Journal of Acoustic Emission, Volume 30, 2012
CONTENTS
30-001 -Analysis of AE Signals during Scratch Test on the Coated Paperboard
Hiroyuki Kishi, Akio Matudoe, Ken Yamashita, Shigeru Nagasawa and Yasushi Fukuzawa
30-011 Acoustic Emission Related to Drought Stress Response of Four Deciduous Broad-Leaved Woody
Species
Sabine Rosner30-021 Micro-mechanical Damage in Tool Steels Analyzed by Acoustic Emission Technique
Ingrid Picas, Eva Martinez-Gonzalez, Daniel Casellas and Jordi Romeu
30-029 Viable Precursors of Paroxysmal Phenomena as Detected by Applying RQA to Acoustic
Emission Time Series
Maurizio Poscolieri, Claudio Rafanelli and Giovanna Zimatore
30-040 Artificial Neural Network Analyses of AE Data during Long-Term Corrosion Monitoring of a
Post-Tensioned Concrete Beam
Lucio Bonaccorsi, Luigi Calabrese, Giuseppe Campanella and Edoardo Proverbio
30-054 On the Use of the Acoustic Emission Technique for In-situ Monitoring of the Pulverization ofBattery Electrodes
Aurélien Etiemble, Hassane Idrissi and Lionel Roué
30-064 AE Wavelet Processing in Dynamical Tests of a Reinforced Concrete Slab
Miguel E. Zitto, Rosa Piotrkowski, Antolino Gallego and Francisco Sagasta330-076 Recent Advances of AE Technology for Damage Assessment of Infrastructures
Tomoki Shiotani
30-100 Preliminary Investigations of Acoustics Emission Signal from Snow and its Wavelet Transform
Karmjeet Singh, Yogesh C. Nagar, Jagdish C. Kapil, Pramod K. Satyawali and Ashwagosha Ganju
30-109 Attenuation of Lamb Waves in CFRP Plates
Kanji Ono and Antolino Gallego30-124 Application of ICI Principle for AE Data Processing
Vera Barat, Sergey Elizarov, Irina Bolokhova and Evgeniy Bolokhov
30-137 Modeling of the Far-field Acoustic Emission from a Crack under Stress
Warida Ben Khalifa, Karim Jezzine, Sébastien Grondel, Gaëtan Hello and Alain Lhémery
30-152 Primary Calibration of Acoustic Emission Sensors by the Method of Reciprocity, Theoretical andExperimental Considerations
Thomas Monnier, Seydou Dia, Nathalie Godin and Fan Zhang
30-167 Neural Network Burst Pressure Prediction In Tall Graphite-Epoxy Pressure Vessels From
Acoustic Emission Data
Eric v. K. Hill, Christopher J. Foti, Ning Y. Leung and Andrea E. PalaciosI-2

30-180 Research and Applications of AE on Advanced Composites
Kanji Ono and Antolino Gallego
Contents30

Contents of Volume 30 (2012)
Meeting Calendar - AE Activity (EWGAE30/ICAE7)
Announcement
AUindex30
Authors Index of Volume 30
AusNotes
Policy/Author’s Notes
In Memoriam
Professor Steve Carpenter
Cover illustration
From EWGAE30-ICAE7, Granada, Spain.

I-1 – I-2
I-2
I-2
I-3
I-4 – I-5
I-6

JAE Index Folder*

Cumulative Indices of J. of Acoustic Emission, 1982 – 2012
JAE-Contents1-30
Contents of Volumes 1-30
JAE-Authors Index1-30
Authors Index Volumes 1-30
Comprehensive Authors Index (2012) is also included.
* indicates the availability in CD-ROM only. Indices are also available for download fromwww.aewg.org.

Meeting Calendar
AEWG 55
The 55th Meeting on Acoustic Emission Working Group will be held on June 11-12, 2013 at Garden
Grove, CA. The host is Dr. Jerry Huang of Boeing (jerry.q.huang@boeing.com). More information isavailable at www.aewg.org. For accommodation, contact Crowne Plaza Anaheim Resort at
http://www.anaheim.crowneplaza.com/

AE Activity
EWGAE30-International Conf. on AE 7, 2012
Information on this successful conference is available at www.ewgae.es, including Program andProceedings in downloadable pdf file. See also a report on the conference at
http://www.ndt.net/search/docs.php3?id=13636&content=1

Announcement
With the publication of this volume, this Journal will shift to the open-access mode, terminating
the paper/CD distribution and subscription services. I wish to thank all of you, authors, reviewers,
co-editors, book agencies and readers, who have supported this Journal for the past 32 years.
Some of the reasons for the founding of this publication have faded as there are so many venues
for quality publications. In particular, academic authors require impact factors, which areincompatible with our goals. We will continue providing a forum in timely manner with webbased distribution, rather than annual basis, with rigorous review as in the past to maintain thequality we have sustained. We hope future contributors and readers keep the confidence in
Journal of Acoustic Emission in the new approach for disseminating the information useful to theglobal AE community.
Kanji Ono, Editor and Publisher
I-2

Authors Index, Volume 30, 2012
Vera Barat
Evgeniy Bolokhov
Irina Bolokhova
Luigi Calabrese
Giuseppe Campanella
Daniel Casellas
Seydou Dia
Sergey Elizarov
Aurélien Etiemble
Christopher J. Foti
Yasushi Fukuzawa
Antolino Gallego
Ashwagosha Ganju
Nathalie Godin
Sébastien GrondelGaëtan Hello
Eric v. K. Hill
Hassane Idrissi
Karim Jezzine
Jagdish C. Kapil
Warida Ben Khalifa
Hiroyuki Kishi
Ning Y. Leung
Alain Lhémery
Eva Martinez-Gonzalez
Akio Matudoe
Thomas Monnier
Yogesh C. Nagar
Shigeru Nagasawa
Kanji Ono
Andrea E. Palacios
Ingrid Picas
Rosa Piotrkowski
Maurizio Poscolieri
Edoardo Proverbio
Claudio Rafanelli
Jordi Romeu
Sabine Rosner
Lionel RouéFrancisco Sagasta
Pramod K. Satyawali
Tomoki Shiotani
Karmjeet SinghKen Yamashita
Fan Zhang
Giovanna Zimatore
Miguel E. Zitto

30-12430-12430-12430-04030-04030-02130-15230-12430-05430-16730-00130-064, 30-109, 30-180
30-10030-15230-13730-13730-16730-05430-13730-10030-13730-00130-16730-13730-02130-00130-15230-10030-00130-109, 30-180
30-16730-02130-06430-02930-04030-02930-02130-01130-05430-06430-10030-07630-10030-00130-15230-02930-064I-3

JOURNAL OF ACOUSTIC EMISSION
Editor: Kanji Ono
Associate Editors: A. G. Beattie, T. F. Drouillard, M. Ohtsu and W. H. Prosser
1. Aims and Scope of the Journal
Journal of Acoustic Emission is an international journal
designed to be of broad interest and use to both researcher andpractitioner of acoustic emission. It will publish original contributions of all aspects of research and significant engineering
advances in the sciences and applications of acoustic emission.
The journal will also publish reviews, the abstracts of papers
presented at meetings, technical notes, communications andsummaries of reports. Current news of interest to the acoustic
emission communities, announcements of future conferences andworking group meetings and new products will also be included.
Journal of Acoustic Emission includes the following classesof subject matters;
A. Research Articles: Manuscripts should represent completed original work embodying the results of extensive investigation. These will be judged for scientific and technical merit.
B. Applications: Articles must present significant advancesin the engineering applications of acoustic emission. Material will
be subject to reviews for adequate description of procedures,substantial database and objective interpretation.
C. Technical Notes and Communications: These allow publications of short items of current interest, new or improved experimental techniques and procedures, discussion of published articles
and relevant applications.
D. AE Program and Data Files: Original program files anddata files that can be read by others and analyzed will be
distributed in CD-ROM.
Reviews, Tutorial Articles and Special Contributions willaddress the subjects of general interest. Nontechnical part will
cover book reviews, significant personal and technical accomplishments, current news and new products.

policies and other aspects. The Board presently includes thefollowing members:
A. Anastasopoulos
F.C. Beall
J. Bohse
P. ColeM.A. Hamstad
R. HayK.M. Holford
O.Y. Kwon
J.C. Lenain
G. Manthei
P. Mazal
C.R.L. Murthy
A.A. Pollock
F. Rauscher
J. Schmidt
T. Shiotani
P. Tschliesnig
H. Vallen
M. Wevers
B.R.A. Wood
4. Publication
Journal of Acoustic Emission is published annually in CDROM by Acoustic Emission Group, PMB 409, 4924 Balboa Blvd,
Encino, CA 91316. It may also be reached at 2121H, Engr. V,
University of California, Los Angeles, California 90095-1595(USA). tel. 310-825-5233. FAX 310-206-7353. e-mail:aegroup7@gmail.com or ono@ucla.edu
5. Subscription

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

ISSN 0730-0050 Copyright©2012 Acoustic Emission Group(Greece)(USA)(Germany)
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6. Advertisement
No advertisement will be accepted, but announcements forbooks, training courses and future meetings on AE will be
included without charge.

I-4

CODEN: JACEDO All rights reserved.

Notes for Contributors
1. Generalat the end of the paper. Journal references should bearranged as below. Titles for journal or book articles arehelpful for readers, but may be omitted.

The Journal will publish contributions from all parts of theworld and manuscripts for publication should be submitted
to the Editor. Send by e-mail or to:Professor Kanji Ono, Editor - JAE
Rm. 2121H, Engr. V, MSE Dept.
University of California
420 Westwood Plaza,
Los Angeles, California 90095-1595 USA
e-mail: kanjiono1@gmail.com

H.L. Dunegan, D.O. Harris and C.A. Tatro (1968), Eng.
Fract. Mech., 1, 105-122.
Y. Krampfner, A. Kawamoto, K. Ono and A.T. Green(1975), "Acoustic Emission Characteristics of Cu Alloys
under Low-Cycle Fatigue Conditions," NASA CR-134766,
University of California, Los Angeles and Acoustic Emission Tech. Corp., Sacramento, April.

Authors of any AE related publications are encouraged tosend a copy to:
Acoustic Emission Collection
Grainger Engineering Library
University of Illinois at Urbana-Champaign.
c/o: Mary C. Schlembach, Engineering Physics &Astronomy Librarian
154 Grainger Engineering Library Information Center
1301 West Springfield Ave. Urbana, IL 61801
Email: schlemba@illinois.edu

A.E. Lord, Jr. (1975), Physical Acoustics: Principles andMethods, vol. 11, eds. W. P. Mason and R. N. Thurston,Academic Press, New York, pp. 289-353.
Abbreviations of journal titles should follow those used in
the ASM Metals Abstracts. In every case, authors' initials,
appropriate volume and page numbers should be included.
The title of the cited journal reference is optional.
Illustrations and tables should be planned to fit a single
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these need not be of high quality, but submit glossy prints
or equivalent electronic files with the final manuscript.
Lines and letters should be legible.

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

4. Review

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

All manuscripts will be judged by qualified reviewer(s).
Each paper is reviewed by one of the editors and may besent for review by members of the Editorial Board. The
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This Journal will be primarily distributed electronically by
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References to published literature should be quoted in the
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I-5

In Memoriam

Steve H. Carpenter (1938 – 2012)
Steve	  H.	
  Carpenter,	
  John	  Evans	
  Professor	
  Emeritus,	
  Department	
  of	
  Physics,	
  University	
  of	
  Denver,	
  of	
  Littleton,	
  CO,	
  passed	
  away	  June	  10,	  2012.	
  He	
  was	
  74.	  He	  was	
  internationally	
  known	
  for	
  his	
  acoustic	
  emission	
  research,	
  especially	
  on	
  the	
  plastic	
  deformation	
  of	
  metals	
  and	
  hydrogen	
  effects	
  and	
  for	  resonance	  measurements	
  of	
  crack	
  detection.	
  He	
  contributed	
  over	  one	
  hundred	
  papers	
  on	
  
AE	
  alone.	
  Born	
  May	
  15,	  1938,	
  in	
  Cedar	
  City,	
  UT,	  he	
  has	  been	  an	
  active	
  member	
  of	
  the	
  Church	
  of	
  Jesus	
  Christ	
  of	
  Latter-­‐day	
  Saints	  and	  is	
  survived	
  by	
  his	
  wife,	
  Marie,	
  by	
  his	
  four	  children,	
  Michelle,	  Kerri,	
  Stephanie	
  and	
  Christopher,	  and	  15	  grandchildren.	
  	
  After	
  getting	  BS	
  (1959)	
  and	  PhD	  (1964)	
  in	
  Physics	
  from	  University	
  of	
  Utah,	  Steve	
  was	  at	
  Aerojet	
  General	
  Corp.	
  	
  and	
  joined	  the	
  faculty	
  of	
  University	
  of	
  Denver	  in	
  1965,	
  and	
  has	  been	
  full	  professor	
  since	
  1974.	
  From	  1984,	  he	
  also	
  worked	
  part time at Rocky Flats Plant. 	
  He	
  gave	  1987	
  University	
  Lecture	
  on	
  AE	
  entitled	
  “Materials	
  Cry	
  when	  Stressed”.	
  He	
  served	
  as	
  chairman	
  of	
  Physics	
  Dept.	
  and	  as	
  head	  of	
  DRI.	
  Steve	
  lectured	  in	
  many	  countries	
  as	
  visiting	
  professor/scientist,	
  including	
  New	
  Zealand,	
  Australia,	
  Europe,	
  USSR	
  (now	
  Belarus),	
  China	
  and	  Japan.	
  He	
  was	  a	
  Fulbright-­‐Hayes	
  scholar	  and	
  the	  recipient	
  of	
  the	
  AEWG	
  Achievement	
  and	
  Gold	  Medal	
  Awards	
  for	
  outstanding	
  contributions	  in	
  scientific	
  understanding	  of	
  acoustic	
  emission.	
  Steve	  also	  received	
  the	
  AEWG	
  Publication	
  Award	  with	
  Clint	
  Heiple.	
  He	
  was	  a	
  fellow	
  of	
  APS,	
  a	
  charter	
  fellow	
  of	
  AEWG	  and	
  also	  member	  of	
  ASM	
  International,	
  TMS-­‐AIME,	
  and	
  ASNT.	
  Steve	
  served	
  as	
  Chairman	
  of	
  AEWG	
  in	
  1990-­‐1992.	
  
	
  
He	
  will	  be	
  dearly	
  missed	
  by	
  his	
  family,	
  friends	
  and	  colleagues	
  from	  around	  the	
  world.	
  	
  Kanji	
  Ono	
  I-6

Analysis of AE Signals during Scratch Test on the Coated Paperboard
Hiroyuki Kishi, Akio Matudoe, Ken Yamashita, Shigeru Nagasawa and Yasushi Fukuzawa
Department of Mechanical Engineering, Nagaoka University of Technology,1603-1 Kamitomioka, Nagaoka, Niigata, 940-2188, Japan
Abstract
The deformation and fracture behavior of ink-printed layers on clay-coated paperboards were
estimated using the scratch tester with the acoustic emission (AE) method. The designed scratch
tester was custom-designed made for the paper. To investigate the boundary effects of coatedand printed layers on the fiber matrix, two kinds of paperboards were prepared. Scratch tests
were carried out using three types of machined spherical indenters with an AE transducer attached on the surface of the paperboard. The shape of compressed marks was observed by thelaser microscopy. The measured AE signal was analyzed by amplitude and peak frequency andrelated to the deformation and fracture behaviors. The detected AE generation was divided into
two zones: one corresponded to the micro-crack generation and the other was the generation ofdelamination. This AE generation behaviors varied with the indenter tip radius.
Keywords: Scratch test, paperboard, coated layer, micro-crack, delamination
Introduction
Paperboard has received much attention since it is easily and cheaply recycled. Further, inorder to meet modern packaging needs, the coloring and texture have both been enhanced using
different base papers and coating substances. Printed paperboard surfaces are damaged by scuffing and abrasions during careering operation in the shape forming and carton forming processes.
The fracture behavior of printing surfaces needs to be easily quantified in order to evaluate theirmechanical properties. Scratching tests are available to evaluate the mechanical properties of
coating substances. These tests have been used to evaluate the strength of adhesion between ahard membrane and a matrix such as a deposited film or plating layer on a metallic surface [1]. It
has also been reported that the scratching test is useful for evaluating the mechanical propertiesof paperboards [2, 3]. However limited results were obtained regarding the strength of a coatedlayer or paper substrate, based on the compressive load or width of scratching indent. These results could not be used to completely evaluate the adhesion strength of the printing layer. Furthermore, the relationship between the adhesion strength and the fracture behavior is not clear,and is insufficiently understood to confirm the effect of indenter shape on the fracture behavior
during the scratching process.
Acoustic emission (AE) is useful in detecting fracture behavior in dynamically deformedmaterials. It reveals AE signals, which correspond to the deformation and fracture behaviors
during the paperboard cutting process [4, 5]. AE signals during the cutting, ruling and embossing
machining have been collected and analyzed in the same manner [6-9]. However, AE signals
from surface fractures such as scuffs and abrasions have not been widely studied.
In this study, scratching tests were examined on two types of paperboards, a black-ink layer
printed surface and a white-clay layer coated surface, with three scratching indenter diameters
under constant scratching rates to investigate the abrasion phenomenon and the fracture behaviors within the very thin depth region of 50 µm. AE signals were detected in the respective
J. Acoustic Emission, 30 (2012)1© 2012 Acoustic Emission Groupscratching tests, and the compressed traces were observed using microscopy. These signals andmicroscopic images were synchronized and verified to recognize the fracture behaviors of coatedlayers.
Experimental
Materials: The fundamental structure of the paperboard is displayed in Fig. 1. Paperboard iscomposed of a pulp fiber structure matrix and clay structured coated layer having a color printed
layer and varnish. To investigate the boundary strength of the printed paperboard, two patternpaperboards were prepared for the scratch test. One was conventional coated paperboard of 230
g/m2 nominal basis weight and 0.3 mm thickness. Specimens were cut to 100 mm in length and
15 mm in width. The second was printed paperboard of carbon black pigment with a varnish
layer. It was the same size and weight of unprinted paperboard.

Fig. 1 Fundamental structure of the printed paperboard.
Load cell
Wire

WeightSpecimen
SUS630
V:Feed velocity

Fig. 2 Illustration of the dynamic friction test machine.
Estimation of mechanical properties: The machining strength of the base paperboard was examined using the tensile test along the machining direction (MD) and cross direction (CD) following the JIS P8113 specification. To estimate the friction coefficient between the surface of thepaperboard and the indenter material, the dynamic friction test (horizontal method) was carriedout with a specially designed machine. Figure 2 shows a schematic illustration of the frictional
system.
Scratching test and AE detection system: Figure 3 shows a schematic illustration of scratching
test configuration and AE system using the universal testing machine. The automatic X-stage
was attached to the lower crosshead, and the specimen was placed upon the SUS630 steel faceplate embedded on the X-stage. Indenters were made of SKD tool steel and were shaped with2

tip radii of 0.05, 0.1 and 0.5 mm. The spherical shaped indenters were attached below the upper crosshead with a load cell. The universal testing machine was operated by lowering the upper
crosshead at a constant speed of 0.001 mm/s in the vertical direction and the X-stage was synchronized at a constant speed of 0.1 mm/s in the horizontal direction. The applied load wasmeasured over the duration of indenter contacts with the surface for a penetration depth of 50µm.
The AE waves were detected using AE sensors attached to the sides of the indenter. The AE
detection and analysis system is shown in Fig. 4. The AE sensor has a resonance frequency of
220 kHz, and signal processing was operated with a 10 kHz high-pass filter and a 400 kHz
low-pass filter. These examinations were performed at 296 K, 50% RH in the thermo-hygrostat
room, and were repeated ten times. The scratching traces of the width and depth were observed
using laser microscopy. The images of the scratching were synchronized to the time scale ofthe scratching and AE signals, and the elastic waves were related to the fracture behaviors. The
scratching mark profiles were also traced from the surface texture measurements.

Fig. 3 Schematic illustration of the scratching test with the AE analysis system.

Fig. 4 Schematic illustration of the AE analysis system.
Results and Discussion
Characteristics of the paperboard: To evaluate the structure of the printed paperboard, the crosssectional view was observed using a laser microscope. The observed result is shown in Fig. 5.
A paperboard is composed with a pulp fiber matrix, clay layer, printed ink layer and varnish. The
clay is adhered on the pulp matrix with 10 to 20 µm thickness, and ink layer has a few µm thick3

ness. Figure 6 shows the surface conditions on each paperboard surface for (a) non-coated surface, (b) surface of the coated layer with clay and (c) printed surface with black ink (Carbon
black) and adhered to the varnish on the ink layer. In this observation, the surface roughness
could also be evaluated. The results are shown in Fig. 7. On the non-coated surface, many fibers
are adhered and arrayed in one direction. The surface roughness became large. In the clay layer
coated surface, as the space of the jointed fiber structure is filled with clay, the surface roughness
become less than the non-coated surface. On the other hand, when the printed surfaces are covered with black carbon ink and varnish, the surface roughness depended on the printed ink elements, thickness and flatness. The surface roughness value of the printed paperboard improved
much more than other surfaces. This indicates that, if the adhesion strength between the coatedand printed ink layer could be estimated with some mechanical method, the printing condition
could be evaluated on every printed paperboard. The scratch test and AE method was applied so
this evaluation could be realized.

Fig. 5 Sectional view of the paperboard using a laser microscope.

100μ
mま

(a) No coated surface,	 (b) Coated surface,

100μ
mμ

100μ
mま

(c) Printed surface,Fig. 6 Observed top surface using a laser microscope.
Strength of paperboard: To evaluate the strength of the paperboard, the machine direction (MD)and cross-machine direction (CD) tensile tests were carried out on the coated and printed paperboard, using the tensile machine following the JIS-P8113 specification. The results are shown in
Table 1. As the difference in tensile strength values between coated and printed paperboard foreach tensile direction was small, the strength of the printed layer on the coating layer could not
be evaluated using the tensile test.

4

Fig. 7 Results of surface roughness of Ra.
Table 1. Tensile strength of coating and printed paperboard on MD and CD.
Material

CoatedTensile Strength (MPa)

MD
36±3

CD
19±1

Printed (black ink)
MD
CD
36±2
19±1

Table 2 Results of friction test on coated and printed paperboard.
Material
Dynamic friction coefficient

Coating0.24 ~0.25

Printed (black ink)
0.23 ~0.24

The results of the friction coefficient tests are shown in Table 2. The friction coefficient ofprinted paperboard is a little less than that of the coated paperboard. It was concluded that thesurface roughness slightly affected the friction coefficient. As the scratch test could be carriedout under small friction force, the deformed depth and width value seem to be almost independent to the surface conditions. Hence, when the indentation force is sufficiently small, the AE
signal would be detected under a low noise level without any effects of compressed inside layerof paperboard.
Scratch test: Figure 8 shows a typical AE signal with the vertical forces on the same time scale.
The corresponding scratching trace image on the clay layer is shown in Fig. 9 with an indenterradius of 0.1 mm.

Fig. 8 Relation between AE amplitude,
vertical force and scratch depth

Fig. 9 Compressed figure of coating layer
surface.
5Coated paperboard: On the clay layer, AE signals were generated from the moment of indentercontact and continued during the scratching process. In the observed compressed surface, many
cracks could be detected inside the scratched surface. This indicates that the AE signal resulted
from the friction behavior of the clay layer surface and the flaking behavior of the boundary region of the fiber layer. To confirm the separation behavior of the coating layer from fiber matrix,
a one-dimensional compression test was carried out with the same tensile machine on the printed
layer. Figure 10 shows the penetrated hole shape after the compression test. Figure 10(a) and (b)respectively show the results when using a 0.5 and 0.1 mm radius tip. In Fig. 10(a), it is assumed that the hole surface was evenly deformed, but in case of the smaller tip radius, 10(b),some micro cracks could be detected on the hole wall. This shows that the printed and clay layer
did not break during the compression process in the large tip. An AE signal could not be observed during these tests, but was detected during the scratch tests. Figure 11 shows the typical
AE signal and vertical forces on the same time scale, and the corresponding scratching trace images on the printed layer are shown in Fig. 12 with an indenter radius of 0.1 mm.

Fig. 10 Compressed hole with a tip radius of (a) 0.5 and (b) 0.1 mm.

S2

S1

S3

Fv

AE

Fig. 11 Typical relation between AE generation and vertical forces during the scratch test on theprinted paperboard. (tip radius of 0.1 mm)
6

Fig. 12 Observed compressed trace image during scratch test on printed paperboard.
For indentation depth below 40 µm, no AE signal was observed (region S1). In the larger
scratch depth region of 40 to 100 µm, AE was continuously generated (region S2). In this region
many micro-cracks could be observed on the surface edge of the scratch mark, as shown in Fig.
12. When the scratched depth became larger than 100 µm (region S3), AE signal generation increased abruptly. It is assumed that the AE behavior corresponded to the separation of the printed
layer from the coating layer and the fracture of fibers.
Effects of indenter tip radius: The compressed trace shape of the scratched surface changed with
the indenter tip radius. To investigate the effects of tip radius, three indenters were machined forthis experiment, and they were compressed to a depth of 50 µm (50 sec). The scratching tests
were carried out on a printed surface with carbon black pigment ‘black’, using the indenters witha tip radius of (a) 0.5 mm, (b) 0.1 mm, and (c) 0.05 mm, respectively. Relationship between thescratch depth, AE amplitude, and vertical force, is shown in Fig. 13 for each indenter tip radius.
Every vertical force increased proportionally with the penetrating depth, and values in the clay
layer were always less than the printed layer. This indicates that the clay layer was harder andstronger than the printed layer. Figure 13(a) shows the result of the largest indenter radius (0.5mm). The AE signal could not be observed under this penetration depth, and only the compressed shape was impressed on the surface. On the compressed trace, no micro-cracks were observed by the estimation system. This indicated that the indenter penetrated into the printed layersmoothly in this large depth region.
In Fig. 13(b), when the indenter tip radius was 0.1 mm, the force curve increased withscratching depth more smoothly than the 0.5 mm radius case. Further, the first AE signal detected, corresponding to micro-crack generation, could be observed around 40 µm, as shown in the
SEM observation result. The observed AE amplitude existed between the region of 30 to 40 dB.
In the smallest radius case (0.05 mm), the force curve trended upwards, and AE signal was detected around a depth of 20 µm. The amplitude of the AE signals was observed in the wider region to be from 30 to 60 dB (compared to the 0.1 mm radius). Micro-cracks and delamination
were detected in the same compressed region. Comparing the results of Fig. 13(b) and (c), thecracks are generated by a vertical force of over 0.3 N and the corresponding AE signal could beobserved in the amplitude range of 30 to 40 dB. On the other hand, delamination was also detected on the compressed trace surface. It appears that micro-cracks occurred during
7Fig. 13 AE signals and the vertical force in the scratching test and a typical image. (a) Black,
r=0.05mm, (b) Black r=0.1mm, and (c) Black r=0.5mm.
delamination under this tip radius condition. Comparing Figs. 13(c) with (b), AE signals near 40
dB were not detected, and delamination did not occur in Fig. 13(b). It is obvious that the greater
AE signals can be attributed to the occurrence of delamination. It was also confirmed that deformation without fracture could not be detected in the AE signals in Fig. 13(a). As a result, theAE signal amplitude on the coated paperboard can be classified into three ranges from thescratching test: elastic compressive deformation at less than 28 dB (A1), crack initiation in therange of 28 to 40 dB (A2) and the occurrence of delamination above 40 dB (A3).
It is assumed that the three deformation behaviors would be generated by:
1. Elastic deformation of the entire structure.
8

2. Plastic deformation of the printed surface layer after a yield point.
3. Break-down of the surface and clay layer.
The generation point of these deformation behaviors varied with indentation tip radius. The deformation behavior of advanced indentation surface area changed with the applied load surface
area, which depended on the stress concentration. On the other hand, a notable number of AE
signals are observed in Fig. 13(a). These AE signals can be classified by their amplitude,
greater than 40 dB and less than 30 dB. In Fig. 13(b), the AE signals decreased drastically, andthe upper level signals did not appear on this time scale. Despite the high vertical force, AE
signals could not be detected, as shown in Fig. 13(c). The detected AE signal corresponded toaccelerated motion phenomena such as brittle crack generation and delamination. As the resultsof AE generation behavior on Fig. 13(a), the AE signal generation level were divided into three
region in comparison with the crack and delamination. The schematic AE generation behavior on
small tip radius of 0.05 mm, is shown in Fig. 14. In the early stage (A1), there were no AE signals but only deformation was observed. Cracks generation occurred in the lower AE amplitude
region (A2), and the delamination occurred higher amplitude region (A3), almost same indenter
compressed depth area.

Fig. 14 The classification of the AE signals corresponding to fracture behaviors.
Summary
To evaluate the deformation and fracture behaviors of printed paperboard surfaces, thescratch test was carried out and the AE method was used simultaneously. The main results are
summarized below.
1. The strength of the surface layer on the printed paperboard could not be evaluated using thenormal mechanical method.
2. On the clay coating layer, many cracks occurred after a very low applied load, and many AE
signal were detected at the same time.
3. During the compression test, micro-cracks were detected in the penetrated wall when a small
indentation radius was used, but no AE signal was observed.
4. When an indentation radius of 0.1 mm was used AE signal generation began at 40 µm, whichcorresponded to the occurrence area of micro-cracks. Abrupt AE generation occurred over 100µm, which was attributed to delamination.
5. The deformation and fracture behavior of surface printed layers changed with the indentation
tip radius, and the generation of AE signals also varied with changes in fracture behavior. It wasconsidered that stress concentration effects in the advanced deformation surface area, depended
on the micro-crack occurrence phenomena.

96. The AE signals could be detected and categorized into three regions in order of amplitude,
and on the same time scale corresponded to compressive deformation, crack initiation, and delamination. It was revealed that the AE method provides an effective evaluation of the printed
layers on paperboards.
References[1] N. Shinke, A. Yonekura, M. Takuma, H. Yoshida, and S. Kawai, “Evaluation of Fracture
Characteristics of TiN Ceramics Film with Scratch Testing”, JSME International Journal SeriesA, 44 (1), 2001, 123-129.
[2] D. Arai, “Adhesion Strength Evaluation Method for Thin Films Using Micro-Scratch Tester”,
The Surface Finishing Society of Japan, 58, Feb. 1992, 295.
[3] S. Suzuki, M. Murayama, S. Nagasawa, Y. Fukuzawa, I. Katayama, “Estimation of Mechanical Properties of White-Coated Paperboard by Scratch Test and Push Shear Test”, J. Jpn, Soc.
Technol. Plast., 46-530, Mar. 2005, 230-234.
[4] S. Suzuki, Y. Fukuzawa, S. Nagasawa, I. Katayama and H. Iijima, “Acoustic Emission
Characteristics on Variation of Cutter Tip Thickness during Cutter Indentation on Paperboard,” J.
JSTP, 46, 2005, 1061-1065.
[5] A. Sadamoto, T. Yamaguchi, S. Nagasawa, Y. Fukuzawa, D. Yamagushi, I. Katayama,“Sensing for Machining Using Sound Radiated by Paperboard Die Cutting,” J. Jpn, Soc. Precision Eng., 68 (12), 2002, 1554-1558.
[6] H. Katayama, “Point of Paper Container Processing Technology,” Japan TAPPI Journal, 62
2008, 115-120.
[7] S. Suzuki, Y. Fukuzawa, S. Nagasawa, H. Sakayori, I. Katayama, “Characterization of Paperboard Deformation by AE,” Progress in AE XI, The Jpn. Soc. Non-Destructive Inspection,
2002, pp. 37-44.
[8] S. Nagasawa, Y. Fukuzawa, T. Hine, I. Katayama, A. Yoshizawa, T. Furumi, “Effects ofEdge Clearance and Board Thickness on Shearing Characteristic of Paperboard Diecutting,” J.
Jpn. Soc. Technol. Plast., 41-469, 2000, 126-130.
[9] S. Nagasawa, Y. Fukuzawa, D. Yamaguchi, S. Nagae, I. Katayama, and A. Yoshizawa, “Deformation Characteristics on Creasing of Paperboard Under Shallow Indentation,” Advances in
Fracture Research, ICF100202OR(CD-ROM), Elsevier Science, 2001.

10

Acoustic Emission Related to Drought Stress Response of Four Deciduous
Broad-Leaved Woody Species
Sabine RosnerInstitute of Botany, BOKU, Vienna, Austria
Abstract
The aim of the present study was to test if acoustic emission (AE) detected on leaves andstem surfaces of young trees gives information about stomatal closure and thus about a tree’s performance under drought stress. Two diffuse-porous (conduit size shows little variation within anannual ring) and two ring-porous (conduits in earlywood are much bigger than in latewood) species were tested for their reaction to drought stress. In both diffuse (Populus alba L., Fagus sylvatica L.) and ring-porous (Fraxinus excelsior L., Quercus pubenscens WILLD.) species, AE
activity of leaves ceased when stomatal conductance reached minimum values. In some species,
AE rate and amplitude distribution can be a useful parameter to discriminate between AE resulting from conductivity loss and from other processes such as shrinkage and wilting. AE activity ofstem wood showed a clear pattern with two amplitude peaks in ring-porous species whereas nosuch pattern was found in diffuse-porous species. The results are discussed with respect to thespecies-specific reaction to drought stress.
Keywords: Drought stress, stomata response, stomatal closure, vulnerability to cavitation
1. Introduction
Knowledge about the hydraulic performance of long-living plants under drought stress is ofhigh importance with respect to global change. Water is transported from the roots to the leavesof the crown in a metastable state (i.e. under tension) [1]. Due to this metastable state the watercolumn inside the small conduits is likely to break when the water tension increases above a certain level. Breakage of the water columns (cavitation) produces acoustic emission (AE) in thehigh frequency range [2, 3]. Cavitation induces a reduction in the hydraulic conductivity of theplant, which impairs water supply of the transpiring leaves. Cavitation would result in total lossof hydraulic conductance in the absence of any regulative mechanisms of transpiration [4]. Plants
therefore respond by closing their stomata, which should reduce transpiration and keep the water
potential (i.e. tension) above the species-specific cavitation threshold value, preventing further
loss of conductivity [1]. Limitation in gaseous exchange due to stomatal closure restricts, however, simultaneous carbon assimilation by the plant and thus growth [5].
Drought stress induced stomatal behavior differs widely among species and may be a criticalfactor in the regulation of survival and mortality during extended drought periods and thus for
species distribution [6]. Plants fall into two categories according stomatal regulation of transpiration; “drought avoiders of the saving type” (isohydric plants) and “drought avoiders of the spending type” (anisohydric plants) [7]. Isohydric plants maintain high leaf water potentials during the
day for extended periods of drought, thus preventing cavitation. Anisohydric species let midday
leaf water potentials decline as soil water potential declines with drought. They can keep theirstomata open because their xylem is either less prone to cavitation or they are able to compensate
rapidly for water loss, due to a deep and large root system [6, 8]. Ring-porous woody species,
with a band of wide vessels in earlywood followed by much narrower vessels in latewood should
be successful if plants maintain similar water status throughout the season. This strategy would
J. Acoustic Emission, 30 (2012)11© 2012 Acoustic Emission Grouphowever be less essential for diffuse-porous species, with narrower conduits, packed more densely per unit wood area [10].
The control of stomata under conditions of drought is achieved due to chemical or hydraulicsignaling processes [4, 11]. ABA (abscisic acid) is the most likely candidate for chemical rootto-shoot signaling [12]. It might however also be possible that a combination of both hydraulicand chemical signaling is important in the stomatal regulation in plants [4, 9, 13]. Stomata haveto respond directly to some component of the leaf water status because a lack of response to distal shoot water potentials would be dangerous and potentially fatal to plants. Moreover, ABA
transport will be limited under severe drought by increased hydraulic resistance in the xylem[14]. Stomata might not respond to leaf water potential per se [15], but to factors affecting leaf
water status, such as cavitation, which decreases the hydraulic conductivity in stems [13, 16] andleaves [17, 18]. The onset of stomatal closure is in some species reported to coincide well withthe onset of cavitation [19-21], whereas in other species no such relationship was found [16]. AE
counting combined with water potential measurements is a reliable method to detect cavitations
in different plant organs, but provides little information on the loss in hydraulic conductance [16,22]. If conductivity loss is a trigger for stomatal closure [13, 18-20], AE testing with subsequentfeature analysis could give additional information on stomata’s responses to drought stress [22].
Within this study, the response of stomata to drought stress induced cavitation in leaves andin stem wood was tested in two ring-porous and two diffuse-porous woody species with different
demands on water availability. The aim was to test if AE testing combined with AE feature analysis gives information about the species-specific strategy to avoid severe losses in hydraulicconductivity.

Fig. 1. Species investigated were European ash (Fraxinus excelsior L.) (A), Downey oak(Quercus pubescens WILLD.) (B), silver poplar (Populus alba L.) (C) and European beech(Fagus sylvatica L.) (D).
12

2. Experiment
2.1 Plant Material
Young trees (2-3 years) grown in the botanical garden at BOKU, Vienna, were harvested
during the morning hours and re-hydrated for 24 hours in the dark in order to refill all sapwood
conduits with free water (Fig. 1). Species used within this study were European (common) ash(Fraxinus excelsior L.), occurring in riparian mixed forests along big rivers in central Europe,
Downey oak (Quercus pubescens WILLD.), a submediterranean species native to southern andeastern Europe, silver poplar (Populus alba L.), a relatively drought tolerant riparian pioneer
species, and European (common) beech (Fagus sylvatica L.), which can be found in pure as wellas in mixed stands across central and northern Europe [23]. Common ash and Downey oak are
ring-porous woody species, whereas silver poplar and beech are diffuse-porous woody species.
In ring-porous wood, wide conduits are produced in the early growing season and narrow conduits during the rest of the growing season. In diffuse-porous species, all water-conducting elements have quite similar diameters and are evenly spread across the annual ring.
2.2 Bench drying experiment
Whole above-ground biomass of young trees was dehydrated at the laboratory bench (“benchdrying”) at ambient conditions (25°C, 50% r.h.) for 24 h. During dehydration, AE from leaves
and the main trunk as well as changes in water potential and stomatal conductance of leaves wereacquired. Prior to the dehydration experiment, cut ends of the small trees were tightly wrapped in
Parafilm® (Pechiney Plastic Packaging, Chicago, IL, USA).

Fig. 2. Experimental setup: Drought stress experiment on silver poplar (Populus alba L.) shoot.
AE testing was performed with the µDiSP™ digital AE system from Physical Acoustics Corporation (PAC, Princeton Jct, USA). Preamplifiers (40 dB) were used in connection with WD
transducers (100 to 1000 kHz). One WD transducer was positioned on the upper side of the leaf,
at the proximal third of the leaf midrib [24], except in common beech, where better results wereobtained when the sensor was positioned on the lower side of the leaf. AE signals were recordedwith a detection threshold of 32 dB (0 dB = 1 µV input). Data analysis was done with Vallen
VisualAE™ software (Vallen Systeme GMbH, Munich, Germany).
Bark was thereafter removed from the young stems and the second WD transducer was positioned directly on the wood surface (Fig. 2). Silicone grease served as a coupling agent (Wacker,
Burghausen, Germany). Open cut faces, which were not covered by the transducer, were sealed
13with Parafilm®. All preparation steps were performed while the main parts of the small trees
were covered by dark polyethylene sheets in order to avoid moisture loss due to transpiration.
During dehydration, water potential was measured at intervals of 10-120 min by means of aplant water status console (“Scholander pressure bomb”, Soilmoisture Equipment Corp. Santa
Barbara, CA, USA). Continuous changes in stomatal conductance in leaves were measured with
the AP4 leaf porometer (Delta-T Devices Ltd, Cambridge, GB) in intervals of 1-20 min.
2.3 Anatomical investigations
Wood sections and sections from the midrib of leaves (20 µm) were produced on a sliding
microtome (Jung Reichert, Germany). Sections were stained with Astrablue/Safranin dye andmounted in Entellan (Merck, Darmstadt, Germany). Microscopic views of tree stems and leaf
midribs were produced by means of a Leica DM4000M microscope interfaced with a digital
camera and Leica image analysis software (Leica Microsystems Wetzlar GmbH, Germany).
3. Results and Discussion
3.1 AE characteristics of tree stems
AE from tree stems of ring-porous species (ash and oak) showed different amplitude distribution compared to diffuse-porous species (poplar and beech) (Fig. 3). In ash and oak, a distinct
amplitude cluster could be detected, whereas poplar and beech wood had a similar amplitude distribution throughout the whole dehydration experiment. The amplitude cluster of ring-porous
species coincided with their first AE rate peak. A distinct rate peak was also found in poplar but
not in common beech. It is suggested that AE of the first peak were produced from the breakageof the water columns in conducting elements, whereas AE detected thereafter represents soundproduced from capillary action in wood fibers (non-water conducting wood elements), wood
shrinkage or crack formation [25]. In Fig. 3, AE results are shown only for the first 10 hours ofdehydration, since AE production thereafter is of no physiological relevance, meaning that mostof the hydraulic conductivity was lost already by then.
Dehydration of whole plants on the laboratory bench simulates extreme drought stress. Out inthe field, all these reactions will proceed much slower and plants will have the chance to refill
emptied conduits during the nighttime provided that water availability from the ground is sufficient. Nevertheless, AE monitored from living, extremely drought stressed trees should show asimilar pattern. If it is assumed that almost all hydraulic conductivity is lost when the AE rate
reaches again very low values after the first AE rate peak, decreasing AE rates indicate a beginning of dramatic loss in hydraulic conductance. Common ash and especially silver poplar developed sufficient mechanisms to prevent further cavitation in stem wood, when most of the hydraulic conductivity was lost already, whereas Downey oak and common beech exhibited quite
high AE rates after the first rate peak.
3.2 AE characteristics of leaves
Stomatal closure and cavitation started right from the beginning of the drought stress experiment [19-21]. AE rate of drought stressed leaves showed a distinct peak during the first twohours of dehydration [26], which coincided with a first AE amplitude cluster in both ring- anddiffuse-porous species and the course of stomatal conductance (Figs. 4 and 5). At the AE ratepeak, single hits with highest amplitudes were detected. Highest amplitudes were found in oakleaves, which might be explained either by bigger lumen diameters of the sound producing
sources, the xylem conduits [22, 27, 28], or by less attenuation of the (denser) plant tissue.
14

Fig. 3. AE amplitudes and AE rates/10 min (red lines) received from stems of different broadleaved species (common ash, Downey oak, silver poplar, common beech plotted against time.
Microscopic views of transverse cuts (20 µm) of the same stems where the AE transducers werepositioned are show below the AE plots. The reference bars represent 250 µm.

15Fig. 4. AE amplitudes (blue dots) and AE rates/10 min (red lines) received from leaves of two
different ring-porous species plotted against time. The courses of the water potential (green line)
and the stomatal conductance (pink line, in % of the maximum value measured during dehydration) and a microscopic view of the leaf midrib (where the transducer was positioned) for eachspecies are shown below the corresponding AE plots. On the left side, results for common ash(Fraxinus excelsior L.), and on the right side, results for Downey oak (Quercus pubenscens
WILLD.) are presented.
(Total) stomatal closure is reached when stomatal conductance approaches towards minimum
percentage values. Thereafter, AE rate decreased rapidly, but increased again when leaves started
to wilt. Most of the hydraulic conductivity was probably lost already by then. In common ash
and silver poplar, stomatal closure proceeded very fast and was quite efficient; after the firstpeak, AE rate did not increase within the next 6 hours. In common beach and Downey oak, however, AE rate increased again after another hour of dehydration. These results coincided with thedrop in the water potential: whereas Downey oak and common beech reached quite negative water potentials between -5 and -4 MPa, water potentials of silver poplar and common ash did not
drop below -2.5 MPa, although leaves started to wilt already.
16

Fig. 5. AE amplitudes (blue dots) and AE rates/10 min (red lines) received from leaves of two
different diffuse-porous species plotted against time. The courses of the water potential (greenline) and the stomatal conductance (pink line, in % of the maximum value measured during dehydration) and a microscopic view of the leaf midrib (where the transducer was positioned) for
each species are shown below the corresponding AE plots. On the left side results for silver poplar (Populus alba L.), and on the right side results for common beech (Fagus sylvatica L.) arepresented.
Thus, a decline in the AE rate of leaves after a pervious rise indicates that plants reacted todrought stress by closing their stomata. Common ash and silver poplar had the most efficient
strategy to prevent transpiration, since water potential reached a quite stable high (less negative)
value; furthermore, in silver poplar no increase in AE rate of stems could be detected after total
stomatal closure.

17

3.3 Drought avoiders of the spending or of the saving type?
Based on these preliminary results, common ash and silver poplar would be classified as isohydric species; as “drought avoiders of the saving type”, whereas common beech and Downey
oak as anisohydric species; as “drought avoiders of the spending type” [7].
Isohydric plants have a high degree of stomatal control enabling them to maintain high leaf
water potentials during the day for extended periods of drought, thus preventing cavitation. Anisohydric plants can keep their stomata open because their conducting elements in the xylem areeither less prone to cavitation or they compensate for water loss due to a deep and large root system [6, 8, 9]. A deep root system is typical for oak species [23]; however, ring-porous species,
with a band of wide vessels in the earlywood followed by much narrower vessels in latewood,should show in theory isohydric behavior of stomatal control, i.e., a tighter stomatal regulation oftranspiration to extremes in vapor pressure than diffuse-porous species and conifers [15, 29-32].
Although isohydry would be less beneficial in diffuse-porous species [10], silver poplar exhibited an efficient stomatal control, thus a near-isohydric behavior in order to avoid cavitationand keep the water potential at high (less negative) values. It has been reported in literature that
diffuse-porous species with a tendency to half-ring-porosity, such as beech (Fagus sylvatica L.)
and poplars (Populus spp.), might exhibit isohydric [33-35] as well as anisohydric behavior [30].
4. Conclusions
At least in some broad-leafed species, AE rate and amplitude distribution of dehydratingstems can be a useful parameter to discriminate between AE resulting from conductivity loss andfrom other processes such as cavitation from non-conducting wood elements, wood shrinkage
and wilting. The pattern of AE activity of dehydrating stem wood was strongly related to wood
anatomy, where ring-porous species show clear clusters, which can be separated by their amplitude characteristics. AE activity of dehydrating leaves gives information on the (a) onset of stomatal closure and (b) achievement of maximum stomatal closure. Together with water potential
measurements, AE activity and peak amplitude analysis should give thus a quite good insight
into the species-specific strategy to survive drought stress.
Acknowledgements
This study was financed by the Austrian Science Fund (FWF): V146-B16.
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1-11.
SE Bush, DE Pataki, AG Hultine, AG West, JS Sperry and JR Ehlinger. 2008. Wood anatomy constrain stomatal responses to atmospheric vapor pressure defict in irrigated urban
trees. Oecologia Vol. 156, pp 13-20.
H Taneda and JS Sperry. 2008. A case study of water transport in co-occurring ring- versus
diffuse-porous trees: contrasts in water-status, conducting capacity, cavitation and vessel
refilling. Tree Physiol. Vol. 28, pp 1641-1651.
U Hacke and JJ Sauter. 1995. Vulnerability of xylem to embolism in relation to leaf water
potential and stomatal conductance in Fagus sylvatica f. purpurea and Populus balsamifera. J. Exp. Bot. Vol. 46, pp 1177-1183.
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A Gallé and U Feller. 2007. Changes of photosynthetic traits in beech saplings (Fagus sylvatica) under severe drought stress and during recovery. Physiologia Plant. Vol. 131, pp
412-421.

20

Micro-mechanical Damage in Tool Steels Analyzed by Acoustic Emission
Technique
Ingrid Picas1, Eva Martinez-Gonzalez2, Daniel Casellas1 and Jordi Romeu2
Fundació CTM Centre Tecnològic, Av. Bases de Manresa 1, 08242 Manresa, Spain
2
Laboratori d’Enginyeria Acústica i Mecànica (LEAM), Departament d’Enginyeria Mecànica
(DEM)m Universitat Politècnica de Catalunya (UPC), EUETIB, Urgell 187,
08036 Barcelona, Spain
1

Abstract
The carbides in tool steels act as hard particles and dictate the wear resistance. The origin of
failure of these steels is mostly related with the carbide cracking, as a nucleation site of final
cracks. This paper shows the acoustic emission (AE) signals related with the nucleation (carbide
cracking) and the crack growth (in the metallic matrix) during a monotonic bending test of a toolsteel obtained by ingot metallurgy routes (DIN 1.2379). The paper presents a relationship between the frequency spectrum of AE signals obtained and the microscopic images during thetest.
Introduction
Understanding the fracture events of tools in the manufacturing process is crucial to foresee
tool lifetime and develop tool steels with improved mechanical performance [1]. Failure detection helps in predicting premature failure of the tool, which directly affects the price of the manufactured part. The interaction between the two main constituents of the tool steel microstructures: the primary carbides and the metallic matrix, determines their mechanical properties andthe performance of tools.
Carbides play an import role in the mechanical response of these steels, since they act as hardparticles and dictate the wear resistance. The origin of fracture and fatigue cracking of cast toolsteels is usually associated with the primary carbides, which break under the applied stress andact as initiation sites [2]. However, there is a lack of knowledge about the mechanical behaviorof carbides in tool steels, mainly due to the experimental difficulties associated with its measurement [3]. With conventional non-destructive testing methods, it is difficult to identify the
moment when carbides begin to break and even more difficult to identify when the crack starts to
propagate in the metal matrix because it is necessary to stop the test and inspect the trial.
The acoustic emission (AE) technique “listens” what is happening within the material during
the test; because the AE is based on the phenomenon of transient elastic-waves generated due toa rapid release of strain energy caused by a structural alteration in the solid material. So, it serves
as a powerful method to monitor progressive damage accumulation in different materials [4-7].
Only scarce data exist in the literature concerning the application of AE for the analysis of
micro-damage of tool steels. Fukaura et al. [8, 9] and Yokoi et al. [10] are amongst the few authors, who employed this technique to determine the progression of internal damage on toolsteels. These authors successfully detected AE signals from carbide cracking; the signals started
at a certain applied load and the event rates continually increased until reaching the fracture
stress. No continuous AE signals existed (related with plastic deformation processes), but thatnumerous burst emissions at close intervals were recorded instead. Yamada and Wakayama [11]J. Acoustic Emission, 30 (2012)21© 2012 Acoustic Emission Groupobserved a rapid increase in cumulative AE energy prior to the final fracture and attributed thisphenomenon to the main crack formation. They also distinguished two types of AE signal: one
was a burst-type signal with high frequency and the other was a low frequency and continuoustype signal. The former was considered to be emitted from micro-cracking while the latter was
due to plastic deformation of the binder phase.

Fig. 1. Example of broken carbides observed by means of CM (Confocal Microscopy) in thetensile stressed surface of DIN 1.2379 specimens. [12]In a previous work, Martinez et al. [12] applied AE in a three-point bending test. Three different zones were distinguished during a bending test in a tool steel sample with regard to the AE
events, and the stress level at which cracking carbides start was also found (Fig. 1). This work
dealt with real time detection of elastic waves signals generated during a monotonic mechanicaltest, a three-point bending test, in case of a tool steel named DIN 1.2379. The main aim is tocluster the recorded signals during the test, based on the spectral characteristics, and correlate theclassification with the failure mechanism that generates the reference waveform, identified by
microstructural inspection of the specimen.
Experimental Procedure
The material studied was a conventional ledeburitic high-carbon, high-chromium tool steel
named DIN 1.2379 (AISI D2) obtained by ingot metallurgy routes. The main alloying elements
found in their chemical composition are shown in Table 1.
Table 1. Main alloying elements in the chemical composition of DIN 1.2379 (in wt %).
Steel
1.2379C
1.5-1.6

Cr
11.0-12.0

Mo
0.6-0.8W-V
0.9-1.0

Prismatic samples were machined from forged and annealed commercial bars parallel to theforging direction. Heat treatment was applied to the sample material in order to get a hardness
level of 60 – 62 HRC, as summarized in Table 2. The bending strength, σR, was reported by22

Picas et al. [1], and the fracture toughness, KIC, was determined as specified in the ASTM E 39990 standard. These values are also shown in Table 2.
Table 2. Heat treatment and obtained hardness and bending strength.
Steel1.2379Austenitizing (oil quench)
1050 ºC for 30 min

Tempering
550 ºC for 2 h (x2)HRC
60 - 62

σR [MPa] [1]2847 ± 96

KIC [MPa·m1/2]
28

Fracture tests were performed by means of three-point bending tests with a constant span
length of 40 mm. Samples dimensions were 8 mm x 6 mm x 120 mm. Samples were mechanically ground and their corners were rounded to avoid stress magnifications and remove any defectintroduced during sample preparation. Faces subject to tensile stress during three-point bending
tests were carefully polished to mirror-like finish using colloidal silica particles with approximately 40 nm sizes.
Microstructural inspection of the samples was carried out using a FE-SEM (Field Emission
Scanning Electron Microscope) and the fracture tests were performed in a universal testing machine using an articulated fixture to minimize torsion effects. The applied displacement rate was
0.01 mm/min.
The test was monitored using sensors of a fixed resonance frequency of 700 kHz (VS700D,
Vallen System GmbH). Three pre-amplifiers with a 34 dB gain of the same brand were also used
(AEP4). Acoustic Emission (AE) signals were recorded and analyzed using the Vallen Systeme
GmbH AMSY5 analyzer. The experimental set-up is schematized in Fig. 2.

Test setup

F

Test setup equipment

Pre-amplifiers

AE acquisition and analyser equipment

Fig. 2. Experimental set-up for the AE-monitored bending tests.

23During the measurements, digital filters of 95-850 kHz were applied. In order to avoid themultiple reflections due the small dimensions of the specimen, only the first counts of each hit
were analyzed as purely representative of micro-damage phenomena.
Using this set-up configuration, 3 to 5 samples of each material were monitored until final
fracture. Later, 2 to 3 samples were analyzed by means of stepwise loading (Fig. 3a)) in order torelate each type of AE characteristic signal pattern to the generated damage in the microstructure. Surface inspection of samples was carried out after each load increment in a Confocal Microscope (CM) (Fig. 3b)).
b)

Surface inspection

2 mm

σR

Stress (MPa)

a)

40 mm

Tensile face…

Inspected zone

Time (s)Fig. 3. a) Stepwise loading to final fracture of the sample, σR; b) schema of the micrographicallyinspected zone of samples.
Results and Discussion
A. Microstructural Analysis.
In Fig. 4 the microstructure of the studied steel can be observed. The microstructure was markedly anisotropic, with large carbide stringers forming bands in the metallic matrix. The primary
carbides of this steel were rather large and had irregular morphologies.

Fig. 4. Microstructure of tool steel, DIN 1.2379.
B.1. Identification of Characteristic AE Signal Patterns in Bending Tests of 1.2379 under Monotonic Loading.
Figure 5a) shows the results of the AE signals registered in bending tests under monotonic loading for 1.2379. This diagram plots the cumulative number of hits as a function of the stress applied and the location of each signal on the sample surface (with respect to the center of the sample). As it can be observed, the highest amount of signals was generated at the center of the24

sample (X-Loc. = 0 in Fig. 5a), where the applied stress was the highest during the three-point
bending test, and the quantity of emitted signal continuously increased with the applied stress.
The Y axis refers to the applied stress and the Z axis to the cumulated number of hits registered).
a)

b)

c)

Signal type 1º type 2
Signalb) Signal
type 1Signal type 2

Fig. 5. a) Cumulative number of hits (Z) as a function of the stress applied during the bending
test (Y) and the location of the signals (X). b) Cumulative number of hits vs. applied stress during a monotonic bending test in which two different types of AE signals could be identified; c)Cumulative number of hits vs. frequency for the two signals registered.
A closer look to the AE signals obtained allowed us to classify them into two categories depending on the frequency spectrum. As shown in Fig. 5b), at the beginning of the test, no AE
signals were detected. At a certain applied stress level, a first type of AE signal started to be recorded (green line in Fig. 5b)). These signals were not continuous but they were emitted in aburst-like manner, and the quantity of hits registered increased along with the applied stress. Later as the stress increased, a second type of signal was distinguished (red line in Fig. 5b)). Thissignal also increased in number of hits together with the applied stress, but at the moment of final
fracture it attained lower hit values than the first signal type.
These two signals identified not only differed because of the number of hits, but also they
had very different characteristic frequencies and waveforms. As shown in Fig. 5c), the first typeof signal had a main frequency of 280 kHz, while the frequency of the second type was around
650 kHz. These different frequency ranges of the two signals indicated that the responsible
mechanisms for emitting them took place at different velocities in the microstructure, i.e. thesecond mechanism would be much faster than the first one.
B.2. Relationship between AE Signals and Micro-Damage during Bending Tests of 1.2379 a under Monotonic Loading.
Stepwise bending tests permitted to inspect the tensile surface of the samples at different increasing stress levels, and correlate the registered AE data (namely the two different identified signal
types) to the micro-damage observed in the microstructure.
In Fig. 6a) the cumulative number of hits as a function of the stress applied at the first load
step can be observed. This test was stopped at 800 MPa, when the first signals were detected.
These signals indicated the same pattern as those of type 1 identified before. However, no damage could be observed at the sample surface, as shown in Fig. 7a); it is likely that something
happened at the microstructure but it could not be detected yet by microscopy.
The next test was stopped at 2200 MPa, when a higher quantity of AE signal was detected.
Practically all signals responded to the characteristics of the type 1 identified before, and a small
number of hits of characteristic type 2 signals were first detected (Fig. 6b)). In this case, the first25

cracks were observed in the microstructure and they were located at primary carbides (Fig. 6b)
and c)). However, despite many carbides were broken, none of the cracks nucleated from themwere observed to have started propagating through the metallic matrix surrounding the broken
carbide. The last load step at 2600 MPa revealed a notable increase of the type 2 signal, eventhough the number of hits of the type 1 had not ceased to increase (Fig. 6c)), as well as the number of broken carbides in the sample. The inspection of the surface permitted to observe thatsome cracks had now propagated through the metallic matrix (Fig. 7d)).

Fig. 6. AE signal results of monotonic stepwise tests in 1.2379 in terms of the cumulated numberof hits vs applied stress to: a) 800 MPa; b) 2200 MPa and c) 2600 MPa.
26

Fig. 7. Images of the microstructure of 1.2379 at: a) 800 MPa; b)-c) 2200 MPa and d) 2600 MPa.
As it followed from the obtained results, the first and the second AE signal types were related
to different damage mechanisms occurring in 1.2379 samples as the applied stress increased. The
first type of signal corresponded to the breakage of carbides in the microstructure, i.e. the nucleation of cracks, while the second type was emitted by the subsequent propagation of these cracks
through the metallic matrix.
The data shown above demonstrate that the AE technique, coupled to the bending tests undermonotonic conditions, was able to provide accurate information regarding the acting micromechanical and damaging mechanisms of 1.2379. In this case, the nucleation and propagation of
cracks in the microstructure was well identified by means of two different types of AE signals
reporting respectively, the breakage of carbides in the microstructure, i.e. the nucleation of
cracks, and the moment when these cracks left the carbide and grew through the metallic matrix,
i.e., the propagation of cracks. Therefore, this technique provided a unique and very accurate tool
to determine the threshold stresses, at which carbides started to break and the stresses at which
the first cracks were nucleated.
Summary
The method developed in this study by which bending fracture tests were coupled to AE
techniques provided helpful results to understand in great detail the failure mechanisms of toolsteels under such applied loading, as well as the interaction of their microstructural constituents.
27

In the 1.2379 steel studied, two different AE signal wave patterns were identified during a fracture test. These signals were respectively assigned to crack nucleation in primary carbides, andthe propagation of these through the metallic matrix.
Acknowledgements
Authors from Fundació CTM Centre Tecnològic acknowledge the Catalan government forpartially funding this work under grant TECCTA11-1-0006.
References[1] Picas, I., Hernández, R., Casellas, D., Valls, I.: 2010; Strategies to increase the tool performance in punching operations of UHSS; 50th IDDRG Conference (50th International DeepDrawing Research Group), Graz, Austria; June 2010; p.325.
[2] Picas I., Cuadrado N., Casellas D., Goez A., Llanes L.; 2010; Microstructural effects on thefatigue crack nucleation in cold work tool steels. Proc Eng 2:1777-1785.
[3] Casellas D., Caro J., Molas S., Prado J.M., Valls I.; 2007; Fracture toughness of carbides intool steels evaluated by nanoindentation. Acta Mater 55:4277-4286[4] Waller J.M., Saulsberry R.L., Andrade E.; 2009; Use of acoustic emission to monitor progressive damage accumulation in Kevlar 49 composites. AIP Conf Proc 1211:1111-1118.
[5] Lugo M., Jordon J.B., Horstemeyer M.F., Tschopp M.A., Harris J., Gokhalen A.M.: 2011;
Quantification of damage evolution in a 7075 aluminium alloy using an acoustic emission
technique. Mat Sci A 528:6708-6714[6] Pollock A.A.; 2007; Some observations on acoustic emission – stress – time relationships.
In: Carpinteri A, Lacidogna RG (ed) Acoustic emission and critical phenomena: from structural mechanics to geophysics. Boca Raton, London: CRC Press/Taylor&Francis,
ISBN:9780415450829[7] Maillet E., Godin N., R’Mili M., Reynaud P., Lamon J., Fantozzi G.; 2012; Analysis of
acoustic emission energy release during static fatigue test at intermediate temperatures onceramic matrix composites: towards rupture time prediction. Compos Sci Technol 72:10011007.
[8] Fukaura, K., Yokoyama, Y., Yokoi, D., Tsujii, N., Ono, K.; 2004; Fatigue of cold-work toolsteels: effect of heat treatment and carbide morphology on fatigue life formation, life andfracture surface observations; Metallurgical and materials transactions A; vol.35A, April
2004, pp. 1289-1300.
[9] Fukaura K. and Ono, K.; 2001; Acoustic emission analysis of carbide cracking in tool steels,
Journal of Acoustic Emission (JAE); vol.19, pp. 91-99.
[10] Yokoi D., Tsujii N., Fukaura F.; 2003; Effects of tempering temperatures and stress amplitude on low-cycle fatigue behavior of a cold work tool steel. Mater Sci Res Int. 9: 216-222.
[11] Yamada, K. and Wakayama, S.; 2009; AE Monitoring of microdamage during flexural fracture of cermets; In Proceedings International Congress and Exhibition EURO PM2009, Copenhagen, Denmark; October 2009; p. 247-252.
[12] Martinez-Gonzalez, E.; Picas, I.; Casellas, D.; Romeu, J.; 2010; Analysis of fracture resistance of tool steels by means of acoustic emission; Journal of acoustic emission (JAE);
vol.28; pp.163-169.

28

Viable Precursors of Paroxysmal Phenomena as Detected by Applying RQA
to Acoustic Emission Time Series
Maurizio Poscolieri, Claudio Rafanelli and Giovanna Zimatore
CNR-IDASC – Institute of Acoustics and Sensor "Orso Mario Corbino" – ICES Group
Area della Ricerca "Roma - Tor Vergata", via del Fosso del Cavaliere, 100, Rome, Italy
Abstract
Recurrence quantification analysis (RQA) represents a recent method for processing nonlinear time series, already addressed to different topics concerning earth sciences and others.
RQA is applied in this work to analyze time series of acoustic emission (AE) triggered by
Earth’s crust phenomena, so to detect feasible precursors of catastrophic events. The AE recordsof rms values were acquired at two different sites, placed close to the Peteroa volcano (Argentina) and at Valsinni (Matera, Italy), considering two ultrasound frequencies (25 and 150 kHz)with 30-sec sampling rate. The preliminary results of the application of RQA to the AE data of
Valsinni with respect to the L’Aquila earthquake (Italy, on April 6, 2009), and to those related tothe activity of the Peteroa volcano (Argentina) are described and appear suited to stress relationships with an impending catastrophic event.
Keywords: AE sensor, AE time series, signal analysis, RQA, precursors, paroxysmal events.
1. Introduction
Humankind often faces the hazard due to catastrophic natural phenomena, such as earthquakes and volcanic eruptions. They occur mostly with no feasible warning and cause often victims, serious damage to buildings and civil structures according to the involved energy. As regards the seismic events, the best protection is to plan earthquake-resistant dwellings, while
against volcanic eruptions, especially for the explosive ones, the main safety suggestion is related to the definition of an appropriate escape strategy and of a land planning that foresees an urban development enough far away from the volcanic edifice.
Hence, reduction of danger for people, goods and structures should be a main task of society
having available tools addressed to highlight impending earthquakes or volcanic eruptions. Thisis possible by identifying a so-called precursor, that is, a timely index providing a warning for
approaching paroxysmal events. One of such parameters yielding suitable results is based onmonitoring and analysing acoustic emission (AE) signals [1]. The AEs are elastic waves, in ultrasound frequencies (25 - 800 kHz), due to the energy release within natural or manmade structures affected by mechanical stress [2]. AE frequencies lower progressively almost up to the infrasound when the collapse occurs (Fig. 1).
The catastrophic stage is preceded (months, weeks, days, hours in advance) by the occurrence of ultrasonic AE according to the force that yields the structural stress and to its strength. A
constant monitoring of AE signals and their time-series analysis allows to achieve anticipated information on the collapse risk choosing appropriate threshold values, defined on the basis of laboratory studies [3]. This way, the AE technique can be applied to geological structures, providing information about impending seismic, volcanic and hydrogeological (landslides, mudflows)
events.
J. Acoustic Emission, 30 (2012)29© 2012 Acoustic Emission GroupFig. 1. The decrease of the frequencies sequence of the observed AE vs. time [1].

Fig. 2. Present configuration of an AE acquisition station.
Consequently, a suitable surveillance service for early warning handling can be provided by
an AE recording equipment connected to an ICT network (Fig. 2), thus exhibiting AE as a challenging precursor. AE signal amplitude varies with the acoustic impedance, related to local rockstress conditions and particularly sensitive to fracture density and water content; therefore, the30

application of a method such as Recurrence Quantification Analysis (RQA) [4-6] to the AE timeseries is particularly suited; in fact, RQA is focused on pinpointing peculiar recurrence pattern
without taking into account the amplitude. RQA allows studying the change in correlation structure of the observed phenomenon; a relevant increase of correlation is known to precede the catastrophic event in many different systems ranging from physiology to economy [7].
This technique belongs to a category of recent methods of analysis of so-called disorderedsystems. Such methods show that many objects and processes that earlier were considered ascompletely random reveal clear evidence of having some ordered structure in both time andspace [8]. Hence, this paper shows the absolutely new application of RQA to AE time series
recorded in areas undergoing earthquakes and volcanic eruptions. The RQA method is applied to
the non-stationary AE signals, in order to investigate the evolution features of the quiescence andactivation status of the crustal system. Two case histories, recorded at Valsinni (Matera, Italy)
and at Peteroa volcano (on the Argentina – Chile border), are presented.
2. RQA Method
2.1 RQA general description
In the study of the numerical series, among various statistical analysis techniques, the "dynamic" techniques are relatively new. They are generally based on representing, in a multidimensional space, a one-dimensional or time-dependent signal, through the technique of embedding [5]. This way, with the same analytical techniques, it is possible to identify in the numerical series members, i.e. the profile of hydrophobicity of the protein sequences or the variation over time of biopotentials such as the ECG (electrocardiogram), those 'hidden' variables,
which are supposed to help define the dynamic characteristics of the system. These characteristics are analyzed individually, and this allows detecting subtle periodicities and recurrences notcaught by the traditional methods (e.g. Fourier analysis or wavelet analysis [9]). A further advantage is that the reliability of the results is not conditioned by any assumptions of stationary or
minimum length of the signal [5, 10].
Among these methods, the RQA includes an embedding procedure that allows expanding a
mono-dimensional signal into multi-dimensional space, thus permitting the identification of finepeculiarities of the sampled series that in turn are described by few global parameters allowing
for a synthetic description. RQA has been recently applied in medicine for the analysis of timeseries of oto-acoustic emissions [11, 12]. It introduces some parameters of the overall complexityof the signal, derived from the so-called "Plot of Recurrence" (RP). The trend of the original signal over time is represented by a set of n points equally spaced (e.g. {a1 a2 .... an}, where ai is thesignal value at time i. Next, the series is copied into successive columns (the number of columns
is defined as a dimension of embedding, N), each one shifted by a given number of points (lag):this way, the embedding matrix is created. Finally, the graph of recurrence is built by drawing a
dot in the space that represents the distances between the corresponding rows if the distance between the jth and the (j + 1)th rows of the embedding matrix is less than a fixed value (called radius), producing a graph pattern. In the so obtained plot, the horizontal and vertical axes represent the relative position of the n points of the time series. The presence of horizontal and vertical lines in the recurrence plot shows that part of the considered signal matches closely with asequence farther along the time.

31

The RQA descriptors are calculated based on the number and location of dots on the plot. In
particular, percent of recurrence (%Rec) is the percentage of recurrence points in a recurrent plot;
percent of determinism (%Det) is the percentage of recurrence points, which form diagonal lines
and it indicates the degree of deterministic structure of the signal. Entropy (Ent) is the Shannon
entropy of the probability distribution of the diagonal line lengths and is linked to the richness ofdeterministic structure [11]. Laminarity (LAM) is the percentage of recurrent points that are included in line segments vertical to the upward diagonal and whose length meets or exceeds theminimum length threshold. It measures chaotic transitions and is related to the amount of laminarphases in the system (intermittency) [6, 10].
Within this framework, Chelidze’s team applied RQA to study the dynamics of earthquakes’
temporal distribution by considering the inter-arrival times between two events of Caucasianearthquakes with Magnitude 3 [13]. They calculated the correlation dimension of the integral
time series (14,100 time intervals) for a large period of observations; the possible control of dynamics of a complex seismic process by strong electromagnetic impacts in the temporal domain
was observed. The result obtained by Chelidze et al. was that the qualitative methods testify thepresence of some non-random nonlinear structure in energetic, spatial and temporal distributionsof earthquakes. The predictive potential of complexity analysis of seismological time series is recently considered in the review by Chelidze and Matcharashvili [8].
2.2 RQA input parameters for AE application
For this work an average on 120 points (1 hour) as a first smoothing of the recorded AE signals is reckoned, reminding that sampling rate was 30 seconds. Then, the optimization proceduresuggested the following parameters: the delay (lag) is set to 1; the number of the embedding matrix columns (embedding dimension) is set to 10 and the cut-off distance (radius) is set to 15.
If RQA is carried out by the computation of many small distance matrices corresponding toconsecutive and overlapping sliding windows (epochs) along the series, the changing values of
RQA variables in the subsequent windows allow for the detection of abrupt changes in the dynamical regime of the signal. This procedure, Recurrence Quantification over Epochs (RQE)
was used to test the presence of phase changes in AEs.
RQE analysis was carried out by adopting the same parameters setting as for the global
mode (RQA), plus the definition of windows having length of 24, 150 and 300 points (1 point =1 hour) and shifting of 24, 150 or 300 points between consecutive windows, respectively (to obtain non-superimposed windows).
3. Case Histories
Figure 1 shows how a quite extensive range of frequencies in the ultrasound spectrum must
be monitored in order to collect a satisfactory AE data set. This is an enough hard task taking
into account the difficulty of simultaneously handling different piezoelectric sensors in thenatural world. However, the skill acquired on laboratory experiments has allowed to define thebest selection of transducer frequencies in correspondence of 25 and 150 kHz that depict thecrack evolution in different materials [14]. Figure 2 shows the instrument sequence for AE datacollection as well as a typical installation case, at Valsinni site.

32

In the present configuration of an AE acquisition site the two sensors are connected to a
rocky outcrop through a glass bar, transparent to ultrasonic signals; then, the AE acquisition
apparatus’ chain includes a pre-amplifier, an amplifier, a data logger and a remote connection
module based on GPRS protocol. The incoming AE signals are sampled at 3 kHz, averaged andthen recorded at 30-seconds time steps as rms values.
Within this context, two case studies of AE recording sites are described: one is Valsinni,
Italy (Basilicata region; 40°10’05” Lat. and 16°26’35” Long.), a highly seismogenetic area, andthe other, along the Argentina/Chile boundary, the Peteroa volcano (35º15´S Lat. and 70º35´ W
Long.), exhibiting either seismic events or explosive eruptions. By analyzing both AE timeseries, quite remarkable anomalies of paroxysm precursors showed up. Henceforth, the AE datacollected at 25 kHz will be termed as LF AE, while the AEs recorded at 150 kHz as HF AE.

Fig. 3. Input AE signals recorded at Valsinni acquisition site during Springs 2008-2009 period.
Blue line depicts LF AE data, while red one shows HF AE data. L’Aquila earthquake (Magnitude = 6.3) occurrence is also reported.
The AE time series acquired at Valsinni site between May 2008 and April 2009, just after the
L’Aquila seismic event, are depicted in Fig. 3. In this plot the HF AEs (red line) show aconsiderable activity up to November 2008 and a subsequent depletion until March 2009, onemonth before L’Aquila earthquake; later, they start to increase again. Conversely, LF AEs
exhibit evident activity from May to July 2008 and a following depletion up to the end of
September 2008 when a rather remarkable activity begins, decreasing only when the seismic
event comes up [15].
Next, the two AE time series acquired at the HF and LF frequencies on Peteroa volcano between April 2009 and March 2011 are depicted in Fig. 4. The plots show a periodic pattern of the
LF AEs (right), as long as six months, perhaps related to Earth’s tide force [16]. The HF AEs(left), instead, show a more subdued periodic trend, and exhibit, as main characteristic, an abrupt
33

increase on November 2011, in correspondence to the first eruption phase. Better pictures of thetwo whole AE temporal distributions are shown in the left panels of Figs. 5 and 6.

Fig. 4. The AE collected on Peteroa volcano between 17 April 2009 and 23 March 2011. (Left)
HF AE; (Right) LF AE data. The green vertical lines point out the end of every year.

Fig. 5. (Left) Time-series of HF AE (red lines) collected on Peteroa volcano between Falls 2009
and 2011. Black lines point out the earthquakes (1/10 of Magnitude values larger than 0.3)
occurred within 300 km from Peteroa volcano (USGS catalogue). Blue line refers to the verystrong earthquake of Maule (Magnitude = 8.8), while the orange lines identify the explosive
eruptions periods of Peteroa. (Right) RQA parameters calculated from the HF AE series: thegreen line points out the %DET descriptor, while the brown line the %LAM (51 non-overlapping
epochs; 1 epoch=300 hours).
4. Results of the RQA Application to AE Data
4.1 Valsinni case history
The RQA application to LF AE data collected at Valsinni site during the Springs 2008-2009
period (see Fig. 3) has yielded four RP, depicted in Fig. 7, relative to four different time lags.
The top-left and bottom right graphs score low values (%DET ~70) before, about six months,
and just after the L’Aquila earthquake and, moreover, exhibit much similar patterns, which appear to resemble the one of a sinusoidal trend. On the contrary, the other two RP, related to a34

%DET ~90, exhibit quite complex patterns quite different from the other two. Thus, the incoming catastrophic event is observed in terms of a change in dynamic regime of AEs.

Fig. 6. (Left) Time-series of LF AE (magenta lines) acquired on Peteroa volcano between Falls
2009 and 2011. Black lines point out the seismic events (1/10 of Magnitude values larger than0.3) recorded within 300 km from Peteroa volcano (USGS catalogue). Blue line is referring to
the greatest earthquake of Maule (Magnitude = 8.8), while the orange lines define the explosive
eruptions periods of Peteroa. (Right) RQA parameters calculated from the LF AE series: thegreen line points out the %DET descriptor, while the brown line the %LAM (51 non-overlapping
epochs; 1 epoch= 300 hours).

Fig. 7. Comparison of the RP of the LF AEs recorded at Valsinni site in four different time periods, between September 2008 and April 2009. (Top left) Sep. 26-Oct. 03, 2008; (Top right) Dec.
12-Dec. 28, 2008; (Bottom left) Feb. 27-Mar. 09, 2009; (Bottom right) Apr. 13-Apr. 19, 2009.
35

Modification in dynamic regime of AEs is observed in Fig. 8, where the trend of the %DET
values since the installation of the Valsinni AE station (May 2008) is reported. It can be notedthat there is an almost continuous raise of %DET values until the occurrence of L’Aquila earthquake (Apr. 6, 2009) and a successive sudden depletion afterward.

Fig. 8. Trend of the RQA %DET values for the LF AEs recorded at Valsinni station since June2008 to middle April 2009. The red line indicates the occurrence of the L’Aquila earthquake.
The time intervals reflect the different periods in which the %DET has been calculated.

Fig. 9. Peteroa volcano. Top: An early record of LF AEs (150 hours: 17-23 April 2009). Bottom:
Corresponding Recurrence plot (RP): the inset on 300 epochs (1 epoch=24 h=24 points). A periodic signal and the very regular pattern of RP can be observed.
36

4.2 Volcano Peteroa case history
As already mentioned in Sec. 2.2, the volcano Peteroa AE time series were analyzed in different time windows, in order to fine-tune its RQA parameters and to examine events at various
time scales (Figs. 5, 6, 9, 10). In particular, the results obtained for the LF AE data with two different non-superimposed windows (termed epochs), of 24 and 300 points, are described. In theupper panel of Fig. 9, the pattern of the earlier record (the first 150 epochs corresponding to 6.25
days) of LF AEs is reported, while the upper panel of Fig. 10 shows the pattern of the entire recording period (16,146 points; corresponding to 2 years) of LF AEs. The lower panels of both
Figs. 9 and 10 exhibit the corresponding RP.
Figure 9 points out a daily Earth’s tide oscillation in the AEs while its RP shows the typical
pattern of sinusoidal trend, well observed plotting only the first 150 points (top of Fig. 9). Conversely, Fig. 10 for the whole monitored period shows a seasonal fluctuation of the LF AE data.
The corresponding RP (lower panel of Fig. 10) exhibits a blank area around the middle, placed
between two complex patterns, probably reflecting some modification in the internal structure of
the volcano. It could correspond to sharp transition of the regime, probably being a thresholdvalue in the AE time series, quite coinciding with the Chile Maule earthquake (Magnitude = 8.8;
Feb. 27, 2010).

Fig. 10. Peteroa volcano, (Top) LF AEs recorded in the whole period between Apr. 17, 2009 andMar. 23, 2011 (16,146 points). (Bottom) RP on 600 epochs (1 epoch = 24 points = 24 h).
A better interpretation of RQA results about Peteroa volcano has been obtained by overlapping the time distribution of the RQA descriptors %DET and %LAM to the original AE time series and to the earthquakes occurred in the neighborhood of the volcano within a radius of 300
km. In Figs. 5 and 6, respectively, these data for HF and LF AEs are shown with the date of theMaule seismic event and the volcanic eruption periods. The HF AEs (right panel of Fig. 5) exhibit three significant patterns of both %DET and %LAM for different periods. The first one, lasting about seven months, shows uniform patterns of high values for both parameters, in accord37

ance with the smoothed sequence of seismic events. Then, the %DET and %LAM trends, about
after middle December 2009, drop suddenly until the Maule earthquake occurrence, followed bya subsequent increase of both parameters. Next, after the intense Maule aftershocks seismic series, the RQA parameters present both remarkable fluctuations, perhaps driven by the explosive
eruption sequences occurred on Spring 2010 and Easter-Fall 2011.
The LF AEs (Fig. 6) show a less evident pattern. The %DET and %LAM trends before theoccurrence of the Maule earthquake appear less uniform than those exhibited by the HF AEs
with strong fluctuations; they show (particularly %LAM) a remarkable decrease of values as theearthquake is approaching. Next, after the Maule earthquake, both RQA parameters exhibit aquite variable trend achieving a relative maximum during the Spring 2010 eruption period
and a strong value depletion (especially the %LAM) during the inter-period between the two
eruption phases and a successive abrupt increase before the second eruption period.
These results seem to confirm the capabilities of RQA method to depict the modifications in
the volcanic edifice due to the combined action of seismic and volcanic activities. Particularinterest has to be devoted to the relations between the RQA parameters trends and the occurrenceof the Maule earthquake (Magnitude = 8.8), while it is difficult to define peculiar relations withthe volcanic explosive activity.
5. Concluding Remarks
The results of the application of RQA technique to AE time series within two frequencies(HF = 150 kHz and LF = 25 kHz) in seismogenetic and volcanic areas are given considering different time scales. The temporal distribution of the obtained RQA parameters (Figs. 5, 6, 8) exhibits remarkable variations as a paroxysmal event is approaching.
Despite any possible warning due to the uncertainty of the underlying relationships with volcanic activity and earthquakes and to the merely episodic nature of the observations (more casestudies are to be analyzed to confirm the obtained results), the anomalies detected in the RQAparameters could represent a likely signature of an incoming catastrophic event in terms of AE
dynamic regime modifications, possibly providing a remarkable finding. The arrangement of dynamical processes can be analyzed, so reckoning noteworthy “predictive” RQA parameters. The
obtained results appear quite interesting, but a fine-tuning of the analysis and more data sets fromdifferent seismic and volcanic areas are needed to assess the impact.
Acknowledgements
The authors are very thankful to Dr. J. Ruzzante and his team of ICES-CNEA (Argentina) for
providing the AE data collected on Peteroa volcano monitoring site within the framework of the
project PICT 2007-2009 “Emissión Acústica y precursors sismicos”. They also thank the Civil
Protection of Basilicata for the financial support for the installation of the Valsinni AE monitoring station in the framework of the Research Agreement between Regional Civil Protection of
Basilicata and CNR IDASC. They are grateful to G. Ventrice of the SME P.M.E. srl (Italy) for
his contribution to the instrument development, installation and maintenance at Valsinni site.

38

References[1] G.P. Gregori, G. Paparo, M. Poscolieri, C. Rafanelli, G. Ventrice, ‘Acoustic Emission (AE)for monitoring stress and ageing in materials, including either manmade or natural structures,
and assessing paroxysmal phases precursors’, in: Acoustic Emission, Intech Pub., Rijeka,
Croatia, (2011), 365-398.
[2] K. Ono, ‘Trends of recent acoustic emission literature’, J. of Ac. Emission, 12 (1993), 177198.
[3] G. Paparo and G.P. Gregori, ‘Multifrequency acoustic emissions (AE) for monitoring thetime evolution of microprocesses within solids, reviews of quantitative nondestructive evaluation’, 22, AIP Conference Proceedings, D.O. Thompson, and D.E. Chimenti (eds.), (2003),
pp. 1423-1430.
[4] J.P. Eckmann, S.O. Kamphorst and D. Ruelle, ‘Recurrence plots of dynamical systems’, Europhysics Letters, 4 (1987), 973-977.
[5] C.L. Webber and J.P Zbilut., ‘Dynamical assessment of physiological systems and states using Recurrence Plots Strategy’, J. Appl. Physiol., 76(2) (1994), 965-973.
[6] N. Marwan, N. Wessel, U. Meyerfeldt, A. Schirdewan and J. Kurths, ‘Recurrence plot based
measures of complexity and its application to heart rate variability data’, Phys. Rev. E, 66(2)(2002), 026702.1-026702.8.
[7] A.N. Gorban, E.V. Smirnova and T.A. Tyukina, ‘Correlations, risk and crisis: fromphysiology to finance’, Physica A, 389 (2010), 3193-3217.
[8] T. Chelidze and T. Matcharashvili, ‘Complexity of seismic process; measuring and applications - A review’, Tectonophysics, 431 (2007), 49-60.
[9] M. Takemoto, H. Nishino and K. Ono, ‘Wavelet Transform - Applications to AE Signal
Analysis’, in: Acoustic Emission - Beyond the Millennium, Elsevier, (2000), pp. 35-56.
[10] http://homepages.luc.edu/~cwebber/ (looked on Dec. 4, 2012)[11] G. Zimatore, A. Giuliani and A. Colosimo, ‘Revealing deterministic structures in clickevoked otoacoustic emissions’, J. Appl. Physiol., 88(4) (2000), 1431-1437.
[12] G. Zimatore , A. R. Fetoni, G. Paludetti, M. Cavagnaro, M. V. Podda, and D. Troiani. ‘Early
detection of hearing impairment by using transient-evoked otoacoustic emissions- a pilot
study’, Med. Sci. Monit., 17(6) (2011), MT41-49.
[13] T. Chelidze, T. Matcharashvili, ‘Electromagnetic control of earthquake dynamics?’ Computers & Geosciences, 29 (2003), 587-593.
[14] G. P. Gregori and G. Paparo, G., ‘Acoustic emission (AE). A diagnostic tool for environmental sciences and for non destructive tests (with a potential application to gravitational antennas)’, in: Meteorological and Geophysical Fluid Dynamics (A Book to Commemorate theCentenary of the Birth of Hans Ertel), W. Schröder,(ed.), Arbeitkreis Geschichte der Geophysik und Kosmische Physik, W. Schröder/Science, Bremen, Ger., (2004), pp. 166–204.
[15] G.P. Gregori, M. Poscolieri, G. Paparo, S. De Simone, C. Rafanelli, G. Ventrice, ‘“Storms
of crustal stress” and AE earthquake precursors’, Nat. Hazards Earth Syst. Sci., 10 (2010),
319-337.
[16] J. Ruzzante, M.I. Lòpez Pumarega, M. Armeite, R. Piotrkowski, G.P. Gregori, I. Marson, G.
Paparo, M. Poscolieri, A. C. Catellani, ‘Análisis sobre el comportamiento del volcán Peteroa(Argentina), por metodos de Emisión Acústica (EA)’, Proceedings of the Workshop
AAGG2009, 1st ed., Buenos Aires (Arg), Asociación Argentina de Geofísicos y Geodestas,
ISBN 978-987-25291-1-6, Ciencias de la Tierra. I (2009), 221-227.

39

Artificial Neural Network Analyses of AE Data during Long-Term Corrosion
Monitoring of a Post-Tensioned Concrete Beam
1

Lucio Bonaccorsi1, Luigi Calabrese1, Giuseppe Campanella2 and Edoardo Proverbio1
University of Messina, Dept. Industrial Chemistry and Material Engineering, Viale F. Stagnod’Alcontres 31, S. Agata di Messina, 98166 Messina, Italy
2
NDT consultant, Via San Giuseppe 7 is. 297, 98122 Messina, Italy

Abstract
Acoustic emission (AE) technique is well suited for real-time control and detection of active
defects within a structure. We used AE to monitor a post-tensioned concrete beam for about
seven months, undergoing stress corrosion cracking (SCC) assisted by hydrogen embrittlement,
activated by ammonium thiocyanate solution during a laboratory corrosion test. By multivariate
analysis, three stages of damage mechanisms were identified: activation, propagation andrupture. An artificial neural self-organizing map (SOM) analysis was used to identify the
relationship between the AE variables and to classify AE events. This methodology has in factproved particularly powerful in identifying the evolution and extent of damage of the monitored
post-tensioned concrete beam with the use of exemplified topological maps. The SOM analysis
made it possible to correlate each AE stage, with unambiguous significant variables, to a specificdegradation phase.
Keywords: Signal discrimination, corrosion, damage, prestressed concrete, principal component
analysis, Kohonen map, SOM analysis
1. Introduction
Post-tensioning, as well as pre-stressing technology, is widely adopted in civil engineering,esp. in bridge engineering [1,2], since it allows to obtain slender but high performing structures.
Although this technology is effective from a structural standpoint, it is sensitive, depending on
environmental conditions, in which the structure operates, to corrosion damages. In particular,
several cases have been described in the literature of premature failures of structures due tolocalized attack such as corrosion fatigue, wear-corrosion, but mainly stress corrosion cracking(SCC) [3-5]. This SCC mechanism is more critical than others because it induces a prematureand unexpected rupture of the steel cables, affecting the mechanical integrity of the structure [6].
The development of reliable and affordable technique for monitoring and for damage
evaluation of reinforced concrete structures, and in particular of prestressed concrete structures,is therefore becoming a pressing demand since the increasing age of the structures, theprogression of deterioration processes and the ever increasing demanded performance. Acoustic
emission (AE) technique is very promising in this field since it is not invasive, allows a volume
evaluation and at the same time offers the possibility to locate discrete defects.
AE was widely used in studying corrosion phenomena and in particular SCC since the earlystage of the development of the technique [7]. More recently AE technique was introduced in thecivil engineering in evaluating SCC of reinforcing cables in prestressed concrete structure [8] aswell as in the detection of corrosion of reinforcing bar, in the identification of corrosion
processes [9] and for damage evaluation [10].
J. Acoustic Emission, 30 (2012)40© 2012 Acoustic Emission Group

Concerning SCC phenomena it has to be underlined that in prestressing steel they evolve
through complex and different mechanisms, where anodic metal dissolution and hydrogen
embrittlement (HE) are not unique stages [11-12]. However, many mechanisms can be identified
by AE [13-14]. AE signals are generated mainly by crack initiation and growth due to metal
dissolution or HE. Furthermore the hydrogen evolution induced by cathodic reaction within thecrack, the breakdown of thick surface-oxide films, the fracture or decohesion of phases (such as
precipitates, second-phase particles and non-metallic inclusions), twin or slip deformation in theplastic zone at the crack tip are all mechanisms that can produce detectable AE signals [15].
By considering the above-mentioned aspects it is clear that traditional AE analysis techniques
are not sufficient to discriminate the different stages of damage in a corroding structure. The
adoption of new statistical methods of data analysis is necessary to find out the significant
parameters capable of discriminating the different forms of damage. Pattern recognition
techniques can be used with this aim [16], and signal processing is performed at the waveform
level, either by applying digital filtering, Fourier transforms or other processing such as wavelet
transform, or by extracting AE features as a mean to describe the shape and content of a detected
AE waveform. However, waveform acquisition requires high data storage capacity andsophisticated equipment while feature analysis is based only on the evaluation of the main AE
signal parameters such as energy, counts, amplitude, without the need of the waveform record
and is carried out by mean of the cross evaluation of different “health indexes” related to AE
signal intensity and to loading condition [17].
Aim of this work is to identify the different damage mechanisms in steel cables in a posttensioned concrete beam due to hydrogen assisted cracking by means of AE technique. A selforganized map (SOM) procedure was applied in order to interpret the experimentally acquired
data. It was possible to group events into sub-clusters, each being an identifier of a specificdegradation condition. The topological neural map was implemented in identifying the temporal
evolution of damage and its related structural risk.
2. Material and methods
2.1 Experimental set-up
Table 1: Steel wire composition and mechanical properties
Nominal chemical composition of eutectoid steel
C

Si

S

P

Cr

Ni

Mn

Cu

Fe

0.81

0.24

0.002

0.008

0.12

0.04

0.65

0.1

Bal.

Mean mechanical properties of cold drawn steel
Steel type

Yield stress fp 0.1k (MPa)UTS fpk (MPa)Elongation (%)

Cold drawn

1670

1860

3.5

AE monitoring was carried out on a post-tensioned concrete beam during long-termhydrogen assisted stress corrosion cracking test (HASCC). The beam had a length of 6.30 m anda cross section of 0.40 x 0.25 m (Fig. 1a). It was made by a concrete mix with 430 kg/m3 ofordinary Portland cement (OPC) content having the characteristic compressive strength of Rck =41

55 MPa. It was reinforced with four 18-mm steel bars and post-tensioned with four 7-wire 6/10”
strands (Fig. 1b). Steel wire composition and mechanical properties are reported in Table 1.
Post-tensioning force induced a stress level on the strand of about 60% of the UTS, of about
52% UTS after deducing losses due to elastic shortening, creep and shrinkage in the concrete andrelaxation in the steel. The tendon was completely grouted with the exception of a small
corrosion cell of 25 cm in length in the middle of the beam [18] (Fig. 1c). The corrosive solution
used was composed with 250 g/l of ammonium thiocyanate (NH4CNS) in accordance to ISO
15630 [19]. The solution was kept at room temperature, and with constant liquid re-circulationfrom an external reservoir by a pump.

(a)

(b)

(c)

Fig. 1: a) View of test setup used for corrosion test. b) Close-up view of strands anchorage.
c) Corrosion cell obtained in the metal duct.
AE signals were recorded by a 10-channel Vallen AMSY-5 measurement system. It was usedwith 8 piezoelectric transducers for steel, VS150-M type that worked in 100-450 kHz frequency
range with resonance at 150 kHz. The sensors were positioned on ends of steel wires (Figure 1c).
The AE acquisition covered a period approximately of 7 months, with a daily 24h monitoring.
Before analysis, raw data were de-noised as reported in a previous work [20]. The neuralnetwork analysis has been proposed to provide meaningful information on the quality ofacquired signals and their interpretation on the status of the structure and damage evolution. In
particular a self-organizing map (SOM) was constructed. It allowed to homogenize the acquired
data and to interpret them in explicit form with a topological map that exemplifies the damage
evolution of the structure during the acquisition time.
42All specific algorithms proposed to analyze AE data were written by using Matlab 7.5
software. The SOM analysis was carried out by using 6 uncorrelated variables (i.e. averagefrequency, amplitude, duration, energy, time and event frequency) extrapolated from theacquired AE signal, as resulted by correlation matrix analysis.
2.2 Self Organizing Map
Self-organizing map (SOM) or Kohonen's map is a neural network used in the classificationof complex multi-dimensional data [21]. The algorithm could be unsupervised, requiring no userintervention. The self-organizing map (SOM) is one of the most prominent artificial neuralnetwork models adhering to the unsupervised learning paradigm. The model consists of anumber of neural processing elements, i.e. units. Each of the units i is assigned an n-dimensional
weight vector mi, mi Є Rm. The training process of self-organizing map may be described interms of input pattern presentation and weight vector adaptation. Each training iteration t starts
with the random selection of one input pattern x(t). This input pattern is presented to the selforganizing map and each unit determines its activation. Euclidean distance between the weightvector and the input pattern was used to calculate a unit’s activation. In this particular case, theunit with the lowest activation is referred to as the winner, c, of the training iteration, as given inequation 1.
c:mc ( t ) = mini  x ( t ) − mi ( t ) (1)Subsequently, the weight vector of the winner as well as the weight vectors of selected units inthe vicinity of the winner are adapted. This adaptation is implemented as a gradual reduction of
the difference between corresponding components of the input pattern and the weight vector, as
shown in equation 2.
mi ( t +1) = mi ( t ) +α ( t ) ∗ hci ( t ) ∗ ⎡⎣ x ( t ) − mi ( t ) ⎤⎦(2)
Geometrically speaking, the weight vectors of the adapted units are moved slightly towards
the input pattern. The amount of weight vector movement is guided by a so-called learning rate,
α, decreasing in time. The number of units that are affected by adaptation as well as the strengthof adaptation is determined by a so-called neighborhood function hci. This number of units
decreases in time. Typically, the neighborhood function is a unimodal function, which issymmetric around the location of the winner and monotonically decreasing with increasing
distance from the winner. A Gaussian is used to model the neighborhood function. At the
beginning of training a wide area of output space is subject to adaptation. The special width ofunits affected by adaptation is reduced gradually during the training process. Such a strategy
allows the formation of large clusters at the beginning and a fine-grained input discrimination
towards the end of training process. The training process of the self-organizing map leads to a
spatial arrangement of the input pattern such that alike input are ordered in a topological map (Umatrix) [21,22]. Training has enabled the convergence of the system towards a model clustering
the internal nature of data (Fig. 2).
The default training algorithm in SOM script is this batch algorithm; this because it needs amuch smaller number of iterations than the classic one, and each iteration uses a large number of
input data points. The whole training set is gone through at once and only after this the map isupdated with the net effect of all the samples. Actually, the updating is done by simply replacing
the prototype vector with a weighted average over the samples where the weighting factors are
the neighborhood function values.

43nmi ( t +1) =∑ h ( ) (t ) x
ic j

j

j=1
n

(3)

∑ h ( ) (t )
ic j

j=1

where c(j) is the BMU of sample vector xj, hi,c(j) is the neighborhood function (the weighting
factor), and n is the number of sample vectors.

Fig. 2: Scheme of SOM training operation.
Additional information can be obtained by plotting the hit histogram U-matrix. This map
shows the projection of data samples into map. The data are calculated by finding the BMU(Best Matching Unit) of each data sample from the map, and increasing a counter in the map unit
each time it is the BMU. The colors are related to a specific level of a variable and hexagon size
is related to the numbers of AE hits related to that cluster point.
3. Results and discussion
Figure 3 shows the cumulative hits plot against acquisition time, with the purpose to indicate
the AE activity evolution with time. We can identify three relevant steps that could be related to
specific damage evolution of the steel.
Region I: During the early stages of the test there was a significant AE activity due to initial
electrochemical interaction between the steel wires and the electrolyte solution. This
period of activity is characterized by several sub-steps due to the activation andstabilization of different corrosive phenomena. At first the AE activity is related to thehomogenization and stabilization of reactions at the metal surface in the corrosive
solution [23]. Region I is characterized by events in a wide range of amplitude (in therange 40-70 dB) and usually medium-high rise-time (> 30 µs). The low amplitude
events could be attributed mainly to hydrogen formation at the steel surface [24,25].
As reported in literature, AE with amplitudes in the range of 40-50 dB with high rise44

time can be induced by hydrogen evolution phenomenon [26]. Besides, the presenceof hydrogen favors the breakdown mechanism of a pseudo-passive layer at the metal
surface of the strand and the beginning of hydrogen diffusion inside the metal,
inducing a strong AE activity [27]. During this incubation period, corrosion
mechanisms are activated, as random pit formation on the surface of the metal [28].
The localized corrosion is however not sufficiently energetic to be detected by AE, but
it could also favor the energetically more relevant SCC [29]. The SCC initiation
phenomenon induces events with low duration, counts and rise-time and amplitude in
the same range of hydrogen evolution events (30-50 dB) [30]. The crack initiation isfollowed by short crack propagation, identified with AE at higher amplitude (in therange 60-70 dB) [30].

Fig. 3: Cumulative hits plot versus time.
Region II: After 35 days a new swarm of significant events in a short period occurred (in about
15 days over 5000 hits were observed) and the cumulative AE hit curve increases
significantly. Afterwards the structure has a new quiescent phase where the increase of
AE hits is very low. At this stage, at the strand surface, micro-cracks gradually initiate,
increase and coalesce [14,31,32]. This phase is the evolution stage concerninghydrogen embrittlement and propagation of cracks [23]. This is confirmed by therelevant amount of events in the range amplitude of 40-50 dB with the rise-time about
100 µs. The hydrogen diffusion through the metal lattice occurs. When the hydrogen
uptake is sufficient for the steel to overcome its threshold stress intensity factor a new
AE activity will take place, due to initiation and propagation of cracks [23]. In region
II the time period between two AE events increases significantly; this phenomenon
could be related with the formation of plastic zone at the crack tip during the crack
propagation stages [26]. With increasing the crack length an increase of stressconcentration occurs, which induces larger plastic zone ahead of the crack tips. This
results in a greater blunting of the crack tip. Larger plastic zone implies that a longer45

time is needed for crack to resharpen by dissolution for further crack propagation. The
period of time between two AE events corresponds to the period of material
dissolution that induced the crack growth [26]. This could explain the larger time gaps
between two AE events during later stages of crack growth.
Region III: High-intensity AE events were recorded after four months. Some steel wires wereapproaching final rupture and the post-tensioned concrete beam was undergoing astabilization phase due to a new redistribution state of stress levels. This phase ofreassessment was the prelude to new damaging phases progressively much more
destructive. After this period we acquired about 25,000 AE events for a total of about
800,000 cumulative counts. In this phase the AE were mainly generated by crack
propagation. However, corresponding to wire failure, the intense AE activity wasprobably generated due to rapid reduction of the cross-section during its rupture [28].
The large burst of activity characterized by very high energy and amplitude up to 90
dB is symptomatic of final ductile fracture of a strand [30].
These considerations are summarized in Fig. 4 that shows the rise time versus amplitude
distribution of the data discriminated according to the three steps identified in Fig. 3. In Region I,
it is possible to identify the AE phenomena related with corrosion activation and crack initiation(oxide breakdown, hydrogen evolution, SCC initiation). In Region II the acoustic events,characterized by low amplitude and medium risetime, were associated to hydrogen embrittlement
mechanisms. Finally in Region III we have a large amount of data at medium amplitude relatedwith crack propagation and few AE events with very high amplitude due to the final fracture of
the strand.

Fig. 4: Amplitude vs. risetime grid maps; corrosion mechanisms identification was done
following literature data.
The Kohonen's self-organizing map algorithm was applied to the uncorrelated variables
resulting from the analysis of the correlation matrix, i.e. average frequency, amplitude, duration,energy, time and event frequency. In Fig. 5 the U-matrix map resulting from SOM analysis isreported. The U-matrix map shows distances from map units and their nearest neighborhood,46

evaluated by Euclidean method. High values of U-matrix map (red and yellow pixels) mean
large distances between neighboring map units. Elements belonging to the same cluster are,therefore, identified by uniform areas of low value (blue pixels). In this specific case, we
identified many homogeneous areas associated with cluster areas. It is interesting to note thatusing the topological maps of the variables (reported in Fig. 6) it should be possible, on the basisof variable magnitude distribution, to relate data cluster to local area of specific variables. In
particular, according with the regions identified in Fig. 3 and on the basis of time variable maps
we defined three regions on the U-matrix map. The definition of the area associated to the firstregion is heavy influenced by the event frequency distribution as can be observed in the
topological map (Fig. 6).

Fig. 5:U-matrix resulting from the application of the Kohonen self-organizing map algorithm.
In fact, the events characterized by high event frequency (red and yellow pixels in the event
frequency topological map) are grouped in the right and central area of the map. The second
region is a left bottom area in the U-matrix map. The events grouped there have a low event
frequency and middle/average frequency and duration. The third region includes the top andright areas of the map. These events are characterized by energy and amplitude variables. In
particular the AE events with highest energy are grouped in the top-right corner of the map.
Furthermore, a large variation of duration and average frequency can be observed. In particularon the top of the map AE events with high duration and low average frequency are grouped; viceversa AE events with low duration and high average frequency are grouped in the right side of
the U-matrix map.
Based on these considerations it is possible to say that the U-matrix identifies some clusters
related to the damage evolution of the steel strand. But further investigation are necessary tobetter clarify the discrimination of that damage phenomena and its time evolution. With thispurpose a so-called hits-U-matrix (Fig. 7) was performed using the data collected in the regions
identified in Fig. 3. This graph shows the projection of data samples into the U-map. The
projection is obtained by finding the BMU (best matching unit) of each data sample from themap, and increasing a counter in the map unit each time it is the BMU. The colors are related to a47

specific event group (activation or quiescence phase as defined in Fig. 3) and colored hexagon
size is related to the numbers of AE hits related to that cluster point. Figure 7 shows that theinterpretation of the U-matrix map could be much more complex than the previous analysis.

Fig. 6: Topological maps of the variables as resulted from the application of the Kohonen's selforganizing map algorithm.
The clusters associated with the regions I, II and III are characterized by several events,
which could create internal sub-cluster or interference regions where multiple families of events
coexist.
Region I: About the data collected in region I, the activation AE are located mainly in the zone
Ia (characterized by high energy and high duration) and Ib (characterized by low
energy and low amplitude); in contrast, the quiescence data are not well identifiable ina specific area, although a group of hits of these events is identifiable in the zone Ic
and Id. The area Ic includes AE events of the region I characterized by low energy,
medium low amplitude and high average frequency. The area Id is related mainly withquiescence AE with high amplitude and relatively high average and event frequency.
Region II: Other considerations can be extrapolated analyzing the hits distribution for region II
data. A large amount of activation AE are located in IIa and IIb region, with theformer related with lower event frequency than the latter one. The zone IIc could be
related with the quiescence phase of region II.
Region III: The analysis of the hits map for region III data confirms that the AEs of this region
are located in the top of the map. In particular a large amount of activation data arelocated in the top corner. The AE with very high energy, generated during theactivation of region III, are located on the top-right corner of the map (area IIIa). The
quiescence phase is located in peripheral region of this section of the map. In
particular in the quiescence phase two sub-cluster can be identified. Events with highduration and low average frequency are mainly grouped in area IIIb, while events withhigh amplitude and low duration are predominant in area IIIc.
48

With the purpose to verify the hits distribution and to better clarify the discriminating
performances of the SOM analysis about the event evolution on SCC tests on post-tensioned
strands, a hits validation map was analyzed by using a fuzzy response parameter to show the
relative goodness of each map unit in representing the data. The fuzzy response was calculated
by summing a function of quantization error as follow [33]:1g( x , ui)=2
(4)
(1+ ( qi /a) )where qi = x − ui is the quantization error (i.e. the distance between the sample x and map unitui). The scaling factor a is the average distance between each training data sample and its BMU.
Consequently, a high value of the fuzzy response is related to high affordability of theprevisioned hits.

Fig. 7: Hits U-matrix for the data regions identified in Fig. 3.

Fig. 8: Fuzzy response index distribution for data regions identified in Fig. 3.
Figure 8 confirm that the SOM analysis identified the damage phase of the SCC strand well
as reported in Fig. 7. The activation and propagation data for each region are well clustered inlocal areas on the U-matrix map. All configurations showed a high fuzzy response index, related49with high accuracy of the numerical analysis. A higher uncertainty was observed only for region
II quiescence data, where we observe a low fuzzy response value (maximum value is only 480).
Consequently, only for this dataset, we believe that not enough information was available todiscriminate with good affordability their location on the U-matrix map. Furthermore thisinvestigation showed that in the middle of the U-matrix map an interference area generated by
AE of region I and II was present.

Fig. 9: Scheme of damage detection areas by SOM.
On the basis of the above reported considerations it is possible to divide the U-matrix map
into specific damage mechanism areas, according to the schematic representation shown in Fig.
9. Here, we can distinguish the activation, propagation and critical damage areas, respectively,
related with areas Ia and Ib, IIa and IIb and finally IIIa. The peripheral regions, not directly
related with a specific variable (and characterized by multiple sub-clusters) represent thequiescence areas. In particular, the peripheral area Ic includes quiescent events that occurred
after the electrochemical activation of steel surface characterized by high level of averagefrequency. The so-called pre-critical damaging quiescent area (IIc) was instead characterized bylow event frequency and middle duration events. Consequently, the lower peripheral region in
the map is related with the quiescent phase events before the critical damaging of the structure,
while the peripheral regions at the top and right of the map (IIIb and IIIc) are related to thequiescent phase following the critical damaging and characterized mainly by high amplitude
values.
The changes in waveform patterns of AE hits during the above described damage evolution
are well visualized in Fig. 10. Starting from the activation phase (Ia) where AE hits arecharacterized by low average frequency, medium amplitude and high duration, in region II (IIa)
we can observe an AE hit population defined by medium-high average frequency, low amplitude
and medium duration, while the final damage step (post critical quiescent phase in region III, i.e.
IIIc) is characterized by very high average frequency, medium high amplitude and very low
duration.
50

Globally, the authors believe that the self-organizing map algorithms and the related Umatrix maps are powerful instruments for analyzing multivariate data set. In the specific cases
their application allowed not only to validate preliminary hypotheses formulated on the basis of
the interpretation of cumulative hit curve but permitted to discriminate between different damage
mechanisms occurring over time on steel strands on the basis of the correlation between specific
AE wave attributes.

Fig. 10: Trajectory plots of some AE hit patterns during damage evolution.
4. Conclusions
Acoustic emission (AE) technique was used to monitor hydrogen-assisted stress corrosion
cracking of post-tensioned strands stimulated by using accelerated test with an ammonium
thiocyanate solution. Three damage phases were clearly distinguished from the cumulative hit
plot, but a more detailed interpretation of the damage mechanisms occurring on steel wires
during aging time was obtained by using a self-organizing map (SOM) methodology. The
numerical results show that it is possible to integrate in an effective and synergistic way by using
SOM with the traditional univariate analysis. Moreover this methodology has proved particularly
effective in identifying, using exemplified topological maps, the evolution and intensity ofcorrosion damage on steel wires in the monitored post-tensioned concrete beam. Initiation,
propagation and critical rupture phases were clearly identified and associated to specific featuresof the AE events. Such results allow, also on the basis of further refining of the methodology, tohypothesize the use of such a procedure for in situ recognition of damage processes.
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Concrete', in Advances in Construction Materials, Grosse, Christian U. ed., Springer
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A. Nair, C.S. Cai, 'Acoustic emission monitoring of bridges: Review and case studies',
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steel', Materials and Corrosion Vol 49, pp 34-38, 1998.
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from SCC propagation', Corrosion Science, Vol 55, pp 5-9, 2012.
S. Yuyama, T. Kishi, Y. Hisamatsu, 'AE analysis during corrosion, stress corrosion
cracking and corrosion fatigue processes', Journal of Acoustic Emission 2 (1983) 71–93.
S. Yuyama, 'Fundamental aspects of acoustic emission applications to the problems causedby corrosion'. In: Moran G.C. , Labine P., 'Corrosion Monitoring in Industrial Plants Using
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E. Proverbio, 'Evaluation of deterioration in reinforced concrete structures by AE
technique,' Materials and Corrosion, Vol 62, No 2, pp 161-169, 2011.
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Supervised and Unsupervised Classifiers', NDT&E Int, Vol 37, pp 253-264, 2004.
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53

On the Use of the Acoustic Emission Technique for In-situ Monitoring of thePulverization of Battery Electrodes
1

Aurélien Etiemble 1,2, Hassane Idrissi 2 and Lionel Roué 1
INRS-Énergie, Matériaux et Télécommunications, 1650, bd. Lionel Boulet,
Varennes, Québec, J3X1S2 Canada.
2
INSA-Lyon MATEIS CNRS UMR5510, F-69621 Villeurbanne, France

Abstract
Acoustic emission coupled with electrochemical measurements have been successfully
applied for the in situ monitoring of the cracking upon cycling of metal hydride electrodes for
Ni-MH batteries and Si-based electrode for Li-ion batteries. This method offers the possibility tocharacterize the electrode disintegration process depending on the electrode composition andcycling conditions and then, it would become a powerful tool in the evaluation/optimization of
new electrode materials for different types of batteries.
Keywords: particle cracking, metal hydride, silicon, Ni-MH battery, Li-ion battery
1. Introduction
In various batteries, the electrode cracking due to the volume variation of the active materials
with cycling has a negative effect on the battery durability by accelerating the electrodecorrosion by the electrolyte and/or by inducing a loss of electronic connectivity. For instance, for
LaNi5-based alloys used as metal hydride materials in commercial Ni-MH batteries, thepulverization phenomenon induces the breaking of the native surface oxide layer and increases
the effective surface area of the electrode. This favors the hydriding reaction but it also decreases
the MH cycle life by increasing the alloy corrosion rate [1, 2]. For Mg-based alloys, which
present higher hydrogen storage capacities than the conventional LaNi5-based alloys, thepulverization process intensifies the irreversible oxidation of the active material by the KOH
electrolyte. For instance, amorphous MgNi and crystalline MgTi-10%w Pd alloys obtained byhigh-energy ball milling have a maximum discharge capacity close to 500 mAh g-1, but retain
less than 50 % and 70 % of it after only 10 cycles, respectively [3, 4].
For Li-ion batteries, silicon-based anode appears as an attractive candidate to replacedcarbon-based anode, thanks to a ten-times higher capacity (3572 mAh g-1) and safer behavior
than that of graphite electrodes [5]. However, the cracking of the silicon-based anodes due to theimportant volume variation of the Si particles (up to ~300 %) with lithiation has a negative effecton their cycle life by inducing a loss of electronic connectivity within the composite electrode in
addition to be unfavorable to the formation of a stable solid electrolyte interface (SEI) resulting
in severe electrolyte degradation at the surface of the Si particles.
To date, the study of the electrode cracking in batteries is generally limited to a post mortem
examination of the electrode by microscopy. This does not allow a detailed analysis of thecracking process, which can significantly vary depending on the electrode composition andprocessing, and the charge/discharge conditions. However, we have recently shown that the
acoustic emission (AE) technique is a relevant tool for in situ monitoring of the cracking of
MgNi and LaNi5-based electrodes for Ni-MH batteries [6, 7]. We have demonstrated that MgNi
J. Acoustic Emission, 30 (2012)54© 2012 Acoustic Emission Groupand LaNi5-based alloys present different pulverization mechanisms. It was also shown that thecracking of MgNi increases with the particle size and the charge rate [7]. A new experimental
set-up constituted of an electrochemical cell connected to a compression load cell and an AE
equipment was also used to characterize the force and the cracking of LaNi5-based and MgNielectrodes generated by their volume expansion/contraction during their cycling [8].
In the present paper, acoustic emission coupled with electrochemical measurements is
performed on different metal-hydride electrodes for Ni-MH batteries and on a Si-based anode for
Li-ion batteries. It will be shown that this methodology is very effective for determining the mainprocess responsible of their decrepitation.
2. Experimental
2.1 Metal hydride electrodes
A commercial LaNi5-based alloy (MmNi3.68Co0.78Mn0.36Al0.28) powder from Japan Metals &Chemicals Co, MgNi and MgTi-10%w Pd alloy powders obtained by high energy ball milling [3,4] were studied as active materials. 0.2 g of active material was manually mixed with 0.2 g ofcopper powder. The mixture was then cold pressed on 2 g of copper powder at 6 tons cm-2 for 10
minutes in a stainless steel die to form a pellet (16 mm in diameter, ~2 mm in thickness), which
is used as working electrode.
The experimental set-up coupling AE and electrochemical measurements has been described
elsewhere [6]. The reference electrode and the counter electrode were respectively an Hg/HgO
electrode and a nickel wire. The three-electrode cell was monitored by a Voltalab PGZ 301
galvanostat/potentiostat. The working electrode was charged at -100 mA g-1 for 3 h for the
LaNi5-based alloy and 5 h for MgNi and MgTi-10%w Pd. It was discharged at 20 mA g-1 until
reaching -0.6 V vs. Hg/HgO for the LaNi5-based and MgNi alloys and -0.5 V vs. Hg/HgO for
MgTi . All the experiments were carried out at room temperature in a 6 mol l-1 KOH solution.
Before the first charge, the potential of the working electrode was maintained at -0.85 V vs.
Hg/HgO for a few minutes to reduce the native oxide layer present on the Cu and active material
powders.
The AE signals were recorded during cycling by a wide band sensor EPA micro 80(frequency range 100-1000 kHz) and transmitted via an EPA USB Node acquisition card to thecomputer. For all the experiments, the gain G and the threshold S were fixed at 40 dB and 27 dB,
respectively. The recorded AE signals were treated and the waveforms were obtained with
AEWin software (EPA). The AE signals are separated in two classes using Noesis software(EPA). The first one, labelled P1, is attributed to cracking of MH particles and the second one,labelled P2, is due to the release of H2 bubbles [6, 7]. The temporal and energetic characteristicsof the P1 and P2 classes were described in detail in ref. [6].
2.2 Silicon electrode
Pure Si (99.999%, 20 mesh) ball milled for 20 h with a ball-to-powder ratio of 5:1 using a
SPEX 8000 mixer was used as active material, Super P carbon black (CB) as the conductive
agent and carboxymethyl cellulose (CMC) as the binder. A mixture of 200 mg of active material+ CB + CMC in a weight ratio of 80:12:8 with 0.5 ml of citric acid + KOH buffer solution at pH55

3 was mixed at 500 rpm for 1 h using a Fritsch Pulverisette 7 mixer. The slurry was tape cast
onto a 25 µm thick copper foil and dried for 12 h at room temperature and then 2 h at 100°C in
vacuum. Structural and morphological characteristics of the Si powder and electrochemical
behavior of the Si/CB/CMC anode were described in detail in ref. [9].
Two-electrode Swagelok cells were used for the cycling tests performed at room temperatureusing a VMP3 potentiostat/galvanostat. The cells were assembled in a glove box under argon
atmosphere and comprised (i) a 1 cm² disc of composite working electrode containing 0.7 mg±10% of active material, (ii) a Whatman GF/D borosilicate glass-fiber sheet saturated with theelectrolyte (1M LiPF6 dissolved in 1:1 dimethyl carbonate/ethylene carbonate and (iii) a 1 cm2 Li
metal disc as the negative and reference electrode. Cycling tests were performed in galvanostatic
mode between 1 and 0.005V versus Li+/Li at a current density of 400 mA g-1. The AE signals
were obtained and treated as before using AEWin and Noesis softwares (EPA).
3. Results and discussion
3.1 Metal hydride electrodes for Ni-MH batteries
3.1.1 Electrochemical behaviour
The evolution of the discharge capacity with cycling for LaNi5-based, MgNi and MgTi10%w Pd electrodes is shown in Fig. 1. In the case of the LaNi5-based electrode, a slight increaseof the discharge capacity is observed during the two first cycles. This increase can be attributed
to the particle cracking due their volume expansion with hydriding, leading to their activation bybreaking the native surface oxide layer and by increasing the effective surface area of theelectrode [1]. For the MgNi electrode, the discharge capacity decreases strongly with cycling.
This is related to the irreversible oxidation of the active material by the KOH electrolyte (i.e.,
formation of Mg(OH)2), which is intensified by the creation of new surfaces with the particle
cracking [3]. For the MgTi-10%w Pd electrode, the discharge capacity increases during the two
first cycles. This activation is associated with the phase transition from a bcc+hcp initialstructure to an fcc phase induced by the hydriding reaction [4]. After the third cycle, theFig. 1 Evolution of the discharge capacity with cycling for LaNi5-based, MgNi and MgTi 10%w
Pd electrodes.
56discharge capacity decreases with cycling due to the irreversible oxidation of the active material
by the KOH electrolyte as observed for the MgNi electrode.
3.1.2. AE Measurements
The evolution of the P1-type normalized AE activity (related to the particle cracking) and P2type normalized AE activity (related to the formation of H2 bubbles) during the first charge on
LaNi5-based, MgNi and MgTi-10%wPd electrodes are shown in Figs. 2a and 2b, respectively.
Note that no significant acoustic activity was observed during the discharge step (not shown).

Fig. 2 Evolution of the P1-type (a) and P2-type (b) normalized acoustic activities as a functionof the charge input on LaNi5-based, MgNi and MgTi-10%w Pd electrodes.
For the LaNi5-based electrode, an abrupt increase of the P1-type acoustic activity is observed
from ~5 to 25 mAh g-1 (Fig. 2a). Almost no additional P1 events are observed during the rest of57

the charge period, except at the end of the charge (i.e., from ~260 mAh g-1) where a small
increase in P1 activity is again observed. This indicates that the mechanical stress inducing thecracking of the LaNi5-based alloy is concentrated at the beginning of the charge, i.e. in the α-to-βphase transition region. This is in accordance with in situ generated stress measurements [8] andin situ XRD investigations [1] on LaNi5-based alloys showing that the volume expansion does
not increase linearly with the H-content in the alloy but occurs abruptly when the α-to-β phasetransition is initiated.
For the MgNi electrode, the evolution of the P1 activity with the charge input differs notably
from that observed for the LaNi5-based electrode. Indeed, a small increase of the P1 activity isobserved from ~25 to 100 mAh g-1. Then, no P1 activity is detected before reaching a charge
input of ~365 mAh g-1 where an abrupt and large rise of the P1 activity is observed. At the sametime, an important increase of the P2 acoustic activity related to the release of H2 bubbles
through the hydrogen evolution reaction (HER) is detected as shown in Fig. 2b. Theseobservations demonstrate that the MgNi particle cracking is mainly induced by the HER rather
than by the α-β lattice expansion in contrast to that was previously observed for the LaNi5-based
electrode. It can be explained by the fact that the MgNi alloy produced by ball milling has anamorphous structure, which results in the absence of abrupt volume expansion during thehydrogen absorption reaction (HAR) [8]. On the other hand, ball-milled MgNi powder isconstituted of porous agglomerates made up of many particles cold-welded together (Fig. 3a)
whereas LaNi5-based powders appear much denser (Fig. 3c). These agglomerates are likely to bebroken down by the mechanical stress induced by the accumulation of H2 bubbles into theagglomerate pores during the HER.
For the MgTi-10%w Pd electrode, no P1 activity is detected before reaching a charge input of~300 mAh g-1 where an abrupt and large rise of the P1 activity is observed (Fig. 2a). At the sametime, an important increase of the P2 acoustic activity related to the release of H2 bubbles is
detected (Fig. 2b). These observations suggest that the MgTi-10%w Pd electrode pulverization ismainly induced by the mechanical action of the H2 bubbles as observed for the MgNi electrode.
This is not surprising since both materials are elaborated by ball-milling and present the sameporous agglomerate morphology (Fig. 3b).

(a)

(b)

(c)

Fig. 3 SEM images of the MgNi (a), MgTi-10%w Pd (b) and LaNi5-based alloy (c) powders.
The evolution of the P1 acoustic activity with cycling on LaNi5-based, MgNi and MgTi10%w Pd electrodes is shown in Fig. 4. On the three electrodes, the P1 activity is maximum
during the first charge. This clearly indicates that the pulverization of the LaNi5-based, MgNi
58

and MgTi-10%w Pd electrodes mainly occurs during the first charge. Note that the P1 activity islower for the MgTi-10%w Pd electrode, which may indicate that this alloy is less sensitive to
cracking. In addition, the fact that its P1 activity is maximum at the first cycle despite its low Hstorage capacity (60 mAh g-1, see Fig. 1) tends to confirm that its pulverization is mainly related
to HER and not to the HAR.

Fig. 4 Evolution of the P1 AE activity with cycling for LaNi5-based, MgNi and MgTi-10%w Pdelectrodes.

Fig. 5 Evolution of the electrode potential and AE activity as a function of the charge capacity
during the first lithiation of the silicon electrode.
593.2 Silicon electrode for Li-ion batteries
3.2.1. Evolution of the acoustic activity during the first charge (lithiation)
The evolution of the electrode potential and the acoustic activity as a function of the charge
capacity during the first lithiation of the silicon electrode are shown in Fig. 5. During the firstlithiation, the electrode potential first decreases quickly up to reach a sloping plateau between~0.15 and ~0.05 V. During this stage, the crystalline Si reacts with lithium to form amorphous
SiLix [10]. At the end of the charge (i.e., below ~0.05 V), the crystalline Li15Si4 phase is formed[10]. Regarding the acoustic activity, a small increase is observed at the beginning of the charge
and may be related to surface reactions such as the reduction of the native Si oxides and/or theformation of the solid electrolyte interface (SEI). Then, from a charge capacity of ~600 mAh g-1
to ~2700 mAh g-1, the AE activity increases almost linearly and could be associated with the Siparticle cracking induced by their volume expansion with lithiation. Then, at the end of thedischarge (i.e., at ~0.05 V), a abrupt increase of the acoustic activity is observed, which can becorrelated to the formation of the crystalline Li15Si4 phase [10].
3.2.2. Evolution of the acoustic activity during the first discharge (delithiation)
The evolution of the electrode potential and the acoustic activity as a function of thedischarge capacity during the first delithiation of the silicon anode is shown in Fig. 6. An abrupt
increase of the acoustic activity attributed to the particle cracking is observed when the electrodepotential reaches 0.4 V, corresponding to a discharge capacity of 1100 mAh g-1. At this potential,
the phase change from crystalline Li15Si4 to amorphous silicon occurs [10]. This tends to confirm
that the electrode decrepitation is mainly induced by the abrupt volume variation related to theformation/disappearance of the Li15Si4 phase.

Fig. 6 Evolution of the electrode potential and the AE activity as a function of the discharge
capacity during the first delithiation of the silicon anode.
60

3.2.3. Evolution of the acoustic activity and charge capacity with cycling
The evolution of the acoustic activity with cycling is shown in Fig. 7a. The acoustic activity
is maximum during the first charge (lithiation) and discharge (delithiation). Then, the acoustic
activity decreases rapidly with cycling to reach less than 50 events per cycle after the third cycle.
Similar evolution with cycling is observed for the electrode charge capacity as shown in Fig. 7b.
This clearly indicates that the decrepitation of the silicon electrode mainly occurs during the first
three cycles and greatly affects the electrode cycle life. After the third cycle, it is assumed that Si
particles have reached a critical size, which limits further particle cracking. Additional AE study
about the influence of the initial Si particle size on their cracking sensitivity will be performed toconfirm this issue.

Fig. 7 Evolution with cycling of the AE activity (a) and charge capacity (b) of the Si electrode.
61Fig. 8 Peak frequency histogram of the AE signals detected during the ten first cycles of thesilicon electrode.
3.2.4. Characterization of the AE classes
From the analysis of the AE signals performed using Noesis software, the peak frequency
appears as the most discriminate acoustic parameter. The peak frequency histogram of the AE
signals detected during the ten first cycles on the Si electrode is shown in Fig. 8. Three classes of
AE signals can be distinguished. The first one, labeled PA, exhibits peak frequencies between 80
and 250 kHz with a maximum number of events at ~150 kHz. The second one, labelled PB, ischaracterized by peak frequencies between 250 and 400 kHz with a maximum number of events
at ~330 kHz. These PB-type signals also exhibit short duration and high energy. This ischaracteristic of AE signals related to cracking phenomenon, as observed on metal hydride
electrodes [6] and during aluminium exfoliation corrosion [11] or hydrogen embrittlement
process [12]. The third AE class, labelled PC, exhibits high peak frequencies, between 500 and
950 kHz. Further investigation using different electrode materials, electrolytes, particle
morphology, etc. are planned in order to identify the main sources of these three AE classes andto evaluate their respective influence of the electrode cycle life.
4. Conclusion
The study confirms that acoustic emission techniques coupled with electrochemicalmeasurements is an effective in situ method for studying the pulverization of electrodes for NiMH and Li-ion batteries. With metal hydride electrodes for Ni-MH batteries, it wasdemonstrated that the process inducing the pulverization of MgNi and MgTi-10%wPd producedby ball milling differs notably from that of LaNi5-based material. The pulverization of this latteroccurs at the beginning of the charge step and can be related to the abrupt volume change in the
α-to-β phase transition zone whereas for MgNi and MgTi-10%wPd, it is mainly due to themechanical action of the H2 bubbles produced at the end of the charge step. On silicon electrode62

for Li-ion batteries, the AE measurements tends to indicate that the electrode decrepitation ismainly due to the abrupt volume variation related to the formation (in charge) and disappearance(in discharge) of the crystalline Li15Si4 phase.
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8. A. Etiemble, H. Idrissi and L. Roué, 'In situ investigation of the volume change andpulverization of hydride materials for Ni-MH batteries by concomitant generated force andacoustic emission measurements', J. Power Sources, 205, 500-505, 2012.
9. S. Rousselot, M. Gauthier, D. Mazouzi, B. Lestriez, D. Guyomard and L. Roué, 'Synthesis ofboron-doped Si particles by ball milling and application in Li-ion batteries', J. Power
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cables', Corr. Sc. , 52, (6), 1915-1926, 2010.

63

AE Wavelet Processing in Dynamical Tests of a Reinforced Concrete Slab
Miguel E. Zitto1, Rosa Piotrkowski1,2, Antolino Gallego3
and Francisco Sagasta3
1
Mathematics Department, Engineering Faculty, University of Buenos Aires, Argentina.
2
School of Science and Technology, University of San Martin, Argentina
3
Department of Applied Physics, University of Granada, Spain
Abstract
Different processing techniques have been previously applied to AE signals coming fromdynamic tests of reinforced concrete structures on a shaking table. The dynamic test simulated
the earthquake of Campano-Lucano recorded in Calitri, Italy. After the test, the recorded AE had
been classified into fracture and noise hits according to various parameters: autocorrelation,
power wavelet in different frequency bands, kurtosis, RMS from different parts of the signal andapproximate entropy value. The results of comparing the classifications of the hits according todifferent parameters encouraged the convenience of using the filtered AE signals to obtaindamage indices in RC structures subjected to earthquake loads. Values of the parameters thus
obtained are used in the present paper as guidelines of our work, but the philosophy of theprocessing is now different. The hits are not already classified into fracture or noise hits
according to different parameters, but instead relevant information is extracted from each hit byadequate processing and filtering. Continuous wavelet transform, most suitable for nonstationary phenomena, was applied to each hit; various wavelet bases were tested in order toimprove scale resolution. Different scale (frequency) bands could be ascribed to different AE
sources; the bands were separated using statistical clustering methods. The comparison with the
cumulative dissipated energy related to damage permitted to clearly identify the band, whichcorresponds to concrete fracture. The proposed algorithm is automatic and direct and it improvesprevious procedures, besides providing a deeper physical understanding.
Keywords: Dynamical tests, continuous wavelet transform, signal processing, modeling.
1. Introduction
The applicability of acoustic emission (AE) to assess the damage in reinforced concrete (RC)structures subjected to earthquake loading was investigated in previous papers [1-3]. One of themain challenges in applying the AE technique to measurements obtained from such complex
dynamic loading is to remove the spurious information not related to concrete damage. A
procedure was applied in [1-3] to the measured AE signals obtained from dynamic shaking table
tests conducted on a RC slab supported on four steel columns. The proposed procedure consisted
in applying two filters; one of them was a polygonal filter over the Amplitude-Duration diagram,and the other one was based on the calculation of the RMS of the AE signal in different temporalwindows. The filters were applied in order to separate the short-duration signals (associated withconcrete fracture and, thus, with cumulative damage to the structure) and the long-duration
signals (corresponding to other noisy mechanisms). A quite good correlation between the
cumulative AE energy thus calculated and the cumulative damage on the concrete measured interms of cumulative dissipated energy was obtained.
In the present paper we intend to achieve the same or better results applying algorithms thatinvolve straight, automatic and physically grounded procedures to each AE signal. In this way
J. Acoustic Emission, 30 (2012)64© 2012 Acoustic Emission Groupwe hope to obtain at the same time more efficient codes to assess damage and a deeper
understanding of the basic phenomena connected with damage generation in RC structuressubjected to earthquake loads.
In the line of previous work [4,5] performed on scratch-tests on coatings, specific frequency
bands were intended to be assigned to different failure mechanisms applying the Continuous
Wavelet Transform (CWT) to AE signals.
2. Modeling and experimental background
Modeling and experimental details are fully explained in [1-3], and the AE signals processedin the present work are some of those used in previous work. A brief description is provided forclarity.
The objective of our work is to calculate the cumulative AE energy (named AE in what
follows for simplicity) and to perform the comparison with the cumulative dissipated energy
related to damage (named DE) that was previously obtained in [1,2] and was named WP in thoseworks.
From basic principles of dynamics, and assuming that the inherent damping of the structure is of
the viscous type, in a one-dimensional model, the equilibrium equation can be expressed by(1)m xt + c x + Fr = 0
where x is the relative horizontal displacement between the shake table and the slab, xt is theabsolute acceleration of the slab, c is the viscous damping coefficient (c = 4πξm/T), m is the
structure mass, T is the vibration period, ξ is the viscous damping fraction of the structure (seeTable 1) and Fr is the restoring force opposed by the tested structure against the relative
displacement x. Solving Eq. (1) for Fr the corresponding Fr-x curves were obtained, and then, thecumulative energy DE dissipated by the test model was calculated by integration.
2.1 Experimental setup and seismic simulation
Here, the experimental acquisition of parameters is described. The experimental setup was a
one-story (2.8m height) and one-bay (4.8m length) prototype structure consisting of an RC slab
supported on four box-type steel columns. It was designed following current Spanish codes
NCSE-02 and EHE-08. From the prototype structure, the corresponding test model was derived
by applying the following similarity laws: λl =1/2, λa =1 and λσ =1, where λl , λa and λσ are thescaling factors by which the geometry, the acceleration and the stress in the prototype must be
multiplied to obtain the corresponding dimensions in the test model. The thickness of the slab
was 125 mm and it was reinforced with steel meshes, one on the top made with 6 mm diameterbars spaced 100 mm, and another on the bottom consisting of 10 mm diameter bars spaced 75
mm. The test model was prepared in the laboratory. The average yield stress fs of the reinforcing
steel was 467 MPa, and the average concrete strength fc was 23.5 MPa.
The test model was tried with the uniaxial MTS 3 × 3 m2 shaking table of the University ofGranada (Spain). The bottom ends of the columns were fixed to the table by bolts. Similitude
requirements between prototype and test model and the dead and live gravity load were satisfied
by attaching additional steel blocks on the top of the RC slab. The total mass of the slab
including the added steel blocks was m =7390 kg. The acceleration record used for the shake65

table tests reproduced the NS component of the 1980 Campano-Lucano earthquake recorded at
Calitri (Italy).
Two series of seismic simulations were applied to the test model. The same accelerogram
was used in all simulations, the scaling factor of the peak accelerations (PA) being the only
difference (Table 1). The first series consisted of eight simulations with PA increasing
progressively from 0.08g to 0.58g (g: gravity of acceleration). The second series consisted of six
simulations with PA increasing from 0.19g to 0.95g. The second series started with values of PA
smaller than the maximum obtained in the first series, that is, in several simulations of the second
series the test model was subjected to load levels smaller than those it had been previously
exposed to. This was intentionally done so that the simulations reproduced two types ofsituations on the structure: (i) that in which the AE energy and the dissipated energy related todamage are dominated by the new damage associated with the opening and extension of cracks;and (ii) the situation in which AE energy and hysteretic energy are dominated by friction
generated from existing damage. Both situations are realistic scenarios that the structure may
experience over its lifetime. During the seismic simulations the test model was driven very close
to the limit commonly acceptable on an RC structure subjected to moderate earthquakes. The
measurements of the strain gages attached to the reinforcing bars indicated two facts: themaximum strain in the reinforcing bars ε max,reinf closely reached the yield strain (2200 µε); slip
occurred between the longitudinal reinforcing bars and the surrounding concrete. The variables ξ(damping fraction) and T (vibration period) of Table 1 were determined in free vibration tests.
Table 1: Seismic simulations
Test series
1
2
Simulation(in order ofapplication)
A1
B1
C1
D1
E1
F1
G1
H1
A2
B2
C2
D2
E2
F2

T(s)

ξ(%)

εmax,reinf
(µε)

0.26
0.29
0.30
0.31
0.31
0.31
0.31
0.31
0.32
0.32
0.32
0.32
0.32
0.32

1.10
1.14
1.20
1.26
1.30
1.42
1.48
1.60
1.66
2.11
2.55
3.16
3.24
3.50

497
568
638
909
1150
1361
1537
1670
836
1295
1350
1460
1540
1800

PA
(g)

0.08
0.10
0.12
0.19
0.29
0.38
0.44
0.58
0.19
0.38
0.58
0.66
0.74
0.95

2.2 Monitoring
2.2.1 Displacement, strains and acceleration monitoring
Displacement, strain and acceleration were simultaneously acquired during each seismic
simulation. The relative horizontal displacement x between the shake table and the slab wasmeasured by displacement transducers. Electrical resistance strain gages were attached tonineteen (top and bottom) longitudinal reinforcing bars near the corner of the slab prior to
casting the concrete. Strain gages were also attached at the upper and lower ends of the columns.
66

Accelerometers were fixed to the shake table and to the slab, which measured the absolute
acceleration of the table and the absolute response acceleration of the slab, in the direction of
shaking. All data were collected continuously with a sampling rate of 200 Hz by a dataacquisition system. In addition to the electronic data, detailed visual inspections of the slab weremade after each seismic simulation to identify the propagation of the cracks.
2.2.2. AE monitoring
A Vallen Systeme ASMY-5 was used to measure the AE signals during the tests. Eight lowfrequency AE sensors, type VS30 set in the range 20-100 kHz, were placed on the test model;
four positions were chosen along the four lateral sides of the specimen, while the other four
positions were on the bottom of the specimen. In all channels, the 25-180 kHz frequency band
was used during signal acquisition. During acquisition, a sample period of 1.6 µs and 1024 data
were used for signal recording (200 of them, before the arrival time). Thus, the entire duration of
the record window was tmax=1318 µs. Silicone adhesive bonding agent was used for the couplingof sensors on concrete. Before testing, the electric noise in the laboratory was measured and acalibration test by breaking pencil leads (AE Hsu-Nielsen source) along the specimen was
carried out. Thus, it was established that using 45 dB as the threshold of detection, pencil leads
broken at any place of the specimen could be recorded by all the sensors. Also to prevent
undesired noise generated by the contact between the base plate of the columns and the shake
table, four guard sensors were placed near the bottom end of the columns.
Attenuation tests were carried out by breaking pencil leads along the specimen andmeasuring the signals in all the 8 sensors. Five leads were broken at each position, and the meanvalue was calculated. Thus, it was established that 0.11 dB/cm was the mean attenuation. Then,
keeping in mind that the point farthest from an AE sensor (the center of slab) is 94 cm away, themaximum attenuation of a source located at that point would be 10.3 dB. Since a threshold of 45
dB was used, it can be stated that all the sources producing AE signals with amplitude higher
than 55.3 dB could be recorded by at least one sensor. Note, however, that the center of the slab
is the place where less cracking is expected, since the bending moment under lateral loads isapproximately zero.
In general, different modes of propagation can be expected in the AE waves generated by thecracking of a concrete slab (i.e. longitudinal waves, transversal waves, Rayleigh waves). These
modes of propagation superpose and are influenced by many factors such as the mechanics of thedamaging process in concrete. Under earthquake-type cyclic loadings, the concrete experiences
both damage associated with the opening (new cracks) and extension of cracks, and damage due
to friction between the planes of fracture of previous cracks. Other factors such as the orientationof the cracks, the geometry of the specimen, the type of sensor, the multiple reflections occurring
along the wave path between the source and the sensor etc., play an important role. Most of thesefactors are random and they can hardly be controlled. All these factors make the signal very
cumbersome and modify the vibration modes. The analysis of a complex material such asconcrete or of a reinforced concrete structure based on the study of the vibration modes ismeaningless, since it is very difficult to identify the different vibration modes.
For the depth of the slab tested in this study (125 mm), the frequency of the propagation
modes corresponding to the Lamb waves is below 20 kHz, that is, outside the frequency range ofsensitivity of the two types of sensors used. Therefore, it can be concluded that the recorded
waves are a mixture of longitudinal and transversal waves. Besides, the velocity of propagationof the longitudinal waves was measured by breaking pencil leads, giving an average value
67

(obtained with six measurements in the longitudinal direction of a concrete bar made of the samematerial of the slab) of 3200 m/s.
Due to the complexity of the test and of this type of dynamic loading, a great quantity ofundesired friction noise and mechanical noise was expected. For this reason, different
precautions were taken to deal with such spurious signals before and after the test. Considerable
mechanical noise coming from the oil flow in the actuator that moved the shaking table was
detected. The level of this noise was above 100 dB in the actuator, and about 70 dB at the base of
the four steel columns that formed the specimen. For this reason, one guard sensor was placed at
the bottom of each column, in order to filter out this noise and the friction noise generated in theconnection between the base plate of the columns and the surface of the shaking table.
3. AE signal processing
3.1 Continuous Wavelet Transform (CWT)
The wavelet transform is widely used to analyze time series that contain non-stationary
power at many different frequencies. We next describe the method used for the wavelet analysis,
including a discussion of different wavelet functions and details for the analysis of the wavelet
power spectrum. The proposed equations, obtained from the continuous formulas, have been
adapted to discrete notation according to results obtained by other authors [6, 7]. The CWT of afunction f(t) is defined as the integral transformation in Eq. (2)∞(W f ) (s, b) = ∫ f (t ) ψ s,b (t ) dt

s≠0

(2)

−∞

with the wavelet function ψ (t ) and Eq. (3) has to be fulfilled.

ψ s,b (t) =⎛ t - b⎞ψ⎜⎟s ⎝ s ⎠

1

(3)

In order that ψ (t ) is “admissible” as a wavelet, this function must have a zero mean value and it
has to be localized both in time and frequency spaces. An example is the Morlet wavelet,
consisting of a plane wave modulated by a Gaussian, i.e.

ψ (η ) = π −1/4eiω0 η e−η /22(4)
where ω0 is a non-dimensional frequency, here taken as 6 in order to satisfy the admissibility
condition.
The CWT of a discrete sequence x(n ⋅ δ t ) is defined as the convolution of x(n ⋅ δ t ) with ascaled and translated version of ψ(η) given in Eq. (5), (see [8])⎡ ( n '− n ) δ t ⎤(5)⎥sn '= 0⎣⎦Although it is possible to calculate the wavelet transform using the given equations, it isconsiderably faster to do calculations in the Fourier space using a discrete Fourier transform(DFT). The DFT of a sequence xn is given by
N −1(W x ) (s, n) = ∑ x(n 'δ t ) ψ ⎢N −1

1x k = ∑ xn exp(−i2π k n / N )
N n=0

(6)
68

The following Eq. (7) was obtained by three steps: discretization of Eq. (2); DFT of Eq. (5);inverse DFT (IDFT).
N −1(W x ) (s,n) = s ∑ x ψ (s ω ) exp(iω n δ t)
k

k

k

k

(7)

k=0

⎧ 2π k⎪⎪where the angular frequency is ωk = ⎨ Nδ t
⎪ −2π k⎪⎩ Nδ tk = 0,K , N / 2(8)

k = N / 2 + 1,K , N − 1

We can calculate the CWT for a given s and for all n simultaneously using Eq. (7) and astandard Fourier transform routine. It is then possible to reconstruct the original time series fromthe CWT. This is straightforward for an orthogonal wavelet transform, but it is complicated for
the CWT because of the redundancy in time and scale. However, this redundancy has theadvantage of allowing the reconstruction of the time series using a completely different wavelet
function, the simplest of which is a delta function [7]. For a real time series

xn =

{

}

δ j ⋅ δ t1/2 J ℜ (W x ) ( s j , n )∑Cδ ⋅ψ (0) j =0
s j1/2(9)where the index j is the one corresponding to the scale s j = s0 2 j⋅δ j , with j=0,1,…,J. The factor Cδ comesfrom the reconstruction of a δ function from its wavelet transform using the function ψ(η). This factor isa constant, different for each wavelet function; for the Morlet wavelet it is Cδ = 0.776 .
Summarizing, the calculation is essentially described in Scheme 1.
Scheme 1
1. Selection of the wavelet function
2. Discretization and scale selection
3. DFT calculation of the signal and the wavelet
4. IDFT calculation of the product of both Fourier transforms
5. Eventual reconstruction of the filtered signal

3.2 Statistical clustering (K-means algorithm)
A very popular approximate non-hierarchic method is the K-means method [9-11], which has
been used in previous occasions [12-15] with very good results. The idea of this algorithm is todivide the n samples into k groups/clusters according to how far the sample is from certain meanvalues, which are found in an iterative procedure. In this way, the concept of metric is introduced
and a distance definition is needed. In our case, we used the Euclidean distance. The number ofclusters k should be selected from the beginning according to physical criteria.
For a given a set of samples, each one consisting of n samples {xi}, the K-means algorithm
will try to find the vector of k-means values {µi}. The algorithm starts with random seeds for the{µi} values and from them all the samples are classified according to the nearest µi. Once it isdone, the mean values are recomputed applying maximum-likelihood criteria until the differencewith the previous set of {µi} is practically zero. The calculation is concisely described in Scheme
2.

69Scheme 2
1. begin
2. initialize variables n, k, µ1, µ2,… µk
3. do
4. classify n samples according to nearest µi
5. recomputed µi
6. until no change in µi
7. return µ1, µ2,… µk
8. EndIn the present work the selected number of clusters was 2, because we considered that relevant
information related to concrete damage corresponded to a specific frequency band.
4. Results and Discussion
4.1 Wavelet selection
The seismic simulations B1, D1, F1, H1, D2, and F2 were selected from Table 1. The firststep in the wavelet analysis was the selection of the wavelet function among different
possibilities: orthogonal or non-orthogonal, complex or real, shape. We decided to use the nonorthogonal transform because we did not expect very sharp variations in the absolute values of
the wavelet coefficients. Different wavelets were tested: Morlet, Paul of order 4, (both complex)
and Derivative of a Gaussian (DOG) of order 2 (real). In all cases the wavelet power spectrum,
defined as |(W x)(s, n)|2, was plotted on a time-scale mesh. Although similar results wereobtained for the three wavelets, the Morlet wavelet was selected because of the best scale
resolution.
Table 2. Morlet wavelet index and scale and Fourier period and frequency
Index
(j)
0
1
2
3
4
..
8
9
10
11
12
13
14
15
..

Scale
(s)
3.2
3.8
4.5
5.4
6.4
..
12.8
15.2
18.1
21.5
25.6
30.4
36.2
43.1
..

Period
(µs)
3.3
3.9
4.7
5.6
6.6
..
13.2
15.7
18.7
22.2
26.4
31.4
37.4
44.5
..

70

Frecuency
(kHz)
302
254
214
180
151
..
76
64
53
45
38
32
27
22
..

4.2 Wavelet scale and Fourier frequency
It is convenient, from the computational point of view, to express the scales as potentialfunctions with fractional exponents and base 2. In all the cases the scales were taken as:
s j = s0 ⋅ 2 j δ j , with j = 0, 1, …, J, the lower scale being so = 2 δt, where δt is the sampling period of
the signal (1.6 µs) and J = δ j −1 log2 ( N δ t / s0 ) is the index that allows to obtain as highest scalevalue smax = N ⋅ δ t . Table 2 shows the scales used for a sampling period of 1.6 µs that was thesame for all the seismic simulation signals, and δj = ¼. To find the relationship between the
wavelet scale and the period we performed the wavelet transform of a cosine wave of known
frequency with a particular wavelet function (Eq. 7). Then, the scale at which the wavelet power
spectrum reaches the maximum is calculated, and this period is known as the equivalent Fourier
period [16]. Using the Morlet wavelet, with ωo = 6, the scale has to be multiplied by 1.03 toobtain the period.

Fig. 1. Typical wavelet power spectra. Test D2. Time in microseconds.
Figure 1 shows typical wavelet power spectra from signals obtained in the seismic simulation
D2. The highest values of absolute wavelet coefficient occur at characteristic scales. This was ageneral result.
4.3 Statistical clustering (K-means Algorithm)
In order to clearly recognize the wavelet scales related to different processes the highest
value in each CWT matrix was considered. The wavelet scale location of these maxima was71

defined as a component of the data vector, to which the one-dimensional (1D) K-means
algorithm was applied [15]. We found that the maxima occurred at the scales: 12.8; 15.2; 18.1;
21.5; 25.6; 30.4; 36.2. Figure 2 displays a graph where the time (horizontal axis) refers to the hit
occurrence and the wavelet scale (vertical axis) refers to the scale, for which the absolute valueof the wavelet coefficient is highest for the given hit. We then applied the K-means algorithmwith different number of centroids k, finding that for k = 2 the scales 36.2 and 18.1 (see Fig. 2)
were the most significant. In fact, the centroids were located at these positions and they can beclearly seen as two horizontal lines in Fig. 2. From previous works [1-3], we knew that highscales are associated with noise. For this reason, the scale 18.1 was pre-assigned to concrete
fracture. This result was validated in our work as it is explained in Section 4.4.

Fig. 2. 1D Clustering applied to the scale location of the maximum absolute values of CWTversus the time of hit occurrence. Test D1 in Table 1.
4.4 AE Energy (AE)The cumulative dissipated energy in damage processes, obtained by integrating Fr over x ,that is to say DE, is considered an appropriate parameter for characterizing low-cycle fatigue
damage in RC components and is used for well–established RC damage indices [17]. Correlation
between the AE energy obtained from filtered signals (AE) and DE was previously performed
and demonstrated in [1,2]. Our objective is to perform the same correlation with signals filteredwith the method proposed in the present work. For that, from the wavelet transform of the signal
we picked up the coefficients of scales assigned to fracture and then reconstructed the filteredsignal (Eq. 9). AE of the filtered signal was calculated; this procedure is necessary for nonorthogonal wavelet functions. For all the tests analyzed in this work the best correlation between
cumulative AE and DE was obtained when the 18.1 scale-53 kHz band was considered. This
result could not be improved with other bands or by adding different bands. Figure 3 shows as an
example the evolution of cumulative AE and DE for test D1, considering for AE only the scale
18.1, only the scale 36.2 and the whole signal (not filtered signal).
Figure 4 shows the comparison of cumulative AE of the band 18.1 and cumulative DE for all
the seismic simulation considered in the present work. It can be seen that both curves follow
similar trends, like in paper [1,2]. The advantage of the present results is that they were obtained72with a more direct and faster procedure, which is implemented in a totally automatic way and a
deeper understanding is possible.

Fig. 3. Correlation between cumulative AE (blue) and cumulative DE (green) for test D1.
A portion of the mechanical energy provided by the actuator dissipates into heat; this portion
can be obtained by integrating the viscous force over x. Another portion of the mechanicalenergy dissipates into damage; it can be obtained by integration of the restoring force over x.
This dissipation occurs when irreversible microstructural changes take place: fracture processes
in concrete and deformation processes in the reinforcing steel. These damage processes
altogether contribute to DE. On the other hand, the cumulative acoustic energy AE is mainly due
to the fracture of concrete and friction between faces of previous cracks. By comparing the blue
and the green curves in Fig. 4, we can think that in the case that a jump in DE is notaccompanied by a similar jump in AE it is because the deformation of reinforcing steel isprevalent. When a jump in AE occurs that is not accompanied by a jump in DE, this would
indicate a process connected with previous fracturing; it could be ascribed to friction between thefaces of previous fractures. In general we can infer from the graphs that concrete fracture isprevalent and/or fracture of concrete and deformation of reinforcing steel are correlated; thiswould explain the similar trends. These facts deserve to be quantitatively established in futurework.
5. Conclusions
1. The wavelet power spectra obtained with the Morlet wavelet reaches maximum values
mostly at certain scales (frequencies). This was observed visually and then fully
automatically corroborated using the K-means algorithm.
73

Fig. 4. Normalized AE (blue) reconstructed with scale 18.1 and DE (green). Curves show theevolution of cumulative energy.
2. We could identify the AE frequency band associated with concrete damage mechanisms(opening and extension of new cracks and friction of previous fracture faces) in
reinforced structures submitted to load tests simulating moderate earthquakes.
3. We found that filtering the acoustic signals by the CWT and reconstructing the signal bytaking the coefficients corresponding to the scale 18.1 (frequency 53.3 kHz) we obtained
a quite good correlation between the cumulative AE and DE for all the seismic
simulations considered in the present work.
Acknowledgements
This research received financial support the Education Ministry of Argentina(Fortalecimiento De Redes Interuniversitarias V), from the local government of Spain,74

Consejería de Innovación, Ciencia y Tecnología, Project P07-TEP-02610 and from the European
Union (Fonds Européen de Dévelopment Régional).
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Evaluation, 31, (2), 169-180.
14. R. Gutkin, C.J. Green, S. Vangrattanachai, S.T. Pinho, P. Robinson, P.T. Curtis (May 2011),“On acoustic emission for failure investigation in CFRP: Pattern recognition and peak frequency
analyses”, Mechanical Systems and Signal Processing, 25 (4), 1393-1407.
15. E. Zitto, M. Scaramal, F. Sagasta, R. Piotrkowski, A. Gallego, E. Castro, (2012), “AE Signal
Processing in Dynamical Tests of Reinforced Concrete Structures”, EWGAE 30, Granada,
Spain. www.ndt.net.
16 S. D. Meyers, B. G. Kelly and J. J. O’Brien (1993), “An introduction to wavelet analysis inoceanography and meteorology”, Monthly Weather Review, 121, 2858-2866.
17. Y.J. Park, A.H.S. Ang, (1985), “Mechanistic seismic damage model for reinforced
concrete”, Journal of Structural Engineering, ASCE, 111, 722-739.

75

Recent Advances of AE Technology for Damage Assessmentof Infrastructures
Tomoki Shiotani
Graduate School of Engineering and Graduate School of Business, Kyoto University
Nishikyo, Kyoto, Japan
Abstract
Acoustic emission is a powerful NDT to detect the internal cracking process of civil engineering materials qualitatively, which cannot be readily realized by other NDT methods. Identified AE sources through the AE measurement help reveal further information on fracturing behavior based on the energy-related AE parameters, slope of the waveform as well as frequency
features of the waveform. From these experimental findings, several promising AE procedures
may be applied for the damage assessment of actual infrastructures. These AE activities of insitu applications are typically ‘secondary emission’ produced by friction among existent cracks
or damage. Thus, fracture-originated AE activity, primary AE activity, is not applicable to theinfrastructure damage assessment. In this paper, several AE findings toward in-situ infrastructural assessment for their current status are mainly described. Specifically AE parametric features
useful for the damage assessment are summarized; elimination of ambient noise when utilizing
active mobile loads is clarified; strategic AE monitoring procedure for a large structure isdemonstrated; several promising AE waveform features are introduced with fatigue tests of concrete bridge deck; and the latest on-going studies for a global inspection over the whole of structures with AE technique are demonstrated.
Keywords: Damage assessment, infrastructures, secondary AE activity, transfer function, quantitative NDT
1. Introduction
Acoustic emission (AE) is an elastic wave generated due to crack occurrence or crack coalescence. As AE activity characterizes such fracture states as early, intermediate and final stage, it
has been employed as a precursor alerting the impending failure of materials or structures. When
the AE technique is applied for damage assessment in infrastructures, e.g., civil engineering
structures, one shall pay his/her attention to other types of AE activity in distinction from theformer AE activity, namely primary AE activity. In Fig. 1, schematic behavior of damage progress as a function of time is demonstrated, where the circles show the time of AE monitoring as
in the most ideal case for an important infrastructures but in actual, AE monitoring has been
conducted so far only for the experimental case or the case of crucially damaged important structures. As in the open circles of the chart, AE monitoring has sometimes been utilized as to expect
a real time trace for the eventual failure. The author has been involved in the rock failure monitoring on the basis of this expectation, and reported that internal deformation of rock slope could
be evaluated by several promising AE parameters, predicting resultant final failure [1-4]. Indeed
this continuous AE monitoring is in demand for important infrastructures; however, it was forthe limited number of infrastructures, and the actual requirement of AE monitoring lays in otherpurposes i.e., evaluation of current condition is the highest demand and which shall be conductedin such a short period as a coupe hours or several days at the longest. In this monitoring, obtaining AE activity is not from newly crack occurrence but emerged due to reversible motion of existent damage. This is referred to as secondary AE activity while the other is defined as primary
J. Acoustic Emission, 30 (2012)76© 2012 Acoustic Emission GroupAE activity as aforementioned. In this paper, with secondary AE activity, recent advances of AE
technology for damage assessment in infrastructures are drawn. Specifically in the following: AE
parametric features being useful for the damage assessment are summarized; elimination of ambient noise when utilizing active mobile loads is clarified; strategic AE monitoring procedure fora large structure is demonstrated; several promising AE waveform features are introduced with
fatigue tests of concrete bridge deck; and the latest on-going study utilizing the transfer functionof AE waveforms is demonstrated.

Microscopic	
  failure

DamageRuptureMesoscopic
failureMacroscpic	  failureElapsed	
  timeFig. 1. General AE behavior approaching final failure.
2. AE based damage assessment parameters
2.1 Load ratio, Calm ratio, RTRI, i-Load ratio and i-Calm ratio derived from Kaiser effect
Under incremental cyclic load application to materials, AE activity can be represented withdamage progress as shown in Fig. 2, where the damage progress is assumed to be classified into
four different levels: intact; almost intact; slightly damage; and heavily damage. Due to crack
occurrence, AE activity starts to be observed e.g., one in the first cycle. And in subsequent second load cycle, the AE activity starts at the load level being equivalent to maximum prior load.
This coincidence between pre-stress and applying stress is referred to as Kaiser effect and havebeen studied in civil engineering to evaluate initial rock stress (e.g., Seto et al [5]). During thethird load cycle in which the material exhibits slightly damage, the onset of AE appearance is ata smaller level than previously. Decrease of effective areas against external force or accumulation of microcracks within materials appears to play a significant role in this fact. Considering
the relation of the stress showing the onset of AE activity to the stress experienced, such damage-indices as Felicity ratio (Fowler [6]), CBI ratio (Yuyama et al [7]), and Load ratio (JSNDI[8]) have already been proposed. With damage evolution, not only the AE activity during uploading, but that during unloading becomes more intense. Therefore, it is also important to focus
on unloading processes (Shiotani et al [9]). Accumulation of shear type of cracks seems to be
attributed to this phenomenon. The ratio of accumulated number of AE activity during uploading
to that during a whole loading cycle is referred to as Calm ratio (JSNDI, [8]).
For the ratios mentioned previously, they may be difficult to apply for in-situ monitoringsince evaluation of the maximum stress in which materials have experienced is not readily assumed. Thus, a RTRI ratio has been proposed instead (Luo et al [10]). The RTRI ratio can be77

defined with regard to any measured mechanical parameters, such as stress/load,strain/deformation. Specifically it is the ratio of the value of the parameter when AE is initiateddivided by the maximum value of this parameter during the inspection period, regardless of the
maximum load in the whole loading history of structures.
P4

Load

AE activity during loading
AE activity during unloading

P3

P2
P1

Intact state

Almost intact

Slightly damaged

Heavily damaged

Progress of damage
Fig. 2. AE activities with application of incremental cyclic loads.

Fig. 3. Identification of the onset of AE activity in i-Load ratio.
Using these AE damage indices, current damage status of structures can be evaluated qualitatively; while recovery, suggesting how much degree the structures were repaired, could not be
quantified. Since both Calm ratio and Load ratio are relative values, normalized by an accumulated number of AE activity during the whole loading cycle or by the maximum value of referredparameter like pressure, respectively, these indices do not take into account substantial differences in the obtained number of AE hits before and after repair. Therefore, the recovery rate has
been proposed considering the numbers of AE hits obtained before and after repair [11]. Specifically to obtain the recovery rate, Load and Calm ratios are modified based on the AE activity observed as in demonstrated in Fig. 3 for the modification of Load ratio. Here, two typical AE hits78

vs. load curves (before and after repair) are shown. Conventional “Load ratios” are defined by
dividing Pb, or Pa values (before and after repair, respectively) with the previous maximum load,
Pmax. Instead, we introduce the threshold value (in percent a) to determine the onset of cumulative AE curve. Suppose total AE hits before repair, TAEb, is 100 and we set a to be 10%. Thus,
the threshold hit value or TAEt is 10. This defines PAEb for AE hits vs. load curve before repair.
After the repair, we use the same threshold value as before, i.e., 10 in this case and we obtain
PAEa. By dividing PAEb and PAEa with the previous maximum load, Pmax, we define improved
Load ratios or “i-Load” ratios. As found we employ an identical condition for judging the onsetof AE curves for before and after repair.
For modification of Calm ratio, we multiply a conventional Calm ratio with the ratio of cumulative AE activity after repair to that before repair. This allows us to factor in the variation of
AE hits before and after. This new index is named improved Calm ratio or “i-Calm” ratio.
2.2 Grade providing fracture types
Figure 4 illustrates the variation of waveforms with fracture processes. In the process of progressing fracture within materials, tensile type of cracks would be mostly obtained in early stage,then mixed mode of tensile and shear type, followed by shear type of cracks (Yuyama et al [12]).
It is known that tensile fracture velocity ranges from P-wave to 60-70% of S-wave velocity andshear fracture velocity would be 60-70% of S-wave velocity (Kanamori [13]). Corresponding to
the variation of these crack types, it is expected that fracture velocity becomes smaller with progress of fracture although dislocation-scale becomes larger. This implies two important facts in
AE waveforms: one is that gradients of ascending parts of waveforms become smaller with theprogress of fracture; and the other suggests that low-frequency components would be dominantwith progressing fracture. Therefore ‘grade’ is introduced [14], which is defined as the peak amplitude divided by the rise time. The large values of grade suggest the early stage of fracture
where cracks of tensile type are dominantly generated, while smaller ones imply the approach tothe final stage where cracks of shear type primarily occur (see Fig. 4). Indeed as shown in Fig. 5,
the grade due to tensile type of failure exhibits the largest value followed by mixed type and theshear type of fracture. It is noted that applicability of the gradient has also been studied on theprocess of concrete materials (Iwanami et al. [16]) or on classification of crack types in combination with average frequency [17].

Tensile crack

Mixed mode crack■ Early stage■ Intermediate stage

Shear crack■ Final stage

Fig. 4. Variation of AE waveforms with damage progress.
792.3 Improved b-value obtained from peak amplitude distributions
Because AE peak amplitude is associated with the scale of fracture, the b-value defined as aslope of the amplitude distribution is known as an effective index related to the states of fracture(Mogi [18], Scholz [19]). Larger b-values show the state of materials where microscopic fractures occur more predominantly than macroscopic fractures, whereas smaller b-values indicatethat the occurrence of macro-fractures prevails. Since the b-value was originally defined in seismology i.e., obtained in a long time span with a lot of numbers of observatories, and therefore
the precise evaluation was possible, being different from the AE applications where there existed
issues to be addressed. Thus, instead of the seismic b-value, an improved b-value (Ib-value) has
been proposed (Shiotani et al [1]), which is suitable for AE applications dealt with any kinds andscale of materials/ structures.

Fig. 5. Variation of grade (dB/µs) due to different types of fracture in four point bending test of
RC. Fracture type characterization was implemented by SiGMA procedure ([15]).
In the improved b-value analysis, the number of AE data to be used should be first set.
Roughly, two methods for determining the calculation number have conventionally been employed: a) accumulated numbers from the beginning data; and b) numbers per unit time. In theformer case, the number of data used to determine the b-value is increasing with elapsed time. In
the latter case, because the AE activity increases exponentially with approaching final failure, it
is apparent that the number of AE data determining the b-value is differed in each unit time. Statistically it is so important to use the constant number of AE data when determining the b-valuethat the number of AE data: b, is introduced as in (1) to improve the calculation of the b-value:∞∫ n(a)da = β(1)0where a is an amplitude, n(a) is a number of AE at da and b is a number of AE data. Values ofb between 50 and 100 are considered appropriate as suggested by other studies (Shiotani et al[20]). In actual, in the improved b-value calculation, a fixed number of b between 50 and 100
should be used throughout the whole experimental data.

80

The value of AE peak amplitude varies with monitoring conditions such as sensor installations, propagation media, and occurrence locations, resulting that the AE amplitude distributionis also dependent on these conditions. In order to obtain the b-value qualitatively, a method todetermine the amplitude interval being independent on the distributed amplitude is necessary. In
the improved b-value analysis, the range of AE amplitude is determined based on such statisticalvalues as the mean m and standard deviation σ, where the upper amplitude w2 and lower w1 areformulated as m+a1σ and m-a2σ, respectively. We define the accumulated numbers of amplitude
over w1 and w2, as N (w1) and N (w2), which is obtained by,
N ( w1 ) = N ( µ − α 2σ ) = ∫∞n(a)da(2)n(a)da(3)µ −α 2σN ( w2 ) = N ( µ + α1σ ) = ∫∞µ +α1σwhere the interval of amplitude analyzed would be (a1+a2)s. Then the Ib-value is given by,

log10 N(w1 ) / N(w2 )(4)(α1 + α 2 )σwhere a1 and a2 are empirical constants. It is noted that since Ib-value is calculated on the basisof decibel unit. When comparing with seismic b-value, the Ib-value shall be multiplied by a coefficient of 20. The Ib-value has successfully been applied to evaluate the developing process offracture in such materials as soil (Shiotani et al. [2]), rock (Shiotani et al. [21]), and concrete(Shiotani et al. [20, 22, 23]). Several similar attempts have so far been found elsewhere [e.g., 24]
but it is noted that improved b-value is the original employing constant numbers of AE data in
combination with statistical values of amplitude distribution to the AEs’ b-value analysis. Also it
is very important to recognize when applying the AE technique to low attenuation materials withplural numbers of AE sensors or to the materials where numerous macroscopic failures happen
over the area of interest that: for the former case, as the b-value analysis shall be carried out
based on the AE source magnitude i.e., calculation based on a specified sensor shall be avoided
and source magnitude, and AE source amplitude, average amplitude with all contributed sensor
to the source location, or AE amplitude with first arrival sensor shall be employed; and for thelatter case, in addition to the former consideration, the b-value shall be obtained individuallywith sectioning area of interest into several parts.
Ib =3. Acceleration of mobile traffic with train passage
When secondary AE monitoring is carried out utilizing such mobile loads as railway traffic[25, 26], it is worth understanding that what frequency components are directly generated due tothe train passage. In order to know the frequency components, accelerometers were placed on thebottom of an elevated arch brick bridge. The acceleration due to the train passage was measured
with two accelerometers (750WI, TEAC) of which the frequency response ranges from 3 Hz to
10 kHz. To study higher frequency spectrum, the acceleration signals were recorded in a signal
recorder (GX-1, TEAC) with a sampling of 200 kHz.
As a result, the frequency spectrum showed a slightly difference with the direction of train
movement, namely in-coming trains or out-going trains. The frequency due to the train passage
ranged from 80 Hz to 130 Hz. Figure 6a shows a typical waveform detected and its FFT result
due to train passage as shown in Fig. 6b.
81

It is generally recognized that this range of frequency, namely 50- 180 Hz, is far below thefrequency response of AE monitoring system, suggesting no influence on AE monitoring. To
verify this assumption the experimental study with a reaction type shaker were conducted.
5.0

0.7

0.5
mV ・sec

Amplitude (mV)

0.6
2.5

0.0

0.4
0.3
0.2

-2.5

0.1
-5.0

0
0

0.05

0.1

0.15

0

100

200

Time (sec)

300

400

500

Frequency (Hz)(a) Wafeform(b) Fourier spectrum
Fig. 6. Typical detected AE signal due to train-induced acceleration.
Three frequencies (with the force output in newtons), 30 Hz (300 N), 40 Hz (160 N) and 50
Hz (100 N), were excited by a reaction-type shaker (Model F4/Z820WA, Wilcoxon Research,Inc.). The shaker used has a reproducible frequency ranging from 10 Hz to 7.5 kHz. A brick wall
was employed as a propagation medium, which has a height of 1,530 mm, a width of 1,530 mm
and a thickness of 430 mm. The shaker was firmly stud-mounted on one side, and an AE sensorof 60 kHz resonance (R6, PAC) was set on the other side of the brick wall with wax, resulting in
propagation distance of 430 mm. AE signals detected were recorded with Mistras AE system under the condition of 2 MHz sampling and a 1k words length. It is noted that the frequency response of the AE monitoring system ranges from 10 kHz to 10 MHz.

0.08
0.06
0.04
0.02
0.00
-0.02
-0.04
-0.06

Volt ・sec

Amplitude (volt)

Surprisingly, although the accelerations were excited with a range of frequency between 30
Hz to 50 Hz, showing far below frequency range of that in in AE monitoring system, AE waveforms detected had frequency peaks ranged from 7.8 kHz to 9.8 kHz, suggesting increase of twoorders of magnitude in the frequency of excitations. A typical waveform and its FFT result incase of 50 Hz excitation can be found in Fig. 7. A peak frequency was observed at 9.8 kHz incase of 50 Hz.

0

500

1000

Τ ime (µsec)

0.014
0.012
0.010
0.008
0.006
0.004
0.002
0.000
0

20

40

60

80

100

Frequency (kHz)Fig. 7. Typical waveform and FFT result in acceleration of 50 Hz.
This suggests that even when the lower frequency accelerations than that of the response of
AE system were generated, the AE sensors detected the induced accelerations as AE signals for82the case of large energy. From this discussion, it leads to a conclusion that train passage or compatible passage by vehicles has a potential to generate indirect AE signals that have a peak frequency around the lowest limit of the AE system, namely 10 kHz in our experimental condition.
Thus, to avoid such indirect emissions due to passage-induced noise superimposed to AE signals
due to crack behavior, spatial filter using AE source location might be crucial since the frequency of the secondary AE activity includes these lower range of AE frequencies.
4. Strategic phased AE monitoring
With sparsely arrayed AE sensors at low frequency, wide range monitoring for infrastructures is possible, suggesting most-likely deteriorated areas, while more precise investigation ispossible when employing high-frequency resonant AE sensors with dense arraying. In this way
an objective area and the resolution of monitoring can be arranged by using suitable type of AE
sensors according to the demand. The following is an example when AE monitoring was applied
to such large infrastructures as bridges, where the preliminary investigations showed little deterioration in a 45-m bridge span by both surface-crack observation and physical tests of excavatedcores [27].

Fig. 8. AE monitoring details with crane mobile load. Positions both of AE sensors and strain
gauges are shown.
In order to know the relatively damaged area for the longitudinal structure, a linear arrangement of AE sensors with a space of 1.5 m over 45 m in a longitudinal direction is carried out asshown in Fig. 8. A total of 28 sensors of 60-kHz resonance are placed on the bottom surface ofthe bridge with wax. The AE testing was performed while moving a heavy vehicle of 20 tons
crane over the bridge. Based on the AE activity the part of structure being most likely to exhibit
higher degree of damage than the other areas was followed to select the detail investigation, or
end up in this first monitoring when no active parts of AE were found. In the former case, theselected area is again measured for AE activity with a denser sensor array in 2D. Using the 2D
arrayed AE sensors, a surface ultrasonic examination is also conducted in order to investigate theP-wave velocity of concrete at the area of interest.
As stated already, in order to calculate the Calm ratio, the AE activity should be correlatedwith a mechanical parameter. In this case, it was conducted by the measured strain at the middle
point of the bridge span and the AE activity recorded by the two sensors placed closest to thecenter (#13 and #14 of Fig. 8).
83

The number of hits for three different trips of the crane exhibited the resultant Calm ratios
ranging from 0.3 to 0.45 for any individual passage of the crane, indicating serious damage according to past studies [26].
40
AE sensor locations

AE event

30
1st trip
3rd trip
5th trip

20

10

0
0

5

10

15

20
25
Distance (m)

30

35

40

Fig. 9. 1D AE sources along with repeated mobile loads.
One-dimensional AE sources are determined by the 28 linearly placed sensors. The locationsof AE sensors and resultant AE events activity within each 10-cm segment are shown in Fig. 9.
Intensive AE activity was always emerged at about 3 m, irrespective to the trip, and therefore anarea around 3 m in the figure was chosen for the subsequent detail monitoring, where the separation distance of sensor is 1.5 m, forming the examined area of 3 m by 3 m of three parallel arraysof three as shown in Fig. 10. Nine sensors were used for this detail test.

1.5m

1.5m
1.5m
1.5m

Bottomsurfacesensors

Fig. 10. Arrangement of AE sensors (60 kHz resonant) both for 2D AE monitoring and UT.
As shown in Fig. 11, remarkable numbers of AE events ranging from small to large scale
were obtained. Specifically those appeared along the tires’ trace of the mobile vehicle employed.
Velocity tomogram leads to a characteristic low velocity zone, developed diagonally in the areaof interest. The low velocity zone might be attributed to a subsurface defect, although the depth
cannot be easily determined. Considering the fact that the AE events were intensively generated
on this low velocity zone, it can result from concrete cracks, delaminations of different layers(e.g. asphalt on concrete) or friction between the tendon ducts. Also matrix concrete would bepossible origins to induce AE activity due to the loading. As found in this phased AE monitoring,84

this sequential investigation, started with global AE monitoring then local AE monitoring, followed by detailed measurements of ultrasonic P-wave velocity are very useful in characterizing
the quality of large-scale concrete structures.

22.5
23.0
23.5

Y location (m)

24.0
24.5
25.0
25.5
26.0
26.5
27.0
27.5
-1.0

0.0

1.0

2.0

3.0

4.0

X location (m)Fig. 11. Identified 2D AE sources along with mobile load (see circles) and velocity tomogram
obtained from surface UT measurement. The vertical direction of the figure corresponds to thelongitudinal direction of the bridge.
5. Evaluation of fatigue damage of concrete bridge deck
5.1 Experimental set up
Repeating wheel loading, particularly by heavy vehicles, is known to be the major accelerator to cause serious fatigue damage of reinforced concrete (RC) deck on road bridges [e.g., 28].
A visual investigation from the bottom surface of the deck so far assesses the integrity of thebridge deck; however, this visual evaluation is only possible after the emergence of surface cracki.e., internally evolving cracks due to rebar corrosion or delamination among layers are only recognized when they develop into the critical stage as to be observed on the surface. In addition
this visual observation cannot be applied for the deck, of which the bottom surface has alreadycovered with such reinforcing sheets as steel and FRP [e.g., 30]. In the following, AE testing is
experimentally applied to the fatigue failure of RC deck with steel-wheel loading apparatus. Notethat AE monitoring is conducted applying static loads when repeated wheel loading is suspended.
Specifically the static loads are applied after the initial loading, 10,000 loading and 20,000 loading. In addition to the AE monitoring, internal conditions were also assessed by ultrasonic tomography. The experimental detail can be found in [29].
The RC test specimen being subject to the cyclic load, having dimensions of 3,000 x 2,200 x
180 mm was set on an experimental apparatus. To accelerate the subsequent fatigue damage,85

initial load up to 150 kN was applied to the center of the specimen, followed by repeated wheelload of 100 kN. The wheel loading is applied to the central part in the longitudinal axis up to
20,000 times of passage. After each stage of initial state, 10,000 times and 20,000 times of passage, the test was stopped for elastic wave tomography tests to obtain the internal velocity distribution. Subsequently incremental cyclic loads by 20 kN up to 100 kN are applied at the center ofthe specimen to induce the secondary AE activity from existent defects. AE monitoring was conducted with 12 AE sensors of 60-kHz resonance, placing six on the top and six on the bottom asshown in Fig. 12. The signals detected by the sensor were amplified by 40 dB, and processed andrecorded by AE monitoring system (SAMOS, PAC) with 1 MHz sampling and 2k samples. With
regard to the tomography, totally four sections of 50 x 18 cm each avoiding the loading area wasset for the measurement as shown in Fig. 13. In each section, elastic waves were generated by thepulser on both surfaces placing five-sensor with a space of 50 mm on the other surface in turn.
The sensors and monitoring system used are the same as in the AE monitoring as shown previously. To input transit time of the entire ray paths into the tomography program [31], the first
motion of the detected waves was extracted.

Fig. 12. Arrangement of 12 AE sensors.
5.2 Ultrasonic tomography
As a typical case, velocity distributions in L1 section (see Fig. 13) are shown in Fig. 14. In
general, concrete with the P-wave velocity of more than 3,500 m/s is evaluated as good, and incase of more than 4,500 m/s as excellent, while those of less than 3,000 m/s are considered aspoor and as very poor when less than 2,000 m/s [32]. As seen in Fig. 14, the evolution of damage
areas due to increase of wheel loadings could be identified with low velocity zones denoted by86yellow to red colors, corresponding to less than 3,000 m/s in the velocity. As in our past studies[e.g., 33], the damage areas could be identified with low velocity zones of elastic wave.

Fig. 13. Sensor array of elastic wave tomography.

(a) after initial loading(b) 10,000 passage(c) 20,000 passage
Fig. 14. Tomogram in L1 section. Legend shows the velocity in m/s.
5.3 Limitation of solely damage-evaluation with Calm ratio and RTRI ratio
The Calm and RTRI ratios both from incremental cyclic load tests can be found in Fig. 15
and Fig. 16, respectively. Again, AE measurement was conducted with incremental cyclic loads
following the specified numbers of wheel loading. Therefore no mechanical noise due to wheelloading was included in these AE activities. In these figures, diamonds denoting the case of ‘after initial loading,’ exhibit the AE activity in the second loading after the initial wheel loadingwith 150 kN, i.e., the specimen had already experienced the load up to 150 kN. In the same way,squares indicate the result when 10,001st loading and 20,001st for triangles. It is noted that if
damage degree could be obtained quantitatively based on the past stress-experience, the ratios ina certain situation, e.g., 10,000 passage in the figures, must stand as an intrinsic value, irrespective to the load applied; however contrary to this assumption, the ratios varied corresponding to
the load applied. Specifically in the Calm ratio as shown in Fig. 15, a large applied load gives
large Calm ratio, indicating the development of damage due to load increase. For the RTRI ratio
shown in Fig. 16, large RTRI ratios, implying damage mitigation, were obtained as load increased. In either case, both ratios could not well quantify the damage by their values. These indices are plotted in Calm-RTRI ratios chart as shown in Fig. 17. In the window, provisional lines
are drawn to classify the damage. As seen, the plots in the case of initial loading reveal in thebottom-right indicating intact/minor damage, and these plots shift to the top-left as passage
87Calm ratio

proceeds, implying the progress of failure. Combination plots of these two indices of Calm and
RTRI ratios have a potential to quantify the fatigue damage.

1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0

Initial loading
10,000 passage
20,000 passage
In-situ #1
In-situ #2

20kN
	
 

40kN

60kN

80kN

100kN

Load (kN)
Fig. 15. Calm ratios in three damage cases.

Fig. 16. RTRI ratios in three damage cases.
5.4 Peak frequency
The frequency features such as peak and centroid provide valuable information on materials’
damage status [34]. This fact was readily confirmed when controlled artificial signals, of which
the frequency components are known already, were excited. In this case unique variations of frequency component through travelling are only dependent on the materials properties. Ultrasonicwaves lose their high frequency components when they travel through the media, influenced by
the attenuation/scattering effect from defects, and as a result the lower frequency components ofdetected signals are enhanced for the severely damaged cases. As for AE waveforms, however,resultant frequency components are influenced also by the source types, namely fracture behavior causing various displacement-time function in addition to the materials properties, i.e., thefrequency components of the measured AE signal do not always reflect the materials damage
88

condition alone. Fortunately in this study we are dealing with the secondary AE activity obtained
mainly from identical types of source, namely, friction between existent crack interfaces, the frequency components of detected AE waveforms can be regarded as influenced principally by thequality of propagation media. Figure 18 shows the average of peak frequencies of detected AE
waveforms in each case. Undoubtedly the peak frequency decreases with damage progress, and it
was confirmed that the peak frequency of measured AE signals could be a clue to provide thedegree of damage.
1.0

Initial loading
10,000 passage
20,000 passage
In-situ #1
In-situ #2

0.9

Calm ratio

0.8
0.7

0.6
0.5
0.4
0.3

0.2
0.1
0.0

0.0

0.2

0.4

0.6

0.8

1.0

RTRI ratio
Fig. 17. Calm and RTRI ratios in three damage cases.

Average peak frequency
(kHz)

140
120
100
80
60

40
20
0

Initialloading

10,000passage

20,000passagein-situ #1 in-situ #2

Fig. 18. Average peak frequencies of AE waveforms.
5.5 Specimens cut from actual bridge deck
Elastic wave excitations for tomography and AE measurement were both conducted for two
specimens cut from in-situ concrete deck. In this particular case, one of the most complex deterioration patterns to investigate is taken. Specifically to reinforce the deck after severe deterioration, an additional concrete layer over the original deck was cast; however, invisible lateralcracks were also generated as shown in Fig. 19. The specimens are referred to as No. 1 for seriously damaged case and No. 2 for intermediately damaged case based on the sectional observa89

tion. For these in-situ specimens, Calm ratio and RTRI ratio are given in Figs. 15 and 16, respectively. With in-situ specimens, variations of both ratios due to applied load could be observed as before; it is noted that two results of in-situ specimen #1 & #2 enclose those of 10,000
and 20,000 passage damage, and those are quite different from that of initial loading.
As noted earlier, in-situ specimen #1 appeared to be more deteriorated than that of #2; however, the findings from Calm ratio, RTRI ratio and the combination of both ratios (see Figs. 1517) could not separate the damage of the two specimens adequately. This might be caused by theheterogeneity of damage evolution. The damage condition observed from the side could not always reflect the internal damage.

Fig. 19. Side view of in-situ specimen #1 cut from actual concrete bridge deck.
The average peak frequencies from the in-situ specimens again show their similarity (Fig. 18).
Low average frequencies were obtained from both of the in-situ specimens. Although the evolution pattern of the cracks were different from ones of the wheel loading test, the average peakfrequencies in the in-situ cases is equal to or even less than the value of 20,000, suggesting thein-situ specimens were damaged as equivalent or more to the case of 20,000 passage.
As described above, using sparsely arrayed AE sensors, followed by extracting AE frequency
features, global investigation of bridge decks’ integrity can be carried out. Once the area of interest is determined, further detailed information as sectional damage can be visualized by ultrasonic tomography procedure.
6. New approach for assessing damage with transfer functions of AE waveforms [35]The frequency characteristics of AE waves could imply the damage condition of infrastructures in comparison to other features of elastic waves. In this case, exact quantitative evaluation
for the damage is only possible in case where every AE waves travelled in the compatible distance. As the frequency characteristics of AE waves are also known, which depend on the resultant propagation distance, obtained frequency does not always demonstrate the damage condition,
providing the rough evaluation [36]. Thus as one of on-going studies, transfer functions of propagation media are briefly described here to quantify the damage with AE waveforms.
6.1 Conceptual configuration
AE waveforms are obtained as convolution of functions of source, propagation media, sensor
and acquisition system in the time domain, and those frequency responses can be formulated by asimple multiple equation in the frequency domain:90

X ( f ) = U ( f )T ( f ) D( f ) S ( f )(5)Here X(f), S(f), D(f), T(f), U(f) are Fourier transforms of detected AE waveforms, AE source,
propagation media, sensor and acquisition system, respectively. D(f) in Eq. 5 is the target toquantify the damage; however, as S(f) is not readily obtained in AE technique, an approach toidentify D(f) irrespective to source time function is crucial. In AE application, plural numbers of
sensors are employed to locate the AE source, and therefore the comparison of waveforms detected among different sensors for an AE source could suffice this requirement as in Eq. 6 assuming the frequency responses of all the sensors employed are compatible.
X 2 ( f ) T2 ( f ) D2 ( f ) D2 ( f )=⋅≅X 1 ( f ) T1 ( f ) D1 ( f ) D1 ( f )(6)

On the other hand, when D(f) is dependent on propagation media attenuation, Eq. 7 can be defined as well, where f is a frequency (Hz), V is a P-wave velocity (m/s) and Q is a normalized
value demonstrating attenuation rate.
⎛ πf ⎞d ⎟⎟D( f ) = exp⎜⎜ −⎝ VQ ⎠(7)⎛ πf⎞
X i ( f ) Di ( f )≅= exp⎜ −Δd i ⎟⎜⎟X 1 ( f ) D1 ( f )
⎝ V Q
⎠

(8)

By combining Eq. 6 and Eq. 7, Eq. 8 is obtained.

Δd i = d i − d1 (d i ≥ d ), (i = 2,, n)Equation 8 shows that a function of frequency response can be expressed by an exponential function dependent on the difference of distance Δd and frequency f.
6.2 Preliminary examination
As shown in Fig. 20, concrete specimen of 500 x 500 x 100 mm was prepared placing four
AE sensor of 60 kHz resonance at the four corners on the bottom surface. Ultrasonic wave excitation was made using the wideband AE sensor (1045S, Fuji Ceramics), where electric pulse wasmade by PUNDIT (-500 kHz, CNS Electronics) .

Fig. 20. Sensor arrangement and positions of pulse excitations.
To examine the potential influence of heterogeneity of the concrete specimen on the transfer
function in the intact specimen, excitations at the intersectional points in the line between Ch1
91and Ch4 sensors were conducted with Ch2 and Ch3 operated as receivers. Switching of sensor
pairs was also used. Figure 21 shows the ratio of Ch1 to Ch4 in frequency response when the
excitations were made along the line between Ch2 and Ch3. As many excitation points were employed, three lines for the mean value and mean ± standard deviations σ were drawn. Two important facts can be found: even for intact condition the frequency response showed difference
from the point measured, and the frequency response needs to be discussed in the range of effective frequency response, i.e., this case the range of 60 to180 kHz.

Fig. 21. Ratio of Ch1 to Ch4 in frequency response when the excitations were made along theline between Ch2 and Ch3.

Fig. 22. Ratios of frequency responses for the differences of propagation distances.
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To verify the frequency response corresponding to the propagation distance, other intersectional points than above were used to excite the signal. The ratios of frequency responses for thedifferences of propagation distances can be found in Fig. 22. The legends of the figure show thedifference of propagation distances. The decrease rate for higher frequency above 100 kHz becomes more remarkable as the difference of propagation distances becomes larger. As this trend
had a good accordance with Eq. 8, the slope of the ratios, α in Fig. 22 were calculated by the approximation, where the slope can be defined as Eq. 9.

α=

π Δd

(9)
VQ
Figure 23 shows the relation between the slope α and differences of propagation distances. Surprisingly the relation can be approximated by a linear equation, resulting in good agreement with
Eq. 8. Of course this slope α correlates well to the difference of propagation distance, but more
likely to demonstrate the properties of propagation media. This is the very concept to quantify
the damage of the materials with transfer functions using AE waveforms.

Fig. 23. Slopes of frequency response with the differences of propagation distances.
6.3 Application to the mortar specimens with several degrees of artificial damage
To verify whether the concept of the ratio of frequency responses is applicable to the damage
assessment of concrete, homogeneous standard mortar with formed polystyrene spheres simulating damage was prepared as test specimens. As shown in Fig. 1, the prism mortar specimens
have dimensions of 150 x 150 mm with different heights: about 100, 200, 300, 400 and 500 mm.
In order to simulate the damage, spherical foamed styrene material (6 mm in diameter) was used
as a false cavity, with four types of volumetric percentages as 0%, 1%, 5% and 10%. Two typesof elastic wave excitation were made by pencil-lead break and electric pulser (PAC) with awideband AE sensor (1045S, Fuji Ceramics). Piezoelectric sensors of 60 kHz resonant (R6,
PAC) are employed for the receiver. The signals detected by the sensor were amplified by 40 dB
at the amplifier, and processed and recorded by AE monitoring system (SAMOS, PAC) with 1
MHz rate and 1k words samples. The elastic wave was excited at the center on the top surface ofthe specimen, and received by the sensor installed in the bottom surface93

As a typical case, the result of damage content of 5% is shown as in Fig. 25. Figure 25ashows the frequency distribution for the case of elastic wave excitation by electric pulser, and bshows that by pencil lead break. As can be found, the frequency spectra decrease with the increase of propagation distance, and the decrease rate is large as the frequency becomes high. It
can be thus obvious that this result accords well to the theoretical formula as shown in Eq. 7
demonstrating the decay with the propagation distance. As for the difference of propagation distances, the attenuation trend as a function of frequency becomes remarkable when the propagation distance becomes longer even in the case of the same degree of damage (5% in this case),suggesting that it is not easy to evaluate degradation only by the frequency related parameters as
the frequency centroid and the peak-frequency which are well used to relate damage with elastic
waves’ parameter.
The ratio of frequency spectrum response is determined based on the cases of at least 100mm propagation distance. Specifically, the ratio of frequency response for the 96-mm propagation is obtained from the spectra of 196-mm and 100-mm propagation distances. Other ratios ofthe spectral difference of propagation distance for 208 mm, 320 mm and 444 mm are obtained ina similar manner from the frequency spectra of 200-500 mm in propagation distances (or theheight of specimens). Calculated results (for 96 mm, 208 mm, 320 mm, and 444 mm) as a function of the frequency can be found in Fig. 26. Two straight lines in the figure show the linear approximation over the frequency between 0 to 200 kHz for the ratio of frequency responses of distance difference 96 mm and 444 mm. In this experiment, as the same type of receiving sensor
were used for all the measurement, T2(f)/T1(f) in Eq. 2 can be regarded as 1 in all the frequency
bands. Because of the heterogeneity of the mortar specimen, the ratio of frequency responses
does not form a straight line like a theoretical formula. Therefore, the regression line is used bycarrying out linear approximation of the frequency response ratio.

Fig. 24. Mortar specimens with simulated damages (Damage content: 1%).
As found in Fig. 26, the larger the difference of propagation distance, the larger the slopes of
the ratio of frequency responses. Here, it was verified experimentally that the theoretical formula,
Eq. 8, simulates the practical behavior well. By using the slopes obtained from Fig. 26, the ratioof frequency responses and the relation of a distance difference to each mix rate of damage simulation are shown in Fig. 27. We find that correlations were uniquely acquired in all damage mixture rate and in both excitations. The frequency slope obviously becomes large with the increaseof damage volume.

94

(a) Pulser(b) Pencil-lead break
Fig. 25. Frequency distributions with different distances (damage content: 5%).

(a) Pulser(b) Pencil-lead break
Fig. 26 Ratio of frequency responses with differences of propagations (damage content 5%).
Q-value is also determined by using Eq. 8, substituting P-wave velocity, V, which was obtained by another measurement, and it is shown in Fig. 28. As mentioned previously, the velocity
of a P-wave shows the dispersion depending on the frequency, e.g., the velocity becomes large toa certain frequency then decreases, and this tendency appears more noticeable when more damage was included [37]. In addition, since Q-value also depends on the frequency, showing thedispersion [38], the value depends on frequency. Q values by two excitation methods (pencillead and pulser) were different as shown in Fig. 28; the damage could also be evaluated by Qvalue, e.g., Q-value showing between 170-270 demonstrates intact condition of the material, 130
stands for 5%, and about 100 for 10% damage. The dependency of Q value on the frequency andallowable range of Q-value in regard to damage will be clarified in a subsequent paper.
7. Conclusions
In this paper, recent advances of AE technology for damage assessment in infrastructures
were given mainly utilizing secondary AE activity. The phased AE measurement followed byultrasonic tomography enables us to detail the damage; however, since it takes time and cost, aninnovative study project being referred to as ‘AE tomography,’ which implement AE source location simultaneously with tomographic approach are on-going [37, 38]. The detail will be presented in follow-up papers.

95

(a) Pulser(b) Pencil-lead break

Fig. 27 Relation between slopes of the ratio of frequency responses and difference of propagation distances.

Fig. 28 Q-values with damages for pulser and pencil-lead break.
In infrastructural asset management, performance assessment of the structure with monitoring is regarded as a key subject and this will be incorporated in the coming ISO5500X series‘Asset Management’ in 2014 [39]. AE technique could be a major contributor for the standards;
however, without careful examinations and comprehension on the AE technique, especially theuse of secondary AE activity and its application to in-situ facilities, it will come to naught. This
paper concludes enumerating following must-issues to be studied from now on in considerationof ultrasonic technique:- The relation of the damage scale and frequency employed;- The relation of damage volume with elastic wave parameters;- Exploration of elastic waveforms’ parameter to represent the damage;- Suitable frequency of sensors based on the present findings;- Adequate sensor arrangement corresponding to the objective scale of damage; and- In practice demonstration of the priority of AE measurement in comparison with other NDT
in terms of adaptability and sensitivity for a various magnitude of damage.
96

It must be noted as well that all the above issues shall be implemented in conjunction with othersubjects on design; definition of damage by engineer, on finance; estimation of life cycle costwhen applying monitoring, and on risk: consideration of probability of failure together with consequent damage.
Acknowledgements
The works presented here were findings through the joint studies with other distinguishedresearchers: Prof. M. Ohtsu (Kumamoto University), Dr. X. Luo (Railway Research Institute),
Dr. R. Kawase (Koken Engineering), Dr. D.G. Aggelis (Ioannia University), Mr. S. Momoki(Tobishima Corporation), Prof. T. Kamada (Osaka University), Graduates from Kyoto University, and others. Valuable contributions from them are extremely appreciated.
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Preliminary Investigations of Acoustics Emission Signal from Snow and its
Wavelet Transform
Karmjeet Singh, Yogesh C. Nagar, Jagdish C. Kapil, Pramod K. Satyawali and
Ashwagosha Ganju
Snow and Avalanche Study Establishment, Manali (H.P.), 175 103, India
Abstract

A new approach is presented for the detection of deformation of snow with the help of platewaveguide for avalanche warning. Avalanches are difficult to predict due to heterogeneity of thesnow media and the uncertainties associated with triggering mechanisms. Monitoring of acoustic
emission (AE) is a very effective non-destructive technique, which is applied to identify micro
and macro-cracks and their temporal advancement in snow. The propagation of these elastic
waves in attenuated media to long distance is helpful for extracting useful information for slope
monitoring. Since the snow exhibits a high attenuation during wave propagation, a lessattenuative media like plate sensors (solid steel and aluminium waveguide) is essential.
Preliminary experiments towards the formation of Lamb waves in a plate coupled with snow was
performed with the help of piezoelectric sensors. Different deformations (1-100 mm/min) wereapplied to various snow samples having different densities (300-450 kg/m3). The dependence ofstress-strain with AE and visible crack is examined along with the wavelet transform. The wavelet transform (WT) was used and applied on the time-frequency of AE present in plate waveguide. Lamb waves are detected from a long distance, which help in understanding the physicalcondition of snow stability with the help of waveguide.
Keywords: Snow, waveguide, wavelet transform
1. Introduction

The effect of climate change has resulted in significant impact on catastrophic events such as
snow avalanche in the cryospheric regions of Himalaya. Summer temperature rise, changes insnowfall pattern, fluctuation in mean seasonal temperature, heavy precipitations are few
indications of global climate changes and these have adverse effect on the avalanche activity.
Avalanches are difficult to predict due to heterogeneity of the snow media and the uncertainties
associated with triggering mechanisms. In most cases, initially, there is a formation of crack at
micro/macro scale inside the snow pack, which leads to the fracture initiation and finally theformation of an avalanche. During this crack formation acoustic emission (AE) are emitted.
Acoustic emissions are elastic wave, which are formed when snow is under critical stress andgive the information prior to any failure [1, 2]. Monitoring of AE is a very effective and nondestructive technique, which has been applied to recognize instability and their temporal advancement in snow [3]. Acoustic emission in porous media (like snow and soil) is difficult tomeasure because of the high attenuation of the source signal during propagation [4, 5]. High frequencies are more attenuated than lower frequencies due to small wavelength [6]. The prime
challenge in this kind of study is to detect the behavior of such changes (AE signal) prior to large
failure in snow. The phenomenon of propagation of acoustic wave inside the snow takes placethrough the solid skeleton of snow and pore structure. Mainly three types of waves travel throughthe snow; two are longitudinal and one is shear (transverse) waves. The shear wave travels
through the solid snow and the longitudinal wave travel through the pore space as well as solid
snow structure. As shear waves are not supported by air of pore space so they do not travel
J. Acoustic Emission, 30 (2012)100© 2012 Acoustic Emission Group	
  

	
  

through pores. Propagation of wave through pore is again depends on the pore structure of snow
which might be opened or closed and the propagative wave is highly attenuated [7, 8]. If a solid
media is in contact with snow the transmission of the acoustic wave can take place, which help
us understand the behavior and physical status of snow. The propagation of these elastic waves
in attenuative media up to a long distance is useful for extracting important information for slope
monitoring. Since the snow exhibits a high attenuation during wave propagation [4, 5, 9, 10], aless attenuative medium like plate waveguide (solid steel and aluminum plate guide) is required[11]. The associated elastic waves travel through the snow medium and enter into the plate structure in the form of longitudinal, shear and Lamb waves [12]. Shear wave propagation depends
upon the couplant used between snow and plate. Out of these three longitudinal and Lamb waves
are dominated in plate. Lamb waves are formed inside the plate when the thickness is comparable to the wavelength of guiding plate media. These waves travel up to long distance due to multiple reflections from boundaries of plate or structure. The low attenuation feature of Lamb wave
makes it a perfect choice for guiding wave, which can travel up to long distances [13]. Various
kind of waveguide is developed for different length and shapes and the thickness of waveguide isa key parameter for good signal strength [11].
2. Approach

Preliminary studies towards the formation of Lamb waves in a plate coupled with snow were
performed with the help of piezoceramic sensors. The dependence of AE and visible crack withstress-strain was examined along with the wavelet transform. Due to various types of deformations, AE activity develops in the snow sample. The associated elastic waves travel throughthe snow medium [7] and enter into the plate structure where these can be dominated in the formof Lamb waves [12]. The application of guided waves for evaluation of material properties involves modes of propagation in a material (plates), which are given by the well-known dispersion curves (dependence of velocity on frequency)[14]. The present work describes the AE response in snow as a function of stress and strain. The AE in snow indicates qualitative information of major crack, creep-glide rupture, inter-granular deformation and friction between various grains of snow [1]. Different deformation rates were applied to various snow samples having
different densities. The wavelet transform (WT) was applied to Lamb wave, which is a potential
technique to detect attenuation of source signal, presence of cracks, defects and different modesof Lamb waves in plates [15-20]. Wavelet transformations have been performed on different
kind of AE signal. Results show that various types of Lamb wave modes and their dominancewith time. The WT was applied to the time-frequency analysis of both symmetric and antisymmetric Lamb wave modes. Experiments were performed using the artificial AE signal produced on the surface of waveguide plate to ensure sensors coupling and attenuation of source
signal [21]. WT and frequency components of AE signal can give important information about
energy carried by different frequency components.
3. Instrumentation
In order to see the structural deformation in snow, a compression test was performed with 2.5
kN load cell on various snow samples (with density 300-450 kg/m3) under Universal Testing
Machine (UTM). All snow samples were cylindrical in shape having 65 mm in diameter and 150
mm in length and were loaded uniaxialy under a constant strain rate from 10–2 to 10–4 s-1.
Variable deformation rates of 1, 5, 10, 100 mm/min. were applied.

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AE signals were detected by a set of seven piezoceramic sensors. The sensors were attachedto the plate with the help of H210 high-vacuum compound grease in a circular pattern with adistance of 50.5 mm from the center of the circular plate waveguide (thickness 2 mm, diameter
130 mm) as shown in Fig. 1a and 1b. In order to have the proper contact, the air between thesensors and waveguide plate was removed. Similarly a thin ice layer was used for contact
between snow sample and waveguide plate. In order to see the proper contact between the AE
sensors, and waveguide plate, a standard procedure [Hsu-Nielson source] was followed, i.e. apencil lead (0.5 mm, HB) was broken close to the contact. The AE instrumentation consists ofPhysical Acoustic Corporation (PAC) MISTRAS-2003 (SAMOS AE system), which has afrequency range from 1 kHz to 530 kHz. The analysis has been carried out on single wideband
piezoceramic transducers (PAC R30I-AST transducers) with operating range 125−450 kHz(resonance frequency 300 kHz; dimensions, 29x31 mm). To avoid the chamber and otherbackground noise the threshold level of the sensors was set to be 45-50 dB. The peak sensitivityof sensors was -22 dB ref. 1 V/µbar. Other parameters, viz. Peak Definition Time (PDT), Hit
Definition Time (HDT), Hit Lockout Time (HLT), were set to be 200 µs, 800 µs and 1000 µs,
respectively.

Fig. 1. a) Deployment scheme of deformation of snow sample. b) piezoceramic transducers on
aluminium plate used for testing.

Fig. 2. Load-time curves of snow under three deformation rates.
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4. Results and Discussion
4.1 Mechanical deformation of snow sample
It has been observed that snow behaves differently under different strain rates (Fig. 2). Whenthe deformation rate is small (5 mm/min) snow starts to deform due to the basal slip inside thesnow grains [23]. For higher deformation rates, brittle transition occurred, which corresponds tothe complete breakage of snow grains inside the sample (disjointment) [23, 24]. Both the brittle
transition at 10 and 100 mm/min took place below 5% strain level. The same behaviour wasobserved after repeating the experiment for multiple times with same parameters.

Fig. 3. Amplitude distributions of AE events with load applied to Snow having density 401
kg/m3 (grain sizes 0.7-1.0 mm) with deformation rate 10 mm/min at -10ºC under uniaxial
compression tests. Photographs below show the progressive formation of major cracks, 1, 2, 3.
Figure 3 gives load versus time, along with AE activity and shows the typical nature of AE
events, which were irregular for this snow sample. The amplitude of each AE event is also
indicated. This was due to the heterogeneity of snow sample. From the load-time analysis it was
found that sample was getting irregular deformation with applied stress before pre-failure stage[25]. Similar behavior could not be observe in others sample with density lower than 400 kg/m3.
Therefore, it was speculated based on the observations that load behavior of snow is changed
close to 400 kg/m3 [26]. The AE increases with the applied stress and the reason for this may be
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due to deformation of snow, crack, creep-glide rupture, inter-granular deformation and friction
between various snow grains. When sample changes its behavior from less dense to more dense
state, the AE activity lasts between 10−40 second. This directly corresponds to structuraldeformation of snow sample before complete failure. The AE growth developed prior to the first
crack. This is at 12 sec and is marked as “1”. It was found that during the formation of second
and third crack (marked 2 and 3 at 18 sec and at 24 sec), there was large increases in AE
intensity (Fig. 3). Almost complete deformation of snow sample took place after the appearanceof the third crack, and the next peak in load diagram corresponds to mixed effect of all threecracks. Afterwards small internal crack events took place, which contain mainly burst kind of AE
signals. In other samples, changes of AE behavior was seen. The behavior was similar only ininitial stage where they follow load curve before densification. No major visible crack was foundin low deformation rates, i.e. 1 and 5 mm/min. It was found that after three main cracks, AE rate
was quite scattered. A number of spikes of AE events were observed in later part, whichcorresponds to small sliding events in deformed snow and no visible crack were found during therest of experiment. At last when the sample is released from the load a few relaxation events ofinter-granular grain were observed at ~140 sec. During the process of major cracking, large
continuous events were observed, which show the sliding friction, shearing and slip orintergranular breaking of snow grains.
4.2 Wavelet analysis of acoustic emissions
AE signals from snow samples consist of various frequency components. Two types of AE
signal were captured. First was in continuous mode and second was in burst mode (Fig. 4a and
4b).

Fig. 4 Typical AE signals (a) in continuous mode. (b) in burst mode.
Wavelet analysis (WT) was done to decompose these emitted signals into different
frequencies component to understand the composition of various frequency components andtheir temporal variation. Freeware software AGU-Vallen Wavelet from Vallen-Systeme GmbH,
2001 [27] was used for time-frequency and wavelet analysis. It uses Gabor (Gaussian form)
wavelet as a mother function where correlation between frequency, time and amplitude can beeasily achieved. It has been observed during the compression test of snow that continuous AE
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signals have generally small amplitude, which occurred near the yield stress of the snow. The
acoustic impulses of small amplitude are released close to each other, which reflect that all theevents form a band [28]. This shows the continuum behavior of snow before any major failure
inside the sample. The continuous emission was sensitive to strain rate, as the strain rates varied
from 10-4 - 10-3 s-1. These activities show that the snow deformation and failure were strain ratedependent. The strain rate dependencies were characterized as brittle behavior at higher strain
rates and plastic deformation corresponding to low strain rates. The burst type signals are the isolated events during the failure of snow and can be characterized as crack growth and catastrophic
failure. The burst type emissions were observed during the brittle transition, which is due to the
disjointment of snow grain bond in the sample [23]. It was observed that burst signals were dominant during the crack formation. In all failure cases there were emissions of continuous signals,
which were followed by burst signals. Therefore, acoustic emission spectra can be used to characterize the brittle and plastic behavior of snow.
For wavelet analysis burst signal was analysed and a typical of this is shown in Fig. 4b. It
was found from the wavelet coefficient that it has maximum amplitude at 125 µs. Wavelet
coefficients with respect to frequency have been drawn for 125 µs. It was found that signal
mainly contains of two major frequencies of 32 kHz and 160 kHz as shown in Fig. 5. This showsthat signal is dominant in lower frequency ranges. In the present case, most signals (bothcontinuous and burst) were in the lower frequencies ranges below 200 kHz.

Fig. 5 Three-dimensional diagram of wavelet coefficient of a burst AE signal (Fig. 4b) against
time and frequency.
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Fig. 6. Time- frequency plots of a signal between 0 to 200 kHz and time 0 to 250 µs (for threesegments of a burst signal).
Figure 6 shows the time-frequency plots of a single burst signal at different time segments.
Color code scheme on the right-hand side shows the magnitude of WT coefficient. Symmetric(S0) and anti-symmetric mode (A0) dispersion curves were drawn for circular plate waveguide of
thickness 2 mm and shown in Fig. 6. The anti-symmetric mode is dominant in the frequency
ranges 20-70 kHz and 120-170 kHz. In the present experiment, disturbances comes through thesnow sample, which is out of plane with respect to plate waveguide. Thus, anti-symmetricmode’s domination should be expected, as found in the time-frequency diagrams. A0-mode
domination is an important factor for analysis, helping to select suitable piezoceramic sensors forbetter avalanche forecasting.
5. Conclusion
An attempt was made to correlate AE with the various damage mechanisms occurring insidesnow with varying parameters. The AE activity is a strong function of strain rate, density andgrain size of snow sample. Snow shows continuous AE behavior throughout the deformation. AE
increased near the yield point of snow in the stress-time curve. AE can be used to continuously
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monitor the evolution of the damage in snow sample. Burst type AE signals were related to crack
formation and failure of snow sample regardless of the others parameters. WT was used to
identify the AE s during major crack formation.
Acknowledgement
We would like to thank Sh. Prem Datt, Sh. Paramvir Singh, Sh. Siddheshwar Kumar and Sh.
Vinod Kumar for their help and support during this study.
References[1] Sinha, N.K., “Acoustic Emission and Micro cracking in Ice”, Proceedings 1982 SESA/ Japan
Society for Mechanical Engineers, Honolulu/Maui, Hawaii. 1982, pp. 767-772.
[2] Carlo Scapozza, Felix Bucher, Peter Amann, Walter J. Ammann, Perry Bartelet, “Thetemperature and density-dependent acoustic emission response of snow in monoaxial
compression tests”, Annals of Glaciology (International Glaciological Society), 38, 2004, 291298.
[3] Masera, D., Bocca, P. and Grazzini, A., “Frequency Analysis of Acoustic Emission Signal to
Monitor Damage Evolution in Masonry Structures”, Journal of Physics (Conference series)
305/012134, 2011.
[4] Albert, D.G., “Reduction of blast noise by a snow cover”, Noise Control Engineering Journal
50, 2002, 200-203.
[5] Iwase, T., Sakuma, T. and Yoshihisa, K., “Measurements and simulations on soundpropagation above snow field”, The 2001 International Congress and Exhibition on NoiseControl Engineering, Hague, Netherlands, 27-30 August, 2001.
[6] Biot, M.A., “Theory of Propagation of Elastic Waves in a Fluid-Saturated Porous Solid. II.
Higher Frequency Range”, Journal of the Acoustical Society of America, 28 (2), 1956.
[7] Zhekamukhov, M.K. and Malkandueva, L.M., “Propagation of elastics vibration in snow”,
Journal of Engineering Physics and Thermo physics, 77, (6), 2004, 1205.
[8] Johnson, J.B., “On the application of Biot’s theory to acoustic wave propagation in snow”,
Cold Region Science and Technology, 6, 1982, 49-60.
[9] Ishida, T., “Acoustic Properties of Snow”, Contributions from the Institute of Low
Temperature Science, (Bulletin), A20: 1965, 23-63.
[10] Marco, O., Buser, O., Villemain, P., Touvier, F., Revol, H.P. “Acoustic impedance
measurement of snow density”, Annals of Glaciology (International Glaciological Society), 26,
1998, 92-96.
[11] Ono, Kanji and Cho, Hideo, “Rods and tubes as AE waveguide”, Journal of Acoustic
Emission, 22, 2004, 243-252.
[12] Su, Z. and Ye. L., Identification of damage using Lamb waves, Springer, Berlin, 2009.
[13] Greve, D.W., Oppenheim, I.J. and Zheng, Peng, “Lamb Waves and Nearly-Longitudinal
Waves in Thick Plates”, Dept. of Electrical and Computer Engineering, Carnegie Mellon
University, Pittsburgh, PA, 15213.
[14] Rose, J.L., “Dispersions Curve in guided wave testing”, Ultrasonic Laboratory, Departmentof Ultrasonic and Mechanics, Pennsylvania State University, PA 16802, 2003.
[15] Manthei, G., Eisenblatter, J. and Spies, T., “Determination of wave attenuation in rock salt
in the frequency ranges 1-100kHz using located AE events”, Journal of Acoustic Emission, 24,
2006.
[16] Finkel, P., Miller, R.K., Friesel, M.A., Finlayson, R.D., Cole, P.T., Barsoum, M.W. and ElRaghy, T., “Characterization of the damage and fracture mechanisms in Ti3SiC2 using Acoustic
emission”, J. Acoustic Emission, 18, 2000, 61-67.
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[17] Surgeon, M., Buelens, C., Wevers, M. and Meester, P.D., “Waveform based analysis
techniques for the reliable acoustic emission composite structures”, Journal of Acoustic
Emission, 18, 2000, 34-40.
[18] Nakamurai, H., Arakawa, T., Yamada, M., “Examination of AE wave propagation routes insmall model tank”, Journal of Acoustic Emission, 23, 2005, 243-248.
[19] Nishino, H., Uchida, F., Takashina, S., Takemot, M. and Ono, K., “A new method of AE
source location in pipes using cylindrical guided waves”, Journal of Acoustic Emission, 18,
2000, 102-110.
[20] Bayray, M., “Investigation of acoustic emission waveform on pressure vessel”, Vienna
University of Technology, Austria, Journal of Acoustic Emission, 19, 2001, 241-257.
[21] Yamada, H., Mizutani, Y., Nishino, H., Takemoto, M. and Ono K., “Lamb wave source
location of impact on anisotropic plates”, Journal of Acoustic Emission, Vol. 18, 2000, 51-60.
[22] Hamstad, M.A., Gallagher, A.O. and Gary, J.A., “Wavelet Transform Applied to Acoustic
Emission Signals: Part 2:” Journal of Acoustic Emission, 20, 2002, 39-61.
[23] Kinosita, Seiiti, “Compression of Snow at Constant Speed”, Physics of Snow and Ice:
proceedings, 1(2), 1967, 911-927.
[24] Narita, H., “Mechanical behaviour and structure of snow under uniaxial tensile stress”,
Journal of Glaciology, 26(94), 275–282, 1980.
[25] Sinha, N.K., Shkhinek, K. and Smirnov, V., “On borehole indentor (BHI) measurement andanalysis”, Cold Regions Science and Technology, 76-77, 2012, 109-120.
[26] Abele, G., and Gow, A.J., “Compressibility characteristics of compacted snow (CRREL
Report)”, Cold Regions Research and Engineering Laboratory, USA, 1976.
[27] AGU-Vallen Wavelet , Vallen-Systeme GmbH, Munich, Germany,
http://www.vallen.de/wavelet/index.html, 2001.
[28] Ono, K., Cho, H. and Takuma, M., “The origin of continous emissions”, Journal of Acoustic
Emission, 23, 2005, 206-214.
[29] Hamstad, M.A., “Small diameter waveguide for wideband acoustic emission”, Journal of
Acoustic Emission, 24, 2006, 234.

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Attenuation of Lamb Waves in CFRP Plates
Kanji Ono1 and Antolino Gallego2
1
Department of Materials Science and Engineering, University of California, Los Angeles(UCLA), Los Angeles, CA 90095
2
Department of Applied Physics, University of Granada, Granada, Spain	
  Abstract
The paper presents the Lamb wave attenuation of Carbon Fiber Reinforced Plastic (CFRP)
plates on the basis of an experimental transmitter-sensor scheme. We measured separately the S0
and A0 Lamb modes of CFRP plates with three different kind of laminate lay-ups; unidirectional[0]8s (and [90]8s), cross-ply [0, 90]4s, and quasi-isotropic [0,45,-45,90]2s. Several ultrasoundtransmitters were used as actuator and short-duration high-voltage pulses were used for excitation. Two kinds of AE resonant sensors with resonance frequency around 260 and 500 kHz, respectively, were used to record the Lamb waveforms of the plates. By using sensors on bothsides of the plate, the confirmation and separation of both modes, S0 and A0, were successfully
carried out. This result was also confirmed by using the wavelet transform of the recorded signals. The experiment was carried out at several transmitter-sensor distances, so both phase andgroup velocities and attenuation could be obtained. By means an appropriate fitting of a theoretical model of attenuation, including the geometrical attenuation, the damping factor of bothmodes was obtained for each laminate and sensing frequency. The experimental results for velocities and damping factors were compared with previously published and theoretical ones. Directivity results are also reported, showing complex behavior dependent on laminate layups as
expected.
Keywords: Composites, CFRP, Lamb waves, attenuation, directivity	
  1. Introduction
In recent years, composite structures are increasingly used in strength-critical applications inaircraft, requiring more effective NDE methods. These materials, however, have high stresswave attenuation characteristics and AE has encountered stiff barriers against its uses in the aircraft industry [1,2]. Problems are mounted as typical composites are used in anisotropic sheets,
requiring more elaborate Lamb-wave theories than usual ultrasonic body or surface waves. Some
attenuation studies have been published [3,4], but only recently systematic works have appeared[5-12] on Lamb (or plate) waves in composite materials. However, mode-frequency specific attenuation data is scarce and we conducted an experimental study to fill the gap. Another motivation is to use such data as the basis for an improved NDE method based on the acoustoultrasonics approach [13], which often disregarded the nature of the waves it is relying on. This
trend seems to be inherited in many SHM methods as these often ignore real conditions of wave
generation and detection using only idealized models.
In searching for the base attenuation data in isotropic materials, we found the scarcity of
Lamb wave data even for Al. Kasama et al. [14] used a laser method (YAG laser generation,laser interferometer detection) to obtain So-wave propagation data. The attenuation results areshown in Fig. 1 for PMMA and Al thin sheets (0.5 and 1 mm data shown; for Al, 0.3 and 0.7 mm
J. Acoustic Emission, 30 (2012)109© 2012 Acoustic Emission Groupdata also obtained). The data has eliminated the geometrical spreading effect of the inversesquare-root distance. For Al, the attenuation coefficient α (of the exponential decay with distance) is 0.25 to 2 Np/m (or 2.2 to 17 dB/m) at 0.5~5 MHz, whereas it is much higher for PMMAby a factor of 10~50. In Fig. 1, x marks represent corresponding P-wave attenuation values. Notethere are significant frequency effects (higher for viscoelastic PMMA) and also a thickness effect. Aljets found 2 Np/m using pencil-lead break source and PAC µ80 sensor [11].

Fig. 1 Lamb wave attenuation in PMMA and Al sheets. Redrawn data of Kasama et al. [14].
Kasama’s experimental setup is shown schematically in Fig. A1 (in Appendix) from his thesis [15]. YAG laser beam is focused on a plate edge in the in-plane orientation and a laser interferometer detected the surface displacement as a function of distance from the source. They used
only the initial S0-mode segment as later segment includes both modes, as shown in Fig. A2.
Wavelet amplitude at a selected frequency, P, is then determined and its peak (also see Fig. A2)
and the integrated WT energy of E = ∫P2 dt were plotted against the distance of propagation.
Typical examples of the peak-amplitude fit and WT energy fit are shown in Figs. A3 and A4,
respectively. The peak amplitude-distance curves were fitted to the inverse square-rootexponential attenuation that includes the geometrical spreading effect. The attenuation coefficient, α, is obtained from the best-fit equation. The integrated WT energy data was fitted to thesquare of the above expression, again yielding the attenuation coefficient, α. These are eqns. (1)and (2) below.
𝑃 = 𝑃! 1

exp  (– 𝛼𝑥)(1)

𝐸 = 𝐸! 1 𝑥 exp  (– 2𝛼𝑥)

(2)𝑥Both of these equations are based on the wave radiation pattern of a point source [16]. Kasama
confirmed the predicted directivity of [16] by measuring the peak amplitude of S0 waves at 50mm from the source. The polar plot shows good agreement with theoretical shape of the P-wave
radiation pattern [16, 17]. Note that the directivity of a point source can be approximated withthe cos-θ angular dependence (equivalent to the half of a dipole point source) [18].
110In the present work, three CFRP plates were used to determine their wave attenuation behavior. An aluminum alloy sheet was used to generate companion data. For this phase, we used theresonance nature of sensors to define the frequency ranges of waves. Orientation effects werealso evaluated and results given, but this part is complex and requires more extensive investigation with modeling calculations.
2. Experiment: methods and results
Attenuation: Carbon Fiber Reinforced Plastic (CFRP) plates with three different kind of laminate
lay-ups were obtained from Dr. W. Prosser of NASA Langley Research Center; AS4 fibers, 3502
epoxy; unidirectional [0]8s (and [90]8s) [UD], cross-ply [0, 90]4s [XP], and quasi-isotropic [0,45,45,90]2s [QI]. Their dimensions were 508x382 mm and 2.2 mm thick (16 plies). For comparison,
7475 Al plate (in cold-worked condition, 610x370 mm) of 3.1 mm thickness was used. S0 and A0
Lamb mode waves were generated on the CFRP plates by using an actuator of one edge. For Ao
component, an wedge (7º) was used so that only one edge is in contact with the actuator surface(Fig. 2). Several ultrasound sensors were used as actuator and fast high-voltage step-down pulses
were used for excitation. Panametrics V101 (0.5 MHz, 25 mm), PAC Pico (0.5 MHz, 3.2-mm
element, 5-mm case diameter), PAC µ30 (0.3 MHz) and DECI SH225 shear sensor (details notknown) were used. As receivers, PAC Pico and PAC µ30 were mainly used. These were coupleddirectly to digital scope board inputs (Gage Applied, 1450, 1 MΩ input impedance) using its
signal averaging function. The receivers have the resonance frequency around 260 and 500 kHz,
respectively, and recorded Lamb waveforms have approximately the same values. However, theactual frequency varied systematically and we need analysis by wavelet transform to get a single
frequency attenuation data. This part is not finished and will be reported later. By using sensors
on both sides of the plate, the confirmation and separation of both modes, S0 and A0, were successfully carried out. However, placing sensors on both sides simultaneously, amplitude was
reduced. Thus, experiments were done on one side at a time. Distance was defined as that fromplate edge to the center of receiver, though attenuation was measured starting from the sensor
closest to the transmitter to minimize distance error.

Fig. 2 Sensor placement for So (left) and Ao (right) wave generation. Transmitter (T). Sensor (S).
Figure 3 shows waveforms for Al case using V101 actuator at propagation distance x = 100400 mm. Receiver was Pico sensor showing ~500 kHz frequency. Figure 4a shows the sum of
two sensor signals from two sides, while Fig. 4b shows the difference of two side signals. These
give So and Ao propagation characteristics. Figure 5 illustrates the wavelet transform coefficients111with color showing the amplitude (used AGU-Vallen WT). The maximum energy frequency was
475 kHz for So and 430 kHz for Ao propagation mode, respectively. Note that dispersion effect isshown for the So mode, but it is absent for Ao mode.
The peak amplitude (ignoring the dispersion effect) and the usual attenuation model with theinverse-square-root distance relation, eqn. 1, were used. For V101 transmitter, using Pico or µ30
receiver, we found the attenuation coefficient (damping factor), α, to be as below in Np/m:
So
Ao

1.397 (Pico)
0.560 (Pico)0.676 (µ30)
1.112 (µ30)

Using Pico as a receiver and changing transmitter between V101 and Pico, we found α for So to
be as follow:
V101 (transmitter)Pico (sensor)

1.397; Pico (transmitter) Pico (sensor) 1.625

All the α values for So mode are much higher than Kasama’s values (even though Al plate here
was at least 3 times thicker). This must be due to the dispersion effect. In fact, for V101-Picocase, Ao value was less than a half So value of α.

Fig. 3 Waveforms for Al case using V101 transmitter at propagation distance d = 100-400 mm.
Similar experiments were conducted using the identical setup, but with CFRP plates. For the
UD plate, measurements were taken along the fiber direction (0º) and at the transverse direction(90º). Waveforms similar to Fig. 4a and 4b are given in Fig. 6. On the right side, reflected waves
are seen (these were cut out for So waves for clarity). The UD-So case turns out to be a special
case because of the near-field effect of the large aperture actuator (25-mm diameter). Peak amplitude actually increased up to 250 mm. Fit to the attenuation expression is shown in Fig. 7 for
UD-So case with V101-Pico combination and using reflected large distance data as well. For Aomode in the same plate, results are shown in Fig. 8. Again the initial part does not fit the attenuation expression well, but shows the attenuation coefficient over ten times that of So case is found.

112

Fig. 4a The sum of two sensor signals from two sides, giving So mode.

Fig. 4b The difference of two sensor signals from two sides, giving Ao mode.
Table 1. Attenuation coefficients of Lamb waves (in Np/m) with V101 transmitter.
Plate
Aluminum 7475
C1-[0]8s
C1-[90]8s
C2-[0,90]4s
C3-[0,45,45,90]2s

S0 (Pico)
1.397
0.560
12.21
1.082
4.254

S0 (µ30)
0.676
0.552
9.471
0.237
1.455

113

A0 (Pico)
0.560
6.123
-

A0 (µ30)
1.112
6.935
9.535
9.105
10.577

Fig. 5 AGU-Vallen wavelet transform coefficients with color showing the amplitude for So andAo propagation. Al V101—Pico. Left used Fig. 4a waveforms. Right used Fig. 4b waveforms.

Fig. 6 Waveforms for So and Ao propagation. UD CFRP in the fiber direction. V101—Pico.
Left: sum of two side waveforms. Right: difference of of two side waveforms. Reflections arevisible for the right figure.

114

Fig. 7 Fit to the attenuation expression using peak amplitude beyond 200 mm. UD-So case with
V101-Pico combination.

Fig. 8 Fit to the attenuation expression using peak amplitude. UD-Ao case with Pico-Pico combination.
Curve fittings for other CFRP cases are better than those for UD-0º cases. Results for theattenuation coefficients are summarized in Table 1. For S0 mode, attenuation was lower for µ30
data in all cases, reflecting the lower nominal frequency of 260 kHz. For A0 mode in Al plate,attenuation was lower for µ30 in the two cases we can compare, indicating that the dispersion
115

effect is higher at 260 kHz for this mode. On the other hand, attenuation was very high for A0
mode using Pico sensor (nominally at 500 kHz) for 90º-orientation of UD, XP and QI plates andcould not be measured.
Directivity: The directivity of Lamb wave propagation was explored. First, we examined the directional behavior in an isotropic Al plate as shown in Fig. 9. Data points for Al are given withsquares. It follows the usual piston source directivity, matching well to the case of ka = 2.9,
where k is the wave number. For both transmitter and receiver, we used Pico sensors, giving thenominal frequency of 500 kHz. The sensor case diameter is 5 mm and the PZT element diameteris 3.2 mm. Using a = 2.5 mm, ka value is equal to 1.75 at the S0-mode velocity of 4.5 mm/µs.
This indicates that the directivity of S0-mode waves is narrower than that of P-waves.

Fig. 9 The directivity of S0 waves, Al, 500 kHz. P- Fig. 10 The directivity of S0 waves, CFRPwave directivity is plotted for ka of 1.75 and 2.9. UD. 0˚ angle = fiber direction. Propagation
Propagation distance = 50 mm.
distances of 50, 100 and 150 mm used.
The directivity in unidirectional CFRP is much sharper than in Al as shown in Fig. 10. The
0º-propagation angle corresponds to the fiber direction in the UD composite plate. Again, two
Pico sensors were used as transmitter and receiver. Half-value angle was 12.5º at 50-mm distance of propagation, decreasing further to 8º at 150 mm. The latter case is comparable to Pwave directivity at ka ≈ 15. The observed dependence on the propagation distance originatesfrom attenuation effects that increase with the angle away from the fiber direction. Suzuki
showed that P-wave attenuation doubled from 0º to 30º in UD-GFRP [19].
The directivity in a quasi-isotropic CFRP (QI) plate is much more complex. When the 0ºfiber direction (located at the top and bottom plies) coincides with the 0º propagation angle, observed amplitude changes were moderate at 50-mm distance, as shown in Fig. 11. The variation
became larger with larger distance with the normalized amplitude increasing to a peak at ~10º.
This is followed by a gradual decrease. The angular dependence varied with the propagation distance, which raised the relative peak height to 1.74 times the 0º value at 150 mm. This angulardependence suggests that the 45º ply immediately below the 0º ply causes this effect. Here, positive values were arbitrarily taken for angles measured counter-clockwise. We refer this as QI-0
configuration.
When the QI plate is rotated 90º and the 0º propagation angle is oriented along the 90º-fiberplies (at the mid-thickness), the directivity characteristics were different as shown in Fig. 12.
That is, waves at 0º angle is propagating along the fiber direction in the 90º plies. In this case, the116orientation dependence was nearly absent (<±20%). Effects of propagation distance between 50
and 100 mm were also small. These are marked as 90-50 and 90-100 in the figure [this is referred to as QI-90.] and two curves from Fig. 11 are re-plotted for comparison (as 0-50 and 0100). This finding shows that the positions of various plies have large effects on radiation andpropagation patterns. Here, 45º-direction (on QI-90) is perpendicular to the 45º-direction (on QI0) in Fig. 11.

Fig, 11 The directivity of S0 waves in CFRP-QI plate. T/R: Pico sensors; 0º propagation angle isparallel to the 0º-ply orientation on the outside. Propagation distance = 50, 100 and 150 mm.

Fig, 12 The directivity of S0 waves in CFRP-QI plate. T/R: Pico sensors; 0º propagation angle isparallel to the 90º-ply orientation at mid-thickness. Propagation distance = 50 and 100 mm. Twocurves from Fig. 11 are also shown for comparison (marked 0-50 and 0-100).
Another series of directivity measurement was repeated with the range of angle into the negative side. Figure 13 shows the results. The positive side is essentially the same as in Fig. 11 (amplitude for 100-mm propagation is lower, however). On the negative side, the normalized amplitude is relatively unchanged; to be precise, it dips to ~0.8 at 5-10º, then slowly rises to ~1.2 at117

–30º. The directivity of this QI plate is asymmetric even though the wave velocity is orientationindependent. It appears that subsurface plies are responsible for this asymmetric behavior.
When this experiment is run using a larger transmitter, Panametrics V101 (500 kHz, 25-mm
element size), with Pico as a receiver, the directivity is strongly affected as shown in Fig. 14. Incomparison to Pico transmitter, the ka value is eight times larger for V101. The amplitude was
reduced to 0.2~0.3 at 30-40º on both sides and the peak height at θ = 10º reached only 1.2. Obviously, this behavior is due to narrower beam generated by a larger transmitter, accentuating theasymmetric behavior.

Fig. 13 Asymmetric directivity in CFRP-QI plate. 0º orientation. 100 mm () and 150 mm ()propagation distance. Transmitter: Pico, Receiver: Pico.

Fig. 14 Asymmetric directivity in CFRP-QI plate. 0º orientation. 150 mm propagation distance.
Transmitter: V101, Receiver: Pico.

118

Fig. 15 The directivity of S0 waves in CFRP-UD plate. T/R: Pico sensors; 0º propagation angle isperpendicular to the fiber orientation. Propagation distance = 100 mm.
Yet another unexpected behavior was found in the CFRP-UD plate. Here, the 0º angle corresponds to the normal direction of the fiber. Observed amplitude increased with angle, reaching apeak at ~30º, as shown in Fig. 15. This is in sharp contrast to Fig. 10, where the directivity ofwave showed a narrow focus around the 0º orientation. Obviously, initial wave entry to thebroadside of fibers produces substantial differences from the case where wave propagation starts
from the ends of the fibers in the 0º-fiber direction. Still, no apparent explanation is available.
Discussion
Attenuation: The data in Table 1 are apparent attenuation coefficients since specific frequency
components are not compared by the use of wavelet transform. On the other hand, in dealing
with practical attenuation case, these values are representative of observed attenuation (with dispersion effect, but without the geometrical spreading effects). We will in the near future conduct
such analysis and report the frequency dependent attenuation data removing the dispersion induced amplitude loss. The above data are still interesting:
1. Attenuation of Ao or flexural mode is much higher than So or extensional mode. Exception is when waves propagate along the transverse direction to the fiber in the UD plate.
2. Pico data are higher than µ30 data most of the time. The difference in frequency is from
260 to 430-500 kHz, which is about twice and not really that large.
3. In the UD-90º direction, both So and Ao propagation modes attenuate greatly.
4. While QI plates are useful in design, stress wave inspection is difficult due to high attenuation of both Lamb modes.
5. XP plates can be a good compromise in inspectability and design freedom.
Some of these values can be compared to theoretical studies, and are comparable in cases ofsimilar materials/layups within a factor of two to three. However, theory is for plane waves andat a fixed frequency. Thus, we must first eliminate dispersion effect in our data. Thickness effectfound in [14] was noted in Prosser’s work [3] as well. So this may make a comparison difficult.
119

We attempted to use lower frequency sensors at 30-150 kHz, but mode mixing prevented us fromgetting meaningful data. Larger plates are also necessary for such experiments.
For the case of XP plate, we can compare with Aljets’ data for twill-weave CFRP of 2.5-mm
thick [11]. Along the fiber direction, he obtained 1.78 Np/m for S0 waves at 250-300 kHz, and6.17 Np/m for A0 waves at 150-200 kHz, respectively. These values were recalculated from his
Fig. 56. Signal source was pencil-lead breaks. At the direction 45º to the fibers, attenuation coefficients were slightly higher at 2.67 and 6.49 Np/m for S0 and A0 waves, respectively. The attenuation coefficients for S0 waves were significantly higher than our XP results. A possible origin
is their use of twill-weave, which produces undulations of fibers and higher resin contents. The
differences for A0 waves were smaller; our µ30 data is only 50% higher than Aljets data.
What this work shows is that it is indeed difficult to conduct AE or acousto-ultrasonic experiments at frequency above 250 kHz due to high attenuation. As CARP’s procedures for FRP tankinspection used under 100-kHz range, aerospace composite NDE should look for the lower frequency range to provide workable and practical solutions. In acousto-ultrasonic methods, typicalexcitation mode is surface-driven sensor. This generates mainly flexural mode, which attenuateshighly. We need to devise a means to excite extensional mode with good efficiency and movability.
Directivity: Preliminary examination of directivity in CFRP has revealed a complex behavior. In
UD-composite along the fiber direction, waves travel along the fibers with narrow beam spread.
In most other cases, ready understanding is unavailable except that the transmitter radiation pattern and the directional attenuation of CFRP plates jointly produced the observed characteristics.
It is necessary to accumulate more experimental results before consistent interpretations can be
developed. It will be helpful if we can separate radiation pattern and orientation dependent attenuation as a function of frequency and propagation modes. Obviously, high-level modeling isneeded to explain these findings. However, understanding these characteristics will benefit future
AE applications on composite materials.
Conclusion
Apparent attenuation of Lamb wave propagation was measured for specific modes on CFRP
plates of three different layups. Generally, attenuation is high limiting the reach of stress-wave
based NDE methods. It is essential to utilize a lower frequency range for practical NDE methods.
Directivity of wave propagation revealed complex behavior, requiring further studies.
Acknowledgement
Assistance of Zhiyuan Wu in experiment and of Elisabet Suárez Vargas in analysis is highly
appreciated.
References
1. U. Schnars, R. Henrich, “Applications of NDT methods on composite structures in aerospaceindustry”, Online-Proc. Conf. on Damage in Composite Materials – CDCM2006, Vol. 12, No. 12(2006). http://www.ndt.net/article/cdcm2006/
2. T. Ullmann, T. Schmidt, S. Hofmann, R. Jemmali, “In-line quality assurance for the manufacturing of carbon fiber reinforced aircraft structures”, 2nd Intl Symp. on NDT in Aerospace 2010 120

Tu.1.A.4. http://www.ndt.net/article/aero2010/papers/tu1a4.pdf
3. W.H. Prosser, “Advanced AE techniques in composite materials research”, J. Acoustic Emission, vol. 14, 1996, pp. S1-S11.
4. A.K. Mal, Y. Bar-Cohen and S.S. Lih, "Wave attenuation in fiber-reinforced composites,"
M3D: Mechanics and Mechanisms of Material Damping, ASTM STP 1169, American Society
for Testing and Materials, 1992, pp. 245-261.
5. M. Castaings, B. Hosten, “Air-coupled measurement of plane wave, ultrasonic platetransmission for characterising anisotropic, viscoelastic materials”, Ultrasonics, vol. 38, 2000,
pp. 781–786.
6. M. Castaings, B. Hosten, T. Kundu, “Inversion of ultrasonic, plane-wave transmission data incomposite plates to infer viscoelastic material properties. NDT & E International, vol. 33, no.6,
2000, pp. 377–92.
7. B. Hosten, “Heterogeneous structure of modes and Kramers–Kronig relationship in anisotropic viscoelastic materials”, J. Acoust. Soc. Am. vol. 104 (3), September 1998, pp. 1382-1388.
8. M. Castaings, M.V. Predoi, B. Hosten, “Ultrasound propagation in viscoelastic material
guides”, Proceedings of the COMSOL Multiphysics User's Conference 2005 Paris, 6 p.
9. M. Calomfirescu, “Lamb Waves for Structural Health Monitoring in Viscoelastic Composite
Materials”, Logos Verlag, Berlin, Germany, 2008. 119 p.
10. M. Sause, Identification of failure mechanisms in hybrid materials utilizing pattern recognition techniques applied to acoustic emission signals, Augsburg Univ. doctoral dissertation,
Mensch und Buch Verlag, 2010, 305 p.
11. D. Aljets, “Acoustic Emission Source Location in Composite Aircraft Structures using Modal
Analysis”, Ph.D. Thesis, Faculty of Advanced Technology, University of Glamorgan, June 2011,163 p. http://dspace1.isd.glam.ac.uk/dspace/bitstream/10265/575/1/PhD%20Thesis%20Dirk%20Aljets.pdf

12. M.A. Torres-Arredondo, C.-P. Fritzen, “A viscoelastic plate theory for the fast modelling of
Lamb wave solutions in NDT/SHM applications”, Ultragarsas (Ultrasound), vol. 66, No. 2,
2011, pp. 7-13.
13. Acousto-Ultrasonics, Special Issue, J. Acoustic Emission, vol. 12, nos. 1/2, pp. 1-103.
14. H. Kasama, M. Takemoto, K. Ono, “Attenuation measurement of Laser excited So-Lambwave by the wavelet transform and porosity estimation in superplastic Al-Mg plate”, Hihakaikensa (Nondestructive Testing), vol. 49, no. 4, 2000, pp. 269-276.
15. H. Kasama, MS Thesis, Dept. Mechanical Engineering, Aoyama Gakuin Univ., Sagamihara,
Kanagawa, Japan, 1999, 102 p.
16. D.A. Hutchins, R.J. Dewhurst, and S.B. Palmer, “Directivity patterns of laser‐generated ultrasound in aluminum”, J. Acoust. Soc. Am. vol. 70, 1981, pp. 1362-1369.
17. S.J. Davies, C. Edwards, G.S. Taylor and S.B. Palmer, “Laser-generated ultrasound: its
properties, mechanisms and multifarious applications“, J. Phys. D: Appl. Phys. vol. 26 (1993) 329348.
18. X.M. Tang, Zhenya Zhu, and M.N. Toksöz, “Radiation patterns of compressional andshear transducers at the surface of an elastic half-space’, J. Acoust. Soc. Am. 95 (1),1994, 71-76.
19. H. Suzuki, “Fracture dynamics via inverse analysis of viscoelastic waves”, doctoral thesis,
Tokyo Inst. Technology, 2000, 176 p.

121

Appendix

Fig. A1 Laser generation and detection of Lamb waves in Al sheet. [15]Fig. A2 Extraction of So component, WT analysis and fitting to geometrical spread-attenuation
curve (eqn. 1)[15].
122Fig. A3 Curve fitting of WT peak amplitude to eqn. 1. Three examples for 1.0-mm thick Al [15]Fig. A4 Curve fitting of WT energy to eqn. 2. Three examples each for 0.7- and 1.0-mm thick Al
[15].

123

Application of ICI Principle for AE Data Processing
Vera Barat1, Sergey Elizarov1, Irina Bolokhova2 and Evgeniy Bolokhov2
1
Interunis Ltd., Moscow, Russia
2
National Research University ”Moscow Power Engineering Institute”, Moscow, Russia
Abstract
During AE data processing, it is often necessary to determine the change points of parameters. The most important problems are arrival time detection and AE data segmentation. The
first-arrival time of acoustic emission (AE) signal is very important parameter, which is used forevent location and identification. At present there are many advanced arrival time determination
methods, which significantly increase accuracy of estimation in comparison with traditional
threshold crossing method. However, advanced methods have several disadvantages connectedwith algorithm parameters selection. Arrival time estimation algorithms are based on a calculation of several statistical parameters within a sliding time window. The time-window length isoften chosen empirically by the operator. When criteria for window length selection are undefined an incorrect length selection is possible, as well as incorrect AE signal arrival time definition. This paper describes the method based on intersection of confidence interval (ICI) principle. This method allows one to select time parameters optimally and in automatic mode. Application of ICI principle increases the accuracy and reliability of first-arrival time estimation. The
problem of segmentation data arises in connection with the necessity to identify the data.
Specifically the definition in the data stream of time intervals corresponding to different stagesof the observed processes.
Keywords: Signal processing, AE source location, intersection of confidence interval (ICI)
1. Introduction
The signal processing holds a significant place in theory and practice of nondestructive testing, since the accuracy of estimation of the tested equipment depends on the correctness of processing and analysis of diagnostic information. Such problems of signal processing like signal
detection, filtering and parameters estimation are urgent for any type of nondestructive testing.
The AE testing has many distinctive features as compared with other types of testing. The distinctive characteristics of AE testing generate specific problems of the AE data processing.
Among such problems we can mention the necessity of separation of AE signal components,
which correspond to different wave modes, clustering of AE signals and impulses and determination of the signal arrival time.
The necessity of determination of the signal arrival time stems from the fact that the AE testing is remote. It allows for detecting defects at the distance up to several tens meters from theplace of sensor location. The defect location can be determined by location, which is based, as arule, on the time-of-arrival technique wherein the defect coordinates are determined from the difference of arrival times for signals at different sensors and from the speed of AE wave propagation. With such techniques, the accuracy of location, and as consequence, the reliable results of
AE testing as a whole are governed by the accuracy of determination of the signal arrival time.
The conventional method of signal arrival time definition is the threshold method based on
the first threshold crossing. The threshold method is very simple in implementation but has low
J. Acoustic Emission, 30 (2012)124© 2012 Acoustic Emission Groupaccuracy. The error in signal arrival time determination by the threshold method can reach hundreds of microseconds, and the error in determination of defect coordinates can be several meters.
Most modern AE systems, in addition to the conventional threshold processing of AE data,
are supplied with an advanced floating threshold method. The floating threshold varies according
to a noise level registered by the AE system. The signal start point will be detected, when thesignal samples exceed an averaged value of noise amount by a preset number of times. An adaptive change of the threshold value allows for defining more precisely the arrival time of a signal
through reducing an influence of the noise level. A limitation of the floating threshold method is
the low accuracy during determination of the arrival time for far-field signals, which are emitted
by the AE sources far removed from the sensors [1].
At present, a number of advanced time arrival determination methods [2] based on different
principles of digital signal processing (wave modes isolation filtering, wavelet thresholding,cross-correlation analysis) and statistical time-series analysis (auto-regressive analysis, STA/
LTA methods, high order statistics approach) have been developed. This paper is devoted to thecomparative analysis of existing statistical methods and the description of the signal arrival time
determination algorithm by using the ICI principle.
2. Review of advanced statistical methods
During the statistical analysis of time series, the waveform of AE signal can be considered asa stochastic, as a rule, stationary noise process, against which impulses of acoustic emission are
observed. Since the AE impulses create a short-term variability in the properties of noise process,the problem of signal arrival time determination can be considered as change point detection.
Among a great number of change point detection methods, we selected those, such as Likelihood-ratio procedure, Akaike information criteria [3], and high order statistics (HOS) approach[4], of which application in AE signals processing has been successfully tested.
2.1 Maximum likelihood estimation method
The maximum likelihood estimation (MLE) method is a parametrical method of statisticalanalysis [5] used for estimation and check of statistical hypotheses. Advantage of MLE method
is the possibility to construct optimum statistical criteria. The basis for this method is the likelihood function, eqn. (1) expressing the joint probability in a signal of samples x1, x2, …, xn. The
likelihood function is equal to the product of probability of each value of sample:L( x1 , x2 ,..., xn ,θ ) = ϕ ( x1 ,θ ) ⋅ ϕ ( x2 ,θ )...ϕ ( xn ,θ ) ,(1)

where φ(x) = probability density function, and θ = a set of parameters of time series distribution.
To construct statistical conclusions, in regard to parameters of the analyzed time series θ, a maximum of likelihood function or a maximum of its logarithm should be determined.
To determine the AE signal arrival time, MLE can be used as a criterion for testing hypothesis that the AE signal parameters do not change over the period of observation. The alternative
hypothesis is the hypothesis according to which there exists a change point of the signal k properties, corresponding to the moment of AE signal arrival, eqn. (2). As a parameter changing with
the AE signal arrival it is reasonable to select the dispersion.
125

H 0 : σ 12 = σ 22 = ... = σ n2 = σ 2
2
H 1 : σ 12 = ... = σ k2 ≠ σ k+1= ... = σ 22(2)

The logarithm of maximum likelihood function L is defined by eqn. (3). The argument k at which
log L function reaches a maximum, determines the moment of AE signal arrival [6]. As the MLE
method is parametrical, σ1 and σ2 should be preliminarily estimated.
k∑ (x − x )nkn−klog L(k, σ 12 , σ 22 ) = − log 2π − log σ 12 −log σ 22 − i=1
2
2
2
2σ 12
i

n

2

∑ (x − x )− i=k+1

2

i

2σ 22

(3)
2.2 Akaike information criteria
Many researchers have proved that the AE signal can be considered as an autoregressive(AR) process, whose current values depend on previous values; see eqn. (4). The process structure is represented by the coefficients of autoregressive equation, which form an autoregressivemodel.
(4)
To check the signal conformance to the model, different criteria are used. The most popular
is the Akaike information criterion (AIC). The application of the Akaike information criterion for
defining the signal arrival time lies in the fact that the noise and the AE signal correspond to different autoregressive models, and the moment of signal origin corresponds to the maximum
change point of the autoregressive equation coefficients. Therefore, the extreme of Akaike information criterion will correspond to the signal arrival time. As a classic case, values of AIC
criterion are calculated through the autoregressive equation coefficients. In contrast, Maeda [4]calculated the AIC function directly from the signal without using the AR coefficients. For timeseries x of length N, the AIC is defined as:

AIC(k) = k ⋅ log(var(x[1, k])) + (N − k − 1)⋅ log(var(x[k + 1, N ]))(5)

The signal time-of-arrival determination algorithm has been well studied and actively engaged in practice. Sedlak et al. [3] developed a two-stage AIC based approach for determining
the arrival times of AE signals, in which the AIC function is applied to a characteristic functionthat represents the raw signal, instead of directly to the raw signal.
2.3 HOS approach
The HOS approach belongs to statistical methods using a sliding time window. The signal
arrival time is determined with the help of the HOS approach as the change point of signal distribution nature. Noise has a normal distribution with zero high-order moments. At the moment ofsignal arrival the distribution character is changed, as demonstrated by a sharp increase of any ofhigh-order statistic parameters. Lokajicek and Klima [4] analyze the possibility of applying thedifferent order moments. The most exact results of the time-of-arrival determination can be obtained when using the sixth order derivative of the moment is used.

126

2.4 Comparative analysis of time determination estimation (TDE) methods
Notwithstanding the fact that time determination methods of the signal origin described in
2.1-2.3 are based on different approaches, all of them fall in the same class, namely, the change
point detection methods. In the MLE method a variance change is determined, a minimum valueof AIC criterion corresponds to the change point of autoregressive equation coefficients, while
using the HOS approach, a change point of the process probability distribution law is determined.
Let us consider application of the above methods for definition. The AIC and MLE methods
are applied to the total length of a signal, and the signal time-of-arrival is determined by an extremum of the corresponding function. The higher order moments are calculated inside the sliding time window, and to the origin of AE signal there corresponds a sharp increase of the statistical parameter. By way of illustration, Fig. 1 shows application of the above methods for determination of the time-of-arrival of a model signal (Fig. 1а). The times of signal arrival are marked
with red point.
0.003

0
0.002

-20000000
0.001-40000000

0-0.001

0

200

400

600

800

-60000000

1000-80000000-0.002-100000000
0-0.003
time, µs

400

600

800

1000

time, µs

a.

-6000
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200

b

10000

0

200

400

600

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time, µs

200

400

600

800

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time, µsc..
dFig. 1 a. Initial AE signal, b. MLE function (eq.3), c. AIC criterion, d. sixth order moment
3. ICI change point detection method
The ICI principle has been widely and successfully used in recent years for solving the mostdiverse problems from the field of data processing; for filtering [8-10], classification [7], and estimation of various signal parameters. In essence, the ICI principle is a universal method ofchange point determination, wherein the decision of changing any signal parameter is made by
the absence of the intersection of confidence intervals constructed for this parameter at different
sections of the considered signal.
The ICI method can be successfully used for determination of the signal arrival time, since
the AE signal parameters, such as dispersion, high order moments, and frequency characteristics,
127differ from the noise parameters. Therefore, the confidence intervals constructed for these parameters to the right and to the left of the point of signal origin will not intersect (Fig. 2).

Fig. 2 Confidence intervals of informative parameters of the AE signal and the noise should notintersect.
The simplest usage of ICI method is the calculation of confidence intervals for the selected
informative parameter in two sliding windows, and determination of the signal time-of-arrivalfrom the absence of the intersection of intervals. The more complicated algorithm of ICI method
consists in application of the cascade of nested windows with the different length (Fig. 3). For
each fragment of the signal under study, the window with the optimal length is selected. The optimum window length is selected as follows. The informative parameter confidence intervals arecalculated for all nested windows. Further, the windows of which the confidence interval doesn'tintersect with the confidence interval of minimal window are excluded from consideration. In
accordance with ICI principle the optimal window length is equal to the maximal length of otherwindows with the intersected confidence intervals. Thus, in the ICI method the sliding window
with the variable length is realized. The length of window adapts for the change in signal properties.

Fig. 3 The nested windows used in the ICI method.
128

1.4E-06

1.2E-06

1.2E-06

1.0E-06

1.0E-06

8.0E-078.0E-07

variation

variation

1.4E-06

6.0E-074.0E-07

6.0E-074.0E-072.0E-072.0E-07

0.0E+00

0.0E+00
0

200

400

600

800

1000

time, µs

498

500

502

504

time, µs

a.

506

508

b.

125

window length, µs

100
75
50
25
0
0

200

400

600
time, µs

800

1000

c.

Fig. 4 a. AE signal variance calculated using sliding window with variable length, b. Initial partof variance rising, c. length of the sliding window.
If the signal part under consideration represents a stationary process, all confidence intervals,
from the minimum window to the maximum one, will intersect, and according to the ICI principle the window optimum length will be equal to the maximum value. No intersection of the confidence intervals indicates that the signal fragment under study contains the change point, that is,in the case concerned, the start point of acoustic emission impulse. The optimum window length
defines the distance to the change point, as the change point becomes closer, the window length
decreases reaching the minimum value.
As described above, the ICI principle can be realized in combination with any signal processing algorithm, which requires that the informative parameter be calculated within the sliding
window. As the most evident parameter distinguishing the noise and the impulse of acoustic
emission is the dispersion, we will consider the ICI principle application for determination of thesignal time-of-arrival from the change of dispersion in the cascade of nested windows of different length. Figure 4 shows that the values of the local variance of the AE signal are calculatedusing a sliding window of variable length. As seen in fig. 4c, in the neighborhood of the signal
start point the length of sliding window starts to decrease, reaching its minimum value at the
point corresponding to the arrival time of AE signal. Due to the adaptive changes in the size of
the window, there is a sharp increase in the local value of dispersion. Duration of growth fromzero to its maximum value is about 3 µs.
The ICI-based method is rested on various statistical approaches to determine the time-ofarrival of AE signal. It uses calculation of informative parameters in a sliding time window. In
129

addition, the ICI method uses elements of LTA/STA technique by comparing properties of signals in different time scales, and a comparison is made between values of informative parameters
calculated using windows with the different lengths.
The disadvantage of this method is more calculations in comparison with the other methods
for the signal arrival time determination. Its main advantages are adaptability, reducing an error
in the signal time-of-arrival determination, and information redundancy. When using the ICI
principle, the signal arrival time can be determined from the sharp increase of values of informative parameters in the concerned case of dispersion, and from the moment when the optimum
size of window reaches a minimum value.
3.1 Selection of window length
Adequacy of results received when employing the ICI principle depends on a correct selection of its parameters – lengths of nested windows. The window length is an important parameter, since the maximum and minimum lengths of windows define accordingly the maximum andminimum scale of signal observation. Under selection of the observation scale, the error leads tothe uncertainty of results.
With numerical experiments, within the limits of this paper, principles for selection of the
maximum and minimum window length have been defined. The minimum length is selected withthe help of a stationary noisy signal. As the stationary signal parameters do not vary with time,the minimum length of window should provide the intersection of confidence intervals with thewindow of any arbitrary large length. Otherwise, “false detections” of AE impulses are possible.
The maximum length of window should not exceed the characteristic length of the AE signal.
Alternatively, the significant errors in determination of the signal time-of-arrival or the AE signal
omission can occur.
3.2 Application of ICI method in combination with HOS approach
The ICI method can be used in combination with any informative parameter, such as an average value, a variance, a median, and other statistical characteristics of signal. If an informative
parameter of the high-order moments is used, application of the ICI principle can be consideredas the improved HOS method, which allows for making an adaptive selection of the sliding window length. Since the result of HOS calculation depends on the time window length, the methodof adaptive change of its length will allow for increasing the accuracy and validity of the HOS
approach in determining the signal origin time.
In implementing the ICI principle, the calculation of confidence intervals of high-order moments presents a certain difficulty. In default of analytical formulas for the confidence intervalsof the above fourth-order moments, we offer to use the bootstrap processing technique [11]. According to the bootstrap technique, a set of samples is formed on the basis of the main analyzed
sample of signal values by eliminating some elements from it in a random manner. On the set ofsamples, a sampling empirical distribution of an informative parameter (for example, the sixth oreighth order moment) is constructed, while on the basis of the empirical distribution constructed
is a sampling confidence interval.

1300.003

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1000

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0.003
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600

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0.003

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time, µstime, µs

00medium rise time high SNR ratio

200

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long time high SNR ratio

Fig. 5. Examples of model AE signals.
4. Arrival time estimation results
To compare the accuracy of signal time-of-arrival determination using different methods, anumerical experiment has been carried out wherein models of AE signals are presented by analytical formula (6),Ns(t ) = A(t − t 0 )∑ sin(ωi t + ϕ i ) + noise(t )

(6)

i =1

where A(t) = nonsymmetrical envelope of harmonic process, and noise(t) = Gaussian noise.
In model signals the parameters varied and had an effect on the accuracy of signal arrival
time determination, such as signal rise time and SNR ratio. It is obvious that the high level ofnoise and long rise time interfere with the exact definition of rise time of AE signals. Figure 5
shows examples of the model signals. In total, about 500 signals were analyzed with the rise timefrom 1 to 100 readings and with SNR ratio from 50 to 2.
The arrival time for each of the signals was estimated by three methods: by a derivative of 6thorder statistical function with the constant length of time window equal to 100 µs (HOS approach), by a derivative of variation function with the variable length of time window (ICI131

approach) and by means of the Akaike criterion. Figure 6 illustrates the error in determination of
the signal time of origin by different methods.
Figure 6a illustrates the error in the method based on calculation of the sixth-order moment.
As is obvious from the figure, the value of error depends on the signal rise time. The less is therise time, the more exact is the determination of signal origin. The delay of estimated time-ofarrival as compared with the true value follows from the final length of time window that reduces
the method sensitivity. However, it should be noted, that decrease in the time window length will
not lead to increase in the method accuracy, since at the small window length "false extrema"appear, making the interpretation of result difficult and reducing the validity of this method.

25
20

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15

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error, µs

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5

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30 15
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30 15

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error, µs

a.

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3
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30 15

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rise time, µs

310

SNR ratio

1c.
Fig. 6 Error in time-of-arrival determination by different methods depending on signal rise timeand SNR ratio. a. HOS approach, b. ICI method on the basis of signal variance, c. AIC criterion.
If a variable-size sliding window is used in accordance with the ICI principle (Fig. 6b), theerror in time-of-arrival determination decreases for signals with the long rise time at the cost of
the window size adaptation to the shape of the signal analyzed. However, this case shows the
SNR ratio dependence of the method accuracy not observed in the previous method.
The best results for signals of any shape can be obtained by using the Akaike criterion (Fig.
6c). The method accuracy is defined by the fact that the Akaike criterion reflects changes inspectral characteristics of the signal not associated directly with increase of signal values. Therefore, the errors in the signal time-of-arrival estimation, which are received by means of AIC do
not depend directly on the shape of signals under analysis and on the level of noise containing in132them. In spite of the fact, that Akaike criterion shows the minimal error level, this method has
disadvantages, which are not apparent from this study. The main disadvantage is that this method
requires a preliminary choice of the region of interest, the signal part that contains the starting
point of AE signal. The two-stage method implementation scheme makes on-line use difficult.
Table 1 shows the typical values of error of different methods.
Table 1. Arrival time error values
Arrival time error values, µs
for signals with a long rise time

HOS approach
13.7

ICI method
8.1

AIC criteria
3.6

for signal with a low SNR ratio

10.3

8.1

3.0

average error

7.8

4.7

2.1

5. ICI-based acoustic emission data classification.
5.1 Supervised and unsupervised classification.
A convenient way of AE event identification is clustering, that is a self-organized data structuring method. While being clustered, AE pulses are joined into groups on the ground of the distance measure, provided by some metric in feature space. If the clustering parameters are chosen
successfully, AE events are clustered in such way, that each cluster corresponds to the particularcertain process in testing structure, or to the certain process stage, that differs from other stages
by its quantitative adjectives. After AE impulses are clustered, AE source that generated eventsin certain cluster could be identified by expert review.
Nevertheless, clustering is an out-of-control process, and even advanced methods could notguarantee data grouping correctness in line with physical model of ongoing processes. Clustering does not involve usage of information given, a priori, that is, on one hand, an advantage of
method, because it can be implemented without such information, and, on the other hand, a disadvantage, as a priori information could not be taken into account even if it is available to theresearcher.
Modern stage of AE testing development is confined to its possibilities enhancing, foremostby identification of testing structure internal processes. These processes could be material lapsing or destruction, or technological processes. Many investigations are being established in orderto reveal the nature of AE as a result the dependence between AE parameters and mechanicalproperties of material is being stated. This dependence is a diagnostic model, used to estimate thestate of testing structure, and this model might be taken into account as a priori information
while classifying AE events.
Availability of a priori information involves usage of supervised classification methods. The
distinction between supervised classification and clustering is a preliminary definition of all classes for supervised classification, while this stage is not performed during clustering. A priori information could be represented as a learning sample, consisting of typical for each class AE133

events, or as a knowledge base with testing structure internal processes, which generate certain
AE events.
AE data identification and classification special feature is a particular AE events ensemble(not single event or single quantitative criteria) characterizing each testing structure internal process. That is why classification objects are ensembles of AE events, detected during certain period, not single events. Such time sequence of events could characterize some process, or its stage,in testing structure. For each process stage AE events parameters are spaced under the particular
distribution law that could be stated empirically in experimental research.
5.2 ICI – based classification algorithm.
Aforesaid allows to suggest an algorithm of AE data supervised classification that implements two main functions [1]: the first is detaching of time periods in AE events flow with certain empirical distribution laws of informative AE parameters, and the second is AE events classification within detached time windows.
Results of laboratory experiments on corrosion process are shown on Fig. 7. Due to shortnessof experiment duration, this results are not of diagnostic concern, but it allows to present application of supervised classification algorithm introduced above.

I

II

III

I

II

III

Fig. 7 AE data obtained during corrosion growth. a. AE amplitude, dB; b. AE event run-on.
For illustration purpose one channel data is chosen. Figure 7a contains amplitude vs. time,and Fig. 7b, AE events run-on vs. time during experiment. It is possible to detach three stages ofcorrosion process development; those are going along with quality changes of anode and cathodereactions behavior. Certain process stage is typified by amplitude and AE activity distribution(Fig. 8). Empiric two-dimensional distribution of this features φ(A,Ev) could be used as a priori
information for Bayesian classification [13].
5.2.1 ICI segmentation.
Supervised stage classification algorithm could be implemented in corrosion monitoring system.
It contains two main stages: the first one is detaching of process stages while registering AE data, the second one is detached stages identification by Bayesian classification with properties statistical distributions typical for each stage as a priori information. Stages detaching by ICI method allows to effectively get stages of various continuance, from order of milliseconds to order of

134

50

35

70
60

25
20
15
10

probability, %40

probability, %probability, %

30

30
20
10

5

40
30
20
10

0

0

50

0

34 36 38 40 42 44 46 48 50

34 36 38 40 42 44 46 48 50

34 36 38 40 42 44 46 48 50

amplitude, dB

amplitude, dB

amplutude, dB

Fig. 8 AE impulse amplitude distribution for I, II and III stage of corrosion process.
hours. Several observation intervals of different scale should be chosen in accordance with ICI
method [12]. The shortest interval duration is determined by expected shortest stage of the process. For segmentation purposes mean values and confidence intervals of informative features
are calculated in each interval of specified durations. If all of get confidence intervals intersect,
the whole observed interval corresponds to one process stage. On the contrary, if some confidence intervals do not intersect, it reveals existence of change point, corresponding to second
stage start, within AE data.
T0

T1

T2

T3

T4

Fig. 9 Embedded time windows for ICI segmentation.
Four embedded time windows are shown on Fig. 9. Confidence intervals {AAE_T1}, {AAE_T2},{AAE_T3}, {AAE_T4} for informative AE data feature – amplitude AAE – are calculated within eachtime interval. If intersection of minimal and maximal duration windows confidence intervals is
not null {AAE_T1}∩{AAE_T4}≠0, then process stage has not changed within this time interval. If intersection of all windows confidence intervals does not occur, it means that time period from T0
to T4 contains more then one process stage. Change point is located in interval between end pointof maximal window where confidence intervals do intersect and end point of first window,
where they do not. For instance, if {AAE_T1}∩{AAE_T3}≠0 and {AAE_T1}∩{AAE_T4}=0, then change
point occurs in [T3,T4]. To find its location more preciously the procedure with time windows(obviously with smaller time scale) and confidence intervals should be repeated within foundinterval [T3,T4].
5.2.2 Bayesian classification
After stage separation it is possible to perform data identification. In accordance with suggested
algorithm stage identification could be implemented by Bayesian classification. By Bayes theorem, on account of empiric informative properties distributions the object belongs to one class{wi} with probability p(wi), where qi = priory probability for each class, and φi(A,Ev) = twodimensional empirical distribution of informative features for ith stage of investigated process:q ϕ ( A, Ev).
p( wi ) = k i i∑ q jϕ j ( A, Ev)

(7)

j =1

135

6. Conclusion
In this paper different methods for change point detection in AE data have been considered,and a comparative analysis of accuracy of different methods has been made. Furthermore, a newalgorithm of signal arrival time determination and AE data classification based on ICI principle
is offered. These methods allow for increasing the estimation and classification accuracy.
References
1. J.M. Rodgers, The “Use of a Floating Threshold for Online Acoustic Emission Monitoring of
Fossil High Energy Piping”, AE Consulting, 1994
2. Karen M Holford and Mark Eaton, “Recent Advances in Acoustic Emission”, Proceedings ofWorld Conference of Acoustic Emission, 2011, Beijing, pp. 58-66
3. P. Sedlak, Y. Hirose, S.A. Khan, M. Enoki and J. Sikula, “New Automatic Localization
Technique of Acoustic Emission Signals in Thin Plates”. Ultrasonics, 49, 2009, 254-62.
4. T. Lokajicek and K. Klima, “A First Arrival Identification System of Acoustic Emission(AE) Signals by Means of a Higher-Order Statistics Approach”. Measurement Science andTechnology. 17, 2006, 2461-66.
5. J. Chen, A.K. Gupta, Parametric statistical change point analysis. Birkhaeuser, 2000, 184р.
6. S.K. Yung and L.T. Ikelle, An Example of Seismic Time Picking by 3rd Order Bicoherence.
Geophysics, 62, 1997, 1947-1952
7. C. Alippi, G. Boracchi, M. Roveri, A just-in-time adaptive classification system based on theintersection of confidence intervals rule. Neural Networks, 24 (2011), pp. 791-800.
8. Vladimir Katkovnik, Karen Egiazarian, Ilya Shmulevich, “Adaptive varying window size
selection based on intersection of confidence intervals rule ”, Signal Processing Laboratory,
Tampere University of Technology, Tampere, Finland
9. P. Stachel, R. Zivanovic and P. Schegner, Enhanced Segmentation of Disturbance Records
by Adaptive Thresholding, Proceedings of Power Systems Computation Conference(PSCC’08), Glasgow, Scotland, July 2008.
10. V. Barat, D. Grishin, M. Rostovtsev, Detection of AE signals against background friction
noise, Journal of Acoustic Emission, 29, 2011, pp. 133-141
11. B. Efron and R.J. Tibshirani, An introduction to the bootstrap. Chapman and Hall, London,
1993.
12. Cesare Alippi, Giacomo Boracchi, Manuel Roveri: A just-in-time adaptive classification system based on the intersection of confidence intervals rule. Journal Neural Networks, 24, Issue 8, October, 2011 Pages 791-800
13. A. Afifi, S. P. Azen. Statistical analysis: a computer oriented approach. Academic Press,
1972

136

Modeling of the Far-field Acoustic Emission from a Crack under Stress
Warida Ben Khalifa1, Karim Jezzine1, Sébastien Grondel2, Gaëtan Hello3
and Alain Lhémery1
1
CEA, LIST, centre de Saclay, bât. 611, point courrier 120, 91191 Gif-sur-Yvette cedex, France
2
Département OAE, IEMN, UMR CNRS 8520, Université de Valenciennes et du Hainaut Cambrésis, Le Mont Houy, 59313 Valenciennes Cedex 9, France
3
IUP GSI-GEII-GM, LMEE, 40 rue du Pelvoux, CE1455 Courcouronnes, 91020 Evry Cedex,
FranceAbstract
Models for predicting the received time-domain waveform by a transducer as a result of an
acoustic emission (AE) event are proposed. They aim at simulating AE examinations of large
structures subjected to various stresses (e.g. mechanical loadings) in different domains (aerospace, pressure-vessel industries, etc.). The energy released by a crack under stress can propagate
as guided waves in thin structures. In thick ones, the acoustic far field radiated by a crack understress is dominated by the presence of Rayleigh wave. 2D models for guided waves in plate-likestructures are proposed as well as 3D models for Rayleigh wave. A piston-like model is used toobtain the delivered voltage at the transducer output, whereas a formulation is proposed coupling
models for the AE sources and for the propagation of guided or Rayleigh waves. Specifically, thecoupling of the expression of the crack-opening displacement and Green’s functions for Rayleighwave is achieved through an integral formulation relying on the elastodynamic reciprocity principle. For the AE source model, a constant crack propagation velocity is considered. The exact
analytical solution from a fracture-mechanics based model is used to obtain the crack opening
displacement. Green’s functions for Rayleigh wave are calculated using reciprocity considerations without the use of integral transform techniques. Predictions computed with this 3D model
are compared to results from the literature for validation purpose on simple geometries and for apenny-shaped tensile crack. For thin structures, a similar coupling formulation based on the elastodynamic reciprocity principle has been derived, allowing the amplitudes of the possibly propagating guided modes at a given frequency to be predicted.
Keywords: Rayleigh wave, guided waves, AE sensor, crack propagation.
1. Introduction
Acoustic emission (AE) in a thick structure is dominated by the presence of Rayleigh wave
in the far field. Indeed, Rayleigh wave decays as 1/ r whereas bulk waves decay as 1/r. In a thin
structure, the energy released by a defect under stress propagates as guided waves.
Different models have been developed to simulate AE from buried crack under stress. For
instance, Harris and Pott [1] developed a Rayleigh wave AE model from a buried crack that predicts Rayleigh waves excited by the starting of a faulting event. The surface wave is expressedby an integral formulation relying on the elastodynamic reciprocity theorem. This formulation
combines bulk waves emitted by the starting event and the Rayleigh wave components of theGreen’s tensor, calculated in [2] from the coupling between the P-wave and S-wave component(SV) polarized in the plane of incidence. The Rayleigh wave is then evaluated by means of thestationary phase technique and the particle velocity of the emitted wave is approximated near theRayleigh wave arrival time.
J. Acoustic Emission, 30 (2012)137© 2012 Acoustic Emission GroupA method using a seismic moment tensor approach [3] has been also adopted in the AE field
to describe the damage processes since micro-damage and seismic rupture share the same source
mechanism, at different time and length scales. The moment tensor method was used for describing different types of point sources and equivalent body forces for displacement discontinuities.
Aki and Richards [3] summarized studies of elastic waves generated by seismic sources, where
surface wave terms of Green’s tensor have been calculated in cylindrical coordinates in the caseof a vertically heterogeneous half space.
In the case of a thin structure, the prediction of guided waves emitted by a crack under stress
was calculated by reciprocity considerations [7], a moment tensor method [4] or a finite element
method [5]. Bogert [4] calculated the AE in plates using a seismic moment tensor method andGreen’s function. He compared different crack types and different propagation models. In addition, he studied the effect of the use of first and third order plate theory solutions on the AE signal. Achenbach [7] calculated the AE from a surface-breaking crack in a plate. The amplitudes ofsymmetric and antisymmetric modes were calculated separately by an integral formulation as afunction of the crack opening displacement.
In this paper, AE from a crack under stress is obtained by applying the elastodynamic reciprocity principle. In the first part, we present a 3D AE model of Rayleigh wave emitted by a defect under stress. This model couples a fracture-mechanics source model and a Rayleigh-wave
Green’s function. Green’s functions are obtained by applying the elastodynamic reciprocity principle following a method presented by Achenbach in [6]. The AE source model used in the coupling formulation is determined from the exact solution of the crack opening displacement as afunction of the initial and final crack length and the crack propagation velocity.
In the second part we present a model for the AE of guided waves using an integral formulation method proposed by Achenbach [7]. This formulation combines a guided wave propagation
model and a fracture mechanics source model. The modal solution is calculated by the SAFE
method (semi analytical finite elements method).
The originality of the method lies in the fact that this formulation combines a propagation
model and a fracture-mechanics source model with an arbitrary crack orientation. The work presented in this paper will be integrated in CIVA, a commercial NDE simulation package developed at CEA-LIST.
2. Rayleigh wave AE from buried crack in a thick structure
2.1 Application of the reciprocity theorem for computing the emitted Rayleigh wave
In a 3D geometry, the particle displacement associated to the Rayleigh wave emitted by a
growing crack under stress can be calculated by applying the elastodynamic reciprocity principle. In the frequency domain, the elastodynamic reciprocity theorem for a body of volume V andsurface S is written as:∫ ⎡⎣ f u − f u ⎤⎦ dV = ∫ ⎡⎣τ u −τ u ⎤⎦ n dS
V

A B
j
j

B
j

A
j

S

B
ij

A
j

A B
ij j

i

(1)

where ni are the components of the outward normal to S, fj, uj and τij are the components of bodyforces, displacements and stresses.

138

(a)

(c)

(b)

	
  

Fig. 1: (a) Geometry of the AE problem, (b) definition of the global cylindrical coordinate system (r, θ, z) and (c) geometry of the crack and definition of the local cylindrical coordinate system (ρ, ϕ, x1)
We define state A as the solution of the acoustic emission problem and state B as the Rayleigh wave emitted by a point source applied in the xk direction (xk = x1, x2 or z). We apply thereciprocity equation to the region of the half space defined in Fig. 1 where Σ is the surface of thecrack located in the (x2, z) plane. The integrals over the free surface and the hemisphere of radius
R as R → ∞ vanish and the reciprocity equation leads to:ukA (ξ ) = ∫ ⎡⎣uiG,k ( X , ξ )τ ijA − uiAτ ijG,k ( X , ξ )⎤⎦ n j ( X ) d Σ ( X ) 	
  

(2)

Σ

where uiG, k and τ ijG, k denote the displacement and stress components of the Rayleigh Green’s tensor,
respectively. X =(r, θ, z) and ξ = (r0,θ0, z0) are the positions of the observation point and of thesource, respectively.
2.1.1 Rayleigh Green’s function
The particle displacement of the Rayleigh wave generated by a point load in a cylindrical coordinate system can be calculated by applying the elastodynamic reciprocity theorem [6].

Fig. 2: Half space subjected to a point load at z=z0.
The displacement components of Rayleigh wave generated by a point load of magnitude Q(Figure 2) applied at z=z0 in the x1 direction are:k QV ( z0 ) R
ur ,1 = R
V ( z ) Φ' ( k R r ) cosθ4i
IR

⎛ −1 ⎞
k R QV ( z0 ) R
V ( z )⎜⎟ Φ ( k R r ) sinθI
4i
⎝ rk R ⎠(3)

Ruθ ,1 =139(4)

k QV ( z0 ) R
u z ,1 = R
W ( z ) Φ ( k R r ) cosθ
4i
I
R

(5)

where Φ ' ( x) = d Φ . In the case of a point load of magnitude M in the x2 direction, we have:dx

k MV ( z0 ) R
ur ,2 = − R
V ( z ) Φ' ( k R r ) sin θ
4i
I
R

R
⎛ −1 ⎞k R MV ( z0 ) R
V ( z)⎜⎟ Φ ( k R r ) cos θI4i⎝ rk R ⎠uθ ,2 =

(6)
(7)

k MV ( z0 ) R
u z ,2 = − R
W ( z ) Φ ( k R r ) sin θ
4i
IIn the case of a point load of magnitude P in the z direction, we have:(8)

kR PW ( z0 ) R
V ( z ) Φ0' ( k R r )
4i
I

(9)

R

R

ur , z = −uθ , z = 0(10)

k PW ( z0 ) R
uz, z = − R
W ( z ) Φ0 ( k R r )
4i
I
R

(11)

where

I=

∞

∫ ⎡⎣T (z)V (z) − T (z)W (z) ⎤⎦ dz.
0

R
rr

R

R
rz

R

Expressions of V , WR, TrrR and TrzR are available in [6] andR(12)Φ ( kR r ) = H1(1) (kR r )(13)Φ0 ( kR r ) = H 0(1) (kR r )(14)

H n(1) is the first kind Hankel function of order n. The Rayleigh wave number is defined as:kkR = t(15)

ηR

where ηR is given using a standard approximate formula for the Rayleigh wave velocity byηR =0.87 + 1.12v
1+ v

(16)

where kt is the shear wave number:1⎛ ρ ⎞2kt = ω ⎜ ⎟(17)⎝µ⎠ρ, ν, λ and µ denote the density of the medium, the Poisson ratio and the elastic Lamé constants
defined by: λ =Eν
Eand µ =
, where E is the Young's modulus.
(1 +ν )(1 − 2ν )2(1 +ν )We express the Green tensor in cylindrical coordinates using the superposition principle:⎛ ur ,r ur ,θ ur , z ⎞⎜⎟R
G = ⎜ uθ ,r uθ ,θ uθ , z ⎟
(18)
⎜u
⎟
⎝ z ,r u z ,θ u z , z ⎠140wherek R V ( z0 ) R
V ( z ) Φ' ( k R r ) ,4i
Iuθ ,r = 0,(19)

k R V ( z0 )W ( z ) Φ ( kR r ) ,4i
Iur ,θ = 0,

(21)

R

ur , r =(20)Ru z ,r =⎛ −1 ⎞k R V ( z0 )2
V ( z )⎜⎟ Φ ( k R r ) 1 − 2(sin θ )
I
k
4i
r
⎝ R⎠
R

uθ ,θ =

(

(22)

)

(23)

u z ,θ = 0.

(24)
We neglect terms attenuating with distance more rapidly than 1/ r and we use the asymptotic
expansion for Hankel functions in the next calculations. These approximations were used by Aki
and Richards in [3].
2.1.2 Acoustic emission source
We assume that the crack is a surface of displacement discontinuity. In the case of tensile circular crack of radius a loaded by uniform pressure σ on its faces, the crack opening displacement(COD), expressed from the complex solution issued from fracture mechanics in the local cylindrical coordinate system (ρ, ϕ, x1) can be looked up in text books. One has:Δu1 ( ρ ) = 2

4(1 − v 2 )σ
πE

(25)

a2 − ρ 2

2.1.3 AE from tensile crack
Equation (2) can be written as:
ukA (ξ ) =∫ ⎡⎣u ( X , ξ )τ − u τ
G
i,k

A
ij

Σ+

+

∫

Σ−

⎡uiG, k⎣A G
i ij , k( X , ξ )⎤⎦ n j ( X ) d Σ+ ( X )( X , ξ )τ ijA − uiAτ ijG,k ( X , ξ )⎤⎦ n j ( X ) d Σ− ( X )(26)

Σ+ and Σ- are respectively the crack surface on x1= 0+ and x1= 0-.
In the case of a tensile stress (mode I), the displacement at the surface of the crack in the
global cylindrical coordinate system is:(3)uθA = u1A
Eq. (26) can be written as:
ukA (ξ ) =∫ ⎡⎣−Δuθ τθθ ( X , ξ )⎤⎦ nθ ( X ) d Σ ( X )

(4)

− uθA

(5)

A G

Σ

where

A

A

Δuθ = uθx1 = 0+,k++x1 = 0−The integral over the surface Σ+ can be written as:

(30)
141

As uiG,k ( X , ξ ) = ukG,i (ξ , X ) ,ukA (ξ ) =G⎛ ∂u G (ξ , X )⎞∂ukG, z (ξ , X ) ⎞ +1 ⎛ ∂uk ,θ (ξ , X ) G
k ,r⎟ dΣ ( X )−ΔuθA ⎜ λ+ ( λ + 2µ ) ⎜+ u k , r (ξ , X ) ⎟ + λ
⎟
⎟
⎜
r ⎜⎝
∂r
∂θ
∂z
⎠
⎠
⎝
Σ+

∫

(31)
We simplify the expression by considering a single dipole acting at the center of the crack, i.e. at
X= (0, 0, h) (see Fig. 1):G⎛ ∂u G (ξ , h )⎞∂ukG, z (ξ , h ) ⎞1 ⎛ ∂uk ,θ (ξ , h )k ,r⎟+ ( λ + 2µ ) ⎜+ ukG,r (ξ , h ) ⎟ + λukA (ξ ) = ⎜ λ
⎟
⎜
⎟
∂r
∂θ
∂z
r ⎜⎝
⎠
⎝
⎠

×

a

2π

0

0

∫ ∫ −Δu ρ d ρ dϕ .
A
1

(6)

2.2 Simulation and comparison with literature
We assume that the crack diameter evolves from l0 = 1 mm to l =5 mm at a velocity V =2000 m/s during T = 0.8 µs, and we consider a sampling frequency Fe = 50 MHz. We define rj
and tn as:
rj = jV Δt(33)

1
tn = t0 + n
Fe

(34)

j varies from 0 to J/2, and n varies from 0 to N where:Δt = 1/ Fe

(35)

T
N=
Δt

(36)

l
J=
V Δt

(37)

Eq. (25) can be discretized as follows:

(

)

(

)

Δu1 rj , tn = 2σand4(1 − v2 )((l0 + nV Δt ) / 2)2 − rj2 , for rj ≤ (l0 + nV Δt ) / 2
πE

Δu1 rj , tn = 0, for (l0 + nV Δt ) / 2 ≤ rj ≤ l / 2

(38)
(7)

We take the Fourier transform of the COD given by eq. (38) to obtain the displacement fieldof the emitted Rayleigh wave in the frequency domain given by eq. (32). The displacement fieldin the time domain is then obtained using the inverse Fourier transform of eq. (32).
We have simulated the displacement field of the Rayleigh wave emitted by a defect evolving
at different velocities V. Figure 3 presents the particle velocity of the emitted wave in the time
domain obtained by deriving eq. (32) in the time domain.
The amplitude of the emitted wave increases and the arrival time decreases when the crack
propagation velocity increases. This observation can be explained by analyzing eq. (40). The
displacement of the emitted wave in the time domain is obtained from the convolution of thespatial derivative of the Green’s function with the integral of the COD over the crack faces, as

142

(a)

(b)

(µs)

(µs)
(c)

(d)

(µs)

(µs)

Fig. 3: Rayleigh wave particle velocity emitted by a crack under 200 MPa, normal component in
red and radial component in blue. (a) V= 500 m/s, (b) V= 1000 m/s, (c) V= 2000 m/s and (d) V=
2500 m/s.
S=

a

2π

0

0

∫ ∫ −Δu ρ d ρ dϕ
A
1

(40)
Figure 4 presents the quantity S in the time domain. The main contribution arises at a time corresponding to the end of the emitting process, which is inversely proportional to V.

(µs)
Fig. 4: Integral of the COD over the crack faces for V= 500, 1000, 2000 and 2500 m/s.
143

(a)

(b)

Fig. 5: Comparison of the Rayleigh wave acoustic emission normalized particle velocity obtained by our method (a) with a result from [1] (b).
We have compared our results with a result from literature [1] on Fig. 5. There is a good
agreement between the two results. Differences can be explained by the assumptions used in thetwo models. Our integral formulation combines directly the COD and the Rayleigh wave Green’s
functions. This is not the case for Harris and Pott model [1]. Their integral formulation combines
the body waves emitted from the crack approximated at the Rayleigh wave arrival time and Rayleigh wave Green’s functions.
Our results presented in Fig. 5(a) are obtained by using the crack opening displacement at all
points of the crack obtained from a fracture-mechanics exact model whereas results from theliterature [1] in Fig. 5(b) present the velocity of the emitted wave from the starting of faulting
event considering only the crack tip velocity.
3. Simulation of the reception by a piston-like transducer model
We have simulated the delivered voltage by an AE sensor using a piston-like model. In fact,
only the sensitivity to the normal component of the particle velocity of the emitted wave has
been considered. In this case, the delivered voltage can be written as [8]:E = 2π ∫ m(r0 )vz (r0 ) dr0 .

(8)

S

144

We consider a crack propagating at a velocity V = 2500 m/s. The crack diameter is evolvingfrom 1 mm to 5 mm under 200 MPa. The receiver is an AE sensor of radius R=5 mm and a constant sensitivity m(r0) = m0 = 600 V/m/s. The AE sensor is positioned at a distance r = 100 mm
away from the epicenter of the crack. Figure 6 illustrates the geometry of the AE problem andthe reception by an AE sensor.

Fig. 6: Reception by an AE sensor.
The delivered voltage has been simulated using the method described in Sec. 2 and integrating the particle velocity over the sensor surface. Figure 7 presents the received waveform by thesensor, which results from the integration of the Rayleigh normal particle velocity over the sensor area.
delivered voltage (V=2500 m/s)

delivered voltage (V)

0.01

0.005

0

-0.005

-0.01

0

10

20

30

40
t(µs)

50

60

70

80

Fig. 7: Delivered voltage by an AE sensor.
4. Guided waves AE from buried crack in thin structureThis paragraph describes the AE in a plate by a crack of arbitrary orientation. The geometry of the problem is shown on Fig. 8. Guided modes generated by a crack under stress are calculated again by applying the elastodynamic reciprocity theorem following a method presented by
Achenbach [7]. The acoustic field emitted by a crack under stress is now composed of both145

symmetric and antisymmetric modes [7], whose displacement and stress fields can be written as
follows:

Fig. 8: Geometry of the acoustic emission problem in thin structure.
∞⎡∞⎤u xA = ±i ⎢ ∑ AmaVam ( z ) e± ikm x + ∑ Ams Vsm ( z ) e ± ikm x ⎥m =0⎣ m =0⎦(9)∞⎡∞ a m⎤± ikm xu = ⎢ ∑ AmWa ( z ) e+ ∑ Ams Wsm ( z ) e ± ikm x ⎥m =0⎣ m =0
⎦

(10)

A
z

⎡∞

∞

⎤

τ xxA = ±i ⎢ ∑ Ams Txxsm ( z ) e± ik x + ∑ AmaTxxam ( z ) e± ik x ⎥mm⎣ m =0

⎦

m =0

⎡∞

∞

⎤

τ xzA = ⎢ ∑ Ama Txzam ( z ) e± ik x + ∑ Ams Txzsm ( z ) e± ik x ⎥m⎣ m =0

m

⎦

m =0

(11)

(12)

where km is the wave number of the mth guided mode. Expressions of Vam , Vsm , Wam , Wsm , Txxam ,Txxsm , Txzam and Txzsm are available in [7].
4.1 Application of the reciprocity theorem for the calculation of the emitted guided waves
We select state A as the solution of the acoustic emission problem presented by equations(42) to (45) and state B, as a virtual symmetric or antisymmetric wave.
4.1.1 Amplitude of symmetric modes:
We select state B as the nth symmetric mode propagating in the positive x1 direction. Displacement and stress components can be written as:(13)uxB = iVsn ( z ) e−ikn x

uzB = Wsn ( z ) e−ikn x(14)

τ xxB = Txxsn ( z )e−ik x(15)

τ xzB = iTxzsn ( z )e − ik xn(16)

τ zzB = Tzzsn ( z )e −ik x

(17)

n

n

We apply the reciprocity relation to the region surrounding the crack defined in Fig. 9. In theabsence of body forces, equation (1) may be written as:
146

Fig. 9: Contours of integration used in the integral formulation.

∫ ⎡⎣τ u −τ u ⎤⎦ n dS = 0 .
B
ij

S

A
j

A B
ij j

(18)

j

We define J1 as the integral along the line −h ≤ z ≤ h , x = a , J2 along −h ≤ z ≤ h , x = b and J3,
over Σ. Integrals along the free surfaces z = ± h vanish. J1 may be written as:h(19)
J1 = − ∫ ⎡⎣τ xxB u xA + τ xzB u zA − τ xxA u xB − τ xzA u zB ⎤⎦ dz−hWe replace displacement and stress components by their expressions from equations (42) to (45)and (46) to (50), so that integral J1 can be expressed as follows:
∞

∞

m =0

m =0

s
s
as
as
⎤⎦ + ∑ i AmA ⎡⎣ I mn⎤⎦ ei ( km −kn ) a+ I nm+ I nmJ1 = ∑ i Ams ⎡⎣ I mn

(20)

where:
h

and

as
I mn
= ∫ ⎡⎣Txxs m ( z )Vsn ( z ) − T axzm ( z )Wsn ( z ) ⎤⎦ dz = 0
−h

(21)

h

s
(22)
I mn
= ∫ ⎡⎣Txxs m ( z )Vsn ( z ) − Txzs m ( z )Wsn ( z ) ⎤⎦ dz−hAs the summation produces non zero result only for n = m, the integral can be simplified asfollows:(23)
J1 = 2iAns I nns
The contribution from x = b leads to:
(24)
J =0
2

Integration over the crack surface can be written as:
J 3 = ∫ ⎡⎣τ uuA uuB + τ uvA uvB − τ uuB uuA − τ uvB uvA ⎤⎦ d Σ

(25)

Σ

J3 is calculated from the contribution of the two line elements that define Σ, as follows:

J3 = ∫

v0 + lv0+∫⎡⎣τ uuA uuB + τ uvA uvB − τ uuB uuA − τ uvB uvA ⎤⎦ + dv
u =0

v0

⎡τ A u B + τ uvA uvB − τ uuB uuA − τ uvB uvA ⎤⎦ − dv
v0 + l ⎣ uu u
u =0

(26)

We express the integral over the crack faces as a function of the displacement and stress discontinuities:
v0 +l
(27)
J 3 = ∫ ⎡⎣ Δτ uuAVsn (0,v) + Δτ uvAWsn (0,v) − Tuusn (0,v)ΔuuA − Tuvsn (0,v)ΔuvA ⎤⎦ dvv0
whereVsn (0,v) = uuB (0,v)
(28)
147

Wsn (0,v) = uvB (0,v)
T sn (0,v) = τ B (0,v)(29)

Tuvsn (0,v) = τ uvB (0,v)

(31)

uu

(30)

uu

The amplitude of symmetric waves can be obtained from the condition that the total contour
integral must vanish [eq. (51)]. One gets:−i v0 +l
AmS = S ∫ ⎡⎣ Δτ uuAVsn (0,v) + Δτ uvAWsn (0,v) − Tuusn (0,v)ΔuuA − Tuvsn (0,v)ΔuvA ⎤⎦ dv
2I mm v0(32)
4.1.2 Amplitude of antisymmetric modes:
Similarly, the amplitude of antisymmetric modes can be obtained by selecting the nth antisymmetric mode as a virtual wave for state B.
−i v0 +l
AmA = A ∫ ⎡⎣ Δτ uuAVan (0,v) + Δτ uvAW an (0,v) − Tuuan (0,v)ΔuuA − Tuvan (0,v)ΔuvA ⎤⎦ dv
2I mm v0
(66)
where
h

A
I mn
= ∫ ⎡⎣Txxa m ( z )Van ( z ) − Txza m ( z )Wan ( z ) ⎤⎦ dz−hnVa (0,v) = uuB (0,v)
W n (0,v) = u B (0,v)
avTuuan (0,v) = τ uuB (0,v)
T an (0,v) = τ B (0,v)
uv

(33)
(34)
(35)
(36)
(37)

uv

4.2 Simulation and comparison with literature
We consider an isotropic plate of 3 mm thickness and Poisson’s ratio ν = 0.33. We have simulated the surface strain of the first symmetric (S0) and antisymmetric mode (A0) emitted by avertical surface-breaking tensile crack as shown in Fig. 10 and compared our simulation with aresult from literature [5] obtained from a finite element method (). We assume that the time dependence of the crack opening displacement is a unit Heaviside step function.

Fig. 10: Surface-breaking tensile crack.
The modal solutions were calculated by the semi-analytical finite element method (SAFE)[9]. Figure 11(a) presents the filtered surface strain for modes S0 and A0 generated by the surface-breaking tensile crack using a Gaussian window with 250 kHz center frequency and band148

width of 350 kHz (measured at -40dB points). The comparison of our model with the finite element results [5] shows a good agreement.
We have also performed a parametric study giving the mode amplitudes as a function of thecrack depth. Results obtained with our model have also been compared with finite element results taken from [5]. A qualitative agreement between both models is observed as A0 shows thehighest amplitude for the most of the considered crack depths and S0 amplitude increases slowly
with the crack depth. Nevertheless, our results seem to underestimate the amplitude of the S0
mode in comparison to finite element ones.

Fig. 11: Surface strain transient response from a surface-breaking crack of 1.5-mm depth at a
distance x= 300mm from the plane of the crack (a) our result, (b) Result in [5] using a finite element model.
5. Conclusion
A formulation coupling an acoustic emission source model and a propagation model has been
proposed in the case of thick structures in order to predict the acoustic emission from the propa149

gation of a crack under stress and to quantify the influence of the crack propagation velocity on
the Rayleigh wave AE signals. Comparisons of our simulated results with results from the literature showed satisfying agreement even if the two models are different and not based on the same
approximations and hypotheses. A similar formulation has been derived for guided wave andcompared with results from the literature. In addition, the reception by an AE sensor has been
simulated using a piston-like model considering only the sensitivity to the normal component of
the emitted wave particle velocity.

Fig. 12 : Amplitude of the first symmetric and antisymmetric emitted modes as a function of thesurface-breaking crack depth at a distance x= 300 mm from the plane of the crack (a) our result,(b) Result in [5] using a finite element model.
Acknowledgements
The work presented in this paper was partly supported by ANR (French National Research
Agency) for the project MACSIM - ANR-08-COSI-005-01.’
150

References
1. G P Harris and J Pott, 'Surface Motion Excited by Acoustic Emission from a Buried Crack',
J. Appl. Mech. Vol 51, pp 77-83, March 1984.
2. J D Achenbach , A K Gautesen and H McMaken, 'Ray methods for waves in Elastic solids',
Pitman publishing INC, 1982.
3. K Aki and P G Richards, 'Quantitative Seismology', University Science Books, 2002.
4. Ph B Bogert, 'Transient Waves from Acoustic Emission Sources in Isotropic Plates Using a
Higher Order Extensional and Bending Theory', Doctoral Thesis, North Carolina State University, 2010.
5. C K Lee, J J Scholey, P D Wilcox, M R Wisnom, M I Friswell, B W Drinkwater, 'Guided
Wave Acoustic Emission from Fatigue crack growth in Aluminium Plate', Advenced Material Research. Vol 13-14, pp 23-28, 2006.
6. J D Achenbach, 'Reciprocity in elastodynamics', Cambridge University Press, 2003.
7. J D Achenbach, 'Acoustic Emission from a Surface-breaking Crack in a Layer under Cyclic
Loading', J. Mech. Mat. and Struct., Vol 4, No 4, pp 649-657, 2009.
8. L Goujon and J C Baboux, 'Behaviour of acoustic emission sensors using broadband calibration techniques', Meas. Sci. Technol. Vol 14, pp 903-908, 2003.
9. K Jezzine, 'Approche modale pour la simulation de contrôle non destructifs par ondesélastiques guidées', doctoral thesis, Université de Bordeaux I, 2007.

151

Primary Calibration of Acoustic Emission Sensors by the Method of
Reciprocity, Theoretical and Experimental Considerations
Thomas Monnier1, Seydou Dia2, Nathalie Godin2 and Fan Zhang3
Université de Lyon, INSA-Lyon, LVA EA 677, F-69621 Villeurbanne, France
2
Université de Lyon, INSA-Lyon, MATEIS UMR 5510, F-69621 Villeurbanne, France
3
CETIM; Senlis, France
1

Abstract
This paper is focused on the calibration of acoustic emission (AE) sensors by means of thereciprocity method. Therefore, the problem of the reciprocity criterion in the case of ultrasonicwaves in solid is addressed. This latter criterion is well established and widely used for thecalibration of electro-acoustic transducer. However, and despite that the first papers have been
published forty years ago, its application in the case of ultrasonic wave in solids now raises anumber of discussions in the community of acoustic emission. To start with, we present a reviewof the concept of reciprocity since its origin in electromagnetism to its application to electroacoustic transducers. We follow a critical analysis on the validity of the reciprocity theorem inthe case of AE sensors. Next, a new aperture function will be presented, reconsidering theclassical assumption of a constant sensitivity of the transducer’s sole by achieving anexperimental estimation of its actual vibration using contactless laser interferometry. Finally, an
original method for shear wave calibration of AE sensors will be presented.
Keywords: AE sensors, calibration, reciprocity method, aperture effect.
1. Introduction
The reciprocity method is based upon the reciprocity theorem. In the field of electrical
networks the following statement can translate the reciprocity theorem: “a given e.m.f. in the pth branch will produce the same current in the q-th branch of a circuit as the same e.m.f. in the qth branch would produce in the p-th branch” [1]. As a consequence of the principle of electromechanical equivalence, this theorem is applied to ultrasonic transducer.
McLean [2] was the first to propose a reciprocity-based technique for the calibration ofelectroacoustic transducers. The reciprocity technique has since then been developed andextended to carry out primary calibration of microphones, accelerometers and ultrasonic sonartransducers. It appears that this technique can be developed further to carry out calibration oftransducers operating on an isotropic solid medium [3], and in particular for acoustic emission
sensors as first proposed formally by Hatano [4].
The basic principle of the reciprocity calibration method is that three transducers, all ofwhich are uncalibrated, are used as emitter and receiver successively. The basic experimental
arrangement is shown in Fig. 1. The conditions of validity of this method are defined as follow:
1. AE sensors are assumed linear and reversible.
2. The coupling of the sensors must be reproducible (defined materials, constant thickness). [5]3. The propagation medium is free of interference referred to the wave (attenuation, reflections, conversions, etc.).
J. Acoustic Emission, 30 (2012)152© 2012 Acoustic Emission GroupAs shown in Fig. 1, reciprocity calibration process consists of three steps. Each step involves
two sensors: the emitter and the receiver. At each step current absorbed by the emitting sensor
and voltage across the receiving sensor are measured. The sequence is repeated for the three AE
sensors pairs.
Finally, computation performed on the measured voltages and currents yields the sensitivitiesof all three sensors. The reciprocity calibration process is relatively straightforward. The
receiving sensitivity of sensor 2 can be written as:

where H is the reciprocity parameter. H is frequency dependent and is defined as the ratio M/S
for a given transducer. M is the receiving sensitivity of the transducer, and S the emitting
sensitivity of the transducer. In this paper we review the theoretical basis of the reciprocity
parameter and reciprocity calibration of AE sensors.

Fig. 1: Methodology of reciprocity calibration of AE sensors.
Using this methodology, we obtain the reception sensitivity curve of the sensor to becalibrated with reproducibility on the sensitivity to the resonant frequency of the order of 2dB,
with some limitations:- Only the surface wave response is available in the frequency band of 50 kHz to 1 MHz.
- Some difficulties remain to calibrate sensors low frequencies.
For what concerns Rayleigh waves calibration, our results are consistent with those obtained by
the original work of Hatano [6] and those recently shown by other research teams [7-9].
This article is divided into three parts. The first section discusses the question of reciprocity
criterion in the case of AE sensors. Then follows the introduction of a new aperture function.
Finally, an original method for shear wave (S-wave) calibration of AE sensors will be presented.
2. The reciprocity parameterTo give a proper definition of the reciprocity parameter is not a simple task. Generally, thereciprocity parameter is defined by either quoting an equation or quoting a definition, neither ofwhich gives any physical insight into the meaning of the parameter. The literature on acoustic
reciprocity calibration is also confusing although the measurement procedure is relatively
straightforward.
153

To our knowledge the only theoretical work on the reciprocity method in the case of acoustic
transducer is owed to Bobber [10]. He showed that the reciprocity parameter can be expressed as
the ratio of acoustic volume velocity to the mean pressure at the face of the transducer. He also
proved that the reciprocity parameter is actually the transfer admittance of the medium. He
deduced from that, reciprocity parameter for: (i) spherical wave, (ii) plane wave, (iii) acoustic
couplers, (iv) diffused sound, (v) tubes.
Bobber's conclusions are relevant to the case of transducers totally immersed in fluid
medium. Since in standard operation AE sensors are stuck to the external surface of solid
medium, how reciprocity parameter can be derived?
Table 1: Expressions of reciprocity parameter
Medium	  Fluid	  Geometrical	
  arrangement	  Plane	
  wave	  Solid	
  Reciprocity	
  Parameter	  H	
  Spherical	
  wave	  Rayleigh	
  wave	  (R-­‐wave)	
  Longitudinal	
  wave	  (L-­‐wave)	
  Shear	
  wave	  (S-­‐wave)	
  

	
  

	
  

	
  

	
  

	
  

d is the propagation path distance, ρ is the material density, f is the calibration frequency, C is thecompliance of coupling medium, K and Y are numerical solutions to Lamb's equation for specificvalues of Poisson's ratio, σ is the material Poisson's ratio, E is Young's modulus.

Hill and Adams [5] gave the answer to that question, by defining AE measurement procedurein terms of transfer functions. Dimensional analysis of the equations obtained yields that themedium is excited by a surface velocity. Hill and Adams noted that this observation is consistent
with the transformation of electrical to acoustical quantities. Most of the theories relating to thecalibration of ultrasonic transducers in fluid use volume velocity for the source output andacoustic pressure as the received quantity. However, Hill and Adams have shown that other
input and output quantities are possible. Among these possibilities, the ratio Velocity/Force isindicated for AE sensors. As a result R-wave reciprocity parameter is obtained from the basic
theory of surface wave propagation on a semi-infinite half space – Lamb [11]. Hill and Adams
showed that considering a point force driving the surface derives the reciprocity parameter,
yielding vertical velocity a distance d away. This definition is the starting point for the finite
element simulations we conducted in our laboratory. However, the results are not consistent with154

the conclusions of Hill and Adams. Further investigations are currently conducted. Various
expressions of the reciprocity parameter according to the type of wave and the propagation
medium are summarized in Table 1, all coming from Hill [12] except for S-wave. In thatparticular case, we simply propose to adopt the same definition as for L-waves but replacing theexpression of longitudinal wave velocity by the shear wave velocity.
So far we have pointed to the question of the definition of the reciprocity parameter. Now, it
is necessary to make some special remarks about the fulfillment of the reciprocity criterion in thecase of AE sensors.
The case of bulk waves (e.g. L-wave) is handled in Bobber's paper. In that case thereciprocity criterion is met for AE sensors. In the case of surface wave (R-wave), a phenomenon,
called the aperture effect, makes possible the existence of frequencies, at which the sensor
response is null. Thus, for these cut-off frequencies, the sensor can emit but cannot hearanything. Consequently reciprocity is not met.
Bobber defined a general reciprocity parameter for electro-acoustical transducer. Hill andAdams have defined the reciprocity parameter for AE sensors calibration. Although AE sensors
in L-wave configuration met the reciprocity criterion, it is not the case for R-wave configuration
due to aperture effect. In the next section we shall go into more details about the aperturefunction. First, a classical approach will be used to define it. Next, a new aperture function basedon an original approach will be presented.
3. The aperture effectLet us consider a wave, propagating on the surface of a propagation medium. An AE sensor
is positioned on the propagation medium. When the sensor is submitted to the surface wave, thenormal vibration velocity at each point of its face is not uniform. It can be derived that thevoltage across the sensor is proportional to the average vibration velocity. Such average depends
on the wavelength of the incident wave and the sensor diameter. This phenomenon, called theaperture effect, acts as a filter applied to the R-wave sensitivity of the sensor. The complex valueof this filter in the frequency domain is called the aperture function. It has been shown thatremoving the aperture effect by combining the sensor’s aperture function with its R-wave
sensitivity yields its L-wave sensitivity.
To illustrate this phenomenon, Fig. 2 shows the displaced surface of a sine wave, for which
the average movements gives zero values for the sensor’s response at certain cut-off frequencies.
For AE sensor larger than the wavelengths of interest, the classical recommendation to overcome
this limitation simply consists in choosing sensing elements with a diameter as small as otherconstraints allow.
It has been shown [13, 14] that the voltage output of AE transducers is proportional to thenormal surface velocity since these sensors have a dominant response to particle motion normal
to the surface. Moreover, in Rayleigh wave-based reciprocity calibration, the sensor is mainly
submitted to a normal vibration velocity because the fluid couplant strongly attenuates the inplane component of the vibration [15]. A first approach to the calculation of the aperture function
is provided by the ASTM [16], which consider that the sensor is hit by an ideal plane wavefront.
Goujon et al. [17] refined the calculation of the aperture function by considering the case of acircular wavefront. In this paper, the size and shape of the sensor surface as well as the155inhomogeneity of its sensitivity will be taken into account to estimate the output voltage. The
influence of the size and shape of the sensor on the output voltage is given by the following
integral:!
U=ω r m(r)dS(1)! !
where:
U is the open circuit voltage across the receiving sensor,
S the active surface of the sensor,
ω is the normal velocity of a point of the sensor surface,m is the sensitivity of a point placed at the distance r from the center of the sensor’s surface.

Fig. 2: Illustration of the aperture effect.
In the literature, a common assumption is to consider that m(r) is uniform over the wholesurface of the sensor. Consequently, in the case of cylindrical wave of amplitude A, thehypothesis m(r) = m0 leads (1) to become:U=!!!!! !!

!! !
!

!

!!

rdr dθ

(2)

Fig. 3: Geometry used in the calculation of the aperture function of the AE sensor.
156

where r0, r and D are defined in Fig. 3, which shows the geometry used in the calculation of theaperture function.
The receiving sensitivity of a sensor in Rayleigh waves MR is defined as the ratio between the
open circuit voltage of the sensor U and the normal component of the Rayleigh-wave velocity at
the center of the sensor (r0=D), which can be written as:
𝜔 𝐷 =

!
!

𝑒 !!!! !

(3)

Then, the expression of MR is:! !!!! !!

!

𝑀! = !! 𝐷𝑒 !!! !

!

!!

𝑟d𝑟 dθ

(4)

With 𝑘! = 2𝜋𝑓 𝑐! the Rayleigh wavenumber, 𝑐! being the velocity of Rayleigh wave in steel, it
follows immediately:!! !!! !

𝑀! = 𝑚! 𝐹 𝑓, 𝑆 = 𝑚!

!

! !!!! !!
!

!!

𝑟d𝑟 dθ

(5)

which defines the aperture function F of the sensor.
The aperture function depends not only on the geometry of the sensor as well as on thefrequency and geometry of the incident wavefront, but also on spatial variations of the sensitivityof sensor surface. Indeed, one can find in [17] mentions of experimental deviations between theexperimental and theoretical aperture function, which could be attributable to the false
assumption of uniform sensitivity m0. Considering that the sensitivity of the sensor depends on
the amplitude and phase of the free-end vibration of the sensor’s surface, the new expression of
the Rayleigh-wave sensitivity is thus:
𝑀! =!! !!! !
!

𝑚(𝑟)!

! !!!! !!
!!

𝑟d𝑟 dθ

(6)

Here is made the assumption that the disc-shaped transducer exhibits an axisymmetric vibration
profile, that is to say that m(r) is only function of the distance r from the center of the sensor’s
surface. Thus the complex expression of m(r) is:𝑚 𝑟 = 𝑚! 𝜓(𝑟)𝑒 !!"(!)(7)

where  𝑚!   is the maximum sensitivity to normal vibration of the sensor surface and φ the phaseshift of this vibration. 𝜓(𝑟) is a real number comprised between 0 and 1 so that eqn. (5) holds
with:𝐹 𝑓, 𝑆 =!! !!! !
!

𝜓 𝑟
!

!

!! ! ! !!! !!

!!

𝑟d𝑟

(8)

In the next section we present the approach that has been used to experimentally determine
𝜓(𝑟) and 𝜑(r), which are subsequently used in the computation of the new aperturefunction  𝐹 𝑓, 𝑆 .
3.1 Experimental setup
A laser scanning velocimeter was setup in order to determine the vibration profiles of the AE
transducer as well as the vibration of the surface of a steel block used for Rayleigh-wave sensor
calibration. These measurements will allow combining the actual sensor sensitivity with the157theoretical diffraction effects due to the size of the transmitting and receiving areas, with regards
to the wavelength of the Rayleigh waves.
As reminded in (1), the AE sensor voltage is the average of the normal velocity under its
surface, weighted by its receiving sensitivity MR defined in (4). MR being referred to the Rayleighwave vibration velocity at the center of the face of the sensor, it is independent from theamplitude A of the incoming wave. The sensitivity m(r) has the same dimension as MR as seen in(6) and the aperture function F is dimensionless. In comparison with the definition of (5), thedimensionless parameter 𝜓(𝑟) and 𝜑(r) are the only new parameters to be determined for the
calculation of the new aperture function F defined in (8). Indeed, only the relative behavior of
the sensor surface has to be determined in order to improve the representativeness of the aperturefunction since quantities such as the amplitude of the Rayleigh wave or the magnitude of thesensitivity m0 do not appear in (8).
The laser interferometer detects the quasi-punctual normal velocity of the sensor, which is let
to freely vibrate in air at atmospheric pressure. It is here assumed that the vibration behavior
measured under free-boundary condition is representative enough of the one that could be
expected when the transducer is coupled to the block with the fluid couplant. This question wasaddressed by measuring the vibration of the sensor when coupled to a glass block, which be
more representative of the boundary conditions of the calibration method, but the laser
measurement through the transparent block did not show a sufficient signal to noise ratio.
Thus, extrapolating the relative sensitivity of the sensor to be proportional to theexperimentally determined normal velocity vibration profile allows recalculating the complex
aperture function of the transducer with a non-uniform sensitivity m(r).
3.1.1 Contactless measurement of the sensor’s surface sensitivity
The acquisition of the velocity profile of the sensor surface was performed using a Polytec OFV
3001 laser velocimeter equipped with an OFV 303 head. The AE sensor used is a typicalcommercial PAC µ80 sensor from Euro Physical Acoustics driven by an Agilent 33250A
arbitrary waveform generator. For each point of the surface, the time signal is digitized by aTektronix TDS3012B oscilloscope and stored in a personal computer for post processing (Fig.
4).

Fig. 4: Measurement of the AE sensor normal velocity.
158

The normal velocity of each point of the sensor surface is measured with 100-µm spacing,when the electrical input is a sinusoidal excitation at 300 kHz. Fig. 5a represents the peak-topeak amplitude, in mm/s, of the velocity while Fig. 5b represents its phase relative to the one
measured at the center of the sensor. The dashed pink line shows the outer circumference of the
9.5 mm diameter transducer. It can be seen that the local velocity at the sensor surface is far frombeing uniform, contrary to the classical assumption of a piston-like behavior. The center area
vibrates out-of-phase and with much higher velocity than that the outer ring of the sensing
surface. It has a diameter close to the 6.5 mm diameter of the piezoceramic, which is enclosed
inside the transducer.
The magnitude and phase of the vibration of the surface being rather axisymmetric, it isdecided to extract their profiles under this assumption, which meets the supposition made about
the sensor being axisymmetric in (7).

(a)

(b)

Fig. 5: Amplitude (a) and phase (b) of normal velocity of the sensor's surface in free condition.
Since the PAC µ80 sensor is fairly resonant, the local sensitivity m(r) at the sensor face isderived from the local velocity measured at its central resonance frequency of 300 kHz. A
proportionality relationship between those quantities is assumed. Fig. 6 shows the modulus of theFourier spectrum profiles extracted at various frequencies in the case of a pulsed excitation of thesensor, and also that the sensitivity profile of the sensor is also a function of frequency.
Consequently, the implementation of the proposed technique for a truly broadband sensor, whichis quite rare in AE experiments, would require a procedure for extracting the complex sensitivity
profile for each frequency component within the frequency band of the sensor.
In the present paper, it is decided to extrapolate the qualitative behavior of the sensor in theuseful band as identical to the 𝜓(𝑟) and 𝜑(r) measured at 300 kHz. Indeed, by virtue of theparticularly resonant behavior of this sensor, we assume that the qualitative effect of thedifference between off-resonance velocity profiles is of the second order with regards to thequantitative effect of the variation of 𝑚!   with the frequency, this later effect being actually taken
into account.
159

Fig. 6: Amplitude profiles of normal velocity at various frequencies.

Fig. 7: Snapshots at consecutive instants showing the curvature of the wavefront.
3.1.2. Visualization of Rayleigh wavefront curvature in Rayleigh wave calibration
The Rayleigh wavefront that passes under the AE sensor is visualized using the same setup thatthe one described in [17], where a PAC µ80 sensor, coupled with glycerin on a 300 × 300 × 400
mm steel transfer block, is used as an emitter and placed with its center at 30 mm from the centerof the surface of interest. The transducer is driven with a short-pulse excitation provided by a160

Panametrics 5052 pulser-receiver. The interrogated surface has the same area that the one of Fig.
5 and Fig. 6 and is also scanned with 100-µm spacing in both X and Y directions. Snapshots ofFig. 7 taken at close instants show that the wavefront propagating from left to right has a low
curvature despite the relatively small distance between the transmitter and the reception area.
This helps to understand the weak influence of the calculation proposed in [17] compared to theclassical calculation involving a plane wavefront [16], as it will be shown in the next paragraph.
3.2 Application towards a better consideration of the actual aperture of AE sensor
Fig. 8 collects, in the 1 MHz frequency range, the results in terms of aperture function
obtained for the two referenced models and for the present model, where the information of
amplitude of vibration was first only added, then complemented by the information of phase. The
solid line in Fig. 8 represents the aperture function proposed by the ASTM model [16] of a 300kHz plane Rayleigh waves on stainless steel, by taking into account the actual active area of thesensor of about 6.5 mm in diameter instead of the 9.5-mm diameter of the sensor face. Thisfunction behaves like a squared cardinal sine, and exhibits one null value within the measured
frequency range of 1 MHz, which is the upper frequency commonly used for the calibration ofsuch kind of AE sensors. The dashed line in Fig. 8 represents the aperture function obtained by
Goujon et al. model when the radius of curvature of the Rayleigh-wave beam from a remotetransmitter is also taken into account. It is almost the same as the solid one because thewavefront of the cylindrical wave is here relatively flat, as one can observe in Fig. 7. So, theincoming wave from the actuator placed at a distance of 30 mm is quite close to the plane wave
considered in the ASTM model.

Fig. 8: Aperture function in the 0 to 1 MHz frequency range.
Taking into account the actual amplitude of vibration velocity sensor, the first cut-off
frequency of the aperture function occurs at about 1.05 MHz. Moreover, if one also reflects thephase shift of vibrations of the surface, still under the assumption of axial symmetry, thewidening of the first lobe of the aperture function is slightly less than when accounting for the161amplitude only. Knowing the difficulty to accurately measure the phase, it can be suggested that
only considering the amplitude information may be sufficient to partially correct the aperturefunction without significant practical difficulty.
Aiming at performing a calibration of AE sensors on the surface of a block transfer(Rayleigh-wave calibration) instead of using the usual bulk-wave calibration method requires
taking into account the aperture effect of the sensor.
In order to check the validity of the aperture effect correction in surface wave calibration, the
PAC µ80 sensor sensitivity to Rayleigh waves is compared to the one obtained with longitudinalbulk waves. To produce the incident bulk wave, a longitudinal wave transducer was set to the
opposite face of the block. The 300-mm thickness of the block is more than fifteen times the
wavelength of longitudinal waves in steel and according to the relationship d2/(4λ)=1.25 mm
defining the near field limit of the sensor, the sensor is here in the far-field of the emitter. The
radius of curvature of the wavefront travelling through the thickness of the propagating medium
is large enough to consider it as a perfect plane wavefront. Since there is no aperture effect to beaccounted for in the presence of such a plane wave of amplitude B hitting the sensor at normal
incidence, the voltage across the sensor is:
U =

!

!

m(r)rdr dθ = ∙ 𝑚!

!

!

!

!

𝜓 r 𝑒 !!" ! rdr dθ(9)Under the hypotheses of the absence of geometrical influence of the frequency and axial
symmetry of the d diameter sensor, (9) becomes:!

!

U = 𝑚! 2π ! ! 𝜓 r 𝑒 !!" ! rdr = 𝑚! 𝐵𝐾 𝑑!

(10)with K(d) a constant value that is numerically computed. Hence the sensitivity to longitudinalbulk wave ML versus frequency shows a flat curve with expression of:
  𝑀! = 𝑚! 𝐾

(11)

Therefore, using (5) yields:
𝑀! =!
!(!,!)∙ 𝑀!

(12)

The aperture function 𝐹(𝑓, 𝑆) is the only quantity to be determined in order to validate the Rwave calibration method. The sensitivity to longitudinal bulk wave should thus be proportional
to the ratio of the sensitivity to longitudinal bulk wave and the aperture function.
Fig. 9 shows various sensitivity spectra of the sensor PAC µ80. The curves represent themoduli of the complex sensitivities in the 1 MHz range. The solid curve shows the sensitivity
obtained by bulk longitudinal-wave calibration whereas the dashed line represents the Rayleighwave sensitivity, which we measured by applying the surface-wave reciprocity method proposed
by Hatano [18]. According to (12), the bulk-wave sensitivity curve must be inferred from theRayleigh-wave curve by simple division term by term.
In [17], Goujon et al. did not account for any geometrical variation of m, assuming m=m0;
e.g. ψ r =1 and φ r   = 0, yielding K = 1. The dashed-dotted curve with cross markers is
obtained according to the piston-like behavior model, which considers a uniform and in-phasedisplacement of every point of the surface of the sensor, e.g. considering K = 1 and substitutingin (12) the aperture function F given by (5). The sensitivity curve thus obtained clearly does not
fit the sensitivity measured with bulk longitudinal waves, invalidating the Goujon et al. model.
162Without detracting from the quality of the work of these authors, their assumptions prevent tocorrectly account for the aperture effect outside the low-frequency range. Indeed, the too fast
decay of the aperture function depicted in Fig. 8 makes the recalculated curve deviate from theexperimental curve when the frequency exceeds about 400 kHz. In addition, the vanishing of thedenominator for the cut-off frequency at f = 550 kHz causes the divergence of the recalculated
ML sensitivity. We believe that the presentation in [17] of results only for frequencies below thisfirst cut-off frequency, combined with the fact that the approach was to calculate the sensitivityof Rayleigh waves from the sensitivity of bulk waves by multiplying the aperture function, thus
avoiding the above mentioned problem of divergence, has overshadowed the limitations of thepiston–like model.
The curve with black dot markers in Fig. 9 shows the bulk-wave sensitivity spectra obtained
from the Rayleigh-wave sensitivity spectra divided by the new aperture function model (8). It
shows a good agreement with the experimental curve for frequencies up to 450 kHz and areasonable agreement for frequency above this value. As no normalization of the measured bulkwave or Rayleigh-wave sensitivities were performed, one can deduce that the value of K(d) ishere close to 1. The good fit between the experimental curves and those derived through ouraperture function, itself based on precise measurements of the vibrational behavior of the sensor,
confirms our approach and validates the proposed model.

Fig. 9: Aperture effect correction of R-wave sensitivity and comparison with L-wave sensitivity
in the 1 MHz range.
In the previous section we mentioned that aperture effect undermines the reciprocity
criterion. With the new aperture function, we showed that L-wave sensitivity can be derived
from R-wave sensitivity. In other words R-wave configuration for AE sensor meets reciprocity
criterion.
163

4. Shear wave calibration of AE sensors
To our knowledge there are no previous attempts of S-wave calibration of AE sensors. Our
first attempt was the classic arrangement for reciprocity calibration: three AE sensors and a
propagating medium. The experimental arrangement is the same as the one Hatano used for Lwave calibration of AE sensors in [18]. With that method, it is possible to detect S-wave but it is
not possible to isolate it from the whole signal. Indeed S-wave is partly buried in the preceding
L-wave. To workaround that issue, normal incidence S-wave transducers (Panametrics V151)
have replaced two of the AE sensors. As S-wave calibration requires repeatable sensor coupling,a solid cement was used to validate the method. For practical reasons, namely to ease the sensor
repositioning, a thick couplant was also used. This shear wave couplant from Sofranel showed
good repeatability and produced comparable result to the one that was get from the use of thecement.
As shown in Fig. 10, the L-wave component became negligible compared to S-wave. Before
computations the L-wave component is removed by proper windowing. Conventional hardware
has been used for the calibration. Current is sent to the emitting sensor by means of the Agilent
33210A arbitrary waveform generator. Philips PM9355 current probe/amplifier is used tomeasure current absorbed by the emitting sensor. A Bruel & Kjaer 2638 amplifier is used topreamplify the voltage across the receiving transducer. Finally, a Tektronix Tds-340 oscilloscope
records voltages with 512 averages.

Fig. 10: Current absorbed by the emitter and voltage across the AE sensor in S-wave calibration.
What can be observed in Fig. 11 is the comparison between L-wave and S-wave sensitivities.
It appears that the S-wave sensitivity is 15 dB below L-wave sensitivity. However bothsensitivities have the same evolution. Tests conducted with other sensors, for instance with a
PAC R-15alpha sensor, and different coupling fluids confirmed this trend.
S-wave AE calibration is a prime. We proposed a method with one AE sensor and two
normal-incident S-wave transducers. Obtained results have demonstrated the technical feasibility164

of that method. It is also important to note that S-wave sensitivity and L-wave sensitivity arelinked. As a result, relying on R-wave sensitivity and the aperture function, one can derive Lwave sensitivity and finally S-wave sensitivity.

Fig. 11: S-wave calibration of a PAC µ80 sensor: comparison with L-wave sensitivity.

Fig. 12: S-wave calibration of a PAC R15alpha sensor: comparison with L-wave sensitivity.
5. Conclusion
Although proper definition of the reciprocity parameter is not a simple task, the objective of
this paper was to present elements that are essential to a better understanding of the reciprocity
parameter. First we have seen that Bobber's conclusions give physical insight to the definition ofthis parameter and that Hill and Adams extended it to AE sensors calibration. Next we presented
a new aperture function. We have seen that aperture effect correction of R-wave sensitivity canbe used to derive L-wave sensitivity. Finally, an original method for S-wave calibration of AE
165

sensors has been presented. The first results showed that it might be possible to derive S-wave
sensitivity from L-wave sensitivity. As a result it can be inferred that knowledge of R-wave
sensitivity is sufficient for the complete characterization of AE sensors.
Acknowledgements
This work was conducted as part of the MASCIM project, dealing with modeling of acoustic
emission testing and funded by the French National Research Agency. The project partners are
CEA LIST, CETIM, DCNS, EADS IW, and EXTEND UTC
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Bleaney, B. I., Bleaney, B., Electricity and magnetism, 2nd édition. Oxford University Press,
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Amer. 12 [1940], 140.
3 Breckenridge, F. R., Watanabe T., Hatano H., Calibration of acoustic emission transducers:
comparison of two methods. Progress in Acoustic Emission, Vol. 1, [1982], 448-458.
4 Hatano H., Mori E., Acoustic-­‐‑emission transducer and its absolute calibration, J. Acoust. Soc.
Amer. 59 [1976], 344.
5 Hill, R., Adams N. L., Reinterpretation of the reciprocity theorem for the calibration of
acoustic emission transducers operating on a solid. Acoustica 43 [1979], 305-312.
6 Hatano H., Chaya T., Watanabe S., Jinbo K., Reciprocity Calibration of Impulse Responses of
Acoustic Emission Transducers, IEEE Transactions on ultrasonics, ferroelectrics andfrequency control, Vol. 45 (5) [1998], 1221-1228.
7 Keprt J., Benes P., Determination of Uncertainty in Calibration of Acoustic Emission Sensors,
Hellenic Society for NDT, [2007], Chania, Crete-Greece
8 Keprt J., Benes P., Progress in Primary Calibration of Acoustic Emission Sensors, Acoustic[2008], Paris, 2217-2222.
9 Keprt J., Benes P., Primary calibration of acoustic emission sensors, Fundamental andApplied Metrology, [2009], Lisbon, Portugal
10 Bobber, R. J., General reciprocity parameter. J. Acoust. Soc. Amer. 39 [1966], 680.
11 Lamb H., On the propagation of tremors over the surface of an elastic solid., Philos. Trans.
A203 [1904], 1.
12 Hill R., Reciprocity and other Acoustic Emission Transducer Calibration Techniques, J.
Acoustic Emission 1 [1982], 73-80.
13 Gorman M.R., Ziola S. M., Plate waves produced by transverse matrix cracking, Ultrasonics
29 [1991] 251.
14 Gorman M.R., Plate Wave Acoustic Emission, J. Acoust. Soc. Amer. 90 [1991] 358-364.
15 Theobald P., Zeqiri B., Avison J. Couplants and their influence on AE sensor sensitivity, J.
Acoustic Emission [2008] 26
16 ASTM Non destructive Testing Handbook: Acoustic Emission Testing 2nd ed. (Columbus,
OH: American Society for Non Destructive Testing) [1987] 126-30
17 Goujon L., Baboux J. C., Behaviour of acoustic emission sensors using broadband calibration
techniques, Meas. Sci. Technol. 14 [2003] 903-908.
18 Hatano, H. and Watanabe, T., Reciprocity Calibration of Acoustic Emission Transducers inRayleigh-Wave and Longitudinal-Wave Sound Fields, J. Acoust. Soc. Am., Vol. 101 [1997],
1450-1455.
166

Neural Network Burst Pressure Prediction In Tall
Graphite-Epoxy Pressure Vessels From Acoustic Emission Data
Eric v. K. Hill1, Christopher J. Foti2, Ning Y. Leung2 and Andrea E. Palacios2
1
Aura Vector Consulting, 3041 Turnbull Bay Road, New Smyrna Beach, FL 32168
2
Aerospace Engineering Department, Embry-Riddle Aeronautical University,
600 S. Clyde Morris Boulevard, Daytona Beach, FL 32114
Abstract
The goal of this research was to find an accurate and reliable method to predict the burst
pressures of various undamaged tall graphite-epoxy pressure vessels at low proof pressures. A
total of fifteen bottles were monitored with acoustic emission (AE) transducers while beingpressurized to failure. The AE flaw growth data were then analyzed using artificial neuralnetworks. A Kohonen self organizing map (SOM) was first used to classify the AE data into its
various failure mechanisms, then a back-propagation neural network (BPNN) was used for burst
pressure predictions. Both the unclasssified AE data and the SOM classified AE data were input
into two different software packages – NeuralWorks Professional II+ and MATLAB – yielding a
total of four different predictions. Each prediction was tested using 12%, 25%, and 50% of theinitial AE data. The final optimized predictions used the first 12% of the classified data, which
resulted in essentially identical worst-case errors of about 6%. These lowest worst-case errors
were produced using both the AE input data that were pre-classified into the four failure
mechanisms using the Kohonen SOM plus categorical variables to identify the five different
resin cure methods. The tall graphite-epoxy bottles tested had to be pressurized to almost 50% of
their burst pressures to obtain sufficient AE data for accurate burst predictions. The use of low
proof pressures (≤25%) was inadequate in obtaining accurate burst pressure prediction.
Keywords: Back-propagation, self-organizing map, failure mechanisms, burst pressureprediction, composite pressure vessels, graphite/epoxy, neural networks, nondestructive
evaluation
Introduction
In recent years, there have been many engineering applications that required the use ofpressure vessels to store compressed gases and fluids. These applications range from the large
storage tanks found on natural gas vehicles to the small, lightweight inert gas storage tanks usedon modern spacecraft. For most purposes, a metallic pressure vessel is an effective and cost
efficient solution. However, when dealing with aerospace applications it is very desirable to use
lightweight structures. Even though the manufacture of a lightweight pressure vessel is
expensive, the increased operational performance and decreased cost of operation due to avehicle’s lower mass make the use of lightweight pressure vessels advantageous, particularly inaerospace applications. For this reason composite overwrapped pressure vessels (COPVs) or
fully composite pressure vessels are commonly used on modern aircraft and spacecraft. This
includes extensive use on the Space Shuttle (see ref. 1). This project focuses on the evaluation of
fully composite pressure vessels only. Composite pressure vessels have added weight savings
over COPVs. However, they can be more difficult to manufacture and composite materials havea tendency to fail unpredictably due to variability in cure methods, layup techniques, and othermanufacturing processes. Because of this variability in manufacturing quality, it is important todevelop a practical method to nondestructively evaluate the ultimate strength or burst pressure of
J. Acoustic Emission, 30 (2012)167© 2012 Acoustic Emission Groupeach individual composite pressure vessel, and therefore, that is the primary focus of thisresearch.
The data for this project were gathered in 1996 at the NASA Marshall Space Flight Center(Walker et al., 1995). Here, fifteen 146-mm diameter, tall IM7/977-2 graphite-epoxy pressure
vessels were manufactured and tested. A tall pressure vessel is defined as a pressure vessel
having a cylindrical section at least three times the length of vessel. In this case, the cylindricalportion of the bottles was 305 mm long, as opposed to the typical 100 mm long cylindricalregion. The fifteen bottles were split into five groups of three bottles, in which each group was
manufactured using a different resin cure method. These methods included autoclave cure,vacuum bag cure, low temperature cure with polyvinyl alcohol (PVA) removed, rotisserie cure atroom temperature, and rotisserie cure at 177°C. Four acoustic emission transducers were affixed
to each bottle as shown in Fig. 1. Transducer 1 was attached to the polar boss, while the threeremaining transducers were attached around the periphery of the pressure vessel along the hoop
fibers. The bottles were first pressurized at a rate of 68.95 kPa/sec up to 6.895 MPa, which isroughly 25% of the design or rated burst pressure. The bottles were held at this pressure for two
minutes to allow them sufficient time for any creep to stop. The pressure was then brought backto zero and finally ramped up to failure, again at a rate of 68.95 kPa/sec. See Fig. 2. Acoustic
emission data were gathered during the entire test using the AE transducers and a multi-channel
AE analyzer. Finally, a portion of the AE data were input into a back-propagation neuralnetwork (BPNN) in order to predict the burst pressures of the graphite-epoxy pressure vessels.
Although the BPNN successfully predicted the burst pressures for most of the bottles with lessthan a 20% error, the prediction on one bottle was in error by 37.8%. These errors are
summarized in Table A1 in the Appendix.

Fig. 1. AE transducer placement.

Fig. 2. Pressurization scheme.

The primary goal of this research was to use the data previously collected to devise a methodof prediction yielding lower errors, with the specific goal of attaining a worst-case error of lessthan ±10%. This was attempted by modifying the input to the artificial neural networks. Unlike
the previous research, the AE data herein were first classified into the different failure
mechanisms that occur during pressurization of composite pressure vessels using a Kohonen selforganizing map (SOM) neural network. Both the original unclassified data and the classified
168

data generated by the SOM were then input into a back-propagation neural network, which
predicted the burst pressures of the bottles. Two different neural network software packages
were used – Neural Works Professional II+ and MATLAB – and the amount of AE data input
into the neural networks was varied from the initial 12%, 0-25% and 0-50% of the AE data taken
up to burst. The variability of the networks allowed for the testing of many different neuralnetwork configurations and helped to achieve a much improved burst pressure prediction. The
secondary goal was to obtain accurate burst pressure predictions at low proof loads such thatminimal damage is done to the composite pressure vessel. Details of the neural network analyses
will be presented later.
Acoustic Emission Nondestructive Testing and Evaluation
Acoustic emission testing is a method to test and evaluate the integrity of a structure
undergoing stress. In composites the matrix will begin to crack first, then the cracks will couple
into micro-delaminations, following which the layers or plies of the composite will delaminate
from one another on a macroscopic scale, then the fiber bundles within the composite will begin
to break, and finally rupture will occur (Reifsnider et al. 1983). Each one of these failure
mechanisms will release energy throughout the structure in the form of elastic mechanical waves
that will have unique characteristics when compared to waves produced by other mechanisms.
By measuring the acoustic emission hits using a piezoelectric transducer, the modes of failure
occurring can be determined and the state of the structure can be inferred.
Two major aspects of AE data analysis that must be taken into account are noise and multiple
hit data. Acoustic emission tests are highly prone to gathering noise from sources such as
electromagnetic interference and mechanical rubbing. The noise must be filtered out byrecognizing their characteristics such as very high or very low average frequencies. When AE
hits occur in rapid succession, it is possible for the AE data acquisition system to mistake
multiple independent hits as one long combined hit. This is referred to as multiple hit data
(MHD). Multiple hit data tend to have high energies, long durations, and low averagefrequencies. In order to get accurate results, noise and MHD have to be removed from the data
set prior to any analysis. Fortunately, the data used in this research had very little noise.
However, there were some MHD that needed to be removed in order to attain more accurate
predictions.
Artificial Neural Networks
In order to analyze the data gathered herein, artificial neural networks were employed. The
method, by which an artificial neural network (ANN) analyzes data, is modeled after the
biological neurons and synapses in the human brain. The ANN’s interconnected neurons, orprocessing elements (PEs), are assigned random initial weights. Each PE sums together all of its
weighted inputs and passes it through a transfer or activation function before it is output to thenext neuron. The network optimizes the weights by going through iteration while trying to lower
the error of the network’s output. The main advantage of using neural networks is that very little
needs to be known about the data to achieve good results. For example, if trying to curve fit datausing statistics, the general shape of the curve must be known. It must be first determined if thecurve should be linear, parabolic, exponential, or some other function. An artificial neuralnetwork will come up with its own way to fit the data and does not depend on the user’s curve
fitting intuition. Also, it is virtually impossible to employ statistics when there is noise present in
the data. Noise will cause the analysis to “blow up” and result in unacceptably large prediction169

errors. An ANN, on the other hand, can easily handle noisy data. Typically, the weights
corresponding to the noisy data points will approach zero after many iterations, causing this datato have little effect on the output. Two kinds of ANN’s were used to analyze the low pressure
AE data. First, a Kohonen self organizing map was used to separate or classify the data into
different failure mechanisms. Following classification, the AE data were input into a backpropagation neural network to predict burst pressures.

Fig. 3. Basic SOM architecture.

Fig. 4. BPNN architecture with amplitude histogram input.

Kohonen Self Organizing Maps
A Kohonen self organizing map (SOM) is a type of ANN which is used to categorize orclassify data. The architecture of a SOM can be seen in Fig. 3. Data are first applied at the input
layer of the SOM. There is one input neuron for each parameter of a data point. For example,
here the AE data were categorized in the SOM by using amplitude, counts, duration, energy, andrise time; hence, there were 5 input neurons. The Kohonen layer, which in this case was onedimensional, will then process and classify the data into a number of categories. The number ofcategories possible is defined by the size of the Kohonen layer. For example, four categories or
failure mechanisms were expected for the graphite-epoxy bottles: matrix cracking, crack
coupling, delaminations, and a mixture of fiber breaks with multiple hit data (Reifsnider et al.,1983). This resulted in the need to use a 4x1 Kohonen processing layer, which yielded four
possible classifications. If the number of categories is unknown, it is best to use more categories
than expected. The network will not necessarily use all of the categorizations possible, but thesize of the processing layer should be optimized to yield the best classification. Finally, theoutput layer shown in Fig. 3 gives an x-y coordinate representing the categorization of each data
point or AE hit.
Back-propagation Neural Networks
Unlike an SOM, a back-propagation neural network (BPNN) is used for prediction. A
BPNN’s architecture consists of an input layer, one or more hidden or processing layers, and asingle neuron output layer as seen in Fig. 4. Data are first input to the BPNN through the input
layer. When using AE data, typically an amplitude histogram is input into this layer with one
neuron per each 1 decibel amplitude interval. For this application, the amplitude range of the170pressure vessel AE data ranged from 60-100 dB. With each decibel level requiring one input
neuron, this yielded a total of 41 input neurons. The processing hidden layer then sums theweighted inputs from all the input neurons and passes them through the each hidden layer neurontransfer function; these outputs from the hidden layer then become the inputs to the single output
layer neuron. In this case, the output from the single output layer neuron is the predicted burst
pressure.
In order to produce a BPNN capable of accurately predicting burst pressures, the test data
must be split into training and testing files. Since there were fifteen pressure vessels tested inthis project, eight bottles were used to train the network, while the remaining seven were used totest it. The way the BPNN learns to predict is analogous to the method, in which a student learns
to solve a problem. The student will first learn a subject and learn how to solve a set of problems
relating to it. Later, the student will be tested on different, but similar, problems to determine ortest how well the subject has been learned. Likewise, a portion of the AE data was used to train
the network. This portion of data included the appropriate AE amplitude histograms for eight ofthe bottles along with their actual burst pressures. The network will predict the burst pressuresby using randomly assigned initial neuron weights. In order to improve the prediction results,
the output error is computed, and the BPNN will iterate until either a defined number of
iterations occur or a specified RMS error is achieved between the training bottles’ burst pressuresand the BPNN’s prediction results. The BPNN will finally be tested on the remaining bottles tocheck the prediction ability of the network. Here, the burst pressures are not supplied, butinstead they must be predicted using only the untested bottles’ amplitude distribution histograms
as inputs.
Application of Artificial Neural Networks
Multiple approaches were taken to optimize the artificial neural network parameters to
improve their burst pressure prediction ability. As stated earlier, the data were classified into
four failure mechanisms using the SOM. Obtaining well categorized data proved to be a
relatively simple task. However, determining the best way to input data into the BPNN wasmuch more difficult. Two different neural network programs were used to determine which
produced better results. NeuralWorks Professional II+, a program built solely for ANN’s, andthe neural networks application in MATLAB were both used. For each program, bothcategorized and uncategorized amplitude histograms were input into the BPNN’s for burst
pressure prediction. The hypothesis was that the classified data, which provided no data overlap
and removed MHD, would result in lower errors when input into the BPNN. Additionally, theamount of amplitude data input into the SOM and BPNN was varied between the initial 12%,25%, and 50% of the channel 1 AE hits up to burst. It was believed that more AE data would
result in less prediction error; on the other hand, less data correlated to an earlier prediction,
which meant less damage to the bottles. Finally, the method in which the input file was set up
was varied. This included appending all four of the amplitude histograms to one another,removing the MHD and fiber breaks, using only the matrix cracking data, and using a categoricalvariable to specify cure type. All of these approaches will be discussed in further detail later.
NeuralWorks Professional II/PLUS Predictions
NeuralWorks Professional II/Plus is a software package that is specifically designed togenerate artificial neural networks. For this project, this software was used to generate a series of
BPNN’s to predict burst pressures in tall graphite epoxy bottles using low-pressure acoustic

171

emission data. An SOM was also used to classify failure mechanisms within the AE data sets
and thereby allow the BPNN to make more accurate predictions.
The SOM categorized the data using five different AE parameter inputs: rise time, duration,
counts, energy and amplitude. This SOM classification process iterated 30 times through thetotal number of AE hits for each bottle, resulting in the four different classifications previously
mentioned. Shown in Fig. 5 is an amplitude distribution histogram of the categorizations
obtained from the SOM. Figure A1 in the Appendix is a three dimensional plot of amplitude,energy and duration of one of the bottles in the data set. It was expected that some of the data
would overlap due to the fact that different failure mechanisms can appear similar whenanalyzing the various AE parameters. It is clearly seen that the largest group of the data from theamplitude histogram was categorized as matrix cracking.

Fig. 5. Categorized amplitude distribution.
Once the SOM classified the data, it was necessary to determine the format in which the
BPNN input files would be created. An input file for the uncategorized data would only haveone amplitude histogram as an input. Because of this, the input file would have 41 columns: onefor each amplitude level. On the other hand, the classified data had three appended amplitude
histograms representing the first three failure mechanisms. This resulted in 123 columns.
Further improvement of the input file resulted in the addition of three more columns, which useda binary categorical variable to indicate the cure type of the bottle. This variable can be seen in
Table A2 of the Appendix. The additional columns made for a total of 126 columns to thecategorized input file. An example of the format of the categorized file can be seen below in Fig.
6.
The BPNN was trained on seven of the fifteen bottles and tested on the remaining eight. To
maximize the effectiveness of the training data, the bottle with the maximum burst pressure aswell as the bottle with minimum burst pressure were used in the training file. Furthermore, onebottle of each of the cure types was included in the training files. The remaining bottles werechosen at random. By choosing these different types of bottles to train on, more accurate
predictions were obtained. The bottles that were trained and tested on can be seen in Table A3of the Appendix.
172

Fig. 6. BPNN categorized input file format.
The BPNN prediction was accomplished using two different methods. In the first method,
uncategorized data were used as an input, while in the second, categorized data with MHD
removed were used instead. Each method was tested with the initial 12%, 0-25% and 0-50% of
the AE hits. Other parameters such as the number of neurons per layer, learning coefficients,and momentum were optimized in order to yield the best possible burst pressure predictions. An
effort was made to train and test the network to predict failure using the least amount of data
possible. It was expected that less data supplied to the BPNN would result in higher prediction
errors but minimize damage done to the bottle during testing. To optimize the results for bothmethods, the number of neurons in the hidden layer of the BPNN was varied from 4 to 20
neurons. The learning coefficient value was optimized by varying it from 0.3 to 0.9, and themomentum was varied from 0.4 to 0.9. The root mean square (RMS) error for the seven training
bottles was also varied from 0.1% to 5%. The normalized cumulative delta (norm-cum-delta)
learning rule was used along with a hyperbolic tangent transfer function. The epoch size for the
BPNN was set to seven, this being the number of files or bottles that the BPNN trained on.
The prediction accuracy for the uncategorized AE amplitude histogram data was better than
expected. All of the cases resulted in less than a 20% error. This was unexpected, since the data
were believed to include high amounts of multiple hit data. Here the optimum worst-case error
occurred when 25% of the data were input to the BPNN. This resulted in an error of 7.94%. The
errors and the neural network parameters that were used to achieve them can be seen in Table 1.
Regarding the categorized data, the optimum worst-case error was 5.73%, which corresponded to
25% of the data input into the BPNN. This prediction was a 560% improvement over theoriginal NASA Marshall Space Flight Center research predictions (Table A1, Appendix). The
BPNN parameters used to attain these accuracies are summarized in Table 2.
Table 1: Uncategorized NeuralWorks prediction errors.

Table 2: Categorized NeuralWorks prediction errors.

173

MATLAB Neural Network Predictions
The primary purpose of using MATLAB in addition to NeuralWorks was to compare theburst-pressure prediction accuracies of both programs. The input data used for the MATLAB
neural networks and the methodology of optimization were the same as those used previously for
the NeuralWorks software. The only difference is that the neural network toolkit for SOMs in
MATLAB was used to classify the data into their respective failure mechanisms. The classified
data were subsequently input into MATLAB’s BPNN toolkit for burst pressure prediction.
The SOMs for the different bottles were generated using the programming command called“newsom” in MATLAB. This program allowed the user to choose how MATLAB categorized
the data. Since the tall bottles were made of graphite-epoxy, the SOMs were configured to havefour failure mechanism classifications: matrix cracking, crack coupling, delaminations and fiber
breaks with multiple hit data (Reifsnider et al., 1983). The different failure modes were colorcoded and the data were manipulated into various plots using MATLAB. This software also
allowed three-dimensional plots to be generated for the purpose of observing the energy,
amplitude and duration of the bottles as they approach their burst pressures as shown in Fig. A2of the Appendix. The classified AE hit data showed four different failure mechanisms in theamplitude histograms, but the classified data for the various failure mechanisms had considerable
overlap as can be seen in Fig. 7.

Fig. 7. MATLAB BPNN categorized amplitude distribution.
The BPNN in MATLAB runs the “newff” command from the neural network toolkit. This
command required the input and target data sets for training and testing. The input for thisparticular case was the amplitude distributions acquired from the SOM, and the target was theburst pressure of the various bottles during AE recording. Again the number of hidden layer
neurons was optimized for the BPNN. For burst-pressure prediction of the tall graphite-epoxy
bottles, a loop was created to vary the number of hidden layer neurons from 1 to 20. The outputof the BPNN was a matrix of errors in reference to the different number of hidden layer neurons.
The optimum worst-case error was selected out of this group. It is important to note that the174

MATLAB BPNN will randomly assign bottles to train, test and validate the network. To ensurethat the network is set up properly, the bottles for the training, testing and validating can bechosen manually by splitting the bottles evenly according to the actual measured burst pressures(see Table A4 of the Appendix). This was done to ensure that high and low burst-pressure
bottles were spread evenly among the test bottles. This prevented a scenario in which all thebottles chosen for training were those of high or low burst pressures, thus distorting the results.
As stated previously, the data were run in a similar fashion to that of NeuralWorks. Bothcategorized data and uncategorized data were input into the BPNN with various percentages of
initial data used. However, the MATLAB BPNN experienced better results when predicting on
matrix cracking only data. This is the only major difference in input data between the two
methods of prediction. The results of the MATLAB uncategorized prediction can be seen in
Table 3, and the results of the categorized prediction can be seen in Table 4.
Table 3: Uncategorized MATLAB prediction errors.
BPNN
HiddenPredicted
Input Bottle
Learn
Layer
Momentum
Burst
Data ID
Coeff.
Neurons
Pressure
(Mpa)
12%
4B
4
0.5
0.8
19.27
25% 8A
11
0.6
0.6
18.63
50%
7B
4
0.9
0.41
20.12

Actual Burst
Pressure% Error
(Mpa)
17.74
20.92
17.74

8.63
-10.94
13.41

Table 4: Categorized MATLAB prediction errors.
BPNN
HiddenPredicted
Input Bottle
Learn
Layer
Momentum
Burst
Data ID
Coeff.
Neurons
Pressure(Mpa)
12% 8A
11
0.007
0.9
19.71
25%
6B
6
0.007
0.9
20.63
50%
4B
5
0.007
0.9
22.67

Actual Burst
Pressure% Error
(Mpa)
20.92
22.34
21.06

-5.79
-7.65
7.63

Conclusions and Recommendations
The low error achieved from the classified and the unclassified AE amplitude histogram data
demonstrated that accurate pressure predictions can be obtained using both the NeuralWorks
Professional II+ and MATLAB neural network software packages. NeuralWorks produced thelowest worst-case error of 5.73%, while MATLAB followed closely with a -5.79% error. Both
SOMs effectively categorized the data into failure mechanisms, while the BPNNs used thiscategorized data to accurately predict burst pressures. After optimization of the network
parameters, the BPNNs predicted burst pressures with less error when the AE data werecategorized in almost every case. The two parameters that were not varied in this research were
the hyperbolic tangent transfer or activation function and the normalized cumulative deltalearning rule. It is possible that with a change in these two parameters that the prediction errors
could be lowered even further. In general, it was found that NeuralWorks provided slightly
175better, more refined results, while MATLAB was easier to use and produced results faster than
NeuralWorks.
Besides the SOM classification of the failure mechanism data, the other factor that may havecontributed even more significantly to the accuracy of the burst pressure predictions was thethree digit categorical variables used to identify the five different cure types for the bottles.
These can be seen in Table A2 of the Appendix. The addition of such categorical variables tothe input data allows the BPNN to fit the data for each cure type separately rather than treating
all the bottles as being from the same batch. This may explain a large part of the error obtainedin the original analysis as can be seen in Table A1.
Another topic that was explored was the nondestructive testing of additional bottles. From
Table A5 of the Appendix, it can be seen that the minimum burst pressure for any bottle in thedata set was roughly 16 MPa. The highest pressure required to collect 12% of the total AE
activity for any bottle was slightly below 13 MPa. It can be concluded from this that loading any
future bottle of the types that were tested in this research to 13 MPa would result in at least 12%of the total AE activity being recorded. This should allow a burst pressure prediction within a±10% worst-case error while preventing any bottle from failing during the hydroproof test.
However, the data in Table A5 also indicate that the tall graphite-epoxy bottles tested herein, on
average, had to be pressurized to almost 50% of their burst pressures to obtain sufficient AE data
for accurate burst predictions. As such, the goal for obtaining accurate burst pressures at low
proof pressures (≤25% of the average burst pressure (Hill et al., 1996)) was not met for thisresearch.
For future tests, perhaps the best way to both lower proof pressures – and thereby minimize
damage to the test article – plus ensure that there are sufficient AE data for burst pressurepredictions would be to lower the threshold at which the AE data are taken from 60 dB down to
50 dB. This would substantially increase the amount of AE data available for processing andpossibly allow AE data to be taken up to only 25% of the average burst pressure while
generating worst-case prediction errors within ±5%, values which are more typical for such tests.
Appendix

Figure A1: NeuralWorks SOM classification 3D plot.
176

Table A1: Original MSFC neural network burst pressure predictions [2].

Table A2: Cure type categorical variables.

Fig. A2. MATLAB SOM classification 3D plot.
177

Table A3: NeuralWorks BPNN training and testing bottles.

Table A4: MATLAB BPNN training and testing bottles.
Burst
Pressure
(MPA)
22.93
22.53
22.34
21.83
21.8
21.4
21.06
20.92
20.91
20.61
20.33
17.86
17.74
16.05
15.79

Input Type
Train
Test
Validate
Train
Train
Train
Test
Validate
Train
Train
Train
Train
Validate
Test
Train

Bottle Name
GBT5A
GBT5B
GBT6B
GBT6C
GBT5C
GBT8B
GBT4B
GBT8A
GBT6A
GBT4A
GBT8C
GBT7A
GBT7B
GBT7C
GBT4C

178

Bottle Number
4
5
8
9
6
14
2
13
7
1
15
10
11
12
3

Table A5: Pressurization requirements with critical values highlighted.

Acknowledgements
The authors would like to thank James L. Walker II, NASA Marshall Space Flight Center for
performing the hydrostatic testing on the 15 bottles, taking the original acoustic emission data,
and making it available for this research effort.
References
1. “Orbiter Tank Locations” NASA Operations Project Office [online database], URL:
http://microgravity.grc.nasa.gov/SpaceOps/Shuttle/COPV/ [cited 10 December 2010].
2. J.L. Walker, G.L. Workman and S.S. Russell, “An Acoustic Emission and AcoustoUltrasonic Analysis of Impact Damaged Composite Pressure Vessels,” National Aeronautics
and Space Administration, Marshall Space Flight Center, AL 35812, May 1996.
3. K.L. Reifsnider, E.G. Henneke, W.W. Stinchcomb and J.C. Duke, “Damage Mechanics and
NDE of Composite Laminates,” Mechanics of Composite Materials: Recent Advances, Z.
Hashin and C.T. Herakovich, Editors, Pergamon Press, NY, 1983, pp. 399-420.
4. E.v.K. Hill, J.L. Walker II and G.H. Rowell, “Burst Pressure Prediction in Graphite/Epoxy
Pressure Vessels Using Neural Networks and Acoustic Emission Amplitude Data,” Materials
Evaluation, Vol. 54, No. 6, 1996, pp. 744-748, 754.

179

Research and Applications of AE on Advanced Composites
1

Kanji Ono1 and Antolino Gallego2
Department of Materials Science and Engineering, University of California,
Los Angeles, CA 90095, USA
2
University of Granada, 18071 Granada, Spain

Abstract
This paper reviews progress in acoustic emission (AE) research and its applications to highperformance composite materials and structures. The achievement and inadequacy in understanding of AE from composites are examined along with cases of successful usage in commercial and aerospace fields, taking into account limitations due to high attenuation and anisotropy.
New goals for the future are also discussed in view of new analytical tools and vastly advanced
instrumentation.
Keywords: attenuation, localization of damage, inspection, Lamb waves, wave propagation, finite element method (FEM), frequency analysis, modeling and simulation, pattern recognition,
signal processing, aerospace, chemical and petrochemical, advanced composites, fiber reinforced
materials, pressure vessel, structural health monitoring, composite structural evaluation, composite materials NDT.
1. Introduction
Acoustic emission (AE) played an important role in the development of glass-fiber reinforced
composite (GFRP) rocket motor cases for Polaris A3 circa 1962 [1]. It was the beginning of AE
applications that have expanded greatly over the last 50 years. AE was next deployed in testing
propellant tanks for the Apollo lunar module and other aerospace applications [2]. Aerojet engineers recognized the variation in AE to originate from resin, interlaminar and fiber failures [1, 2]
and modified manufacturing processes, e.g., by eliminating the geometrical inflection points indesign to reduce the interlaminar shear stresses [3]. They used the summation of AE amplitude tosuccessfully predict the burst pressure and developed practical AE source location equipment [1].
Continued work on rocket motor cases at Thiokol introduced Kaiser effect and load-hold emissions to the burst pressure prediction [4]. These pioneering accomplishments still form the backbone of AE pressure vessel inspection technology today.
Research on fiber-reinforced composites started in early 1970s [5-7]. Results verified correlations of AE to micro-fracture mechanisms. In one of the earliest AE papers on composite materials research, Liptai [5] showed the breakdown of Kaiser effect (this is now known as Felicity effect) and gradual reduction of Felicity ratio (aka Load ratio per JSNDI for concrete applications)in cyclically loaded fiberglass reinforced plastic (FRP) NOL rings. The latter was recognized asstatic fatigue (or stress rupture, a well-known glass failure mode due to load hold) of glass fibers,
but no follow-up AE study was apparently published. Significance of these findings was initiallyunnoticed, however. Kim et al. [6a] clearly observed Felicity effect in carbon fiber reinforced
plastic (CFRP) as well, but dismissed this effect while Kimpara [6b] also found Felicity effect,
but treated it as error. The first peer-reviewed paper on composite research seems to be from
UCLA’s Tetelman group on boron-epoxy composite [7]. They correlated fiber fracture to AE,
electrical resistance and compliance changes.

J. Acoustic Emission, 30 (2012)180© 2012 Acoustic Emission GroupA breakthrough came in 1977 when Fowler recognized that AE activities found before reaching prior maximum load indicate the degradation of FRPs [8, 9]; for this behavior, he coined a
new term, Felicity effect, and defined Felicity ratio as (load at AE re-start during reloading)/(prior maximum load)[see also the follow–up work, 10-12]. Subsequently, the Committeeon Acoustic Emission from Reinforced Plastics (CARP) was organized, including Fowler, Teti,
Crump, Hagemeier among others [13, 14]. Through extensive collaborative field tests, CARP
developed FRP inspection technology and published method documents through Society of the
Plastics Industry, then through ASME/ASTM [15, 16]. This Recommended Practice spelled out
loading scheme, AE analysis and interpretation methods and evaluation guideline, enablingwidespread AE applications of FRP vessel examination, in chemical industry in particular.
Fowler [12] documented success achieved in reducing major accidents via this AE method. In arevised document published in 1995 [17], intensity analysis method is used, relying on severity
and historic index, along with emission during load hold, total counts, high amplitude emission,
Felicity effect as key elements of evaluation. Dr. Fowler states (e-mail, 2012) “In	
  the	
  absence	  of	
  CARP,	
   the	   revised	
   procedure	
   became	
   an	
   open	
   document.	
  	
   A	
   number	
   of	   organizations	
   modified	
   it	
   for	  their	
   own	
   in-­‐house	
   needs.	
  	
   Some	
   equipment	
   vendors	
   programmed	
   it	
   for	   use	   with	
   their	   proprietary	  equipment.	
  	
   Even	
   though	
   not	
   a	
   public	
   document,	
   the	   technique	
   has	   widespread	
   use.”	
   His students ex-

tended its uses in FRP design and analysis [18, 19]. Fowler [20] and Davies [21] gave details of
the intensity analysis method, which has also been used in MONPAC system for examining metal chemical processing equipment [22].
With improved FRP technology combined with the advances in AE instrumentation, numerous studies on research and applications followed and have been documented in AE Testing volumes of ASNT Nondestructive Testing Handbooks [23], where one can find many AE uses, suchas for testing bucket-truck FRP booms. Also available are six-volume conference proceedings of
AECM [24a-f], ASTM and ASME codes and standards, and many journal/conference articles
that reach into thousands. Hamstad contributed important reviews [25, 26] along with his many
papers with his extensive composite research since early 1970s. See also other notable reviews[12, 20, 27, 28]. Gorman [29] and Wevers [30] reviewed AE methods for structural health monitoring (SHM) field. See [31] for a review of newer AE analysis techniques. Drouillard and Hamstad indexed early AE papers in [32].
We must realize first that AE has not provided an effective NDT tool for some parts of composite industry. Such a tool is sensitive to existing defects, and is globally applicable to a large
component in real time monitoring condition. While many success stories exist in FRP industry,
as noted above by Dr. Fowler, AE has only played limited roles in many aerospace sites. We
know AE can do better. In this paper, selected aspects of research and applications will be reviewed to highlight the progress achieved and to point to areas in need of more work. The main
focus will be on development from the last 10-15 years since references cited above [23-30] haveprovided adequate coverage for the earlier period. Hopefully, we can go to the next level of understanding to give better solutions. Applications of advanced composites are expanding beyond
aerospace and chemical industries into transportation industry and utilities. Such expansion presents renewed challenges for AE to be a worthy NDT method. We do have to overcome problems due to high signal attenuation in composites and need to explore the causes of Felicity effect and stress rupture, just listing a few. Collectively, we should resolve these obstacles, andmake AE into a truly indispensable NDT method.

181

Research
Fiber fracture
From the early days of AE, glass fiber (GF) fracture has been recognized as energetic AE source
detected close to the fracture load. AE from single fibers, tows (or strand) and unidirectional(UD) composites was initially characterized with amplitude distribution in order to compare withother failure mechanisms. By now, the difficulty of using AE amplitude by itself in signal discrimination is well recognized. However, a misconception that fiber fracture always produces
high-energy event still persists to this day and this section is intended to clarify the situation.
For typical 10-µm diameter UD GFRP, peak amplitude ranges from 60-80 dB (ref. 1 µV=0
dB; literature in the 80s often used 1 µV=20 dB and one needs to reduce amplitude values by 20dB) [33-35]. This range depends on the fiber diameters and sensors used, but GF results are fairly consistent over the years when sensor difference is accounted for. Wolters [33] used sensitive
resonant sensors with less than 10-mm propagation distance and found 40-60 dB for 6 µm, 65-85
dB for 13 µm and 80-100 dB for 24 µm fibers, while another reported 50-70 dB for 13.5 µm GF[34]. In our test of woven GFRP [36], high amplitude AE signals of 60 dB mean level were
found at above 90% of the failure load, verified as fiber fracture microscopically. Woven GFRP
failure mechanisms are the subject of a detailed study [37], and complementary AE study should
allow better understanding of woven composites, which are increasingly used in aircraft fuselage
with carbon fibers, such as Boeing 787 (A.K. Mal, July 2012, private communication). Recent
studies [38-40] reexamined single fiber composite (or SFC, with epoxy matrix) tests. Nordstrom[38] used 18-µm glass fibers and found peak amplitude of fiber fracture at 80 dB (150 kHz sensor). He also obtained Weibull moduli of 7-9 and correlated the shape of stress-strain curves toprogressing fiber fracture (see also [41]). Bohse [39] used 20-µm glass fibers with peak amplitude of fiber fracture to be 58-81 dB (PAC-WD sensor used). More recently, de Oliveira [40]conducted careful SFC tests determining the amplitude of fiber breaks along with source location
data. Such amplitude correction with source location was introduced earlier [42], but has seldom
been used since. Carey [43] also introduced distance correction in his AE signature analysis
work on CFRP. Using 21-µm GF, SFC showed 20-dB attenuation over 45 mm and fiber-break
AE mean amplitude of 73 (53-79) dB (58 dB for 11-µm GF); the lower values are due to the useof a wideband sensor (DWC B1025). In single tow 9-µm GF composite experiment, Nanjyo et al.
[44] obtained lower amplitude values ranging from 46-56 dB (Pico sensor). Diameter and sensor
differences are responsible for the observed variation in amplitude ranges since GF strength andmodulus are essentially unchanged. Berthelot obtained comparable amplitude range for 13-15µm GF and for 7-µm T300 CF [45, 46].

Fig. 1 Peak amplitude distribution of fiber fracture at 40-60% of the fracture load from 9-ply UD
sample with pre-cut mid-plies [48]. Celion G50 fiber.
182

When carbon fibers (CF) were tested initially, peak amplitude of AE signals for T300 CF
was 30 dB less than glass fiber [34, 47]. In part, this is because most carbon fibers have 7-µm
diameter, and earlier CF strength was lower (T300 tensile strength = 2.65 GPa circa 1982). In
our study [48, 49], the peak amplitude range was 30-50 dB for Celion G50 UD composites ofvarious cut-ply arrangements (MAC175 sensor). This is 10+ dB lower than our GF results [36].
These AE signals from CF started to occur at 25-33% of the composite strength and also had
short duration (mostly <50 µs) [34, 47, 48]. Figure 1 shows the peak amplitude distribution due
to fiber fracture at 350-530 MPa, corresponding to 40-60% of the fracture stress from 9-ply UD
sample with the middle 3 plies pre-cut [48]. Berthelot [46, 75] and Awerbuch [50] observed similar amplitude range (30-50 dB) during tensile and fatigue tests of CFRP coupons (CF not specified in [50], but probably T-300). Nanjyo’s tow composite tests in 1992 [44] were also conducted for PAN (M50, 5-µm diameter) and pitch (XN50, 10 µm diameter) CF as well, giving comparable amplitude as GF data cited above; 44-56 dB for M50 and 38-58 dB for XN50. These CF
were stronger than T300 or G50; 4.2 GPa for M50 and 3.7 GPa for XN50, accounting for thehigher amplitude by nearly 20 dB (Fig. 2). In this study [44], the Weibull analysis of CF fracture
strength and AE hits-strength distributions was reported. For three lots of CF, Weibull moduli of
5.7 to 13 were found for the fracture strength, while AE hits had higher Weibull moduli, m = 7 to35, indicating multiple CF fracture in the tow tests [44]. A recent statistical analysis of AE signals from non-bonded GF bundles showed m to be 2.8-4.0 for 15-µm diameter fibers [41], which
are about a half of m = 5.6 to 9 in tow tests [38, 40].
Recently, a new method for composite fracture study appeared [51, 52]; this is “High resolution Synchrotron Radiation Computed Tomography (SRCT)” and was used to capture fiber damage progression in a cross-ply CFRP laminate. Wright [51] showed that transverse-ply cracks
initiate at 20% of the failure load, whilst 0º-splitting initiates at ~40% and large delaminations at~60%. It is important to note that these cracking events occur in advance of significant fiber
breaks that start after 60%. In Scott [52], fiber fracture was analyzed and compared with otherstudies, including AE. AE results for fiber fracture accumulation showed the same trend as
SRCT and Weibull moduli are similar (also similar to [38, 40, 44]). The number of fiber breaks(Nf) was found to follow
Nf = C (σ/σο)m,(1)
where σ is stress and σο is characteristic stress and m is Weibull modulus. See also [38]. It is
hoped this method can be used widely leading to more definitive AE mechanistic studies.

Fig. 2 Peak amplitude distribution of fiber fracture of a) XN50 pitch CF, b) M50 PAN CF, c) Eglass fiber tow-composite samples. Pico sensor. [44]SFC tests with 7-µm carbon fibers, Bohse [39] showed amplitude of fiber fracture at 45-75
dB with the median value of 65 dB (WD sensor used; fiber unidentified, but ~late-90s vintage),comparable to usual GF fracture amplitude. In this work, CF diameter is ~1/3 of GF (–18 dB in
183area) and less than 10 dB amplitude difference implies that higher CF modulus (by a factor of
3.5) accounted for the comparatively stronger AE in CF fracture (assuming comparable fracturestrains). This CF SFC fracture result is 20-30 dB higher than previous studies from the 1980s[34]. This higher range of AE amplitude for CF fracture seems to reflect higher CF strength (2.5
GPa for G50 to 6.3 GPa for Toray T1000) and higher fracture strain values (0.7% for G50 to2.2% for T1000). This is obviously due to dramatic improvements of CF properties in the last
three decades. Thus, if one uses newer CF fracturing at 3-5 GPa (50-80% of T1000 fiber
strength) in lieu of the G50 CF fractured at 350-530 MPa in 1988, 40 dB stronger AE signals are
expected. Thus, we should observe CF fracture amplitude of similar or higher magnitude as 10µm GF. In fact, Sause et al. [53] showed this recently. Comparing E-GF (6.5 µm) and T800-CF(5.2 µm), he obtained fiber fracture energy of 12.6 and 13.1 mNm3/s2. Assuming similar signal
durations, the amplitude is expected to be comparable for comparable fiber diameters. It should
be clear that AE amplitude from fiber fracture can vary widely. Small diameter, low strength CF
produces AE amplitude comparable to the matrix sources (in 30-40 dB), while larger diameter
fibers lead to higher amplitude to 80 dB. For thicker filaments, it can be even higher; AE signals
from 150-µm boron filaments produced 95+ dB signals [54]. Fiber optic samples also give similar strong AE.
Recent tow composite tests using T1000 CF indicates the number of CF fracture at stresses
below 60% of the fracture strength is grossly reduced with improved CF quality (no amplitude
data was reported unfortunately) [55]. Figure 3 shows this behavior in tensile testing with intermittent load hold (ILH) for characterizing Felicity ratio, but cumulative hits start to rise rapidly
after the fifth load hold or at 213 lbs (failed at 352 lbs) or 60.5% of 6.77 GPa fracture strength.
The same NASA group examined Kevlar tow composites as well. In this case, fibers start to failat ~90% of the fracture strength [56]. This behavior is in common with GF composites. Detailed
analysis of these experiments of UD composites should clarify this situation on amplitude levels.

Fig. 3 AE of T1000 CF tow-composite test using ILH schedule at NASA. Red: Applied load (inlbs), Blue: AE. [55] (1 lbs = 4.54 N)
184

When a carbon or glass fiber fractures, it does so in brittle manner. Typical crack velocity inbrittle fracture is several tenths of mm/µs or higher (e.g., 0.52 mm/µs for PMMA [57] and1.6 mm/µs for glass [58]). Actual cracking velocity is expected to be much smaller as a static
crack needs to accelerate under stress. For GF and CF, fracture time is expected to be 0.7~2 µs
assuming crack velocity of 5~10 m/s (or µm/µs). The lower velocity limit is for GF [58], while
CF crack velocity is expected to be twice that since CF moduli are ~4 times that of GF (crack
velocity scaling with the Rayleigh velocity [57]). This translates to the center frequency of
0.5~1.4 MHz for AE signals from fiber fracture, which is partially within the high frequency
range of common AE sensors, such as PAC WD, Pico and DWC B1025/B1080. Indeed, Gorman[59] reported such fiber-break signals from a COPV cover the entire range of flat sensor response (20-400 kHz), in contrast to matrix cracks (splitting), for which detected signals had frequency contents confined below 50 kHz. He also observed that, in hundreds of pressure vessel
tests, including both CFRP and FRP vessels, waves with the highest frequencies appeared when
the load or pressure level exceeds above 80% of ultimate stress. Thus, tow composite behavior
extends to the windings of COPV. SFC samples in de Oliveira’s study also showed highfrequency components [40]. His signals contained strong 300-1050 kHz components (using
DWC B1025), especially when debonding accompanies a fiber break or when 7-µm CF SFC has
a fiber break. He attributes the effect of debonding to the higher freedom of the fiber ends at
fracture. In contrast, GF fiber break signals without simultaneous debonding (11 and 21 µm GF)
have the highest peak at ~170 kHz with gradually decreasing high-frequency components. Gorman [59] observed similar vibration-induced AE from broken ends of tows and further clarification of debonding may be worthwhile in providing new inspection scheme. It should be noted
here that Kim and Nairn [60] showed that fiber-matrix debonding always accompanies a fiber
fracture in GF and CF SFC. Their epoxy resin (Epon 828) did not exhibit flat or conical fractureenvisioned by earlier work [34]. We must have independent proof before assigning to an AE
cluster a mechanism of fiber fracture with or without debonding.
When using broadband sensors, the observed peaks in the frequency spectra have to be treated with caution. Unless one has broadband flat-in-frequency sensor and propagation path, thepeak frequency differs from the source-defined frequency. Most available sensors today have
limited frequency range and have resonance peaks. Sheet or plate geometry has preferential frequency ranges of wave propagation. For example, Bohse [39] correlated fiber fracture to 500kHz peak using WD sensor while matrix fracture contains lower frequency peaks only. This
work and many others clearly demonstrated that one can often identify AE mechanisms via thefrequency domain information. In fact, it is an important part of our pattern recognition analysis
studies on composite AE [61a-c]. On the other hand, one cannot conclude fiber fracture has sucha frequency peak because the WD sensor used is the origin of this peak (WD has three main resonances including one at 500 kHz [62]). Such arguments assigning frequency peaks to some specific AE sources have shown up too often in the past [63-65], yet most such claims revert to specific sensor or sample resonances and sensor-source path has significant effects. For example, deGroot et al. [64] examined a variety of CFRP and assigned frequency ranges to specific mechanisms. Such correlation is valid for the test condition they utilized, but the particular peaks originated from the use of WD sensor again, which was also the sensor used in Giordano et al. [65].
Eaton et al. [66] warned that “the overriding effects of specimen geometry and sensor response
can produce misleading results” in their study to use frequency for separating source mechanisms having the differing relaxation time. One should not expect specific frequency peaks inother test arrangements, except for the general concept of faster source events to have higher frequency contents. It is instructive to refer to the most broadband AE study reported to date by
Scruby and Battle, extending the limit to 3 MHz (–6 dB point) [67]. They did find two peaks for185the signal from fatigue crack propagation, the main one at 600 kHz. However, these peaks are
due to the normal modes of the sample. Even more broadband system is needed to identify the
key frequency of fiber fracture via the frequency spectrum analysis since fracture time is quite
short, on the order of 0.1-1 µs.

Fig. 4 Waveforms of fiber fracture (theory-broken line; experiment-solid line). [68]This situation regarding the frequency peaks also arises in theoretical studies. For example,
Guo et al. [68] obtained the solution of the wave motion produced by a general source in a composite plate using laminate theory with transverse shear correction. He identified the distinguishing features of the wave motion produced by various micro-fracture events (e.g., matrix cracking,delamination, fiber break, etc.) in thin composite laminates. Guo represented fiber fracture with
0.125-µs cracking time in a model CFRP plate among others. Using the experimental transfer
function of a broadband sensor (B1025) showing 200-2000 kHz bandwidth (within 3 dB of thepeak at 1 MHz,), the calculated waveform for CF fracture shows dominant ~1 MHz oscillations.
See Fig. 4. The apparent frequency in this waveform is much lower than the expected center frequency of the original crack model, or 8 MHz. Note that Guo did not claim fiber fracture AE tohave 1 MHz peak frequency. Recently, Sause [69] used finite-element modeling of laminatedcomposites and crack models for CF fracture (in-plane) and resin fracture (out-of-plane) with
0.1-µs cracking time. The CF crack produced displacement signals dominated by ~1-MHz So
waves (where So velocity starts to decrease sharply), whereas the resin crack produced 100-300
kHz Ao waves. His model included Lamb wave propagation of 50 mm from the source. Here, theLamb-wave dispersion effects play the central role in limiting the upper frequency limit of thesimulated AE signal and deciding the centroid frequency. Sause has presented more extensive
analysis of related modeling in his thesis work [70]. His results point to the additional factor wemust consider in trying to assess the source function when AE moves through thin sheets/plates.
[It is noted that Ichikawa et al. [A1] reported theoretical calculations for the intensity of Lambwave modes and dispersion effects as a function of frequency-thickness product. They used steel
as the medium, but confirmed the Lamb-wave behavior observed by others. See Appendix 1.]
In considering the frequency contents of AE signals, one must not forget that high frequency
components are attenuated rapidly as the signals travel from the source to sensors. Unless thesensors are almost over the source, the high frequency components above 200-300 kHz are reduced to background beyond 0.5~1 m distance. This has been known for 30 years and was incorporated in the CARP procedures, but we have no effective remedy applicable to typical applications. In laboratory, sensors are close to sources and we can try to understand the basic behavior.

186

Fig. 5 Observed and matched calculated waveforms with source parameters. UD GFRP test. [71]Another approach to the study of fiber-AE originated from Takemoto group at Aoyama
Gakuin University. Suzuki et al. [71] conducted elaborate experimental analysis of GF fracture,
deducing the source function for detected AE signals during tensile testing of UD FRP. The goalof getting the force-time function of an AE source is similar to the deconvolution approach fromthe 1980s, but waveform simulation and matching of source parameters distinguish this methodfrom the past attempts [72]. Suzuki [71] incorporated the viscoelastic nature of FRP by introducing a frequency independent relaxation function, R(t), after Weaver [73] and extended theGreen’s function approach of Ohtsu and Ono [74]. Suzuki [71] used a NIST-type displacement
sensor, as well as an 8-channel source location system that identified the source position (using 8
Pico sensors). Both displacement waveform and location data were fed as input to waveform
simulation algorithm. Adjusting the source rise time and crack volume, theoretical and experimental waveforms were matched. Here, the latter waveform was obtained by a conical displacement sensor of NIST design derivative (PAC S9208). Examples of such pair are shown in Fig. 5
for fiber-break cases. A cross plot of source parameters, crack volume vs. rise time, in Fig. 6,
indicates the crack velocity limits of 2-22 m/s. The median crack velocity is 6.5 m/s in a good
agreement with the measured crack velocity in glass of 5 m/s [58].
Note that, in elastodynamic theory [73, 74], the source parameter representing its magnitudeis given by the product of the source area (A) and displacement (bi), or crack volume. In fiber
fracture, fiber opening equals bi, not the released elastic energy. Thus, the energy analysis givenin the past, such as by Lorenzo [70] and Berthelot [75] needs to be modified. Another overlooked
aspect of AE signal analysis is the modeling of sensor input. For example, Berthelot [75] used arectangular pulse to represent a crack signal, which is a sensible first-order approximation forsurface displacement due to an opening crack detected at the epicenter. Note that the source displacement function is step-wise, but with a slope (Fig. 4 in [75]). Because Green’s function is
convolved with the source [74], surface displacement becomes a Gaussian-type mono-pole pulse(left: Fig. 5, [75]). It was simplified to a rectangular one (right: Fig. 5, [75]). AE sensors, however, typically respond to the velocity component. Thus, the sensor responds to two sharp pulses of
opposite polarity separated by the duration of the rectangular pulse (Tf). In this case, the FFT ofthe velocity response has the center frequency of 1/2Tf, instead of the displacement response that187

peaks at zero and vanishes at 1/Tf. When one uses a Gaussian shaped displacement pulse (a halfwidth of 0.22 µs; Appendix 2, Fig. A2a), its FFT gradually vanishes without multiple zero points
as can be seen in Fig. A2b. Note the half-width is 2 MHz, equaling the inverse of the base widthof ~0.5 µs in Fig. A2a. The derivative of the Gaussian pulse is known as a Gaussian mono-pulse
and is shown in Fig. A2c. The FFT pairs of Gaussian mono-pulses of 0.2 to 2 MHz are shown in
Fig. A3. It is evident that these pulses are broad and one can hardly use the spectra for source
discrimination. Even recent AE modeling efforts have not taken these points raised here andreevaluation is called for.

Fig. 6 Crack volume vs. source rise time. Data points are within the zone between crack velocities of 2.3 and 22 m/s. [71]Still another issue evolves from the examination of Fig. 5. Note the three waveforms originated from fiber fracture, but with different propagation paths and distances (6.2 to 12.2 mm).
These differences resulted in P, SP and S waves arriving at variable timing. Typical argument for
the frequency contents relies on the P-wave arrival. However, a sensor detects all arrivals andtime differences dictate the frequency spectra of the received AE signals. This aspect must be
accounted for as well in discussing the frequency content of an AE signal when it consists ofbulk waves.

Fig. 7 Typical waveform from a transverse matrix crack in [0/90n/0] CFRP. [77a]188Matrix and interfaces
AE signals originating from the matrix and interfaces are varied and range from weak to strong,making unambiguous characterization difficult in practical composite structures. Several different mechanisms are expected to overlap as well. In model composites, however, clear distinction
is possible.
Transverse matrix cracks: In 1979, as a part of an extensive investigation on composite failure
mechanisms, Bailey et al. [76] first reported using AE for detecting transverse crack initiation in
GFRP, while CFRP produced no AE due to pre-failure degradation. Prosser [77a] reported oneto-one correlation between observed So Lamb wave signals and transverse matrix cracks incross-ply composites (Fig. 7) when 90º-ply thickness exceeds 0.4 mm (3 plies). Notice that thissignal is of high-frequency type (~700 kHz) and in thick cases (>6 plies) of very high amplitude(>80 dB). Gorman and Ziola [77b] showed even stronger events in (0/90)s CFRP, ranging up to
98 dB, but with >100 µs duration. [Note this work is the first to exploit the plate wave characteristics of AE signals in typically thin composite samples.] Some of these signals with short duration are hard to differentiate from fiber-break signals. When transverse matrix cracks occur, threepossible micro-mechanisms exist; i) matrix (resin) cracking, ii) debonding of fiber-resin boundaries and iii) fracture of bridging fibers. Matrix is low strength and slower fracture produces only
low energy (amplitude) AE. See [39] for epoxy cracks having ~55 dB AE even for large area
cracks. Matrix cracks are present in notched woven-roving fracture surfaces in [36, 37] as resinrich areas of the size of roving diameter exist throughout the composite. In laminates made fromUD prepregs, only the interlaminar layers have contiguous resin constituent and the contributionof mechanism (i) is limited. Debonding is expected to be the main mechanism for the transverse
matrix cracking in laminates with straight fibers and as the source of first ply failure. Mechanisms i) and ii) typically coexist in woven composites [36, 37]. Woo and Choi [78] characterized
debonding AE to have low amplitude (under 70 dB) and low frequency (lower frequency peaks
than the sensor resonance of 265 kHz for PAC µ30) using in situ microscopical observation of
the notch-tip zone. They showed, however, that once macro-cracking begins, higher amplitude/frequency (>70 dB, 430-450 kHz) peaks are dominant, implying bridging fiber failures contribute substantially. Fracture surface showed both of these (ii and iii) mechanisms. Elevatedtemperature exposure (wet or dry) affects the matrix-bonding behavior, which was detected with
AE. Schoßig et al. [79] used environmental SEM to assess damage mechanisms of short-fiber
GFRP and identified 14 different mechanisms and recorded AE simultaneously. However, too
many processes occur together. No clear correlation to AE signal characteristics is established sofar. Bussiba et al. [80] exposed CFRP (0/10 layup) to 100ºC for 2 weeks and saw accelerated
debonding with the knee strain down from 1.5% to 0.6%. Wet exposure of GFRP was reported
in [61c, see also 81].
Godin et al. [82-85] made a series of well-designed studies using UD, angle-ply and crossply composites with advanced pattern recognition analysis. Using Kohonen map and k-means
methods, they identified four distinct signal types and attributed them to matrix cracking, interfacial debonding, fiber fracture and delamination. Waveforms of the 2nd and 3rd types are visuallyindistinguishable, however. The use of Kohonen maps appears effective in identifying clusters of
AE signals and will be examined in a later section. In de Oliveira’s thesis [40], he summarized
nearly 20 other AE works on transverse matrix cracking and also reported own cross-ply GFRP
testing [see also 60]. He developed artificial neural networks based on self-organizing maps withwaveform and wavelet-transform data as input. Among six clusters identified, transverse matrix
cracking produced two types of AE signals having characteristic So waves centering at 350-400
kHz lasting 30~60 µs. The low frequency part arrived first as expected for So waves. Shorter
189

Fig. 8 Waveforms and frequency spectra of 6 different types, classified by pattern recognition
analysis of AE signals from QI CFRP laminate. Time scale: 41.0 µs/div, Frequency: 97.7
kHz/div, Amplitude scale varies. WD sensor at the sample center. Huang [61b]190

duration signals (type II) are attributed to the initiation, while longer ones (type I) to the propagation of transverse matrix cracks [60].
Delamination-Shear: Ono [48] reported AE from the delamination of UD CFRP with pre-cut
plies with several different shearing zones. Shear strength level was 5 – 24 MPa. Overlap length
ranged from 25 to 75 mm (13 mm width). Tensile loading produced initially medium amplitude
signals (50 - 70 dB) with an average event duration of ~120 µs from the initiation of Mode II delamination. At higher loads, high amplitude events (70-130 dB) of long (> 200 µs) event duration
were found. These are caused by rapid advances of delamination under mainly Mode II loadingwith out-of-plane stress near cut edges. Lagunegrand et al. [86] examined free-edge delamination
on ±30º angle ply CFRP and identified 90-100 dB signals as Mode-III delamination induced AE.
This delamination occurred between +30º and –30º plies in shear, while mixed mode shear was
reported in [15n/90m/-15n]s laminates. Scholey [87] examined delamination in large-sized QI
CFRP samples with cut plies, getting 10-50 pm Ao-wave amplitude as the area of delamination
reaches ~30 x 50 mm after loading. Surprisingly, duration is only ~100 µs, implying delamination expands in small steps. Huang [61b,c] examined waveforms of various AE origins of CFRP
tensile samples (UD, cross-ply, quasi-isotropic) in conjunction with pattern recognition analysis.
One of six types identified is due to delamination, which becomes active just prior to sample
failures. As shown in Fig. 8, type-D delamination signal has the lowest frequency content (~100kHz) and long duration, while fiber-break AE (types A and B, latter with larger matrix debonding) has highest frequency content and short duration. (Type C is from transverse matrix crackwith ~100 kHz peak, type E from splitting with ~400 kHz peak and type F from shear crack in±45˚ plies at medium frequency.) Corresponding power spectra show features originating fromWD sensor resonances (esp. at ~100, ~230, ~300 and ~400 kHz) as the sensor primarily detectedsignals under its face in the middle of a narrow sheet sample. Effects of Lamb wave types may
be present for types A and D, in particular.
De Olveira [40, 42] identified two types (IV and V) of delamination-induced AE signals incross-ply GFRP tests. Both are Ao-mode waves with low frequency contents under 400 kHz(down to 20 kHz in type V) and strongest contents at 150-230 kHz. Type IV was observed atlower loads, attributed to matrix-fiber decohesion as well as delamination. Type V appeared near
final fracture when delamination activity was high.
Delamination-Opening: Bohse and coworkers [88, 89] studied Mode I (crack-opening type) delamination using DCB specimens and correlated AE energy with strain energy release rate (GIc),
supporting the concept of damage zone concept ahead of the crack tip. They consider the presence of matrix cracking and matrix-fiber interfacial debonding, the latter in stronger interface
condition. Some bridging fiber fracture is possible as well as in [78]. This also has more highfrequency component, allowing the discrimination of the two mechanisms. In both, however, themain frequency feature is the dominant low frequency components below 200 kHz. Amplitude ofthese AE is mostly (~90%) in 60-80 dB range. Another CFRP DCB tests [64] reported essentially same results with a comparable low frequency peak (120 kHz with minor one ~300 kHz, bothcharacteristic of WD sensor). This low frequency peak was also seen in epoxy resin crack and in
lap-shear AE.
Recently, Sause et al. [53] published a comprehensive study of DCB tests with AE analysis
examining interlaminar crack propagation in GFRP and CFRP. They conducted pattern recognition analysis to identify three clusters, corresponding to matrix cracking, interfacial failure andfiber breakage, using mostly frequency-based features, such as average, centroid, initiation, peak,191weighted peak frequency, six partial powers, etc. Cross plot of partial power at 450-600 kHz vs.
weighted peak frequency easily separated the 3 clusters. Figure 4 in [53] compares observed andFEM-simulated signals and wavelet spectrograms, showing good agreement. Interfacial failure
dominates cumulative signal amplitude, followed by matrix cracks. Fiber breaks contribution is
low as this comes from bridging failure. This theory-test-model hybrid approach holds great
promise in assessing complex structural composite design under various loading conditions, integrating AE as a part of health-monitoring scheme.

Fig. 9 Amplitude vs. duration of tow-composite samples with (a) no and (b) maximum surface
treatment. [44]Fiber-pullout: In tow composite tests, Nanjyo [44] used 4 different levels of (pitch-)CF surface
treatment. Without treatment (level 0), many fiber pull-outs of up to 100 µm were observed after
failure. With maximum treatment (level 5), fiber-matrix bonding improved and few fiber pullouts were found producing flat fracture surface. Comparison of these two conditions on amplitude-duration plots indicates events with 60-80 dB amplitude, longer than 80 µs duration increased more than ten-folds in the level-0 sample. These long duration signals are due to fiber
pull-outs. This means pull-out AE amplitude is 9 dB higher to fiber fracture (Fig. 9) on average.
However, differentiation must rely on duration. Single glass-fiber pull-out experiment by Mielke[90] produced similar results regarding amplitude, though fiber diameters of 20-25 µm were used
and fiber-break amplitude was ~10 dB higher than pull-out signals while another study examinedpull-out of SiC fiber from glass matrix [91]. A more recent work used AE in single Kevlar-fiber
pull-out tests, and showed energetic signals of 50-90 dB (Pico sensor). However, AE part is difficult to evaluate lacking key details [92].
Mixed mode cases: Mizutani et al. [93] examined fracture of cross-ply CFRP under central point
loading and observed four separate types of AE signals. Signal simulation using laser sources
allowed him to establish source mechanisms of fiber fracture, transverse crack, delamination(shear) and matrix crack, designated as type-A, -B, -C and -D. These 4 signal types are shown in
Fig. 10 together with wavelet spectrograms. Two sensors placed at 90º angle from the loading
point (along 0º and 90º relative to the surface fiber axis) showed vastly different waveforms,
helping the identification process. Of 327 total, initial 70 events showed the following: Mode-I
type fiber fracture in the front layer (the first event + 3), followed by transverse matrix cracks inthe mid-lamina (27), from the middle part on, delamination (15) and splitting-type matrix cracks(11), (13 unidentified). They also studied the same experiment, but under dynamic loading192

condition. Details can be seen in JAE [94]. Four types of cross-ply CFRP plates were impacted.
Lamb-wave AE signals were detected by small AE sensors on both surfaces to separate So andAo modes. Only Impact-AE is obtained when the impact energy is below a fracture threshold.
When internal fracture occurs, both Impact-AE and fracture-induced AE (or Fracture-AE) weredetected. Most of strong AE signals were correlated with splitting matrix fracture source (type D
in Fig. 10).

Figure 10 Waveforms and wavelet spectrograms of 4 types of signals from point-loaded crossply CFRP. [87]There are other types of matrix fracture. For example, matrix fracture similar to the transverse matrix crack, but fibers oriented along the loading direction, also occurs. This is usuallycalled splitting and corresponding waveform is that of type E in Fig. 8 [61b,c] (also type III in[40, 42]). Another type of matrix fracture occurs under shear stress along the fiber-matrix interfaces in angle plies, such as those ±45º plies in quasi-isotropic laminates. This has a characteristic waveform of type F in Fig. 8. Both E and F types have slowly rise time, reflecting slower
growth of such cracking (WD sensor was on top of these sources 0.5 mm away, so no propagation induced slow rise time). The characterization of signal types relied on the laminate types andwhen these signal types were emitted during loading. (Note these features are dependent of test
conditions: these tests used thin (~1 mm thick), narrow (13 mm wide) CFRP samples with WD
sensor attached at mid-section. See also [95].)

193

Felicity effects
When damage accumulates in materials, Kaiser effect breaks down. This is usually characterized
by Felicity ratio; the stress of AE restart/prior maximum stress. Felicity ratio has been linked to
the residual strength of composites, and has been studied over many years: see Hamstad’s thorough discussion in 1986 [96]. Changes in Felicity ratio on laminates are shown in Fig. 11a and b.
In Fig. 11a, Hamstad [96] shows a schematic plot of GFRP data by Fowler and Gray [10], where
it drops with applied load. Awerbuch’s data on a multi-ply CFRP decreased with increasing priorstress above 1/3 of the tensile strength of 272 MPa [97]. Felicity ratio was down to 0.7 with priorstress of 220 MPa or 80% of the maximum. These are data from tensile coupons. On bend testing,
GFRP also exhibited Felicity effect, but stress level at the start of decreasing Felicity ratio ishigher (75-80%) instead of 45-60% in Fig. 11a, as shown in Fig. 12 [98]. Obviously, the difference arises from different failure mechanisms between tension and bend tests, but exact cause isunknown. AE monitoring in compression-after-impact or bending tests with damages showed the
reduction of FR values [e.g., 99, 100]. Various AE parameters including FR were combined withneural networks for burst pressure prediction [e.g., Hill, [23], pp. 382-387].
The origins of Felicity effects need to be explored further as we find few articles beyond
Hamstad [96]. It is obvious that the stiffness of fiber-damaged zone is reduced and stress/strain
differentials develop against the surrounding sound zones upon reloading. This is realized during
any unload-reload step [cf. 41], since the stiffness is proportional to the fraction of intact fibers.
A damaged zone has a lower stiffness and deforms more. The stress/strain differentials, in turn,produce additional interlaminar stresses, leading to premature delamination. This can be thesource of Felicity effect in impact-damaged vessels. In the absence of fiber damage, delamination itself should give rise to frictional AE during an unload-reload step. At any interlaminar, resin-rich layer between plies of different fiber orientations, interlaminar shear stresses are always
present. While the shear fracture strain is of the order of 1% in most resins used today, it iscommon to use longitudinal-hoop fiber angles of 60-70º and substantial interlaminar shear
stresses result upon loading. With our current AE methods, separating delamination and frictional AE signals is feasible. With the above scenarios for Felicity effect, we should expect strong
FR dependence on lay-up sequence.

Fig. 11 a) FR vs. applied load of GFRP [96].

b) FR vs. applied stress of multiply CFRP [97].

Felicity ratio (FR) provided sensitive measure of developing damage under compressive tests[101]. CFRP laminates with 914 or PEEK resin matrix having a seeded defect were cyclicallyloaded and f = 1 – FR was used as damage indicator (of internal delamination). Applied load P
194and f are related with laminate specific manner with high R2-values >0.98. See Fig. 13. As themarks for visible crack indicates, f-value gives the warning of internal damage.

Fig. 12 FR vs. stress ratio of a GFRP under bending load [98].

Fig. 13 Cyclically applied peak load vs. f = 1–FR for CFRP. a) with different resin, b) different
orientation [101] Loading is compressive and CFRP has an internal defect to initiate delamination. Appearance of macro-cracks is marked by squares.
Several stressing sequences are used in obtaining FR. The usual one is to unload from theprevious maximum as in [101], whereas incomplete unload sequences are more convenient toperform. One such example is shown in Fig. 3, called intermittent load hold (ILH), while CARP
procedure calls for 10% unload with stepped loading (Fig. 4, [15]). The origin of Felicity effects
is most likely from frictional (secondary) AE [97], but the link between Felicity effects and residual strength is empirical. Toward clarifying this link, NASA has embarked on extensive basic
study of FR as they view this as one key element in the prediction of composite over-wrappedpressure vessel (COPV) performance. Specifically, it was found that some COPV failed in accelerated stress rupture testing; that is, COPV is pressurized and held for extended periods. They
cannot afford such a failure in space [102]. Tow composite tests using Kevlar 49 and IM7 andT1000 carbon fibers were initiated with 4 sensors mounted on 250 mm long samples [55, 103105]. AE results for a T1000 tow test was seen in Fig. 3. FR from this and similar tests showed
quite different behavior compared what we have observed previously. Figure 14 shows the new
findings. Data for an IM7 COPV is also included for comparison. Felicity ratios given here were
determined using the first AE event for Kevlar-epoxy tow, and the mean of the first 15 events for
T1000 tow, IM7 tow, and the single IM7 COPV. FR values are in all 4 cases higher than 0.9
even very close to the failure load. FR is above 1.0 up to load ratio of 0.6-0.7 for CF and 0.85 for
Kevlar fiber. For COPV, FR stays above 1.05 to load ratio of 0.78. First, these finding again
195

imply that fibers remain intact to much higher stress levels than older CFs like G50 used in [48]
(IM7 is relatively old, however) as discussed earlier in conjunction with Fig. 3. A recent study[106] of T1000 Weibull modulus indicates that m-value at lower strength range exceeds 30(overall m = 5.9), thus virtually eliminating fiber fracture below 80% of the average strength.
Second, frictional mechanisms are not as active as in old CFRP, reflective of better interfacial
bonding. Interlaminar shear strength has doubled from previously common AS4/3501 (76 MPa)to newer T1000 CFRP (150 MPa). This condition is true even in COPV, in which windings ofvarious orientations are expected to lead to interlaminar shearing and friction (it is unclear if thisparticular COPV was subjected to autofrettage* (*an over-pressurization step to put the metal liner in compression), which can suppress AE at lower loads.) Third, flaws like splitting or delamination are
not expected in tow composites, although this is not the case for COPV. As higher FR values arelinked to low damage state and higher failure strength, more extensive studies on composite Felicity effects seem to be highly advisable. These should point to improved materials and manufacturing processes. FR on pressure vessels will be discussed later.

Fig. 14 FR vs. load ratio for T1000 carbon-epoxy tow (black-dotted), IM7 carbon-epoxy tow(grey-dashed), Kevlar 49 -epoxy tow (white-solid lines), and an IM7 composite overwrappedpressure vessel (large crossed hexagonal symbols). 95 % confidence intervals also shown. [105]
Wave propagation
Typical composite structures comprise thin members and AE signals are transmitted as Lamb orplate waves of extensional (or symmetric) and flexural (asymmetric) modes. Many composite
members are anisotropic as well. Basic aspects on plate waves have been covered well in [12]and standard textbook [107]. In composite AE field, work by Gorman and coworkers [108-110]
brought the focus on the peculiar wave propagation behavior of AE signals in thin composites,
especially on the aspect of source orientation producing different wave modes. Hamstad and his
NIST colleagues enhanced our understanding with extensive finite element modeling studies toshed light on the source types and resultant plate waves [111-114]. Large wave attenuation incomposites has been accounted for in CARP methods [8-17, 20, 21]. Downs and Hamstad [115]showed the sharp amplitude drop (20 dB in 6 mm) due to geometrical spreading in CFRP vessels
of inverse-square-root distance dependence. More recently, the anisotropic nature of composite
plates has been included in analysis. Mal et al. [116] used a classical damping factor approach to196

anisotropic wave analysis and found satisfactory results between theory and experiment (though
this work is difficult to transfer to AE study). Sause incorporated suitable source models andwave propagation analysis on such CFRP model sheets [53, 69, 70]. Using complex elastic modulus obtained by air-ultrasonic technique [117, 118], some wave propagation analysis included
attenuation factors as well [119-122]. Anisotropic velocity calculations are also reported forcommon CFRP layups [123-126].
Actual measurements of attenuation are limited. Suzuki [71b] measured P-wave attenuationin 60% UD-GFRP as a function of propagation direction and frequency. Along the fibers (at 0˚),
attenuation was 60 dB/m for 0 – 1 MHz and 180 dB/m normal to fibers (at 90˚), 0 – 1 MHz. See
Appendix 3. Suzuki also showed the relaxation function model of Weaver [73] fits the data
above 1 MHz, but measured attenuation did not diminish at low frequencies contradicting model
prediction. Prosser [76a] obtained apparent attenuation of So and Ao waves in IM7/977-2 CFRP
of 1.2 and 3.7 mm thick quasi-isotropic laminates. For So mode in the thinner plate, attenuation
was 42 dB/m (410 kHz) and for Ao mode it was 83 dB/m (85 kHz). For the thick plate, corresponding values were 35 dB/m (230 kHz) and 51 dB/m (90 kHz), indicating the effect of thickness and frequency. Attenuation observed on a CFRP pressure vessel [115] was higher. These
values change when geometrical spread of inverse-square-root (Lamb waves) is accounted for.
Gallego and Ono [127] reported attenuation measurements on CFRP plates of three types oflayups (UD, XP, QI). So and Ao wave modes are separately evaluated and frequency effects are
examined. Attenuation is also high. It is evident that any AE analysis must consider frequencydependent signal loss through the transmission. It must be pointed out that some of theoretical
papers on Lamb wave attenuation [117-126] state that guided waves propagate long distances.
This is contrary to their results, many showing 100+ dB/m, and without experimental support.
Applications
Pressure vessels and tanks - 1
When AE applications mature, we find few reports in publications especially after these arecompiled in reference books, like NDT Handbook [23] and codes and standards established. Applications to chemical tanks and vessels fall into this category after the first three AERC/AECM
conferences [24a-c]. Rocket motor case applications are at a similar state [see [23], pp. 377-381];
actually, few reports had appeared from the beginning due to their sensitive nature. In this connection, it is interesting to find a review on the burst pressure prediction; Joselin [128] covers the
subject quite comprehensively, introducing each paper adequately without inserting own interpretation. Thus, a reader must judge which approach is successful/unsuccessful on his/her own.
Also many papers from AECM series conference proceedings are missing, as access to them
may be difficult outside the US. Still this review is a recommended reading on this topic.
There are two important issues at this time. One is the evaluation of impact damage and theother is estimating the residual life of COPV. The first topic is not limited to pressure vessels,
but it is one of the critical problems in aerospace circle. This has been studied with AE for morethan 20 years [e.g., 129-135]. It is known that impact energy links to the burst pressure. Many
AE parameters have been “successful” in predicting the burst pressure [23, 128]. An example ofburst pressure-Felicity ratio correlation is shown in Fig. 15 [130]. While the sample size of thisstudy is small, it is believed a larger database has been prepared to show the same trend within
the aerospace circle. Yet industrial practice still uses AE as a supplementary method. As a tool
for space COPV inspection, Chang [136] in 2003 assessed AE to be better on “inspection time”
and “field use”, as average on “flaw characterization”, “simplicity” and “COPV preparation” and197

as weak on “whole field”, “data evaluation” and “sensitivity”. In the AE field, we think “whole
field”, and “sensitivity” as AE’s major strong points, but actual judgment by the practicing engineers is much more critical.
As mentioned in Felicity effect section, NASA is attempting to use AE for COPV burst pressure prediction [102-105]. This is now based on the higher levels of newly measured FR valuesof near unity. Figure 16 shows Felicity ratio with increasing previous highest pressure for an IM7
COPV during two separate pressurizations to 6800 psi (circles) and to burst (crosses). A linear fit
is made to both pressurization data. Once the FR value at burst is established with confidence,
this will allow burst pressure prediction from lower pressure ILH loading schedule. As noted before, this FR-pressure relation is comparable to tow composite behavior. This surprising similarity is promising in a sense that newer COPV manufacturing is eliminating many sources of flaws
that previously decreased FR values, and also reduced COPV strength. It is hoped to see evenbetter correlation develops in the case of newer T1000 fibers.

Fig. 15 FR values are almost linearly related to failure strength of spherical Kevlar COPV [130].

Fig. 16 Felicity ratio vs. increasing previous highest pressure for an IM7 COPV. Data from [105].
In connection to the use of FR values, it should be noted that another parameter is useful insome cases. Downs and Hamstad [133] defined Shelby (countdown) ratio, which is (the load198

during unload when a predefined AE activity level is reached) divided by (the previous maximum load). This ratio was used much like FR values in correlating to the burst strength of CFRP
pressure vessels. This ratio is being used in the NASA study discussed above [102-105].
The second problem of estimating the residual life of COPV comes from stress rupture behavior of high strength fibers. It is well known that silicate glass suffers from static fatigue, i.e.,time dependent fracture due to the presence of moisture, aka. stress corrosion cracking. Similareffects exist in COPV made with Kevlar and carbon fibers. Figure 17 shows sustained load design curves for three types of COPV. These are used for determining the allowable sustainedload operating stress for a specified time at load using a probability of survival of 0.999. The
time at pressure represents the sum of the time that the COPV is pressurized at or above 60% ofmean expected operating pressure [136]. Over a long period of service, allowable applied stresses are quite low relative to the static strength. Of the three fibers, carbon fibers give the best performance at longer times (>100 hrs).

Fig. 17 Sustained load design curves for three types of COPV. Data from [136].
Since fiber fracture is the root cause, AE can play a significant role in resolving this problemof estimating the residual life. From the earliest days of AE, the stress rupture issue was recognized and Liptai [5] reported the process of stress rupture of a GFRP NOL ring. See Fig. 18a,
which follows two hrs of loading before fracture with rising AE counts. This sample was loaded
to 80% of the expected fracture load. Another NOL ring was loaded in fatigue at 50% of the ultimate load. At each 2000 cycles, it was loaded to 75% with AE monitoring, shown in Fig. 18b.
As listed in the figure, estimated FR values decreased with loading cycles, quickly dropping to~0.5 (as noted earlier in Introduction). Since then, however, only a few reports of AE monitoringof stress rupture of FRP appeared in the last 40 years [136-140]. There are reports of FRP stressrupture studies [e.g., 141-143]. NASA conducted wide-ranging study of Kevlar COPV used inSpace Shuttles (24 used in each orbiter vehicle) after the Columbia accident and reestablished thereliability to be 0.998 relying mainly on accelerated pressurization testing. Raman spectroscopy
was used to evaluate the residual stress state of Kevlar [144]. No mention of AE appeared in thereport. However, another program is in progress [55,102-105,140] at NASA. On stress rupture
tests, only a glimpse of data can be viewed so far [145], showing the usual increase in AE justprior to failure. In some cases, other data appear inconsistent [140] and we need to wait for thecompletion of the program. Still the location data on a successful test of a COPV shows several
199

concentrations of energetic AE events within 24 hrs of final failure (slide 19, [145]). Two views
are given in Fig. 19. Such data point to the availability of information that warns incipient failure
if continuous AE monitoring is used. Without monitoring, projecting 0.999-level reliability willprobably require a new strategy yet to be formulated.

a)b)Fig. 18 a) Cummulative AE counts vs. time at 80% of fracture load applied. b) AE counts vs.
load under fatigue load with intermittent proofing. Data from [5].

Fig. 19 Distribution of energetic AE events in 24-hrs period before the final fracture. Kevlar
COPV. Data from [145] with color inverted for easier viewing.
Pressure vessels and tanks – 2
AE tests of smaller tanks have occupied some European AE groups of late. These have to be200

done at low cost and rapidly. See [146-148]. A similar effort was undertaken by Propane Education & Research Council in the US, using ISO11119-3 as the basis. They chose to use only visualinspection, relying instead on the safety factor [149]. They stated in their Exemption request to
the US Dept. of Transportation (prepared by Battelle) that “Electronic methods which have been examined to some level of detail in the laboratory include acoustic emission and acousto-ultrasonics (sic). Other potential
inspection methods include holography, shearography, and thermography. These methods are used by highly trained
staff in controlled laboratory and manufacturing plant conditions for quality control and special purpose applications.
However, available data indicate the quality of damage detection in composite cylinders by these methods is inconsistent and varies significantly with the material selection, design, and manufacturing techniques. Consequently,
unique NDI test criteria will be necessary for each different cylinder design, even though they may be from the same
manufacturer. Practical application of these methods will be erratic and inconsistent for operations which inspect
more than one cylinder type at one time, as is typically done in cylinder and propane industries.” Their products,

LPG/CNG cylinders, are inexpensive, requiring the lowest cost inspection method. Yet, thisseems to be the state of general NDT community that is so ignorant of the state-of-the-art in AE
technology. The safety factors they incorporated are still of interest; This value, minimum stress ratio,
is defined as the ratio of the fiber stress at calculated design minimum burst pressure divided by the fiber stress at
2/3 times test pressure (i.e. working pressure). For cylinders without liners or with non-load-sharing liners, the stressratios are:
• Glass fibers–
3.4• Aramid fibers –3.1• Carbon fibers –2.4

These cylinders are designed to last 15 to 50 years and the minimum stress ratio is to assure
against failure due to stress rupture. Note CF can be stressed the highest. It is not known if thishas received DOT approval.
On the side of chemical vessels, AE is well accepted thanks to the efforts of Dr. Fowler andthe CARP, as mentioned in the Introduction. A manufacturer of FRP vessels [150] recommends
AE testing of their product as follow: “Acoustic emission tests on used equipment are most effective when
there is baseline information available. It is therefore recommended that all critical FRP equipment be baseline AE
tested soon after start-up. Once in service, the amount of AE testing that is done is the owner’s prerogative. Often,
equipment is tested at a point in time approximately halfway through the estimated life span of the equipment. Ongoing testing will depend on the results of this test. When critical equipment is nearing the end of its service life,
regular AE testing can provide a meaningful structural evaluation that may prolong equipment use and prevent unexpected failure. Acoustic emission testing identifies and locates active defects in laminates by detecting minuteacoustic impulses that are generated as a defect propagates under load. A major advantage of this procedure is its
ability to monitor an entire piece of equipment quickly.” This is a welcome testimony for the validity of

AE testing.
Small, but extremely high-pressure cylinders also present technical challenge, as in the caseof 100-MPa hydrogen cylinders [151]. Also see Gorman [152] for AE specific discussion. A new
ASME Code case was developed based on AE to provide the accept-or-reject criteria. These arebased on the curvatures of cumulative events and energy curves that quantitatively measure pressure vessel stability. These are COPV, but the liners do not carry load, unlike the space counterpart discussed in the previous section (these carry 20-30% load [144]). An example of AE cumulative event (ΣAE during load hold) analysis is shown in Fig. 20. Left curves are AE events
curves for 4 channels. At 320-570 s, load is held and event rates started to decline, which are fitted with exponential functions of the form
ΣAE = A exp (B t) + C,

(2)

t = time and A, B, C constants. For the curve on the right, shape factor B is –0.0183 and goodness of fit is R2 = 0.91. When B is negative, AE event rates decay and the vessel is accepted asstable. Other factors used include the number of fiber breaks, fiber break energy, background201

energy oscillation effect (which begins at 60-75% of burst pressure) and frictional AE. This
Code is now published from ASME (Section X, Appendix 8, 2010) [151b] and being applied in
the field. This Code requires for vessel acceptance that the value of B falls between –0.1 and –0.001 and R2 ≥ 0.80. Alternately, it is possible to use time required for the structure to emit 99%
of AE events on a dwell, namely, t99%. Here, t99% = ln (0.01)/B and takes values between 25 and40 min. For various non-AE requirements, see [151a and b].

Fig. 20 a) Cumulative events and pressure vs. time. Load is held between two vertical lines. b)
Exponential fitting of the cumulative events vs. time for channel 1 during pressure hold period.
[152]
Wind turbines
Increasing size of wind turbine power generators demands constant monitoring and maintenance.
Articles of applicable NDT methods and of AE methods have appeared [153-157]. Ciang et al.
[153] reviewed various SHM methods, with a broad discussion on AE with a dozen references.
Some new approaches are suggested. Reference [154] suggests somewhat limitedly “AE is considered more robust for low-speed operation of wind turbine compared to the classic vibration
based methods. This approach is also more ideal for identifying early faults in gearbox bearings.”
Reference [155] is a brief summary, while [156] reported on an AE test of 25-m long blade witha static step loading schedule and noted locations of AE events at 77% of the fracture load. Sensor placements tightly (20-50 cm spacing) surrounded the eventual failure site.

Fig. 21 Surface damage above the spar-cap skin interface with 3 R6I AE sensors (#18-20). [100]202Fig. 22 AE energy rate vs. fatigue cycles. [157]Fig. 23 AE event locations near the spar cap
end. [157]

Rumsey et al. [157] conducted high-cycle fatigue loading of TX-100 blade of 9-m length toover 4 million cycles. Dr. Beattie performed AE testing portion. This blade has 6-mm balsa
wood at center, a glass cloth supporting a single ply of UD carbon fibers at 20º, then the top mat
layer on both sides. At mid-length, spar cap is joined to the base side, where eventual failure waspredicted and AE monitored. At ~2.5 million fatigue cycles, a large visible crack developed parallel to the chord axis on the high-pressure side at ~4.5 m. The crack then grew along the 20º offpitch-axis carbon fiber direction until the test was stopped at 4 million cycles. Figure 21 shows
the surface damage above the spar cap skin interface with 3 AE sensors (PAC R6I) nearby. Almost immediately with test start, AE detected AE events and followed the evolution of blade
failure around the end of the spar cap at 4.5 m, but no significant damage in any other monitored
areas (24-channel system used). Significant AE appeared at 3.55 million cycles and extreme
damage indications started at 3.78 million cycles (see AE energy rate in Fig. 22). The locationsof the AE events (Fig. 23) matched the visual damage shown in Fig. 21. The most significant result of this test was that only AE was able to predict and identify the damage location and severity while a few other SHM sensors failed to see the damage.
Another approach for AE characterization, i.e., pattern recognition/neural network, has beenused for AE signals from turbine blade tests. [158-160] This approach provided damage grade
assessment, relatable to the state of cracks and other flaws developed in fatigue loadings [158].
Load-hold emissions were useful in setting up the classifiers. Other methods evaluated different
aspects of blade state assessment [159, 160]. This application is still developing and new methods are expected in this Conference.
Space Shuttle
NASA tried to develop and implement on-board impact detection technologies after the loss of
the Space Shuttle Columbia. Prosser [161, 162] described a system called Shuttle Wing Leading
Edge Impact Detection System (WLEIDS). It had been installed on all Space Shuttle missions
till recent retirement of the fleet. The system tested accelerometers and AE sensors for detecting
the impact of launch foam debris on the leading edge made of reinforced carbon-carbon and on
thermal protection tiles. Even though a foam block tested was ~0.8 kg, its speed is 240-290 m/s
and produced detectable damages with the sensors. Some hypervelocity (6.8 km/s) tests weredone, indicating different waveforms from the slow impacts. Implementation on shuttle fleet relied on flight-qualified accelerometers that proved to function at lower frequency range than AE
sensors, while reducing the signal acquisition frequency down to 20 kHz, beneficial in data203

storage and transmission to the Mission Control on earth. For each Space Shuttle, 66 sensors
were on board and 22 battery-powered data acquisition/wireless transmission units were mounted in each wing cavity. The system recorded data from all sensors continuously during launch
and ascent to orbit. The data was transmitted wirelessly to a laptop computer in the crew compartment and then downlinked to ground. The monitored data consists of rms values of g-force.
A summary file of the largest peaks is also stored and used for preliminary analysis. When warranted, detailed analysis follows. For all flights since Columbia, the WLEIDS has performed exceptionally well. None of probable impact events have been of amplitude consistent with criticaldamage to the RCC leading edge and in-flight inspection at the suspected impact locations using
the Orbital Boom Sensing System has not revealed damage. Post-analysis of the entire records
evaluated the system performance on flight for future deployment. Some of the waveform records in the ground tests can be seen in [161] and key components of the Shuttle Wing Leading
Edge Impact Detection System are shown in Fig. 24.

Fig. 24 Key components of the Shuttle Wing Leading Edge Impact Detection System. [161]
Sandwich structures
In practical composite design, sandwich structures are widely used [163]. Aluminum or Nomax
honeycomb core is widely used in aerospace applications and foam core is common in less demanding design. AE studies of such structures are not many. Burman [164] conducted an extensive study of sandwich composites under static and fatigue loading conditions and used AE for
monitoring crack initiation and growth in the core. AE location aided failure process study on
invisible subsurface damages. AE behavior is typical one with rising AE near fracture, and amplitude reaching into >80-dB range. Sandwich composite panels are finding new applications inrehabilitating aging infrastructures [165, 166]. Reference [165] used a small AE system and obtained some AE results on tensile samples, but only rough data outline is given. Larger samples
with cyclic loading are also tested, but results are not clearly analyzed or presented. [166] used
16-channel AE system and obtained some AE results during stepwise loading and signal attenuation on the panel, but definitive results are yet to come. In these ambitious projects, it is hopedthat past AE experience in the literature is used to better outcomes.
204In similar composites with 30+% GFRP skin and balsa-wood core, Hoa [167] studied their
AE behavior extensively, producing an ASTM document (ASTM E1888-12). It is worth noting
that, starting from FR concept, Hoa defined a trend number Z = A2/A1, where
A1 is AE parameter (counts, hits) upon loading to P1.
A2 is AE parameter (counts, hits) upon reloading to P1.
Under cyclic loads, Z tends to vanish in a good vessel (or a zone), while Z in damaged one starts
to rise. This parameter is useful in judging this type of sandwich composite and may be applicable for other types.
Aircraft applications
In 1984, we were invited to monitor the testing of a prototype all Kevlar composite aircraft(Avtek) using AE. See Fig. 25, which shows an 8-channel AET-5000 system. The Avtek plane
was supported at the main wings and canard wing tips while the mid-fuselage was pulled down.
30- and 175-kHz sensors were distributed, especially at the canard attachments. This position
turned out to be much weaker than design load, producing significant AE and stopping the test
before damage. This demonstrated the AE test’s worth, but the Kevlar plane project did not continue long unfortunately.

Figure 25 AE monitoring of prototype Avtek aircraft in 1984. Dr. I. Roman, on leave from Hebrew University of Jerusalem, operated AET-5000 system. [The original of the cover photoshown in JAE vol. 3]
AE applications in aircraft industry appear to have advanced according to the anecdotes fromAE equipment makers, but little is known in the open literature for obvious reason. Only about adozen papers appeared in JAE in the past 30 years with direct connection to airframe, but only
metallic. Composite structural components are increasing their importance even in commercial
aircraft, but the role of AE is apparently limited. Pfeiffer and Wevers [168] detailed AE andLamb wave methods for aircraft SHM applications as a part of AISHA project under European
Research Area. For critical damages, appropriate Lamb modes are being identified for such parts
as helicopter tailbooms and Airbus slat tracks. Takeda [169] reviewed small-diameter optical fiber sensors for damage monitoring and structural health monitoring (SHM) of composite structures. They also reported on results from ACS-SIDE project in Japan with optical SHM. FiberBragg grating (FBG) sensors are combined with PZT actuators for Lamb wave inspection of
CFRP structures. Several articles on NDT in aircraft manufacturing appeared recently [170-174],
but only one briefly mentioned AE’s use in predevelopment-design phase [170]. Our concerted
efforts to change the perception of key people in this industry are needed. For example, an improved activation method of impact/delamination damage for AE diagnosis will be needed toprovide a better technique to aircraft people. On composite panels, the benefit of global
205inspection with AE often evaporates from high wave attenuation behavior. An innovative method is required also.
Figure 26 shows expanding uses of composite in modern commercial and military aircrafts.
This poses a formidable challenge for NDE/NDT of composite components during manufacturing, service inspections, flight conditions and SHM. Numerous laboratory tests have been carriedout during last decades on large composite components for aircraft structures. At the beginningof the 1990s, Boeing designed and built a 64-channels AE system, called AE Pre-failure Warning System (AEPWS), which was used on a prototype of a low tail horizontal stabilizer (LTHS)
box [175]. AEPWS, based on calculation of the AE energy (MARSE), was used to monitor selected flights for the first lifetime (50,000 flights) of fatigue cycling of the LTHS. At the end of
the 1990s, Saab Aerospace (Sweden) monitored by AE two full-scale test on the JAS 39 Gripen
combat aircraft, partially manufactured from CFRP [176]. In the first one, the wings and the vertical stabilizer (which are primarily manufactured from CFRP) were monitored by a 72-channels
PAC AE system in order to localize damage in the early stage. It was possible even without any
false alarms and no more damage was detected by other NDT techniques. During the more than
100 real-time tests FR index was checked.

Fig. 26 Composites ratio in structural aircraft weight during last decades in commercial andmilitary aircrafts.
Toward the goal of more active AE utilization in composite aircraft area, some papers are ofinterest [177-179]. Leone et al. [177] monitored AE in several honeycomb sandwich composites
curved fuselage panes, each containing a different scenario of damage, using the Full Scale Aircraft Structure Test Evaluation and Research (FASTER) placed in Atlantic City (US). In particular, the evolution of particular notches artificially made on the full-scale T700-CFRP sandwich
panels was monitored by AE using an 8-channel 150 kHz resonant sensor configuration locatedin circle (see Fig. 27). A good correlation was found between the notch progression (see Fig. 28)
and AE accumulated and during loading of the panels (see Fig. 29). The most of the events recorded were below 70 dB (between 85-97 %). Figure 29 shows that the percentage of highamplitude events, normally associated with fiber breakage, was less than 5% of the total AE
events recorded.
206

Fig. 27 Left: Honeycomb sandwich composite panel. Right: A segment of the panel showing the
AE sensors arrangement, a photogrammetry system and an artificial notch. [177]Fig. 28 Notch-tip damage after complete final fracture in two sandwich panels. [177]Fig. 29 Load-time history and accumulation of AE events and amplitudes recorded during loading two panels. Each dot signifies a three-hit event. [177]Sensors and Sensing
Aljets [178, 179] completed a thesis with new approach to source location methods and validated
them on large CFRP plates. One is to use an array of 3 closely positioned (45 mm) sensors. Using this sensor array, wave modes and source locations are obtained with two different methods.
The array sensor approach is similar to Sachse’s patent of 4-sensor array, but the latter was forsource location purpose [180]. As we saw earlier in this review, the knowledge of wave modes is207

very important information today. More such work on large samples must be pursued to makeAE acceptable to industry engineers. In this connection, a new method of Lamb-wave detection
should be of interest. Yeum et al. [181] used a sensor of two coaxial PZT elements, a center discand a surrounding ring. This sensor needs to be placed only on one surface. The mode decomposition can be performed at any selected frequency without changing the PZT size and/or spacing
configuration, aided by solving 3D Lamb-wave propagation equations with the PZT size andshape as input. This technique was demonstrated for Lamb waves in an aluminum plate, butshould be applicable to composite plates as well.
Optical sensing of AE signals relied on bulky components initially, but the size of sensors
and associated equipment started to shrink. Wevers et al. [182] introduced an intensity modulated optical sensor based on the microbending into a CFRP plate and evaluated AE from developing damage, though S/N ratio needed improvements. Surgeon and Wevers [183] evaluated effects of adding optical sensors into CFRP plates. When optical fibers are near the stress-carrying
plies, degradation was detectable. Subsequent reports from this group [184, 185] examined signal processing aspect for S/N improvements and a different sensing principle, using polarimetric
single-mode optical fiber sensors. Other approaches also appeared more recently [186-188] withinterferometric techniques. FBG sensors are from a different principle and is amenable to source
localization. See Tsuda’s article [189] on its use in SHM applications along with [169]. For embedded usage, see [190]. The main obstacle for wider uses of optical sensing is still the cost ofequipment.
Pattern recognition – Clustering
When we initiated using pattern recognition analysis (PRA) software, called ILS, in the early
1980s [61a, 191, 192], we relied on autoregressive modeling to obtain the signal feature vectors
and utilized principal component analysis. It was cumbersome, but the promise of signal discrimination was encouraging. We next used Icepak software from TISEC in the 1990s, which vastly
ran faster and reported CFRP signal classification results discussed earlier [61b,c, 95]. With Icepak, several algorithms including artificial neural network were available, but we primarily used
k-NN. In these studies, we had to supply the likely clustering scheme based on separate aspects,
such as special specimen geometry, loading range, microscopy, etc. Unsupervised PRA algorithm started to appear [193] and Noesis software improved the coordination with PAC’s dataacquisition hardware in an integrated operation. This approach basically uses the standard AE
parameters along with partial power spectral density, chosen to represent different AE source
features. A different approach is taken in Vallen’s VisualClass that relies on time-staggered
power spectral features. In the past decade, PRA has become a common tool in AE examination[194, 195; see also Hill, [23], pp. 382-387].
Kohonen introduced a new concept in classifying a large body of diverse data [196]. This is
known as the Self-organizing Map (SOM). It has been incorporated into PRA scheme for AE
data analysis [40, 82-85, 197-199]. For non-specialists, it is easiest to start with [40, 84]. In thesimplest term, SOM helps achieve “Birds of a feather flock together” phenomenon by directing adata vector (characterized by multiple features) into an area of a map where other data vectors ofsimilar characteristics are already present. Godin [84] combined SOM with k-means method toimprove further AE data classification. [200, 201] are recent studies that take advantage of the
new PRA method with SOM. SOM Toolbox for MATLAB is available from Prof. Kohonen’sgroup [http://www.cis.hut.fi/somtoolbox/links/somsoftware.shtml] and can be incorporated into
own data analysis package. With the SOM, robust unsupervised PRA is possible and this should
become a practical AE analysis tool shortly.
208

Concluding Remarks
During the past ~50 years, we have expanded the knowledge base on AE. Still, further advances are critically needed to make AE an indispensable tool for composite industry. This article has attempted to review “composite AE” from materials researchers’ perspectives and to survey how AE has contributed to various applications. The first part was reasonably achieved but
the second part was only partially fulfilled, as it has been difficult to gain information of AE
technology practiced in various industries. We have also identified a few crucial problems thathave to be resolved to reach our goal of making AE a useful tool for industry. These include displacement-velocity sensor response, high wave attenuation in composites and the lack of understanding of Felicity effect and stress rupture. Promising developments include new methods forhigh-pressure vessel monitoring sanctioned by ASME, SOM-based unsupervised PRA, and tackling of issues with wave attenuation and stress rupture. We trust this article presents a freshsummary of what has been discovered and utilized, points out obstacles to surmount and gives
some suggestions to future work, hopefully serving as inspiration for developing a new technology.
Acknowledgment: The author (K.O.) wishes to thank Drs. Timothy Fowler and Michael Gorman for criticallyreviewing this manuscript.

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Appendix 1: Lamb wave propagation
In 1986, Ichikawa et al. [A1] investigated the optimum conditions for using Lamb waves forinspection of steel sheets, which fail from nonmetallic inclusions under heavy deformation. Using theoretical results, they developed an improved inspection system for use in steel productionlines. From the basic Lamb wave behavior, they noted complexity, such as 1) some modes are
difficult to excite or detect depending on frequency-thickness product, or f·d values, 2) the
sharpness of flaw reflection varies strongly with f·d values, and 3) flaw reflection pulse has a different frequency from that of the excitation pulse.
Pulse excitation and detection typically uses piezoelectric sensors that respond to normal displacement; on the plate surfaces, one needs vertical particle displacements from Lamb waves.
For a plate of thickness, d, scalar and vector potentials are introduced to represent displacement
and stress within the plate using free boundary conditions on the surfaces. The components of adisplacement vector (U, W) for the wave propagation direction, x, and for the normal (thickness)direction, z, are given by:
For symmetric modes:

For asymmetric modes

where d = 2δ, 𝑞!! = 𝑘!! − 𝑘!! , 𝑞!! = 𝑘!! − 𝑘!! , 𝑠!! = 𝑘!! − 𝑘!! , 𝑠!! = 𝑘!! − 𝑘!! , ω = 2πf : angularfrequency, ks (a, l, t): wave number for symmetric (asymmetric, longitudinal, transverse) wave, α, β= constant. Wδ is defined as an index to express the magnitude of the surface normal displacement, W, relative to the integrated mean-square displacement as:

Using equations 3 and 4 with the shear modulus of steel = 80.3 GPa, density = 7.8 Mg/m3, Poisson’s ratio = 0.28, the phase velocity and Wδ in dB scale are shown against f·d in Fig. A1 (a) and(b). These are reproduced from Figs. 4 and 5 in [A1]. It is evident that Wδ varies widely
depending on the wave mode and f·d values. It is significant that the surface normal displacement221

of So mode is less than –17 dB for f·d ≤ 1 MHz·mm, while that for Ao is always above –6 dB.
This explains why So mode is usually not observed at low frequencies, especially for thin structures.
The wave dispersion also affects the pulse amplitude. The group velocity Cg is given by
!

!!

!

!"

!

!"

= 1 −   ( ),(5)where C is the phase velocity. When a pulse with the center frequency fo and its spread in thefrequency domain Δf (in half-value width) travels time t over distance l, resulting in pulse spreadin time of Δt (in half-value pulse width) of:

where Ao is a constant. A parameter Dg defined below indicates the sharpness of a pulse affectedby the velocity dispersion. With the transverse velocity Ct, we have:

Results of calculation are shown in Fig. A1(c) (which is Fig. 6 in [A1]). When the value ofDg vanishes, the dispersion effect is minimal. Again, Dg varies strongly with f·d values, depending on the wave mode. Open circles marked P1 to P8 (in Fig. A1 (b) and (c)) indicate the points
where Dg = 0. Table 1 in [A1] gives the f·d values (along with C/CL, Cg/CL and Wd (in dB)) corresponding to P1 to P8 :Pi
f·d value
wave mode
C/CL
Cg/CL
Wd (in dB)

P1

P2

P3

P4

P5

P6

P7

P8

1.42

2.52

2.80

4.81

6.97

7.26

7.41

9.09

Ao

So

A1

A1

S1

A3

A3

A2

0.79
1.01
-6.0

1.23
0.54
-4.0

2.00
1.18
-8.5

1.33
0.69
-5.1

1.38
0.70
-5.2

2.80
0.78
-3.8

2.60
0.78
-3.9

1.40
0.71
-5.1

There seems another vanishing Dg point at f·d = 4.12 for S1 mode, but Wδ is low here. Judging from various wave propagation data, both theoretical and experimental, it appears that when
Dg ≤ ±0.3~0.5, pulse propagation is possible without suffering excessive dispersion effects.
Considering both Wδ and Dg, this work leads to approximate ranges where good pulse detection with low dispersion loss can be expected:
So modeAo modeS1 modeA1 modeS2 modeA2 modef·d = 1 ~ 2, >3.5
> 0.5
~2.7
2~4
3.5 ~ 7> 5.8
222

Fig. A1. a) Phase velocity of Lamb waves vs. f·d values. b) Index for surface normal displacement Wδ in dB scale vs. f·d values. c) Parameter for dispersion effect Dg vs. f·d values. Thesefigures were assembled from Figs. 4-6 in Ichikawa et al. [A1], matching the abscissa.
223

Since Wδ is in dB scale, it should dictate the outcome, while effects of Dg parameter needs to be
explored experimentally.
This result of Ichikawa et al. [A1] is for isotropic medium and not directly applicable to fiber
composite materials. However, this gives for the first time explanations for oft-peculiar Lambwave propagation behavior. In practical inspection setups, one also needs to consider frequencydependent attenuation effects.
Appendix 2: Gaussian Pulses
A Gaussian pulse is defined as a function of time t
𝐺 𝑡 =   exp  [−𝑡 ! /2𝜎 ! ](8)where σ is a pulse width parameter [A2]. The pulse width at one-half of the peak height, t1/2, (also called the full width at half-maximum or FWHM) is given by𝑡!/! =    8  ln  (8)  𝜎 = 2.35482  𝜎.
(9)Figure A2 (a) shows a Gaussian pulse with t1/2 = 0.22 µs [A3]. The power spectral density ofthis pulse is given in Fig. A2 (b). It peaks at f = 0 and the spectral width at one-half of the peakheight, f1/2, is equal to 2.0 MHz. The half-height widths are related to each other by [A4]t1/2 x f1/2 = 0.44.

(10)

The time derivative of a Gaussian pulse defines a Gaussian mono-pulse (aka monocycle).
Using the center frequency, fc, it is given by [A5]
𝑉 𝑡, 𝑓! , 𝐴 =   2 𝑒  𝐴𝜋𝑡  𝑓!   exp  [−2 𝜋𝑡𝑓! )! ,(11)

where A is the peak height. A Gaussian mono-pulse at 2 MHz is shown in Fig. A2 (c) [A6]. In
the frequency domain, this is given by
𝑉 𝑓, 𝑓! , 𝐴 =   

!

!! !

!

! !!

exp  [−

!

! !

! !!

) .

(12)

The inverse of the center frequency is essentially pulse duration, τ. That is,(13)
τ x fc ~ 1.
A pair of waveform and its frequency spectrum from [A5] was scaled to 2 MHz to 200 kHz center frequencies. Four such pairs are illustrated in Fig. A3, (a) to (d).
The most striking feature of Fig. A3 is the broadness of the spectra. For a 2 MHz pulse (of0.5-µs duration), –3 dB half-power points are at 1 and 3.2 MHz. At 200 kHz (5-µs long pulse),
the corresponding frequency range is from 100 to 320 kHz. Thus, a variation in source duration
has to be quite large to be noticeable in the detected frequency spectra. Since most sensors andstructures have natural frequencies, their dominance will be difficult to overcome.

224

Fig. A2. a) 2-MHz Gaussian pulse. b) Its power spectral density. [A3] c) 2-MHz Gaussian monopulse. [A6]225

Fig. A3. a) 2-MHz Gaussian monopulse and its power spectral density. b) Same for 1 MHz. c)Same for 500 kHz. d) Same for 200 kHz. [A5]226

Appendix 3: Directionality of Attenuation and Source Types
Suzuki [71b] measured P-wave attenuation in UD-GFRP with 60% fiber content using a laser
technique. Figures 4.15(g), 4.16 and 4.17 from his thesis are shown in Fig. A4 below. Theseshow high attenuation especially for 90º orientation at 160-180 dB/m, but even for the orientation along fibers (0º) it is 60~80 dB/m. These are still 2 to 5 times less than in epoxy.

Fig. A4. a), b) Frequency dependence of P-wave attenuation in GFRP. c) Orientation dependence
at 1.4~1.65 MHz. [71b]Downs et al. [A7] examined the directionality of AE sources in an Al plate, that is, the orientation dependence of FEM-generated AE signals due to three types of sources, i.e., in-plane dipole, microcrack initiation, and shear at 45° about the y-axis without a moment. They used wavelet transform to characterize the absolute WT peak magnitude as a function of radiation angle
and source depth. The fall-off with increasing angle is dramatic for the in-plane dipole (nearly
100 %), as shown in Fig. A5. It is least for the microcrack initiation, except for a source at the
mid-plane, where the 45° shear has the least fall-off. The results also demonstrated that the WT
absolute peak magnitudes have their greatest values when the source is close to the surface andhave their lowest values when the source is located at or near the mid-plane.
227

Absolute Peak WT
Magnitude for Entire Signal

The directionality WT magnitude of microcrack sources is shown in Fig. A6, where 0ºamplitude was used to normalize other radiation angles. Here, frequency: 270 kHz, propagation
distance: 180 mm, plate thickness: 4.7 mm. S0 and A0 data behaved differently; Source depth
effect was present for the A0 mode, but not for the S0 mode. In both modes, the magnitude did
not vanish at 90º.

200,000

0.47 mm IPD
0.783 mm IPD
1.097 mm IPD
1.41 mm IPD

150,000
100,000

1.723 mm IPD
2.037 mm IPD
2.35 mm IPD

50,0000030
60Radiation Angle (degrees)

90

Fig. A5. Absolute peak WT magnitudes for various source depths for in-plane dipole source at
180 mm propagation distance [A7].

Fig. A6. Normalized peak WT magnitudes for microcrack source at 270 kHz; legend lists source
depth (mm), propagation distance (mm), and mode [A7].
228References for AppendicesA1. F. Ichikawa, K. Kurita, H. Maruyama, S. Takahashi, “Study on optimum conditions for
Lamb wave ultrasonic testing and its application to product line”, Kawasaki Steel Tech. Report,
vol. 18, 1986, pp. 192-198.
A2. S. Smith, Gaussian Pulses. http://gamma.ethz.ch/download/pdf/pulses/gaussian.pdf
A3. Introduction to Optical Pulses, Optipedia, Free optics information SPIE. Excerpt from R.
Paschotta, Field Guide to Laser Pulse Generation, SPIE Press, Bellingham, WA (2008).
spie/org/x32442/xml?pf＝true	  A4. Gaussian pulses, Encyclopedia of Laser Physics and Technology.
www.rp‑photonics.com/gaussian_pulses.html	
  A5. P. Withington, Impulse Radio Overview, Time Domain Corp.
user.it.uu.se/~carle/Notes/UWB.pdf	
  A6. Gaussian monopulse – Matlab: www.mathworks.com/help/toolbox/signal/ref/gmonopuls.html	
  A7. K.S. Downs, M.A. Hamstad and A. O’Gallagher, “Practical aspects of acoustic emission
source location by a wavelet transform”, J. Acoustic Emission, vol. 21, 2003, pp. 52-69.

229