sensitivity becomes comparable (Fig. 5).
129

60

10'000'000

SE150-M #1012
SE150-M #510
AFC #80

1'000'000

20
phase angle [°]100'000¦Z¦

10'000
1'000

SE150-M #1012
SE150M #-510
AFC #80

40

0
-20
-40

100

-60

10

-80

1

-100
0

100'000

200'000
300'000
Frequency (Hz)400'0000

500'000

100'000

200'000

300'000

400'000

500'000

Frequency (Hz)Fig. 2 Electrical impedance measurements on impedance analyzer (HP4194A) as a function of
frequency from 100 Hz to 500 kHz for two AE sensors (SE150-M) and one AFC. (left) Absolutevalue of impedance |Z|; (right) Corresponding phase angle.

Fig. 3 The phase angle vs. frequency from 100 Hz to 500 kHz for a range of AFC elements withdifferent lengths of the piezoelectric fibers.
70'000

SE150-M #1012
R-15 #AA23
AFC #79

2'500

70'00050'000
1'500

40'00030'000

1'000

20'000500

10'000

SE150-M #1012
AFC #79
AFC #80

60'000

2'000
Sensitivity [µV]

Sensitivity [µV]

60'00050'000100'000

200'000

300'000

400'000

700
600
500

30'000400
300

20'000

200

0

0

800

40'000

10'000

0

900

100

0

500'000

0Frequency [Hz]100'000

200'000
300'000
Frequency [Hz]400'0000
500'000

Fig. 4 Sensor tester characterization (VST, with V-101) for AE and AFC sensors. Blue curves
scale on the left, red/purple curves on the right. (left) Face-to-face transducer coupling with AFC
on Al plate, (right) face-to-back coupling through Al plate (all scales linear, offset removed).

130

1'600

1'600

SE150-M #1012
AFC #79
AFC #125

1'400
1'200

1'200
Sensitivity [µV]

Sensitivity [µV]

SE150-M #1012
AFC #79
AFC #124

1'400

1'000
800

AFC fibers vertical

600

1'000
800

AFC fibers horizontal

600

400

400

200

200
0

0
0

100'000

200'000
300'000
Frequency [Hz]400'0000

500'000

100'000

200'000
300'000
Frequency [Hz]400'000

500'000

Fig. 5 Sensor tester characterization (VST, V-101), AE sensors and AFC coupled on a PMMA
wedge (see text for details). (left) AFC fibers oriented vertically, (right) AFC fibers oriented
horizontally on the inclined plane of the PMMA wedge.
3.3 Measurements with simulated AE
Figure 6 shows the fast Fourier transforms (FFT) of waveforms from simulated AE (HsuNielsen sources) applied at increasing distance from the center of the AFC element along a line
parallel to the orientation of the piezo-fibers. Frequency band-pass filtering between 30 and 1000
kHz has been used. The magnitude of the FFT at frequencies above about 100 kHz is increasingly attenuated relative to the main peak with increasing distance from the source.
Figure 7 shows the polar diagrams obtained from Hsu-Nielsen sources applied on a PMMA
plate. The values indicated in the diagram are maximum AE signal amplitude averages of at least
three pencil-lead breaks per polar angle performed by a single operator and recorded by the AE
sensor or AFC at 20 cm distance from the source location. Analogous polar diagrams (notshown) were obtained with the AFC recording AE signals generated by the auto-calibration
function of the AE equipment by applying transient voltage pulses to AE sensors (DECI SE150M or SE1000-H) coupled at 20 cm distance from the center of the AFC to the PMMA plate. The
maximum amplitude, and hence the shape of the polar diagram for the AFC element, are strongly
affected by the frequency filter that is applied. Frequency filtering between 100 and 1000 kHz
yields relatively low AE signal amplitudes and a strongly anisotropic shape of the polar diagram.
Frequency filtering between 30 and 1000 kHz, however, only yields a slight anisotropy. The polar diagram for the AE sensor essentially is isotropic and yields the same maximum AE signal
amplitudes for both types of frequency filter. It is noteworthy that the AFC element yields higher
maximum AE signal amplitudes than the AE sensor, if the 30 to 1000 kHz band-pass frequency
filter is applied.
The different shapes of the polar diagrams for the AFC are explained by the FFT in Fig. 8.
Shown are selected frequency spectra from FFT from Hsu-Nielsen sources on a PMMA plateand recorded with the AFC at 20 cm. Both orientations of the AFC, i.e., AFC fibers parallel (//)
and normal ( | ) to the source-AFC center line, frequency filtered between 30 kHz and 1000 kHz,as well as between 95 kHz and 1000 kHz are shown.

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10 cm distance// to piezofibers

20 cm distance// to piezofibers

30 cm distance// to piezofibers

40 cm distance// to piezofibers

Fig. 6 FFT (30 to 300 kHz range) of Hsu-Nielsen sources applied along the center of the AFC
parallel to the piezo-fibers on a PMMA plate and recorded with an AFC element (fiber lengtharound 31 mm) at various distance.
90
105
120

90

75
60

80

135

45

70

150

30

60
50

165

15

40
30

180

0

195

345

210

330
225

315
240

300
255

285
270

Fig. 7 Polar diagrams of maximum AE signal amplitudes from Hsu-Nielsen sources applied on
PMMA 20 cm from the AE sensor (SE150-M, circles) and the AFC (triangles) with its fibers
oriented along the 180°-to-0° axis. Open and closed symbols indicate 95 kHz to 1000 kHz and
30 kHz to 1000 kHz band-pass filters, respectively.
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AFC fibers ||filter 30 kHz

AFC fibers ( | )filter 30 kHz

AFC fibers ||Filter 95 kHz

AFC fibers ( | )Filter 95 kHz

Fig. 8 FFT spectra from AFC recorded waveforms generated by Hsu-Nielsen sources located 20
cm from the AFC on a PMMA plate. (left) AFC fibers parallel (//) and (right) normal to thesource-AFC center line ( | ); filter 30 kHz to 1000 kHz (top), filter 95 kHz to 1000 kHz (bottom).
Figure 9 shows attenuation curves obtained from simulated AE (Hsu-Nielsen sources) on a
PMMA plate as a function of distance from the center of AFC elements with different distances
between the interdigitated electrodes but similar width of the electrodes (about 200 µm). Even
though the anisotropy of the AFC elements with respect to simulated AE applied along a line
parallel and normal to the orientation of the piezo-fibers, respectively, yields a higher attenuationin the latter case, no significant dependence on electrode distance is observed, at least for therange of distances considered here (0.7 mm to 1.3 mm).
Finally, Fig. 10 shows selected waveforms and corresponding FFT spectra generated by applying auto-calibration pulses to the AFC and recording the emitted signals with an AE sensor(DECI SE1000-H) located on the PMMA plate 10 cm and 20 cm, respectively, from the center ofthe AFC. First, it can be noted that AFC elements act as emitters of transient mechanical waves
comparable to conventional PZT-based AE sensors, when excited by electric voltage pulses.
Second, the dominant contribution of the frequency spectrum of these waves coincides with thefrequency range of sensitivity of the AFC element. However, in the FFT recorded along a line
parallel to the orientation of the piezo-fibers, significant additional contributions above 100 kHz
appear that are largely absent in the FFT recorded normal to the orientation of the piezo-fibers.
4. Discussion
Both impedance and sensor tester measurements indicate that the AFC is sensitive to AE signals similar to the commercial resonant AE sensors. Most of the data shown in the present paper
133

Maximum AE Signal Amplitude [dBAE]

100

90

80

70

60
0

10

20

30

40

50

60

70

80

90

Distance from Center of Sensor [cm]Fig. 9 Maximum AE signal amplitudes of Hsu-Nielsen sources applied on a PMMA plate atincreasing distances from three AFC elements (fiber length ~31 mm) with varying electrode distance, triangles = 0.7 mm, circles = 0.9 mm, squares = 1.3 mm; Hsu-Nielsen sources applied
parallel (filled symbols), and normal (open symbols) to the piezo-fibers, respectively.
are from AFC elements with a fiber length of 31 mm. The dominant contributions in the FFT of
AFC elements lie at frequencies in the range between about 50 and 270 kHz with a typicalbandwidth on the order of a few tens of kHz. AFC elements can hence be regarded as planar AE
sensors for typical AE applications [7].
The face-to-face and back-to-face data from the sensor tester indicate that, in this set-up, theAFC is considerably less sensitive than AE sensors with a comparable resonance frequency.
However, when mounted on the PMMA wedge, AFC and AE sensor yield comparable sensitivity. The main difference between face-to-face and wedge mounting the devices is quite likely the
relative amount of out-of-plane to in-plane excitation. The pronounced increase in sensitivity
observed for the AFC when switching from face-to-face to wedge mounting is tentatively attributed to the d33-polarization of the piezoelectric fibers. This is consistent with the effect of the d31polarization of the AE sensor, yielding a clearly lower sensitivity on the PMMA wedge thanmounted face-to-face on the transducer.
The PMMA wedge used to compare the sensitivity of AFC and AE sensors with respect toin-plane excitation clearly has to be regarded as an empirical tool for limited comparative purposes. Developing this approach into a standardized method for sensor response characterization
would require detailed investigations, among others, e.g., of the influence of the geometry andsize, as well as possible, multiple reflections, and mode conversions.
The AFC sensor tester measurements on the PMMA wedge (Fig. 4) also provide an explanation for the observed anisotropy in the polar diagrams on a PMMA plate, when the AE signals
are band-pass filtered between 95 kHz and 1000 kHz (Fig. 5). Changing the orientation of the
AFC fibers on the PMMA wedge from vertical to horizontal clearly reduces the intensity of thepeaks observed at about 130, 210, and 310 kHz. This yields a sensitivity differing for signals
arriving parallel and normal, respectively, to the orientation of the AFC fibers. If the signals are13410cm distance// to piezofibers

10 cm distance| to piezofibers

20 cm distance// to piezofibers

20 cm distance| to piezofibers

Fig. 10 Waveforms and corresponding FFT of simulated AE signals emitted by an AFC element(fiber length ~31 mm) on a PMMA plate with pulses (~60 V peak-to-peak, 1 µs duration) andrecorded with an AE sensor (DECI SE-1000H), located at 10 (top) and 20 cm (bottom), andalong a line parallel (left) and normal (right) to the orientation of the piezo-fibers, respectively.
135

band-pass filtered between 30 kHz and 1000 kHz, where the intensity of the dominant sensitivity
peak around 50 kHz changes much less with AFC fiber orientation (Fig. 8), this anisotropy ishardly noticeable.
In measurements of the so-called “free” strain of AFC elements operated as actuators, increasing electrode distance had yielded increasing free strain, presumably due to the more homogeneous electric field between the electrodes [3]. The sensor characteristic, on the other hand,does not seem to strongly depend on electrode distance (Fig. 9).
The waveforms and FFT spectra shown in Fig. 10 indicate that the AFC, when acousticallycoupled to a solid and excited by transient voltage pulses (using the auto-calibration feature of
the AMS-3 equipment) emits transient elastic waves into the solid. The emission seems to
mainly occur in the frequency range around 50 kHz, i.e., the same range where the maximum
sensitivity of this AFC element was observed. Recording the AFC emissions along the fiber axisof the AFC at 20 cm distance also yields noticeable frequency components above 100 kHz thatare missing when emissions normal to the AFC fiber axis are recorded. Therefore, the anisotropy
noted in the sensitivity of the AFC in the frequency range above 100 kHz also occurs when the
AFC is used as acousto-ultrasonic emitter. At frequencies below 100 kHz, this effect is muchless pronounced. Of course, the PMMA plate used in the experiment induces some frequencydependent attenuation, and the impedance of the AFC will change when coupled to a solid. Possible explanations for the observed anisotropy hence are frequency dependent wave propagationin the PMMA plate used as substrate, coupling among the different polarization coefficients ofthe piezoelectric fibers, as well as in the epoxy matrix used to embed the piezoelectric fibers.
A direct comparison of AFC with other types of planar, piezoelectric AE sensors, e.g., PZTwafers or made from piezoelectric polymers such as poly-vinylidene-fluoride (PVDF) [4, 5],
however, is still lacking. On the other hand, some specimens of AFC elements fully integrated
into glass-fiber reinforced laminates were manufactured and successfully tested, both as sensors
for AE signals and acousto-ultrasonic (AU) emitters [6].
Overall, the characterization with various methods indicates that AFC elements roughly perform similar to low-frequency AE sensors, e.g., such as those used for leak testing in pipelines.
Areas of application where the specific properties of AFC elements show potential for replacing
conventional PZT-based transducers, such as AE sensors, are discussed in [7].
5. Summary and Outlook
Commercial 150 kHz resonant AE sensors have been compared with new Active Fiber Composite (AFC) elements manufactured from piezoelectric fibers. Properties such as AE signal sensitivity and others have been compared for a range of characterization methods. It has beenshown that the AFC elements investigated in this paper essentially function as AE sensors. Bandpass filtering eliminating the frequency range below 95 kHz, on one hand reduces the sensitivityof the AFC but, on the other, yields an anisotropic sensitivity characteristic. Due to their conformability, both, parallel and normal to the fiber direction, these AFC elements are promisingfor continuous AE monitoring for, e.g., leak detection in pipelines, at least for pipe diameters >70 mm. The process for manufacturing the AFC is compatible with that used for composite
laminates and integration of these thin, planar, light-weight AFC elements into glass- and carbon-fiber laminates has been achieved. Therefore, continuous health monitoring of composite
136structures with AE testing or periodic inspection with AU or low-frequency ultrasonics using
AFC elements does seem feasible.
Acknowledgments
The support of B. Schechinger, Institute of Structural Engineering, ETH Zürich for the sensor
tester measurements, of A. Belloli, Institute for Mechanical Systems, ETH Zürich, for the impedance measurements, of G. Rössler, Electronics/Measurement Laboratory and Center for Nondestructive Testing, EMPA, for characterization of the PMMA wedges, and of K. Ruf, Polymers/Composites Laboratory for providing special fixtures and PMMA wedges, as well as discussions with X. Kornmann, Polymers/Composites Laboratory, EMPA at the time (now withABB Research Ltd) and J. Neuenschwander, Electronics/Measurement Laboratory and Center
for Nondestructive Testing, EMPA, are gratefully acknowledged.
References[1] A. Bent, N.W. Hagood, “Piezoelectric fiber composites with interdigitated electrodes”, Journal of Intelligent Material Systems and Structures, 8 (11), 903-919, 1997.
[2] M.J. Sundaresan, A. Ghoshal, W.N. Martin, M.J. Schulz, “A continuous sensor to measureacoustic waves in plates”, Journal of Intelligent Material Systems and Structures, 11 (1), 41-56,
2001.
[3] X. Kornmann, C. Huber, M. Barbezat, A.J. Brunner, “Active fibre composites: sensors andactuators for smart composites & structures”, Proc. 11th European Conference on Composite
Materials, ECCM-11, Paper No. B047, 9 pp., 2004.
[4] R. Stiffler, E.G. Henneke, “The application of polyvinylidene fluoride as an acoustic emission transducer for fibrous composite materials”, Materials Evaluation, 41, 956-960, 1983.
[5] N.B. Spedding, “The effect of sensor geometry on the use of polyvinylidene fluoride as anacoustic emission sensor”, INSIGHT, 38 (1), 37-50, 1996.
[6] M. Barbezat, A.J. Brunner, M. Melnykowycz, X. Kornmann, Ch. Huber, P. Flüeler, “Integrated active fiber composite elements for structural health monitoring in glass fibre reinforced
laminates”, Proc. International Conference on High Performance Structures and Materials,
HPSM2004, WIT Press, Southampton, pp. 13-18, 2004.
[7] A.J. Brunner, M. Barbezat, Ch. Huber, P. Flüeler, “The potential of active fiber composites
made from piezoelectric fibres for actuating and sensing applications in structural health monitoring”, Materials and Structures / Matériaux et Constructions (submitted 2004).

137

PROCESSING OF AE SIGNALS IN DISPERSIVE MEDIAMICHAL BLAHACEK, ZDENEK PREVOROVSKY, MICHAL LANDA
Institute of Thermomechanics AS CR, Prague, Czech Republic
Abstract
Acoustic emission (AE) wave propagating in thin wall structure (plate, tube, vessel, etc.) is
substantially distorted during its pass from AE source to the transducer. AE signal processing israther difficult under such conditions (dispersion), and AE source characteristics (location, amplitude, energy, etc.) may be interpreted incorrectly, when signal distortion effects are not considered in the evaluation of source parameters. Wave dispersion effects, i.e. frequency dependence of wave mode velocity, represent the most serious problem in AE signal parameters evaluation. In the paper, construction of guided wave dispersion curves using experimental data is presented. The method, based on wavelet transform, is efficient tool in both isotropic and anisotropic materials, even when current theoretical predictions give no satisfactory results.
Keywords: dispersion curves, wavelet transformation, guided waves
1. Introduction
Most of ultrasonic NDT methods are based on monitoring of artificially generated and thenreceived longitudinal or transversal (shear) wave in material [1]. In contrast, the acoustic emission (AE) method uses detection of waves generated by internal AE source in the material, which
requires correct location and interpretation. Recently, the guided waves, i.e. waves created bymultiple echoes in a thin structure wall, are used in nondestructive testing [2]. The main advantage of guided wave applications is their possibility to detect defects in material on a long distance. The guided waves are dispersion waves, so the wave velocity depends on frequency. Neglecting of the dispersion effects in AE monitoring can lead to relevant mistakes in conclusion
about the presence of critical defects in tested structural part.
The application of guided waves in ultrasonic NDT and in evaluation of material properties
requires good knowledge of dominant wave modes propagating in a structure, which are characterized by the well known dispersion curves (mostly presented as the dependence of velocity on
frequency). To simplify the problem, measurements are usually done with respect to potentialityof one dominant mode wave generation. Signal filtration is also used to emphasize one guided
wave mode. Numerical algorithms for the computation of dispersion curves are available forboth homogenous plates and composite like structures. Material parameters are the input data insuch algorithms. It doesn't represent a problem in a case of homogenous plates manufactured
from well-known material, but it could be potential trouble in composite structures. There areoften many undetermined factors in composite structures (e.g. precise chemical composition of
fillers, stresses in matrix and fibers, etc.). Material parameters of such structure may vary significantly as a result of complicated manufacturing process. In such situations, the determination ofdispersion curves of basic guided modes (s0, a0) from experimental data is very useful and evennecessary.
The problem of dispersion curves computation from experimental data is not yet completely
solved, especially in anisotropic materials. However, two numerical methods are promising:J. Acoustic Emission, 22 (2004)

138©2004 Acoustic Emission Group1. The first method is based on 2-D Fourier transform utilization [5]. The problem of themethod is in necessity to record a lot of signals (64 at least, 128 better) from artificial wave
sources placed in well-defined points on the structure. Short distance between successive
sources (below 1 mm) is advantageous.
2. The second method is based on continuous wavelet transform application [6, 7]. Special
wavelets, e.g. Gabor or Morlet wavelet, have well-defined basic frequency. Continuous
wavelet transform amplify the frequency in digitized ultrasound wave record, which enables
dispersion curve computation of wave mode with highest amplitude.
The second method using continuous wavelet transform will be discussed in this paper. Glass
plates are used as a sample of thin-wall structure, as to compare dispersion curves obtained bytheoretical and experimental data. Theoretical dispersion curves of guided waves propagating inglass plate are well known.
2. Experiment
Laser generated ultrasound is most suitable method for obtaining experimental data. Main
advantage of the laser generated ultrasound method is good reproducibility of ultrasound waves[4]. Nd:YAG pulse laser was used to generate ultrasonic waves. The laser beam was split bysemi-permeable mirror. The first sub-beam has triggered digital oscilloscope, which recorded
signals from miniature piezoelectric transducers placed on a glass plate surface. The second subbeam excited acoustic wave in the plate. The experimental details are given in [4].
Two identical AE transducers were placed on opposite sides in the plate center. Using thistransducer configuration, along with accurate synchronization of oscilloscope channels enables
symmetric or anti-symmetric guided waves modes emphasis in recorded signals. Sum of bothrecorded signals results in signal with emphasized symmetrical modes, and the signal differenceemphasizes anti-symmetric modes of guided waves (see Fig. 1).
3. Continuous Wavelet Transform
Continuous wavelet transform (CWT) is a kind of time-frequency transform defined by theformulaWf (a, b) =∞* t − b 
(
)
Ψ
f
t

dt,
∫
a −∞
 a 

1

(1)

where a is scale parameter and b is time. Ψ is a wavelet and f is transformed function. Symbol *represents complex conjugate value. The wavelet transform is used to identify hidden signal
properties, such as small discontinuities, frequency changes and transient effects. Reciprocal
scale parameter a has a meaning of frequency in intuitive sense, but the wavelet Ψ is usually not
the periodic function, so it has no frequency in mathematical sense. Fortunately, there exist also
wavelets based on periodic functions with well-defined frequency.
Morlet wavelet (fig. 2) is an example of the periodic function based wavelet. It is given
by formula2

Ψ ( x) = e − x / 2 cos(5 x),(2)which represents product of attenuation term exp(-x2/2) and periodic term cos(5x).

139

If the function f is a digitised AE signal with sampling frequency F, then f(t) is real and thefunction is represented by its discrete values fn. In addition Morlet wavelet frequency expressedusing scale parameter a isFΨ (a) =

5
F.
2πa

(3)

Fig. 1 Acoustic waves with symmetric (top) or anti-symmetric (bottom) wave modes
emphasized. Each division = 10 mV and 50 µs.

Fig. 2 Morlet wavelet140

4. Computation of Dispersion Curves
Continuous wavelet transform Wf(a,b) of digitised signal using Morlet wavelet is a signal
with highlighted Fψ(a) frequency (see equation (3)). Because guided wave amplitude is mostly
rather high (especially the amplitude of first guided wave modes a0, s0), the waves arrivals arewell perceptible in CWT signals. Using guided-wave arrival time (detected in transformed signal) and AE wave source – transducer distance, the wave velocity is computed and point on dispersion curve with frequency Fψ(a) is known. Dispersion curve is then sampled by changing thescale parameter a.
Some potential troubles are connected with the algorithm application. The first problem is inwave-arrival time detection. The arrival time is given by corresponding wave packet maximum(see upper part of Fig. 3). But it is not always easy to determine the maximum. Solution of the
problem is in the use of Hilbert transform (bottom part of Fig. 3). That transform represents thesignal envelope where the wave packet maxima are evident. The second problem, illustrated inFig. 4, is interaction of guided-wave modes. First part of Fig. 4 shows transformation of signal
with anti-symmetric wave modes emphasized. Maximum, which corresponds to a0-wave mode,
is obvious. On the other hand, the maximum corresponding to s0-wave mode is overlapped by
the a0 maximum (bottom part of Fig. 4), even though the signal with symmetric modes emphasized was used. To solve the problem partially, filtration of original signal and/or the use of selected signal is suggested.
It is well known that the spectra of symmetric and anti-symmetric mode guided waves differ
substantially [8]. That property is illustrated in Fig. 5, where the normalized spectra of signals
with emphasized symmetric or anti-symmetric wave modes are plotted. Anti-symmetric guided
wave modes contain much more of low frequencies than the symmetric ones. Band-pass filtering
is often used to separate both symmetric and anti-symmetric wave modes. But this procedure isnot suitable for the above described construction algorithm of dispersion curves. To obtain thewhole dispersion curve, we need all frequencies contained in the signal. If set of plates with different thicknesses is available, a thicker plate looks better when we compute the dispersion
curves of symmetric wave modes. But the key factor is still proper maximum selection in thetransformed signal (see Fig. 4).
Outputs of the described algorithm – a0 and s0 wave modes dispersion curves in glass plate –are plotted in Figs. 6 and 7. The a0-wave mode dispersion curve is obtained quite easily (see Fig.
6). Difference between theoretical and computed dispersion curve is negligible. Small divergence may be caused by AE transducer size; there is no obvious effect of the source - transducer
distance. On the other hand, symmetric wave mode (s0) dispersion curve computation encountersrelatively tough problem (Fig. 7). Finally we used the 8-mm thick glass plate, because it is easier
to obtain the curve than in a case of 2-mm thick plate. No original signal filtration is applied.
Difference between theoretical and computed curve is now bigger, but the shape of curves issimilar. The results of described algorithm are perfect for higher frequencies (above 600 kHz), as
it was expected.
5. Computation of Dispersion Curves in Polymer Samples
To demonstrate wavelet algorithm potential and limits, data from tensile tests of polymer
samples was used as algorithm input data. 100 × 10 × 4 mm polymer samples were used in theexperiment. The samples were manufactured from pure polyethylene (PE), pure polypropylene
141

Fig. 3 Continuous wavelet transform of AE wave (Morlet wavelet is used) and the signal after
Hilbert transform. Time scale = 20 µs per division.

Fig. 4 CWT coefficients transformed using Hilbert transform. Original signal had antisymmetric or symmetric wave modes emphasized. Time scale = 20 µs per division.
(PP), from 50%/50% PE/PP blend and from blend of PE, PP and additive VISTALON. Tensile
tests of PP and its blends with PE and VISTALON have been recorded by digital camcorder.
Acoustic emission (AE) was monitored during tests by two transducers attached to test samples,allowing linear location of AE sources. The sources were recorded using transient recorder
ADAM-MAURER (Fig. 8c, d), AE parameters were also recorded during test by digital analyzer
DAKEL-XEDO (Fig. 8b)). Tested materials exhibit large localized plastic deformation – necking
142

Fig. 5 Spectra sum (thin line) and difference (thick line) of AE signals. The signals wererecorded from two transducers placed on opposite plate sides.

Fig. 6 a0-wave mode dispersion curve in 2 mm thick glass plate. Theoretical curve is plottedusing solid line, curve computed from experim. data is plotted using circles– reaching from tens to hundreds of percent before failure. Dispersion waves play significant rolein complex wave field observed in the samples, so the knowledge of dispersion curves improves
significantly conclusions about AE events and sources and finally about tested polymer sample
damage process.
143

Fig. 7 s0 wave mode dispersion curve in 8 mm thick glass plate. Theoretical curve is plottedusing solid line, curve computed from experim. data is plotted using circles
Described wavelet algorithm result, using AE signal from Fig. 8d as the algorithm input data,is shown in Fig. 9. Dispersion curve of wave mode with the highest amplitude was computed
only as this is tensile test configuration and AE transducer positions did not allow the emphasisof symmetric and anti-symmetric wave modes in the similar way as in Fig. 1. Moreover recorded
signals have background noise, although only the best signals (see Fig. 8) were utilized.
The difficulties (background noise, dispersion wave modes interaction) affect a0-wave mode
dispersion curve approximation (Fig. 9). There are visible some discontinuities and drapes in Fig.
9. However, despite really unfavorable experimental conditions, the wavelet algorithm gives important result applicable for damage-process investigation of polymer samples.
6. Conclusion
Dispersion curves of basic guided-waves modes (a0, s0) in glass plates were computed using
the experimental data. The computational algorithm is described, which is based on continuous
wavelet transform. Signals recorded during the experiments with laser-generated ultrasound wereused as input data in the algorithm. Advantages of laser-generated ultrasound are good reproducibility of the wave source and the exact source positioning, which are important conditions
for reliable algorithm application.
Some additional steps were done as to improve the computation of dispersion curves. The
first improvement of the wavelet-transform-based algorithm is in the Hilbert transform utilization. The guided-wave mode arrival time is much more evident in Hilbert-transformed signal.
The second step is the use of glass plates of different thickness. The reason of that is to increase
specific wave mode fraction in the complex signal. The same purpose had a special experimental
configuration using the pair of piezoelectric transducers placed on opposite sides at the center oftested plate. Such configuration along with precise synchronization of recorder channels enables
the emphasis of symmetric or anti-symmetric guided-waves modes in signal.

144

Fig. 8 PE/PP/VISTEON sample tensile test: a) diagram of loading force, b) AE activity
monitoring using RMS and two levels of counts, c), d) recorded AE events.

Fig. 9 a0-wave mode dispersion curve approximation in 4-mm thick PE/PP/VISTALON
polymer sample. Recorded AE event from Fig. 8d) was used as algorithm input.
145

Described CWT-based algorithm is a useful tool for the computation of dispersion curves ofmain guided-wave modes (a0, s0). The algorithm was tested using glass plates mainly, but it canbe used in other situations, e.g. in damage process investigation of polymer samples.
Acknowledgement
The present research is supported by the Grant Agency of Czech Republic under the grantNo. 201/04/2102 and grant No. 205/03/0071. The Czech Society for NDT is also acknowledged
for supporting participation of the first author at the conference.
References[1] J. Krautkrämer, H. Krautkrämer, Ultrasonic Testing of Materials, Springer-Verlag,
Heidelberg 1990.
[2] D.E. Chimenti, Guided Waves in Plates and Their Use in Material Characterization, Appl.
Mech. Rev. 50: 247-284, 1997.
[3] Z. Prevorovsky, M. Landa, M. Blahácek, D. Varchon, J. Rousseau, L. Ferry and D. Perreux.
Ultrasonic Scanning and AE of Composite Tubes Subjected to Multiaxial Loading, Ultrasonics, 36(1-5): 531-537, 1998.
[4] M. Blahácek, Z. Prevorovsky, Elastic wave attenuation in thin-wall structures. 8th European
conference on NDT, Barcelona 2002.
[5] D. Alleyne, P. Cawley, A two-dimensional Fourier transform method for the measurement ofpropagating multimode signals. J. Acoust. Soc. Am. 89(3): 1159-1167, 1991.
[6] H. Inoue, K. Kishimoto, T. Shibuya, Experimental Wavelet Analysis of Flexural Waves in
Beams. Experimental Mechanics, 36(3): 212-217, 1998.
[7] T. T. Wu, Y. Y. Chen, Wavelet Analysis of Laser-Generated Surface Waves in a Layered
Structure With Unbond Regions. Journal of Appl. Mechanics, 66: 507-513, 1999.
[8] S. Ziola, M. Gorman, Source location in thin plates using cross-correlation, Journal Acoustic
Soc. Am., 90(3), 1991.

146

BASIC PRINCIPLES OF ACOUSTIC EMISSION TOMOGRAPHYFRANK SCHUBERT
Fraunhofer-Institute for Nondestructive Evaluation (IZFP), Dresden, Germany
Abstract
The present paper describes the basic principles of acoustic emission (AE) tomography. Thismethod uses acoustic emission events as point sources and combines the usual iterative localization algorithm of AE testing with algorithms for travel time tomography like ART (algebraic
reconstruction technique). The procedure is equivalent to the solution of the generalized inverse
localization problem in locally isotropic heterogeneous media and leads to a new imaging technique where in addition to the source positions the volume of the specimen is visualized in termsof a locally varying wave speed distribution. It is shown by numerically obtained data sets thatthe algorithm leads to a more accurate localization of acoustic emission events and offers totallynew perspectives for acoustic emission imaging and for acoustic tomography in general.
1. Introduction and Outline
Localization algorithms in acoustic emission (AE) testing mostly use the assumption of ahomogeneous background medium with constant wave speed in order to determine the locationof AE events. However in practice, the structures under investigation are inhomogeneous inmany cases, i.e. wave speeds are changing in space and time due to heterogeneities of the microstructure (e.g. grains and pores), the effect of structural components (e.g. tendon ducts in concrete), and material changes caused by the damage mechanism itself (e.g. crack growth). These
heterogeneities limit the accuracy of source localization algorithms.
In order to overcome these drawbacks the usual localization algorithm of AE testing can be
combined with travel time tomography by using the AE events as acoustic point sources. In thiscontext a re-localization, i.e. an update of the current source positions, has to be performed after
each tomographic inversion resulting in an iterative procedure with alternating steps of source
localization and tomography. This method is in principle known from geophysics where earthquakes are located and used for tomographic imaging of the earth’s interior.
Section 2 first summarizes the fundamentals of iterative AE localization and describes how
the underlying equations can simply be generalised to heterogeneous media. Section 3 briefly
describes the different approaches of tomographic imaging with diffracting and non-diffracting
sources paying particular attention to algebraic reconstruction techniques (ART). Section 4shows how the two concepts of localization and travel-time tomography can be combined resulting in an iterative algorithm for acoustic emission tomography called AE-TOMO. In Section 5
numerical AE data obtained by the elastodynamic finite integration technique (EFIT) are used todemonstrate the physical soundness of the proposed method. It is further shown that the AETOMO approach offers totally new perspectives, not only for AE imaging but also for traditional
acoustic tomography since AE events can also be produced artificially at the outer surfaces of thespecimen under investigation (e.g. by pencil-lead breaks or hammer impacts). Finally an outlook
is given how the present algorithms could be improved by using further advanced tomographic
imaging techniques and how AE tomography can be verified experimentally.
J. Acoustic Emission, 22 (2004)147© 2004 Acoustic Emission Group2. Source Localization
Let us suppose we have i = 1,…,N sensors and the P-wave arrival times of a single AE event
have been determined by using an appropriate picking algorithm. In a first approximation thearrival time at sensor i is given by
S

t =
A
i

ri − rci(x − x ) + (y − y ) + (z − z ) + t
+t =
S
i

2

2

S
i

S
i

2

ci

,

(1)

where riS = (xiS, yiS, ziS) is the known position of sensor i and r = (x, y, z) is the unknown locationof the AE event to be determined by the localization algorithm. t represents the source time of
the AE event and is usually measured relative to a trigger level.
Eq. (1) is based on a simple straight ray model regarding the source-sensor travel path. In thiscontext ci is the mean effective wave speed along ray i. In most cases, AE localization is done
under the assumption of a homogeneous background medium and thus, ci = c = constant for all
rays, i = 1,…,N. On the other hand, in a heterogeneous medium the effective wave speed can bedifferent from one ray to another, i.e. ci ≠ cj for i ≠ j, in general.
In Eq. (1) we have four unknowns, namely the three source coordinates x, y, z and the source
time t. Thus, at least four different sensor arrival times are needed to solve the underlying systemof equations. Since Eq. (1) represents a nonlinear system of equations, a closed analytical solution is not available in general and thus, it has to be solved by an iterative method.
For that purpose we start with some initial values x0, y0, z0, t0 and suppose that the effective
wave speeds ci are all known. By using the same straight ray model as explained above we can
now calculate the theoretical arrival times t iA, 0 using Eq. (1). In general, these theoretical times
will be different from the measured arrival times, TiA. From the differences ∆tiA = TiA − t iA, 0 , thecorrection values for the next iteration ∆x, ∆y, ∆z, ∆t can be obtained. Since the measured arrival
times can be written as a function of x, y , z, and t,

Ti A = f i ( x, y, z , t ) = t iA,0 + ∆t iA = t iA,0 +

∂f i
∂f
∂f
∂f
∆x + i ∆y + i ∆z + i ∆t
∂x
∂y
∂z
∂t

,

(2)

the arrival time difference ∆tiA is expressed as total differential of fi. In matrix form we have ∆t1A   ∂∂fx1
 
 .   .
 . = .
  .   .
 A   ∂f N
 ∆t N   ∂x

∂f1
∂y

∂f1
∂z

.
.
.

.
.
.

∂f N
∂y

∂f N
∂z


  ∆x .     ∆y . ⋅ ∆z.   ∆t∂f N 
  
∂t 
∂f1
∂t

,

(3)

or in short,
∆t

A

=

F

⋅ ∆s

,

(4)

where ∆t and F are known while ∆s is unknown. If the number of sensors is N = 4, the solutionof Eq. (4) is well-defined, namely−1A∆s = F ⋅ ∆t
.
(5)
A

148

If N > 4, the problem is over-determined. In this case a least-square approximation with regard toarrival time differences is given by the normal equation,(T∆s = F ⋅ F) ⋅ F ⋅ ∆t
−1

T

A

.

(6)

Using the result of Eq. (6), the update of source coordinates and source time from iteration 0 to
iteration 1, or more general from iteration k to k+1 can now be expressed asx ( k +1) = x ( k ) + R x ∆xy ( k +1) = y ( k ) + R y ∆yz ( k +1) = z ( k ) + R z ∆z,(7)t ( k +1) = t ( k ) + Rt ∆twhere the Rj’s with 0 ≤ Rj ≤ 1 and j = x, y, z, t are relaxation parameters. In order to ensure convergence of the iterative method, values of R ≈ 0.1 are typically used.

3. Acoustic Tomography

In acoustic tomography, data collected from sending acoustic waves through an object atmany different angles are used to compute an image of changes of a physical quantity within theobject under investigation (e.g. attenuation or wave speed in transmission tomography or acoustic impedance mismatch in reflection tomography). In traditional acoustic tomography the soundwaves are passed from a series of sources to receivers at various locations around the specimen(Fig. 1).

Fig. 1 Source/receiver geometry for traditional acoustic tomography.
Usually each transducer is used as both, source and receiver, resulting in at most N2 ray paths(exact number depends on transducer configuration and operation mode (reflection or transmission)) if the number of transducers is N. In order to ensure a high depth of ray coverage, a large
number of evenly distributed transducers are necessary. For example, the travel times between
each source and receiver are measured. In this case the resulting tomographic images represent
the locally varying wave speed distribution inside the specimen. Alternatively, the amplitudes of
the transmitted time-domain signals at the receivers can be measured and the variations in attenuation may be reconstructed. The latter method, however, makes high demands on the trans149

ducer coupling conditions. Another possibility is the measurement of reflected signals and thereconstruction of the acoustic impedance mismatch between matrix and scatterers (“reflection
tomography”).
Mathematically, there are two major classes of tomographic reconstruction techniques [1].
One class, the transform based methods are using the Fourier-slice theorem for non-diffracting
sources and the Fourier-diffraction theorem for diffracting sources, respectively. These methods
are fast but have the restriction that the data must be acquired on evenly spaced sets of straightrays (“projections”).
A second type of methods employs iterative procedures to reconstruct an image. The mostwidely used of these methods is called algebraic reconstruction technique (ART). Other related
iterative techniques are SIRT (simultaneous iterative reconstruction technique) and SART (simultaneous algebraic reconstruction technique). These techniques are less efficient than transform-based methods but they have several advantages. They can be used with irregular samplinggeometries, incomplete data sets, and may incorporate curved ray paths. In recent years powerful
ray-tracing algorithms have been developed correcting for both, diffraction and refraction of the
wave field. In the present paper, a transmission ART algorithm with straight ray paths has beenused in a first step due to its simplicity and its potential for further ray-tracing enhancements asmentioned above. This implies by no means that ART represents the best method for the problem
described in this paper.
The ART algorithm adjusts the estimated slowness values, sij = 1/cij, of the discrete tomography cells (i,j) in a systematic fashion until the computed arrival times, tpA, for ray p (p = 1,…,Np)
match the measured arrival times, TpA. For that purpose, the path lengths, lijp, of each ray p intomography cell (i,j) and the computed arrival times for iteration k are used to obtain a new estimate of the slowness for iteration k+1:
s ij( k +1) = s ij( k ) + R ⋅ ∆sij ,(8)with∆sij = (T pA − t pA )l ijp

∑ (l )

p 2
ij

,

(9)

i, j

and

t pA = ∑ sij( k ) l ijp

.

(10)

i, j

The summation in Eqs. (9) and (10) is performed over all tomography cells (i,j) passed by ray p.
R is a relaxation parameter used to improve stability and convergence of the method. Small values of R between 0.01 und 0.1 are typical and provide some filtering behavior.
ART makes the slowness adjustment on a ray-by-ray basis [2]. In contrast to that, the SIRT
method uses an average correction for all rays applied to each tomography cell [3], but was not
employed here.
4. Acoustic Emission Tomography

With the basic principles described in the previous two sections it is now easy to depict theconcept of AE tomography using AE events inside the specimen as tomographic point sources(Fig. 2). Only sensors are needed at the outer surfaces of the specimen. Naturally, a high depth of150

ray coverage is reached since the number of AE events typically lies in the range of some hundreds or thousands.

Fig. 2 Source/receiver geometry for AE tomography.
We start with an initial guess of the slowness distribution, typically a homogeneous medium
with constant wave speed (step 0). In step 1 we perform an initial localization of the AE sources
using the method described in Section 2. The initial source locations are then used to perform an
ART tomography as described in Section 3 (step 2). The result is an updated heterogeneousmodel with locally varying slowness and wave speed, respectively.
This heterogeneous model can now be used to perform a re-localization of the AE sources
using different effective wave speeds for each ray (step 1 again). In a further step the modified
and improved source positions permit an improved tomography result (step 2 again) which inturn leads to better source positions (step 1) and so on (step 2 ↔ step 1).
This iterative procedure is repeated until the squares of arrival-time differences between
measured and computed times (residues) reach a minimum or a predefined accuracy level. The
procedure is equivalent to the solution of the generalized inverse localization problem in locallyisotropic heterogeneous media and leads to a new imaging technique where in addition to thesource positions the volume of the specimen is visualized in terms of a locally varying wave
speed distribution.
While in traditional acoustic tomography the number of available ray paths and thus, thedepth of coverage is limited by the number of actuators and receivers, in AE tomography hundreds or thousands of AE events are typically available. Therefore, a relatively small number of
sensors, e.g. N = 8 or less, can be compensated by picking a correspondingly larger number ofAE events. Of course, the locations of AE events are usually restricted to some active areas likedamage zones leading to a more or less non-uniform coverage of rays. However, the large number of rays should also compensate for this inhomogeneity to some extent. Moreover, imaging of
the immediate vicinity of damage zones is of high practical interest and in these zones, an extremely high depth of ray coverage and thus, a high spatial resolution of the tomographic image
should be achievable.

151Another important advantage of AE tomography compared to traditional AE testing is connected with the use of “unwanted” AE events. For example in an aircraft usually the majority of
AE events is produced by sources of interference like joints and great effort is required to separate these unwanted signals from wanted signals generated by cracks or other defects. In AE tomography, these unwanted sources can also be used for tomographic imaging provided that they
can also be treated as transient point sources. While traditional AE analysis provides information
about the location of active AE regions, AE tomography visualizes both, active and non-active
regions of the specimen. Therefore, acoustically “passive” defects in the structure can also be
identified in principle.
The list of advantages of AE tomography as described above is by no means complete. Since
AE events can also be produced artificially, e.g. by pencil-lead breaks or hammer impacts at thesurface of a specimen, new paradigms in acoustic NDT tomography in general are imaginable as
explained in somewhat more detail in the outlook.
At the end of this section it is important to point out that AE tomography represents a purely
algorithmic extension of traditional AE analysis using exactly the same raw data, i.e. no additional expenses concerning data acquisition are necessary in general.
5. AE Tomography with Numerical Data

In order to demonstrate the physical soundness of AE tomography, numerical AE data sets
calculated by the elastodynamic finite integration technique (EFIT, [4]) were used. For that purpose, the cross-section of an existing concrete specimen (440 × 440 mm2) with steel reinforcement and tendon duct was chosen (see Fig. 2 and [5]). The specimen was used for fatigue tests
and was assigned for AE measurements as well.
The cross section in Fig. 2 shows four steel reinforcement bars with diameters of 22 mm in
the corners (cP = 5900 m/s, cS = 3200 m/s, ρ = 7820 kg/m3) and an ungrouted polyethylene tendon duct with inner diameter of 100 mm and a wall thickness of 3 mm in the middle of the model(cP = 2300 m/s, cS = 1200 m/s, ρ = 950 kg/m3). 16 sensors were placed at the outer surfaces ofthe specimen using a distance of 11 cm to each other. In this first step the concrete matrix wasapproximated as homogeneous background medium (cP = 3950 m/s, cS = 2250 m/s, ρ =2050 kg/m3), the sensors as point detectors. Moreover idealized isotropic AE sources were realized, generating P-waves only. As shown in [5] these simplifying assumptions can easily be
dropped and more realistic models including aggregates, pores, grouted tendon ducts filled withmortar and steel strands as well as AE sources with more realistic crack mechanisms can be
simulated in further investigations but are omitted here.
A total of 40 AE events with random locations inside the model (but restricted to the concrete
matrix) were calculated by the numerical EFIT code. Figure 3 exemplary shows the wave front
snapshots of six AE events taken at a time of 25.3 µs after source excitation in each case. The
pictures clearly show the interaction of the pressure waves with tendon duct, steel reinforcement,
and outer surfaces of the specimen, respectively. The normal components of particle velocity
were calculated at the individual sensor positions shown in Fig. 2. After that, the first-arrival
times of the time-domain signals were determined by using an automated picking algorithm
based on the Hinkley criterion [6]. These arrival times then served as input for the AE tomography algorithm called AE-TOMO.
152

The ray coverage due to the 40 AE sources is shown in Fig. 4. A total of 40 × 16 = 640 rays
was used for AE tomography. Due to the random locations of the AE events a more or less uniform coverage of the model was reached. Only the corners are not covered. It can also be seenthat the steel cable in the bottom left corner is covered less than the remaining three cables due toa lower density of AE events in this region.
Since the locations of all AE events in the simulation are exactly known, the accuracy of the
traditional localization algorithm as described in Section 2 can be evaluated and compared with
the AE-TOMO algorithm. Since the traditional algorithm uses a homogeneous background medium, the results strongly depend on the chosen P-wave velocity of the matrix as shown in Fig. 5.
In this case, a minimum source location error of ± 2 mm is reached at cP ≈ 3930 m/s.

Fig. 3 Wave front snapshots of six different AE events.

Fig. 4 Ray coverage of the model according to 40 AE events and 16 sensors.
153If the mean effective wave speed varies in the range of a few percent – which is the typicalinaccuracy in experimental measurements – the mean error of source location increases by a few
hundred percent as can be seen in Fig. 5. Deviations of only 3-4% in acoustic P-wave speed (≈3930 ± 150 m/s) lead to an increase of source location error by 300-500% (from 2 mm at theminimum up to 6 and 10 mm, respectively). In order to avoid this problem, the wave speed could
be incorporated as a further unknown into the traditional localization algorithm. However, ourinvestigations revealed that in this case the minimum error of source location amounts to about ±4.5 mm which is significantly larger than that obtained by the normal localization algorithm withconstant wave speed (±2 mm). Moreover, the enhanced algorithm turned out to be numericallyless stable. The results of both localization algorithms are summarized in Table 1 and compared

Fig. 5 Mean error of source location as a function of matrix wave speed as obtained by the
traditional (normal) localization algorithm.
Table 1 Results for normal localization, enhanced localization, and AE tomography.
Normal localisation(homogeneous matrix);
Unknowns: x,y,(z),tEnhanced localisation(homogeneous matrix);
UnknownsÊ: x,y,(z),t,cP

Mean residual
Minimum residual
Maximum residual

0.71 µs
0.42 µs
1.17 µs

0.72 µs
0.42 µs
1.14 µs

Acoustic emission
tomography(heterogeneous matrix);
Unknowns: x,y,(z),t,cP(x,y,z)0.12 µs
0.04 µs
0.35 µs

Mean source
location error
Minimum source
location error
Maximum source
location error

2.20 mm

4.54 mm0.90 mm0.31 mm0.17 mm0.10 mm

4.98 mm10.0 mm3.82 mm

Mean source
time error
Minimum source
time error
Maximum source
time error

0.25 µs

1.41 µs

0.11 µs

0.04 µs

0.01 µs0.001 µs

0.53 µs

4.25 µs

0.43 µs

154with the results of AE tomography. The spatial coordinate z is given in parentheses since theunderlying problem is effectively 2-D. For the normal localization the values at cP = 3950 m/s
are listed (compare Fig. 5).
The results for AE tomography, obtained by the model in Fig. 4 (40 AE events, 16 sensors),
are significantly better than for the traditional algorithms since heterogeneity of the medium istaken into account. Moreover, each new AE event leads to a better approximation of the spatial
velocity field and thus, also to a better localization of the preceding AE events. Therefore, increasing the number of AE events in AE tomography improves the overall accuracy of localization while traditional localization of AE events is performed independently from each other, i.e.
the mean error is more or less independent from the number of AE events.
0 iterations

5 iterations

15 iterations

10 iterations

20 iterations

Fig. 6 Tomographic images of the model after 0, 5, 10, 15, and 20 iterations of the AE-TOMO
algorithm (28 × 28 pixels with anti-aliasing).
Figure 6 shows tomographic images after 0 (homogeneous start model), 5, 10, 15, and 20 iterations of the AE-TOMO algorithm obtained at the model from Fig. 4 (40 AE events, 16 sensors). One can see that after about 10-15 iterations the wave speed model becomes more andmore stable. The steel reinforcement cables with locally increased wave speed (red color) in thecorners as well as the ungrouted tendon duct with effectively decreased wave speed (blue color)in the middle of the model are clearly visible. It is obvious that the reconstruction of the steel
cable in the bottom left corner is worse than for the remaining three cables, which is most likely
caused by the worse ray coverage as shown in Fig. 4.

155

Figure 7 shows the influence of the number of AE events on the tomography result (after
20 iterations in each case). The corresponding pictures represent the wave speed model using 5,
10, 20, 30, and 40 AE events for tomographic reconstruction. If the number of available sources
and thus, the number of rays is increased the model becomes better and better. However, similar
to Fig. 6 a saturation behavior can be observed if the ray coverage of the tomography cells
reaches a certain level. A further increase of the number of rays would not automatically improve
the quality of the image if we keep the number of tomography cells constant. Instead we could
increase the number of cells and thus, the spatial resolution of the tomographic image. With typical numbers of AE events in the range of hundreds or thousands image resolutions that can neverbe reached with traditional acoustic tomography seem to be possible.
5 AE events

10 AE events

30 AE events

20 AE events

40 AE events

Fig. 7 Tomographic images of the model using 5, 10, 20, 30, and 40 AE events for reconstruction (28 × 28 pixels with anti-aliasing).
However, as can be seen in Figs. 6 and 7 there are still some artifacts in the tomographic images most likely caused by the simple straight-line approximation of the ray model. Due to thelarge differences in acoustic impedance between the matrix and the scatterers (steel reinforcement and ungrouted tendon duct, respectively) this straight-line approximation is not sufficiently
fulfilled in the present case. Thus, taking diffraction and refraction effects with curved ray paths
into account should lead to significantly improved images in the future.
Another reason for some of the artifacts is the non-uniform ray coverage of the model leading to large differences in the number of rays passing through the equally sized tomographycells. As a consequence, working with adaptively sized tomography cells should lead to a moreeven ray density per cell and thus, to a better tomographic image.
156

6. Summary and Outlook

In the present paper it has been shown that AE tomography represents an important improvement of traditional AE analysis leading to significantly better localization of AE events due
to consideration of heterogeneous media. The method leads to a new imaging technique where in
addition to the source positions the volume of the specimen is visualized in terms of a locallyvarying wave speed distribution.
Apart from that, AE tomography has self-contained relevance since in the traditional sense“unwanted” AE events can be used for tomographic imaging, too. Moreover, structural elements
and acoustically “passive” defects can also be visualized. Finally, also AE events artificiallygenerated, e.g. by pencil-lead breaks or hammer impacts at outer surfaces of the specimen can beused for tomographic imaging. Thus, it seems that in many cases traditional acoustic tomography
with fixed and inflexible ray coverage could be replaced by adaptive AE tomography where tomographic inversion takes place on a ray-by-ray or rather an event-by-event basis. Based on theprevious iteration the investigator could interactively decide if and where a higher resolution of
the tomographic image is necessary and consequently, where the next AE event should be placed
manually.
As has been shown in the discussion of the tomographic images in Figs. 6 and 7, there is stillmuch room for improvements of the underlying algorithms. Varieties of algebraic reconstruction
techniques using curved ray paths should be tested for applicability in AE tomography in the
future. Also the development of transform based tomography algorithms similar to the filtered
back-projection algorithm seems to be worthwhile. Moreover, since in nondestructive evaluationstrong scatterers are typical, the development of algorithms for AE reflection tomography (e.g.
filtered back-propagation or synthetic aperture focusing technique, SAFT [7]) should be considered.
So far, the physical soundness of AE tomography has been shown by idealized numerical
data only. In the future, more realistic models including heterogeneity of the concrete matrix andmore complicated crack mechanisms will be investigated. Real measurements on concrete beams(using pencil-lead breaks as AE sources) and aluminum plates (using Lamb waves for tomography) are under way in order to verify AE tomography experimentally. Based on the results obtained so far it can be expected that AE tomography has great potential for future applications
offering totally new perspectives for AE imaging and acoustic tomography in general.
References[1] Kak, A.C., and Slaney, M., Principles of Computerized Tomographic Imaging, IEEE Press,
New York, 1988.
[2] Schechter, R.S., Mignogna, R.B., and Delsanto, P.P., “Ultrasonic tomography using curved
ray paths obtained by wave propagation simulations on a massively parallel computer”,
J. Acoust. Soc. Am. 100 (4), 2103-2111, 1996.
[3] Mignogna, R.B., and Delsanto, P.P., “A parallel approach to acoustic tomography”,
J. Acoust. Soc. Am. 99 (4), 2142-2147, 1996.
[4] Fellinger, P., Marklein, R., Langenberg, K. J., and Klaholz, S., “Numerical modelling ofelastic wave propagation and scattering with EFIT – Elastodynamic finite integration technique“,
Wave Motion 21, 47-66, 1995.
157

[5] Schubert, F., and Schechinger, B., “Numerical modelling of acoustic emission sources andwave propagation in concrete“, NDTnet, the e-Journal of Nondestructive Testing, 7 (9), 2002
(http://www.ndt.net/article/v07n09/07/07.htm).
[6] Reinhardt, H.W., and Grosse, C.U., “Continuous monitoring of setting and hardening of mortar and concrete”, Construction and Building Materials, 18, 145-154, 2004.
[7] Langenberg, K.J., Berger, M., Kreutter, T., Mayer, K., and Schmitz, V., “Synthetic aperture
focusing technique signal processing“, NDT International, 19 (3), 177-189, 1986.

158

ACOUSTIC EMISSION BEHAVIOR OF MARTENSITIC TRANSFORMATION DURING DEFORMATION OF Cu-Al-Ni
SHAPE-MEMORY ALLOY SINGLE CRYSTALSKENICHI YOSHIDA, KOTARO HANABUSA AND TAKUO NAGAMACHI
Department of Mechanical Engineering, Tokushima University,
Tokushima 770-8506, Japan
Abstract
Cu-Al-Ni shape memory alloy exhibits a super-elasticity that shows perfect recovery of plastic deformation. The super-elasticity used in this study results from a perfect reversible martensitic transformation. AE behavior during the stress-induced martensitic transformation has been
investigated using two types of specimens of Cu-Al-Ni shape-memory alloy single crystal, eachof which have different stress concentration factor. The rise time of martensitic transformation in
the early stage during tensile deformation was evaluated using a wavelet transform analysis instead of the frequency-domain slope method of whole waveform. Shorter rise time of AE waveform was found in the early stage and associated with the nucleation of the martensite regardlessof the specimen shape.
Keywords: Cu-Al-Ni, shape-memory alloy, martensitic transformation, rise time, AE waveforms,
FFT, wavelet transform
1. Introduction
Martensitic transformation is known as a transient shear process in materials. The kinetic
property of the transformation must give important information to study the nature of the transformation [1]. In general, the martensitic transformation proceeds both by nucleation and growthof martensite plate [2]. The kinetics of the martensitic transformation phenomenon has beenmeasured using transient electrical resistance change in Fe-Ni alloy [3], magnetic permeabilitychange in Fe-Ni alloy [4] and AE method in austenitic stainless steel [5, 6]. These results have
two shortcomings to explain the kinetic behavior of individual martensite plates. One is thatthose included the constraints due to grain boundaries because materials used were polycrystalline. Another is that those could not distinguish between the nucleation and growth of martensite
plates because of difficulty of in-situ measurement.
In this paper, the kinetic properties of the stress-induced martensitic transformation in two
types of specimens of Cu-Al-Ni shape-memory alloy single crystal were investigated using AE
waveform generated in the early stage during tensile deformation. The AE waveform analysis
where the rise time of each martensitic transformation can be obtained due to the frequency-domain slope method has been already conducted in the previous papers [7, 8]. However,
the rise time was modified due to a wavelet transform in the finite specimens because the analysis can be used only in infinite elastic media. The martensitic transformation used in this study isthe ß1(DO3 structure)⇔ß1’(18R structure) transformation which can be observed by an optical
microscope during tensile deformation at room temperature. Therefore, the kinetics of martensite
plates such as nucleation and growth was examined in detail.
J. Acoustic Emission, 22 (2004)159© 2004 Acoustic Emission Group2. Experimental Procedures
Material used was a Cu-14.1mass%Al-4.1mass%Ni alloy for stress-induced martensitic
transformation. It was melted in argon in a high-frequency induction furnace and cast into aplate-shaped copper mold. After casting it was put into a graphite mold. An oriented single crystal of the Cu-Al-Ni alloy was grown using a seed crystal with the Bridgman method at a speed of
32 mm/h. They were solution-treated at 1273 K for 1 hr. and then quenched into water at room
temperature in order to keep the ß1 matrix phase (Ms = 250 K, Af = 280 K). Two kinds of specimens which consist of smooth (specimen No. 1, gage size: 12 x 4 x 1.8 mm) and two-side
90˚-notched (specimen No. 2) specimens to examine the effect of stress concentration on martensitic transformation dynamics were formed with an electric-discharge machine. These were
polished mechanically and then electro-polished in a solution of phosphoric acid saturated withchromium trioxide. The stress concentration factors in these specimens were 1.01 and 2.65, respectively. The orientation of the single-crystal specimen was determined by the back-reflection
Laue method. The specimen had the surface orientation of (001) and [100] tensile direction, respectively. Tensile test was carried out with a screw-driven testing machine at a strain rate of 5.6
x 10-5 s-1. Surface appearance during tensile test was observed and photographed microscopicallyby an optical microscope.
AE measurements were performed using 2-channel AE monitoring system (PAC: MISTRAS-2001). The AE signals were detected by two wideband AE transducers (M5W) that were directly attached on the grip part of specimen with fast-curing glue. The distance between two transducers was 20 mm. Detected AE signals were amplified by a 60 dB preamplifier with a band-passfilter of 0.1 to 1.2 MHz. AE events and the peak amplitude were obtained and the waveforms recorded. Threshold level was 50 dB, which corresponded to 316 µV at the preamplifier input.
3. Results and Discussion
3.1 Stress-elongation curves and surface appearances during loading and unloading
Relations between load and elongation, in the smooth and notched specimens, during loading
are shown in Fig. 1 including typical surface appearances under the stress condition indicated byarrow. All the specimens exhibited super-elasticity and perfectly recovered the matrix phase after
unloading due to low Af temperature of the material. The nucleation in both specimens was recognized to start in the elastic region or in the early stage of the tensile deformation.
3.2 AE activity under the different stress conditions
The AE activities were explained in detail in the previous paper [9]. Less number of AE eventsin the smooth specimen were detected than those in the notched specimen, but higher amplitude in
the smooth specimen. It means that the AE activity is related to nucleation and growth of large
and not many martensite plates in the smooth specimen and to only nucleation of small and manymartensite plates in the notched specimen according to these surface appearances in Fig. 1. Here,surface appearance at each stress condition indicated by arrow. If the given plastic strain is almost
only performed due to the martensitic transformation, it is considered that an amount of transformation is proportional to the given plastic strain energy. So effect of specimen volume on AE activity has been examined with all the specimens given the same plastic strain energy per unit volume and an amount of transformation changed by the specimen volume. Total AE event counts
tend to decrease with an increase of the specimen volume. There is no specimen volume effect.
Therefore, AE activity does not seem to depend on all the martensitic transformation of material
160

(a)

(b)
Fig. 1 Load-elongation curves during loading with surface photographs; (a) Smooth (b) Notched.
161

(a)

(b)
Fig. 3 A typical example of (a) wavelet transformation of a detected waveform and (b) the frequency spectrum of the first arrival waveform at 20 µs from the starting point. The estimated risetime was 0.604 µs.
The rise time distributions obtained during loading and unloading in both specimens are shownin Fig. 4. The waveform analysis was performed in the early stage during tensile deformation
where the maximum elongation was 0.4 mm. The rise time distribution during loading was
broader than that during unloading regardless of the specimen shape. Especially no AE waveform with the rise time of less than 0.5 µs existed during unloading. The shorter rise time is considered to be associated with the nucleation of the martensite plates in a single crystal with lessconstraint for the martensitic transformation [10], so the same number of waveforms with therise time of less than 0.5 µs were included during loading in both specimens with the different
shapes under the different stress conditions. If the nucleation speed is assumed to be 1000 m/s[8], the initial scale of martensite plate during loading is likely to be of the order of several hundreds µm. On the other hand, there was the asymmetry of AE event counts detected between
163

loading and unloading. That is to say, AE event counts during unloading enhanced those duringloading as shown in Fig. 4. It is expected that the reverse transformation is easy to nucleate oroperate because of the strain energy stored during loading. The nucleation is important to explain
the asymmetry between loading and unloading, because the rise time distribution during unloading was at higher level than that during loading. These results show that AE research can play animportant role in understanding martensitic transformation.

(a) ) smooth specimen.

Fig. 4(b) notched specimen.
Rise time distributions during loading (left) and unloading (right) .

4. Conclusions
AE behavior during stress-induced martensitic transformation of Cu-Al-Ni shape-memory
alloy single crystals that exhibit super-elasticity has been investigated using the smooth andnotched specimens. Results obtained are as follows:(1) There was no effect of specimen shape on acoustic emission activity.
(2) Time-dependence of AE activities that consisted of the AE event counts, the amplitude andthe waveforms in the smooth specimen were different from those in the notched specimen because of different stress concentration.

164

(3) AE method is effective in evaluating such kinetic processes such as nucleation and growth ofmartensitic transformation.
References
1) T. Saburi, C.M. Wayman, K. Takata and S. Nenno: Acta Metall., 28 (1980), 15-32.
2) K. Otsuka, C.M. Wayman, H. Sakamoto and K. Shimizu: Acta Metall., 24 (1976), 207-226.
3) R.F. Bunshah and R.F. Mehl: Trans. AIME, 197 (1953), 1251-1256.
4) T. Kakeshita, K. Shimizu, T. Maki and I. Tamura: Scripta Metall., 14 (1980), 1067-1070.
5) T. Takashima, Y. Higo and S. Nunomura: Phil. Mag., 49A (1984), 231-241.
6) Z.Z. Yu and P.C. Clapp: Metall. Trans., 20A (1989), 1601-1615.
7) T. Takashima, Y. Higo and S. Nunomura: Phil. Mag., 49A (1984), 221-229.
8) K. Yoshida, H. Takagi and K. Sakamaki: J. JSNDI, 46-11 (1997), 821-829.
9) K. Yoshida, S. Kihara and K. Sakamaki: Proc. 24th European Conference on Acoustic Emission Testing (EWGAE 2000), 24-5 (2000), pp. 107-112.
10) K. Yoshida, A. Takahashi, K. Sakamaki and H. Takagi: Progress in Acoustic Emission VI(Proc. of 11th IAES, Fukuoka, Japan), JSNDI, Tokyo, (1992), 497-503.

165

ACOUSTIC EMISSION MONITORING OF CONCRETE HINGE JOINTMODELSK. M. HOLFORD, R. PULLIN and R. J. LARK
School of Engineering, Cardiff University, UK
Abstract
Currently in the United Kingdom there are over 100 bridges containing hinge joint components, introduced to simplify and standardise designs. Visual inspection of hinge joints noted
bridge deck waterproofing failure, which can cause steel reinforcement bar corrosion. This reinforcement is crucial to the integrity of the joint, and the loss of reinforcement section can induce
higher stresses leading to eventual failure by yielding. The Transport Research Laboratory created a model of two hinge joint assemblies typical of those found in reinforced concrete slab
bridges. The model had notched reinforcement bars at the hinges. The aim of this investigation
was to determine the effectiveness of the acoustic emission (AE) technique in detecting and locating the cracking of concrete at the throat. AE sensors were attached to the steel reinforcement
bars using waveguides and to the concrete face of the model. The model was statically loaded
until failure. To allow the visual inspection and logging of concrete cracking around the throat,
the load was applied in stages with a brief hold period between each stage. AE results showedthat it was possible to identify and locate cracking of the concrete around the throat and that thistechnique needs to be further evaluated on bridge structures under live loading.
Keywords: Acoustic emission, bridges, concrete hinge joints
1. Introduction
Concrete hinge joints were introduced into bridge decks as a means of simplifying the design, and standardising details on bridges having a range of span and functional requirements. It
is thought that the hinge joints transfer shear and accommodate small angular movements but
restrict longitudinal movement (Wilson, 1995). The hinges also enabled the bridges to cope withpossible differential settlement. The disadvantages with hinge joints are that they are not easily
accessible for inspection or maintenance due to their form and their location over or under live
traffic lanes.
Previous attempts to investigate the deterioration of hinge joints by visual inspection, which
involves the removal of structural concrete around the joint to expose the reinforcement bars,
have noted particular defects; the majority have cracks running through the throat and a loss ofwaterproofing. Waterproofing failure can lead to chloride-rich seepage through the joint that cancause reinforcement bar corrosion leading to eventual failure.
The Transport Research Laboratory (TRL) created a model, of two hinge joint assemblies.
The model created was part of a larger testing programme commissioned by the Highways
Agency to investigate the behaviour of the hinges under load (Daly, 2004). The hinge joint
model is typical of those found in reinforced concrete slab bridges. The model reinforcement wasnotched at the hinge to ensure failure of the steel reinforcement. The investigation presented herereports the results the effectiveness of the acoustic emission (AE) technique in detecting bothJ. Acoustic Emission, 22 (2004)166© 2004 Acoustic Emission Groupdamage in the steel reinforcement and cracking of concrete at the throat in the notched hinge
joint model.
2. Experimental Procedure
The hinge joint specimen was restrained as shown in Fig. 1 and loaded using a hand-operated
hydraulic actuator. Load was measured using a load cell and the vertical displacement of the centre span was measured using a displacement gauge. The model was loaded in 10 kN intervals to
125 kN followed by 15 kN interval to 290 kN and then 0.5 mm increases in deflection until failure. Following each load step there was a brief hold period to log cracking of the concrete. The
position of the crack tip and the respective load was marked on the beam.

Fig. 1 Test specimen and instrumentation.
Nine AE sensors operating in the 70-300 kHz frequency range were attached to the internal
reinforcement bars of the model (sensors 1-9). The sensors were attached via waveguides withgrease as a couplant. Prior to casting of the concrete a hole suitable for 5 mm studding was
drilled and tapped into the reinforcement bar at the required positions. The studding was screwed
into the hole and cut so the studding finished above the surface allowing a waveguide to be attached. This type of connection allows signals emitted from the steel to travel directly to the sensors. Three low-frequency sensors operating in the 20-50 kHz range (sensors 10-12) were used toevaluate the cracking of the concrete. Steel pieces were bonded to the concrete using a two part167

hardening system, enabling magnetic clamps to hold the sensor in place. Grease was used as anacoustic couplant. The concrete sensors were attached on the opposite side of the beam to the
waveguides.
Sensor response, attenuation and signal location were evaluated using the pencil-lead fracture(PLF) technique. Signals resulting from the PLF adjacent to each sensor were recorded to evaluate response. To assess attenuation a PLF adjacent to sensor 1 was recorded on all other sensors.
Location of signals from the concrete using sensors attached to steel reinforcement was evaluated using PLFs at the centre of each joint. The specimen was monitored for the entire loading at
a threshold of 35 dB on the sensors attached to the steel and 45 dB on the sensors attached to theconcrete. (0 dB = 1 µV) Waveforms over 65 dB were recorded. Time marks were logged within
the data to signify the start and finish of each load step increment.
3. Results and Discussion
The response of all sensors to PLFs adjacent to the sensor was above 97 dB. This demonstrates that all sensors were attached correctly. The responses to a PLF at sensor 1 as recorded by
sensors 1, 2 and 3 were 97 dB, 78 dB and 43 dB, respectively. This suggests that the location of
signals from the steel reinforcement at the hinge will be possible as the loss of signal between
sensor pairs is relatively low. Figure 2 shows the results of the PLF test to establish the accuracyof the time-of-arrival location method using sensors attached to the steel reinforcement to locate
concrete damage. These results indicate that the location of damage in the concrete using thesteel mounted sensors will be possible but has an error of 200 mm.
Details of the load and direction of concrete cracking based on post-test photographs are presented in Fig. 3. Figure 4 shows the AE activity of the joints as recorded by the steel mounted
sensors in terms of the amount of energy detected on all sensors. The plots show that higher levels of energy were recorded during the loading periods compared with the hold periods (hold periods are shaded), showing that AE is detecting the damage occurring during each load stage.

Tension Bar
Energy(aJ)

Dowel Bar

Compression Bar
X-position (m)Fig. 2 Location of pencil lead fractures from centre of each hinge joint.
168

Fig. 3 Location and direction of cracks with respective loads.

Fig. 4 Energy detected and located from reinforcement mounted sensors.
H1

H2

Tension Bar
Energy(aJ)
Dowel Bar

Compression Bar
X-position (m)Fig. 5 Location of signals detected from sensors attached to steel reinforcement
during 10-20 kN load stage.
169

H2

H1

Tension Bar

Energy(aJ)

Dowel Bar

Compression Bar
X-position (m)Fig. 6 Location of signals detected from sensors attached to steel reinforcement
during 250-353 kN load stage.

Fig. 7 Final failure of notched reinforcement model at Hinge 2 (photograph taken from surface
sensor mounted side).
The location and magnitude of energy recorded during the 10-20 kN load stage is shown in
Fig. 5. Comparing the location graphs with details of concrete cracking (Fig. 3), it can be seenthat the sensors attached to the steel reinforcement bar via waveguides can detect and locate theconcrete cracking. The last loading stage of the model is shown in Fig. 6 (350-353 kN), and anumber of emissions were located. The highest energy level emission is at Hinge “2” suggesting
the final failure as detected by AE must be at Hinge “2”. Figure 7 shows the ultimate failure at
Hinge “2”. The remaining energy also located at failure may be due to the de-bonding of the bars
170

as a result of the failure. This cannot be validated currently as it would require the removal of
concrete from the bars.
Figure 8 shows the energy recorded during the monitoring of the model by the concrete sensors. The plots again show that higher levels of energy were recorded during the loading periods
compared with the hold periods (hold periods are shaded), showing that AE detects the damage
occurring during each load stage. The concrete sensors did not record the total duration of thetest as surface cracking of the specimen caused the failure of the hardening compound under theclamps, dislodging the central sensor.

Energy(aJ)Time (s)Fig. 8 Energy detected and located by the concrete mounted sensors.
From Fig. 9, showing location of signals recorded by concrete mounted sensors in the 0-50
kN loading stages, the initial cracking at Hinge “1” is detected at the load at which it was firstseen (50 kN). However the cracking at Hinge “2” is detected prior to visual observation. Notethat emissions are detected at locations not included in Fig. 3 as the sensors detect damage in theconcrete throughout the model and not just surface cracks logged.
The disadvantage with hinge joints is that they are not easily accessible for inspection or
maintenance due to their form and their location over or under live traffic lanes. Results reported
in this paper shows that the damage of concrete as it occurs in model hinge joints can be identified using sensors attached to the structure using waveguides or surface mounted sensors. The
use of surface-mounted sensors does not further damage the structure and can be implemented
during night closing of motorway. It is then possible to monitor a structure under normal loading
conditions avoiding further bridge closures during heavy traffic periods.
4. Conclusions
This trial shows that it is possible to detect and locate concrete cracking in concrete model
hinge joints using sensors attached to the steel reinforcement bars and sensors attached to theconcrete surface
Acknowledgements
The authors wish to express their gratitude to the Highways Agency for their financial support of this research and to the technician staff of the Transport Research Laboratory for theirassistance.
171

References
Daly, A. F., 2004. “Durability and Performance of Deck Hinges: Report on Preliminary Load
Tests”, Transport Research Laboratory, TRL Project Report
Wilson, C.B., 1995. “Assessment of the Reinforced Concrete Hinges on Five M6 Overbridges in
Staffordshire”, Proceedings Institution Civil Engineers, 110 (1995) 4-10.
H1

H2

Energy(aJ)
X-position (m)0-10kN
Energy(aJ)

X-position (m)10-20kN
Energy(aJ)
X-position (m)20-30kN
Energy(aJ)
X-position (m)30-40kN
Energy(aJ)
X-position (m)40-50kN
Fig. 9 Location of signals detected from sensors attached to concrete surface
during 0-50 kN loading stages.

172

CHARACTERIZATION OF ACOUSTIC EMISSION SOURCES IN A
ROCK SALT SPECIMEN UNDER TRIAXIAL LOADGERD MANTHEI
Gesellschaft für Materialprüfung und Geophysik, Dieselstr. 6a, D-61239 Ober-Mörlen, Germany
Abstract
This study presents the application of an automated moment tensor analysis procedure on
microcracks, which were generated during a triaxial compression test of a cylindrical rock salt
specimen (diameter 150 mm, length 300 mm). The acoustic emission (AE) signals were detected
in a frequency range between 20 kHz and 1 MHz using 12 AE sensors mounted on the surface ofthe specimen cylinder. The moment tensor analysis was applied to about 30,000 events, whichwere precisely located using at least 16 P- and S-wave arrival times. For more than 40% of these
events, i.e. approximately 12,500 events, stable moment tensor solutions could be evaluated using the first motion of the P-wave radiation patterns. Most of the evaluated events showed significant isotropic source components, which are in good agreement with dilatation of rock during
compressional loading. The majority of the events were caused by tensile opening, which leads
to dilatation of the rock. The tension (T) axes, which are normal to the crack plane of these tensile microcracks, were predominantly oriented in radial direction of the cylindrical specimen,
perpendicular to the maximum principal stress, which is in axial direction. The direction of thetensile opening calculated by the moment tensor evaluation coincides very well with the direction of the minimum principal stress. The applied collapsing method discovers cellular structures with a cell size in the range of a few cm. However, it seems that the events occur only inzones where the cell interfaces are favorably oriented in the stress field. These events are attributed to cracking at grain interfaces, which occur in rock salt under very slow creep loading above
the dilatancy boundary.
1. Introduction
The scope of this work is to determine mechanisms of acoustic emission (AE) events, whichwere recorded during a compression test of a rock salt specimen under confining pressure. In
analogy to earthquakes, AE sources (like microcracks) radiate elastic energy. Consequently, it ispossible to investigate them by similar techniques as used in seismology. In other words, themechanisms of earthquakes and AE sources are in principle the same, in spite of the fact thatstrengths and frequencies differ by orders of magnitude (Eisenblätter, 1980). Therefore, it is possible to apply the moment tensor method, which describes mechanisms of general seismic
sources, to these events whose frequencies range between 20 kHz and 1 MHz. The moment tensor method was introduced into seismology by Gilbert (1970). A review about several applications of AE measurements in rock mechanics studies is given in Manthei et al. (2000).
In principle, two methods are in common use in source mechanism studies. In earthquake
seismology, the classical method is the fault-plane solution method. This method uses polarities
and ray directions of the P-wave to constrain possible double-couple (DC) fault-plane solutions
and requires very good sensor coverage of the focal sphere. The fault-plane solution method assumes pure shear slip along the fault plane, which is realistic for tectonic earthquakes. Therefore,
the fault-plane solution method is especially adapted to the situation of tectonic earthquakes
J. Acoustic Emission, 22 (2004)173© 2004 Acoustic Emission Group

within the earth where volume changes normally cannot occur. On the contrary, in the case oflaboratory investigations on rock specimens under uniaxial or triaxial compressional load, volume changes in the source play an important role. This fact was confirmed in many laboratory
experiments on rock samples under compressional stress conditions. The experiments figured outthat microcracking leads to a non-reversible increase of the rock volume, which is called dilatancy (Brace et al., 1966, Scholz, 1968a, Lockner et al., 1977, Hunsche et al., 1999, Alkan et al.,2002). Dilatancy can be explained as volume expansion, which is caused by tensile opening.
In contrast to the fault-plane solution method, the more complex moment tensor method iscapable to describe sources with isotropic components like tensile cracks as well as deviatoric
sources like shear cracks or a mixture of both source types. The isotropic source components canbe easily obtained using the isotropic part defined as one-third of the trace of the moment tensor.
With the moment tensor method the source mechanisms are estimated in a least-square inversion
calculation from amplitudes of the first motion as well as from full waveforms of compressional(P) and shear (S) waves which require additional knowledge about the transfer function of themedium (the so called Green's function) and sensor response. In this paper we would like todemonstrate the application of the moment tensor method to analyze automatically thousands ofAE events. The automatic procedure is separated into source location, data extraction, and stability check of the inversion calculation.
2. Theory
In the following, a short description of the theory of moment tensor representation for point
sources in homogeneous media will be given. A detailed description of the theory and generalproperties of moment tensor is given in the textbook of Aki and Richards (2002) and in the student's guides of Pujol and Hermann (1990) or Jost and Hermann (1989).
The n-component of observed displacement at an observation point specified by coordinatesrx and time t is given with the moment tensor representation of a seismic point source as:
+∞
r
r
r
∂
un ( x , t ) = ∫ M pq (τ )Gnq ( x; t − τ ; ξ ,0) dτ .
∂ξ q
−∞

(1)

In this formula un is the displacement (n = 1…3), Mpq the seismic moment tensor, Gnp the Green'srrfunction tensor, x and ξ are space coordinates. Equation (1) can be used to describe the excitation of a source in a material where the forces and its moments in the source region must be selfequilibrating. In the case in which each of the components Mpq. shares the same source-timefunction S(t), Equation (1) becomes in the far field•r
un ( x , t ) =  M pq ⋅ S (t ) ∗ Gnp ,q .



(2)

The asterisk denotes the convolution in the time domain and S& (t ) is the time derivative of thesource time function. In the ray-theoretical limit, the Green's function tensor in source equation(2) can be further simplified. For this purpose, we introduce a local coordinate system with origin at the source location. The azimuth angle ϕ (angle in horizontal plane) is measured clockwisefrom north and θ is the angle between the normal vector of the horizontal plane and radius vector
between source and sensor positions (Aki and Richards, 2002). The n-component of displacement of the P-wave is:174

u nP = I n

6∑m ⋅ a ,
k

nk

(3)

k =1

with

an1 = sin2 ϑ ⋅ cos2 ϕ, an2 = sin2 ϑ ⋅ cos2ϕ, an3 = sin2 ϑ ⋅ sin2 ϕ,
an4 = sin2ϑ ⋅ cosϕ, an5 = sin2ϑ ⋅ sinϕ, an6 = − sin2 ϑ +1
and

m1 = M 11 , m 2 = M 12 , m3 = M 22 , m 4 = M 13 , m5 = M 23 , m6 = M 33 .
3. Experimental Set-Up and Performance of The Experiment
The experiment on the cylindrical salt rock specimen (diameter 150 mm, length 300 mm)
from the salt mine Asse in Germany was carried out in the laboratory of the Department for
Waste Disposal Technology and Geomechanics at the Technical University of Clausthal, Germany. A triaxial hydraulic loading system was used for the test, which contains the hydraulic cell
where cylindrical rock samples covered by a viton sleeve are subjected to high hydraulic pressure up to 75 MPa and additional axial
force up to 2,500 kN (Lux, et al. 2000).
During the test the axial displacement
and axial force, the radial pressure, andthe volume change (dilatancy) of thespecimen can be measured and displayed
on-line on the screen of the computer.
The digitized data are stored every fiveseconds on the hard disk. The pore volume of the specimen corresponds to thechange of oil volume in the triaxial cell,which is measured using a double acting
measuring hydraulic cylinder. The accuracy of the dilatancy measurement isabout 0.0025% of the total volume
within the load cell. At the beginning of
the test the specimen was compacted for
16 hours over night under isotropic
stress conditions at a pressure of 20 MPa
in order to close open fractures at thesurface of the specimen, which were
eventually caused by manufacturing of
the sample. After the compaction phasethe test was performed applying a constant axial displacement rate of 0.15 mm
per minute and a radial pressure of 5
MPa.
Fig. 1 Geometry of the rock salt specimen and location of AE sensors positions.

175

Fig. 2 Event originating from the lower half of the specimen. The onsets of the picked and calculated first (P-wave) and second (S-wave) arrivals are marked by ticks above and below the signal
trace, respectively.
The AE sensor arrangement is shown in Fig. 1. Stiff strain gauge glue was used to fix thesensors to the surface of the boreholes. The AE measuring system includes three four-channeltransient recorder cards in a personal computer. The transient recorder cards (sampling rate 10
MHz, resolution 14 bit, storage capacity 512 kByte per channel) were read each time when atleast two channels were hit. The digitized signals were stored on the hard disk of the personal
computer. The overall duration of the test was about 4.3 hours.
4. Source Location
During the experiment about 64,000 events were recorded by more than five channels, 90%of these were locatable. At about 21 mm axial compression most of the sensors were damaged or
the measuring cables were ruptured due to the high deformation. Therefore, after this point a
three-dimensional location was no longer possible.
The location of the events was made off-line after the test. As the signals showed a surprisingly good signal-to-noise ratio (Fig. 2), automatic location was possible. The location procedure
starts with low-pass filtering of the signals at a corner frequency of 500 kHz. After filtering, allsignals showing a signal-to-noise ratio greater than 5 were taken into consideration for source
location. In the first step, events with at least five P-wave arrival times detected at a very low
amplitude threshold of 2.5% of the peak amplitude were located. The location was consideredvalid if the location error was smaller than 5 mm. Otherwise the location was repeated twice. In
the first variant the first hit channel was ignored and in the second variant the channel with the176greatest P-wave travel time residuum was ignored. The variant with smaller location error was
kept. This procedure was repeated as long as the location error was smaller than 5 mm or lessthan five P-wave onsets were left for location. In the latter case the event was rejected.
After source location using only P-wave arrival times, in the second step, the location procedure was repeated using P- and S-wave arrivals. To this end, the onsets of the S wave weresought around the calculated P-wave arrival times. The trigger threshold for the S wave was chosen in the interval between noise level after the calculated P-wave arrival and peak amplitude of
the signals. The applied S-wave trigger threshold at 35% of the said interval is much higher than
the P-wave threshold. Similar to the previous location using only P-wave onsets, the channel
with the greatest S-wave travel time residuum was ignored. An event was rejected when the location error was greater than 5 mm or less than eight P- and S-wave onsets had remained for location.
Figure 2 shows the automatically detected P-wave arrivals and S-wave arrivals, which aremarked by ticks above the signal trace. The calculated onsets of the P- and S-waves at the various channels (ticks below the signal traces) could be used to assess the quality of location. In thisfigure the angle of incidence, the peak amplitude, and the source-receiver distance are given at
the end of each signal.
In order to remove systematic location errors associated with picking errors and inaccuracyof assumed velocity we applied the joint-hypocenter determination (JHD) method (Douglas,
1967, Frohlich, 1979). In this method, we can determine "station corrections" that account for the
inaccuracies of the wave velocity along the travel path especially near sensor positions. The
source locations are iteratively calculated involving corrections of onset times. These corrections
are estimated as the mean residual at each channel before each iteration. The iteration process isfinished when corrections are becoming small enough.
5. Moment Tensor Evaluation
5.1 Pre-processing and extraction of data
We applied the moment tensor method to a selection of 15,526 events, which were locatedusing at least fifteen P- and S-wave arrival times. Signals striking the sensors from behind wereignored in the evaluation. In this case the sensor radiation pattern is poorly known. Since ourgoal is to estimate source mechanisms of many events in an automatic procedure, we have madea further selection in such a way that only events with a good signal-to-noise ratio were considered.
Input data for the moment tensor calculations are peak amplitudes and polarities of the first
motion of the P-wave signal. We developed an automatic procedure for data extraction because
inspection by eye is too time consuming for such a huge number of events. The data extraction
procedure involves the following steps:•••

Division of the signal amplitudes by the calibration factor of 335.1 V/(m/s) (the physicalquantity measured by the sensors is supposed to be velocity).
Conversion of velocity signals into displacement signals by integrating the data in thetime domain.
Band-pass filtering in the frequency range between 10 kHz and 500 kHz to remove highfrequency noise and signal offsets which eventually occurs due to integration.
177

•

•
•

•

Cutting out traces using a definedtime window of ±7.5 µs (151 samples) around the theoretical P-wave
onsets.
Only traces with a signal-to-noise ratio greater than 10 were considered
for data extraction.
Determination of first crossing of athreshold level of 10% of the interval
between peak amplitude within the
7.5-µs time window and noise level
before signal onset at the rectified
signal trace.
Determination of the P-wave first
motion, which is the first minimum
or maximum amplitude after thetrigger point.

Figure 3 shows windowed time tracesaligned at the times of first motion of the Pwave (ticks below the signal traces). It canbe seen that the polarities of the P-wave first
motion are in this case compressional at allchannels. The following steps outline thetreatment of data before moment tensor inversion calculations:
•
•
•

•

Data were corrected for the measured sensor directivity.
Considering geometrical wave attenuation and damping (approximately –0.435 dB per 100 mm).
Transformation of the source location coordinates and the normal vector components of the sensors into
the north-east-down coordinate system.
Calculation of the coefficient matrix
ank from Equation (3).

•After the data treatment the over-determined
equation system (3) was solved for the six
moment tensor components using singularvalue decomposition (SVD).

Fig. 3 Aligned P waveforms in a time windowof 25 µs around the automatically picked first
motion amplitude of the P-wave (marked byticks above the signal traces).
1785.2 Case study of moment tensor evaluation
In the following, the inversion method will be exemplarily applied to a data set of one event
whose signals are displayed in Fig. 2. The source mechanism is displayed by plotting the orientation of the principal axes, which are obtained from the eigenvalues of the moment tensor (seeFig. 4). The eigenvector of the largest eigenvalue gives the T (or tensional) axis direction; theeigenvector of the smallest eigenvalue gives the P (or compressional) axis direction, while theeigenvector associated with the intermediate eigenvalue gives the direction of the B (or null)axis. Note, that the P and T axes which are obtained from the motion on the fault plane, only
coincide with the axes of maximum stress if the motion takes place on a newly formed fault
plane. This is not the case if the motion takes place on a pre-existing plane of weakness. In principle, it is not possible to distinguish between motion on new planes or motion on pre-existingfault planes of weakness from the seismic radiation. Additional knowledge from stress measurements, for example, is necessary. Under consideration of this kind of knowledge, it is assumed
that P and T axes, which were found from seismic radiation are somewhat indicative of the direction of stress.
The inversion method as presented in this work may lead to biased estimates if, for instance,
the applied Green's functions are not appropriate to describe the wave propagation in the material, or the input data are inexact, or there is a large variability in sensor coupling, or if the source
locations are not accurate enough. These uncertainties may lead to stability problems of the in-Fig. 4 Position of the mean T, B, and P axes (big dots) and their confidence areas (marked
by small dots) which were estimated by the Jackknife test and bootstrap analysis.
version calculations. Other stability problems may occur due to the small number of stations. An
advantage of the sensor arrangement as presented in Fig. 2 is good coverage of the focal spherein all directions, which is essential for the use of P-wave first motion data.
However, all mentioned uncertainties lead to errors in the estimated source mechanisms.
Therefore we applied a so-called Jackknife test (Efron et al., 1986). The Jackknife test is a suit179

able test to check the influence of a single sensor onto the solution of the inversion calculations
and to estimate their stability. As a means to visualize and quantify the scatter of principal axes
orientations a bootstrap analysis (Efron et al., 1986) was used. For each event 250 moment tensor
sets were generated by adding random errors to the moment tensor components within a confidence level of 99%, i.e. within 2.35 standard deviations obtained from the Jackknife test. The
eigenvectors of all 250 moment tensors were calculated and their directions P, B, and T wereplotted as small dots in an equal-area projection onto the lower hemisphere. Figure 4 shows sucha plot for the event of Fig. 2. The big dots in this figure represent the average direction of theprincipal axes. The two dotted concentric circles indicate plunge angles (measured from horizontal direction downwards) of 30° and 60°. The figure shows that the T axis is nearly horizontal
and its confidence area is fairly concentrated, whereas the overlapping confidence areas of the P
and B axes form a broad strip which extends over the whole focal sphere in perpendicular direction to the T axis. The width of the confidence area is a measure of the stability of the solution.
In the shown case, their width was not acceptable. Therefore, we looked for a more stable solution by trials where each time the amplitude of one channel was removed from the input data set.
The above-mentioned procedure (Jackknife test plus bootstrap analysis) was repeated for eachnew data set obtained in such a manner.
The results of these tests are shown in Fig. 5 for the event from Fig. 2. The plots in the upperrow display calculations without channels 1 to 4 (from left to right), the plots in the second row
display calculations without channels 5 to 8, and the plots in the third row display calculations
without channels 9 to 12. The solution with the smallest width of the confidence area is obviously that without channel 10 (second plot in the third row), which consequently was excluded
from the input data set in order to get stable solutions.
In order to quantify acceptable principal-axes scattering we derived stability criteria on a setof 50 randomly selected events. Events were considered to be stable if the standard deviation ofat least one of the three principal axes was less than 4°. Additionally the deviation between theobserved and calculated first motion amplitudes must be less than 12%. lf these conditions were
not true the event was rejected and not considered for interpretation.
5.3 Influence of source type on extension of principal axes orientations
In some cases the confidence area is primarily enlarged in a specific direction depending on
the source mechanism. There is obviously a direct relation between the extension of the confidence areas of the principal axes and the absolute values of the corresponding eigenvalues. For apure double couple with two eigenvalues of equal absolute values the confidence areas of the T
and P axes are similar. In the case of a pure tensile mechanism which has two equal eigenvalues
having the same sign and a third eigenvalue of same sign but approximately three times as large,
only the confidence area of principal axis which corresponds to the dominant eigenvalue (T axis)
is fixed. For a purely isotropic source, the three eigenvalues are equal; the positions of the principal axes are not determined and, therefore, their confidence areas extend over the whole focal
sphere.
Under the assumption that we have found events with stable inversion solutions through limitation of the confidence areas of the principal axes we can easily classify source mechanisms
through the parameter ε = e2/max(|e1|,|e3|) where ei are the eigenvalues of the moment tensor (e1≥ e2 ≥ e3). ε is zero for a shear mechanism, approximately 0.37 for a tensile mechanism for elastic parameters of rock salt, ±l.0 for an explosion and implosion mechanism, respectively, and 0.5
for a compensated linear vector dipole (Knopoff et al. 1970). Figure 6 shows the histogram of ε180

Fig. 5 Positions of the mean T, B, and P axes (big dots) and their confidence areas (small dots)
which were estimated by the Jackknife test and bootstrap analysis with excluded channels 1 to 4
(plots at the first row), with excluded channels 5 to 8 (plots in the second row), and with excluded channels 9 to 12 (plots in the third row).
values of 12,562 events which fulfilled the required stability criteria. The distribution is shiftedto positive values with maximum at ε = 0.32 with tendency to values of a pure tensile mechanism(ε = 0.37).
5.4 Decomposition of the moment tensor
According to the results of the eigenvalue analysis, the focus of the evaluation of source
mechanisms is on volume change in the sources and orientation of the fracture planes. For thispurpose we choose a general decomposition of the moment tensor, which is defined as:00  3 tr ( M )1 
M = ⋅ 0
tr ( M )0  + ∑ mi*
3 
i =1
0
tr ( M ) 
 0

(6)

where tr(M) = m1 + m2 + m3 is the trace of the entire moment tensor and mi* = mi – 1/3·tr(M)are deviatoric eigenvalues. The first term describes the isotropic part of the moment tensor,
which is important for quantifying the volume change in the source. The second term describes
the deviatoric part of the moment tensor. The deviatoric part can be further decomposed. Various
decompositions are possible. In the field of AE we choose a frequently used decomposition into
double couple (DC) and compensated linear vector dipole (CLVD). Of course, the DC
181

mechanism is a realistic one for microcracks. The CLVD mechanism raises the question: is thismechanism realistic for microcracks or not? In seismology, significant CLVD mechanisms canbe found for deeper earthquakes or earthquakes with two or more sub-events at a complicatedfractal geometry of the fault plane (Frohlich, 1994, Miller et al., 1998).

Fig. 6 Histogram of the ε values calculated by the eigenvalue of the moment tensor.
The combination of isotropic and CLVD components describes a tensile source. A pure tensile source can be represented by setting the isotropic part as 79.6% (for elastic constants of rocksalt) and the CLVD part as 100%. In a broader sense, CLVD source components can be interpreted as the deviatoric part of a tensile source in case of microcracks.
In addition to the above mentioned decomposition, the source mechanisms can be quantified
using the R value which is defined after Feignier et al. (1992) as the ratio of isotropic component
and sum of isotropic and deviatoric component:R=tr ( M )3tr ( M ) + ∑ m
i =1

(7)

*
i

The ratio varies from –100% for pure-implosion sources up to 100% for pure-explosion
sources. A pure-shear failure mechanism is indicated by R = 0 and a pure-tensile mechanism isindicated by R value of approximately 67% for the elastic parameters of rock salt.
The volume change in the source can be estimated from the isotropic moment tensor MISO =(λ + 2/3µ)∆V (λ, µ are the Lamé’s constants), which is applicable to plane cracks with tensile
source components (Müller, 2001). According this relationship the crack volume can be estimated from the scalar moment MISO of the isotropic moment tensor as: ∆V = MISO/(λ + 2/3µ).
182

After Silver and Jordan (1982) the scalar moment M is defined as:
3

M =

∑e
j =1

j

,
6
where ej are the eigenvalues of the isotropic moment tensor. It should be noted that alternative
definitions of the scalar moment were considered in literature (see Dziewonski et al., 1983, Silver et al., 1982, and Hudson et al., 1989). Bowers et al. (1999) compare various published definitions of scalar moments, which were derived from the seismic moment tensor. They found differences between seismic moments suggested by Silver and Jordan (1982) and Hudson et al.
(1989) by a factor that varies between 1.2 and 0.8 (for a Poisson solid).

6. Results of The Moment Tensor Calculations
6.1 Effectiveness of automated evaluation procedures
Table 1 presents the number of events, which were evaluated with various automatic procedures. The table shows, that 57% of all 105 detected events were located using at least eight Pand S-wave arrivals times, 30% of all events were more precisely located using 16 arrivals atleast. The source mechanism could be evaluated using the moment tensor method on about 15%of all events, and stable moment tensor solutions were obtained on 10% of the events.
Table 1 Number of events which were obtained in automatic procedures such as registrationof events up to moment tensor determination.
Procedure

Requirements

Number of events

Registration of eventsat least one channel was hit

105 (100 %)Source location

at least eight P- and S-wave consistent arrival times

5.7·104 (57 %)Precise source location

at least 16 P- and S-wave consistentarrival times

3·104 (30 %)Moment tensor method

at least 10 clear discernible P-wave
first motions

1.5·104 (15 %)Stable moment tensor
solutionsat least 10 clear discernible P-wave
first motions

1.25·104 (12.5 %)6.2 Source mechanism and source orientation
As mentioned above, the moment tensor method was applied to 12,562 events which fulfilled
the required stability criteria. The following analysis exclusively concentrates on these events. In
a more detailed analysis the AE activity was divided in five successive stages of the test. At low
axial stress up to 20.8 MPa the rock behaves nearly linear elastic. Above this axial stress the rockbecomes dilatant. In the dilatancy region where most of the events were recorded, stages withequal number of events were selected. The four further stages are from 20.8 to 30.2 MPa, from30.2 to 34.1 MPa, from 34.1 to 37.8 MPa, and from 37.8 to 44.1 MPa, respectively. Please notethat the highest axial stress of 44.1 MPa does not correspond to the stress at fracture. The ultimate axial stress is approximately 55 to 60 MPa. Above 44.1 MPa most of the AE sensor cables
were ruptured due to the high deformation of the specimen and, therefore, the AE measurement
has been stopped.
183

Fig. 7 At top: position of T axes marked by small dashes which are lying in the vertical x-zprojection plane in five successive stages of loading. The black boxes are the contour of thespecimen after reaching the final stress at each stage. At bottom: distribution of the T axes in an
equal-area projection on the lower hemisphere.
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Figure 7 displays in its upper part the positions of T axis in a vertical coordinate plane (x-zplane) and in its lower part the density of T axis in an equal-area projection to the lower hemisphere in the above-mentioned five stages. The figure shows at top the T axes by means of small
dashes, which were rotated in such a way that the axes lie in the vertical coordinate planes. The z
axis corresponds to the loading direction. The shape of the contour of the rock specimen at theend of each stage is plotted as a box. Apart from few events, which were recorded at the beginning of the test (figure at the left-hand side) most of the T axes are nearly horizontal oriented.
The T axes distribute around the focal sphere at plunge below 30°. One accumulation of events
has been formed in direction of approximately 54° in azimuth (measured clockwise from top)
and of 15° in plunge which becomes stronger with increasing axial stress.
In order to study the distribution of the isotropic and double-couple components, the eventswere separated into two fractions: events with exclusively compressional first motions andevents with dilatational first motion at least at one channel. The latter fraction includes only
about 2.5% of all events. The histograms of the isotropic and double-couple components of both
fractions are shown in Fig. 8. The distribution of the fraction with compressional first motion isindicated by unfilled bars, while the other with partly dilatational first motion is indicated byfilled bars. Apart from the events with pure deviatoric components which are indicated by thepeak at zero %, the distribution of the isotropic part (Fig. 8a) is very wide with its maximum at
50%. Most events with dilatational first motion show lower isotropic components with its maximum near zero %.

Fig. 8 Histogram of isotropic source component (a.) and double-couple component (b.) of all
evaluated events. Unfilled bars are indicating events with compressional first motions only andblack bars are indicating events with at least one dilatational first motion.
Figure 8b shows the distribution of the DC components. The frequency of events increases
up to approximately 25% and after a broad maximum slightly decreases. Events with partly dilatational first motion show a fairly flat distribution beginning at about 10%.
Figure 9 shows the distribution of the R value of all evaluated events. Additionally, the specific R values of a pure-implosion source at -100%, pure-explosion source at 100%, pure-shear
source at R = 0%, and pure-tensile source at 67% are marked by arrows. The R values show apeaked distribution with maximum at 54% close to the R value of a pure-tensile source. At
higher values the distribution quickly tails off. At lower R values the tail reaches down to negative values indicating the presence of few events with partly implosion-source components. It
185

can be seen that the predominant source mechanism is of tensile type. Pure-explosion sources do
not occur.
The distribution of the volume change (Fig. 10) shows that most of the events have volume
changes below about 50 mm3 with a pronounced increase to very small values. The total amountof dilatancy calculated from the moment tensors by adding up the volume change of all events isabout 150 ml. The survey of the rock specimen after the test pointed out that the average diameter of the rock specimen had increased from originally 150 mm to 158.2 mm and that the final
axial deformation was about 26.4 mm. According to these dimensions the actual dilatancy of thespecimen was about 75.6 ml. That means, that the calculated dilatancy is two times higher thanthe measured one. It can be even more, because small events, which are not considered in the
moment tensor evaluation, contribute to the volume, but with much smaller amount than the big
events. This discrepancy between measured and calculated dilatancy can be explained by the factthat open cracks were partly closed due to the applied confining pressure. This suggests thatsome of the opened cracks close during further loading of the specimen.
As mentioned before the majority of evaluated events have compressional P-wave first motions at all channels, which is a clear indication for tensile fracturing. The few events with partly
dilatational first motion tend to higher DC components and lower isotropic components. The
volume change in the source of these events is nearly zero. This is an indication that particular
events with high shear components occurred. If such sources exist they could be generated bystress deviations at increasing axial load. Under deviatoric stress conditions planes of maximum
shear stress are orientated at 45° from planes of maximum normal stress. But planes of maximum shear stress are not generally free of normal stress. Additional information e.g. from Swave first motions, could better explain the existence of DC components, because sources ofFig. 9 Histogram of R value of all evaluated events. The R values of pure-explosion sources,pure-implosion sources, pure shear sources, and pure tensile sources are marked by arrows.
186

Fig. 10 Histogram of volume change (in mm3) of the sources.
dominant shear type radiate more energy in S waves than in P waves. Unfortunately, S-wave first
motions are not clearly discernible in our recorded signals (cf. Fig. 2) because of the small time
separation between P- and S-wave arrivals and the presence of P waves reflected at the specimen
walls which can arrive earlier or at the same time as direct S waves.
7. Discussion
Alkan et al. (2002) performed model experiments on 10 mm thick rock salt slices to investigate the occurrence crack types during dilatant loading. Under an optical microscope with a total
magnification of 180 they observed intercrystalline cracks (Type 1) on grain boundaries. Increasing the deviatoric stress resulted in growth and opening of these cracks. Contrary to this, transcrystalline cleavage cracks (Type 2) which formed during loading remained closed. Consequently, Type 1 cracking seams to be mainly responsible for dilatancy and an increase of permeability for fluids during deviatoric loading of rock salt.
We attribute the evaluated AE events in this study mainly to Type 1 cracking. There are several indications, which confirm this opinion. First, the source location discovers a cellular structure with a cell size in the range of some grain diameters. The events occurred in zones where
grain interfaces are favorably orientated in the stress field, i.e. the minimum principal stress isnormal to the interface. Second, most of the events (approximately 90%) show significant isotropic source components. This is in good agreement with dilatation of salt rock during compression. Their ε values and R values (Figs. 6 and 9), which were obtained from eigenvalues ofthe moment tensors tend to values of pure-tensile mechanisms (ε = 0.37 and R = 67%). And
third, we found that the tension (T) axes which are normal to the crack plane of tensile cracks
187were predominantly radially directed, that means normal to the direction of the maximum principal stress as expected for such kind of crack type.
Acknowledgement
The author thanks his colleague Dr. J. Eisenblätter and Prof. T. Dahm from the University of
Hamburg, Germany, for discussion and their suggestions and interests. The author would like tothank Dr. U. Düsterloh and Mr. R. Niens from the Technical University of Clausthal, Germany,for the performance of the experiment. We are grateful to Dr. T. Spies and Dr. J. Hesser (Federal
Institute for Geosciences and Natural Resources, Hannover, Germany) for providing us with the
AE measuring system.
References
Aki, K., Richards, P.G., 2002: Quantitative Seismology, Second Edition, University Science
Books, Sausalito, California.
Alkan, H., Pusch, G., 2002: Definition of the Dilatancy Boundary Based on Hydro-MechanicalExperiments and Acoustic Detection, SMRI Fall Meeting, Bad Ischl, Austria, Oct. 7-9, 2002.
Bowers, D., Hudson, J.A., 1999: Defining the Scalar Moment of a Seismic Source with a
Gerneral Moment Tensor, Bull. Seis. Sc. Am., 89 (5), 1390-1394.
Brace, W.F., Paulding, B.W., Scholz, C., 1966: Dilatancy in the fracture of crystalline rocks, J.
Geophys. Res., 71 (16), 3939-3953.
Douglas, A., 1967: Joint hypocenter determination, Nature, 215, 47-48.
Dziewonski, A.M., Woodhouse, J.H., 1983: An experiment in systematic study of global seismicity: centroid-moment solution for 201 moderate and large earthquakes, J. Geophys. Res., 88,
3247-3271.
Efron, B., Tibshirani, R., 1986: Bootstrap methods for standard errors, confidence intervals andother measures of statistical accuracy, Statistical Science, 1, 54-77.
Eisenblätter, J., 1980: The origin of acoustic emission – Mechanisms and models, in: Acoustic
Emission, Conference in Bad Nauheim, Deutsche Gesellschaft für Metallkunde e.V., pp. 189204.
Feignier, B., Young, R.P., 1992: Moment Tensor Inversion of Induced Microseismic Events:
Evidence of Non-Shear Failure in the -4<M<-2 Moment Magnitude Range, Geophys. Res. Letters, 19 (14), 1503-1506.
Frohlich, C., 1979: An Efficient Method for Joint Hypocenter Determination for Large Groups ofEarthquakes. Computers and Geosciences, 5, 387-389.
Frohlich, C., 1994: Earthquakes with non-double-couple mechanisms. Science, 264, 804-809.
Gilbert, F., 1970: Excitation of the normal modes of the earth by earthquake sources. Geophys. J.
R. Astr. Soc. 22, 223-226.
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Hudson, J.A., Pearce, R.G., Rogers, R.M., 1989: Source plot for inversion of moment tensors, J.
Geophys. Res., 94, 765-774.
Hunsche, U., Hampel, A., 1999: Rock Salt – the mechanical properties of the host rock material
for a radioactive waste repository, Engineering Geology, 52, 271-291.
Jost, M.L., Herrmann, R.B., 1989: A student’s guide to a review of moment tensors, Seis. Res.
Letters, 60, 37-57.
Lockner, D., Byerlee, J., 1977: Hydrofracture in Weber Sandstone at high confining pressure anddifferential stress, J. Geophys. Res., 82, 2018-2026.
Lux, K-H., Düsterloh, U., Hou, Z., 2000: Increasing the Profitability of Storage Caverns by
Means of a New Dimensioning Concept (I), Erdöl Erdgas Kohle, 118, 294-300.
Manthei, G., Eisenblätter, J., Spies, T., 2000: Acoustic Emission in Rock Mechanics Studies, in:
Acoustic Emission – Beyond the Millennium, Eds.: Kishi, T., Ohtsu, M., Yuyama, S., Elsevier,pp. 127-144.
Miller, A.D., Foulger, G.R., Julian, B.R., 1998: Non-double-couple earthquakes. 2. Observations, Rev. Geophys., 36, 551-568.
Pujol, J., Herrmann, R.B., 1990: A student’s guide to point sources in homogeneous media, Seis.
Res. Lett., 61 (3-4), 209-224.
Scholz, C., H., 1968a: Microfracturing and the Inelastic Deformation of Rock in Compression, J.
Geophys. Res., 73 (4), 1417-1432.
Silver, P. G., Jordan, T. H., 1982: Optimal estimation of scalar seismic moment, Geophys. J. R.
Astr. Soc., 70, 755-787.

189

TESTING OF DIAMOND-LIKE CARBON COATINGS UNDER SLIPROLLING FRICTION MONITORED BY ACOUSTIC EMISSIONMANUEL LÖHR
Federal Institute for Materials Research and Testing (BAM), Berlin, Germany
Abstract
Wear tests were carried out on different diamond-like carbon (DLC) coatings under lubricated and dry slip-rolling friction in a twin-disc testing rig. The analysis of acoustic emission(AE) provided an easy and comfortable tool for monitoring the wear life of DLC coatings. A
DLC coating in-service was indicated by the appearance of no or just a single AE event due tosingle damage (crack, small spalling, etc.) to the coating and the counterbody. A high AE activity consisting of AE events of low energy was due to large spallings in the coating, which led tothe undesirable contact between steel substrate and steel counterbody. However, a high AE activity caused by events of high energy indicated the end-of-service of the coating. In each and every
test this AE activity was observed, the coating failed in the end. The AE responded much quicker
and was much more sensitive than any changes in the coefficient of friction.
Keywords: Wear life, DLC coatings, slip-rolling friction, acoustic emission testing
1. Introduction
Diamond-like carbon (DLC) coatings show good resistance to wear and low coefficients offriction (COF) under sliding friction, especially under dry conditions (1). DLC coatings also leadto low wear on counterbodies made of steel (2). DLC coatings are already used in ball bearing
races or cages, as protection of hard disc drives, as well as in a number of moving parts insideautomobile engines (e.g. in diesel engine injection systems) (3). The field of applications can be
extremely widened, if they are resistant to slip-rolling conditions at contact pressures P0 > 2.2
GPa. As the wear life of such DLC coatings is important for the performance of the respective
ball bearings, etc., DLC coatings were tested here. The performance of different DLC coatings
under slip-rolling friction was examined.
The well-known analysis of acoustic emission (AE) is already used successfully in tribology.
Sliding tests of CrN (4) and DLC (5, 6) coatings were monitored using AE. In contrast, for sliprolling conditions both samples are in motion and as a consequence the application of AE ismore demanding. The AE was introduced to monitor in-situ the tests of different DLC coatings
under slip-rolling friction.
2. Tribology Setup
In an Amsler-type twin-disc wear tester, steel samples with a thin DLC coating were tested
under lubricated and dry slip-rolling friction. Paraffin oil without any additives was used as lubricant (boundary lubricated conditions). In Fig. 1 the testing condition is illustrated. The tribological test conditions are listed in Table 1. Two disc specimens form a test set and are in motion rolling on each other with a slip of about 10%. Maximum initial Hertzian pressures P0 = 1.5
GPa. Pressure of 1.9 GPa and 2.3 GPa could occur in the centre of the contact area. The tests under dry slip-rolling conditions were carried out with a Hertzian pressure P0 = 1.5 GPa.
J. Acoustic Emission, 22 (2004)190© 2004 Acoustic Emission GroupA bearing steel (100Cr6) served as the substrate of the DLC coating and also as the materialof the counterbody. Only the cylindrical specimen was coated with an interlayer and a DLC coating with a thickness of about 1-10 µm. The coatings of six different suppliers were monitored
using AE. The properties of these coatings are listed in Table 2. Their tribological behavior in thetests is discussed in detail elsewhere (7). For further information about each test, use the code
number presented here. In the code numbers the different letters denote different suppliers andthe numbers indicate the test number of a DLC coating tested of one supplier.

Fig. 1 Picture of a test sample in the Amsler type twin-disc wear tester and sketch of the acoustic
coupling of the AE sensor under lubricated slip-rolling friction.
3. Acoustic Emission Setup
The AE signals were detected by means of an AE sensor (PAC-µ30) with a resonant frequency of about 270 kHz. The sensor was arranged in a distance of about 1 mm to the center of thecontact area. The sketch in Fig. 1 shows that the oil served also as couplant. Under dry sliprolling conditions the ambient air served as couplant. The output of the sensor was amplified 40
dB (lubricated) or 60 dB (dry) by a PAC 2/4/6 preamplifier. Every AE signal which exceeded thethreshold of 61 dB or 45 dB originated from the contact area was stored as a hit by an AE measurement system (PAC-DISP). The energy in aJ (atto = 10-18) of each hit was used. The energy E
was calculated byt*
1
E = ⋅ ∫  A(t)2  ⋅ dt
R t =0with R = 10 kΩfor the input resistance of the preamplifier, t the time in seconds, A the timedependent amplitude in V that exceeds the threshold of 61 dB or 45 dB and t* the duration inseconds. The configuration of the AE measurement is listed in Table 3.

191

Substrates coateda) perfect in-service

Counterbodies

AE graphsb) in -servicec) end-of-serviced) out-of-service

Fig. 2 AE indicated the different stages of the wear life of DLC coatings in steel contact underslip-rolling friction. The undesirable contact between steel against steel was indicated immediately by a high AE activity (base noise). (a) D2, lubricated, P0= 1.9 GPa, n = 0-3,000 revolutions;(b) D2, lubricated, P0= 1.9 GPa, n = 615,000-1,250,000 revolutions; (c) D9, lubricated, P0= 1.9
GPa, n = 30,000-60,000 revolutions; (d) uncoated, lubricated, P0= 2.3 GPa, n = 320,0001,000,000 revolutions.
4. Wear Time History and Sources of Acoustic Emission
DLC coatings were tested here as a wear protection coating in steel contacts. The coating had
to avoid a contact between the steel surfaces of the substrate and the counterbody. In doing so,
the DLC coating was in-service. The service life ended when a coating exhibited spallings,
which were large enough to render the direct contact of steel against steel. Figure 2 shows thedifferent stages of the use of a DLC coating and the corresponding AE under lubricated sliprolling friction:
192

Fig. 2(a): DLC coating is perfect in-service. At the beginning of the test the counterbody waspolished from rough (RZ~ 3 µm) to fine (RZ< 3 µm). The coating showed no damage. Aslong as no damage occurred either to the coating or to the steel, no AE was recorded due tothe proper threshold setting.
Fig. 2(b): DLC coating is in-service. The coating showed only minor defects after a total numberof 1 million revolutions. The corresponding counterbody stayed polished. In accordance tothis desirable tribological behavior only a few AE hits were recorded due to single damage
events to the coating.
Fig. 2(c): DLC coating is at end-of-service. After a total number of 60,000 revolutions the coating showed a spalling about 1 mm2 in size. A high AE activity occurred after a total numberof ~40,000 revolutions and stayed till the end of the test. The high AE activity, which formed
the basic noise was the result of the direct contact between the steel of the substrate and thesteel of the counterbody. In addition, a few hits with AE energies of 10,000 aJ and more wereregistered. These hits were associated with individual damage events to the coating, because
the counterbody here stayed polished without damage. In general, spallings larger than 1
mm2 led to heavy damages like plastic deformations to the counterbody (see Sec. 5.2).
Fig. 2(d): DLC coating is out-of-service. As reference, a test sample without any coating in thesteel contact was examined. The corresponding counterbody and the uncoated substrate
showed small spallings, plastic deformations and cracks after a total number of 1 million
revolutions. These events were associated with single hits mainly with AE energies of 10,000
aJ and larger. A high AE activity as a basic noise was observed due to the direct contact ofsteel against steel.
Figure 3 shows the different AE sources, which can occur in the lubricated tribological contact. The AE hits showed AE energy in a range of 100 to 100,000,000 aJ. The different AE
sources were located in the DLC coating and steel. AE sources “subsurface” to the coating (under the coating surface) were difficult to observe. However, AE activity without damage to thecoating and the counterbody would be assumed as AE hits due to delaminations, cracks and otherAE sources “subsurface” to the coating. Considering the wear time history a separation of different AE sources and locations were possible. As long as a DLC coating was in service all AE hits
had one origin (location): the coating itself. Only cracks which occurred in the wear track of thecounterbody led to hits mainly with AE energy above 10,000 aJ. These cracks were relief cracks
and were not critical. However, they only appeared rarely. Spallings with a critical size led to ahigh AE activity. The base noise indicated the undesirable direct contact between steel against
steel (end-of-service) of a DLC coating. Furthermore, the AE activity got more intensive due tothe number of separate damage events from the coating and the counterbody.
The tests under dry slip-rolling friction conditions were too small for an analysis of the AE
sources. Only five tests were taken out. More details are discussed elsewhere (8). Fretting was asource of intense AE with large AE energies (see Fig. 7).
5. Wear Tests Monitored by Acoustic Emission
The classical cause for the failure of rolling elements in bearings is localized defects, inwhich large pieces of the contact surface are dislodged during operation. The appearance of sucha defect with the size of 1 mm2 was chosen as the criterion to mark the end of the wear life of the
DLC coating tested (1 mm2-criterion) (7, 8). If a DLC coating did not exhibit a defect of such asize after about one million revolutions, it was called resistant to slip-rolling fatigue. In order todetermine the end of the wear life the tests were divided into logarithmic intervals to examine thesurfaces with an optical microscope. This procedure led only to a rough estimation of the wear
193

Fig. 3 AE energy of hits from different sources and locations under lubricated slip-rolling
friction.

Fig. 4 Results of the wear tests of the DLC coating under lubricated slip-rolling friction. Test
samples monitored by AE are marked in bold and are underlined.
194

life, because after the test the size of spallings larger than 1 mm2 varied in a large scale. In orderto get a finer estimation of the wear life and to save time, it was important to have a technique,which was able to detect defects in-situ, while the tests were in progress. The wear tests weremonitored by AE and the coefficient of friction (COF) was recorded. In general, if a system isnearing the end of its wear life, the friction torque dramatically increases. However, under lubricated slip-rolling friction conditions the COF did not change significantly, even if the DLC coating was removed completely from inside the wear track (7). Under dry conditions the COF was
not able to detect undesirable damages to the DLC coatings clearly. Complicated tribochemical
reactions were probably the reason for the different behavior of the COF (7). In contrast, the tests
were monitored successfully with AE.
5.1 Results
In Fig. 4 all results are presented of the tests under lubricated slip-rolling friction. Test samples with a bold and underlined number were monitored by AE. In all cases in which test samples reached the 1 mm2-criterion, a high AE activity could be registered, too. In many cases theAE criterion indicated the end-of-service very early.
Substrates coatedCounterbodies

AE graphsa) AE criterionb) 1 mm2- criterion

Fig. 5 Test results for specimen code A10, showing early spalling detection by AE. (a) A10, lubricated, P0= 2.3 GPa, n = 16.000-30.000 revolutions; (b) A10, lubricated, P0= 2.3 GPa, n =490,000-690,000 revolutions.
5.2 Early detection by acoustic emission
Spallings smaller than 1 mm2 and damage events “subsurface” to the DLC coatings could
lead to a high AE activity (AE criterion). In Fig. 5 the test sample with the code number A10 isshown. This test was terminated after a total number of 690,000 revolutions and a high AE activity was registered. This high AE activity occurred already after a total number of 23,000 revolutions, too. The base noise was caused mainly by the direct contact steel against steel throughspallings with a size less than 1 mm2. The small damage events in the wear track of the195counterbody were mainly caused by the hard edges of the spalling in the coating. The damage
events of the coating and of the counterbody were indicated by individual AE hits. The AE energy of these hits was in most cases higher than the base noise.
Damage events “subsurface” to the DLC coating were another reason of an early detection.
These damage events indicated the oncoming termination of the test (1 mm2-criterion). Somesamples, which by definition were called resistant to slip-rolling fatigue, were tested further tothe 10,000,000 revolutions. The test samples B7, E3, F1 and G3 reached the 1 mm2-criterion after 1,000,000 revolutions. A high AE activity was recorded much earlier. Figure 6 shows the results of the test sample B7 and G3. The AE graphs show the AE in the test interval, in which thehigh AE activity began. The sources of AE had to be “subsurface” to the DLC coating likecracks and delamination, because the amount of “visible” damage events were too small for thehigh AE activity observed.
Substrates coateda) failed: 3,000,000

Counterbodies

AE graphsb) failed: 9,600,000

Fig. 6 Early AE detection of subsurface defects. (a) B7, lubricated, P0= 1.9 GPa, n = 150,000200,000 revolutions; (b) G3, lubricated, P0= 2.3 GPa, n = 1,000,000-2,600,000 revolutions.
5.3 Larges spallings
Spallings larger than 1 mm2 could heavily damage the wear track of the counterbody. Underlubricated slip-rolling friction the counterbody showed plastic deformations and spallings. Under
dry conditions a DLC coating, which was partly rubbed out off the wear track, could lead to fretting conditions between the steel surfaces. Such damage events led to a high AE activity in addition to the base noise. The base noise became more intense with increasing size of the spalling.
Figure 7 shows three DLC coatings with a spalling larger than 1 mm2. In all cases a very powerful and intensive AE activity was recorded:196

Substrates coatedCounterbodies

AE graphs

2a) 1 mm -criterionb) 1 mm2-criterionc) 1 mm2-criterion

Fig. 7 Heavy damage of the counterbody from large spallings, producing intense AE activity.
(a) D1, lubricated, P0= 2.3 GPa, n = 0-3,000 revolutions; (b) F2, lubricated, P0= 1.5 GPa, n =100,000-483,000 revolutions; (c) D4, dry, P0= 1.5 GPa, n = 30,000-130,000 revolutions.
Fig. 7(a) The edges of the large spalling in the coating had dramatically damaged the respective
counterbody. The high AE activity, which was recorded immediately, was due to the directcontact of steel against steel and to the observed plastic deformations and spallings of thecounterbody.
Fig. 7(b) After a total number of 483,000 revolutions this sample reached the 1 mm2-criterionwith a very large spalling. Most of the coating was removed suddenly inside the wear track.
The counterbody showed plastic deformations. The AE graph showed the AE activity from anumber of 100,000 to 483,000 revolutions. In this test interval the AE activity changed froma high AE activity to a very high AE activity. The very high AE activity was due to the contact of steel against steel and to the heavy damaging of the counterbody and the coating, both
appeared suddenly. Probably the high AE activity occurred earlier due to crack events in thecoating.
Fig. 7(c) The DLC coating was partly rubbed out off the wear track. Parts of the coating covered
the transfer layer in the wear track of the respective counterbody. The AE graph shows theevolution of the damages which could be observed after the tests. The removal of the coating197

happened suddenly. Nearly no AE activity was registered before a very high activity level of
AE was reached. The high AE activity was mainly caused by fretting events through the steel
against steel contact and appeared suddenly at a total number of 90,000 revolutions.
6. Conclusion & Discussion
Wear life tests of DLC coatings on a steel substrate under slip-rolling friction conditions
were carried out. The coated substrate ran against a counterbody made of steel. The test parameter changed as follows:• DLC coatings of different suppliers• Hertzian pressures P0 in a range of 1.5 – 2.3 GPa• Lubricated with paraffin oil without additives and dry• Coating thickness/ Surface finishing of the counterbody
The individual tests were terminated by the 1 mm2-criterion. The AE was an excellent toolfor in-situ estimation for the end of the wear life of the DLC coatings tested here. Under lubricated conditions a high AE activity (AE criterion) indicated in-situ all samples which actually
failed later on in the tests. A high AE activity was capable to detect the failure much earlier than
the 1 mm2-criterion. The sources of a high AE activity were as follows:• Mainly originating from the contact of steel against steel. Sizes of spallings in the coating
less than 1 mm2 could lead to a contact of the substrate and the counterbody, both made ofsteel. The high AE activity appeared as a base noise.
• Continuous damaging to the surface of the counterbody. The edges of larger spallings in thecoating were able to damage the counterbody plastically.
• Many events, like cracking and delamination, under the coating surface. These events could
indicate the catastrophic failure, which appeared suddenly.
The spallings showed different sizes above 1 mm2 after the termination of the tests. The total
amount of coating material removed changed with the type and especially with the individual
quality of each coating. The AE reacted very sensitively to the end-of-service of a coating. A
high AE activity as a base noise indicated the undesirable contact between steel against steel due
to spallings in the coating. The AE turned out to be a quick and easy monitoring tool of wear
tests to save test time and to get more comparable results. Furthermore, the AE provided a useful
tool for the observation of the wear time history. Tests could be stopped, if a damage event
occurred. Regarding the wear time history of the DLC coatings in a steel contact a separation ofdifferent locations of the AE sources was possible.
Further tests under dry conditions have to be carried out. However a high AE activity occurred due to fretting conditions, which were induced by a large and deep spalling in a DLC
coating.
•••••

The advantages of using the AE were as follows:
Detecting the onset of undesirable wear
Early detection of the end-of-service
In-situ view of the tribological history
Remote monitoring
Save time and money, especially in long time tests.

198

Table 1: Test conditions in the Amsler type twin-disc wear tester.
Disc dimensions

Diameter D: 42 mm, Thickness t: 10 mm

SampleSubstrate: cylindrical lateral area
Counter body: spherical lateral area (R: 30 mm)

Tested materials

Substrate: bearing steel (100Cr6), fine polished with a thin DLC coating
Counter body: bearing steel (100Cr6), fine or rough polished
1.5 GPa, 1.9 GPa, 2.3 GPa

Initial normal loads(Hertzian pressure P0)
Speed

Substrate: 390 RPM; Counter body: 354 RPM

Tribological use

Slip-rolling friction with a sliding velocity of 0.08 m/s (10% slip)

Test atmosphere

Ambient air (temperature 20 °C, relative humidity 50 %)LubricantParaffin oil (without additives, η20°C: 110-230 mPa⋅s), dry

Table 2: Properties of the different DLC coatings, which were monitored using AE.
Code num-Type of DLC

interlayer Doping

Thickness of the DLC

modulus of elasticity

bers

coating

coating

E

A7 – A11

a-C:H:Si

Si

Si

10 µm

161 GPa

B5 – B7

a-C:H:Me

Ti, W

Ti, W

2-3 µm

No information

D1 - D4

a-C:H

Ti

No

2 µm

256 GPa

D6 - D9

1 µm

E2 - E5

a-C:H:MeCrW

1-2 µm150 – 170 GPa

F1 – F4

a-C:H:SiSi

Si

3-5 µm251 GPaF5 – F7
G1, G3, G4

No information
a-C

Ti

No

199

3 µm

500 GPaTable 3: Configuration of the Acoustic Emission (1 dB corresponds to 1 µV).
Sensor

PAC µ30 (resonant frequency fR of about 270 kHz)

Couplant

Dry:ambient air:Lubricated:paraffin oil

Preamplifier PAC 2/4/6(filter: 100-1200 kHz)

60 dB

40 dB

Threshold

45 dB

61 dB

Front-end filter

2 Counts

3 Counts

AE parameterAE energy of each hit in aJ (atto = 10-18)

Acknowledgement
This work was carried out at the laboratory VIII.11 of the Federal Institute for Materials Research and Testing (BAM) in Berlin and was supported by the Deutsche Forschungsgemeinschaft (DFG SA 645/4).
References[1] K. Holmberg, H. Ronkainen, A. Matthews, Coatings tribology-contact mechanisms and surface design, in: I.M. Hutchings (Ed.), New Directions in Tribology, Mechanical Engineering
Publication Ltd., Bury St. Edmunds and London, 1997
[2] M. Sellschopp, G. Durst, H.D. Hildebrandt, Herstellung und Charakterisierung vonmodifizierten DLC-Schichten, in: H. Dimigen and W. Paatsch (Ed.), Oberflächentechnik,
Werkstoffwoche 1998, Symposium 11, Wiley-VCH Verlag, Weinheim, 1999
[3] H. Ronkainen, Tribological properties of hydrogenated and hydrogen-free diamond-like carbon coatings, VTT Publications 434, Technical Research Center of Finland, Espoo, 2001
[4] C. Chung-Woo, L. Young-Ze, Wear life evaluation of CrN-coated steels using acoustic emission signals, Surface and Coatings Technology 127 (2000) 59-65[5] F.M. Kustas, J.N. Schwartzberg, S.H. Carpenter, C.R. Heiple, D.L. Armentrout, Acoustic
emission monitoring of diamond-like carbon coating degradation during sliding wear, Surface and Coatings Technology 67 (1994) 1-7[7] D. Spaltmann, M. Löhr, U. Effner, M. Woydt, Experimentelle Untersuchungen und
numerische Modellbildung zur Beschreibung des Funktionsverhaltens wälzbean-spruchter
Schichtsysteme, Forschungbericht der Bundesanstalt für Materialforschung und –prüfung
(BAM), N&W Verlag, Berlin 2004[8] M. Löhr, E. Santner, D. Spaltmann, M. Woydt, In-situ wear life detection of Diamond-Like
Carbon coatings during slip-rolling friction by using Acoustic Emission, Wear (to be published).

200

FIELD TESTING OF FLAT BOTTOMED STORAGE TANKS WITH
ACOUSTIC EMISSION – A REVIEW ON THE GAINED EXPERIENCEGERALD LACKNER and PETER TSCHELIESNIGTUV Austria, Vienna, Austria
Abstract
A new testing technique for corrosion and leakage testing of the flat bottoms of atmospheric,aboveground storage tanks (AST) with acoustic emission (AE) was developed under an EC
funded project. Frequency-domain-based pattern recognition is used to identify the source
mechanism of the found AE sources located at the tank floor. The basics of the applied method
and the testing technique itself are outlined as well as results of performed tests are given together with statistical numbers regarding the grading of tested tank floors. Conventional nondestructive testing of tank floors relies mainly on visual testing and wall thickness measurements
and therefore reflects the past service periods of the tank in total. On the other hand AE enables
to monitor the active processes taking place at the time of test on the tank floor at conditions
similar to normal tank service. The combination of both leads to a complementary view of thetank floor condition, taking advantage of both approaches: determination of the total degradationof the tank floor and monitoring of the actual degradation processes taking place under current
service conditions.
Keywords: Corrosion and leakage testing, aboveground storage tank (AST), tank floor
1. Introduction
We have tested more than 200 tanks at refineries, chemical sites and tank farms since theacoustic emission (AE) testing technique, developed under an EC funded project [1], was introduced into the maintenance and inspection market mainly in Germany, Austria and Italy. Whereavailable, results obtained with other non-destructive testing (NDT) methods are compared withAE test results. In order to be able to combine the information from different NDT techniques aprecise knowledge of the service conditions, current as well as previous, of all performed maintenance actions and of all preparation works in order to clean the tank for inside inspection isnecessary. Compared to an AE test, which occupies the tank with the stock product for only one
or two days, the cleaning procedure and the inside inspection leads to a downtime of the tank forweeks or even months. This underlines the increase in tank availability and the benefit related to
employing AE corrosion and leakage testing of aboveground storage tanks (ASTs).
In many countries the duration of the service period to the next inside inspection is restricted
to a few years. Then the tank has to be put out of service and very often the inside inspection iscarried out to confirm again a good floor condition. This procedure is still questioned by manytank operators, who know the overall conditions of their tanks according to documented service
histories very well. Floor degradation due to corrosion, especially general corrosion, can be prevented quite effectively by appropriate measures, e.g. inside coating and cathodic protection. So
if the service conditions are kept well inside the known boundaries of safe operation, the tankfloor should not suffer from serious degradation.

J. Acoustic Emission, 22 (2004)201© 2004 Acoustic Emission GroupHowever, still unforeseen situations might occur, which lead to more severe types of corrosion with higher corrosion rates than expected. This could be the case at areas on the tank floor
where the inside coating has been damaged or due to contamination of the stock product withcorrosive agents accumulating at the tank floor. Thus, the time driven inside inspection intervals
may detect the onset of corrosion only by accident, more likely the present corrosion damage
may just be documented in order to repair the affected floor areas.
In contrary acoustic emission testing (AT) enables one to indicate tanks with active corrosion
processes and does not severely interfere with tank operation. AT is therefore a valuable maintenance and inspection tool supporting the tank operator to prevent serious damage. Optimal conditions for this kind of monitoring are given, when AT starts after the tank has been put back into
service after an inside inspection. At that occasion the current status of the tank floor is usuallyestimated precisely by floor scanning techniques, hence it follows that an early measurementwith AE gives an ideal reference for future repetition tests. A tank history built up in such a way
may be exploited by prolonging substantially the service period to the next intrusive tank maintenance activities. The costs of preparing the tank for inside inspection may reach easily some
100,000 Euro, therefore significant savings may be gained by using non-intrusive AT.
2. Testing Technique
The sources of costs due to corrosion are manifold and the economic costs of corrosion are
obviously enormous [2]. Beside direct costs, indirect costs, e.g. plant downtime or loss of product, are important factors. The presented testing technique can detect and locate active corrosion
processes at the tank floor and, in case of an already penetrated floor, it can detect and locate
active leakage. Thus, it is a useful maintenance tool to handle tank floor degradation economically. Acoustic emission is in general limited to the detection of active defects. But this limitation turns out to be strength, since this NDT method indicates only defects, which have an impact
on tank floor degradation (progressive loss of wall thickness) or on tank safety (actual loss of
product).
It has to be stated, and this will be underlined by statistics later on in this paper, that the majority of tanks are in good condition when continuously well maintained. These tanks should be
kept in operation and the floor condition should be monitored further. The test result gives thecondition of the tank floor regarding the defects under consideration in terms of grades. The applied grading system is given in Table 1.
The recommended maximum duration of the subsequent service is with respect to the service
condition present at the time of test. This is communicated clearly to the responsible contact person on-site, such that the conditions set by the tank operator in preparing the tank for the AE test
have to be as close as possible to usual service conditions. If the service conditions are mainTable 1: Grading system for AE testing on ASTs
Grade
I
II
III
IV

Description
Recommended service period duration
No active sources
5 years
Low active corrosion
3 years
Medium active corrosion
1 year
Leakage and/or high active corrosion--202

tained as before the test, then a prediction for the floor condition is possible. It follows that after
a change in service conditions the tank floor grading is not valid any more and a repetition AE
test is necessary to assess the new service conditions. If the tank floor is assigned to grade IV,then an inside inspection is recommended in order to verify the indications found with AT.
The preparation works for AT start with the determination of the tank properties (construction details, storage product, etc.). For this purpose we usually send out a questionnaire together
with basic information regarding our testing technique. After evaluating the filled-in statementsof the responsible contact person on-site, all necessary resources have to be allocated. Our testing
personnel is certified for AT according to EN 473 and state-of-the-art AE equipment is used.
It is also communicated before each test to the responsible customer representative that every
AE measurement is affected by noise of different origin (mechanical, electrical, etc.). Muchnoise data can be identified by software procedures and therefore it can be filtered and does not
fault the test result. But the most important part in preparing a tank on-site is simply to avoid
noise so that the AE sensors do not picked up noise. If appropriate measures are taken before thetest, e.g. elimination of noise sources or avoiding noisy time periods, then the noise content isalready limited and the noise rejection algorithms need not be invoked to their limits. Thus, thereliability of the test result increases, which is of major importance, especially if costly follow-up
actions are recommended.
The standard arrangement requires at least an AE sensor for every 15 m along the circumference of AST at a height of approximately 1 m. This row of sensors locate AE events within thehorizontal cross-section of the tank (2-D location). The considered path of sound wave propagation (see Fig. 1) from the source at the tank floor to the AE sensor on the tank shell is via theliquid stock product, therefore the location uses the speed of sound of the liquid stock product.
This arrangement is suitable when AE events originating at other parts than the floor (e.g. a floating roof) can be ruled out. In every other case we strongly recommend a sensor arrangement of
two rows of sensors. With the help of these additional sensors at a height of 3 to 5 m above thefloor and exactly above the sensors near the bottom, it is possible to reject AE events from upper
regions of the tank. This also increases the reliability of the floor grading. An example for AE
sensors mounted onto the tank shell may be seen in Fig. 1 (left).
2nd row sensor

Bottom row sensor

Fig. 1 AE sensors mounted onto the tank shell (left), and path of wave propagation (right).

203

Once an AE source is detected at the tank floor, it is important to state the source mechanism
in order to grade the tank floor properly. Source mechanism identification involves essentiallythe three following aspects:
1. data acquisition and pre-processing,
2. data representation and3. decision making.
These steps lead to the design of a pattern recognition system [3]. For choosing the best way ofdata representation one has to consider that a tank floor covers an area up to some 1,000 square
meters. Thus, the distance from the source at the tank floor to the sensor at the tank shell varies
over a wide range and so do AE signal parameters like signal peak amplitude, signal duration,
signal energy etc. accordingly. The applied testing technique takes advantage of the waveforms,
which are acquired and stored along with the other AE signal parameters. From the waveform
data the frequency response of the sound waves may be calculated and used for data representation. Figure 2 shows a typical AE signal waveform together with its frequency response. Sound
wave propagation in liquids is free of frequency dispersion; thus, the frequency response is independent of the distance from the source to the sensor. Furthermore, the attenuation of the concerned frequency bandwidth around 30 kHz may be regarded to be uniform. Hence it follows
that the frequency response of the AE signal does not depend on the distance from the source to
the AE sensor. A classifier was designed with the help of a reference database comprising signal
waveforms with well-known source mechanisms. This tool is then applied on unknown data inorder to decide whether the detected AE source is related to corrosion or to leakage. If the source
mechanism turns out to be leakage, then the tank floor is of ‘grade IV’ and it is recommended toopen the tank as soon as possible. If corrosion is detected, the test result may vary from ‘grade II’to ‘grade IV’ according to the source activity. Moreover, in a second classification step the detected corrosion source is assigned either to well-established corrosion, which is indicated by thepresence of a layer of corrosion product, or to the onset of corrosion, when a scaling layer isabout to develop. In case no active source is detected, then the tank is free of any active defectand therefore from the AE point of view it may be operated for another five years without any
further maintenance.

Fig. 2 AE signal waveform and its frequency response.
204

3. Statistics of The Performed Tests
Among the more than 200 tanks tested, the diameter range of the tested ASTs (fixed roof or
floating roof) is from 4.5 m (product tank of a chemical plant) to 98 m (crude oil tank of a pipeline tank farm). The stock product had been at ambient temperature as well as at elevated temperatures (80 °C maximum surface temperature at the sensor position) with tank shells thermallyisolated or not. Figure 3 shows the distribution according the stock product, grouped into thethree main categories. Distillates like naphtha, gasoline, gas oil or fuel oil have been assigned to‘refinery products’, whereas chemicals like acetone, trichloroethylene, propylene oxide and also
caustic soda have been added to ‘products’.

Fig. 3 Stock product distribution of tested ASTs (total of 218 tanks).
Altogether the applicability of the testing technique covers a broad variety of stock products,
which are used frequently in different industries and the respective ASTs have been tested already with AE successfully. Figure 4 gives a distribution of the test results according the four
different grades from Table 1.
One can take from this distribution that the majority of tanks (52%) is assigned to ‘grade I’,
indicating the best floor condition. This underlines the experience of many tank operators thatthe time-driven inside inspection intervals lead to costly openings of tanks being still in goodcondition. By integrating AT into the regular maintenance and inspection program, it is possible
to indicate that some tanks can have their service period prolonged.
The reliability of our testing technique has been proven twofold: by safe tank operation for
the recommended service period and by many follow-up inspections. Since AT results in astatement regarding active defects at conditions present during the test, it is complementary to
the result of an inside inspection, which aims on the estimation of the total tank floor degradation. This has to be kept in mind when comparing both kind of testing. One needs detailed
knowledge of the tank service history and also the actions performed in order to clean the tankhave to be considered when finally assessing the floor condition in comparison to the AT result.
As an example we experienced one case where the inside coating has been removed by the cleaning procedure due to insufficient bonding at an area of already established corrosion damage.
Since the tank floor had been assigned to ‘grade I’, it appeared at first glance that this defect has
not been detected. After studying the tank history it was found well documented, that the insidecoating was applied onto the given corrosion damage after grit-blasting but without repair.
205

Fig. 4 Result distribution of tested ASTs (total of 218 tanks).
Finally it turned out that the AT result was true and it represented the actual tank floor condition
present before the tank cleaning procedure. Of course the result of the inside inspection was trueas a matter of fact, and it showed the total tank floor degradation. This case should just underlinethat the beneficial combination of both results requires sometimes a precise knowledge of thetank history and of the preparation activities before entering the tank. In the majority of experienced cases this detailed discussion could be dropped due to congruent results: a tank floor is ingood condition.
According to Fig. 4, quite a number of ASTs in rather poor condition have been tested. Tank
floors have been found with severe general corrosion as well as tank floors with localized formsof corrosion and leaking tanks. As one may have already assumed, at other end of the grading
system some indications have to be clarified first before taking further actions. Since AT is capable of detecting active corrosion, it does detect sacrificial anodes within the tank. Together withthe tank operator we could identify the found corrosion indications to be at locations were sacrificial anodes had been placed. At another site a serious corrosion source was detected not on thetank floor but at the floating roof. Since a sensor arrangement in two rows as shown in Fig. 1 hadbeen applied, the detected corrosion source could be assigned definitely to the floating roof.
There a weld had already been penetrated and product was found on the sheets. Floating roof
corrosion causes AE indications and therefore faults the grading of the tank floor if not properly
identified. With the help of two rows of sensors, this can be done simply and effectively. In general every indication for a serious defect is first analyzed regarding other possible causes to avoid
costly false calls. Figure 5 shows both kinds of evaluation for the roof corrosion example, without and with the help of the second row sensors. The horizontal cross-section of the tank isshown in the x-y plane together with the sensor positions and identifications of the bottom row,whereas in the vertical direction the number of located AE events within the reference area perhour is given. It can be seen easily that the indication (red column on the left) disappears when
taking advantage of the improved sensor arrangement (right).
4. Conclusion
Non-intrusive tank floor testing with AE can detect active corrosion (progressive loss of wall
thickness) and active leakage (actual loss of product). The testing covers 100 % of the tank floor
as well as bottom side and top side of the floor sheets. Integrated into the regular maintenanceand inspection program, it is a valuable tool to separate tanks in good condition, capable for206

Fig. 5 3-D diagram of location result without and with two rows of sensors.
some further years of safe operation, from tanks in poor condition, which should be opened forinside inspection and repair.
The application of the AE testing technique enables the operator to open tanks based on thetank floor condition and not on a time-driven schedule. Thus, maintenance may be focused ontanks, which show an indication of a severe defect.
The regulations for tank operation are not the same in all European countries. There are different restrictions for the duration of the service period to the next inside inspection. To receive
an official approval for prolonging a service period requires the tank operator to convince theresponsible authority, which has already led to first successful results.
Corrosion testing with AE is not restricted to ASTs. A new research project funded by theEuropean Commission (EVG1-CT-2002-00067) has been started to test ship tanks, especiallythose of crude oil tankers, for active corrosion [4]. It has already been shown, that this application is feasible despite the harsh environment present on sea. Ongoing research activities together
with the experience from hundreds of tests ensure continuous improvements, so that AT is able
to strengthen its position for supporting economical and safe plant operation.
References[1] G. Lackner and P. Tscheliesnig, ‘Acoustic emission testing on flat-bottomed storage tanks:
How to condense acquired data to a reliable statement regarding floor condition’, J. Acoustic
Emission, 20 (2002)[2] D.A. Jones, ‘Principles and Prevention of Corrosion’, 2nd ed., Prentice Hall, Inc., NJ (1996),
p. 3.
[3] A.K. Jain, R.P.W. Duin and J. Mao, ‘Statistical Pattern Recognition: A Review’, IEEE
Transactions on Pattern Analysis and Machine Intelligence, 22 (1) (2000)[4] M.V. Veazey, ‘Preventing another Erika’, Materials Performance, 43 (2) (2004)
207

ACOUSTIC EMISSION EXAMINATION OF POLYMER-MATRIXCOMPOSITESJÜRGEN BOHSE
BAM, Federal Institute for Materials Research and Testing
Unter den Eichen 87, D-12200 Berlin, Germany
Abstract
This article shall illustrate which general information about initiation and accumulation ofdamages, critical damage stages and stable macro-crack propagation in polymer-matrix composites arise from conventional AE features and analysis of AE signals. It is referred to currently
used methods for classification and identification of AE sources. Studies of stressed specimens
cut from laminates with different lay-ups are performed to receive basic information about the
AE indication of micro- and macro-failure processes of materials used for the design of composite structures. Micro-failure by inter-fiber fracture indicates a critical stress/strain state, whichshould not be exceeded for a long life-time. Results of the theoretical analysis of inter-fiber andfiber fracture in laminates under tension are compared with monitored stress dependent AE behavior. Another important damage process that can significantly reduce the residual strength orlife-time of composite structures is the macroscopic delamination within weak areas. AE examinations of the static interlaminar fracture behavior of unidirectional, continuous fiber-reinforced
laminates under different fracture modes are discussed. AE results from pressure tests of externally non-damaged and damaged filament wound all-composite pressure cylinders are presented
and criteria for identification of “heavy” damages such as impacts and flaws are suggested. Finally, particular problems associated with the distortion of stress waves at wave propagation andby the AE transducer are pointed out. The affiliated problems of the identification of AE source
mechanisms in polymer-matrix composites using AE signals are discussed.
1. Introduction
AE measurement technique is based on generation of acoustic (elastic stress or pressure)waves by fast propagating micro-failure processes or other sources. Highly sensitive piezoelectric transducers passively detect these waves by dynamic surface motion on sub-nanometer scaleand convert it into an electric signal. The practically used frequency range is about 50 kHz to 1
MHz. Lower frequencies are often associated with extraneous noise sources or resonance effectsof the transducer case. Higher frequencies are excessively attenuated by polymer-matrix materials and, hence, these high-frequency parts of waves are carried to a distance no longer than a few
centimeters from the source location.
AE testing is a real-time measuring and on-line evaluation technique. It basically shall giveinformation as to when (time, external loading parameter), how many (rate), how intense (amplitude, energy) and where (location of AE sources) stress-wave emitting damage processes in
specimens or structures occur. However, the interpretation of AE signals and the separation of“true” damage sources from extraneous noise or rubbing of fracture surfaces is still a major problem of this method. For a long time only conventional AE features, e.g., peak amplitude, AE
counts, duration, etc. of the signals or its distributions and correlation plots were used. Recent
developments in hardware components for AE monitoring and new methods of AE signal analysis seem to yield additional information for the identification of AE source mechanisms.
J. Acoustic Emission, 22 (2004)208© 2004 Acoustic Emission Group2. AE Tools for Identification and Evaluation of Damage Stages
Currently used AE tools for the identification and evaluation of damage or fracture stages ofpolymer composites are summarized in the following overview (see also Fig. 1):
Changes in AE activity or AE intensity with time or external parameter
First AE hits/events (1)→ onset of damage processes
Characteristic changes of AE hit/count/event/energy rate or cumulative sum (2)→ progressive increase – “knee point” (progressive damage and/or occurrence of new
failure mechanisms)→ continued holding or increasing AE activity and intensity during hold periods of loading (achievement of unstable damage stages)→ increasing AE activity during unloading (rubbing of damaged areas, e.g. delaminatedsurfaces)
AE amplitude distribution (3)→ number and types of failure mechanisms→ high amplitude hits
Felicity ratio(ratio of the load at onset of significant AE during reloading to the previously applied
maximum load)→ ultimate failure warning if it falls below a certain value (≤ 0.90 - 0.95)
Location of AE sources
Planar or zonal location of single events or event clusters→ location, size, shape and growth direction of AE active zones, which are characteristic for individual forms of damage (delamination, impact, flaw)
Identification of AE sources
Tools:• conventional AE features (peak amplitude, counts, duration and energy of the signal
are dependent on transducer, frequency filter and/or threshold)• AE feature histograms and correlation plots (e.g. log duration (or counts) vs peak
amplitude)• waveform / wave mode analysis (wavelets)• frequency spectrum (FFT)• pattern recognition by means of neuronal networks• inverse moment tensor analysis• modeling of AE sources
identification of AE source mechanisms→ separation of "true" damage mechanisms from extraneous noise sources(hydraulic noise, water flow, rubbing etc.)
The interpretation of AE results is also supported by finite element (FE) modeling of local
stress/strain conditions as well as by micro-mechanical modeling of failure processes in laminates (inter-fiber failure or fiber break).
209

Fig. 1 AE from unidirectional aramide-fiber-reinforced polyamide composite during tensile test.
Active failure mechanisms in polymer-matrix composites which should be detected by the
AE method are• matrix cracking• fiber/matrix interface debonding• fiber break• intra- or interlaminar crack (delamination) propagation.
A number of general approaches used for identification of AE sources are described in theliterature [1, 2]. In reference [2] also specific problems and ways to the interpretation of AE signals from composite materials are discussed. From composites mostly thin-walled structures are
manufactured. Therefore, mainly plate waves are generated from AE sources. Modal analysis ofplate waves is based on a preferred stimulation of specific wave modes by different fracturemodes, fracture geometries and location of sources (bulk or surface) and has the purpose to recognize out-of-plane (delamination mechanism) and in-plane (fiber break) sources. Longitudinal(extensional mode) and shear components (flexural mode) of the signals are analyzed by wavelet
transform (time-frequency domain) which is suitable for AE signals whose statistical properties
change with time as in the case of plate waves. The method of inverse moment tensor analysis of
AE signals from volume waves of sources tries to separate shear cracks from tensile cracks during nucleation of the fracture process zone and crack propagation in larger isotropic structures.
A comparative analysis of selected international and national standards and guidelines on AE
testing yields an assessment of the current status of standardization [3].
210

3. Analysis of Micro-Failure Processes
For dimensioning of structures it is important to have reliable failure models. However, modern physically interpretable failure criteria should be based on specified modes of failure [4, 5].
The referred criterion describes initial failure by an inter-fiber fracture mode and ultimate macrofailure by fiber fracture. Therein, inter-fiber fracture means both interfacial failure (fiber/matrix
debonding) and matrix cracking. Aims of the studies were to check the fitness for use of failure
models and a better interpretation of AE signals by theoretically expected modes of microfailure. Another interesting question is what stress level is relevant for a significant reduction of
fatigue life-time and how it is expressed in the AE characteristics.

Fig. 2 AE behavior (signal amplification = 60 dB) and failure analysis of two carbon-fiber/epoxy
laminates by means of modeling of inter-fiber failure and fiber break (arrows). Here, #45 indicates ±45˚ plies.

211

The stress for inter-fiber fracture strongly depends on fiber/matrix adhesion, matrix strength,
fiber orientation and volume of fibers. Due to the problem with a reliable analytical approximation of the intra-laminar failure the database for modeling were extracted from experimental results.
Figure 2 shows AE test results compared with theoretical results of a layer-by-layer stressand failure analysis of carbon-fiber/epoxy laminates by use of the inter-fiber fracture (IFF) andfiber break criterion. For calculation of inter-fiber fracture of laminates the tensile strength forpure 90° layers with load application transverse to the fiber orientation and the shear strength of
0° layers parallel to the fiber orientation (characterizing shear failure of #45° laminates: #45˚ =±45˚) were used.
Because of its high sensitivity the AE technique indicates the occurrence of micro-damages
long before ultimate macro-failure. Naturally, the sensitivity of detection and, hence, the determination of stresses where first AE hits are observed depends on the detection threshold. During
our tests AE signals of different characteristics are detected at about 60-70% of the theoretical
approximated loading levels for initiation of inter-fiber fracture and fiber break. Thereby different failure processes are indicated by its individual AE characteristic (here the dimension of AE
activity means hits/s and AE intensity is given by mVs/hit):••

Laminate (90°)6 / (#45°)3 / (90°)6: transverse failure of fiber/matrix interfaces and matrix in
90° laminated layers are of low AE activity and AE intensity whereas shear failure of matrix and interfaces in #45° woven layers superposed by delamination processes between
90° and #45° layers are of medium AE intensity but high AE activity; pure shear deformation in #45° layers produce very high AE activity after ultimate fracture of 90° layers at thestress maximum.
Woven fabrics (0°/90°): transverse failure of fiber/matrix interfaces and matrix (90° direction); breaks of fiber bundles in 0° direction is associated with very high AE intensity atstresses higher approximately 70% of the ultimate laminate strength.

From this an energy criterion for separation of inter-fiber and fiber failures in specimens canbe derived by means of the conventional AE features signal peak amplitude and signal energy.
Breaks of fiber bundles are detected by AE signals with ten to hundredfold higher amplitudes
and energy rates than matrix cracking.
The comparison of results from fatigue tests and AE examination of the damage progressshows that the critical stress/strain state which should not exceeded for a long life-time is determined by the onset of first and continuous AE. In carbon-fiber reinforced epoxy composites, thatmeans a stress level of about 20-25% of the ultimate laminate strength.
The methodology of a layer-by-layer stress and micro-failure analysis combined with AE examination naturally can be applied to composite structures to approximate maximum stress levels
for a long life and to support the optimization of laminate lay-ups.
4. Studies of Delamination Behavior
Several standardized fracture mechanics tests are used for the purpose to determine the resistance against inter-laminar crack propagation. The delamination behavior of unidirectional fiberreinforced polymer laminates made from glass-fibers or carbon-fibers with thermoplastic or212

epoxy matrix were studied using different fracture tests such as Double Cantilever Beam test
(mode I), End Notched Flexure test (mode II) und Mixed Mode Bending test (mixed mode I/II)[6, 7].
The application of AE examination has two aims. On the one hand this technique shall contribute to a more objective determination of initiation loads (for micro-crack initiation and onsetof macroscopic delamination) and to overcome the problems arising from the difficult and subjective optical observation of the delamination growth on the edge of the specimen. On the other
hand AE examination gives additional information on micro-processes which appear during delamination for a better micro-mechanical understanding of the toughness behavior. Those experiments exemplify one of the most frequent macroscopic failure processes in composite structures.
An example is given in Fig. 3. It shows the AE behavior of a unidirectional reinforced composite during mode I delamination.

Fig. 3. Loading (top left) and fixed AE transducer position for location of AE sources in delamination tests (bottom left). Time-dependent delamination load (line) and AE signal energy(points) from DCB (mode I) tests on unidirectional reinforced glass-fiber/polypropylene (topright). Time-dependent position and size of the damage zone determined by location of AE
events with amplitudes at the transducer ≥ 60 dBAE (bottom right).
213

For the first objective following statements resulted:• Location of AE sources around the delamination tip enable the determination of° position of the delamination front° initiation points (loads):
INITlocAE: micro-crack initiation at the delamination tip
INI-PROPlocAE: onset of macroscopic delamination propagation° delamination speed° size and shape of energy-dissipative zones around the delamination tip (with the assistance of FE modeling).
• If the delamination is connected with similar failure processes such as matrix cracking
and fiber/matrix interface debonding a correlation between AE energy and mechanicallyreleased energy during stable delamination propagation exists. In the case of fiber fracture in the bent cantilevers or break of bridging fibers the correlation becomes non-linear– AE burst signals with high energy are produced but only a small mechanical energy isreleased (Fig. 4). Breaks of bridging fibers between the opened delamination surfaces
frequently occur specially in composites with weak interface adhesion and result in AE
sources behind the delamination tip (Fig. 5).
• Number and intensity of AE signals are dependent on matrix and fiber material as well as
fiber/matrix adhesion. Therefore, above-mentioned energy correlation has to be established for each composite material.

Fig. 4 Correlation between mechanical energy release and AE energy during mode I delamination of glass-fiber/polypropylene uni-directional (UD)-composites: (left) for one specific delamination test; (right) for stable delamination propagation in different composites with varied polypropylene matrices and adhesion .
With reference to detectability and size assessment of propagating delamination areas incomposite structures it can be concluded that mode I delamination generates most and strongest
AE sources associated with the largest process zone around the delamination tip compared withother fracture modes. Maximum AE peak amplitudes detected a few cm from the source are 90
to 100 dBAE, but maximum amplitudes from shear fracture mode II are only 70 to 80 dBAE. Consequently, the detectability of propagating delamination tips should be good. Most of the AE
sources are positioned at areas behind the delamination tip. Unstable fracture stages with highdelamination speeds and development of large delamination cracks (e.g., fast splitting of UDfilaments at hoop-wrapped pressure cylinders) yield AE signals with high peak amplitudes andhigh energies.
214

Fig. 5 Images of fracture surface (left) and bridging fibers (right) from mode I delamination.
5. AE Inspection of Pressure Cylinders
In order to investigate the AE characteristic of damages in all-composite cylinders (made of aplastic liner and fully-wrapped with a filament wound carbon-fiber/epoxy shell) the cylinders
were externally damaged by impacts or flaws and were pressurized up to the allowed test pressure. After a varying number of internal pressure cycles the cylinders were inspected performing
pressure tests accompanied by AE testing (Fig. 6). For the purpose of comparison also a nondamaged cylinder whose micro-damages only results from pure pressure cycling was inspected.

Fig. 6 Transducer mounting for separate AE testing of cylinder No. 3.
215

For example, Fig. 7 shows AE results from a pressure test of an impact damaged cylinder(impact with Wkin. = 2 x 70 J in the central part of an oil-filled cylinder) after 5000 pressure cycles (∆p = 5 – 300 bar) up to a test pressure of 360 bar.

Fig. 7 AE testing of MCS 6.8litre/300bar all-composite cylinder after 5000 cycles; (left) without
external damage; (right) with impact damage.
top:
test pressure, AE hit rate, peak amplitude and energy of AE signals vs. time
middle: energy sum of hits detected by different transducer groups vs. test pressure
bottom: planar located AE sources; cluster size: range/20 [5%] and minimum level: E > 10.000216

At the first pressurization after damage a dramatic rise of AE activity is detected comparedwith a pressure test before the damage. A subsequent pressure cycling up to the working pressure
should simulate refuels and lead to a local stress reduction due to a further degradation by microdamaging in the region of macro-damages. Caused by local stress relief following periodic AE
tests after predetermined pressure cycles, AE activities and AE intensities decrease with increasing number of cycles (Fig. 8).

Fig. 8 AE activity during pressure tests of cylinders with different types of damage after specificnumbers of pressure cycles.
From our test results it can be concluded that impact and critical flaw damages are clearly detectable also after longer service intervals. Maximum AE peak amplitudes of 90 to 100 dBAE at
the source location were calculated by means of a distance-amplitude correction procedure. This
is in agreement with peak amplitude, which appears during delamination tests of specimens.
Following possible qualitative criteria for identification of “heavy” damages in filamentwound pressure cylinders using AE features from periodic inspections with only one pressure
ramp are suggested:• Much higher cumulative sums of AE hits over the complete pressure test (Fig. 8)• AE transducers which are located near damaged regions- record more AE hits of higher energy (energy sum of planar located sources or derived from zonal location) (Fig. 9)- detect higher AE hit sums during depressurization compared with those of the previous increase of the pressure ramp (Fig. 7)- show a progressive increase of AE energy during depressurization (sources from rubbing of damaged zones!?)• Formation of clusters of AE sources with much higher energy release than from surrounding area (if a planar location is successful applicable).
However, a distinction of AE source mechanisms from loading and unloading stages basedon conventional AE features was not achieved.
6. Identification of AE Source Mechanisms
As discussed above the advantage of fiber composites compared with other materials is thegeneration of very strong stress waves associated with fiber breaks and delamination processes.
217

Fig. 9 Transducer location dependent AE monitoring (transducer 3 and 4 near the damaged region).
In previous papers [8, 9] we suggested a method to distinguish matrix cracking and fiberbreak sources by a power spectrum analysis of the monitored AE waveforms. It was assumed
that matrix cracks have at least 70 % of the signal power in the 100 to 350 kHz and fiber break inthe 350 to 700 kHz frequency interval.
New results and a better understanding of the effects of wave propagation show that we must
be careful with the conclusion drawn from this method. Effects of wave propagation such as
geometrically determined attenuation, wave-mode interactions, reflections, mode conversions atfree surfaces, etc. cause a clearly marked change of the wave in time and frequency domain on
its path from the source location to the transducer position. In addition composites with a viscoelastic polymer matrix possess a comparatively high material attenuation, which is dependent on
frequency, temperature and wave modes especially for frequencies greater than about 400 kHz.
Figure 10 demonstrates the strong difference in the waveforms and frequency spectra withdifferent transducer spacing to one and the same AE source. These results show that our abovementioned method of identification of AE source mechanisms from its power spectra is strongly
restricted to a very short distance of a few mm from the source.
In addition to geometrical and materials influences, modulations of waveforms and spectraby the strong frequency dependent sensitivity of AE transducers and electronic frequency filters
must be accounted for. All these effects cause spectra of AE signals, which have no similarity
with theoretically assumed AE sources. The main influence in the frequency domain comes from218

Fig. 10 Distance dependent waveforms and power spectra of AE signals from single glass-fiber
fragmentation (fiber diameter 80 µm) in epoxy matrix (recorded with PAC-Pico transducers ofshown frequency response, amplifier with HP-filter 20 kHz, sampling 5 MHz) at a distance of 7mm (top) and 43 mm (bottom; only the first counts of the waveform are shown) from the source.
the more or less resonant behavior of piezoelectric AE transducer (Fig. 11). From activation ofhigh-frequency resonance of AE transducer it only can be concluded that the AE source isbroadband beyond this frequency range.
Technical structures from composite materials have plate-like geometries and the transport ofmechanic/acoustic energy is realized by plate waves (Fig. 12).
Hence, an alternative method for separation of different modes of fracture seems to be themodal analysis of plate waves. That means the main direction of source movement stimulates apreferred plate wave mode. For example, Fig. 13 shows waves generated by Hsu-Nielsen sourcesof different directionality. A pencil-lead break parallel to the plate (as fiber-break in-plane)219

Fig. 11 Calculated spectrum of AE signal from the product of transfer functions Hi of the AE
source, propagation medium (100 mm in PMMA rod), AE transducer and electronic system forwaveform recording in the frequency domain (HAE = HS · HM · HT · HE).

Fig. 12 Velocity of Lamb waves (S0: extensional wave mode, A0: flexural wave mode) for wave
propagation parallel to fiber orientation .
stimulates a much stronger S0-wave mode than a lead break perpendicular to the plate (as delamination out-of-plane). But the problem of waveform and spectrum influences caused by highmaterials attenuation especially of high-frequency parts and by the AE transducer thereby also
must be taken into account. That means for polymer-matrix composites there is practically nochance of source identification from frequency spectra of AE signals after long wave travel andalso unless really flat and broadband transducers are used.
220

Fig. 13 Wavelet transform (time-frequency domain) of signals generated by Hsu-Nielsen sourcesin UD-glass-fiber/polypropylene at a distance of 300 mm from the broadband AE transducer
PAC-S9208 (wave propagation parallel to fiber orientation).

221

7. Conclusions
•
•
•
•
•
•
•

•

AE testing is a powerful method for online detection and analysis of matrix, fiber and interface related active fracture processes in composite materials.
A number of tools for identification and evaluation of damage stages and failure mechanisms
exists. They are based on changes in AE activity or intensity features, location of AE sources
as well as the analysis of waveforms, spectra and wave modes from AE signals.
The methodology of a layer-by-layer stress analysis of laminates combined with AE examination is a good instrument to calculate critical stresses for inter-fiber and fiber fracture andof maximum allowable stress levels for a long life-time.
Combination of fracture mechanics studies of delamination and AE measurement gives additional information on micro-processes and a better micro-mechanical understanding of thetoughness behavior.
Stress waves emitted by fiber breaks cause much higher AE amplitudes than other mechanisms such as matrix cracking. Propagating delamination also produces strong AE sourcesfrom areas near the delamination tip.
At pressure tests “heavy” damages in structures, such as impacts and flaws, are detectable byformation of cluster of located high-energy AE sources and a significantly higher AE activity
and intensity, especially during depressurization periods.
From studies of transfer functions of components of the measuring chain it must be concluded that it is difficult to separate micro-failure mechanisms in polymer-matrix composites
from frequency spectra of AE signals. The source information is mainly distorted by the highattenuation of polymer materials related to high-frequency parts of stress waves and thetransfer function of the AE transducer.
Due to all discussed problems it is deduced that an identification of AE sources from frequency spectra of AE signals after long wave travel is not possible. Possibly this could beachieved by means of a numerical modeling of AE sources and the wave propagation in timeand place. For this purpose source function, transfer function of wave-guiding medium, geometry of the examined structure and characteristic of used AE transducer must be clearly
defined.

References
1. Kishi, T., Higo, Y., Ohtsu, M. and Yuyama, S. (Eds.) (2000), Progress in Acoustic Emission
X, 15th International Acoustic Emission Symposium, 11-14 September 2000, Tokyo, Japan,
Japanese Society for Non-Destructive Inspection.
2. Kishi, T., Ohtsu, M. and Yuyama, S. (Eds.) (2000), Acoustic Emission – Beyond the Millennium, 11-14 September 2000, Tokyo, Japan, Elsevier Science Ltd..
3. Brunner, A.J. and Bohse, J. (2002), “Acoustic Emission standards and guidelines 2002: acomparative assessment and perspectives”, NDT.net – September 2002, 7 (09).
4. Hinton, M.J., Kaddour, A.S. and Soden, P.D. (2002), “A comparison of the predictive capabilities of current failure theories for composite laminates, judged against experimental evidence”, Compos. Sci. Technol. 62, 1725-1797.
5. Puck, A. and Schürmann, H. (2002), “Failure analysis of FRP laminates by means of physically based phenomenological models”, Compos. Sci. Technol. 62, 1633-1662.

222

6

Bohse, J., Krietsch, T., Chen, J. and Brunner, A.J. (2000), “Acoustic Emission Analysis andMicro-mechanical Interpretation of Mode I Fracture Toughness Tests on Composite Materials”, 2nd ESIS Conference on Fracture of Polymers, Composites and Adhesives, Les
Diablerets, Switzerland, September 13-15, 1999, ESIS Publication No. 27, Elsevier Science
Ltd., 15-26.

7. Bohse, J. and Chen, J.H. (2001), “Acoustic Emission Examination of Mode I, Mode II and
Mixed-Mode I/II Interlaminar Fracture of Unidirectional Fiber-Reinforced Polymers”, J.
Acoustic Emission, 19, 1-10.
8. Krietsch, T. and Bohse, J. (1998), “Selection of Acoustic Emissions and Classification of
Damage Mechanisms in Fiber Composite Materials”, Progress in Acoustic Emission IX, Big
Island, Hawaii, USA, August 9-14, IV30-IV39.
9. Bohse, J. (2000), “Acoustic emission characteristics of micro-failure processes in polymer
blends and composites”, Compos. Sci. Technol. 60, 1213-1226.

223

ACOUSTIC EMISSION FROM RUST IN STRESS CORROSIONCRACKINGHIDEO CHO and MIKIO TAKEMOTO
Aoyama Gakuin University, Sagamihara, Kanagawa, JAPAN
Abstract
Petrochemical and nuclear plants are located in coastal area in Japan and tend to suffer
chloride stress corrosion cracking (SCC) in heat-affected zones (HAZ) of welded stainless steels.
SCC on the outer surfaces (external SCC or ESCC) of aged storage tanks and pipelines is
becoming serious problems. Acoustic emission (AE) detection of ESCC of insulated equipment
is desirable, but no AE signals are produced by chloride-induced transgranular SCC (TG-SCC).
SCC progression can be monitored by AE only if the fracture of rust in cracks produces strong
AE signals. We studied characteristics of Lamb-wave AE signals during rust growth in SCC of
butt-welded SUS-304 stainless steel. Both pipe and shells were studied as a function of wall
temperature from 298 K to 253 K. Though the amplitude of AE was low due to small crack
volumes, we can monitor the progression of SCC by AE technique.
Keywords: External stress corrosion cracking (ESCC), chloride SCC, rust fracture, volumetric
expansion, Lamb-wave AE, cylinder-wave AE
1. Introduction
Among various types of stress corrosion cracking (SCC), SCC of austenitic stainless steels(SS) by chloride anions is the most serious problem in chemical, petrochemical, food and nuclear
power plants. SCC on the outer surface of equipment and tubing, called external SCC (ESCC), is
becoming important problems for aged plants. Also health monitoring of new storage vessels ofspent nuclear fuel for over 40 years is another important technology where SCC can be a
problem.
Chemical causes of ESCC of austenitic SS are chloride ions from airborne salts and in theinsulation such as hard urethane rubber, and sometimes vinyl-chloride adhesive tapes in nuclear
power plants. As the most chemical and petrochemical plants in Japan are located in the coastal
areas, thermal insulations are contaminated by airborne salts and un-acceptable chloride levels
are commonly found at present. ESCC tends to occur at equipments whose surface temperatures
are below approximately 340 K and cracking is generally transgranular. Wet insulation offers theplace in which chloride and sometimes fluoride ions concentrate. Takemoto [1] first reported theharmfulness of fluoride ions in ESCC. Fluoride ions appear to come from welding fume and are
detrimental to sensitized stainless steels below 340 K.
Visual inspection of ESCC, especially under thermal insulation, is time-consuming andcostly. This inspection is impossible in nuclear-power equipments. Some petrochemical plantowners have recently utilized AE for monitoring the chloride ESCC, but could not detect AE
signals. Okada et al. [2] reported no AE from chloride-SCC of SUS-304 stainless steel. They
attributed this to the active-path-corrosion (APC) mechanism [2]. We recently confirmed that thetransgranular chloride SCC of SUS-304 SS does not produce AE signals while intergranular SCC(IG-SCC) of the same steel does produce low-level AE signals [3].
J. Acoustic Emission, 22 (2004)

224© 2004 Acoustic Emission GroupFor the ESCCs of process equipments, we have noticed much rusting around the SCC
observed. Rust growth in fine crevice or cracks, as well those on the free surface, produces
strong AE signals due to their volumetric expansion during growth [4]. Hard rust in crevice
sometimes induces additional tensile stresses and resulted in complicated SCC propagation. Thissuggests that we can monitor the progression of SCC and ESCC by detecting the secondary AE
signals produced by rust fracture in the fine crevice.
We first studied by AE source inversion the fracture dynamics of natural rust produced oncarbon-steel plates exposed to outdoor weathering for 500 days. Next we monitored AE signals
from rusting under SCC. Three types of experiment were conducted. One is AE monitoring of
SCC of a butt-welded SS pipe exposed to chloride solution and to heating-and-cooling cycles.
Second is the AE monitoring of SCC during wet-and-dry operation of a welded shell. Third is
the AE monitoring of a clad steel plate (304 SS clad on carbon steel) which suffered severe SCC
during 12-years of service in salt-producing plants.
2. Fracture Dynamics of Natural Rust by AE Source Inversion
AE signals from natural rust fracture are important not only for SCC monitoring of processequipment but also for corrosion monitoring of steel structures exposed to weathering. We firststudied fracture dynamics of natural rust by waveform simulation of the Lamb-wave AE signals.
We exposed rectangular carbon-steel plates (40 x 200 x 1 mm) to the outdoor weathering for 500
days forming on the steel samples with natural rust of approximately 0.4-mm thickness.
Reduction of the plate thickness was approximately 0.1 mm. Natural rust is composed of γFeOOH (lepidocrysite), Fe3O4 (magnetite, black rust) and Fe2O3 (hematite, red rust).

Fig. 1 Atmospheric rust produced on a carbon-steel plate exposed to outdoor weathering for 500
days.
Using four AE sensors (PAC, PICO) mounted on the surface of the plate as shown in Fig. 1,
we monitored Lamb-wave AE signals during four-point bending. Figure 2 shows cumulative AE
counts as a function of the surface strain. Strong AE signals, as shown in Fig. 3, were detectedfrom small surface tensile strains. Waveform simulation of So-packets revealed crack opening of
1.4 x 10-14 m3 with a rise time of 0.7 µs. It is noted that this source rise time is similar to those ofhydrogen-induced microcracks in a low-alloy steel [5], but the crack volume is thousands times
larger than those of the microcracks. As shown in Fig. 4, Mode-I fracture of the surface rust with
an opening vector in the axial direction is apparent, and this produced strong AE. We could also
detect strong AE signals from growing natural rust of steel plate exposed to outdoors weathering[6]. As the rust growth takes time, we did not detect the initiation of corrosion by AE, but canmonitor the progression of corrosion by AE.
225

Fig. 2 Cumulative AE counts vs. substrate surface strain.

Fig. 3 Lamb-wave AE signals due to Mode-I fracture of the rust produced by atmosphericcorrosion.

Fig. 4 Transverse structure of the atmospheric rusts after tensile bending test.
3. AE Signals from Rust Fracture in ESCC of Butt-Welded 304 SS Pipe
In order to study the AE signals from rust growth in SCC, we first produced ESCC on theouter surface of a butt-welded 304 SS pipe of 3/4-inch (19-mm) diameter by immersing the pipe
vertically in a boiling 35 mass% magnesium chloride solution for 410 h. Circumferential SCC of
25-mm long was produced by residual tensile stresses in HAZ as shown in Fig. 5. We also
226

observed severe red rust over the splash zone of the pipe along axial scratch. This rust was later
found to be produced by axial SCC caused by scratch-induced residual stresses. It is noted thatthe 304 SS does not suffer any rust over the polished free surfaces in the chloride solution, butdoes suffer severe rusting along scratch and crack by the wet-and-dry condition.

Fig. 5 SCC damaged butt-weld 304
pipe.

•

Fig. 6 Experimental setup for butt-weld 304 pipe sample.

Next we monitored cylinder-wave AE signals of this pipe during heating–cooling cycles inair. Two monitoring methods were utilized as shown in Fig. 6. Two AE sensors (PAC R3I) withan integral-amplifier were mounted on the pipe edges, and two PICO sensors on the pipe surface
at 50 mm (left) and 55 mm (right) from the weld line. It is noted that the right R3I sensor was
150 mm from the weld line and the left one from 70 mm.
227

Fig. 7 Cumulative AE
counts vs. time.

Fig. 8 Typicalcylinder-wave AE
signals from rustcracking.

Outputs of these sensors were amplified by 40 dB and digitized and stored in a computer
simultaneously. A solution of 35 mass % MgCl2 was brushed once on the pipe surface around
HAZ. The pipe was heated by an infrared lamp to 333 K for 42.6 ks and then cooled. Figure 7
shows the cumulative AE counts by R3I sensors during a cooling (OFF) and heating (ON) cycle.
Here, solid line of “near Ch.1” designates the AE signals whose sources were close to thechannel-1 sensor or the weld line. Broken line indicates AE signals from the rust on the rightsurface close to the channel-2 sensor. We observed several hundreds AE events at later period ofheating. Figure 8 presents an example of cylinder-wave AE signals. First arrival packet of228

waveforms detected by the R3I sensors represents the L(0,1) or breathing mode. Theoreticalgroup velocity of L(0,1) is calculated as 4800 m/s and agrees well with the experimental velocityof 4700 m/s. Source location in the axial direction of the pipe is shown in Fig. 9. Sources arelocated near the SCC and the right rust on the surface. We next cut the pipe at line b-b’ andexamined the cross-sectional structure. As shown in Fig. 10, we found SCC under the rust (a).
The SCC was filled with rust (b). This suggests that the AE events shown by the broken line
(near Ch. 2) of Fig. 6 are from the rust on the pipe surface as well as the rust fractures in the
SCC. It is noted that the free surface of the pipe are free from any rust even in the SCC test andfollowing heating-cooling cycles, but the SCC accelerates significantly the rust formation.

Fig. 9 Estimated AE source location on the pipe.

Fig. 10 SCC in the cross-section of pipe (a) and crack in the rust in SCC (b)As the AE amplitude of the PICO sensor (Fig. 8) was low, we next monitored AE signals
using M201 sensor (JT-Toshi, with an integral-amplifier). Two M201 sensors were mounted on
the pipe surface. One sensor was on the left surface at 15 mm and another on the right surface at
47 mm from the weld line. Now the left R3I sensor is at 28 mm from the weld line and the right
one from 71 mm. Figure 11 shows an example of Lamb-wave AE signals by R3I and M201
sensors. Amplitudes of AE signals from rust fracture are weak due to small crack volumes thanthat of natural rust formed on the free surface. We, however, can detect AE signals from the rustin SCC.
229

Fig. 11 Detected AE waveform by another small sensor (JT-Toshi)

Fig. 12 Thin-wall hollow cylinder with SCC damage and sensor location.
230

4. AE Signals from Rust in ESCC of Welded 304 Thin-Wall Shell
AE signals from rust growth in SCC of a thin-wall shell of 304 SS were monitored forapproximately 16 days. SCC was previously induced by a MgCl2 solution at 373 K as shown in
Fig. 12, and more than seven branched cracks were produced across the weld line after 30 days
exposure to the chloride solution. Lamb-wave AE signals were monitored by four R6I sensors(gain 40 dB) mounted in the cylindrical jigs as shown in Fig. 12. Inside of the shell was heatedby an infrared lamp for 8 hours every day. Outer surface of the SCC was subjected to the water
mist spray for 0.5 h and dried for 3.5 hr cycle. Figure 13 shows typical AE waves, which weremoderately strong. Amplitudes of AE were in the range of 80 to 100 mVp-p after 40 dB
amplification. Still So-packets were clearly detected by only Ch.-4 sensor, and we located AE
sources from the arrival times of the first Ao mode waves.

Fig. 13 Typical Lamb-wave AE signals from rust fracture.
Figure 14 shows AE source location map and AE sources were located near the SCC area.
Figure 15 shows cumulative AE counts during the test. Masked areas designate the heating
periods. Surface temperature reached 353 K by heating. AE generation rate gradually increased
with time, probably due to the observed rust growth. After 900 ks (Fig. 16), many AE signals
were generated during mist spraying with temperature cooling down. This suggests that AE
signals are generated by rust fracture not only from the rust growth but also from the stresses
induced by crack faces.
231

Fig. 14 Estimated AE source location of the thin-wall hollow cylinder sample.

Fig. 15 Cumulative AE count and heating cycle history during AE monitoring.

232

Fig. 16 Expanded history of cumulative AE counts after 900 ks.

Fig. 17 Surface appearance of SCC on SUS304 steel cladding taken from an evaporator
circulation pipe of salt-producing plant.
5. AE from Rust in SCC on a Stainless-Steel Clad Plate from Salt Producing Plant
The sample tested is a stainless-steel clad steel plate taken from a pipe in salt-producing
plant. This SUS304 stainless-steel cladding suffered severe SCC in HAZ after 12 years ofservice, as shown in Fig. 17. Stainless-steel layer (2-mm thick) exfoliated partially from thecarbon steel base (6-mm thick). SCC reached the carbon steel and is filled with dense rust(magnetite) produced by severe galvanic corrosion (Fig. 18). Dense and hard rust (Hv=280)
233

produced tensile stresses to cause new lateral SCC and accelerated the exfoliation of stainlessclad layer. This is a sample of internal SCC, but expected to produce strong AE signals by further
rusting in the cracks. We monitored AE signals using two R6I sensors mounted on the SScladding surface. Rusting was produced by brushing on a 35-mass% MgCl2 solution in an area
limited by an O-ring over the weld line. This solution is deliquescent and remains liquid overextended period. The sample was heated to 308 K to 318 K by a lamp. Figure 19 shows AE event
count vs. exposure time. AE events increased after 50 ks exposure, and increased with time
thereafter. First 50 ks is the incubation time during which new rust is produced. Typical Lambwave AE signals are shown in Fig. 20. Sources of 20 events are located inside the O-ring.

Fig. 18 Transverse SCC through clad SUS304 with hard rust in it and surface parallel cracks
produced by wedge effect.

Fig. 19 cumulative AE count vs. time.

Fig. 20 Typical AE waveform of Lamb wave.
234

6. Conclusions
With the aim of remote monitoring of SCC progression by AE, we first studied fracture
dynamics of natural rust on steel plates by the waveform simulation of So-mode Lamb-wave AE
signals. Source rise-times of natural rust fracture are almost the same as those of micro-cracks indelayed fracture of high-strength steel, but the crack volumes are thousands times larger thanthose of the delayed fracture. Next, we detected AE signals from the rust fracture in SCC of buttwelded and clad stainless steel. We detected a number of AE signals during heating-cooling andwet-dry operation of SCC. These AE signals are produced by rust fracture due to the volumetric
expansion of the rust and due to the fracture forces from the crack wall by thermal cycling.
Though the amplitude of AE was low due to small crack volumes, we can monitor the
progression of SCC by AE technique.
References[1] M. Takemoto, External Stress Corrosion Cracking (ESCC) of Austenitic Stainless Steel,Materials Performance, 24-6, (1985) 26-32.
[2] H. Okada, Y. Yukawa and H. Tamura, Corrosion, 30, 253 (1974).
[3] S. Fujimoto, M. Takemoto and K. Ono, J, Acoustic Emission, 19, 63 (2001).
[4] M. Takmeoto, T. Sogabe, K. Matsuura and K. Ono, Progress in Acoustic Emission XI, p.45
(2002).
[5] M. Takemoto, Y. Hayashi and M. Takemoto, J. Jpn. Soc. Nondestructive Inspection (inJapanese), 43, 637 (1994).
[6] T. Sogabe and M. Takemoro, J. Japan. Soc. Nondestructive Inspection (in Japanese), 53, 35(2004).

235

ACOUSTIC EMISSION MEASUREMENT SYSTEM FOR THE ORTHOPEDIC DIAGNOSTICS OF THE HUMAN FEMUR AND KNEE JOINT
R.P. FRANKE1, P. DÖRNER2, H.-J. SCHWALBE3 and B. ZIEGLER3
1
University of Ulm, Dept. of Biomaterials, Ulm, Germany, 2Bad Griessbach, Germany,3
University of Applied Science Giessen, Giessen, Germany
Abstract
Orthopedic diagnostics per DIN EN ISO 9000 ff requires methods of nondestructive processcontrol. Acoustic emission (AE) signals allows the prediction of bone rupture and assessment of
the tribological status of the knee joint. We have developed an adaptive AE measurement system
named Bone Diagnostic System (BONDIAS), making the in vivo analysis of the medical statuspossible.
1. Introduction
Quality control in the orthopedic diagnostics according to DIN EN ISO 9000 ff requires
methods of nondestructive process control, which do not harm the patient either by radiation or
by invasive examinations. To improve health economy, quality-controlled and nondestructive
measurements are needed in the diagnostics and the therapy of human joints and bones. Noninvasive evaluation method for the state of wear regarding human joints and the cracking tendency of bones is not established yet.
The analysis of acoustic emission (AE) signals allows the prediction of bone rupture far below the fracture load. The evaluation of dry and wet bone samples revealed that it is possible toestimate the bone strength from crack initiation and thus to predict the probability of bone rupture. Besides the fracture probability of bone, AE allows to assess the tribological status of theknee joint. Simple states of wear without inflammation can be separated from states of wear
complicated by inflammation (arthritis). For the assessment of the tribological knee function andby the probability of fracture of the femur, an adaptive AE measurement system named BoneDiagnostic System (BONDIAS) was developed. This system makes the in vivo analysis of the
medical status possible.
2. AE Assessment of Crack Initiation and Crack Propagation in the Human Femur.
Mechanical loading of the femur is accompanied by elastic strain. Due to differences in compliance of the compacta and the trabecular system of bone shear stresses arise in the interface ofthe compacta and the trabecular system, eventually leading to crack initiation. Different mechanisms of cracking were accompanied by different AE from human femora as shown in literature[1-6, 8]. An AE signal typical of crack initiation is shown in Fig. 1. It is characterized by a very
short rise time and an exponential decrease of the amplitudes.
The assessment of crack initiation is very important in the healing process after the bone
fracture, or during the implantation of an endoprosthesis of the hip, or in cases of osteoporosis.
Equally important is the development over time of the threshold of crack initiation during thehealing process or in the cause of a disease. For the assessment of crack initiation of the femur acertain mechanical load is afforded. The BonDiaS-System allows applying different ranges ofJ. Acoustic Emission, 22 (2004)

236© 2004 Acoustic Emission Groupmotion and loads especially those, which are typical for the patient´s day-to-day life. These comprise e.g. rising from a chair, knee bending, climbing up or down staircases. From the medical
point of view such mechanical loads are regarded as non-destructive although there is alreadycrack initiation in the interface of the compacta and the trabecular system of the bone. Thesemicro cracks seem to be essential for the physiological bone remodeling. For the description of
the development of bone strength over time it is necessary to assess both the threshold of crack
initiation and the conditions for crack propagation. For the evaluation of fracture toughness further examinations of the crack initiation and the crack propagation are necessary. Assessment of
the geometrical structure by computer tomography (CT) in combination with an experimental
calibration of compliance allows the evaluation of the fracture mechanics. The two thresholds ofcrack initiation and stable crack propagation, which are needed for this evaluation, follow fromAE analysis.

Fig. 1 Acoustic emission caused by crack initiation in the compacta-spongiosa interface.
According to the AE analysis, stable crack propagation appears in the transit from area I toarea II as demonstrated in Fig. 2. This graph shows the total AE counts according to the applied
load. There is a clear distinction of the transition, which is defined as the load critical for cracking. Based on results gained from explanted femora these evaluations can be performed in vivo,
now.
The fracture toughness can be calculated from:K IC = σ a  W 

π *a* f 

Here σ describes the normal stress with respect to the crack plane and a the depth of the crack.
f(a/W) is a correction function gained by calibration of compliance to take into consideration theindividual femur geometry and the length of the crack, W. The values of fracture toughness are• KIC= 220 [N-mm-3/2] at the transition from area I to area II,• KIC= 330 [N-mm-3/2] at fracture load.
The normal stress σ is calculated individually by FEM analysis based on CT data. To optimize
the FEM analysis a variety of grid structures are tested at the moment.
This system is well suited to assess for each patient the individual critical load for cracking.
To know these individual critical thresholds of bone cracking is the key to assess the bonestrength e.g. in patients recovering from bone fracture or under therapy in cases of osteoporosis237

or during the implantation of a hip endoprosthesis. It is also the key for all those participating insport activities to the limit of endurance or for those who have the training of bone strength inmind. Of course, it is necessary to apply the knowledge gained with this system in all the medical fields where structure and function of the human skeleton is affected or impaired.

Fig. 2: Accumulated momentum of acoustic emission over the applied bending load.

Fig. 3: AE from a knee joint during knee bending, correlated to the angle of knee flexion.
3. Surveillance of the Human Knee Joint
A natural center of the surveillance of joints is the analysis of the AE from joints moving under typical daily load. Here again, the typical loads comprise e.g. knee bending, climbing up or
down stairs, but also ergometric examinations. The AE analysis of the knee joint clearly reveals
cartilage lesions, arthritic degeneration of the knee joint with more or less inflammatory contributions and damage caused by the change of the inclination of the line of thrust [7-9]. Acoustic
emission from the knee joint (Fig. 3) is registered by a sensor, which is fixed by tapes to the skinover the medial condyle of the femur (Fig. 4) during application of the natural load.
As shown in Fig. 3 the AE is registered over time (upper part of Fig. 3) and correlated to theangle of knee flexion, i.e., thin line in the diagram in the lower part of Fig. 3. The kinetics of load238

and motion can reveal non-stationary characteristics, which can be typical of certain diseases.
Knowing the kinetics of load and motion, the AE offers potential causes for the measured phenomena. Whether the medial or the lateral femoral condyles or both are damaged can be tested
by changes of the distribution of load and by the concomitant registration of the emission.

Fig, 4 Position of an AE sensor at
the medial femoral condyle during
the measurement.

Fig. 5 AE from cartilage deformation in a healthy knee joint after the sudden change from a twolegs stand to a one-leg stand.
The AE analysis allows for a multifaceted assessment of joint defects depending on the range
of knee flexion medial or lateral condyles can be changed thereby. A short rise time of the AE
characteristic for cartilage defects is correlated to a low signal damping by the cartilage layers. If
in that case a cartilage lesion can be verified such a signal is really indicative of a low thicknessof the cartilage layer in the damaged area. To reach this diagnosis the individual damping characteristics of the knee cartilage have to be assessed. This information is drawn from a simple test.
The instrumented patient is standing relaxed on the two legs and then he quickly raises one leg.
The fast increase in load of the loaded leg also initiates reactions in the additionally loaded knee
cartilage. Acoustic emissions typical of normal cartilage, of arthritis with more or less inflammatory contribution and of cartilage lesions are demonstrated in Figs. 5 to 7.

239

Figure 5 demonstrates the AE from a knee joint caused by cartilage deformation due to thesudden change from a two-legs stand to a one-leg stand. The intermittent cartilage deformation isof visco-elastic nature. The graph of AE over time shows a correlation to the thickness of thedeformed cartilage. Short signal duration is indicative of a thin cartilage layer.
Acoustic emission from a cartilage lesion is shown in Fig. 6. Articulating cartilaginous counterparts literally “fall” into the cartilage lesion. In reality this process has to be considered as asliding one. Sliding into the lesion (indicated by region 1) over the ingoing visco-elastic edge ofthe cartilage lesion is accompanied by a low energy transfer. The concomitant AE is of low energy and amplitude. Sliding out of the lesion, however, as shown in region 2, the outgoing edgeof the lesion is strongly deformed. A higher volume of the cartilage is deformed visco-elasticallywith high energy. This is accompanied by AE with a long rise time representing both the sequence of motion and the deformation process of the cartilage. The latter is responsible also forthis type of amplitude descent.

Fig. 6: Acoustic emission from a cartilage lesion.
The AE from an arthritic defect is represented in Fig. 7. Arthritic defects are characterized bydifferent events in the course of AE. This can be a signal typical of cartilage lesions where needle-like signal peaks are superimposed. These signal peaks are usually due to stick-slip effects orto the interaction of bone structures in the contact areas.
4. Measurement System BONDIAS
The measurement system BONDIAS has been developed for the automated assessment andevaluation of the AE from the human femur and knee joint for the orthopedic diagnosis. Kneebending of a patient will release AE in high temporal resolution and well correlated to the angleof knee flexion. However, the physician is not left alone with a bundle of data and the task toevaluate the AE. He will get the relevant information concerning:240

Fig. 7 Acoustic emission from an arthritic defect.
•••

Arthritic lesions in the knee joint: well characterized AE, singular events without a follow
up of further emission.
Acoustic emission due to elevated intra-articular friction caused by e.g. cartilage lesions,inappropriate surface roughness, a lack of synovial fluid or other defects: a plethora ofcontinuous emission.
Crack initiation in the femur: a burst type AE followed by continuous emission, which istypical of relaxation phenomena in the crack banks

The energy and the frequency of signals are mostly indicative of the originating events andimportant characteristics for the evaluation of defects. Added also is an analysis of the center ofthrust under the foot, which reveals [uniformity/non-uniformity] of the motion.
5. Benefits for the Use in Medical Therapy
The non-invasive diagnosis is based on the analysis of AE caused by day-to-day motion andload in a well-defined manner. There are several advantages of this diagnostic procedure whencompared with the established conventional methods:
•
•
•
•
•
•

No pain is caused by this procedure.
This procedure is non-destructive. Mechanical load even beyond the crack initiationthreshold are typical of day-to-day life and necessary for the physiological bone remodeling to avoid the degeneration of the bone and joint system.
There is no health burden through ionizing irradiation as is unavoidable with X-ray examination and CT.
There is no danger of infection since this is a non-invasive examination.
The time needed for the assessment of the acoustical emission and the validated analysisof data is of the order of seconds to minutes.
The expenses for the AE measurement system are small compared to X-ray systems.

241

•
•

The costs per examination including a detailed diagnosis are well below costs of other diagnostic procedures and there is no danger of causing further costs by infection as occurs
with invasive methods, e.g. endoscopic examinations.
Diagnostic (real time) monitoring of bone and joint training of e.g. sport professionals
becomes possible.

References
C E. Cann, Quantitative CT for Determination of Bone Mineral Density; A Review Radiology,
166 (1988) 509-521.
S. Hanagud, R.G. Clinton, J.P. Lopez, Acoustic emission in bone substance; Proceedings of the
ASME Symposium on Biomechanics, (1973) pp. 79-81.
I. Leichter, A. Bivas, J.Y. Margulies, I. Roman, A. Simkin, Acoustic emission from trabecularbone during mechanical testing: the effect of osteoporosis and osteoarthritis; Proc. ind. mech.
eng., 204 (1990) 123-127.
M. Thomas, J.H. Evans, Acoustic emission from vertebral bodies; Journal of materials science
letters, 7 (1988) 267-269.
R.A. Thomas, H.S. Yoon, J.L. Katz, Acoustic emission from fresh bovine femora; IEEE Ultrasonics Symposium Proceedings, IEEE, New York, (1977) pp. 237-241.
T. M. Wright, F. Vosburgh, A.H. Burstein: Permanent deformation of compact bone monitoredby acoustic emission; Journal of biomechanics, 14 (1981) 405-409.
H.-J. Schwalbe, G. Bamfaste, R.-P. Franke: Non-destructive and non-invasive observation offriction and wear of human joints and of fracture initiation by acoustic emission; Proc. Instn
Mech Engrs, 213 Part H, 1999, 41-48.
H.-J. Schwalbe, R.-P. Franke: Orthopädische Diagnostik und Qualitätskontrolle mit Hilfe derSchallemissionsanalyse; 12. Kolloquium Schallemission, DGZfP, 2000, pp. 151-160.
K. Wierzcholski, H.-J. Schwalbe: A review of Friction Forces in Human Joints: Tribologia
1/2000 (169), 39-62.

242

RODS AND TUBES AS AE WAVEGUIDES
KANJI ONO1 and HIDEO CHO2
1
University of California, Los Angeles, California, USA
2
Aoyama Gakuin University, Sagamihara, Kanagawa, Japan.
Abstract
We have examined the wave propagation behavior in aluminum rods and tubes using laser
techniques along with conventional sensors. The velocity characteristics agree between theory
and experiment, but substantial signal dispersion effects were observed. Waveform fidelity iscompletely absent with the use of waveguides. Pointed waveguides often used in practice
showed less signal broadening. The wave attenuation is high in certain frequency ranges. Tube
waveguides showed similar pulse broadening as the solid rod type, but with more attenuation.
Implications of the observed behavior for the application of AE waveguides are discussed.
Keywords: Waveguides, rods, tubes, wave propagation, dispersion, waveform fidelity
1. Introduction
Waveguides have been used in acoustic emission (AE) testing to protect sensors from extreme environment. Common examples include AE sensing at high and low temperatures, andunder corrosive chemical solutions and gases. The presence of intense radiation and magnetic
fields also necessitates the use of waveguides. Rods are typically employed as AE waveguides.
One end of a waveguide is glued, welded, brazed, soldered, fastened, or pressed to structure,
while a sensor is coupled to the opposite end. Among many reports of successful uses ofwaveguides, Hsu and Ono (1) used brazed rod waveguides to conduct high-temperature deformation studies; Rodgers and Tilley (2) are using stud-welded rod waveguides in their work on creep
crack detection in fossil-fuel power plant piping; Allevato (3) has been using a pointed-end
waveguides, pressure-coupled to petroleum-refinery vessels. However, no systematic study on
AE waveguides has been reported (4). Thus, questions remain; Is the rod the best shape? If so,
what diameter to use? Is tube a better waveguide? Can we get more design choices? In some applications (e.g., cryogenics), tubes can be advantageous due to lower heat conduction and lessmass. This provided an impetus to compare rod and tube waveguides in typical AE application
environment.
For the history of wave propagation theory, see (4). Also consult Redwood (5), Graff (6) and
Rose (7). In rods, simple kinematical theory says that the velocity of wave propagation has afixed value at the low frequency limit, in terms of the Young’s modulus E and mass density ρ, asgiven byVrod = E / ρ .
This is called rod velocity or bar velocity and applies to both phase and group velocities. At
higher frequencies, wave propagation is dispersive and the velocities vary widely. Wave propagation in tubes (or hollow cylinders) has a higher level of complexity due to the presence of inner surfaces. Gazis (8) established the theoretical basis of wave propagation in tubes and derived
characteristic frequency equations. This theory is the foundation of current understanding ofwaves in tubes. Nishino et al. (9) examined Gazis theory and clarified the modes of wave propaJ. Acoustic Emission, 22 (2004)243© 2004 Acoustic Emission Groupgation, differentiating certain flexural and torsional wave modes. They also developed computational software and calculated dispersion relations for several sets of tube geometry. A commercial software package (DISPERSE) is also available.
In this study, we examine the wave propagation behavior in rods and tubes in conditions that
may arise in typical AE application environment for waveguides.
2. Experiment

Experiment on rod/tube waves mostly utilized a through-transmission technique. A series ofbasic tests utilized a YAG laser (Continuum; 3 mJ output) and a laser interferometer (Thalen Laser; SH-140) with 20 kHz to 20 MHz bandwidth. A 140-kHz PZT sensor (Dunegan S140B) wasused as a receiver with a preamplifier (NF 9913A) or as a transmitter with a 150V, 0.6-µs risetime pulser. A more practical series used piezoelectric sensors on both ends. Some pulse-echo
experiments were conducted as well, especially for higher frequencies. Pulse generator was either AE simulator (AE Associates) or ultrasonic pulser/receiver (Panametrics 5050PR or
5052PR). For transmitter and receiver, AE or ultrasonic sensors of various resonance frequency
were used; 30 kHz (AET AC30L), 150 kHz (PAC R15), 175 kHz (AET AC175L), 375 kHz(AET AC375L), 500 kHz (PAC PICO), 750 kHz (AET AC750L), 1 MHz (Automation Ind., 1
MHz-0.5”), 2.25 MHz (Automation Ind., 2.25 MHz-0.5”), 5 MHz (Automation Ind., 5 MHz0.25”), 10 MHz (Automation Ind., 1 MHz-0.25”). Most AE sensors were used in pair. Waveforms, both input pulse and received and amplified signals, were recorded using a transient recorder. Wavelet transform (WT) analysis was performed using AGU-Vallen WT program.
Sample rods used are of aluminum alloy (2024-T3) with diameter of 3.2 mm, 6.4 mm, 9.5mm, 11.1 mm and 12.7 mm. Metric rods of 300-mm length were also used and a 10-mm rod had
a tapered end (1.6 mm end diameter, reduced over 22 mm). Tubes of 6.4 mm, 9.5 mm and 12.7mm were used and their diameter-to-wall-thickness (d/t) ratios ranged from 5 to 10. In most experiment, the rod/tube length was ~150 mm, but rods/tubes of shorter and longer length werealso used.
3. Results and Discussion
3.1 Laser experiments
Figure 1 summarizes a set of results from laser experiments. YAG laser pulse imparts a fastrise-time, short impulse, containing a broad range of frequency. The spot size is ~1 mm in diameter. Laser excitation and detection require no contact.

When the rod diameter is large, we found a “thick-rod” behavior. Figure 1(a) and (b) show
the waveforms and WT of 10-mm rod (300 mm length). This is essentially the Green’s functionof the rod waveguide. The left end corresponds to the laser excitation (at 20 µs). For 10-mm rod,the initial waves arrive with the rod velocity at high frequency above 800 kHz, followed bystronger packets at 2.8-3.3 mm/µs (arriving at 106 µs). The strong peaks have the peak frequency
above 1 MHz. A long 300-kHz slow component starts at 145 µs (2.07 mm/µs) and peaks at~290 µs (~1 mm/µs). This 300-kHz component corresponds to the L(0,1)-rod wave and its frequency is equivalent to the longitudinal waves resonating across the rod cross section. The same
wave modes were found at proportionately higher frequencies in thinner rods. Lower frequency
components below 225 kHz are absent. Reverberation continues beyond 10 ms. Initial 3.9-ms
portion is shown in Fig. 1(b). In this figure, the digitization rate is lower and the first strong peak244

amplitude is reduced. The observed reverberation has irregular amplitude variation without any
end reflections. This is apparently caused by the spot excitation, producing complex wave-mode
coupling. These figures represent the thick-rod behavior.

Fig. 1 (a) Laser input on 10-mm rod, laser sensing; Time: 390 µs, Frequency: 0 – 1000 kHz.
When the rod diameter is small, we observed a “thin-rod” behavior. In 3-mm rod waveguide,
the waveforms and WT show much more regularity, as shown in Fig. 1(c). Here, the lowfrequency (50-250 kHz) reverberation is the dominant feature. The main wave packet arrives at
the rod velocity of 5 mm/µs and has the peak frequency of 100 kHz. The wave packets have WT
coefficient extending to 500-750 kHz. This higher frequency tail follows the predicted disper245

(b) Waveform to 3.9 ms, laser-detected on 10-mm rod from laser excitation. Extension of (a).

Fig. 1(c) Laser input on 3-mm rod, laser sensing; Time: 440 µs, Frequency: 0 – 1000 kHz.
246

(d) Laser input on pointed 10-mm rod, laser sensing; (e) Laser input on 10-mm rod, S140B;
Time: 390 µs, Frequency: 0 – 1000 kHz.
Time: 1200 µs, Frequency: 0 – 500 kHz.
dispersion curve. Much weaker, slow L(0,1)-component was found at 1 MHz. This thin-rod behavior provides much higher amplitude for the same excitation and a narrower wave packet. For
6-mm rod, this L(0,1)-component was more prominent at 400-650 kHz and the reverberating partwas barely visible. That is, it is basically the thick-rod waveguide. For 5-mm rod, reverberation
was stronger, showing the transition behavior between thin and thick rods.
These findings indicate that thin rods allow low frequency signals to propagate with low attenuation. These signals reverberate for more than several ms. In thick rods, higher rod wave
modes are excited and propagated, but the low frequency signals are absent. This may originate
from the spot laser excitation. The slow component observed at 300 kHz for 10-mm rod, and at
1 MHz for 3 mm rod propagate at 2 mm/µs or slower. This part is L(0,1) mode, having the frequency-radius product of 1500 kHz-mm. Since L(0,2)-mode has diminishing group velocity at
1800 kHz-mm, this is unlikely to be the origin of the slow component. As noted previously, thefrequency matches the wave transit time across the diameter, suggesting a resonant mode. In
fact, this slow mode is absent when a pointed waveguide was excited as shown in Fig. 1(d). Thismay be also related to the small spot size of laser excitation. When laser impulse hit the pointed
end, excited wave starts to spread in the waveguide, helping to establish the L(0,1)-mode from300 kHz to 1 MHz. This shortens the wave packets in the pointed waveguide. While reflected
waves are still observed, an extended display in Fig. 1(f) shows that pointed waveguides provide
a better signal definition than normal thick-rod waveguides.
Figure 1(e) shows the waveform detected using a resonant sensor (Dunegan S140B) on laserexcited 10-mm rod. Here, only 300 kHz WT coefficients are found. Notice the complete247absence of 140-kHz component in the 10-mm rod. 300-kHz waves are extended over a long period, obscuring the reflected waves. Sensor coupling, however, produces damping effect andlimits the reverberation to a few ms.

(f) Waveform to 1.5 ms, laser-detected on pointed 10-mm rod from laser excitation. Same as (d).
This set of experiment shows that, in all cases, waveform fidelity is lost. Further, the observed waves in rod generally agree with the calculated dispersion curve, except for the trailingpart of the L(0.1)-component. However, it appears difficult to understand which parts of the
waves have propagating modes and to predict high amplitude modes. This is especially true inthe thick-rod behavior. Further, the low-frequency parts found in thin rods propagate with low
attenuation, producing many reverberating pulses. The absence of low frequency components inthick rods may be an anomaly due to the spot excitation, but further study is needed.
3.2 Rod waves with sensor input
Typical results are shown in Fig. 2 when a pulsed sensor generates input waves and anothersensor detects at the opposite end. Input pulse (30 kHz, 150 µs duration) was fed to a 30 kHz
sensor and received directly or through a rod of various diameters (152-156 mm length). Received pulses are shown with the plot length of 2.5 ms and show substantial broadening (1 to>2.5 ms) and multiple reflections from the rod ends. At 370 kHz, the individual packets are more
distinct, but the received pulse trains are still much longer than the input. Considering the impedance mismatch between the sensor faces and aluminum, attenuation of 12 dB is expected, but
generally the amplitude loss is less as shown in Fig. 3. Amplitude loss due to smaller diameterrod is expected to be proportional to diameter, and this trend is observed for 370 kHz. At otherfrequencies examined, this loss was less than expected. Group velocity measurements mostly
agree with the prediction from theory, as seen in Fig. 4. These values were obtained using thereflected wave packets, rather than the comparison between the main peaks with or without
waveguide. Apparently, main peaks are comprised of different modes, especially in the case of
face-to-face experiment and direct comparison is not feasible.

From these observations, we find that 6.4-mm (1/4”) rod is best as a waveguide for typicalAE frequencies. Here, the rise time is short, the main peak is well defined and amplitude loss isonly slightly lower that the best cases. Still, it is necessary to utilize long duration settings for hit
definition because the pulse broadening is substantial. Fortuitously, 6.4-mm waveguides appear
to be the choice of industry due to adequate rigidity and ease of handling.

248

Fig. 2 Typical transmission waveforms. 30 kHz. Insert at top left is the input on the same timescale. Rod waveguides of 12.7, 11, 9.5, 6.4 and 3.2 mm diameter had length of 152 to 156 mm.
3.2-mm waveguide data magnified 10x. 2.5 ms plot length with 2 Msample/s rate.

Fig. 3 Observed insertion loss due to rod waveguide. Insert is a log-log plot.
In reference to the dispersion curves, we should avoid using a waveguide corresponding to
the slow velocity trough of the L(0,1) dispersion curve and to rapidly varying velocity. High attenuation occurs due to dispersion. Conversely, ideal range is the low frequency limit, where thedispersion effect is minimal. In Fig. 4, when the frequency-thickness product, ft, is below 500
249

kHz-mm, the group velocity variation is small. Another region is when multiple modes coincide;e.g., in Fig. 4, this corresponds to ft = 2000 kHz-mm when L(0,1) and L(0,2) overlap. Goodtransmission of 12.7-mm waveguide at 370 kHz appears to result from the mode overlap.

Fig. 4 Group velocity for rod waves, L(0,1) and L(0,2), and experimental results (+).
3.2 Tube waves
Waves in tubes have many different modes; L-mode: axially symmetric: F-mode: non-axiallysymmetric: and T-mode: displacement in the circumferential direction. For AE waveguides, Land F-modes are involved and they are equivalent of S- and A-modes of plate waves.

Waveforms again exhibit strong broadening as shown in Fig. 5. These four waveforms arefor a rod of 12.7-mm diameter and for tubes of diameter-to-wall-thickness (d/t) ratio = 5, 8 and10 with the center frequency of 370 kHz. Additional attenuation loss of 11-12 dB is found incomparison with the solid rod case (d/t = 2). The d/t ratio has small effect on the peak amplitude. At high d/t, this represents better transmission per unit mass. At 175 kHz, pulse broadening
is particularly strong and the attenuation varies with d/t. For d/t = 8~11, the variation of diameter
affects the broadening and attenuation in unexpected manner. For tubes of 7.9 and 9.5 mm,broadening stretched to 400 µs, but for those of 12.7 and 6.4 mm pulse width was ~200 µs. The
peak amplitude was the highest for the 6.4 mm tube. These effects are naturally dependent on
frequency.
From experiment covering 30-1500 kHz, 6.4-mm tube is the best among tubes tested. It provides the least broadening, relatively short rise time and the lowest attenuation. It is clear, however, that waveform fidelity cannot be maintained. Significant pulse broadening is found in all
the cases examined. Thus, waveform-based AE cannot be practiced using waveguides. Another250

Fig. 5 Waveforms from transmission experiment with tubes of different diameter at 370 kHz. d/tratios ranged from 7.8 to 11.2. Note high amplitude on 6.4 mm data. Display range: 422 µs.
rule of waveguide usage is to avoid the frequency-thickness ranges that result in a dip on the velocity vs. ft curves. Such a region produces strong dispersion effect and results in high attenuation and pulse broadening as in the case of rod waveguide. This can be found from calculated
dispersion curves, but trials may be needed when the calculation is unavailable.

Fig. 6 Group velocity data; theory (for d/t = 3, 5 and 10). Experiment with d/t = 7.8 (solid circle)
and d/t = 8.6 (circle with +).
251

As for the dispersion behavior, experimental velocity generally agrees well with calculation.
This is illustrated in Fig. 6, which shows typical data for measured group velocity for tubes withdiameter-to-wall-thickness (d/t) ratio of 8.6 and 7.8. Again, it is difficult to predict the range of
frequency or mode that provide low attenuation.
4. Conclusions

1. Waveguides are useful in expanding the condition of AE sensor usage. However, significant
pulse broadening occurs. One must avoid using waveguides for achieving waveform fidelity. In
fact, some common sample shapes must be reevaluated for use in waveform-based AE testing.
2. In using rod waveguides, pick low “ft” region with Vrod or when L(0,1) and L(0,2) curves intersect and avoid “Velocity Trough”, where propagation velocity dips sharply. Tubes add additional attenuation (of up to 12 dB in the conditions evaluated).
3. As practical AE waveguides, commonly used 6.4-mm rods are apparently the best choice anda pointed end improves pulse definition.
4. Consider pulse broadening in AE setup so that individual events are counted appropriately.
For detecting surface or plate waves, other design guide is needed as attachment or coupling design influences the transfer of waves onto the waveguide.
References

1. S. Hsu and K. Ono, Proc. 5-th International Acoustic Emission Symp. 1980, Japanese Society
NDI, Tokyo, pp. 294-303.
2. J. Rodgers and R. Tilley, Proc. EPRI Plant Maintenance Conference, 1999, EPRI, pp. 6D-1 to
6D-13.
3. C. Allevato, presented at 46-th Meeting of AEWG, Portland, Oregon, 2003.
4. K. Ono, H. Nishino and H. Cho, Proc. 14-th AE Conference, JSNDI, 2003, pp. 29-38.
5. M. Redwood, Mechanical Waveguides, 1960, Pergamon Press, New York.
6. K.F. Graff: Wave Motion in Elastic Solids, 1975, Dover, New York. (Ch. 8)
7. J.L. Rose: Ultrasonic Waves in Solid Media, 1999, Cambridge University Press, Cambridge.
8. D.C. Gazis, J. Acoust. Soc. Am., 31, 1959, 568.
9. H. Nishino, S. Takashina, F. Uchida, M. Takemoto, and K. Ono, Japanese J. Applied Physics,
Part 1, 40 (1), 2001, 364-70.

252

ACOUSTIC EMISSION FOR ON-LINE MONITORING OF DAMAGE IN
VARIOUS APPLICATION FIELDSMARTINE WEVERS1, GERT VAN DIJCK1,2, WENDY DESADELEER1,
MARK WINKELMANS3 and KOEN VAN DEN ABEELE4
1
Katholieke Universiteit Leuven, Department of Materials Engineering, Kasteelpark Arenberg
44, B-3001 Leuven, Belgium; 2 Katholieke Universiteit Leuven, Computational Neuroscience
Research Group, Campus Gasthuisberg, Herestraat 49, B-3000 Leuven, Belgium;3 BASF AG,
Carl-Bosch-Straβe 38, D-67056 Ludwigshafen, Germany; 4 Katholieke Universiteit Leuven,Afdeling Kortrijk, Subfaculty of Sciences, Sabbelaan 53, B-8500 Kortrijk, Belgium
Abstract
Structural damage in materials is a “top priority” to be investigated by material scientists allover the world. The future or limit of a material to be used in structural applications depends
upon it, as safety and environmental protection are important issues in national policies. The
acoustic emission (AE) technique offers the possibility to listen to the damage process when andwhere it occurs. This paper focuses on a number of cases investigated at the department MTM
using AE to analyze damage in various application fields. The tools handled to interpret the AE
signals, the relation between AE and the residual properties and the additional understanding of aparticular material behavior will be outlined. The fatigue damage of CFRP, the curing of
concrete and the corrosion of carbon steel and stainless steel will be discussed as case studies.
Introduction
The acoustic emission technique is a “passive” method, which detects the elastic energy
released when microstructural changes in a material or a process take place. The AE sensors,
mostly PZT transducers, resonant or broadband, are used to pick up the elastic stress waves fromthe material and convert them into an electrical signal. This electrical signal is further processedby filters, amplifiers and a signal processing unit on a PC. Depending on the application thecorrect frequency bandwidth has to be chosen to ensure the best sensitivity of the detection
system for the phenomena to be measured. The sampling rate also depends upon the latter. The
further tuning of the signal-to-noise ratio (filters/amplifiers) can be done to optimize themonitoring conditions. For the analysis of the AE data it can be of help that with the AE signal
also a recording is done of the time of loading or the loading itself (mechanical or thermal).
From the AE signal itself the following parameters or AE characteristics are recorded: amplitude,
duration, AE (ringdown) counts, rise time, energy, slope, frequency, modal content of the AE
signal, AR (auto regressive) and wavelet parameters.
Case study 1: Fatigue damage monitoring of carbon fiber-reinforced epoxy laminates with AE
Tension-tension fatigue tests have been performed on quasi-isotropic (0°, +45°, 90°, -45°)scarbon-epoxy laminates at different R-ratios. The typical damage development curve of this typeof laminate lay-up is depicted in Fig. 1. From this curve it is said that the first increase in damage is caused by matrix cracking (stage I). Matrix cracking is then followed by a steady state
growth of delamination in stage II and in the final stage III fibers are broken leading to the final
failure of the composite.
J. Acoustic Emission, 22 (2004)

253©2004 Acoustic Emission Groupstage IIstage III

Damagestage I

cycles n
Fig. 1 Typical damage development curve for quasi-isotropic composites.

In Fig. 2, where the total counts of the “filtered” AE are depicted as a function of fatigue cycles of a fatigue test run at Smax = 70% of the UTS and R = 0.3 at a frequency of 3 Hz, the shapeof Fig. 1 can be identified as well. Elaborate studies of the damage development, using replica’s,
X-ray radiography with penetrant and ultrasonic C-scan identified matrix cracking in stage I,
more matrix cracking and delamination in stage II and fiber failures in stage III. The filter consisted of only counting the AE counts in the upper 20% of the sinusoidal loading cycle, which
hereby excludes the AE signals generated by crack closure.

Fig. 2 Cumulative counts versus cycles
for filtered AE. Smax = 70% UTS, R = 0.3.
Taking this evidence into account an attempt was made to model the fatigue behavior of thecomposite using the AE counts as an input.
For stage I it was obtained that: nI = nini + nf,
where nini is the cumulative counts during quasi-static loading and nf the cumulative counts during fatigue loading. Here, dn/dN = A1 σ2cmax / cnc-1,
so
nI = (A1 σ2cmax N + ncini)1/c
and A1 and c constants to be defined as a function of the applied stresses.

254

For stage II the variation of the cumulative counts with fatigue cycles
dnII/dN = A2 (∆σ/σu)γ
with A2 and γ the constants to be defined by plotting dnII/dN as a function of ∆σ/σu, being thefatigue stress amplitude divided by the tensile strength of the composite. It was found that forthis composite A2 = 32.3 and γ = 10.2.
For stage III a problem arises when the final damage development is not located in between
the monitored region as was the case for a ‘certain’ number of fatigue tests. [1]The results presented indicate clear that the damage development in a composite can bemonitored with the acoustic emission technique whenever the whole critical area is being monitored and the modeling of non-linear behavior (D) can be done based on AE data from the critical region when the modeling parameter is related to physical damage. Once the material behavior is characterized by such curves, monitoring the slope of the AE cumulative counts with thenumber of fatigue cycles can identify the damage stage the quasi-isotropic composite material isin.
AE system used: Vallen AMS-3 AE System, DECI 375M AE-sensors
Case study 2: Cure monitoring of concrete with AE
Another application of the use of AE is the on-line monitoring of curing processes in freshly
poured concrete. The ultimate goal for monitoring concrete during its setting is to predict thelong-term behavior of concrete and to interpret the dependence of this behavior on the initial
composition and initial drying conditions of the concrete. [2]Preliminary monitoring tests were performed in the laboratory, using a curing cell (Fig. 3) of
200 x 150 x 100 mm, instrumented with 2 AE sensors, a temperature sensor (T) and 2 ultrasonictransducers (U). Concrete of the following composition was used: 1.88 kg sand, 3.57 kg aggregates, 1.35 kg CEM I 42.5R, 0.45 l water and 13.5 ml Rheolmild. The curing cell was placed in
an environmental chamber (Heraeus-Vötsch Climate Cabinet VLK 04/150) to isolate it fromvariations in outside temperature and humidity. However, the climate conditions were not controlled, allowing variations in temperature inside the concrete sample.

Fig. 3 Top view of the curing cell instrumented with 2 AE sensors, a temperature sensor (T) and2 ultrasonic transducers (U).
The curing process during the first 72 hours was monitored, measuring the cumulative countsof the AE events due to cracking, which is caused by the setting of the material during255

hardening. Apart from real curing related AE events, the received AE signals (Fig. 4a: originaldata) also consist of mechanical noise and EMI signals. The high frequency signals (Fig. 4b) can
easily be distinguished from the low frequency AE events (Fig. 4c) and were filtered out by applying a low pass filter on both channels, generating the “filtered” AE data (Fig. 4a).
a.

b.

c.

Fig. 4 (a) Cumulative counts of the original AE data and the “filtered” events: (b) Example of ahigh frequency signal (spurious AE event): (c) Example of a low frequency AE event.
Simultaneously with the AE measurements, the temperature inside the concrete cell was registered and the evolution in the compressional wave velocity during hardening was derived fromthe time of flight of an ultrasonic compressional pulse, which was sent through the sample every
10 minutes. Figure 5(a) illustrates the correlation between the cumulative counts of the filtered
AE events and the temperature variations inside the concrete sample. A first increase in the AE
events after 9 hours corresponds with an increased inside temperature. Figure 5(b) shows that
this increase in AE events occurs when the compressional wave velocity is almost stabilized.
The first, and most important, increase in temperature is caused by an acceleration of the exothermal hydration reactions, which reach a peak value after 11 hrs, when the hydration reaction
is at its maximum efficiency. This acceleration is also noticed in the evolution of the compressional wave velocity in time, indicating a stage of increased hardening. When the hydration reaction is at its maximum efficiency, the concrete sample is almost completely cured, and the velocity remains at a more stable level after 11 hrs. The first increase in the cumulative counts ofthe filtered AE events can thus be explained by intensification in microcrack generation at the
final phase of the main concrete setting and shrinkage process (<20 hrs). At later stages,a.

b.

Fig. 5 Cumulative counts of filtered AE events in comparison with (a) the variations in insidetemperature and (b) the evolution of the ultrasonic wave velocity.
256microcracking is caused by further curing and by temperature variations due to day/night cycles,
which are also detected in the cumulative counts of the filtered AE events.
AE system used: Fracture Wave Detector, Digital Wave Co., VS375-M 340 AE-sensors
Case study 3: Corrosion monitoring of carbon steel and stainless steel by means of advanced
data analysis and AI techniques on AE signals
Many failures in chemical process installations are due to corrosion processes. Literature [3]mentions that annually 3 to 5 % of the G.D.P. in industrialized countries is lost directly or indirectly due to corrosion processes. Conventional non-destructive testing techniques are most frequently used to evaluate corrosion in chemical installations [4]. All these techniques are typicallyapplied when the installation is taken out of service during periodic inspections. These techniques are insufficient when a considerable amount of corrosion has taken place between two
periodic inspections.
A continuous corrosion monitoring system obtrudes. Acoustic emission offers the possibility
to listen continuously to the materials while damage is developing. Typical AE signals could offer operators the alarm to inspect installations while damage is occurring. By immediate responseto the occurring damage one can avoid that aggressive chemicals are liberated such that risk forpeople and environment is minimized.
This case study focuses on identifying 3 most important corrosion processes for chemical industry by means of the acoustic emission technique: uniform corrosion, pitting and stress corrosion cracking (SCC). Corrosion processes were induced on carbon steel (DIN 1.0038) andstainless steel (DIN 1.4541). The number of selected signals for each corrosion process is:• 102 pitting events (brackish water + FeCl3 1% at 60°C)• 43 SCC signals in stainless steel (CaCl2 40% at 80°C)• 42 SCC signals in carbon steel (Ca(NO3)2 60% at boiling point)• 81 uniform corrosion signals in carbon steel (H3PO4 10% at room temperature)
The generated AE signals in the different corrosion processes are highly dependent on thegeometry of the material. In order to guarantee reproducible results and to facilitate up scalingfrom laboratory to chemical plant installations the research focused on a fixed geometry of a reference probe to monitor the signals. The corrosion damage on the probe needs to be representative for the damage on the chemical installation. This implies that the material of the probe underwent the same treatment as the material of the installation. Critical stress locations in the installations need to be identified, the same stresses need to be applied to the reference probe. The
experimental set-up has been published in [5, 6].
Two reference probes are partially submerged in the corrosive environment. When transient
signals are detected simultaneously these are likely due to system influences (flow of the chemical or pressurized air) or due to environmental influences (electromagnetic interference, mechanical vibrations, etc.). The third probe is placed in a non-corrosive environment. Simultaneous transients on all 3 probes are due to environmental influences. Transients that could be related to system or environmental influences are not retained within the data analysis.

257

Data analysis
Different steps were undertaken to achieve an automatic classification of the different corrosion processes. First, different filtering (denoising) techniques were tried out to increase the signal-to-noise ratio. The filtering techniques applied are: spectral subtraction [7], NLMS (normalized least-mean-squares adaptive filtering) [8], signal subspace filtering and an enhanced form ofsignal subspace filtering [9].
Second, the denoised time signals are transformed for feature extraction. The transformations
applied are: FFT (fast Fourier transform), AR model (auto regressive model) [10] and a wavelet
transform [11]. The FFT allows characterizing the time signals in frequency domain, the AR
model in time domain and the wavelet transform in frequency-time domain. The best results inclassification were obtained in frequency domain (FFT). 512 samples were selected in each timeseries and the FFT was applied. This results in 512 samples in the frequency domain. Only halfof the spectrum is retained because real time series result in a symmetric spectrum, the second
half contains no additional information.
Third, the information in the frequency domain, 256 frequencies with corresponding amplitudes, is compressed in a few parameters. The principal component analysis (PCA) [12] was applied to extract important parameters from the frequency domain. PCA finds projections of thedata such that variance in data is maximal after each projection, with the constraint that projection axes are orthogonal.
In a fourth step the visualization of the data is done by estimating the probability density
function (PDF) of the data in 2D space, after projecting the frequency data on the 2 most important principal axes (vectors) of the PCA. If different corrosion processes fall apart in different
clusters, a classification machine should be successful in separating different corrosion processes. Different clusters can be detected by means of the maxima in the PDF. This allows determining qualitatively the quality of the transform on the time series, after PCA, independent of
the chosen classification procedure. The density was estimated by means of an unsupervised neural network: Kohonen self-organizing map [12], see Figs. 6 and 7.

Fig. 6 Visualization of data. Every time series is reduced to 2 parameters (by means of PCA) for
which the values are put on the X- and Y-axis. The grid overlaying the data is the Kohonen selforganizing map. The crossings of the grid are the neurons of the neural net. The neurons are distributed over the data in order to represent the density of the data.
258

Fig. 7 Probability density estimate, derived from data in Fig. 6 by putting Gaussian kernels on
the grid crossings over the neural net. Maxima in the density estimate represent different clusters.
Every cluster corresponds with a different corrosion class. The density estimate allows describing the transform (and PCA) qualitatively independent of the classification procedure.
The kernel widths of the Gaussian kernels, which are centered on the neurons, are tuned
automatically by using the least-squares cross-validation method [13]. Only for the FFT different
corrosion classes resulted in separate clusters, see Fig. 7. The wavelet transform merged all datain 1 cluster. The AR model merged pitting and SCC in 1 cluster and resulted in a separate cluster
for uniform corrosion. From this it can be concluded qualitatively that the FFT is the most appropriate transform to distinguish between different corrosion classes.
For quantitative assessment of separability, the least squares support vector machine (LSSVM) [14] classification procedure is used. The function to be minimized in LS-SVM’s is defined by:
1
1 N
min Jp(w, e) = w T .w + γ ∑ ek2(1)
w ,b , e2
2 k =1such that()

y k . w T .ϕ (c k ) + b = 1 − ek , k = 1,..., N(2)

For a binary classification problem the classifier y(c) takes the form:y (c) = sign(w T .ϕ (c) + b )(3)

The w in (1) is related to the margin between different classes in the feature space. Minimizing
1 T
w .w results in maximizing the margin, see Fig. 8.
2
In SVM’s the input space is defined by the data points: the ck. The feature space is defined by
the ϕ(ck), see Fig. 8. The ϕ-mapping is a transformation to a higher dimensional feature space.
In this higher dimensional feature space a linear separation is made between the different classes.
259

Fig. 8 In the feature space a linear decisionboundary is computed. 2/||w||2 is the margin between the 2 different classes.
X : data points of a 1st class.
+ : data points of a 2nd class.
The second term in (1) tries to minimize the classification error. When γ is large less training
examples will be misclassified, but this might result in over-fitting. The margin will allow for
generalization. yk in (2) is the class label, for a first class this is chosen +1 for a second class –1.
The 1 on the right hand side in (2) is a target value, and in case of overlapping distributions between classes some error ek is allowed on the target value.
The constrained optimization problem (1)-(2) can be solved easily by defining the Lagrangian of the problem:N{

L(w, b, e;a) = J p (w, e) − ∑ α k yk  wT .ϕ (c k ) + b  − 1 + ek
k =1

}

(4)

In the general case the ϕ-mapping can become infinite dimensional and a so-called dual
problem is solved. The dual problem is defined by the search for the optimal Lagrangian multipliers: α1, α2… αN of the constrained optimization problem, where a = {α1, α2… αN}. Solving
the primal problem corresponds to searching for the optimal w. It can be shown that optimal α isfound by solving a set of linear equations. The advantage of the dual problem is to circumventthe problem of the possible infinite ϕ-mapping. The classifier in the dual space takes the form:

with

N

y (c) = sign ∑ α k . y k .K (c, c k ) + b  k =1(5)

K(c,ck) = ϕ(c)T.ϕ(ck)(6)

By solving the dual problem all ϕ disappear and are replaced by kernel functions K(c,ck) of
the form in (6). This is known as the kernel trick. In practice different kernels can be chosen: linear, polynomial, radial basis function (RBF), etc. The RBF kernel takes the form: − c − ck 2 
2 
K (c, c k ) = exp


σ2



(7)

For the classification problem both the linear kernel and the RBF-kernel were tried out, results were slightly better with the RBF-kernel. Classification results were always better for thedata after the FFT compared to the wavelet transform and the AR-model. This could be expected
while the FFT was the only transform that resulted in separate clusters for each corrosion class.
260

LS-SVMRBF, with 4 different classes
2=2, =0.35
γ

σ

2

20
10

2

0

3

3
2

3

-10
-20
2

Projection on 2nd PCA-axis

30

-50

-40

-30

-20

-10

0

10

20

30

Projection on 1st PCA-axis
Fig. 9 Decision boundaries computed by the LS-SVM. These time series are obtained from experimental conditions when no corrosion process could be expected. All symbols are time series,
with information compressed in 2 parameters after FFT and PCA. Ck’s are represented by thedata points.
*: Pitting; : SCC; + : uniform corrosion; ○ : time series without active corrosion processes.
MML density estimation: decision boundaries

Projection on 2nd PCA-axis

30
20
10
0
-10
-20
-30
-40

-30

-20

-10

0

10

20

30

40

Projection on 1st PCA-axis
Fig. 10 Decision boundaries computed by the Bayesian classifier. Observe thatthe decision boundaries in Figs. 9 and 10 are very similar.
261

The separation between different corrosion classes: SCC, pitting, uniform corrosion and time series in absence of corrosion could be achieved with about 2% classification error on average.
The classification performances are obtained using the 10-fold cross-validation technique. Best
results are obtained when using the spectral subtraction filtering technique. Figure 9 shows anexample of the decision boundaries computed by the LS-SVM. Figure 10 shows an example ofthe decision boundaries computed from a Bayesian classifier. In this case density estimations are
obtained from the four different classes and the c’s are assigned to the class with maximum aposterior class probability:
P(c | θi , Ci ) P(Ci )j = arg max P(Ci | c) =(8)
P(c | θ)i
Ci represents the class and θi is the set of parameters that determines the class conditional density, P (c | θi , Ci ) . θ is the set of θi parameters: θ = {θ1 , θ 2 , ... θ N } , where N is the number ofclasses.
Density estimations are obtained by the minimum message length (MML) criterion for Gaussian
mixture models (GMM) [15]. Observe that the decision boundaries obtained in Figs. 9 and 10 are very
similar. There is no statistically significant difference in classification performance obtained between
both classification paradigms for this particular data set.
All programs were partially developed in the C-programming language (Kohonen selforganizing map) and MATLAB. The LS-SVM was available as a software tool, developed at the
Department of Electrical Engineering (ESAT), at the Katholieke Universiteit, Leuven.
AE system used: Fracture Wave Detector, Digital Wave Co., B1025 Digital Wave AE-sensors
Conclusions
Modeling the material behavior based on NDT data is gaining more interest as the confidence
in the techniques increases, as the relation with physical damage is proven. The on-line monitoring of fatigue in CFRP laminates and of the curing of concrete with AE results in cumulative AE
counts which after filtering offer the input data to model the damage behavior of the material.
The AE technique offers parameters linked with physical damage to do the modeling with.
Sophisticated signal processing of the AE signals is also presented in order to extract featuresfrom the time signals; subsequently these features are used as input for a classification machinein order to identify different corrosion processes.
Case study 1: Research funded by the EC human capital and mobility fellowship,
Case study 2: Research funded by the FWO, Flemish Fund for Scientific Research,
Case study 3: Research funded by a Ph.D. grant of the Institute for the Promotion of
Innovation through Science and Technology in Flanders (IWT-Vlaanderen).

262

References
1. Tsamtsakis, D. and Wevers, M., “Acoustic emission to model the fatigue behavior of quasiisotropic carbon-epoxy laminate composites”, INSIGHT – Non-Destructive Testing andCondition Monitoring, Journal of British Institute of Non-Destructive Testing, 41 (8), August
1999, 513-516.
2. Lacouture, J.-C., “Modélisation de l'évolution des coefficients mécaniques du béton pendantla prise. Liaison avec les mesures ultrasonores linéaires et non linéaires” Thèse, UFR De
Physique, 2002, Université Paris 7 – Denis Diderot, 201p.
3. National Bureau of Standard/US Department of Commerce, “Economic effects of metallic
corrosion in the United States”, NBS, Special Publication 511-1, Washington DC, 1975
4. Rudlin, J.R., “Review of existing and possible techniques for corrosion under insulation andwall thickness measurement in steel pressure containments”, Insight, 39 (6), 1997, 413-416.
5. Winkelmans, M. and Wevers, M., “Non-destructive testing for corrosion monitoring inchemical plants”, Journal of Acoustic Emission, 20, 2002, 206-217 (CD-ROM).
6. Winkelmans, M., Ph.D., “Fusion of non-destructive inspection techniques for corrosion
monitoring in chemical process installations”, Katholieke Universiteit Leuven, ISBN 905682-484-8, UDC 620.179:620.193.4, University of Leuven, 2004.
7. Boll, S.F., “Suppression of acoustic noise in speech using spectral subtraction”, IEEE Transactions on Acoustics, Speech and Signal Processing, ASSP-27 (2), April 1979.
8. Moonen, M., Proudler, I., Introduction to adaptive signal processing.
9. Hu, Y., Loizou, C., “A subspace approach for enhancing speech corrupted by colored noise”,
IEEE Signal Processing Letters, 9 (7), July 2002.
10. Chatfield, C., The analysis of time series, Chapman & Hall, 1987.
11. Daubechies, I., Ten lectures on wavelets, Society for Industrial and Applied Mathematics,
Philadelphia, Pennsylvania 1999.
12. Ham, F.M., Kostanic, I., Principles of neurocomputing for science & engineering, McGrawHill Higher Education, 2001.
13. Silverman, B.W., Density estimation for statistics and data analysis, Chapman & Hall, pp.
106-107, 1992.
14. Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, J., Vandewalle J., Least squares
support vector machines, World Scientific Pub. Co., Singapore, 2002.
15. Figueirido, M.A.T. and Jain, A.K., “Unsupervised learning of finite mixture models,” IEEE
Trans. On Pattern Analysis and Machine Intelligence, 24, (3) 381-396, March 2002.

263

THE EFFECT OF WAVEGUIDE MATERIAL AND SHAPE ON
ACOUSTIC EMISSION TRANSMISSION CHARACTERISTICS
PART 1: TRADITIONAL FEATURESJOANNA SIKORSKA and JIE PAN
University of Western Australia, Perth, Australia
Abstract
This paper presents the effects of varying waveguide material and/or shape on traditional
acoustic emission characteristics of pulsed events. Forty-eight solid cylindrical waveguides were
fabricated from alumina ceramic, mild steel, stainless steel (SS316), 2024-T3 aluminium or extruded Delrin. Three different lengths, two diameters and four sensor face angles were tested.
The effects of a point at the source end of the waveguide were also verified. Results show that
although ceramic guides attenuate the signals more than commonly used grades of steel and aluminium, waveform fidelity is less affected. Points also had a negative effect of all AE features
and waveform profiles.
Keywords: acoustic emission testing, waveguides, buffer rods, wave propagation.
1. Relevant Ultrasonic Theory
The solutions to the general wave equation for acoustic waves travelling through infinitely
long isotropic cylinders are detailed in references [1-3]. However, there are several key points
applicable to this research work that will now be summarised.
Longitudinal (axially symmetric) wave modes in an infinitely long cylinder vary with radial
and longitudinal distance along the cylinder but are symmetrical around the circumference. The
relationship between longitudinal phase velocity and frequency is known as the PochammerChree equation and resembles the Lamb dispersion relationship in plates. Its solution is well
documented and can be found in the aforementioned references.
Group velocities also vary with frequency in a similar fashion. Fig. 1 depicts the first two
longitudinal phase and group velocities for one of the materials tested in this study. It can beseen that the fundamental mode approaches a constant speed as frequency approaches zero. This
limiting speed corresponds to the longitudinal rod speed, c L = E ρ where E is the Young’s
modulus for the material and ρ is the density. As frequency increases the first longitudinalmodes asymptote to the Rayleigh velocity. Rayleigh waves are surface acoustic waves in whichlongitudinal and shear modes couple together and travel at a common speed; it is always lessthan the transverse speed by 5 to 15% [4]. They also decay rapidly with depth and therefore displacements are confined to within one order of Rayleigh wavelengths from the surface. Higher
longitudinal modes have cut-off frequencies below which they will not propagate, and above
which they are dispersive. Torsional waves (through an infinite cylinder) propagate with one
constant phase velocity equal to the shear (or transverse) speed:cT =J. Acoustic Emission, 22 (2004)E1
ρ 2(1+ ν )

(1)

⋅

264

©2004 Acoustic Emission Groupwhere ν is the Poisson’s ratio. Again, higher phase modes are dispersive and each has a unique
cut-off frequency below which these modes will not propagate as evident in Fig. 2. Cut-off frequencies are a function of the rod diameter (due to the changing velocity), but independent oflength. All torsional group velocities are dispersive. Similar, but mathematically more complex
relationships govern the dispersive characteristics of flexural (anti-symmetric) waves in rods;
these are described in reference [4].
3.0Phase, Mode 1
Phase, Mode 2Group, Mode 1
Group, Mode 2

2.5

c/cT

2.0

1.5

1.0

0.5

kHz

0.0
0

500

1000

1500

2000

Fig. 1 First two longitudinal modes showing normalised phase and group velocities (latter are
shown as dashed lines) for an 8-mm SS316 infinitely long rod waveguide. Wave speed is normalised with respect to transverse wave speed, given in equation (1), whilst frequency is in kHz.

Fig. 2 First four normalised torsional phase velocities for 8-mm diameter, infinitely long waveguides of different materials. Wave speed is normalised with respect to transverse wave speed,
given in equation 1, whilst frequency is in kHz.
265

At very narrow diameters, cut-off effects act like a high pass filter, leaving only lower ordermodes as the means by which low frequency longitudinal, torsional and flexural waves can betransmitted. Conversely, above a certain frequency, which is a function of rod diameter and material properties (namely E, ρ, and ν) speeds approach their limiting values and dispersion will
no longer occur. In this interim region however, where wavelength is in the same order as therod diameter, significant dispersive effects can be expected.
In addition to dispersion, AE bursts are attenuated as they travel through the material. Someof this energy loss is caused by friction and thermal conductivity and occurs in all materials, independent of frequency. However in compounds with larger grain sizes, such as plastics orcomposites, frequency-dependant attenuation can increase significantly (by up to 3 orders ofmagnitude). Attenuation is also generally greater for transverse waves than for longitudinalmodes. Finally, it is important to recognise that AE sensors are not equally sensitive to all wave
modes, causing further apparent signal reduction of various modes.
One side effect of dispersion is the tendency of AE bursts, which are made up of numerous
waves of different polarizations and consequently different speeds, to lengthen in time as they
move further from the source. Called mode separation, continuous transformation between
modes exacerbates this effect [5].
In a finite rod, wave motion becomes even more complicated due to reflections from, andresonance at, both ends [6]. Acoustic waves are partially reflected and transmitted at every interface, the relative amounts of which depend on the ratio of the relative characteristic impedancesof each pair of boundary materials. This impedance is a function of material density and the
speed wave hitting the interface. If material attenuation is low, these reflections will combinewith late arriving components of the original signal, thus complicating feature identification.
For non-zero angles of incidence, the character of the wavefront will also change as it hits theinterface. Called mode conversion, the change in angle of the wavefront will convert part of theincoming wave from longitudinal to shear and vice versa. Flexural waves that may not havebeen present in the original waveform can also be excited.
Finally, cylinders have natural frequencies that are a function of their length and wave velocity. As an AE burst is made up of numerous waves of different frequencies, resonance can be
expected. Another interesting effect of dispersion is that higher order resonant modes are nolonger integer multiples of the fundamental frequency. This can complicate identification ofthese artefacts.
In traditional AE processing, various AE parameters are used for describing AE signals. AE
energy is calculated by integrating the signal over time. Amplitude and frequency analysis are
more meaningful parameters for measuring continuous emissions, whilst count rate and energy
analysis are useful for burst emission characterisation [7]. Defining a burst as an event that exceeds a certain threshold, the following features will be extracted and analysed in Part 1 of thisstudy:
Peak amplitude
Burst energy
Duration and/or total counts
Rise time and/or counts to peak
Signal strength.
266

2. Experimental Method
A variety of short waveguides were tested to illustrate the effects of (a) material properties;(b) length; (c) diameter; (d) face angle and (e) source point on the transmission and reception ofpulsed AE bursts.
Forty-eight (48) waveguides were tested in total, fabricated from five materials: Delrin acetal
resin (white extruded bar, exact grade unknown), common mild steel (exact grade unknown),
SS316, 2024-T3 aluminium and sintered 99.9% alumina ceramic (α-Al2O3). Approximate material properties are summarised in Table 1. Sensor face angles were 0˚, 30˚, 45˚ or 60˚; lengths
were 30, 43 or 51 mm; and diameters were 5 or 8 mm. Unless otherwise stated, the rod face attached to the pulser was flat.
Test specimens were grouped as follows:(a) 5 sets of 8-mm diameter waveguides with face angles of 0˚, 30˚, 45˚ and 60˚ from each of thefour materials (20 waveguides in total). Lengths were set at 43 mm between centres (atwhich pulser and receiver were located).
(b) 3 sets of 8-mm diameter, shorter (30 mm) waveguides with face angles of 0˚, 30˚, 45˚ and60˚; from Delrin, 2024-T3 aluminium and SS316.
(c) 2 sets of narrower (5 mm) mild steel rods, 30mm and 43mm in length, with face angles of 0˚,
30˚, 45˚ and 60˚.
(d) 3 longer (51 mm) rods from Delrin, 2024-T3 aluminium and SS316, 0˚ sensor face angle.
(e) 5 8-mm diameter, 43-mm long rods from mild steel (1 of), SS316 (1 of) or AL2024-T3 (3 of),each with one end tapered (45˚) to a point (pulser end). The sensor end was always flat.
More specifically the details of each were:• 1 x MS waveguide with dull point• 1 x SS316 waveguide with sharp point• 1 x AL2024-T3 waveguide with a flat point (1.6 mm in diameter)• 1 x AL2024-T3 waveguide with dull point• 1 x AL2024-T3 waveguide with sharp point
The following designation is used to describe samples:
Material x Diameter in mm x Nominal Length in mm “PT“ (optional).
To quantify the effect of each waveguide, data was also collected from the sensor mounted directly on the pulser face. The resulting values were used to normalise all data.
Table 1: Approximate acoustic properties of test materials used in theoretical calculations.
* Properties given for Delrin [9].
Abbreviation

Density(g/cm3)cL(mm/µs)cT(mm/µs)ZL

Delrin *

DR

1.42

1.8

0.9

2.6

Mild Steel

MS

8.00

5.6

3.1

45

SS316

SS

7.89

5.7

3.0

45

AL2024-T3

AL

2.77

6.4

3.1

17.7

α-Al2O3

ALM

3.9

10.6

3.5

40.6

Material

267

Longitudinal pulses, representative of AE bursts, were generated by a DECI Model SE25-P
sensor driven by a DECI Model 600 pulser, set at 150V. A waveguide holder (Fig. 3) was fabricated to ensure that the relative positions of the sensor, waveguide and pulser were kept constant.
The pulser was attached to the underside of the holder with a Delrin collar containing 8 rare earthmagnets and two locating pins to ensure repeatable orientation. Incorporated into the sensor
mount was a screw and spring that held the sensor firmly against the top waveguide face. This
could be oriented to any angle. O-rings prevented the waveguides from contacting the sides ofthe sample holder. Silicone grease was used as a couplant between the pulser-waveguide andwaveguide-sensor faces.
Bursts were detected using a B1080 Digital Wave Corp. single-ended wideband AE sensor
connected to a PAC 2/4/6 preamplifier supplying a gain of 20 dB and fitted with 100-1000 kHz
bandpass filters. Data was recorded with a PAC PCI-2 card, which was housed in a 2.8-GHz
Pentium-IV desktop computer. Prior to digitisation, signals were bandpass filtered onboard at 13000 kHz. A fixed trigger threshold was set at 50 dBAE. For each waveguide, between 128 and
300 triggered waveforms, each consisting of 8192 samples with 256 pre-trigger samples, werecollected and digitised at 10 MHz.
Various burst features were collected using PAC’s AEWin software for each triggered event,including peak amplitude (in dBAE), AE energy, signal strength, duration, rise time and AE
counts. These were then averaged and graphed. All values used in any particular average varied
by less than 2% indicating high pulser repeatability. Individual waveforms were also captured
and averaged to remove the effects of random instrumentation noise.

Fig. 3 Waveguide holder.
3. Results and Observations
3.1 Burst profiles
As can be seen clearly in Fig. 4, waveform profiles being received by the sensor vary significantly between materials. Alumina ceramic appears by first inspection most like the face-to-facesignal but greatly reduced in amplitude. On the other hand, waveforms associated with bothsteels are significantly protracted and full of broadband artefacts. Aluminium wavefront also268

Fig. 4 Time waveforms for flat waveguide samples. Different y-scales are used to facilitate better
appreciation of waveform profiles. Time scales are identical.
appears filled with similar broadband artefacts. This loss of waveform fidelity was also observedby Ono, in his tests on longer samples of the same material using lasers as a source of AE [10].
Mode separation is most obvious in the Delrin sample, which has also lost the pulse’s sharp
wavefront.

Fig. 5 Amplitude for different flat-faced waveguides.
269

Fig. 6 Amplitude versus face angle for all sets of angled samples.

Fig. 7 Effect on amplitude by using a pointed waveguide.
3.2 Amplitude and energy
The effects on amplitude were very similar for most waveguides as can be seen from Figs. 5
to 7. The trend for energy was similar. These show that both parameters decreased with length,
increased diameter and increased face angle. As expected, attenuation was higher for α-Al2O3
and Delrin. For the three materials from which different length waveguides were tested, approximate values for attenuation were calculated (see a table below).
Although these numbers are very approximate, they do indicate that Delrin’s broadband attenuation is highly non-linear and in this application (across the frequency range studied) significantly less than predicted by the literature.
270

Average peak amplitudes of samples in dBAE

Attenuation

30 mm

51 mm

Difference

dB/m

SS316

107.7

101.0

6.7

319

AL2024-T3

105.6

100.2

5.4

257

Delrin

95.3

90.5

4.8

229

3.3 Rise time, duration and counts
Rise-times (and counts to peak) were greatest for Delrin, due to its much lower speed, andobvious mode separation. Otherwise material effects on rise-time were minimal. There werealso no clear relationships between length, face angle and rise-time. The same cannot be said forpointed waveguides, as shown in Fig. 8, which caused rise-times to increase significantly in all
three materials tested.

Fig. 8 Effect of pointed end on rise-times.
Durations (and AE counts) were affected by material properties, as shown in Fig. 9. Results
support initial observations from time waveforms, with steel samples showing the highest durations, which increased with both length and diameter.
However, results for other materials were less consistent. Although alumina appeared tohave a very similar waveform to the originating pulser burst, its much smaller duration value
suggests that its calculation is as much a feature of the fixed threshold value as it is the waveformprofile: a small signal takes less time to drop below the fixed 50 dBAE trigger level. (A smaller
threshold, closer to the true noise floor, may have lessened this affect.)Neither face angles, nor
end points had any appreciable effect on durations.
4. Discussion
4.1 Effect of material
Little difference in the time domain can be seen between mild steel and stainless steel
waveguide responses, especially when compared to other material samples. AE features analysed
were also consistent between the two steel grades, indicating that neither material has any obvious271

Fig. 9 Material effects on duration.
acoustic superiority over the other. Durations and counts were substantially higher in both
grades of steel and both features increased with rod length. The reason for this is not immediately obvious from the tests in this paper but is the subject of investigations presented in Part 2.
Of the metallic specimens, 2024-T3 aluminium samples transmitted pulses with the least signal distortion. However waveforms still appeared protracted and filled with broadband artefacts
not present in the original signal, although not as much as in either steel. Alumina ceramic was
the least distorted of all materials, with signals appearing similar to the originating bursts. Delrin, due to its much slower wave speed, showed the clearest mode separation of all materials.
As predicted, overall attenuation of both energy and peak amplitude was greatest in nonmetallic waveguides, however rudimentary calculations based on drop in peak amplitude for different lengths indicated that this was not as severe as was expected. Furthermore, any attenuation present in Delrin seemed to be non-linear and therefore longer waveguides caused proportionally less attenuation than shorter guides across the traditional AE bandwidth.
4.2 Effect of length, diameter and face angle
Attenuation also increased with length and diameter (where measured). Reasons for the latter are not clear, and were only tested for one material (mild steel) but may be due to the increased number of modes present in wider rods (below cutoff). This will be discussed further inPart 2. In all materials, attenuation increased significantly with face angle, particularly above 30
degrees. This is probably because mode conversion is causing redirection of energy into modes
that the transducer is less able to detect.
4.3 Effect of pointed source end
Pointed 43-mm waveguides attenuated the signals by over 12 dB when compared to similar
flat waveguides with adequate couplant. This was greater than the attenuation quoted by otherresearchers [10, 11]. The type of point also made a significant difference on amplitude and energy attenuation with finer (sharper) points causing greater signal reduction than rounder or flat272

points. This may allude to the discrepancy between these values and those published in otherwork.
5. Conclusions
Although signals passing through metallic waveguides were not attenuated significantly, signals were protracted and appeared as if contaminated by reflections and resonance artefacts.
Conditions deteriorated with length and diameter. It is possible that much longer waveguides(>100mm) might avoid this problem due to the additional attenuation that would be incurred;however this requires further verification. On the other hand, α-Al2O3 did not suffer from thisproblem, with primary bursts being easily separated from subsequent reflections. Although alumina had a higher overall attenuation, measured bursts showed significantly less distortion and(except for amplitude and energy due to its increased attenuation) best-matched AE features offace-to-face signals.
Unfortunately, use of waveguides in AE testing cannot be avoided, however their implementation must be properly considered. Whenever feasible, specific designs should be tested prior
to installation to determine actual transmission characteristics.
Further effects on transmission of modal information will be investigated in Part 2.
Acknowledgements
The authors thank IMES Group for supplying the PCI-2 board and software used for data acquisition.
References
1. Graff, K.F. (1975) Wave motion in elastic solids, London: Oxford University Press.
2. Redwood, M. (1960) Mechanical Waveguides, London: Pergamon Press.
3. Meeker, T.R. and Meitzler (1964) Guided wave propagation in elongated cylinders andplates, Physical Acoustics, 1(A): pp. 112-169.
4. Pao, Y.-H. and Mindlin, R.D. (1960) Dispersion of Flexural Waves in an Elastic, Circular
Cylinder, Journal of Applied Mechanics - Trans. ASME, 27(9): pp. 513-520.
5. Cheeke, J.D.N. (2002) Fundamentals and Applications of Ultrasonic Waves, Boca Raton,
Florida: CRC Press.
6. Onoe, M., McNiven, H.D. and Mindlin, R.D. (1962) Dispersion of Axially Symmetric
Waves in Elastic Rods, Journal of Applied Mechanics - Trans. ASME, 29(12): pp. 729-734.
7. Raj, B. and Jha, B.B. (1994) Fundamentals of acoustic emission, British Journal of NDT,
36(1): pp. 16-23.
8. Holroyd, T. (2000) Acoustic Emission & Ultrasonics, Oxford, UK: Coxmoor Publishing Co.
9. Dupont Delrin Design Guide - Module III [online], Ed. Available from:http://plastics.dupont.com/plastics/pdflit/americas/delrin/230323c.pdf[Accessed
15.03.2004].
10. Ono, K. and Cho, H.-J., (2004) 'Rods and Tubes as AE Waveguides' in Proceedings of the
26th European Conference on Acoustic Emission Testing, Berlin: DGZfP, pp. 593-603.
11. Wood, B.R.A., Flynn, T.C., Harris, R.W. and Noyes, L.M. (1991) The use of waveguides inacoustic emission monitoring projects, Acoustics Australia, 19(3): pp. 87-89.
273

THE EFFECT OF WAVEGUIDE MATERIAL AND SHAPE ON AE
TRANSMISSION CHARACTERISTICS
PART 2: FREQUENCY AND JOINT-TIME-FREQUENCY CHARACTERISTICSJOANNA SIKORSKA and JIE PAN
University of Western Australia, Perth, Australia

Abstract
This paper presents the effects of varying waveguide material and/or shape on the frequency
and joint-time-frequency characteristics of pulsed acoustic emission (AE) events. Forty-eightsolid cylindrical waveguides were fabricated from alumina ceramic, mild steel, stainless steel(SS316), 2024-T3 aluminium or extruded Delrin. Three different lengths, two diameters andfour sensor face angles were tested. The effects of a point at the source end of the waveguidewere also verified. Analysis methods included continuous wavelets, short time Fourier transforms and standard Fourier transforms. Results showed that detected signals are dominated by
the resonant harmonics of the waveguides, particularly with commonly used waveguide materials such as aluminium and steel.
Keywords: acoustic emission testing, finite waveguides, wave propagation, modal AE, wavelets.
1. Relevant Theory
The solutions to the general wave equation for acoustic waves travelling through infinitely
long isotropic cylinders are detailed in references [1 - 3]. Additionally, key points applicable tothis research work were summarised in Part 1 [1].
1.1 Effect of finite ends
Finite cylinders have natural frequencies according to the following:
c
fn =
(1)
2nL
where c is the wave speed, n is an integer and L is the length of the waveguide. For materials
analysed in this study, the corresponding values based on the longitudinal wave speed are listed
in Table 1.
Table 1 Approximate natural frequencies of cylindrical rods with differing diameters.
Material(abbreviation)
Delrin (DR)Mild Steel (MS)SS316 (SS)
AL2024-T3 (AL)
α-Al2O3 (ALM)

J. Acoustic Emission, 22 (2004)

cL

fn in kHzfor length in m

(mm/µs)

0.03

0.043

0.051

1.8

30

21

18

5.6

93

65

55

5.7

95

66

56

6.4

107

74

63

10.6

177

123

104

274

©2004 Acoustic Emission Group

1.2 Frequency signal processing
Traditionally, frequency analysis has not been used to describe AE signals because material
induced AE is generally broadband (energy is distributed across a wide range of frequencies) or
unduly influenced by the natural frequency of the sensor. However, in certain cases amplitude
spectra can be useful to quickly ascertain the presence of specific frequencies. Additionally,some researchers have observed that certain types of faults change the relative distribution of energies between different frequency bands [2].
1.3 Advanced joint-time-frequency techniques
Fourier transformation (commonly referred to as FFT), used in traditional vibration analysis,
destroys all time information: a FFT calculation returns all frequencies that are present in thesignal irrespective of whether they exist for its entire duration or are only transitory. Therefore,
in general, FFT analysis should only be performed on stationary and ergodic signals.
Burst emissions are neither ergodic, nor stationary. Therefore over the last decade many researchers have attempted to characterise AE signals by more advanced techniques such as wavelets [3-8], Wigner-Ville functions [9], short-time Fourier Transforms (STFT) [10] and Kalmanfiltering [11], of which wavelets are the most common. Known collectively as joint-timefrequency-analysis, these techniques investigate how frequencies change with time, but requiresignificantly more computing power to implement than standard frequency analysis.
STFTs can be thought of as simply an extension of traditional FFTs; the time-frequency
space is effectively divided into a number of equal blocks and then a windowed Fourier transform is performed on each block. Blocks are overlapped slightly to overcome errors caused byapplying the window function. Results are typically displayed on a contour plot, called a spectrogram, or a 3-D x-y-z (time-frequency-amplitude) plot. Unfortunately, ultimate resolution islimited by the uncertainty principal ( ∆t ∆ω ≥ 0.5 ) so the only way to increase temporal resolution is to decrease frequency resolution and vice versa.
Continuous-time wavelet transforms (CWT) on the other hand, are special mathematical
functions that split a signal into non-uniform sections (windows), localised in time and scale[12]. Scale is related to, but not synonymous with, frequency. The relationship between a
block’s central frequency and its bandwidth is always constant and decreases exponentially.
To determine values for each block, wavelet transformation dilates and translates a basis
function (called a mother wavelet). The CWT is simply the cross correlation of the time signalx(t) with this dilated and translated wavelet: w (( t − b a ) / a ) or in layman’s terms: how well the
dilated, translated wavelet matches the original signal. The results of this process are then displayed as a spectrogram or xyz plot, very similar in appearance to the STFT.

CWT generally has superior resolution over STFT because of this non-uniform division oftime and scale: at higher scales, signals are divided into very short blocks of time, allowing identification of very quick, impulsive events, whilst at lower scales they are highly localised inscale, facilitating identification of specific frequencies. This makes them incredibly useful for
identifying sharp (i.e. high frequency) bursts and discontinuities that may be masked by larger
stationary signals or noise (low frequency). Unfortunately a CWT generally takes more time tocompute than an STFT.

275

One commonly used wavelet in AE analysis is the Gabor (otherwise known as Morlet) function [8], also used by the AGU-Vallen freeware. Its function is defined by the following:w(t ) = exp(−t 2 / 2)cos(5t )(1)
The relationship between scale, a, and centre frequency, fc, for the Morlet wavelet is given by thefollowing [13]: fs
where a=0
(2)
fc =  5 fs
a>0
 2π awhere fs is the sampling rate in Hertz. The relationship between centre frequency and bandwidth
therefore becomes:
centre frequency 5 f s / 2π a 5 f s
=
=
≈ 0.8
(3)
1/ a
2π
bandwidth
Similarly, the translated position on the x-axis, b, is determined by:
b × Nx(4)
xb =where b=0,1,2...,N/N x
fs
where Nx is the number of time samples per time division and N is the total number of samples.
For efficient computation, these should both be integral powers of 2 (eg. 64, 1024, 8192 etc).
1.4 Modal AE
It is assumed that specific faults propagate by a defined set of one or more dispersive modes,
the characteristics of which are a function of material properties and dimensions [1]. Modal AE
involves acquiring and digitizing AE time signals, from which individual propagation modes arethen identified and extracted. Information about particular modes can then be quantified in an
attempt to identify and/or source the underlying faults.
2. Experimental Method
A variety of short waveguides were tested to illustrate the effects of material properties,
length, diameter, face angle and source point on the transmission and reception of pulsed AE
bursts. Details of samples tested and experimental method was described at length in Part 1.
2.1 Additional post-processing
After capturing burst data using AEWin (Physical Acoustics software for PCI-2 board), 32
sequential bursts of 8192 samples each (digitised at 10 or 40 MHz) were synchronously timeaveraged and post-processed using programs written in National Instrument’s graphical programming language, LabVIEWTM (Version 6.1). Due to the highly transient nature of the AE
data, no window or overlapping was applied prior to performing an FFT; as bursts were always
captured completely within each dataset, spectral leakage was considered highly unlikely.
To exclude the effect of sensor-pulser response, cross-spectra were performed against theface-to-face averaged signal and results determined in terms of the magnitude and phase output.
Coherence was checked, but always found to be equal to one. Paradoxically, the effect of thesensor-pulser was easier to appreciate by superimposing its FFT over waveguide amplitude spectra, so this is the selected presentation method implemented here.

276

(a) 10 MHz(b) 40 MHz

Fig. 1 Wavelet transforms at different sampling rates (Face-to-face signals).
STFT and CWT analysis was performed using programs written in LabVIEW, using standard modules from the LabVIEW Signal Processing Toolset (Version 7)[6]. Wavelet results
were also compared with the output from AGU-Vallen freeware and found to be comparable(see Fig. 3). Improved resolution obtained by increasing sampling rate from 10 MHz to 40 MHz
can be seen in Fig. 1. Unless otherwise indicated, wavelet spectrograms in this paper were digitized at 40 MHz.

Fig. 2 Difference in resolution between (a) STFT in dBAE and (b) CWT, log scale (Delrin 51-mm
sample). Computing times were 594ms and 15141ms respectively.

Wavelet processing used a Mortlet wavelet as the mother wavelet and for this work computation was based on 600 scales and 100 samples per time window.
277

Fig. 3 Comparison of results obtained from the custom written programs in LabVIEW and
the AGU-Vallen wavelet tool. LabVIEW parameters: 600 scales, 100 samples per time increment, ~30 seconds processing time. AGU-Vallen parameters: frequency resolution of
5kHz, 1000 wavelet samples, ~60 seconds processing time.
278

STFTs were undertaken using 1024 frequency increments and a Hanning window. As thesimultaneous time-frequency resolution of the STFT has functional limitations, two sets ofgraphs with time increments of 128 or 512 samples per time window to extract improve temporaland spatial resolution respectively, were created for each sample.
As expected, CWT provided better simultaneous time-frequency resolution (see Fig. 2) thanany one STFT but took significantly longer to compute; the respective STFT and CWT functions
behind the spectrograms shown in Fig. 2 took 594 and 15141 ms to calculate respectively (i.e.
wavelets took almost 30 times longer). Nevertheless, wavelets offer a visually more economic
display of time-frequency information and therefore are predominantly shown herein. It should
be noted however, that computing multiple STFTs, with different time and frequency intervals
provides the same information in a much shorter time (albeit in two graphs), and therefore would
be more appropriate for real-time feature extraction.
2.2 Modal analysis
A commercially available software package called DISPERSE (Version 2.0.16c) was used togenerate individual modes for the materials being analysed. These were then superimposed overthe signals using a standard drawing package; frequency and time scales were matched appropriately.
Unfortunately, exact material properties for the various materials (particularly Delrin andmild steel) were not known and due to limited access to DISPERSE, examining slightly different
values to obtain better modal matching was not possible. Therefore some discrepancies can be
expected, particularly for Delrin and mild steel.
3. Frequency Results
3.1 Flat waveguides
Resulting averaged amplitude spectra for all flat 43-mm waveguides are given in Fig. 4. The
face-to-face spectrum is also plotted to show the effect of its non-uniform frequency response on
other results. (Wavelet plots are given in Fig. 11.) Spectra of metallic waveguides are dominatedby harmonics of longitudinal rod frequencies. The first of these peaks (~60 kHz) matches theresults listed in Table 1. This is confirmed by observing the changing location of these peaks fordifferent rod lengths (see Fig. 5); modal frequencies are independent of length.
Alumina is also affected by rod resonance, but due to its much higher wave speed, there arefewer harmonics permeating the bandwidth of interest. Additionally, the material’s frequencydependant damping seems to reduce the effect of rod resonance above 600 kHz. Similarly, Delrin’s high damping [1] is probably the reason no resonant effects can be seen in its amplitude
spectra.

3.2 Pointed Waveguides
The effects of points at the source of the waveguide are depicted in Figs. 6 and 7. This showsthat all points reduced low and high frequency content and amplified rod resonances, contrary toother reported results [15]. The magnitude of these effects depended on the type of point, withsmaller, sharper points being most deleterious.
3.3 Angled waveguides
Changes in spectral content resulting from different sensor face angles can be seen in Figs. 8
and 9. (Results from 43 mm x 8 mm SS316 samples are shown, but similar findings were found279

Fig. 4 Amplitude (in dB) – frequency (kHz) spectra of averaged time signals. (Face-to-face spectrum shown as a shadow on each graph.)Fig. 5 Increasing rod length decreases frequency of resonant peaks (aluminium samples).
for other materials). As angle increases, overall amplitudes decrease and resonant peaks become
more pronounced. For metallic guides new spectral components become noticeable, particularly
above 45˚. These were not obvious in narrow (5-mm diameter) mild steel rods, alumina or Delrin guides. In the latter case, virtually no changes to spectral content were observed as angles increased. Narrow MS rods in fact showed patterns very similar to alumina waveguides, implyingthat these additional spectral artefacts are indeed due to higher order modes below their cut-off
frequencies.
It was difficult to relate mild steel and Delrin theoretical modes with experimental results,
implying that the actual material properties differed to those given (Table 1 in Part 1). (Mildsteel’s spectrogram is not shown, but the analysis of the 43-mm samples showed it was almost
identical to that of SS316.)
280

Fig. 6 Effects of different types of points on the resulting frequency spectrum (43-mm long, 8mm diameter aluminium samples).

Fig. 7 Effect of points on wavelet spectrograms.
Although no correlation with theoretical results was obtained for Delrin, it is deduced that its
CWT spectral peaks are nevertheless most likely from transmission of the originating burst via
two different dispersive modes, because samples clearly demonstrated mode separation (see Fig.
10) and no discernible change in frequency.
Early arrival of high frequency information (~700 kHz), which cannot be matched to any
modes, is most obvious in the SS316 spectrogram, but can also be seen in plots for other materials. This is also present in the face-to-face spectrogram (Fig. 1), indicating that these are probably frequencies from the pulser or resonance in the sensor (neither being truly broadband). Similar peaks can sometimes be seen at approximately 110 kHz, 350 kHz and 580 kHz, depending on
the amplitudes of neighbouring spectral highlights.
281

Fig. 8 Changes in wavelet spectrograms for changing face angles (30-mm aluminium samples).

Fig. 9 Effect of face angles of FFT spectra (43-mm long SS316 samples).
282

Fig. 10 Mode separation is clearly evident in Delrin waveguides.

Fig. 11 Wavelet spectrograms with longitudinal mode frequencies. 43 mm x 8 mm waveguides(Phase modes are shown as dotted lines, group modes as solid lines).
283

Fig. 12 CWT from 43-mm long, 60˚ waveguides (Data sampled at 10 MHz)As already discussed, face angles caused significant mode conversion and energy redistribution from the first longitudinal group mode. Interestingly, no appreciable energy can be observed in flexural modes at non-zero face angles, contrary to the authors’ expectations. Instead
most energy is contained in the first few rod resonances, whilst in the case of metallic
waveguides, new spectral artefacts are also visible (Fig. 13). These are much more prevalent inwider guides, at face angles of 45˚ and 60˚ and may be due to the excitation of radial waveguideresonances. For 8-mm diameter SS316 rods, theory predicts this to be around 400 kHz, whichcorresponds with frequencies seen in Fig. 13d).
The actual frequency of the second longitudinal rod resonance peak also reduces slightly as
angle increases, probably because the absolute waveguide length increases (centre-centre distances are kept constant).
4. Discussion
4.1 Effect of material
In all metallic and alumina waveguides, the first longitudinal group mode was the mechanism
by which bursts were transmitted through the waveguides. Modal identification in Delrin samples was inconclusive.
Of all materials tested, alumina ceramic best retained the shape and frequency characteristicsof the originating (face-to-face) waveform, albeit with significantly reduced amplitude (discussed in Part 1). Although some reflections could be seen in its CWT spectrogram, these were
easily separated from the original pulse. Some excitation of the waveguide’s first and second
resonant frequencies was also observed. Narrow (5mm) steel waveguides were almost as effective in retaining original waveform shape, without the attenuation.
Lower damping and slower wave speeds caused larger diameter metallic waveguides to lose
all waveform fidelity; late arriving components of the first longitudinal group mode merged withsubsequent reflections and numerous harmonics of the waveguide’s natural frequency. The result was a protracted and irregular waveform, with a highly resonant spectrum.
284

Fig. 13 Changes due to different face angles for 30-mm SS316 waveguides do not correspondwith flexural modes (only one shown for clarity). Standard amplitude spectrum showingchanges in location of 2nd resonance peak is shown in (e).
285Delrin waveguides showed little or no resonant effects due to the material’s high damping,although waveforms had little resemblance to the originating pulses in either temporal appearance or frequency content.
4.2 Effect of length and diameter
Longer rods accentuated mode separation and decreased the frequency of resonant modes.
Unfortunately, harmonics of fundamental resonant frequencies permeated the entire bandwidth,
affected only by the material’s inherent damping, implying that lengthening rods to reduce thesefrequencies, in unlikely to be successful. Conversely, shortening rods so that fundamental frequencies are above the frequency range of interest is impractical in all but the fastest acoustic
materials (even a 10-mm alumina waveguide would still have a fundamental frequency at around
530 kHz). Modal content was otherwise unaffected by length.
Reducing diameter had a significant effect of the frequency and modal content of resulting
waveforms by limiting the number of modes by which signals could be transmitted. Although
individual modes (above the first longitudinal group mode) could not be identified in CWT spectrograms, narrower waveguides had significantly fewer spectral artefacts than their wider counterparts.
4.3 Effect of face angle
Face angles of 45˚ or 60˚ caused energy to be transferred from the first longitudinal velocity
to longitudinal and/or radial resonant modes. Only waveforms being transmitted through Delrin
waveguides remained unaffected by face angle, although amplitudes did decrease as shown inPart 1.
4.4 Effect of a pointed source end
Contrary to results published by others, pointed waveguides affected waveform profiles andfrequency content significantly. Not only were amplitudes and energies attenuated, resonant effects were amplified and certain high and low frequency regions were filtered. Steels and aluminium were affected similarly. The severity of changes depended on the shape of the waveform tip, with sharper points being most detrimental.
5. Conclusions
This work indicates that waveform integrity is affected by the number of modes available forsignal propagation. This in turn is directly related to acoustic wave speed and inversely proportional to waveguide diameter. Therefore, narrow waveguides fabricated from materials withvery high acoustic velocities (ceramics) should best retain waveform properties.
In commonly used materials such as aluminium, mild steel and stainless steel, longitudinalresonance artefacts dominate frequency responses in all but very narrow guides. These effects
are magnified by application of a point on the end of a waveguide, or by angling the sensor face,which also excites radial resonances. Consequently, both should be avoided if trying to maintainwaveform integrity.
Although use of waveguides in AE testing cannot be avoided, their implementation must be
properly considered; dispersion, attenuation, mode conversion and/or waveguide reflections occurring within the waveguide will affect the signals being detected by the sensor. If possible,
ends should be square and parallel and materials should be selected depending on the transmis286

sion characteristics required for a particular monitoring task. If possible, specific designs shouldbe tested prior to installation so that transmission characteristics can be verified.
Acknowledgements
The authors thank IMES Group for supplying the PCI-2 board and software used for data acquisition and to Dr. Mike Lowe from the NDT Lab at Imperial College in London for determining propagation modes using DISPERSE.
References
1. Sikorska, J.Z. and Pan, J. (2004) The effect of waveguide material and shape on AE transmission characteristics, Part 1: Traditional AE parameters, Journal of Acoustic Emission, 22.
264-273.
2. Neill, G. (1998) "PC Based diagnostic system for the condition monitoring of rotating machines", PhD, Heriot-Watt University: Edinburgh, 239 pages.
3. Wang, Q. and Chu, F. (2001) Experimental determination of the rubbing location by meansof acoustic emission and wavelet transform, Journal of Sound and Vibration, 248(1): pp. 91103.
4. Takemoto, M., Nishino, H. and Ono, K. (2000) Wavelet Transform - Applications to AE Signal Analysis, Acoustic Emission - Beyond the Millennium: Elsevier, Amsterdam, pp. 35-56.
5. Tognola, G., Ravazzani, P., Ruohonen, J. and Grandori, F., (1995) 'Time-frequency analysisof acoustic emissions: a wavelet approach' in Proceedings of the IEEE 17th Annual Conference of the Engineering in Medicine and Biology Society, pp. 1067-1068.
6. Paul, D., (1994) 'Detection of change in processes using wavelets' in Proceedings of theIEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis, pp. 174179.
7. Khalfallah, R. and Simard, P., (1996) 'An algorithm for the automatic segmentation of acoustic emission signals' in Proceedings of the 14th World Conference on Non Destructive Testing (14th WCNDT), New Delhi, pp. 2555-2558.
8. Hamstad, M.A., Downs, K.S. and O’Gallagher, A. (2003) Practical Aspects of Acoustic
Emission Source Location by a Wavelet Transform, Journal of Acoustic Emission.
9. Dvoracek, J., Petras, J. and Pazdera, L. (2000) The phase of contact damage and its description by help of acoustic emission, Journal of Acoustic Emission, 18: pp. 81-86.
10. Ziola, S. and Searle, I. (1997) Automated source identification using modal acoustic emission, Review of progress in quantitative non-destructive evaluation, 16: pp. 413-419.
11. Perez, L.V., D'Attellis, C.E. and Ruzzante, J.E. (1997) A model for acoustic emission signals
and burst occurrence estimation, Insight, 39(2): pp. 83-87.
12. Qian, S. (2002) Introduction to time-frequency and wavelet transforms, Upper Saddle River,
NJ: Prentice Hall PTR.
13. Phillips, W.J. (2003) Wavelets and filter banks course notes [online], Ed. 1, Available from:http://www.engmath.dal.ca/courses/engm6610/notes/notes.html [Accessed 15-07-2004].
14. Dupont Delrin Design Guide - Module III [online], Ed. Available from: http://plastics. dupont.com/plastics/pdflit/americas/delrin/230323c.pdf [Accessed 15.03.2004].
15. Ono, K. and Cho, H.-J., (2004) 'Rods and Tubes as AE Waveguides' in Proceedings of the
26th European Conference on Acoustic Emission Testing, Berlin: DGZfP, pp. 593-603.

287

JOURNALOF
ACOUSTIC EMISSION
VOLUME 22, 2004

EWGAE 2004 Welcome
Hartmut Vallen, Organizer, EWGAE2004
Good morning Ladies and Gentlemen.
Welcome to Germany, welcome to Berlin Adlershof, welcome to the 26th Conference of
the European Working Group on Acoustic Emission.
Before the chairman of EWGAE presents the formal greetings I would like to mention a
few organizational items.
We are happy to announce that 150 participants have registered for the conference,that is the highest number of participants EWGAE ever had. 23 countries are
represented:
Germany
UK
FranceCzech Republic
Japan
RussiaUkraineUSA
PolandSpain
Austria

33,
27
12,
12
8
8
8
6
6
6
5

Three or less participants come from
Sweden3
Netherlands
3
Belgium
2,
Greece2
Switzerland
2
Finland1,
Italy
1,
Israel1,
Saudi Arabia
1,
Thailand1,
Korea
1
And farthest away: Australia 1,

You are all welcome.
The response to our Call for Papers was very exciting. We received much more
abstracts than expected. So we had to decide for parallel sessions. We could develop a
programme with 75 lectures, 7 in common sessions and 68 in parallel sessions. Thatmeans you can only see and hear 41 of the 75 presentations. We must ask for yourunderstanding, if two presentations of your interest are scheduled for the same timeslot.
You will see that a few titles do not well fit to their session theme. This is due to rearrangements caused by last minute cancellations. We ask the concerned colleagues
for their understanding.
Moving from one room to the other between the presentations should be possible. We
ask the session chairs to grant 15 minutes presentation time, 4 minutes discussion and
1 minute for the move between the rooms.
If a presentation must be cancelled, the session will be interrupted and shall continue atthe time foreseen for the next presentation. We ask those who wish to move between
the rooms to leave in time to be in place before the new presentation starts. Please do
not move if you see you will be late and disturb the beginning of the other presentation.
Coffee and Lunch buffet will be served in the Newton Cabinet, Posters are presented in
the Pasteur cabinet. In the foyer in front of Pasteur Cabinet, the exhibitors await yourvisit.

Concerning the social programme:
Tonight, we will enjoy a guided bus tour through Berlin’s center. The last session ends
on 16:50. Busses will wait in front of the entrance. They will depart at 17:15. After thesight seeing programme, we will have a dinner buffet at the Ratskeller in Köpenick.
From there the busses will leave at 22:45 for the Dorint Hotel. Those who want to go toanother place can leave the bus at S-Bahn station Adlershof.
Tomorrow, the last session will end at 17:10. You will go to Opernpalais “Unter den
Linden” in City Center on your own. Tickets are already provided. The travel by S-Bahn
takes about 40 minutes. You have enough time for looking around and see otherplaces, if you like. Please arrive a quarter before 8pm to enjoy an aperitif, before thedinner starts.
During the banquet, we will listen to Professor Hans Maria Tensi, who, 50 years ago,continued Joseph Kaiser’s studies at Technische Hochschule München. He coined theterm “KAISER EFFECT.” While that banquet presentation will provide us with relaxed
entertainment, I would like to thank Professor Tensi for his contribution to theProceedings, the keynote paper: “The KAISER-EFFECT and its Scientific Background.”On Friday noon, immediately after the closing of the technical session, the businessmeeting will be held. All members of EWGAE are invited to join that meeting. Most
important topic is to vote for the next meeting place.
After Mr Svoboda’s greeting address, the chairmen of the American and Japanese AEworking groups, Mr Drouilard and Professor Ohtsu will present their greetings. Then the
President of DGZfP will open the Conference.
I wish all of us a successful, interesting meeting and good talks.
Now, I would like to hand over to Vaclav Svoboda, acting chairman of the EWGAE.
Thank you.

OPENING REMARKS
Thomas F. Drouillard, Chairman, AEWG (USA)Good morning.
On behalf of the American Acoustic Emission Working Group, I bring you greetings.
This is indeed a great honor and pleasure for me to attend my first EWGAE Conference. I would like to take this opportunity to introduce some of our AEWG members
who are here with us today:
From the Czech Republic: Mr. Vaclav Svoboda - His application for membership wasjust approved at our last meeting.
From Germany: Mr. Jochen Vallen, who is our Vice Chairman. Jochen also was corecipient of the Publication Award last year.
Mr. Hartmut Vallen - who last year was elected a Fellow of the AEWG.
From Japan: Prof. Yasuhiko MoriProf. Masayasu Ohtsu, Publication Award recipient, and Fellow of the AEWG.
Prof. Kenichi Yoshida, who was elected a Fellow at our last meeting.
Prof. Mikio Takemoto, who was co-recipient of last year's Publication Award.
Dr. Tomoki Shiotani
From the United States:
Mr. Allen Green - co-founder, Gold Medal and Special Recognition Award recipient,and Fellow of the AEWG.
Prof. Kanji Ono - Gold Medal, Achievement, and Publication Award recipient, andFellow of the AEWG. Kanji is also Editor of the Journal of Acoustic Emission.
Prof. Marvin Hamstad - Gold Medal and Achievement Award recipient, and Fellowof the AEWG.
As you can see, we really are an international organization. Incidentally, both cofounders of EWGAE - Dr. Adrain Pollock and the late Dr. Don Birchon are members of
the AEWG.
The AEWG was founded in 1967. In July of this year, we held our 47th meeting at Penn
State University in University Park, Pennsylvania. Our 48th meeting will be held next
May, 2005, in Houston, Texas, and our 49th meeting in 2006, at the University of California in Berkeley, California. You can find information on the AEWG and on thesemeetings on our Website:www.aewg.org
You are all cordially invited to attend any and all of our meetings. We welcome our international colleagues, and hope you will plan to give a technical presentation of yourcurrent research and applications of acoustic emission technology.
I would also like to remind you of the Journal of Acoustic Emission. If you have any
questions or would like to submit a paper for publication, please see Prof. Kanji Ono.
And finally, let me remind you that for a valuable resource on AE, you are invited tosearch the AE Database at the Grainger Engineering Library, at the University of Illinois:<http://shiva.grainger.uiuc.edu/ae/opent1.asp>
If you have any questions or want further information about the AE collection, please
see Allen Green.

I thank you for the privilege of speaking to you this morning and wish you a very successful meeting.
Thank you.

OPENING REMARKS
Professor M. Ohtsu, Chairman, Japanese Comm. on AE
Guten Morgen
I am very pleased to be able to participate the 26th European Conference on Acoustic
Emission Testing. My name is Masayasu Ohtsu. Currently, I am the chairman of Japanese AE Working group organized in the Japanese Society of Nondestructive Inspection, JSNDI. Also from this year, I am a chairman of RILEM Technical Committee,
TC-ACD: Acoustic Emission and NDT for Crack Detection and Damage Evaluation inConcrete.
In Japan, we have held the International Acoustic Emission Symposium (IAES) every
two year, since 1972. This year in November, we are going to hold the 17th Symposium(IAES-17).
Concerning IAES series, we really appreciate that many members of European Working Group on Acoustic Emission have participated and contributed to our symposia.
So, on behalf of Japanese AE committee, I thank you for your cooperation with AE research activities in Japan and we are willing to cooperate further in order to promote AE
research and to keep our beautiful friendship.
Danke schon.

EWGAE 2004 History

About EWGAE-40813.Doc

European Working Group on Acoustic Emission (EWGAE)
The European Working Group on Acoustic Emission (EWGAE) was formed in 1972 by anumber of people throughout Europe who were already engaged in acoustic emission
research.
At that time, Acoustic Emission research was performed
in England by Adrian Pollock at Cambrige Consultants, by Don Birchon at the Admiralty
Materials Laboratory, by Peter G. Bentley at Risley Engineering and Materials Laboratory of
the U.K. Atomic Energy Authority, by Brian Harris at the University of Sussex;
in Germany by Jürgen Eisenblätter and Peter Jax at Batelle Institut;in France by P. F. Dumousseau at CETIM, by Madame Nicole Chretien and E.G.
Tomachevsky at Centre d’Etudes Nucleaires de Saclay, Commissariat a l’Energie Atomique;in Italy by M. Mirabile at Centro Sperimentale Metallurigco in Rome;in Denmark by Arved Nielsen at the Research Establishment Riso, Danish Atomic Energy
Commission;in The Netherlands by J.C.F. DeKanter at the Technische Hogeschool Delft.
(This list may not be complete)
The first European meeting on the subject was the “Institute of Physics Conference on
Acoustic Emission” which was organized by Adrian Pollock and held in March 1972 at
Imperial College in London. Fifteen papers from throughout Europe and USA were presented.
The success of this conference established both the interest and the need for forming aworking group. Consequently, Pollock and Birchon organized the European Stress Wave
Emission Working Group which held its first meeting in November 1972, at the Admiralty
Materials Laboratory in England. During their second meeting at Batelle-Institut in September
1973, the group formally adopted their present name, European Working Group on Acoustic
Emission. The letterhead logo was designed by Patricia Preston, graphics designer at
Cambridge Consultance.
During the late 1970s and early 1980s, the Codes Subgroup of EWGAE published 5 codes ofpractice. In 1991, the work of this group was taken up by a new working group on AE (WG7)
within TC138, the Technical Committee for NDT within CEN. This working group iscomprised of national representatives who have been nominated by their respective National
Standards Institutions. The first Convenor of WG-7 was Emilio Fontane of CISE, Italy. After
his retirement in 2003, Peter Tscheliesnig of TÜV Austria was appointed and is currently
serving as Convenor of the group. Since 2000, the following standards for AE, developed by
TC138-WG7 have been published:• Acoustic Emission Terminology (EN1330-9),• General Principles (EN 13544),• Equipment Description (EN 13477-1),• Equipment Characterization (EN 13477-2),• Examination of Metallic Pressure Equipment during Proof Testing (prEN14584).
These Standards have significantly contributed to the today’s acceptance of the AE testing
method in Europe.

During the last 8 years, active members within the CEN TC138-WG7 were:
E. Fontana (Italy), P. Tscheliesnig (Austria), H. Vallen, (Germany), L. Rogers and P. Cole
(U.K.), C. Herve and J.C.L. Lenain (France), A. Anastasopoulos (Greece). New active
members of the group are J. Bohse (Germany) and H. Schoorlemmer (The Netherlands).
Since 2000, the European Standard for Qualification and Certification of NDT Personnel,
EN473, covers the AE testing method (abbreviation: AT) and defines details for theeducation. DGZfP in Germany has already held a remarkable number of certification courses
for level 1, 2 and 3 in accordance with the rules of EN473 in German language. Mainlecturers of these courses were J. Eisenblätter, P. Tscheliesnig, H. Vallen. An EN473-conform
course in English language is in preparation.
The following table gives an overview of the positive development of the EWGAE
conferences held since 1990 and demonstrates the increasing desire to exchange ideas andexperience:
No.
19th
20th
21st
22nd
23rd
24th
25th
26th

Year
1990
1992
1994
1996
1998
2000
2002
2004

Month City
Oct
Erlangen
April
LeuvenNice
May
Aberdeen
May
ViennaJune
Senlis/Paris
Sept
PragueSept.
BerlinCountry
Germany
Belgium
FranceU.K.
Austria
FranceCzech Republic
Germany

No. of Participants
33
45
Combined with ECNDT
78
83
99
108
150

AE Testing in Europe is now well established in several important areas of proof testing, inservice monitoring, corrosion, and leak detection. Development of the technology continues
to be driven by the needs of the industry to reduce inspection and maintenance costs while
preserving its assets and personnel safety. The advancements of PC technology has positively
influenced new developments in hardware and software for powerful and user-friendly
testing equipment.
Members of the present EWGAE Executive Committee:
Name
Country
Vaclav Svoboda
Czech RepublicPeter Tscheliesnig
Austria
Len Rogers
U.K.
Hartmut Vallen
Germany
Catherine Herve
FranceJean Claude Lenain
FranceFunction
Chairman
Secretary
Treasurer
Organizer of EWGAE-2004

Information gathered by:Hartmut Vallen,
Some of the information used in this article has been taken from the Introduction in "Acoustic
Emission: A Bibliography with Abstracts" by T.F. Drouillard (Plenum 1979) and earlier
EWGAE conference proceedings.

CONFERENCE AT A GLANCE
Tuesday, September 14, 2004
14.00

Registration and Installation of Exhibition

17.00

informal Come-Together at WISTA

Wednesday, September 15, 2004
Room 1Room 2

09.20

Opening

10.00

Start Session

11.30

A1 Pressure
Equipment

13.40

A2 Pressure Equipment / B2 Acoustic Emission
Medical andfrom Rock
BiologicalApplications

15.30

A3 Tanks, Civil
Structures

17.15

Sightseeing Tour followed by dinner at „Ratskeller Köpenick“

B1 Acoustic Emission
from Concrete

B3 Acoustic Emission fromother Materials

Thursday, September 16, 2004
09.00

A4 Other Industrial
Applications

B4 Acoustic Emission
from Leak / Composites 1

11.10

A5 Process
Monitoring /Rotating
Machineries

B5 Acoustic Emission
from Composites 2

14.00

A6 Acoustic Emission
from Gears /Wave Guide

B6 Acoustic Emission
from Corrosion

15.50

A7 New Sensors /Other Testobjects

B7 Other Advanced
Analysis Methods

19.30

PROGRAMME & INVITATION

26th European Conferenceon Acoustic Emission Testing

Co-sponsored by:
BUNDESANSTALT FÜR
MATERIALFORSCHUNG
UND -PRÜFUNG

Conference Dinner at „Opernpalais“

Friday, September 17, 2004
09.00

A8 Wave propagationand location

B8 Data Management /Vendor Applications

11.10

A9 Calibration of
Sensors, Equipment

12.30

Closing of technical sessions

12.40

Business Meeting followed by light lunch

Friedrich-Schiller-Universität Jena

Equipment Exhibition and Poster Presentation are open during theConference for Delegates.

September 15-17, 2004

. Berlin, Germany

ORGANIZATION
Venue

WISTA MANAGEMENT GmbH
Einstein- + Newton-Kabinett
Rudower Chaussee 1712489 Berlin (Adlershof)
Germany

Language

All technical papers will be presented in
English, simultaneous translation will not beprovided.

Exhibition

An exhibition linked to the conference subjects
will be open to conference delegates.
Interested companies or institutes should please contact the Conference Secretariat.

Location: http://www.wista.de

Proceedings The proceedings will include all papers presented during the conference (if manuscripts havebeen submitted in time). The proceedings will
be published on CD-ROM and as booklet andwill be available to the conference.
Manuscripts Guidelines for preparing and submitting
manuscripts are published on the conference
web-site.
web-sitewww.ewgae2004.de

SocialSeptember 14, 2004
17:00 Informal Come together at WISTA
September 15, 2004
17:15 Sightseeing Tour followed by dinner at„Ratskeller Köpenick“
September 16, 2004
19:30 Conference Dinner at „Opernpalais“

Accommodation
The conference organizers have arranged special rates with
Dorint Hotel (Phone: +49 (0) 30 678 220, Fax: +49 (0) 30
678 22 1000) situated in walking distance to the conference
venue. For reservation please use the registration form on thewebsite.
More hotels of different categories can be found at the DGZfP
Homepage.
Berlin information: www.berlin.de

SCIENTIFIC & ORGANIZING COMMITTEE
Tscheliesnig, P.
Surgeon, M.
Wevers, M.
Svoboda, V.
Lenain, J.C.
Herve, C.
Bohse, J.
Große, C.U.
Hueck, W.
Vallen, H.
Caneva, C.
Yoshida, K.
Gradin, P.
Brunner, A.J.
Schoorlemmer, H.
Rogers, L.
Ono, K.

Austria
Belgium
Belgium
Czech Republic
FranceFranceGermany
Germany
Germany
Germany
Italy
Japan
SwedenSwitzerland
The Netherlands
United Kingdom
USA

Chairman

Conference Secretariat:
Mrs. Steffi Schäske
DGZfP - Deutsche Gesellschaft für Zerstörungsfreie Prüfung e.V.
German Society for Nondestructive Testing
Max-Planck-Straße 612489 Berlin, Germany
Phone: +49-(0)30-678 07 120
Fax:+49-(0)30-678 07 129
e-mail: tagungen@dgzfp.deNational Organization Committee:
EWGAE:
Mr. Hartmut Vallen
Vallen-Systeme GmbH
Schaeftlarner Weg 2682057 Icking, Germany
Phone: +49-(0)8178 9674 400
Fax:+49-(0)8178 9674 444
e-mail: sales@vallen.de
DGZfP:
Wilfried Hueck, Member of the Board of DGZfP
Jürgen Dieter Schnapp, Chairman of national expert
committee „SEP“
Jürgen Bohse, Chairman of national expert committee „UA SEvP“
Christian U. Große, Vice-Chairman of national expert committee „SEP“

PREFACE

The primary objective of the EWGAE is the exchange of information on acoustic emission (AE), with particular emphasis on
scientific and technical development. It is the pleasure of
EWGAE and DGZfP to invite everybody interested in AE, experienced specialists as well as beginners, scientists as well aspractitioners, AE service providers as well as AE equipment
manufacturers, to discuss latest developments at the26th European Conference on Acoustic Emission Testing(EWGAE 2004),
to be held on September 15 -17, 2004, in Berlin, Germany.
We are happy to submit a programme with a wealth of information about very different applications. The participants will
enjoy presentations and discussions about the traditional use of
AE in pressure vessel testing, various industrial applications, theuse of AE in medical and biological fields, and on objects ofvery different size and materials: metals, composites, concrete,rock, and others. Other important topics are AE from corrosion
and other source mechanisms, wave propagation and location,
new sensor technologies, sensor calibration, new equipment
and equipment verification.
The social events include: on the first evening a guided sightseeing tour through Berlin, one of the most exciting cities in
Europe, and a festive dinner evening on the second.
The organizers hope the participants will remember that conference as one of the most interesting and exciting events.

Hartmut Vallen
Member of
Executive Committee of EWGAE

Wilfried Hueck
Member of Boardof DGZfP

1

EXHIBITION

The conference will be accompanied by an exhibition, which
will show innovative equipment in the fields related to the conference topics. Equipment producers, distributors and service
providers are invited to use this broad international forum topresent their products.
We offer a small and a large variant of exhibition sites:
The small site (about 4 square meter) includes a table, 230 V
AC Power, a surface. The rate is € 400 plus 16% VAT and includes the participation fee for one person (no paper presenter)caring for the exhibition and frequenting the sessions.
The large site includes up to three tables, 230 V AC Power andsurfaces for three posters. The rate is € 1350 plus 16% VAT
and includes the participation fee for two persons (no paper
presenters) caring for the exhibition and frequenting the sessions plus a one page advertising in the proceedings.
The deadline for registration is May 15, 2004
Advertisment in the proceedings:
1 page format DIN A 5 inside, black and white € 250,00 plus
16% VAT (a page is included in the fee of the large exhibition
site)
Deadline for delivery of data files for advertisement is May 15,
2004
For registration please (Exhibition and/or advertisement) use
the electronic registration form under www.ewgae2004.de orcontact the Symposium Secretariat:
Phone: +49 (0) 30 67807120; Email: tagungen@dgzfp.de

2

Wednesday, September 15, 2004

9:20

Session A

Opening
H. Vallen, National Organizing Committee (Germany)
V. Svoboda, Chairman EWGAE (Czech Republic)
T. Drouillard, Chairman AEWG (USA)M. Ohtsu, Chairman AEWG (Japan)
J. Völker, President DGZfP (Germany)

A Start Session
Chair: V. Svoboda, Prague (Czech Republic); L. Rogers, London (United
Kingdom)
1
10:00

Acoustic Emission Measurement System for the
Orthopedical Diagnostics of the Human Femur andKnee Joint
H.-J. SCHWALBE, GIEßEN; R.-P. FRANKE, ULM; H.-P. DÖRNER, BAD
GRIESBACH; B. ZIEGLER, GIEßEN (GERMANY)

2
10:20

Corrosion Testing of Ship Building Material with
Acoustic Emission
P. TSCHELIESNIG, WIEN (AUSTRIA)

3
10:40

An Investigation into the Comparison of Maintenance
Strategies Past, Present and Future by enhancing
Periodic Proof Testing with Acoustic Emission to generate reliable through Life Structural Integrity Assurance
G. DRUMMOND, ABERDEEN (UNITED KINGDOM)

11:00

Coffee break

A1 Pressure Equipment
Chair: A. Anastasopoulos, Athen (Greece); C. Caneva, Rome (Italy)
4
11:30

Acoustic Emission Experience with AE Monitoring of
New Vessels during Initial Proof-Test
P. COLE, S. GAUTREY, N. WAY, CAMBRIDGE (UNITED KINGDOM)

5
11:50

Defect Detection by Acoustic Emission Examination ofMetallic Pressure Vessels
F. RAUSCHER, WIEN (AUSTRIA)

6
12:10

Experience of Acoustic Emission Method (AE)Application at Equipment Diagnostics
E. NEFEDJEV, A. SUDAKOV, ST. PETERSBURG (RUSSIA)

12:30

Lunch break4

Session B

Wednesday, September 15, 2004

B1 Acoustic Emission from Concrete
Chair: T. Shiotani, Chiba (Japan); F. Finck, Stuttgart (Germany)7
11:30

Quantitative Damage Estimation of Concrete Core
based on AE Rate Process Analysis
M. OHTSU, T. SUZUKI, KUMAMOTO (JAPAN)

8
11:50

Acoustic Emission for Monitoring Damage
Accumulation in Reinforced Concrete Structures
B. SCHECHINGER, T. VOGEL, ZÜRICH (SWITZERLAND)

9
12:10

Acoustic Emission Monitoring of the Fracture Behaviourof Concrete containing various Size and Shape of Glass
Aggregates
A.M. SIDDIQUI, A. CHOUDHURY, I. MERCHANT, ABERDEEN (UNITED
KINGDOM)

12:30

Lunch break5

Wednesday, September 15, 2004

Session A

A2 Pressure Equipment / Medical and Biological Applications
Chair: F. Rauscher, Wien (Austria); C. Herve, Senlis (France)
10
13:40

Use of AE Method Abilities for Petrochemical
Equipment Inspection
B.S. KABANOV, V.P. GOMERA, V.L. SOKOLOV, A.A. OKHOTNIKOV,
V.P. FEDOROV, KIRISHI (RUSSIA)

11
14:00

In Service Inspection of Spherical Tank using AE in Wave
Guide Configuration
J. CATTY, M. MEDIOUNI, J. MENETRIER, C. HERVE, SENLIS (FRANCE)

12
14:20

Acoustic Emission of Transpiring Plants - new Resultsand Conclusions
R. LASCHIMKE, M. BURGER, STRASSBERG; H. VALLEN, ICKING (GERMANY)

13
14:40

Ultrasound Acoustic Detection of Cavitation Events inWater Conducting Elements of Tree Stems
S. ROSNER, S. KIKUTA, WIEN (AUSTRIA)

15:00

Coffee break

A3 Tanks, Civil Structures
Chair: P. Tscheliesnig, Wien (Austria); R.-P. Franke, Ulm (Germany)
18
15:30

Field Testing of flat bottomed Storage Tanks with
Acoustic Emission - a Review on the gained Experience
G. LACKNER, P. TSCHELIESNIG, WIEN (AUSTRIA)

19
15:50

Acoustic Emission Monitoring of Concrete Hinge Joint
ModelsK. HOLFORD, R. PULLIN, R. LARK, CARDIFF (UNITED KINGDOM)

20
16:10

Noise Localization in large Civil Structures using AE
D. PRINE, EVANSTON (USA)

21
16:30

Damage Quantification of Railway Structures using AE
Activity
T. SHIOTANI, CHIBA; H. HAYA, TOKIO (JAPAN)

16:50

End of session

6Session B

Wednesday, September 15, 2004

B2 Acoustic Emission from Rock
Chair: J. Eisenblätter, Ober-Mörlen (Germany); M. Ohtsu, Kumamoto (Japan)
14
13:40

AE Kaiser Effect and Electromagnetic Emission in the
Deformation of Rock Sample
Y. MORI, Y. OBATA, CHIBA, NARASHINO (JAPAN); J. SIKULA, BRNO(CZECH REPUBLIC)

15
14:00

Characterization of Acoustic Emission Sources in a Salt
Rock Specimen under Triaxial Load
G. MANTHEI, OBER-MÖRLEN (GERMANY)

16
14:20

Possibilities of Forecasting of Rock Sample Total Fracture
by Application of Correlation Analysis of Acoustic
Emission Events Series
J. VILHELM, T. LOKAJICEK, V. RUDAJEV, PRAGUE (CZECH REPUBLIC)

17
14:40

The Migration of Acoustic Emission Foci in the
Dependence on Acting Load of Rock Samples
J. VEVERKA, V. RUDAJEV, PRAGUE (CZECH REPUBLIC)

15:00

Coffee breakB3 Acoustic Emission from other Materials
Chair: G. Manthei, Ober-Mörlen (Germany); M. Takemoto, Sagamihara
Kanagawa (Japan)
22
15:30

Phase Transition Studies in PZT Ceramics using Acoustic
Emission
K. PRABAKAR, A. DODE, S. MALLIKARJUN RAO, S. MANJUNATH RAO,HYDERABAD (INDIA)

23
15:50

Acoustic Emission Behavior of Martensitic
Transformation in Early Stage during Deformation of
Cu-Al-Ni Shape Memory Alloy Single Crystals
K. YOSHIDA, K. HANABUSA, TOKUSHIMA CITY TOKUSHIMAPREFECTURE; K. HORIKAWA, TOYONAKA OSAKA (JAPAN)

24
16:10

Acoustic Emission Testing of Diamond-Like Carbon Coatings under Slip-Rolling Friction
M. LÖHR, BERLIN (GERMANY)

25
16:30

AE Monitoring from CVD-Diamond Film-subjected to
Micro-Indentation and Pulse Laser Spallation
R. IKEDA, M. TAKEMOTO, H. CHO, SAGAMIHARA KANAGAWA(JAPAN); K. ONO, UCLA (USA)16:50

End of session

7

Thursday, September 16, 2004

Session A

A4 Other Industrial Applications
Chair: P. Cole, Cambrigde (United Kingdom); G. Drummond, Aberdeen (United
Kingdom)26
9:00

Acoustic Emission of Fatigue-deformed Aluminum Alloys
for Automotive Panels
K. HORIKAWA, TOYONAKA OSAKA; Y. TAKEUCHI, K. YOSHIDA,
TOKUSHIMA CITY TOKUSHIMA PREFECTURE; H. KOBAYASHI, TOYONAKAOSAKA (JAPAN)

27
9:20

Detection of Fatigue Crack Growth in Aircraft Landing
Gear
K. HOLFORD, R. PULLIN, M. BAXTER, CARDIFF (UNITED KINGDOM)

28
9:40

Condition Monitoring using Acoustic Emission Method
R. MURRAY, V. SVOBODA, F. ZEMLI`ĆKA, PRAGUE (CZECH REPUBLIC)

29
10:00

Cracks Detection on Metal Parts by Acoustic Emission
C. DELALANDE, O. RENCKERT, CHALETTE SUR LOING LOIRET (FRANCE)

30
10:20

Identification of Common Faults in a small HSDI Diesel
Engine using Acoustic Emission
A.K. FRANCES, ABERDEEN (UNITED KINGDOM)

10:40

Coffee break

A5 Process Monitoring / Rotating Machineries
Chair: J.-C. Lenain, Sucy en Brie Cedex (France); D. Mba, Cranfield (United
Kingdom)36
11:10

AE Signals from Process Monitoring
T. BRADSHAW, P. COLE, CAMBRIDGE; D. MBA, CRANFIELD,
BEDFORDSHIRE (UNITED KINGDOM)

37
11:30

Monitoring of Gas Turbine Operating Parameters using
Acoustic Emission
R. DOUGLAS, M. JENKINS, J. STEEL, R. REUBEN, P. KEW, EDINBURGH(UNITED KINGDOM)

38
11:50

Determining the Size of a Bearing Defect with Acoustic
Emission
A. AL-GHAMDI, D. MBA, CRANFIELD, BEDFORDSHIRE (UNITED
KINGDOM)

39
12:10

Detection of Incipient Cavitation in Centrifugal Pumps
using Acoustic Emission
L. ALFAYEZ, D. MBA, CRANFIELD, BEDFORDSHIRE (UNITED KINGDOM)

40
12:30

Semi-Real Time Neural Classification of Acoustic Emission
Signals for Drive System Coupling Crack Detection
A. TSIMOGIANNIS, A. ANASTASOPOULOS, ATHEN (GREECE) V.
GODINEZ, F. SHU, R. FINLAYSON, LAWRENCEVILLE (USA), B.
O´DONN, PATUXENT RIVER MD (USA)

12:50

Lunch break8

Session B

Thursday, September 16, 2004

B4 Acoustic Emission from Leak / Composites 1
Chair: A.J. Brunner, Dübendorf (Switzerland); P. Gradin, Sundsvall (Sweden)
31
9:00

Frequency Characteristics of Acoustic Emission
Waveforms during Gas Leak
K. YOSHIDA, T. KAWANO, TOKUSHIMA CITY TOKUSHIMA PREFECTURE;
K. HORIKAWA, TOYONAKA OSAKA (JAPAN)

32
9:20

Differentiation of Micro-Failures in Glass Polypropylene
Composites by Means of Acoustic Emissions Technologies
C. RAMIREZ, M. PHARAOH, N. REYNOLDS, N. PAPADAKIS, COVENTRY
WARWICKSHIRE (UNITED KINGDOM)

33
9:40

Damage Analysis of Polymer Matrix Composites by
Acoustic Emission Testing
J. BOHSE, BERLIN (GERMANY)

34
10:00

Acoustic Emission Technique to assist the Formula One
Designer in Structural Design
C. ROWLAND, L. BULTER, M. PRESTON, HUNTINGDONCAMBRIDGESHIRE (UNITED KINGDOM)

35
10:20

Acoustic Emission Monitoring of Buckling Behavior in
Impact-Damaged Composite Plates
K. HOLFORD, C. FEATHERSTON, M. EATON, CARDIFF (UNITED
KINGDOM)

10:40

Coffee break

B5 Acoustic Emission from Composites 2
Chair: J. Bohse, Berlin (Germany); K.Yoshida, Tokushima (Japan)
41
11:10

Acoustic Emission applied to mechanically loaded Paper
D. GRAHAM, D. KAO, B. KNIGHT, GREENWICH (UNITED KINGDOM);
P. GRADIN, P. ISAKSSON, S. NYSTRÖM, SUNDSVALL (SWEDEN)

42
11:30

On the Application of Wavelet Transform of AE Signals
from Composite Materials
T. LOUTAS, V. KOSTOPULOS, RIO PATRAS (GREECE)

43
11:50

Acoustic Emission Characteristics of Pultruded Fiber
Reinforced Plastics under Uniaxial Tensile Stress
N. ATIVITAVAS, T. POTHISIRI, BANGKOK (THAILAND); J. FOWLER, AUSTIN
(USA)

44
12:10

Acoustic Emission Studies on Fracture Behaviour of
CFC-Materials under various Loads
P. MAJERUS, T. HIRAI, J. LINKE, JÜLICH (GERMANY); J. COMPAN,
PARIS (FRANCE)

45
12:30

Dynamic Health Monitoring of Metal on Metal
Hip Prostheses Using Acoustic Emission
C. ROWLAND, A. TAYLOR, M. BROWNE, SOUTHAMPTON, HANTS(UNITED KINGDOM)

12:50

Lunch break9Thursday, September 16, 2004

Session A

A6 Acoustic Emission from Gears / Wave Guide
Chair: M. Wevers, Leuven (Belgium); J. Sell, Wolfegg (Germany)
46
14:00

Acoustic Emission Source during a Gear Mesh
C.K. TAN, D. MBA, CRANFIELD, BEDFORDSHIRE (UNITED KINGDOM)

47
14:20

Acoustic Emission for Identifying Seeded Defects inGearboxes; Limitations
D. MBA, C.K. TAN, CRANFIELD, BEDFORDSHIRE (UNITED KINGDOM)

48
14:40

The Effect of Waveguide Shape on AE Transmission
Characteristics
J. SIKORSKA, J. PAN, CRAWLEY (AUSTRALIA)

49
15:00

Rods and Tubes as AE Waveguides
K. ONO, LOS ANGELES (USA); H. CHO, SAGAMIHARA KANAGAWA(JAPAN)

15:20

Coffee break

A7 New Sensors / Other Testobjects
Chair: D. Prine, Evanston (USA); H. Vallen, Icking (Germany)
54
15:50

Active Fiber Composites for Application as Acoustic
Emission Sensors: Principles and Characterisation
A.J. BRUNNER, M. BARBEZAT, P. FLUELER, C. HUBER, X. KORNMANN,
DÜBENDORF (SWITZERLAND)

55
16:10

Steel Plate Coupled Behavior of MEMS Transducers
Developed for Acoustic Emission Testing
D. OZEVIN, S. PESSIKI, BETHLEHEM; D.W. GREVE, I.J. OPPENHEIM,
PITTSBURGH (USA)

56
16:30

Optical Fibre Sensor for Damage Monitoring in
Composite Laminate
R. DE OLIVEIRA, O. FRAZAO, J.L. SANTOS, A.T. MARQUES, PORTO(PORTUGAL)

57
16:50

Acoustic Emission to assess the structural Condition ofBronze Statues. Case of the „Nike“ of Brescia
C. CANEVA, F. DOMENICHINI, S. VISKOVIC, A. PAMPALLONA, ROME
(ITALIY)

17:10

End of session

10Session B

Thursday, September 16, 2004

B6 Acoustic Emission from Corrosion
Chair: G. Lackner, Wien (Austria); A.M. Siddiqui, Aberdeen (United Kingdom)
50
14:00

Acoustic Emission from Rust in Stress Corrosion Cracking
H. CHO, M. TAKEMOTO, SAGAMIHARA KANAGAWA (JAPAN)

51
14:20

Acoustic Emission during Stress Corrosion Cracking
Tests
M. TAKEMOTO, H. CHO, SAGAMIHARA KANAGAWA (JAPAN)

52
14:40

A comparative Study of Corrosion in an Aluminium Alloyand Mild Steel using Acoustic Emission and Microscopic
Techniques
A.M. SIDDIQUI, S. JIHAN, A. CHOUDHURY, A. ABDU, ABERDEEN(UNITED KINGDOM)

53
15:00

Acoustic Emission during Hydrogen Charging of Alloys
K. TRAPL, P. SAJDL, P. KU`ĆERA, PRAGUE (CZECH REPUBLIC)

15:20

Coffee break

B7 Other Advanced Analysis Methods
Chair: Y. Mori, Narashino Chiba (Japan); H. Schoorlemmer, Rotterdam(Netherlands)
58
15:50

Basic Pprinciples of Acoustic Emission Tomography
F. SCHUBERT, DRESDEN (GERMANY)

59
16:10

Acoustic Emission for On-Line Monitoring Damage invarious Application Fields
M. WEVERS, LEUVEN (BELGIUM)

60
16:30

Acoustic Emission Technique and Potential Difference
Method for Detecting the Differents Stage of the Crack
Propagation in Carbon and Stainless SteelC. ENNACEUR, A. LAKSIMI, C. HERVE, M. MEDIOUNI, SENLIS(FRANCE)61
16:50

Influence of the Weak Mechanical Disturbances on the
Fracture Nucleation Behavior according AE
Measurements
S. ELIZAROV, MOSKAU; V. KOUXENKO, N. TOMILIN, ST. PETERSBURG(RUSSIA); X.-C. YIN, PEKING (CHINA)

17:10

End of session

11

Thursday, September 16, 2004

Session A

A8 Wave Propagation and Location
Chair: K. Ono, Los Angeles (USA); K. Holford, Cardiff (United Kingdom)
62
9:00

Electronic Noise Effects on Fundamental Lamb-Mode AE
Signal Arrival Times Determined using Wavelet
Transform Results
M. HAMSTAD, DENVER (USA)

63
9:20

Advanced Real Time Source Location Applications
H. SCHOORLEMMER, ROTTERDAM (NETHERLANDS)

64
9:40

Investigation of Fracture Processes using Moment Tensor
Inversion Technique
F. FINCK, C.U. GROßE, H.-W. REINHARDT, STUTTGART (GERMANY)

65
10:00

Source Location on Cylinder Liners using Acoustic
Emission
A. ROBERTSON, P. NIVESRANGSAN, R. DOUGLAS, J. STEEL,
R. REUBEN, EDINBURGH (UNITED KINGDOM)

66
10:20

Processing of Acoustic Emission Signals in dispersive
Media
M. BLAHACEK, PRAGUE (CZECH REPUBLIC)

10:40

Coffee breakA9 Calibration of Sensors, Equipment
Chair: M. Hamstad, Denver (USA); V. Svoboda, Prague (Czech Republic)
72
11:10

Towards traceable Calibration of Acoustic Emission
Measurement Systems: Development of a Reference
Source at the UK's National Physical Laboratory
P. THEOBALD, S. DOWSON, TEDDINGTON MIDDLESEX (UNITED
KINGDOM)

73
11:30

Monitoring Techniques based on Wireless AE
Sensors for large Structures in Civil Engineering
C.U. GROßE, F. FINCK, J.H. KURZ, H.-W. REINHARDT, STUTTGART(GERMANY)

74
11:50

Development of a Finite Element Model for the UK's
National Physical Laboratory Acoustic Emission
Reference Facility
P. THEOBALD, P. GELAT, TEDDINGTON MIDDLESEX (UNITED
KINGDOM); F. SCHUBERT, DRESDEN (GERMANY)

75
12:10

Possibilities and Limits of an automated AE-ChannelVerification Process
H. VALLEN, J. FORKER, G. CORNEANU, ICKING (GERMANY)

12:30

Closing of technical sessions

12:40

Business Meeting followed by light lunch

12Session B

Thursday, September 16, 2004

B8 Data Management / Vendor Applications
Chair: H. Kühnicke, Dresden (Germany); L. Sodomka, Liberec (Czech Republic)67
9:00

Using a Data Base Tool for the Efficient Administrationof AE-Data
H. VALLEN, J. VALLEN, J. FORKER, G. CORNEANU, ICKING (GERMANY)

68
9:20

Overview of new Acoustic Emission (AT) Systems, PACDevelopments
J. SELL, WOLFEGG (GERMANY)

69
9:40

Evaluation of Acoustic Emission Signals during
Monitoring of Thick-Wall Vessels Operating at ElevatedTemperatures
A. ANASTASOPOULOS, A. TSIMOGIANNIS, ATHEN (GREECE)

70
10:00

Acoustic Emission (AE) Loose Parts Monitoring
Application in a Nuclear Power Plant
J. LENAIN, SUCY EN BRIE CEDEX (FRANCE); M. CARLOS, PRINCETON
(USA)

71
10:20

About the Experience of our Acoustic Emission
Equipment Application in Various Industries
M. PODLEVSKIKH, V. BELOV, A. GLUSHKO, O. TARUTIN, MOSKAU(RUSSIA)

10:40

Coffee break

13

POSTERP1

Wavelet Processing of Acoustic Emission Signals from
Scratch Tests on Nitride Coatings
A. Gallego, Granada; M. Garcia-Hernandez, Jaen (Spain), R.
Piotrkowski, San Martin, J.E. Ruzzante, Buenos Aires(Argentina)

P2

Acoustic Emission during the Scratch-Test on galvanized
Steel
A. Gallego, J. Gil, J. Piqueras, J. Rodriguez, J. Vico, Granada(Spain), R. Piotrkowski, San Martin;
J.E. Ruzzante, Buenos Aires (Argentina)

P3

Comparison between Concrete-Black Steel and ConcreteGalvanized Steel Bond via the Pull-Out Test supplied with
Acoustic Emission
A. Gallego, J. Rodriguez, J. Vico, M. Ortega, J. Piqueras, J. Gil,
V. Jimenez, Granada (Spain)

P4

Similarity Matrices as a new Feature for Acoustic Emission
Analysis on Concrete
J.H. Kurz, F. Finck, C.U. Große, H.-W. Reinhardt, Stuttgart(Germany)

P5

Acoustic Emission Method as a Tool to Diagnostic theCorrosion Cracking in Reinforced Concrete Beams
M. Korenska, L. Pazdera, K. Pospisil, L. Manyochowa, Brno
(Czech Republic)

P6

Application of Acoustical Emission in Textile Fields
L. Sodomka, Liberec (Czech Republic)

P7

Area Textile Anisotropy estimated also using Acoustic
Emission
L. Sodomka, Liberec (Czech Republic)

P8

The Evaluation of Fatigue Crack Propagation by Acoustic
Emission Technique
G.-W. Song, S.-M. Park, J.-S. Ham, Daejeon (Korea)

P9

Application of Advanced AE Signal Treatment in the Areaof Mechanical Cyclic Loading
P. Mazal, L. Pazdera, Brno (Czech Rpublic)

P10

Advanced Acoustic Emission Analysis of Rock Fracture
S. Stanchits, G. Dresen, Potsdam (Germany)

P11

Characterization of Adhesive Joints between Steel and
Fiber-Reinforced Polymers
H. Kühnicke, Dresden (Germany)

P12

In-situ Investigation of Excavation disturbed Zones close to
Drives in Salt Mines
H. Kühnicke, E. Schulze, F. Schubert, Dresden (Germany)

P13

Optimization of the AE Behavior of Transformation indued
Plasticity (TRIP) Steels
H. Kühnicke, U. Waag, Dresden (Germany)

14

POSTER
P14

Assessment of Non Visible Damage on Multistrand Cable
using Acoustic Emission
L. Gaillet, C. Tessier, H. Zejli, Bougenais Cedex (France)

P15

The Test of Acoustic Emission Effects during Stress Corrosion
Cracking of Copper Alloy used for Marine Screw Propellers
M. Smajdor, H. Buglacki, Gdansk (Poland)

P16

Acoustic Emission Analysis for Evaluation of the SCCProcess of Brass
U.-D. Hünicke, M. Schulz, Rostock (Germany)

P17

Methods for Investigating Austenite Stability during
Tribological Stressing of FeCrNi Alloys
K. Aßmus, W. Hübner, Berlin (Germany)

P18

Acoustic Emission Model for Determination ofDelamination Stages in bimetallic Compositions
V. Skalsky, O. Andreykiv, O. Serhiyenko, Lviv (Ukraine)

P19

Application of Wavelet Analysis to Acoustic Emission Pulses
Generated by Electrical Discharges in Air
T. Boczar, Opole Opolskie (Poland)

P20

Magneto-Acoustic Emission in Wide Ribbons of Metglas
2605SC
R. Lopez, Santander (Spain)

P21

Testing of Frost Resistance of Fired Roofing Tiles by using
Acoustic Emission Method
M. Korenska, Z.Chobola, P. Mikulkova, J. Martinek, Brno
(Czech Republic)

P22

The Acoustic Emission Technique Applied to the Test Pipes
of GFRP Destinated to Transport Chemicals
A. Valea, I. Mondragon, J. Martinez, M. Gonzalez, Bilbao(Spain)P23

Investigation of the Failure Development in CFRP
R. Kuhnel, E. Schmachtenberg, Aachen (Germany)15CUMULATIVE INDEX
J. of Acoustic Emission, Volumes 1 - 10, 1982 - 92
Authors Index
Authors with four or more contributions
Baldev RajFrank C. Beall
A. G. Beattie
W. D. Brosey
D. J. Buttle
S. H. Carpenter
Roger W.Y. ChanDavid A. Dornfeld
Thomas F. Drouillard
Davis M. Egle
Mark A. Friesel
G.L. Goswami
M. A. Hamstad
H. R. Hardy, Jr.
R. W. Harris
D. Robert Hay
C. R. Heiple
Roger Hill
Nelson N. Hsu
David A. Hutchins
P. H. Hutton
T. Jayakumar
Teruo Kishi
R. A. Kline
R. M. Koerner
Oh-Yang Kwon
Richard Lemaster
Arthur E. Lord, Jr.
J. W. Maclachlan
Stuart L. McBride
Winfred Morgner
C. R. L. Murthy
Masayasu Ohtsu
Kanji Ono

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

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

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

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

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

G. Buzzacchi

II-11

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

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

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

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

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

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

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

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

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

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

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

Robert A. Haack

IX-181III

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

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

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

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

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

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

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

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

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

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

J.-C. Laizet
V. Lalitha

IX-97
IV-S26IV

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

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

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

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

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

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

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

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

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

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

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

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

Stephen L. Quarles

VIII-134, IX-17, IX-189

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

V

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

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

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

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

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

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

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

Taketo Uomoto
T. I. Urbancic

VI-137
VIII-S254

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

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

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

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

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

VIII-S246
IV-S215
III-81

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

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

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

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

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

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

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

Page 7

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

Page 21

An Acoustic Measurement of Boiling Instabilities in a Solar ReceiverPage 29

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

Page 35

A Simple Tape Recording System for Acoustic Emission

Page 37

Earthquakes as Acoustic Emission - 1980 Izu Peninsula Earthquake ln Particular

Page 45

AE Literature

Page 67
Page 68

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

Page 69
Page 72

The Tenth European Working Group on Acoustic Emission
Future Meetings

Number 2
Page 73

Reciprocity and Other Acoustic Emission Transducer Calibration Techniques

Page 81

Production Acoustic Emission Testing of Braze Joint

Page 87

Acoustic Emission Transducer Calibration by Means of the Seismic Surface Pulse

Page 95

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

Alan G. Beattie

P. G. Bentley and A. Plevin
Kiyoo Mogi

T. F. Drouillard

VII

H. R. Hardy, Jr.

K. OnoRoger Hill

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

Page 103

Source Mechanism and Waveform Analysis of Acoustic Emission in Concrete

Masayasu Ohtsu

Page 114

Acoustic Emission in Aircraft Structural Integrity and Maintenance Programs

J. M. Rodgers

Page 121

Page 149

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

Number 3
Page 151

Testing Fiber Composites with Acouatic Emission Monitoring

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

Marvin A. Hamstad

Page 165

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

Page 173

Some Details on the NBS Conical Transducer

Page 179

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

Page 183

Acoustic Emission Behavior of Nickel during Tensile Deformation

Page 191

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

Page 193

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

Page 195

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

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

Number 4
Page 223

Thomas M. Proctor, Jr.

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

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

Page 229

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

Page 237

Leak Detection Using Acoustic Emission

Page 244

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

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

VIIIPage 251

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

Page 263

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

Page 266

Classification of NDE Waveforms with Autoregressive Models

Page 271

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

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

Ronald B. Melton

Volume 2 (1983)
Number 1/2
Page 1

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

Page 11

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

Page 19

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

Page 29

Detection of Corrosion Fatigue by Acoustic Emission

Page 39

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

Page 47

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

Page 57

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

Page 64

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

Page 67

Acoustic Emission Couplants I: The ASTM Survey

Page 69

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

Page 71

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

Page 96

Acoustic Emission, Principles and Instrumentation

Page 129

AE Literature

Thomas F. Drouillard
IX

P. Jax and B. Richter

M.A. Hamstad

A.G. Beattie

A.G. Beattie

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

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

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

Page 159

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

Page 169

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

Page 179

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

Page 187

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

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

Page 195

Acoustic Emissions in Geological Materials

Page 221
Page 239
Page iPage ii

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

Number 4
Page 247

Resonance Analysis of Piezoelectric Transducer Elements

Page 261

Acoustic Emission Evaluation of Metal-Elastomer Junctions

Page 267

Partial Discharge Detection in Bushings by an Acoustic Emission Method

Page 272

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

Page 275

Acoustic Emission Sensors

Page 281

Mechanical Properties and Acoustic Emission in Laser Welded HSLA Steel

Page 289

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

Page 292
Page 319
Page 319
Page 327

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

Page I-1

Index to Volume 2

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

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

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

Volume 3 (1984)
Number 1
Page 1

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

Page 11

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

Page 19

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

Page 27

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

Page 41

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

Page 46
Page I
Page ii

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

Number 2
Page 51

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

Page 59

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

Page 69

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

Page 81

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

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

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

Number 3
Page 108

Monitoring of Metal Cutting and Grinding Processes by Acoustic Emission

Y. Kakino

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

Page 130

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

Page 144

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

Page 158

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

Page 164
Page 172
Page 172
Page 173
Page 174

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

Page 117
Page 157
Page 175
Number 4
Page 176

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

Page 182

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

Page 190

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

Page 204

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

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

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

Page I-1

Index to Volume 3James D. Leaird

Volume 4 (1985)
Number 1
Page 1

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

Page 9

Acoustic Emission Measurments Using Point-Contact Transducers

Page 19

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

Page 31

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

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

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

XII

C. B. Scruby

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

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

S11

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

S17

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

S19

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

S21

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

S26

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

S30

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

S32

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

S35

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

S38

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

S42

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

S46

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

S50

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

S54

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

S58

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

S62

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

S64

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

S69

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

S74

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

S77

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

S78

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

S82

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

S86

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

S90

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

S94

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

S98

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

S102

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

S106

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

S111

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

S116

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

S119

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

S123

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

S127

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

S131

Acoustic Emission Monitoring of Drilling, M. W. Hawman

S132

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

S134

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

S135

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

S137

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

S138

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

S142

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

S147

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

S151

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

S155

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

S157

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

S161

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

S165

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

S166

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

XIV

S170

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

S174

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

S178

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

S182

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

S186

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

S191

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

S195

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

S199

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

S203

Fatigue Crack Closure Study, Guozhi Lu

S207

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

S211

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

S215

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

S220

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

S224

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

S228

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

S232

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

S236

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

S240

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

S244

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

S247

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

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

S255

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

S259

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

S263

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

S269

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

S270

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

S274

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

S278

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

S282

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

S286

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

S290

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

S294

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

S296

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

S300

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

S304

Detection of Fatigue Cracks in Highway Bridges with Acoustic Emission, David W. Prine and Theodore
Hopwood II

S307

Laboratory Acoustic Emission Investigation of Full Size ASTM A-588 Bridge Beams, Al Ghorbanpoor andDonald W. Vannoy

S311

Acoustic Emission Made Audible by Time Dilation of Digitally-Recorded AE Signals, Nelson N. Hsu andSteven E. Fick

S312

Magnetoelastic Resonance Spectroscopy, Wolfgang Stengel

S316

Discrimination of Fracture Mechanisms via Pattern Recognition Analysis of AE Signals during Fracture
Testing, Kanji Ono and Masayasu Ohtsu

S321

Some Design Concepts for an Accurate, High Speed AE Signal Acquisition Module, T. Kevin Bierney

S325

Advanced Acoustic Emission Monitoring System by Distributed Processing Waveform Microdata and the
System Configuration, Kusuo Yamaguchi, Takashi Hamada, Hatsuo Ichikawa, Hirotada Oyaizu, Teruo
Kishi and Hisashi Ishitani

S329

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

S330
Number 4
Page 71

Evaluation of Pattern Recognition Analysis of Acoustic Emission from Stressed Polymers andComposites
R. M. Belchamber, D. Betteridge, Y. T. Chow, T. Lilley, M. E. A. Cudby and D. G. M. Wood

Page 85

The Use of Acoustic Emission to Measure the Ductility of Hardened Surface Layers
J. Roget and D.P. Souquet

Page 93

The Detection of Longitudinal Rail Force via Magnetomechanical Acoustic Emission
M. Shibata, E. Kobayashi and K. Ono
XVI

Page 103

Application of Acoustic Emission on Railroad Car Cushioning Devices
V. Godinez and W.D. Jolly

Page 107

Methods of Calculating Attenuation and Dispersion Effects on Acoustic Emission Signals
R. A. Kline and S. S. Ali

Page 115Classification of Acoustic Emission Signals Generated during Welding
Roger W.Y. Chan, D. Robert Hay, Victor Caron, Michel Hone and R. Douglas Sharp

Page 124
Page 124
Page 124
Page 125
Page S333

Conferences and Symposia
The 2nd International Conference on AE/28th AEWG Meeting
29th AEWG Meeting/AE Primer/The 8th International AE Symposium
AE Training Course Announced
Extended Abstracts from The Second International Conference on AE

Page I - 1

Index to Volume 4

Volume 5 (1986)
Number 1
Page 1

Nondestructive Monitoring of Installed Refractories by Acoustic Emission

David A. Bell

Page 7

Improving the Reliability of Critical Parts by Acoustic Emission Surveillance
during Proof Testing
Edward Goliti

Page 15On the Applicability of Amplitude Distribution Analysis to the Fracture
Process of Composite Materials
Luis Lorenzo and H. Thomas Hahn

Page 25

Detection, Measurements and Location of Partial Discharges in High Power Transformers using Acoustic
Emission Method
Jerzy Skubis, Jerzy Ranachowski and Boguslaw Gronowski

Page 31

Is it Time for Acoustic Emission Surveillance of Operating Nuclear Reactors?

W. F. Hartman

Page 39

Fracture Toughness Measurement of a NiCrMoV Steel by Acoustic Emission

V. Dal Re

Page 45

Uncommon Cries of Cast Iron Elucidated by Acoustic Emission Analysis
Winfred Morgner and Hartmut Heyse

Page 51
Page 53
Page 54
Page ii
Page 50
Page iPage 6
Page 14
Page 59
Page 60

The XVth and XIVth EWGAE Meetings Roger Hill
ASTM E7.04 Meeting A. G. Beattie / French AE Codes / E. German AE Colloquium
The Second International Conference on AE from Reinforced Plastics
29th AEWG Meeting / AE Primer
Meeting Calendar
Appreciation K. Ono / European Perspective Roger Hill
Available Books on AE
EWGAE Provisional Form
Montreal Conference Information / Cover Photo
AE Training Courses

Number 2
Page 61

Prediction of Lumber Checking during Drying by Means of Acoustic Emission TechniqueShigeru Ogino, Koji Kaino and Masahiko Suzuki

Page 67

On the Acousto-Ultrasonic Characterization of Wood Fiber Hardboard
Henrique L. M. dos Reis and D. Michael McFarland

Page 71

Effect of Moisture Conditioning on Acoustic Emission from Particleboard

Page 77

Acoustic Emission During the Deformation and Fracture of Molybdenum at Low Temperatures
Jay B. James and Steve H. Carpenter
XVII

Frank C. BeallPage 85

Acoustic Emission Produced by the Deformation of Uranium

Page 95

A Discussion of the Basic Understanding of the Felicity Effect in Fiber Composites

Page 103
Page 110Page 111

AE Literature - ConcreteThomas F. Drouillard
Book Review
M. A. Hamstad
The 29th Meeting of Acoustic Emission Working GroupThe Second International Symposium on Acoustic Emission from Reinforced Composites
XVth Meeting of The European Working Group of Acoustic Emission (EWGAE)
The 8th Int'l AE Symposium / The 30th Meeting of AEWG
Acousto-Ultrasonics: Theory and Application
Meeting Calendar
The Second CARP Symposium
Cover Photogragh
AE Events of Interest

Page 66
Page 94
Page 102
Page iC. R. Heiple and S. S. Christiansen
M. A. Hamstad

Number 3
Page S1Page S42
Page 113

Page 114
Page 116
Page 122
Page 123
Number 4
Page 124

A SPECIAL ISSUE ON AEWG AND EWGAE MEETINGS - EXTENDED ABSTRACTSThe 29th Meeting of Acoustic Emission Working GroupXVth Meeting of The European Working Group of Acoustic Emission (EWGAE)
The 29th Meeting of Acoustic Emission Working Group (AEWG)
XVth Meeting of The European Working Group of Acoustic Emission (EWGAE)
The 8th International AE Symposium/ The 30th Meeting of AEWG
Acousto-Ultrasonics: Theory and Application
Program of The 29th Meeting of Acoustic Emission Working Group
Abstracts of The 29th Meeting of Acoustic Emission Working Group Page 121
Program of XVth Meeting of The EWGAE
Program of The 8th International AE Symposium
The 2nd International Symposium on AE from Reinforced Composites M. A. Hamstad
The Generalized Theory and Source Representations of Acoustic Emission
Masayasu Ohtsu and Kanji Ono

Page 134

More Recent Improvements on the NBS Conical Transducer

Page 144

Nondestructive Evaluation of Adhesive Bond Strength Using the Stress Wave
Factor TechniqueHenrique L. M. dos Reis and Harold E. Kautz

Page 148

A Note on the Prediction of Fatigue Life of Metal Structures by Use of the
Felicity EffectJ. W. Whittaker

Page 152

Vibro-Acoustic Emission in Plastic Bars

Page 156

Acoustic Emission Testing of Glass Fiber Reinforced Plastic Components

Page 162

Correlation of Acoustic Emission to Microstructural Sources
James Mohr and Amiya K. Mukherjee

Page 172

Acoustic Emission Characterization of Pinch Welds

Page 184

XVth EWGAE Meeting/ 8th Int'l AE Symp.
DGM Symp. on AE/ 16th Symp. on NDE/ Prog. QNDE/ AE in Sci. & Tech.
30th AEWG Meeting/ Acousto-Ultrasonics/ XVIth EWGAE Meeting
9th Int'l AE Symp./ 3rd Int'l AE Conf./ 3rd Int'l Symp. on AE - Composites
Instruction for AEWG-EWGAE Extended Abstracts
Abstract; XVth EWGAE Meeting
Meeting Calendar
Call for Papers - 30th AEWG Meeting and AEWG Short Course
Registration Forms for 30th AEWG Meeting and AEWG Short Course
Call for Papers/ Announcements/ AE Training Courses

Page 185
Page 186
Page S69
Page 155
Page 143
Page 171
Page 185

Thomas M. Proctor, Jr.

A. E. Lord, Jr.

XVIII

R. TetiA. G. Beattie and C. W. Pretzel

Page 151
Page 161
Page iPage ii

Available Books on AE
Progress in Acoustic Emission III
AE Events of Interest
Acoustic Emission of a Kouros
Kanji Ono

Page I - 1

Index to Volume 5

Volume 6 (1987)
Number 1
Page 1

Fracture-Induced Acoustic Emission during Slow Bend Tests of A533B Steel H.B. Teoh and Kanji Ono

Page 13

Real-Time Monitoring of Multi-Pass Welding by Acoustic Emission K. Ishihara and K. Yamada

Page 19

Application of Pattern Recognition Concepts to Acoustic Emission Signals Analysis
C.R.L. Murthy, B. Dattaguru and A.K. Rao

Page 29

Punch Stretching Process Monitoring Using Acoustic Emission Signal Analysis--Part 1:
Basic Characteristics Steven Y. Liang and David A. Dornfeld

Page 37

Punch Stretching Process Monitoring Using Acoustic Emission Signal Analysis - Part 2:
Application of Frequency Domain Deconvolution
Steven Y. Liang, David A. Dornfeld and Jackson A. Nickerson

Page 43

Modeling Concrete Damage by Acoustic Emission

Page 61

Pattern Recognition Analysis of Acoustic Emission from Unidirectional Carbon Fiber-Epoxy
Composites by using Autoregressive Modeling
Masayasu Ohtsu and Kanji Ono

Page 73

A New Approach to the Use of Acoustic Emission Peak Amplitude Distribution as a Tool of
Characterizing Failure Mechanisms in Composite Materials
A. Mittelman and I. Roman

Page 84
Page 79
Page 80
Page 81
Page 83
Page 83

Waveform Digitizers Kanji Ono
30th AEWG Meeting/8th Int'l AE Symp.
M. Ohtsu
Int'l Conf. of NDE with AE Technology
Letter to Editor/An Erratum/ AE Training Courses
31st AEWG Meeting and AEWG Short Course
Progress in Acoustic Emission III

Number 2
Page 85

J.M. Berthelot and J.L. Robert

Acoustic Emission Wave Characterization: A Numerical Simulation of the Experiments on Cracked
and Uncracked Specimens T. Aizawa, T. Kishi and F. Mudry

Page 93

Flaw Growth in Alumina Studied by Acoustic Emission
M.A. Hamstad, P.M. Thompson and R.D. Young

Page 99

Acoustic Emission Characteristics in Concrete and Diagnostic Applications

Page 109

Measurement of the Maximum Applied Loads to Automobile Components by Acoustic Emission
Technique Tatsuhiko Yoshimura and Shigeto Kano

Page 115

Effects of Heat Treatment on the Acoustic Emission Generated During the Deformation of 7075-T651
Aluminum Alloy Zu-Ming Zhu and S.H. Carpenter

Page 121

An Overview of Acoustic Emission Codes and Standards

Page 125

Acoustic Emission Applied to Pressure Vessels

Page 133
Page 133
Page 135
Page 136

Third Symposium on AE W. Morgner
Fourth European Conf. on NDT and 16th EWGAE
AE Codes/Standards Activity within ASME and ASNT
Prof. S.H. Carpenter, 1987 University Lecturer
XIX

J.C. Spanner, Sr.

Brian R.A. Wood

J. R. Mitchell

Masayasu Ohtsu

Number 3
Page 137

Application of Acoustic Emission to the Field of Concrete Engineering

Taketo Uomoto

Page 145

A Method of Rapidly Estimating the Fatigue Limits by Acoustic Emission
Tatsuhiko Yoshimura and Shigeto Kano

Page 151

Preliminary Investigation of Acoustic Emission from Wood During Pyrolysis and Combustion
Frank C. Beall

Page 157

Preliminary Investigation of the Feasibility of Using Acousto-Ultrasonics to Measure Defects in Lumber
R.L. Lemaster and D.A. Dornfeld

Page 167

Development and Application of Acoustic Emission Methods in the United States - A Status Review
P.H. Hutton

Page 177

Acoustic Emission Produced by Deformation of Metals and Alloys - A Review: Part I
and S.H. Carpenter

Page 205
Page 205
Page 206
Page 166
Page 156
Page 176
Page 208

30th Meeting of AEWG D.M. Egle and R.A. Kline
A Brief Note on the Kaiser and Felicity Effects
R.A. Kline and D.M. Egle
The Third Int'l Workshop on Composite Materials / The Sixth (Japanese) Conference on AE
31st Meeting of AEWG
Available Books on AE
Cover Photograph
AE Training Courses/Announcements

Number 4
Page 209

C. R. Heiple

The Application of Acoustic Emission Techniques in High-Temperature Oxidation Studies A. S. Khanna,
B. B. Jha and Baldev Raj

Page 215

Acoustic Emission Produced by Deformation of Metals and Alloys - A Review: Part II
and S.H. Carpenter

C. R. Heiple

Page 239

Acoustic Emission During Deformation and Crack Initiation of Pipeline Steels M. W. Drew, B. R. A.
Wood and R. W. Harris

Page 249

Acoustic Emission Waveform Analysis to Identify Fatigue Crack Propagation in a Mirage Aircraft
C. M. Scala and R. A. Coyle

Page 257

The Detection of Hydrogen-Assisted-Crack Formation in U - 0.8% Ti Alloy Electron-Beam Weldments
J. W. Whittaker and M. W. Richey

Page 261

The Detection of the Fracture of Autoclaved Aerated Concrete during Autoclave Curing Process by
Acoustic Emission Satoshi Teramura, Koichi Tsukiyama and Hideaki Takahashi

Page 267
Page 273
Page 238
Page 256

31st Meeting of AEWG - Abstracts
Meetings on AE and related topics
9th Int'l AE Symp/World Meeting on AE
Cover Photograph

Page 273

INDEX to Volume 6

Volume 7 (1988)
Number 1
Page 1

Evaluation of Fracture Toughness of Autoclaved Lightweight Concrete by Means of Acoustic
Emission Technique, Satoshi Teramura, Koichi Tsukiyama and Hideaki Takahashi

Page 9Identification of Crack Propagation Modes in 304 Stainless Steel by Analysis of Their
Acoustic Emission Signatures Daniel R. Smith Jr. and Steve H. Carpenter

Page 21

A New Sensor for Quantitative Acoustic Emission Measurement
XX

Chung Chang and C. T. Sun

Page 31

Acoustic Emission Propagation and Source Location in Small, Spherical Composite Test Specimens
J. W. Whittaker, W. D. Brosey, O. Burenko and D. A. Waldrop

Page 41

A High Fidelity Piezoelectric Tangential Displacement Transducer for Acoustic Emission
Thomas M. Proctor, Jr.

Page 49

Acoustic Emission from Fatigue Cracks in Chrome-Molybdenum Steel Cylinders
P.R. BlackburnPage 57
Page 20
Page 40A
Page 48
Page 30

31st Meeting of AEWG
Meeting Schedule
World Meeting on AE
Instructions to Authors - World Meeting on AE
Books Available

Number 2
Page 59

Acoustic Emission Measurements on PWR Weld Material with Inserted Defects using Advanced
Instrumentation P. G. Bentley and M. J. Beesley

Page 81

Acoustic Emission Measurements on PWR Weld Material with Inserted Defects
C. B. Scruby and K. A. Stacey

Page 95

Characterization of Acoustic Emission from Thermally-Cycled Lithium Hydride
J. W. Whittaker and D. G. Morris

Page 103

Apparatus for Coupling an Acoustic Emission Transducer to a Rotating Circular Saw
Richard L. Lemaster and David A. Dornfeld

Page 111

Measurement of Density Profiles in Wood Composites Using Acoustic Emission
Richard L. Lemaster, Michael F. Gasick and David A. Dornfeld

Page 119

Acoustic Emission during Intergranular Stress Corrosion Cracking of Iron
M. A. Friesel and R. H. Jones

Page 129
Page 129
Page 80
Page 94
Page 102

XVIIth Meeting of EWGAE - Program
The 9th International Symposium on AE - Program
Meeting Schedule
World Meeting on AE
Books Available/Training Courses

Number 3
Page 135

Acoustic Emission from Porous Films of Aluminum Oxide during the Application of Electrical
Voltage J. Sampath Kumar and S.P. Mallikarjun Rao

Page 139
Page 140
Page 141

Third Domestic Conf. on Subsurface Acoustic Emission (Sendai, Japan) - Program
Acoustic Emission (UK) - Abstracts
Seminar on Acoustic Emission (India) - Abstracts

Page S1

Extended Abstracts of Presentations at AEWG(I) Seminar on ACOUSTIC EMISSION
Acoustic Emission during Tensile Deformation and Fracture in Austenitic Stainless Steels
Baldev Raj and T. Jayakumar

Page S13

Detection of Breakaway Oxidation of Zircaloy-2 by Acoustic Emission Technique
B. K. Gaur, A. K. Sinha, B. K. Shah, P. G. Kulkarni and R. Vijayaraghavan

Page S18

Magnetomechanical Acoustic Emission Behaviour of Some Structural Steels
S. G. Savanur and C. R. L. Murthy

Page S29

A Study of Acoustic Emission Activity in Granites during Stress Cycling Experiments

Page S35

Acoustic Emission Studies on Adhesive Potted Inserts of Honeycomb Sandwich Panels
T.S. Sriranga and R. Samuel
XXI

M.V.M.S. Rao

Page S40

Location of Weld Defects by Acoustic Emission

Page S43

Thermally Induced Relaxation Behaviour in a Metallic Glass
G. L. Goswami, S. K. Jha and G. P. Tiwari

Page S47
Page S48

Meeting Schedule/Training Courses
Announcements

Number 4
Page 145

G.L. Goswami and P.R. Roy

Acoustic Emission During Quasi-Static Loading/Hold /Unloading in Notched Reinforced
Fiber Composite Materials S. V. Hoa and L. Li

Page 161

The Acoustic Emission Generated during the Plastic Deformation of High Purity Zinc
S. H. Carpenter and Chung-Mei Chen

Page 167

Evaluation of Concrete Structure Deterioration via AE Observation of Core Tests
Masayasu Ohtsu, Tatsuro Sakimoto, Yutaka Kawai and Syuro Yuji

Page 173

Diagnosis of Rotating Slides in Rotary Compressors using Acoustic Emission Technique
Ichiya Sato, Takao Yoneyama, Kouichi Sato, Toshiyuki Tanaka and Hiroaki Hata

Page 179

AE-Monitoring Systems for the Detection of Single-Point and Multipoint Cutting Tool Failures
Thomas Blum, Ippei Suzuki and Ichiro Inasaki

Page 185

Direct Measurement of Water Hammer Pressure by AE Source Wave Analysis
Mikio Takemoto and Yasuhisa Hayashi

Page 193

Acoustic Emission from an Industrial Applications Viewpoint

Page 201

Downhole AE Measurement Technique and Its Application to Geothermal Fields

Page 211

Acoustic Emission Source Location in Fiber Reinforced Plastic Composites
D. J. Buttle and C. B. Scruby

Page 225

The Acoustic Emission Source Mechanism for Fatigue Crack Propagation in 7075 Aluminum
S. L. McBride, P. Bowman and K. I. McRae

Page 231
Page 231
Page 192
Page 200
Page 210
Page 224

Conferences and Symposia
Third International Symposium on AE from Composite Materials/EWGAE Meeting
Meeting Schedule
Books Available
Progress in AE IV, Proc. The 9th International AE Symposium
The World Meeting on AE/ASNT Recognition/Codes and Standards Committee

Page I-1

Index to Volume 7

Trevor J. Holroyd
Hiroaki Niitsuma

Volume 8 (1989)
Numbers 1 and 2
Pages S1-S338 Proceedings of the World Meeting on Acoustic Emission, March 20-23, 1989
S1
"Acoustic Emission Technology using Multi-parameter Analysis of Waveform and the Applications to
Fracture Modes and Growth Recognition in Composites", K. Yamaguchi, H. Oyaizu, J. Johkaji and Y.
Kobayashi
S4"Acoustic Emission Detection of Crack Presence and Crack Advance During Flight", S. L. McBride, M. D.
Pollard, J. D. MacPhail, P. S. Bowman and D. T. Peters

S8"Time-frequency domain (3-D) Analysis of AE Signals using Simple Instrumentation Techniques", S. V.
Subba Rao, K. V. Srincivasan and M. Annamalai

S12"Improving Acoustic Emission Crack/Leak Detection in Pressurized Piping by Pattern Recognition
Techniques", R. W. Y. Chan, D. R. Hay, J. R. Hay and H. B. Patel
XXII

S16

"An Efficient Unsupervised Pattern Recognition Procedure for Acoustic Emission Signal Analysis", M. A.
Majeed and C. R. L. Murthy

S20"Solving AE Problems by a Neural Network", I. Grabec and W. Sachse

S24"The General Problems of AE Sensors", Y. Higo and H. Inaba

S28"Acoustic Emission Transducer Modelling Using System Identification Techniques", S. Kallara, P. K.
Rajan and J. R. Houghton

S32"Design of 3-Dimensional AE/MS Transducer Arrays", M. Ge and H. R. Hardy

S38"Simultaneous Velocity Tomography and Source Location of Synthetic Acoustic Emission Data", S. C.
Maxwell, R. P. Young, and D. A. Hutchins

S42"Use of Mechanical Waveguides and Acoustic Antennae in Geotechnical AE/MS Studies", H. R. Hardy,
Jr., F. Taioli and M. E. Hager

S49"Linear Location of AE Simulated Sources on Steel Pipelines with Waveguides", B. Q. Zhang

S53"AE Application and Recent Results on Nuclear Components", P. Jax and V. Streicher

S57"Acoustic Emission from Steel Structures", A. Nielsen

S62"AE Role in the Diagnosis and Prognosis of Defects in Industrial Plant Steel Components", F. Tonolini

S66"Structural Integrity Evaluation Using Acoustic Emission Techniques", B. R. A. Wood and R. W. Harris

S70"On-line Application of Acoustic Emission Analysis", W. Morgner

S71"AE - For when you absolutely cannot afford a Failure of a Critical Pressure Boundary", H. L. Dunegan

S75"Periodic Inspection of Compressed Gas Cylinders and Transport Vessels by Using Acoustic Emission
Testing", H. Barthelemy

S79"Locating Fatigue Cracks by Acoustic Emission Testing", P. M. Horrigan, J. F. Finn, F. R. Tuler and J. H.
Smith

S84

"A New Acoustic Emission Measurement System and its Application to the Local Monitoring of a Crack in
a Pressure Vessel", B. Tirbonod and L. Hanacek

S88"An Approach for the Integrity Assessment of M250 Maraging Steel Pressurized Systems", T. Chelladurai,
R. Krishnamurthy and A. R. Acharya

S93"Detectability of Defects in Reactor Pressure Components by Location and Interpretation of AE-Sources",
C. Sklarczyk and E. Waschkies,

S97"Application of Acoustic Emission Technique during In-service Pressure Vessel Inspection", S. Liu, G.
Shen, Y. Wan and Q. Duan

S101

"Acoustic Emission Leak Monitoring in Pressurized Piping", H. B. Patel and A. W. Cook

S103

"Application of Acoustic Emission Technique in the High-temperature Oxidation Studies - A Review", A.
S. Khanna

S105"Acoustic Emission during Phase Transformation in Cr12MoV Steel and Fe-33Ni-4Ti-10Co Alloy", B. Q.
Zhang, C. K. Yao and C. G. Jiao

S109

"Effect of Pre-exposure to Water on the Acoustic Emission Behavior of 2091-T3 Al-Li Alloy", F. M.
Zeides and I. Roman

S114"Acoustic Emission from Weld-seam of 16MnR Steel during Stress Corrosion", B. Q. Zhang and J. Q. Sun
XXIII

S118

"The Influence of Crack Front Geometry on Acoustic Emission During Fatigue of Al2024-T351", F. A.
Veer and J. Zuidema

S122"Acoustic Emission Monitoring of Incipient Crack Propagation and its Growth Rate in EN-24 Steel under
Fatigue", S. C. Pathak and C. R. L. Murthy

S126"Acoustic Emission during Tensile Deformation and Fracture in Austenitic Alloys", B. Raj and T.
Jayakumar

S131"Relationship between Acoustic Emission and Flaw Size in Si3N4 Ceramics", Y. Mori, M. Nishino, K.-I.
Aoki, T. Kishi and K. Kitadate

S135"A Comparison of the Acoustic Emission Generated from the Fracture and Decohesion of Graphite
Nodules with Theoretical Predictions", S. H. Carpenter and Z. Zhu

S140"Influence of Grain Size on Frequency Spectra of AE Signal Generated during Tensile Deformation in an
AISI Type 316 Stainless Steel", B. Raj, P. Kalyanasundaram, T. Jayakumar, P. Barat and P. Rodriguez

S145"Evaluation of Fatigue Crack Growth Rate of Carburized Gear by Acoustic Emission Technique", Y.
Obata, H. Kobayashi, K. Aoki, T. Yamaguchi and K. Shibata

S149"Acoustic Emission Investigations in Poland", J. Ranachowski, Poland "Comparative Studies on Acoustic
Emission Generated During Lüder's Deformation in Mild Steel and Portevin-Le Chatelier Effect inAustenitic Stainless Steel", B. Raj, T. Jayakumar, P. Kalyanasundaram, P. Barat, B. B. Jha, D. K.
Bhattacharya, P. Rodriguez

S154"Development and Future Aspects in AE Source Characterization", M. Enoki and T. Kishi

S158"AE Source Modelling-Comparison of Inversion and Forward Techniques", D. J. Buttle and C. B. Scruby

S162"Source Inversion of Acoustic Emission for the Determination of Crack Kinematics and Kinetics", M.
Ohtsu

S166

"Acoustic Emission Analysis and Ultrasonic Velocity Imaging in the Study of Rock Failure", S. D. Falls, T.
Chow, R. P. Young and D. A. Hutchins

S170"Thin-Film Acoustics: Line and Point Sources Generation and the Testing of thin Films", K. Y. Kim and
W. Sachse

S175"Acousto-Ultrasonics: An Update", A. Vary

S179

"Theoretical Basis of Acousto-Ultrasonics", M. T. Kiernan and J. C. Duke

S184

"Fracture of Boron Particles in 2219 Aluminum as a Known Acoustic Emission Source", C. R. Heiple, S.
H. Carpenter and S. S. Christiansen

S188"Surface Analysis by Tribo-acoustic Emission", M.-K. Tse and P.-Y. Gu

S192"Acoustic Emission Measurements of Rubbing Surfaces", S. L. McBride, R. J. Boness, M. Sobczyk and M.
R. Viner

S197"Vibro-Acoustic Emission Rubbing Sources", J. R. Webster

S201"Acoustic Characterisation of Small Particle Impact", D. J. Buttle and C. B. Scruby

S205"Estimation of Impact Force between Rough Surfaces by Means of Acoustic Emission", I. Kukman and I.
Grabec

S209"Acoustic Emission Monitoring of Magnetic Hard Disks during Accelerated Wear Testing", J. C. Briggs,
M. M. Besen and M.-K. Tse

XXIV

S213

"Applications of Acoustic Emission Techniques for Diagnosis of Large Rotating Machinery and Mass
Products", I. Sato, T. Yoneyama, K. Sato, T. Tanaka, M. Yanagibashi and K. Takigawa

S217"Quality Inspection of Rolling Element Bearings using Acoustic Emission Technique", V. Bansal, A.
Prakash, V. A. Eshwar and B. C. Gupta

S219"Stress Wave Sensing - Affordable AE for Industry", T. J. Holroyd

S223"Cavitation Monitoring of Hydroturbines with True-RMS Acoustic Emission Measurement", O.
Derakhshan, J. R. Houghton, R. K. Jones and P. A. March

S227"Monitoring of the Cutting Process by Means of AE Sensor", D. A. Dornfeld

S231"Tool Monitoring by Acoustic Emission", J. Roget, P. Souquet, M. Deschamps and N. Gsib

S236

"MONPAC - An Acoustic Emission based System for Evaluating the Structural Integrity of Metal Vessels",
T. J. Fowler, J. A. Blessing and T. L. Swanson

S238"ICI's Perspective on Acoustic Emission Monitoring", S. Hewerdine

S239"MONPAC - Condition Monitoring for Static Plant - Case Histories", P. T. Cole

S240

"Acoustic Emission Monitoring of a Large Pressure Vessel during a Pneumatic Re-Qualification Test", M.
Peacock

S241"Summary of Experiences with MONPAC Testing by MQS/Dunegan Testing Group", R. K. Miller

S242"Microseismics and Geotechnical Applications", M. Ohtsu

S246"Acoustic Emission Phenomena in Geological Media", R. A. Kline, A. S. Khan, Y. Xiang, J.
Shlyapobersky and F. Irani

S250"Acoustic Emission/Microseismic Activity at very low Strain Levels", B. H. Armstrong

S254"Focal Mechanism Determinations for a Sequence of Mining-Induced Seismic Events Recorded at a Hard
Rock Mine", T. I. Urbancic, S. Talebi and R. P. Young,

S258"Effects of Porosity on Acoustic Emission Signatures", R. J. Watters and D. M. Chuck

S262"Acoustic Emission Monitoring and Analysis Procedures Utilized during Deformation Studies on Geologic
Materials", X. Sun, H. R. Hardy, Jr. and M. V. M. S. Rao

S266"PDP 11/34 Based Microseismic Monitoring System for Kolar Gold Fields, India", G. Jayachandran Nair

S268

"Fracture Mechanism Studies of Carbon/PMR-15 Composites by Acoustic Emission", J. S. Jeng, K. Ono
and J. M. Yang

S272

"Assessment of Fatigue Damage in Carbon Fibre Reinforced Epoxy Laminates with the Energy
Discrimating Acoustic Emission Method", M. Wevers, I. Verpoest, P. De Meester and E. Aernoudt,

S276"NDE Procedure for Predicting the Fatigue Life of Composite Structural Members", M. J. Sundaresan, E.
G. HennekeII and A. Gavens

S280"Detection of Impact Damage in Composite Structures by Use of Thermally-activated Acoustic Emission",J. W. Whittaker and W. D. Brosey

S284"Characterization of Failure Modes in Glass Fibre Reinforced Plastic Composites", M. N. Raghavendra Rao
and C. R. L. Murthy

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

S292

"Analysis of AE Signals in Time and Frequency Domains Coupled to Pattern Recognition to Identify
Fracture Mechanisms in CFRP", A. Maslouhi and C. Roy

S297"Some New Results in the Damage Identification in Kevlar-Epoxy Composites", D. S. Rajan, N. N.
Kishore and B. D. Agarwal

S301"Monitoring Initiation and Growth of Matrix Splitting in a Uni-directional Graphite/Epoxy Composite",
S. Ghaffari and J. Awerbuch

S306"Correlation of Internal Bond Strength of Particleboard with Acousto-Ultrasonics", A. T. Green

S311"Acoustic Emission during Contact Drying of Southern Pine Veneer", F. C. Beall

S314"Nondestructive Evaluation of Adhesively Bonded Joints using Acousto-Ultrasonics", S. Tanary, A. Fahr
and Y. Haddad

S317"AE Research on Adhesion in Composite Materials", H. G. Moslé

S318"Acoustic Emission Testing of Flexed Concrete Beams Reinforced with Bonded Surface Plates", D. P.
Henkel and J. D. Wood

S322

"Acoustic Emission Investigation into some Concrete Construction Problems", J. D. Leaird and M. A.
Taylor

S326"Correlation of AE and Fracture Intensity during Impact Indentation of Coal Block", S. J. Jung and A. W.
Khair

S330

"Acoustic Emission Monitoring of Pressure Vessel Preventing Catastrophic Failure", S. F. Botten

S334

Limiting State Prediction from AE Signals for Large-size Structures under Static, Cyclic and Thermocyclic
Loads, V.A. Strizhalo and V.A. Strelchenko

S337

Authors Index

Number 3
Page 1The MONPAC System

Timothy J. Fowler, James A. Blessing, Peter J. Conlisk and Terry L. Swanson

Page 11

Acoustic Emission Monitoring of a Large Pressure Vessel during a Pneumatic Re-qualification Test
M. J. Peacock

Page 21

ICI's Perspective on Acoustic Emission Monitoring S. Hewerdine

Page 25

A Summary of Experiences with MONPAC™ Testing by the MQS/Dunegan Testing Group
R.K. Miller, R.G. Tobin, D.J. Gross and D.T. Tran

Page 31MONPAC - Condition Monitoring for Static Plant - Case Histories

Page 35

Detection of an Impulse Force in Head-Disk Media Contact using Small Piezoelectric Transducer
Kenji Mochizuki, Isamu Sato and Takefumi Hayashi

Page 41

Investigations of Sensor Placement for Monitoring Acoustic Emission in Machining
David V. Hutton and Qing Huan Yu

Page 47

Use of Plane-Strain Compression for the Diagnosis of Acoustic Emission Source during Plastic
Deformation F. Zeides and I. Roman

Page 51

Transverse Cracking and Longitudinal Splitting in Graphite/Epoxy Tensile Coupons as Determined byAcoustic Emission Steven M. Ziola and Michael R. Gorman

Page 8Page 9Cover Photographs
Meeting Calendar -- 33rd AEWG Meeting, 10th IAES, Kumamoto Workshops
XXVI

Phillip T. ColePage 61
Page 61
Page 34
Page 20, 30, 40
Page 50, 50, 62
Number 4
Page 65

Third International Symposium on AE from Composite Materials (AECM-3)
EWGAE Meeting/Future Meetings/Errata
The Status of EWGAE Panel on Standards J. Roget /Codes and Standards Committee
Abstracts of Third International Symposium on AE from Composite Materials (AECM-3)
Selected Abstracts of AECM-3 (continued)
A Review of International Research Relative to the Geotechnical Field Application of Acoustic Emission/
Microseismic Techniques H. Reginald Hardy, Jr.

Page 93

A Review of Acoustic Emission in Civil Engineering with Emphasis on Concrete
Masayasu Ohtsu

Page 99

Application of Acoustic Emission for the Evaluation of Microseismic Source Location Techniques
M. Kat and F. P. HassaniPage 107

Acoustic Emission Characteristics of Unstable Slopesand M. Kamata

Page 113

Experimental Studies on the Effect of Stress History on Acoustic Emission Activity -- A Possibility for
Estimation of Rock Stress
Sumio Yoshikawa and Kiyoo Mogi

Page 125

Acoustic Emission from Phase Transformations in Au - 47.5 at. % Cd
C. R. Heiple and S. H. Carpenter

Page 135

The Effect of Same-side and Through-Thickness Transmission Modes on Signal Propagation in Wood
Richard L. Lemaster and Stephen L. Quarles

Page 92
Page 143
Page 200
Page 210

Meeting Schedule
Abstracts of Third International Symposium on AE from Composite Materials
EWGAE Meeting/AEWG -33 Books Available
Progress in Acoustic Emission IV, Proc. The 9th International AE Symposium

Page I-1

Index to Volume 8

A. Chichibu, K. Jo, M. Nakamura, T. Goto

Volume 9 (1990)
Number 1
Page 1

Origin of Acoustic Emission Produced during Deformation of Beryllium
C. R. Heiple and S. H. Carpenter

Page 9Optimal Waveform Feature Selection Using a Pseudo-Similarity Method
K. J. Siddiqui, Y.-H. Liu, D. R. Hay and C. Y. Suen

Page 17

The Effect of Same-Side and Through-Thickness Transmission Modes on Signal Propagationin Wood Richard L. Lemaster and Stephen L. Quarles

Page 25

Defect Detection in Rolling Element Bearings by Acoustic Emission Method
N. Tandon and B. C. Nakra

Page 29

Monitoring of a Pressure Vessel (ZB2) by means of Acoustic Emission
H.-A. Crostack and P. Böhm

Page 37

A Procedure for Acceptance Testing of FRP Balsa Wood Core Pressure Vessels
P. Ouellette, S.V. Hoa and L. Li

Page 45

AE Literature
A Comprehensive Guide to the Literature on Acoustic Emission from Composites,
Supplement II Thomas F. Drouillard

XXVII

Page 69
Page 72
Page 73
Page 8Page 31
Page ii

Conferences and Symposia
Abstracts of 33rd Acoustic Emission Work Group Meeting
ASNT Spring Conference
1st International Conf. on AE in Manufacturing, AECM-4
Available Books on AE
Cover PhotographMeeting Calendar

Number 2
Page 75

Composites
Felicity Ratio Behavior of Pneumatically and Hydraulically Loaded Spherical Composite Test Specimens
J. W. Whittaker, W. D. Brosey and M. A. Hamstad

Page 84

Correlation of Felicity Ratio and Strength Behavior of Impact-Damaged Spherical Composite Test
Specimens J. W. Whittaker, W. D. Brosey and M. A. Hamstad

Page 91

Delayed Acoustic Emission: A Rheological Approach
P. Fleischmann

Page 97

Acoustic Emission Analysis of the Accumulation of Cracks in CFRP Cross-ply Laminates under Tensile
Loading J.-P. Favre and J.-C. Laizet

Page 103

Analysis of Acoustic Emission Events from Single Fiber Pullout Experiments
W. Mielke, A. Hampe, O. Hoyer and K. Schumacher

Page 109

Digital Signal Analysis of Acoustic Emission from Carbon Fiber/ Epoxy Composites
Kenji Kawamoto

Page 117

Acoustic Emission: A Micro-Investigation Technique for Interface Mechanisms in Fiber Composites
D. Rouby

Page 123

Acoustic Emission Characterization of the Deformation and Fracture of an SiC-Reinforced, Aluminum
Matrix Composite Oh-Yang Kwon and Kanji Ono

Page 131

Burst Prediction by Acoustic Emission in Filament-Wound Pressure Vessels
Michael R. Gorman

Page 140

Critical AE Problems for the Researcher

Page 142

Quality Inspection of Rolling Element Bearing using Acoustic Emission Technique
Vibha Bansal, B.C. Gupta, Arun Prakash and V.A. Eshwar

Page 147
Page 152
Page 153
Page 154
Page 122
Page 130
Page 154

N. Rochat, R. Fougeres andAdrian A. Pollock

Conferences and Symposia
XIX Meeting of EWGAE, 10th International Acoustic Emission Symposium
1st Symp. Evaluation of Adv. Materials by AE
Intl Joint Meeting at Kumamoto
Korean Working Group on Acoustic Emission (KWGAE), AE Training Courses
Meeting Schedule
34th AEWG Meeting
Cover Photograph

Number 3
Page 155

Wood
Frank C. Beall, Topical Editor
Anecdotal History of Acoustic Emission from Wood

Thomas F. Drouillard

Page 177

An Experiment on the Progression of Fracture (A Preliminary Report)
Fuyuhiko Kishinouye (Translated by Kanji Ono)

Page 181

Acoustic Emission from Drought-Stressed Red Pine (Pinus resinosa)
Robert A. Haack and Richard W. Blank

XXVIII

Kanji Ono andPage 189

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

Page 197

Nondestructive Evaluation of Adhesive Bond Strength of Finger Joints in Structural
Lumber Using the Acousto-Ultrasonic Approach Henrique L. M. dos Reis,
Frank C. Beall, Michael J. Chica and Dick W. Caster

Page 203

Determining the Abrasiveness to Tools of Wood-Based Composites
with Acoustic Emission Richard L. Lemaster

Page 209
Page 215

Lumber Stress Grading utilizing the Acoustic Emission Technique Keiichi Sato,
Hajime Takeuchi, Katsuya Yamaguchi, Naoto Ando and Masami Fushitani
AE Literature - Wood Thomas F. Drouillard and Frank C. Beall

196, 214, 223
Page 226
Page 188
Page 180

Conferences and Symposia
10th International Acoustic Emission Symposium (abstracts)
Intl Joint Meeting at Kumamoto/ KWGAE/ 34th AEWG Meeting, AE Training Courses
Meeting Schedule
Fuyuhiko Kishinouye (biography) / Cover Photograph

Number 4
Page 227

Detection of Irradiation Effects on Reactor Vessel Steels by Magneto-Acoustic Emission
Oh-Yang Kwon and Kanji Ono

Page 237

New Algorithm for Acoustic Emission Source Location in Cylindrical Structures
Dong-Jin Yoon, Young H. Kim and Oh-Yang Kwon

Page 243

Characterization of Fatigue of Aluminum Alloys by Acoustic Emission, Part I Identification of Source Mechanism D. J. Buttle and C. B. Scruby

Page 255

Characterization of Fatigue of Aluminum Alloys by Acoustic Emission, Part II Discrimination Between Primary and Other Emissions D. J. Buttle and C. B. Scruby

Page 271

Acoustic Emission Source Location Using Simplex Optimization
M. P. Collins and R. M. Belchamber

Page 277

Acoustic Emission during Lumber Drying

Page 283

AE Source Orientation by Plate Wave Analysis
and William H. Prosser

Page 289
Page 270
Page 276A

Conferences and Symposia
10th International AE Symp.
Cover Photograph AE Inspection of Monorail Trains
Meeting Calendar

I-i

Index to Volume 9

S. Ogino, K. Kaino and M. Suzuki
Michael R. Gorman

Volume 10 (1991/92)
Number 1/2
Page iCurrent Research and Future Trend of AE Applications to Civil Engineering and Geological Technology
Masayasu Ohtsu, Topical Editor

S1-S12

Variety of Acoustic Emission Waveforms Produced by Discrete Crack Growth in Rock
Steven D. Glaser and Priscilla P. Nelson

S13-S17

Estimation of Maximum Stress in Old Railway Riveted I-Girder Bridges using Acoustic Emission Signals
Hisanori Otsuka, Hiroshi Hikosaka, Hiroyuki Miyatake and Syouzou Nakamura

S18-S21

Another Look at Booming Sand

Marcel F. Leach and Gottfried A. Rubin

XXIXS22-S28

Expected Acoustic Emission from Around a Shaft in Intact Rock

S29-S34

Acoustic Emission Monitoring during Microseismic Activity Caused by Mine Subsidence
Brian R. A. Wood and Robert W. Harris

S35-S41

Acoustic Emission/Microseismic Activity Monitoring of Salt Crystallization for Stone Conservation
M. Montoto, R.M. Esbert, L.M. Suárez del Río, V.G. Ruiz de Argandoña and C.M. Grossi

S42-S48

Acoustic Emission Monitoring during In-situ Heater Test of Granite
T. Ishida, K. Kitano, N. Kinoshita and N. Wakabayashi

S49-S54

Using Acoustic Emission Testing in Seepage Investigations

S55-S58

Acoustic Emission of Penetration Experiments to Judge Soil Condition
S. Naemura, M. Tanaka, S. Nishikawa, M. Nakamura, K. Jo and T. Kishishita

S59-S62

A Laboratory Investigation of AE from Coal

S63-S76

Determination of the Initial Stresses on Rock Mass using Acoustic Emission Method
K. Michihiro, K. Hata, H. Yoshioka and T. Fujiwara

S77-S89

U.S. Bureau of Mines Research on the Kaiser Effect for Determining Stress in Rock
Michael J. Friedel and Richard E. Thill

S90-S96

Evaluation of Joint Properties of Anti-washout Underwater Concrete by Acoustic Emission Measurement
Kazuya Miyano, Tatsuo Kita, Yuji Murakami and Takako Inaba

S97-S103

Automated Determination of First P-Wave Arrival and Acoustic Emission Source Location
E. Landis, C. Ouyang and S. P. ShahS104-S109

Application of Acoustic Emission Techniques in the Evaluation of Frost Damage in Mortar
Hisatoshi Shimada and Koji Sakai

Number 3/4
Pages 1-11

B.J.S. Wilkins and G.L. Rigby

Andrew R. Blystra

R. W. Harris, B. R. A. Wood and T. Flynn

Acoustic Emission of the 45HNMFA Structural Steel during Low-Cycle Fatigue
J. Siedlaczek, S. Pilecki and F. Dusek

Pages 13-17

Parameter Estimation in Acoustic Emission Signals
C. E. D'Attellis, L. V. Perez, D. Rubio and J. E. Ruzzante

Pages 19-23

Acoustic Emission Technique at Proof Tests of Nuclear Pressure Vessels in Hungary
Peter Pellionisz and János Geréb

Pages 25-29

Effects of Wave Velocity Change on Magnetomechanical AE in Sintered Iron
Noboru Shinke and Yoshitugu Ohigashi

Pages 31-33

Origin of Acoustic Emission in Naturally Aged Aluminum-Lithium Alloys
F. Zeides and I. Roman

Pages 35-41

Analysis of Artificial Acoustic Emission Waveforms Using a Neural Network
Hironobu Yuki and Kyoji Homma

Pages 43-48

Source Force Waveforms: The Use of a Calibrated Transducer in Obtaining an Accurate Waveform of a
SourceThomas M. Proctor, Jr. and Franklin R. Breckenridge

Pages 49-60

Acoustic Emission Monitoring of a Fatigue Test of an F/A-18 Bulkhead
C. M. Scala, J. F. McCardle and S. J. Bowles

XXX

Pages 61-65

Maximum Curvature Method: A Technique to Estimate Kaiser-Effect Load from Acoustic Emission Data
M. Momayez, F. P. Hassani and H. R. Hardy, Jr.

Pages 67-70

Acoustic Emission Measurements on Bridges

Pages 71-82

Acoustic Emission Monitoring of the Sensitivity of Chemicals to Impact
Timothy G. Crowther, Adrian P. Wade and Nancy Brown

Pages 83-89

Study of Acoustic Emission Generation in Sliding Motion
Musa K. Jouaneh, Richard Lemaster and Frank C. Beall

Pages 91-95

The Role of Acoustic Monitoring as a Diagnostic Tools in Nuclear Reactors
Shahla Keyvan and Ron King

Pages 97-101

Acoustic Emission Produced by Sliding Friction and its Relationship to AE from Machining
S. H. Carpenter, C. R. Heiple, D. L. Armentrout, F. M. Kustas and J. S. Schwartzberg

Pages 103-106

Comments on the Origin of Acoustic Emission in Fatigue Testing of Aluminum Alloys
C. R. Heiple, S. H. Carpenter and D. L. Armentrout

Pages 107-111

Acoustic Emission of Wood during Swelling in Water
Stefan Poliszko, Waldemar Molinski and Jan Raczkowski

Pages 113-116

Characterization of the ASL Parameter

Pages 117-121

Acoustic Emission from Bubbles in a Water Column,

Pages 122-124

In Memoriam, Dr. Raymond W. B. Stephens (1902-1990)

12, 18, 24, 30, 34
42, 66, 90, 96, 102
Page I-i

H. Hick, H. Willer, E. Winter and F. Simacek

J. W. Whittaker
Mark A. Friesel and Jack F. Dawson

Conferences and Symposia
Progress in Acoustic Emission VI, Proceedings of The 11th International AE Symposium
36th Meeting of Acoustic Emission Working GroupIndex to Volume 10

XXXI

INDEX to Journal of Acoustic Emission, 1993-2002
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
I -1

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-5I -2

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

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

Pages 171-176

Improving the Coupling Reproducibility of Piezoelectric Transducers

Pages 177-198

Trends of Recent Acoustic Emission Literature

AE Literature
I -3

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
I -4

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
I -6

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 a
I -6

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
I -7

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
I -8

Page S115
Page S125
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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
I -9

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
I -10

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
I -13

Page 29
Page 34
Page 41
Page 51
Page 61

Page 68
Page 75
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Page 189

A. Tsimogiannis, B. Georgali, and A. Anastassopulos (Envirocoustics S.A. - Greece)
Concrete Crossbeam Diagnostic by acoustic emission method Z. Weber, P. Svadbik,
M. Korenska and L. Pazdera
Waveform based analysis techniques for the reliable acoustic emission testing ofcomposite structures
M. Surgeon, C. Buelens, M. Wevers, and P. De Meester
Optical fibres for in situ monitoring the damage development in composites and therelation with acoustic emission measurements
M. Wevers, L. Rippert, and S. Van Huffel
Lamb-wave source location of impact on anisotropic plates
H. Yamada, Y Mizutani, H Nishino, M. Takemoto and K. Ono
Characterisation of the damage and fracture mechanisms in Ti3SiC2 using acoustic
emissionP. Finkel, R.K. Miller, M.A. Friesel, R.D. Finlayson, P.T. Cole, M.W. Barsoum and
T. El-Raghy
Evaluation of martensitic transformation dynamics of Cu-Al-Ni shape memory alloy single
crystals by acoustic emission method K. Yoshida, S. Kihara, and K. Sakamaki
The identification of basic fatigue parameters on electroresonance pulsator with help of
acoustic emission technology P. Mazal, and J. Petras
The phase of contact damage and its description by help of acoustic emission
J. Dvoracek, J. Pazdera, and L. Petras (Brno University of Technology - Czech Republic)
Acoustic emission monitoring of delayed hydride cracking in Zirconium
A. Barron and C. Rowland
Examination of plate valve behaviour in a small reciprocating compressor using acoustic
emission
J.D. Gill, R.D. Douglas, Y.S. Neo, R.L. Reuben and J.A. SteelA new method of acoustic emission source location in Pipes using cylindrical guided waves
H. Nishino, F. Uchida, S. Takashina , M. Takemoto and K. Ono
Acoustic emission method for pressure vessel diagnostics at a refinery
B.S. Kabanov, V.P. Gomera, V.L. Sokolov, and A.A. Okhotnikov
Optimisation of acquisition parameters for acoustic emission measurements on smallpressure vessels F. Rauscher
Monitoring of weld's defects evolution submit to static and dynamic loading thanks tothe acoustic emission method C. Hervé, R. Pensec, and A. Laksimi,
Acoustic emission due to cyclic pressurisation of vessels with partially penetrated
longitudinal seams M. Bayray
Inspection of LPG vessels with AE examination
P. Tscheliesnig, and G. Schauritsch
Inspection of pressure vessels used in refrigeration and air conditioning systems
A. Skraber, F. Zhang, M. Cherfaoui, and L. Legin
The new Russian standards in the field of acoustic emission
V.I. Ivanov, and L.E. Vlasov
Using acoustic emission to monitor metal dusting
F. Ferrer, E. Andres, J. Goudiakas, and C. Brun (Elf Atochem - France)
Use of acoustic emission to detect localised corrosion philosophy of industrial use,
illustrated with real examples A. Proust, and J. C. Lenain
Inspection of flat bottomed storage tanks by acoustical methods. Classification of
corrosion related signals
P. Tscheliesnig, G. Lackner, M. Gori, H. Vallen and B. Herrmann
Screening of tank bottom corrosion with a single point AE detector: AE-Simple
P.J. Van De Loo, and D.A. Kronemeijer
Case histories from ten years of testing storage tank floors using acoustic emission
S.N. Gautrey, P.T. Cole, and H.J. Schoorlemmer,
Acoustic emission detection of damage in reinforced concrete conduit H.W. Shen,
S. Iyer, M.A. Friesel, F. Mostert, R.D. Finlayson, R.K. Miller, M.F. Carlos
I -13

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Page 307

and S. Vahaviolos,
Comparison of artificial acoustic emission sources as calibration sources for tool wear
monitoring in single-point machining A. Prateepasen, Y.H.J. Au and B.E. Jones
Development of an equipment to monitoring and control the quality of resistancewelding (CRAFT Project) J. Catty,
A study of small HSDI Diesel engine fuel injection equipment faults using acoustic
emission J.D. Gill, R.L. Reuben, J.A. Steel, M.W. Scaife and J. Asquith
Unsupervised pattern recognition of acoustic emission from full scale testing of a windturbine blade D. Kouroussis, A. Anastassopoulos, P. Vionis and V. Kolovos
Acoustic emission proof testing of insulated aerial man lift devices
A. Anastassopoulos, A. Tsimogianis and D. Kourousis
BOXMAP - Non-invasive detection of cracks in steel box girders
J.R. Watson, K.M. Holford, A.W. Davies and P.T. Cole,
Monitoring failure mechanisms in CFRP orthopaedic implants during fatigue testing
A. Taylor, S. Gross, C. Rowland and P. Gregson
Continuous monitoring of rock failure by a remote AE system
T. Shiotani, S. Yuyama, M. Carlos and S. Vahaviolos
New AE signal conditioner for industrial use H. Vallen, J. Forker and J. von Stebut,
New software tools for the AE-practitioner
H. Vallen and J. Vallen,
Improved source location methods for pressure vessels V. Godinez, S. Vahaviolos,
R.D. Finlayson, R.K. Miller, and M.F. Carlos
Neural network localization of noisy AE events in dispersive media
M. Blahacek, Z. Prevorovsky, and J. Krofta
Dynamics and damage assessment in impacted cross-ply CFRP plate utilizing thewavaform simulation of Lamb wave acoustic emission
Y. Mizutani, H. Nishino, M. Takemoto and K. Ono
Acoustic emission detection during stress corrosion cracking at elevated pressure andtemperature R. Van Nieuwenhove, and R.W. Bosch
Detection of pitting corrosion of aluminiurn alloys by acoustic emission technique
H. Idrissi, J. Derenne, and H. Mazille
Reliability of acoustic emission technique to assess corrosion of reinforced concrete
H. Idrissi, and A. Limam

AEWG43 Presentation
Page S1Page S7

Theoretical Treatment of AE in Massive Solid….M. Ohtsu
Diagnosis of Concrete Structures by AE….M. Ohtsu

EWGAE 2000 (EWGAE.pdf)
Page i – v
SOMMAIRE -- Content of EWGAE 2000 Proceedings
Page vi
Page vii

AVANT-PROPOS
Mohammed CHERFAOUI, Christel RIGAULT
Présentation – EWGAE
Authors Index (18Auindx.pdf)
Contents of Volume 18, 2000 (18Conts.pdf)

Page I-1 – I-2
Page I-3 – I-6

e-mail Addresses of Authors (e-mail.pdf)
EWGAE 2000 Participant List (Senlis.pdf)

I -13

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
I -14

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

I -15

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 Mier153

I - 16

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. RaabI - 17274

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

I - 18

Authors Index
Satoshi Abe,
M. Akiyama
Bernhard Allemann
A.F. Almeida
Masashi Amaya,
A. A. Anastassopoulos,
E. Andres,
D. Armentrout
Baxter H. Armstrong,
M. Asano,
J. Asquith,
Y.H.J. Au,

19-04516-S150
14-11915-S107, 15-S108
13-S3513-011, 18-021, 18-217, 18-224, 20-229
18-15515-43, 16-S10
12-117
19-13418-211
18-196

R.H. Bannister
I. Baran,
A. Barron,
M.W. Barsoum,
B. L. Baskin,
M. Bayray,Frank C. Beall,
Alan G. Beattie
S. Béland,
S. H. Benabdallah
Avraham Berkovits
C. C. Berndt
J.-M. Berthelot,
Yves H. Berthelot,
D. K. Bhattacharya,
Thomas Bidlingmaier
Jacek M. Biernacki,
Jan Bisschop
Michal Blahácek
M. J. Blanch
T. Boczar
Jurgen Bohse
Yasuyuki Bokoi
R. J. Boness
R.W. Bosch,P. Bowen,
John Brandon
C. Brun,A.J. Brunner
G. Budenkov
R. Budzier
C. Buelens,
E. Bulatova

19-20917-S37
18-87
18-61
12-14918-131, 19-241, 20-188
12-i (1/2), 12-55
15-63, 15-S111
12-45
15-S117
11-85
15-S117
11-11
12-27
11-43
14-S47
12-55
20-153
18-279, 20-163, 20-274
20-22917-S7
15-S108, 16-S233, 16-S343, 19-001
16-S196
15-S117
18-29313-S0817-49
18-15515-S108
17-S13, 17-S51
20-172
18-34
17-S13I - 19

CARP Aerospace/Advanced Composites Subcommittee
11-C1
M. Carlos
15-S104, 15-S107, 15-S109, 18-189, 18-248, 18-272
Steve H. Carpenter,
11-5, 12-141, 13-S01, 15-43, 16-S10
Damian Carter
17-49
J. Catty,
18-205
M. Cerny
17-S20J. Cerv
20-025
Jihua Chen
Chang-Sheng Chien,
Y. S. Chin
Min-Hwa Chung,T. Chelladurai,
M. Cherfaoui
Akiyoshi Chichibu,
Milan Chlada
Frantisek Chmelík
Hideo Cho
N. S. Choi
A-A. R. Choudhury
M.A. Clark
P. ColeP.T. Cole,David B. Cook
R. Daniel Costley, Jr.,
J. Crha19-001
15-S118
11-71
13-1
12-111
11-1, 18-144
11-S47, 12-S1
17-S57, 20-134
17-S29, 20-108
16-S115
16-S324
16-S125
15-S107
15-S109
18-61, 18-180, 18-232, 19-191
17-83
12-27
17-S45

A.W. Davies,
S. De Bondt,
P. De Meester
L. Delaey
J. Derenne,
A. Deruyttere
John J. Ditri,
Jason W.P. Dong
Henrique L. M. dos Reis,
R.D. Douglas,
Karyn S. Downs,
T.F. Drouillard,
Qingru Duan
R. Dubiel,
H.L. Dunegan
C. Divaker Durairaj
A. G. Dutton
J. Dvoracek,
Yuris A. Dzenis

18-232
11-95
13-79, 15-S105, 18-34
11-95
18-299
11-95
12-23
16-S1
12-15
18-96
13-31, 13-45, 13-56, 14-S61, 15-S109, 16-iv, 16-S333
11-53, 12-71, 12-79, 13-42, 14-1, 16-v
16-S243
18-15
15-53, 15-S106, 16-v
17-15
20-22918-81
15-S112, 20-016, 20-099

J. Eberlein
Gernot Eilers
J. Eisenblätter
T. El-Raghy,

20-172
19-100
15-S119, 16-S85, 19-100, 19-153
18-61
I - 20T. Ely
Manabu Enoki,
W. T. Evans

16-S1013-S29, 15-S90, 15-S120, 16-S269
11-71

A. Fahr,
Daining Fang
Ahmed M. Farahat
V. P. Fedorov
G. Fernando
F. Ferrer,
Chantal Filion
P. Finkel,
R.D. Finlayson,
P. Fleischmann
J. Forker,
D. S. Forsyth
H. Frackiewicz,
M.A. Friesel,
L. Froyen,
Taisaku Fujioka
Tetsuro Fujiwara
T. Fuketa,
Roy D. Fultineer, Jr.
Masami Fushitani,

12-39, 15-S122
11-85
11-S3720-21820-22918-15516-S186
18-61
18-61, 18-189, 18-272
19-22918-25815-S122
12-14918-61, 18-189
11-95
15-S31
11-S65, 15-S40, 15-S113
11-21
15-S10313-S42

M. Gakumazawa
John Gary,

16-S150
12-157, 14-103, 15-S115, 16-S251, 17-37, 17-97, 19-258,
20-039, 20-062
14-11918-180, 19-191
15-S105
17-S37, 19-285, 20-083
12-171
18-21
18-96, 18-211
18-272
17-S37, 19-285, 20-083
16-S186
18-111, 20-218
13-S21
12-117
18-167
13-S01, 14-i (3/4), 17-29
18-155
13-97
11-79
18-23918-23914-S7414-S19, 16-S222

Ludwig Gauckler
Stephen N. Gautrey
Maochen Ge
P. Gebski
D. Geisse,
B. Georgali,
J.D. Gill,
V. Godinez,
L. Golaski
Douglas E. Goldsack
V.P. Gomera,
Javier Gomez,
Carlos M. Valdes-Gonzalez,
M. Gori,Michael R. Gorman
J. Goudiakas,
Per A Gradin,
Igor Grabec
P. Gregson,
S. Gross,
C. U. Grosse
Dawei GuoI - 21

Yanfeng Guo

16-S317

P. Hähner
20-265
Shigenori Hamada
15-S40M. A. Hamstad
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
Mineyuki Hanano
11-S75L. D. Hall,
19-209V. Hänel
14-115J. J. Hanley
11-27
H.R. Hardy, Jr.
15-S105, 15-S118, 16-S277
Robert W. Harris
15-S113, 17-121
H. Nayeb-Hashemi,
12-1
Toshiyuki Hashida,13-S68
Hajime Hatano
15-S115
F. Havlícek
17-S45
Yoshie Hayashi,
19-035
Yasuhisa Hayashi
14-69, 15-S108
Jianmei He
20-194
Edmund G. Henneke II
14-53
B. Herrmann,
18-167
C. Hervé,18-125
Stefan Heusermann
16-S85
Kazuo Hiekata
19-045
Y. Higo16-S150, 16-S196, 19-085
R. Hill
16-S125
Koji Hisamatsu
11-S1
S.V. Hoa
11-65
Karen Holford
17-49, 18-232
Ki-Jung Hong,
13-S61
K. Horikawa,
19-022
J.R. Houghton
14-61
Nelson N. Hsu,
13-23
J. Hu
16-S150
J. H. Huh
15-S80
Donald H. Humes
17-29
Wolfgang Hundt
14-119
U.-D. Hünicke
20-172
Y. Ichimura
Makoto Ichinose
H. Idrissi,
Hidehiro Inaba,
T. Inamura,
Yoshiki Inoue
Akichika Ishibashi
C. Ishiyama
Ken-Ichi Itakura,
V.I. Ivanov,Y. Iwata

19-14220-001
18-299, 18-307
19-19619-085
11-S89
11-S65, 15-S40, 15-S113
16-S150, 16-S196
13-S54, 13-S75, 19-109
18-14416-S142I - 22

S. Iyer,
Takeshi Izuta,

18-18913-S42

Laurence J. Jacobs,
T. Jayakumar,
Hee-Don Jeong,
Kyung-Young Jhang
C.Y. Jian,
Malcolm J. S. Johnston,
B.E. Jones,
L. E. Jones
Young-Chan Joo
P. A. Joosse,

12-27
11-43
13-S61
16-S261
13-S68
12-117
18-19620-22915-S1, 16-S212
20-229B.S. Kabanov,
Kazuro Kageyama,
Karl-Ulrich Kainer
K. Kajiyama
T. Kamada,Peter Kamlot
Yasuhiro Kanemoto
Elijah Kannatey-Asibu, Jr.
Chiaki Kato
K. Katsuyama
Kunihisa Katsuyama
Harold E. Kautz,
Yoshiaki Kawaguchi,
Shahla Keyvan
A. Wahab Khair
E. W. Khokhlova,
Jens Kiehn
S. Kihara,
Tadashi Kikuchi
Cindi Kilkenny
Byoung-Geuk Kim
Byung-Nam Kim
Dal-Jung Kim
Kyung-Woong Kim,
Sang-Hyo Kim
Isao Kimpara,
Tetsuo Kinjo
Teruo Kishi
Takahiro Kishishita
P. Kisnomo
Kiyoshi Kiuchi
W. Knabl
Markku Knuuttila
Miroslav Koberna
Takao Koide,

18-111, 20-218
13-S89, 19-045
20-10819-022
19-13416-S85
15-S40
15-S118
19-053
11-S1911-S2712-65
12-127
14-97, 15-79
15-S105, 16-S53
12-14920-108
18-68
11-S47
16-S186
15-S120
12-S24, 15-S90, 16-S269
16-S261
13-1
15-S11
13-S89, 19-045
15-19, 14-69
12-127, 13-S29, 15-S90, 15-S120, 16-S269
11-S47
15-33
19-05320-257
19-162
11-61
13-S47I - 23

Masami Koike,
B. Koktavy,
J. G. Kolaxis
V. Kolovos,
Hidemichi Komura
Ja-Ho Koo
T. M. Kooistra
Atsushi Korenaga,
M. Korenska,
I. Kosiková
T. Kossivas
V. Kostopoulos
A. Kotolomov
D. A. Kouroussis,
Torsten Krietsch
Vladimir Krivobodrov,
J. Krofta,
J. Królikowski,
D.A. Kronemeijer,
Joseph Krynicki
Takafumi Kubo,
M. Kumosa
M. Kunieda
Yasufumi Kusano,
Oh-Yang Kwon
H. Kwun,19-202
17-S100
17-69
18-217
19-19615-S90, 16-S269
20-23819-196
18-29
17-S100
20-22920-265
17-S51
18-217, 18-224, 20-229
16-S233, 16-S343
13-87
18-279, 20-274
12-14918-17415-S116
13-S42
16-S1019-134
13-S75
13-1, 13-S83, 15-S1, 15-S19, 16-S212
11-27

G. Lackner,
A. Laksimi,
M. Landa
E. Landis
Terence G. Langdon
A. Lavrov
Marcel F. LeachJames D. Leaird,
R. D. LeblancChong Soo Lee,
Joon-Hyun Lee,K. A. LeeP. Y. Lee
Sang-Ho Lee
Seung-Hwan Lee
Sekyung Lee
Seung-Seok Lee
Weon-Heum Lee
L. Legin
D. J. Lekou
A. M. Leksowskij,
Richard L. Lemaster,

18-167, 20-179
18-125
17-S57, 20-025, 20-163
15-S104
20-10820-292
11-19, 16-S186
12-11715-S122
13-S61, 15-S80
13-S8315-S80
15-S117
15-S11
15-S1915-S120
15-S11
16-S261
18-14420-22912-14912-55
I - 24

J. C. Lenain,
Luidmila Lezvinsky
Bang xian Li
Z. W. Li
S. Lim,Jae-Kyoo Lim
R. Lima
A. Limam,
C. K. LinDan Lindahl
J. Liöka
Shifeng Liu
T. LiuXi-qiang Liu
David A. Lockner
T. Lokajícek
M. I. López Pumarega
E. Lowenhar
M.G. Lozev
P. Lukác
D. Lupascu

18-161
16-S35
16-S243
19-11819-13416-S309
15-S11718-30715-S117
19-162
17-S108
15-S124
15-S117
15-S123
14-S8817-S100
17-61
15-S109
15-S104
17-S29, 20-108
17-S78A. Machová
Morihiko Maeda,
Ian Main,
M. A. Majeed,
Z. J. Majewska,
Zofia Majewska
S.A. Majewski,
Arup Maji
Ajit Mal
Gerd Manthei
H. Marcak,
K. Marsh
A. Maslouhi,
S. Mata
O. Matal
Kristian Mathis
Kimitoshi Matsuyama
P. Mazal
H. Mazille,
D. MbaJ.D. McColskey
P. G. Meredith,Philip Meredith,R. (K.) Miller
O. Minemura
J. Mitchell

20-02519-202
13-S21
12-10712-S7
16-S105, 18-1
12-S7
15-S116
14-S19, 16-S222
15-S119, 16-S85, 19-100
12-S7
15-S104
12-45, 15-S122, 16-S299
17-23
17-S6520-108
11-S65, 15-S40
17-S2, 17-S20, 17-S70, 18-75
18-299, 19-229
19-20915-1, 15-S108, 15-S111, 15-S119
12-S12
13-S21
15-S104, 15-S107, 15-S108, 18-61, 18-189, 18-272
16-S7515-S109

I - 25

S. Miwa,
Makoto Miwa,
Kouitsu Miyachika,
Souichi Mizutani
Yoshihiro Mizutani
K. Mogi
Florian Moll
Keiichi Monma
Bryan C. Morgan
L. Morgan
Y. Mori
Yoshiki Morino
Hiroyuki Morishima
Zofia Mortimer
W. J. Moś cicki,
F. Mostert
M. C. Mumwam
K. Murayama
Boris Muravin
Gregory Muravin
S. A. F. Murrell,
C. R. L. Murthy,

19-142
13-S42
13-S47
20-19416-S115, 18-51, 18-286, 19-035, 20-194
16-S4520-10815-S113
15-69, 15-S103
16-S125
16-S45, 20-248
20-19420-14516-S105, 18-1
12-S7
18-18916-S65
16-S75
16-S35
16-S35
12-S1212-107

Koji Nagano,
S. Nagano,
Jyothi Nagaraj
Kenji Nagashima,
M. M. Nagl,
Hidehumi Naito,
Yasuhiro Nakanishi
Hiroyasu Nakasa
Eisaku Nakashima,
Noritaka Nakaso,
H. Nayeb-Hashemi,
O. Nedzvetskaya
Y.S. Neo,Eng T. Ng
Shigeto Nishimoto
Hiroaki Niitsuma,
V. N. Nikolaidis,
Minoru Nishida,
H. Nishino,
Osamu Nishizawa
M. Noguchi
C. Nojiri
Richard Nordstrom
Václav Novák
J. Nuffer
Staffan Nyström,

11-S1, 13-S54, 13-S75
19-085
14-97
19-011
11-71
12-S2420-145
16-S2519-202
13-23
12-1, 14-85, 15-33
17-S13, 17-S51
18-96
19-27519-20211-i(4), 11-S1
17-69, 20-229
12-S1818-51, 18-102, 18-286, 19-011, 19-035, 19-075
11-S27
11-21
16-S150
15-S108, 16-S204
20-16317-S7813-97
I - 26

Y. ObataSatoshi Oda,
A. O’Gallagher

16-S45, 20-248
13-S47
14-103, 15-S115, 16-S251, 17-37, 17-97, 19-258, 20-039,
20-062
Eu Seok Oh
16-S277
Tatsuma Ohnishi
19-109Isamu Ohsawa,
13-S89, 19-045
Shiro Ohta
15-S40M. Ohtsu
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
Takahisa Okamoto,
13-S14
T. Okamoto
16-S75
A.A. Okhotnikov,
18-111, 20-218
S. Okumura
11-21
L. Okushima
17-15
Kanji Ono,
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
P. Ouellette
11-65
Philip Park
Young-Jin ParkJ. Pavelka
J. Pazdera,
L. Pazdera,
R. Pensec,
S. Peteves
J. Petras,
L. Petras,
J. Petrasek
Christian Pfleiderer,
T. P. Philippidis,
Rodney G. Pickard
Aleksander Pilarski,
S. Pilecki,
J. Pininska,
R. Piotrkowski
T. Pocheco
A.A. Pollock
Elizabeth L. Porter
A. Prateepasen,
D. Prevorovsky
Zdenek Prevorovsky
David W. Prine
William H. Prosser
A. ProustJose Pujol
M. I. López Pumarega

15-S11
15-S11
17-S100
18-81
18-29
18-12520-265
18-75
18-81
17-S8312-141
13-11, 17-69, 20-229
15-79
12-23
12-149
18-8
17-61
14-85
15-S107, 15-S108
17-121
18-19620-285
17-S57, 18-279, 20-134, 20-274, 20-285
15-S106
14-S1, 15-S106, 15-S115, 17-29, 17-37
17-S83, 18-161, 19-229, 20-229
17-111
17-61
I - 27

K. K. Purushothaman,

12-111Gang Qi
Jie Qian

17-111, 19-275
20-016, 20-099

M. RaabAmani Raad
Baldev Raj,
S. P. Mallikarjun Rao
F. Rauscher
Henrique L. M. dos Reis,
F. Rehsteiner
H. W. Reinhardt
R.L. Reuben,
L.E. Rewerts
J. Richter
L. Rippert,
Steffen Ritter
J. L. Robert
R.A. Roberts
J. RödelP. Rödhammer
P. Rodriguez
K. Rokugo
Joseph L. Rose,Z. Rosecky
J. N. Rossettos,
R. Rothea
C. Rowland,
C. Roy,Gottfried A. Rubin
J. E. Ruzzante

20-27420-300
11-43
11-101
17-S92, 18-118, 20-188
11-107, 12-15, 17-83
14-11914-S7418-96, 18-211
15-S107
17-S70
18-41
14-S47
11-11
15-S107
17-S7820-257
11-43
19-134
12-23
20-02512-1
19-22918-87, 18-239
12-45
11-19
17-61

H. Saadaoui,
Wolfgang Sachse
Masahiro Saito,
Naoya Saito
Norio Sakaino
K. Sakamaki
N. Sakata
Yasunori Sakata
Takumi Sakuma
H. M. Sallam
Peter Sammonds,
A. S. Sankaranarayanan,
N. Saniei
Hiroaki Sasaki
S. SaseL. Hanumantha Sastry

12-45
11-79
13-S6815-19, 16-S289
15-S50
16-S134, 18-68
16-S75
15-S70
16-S196
14-85
13-S21
12-111
15-33
16-S25
17-15
11-101I - 28

Akihiro Sato,
Kazuhiko Sato,
K. SatoKeiichi Sato,
M.W. Scaife,
G. Schauritsch,
R. Schelling
H.J. Schoorlemmer,
M. Schulz
G. P. Sendeckyj
Hirofumi Sentoku
U. Senturk
M. SetoMasahiro Seto,
V.V. Shemyakin,
Gongtian Shen
H.W. Shen
Ping Shen
Nanling Shi
Mitsuhiro Shigeishi,
Shigeo Shimizu,
Tatsuya Shimizu,
Takayuki Shimoda
J. Sikula
T. Simo
Masayuki Shimojo
Noboru Shinke
Takuo Shinomiya
Tomoki Shiotani
Ménad Sidahmed
Petr Sittner
A. Skraber,
V.L. Sokolov,
Jun-Hee SongM. Ben Souda
Th. Spies
J.A. Steel,H. Strauven,
S.A. Strizkov
C. E. Stuart,
V. Suba
Katsuhiro Sugawara,
M. Surgeon
Hiroaki Suzuki
Toshio Suzuki,
P. Svadbik,
V. Svoboda

19-202
13-S54, 13-S75, 19-109
16-S45
13-S42
18-211
18-13820-23818-180, 20-238
20-172
11-33
16-S1915-S117
11-S1911-S27
19-172
16-S243
15-S105, 18-189
15-S123
16-S317
11-S57, 13-S14, 15-S50, 15-S60, 16-S65
19-196
13-S6820-19417-S100
17-S6516-S196, 19-085
16-S134
20-145
15-S50, 16-S95, 18-248, 19-118, 19-142, 20-145, 20-153
20-30020-16318-144
18-111, 20-218
16-S309
11-11
15-S119, 19-100, 19-153
18-96, 18-211
13-79
19-172
12-S12
17-S2013-S5415-S105, 18-34
11-117, 14-35, 14-69, 15-19, 15-S108, 16-S178, 16-S289
13-S8918-29
17-S2, 17-S83

A.N. Tafuri

15-S104
I - 29

V.L. Tahiri
16-S299
H. Takagi
16-S142
Hideaki Takahashi,
13-S68K. Takahashi
16-S324
Atsushi Takashima,
19-109Kazuki Takashima,
12-S18, 13-S08, 16-S150, 19-085
S. Takashina,
18-102
Nobuo Takeda,
12-127
Mikio Takemoto
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
Masanori Takuma
16-S134
Okiharu Tamura
16-S178
S. Tanary,
12-39
N. Tandon
17-23
O.B. Tarutin
15-S120
Masayuki Tateno,
11-S75
A. Taylor,18-239C. M. Teller
11-27
Lawrence W. Teufel
15-S124
W. Thelen
14-115
Peter G. Thwaite
14-vi (3/4)
Anil Tiwari
14-53
S. Tomecka-Suchoń ,12-S7
Yuichi Tomoda
15-S31
H. Tonda,
13-S08
H. Traxler
20-257
D. Tsamtsakis,
13-79
P. Tscheliesnig
17-S108, 18-138, 18-167, 20-129, 20-179
A. Tsimogiannis,18-21, 18-224
Nobuyuki Tsuji
15-S60
Yusuke Tsukahara,
13-23
F. Uchida,
Atsushi Uchiyama,
Toshiyuki Uenoya,
Takateru Umeda
E. Uria,

18-102, 19-075
13-S42
13-S95, 15-S112
20-248
13-79

S.J. Vahaviolos
Carlos M. Valdes-Gonzalez,
H. Vallen,
J. Vallen,
P.J. Van De Loo,
D. R. V. Van Delft
S. Van Huffel,
J. G. M. Van Mier
R. Van Nieuwenhove,
D. Varchon
Alex Vary,

15-S109, 18-189, 18-248, 18-272, 19-172
12-117
18-167, 18-258, 18-265
18-265
18-174, 20-238
20-22918-41
20-15318-29320-28512-i (1/2), 12-71, 12-79, 14-53
I - 30Vasisht Venkatesh
E. Verbrugghe
A. Vervoort
A. Vinogradov
A. Yu. Vinogradov
P. Vionis
L.E. Vlasov,Aaron C. Voegele
J. von Stebut,
Y. Vougiouklakis

14-61
11-1
20-292
17-1
16-S158
18-217, 20-229
18-150
17-83
18-25820-265

Shuichi Wakayama,
Teruyuki Waki,
Akimasa Waku,
Y. Wang,
Alexander Wanner
Kris A. Warmann
G. Washer
Hiroshi Watanabe
Naoaki Watanabe,
J.R. Watson,
D.J. Watts
Z. Weber,
Qiang Wei
B. Weiler
M. Wevers,
M. Winkelmans
A. Wolfert
Brian R. A. Wood
Min Wu12-S24, 16-S170
12-S1
12-S1
19-02214-i, 14-v, 14-vi (3/4), 14-S47
11-10715-S104
20-001
13-S42
18-232
15-S104, 19-172
18-29
11-S75
14-S7413-79, 15-S105, 18-34, 18-41, 20-206, 20-292
20-206
20-23815-S113, 17-121
11-5

H. Yamada,
Yoshiaki Yamade,Koji Yamamoto
Akihiko Yamashita,
T. Yamauchi
Tinghu Yan
W. YanMasa-aki Yanaka,
Xuanhui Yang
Daisuke Yasuoka
J.J. Yezzi, Jr.
Dong-Jin Yoon,
K. YoshidaToshikatsu Yoshiara
Hiroshi Yoshino,
Takeo Yoshioka,
Y. Youssef,
Kunihiro Yuno

18-51
12-127
19-196
13-S89
11-21
17-49
15-S109
13-23
15-S123
15-S60
15-S104
13-S83, 15-S11
16-S142, 18-68, 19-022
19-202
12-S1
19-196
12-39
11-S89I - 31

Shigenori Yuyama,

13-S14, 15-S107, 16-S75, 18-248, 19-118, 19-184

J. Zaloudek
Kornelija Zgonc,
Fan Zhang
Zhizhen Zheng
Zuming Zhu
J. Zietek,
Steve Ziola
B. Zogala,17-S65
11-79
18-144, 20-300
15-S123
16-S317
12-S7
14-S12
18-15

I - 32JOURNAL OF ACOUSTIC EMISSION
Volume 21 - 22; 2003 - 2004
VOLUME 21, 2003
21-001

Identifying Acoustic Emission Sources in Aging Bridge Steel
Takao Kobayashi and Donald A. Shockey

1

21-014

Analysis of Source Location Algorithms, Part I: Overview and Non-Iterative Methods
Maochen Ge
14

21-029Analysis of Source Location Algorithms, Part II: Iterative Methods
Maochen Ge29

21-052

Wavelet Transform Signal Processing to Distinguish Different Acoustic Emission
SourcesK. S. Downs, M. A. Hamstad and A. O’Gallagher
52

21-070Practical Aspects of Acoustic Emission Source Location by a Wavelet Transform
M. A. Hamstad, K. S. Downs and A. O’Gallagher
70

21-A01

Appendices: Practical Aspects of Acoustic Emission Source Location by a Wavelet
Transform
M. A. Hamstad, K. S. Downs and A. O’Gallagher
A1

21-095

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 inSimulated Body Environment
Shuichi Wakayama, Teppei Kawakami, Satoshi Kobayashi,
Mamoru Aizawa and Akira Nozue

149

21-11221-12021-13121-14221-149I-1

21-157

21-16621-176

Corrosion Monitoring in Reinforced Concrete by Acoustic Emission
Masayasu Ohtsu and Yuichi Tomoda

157

Evaluation of Bond Behavior of Reinforcing Bars in Concrete Structuresby 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

21-187

Acoustic Emission Evaluation of Corrosion Damages in Buried Pipes of Refinery
S. Yuyama and T. Nishida
187

21-197

New 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

21-206

Acoustic Emission Caused by Environmental Embrittlement of an Al-Mg-Si Alloy
Keitaro Horikawa, Kenichi Yoshida, A. Ohmori and Kiyoshi Sakamaki
206

21-213

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

21-223

Effect of Pinhole Shape with Divergent Exit on AE Characteristics during Gas Leak
K. Yoshida, Y. Akematsu, K. Sakamaki and K. Horikawa
223

21-230Operation Monitoring of Roll Cover by Acoustic Emission
Juha Miettinen and Pekka Salmenperä

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

230
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

THE KAISER-EFFECT AND ITS SCIENTIFIC BACKGROUND
HANS MARIA TENSIS1MODAL-BASED IDENTIFICATION OF ACOUSTIC EMISSION SOURCES IN THE
PRESENCE OF ELECTRONIC NOISE
M. A. HAMSTAD and A. O’GALLAGHER
1

I-2

22-A01*

Appendix A: DETAILS OF THE APPLICATION OF THE SOURCE IDENTIFICATIONSCHEME
M. A. HAMSTAD and A. O’GALLAGHER
A1

22-022

EXPERIENCE WITH ACOUSTIC EMISSION MONITORING OF NEW VESSELS DURING
INITIAL PROOF TESTPHILLIP COLE and STEPHEN GAUTREY
2222-030QUANTITATIVE DAMAGE ESTIMATION OF CONCRETE CORE BASED ON AE RATEPROCESS ANALYSIS
MASAYASU OHTSU and TETSUYA SUZUKI
30

22-039DAMAGE ASSESSMENT IN DETERIORATED RAILWAY SUB-STRUCTURES USING AETECHNIQUETOMOKI SHIOTANI, YASUHIRO NAKANISHI, XIU LUO and HIROSHI HAYA
39

22-049DEFECT DETECTION BY ACOUSTIC EMISSION EXAMINATION OF METALLICPRESSURE VESSELSFRANZ RAUSCHER
49

22-059EVALUATION OF ACOUSTIC EMISSION SIGNALS DURING MONITORING OF THICKWALL VESSELS OPERATING AT ELEVATED TEMPERATURES.
ATHANASIOS ANASTASOPOULOS AND APOSTOLOS TSIMOGIANNIS59

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
71

22-077

DETECTION OF INCIPIENT CAVITATION AND BEST EFFICIENCY POINT IN A 2.2MW
CENTRIFUGAL PUMP USING ACOUSTIC EMISSION
L. ALFAYEZ and D. MBA
77

22-083INVESTIGATION OF FRACTURE PROCESSES USING MOMENT TENSOR INVERSIONTECHNIQUE
F. FINCK, C. U. GROSSE and H.-W. REINHARDT
83

22-091

AE KAISER EFFECT AND ELECTROMAGNETIC EMISSION IN THE DEFORMATION OF
ROCK SAMPLEYASUHIKO MORI, YOSHIHIKO OBATA, JAN PAVELKA, JOSEF SIKULA and THOMAS
LOKAJICEK91

22-102

ACOUSTIC EMISSIONS FROM TRANSPIRING PLANTS– NEW RESULTS AND
CONCLUSIONSRALF LASCHIMKE, MARIA BURGER and HARTMUT VALLEN

102

ACOUSTIC DETECTION OF CAVITATION EVENTS IN WATER CONDUCTINGELEMENTS OF NORWAY SPRUCE SAPWOOD
SABINE ROSNER

110

22-110I-3

22-119

AE MONITORING FROM CVD-DIAMOND FILM SUBJECTED TO MICROINDENTATION AND PULSE LASER SPALLATION
R. IKEDA, H. CHO, M. TAKEMOTO and KANJI ONO

119

22-127COMPOSITES FROM PIEZOELECTRIC FIBERS AS SENSORS AND EMITTERS FOR
ACOUSTIC APPLICATIONSANDREAS J. BRUNNER, MICHEL BARBEZAT, PETER FLÜELER and CHRISTIAN
HUBER
127

22-138PROCESSING OF AE SIGNALS IN DISPERSIVE MEDIAMICHAL BLAHACEK, ZDENEK PREVOROVSKY, and MICHAL LANDA

138

BASIC PRINCIPLES OF ACOUSTIC EMISSION TOMOGRAPHYFRANK SCHUBERT

147

22-14722-159ACOUSTIC EMISSION BEHAVIOR OF MARTENSITIC TRANSFORMATION DURING
DEFORMATION OF Cu-Al-Ni SHAPE-MEMORY ALLOY SINGLE CRYSTALSKENICHI YOSHIDA, KOTARO HANABUSA and TAKUO NAGAMACHI
159

22-166

ACOUSTIC EMISSION MONITORING OF CONCRETE HINGE JOINT MODELS
K. M. HOLFORD, R. PULLIN and R. J. LARK

166

CHARACTERIZATION OF ACOUSTIC EMISSION SOURCES IN A ROCK SALTSPECIMEN UNDER TRIAXIAL LOADGERD MANTHEI

173

22-17322-190TESTING OF DIAMOND-LIKE CARBON COATINGS UNDER SLIP-ROLLING FRICTIONMONITORED BY ACOUSTIC EMISSIONMANUEL LÖHR
190

22-201FIELD TESTING OF FLAT BOTTOMED STORAGE TANKS WITH ACOUSTIC EMISSION– A REVIEW ON THE GAINED EXPERIENCEGERALD LACKNER and PETER TSCHELIESNIG
201

22-208

ACOUSTIC EMISSION EXAMINATION OF POLYMER-MATRIX COMPOSITESJÜRGEN BOHSE

208

ACOUSTIC EMISSION FROM RUST IN STRESS CORROSION CRACKINGHIDEO CHO and MIKIO TAKEMOTO

224

ACOUSTIC EMISSION MEASUREMENT SYSTEM FOR THE ORTHOPEDICDIAGNOSTICS OF THE HUMAN FEMUR AND KNEE JOINT
R.P. FRANKE, P. DÖRNER, H.-J. SCHWALBE and B. ZIEGLER

236

RODS AND TUBES AS AE WAVEGUIDES
KANJI ONO and HIDEO CHO

243

22-22422-23622-24322-253

ACOUSTIC EMISSION FOR ON-LINE MONITORING OF DAMAGE IN VARIOUS
APPLICATION FIELDSMARTINE WEVERS, GERT VAN DIJCK, WENDY DESADELEER, MARK
WINKELMANS and KOEN VAN DEN ABEELE
253

I-4

22-264

THE EFFECT OF WAVEGUIDE MATERIAL AND SHAPE ON ACOUSTIC EMISSION
TRANSMISSION CHARACTERISTICS - PART 1: TRADITIONAL FEATURESJOANNA SIKORSKA and JIE PAN
264

22-274

THE EFFECT OF WAVEGUIDE MATERIAL AND SHAPE ON AE TRANSMISSION
CHARACTERISTICS - PART 2: FREQUENCY AND JOINT-TIME-FREQUENCYCHARACTERISTICSJOANNA SIKORSKA and JIE PAN
274

Contents22.pdf
AuIndex22.pdf
AusNotes.pdf
Berlin2004.pdf
InMemorium.pdf
Tensi.pdf

Contents of Volume 22 (2004)
Authors Index of Volume 22
Policy/Author’s Notes/Meeting Calendar/Subscription Information
Activities at 2004 EWGAE Meeting;
Dick Blackburn (T.F. Drouillard)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

* indicates the availability in CD-ROM only.

I-5

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

Authors Index of Volume 21 (2003) and 22 (2004)
MAMORU AIZAWA
Y. AKEMATSU
L. ALFAYEZ
A. ANASTASOPOULOS

21-14921-22322-07722-059MICHEL BARBEZAT,
A. G. BEATTIEMICHAL BLAHACEK,JÜRGEN BOHSE
ANDREAS J. BRUNNER,
MARIA BURGER

22-127
21-09522-13822-20822-12722-102

M. CHERFAOUIHIDEO CHO
PHILLIP COLE

22-071
21-112, 22-119, 22-224, 22-243
22-022K. S. DOWNSWENDY DESADELEER,
P. DÖRNER,
T.F. DROUILLARD
C. ENNACEUR,
M. ENOKI

21-052, 21-070, 21-A01
22-25322-23622-I1022-07121-142

F. FINCK,PETER FLÜELER
R.P. FRANKE,KENZO FUKAURA

22-08322-12722-236
21-112

STEPHEN GAUTREY
MAOCHEN GE
C. U. GROSSE
Y. HAYASHI
M. A. HAMSTADKOTARO HANABUSAHIROSHI HAYA

22-02221-014, 21-029
22-08321-13121-052, 21-070, 21-A01, 22-001, 22-A01
22-15922-039

C. HERVÉ,
K. M. HOLFORD,
KEITARO HORIKAWACHRISTIAN HUBER

22-07122-16621-206, 21-223
22-127

R. IKEDA

21-131, 22-119

I-6

K. IWAI
K. IWAKI
KAZURO KAGEYAMAMAKOTO KANAITEPPEI KAWAKAMISATOSHI KOBAYASHITAKAO KOBAYASHI
T. KUSUGERALD LACKNER

21-197
21-16621-17621-17621-14921-14921-00121-14222-201

A. LAKSIMI,MICHAL LANDA
R. J. LARKRALF LASCHIMKE,
Z. W. LIMANUEL LÖHR,
THOMAS LOKAJICEKXIU LUO

22-07122-13822-16622-10221-21322-19022-09122-039A. MAIE
O. MAKISHIMAGERD MANTHEIK. MATSUURA
D. MBA
M. MEDIOUNIJUHA MIETTINEN
Y. MORI
K. MOROFUJI

21-21321-16622-17321-12022-07722-071
21-23021-19721-213

YASUHIKO MORI,TAKUO NAGAMACHIYASUHIRO NAKANISHI,
M. NAKANO
T. NISHIDAAKIRA NOZUE

22-09122-15922-03921-197
21-187
21-149YOSHIHIKO OBATA,
A. O’GALLAGHER
A. OHMORIISAMU OHSAWA
MASAYASU OHTSU
KANJI ONO
K. OZAWA22-09121-052, 21-070, 21-A01, 22-001, 22-A01
21-20621-17621-157, 22-030
21-112, 21-120, 22-119, 22-243
21-166JIE PANJAN PAVELKA,

22-264, 22-274
22-091

I-7

ZDENEK PREVOROVSKY,
R. PULLIN

22-13822-166RANZ RAUSCHER
H.-W. REINHARDTSABINE ROSNER

22-04922-08322-110KIYOSHI SAKAMAKIPEKKA SALMENPERÄFRANK SCHUBERT
H.-J. SCHWALBE
T. SHIOTANIM. SHIWADONALD A. SHOCKEYJOANNA SIKORSKAJOSEF SIKULA
T. SOGABETETSUYA SUZUKI

21-206, 21-223
21-23022-14722-23621-166, 22-039
21-19721-00122-264, 22-274
22-09121-12022-030M. TAKEMOTO
H. TANAKA
HANS MARIA TENSIYUICHI TOMODAPETER TSCHELIESNIG
A. TSIMOGIANNISYUKIYA TSUCHIDA
N. TSUI

21-120, 21-131, 22-119, 22-224
21-16622-S0121-15722-20122-05921-17621-213

HARTMUT VALLEN
KOEN VAN DEN ABEELEGERT VAN DIJCK,

22-10222-25322-253SHUICHI WAKAYAMAMARTINE WEVERS,MARK WINKELMANS

21-14922-25322-253

M. YAMADAKENICHI YOSHIDAS. YUYAMA

21-21321-206, 21-223, 22-159
21-187, 21-213

B. ZIEGLER

22-236

I-8