JOURNAL OF ACOUSTIC EMISSION
VOLUME 20, 2002
VIEWING AN ARTICLE
You need a software that can read a pdf-file. Two folders are provided for Windows 95/98/ME/2000 and
for Macintosh. Each contains Adobe Acrobat Reader® Installer (ACRD5ENU.EXE or Reader Installer).
Install one first before going further. Use of Adobe Acrobat ® (not provided) allows one to utilize the
Search function. This gives the power to search for a name or a word in the entire volume.
PDF Files: Contents are listed below. Individual papers have file names of 20-xxx.pdf, with xxx as theirfirst page number.
ISSN 0730-0050

Copyright©2002 Acoustic Emission Group

Coden JACEDO

Contents
20-001DAMAGE ESTIMATION OF CONCRETE BY AE RATE PROCESS ANALYSIS
Masayasu Ohtsu, Makoto Ichinose and Hiroshi Watanabe
120-016EXTRACTION OF HISTORIES OF DAMAGE MICRO-MECHANISMS IN
UNIDIRECTIONAL COMPOSITES BY TRANSIENT-PARAMETRIC ANALYSIS
Jie Qian and Yuris Dzenis
16

20-025

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

20-03920-06220-08320-099ANALYSIS 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
I -1

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

DAMAGE DIAGNOSIS TECHNIQUE FOR BRICK STRUCTURES USING
ACOUSTIC EMISSION
Takuo Shinomiya, Yasuhiro Nakanishi,
Hiroyuki Morishima and Tomoki Shiotani

145

20-14520-153

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

20-163

TRANSFORMATION PROCESSES IN SHAPE MEMORY ALLOYS BASED ON
MONITORING ACOUSTIC EMISSION ACTIVITY
Michal Landa, Václav Novák, Michal Blahácek and Petr Sittner
163

20-172CHARACTERIZATION 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 STATEMENTREGARDING 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

20-194

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

20-20620-218AE TECHNOLOGY AS A KEY ELEMENT OF THE OPERATION SAFETY SYSTEMAT 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
I-2

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
ACOUSTIC EMISSION
H. Traxler, W. Arnold, W. Knabl and P. Rödhammer257

20-265DAMAGE MODE IDENTIFICATION AND ANALYSIS OF COATED GASTURBINE MATERIALS USING A NON-DESTRUCTIVE EVALUATIONTECHNIQUE Y. Vougiouklakis, P. Hähner, V. Kostopoulos and S. Peteves

265

ACOUSTIC EMISSION DURING STRUCTURE CHANGES IN SEMICRYSTALLINE POLYMERSJ. Krofta, Z. Prevorovsky, M. Blahácek and M. Raab274

20-27420-285

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
AuIndex20.pdf
AusNotes.pdf

Contents of Volume 20 (2002)
Authors Index of Volume 20
Policy/Author’s Notes/Announcement/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
Presentation at AEWG45 by W. Prosser (NASA Langley)
PAC Folder Presentation at AEWG45 by T. Tamutas (Physical Acoustics Corp.)
Vallen Folder Presentation at AEWG45 by H. Vallen (Vallen Systeme)
AGU-Vallen Wavelet Transform Freeware and Introduction by M. Hamstad
I-3

Authors Index of Volume 20 (2002)
A. A. Anastassopoulos,
W. Arnold
J. Asserin
Mulu Bayray
Jan Bisschop
Michal Blahácek
M. J. Blanch
R. Budzier
J. Cerv
Milan Chlada
Frantisek Chmelík
A. G. Dutton
Yuris Dzenis
J. Eberlein
V. P. Fedorov
G. Fernando
L. E. Jones
D. J. Lekou
P. A. Joosse,
T. Kossivas
T. M. Kooistra
J. Gary
Pawel Gebski
Leszek Golaski
V. P. Gomera
P. Hähner
M. A. Hamstad
Jianmei He
U.-D. Hünicke
Makoto Ichinose
B. S. Kabanov
Karl-Ulrich Kainer
Jens Kiehn
W. Knabl
D. A. Kouroussis,
V. Kostopoulos
J. Krofta
G. Lackner
M. Landa
Terence G. Langdon
A. Lavrov
Pavel Lukác
A. Machová
Kristian Mathis
Souichi Mizutani
Yoshihiro Mizutani,
Florian Moll

Yasuhiko Mori
Yoshiki Morino
Hiroyuki Morishima
Yasuhiro Nakanishi
V. N. Nikolaidis,
Václav Novák
Yoshihiko Obata
A. O’Gallagher
Masayasu Ohtsu,
A.A. Okhotnikov
Kanji Ono
S. Peteves
T. P. Philippidis
D. Prevorovsky
Zdenek Prevorovsky
A. ProustJie Qian
M. RaabAmani Raad
Franz Rauscher
P. Rödhammer
Z. Rosecky
R. Schelling
H. J. Schoorlemmer
M. Schulz
Takayuki Shimoda
Takuo Shinomiya
Tomoki Shiotani
Ménad Sidahmed
Petr Sittner
V. L. Sokolov
H. Traxler
P. Tscheliesnig
Takateru Umeda
P. J. Van De Loo
D. R. V. Van Delft
J. G. M. Van Mier
D. Varchon
A. Vervoort
P. Vionis
Y. Vougiouklakis
Hiroshi Watanabe
M. Wevers
M. Winkelmans
A. Wolfert
Fan Zhang

229
257
285
188
153
163, 274
229
172
25
134
108
229
16, 99
172
218
229
229
229
229
229
238
39, 62
83
83
218
265
39, 62
194
172
1
218
108
108
257
229
265
274
179
25, 163
108
292
108
25
108
194
194
108
I-4

248
194
145
145
229
163
248
39, 62
1
218
83
265
229
285
134, 274, 285
229
16, 99
274
300
188
257
25
238
238
172
194
145
145, 153
300
163
218
257
129, 179
248
238
229
153
285
292
229
265
1
206, 292
206
238
300

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
Drouillard is responsible for the AE Literature section. The membersof the Editorial Board are selected for their knowledge andexperience 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 at meetings, technical notes, communications andsummaries of reports. Current news of interest to the acoustic
emission communities, announcements of future conferences andworking group meetings and new products will also be included.
Journal of Acoustic Emission includes the following 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
will also be presented.
E. AE Program and Data Files: Original program files and data
files that can be read by others and analyzed will be distributed in
CD-ROM.
Reviews, Tutorial Articles and Special Contributions willaddress the subjects of general interest. Non-technical part will
cover book reviews, significant personal and technical accomplishments, current news and new products.

P. ColeL. Golaski
M.A. Hamstad
H.R. Hardy, Jr.
T. Kishi
O.Y. Kwon
J.C. Lenain
P. Mazal
S.L. McBride
C.R.L. Murthy
A.A. Pollock
F. Rauscher
I. RomanC. Scala
C. ScrubyP. Tscheliesnig
H. Vallen
P. J. Van de Loo
M. Wevers
B.R.A. Wood
4. Publication

Journal of Acoustic Emission is published annually in CD-ROM
by Acoustic Emission Group, PMB 409, 4924 Balboa Blvd, Encino,
CA 91316. It may also be reached at 6531 Boelter Hall, Universityof California, Los Angeles, California 90095-1595 (USA). tel. 310825-5233. FAX 818-990-1686. e-mail: ono@ucla.edu
5. Subscription

2. Endorsement

Subscription should be sent to Acoustic Emission Group. Annual
rate for 2003 and 2004 is US $111.00 including CD-ROM delivery,
by priority mail in the U.S. and by air for Canada and elsewhere.
For additional print copy, add $40. Print only +shipping is $140.
Overseas orders must be paid in US currencies with a check drawn
on a US bank. VISA/MC accepted. Inquire for individual (withinstitutional order) and bookseller discounts.

Acoustic Emission Working Group (AEWG), European
Working Group on Acoustic Emission (EWGAE), and Committeeon Acoustic Emission from Reinforced Composites (CARP) haveendorsed the publication of Journal of Acoustic Emission.
3. Governing Body
The Editor and Associate Editors will implement the editorial
policies described above. The Editorial Board will advise the edi
tors on any major change. The Editor, Professor Kanji Ono, has
the general responsibility for all the matters. Associate Editors
assist the review processes as lead reviewers. Mr. Thomas F.

ISSN 0730-0050(UK)(Poland)
(USA)
(USA)
(Japan)(Korea)(France)(Czech Republic)(Canada)(India)(USA)(Austria)(Israel)(Australia)(UK)(Austria)(Germany)(Netherlands)(Belgium)(Australia)

Copyright©2002 Acoustic Emission Group

6. Advertisement
No advertisement will be accepted, but announcements for books,
training courses and future meetings on AE will be included withoutcharge.

I-5

CODEN: JACEDO

All rights reserved.

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
Los Angeles, California 90095-1595 USA
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
11791 Spruce Canyon Circle
Golden, Colorado 80403 USA

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

Only papers not previously published will be accepted.
Authors must agree to transfer the copyright to the Journal
and not to publish elsewhere, the same paper submitted toand accepted by the Journal. A paper is acceptable if it is arevision of a governmental or organizational report, or if it
is based on a paper published with limited distribution.

Illustrations and tables should be planned to fit a single
page width (6.5"). For the reviewing processes, these neednot be of high quality, but submit glossy prints orequivalent electronic files with the final manuscript. Lines
and letters should be legible.

Compilation of annotated data files will be accepted forinclusion in CD-ROM distribution, so that others can sharein their analysis for research as well as training. ASCII or
widely accepted data formats will be required.

4. Review
All manuscripts will be judged by qualified reviewer(s).
Each paper is reviewed by one of the editors and may besent for review by members of the Editorial Board. The
Board member may seek another independent review. In
case of disputes, the author may request other reviewers.

The language of the Journal is English. All papers should
be written concisely and clearly.
2. Page Charges
No page charge is levied. One copy of CD-ROM will be
supplied to the authors free of charge.

5. Electronic MediaThis Journal will be primarily distributed electronically by
CD-ROM. In order to expedite processing and minimize
errors, the authors are requested to submit electronic files
or floppy disk copy of the paper. We can read Macintosh
and IBM PC formats. On the INTERNET, you can send an
MS Word file or the text portion to "ono@ucla.edu".

3. Manuscript for Review
Manuscripts for review need only to be typed legibly;
preferably, double-spaced on only one side of the page
with wide margins. The title should be brief. An abstract of
100-200 words is needed for Research and Applications
articles. Except for short communications, descriptive
heading should be used to divide the paper into its
component parts. Use the International System of Units(SI).

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

References to published literature should be quoted in the
text citing authors and the year of publication. These are toI-6

e-MAIL ADDRESSES OF SELECTED AUTHORS
Athanasio Anastasopoulos < nassos@envirocoustics.gr >Mulu Bayray <mbayray@iaa1.iaa.tuwien.ac.at>
Michal Blahácek <blahacek@it.cas.cz>
Franticek Chmelík <chmelik@met.mff.cuni.cz>
Yuris Dzenis <ydzenis@unl.edu>
Leszek Golaski <bmtlg@tu.kielce.pl>
Gerald Lackner <lag@tuev.or.at>
Michal Landa <ml@it.cas.cz>
Pavel Mazal <mazal@uk.fme.vutbr.cz>
Yoshihiro Mizutani <mizutani.yoshihiro@nasda.go.jp>
Yasuhiko Mori <mori@me.cit.nihon-u.ac.jp>
Masayasu Ohtsu <ohtsu@gpo.kumamoto-u.ac.jp>
Kanji Ono <ono@ucla.edu>
Zdenek Prevorovsky <zp@it.cas.cz>
Amani Raad <amani.raad@utc.fr>
Franz Rauscher <f.rauscher+e329@tuwien.ac.at>
Takuo Shinomiya <shinomiya@jreast.co.jp>
Tomoki, Shiotani <shiotani@mx1.alpha-web.ne.jp>, tomoki_shiotani@tobishima.co.jp
Michaela Schulz <Michaela.schulz@mbst.uni-rostock.de>
Hannes Traxler <hannes.traxler@plansee.at>Peter Tscheliesnig <tsc@tuev.or.at>Peter Van de Loo <peter.vandeloo@shell.com>
Yannis Vougiouklakis <vougiouklakis@jrc.nl>
Martine Wevers <martine.wevers@mtm.kuleuven.ac.be>Mark Winkelmans <mark.winkelmans@notes.basant.be>

MEETING CALENDAR:

The 46-th Meeting of AEWG

The next meeting of AEWG will be at Porltand State University on Aug 5 and 6, 2003. The primer will
be held on Aug 4, 2003. Send inquiry to "Richard Nordstrom" <nordsr@cecs.pdx.edu>.
Registration forms, as well as other information regarding this conference, can be found at:
http://www.cecs.pdx.edu/~nordsr/AEWG46/aewg46.htm

2003/2004 SUBSCRIPTION RATE
Base rate (CD-ROM) for one year
$ 103.00
Add Postage of
U.S. - Priority rate
$ 8.00All others - Air mail rate
$ 8.00
CD-ROM + printed copy, surface mail: add$ 40.00
Print copy only with priority/air shipping$140.00
Payment must be in U.S. dollars drawn on a U.S. bank. Bank transfer accepted at
California Bank and Trust, San Fernando Valley Office (Account No. 080-03416470)
16140 Ventura Blvd, Encino, CA 91436 USA
Back issues available; Inquiry and all orders should be sent to (or Fax 1-818-990-1686):
Acoustic Emission GroupPMB 409, 4924 Balboa Blvd. Encino, CA 91316 USA
For inquiry through Internet, use the following address:
Editor-Publisher
Publication Date of This Issue (Volume 20):
I-7

ono@ucla.edu
Kanji Ono Tel. (310) 825-5233
31 March 2003.

DAMAGE ESTIMATION OF CONCRETE BY AE RATE PROCESSANALYSIS
MASAYASU OHTSU, MAKOTO ICHINOSE and HIROSHI WATANABE
Graduate School of Science & Technology, Kumamoto University, Kumamoto 860-8555, Japan
Abstract
Damage estimation of structural concrete from concrete-core samples is developed, applying
acoustic emission (AE) activity and damage mechanics. Generating behavior of AE events froma concrete sample in a uniaxial compression test is quantitatively analyzed, based on the rate
process theory, because notable discrepancy of AE activity is observed between damaged andundamaged concrete. It is confirmed that the rate is substantially associated with pore volume
responsible for the damage in concrete. The damage is quantitatively defined by a scalardamage parameter in damage mechanics. Correlating the rate with the damage parameter,
quantitative estimation of the damage is proposed in terms of the relative modulus of elasticity.
An applicability of the procedure is confirmed in concrete samples of controlled damage due tofreezing and thawing and of various cured ages. Further, concrete-core samples taken fromreinforced concrete columns of an existing bridge are tested. The results suggest that the damageof concrete at the current state could be quantitatively estimated from AE rate process analysis
without knowing the original state of the concrete at construction.
1. Introduction
Lately it is widely realized that concrete structures are no longer maintenance-free. As aresult, evaluation techniques for diagnostic inspection are in great demand in concrete
engineering. As a detailed inspection of a concrete structure in service, core samples are usually
drilled out and then both chemical and physical properties are measured. Concerning mechanicalproperty of the physical property, the compressive strength and the modulus of elasticity(Young’s modulus) are normally determined by conducting a uniaxial compression test. These
values are then compared, if possible, with those of the specification. Otherwise, there is no
qualified procedure to estimate mechanical properties responsible for the durability and thedeterioration of concrete. In most cases, only the strength is evaluated whether the obtained
value is good enough against designed stress.
One example is shown in Fig. 1. These are results of tests on deteriorated concrete samples
due to freezing-thawing cycles. The decrease of Young’s modulus is reasonably observed with
the increase in freezing-thawing cycles, whereas the decrease of the strength is unconfirmed.
Because the tested concrete contained high percentage of entrained air (over 5%), the damages
due to freezing and thawing cycles were minor up to 300 cycles. Ordinary concrete is acomposite material of cement paste and aggregate. Even though cementation in the intergranularlayer is deteriorated, the decrease of the strength might be not yet dominant due to load-bearing
capacity of aggregate. Poor cementation, however, could lead to the decrease of the modulus
and readily to loss of the durability. Not relying only on the strength, therefore, it is desirable toestimate the decrease of the modulus of elasticity in the existing concrete structures.

J. Acoustic Emission, 20 (2002)

1© 2002 Acoustic Emission GroupFig. 1 Variations of the modulus of elasticity and the compressive strength during the process offreezing and thawing.
To inspect existing structures for maintenance, acoustic emission (AE) techniques deserve todraw a careful attention. This is because crack nucleation and extension are readily detected andmonitored. In this respect, measurement of AE activity in the uniaxial compression test of coresamples was proposed (Ohtsu et al., 1988; Ohtsu, 1992; Farahat and Ohtsu, 1994).
In order to formulate AE generating behavior under compression, the rate process analysis
was introduced. It is demonstrated that the result of AE rate process analysis is closely
associated with the presence of micro-cracks in concrete (Ishibashi et al., 1995). In concrete, thedamage of defects and cracks are primarily nucleated due to chemical reaction and fatigue. It isalso confirmed that the procedure based on the rate process analysis is applicable to these typesof the damage in concrete (Yuno et al., 1995).
In the present paper, damage mechanics is introduced and the damage parameter is correlatedwith AE rate process analysis to estimate the decrease of the modulus in concrete. An
applicability of the procedure is investigated by employing concrete samples of controlled
damage, and then is applied to a field measurement.
2. Rate Process Analysis
When concrete contains a considerable amount of critical micro-cracks, AE activity isobserved from low stress level in the compression test. In contrast, AE activity in intact or soundconcrete is mostly observed right after dilatancy occurs due to micro-cracking prior to final
failure. Thus, the presence and the nucleation of critical micro-cracks in concrete are closely
associated with AE generating behavior under compression.
2

Fig. 2 Probability functions f(V) in the rate process analysis of AE activity.
To formulate AE activity under compression, the rate process theory was introduced (Ohtsu,
1992). Probability function f(V) of AE occurrence from stress level V(%) to V+dV (%) isrepresented as a function of the incremental number of AE counts or hits, dN,f(V) dV = dN/N,(1)

where N is the accumulated number of AE counts (hits) up to stress level V(%), which isnormalized by the compressive strength. Equation 1 is known as the governing equation of therate process, because the gradient dN/dV is not only a function of the probability f(v), but also of
the accumulated number N. To discriminate AE activities at low stress level, the function f(V) isapproximated as a hyperbolic function,f(V) = (a/V) + b,(2)

where a and b are empirical constants. In Fig. 2, typical results obtained in uniaxial compression
tests are shown. Histograms on the values f(V) were directly determined as dN/(N dV) at eachstress increment dV. In order to eliminate the noise due to fretting between the sample andloading plates in early stage as well as AE activity at final failure, only the stress range from
30% to 80% is taken into consideration. Applying Eq. 2, the distribution was approximated by
the hyperbolic function with the least-square-error estimation. In Case A of which concrete
sample was damaged due to freezing–thawing action, AE activity is observed to be high at low
stress level. Thus, the probability function is modeled such that the coefficient ‘a’ is positive.
Hereinafter, this coefficient is called the rate. In Case B of newly-cast concrete sample, thefunction is modeled as the negative rate (a < 0), presenting low AE activity at low stress level.
These results show that AE activity at low stress level could be discriminated from the rate ‘a’.
3

Fig. 3 Relation between total number of AE events and stress level, analyzed by the rate process
analysis.
Substituting Eq. 2 into Eq. 1 and integrating it, a relationship between the total number of AE
counts N and stress level V(%) is derived as,
N = C Va exp (bV).

(3)

Here, C is the integration constant. One example of the rate process analysis in the uniaxial
compression tests is shown in Fig. 3. AE activity under compressive stress is approximated inreasonable agreement by Eq. 3, as confirmed in the previous research (Ohtsu, et al., 1988; Ohtsu,
1992; Farahat and Ohtsu, 1994).
3. Damage Parameter and the Rate ‘a’From the continuum damage mechanics (Kachanov, 1980), the state of damage is represented
by the scalar damage parameter Ω. In a simple model, modulus of elasticity (Young's modulus)
E of a damaged material is expressed as,
E = E*(1 - Ω),(4)

where modulus E* is that of an intact material. In the uniaxial compression test of a concrete
sample, a relation between stress and strain is obtained as shown in Fig. 4(a). Taking into
account an initial or current damage when the core is drilled out, modulus Eo is defined as atangential modulus and is associated with the degree of initial damage Ωo as,
Eo = E*(1 – Ωo).

(5)

During the uniaxial compression test, the damage increases from Ωo to Ωc as shown in Fig.
4(b), where Ωc is the damage at the ultimate strain εc. In order to define the stress (σ) – strain(ε) relation as σ = Eε, modulus Ec is defined as a secant modulus as shown in Fig. 4(a) and ispresented,

4

Fig. 4 (a) Stress-strain relation and elastic moduli: Eo and Ec. (b) Relation between damage andstrain.
Ec = E* (1 – Ωc).

(6)

In order to relate damage evolution Ωc - Ωo with AE activity in the compression test, therelationship on amplitude distribution is introduced (Mogi, 1962),
n(A) = k A–m.

(7)

Here n(A) is the number of AE events of the amplitude from A to A+dA. k and m are empiricalconstants. Assuming that the crack volume is proportional to the amplitude of AE event, theevolution is formulated asΩc - Ωo =∫ n(A) A dA.

(8)

Substituting Eqs. 3 and 7 into Eq. 8, the damage evolution is eventually represented asΩc - Ωo = (1 - m) Ao C (100)a exp (100b) / (2 - m),(9)where Ao is the threshold amplitude of the measuring system. This implies that the damage
evolution is closely correlated with parameters m, a, and b. The correlation of these parameterswith the damage evolution was previously studied in concrete samples of various damage levels(Iida et al., 2000). It is found that the evolution is highly correlated with the rate ‘a’, and that theparameter m is primarily associated with failure mechanisms (Ohtsu, 1987). Because failuremode of only uniaxial compression is generated in the test, the parameter m does not vary muchduring the evolution process. The parameter b is normally small and to be less correlated thanothers. Thus, it is concluded that the damage evolution Ωc - Ωo is predominantly associated withthe variation of the rate ‘a’.
In concrete core samples taken from harbor structures, uniaxial compression tests wereconducted (Ishibashi et al., 1998). After determining the pore distribution by the mercury
intrusion method from the same core samples, the ratio of pore volume over 0.1 µm radius wascompared with the rate ‘a’. Results are given in Fig. 5. Strong correlation between the rate ‘a’and the ratio of pore volume is observed. Because it is considered that the volume of large poresis closely associated with the deterioration of concrete (Matsuyama and Ohtsu, 1992), this result5Fig. 5 Relation between the rate ‘a’ and pore volume.

Fig. 6 Locations of core samples core-drilled in a nuclear power plant.

6Fig. 7 Relations among the pore volume, the compressive strength, and the rate ‘a’ of the coresamples.
confirms the feasibility of the relation in Eq. 9 and the rate ‘a’ is to be a key factor of the damage
in concrete.
In order to confirm the relationship in Eq. 9, core samples were taken from an aqueduct of a
nuclear power plant shown in Fig. 6. To drill core samples out, three sites were selected: at thegate (Site A), 20 m inland (Site B), and 30 m inland from the gate (Site C). Three core samples
at each site were mold-cut of dimensions 7.4 cm diameter and 14.5 cm height. The uniaxial
compression tests of the samples were conducted. AE events generated under compression werecounted up to final failure by AE processor (LOCAN; PAC). An AE sensor of 1 MHz resonance
was attached at the middle of the sample, where the surface was ground flat. For event-counting,the threshold level was set to 150 mV and the dead-time for event-counting was 1 ms. The filter
frequency range was from 10 kHz to 300 kHz, and total amplification was 60 dB.
The compressive strength and the rate ‘a’ were determined as the averaged value of the three.
Distribution of pore radii was also measured by the mercury intrusion method from concrete
fragments at the three sites. After determining the pore distribution, the total volume of porewith the radii over 0.5 µm was determined, because the pore volume from 0.1 µm to 0.5 µm was
negligible. Results of the pore volumes, the rate ‘a’, and the compressive strength aresummarized in Fig. 7. From Site A to Site C, the pore volume decreases monotonously with theincrease in the distance from the sea. This implies that the heaviest damage was introduced inconcrete at the gate (Site A), where concrete was frequently deteriorated by seawater. Leavingfrom the seaside, the damage of concrete decreases. In accordance with the increase in the porevolume, the rate ‘a’ increases from Site C to Site A. Thus, the increase of the pore volume (over0.5 µm radius) corresponds remarkably to the increase in the rate ‘a’. It confirms that with the7increase in the rate ‘a’, the volume of the micro-voids responsible for damage increases. Because
the rate ‘a’ is still negative, the degree of damage is supposed to be minor in these concrete
samples. This is a reason why compressive strengths did not correlate with the rate ‘a’ or withthe pore volume. It is again observed that the strengths are not explicitly associated with thedamages defined by the pore volumes, as is the case of freezing-thawing damages. Even though
the damage is minor, the durability of concrete definitely decreases. These results suggest thatthe strength may not be a key factor for the durability, but the rate ‘a’ is sensitive to it.
4. Estimation of Damage(1) Samples of controlled damage
To prepare concrete samples of controlled damage, cylindrical specimens of 10 cm diameterand 20 cm height were made. Mixture proportion of concrete was water: cement : sand : gravel= 0.55 : 1.0 : 2.5 : 3.2 and the maximum size of gravel was 20 mm. The compressive strength
after 28 day moisture-cure was 45.4 MPa. Controlled damage was induced in the concrete
samples, by the freezing and thawing test. After 28 day moisture-cure, freezing and thawing
cycles from -16o C to 3o C in three hours interval were applied. The damaged samples wereprepared and tested after 100 cycles, 200 cycles and 300 cycles.
Since initial modulus Eo should be determined as the tangential modulus: dσ/dε at ε = 0, thestress versus strain (σ-ε) relation was approximated by a parabolic equation which is adopted in
the Standard Specification for Concrete Structures of JSCE and the CEB-FIP Model Code 1990.
Consequently, it is represented as,
σ = a0 + a1ε + a2ε2.

(10)

One example is shown in Fig. 8 as the stress-strain relation is approximated by Eq. 10 and
the initial modulus Eo is determined as the coefficient a1. It is confirmed that the stress-strain
curve is approximated by the parabolic equation with high correlation (0.9998).
In the damage-mechanics model of a pore-cracked material, a relation between the modulus
of elasticity and the strength is mathematically derived (Nielsen, 1998),
σc/σc* = exp[-0.75 (E*/Eo –1)],(11)

where σc is the compressive strength of a cracked material and σc* is the strength of the intact(zero porosity) material. In an ordinary cracked material, as the limiting case for which voids are
squeezed out, and Eq. 11 is simplified toσc/σc* = 0.25 + 0.75Eo/E*.

(12)

Taking the average of three specimens tested, the modulus of elasticity Eon and thecompressive strength σcn were determined after n freezing-thawing cycles. To estimate thedamage introduced, the ratios σcn/σc0 were compared with σc/σc* in Eqs. 11 and 12. Relative
moduli were also estimated from Eon/Eoo, where Eoo is the modulus of elasticity of concrete
after 28 day moisture-cure (but prior to the freezing-thawing cycles) and Eon is that after nfreezing-thawing cycles. Results are plotted in Fig. 9. The relation given by Eq. 11 is indicatedby a broken curve and that of Eq. 12 is shown by a solid line. The damages introduced are8

Fig. 8 Stress-strain relation approximated by a parabolic equation.

Fig. 9 Relative moduli Eon/Eo0 and ratios of the strength σcn/σc0 in the freezing and thawing test.
reasonably approximated by the relations of damage mechanics. The strengths decrease evenlower than the relative moduli in Eq. 11, while the relative moduli are lower than the strengths in
Eq. 12. It is observed that the damages introduced are moderate so that results are plotted on theboth curves with reasonable agreement
From Eqs. 5 and 6, the damage evolution is expressed as,Ωc - Ωo = (Eo - Ec)/E*.
9(13)

As mentioned in Sec. 3, it is found that the damage evolution is closely associated with the rate
‘a’. Since modulus E* of an intact concrete is unknown in an actual case, a relationship between
the difference Eo - Ec and the rate ‘a’ of the samples is studied and plotted as shown in Fig. 10.
For the sake of simplicity, a linear correlation is approximated as shown in the figure, where thedifference Eo - Ec is represented as,
Eo - Ec = E*(Ωc - Ωo) = X a + Y.

(14)

Because the rate ‘a’ varies depending on the degree of the damage, it is assumed that the initialmodulus Eo is identical to intact modulus E* in the case where the rate ‘a’ is equal to 0. From
the figure, intact modulus E* is determined by
E* = Ec + Y.

(15)

Fig. 10 Correlation between the rate ‘a’ and the difference between the tangential modulus Eo
and the secant modulus Ec in the concrete samples of controlled damage.
Through this procedure, relative modulus Eo/E* was estimated at each freezing-thawing
cycle as an average value of three samples. In Fig. 11, results of relative moduli Eo/E* arecompared with ratios Eon/Eo0, which were determined as the ratios of the tangential moduli Eo
after n cycles to that of 0 cycle in the experiment. It appears that the ratios Eo/E* obtained fromthe above procedure slightly overestimate the actual ratios Eon /Eo0, while the trend of thedecrease due to deterioration is in reasonable agreement. This result implies that the damage ofconcrete can be practically estimated without knowing the modulus of elasticity at the initialconstruction stage. Because the mechanical properties of concrete at the time of construction aremostly missing when inspection is conducted years later, the procedure is promising to estimate
the degree of damage using the relative modulus Eo/E* determined via the AE rate process
analysis described here.

10Fig. 11 Relative moduli Eo/E* and Eon/Eo0 in the freezing and thawing test.
(2) Concrete of various ages
Casting concrete samples of the same mixture as in (1), the effect of cementation due to age
is studied. First, in the early age after 7, 14, 21, and 28 days, uniaxial compression tests wereconducted. It is noted that the designed strength of concrete is estimated at the age of 28 days.
All samples were moisture-cured in the standard room (20oC), and were taken from a reservoir
one day before the test. In Fig. 12, Eon/Eo28 represents the relative moduli actually measured in
the experiment as the modulus of elasticity Eon at n days elapsed. Eo/E* represents the relative
modulus of elasticity, which is estimated from AE rate process analysis. It is found that relative
moduli measured and those estimated from the rate process analysis agree after 21 days. Thus, it
is confirmed that the damage due to weak cementation or inadequate hydration could be
estimated by this method.
Although the rate process analysis is originally investigated to estimate the deterioration of
hardened concrete, the degree of soundness is also studied in the ages after 28 days. In Fig. 13,
results of moisture-cured samples after 7, 9, 11, 13, 15, and 17 months are summarized.It is wellknown that hydration process of concrete still continues even after 50 years. Accordingly, theratios Eon/Eo28 becomes over 1.0 after 7 months. In this case, the ratios Eo/E* underestimate theactual ratios Eon/Eo28, although the trend of the relative moduli in hardening process is similar to
the actual ratios. Thus, the procedure is not quite effective to estimate the more sound concretedue to hardening.
(3) Field Survey
The procedure was applied to concrete-core samples taken from concrete columns of an
existing bridge. A sketch of pier and anchor of the bridge is given in Fig. 14. From eachportion, three cores were taken and tested. Results of relative moduli Eo/E* estimated are11

Fig. 12 Relative moduli Eo/E* and Eon/Eo28 in early-age concrete.

Fig. 13 Relative moduli Eo/E* and Eon/Eo28 in long-age cured concretesummarized in Fig. 15. It is observed that the relative moduli of cores in the anchor A2 arelarger than 1.0, while the relative moduli of cores in the piers P1 and P2 are slightly smaller than
1.0. From Eq. 5, it is derived thatΩo = 1 – Eo/E*.
(16)
12

Fig. 14 Sketch of piers and an anchor of a concrete bridge.

Fig. 15 Relative moduli Eo/E* estimated from core samples.
Consequently, the degree of current damage Ωo was estimated from the average values
Eo/E* at three locations, although a negative value is set to zero in Eq. 16. Results are
summarized in Fig. 16. This suggests that the concrete in the anchor A2 is undamaged, while theconcrete of the piers P1 and P2 is a little deteriorated. The trend of these results is found to be ingood agreement with a numerical study on fatigue damage of the bridge columns. It was also
confirmed that the strengths of these core samples were similar and higher than the designed
strength. Based on these results, it was decided to retrofit this bridge with slightly repairing thetwo piers.
13

Fig. 16 The damage parameter estimated from the core samples.
5. Conclusion
For quantitative estimation of the damage in structural concrete, AE measurement is applied
to the uniaxial compression test of concrete samples. AE generating behavior, which is closely
associated with the damage inside concrete, is quantitatively formulated by the rate process
theory. Then, the damage is defined from the damage mechanics. Correlating the rate process
analysis and damage mechanics, the procedure is developed to quantitatively estimate thedamage of the concrete sample taken out of the structure. From the results, it is demonstrated
that the damage of concrete at the current state could be estimated from AE rate process analysis
without knowing the original state at construction. All results obtained are summarized, as
follows:
1) From the results of core samples taken out of a nuclear power plant, it is confirmed that with
the increase in the rate ‘a’, the pore volume of the micro-voids responsible for the damage
increases. This demonstrates that the rate ‘a’ could give a quantitative reference on damage levelof concrete.
2) A procedure to estimate the damage of the current state is studied, introducing damage
mechanics and correlating the rate ‘a’ with the scalar-damage parameter. From the correlation
between the damage evolution and the rate ‘a’, the modulus of elasticity in the intact state isestimated.
3) By using concrete samples of controlled damage and of various ages, it is confirmed that
relative moduli estimated by the proposed procedure are in reasonable agreement with actual
moduli in the tests for damaged concrete.

144) In order to check the feasibility, the procedure was applied to concrete-core samples takenfrom an existing bridge. It is suggested that concrete of the anchor is not deteriorated, but
concrete of the piers is slightly deteriorated. Because the trend of these results is found to be ingood agreement with analytical study, the retrofit plans of the bridge were decided, based on the
result of AE rate process analysis.
References
Farahat, A. M. and Ohtsu, M. (1994), ”Assessment of Concrete Deterioration using Plastic
Analysis and Acoustic Emission Technique,” Journal of AE, 11(4), 37-46.
Iida, T., Watanabe, H., Tomoda, Y. and Ohtsu, M. (2000), ”Damage Estimation of ConcreteCore by AE Rate Process Analysis,” Proc. Japan Concrete Institute, 22(1), 271-276 (inJapanese).
Ishibashi, A, Hidaka, E., Farahat, A. M. and Ohtsu, M. (1995), ”Deterioration Evaluation by AE
in Concrete Samples,” Proc. Sixth Int. Conf. Structural Faults and Repair, 2, 69-74.
Ishibashi, A., Matsuyama, K., Ohta, S. and Ohtsu, M. (1998), ”Acoustic Emission Application
for Diagnosis of Deteriorated Concrete of Harbor Structures,” Proc. Sixth Int. Sym. on AE from
Composite Materials, AECM-6, ASNT, 145-152.
Kachanov, M. (1980), "A Continuum Model for Medium with Cracks," J. Engineering
Mechanics, ASCE, 106(5), 1039-1051.
Matsuyama, K. and Ohtsu, M. (1992), "Rate Process Analysis of AE Activity to Evaluate theDeterioration of Concrete by Core Tests," Proc. 4th Int. Sym. on AE from Composite Materials,
AECM-4, ASNT, 132-138.
Mogi, K. (1962), ”Magnitude Frequency Relation for Elastic Shocks accompanying Fracture of
Various Materials and Some related Problems in Earthquakes, “ Bull. Earthq. Res. Inst., 40, 831853.
Nielsen, L. F. (1998), ”On Strength of Porous Material: Simple Systems and Densified Systems,”
Materials and Structures, 31, 651-661.
Ohtsu, M. (1987), ”Acoustic Emission Characteristics in Concrete and Diagnostic Applications,”
Journal of AE, 6(2), 99-108.
Ohtsu, M., Sakimoto, T., Kawai, Y. and Yuji, S. (1988), ”Evaluation of Concrete Structure
Deterioration via AE Observation of Core Test,” Journal of AE, 7(4), 167-172.
Ohtsu, M. (1992), "Rate Process Analysis of Acoustic Emission Activity in Core Test ofConcrete," Concrete Library of JSCE, No. 20, 143-153.
Yuno, K., Inoue, Y. and Ohtsu, M. (1995), ”Evaluation of Deterioration in Concrete by
Stochastic Analysis of AE Activity under Uniaxial Compression,” J. Materials, Concrete
Structures and Pavement, JSCE, No. 520/V-28, 13-24.

15

EXTRACTION OF HISTORIES OF DAMAGE MICRO-MECHANISMS IN
UNIDIRECTIONAL COMPOSITES BY TRANSIENT-PARAMETRICANALYSISJIE QIAN and YURIS DZENIS
Department of Engineering Mechanics, University of Nebraska, Lincoln,
Lincoln, NE 68588-0526, USA
Abstract
The problem of micro-damage evolution to failure in advanced composites is addressed. A
combination of transient waveform classification and multi-parametric filtering was used to
extract the histories of damage evolution in a unidirectional graphite/epoxy composite loaded in
the fiber and transverse directions. Three characteristic AE waveforms with different amplitudes,
durations, and frequency spectra were identified based on the transient analysis. The waveforms
were associated with matrix cracks, fiber breaks, and longitudinal splitting in the composite.
Parametric regions for the characteristic waveforms were identified in the amplitude-rise timeparametric space. Multi-parametric filtering was applied to extract the micro-damage evolution
histories. The method is expected to be advantageous for the real-time damage and fracture
monitoring of composite structures.
1. Introduction
Advanced composite materials under loading gradually accumulate several types of damage.
These include matrix cracks, fiber breaks, fiber-matrix debonding, longitudinal splitting, anddelamination. Studies of damage evolution are needed for better understanding of mechanisms of
the ultimate failure and life of composites and structures. Acoustic emission has been used toanalyze overall damage accumulation in composites by many authors (Bakuckas et al., 1994; Ely
and Hill, 1995; Luo et al., 1995). Different damage mechanisms have been reported to produce
AE signals with different parameters. However, attempts to apply single-parameter filters toseparate damage mechanisms have been largely unsuccessful due to the overlap of theparametric ranges for different damage micro-mechanisms (Kouvarakos and Hill, 1996). Thisoverlap is due to high unpredictability of AE signals caused by the variability and complexity ofboth damage and wave propagation phenomena in composites (Chang and Sun, 1988; Gorman,
1992). In this paper, a hybrid transient-parametric method was applied to separate the overall AE
histories from unidirectional composites into the histories for different damage mechanisms. The
method is based on a combination of transient AE classification and multi-parametric filtering.
2. Technical Approach
Recently, Ono and Huang (1994), Prosser et al. (1995), and de Groot et al. (1995) haveapplied transient AE analysis for AE source recognition. Methods of pattern recognition andneural networks have been used for signal classification. These methods can be used to extract
histories of AE signals with different waveforms based on the transient AE data. However, thebulk of the AE evaluations at present are performed by a parametric analysis that requires
simpler and cheaper hardware and may be especially advantageous for long-term AE monitoring
J. Acoustic Emission, 20 (2002)16© 2002 Acoustic Emission Groupwhen the accumulation and analysis of huge amounts of transient data is impossible or
impractical.
The following hybrid approach for the AE analysis of damage evolution histories incomposites was applied in this work. AE transient waveforms were acquired along with theparametric AE data. The waveforms were screened and the characteristic waveforms wereidentified. AE parametric spaces were then analyzed and parametric regions occupied by signalsof different types were identified. Evolution histories for different waveforms were thenextracted by multi-parameter filtering. Finally, damage mechanisms associated with different
waveforms were identified based on the expected and observed damage in composites.

Fig. 1. Mechanical response. Above: [0]8 and below: [90]16.

17

3. Experimental
Damage evolution in unidirectional composites subjected to tensile loading was analyzed.
Two composite panels, [0]8 and [90]16, were manufactured from Hexcel T2G190/F263
graphite/epoxy prepreg following the manufacturer-recommended curing cycle. The panels weretabbed and rectangular 250mm x 15 mm ([0]8) and 175mm x 25 mm ([90]16) specimens weremachined by a high-speed diamond saw. Tensile testing was performed by a MTS testingmachine retrofitted with a digital Instron test control and data acquisition system. The constantdisplacement rate of 0.5 mm/min was applied. The AE was acquired and analyzed by a twochannel Vallen AMS3 acoustic emission system equipped with a transient recorder. Two
wideband, high fidelity B1025 AE sensors by Digital Wave were used. The AE gage zone (thedistance between the AE sensors) was 80 mm for the [0]8 composite and 60 mm for the [90]16
composite. The AE source location analysis was performed on the incoming signals and thesignals originating outside the acoustic gage zone were eliminated from the analysis. A 34.5 dB
system gain and a 40.5 dB threshold were used in the AE data acquisition. The load and strain
data were fed from the testing machine and recorded as parameters. Several specimens of eachtype were tested.
4. Results and Analysis
The typical mechanical response of the unidirectional composites is shown in Fig. 1. The
overall, unfiltered AE histories are shown in Fig. 2a and b. The AE signal location distributions
were rather uniform (within the acoustic gage zone) for all tests. It is seen that the overall AE
histories for the [0]8 and [90]16 composites were qualitatively different. Loading in the fiber
direction produced very little emission at the load levels below 50% of the ultimate load. At
higher loads, the accumulation rate was non-uniform with considerable jumps observed at 65%
and 90% of the ultimate load. Loading in the transverse direction produced the highest
accumulation rate at the low load levels, from 10 - 20% of the ultimate load. As the loadincreased, the AE accumulation rate decreased and stayed almost constant to failure.
Analysis of the transient AE data for both composites revealed three characteristic
waveforms with different amplitudes, durations, and frequency spectra. Examples of thesewaveforms are shown in Fig. 3. The A-type signals had peak frequency in the range from 100220 kHz. The B-type signals had higher amplitude and peak frequency between 300-700 kHz.
The C-type signals had high amplitude, extremely long duration (>1000 µs), and a wide
frequency spectrum. These three types of signals accounted for 65% to 90% of the overall AE,
depending on the specimen. The balance contained signals with random or mixed waveforms andfrequency spectra that could not be easily classified.
The C-type signals were extracted from the overall parametric AE data by a simple duration
filter. Several parametric spaces were then analyzed in an attempt to separate the signals of thetypes A and B. The best separation was achieved in the amplitude-rise time space. The
parametric regions for the A-type and B-type signals in this space for the two specimens areshown in Fig. 4. The multi-parameter filtering technique was then applied to separate theaccumulation histories for these AE signals. Extracted histories of the AE with different
waveforms for the two composites are shown in Fig. 5. The failed composite specimens areshown in Fig. 6.

18

Fig. 2a Overall AE count histories for [0]8.

Fig. 2b Overall AE count histories for [90]16.
The comparison of the classified AE histories with the damage mechanisms identified byboth on-line observations and fractographic analysis of the broken specimens allowed the19Fig. 3 Characteristic waveforms for Types A, B and C.
correlation of the damage mechanisms with the characteristic AE signals. The A-type signals
were associated with the matrix damage, the B-type signals with the fiber breaks, and the C-typesignals with the macroscopic longitudinal splitting. The analysis of the classified AE histories for20

Fig. 4. Parametric regions for the A-type and B-type AE signals in the amplitude-rise time
space. Above: [0]8 and below: [90]16.
the [0]8 composite (Fig. 5a) showed that the two jumps on the overall AE history plot (Fig. 2)
were due to longitudinal splitting. The fiber breaks and matrix cracks in this composite
accumulated rather smoothly, except for the jump in the fiber damage caused by the second
splitting event at 90% of the ultimate load. The early start of the fiber break accumulation in thegraphite/epoxy composite (at around 40-50% of the ultimate load) was consistent with the21

Fig. 5a. Classified histories for various AE signals for [0]8 composite.

Fig. 5b. Classified histories for various AE signals for [90]16 composite.

22

observations made earlier by other authors (Ono and Huang, 1994). It is different from thesituation with glass fiber reinforced composites where fiber breaks were sometimes observed tostart accumulating at the loads as high as 90% of the ultimate. The damage in the [90]16
composite (see Fig. 5b) consisted almost entirely of matrix cracks with a small amount of fiber
breaks developed near the final failure. The differences in the observed AE behavior are readilyreflected in the failed composite specimens, shown in Fig. 6. Fractured fibers and numerous
fiber-matrix failures along the 0û-direction are found in the 0û-sample failure, while clean fibermatrix fracture characterizes the 90û-sample failure.

Fig. 6 Failed composite specimens. Above: [0]8 and below: [90]16.
5. Conclusions
A new approach for the AE damage evolution analysis of composites is proposed. The
approach combines the advantages of the transient and parametric AE methods. Capabilities of
the new method are illustrated on extraction of the histories of damage micro-mechanisms in theunidirectional graphite/epoxy composites. Applications of the new method to static and fatigue
damage evolution studies in advanced laminated composites are in progress. The method is
expected to be advantageous for real-time monitoring of damage and fracture in composites
subjected to long-term loadings when massive transient AE data analysis is impractical.
Acknowledgment
The support of this work by the Air Force Office of Scientific Research is gratefully
acknowledged. The graphite/epoxy prepreg for the experiments conducted was provided by
Hexcel Corporation.
23

References
Bakuckas, J.G., Jr., Prosser, W.H., and Johnson, W.S., “Monitoring damage growth in titanium
matrix composites using acoustic emission”, J. Composite Materials, 28(4), 305-328 (1994).
Chang, C. and Sun, C.T., “Acoustic emission and transient elastic waves in an orthotropic
laminate”, Composites Science and Technology, 22, 213-236 (1988).
de Groot, P.J., Wijnen, P.A.M., and Janssen, R.B.F., “Real-time frequency determination of
acoustic emission for different mechanisms in carbon/epoxy composites”, Composite Scienceand Technology, 55, 405-412 (1995).
Ely, T.M. and Hill, E.v.K., “Longitudinal splitting and fiber breakage characterization in graphite
epoxy using acoustic emission data”, Materials Evaluation, 53, Feb, 288-294 (1995).
Gorman, M.R., “Plate wave acoustic emission; Composite materials”, Proc. 4th Int. Symp on AE
from Composites, Seattle: ASNT, Colombus, OH, pp. 395-400 (1992).
Kouvarakos, M. and Hill, E.v.K., “Isolating tensile failure mechanisms in fiberglass epoxy fromacoustic emission signal parameters”, Materials Evaluation, 54, Sept, 1025-1031 (1996).
Luo, J.J., Wooh, S.C., and Daniel, I.M., “Acoustic emission study of failure mechanisms in
ceramic matrix composite under longitudinal tensile loading”, J. Composite Materials, 29(15),1946-1961 (1995).
Ono, K. and Huang, Q., "Pattern Recognition Analysis of Acoustic Emission Signals", Progressin Acoustic Emission VII, eds. Kishi, T. et al., Japan Soc. NonDestructive Inspection, Tokyo,
1994, pp. 69-78.
Prosser, W.H., Jackson, K.E., Kellas, S., Smith, B.T., McKeon, J., and Friedman, A., “Advanced
waveform-based acoustic emission detection of matrix cracking in composites”, Materials
Evaluation, 53, Sept, 1052-1058 (1995).

24

ACOUSTIC EMISSION SOURCES BY ATOMISTIC SIMULATIONS
M. LANDA, J. CERV, A. MACHOVÁ and Z. ROSECKY
Institute of Thermomechanics AS CR, Dolejskova 5, 182 00 Prague 8, Czech Republic
Abstract
We present acoustic emission (AE) sources at the crack tip obtained from atomistic
simulations by molecular dynamic technique in bcc iron under plane strain conditions. We showthat the character of acoustic emission is different when different processes operate at the crack
tip. Wave processes during brittle fracture, twin extension and dislocation emission from thecrack tip are visualized and analyzed. For better understanding of the basic feature of theatomistic results, we also include a simplified reconstruction of the most important wave patterns
in anisotropic elastic continuum.
1. Introduction
Stress wave radiation caused by crack extension (Freund and Rosakis, 1991; Prakash et al.,1992), by motion of dislocations (Trochidis and Polyzos, 1995) and at twin extension (Rosakis
and Tsai, 1995) have been analyzed recently in the framework of isotropic continuum. Unlike
isotropic continuum, where only two different velocities of longitudinal and shear wave exist,
such an analysis in anisotropic crystals has not been performed due to a very complex characterof wave phenomena in the crystals. Pure longitudinal and transverse modes exist here only insome special crystallographic directions; in other directions, quasi-longitudinal or quasitransverse waves transfer the energy. Their velocity is different in dependence on the orientationof wave vectors. Atomistic simulations by molecular dynamic technique offer an opportunity tovisualize the radiation of stress waves in anisotropic crystals with defects in a relatively simple
way (Holian and Ravelo, 1995; Gumbsch and Gao 1999a, b). Moreover, such simulations may
also bring information in the framework of the non-linear dynamic approach, since theinteratomic forces are of a non-linear character.
In molecular dynamic (MD) simulations, Newtonian equations of motion are solved for theset of atoms using an explicit time integration scheme and a very fine time integration step. The
equations for individual atoms have generally a form:(d2m/dt2 ) uα(l) + Rα(l) = 0 at interior atoms, or= Fαext(l) at surface atoms, respectively, α = 1, 2, 3.
The resulting force Rα(l), acting at an atom l in the direction xα, is calculated as a sum of the
non-linear interatomic forces between individual atoms l and k. The interatomic forces arederived from the potential energy with limited range of interaction. The forces and potentials
have to secure at least: the mechanical stability, the basic energetics of the lattice and to describe
well the elastic constants in anisotropic crystals. The other quantities denote: uα thedisplacement, Fext the external force and m the mass of the atom. MD-technique enables thesimultaneous mapping of the displacement field and the local potential and kinetic energies ofindividual atoms during defect growth in a loaded crystal. The displacement field describes wellJ. Acoustic Emission, 20 (2002)

25© 2002 Acoustic Emission Groupthe type of defect (Machová et al., 1999), while the local kinetic energies of individual atoms
describe well the wave processes during defect growth (Landa, et al., 1998; Machová andAckland, 1998; Cerv et al., 2000), it means AE on the atomic level.
In our MD simulations, relatively large bcc iron crystals are oriented along the coordination
axes x1 = [-110], x2 = [001] and x3 = [110]. We use short-ranged interatomic potentials andforces (Landa et al., 1998; Machová and Ackland, 1998) with the so-called cut-off radius rc lying
between the second and the third nearest neighbors in the bcc lattice. If the distance r between
individual atoms is smaller than the rc the interaction exists, if r ≥ rc the interatomic force is zero.
Beside the elastic constants and basic energetics, our interatomic potentials and forces also
describe well the phonon frequency spectra (lattice vibrations), in agreement with theexperimental data for bcc iron (Machová and Ackland, 1998). It secures a correct propagation of
the stress waves in the model, in agreement with the expectation according to the elastic
anisotropic continuum. The bcc lattice is projected onto a (110) plane. Plane strain conditions are
considered along x3, therefore in this direction the atomic motion is not allowed and periodic(translational) boundary conditions are imposed. The atoms are free to move in x1 and x2. Eachprojected atom represents a chain of atoms in the perpendicular [110] direction, moving
simultaneously with the projected atom. The initial temperature corresponds to 0 K and further
atomic thermal motion is not controlled. Central difference scheme and time integration step h =1 x 10-14 s have been used for time integration of Newtonian equations of motion in all thesimulations. The magnitude of the time step is sufficient to secure numerical stability in thesystem and also a reliable transfer of the elementary AE events during defect growth studied. We
consider a pre-existing crack loaded in mode I. The crack front is oriented along the direction x3= [110]. The crack plane lies either on (001) or on (-110) planes. In dependence on the type ofloading, this crystal orientation enables the observation of both a brittle fracture and important
shear processes in the slip systems <111>{112}. Before loading, surface relaxation of thecrystals is performed to avoid its influence on the kinetics of the microscopic processes studied.
The wave patterns obtained from MD simulations are discussed in the light of existing
predictions following from isotropic continuum (Freund and Rosakis, 1991; Prakash et al., 1992;
Trochidis and Polyzos, 1995; Rosakis and Tsai, 1995). To understand the basic feature of theanisotropic atomistic results, we also include a simplified reconstruction of the most important
wave patterns by means of the expected wave processes in anisotropic elastic continuum underplane strain.
2. Brittle Crack Extension
Brittle (Machová et al., 1999) or quasi-brittle (Machová and Ackland, 1998) fracture isobserved in atomistic simulations when a crack is loaded in elastic regime. We present here
brittle crack extension in a relatively long sample of the SEN-type with an edge pre-existing
crack. The sample is loaded linearly by external forces (PE) acting in the x2 direction with aloading rate dPE/dt ≈ 2.5 kN/s. It simulated a fracture tension test on a single crystal of bcc ironpresented in (Landa et al., 1998). The atomic sample has 200 atomic planes in x2 and 50 atomic
planes in x1. The initial crack is atomically sharp, the crack plane is (001) and the initial crack
length is lo = 15 d110, where d110 = ao/√2 is the interplanar distance in a <110> direction and ao is
the lattice parameter. The edge crack (001)/[110] (crack plane/crack front) propagates in the
direction x1 = [-110]. Crack initiation has been monitored at the applied stress intensity KIC=1.23 MPa m1/2 (Landa et al., 1998).
26

Fig. 1 Brittle initiation of an edge crack (001)/[110] in tension mode I from MD simulations.
Detail of atomic configuration at the crack tip at time step Nstep =1650 (stationary crack) and at
Nstep = 2000 (brittle crack extension).
The details of atomic configuration at the crack tip before crack initiation (time step 1650)
and during crack extension (time step 2000) are shown in Fig. 1. According to Fig. 1, the crack
extension corresponds to 8 elementary displacements d110 in the atomic lattice, i.e. when 8elementary AE events have been realized.
Figure 2 presents a relief of the kinetic energy distribution inside the whole atomic sample,
created from the mapping of local kinetic energies at individual atoms (li) and scaled as(Ekin(li)/10-22)1/2 . The black horizontal line denotes the actual crack length. The configuration attime step 1650 illustrates the situation before crack growth, where wave patterns arise only due
to the loading and scattering of loading waves at the free crack faces (Machová and Ackland,1998) (including the crack tip). The loading waves arrive to the crack faces continuously after
each increment of loading at external sample borders. The situation at time step 2000 is already27

Fig. 2 A relief of the local kinetic energies at individual atoms obtained from MD for thestationary crack (Nstep = 1650 – scattering of loading waves at the crack) and after brittle crack
extension in mode I (Nstep = 2000 – stress wave radiation from the moving crack, quasilongitudinal waves).
influenced by the crack extension. The 8 partial AE events may be recognized in the [001]direction, where the 8 wave fronts propagate to the upper and lower sample border being underload. Acoustic emission from the crack tip in the [001] directions is well visible at the time step
2000, since the first reflection of the wave front in the [001] directions appears at the [001]borders later, nearly at time step 2100. The character of the acoustic emission is given mainly by
the relaxation of the tension stress components in front of the crack tip and by bond breakage
during crack extension. The position of the individual wave fronts well corresponds to thevelocity of longitudinal waves CL<001>. The distances between the individual wave fronts wellcorrespond to average crack velocity Vcrack = d110/∆t = 1013 m/s, where ∆t ≈ 0.2 ps means theaverage time needed for the change of the crack front position in the lattice. Figure 2 shows that
the AE-source during brittle fracture can be characterized as a sequence of high frequency
pulses, emitted after each elementary crack tip hop, with a repetition frequency ν = 1 / 0.2 ps = 5
THz.
The patterns in the <110> and <111> directions in Fig. 2 at Nstep = 2000 are alreadyinfluenced by the reflections of the acoustic waves from the left and right sample borders. They28

have a more complicated character due to the interaction of the emitted waves from the crack tip
with the reflected waves. A similar situation arises also in experimental fracture tension tests but
at different times.
Acoustic emission during crack extension, unimpeded by back wave reflections from sample
borders, may be found also in the atomistic simulation (Holian and Ravelo, 1995) and (Machová
and Ackland, 1998). The character of acoustic emission during crack extension presented here
and in (Holian and Ravelo, 1995; Machová and Ackland, 1998) is in a qualitative agreementwith the expectation by continuum model, presented in (Freund and Rosakis, 1991). The wave
patterns in (Machová and Ackland, 1998) are somewhat influenced by transient local twinning at
the crack tip.
3. Transonic twinning from the crack tip
In agreement with experimental observations in bcc iron (Hertzberg, 1983; Smida andBosansky 2000), twinning in atomistic simulations occurs at some stress concentrators either
under high strain rates (Machová and Ackland, 1998) or at sufficiently large applied stress at low
temperatures in a quasi-static (Machová et al., 1999; Hu et al., 1998) or static case (Cerv et al.,2000).
We present here unpublished results on the character of acoustic emission during fast
twinning from the crack tip under a gradual quasi-static loading from Machová et al. (1999). A
central pre-existing crack (001)/[110] is considered in an initially rectangular sample. Due to thesymmetry of the problem, only one half of the sample is treated in the simulations. The initialhalf crack length is lo = 20 d110. The simulation box consists of 200 atomic planes (-110) in the x1
direction and 400 planes (001) in the x2 direction, parallel to loading. The sample was loaded
gradually (during 4000 time steps) by external forces distributed at the upper and lower (001)sample borders up to the level corresponding to Griffith stress σG.
Twin formation at the crack tip in the slip systems <111>{112} (Fig. 3) and a fast twin
extension into bulk crystal is already observed during the linear phase of loading between time
steps 3000 and 3360. As follows from Machová et al. (1999), the unstable three layers stacking
fault (3SF) at the crack tip has been transformed to a thin twin at time step 3000. At this moment,
the relative shear displacements between interior neighboring planes k and l in the <111>{112}shear systems near the crack tip reach the expected value for ideal twinning ukl = b/3, where b =ao/2 <111> is the Burgers vector of the magnitude ao√3/2. As mentioned in Cerv et al. (2000),
the length of the initial twins in the <111> direction corresponds to about 10b at time step 3000.
The twins extended up to 30b at time step 3100, at time step 3200 the corresponding length of atwin lamella is 50b, at 3300 it is 70b and at final step 3360 (see Fig. 1 in Cerv et al., 2000) thetwins are extended in the <111> directions up to the distance 82(±1)b from the crack tip. (Notethat 100 time steps represent 1 ps). The twins extend from the crack tip toward the crystal with aconstant velocity Vtwin = 1.65 CT , where CT = 3007 m/s is the velocity of the shear waves in the<111>{112} slip systems. The slip system is inclined with respect to the [-110] direction at theangle θ ≈ 35o. Note that Burgers vector b corresponds to an elementary distance between atoms
in the <111> direction.
Figure 4 shows a local map of kinetic energies of individual atoms, normalized by 1J. The
individual figures correspond to the situation at time steps 3100, 3200, 3300 and 3360. The wave
patterns at the upper and lower sample borders come from the gradual (step by step) external
29

Fig. 3 Atomic configuration at the crack tip during twin generation (a) and fast twin extension(b) at a crack (001)/[110] loaded in mode I.
loading. These patterns do not occur in Cerv et al. (2000), where transonic twinning under static
loading is presented. Exchange of interactions occurs during twinning, i.e. some bonds arebroken and new created in the <111>{112} slip systems. The bond breakage and relaxation of
the shear stresses contribute to acoustic emission during twin extension. Our results are notinfluenced by the back wave reflections from external sample borders, caused by the shear bond30

Fig. 4 Local map of kinetic energies of individual atoms from MD in the simulated sample
loaded gradually in mode I at time steps 3100, 3200, 3300 and 3360. The pre-existing crack(001)/[110] is shown as the horizontal black line. The oblique lines emanating from the black
twin tips are shock waves generated by transonic twinning.
31

Fig. 5 Sections of the ray (wave) surfaces (quasi-longitudinal (qL) and quasi-transverse (qT)) inthe (110) plane in bcc iron under plane strain.
breakage. When twins expand with a velocity higher than the velocity of shear waves, shockwaves are expected, both in continuum model (Rosakis and Tsai, 1995) and in our atomistic
model. Although the situation in Fig. 4 is influenced by gradual loading, bond breakage andstress relaxation mentioned above, the shock waves arising during transonic twin extension are
clearly visible as oblique lines at twin tips. The oblique lines represent the envelopes of
elementary shear waves, generated by the twin tips and by the twin corners (Rosakis and Tsai,
1995) when they propagate with a speed higher than the velocity of shear waves in the shear
direction <111>. Contrary to isotropic medium, the elementary waves have slightly complicatedwave fronts due to the anisotropic cubic symmetry (Fig. 5). The oblique lines in Fig. 4 make with
twin boundaries so called Mach angles of about 46o. It is in a reasonable agreement with angles
47o-55o coming from a reconstruction (Fig. 6) of the fast moving ray (wave) surfaces, generated
by the upper moving twin tip in the <111> direction in elastic anisotropic continuum of the sameorientation as in our case. The results for a static loading presented in (Cerv et al., 2000) showthat the character of acoustic emission at twin boundaries is similar, i.e. in our case transonic
twinning is not significantly influenced by the non-zero loading rate. The hot (black) finestructure at the twin tips is probably caused by fast structural changes at the twin tips and also by
32

Fig. 6 Reconstruction of the shear wave patterns for fast twinning in anisotropic continuum of
the same orientation as in Fig. 5. The crosses denote the position of the moving twin tip (at theupper twin in Fig. 4) with a constant transonic velocity Vtwin = 1.65 CT. The distance between
crosses corresponds to Burgers vector b = ao/2 <111> and the time corresponds to 0.5 ps after the
beginning (start) of the fast twin extension. The oblique lines under the angles 47o and 55odenote the envelopes of the individual shear waves fronts and they represent the shock waves
arising after transonic motion of a local source of the shear stress waves.
partial twinning dislocations that operate at interfaces with transonic velocities. Note that in thiscase twinning occurs in the easy twinning direction, described e.g. in Hertzberg (1983).
4. Dislocation emission from the crack tip
Emission of complete edge dislocations with Burgers vector b = ao/2 <111> in the slip
systems <111>{112} has been observed recently (Rosecky et al., 2001) in atomistic simulations
with a different crack orientation (-110) / [110] – see Fig. 7. In this section we present new
results on the character of acoustic emission during subsonic motion of dislocations away fromthe crack tip.
33

Fig. 7 Detail of atomic configuration at the crack tip: (a) during dislocation generation at a preexisting crack (-110)/[110], (b) at pair emission of complete edge dislocations in the slip
systems <111>{112}. The crack is loaded in tension mode I.
34

A central pre-existing crack (-110) / [110] is embedded in a rectangular sample. Due to thesymmetry, only one half of the sample is treated in the simulations. The initial half-crack length
is lo = 30 ao/2. The simulated crystal consists of 300 planes in the x1 = [-110] direction (parallel
to loading) and 300 planes in the x2 = [001] direction. The sample is loaded gradually (during6000 time steps) by external forces up to the level corresponding to Griffith stress σG. After thatthe external forces are kept at the constant level.
Dislocation generation at the crack tip (Fig. 7a) has been observed at time step 6500. In thiscase, the slip system <111>{112} is inclined with respect to the [001] direction at an angle θ ≈55o and dislocation motion away from the crack tip is realized in the anti-twinning direction(Hertzberg, 1983). During the dislocation generation, the relative shear displacements U01, U12,
U23 and U34 at the crack tip have been monitored between the planes 0,1,2,3,4 respectively (seeFig.7a). The most significant component is the U01, the other relative shear displacements were
negligible (small) during the process. The relative shear displacement U01 reached the expectedvalue U01 = b at time step 6549, when the generation process finished and the two complete edge
dislocations in Fig.7b were emitted from the crack tip. The emission is realized as a fast block
like shear of the lower crystal parts lying below the slip planes, which enlarges crack opening
displacements and causes crack tip blunting and crack stability. At this moment (time step 6549),
the position of the dislocation nucleus in the <111> direction corresponds to a distance 8b fromthe crack tip and the dislocations start to move in the <111>{112} slip systems away from thecrack tip. At time step 6700, the position of the dislocation nucleus already corresponds to 23b.
At time step 7000, the dislocations occur at a distance 53b from the crack tip. The averagedislocation velocity is Vdisl = (53 – 8) b/(7000 – 6549) h = 2476 m/s; i.e. it is subsonic, smaller
than the velocity of the shear waves in the slip system <111>{112}.
Shear bond breakage and exchange of interactions exist in the slip systems also duringdislocation emission and dislocation motion. The presented results are not influenced by the backwave reflections from the external sample borders.
The local kinetic energies of individual atoms in the whole crystal are visualized at thementioned time steps 6500, 6549, 6700 and 7000 in Fig. 8. Unlike Fig. 4, the loading waves are
not visible in Fig. 8 since the external forces are kept at a constant level after the time step 6000.
The higher kinetic energy, the darker color in Fig. 8 occurs, similar to Fig. 4. The crack length in
Fig. 8 is shown as a vertical short line. The oblique dark patterns in figures R6500 and R6549
denote the “hot” slip systems <111>{112} where dislocation generation is realized and the local
kinetic energies of the participating atoms are very high. Small shock patterns are visible atdislocation positions in figure R6549, since the emission process is very fast, as alreadymentioned in (Holian and Ravelo, 1995). The fast crack opening after the emission significantly
contributes also to the generation of longitudinal waves. The wave front of the longitudinal
waves emanating from the crack is well visible in figure R6700 as a light half ring around thecrack, located in front of the dark patterns. The dark oblique line patterns represent the slip
systems. The darkest place at the end of each slip system is the moving dislocation nucleus. The
wave fronts of the shear waves generated during subsonic motion of the dislocations are visible
almost perpendicularly to the slip systems. While the analysis (Trochidis and Polyzos, 1995)
brings information on transverse elastic displacements caused by motion of a dislocation in
isotropic continuum, we are concerned with the field of atomic velocities in anisotropic medium.
The reconstruction of the shear wave processes arising in anisotropic continuum due to a moving
source with the speed Vdisl = 0.82 CT is shown in Fig. 9. This figure shows that the darker region
at the end of the lower slip system in Fig. 8 is created as a diffusive wave front of the shear35

Fig. 8 Local kinetic energies of individual atoms from MD in the simulated cracked crystal:
R6500 - dislocation generation at the crack tip at time step 6500; R6549 - emission of thecomplete edge dislocations in the slip systems <111>{112}) at time step 6549; R6700 and
R7000 - subsonic motion of the two dislocations with a velocity Vdisl = 0.82 CT away from thecrack tip. The crack (-110)/[110] is shown by vertical short lines, the slip systems are visible as
the thin oblique patterns starting from the crack tip.
waves emitted by the moving dislocation. A larger level of the kinetic energy and of atomic
velocities is expected in the region of the shear wave front and so, this region in Fig. 8 is dark.
The shear waves are generated during the shear bond breakage and relaxation of the shear stressin the <111>{112} slip systems. They represent the most intensive processes in the system. The
relaxation of the normal stress components (dilatation above and compression below thedislocation slip plane) does not seem to contribute significantly to acoustic emission from themoving dislocations. It indicates figure R7000, where the wave front of the longitudinal waves isalmost already invisible.
The results on transonic twinning and subsonic motion of dislocations in the equivalent slip
systems <111>{112} illustrate how different shear wave patterns arise during transonic andsubsonic motion of a local AE source.
36

Fig. 9 Reconstruction of the shear wave patterns in anisotropic elastic continuum for subsonicdislocation motion. The crosses in the direction <111> denote the position of the dislocation (thelower dislocation in Fig. 8) moving with the velocity Vdisl = 0.82 CT. The distance between
crosses corresponds again to Burgers vector. This figure shows that the darker region at the endof the lower slip system in Fig. 8 represents a diffusive wave front of the shear waves emitted by
the moving dislocation.
MD simulations offer an opportunity to study acoustic emission during a defect growth on
atomic scale level, where very high frequencies in THz region may occur. It is not yet directly
accessible experimentally, where to at most GHz frequency of AE can be detected. However, theemitted pulses still contain low-frequency components, which have low intensity, but may be
detectable.
Acknowledgements
The work was supported by the grant agencies of the Czech Republic under a contract
GA106/00/D106 and of the Academy of Sciences CR under contracts A2076001 and A2076201.
37References
J. Cerv, M. Landa and A. Machová (2000), “Transonic twinning from the crack tip”, Scripta
mater., 43, 423-428.
L. B. Freund and A. J. Rosakis (1991), “The structure of the near tip field during transient elastodynamic crack growth”, research report R.I.02912, Brown University, Division of Engineering,
April 1991, pp. 1-23.
P. Gumbsch and H. Gao (1999a), “Dislocations faster than the speed of sound”, Science, 283,
965-968.
P. Gumbsch and H. Gao (1999b), “Driving force and nucleation of supersonic dislocations”, J.
Computer-Aided Materials Design, 6, 137-144.
R. W. Hertzberg (1983), Deformation and fracture mechanics of engineering materials, John
Wiley, New York, p. 119.
B. L. Holian and R. Ravelo (1995), “Fracture simulations using large-scale molecular
dynamics”, Phys. Rev., B 51, 11 275-11 289.
S. Y. Hu, M. Ludwig, P. Kizler and S. Schmauder (1998), “Atomistic simulations of deformationand fracture of alpha-Fe”, Modelling Simul. Mater. Sci. Eng., 6, 567-586.
M. Landa, A. Machová, Z. Prevorovsky, J. Cerv and J. Adámek (1998), “Crack growth in single
crystals of α-iron (3 wt% Si)“, Czech. J. Phys., B 48, 1589-1606.
A. Machová and G. J. Ackland (1998), “Dynamic overshoot in α-iron by atomistic simulations”,
Modelling Simul. Mater. Sci. Eng., 6, 521-542.
A. Machová, G. E. Beltz and M. Chang (1999), “Atomistic simulations of stacking fault
formation in bcc iron”, Modelling Simul. Mater. Sci. Eng., 7, 949-974.
V. Prakash, L. B. Freund and R. J. Clifton (1992), “Stress wave radiation from a crack tip during
dynamic initiation”, J. Appl. Mech., 59, 356-365.
P. Rosakis and H. Tsai (1995), “Dynamic twinning processes in crystals”, Int. J. Solids Structure,32, 2711-2723.
Z. Rosecky, A. Machová and J. Cejp (2001), “Dislocation emission from the crack tip in bcc
iron”, CD-ROM Proceedings of the 3rd International Conference on Materials Structure &Micromechanics of Fracture, Brno, Czech Republic, June 27-29, 2001.
T. Smida and J. Bosansky (2000), “Deformation twinning and its possible influence on theductile brittle transition temperature of ferritic steels”, Mater. Sci. Eng., A287, 107-115.
A. Trochidis and P. Polyzos (1995), “Dislocation dynamics and acoustic emission during plastic
deformation of crystals”, Wave motion, 21, 343-355.
38

A WAVELET TRANSFORM APPLIED TO ACOUSTIC EMISSION
SIGNALS: PART 1: SOURCE IDENTIFICATION #M. A. HAMSTAD+, A. O’GALLAGHER and J. GARYNational Institute of Standards and Technology, Materials Reliability Division (853), Boulder,
CO 80305-3328;+
Also Department of Engineering, University of Denver, Denver, CO 80208.
Abstract
A database of wideband acoustic emission (AE) modeled signals was used in Part 1 to examine the application of a wavelet transform (WT) to identify AE sources. The AE signals in the
database were created by use of a validated three-dimensional finite element code. These signals
represented the out-of-plane displacements from buried dipole sources in aluminum plates 4.7mm thick and of small and large lateral dimensions. The surface displacement signals at three
far-field distances were filtered with a 40 kHz high-pass filter prior to applying the WT. The
WTs were calculated with AGU-Vallen Wavelet, a freeware software program. The effects ofpropagation distance, AE source type, and the depth of the AE source below the plate surface
were examined. Specifically, a ratio of the WT magnitude (WT coefficient) from the fundamental anti-symmetric mode to that from the fundamental symmetric mode was studied for correlation with the AE source type. The WT magnitudes were those corresponding to a particulargroup velocity and signal frequency for each mode. For sources in the large plate located at thesame depth, the ratios were able to distinguish different source types and exhibited only smallchanges with increasing propagation distance. But, when the variable of depth of the source wasintroduced, the ratios did not uniquely classify the AE source type. In the case of the small coupon plate specimen, reflections from the specimen edges distorted and complicated the WTs.
Since the current coupon database excludes (except for one case) the parameter of changes in the
distance of the source from the coupon sides, a full examination of these complications was notpossible.
1. Introduction
Since early in the history of AE, a goal of AE practitioners has been to use AE signals as themeans to identify the type of source that generated the signals (Mehan and Mullin, 1971). Papers
have been published indicating the successful identification of AE sources, and commercial AE
companies offer software for the purpose of identifying AE sources. These efforts are often controversial because they lack an analytical justification (based on the theory of AE) of the signal
features used to sort the experimental signals into different types of sources. Alternatively, some
AE source-identification experiments have been carried out with specimen geometries and sensor locations such that signals are obtained from the direct longitudinal bulk waves in several
directions of radiation (Buttle and Scruby, 1990a). The analysis that is used to sort these signals
into different source types (or combinations of source types) is based on analytical calculations(forward modeling) that determine the relative amplitudes of the first bulk longitudinal signals in
different radiation directions. By comparing relative experimental bulk wave amplitudes in different directions with the calculated results for a series of possible sources, the experimental#

Contribution of the U.S. National Institute of Standards and Technology; not subject to copyright in the US.

J. Acoustic Emission, 20 (2002)

39© 2002 Acoustic Emission Groupsources were identified in a more satisfying fashion. However, in many AE applications Lambwaves are present due to plate-like test specimens and observation of the AE signals in the farfield. In this case, this more satisfying approach is not directly applicable. Some AE researchers(Weaver and Pao, 1982a, b; Guo et al., 1998) have presented analytical results (forward modeling) for Lamb waves in infinite plates, but to date they have not published an extensive databasethat could lead to source identification of experimental signals by appropriate comparisons.
The AE source-identification research presented here uses an extensive database of modeled
Lamb-wave AE signals. These signals were obtained by use of a validated finite-element modeling code (Hamstad et al., 1999). Since the exact source type and all its characteristics are
known for each signal, the features of the AE signals can be unequivocally associated with particular source types. In the case of experimental signals, this is not the case. In this paper we examine the possibilities of extracting meaningful source identification features by the use of awavelet transform (WT). To retain the possibility of a direct method of source identification(rather that an artificial-intelligence method), this paper examines the extraction of limited data
sets from the WT results. In addition, if the direct method is not completely successful, these results could provide insight into the most relevant WT results to input into an artificialintelligence approach.
2. Description of AE Database
The AE signals that comprised the analyzed database were all calculated by the NIST- Boulder finite-element modeling (FEM) code. This code has been validated (Hamstad et al., 1999;
Prosser et al., 1999) for buried dipole-type point sources operating in plate specimens with infinite lateral dimensions and for surface and edge monopole sources on plates with small and large
lateral dimensions. The specimen domains in the database were aluminum plates 4.7 mm thick.
These plates had lateral dimensions of either 1000 mm by 1000 mm (representing the infiniteplate case) or 480 mm by 25.4 mm (representing a small coupon specimen). Figure 1 shows adrawing of the small specimen superimposed on the large specimen. The lateral position of the
AE source and the positions where the plate top surface out-of-plane displacement was determined as a function of time are shown. These displacement signals provide the AE signals thatwould be obtained in a single direction of radiation (toward sensors 1, 2 and 3) or the opposite
direction (toward sensors 5, 6, and 7). All the sensor positions were 60 mm apart.
The calculated signals provided a unique and ideal database that was far superior to experimental AE signals for the study of AE source identification. The reasons for this superiority are
due to exact knowledge of key information not always available in experimental data: pointsource location in three dimensions; source rise time; magnitude and orientation of source dipoles; absolute out-of-plane displacement of a perfect wideband point sensor at an exact location; known filtering of the AE signal; signals both with specimen edge reflections and without
such reflections; and signals that are largely free of noise.
For this study of AE source identification, the analyzed database included three types ofburied point-sources: (1) in-plane dipole, aligned with the propagation direction to the sensors,(2) out-of-plane dipole, and (3) crack initiation (three dipoles) with the largest dipole alignedwith the propagation direction to the sensors. The dipole forces (body forces) were all 1 N except for the two smaller dipoles in the microcrack case, which were 0.52 N (based on the elastic
constants of aluminum). Each dipole was made up of a “central” cell having no body force,along with single cells on each side of the “central” cell having body forces. All the
40

1000
500

Source below sensor 4

Sensornumbers

500
1

2

3 4

5

6

7

1000
90

30

25.4

Fig. 1 Large specimen with superimposed small coupon specimen. Sensor positions, spaced 60
mm apart, show points where out-of-plane displacement was determined versus time. All dimensions in mm.
calculations were based on a uniform three-dimensional cell size of 0.313 mm, which was chosen to conform to the requirements for convergence (Hamstad et al., 1999). For the examinationof propagation distance, the database included three propagation distances (60, 120, and 180mm). The three distances were all in the far-field for the 4.7 mm thick plate. In addition, thedatabase included the very important (as will be seen below) variable of depth of the point source
below the top surface of the plate. All the sources had a rise time (of the dipole) of 1.5 µs with a“cosine bell” temporal dependence (Hamstad et al., 1999). This rise time resulted in AE signals
with frequencies up to about 1 MHz (as determined from spectra calculated by a Fast Fourier
Transform (FFT)). To provide a realistic database, all the AE signals were filtered with a 40 kHz
high-pass four-pole Butterworth filter prior to analysis. Also, to represent experimental practice,
all the calculated digital signals were resampled to a time step of 0.1 µs from the original calculation time step of 0.045 µs. Comparisons demonstrated that this resampling had no apparent
effect on the waveforms or their FFTs. The calculated total signal length in each case was 200µs beginning from the start of operation of the AE source.
3. General Examination of WT Results
Figure 2 shows an example of an AE signal without edge reflections and its calculated WT
magnitude (WT coefficients) as a function of frequency versus time. The source for this signal
was an in-plane dipole centered at a depth of 1.723 mm (the center of the “central” cell) below
the plate top surface. The signal represents the top-surface out-of-plane displacement at 180 mmfrom the epicenter of the source. All the WTs were calculated with AGU-Vallen Wavelet, afreeware software program (Vallen-Systeme GmbH, 2001; H. Suzuki et al., 1996). AGU-Vallen
41

A0

S0

Fig. 2 Typical calculated AE signal from an in-plane dipole source with corresponding WT.
Superimposed fundamental symmetric, So, and anti-symmetric, Ao, modes after convertinggroup velocity to time based on 180 mm propagation distance. Red color corresponds to highest
magnitude of the WT. Source depth is 1.723 mm in large specimen. (WT scale: 1 MHz: 150 µs)
Wavelet has been developed in collaboration between Vallen-Systeme GmbH and Aoyama
Gakuin University (AGU), Tokyo, Japan. The AGU group has pioneered in the research ofwavelet analysis in the field of acoustic emission (Suzuki et al., 1996; Takemoto et al., 2000;
Yamada et al., 2000). This program has a Gabor function as the “mother” wavelet with a central
frequency of 7 MHz. The software program also includes a program to calculate the relevantgroup-velocity curves for the lowest ten modes of the infinite number of Lamb modes that govern the far-field wave propagation in a plate. Figure 2 shows the two lowest modes (fundamental
symmetric, So, and anti-symmetric, Ao) superimposed on the WT. This superposition is facilitated by an option that converts the group velocity scale to a time scale using the known exact
propagation distance. The group-velocity curves were calculated by use of the same bulk velocities that were used in the finite-element computations (6,320 m/s bulk longitudinal velocity
and 3,100 m/s bulk shear velocity). The FEM calculation also uses the material density of 2700
kg/m3. The color scale in Fig. 2 is a linear scale with red representing the highest-magnitude region of the WT and pink the smallest or zero-magnitude region. The display includes an option
to change the color scale (called the color factor) so as to include a greater or lesser portion of
the maximum magnitudes in the “red” region. The WT in Fig. 2 has a color-factor (CF) value of
0.8. A color factor of less than 1 (the default value) means that a wider range of the WT peakmagnitudes are displayed in red. The converse applies for color factors greater than 1.
42

Fig. 3 FFT of the waveform shown in Fig. 2.
Clearly, Fig. 2 shows the presence of AE signal energy in portions of both the fundamental
symmetric and anti-symmetric modes. The WT also demonstrates that the AE signal energy is
not uniformly distributed between the modes that are present, nor is it uniformly distributed as afunction of frequency along each of the dominant modes. These aspects demonstrate a key advantage of the WT as compared to an FFT. The WT shows how the signal energy is distributed
as a function of frequency, time (or group velocity), and mode. In contrast, as is well known andillustrated in Fig. 3, the FFT of the same AE signal provides only the frequency content of thewhole signal and does not allow one to easily see the modal division or how the intensity of theenergy in particular frequency ranges and modes varies as a function of time (or group velocity).
Thus, Fig. 2 shows that this AE source has the greatest concentration (most red color) of energy
in the anti-symmetric fundamental mode in a frequency range of about 40 to 80 kHz over a rangeof group velocities from 1.8 to 2.7 mm/µs. Another large amplitude region of the WT is the partof the fundamental symmetric mode in a frequency range from about 500 to 540 kHz, which iscentered about a group velocity of about 1.9 mm/µs. With regard to these frequency ranges andgroup velocities, we point out that the WT algorithm allows the user to select both the frequency
resolution and the wavelet size. The effects on the WT results of different choices for these parameters are discussed next.
4. Parameter Selection to Enhance the Resolution/Smoothness of WT Results
The wavelet transform program, AGU-Vallen Wavelet, used in this research is that freely
distributed on the World Wide Web (see web site www.vallen.de). There are several parametersthat need to be selected prior to the wavelet calculation. Figure 4 shows the setup screen for thiswavelet transform. Two parameters must be chosen properly to obtain sufficient resolution of
the WT. These parameters are the “Frequency Resolution” and the “Wavelet size”. The “Freq.
Resolution” gives the frequency interval for the WT calculation, and has a default value of 10
43

Fig. 4 “Wavelet Setup” screen for AGU-Vallen Wavelet.
kHz. This value is not necessarily the best, as is shown in Fig. 5. In this case the WT was calculated for an in-plane dipole centered at a depth of 0.47 mm in the large plate with a source-tosensor propagation distance of 180 mm. Figure 5 (a) shows the WT result (CF = 1) with the default value of “Freq. Resolution” of 10 kHz and a “Wavelet size” of 800 samples, and Fig. 5 (b)shows the WT result (CF = 1) using a “Freq. Resolution” of 2 kHz and the same “Wavelet size”.
Clearly the 10 kHz frequency steps can be seen in Fig. 5 (a). These steps are not present in Fig.
5 (b), which shows a much smoother result. The disadvantage of using the smaller value for thefrequency resolution is that the calculation takes longer. For example, on a personal computerwith a CPU speed of 0.7 GHz, the WT shown in Fig. 5(a) took about 5 s and that shown in Fig.
5(b) took about 18 s.
The “Wavelet size” has a default value of 200 samples. Figure 6 shows the WT results of thesame AE signal used for Fig. 5 with the default value (a) and a size value of 800 samples (b).
For this figure the “Freq. Resolution” of 2 kHz was used along with a color factor of 1. It can beseen that the larger “Wavelet size” gives a smoother result. With a larger “Wavelet size” value,
the calculation requires more time. To date, our observation has been that an increase in the“Wavelet size” is advisable when the larger amplitudes in the WT are at frequencies below about
100 kHz. Thus, we conclude that it is worthwhile to enhance the resolution and smoothness ofthe WT results at the cost of slightly longer computational times. For example, on a modern personal computer, the WT shown in Fig. 6 (a) took about 6 s and that shown in Fig. 6 (b) took
about 18 s. When WT results were used to extract quantitative results, the parameters were selected to provide better resolution. In other cases lower resolution was used for more qualitative
results.
44

(a)

(b)

Fig. 5 WT results with low “Frequency resolution” (default) of 10 kHz (a) and high “Frequency
resolution” of 2 kHz (b). Both results with a “Wavelet size” of 800 samples. Figures show frequency (0 to 500 kHz) versus time (0 to 150 µs).
5. Correlation of WTs with Source Type (Large Plate)
To begin to study source identification using the FEM database, we first focus on WT resultsof calculated AE signals at three different propagation distances from a single source. Figure 7
shows the WT results (“Freq. Resolution” of 2 kHz, “Wavelet size” of 600 samples and CF = 1)
at the three available distances for an in-plane dipole source (dipole forces are aligned with thedirection of propagation to the sensors) at a depth of 1.723 mm in a large plate. This figure
45

(a)

(b)

Fig. 6 WT results with small (default) “Wavelet size” of 200 samples (a) and large “Waveletsize” of 800 samples (b). Scales are the same as Fig. 5.
demonstrates that the WT results look very similar at different propagation distances when thetime axis has been adjusted for the different distances. Due to this similarity with propagation
distance, a limited data set could be extracted from the WT results that potentially shows thatthese three signals are from the same source. This potential was examined by extracting the WT
peak magnitudes at two pairs of frequency and approximate group velocity. The frequencies andapproximate group velocities were selected, one from each of the dominant WT magnitude regions of the fundamental modes. The circles in Fig. 7 show the two modal regions where theWT peak magnitudes and associated arrival times were extracted. The selected pairs of fre46

60 mm

120 mm

180 mmFig. 7 WT results at three propagation distances 60 mm, 120 mm, and 180 mm for an in-plane
dipole source at a depth of 1.723 mm. Circles show approximate regions from which the maximum WT magnitudes and group velocities were extracted at 50 kHz (Ao) and 522 kHz (So).
Figures show frequency (0 to 1 MHz) versus time (0 to 80, 120, and 150 µs for the three propagation distances).
47

Table 1. WT magnitudes (peak value) for specified mode and frequency at indicated group velocity for in-plane dipole source at a depth of 1.723 mm.
Ao mode at 50 kHz
Propagation Indicated group
WT
distance, mm velocity, mm/µs Magnitude60
1.76
83,454120
2.02
58,265180
2.17
46,415

So mode at 522 kHz
Indicated group
WT
velocity, mm/µs Magnitude
1.88
77,724
1.86
45,904
1.84
33,225Magnitude ratio,
Ao/So
1.1
1.3
1.4

Table 2. WT magnitudes (peak value) for specified mode and frequency at indicated group velocity with sources at a depth of 1.723 mm and a propagation distance of 180 mm.
SourceType
Out-of-plane
In-plane
Crack initiation

Ao mode at 50 kHz
Indicated group
WT
velocity, mm/µs
Magnitude
2.18
22,8192.17
46,415
2.14
34,603

So mode at 522 kHz
Indicated group
WT
velocity, mm/µs
Magnigude
1.84
35,924
1.84
33,225
1.83
14,546

Magnitude ratio,
Ao/So
0.64
1.4
2.4

quency and group velocity were 50 kHz and about 2.16 mm/µs for the flexural mode (Ao) and
522 kHz and about 1.84 mm/µs for the extensional mode (So). The extraction of the peak WTmagnitudes and their arrival times (in the approximate group-velocity region) was facilitated by
an option that allows exporting the WT results into spreadsheets. Table 1 shows the extracted
results. The indicated group velocity was obtained by dividing the extracted arrival time into the
propagation distance from the source epicenter to the sensor location. Table 1 also shows theratios of the Ao/So peak WT magnitudes at the selected frequencies and group velocities. The
use of such a ratio provides a measure that is independent of the original source strength.
Although the Ao/So ratio experiences an increase with increasing propagation distance, thischange could be corrected for by developing approximate rates of travel-distance attenuation of
the WT magnitudes for the different modes and frequencies being used. Thus, it seems a limited
set of data can be extracted (from WT results) that indicates the same source was observed at different propagation distances. However, it should be pointed out that the current dataset studies
only the effect of propagation distance in one source-radiation direction (and the direction 180
degrees opposed; a symmetrical direction). Since a source in a plate emits different amounts ofenergy of the bulk modes in different radiation directions, future research should check the above
conclusions (for source identification) when the propagation distance varies along with the twodimensional radiation direction in the plane of the plate. Due to the typical symmetries of radiation patterns of sources aligned with the plate coordinate axes, this check will need to be done for
only one quadrant rather than for a full 360 degrees.
To extend the study of extraction of a limited database for AE source identification from WT
results, the next focus was on three different source types and their signals at a fixed propagation
distance of 180 mm. Figure 8 shows (CF = 1) the WTs for an in-plane dipole (a), out-of-plane
dipole (b), and a microcrack initiation (c) with all the sources centered at 1.723 mm below the
top surface. These WTs were calculated with a “Frequency Resolution” (FR) of 2 kHz and a“Wavelet size” (WS) of 600 samples. Following the same procedure that was used with Fig. 7 toobtain the data in Table 1, the data shown in Table 2 were obtained from the WTs in Fig. 8.
Again circles have been drawn in Fig. 8 about the regions from which the modes, approximategroup velocities and frequencies were selected. Examination of Table 2 indicates that the sig48

(a)

(b)

(c)

Fig. 8. WT results from out-of-plane displacement signals for three different source types: (a)in-plane dipole, (b) out-of-plane dipole and (c) crack initiation. Sources are all at a depth of
1.723 mm. Circles show approximate regions from which the maximum WT magnitudes andgroup velocities were extracted at 50 kHz (Ao) and 522 kHz (So). Figures show frequency (0 to
1 MHz) versus time (0 to 150 µs) for the signals at 180 mm propagation distance.
nificant changes of the simple Ao/So ratio potentially could be used for source identification.
The out-of-plane dipole source had the smallest ratio, and the crack-initiation source had thelargest ratio.
49

@ 2.35 mm,
mid-plane@ 1.41 mm@ 1.097 mm@ 2.037 mm@ 0.47 mm@ 1.723 mm

Fig. 9. WT plots as a function of the indicated source depths below the plate top surface. The
source was an in-plane dipole with a 180 mm propagation distance. Figures show frequency (0to 1 MHz) versus time (0 to 150 µs).
Examination of WT results for a single source type as a function of source depth reveals
some potential difficulties in the approach suggested above. Figure 9 demonstrates the changes
in WTs (with default values and CF = 1 for the AE signals at 180 mm) for an in-plane dipole as afunction of the depth of the source below the top surface of the plate. Even a casual examinationof the results in this figure shows that the WT result varies substantially as the depth of thesource changes. For the source located at the mid-plane (at 2.35 mm) the fundamental symmetric (extensional) mode dominates. As the source is moved closer to the surface, it is clear thatthe energy carried in the extensional mode decreases and most of the energy is carried in thefundamental anti-symmetric (flexural) mode. This dependence of the dominant Lamb modes
and associated frequencies can also be seen in the signal waveforms and their FFTs. These results are shown in Fig. 10 as a function of source depth for the same series of depths. The dependence of the WT results on source depth also is apparent for the out-of-plane dipole source
and the more complicated microcrack initiation source. These WT results (default parameters
and CF = 1) for the AE signals at 180 mm are shown in Fig. 11 for two different depths (0.783
and 1.723 mm).
50

500

0

250

-10
10

0
500

0

250

-10
10

0
500

Spectral amplitude, fm

Displacement, pm

10

0
-10
10
0
-10
10

@ 2.35 mm,
mid-plane@ 2.037 mm@ 1.723 mm

250
0
500

@ 1.41 mm

250
0
500

0

250

-10
10

0
500

@ 1.097 mm@ 0.47 mm

250

0

0

-10
20

50

80
110
Time, µs

0

140

0.2

0.4
0.6
0.8
Frequency, MHz

1

Fig. 10. Calculated AE signals with corresponding FFTs (in the same sequence top to bottom)
for same cases shown in Fig. 9.
The possibility of extracting from WTs (of the out-of-plane displacement signals) a more
limited data set to uniquely identify the different AE sources certainly exists. However, it iscomplicated by the dependence of the AE signals and their WTs on the depth of the sources.
There are two primary reasons for this complication. First, the Ao/So ratio changes not just withsource type but with source depth. The primary change (for the three source types considered)that takes place when the depth of a source changes is a transfer of more energy to either the extensional mode or the flexural mode from the alternate mode. Thus, it does not seem possible toextract simple Ao/So ratio information, such as in Tables 1 and 2, from the WTs that is unique toa particular source type. Second, since at a fixed depth there are clear differences between
source types (see Fig. 8 and Table 2), it is likely, as a consequence of the transfer of energy between modes, that two different sources at two different source depths could have very similar
WTs. This is in fact the case, as Figs. 12 and 13 show (calculated using default WT parameters)
for signals at 180 mm from the sources. Figure 12 shows (CF = 1) that a microcrack initiation
source (a) at a depth of 0.783 mm has nearly the same WT as an in-plane dipole source (b) at a
depth of 0.47 mm. Figure 13 demonstrates (CF = 1) the close WT similarities between an outof-plane dipole source (a) at a depth of 1.723 mm and an in-plane dipole source (b) at a depth of
2.037 mm.

51

Microcrack

Out-of-plane dipole

Source @0.783 mmdeep

Source @1.723 mmdeep

Fig. 11. WT plots showing the distinctions between a microcrack initiation source and an out-ofplane dipole compared at the same two different source depths and at 180 mm propagation distance. Plots show frequency (0 to 1 MHz) versus time (0 to 150 µs).
One might consider whether the lower-magnitude regions of the WTs in Figs. 12 and 13
might provide suitable simple features that would distinguish these sources. This might be thecase as shown in Fig. 14, which shows WTs for the same signals used for Fig. 13 calculated with
WS = 600 samples and FR = 2 kHz. Also in Fig. 14, the CF was set at 0.8 to provide a widermagnitude range for the red color region. This setting of the CF effectively shows more featuresof the lower-magnitude parts of the WTs. The arrows in Fig. 14 point to regions where the WTof the in-plane dipole (b) has a larger magnitude than the WT of the out-of-plane dipole (a). But,
this aspect that shows up very well in the FEM modeled data may not be nearly as clear when the
typical signal-to-noise ratios of real AE signals are considered. In the modeled case, data has little or no apparent noise. Thus, examining differences in regions of lower WT magnitude may
not be a practical solution for real AE signals.
To examine the depth effects more systematically and quantitatively, high-resolution WTs
were calculated from signals at a propagation distance of 180 mm for the different source types
and depths in the available database. The WT peak magnitudes at two pairs of frequency andapproximate group velocity were then extracted for each case. The frequencies and approximate
52

(a)

(b)

Fig. 12. WT plots showing the similarities for two different source types at different depths. At
180 mm propagation distance. Microcrack initiation (a) at 0.783 mm depth and in-plane dipole(b) at 0.470 mm depth. Plots show frequency (0 to 1 MHz) versus time (0 to 150 µs).

(a)

(b)

Fig. 13. WT plots show the similarities for two different source types at different depths. At 180
mm propagation distance. Out-of-plane dipole (a) at 1.723 mm depth and in-plane dipole (b) at
2.037 mm depth. Plots show frequency (0 to 1 MHz) versus time (0 to 150 µs).
53

(a)

(b)

S14_2729

S14_2698

Fig. 14. Same cases and scales as Fig. 13 with color factor changed to 0.8 to examine differences in WTs in their lower magnitude regions. Arrows point to higher magnitude regions in thein-plane dipole (b) versus the out-of-plane dipole (a).
100
Microcrack
In-plane dipole

WT magnitude ratio A0/S0

10

1Out-of-plane dipole
0.1

0.01

0.001
0

0.5

1

1.5

2

2.5

Depth below surface, mm

Fig. 15. Ao/So ratio of peak WT magnitudes versus AE source depth for the microcrack initiation, in-plane dipole and out-of-plane dipole sources at 180 mm propagation distance. Peakmagnitudes at 50 kHz and approximate group velocity of 2.16 mm/µs for Ao and at 522 kHz andapproximate group velocity of 1.84 mm/µs for So.
54

Table 3a. WT magnitudes (peak value) for specified mode and frequency at indicated group velocity for in-plane dipole source at 180 mm. (Note * indicates a peak was not present)
Sourcedepth, mm
2.35
2.037
1.723
1.41
1.097
0.783
0.47

Ao mode at 50 kHz
Indicated group
WT
velocity, mm/µs Magnitude
2.18
160*
2.18
23,1042.17
46,415
2.14
70,427
2.14
95,2092.14
121,1442.15
148,578

So mode at 522 kHz
Indicated group
WT
velocity, mm/µs Magnitude
1.84
37,124
1.84
36,1381.84
33,225
1.84
28,398
1.83
21,738
1.83
13,331
1.82
3,357

Magnitude ratio,
Ao/So
0.0043
0.64
1.4
2.5
4.4
9.1
44

Table 3b. WT magnitudes (peak value) for specified mode and frequency at indicated group velocity for microcrack initiation at 180 mm. (Note * indicates a peak was not present)
Sourcedepth, mm
2.35
2.037
1.723
1.41
1.097

Ao mode at 50 kHz
Indicated group
WT
Magnitude
veloicty, mm/µs
2.14
166*
2.14
17,2482.14
34,603
2.18
52,296
2.14
70,490

So mode at 522 kHz
Indicated group
WT
velocity, mm/µs Magnitude
1.84
16,535
1.84
16,035
1.83
14,546
1.83
12,084
1.83
8,669

Magnitude ratio,
Ao/So
0.004
1.1
2.4
4.3
8.1

Table 3c.
WT magnitudes (peak value) for specified mode and frequency at indicatedgroup velocity for out-of-plane dipole at 180 mm.
Sourcedepth, mm
2.35
2.037
1.723
1.41
1.097
0.783

Ao mode at 50 kHz
Indicated group
WT
velocity, mm/µs
Magnitude
2.16
132.7
2.14
11,3432.18
22,8192.14
34,8432.14
47,5102.14
61,099So mode at 522 kHz
Indicated group
WT
velocity, mm/µs
Magnitude
1.84
39,605
1.84
38,676
1.84
35,9241.84
31,402
1.84
25,1771.84
17,365Magnitude ratio,
Ao/So
0.0034
0.29
0.64
1.1
1.9
3.5

group velocities selected were those from the dominant WT magnitude regions of both fundamental modes. The selected values for the flexural mode (Ao) were 50 kHz and about 2.16mm/µs. For the extensional mode (So), the values were 522 kHz and about 1.84 mm/µs. Then,as before, at each frequency the maximum WT magnitude and its arrival time were determinednear the selected approximate group velocities. For certain cases the magnitudes of the WT are
relatively small at the selected frequency and approximate group velocity, and in some cases the
WT did not have a clear maximum in the approximate group-velocity region. In these latter
cases, the WT magnitude at the selected frequency was selected at the same arrival time that thepeak magnitude occurred when the same source type was at the nearest depth where the WT as afunction of time had a true maximum in that region. The values determined are shown in Tables
3a, b and c for the three different source types. Also, these tables include the calculated magnitude ratio Ao/So for the range of source depths and source types. Figure 15 shows a plot of
Ao/So versus source depth with the source type as a parameter. It is reasonable to conclude that
there is a definite ordering of the Ao/So ratio as function of source depth. The microcrack initia55

tion source results in the highest values and the out-of-plane dipole has the lowest values, with
the in-plane dipole source in between. The relative differences between the ratios at a fixed
depth are not small, since the ratio scale in the figure is logarithmic. At the mid-plane depth (2.35mm), the ratio values are likely not reliable due to the very small WT magnitudes for the Aomode. Also, these Ao values were in most cases arbitrarily selected as described above since theAo mode did not have a peak value for the approximate group velocity region of the mode.
It is clear in the case of experimental data when the source depth is unknown that the Ao/So
ratio alone will not uniquely define a source type. But, if one considers the data in Fig. 15 to bethat for a radiation direction of zero degrees, then it may be possible that results of Ao/So ratiosfrom other radiation directions could provide sufficient additional information to uniquely identify the source type. This expectation, not unlike the approach of Buttle and Scruby (1990a),
uses the fact that the radiation pattern is different for different source types. Since in experimental situations typically three or four sensors are hit when two-dimensional source location isdetermined (a prerequisite for the above approach, since the selected approximate group velocity
needs to be converted to an approximate arrival time), signals are typically available in several
two-dimensional radiation directions. For these different radiation directions, the Ao/So ratio
could be extracted from WTs of the signals. These ratios could then be compared as a functionof the radiation angle with modeled results of the Ao/So ratio versus radiation angle determinedfor different source types and depths. This method could possibly add sufficient information touniquely identify the source type and source depth. Based upon these observations in future research we expect to examine the above approach with a FEM database that includes other radiation directions.
The application to experimental AE data of the Ao/So ratio approach will likely experience
difficulties when the source is located very near the plate surfaces or very near the plate’s midplane. As the first and last rows of Tables 3a, b and c show, in these cases either the Ao or So
WT-based magnitude is small, and they do not always have a local maximum (at a given frequency and associated approximate group velocity). Thus, for experimental data from sources
near the mid-plane, effects of low signal-to-noise ratios will likely eliminate the possibility ofcalculating meaningful Ao/So ratios. A possible solution to this problem might be to focus on the
single dominant mode for these source-depth cases. For example, for a source located near or at
the mid-plane of the plate two or more frequencies and associated approximate group velocities
could be selected from the So mode. At these frequencies and velocities the maximum magnitudes of the WT could be determined within the So mode. Then ratios such as So (at 522 kHz)to So (at say 325 kHz) could be calculated. It is possible that this ratio or other appropriate ones
might result in distinguishing different source types located near the plate mid-plane. And for asource located near the plate surfaces, a ratio from two frequencies of the Ao mode could be
used. Hence, in future research we expect to examine such an approach using the current FEM
database extended to include other source types, such as a shear source, and various radiation
angles.
6. WT Data Subsets for Source Identification in Specimens with Nearby Edges
When the lateral size of the test specimen is decreased so that nearby edges are present, theAE signals and their WTs become much more complicated. This result is clearly seen in Fig. 16,
which compares WTs of the AE signals (40 kHz high-pass) at 180 mm from in-plane and out-ofplane dipole sources in the small coupon specimen (Fig. 16(a)) and the large specimen (Fig.
16(b)) both at a depth of 1.723 mm. Figure 17(a) also shows as a function of propagation dis56

In-plane dipole

Out-of-plane dipole

(a)

(b)

Fig. 16. WT results (40 kHz high-pass AE signals) for coupon specimen (a) with multiple edge
reflections compared to large specimen (b) without edge reflections. Results shown for an inplane dipole source and an out-of-plane dipole source with a propagation distance of 180 mm.
Sources centered at 1.723 mm below the top surface of the plate. Frequency (0 to 1 MHz) versus
time (0 to 150 µs). Superimposed fundamental modes shown.
tance the significant distortion in the WT results (of AE signals from an in-plane dipole) for the
coupon specimen compared to the large specimen (Fig. 17(b)) with sources at a depth of 1.723
mm. The distribution of signal energy in the coupon as shown in Figs. 16(a) and 17(a) does notclearly follow the shapes of the superimposed fundamental Lamb-mode curves from the dispersion relations. Thus the extension of the possible extraction approaches proposed for source
identification in the large plate is not straightforward for the coupon specimen. Further, since thedistortion of the WT results is due to edge reflections (sides and at later times the specimenends), moving the source from side-to-side across the 25.4 mm dimension of the coupon specimen will change the reflections in the AE signals. These source-position changes will alsochange the associated WTs as shown in Fig. 18 with superimposed fundamental modes. This
figure compares the WTs for in-plane dipole sources as a function of the transverse position of
the AE source. In Fig. 18(a), the source was located half-way across the specimen at 12.7 mmfrom the coupon side edge. In Figs. 18(b) and 18(c), the source was successively at 6.13 mm and3.31 mm from the coupon side edge. The WTs (CF = 1, WS = 600) in these figures were calculated from the AE signals at 180 mm from the sources. The source depth was 2.35 mm. The
57

60 mm

120 mm

180 mm(a)(b)

Fig. 17. WT results (40 kHz high-pass AE signals) for coupon specimen (a) with multiple edge
reflections contrasted to large specimen (b) without edge reflections at three propagation distances: 60 mm, 120 mm, and 180 mm. Source in-plane dipole centered at a depth of 1.723 mm
below the top surface of the plate. Frequency (0 to 1 MHz) versus time (0 to 80, 120, and 150 µs
with increasing propagation distance). Superimposed fundamental modes shown
current coupon database, except for the case illustrated in Fig. 18, does not include the side-toside variation of the source position.
We believe the possible use of a WT for source identification in small specimens needs study
because researchers use such specimens. Thus the necessary FEM database should be developed. But, it is prudent in the case of source identification that such studies be done after the
potential difficulty of the dependence of the AE signals on the depth of the source is resolved. If
the resolution of source depth difficulties requires the use of AE signals in several directions ofradiation, then source identification in the small coupon specimen will likely require an alternate
approach. This conclusion follows from the fact that in the coupon specimen far-field signals
will not be available for different radiation directions. Researchers might be forced to use thebulk wave approach mentioned earlier (Buttle and Scruby, 1990a) if information from different
propagation directions is required. This approach has been used in a 8-mm-thick coupon byplacing the sensors in very close proximity to known crack tips (Buttle and Scruby, 1990b). Reducing the coupon thickness will eventually result in a sample that is too thin to allow one to
58

(a)

(b)

(c)

Fig. 18. Comparison of WT results (40 kHz high-pass AE signals) as a function of transverse
position of the in-plane dipole source in the coupon specimen. The WTs with superimposed
fundamental modes were calculated from the displacement signals at 120 mm for the mid-plane
depth source. The sources were located half-way across the specimen (a) at 12.7 mm from theedge, (b) at 6.13 mm from the edge, and (c) at 3.31 mm from the edge. Plots show frequency (0to 1 MHz) versus time (0 to 120 µs).
distinguish the bulk-wave arrivals from sample surface reflections. Also, to practice the bulkwave approach, the AE sources of interest must be located in a small known region.
59

7. Conclusions
A. Source Identification−Large Plates!

!
!

For the 4.7 mm aluminum plate thickness of the current FEM database, a potentially usefulparameter for source identification has been found. Using a separate selected frequency andapproximate group velocity for each fundamental mode, the WT coefficients (usually local
maxima for the frequency and group velocity) can be combined to form an Ao/So magnitude
ratio. This ratio was found to distinguish different source types when the sources were allcentered at the same depth below the plate surface and with the same propagation distance.
But, since the values of this ratio overlap for different source types at different depths, it is
not possible to uniquely identify the source type with this small set of WT-based data.
The current database indicates that the value ranking of the Ao/So magnitude ratio for different source types remains in the same order as a function of source depth. Since this result isfor a single radiation direction, it is to be expected that obtaining this ratio for other radiation
directions will supplement the WT-based data with possibly sufficient information touniquely classify the AE source type even with changing source depth. This expectation isbased upon the fact that different source types have different AE-energy radiation patterns.
Thus a series of WT-based magnitude ratios from a total number, n, of different radiation angles (e.g., (Ao/So)1, (Ao/So)2, …, (Ao/So)n) could form an input vector to an artificial intelligence (AI) software program that would determine the AE source type. The AI program
could be initially trained using a FEM-generated database.

B. Source Identification−Small Coupon Specimens!

The inherent multiple edge reflections present in small coupon specimens complicate the useof WT coefficients for source identification. Since the current small coupon FEM database
does not include (except for one case) the important parameter (relative to edge reflection effects) of changes in specimen side-to-side position of the source, the current database did notallow a full examination of these complications.

Acknowledgement
This work was partially supported by NASA Langley. We wish to express our gratitude toProf. Takemoto, who released the source code of the wavelet transform software his group had
developed and made AGU-Vallen Wavelet available. We also thank Dr. Y. Mizutani and Mr.
Jochen Vallen for making the program into a highly usable form. Their contributions have significantly advanced the field of AE.
References
D. J. Buttle and C. B. Scruby, “Characterization of Fatigue of Aluminum Alloys by Acoustic
Emission, Part 1-Identification of Source Mechanism”, Journal of Acoustic Emission, 9, No. 4,
1990a, 243-254.
D. J. Buttle and C. B. Scruby, “Characterization of Fatigue of Aluminum Alloys by Acoustic
Emission, Part II – Discrimination Between Primary and Secondary Emissions”, Journal of
Acoustic Emission, 9, No. 4, 1990b, 255-269.
60

Dawei Guo, Ajit K. Mal and Marvin A. Hamstad, “AE Wavefield Calculations in a Plate”, inProgress in Acoustic Emission IX, Acoustic Emission Working Group and Acoustic Emission
Group, 1998, pp. IV-19 to IV-29.
M. A. Hamstad, A. O'Gallagher and J. Gary, "Modeling of Buried Acoustic Emission Monopoleand Dipole Sources With a Finite Element Technique", Journal of Acoustic Emission, 17, No. 34, 1999, 97-110.
R. L. Mehan and J.V. Mullin, “Analysis of Composite Failure Mechanisms Using Acoustic
Emissions”, Journal of Composite Materials, 5, April 1971, 266-269.
W. H. Prosser, M. A. Hamstad, J. Gary and A. O'Gallagher, "Reflections of AE Waves in Finite
Plates: Finite Element Modeling and Experimental Measurements", Journal of Acoustic Emission, 17, No. 1-2, 1999, 37-47.
H. Suzuki, T. Kinjo, Y. Hayashi, M. Takemoto and K. Ono, “Wavelet Transform of Acoustic
Emission Signals”, Journal of Acoustic Emission, 14, No. 2, 1996, 69-84.
M. Takemoto, H. Nishino and K. Ono, "Wavelet Transform - Applications to AE Signal Analysis", Acoustic Emission - Beyond the Millennium, Elsevier (2000), pp. 35-56.
Vallen-Systeme GmbH, Münich, Germany, http://www.vallen.de/wavelet/index.html, 2001.
R. Weaver and Y. H. Pao, “Axisymmetrical Waves Excited by a Point Source in a Plate”, Journal of Applied Mechanics, 49, 1982a, 821-836.
R. Weaver and Y. H. Pao, “Spectra of Transient Waves in Elastic Plates”, Journal of the Acoustical Society of America, 72, 1982b, 1933-1941.
H. Yamada, Y. Mizutani, H. Nishino, M. Takemoto and K. Ono, "Lamb Wave Source Locationof Impact on Anisotropic Plates", Journal of Acoustic Emission, 18, (2000, December), 51-60.

61

A WAVELET TRANSFORM APPLIED TO ACOUSTIC EMISSION
SIGNALS: PART 2: SOURCE LOCATION#
M. A. HAMSTAD+, A. O’GALLAGHER and J. GARYNational Institute of Standards and Technology, Materials Reliability Division (853),
Boulder, CO 80305-3328;+
Also Department of Engineering, University of Denver, Denver, CO 80208.
Abstract
In Part 2, the same finite-element-generated database of acoustic emission (AE) signals wasused, as in Part 1: Source Identification, to examine the application of a wavelet transform (WT)
to improve the accuracy of AE source location. These signals represented the top surface out-ofplane displacement versus time from buried dipole sources in aluminum plates 4.7 mm thick.
The method utilizes a WT result to select AE signal-arrival times for a single group velocity
from energetic modes. The cases of both the large plate without edge reflections and the small
plate (coupon) with multiple edge reflections were examined. The arrival time of a specific frequency of an energetic fundamental mode of the far-field signals was determined from the WT.
Using these arrival times at three propagation distances, a group velocity was determined forcomparison with the appropriate group velocity based on dispersion curves. Both filtered narrow-band (100 to 300 kHz) and wideband (40 kHz high-pass) signals were examined. In addition, in the large-plate case, experimental sensor/preamplifier electronic noise was added to theAE signals to examine the effect of noise on the determination of accurate arrival times as afunction of signal-to-noise (S/N) ratios. Results for the large plate indicate that very accurate arrival times can be determined that correspond to a particular group velocity. In the coupon case,the results indicate significant distortions in the arrival times due to the multiple edge reflections.
The perturbation due to the presence of electronic noise was relatively small for the case of the
wideband signals in the large plate until signal-to-noise ratios reached levels where an AE hit
would likely not be recorded.
1. Introduction
One of the important aspects of acoustic emission (AE) technology is the ability to use the arrival times of the signals (from a single event) at three or more sensors to calculate the locationof the source in the test object. Often the two-dimensional location in a plate is determined. The
standard technique combines the measured arrival times with an appropriate single propagation
velocity to calculate the source location. In most AE applications, the arrival times of the signals
are obtained from the time that the signals first penetrate a fixed voltage threshold. As has been
documented (Hamstad, 1986; Gorman, 1990; Hamstad and Downs, 1995), this approach leads tolocation errors. In particular, the arrival times at the different sensors often are determined fromdifferent unknown velocities. This situation is a direct result of both the AE source characteristics (for example the source strength) and effects of the propagation of dispersive Lamb waves
along with geometry-based attenuation. The net result is that source location may be calculatedon the basis of inconsistent data. Specifically, the arrival times are not all obtained at the same#

Contribution of the U.S. National Institute of Standards and Technology; not subject to copyright in the US.

J. Acoustic Emission, 20 (2002)

62© 2002 Acoustic Emission Groupvelocity, and the velocity used in the location calculation is not the correct one for each sensor
hit.
Some previous research has demonstrated an approach to decrease the location errors (Ziola
and Gorman, 1992). In this approach, cross-correlation techniques were used to determine thearrival times for a particular mode (typically the flexural mode) and frequency. The AE signals
analyzed were obtained from sensors responding to out-of-plane surface displacements. These
arrival times were combined with a corresponding theoretical group velocity to calculate a moreaccurate source location. The resulting improvement in location accuracy was demonstrated byout-of-plane pencil-lead breaks on the surface of a plate test specimen. This validation approachis potentially incomplete. First, out-of-plane lead breaks create relatively large flexural mode(anti-symmetric) components of the AE signals as compared to real AE sources, which can be
dominated by in-plane dipoles with smaller flexural-mode signals. Second, the surface leadbreaks do not account for the substantial differences in the dominate mode in AE signals as afunction of the depth of a real AE source through the plate thickness, as was shown in Part 1(Hamstad et al., 2002).
In the research reported here, we examine the use of a wavelet transform (WT) to accurately
obtain arrival times at known group velocities from realistic modeled AE signals. We utilized
AGU-Vallen Wavelet program for obtaining WTs as in Part 1 (Hamstad et al., 2002). The AGU
group first used WT for determining the group velocity dispersion of the Lamb waves (Cho etal., 1996; Hayashi et al., 1999) and for AE source location on anisotropic CFRP plates (Yamada
et al., 2000; Takemoto et al., 2000). Yamada et al. (2000) determined the group velocity offlexual Lamb waves (generated by pencil-lead breaks or ball drops) from the maxima of WT coefficients of a certain frequency. A few other research works used a WT for AE source location(Yamada et al., 2000; Takemoto et al., 2000; Kwon and Joo, 2000, Jeong and Jang, 2000). The
validation approach used in these experimental studies is incomplete due to the reliance on monopole sources from out-of-plane pencil-lead breaks or ball drops on the plate surface. In thiswork, we obtained the AE signals in plate samples by finite-element modeling (FEM), and thesources of these signals include realistic dipoles as well as the important variable of source
depth. The modeled signals also have the advantage that the exact three-dimensional source location is known. Thus, WT-based signal-arrival times at different propagation distances can bequantitatively evaluated for their correspondence to a single group velocity.
2. Signal Database and Wavelet Transform
An AE signal database of plate top surface out-of-plane displacement as a function of time atthree propagation distances was used. The AE signal database, specimen geometry and the relevant WT information were described in detail in Part 1 (Hamstad et al., 2002) of this two-partpaper. Hence, we have not repeated that description here.
−Based Source Location (Large Plates)
3. WT−The extraction of arrival times from the WTs of AE signals was successively examined byseveral approaches. The first approach was based on the results of a WT of the simulated wideband AE signals that had been filtered with a 40 kHz high-pass filter. In this case, the information extracted from the WT result consisted of data pairs of arrival time and frequency based
upon absolute peaks of the WT’s magnitude (WT coefficient). When sets of such data pairs
were compared for three different propagation distances from a given AE source, it was deter63

(a)

(b)

(c)

Fig. 1 WT plots used to determine AE signal arrival times from the peak WT magnitudes of the
So mode at 522 kHz. The AE source was an in-plane dipole at a depth of 2.037 mm with propagation distances of (a) 60 mm, (b) 120 mm, and (c) 180 mm. AE signals filtered with 40 kHz
high pass filter prior to WT. Frequency (0 to 1 MHz) versus time (a: 0 to 80 µs, b: 0 to 120 µs,
and c: 0 to 150 µs from top to bottom).
64

(a)

(b)

(c)

Fig. 2 WT plots used to determine AE signal arrival times from the peak WT magnitudes theAo mode at 66 kHz. The AE source was an in-plane dipole at a depth of 0.783 mm. The propagation distances and filter were the same as for Fig. 1(a), (b), and (c). Frequency (0 to 400 kHz)versus time (same scales as Fig. 1; 1 MHz vs. a: 80 µs, b: 120 µs, and c: 150 µs top to bottom).
65

40000WT Magnitude at 522 kHz

Maximum
35000
30000
25000
20000
15000
10000
5000
0

0

20

40

60

80
100
120
140
Time, µs
Fig. 3 Example of the WT magnitude versus time for the So mode at 522 kHz from the WT result shown in Fig. 1(c). Plot illustrates the determination of the arrival time of the peak magnitude near the selected group velocity of the mode.
mined that there were differences in the frequency values of the maximum magnitude as a function of propagation distance. Since determining arrival times at different frequencies and thus
different group velocities at each sensor would complicate the process of source location, it
seemed best to examine alternative approaches so that the simplest use of WT data to extract arrival times could be developed.
The second approach that was examined was based upon choosing a single frequency for aparticular fundamental Lamb symmetric (So) or anti-symmetric (Ao) mode. Based upon themodal region of the WT that had a large magnitude, a single frequency was chosen in the same
fashion as the source-identification approach in Part 1 (Hamstad et al., 2002). Then, using thisfrequency, the arrival time of the maximum magnitude of the WT at that frequency (and associated mode) was determined, as was done by Yamada et al. (2000). It was found that choosing
the parameters of the WT to enhance the resolution and smoothness (as discussed in Part 1:
Hamstad et al., 2002) of the WT result was important to facilitate the determination of the maximum at the selected frequency and mode. To initially examine this approach in more depth, twodifferent in-plane dipole source cases were chosen. In one case the source depth was chosensuch that the maximum magnitudes of the WT were in the lower-frequency regime of the fundamental anti-symmetric mode. In the other source-depth case, the maximum magnitudes of the
WT were in the higher-frequency regime of the fundamental symmetrical mode. Table 1 shows
the arrival times obtained at the indicated frequencies and modes (Ao, 66 kHz and So, 522 kHz)for the three available propagation distances (60, 120, 180 mm). In contrast to Part 1, a higher
frequency was used for Ao to be more certain that the arrival times would not be altered by the
40 kHz high-pass filter. Figures 1 and 2 show the high-resolution WT results, which were used in
the two cases. The WT-parameters used were Wavelet size (WS) = 600 samples, Maximum Fre66

Table 1. Arrival times (µs) of the peak WT magnitude for in-plane dipole source signals filteredwith a 40 kHz high-pass filter in the large plate.
Sourcedepth, mm
2.037
0.783

So mode at 522 kHz
Distance, mm
Velocity, mm/µs
60
120
180
Slope Theoretical
31.9
64.6
98
1.82
1.78
-

Ao mode at 66 kHz
Distance, mm
Velocity, mm/µs
60
120
180
Slope Theoretical
29.1
51.9
75.1
2.61
2.61

quency (MF) = 700 kHz, Frequency Resolution (FR) = 3 kHz for the signal dominated by the So
mode and WS = 600 samples, MF = 400 kHz, FR = 2 kHz for the signal dominated by the Aomode. The sources were at depths of 2.037 mm (Fig. 1 for the dominant symmetric mode) and
0.783 mm (Fig. 2 for the dominant anti-symmetric mode). Figure 3 shows a plot of the WT
magnitude as a function of time at a single frequency of 522 kHz for the 180 mm distance caseof Fig. 1. The time of arrival of the maximum WT magnitude for the selected mode and frequency was obtained from the available spreadsheet that gives the WT magnitude (WT coefficient) as a function of time and frequency. The time resolution of the spreadsheet was 0.1 µs,
which is the same resolution as the re-sampled simulated AE signal. Since the exact locations of
the source and sensors are known, the information in Table 1 can be plotted to form distanceversus-time plots. The slopes of these plots provide group velocities as well as coefficients ofdetermination (R2) for the straight line fits. Table 1 shows these slope-based group velocities aswell as the group velocity (for the mode and frequency) available from the relevant theoreticaldispersion curves. Figure 4 shows an example of one of the plots (for the Fig. 1 case, 2.037 mm
source depth) of distance versus arrival time along with the equation that describes the straightline fit. The plot (not shown) of the other case (0.783 mm depth) gave a similar straight-line fit.
The excellent straight-line fits imply that the arrival times correspond very closely to a single
group velocity that is quite near the theoretical group velocity.
200
y = 1.815x + 2.30

180

R2 = 1

160
Distance, mm

140
120
100
80
60
40
20
0

0

20

40

60
Time, µs

80

100

120

Fig. 4. Plot of propagation distances versus arrival time of peak magnitude of the So mode at
522 kHz for the WTs in Fig. 1. Equation of straight-line fit with slope of 1.815 mm/µs representing the slope-based group velocity. R2 = 1, correlation coefficient.
67

Table 2.
Arrival times (µs) of the peak WT magnitude for in-plane dipole source signals
filtered with a 100 to 300 kHz bandpass in the large plate.
Sourcedepth, mm
2.037
0.783

Velocity, mm/µs
Distance, mm
60
120
180 Slope Theoretical
15.9 28.2 40.5 4.88
4.88-

Distance, mm
60
120 18025.2 45.7 65.7

Velocity, mm/µs
Slope Theoretical
2.96
3.00

It is worthwhile to point out that the straight-line fit has an intercept that does not passthrough the source origin (even with an excellent fit, R2 = 1, of the data to a straight line). The
reason for this behavior is likely due to the fact that the wave propagation needs to progress oversome number of plate thicknesses in order to fully develop the Lamb modes at their propagation
velocity. We also note that the group velocities determined from the WTs of the finite element
data did not always correspond exactly to those determined from the theoretical group velocity(dispersion) curves. For example, when the arrival times used to calculate the indicated velocity
in Table 1 of Part 1 (Hamstad et al., 2002) were used along with the propagation distances toobtain a plot of distance versus time the slope-based group velocity was 2.45 mm/µs. This valueis 1.2 % faster than the dispersion curve velocity of 2.42 mm/µs for the Ao mode at 50 kHz. As
will be seen later in the description of a proposed source location procedure, we suggest thegroup velocities determined from WT results might be expected to provide more accurate results.
But the correctness of this assumption will need to be determined through additional research.
A third approach was also examined. This approach was really just a deviation of the second
one. In this case the modification was to filter the FEM-based signals with a 100 to 300 kHz
bandpass filter before calculating the WTs. This frequency range was chosen since it has often
been used in experimental AE applications. Figure 5 (Fig. 1 case) and Figure 6 (Fig. 2 case)show the resulting high-resolution WT results for the same two sources. These WT results were
calculated for the filtered signals, which are also shown in Figs. 5 and 6. The WT-parameters
used were WS = 600 samples, FR = 2 kHz, and MF = 400 kHz. In this case the maximum magnitudes of the WTs are in a different frequency range. Table 2 shows the selected frequencies
and modes (Ao, 134 kHz and So, 262 kHz) for each case as well as the arrival times determinedfrom the WTs; also shown are the group velocities from the plots of distance versus time and thegroup velocities from the dispersive group-velocity curves at the selected frequencies and modes.
The slope-based group velocities shown in Table 2, again, were quite close to those obtained
from dispersion curves. As before, the plots of distance versus arrival time had excellent fits but
again did not pass through the origin, presumably due to the need to propagate several plate
thicknesses to develop the Lamb modes and their group velocities. Thus, in this range of bandpass frequency, the arrival times correspond very closely to a single group velocity very near thetheoretical velocity.
4. Proposed Method of WT-Based Source Location for Experimental Data (Large Plate)
The results above indicate that the approach of using a fixed frequency of an energetic modeto determine an arrival time at the maximum magnitude of a WT (Yamada et al., 2000) could
result in accurate arrival times that are associated with a particular wave mode and corresponding
group velocity. But, since the calculated group velocities from the finite-element data did notalways correspond exactly to the dispersion-curve values, it may be useful to account for this aspect. By providing for this aspect, it may be possible to obtain the best possible experimental
location accuracy with a WT-based technique. The approach we suggest would use the group
68

(a)

Displacement, pm

3

0

-3

0

16

32
48
Time, µs

64

80

0

24

48
72
Time, µs

96

120

0

30

60
90
Time, µs

120

150

(b)

Displacement, pm

2

0

-2

(c)

Displacement, pm

1.3

0.0

-1.3

Fig. 5 WT results (frequency 0 to 400 kHz versus time (0 to 80, 0 to 120, and 0 to 150 µs)) for
the same cases shown in Fig. 1 but with a 100 to 300 kHz bandpass filter prior to calculating theWT. Fig. 5(c) also shows superimposed Ao and So group-velocity-based dispersion curves afterconversion to arrival times based on the propagation distance of 180 mm. Also shown are thecorresponding filtered AE signals.
velocity from the dispersion-based curves as an initial group-velocity estimate. Possibly a bettergroup-velocity value could be determined as described below by pre-experiments. The preexperiment approach would replace that illustrated in Fig. 4 since the propagation distances used
to construct that figure are available only for the FEM data. We point out that the “calibrated”
group velocity extracted from Fig. 4 could be viewed as compensating for small differences ingroup velocity (compared to dispersion-based values) induced by the WT algorithm. Presumably
these algorithm-induced differences would be a part of each WT calculation with a particular algorithm. Also, the pre-experiments would be necessary when accurate group velocities cannot becalculated.

69

Displacement, pm

11

(a)

0

-11

0

16

32
48
Time,µs

64

80

0

24

48
72
Time, µs

96

120

0

30

60
90
Time, µs

120

150

(b)

Displacement, pm

7

0

-7

(c)

Displacement, pm

5

0

-5

Fig. 6 WT results (same scales as Fig. 5) for the same cases shown in Fig. 2 but with a 100 to
300 kHz bandpass filter prior to calculating the WT. Fig. 6(c) shows the superimposed Ao and
So group-velocity-based dispersion curves after conversion to arrival times based on the propagation distance of 180 mm. Also shown are the corresponding filtered AE signals.
5. Comments Relative to the Location Approach for Real AE Signals
With actual AE data, in particular narrow-band data, the preliminary task of selecting thewave mode to associate with the WT maximum magnitude region may require some preexperiments. The purpose of conducting pre-experiments with pencil lead breaks and the real
sensors would be to identify the mode(s) associated with the peak magnitude frequency range(s)of the WT and to determine corresponding WT-based group velocities. A large plate of the same
thickness and material could be used for these pre-experiments. Good control of the position andforce direction of the lead break is necessary. But in principle, an in-plane pencil break on theplate edge close to the top or bottom surfaces would allow identification of the dominant-energy
WT frequencies for the anti-symmetric modes (typically for thin plates this will be the fundamental anti-symmetric mode). And an in-plane lead break located almost exactly at the mid70

plane could allow (with careful filtering) the determination of the dominant frequencies of thepeak-magnitude region of the WT for the symmetric modes (typically for thin plates this is expected to be the fundamental symmetric mode). These frequencies and modes could then be
used with dispersion-based group-velocity curves to provide the initial estimates of the groupvelocities to be used to calculate the source location. In addition WTs from sensors at several
accurately known distances from these experimental sources could be used to obtain “calibrated”
group velocities (as referred to above).
In contrast to the source-identification task with a subset of WT data, the dependence of the
WT results on source depth does not cause significant problems in determining source location.
It is necessary only to add a step in the calculation of source location that determines whichmode is dominant in an AE signal by calculating appropriate Ao/So ratios as described in Part 1(Hamstad et al., 2002). Based on the ratio determined, the most energetic mode and associatedfrequency can be used to extract the WT-based arrival times. Also, the correct group velocity forthe dominant mode and frequency for each AE signal can be selected for the calculation of thesource location.
6. Effects of Electronic NoiseIn experimental cases, the AE signal is a combination of signal (from the AE source) andnoise (from the electronics). Thus, to be useful, the above method of WT-based source locationmust retain its determination of accurate arrival times for a single group velocity in the case ofexperimental signals that combine signal and noise. To examine this issue we added electronicnoise to the calculated FEM signals and studied the WT-based determination of arrival times.
The study was carried out as a function of the signal-to-noise (S/N) ratio.
The noise signals were taken from two different wideband sensor/preamplifier combinations.
These experimental signals were obtained with the wideband sensors coupled only to air. In addition, the sensors were protected from any possible laboratory airborne signals. Prior to being
added to the FEM calculated signals, the noise signals were filtered with a six-pole digital Butterworth filter from 40 kHz to 1.2 MHz. As Fig. 7 shows, the typical spectra of the noise signals
were considerably different from each other. In one case the electronic noise increases significantly with decreasing frequency. In the other case, the electronic noise amplitude is distributed
more uniformly with frequency, with a peak region near the middle of the frequency range. By
using noise signals with these two different spectrums, we attempted to broaden the results of thestudy.
Also to broaden the results of the study, two distinctly different FEM calculated signals wereselected. In one case, a higher-frequency region of the fundamental symmetric mode dominated
the AE signal energy. In the second case, a lower-frequency region of the fundamental antisymmetric mode dominated the AE signal energy. The source for both signals was an in-plane
dipole located at 180 mm from the sensor. In the case of the dominant symmetric mode thesource was at a depth of 2.35 mm (mid-plane). The dominant anti-symmetric-mode source was at
a depth of 0.47 mm below the top surface of the large plate.
In order to quantitatively characterize the S/N ratio, the peak amplitudes of the noise and thecalculated AE signals (40 kHz high-pass) were determined over the 150 µs portion of each signal
used in the WTs. Then the noise signals were multiplied by appropriate factors to obtain the desired range of S/N ratios, based on the peak amplitudes.
71

50

(a)

Amplitude, fm

25

0
15

(b)

10
5
0
0

0.2

0.4

0.6

0.8

1

Fig. 7 Typical frequency spectra for the two experimental
noise signals calculated froman 819.2 µs signal length. Lowfrequency emphasis (a) anddistributed frequencies (b).

Frequency, MHz
Figure 8 shows the WTs of the two calculated AE signals ((a) and (b) without added noise)and the two noise signals ((c) and (d)). These WTs were calculated with the following highresolution parameters (WS = 600, FR = 3 kHz [0-600 kHz case] and 2 kHz [0-400 kHz case]).
Following the approach used earlier in this paper, the arrival times of the peak magnitudes of theWTs were determined at 522 kHz (AE signals dominated by the symmetric mode) and at 66 kHz(AE signals dominated by the anti-symmetric mode). Table 3 shows the determined arrival times
at 522 kHz for the two different noise types at the selected S/N ratios. Table 4 shows the arrival
times at 66 kHz for the two noise cases. No arrival times are shown in the tables when the peakmagnitude of the WTs was not obvious. In all cases the WTs were calculated with the highresolution parameters used to produce Fig. 8.
Table 3. Arrival times (µs) determined from the peak WT magnitude at 522 kHz for the originalAE signal dominated by a higher-frequency region of the fundamental symmetric mode.
S/N ratio

Noise dominated by low frequen- Noise with distributed frequencies
cies
no noise
97.9
97.9
10:1
97.9
97.9
5:1
97.9
97.9
2:1
97.9
97.9
1:1
97.9
97.8
1:2
97.9
87.8
1:5
101.8
117.2
1:10
101.8
–
Noise signal
33.1, 101.5
115.9, 15.5
72

(a)

(c)

(b)

(d)

Fig. 8 WTs of the calculated AE signals and the experimental electronic noise for 150 µs length
signals: (a) for AE signal at 180 mm propagation distance with dominant fundamental antisymmetric mode; (b) for AE signal at 180 mm propagation distance with dominant fundamental
symmetric mode; (c) for electronic noise with low-frequency emphasis; and (d) for electronicnoise with distributed-frequency emphasis.
Table 4. Arrival times (µs) determined from the peak WT magnitude at 66 kHz for the originalAE signal dominated by a lower frequency region of the fundamental antisymmetric mode.
S/N ratio

Noise dominated by low frequen- Noise with distributed frequencies
cies
no noise
75.1
75.1
10:1
75.6
75.2
5:1
76.1
75.3
2:1
77.1
75.6
1:1
80.3
76.1
1:2
47.7
76.2
1:5
–
74.6
1:10
–
26.7
Noise signal
49.2
66.6, 24.4

73

(a)
5

No noise

No noise

0

0
-5
5

-5
5

2:1

2:1

0

0
-5
5

Displacement, pm

Displacement, pm

(b)

5

1:1

0
-5
10

1:2

0
-10
20

-5
5

1:1

0
-5
10

1:2

0
-10
20

1:5

1:5

0

0

-20

-20
0

30

60

90

120

0

150

30

60

90

120

150

Time, µs

Time, µs

Fig. 9 Waveforms of signal-plus-noise at the indicated S/N ratios for the AE signal at 180 mm
propagation distance dominated by the fundamental symmetric mode: (a) noise with lowfrequency emphasis; (b) noise with distributed-frequency emphasis.
(a)

(b)

10

10

No noise
0

0

-10
10

-10
10

5:1

0

2:1

0

-10
10

Displacement, pm

Displacement, pm

No noise

2:1

0
-10
20

1:1

0

-10
20

1:1

0
-20
20

1:2

0

-20
20

-20
50

1:2

0

1:5

0
-50

-20
0

30

60

90

120

150

0

30

60

90

120

150

Time, µs

Time, µs

Fig. 10 Same types of waveforms as Fig. 9 for the AE signal at 180 mm propagation distancedominated by the fundamental anti-symmetric mode.
74

(a)

(c)

(b)

(d)

Fig. 11 WTs of waveforms of signal plus noise. Signals at 180 mm from the in-plane dipole
source. The noise in (a) and (b) was dominated by low frequencies and in (c) and (d) by distributed frequencies. The signal in (a) and (c) was dominated by the fundamental anti-symmetricmode from the source at a depth of 0.47 mm. In (b) and (d) the signal was dominated by the fundamental symmetric mode from the source at a depth of 2.35 mm. The signal-to-noise ratio in
each case was 1:1.
To provide a clearer understanding of the results in Tables 3 and 4, Fig. 9 shows selected
waveforms of signal-plus-noise for the two noise cases when the original AE signal was dominated by a higher-frequency region of the symmetric mode. Figure 10 shows the same type of
selected results when a lower-frequency region of the anti-symmetric mode dominated the original AE signal. Examination of the above figures and tables reveals several key aspects about theeffects of electronic noise on the WT-determination of arrival times from a particular mode andfrequency. First, when the S/N ratio becomes too small, the arrival time of the WT magnitude
peak typically moves toward the WT peak magnitude of the noise signal (at the selected frequency). Second, even with a relatively poor S/N ratio of 1:1 as shown in Figs. 9 and 10, theWT-based arrival time is still reasonably accurate. As Figs. 9 and 10 show for these cases, it is
becoming difficult to extract by eye the AE signal. And clearly, a threshold-based techniquewould lead to inaccurate arrival times (or no hit detected) not associated with a known groupvelocity. The worst result at this 1:1 ratio is the case of the signal dominated by the lowfrequency anti-symmetric mode and the noise dominated by low frequencies. Since WTs result
in better time resolution at higher frequencies, it is possible that the slightly poorer accuracy at
66 kHz is due to the characteristics of the WT. More likely, the result is due to the frequency
peaks of the original AE signal and the noise being in the same frequency range. This conclusion75

is based on the observation (see Tables 3 and 4) that errors in the arrival times appear at higher
S/N ratios when the noise and original signals have peaks in the same frequency region.
The reason why reasonably accurate arrival times can be extracted from the WT results at
S/N ratios of 1:1 or less seems to be due to the fact that the WT spreads out the noise over the
bandpass frequency range while preserving the spread of the noise in time. Determinations ofarrival times by penetration of a fixed threshold have the noise spread out only in time. Thus,
such approaches typically cannot even detect an AE hit at S/N ratios as low as 1:1. Figure 11
demonstrates at a S/N ratio of 1:1 that the WT of the AE signal plus noise is dominated in all
four cases by the same WT region that dominated when only the AE signal was present (compare
Fig. 11 with Figs. 8(a) and 8(b)). In Fig. 11 the four cases of AE signal plus noise are: (a) lowfrequency noise and low-frequency AE signal; (b) low-frequency noise and high-frequency AE
signal; (c) distributed noise and low-frequency AE signal; (d) distributed noise and highfrequency AE signal.
We did not investigate the narrow-band S/N ratio case, since we did not have available thedigital displacement calibration of a typical 150 kHz resonant commercial AE sensor. Thus, we
could not properly alter the calculated sensor signal for the AE source to correspond to the sensor
resonance. We expect that the above WT-based method of detection of arrival times would not
be as robust at low S/N ratios, since both the AE signal and the noise would be concentrated in
the same relatively narrow frequency range.
An additional positive of the WT-based method, in the case of wideband waveform-based AE
measurement systems, may be that fewer AE events would have only one useable hit. If themeasurement system were configured so that all the waveforms from a group of sensors wererecorded when one sensor was hit (i.e. a signal above the threshold), then WT-based processing
could extract relevant arrival times from the sensor channels where threshold-penetration hits
would not occur. Thus, signal arrival times corresponding to a single group velocity could be
extracted from the channels where the AE signal had attenuated into the background electronicnoise. And potentially a source location could be calculated for an event where it could not becalculated with a threshold-based AE system.
7.

WT for Source Location in Samples with Nearby Edges

When the lateral size of the test specimen is decreased so that nearby edges are present, theAE signals and their WTs become much more complicated. This result was illustrated in Part 1(Hamstad et al., 2002) in Figs. 16 and 17. Since the distribution of WT-illustrated signal energy
in these figures does not clearly follow the shapes of the superimposed fundamental Lamb-mode
curves from the dispersion relations, the use of the approaches developed for source location in
the large plate might be expected to experience difficulties for the coupon specimen. Further,since the distortion of the WT results is due to edge reflections, moving the source from side-toside across the 25.4 mm width of the coupon specimen will change the reflections and WTs as
was illustrated in Fig. 18 of Part 1 (Hamstad et al., 2002). The current coupon database, except
for the case illustrated in Fig. 18 of Part 1 (Hamstad et al., 2002), does not include the side-toside variation of the source position.
In spite of the limited database (side-to-side source position), we examined for the coupon
specimen the application of the techniques applied to the large-plate signals. The results of thisexamination in the coupon (using 40 kHz high-pass signals) for the same source cases used in the
76

(a)

(a)

(b)

(b)

(c)

(c)

Fig. 12 WT plots used to determine AE signal arrival times from the peak magnitudesof the So mode at 522 kHz for the coupon
specimen. The AE source was an in-plane
dipole at a depth of 2.037 mm with propagation distances of (a) 60 mm, (b) 120 mm,
and (c) 180 mm. AE signals filtered with 40
kHz high-pass filter prior to WT. Frequency(0 to 700 kHz) vs. time (0 to 80, 0 to 120, 0
to 180 µs from top to bottom). Superimposed group velocity curves of fundamental
modes.

Fig. 13 WT plots used to determine AE signal
arrival times from the peak magnitudes of the Aomode at 66 kHz for the coupon specimen. The
AE source was an in-plane dipole at a depth of
0.783 mm. The propagation distances and filter
were the same as for Fig. 12 (a), (b), and (c).
Frequency (0 to 400 kHz) versus time (samescales as Fig. 12, top to bottom).

77

Table 5. Arrival times (µs) of the peak WT magnitude for in-plane dipole source signals filteredwith a 40 kHz high-pass filter in the coupon specimen.
Sourcedepth, mm
2.037
0.783

So mode at 522 kHz
Velocity, mm/µs
Distance, mm
60
120
180 Slope Theoretical
33.8
60
99
1.82
1.78
-

Ao mode at 66 kHz
Velocity, mm/µs
Distance, mm
60
120
180 Slope Theoretical
33.8 61.9 91.2 2.09
2.62

large plate (those that lead to Figs. 1 and 2 and Table 1) are shown in Figs. 12 and 13 and Table
5 for the coupon. For the WT calculations the WS = 600 samples was used for both modes; andthe FR and MF values used were 2 kHz and 400 kHz for the Ao mode and 3 kHz and 700 kHz
for the So mode. The maximum WT magnitude arrival times were selected while ignoring reflections from the ends of the coupon specimen. Figure 12 shows the WTs for the coupon for thein-plane dipole at a depth of 2.037 mm. Figure 13 shows the coupon WTs for a depth of 0.783
mm. The source position for the results shown in Figs. 12 and 13, as well as Table 5, was locatedhalf-way across the coupon (12.7 mm form the side). Table 5 indicates the results were mixed.
For the 2.037 mm depth and the So mode at 522 kHz, the slope-determined velocity of 1.82mm/µs was the same as the large plate velocity of 1.82 mm/µs, but in the plot of distance versus
arrival time the data did not fit the straight-line slope very well. For the 0.783 mm depth and theAo mode at 66 kHz the slope-determined velocity in the coupon was 2.09 mm/µs (again datawith a poor fit) which was 20 % lower than the value of 2.61 mm/µs in the large plate.
Table 6. Arrival times (µs) of the peak WT magnitude for in-plane dipole source at 2.35 mm
depth and different distances from the coupon side. So mode at 522 kHz.
Case
Coupon, 3.31 mm
Coupon, 6.13 mm
Coupon, 12.7 mm
Large plate

60
32.4
36.4
33.9
31.9

Distance, mm
120
180
65.1
87.8
77.7
103.8
60
98.7
64.6
97.9

Velocity, mm/µs
Slope
Theoretical
2.13
1.78
1.75
1.78
1.82
1.78
1.82
1.78

To study the effect of side-to-side variation of the source position, we applied the same approach (522 kHz WT-peak arrival time) to the cases for the in-plane dipole at the mid-plane
depth of 2.35 mm. Table 6 (same WT-parameters as for Table 5) shows the results (40 kHz
high-pass data) for the coupon with the source at three different distances (12.7, 6.13, and 3.31mm) from the coupon side compared to the equivalent large plate case. For convenience, thepropagation distances are reported as 60, 120, 180 mm, but in determining the slope-based groupvelocities the actual straight-line distances from the source to the sensors were used. Table 6
clearly shows significant differences in the slope-determined group velocity (of the fundamental
symmetric mode at a frequency of 522 kHz) as a function of transverse source position. Compared to the velocity determined from the large-plate signals (for the same case) the group velocity changes range from 0.6 % to 17 %. It is also clear from Table 6 that the arrival times atthe different sensors vary over a considerable range compared to the large plate values, whichwere not influenced by the side-edge reflections. Figure 14 compares the differences in the WT
magnitude versus time at 522 kHz for the large plate compared to the coupon specimen with thesource at 3.31 mm from the coupon side. This figure is for the sensor located at the nominal
propagation distance of 180 mm. The alteration of the peak arrival time in the coupon specimen
is clearly visible along with the generally significantly larger magnitudes of the WT in that case.
78

WT magnitude at 522 kHz

90000

Coupon sample peak

80000
70000
60000

Large plate peak

50000
40000
30000
20000
10000
0

0

20

40

60

80
100
Time, us

120

140

160

Fig. 14 WT magnitude at 522 kHz versus time for a large plate and coupon specimen. In bothcases the source was an in-plane dipole at a depth of 2.35 mm and a propagation distance of 180
mm. The coupon source was located at 3.31 mm from the coupon side.
We conclude that significant and variable errors can be present in the arrival times for the coupon specimens when they are determined by the WT approach examined here. The errors incoupon-determined group velocities could be overcome by appropriate pencil lead-break datafrom a large-plate specimen of the same material and plate thickness as the coupon specimen, butarrival-time errors would remain. These errors would result in poor accuracy for source location
calculations.
The effect of the side-to-side transverse source position was also examined using signals that
had been filtered from 100 to 300 kHz. The WTs were calculated with high-resolution parameters (WS = 600 samples, MF = 400 kHz and FR = 2 kHz) for the in-plane dipole at a depth of
2.35 mm. Table 7 compares the three small coupon cases with the equivalent large-specimen results and the dispersion-curves-based group velocity at 262 kHz. As was the case above, the arrival time at maximum WT magnitude was selected by ignoring a magnitude maximum caused byreflections from the coupon-specimen’s ends. The propagation distance versus the arrival times
was plotted to determine the group velocity for each case of transverse source position. Compared to the large-plate data, the straight-line fits to the data for distance versus time were poor.
The best fit was for the symmetrical center-line source position. But, even in that case, the groupvelocity was reduced by 13 % compared to the large-plate velocity. The group velocity determined when the source was located 6.13 mm from the coupon edge was very close to the result
determined for the large plate. However, this was happenstance as the straight-line fit was very
poor. Figure 15 shows the excellent fit and equation for the large plate as well as the significant
error and typical scatter (relative to a straight line) of the three small-coupon cases. Thus, again,
we conclude that side-edge reflections distort the determination of accurate arrival times. Obviously, inaccurate arrival times would lead to significant errors in source location.
79

Table 7. Arrival times (µs) of the peak WT magnitude for in-plane dipole sources at 2.35 mm
depth and different distances from the coupon side. Signals were filtered from 100-300 kHz and
the So mode at 262 kHz was used.
Case
Coupon, 3.31 mm
Coupon, 6.13 mm
Coupon, 12.7 mm
Large plate

60
15.3
26.4
19.4
15.8

Distance, mm
120
180
34.8
46.8
44.5
47
34.4
47.6
28.3
40.5

Velocity, mm/ µs
Slope
Theoretical
3.74
4.89
4.89
4.89
4.25
4.89
4.86
4.89

200
180

Large plate
Coupon: Source @ 12.7 mm
Coupon: Source @ 6.13 mm
Coupon: Source @ 3.31 mm
Linear (Large plate)160

Distance, mm

140
120
100

y = 4.8581x - 16.997

80
60
40
20
0
0

5

10

15

20

25
30
Time, µs

35

40

45

50

Fig. 15 Excellent fit of distance versus arrival time from WT peak magnitude at 262 kHz forlarge plate as compared to scatter and consequent lack of straight line fit for coupon specimen
cases with variation in source distance from specimen side. AE signals were filtered from 100 to
300 kHz prior to the WT. Source was an in-plane dipole at a depth of 2.35 mm below the platesurface.
8. Conclusions
A. Source Location−Large Plates

Excellent straight-line correlations of propagation distance versus the WT-based arrival timesof the fundamental modes at fixed frequencies indicate that source location in large plates

80

!

!

!
!

can be significantly enhanced by use of WT results compared to that possible with fixed
thresholds and an assumed constant group velocity.
Since the amount of energy in a particular mode depends on the source depth, it will be necessary to first select the mode with the dominant energy in each AE signal. Then WT-based
arrival times of the maximum WT magnitude of the experimental AE signals can be obtained
from that mode at a selected frequency.
In experimental situations, carefully applied pencil-lead breaks can be used to determine thekey frequency to use for each dominant mode. Then only the WT-based arrival times andpeak magnitudes at these frequencies need to be extracted. The source location can then be
directly calculated from the arrival times for the mode with the dominant peak magnitude.
The group velocity for the calculation could be taken from dispersion curves for the selectedmode and frequency or from a WT-based group velocity obtained during pre-experimentswith careful pencil lead breaks.
The technique for extraction of WT-based arrival times corresponding to a single group velocity was found to be applicable for both wideband and narrow-band signals.
In the wideband case, the extraction of single-velocity arrival times was found to be robust in
the presence of electronic noise even for signal-to-noise ratios as low as 1:1.

B. Source Location−Small Coupon Specimens!

The inherent multiple side-edge reflections present in small-coupon specimens complicate
the use of the WT for the purpose of obtaining accurate source locations.

Acknowledgement
This work was partially supported by NASA Langley.
References
H. Cho, S. Ogawa and M. Takemoto, "Non-contact Laser Ultrasonics for Detecting Subsurface
Lateral Defects”, NDT and E International, 29-5, 1996, 301-306.
Hayashi et al., 1999
M. R. Gorman, “Plate Wave Acoustic Emission”, Journal of the Acoustic Society of America, 90(1), 1990, 358-364.
M. A. Hamstad, “On Determination of Arrival Times for Acoustic Emission Source Location in
Composites”, Proceedings AECM-2, Society of the Plastics Industry, New York, New York,1986, pp. 202-208.
M. A. Hamstad, A. O’Gallagher and J. Gary, “Examination of the Application of a Wavelet
Transform to Acoustic Emission Signals: Part 1. Source Identification”, Journal of Acoustic
Emission, 20, 2002, 39-61. (preceding paper)
M. A. Hamstad and K. S. Downs, “On Characterization and Location of Acoustic Emission
Sources in Real Size Composite Structures – A Waveform Study”, Journal of Acoustic Emission,
13, No. 1–2, 1995, 31-41.

81

Yasuhisa Hayashi, Shingo Ogawa, Hideo Cho and Mikio Takemoto, “Non-contact estimation of
thickness and elastic properties of metallic foils by the wavelet transform of laser-generated
Lamb waves”, NDT and E International, 32-1, 1999, 21-27.
Hyunjo Jeong and Young-Su Jang, “Fracture Source Location in Thin Plates Using the Wavelet
Transform of Dispersive Waves”, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 47-3, 2000, 612-619.
O.Y. Kwon and Y.C. Joo, “Source Location in Plates by Using Wavelet Transform of AE Signals”, Journal of Acoustic Emission, 18, 2000, S212-221.
M. Takemoto, H. Nishino and K. Ono, "Wavelet Transform - Applications to AE Signal Analysis", Acoustic Emission - Beyond the Millennium, Elsevier, 2000, pp. 35-56.
H. Yamada, Y. Mizutani, H. Nishino, M. Takemoto and K. Ono, "Lamb Wave Source Locationof Impact on Anisotropic Plates", Journal of Acoustic Emission, 18, 2000, 51-60.
Steven M. Ziola and Michael R. Gorman, “Acoustic Emission Source Location in Thin Plates
Using Crosscorrelation”, Proceedings AECM-4, The American Society for Nondestructive
Testing Inc., Columbus, OH, 1992, pp. 411-417.

82

DIAGNOSTICS OF REINFORCED CONCRETE BRIDGESBY ACOUSTIC EMISSIONLESZEK GOLASKI1, PAWEL GEBSKI1 and KANJI ONO2
1

2

Kielce University of Technology, Kielce, Poland
University of California, Los Angeles, California 90095-1595 USA

Keywords: acoustic emission, concrete structures, damage monitoring
Abstract
Our work on concrete bridges will be reported. We found that AE is the most suitable
method for the purpose of old bridge inspection and further development effort was conducted.
This culminated in a draft document, “Recommended Practice for Testing Reinforced and Prestressed Concrete Structures by Acoustic Emission” (RP), following the lead of documents of asimilar nature in Japan and in Texas. We will report here selectively the test results of five fullscale bridges following the procedures we have proposed. These bridges were of different typesof construction: reinforced concrete, pre-stressed concrete (post-tensioned and pre-tensioned)
and combined concrete-steel construction. These justified the basic principles of RP, but also
demonstrated needs of individual modification fitting the structures and testing conditions.
Introduction
Extensive acoustic emission (AE) studies of concrete structures have been reported [1-5].
However, most concrete bridges and other structures are still inspected using traditional methods.
In Poland, there are about 30,000 concrete bridges, many of which were built over 30 years ago.
These structures have sustained various degrees of damage and their continued usage requires
maintenance and renovation. We need a method to select the bridges that must be renovatedurgently, but conventional testing methods for concrete structures do not provide the full
information about the severity of defects. With the support of the Polish construction industry
and the administrators of highways and bridges, we have embarked on developing a procedurethat can be applied for testing a large number of bridges.
“Recommended Practice” documents for concrete structures have appeared in Japan [6] andfrom Texas Department of Transportation [7]. We have prepared a similar document for use in
Poland and conducted some tests according to the procedures proposed. These bridges tested
were of different types of construction: reinforced concrete, pre-stressed concrete (post- and pretensioned) and combined concrete-steel construction. Selected results will be given here.
Philosophy of Testing Concrete Bridges By Acoustic Emission
Before any reliable tests on bridges could be done, it was necessary to perform a series ofmeasurements on model elements and full-scale structures under controlled loading condition.
Testing elements in a laboratory enable us to learn more about the features of the observed
phenomenon. It also gives important information concerning the setup of the testing equipment.
The research was performed following the scheme shown in Fig. 1. We have utilized a standard
12-channel PAC MISTRAS system for AE data acquisition and analysis.
J. Acoustic Emission, 20 (2002)

83© 2002 Acoustic Emission GroupFig. 1. Philosophy of testing concrete bridges by AE.
Model structural element testing by AE is well recognized (especially for reinforced concretebeams). Test results have been published in a number of papers [e.g., 5] and results of our tests
are in general agreement. However, the results obtained during model testing cannot be directly
moved to in situ monitoring.
The model tests generated useful information about the wave propagation and attenuation in
concrete, which was helpful for setting up the measuring equipment. The attenuation has acritical influence on the values of AE signal parameters: amplitude, duration and energy (orMARSE1), which are the most important in evaluating the damage severity of structure [6, 7].
The attenuation depends on tested concrete structures. For example, in pre-stressed concreteelements, the attenuation of AE signals is relatively low (2~3 dB/m at 55 kHz), while in old
concrete or concrete containing numerous tiny cracks or porosity, severe attenuation (greaterthan 10 dB/m) essentially limits AE wave propagation. Therefore, the sensor placement scheme
must be designed individually for each of the tested structures or elements. For most structures,
we selected zonal location and zonal analysis of AE signals. The tested element (beam, slab, etc.)
shall be divided in zones and AE signals from each zone are recorded by one sensor. In the RP, it
is specified that the attenuation within each zone must be precisely determined and must notexceed 10 dB in amplitude. This assures adequate coverage of the structure under test in
evaluating the integrity of the monitored structure. It also enables us to compare results obtained
from different tests. In addition, planar or volume locations are recommended when AE sources
are located in the vicinity of a known damage.
In the present RP, the following four AE parameters, the number of AE events, energy of AE
signals, duration and amplitude, are taken into consideration in damage evaluation of concrete
structures. If any of those parameters significantly exceeds the threshold value, more carefulinspection is required. Furthermore, in this procedure, multi-parameter analysis of these AE
features is performed. It is also recommended to compare the results for the test structure withresults from previously performed tests on similar objects and other information, such as damage
1

MARSE – Measured Area of the Rectified Signal Envelope
84

identified post-test. This analysis procedure depends on the tested structure and testing
conditions. Thus, it is important to accumulate the database of differently constructed bridges or
structures in properly assessing the integrity of the tested structure.
Another important problem in the RP is the AE sensor selection for RC testing. For thispurpose, we have selected two types of DECI AE sensors for these tests: resonant sensors with thecenter frequency of 55 and 150 kHz. However, it has been noticed that AE signals acquired fromdifferent structures vary significantly, and depend as well on type of cracking, type ofreinforcement and parameters of concrete. Wide-band sensors are currently used rarely due to thelow sensitivity (and additional background noise recorded, which are not caused by damage
propagation), but using resonant sensors of a particular frequency may result in losing some
important signals. In such cases, a solution is to perform an initial AE measurement on similar
concrete beams or bridge monitoring during service using wideband sensors, choosing thesuitable resonant frequency of test sensors.
Care should be taken when new pre-stressed prefabricated beams or structures are examined.
These beams exhibit significantly higher AE activity, which diminish over time. During theperiod of high AE activities, which typically last about 1 month, these should not be included in
AE tests of structures. In addition it is recommended to compare the AE signals during bridge
testing with those generated during destructive tests of full-scale beams of similar construction.
Starting at the beginning of year 2003 the AE characteristics of selected pre-stressed beams (onebeam taken from each series) will be provided.
We decided to give special emphasis to the full-scale element testing, namely (12 and 18meters-long) pre-stressed concrete beams loaded on stands up to fracture under smooth andrepeated loading. Pictures of these tests are shown in Fig. 2. During loading the parameters of
AE signals were analyzed and compared with other parameters such as: crack appearance,
deflection and strain, in order to obtain the full range of failure processes that occur in the tested
beams.

Fig. 2. Left: Pre-stressed beam (type KUJAN), Right: AE sensor attached to the bottom surface.
As expected, the features of conventional parameters of AE signals depend on the load
applied (as a consequence of changes in integrity of tested element). Hits rate, values ofamplitude, energy and duration increased while load was raised. However, because of the large
scale of the element, it was impossible to assign each acquired signal to the particular crack
occurred. Due to this difficulty, a different method of data analysis had to be developed; namely,85

a zonal location was used. Each of the tested pre-stressed beams was divided in 10 measuring
areas (10 sensors were used). The structural integrity together with the intensity of AE wasanalyzed separately for each area. The tested beams were loaded in 4-point-bending. This
scheme of loading means that in the middle part (1/3 of the beam’s length) the tensile stress had
the highest value. Those measuring areas were inspected more carefully (both by AE andvisually). The analysis of only conventional AE parameters could provide qualitative results. To
evaluate the load capacity or the level of deterioration more precisely, the quantitative analysis isnecessary. We suggest examining the intensity (based on historic index and severity) as ameasure of deterioration. In order to calculate the intensity of an emission source, the historicindex and severity must be calculated on data taken during the loading. To determinate theintensity, the maximum historic index (HI) and severity values are plotted on a chart.
Historic index is a measure of the changes in Signal Strength throughout the test, defined byN∑S
N i = K +1 Oi
H (t ) =
N−K N
∑ Soi
i =1

where H(t) is the historic index at time t, N is the number of hits up to and including time t, SOi is
the signal strength of the i-th event, and K is an empirically derived factor that varies with thenumber of hits. For N < 50, K = 0: for 50 ≤ N < 200, K = N – 30: for 201≤ N < 500, K = 0.85N:
and for 501 ≤ N ≤ 2000, K = N – 35 [8]. The second parameter used for this analysis is known as
severity (Sr) and is defined as the average signal strength for the 50 events having the largest
numerical value of signal strength and is defined by the following equation:Sr =1 i =50∑ S Oi
50 i =1

where Sr is severity and SOi is the signal strength of the i-th event as above. CARP first
introduced this procedure for damage evaluation in large laminated structures such as FRP tanks
and pipelines [8].
Intensity for different level of loading was analyzed. Although all the intensity parametersand curves were experimentally derived for composite materials, our experiments show that they
may be applied for concrete structures as well. Surely, the more accurate evaluation based on
those criteria requires performing much more research. The intensity curves must be corrected inorder to provide absolutely reliable results. In Fig. 3 intensity for selected channels is shown(historic index on horizontal axis and severity on vertical axis) for different levels of loading.
The applied gradation scale and the boundary values of severity and historic index were thesame as given by Fowler [8]. The general descriptions of the plotted zones are as follows:
A – minor emission, not for further reference,B – “small” defect,
C – significant defect, further evaluation required,D, E – major defect, immediate shutdown and follow-up nondestructive examination needed.
Each dot plotted in Fig. 3 represents a group of measuring zones producing AE signals
characterized by the similar features. As shown in these graphs, at the early stages of loading,signals recorded by all sensors are graded as A (up to 40% of the failure load), which means no86

serious deterioration within monitored zones. For this loading, no cracks were observed. The
analysis shows that as the load exceeds 60% of the beam loading capacity, intensity plots areplaced in area B, that means that small scale cracking took place and indeed a number of tiny
cracks were observed. The last graph shows the intensity at failure, which took place in compres-Fig. 3. Intensity plots for different stages of loading: 25% of failure load (upper left), 40% of
failure load (upper right), 60% of failure load (lower left), failure load (lower right). Different
colored dots indicate different measuring zones or sensor positions.
sion zone, where no reinforcement was situated. However, at the side surfaces, the tensile crack
could be seen. No fracture of reinforcing wires was observed.
The method of data analysis, which was described above, is a new way in the field of fullscale concrete elements or structures evaluating by AE [7]. The zonal analysis and calculating of
historic index together with severity is the only method that provides results based on
quantitative criteria. It was used for evaluating the integrity of five bridges in Poland.
Examples of Reinforced Concrete Bridges Tested by AE
Acoustic emission method enables the testing of the structural integrity of concrete bridges infollowing cases:
87

•
•
•
•

under regular traffic,
under nominal moving load (dynamic),
under nominal stationary load (static),
special cases (during overloading).

The basic tests are performed during regular traffic. This approach does not require closing
the bridges and does not cause any traffic problems. If the bridge is graded as A or B class (using
Fowler’s scale), no further examination is needed. In other cases the bridges are monitored undernominal loading (static and dynamic). A different procedure is required in special cases, forexample during overloading. Such type of AE monitoring is also described below. While testing
new bridges (before regular traffic is applied), the examination under nominal loading only is
performed.
A. New bridge built of pre-stressed beams.
One of the first bridges in Poland that was tested by AE was a new pre-stressed bridge shownin figure 4. This was a 2 span structure built of 30 pre-stressed beams – type KUJAN. Beams of
the same type were examined by acoustic emission in a laboratory up to fracture. The bridge was
loaded in two modes (dynamic and static) by two heavy trucks (the load was equal to thenominal capacity of this structure). As expected, the new bridge did not show any signs ofdamage. During this AE monitoring, the bridge was loaded for the first time and this was thereason of some initial friction, which produced AE signals at the supports typical for new
structures. This emission diminished during the following stages of loading.

Fig. 4. New pre-stressed bridge tested by AE.
The main purpose of these AE measurements was to get the initial AE features of thestructure. In the future the periodical tests will be performed. The results of all those tests will becompared and in this way the deterioration of the bridge will be evaluated.
88The results obtained from the bridge are similar to those acquired during the early stages ofloading of the pre-stressed beams. Both the conventional AE parameters and intensity (Fig. 5)(based on severity and historic index) were determined. Again, the zonal location was appliedand each dot plotted in Fig. 5 represents a group of zones producing AE signals characterized by
the similar features. As shown, AE signals recorded by all sensors were graded as A, whichmeans no damage or a minor one.

Fig. 5. Intensity parameter for tested bridge.
The testing of a new object became surprisingly one of the most often performed in situ tests
in Poland. Many investors and supervisors see a great need for evaluation of the quality ofengineering structures and the initial damage level of the structure. For a bridge that was gradedas A-intensity, a so-called “AE Certificate” is provided.
B. Severely Damaged Bridge Built of Post-tensioned Beams.
This AE measurement was intended to evaluate the damage of a bridge before repairing(shown in Fig. 6) during loading. This bridge deteriorated severely and was selected for repair.
The visual inspection revealed: a) uncovered reinforcement, b) corrosion of the reinforcing
cables, c) corrosion of concrete, d) cracks in concrete. Those damages are shown in Fig. 7.
During field AE examination, the bridge was loaded by two heavy trucks placed closely toeach other (15 tons each – 30 t was the nominal loading capacity for this bridge). The structure
was subjected to two loading modes: dynamic and static. During the dynamic loading, the trucks
passed the bridge with the speed of 0.5 km/h, while during static loading the trucks were placedat the center of the span in order to cause the highest stress in tested beams. To record AE
activities, 12 resonant (55 kHz) AE sensors were attached to the bottom surface of one of thebeams and zonal location was applied. The length of the beams was equal to 14.85 m. The sensor
spacing was 1.25 m.
89

Fig. 6. Monitored bridge built of post-tensioned beams.

Fig. 7. Existing damages of the tested bridge.
Because of numerous visible damages, a large number of AE hits was expected during thebridge loading. The purpose of this AE test was to identify the active damage (propagating
throughout the test) and their location of the most damaged zones. Using zonal location mode
does not require determining precisely the velocity of stress wave propagating in the tested
element. Direct source location would be very difficult especially in old fractured concreteelements.
Results of AE activity recorded only for one of tested beams will be presented. AE from thesecond beam did not indicate any active damage that would be detrimental for the structural
integrity of the beam. Due to this the description of those results has been omitted.
Results of AE activity during dynamic loading.
In Table 1, the number of hits and the maximum values of AE signal parameters that wererecorded by the channels during a dynamic loading are given. The other channels recorded no
hits.
90

Table 1. Number of hits and maximum values of AE signal parameters during dynamic loading.
AE sensor
number3
91112Numberof Hits
79
1
38
1

Amplitude
[dB]
55
42
55
50

Energy20
1
727

Duration
µs]
[µ
1300
32
828
1958

Rise Time
µs]
[µ
346
10
141
369

Counts242
20
56

Relatively weak AE activity indicates that there is no danger for the structural integrity ofthis beam under applied loading conditions. No AE signals were produced in the middle part ofthe beam, although that part received the highest stress. All the acquired signals are characterized
by low amplitude (not greater than 55 dB), low energy, and relatively short duration.
The graphs of AE parameters (number of hits rate, amplitude, energy and duration) vs. time,
given in Fig. 8a and Fig. 8b, indicate that AE activity took place when the trucks were passing
through the middle part of the beam. However, during the following passes the level of AE did
not increase. The surface of beam in zones 3 and 11, where most of AE events were recorded,
were visually examined in detail and no cracks were observed. This suggests the friction between
concrete and uncovered reinforcing cables under the heavy loading was the main sources ofrecorded emission.
Results of AE measurements under static loading.
The second stage of this AE examination consisted in measuring AE produced within the
beam under static loading. Two heavy trucks were positioned over the tested beam, and then AE
processor was switched on. After two minutes, it started to record AE activities. Almost all AE
signals were recorded on channel 4, meaning that these were produced within the zone about 4 mfrom the supported end of the beam. A very intensive AE activity was observed at the beginningof the third minute of the test and it has been clearly shown in Energy vs. Time and Duration vs.
Time plots in Fig. 9. Duration of some of the recorded signals amounted to 500 ms. Althoughstrong AE observation clearly indicates the development of active damage, the loading has notbeen stopped because the signals’ amplitude did not exceed 80 dB at first. In later part of the test,
such high amplitude signals were observed. The loading was removed when the number ofstrong AE hits (>80 dB) exceeding a critical value (derived experimentally). Channels 3, 9, 11
and 12 recorded only one or two AE hits throughout the test. In Table 2, the number of hits andmaximum values of their parameters are given. The other channels recorded no hits.
Table 2. Number of hits and maximum values of AE signal parameters during dynamic loading.
AE sensor’s
number34
91112

Numberof Hits
1
70950
2
2
1

Amplitude
[dB]
45
85
45
73
45

Energy
1
11250
10
200
9

Durationµs][µ
30500000
1310
3200
1090

Rise Time
µs]
[µ
4
1600
80
290
700

Counts
1
17200
11
100
200

The increase of the AE hits at sensor 4 recorded under the constant loading indicated theserious damages were taking place in the vicinity of sensor 4. Visual examination of beam
surface in zone 4 did not reveal new cracks. Therefore, it was suggested that this emission musthave been caused by sources located in concrete-reinforcement interface. It was concluded thatthe bridge has to be repaired (by constructing a continuous slab for all three spans) or reinforced.
91

Fig. 8a. AE parameters (hits rate, amplitude) vs. time during dynamic loading.

Fig. 8b. AE parameters (energy, duration) vs. time during dynamic loading.
In rare cases, it is relatively easy to evaluate the structural integrity of the examined structure.
In this particular case, the real-time analysis of AE parameters described here clearly indicated aserious damage in one girder. However, more often, only a multi-parameter analysis enables the
final reliable evaluation. The important conclusion to note from this test is that the dynamic
loading did not reveal any serious defects that were detected under static loading.
The same bridge was tested after it was repaired. The design of repair was preparedfollowing our suggestions. Next, selected beams were tested by AE (the same beams aspreviously). The purpose of this action was to compare the parameters of AE signals obtained
from a damaged bridge and a renovated one. As expected, the multi-parameter analysis ofacquired data did not indicate any active damage. This example shows the potential of using AE
as a method for testing the quality of repair. This advantage of AE will be developed in cooperation with Polish Management of Highways and Bridges.
92

Fig. 9. AE parameters (Hits rate, Amplitude, Energy, Duration) vs. time during static loading.
C. An old viaduct built of pre-stressed concrete beams.
Some results obtained during the monitoring of a pre-stressed concrete viaduct together withthe evaluation criteria are given. On this viaduct, several pre-stressed concrete beams (17.5meters-long) were chosen for testing by AE technique. The examination was performed under
the loading from regular traffic. Resonant sensors (frequency = 55 kHz) were used. After
preliminary measurements (concerning the attenuation of produced stress waves), it was decided
that the sensors should by placed at the bottom surface of a tested beam. The distance between
the neighboring sensors was equal to 145 cm. Zonal location was performed.
The main goals of these measurements was to select beams that need to be removed (if there
are any) and to indicate seriously damaged zones of the beams. To achieve these goals, eachbeam was divided in 12 measuring zones (145 cm long) according to the scheme of sensors’
placement. The conventional AE parameters (Amplitude, Duration and Energy) together with
Signal Strength were recorded. In Fig. 10, Amplitude vs. Time point plot is shown for 12 sensors(for one beam only). It can be seen that only channel-2 signals exceeded the value of 70 dB.
93

Other conventional AE parameters were higher in this zone as well. However, the number of hits
and the values of its parameters were higher on channels 4, 7, 8 and 10 than on others. It was
decided that the best solution for evaluating the structural integrity is to use a “comparative
criterion”. This method is based on comparing values of AE parameters acquired on individual
independent channel with the mean values of those parameters form all channels. Based on thosecriteria, the measuring areas were divided in three types described as:• low (or zero) AE intensity,• medium AE intensity (comparable with the medium value),• high AE intensity (greater than double mean value).
In Fig. 11, these areas for one of the tested beams are shown. These areas correspond withthe plots shown in Fig. 10. The final evaluation of the tested structure proved the usefulness ofthe criteria described in the RP. This shows that preparing and using the RP was a successfulattempt of applying AE results obtained in laboratory to practical cases. In addition, for selectedchannels, Historic Index (HI) was analyzed. It was assumed [7] that HI value exceeding 1.85
indicates the presence of active damage in pre-stressed concrete beams. However, the analysis of
this HI parameter does not always provide proper and reliable results in our tests and much work
remains to be done to derive the universal coefficients that allow the use of Historic Index for
reinforced concrete girders. Such experiments are currently performed at Kielce University ofTechnology. For the examined structure, the value of HI did not exceed 1.85 for any zone.
It was noticed, that for some structures (especially the seriously deteriorated ones) the stresswave’s velocity and attenuation might be good and reliable criteria for evaluating the level ofdamage of concrete. It is well recognized that the higher the level of deterioration is, the stronger
attenuation of AE signals is observed. It is also known that, in new concrete structures orelements, the velocity of AE signals is much higher that in seriously damaged ones. The aspects
listed above are very helpful during the initial in-situ selection of the most damaged zones.

Fig. 10. Amplitude vs. Time point plot.
94

Fig. 11. Measuring areas with different level of AE intensity.
As mentioned above, AE parameters strongly depend on the type of structure and theloading mode. Using “comparative criteria” of evaluation gives very good results and is probably
the most reliable method of evaluation. For this particular structure, based on the selected
criteria, the monitoring of selected zones under nominal loading was suggested in order toprovide the final evaluation.
D. Monitoring of a reinforced concrete bridge during overloading.
A small reinforced concrete bridge was monitored during overloading. The pictures taken
during this measurement are shown in Fig. 12.
The purpose of this test was to monitor the behavior of the bridge during transportation of aheavy electrical transformer (470 tons) transported on two trailers. The monitoring was divided
in two stages: passing of the truck together with the first trailer and passing of the second trailer.
The number of hits and hits rate obtained during this transportation is shown in Fig. 13. It can beseen that during both stages, the intensity of AE was similar. It means that there was no damage
caused by the heavy load.
It should be mentioned that AE was also recorded before and after the transportation underregular traffic. The comparison of the results obtained during those measurements also did notsuggest any new damage. The evaluation based on AE was than confirmed by analyzing anotherparameter: deflection.
The promising results of the first AE real-time monitoring had strong effects in thedevelopment of this method. In the near future the Polish Management of Highways and Bridges
is going to use AE as a mandatory method for monitoring overloaded transports and selecting
routes for them.
E. A New Steel-Concrete Bridge.
The main purpose of this test was to obtain the initial AE level of a new combined steelconcrete structure (shown in Fig. 14) in order to provide the “AE Certificate”. Selected areas of
the reinforced concrete slab were tested under nominal loading equal to 30 tons. As expected, nodamage was indicated. During the loading of the bridge, some emissions were recorded close to
the supports and steel girders, but these were of low levels and in Grade A.
95

Fig. 12. Reinforced concrete bridge monitored during overloading.
During post-test analysis, intensity for each sensor was calculated. All measuring areas wereclassified with grade A, which means low emission and no damage.
Conclusion
Acoustic emission method is useful for the evaluation of the integrity of reinforced concrete
structures. It can be successfully used for selecting bridges or their elements that need to berenovated or scrapped. However, for each type of structures, individual evaluation criteria must
be selected. This requires performing model tests of reinforced and pre-stressed concrete beams
in different loading modes. In laboratory, it is possible to compare AE parameters with otherphysical parameters such as: width of cracks, deflection and strain. The combined analysis ofthese values gives a comprehensive view of processes occurring in tested elements and allows
the use of AE method for in situ structure testing. We strongly believe that, in the near future, theowners of bridges and bridge industry in Poland will accept the „Recommended Practice for
Testing Reinforced and Pre-stressed Concrete Structures by Acoustic Emission”.
In many reports, the Felicity Ratio (FR) is recommended as a useful criterion. Although it
gives good results in laboratory tests, it cannot be used for evaluating in situ structures. The main
disadvantage of using FR for evaluating full-scale structures is the requirement of an accurate
96Fig. 13. AE hits and hit rates: First stage of transportation – on the left,
Second stage of transportation – on the right.
control of loading. This is impossible in practice while testing existing objects in regular traffic.
Possible inaccuracies that may occur make this method useless.
We need to perform AE examination for each different type of reinforced and pre-stressed
beams, which are currently used in full-scale structures. Collecting their acoustic emission
characteristics will be useful in evaluating AE data recorded during bridge monitoring. Such anarchive for beam designs used in Poland is currently being prepared at Kielce University ofTechnology and will be assessable for our partners in this cooperative effort. A series of new
bridges will be tested as well in order to estimate their level of damage during subsequent
periodic monitoring.

97

Fig. 14. New steel-concrete bridge tested by AE.
References
1. M. Ohtsu, “Acoustic Emission Characteristics in Concrete and Diagnostic Applications”, J.
of Acoustic Emission 6, 99, 1987.
2. C. U. Grosse, B. Weiler and H. W. Reinhardt, “Relative Moment Tensor Inversion Applied
to Concrete Fracture Tests”, J. of Acoustic Emission, 14, S74-S87, 1996,
3. K. Matsuyama, T. Fujiwara, A. Ishibashi and M. Ohtsu, “Field Application of Acoustic
Emission for the Diagnosis of Structural Deterioration of Concrete”, J. of Acoustic Emission,
11, S65-S73, 1993.
4. T. Bradshaw, K. Holford, P. Cole and A. Davies, “Ball bearing calibration of concrete
structures using AE”, Proc. 25th European Conference on AE Testing, Prague, Czech
Republic, 2002, I/91.
5. S. Colombo, I. G. Main, M.C. Forde and J. Halliday “AE on bridges: experiments on
concrete beams”, Proc. 25th European Conference on AE Testing, Prague, Czech Republic,
2002. I/127.
6. S. Yuyama, T. Okamoto, M. Shigeishi, M. Ohtsu and T. Kishi, “A Proposed Standard for
Evaluating Structural Integrity of Reinforced Concrete Beams by AE”, ASTM-STP 1353,
ASTM, West Conshohocken, PA, 1999; pp. 25-40.
7. Texas Department of Transportation “Procedure for Acoustic Emission Monitoring of Prestressed Concrete Girders”, 2001. (J. of Acoustic Emission, volume 19 CD-ROM)
8. T. J. Fowler, J. A. Blessing and P. J. Conlisk, “New Directions in Testing”, Proc. 3rd
International Symposium on AE from Composite Materials, Paris, France, 1989.

98

ANALYSIS AND IDENTIFICATION OF ACOUSTIC EMISSION FROMDAMAGE AND INTERNAL FRETTING IN ADVANCED COMPOSITESUNDER FATIGUEYURIS DZENIS and JIE QIAN
Department of Engineering Mechanics, University of Nebraska-Lincoln, Lincoln, NE 68588
Abstract
Acoustic emission (AE) from advanced composites under fatigue is addressed in this paper.
It is well known that composites subjected to cyclic fatigue loading exhibit gradual damage accumulation to failure. Acoustic emission analysis can be applied, in general, to characterize andquantify the damage evolution process. However, under fatigue loading, substantial AE in composites can be due to internal fretting. This emission is generated by crack face fretting andsimilar mechanisms and its amount can be large enough to obscure useful information on damage development in the overall AE histories. It is important to be able to recognize and eliminate
this emission in order to apply the AE method to fundamental fatigue damage analysis of composites. In this paper, acoustic emission from low-cycle tension-tension fatigue of a quasiisotropic graphite-epoxy composite laminate was analyzed. The overall AE was separated into
the emission from loading and unloading. The AE count, location, and load histories, and theload range distributions were generated and analyzed. The emission from the higher and lower
load levels during loading and unloading was studied. It was concluded that the AE from thehigher load levels during both loading and unloading was mostly from the new damage events.
In contrast, the emission from the lower load levels during unloading was attributed to internal
fretting. The amount of this emission comprised 13% of the overall AE for the composite studied. The origin of the emission from the lower load levels during loading could not be positively
identified, based on the available data. The amount of this emission comprised 7% of the overall
AE. The results provide insights on the AE behavior of composites under fatigue and can be used
for the analysis of damage micromechanisms and life and for the development of predictivemodels.
Introduction
It is well known that advanced composite materials subjected to loads exhibit gradual accumulation of damage of several types. These include matrix cracks, fiber breaks, fiber-matrix
debonds, longitudinal splitting, and delaminations. Scattered damage development can be especially pronounced under fatigue. Experimental information on damage evolution in composites isneeded for better understanding of micromechanisms of their fatigue failure and life and for thedevelopment of predictive models.
Acoustic emission has been shown to provide useful information on damage development
under cyclic loading [1-9]. One problem with AE studies of fatigue damage in composites is thelarge amount of acoustic noise that can be generated by the testing machine and grips. In the previous studies, this noise has been reduced or eliminated by load gating [1, 8], amplitude discrimination [9], or location filtering [2, 3, 5]. The latter method, utilizing two or more AE sensors and the AE location analysis to filter out the emission originating outside of the designatedacoustic gage zone (usually, part of the specimen between the sensors) is especially attractive as
J. Acoustic Emission, 20 (2002)99© 2002 Acoustic Emission Groupit does not exclude any signals originating within the gage zone and, thus, prevents the loss ofuseful information on damage development.
Another, more complicated problem with the AE analysis of composites under fatigue is theemission from internal fretting [10]. It has been shown that friction or fretting between the facesof the previously developed cracks can produce substantial AE [2-5]. This emission can obscureuseful information from the signals generated by the damage events. It is important to be able torecognize and separate the AE from damage and fretting in order to use the AE method for
fundamental fatigue damage studies of composites.
Often, the AE signal parameters are being used to recognize the signals from different
sources. It has been shown, however, that the amplitude of the fretting signals can exceed theamplitude of the signals from the new damage. It has been suggested [2, 3] that other AE
parameters, such as the count number or signal duration, can be used to distinguish the signals
from friction. However, the threshold values of these parameters, necessary for the parametricdiscrimination, are difficult to determine. Also, due to the inherent scatter of both fretting anddamage events in composites, the ranges of the AE signal parameters for these two types of
events can be expected to overlap.
A study of the AE from loading and unloading under fatigue can shed light on the emission
from internal fretting and lead to the development of methods of its elimination. One would
expect more new damage to be developed during loading and more fretting noise to be generated
during unloading. The literature data on the AE from composites during fatigue loading andunloading is limited. The AE has been recorded during several cycles of the quasi-static loadingunloading with the increasing load amplitude in [10]. Eight to eleven load cycles have been
applied to failure. Substantial AE observed during the unloading has been attributed to internal
fretting. The absolute time scale of the acoustic and mechanical tests has been used to correlate
the acoustic events with the load levels over the short time intervals (about three cycles) during
the fatigue process [4]. Substantial AE has been observed in the unloading phase. Some of thisemission has been attributed to the matrix cracking in compression. More detailed studies of the
AE from loading and unloading are needed to produce additional information.
In this paper, such studies are conducted on an unnotched quasi-isotropic graphite-epoxy
laminate subjected to low-cycle tension-tension fatigue loading. The overall AE is separated into
the emission from loading and unloading by the loading phase filtering. The AE count, location,and load histories, and the load range distributions are generated and analyzed. The emission
from the higher and lower load levels during loading and unloading is studied. Conclusions are
drawn on the origin of the emission from different load phases and levels.
Experimental
Quasi-isotropic [90/±30]3S laminated composite panel was manufactured from Hexcel
T2G190/F263 graphite/epoxy prepreg following the manufacturer-recommended curing cycle.
The panel was tabbed and the rectangular 200 x 25 mm specimens were machined by a highspeed diamond saw. Tension-tension fatigue testing was performed by an MTS testing machine
retrofitted with a digital Instron test control and data acquisition system. The tests were load
controlled, with the fatigue stress ratio of 0.1 and frequency of 0.5 Hz. The maximum loading
was approximately 90% of the ultimate static strength for this composite.
100The AE was acquired and analyzed by a two-channel Vallen AMS3 acoustic emission system. Two wide-band, high fidelity B1025 AE sensors by Digital Wave were used. The AE gage
zone (the distance between the AE sensors) was 80 mm. The AE source location analysis was
performed on the incoming signals and the signals originating outside of the acoustic gage zone
were eliminated from the analysis. A 34.5 dB system gain and a 40.5 dB threshold (ref. 0 dB at 1µV) were used in the AE data acquisition. The load and strain data were fed from the testing machine and recorded as parameters.
Results and Discussion
Overall Acoustic Emission
Figure 1 shows the overall AE from a representative fatigue test. The specimen failed in the
432nd cycle of fatigue loading. Subplot 1 of Fig. 1 presents the history of the accumulated AE
counts as a function of cycle number. Subplot 2 shows the distribution of the AE over the loading range. The AE location history is shown in subplot 3 and subplot 4 presents the AE load
history. Each dot in the correlation-type subplots 3 and 4 corresponds to an AE event.

Fig. 1. Overall AE during low-cycle fatigue test.
The cumulative AE count history (subplot 1) exhibited three classical stages in the fatigue
process in composites, i.e. initial fast damage development, gradual steady damage accumulation, and final failure development. The load range distribution (subplot 2) showed that most of
the emission was generated at higher loads. However, some of the emission was generated atlower loads. In particular, substantial emission was generated in the range from 0.4-0.45 of the101

maximum fatigue loading. It is interesting that few AE events were generated between the two
peaks on the load range distribution.
The location history (subplot 3) showed that most of the AE events were scattered in respect
to both location and time. A few spatial clusters of events were noticed at locations between 5
and 7 cm in the beginning of the fatigue process, but these clusters existed only through a few
loading cycles. A long-lived cluster was detected near 0 cm location. This cluster lasted throughabout a third of the fatigue life.
The load-range history plot (subplot 4) showed that the AE events at the higher loads weregenerated uniformly throughout the fatigue life, with slightly higher rates in the beginning of thefatigue process. The events at the loads near the maximum load were not clustered. In contrast,
most of the events at the lower loads were clustered, i.e. they occurred at the same load levels
over the number of loading cycles. These AE events might be due to a well-characterized damage, such as, for example, matrix cracks in the 90˚ plies. They could also be due to a gradual extension of fatigue cracks or internal fretting.
Further analysis was conducted by separating AE signals from loading and unloading.
Acoustic Emission from Loading and Unloading
It can be expected that new damage is more likely to be developed during loading than during unloading. The overall AE described above was separated into the AE from loading and unloading using a unique phase filtering capability of the AMS3 system. This system can analyze
an external analog signal, such as load, and determine the sign of its derivative. In the case of
cyclic fatigue loading, this sign uniquely identifies the loading phase, i.e. loading or unloading.
This sign can be recorded as a flag parameter, along with other AE parameters. This flag wasused to separate the emission from different loading phases in the current study.
Figure 2 presents the AE from the loading phase of the cyclic loading, in the format similar
to the format of the overall AE in Fig. 1. Subplot 1 of Fig. 2 showed that the emission fromloading exhibited classical fatigue stages that were even more pronounced than the stages observed in the overall AE count history (Fig. 1). The load range distribution (subplot 2 of Fig. 2)
showed a single maximum at the higher loads with most signal developed at loads exceeding 0.8
of the maximum fatigue load. The location history of the AE from loading (subplot 3) was wellscattered while the load history (subplot 4) exhibited both dense scattered signals at the high load
levels and clustered signals at the lower loads.
The results for the emission from unloading (Fig. 3) were substantially different. The accumulation rate (subplot 1) was almost uniform throughout the fatigue life, with slightly higher
rates in the beginning but without a pronounced final failure development stage. The load distribution (subplot 2 of Fig. 3) exhibited two peaks at the medium and high loads. The intensity ofthese peaks was comparable. The location history (subplot 3) showed that the clusters observedin the overall AE were generated during unloading. The load history (subplot 4) showed fewer
signals generated at the higher loads. The density of these signals was higher in the beginning of
the fatigue process. This was probably responsible for the higher initial accumulation rate seen in
subplot 1. Most of the signals generated at the lower loads were clustered.

102

Fig. 2. AE during loading.

Fig. 3. AE during unloading.
103The comparison of Figs. 2 and 3 with Fig. 1 showed that most of the emission at the medium
load levels (a low peak in subplot 2 of Fig. 1) was generated during unloading. The signals from
unloading were also more clustered. The total amount of the emission from unloading was almost half of that from loading (see maximum cumulative counts in subplots 1 of Figs. 2 and 3).
Further analysis was conducted by separating signals from higher and lower load levels under loading and unloading.
Acoustic Emission from Higher and Lower Load Levels
Figures 4 and 5 show the AE generated at higher (over 0.8 of the maximum load) and lower(under 0.8 of the maximum load) load levels during loading. The emission from the higher load
levels (Fig. 4) showed pronounced fatigue staging (subplot 1) and was well scattered (subplot 3).
The emission from the lower load levels (Fig. 5) was distributed over the loading range (subplot2). It exhibited irregular accumulation history (subplot 1) with high accumulation rates in thefirst several cycles and much slower, varying accumulation rates afterwards. Although the AE
events were clustered in respect to load (subplot 4), no spatial clusters were recorded (subplot 3).
The emission from the low load levels accounted for about 10% of the total emission from theloading phase or about 7% of the overall emission in this composite.

Fig. 4. AE from high load levels under loading.
Figures 6 and 7 show similar analysis of the emission generated during unloading. The emission from the higher load levels (Fig. 6) showed gradual accumulation with higher accumulation
rates in the beginning of the process (subplot 1) and was well scattered (subplots 3 and 4). The
emission from the lower load levels (Fig. 7) exhibited increasing accumulation rate (subplot 1).
104

Fig. 5. AE from low load levels under loading.

Fig. 6. AE from high load levels under unloading.
105Fig. 7. AE from low load levels under unloading.
Most of this emission was generated in the narrow loading range (subplot 2) and the AE eventswere clustered in respect to both load (subplot 4) and location (subplot 3). The emission fromthe lower load levels accounted for about 46% of the total emission from unloading or about
13% of the overall AE in this composite.
Analysis and Identification of Emission from Damage and Fretting
The fatigue damage in unnotched composites is expected to exhibit scattered location andcharacteristic staging in the cumulative history. Also, the damage rate is expected to increase
with the load level. The AE from the higher load levels (>0.8 of the maximum fatigue load)
during both loading and unloading phases of the fatigue loading (Figs. 4 and 6) correlated wellwith these expectations. This emission was therefore attributed to the new damage development.
Note that the final failure development appeared to take place mostly during the loading phase of
the fatigue loading.
On the other hand, the emission from the lower load levels in the unloading phase (Fig. 7)
was developed mostly in the narrow load range from 0.4-0.45 of the maximum fatigue load. In
general, both new damage and fretting could be responsible for the emission at the lower load
levels. However, the new damage events are expected to be distributed over the load range, withhigher rates at higher loads. In contrast, the density of the fretting signals can be higher at lower
loads. The accumulated cracks open and close repeatedly during cyclic loading. Their faces
come into contact during unloading that can generate the emission. Depending on the type ofdamage, this may occur at a particular load level. The emission from friction is also expected tobe less scattered and more clustered, as the fretting noise will be generated by the same cracks at106

the same locations. Finally, it is also expected to exhibit the gradually increasing cumulative
history, as more and more cracks will contribute to friction with the increasing fatigue damage.
Based on the above, the emission from the lower load range during unloading (Fig. 7) was attributed to internal fretting.
The emission from the lower loading levels during the loading phase (Fig. 5) differed fromthe unloading emission. The cumulative history (subplot 1) showed elements of the classical fatigue staging. Some irregularity could be due to the overall low amount of this emission. The
emission was distributed in a wide load range (subplot 2) with several peaks at both lower andhigher loads within this range. Although clusters were observed in the load history (subplot 4),
no spatial clusters could be identified in the location history. In general, the information above is
not sufficient to make a positive identification of the emission from the lower load levels duringloading. More studies are needed. It can be noted, that the cumulative content of this emission in
the overall AE from the composite studied was only about 7%.
Conclusions
The conducted analysis of the AE from an advanced laminated composite subjected to fatigue loading showed that the emission from the higher load levels during both loading and unloading phases of the cyclic loading was due to new damage development. The emission fromthe lower load levels during unloading was from internal fretting. The emission from the lower
load levels during loading could not be positively identified.
The results provide insights on the AE behavior of composites under fatigue and can be usedin fundamental studies of damage micromechanisms [11] in composites under fatigue and for thedevelopment of predictive models.
Acknowledgments
This work was supported by the Air Force Office of Scientific Research. The graphite/epoxy
prepreg was provided by Hexcel Corporation.
References
1. Williams, R.S. and Reifsnider, K.L., Journal of Composite Materials, 1974, 8, 340.
2. Awerbuch, J. and Ghaffari, S., Journal of Reinforced Plastics and Composites, 1988, 7, 245.
3. Eckles, W. and Awerbuch, J., Journal of Reinforced Plastics and Composites, 1988, 7, 265.
4. Shiwa, M., Carpenter, S., and Kishi, T., Journal of Composite Materials, 1996, 18, 2019.
5. Gustafson, C.-G. and Selden, R.B., Delamination and Debonding of Materials, ASTM STP
876 (ed. W.S. Johnson), American Society for Testing and Materials, Philadelphia, 1985, p. 448.
6. Wevers, M., Verpoest, I., De Meester, P., and Aernoudt, E., Acoustic Emission: Current Practice and Future Directions, ASTM STP 1077 (eds. W. Sachse, J. Roget, and K. Yamaguchi),
American Society for Testing and Materials, Philadelphia, 1991, p. 416.
7. Kloua, H., Maslouhi, A., and Roy, C., Canadian Aeronautics and Space Journal, 1995, 41, 21.
8. Edwards, J.H., Composite Materials: Testing and Design, ASTM STP 972 (ed J.D. Whitcomb), American Society for Testing and Materials, Philadelphia, 1988, p. 369.
9. Takemura, K. and Fujii, T., JSME International Journal, 1994, A37, 472.
10. Awerbuch, J., Gorman, M.R., and Madhukar, M. Materials Evaluation, 1985, 43, 754.
11. Qian, J. and Dzenis, Y.A., Journal of Acoustic Emission, 2002, 20, 16.
107

ACOUSTIC EMISSION FROM MAGNESIUM-BASED ALLOYS
AND METAL MATRIX COMPOSITES
FRANTISEK CHMELÍK1, FLORIAN MOLL2, JENS KIEHN2, KRISTIAN MATHIS3,PAVEL LUKÁC1, KARL-ULRICH KAINER4 and TERENCE G. LANGDON5
1

Department of Metal Physics, Charles University, CZ 121 16 Prague 2, Czech Republic2
Technical University Clausthal, D 38678 Clausthal-Zellerfeld, Germany
3
Department of General Physics, Eötvös Loránd University, Budapest, H-1117, Hungary
4Institute for Materials Research, GKSS Research Center, D 21502 Geesthacht, Germany
5
University of Southern California, Los Angeles, CA 90089-1453, U.S.A.
Abstract
The acoustic emission (AE) technique was used to monitor structural changes and related
plastic deformation in prospective Mg-based light alloys and metal matrix composites subjectedto thermal loading and/or mechanical stress (tensile or creep testing). It is shown that the AE
response depends significantly on the testing conditions. The deformation mechanisms and theircorrelation with the testing parameters are discussed. It is shown that the AE response and theresidual strain after thermal cycling depend significantly on the upper temperature of cycling.
The results are explained in a quantitative analysis using a model that correlates the internal
thermal stresses with the changes in temperature. The AE response in deformation tests is
discussed in terms of the contemporary knowledge of plastic deformation processes in metallic
materials.
1. Introduction
Several magnesium-based alloys and metal matrix composites (MMCs) have been developed
and manufactured over the last decade for potential use as light-weight high-performancematerials in a range of applications including, for example, the automotive industry (Fritze et al.,1998). The standard operating conditions for most alloys and MMCs involve thermal and/or
mechanical loading. This may induce microstructural changes and matrix plastic deformation
characterized by dislocation generation and motion. In addition, for the case of MMCs thermalloading induces internal stresses owing to the often substantial mismatch between the thermal
expansion coefficients of the matrix and the reinforcement. Under higher load and/or long-term
exposure, structural damage (for example, interface de-cohesion, fiber fracture) may also occur.
The mechanical properties of Mg-based materials are dependent on the mechanisms ofplastic deformation in hexagonal close-packed (hcp) metals. Magnesium exhibits an axial ratioof c / a equal to 1.623, which is slightly less than the ideal value of 1.633. The main deformation
mechanisms are dislocation glide in the basal planes (0001) along < 1120 > directions andtwinning in the pyramidal planes {10 1 1} (ASM Handbook, 1999). Twinning may also reorient
the basal planes so that they become more favorably oriented for slip (Zhang et al., 2000). The
relative importance of these various mechanisms depends strongly upon temperature. With
increasing temperature, twinning becomes easier as the metal strength decreases. However,
above a temperature of 250°C additional non-basal slip systems (pyramidal planes) are activatedand deformation becomes much easier so that twinning is then less important. In addition,
J. Acoustic Emission, 20 (2002)

108© 2002 Acoustic Emission Grouprecovery processes gain importance with increasing temperature and in polycrystalline materials
there is the increasing importance of grain boundary sliding at the higher temperatures.
Acoustic emission (AE) is a non-destructive experimental technique, in which transient
elastic waves are generated within a material due to sudden and irreversible structural changes. It
has been established that AE is a viable procedure for monitoring the development of
microstructural changes and related plastic deformation and/or structural damage in many classesof materials. Specifically, dislocation motion and microstructural damage are generally
recognized to produce significant AE in most metals and alloys (Heiple and Carpenter, 1987).
Hence, there is a possibility of using AE monitoring to identify and characterize themicrostructural changes occurring within the Mg-based materials and to correlate thesemeasurements with the associated temperatures or other testing parameters.
To date, there are only limited results demonstrating the use of AE in monitoring thestructural response and matrix deformation of Mg-based alloys (Fisher and Lally, 1967, Siegel,
1977, Friesel and Carpenter, 1984, Kato et al., 1986). During the deformation of pure Mg, an
AZ31B and various Mg-Al alloys at room temperature, there was a distinct correlation between
the AE activity and the sample orientation, purity, strain rate and the mode of testing (tension,
compression). In all cases, deformation twinning and dislocation glide were found to be themajor sources of AE. By contrast, AE evidence for Mg-based MMCs is almost entirely lacking.
This paper provides a comprehensive review of our recent work in this area. The present
experiments were conducted to investigate the use of AE as a non-destructive tool for theexamination and evaluation of Mg-based materials. Special emphasis was placed on theinfluence of the matrix composition, the matrix reinforcement and temperature on AE, and therelated microstructural evolution and plastic deformation of a variety of Mg-based alloys and
MMCs. To simulate thermal and/or mechanical loading of structural parts, the specimens were
subjected to thermal cycling, tensile tests at constant cross-head speeds and tensile creep tests atelevated temperatures. In addition, the potential for analyzing the experimental data using
established models is evaluated.
2. Experimental Procedure
The matrix materials used in this investigation were Mg of commercial purity, magnesium
alloys ZE41 alloy containing 4 wt. % Zn and 1 wt. % rare earths, an AZ91 alloy containing 9 wt.
% Al and 1 wt. % Zn, a QE22 alloy containing 2 wt. % Ag and 2 wt. % rare earths and an AM60
alloy containing 6 wt. % Al-0.5 wt. % Mn, where all of these alloys have Mg as the balance. The
AM60 alloy received a standard T6 thermal treatment prior to testing.
The matrix materials were reinforced with 20 vol. % of Saffil δ-Al2O3 short fibers throughthe use of a squeeze-casting procedure, in which the molten Mg or Mg alloy is infiltrated underpressure into Saffil short fiber pre-forms having planar isotropic fiber distributions. The MMCs
showed planar isotropic fiber distributions with fiber diameters of ~3-5 µm and fiber lengths up
to ~150 µm after casting. There was a slight chemical reaction between the matrix and thereinforcement, which led to chemical bonding at the interfaces. In addition, the matrix in thevicinity of the interfaces became enriched in aluminum. The AZ91-Saffil MMC received astandard T6 thermal treatment prior to testing. A typical optical micrograph of the as-received
state is shown in Fig. 1. The surfaces of selected specimens were examined after fracture using a
109

Fig. 1. Typical optical micrograph of the AZ91-Saffil in the as-received condition.
TESLA BS 343 scanning electron microscope operating at an accelerating voltage of 15 kV.
Fracture surfaces were cleaned in acetone before making any observations.
An unreinforced QE22 alloy and MMCs QE22-15 vol.% SiC and AZ91-15 vol.% SiC(globular particles with a mean diameter of 30 and 10 µm, denoted as 320BL and 600BL or
600HD, respectively) were also produced by means of hot extrusion after mixing and milling of
the matrix powder and particles. Almost no defects and no products of any chemical reaction
were detected between the matrix and the particles at the matrix-particle interfaces. Examination
revealed that the materials exhibited a wide distribution of the matrix grain sizes so that there
were regions with a grain size less than 1 µm as well as regions with grain sizes up to ~20 µm.
These materials received a standard T6 thermal treatment prior to testing.
Specimens were machined for thermal cycling in the form of rods with lengths of 50 mm anddiameters of 5 mm: the reinforcement planes in these samples were parallel to the longitudinalaxes. Thermal cycling was conducted in situ by placing the specimens within a dilatometerequipped with a radiant furnace permitting temperatures from ambient to 400ºC. The residualstrain was measured after each cycle and the AE signal was transmitted through a quartz rod incontact with the specimen.
Cylindrical specimens with a gauge length of 29 mm and a diameter of 4.95 mm were
deformed in tension. The tensile tests were performed in furnace-equipped MTS and Instronmachines in the temperature range from 20 to 300°C and at a constant crosshead speed giving an
initial strain rate of 8.3 x 10-5 s-1. A steel wave-guide was used to transmit AE. One end had
cylindrical envelope-shaped that fitted tightly to the round specimen surface and it was coupled
to the sample surface using heat-resistant silicone paste. The other end of the wave-guide has aconical shape, to which the transducer was placed using vacuum grease and spring holders.
110Tensile creep specimens were machined with gauge lengths of 25.4 mm and M10 screwheads. The longitudinal specimen axes were within the plane containing the long axes of thefibers. All of the creep testing was conducted under conditions of constant stress using a creep
machine equipped with a contoured lever arm. Tests were performed at stresses in the range
from 35 to 95 MPa and at temperatures from 423 to 583 K (150-310ûC) with the temperature
controlled during each test to ±2 K. Some tests were conducted at a single stress until failure andothers used progressive loading, in which the stress was increased at selected time intervals until
ultimate failure. The AE signal was monitored during each creep test using a steel wave-guideof cylindrical shape that fitted tightly to the specimen. This wave-guide was coupled to thesample surface using heat-resistant silicone paste and steel springs.
The AE was monitored directly using computer-controlled DAKEL-LMS-16 (thermal
cycling and creep tests) and DAKEL-XEDO-3 (dynamic tensile tests) facilities developed by
DAKEL-ZD Rpety, Czech Republic. The facilities incorporated a highly sensitive LB10A
transducer (with a flat response between 100 and 500 kHz and a built-in preamplifier giving again of ~30 dB) and a miniaturized MST8S piezoelectric transducer (with a flat response
between 100 and 600 kHz). As described earlier (Chmelík et al., 1998a), the AE analyzers
detected the AE signals at two threshold levels, corresponding to gains of 100 and 80 dB,
respectively.
3. Experimental Results and Discussion
3.1. Thermal cycling of Mg-based metal matrix composites& , and the specimen
Figure 2 shows the variation with time of the AE count rate, N
C1
deformation, ∆l, with the temperature, T, for two MMCs during a single temperature cycle
having upper temperatures, Ttop, of 300°C and 400°C, respectively. It is apparent from Fig. 2athat, for the Mg-Saffil MMC, there is moderate AE during the heating within a temperature
range from ~140°C to ~220°C and significant AE during the cooling of the sample attemperatures from ~180°C to room temperature. The AZ91-Saffil MMC (Fig. 2b) shows AE
only during cooling at the lower temperatures from ~60°C to room temperature. Following thecycles, a residual contraction was measured in the samples.

111

& , temperature, T, and sample deformation, ∆l, as
Fig. 2. Value of the AE count rate, N
C1
measured in the Mg-Saffil (a) and the AZ91-Saffil (b) during temperature cycles to upper
temperatures of 300°C and 400°C, respectively.

Fig. 3 (a) and (b)112

Fig. 3. Residual strain, ∆l/l0, and AE counts for two different detection levels, NC1 and NC 2,
versus the upper cycling temperature, Ttop: the residual strain was estimated with reference to atemperature of 30°C and the AE counts were evaluated for the entire cooling period of eachcycle. (a) Mg-Saffil, (b) ZE41-Saffil, (c) AZ91-Saffil, (d) QE22-Saffil.
The behavior of the composites during thermal cycling was characterized in detail by using a
stepped incremental temperature technique. The results are documented in Fig. 3 where theresidual strain, ∆l/lo, and the AE count per cycle, NC1 are plotted against the upper cycle
temperature, Ttop, where lo is the original length of the sample and one cycle was performed foreach upper temperature corresponding to each separate experimental point recorded for the
residual strain. These plots demonstrate that there is no residual strain up to a certain upper
temperature, followed by a slight tendency for a compressive contraction for the Mg-Saffil and
the ZE41-Saffil. For all MMCs, residual elongation prevails in a certain temperature interval andfor higher upper temperatures there is a residual contraction that increases in magnitude withincreasing values of Ttop. The AE response also depends on the upper temperature, increasing
significantly at a critical value of Ttop and exhibiting intense AE bursts, which appear with afurther increase in Ttop (with the exception of the QE22-Saffil MMC, which does not show burstsof AE).
It has been shown that, under conditions similar to those used in these experiments, morethan 1000 thermal cycles are needed in order to produce any measurable damage in the samples
113

(Kiehn et al., 1994). Thus, the AE counts recorded in these experiments must be attributed to theoccurrence of structural changes in the matrix and to any associated plastic deformation. Since
the MMCs were fabricated by squeeze-casting at an elevated temperature, the composites
contain thermal residual stresses at room temperature due to the mismatch in the thermal
expansion coefficients between the matrix and the reinforcement (Arsenault and Taya, 1987; Xia
and Langdon, 1994) and the magnitude of these stresses is of the order of the minimum stressrequired for creep in the matrix. In practice, the matrix in these MMCs experiences tensilestresses whereas the fiber reinforcements experience compressive stresses. Therefore, when the
MMC is heated, the internal tensile stress acting on the matrix reduces to zero and, on further
heating, there is a build up of compressive stresses; whereas on cooling, the internal stresses
behave in the opposite sense. It is anticipated these stresses will be concentrated near the matrixfiber interfaces and at the ends of the reinforcing fibers. These thermal stresses may also exceed
the matrix yield stress within discrete temperature ranges and relaxation will then occur throughthe generation of new dislocations and plastic deformation within the matrix. This plastic
deformation may appear as dislocation glide, as twinning or possibly as grain boundary sliding at
the higher temperatures depending upon the precise temperature and the crystallographic
structure of the matrix. In general, it is reasonable to anticipate that the compressive deformationthat appears on heating will give some form of diffusion-controlled high temperature creep
whereas the tensile deformation appearing on cooling will lead to dislocation glide and twinning.
Thus, and in support of the experimental observations, a larger AE is expected during cooling at
the lower temperatures.
It was suggested earlier that it may be possible to correlate the AE response and the internal
thermal stresses produced during thermal cycling (Chmelík et al., 1998b). With respect to thedependence of the residual strain on the upper cycle temperature as plotted in Fig. 3, aquantitative analysis of internal thermal stresses has been developed for a short-fiber reinforced
aluminum composite exhibiting chemical bonding at the interfaces (Urreta et al., 1996) and more
recently this approach has been further developed and applied to experimental data (CarreñoMorelli et al., 1997, 2000). The analysis predicts that the thermal stresses, σTS, produced by atemperature change at the interfaces of ∆T are given by
σ TS =

Ef EMf∆α∆T(E f f + E M (1 − f ))(1)

where Ef and EM are the values of Young's modulus for the reinforcement and the matrix,
respectively, f is the volume fraction of fibers and ∆α is the difference between thermal
expansion coefficients of the matrix and the reinforcement between the matrix and thereinforcement associated with the coefficients of thermal expansion.
For the MMCs used in these experiments, Ef = 300 GPa and EM = 45 GPa at room
temperature, f = 0.20, ∆α = 20 × 10-6 K-1 and the decrease in EM with increasing temperature is~50 MPa K-1. Thus, eqn. (1) predicts that a temperature change by 1ºC produces an increment in
the thermal stress of ~0.6 MPa and at temperatures above ~250°C this increment decreases to~0.4 MPa K-1.
It is shown in Fig. 3a that slight but measurable compressive deformation occurs in Mg-Saffil
during cycling at upper temperatures above ~100°C and tensile deformation appears during
cycling at upper temperatures above ~140°C. It follows from eqn. (1) that a temperature changeof 70°C produces a thermal stress of ~40 MPa and, by comparison with the reported compressive
114

acoustic yield (micro-yield) stress of ~20 MPa for the squeeze-cast unreinforced Mg at 100°C(Trojanová, 2002), this implies the presence of an initial internal tensile stress of ~20 MPa. A
similar calculation may be performed for cooling of the sample from ~140°C since, on heating, atemperature change of 110°C produces a thermal stress of ~60 MPa so that the matrix
experiences a compressive stress of ~40 MPa at 140°C. This stress exceeds the matrix yieldstress at this temperature and is relaxed by matrix plastic deformation (cf. observed AE) tovalues below ~15 – 20 MPa. Cooling of the composite to room temperature produces anestimated thermal stress of ~70 MPa. Consequently, a tensile stress approaching ~40 MPa should
appear at temperatures near to room temperature and this will correspond to the macroscopic
yield point. This effect will lead to AE, as observed experimentally. It is noteworthy that thedisappearance of AE during heating at 220°C indicates a change in the deformation mechanism
towards high temperature creep. A similar calculation may be performed for the ZE41-Saffil,
which exhibits a similar behavior to the Mg-Saffil (Fig. 3b).
For the AZ91-Saffil, Fig. 3c shows that reaching a Ttop of 260-280°C leads to the appearanceof compressive deformation. The corresponding temperature change of 230°C produces athermal stress of ~110 MPa. Since the compressive yield point of the squeeze-cast AZ91 alloy is~75 MPa at 270°C (Trojanová, 2002), the initial internal tensile stresses of 35 MPa may beassessed. A similar calculation may be done for cooling of the specimen from Ttop. Figure 3cshows that a measurable tensile deformation appears during cooling when Ttop of 200°C isreached. On heating, the corresponding temperature change of 170°C produces a thermal stressof ~80 MPa, which demonstrates that the matrix experiences a compressive stress of ~45 MPa at
200°C. Cooling the composite to room temperature produces a thermal stress of ~90 MPa.
Consequently, a tensile stress approaching ~45 MPa should appear close to room temperature,
which corresponds to the acoustic yield point (Trojanová, 2002). Hence, it is reasonable toanticipate that a slight tensile deformation will appear, which was indeed observed. Figure 3cshows also that AE first appears after cooling from Ttop of 320°C. On heating, a temperaturechange of 290°C produces a thermal stress of ~125 MPa so that the matrix experiences acompressive stress of ~90 MPa. In practice, this stress is probably relaxed by compressive
deformation to below 20 ~ 30 MPa. If the composite is cooled to 60°C where significant AE first
appears, a thermal stress of ~160 MPa is produced. Consequently, a tensile stress approaching~140 MPa should appear at 60°C and this is of the same order as the measured macroscopic
tensile yield stress of the matrix alloy (Trojanová, 2002). This calculation suggests there will be
an intense AE on cooling to near room temperature, as is clearly evident in Fig. 2. Similar results
to those for AZ91-Saffil were also obtained for QE22-Saffil.
It follows from these calculations that there is a very good correlation between the present
experimental data and the predictions of a model developed earlier to explain the characteristics
of internal damping in an aluminum composite containing short fibers (Carreño-Morelli et al.,2000).
3.2 Tensile deformation at elevated temperatures
To clarify the nature of the matrix plastic deformation during thermal cycling of Mg-based
MMCs, tensile tests of an aluminum-rich AM60 alloy were performed as a function oftemperature. The stress–strain curves and corresponding AE count rates ( N& C1 and N& C 2 ) for the
AM60 alloy deformed at different temperatures are depicted in Fig. 4a, b, respectively. The
deformation curves are smooth and indicate a substantial strain hardening at temperatures lower
than 200°C. They also show a fairly large elongation to fracture of about 0.2. With increasing
temperature, the flow stress decreases and softening occurs. At temperatures above 200°C, the115

Fig. 4. (a) True stress-true strain curves for AM60 deformed at different temperatures. (b) Strain
dependencies of the AE count rates N&C1 and N&C 2 for different temperatures.
116

work hardening rate is close to zero and a dynamic balance occurs between hardening andsoftening. The strain dependencies of the AE count rates show a characteristic shape with a peakclose to the yield point and a subsequent decrease in the AE activity. The AE activity vs.
temperature shows a distinct maximum at 200°C. This behavior thus demonstrates clearly thetemperature dependence of the plastic deformation in Mg-based alloys as discussed in Section 1.
It is noteworthy that these results are consistent with the AE observations during heating of the
Mg-Saffil MMC where AE vanished when the temperature exceeded 220°C (Fig. 2a).
3.3 Creep of an AZ91–Saffil composite
There are a number of reports describing the creep behavior of Mg-based MMCs reinforced
with short fibers and using AZ91 (Mordike et al., 1997, 1998; Li and Langdon, 1999a and b; Li
et al., 1999; Sklenicka et al., 2000; Pahutová et al., 2000), AS41 (Mordike et al., 1998) or QE22
(Mordike et al., 1997) as the matrix alloys. The present investigation was initiated to provide
more information on the creep of an MMC with an AZ91 matrix alloy and especially to criticallyevaluate the potential for making use of AE as a monitoring tool for characterizing creep
deformation and the development of creep damage during testing. Because very slow strain rates
are an inherent feature of long-term creep investigations, a series of specific tests was judiciously
selected and undertaken to provide information on three separate issues: (1) the ability of thefiber reinforcement to provide additional creep strengthening by comparison with theunreinforced alloy, (2) the testing conditions required to reveal a meaningful response using AE
monitoring and (3) the effect on the AE response of testing at different temperatures.

Fig. 5. Creep curves of strain versus time at 473 K with an applied stress of 40 MPa for theunreinforced AZ91 alloy and the composite: both materials were tested in a T6 condition.
When an MMC is subjected to mechanical loading, creep occurs in the matrix alloy throughthe generation and motion of dislocations and, in addition, there may be structural damage at thehigher stress levels through debonding at the fiber-matrix interfaces or fracturing of theindividual fibers. It is reasonable to anticipate that AE will provide a meaningful and measurable
response to some deformation and damage mechanisms (Heiple and Carpenter, 1987) and/or
flow changes. This suggests the potential for using AE measurements to characterize the flow
and damage processes occurring within the material during testing.
117

To evaluate whether additional creep strengthening is introduced through the presence of thereinforcement, Fig. 5 shows the initial portions of the creep curves for tests conducted on theunreinforced AZ91 alloy and the composite material at a stress of 40 MPa and a temperature of
473 K: both of these materials were tested in the T6 condition. It is apparent from Fig. 5 that,when the applied stress is the same on each specimen, the creep rate is substantially slower in thecomposite due to an apparent strengthening effect arising from the presence of the Saffil fibers.
In practice, however, care must be taken in making a comparison of this type because there isexperimental evidence for a threshold stress, σo, when creep testing this MMC (Li and Langdon,
1999; Li et al., 1999; Sklenicka et al., 2000). This means in practice that the effective stress, σe(= σ − σo), acting on the composite may be significantly lower than in the unreinforced material
where there is no threshold stress and σo = 0. In the MMC used in these experiments, an earlier
analysis suggested the occurrence of load transfer (Sklenicka et al., 2000) whereby part of theexternal load is transferred to the reinforcement. It is instructive to note that experiments on
numerous Al alloys and Al-based MMCs have often revealed slower creep rates in thecomposites even when creep data are compared at the same values of the effective stress(Mohamed, 1998; Li and Langdon, 1999).
To elucidate the nature of the AE response in unreinforced and reinforced materials, tests
were conducted on the unreinforced alloy and on the MMC at 473 K and at the same initial levelof the applied stress of 40 MPa. In each test, the stress was increased progressively at selected
time intervals. The results are shown in Fig. 6 for (a) the unreinforced AZ91 alloy and (b) thecomposite material: again, both materials were in a T6 condition. The count rate for AE isdocumented along the lower axes.
Inspection of Figs. 6(a) and (b) shows there is a well-defined AE response throughout the
total lifetime of the unreinforced alloy whereas creep of the MMC is not accompanied by ameasurable AE except only at the instances associated with the stress increases and at the very
highest stress increment of 80 MPa. Several AE pulses in the MMC are observed at an appliedstress of 70 MPa. It is important to note the creep lifetime of the unreinforced alloy and the
MMC. There is also a large difference between the count rate associated with the stress increases
during creep of the unreinforced alloy and the MMC. The AE count rate change due to the stressincrease in the MMC is several times lower than for the unreinforced alloy.
The presence of a measurable AE response must be associated either with the creep
deformation occurring within the material or with the advent of creep damage in the form ofdebonding or breaking of the fibers. However, there are no fibers in the unreinforced alloy and,since there is an AE response throughout the creep lifetime of this material, it must be associatedwith the flow mechanism occurring within the alloy. There are several experiments showing a
stress exponent of n = 3 for the unreinforced AZ91 alloy (Dargusch et al., 1998, Blum et al.,
1997; Pahutová et al., 2000) and this suggests that a viscous glide process is dominant whereby
the movement of dislocations is impeded by the presence of aluminum solute atom atmospheres.
In addition, there have been similar reports of n = 3 in Mg solid solution alloys containing 0.8%
Al (Vagarali and Langdon, 1982) and 3% and 5% Al (Sato et al., 1993), respectively.
If viscous glide is the rate-controlling flow process in the unreinforced AZ91 alloy with n =3, it is reasonable to assume there will be no AE response: but an AE response is anticipated at
higher stress levels when the dislocations are able to break away from their solute atom
atmospheres (Yavari and Langdon, 1982). Therefore, in order to explain the experimental results118

Fig. 6. Creep curves and the AE response for (a) the unreinforced alloy and (b) the composite,where both materials were tested in a T6 condition at 473 K with the same initial applied stressof 40 MPa.
documented in Fig. 6, it is necessary to demonstrate that flow occurs in the unreinforced alloy
within the region of dislocation breakaway at stress levels as low as 40 MPa. The breakaway
stress, σb, in solid solution alloys is given by an expression of the form (Friedel, 1964):

Wm2 c
σb = 35b kT(2)where c is the concentration of the solute, b is the magnitude of the Burgers vector, k is
Boltzmann’s constant, T is the absolute temperature and Wm is the binding energy between the119solute atom and the dislocation, which may be written as
1 1+ µ 
Wm = − G ∆Va(3)2π  1 − µ 
where µ is Poisson’s ratio, G is the shear modulus and ∆Va is the difference in volume between
the solute and the solvent atoms.
Equation (2) may be solved through the use of µ = 0.34 and ∆Va = 8.2 × 10-30 m3 (King,
1966) for aluminum atoms in magnesium and with b = 3.2 × 10-10 m and G = {(1.92 × 104) 8.6T} MPa (Slutsky and Garland, 1957) for pure magnesium and noting that the initialconcentration of aluminum in the alloy is ~9 wt. %, which corresponds to ~8 at. % so that c ≈0.08. Following this procedure, the value of the breakaway stress is estimated as σo ≈ 110 MPa
and this is higher than any of the applied stresses used to obtain the data in Fig. 6(a). In practice,
however, it has been shown that Eq. (2) tends to overestimate the magnitude of σb by a factor of~2 because it fails to include the variation in the interaction energy with the solute concentrationand the influence of the different spacings between the solute atoms and the line of thedislocation (Endo et al., 1984). The introduction of these improvements reduces the value of σb
to ~55 MPa, and in practice an additional reduction is necessary because the T6 heat treatment
leads to a fine dispersion of Mg17Al 12 precipitates (Clark, 1968; Solberg et al. 1991), which
serves to deplete the concentration of aluminum atoms remaining in solid solution within thematrix and thereby it reduces the value of c in Eq. (2). The total extent of this aluminum
depletion is not known at the present time but it is reasonable to conclude that all of the stresses
used in Fig. 6(a), including the lowest applied stress of 40 MPa, are within the region of
dislocation breakaway and therefore the occurrence of breakaway accounts for the AE response,
which is clearly visible throughout the test. This conclusion is consistent also with experimental
creep data on the composite material where it was shown that breakaway occurred, and n
increased above a value of 3, at an effective stress level of ~14 MPa at the slightly higher
temperature of 573 K (Li and Langdon, 1999a and b).

Fig. 7. Fracture surface of the composite after testing as shown in Fig. 6(b).
120

Fig. 8. Optical micrograph showing the distribution of fibers in the perpendicular plane near thecrack surface shown in Fig. 7.
By contrast, no AE response is visible in the composite material shown in Fig. 6(b), at least
below 70 MPa, although these tests were conducted at the same temperature of 200°C and withthe same lowest applied stress of 40 MPa using material subjected to the same T6 heat treatment.
This difference arises because there is a significant threshold stress in the composite and, strictly,
this requires comparing the unreinforced and the reinforced materials at the same values of theeffective stress acting on the dislocations. No detailed information is available on the magnitudeof the threshold stress, σ o, in the composite material at a testing temperature of 473 K but
scattered results from tests conducted at this temperature under shear conditions (Li and
Langdon, 1999a and b) suggest the threshold stress is of the order of σo ≈ 30 MPa. Practically thesame value of the threshold stress may be obtained from results reported in other work
(Sklenicka et al., 2000a and b). If it is now assumed that the lowest applied stress level of 40
MPa used for the unreinforced alloy in Fig. 6(a) is probably close to the lowest stress for abreakaway condition in the AZ91 matrix alloy at a temperature of 473 K, it follows thatbreakaway in the composite will require an effective stress, σe, of ~40 MPa which is equivalent,
when incorporating the threshold stress, to an applied stress of the order of (σe + σo) ≈ 70 MPa.
These calculations are therefore consistent with Fig. 6(b) and with the occurrence of a significant
AE response in the composite only at the highest applied stress of 80 MPa.
Figure 7 shows the appearance of the fracture surface of the composite material after testing
through the progressive loading shown in Fig. 6(b). An optical micrograph showing the
distribution of fibers in the perpendicular plane near the crack surface is shown in Fig. 8. It isseen that the majority of fibers are perpendicular to this plane but some fibers lie also in theplane. It is also seen that the distribution of fibers is not homogeneous in the volume of thespecimen. Thus, some areas of the volume are almost free of fibers and, by contrast, in someareas the volume content of fibers is much higher than the nominal volume of 20%. The SEM
micrograph of the fracture surface (Fig. 7) shows no clear evidence for either fiber breaking or
fiber pull-out. Some fibers lying in the perpendicular plane might appear to correspond to the121Fig. 9. Creep curve and the AE response for the composite tested at 473 K with an initial appliedstress of 70 MPa after a T6 heat treatment.
occurrence of fiber fracture but these fibers broke during the squeeze-casting of the composite
material. Drozd (2000) has observed similar features in a non-deformed AZ91 composite.
To evaluate the effect of increasing the applied stress and the test temperature, identical tests
were conducted on the composite material using a temperature of 473 K and an applied stress of
70 MPa for samples after the T6 heat treatment. It is reasonable to expect that breakaway will
occur in the composite under this applied stress and the result is shown in Fig. 9. Inspection
shows the character of the AE count rate changes drastically and the AE response in Fig. 9 issignificantly greater for the sample crept at 70 MPa than after creep at 40 MPa. In practice, there
is a large increase in the AE activity in the later stage of tertiary creep immediately prior to
fracture. The decrease in the level of the aluminum solute due to the precipitation of Mg17Al12
during the T6 heat treatment combined with the increase in the applied stress serve to increase
the potential for dislocations to break away from their solute atmospheres and this gives anincrease in the AE response.
An increase in the test temperature should result in a decrease of the effective stressnecessary for dislocations to break away from their solute atmospheres. Tests were conductedon the composite at 583 K with an initial applied stress of 40 MPa after the T6 heat treatment.
Figure 10 shows a faster creep rate and a larger AE response throughout the creep lifetime of the
MMC by comparison with the results obtained at 473 K. The fracture surface is shown in Fig.
11 and there is evidence for a pull-out of the fibers corresponding to local debonding and thelarge AE response observed prior to failure in Fig. 10. In practice, the distribution of fibers in theperpendicular plane near the crack surface is similar to that shown in Fig. 8.
3.4 Creep of QE22 and AZ91 reinforced with SiC particles
Since no load transfer is anticipated in particle-strengthened MMCs, the creep data wereanalyzed using the standard relationship (Cadek, 1988):
ε&ss = A σn exp (-Qc/RT)(4)122

Fig. 10. Creep curve and the AE response for the composite tested at 583 K with an initial
applied stress of 40 MPa after a T6 heat treatment.

Fig. 11. Fracture surface of the composite after testing at 583 K with an initial applied stress of
40 MPa after a T6 heat treatment.
where ε&ss is the minimum creep rate, A is a constant, σ is the applied stress, n is the stressexponent, Qc is the activation energy for the creep process and R is the gas constant (8.314
J/Kmol).
The analysis of the creep data of the unreinforced QE22 alloy shows, for the temperature
range from 423 to 473 K, an increase in the stress exponent from 2.0 to 5.8. Also, progressive123Fig. 12. The creep curve and the AE count rate tested under progressive loading: (a) the
unreinforced QE22 alloy at 473 K, (b) QE22-15 vol. % SiC (320BL) tested at 448 K, (c) QE2215 vol. % SiC (600BL) tested at 473 K. In all cases, stress changes are indicated by arrows.
124

Fig. 13. Transmission electron microscopy micrograph of the QE22-SiC composite after creep
deformation at 473 K showing distinct cracking (indicated by arrows) at interfaces.
loading from 35 to 70 MPa gives in an increase in the activation energy from ~97 to ~184
kJ/mol. The analysis of the creep data of the QE22-SiC reveals no clear dependence of the stressexponent and the activation energy on temperature and the applied stress. Thus, the measured
values of the stress exponent are within the range from ~2.0 to ~9.4 and the measured values of
the activation energy are within the range from ~74 to ~246 kJ/mol. The AZ91-SiC behaves in avery similar manner to QE22-SiC. Detailed information on the creep properties has beenreported (Moll et al., 2000).
The creep rate and the AE response are shown in Fig. 12a for a test on the unreinforced
QE22 alloy under progressive loading at 473 K. The test was stopped after 17 days because thelimits of the AE and strain measurements were reached. Only very few AE signals appeared
rather randomly during the test and these signals could not be correlated with the creep curve.
Figure 12b shows the creep rate and the AE response of the QE22-SiC (320BL) MMC underprogressive loading at 448 K. The creep rate is significantly higher than for the unreinforced
sample deformed at 473 K. Pronounced AE activity is observed at events associated with stressincreases and at the stress level of 70 MPa. In the last stage of the test, AE activity increases
steadily. Figure 12c shows that a slight increase in temperature to 473 K and a reduction in theparticle size to 600BL lead to a significant reduction in the creep resistance. As previously seen,
intense AE is observed at a stress of 70 MPa and the AE activity is higher than shown in Fig.
12b. The AZ91-SiC MMC exhibited similar results.
The main deformation mechanism of the QE22 alloy and the QE22-SiC and AZ91-SiC
MMCs is very probably diffusion-controlled dislocation motion and, possibly, also grainboundary sliding. Grain boundary sliding was not observed directly but, at a homologous
125

temperature of 0.5, this process tends to be unavoidable in fine-grained materials. The MMCs
show a pronounced ductility and rather high creep rates in comparison with the unreinforced
sample. It is reasonable to suggest, therefore, that interfacial sliding contributes significantly tothe creep deformation. Since diffusion-controlled dislocation motion and grain boundary sliding
are not important AE sources (Heiple and Carpenter, 1987), the observed increase of the AE
activity at increasing load may indicate a growing share of interfacial sliding. Since the SiC
particles are rigid, interfacial sliding is not easily accommodated sufficiently so that creep
damage can take place. Indeed, many cavities and cracks were revealed at the interfaces after
creep testing in examinations using transmission electron microscopy: a typical example of thiscracking is shown in Fig. 13.
5. Summary
1. A number of Mg-based composites were subjected to thermal cycling and measurements
were taken, using acoustic emission (AE) and dilatometry, to record the internal thermal stresses
and the related structural changes and matrix plastic deformation. It is shown that the AE
response and the residual strain recorded after cycling depend significantly on the upper
temperature of cycling. Using a recent model developed for internal damping, it is demonstrated
that the experimental data may be successfully correlated in terms of the variations of both the
AE count rate with temperature and the residual strain with the upper cycling temperature.
2. The AE response during plastic deformation of the AM60 magnesium alloy issignificantly dependent on the temperature. Peak AE activity is found for a deformation
temperature of 200°C, which can correspond to the maximum twinning activity.
3. The introduction of short alumina fibers into an AZ91 magnesium alloy improves the
creep resistance because of the introduction of a threshold stress that serves to reduce theeffective stress acting on the material. The creep deformation may be monitored using AE.
There is little or no AE response when deformation occurs by the viscous glide of dislocations
with n = 3, but an AE response is visible at stress levels, which are sufficiently high such that thedislocations are able to break away from their solute-atom atmospheres. There may be also avery large AE response immediately prior to failure due to a local debonding around the fibers,
which is visible as a pull-out of fibers at the fracture surface.
4. Creep behavior was investigated in a QE22 alloy in the unreinforced condition and QE22
and AZ91 alloys reinforced with 15 vol. % SiC particles. The creep of the unreinforced alloy
appears to be governed by grain boundary sliding and dislocation motion. The reinforced alloyshows poor creep resistance in comparison with the unreinforced alloy at comparable stresslevels and temperatures. This may be explained by taking into account interfacial sliding as anadditional creep mechanism. As a consequence of this interfacial sliding, many cavities andmacroscopic cracks occur at the interfaces.
Acknowledgements
This work was conducted within the framework of Research Goal No. 190-01/206054
supported by the Ministry of Education, Youth and Sports of the Czech Republic. Additional
support was provided by the Grant Agency of the Czech Republic under Grant No. 103-01-1058
and the Grant Agency of the Academy of Sciences of the Czech Republic under Grant No.
A2041902. Two of the authors (F.C. and P.L.) gratefully acknowledge the support of theAlexander von Humboldt Foundation (Germany). Thanks are also due to Prof. Dr. Z. Trojanová
of Charles University, Prague for many stimulating discussions.

126

References
R.J. Arsenault and M. Taya (1987), Acta metall., 35, 651-657.
ASM Handbook (1999) Magnesium and Magnesium Alloys. ASM International, Materials
Park, OH (1999).
W. Blum, P. Weidinger, B. Watzinger, R. Sedlácek, R. Rösch and H.-C. Haldenwanger (1997),
Z. Metallkd., 88, 636-641.
J. Cadek (1988), Creep in Metallic Materials, Elsevier, Amsterdam, the Netherlands.
E. Carreño-Morelli, S. E. Urreta and R. Schaller (1997), Proceedings of the International
Conference on Fatigue of Composites (S. Degallaix, C. Bathias and R. Fougères, eds.), Société
Française de Métallurgie et de Matériaux, Paris, France, pp. 112-119.
E. Carreño-Morelli, S. E. Urreta and R. Schaller (2000), Acta mater., 48, 4725-4733.
J.B. Clark (1968), Acta metall., 16, 141-152.
F. Chmelík, E. Pink, J. Król, J. Balík, J. Pesicka and P. Lukác (1998a), Acta mater., 46,4435-4443.
F. Chmelík, J. Kiehn, P. Lukác, K.U. Kainer and B. L. Mordike (1998b), Scripta Mater., 38, 8187.
M.S. Dargusch, G.L. Dunlop and K. Pettersen (1998), Magnesium Alloys and their Applications,(B. L. Mordike and K. U. Kainer eds.), Werkstoff-Informationsgesellschaft, Frankfurt, pp. 277284.
Z. Drozd (2000), PhD. Thesis, Charles University, Prague, Czech Republic.
T. Endo, T. Shimada and T.G. Langdon (1984), Acta metall., 32, 1991-1999.
R.M. Fisher and J.S. Lally (1967), Can. J. Phys., 45, 1147-1159.
J. Friedel (1964), Dislocations, Pergamon Press, Oxford.
M. Friesel, S. H. Carpenter (1984), J. Acoustic Emission, 3, 11-17.
C. Fritze, H. Berek, K.U. Kainer, S. Mielke and B. Wielage (1998), Magnesium Alloys and their
Applications (B.L. Mordike and K.U. Kainer, eds.), Werkstoff-Informationsgesellschaft,Frankfurt, Germany, pp. 635-643.
C. R. Heiple and S. H. Carpenter (1987), J. Acoustic Emission, 6, 177-237.
H. Kato, T. Tozawa and Y. Takayama (1986), Progress in Acoustic Emission III, (Y.
Yamaguchi, K.Aoki and T. Kishi eds.), Japanese Society for Nondestructive Inspection, Tokyo,
Japan, pp. 514-521.
J. Kiehn, C. Köhler and K. U. Kainer (1994), Key Engng Mater., 97-98, 37-42.
H.W. King (1966), J. Mater. Sci., 1, 79-90.
Y. Li and T.G. Langdon (1999a), Metall. Mater. Trans., 30A, 315-324.
Y. Li, T. G. Langdon (1999b), Metall. Mater. Trans., 30A, 2059-2066.
Y. Li, V. Sklenicka and T. G. Langdon (1999), Creep Behavior of Advanced Materials for the
21st Century, (R. S. Mishra, A. K. Mukherjee and K. L. Murty eds.), The Minerals, Metals andMaterials Society, Warrendale, PA, pp. 171-178.
F.A. Mohamed (1998), Mater. Sci. Eng., A245, 242-256.
F. Moll, F. Chmelík, P.Lukác, B. L. Mordike, K. U. Kainer (2000), Mater. Sci. Eng., A291, 246249.
B. L. Mordike, K. U. Kainer and B. Sommer (1997), Proceedings of the Third International
Magnesium Conference (G. W. Lorimer, ed.), The Institute of Materials, London, pp. 637-645.
B. L. Mordike, K. U. Kainer, F. Moll and B. Sommer (1998), Magnesium 97, (E. Aghion and D.
Eliezer eds.), Magnesium Research Institute Ltd., Beer-Sheva, pp. 178-185.
M. Pahutová, V. Sklenicka, K. Kucharová, J. Brezina, M. Svoboda and T. G. Langdon (2000),
Magnesium 2000, (E. Aghion and D. Eliezer eds.),Magnesium Research Institute Ltd., BeerSheva, pp. 285-292.
127

H. Sato, Y. Masada and H. Oikawa (1993), Aspects of High Temperature Deformation andFracture in Crystalline Materials, (Y. Hosoi, H. Yoshinaga, H. Oikawa and K. Maruyama eds.),
The Japan Institute of Metals, Sendai, pp. 107-114.
E. Siegel (1977), Acta metall., 25, 383-394.
V. Sklenicka, M. Pahutová, K. Kucharová, M. Svoboda and T. G. Langdon (2000a), Key Eng.
Mater., 171-174, 593-600.
V. Sklenicka, M. Pahutová, K. Kucharová, M. Svoboda and T. G. Langdon (2000b), Magnesium
Alloys and their Applications, (K. U. Kainer ed.), Wiley-VCH, Weinheim, pp. 246-251.
L.J. Slutsky and C.W. Garland (1957), Phys. Rev., 107, 972-976.
J.K. Solberg, J. Tørklep, Ø. Bauger and N. Gjestland (1991), Mater. Sci. Eng., A134, 1201-1208.
Z. Trojanová (2002), Charles University, Prague, private communication.
S.E. Urreta, R. Schaller, E. Carreño-Morelli and L. Gabella (1996), J. Physique, IV 6 C8, 747750.
S.S. Vagarali and T.G. Langdon (1982), Acta metall., 30, 1157-1170.
K. Xia and T.G. Langdon (1994), J. Mater. Sci., 29, 5219-5231.
P. Yavari and T.G. Langdon (1982), Acta metall., 30, 2181-2196.
P. Zhang, B. Watzinger, P.Q. Kong and W. Blum (2000), Key Eng. Mater., 171-174, 609-616.

128

EWGAE2002

TRAINING AND CERTIFICATION ON THE FIELD OF ACOUSTICEMISSION TESTING (AT) IN ACCORDANCE WITH THE EUROPEAN
STANDARDISATION (EN 473)
P. TSCHELIESNIG
Institute for Technical Physics, TÜV Austria, Vienna, Austria
Abstract
A brief overview of education and training of NDT personnel on acoustic emission (AE) in
Europe is given. New courses prepare Levels 1 to 3 in accordance with EN 473.
Introduction and Historical Overview
The education and training of NDT personnel on acoustic emission (AE) in Europe was
first given in Germany. The courses were held at the “Battelle Institut” in Frankfurt from 1975
till 1981. Through these courses, approximately 250 persons within the German speaking
countries learned about the fundamentals of AE. This situation deteriorated in the following
years, because of the growing refusal of AE within Germany.
Although this was a German problem, the training of AE personnel, which is one important
step for the acceptance of a non-destructive testing method, was more or less absent within
Europe. Also the new attempt by the German Working Group for AE failed especially due tothe fact that within the EN 473/1993, “qualification and training of NDT personnel” was not
accepted as a standardised NDT method, although Acoustic Emission Testing (AT) was
mentioned under all NDT methods, for which the rules of EN 473 could be used. This decision
was a considerable setback for the efforts to establish a common European and comparable
training for AE. The efforts continue and the first European training course in acoustic
emission was given at CETIM in France. This course combined the training of the Level 1 andLevel 2. The new attempt in Germany to establish an “Information course about AE” led in
1999 to the first training course in AT Level 1 (AT1) in Germany, organised under theumbrella of the German Society of NDT.
Basis for the AT Training and Certification
During preparation of the training course and the examination for an AT1, the new editionof the EN 473 was under preparation. In this new standard, AT became a standardised NDT
method with requirements for all three levels of certification.
Based upon the EN 473, minimum requirements for experience and training time were
defined. Furthermore, beside the number of questions for the general and specific examination
the minimum number and type of test specimens for Level 1 and Level 2 practical examination
were standardised. Because the personnel, which shall be trained, was coming from mainly
from service companies and the chemical and petrochemical industry, the examination shall be
performed in the industrial sector of “Pre- and in-service testing of equipment, plant andstructure.”
J. Acoustic Emission, 20 (2002)129© 2002 Acoustic Emission GroupThe content of the training courses was based on an ICNDT draft about the minimum
requirement for the training of NDT personnel “Acoustic Emission”. Meanwhile the TC138
AHG8 established a training syllabus for NDT methods – minimum requirement, which shallbe published as CEN Technical report in 2003. These are shown in Tables 1 - 3.
NDT-method
AT

Table 1: Minimum experience requirements
Level 1
6 months

Level 2
12 months

NDT-method
AT

Table 2: Minimum training requirements
Level 140 h

Level 264 h

Table 3: minimum number and type of test specimens for the practical examination
NDT method Number and type Declarationand levelof specimen
AT1
1
For AT examinations the discontinuities are normallyreplaced by artificial sources. The level 1 candidate shalldemonstrate the ability to install the equipment, verify
its sensitivity and record the test data.
AT2
1 + 2datasets
The level 2 candidate shall also demonstrate the ability
to interpret and evaluate previously recorded test data.
EN 473/2000 “NDT – Qualification and Certification of NDT Personnel – General
Principles”
The EN 473 establishes a system for the qualification and certification of personnel, who
perform industrial NDT. In this standard, levels of qualification with defined competencies
will be introduced.
The Level 1 personnel may be authorised to:a) set up NDT equipment;b) perform the tests according to written instructions;c) record and classify the results of the tests in terms of written criteria, andd) report the results.
Level 1 certified personnel shall not be responsible for the choice of test method or technique
to be used, nor for assessment of test results.
The Level 2 personnel may be authorised to:a) select the NDT technique for the test method to be used;b) define the limitations of application of the testing method;c) translate NDT standards and specifications into NDT instructions;d) set up and verify equipment settings;e) perform and supervise tests;f) interpret and evaluate results according to applicable standards, codes or specifications;g) prepare written instructions;h) carry out and to supervise all Level 1 duties;i) provide guidance for personnel at or below Level 2, andj) organise and report the results of non destructive tests.
130

An individual certified to Level 3 maya) assume full responsibility for a test facility and staff:b) establish and validate NDT instructions and procedures;c) interpret standards, codes, specifications and procedures;d) designate the particular test methods, procedures and NDT instructions to be used, ande) carry out and supervise all Level 1 and Level 2 duties.
This clear regulation for the distribution of the different duties sets forth that a test
organisation needs all three levels of NDT personnel and also defines which competence haveto be trained and examined during the required courses.
Beside the technical knowledge about AE, the Level 1 has to be trained in the equipment
set up and how to use a written test instruction properly. The Level 2, which must have gained
test experience during the 12-month experience time, has to learn how to write a test
instruction based on the QM – NDT procedures and shall be able to perform the assessment of
the test results. The Level 3, which is an absolute must for a testing organisation to retain, has
to be the head of the complete organisation with the competence to evaluate and interpret
results in term of existing standards, codes and specifications and a general familiarity withother NDT methods.
CEN/TC138 AHG8 “Training Syllabus”
Because the comparable applications all over Europe need for the training and certification
the same minimum requirements for the education, the CEN/TC138 establish the AHG8,
which became responsible for the creation of a training syllabus for all NDT methods, asmentioned in the EN 473. Table 4 shows the unified format for all NDT methods, which was
created by the AHG8 during its “kick-off” meeting last year (2001) in Paris.
Table 4: Unified format
Contents
Level 1

Level 2

Level 3

1

introduction, terminology, purpose andhistory of NDT a

X

X

X

2

physical principles and associatedknowledge b

X

A

A

3

product knowledge and capabilities ofmethod and its derivate techniques c f

X

A

A

4

equipment f

A

X

X

A

X

X

A

X

X

A

X

X

A

X

5

information prior of test

df

f

6

testing

7

evaluation and reporting f

8

assessmentf

9quality aspects e f

X

A

A

10

environmental and safety conditions

A

A

X

11

developments

X

A

131

A

for the considered level, it is a very important point.

a(Brief history of NDT in general + introduction to the various NDT method, common
standardised terminology)(chemistry, mathematics, …)(include metallurgy)(including e.g. written instruction, standards, procedures, acceptance criteria, relevant
indications, performances, …)
is dealing with responsibilities and guidance
include codes, standards and procedures

b
c
d
e
f

note: For Level 3 basic the contents have to be taught for at least four methods including atleast one volumetric method.
Based upon this unified format the different Working Groups of CEN/TC138 started toestablish minimum requirements for the content of the training courses and the examination.
These minimum requirements shall be used also as a basis for an ISO standard, which shallmake the comparable training applicable worldwide.
AT Training within the German-speaking Countries
As an example for the first EN473 conform training courses on the AT field, the following
training courses were given under the umbrella of the German Society of NDT (DGZfP) in cooperation with the Austrian Society of NDT (ÖGfZP).
The training courses given in accordance with the EN473 before June 30th, 2002.
Level
year
location
number of candidates
1
1999
Ismaning (D)
15
2
2000
Dortmund (D)
10
3
2000
Puchberg (A)
15
1
2001
Wien (A)121
2001
Dortmund (D)
10
2
2002
Dortmund (D)11
The courses are under preparation.
Level
year
1
2002
1 or 22003
3
2003

location
Wien (A)
Dortmund (D)Puchberg (A)We shall be proud on the fact that till today no other testing was able to hold the firsttraining courses with examinations starting from level 1 to 3, within a time period of one year,with attendees from Germany (D), Austria (A) and Switzerland (CH).
Prospects of the Future
The training and examination in conformity with the EN473/2000 is a very important stepfor AT to become a more accepted non-destructive testing method. Because this bring AT on132

the way from the “so-called experts” to real trained and certified NDT personnel not only inthe service organisations as well as in the user industry.
In the future the user industry shall have at minimum one trained AT1, 2 or 3, which is able
to recognise the problem and to evaluate a possible solution in conjunction with a service
organisation. He also shall be able to validate the service organisation and the proposed written
procedure and supervise the testing people during their work.
A further demand for the future is, that all AT testing organisation have the responsible
AT1, AT2 and AT3, because only their teamwork, with their different competence canguarantee a proper execution of the test order. This give the potential customer the opportunity
to decide, which testing organisation is able to perform the job, this one, which is accredited
and have certified AT testing personnel, or this, which nominates themselves as experts,
References
EN473/2000 Non-destructive testing – Qualification and certification of NDT personnel –General principles.
CEN/TC138 AHG8 training syllabus “unified format”.
“Die Ausbildung auf dem Gebiet der Schallemissionsprüfung (AT) – Historie, Inhalte und
Ausblick” - P. Tscheliesnig, TÜV Österreich, Wien (A); W. Schmidt, DGZfP, Dortmund (D)presented at the annual DGZfP conference, Berlin (D) 2001.

133

EWGAE2002

THRESHOLD COUNTING IN WAVELET DOMAINMILAN CHLADA and ZDENEK PREVOROVSKYInstitute of Thermomechanics AS CR, Prague, Czech Republic
Abstract
Threshold counting of AE signal is the simplest and most frequently used type of AE signal
parameterization. As the wavelet transform of AE signal appears to be useful tool for AE analysis, the threshold counting applied to the wavelet decomposition represents a logical extension ofcommonly used procedures. New AE signal parameters (wavelet counts) are introduced using a
two-level threshold counting of wavelet coefficients. The application of wavelet counts is illustrated in three examples of both real and simulated AE data. The significance of various classicaland newly introduced AE signal parameters used to AE source identification is tested using theneural network sensitivity and factor analyses. The wavelet counts, carrying the global information in both time and frequency domains, can replace ill-defined frequency spectrum parametersof AE signal in a more efficient way.
1. Threshold Counting
1.1 AE signal parameterization
Threshold counting (TC) of AE signal crossings of predetermined voltage levels is one of theoldest and most common parameterization methods in the AE analysis. The number of thresholdcounts Nc or count rate, dNc/dt, simply characterizes both the continuous and burst AE signals intime domain. TC substantially reduces information about AE signal, and saves good knowledge
on the global AE activity. TC methods are very effective as simple devices and/or digital signal
processing (DSP) procedures can be used for AE detection and quantitative evaluation. The mostefficient seems to be the dual TC (low and high amplitude threshold level, [1]), as it allows distinguishing solitary high amplitude signal bursts in quasi-continuous AE. In the most cases, thetime evolution of count rate closely correlates with, e.g. that of signal RMS value, as both quantities are related to the instantaneous signal energy. The main information differences between
both signal energy measures consist in a constant noise level suppression at the TC, i.e., d.c.
component of RMS curve is eliminated. Another difference deals with the signal transform linearity. Contrary to the RMS measure, TC represents a highly nonlinear signal transform, which
may cause some difficulties (it is surprising that rigorous mathematical treatment of TC transform properties is still insufficient). On the other hand, due to its non-linearity, the TC allows
often better recognition of some critical events (e.g. in leak detection) than linear signal treatments.
The purpose of AE signal parameterization is to reduce registered signal waveform data into
the lowest possible number of signal features carrying maximum of important signal information
in a simplest form. The number and type of signal features are chosen so as to conform to various criteria (diagnostic purpose, device capabilities, simple and effective DSP, etc.). Together
with a number of signal parameters in time domain, parameters of frequency spectra (parameterization in frequency domain) are often used by AE source identification procedures. The
time-frequency representation (windowed FFT, Wigner-Ville distribution, etc.) combines bothaspects of AE signal processing [2]. The most advanced processing of AE signal in the wavelet
J. Acoustic Emission, 20 (2002)

134© 2002 Acoustic Emission Groupdomain [3] may be considered as an enhancement of the time-frequency representation. The
governing wavelet transform (WT) is extremely useful in analyzing time series containing nonstationary power at many different frequencies. In addition to its main destination (signal denoising and compression), the WT has shown to be very effective tool for AE signal processing(e.g. source location in plates [4], fracture mode classification in composites [5], etc.). Also fromthe computational point of view, the WT seems to be more effective than e.g. FFT used in realtime DSP of AE signals. Nevertheless, for the purpose of AE signal classification, the WT signal
representation should be parameterized to reduce the extent of input data to pattern recognition
analysis (e.g. by artificial neural networks). The simplest way to extract general features from theAE signal decomposed by WT is threshold counting at various decomposition levels. This procedure may be introduced as an equivalent to the TC of AE signal in different frequency bands.
1.2. Definition of new AE parameters
Let us suppose, that we have a discrete, multi-level one-dimensional wavelet decompositionof a sampled signal s(i). Let Cm(j), j=1…Nm be the approximation coefficients (m=1) or detailwavelet coefficients at specific level m (m=2,...,L+1, where L is a number of decomposition levels). The three new parameters NC1(m), NC2(m) and RCGm of each detail level and/or approximation are computed as follows:

where THk(m) (k=1,2) are counting two threshold levels. They differ with each wavelet decomposition level. The value TH1(m) is usually set just under the level of noisy coefficients andTH2(m) around the first quarter between the noise magnitude and maximum coefficient of corresponding wavelet decomposition level. Parameter RCGm is relative gravity center of wavelet coefficients in analyzed signal sample:

By the computation above, we obtain 3(L+1) new parameters, which are then compared withthe following classical AE parameters (signal features) [6] in time and frequency (spectral) domain:• Time domain: (1) Amplitude, (2) Rise time, (3) RMS, (4) Energy moment, (5) Relative
center of gravity, (6) ASL, (7-9) Second-order statistical moments.
• Frequency spectrum parameters: parameters of power spectral density function f(ω)
PX =100 ⋅ ∫ f (ω ) dω ∫ f (ω ) dω , X ∈ {A, B, C, D, E , F }X

G

where arbitrarily chosen six frequency bands X are related to the Nyquist frequency, ωN. A:(0 - 0.12)*ω N; B: (0.12 - 0.24)*ωN; C: (0.24 - 0.36)*ω N; D:(0.36 - 0.48)*ω N; E: (0.48 0.6)*ωN; F:(0.6 - 1)*ωN.

135

2. Analysis of AE Signal Parametrization
The main goal of any parameterization is to extract maximum of useful information on given
data. On the other hand, the redundancy of computed parameters should be minimized. Two
methods, the factor analysis and the neural network sensitivity analysis are helpful tools as toreduce the number of extracted parameters. Both methods show us which features support themost important information about existing problem and whether the newly developed parameters
are independent with one another.
2.1 Factor analysis (FA)
FA is a method frequently used to find linear relations among parameters and to computenew, hypothetical variables (factors) explaining variance of parameters. FA is based on principal
component analysis (PCA). PCA has three effects:
1. orthogonalizes components of transformed input vectors so that they become uncorrelated;
2. organizes resulting orthogonal components (principal components) so that the component
with the largest variance is the first; and3. eliminates components that contribute only little to the variance of data set.
The loss of information is minimized, and corresponds to the difference between the original andtransformed data. PCA represents linear data transform (orthogonal rotation followed by scaling). It can be expressed as a matrix-multiplication Z = AP, where the original data are stored inmatrix Z, new hypothetical variables (factors) in matrix P and the factor scheme A represents regression coefficients of factors to the original variables.
2.2 Sensitivity analysis of neural networks
One of the most important problems in NN-based classification systems [7] is the appropriate
choice of input pattern parameters called features. The problem of optimal feature selection consists in identification of significant parameters and deletion of remaining ones from the initiallylarge, redundant set of features. Some pattern features can be redundant for making the correct
decision. When the number of features is relatively small, exhaustive or quasi-exhaustive search
may be used to select the best feature subset. But increasing number of attributes results in rapid
growth of the number of possible combinations. Therefore, Fidalgo [8] suggested an alternative
approach to the feature subset selection, comprising of the following steps:
1. Train the BP (back propagation)-network with outputs y (j = 1…m) using all possible
candidate features.
2. For all training patterns, compute ∂yj/∂xi, which is the derivative of the network outputs
yj with respect to its inputs xi (i = 1…n).
3. Compute the mean absolute value of the above derivatives for each input x i to define thesensitivity coefficient sij as:
1 P ∂ysij = ∑ pj
P p =1 ∂x pi
where p represents the pattern index.
High values of sensitivity coefficients s ij indicate “important“ features for the trained BPnetworks. The differences of s ij values among significant and dummy features become smallerwith increasing amount of noise. If dummy features are eliminated and BP NN training is repeated using only the remaining ones, the performance error becomes lower. This confirms theadvantage of eliminating dummy variables that just introduce noise. Sensitivity coefficients ofthe trained BP-networks can also be calculated by BP technique algorithm. These coefficients
express the network sensitivity to the considered set of input patterns.
j136

3. Comparison of Parametrized AE Data
Performance of various AE signal parameterization and classification procedures including
parameterization in wavelet domain has been tested on examples of both experimental andsimulated AE data obtained by different ways.
3.1 Continuous AE data
The first example is related to relatively long records of quasi-continuous AE. AE signals
were recorded during the metal-sheet punching process described in [9]. Punching is usually accompanied by AE of high intensity, and AE monitoring can be used to identify occurrence of
defects on cutting tools. Computer-controlled hydraulic press has been used to cutout circle andsquare pieces from 1-mm-thick carbon steel sheets (both the diameter of circle and side of thesquare were 160 mm). Punching tests were performed using intact and damaged cutting tools.
The artificial damage was produced by grinding small parts of cutting tool edge (1% of circumference). Four AE transducers were placed at various positions on cutting tool (matrix). Signals
from AE transducers were filtered (high-pass 100 kHz), pre-amplified (40 dB), and recorded by atransient recorder YOKOGAWA DL708 (10 MS/s at 12 bits). The length of stored AE signals
was up to 0.5 s, so very large data were treated. Scheme of punching process accompanied by
AE signals from four transducers (A to D) is shown in Fig. 1. Localization ANN has been trainedon Pen - test data. Hundreds of AE events superimposed on continuous AE background wereextracted from each record, and were localized using fuzzy neural networks.
The initial idea of these tests was to identify process changes and small cutting tool defects
directly according to the changes of some typical (spectral) AE signal features. Hundreds ofpunching tests have been performed and analyzed by using classical AE signal parameterization
mentioned above. Neither FA nor NN sensitivity analysis showed any important differences
between tests with intact and damaged tools (the energy weighted probability summations of the
fuzzy located events were finally used to recognize and localize cutting tool defects, which is
discussed elsewhere [9]). Recently, the stored signals were re-analyzed using the TC parameterization in wavelet domain. Figure 2 illustrates the time evolution of resulting wavelet domaincounts NC1(m) derived from one AE signal recorded during the test #V005. The whole record

Fig. 1a. Scheme of metal punching process.
137

Fig. 1b. AE signals of metal punching process, recorded by four AE transducers (zoomed part bof the process in Fig. 1a) placed on cutting tool [9].

Fig. 2. Time development of AE signal parameters NC1 (m = 1 to 6) during the metal sheet
punching (NCi(m) are plotted in logarithmic scale).
138

Fig. 3. Logarithmic NC1 and NC2 curves (solid and dashed lines, respectively) during the tests#V005 (left), #V007 (middle), and #V010 (right). Counts of the wavelet approximations are at
the top, and counts of wavelet details m = 2 to 6 are below.
of about 400 ms was first eight-times down-sampled, which resulted in 500,000 signal samples.
Down-sampled signal was then divided into 24 equidistant time intervals (windows), and thewavelet decomposition was applied to each interval (mother wavelet db1, L=5 detail levels).
Subsequently, two-level threshold counting was performed on the wavelet coefficients of fivedetails and low frequency approximation, which resulted in 2(L+1) parameters in each time interval (144 data in sum). This procedure represents ca. 3500 times data reduction, saving a global
information on time and frequency changes of the whole process. The detailed NC1,2(m) curves
are shown in Fig. 3: The wavelet parameters NC1(m) and NC2(m), evaluated in 24 time windows, are plotted so that counts of the wavelet approximation (m=1) are on the top, and below
are counts of wavelet decomposition details of m = 2 to 6 (down from low to high frequency details). Low-amplitude level counts NC1 are plotted as solid lines, and high-level NC2 curves asdashed lines. NC i curves, evaluated at all three tests in the same manner, can be compared.
Threshold counting levels and other parameters of all tests were the same. Punching tests were
performed using the cutting tool with very small damage. The only difference was the quality ofpunched metal sheets (degree of surface corrosion), which is pronounced mainly at the wavelet
approximation level (no visible corrosion was at the sheet #V007 in the middle of Fig. 3). At all
three parts of Fig. 3 we can see that NCi curves have very similar form at all wavelet decomposition levels. This means that no remarkable changes in signal spectrum are observed during therunning process, and an attempt to recognize different process stages by using spectral parameters has no sense.
3.2 Classification of simulated AE data
In this section, the use of new wavelet-domain parameters is demonstrated also on a simulated AE data, generated for the testing of NN-based AE source classification procedure [10].
Different time functions of AE sources were assumed to simulate and classify various emitting
defects. At first, it was necessary to train a BP-NN, to classify the original source functions, andfinally, the most significant AE parameters of generated waveforms were tested.
139

The training AE waveforms (input data) were generated as a convolution of the source function (having different forms) with a Green's function. The Green's function has been obtained
experimentally as a transfer function between the AE source and sensor measured during thepulse laser excitation of a steel plate (representing the impulse source of propagating elastic
waves) at a distance 140 mm from the source [10]. Its graph is shown at the top of Fig. 4. Input
patterns for the classification BP-network are composed of the following 14 parameters extracted
from the “input signal“ z(t): amplitude, rise time, RMS value, energy moment, ASL, gravity
center of signal, second to fourth statistical moments, six spectral parameters, and above definedparameters in wavelet domain.
Different AE signal sources Sp (p indicates the pattern index) were simulated as a linear
combination of three source functions ("waves"): wave1, wave2, wave3 , weighted by randomly
generated coefficients ap, bp, cp:
Sp = ap . wave1 + bp . wave2 + cp . wave3
In order to obtain more comparable results, these coefficients are normalized so that theirsum equals to one. The graphs of the source "waves" and their combination are also shown in
Fig. 4, along with the “input signal“ zp(t) computed as a convolution of the AE source function
Sp with the Green function G:
zp(t) = conv(Sp ,G).
500 training patterns were generated for the training of 50 neural networks of the same architecture (topology 45-19-3, resulting error < 0.01). Only initial weights were set differently among
the trained NN's. The averages of sensitivity coefficients calculated for all tested networks arepresented in Table 1 (shaded by values).
Each of the three columns in Table 1 contains 24 values corresponding to input signal features. Only 9 wavelet parameters at 3 highest frequency detail levels were considered in thisanalysis, as the low frequency wavelet coefficients differ little for various model sources. From
Table 1 we can see that the most important are pattern features number 6, 11, 14, 15 and 24. It
means that the generated signal waveforms differ mostly at higher frequencies and a linear combination of different source "waves" can be estimated ("source classification") using the relative
signal gravity centers in time domain and gravity centers of wavelet decompositions.
The application of PCA has proven (see Table 2) that the new “wavelet counting parameters“(parameters number 16 to 24) represent new, linearly independent information. Nevertheless, thisinformation seems to be unimportant for the estimation of the composed signal "weights" as expected, while the linearly dependent classical parameters appear to be more important in thiscase.
Finally, numerical experiment with NN training has proven that for successful learning it ispossible to use only the spectral parameters (features number 10 - 15) or just wavelet decomposition parameters (features number 16 - 24) giving very similar results.
3.3. Recognition of AE sources in polymer composite samples
The last example demonstrates importance of various AE parameters in recognition of AE
signals coming from three types of model GFRP composite specimens. Unidirectional reinforced
composite samples of three special forms were prepared and tested in [11] so that the three different damage mechanisms prevailed during the specimen loading (delamination, fiber breaks,
140

GREEN FUNCTION
0.05

0- 0.05- 0.1- 0.15

- 0.2

100

1

WAVE 1

150

1.5

0.5

1

0

0.5

-0.5

0

-1
0
1.5

10

20

WAVE 3

30

-0.5
0

0.5

0

0

-0.5
20

0.1

300

INPUT SIGNAL (a=0.3, b=0.2, c=0.5)

0.05

0

10

20

30

1

0.5

10

WAVE 2250

0.3*WAVE1 + 0.2*WAVE2 + 0.5*WAVE3

1

-0.5
0

200

30

-1
0

-0.05

-0.1

10

20

30

-0.15
0

50

100

150

Fig. 4. Graphs of the source model functions ("waves" 1,2,3, and their linear combination) andthe input signal resulting from the convolution of the source and Green's function.
and matrix cracks). The data (AE signal parameters) from specimens of different forms are used
to see the significance of various AE parameters for the recognition of the three different AE
source mechanisms. The NN with AE parameter inputs was trained to indicate the highest value
on particular output corresponding to each specimen form (AE source mechanism). In Table 3
we can see that resulting NN sensitivity coefficients show similar picture as in Table 1 for simulated signals. Again, as the most important features (all five wavelet decomposition levels wereconsidered in this case) seem to be the centers of gravity of spectral or wavelet parameters.
4. Conclusions
The wavelet transform of AE signal is now broadly used as very useful tool for AE signal
analysis in time-frequency domain. New AE signal parameters based on wavelet transform(wavelet counts) were introduced here as a logical extension of commonly used AE signal

141

Table 1. Sensitivity analysis of trained BP-networks.

Table 2. Factor scheme (PCA) of tested signal parameters.

142threshold counting procedures. These new AE signal parameters are defined as numbers ofthreshold crossing counts of wavelet coefficient amplitudes at various wavelet decompositionlevels. Two predetermined threshold values are used in a simplest case, which can differ at various wavelet decomposition levels. Thresholds should be selected in a similar way as recommended in standard threshold counting of original AE signal. The applications of wavelet counts
have been illustrated on three examples of both real and simulated AE data. Numerical tests havebeen performed using the neural-network sensitivity and factor analyses. Test results show whichof various classical and newly introduced AE signal features are significant for AE source identification procedures. As the most important features seem to be the centers of gravity of spectralor wavelet parameters. The wavelet counts, carrying the global information in both time and frequency domains, can replace ill-defined frequency spectrum parameters of AE signal in a more
efficient way. The strategy of neural network sensitivity analysis seems to be very useful to reveal the most significant AE signal features in various applications.
Table 3. NN sensitivity analysis results of tested AE parameters significance for the recognitionof three different AE source mechanisms acting in loaded composite samples.

143References[1] Standard Practice for AE Examination of FRP Tanks/Vessels. (ASTM E 1067).
[2] Mertins A.: Signal Analysis: Wavelets, Filter Banks, Time-Frequency Transform and Applications. J. Wiley & Sons, Ltd., Chichester, 1998.
[3] Poularikas A.D.: The Handbook of Formulas and Tables for Signal Processing. CRC Press &IEEE Press, 1998.
[4] Ou-Yang Kwon, Young-Chan Joo: “Source location in plates by using wavelet transform of
AE signals.” Progress of AE IX, The Proc. of 14th Internat AE Symp. and 5th AE World Meeting,
Ono K., ed., Hawaii, August 9-14, 1998.
[5] Kinjo T., Suzuki H., Saito N., Takemoto M., Ono K.: “Fracture-mode classification using
wavelet-transformed AE signals from a composite.” J. of Acoustic Emission, 15(1-4), pp.19-32,
1997.
[6] Yamaguchi K., Oyaizu H.: “Advanced AE Instrumentation System for Versatile and Precise
Use by Waveform Microdata Processing.” Proc. of the 3rd Congress on AE from Composite materials AECM 3, Paris, July 17-21, 1989.
[7] Bishop C.M.: Neural Networks for Pattern Recognition. Oxford University Press, 1996.
[8] Fidalgo, J.N.: “Feature Subset Selection Based on ANN Sensitivity Analysis – a Practical
Study.” Advances in Neural Networks and Applications, Mastorakis N.,ed.:WSES Press, pp 206211, 2001.
[9] Prevorovsky Z., Fischer J., Krofta J., Chlada M., Blahácek M.: “Fuzzy–Probabilistic
Evaluation of AE During Steel Plate Punching.” Proc. COST P4 Conf. on Characterization of
Manufacturing Processes "Synergetics and Data Processing Methods", Govekar E, ed., Ljubljana, Slovenia, 25-26 May 2000, pp. 279-285.
[10] Krofta, J., Chlada, M., Prevorovsky, Z.: “Probabilistic and Neural Network Classifica-tionof Simulated AE Data,” EUROMECH 419 Colloquium on Elastic Waves in NDT, Prague, 3-5
Oct. 2000, Book of Abstracts, p. 47.
[11] Klimes O.: “Modeling of AE sources in fiber reinforced composites.” MS Thesis, Faculty of
Nuclear Sci. and Phys. Eng., Czech Technical University, IT CAS, Prague, 1996.

144

EWGAE2002

DAMAGE DIAGNOSIS TECHNIQUE FOR BRICK STRUCTURES USING
ACOUSTIC EMISSIONTAKUO SHINOMIYA1, YASUHIRO NAKANISHI2, HIROYUKI MORISHIMA1 andTOMOKI SHIOTANI3
1

Technical Center, East Japan Railway Company, Tokyo, Japan
Research Institute of Technology, Tobishima Corporation, Chiba, Japan
3
Faculty of Civil Engineering and Geosciences, Delft University of Technology, The Netherlands (on leave from Tobishima Corporation)
2

Abstract
Acoustic emission (AE) is known as a non-destructive test under external load/variation ofstress distribution within materials; AE waves are difficult to generate without those stress variation. Railway bridges are the structures where the external load variation can be easily applied
due to over-passing railway traffic. In this study, in order to apply AE technique to the damage
evaluation in railway brick structures, fundamental experiments using brick specimens sampledfrom an actual old brick foundation of a bridge are carried out. This is performed to find the applicability of AE technique to brick materials. Next, in order to know the deterioration process
and to establish a damage evaluation table, brick specimens made in laboratory are subjected tocyclic loads, and AE activities with crack growth are examined. Finally, in-situ AE monitoring is
performed on an “elevated arch railway bridge” where obvious penetration-cracks are observed.
For in-situ monitoring, AE activities directly generated from the observed cracks are distinguished from those induced by the railway traffic.
Key words: brick, calm ratio, damage diagnosis/degree, load ratio, railway structures
1. Introduction
In Japan, many masonry structures using bricks have been constructed over 60 years ago.
During service, they have sustained damage from several earthquakes and long-term weathering.
In order to confirm the safety for service, it is essential to investigate the deterioration and degreeof damage of the structures. Also the investigation result is important in performing appropriate
rehabilitation/reinforcement for the deteriorated structures. Thus far, such techniques as visualobservations and crack displacement have been used in the investigation of elevated bridges
made of bricks. As for the lower portions of the elevated bridges, however, since they are normally utilized as commercial buildings, the same techniques are difficult to apply. Again, it isrequired to establish another technique to diagnose the integrity of brick structures even whereconventional techniques are difficult to apply. Several NDT techniques, radar, ultrasonics, electric exploration and acoustic emission (AE), had been performed in brick structures as preparation. However, the results except from the AE technique were useless. Thus, this study only focuses on the AE technique.
When the AE technique is applied to railway brick structures, it has the following unresolved
points:1) How the external loadings or variation of internal stress are applied to induce AE activity?
2) Is AE signal from cracking in bricks really obtainable?
J. Acoustic Emission, 20 (2002)

145© 2002 Acoustic Emission Group3) How do the types of crack affect AE activity?
4) AE activity might decrease with repetition of loads; i.e., Kaiser effect may exist.
5) Detected AE activity might include AE signals, which is not related to crack behavior directly but related to the dynamic behavior due to railway traffic loads.
In this paper, answers to these points are explained with the following procedure. First, fundamental experiments using brick specimens that are sampled from an actual old brick foundationof bridge are carried out (answer point 2). Second, in order to evaluate the deterioration process
and to establish the damage evaluation method, brick specimens made in laboratory are subjected
to cyclic loads, and AE activities with crack growth are examined (for points 3 and 4). Finally, insitu AE monitoring is performed in an “elevated arch railway bridge” where obvious penetration
cracks are observed (for points 1 and 5).
2. Experiments
2.1 Fracture tests using sampled specimens [1]There exists a brick bridge of over 80 years old. Four beam specimens were sampled fromthe substructure of the bridge. Figure 1 shows the configuration of the specimen with the arrangement of AE sensors. The tests are performed with 4 different specimens and 3 different
conditions of axial force as shown in Table 1. The axial stress of 1.0 MPa in No. 1 specimen is
the maximum load when the train passes on the arch, in which the span is 7.8 m; the axial forceof No. 2 is 50% of the assumed maximum load. Monotonic bending load was applied to thespecimen with AE monitoring. 12 AE sensors of 60-kHz resonance were placed on the surface
with wax couplant (see Fig. 1). The signals detected were pre-amplified 40 dB and processed by
a DSP-based MISTRAS AE system (Physical Acoustics Corp.) with the 40-dB threshold (ref. 0 dB
= 1 µV at sensor). Both AE parameters and waveforms are recorded; the latter with 1-MHz sampling and 1-kword length. In this experiment, the AE activity corresponding to the fracture stages
classified by using the load-displacement relation is studied, considering with/without axial
force. The monotonic load was applied in a step-wise manner with 5 kN increment.

No.

b
(mm)

1
2
3
4

350

Table 1. Specification of specimens.
h
L
a
(mm)
(mm)
(mm)
1,000
350
1,200
450
350
1,350
525
1,500
600

Axial force
(MPa)
1.0
0.5
0.0
0.0

2.2 Cyclic tests using laboratory-made specimens
In general, AE activity generated due to crack nucleation and formation is referred to as“primary AE activity,” whereas AE activity due to the friction of pre-existing crack interfaces isreferred to as “secondary AE activity.” In this cyclic test, in order to study the secondary AE activity, the cracks are first introduced into the newly made specimens with loading. The specimen
has a length of 1776 mm, a width of 360 mm and a depth of 330 mm as shown in Fig. 2. 12 AE
sensors are placed on the surface. The condition of AE monitoring system is the same as in theprevious test. Lateral force (i.e., axial stress) of 0.33 MPa was first applied to the specimen, thenthe vertical load was applied until a penetration crack was observed. Changing the shear span,

146

Fig. 1. Configuration of sampled specimen in fracture test.
6

9
3

10
1

360

3

10
1

2

1,776

6

4

1

12

3

8

A E sensor
C rack10

2
4
V erticalL oad

9
LateralLoad

４ｔ
11

5
9

3

2
4
VerticalLoad

9
S ide view

7

8
330

5

360

T op view

330

7

10
1

2

4

Lateral Load

４ｔ
1,776

11

12

Fig. 2. Specimens and arrangements of AE sensors in cyclic tests. (left: bending, right: shear)bending and shear loads were applied (see Fig. 2). After introducing the penetration crack, thespecimen was subjected to cyclic loads. We applied five repetitions of cyclic load at 10, 50 and
75% of the load, at which the penetration crack was observed. After cycling, monotonically increasing load was applied until the specimen broke and dropped from the stand.
2.3 In-situ AE monitoring at an elevated arch railway bridge
In order to generate AE activity, it is essential to apply the external load or to give the variation of stress distribution within materials. For railway structures, both can be possible by thetrain passage. The monitored structure is a double-tracked brick arch elevated bridge constructed
in 1910. The bricks used for the foundation are laid by means of English bond. A penetration
crack was already visible in the direction of railway (see Fig. 3), and out-of-plane displacementof approximately 0.2 mm was measured with each train passage. Thus, the AE activity from thebridge is expected due to the crack movement. A set of AE sensors with 15-kHz resonance wereplaced on the bridge bottom as shown in Fig. 3. Subsequently, these were exchanged with 60kHz sensors. To monitor the acceleration due to the train passage, accelerometers were also set
up. The AE system used and the monitoring condition are the same as in the previous tests.
3. Results and Discussion
3.1 Fracture tests using sampled specimens
A typical behavior of displacement and AE activity are shown in Fig. 4. This is the result ofspecimen No. 1, where axial force was applied. In the chart, the vertical displacement at the147

Fig. 3. A penetration crack and an arrangement of AE sensors in an arch bridge.

Fig. 4. Displacement and average frequency in specimen No. 1 with axial force. The arrows
show the times when a crack was first observed and then reached the yield point.
loading point and AE average frequency obtained by AE (ring-down) counts and duration are
given as a function of elapsed time. The average frequency is the average of the latest 100 AE
hits; i.e., using a method of moving average [2]. As seen in Fig. 4, the axial force was imposed
during the initial 430 s. With subsequent loading, the average frequency increased, accompaniedby the repetition of rise and fall until a crack was observed. The manner of the repetition corresponded well with the stepwise increase of the load step of 5 kN. The average frequency ranges
from 20 to 60 kHz in the cracking process (i.e., 430 - 1775 s). The repetition was also found after
the yield point at 1775 s. However, the average frequency became smaller to around 20 kHz.
Thus, the progress of fracture in brick structures can be classified into the following three processes: 1) Micro-cracking due to tensile/shear stresses accompanied by the AE activity of highaverage frequency; 2) Formation of macro-cracks by connecting the micro-cracks, and local generation of macro-scale shear fracture due to yielding, accompanied by the AE activity of low average frequency; 3) The friction between pre-existing fracture planes, accompanied by the AE
activity of low average frequency.
148

Fig. 5 AE hit rate and load applied with elapsed time in the shear cyclic test.

Fig. 6 Average frequency and load applied with elapsed time in the shear cyclic test.
3.2 Cyclic tests using laboratory-made specimens
Figure 5 shows the applied load and AE hit rate per 10 s against elapsed time in the shear cyclic test. In this figure, the AE activity increases with the first applied monotonic load, and there
is no active AE occurrence during cyclic load at 25% of the nominal cracking load. The no AE
activity during the cyclic load at 25% was caused by the following: 1) Shear stress due to bending was too small. 2) Kaiser effect was observed. During cyclic load at 50%, however, AE activity with loading can be observed in every repetition. AE hits are actively generated during 75%
cyclic load both in loading and unloading. These suggest that Kaiser effect started to break downfrom 50% of the cracking load, and continuous AE activity was obtained not only in loading but
also in unloading to and from 75% of the maximum load.
Figure 6 shows the average frequency and the load applied against elapsed time. After the
penetration of cracks, the average frequency increases from 20 to 30 kHz without the final buckling process. This range of the average frequency is almost the same as in the bending specimen,
and agrees well with the result in the sampled specimen after yielding (see Fig. 4). Figure 7
shows results of 3D source locations projected to the front surface of the specimens with observed cracks. The sources are classified with scale of the peak amplitude. In both bending andshear specimens, cracks are generated not only in direction of the axial load (i.e., vertical), butalso parallel to the axial force. This implies that even when the bending loads are applied, cracks
149

Fig. 7. 3D source locations projected to the front surface. AE sources are classified with the peak
amplitude.

Fig. 8. Damage degree map. (S: damage is given by shear load. B: damage is given by bendingload)
are sometimes generated horizontally along the interfaces among bricks; the shear type of cracks
also occurred even in the bending test. Thus, the reason why the same average frequency was
obtained in both types of specimens is that the same shear fracture had occurred in both bending
and shear specimens. In addition, the location of cracks observed is identical to the AE sources
with large peak amplitude. This leads to a conclusion that macro-scale cracks could be estimatedfrom AE activity with large peak amplitude.
150

Fig. 9. Typical frequency spectra of detected AE waves with 15-kHz (left) and 60-kHz (right)
resonant AE sensors.

Fig. 10. 2D AE sources classified with the average frequency (left: with 15-kHz, right: with 60kHz resonant AE sensors).
From all of the results, a damage map of brick structure is constructed as shown in Fig. 8.
The map is based on the NDIS 2421 [3], “Load” stands for ratio of load at the onset of AE activity to previous load, and “Calm” stands for ratio of cumulative AE activity under unloading tothat of previous maximum loading cycle. With an increase of the degree of damage, the plots
distributed toward the upper left side of the map, irrespective of the types of loading conditions.
Thus, it is found that evaluation of integrity in brick structures is possible using the damage degree map based on the AE activity.
3.3 In-situ AE monitoring at an elevated arch railway bridge
With each train passage, AE signals could be detected in AE sensors of both 15-kHz and 60kHz resonance. Figure 9 shows the typical frequency spectra of detected AE waveforms with 15kHz (left) and 60-kHz (right) sensors. In each chart, a comparison is made between Ch-4 and Ch5, which are the AE sensors close to the observed crack and far from the observed crack, respectively. Both sensors have the common frequency component of 10 kHz, irrespective of the location from the crack. In the 60-kHz sensor data, however, the frequency component from 25 to 35
kHz is only found at Ch-5 being close to the crack. This implies that AE signals having the151frequency component ranging from 25 to 35 kHz are generated from the existing crack. Here, the2D source locations obtained are classified with average frequency as shown in Fig. 10. The AE
signals for the first arrival are used in the classification. In the arrangement of 15-kHz resonant
AE sensors, AE sources disperse over the monitoring area, whereas for 60-kHz resonant AE sensors, AE sources higher than 25 kHz are distributed around the center of the monitoring area.
The AE sources distributed in the center area appear to be the AE events generated from the existing cracks, although this area is not completely identical to the location of the crack observed.
This is because; 1) the heterogeneous velocity-distribution existed in the vicinity of the large
cracks, and 2) the actual cracks distributed in 3D space, normally with curved crack surfaces.
Thus, it seems reasonable that the distribution of AE sources, higher than 25 kHz, may not becompletely identical to the position of the surface-breaking crack. It becomes clear that by using
AE sensor of 60-kHz resonance and focusing on AE signals around 30 kHz, the secondary AE
activity due to the friction between the pre-existing crack interfaces can successfully be detected.
4. Conclusions
In this study, the AE technique was applied for the damage evaluation in brick structures. It
can be concluded that:(1) AE signals could be obtained during the cracking process of bricks, and there exists thesecondary AE activity generated due to the friction of pre-existing crack faces;(2) Deterioration/cracking process could be evaluated using AE parameters;(3) Using the proposed damage map based on AE activity, the degree of damage of brickstructures could be reasonably evaluated;(4) With the sensor of 60-kHz resonance and using the high pass filtering over 25 kHz, AE
activity directly related to the crack behavior could be distinguished from those related to the
dynamic behavior due to railway traffic loads;(5) Railway traffic could induce AE activity generated from pre-existing crack faces, and thedamaged area could be estimated as a distribution where higher frequency AE sources over
25 kHz were generated; and(6) From all of the findings, the AE monitoring range is limited to 1.5 m using AE sensor of
60-kHz resonance, and the threshold of 40 dB. For the future work, additional ideas to expand the monitoring areas would be needed.
References[1] Kino, J., Kanno, T. and Furuya, T., “The strength test of cored samples from brick structures,” Proceedings of 56th JSCE Annual Conference, Japanese Society of Civil Engineers, pp.
222-223, 2001, (in Japanese).
[2] Shiotani, T., Ohtsu, M. and Ikeda, K., “Detection and evaluation of AE waves due to rockdeformation,” Construction and Building Materials, 15, 235-246, 2002.
[3] The Japan Society for Non-Destructive Inspection, “Recommended practice for in situ monitoring of concrete structures by acoustic emission,” Standard of the Japan Society for NonDestructive Inspection (NDIS), NDIS 2421, 2000, (in Japanese).

152

EWGAE2002

EVALUATION OF DRYING SHRINKAGE MICROCRACKING IN CEMENTITIOUS MATERIALS USING ACOUSTIC EMISSIONTOMOKI SHIOTANI1,2, JAN BISSCHOP1 and J. G. M. VAN MIER3
1

Microlab, Faculty of Civil Engineering and Geosciences, Delft University of Technology,
The Netherlands. 2 Research Institute of Technology, Tobishima Corporation, Chiba, Japan.
3
Institute for Building Materials, ETH, Zürich, Switzerland.
Abstract
To determine the temporal evolution of shrinkage microcracking in cementitious materials,
acoustic emission (AE) technique is applied to the materials during drying process. Initial studies
on wave propagation are carried out for the materials. Geometry of generated cracks is recordedwith FLM (fluorescent light microscopy) both in plain cement paste and glass-particle cement
composites. The results indicate two mechanisms of drying shrinkage microcracking; selfrestraint of the specimen/structure and aggregate restraint. In plain cement paste, the cracks due
to self-restraint initially develop perpendicular to the drying surface, but may subsequently growparallel to the drying surface. AE monitoring has revealed that the self-restraint cracks have developed almost instantaneously at the onset of drying. In composites containing glass particles,
however, aggregate-restraint cracking continuously develops throughout the experiment.
Keywords: cementitious materials, cracking, drying shrinkage, restraint, temporal evolution
1. Introduction
Drying shrinkage leads to stresses and cracking in cement-based materials when the deformations are restrained. The mechanism of drying shrinkage cracking strongly relates to the typeof restraint that is caused by the material and structure. This paper focuses on only the cracking
due to internal restraining of the material. There are two internal restraint mechanisms of microcracking due to drying shrinkage in cement-based materials [1]. The first mechanism is called
self-restraining of the material, and is caused by moisture/shrinkage gradients that develop in a
specimen/structure. This mechanism is studied in plain hardened cement paste specimens. The
second type of internal restraint in cement-based composites is caused by the presence of stiff
aggregate particles. Composites containing glass particles are used for studying this type of restraint. In this paper, AE technique is applied to determine the temporal evolution of shrinkage
cracking. Fundamental issues on wave propagation within the materials are studied first. Twotypes of specimens, hardened plain cement paste and cement composite containing mono-sized
glass spheres, are used to reproduce shrinkage due to the self-restraint and due to the aggregaterestraint respectively.
2. Experiment
2.1 Materials
As mentioned, drying shrinkage micro-cracking is studied in two types of materials: plain
hardened cement paste and cement-based composite with mono-sized 6-mm glass spheres. The
cement paste consists of ordinary Portland cement (CEM I 52.5R) with a water-cement ratio of
0.45. The composite consists of the same cement paste with a 35% volume percentage of 6-mm
J. Acoustic Emission, 20 (2002)

153© 2002 Acoustic Emission Group

A

30

#5

#6
#4

#3
#1

A

#2
90 mm

#3

# 2&6

AE sensor, # 4

42

# 1&5

235 mmFig. 1. Cylindrical specimen with the location of AE sensors.
glass spheres with smooth surfaces. The Young’s Modulus of the glass spheres is 77 GPa. The
cement-based materials are cast in cylindrical moulds as shown in Fig. 1. Note that the specimens for the initial studies are cast in smaller prismatic moulds (h x w x l = 40 x 40 x 160 mm).
The specimens remain in the moulds for 24 hours at room temperature, sealed with a plastic foil.
After demoulding, the specimens are placed for 6 days in calcium hydroxide saturated tap waterat room temperature. At an age of 7 days, the specimens are removed from the water and allsides, except the top-surface, are sealed with 3 layers of adhesive tape, creating one-dimensional
drying in the specimens. Subsequently, six AE sensors (M65, Fuji Ceramics Corp.) with a 500kHz resonant frequency are placed with wax couplant onto the drying surface in the arrangement
given in Fig. 1. The specimens are dried in an environmental cabin ventilated with air of 25% ±5% RH, at temperature of 31˚C (± 0.5˚C). The drying continues for 16 hours.
2.2 AE monitoring
The used AE monitoring system is a MISTRAS AE system (Physical Acoustics Corp.). The
detected AE signals are amplified 40 dB using preamplifiers (PAC, 1220). Both parameters andwaveforms of AE signals over the threshold of 30 dB (ref. 0 dB at 1 µV at sensor) are recorded.
Three parameters; grade [2], initial frequency [3] and improved b-value [4], are employed to
study the fracture behavior during shrinkage. The initial part of the AE waves is extracted fromthe waveforms because it is least affected by the resonance characteristic of AE sensors andcontains the cleanest information about fracture characteristics. A higher initial frequency is expected to occur in a situation where cracks rapidly generate (e.g. mode I), while a lower initialfrequency is expected when the cracks develop slowly (e.g. mode II). Grade is defined as a gradient of the AE waveform up to the peak amplitude. A rapid growth/development of cracks
154corresponds to large values of grade. In contrast, slow development of cracks corresponds tosmall values of grade. The improved b-value, which is defined as the negative slope of peak amplitude distribution, is used to quantitatively evaluate the fracture state. Improved b-values tend
to increase where small-scale fracturing occurs in comparison to large-scale fracturing. When
predominantly large-scale fractures are generated in place of small-scale fracture, improved bvalues tend to decrease [4].
After experiment, the generated crack-pattern of the specimen is recorded by FLM [1]. Manual crack tracing is subsequently applied to obtain a detailed crack map, which is digitized to extract characteristic crack data.
3. Initial Studies for AE Monitoring
3.1 Wave velocity
The velocity of elastic waves within the hardened plain cement paste is studied first. Two
conditions of the specimen: before drying (intact state) and after 16 hours drying (cracked state)
are prepared. The measurement of the velocity was carried out in the transverse direction of thespecimen using AE sensors. With an artificial pulse generator of 240-V peak voltage, signals
were excited 30 times. The measured wave velocities in both the intact and cracked specimens
are shown in Fig. 2. A slight decrease of wave velocity is observed with progress in microcracking. The difference of wave velocities between intact and cracked specimens is approximately 340 m/s. How does the velocity difference influence the source location? If the distance
between AE sensors is 5 cm, the maximum difference of arrival times would be 14.3 to 15.6 µs
when the wave velocity ranges from 3200 to 3500 m/s. This error of the time difference isequivalent to 4.2 to 4.6 mm and the source location error due to variation of wave velocity (i.e.,
progress of cracks) would be 4.2 to 4.6 mm.

Fig. 2. Measured wave velocities. The height of bars shows the average of 30 waves with standard deviations (12.75 for intact and 13.06 for cracked).
3.2 Wave attenuation
To obtain the attenuation characteristics of the AE waves, six AE sensors are placed in three
dimensions on a plain cement paste specimen. The enlarged view of the arrangement of the AE
sensors is shown in Fig. 3. The prismatic specimen is subjected to 16 hour drying and AE signals
due to cracking are detected. The attenuation characteristics are determined as follows:155AE sensor

40

30

25

25

40

m
m

Fig. 3 Enlarged view for the arrangement of AE sensors.

Fig. 4. Attenuations of 12 AE sources.
1) 3D source locations are performed; 2) The distances between the source and each AE sensors
are calculated; 3) The relation between peak amplitude (dB) and propagation distance (mm) is
plotted and a linear approximation is carried out, and 4) The attenuation characteristics are obtained. Figure 4 shows the attenuation characteristics of 12 AE sources. With extrapolations, thepeak amplitudes of AE sources range around 60 dB. This suggests that AE signals with large
amplitude are difficult to generate/obtain due to the shrinkage cracking. For a threshold of 40 dB,
the monitoring area is limited to 5-15 cm. If a comparison is made with conventional concrete[5], AE signals generated by shrinkage cracking is observed to attenuate sharply.
4. Results
Figure 5 and 6 show the digitized crack maps on the drying surface and cross-sections ofplain cement paste and the composite specimen, respectively. In the plain cement paste, a celllike crack-pattern developed. The crack-widths ranged between 20 and 40 µm. In the composite,
there is no clear cell-like crack-pattern as on the surface of the plain cement. However, a similarcell-like pattern is believed to exist as in the cement paste, but since the top layer of the composite specimen was removed by grinding, just lightly to remove the upper cement layer to reveal
the positions of the glass spheres, the original pattern can only be inferred. Cracks were generally156

Fig. 5 Digitized crack-map in the plain cement.

Fig. 6 Digitized crack-map in the composite.
157

8.E+06300

6.E+06Composite

200

4.E+06
Plain cement paste

100

2.E+060Absolute energy (aJ)

Cumulative AE events

400

0.E+00
0

2

4

6

8

10

12

14

16

Drying time (hours)

Fig. 7. Cumulative AE events and absolute energy.
perpendicular to the boundary of glass spheres indicating that they were partly caused by aggregate
restraint. Self- restraining also took place in this composite (see Fig. 6b-d): a significant part ofcracking had an orientation perpendicular to the drying surface.

80
#3

60
40

# 6, 2

20

0

2

4

6
8
10 12
Drying time (hours)

14(a) AE events in the plain cement.

40# 6, 2

20
#4

0

16

0

2

4

6
8
10
12
Drying time (hours)

1416(b) AE events with signal strength in the plain cement.

100
Horizontal location (mm)

# 1, 5

#3

60

#4

0

legend1,800,000
(pVs)

80

100
# 1, 5

80
#3

60
40

# 6, 2

20
#4

0
0

2

4

6

8

10

12

14

# 1, 5

Horizontal location (mm)Horizontal location (mm)

# 1, 5

Horizontal location (mm)

100

100

80

40
# 6, 2

20
#4

0

16

0

2

4

6

8

10

12

14

16

Drying time (hours)

Drying time (hours)(c) AE events in the composite.

#360(d) AE events with signal strength in the composite.

Fig. 8. One-dimensional source locations as function of drying time.
158

Figure 7 shows the cumulative AE events (black lines) and cumulative absolute energy (dotted lines) as function of drying time for plain cement paste and the composite. In comparison to
the plain cement paste, approximately 3.5 times more AE events (accompanied by 4.5 times
more absolute energy) are recorded in the composite after 16 hours drying. In both specimens, a
rapid increase of AE events is observed in the first hour of drying. In the plain cement after thisinitial activity during approximately one hour drying, no further AE events are recorded. In contrast, AE events are continuously recorded, and a stepwise increase of absolute energy is found in
the composite throughout the 16-hour drying experiment.
Figure 8 shows 1D source locations as a function of drying time for plain cement paste andcomposite. Source locations projected along line A-A in Fig. 1 are shown along the y-axis in Fig.
8. Figure 8b and d are shown with the scale of signal strength using the first arrival AE signals.
In the plain cement (see Fig. 8a), AE events are dispersed along the A-A-axis in the first hour ofdrying. In the composite, more AE events are recorded from the start of drying and continue tobe generated throughout the drying experiment. An important aspect is also found when thecomparison is made between AE sources (see Fig. 8a) and those with signal strength (see Fig 8b)
in the plain cement. There are a number of AE sources observed in instantaneous drying in Fig.
8a, although they could not be found in Fig. 8b. This implies that the instantaneously generated
AE sources had very small signal strength.
Initial

Reverberation

Fig. 9. Three extracted
components of AE waveform to obtain individual
frequencies.

Threshold

Average frequency

Average frequency (kHz)1000
avg I-freq
avg r-freq
avg a-freq

800
600

R = 0.998
R = 0.936

400
R = 0.258

200
0
0

10

20

30

Average grade (dB/µs)
Fig. 10. Relationships between average frequency and grade.
159

40

30

30

25

25
Grade (dB/ms)

Grade (dB/ms)

Figure 10 shows the relations between average frequency and average grade. 1515 AE signals
obtained from shrinkage cracks of 16 hours drying are used for constructing the chart. Threecomponents of the waveform as shown in Fig. 9 are extracted to obtain the frequencies. Note thatthese are apparent frequencies based on threshold-determined parameters such as ring-downcounts, rise time and duration. As mentioned before, the initial part of the AE waves (initial frequency) and gradient of the AE waves up to peak (grade) are supposed to be closely related eachother. Consequently, the correlation coefficients are 0.998, 0.936 and 0.258 for initial frequency,
average frequency and reverberation, respectively. These results show explicitly the close relationship between initial frequency or average frequency and grade. As expected, reverberation
remains essentially unchanged with grade.

20
15
10
5

20
15

2

10
5

0

0

0

2

4

6

8

10

12

14

16

0

2

4

Drying time (hours)

8

10

12

14

16

14

16

Drying time (hours)(a) Grade in the plain cement.

(b) Grade in the composite.

0.25

0.25

0.20

0.20

Improved b -value

Improved b -value

6

0.15
0.10
0.05

0.15
0.10
0.05
0.00

0.00
0

2

4

6

8

10

12

14

0

16

2

4

6

8

10

12

Drying time (hours)

Drying time (hours)(c) Improved b-value in the plain cement.

(d) Improved b-value in the composite.

Fig.11. Grade and improved b-value as function of drying time.
Figure 11 shows the grade and the improved b-value with respect to the drying time. The
grade sharply decreases only for the first one-hour of drying in the plain cement, whereas repetition of rising and falling values is found in the composite. The same trends are also observed in
the charts of the Ib-value (see Fig. 11 c and d).
Finally in Fig. 12, the AE activity for the first one-hour of drying in the plain cement is enlarged showing Ib-value and initial frequency. A sudden drop is found after 0.1 hour (6 minutes)
drying in both parameters, which suggests that a large scale fracturing and a slower deformation
due to cracking have started at that moment.
160

0.20
Improved b -value

800

0.16

600

0.12
Initial frequency

400

0.08

200

0.04

0

0.00
0.0

0.2

0.4

0.6

0.8

Improved b -value

Averaged initial frequency (kHz)

1000

1.0

Drying time (hours)
Fig. 12. AE activity for the first one-hour of drying showing Ib-value and initial frequency.
5. Discussion
The microscopy result shows that cracking in plain cement paste is due to self-restraining
only, while cracking in composite is the result of combined self-restraint and aggregate restraint
stresses. The AE event data show that in the plain cement paste most micro-cracking was generated within 1 hour, while micro-cracking in the composite continued throughout the drying experiment of 16 hours. The grade, initial frequency and the Ib-value analysis confirm this earlybehavior; large scale fracturing has already started at 6 minutes and continues to occur up to one
hour in the plain cement paste. Similarly in the composite, large scale fracturing has started justafter 6 minutes. Consequently, the self-restraint cracking in the plain cement paste is a very early
drying phenomenon, while cracking due to aggregate restraint is a continuous process upon further drying. The development of the moisture gradient seems to be the main cause for the early
crack-growth due to self-restraint in plain cement paste. The moisture gradient is steepest at theonset of drying, and starts to flatten already from (at least) 8 hours onwards [6]. This means thatfrom this moment onwards, self-restraint stresses become less and cracking comes to a halt. In
the composite, an initial peak in cracking is also observed, which can be explained by a significant contribution of self-restraint stresses. At later ages, aggregate restraint is the cause ofcracking in the composite. Aggregate restraint cracking is not related to the moisture or shrinkage gradient and depends only on the ‘local’ magnitude of matrix shrinkage.
For the AE activity, which was generated during the initial 6 minutes of drying, the mechanisms producing the AE activity could not be ascertained. However, these initial AE signals havevery small energy (from the observed absolute energy, signal strength and Ib-values), and they
are caused by rapid phenomena judging from the observed initial frequency and grade. This AE
mechanism will be clarified in a future paper.
161

6. Conclusions
Early behavior of micro-cracking due to drying shrinkage in cementitious materials has beenstudied by means of acoustic emission. The results are compared to findings from fluorescentlight microscopy, and are summarized as follows:(1) Self-restraining caused cracking in the very early stage of drying in both the plain cement
paste and the composite; surprisingly and quite unexpectedly, drying cracks already were shown
to form after 6 minutes of drying.
(2) Aggregate restraining occurred continuously throughout the drying experiment of thecomposite up to 16 hours.
(3) AE parameters such as grade, initial frequency and improved b-value provide valuable information on fracture mechanisms.
Acknowledgement
T. Shiotani received a TU Delft Senior Fellowship, which is gratefully acknowledged.
References[1] Bisschop J, van Mier J G M. “How to study drying shrinkage micro-cracking in cementbased materials using optical and scanning electron microscopy,” Cement Concrete Research,
32, 279-287, 2002.
[2] Shiotani T, Ohtsu M, Ikeda K. “Detection and evaluation of AE waves due to rock deformation,” Construction and Building Materials 15, Elsevier Science, pp.235-246, 2001.
[3] Shiotani T, Bisschop J, van Mier J G M. “Temporal and spatial development of drying
shrinkage cracking in cement-based materials”, Engineering Fracture Mechanics (in press).
[4] Shiotani T, Yuyama S, Li Z W, Ohtsu M. “Application of AE improved b-value to quantitative evaluation of fracture process in concrete materials,” Journal of Acoustic Emission, 19. 118133. 2001.
[5] Shiotani T, Miwa S, Monma K. “Characteristics of elastic waves induced by raindrops,”
Proc. 6th Domestic Conference on Subsurface and Civil Engineering Acoustic Emission, MMIJ,
pp. 7-12. 1999 (in Japanese).
[6] Bisschop J, Pel L, van Mier J G M. “Effect of aggregate size and paste volume on drying
shrinkage microcracking in cement-based materials,” Creep, Shrinkage and Durability Mechanics of Concrete and Other Quasi-Brittle Materials, Proc. CONCREEP-6, Elsevier Science, pp.
75-80, 2001.

162

EWGAE2002

TRANSFORMATION PROCESSES IN SHAPE MEMORY ALLOYSBASED ON MONITORING ACOUSTIC EMISSION ACTIVITYMICHAL LANDA1, VÁCLAV NOVÁK2, MICHAL BLAHÁCEK1 and PETR SITTNER2
1

Institute of Thermomechanics, AS CR, Dolejskova 5, 182 00, Praha 8, Czech Republic
2
Institute of Physics, AS CR, Na Slovance 2, 182 21 Praha 8, Czech Republic

Abstract
Stress induced martensitic transformations (MT) in Cu-based shape memory alloys at room
temperature were studied by acoustic emission method (AE). AE phenomenon is indicative particularly of the kinetics of MT’s and can be used for nondestructive in-situ recognition of thevarious transformation processes. In this paper, correlations of structural processes and accompanied AE activity during forward and reverse MT in single crystals are shown and discussed.
Introduction
The functional materials are not being produced for their structural properties but for theunique functions they exhibit in thermomechanical loads. Shape memory alloys (SMA) are generic class of intermetallic alloys that display mechanical memory effects upon stress/thermal
cycling. These properties are derived from a solid phase change - thermoelastic martensitic transformation (MT) taking place upon changing external stress and/or temperature. Nondestructive
investigation of such materials requires novel experimental and theoretical approaches particularly in-situ studies under the external loads.
The ultrasonic techniques are based on detection and evaluation of acoustic properties
changes taking place during MT’s. Changes of acoustic wave velocities and attenuation bringinformation about the elastic moduli and structure variations (direct structure state characterization) [1]. Structure anisotropy and elastic properties of phase compositions, changing in thetransformed polycrystalline material, need to be known and this is why single crystal experiments play an important role. The ultrasonic nondestructive techniques have a potential to provide (namely bulk) phase information complementary to classical phase identification methods(e.g. chemical analysis, TEM, X-ray).
Kinetics of MT’s is characterized by acoustic emission (AE) activity. AE studies available in
the literature (e.g. [2]) mainly focus on the thermally induced transformation. We have been
studying AE accompanying stress induced MT in [011] and [001] oriented Cu-Al-Ni single
crystals loaded in compression at a constant temperature.
Specimens
In the particular case of Cu-based SMA’s, the high temperature austenitic ordered phase ofhigh symmetry bcc structure (β1) transforms mainly into lower symmetry martensitic phases with
18R monoclinic (β′1) and/or 2H orthorhombic structures (γ′1).

J. Acoustic Emission, 20 (2002)

163© 2002 Acoustic Emission GroupThe different processes of MT are demonstrated on prismatic specimens (10x3x3 mm3) prepared from Cu-Al-Ni single crystals with orientation near [001] (sample C110t) and [011] (sample C101t) directions. Depending on temperature, the three stage may be distinguished: above Af,
between Af and Ms and under Ms. The stress-free specimen above the temperature Af is in theaustenite phase (β1) and its deformation exhibits super-elasticity effect; i.e. after unloading, thestructure returns again to the austenite. Both specimens studied display this phenomenon at room
temperature (RT), as shown in Fig. 1. The hysteresis of the stress-strain loop depends not only on
the loading orientation but also on the maximal strain level (εmax). The four different transition
processes (β1→ β′1, β1→γ′1, β′1→ β1 and γ′1→ β1) were realized during the first (partial) and thesecond (complete) loading cycles. Accompanying AE activity of these separate processes isanalyzed below.

Fig. 1. Compression tests of Cu-Al-Ni single crystals. Samples C101t (left) and C110t (right).
Experimental Procedure
The experimental arrangement is described in [3]. Miniature AE sensors (PZT transducers,
frequency band 1.4 MHz) were placed in the special designed loading heads of the testing machine TiraTest 2300. The heads had a shape of hollow rods, which enable one to place the sensors in the specimen axis. The strain rate control of compression tests was 0.1%/min. The classical AE parameters (count rate dNc/dt, event rate dNE/dt, AE amplitude distribution) were monitored each time step (0.5 s) by AE analyzer Dunegan-Endevco 3000 interfaced to PC via GPIB.
The AE waveforms were recorded (12 bits/10 MHz) simultaneously by a transient recorder
ADAM (Maurer). The AE events recording rate of the system is very low (several events per second). The AE parameters were resumed to the period ∆τ = 5 s and additional AE parameters
were evaluated by post-processing: averaged count rate per events ∆Nc/∆NE during the time interval ∆τ and characteristics of AE amplitude distribution in the interval ∆τ (see Fig. 2; themaximum amplitude Amax, the averaged amplitude AAVG and the best-fit slope b of the cumulative amplitude distribution; Fig. 2b). The AE amplitude is restricted by the threshold level of 44dB and by the maximum of 100 dB. (In Fig. 2b, this range is from 2.2 to 5.0.)
The AE activity from forward and reverse MT’s of both crystal orientations in the form of
the AE parameters history and the stress curve are plotted together in Figs. 3 and 4. The observation of optical changes on the specimen surface was recorded, synchronously with the acoustomechanical measurements (Fig. 5). The snapshots were obtained during the first loading cycle atthe marked times in Figs. 3 and 4.
164

a.

b.
Fig. 2. Parametrization of instantaneous AE amplitude distribution. a. Differntial; b. Cumulative
distribution. Note Amplitude A (dB) = 20 log Relative Amplitude.
Results and Discussion
Sample C101t
At first, we notice the partial loading cycle of the sample C101t in Fig. 3. The AE indicatesformation of the thin individual lamellae of the martensite β′1, in the austenite single crystal(Fig. 5b). The martensite appears randomly at the characteristic orientation along the whole
length of the sample. AE may be characterized by relatively averaged level of count and event
rate, higher amplitudes and low level of the slope b of 1.5. During the process, the number of
newly arisen lamellae decays and lamella width grows. This advanced stage of the forward MT
is accompanied by lower AE activity (namely ∆Nc/∆NE and Amax, note also dNc/dt). During theunloading part of the first cycle AE activity changes dramatically: High level of dNc/dt, dNE/dt,∆Nc/∆NE; i.e. high number of low amplitude events causes a higher level of the averaged amplitude AAVG and high values of b up to the value of 6.1. The martensitic lamellae transform by thesame way and synchronously.
165

Fig. 3. AE activity during partial and full compression cycles of the sample C101t.
166

Fig. 4. AE activity during partial and full compression cycles of the sample C110t.
167

Higher level of AE activity indicates formation and growth of the martensite. It is in agreement with the expectation of the 2H-phase in the sample loaded over strain level 5%. The existence of high amplitude signals up to 100 dB causes lower values of b or the slope of cumulative
amplitude distribution and higher values of AAVG. High amplitude AE events correspond alsowith the optical observation. The γ′1 martensite growth in the whole cross-section is found along
the sample length, alternatively in both directions from the origin area. The AE decays quickly
during the final linear part of the stress-time curve, as the forward MT is finished. The resulting
structure is a single crystal of the γ′1-martensite having the orientation forced by the unidirectional stress. The AE from the reverse transformation (γ′1→ β′1 + β1) of the second circle is different from the process β′1 → β 1 (the first cycle). The reverse MT from the γ′1-martensite ismore or less symmetric to the forward MT in the sense that γ′1-phase area transforms in wholecross-section and decreases along the sample axis. Nevertheless, the unloading path is longer(greater hysteresis in the stress-strain diagram), the MT is distributed over longer time and AE isless intensive than during the forward process. AE amplitudes of up to 80 dB are found and theparameter b reaches up to the value of 4.5 at the starting part and then moves around the value of
2 during the following constant stress period.
Sample C110t
The MT of the sample C110t (Fig. 4) differs from that of C101t described above. The 2H
martensite appears shortly after starting β1→ β′1 transformation, Fig. 5c. The phase β′1 exists fora short period and the martensitic transformation proceeds to β1→ γ′1 directly after the stressstep, which is notable in the loading curve (Fig. 4). From this moment, the continual martensite
area spreads along the specimen. The β1/ γ′1 interfaces move gradually in parallel planes.
The AE activity reaches the same level during the final unloading stage of the first partial cycle. The reverse MT from single martensite γ′1 (the second full cycle) is accompanied by relatively stable AE activity with notable increase of high amplitude AE events the moment after
starting and before finishing the reverse transformation process. The complete forward MT into
the 2H-phase seems to be more acoustically efficient than the reverse one, but not as radically asin the case of β1↔β′1.
AE source localization
The character of the β1→ γ′1 transformation in the form of the interfaces movement along thespecimen length leads us to an idea of localizing this process by AE. The first wave arrivals were
determined by AE signal records from the two AE sensors placed in the loading heads. The numerical procedure utilized adaptive threshold and denoising technique described in [4] and modified in [5]. The obtained results are plotted in Fig. 6a. The localization map brings only qualitative information; therefore the initial austenite structure is strongly anisotropic (anisotropy factor
is about 12) and the elastic properties changed during MT. Nevertheless, the specimen length isdominant (prismatic bar shape), reducing anisotropy effect, and directions of the austenite/martensite interfaces (marked by arrows) are notable.
The more convincing result is obtained from the test of the sample C44t, which can be seenin Fig. 6b. The specimen is made also of the Cu-Al-Ni alloy and with the same orientation as
C110t ([011]), but the transformation temperature Af is shifted to ~40ûC by a thermal treatment.
It exhibits the shape memory behavior at RT. The specimen is driven to the martensite (2H) bycompression and the structure stays permanent after unloading. The γ′1-phase is formed after themain peak during the loading stage and the only one interface, bordering the γ′1 phase,168

a. Observed field position on the specimen.

b. Sample C101t. Title corresponds to the snapshot number marked in Fig. 3.

c. Sample C110t. Title corresponds to the snapshot number marked in Fig. 4.
Fig. 5. Optical observation of MT-induced surface changes during compression.
propagates dominantly from the one side of the specimen to the other. The AE localization corresponds to only one active place, in contrast to the previous case of the sample C110t, where bothsides of the γ′1 area are active at the same time.
Conclusions
Acoustic emission during stress induced martensitic transformations in Cu-Al-Ni single
crystals at room temperature was analyzed and correlated with structural changes observed on
the specimen surface by optical microscopy. Understanding of martensite transformation processes in single crystals contributes to identification of structure changes in polycrystalline SMA’s
from acoustic emission.
AE signals of the forward and reverse β1↔ β′1 MT were found to be different. This indicatesthat this transformation, though otherwise fully reversible, has different kinetics in the forward
169

Fig. 6. Results of AE source localization and interpretation. C110t (left) and C101t (right).
and reverse processes. Similar behavior of AE activity was observed also during tensile tests ofsingle crystals [6]. A bi-crystal with crystalline interface along the specimen axes has the samecharacter of AE.
AE signals of the β 1↔ γ′1 MT in forward and reverse compression loads are similar. But
complete and partial loading cycles give different results due to difficult nucleation of the stressinduced γ′1 phase. By changing of the loading direction, the martensite phase may be reoriented(it is symbolized like γ1↔ γ′1). The reorientation is a much less active AE source than transformation processes described above [7].
Acknowledgements
The authors wish to express their thanks to Ing. Premysl Urbánek (Inst. of Thermomechanics,
AS CR) for assistance during the experiments, Mr. Vladimír Novák (Inst. of Physics, AS CR) for
preparation of the specimens. This work has been supported by Grant Agency of the Czech Republic under the project No. 106/01/0396.
References[1] M. Landa, V. Novák and P. Sittner, Proceedings of 15th World Conference on NonDestructive Testing, Rome 2000, http://www.ndt.net/article/wcndt00/index.htm[2] E. Vives, I. Raifols , Ll. Mañosa, J. Ortín and A. Planes, Phys. Rev. B, 52 (1995), 12,64412,650.
[3] M. Landa, M. Chlada, Z. Prevorovsky, V. Novák and P. Sittner, J. Acoustic Emission, 17(1999), S57-S64.
170

[4] M. Landa, F. Chmelík and Z. Prevorovsky, Linear localization of acoustic emission sources,
Research Report Z1219/96, IT AS CR Prague 1995.
[5] M. Blahácek , AE source localization using artificial neural network approach (in Czech),
PhD Thesis, IT AS CR, Prague 2000.
[6] Novák, M. Landa and P. Sittner, Proc. International Conference on Martensitic Transformations (ICOMAT’02), Helsinki, June 10-14, 2002 (in press).
[7] V. Novák, M. Landa and P. Sittner, Proceedings of 1st European Conf. on Shape Memory
and Superelastic Technologies (SMST’99), Antwerp 1999, pp. 242-247.

171

EWGAE2002

CHARACTERIZATION OF STRESS CORROSION CRACKING OF
CuZn-ALLOYS BY ACOUSTIC EMISSION TESTING
U.-D. HÜNICKE1, M. SCHULZ1, R. BUDZIER1 and J. EBERLEIN2
1

2

University of Rostock, Rostock, Germany
Mecklenburger Metallguss GmbH, Waren, Germany

Abstract
This paper presents the results of the investigation for the characterization of stress corrosion
cracking of CuZn-alloys using acoustic emission (AE) testing. The slow strain-rate test and theconstant load test of various brass-alloys in a 0.5-molar sodium nitrite solution are carried out
and the AE activity during these tests is recorded. The information provided by the AE hit
counts, the amplitude and AE energy as well as the waveform of the AE signal is analyzed. The
results show the possibility to predict the SCC-susceptibility of brass using AE testing.
Keywords: stress corrosion cracking; acoustic emission; CuZn-alloys
Introduction
The stress corrosion cracking (SCC) is an important engineering problem especially in the
nuclear power and chemical industry. CuZn-alloys are known to undergo SCC both inammoniacal and in some non-ammoniacal solutions. NaNO2 was found to be one of the strongest
promoters of SCC of brass in the non-ammoniacal solutions [1]. The process of SCC of brass in
sodium nitrite solution has been extensively studied. Several models were suggested for the SCC
mechanism of brass in sodium nitrite solution. The following models have achieved the widest
acceptance: The film-rupture and dissolution model [2-7], the film-induced cleavage model [2-4,7, 8] and the surface-mobility model [2, 4, 9]. Also some authors investigated the surface
reaction of NO2-solutions on metals [10-13].
The SCC is a process of microcracking and crack propagation. The initiation, multiplicationand propagation of micro-cracks act as acoustic emission (AE) sources. Therefore, the AE
analyses can be applied to evaluate the SCC-process in brass [14].
This paper describes the first part of a methodical investigation of AE signals for thecharacterization of SCC-susceptibility of different CuZn-alloys in sodium nitrite solution, which
was realized by a slow strain-rate test and a constant load test.
Experimental Details
The specimens were cut from commercial rolled plates of brass with different Zn-contents
with a thickness of 1 mm. The chemical composition of the test materials obtained by emissionspectroscopy and some mechanical properties are given in Table 1. The dimension of the tensile
specimen is 250 mm x 30 mm. The width of the sample, which is exposed to the medium, isreduced to 12 mm.

J. Acoustic Emission, 20 (2002)

172© 2002 Acoustic Emission GroupTable 1. Chemical composition and mechanical properties of the tested brass.
Chemical composition

CuZn20CuZn30CuZn37
CuZn40Cu [wt%]Zn [wt%]Pb [wt%]

80.45
70.47
64.58
61.88

19.49
29.48
35.33
37.91

0.009
0.006
0.012
0.137

Mechanical properties
Tensile strength
Yield strength
Elongation
Rm [N/mm2]
Rp0.2 [N/mm2]
A10 [%]
517
463
13
538
493
16
525
497
12
545
495
17

A resonant piezoelectric sensor (PAC R80, resonant frequency 200 kHz) was mounted
directly on the test specimens above the liquid level preventing a destruction of the sensor due tothe aggressive medium. The AE signals are pre-amplified (40 dB, 100-1200kHz) and processedusing PAC’s PCI-DISP4 AE-System. The threshold was set as low as possible.
The test medium of 0.5-molar sodium-nitrite solution is prepared from reagent grade
chemical and distilled water, and the pH-value was 6.8.
Two different test techniques were used: The slow strain-rate test (SSRT) with a nominal
strain rate of 1.5 x 10–4 s -1 and a constant load test (CLT). During the tests the samples are
strained to rupture although the attention is directed to the first part of the experiment. Figure 1
shows a general overview of the experimental set-up.

Fig. 1. Schematic experimental set-up for tensile tests in 0.5-mol NaNO2 solution.
173

The CuZn-alloys, CuZn37 and CuZn20, were used for the SSRT in air and in a 0.5-molar
NaNO2 solution. Due to some noise emissions at the beginning of the tensile test caused by the
set-up the analysis of the registration of the AE was started at a stress of R = 200 MPa.
The change of AE (ring-down) counts, hit counts, peak amplitude, energy, duration and otherwaveform parameters were evaluated. The CLT with an applied stress of R = 0.9.R p0.2 andimmersed in the solution of 0.5-molar NaNO2 was performed for CuZn20, CuZn30 and CuZn40.
Results and Discussion
Slow strain rate test
The AE-signal-analyses represented by the cumulative hit counts during a SSRT at CuZn37
in air and in 0.5-mol NaNO2 is shown in Fig. 2. An increase of the AE activity near the yieldstrength could be detected in both media. During the plastic deformation the specimen tested in
air shows only a small amount of AE signals, while the AE in NaNO2 intensified when the
incubation time is reached.
Compared to the tensile test in air the influence of the sodium-nitrite solution caused adecrease of the tensile strength, yield strength and fracture elongation. The step-growth of the
cumulative hit counts indicates the process of crack propagation and arrest during SCC of brassin the NaNO2 solution.

Fig. 2. Cumulative counts of AE signals during SSRT for CuZn37 in air and in 0.5-molar NaNO2
solution.

174

The aggressive influence of the 0.5-mol NaNO2 solution is also verified by SEM techniqueof the sample surface near the fracture zone, which is shown in Fig. 3. The sample stressed under
the NaNO2 solution features typical stress corrosion cracks normal to the applied tensile stress.

Fig. 3. Scanning electron micrograph of a CuZn37 near the fracture zone. a) in air (top) b) in 0.5
molar NaNO2 (bottom).
The slow strain-rate technique is only useable for a characterization of an SCC-susceptibility
when the incubation time, which in this case is defined as the time an increase in AE-activity canbe detected, is shorter then the duration of the test. This is demonstrated in Fig. 4, which shows
the cumulative counts during the SSRT in sodium-nitrite solution of CuZn20. Due to the low Zncontent the SCC-susceptibility is much lower. No significant AE activity could be detected after
the increasing AE activity around the yield stress because the test duration was too short.
Constant load test
To characterize brass with a low SCC-susceptibility, a constant load test is more appropriate.
The results of the CLT at a stress of R = 0.9.Rp0.2 in 0.5-molar NaNO2 solution of the α-brass(CuZn20 and CuZn30) and the (α+β)-brass (CuZn40) are illustrated in Fig. 5. During the tests
the applied stress is falling at first caused by the relaxation and later due to the crack
propagation. The time to final fracture decreases with increasing Zn-content of brass. Also thetime where the AE-activity increased visibly depends on the Zn-content. The higher the Zn175

content of the brass the earlier starts the AE-activity. Therefore, the point, where the graph of the
cumulative counts is rising noticeably, could be interpreted as the incubation time. This could be
used to characterize the SCC-susceptibility of brass.

Fig. 4. AE cumulative counts during an SSRT for CuZn20 in 0.5-mol NaNO2 solution.

Fig. 5. AE cumulative counts during a CLT with R = 0.9.R p0.2 for 3 CuZn alloys in 0.5-mol
NaNO2 solution.
176

The influence of the β-phase in CuZn40 is evident. Although the SCC-susceptibility of
CuZn40 is higher (tinc ~1.4 h) than for the single-phase CuZn30 (tinc ~ 2 h), the AE activity islower in CuZn40. This could be explained by the differences of the microstructures. The CLT of
CuZn20 shows a SCC-susceptibility to NaNO2 even though it is much less than for the alloys
with higher Zn-content.

Fig. 6. Cumulative counts and peak amplitude of AE-signals during a CLT with R = 0.9.Rp0.2 for
CuZn30 in 0.5-mol NaNO2 solution.
A more specific view to the character of the AE signals detected during the CLT of CuZn30
is given in Fig. 6 and Fig. 7. As a result of the increasing crack propagation the peak amplitudeof the AE signals is also rising (Fig. 6). The same observation could be made for the duration of
the signals shown in Fig. 7. Although there are two areas with an accumulation of AE signals,
the majority of the detected AE signals possess a short duration up to 40 µs or between 150 and
200 µs. This could indicate two different processes during the SCC.
Conclusion
The presented results show the possibility to characterize the SCC-process for brass in 0.5molar NaNO2-solution by AE testing. Depending of the SCC-susceptibility of the specimen, it ispossible to use the SSRT or CLT method with a load near the yield strength. The AE analysis isable to evaluate the SCC-susceptibility of various brass alloys under different conditions.

177Fig. 7. Cumulative counts and duration of AE signals during a CLT with R = 0.9.R p0.2 for
CuZn30 in 0.5-mol NaNO2 solution.
References[1] M.G. Alvarez, C. Manfredi, M. Giordano and J.R. Galvele, Corros. Sci., 23, 769-780 (1984).
[2] A.J. Bursle and E.N. Pugh, Environment-Sensitive Fracture of Engineering Materials, AIME,
Warrendale, Pennsylvania, pp. 18-47 (1979).
[3] E.N. Pugh, Corrosion NACE, 41(9), 517-526 (1985).
[4] F. King, The Potential for Stress Corrosion Cracking of Copper Containers in a Canadian
Nuclear Fuel Waste Disposal Vault, AECL-11550, COG-96-94, 1996.
[5] J. Yu and R.N. Parkins, Corros. Sci. 27(2), 159-185 (1987).
[6] J. Yu, R.N. Parkins, Y. Xu, G. Thompson, Corros. Sci., 27(2), 149-157 (1987).
[7] D. Wu, H.S. Ahluwalia, A. Cai, J.T. Evans, R.N. Parkins, Corros. Sci., 32(7), 769-794,(1991).
[8] K. Sieradzki, R.C. Newman, Philos. Mag. A. 51(1), 95-132 (1985).
[9] K. Sieradzki et al., J. Electrochem. Soc., 134, 1635-1639 (1991).
[10] R.B. Rebak, R. M. Carranza, J.R. Galvele, Corros. Sci., 28, 1089-1106 (1988).
[11] P. Eriksson et al. J. Electrochem. Soc., 138(5), 1227 (1991).
[12] R. Holm, E. Mattsson, Metal corrosion in the atmosphere, ASTM STP 767, pp. 85-105(1982).
[13] R. Schubert, J. Electrochem. Soc., 125(7), 1114-1116 (1978).
[14] U.-D. Hünicke, M. Schulz, R. Budzier, Proceedings of the DGZfP, Jahrestagung, Weimar,
6-8 May 2002.
178

E WGAE2 0 0 2

ACOUSTIC EMISSION TESTING ON FLAT-BOTTOMED STORAGETANKS: HOW TO CONDENSE ACQUIRED DATA TO A RELIABLE
STATEMENT REGARDING FLOOR CONDITION
G. LACKNER and P. TS CHELIES NIG
Institute for Technical Physics, TÜV Austria, Vienna, Austria
Abstract
Acoustic emission testing on flat-bottomed storage tanks is a beneficial method for tank operators to obtain information about possible corrosion or even leakage of the floor without opening thetank. Within a research project funded by the European Commission (SM T4-CT97-2177), a new
testing technique was developed. It is based on specially designed measuring equipment as well as
on sensor arrangement in combination with suitable location algorithms and on frequency analysisof the waveforms. After successful completion of the research project, the method is now in its
introduction phase on the European market. M any tanks have been tested in refineries and tankfarms. The new technique is described, including the outlines of AE testing, evaluation principle,
data treatment, test examples and final report.
Keywords: Acoustic emission testing, storage tank, corrosion, leakage, maintenance.
1. Introduction
To have the necessary storage capacity available is of vital importance for every tank operator.
One main task of the maintenance department is to take proper actions for ensuring the availabilityof the tanks. There are different methods to estimate the duration of a service period for a certain
tank, most of which are based on experience. The common link between the experience-based
methods is that they do not take into account the actual tank condition. Knowing the actual tankcondition would enable the maintenance department to point out the tanks, which have to be repaired while leaving the others in operation. That would give an economic advantage by opening
only tanks in bad condition for inside inspection and appropriate repair.
Tank degradation is mainly caused by corrosion. Physical and chemical conversions take placeat the boundary area between the liquid storage product and the floor sheet. These processes are
non-ideal. Therefore, some parts of the process energy are transformed into acoustic emission (AE)and may be detected with an appropriate measuring system. Within a research program funded by
the European Commission (project identification: SM T4-CT97-2177, project partners: TÜV Austria/ENEL, Italy/Vallen-Systeme and Dow, Germany/Shell, Netherlands), a measuring system fordetecting active corrosion and active leakage was developed. It was shown by laboratory and onsite measurements that AE signals contain information to obtain the actual tank condition [1-3].
Based on these results a suitable AE testing technique was developed. It consists of two parts:
J. Acoustic Emission, 20 (2002)179© 2002 Acoustic Emission Group1. Testing procedure to set up and to perform the measurement.
2. Evaluation procedure to process the acquired data and to grade the tank floor.
The tested tank floor is assigned to one out of four possible grades given in Table 1. The recommended duration of the following service period after the AE test is with reference to the service
conditions given before and at the test. If the tested tank is grade IV, it is recommended to open thetank in order to clarify the indications found with AE. Costs for opening a tank and preparation for
inside inspection differ according to the size and the storage product. In any event, significant expenses have to be spent. A loss of storage capacity for weeks or months might have a serious effect on production facilities. Therefore, a negative test result has to be as reliable as possible. However, a positive result has to be of highest confidence as well, because unexpected problems areeven worse than an expected shut-down of a tank.
Table 1: Grading system for AE testing on flat-bottomed storage tanks.
Grade
Description
Recommended service period durationI
No active sources
5 yearsII
Low active corrosion
3 yearsIII
M edium active corrosion
1 yearIV
Leaks and/or high active corrosion-2. The AE Equipment
As an example for the standard sensor arrangement, 24 sensors have to be applied on a 100-m
diameter tank, equally distributed along the circumference and at a level of 1 m above the tank floor.
The used sensor is the VS30-V of Vallen-Systeme, which has a flat response especially within theused bandwidth centreing at 30 kHz. The sensitivity of each measuring channel is checked with the
Hsu-Nielsen source before the test. The AE system is an AM SY 4 of Vallen-Systeme, which iscapable of acquiring AE data and waveform data simultaneously for each channel. It has an internal
pulsing unit, which sends on request an electric pulse to a sensor. That electric pulse is transferred
into an acoustical pulse by the sensor. The other sensors may detect the emitted pulse after transmission through the liquid stock product and along the metallic tank shell. The AE system measures the time period for travelling from the emitting to the receiving sensor and that gives together
with the distance between emitter and receiver the speed of sound.
The software for data acquisition, visualisation and frequency analysis are also provided byVallen-Systeme. With VisualAE 2D and 3D plots may be produced to show the tank floor activity. With VisualClass classifier, files for analysing waveform data were created in order to
identify the source mechanism, as shown in Figs. 1a and 1b. That may be either leakage or corrosion. If corrosion is given, further analysis assigns the corrosion process to either the onset of corrosion or to an already established 'old' corrosion indicated by the presence of a scaling layer.
One can take from Fig. 1 that the source mechanism is given in x-direction. The classifier file
was created by using well-known and reliable training data sets. Then, independently acquired test
data given at the y-axis were classified and the resulting assignment percentages given in z-direction
180

a)

b)
Fig. 1. Source mechanism identification with VisualClass classifiers.
a) leakage - corrosion, b) old corrosion - new corrosion.

were obtained. Due to the fact that the method is based on statistical methods it is very unlikely to
get a 100 % assignment. The identified source mechanism is indicated by the maximum assignment
percentage. The source mechanism was identified properly for every test data set in Fig. 1.
3. The Test S et-Up
Today’s AE systems offer many features to filter the acquired data in order to eliminate noise
as well as signals related to effects different from corrosion and/or leakage of the tank floor. There
are possibilities for data filtering of each AE channel independently using criteria derived fromcombinations of various AE signal parameters. Optimised sensor arrangements in combination withappropriate location algorithms enable to focus on sources located on the tank floor by rejecting
AE events with origins on the floating roof or drops falling from the fixed roof onto the liquid.
All these AE system capabilities may lead to the conclusion that the data treatment during theevaluation procedure is the most important part of the testing technique. But this is definitely not
the case. The key to a successful test is always a proper set-up. The better the raw data of the test,
the easier and more reliable is the data reduction down to the tank-floor condition representing located AE data. Following this straightforward concept, no measurement shall be taken during andshortly after periods of rain and/or strong wind. Furthermore, it is the aim before every test to identify and eliminate noise sources at and in the surrounding of the tank. The next step to avoid too
much noise within the raw data is to find the best suited testing period. Very often this is during
night; therefore a working permission covering 24 hours a day has to be provided. The AE sensors
have to be applied onto the metallic surface of the tank shell; therefore small areas of the coating
have to be removed at the sensor positions. The benefit of all these efforts is to be able to test thetank with the lowest possible threshold and with the highest possible sensitivity of the measuring
channels. It has to be stressed that at the time being no data treatment is able to convert bad raw
data into useful data.
To perform the test after a proper set-up is the easiest task of the testing technique. During thetest the boundary conditions of the measurement have to be observed and remarks have to be writ181

ten into the measuring file to identify periods of disturbance. These periods are rejected from
evaluation and do not add to the minimum data recording time of 60 minutes. That gives up to 650
M B data (AE data and waveform data) for one measurement, which has to be stored on CD-R afterwards.
4. The Evaluation Principle
At the beginning of the research works, the demands for the testing technique were defined.
Source mechanism identification was one of the main demands of the industrial partners. One possible approach was to use AE parameters like signal duration or peak amplitude for this purpose.
One weak point of this approach was that the dependence of these parameters on the distance between source and sensor had to be estimated somehow. Since the frequency content of the emitted
sound wave may be regarded as nearly unaltered starting from the source to the sensor on the tankshell, the partners have chosen another approach for source identification: the frequency-domainbased pattern recognition. Therefore, the evaluation of the test focuses on source location. This isagain a straightforward concept. One can keep the link between the active source of the tank floor
and its visualisation by location plots. Figure 2 shows as an example a 55-m diameter crude oil tank(floating roof) with a high active source near sensors 1 and 2.

Fig. 2. Tank floor activity, 3D view of located AE events, 55 m diameter, floating roof.
182

The evaluation results in the location (coordinates) of the sources and their activity (low, medium, high). M oreover, it is obtained by frequency-content analysis of the acquired waveforms, if
the sound waves were emitted by corrosion or leakage sources. In case of a corrosion source thepresence of a scaling layer will be examined in another evaluation step. If a scaling layer is indicated,
the source is assigned to an already established long-term corrosion process. If no scaling layer isindicated, then the source is identified as the onset of corrosion, which does affect the tank floor
condition less than ‘old’ corrosion. But of course, if the activity is high, it has to be taken into account, lest this source might turn into a serious defect soon.
5. Measuring Data Treatment
Before the location result is ready for activity and frequency analysis, the measuring data has tobe filtered in order to reject noise data or other signals not related to corrosion and/or leakage of thetank floor. Figure 3 shows the different stages of data reduction.

Fig. 3. Data reduction of raw data down to located AE data.
The first filtering step is applied to the raw data in order to get rid of spike signals. These signals are characterised by a very short rise time and are acquired in large numbers due to the fact thatthe measuring threshold is set very low. As already mentioned above, every test shall be performed
with the lowest possible threshold. One consequence is that the test sensitivity is at its maximum;on the other hand, many acquired signals are related to arbitrarily occurring spikes of the background noise. Since these signals are without any significant source location potential, they are rejected from further evaluation. The remaining data is called filtered data.
The second filtering step is aimed at noise signals. These are AE signals, which are not related
to corrosion or leakage sources. It was shown by laboratory tests that AE activity due to corrosion
or leakage is evolving steadily in time without any sudden activity steps. Hence, it follows thatevery sudden step in AE activity indicates noise interference. One example of this noise interference during on-site tests is floating roof movement caused by gusts. Besides wind, electronic noiseinterference may lead to such a fluctuation in AE activity. In any case, noise shall not have an influence on the grading of the tank floor and those data has to be rejected from further evaluation. The
remaining data after this step is called extracted AE data.
183

The final step of the data reduction process is the application of the location algorithm. It
analyses the extracted AE data for hit sequences (AE events) with proper differences in arrival
time. The main parameters of the calculations are the speed of sound, the size of the tank and ofcourse the sensor positions. An AE event turns into a located AE event, if the algorithm finds correspondence of the measured arrival time differences with values derived from a certain location
within the tank. The remaining data after this filtering step, the located AE data, are represented inFig. 3 by the centre area of the disk.
At this stage one can be sure that the data is cleared from spikes, noise and signals, which cannot be combined to one single sound wave emitting physical event within the tank floor area. Figure 4 shows the extent of data reduction for some tank floors of different conditions according to
the described filtering steps.

Fig. 4. Data reduction according to filtering stage.
It can be taken from Fig. 4 that the raw data contain only a few locatable AE data in the rangeof some percents (3 % for ‘no active sources’ to 9 % for ‘extensive corrosion’). But still the located AE data are not necessarily representing the actual tank floor condition regarding corrosion
and/or leakage. In some cases further considerations are required.
6. Examples of Tested Tanks
Assuming a highly active corrosion source is located within a tank like that shown in Fig. 2,
then the recommendation for tank maintenance according the grading system given in Table 1 would
be to open the tank as soon as possible. Before presenting this kind of result to the tank operator184other possibilities for corrosion within the tank should be taken into account. One potential sourceof corrosion influencing the test result is the floating roof. An inspection of the roof can solve thisproblem only partly; much better is to use a sensor arrangement consisting of two rows of sensors.
Figure 5 shows the same floating roof tank like Fig. 2 but the measurement was evaluated with thehelp of the applied second row sensors.
The first row sensors are applied at a height of 1 m above the tank bottom as usual, and thesecond row sensors are mounted above the bottom row sensors in a vertical distance of 2 m to 4 m.
The value depends on the filling height and the tank size. With this set-up AE events from above
may be identified easily and reliably by using the second row sensors to guard the bottom row. It
can be taken from Fig. 5 that no active source is remaining after this final evaluation step, the located AE source in Fig. 2 was identified as defect of the weld between two roof sheets.

Fig. 5. Tank floor activity, 3D view of located AE events, 55-m diameter, floating roof, evaluation with two rows of sensors to reject AE events from the roof.
Another application of the second row of sensors is for identifying drops falling on the liquid
product inside a fixed roof tank. This might occur if the gaseous phase of the product condenses atcold parts of the roof structure. Some drops interfering the measurement may be identified manually; for large numbers of drops more efficient methods have to be used in order to finish evaluation
within a reasonable period of time. Figure 6a shows the tank floor activity of a test interfered by185drops obtained by the bottom row only. Figure 6b shows the same tank but evaluated with thehelp of two rows of sensors, and no active source is remaining.

a)b)Fig. 6. Test interfered by drops, evaluation using a) one row and b) two rows of sensors.

The feasibility of the application of this special sensor arrangement is limited only by the increase of the background noise caused by countless drops. The corrosion or leakage signals would
be drowned by noise signals without any chance to get them by a threshold depended AE system.
But for the time being this is the same for all other noise sources, which cannot be eliminated.
7. Conclusion
Acoustic emission tests are suited for obtaining the actual tank floor condition of flat-bottomed
storage tanks regarding active corrosion and/or active leakage. The presented testing technique focuses on location of AE sources and the identification of the source mechanism, either leakage or
corrosion, by frequency-content analysis of the gathered waveforms. If corrosion is detected, further analysis results in distinguishing the onset of corrosion (no scaling layer) from already established long-term corrosion (presence of a scaling layer).
Several demands have to be fulfilled to obtain a test result, which represents the actual tankfloor condition. One important part is to filter the raw data of the performed measurement subsequently in different steps. The strongest filter criterion is given by the location algorithm. It was
shown that the raw data of the measurement contains from 3 to 9 % locatable AE data dependingon the tank floor condition and the boundary conditions of the measurement. It follows that thelocation algorithm and the parameters to be set for its application have to be well known and wellchosen. The standard sensor arrangement is able to locate within a horizontal plane and does notgive any information about the position of the source origin in vertical direction. With a second row
of sensors the location algorithm used is capable of rejecting AE events located above the sensor
arrangement.

186

M eanwhile more than 100 tanks have been tested with this testing technique within the EU.
The database and the knowledge of the given tank populations increase steadily. This again enables
further improvements of the testing as well as the evaluation procedures, which shall lead to abroad acceptance of AE testing.
References[1] P.J. van de Loo and B. Herrmann, “How Reliable is Acoustic Emission (AE) Tank Testing?“ The Quantified Results of an AE User Group Correlation Study, presented at the 7th European
Conference on NDT, 1998, Copenhagen (DM ).
[2] P. Tscheliesnig, G. Lackner, M . Gori, H. Vallen, P.J. van de Loo, and B. Herrmann, “Inspectionof Flat Bottomed Storage Tanks by Acoustical M ethods. Classification of Corrosion Related
Signals”, presented at the 24th EWGAE Conference, 2000, Senlis (F)[3] Synthesis Report of the EC Standards, M easurements and Testing Contract Nr. SM T4-CT972177, “Inspection of Flat Bottomed Storage Tanks by Acoustical M ethods“, issued November
1st , 2000

187

EWGAE2002

ACOUSTIC EMISSION MEASUREMENTS ON SHELL STRUCTURES
WITH DIRECTLY ATTACHED PIEZO-CERAMICFRANZ RAUSCHER and MULU BAYRAYInstitute of Pressure Vessels and Plant Technology
Vienna University of Technology, Vienna, Austria
Abstract
When coupling acoustic emission sensors to curved shells by means of fluid coupling media,
as often done in pressure equipment applications, disadvantages arise. Due to fluid coupling, thein-plane movements are not transferred to the sensor, and due to the coupling to a curved surface
the repetitive accuracy is relatively low. The investigation dealt with the idea to build a simple
sensor by gluing a piezo-ceramic directly to the vessel. To protect the simple sensor from electrical noise, a simple case was also glued to the shell, such that a shield was built around the ceramic by this case and by the tested shell itself. To compare with a sensor using the same piezoceramic, a simple sensor was built. This simple sensor was coupled by gluing, and also, in a
separate test, with silicon grease to the test specimen. The response of the investigated arrangements to narrow-band pulses was recorded and plotted versus the centre frequency of the pulses.
The ratio of the sensitivity to the in-of-plan waves to the sensitivity to the out-of-plane wave was
evaluated using pencil lead breaks. In addition, the sensitivity to electrical noise of the arrangements was checked.
Keywords: Acoustic emission, sensors, plate waves, shell structure, pressure vessel
1. Introduction
Recent investigation [1-6] showed that the ratio of amplitude of the in-plane wave (S0) to theout-of plane (A0) wave is a good parameter for the evaluation of acoustic emission (AE) signals
measured on shell structures like pressure vessels. The in-plane wave (S0) in this context is thefirst symmetrical mode of the Lamb waves and the out-of-plane wave is the first asymmetricalmode of the Lamb waves.
To evaluate this ratio, it is necessary to detect the in-plane wave and to separate it from theout-of-plane wave. When using the usual AE sensors, coupled by some fluid to the vessel, thesensitivity to the in-plane wave is relatively low. Since in the case of the in-plane wave the inplane movement at the surface is larger than the out-of-plane movement, a connection of thesensor to the vessel, which can carry shear forces, is desirable. This can be achieved by a sensor,
which is glued to the surface of the structure, or even better by gluing the piezo-ceramic itself
directly to the structure.
2. Tested Arrangements
2.1 Commercial sensor VS150 coupled by silicon grease
As a reference a commercial sensor (Vallen VS150) was coupled by silicon grease, as usual
when testing pressure vessels. This sensor was not coupled to the vessel by gluing, because inthis case it would not be possible to remove it without damage. Like in all the arrangements
J. Acoustic Emission, 20 (2002)

188© 2002 Acoustic Emission Grouptested here, at the location of the sensor the painting was removed and the surface was groundalmost flat (as far as possible with simple means).
2.2 Directly glued piezo-ceramics
All the painting was removed from the metallic surface before gluing the piezo-ceramic tothe surface. The glue and the gluing procedure were similar to the ones, which are used when
applying strain gauges. Afterwards, for shielding the sensor from electrical noise, a case wasglued to the surface of the tested plate in such a way that the piezo-ceramic is enclosed by a metallic shield (Fig. 1). A cable connected to the case by a BNC-connector and a short piece of barewire, which touches the upper electrode of the piezo-ceramic, builds the electrical contact to thepiezo-ceramic. The whole procedure of applying this kind of arrangement is as simple as applying strain gauges. No damping was applied to achieve high repetitive accuracy, but, because ofthis arrangement only narrow-band resonance sensors can be built. The resonance frequency of
the arrangement can be corrected by adding mass at the top of the piezo-ceramic. The tested
configuration is shown in Table 1.

Fig. 1. Directly glued piezo-ceramics.
Table 1: Tested configuration with directly glued piezo-ceramics
Abbreviation
Form of piezo-ceramic
Dimensions
CD54-dirCircular discD5 x 4 mm
2.3 Simple self made sensor
To determine the influence of some layers between the piezo-ceramic and the tested plate, asimple sensor based on the same piezo-ceramics as above was built. The whole arrangement was
the same as above, only the piezo-ceramic was glued to the sensor case instead of gluing it directly to the tested plate (Fig. 2). This case is called CD54-sen because the same piezo-ceramic
as in the configuration CD54-dir was used. When the sensor is glued to the vessel a “–gl” andwhen it is coupled by silicon grease a “–sil” is added to the name of the sensor to describe thearrangement (CD54-sen-gl, CD54-sen-sil).

Fig. 2. Simple self-made sensor.

189

3. Test Procedures
All the tests were performed on an old vessel with an outside diameter of about 600 mm anda wall thickness of 5 mm. There were cut-outs in the vessel, so that pencil lead breaks could be
performed in in-plane direction at the edge of the shell. The arrangement of the different sensors
is shown in Fig. 3.

Fig. 3. Sensor arrangement for the different tests (drawing of shell simplified).
3.1 Sensitivity tests with narrow band pulses
For this test, narrow-band pulses were generated using a reference pulser (PANAMETRICS
V103 ultrasonic transducer). The narrow-band electrical pulse has the modulated sine waveform(Fig. 4) with 10 oscillations and maximum peak-to-peak amplitude of 2 V. The centre frequencyof the pulse was changed from 100 kHz to 1 MHz in steps of 10 kHz.
The maximum amplitude of the signal received at the tested sensor was plotted against thecentre frequency of the electrical pulse (Fig. 5). When a pulser is coupled to the surface, likehere, only a very small in-plane wave component is generated. Therefore, the results of this test
are dominated by the sensitivity of the tested sensor to the out-of-pane wave.

y ( x)

x

Fig. 4. Narrow band electrical pulse for input to V103 (voltage vs. time).
Due to the coupling with silicon grease to the vessel surface, which was not perfectly plane,
the response of the VS150 is worse than when it is coupled to a perfectly flat surface. The sensitivity of all the arrangements with the circular disc CD54 is relatively high, but it should not bedirectly compared to the commercial sensor VS150, which is much better with respect to otherproperties.
190

Fig. 5. Results of the tests.
When the data for the directly glued piezo-ceramic (CD54-dir) is compared to that of CD54
sensor, glued to the vessel and using the same piezo-ceramic (CD54-sen-gl), the peak sensitivities at the resonance frequencies are almost identical. Between the two resonance frequencies
and in the high frequency range the directly glued piezo-ceramic is more sensitive than the CD54
sensor. When silicon grease is used for coupling the sensor (CD54-sen-sil), the sensitivity at highfrequencies further decreased and also the sensitivity at the first resonance frequency decreased.
3.2 Ratio of the sensitivity to in-plane waves to the sensitivity to out-of-plane waves
To determine the sensitivity of the tested sensors to in-plane waves, pencil-lead breaks were
performed at the edge of the shell (Fig. 3). To generate in-plane as well as out-of-plane waves
the pencil-lead breaks were performed one quarter of the wall thickness from the surface. At the
selected sensor locations, which are about 500 mm from the source of the pencil-lead breaks thein-plane wave can be easily distinguished from the out-of-plane wave and the amplitudes of thegenerated signals due to both waves can be evaluated.

Time (µs)
Fig. 6. Evaluation of the amplitude of the in-plane and out-of-plane waves.
191

Table 2: Sensitivity to in-plane and out-of-plane waves (pencil-lead breaks).
Sensor andNumber of Min/average/max Min/average/max
Min/average/max
Configuration pencil-lead amplitude of in- amplitude of out of ratio of amplitude:
breaksplane wave [mV] plane wave [mV]in-plane to out-ofplane waves
VS150
5
2.8/3.16/3.5
6/7.3/9
0.33/0.44/0.47
CD54-dir5
14/15.6/17
18/23.6/27
0.56/0.67/0.83
CD54-sen-sil
5
3.5/3.6/4
6/9/12
0.29/0.42/0.58
CD54-sen-gl5
8/9/108/9.2/10
0.8/1.0/1.25
Considering the arrangements with the CD54 ceramic, the following conclusions can be
drawn: The best sensitivity is reached with the directly glued piezo-ceramic (CD54-dir). Using asimple sensor based on this ceramic and gluing it to the surface (CD54-sen-gl) decreases the sensitivity, but the ratio of the sensitivity to the in-plane wave to that to the out-of-plane wave improved. When the same sensor is coupled by silicon grease (CD54-sen-sil) the sensitivity as wellas the amplitude ratio decreased.
3.3 Noise tests
To achieve comparable numbers for the sensitivity to noise, especially to electrical noise is
not simple. Here, two tests were used: At first the RMS-level of the sensors was recorded. Because always a VS150 was used at the same time and it had an almost constant RMS level, thesevalues can be compared. To check for the sensitivity to electrical noise, electrical pulses withamplitude of 10 V in the same form as used for pulsing a V103 in Sec. 3.1 were applied to thevessel. A galvanic isolated waveform generator was used, with the plus pole connected to thetested vessel and the minus pole was not connected at all. The vessel itself was connected to theground of the AE measurement equipment.
Configuration
VS150
CD54-dirCD54-sen-sil
CD54-sen-gl

Table 3: Results of noise tests.
RMS-Level µV Electrical noise test
0.3
Hits up to 37dB from 800kHz to 1 MHz, from
100kHz to 800kHz no hits above 35 dB
1.5
Hits of 45 dB at 150kHz, increasing amplitude withfrequency and 57dB max. at 850kHz
1.1
Similar to VS150
1.1
Similar to VS150

Table 3 shows that the RMS noise of the self-made sensor is twice the RMS noise of theVS150 (+6dB). The arrangement using the directly glued piezo-ceramic is relatively sensitive toelectrical noise. Using a closed sensor case improves this. If the ground connection of the vessel
is removed, the sensitivity evaluated in the electrical noise test of all the arrangements increased,
but, in this case, VS150, which is isolated from the vessel, performs much better than the rest.
This noise tests are very sensitive to small changes in the configuration (small gaps in thecases, etc.) so that the results can only give some hints on what is going on.
4. Conclusions
Direct gluing of piezo-ceramics to a test specimen improves the overall sensitivity, especiallythe sensitivity to the in-plane wave. The disadvantage of this arrangement is, beside the relatively
192

complicated application procedure, the high sensitivity to electrical noise. A considerable improvement to the sensitivity to the in-plane waves can also be reached, when the sensor is glued
to the vessels surface instead of liquid coupling. In most cases, direct gluing of a sensor to thetest vessel seems to be the better alternative, but sensors need to be robust so that they could be
removed after gluing them to the vessel. All the tests described here were performed with simple
arrangements. Thus, modification of the procedures, especially the sensor case design, likely
improves the results.
References[1] Bayray, M., “Crack growth investigations in pressure vessels using acoustic emission technique,” Ph.D. dissertation, Vienna University of Technology, April 2002.
[2] Bayray, M., “Investigation of AE waveforms in a pressure vessel,” Journal of Acoustic Emission, 19, 246-257, 2001.
[3] Gorman, M. R., “Plate wave acoustic emission,” Journal of Acoustical Society of America,
90(1), 358-364, 1991.
[4] Dunegan, H. L., “Modal analysis of acoustic emission signals,” Journal of Acoustic Emission, 15, 53-61, 1997.
[5] Huang, M., “Application of Mindilin plate theory to analysis of acoustic emission waveforms
in finite plates,” Review of Progress in Quantitative Non-destructive Evaluation, 107, 493-500,
1998.
[6] Bayray, M. and Rauscher, F.: “Window Fourier Transform and Wavelet Transformin Acoustic Emission Signal Analysis”, Proc. EWGAE 2002 25th European Conference and Exhibition, 11-13 September 2002 – Prague. pp. I/37-I/44.

193

EWGAE2002

AE MONITORING OF CRYOGENIC PROPELLANT TANKYOSHIHIRO MIZUTANI1, TAKAYUKI SHIMODA1, JIANMEI HE1,YOSHIKI MORINO1 and SOUICHI MIZUTANI2
1

National Space Development Agency of Japan, Sengen, Tsukuba, Ibaraki 305-8505, Japan
2
Advanced Engineering Services Co., Ltd., Takezono, Tsukuba, Ibaraki 305-0032, Japan

Abstract
In order to study the cryogenic properties of CFRP tank, we conducted pressurization test ofa small filament-wound (FW) tank at cryogenic temperatures. We first investigated the orientation dependence of acoustic emission (AE) signals at both room temperature and LN2 temperature by using artificial source. Lamb-mode dispersive AE signals were monitored in the CFRP
tank. In tests at room temperature, we used A0-mode Lamb waves for source location. However
A0-mode Lamb waves at several angles were hardly observed at LN2 temperature. In this study
S0-mode were used for source location. 60% of tank wall was damaged before this test, andmany AE signals are generated from this damaged zone. We developed a new method to separate AE signals generated at damaged zone utilizing signal duration. When the AEs from monitoring (or non-damaged) zone are evaluated, detail source location is possible using arrival timedifferences of AEs. Next we conducted pressurization test of the CFRP tank at LN2 temperature.
About 660 AE events were visually extracted from detected 2800 AE hits. We then investigatedthe duration of AE events and 100 AE events are evaluated as AE generated at non-damaged
zone. Source locations of 7 AE events are obtained from the area where leakage was identified
by snoop test conducted after the pressurization test.
Keywords: CFRP tank, Lamb wave, cryogenic pressure test, source location
Introduction
It is essential to develop a cryogenic CFRP tank in order to realize the drastic weight reduction needed for reusable launch vehicle (RLV) [1]. In order to study the cryogenic properties ofsuch CFRP tanks, we conducted pressurization test of a small filament-wound (FW) tank [2] andmonitored micro-cracks, which possibly grow to provide leak path of the propellant. We usedacoustic emission technique for micro-crack monitoring. Several studies regarding AE monitoring of FRP tank were reported [3 - 6], but the study under cryogenic temperature is limited.
We conducted 4-channel AE monitoring during cryogenic pressurization test of a CFRP tank.
We first compared characteristics of AE signals detected at room temperature and LN2 temperature by using artificial AE source. After that, we constructed source location method, which
utilizes the signal duration and arrival time difference of AE signals. The proposed source location method is applied to the AEs detected during a cryogenic pressurization test of the CFRP
tank. Estimated source location is compared to the leak point, which was found by snoop test
conducted after the pressurization test.
CFRP Tank and Experimental Setup
A 300-mm-diameter CFRP tank was fabricated using filament-winding method. The prepreg
tape was BESFIGHT IM600#133 with 3.5-mm width. The tank shape was 320-mm long cylinJ. Acoustic Emission, 20 (2002)

194© 2002 Acoustic Emission GroupFig. 1. Typical RLV concepts. Drastic weight reduction is needed for realization.
der with isotensoid dome at both sides (see Fig. 2). The tape was wound on a plaster mandrel
and cured in autoclave at 180˚C for 2 hours. The layer construction of the cylindrical part is[±30o, 90o2, ±30o, 90o2, ±30o]. The thickness of the cylinder is 1.2 mm.
We conducted water-proof testing and cryogenic low-level pressurization tests on this tank in
the past two years. After these tests, some areas were damaged and we patched additional
prepreg sheets on these areas from the inner side of the tank wall. Epoxy resin adhesive (HYSOL
EA9394) was also pasted on the serious damage zone on the outside surface to prevent leakages.
The photo images of damaged and non-damaged zones are shown in Fig. 2. In this study, weinvestigated cryogenic characteristics of the non-damaged zone. We call non-damaged zone as“monitoring zone” in this paper.
Figure 3 shows the schematic illustration of the CFRP tank used and experimental setup for
cryogenic pressurization. Nickel-steel flanges are attached to both sides of the tank. Five pipes
are connected to the top flange. Three of the five pipes are connected to pressure gauge, liquid
level meter and safety valve. The other two pipes are used for filling and exhausting liquid
nitrogen. These two pipes are also utilized for controlling the internal pressure during pressurization tests.
Liquid level meter output was connected to the monitor. The meter roughly indicates theliquid nitrogen level. The pressure gauge was connected to a digitizer and the data was digitizedat 1-Hz sampling rate. Digitized data was fed to the computer. Strain and temperature of theouter surface were also measured, but we will not discuss these data here.
We used four 150-kHz resonant-type AE sensors (PAC, R15) in the test. These sensors were
mounted on the corners of a 200-mm square on CFRP tank as shown in Fig. 3. Outputs of thesensors were amplified 40 dB and digitized at an interval of 250 ns with 4096 points at 16-bit
amplitude resolution, and fed to a PC for analysis. We set the threshold level at 65 dB (1.78 mVat sensor output) during the pressurization test.
195

Non-damaged zone

Damaged zone

Adhesive

Fig. 2. Appearances of the non-damaged and damaged zones. Epoxy resin was pasted on thedamaged zone. In this study, we evaluated non-damaged (monitoring) zone only.
Safety valve

Liquid level meter

LN2

Pressuregauge
Monitor
Temperature
StrainCh.4

Ch.3

Monitoring area
AE sensor
Ch.1

200 mmCh.2

PC

pre-amp. (40 dB)

200 mm

542 mm
320 mmdigitizer

digitizer
PC

Fig. 3. Schematic illustration of the CFRP tank and experimental setup. 4-channel AE monitoring used 40-dB preamplifiers and Mistras system (PAC).
196

y
Ch.4

Ch.3

y-axis, mm

10
0

150

PC
digitizer

Sensoramplifierθ

attenuator

100
MONITORING
AREA

50
0

Ch.1
0

pulser
Ch.2

50

100
150
x-axis, mm

x200

Fig. 4. Experimental setup for measuring orientation dependence of AEs on the shell wall of cylindrical section (see Fig. 2). Artificial sources generated by small transducer were used.
Procedure for AE Monitoring
We first measured the orientation dependence of AE signals propagating in CFRP tank atboth room temperature and LN2 temperature. We filled water or LN2 into the tank at atmospheric pressure. As shown in Fig. 4, pulses were generated by a compression-type piezoelectrictransducer (PAC, PICO, 4-mm diameter, nominal resonant frequency 0.45 MHz) and detected byan R15 AE sensor mounted on circles of 100-mm radius at angle θ. Examples of detected AEs
are shown in Fig. 5. Left figure represents AEs at room temperature and right at LN2 temperature. Small amplitude S0-mode Lamb waves followed by large amplitude A0-mode are observedat room temperature, but A0-mode Lamb waves are hardly observed for several direction (θ =150˚, 180˚ in Fig.5) at LN2 temperature. In a previous paper, Mizutani et al. [4] used wavelet
coefficients of a specific frequency of A0-Lamb waves for source location in room temperature
tests, but this method cannot be applied to the cryogenic tests. Though some difficulties were
expected (large source location error due to fast velocity of AEs, arrival time reading error due tosmall S/N ratio, etc.), we decided to use the arrival times of initial S0-mode Lamb AEs in thisstudy. The orientation dependence of the S0-mode velocity was determined by dividing the intertransducer distance (100 mm) by the first-peak arrival time. Measured orientation dependence isshown in Fig. 6. We fitted data by a quadratic equation, V(θ) = 7.97 x 10-2θ2 – 1.49 x 10 θ +6.40 x 103 (m/s), shown by a solid line in Fig. 6 and used this function for the source location.
Next, we examined the accuracy of the source location method by using two types of artificial sources. Figure 7 shows top view and development of the tank. Monitoring area surroundedby 4 sensors is shown in tan color. About 60 % of the tank wall was patched by additional CFRP
prepreg sheets from inside. The area which epoxy resin adhesive (HYSOL EA9394) was pasted
from the outer surface is also indicated in the figure (shown in green color). We artificially generated AEs by pencil-lead breaks and a PICO sensor in the monitoring zone and damaged zone.
Source location is shown by the symbol “X” in the figure.
197

LN2 temperature

Room temperature

θ=0°
θ=0°
0.5 [V]

0.4 [V]

θ=30°
θ=30°
θ=60°

θ=60°

A0-modeA0-mode
θ=90°

θ=90°

S0-mode0100

S0-mode200
Time, μsθ=120°

θ=120°

θ=150°

θ=150°

θ=180°

θ=180°

300

0

100

200
Time, μs

300400Fig. 5 Examples of Lamb wave AEs detected at room temperature (left) and LN2 temperature(right) as a function of the propagation direction θ. A0-mode Lamb waves are hardly observed insome directions at LN2 temperature.
198

Fig. 6 Orientation dependence of S0-Lamb velocities in the tank. Solid line indicates fitted
curve used for the source location.

Artificial source

Ch.2&3x=200

Top view

Mechanicalpencilmonitoring zone

x=400

Ch.1&4x=0 [mm]Development
x=600

tank wall
Small transducer

x=800
monitoring zone

patched

0
600
700
800
x=620 720
x=670

ve
ad h
esi

100

ve

(x,y)=(510,100)adhesi

Y-axis [mm]200

patched

OR

900 0

100
200
X-axis [mm]

300

400

500

600

Fig. 7 Top view and development of the tank with AE sensor locations. The monitoring zone isshown in the figure by tan color. Source locations are indicated by symbol “X”. Two types ofartificial sources are shown in top right.
Figure 8 shows detected AE signals induced at (x,y) = (50,50) and (620,100) by a sensor(left) and pencil-lead breaks (right). It is noted that arrival-time differences of AEs induced atdamaged zone (620,100) is at the same level as AEs induced at the monitoring zone (50, 50).

199

Transducer

Pencil lead break

Output, V

Ch. 2

Output, V

-0.6
0.6

Ch. 3

-0.6
0.6

Output, V

Ch. 4

-0.6
Ch. 1

-150
150

Ch. 2

-150
150

Ch. 3

-150
350

-350
0

Ch. 4
largestCh. 2

Output, V

-3.0
2.0

Ch. 3

-2.0
3.0

Ch. 4

Ch. 1

(x,y)=(620,100)

Output, mV Output, mV Output, mV Output, mV

150

Ch. 1
largest-4.0
3.0

Output, V

largest-1.6
0.6

Output, V

Ch. 1(x,y)=(50,50)
4.0

Output, V

1.6

Output, mV Output, mV Output, mV Output, mV

Output, V(x,y)=(50,50)-3.0(x,y)=(620,100)
500

-500
400

-400
300

Ch. 3

-300
500

Ch. 4

-500
0

200
400
Time, μs

Ch. 2

largest200
400
Time, μs

Fig. 8 Detected AEs induced at the monitoring zone (x,y)=(50,50) and damaged zone (620, 100)due to artificial source. Two types of artificial sources were used.
Therefore, we have to separate AEs generated at the damaged zone before the source location isconducted. Duration of the largest amplitude AEs for all events are investigated. The result isshown in Tables 1 and 2. Definition of duration is shown in Fig. 9. We defined the threshold as200

50 % of the maximum value. Duration of AEs generated far from the monitoring zone is much
longer than those for AEs from the monitoring zone. It is also noted that the duration of AEs
generated by the PICO sensor (possibly generated high frequency AEs) and by pencil-lead breaks(generated low frequency AEs) is similar. This is due to the narrow-band AE sensor working asa band-pass filter. We set the threshold duration as 200 µs and try to separate AEs from the damaged zone. The result is shown in Table 1 and 2. Colored areas in the tables indicate the long
duration AE. This clearly shows that AEs generated far from the monitoring area can be separated by utilizing duration.
Table 1 Duration of AEs produced by transducer

Unit: μs

50
100
150
200
250
300
510
620
670
720
y x 0
50
18 101.25 45.25 110 102.25 102.5 117.75
966.75 896
100 109 118.5 105.25 106.75 76
151.5 62.5 1004 258
914
150 66.5 105.5 52.5 39.75 128.25 60
101.5
237.75 482.75
200
227.75

Table 2 Duration of AEs produced by pencil-lead break

x 0
50
100
150
200
250
300
50 31.5
9149
54 147.25 47.75 97.25
100 65.5 161.75 52.5 106.5 1.25 93.75 189.75
150 115.25 51.75 25.75 104
113
73.5 201.25
200

Ma
xi
val
mu
mue

Maximum
value

Output, Vy641

Unit: μs

620
670
720
790 837.75
850.5 520
571.25 609.25
339.75

800
238

850
900
93.75 73.25
117.5 16.5
90.75 101

850
900
57.75 84.25
93.75 149.25
94.5 111.25

Duration time
50% of91[μs]
max. value

Source (50,50)
Ch. 1

2.0 V
0

510

800
314

200
400
Time, μs600Fig. 9. Procedure for determining AE signal duration.
When detected AE is determined as AE from the monitoring zone, detail source location isconducted by the following steps; 1) Monitoring area is divided into 4 areas as shown in Fig. 10.
2) The channel that detected the largest AE is searched. The area that involves the maximum
channel is selected. 3) One size larger area is defined as a scanning area of virtual source.
(shown by dark rectangular in Fig. 9.) 4) Determine the arrival times of detected AEs (S0-mode
arrival time). 5) Determine the source location by minimizing the differences of the measured
arrival times and the arrival time differences computed by moving a virtual source position in thescanning area in 1.0-mm step. Orientation dependence of the S0-mode velocities (Fig. 6) is incorporated.
201

Detailed results of source location are shown in Fig. 11 for transducer (left) and pencil-leadbreak (right). A square indicates an original position and a circle the located source position. The
source locations agree well with the input positions. The maximum error of 12.1 mm and average error of 6.2 mm were obtained for the transducer sources. For the pencil-lead break sources,
the maximum error of 18.0 mm and average error of 9.2 mm were obtained.

Virtualsource

50

Ch.4

t4

t3

AREA 4

AREA 3
t2
50

200
t1

Ch.3

50

AREA 150

AREA 2

Ch.2

Ch.1

200

Fig. 10. Algorithm for source location. Virtual source is moved in 1-mm step. Arrival time istheoretically calculated by using the fitted curve shown in Fig. 6.

Transducer
Ch.4

Ch.3

200

150

y-axis, mm

y-axis, mm

200

PENCIL lead break

100
50

Ch.2

Ch.1
0

50

100

150

x-axis, mm

Ch.1

Ch.2

100
50

0

200

Ch.3

150

0

0

Ch.4

50

100

150

200

x-axis, mm

Fig. 11. Comparison of input location (■) and source location (●) of transducer (left) and pencil lead break (right) on the tank wall.

202

Pressure, MPa

1.5

AE (hit)2400

AE at AREA 1

1.0

1600

Inner pressure

AE (event) 800
AE (monitoring area)
0
800
1200
Time, sec

0.5
0

3200

400

Cumulative AE number

max. pressure(2.0 MPa)

2.0 Startpressurization

Fig. 12. The time history of an inner pressure and the cumulative AE counts. The timing of AE
occurrence in area 1 is indicated by the blue arrow symbol.

y150

204[s]

183[s] 183[s]449[s]

220[s]

y-axis, mm

100
50272[s]

149[s]

232[s] 289[s]

0
-50

Ch.1
0

50
100
x-axis, mm

150xFig. 13. Overlapping of source location result (indicated by symbol “●”) and leak point (“×”)
at area 1.
Cryogenic Pressurization Test
The tank was filled with liquid nitrogen under atmospheric pressure. After the filling, weclosed the valve and kept it for 5 minutes. The tank was chilled down to LN2 temperature andboiling of LN2 stopped. After that, we started AE monitoring and the tank was pressurized bygaseous N2 to 2.0 MPa. Figure 12 shows the pressure history and AE counts. When the innerpressure reached 1.0 MPa, the pressure was held for 60 seconds. Pressurization was stopped at
2.0 MPa when a serious leakage from the damaged zone occurred. After the cryogenic pressurization test, we conducted snoop test by using soapsuds for identifying the leak points. Several
leak points were observed in the damaged zone (x = 300 to 850 mm) and doom parts. Only one
leak point was found near the monitoring area at (x,y) = (-20,90) on a wrinkle. (shown by symbol “×” in Fig. 13).
203

Output, V

80.75
[μs]

Ch. 1

104[μs]0.3 [V]

0.1 [V]
Ch. 2

103[μs]

Output, V

Ch. 3

Ch. 4

83.5[μs]

0.05 [V]0100

0.2 [V]
200
Time, μs

300

0

100

200
Time, μs

300400Fig. 14. Detected AEs located at area 1. Arrival time differences were used for detailed source
location.
Each AE sensor detected about 2800 AEs. After the test, we visually extracted 663 AEs due
to fracture (event), separating them from noise AEs and continuous AEs due to leakage. 123 AE
are estimated to be the AEs generated within the monitoring zone by the zone location method
explained previously. The time history of AE event and AEs in the monitoring zone are also
shown in Fig. 12. An example of detected AEs from the monitoring zone is shown in Fig. 14.
Detail source location is estimated for 9 AEs that were found to be in area 1. Source location
results are shown in Fig. 13. Seven of the 9 AEs are located near the leak point. The timing ofthese 9 AEs are shown in Fig. 12 by arrows. It is noted that most AEs were generated at low
pressure levels (<1.0 MPa) and this result shows that micro-cracks were produced at low stresslevels under cryogenic condition at wrinkled parts.
Conclusion
AE monitoring was conducted during cryogenic pressurization test of a CFRP tank. Before
the test, we investigated characteristics of AEs in the CFRP tank using artificial source and develop source location method. The results are summarized below.
1) A0-mode Lamb waves were hardly observed at several directions at LN2 temperature andcould not be used for source location.
2) Duration of AE signals produced at sources far from monitoring zone became longer. AE
signals from non-monitoring zone can be separated by using signal duration.
3) Virtual source scanning method is used for source location. The source locations of transducer inputs were determined with the average error of 6.2 mm. Pencil-lead break sourceswere located within 9.2 mm on average.
4) 7 AE events monitored at low inner pressure levels are located near the leak point on thewrinkle on the tank. Micro-cracks were possibly produced at low stress levels at cryogenic
temperature.

204

Acknowledgment
Professor K. Ono of UCLA gave valuable advice regarding this test and also kindly revised
this paper.
References
1. Y. Morino et al., “Applicability of CFRP materials to the cryogenic propellant tank for reusable launch vehicle (RLV),” Adv. Composite Mater., 10-4, 339-347 (2001).
2. T. Shimoda et al., “Study of CFRP Application to Cryogenic Fuel Tank for RLV,” Proc. 23rd
Intl. Symp. Space Tech. and Science, ISTS 2002-c-13, (2002).
3. Y. Promboon and T.J. Fowler, “Source Location on Fiber Reinforced Composites”, paper delivered at 6-th Int. Sym. on AE from Composite Materials at San Antonio, Texas, (1998).
4. Y. Mizutani et al., “Damage Zonel Location in a GFRP Storage Tank by Lamb Wave AE
Analysis,” J. of the Japanese Society for NDI, 50-5, 314-320 (2001). (in Japanese)
5. H. Yamada et al., “Lamb Wave Source Location of Impact on Anisotropic Plates,” Proc. EWGAE 2000 European Working Group on Acoustic Emission, CETIM, Senlis, France, (2000), pp.
77-83.
6. Y. Kwon et al., “Source Location in Plates by using wavelet transform of AE signals,” Progress in Acoustic Emission IX, AEWG/AEG, Los Angeles, (1998), pp. IV9-IV17.

205

EWGAE2002

NON-DESTRUCTIVE TESTING FOR CORROSION MONITORINGIN CHEMICAL PLANTS
M. WINKELMANS1 and M. WEVERS2
1

BASF Antwerpen N.V., Haven 725, B 2040 Antwerpen 4, Belgium
2
Departement MTM, KU Leuven, B 3001 Heverlee, Belgium

Abstract
An appropriate and reliable tool for corrosion monitoring is necessary to guarantee thestructural integrity of chemical production units. By combining the electrochemical noise measurements and the acoustic emission technique, the active corrosion processes are revealed. By
combining acousto-ultrasonics and fibre optics a reliable quantification of the corrosion damage
can be obtained. Electrochemical noise measurements, the acoustic emission technique, acoustoultrasonics and fibre optics are very complementary non-destructive testing methods for monitoring the ongoing corrosion in chemical production units.
Keywords: Corrosion monitoring, electrochemical noise measurements, acoustic emission technique, acousto-ultrasonics, optical fibres
Introduction
Every day the chemical industry has to face new failures due to corrosion. In fact the majorpart of failures in the chemical industry are due to corrosion. In this way corrosion costs a lot tothe chemical industry. Corroded parts of the production units have to be replaced and bringabout production losses due to the shutdown of facilities. Corrosion failures also imply a considerable hazard to people and the environment. An appropriate and reliable tool for corrosion
monitoring is necessary to guarantee the structural integrity of chemical production units.
Since 1999, BASF Antwerpen N.V. (Belgium) and the research group "Materials Performance and Non-Destructive Evaluation" of the Department of Metallurgy and Materials Engineering, K.U. Leuven (Belgium) are partners in developing a sensor to identify the active corrosion processes destructive to the equipment and to quantify the degree of corrosion damage in anindustrial environment.
The research project is focused on the most important types of corrosion for the chemical industry. General corrosion of carbon steel, general corrosion of stainless steel, pitting of stainlesssteel, stress corrosion cracking of carbon steel and stress corrosion cracking of stainless steel arestudied by four non-destructive testing methods. Electrochemical noise measurements, theacoustic emission (AE) technique, acousto-ultrasonics (AU) and fibre optics are the nondestructive testing methods used in this research project.
Experimental set up
Two completely different measurement principles are possible for a corrosion sensor. On the
one hand a sensor can be developed so that it has to be fixed on the outside of the structure
whose corrosion behaviour is of interest. The structure itself corrodes and the sensor reveals
J. Acoustic Emission, 20 (2002)

206© 2002 Acoustic Emission Groupinformation about this corrosion behaviour. On the other hand it is possible to develop a sensor,whose sensor body itself corrodes. Information can only be obtained about the corrosion behaviour of the sensor body itself. In this case the sensor has to be built in such a way that its corrosion behaviour is representative for the ongoing corrosion in the equipment of interest.
The decision has been made to develop a sensor, whose sensor body itself corrodes and reveals
information about the ongoing corrosion. Figure 1 illustrates this principle. The four measurement techniques are incorporated on the same sensor body. The two passive non-destructive
testing methods (electrochemical noise measurements, the AE technique) are activated during thewhole life-cycle of the sensor body. The two active non-destructive testing methods (AU andfibre optics) are periodically activated. The periodicity is adapted as a function of the information revealed by the two passive non-destructive testing methods.

Fig. 1. Sensor principle
The basic research was done in the laboratory. The corroding sensor body was placed in a
Faraday cage to prevent electromagnetic interference. Temperature and flow rate were controlled. The conversion to industrial environment has been done in two steps. First a mini-plant
was build to study the influence of temperature fluctuations, fluctuations in flow rate and interference by other measurement equipment, pumps, heating systems, etc. Finally there were someexperiments in a bypass of the chemical facilities themselves. Here the basic measurement concept was tested under the rough industrial conditions.
Carbon steel 1.0038 according to DIN EN 10025 and stainless steel 1.4541 according to DIN
EN 10088 part 1 are used in the experiments. Figure 2 gives an overview of the sample geometry used for the experiments.
207

(a) Sample geometry for corrosion monitoring (general and pitting).

(b) General corrosion.

(c) Pitting corrosion.

(d) Sample geometry for SCC monitoring.

(e) Stress corrosion crack.

Fig. 2. Sample geometries used for the corrosion and SCC experiments.

208

Electrochemical Noise Measurements (EN)Due to the oxidation and reduction reactions of the corrosion processes at the interface of
metal and electrolyte, a potential difference arises across this interface. The short and long-term
evolution of this potential reveals information about the active corrosion processes [1, 2].
A nanovoltmeter (Keithley 2182) with an input resistance > 10 GΩ and a resolution of 100
nV is used for the potential noise measurements. The nanovoltmeter is connected to a computerby a GPIB-interface. The nanovoltmeter is controlled by the computer and the measured data are
stored on the hard disk. The sample frequency is 10 Hz. The potential differences are measured
between the sensor body and an Ag/AgCl reference electrode (Fig. 3).

Fig. 3. Experimental set up electrochemical noise measurements.
Figure 4 shows typical EN signals. General corrosion of carbon steel, general corrosion ofstainless steel, pitting of stainless steel and stress corrosion cracking of stainless steel can be distinguished by pattern recognition techniques in combination with statistical analysis in the time
domain. Any information about pit growth during pitting and about crack growth during stresscorrosion cracking cannot be obtained. Electrochemical noise measurements have a higher accuracy than more conventional non-destructive testing methods like dye penetrant, magnetic interference or ultrasonic testing.
During the scaling-up of the experiments the potential noise measurements have been foundto be very reliable. No additional difficulties arose. In fact no additional filtering of the measured data was necessary. Apparently the developed measurement concept for the potential noise
measurements can be used in an industrial environment.
Acoustic Emission TechniqueCorrosion processes cause microstructural changes at the metal/electrolyte interface. The energy released by these microstructural changes results in transient elastic waves. Sounds ofhigher frequency and lower intensity than audible sounds can be measured by the acoustic emission technique [3, 4]. The Fracture Wave Detector (Digital Wave Co., U.S.) is used for the experiments. Figure 5 shows the basic components of this system. The typical sample frequency
for the experiments is 20 MHz. Broadband sensors (Digital Wave B1025) with a frequency
range from 50 kHz to 2 MHz are used.
209

-0.245V-0.250time [minutes]-0.2552475.02477.52480.02482.52485.02487.52490.0no corrosion
stainless steel - CaCl2 40% - 85°C
-0.470V

time [minutes]-0.475
360.0

362.5

365.0

general corrosion
0.095

367.5

370.0

372.5

carbon steel - H3PO4 10% - 20°C

V

0.090

0.085

0.080

0.075
time [minutes]
0.070
2160.02162.52165.02167.52170.02172.52175.0pitting - stainless steel brackish water+FeCl3 1%- 45°C
Fig. 4. Electrochemical noise measurements - typical signals.

210

375.0

stress corrosion cracking stainless steel - CaCl2 40% - 85°C
Fig. 4 (continued). Electrochemical noise measurements - typical signals.

Fig. 5. Experimental set up for AE technique.
Figure 6 shows typical AE signals. General corrosion of carbon steel, general corrosion ofstainless steel, pitting of stainless steel, stress corrosion cracking of carbon steel and stress corrosion cracking of stainless steel can be distinguished by pattern recognition techniques, statisticalanalysis and the energy content in the time domain in combination with frequency analysis. The
pit initiation or crack initiation as well as the pit growth or crack growth can be monitored by the
acoustic emission technique. This is important additional information revealed by the acoustic
emission technique in comparison to the electrochemical noise measurements. Apart from thisadditional information, the accuracy of the acoustic emission technique is higher than that ofmore conventional non-destructive testing methods like dye penetrant, magnetic interference orultrasonic testing.
The scaling-up of the AE experiments turned out that the development of an additional andvery accurate filtering system is necessary to use the AE technique as a reliable measurement
tool in an industrial environment.

211

0.5
V
0.4

0.3

0.2

0.1

0.0
0

50

100

150

200

250

300

350
µsec

-0.1

-0.2

-0.3

-0.4

-0.5

general corrosion
carbon steel - H3PO4 10% - 20°C
0.5

V

0.4

0.3

0.2

0.1

0.0
0

50

100

150

200

-0.1

-0.2

-0.3

-0.4

-0.5

pitting - stainless steel
brackish water + FeCl3 1% - 45°C
0.6
V

0.4

0.2

0
0

50

100

150

200

250

300

350

-0.2

-0.4

time [µsec]-0.6

stress corrosion cracking
stainless steel - CaCl2 40% - 85°C
Fig. 6. AE technique - typical results.
212

250

300

350
µsec

Acousto-Ultrasonics
A calibrated ultrasonic pulse with a frequency of 300 KHz (Fig. 7a) is introduced in themetal. This ultrasonic pulse is introduced by a function generator (PHILIPS PM 5138). Changes
in the metal morphology, the metal thickness and the metal microstructure change the wave
propagation and therefore the signal measured after propagation through the metal (Fig. 7b) [5,6]. The propagated signal is measured by Fracture Wave Detector (Fig. 5). The typical sample
frequency is 20 MHz. The complete experimental set up for the acousto-ultrasonic technique isshown in Fig. 8.
1

0.6

V

V

0.8
0.4

0.6

0.4
0.2

0.2

0

0
0

100

200

300

400

500

600

0

700
µsec

50

100

150

200

250

300

350
[µsec]

-0.2
-0.2

-0.4

-0.6
-0.4

-0.8

-0.6

-1

a injected pulse

b pulse after propagation through the metal

Fig. 7. Injected pulse and pulse after propagation through the metal; general corrosion, carbon
steel - H3PO4 10% - 20°C.

sensor B1025
sendingsensor B1025
listening

computerfunction
generator

preamplifier

triggering&filtering

Fig. 8. Experimental set up acousto-ultrasonic technnique
Knowledge of the wave type and the interference between several wave types is necessary to
select the most interesting part of the signal for further calculation. An energy loss in this particular part of the signal is measured during ongoing corrosion. (Fig. 9) Corrosion damage due togeneral corrosion, pitting or stress corrosion cracking can be quantified in this way. Figure 10
illustrates this quantification for general corrosion. The correlation between the calculated energy losses of the acousto-ultrasonic measurements and the calculated loss in thickness based on
the weight loss of the specimens during the tests has a very high accuracy. The highest accuracy
is obtained for intermediate and high corrosion rates. For low corrosion rates the accuracy is notthat high.
213

20
rms [V]

16

12

8

4

0
0

50

100

150

200

250

300

350

time [h]general corrosion carbon steel – H3PO4 10% - 20°C
Fig. 9. Energy loss during corrosion attack.
0.50
rms /rms initial [%]

0.40

0.30

0.20

-1.4612y = 23.036x
R2 = 0.9749

0.10

0.00
10

20

30

40

50

60

70

80

thickness loss [%]general corrosion

carbon steel – H3PO4 10% - 20°C
Fig. 10. Correlation energy loss - thickness loss.

The highest regression coefficients (Fig. 10) could be obtained in the laboratory. Howeveralso for the experiments in the mini-plant and in the industrial environment high regression coefficients can be obtained when additional filters are applied. In fact an accurate band-pass filter
centred around 300 kHz turned out to be very efficient.
214

Optical Fibres (OF)An optical fibre is attached on the surface of interest. A laser-diode forces a light pulse
through the fibre. Changes in the stress conditions on the surface due to the corrosion activity
change also the stress conditions of the optical fibre. The changing stress conditions of the optical fibre modulate the light pulse. Analysis of this modulation is very useful in quantifying thecorrosion damage [7, 8].
Figure 11 shows the experimental set up. The main components are a 30-mW laser, a transimpedance photodiode preamplifier with a DC bandwidth of 2 MHz and a gain of 1-10 MV/A
and the silica/silica optical fibre. The photodiode is connected to the computer by a PCI Bus
high-speed A/D board (Keithley KPCI-3110). The typical sample rate is 110 Hz.

optical fibre
computer
LASER
beam
photo diodeFig. 11. Experimental set up of optical fibres.
The variation in intensity of the light pulse is an appropriate tool for the quantification of theongoing corrosion. Figure 12 illustrates the rise in intensity for several experiments. The fibre isfixed on the surface of interest under a certain prestress. During ongoing corrosion the prestressdiminishes and the intensity of the laser pulse through the fibre will rise. The linear curve fittingat the initial part of this curve correlates with the corrosion rate. Optical fibres are very interesting to monitor general corrosion with relative low corrosion rates. In this way the optical fibres
are complementary to the acousto-ultrasonics. During the scaling-up of the experiments the optical fibres has been found to be very reliable. No additional difficulties arose.
Conclusion
Electrochemical noise measurements, the acoustic emission technique, acousto-ultrasonics
and optical fibres are very complementary non-destructive testing methods for the monitoring ofgeneral corrosion of carbon steel, general corrosion of stainless steel, pitting of stainless steel,stress corrosion cracking of carbon steel and stress corrosion cracking of stainless steel (Fig. 13).
By combining the electrochemical noise measurements and the acoustic emission results theactive corrosion processes are revealed. The electrochemical part of the corrosion processes isvisualised by the electrochemical noise measurements. The acoustic emission technique moni-

215

1.70
I/Io

1.60

increasing
corrosion rate

1.50

1.40

1.30

1.20

1.10

1.00

0.90
0

25

time [h]5075general corrosion
carbon steel - H3PO4 10% - 20°C
Fig. 12. Intensity rise in optical fibres. (Io is the initial intensity).
tors the mechanical part of the corrosion processes like the breakdown of passive films, crack
growth, pit growth.
By combining acousto-ultrasonics and fibre optics the corrosion damage can be quantified.
Acousto-ultrasonics have the highest accuracy for intermediate and relative high corrosion rates.
Optical fibres have the highest accuracy for relative low corrosion rates.
By combining electrochemical noise measurements, the acoustic emission technique,acousto-ultrasonics and fibre optics a sensor to identify the active corrosion processes and toquantify the corrosion damage in chemical production units has born.
This combination of non-destructive testing methods has accuracy higher than that of moreconventional non-destructive testing method like dye check, magnetic interference or ultrasonic
testing.
Acknowledgements
This research has been realised with financial support from the Flemish Institute for the promotion of scientific-technological research in industry (IWT), project 010261/BASF Antwerpen.

216

Identification Quantification
no corrosion

EN, (AE)

general corrosion carbon steel

EN, (AE)

AU, OF

general corrosion stainless steel

EN, (AE)

AU, OFpitting stainless steel

EN, AEAU, (OF)stress corrosion cracking carbon steel

AE

AU, (OF)stress corrosion cracking stainless steel

EN, (AE)AU, (OF)

Fig. 13 Four complementary non-destructive measurement methods.
References[1] C. Gabrielli and M. Keddam, “Review of Applications of Impedance and Noise Analysis to
Uniform and Localized Corrosion”, Corrosion, National Association of Corrosion Engineers,
October, pp. 794- 811 (1992).
[2] J. Göllner und A. Burkert, „Untersuchung der Korrosionsneigung mit Hilfe des elektrochemischen Rauschens“, Materials and Corrosion, 49, 614-622 (1998).
[3] R.V. Williams, ‘Acoustic emission’, Adam Hilger Ltd, Bristol, 1980.
[4] M. Wevers, I. Verpoest, P. De Meester, and E. Aernoudt, Acoustic emission: current practiceand future directions (ASTM STP 1077), eds. W. Sachse, J. Roget and K. Yamaguchi, American
Society for Testing of Materials, 1991, pp. 416-423.
[5] J.H. Williams Jr. and S.S. Lee, Promising quantitative nondestructive evaluation techniques
for composite materials – Materials Evaluation 43, April 1985, 561-565.
[6] A. Vary and R.F. Lark, Correlation of fibre composite tensile strength with the ultrasonicstress wave factor, Journal of Testing and Evaluation, 7(4), 185-191.
[7] A. Mendez, T.F. Morse, and F. Mendez, “Applications of Embedded Optical Fiber Sensors in
Reinforced Concrete Buildings and Structures”, Proc. SPIE Fiber Optic Smart Materials andStructures, Vol 1170, Sept. 1994.
[8] Monitoring Corrosion On-Line With Fiber Optics, EPRI Journal, November/December 1995,
p. 5.

217

EWGAE2002

AE TECHNOLOGY AS A KEY ELEMENT OF THE OPERATIONSAFETY SYSTEM AT REFINERY
B. S. KABANOV, V. P. GOMERA, V. L. SOKOLOV, A. A. OKHOTNIKOVand V. P. FEDOROVKIRISHINEFTEORGSINTEZ / Kirishi, Leningrad region, 187110, RUSSIA
Abstract
Experiences of AE inspections of pressure vessel at Kirishi refinery are summarized. This is
to demonstrate AE method despite rather complicated interpretation of results can become an
important part of refinery NDT department. AE is shown to be a useful tool in the production
equipment safety system.
Introduction
Pressure vessel AE inspections started at Kirishi refinery at the end of 1980s. At first, suchjobs had been carried out by outside research centers and independent service companies. In
1991, the refinery organized its own AE inspection laboratory, the first one among Russianrefineries. Since 1992, the AE inspection laboratory has conducted the AE monitoring of refinery
equipment. In some cases, some research centers had been involved in complicated vessels tests,
but since 2000 the refinery AE inspection laboratory conducted all the tests.
Some results of 10-year activity of our AE inspection laboratory are presented in this report.
The main purpose of this article is to demonstrate AE method (despite the fact that resultsinterpretation are rather complicated) can become an important part of refinery NDT department.
Moreover, it is quite reasonable for the petroleum and chemical industries, where a large numberof pressure vessels and storage tanks must be tested, to have a specialized AE laboratory.

Fig. 1. Pressure vessels AE inspection results at Kirishi refinery from 1992 to 2002.
J. Acoustic Emission, 20 (2002)

218© 2002 Acoustic Emission GroupStatistics
For AE tests we use two inspection systems – LOCAN AT (PAC, USA) from 1991 andAMSY4 (Vallen-Systeme, Germany) since 1997. Depending on the test tasks, one or both of thesystems have been used. For example, when we test the large-size vessels, we inspect the upper
part with the help of one, and the lower part with the second system. To the present, we havetested more than 250 vessels. Some statistical data are presented in Figs. 1 and 2. The list of themost serious defects, detected with AE method, is shown in Table 1.
Some Application Techniques
On the basis of our experience, we point out some aspects of AE applications at the plant site.
These notes can be useful for the AE specialists involved in similar tests in different branches ofindustry.
1. Some important notes of organization procedure
1.1. Test planning
Based on the recommendations of corrosion engineers, a safety department manager issues alist of pressure vessels to be tested with AE technology within upcoming year. This list is
included in an Annual Preventive Refinery Maintenance Plan. The presence of process causingalkaline cracking is the reason for the vessel to be included in such a Plan. The vessel preparation
for AE tests is also included in the maintenance documents.

Fig. 2. Pressure vessels AE inspection structure according to equipment type.
1.2. Preparation instruction for the AE test of a vessel.
AE test procedures demand the preparation of the object, involving various activities bytechnical staff. A manual for AE test preparation procedure was created in order to avoid thepossible subjective factors, affecting the quality of preparation. In the manual, the order ofpreparatory steps and the persons responsible for those steps are prescribed.
1.3. Vessel pressurization equipment
At the beginning we used vessel's own capabilities to pressurize itself. It created someproblems connected with the rate of pressurization, keeping the pressure and releasing it. In order219

to avoid all these issues, AE inspection laboratory is equipped with a portable electric pump.
Initially, the tested vessel is filled with water. Then we use the pump to create excessive pressure
inside the vessel. The process of pressurization is fully under control by AE laboratory
specialists.
2. Positive image creation for AE
T he creation of a positive image of AE laboratory and finding allies among refinery
specialists are very important. We use any opportunity to inform them about capabilities,
advantages and even physical principles of AE method. It is impossible to overestimate
popularization of AE method.
To achieve these goals the advantages of the method should be shown and proved (throughliterature examples and/or experience) to the specialists, who are responsible for the safe andreliable performance of vessels and pipelines and also to those who are experts in corrosion
protection of equipment. The most effective are the cases, where the emergencies are prevented
with help of AE technology. There have been some cases in our practice, when very serious
defects were detected, which could lead to dangerous emergencies. More over, these defects
could not be detected with any other methods. For example, dangerous defects had been detected
at the amino-treatment absorber just a few hours before the production process started up(Appendix, Table 1, Item 23). Due to the nature of the detected defects, the probability of an
emergency was high. Later, it was found that the defects were similar to those defects, whichcaused the accident of amino-treatment absorber in Romeoville, Illinois, USA, in 1984. There
were 17 victims in that accident and refinery lost more than US$100 million [1, 2].
3. Results interpretation
Formal use of any known system of AE results interpretation for pressure vessel condition
estimation is not the most successful approach. AE method is based on probability approach (asothers NDT methods of complicated objects). Thus, an AE specialist who is in charge foranalyzing AE data has to treat the formal rules, traditional approaches etc as only as a guide. He
must take into account his own subjective thoughts, guesses, feelings etc because they reflect his
experience, knowledge about particular objects under the test.
Applying Bayes approach (subjective/objective approach) we can take "non-formal"
information (such as thoughts, guesses, feelings etc) as an analogue of a priori density
distribution probabilities of Bayes approach, while real data collected during AE test, formal
techniques etc can be taken as an analogue of objective component (truth function) in expression
for a posteriori distribution.
In this connection the ability to use non-formal information can be considered as one of the
key professional skill of AE specialist. Accordingly, the AE equipment has to have capabilities
for developing and applying such skills, for instance, providing maximum options of datacollection and its interpretation. The best modern systems meet this condition and allow building
up any desired structure of data evaluation in accordance to specialist's skills.
Some Pros and Cons of AE Inspections Implemented by Refinery Specialists
Among the main advantages of AE, it may be pointed out:
1. It has provided the maximum flexibility in actions.
220

2. Many Russian refineries are equipped much better than research centers and third party
service companies in Russia.
3. The variety of the objects for AE testing at refinery provides the excellent opportunity for
AE applications research. On the other hand it is difficult to simulate in laboratory conditions orin theoretical model all the variety of real objects.
4. As a rule, the plant specialists know the object's specific features and their characteristics
to be inspected with AE method very well.
Points mentioned in items 3 and 4 lead to the formation of specialized experience of AE
technicians. This experience is limited, perhaps. However, it is often enough for reliable andsuccessful AE inspections of plant equipment. It forms a good base for a comprehensive
approach to the object inspection. Within refinery it is possible to get any operational and agreed
information, instrumental support concerning the vessel operation from technologists,mechanics, maintenance and NDT personnel. The usage of complex approach allows extending
database of knowledge involved into inspection results analysis. In some cases, it allows to
detect the defects, which may be missed in the event that traditional approach would be applied.
As a result, the quality of AE inspections is increasing.
The following disadvantages of AE can be mentioned:
1. The main distinctive feature of AE inspection at the refinery is a tight maintenanceschedule and as a result - a shortage of time for results analysis. As experience says, there isenough time for defect detection, but sometimes there is no time for their detailed investigation.
Moreover, the defects are not investigated at the refinery, but they are fixed, and often, very fast.
It is evident that a part of information concerning the defects is lost, but the production has
higher priority than defects research.
2. Sometimes the AE tasks can be complicated thus leading to mismatching to the knowledge
and experience of refinery personnel. In some cases quick requests to the experts are not possible
and their responses are not available. However, with gained experience, this problem disappears.
ExamplesThe above written thesis may be illustrated with examples from our practice. Let’s consider
the case, when the information about the specific properties of inspected object helps to assess
the inspection results, while the formal approach can lead to mistakes in results assessment. For
example, traditionally, the remarkable emission at low load is related to “bad emission” and isrelated to mechanical noise [3]. But it is not always correct.
In Figs. 3 and 4 the results of AE sources planar location for two different vessels shells arepresented. In the first case, it is the gas fractionation vessel of 40 cubic meters capacity; in thesecond one it is the top distillation column with 200 cubic meters volume.
In Fig. 3, there are 3 zones marked out where the following defects were detected:(1) - the crack in the weld root of T-weld with 55 mm long and 20 mm depth (wall thickness
is 30 mm);(2) - the group of blisters on the inner surface of the shell within area of 170 x 40 mm, 2-3mm depth;(3) - crack of 30 mm length, 5 mm depth in the root of T-weld.
Defect localisation in zones (1) and (2) was performed by planar location. The crack on zone (3)was detected by analysing relative activity of inspection channels at the different load stages.
221

In Fig. 5, there are also 3 defective zones marked out. The zones are numbered from 4 to 6.
Those zones contained the following defects:(4) and (5) - multiple developed pores in inner side of the shell at 2-3 mm depth,(6) - defect in the weld (large non-metal inclusion, due to which the crack developed).

(1)

(2)

(3)

Fig. 3. Vessel V= 40 m3 AE inspection results of planar location sources.
Pores located on two areas with 120 x 120 mm and 120 x 135 mm sizes near the weldingzone. These pores have been located with help of liquid penetrant inspection (LPI) method.
These defects were eliminated by blasting (wall thicknes 36 mm). Defect presence in zone (6)was confirmed with ultrasonic method (Fig. 8). LPI method did not give any results for zone (6).
As it may be seen in Figs. 4 and 5, the defect in zone (6) was detected with planar location,
but defects in zones (4) and (5) were detected by means similar to that used for zone (3): defects
were detected by analysing relative activity of inspection channels at the different load stages.
The most important of given examples is the following: the periods of major relative AE
activities, connected with sources located in zones (3), (4) and (5), were regestered when thepressures were less than maximum operation pressure of the vessel. It may be seen in Figs. 6 and7. Moreover, these AE activities had the highest values among all AE activities registered duringwhole AE inspection, including higher pressure loads.
The change of the behavior of AE activity after applying the exessive operation pressure does
not match the conception which is considered to be the traditional one. In such way, the expectedsource activity at channel #5 of the first example, did not increase at all, on the contrary it
decreased greatly.

222

Zone 1

Fig. 4. Vessel V= 200 m3 AE inspection results of planar location sources.

Fig 5. Vessel V=200 m3 shell segment with defects (Zone 1 in picture 4). Zones (4) - (5) containpores, zone (6) has defect in the weld.

223

Fig. 6. Relative activity behavior of channels 1-5 at different load stages for vessel V = 40 m3.

Fig. 7. Relative AE activity. Vessel V = 200 m3. Sensor #23 is located near a group of defects.
Sensor #26 is located in the zone of high AE activity near the left support. T0 is the time whenthe maximum operation pressure was achieved.
224

The most probable reason of the obtained effect was determined after joint study withtechnology and corrosion engeneeers where sequence of preparation steps had been discovered.
During shut-down procedure and discharging of the vessel, the oxygen and atmospherichumidity went into the vessel and the temperature dropped. During steaming process, condensate
formed and this was aggressive in terms of corrosion. These factors created the conditions forfast defects development. During the shut-down of a top distillation column, the corrosion speed
can be 10-20 mm per year, whereas in case the vessel is in production stage, the speed ofcorrosion is only 0.02 mm per year. This should be taken into account, otherwise the information
can be lost.
The above described situation is a typical one for refineries. That is why high AE activity
registration at the early stages of vessels load should not be considered as a “bad emission”.
Moreover, this effect may be used as one of helpful criteria during defect detection.

Fig. 8. Results of ultrasonic test method. Weld fragment from zone (6) in Fig. 5. Fragment
length is 232-mm. Color index: red - plane defect (crack); green - volume defect (non-metal
inclusion); yellow - volume/plane defect. Defect height ≥ 3mm (3 mm the high limit ofequipment sensitive).
Conclusion
The experience of AE laboratory at Kirishi refinery demonstrates that activity of suchlaboratory can be a very useful tool in the production equipment safety system. The mostimportant condition for effective implementation of AE method at an industry plant is thepermanent cooperation and communication of AE specialists with all other departments of theplant who are responsible for safe equipment operation.
References
1. M.G. Mogul, ”Reduce corrosion in amine gas absoption columns”, Hydrocarbon Processing,78(10), October, 1999.
2. “Amine Absobers: Chemical Time Bombs”, Science News, 130(265), October 26, 1986.
3. A.A. Pollock, “Acoustic Emission Testing”, Metals Handbook, Volume 17 (9th Ed.), ASM
International, 1989, pp. 278-294.

225

Appendix

1994

1993

1992

Year

Table 1 List of the most severe defects, found in vessels with the help of AE method.
Control objects

Detected defects

Actions

1. Ammonia
storage tank.

Longitudinal cracks (3) with 20-30 mm
length in weld, inside.

Weld repair.

2. Reforming unit
columnLocal non-metal intrusions in the low
part of column in zone 600x600 mm.

3.Carbonization
unit column
4. Paraffin
production reboiler

Multi micro cracks distributed in the
body of material. Common material
degradation.
Several parts of intensive alkaline
cracking. Common metal degradation.

Ultrasonic control,
Defects sizes were
determined.
Column replaced.

5. Gas fractionation
unit separator.

Nozzle welding zone crack, some zones
near welding had corrosion cracks.

6. Paraffin
production reboiler
7. Top & vacuum
distillation column
AVT
8. Reformer unit
drum
9 Hydro treatment
columnSore type defects. (2).

Defects elimination
followed by apparatus
change.
Defects elimination
followed by apparatus
change..
Defects elimination

Some crack zones in plucking welds.

Defects elimination

Inner side sore corrosion (2).

Defects elimination

Fatigue crack in charge pipeline weld.

10. Hydro
treatment column

Fatigue crack in charge pipeline weld.

Weld repair. Schemeof pipeline fixation
changed.
Weld repair. Schemeof pipeline fixation
changed.

11. Sphere drum
for liquefied gases
storage

1) Low part of the drum, weld defectwith the length 500 mm, containing
cracks, pours, sludge intrusions.
2) Middle part of the drum, weld defectcontaining pours and sludge intrusion
with the length of 800 mm.
Weld zone 100x120 mm, containing
pores and cracks 2-3 mm depth.

1) Defect is confirmedwith ultra sonicmethod. Repair.
2) Defect is confirmedwith ultra sonicmethod. Weld repair.
Elimination of thedefect.

Cracks in T-route weld (2) zone 170x40mm, 2-3 mm depth.

Weld repair.

Large corrosion zones in low part of thedrum. Wall decreases from 20 to 7-10mm.
Zone with corrosion cracks a=5-10mm., l=2-4 mm in low part of the drum.

Drum is replaces.

12. Sphere drum
for liquefied gases
storage
13. Gasfractionation unit
drum
14. Benzene
production drum
15. Gasfractionation unit
drum

226

Repair.

Remarks
After weld
opened, defects
were confirmed;
could not be
detected withother methods.
Defects wereregistered in a
distance of 8 m.

Cracks maximum
size: l=3mm,
a=130 mm.

For the vessels
NN 9,10, thedefects weresimilar and related
to theirconstruction
features.
Thickness of thewall is 30 mm.

One of cracks
dimension: a=55
mm; l=20 mm(wall thickness is
30 mm).

1996

16. Hydro
treatment separator

150 mm of a bad welded weld.

Weld repair.

17. Reforming unit
columnThe dispersed caverns in zone of
200x300 mm in low part of the column.

Defects elimination.

18. Top distillation
drum
19. Reforming unit
columnCross cracks of the weld, connecting
shell with low bottom.
Crack l=4 mm., a=25 mm, along theupper bottom weld.

Weld repair.

20. Top distillation
unit AVT
21. Reforming unit
columnCracks in cladding near welds.

Defect elimination.

Intensive corrosion cracking of claddingnear welds in upper and middle zones of
the column.
Cross crack of the weld, l=3 mm.
Stressed weld.
Cracks, including, through, arranged on
circumferences in several cross-sectionsof low part of column.
Some zones with cracks in cladding.

Defects elimination.

25. AT unit column

Perforation in low bottom, some zone
with cracks in cladding.

Defects elimination.

26. Paraffin
production unit
column27. Paraffin
production unit
drum
28. Hydro
treatment separator
29. AVT unit
columnSome zones of corrosion cracks in
cladding.

Defects elimination.

A lot of non-metallic intrusions,distributed along the large square.

Confirmation by USC.

Cracks in cladding.

Defects elimination.

Cladding cracks near the upper bottom
weld.

Defects elimination.

30. Reforming unit
column31. Reformer unit
drum

Cladding cracks in upper part of thecolumn.
Cross-cracks in joint welds of bottoms.

Defects elimination.

32. Reformer unit
column33. Reformer unit
heat exchanger

Long zones with cracks in cladding.

Defects elimination.

Round crack in weld of boss.

Defects elimination.

34. Sphere storagetank for liquefied
gases1) Defect weld zone (l=900 mm) in the
middle part of the vessel.
2) Cross crack of the weld (l=6-7 mm).
3) Damaged part of inner surface duringmounting (low part of the vessel.
4) Zones (2) with active sources in upper
part of the vessel.

35. Sphere storagetank for liquefied
gases

Zones (3) with active sources in low partof the vessel.

1) Additional USC.
Defects confirmed,
weld repair. Defects
elimination.
2) Defects elimination
3) Defect eliminated.
4) Defects allowed insize.
4) Defects allowed insize, confirmed with
USC.

1999

1998

1998

1997

22. Liquefied gases
storage drum
23. Hydro
treatment column
24. AT unit column

227

Defect elimination.

The solution- thecolumn should bechanged.

Defects elimination.
Column dismounted
immediately.

The defects could
be detected onlywith AE method.

Defects elimination.

Defects elimination.

The solution - thedrum should bechanged.

The solution drum should bechanged.
The solution – tochange column.
Crack depth 8-10mm (wall thickness is 30 mm).

1) Cross-cracks (3) in upper part (L=3-4mm)2) Zones with active sources in low partof the vessel.
1) Cladding cracks near welds.
2) Penetration of the bottom.

1) Defect eliminated.
2) Defects confirmedwith USC, allowed in
size.
Defects elimination

38. AT unit column

Long zones with cracks in cladding nearseveral welds.

Defects elimination

The solution – tochange column

39. AT unit column

Long zones with cracks in cladding nearseveral welds.
A lot of micro defects distributed in allof the material
Zone with cladding cracks.

Defects elimination

The solution – tochange column
The solution – tochange the drum.

Bundle defects localization(brass corrosion).

Additional bundle
revision. Defective
tubes removal.

43. AVT unit
column44. Reformer unit
columnCladding cracks near the welds of theupper bottom.
Zones of cladding cracks in the upper
part of the column.

Defects elimination.

45. Reformer unit
column
46. Alkylation unit
apparatus
47. Reformer unit
heat exchanger

Intrusions in the upper part of the vessel.

USC; defects size isallowable.
Defects elimination

Bundle defects localization (brasscorrosion)

Defects elimination.

48. AVT unit
columnLong zones with cracks in cladding nearseveral welds.

Elimination of theconsiderable defects.

The solution – tochange apparatus.
The experiencefrom item 42
utilized. Carriedout the complex
testing: both shell& bundle. Usedthe 3-D location.
The solution – tochange column.

49. AVT unit
columnLong zones with cracks in cladding nearseveral welds.

Elimination of theconsiderable defects.

The solution – tochange column.

50. AVT unit drum1) Inner defect of the weld (non-metal
intrusion + crack).
2) Zones (2) with pores.

Defects elimination

36. Sphere storagetank for liquefied
gases
37. AT unit column

2002

2001

2000

40. At unit drum
41. Hydro
treatment separator
42. Hydro
treatment unit heatexchanger

Intrusions in shell

228

Defects elimination.

The solution – tochange column

Defects elimination

Defects elimination.

The main purpose
was the shellrevision. Tube
defects localization was the
additional result.
Used the 3-D
location algorithm

The solution - tochange column

EWGAE2002

STRUCTURAL INTEGRITY EVALUATION OF WIND TURBINEBLADES USING PATTERN RECOGNITION ANALYSIS ON ACOUSTICEMISSION DATA
A. A. ANASTASSOPOULOS1, D. A. KOUROUSSIS1, V. N. NIKOLAIDIS1, A. PROUST2,
A. G. DUTTON3, M. J. BLANCH3, L. E. JONES3, P. VIONIS4, D. J. LEKOU4, D R V VAN
DELFT5, P. A. JOOSSE5, T. P. PHILIPPIDIS6, T. KOSSIVAS7 and G FERNANDO8
1

Envirocoustics S.A., El. Venizelou 7, 14452 Metamorphosi, Athens, Greece
Euro Physical Acoustics SA, ZAC des Portes de Sucy, Sucy-en-Brie F-94373, France
3
Energy Research Unit, CLRC Rutherford Appleton Laboratory, Chilton, Didcot, Oxon, OX11 0QX, UK
4
CRES, Wind Energy Department, Pikermi 19009, Attiki, Greece
5
Delft University of Technology, WMC-Group, 2628 CN Delft, Netherlands
6
Dept of Mechanical Engr. & Aeronautics, University of Patras, Patras 265 00, Greece
7
Geobiologiki S.A., 136 71 Acharnai, Athens, Greece
8
Engineering Systems Dept., Cranfield Univ. (RMCS), Shrivenham, Swindon SN6 8LA, UK
2

Abstract
Current wind turbine (W/T) blade certification practices require static and fatigue testing on new
blades in order to assess whether the blade can sustain the applied loads. Within the scopes of acurrent EC-funded research project, acoustic emission (AE) monitoring has been extensively applied
during testing of various W/T blades of similar design. All blades were loaded to failure by, either,
gradually increasing the static test loads, or fatiguing the blade until it failed. It has already beenreported that AE could locate the damage imposed on the blade during such tests (static and fatigue),and in most cases before the damage had become visible or audible, enhancing the assessment
capabilities and the understanding of the failure process of the blades. Additionally, application of
typical AE load-and-hold proof tests at intermediate loading stages, prior to failure, has enabled theassessment of the damage criticality for the particular proof load, denoted by high AE rates duringload-holds. Furthermore, it has been observed that the AE behaviour of all tested blades during loadholds exhibited very similar trends right prior to failure, despite the fact that blades failed differently.
The present paper reports on the use of (specially created for the Project) pattern recognition (PR)software, which has revealed the existence of a “critical” class of AE data appearing close to failure.
This has enabled the formulation of evaluation criteria used for the automated assessment of theblade’s integrity, based on the amount of hits from critical classes appearing during the hold period.
It is shown that, for similar blades, common grading criteria can be applied successfully, enabling afast and effective “grading” (from “good” to “severely damaged”), and providing successful
warnings of impending failure. This is particularly important for an effective analysis of fatigue tests
that have lasted for months and have produced huge amounts of AE data. The software and theautomated blade evaluation will be verified with future tests on large, commercial scale blades.
Keywords: acoustic emission, wind turbine blade certification testing, pattern recognition
Introduction
Wind turbine (W/T) blades, while in operation, encounter very complex loading sequences,
due to the stochastic nature of wind conditions on wind turbines sites. The suitability of a parJ. Acoustic Emission, 20 (2002)

229© 2002 Acoustic Emission Groupticular W/T blade to operate on a specific site is assessed through a certification procedure,
which entails the conduction of a series of static and fatigue laboratory tests on the W/T blade.
The purpose of such tests is to ascertain that the blade can survive the applied (static and fatigue)loads according to the applicable design standards [1, 2], while the applied static loads aim tosimulate the 1-in-50-years gust (and is applied on the blade for ten seconds during testing),
followed by fatiguing the same blade for an accelerated 20-years fatigue lifetime test.
In the aforementioned procedures the pass/fail criteria for W/T blades have been based on
deflection measurements and strain measurements on the blade’s surface, during static loading.
Damage characterisation is usually performed by visual inspection for evaluating surface
damage. Audible sound emissions heard during such certification tests are considered as
potential damage indications; however, on most occasions, the source of such noises cannot belocated.
It has already been shown and reported [3-6] that application of AE monitoring duringloading of W/T blades has offered considerable advantages towards the understanding of thecomplex damage mechanisms occurring on the blade, as the loading gets more severe, and,
subsequently, has enhanced the tester’s ability to evaluate the tested blade’s condition. Damage
occurring during certification testing (both static and fatigue) can be located with AE, while“weak” areas of the blade are pin-pointed at early loading stages. Additionally, the application of
AE “proof-type” tests (with ten-minute load-holds) before and after each certification-type static
tests as well as before and during the fatigue tests (at various times during testing) has enabled
the assessment of the criticality of the damage (if any) introduced by the test, based on the
traditional “emission during load-hold” criterion.
Within the scope of a current, EC-funded research project (AEGIS) aiming to provide areliable methodology for the assessment of W/T blades’ structural integrity, AE monitoring was
extensively applied during both static and fatigue certification-type testing of W/T blades.
Various proof tests have also been conducted for each blade and at various loading stages prior
to failure. It has been verified that, as damage was propagating and as the load was approaching
the ultimate failure load, emission during load-hold demonstrated increasing trends.
Furthermore, it has been observed that the qualitative characteristics of the recorded AE signals
during load-holds (e.g. Amplitude, Duration, etc.) exhibited similar “patterns” close to failureloads. In general, the intensity of the recorded signals increased as damage was becoming morecritical and as the loads were approaching the failure loads. The consistency in the presence of a
distinguishable family of AE data right before failure has enabled the formulation of specificcriteria for the assessment of the blade’s ability to withstand specific loads. In other words, theabsolute amount of intense AE data on a specific section of the blade during load-hold at a
specific load has been proven to be a very good measure of weakness of this section of the blade
to withstand such load.
With the use of a PR and blade grading software (AEGIS software, especially created for the
Project), critical AE data can be automatically identified and quantified, for any given AE dataset, and the tested blade can be sectionally (zonally) graded from “A” (minor damage) to “E”(severe damage) for a specific load, based on the number of critical AE data recorded per zone
(i.e. per AE sensor) during the ten-minute load-hold.

230

Experimental Procedure
In a series of ten (10) similar, small-scale blades, made of fibre reinforced plastic (FRP),(Fig. 1), specifically manufactured for the project, various loading envelopes have been applied,
while all blades were loaded up to final failure. Four blades were fatigued up to catastrophic
failure, while six blades were deliberately loaded to failure by gradually increasing static loads,
using various loading envelopes. The static testing loading envelopes varied and started from
simple, monotonic, stepwise load increases (Fig. 2, top) up to failure with 10-min. load holds atvarious load levels, while, at later project stages, more complex loadings were applied,
incorporating both “certification-type” loadings (10-s load increase, 10-s load hold, loaddecrease) to gradually increasing loads until failure and, intermediately, 10-min. load-holds (AEexamination or “proof-type” tests) at increasing load levels, to certain percentages of thepreviously applied certification-type test load (Fig. 5, top).
Results from the test to failure of three different blades will be presented below. Blade #1
was monotonically loaded to failure with gradual load increases and 10-min. load holds. The
results from this blade were used to “calibrate” the grading technique. Blade #7 was staticallyloaded to failure, following a complex loading envelope with certification-type tests of gradually
increasing load and intermediate AE-proof tests with 10-min. load-holds, which were used tograde the blade at various loading stages. Blade #8 was fatigue-loaded to failure, but before theapplication of the fatigue test, it had been subjected to AE-proof tests at two load levels, whichwere used to grade the blade.
The tested blades presented herein were manufactured by Geobiologiki S.A. and were tested
to failure at the Centre for Renewable Energy Sources, while additional blades (not presented)
were loaded at Delft University of Technology. For all tests presented, a 10-channel SPARTAN2000 Acoustic Emission system by Physical Acoustics Corporation (PAC) was used, with PACR6I AE sensors.
Unsupervised Pattern Recognition on Blade #1
The blade layout, sensor positions and load application point are presented in Fig. 1. The
applied loading envelope is shown in Fig. 2. The blade final failure area was at 2300 mm fromthe root, between sensors 7 and 8. Delamination was observed at the bottom section of the root
area (sensor 2, not shown). The blade failed by buckling at 7-kN load.
4500mm
3000mm
750mm
AE Sensor

500mm
9

3
1

4

500mm

5

200mm

10
6

7

8

c/4

LOAD
420mm

500mm

500mm

500mm

500mm

80mm

Fig. 1. AE sensor positions, AE channel numbers, and load application point of Blade #1.
231

Fig. 2. Applied loading envelope of Blade #1 (top) and cumulative AE hits vs. time (bottom),with load superimposed.

232Unsupervised pattern recognition (UPR) was applied on the data of the last load-hold (at6.5KN) prior to failure using the AEGIS software [7]. A “first-hit” analysis was followed. A
representative set of AE features was used, comprising Counts to Peak, Energy, Duration,
Amplitude and Average Frequency. Furthermore, all features were normalised individually to arange from 0 to 1, in order to avoid biasing the classification towards the feature exhibiting thehighest physical dimensions. UPR was performed using the K-Means algorithm and yielded
three different classes of AE data [8].
Figure 3 refers to the data from the load-hold at 6.5 kN, which was classified into threeclasses by UPR. A thorough examination of the data of Class “0” reveals the following:a. Data has an intense rate of AE Energy throughout the load-hold, as opposed to the otherclasses (see Fig. 3, top left graph),b. AE hits have high Amplitude throughout the load-hold (Fig. 3, bottom left graph),c. The class appears mainly in the channels close to the failure area (Fig. 3, top right graph),d. The AE hits in this Class have high Amplitude and Counts values (Fig. 3, bottom rightgraph).
Class “0” was characterized as the “critical” AE class; based on it, the grading strategy wasformulated. Following the clustering performed by UPR on the 6.5-kN load-hold AE data, a “kNearest Neighbour” (k-NNC) Supervised Classifier [9] was trained to correspond AE hits, fromany given data set, to one of the three classes based upon the values of their AE features.
Subsequently, the data from each one of the load-holds of the loading envelope of Blade #1 (Fig.
2) was classified separately and the amount of data falling into the critical class was observed
each time. As a result, a colour-coded grading strategy was formulated, which grades the blade(for a specific load) based on the number of AE hits that are classified with the critical class. For
example, for a channel to get a “B” grade, this channel must have recorded 10-25 critical classfirst-hits during the 10-min. load-hold. This same grading strategy was applied on subsequent
blades’ load holds, (at various loading stages) and results from Blades #7 and 8 are presented
below.
Supervised Pattern Recognition and Grading of Blade #7
The blade layout, sensor positions and load application point are presented in Fig. 4. The
applied loading envelope is shown in Fig. 5, where the loading stages and the sustained damage
are indicated. It can be observed that the loading envelope included both certification-type tests(e.g. the MTL11 test) and AE proof-type tests (e.g. tests AE7a to AE7d). Apart from an
artificially imposed skin delamination, which was located between 2 m and 2.2 m from the rooton the sensors’ line, the blade sustained damage during the test at various positions. Visualinspection during and after the test revealed (see also Fig. 5 bottom):• Delamination at the root area, visible at later stages of the test,• Two cracks at the compressive side, perpendicular to the blade axis, at 1.1 m and 1.2 mfrom the root, visible after the MTL11 certification-type test and,• One crack at the compressive side, perpendicular to the blade axis, at 2.2 m from the root,
developed after the MTL11 certification-type test.
The blade failed at the position of the crack at 2.2 m, during the MTL21 certification-type test at
22.8 kN, slightly before this load was achieved.

2334500 mm3000 mm
SADDLE
1740 mm

510 mm8

AE Sensor

9

1
2

4

3

5

6

10 (bottom)
80 mm

80 mm

420 mm

500 mm

500 mm

500 mm

480 mm

100 mmc/4

7

380 mmFig. 4. AE sensor positions, AE channel numbers, and load application point of Blade #7.

Fig. 5. Loading envelope of Blade #7 indicating loading stages, and damage information.
234

Each AE proof test was graded using the AEGIS software and the grading strategy defined in theprevious paragraph, herein. Grading of some characteristic periods is presented in Table 1. It is worth noting
that, prior to loading MTL5, the grading did not reveal any grades other than “A” or “no-grade”, meaning
complete absence of critical AE data and the blade did not exhibit any visible damage. The grading of
sensor 1 (closest to the delamination area) and sensor 3 (closest to the crack formed during MTL11 test)
gradually increases from “A” (period AE7D) to “E” (period AE11E) giving a very good warning of theimpending damage, i.e. the crack first seen after period MTL11. During the periods AE21A to E, sensor 5
was graded “E”, while the blade failed catastrophically during the next test (MTL21) exactly in the area of
sensor 5. Grading of the rest of the periods is not presented due to space limitations.
Period/Loadhold level (kN)
AE7D
12.48

Table 1. Graded Blade #7

AE8D
13.44
AE9D
14.4
AE10D
15.36
AE11D
16.32
AE21D
17.28
Supervised Pattern Recognition and Grading of Blade #8
The blade layout, sensor positions and load application point of Blade #8 were identical to Blade #7(see Fig. 4). The blade was fatigue-tested with a sinusoidal load ranging from 1.2 to 12 kN, at afrequency of 1 Hz (Fig. 6 middle). Prior to fatigue testing, static AE proof-type tests were applied toassess the ability of the blade to sustain fatigue loads. The applied static loading envelope is shown in
Fig. 6, top, where the loading stages are, also, indicated. Apart from an artificially imposed skindelamination, located from 2 m to 2.2 m from the root, there was no other visible damage present on
the blade during the last visual inspection prior to failure, which was estimated to have taken place lessthan one hour prior to failure. The blade failed after about 75K cycles into fatigue testing. Visualinspection after the test revealed that the blade failed with a crack on the compressive side between
1.8m and 2.0m from the root, close to sensor 5 (Fig. 6 bottom). Each preliminary AE proof test wasgraded using the AEGIS software and the grading strategy defined in the previous paragraph, herein.
Grading results are presented in Table 2 and are summarized below. Here, note that:a. Grading of Periods AE1a and b, does not yield any severe grade, indicating that the applied load
(6 kN) could probably be sustained by the blade.
235

Fig. 6. Static loading envelope of Blade #8 indicating loading stages (top), fatigue envelope(middle) and damage information (bottom).
Period /Load-holdlevel (kN)
AE1a
6 (50% of max.
fatigue)AE1b
6 (50% of max.
fatigue)AE2a13.2 (110% of
max. fatigue)AE2b13.2 (110% of
max. fatigue)b. Grading of Periods AE2a and AE2b yield E grades, indicating that the applied load (13.2 kN) is
severe for the blade.
Table 2: Graded Blade #8

236

Conclusions
An automated method for the assessment of the structural integrity of wind turbine blades and of theirability to withstand specific load levels has been developed and verified in a series of static and fatigue
tests to failure of small-scale wind-turbine blades. The method involves static proof-loading of the blade
and load-hold at the load of interest. AE activity during the load-hold is classified by a trained supervisedclassifier, to classes resulted from UPR classification performed on the AE data obtained from the lastload-hold prior to failure of the first tested blade. Each zone of the blade is, subsequently graded, from“A” to “E” based on the number of AE hits classified with the critical class (revealed from the first blade)of each corresponding AE sensor. A grade “A” indicates that there is minor or insignificant (for the
specific load) damage on the blade, while an “E” grade indicates that the applied load causes severe
damage and cannot be properly supported by the blade. The same grading strategy was applied on all
tested small blades under the AEGIS project with encouraging results, considering the fact that different
blades failed in different ways (some of them very suddenly, with buckling) while different loadingenvelopes were applied. In most of the blades, including Blade #7, an “E” grade was assigned right before
ultimate failure (or extreme damage), even during loading levels in which the blade did not exhibit any
visible damage, and where it would have been otherwise unknown whether failure was pending.
Additionally, the applied grading prior to fatiguing Blade #8, implied severity of the fatigue load levels
and the blade failed after 75 k-cycles. Future work will include application of the same grading strategy
during static and fatigue loading of large, commercial-scale blades, in order to assess whether the method
can be applied to different blades, or further refinement and calibration of the method is required. A
finalized version of such a method will provide manufacturers a tool to assist in blade design
improvement and to users a fast and effective means for evaluating a blade’s condition (both new and inservice), increasing, thus, the overall reliability and cost effectiveness of wind turbines.
Acknowledgements
This work was supported by EC, Non-Nuclear energy program under contract number JOR3-CT98-0283.
References[1] IEC 61400-1, Wind turbine generator systems - part 1: safety requirements, 1998.
[2] IEC 61400-23 TS ed. 1: Wind turbine generator systems - part 23: Full scale structural testing of rotor
blades for WTGS's, draft 1999.
[3] Sutherland, H., et al, “The Application of Non-Destructive Techniques to the Testing of a Wind Turbine
Blade”, Sandia National Labs Report, Ref: SAND93-1380. UC-261, NM, U.S.A., June 1994.
[4] Kouroussis, D.A. and Anastassopoulos, A.A., “Analysis of Acoustic Emission Data from a 12-m F.R.P.
Wind Turbine Blade During Flapwise Bending”, Envirocoustics A.B.E.E., Technical Report, Ref: TR-0018/98, Athens, August 1998.
[5] Dutton, A.G., et al, “Acoustic Emission Monitoring from Wind Turbine Blades Undergoing Static andDynamic Fatigue Testing”, Proceedings of the 15th World Conference on Non-Destructive Testing, Rome,
Italy, 15-21 October, 2000.
[6] Joosse, P.A., et al, “Acoustic Emission Monitoring Of Small Wind Turbine Blades”, 21st ASME Wind
Energy Symposium, 14-17 January 2002, Reno, NV U.S.A.
[7] Nikolaidis, V.N., Kouroussis, D.A., Anastassopoulos, A.A., “AEGIS Software Manual”, Envirocoustics
A.B.E.E. Technical Report, Ref: TR-015-10/2000, Athens, October 2000.
[8] Philippidis, T.P., Nikolaidis, V.N., Anastassopoulos, A.A., “Damage Characterisation of C/C laminates
using Neural Network Techniques on AE Signals”, NDT and E International, 31(5), 1998, 329-340.
[9] Anastassopoulos, A.A., et al, "A Comparative study of Pattern Recognition Algorithms for Classificationof Ultrasonic Signals", Neural Computing & Applications, 8, 1999, 53-61.
237

EWGAE2002

LOW ALLOY STEEL METAL DUSTING: DETAILED ANALYSISBY MEANS OF ACOUSTIC EMISSION
P. J. VAN DE LOO1, A. WOLFERT1, R. SCHELLING1, H. J. SCHOORLEMMER2and T. M. KOOISTRA3
1

Shell Global Solutions, Materials and Inspection, Amsterdam, Netherlands
2
Physical Acoustics Limited, Rotterdam, Netherlands
3
HTS Haarlem, Aeronautics

Abstract
The process of metal dusting of low alloy steel subjected to an equal mixture of CO and H2
gas at elevated temperatures has been monitored on-line with the aid of acoustic emission (AE).
To a test tube a programmable temperature gradient was applied. Two AE sensors allowed
continuous monitoring of AE activities as function of time and position/temperature. At regularintervals the tube was opened and examined for the presence of dusty powder. Two clearly
distinctive metal dusting activities were identified: oxide dusting in the range of 380 to 420oC,
and metal (bulk) dusting, being most reactive at temperatures from 425 to 500oC. On the basis of
AE pattern recognition techniques, noise and relevant signals can be distinguished. The
application of AE has considerably contributed to the identification of the dusting processes andinterpretation of the test results. AE will further be used to study different process conditions andinhibition effectiveness.
Keywords: metal dusting, oxide dusting, AE monitoring, pattern recognition, process parameters
1. Introduction
Metal dusting is a detrimental chemical process by which low and high alloy steels canrapidly disintegrate into powdery dust consisting of carbon and metal particles. Detailed
information about this phenomenon is given in References [1 – 9]. Because many processes
contain synthesis gas, consisting of a CO and H2 mixture at various temperatures, material andprocess engineers need a better understanding of all parameters which may lead to thisdetrimental process. This is required to avoid the critical process conditions, to enable propermaterial selection, and, when appropriate, to study the effectiveness of inhibition measures.
In the past, a number of coupon tests had been performed in the Shell laboratories. Effects ofthe test parameters applied were mainly judged at the end of a pre-defined test period. This gave
insufficient insight in the process dynamics. At the EWGAE2000 at Senlis 2000, Elf Autochem
paper showed the feasibility and advantages of the application of AE during metal dusting tests[10]. We decided to apply a similar test set up in our Shell laboratory. Main difference was theselected steel type and cooling of the tube ends where the sensors were mounted (see Sec. 2).
Apart from protecting the sensors to overheating, a temperature gradient was introduced in thistest set up.
Together with the linear localisation technique of the two AE sensors, AE activities could becorrelated to both position and discrete temperature regions on the tube. This appeared to be aneffective approach to get insight in the temperature dependence of the dusting process.
J. Acoustic Emission, 20 (2002)

238© 2002 Acoustic Emission Group2. Experimentel Set-Up
The material of main interest is steel 13CrMo44 with the following composition:
Chemical composition (% weight)
C

Si

Mn

Cr

Mo

Ni

V

W

Others

0.14

0.2

0.55

0.98

0.6

-

-

-

-

The test tube dimensions and external furnace are given in Fig. 1. Details of the AE sensor
mounting and the cooling part are given in Fig. 2.
50 mm

FURNACE
48 mm

38 mm

43 mm

5 mm

1000 mm
Channel 2

FURNACE

0 mm
Channel 1
300 mm1000 mmFig. 1. Tube dimensions.

Fig. 2. AE sensor & cooling part.
The heating and cooling of the furnace is computer controlled. The maximum temperature in
the furnace is set and by calibration measurements the resultant tube skin temperature (which isequal to the internal gas temperature) has been determined. An example of a measured
temperature profile is given in Fig. 3. Away from the centre one can observe very steep
temperature gradients. This implies that with a limited localisation accuracy the correlation withabsolute temperature may have limited accuracy (see Sec. 3).
In all tests, an equal CO/H2 gas mixture was applied at an injection rate of 100 ml/min. In
future tests the mixture will be varied.
239

Temperature along the tube (longitudinal) vs x position
650
600
550
500

Temperature (Celsius)

450
400
350
Temperature (Celsius)
300
250
200
150
100
50
0
0

10

20

30

40

50

60

70

80

90

100

x position (cm)Fig. 3. Temperature profile along the tube.
We used PAC R15I resonant sensors with a peak sensitivity of 109 dB, operating frequency
70 – 200 kHz, resonance frequency 125 kHz, 40 dB gain integral pre-amplifier for 50 Ω load.
The attenuation over the tube section between the sensors was 3.3 dB. Sensor 2 is 1.4 dB moresensitive than sensor 1. Data handling and analysis is done with PAC’s Mistras and Noesis
system [11]. For all tests the threshold was set at 43 dB. The filter range from 10 kHz to 400 kHz
was used. Sample rate was 4 MHz, pre-trigger time 20 µs (= 7.8 % hit length) and hit length 1 ksample (= 256 µs).
3. Results
On the basis of experience and the available literature data initial tests were started atrelatively high temperatures (> 500ûC). We then observed that directly after start-up of theexperiment AE activities were not registered in the middle of the tube where the highest
temperature is present, but observed 20 to 30 cm left and right from the centre (Fig. 4). Aftervarious repetitive tests at the same tube the activity rate decreased and finally stopped.

Fig. 4. AE events as a function of time (vertical axis) and position (horizontal axis). Maximum
tube temperature at the centre (x position = 0.5 m) = 500ûC. Approximate temperature at active
areas: 380 – 420ûC. At these positions dusty powder was found.
240

This could be explained and was later confirmed by more systematic tests: the observed
dusting activities were related to dusting of the tube’s oxide layer. The oxide scale is reduced by
the gas mixture, resulting in highly dispersed iron particles. Iron concentrations of 70 % were
measured. When this layer was consumed, the process stopped. By applying different
temperatures different sections of the tube were consecutively consumed. Figure 5 shows a long
duration tests with the maximum tube centre temperature at 400ûC. We see initially most AE
activities slightly left from the centre (left is the gas inlet side). Figure 5 indicates that most AE
activities at this position occurred in the first 10 000 s (roughly 3 hours). The tube was almost
blocked with loose dust at that position. From 15 000 to 25 000 s, an increased number of events
was observed at the right (downstream) side of the centre. Maybe by the left hand (upstream)
blockage, this process went slower, but also at this position dust was found. The oxide layer had
disappeared. The rest of the signals may be related to scatter by localisation limitations: various
dispersive modes, shifts in threshold passing by attenuation, etc. and maybe some slow dusting
activities (much less active below 380ûC). It is very unlikely that outside the furnace area, metal
dusting will be active.

Fig. 5. Registered events during the oxide dusting process as a function of time and x position.
Maximum tube temperature at the centre of the tube is 400ûC.
The observations that the dusting was limited to the oxide layer alone and that it happened atrelatively low temperatures of the process were rather unexpected. However, we do know thatmetal dusting of the bulk material is an existing problem, with indications that this happens at
higher temperatures. Therefore, long-duration experiments at higher temperatures were
performed on tubes where the oxide layer had been consumed by the above-described process.
Figure 6 shows a typical example. The maximum temperature at the tube centre was 550oC. The
activities left of the centre (at the gas inlet side) occur in an estimated temperature range of 425
to 500oC. Another interesting phenomenon is that the AE event rate increases as given in Fig. 7.
This shows that the whole process requires a certain incubation time and then progresses atconstant rate.
241

Fig. 6. Metal dusting AE events (color scale) on time (s) vs. x-position (in m) plot. Maximum
tube temperature at the pipe centre: 550oC.

Fig. 7. Events (cumulative) vs time. Maximum tube temperature at the pipe centre: 550ûC.
More tests were done. Figures 8 and 9 show test results where the maximum temperature of
the tube was kept at 650ûC. We observe that without incubation first AE activities occur close to
the centre, thus in the temperature range from 600 to 650ûC. After 150 000 s (= 42 hours)
increased activities start further left from the centre (estimated temperatures between 425 and500ûC). With a symmetric temperature profile one would expect also activities at the right (gasoutlet) side of the tube. A rough estimate of the C-balance from the CO injection and the carbon
formation indicates that most of the reactive gas components have been consumed at the left partand that “downstream” a different gas composition will be present. This may explain theasymmetric behaviour.
At the end of the test, black powder and hard solid material (coke formation in a confined
space) was found at the region of AE activities.
242

Fig. 8. Time vs x-position and metal dusting events. Maximum tube temperature: 650ºC.

Fig. 9. Metal dusting events (cumulative) vs time. Maximum tube temperature: 650ºC.
Finally we want to show test results, which were obtained when stepwise temperaturechanges (25oC) were applied (see Fig. 10). We observe that the first significant increase of AE
activities happens when the temperature in the middle of the tube drops from 500 to 475oC. The
activities are slightly left from the centre, thus at slightly lower temperature than the reported
maximum temperatures. The increments are only for a very small amount related to shrinkage
activities: see the minor steps at higher and lower temperatures. This is also confirmed by thesignal characteristics as derived from pattern recognition.
243

Fig. 10. Cumulative events in relation to temperature steps. Events are related to metal dusting(confirmed). Temperatures are maximum tube temperatures at the tube centre.
We have to keep in mind that the duration of the periods with constant temperature was only
4 hours. From Fig. 9 we learned that the incubation time for the metal dusting process may beover 40 hours. So this test cannot be conclusive about process reactivity and temperature.
However, the reason for presenting these test results here is to draw attention on a moreinteresting phenomenon: within the temperature range from 500 to 400oC the increments inactivities occur at the short periods of the temperature steps and level out when the temperature
is kept constant (faster at 475 and 400oC and slower in the mid-temperature region). The rate ofchange is very significant. This may indicate that vulnerability to metal dusting is largest when
favourable process conditions are combined with the presence of certain strain rates, shrinkage orexpansion due to temperature variations. This phenomenon requires further attention, particularly
because this seems to be the fastest process. When the tube was opened, a hard ring of dusty
powder was found, which almost fully blocked the tube.
4. Detailed Analysis/Pattern Recognition
In the previous section, the observed AE events were mainly related to three types ofactivities: oxide dusting, metal (bulk) dusting and noise, mainly due to shrinkage and expansionof the tube during temperature changes. By the controlled tests and follow-up investigations(dust powder analysis) the periods and conditions can be correlated to the distinctive processes.
Now it is interesting to see whether these processes can be distinguished on the basis of theirsignal characteristics. To this end the pattern recognition programme Noeses has been used. First
manual clustering was applied. Within Mistras it is very hard to select a set of data on the basisof their localisation. By judgement it appeared that dusting activities (from the furnace section of244

the tube, where dusting was identified) mostly had energies >50, whereas over the rest of the
tube more random characteristics were present. These data were selected for further analysis.
Figure 11 shows that the counts and duration distribution plots (signals with energy >50) of
the oxide and metal dusting process can clearly be distinguished. (Counts and duration arestrongly correlated). The same can be done, to a lesser degree, on the basis of the othercharacteristics.

Fig. 11. Frequency distribution of counts (top) and duration (bottom). Oxide and metal dusting
can clearly be distinguished.
245

Fig. 12. Vector projection from unsupervised clustering: energy versus counts (top) and energy
versus duration (bottom). The three clustered, related to noise, oxide dusting and metal dusting
can clearly be distinguished.
Based on single characteristics we get the following discriminating power between the twodusting process activities:• If the duration >6000 µs, then the chance that the hit is related to oxide dusting is 160 to 1.
• If the counts >500, then the chance that the hit is related to oxide dusting is 70 to 1.
246• If the average frequency >120 kHz, the chance that the hit is related to oxide dusting is 8 to 1.
• If the energy >500, then the chance that the hit is related to oxide dusting is 4 to 1.
Better distinction can be obtained when a combination of parameters will be used. To investigate
this, unsupervised clustering has been applied to the complete data set of a mixture of oxide
dusting, metal dusting and noise, mainly due to temperature variations. Figure 12 shows vector
projection of energy vs. counts and duration vs. counts. The three distinctive phenomena areclearly separated. As was done with the manual clustering, best separation is obtained at higher
energy levels.
5. Concluding Remarks
Two clearly distinctive metal dusting activities were identified: oxide dusting in the range of
380 to 420ºC, and metal (bulk) dusting most reactive at temperatures from 425 to 500ºC. On thebasis of AE pattern recognition techniques, noise and relevant signals can be distinguished. The
application of AE has considerably contributed to the identification of the dusting processes andinterpretation of the test results. Insight was gained in the process dynamics, incubation times,
etc. AE will further be used to study different process conditions and inhibition effectiveness.
References
1. R. F. Hochman, “Catastrophic deterioration of high temperature alloys in carbonaceous
atmospheres”, Proceedings of Symposium on Properties of High Temperature Alloys, Z.A.
Foroulis and F.S. Petit, eds, Elec. Chem. Soc., 1976, pp. 715 - 732
2. H. J. Grabke, C. B. Bracho-Troconis and E. M. Muller-Lorenz, “Metal dusting of low alloysteels”. Werkstoffe und Korrosion, 45, 215-221 (1994).
3. R. J. Gommans and T. L. Huurdeman, “Experience with Metal Dusting in Waste Heat
Boilers”. DSM Fertilizers, Geleen, The Netherlands, pp. 145-162.
4. P. F. Tortorelli, J. H. DeVan, R. R. Judkins and I. G. Wright, “Solid-state phase stabilities of
relevance to metal dusting in coal gasifier environments”. Materials and Corrosion, 49, 340344 (1998).
5. M. Maier, J. F. Norton and P. D. Frampton, “Metal dusting of 9 - 20% Cr steels in increased
pressure environments at 560o C”. Materials and Corrosion, 49, 330-335 (1998).
6. H. J. Grabke, D. Monceau and E. M. Muller-Lorenz, “Metal Dusting resistance of steels in
the temperature range 500 - 600oC”, MPI für Eisenforschung, Düsseldorf, Germany.
7 . M.J. Bennett, “Carbon deposition: A major technological problem”. Materials andCorrosion, 49, 345-351 (1998).
8 . P.A. Lefrancois and W. B. Hoyt, “Chemical Thermodynamics of High Temperature
Reactions in Metal Dusting Corrosion”. Proc. the 19th Annual Conference of the National
Association of Corrosion Engineers, March 11-15, 1963, New York. pp. 360t - 368t.
9. M. L. Holland and H. J. de Bruyn, “Metal dusting failures in methane reforming plant”. Int.
J. Press. Vessels and Piping, 66, 125-133 (1996).
10. F. Ferrer, E. Andres, J. Goudiakas and C. Brun, “Using acoustic emission to monitor metal
dusting”. Proc. 24th European conference on Acoustic Emission Testing, EWGAE, Senlis
2000.
11. Mistras and Noesis: Acoustic emission data analysis and pattern recognition systems of
Physical Acoustic Corporation.

247

EWGAE2002

RECONSTRUCTION METHOD OF DYNAMIC FRACTURE PROCESSINSIDE THE MATERIAL WITH THE AID OF ACOUSTIC EMISSIONYASUHIKO MORI1, YOSHIHIKO OBATA1 and TAKATERU UMEDA2
1

2

College of Industrial Technology, Nihon University, Narashino, Chiba 275-8575, Japan
Faculty of Engineering, The University of Tokyo, Tokyo, Japan; now at Faculty of Engineering,
Chulalongkorn University, Bangkok 10330, Thailand

Abstract
A method, which combines a confocal-optics-based scanning laser microscope (SLM) andcomputer software to match three-dimensional features of conjugate fracture surfaces and reconstructs microscopic fracture processes that occurred inside the materials, has been developed.
This method can visualize the location of crack initiation and successive micro-crack extension
behavior. The quantitative information about crack, such as length, displacement between crack
surfaces and direction of crack extension are also obtained from the method. The method, however, is unable to evaluate the forces required to initiate or extend a crack observed. In order toresolve this problem, acoustic emission (AE) test was combined with the method. Pearlitic spheroidal graphite cast iron was selected as a model material for this study, and its notched bar sample was broken in tension, where the AE was simultaneously measured. Reconstructed fracture
process revealed that crack initiation and growth started in the middle of the specimen. Microfracture onset load level and successive fracture processes inside the specimen were estimatedand traced by the AE signals measured. This study demonstrates the utility of the AE technique,which rectified the deficiency of another material evaluation method.
Keywords: fracture process, internal crack, cast iron, scanning laser microscope, AE
1. Introduction
Materials development requires an understanding of detailed micro-fracture mechanisms;
i.e., in what part of the material a micro-crack starts, how the growing crack interacts with various constituents, and what mechanism resists crack growth. Crack observation of the sample
under stress is needed to give answers about these fracture processes.
There are a number of reports that in-situ crack growth observation has been done during
fracture toughness test, which was conducted under the scanning electron microscope or ultrasonic microscope [1 - 5]. These applications, however, gave us only the surface or subsurface
information of crack growth behavior. On the other hand, acoustic emission (AE) is a promising
method for monitoring changes that occur inside a material. In addition, in some cases, it is possible to estimate the size and fracture mode of a micro-crack by analyzing the signal waveformof AE signals [6, 7]. However, the AE method is limited to make a complete trace of crack
growth in relation to the material constituents on a crack path, with which the growing crack
might interact.
The fracture surfaces of a broken part record the details and sequence of micro-fracture processes that led to the failure. Kobayashi and Shockey [8] developed a fracture-surface topography
analysis technique, FRASTA, which combines a confocal-optics-based scanning laser microJ. Acoustic Emission, 20 (2002)

248© 2002 Acoustic Emission Groupscope (SLM) and computer software to match three-dimensional features of conjugate fracturesurfaces. This leads to the reconstruction of detailed fracture processes that occurred inside thematerial. We also have developed a technique, which can reconstruct the fracture process in microscopic details by using the computer aided image processing for the topography of conjugate
fracture surfaces obtained by an SLM [9]. However, though the method can characterized thegrowth of micro-fracture events from evaluation of the fracture surface, the method is unable toevaluate the stress or strain level, at which the crack initiation or growth occurred. Thus, the AE
method was combined with the reconstruction method. By comparing AE signals generated
from the fracture surface with the results of fracture surface analysis, it is possible to relate eachmicro-fracture event to a specific AE event; i.e., the stress state at which the micro-fracture event
occurred. This combination technique eventually will make it possible to interpret the physicalmeaning of an AE signal. It should make AE monitoring a more effective method to evaluate thecondition of structure.
This paper reports that the combined SLM/AE method was applied successfully to answer aproblem existed in cast iron. In ductile cast iron, spheroidizing the graphite distributed in thematrix increases the strength of the material. The strength of the cast iron, however, depends on
the sample shape. For instance, in the case of a bar specimen, it has been reported that the existence of notch did not decrease the strength substantially and in fact increased the strength insome cases [10-13]. Several models have been proposed for the explanation of this notchstrengthening; e.g., the effect of the non-uniformity of stress distribution around the notch root
[12, 14, 15], or the internal notch effect of graphite nodule distributed in the matrix [16]. Therefore, the dynamic behavior of the graphite nodule and the internal fracture process are needed todetermine the fracture mechanism of the material.
In the present paper, tensile tests and AE measurements were conducted on bar specimens ofpearlitic spheroidal graphite cast iron. SLM technique was applied for the broken sample to examine how the graphite nodules in the matrix contributed to the fracture process in the material.
Actual dynamic fracture events were estimated by the AE signals. This paper visually demonstrates that the graphite nodule acts as internal crack and proposes a fracture model built aroundthe graphite nodule.
2. Experimental
The material used was a pearlitic spheroidal graphite cast iron, having retained pearlite of
98.9%, nodularity of graphite of 81%, graphite area of 9.5%, number of graphite nodule per areaof 244/mm2, and mean graphite nodule diameter of 22 µm.
Smooth bar with gage diameter of 6 mm and circumferentially notched bars with outer diameter of 10 mm were stressed and fractured in tension. Shape of notches was U- and V-type,
and the elastic stress concentration factor of notches, Κt, was estimated to be 2.0 and 3.8, respectively. Tensile test samples showed that all the specimens ruptured in flat fracture. Fracture
strength (= tensile strength) of the smooth bar specimen was 940 MPa, showing the elongation of
6.6 %. Fracture strength of V-notched (stress concentration factor Κ t = 3.8) specimen was 880
MPa. The observed decrease was 6% compared to the expected value of 74% due to the effect of
Κ t, implying that the notch strengthening exists. On the other hand, the sample failure of the Unotched specimen (Κ t = 2.0) occurred at stress of 980 MPa, which exceeded the tensile strengthof the smooth specimen by approximately 4 %. Again, the notch strengthening exists.
249

Figure 1 shows the applied stress, σ, and the cumulative counts of AE events, N, as a function of elapsed time of tensile test, t, for the U-notched specimen. Applied stress increases continuously until the specimen fractures. Acoustic emissions start at the stress level of about 90 %of the fracture strength of the specimen and increase continuously until the specimen fracture.

Fig. 1. Applied stress, σ, and AE event counts, N, vs. testing time, t. (U-notched specimen).
Fracture surface analysis by using an SEM showed that in the case of the V-notched specimens, fracture initiated at the notch root. AE in the V-notched specimens started at 85% of thefracture stress, similarly to Fig. 1. However, the stress-time curve was linear to fracture (at 880
MPa). V-notch fracture initiated at the notch. In the U-notched specimens, however, the traces ofcrack initiation site were not clearly left on the fracture surface. So the fracture process of the Unotched specimen was reconstructed and analyzed by using SLM technique.
3. Reconstruction of Fracture Process
Figure 2 shows the macroscopic features of conjugate fracture surfaces of the U-notchedsample. SLM observations were performed on the five locations on the conjugate surfaces,
which are labeled I to V in Fig. 2, because of the limitation of SLM field of view.
Figure 3 shows SLM micrographs (top) and corresponding gray-scale image topographs(bottom) of conjugate fracture surfaces of the area location III in Fig. 2. Scanning-laser micrographs show that the graphite nodules are individually distributed in the matrix. Gray-scale images show that lighter areas are higher in elevation than darker areas.
The fracture process of this area was reconstructed using topographic information from Fig.
3 (bottom) in digitized form. Maximum elevation resolution is 0.03 µm, which is determined by
dividing a specified range, i.e., the difference in elevation between the highest and lowest point
within the observation field, by 256. Reconstruction of fracture process is accomplished by thefollowing procedure. (1) Matching the three-dimensional features of conjugate fracture surfaces
in a direction vertical to the specimen axis with the aid of a computer (Fig. 4a through 4d). This
makes a reference state (Fig. 4d) for the successive procedure. If the surface irregularity, i.e., an
250

Fig. 2. Macroscopic fracture surfaces of U-notched specimen broken in tension.

Fig. 3. Scanning-laser micrographs, top, and corresponding topographs, bottom, of conjugate
fracture surfaces of the location III in Fig. 2.
251

Fig. 4. Procedure of fracture process reconstruction, showing conjugate surface irregularities in
two-dimension. Matching process, (a) to (d); reconstruction of fracture process, (d) to (a).
elevation profile on the fracture surface is caused only by the interaction of the crack tip with themicrostructure and without inelastic deformation, the conjugate surface profiles should match
precisely. Any mismatch appearing as overlap between the conjugate surface profiles indicates
inelastic deformation (Fig. 4d). (2) Separating the two surface irregularities from a matchingreference state in a direction vertical to the specimen axis using computer software (Fig. 4d
through 4a). Displacement of the separation (ds) is increased step-by-step at a certain increment.
Gaps, which appeared between the two surface irregularities in this sequential process, are projected on display equipment.
Figure 5 is a result of fracture process reconstructed for the location III shown in Fig. 2,
showing the step-by-step development of the fracture. Black areas, where the conjugate surface
profiles overlap, denote intact material. White areas, where the surface profiles do not overlap,
are considered locations where the fracture surfaces are separated, i.e., cracked. Projection diagrams of fractured areas show that cracking initiated in the matrix around graphite nodules (Fig.
5a). As the applied stress is increased (see Fig. 1), i.e., as the conjugate fracture surfaces are displaced relative to one another, as denoted as “ds” in Fig. 5, micro-cracks grow slightly, debonding starts at the graphite/matrix interface (Figs. 5b and 5c). Increasing the stress further causes
coalescence of micro-cracks (Fig. 5d) and development in the matrix forming a crack (Fig. 5e).
In order to estimate the entire fracture processes in the specimen, fracture processes for thelocations I to V shown in Fig. 2 were reconstructed with a single reference state. The reference
state, that is, the state of the specimen before loading, was found in the location III, at which thematching of the conjugate surface profiles was completed. A series of fractured area projection
diagrams in Fig. 6 show the step-by-step development of the fracture. The reference state is the
top of location III (center of the specimen) in the diagram. The displacement ds, in Figs. 5 and 6,
is correlated with the applied stress level, since the applied stress continuously increased until the
specimen failure, as seen in Fig. 1. Therefore, the result of reconstructed fracture process shownin Fig. 6 clearly demonstrates that as the stress is applied, micro-crack initiates at around thegraphite nodules distributed in the middle of the specimen (location III), as seen in Fig. 5 in de252

Fig. 5. Scanning-laser micrograph, left top, and a series of projection diagrams of fractured areas,(a) to (e), of the location III shown in Fig. 2.
tail. With increasing the stress further, the micro-cracks are also created successively at thegraphite nodules in the locations of II, I, IV and V. Then, a crack is formed by the coalescence of
these micro-cracks and grows outward (toward the notch root of locations I or V), and thespecimen ruptures.
4. Approach from Acoustic Emission
Result of AE measurement during the tensile test of the specimen, of which fracture process
was reconstructed, has been shown in Fig. 1. Generation of AE started at stress level about 90 %of the fracture strength of the specimen and increased continuously until the specimen rupture.
The stress applied to the specimen also continuously increased until the specimen failure. From
the fracture process shown in Fig. 6, the crack initiation and growth started inside the specimen
and successive micro-fracture processes led to the failure. From the AE measurement, the nominal stress level of crack initiation was estimated to be approximately 850 MPa (Fig. 1).
Microstructure of the material tested showed that the graphite area amounted 9.5 % of thetotal and the number of graphite nodule was 244 nodules/mm2. A total of 16 AE events weredetected during the tensile test (Fig. 1). Though the number of AE events depends on the threshold level of AE discrimination, AE event counts measured were so small compared to the number of graphite nodules distributed over the crack path in the material. Therefore, it is expectedthat the generation of measured AE events were due to the matrix cracking; i.e., coalescence of
micro-cracks initiated from each graphite nodule, formation of main crack, or successive crack
growth, rather than micro-crack initiation around each graphite nodule.
5. Fracture Model
Fracture reconstruction revealed that the graphite nodule in the matrix behaves as void orpore in the ductile matrix stressed in tension. A model shown in Fig. 7 could be proposed to explain the fracture process around the spheroidal graphite nodule. As the material (Fig. 7a) is253

Fig. 6. Fractured area projection diagrams for the locations I to V shown in Fig. 2. Reconstructions of fracture process were performed with a single reference state (location III).

Fig. 7. Model of fracture process occurred around the spheroidal graphite nodule.

254stressed in tension and the stress reaches a critical level, a micro-crack initiates in the matrix
around graphite nodule (Fig. 7b). Such Saturn’s-ring-shaped crack perpendicular to the stressaxis could be created where the stress is the maximum. The next step of debonding at the graphite/matrix interface (Fig. 7c) exists only nominally as the graphite strength is so much lower. The
crack grows into the matrix, forming a main crack (Fig. 7d). When this last step occurs almost
simultaneously at neighboring nodules, an observable AE signal is expected.
Cross sectional observation of the fractured specimen by an SEM showed that the crack path
twisted around the surfaces of graphite nodules. Traces of an internal crack initiated from agraphite nodule were also found near the main crack path. These observations suggest the validity of the model proposed in Fig. 7, but further improvement in resolution is needed to follow thecrack spreading that is expected to generate AE.
6. Summary
In order to determine visually how the graphite nodules in the matrix contributed to the fracture process, tensile testing was conducted on notched bar specimens of pearlitic spheroidal
graphite cast iron. Fracture processes were reconstructed by the computer-aided image processing for the topography of conjugate areas of the fracture surfaces observed by a confocal-opticsbased scanning laser microscope. Acoustic emission was simultaneously measured during thetensile test, in order to obtain the dynamic information about the stress state and interpret the reconstructed fracture process occurred inside the material.
Reconstructed fracture process revealed that crack initiation and growth started in the middleof the specimen. Initial micro-crack was created in the matrix around a graphite nodule at astress level of approximately 90 % of the specimen failure strength, estimated by AE. A model,
which explains the fracture process occurring around the spheroidal graphite nodule, was proposed.
This technique, combining AE with SLM, eventually will make it possible to interpret thephysical meaning of an AE signal and make AE monitoring a more effective method to evaluate
the condition of structure.
Acknowledgement
The authors wish to acknowledge the invaluable discussion in this work of Prof. Kanji Onoof University of California, Los Angeles.
References[1] N. Niwa, Bulletin of the Iron and Steel Institute of Japan, 78(4), 1992, 673-679.
[2] A. Nozue, T. Uchimura and T. Okubo, J. Materials and Environment, 40(11), 1992, 728-733.
[3] K. Kouyama, S. Sato and K. Saka, J. Japan Institute of Light Metals, 42(7), 1992, 418-426.
[4] N. Niwa, M. Gunji and K. Itoh, Annual Report of General Institute of Faculty of Engineering,
University of Tokyo, 45, 1986, 255-262.
[5] Y. Obata, K. Aoki, Y. Mori and A. Nozue, Progress in Acoustic Emission VIII, Proc. of 13th
Int. Acoustic Emission Symp., JSNDI, Tokyo, 1996, pp. 231-236.
[6] T. Ohira and T. Kishi, Bulletin of the Iron and Steel Institute of Japan, 70, 1984, 2188-2195.
255

[7] M. Ohtsu, Journal of Geophysics Research, 96, 1991, 6211-6221.
[8] T. Kobayashi and D. A. Shockey, Metallurgical Transactions A, 18A, 1987, 1941-1949.
[9] Y. Mori, Y. Obata, S. Nose, P. Kriengsak and T. Umeda, Proc. of the 10th Intl. Congress on
Fracture, Honolulu, 2001, Elsevier Science, CD ROM, Ref. No. 1014.
[10] Amer. Foundry Soc., Cast Metal Handbook. Des Plaines, Illinois, 1957, p. 92.
[11] F. Nakanishi and S. Okamoto, Trans. of Japan Society of Mechanical Engineering, 17(61),1951, 103-107.
[12] T. Noguchi, J. Society of Materials Science, Japan, 29, 1980, 387-393.
[13] K. Kriengsak, K. Satoh, Y. Mori, B. Paritud and T. Umeda, Proc. of the 3rd. Asian Foundry
Congress, The Korean Foundrymen’s Society, 1995, pp. 296-303,[14] T. Noguchi, Transaction of Japan Society of Mechanical Engineering, A, 54-507, 1988,
1962.
[15] T. Takao and H. Nisitani, J. Society of Materials Science, Japan, 36(409), 1987, 1060-1064.
[16] T. Ishibasi, Transaction of the Japan Society of Mechanical Engineering, 18, 1952, 87-88

256

EWGAE2002

NON-DESTRUCTIVE EVALUATION OF BRAZED JOINTSBY MEANS OF ACOUSTIC EMISSION
H. TRAXLER1, W. ARNOLD2, W. KNABL1 and P. RÖDHAMMER1
1

PLANSEE Aktiengesellschaft, Technology Center, A-6600 Reutte, Austria
2
Fraunhofer Institut für Zerstörungsfreie Prüfverfahren (IZFP),
D-66123 Saarbrücken, Germany

Abstract
Brazing is a key technology in the production of components made of high performance materials. Due to the extreme operating conditions and stringent safety requirements, there is astrong demand for quality assurance by non-destructive evaluation of brazed components such ascomposite X-ray targets. An established method for non-destructive testing is ultrasonic Cscanning in the pulse-echo mode. This method allows one to detect voids in the brazing interface
such as voids, which are caused by inhomogeneous wetting by the filler metal of the parts to bebrazed. Additionally there is an interest for the microstructure of the braze alloy and for testing
its loading capacity. To get information on these properties at present metallography or loading
tests up to destructive load levels are required. The results presented here show that acoustic
emission (AE) during a loading test to a subcritical load level is sensitive to deviations in theprocess parameters causing embrittlement of the brazing alloy. The origin of the AE signals has
been identified by the systematic exclusion of the various possible sources in a comparative experimental study. Furthermore, a correlation between the non-destructive AE test and the destructive load test has been established. Samples, which failed prematurely in the destructive
load test, showed a strongly increased signal rate and a specific type of signals in the AE test.
Keywords: Acoustic emission, Non-destructive testing, Brazing, X-ray target
1. Introduction
The improvement of X-ray imaging systems for medical applications entails increasing
power densities of the electron beam striking the X-ray target. Due to the large energies deposited on the targets, they must be rotated during the application to avoid melting of the target material. A typical design of X-ray targets consists of two major components, a metal disc made of
the molybdenum alloy TZM (0.5 wt-% Ti, 0.08 wt-% Zr), onto which a layer of a tungstenrhenium alloy facing the electron beam is attached, and a graphite disc, which is joined to themetal disc by brazing (see Fig. 1).
Temperature values above 1200°C and rotation speeds up to 10,000 revolutions per minutecharacterize a typical load cycle of X-ray targets. In view of safety risks during X-ray diagnosis
no defect of the brazed joint is allowed. The demand for reliable quality assurance is met by theapplication of different testing methods for the brazed joint. One of them is the non-destructive
evaluation (NDE) using ultrasonic testing in pulse-echo mode. This method allows us to detectvoids in the brazing interface, but gives no information about its mechanical loading capacity.
The latter may be determined by a destructive load test, such as the “crash test”. During this test

J. Acoustic Emission, 20 (2002)

257© 2002 Acoustic Emission Group

TZM

W/Re

Braze

GraphiteFig. 1. Photograph of X-ray target studied.
a target of a production lot is heated up to some hundreds degrees C and afterwards shock-cooledby immersion in cold water. If there is no visible damage at the examined X-ray target after 20
cycles of thermal loading, the production lot is released.
The goal of the present work was the development of a non-destructive load test to gauge thebond strength of brazed X-ray targets by applying acoustic emission (AE), and in consequence
the replacement of the crash test. The method applied in this test is to induce – by way of elastic
deformation of the brazed composite – a specific pattern of tensile and shear stresses at the TZM– graphite brazing interface and to monitor the ensuing AE response of the interface in the highkilohertz regime. Until now, a few examples of NDE of material joints by AE are known [1, 2].
2. Experimental Set-Up
2.1 Mechanical loading of samples
For loading the samples, the outer perimeter of the metal component of the target is supported by a steel ring. When applied along the central axis of the target, a force (F) effects abending with circular symmetry. The set-up and the coupling of the AE sensors to the part to betested are shown in Fig. 2. This set-up is used because the graphite component cannot bear mechanical loads without surface damage. The stress distribution in the interface between the metal
and the graphite component under maximum test load was calculated by means of finite element
analysis. Pre-stresses present after cool-down from the brazing temperature were taken into account. The resulting distribution of normal and shear stresses is shown in Fig. 3. The stress distribution shows a pronounced maximum in the vicinity of the inner diameter for both normal andshear stresses. To validate the finite element simulation, the deformation under load was measured and compared to the calculations. A test load of 40 kN caused a 70 µm deformation in thecenter of the target, in good agreement with the calculations.
2.2 Measurement of acoustic emission
In the present work the AE signals were detected using piezoelectric sensors covering thefrequency range between 200 and 900 kHz (Dunegan SE-650P). To minimize the influence of
external noise in the measurement chain, and hence to improve the signal-to-noise ratio, the sensor signal was pre-amplified by 34 dB with the preamplifiers shielded and close to the AE sensors. For data acquisition, storage and analysis the system Vallen AMSY4 was used. Four AE
sensors were attached to the sample to allow the location of the AE events. To accurately deter258

F
W/Re
AE sensor

AE sensor

Steel ring

TZM
GraphiteFig. 2. Schematic view of the mechanical loading of X-ray targets and coupling of the AE sensors to the sample. The load F is introduced in the centerline of the target.

Fig. 3. Normal (solid line) and shear (dashed line) stresses in the interface between the metal andthe graphite component of an X-ray target caused by mechanical loading up to 40 kN. The target
has circular symmetry. The hatched area indicates the area of the brazed zone, which extends
from 25 mm to the outer perimeter of the target at 70 mm.
mine the location of the AE signals, the threshold was set at the relatively low value of 25 dB (inreference to 0 dB at 1 µV) to ensure that the mode of fastest wave propagation triggers all fourchannels. Experience showed that a higher threshold value led to unreliable results in locating
the AE sources since the different channels were then triggered by different wave modes. With ahigh threshold value the fastest mode triggers only the sensor next to the signal source and themore distant sensors are triggered by a slower wave mode carrying higher signal energy. To
avoid a saturation of the data recording a front-end filter at 40 dB was applied.
3. Results
3.1 Investigation of the signal sources
The first approach to investigate the AE behavior of the X-ray targets was to study the emission of the individual components. To this end the metal component and the graphite component
259

were loaded mechanically. The AE of these parts was then compared to that of the X-ray target,
which had been brazed applying the standard brazing parameters. A micrograph of the metallographic section of the brazed interface of such a target (Fig. 4) shows that the braze metal, zirconium, upon solidification reacts with molybdenum and forms needle-shaped primary crystalsof the brittle intermetallic phase (Mo2Zr). Furthermore, a carbide layer forms at the interface tothe graphite component. Also there exists a skeleton of ZrC within the graphite zone in the vicinity of the interface, due to infiltration of the zirconium melt into the porous graphite disc
(10 % porosity). A target with altered production history was studied in this stage of investigation. This target was brazed at higher temperature, which caused a significant increase in theformation of both Mo2Zr and ZrC brittle phases (Fig. 5).
Fig. 4. Micrograph of a metallographic
section of the brazing interface of an
X-ray target brazed applying standard
conditions.

TZM

Braze

Graphite

100 µmFig. 5. Micrograph of a metallographic
section of the brazing zone of an X-ray
target brazed at higher temperaturethan standard. The larger content ofMo2Zr-crystallites in the brazed zone
and the thicker carbide layer at thebraze-graphite interface are clearlyvisible.

TZM

Braze

Graphite

100 µmThe signal energies of the AE from the metal and the graphite component and of the two target variants are compared in Fig. 6. The rapid increase between 8 and 9 kN in the AE-energy
versus load graph of the graphite component (dashed line) indicates the fracture of the component, after which the test was stopped. Apart from this failure the individual components exhibit
a negligible AE compared to the brazed targets (see Fig. 6). In the frequency range considered
here, the elastic deformation of molybdenum did not cause significant AE also in earlier investigations [3]. Therefore, AE of the brazed composite may for practical purposes be attributed
solely to the area of the braze interface. The elevated brazing temperature leads to a significant
260

Fig. 6. Comparison of the rate of acoustic energy released during mechanical loading for the
metal and the graphite component, and the two different types of brazed targets examined.
On the y-axis the AE energy per 1 kN loading interval is plotted.

Graphite

TZM

Graphite

TZM

Fig. 7. Scanning electron microscope imageof the zirconium braze after the mechanicalloading test. The arrows mark micro-cracks
in Mo2Zr crystals.

Fig. 8. Scanning electron microscopy imageof the zirconium braze after the mechanicalloading test. The arrows mark micro-cracks in
the ZrC layer.

increase of AE during the mechanical loading test (cf. Fig. 6). Further tests showed that twosource mechanisms could be identified: Micro-cracks in the Mo2Zr crystals (see Fig. 7) andcracks in the carbide layer (see Fig. 8).
3.2 Signal location
Typical results for the distribution of AE activity over the brazing interface of an X-ray target(standard brazing configuration and parameters) are shown in Fig. 9. In this plot the number of261

Fig. 9. Locations of AE signals during a mechanical loading test of an X-ray target up to 40 kN.
The target is round. The hatched area indicates the area of the brazed zone, which extends from25 mm to the outer perimeter of the target at 70 mm.
AE signals, called AE-hits, are measured in radial segments of 1-mm width and plotted as afunction of radius. This source location plot shows a maximum at the inner perimeter of thebrazed zone in agreement with the finite element simulation (see Fig. 3) where both the normal
and shear stresses are highest at the inner perimeter. From the AE data it is not possible to distinguish whether the normal or shear stress caused the AE signal. Since compression at the stresslevels calculated is not likely to cause micro-structural failures in the brazing interface, the shear
stresses are held responsible for this damage on a microscopic scale. The signals located outside
the brazing zone (radius smaller than 25 mm or larger than 70 mm in Fig. 9) are an indication for
the relative number of false location due to triggering delays described in Sec. 2.2.
3.3 Correlation to established tests
To compare the results of the AE test with established testing methods, a series of 50 X-ray
targets partially failed in the ultrasonic test (US) or in the crash test were examined further (seeTable 1).
Table 1. Results of the established testing methods for a series of 50 X-ray targets.
Results of the US test
OKOK
failedResults of the crash testOK
failedOK

Number of targets
43
3
4

All targets listed in Table 1 had identical geometry and were produced using standard parameters. The results of the AE test on these targets are summarized in Fig. 10. The histogramshows the AE energy emitted by each target during the mechanical loading test. The distribution
exhibits the maximum at the lowest AE-energy levels. The targets, which passed both the US
and the crash test without failure, emit AE energies up to 2.3 x 106 eu with one outlier above
107 eu.

262

Fig. 10. Histogram of the AE-energy accumulated during a mechanical loading test up to a load
level of 40 kN of the X-ray targets listed in Table 1.
The increase of the AE energy levels of the targets, which failed in the ultrasonic test, wasinsufficient to discriminate them against the distribution of flawless targets. However, targets,
which later failed in the crash test, emitted significantly higher AE energies than the flawlessones. The increase in AE energy release is associated with the emission of a few specific signals
showing amplitude values up to the saturation value of the data acquisition chain at 100 dB. We
attribute such high amplitude bursts to avalanches of micro-failures involving very large numbers of micro-cracks.
4. Conclusions
AE stimulated by non-destructive bending deformation of brazed X-ray targets provides avaluable tool to sensitively detect variations in the brazing process; in particular, deviations in
the temperature profile during the brazing operation. Upon statistical analysis, the results of AE
tests obtained from large samples may be used to maintain the brazing parameters within thestatistical process control window of tolerance.
In addition, we have demonstrated that the accumulated AE energy detected during the nondestructive mechanical load test of X-ray targets allows one to reject with a high confidence
level those targets, which fail in the destructive crash test. Replacement of the latter by the AE
test allows to increase the sampling of test parts up to 100 % at comparable costs, and thereby toeliminate the risk of passing flawed targets. Defects in the joining zone, such as voids, which canbe detected by ultrasonic C-scans, appear to have influence on the AE, but insufficient for discrimination against flawless targets.
In summary, AE under mechanical loading has proven a valuable tool to gauge the mechanical integrity of braze joints.

263

Acknowledgement
We are grateful to H. Klocker for carrying out the finite element analysis and to A. Plankensteiner for fruitful discussions. The assistance of A. Wolf is gratefully acknowledged.
References[1] Schmitt-Thomas Kh. and Waterschek R., “Untersuchung der Güte von Weichlötstellen mit
Hilfe der Schallemissionsanalyse”, Proc. 8th Congress on Material Testing, Vol. 1, OmikkTechnoinform, Budapest (1982), pp. 1180 – 1184.
[2] Sell J. and Weiler L., “Quality Control Proof-test with Acoustic Emission of Ceramic Cooling Plates”, Proc. 23rd European Conference on Acoustic Emission Testing, TÜV Vienna,
Vienna (1998), pp. 140 – 145.
[3] Traxler H., “Schichtprüfung mittels Schallemissionsanalyse”, Diplomarbeit Technische
Universität Wien (1997), pp. 56 – 64, unpublished.

264

EWGAE2002

DAMAGE MODE IDENTIFICATION AND ANALYSIS OF COATED GASTURBINE MATERIALS USING A NON-DESTRUCTIVE EVALUATIONTECHNIQUE
Y. VOUGIOUKLAKIS1,2, P. HÄHNER2, V. KOSTOPOULOS2 and S. PETEVES1
1.

European Commission, Joint Research Center, Institute for Energy, 1755ZG, The Netherlands.
2.
University of Patras, Applied Mechanics Laboratory, Rio GR-26500, Greece.

Abstract
The performance of coated high-temperature material systems under close-to-service
conditions can be assessed by means of thermo-mechanical fatigue (TMF) tests, while acoustic
emission (AE) can be applied for the in-situ monitoring of damage processes occurring during
these tests. This work presents experimental methodologies adopted for assessing the damage
behavior during TMF testing. In view of the complexity of these tests and the lack of detailed
knowledge of the damage evolution in those material systems, AE monitoring has been used, inorder to reveal and interpret the micromechanical behavior during TMF. The results aresuggesting that the use of the AE technique during the thermal and mechanical solicitation isextremely powerful as it enables non-destructive damage monitoring and it offers possibilities
for the life assessment of coated high-temperature material systems.
Keywords: Thermo-mechanical fatigue, AE activity, CM186
1. Introduction
The research and development efforts for enhanced performance of high-temperaturematerials for gas turbines is driven by the need to meet the demands for increased lifetimes andhigher efficiencies at lower costs and reduced environmental impact. These ambitious goals canonly be achieved through a combined approach comprising the development of new material
systems and the use of advanced materials testing methods, in order to improve theunderstanding of the materials behavior and to allow for less conservative designs [1-4]. This
work presents an experimental set-up optimized for assessing the materials systems behavior andlife under thermo-mechanical fatigue (TMF) testing.
Acoustic emission (AE) is a non-destructive method that allows the in-situ monitoring of
damage evolution during combined thermal and mechanical loading of a material. As comparedto other non-destructive techniques, which provide information about the damage accumulated ata certain stage of life, AE gives access to damage processes inside a material in terms of the rateof damage accumulation, and it is a highly sensitive means for the early detection of impending
failure [5]. Hence, the implementation of AE monitoring in a laboratory environment can be
instrumental in interpreting the damage mechanisms, as these are triggered and progressing
during TMF tests. Furthermore, AE is an extremely versatile technique to the experimental setup, has no constraints concerning into the specimen geometry and can be applied even tocomplex material systems.

J. Acoustic Emission, 20 (2002)

265© 2002 Acoustic Emission GroupIn this work, results from AE data analysis of TMF tests on single crystal and directionallysolidified (DS) superalloys in the uncoated and coated conditions are described. Goal of thisstudy is to establish the validity of the information obtained by AE monitoring during TMF andto correlate the AE data with specific damage processes (modes).
Currently, the efforts are focused on a simple material system (substrate with a diffusion type
coating) in order to evaluate the contribution of AE into identifying the damage processes.
Ultimately, the goal of our efforts is to apply this method to a more complex material system (athermal barrier coating system) in order to scan all the damage processes that are activatedduring a TMF test.
2. Experimental Set-Up
Specimens were prepared from single crystal (SC) and DS Ni-based superalloy, CM 186
alloy bars, with the crystallographic orientation of the long axis of the samples in the [001]direction. Threaded end specimens with a solid rectangular cross-section of 12 x 3 mm2 and aparallel length of 9 mm were machined. The rectangular cross section was adopted to enable
observation of the flat surface by means of a computer-controlled video imaging system during
testing, as discussed later. Some of the SC and DS CM186 samples were coated with a modified
aluminide coating, Sermalloy 1515, via an Al/Si slurry diffusion process, with an approximate
thickness of 80 µm.
2.1 Mechanical testing set-up
The strain-controlled TMF tests were carried out in air on a computer controlled electromechanical closed loop-testing machine of 100 kN capacity (Schenck Trebel). Asymmetricaltriangular out-of-phase cycles with mechanical strain ranges ∆εm of 0.8% and 1.0%, respectively,
and a temperature range ∆T=600°C with temperature cycled between 350°C and 950°C werechosen (minimum to maximum mechanical strain ratio R=−∞, cycle period ∆t=200s) (see Fig. 2).
Temperature and mechanical strain both varied linearly with time, but with a phase shift of 180°.
This cycle imposes maximum mechanical strain/stress at minimum temperature and minimum
strain/stress at maximum temperature. Control of the testing machine, the high frequency
induction heating system and data acquisition was performed by means of a dedicated computer
system using LabVIEW Compensation of the thermal strain was achieved by recording thethermal expansion under zero load as a function of time prior to TMF testing using the same
temperature cycle as in the subsequent test. Prior to the actual TMF tests, the temperaturedependence of the Young’s modulus was also determined by isothermal static measurements in
the TMF temperature range.
The tests were started at the minimum temperature and at zero mechanical strain, andconducted until failure, or stopped when the cyclic stress range had dropped at levels 50% below
the stress range during the previously stabilized value. TMF life is then defined as the ultimate
cycle number.
During TMF testing, images of the flat surface of the specimen were taken at pre-selected
cycle numbers by means of a computer controlled video imaging system, allowing contact-less,in-situ and fully automated monitoring of the evolution of surface damage [6]. To enhance thedetection of crack initiation (with a surface length ≥ 15µm), the CCD camera with a flash unit
was synchronized with the TMF cycle and images were taken when the imposed stress on thespecimen exceeded 80% of the maximum tensile stress of the previous cycle. These images266

covered the whole specimen width and the gauge length. All the images were digitized andstored for post-processing and analysis. More details on the TMF set-up can be found in Ref. [2].
2.2 Acoustic emission set-up
Two broadband piezoelectric transducers (PAC WD) were used as sensors to monitor the
acoustic emission during the tests. The sensors were attached on the grips of the machine justafter the two cooling plates used to protect the gripping system and to stabilize the temperature
control. The AE signals were pre-amplified (PAC 1220A with 500-kHz high-pass filter) and fed
into a two-channel AE digital signal processor. The acquisition of the AE signals was performed
by a computer system using MISTRAS-2001 software configured with the specific mechanical setup [7]. The initialization parameters of the AE set-up were optimized with respect to the external
noise (ambient or induced by the induction heating system), the experimental set-up, thespecimen geometry and volume of interest (the bulk inside the gauge length).
AE sensor

Water coolinglens

Induction coil
Water cooling

AE sensor

Fig. 1. Schematic drawing of the acoustic emission set-up.
The AE signals were collected in time-order and seven different AE features were monitoredeach time: rise time, counts, energy, amplitude, duration, average frequency, counts to peak, aswell as the current stress value and the cycle number as input parameters. The evaluation andanalysis of the received signals was performed using NOESIS and SPSS software packages.
3. TMF Tests
The TMF results and post mortem fractographic and microstructural analyses of the samples
have been reported and discussed elsewhere [8]. The identified damage modes are named briefly
as: (1) damage initiation sites, precipitate cracking at the substrate-coating interface, (2)subsurface-crack propagation in the coating and crack interaction, (3) surface cracking, crack
coalescence, (4) oxidation-controlled fatigue crack growth.
As such a few relevant results will be presented here; namely, the pronounced cyclic creep
during the first cycles caused by the accumulation of compressive inelastic deformation due toplastic flow at high temperature (Fig. 2). It was observed that the coatings crack in mode I (Fig.
3). These cracks initiate in the coating at the coating/substrate interface (Fig. 4), grow towards
the surface and finally propagate also into the substrate by Mode I extension, producing a crack
front almost parallel to the specimen surface and a crack path perpendicular to the loading
direction.
267

0.2
0.0

strain [%]

500
-0.2
-0.4

0

-0.6

εm
εin

-0.8
-1.0

0

50

T
σext
100

150

200

-500

stress [MPa]; temp. [°C]

1000

-1000

time [s]Fig. 2. Thermomechanical behavior of uncoated DS CM186 sample during the first cycle (εm :mechanical strain, εin : inelastic strain, T: temperature, σext :external tensile stress).

Fig. 3. Post-mortem scanning electron micrographs from a coated CM186 sample.

Fig. 4. Post-mortem scanning electron micrograph showing the initiation sites of cracks into thecoating – substrate interface.
268

4. AE Analysis
The AE signals were separated according to the mechanical load state at their time ofoccurrence: compressive or tensile signals. Table 1 presents the global information for each test.
A first conclusion resulting from Table 1 is that more AE signals were received from the coatedspecimens.
Table 1. Experimental conditions and AE activity in terms of number of AE events.
Material
Mechanical strain range
TMF lives (# cycles)
AE events in tension
AE events in compression
AE events in Region I
in tension
in compression
End of Reg. I (cycle #)(% of TMF life)
First crack at cycle #

SC
1%
1941
749
2491

SC
0.8%
9165
1309
4238

SC, coated
1%
1121
2393
4813

SC, coated
0.8%
5300
7022
5918

DS
1%
1412
742
850

DS, coated
1%
2404
14693
2629

…
…
…

…
…
…
1000

6484
1640
2508
(47%)
300

…
…
…

600

1675
1475
335
(30%)
200

6453
1892
933
(39%)
200

400

When the temporal evolution of the overall AE activity during the test is considered, twodistinct regimes of AE activity are revealed; Regions I and II (see Fig. 5), which are sometimes
separated by an inactive period. In the case of the coated samples, both regimes are present,
whereas in the case of the uncoated samples only Region II is active during the last 20% of the
TMF life (Fig. 5).

Fig. 5. AE activity for a coated and an uncoated specimen throughout the test.
269

This observation provides a clear distinction between signals originating exclusively from thecoating (Region I) and those involving also the substrate (Region II). Therefore, signals from
Region I relate to damage processes in, or induced by the presence of the coating [9], while
signals from Region II reflect the behavior of the bulk material independently of the presence ofthe coating. These signals are associated with the final stage of the test before the failure of thespecimen, which is brought about by the fatigue crack growth.
The AE data recorded during the late stages of the TMF tests, i.e. events from theaforementioned Region II, are considered for further analysis in terms of a supervised patternrecognition. This stage may reveal precursory events, before the immanent failure due to the fatal
fatigue crack growth becomes obvious from the mechanical response of the specimens.

(a)

(b)
Fig. 6. AE events (symbols) and cyclic maximum and minimum stress envelopes (solid lines)
during TMF tests for SC (a) and DS (b) coated samples.
Figure. 6 shows two examples of the AE activity in the stress vs. cycle number domain. For
the SC sample, Region II is well separated from Region I by an inactive period, which is missingin the case of the DS sample. In both cases, AE activity in Region II starts at an intermediate270

stress level (close to zero stress or slightly compressive stresses), it spreads with respect to its
stress range of occurrence, while its rate of activity increases as TMF cycling continues. At alater stage of TMF life (80-90%) another type of AE activity emerges which relates to the
maximum stresses (min. temp.) available within the TMF cycles. This AE activity accompanies
the ultimate fatigue crack propagation (Fig. 7).

Fig. 7. Last stage (Region II) of AE data under tension for an uncoated sample. (top)Surface images from the video imaging system during the last cycles. (bottom)
Figures 6a and 6b show that Region II may start as early as approximately 40% of theultimate TMF life, thus, providing important information about damage mechanisms preceding
the propagation of the fatal crack. Further analysis of this last part of the AE data is needed, toconsolidate the observed trend for these samples and to relate the Region II AE activity to
specific damage mechanisms.
271

Based on the general understanding of the damage evolution inside the material andconfirmed by post-mortem microstructural analysis (Figs. 4 and 8), it is expected that the
propagation of cracks inside the substrate is initially controlled by the cyclic formation andfracture of an oxide layer grown at the crack tip during the high-temperature parts of the TMF
cycles. This oxidation controlled crack growth will cease when a critical crack length is reachedsuch that the toughness of the un-oxidized bulk material is exceeded. Then the crack will be free
to propagate at a higher rate controlled by the mechanical loading during the low-temperaturepart of the cycles. The difference in nature of these two crack propagation mechanisms results indistinct clusters of AE signals in the stress vs. cycle number domain.
The oxidation-controlled crack growth relates to AE signals that are generated at intermediate
applied stress levels, as low stresses are sufficient to crack the brittle oxide layer (note that
appreciable tensile internal stresses may assist the cracking of the oxide scale, such that the local
stresses at the crack tip are still tensile even if the applied stress is slightly compressive).
Ultimately, when oxidation-controlled crack growth gives way to stress-controlled crack
propagation, the AE signals related to latter growth mode occur close to the maximum cyclic
stress values (Fig. 8).

Fig. 8. Correlation of AE signals with hypothetical damage mechanisms during the last stageand verification of the existence of these mechanisms by a SEM micrograph.
By choosing stress and cycle number as descriptors in addition to the other physical AEfeatures a supervised cluster analysis can be performed, the result of which will be thecorrelation of the two assuming damage mechanisms with different groups of signals, i.e.
clusters. In this case, where clusters are clearly distinct already in the stress vs. cycle number
domain (cf. Fig. 8) the important information provided by the AE activity does not so much
relate to the physical AE parameters, but mainly to the mechanical state of the system and thetime of their occurrence.

272

5. Conclusions
The analysis of the AE data generated during the previously described experiments revealed
valuable information about the validity of AE as a method to monitor and identify damage
processes. Utilizing these methodically analyzed AE data and combining the results withinformation provided by mechanical, video and post mortem microstructural observations, thedamage evolution was assessed in terms of different damage mechanisms associated with distinct
clusters of AE signals. Future analysis is expected to reveal common trends of material systems
subjected to thermo-mechanical fatigue, as far as the signature of AE activity received prior tomacroscopic damage visible on the surface or inferred from the mechanical response isconcerned. This opens up possibilities for life prediction methodologies for high-temperaturematerial systems during thermal and mechanical loading.
References[1] Affeldt E.E., “Influence of an aluminide coating on the TMF life of a single crystal Ni-basesuperalloy”, J. Engineering for Gas Turbines and Power, 121, 687, 1999.
[2] Martinez-Esnaola J.M., Arana M., Bressers J., Timm J., Martin-Meisozo A., Bennett A.,
Affeldt E., “ Crack initiation in an aluminide coated single crystal during thermo-mechanicalfatigue”, Thermo-mechanical Fatigue Behavior of Materials, Vol. 2, ASTM STP 1263, M.J
Verrilli and M.G. Castelli eds., West Conshohocken, PA (1995), p. 68.
[3] Wood M.I., “The mechanical properties of coatings and coated systems”, Materials Scienceand Engineering A, A121, 633-643, 1989.
[4] Burgel R., “Coating service experience with industrial gas turbines”, Materials Science andTechnology, 2, 302-308, 1986.
[5] Pappas Y.Z, Markopoulos Y.P., Kostopoulos V., Paipetis S.A., “Failure mechanism analysis
of ceramic matrix composites on the pattern recognition approach of acoustic emission”, 1stHellenic Conference on Composite Materials and Structures, Ksanthi 1997, pp. 192-206.
[6] Arrell D.J., Wieneke B., “An in-situ optical technique for surface strain evolution
measurement”, Nontraditional Methods of Sensing Stress, Strain and Damage in Materials
and Structures, Vol. 2, ASTM STP 1323, G.F. Lucas, P.C. McKeighan, J.S. Ransom eds.,
West Conshohocken, PA, 2000, pp. 102-115.
[7] Mistras 2001 Manual, Physical Acoustics Corporation, Princeton, NJ[8] Peteves S.D., De Haan F., Timm J., Bressers J, Hughes P.M., Moss S.J., Johnson P.,
Hendersen M., “Effect of coating on the TMF lives of single crystal and columnar grained
CM186 blade alloy”, Proc. of the 9th Int. Symp. on Superalloys (Superalloys 2000), Sept.
17-21, 2000, pp. 655-665.
[9] Vougiouklakis I., Hahner P., Stamos V., Peteves S., Bressers J., “Acoustic emission analysis
of out-of-phase thermomechanical fatigue of coated Ni-base superalloys”, TemperatureFatigue Interaction, L. Remy, J. Petit eds., Elsevier, 2002, pp. 143-156.

273

EWGAE2002

ACOUSTIC EMISSION DURING STRUCTURE CHANGESIN SEMI-CRYSTALLINE POLYMERSJ. KROFTA1, Z. PREVOROVSKY1, M. BLAHÁCEK1 and M. RAAB2
1

2

Institute of Thermomechanics AS CR, Prague, Czech Republic
Institute of Macromolecular Chemistry AS CR, Prague, Czech Republic

Abstract
Tensile tests of polypropylene and its blends with polyethylene and other additives have beenrecorded by digital camcorder. Acoustic emission (AE) was monitored during tests by two transducers attached to test samples, allowing linear location of AE sources. Tested materials exhibit
large localized plastic deformation or necking, reaching hundreds of percent before failure. Image analysis procedure based on displacement of contrast markers on the surface has been used
to quantitatively evaluate axial and lateral strains and strain rates along the sample. Correlationof AE activity with a set of plastic deformation, neck formation and defect development enables
us to evaluate influence of additives on the whole stretching process.
Keywords: Acoustic emission, strain visualization, polymer drawing and necking
1. Introduction
The behavior of polymer materials beyond their elasticity limit is important for many technological operations, such as a production of reinforced fibers, sheets, plates and so on [1]. A
plastic deformation of semi-crystalline thermoplastics is characterized by the creation and development of a neck. In such cases, the localized plastic deformation can reach hundreds or thousands of percents. The mechanisms of plastic or visco-plastic deformation of these polymers arestrongly dependent on supra-molecular structure (content of the amorphous phase, the size andconfiguration of crystallites, etc.). The attention of plastic processing industry is in recent years
focused on cost reduction and improvements of mechanical properties of plastics. It can beachieved by the usage of the controlled co-polymerization or various blending. Typical exampleof the newly developed material is the blend of polypropylene (PP) and polyethylene (PE). Bothmaterials have different molecular and supra-molecular structure and as a result they also havedifferent mechanical behavior. The main task is how to interconnect these two structures so thatthe resulting blend should have the best mechanical properties (the problem of blend compatibilization). Special compatibilizers have been developed for this purpose (copolymer additives). In
this study, e.g., we used a modifier, VISTALON, with variable content of ethylene units.
The influence of compatibilizers or other additives (rubber particles, etc) is relatively small in
the range of elastic (or linear visco-elastic) deformation. But it becomes important when theplastic deformation becomes large near the neck formation and with neck elongation, as well as
in the case of defect (crack) initiation and growth. The creation and development of a localized
plastic deformation under different strain rates can be well documented using video-recording ofa sample during the standard tension test. Then it is possible to analyze the distribution of longitudinal and transverse deformation of the specimen in each moment of the video image record (itenables to localize the place of a neck formation and its continuous elongation). However, theJ. Acoustic Emission, 20 (2002)

274© 2002 Acoustic Emission Groupvideo-recording gives no local information about micro-mechanisms and structural defects related to the neck formation and elongation. Such information may be obtained by the acoustic
emission (AE) method [2].
2. Tension Tests of Polymer Blends
Polymer specimens for standard tension test were prepared; a set was made of pure isotactic
PP and further sets of PP and PE (HDPE) blends with various additives and modifiers. During
tension tests under different strain rates (5 to 200 mm/min.), AE signals were detected by two
sensors placed at the specimen ends. The goal was to monitor changes of plastic deformation
mechanism and beginning of damage and fracture, dependent on the composition of the material
and the strain rate. Many tests were performed with the following specimens: pure PP, PP+PE(50+50%) blend without compatibilizer, blend of PP+PE (47+47%) + 6% compatibilizer VISTALON 407 (i.e. copolymer PP+PE with 40% of ethylene units), blend PPR (PP+22.5% modifier
ROYALEN), blend PPK (PP+22.5% amorphous rubber KELTON) and blend PPB (PP+22.5% semicrystalline rubber BUNA).
Some experimental and procedural problems were encountered during the first tests: The firstquestion concerned with the reproducibility of acoustic coupling of broadband AE sensors placedon a glassy surface of the specimen subjected to large plastic elongation and neck development.
Next problem was related to the linear localization of AE sources during the continuous increaseof the distance between both sensors. Also a relatively high attenuation of tested polymer samples (more than 1 dB/cm) and its growth during the test causes AE detection and evaluation difficult. The detailed analysis of material structure changes requires correlation of AE sources withthe localized plastic deformation. Considering inhomogeneous distribution of plastic deformation
along the sample (neck formation and elongation), and the fact that the total elongation of thespecimens to the fracture can achieve tens to hundreds of mm (deformation is hundreds of %), it
is evident that only a non-contact (optical) strain measurement method can be used. Equidistantmarks were drawn on the specimen surface, and their changes were scanned by digital camcorder. Camera also scanned the position of the AE sensors. So the instantaneous distance between
sensors, necessary for successful AE source location, was obtained from video-records. The
video-record is synchronized with AE registration by the time code and also (for the fast check)by synchronous video-recording of the AE-analyzer audio-monitor output. Due to the high material attenuation and relatively noiseless deformation mechanisms, the AE analyzer had to be set
on high total amplification (of about 85 dB). Also the threshold level for AE counting was set
just above the background noise.
The experimental setup used in tension tests is shown in Fig. 1. Specimen is loaded using thecomputer-controlled electromechanical tensile testing machine TIRATEST 2300 with attached
dynamometer of 10 kN capacity. Signals from AE transducers were parallel-processed by two 2channel AE analyzers: The analog D/E 3000 analyzer served as a "stereo" audio-monitor andlinear locator. Signal waveforms were recorded by the digital analyzer DAKEL-XEDO equippedwith signal processors. It allows registration of global parameters, as well as digital recording
and frequency analysis of sampled AE events. Low-frequency audio-monitor output was recorded by the digital camera (48 kHz/16 bit) at the same time with movies. After the test, thevideo and sound data from the camera are transferred through the interface IEEE1394 (firewire)
into a PC. The changes of distances between calibration marks (initial spacing was 5 mm),changes of specimen widths (lateral deformation) and also changes of distance between AE sensors during the whole test are then analyzed by the special software designed for this purpose.
275

Analyzed details of specimen evolution during the test are shown in Fig. 2 (snapshots bysteps of 200 s). Video-recording is synchronized with presence of emissive event detected by AE
sensors with the accuracy of 40 ms. It makes it possible to correlate diagrams of heterogeneously
distributed instantaneous specimen deformation (strain rates) with that of AE event occurrence.

Fig. 1. Schematic drawing of experimental setup used in tension tests of polymer samples.

Fig. 2. Samples of videorecords during test of pure PP; the creation and elongation of the neck (5mm spaced markers are connected by white lines and neck ends by black lines).
276

3. Evaluation of Video-Recordings
We used a digital camcorder that scans 25 frames/s, with resolution 720 x 576 pixels, andcolor depth 24 bit. Video and sound information are encoded using camera firmware (codec DV
video). Resulting data flow recorded by the camera (DIGITAL-8 format) amounts to 3.6 MB/s.
After the test, all data are transferred to a PC hard disk. One-minute recording matches AVI filewith size of 216 MB. Total time of tension test until fracture of a specimen was as much as 30
minutes, which results in AVI-file size of about 7 GB. With respect to the main research objectives (deformation behavior of tested material, continuous plastic deformation and neck formation) the information content is inefficiently huge. Only selected frames were analyzed fromvideo files (50 to 500 frames at equidistant time intervals of 3 to 30 s). Distribution of longitudinal and lateral strain along the sample was automatically analyzed in selected frames, showing
the neck formation. At very high elongation rates (200 mm/min.) the test duration is only a few s,
hence all saved frames were analyzed.
Algorithm used for automatic evaluation of deformation characteristics from video-records
must take into account various problems associated with image recognition:a) Non-uniform scene illumination during the test (designed algorithm was sufficiently robust to eliminate that influence, so the contrast and brightness corrections were not necessary)b) Analyzed scene is distorted due to the short focal distance. Quite sufficient solution was
based on a simple linear transform (rhomboid transformed to rectangle).
c) During the sample stretching it is difficult to separate specimen from surroundings, andmoreover, both sample heads are moving (constant position have only static parts, e.g.
upper clamp). The problem is solved by suitable editing of the first and last frames (easily
detectable crosses are added to the last markings on a sample). The relative movement ofenlarged sample heads is influenced only by pure elastic deformation proportional to thestrain rate and thereby the sample position in all other scenes can be well localized.
d) The markers drawn on the specimen change not only their lengths (lateral contraction),but their widths (differently in various specimen parts). Variable width of markers disallows the use of classical line detection algorithm, and it must be replaced by the edge
detection (left and right edges are detected and the line position is identified as theirarithmetic mean). The edge detection is performed by the convolution of analyzed scene
with a suitable mask.
e) The light source is not placed in the camera axis, which results in shadowing of thickness
changes (false lines has sometimes more contrast than detected lines). Presence of falselines requires to detect real edges only around their expected position. The prediction isrealized using the edge position in previous scene and knowledge of exact strain rate.
Designed automatic algorithm consists of following steps:
1. Detection of the auxiliary crosses in the first and the last analyzed frame (i = 1, N).
2. Detection and localization of the left and right edges in the first frame.
3. For i = 2 to N:
Detection of the left and right edges in frame (i) using the knowledge about detectededges in figure (i-1). Determination of marker positions in frame (i).
4. For i = 1 to N:
Detection of top and bottom sample edges at places of detected markers in frame (i). Determination of specimens width in places of detected markers.

277

The accuracy of evaluated strain distribution depends primarily on a pixel resolution of camera used. Further errors are caused by the algorithm of video-recording compression, and by theprecision of drawn markings.
4. Discussion of Results
Results of AE analysis revealed that AE activity during the heterogeneous plastic deformation is predominantly concentrated in areas of necking (neck boundaries). After the further neckdrawing, especially if another deformation mechanisms start to develop, the other areas become
also active. Intensity of AE increases rapidly when defects are formed in plastically exhausted
zones. Development of crack-like defects lead to specimen rupture. The typical AE activity distribution along the sample of PP+PE blend is illustrated in Fig. 3a, where the AE activity is characterized by the number of events Ne (3 decades in logarithmic scale) emitted between two subsequent time snapshots taken at 30-s interval. The variation of loading rate from 0.1 to 50
mm/min has no substantial influence on the test history.

Fig. 3. AE activity during the test of blend PP (50%) + PE (50%).
a. Number of AE events Ne (log. scale) distributed along the sample snapshots.
278

Fig. 3. AE activity during the test of blend PP (50%) + PE (50%). b. (top) Loading force vs.
time. c. (middle) AE activity expressed by RMS values of both AE channels, A and B (upper andmiddle curves), and RMS recorded by camcorder (channel A). d. (bottom) Number of counts
Nc1 in channel A.
Samples made of pure PP exhibited very low AE activity, with the maximum in transition toplastic deformation, when the neck with sharp boundaries begins to form. When the neck elongates, the AE activity is rapidly reduced. It re-appears again just before the specimens rupture,when a fibrous structure forms and breaks (axial fibers are drawn out). If the displacement rate ishigher than 100 mm/min, the deformation behavior of the PP specimen becomes completely different, and specimen breaks in a quasi-brittle manner.
279

a.

b.

c.

Fig. 4. Test of the blend PP (77.5%)+ additive ROYALEN (22.5%)
a. Time dependence of the loading force F.
b. AE count rate dNc1/dt.
c. Time development of axial distributionof AE events Ne.
d. Strain rates dε/dt vs. t and x. Distributions
along the sample axis x are related to theinitial markers spaced by 5 mm (sectors).

d.

280

Blends made of PPR, PPK and PPB exhibited similar deformation behavior, but the formed
neck had no sharp boundaries. They are also characterized by the relatively small AE activity as
it is illustrated in Fig. 4.

Fig. 5. Test of the pure blend PP (50%) + PE (50%) (for other characteristics, see Fig. 3). Top:
Time development of axial distributions of AE events Ne. Bottom: Time development of strain
rate dε/dt distribution along sample axis. Distributions are related to initial markers spaced by 5
mm (sectors).
Quite dissimilar AE activity was detected for PP+PE blends. Already initial tests indicateddifferences in deformation behavior of blends with added compatibilizers. Test examples areshown in Fig. 5 (PP+PE without additives, see also Fig. 3) and in Fig. 6 (mixture with 6% ofcompatibilizer VISTALON 407).

281

Fig. 6. Test of blend PP (47.5%) + PE (47.5%) + VISTALON (5%). Top: Time dependence of theloading force F. Middle: AE count rate dNc1/dt. Middle-lower: Time development of axial distributions of AE events Ne. Bottom: Strain rates dε/dt, vs. t and x. Distributions are related toinitial markers spaced by 5 mm (sectors).
282

Detailed analysis of considerably different AE activities at distinct periods of drawing process can help explain structural mechanism of compatibilizer effect. AE activity of samples without modifier markedly increases at the beginning of neck formation without sharp boundaries.
This activity remains very high until the specimens rupture, and can be compared with AE ofpolymer composites. To explain AE activity, we must consider that PP and PE have different
crystallite sizes and the structure with different crosslinks (tie molecules). In a blend, there are
two different networks mechanically joined together, which must be rearranged and uncoupledduring their deep drawing. In contrast to previous pure polymer, the blend PP+PE with added
compatibilizer VISTALON 407, which is completely amorphous copolymer, had in the first deformation phase the behavior similar to the pure blend. When the neck started to draw, the AE
activity became very low, similar to the pure PP. It may be caused by the presence of compatibilizer, which allows better sliding of networks through the amorphous phase. Nevertheless, after
reaching a certain drawing limit, the AE suddenly starts to grow to the level of the blend without
modifier. This relatively complicated AE behavior reflects very sensitively changes of structural
mechanism during various deformation periods in the materials with complicated molecular network structure. Synchronous video-recording with the AE monitoring shows the instantaneous
correlation of the heterogeneously distributed plastic strain rates with the occurrence of active
AE sources.
Recorded AE signals were also subjected to frequency analysis and their evaluated parameters in time and frequency domain were used as input data for the source type classification.
However, no sharply different classes have been recognized. It is probably caused by many geometrical factors affecting the way of elastic wave propagation:a) Dimensions of the active part of test specimen (100 x 10 x 4 mm, initially) are comparable tothe length of detected elastic waves spreading from AE source to a sensor (2 to 20 mm inhundreds kHz frequency range). It results in strong wave dispersion and guided waves aremainly detected, which substantially modify the spectral and other characteristics of received
signal (filtration effects) and partially implicate loss of information about the sources.
b ) Geometrical form and dispersion behavior of the specimen pass through considerable
changes during the course of very large deformations (hundreds of percent). Moreover, thedistance between AE sensors changes. It complicates the attempt of getting precise linear location of AE sources.
c) Tested polymer materials exhibit relatively high attenuation in the used AE frequency range(100 kHz to 1 MHz). Only the AE events with higher amplitudes were detected by bothtransducers. This allowed localization of only about 30% of all registered events. To obtaincorrect interpretation of structural changes, it is therefore necessary to evaluate global AE
characteristics in both channels separately, and make corrections to them for the attenuation
changes.
5. Conclusions
Deformation behavior of semi-crystalline polymer materials, PP and PE, and their blendswith various additives, was investigated by means of video-recording and AE monitoring during
the tensile tests. The video-records were used to evaluate the localized plastic deformation distributed along the sample. Comparison of plastic zone expansion (necking) and spatial distribution of AE events shows that primary sources of AE are places, where the plastic necking starts
to develop, areas of neck drawing, and finally locations where neck stopped its elongation andcrack defects are created. It has been detected that deformation and AE behavior of pure PP and
PP with small addition of rubber-like fillers (very low AE activity) markedly differs from the283

behavior of polymer blends of PP with PE, whose plastic deformation is accompanied by very
high AE activity. Tests also helped to clarify influence of blending compatibilizers on a plastic
zone development: Compatibilizers prolong the first stage of plastic deformation with low AE
activity (comparable with the pure PP), but after reaching some deformation limit, their AE behavior is similar to blends without additives.
Using the low-cost digital camera for video-recording of mechanical tests together withproper evaluation algorithms allows detailed analysis of AE source distribution connected withstructural changes during the localized plastic deformation.
Acknowledgment
The project was supported by the Grant Agency of the Czech Academy of Sciences under the
grant No. A 4050904.
References[1] Raab M., Sova M., Slízová M.: “Importance of polymer morphology in understanding technological processes”, Internat. Polym. Sci. Technol., 21, T81-T88 (1994).
[2] Raab M., Prevorovsky Z., Kardos J.L: “Quiet and noisy polyethylenes: Birth and Failure of acomposite”, 17th Europhysics Conference on Macromolecular Physics "Morphology of Polymers", Prague 1985, Book of Abstracts, p. 95.

284

EWGAE2002

ACOUSTIC EMISSION CHARACTERISTICS OF SURFACE FRICTION
IN BIO-MEDICAL APPLICATION
D. PREVOROVSKY1, Z. PREVOROVSKY1, J. ASSERIN2, D. VARCHON3
1

Institute of Thermomechanics AS CR, Czech Republic; 2 EVIC France, Bordeaux, France
3
LMARC Université de Franche-Comté, Besançon, France.

Abstract
The paper describes the use of acoustic emission (AE) techniques in a study of surface friction properties. All dynamic friction processes generate quasi-continuous AE signal, which canbe used to characterize surface friction. The AE signal is produced by stick-slip motion of twomaterials in contact. Signal intensity depends on contact force, motion velocity, molecular adhesion forces between both materials, and surface properties as roughness, hardness, elastic andplastic properties and surface layer properties (lubrication, dry friction).
A series of tests have been performed using a new prototype of friction brush probe combined with tangential force measurement. This probe has been designed for measurement of human skin friction coefficient, but can be applied also to other materials. Measured signals (AEsignal, tangential and normal forces) were recorded and analyzed by digital processing analyzer
DAKEL-XEDO. Various friction head materials were tested on samples made of different materials. The best results were obtained using carbon-fiber brush friction head. The contact force andmotion velocity were optimized to produce maximal AE signal amplitude at a contact force as
low as possible. Good correlation was observed between frictional forces and global AE activity
parameters.
1. Introduction
Properties of a solid body surface (surface roughness, coarseness, abrasiveness, profile, topology, friction, adhesion tension, etc.) are often characterized by static or dynamic friction
measurements. Friction can be defined as the resistance to the movement of one body in relation
to another body with which it is in contact [1].
The degree of friction between two objects is greatest when they are at rest (static friction).
The direction of static frictional force is along the contact surface and opposite in direction of
any applied force. The magnitude of static friction force fs is given by
fs = µs N,(1)where µs is static friction coefficient, and N is normal force acting between two objects. The
frictional force equals the applied force (in magnitude) until it reaches the maximum possible
value µsN. Then the object begins to move as the applied force exceeds its maximum. When theobject is moving the frictional force is kinetic (kinetic or dynamic friction) and roughly constantat the value µkN, which is below the maximum static friction force. The direction of the kinetic
frictional force is opposite the direction of motion of the object it acts on. The magnitude of the
kinetic friction force fk is proportional to normal force N and the coefficient of kinetic friction µk
fk = µk N.
J. Acoustic Emission, 20 (2002)

(2)
285

© 2002 Acoustic Emission GroupFig. 1. General relation between frictional and applied force
The coefficients of friction depend on the nature of the surface, and the frictional force isnearly independent of the contact area between objects. The plot in Fig. 1 of the frictional force
vs. the applied force illustrates some of the friction features. Friction depends upon the propertiesof the two surfaces in contact. The irregularities in surfaces (the degree of roughness) cause
resistance to movement. The force of friction is a common but complex force. A simple model ofthe friction force can be built up assuming that the coefficient of friction is the sum of two terms,
molecular and mechanical [2];µ = µmolecular + µmechanical.
(3)
Molecular interactions take place in the surface 'film' and affect the surface layers to a depthof a few hundredths of µm. Mechanical interaction takes place in layers with a thickness of a fewµm. As these processes occur at different levels, they are uncorrelated and hence can be mostly
separated. Kinetic friction coefficient is measured throughout the displacement of body over thesurface by a constant velocity v. It depends on normal acting pressure and movement velocity as
well as on properties of both surfaces in contact (molecular adhesive forces, roughness, hardness,
lubrication, etc.). Accompanying friction, physical effects such as thermal effects, electromagnetic and optical effects (triboluminiscence), low frequency vibrations (1 Hz – 10 kHz) andacoustic emission (30 kHz - 5 MHz) are observed and are evaluated for surface characterization.
Excited by dry friction on a rough surface, AE is monitored using AE sensor attached to themoving body (stylus) and/or to the stationary surface base, as Dunegan reported in [3]. Detected
AE signals have relatively broad frequency content ranging mostly from 1 kHz to a few MHz
depending on surface roughness, and on frequency characteristics of AE transducer. It also depends on a wave-path from friction surface to the AE sensor.
In previous laboratory experiments, AE transducer was coupled with stylus (acting as awave-guide) scanning tested surface. Different stylus materials (hard materials ranging frommetals and ceramics to glass, sapphire, diamond and other crystals) of various quasi-point contact radius and form (ranging from sharp tip or needle to a ball) were used in these measure286

ments. Test results were similar to that from surface profilometry, where the registered verticaldisplacement of stylus is proportional to the surface roughness profile.
The newest contact methods of surface properties study are known as atomic force microscopes (AFM) based on diverse sensing devices. The main principle of AFM consists in 2-D
surface scanning by a single stylus or a thin hard wire contacting tested surface. AFM's arehighly precise, very sensitive, expensive and bulky laboratory instruments allowing to scan only
few square mm of studied surface of a small body supported by a rigid base. Except for classicalfriction measurement (displacement of a body over the surface), all other reviewed methods give
only local, instantaneous information (one contact point of stylus or wire with the surface),
which is then spread over the line or area by the scanning. For area characterization, large
amount of data must be analyzed. This led us to design a new approach as described below.
2. Design and Operation of the Acoustic Brush Probe (ABP)
The low-resolution alternative to AFM for the rough characterization of surface properties
has been designed and patented as a "Friction Brush Probe" (ABP) [4, 5]. It represents new typeof direct mechanical contact sensor designed for non-local surface characterization during thesensor movement along a surface line or other path. The basic element of probe is a fiber brush(line or multi-line bundle of thin fibers) joined with AE sensing element. The probe is mounted
to the line or area-scanning device. During constant velocity brush movement over the analyzed
surface, many of brush fibers are subsequently coming into and released from direct contact withthe surface. These rapid processes of fiber-surface interaction (friction) are randomly exciting
piezoelectric AE sensing element, which produces quasi-continuous AE signal. Simultaneouslywith AE signal acquisition, the classical friction parameters - tangential and normal forces acting
on the probe - are sensed by external force sensing elements mounted on a probe holder or, in a
combined probe version, directly by force sensing elements integrated with AE transducer in acommon probe case. This enables direct correlation of AE signal parameters with classical integral surface friction variables, which are less sensitive to small surface disturbances. Schematic
draw of ABP construction is shown in Fig. 2a.
The brush fibers in contact with the test surface are loaded by normal force N acting on theprobe through the scanning head. Throughout the brush movement along the surface, AE transducer transfers the induced dry friction effects into electrical signal, recorded and processed by
AE analyzer. Common AE signal processing methods are adopted in analyzing device (DAKEL XEDO digital AE analyzer and transient recorder) including signal amplification and filtration.
Standard and extended AE signal parameters (e.g., number of AE (threshold-crossing) counts,
RMS, integral energy, average signal level, event counts and signal envelope parameters, asother selected signal features in time, frequency, time-frequency or wavelet domain) are evaluated by AE analyzer connected with a personal computer via LAN (ETHERNET). Evaluated parameters are used to characterize analyzed surface features and to correlate them with othercommonly used surface properties (e.g., tangential and normal friction forces).
In the combined ABP design, the integrated tangential (shear) and normal force sensing elements are considered: The normal force sensing system is realized by deflection of spring membrane joined to the AE sensor. Deflected membrane acts on miniaturized pressure transducer
connected to the A/D card installed in the DAKEL system. Hydraulic force multiplier is used toenhance pressure transducer sensitivity. Tangential force ft is sensed by the deflection of a metallic foil attached to the AE sensor and semiconductor strain gauges connected to an interface287

Fig. 2a. Schematics of acoustic brush probe.

Fig. 2b. A prototype and detailed view of acoustic brush probe.
288

card of the DAKEL-XEDO device. The computer collects data from AE and force sensing elements and also controls the scanning device. One of ABP prototypes is shown in Fig. 2b.
3. Results Obtained by Acoustic Brush Probe
Measurements using ABP have been performed on two kinds of surfaces. First measurements
were made on reference standards of surface roughness; here, we used abrasive papers of 120 to
1200-grit. In this case, the ABP was moved over the horizontally supported tested surface at aconstant velocity v = 10 mm/s on a distance of approximately 50 mm using precisely controlled
scanning device. Three kinds of line brushes have been tested, made from carbon, glass, andKevlar fibers, respectively. Brushes were composed of about 1000 individual fibers of average
diameter 2-10 µm. Best results were obtained with high modulus carbon fibers. Figure 3 illustrates AE activity detected through tests of abrasive papers (120- to 1200-grit with higher number for finer grains). AE activity is characterized by the sum of AE counts Nc. Slope of linear
part of Nc-vs.-t plot, determined by linear regression, represents average count rate dNc/dt.

Fig. 3. AE activity recorded during ABP tests of papers with 120-, 400-, 600-, and 1200-grit.
Except for the roughest abrasive paper (120-grit), the tendency is increased AE activity withfiner grains. The exception for 120-grit paper is explained by the effect of coarse grains causing
stick-slip movement of individual brush fibers accompanied by relatively high-energy release.
On finer surfaces, the fiber movement starts to be quasi-continuous with lower instantaneousenergy release, but growing number of AE events with higher number of surface obstacles(grains) increases integral AE energy. Nevertheless, the growing trend of AE activity is limited
by fibers diameter. When it becomes comparable or greater with respect to dimensions of surface
crimps, then the AE activity decreases, reflecting sliding friction at molecular level only.
As ABP has been developed in particular for the characterization of human skin surface, second series of measurements were realized on human skin using specially developed scanningdevice. These measurements were performed in-vivo on 18 persons with various types of skin(normal, dry, diseased, etc.) at different places of the body (forearm and shank). Different normalloads and scanning velocities were applied to each measured person in order to study the influence of these two factors on AE results. Two main parameters, the number of AE counts Nc1
and a signal RMS value, were evaluated from measured data to characterize skin properties. Figure 4 shows typical dependencies of AE activity on normal load (left) and on ABP displacement
velocity (right). It can be concluded that average slope of Nc1 (dNc1/dt) is about linearly dependent on both factors in measured intervals.
289

Fig. 4. Dependence of AE activity Nc1 on a normal load (left) and velocity (right).

250
200

RMS (mV)100
dNc1/dt[1/s]A factor analysis has been performed to determine which are the main factors influencing the
AE activity. Along with above mentioned velocity and normal load factors, the other parameters
were also analyzed (test person’s age, skin photo type, skin disease, test location, and interchangeable brush parameters). The brush type, loading force, and scanning velocity were themost important factors over the whole set of tests. Figure 5 illustrates the influence of the test
person’s age on the AE activity, when only tests having more parameter constants are considered. Average count rate in logarithmic scale (Fig. 5 - left) shows the growing global tendencywith the person’s age (five persons from 22 to 86 years old are classified), whereas the AE activity characterized by the average RMS value (Fig. 5 - right) tends to go down with the age during
tests of younger persons between 22 and 35 years.

10
Hand
Leg

1
0

50

age (years)

1cm/s
2cm/s

150
100
50
0

100

20

25

30

Age

35

40

Fig. 5. Dependence of AE activity on the age of tested person.
Six persons with normal healthy skin were considered in the second group (right in Fig. 5),
and results for two different scanning velocities (1 and 2 cm/s) are plotted. The differences between both plots in Fig. 5 can be explained by different information included in both AE activity
parameters. While the count rate reflects more pronounced skin features (e.g. bumps), average
RMS value integrates global skin friction properties. Another useful information has been drawn
from AE records, e.g. concerning indication of some skin diseases, the ratio of AE activity parameters obtained in tests with different scanning velocities differs for healthy and ill skin. A
more detailed and enlarged study under constant conditions is necessary to prove and elucidate
the above findings. Also the frequency parameters of detected AE signals, which are evaluated inongoing studies, seem to be useful for the characterization of skin friction properties.
The choice of brush-fiber material has shown to be of great importance as it determines molecular adhesion forces in friction. Elastic modulus and cross-section of fibers (brush rigidity)
influence local forces acting on fibers, and energy stored and released during brush movement290

over the surface. Hence, both the force and AE signal are dependent on brush rigidity. Low axial
modulus and lower fiber cross-section (low brush rigidity) result in lower force and lower AE
activity excited by surface friction. Fiber cross-section and stiffness also determine the resolutionof surface characteristics (scale of details). Circular cross-section of fibers and rectangular formof brush (fiber bundle) are assumed in most cases but other forms may be considered, too. Concerning brush fiber material, the most practical (optimal) results are achieved choosing relatively
short (some millimeters), small-diameter (1 to 10 microns) glass or high-modulus carbon fibers
in a bundle containing thousands of fibers. To obtain well reproducible results, the brush should
be prepared very carefully by the well-defined manufacturing procedure.
4. Conclusions
A new Acoustic Brush Probe (ABP) has been designed for the characterization of surface
friction properties. Basic element of the probe is a brush of carbon fibers attached to AE sensor.
A number of dynamic friction tests were performed using this probe, on both surface roughness
standards (abrasive papers), and on the human skin surface of 18 persons at a dermatologicalclinic. The tests shown that registered AE activity depends on many influencing factors, whose
importance was estimated by the factor analysis. Interesting relations between the global AE
activity parameters and some external factors were found, which must be further proved in moredetailed studies. ABP is suitable to design standard qualitative and quantitative method of rapid
surface characterization and comparison. Standard measurement conditions and devices, alongwith evaluation procedures must be well defined for this purpose. In dermatological applications,
the ABP can rapidly determine effects of a cosmetic product application or a clinical treatment.
Evaluated integral AE activity parameters cannot render fine details of frictional behavior. Differences in spectral contents and time-frequency representation of AE signals, registered in ongoing experiments, seem to provide useful tool for more detailed characterization of material
surface roughness and other surface friction properties.
References[1] Blau P. J.: Friction Science and Technology. Dekker, N.Y., 1996.
[2] Persson B. N.: Sliding Friction: Physical Principles and Applications. Springer-Verlag, New
York, 2000.
[3] Dunegan H.L.: An Acoustic Emission Technique for Measuring Surface Roughness. The
DECI Report No. 5, December 1998, http://www/deci.com/report5.htm.
[4] Asserin J., Prevorovsky Z., Zahouanni H.: “Dispositif d´évaluation de propriétés acoustiquesde friction d´un surface.” Brevet d´invention No. 0009173, 33 INPI Bordeaux, 13 July 2000.
[5] Prevorovsky Z., Prevorovsky D., Asserin J.: Characterization of Surface Friction by AE.
Proc. 6th Internat. Conf. of the Slovenian Society for NDT, Portoroz, Slovenia, September 13-15,
2001, pp. 205-211.

291

EWGAE2002

ACOUSTIC EMISSION DURING MONOTONIC AND CYCLIC DEFORMATION OF A BRITTLE LIMESTONE
A. LAVROV1, 2, M. WEVERS3 and A. VERVOORT1
1

Katholieke Universiteit Leuven, Department of Civil Engineering, Leuven, Belgium
Moscow State Mining University, Physical and Engineering Department, Moscow, Russia
3
Katholieke Universiteit Leuven, Dept. of Metallurgy and Materials Eng., Leuven, Belgium
2

Abstract
An overview of recent experimental research on acoustic emission (AE) during monotonic
and cyclic tests of a brittle rock (crinoïdal limestone) is given. The AE and the Kaiser effect in‘Brazilian’ tests are described. The results of experiments performed to reveal the directional andloading-rate sensitivity of the Kaiser effect are presented. The impact of the results on geotechnical applications of AE is outlined.
Keywords: acoustic emission, limestone, ‘Brazilian’ test, Kaiser effect, fracturing, rock mechanics.
1. Introduction
Deformation of rock specimens is accompanied by acoustic emission (AE). This is usually
produced by the growth of micro- and macro-cracks, twinning processes and other structuralchanges. Together with other non-destructive techniques like electromagnetic emission, ultrasonic velocity and electric resistivity measurements [1-3], AE allows an insight into the fracturing process in rocks at mesoscale, at the early pre-failure stage.
The purpose of this paper is to give a review of our experimental research on AE in rock mechanics in various loading regimes. Special attention is paid to the Kaiser effect as a possible tool
for stress estimation in rocks. The Kaiser effect takes place when rock is subjected to cyclicloading with the peak stress increasing from cycle to cycle [4, 5]. As long as the stress does notexceed the largest previously reached value, AE activity is sparse or there is no AE at all. As
soon as this largest previously reached stress level is attained, AE activity increases dramatically.
Yoshikawa and Mogi conducted a thorough investigation of temperature and water effects on the
Kaiser effect recovery [6, 7]. The effect of sequential loading in mutually-perpendicular directions on the Kaiser effect in cubic specimens was studied by Michihiro et al. [8]. They also investigated the influence of time delay between loading cycles on the Kaiser effect recovery inbrittle rocks.
The Kaiser effect seems to be applicable for stress measurements in rocks and soils. Suchmeasurements are required in geological, geotechnical, mining and tunneling applications, e.g.
for a proper design of underground workings, for regional seismic forecast and monitoring therock mass stability in mines. First attempts of the application of the Kaiser effect for stressmeasurements in rocks were made by researchers from CRIEPI (Japan) in late 1970s [9].
Practical applications of the Kaiser effect for stress measurements require a firm understanding of its features under various loading conditions. One of the most important questions
J. Acoustic Emission, 20 (2002)

292© 2002 Acoustic Emission Groupconcerns the manifestation of the Kaiser effect in cyclic loading regimes in which the directionsof principal axes do not coincide in successive cycles. This question is important because all
Kaiser-effect-based stress measurement techniques proposed so far imply that the specimen isrecovered in the direction of the major in-situ principal stress, σ1. This direction is never known
exactly. Very often, geological methods can provide some information on the approximate direction of the principal stresses in situ. Yet a possible deviation of the real major principal stressdirection may not be excluded. To clarify what happens when the 2nd loading cycle is performed
in a different direction than the 1st cycle, a series of tests is conducted. Specimens having theshape of thin disks are compressed along a diameter, then unloaded, rotated by an angle of θ andreloaded along the new diameter to failure. The loading scheme employed in these tests (diametrical compression of disk specimens) is usually called ‘Brazilian’ test and is widely used in orderto estimate tensile strength of brittle rocks and concrete. Results of these tests are described inSec. 5.
Another important question for practical applications of the Kaiser effect is its loading-ratesensitivity. Loading of rock in the Earth crust (in virgin rock masses or around undergroundcavities) can occur with different loading rates, from relatively slow (in geological history) tovery fast (e.g. during rock blasting). The question is whether it is possible to estimate the firstcycle peak stress value by means of the Kaiser effect in the 2nd cycle, when the loading rates aredifferent between the 1st and 2nd cycles? To clarify this question, a series of tests is performed on
disk specimens in diametrical compression (‘Brazilian’ method). Results of these tests are described in Sec. 6.
2. Material and Equipment
Experiments are performed on a typical brittle rock, crinoïdal limestone. This low-porosity
rock (porosity <1%) has a uniaxial compressive strength of 80 to 120 MPa, tensile strength of 15
MPa, and consists predominantly of calcite. Disk specimens are prepared for the ‘Brazilian’ tests
with a diameter of 113 mm and a thickness of approximately 20 mm.
The loading is performed on the testing machine INSTRON 1196 with a constant displacement
rate. The AE is measured by a two-channel AMS3 system (Vallen Systeme). Two wide-band
sensors of type B1025 (Digital Wave) are coupled to the specimen using a high-vacuum grease.
They are placed on the same plane of the disk, symmetrically with respect to the loaded diameter. This allows an approximate linear location of AE sources in the disk. The system provides asampling rate of 5 MHz, with the ADC dynamic range of 78 dB. The preamplifiers have a gainof 40 dB. The built-in band-pass filters have a frequency range from 100 kHz to 1 MHz. The
threshold level is chosen at 40 dB in all tests. Based on the AE data measured, the dependenciesof cumulative hits versus load (stress) are plotted.
3. AE in One-cycle Brazilian Tests
A typical example of an AE signal measured in a ‘Brazilian’ test is shown in Fig. 1 together
with its wavelet transform. Two energy maxima are well-pronounced in the wavelet, at about 20µs and 70 µs. They correspond to the direct and the reflected wave, respectively.
Linear location of AE sources has shown that about 70% of all epicenters are located in thecentral zone of the disk. The width of this zone is about 30 mm, i.e., 15 mm on each side of the293Energy maxima(b)

Fig. 1. An example of an AE full wave (a) and its wavelet transform showing the energy distribution with respect to the time and frequency (b) measured in ‘Brazilian’ test of a limestone
specimen.
loaded diameter [10]. The concentration of AE sources in the central part of the disk is in agreement with the observed fracture forms, namely that the disks split along the loaded diameter.
In some of the tests, detectable AE starts from the beginning of loading, virtually from zeroload. In other experiments, AE onset is observed at the load level corresponding to 10-30% of
the ultimate strength of the specimen. AE hit rate measured as a number of AE hits per 1 kN loadincrement gradually increases with increasing load (Fig. 2b).
4. AE and Kaiser Effect in Cyclic Brazilian Tests
Cyclic loading of disks in diametrical compression is accompanied with the Kaiser effect: ineach cycle beginning from the second one, AE starts when the load approaches the largest load
value attained in the past (Fig. 3). This phenomenon is very similar to what has been observed inmany other rocks and materials usually tested in uniaxial or triaxial compression (see [11-15]
and references therein). In a ‘Brazilian’ test, a tensile principal stress is acting in the middle partof the disk, in the direction perpendicular to the loaded diameter. Parallel to the loaded diameter,a compressive principal stress is acting. This implies that while load increases, the rock undergoes a proportional loading in each point of the disk. Directions and ratio of the principal stresses
are different at different points of the disk, but stay the same in each point during the loading.
The observation thus shows that when two loading cycles are performed in proportional294

regime (i.e. the ratio of principal stresses σ1/σ3 stays constant during the test), a very wellpronounced Kaiser effect takes place.

Fig. 2. Load vs. time (a) and AE hit rate vs. load (b) in a ‘Brazilian’ test of a limestone specimen.

Fig. 3. Load vs. time (a), cumulative AE hits vs. load in the 1st (b) and 2nd (c) loading cycles of adisk specimen (the two curves in b, c correspond to the data measured by two AE-channels).
Peak load in the 1st cycle 40 kN. Kaiser effect in the 2nd cycle at 37 kN.
295

5. Kaiser Effect Sensitivity against Principal Axes Rotation
A series of tests is carried out in order to clarify the question about the influence of the mutual orientation of principal stresses in successive loading cycles on the Kaiser effect. This isachieved by performing two-cycle experiments on disk specimens with disk rotation between thetwo cycles. In the 1st cycle, a specimen is diametrically compressed to a certain load level, usually corresponding to 50-80% of the rock’s strength in a ‘Brazilian’ test. Then the specimen iscompletely unloaded, rotated by a certain angle θ and then reloaded, in the new direction. The
following values of θ are studied: 3.5°, 7.5°, 15°, 30°, 45°, 60° and 90°. AE is measured in bothcycles.

Fig. 4. AE in the second cycle in the tests with disk rotation between 1st and 2nd cycle. The valueof the rotation angle θ is shown in each graph. The peak value of the first-cycle load is indicatedby an arrow.
296

Experiments show that with increasing θ, the Kaiser effect becomes less clearly pronounced.
The effect can still be recognised in the tests with θ = 7.5°, as Fig. 4 illustrates. As the rotation
angle increases to 15° or more, the Kaiser effect disappears completely. This corresponds well tothe results of the earlier simulation with a brittle fracture mechanics model (Fairhurst-Cookmodel) [15-18] as well as to the boundary-element computation carried out in [19].
6. Loading Rate Dependency of the Kaiser EffectCumulative hitsacMax. first-cycle
load

Kaiser effectload

Cumulative hits

Cumulative hits

‘Brazilian’ tests described in the above sections are performed with the same loading rate in
all cycles (typically 0.2 mm/min). In reality, cyclic loading of rocks may occur with different
loading rates in different cycles. For example, the loading rate in situ can be higher or lower thanthat in the laboratory test. To clarify the question about the influence of the loading rate on the
Kaiser effect in ‘Brazilian’ test, a series of experiments is performed in which disk specimens are
subjected to three cycles of diametrical loading without rotation. The displacement rates in thefirst, second and third cycles are 0.2 mm/min, 5 mm/min and 0.2 mm/min, respectively. It should
be noted that the maximum displacement rate of 5 mm/min used in these tests is still much lower
than that occurring in blasting or impact processes. Typical results are shown in Fig. 5. The Kaiser effect in the second cycle, when the load rate is increased, is observed at the load value only
slightly higher than the largest previously reached load. In the third cycle, when the load rate isdecreased again, the opposite takes place: the Kaiser effect is observed at the load value only
slightly lower than the largest previously reached load [20]. In both cycles, the peak value of theprevious load can be estimated from the Kaiser effect with a reasonable accuracy. Further experimental and theoretical research should clarify the physics of the observed phenomena.

bdMax. second-cycle
load
Kaiser effectloadFig. 5. Load vs. time in three loading cycles (a); cumulative hits vs. load in the first (b), second(c) and third (d) cycles of diametrical compression of a disk specimen. Displacement rate in thefirst and third (“slow”) cycles: 0.2 mm/min; displacement rate in the second (“fast”) cycle: 5mm/min.
7. Impact of the Experimental Results on Stress Measurement Techniques
The results obtained in the experiments are of importance for the Kaiser effect applications
for stress measurements in rocks and brittle materials like concrete. The high sensitivity of the
Kaiser effect against a non-coincidence of the principal axes in successive loading cycles implies
that it is necessary to know a priori the directions of σ1, σ2, σ3 in order to be able to determine
297

their values by means of the Kaiser effect. For example, when measuring the value of σ1, thespecimen must be oriented parallel to the direction of σ1. If the axis of the specimen is inclined
to the direction of σ1 by more than ±10°, there will be no Kaiser effect. The absence of the Kaiser effect in this case may be misinterpreted by the experimentalist. The results presented aboveshow that the absence of the Kaiser effect in some directions does not indicate the absence ofstress memory in rock. It just means that the orientation of σ1 is different from the orientation of
the core specimen. To reveal stress memory, the orientation of the specimen should be parallel orclose (±10°) to the direction of σ1.
The high sensitivity of the Kaiser effect to the mutual orientation of principal axes in successive loading cycles may be used as a basis for a precise determination of the directions of principal stresses in situ. Estimating the directions of principal axes in situ is important in many applications in geology, rock mechanics and tectonophysics. The results presented show that testing aseries of specimens orientated in various directions may allow one to estimate the σ1-direction
with an accuracy of ±5° provided that a sufficient amount of rock specimens is available. The
direction of σ1 corresponds to the direction of the specimen, which shows the best-pronounced
Kaiser effect.
Varying loading rate between the cycles in the range 0.2 mm/min to 5 mm/min does not result in a disappearance of the Kaiser effect. Further study is necessary in order to estimate theKaiser effect applicability for stress estimation in impact and blasting processes in rocks.
References
1. Rabinovitch A., Bahat D., Frid V. (2002) “Similarity and dissimilarity of electromagnetic
radiation from carbonate rocks under compression, drilling and blasting.” International Journal of Rock Mechanics and Mining Sciences, 39(1): 125-129.
2. Couvreur J.-F., Vervoort A., King M.S., Lousberg E., Thimus J.-F. (2001) “Successive
cracking steps of a limestone highlighted by ultrasonic wave propagation.” Geophysical
Prospecting, 49(1): 71-78.
3. Van de Steen B., Wuytens B., Vervoort A., Van Geemert D. (1997) “Variation in resistivity
with stress level in mineral building materials.” International Journal for the Restoration of
Buildings and Monuments, 3: 75-85.
4. Leaird J.D., Dunning J.D., Miller M.E. (1985) “Kaiser experiment in sawcut rock.” Journalof Acoustic Emission, 4(2-3): S11-S16.
5. Lord A.E., Koerner R.M. (1985) “Field determination of prestress (existing stress) in soil androck masses using acoustic emission.” Journal of Acoustic Emission, 4(2-3): S22-S25.
6. Yoshikawa S., Mogi K. (1981) “A new method for estimation of the crustal stress from rocksamples: laboratory study in the case of uniaxial compression.” Tectonophysics, 74: 323-339.
7. Yoshikawa S., Mogi K. (1989) “Experimental studies on the effect of stress history on
acoustic emission activity: a possibility for estimation of rock stress.” Journal of Acoustic
Emission, 8(4): 113-123.
8. Michihiro K., Hata K., Yoshioka H., Fujiwara T. (1991/1992) “Determination of the initialstresses on rock mass using acoustic emission method.” Journal of Acoustic Emission, 10(1/2): S63-S76.
9. Kanagawa T., Nakasa H. Method of estimating ground pressure. U.S. Patent No. 4107981;
1978.

298

10. Lavrov A.V. (2001) “Acoustic emission in ‘Brazilian’ tests on brittle rock.” Proceedings ofthe 11th Session of the Russian Acoustical Society. Moscow: GEOS, vol. 2, pp. 99-102 (inRussian).
11. Filimonov Y., Lavrov A., Shafarenko Y., Shkuratnik V. (2002) “Observation of post-failure
Kaiser effect in a plastic rock.” Pure and Applied Geophysics (PAGEOPH), 159(6): 13211331.
12. Filimonov Y.L., Lavrov A.V., Shafarenko Y.M., Shkuratnik V.L. (2001) “Memory effects inrock salt under triaxial stress state and their use for stress measurement in a rock mass.” RockMechanics and Rock Engineering, 34(4): 275-291.
13. Lavrov A., Vervoort A., Filimonov Y., Wevers M., Mertens J. (2002) “Acoustic emission in
host-rock material for radioactive waste disposal: comparison between clay and rock salt.”
Bulletin of Engineering Geology and the Environment 61(4): 379-387.
14. Li C., Nordlund E. (1993) “Experimental verification of the Kaiser effect in rocks.” RockMechanics and Rock Engineering, 26(4): 333-351.
15. Holcomb D.J. (1993) “General theory of the Kaiser effect.” International Journal of RockMechanics and Mining Sciences & Geomechanics Abstracts, 30(7): 929-935.
16. Lavrov A.V. (1997) “Three-dimensional simulation of memory effects in rock samples.”Proceedings of the International Symposium on Rock Stress. Rotterdam: A.A. Balkema, pp.
197-202.
17. Shkuratnik V.L., Lavrov A.V. (1997) Memory effects in rocks: Physical features and theoretical models. Moscow: Academy of Mining Sciences Press. 159 pp. (in Russian).
18. Lavrov A.V. (2001) “Features of the formation and manifestation of memory effects inrocks.” D.Sc. dissertation. Moscow State Mining University. 582 pp.
19. Lavrov A., Vervoort A., Wevers M., Napier J.A.L. “Experimental and numerical study of the
Kaiser effect in cyclic Brazilian tests with disk rotation.” International Journal of Rock Mechanics and Mining Sciences, 39(3): 287-302.
20. Lavrov A. (2001) “Kaiser effect observation in brittle rock cyclically loaded with different
loading rates.” Mechanics of Materials, 33(11): 669-677.

299

EWGAE2002

LEAK DETECTION BY ACOUSTIC EMISSION USING SUBSPACEMETHODSAMANI RAAD1,3, FAN ZHANG2,3 and MÉNAD SIDAHMED1,3
1

UTC, Laboratoire Heudiasyc, F60205 Compiègne, France
2
CETIM, F60304 Senlis, France
3
LATIM (CNRS/UTC/CETIM)

Abstract
This paper deals with the leak detection by using AE signals. A method called subspacemethod is introduced and compared with the classical cross-correlation method. A brief reviewof the theory of both methods is presented. Application of these methods to industrial signals
from a test bench of CETIM demonstrates the effectiveness of this subspace method for leakdetection and shows the limitation of the cross-correlation method.
Keywords: leak detection, acoustic emission, subspace method, cross-correlation method.
1. Introduction
For a long time, the detection of leaks constitutes a crucial problem due to the diversity of
applications. The consequences induced by the apparition of leaks are multiple and dangerous
because they can generate a local dysfunction, which can lead to the ruin of all the system. It isimperative to detect them as soon as possible. Thus, the need for detecting the leaks in an early
moment is related to safety constraints and environmental protection reliability. The need is alsoof a financial nature, because it is easier and less expensive to secure a small crack than to startan emergency procedure to stop a large leak. Losses are evaluated in terms of maintenance costand non-production cost.
Several systems of detection have already been used but they make it possible only to detect
big leaks [1 - 3]. With an aim of replacing these systems by more powerful digital systems,
various approaches are being studied of which the method of acoustic emission (AE) constitutes
one of the principal candidates because it showed during pressure tests a great sensitivity.
Several studies were carried out, by various authors and especially in CETIM, to detect, localize
and characterize leaks for nuclear or thermal power stations. Nevertheless, most of thetechniques are based only on simple methods such as cross-correlation or interferometry. Somestudies used time-frequency methods [4] or neural networks [5]. The principal objective in thispaper is to research methods of detection and localization, based on signal processing. We
propose in this study to use a method based on high-resolution techniques, which are developed
and applied in the fields of sonar, radar, telecommunications, geophysics and biomedical signal
processing.
This method is under certain conditions insensitive to the signal to noise ratio. This is ofinterest because the classical method based on cross-correlation function has this major drawback. The method is based on the SVD (Singular Value Decomposition) of the cross-spectral
matrix issued from the AE sensors signals. The paper describes this method and compares it, on
J. Acoustic Emission, 20 (2002)

300© 2002 Acoustic Emission Groupthe basis of simulated data and experiments conducted in CETIM, with the classical cross-correlation method. The paper is organized as follows: A review of the method of cross-correlation;
The high-resolution or subspace method; Applications of these two methods, concerning
simulated signals and industrial AE signals from a test bench of CETIM.
2. Correlation Method
The correlation method is a classical method, which was proposed to localize AE sources byusing the delays observed on an array of sensors [6]. Otherwise, the autocorrelation function
presents good performance in pure detection. In a matter of localization, the cross-correlation
function was used to improve the AE techniques used for monitoring of the primary circuits inthe nuclear power stations [1]. Despite the simplicity of the method, it encounters difficulties forthe interpretation, and presents weak performances, which are due to the violation of thedecorrelation assumptions and especially to the presence of parasites, which do not correspond toan effective delay. Moreover, these methods degraded completely in the presence of the noise.
We present a brief review of the method in the case of a linear localization, i.e. two sensors.
The flow of the fluid through the leak generates a noise (fluctuations in pressure), which ispropagated from its origin to both sides, s(t). The AE signals in the two extremities of the pipe A
and B have additive noise, b1(t) and b2(t), respectively. These noises, in general Gaussian, areuncorrelated between them, and with s (t ) . The following equations can then be obtaineddA) + b1 (t )cdB
S B (t ) = α B s (t −) + b2 (t )c

S A (t ) = α A s (t −(1)

αA and αB are the coefficients of diffusion between 0 and 1; dA and dB the distance from the leakto each sensor A and B and c is the propagation velocity.
The cross-correlation between SA and SB can be expressed as the following:
R AB (τ ) = α Aα B R SS (τ − τ 0 )with τ 0 =(2)

dA − dBc

RAB(τ) is no more than the autocorrelation function of SB(t) with a delay of τ0 and amultiplicative coefficient. The maximum of the cross-correlation can predict the leak position by
the determination of the value of the delay.
3. Subspace Methods
3.1 Introduction
We introduce the high-resolution techniques called subspace methods. These methods
constitute a significant tool used in various applications such as sonar, radar, telecommunications, geophysics, biomedical fields and the non-destructive testing [7]. These methods havebeen developed in array processing and especially in submarine acoustics. But they seem not tobe applied to detect leaks. The advantage of these methods comes from their high resolution evenin the presence of noise. Contrary to the traditional methods, its asymptotic performance are notlimited by the signal-to-noise ratio (SNR). They use the decomposition of the observation space
into a signal subspace (or source) and a noise subspace [8, 9].
301

3.2 Assumptions
The object of these methods is to characterize several sources received on an array of
sensors. The model of propagation is linear and stationary. These sources must be perfectly
coherent and statistically independent. In addition to the sources, all the noise sources aresupposed to be known and uncorrelated between them. Moreover, the sources and thebackground noise must be statistically independent. Finally, it is necessary that the number ofsources is lower than the number of sensors in order to be able to separate the sources.
3.3 Description of the method
First, a vectorial signal x(t ) is introduced where the kth component corresponds to the signal
received on the kth sensor. x(t ) can be written as follows:x(t ) = b(t ) +p∑ S (t )

(3)

i

i =1

where Si(t) is the i-th source signal and b(t) is the background noise.
Taking into account the mutual decorrelation between sources and noise, the correlation
matrix is then equal to:
R (τ ) = E[b(t )b(t − τ )] +p

∑ E[s (t )s (t − τ )]
i

+
i

(4)

i =1

The FFT of the equation gives the following result:Γ( f ) = Γb ( f ) + Γs ( f ) avec Γs ( f ) =p∑Γ ( f )
i

(5)

i =1

where Γ(f) presents the total cross-spectral matrix, Γb(f) is the correlation matrix of noise. Γs(f),the total cross-spectral matrix of sources can be decomposed into a sum of Γi(f), the crossspectral matrix of the source i.
The estimation of the source number is based on the decomposition of the cross-spectral
matrix Γ(f) into eigenvalues and eigenvectors. In practice, Γ(f) is an estimated matrix and no
eigenvalue is really zero. The number of higher eigenvalues corresponding to signal subspacedetermines the number of existing leaks.
The decomposition of the matrix Γ(f) into eigenvalues and eigenvectors gives:-

P eigenvectors Vi are the eigenvectors of the source matrix Γs(f), which correspond to thehigher p eigenvalues of Γ(f). These p eigenvectors constitute the signal subspace ofdimension P.
K-P eigenvectors where the eigenvalues correspond to the variance of the noise, andtherefore are less than the other eigenvalues. These eigenvectors constitute the noise
subspace.

4. Applications
4.1 Cross-correlation methoda) Simulated signals
Simulated signals consist of damped sinusoid with additive white Gaussian noise in order toapproximate AE signals. In the case when there is no leak, the cross-correlation of the two
sensors is a sequence of white noise. However, in the other case, the cross-correlation can make302

it possible to determine the leak position. Figure 1 shows the presence of a peak appearing at 300
samples, corresponding to the delay between the first and second sensors. The assumption of
decorrelation must be strictly respected; if not, the result of the cross-correlation could be
distorted. The notion of SNR is a dominating factor for the final comparison of the performancesof the two methods, and a comparison with a case of low SNR proved to be necessary. Figure1(b) shows the result of the cross-correlation in the case of a simulated signal with low SNR. It
appears that the peak that defines the leak position degrades in favor of the peak at zero. Thisconfirms the degradation of the method performances due to the low SNR.

(a)

(b)
Fig. 1. Cross-correlation between two sensors in the case of leak (a) High SNR (b) Low SNR.
b) Real signals
Experiments have been conducted on an industrial test bench of CETIM. It consists of acompressed-air line of 2.5-cm diameter, in which it is possible to control the leak level. Two
broadband AE sensors are used, the leak being generated between the two sensors. The sampling
frequency is equal to 10 MHz. Figure 2 presents 10,000 samples of AE signals for various cases:303

without leak, small and large leak. Figure 3 shows the result of cross-correlation of AE signals
when there is no leak. It is normal to obtain the white noise sequence with a zero delay.
To complete the analysis, tests on AE signals coming from a small and large leak have been
carried out. Figure 4 shows clearly the distinction between presence and absence of leak in the
cross-correlation signals. The correlation tool proves to be a powerful tool for the leak detection.
However, the limitations of the method in localization appear obviously. Indeed, there exist in
the two cases several parasitic peaks distorting the exact value of delay. Moreover, there is nodifference between small and large leaks.

samplesFig. 2. Acoustic emission signals in three cases: (a) no leak, (b) small (c) large leak.

samplesFig. 3. Cross-correlation in the case of no leak.
304

samplessamplesFig. 4. Cross-correlation in the case of (a) small, (b) large leak.
4.2 Subspace methoda) Simulated signals
Simulated signals consist of one simulated sinusoidal source with 60-Hz frequency and 4
sensors with a respective delay of 0, 300, 600 and 900 samples. The subspace signal is one
dimension and the subspace noise is three dimensions. The noise eigenvalues tend roughly
towards zero. Figure 5(a) shows the evolution of the eigenvalue of the signal subspace withfrequency. In this figure, the characteristic peak of the source frequency appears clearly. The
eigenvalue evolution is thus an image of the sinusoidal signal in the frequency domain. To obtain
the delay between sensors and source, the source eigenvector must be computed. It corresponds
to eigenvalue at 60 Hz. Figure 6 presents the evolution of the magnitude and phase of theeigenvector components according to frequency. The delays obtained at frequency 60 Hz are [0,-0.3737, -0.7462, -1.1135]. The subspace method is very robust against the noise. Even if the
SNR degrades considerably, the separation between sources and noise is easy to obtain. The
degradation takes place only in the figure of the eigenvalue evolution. Figure 5(b) shows thepresence of the parasitic peaks in the eigenvalue evolution.

Fig. 5. Evolution of the eigenvalue of source with frequency (a) high SNR, (b) low SNR.

305

Fig. 6. Evolution of the magnitude and phase of the eigenvector components with frequency.

Fig. 7. Evolution of the eigenvalue with frequency for 3 cases: no leak, small and large leak.

306b) Real signals
In this section, tests concern the same industrial signals from CETIM test bench. Filtering isa significant factor in this study. If the AE signals obtained from the sensors are not filtered, thesubspace signal is equal to zero. Therefore, all tested signals are filtered broadband around 100
kHz. Figure 7 shows the eigenvalue evolution of the subspace signal for three cases: no leak,small and large leak. For example, in the case of small leak, the maximum eigenvalue does notcorrespond exactly to the sensor frequency. A broadband subspace method appears to be an
excellent method for classification between small and large leaks.
5. Conclusion
This paper deals with a method for leak detection using acoustic emission. The subspacemethod is introduced and compared with the classical cross-correlation method. The subspacemethod proves to be a powerful tool for the detection and classification of leaks. Its power
derives from the immunity against noise. Comparison of the two methods showed the limitationsof the cross-correlation method due to the signal-to-noise ratio, the high capacity of the proposed
method to detect the leaks, even ‘small’ leaks, and its insensitivity to signal to noise ratio.
We were only concerned with the leak detection and not the localization. This aspect is moredifficult because it requires the knowledge of the propagation speed in the considered medium, atthe specific frequencies. This is however under study.
Acknowledgements
The work is funded within the common laboratory LATIM (CETIM/UTC/CNRS).
References[1] Cécile Cornu, Study of a cross correlation technique for leak localisation on power primarycircuits by acoustic emission, Commissariat à l'énergie atomique, France, 1993.
[2] D.S. Kuppermann, T.N. Claytor, T. Mathieson, and D. Prine, Leak-detection technology forreactor primary systems, Nuclear Safety, 28(2), April-June 1987, 191-198.
[3] Pascal Boulanger, nouvelles méthodes de détection et de localisation de fuites Par émission
acoustique, Thèse de doctorat, Université de Paris XI Orsay, 1993.
[4] E. Serrano, M. Fabio, Detection of a transient signal by the wavelet transform, International
conference Sao Paolo, Brasil, 1992, pp. 94-97.
[5] M.C. Parpaglione, Neural networks applied to fault detection using acoustic emission,
International conference Sao Paolo, Brasil, 1992, pp. 71-75.
[6] James Roger, Essais non destructifs, L'émission acoustique: Mise en œuvre et applications,
AFNOR-CETIM, 1988.
[7] Sylvie Marcos, Les méthodes à haute résolution: traitement d'antenne et analyse spectrale,
Edition HERMES, Paris, 1998.
[8] Georges Bienvenu, Influence de la cohérence spatiale du bruit de fond sur la résolution desources ponctuelles, Colloque national sur le traitement du signal et ses applications, Nice, Avril
1977.
[9] J.L. Lacoume, B. Bouthemy, F. Glageaud, C. Latombe, Use of spectral matrix for sources
identification, Spectral Analysis and its use in underwater acoustics, Proceedings of the Instituteof Acoustics, Imperial College, London, 29-30 April 1982.

307

EWGAE 2002
25th European Conferenceon Acoustic Emission Testing
EWGAE2002 was held 11 – 13 September 2002 at Prague, Czech Republic. It
was sponsored by the Czech Society For Non-Destructive Testing in collaboration with Czech Technical University of Prague and Brno University ofTechnology. Proceedings in two volumes were edited by Dr. P. Mazal and were
published by Brno University of Technology.
At the very successful 25th European working group on AE conference, 55 papers in 10 oral sections and about 15 papers in poster section were given. The
Proceedings include 86 papers and abstracts. 107 participants from 20 countries
attended the conference.
The Proceedings can be ordered from the Czech Society For Non-Destructive
Testing. Contact:
Czech Society for Non-destructive Testing
Technická 2, CZ 616 69 Brno, Czech republic Or
Dr. Pavel Mazal
Brno University of Technology
Institute of Design
Technicka 2
616 69 Brno, Czech RepublicWith the assistance of the editorial Board members who were present at the
EWGAE2002, 22 papers were chosen and reviewed for the technical contents.
These papers are included in this volume of Journal of Acoustic Emission withthe permission of Brno University of Technology. Two papers were extensively
revised and expanded by the authors, while 20 other papers were edited forclarity and/or received minor revisions by the authors.
Selected pages of the Proceedings are included here. EWGAE 2004 has been
set to open at Berlin from September 15 to 17, 2004. See EWGAE2004.pdf enclosed for details. You can also access the conference information at
http://www.EWGAE2004.de.

EditorEWGAE 2002
25th European Conference on Acoustic Emission Testing
11 – 13 September 2002
PragueCzech Republic

PROCEEDINGSVOLUME ICZECH SOCIETY FOR NON-DESTRUCTIVE TESTING
IN COLABORATION WITHCZECHTECHNICALUNIVERSITYIN PRAGUE

Edited by:
P.MazalCopyright „ 2002
Brno University of Technology
all Rights Reserved

BRNO
UNIVERSITY OF
TECHNOLOGY
Faculty of Mech.
Engineering

Editorial

These Proceedings contain contributions that were presented at the 25th conference ofEuropean Working Group on Acoustic Emission (EWGAE), which took place in Prague from11th to 13th of September 2002.
The method of acoustic emission (AE) is one of fast developing methods used for non–destructive testing of constructions, for monitoring of technologic processes, for material
properties evaluation etc. In some areas, it has become standard, fully certified testing
method the utilization of which brings important economic savings. What is even more
important, it improves safety and enables more precise estimate of service life of various
technical constructions. AE methods provide researchers with new possibilities in the area of
research of various materials.
EWGAE conference has become traditional possibility to meet prominent specialists who
have been pursuing both development and practical applications of AE method. We can see
permanent and agreeable tendency of growing number of students and young researchers
who are going to develop this method in forthcoming years.
The problem of acoustic emission has been solved in the Czech Republic and its
predecessor – Czechoslovakia – for over thirty years at several research and application
workplaces. This tradition and very good reputation of Czech specialists were main reasons
why Czech Society for NDT was entrusted with organizing of the 25th conference at previous
conference EWGAE 2000 in Senlis, France.
These Proceedings include more than 70 contributions that prove the width of the problems
that are currently being solved by this unique method. This publication provides a good
overview not only about current application possibilities, but it shows a number of practicaland theoretical problems the solution of which is necessary for further development of AE
method.

Pavel Mazal
President of the CNDT

I/3

EWGAE 2002

25th European Conference on Acoustic Emission Testing
Prague, Czech Republic, 11 – 13 September 2002

CONTENTS- Volume I Contributions can be found in alphabetical order according to the surname of the first author.

Akematsu Y., Yoshida K., Sakamaki K., Horikawa K.
Characteristics of AE waveform during gas leak from a pipe ...............................................

I/15

Anastassopoulos A.A., Kouroussis D.A., Nikolaidis V.N., Proust A., Dutton A.G., Blanch M.J.,
Jones L.E., Vionis P., Lekou D.J., van Delft D.R.V, Joosse P.A., Philippidis T.P., Kossivas T.,
Fernando G.
Structural integrity evaluation of wind turbine blades using pattern recognition analysis
on acoustic emission data.........................................................................................................

I/21

Anastassopoulos A.A., Tsimogiannis A.N., Kouroussis D.A.
Pressure vessel evaluation with pattern recognition acoustic emission data
analysis........................................................................................................................................

I/29

Bayray M., Rauscher F.
Window Fourier Transform and Wavelet Transform in AE signal analysis...........................

I/37

Belov V., Glushko A., Kozyrev O
New AE source location method and its practical application...............................................

I/45

Benes P.
Process analysis on heat transfer surface ..............................................................................

I/51

Bláha J.
Real source detection during continuous monitoring of pressure vessels..........................

I/59

Blahácek M., Prevorovsky Z.
Probabilistic definition of AE events.........................................................................................

I/63

Boczar T.
Comparison of the results of the frequency analysis of electric signals and AE
generated by partial discharges................................................................................................

I/69

Boczar T.
Determining identification possibilities of multisource partial discharges based on
frequency analysis of their AE..................................................................................................

I/77

Bordei M., Dragan V., Dragulin I., Goanta M.A., Constantinescu S.
Researches regarding the role of the ultrasonic control at the fabrication of theguidance rolls curved thread at the continuous casting machines.......................................

I/85

Bradshaw T., Holford K., Cole P., Davies A.
Ball bearing calibration of concrete structures using AE.......................................................

I/91

Cerny L., Jirsík V.
Signal source location by means of neural networks.............................................................

I/99

Chlada M., Prevorovsky Z.
Threshold counting in wavelet domain.....................................................................................

I/107

Chmelík F., Máthis K., Lukác P., Langdon T.G.
Investigating magnesium alloys and composites using the AE technique...........................

I/117

Cole P.T., Gautrey S.N., Lenain J.C.
On-stream monitoring of process plant by AE.........................................................................

I/125

Colombo S., Main I.G., Forde M.C., Halliday J.
AE on bridges: experiments on concrete beams.....................................................................

I/127

Constantinescu S., Drugescu E., Dragan V., Radu T., Bordei M., Buburuzan L.
Practical application of AE of the different grades of steel....................................................

I/135

I/5

EWGAE 2002

25th European Conference on Acoustic Emission Testing
Prague, Czech Republic, 11 – 13 September 2002

Crha J. , Hahn J., Brom J.
Leak monitoring by AE method................................................................................................

I/141

Derjagin B.V., Lipson A.G., Kluev V.A., Toporov Yu.P., Sodomka L.
Mechanoemission of neutron from deuterated materials.......................................................

I/149

Dragan V., Bordei M., Constantinescu S., Tudor B.
Researches regarding the quantitative analysis of the heavy rolling plates of LRS-EH36
steel type, through ultrasonic control.......................................................................................

I/155

Drummond G.R., Fraser K.F., Little J., Watson J.F., Campbell P.
Assessing the structural integrity of crane booms using AE.................................................

I/161

Ennaceur C., Benmedakhene S., Laksimi A., Hervé C., Mediouni M., Cherfaoui M.
Study of mechanical behaviour of 304L steel using AE technique and the method of
potential difference.....................................................................................................................

I/173

Ferrer F., Radigon E.
Using AE in the objective to evidence corrosion rate evolutions during chemical
process in service modifications...............................................................................................

I/185

Fraser K., Drummond G., Little J., Fleisher S., Bailey J., Brown M.
Investigation utilising AE into cracking of stainless steel aircraft tie down linkplates............................................................................................................................................

I/191

Gaillet L., Brevet P., Robert J.L., Tessier C.
Acoustic monitoring of prestressed concrete bridge and other civil structures withcables...........................................................................................................................................

I/201

Golaski L., Gebski P., Ono K.
Diagnostics of reinforced concrete structures by AE.............................................................

I/207

Goujon L., Baboux J.C., Zhang F.
Reciprocity calibration of AE transducers................................................................................

I/217

Hájek K., Sikula J., Tacano M., Hashiguchi S.
Signal to noise ratio and preamplifier noise of AE measuring systems................................

I/223

Hatscher A., Chmelík F., Lukác P., Zenner H.
Cyclic deformation of a magnesium based light alloy investigated by the AE technique...

I/229

Holford K.M., Pullin R., Davies A.W.
An investigation into location of damage in bridge details by AE.........................................

I/237

Horikawa K., Yoshida K., Sakamaki K.
AE due to the intergranular fracture of Al-Mg-Si base alloys.................................................

I/239

Hünicke U.D., Schulz M., Budzier R., Eberlein J.
Characterization of stress corrosion cracking of CuZn-alloys by AE testing.......................

I/247

Kabanov B.S., Gomera V.P., Sokolov V.L., Okhotnikov A.A., Fedorov V.P.
AE technology as a key element of the safe operation system at refinery...........................

I/253

Korenská Ma., Pazdera L., Smutny J., Korenská Mo.
Detection of defects in hourdis sandwitch slabs by means of AE method...........................

I/263

Krofta J., Prevorovsky Z., Raab M.
AE during structure changes in semi-crystalline polymers....................................................

I/269

Kuravsky L.S., Baranov S.N.
Condition monitoring of thin-walled structures suffered high-level acoustic excitation.....

I/277

I/6

EWGAE 2002

25th European Conference on Acoustic Emission Testing
Prague, Czech Republic, 11 – 13 September 2002
Lackner G., Tscheliesnig P.
AE testing on flat bottomed storage tanks: how to condense acquired data to a reliablestatement regarding floor condition..........................................................................................

I/279

Lamark T.T., Chmelík F., Estrin Y., Lukác P.
Plastic deformation of a magnesium based light alloy investigated by the AE technique..

I/287

Landa M., Novák V., Blahácek M., Sittner P.
Investigation of transformation processes in shape memory alloys based on monitoring
AE activity....................................................................................................................................

I/293

Landa M., Wevers M.
AE during constant loading or constant deformation tests of AISI 304 stainless steel in
5N H2SO4 + 0,1M NaCl..................................................................................................................

I/301

Lavrov A., Wevers M., Vervoort A.
AE during monotonic and cyclic deformation of a brittle limestone......................................

I/307

Little J., Drummond G.R., Fraser K.F., Bentley J.A., Jihan S., Siddiqui A.M.
Lifespan prediction for composite cylinders using AE techniques.......................................

I/315

CONTENTS- Volume II van de Loo P.J., Wolfert A., Schelling R., Schoorlemmer H.J., Kooistra T.M.
Low alloy steel metal dusting: detailed analysis by means of AE.........................................

II/7

Loutas T.H., Kontsos A, Sotiriadis G., Pappas Y.Z., Kostopoulos V.
On the identification of the damage mechanisms in Oxide/Oxide Composites using AE...

II/15

March L.
Application of AE transducers in advanced manufacturing and condition monitoring
applications.................................................................................................................................

II/25

Mazal P., Benes P., Pazdera L.
AE laboratories at Brno University of Technology..................................................................

II/27

Mizutani Y., Shimoda T., He J., Morino Y., Mizutani S.
AE monitoring of cryogenic propellant tank............................................................................

II/35

Mori Y., Obata Y.
A method for reconstruction of dynamic fracture process occurred inside the material
with the aid of AE........................................................................................................................

II/43

Nivesrangsan P., Cochrane C., Steel J.A., Reuben R.L.
AE mapping of engines for spatially-located time series........................................................

II/51

de Oliveira R., Marques A.T., Berthelot J.M.
Transverse matrix cracking monitoring in glass-fibre/polyester cross-ply laminates by
AE.................................................................................................................................................

II/59

De Petris C., Agnello P., Lenzuni P., Rosatelli R.
A basic approach to discriminate AE sources in composites................................................

II/67

Poppeová V.
Cutting tool condition monitoring.............................................................................................

II/69

Prevorovsky D., Prevorovsky Z., Asserin J., Varchon D.
AE characteristics of surface friction in bio-medical application..........................................

II/77

I/7

EWGAE 2002

25th European Conference on Acoustic Emission Testing
Prague, Czech Republic, 11 – 13 September 2002
Prevorovsky Z.
Notes on wave and wave–guide concepts in AE....................................................................

II/83

Proust A., Lenain J.C.
AE monitoring of civil infrastructures.......................................................................................

II/91

Pullin R., Holford K.M.
Single sensor source location of AE from fatigue cracks.......................................................

II/95

Raad A., Zhang F., Sidahmed M.
Early detection of faults in gears using AE and vibration analysis.......................................

II/103Raad A., Zhang F., Sidahmed M.
Leak detection by AE using subspaces methods....................................................................

II/111

Rauscher F., Bayray M.
AE measurements on shell structures with directly attached piezo ceramic.......................

II/119Rogers L.M.
Structural and engineering monitoring by AE methods - fundamentals and applications..

II/125

Rowland C., Callaghan T., Coy J.
Conditioning monitoring using AE on multiple roller bearings in Consignia´s letter
sorting machines.........................................................................................................................

II/131

Sagar Remalli Vidya, Raghu Prasad B.K., Kumar Vijaya
Fracture energy of high strength concrete beams using AE technique................................

II/133

Shinomiya T., Nakanishi Y, Morishima H., Shiotani T.
Damage diagnosis technique for brick structures using AE..................................................

II/141

Shiotani T., Bisschop J., van Mier J.G.M.
Evaluation of drying shrinkage microcracking in cementitious materials using AE...........

II/149Shiraishi M., Koizumi T.
Friction control of positioning system by AE detection..........................................................

II/157

Sikula J., Pavelka J., Majzner J., Dobis P.
Cracks characterisation by acoustic and electromagnetic emission....................................

II/163Sikula J., Strunc M., Majzner J., Koktavy P., Pavelka J., Hájek K.
Electrical noise of piezoceramic sensors.................................................................................

II/171

Skalsky V., Andreykiv O., Serhiyenko O.
AE modeling of microcracking in plastically deformed zones of crystalline solids.............

II/177

Sodomka L.
Applications of the acoustical emission to textile fields.........................................................

II/185

Sodomka L.
To the history of acoustical emission.......................................................................................

II/195Spacková H.
Tram track structure acoustic analysis.....................................................................................

II/199

Svoboda V., Zemlicka F., Petrásek P., Proust A.
Damage monitoring in high pressure vessels by means of AE..............................................

II/203

Theobald P.D., Esward T.J., Dowson S.P.
Towards traceable calibration of AE transducers: a new development at the UK´s NPL....

II/211

Trapl K., Sajdl P., Kucera P., Novotny R.
AE and electrochemical noise during stress corrosion cracking..........................................

II/219Traxler H., Arnold W., Knabl W., Rödhammer P.
Non-Destructive evaluation of brazed joints by means of AE................................................

II/225I/8

EWGAE 2002

25th European Conference on Acoustic Emission Testing
Prague, Czech Republic, 11 – 13 September 2002
Tscheliesnig P.
Training and certification on the field of AE testing at according the European
standardisation (EN 473)............................................................................................................

II/233

Vales F., Lasová V., Las V., Adámek V.
Interaction of elastic waves with plastic zone by the various types of loading....................

II/239Vallen H., Forker J.
A new AE-system approach with optimum speed and flexibility for use in field test andlaboratory.....................................................................................................................................

II/247

Vallen H., Vallen J.
Free-of-cost software tools generate dispersion curves, perform wavelet transform andhelp to correlate both kinds of information..............................................................................

II/255

Vougiouklakis Y. Hähner P., Kostopoulos V., Peteves S.
Damage mode identification and analysis of coated gas turbine materials using a nondestructive evaluation technique..............................................................................................

II/263Winkelmans M., Wevers M.
Non-Destructive testing for corrosion monitoring in chemical plants..................................

II/271

Yan T., Jones B.E.
In-situ AE transducer calibration using artificial AE energy sources....................................

II/279

Yoshida K., Horikawa K., Sakamaki K.
Characteristics of AE behaviour during tensile deformation of Ni-Al intermetallic
compouds with boron additive..................................................................................................

II/287

Zizka J., Prukner V., Ogawa D., Sumiya H., Shiraishi M.
Neuronal network for tool wear monitoring on the AE signal basis......................................

II/295

Zuev L.B., Semukhin B.S., Lunyev A.G.
On the acoustic properties of metallic polycrystals under straining.....................................

II/303

Grad L., Polajnar I., Grum J.
On line identification of gas metal arc welding (GMAW) process by detection of acoustic
signals .........................................................................................................................................

II/305

Index of autors.................................................................................................................

II/313

Contact adresses of selected authors ............................................................................

II/315

I/9

Index of authors

Adámek V.
II/239Agnello P.
II/67
Akematsu Y.
I/15
AnastassopoulosA.A.
I/21,I/29
Andreykiv O.
II/177
Arnold W.
II/225Asserin J.
II/77
Baboux J.C.
I/217
Bailey J.
I/191
Baranov S.N.
I/277
Bayray M.
I/37,II/119
Belov V.
I/45
Benes P.
I/51,II/27
Benmedakhene S.
I/173
Bentley J.A.
I/315
Berthelot J.M.
II/59
Bisschop J.
II/149Bláha J.
I/59
Blahácek M.
I/63, I/293
Blanch M.J.
I/21
Boczar T.
I/69, I/77
Bordei M.
I/85, I/135, I/155
Bradshaw T.
I/91
Brevet P.
I/201
Brom J.
I/141
Brown M.
I/191
Buburuzan L.
I/135
Budzier R.
I/247
Callaghan T.
II/131
Campbell P.
I/161
Cerny L.
I/99
Cherfaoui M.
I/173
Chlada M.
I/107
Chmelík F.
I/117, I/229, I/287
Cochrane C.
II/51
Cole P.T.
I/91, I/125
Colombo S.
I/127
Constantinescu S. I/85,I/135,I/155
Coy J.
II/131
Crha J.
I/141
Davies A.
I/91, I/237
van Delft D.R.
I/21
Derjagin.D.V.
I/149
Dobis P.
II/163
Dowson S.P.
II/211
Dragan V.
I/85, I/135, I/155
Dragulin I.
I/85
Drugescu E.
I/135
Drummond G.R. I/161,I/191,I/315
Dutton A.G.
I/21
Eberlein J.
I/247
Ennaceur C.
I/173
Estrin Y.
I/287

Esward T.J.
Fedorov V.P.
Fernando G.
Ferrer F.
Fleisher S.
Forde M.C.
Forker J.
Fraser K.F.
Gaillet L.
Gautrey S.N.
Gebski P.
Glushko A.
Goanta M.A.
Golaski L.
Gomera V.P.
Goujon L.
Grad L.
Grum J.
Hahn J.
Hähner P.
Hájek K.
Halliday J.
Hashigutchi S.
Hatscher A.
He J.
Hervé C.
Holford K.M.
Horikawa K.
Hünicke U.D.
Jihan S.
Jirsík V.
Jones B.E.
Jones L.E.
Joosse P.A.
Kabanov B.S.
Kluev V.A.
Knabl W.
Koizumi T.
Koktavy P.
Kontsos A.
Kooistra T.M.
Korenská M.
Kossivas T.
Kostopoulos V.
Kouroussis D.A.
Kozyrev O.
Krofta J.
Kucera P.
Kumar V. S.
Kuravsky L.S.
Lackner G.
Laksimi A.
Lamark T.T.

II/313

II/211
I/253
I/21
I/185
I/191
I/127
II/247
I/161,I/191,I/315
I/201
I/125
I/207
I/45
I/85
I/207
I/253
I/217
II/305
II/305
I/141
II/263
I/223, II/171
I/127
I/223
I/229
II/35
I/173
I/91,I/237,II/95
I/15,I/239,II/287
I/247
I/315
I/99
II/279
I/21
I/21
I/253
I/149
II/225
II/157
II/171
II/15
II/7
I/263
I/21
II/15, II/263
I/21, I/29
I/45
I/269
II/219
II/133
I/277
I/279
I/173
I/287

Landa M.
I/293, I/301
Langdon T.G.
I/117
Las V.
II/239Lasová V.
II/239Lavrov A.
I/307
Lekou D.J.
I/21
Lenain J.C.
I/125, II/91
Lenzuni P.
II/67
Lipson A.G.
I/149
Little J.
I/161, I/191,I/315
van de Loo P.J.
II/7
Loutas T.H.
II/15
Lukác P.
I/117,I/229,I/287
Lunyev A.G.
II/303Main I.G.
I/127
Majzner J.
II/163, II/171
March L.
II/25
Marques A.T.
II/59
Mathis K.
I/117
Mazal P.
II/27
Mediouni M.
I/173
van Mier J.G.M.
II/149Mizutani S.
II/35
Mizutani Y.
II/35
Mori Y.
II/43
MorinoII/35
Morishima H.
II/141Nakanishi Y.
II/141
Nikolaidis V.N.
I/21
Nivesrangsan P.
II/51
Novák V.
I/293
Novotny P.
II/219
Obata Y.
II/43
Ogava D.
II/295
Okhotnikov A.A.
I/253
de Oliveira R.
II/59
Ono K.
I/207
Pappas Z.
II/15
Pavelka J.
II/163, II/171
Pazdera L.
I/263, II/27
Peteves S.
II/263
Petrásek P.
II/203de Petris C.
II/67
Philippidis T.P.
I/21
Polajnar I.
II/305
Poppeová V.
II/69
Prevorovsky D.
II/77
Prevorovsky Z.
I/63,I/107,I/269,
II/77, II/83
Proust A.
I/21, II/91,II/203
Prukner V.
II/295
Pullin R.
I/237, II/95
Raab M.
I/269

Raad A.
II/103, II/111
Radigon E.
I/185
Radu T.
I/135
Raghu Prasad B.K.
II/133
Rauscher F.
I/37, II/119
Reuben R.L.
II/51
Robert J.L.
I/201
Rödhammer P.
II/225
Rogers L.M.
II/125Rosatelli R.
II/67
Rowland C.
II/131
Sagar R.V.
II/133
Sajdl P.
II/219
Sakamaki K.
I/15,I/239, II/287
Schelling R.
II/7
Schoorlemmer H.J.
II/7
Schulz M.
I/247
Semukhin B.S.
II/303
Serhiyenko O.
II/177
Shimoda T.
II/35
Shinomiya T.
II/141
Shiotani T.
II/141, II/149

Shiraishi M.
II/157, II/295
Sidahmed M.
II/103, II/111
Siddiqui A.M.
I/317
Sikula J.
I/223,II/163, II/171
Sittner P.
I/293
Skalsky V.
II/177
Smutny J.
I/263
Sodomka L.
I/149,II/185,II/195
Sokolov V.L.
I/253
Sotiriadis G.
II/15
Spacková H.
II/199Steel J.A.
II/51
Strunc M.
II/171
Sumiya H.
II/295
Svoboda V.
II/203
Tacano M.
I/223
Tessier C.
I/201
Theobald P.D.
II/211
Toporov Yu.P.
I/149
Trapl K.
II/219Traxler H.
II/225Tscheliesnig P.
I/279, II/233

II/314Tsimogiannis A.N.
I/29
Tudor B.
I/155
Vales F.
II/239Vallen H.
II/247, II/255
Vallen J.
II/255
Varchon D.
II/77
Vervoort A.
I/307
Vionis P.
I/21
Vougiouklakis Y.
II/263
Watson J.F.
I/161
Wevers M.
I/301, I/307, II/271
Winkelmans M.
II/271
Wolfert A.
II/7
Yan T.
II/279
Yoshida K.
I/15,I/239,II/287
Zenner H.
I/229
Zemlicka F.
II/203
Zhang F.
I/217,II/103,II/111
Zizka J.
II/295
Zuev L.B.
II/303

Modeling of AE Propagation in Anisotropic
Composite Plates
W. H. Prosser*, Ajit Mal+, and Sauvik Banerjee+*NASA Langley Research Center+UCLA

NASA Langley Research Center

Nondestructive Evaluation Sciences Branch

Outline
I.

Introduction
A.
B.

II.

Motivation
Previous modeling review

SDPT and FE Modeling
A.
B.

Very thin (1 mm) anisotropic (gr/ep) plate
Simulated AE source
1. Out-of-plane source motion
2. Single cycle sine wave forcing function

III. Experimental Results
A.
B.
C.

Thicker (16 ply) anisotropic (gr/ep) plateOut-of-plane lead break source
NIST sensor (not calibrated)

IV. Summary and Conclusions

NASA Langley Research Center

Nondestructive Evaluation Sciences BranchModeling of AE Signals
•
•
•
•
•

Sensor/instrument calibration
Optimization of sensor placement
Scaling of coupon test results to practical structures
Source inversion difficulties
Train AI methods

Numerical Model (FE) Advantages• Realistic source configurations– Subsurface location– Dipoles, etc.

• Realistic specimen geometries– Reflections– Thickness changes– Etc.

• Anisotropic materials
NASA Langley Research Center

Nondestructive Evaluation Sciences BranchAnisotropic Composite Materials• Increasing aerospace
applications– Polymer matrix composites– Metal matrix composites– Ceramic matrix composites• Need for improved NDE/SHM– X-33 tank failure– Airbus tail failure• Complexities– Properties function of direction– Inhomogeneous– Laminated or layered– Woven or stitched– High attenuation
NASA Langley Research Center

Composite X-33 LH2 Tank

Nondestructive Evaluation Sciences BranchFinite Element Modeling• Gary and Hamstad (1994)– AE signals in thin plates (2D model)– Experimental verification– Examined source size, rise time, sensor aperture, FE cell size

• Hamstad, Gary, and O’Gallagher (1996)– AE signals in thick plates– Experimental verification• Hamstad, Gary, and O’Gallagher (1998)– Scaling for different plate thicknesses and source rise times– Realistic source conditions• Prosser, Hamstad, Gary, and O’Gallagher (1999)– Reflections in finite geometries• Prosser, Hamstad, Gary, and O’Gallagher (1999)– Comparison with plate theory
NASA Langley Research Center

Nondestructive Evaluation Sciences BranchShear Deformation Plate Theory (SDPT)• Mindlin (1951)– Effects of shear deformation and rotatory inertia in isotropic materials• Yang et al. (1966)– Application to anisotropic materials• Tang et al. (1987)– Dispersion predictions in anisotropic laminated composite plates• Lih and Mal (1995 and 1996)– Waveform predictions in composite plates– Integral transform approach– Numerical solutions

NASA Langley Research Center

Nondestructive Evaluation Sciences BranchParameters for Models• Graphite/epoxy composite– Unidirectional “homogeneous” material– 1.0 mm thickness– 1580 kg/m3 density– Elastic properties (GPa)• C11 = 160.7
C12 = 6.44
C22 = 13.9
• C23 = 6.9
C66 = 7.1
– Sine source function applied
normal to plate surface# 2!t %

# 4!t %'
0.5sin$ " &," &– 200 X 200 mm– 10 cells through thickness– Equi-axial cell size (0.1 mm)– Stress free boundary conditions– No attenuation effects– Source diameter of four times cell size (0.4 mm)

0< t < ".

1.5
1

Amplitude (Pa)• FE modelf (t) = sin$

0.5
0
-0.5
-1
-1.5

0

2

4

6

Time (µs)

NASA Langley Research Center

Nondestructive Evaluation Sciences Branch

810

0 Degree Propagation

Time (µs)– SDPT does not predictvery small extensionalmode observed in FE– SDPT arrives slightly
faster (higher velocities
predicted)– Interesting “phasereversal” effectobserved

NASA Langley Research Center

Displacement 10-8 m• All results bandpass
filtered (50-650 kHz)• Excellent agreement

SDPT
FE
Nondestructive
Evaluation Sciences Branch

Time (µs)

45 Degree Propagation– Larger extensionalmode observed in FE– “Phase reversal” effectstill observed but withdifferent distance of
propagation

NASA Langley Research Center

Displacement 10-8 m• Again excellent
agreement

SDPT
FE
Nondestructive
Evaluation Sciences Branch

Time (µs)

90 Degree Propagation– Only one distancecalculated with FE
model– “Phase reversal” stillpresent

NASA Langley Research Center

Displacement 10-8 m• Again excellent
agreement

SDPT
FE
Nondestructive
Evaluation Sciences BranchExperimental Conditions• Graphite/epoxy composite plate– Unidirectional 16 ply laminate (1.016 X 0.762 m)– 2.26 mm thickness– 13.34 cm propagation distance along 0, 45, and 90 degree directions• 0.5 mm lead break source on center of plate– Sudden release of applied force– FE models sudden application of force– 180 degree shift of experimental waveform• NIST wideband AE sensor– 20 kHz to 1 MHz– No calibration available for composite materials– Used metal plate calibration factor for comparison (5.6233 X 10-9 m/V)
– Trigger sensor placed adjacent to lead break with further (2-3 µs)
timing refinement made based on first peak arrivals

NASA Langley Research Center

Nondestructive Evaluation Sciences Branch

FE Model Parameters• Graphite/epoxy composite– Unidirectional material– Treated as “homogeneous” but anisotropic media– 2.26 mm thickness– 1550 kg/m3 density– Elastic properties (GPa)• C11 = 147.1
C12 = 4.11
C22 = 10.59• C23 = 3.09
C66 = 5.97
– Lead break source function time response• FE model– 282.5 X 282.5 mm– 12 cells through thickness– Equi-axial cell size (0.19 mm)– Stress free boundary conditions– Source diameter of four times cell size (0.75 mm)– No attenuation effects included
NASA Langley Research Center

Nondestructive Evaluation Sciences Branch

0 Degree Propagation

4 10-9Finite Element– Large reflections in FE
predictions beyond 140 µs– Uncertainty in actual properties– Smaller amplitude experimental
results• Again, not calibrated• Also, FE does not include
attenuation effects• Extensional mode nearly innoise for experimental signal,
but some correlation observed

2 10-9
1 10-90-1 10-9-2 10-9-3 10-9-4 10-9
0

20

40

60

80

100

120

140

100

120

140

Time (µs)
1 10-9Experiment
Displacement (m)• 20-1500 kHz filtering• Reasonable agreement for
flexural mode

Displacement (m)3 10-95 10-100-5 10-10-1 10-9
0

20

40

60

80

Time (µs)

NASA Langley Research Center

Nondestructive Evaluation Sciences Branch45 Degree Propagation– Large reflections in FE
predictions beyond 150 µs– Smaller relative amplitude ofexperimental results• Larger attenuation along 45
degree direction

Finite Element

1.5 10-9

Displacement (m)• Again reasonable agreement
in shape of waves

2 10-9

1 10-95 10-100-5 10-10-1 10-9-1.5 10-9-2 10-9
0

20

40

60

80

100

120

140

100

120

140

Time (µs)
3 10-10

Experiment

Displacement (m)2 10-10
1 10-100-1 10-10-2 10-10-3 10-10
0

20

40

60

80

Time (µs)

NASA Langley Research Center

Nondestructive Evaluation Sciences Branch

90 Degree Propagation
2 10-9Displacement (m)– Large reflections in FE
predictions beyond 150 µs– Smaller relative amplitude ofexperimental results• Largest attenuation along 90
degree direction• Again some correlation inextensional mode, but notshown

1 10-95 10-100-5 10-10-1 10-9-1.5 10-9-2 10-9
0

20

40

60

80

100

120

140

100

120

140

Time (µs)
Experiment

2 10-10
1.5 10-10

Displacement (m)• Not quite as good agreement
in shape of waves

Finite Element

1.5 10-9

1 10-10
5 10-110-5 10-11-1 10-10-1.5 10-10-2 10-10
0

20

40

60

80

Time (µs)

NASA Langley Research Center

Nondestructive Evaluation Sciences Branch

SUMMARY AND CONCLUSIONS• Preliminary results on the applicability of the FE model for
predicting AE signals in anisotropic composite plates– Good agreement with SDPT
• Flexural plate mode only
• Source normal to plate surface• Single cycle pulse shape• Interesting phase reversal effect seen as a function of
propagation distance– Also interesting correlation with experimental signals• Lead break source normal to plate surface• 0, 45, and 90 degree directions• Future work
– Need calibrated transducer– Incorporation of attenuation effects– In-plane sources– Effects of laminates
NASA Langley Research Center

Nondestructive Evaluation Sciences Branch