Journal of Acoustic Emission, Volume 26, 2008
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Contents
26-001

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

1-13

26-014

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

26-023

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

26-032

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

32-39

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

40-59

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

60-71

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

72-90

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

91-97

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

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

Automated Method for Statistical Processing of AE Testing Data
V. A. Barat and A. L. Alyakritskiy

26-142132-141

Termites Detection via Spectral Kurtosis and Wavelet De-Noising of Acoustic Emission Signals
Juan-José G. De La Rosa, Antolino Gallego, Rosa Piotrkowski, Enrique Castro andAntonio Moreno-Muñoz
142-151ISSN 0730-0050 Coden JACEDO

I-1© 2008 Acoustic Emission Group

26-152

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

152-159

26-160

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

26-172

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

172-181

26-182

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

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

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

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

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

26-20826-22026-229

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

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

26-247

Wavelet Entropy and Power of AE Signals as Tools to Evaluate Damage in Coatings Submitted to
Scratch Test
Rosa Piotrkowski, Antolino Gallego and Enrique Castro
247-261

26-262

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

26-270Experimental Simulation and Dynamic Behavior of the AE due to Martensitic Transformation
Using Shear Wave Transmission Sensor
Takeshi Yasuda, Shinya Kondo, Hideo Nishino and Kenichi Yoshida270-278

26-279Implementation of Acoustic Emission Method to the Conventional NDT Structure in Oil Refinery
V.P. Gomera, V.L. Sokolov and V.P. Fedorov
279-289

26-290An Experimental Analysis of Frequency Emission and Noise Diagnosis of Commercial Aircrafton Approach
S. Khardi
290-31026-311

New Developments of Software for A-line Family AE Systems
Sergey Elizarov, Аnton Bukatin, Мikhail Rostovtsev and Denis Terentyev

26-317311-317

Application of Acoustic Emission in Optimizing the Design of New Generation castings ofHigh-Voltage Electric Devices
Jan Płowiec, Wojciech L. Spychalski, Huber Matysiak and Jakub Michalski
318-325

I-2© 2008 Acoustic Emission GroupContents26
AUindex26
AusNotesContents of Volume 26 (2008)
Authors Index of Volume 26
Policy/Author’s Notes/Meeting Calendar/DVD/Subscription Information

I-1 – I-3
I-4
I-5 – I-7

JAE Index Folder*
Cumulative Indices of J. of Acoustic Emission, 1982 – 2008
Contents1-26
Contents Volumes 1-26
Authors Index1-26
Authors Index Volumes 1-26
PhD Thesis of Runar Unnthorsson, University of Iceland, 2008** indicates the availability in CD-ROM only. Indecies are also available for download from www.aewg.org.
Cover Illustration is from Paper 26-160 by Toshimitsu Hayashi and Shuichi Wakayama.
This figure shows the WT diagram, AE signal, projected WT‐frequency curve and FFT spectrum. While WT
identifies the first major frequency at 0.56 MHz and the second major frequency at 0.32 MHz, FFT shows the
1st and 2nd major frequencies of ~0.3 MHz as these frequency components have long duration. This clearlyshows that FFT analysis failed to detect the highest characteristic frequency at 0.56 MHz.
In using FFT, it is essential to be cognizant of this shortcoming. Read further in the text.

I-3© 2008 Acoustic Emission GroupAUTHORS INDEX, Volume 26, 2008
A. L. Alyakritskiy
Takahiro Arakawa
Janine Avison
V. A. Barat
Bernd-Arno Behrens
Petr Benes
Christophe Binétruy
Andreas J. BrunnerАnton Bukatin
Enrique Castro
Hideo Cho26-13226-10926-09126-13226-20826-06026-12026-15226-31126-142, 26-247
26-072Igor Maria De Rosa
Juan-José G. De La Rosa
Martin DrabMark J. Eaton
Islam El-Galy
Sergey Elizarov
Manabu Enoki
V.P. Fedorov

26-22026-142
26-18926-17226-20826-311
26-18226-279Laurent Gaillet
Antolino Gallego
V.P. Gomera
P. A. Gradin
Janez Grum
M. Gullikson

26-03226-142, 26-247
26-27926-02326-19926-023M. A. Hamstad
Christian Hansmann
Toshimitsu Hayashi
Yuichiro Hirose
Karen M. Holford
Siegfried Horn
Filip Hort
Patrice Hulot
Hassane Idrissi
D.S. Ivanov
Magnus T. Jonsson

26-04026-01426-16026-182
26-17226-001
26-18926-12026-03226-24026-229, 26-262

Bo KarlssonTomaž Kek
Jiri Keprt
S. Khardi
Shinya Kondo
Johannes KonnerthRobert J. Lark
S.V. Lomov

26-01426-19926-06026-29026-27026-01426-17226-240

Takuma Matsuo
Huber Matysiak
Pavel Mazal
Jakub Michalski
Yoshihiro Mizutani
Antonio Moreno-Muñoz

26-07226-31726-18926-317
26-10926-142
I-4

Hideyuki Nakamura
Hideo Nishino
S. Norgren
S. Nyström
Kanji Ono
Marianne Perrin
Rosa Piotrkowski
Jan Płowiec
Rhys Pullin

26-10926-27026-02326-02326-07226-03226-142, 26-247
26-31726-172Christian Rasche
Franz Rauscher
Sabine Rosner
Мikhail Rostovtsev
Thomas P. Runarsson

26-12026-09826-01426-311
26-229, 26-262

Kouki Saiga
Carlo Santulli
Fabrizio Sarasini
Markus G. R. Sause
Daniel SchultheißPetr Sedlak
Josef Sikula
Olivier Skawinski
V.L. Sokolov
Wojciech L. Spychalski
Tomas Slunecko

26-10926-22026-22026-00126-001
26-182
26-18226-12026-27926-31726-189Nobuhito Takizawa
Denis Terentyev
Giovanni P. Terrasi
Christian Tessier
Pete Theobald
Akira Todoroki

26-10926-31126-15226-032
26-09126-109Runar Unnthorsson
I. Verpoest
Shuichi Wakayama
M. Wevers
Takeshi Yasuda
Kenichi Yoshida
Bajram Zeqiri

26-229, 26-262
26-24026-16026-24026-27026-27026-091© 2008 Acoustic Emission Group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.
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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:
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P. ColeL. Golaski
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4. Publication
Journal of Acoustic Emission is published annually in CDROM by Acoustic Emission Group, PMB 409, 4924 Balboa Blvd,
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5. Subscription

2. Endorsement
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3. Governing Body
The Editor and Associate Editors will implement the editorial
policies described above. The Editorial Board will advise the editors on any major change. The Editor, Professor Kanji Ono, has
the general responsibility for all the matters. Associate Editors
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ISSN 0730-0050 Copyright©2008 Acoustic Emission Group(Greece)(USA)(Germany)(UK)(Poland)(USA)(Canada)(UK)(Korea)(France)(Germany)(Czech Republic)(India)(USA)(Austria)(Japan)(Austria)(Germany)(Belgium)(Australia)

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I-5

CODEN: JACEDO All rights reserved.

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

The Journal will publish contributions from all parts of theworld and manuscripts for publication should be submitted
to the Editor. Send to:Professor Kanji Ono, Editor - JAE
Rm. 2121H, Engr. V, MSE Dept.
University of California
420 Westwood Plaza,
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FAX (1) 818-990-1686
e-mail: ono@ucla.edu

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

Authors of any AE related publications are encouraged tosend a copy to:
Mr. T.F. Drouillard, Associate Editor - JAE
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A.E. Lord, Jr. (1975), Physical Acoustics: Principles andMethods, vol. 11, eds. W. P. Mason and R. N. Thurston,Academic Press, New York, pp. 289-353.
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I-6

MEETING CALENDAR:
AEWG-52
52nd Meeting of AEWG will be held at Stone Harbor Resort, Sturgeon Bay, WI, USA, on October
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I-7

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25 April 2009.

ACOUSTIC EMISSION INVESTIGATION OF COATING FRACTURE
AND DELAMINATION IN HYBRID CARBON FIBER REINFORCED
PLASTIC STRUCTURES
1

MARKUS G. R. SAUSE1, DANIEL SCHULTHEIß2 and SIEGFRIED HORN1
University of Augsburg, Institute for Physics, Experimentalphysik II, D-86135 Augsburg,
Germany; 2BMW Group Forschung und Technik, D-80788 München, Germany

Abstract
Nickel-copper-coated and uncoated carbon-fiber reinforced plastic specimens were investigated by acoustic emission (AE) analysis during four-point bending tests and subsequently characterized by scanning electron microscopy. As a function of applied loading, AE originatingfrom coating failure consisting of crack initiation, crack growth and delamination was detected.
The evolution of coating failure observed by AE is consistent with the degradation detected byelectron microscopy. In particular, the AE originating from coating failure turns out to be distinguishable from signals arising from failure of the carbon-fiber reinforced plastic. The combination of pattern recognition techniques and advanced frequency-spectrum analysis shows promise
to distinguish between different failure mechanisms and, therefore, could be a valuable tool forthe investigation of comparable hybrid materials.
1. Introduction
In recent years, the aerospace and automotive industry has developed a lively interest inlightweight applications. Reducing overall weight is the key for energy efficiency, a larger carrying capacity and, therefore, greater competitiveness. In this context, the European project StorHy(Hydrogen Storage Systems for Automotive Application) investigates a liquid-hydrogen tanksystem made of carbon-fiber reinforced plastic (CFRP). While the required mechanical strengthof such tank systems can be achieved by usage of CFRP [StorHy, 2007], the gas permeability isremarkably higher than in conventional metallic storage systems [Schultheiß, 2007]. The mostencouraging approach to decrease gas permeation through CFRP is to apply a metallic coating, aso-called liner. Recently several liner materials and methods for coating of CFRP vessels were
investigated in StorHy. The best liner solution turned out to be chemically deposited copper,
followed by an electroplated copper layer. A closely related liner system also investigated in
StorHy is a nickel-copper coating [Schultheiß, 2007]. The characterization of adhesive strengthand of maximum allowable strain during mechanical loading of such liner materials is of great
importance to optimize the method of liner application and to predict the gas-tightness of a liner
under real environmental conditions.
Commonly used surface-sensitive diagnostic methods like scanning electron microscopy(SEM) are insufficient tools for the detection of all possible failure sources. Due to the thicknessof the metallic liner, which is in the range of 20 - 60 µm, we can only investigate the delamination between liner and CFRP substrate by SEM after completion of mechanical loading and subsequent destruction of the sample. A direct correlation between the applied strain and the first
appearance of delamination and its further progress is impossible. In contrast, acoustic emission(AE) analysis provides a suitable method to obtain the strain dependence of crack formation andcrack growth within the liner and liner delamination. In the following, we refer to these mechanisms as coating failure.
J. Acoustic Emission, 26 (2008)1© 2008 Acoustic Emission GroupIn the past, many papers concerning the distinction between AE signals due to different failure mechanisms were published. Based on classical methods like AE activity changes, amplitude
distribution and source localization, a change in AE signals during loading of CFRP specimens
was observed [Ceysson, 1995], [Sato, 1986], [Komai, 1991]. A further assignment of AE signals
to at least two distinct types of failure was achieved by measurement of waveform, wave modeand frequency distribution [deGroot, 1995], [Ni, 2007]. For fiber failure a significant spectral
weight was observed at frequencies above 350 kHz, whereas the spectral weight of AE signals
relating to matrix failure shifts to lower frequencies (< 350 kHz) [Bohse, 2001].
Several studies investigated crack formation, crack progress and delamination of metallic
coatings on metallic substrates. Here AE signals were used to determine the quality of coatings[Fu, 2000]. It was demonstrated that AE signals due to different failure mechanisms can be separated using wave-mode analysis and AE activity analysis [Lin, 1997], [Ma, 2001], [Nikulin,2005]. In [Miguel, 2003] fatigue evolution for tungsten-carbide/cobalt-coated steel specimens
was investigated and four different stages of fracture could be distinguished.
Unsupervised pattern recognition techniques provide a completely different approach to classify AE data. Here, a set of N parameters characteristic for a waveform are extracted, and in thefollowing this set is used to represent the respective AE signal. The values of these parameters
are arranged into a N-dimensional space and are scanned for accumulation points using clustering algorithms. According to [Anastassopoulos, 1995, 2000] optimization of numerical discrimination between resulting clusters can be achieved using the heuristic ratios τ and Rij as a measure. Rij is the mean value of the ratios, calculated from average within-cluster-distance to distance between any pair of clusters, whereas τ is defined by the ratio of the minimum distance
between any pair of clusters to the maximum of the average within-cluster distances. If the separation between the clusters is large enough the respective AE is likely to be of different origin.
Numerical optimization (minimizing Rij, maximizing τ) of these heuristic ratios as a functionof cluster numbers can be used to estimate the number of associated failure mechanisms recorded
within the dataset [Anastassopoulos, 1995]. In Ativitavas [2006] and Anastassopoulos [2000], itis also shown that clustering techniques using neuronal network algorithms can successfully
distinguish between failure mechanisms in complex CFRP structures and, therefore, are the mostpromising algorithms for pattern recognition in CFRP materials.
In the present investigation we want to establish the potential of AE analysis as a method for
quality assurance and health monitoring of coated CFRP tank systems. Based on advanced AE
analysis techniques like pattern recognition, the long-term objective is to distinguish between AE
signals arising from liner material failure and those due to failure of structural integrity of CFRP.
In this report, we focus on failure mechanisms in the metallic liner. In our special case, we wereable to distinguish AE signals originating from liner and CFRP by comparison of coated anduncoated specimens.
2. Experimental
The present investigations were performed on rectangular CFRP-specimens according to
DIN-EN-ISO 14125 (class IV type), i.e. specimen dimensions are (100±10) mm × (15±0.5) mm
× (2±0.2) mm (l × w × h) [DIN14125]. The matrix consists of a polyurethane-based epoxy resin(Araldite: LY 564/HY 2954). Carbon fibers of type SIGRATEX KDK 8054/120 are arranged in2

six plies (0°- 90° textured) of woven fabric. The CFRP substrates were coated by subsequentchemical deposition of a 10-µm electroless Ni-layer and an electroplated 50-µm Cu-layer. The
position of the coating was slightly asymmetric with respect to the midpoint of the sample (coating offset, see Fig. 1) with dimensions of (25±1) mm × (15±0.5) mm (l × w). This coating dimension avoids contact between the lower supports and coating during four-point bending.
Bending tests were also performed according to DIN-EN-ISO 14125 with an inner distance between supports of (27±1) mm and an outer distance of (81±2) mm, respectively (see Fig. 1).
An Instron machine (Type 8502) was used for loading of the specimens. For all measurements the specimen was oriented with the coating on the tensile loading side (specimen bottom),which corresponds to the outer side of a pressurized vessel. To ensure a statistically representative result, five coated and five uncoated samples were investigated using AE analysis. Anothereight coated specimens were subjected to different strain levels for investigation in an environmental SEM (Type XL30, FEI Company) to compare microscopic observations with results derived from AE-analysis.

Fig. 1: Schematic drawing of experimental setup (left) and photograph of specimen including
dimensions (right).
Acoustic emission was detected by two PAC-WD50 wideband sensors in linear geometry.
For acoustic coupling Baysilone silicone grease (Bayer Material Science) of medium viscosity
was used, the mechanical contact being provided by two clamps. To suppress detection of friction noise a band-pass ranging from 20 kHz up to 3 MHz was used. In addition, hard rubber
plates were positioned below the lower supports, to minimize hydraulic noise arising from thebending machine. The hard rubber plates had no significant influence on the mechanical loading
properties due to their hardness in comparison to the maximum load level. The AE waveforms
were recorded using a PAC PCI-2 data acquisition system with AEwin software at a sampling
rate of 10 MS/s. Data reduction was performed using the software Noesis (Enviroacoustics). To
analyze AE, the AE signals were localized in linear geometry and only the events within a distance of 40 mm around the specimen center were considered in the investigation. As the exact
phase and group velocities depend on the propagating medium and propagation direction [Lee,2005], the sound velocity used for localization was calculated from automatic sensor tests (AST)[AEwin, 2002]. The so determined velocity varied between 5000 m/s and 6000 m/s. Based on thesensors diameter of 10 mm, a time window for delay-time determination was calculated, whichcorresponds to an uncertainty in sound velocity of the order of 1000 m/s. This uncertainty also
includes an error assumption for delay-time determination. In total, the error estimation for linear
location resulted in ±2.5 mm.
The remaining AE signals were analyzed following a classical approach based on waveform
parameters, signal location and activity changes. Following references [Anastassopoulos, 1995,
3

2000], [Noesis, 2006] and [Philippidis, 1998], the same waveform parameters were also investigated using unsupervised pattern recognition techniques to separate the AE of different failure
mechanisms. For this purpose the heuristic discrimination of the AE signals dependent on cluster
numbers was investigated. The heuristic ratios Rij and τ used for the determination of discrimination efficiency during unsupervised pattern recognition are automatically calculated by Noesis
for each partitioning applied to a dataset [Noesis, 2006]. Both values are heuristic criteria basedon the ratio of average within-cluster distances to the distance between clusters. Rij is the meanvalue of the ratios, calculated from average within-cluster distance to the distance between any
pair of clusters, whereas τ is defined by the ratio of the minimum distance between any pair ofclusters to the maximum of the average within-cluster distances. Therefore, minimization of Rij
and maximization of τ results in better discrimination of the resulting data partition. The 12 AE
parameters were chosen to provide a correlation level between 16% and 80%. Eliminating waveform parameters like threshold (constant), channels and runtime parameters, the remaining parameters were projected on their principal component axis using a covariance analysis. K-means
and Kohonen liquid vector quantizers (LVQ) were used as clustering algorithms with several
weighting options for normalization procedures. Finally, using frequency power spectrum analysis a comparison between signal clusters obtained from pattern recognition was performed. For
this purpose, a Fourier transform on each waveform with a rectangular window function and 0.1µs resolution using the Danielson-Lanczos algorithm was performed. This yielded defined frequency power spectra. Subsequently the arithmetic average, standard deviation, maximum andminimum values were calculated for each cluster using self-written software.
3. Results and Discussion
3.1 Scanning Electron Microscopy
Five coated and three uncoated samples were loaded to different strain levels. Their AE wasrecorded and afterwards the structural damage was assessed by scanning electron microscopy.
The SEM investigations were used to correlate AE signals and the corresponding failure mechanism. An overview of the maximum load levels (εmax) and the respective damage as found by
SEM in top view is given in Table 1.
At low strain levels (ε = 0.33%), microcracks develop in the nickel layer (Fig. 2a). With increased loading (ε = 0.65%), the cracks grow beneath the copper layer and cause extensive delamination at the interface region between copper and nickel (Fig. 2b). At even higher strain levels (ε = 1.23%), macroscopic cracks evolve in the maximum strain region of the coating andpropagate over the whole specimen width (Fig. 2c). For a strain level of ε ≥ 1.40%, matrix
cracks, fiber-matrix debonding and fiber breakage are observed (Fig. 2d). In uncoated CFRP
specimens, similar evolution of matrix and fiber failure is observed, but, remarkably, the onset ofsuch failures in coated CFRPs shifts to strain values below ε = 0.65%. This could be due to an
additional tension caused by the coating or by CFRP degradation during the liner application
process. In the following, the results of the microscopic investigation will be used to correlate theAE signal and the observed failure mechanisms of the coating. To this end, we use the strainlevel and the location, at which cracks and delamination develop, as well as their frequency ofoccurrence.
3.2 Classical Approach
To compare AE signals between coated and uncoated specimens loaded to ε = 1.73%, theclassical approach of AE-energy and amplitude distribution analysis was chosen. The waveform
parameters discussed in this investigation are defined according to [AEwin, 2002] as maximum
4

Table 1: Overview of specimens prepared for SEM, their loading stages and associated damage
assessment.
Material
εmax
Results of SEM investigation
CFRP-NiCu 0.33 % Microcracks start in Ni-Layer (Fig. 2a).
CFRP-NiCu 0.65 % Microcracks increase in size, delamination at Ni-Cu interface, matrix cracks start to evolve in CFRP-specimen (Fig. 2b).
CFRP-NiCu 1.15 % Cracks appear at the area of strain maximum of the Cu-Layer, matrix cracks increase in number and size, fiber failure is observed at
specimen edges.
CFRP-NiCu 1.23 % Macroscopic cracks (size ≥ 1mm) evolve inside the Cu-Layer (Fig.
2c).
CFRP-NiCu 1.40 % Coating failure progresses, large matrix cracks and fiber breakages
occur (Fig. 2d).
CFRP
0.65 % No damage detected.
CFRP
1.23 % Minor matrix cracks evolve along fiber directions (0° and 90°).
CFRP
1.40 % Macroscopic matrix cracks appear, fiber breakages and interlaminar
fracture is observed.

Fig. 2: Top view SEM images of Ni-Cu-coated CFRP specimens at different strain levels. (a)Fringe of coating at ε = 0.33%, (b) fringe of coating at ε = 0.65%, (c) center of coating at
ε = 1.23% and (d) CFRP edge at ε = 1.40%.

5

signal amplitude, absolute signal energy and root mean square (RMS) value of the waveform.
Both the stress-strain curve (Fig. 3a) and the onset of AE activity indicate the start of failure in
the uncoated CFRP substrates to occur at a strain level of ε = 1.0 - 1.2%. In contrast, a large
number of additional AE signals arise for coated samples at ε ≤ 1.0% (see Fig. 3b). Since the
CFRP samples are identical for both specimen series, these additional signals are attributed
solely to coating failure. This is supported by the fact that the additional AE signals do not correspond to significant changes in the stress-strain curve (Fig. 3b) in contrast to CFRP failure (Fig.
3a). This is expected since the liner should have a negligible influence on the specimens´ mechanical properties, which are dominated by the matrix-fiber interaction. Although AE signals
from coating and CFRP failure are clearly distinguishable by the strain range of their occurrence,the distribution range of amplitude and energy is comparable (Figs. 3 and 4). For ε ≥ 1.0%, thehighest amplitude and highest energy AE signals arise in both coated and uncoated samples,
which go along with distinct dips in the stress-strain curve. Therefore, these correspond to severe
failures inside the CFRP substrate like single or multiple fiber breakages. Comparable amplitudes and energies are also observed at strain levels ε ≤ 1.0% in coated CFRP specimens. Consequently, no clear distinction between liner and CFRP failure can be achieved based on the range
of energy or amplitude distribution alone.

Fig. 3: Absolute energy of AE signal vs. strain (dots) and stress-strain diagram (line) in (a) un
coated and (b) coated CFRP specimen.

Fig. 4: Amplitude of AE signal vs. strain (dots) and stress-strain diagram (line) in (a) uncoatedand (b) coated CFRP specimen.
In the following we want to correlate the failure positions found by SEM to locations calculated from the arrival time of the AE signals. In Fig. 5, a comparison between the x-location of
the AE signals of coated and uncoated CFRPs is shown. For uncoated CFRP specimens, most6

AE sources are located at two distinct positions, which correspond to the contact positions of theinner supports. At these positions, where the maximum stress level occurs, the highest damagelevel is observed in electron microscopy. In contrast, the locations of AE sources of the coatedspecimens are broadly distributed over the coated area (see Fig. 5b). SEM images as a functionof loading show that cracks occur over the coated area with preferred direction perpendicular tothe sensor alignment. In addition, at ε ≥ 0.65%, extensive delamination is observed. The delamination starts at the fringe of the coating and causes a large amount of AE signals from the coatedarea. Additionally, the distribution of the AE sources reflects the slightly asymmetric position of
the coated area, with respect to the midpoint of the sample (coating offset). At the position of theinner supports, peaks with higher energies arise. These peaks also occur in uncoated CFRP
specimens at equal stress levels and, consequently, are attributed to structural damage at the inner supports (see Fig. 5b).

Fig. 5: Absolute energy of AE signal vs. calculated x-position in (a) uncoated and (b) coated
CFRP specimen.
The previous discussion suggests that the AE signals detected below ε ≤ 0.8% arise solely
from coating failure, and are therefore denoted “Coating AE signals” in the following. Further,AE signals recorded at bending tests of uncoated CFRP specimens are denoted “CFRP AE signals”. In Fig. 6a, the absolute energy of the Coating AE signals (ε ≤ 0.80%) and CFRP AE signals (ε ≤ 1.73%) of two representative datasets are plotted against their RMS-values. While for
all Coating AE signal datasets, a well defined maximal RMS-threshold between 0.04 mV and
0.06 mV was found, the energy value range of the Coating AE signal and CFRP AE signal are
not significantly different. All AE signals above this RMS threshold level are attributed to CFRP
AE signals and arise at defined strain values, at which the stress-strain curve indicates partial or
total structural failure of the CFRP specimen. CFRP AE signals with an RMS value below thethreshold level are attributed to minor failures, like crack propagation through epoxy resin infiber directions or between ply layers (Mode I and Mode II) [Bohse, 2001, 2004]. In summary,
we attribute RMS-values above threshold level to structural failure, whereas signals due to coating failure stay below the RMS-threshold.
For other classical waveform parameters (durations, counts or calculated features likefrequencies), no obvious distinction between AE signals of coating and CFRP failure was found.
For both datasets discussed above (Coating AE signal and CFRP AE signal), all of the investigated waveform parameters are within the same range of values. This is exemplarily shown in
Fig. 6b, in which the absolute energy is plotted against amplitude. All the conclusions discussed
above apply to the complete set of specimens.
7

Fig. 6: (a) Absolute energy vs. RMS-value, including threshold value as discussed in text and (b)absolute energy vs. amplitude plot for two datasets.
3.3 Pattern Recognition
Based on the conclusion that AE signals at low strain levels (up to ε = 0.8%) originate fromcoating failure, a sufficient number of AE signals is available to investigate the coating degradation. In particular we will focus on the involved failure mechanisms of the coating. For this purpose, several datasets of Coating AE signals were investigated by unsupervised clustering algorithms, after manually eliminating AE signals arising from friction and electrical discharges. For
all investigations an amount ≥ 98% of raw data was used.
Following references [Anastassopoulos, 1995, 2000], [Noesis, 2006], [Philippidis, 1998], we
investigated the discrimination behavior of each dataset by optimizing Rij and τ values. The respective dataset was partitioned into clusters using k-means and Kohonen LVQ algorithms available in Noesis 5.1 [Noesis, 2006]. In the following, one representative dataset will be discussed.
In Fig. 7a, the values of Rij and τ of this dataset obtained from both k-means and Kohonen
LVQ algorithms are plotted against cluster numbers. In this investigation, a maximum of ten
clusters was chosen, since saturation of Rij and τ values was already observed within this limit.
Best numerical discrimination with a distinct minimum in Rij and a corresponding maximum in τ
was achieved using Kohonen LVQ at four clusters (see Fig. 7a). For comparison, an analysis on
the same dataset using k-means yielded no obvious discrimination optimum. An accompanying
investigation of other datasets yielded similar results for Rij and τ, which are listed in Table 2.
Figure 7b shows the partitioning based on the Kohonen LVQ in a suitable principal component
covariance projection. The figure suggests that this procedure is suited to discriminate between
recorded AE signals. In the following, AE signals belonging to the four clusters are marked bydifferent colors.
To further discuss the correlation between the obtained clusters and failure mechanisms weplot the absolute energy of AE signals vs. amplitude in Fig. 8a and the absolute energy vs. thestrain, at which they occur in Fig. 8b. Additionally, the calculated x-location for each AE signal
is plotted against its corresponding cluster number (marked by its designated color) in Fig. 9.
AE signals belonging to cluster 1, which are only detected above ε = 0.3%, show the highest
amplitudes and energies. As demonstrated in [Miguel, 2003], the highest signal amplitudes arise
during rapid crack propagation and usually appear at medium stages of coating failure. The AE
8

Fig. 7: Optimization of Rij and τ values using k-means and Kohonen-LVQ algorithms (a) anddataset partitioning after clustering with Kohonen-LVQ in principal component projection (b).

Fig. 8: Optimal dataset partitioning in classical amplitude versus energy plot (a) and strain versus
energy diagram (b).

Fig. 9: AE signal x-Location versus cluster number together with specimen dimensions.
sources of cluster 1 are located around the maximum stress region of a specimen and, therefore,
the signals of this cluster can be associated with macroscopic crack propagation within the Culayer, consistent with SEM observations.
In contrast, AE signals belonging to cluster 2 are broadly distributed in energy and amplitude. This cluster contains the largest number of AE signals. Based upon the micro-mechanicalfracture model, it was shown in [Bohse, 2000] that energy release rate of Mode-I delamination is
proportional to the crack velocity times specimen width. This corresponds to the delaminatedcoating area per time in our case. Due to the extensive delamination observed during loading thisdamage mechanism will produce a considerably higher amount of AE signals than crack propa9

gation. Because of the weak adhesion between coating and CFRP-substrate most specimens investigated showed delamination in large areas. Even full-scale delamination was observed. The
location of the corresponding AE sources shows a broad spatial distribution covering the whole
coated area in agreement with SEM observations (see also 3.2). We, therefore, attribute AE signals belonging to cluster 2 to coating delamination.
An inspection of the recorded AE signals belonging to cluster 3 shows that they exhibit multiple reflections or originate from transient noise. Reflections and transient noise strongly influence the range of amplitude, energy and duration of AE signals. This can cause a large scatteringof the values of these waveform parameters. Therefore, signals of this cluster are declared undefined and are ignored for further investigations. In fact applying the Kohonen LVQ algorithm tothe different datasets resulted in either four or three distinguishable clusters as numerical optimum (see Table 2) depending on the quality of the respective dataset.
The investigation of AE signals of cluster 4 also shows a large scattering of all classical
waveform parameters. In contrast to cluster 3, AE signals of cluster 4 show a characteristic frequency composition and well-defined AE sources location. All signals belonging to cluster 4
originate from fringes of the coated area, and therefore can be correlated with a failure mechanism different from cluster 1 and 2. This correlation could not be achieved using waveform parameters, but instead by investigating the AE power spectrum (see 3.4).
Table 2: Parameters for dataset partitioning using Kohonen LVQ at numerical optimum and corresponding number of clusters.
Specimen Number
1
2
3
4
5

τ
0.728210.81497
0.94123
0.953360.91335

Rij
1.8454
1.85771.86211.3941
1.5313

Achieved Clusters
4
3
4
3
3

3.4 Power Spectrum Analysis
The measured power spectrum is determined by the characteristics of the AE source and thetransmitting medium. Due to anisotropic dispersion, anisotropic attenuation and frequency dependent attenuation in CFRP specimens [Bohse, 2004], the frequency distribution of the AE signals is strongly dependent on its propagation path. To minimize this effect, our experimental
setup ensures that the distance between AE source and sensor does not exceed 30 mm. At thesame time, the used transducers provide a broad range of sensitivity. Based on these two measures we can assume that both transducers are able to detect all frequency contributions to the AE
signal within the used range of 20 kHz – 1 MHz. Hence we think that the influence of propagation path length and frequency dependent dispersion and attenuation can be neglected. In thefollowing an investigation of the averaged frequency power spectra was performed for each cluster derived from pattern recognition techniques. The respective power spectrum of each cluster
averaged over all cluster members is shown in Fig. 10a for one representative dataset. In order tocompare the power spectra of all clusters the intensity of the spectra was normalized to a major
peak occurring at 267 kHz, which is common to all clusters. These normalized frequency power
spectra are presented in Fig. 10b. Normalization procedure using the total area below the power
spectrum yields the same result.

10Fig. 10: Averaged power spectra for each cluster derived from pattern recognition (a) and after
normalization (b).
Due to its radiation direction, cracking (in-plane direction) is expected to cause more spectral
weight at higher frequencies than delamination between coating and substrate (out-of-plane direction) [Bohse, 2004]. The average power spectrum of cluster 4 shows more weight at frequencies above 500 kHz, compared to both other clusters. As already discussed in 3.3, AE signals
contributing to cluster 4 are located at the fringes of the applied coating. In this area the chemically deposited nickel layer is not completely covered by copper and cracks in the nickel layer
were observed in SEM imaging immediately at the onset of mechanical loading (see Figs. 2a and2b). The fact that cracks appear immediately upon mechanical loading reflects the strain dependent evolution of AE signals contributing to cluster 4. These are also observed immediately at theonset and during mechanical loading. The fact that both sensors detected AE signals from both
fringes of coating confirms our assumption that the effects of dispersion and frequency dependent attenuation are negligible in our experimental setup. Therefore we attribute cluster 4 to crack
initiation and growth in the nickel-starting layer.
The power spectra of clusters 1 and 2 behave similarly for frequencies above 500 kHz, but
are distinctly different for frequencies below 140 kHz. Here cluster 2 shows more spectral
weight than cluster 1. This difference in the power spectra reflects the difference of the corresponding failure mechanism as already discussed in 3.3.
4. Conclusions
For metal-coated CFRP substrates investigated here, crack initiation and delamination between coating and CFRP substrate could not be inferred from signatures in stress-strain curves
and, therefore, could not be predicted solely from investigations of mechanical properties. Instead, the onset of acoustic emission provides stress-dependent information about failure initiation in coatings applied to CFRP and can hence be used to determine its load limit. In addition,
AE shows the ability to monitor the evolution of failure mechanisms in functional coatings as
demonstrated by comparing SEM imaging and AE analysis as a function of loading. Based on
RMS-values and localization of AE signals it appears to be possible to distinguish between structural failure of the CFRP substrate and coating.
An application of pattern recognition techniques to investigate the involved failure mechanisms of the coating yielded three distinct clusters of AE signals for all specimens investigated.
A correlation of failure mechanisms (crack initiation, crack growth and delamination) observedin SEM investigations with AE signals could be obtained. This correlation shows good agree11

ment in location, time of origin and frequency of occurrence. AE signals corresponding to crack
initiation and coating delamination are within the same range for most of the common waveform
parameters. In contrast, the average power spectra calculated for each cluster were clearly distinguishable, i.e., signals from coating cracks and delamination could be distinguished.
In summary, we have shown that the combination of pattern recognition techniques andpower spectrum analysis could be a powerful tool to identify failure mechanisms in hybrid materials. This suggests that AE analysis is generally applicable for health monitoring and quality
assurance of hybrid materials, as shown here in case of coated CFRP structures. Therefore, further investigations will focus on the determination of quality criteria of hybrid materials, e.g., todiscriminate between failure mechanisms at the interface and in the bulk, respectively.
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Mixed-Mode I/II Interlaminar Fracture Of Unidirectional Fiber-Reinforced Polymers”, Journalof Acoustic Emission, 19, 01–10 (2001).
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sprayed thermal barrier coatings during four-point bend tests”, Surface and Coatings Technology, 139, 55–62 (2001).
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WC/Co thermal sprayed coatings”, Materials Science and Engineering A, Vol. 352, pp. 55–63(2003).
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13

ACOUSTIC EMISSIONS RELATED TO THE DEHYDRATION STRESSBEHAVIOR OF GREEN NORWAY SPRUCE WOOD
SABINE ROSNER1, BO KARLSSON2, JOHANNES KONNERTH3
and CHRISTIAN HANSMANN4
1
Institute of Botany, Department of Integrative Biology, University of Natural Resources andApplied Life Sciences, BOKU Vienna, Gregor Mendel-Str. 33, A-1180 Vienna, Austria; 2 The
Forestry Research Institute of Sweden (Skogforsk), Ekebo, S-26890 Svalöv, Sweden; 3 Instituteof Wood Science and Technology, Department of Material Sciences and Process Engineering,
University of Natural Resources and Applied Life Sciences, BOKU Vienna, Peter Jordanstr.
82, A-1180 Vienna, Austria; 4 Competence Centre for Wood Composites and Wood Chemistry,
St.-Peter-Str. 25, A-4021 Linz, Austria
Keywords: AE feature extraction, vulnerability to cavitation, wood shrinkage
Abstract
Waveform features of acoustic emission (AE) signals emitted from cavitation events in water-conducting elements of dehydrating wood bear a lot of information about both wood structure
and the behavior under dehydration stress. We present two applications of AE feature extraction
on wood; first, the prognosis of the survival prospects of trees under drought stress by constructing vulnerability curves and second, monitoring of the shrinkage behavior during lumber drying.
AE testing was performed with resonant 150 kHz transducers on Norway spruce wood with different structural characteristics. Radial wood shrinkage was calculated from changes in the contact pressure between the AE transducer and the wood specimen. The reference method to assess
the vulnerability to cavitation was the hydraulic method, where vulnerability curves were constructed by plotting the percent loss of conductivity vs. an overpressure of compressed air. Vulnerability curves were also constructed by relating the cumulated amount of AE energy to theapplied overpressure. Samples with smaller conduit diameters showed lower mean AE energies
and were less sensitive to cavitation and thus to drought stress. Two shrinkage processes can beobserved during wood dehydration; the initial process, induced by tensile stresses, and the final
shrinkage process, which started when most of the tracheids reach relative water contents below
the fiber saturation point. Wood less sensitive to cavitation showed lower shrinkage and mean
AE energy /min but higher total AE activity than wood with higher sensitivity to cavitation. The
negative relationship between maximum AE energy and total AE activity and the positive relationship between maximum AE energy and radial lumen diameters reveals that more elastic energy is stored in bigger conduits when under tension. AE testing in combination with feature
extraction offers an interesting alternative to the labor-intensive conventional methods to assess
the vulnerability to cavitation and can be used to monitor the shrinkage behavior and crack development during lumber drying.
Introduction
Waveform features of AE signals from dehydrating wood bear a lot of information aboutboth wood structure and the behavior of wood under dehydration stress. We present two applications of AE feature extraction on wood; first, a method to assess the vulnerability to cavitation oftrunk wood and second, the monitoring of the wood structure-dependent shrinkage behavior during lumber drying processes.
J. Acoustic Emission, 26 (2008)14© 2008 Acoustic Emission GroupNorway spruce wood consists to 90% of small water-conducting elements with a diameter ofabout 10-40 µm and a length of 1-4 mm (tracheids, Fig. 1). One annual ring, which consists ofearlywood and latewood tracheids, is formed every year. Water in tracheids exists in three states,(1) as “free” liquid water, (2) as water vapor and within the tracheids, and (3) as chemically“bound” water in the cell wall matrix [1]. Water transport from the roots to the crown is achieved
under tension, requiring high mechanical strength of the cell walls in order to avoid implosion,and hydraulic safety against the breakage of the water column in the conducting elements (cavitation). Cavitation is induced when the water tension increases above a certain threshold. Oncecavitated, the conduit does not conduct water any longer and the hydraulic conductivity of theplant is reduced, leading to impairment of water supply of the transpiring needles [2]. Hydraulicsafety depends on wood structure and can be assessed by the construction of vulnerability
curves. A vulnerability curve is a plot of the percent loss in hydraulic conductivity (PLC) vs. thepressure potential in the solution of the tracheids. The most widely used drought-stress parameteris the pressure potential that causes 50% PLC. The common approach to quantify 50% PLC is torelate actual hydraulic conductivity to the hydraulic conductivity at full saturation (hydraulicmethod). The hydraulic method is very time consuming and demands practical experience. A
more direct way to detect cavitation events is to record AE in the high-frequency range of 50
kHz to 1 MHz [3]. AE with the strongest frequencies in the range of 100-300 kHz are supposed
to be induced by rapid tension release in the tracheid lumen as liquid water at negative pressure
is replaced by water vapor very near vacuum pressure.

Fig. 1 (a) Macerated Norway spruce tracheids of the last latewood cell row (below) and the adjacent first earlywood cell rows formed in the following growing season and (b) tracheids of theearlywood formed later in the growing season. “R” marks a ray cell, which achieve the radialtransport in wood. The reference bar represents 200 µm.
During periods of drought stress, decreases in the stem diameter of living trees can be observed [4]. This tensile strain is a consequence of adhesive forces between the water molecules
and the inner conduit walls when “free” water in the tracheids comes under tension [5]. It has
been hypothesized by many authors that the elastic behavior induced by water tension is strongly
related to the elastic properties of the swelling tissues [5, 6] and thus to wood structure. Whenthe moisture content of the tracheids drops below the fiber saturation point (FSP), that is whenlumina contain no “free” water but cell walls are fully saturated with liquid, further dehydration
leads to cell wall shrinkage processes. Checking during lumber drying occurs because wood is ananisotropic material concerning cell-wall shrinkage (radial:tangential = 1:2) and the FSP isreached far earlier in the shell than in the core of a specimen. Some authors suggest, however,that internal checks can even be induced by water tension because checking occurs long before
most of the cells reach the FSP [e.g., 7]. AE testing is an established method for optimizing lumber drying conditions, where the analysis of the amplitude or energy distribution of AE signals
15

has been successfully used to pinpoint checking [8, 9]. Combined shrinkage- and AE tests are,however, scarce in AE literature on wood [10] and on live stems such investigation are performed at moisture contents well above the FSP [4]. Lumber shrinkage assessment starts either atmoisture contents around the FSP or at “green” moisture content [11], defined as the moisture
content at the time of harvesting [1]. “Green” moisture content will not correspond to the moisture content at 100% saturation when trees suffered at the time of harvesting from drought stressor when water was lost by evaporation during harvesting, transport or storage. In the present
study we relate AE of dehydrating Norway spruce (Picea abies (L.) Karst.) sapwood with different structural characteristics to radial wood shrinkage processes from the totally saturated statetill the end of all shrinkage processes.
Hydraulic Method to Assess the Vulnerability to Cavitation
Vulnerability curves were obtained on fully saturated juvenile (age = 1-2 years) and mature(age = 17-19) Norway spruce (Picea abies [L. Karst.]) wood beams (0.6 cm tangential, 0.6 cm
radial, 10.0 cm longitudinal). After determination of the conductivity at full saturation, a positive
pressure was applied to the lateral sides of the samples, while the transverse ends protruded froma double-ended pressure chamber (PMS Instruments Co., Corvallis, OR), to induce cavitation.
After pressure treatment samples were weighed and hydraulic conductivity was measured again.
The continuous pressure treatment leads to a decrease in hydraulic conductivity. Initially, thepressure chamber was pressurized to 0.5 MPa, and the pressure was subsequently increased after
each conductivity measurement in steps of 0.5 MPa till more than 95% loss of conductivity wasreached. The whole measuring procedure is described in [12]. Dry weight of the wood beams
was obtained by drying wood samples at 103°C to constant weight in order to calculate the relative water loss (RWL). The pressure application was related to RWL by cubic functions [12].
AE Testing Procedure and Data Analysis
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 R15
resonant transducers (50 to 200 kHz). AE signals were recorded with a detection threshold of 30
dB (0 dB = 1 µV input). Extraction of AE energy (pVs) was carried out with AEWin software(PAC). AE energy (also referred to as “PAC-Energy”) is defined as the area of the rectified voltage signal over the duration of the AE signal.
AE transducers were positioned on the tangential face of the wood beams using an acrylic
resin clamp. The whole measuring procedure, where coupling pressure during the dehydration
process was recorded with a DMS load cell (Type 8416-5500, range 0 - 500 N; amplification
with an inline amplifier for DMS, Type 9235; Burster, Gernsbach, Germany) and water loss wasmeasured with a balance, is described in [12]. Percent radial shrinkage (given in negative values)
was calculated for 10 min time steps by relating the total radial shrinkage (digital gauge, accuracy 1 µm) to the total coupling pressure decrease. Percent radial shrinkage was referred to thenearest 5% RWL step.
AE data filtering was done with Vallen VisualAE™ software (Vallen Systeme GMbH, Munich, Germany). Mean AE energy values were calculated for 1 minute and 10 minute time steps.
Relative cumulated AE energy data were obtained by relating the stepwise cumulated mean AEenergy values/10 minutes to the sum of all mean AE energy values/10 minutes. AE data were16

Fig. 2. (a) Percent loss of hydraulic conductivity (PLC), (b) cumulated AE signals and (c) cumulated AE energy/10 min plotted against positive pressure. Open symbols represent mature wood(n = 12), closed circles juvenile wood (n = 6).
referred to the nearest 5% RWL and thereafter to 0.5 MPa steps in order to construct vulnerability curves. Values are given as mean values ± standard error. Differences between mean values
were tested for significance by Student’s t-test. Associations between two variables were examined using linear or quadratic regression analysis.
Anatomical investigations
Tangential double wall thickness of adjacent tracheids and lumen diameters were measured
by means of a Leica DM4000M microscope interfaced with a digital camera and Leica image
analysis software (Leica Microsystems Wetzlar GmbH, Germany). Tracheid dimensions of thefirst formed cell rows (earlywood) were measured on microtomed transverse cut faces of juvenile and mature Norway spruce wood (Fig. 5). Mean values of anatomical parameters were derived from 40 single measurements per standard beam, respectively.

17

Fig. 3. (a) Changes of the mean AE energy/10 min and (b) radial shrinkage against relative water
loss (RWL). (c) and (d): same AE energy and radial shrinkage against time. Open circles represent mature wood, closed circles juvenile wood (n = 12, respectively).
Results and Discussion
Assessment of vulnerability to cavitation
Juvenile wood had a lower vulnerability to cavitation than mature wood: For juvenile woodfrom the tree top much more pressure was necessary to result in 50% loss of conductivity (PLC)
than for mature wood from the tree base (3.20 ± 0.16%, 2.26 ± 0.04 MPa, respectively, P <0.001, Fig. 2a). The cumulated number of AE signals was not a good predictive for the PLC,
because 50% cumulated AE signals were reached at much higher pressures (Fig. 2b). 50% of the
cumulated AE energy was induced at 3.73 ± 0.11 MPa in juvenile and at 2.31 ± 0.05 MPa in
mature wood (P < 0.001, Fig. 2c). We assume that the cumulated AE energy is a more reliablemeasure for PLC than the AE number per se, because the hydraulic conductivity is proportional
to the 4th power of the radius of a capillary (Hagen-Poiseuille equation) and tracheid diameters
are variable within a wood specimen. High mean AE energies detected at moderate water losses
or pressures resemble cavitation events of more vulnerable large diameter tracheids [12].
Relationship between shrinkage processes and AE
The AE and radial shrinkage data are given in Fig. 3. Two different radial shrinkage processes were observed (Fig. 3d). First is a shrinkage period at moderate water losses, which wastermed “tension shrinkage”, and second is the cell-wall shrinkage period. Prior to cavitation,
dehydration stress generates tensile strains inside the most vulnerable tracheids and in tracheids
near the specimen surface. Tension shrinkage started therefore right from the beginning of dehydration, before any AE signals were detected (Figs. 3a-b) and ceased in mature wood at18

Fig. 4. Maximum mean AE energy/min related to the number of AE signals by a quadratic regression model. Open symbols represent mature wood, closed circles juvenile wood beams (6mm x 6 mm x 100 mm).
53.99 ± 1.91% RWL and in juvenile wood at 58.94 ± 2.48% RWL. Maximum tension shrinkage
was more than three times higher in mature than in juvenile wood (–1.44 ± 0.11%, –0.42 ±
0.01%, respectively, P < 0.001). Maximum AE energies were detected at moderate water losses,
when already more than 50% of the tension shrinkage had taken place (Figs. 3a-b). Tension
shrinkage ceased when mean AE energies reached constantly lower values, and was even reversible to some extent (Figs. 3c-d). Recovery from shrinkage induced by cavitation seems tooccur also in live stems [4], where it can either occur as a result of cavitation, or because of adecrease in crown transpiration. In debarked sapwood specimens, shrinkage can only be reversed
by a local tension stress release after cavitation, when water is pulled out of the conduit into thesurrounding wood. Recovery from tension shrinkage due to cavitation occurred at higher relative
water losses than one would expect, being probably masked by continuous shrinkage in woodparts, which still contained “free” water. Shrinkage and regeneration from shrinkage should
therefore occur at different locations at the same time in the numerous tracheids within a standard size wood specimen, which might be the reason why there is almost no time lag between
periods of stem diameter decrease and increases in cumulated AE [4].
During late dehydration stages, the reversibility of tension shrinkage was additionally overlaid by cell-wall shrinkage [10]. The fiber saturation point (FSP) refers to the moisture content of
a conduit and not to a whole piece of wood and it should be therefore not the same throughout awhole wood specimen [1]. During late dehydration stages, when the drier shell of the specimen
had already reached moisture contents below the FSP, cell wall contraction could therefore additionally mask recovery from tension. The partial reversibility of tension shrinkage indicates
however that the first shrinkage process at moderate water losses in standard size wood specimens was not entirely induced by cell wall shrinkage of the shell at water contents below thefiber saturation point but by tensile stresses due to water tension.
The final shrinkage process started after the shrinkage recovery peak, at about 80% RWL,
corresponding to moisture contents given for the FSP of spruce species 35 – 37% [13]. Mature
19

wood showed higher total shrinkage than juvenile wood (–3.86 ± 0.13%, –2.31 ± 0.23%, respectively, P < 0.001, Fig. 3d). AE ceased when only 50% of shrinkage had taken place, giving
strong evidence that no crack formation occurred at moisture contents below the FSP [7].

Fig. 5. Simplified model of the transverse view of one annual ring of juvenile (left side) and mature Norway spruce wood (right side). Each annual ring consists of earlywood and latewood.
Juvenile wood has smaller radial and tangential lumen diameters and thinner double cell walls
than mature spruce wood.
Juvenile wood beams emitted significantly more AE (numbers in 106 counts) than maturewood beams (1.70 ± 0.11, 0.62 ± 0.03, P < 0.001) and maximum AE energy/min was lower injuvenile than in mature wood beams (1.17 ± 0.12 pVs, 4.21 ± 0.32 pVs, P < 0.001). The maximum AE energy/min was strongly related to the total number of AEs (Fig. 4), total shrinkage (r= –0.80, P < 0.001) and maximum tension shrinkage (r = –0.88, P < 0.001). The total number ofAE signals was higher in samples with lower total shrinkage (r = 0.80, P < 0.001) and tension
shrinkage (r = 0.83, P < 0.001). The higher total number of AE signals and lower mean AE energies measured in juvenile than in mature wood indicate that lower vulnerability to cavitation
was associated with a higher number of tracheids/volume with smaller lumen diameters [12].
Anatomical investigations showed that juvenile wood had significantly thinner double cell walls(3.34 ± 0.06 µm, 4.41 ± 0.11 µm, P < 0.001) and radial lumen dimensions (21.35 ± 0.60 µm,
39.68 ± 1.57 µm, P < 0.001) than mature wood (Fig. 5). The maximum AE energy/min increased
therefore linearly with the radial lumen diameters (Fig. 6).
Norway spruce trees mainly vary cell size to optimize water transport and mechanical stability whereas modification of the cell wall organization plays a minor role [14]. Mature wood,
which was more vulnerable to cavitation showed higher tension shrinkage and total shrinkage(Fig. 3d). Mature wood was thus more prone to deformation due to the same tension than
20

juvenile wood, indicating that the latter seems to be designed to resist implosion under high tension. High hydraulic vulnerability seems to be related to high AE energy signals, but what is thereason for this relationship? Hydraulically less vulnerable juvenile wood showed much lower
radial lumen diameters than mature wood. Wall thickness increases as well with tree ring age,
however to a much lower extent than the lumen diameters. The reinforcement against collapse
from bending due to tension forces increases when the thickness of the double cell wall increases
relatively to its span [15]. Juvenile wood showed thus a higher wall/lumen ratio than mature
wood, which results in lower susceptibility to elastic deformation due to the same tension stress.
Therefore, more elastic energy is stored in bigger tracheids when under the same tension. This
energy is suddenly released when the water column breaks and liquid water at negative pressure
is replaced by water vapor very near vacuum pressure and later by air. Conduits, which show
higher deformation under tensile stress, will therefore release AE signals with higher energy
when the cell wall relaxes due to cavitation. AE signals from more hydraulically vulnerable mature wood are therefore characterized by higher mean AE energies than those from less hydraulically vulnerable juvenile wood (Fig. 6).

Fig. 6. Radial lumen diameter related to maximum mean AE energy/min by a linear regression
model. Open symbols represent mature wood, closed circles juvenile wood beams (6 mm x 6
mm x 100 mm).
Conclusion
Analysis of the spectrum features of AE signals during wood dehydration offered an interesting alternative to the hydraulic method for assessing the hydraulic vulnerability of spruce trunkwood. Vulnerability curves were constructed by relating the relative cumulated amount of AEenergy during dehydration of standard beams under defined conditions to the xylem tension.
Xylem tension of the dehydrating specimens was calculated with a parallel sample set, where air
overpressures were related to the relative moisture loss by cubic functions. The xylem tension
corresponding to 50% of the cumulated AE energy can be a useful parameter to quantify hydraulic vulnerability. The major advantage of the AE method presented is that it is readily automatedand easier in handling compared to the labor-intensive hydraulic method.

21

Combined AE and shrinkage testing gave strong evidence that the relationship betweenchanges in xylem tension and tensile strains measured at the surface of a tree trunk are strongly
dependent on wood structure. Diurnal stem-diameter changes caused by tensile strain are used to
pinpoint periods of drought stress in trees. Our results underline that the relationship between
water potential and tensile strain is not constant, but requires time-consuming calibrations foreach single tree if different trees should be compared [15]. Another source of error, which mightunderestimate actual drought stress, is that tension shrinkage is not only reversible due to an increase in water potential and refilling of cavitated water conducting elements in the night, butalso due to tension release by cavitation. Combined AE and shrinkage testing could be also a
useful tool to monitor the shrinkage behavior leading to crack development during industrial
lumber drying.
Acknowledgements
This study was financed by the Austrian Science Fund FWF (Project T304-B16).
References
1. C. Skaar, Wood-Water Relations, Springer-Verlag, 1988, 283 p.
2. M.H. Zimmermann, Xylem structure and the ascent of sap, Springer-Verlag, 1983, 143 p.
3. M.T. Tyree, J.S. Sperry, Plant, Cell and Environment 12, 1989, 371-382.
4. T. Hölttä, T. Vesala, E. Nikinmaa, M. Perämäki, E. Siivola, M. Mencuccini, Tree Physiology
25, 2005, 237-243.
5. T.T. Kozlowski. Shrinking and Swelling of Plant Tissues. In: T.T. Kozlowski, ed. Water deficits and plant growth. Vol. III, Plant Responses and Control of Water Balance, Academic Press,
1972, 1-64.
6. J. Irvine, J. Grace, Planta 202, 1997, 455-461.
7. R.E. Booker, Proceedings of the 4th IUFRO Wood Drying Conference: Improving Wood Drying Technology, 1994, pp. 133-140.
8. F.C. Beall, Wood Science and Technology 36, 2002, 197-212.
9. S. Kawamoto, R.S. Williams, Acoustic emission and acousto-ultrasonic techniques for woodand wood-based composites A Review, U.S. Department of Agriculture, Forest Service, Forest
Products Laboratory, Gen. Techn. Rep. FPL-GTR-134, 2002, 16 p.
10. S. Rosner, Journal of Acoustic Emission 25, 2007, 149-.156.
11. H. Sakagami, J. Matsumura, K. Oda, IAWA Journal 28, 2007, 29-37.
12. S. Rosner, A. Klein, R. Wimmer, B. Karlsson, New Phytologist 171, 2006, 105-116.
13. A. Bernhart, Forstwirtschaftliches Zentralblatt 84, 1965, 347-356.
14. M. Eder, K. Jungnikl, I.Burgert, Trees 23, 2009: 79-84.
15 U.G. Hacke, J.S. Sperry, W.T. Pockman, S.D. Davis, K. McCulloh, Oecologia 126, 2001,
457-461.
16 T. Alméras, J. Gril, Tree Physiology 27, 2007, 1505-1516.

22

INVESTIGATION OF THE Z-DIRECTION STRENGTH PROPERTIES OF
PAPER BY USE OF ACOUSTIC EMISSION MONITORING
S. NORGREN, P. A. GRADIN, S. NYSTRÖM and M. GULLIKSONMid Sweden University, FSCN, SE-851 70 Sundsvall, Sweden
Keywords: Z-test, Z-direction strength, cohesive zone, fiber/fiber bonds
Abstract
In the quality assessment of paper, a standard test referred to as the Z-test is often utilised. It
consists in gluing a piece of paper between two circular metallic grips and loading it with a tensile load in the thickness direction of the paper. The fracture load or rather the maximum load thepaper specimen can withstand is then taken as a measure of the Z-direction strength of the paper
tested. The interpretation of the results and how to use the results has been and still is a questionof some controversy. To shed some light over the deformation or damage processes active whena paper is loaded in the thickness direction, specially designed grips were developed and manufactured. The grips are designed in such a way that they admit that acoustic emission (AE) sensors are mounted in the grips.
In this paper, the method is presented together with a simple analytical model, relevant for
the Z-test. Also, some experimental results are given and it is concluded that the analytical model
can, at least in a qualitative way, predict not only the load versus displacement behaviour but
also the total number of AE events versus displacement relation.
Introduction
In the papermaking society, there exist a number of testing methods, the results from which
are believed to say something about the quality of the paper. In for instance a situation where thepaper is to be printed on, or when the paper is coated with some coating color, it might happen
that the paper delaminates due to that coating color or printing ink sticks simultaneously to thepaper and a roller in a roller nip, causing out of plane loading of the paper. One testing method,which is believed to give a measure of the sensitivity to delamination is the Z-strength test [1]. In
essence, the test consists in that a quadratic paper specimen is glued in between two grips with acircular cross section (with a diameter less then the side length of the paper specimen) andloaded in the thickness direction. The maximum force divided by the grip area is defined as theZ-strength. This test is not free from criticism since it is obvious that the stress field in the paper
specimen is far from being homogenous and for instance, the stresses in the thickness direction
will take on comparatively large values in a region close to the circular boundary of the grips.
Since paper is not a homogeneous material, the paper will start to degrade in some weak point,
affected by the high stresses. After a while, the damage will have reached such a level that a realcrack initiates and starts to grow across the grip area. The maximum load and the start of crack
growth probably coincide. Since there are local conditions that are responsible for the fracture of
the specimen, it is questionable to define an average measure of the strength. To gain some further insight into the delamination process, it would be of particular interest to be able to determine at which load the damage growth starts to initiate and also to study how the damage progresses up to the point and after, where a crack is formed. Therefore, in an attempt to extract
more information from a traditional Z-strength test, acoustic emission (AE) monitoring was used
to record the elastic stress waves, emitted due to the breaking of fiber/fiber bonds.
J. Acoustic Emission, 26 (2008)23© 2008 Acoustic Emission GroupAE Monitoring
When some internal event happens fast enough in a structure, the emitted elastic stress wave
can be recorded (provided the amplitude is high enough) by e.g. a piezoelectric resonance frequency sensor. One example of such an internal event can be the breaking of a fiber/fiber bondand in fact, AE monitoring has been used on paper before (c.f. [2-5]) but then only in connection
with ordinary tensile tests. To use AE in a Z-strength test is not as simple since the AE sensors
cannot be attached directly to the paper specimen. To this end, the grips used in the Z-strength
test were modified so that one AE sensor could be attached to each of the two grips. This is ofcourse not ideal, since there is a metallic layer between the sensor and the paper specimen andhence there is a possibility of wave reflection at the paper – grip interface due to acoustic impedance mismatch. In spite of this, it was possible to detect signals from the loaded paper specimeneven though the number of events recorded might not be entirely representative for the true
number of broken fiber/fiber bonds.
Experimental
In the experiments, the AE – unit: Vallen Systeme AMSY 4 was used together with the sensors: Vallen Systeme AMSY 4 PROTO 410 and the preamplifiers: Vallen Systeme AEP 4H- 10kwith an amplification of 34 dB. A threshold value of 40 dB was used in all tests.
A paper sample was first covered with a double side tape on both sides. The paper sample
was attached to the first sample holder as seen in Fig. 1a. The second upper sample holder must
be placed precisely over the first sample holder. A position fixture was used to attain that. The
second sample holder was put on the paper sample as seen in Fig. 1b. The sample holders are
made of steel and have a radius of 17.5 mm. The sample holders are sliced out so the AE sensors
can be placed as close to the paper surfaces as possible. The thickness of the steel layer was
about 2 mm at that position.

Fig. 1a: Sample holder with a paper
sample in position.

Fig. 1b: The upper grip aligned inposition over the lower grip.

The paper sample was compressed with a pressure that during testing forces the paper to delaminate in a “perfect breakage”, i.e., the paper divides into two halves when the MTS machine
pulls the sample holders apart and the whole fracture surface are within the paper structure. Pressure levels between 3 to 7 MPa were applied in the L&W sheet press (see Fig. 2a) to receive
clean breakage. As it was of interest to detect the real elongation in Z- direction an extensiometer(MTS model 632, 110-210) was attached to the sample holder by rubber bands as shown in Fig.
2b.
24

Fig. 2a: Paper sample in holder placed in a
L&W sheet press.

Fig. 3a: View of one of the AE
sensors used in the experiments.

Fig. 2b: A MTS extensiometer attachedto the sample holder.

Fig. 3b: Sample holder with extensometer and AE
sensor attached in position to measurement between moment free hinges in the MTS machine.

Figure 3a shows an AE sensor of model PROTO 410. This sensor is specially designed withmagnetic capability so it can be attached to steel surfaces. Two sensors were used, one in the upper and one in the lower sample holder. The last step before pulling in Z-direction is to fasten
sample holders between two moment free hinges, which are in turn attached to the MTS machine. This is done simultaneously with the placement of the AE sensors. The setup is shown in
Fig. 3b. The pulling speed is 0.1 mm/min.

Fig. 4a: An unwanted type of fracture. The
sample breaks not only in the paper layer
but also between the paper and the tape.

Fig. 4b: Perfectly delaminated samples
collected after testing.

25

As seen in Fig. 4a, a test can go wrong. Here, the paper partly breaks in the middle, which it
should (to the right in the picture), but also shows an unwanted incident. Half the paper has comeloose from the metal surface (left in the picture). This leads to an irrelevant maximum load atbreakage and also incorrect breaking AE events are recorded. Such results are rejected. At least
8 correct samples (Fig. 3b) from each paper sample have been tested to reach relevant data at acceptably low standard deviation.
The paper qualities considered were: Light weight coated (LWC) paper with sw of 60 g/m2
and paper board with sw of 281 g/m2
Cohesive Zone Model
In order to connect the AE output to the state of the paper specimen, a simple analyticalmodel will be employed. During the tests, it was observed that a crack in the paper initiated in apoint on the boundary and propagated over the cross section of the paper sample. For simplicity
the crack front is assumed to be straight and further on, the stress state in the sample is assumed
to be uniaxial and described by a simple cohesive relation according to Fig. 5.

Fig. 5a: A simple cohesive relation.

Fig. 5b: Definition of the geometry anddegrees of freedom.

In Fig. 5a, σ denotes stress and u displacement in the thickness direction of the paper sample.
On the compression side the material can be assumed to behave according to the dotted line since
the grips are very stiff. For simplicity, the stiffness is assumed to be infinite as soon as u becomes negative. When σ becomes equal to σc, the cohesive zone starts to develop and transfersload up to the point where u = uc. At this point it should be noted that in small region around the(weak) point where the cohesive zone starts to develop initially, the cohesive relation will most
likely be different from the relation for the rest of the material in that for example σc will belower. In Fig. 5b are shown the degrees of freedom used to describe the deformations.
The loading of the paper sample can be divided into three cases shown in Fig. 6. With reference to this figure, depending on the load level, the material can be entirely elastic (I), a cohesivezone can have started to develop (II) or the cohesive zone can be fully developed and a crack has
formed (III). The variables used to define a general situation are shown in Fig. 7.
26

Fig. 6: Three consecutive loading cases.

Fig. 7: Angles θa and θc used to describe a general situation.
The elastic case (case I in Fig. 6) is obtained when θa = θc = π/2. Case II is defined by θa =π/2 and θc < π/2. Case III finally is given by θc < θa ≤ π/2. R denotes the radius of the grips. Letting σ denote the stress in the thickness direction of the paper sample, the following two relations
must be satisfied for reasons of equilibrium (see Fig. 5b):

$ !dA = P; $ !"dA = -Ps#

A

(1)

A

where ξ is defined in Fig. 8 and it has been assumed that φ is a small angle. The displacementu(ξ) in the thickness direction can be expressed in the degrees of freedom u0 and φ according to:u(ξ) = u0 + ξφ(2)This is because the grips can be assumed to be rigid. Dividing by the paper thickness t, the strain
ε in the thickness direction is obtained as:ε(ξ) = (u0 + ξφ)/t

(3)

27

In the elastic region, the stress is (since a uniaxial stress state is assumed) given by:σ(ξ) = Eε(ξ)(4)

Introducing the following transformation:
ξ = Rsinθ

(5)will turn (1) in to:
$a

2R 2

$a

% ! cos $d$ = P; 2R % ! cos $sin$d$ = "Ps&2"# / 23

2(6a, b)"# / 2

Putting θa = θc = π/2 will give the elastic case (I) and the value of u0, which will give σ = σc, canbe determined. Remember the note made previously about the initial value of σc.
For reasons of simplicity, the analysis will start when the displacement in θ = π/2 equals uc.
Then a real crack is forming and one enters case III. Over the part of the cross section where areal crack is propagating, the stress is of course equal to zero. Over the cohesive zone, i.e. whenθc ≤ θ ≤ θa, one will have for σ:
σ = σc(sinθa – sinθ)/(sinθa – sinθc)(7)

In addition to equations (6a, b), one must have that for θ = θa, u = uc, which condition with equations (2) and (5), will give:uc = u0 + Rφsin θa(8)

Also, for θ = θc, u = σct/E, which (again with (2) and (5)) will give:σct/E = u0 + Rφsin θc(9)Equations (6a, b), (8) and (9) will give four equations for the four unknowns P, θa, θc and φ(choosing u0 to be the independent parameter in this case). If, during the analysis, the displacement at θ = - π/2 becomes equal to zero one has to proceed in a different manner. Putting thedisplacement at θ = - π/2 equal to zero will give with (2) and (5) that:u0 – Rφ = 0(10)Now, consider Fig. 8:

Fig. 8: Contacting points.
28

The reaction force FR will affect equations (6a, b) such that:
$a

2R

$a

% ! cos $d$ = P + F ; 2R % ! cos $sin$d$ = "Ps& " F R

2

2

3

2

R

R

"# / 2(6’a, b)"# / 2One extra equation is gained, i.e. equation (10) but at the price of another unknown; FR. However five unknowns; F, FR, φ, θa and θa are determined by equations (6’a, b), (8), (9), and (10). If,
at some instant, the reaction force FR goes from a positive value to zero, then FR is put equal tozero, equation (10) becomes invalid and the analysis proceed as before.
It should be pointed out that all results are based on the assumption that unloading over thecohesive zone never occurs.
In order to relate the results from the analytical model to the AE output, it is reasonable to assume that to create one unit stress free area, in average N0 fiber bonds have to be broken. Thismeans that for a given value of θa, one contribution to the number of broken bonds is:
"/2

"/2

# dA(!) = 2N R # cos !d! .
2

N0

2

0

!a

!a

In the cohesive zone the material has failed completely at θ = θa while at θ = θc, the material isintact. If it is assumed that the number of broken bonds per unit area varies linearly over the cohesive zone, this will give another contribution to the total number of broken bonds according to:
!a

2N 0R

2

# ((sin! " sin! )cos ! /( sin! " sin! ))d!
2

c

a

c

!c

such that the total number of broken bonds will be proportional to Ψ given by:
"a

#/2

!=

$ cos "d" + $ ((sin" % sin" )cos " /( sin" % sin" ))d"
2

2

c

"a

a

c

(11)

"c

If there is a direct proportionality between the total number of broken bonds and total number ofAE events ! ev one will have the form of the AE curve given by:! ev = αΨ(12)where α is a constant.
Results

Numerical example
To solve the equations, MATLAB [6] was utilized. The following input data were used:
R = 17.5 mm, s = 50 mm, t = 0.042 mm.
σc = 50 MPa, E = 500 MPa, Gc = σcu0/2 = 0.14 N/mm
In Fig. 9a is shown the applied load P versus the load point displacement u0. while Fig. 9b showsΨ versus the load point displacement u0.
29

Fig. 9b: Ψ versus the load point displacement u0.

Fig. 9a: The load P versus the load point displacement u0.

It should be pointed out that the results given in Figs. 9a and 9b are at best qualitative. However, the experimental P vs. u0 curves shows the same behavior as is shown in Fig. 9b. Also, theexperimental AE curves exhibit a steep slope just after load maximum and another observation isthat as the load point displacement u0 is increased, the AE curve levels out.
One thing that must be remembered is that the model does not account for the initiation ofdamage, i.e. for phase II but only for the propagation of a real crack across the cross section of
the grips.
Experimental Results
The load – displacement curves and corresponding AE curves are shown in Fig. 10 for thetwo paper qualities. These were as mentioned before; light-weight coated (LWC) paper and paper board. The curves represent average values for at least 8 specimens. Note that on the horizontal axis in Fig. 10, time in seconds is the parameter. With a given deformation rate, this can easily be converted to displacement.
800

LWC
Paper board
Hits LWC
Hits Paper board

Load [N]

2800
2400
2000

500
400
300

1600

200
100

800

0

0

1200
400
0

10

20

30

Acoustic events

700
600

40

Time [s]Fig. 10: Curves for load and cumulative AE events for a paperboard and LWC versus time.

30

As can be observed from Fig. 10, there was a big difference between the behavior of LWC
and paper board. The most obvious difference is the peak load level in that the paper board load
curve peaked at about 320 N while the LWC load curve peaked at about 700 N. Also, the shapesof the curves show differences. For example, LWC seems to be quite brittle since the load decreases rapidly after the load peak and there are also very few AE events before the peak load isreached. As the load decreases after the load peak, the AE events increased dramatically (inagreement with the results from the analytical model). The paper board, on the other hand, had amore non-brittle behavior in that damage (AE) started well before the peak load was reached.
Also, the drop in load after the peak load was not as dramatic as for LWC.
Discussion
An experimental device for studying the damage evolution in paper when loaded in the
thickness (Z) direction has been presented. The method, when applied to two different paper
qualities, shows promising results in that it is possible to get some information regarding thetoughness of the paper material. It is also possible to determine the load or deformation whendamage initiates. This together with for example the finite element method, can give information
about stress and strain fields at the onset of damage. An analytical model is also developed
which in spite of its simplicity seems to be able to predict, at least in a qualitative sense, both theload – displacement and AE relations. With a more refined model it might be possible to performparameter estimation, i.e. to estimate from experimental data, the cohesive strength and fractureenergy.
Acknowledgements
We would like to take this opportunity to thank the companies that have supplied us with paper samples (too many to mention here). In addition, the Swedish Energy Agency, the Foundation for Knowledge and Competence, EU Objective 1 Programme (Region of South Forest
Counties), and the Bo Rydin Foundation are greatly acknowledged for their financial support.
References[1] SCAN-P 80:98; Z-directional tensile strength.
[2] Gradin PA, Nyström S, Flink P, Forsberg S, Stollmaier F., Acoustic Emission Monitoring of Light
Weight Coated Paper. J of Pulp and Paper Science, 23, (3), March 1997.
[3] Isaksson P., Hägglund R., Gradin P. A., Continuum Damage Mechanics Applied to Paper.
International Journal of Solids and Structures, 41 (2004) pp 4731 – 4735.
[4] Isaksson P., Gradin P.A. and Kulachenko A., The onset and progression of damage in isotropic paper sheets. Int. J. Solids Struct. 43, 713-726. (2006).
[5] Gradin, P A., Graham, D., Nygård, P. and Vallen, H., The Use of Acoustic Emission Monitoring to Rank Paper Materials With Respect to their Fracture Toughness. Experimental Mechanics, 48, (1), February 2008.
[6] MatLab version 6.5., The MathWorks Inc., Natick, MA, USA; 2002.

31

ASSESSMENT OF STRESS CORROSION CRACKING IN
PRESTRESSING STRANDS USING AE TECHNIQUEMARIANNE PERRIN1,2, LAURENT GAILLET1, CHRISTIAN TESSIER1and HASSANE IDRISSI2
1
Laboratoire Central des Ponts et Chaussées, Centre de Nantes, Div. MACOA, route de Bouaye,
BP 4129, 44341 Bouguenais cedex, France; 2 Laboratoire MATEIS-CNRS UMR-5510, Equipe
RI2S, INSA-Lyon, Bât. L. de Vinci, 21 ave. J. Capelle, 69621 Villeurbanne cedex, France
Keywords: Corrosion AE monitoring, prestressing steel
Abstract
Detecting corrosion of prestressing strands in concrete structures requires non-destructive
techniques like acoustic emission (AE), which allows the monitoring of active defects of thestructures. The aim of this work is to adapt AE to structural characteristics of bridges, to detectand localize stress corrosion cracking (hydrogen embrittlement, HE) of tendons. Accelerated
corrosion tests with ammonium thiocyanate on tensioned cables have permitted to validate AE
system capacity to detect signals coming from hydrogen embrittlement mechanism. Distinction
by AE of the different stages of the mechanism (crack initiation, crack propagation, etc.) occurring on prestressing strands is observed. Limited accessibility to strands on bridges has also beentaken into account by placing AE sensors on strand anchorage. Results show effects of wave
dispersion but the discrimination of AE hits is possible. This work presents the potential of AE
monitoring to detect, in their earliest stage, damages to prestressing strands, but the limitations of
AE technique is also pointed out.
Introduction
Non-destructive testing is multi-faceted and diversified. Most of them allow no real-time
control of structural damage evolution, in contrast to acoustic emission (AE). Due to the collection by sensors placed on the structure of acoustic waves emitted by active defects, the detection
and localization of damages can be done [1, 2]. Naturally, a non-destructive method has to beadapted to the degradation characteristics but also to the monitored structure.
Bridges studied in this work are prestressed concrete bridges. In addition to many bridges,
which benefit from this technology (48% of new structures use this construction technique), thismethod is also used for nuclear power-plant construction or for energy production units. It isbased on concrete compression maintained by the way of tensioned strands (at 80% of the guaranteed ultimate tensile strength, GUTS) of high mechanical strength [3]. Strands are maintainedin ducts placed inside concrete walls of post-tensioned prestressed concrete bridges. A grout isinjected inside the duct to protect strands from corrosion. Concrete compression is realized by
the way of anchorage systems at each side of the concrete unit [4]. Despite the use of protection,
metallic strands can suffer from corrosion, notably stress corrosion cracking [5]. It can be initiated by material surface defects or by ionic species in the interstitial liquid of concrete, nearstrands. In these initiation zones, the presence of high strain conditions associated with an appreciable hydrogen concentration cause the metallic embrittlement [6]. When stresses in the crack
tip reach a critical value, then the crack propagates until rapid wire failure.

J. Acoustic Emission, 26 (2008)32© 2008 Acoustic Emission GroupAE has shown its capacity to detect corrosion phenomenon [7-14]. Besides, it is used on
bridges to detect different damage types [15-18], but also in other areas, especially on metallic
structures suffering from corrosion like storage tanks.
This work offers to validate the capacity of AE technique to detect stress corrosion cracking(hydrogen embrittlement), and the final aim is to be able to characterize the different degradation
stages of prestressing tendons. Characteristic parameters coming from AE analysis are attributed
to each corrosion mechanism. At the same time, a comparison of results obtained on sensors attached at different places on the structure is done to observe the acoustic waves propagation.
Testing Device and Experimental ProcedureSpecimens are prestressing strands of T15.7 type. It is a cable including a straight central
wire with six other wires surrounding it helicoidally (Fig. 1). The steel used to manufacture thesestrands is obtained after cold drawing process. This process results in a microstructure composedof fine pearlitic grains fully oriented in the wire axis. This microstructure offers to the strand
important mechanical characteristics, notably concerning the ultimate tensile strength (UTS)
about 1900 MPa (the force of 285 kN for T15.7 unit).

Fig. 1. T15.7 strand geometry: composed by a central wire and 6 helical wires.
Table 1. Chemical composition and mechanical properties of cold-drawn steel for T15.7.
Chemical composition [wt%]
C
Si
Mn
P
S

0.8
0.25
0.50
0.02
0.02

Mechanical properties
F0,1 [kN]Fm [kN]Elongation [%]Sectional area [mm²]Pitch [mm]

275
295
5.3
150
240

The testing device used to apply a constant elongation to specimens is presented on Fig. 2.
Basically, it is composed of a rigid frame and a hydraulic jack allowing tensioning a cable at
80% of its GUTS (≈ 220 kN).
The corrosion cell permits to apply a corrosive environment to the strand. The corrosive solution chosen is ammonium thiocyanate (NH4SCN) with a concentration of 250 g.l-1, following
EN-ISO Standard 15630-3. The ammonium thiocyanate is known to make steel prone to33hydrogen embrittlement by inhibition of recombination reaction of hydrogen atoms. During thetest, the corrosive solution is kept at a constant temperature (50°C).
Some selection tests of the sensors have permitted to choose the sensor presenting the best
detection/easy use ratio. The sensor having the best results is a resonant type micro-80 (EPA).
For the strand monitoring in laboratory, four sensors have been used (Fig. 2). Sensors 1 and 2 arelocated on each side of the corrosion cell to have reference signals. Sensors 3 and 4 are attachedon the anchorage system: sensor 3 is placed on the wedge system and sensor 4 on the wire end.
The sensor coupling with the structure is done with a rigid paste (HBMX60 paste). Received
signals are amplified and then processed by the AE system. The sampling frequency used for thedata acquisition is 1 MHz. The acquisition threshold used is 26 dB.

Fig. 2. Strand loading system and AE sensor positions.
Results and Discussion
Metallographic observations of the specimen:
The fracture surface of broken wire is of brittle type and shows three propagation stages (Fig.
3a). The primary crack meets several smaller cracks on the surface of the wire. The propagation
depth of the primary crack is relatively shallow, about 370 µm. The second stage of the crack
progress is mode II propagation (Fig. 3b). This propagation process is typical of cold-drawn steel
SCC due to the microstructure, which can be assimilating to an orthotropic material [19-21]. The
last stage of the fracture surface is a shear failure with some radial marks oriented toward theprimary crack (Fig. 3d).
Despite the crack propagation is not a pure mode I, a first approximation of the thresholdstress intensity factor KSCC can be made with the Valiente method [22]. The wire is considered asa bar with a 5.2 mm diameter. The guaranteed ultimate tensile strength of the wire is 1860 MPa.
The KSCC factor obtained is 56 MPa√m. This value is lower than classical stress intensity factor
of cold-drawn steel [23]. It is also lower than Toribio [19] values obtained with a hydrogen embrittlement. It can be explained by the important quantity of hydrogen, which has penetrated insteel in this case by comparison to other works.
34

The hydrogen effect on strand damages is also present on the specimen surface. Lots of small
cracks are visible on the wire surface due to the important introduction of hydrogen inside themetallic matrix. Some longitudinal secondary cracks are also present (Fig. 3c).

a)

b)

c)
d)
Fig. 3. a) Fracture surface with mode I crack propagation, b) fracture surface with radial marks,c) microcracks and longitudinal secondary cracks, d) shear failure and surface cracks.
Hydrogen embrittlement detection:
Sensor having collected the most AE hits is sensor 2 (Fig. 4). Collection ratio differences
between all sensors can be explained:• For sensors 3 and 4, separated from corrosion cell (signal dispersion coming from wave
propagation and multiple impedance crossings).
• For sensor 1, by a less efficient coupling than for sensor 2.
All analysis will be based on results obtained on sensor 2. Figure 5 present cumulative AE number and cumulative energy evolutions with time. We can see four different stages:
Stage 1: from 0 to 15-20 hours. We observe a low number of hits, whose energy is also low.
This stage corresponds to the homogenization and chemical stabilization of specimen in the
corrosive solution after the filling up of the corrosion cell. This stage is then inherent to thetest procedure.
35

Fig. 4. AE hits evolution on each channel.
Stage 2: from 15-20 hours to 110-120 hours. For this period, AE behavior is characterized bya substantial increase of AE hits, but AE energy evolves differently. From the literature, we
can attribute this behavior to hydrogen formation and bubble bursting at the specimen surface[12, 24-26]. Besides, Jomdecha et al. [27] showed that for stress corrosion cracking mechanism, a strong AE activity appears to come from hydrogen. These mechanisms involve thepseudo-passive layer breakdown and the beginning of hydrogen diffusion inside metal matrix.
Stage 3: from 110-120 hours to 275 hours. It can be characterized like a steady state where
the increase of AE hits is very low, so as cumulative energy. It is an initiation stage concerning hydrogen embrittlement and general corrosion occurs. A post-mortem analysis has shown
general corrosion near fracture area. At the end of this stage when hydrogen uptake is sufficient for the steel to exceed the threshold stress intensity factor (KSCC+HE), rapid crack propagation takes place.
Stage 4: after 275 hours. It is pure crack propagation phenomenon affecting a large volume
of metal characterized by low AE hits of high energies. This stage is very brief until final
strand failure (<12 hours).
Characteristics of AE hits coming from each damage stage have been analyzed. First stage
has been omitted from this study because its length is very limited and its detection on bridges isof little interest. Four AE parameters (amplitude, duration, counts, cumulative hits) characterizing evolution during stages 2, 3 and 4 (Table 2) have been selected. All hits have been considered including hits near strand breakage. Best discriminating parameters for each stage are duration and counts. Although high cumulative hits characterize phase 2, this parameter does notallow clear distinction between phase 3 and phase 4. Amplitude is also unable to distinguishbetween phases 2 and 3.
AE monitoring on the anchorage system:
For all tests, sensors 3 and 4 (placed on anchorage and on strand end, respectively) have detected strand corrosion degradation. The evolution for both sensor locations are quite similar andwhen we compare with results obtained for channel 2, the four damage stages are not so clearly36

Fig. 5. AE hits and energy evolutions with time on channel 2.
Table 2. Means values of AE parameters and cumulative hits for damage stages 2, 3 and 4(channel 2).
AE on channel 2
Amplitude (dB)
Duration (µs)
CountsΣAE hits

Stage 2
33
182
8
62138

Stage 3
33
384
22
2627

Stage 4
39
664
67
2793

recognized. Nevertheless, we can identify stages 1 and 4 limits. When we apply now the samedata processing as above (Table 3) we observe that amplitude parameter has nearly the samevalue for sensors 2 to 4 (channels 2 to 4).
Concerning AE hits, their cumulative number is clearly lower for channel 3 and 4 (notably
for stage 2) than for channel 2. Despite a reversal of lower duration value for sensor 4, durationand counts seem to be also valid discrimination parameters for sensors attached on the anchorage
system.
Conclusion
Although SCC mechanisms induced by hydrogen are complex, AE has permitted to distinguish different damage stages. By a rather simple approach using classical AE parameters, AE
monitoring is able to differentiate four stages of the evolution of corrosion damage. Most emitting mechanisms are bursting of hydrogen bubbles or fast crack propagation that can easily be
determined with these parameters.
A comparison of AE signals collected near the damage zone (sensor 2) and behind anchorage
system (sensor 3 and 4) permits us to conclude that a discrimination of the different damage
mechanisms by usual AE parameters is possible placing sensors on anchorage area. This is inmany cases the only accessible zone on structures.
37

Fig. 6. Hits and energy evolutions with time on sensor 3 (channel 3).

Fig. 7 Hits and energy evolutions with time on sensor 4 (channel 4).
Table 3. Mean values of AE parameters and cumulative hits for damage stages 2, 3 and 4.
AE on channel 3
Amplitude (dB)
Duration (µs)
CountsCumulativeAE hits
AE on channel 4
Amplitude (dB)
Duration (µs)
CountsCumulativeAE hits

Stage 2
33
790
34
2470
Stage 2
34
543
41
1902
38

Stage 3
34
1138
68
2553
Stage 3
34
1115
58
2640

Stage 4
39
1303
89
1437
Stage 4
39
757
72
1864

Results obtained are promising and show interesting capacities of AE for the early detectionof stress-corrosion cracking damages in prestressing strands tensioned in bridge configurations.
Acknowledgement
This work has been realized as a part of ACTENA French project. Authors thank EDF(French Electricity) and ANR (National Research Agency) for their financial support.
References
1. Roget J.: Essais non destructifs : l’émission acoustique, mise en œuvre et applications, éditions AFNOR CETIM.
2. Roget J.: Techniques de l’ingénieur, traité Mesures et Contrôles, R 3200, pp. 1-10.
3. Chaussin R.: Techniques de l’ingénieur, traité Construction, C2360, 1990, pp. 1-77.
4. Jartoux P., Fargeot B., Tourneur C.: Techniques de l’ingénieur, traité construction, C2372,
1996, pp. 1-34.
5. Nürnberger U.: Otto Graf Journal, N°13, 2002, pp. 9-25.
6. Schroeder R.M.: Corrosion Science, 45, 2003, 1969-1983.
7. Idrissi H., Liman A.: NDT&E International, 36, 2003, 563-569.
8. Darowicki K., Mirakowski A., Krakoviak S.: Corrosion Science, 45, 2003, 1747-1756.
9. Kovac J., Leban M., Legat A.: Electrochemica Acta, 52, 2007, 7607-7616.
10. Idrissi H., Derenne J., Mazille H.: Journal of Acoustic Emission, 18, 2000, 409-416.
11. Idrissi H., Limam A.: Journal of Acoustic Emission, 18, 2000, 417-423.
12. Fregonese M., Idrissi H., Mazille H., Cetre Y., Renaud L.: Corrosion Science, 43, 2001, 627641.
13. Bellenger F., Mazille H., Idrissi H.: NDT&E International, 35, 2002, 385-392.
14. Ramadan S., Gaillet L., Tessier C., Idrissi H.: Applied Surface Science, 254, 2008, 22552261.
15. Matsuyama K., Fujiwara T., Ashibashi A., Ohtsu M.: National conference on subsurface andcivil engineering acoustic emission, n°5, Kumamoto, Japan, 1993.
16. Zejli H., Lacksimi A., Tessier C., Gaillet L. Benmedakhene S.: Advanced Materials Research, 13-14, 2006, 345-350.
17. Brevet P., Robert J.L., Gaillet L.: Proceedings of the EWGAE 2002 European Working
Group on Acoustic Emission, Prague, Czech Republic, 2002, pp. 11-13.
18. Gaillet L., Tessier C., Bruhat B., Michel R.: Bulletin des laboratoires des ponts et chaussées,250-251, 2004, 55-63.
19. Toribio J., Ovejero E., Corrosion Science, 49, 2007, 3539-3556.
20. Toribio J., Valiente A., Engineering Failure Analysis, 13, 2006, 301-311.
21. Toribio J., Ovejero E., Engineering Failure Analysis, 12, 2005, 654-661.
22. Valiente A., Ph.D. thesis, Polytechnic University of Madrid, 1980.
23. Brevet P., Raharinaivo A., Siegert D., Bulletin des laboratoires des ponts et chausses, 250251, 2004, 75-92.
24. Ramadan S., Idrissi H.: Desalination, 219, 2008, 358-366.
25. Didier-Laurent S., Idrissi H., Roue L.: Journal of Power Sources, 179, 2008, 412-416.
26. Yuyama S., Hisamatsu Y., Kishi T., Nakasa H.: Proceedings of the Fifth International Conference on Acoustic Emission, Tokyo, 1980, pp. 115.
27. Jomdecha C., Prateepasen A., Kaewtrakulpong P.: NDT&E International, 40, 2007, 584-593.
39

COMPARISON OF WAVELET TRANSFORM AND CHOI-WILLIAMSDISTRIBUTION TO DETERMINE GROUP VELOCITIESFOR DIFFERENT ACOUSTIC EMISSION SENSORS #*M. A. HAMSTAD
University of Denver, Department of Mechanical and Materials Engineering, Denver, CO 80208
and National Institute of Standards and Technology, Boulder, CO 80305, USA
Keywords: AE arrival times, AE sensors, Choi-Williams distribution, group velocities, Lambmodes, thin plate, wavelet transform
Abstract
Acoustic emission (AE) sources in a plate generate dispersive Lamb waves. In this research,
pencil lead breaks (PLBs) were made on an edge of a 3.1 mm thick aluminum alloy plate to generate such AE waves. The PLBs were made near the plate mid-plane and near the bottom of theedge. The waves were sensed by pairs of AE sensors (both resonant or both non-resonant wideband). Each sensor of each pair was located at a different distance from the plate edge PLB position. The signals from the different sensors and source positions were analyzed and contrasted.
The recorded signals were processed by a wavelet transform (WT), and the group velocity curves
for the appropriate Lamb modes were superimposed to clearly identify the modes in the signal.
The threshold-independent mode arrival times at specific intense signal frequencies were determined by the time of the peak WT magnitude at the intense frequency. Since the two sensors
were located at different known distances from the PLB position, an experimental group velocity
was calculated based on the difference in arrival times and the difference in distance. This velocity was then compared to the theoretical group velocity for the relevant mode and frequency. In
addition, an alternate frequency/time analysis method was used. This was the Choi-Williams distribution (CWD). The same procedures used for the WT results were followed for the CWD distribution. In addition the experimental results were compared to finite element calculated resultsfor plate-edge monopoles both near the mid-plane and near the top plate edge. Group velocities
within 6.5 % of the theoretical values were obtained with all the sensor types when certain intense resonant frequencies were ignored for two of the three resonant sensor types.
Introduction
The author and his co-workers have published a number of papers on the analysis of finiteelement modeled (FEM) acoustic emission (AE) signals in a 4.7 mm thick aluminum plate [1- 6].
One focus of this work has been to demonstrate the use of a wavelet transform (WT) to obtainaccurate and threshold-independent arrival times from the peak WT magnitude of certain intense
frequencies of the Lamb modes. Since these arrival times were associated with specific known
group velocities, very accurate source locations could be calculated for the dispersive AE waves#

Contribution of the U.S. National Institute of Standards and Technology; not subject to copyright in the United States* Trade and company names are included only for complete scientific/technical description; endorsement is neither intended nor implied.
J. Acoustic Emission, 26 (2008)40© 2008 Acoustic Emission Groupeven in the presence of significant electronic preamplifier noise [6]. This result was due to thefact that the WT spreads out the noise in frequency, while the arrival of a mode in an AE signal
concentrates the signal energy at specific frequencies and specific times. In addition, since WTbased arrival times are obtained from regions of the AE signal that have significant amplitude,
the above approach is also much more robust in the presence of preamplifier noise than AE
analysis techniques that seek to obtain the arrival time from the very first arrival of an AE signal.
This result is due to the fact that the first arrival region of an AE signal is often considerably
lower in amplitude than the peak region of the signal. Thus, the very first arrival may be “lost” inthe noise.
Due to the small spatial resolution of the FEM results (typically a 0.3 mm cell size), the WTbased arrival times were in essence obtained from the signal from a perfectly flat-with-frequency
point-contact “pseudo” displacement sensor. Thus, a question remains with respect to the above
research. What is the accuracy of arrival times obtained in a similar fashion from the signals
from real AE sensors?
The purpose of the research reported here was to examine with signals from real AE sensors
whether experimentally obtained mode arrival times from pencil lead break (PLB) generated
waves accurately corresponded to theoretical group velocities in a thin aluminum plate. Theseexperiments were done using a variety of AE sensor types, and the arrival times were determinedfrom frequency/time analysis, as was done with the FEM-generated results. The sensors included both wideband (with response approximately flat with frequency over certain frequency
ranges) and resonant sensors (this characterization of the sensors was based on information
available from sensor suppliers). Both a WT and the Choi-Williams distribution (CWD) werecompared relative to their use for determination of accurate mode arrival times as measured by
the closeness of the experimental group velocities to theory. These comparisons were made fromthe arrival times of the more intense modal regions in the frequency/time magnitude results.
Experiment
Previous finite element modeled results as well as experimental results demonstrated that
PLBs applied to the edge of a plate generated AE waves that were much more similar to the AEfrom modeled results from interior buried dipole AE sources than PLBs applied to the plate top
or bottom surface [7,8]. Hence, PLBs (0.3 mm diameter, 2H hardness, length of about 2 mm)
were applied to the edge of an aluminum alloy plate having a thickness of 3.1 mm. Two sensorsof each type were mounted at a time on the upper plate surface by use of vacuum grease as thecouplant. They were mounted on a line perpendicular to the plate-edge PLB position (located at
the midpoint along the long edge of the plate). One sensor was mounted at 127 mm (5 in.) fromthe edge, and the other at 305 mm (12 in.). Based on the results of previous modeling thatshowed the modal distribution of energy in an AE signal strongly depended on the source depth
in a plate [1], the pencil lead was broken in contact with the plate edge near the mid-plane andalso near the bottom of the edge. A third AE sensor located on the plate top surface near the PLB
point was used to provide a consistent trigger signal for a 12-bit transient digital recorder. A passive four-pole Butterworth filter was used to high-pass the signals at 50 kHz before the signals
were recorded with a time step of 0.1 µs. A total of six different models of AE sensors wereused, including two commercially available wideband sensors (designated WB #1 and WB #2)
and three commercially available resonant sensors (designated Res #1, Res #2 and Res #3). In
addition, a high sensitivity wideband sensor developed at NIST Boulder was used (designated
FHWA) [9, 10]. Some information about the sensors is shown in Table 1. No special purchase41

Table 1 Listing of sensors used.
Sensor names
FHWA
WB #1
WB #2
Res #1Res #2Res #3

Expected response character
Wideband
Wideband
WidebandResonant
Resonant
Resonant

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

of sensors was made; instead available sensors from other projects were used. Appropriate preamplifiers and preamplifier gains were used with each sensor. In all cases the high-pass frequency of the preamplifiers was either 1 kHz or 5 kHz, and the low-pass frequency was at least 1
MHz. A total of at least three PLBs was made for each source position for each set of sensors tomake sure that representative results were obtained. In the analysis, only the representative signals from one PLB (per source position) were used for each set of sensors. The transverse dimensions of the plate (1220 mm by 1525 mm) were large enough to preclude reflections fromthe plate edges arriving during the duration of the waves propagating directly from the PLB position to the sensors.
In addition, finite element modeled results were also obtained for a 3.1 mm thick aluminum
plate. These results were “converted” from existing results for a 4.7 mm thick aluminum plate byuse of a technique previously demonstrated [11]. Because the “converted” 3.1 mm thick results
were generated from the existing results for a different plate thickness and propagation distance,
the in-plane monopole source depths (below the top surface of the plate) and the propagation distance were directly determined by the “conversion” process applied to the original data. Thus,
the propagation distance to the “pseudo” sensor position was 120 mm from the monopole source
applied to the plate edge. In addition, the two source depths were 0.52 mm (near the plate topedge) and 1.35 mm (near the plate mid-plane) below the top plate surface. After the conversion
process was applied to the unfiltered FEM data, the signal that represented the out-of-plane top
surface displacement versus time was digitally filtered with a four-pole 50 kHz high-pass Butterworth filter and then resampled in time from 29.6 ns to 0.1 µs per point. The resultant data
corresponded to the sampling interval of the experimental data. The domain of the plate for thefinite element modeled (FEM) data was smaller in size (530 mm on the source side by 330 mm
in the propagation direction) than the experimental plate. The plate was still of sufficient size thatedge reflections did not occur during the full arrival of the signal that propagated directly fromthe source to the “pseudo” sensor position.
Group Velocity Curves for a Thin Aluminum Plate and Frequency/Time Analysis ProceduresGroup velocity curves [12] of the fundamental symmetric and the first two antisymmetric
Lamb modes for the 3.1 mm thick plate are shown in Fig. 1. Additional group velocity modes
were not considered because their frequencies were beyond those normally used in AE for thecurrent plate thickness. WTs [12] of the signals from the PLBs and FEM results were calculated.
Group velocity curves were superimposed on the WT results by use of the known propagation
distances. To correctly superimpose the group velocity curves (after the effect of propagation
distance was accounted for), the curves were offset in time to match (by eye) the WT experimental results for the FHWA sensor. Then this same time offset was used for all the other42

Fig. 1. Relevant group velocities vs. frequency for an aluminum plate of 3.1 mm thickness [12].
sensor results. This time adjustment was not necessary for the FEM results, because the displacement versus time data started at the time of the AE source initiation. The key parameterschosen for the WT calculation were a frequency resolution of 3 kHz (frequency band) and awavelet size of 600 samples. In addition, a feature of the WT software was used to determine thefrequencies, arrival times (at the peak magnitude of the frequency band) and the peak magnitudeof the more intense mode arrivals.
An alternate frequency/time analysis was also applied to each experimental signal and to the
FEM results. This approach was the CWD. This computation was made using the LabVIEW implementation (Easy CWD) [13]. Other than the default parameters, the following settings wereused: (i) 2048 frequency bins, (ii) time resolution of 0.1 µs, (iii) analytic signal and (iv) α = 0.1(this parameter controls the balance between resolution and cross-term interference). The CWD
implementation did not allow for the superposition of the group velocity curves, but it was easy
to pick out the modes by eye by use of the insight from the WT results for the same signals.
Again the frequency, arrival times (at the peak of the frequency band) and the peak magnitude of
the more intense mode arrivals were obtained from the CWD results by use of custom additions
to the LabVIEW programming.
The frequencies of the intense mode arrivals were reported as the frequency at the high endof the WT and CWD frequency bands. Because the CWD bands were 2.44 kHz wide, the reported frequency was the largest number without decimal values in each band.
Typical Time Domain Results for Signals from Experimental PLBs and the FEM Data
Because the spread in time between the modes increased as the propagation distance increased, the signals at the farther distance (305 mm) were initially selected for more detailed examination. Thus, mode identification was enhanced. For an edge in-plane PLB near the midplane of the plate, Fig. 2 illustrates typical experimental time domains of the signals from all43

Fig. 2. Time domains for the different sensors at 305 mm propagation distance from PLB near
the mid-plane of plate.

Fig. 3. Time domains for the different sensors at 305 mm propagation distance from PLB near
the bottom edge of plate.
44

the sensor types. Figure 3 shows the typical time domain results for an in-plane edge PLB near
the bottom of the plate.
Figure 2 demonstrates that for nominally the same displacement wave passing beneath thesensors, there was a wide variety of waveforms from the different sensors for the near mid-plane
PLB. Only the wideband FHWA and WB #1 sensor signals were reasonably similar. As expectedfrom the group velocity curves, the S0 (extensional) mode first arrival was clearly observed at the
beginning of all the signals. From the wideband sensor signals, the first arrival of the A0 (flexural) mode can also be identified by eye, as indicated by the arrow in Fig. 2. In contrast, the firstarrival of the flexural mode cannot be easily identified in the resonant sensor signals. Close examination by use of an expanded time scale of the signal from the Res #2 sensor does show the
first arrival of the A0 mode, but it was a little later than with the wideband sensors.
For the PLB source near the bottom edge, Fig. 3 shows the first arrival of the extensionalmode for all the sensor signals. Also, with the exception of the Res #1 sensor, the first arrival
region of the flexural mode can be identified by eye, as indicated by the arrow in Fig. 3. For thissource position, the time domain signals were still quite different from each other, but they weremore similar to each other than for the near mid-plane source. The signals from all three wideband sensors were the most like each other for the near bottom edge source.

Fig. 4. FEM displacement at 120 mm propagation distance and sensor signals at 127 mm for inplane source. Left column source near mid-plane and right column source near a plate surface.
It is of interest to compare the PLB results with the FEM results. Figure 4 shows the FEM
out-of-plane displacement versus time for the in-plane edge monopole source located at two
depths. These results are shown as the top waveforms in part (a) at 1.35 mm depth (near the midplane) and in part (b) at 0.52 mm depth (near the top edge). Figure 4 also shows the PLB waveforms for the different sensors at the 127 mm propagation distance. Although the45

propagation distance of the FEM result was a little less at 120 mm, it is clear that both the extensional and flexural mode signals show clear first arrival regions in the FEM signals, while the
PLB signals in general show mode arrival characteristics similar to those discussed above for thelarger propagation distance. In addition, it is worthwhile to note that the wideband sensor signals
were much closer to those from the FEM computation. The resonant sensor signals were notnearly as close to the FEM-based signals. In closing this paragraph, we point out that the only
significant difference between a source near the top edge (FEM data) and one near the bottomedge (PLB data) is a change in phase of the anti-symmetric modes.

Fig. 5. WT (left column) and CWD (right column) of signals (Fig. 2) resulting from PLB near
the mid-plane of the plate sensed by wideband sensors at a propagation distance of 305 mm.
Typical Frequency/Time Results for Signals from Experimental PLBs and the FEM Results
The left columns of Figs. 5 through 8 show the results after the WT was applied to the sensor
signals. The A0 and S0 modes are labeled in all the figures, and the A1 mode is labeled in figureswhere it was relevant. Figures 5 and 7 respectively show the wideband sensor results for the near
mid-plane and near bottom edge PLBs. Figures 6 and 8 respectively show the resonant sensor
results for the near mid-plane and near bottom edge PLBs. With the help of the group velocity
46

Fig. 6. WT (left column) and CWD (right column) of signals (Fig. 2) resulting from PLB near
the mid-plane of the plate sensed by resonant sensors at a propagation distance of 305 mm.
curves at the known propagations distances, it was easy to identify the modes that were intense
for all the sensors at both source depths. Thus the WT results provide a clearer picture of themodes and their arrivals than do the original amplitude versus time results. This result is particularly the case for the resonant sensors. Of even more importance, the WT results also identified
the frequencies within the modes that were most intense for the different sensors at the two different source depths.
Because the waves that the sensors responded to were very similar for each PLB position, theWT results demonstrate significant distinctions between the response characteristics of the different sensors. First, considering the wideband sensor data for the near mid-plane source, the
FHWA sensor responded most strongly in the A0 mode, while the other two wideband sensors
responded most strongly in the S0 mode (see Fig. 5). Second, with these same three wideband
sensors for the near bottom edge source, the signals responded most strongly in the A0 mode. For
this source depth, Fig. 7 shows that there were two regions of most intensity in the A0 mode forthe signals from the WB #1 and WB #2 sensors, and only one lower frequency region for thesignals from the FHWA sensor.

47

Fig. 7. WT (left column) and CWD (right column) of signals (Fig. 3) resulting from PLB near
the bottom edge of the plate sensed by wideband sensors at a propagation distance of 305 mm.
Examination of the resonant sensors WT results shown in Fig. 6 for the near mid-planesource position showed that the S0 mode was dominant in the signals from each sensor. In addition the figure showed that there was a considerable difference in the dominant frequency withinthat mode for the different sensors. For the source position near the bottom edge, the A0 mode
dominated for all the resonant sensors, as can be observed in Fig. 8. Again there was a wide
range in the most dominant frequency within the mode. When the WT results for the different
resonant sensors were examined in more depth, it was found that not only are there different frequencies within the modes more intense than those that were intense for the wideband sensors,
but there is evidence that points to resonances within the signals from these sensors. For example
for the near mid-plane source, the Res #3 sensor result shows a time-extended high intensity region of the S0 mode at about 150 kHz (see Fig. 6). This resonance was expected based on thecharacteristics of this sensor. Another example of such behavior was present in the WT results of
the Res #1 sensor signal for the source near the bottom edge. In this case, there was an extended
intense region at about 100 kHz, where the intense region starts at the A0 mode curve and thencontinues with intensity (as time increases) at this same frequency (see Fig. 8). These examplesof resonant behavior were not present in the wideband sensor results. For example in contrast to
the Res #1 result, for the same near bottom edge source position, the FHWA sensor’s intense region closely follows the A0 mode curve (see Fig. 7).
48

Fig. 8. WT (left column) and CWD (right column) of signals (Fig. 3) resulting from PLB near
the bottom edge of the plate sensed by resonant sensors at a propagation distance of 305 mm.
Corresponding to the WTs of the signals from the different sensors for the two source positions, the CWD results from the sensor signals are shown in the right columns of Figs. 5 through8 for the 305 mm propagation distance. Based on the adjacent WT results, the intense modes in
the CWD were labeled as shown. The most intense regions for each sensor at each depth weresimilar to the WT results except for a slight difference in the CWD for the FHWA sensor for thein-plane source. In this case, there is an additional intense region within the S0 mode at approximately 650 kHz in the CWD result. This region does not show as strongly in the WT result forthis signal. Further contrasts between the CWD and the WT results will be discussed later in thispaper.
The WT and CWD results were also obtained from the FEM-generated signals at the two
depths. Figure 9 shows these results in the left column for the near mid-plane source results andin the right column for the near top surface results. Due to the fact that the FEM results represent
those from a perfect wideband sensor, it makes the most sense to compare them with the experimental results from the wideband sensors. In this comparison, there was a significant difference
for the near mid-plane result. For the FEM signals, the WT and CWD (respectively Figs. 9(a)and (c)) results show that the most intense region was in the low frequency region (centeredaround 60 kHz) of the A0 mode. In the experimental case for the near mid-plane PLB, the mostintense region, except for the FHWA sensor, was in the S0 mode at both propagation distances49

Fig. 9. WTs of finite element modeled signals shown in top row of Fig. 4; (a) near mid-planesource and (b) near top edge. CWD for the same two modeled signals; (c) and (d) correspondrespectively to the signals resulting in the WTs in (a) and (b).
(results at 127 mm are not shown). For the FHWA sensor the most intense mode was the A0mode, but the intense frequency regions were at higher frequencies than with those from the
FEM results. Specifically the FHWA signal had two nearly identical magnitude WT peaks at 291
kHz and 513 kHz at the 127 mm distance, and at the 305 mm distance the most intense WT peak
was at 405 kHz. For the source position nearer a plate surface, the FEM result (Figs. 9(b) and(d)) was quite similar to the experimental wideband sensor results at both distances (results forthe 127 mm distance are not shown) for the FHWA and WB #1 sensors, as shown in Fig. 7 at the
305 mm distance, with the most intense region being in the low frequency region centeredaround 60 kHz (at the 127 mm distance the results were similar, but are not shown). The WB #2
sensor had its most intense region at a higher frequency of the A0 mode centered at about 138
kHz in the WT result. This observation indicates the WB #2 sensor has a poorer low frequency
response compared to that of the other two wideband sensors.
Group Velocity Results for PLBs near the Mid-plane and Bottom-edge of the Plate
Because, as previously noted, the individual mode/frequency combinations with significant
intensity are most easily characterized at the further propagation distance, the WTs and CWDs
for the 305 mm propagation distance were used to define the intense mode/frequency combinations (i.e., the regions in the frequency versus time plane where the magnitudes of the WT or
CWD were large at mode arrivals) to be used to determine mode arrival times at both propagation distances. It is worth noting that the frequency at the peak within the modes was not exactly
the same for the WT and CWD for a particularly intense modal region. The process of determining of arrival times is demonstrated in Fig. 10 for a signal from the FHWA sensor for a near mid50

Table 2 Mode/Frequency combinations and group velocities for PLB near mid-plane.
Mode/Frequency
Sensor[kHz]
A0/405
A0/463FHWA
S0/627
S0/661
S0/351
S0/371WB
A0/141
A0/146
#1
A0/60
A0/61
WB
S0/375
S0/397#2
S0/306
S0/307
Res #1
S0/441
S0/517
S0/510S0/527
Res #2
A0/195
A0/222
S0/147
S0/153
Res #3
S0/252
S0/251

Magnitude as apercentage ofmaximum
100
100
55
93
100
100
62
36
61
36
100
100
100
83
77
71
100
100
66
22
100
100
94
98

Experimentalgroup velocity[mm/µs]
3.16
3.03
2.78
2.56
5.08
4.91
2.80
2.80
2.34
2.26
4.95
4.89
5.37
5.44
4.49
4.32
4.04
4.03
2.99
3.09
5.40
5.37
5.25
5.26

Theory groupvelocity[mm/µs]
3.14
3.14
2.91
2.46
4.99
4.93
2.85
2.87
2.26
2.30
4.92
4.84
5.12
5.11
4.63
4.13
4.18
4.05
3.00
3.05
5.35
5.34
5.22
5.22

Signed percentage
difference in velocity
0.6
-3.4
-4.7
4.1
1.8
-0.4
-1.7
-2.5
3.6
-1.9
0.7
0.9
5.0
6.5
-3.0
4.5
-3.3
-0.4
-0.5
1.8
1.1
0.6
0.5
0.5

plane PLB. This figure shows the signal, the CWD and WT, and the magnitude versus time ofboth the CWD and the WT coefficients for the 405 kHz frequency band. The time of the maximum magnitude peak was used to define the arrival time of the A0 mode in this case. Using thedifferences in the arrival times (since the signals from the two sensors had a common trigger) an
experimental group velocity was calculated based on the difference in propagation distance of
178 mm. Table 2 summarizes the more intense mode/frequency combinations and their relative
magnitude (from the 305 mm results) at the mode arrival along with the calculated experimentalgroup velocity. The relative magnitude was calculated based on the maximum frequency/time
intensity for the whole signal. In addition, the table shows the theoretical group velocities (fromthe data shown in Fig. 1) and a sign-based percentage difference of the experimental velocity
relative to the theoretical one. In the table, there are two lines for each mode/frequency case. The
first line gives the WT results, and the second line gives the associated CWD results for eachsensor and mode/frequency case. When the local maximum of an intense frequency was near the
50 kHz filter’s high-pass frequency, a frequency of 60 kHz was used for the WT and 61 kHz for
the CWD. Only the intense mode/frequency combinations that had magnitude maxima for thesame mode at both propagation distances at the frequency determined from the 305 mm distancedata were considered. Also, the table does not include intense frequencies where the mode could
not be clearly indentified.
Following the same procedures used to create Table 2, Table 3 was created for the PLB
source near the bottom edge of the plate. In both tables, only the fundamental modes appear.
51

Fig. 10. Illustration of determination of the A0 mode arrival times for the signal (FHWA sensor
for near mid-plane PLB; propagation distance 305 mm) shown in (a). The CWD and WT coefficients at 405 kHz are shown respectively in (b) and (c). The full CWD and WT are shown respectively in (d) and (e) with a line at 405 kHz.
Observations Based on Tables 2 and 3
In spite of the dominance of a particular mode for each of the two source positions, Tables 2
and 3 show that the frequencies that were intense within each mode varied widely over the six
different sensors. Interestingly, this was the case even for the wideband sensors (for the near
mid-plane source and the S0 mode, the minimum and maximum intense frequencies were 60 kHz
and 661 kHz; for the source near the bottom edge and the A0 mode these maximum and minimum frequency values were respectively at 60 kHz and 384 kHz). With the resonant sensors, therange of intense S0 mode frequencies varied from 147 kHz to 527 kHz for the near mid-planesource, and the range of A0 mode frequencies varied from 93 kHz to 466 kHz for the52near bottom edge source. It seems most likely that these wide variations in the intense frequencies within the modes were due to both the intense mode/frequency combinations in the generated Lamb waves and the details of the different sensor’s response sensitivity as a function of
frequency. Thus, the observed intense mode frequencies do not in general directly correlate withthe sensor resonant frequencies.
Table 3 Mode/Frequency combinations and group velocities for PLB near bottom edge.

Sensor
FHWA

WB #1

WB #2

Res #1Res #2Res #3Mode/Frequency[ kHz]
A0/60
A0/61
A0/384
A0/346
A0/60
A0/61
A0/141
A0/146
A0/288A0/317
A0/138A0/151
A0/330A0/349
A0/93
A0/100S0/300---A0/441
A0/466
A0/231
A0/239Magnitude as apercentage ofmaximum
100
100
64
45
100
100
96
88
58
38
100
100
99
100
100
100
43
---100100
100
100

Experimentalgroup velocity[mm/µs]
2.23
2.25
3.16
3.15
2.32
2.27
2.80
2.78
3.09
3.11
2.88
2.87
3.21
3.17
3.77
3.41
5.39
---3.16
3.16
3.08
3.07

Theory groupvelocity[mm/µs]
2.26
2.30
3.14
3.13
2.26
2.30
2.85
2.87
3.11
3.12
2.83
2.88
3.13
3.13
2.59
2.64
5.14
---3.14
3.16
3.06
3.07

Signed percentage difference invelocity
-1.6
-2.2
0.6
0.6
2.4
-1.3
-1.7
-3.1
-0.7
-0.5
1.6
-0.2
2.6
1.2
45
29
4.8
---0.90.7
0.8
0.3

Since the time domain signals processed by both the CWD and the WT were identical, it was
not surprising that there were related pairs (except for one case) of intense regions as shown in
Tables 2 and 3. In the single case in Table 3 for the Res #1 sensor, the CWD did not have thesame peak modal location at both propagation distances; hence no result is included in the tablein this case. Examination of these pairs shows that generally the CWD gives a higher frequency
for the frequency of the intense regions of the modes (ignoring the cases of the arbitrarily selected frequencies of 60 and 61 kHz). This result seems to be consistent with those of the following experiment. Three sine waves of frequencies 60 kHz, 270 kHz and 522 kHz (all with thesame amplitude, with a 0.1 µs time step) were superimposed, and then the WT and CWD wereapplied. For the results shown in Fig. 11, the CWD band magnitudes (that included the sine wave
frequencies) expressed as a percentage from low to high frequency were 100, 100 and 67. On theother hand, the WT magnitudes show an increasingly greater reduction at the higher frequencies,
since their respective values were 100, 48 and 36. Thus the magnitude reduction of higher frequencies does not appear in the CWD result to the same large degree as with the WT. Also as
can be clearly seen in Fig. 11, the WT results show that the single frequency input signal has intensity that increasingly spreads over a wider frequency range in the WT result as the53

Fig. 11. Results for WT (a) and CWD (b) both applied to signal comprised of superposition ofsine waves of equal amplitude at frequencies of 60 kHz, 270 kHz and 522 kHz.

Fig. 12. Illustration of cause of group velocity error with resonant Res #1 sensor at 93 kHz for
PLB source near bottom edge. Shows in (a) the peak is not in the same relation to the A0 groupvelocity curve as shown in (b).
signal frequency increases. In summary, this contrast between the CWD and the WT results canbe expected to result in the CWD frequency of the pair being higher at higher frequency intense
modal regions.
The group velocity results in Tables 2 and 3 show that with the wideband sensors, the difference between the experimental velocity and the theoretical values was less than 5 %. For the
resonant sensors, the difference between experiment and theory was less than 6.5 %, with one
exception. In this case (Res #1 sensor for the near bottom edge source), the most intense region of54

the A0 mode had large differences from the theoretical group velocity (WT – 45 % and CWD –29 %). Using the WT result, Fig. 12 illustrates the cause of this large difference. This figure
shows that at the farther distance the WT peak at 93 kHz was quite close to the A0 curve, but due
to a resonance at the shorter distance, the WT peak was well beyond the A0 curve even though
the intensity began just beyond the A0 curve. It should be noted that the error with the CWD was
still large, but it was considerably less. This result may be due to sharper peaks in time in lower
frequency bands with the CWD compared to the WT, as will be demonstrated later in this paper.
Such large differences in the experimental versus theoretical group velocities, implies that if arrival times were used from this intense region, poor results would be obtained for calculated
source locations.
The results in the tables contribute to a false impression relative to the use of resonant sensors. The reason is that a number of intense regions for these sensors were not included in thetable. They were not included due to several facts: (i) the mode associated with the frequency of
the intense region changed between the 305 propagation distance and the 127 mm distance; (ii)there was an intensity peak near the intersection of all three modes (at about 625 kHz), and thelocation of the peak magnitude changed in time relative to this intersection at the two distances;and (iii) the mode of the intense frequency peak could not be determined. These three characteristics were not observed with the wideband sensors. Figures 13 through 15 illustrate some ofthese aspects with the WT results shown at the two propagation distances. Figure 13 demonstrates a case where the mode corresponding to the peak WT coefficient at a resonant frequencychanged when the propagation distance changed. At the 127 mm distance, the peak at 147 kHz
was near the A0 mode. While at the 305 mm distance, the peak was near the S0 mode. Thus, thesignals from the Res #3 sensor at this intense frequency did not provide arrival times at a fixedgroup velocity for the PLB source near the bottom edge.
Another example with the resonant Res #1 sensor is shown in Fig. 14 for a PLB near the midplane. In this case, the WT coefficient peak at 594 kHz changed its relationship in time relative
to the intersection of the three modes (A0, A1 and S0) at the two different propagation distances.
This change again would yield arrival times from the intense region that did not correspond to asingle group velocity at different propagation distances.
A final example to demonstrate the potential issues is shown in Fig. 15. In this case for the
Res #1 sensor, the mode of the WT coefficient peak at 573 kHz changed from the A1 mode at 127
mm to the A0 mode at 305 mm. This change for a PLB near the bottom edge would not result inpeak-determined arrival times at this frequency for the same mode and group velocity when the
propagation distance changed. We note that the CWD results were similar for these three examples.
Since the intense mode/frequency combinations generated by AE sources in plates vary as afunction of source depth [1] and plate thickness [11], it is not possible to predict ahead of time
when a resonant sensor will yield poor results in the determination of accurate arrival times at aparticular intense frequency. One resonant sensor (Res #2) yielded accurate group velocity results
for the sources at both source depths. This result was likely due to the fact that the intense frequencies were located at points where there was a significant difference in the group velocities of
the two fundamental modes, and these frequencies were away from the intersection of all threemodes. Thus, the results of the current experiment imply that if one is planning to use
time/frequency analysis to obtain accurate mode arrival times, it does not make sense to use
resonant sensors unless some significant experimental effort is expended to determine the55

Fig. 13. Example of nearest mode at 147 kHz peak changing with propagation distance for resonant Res #3 sensor for PLB near bottom edge.

Fig. 14. Example of mode changing relationship in time to intersection of three modes at 594
kHz peak for resonant Res #1 sensor for PLB near mid-plane.

Fig. 15. Example of mode of peak changing with propagation distance at 573 kHz with resonant
Res #1 sensor for PLB near bottom edge.
frequencies that should not be used. But, if such effort is invested, then the results from resonant
sensors can be used to obtain accurate arrival times that correspond to a single group velocity.
Thus, accurate source locations could be calculated by use of resonant sensors with their typicaladvantage in sensitivity. This situation would enhance AE nondestructive examinations that depend on identifying clusters of events originating from a flaw in a structure that does not haveedge reflections (for example a large diameter sphere).
56

Potential Frequencies for the Sensors for the 3.1 mm Thick Plate
Based on the results in Tables 2 and 3 and the discussion relative to the frequencies that were
not included in the table, Table 4 provides potential frequencies (really the high-end frequency ofa frequency band) to use with the WT or CWD to obtain accurate arrival times from real AE signals with the different sensors in a 3.1 mm thick aluminum plate. For each sensor, the first line in
the table provides a frequency for WT analysis and the second line gives one for CWD analysis.
The suggested frequencies are those that were the most intense in the case of the wideband sensors. In the case of the resonant sensors, as can be seen from Tables 2 and 3 the suggested frequencies were not always the most intense for the Res #1 and Res #3 sensors. This table also suggests the frequencies to avoid to prevent obtaining inaccurate arrival times that do not correspondto the same mode arrivals at different propagation distances. Since there are two frequencies (except for one CWD case) associated with each sensor (for the two different depth regions), forreal AE testing it will be necessary to select the best frequency for each AE event and to verify
that the mode of the peak magnitude arrival time corresponds to the expected one at that frequency. Previous modeling work [14] has shown that the ratio of the peak magnitudes at the two
frequencies can be used to choose which frequency will provide the best signal-to-noise ratio. In
addition as shown in previous work [15], the group velocities from the two modes at that frequency can be used to verify the mode when source location calculations are made with eachgroup velocity (for the different source depths) using the determined arrival times. The velocity
that provides the smallest “Lucy” value [15] identifies the mode and results in the most accurate
source location. It is interesting to note in Table 4, that the two resonant sensors, which have frequencies to avoid, result in the frequencies from the intense signal region of the WT and CWD
being relatively close to each other, whereas, with the wideband sensors, the paired intense frequencies always resulted in the CWD frequency being significantly larger than the WT frequency.
Table 4 Suggested frequencies for arrvial times from the WT and/or CWD.
Sensor
FHWA
WB #1
WB #2
Res #1Res #2Res #3Frequency [kHz],mode for near
mid-plane source
405, A0463, A0351, S0371, S0375, S0397, S0306, S0307, S0510, S0527, S0252, S0251, S0Frequency [kHz],mode for nearplate surface
source60, A061, A060, A061, A0138, A0151, A0300, S0---441, A0466, A0
231, A0239, A0

Frequency [kHz]to avoid for near
mid-plane source------------------594610-------------

Frequency [kHz]to avoid for nearplate surface
source------------------93, 573
100, 581------147
151

On the Use of the WT and CWD to Determine Arrival Times for Thicker Plates
If one considers the situation for thicker plates where more modes will be present (in thenormal frequency range used in AE) and will be in closer proximity in time and frequency, the57

Fig. 16. Magnitude of CWD and WT coefficients versus time for nominal 60 kHz band, FHWA
sensor and near bottom edge source for propagation distance of 305 mm.
accuracy of the mode arrival-time results determined with the WT versus those determined with
the CWD may be different. In these thicker plates in the typical frequency range for AE monitoring, it may be expected that the CWD will be more likely to provide results not determined by a
combination of modes. This result is likely to be due to the sharper peaks of mode arrivals withCWD as a function of time at lower frequencies, as shown in Fig. 16. In this case for a near bottom edge PLB source with the signal from the FHWA sensor, the CWD peak corresponding to
the arrival of the A0 mode is sharper than that from the WT for the same signal. Thus there
would be less opportunity for adjacent mode arrivals at this frequency to result in a peak magnitude from more than one mode when the CWD is used.
Further, the potential superiority of the CWD in this plate may be due to sharper peaks at a
fixed signal time as a function of frequency at higher frequencies. As an example one can consider the related pair of intense arrivals from a near mid-plane PLB source and the FHWA sensor
data at a propagation distance of 305 mm. In this case, the WT magnitudes from the same arrival
time at 10 % above and 10 % below the peak frequency of 627 kHz (S0 mode) were respectively
90 % and 88 % of the peak magnitude at the peak frequency. In contrast, the CWD respective
magnitudes from the same arrival time at 10 % above and 10% below the peak frequency of 661
kHz (S0 mode) were respectively 4 % and 21 % of the peak magnitude at the peak frequency.
Finally, based on the current thin-plate results, it may be expected with resonant sensors that
there will be a set of frequencies to avoid in thick plates.
Conclusions
In the case of a relatively thin and large plate:• Use of either the WT or the CWD allows easy determination of the arrivals of the two
fundamental modes with both the resonant and wideband sensors used in this study.

58

•
•

•
•

•
•

Even though the different sensor models responded to nearly the same waves, there was awide range of intense frequencies for the signals from PLBs at each of the two source
depths.
The experimental group velocities as determined with all the sensors (with one exception)
were close to the theoretical values (less than 6.5 % difference) when certain intense frequencies of the resonant sensors were not used to obtain arrival times and group velocities.
Cases that were ignored with the resonant sensors primarily dealt with an inability toidentify any precise and/or consistent mode arrival at each propagation distance.
Typically the high intensity regions determined with the CWD occurred at a higher frequency compared with the WT-determined frequencies for each intense modal region.
This result is consistent with the fact that at higher frequencies the WT (in contrast to the
CWD) spreads the intensity from a single frequency signal into adjacent frequency regions.
At lower signal frequencies, the CWD magnitude results had sharper peaks (as a functionof time) at mode arrivals than the WT. In addition, it also had sharper peaks (as a function of frequency) than the WT at higher frequencies.
It might be expected that the CWD will provide more accurate arrival time results thanthe WT for thicker plates when more modes will be present and will be closer in time toeach other.

Acknowledgement
The loan of the Res #1 sensors from Prof. K. Ono was much appreciated.
References
1. M. A. Hamstad, A. O’Gallagher and J. Gary, J. of Acoustic Emission, 20, 2002, 39-61.
2. M.A. Hamstad, J. Gary and A. O’Gallagher, J. of Acoustic Emission, 20, 2002, 62-82.
3. K. S. Downs, M. A. Hamstad and A. O’Gallagher, J. of Acoustic Emission, 21, 2003, 52-69.
4. M.A. Hamstad, K.S. Downs and A. O’Gallagher, J. of Acoustic Emission, 21, 2003, 70-94 andA1-A7
5. M. A. Hamstad and A. O’Gallagher, J. of Acoustic Emission, 22, 1-21, A1-A7, 2004.
6. M.A. Hamstad and A. O'Gallagher, J. of Acoustic Emission, 23, 2005, 1- 24.
7. M.A. Hamstad, Advanced Materials Research, 13-14, 2006, 61-67.
8. M.A. Hamstad, J. Acoustic Emission, 25, 2007, 92-106.
9. M.A. Hamstad and C.M. Fortunko, Nondestructive Evaluation of Aging Bridges and Highways, Steve Chase, Editor, Proc. SPIE 2456, pp. 281-288, 1995.
10. M.A. Hamstad, Wood and Fiber Science, 29 (3), 1997, 239-248.
11. M. A. Hamstad, J. Gary and A. O’Gallagher, J. of Acoustic Emission, 16, (1-4), 1998, S251
to S259.
12. Vallen, J., AGU-Vallen Wavelet transform software version R2007.0309 and Vallen Dispersion software version R2006.1024, Vallen-Systeme GmbH, Icking, Germany (2007).
13. LabVIEW, Signal Processing Toolkit, Version 7.5, National Instruments, Austin, TX.
14. M.A. Hamstad, ICAE-6, Advances in Acoustic Emission, 2007, pp. 247-252
15. M.A. Hamstad, J. Acoustic Emission, 25, 2007, 194-214.

59

A COMPARISON OF AE SENSOR CALIBRATION METHODSJIRI KEPRT and PETR BENES
Brno University of Technology, Department of Control and Instrumentation, Kolejni 2906/4,
Brno, 612 00, Czech Republic
Keywords: AE sensor calibration, Reciprocity calibration, Step force calibration, Uncertainty
Abstract
The paper reviews the background of the primary calibration of acoustic emission (AE) sensors and the determination of uncertainty in the calibration. The main sources of uncertainty inpractical usage of calibration results are discussed. The comparison of the results of the reciprocity primary calibration, step-force and reciprocity calibration with broadband-pulse excitation
method is presented. The shape of calibration characteristics corresponds well up to 300 kHz.
The calculation of the uncertainty in all the calibration methods is described. The problem of
propagation of uncertainty in the fast Fourier algorithm was solved. Uncertainty of measurement
by primary calibration is determined and the influence of selected sources of uncertainty for eachused method of calibration is presented and practically measured results are discussed. Piezoelectric AE sensor PAC UT1000 was used as the sensor under test. The big influence on uncertainty in calibrations was the influence of remounting sensors and influence of precision of
measuring device. This influence is possible to suppress by correct and precise mounting of thesensors and using of more accurate measuring device with high resolution.
AE Sensors
Transducers used for AE measurement are in general sensitive to motion normal to the surface, to which they are attached. Typically, AE transducers are sensitive to frequencies above
100 kHz. Resonant transducers are highly sensitive to a narrow frequency range, which must be
carefully selected depending on the application. Resonant transducers in the range of 150 to 300
kHz are probably the most widely used. The highest frequencies likely to be of interest to usersof AE transducers are in the range from 800 kHz to 1 MHz.
There are several ways how this transduction can be achieved. The piezoelectric effect, capacitance methods and optical interferometry are common techniques used for the detection of
AE signals. Piezoelectric devices offer the greatest sensitivity and thus they are the most widelyused type of transducer in AE applications. Interferometers and capacitance transducers are often
used as reference sensors in the calibration of piezoelectric AE transducers.
AE Sensor Calibration
A main problem of the calibration is to find the characteristic of the transducer. A frequency
response of a specific sensor in the mechanical input quantity (velocity, displacement) is themost common result of calibration. Output quantity of calibration is voltage relate to unit of mechanical input. The absolute value of input quantity and its shape has to be known for primarycalibration. Good metrology of the AE calibration method is necessary to be able to compare theresults of calibration made by other laboratories or to compare the effects of aging, thermal cycling and so on.
J. Acoustic Emission, 26 (2008)60© 2008 Acoustic Emission GroupStep-function calibration
ASTM E1106 [1] and ISO 12713 [2] outline a step-force method for primary calibration of
AE sensors. The basis for the step function force calibration is that known, well-characterized
displacements can be generated on a plane surface of a test block. A step function force applied
to a point on one surface of the test block initiates an elastic disturbance that travels through theblock. In general it is possible to use configuration of sensor for surface calibration and forthrough-pulse calibration.

Fig. 1 Setup for step-function calibration.
It uses a standard reference capacitance transducer and the step-force is generated by thefracture of a glass capillary. The response of the sensor being calibrated to the step-force sourceis then compared with the reference transducer, which measures the surface displacement due tothe elastic surface waves. The displacement at the position of the reference transducer can also
be calculated using elastic theory – Pekeris’s solution of Lamb’s problem. The surface motion on
the transfer block, determined using either technique, is the free motion of the surface and not theloaded surface displacement, under the transducer being calibrated. The loading effect of thesensor being calibrated, therefore, affects the measurement being made and thus becomes part ofthe calibration.

Fig. 2 Workspace for primary calibration.
61

The measured data are used to calculate a fast Fourier transform to determine values of thespectra from unknown and reference sensor. The response of the transducer under test is as follows:U ( fm )D( f m ) =(1)
S( fm )
where U(fm) is spectrum from unknown sensor and S(fm) is spectrum from standard sensor orfrom solution of Lamb’s problem.
It is generally assumed that a transducer has only normal sensitivity because of its axial
symmetry (an assumption that may not be justified). Calibration by the surface pulse techniquefor a transducer having significant sensitivity to tangential displacement will be in error, because
the surface pulse from the step force contains a tangential component approximately as large as
the normal component [3].
Reciprocity calibration
NDIS 2109 [4] outlines reciprocity calibration. Reciprocity calibration works on reciprocity
theorem that is known from electrical circuits. This principal can be use for electromechanicalsystem and makes relation between transition of the sensor acting as source and later as receiver.
Reciprocity applies to a category of passive electromechanical transducers that have two important characteristics - they are purely electrostatic or purely electromagnetic in nature and they arereversible – can be used as either a source or a receiver of mechanical energy. This category includes all known commercial AE transducer without preamplifiers [3].
The input current and reception signal voltage for tone bursts of varying frequency are established for each pair together with the reciprocity parameter, allowing each transducer to be calibrated by measuring electrical signals only. The transducer characteristics are defined as thetransmission voltage response in the transmitter configuration and the free-field voltage sensitivity in the receiver configuration.
USB

CALIBRATION
SIGNALGENERATOR
HP 33220A

CURRENT PROBETEKTRONIX P6022

CHANNEL 1
VECTOR
SIGNALANALYZER
HP 89410A

AE AMPLIFIER
30 dB
MATRIXSWITCHPC
WITH GPIB
GP IB

INTERFACECHANNEL 2

STABILIZEDVOLTAGE SOURCE
ALU 310

AE SENSORSTESTED BLOCKSTEEL CYLINDER
diameter 94 cm, height 44 cm

Fig. 3 Schema of workspace for reciprocity calibration with HP 89410A.
62

The primary advantage of the reciprocity calibration technique is that it avoids the necessityof measuring or producing a known mechanical displacement or force. All of the basic measurements made during the calibration are electrical. It is important to note that the mechanicaltransfer function or Green function for the transmission of signals from the source location toreceiver location must be known. This function is equivalent to the reciprocity parameter, that
describes a transfer function of a Rayleigh-wave and it takes into account the frequency of theRayleigh wave and the material properties of the propagating medium. It is the frequency domain representation of the elasticity theory solution [3]. Equation 2 shows the calculation of frequency response for sensor 2.
F2 ( f ) =1 U 12 ( f ) U 23 ( f ) I 31 ( f )H ( f ) I 12 ( f ) I 23 ( f ) U 31 ( f )(2)

U: voltage [V], I: current [A], H: reciprocity parameter [m.s-1.N-1] according to Hatano [5].
Reciprocity calibration with broadband impulses
This method is based on a method of reciprocity calibration, modified by Goujon and Baboux [6]. Their method was supplemented by using more than one excitation and final characteristics were calculated from multiple measured characteristics.
The experimental setup is similar to usual reciprocity calibration. When the sensors are working as transmitters, the transducers are driven with a short-pulse excitation (single-period sinusoid or single period Gaussian). The excitation of sensor is provided for example at 100 kHz, 200
kHz … 1 MHz. The voltages and currents required for reciprocity calibration by Hatano [5] arethen calculated from the fast Fourier transforms of the signals recorded with high sampling frequency. Sensitivities of the sensor are calculated for each excitation. The final characteristics foreach sensor is calculated point to point by weighted mean of sensitivities of the two nearest excitation frequency to the calculated point. For example the final point of sensitivity at 110 kHz was
calculated as 0.1 * 110 kHz (200 kHz excitation) + 0.9 * 110 kHz (100 kHz excitation).
This broadband excitation allows a better discrimination between the direct signal and theechoes against the borders [6]. This method of the calibration is much faster than method according to standard reciprocity calibration and can be used to proof quality of sensor’s mounting on
the surface before usual standard reciprocity calibration measurement.
Comparison of the results for different calibration methods is shown on Fig. 4. This figure
shows three measured characteristics for an AE sensor, PAC UT1000, mounted on the surface ofa steel block. The orange curve shows results from step-function calibration, the black from reciprocity calibration and the blue from reciprocity calibration with broadband-pulse excitation.
Analysis of Uncertainty in AE Sensor Calibration
Analysis of uncertainty in step function calibration
Calculations of determination of uncertainty for the step function calibration result from following basic equation for calculation of sensitivity of sensor:
FFT (U cal )(3)
FFT (U ref )Ucal is voltage of calibrated sensor [V], Uref is voltage on reference sensor [V], or determined by
calculation.
U=

63

Fig. 4 Results of three methods of calibration of PAC UT1000.
Main problem was to determine the propagation of uncertainty in Discrete Fast Fourier
Transform algorithm (FFT). The calculations follow [7]. Equation for FFT is
N "1

X ( k ) = ! x ( n) # e

"j

2$
nk
N

(4)

n =0

General complex sequence X(k) can be described as X(k) = R(k) + j I(k). Spectrum modulus
is calculated as:
M (k ) = X (k ) = R 2 (k ) + I 2 (k )(5)

Amplitude of first frequency point
V0 = M(0)/Nand subsequent points
Vim = 2M(i)/N
Uncertainty of modulus M can be determined [7] as:

(6)
(7)
(8)

The partial derivatives according all variable were calculated

The uncertainty of type A and B was calculated for calibrated sensor and following combined
uncertainty(11)
Finally the expanded uncertainty with coverage factor 2 was calculated.
64Analysis of uncertainty in reciprocity calibration
Calculations for the uncertainty of the reciprocity calibration are explained for second sensor.
The equation (2) shows calculation of frequency response for second sensor.
The partial derivatives according all variable were calculated. For example for variable U12
Eq. (12) and I12 Eq. (13):!F2 ( f )=!U 12 ( f )U 23 ( f ) I 31 ( f )1•
H ( f ) I 12 ( f ) I 23 ( f )U 31 ( f ) 2 U 12 ( f )U 12 ( f )U 23 ( f ) I 31 ( f )"F2 ( f )1=•!
"I 12 ( f )H ( f ) I 23 ( f )U 31 ( f )2 U 12 ( f ) 3

(12)
(13)

Evaluation of the uncertainty type A is based on a series at least of 10 measurements. Experimental standard deviation was used as an uncertainty of type A for measurements of voltage
and current. Uncertainty of current probe was determined from measurements of probe characteristics and from manual.
Main source of uncertainty of type B was vector signal analyzer HP 89410A. Its absolute
amplitude full-scale accuracy is ±0.5 dB from full-scale [8]. Also the uncertainty of current
probe was included and uncertainty of AE signal amplifier was included. For measuring devices
PXI 5122 by National Instruments and Handy Scope 3 by TeePee the uncertainty of type B wasdetermined according to manual of producer.
Because of simultaneous measurements of voltage and current for each pair it was assumed
that at least two input quantities are interdependent. So the correlation for each combination ofvariables was calculated according to [9]. The assumption that the variables are correlated was
not confirmed and from calculations followed, that the calculated covariance was negligible. Expanded uncertainty was calculated with the value of coverage factor 2.
Analysis of uncertainty in reciprocity calibration with broadband pulse excitation
The main problem was to determine uncertainty of calculated currents and voltages. Because
these variables were calculated from FFT, so the determination of uncertainty of modulus was
the same as in equation (8) according to [7]. These values were supplement to calculations as theuncertainties of voltages and currents and following calculations were the same as for standard
reciprocity calibration.
Influence of Sources of Uncertainty on Step-function Calibration
Measuring device was driven by a trigger of 0.05 V and 200 µs of input signal was sampled.
Signal of the capillary break was calculated by MatLab with the same length and sampling parameters as real signal and validated by the noisy signal from interferometer. Signal generated
from MatLab was recalculated from displacement to velocity. Final characteristics are calculated
according to equation 1. Calculations of uncertainty for the step-function calibration result were
determined. Main problem was to determine the propagation of uncertainty in discrete fast Fourier transform algorithm (FFT). The calculations follow [7].
Third column shows the mean of uncertainties [%] in range from 60 to 300 kHz and thefourth column for range 300 kHz to 1 MHz. The first row shows the influence of thickness of65

Fig. 5 Results of three methods of calibration of PAC UT1000 with uncertainties.
Table 1. Comparison influences on uncertainty of various sources in step function calibration.
Source of uncertainty
1
2
3

thickness of capillary
speed of capillary breakrotating with UT1000

Median uncer.
type A (region 1)
6.7
4.2
5.1

Median uncer.
type A (region 2)
10.3
7.1
8.0

Max uncer.
type A
17.0
11.0
25.2

used capillary from 0.11 to 0.32 mm. The second row shows the influence of speed of breaking
capillary. It varies from slow to fast and the third shows the influence of slewing with reference
sensor.
Influence of Sources of Uncertainty on Reciprocity Calibration
The analysis of influence of sources of uncertainty on measured characteristics was done. To
be able to compare the impact of the sources to the final characteristics the uncertainty type A
was calculated from the experimental standard deviation of final calculated sensitivities. Calibration of UT1000 (PAC) was measured many times on the same conditions and with the sameequipment and only the one condition was change to determine uncertainty of this source. The
results were summarized to following Table 2.
Evaluation of the uncertainty type A is based on a series at least of 10 measurements. Experimental standard deviation was used as an uncertainty of type A for measurements of voltage
and current. Uncertainty of current probe was determined from measurements of probe characteristics and from manual. Main source of uncertainty of type B was the vector signal analyzer,
HP 89410A. Its absolute accuracy in amplitude is ±0.5 dB at full-scale [8]. Also the uncertainty
of current probe was included and uncertainty of AE signal amplifier was included. For measuring devices PXI 5122 by National Instruments and Handy Scope 3 by TeePee the uncertainty oftype B was determined according to manual of producer.
66

Fig. 6 Influence of slewing with UT1000 sensor on step function calibration.

Fig. 7 Uncertainty of slewing with UT1000 sensor on step-function calibration.
Third column of the following Table 2 shows the mean of uncertainties [%] in region 1 (60 300 kHz) and the fourth column for region 2 (0.3 - 1 MHz). Step of the measurement was 5 kHz.
The duration of the driving signal was 100 µs according to the size of the testing block. The firstrow shows the influence of used channel on matrix switch. Second was measured with and without weight (normal force 10 N). Third shows influence of temperature. The case of sensor was
raised from 25 to 60˚C and the temperature was measured by surface temperature sensor. Fourthshows the influence of setting time of couplant, the value being calculated from more than ahundred measurements during two days.
Fifth row in Table 2 shows the influence of amount of couplant from none to excessive. The
sixth row shows influence of remounting reference sensor, UT1000. The other sensor lies for a
few days. Seventh shows the influence of remounting pair sensors (K2G (s.n. 58507-00797 by
Krautkramer) and Aura (s.n. SV416-416004 by Aura Milevsko). The reference sensor lies for a
few days. Eighth shows the influence of slewing with reference sensor. Ninth shows the influence of moving with weight on the top of the mounted sensor. Tenth shows the influence of incorrect positioning on the surface. The reference sensor was positioned from 2 to 10 cm from thecorrect position to the direction opposite to direction to the center of triangle of sensor and67

reciprocity parameter was calculated for correct position. One of the largest influences was theremounting of the pair sensor.
Table 2 Comparison influences on uncertainty of various sources in reciprocity calibration.
Source of uncertainty
1
2
3
4
5
6
7
8
9
10

used channel on matrix switcher
with and without normal force
temperaturetime stability of couplantamount of binding paste
remounting of UT1000 sensor
remounting of pair sensors
slewing with sensor
moving with weight
incorrect position of pair sensors

Median uncer.
type A (region 1)
0.4
0.1
0.7
0.1
13.8
5.1
8.3
1.6
2.9
6.1

Median uncer.
type A (region 2)
0.3
0.3
2.4
0.2
9.3
5.4
13.7
4.9
3.5
9.2

Max uncer.
type A
0.7
1.7
8.1
1.6
16.7
17.1
31.0
11.2
11.9
21.7

Fig. 8 Influence of remounting of pair sensor on reciprocity calibration.

Fig. 9 Uncertainty of remounting of pair sensor on reciprocity calibration.
68

Comparison of Uncertainties with Error Requirements in ASTM 1106
Figure 10 shows the results of combined uncertainties for all the methods of calibration related to final characteristics of each method. So the results are presented in percent of final characteristics. The orange curve shows errors from step-function calibration, the black from reciprocity calibration and the blue from reciprocity calibration with broadband-pulse excitation.
The error for step-function measurement and reciprocity measurement with broadband-pulse
excitation is low up to 300 kHz and then it grows. The main peaks in error are on defined frequencies caused by aperture effect of finite length of sensor surface of PAC UT1000 having theradius of 0.775 cm. Zero points calculated according to [1] are 237, 433, 628 and 823 kHz forRayleigh wave speed 3006 m.s-1 for our testing block.

Fig. 10 Combined uncertainties for different calibration methods.
ASTM 1106 [1] and ISO 13713 [2] present that the error of measurements between 100 kHz
and 1 MHz should be up to ±10%. The data from repeated calibration with remounting sensor
should be collected and the overall system should produce calibration with precision of ±15%.
From Fig. 10 follows that previous assertion is valid for range 60 kHz to 300 kHz. On higher
frequencies the sensitivity of the sensor is too low and uncertainty grows. For example, at 70
kHz the sensitivity is 62.5 dB ref. 1 V/(m/s) and at 300 kHz the sensitivity is 24.1 dB ref. 1
V/(m/s). So the difference is 38.4 dB ref. 1 V/(m/s). The final uncertainty of step-function calibration and reciprocity calibration with broadband-pulse excitation is in fact dependent on valueof sensitivity of sensor on a certain frequency.
Conclusions
The paper reviews the background, the methodology and the standardization of the primarycalibration of AE sensors. The reciprocity calibration, reciprocity calibration with broadband
pulse excitation and step-function method of absolute calibration were practically realized inlaboratory of vibro-diagnostics at Brno University of Technology. The whole experiment was
69managed by PC with LabVIEW 8.5. The software and measuring apparatus enables primarycalibration of AE sensors by reciprocity method according to NDIS 2109 [4] and by stepfunction method according to ASTM 1106 [1]. Approximately 1600 calibrations and 100 different sensors were measured.
The comparison of the results of all of the method is presented on Figs. 4 and 5. The shapecorresponds well up to 300 kHz. The method of reciprocity calibration with broadband-pulse excitation was based on the modification of reciprocity calibration by Goujon and Baboux [6].
Their method was supplemented by using more than one excitation on different frequencies andfinal characteristics were calculated from more than one measured characteristics. The experimental setup is similar to usual reciprocity calibration. This method of the calibration is much
faster than usual reciprocity method.
The results correspond well with results from basic reciprocity calibration, but the uncertainty is worse especially on higher frequencies where the sensitivity of UT1000 is lower. So themethod can be used to verify the quality of sensor’s mounting on the surface before usual reciprocity calibration measurement.
The uncertainty of all methods was determined. PAC UT1000 (s.n. 169) was used as the sensor-under-test. The problem of the propagation of uncertainty in discrete fast Fourier transform
algorithm was solved. For measurements we used more accurate measuring devices than HP
89410A – PXI 5122 by NI and Handy Scope 3 by TeePee. PXI – 5122 by National Instruments
was most cost-effective. It has the sampling frequency up to 100 MHz, good dynamic range andaccuracy better then HP. Handy Scope 3 is unsuitable for measurements of reciprocity methodwith broadband-pulse excitation and step-function calibration due to its poor dynamic range andlower sampling frequency and resolution.
The main sources of the uncertainty were described and its influence to uncertainty is presented. The big influence on uncertainty in reciprocity calibration was the influence of remounting reference sensor and pair sensors. By correct and precise mounting of the sensors, this influence can be reduced. The repeatability of the step function calibration is in general worse than inreciprocity calibration for high frequency range up 300 kHz. Noise is crucial, so we recommend
using of digitizers with high resolution. For example NI 5922 with 24-bit flexible resolution.
Acknowledgment
This research has been supported by Grand Agency of the Czech Republic under the contract
GACR 101/06/1689 and by the Czech Ministry for Education in frame of the Research Intention
JC MSM 0021630529: Intelligent systems in automation.
References
1. ASTM Standard E1106-86: Standard Method for Primary Calibration of Acoustic Emission
sensors, 1986, ASTM, Philadelphia.
2. ISO 12713: 1998(E) Non-destructive testing - Acoustic emission inspection - Primary calibration of transducers.
3. Miller, R. K., McIntre, P.: Acoustic Emission Testing (Nondestructive Testing Handbook, Volume 5), American Society for Nondestructive Testing, 1987.

70

4. NDIS 2109-91: Method for Absolute Calibration of Acoustic Emission Transducers by Reciprocity Technique, The Japanese Society for Non-Destructive Inspection, 1991.
5. Hatano, H., Chaya, T., Watanabe, S., Jinbo, K.: Reciprocity Calibration of Impulse Responsesof Acoustic Emission Transducers, IEEE Transactions UFFC, 5 (45), September 1998, 1221 –1228.
6. Goujon L., Baboux J. C.: Behaviour of acoustic emission sensors using broadband calibration
techniques, Measurement Science Technology, 14, 2003, 903 – 908.
7. Betta G., Liguori C., Pietrosanto A.: Propagation of uncertainty in a discrete Fourier transform
algorithm, Elsevier Measurement 27, 2000, 231 – 239.
8. HP 89410A/HP 89441A Operator's Guide, Hewlett-Packard Company, 1998.
9. Laaneots R., Mathiesen O.: An introduction to metrology, TUT Press, 2006.

71

TRANSFER FUNCTIONS OF ACOUSTIC EMISSION SENSORS
1KANJI ONO1, HIDEO CHO2 and TAKUMA MATSUO2
University of California, Los Angeles, Los Angeles, CA 90095, USA;
2
Aoyama Gakuin University, Sagamihara, Kanagawa, Japan

Keywords: AE sensors, transfer functions, plane waves, spherical waves, pulse-laser excitation,
deconvolution, interferometer
Abstract
We have obtained the transfer functions of a wide range of AE sensors commonly available
and utilized. These were determined by the excitation of input waves using an ultrasonic transmitter or pulse-laser in conjunction with a transfer block, a laser interferometer and a deconvolution procedure typically in the frequency domain. Sensor responses depend on the wave types;
i.e., plane waves, and spherical waves with different radius of curvature. Using typical source
waveforms and a convolution procedure, one can then visualize waveforms expected out of these
AE sensors. Some sensors showed displacement response, while another gave velocity response.
These response types also depended on incident wave types, requiring additional characterization
steps in sensor calibration procedures.
Introduction
Acoustic emission (AE) sensors play a crucial part in determining AE behavior of materials
and structures. The characteristics of AE sensors control the waveforms of AE signals detected.
Manufacturers usually provide the sensitivity and frequency response of an AE sensor in termsof velocity response in reference to 1 V/µbar or 1 V/m/s. Here, 1 V/µbar can be converted (using aluminum dilatational wave velocity of 6.15 mm/µs) to 1.1 x 108 V/m/s. Typical peak sensitivity values are about –65 dB in reference to 1 V/µbar for normally incident waves and correspond to 6 x 104 V/m/s. Some sensors produce the output proportional to acceleration, while others respond to displacement. These are calibrated differently and reported in mV/Gal or in reference to 1 V/µm, for example [1]. Such information is useful in sensor selection for laboratory
work and for field applications, but is inadequate to characterize the input since a sensor response depends on frequency and other conditions. Thus, in order to understand mechanical incident waves that generated AE waveforms, we need the transfer function of an AE sensor.
Let us consider mechanical input, x(t), to a sensor and assume x represents displacement as afunction of time, t. x(t) is normal surface motion at the epicenter and is generated on the sensing
surface of a transfer block by a laser or a piezoelectric transmitter source on the opposite face (at–Xo). See Fig. 1. Voltage output from the sensor, y(t), is given by the convolution of the inputx(t) and the impulse response of the sensor, h(t), as∞y(t) = ∫ h(λ )x(t − λ )d λ

(1)

−∞

The Fourier transform of h(t), or H(jω) where j2 = –1 and ω is angular frequency, 2πf, is known
as the transfer function or system function and relates the input and output in the frequency domain. Here, we use the time domain representation, h(t), with displacement input as the transfer
function. This allows us to simulate expected waveforms corresponding to arbitrary input functions. Furthermore, we limit the input to be displacement of normal incidence to the sensor face.
J. Acoustic Emission, 26 (2008)72© 2008 Acoustic Emission GroupFig. 1 Experimental schema. Wave excitation from the bottom surface of transfer block, thickness Xo (100-mm and 25-mm plates used). Sensor placed at the epicenter on the sensing surface
side. Relative changes in the radius of curvature for the two plates shown.
Dependence on incident angle is expected and will be examined in the future. It is also possible
to define a similar function for velocity or acceleration applied to the sensor face. However, it is
not feasible to consider surface-wave input or sensor response at this time even though surface
waves are important in AE signal detection and analysis.
In the above definition of the transfer function, x(t) was treated as scalar. In reality, any sensor face has a finite area, typically circular with radius, ro. We must consider spatial dependence
and define the surface displacement as x(r, t), where r is the distance from the epicenter assuming
circular symmetry (cf. Fig. 1). In turn, x(r, t) is given by the convolution of the impulse response(Green’s function) of the transfer block (of thickness, Xo) and forces applied at the opposite face.
The forces are dynamic and can be applied at a point (focused laser sources) or distributed overan area (piezoelectric sources). When a point source is used, the waves spread spherically to radius of Xo or larger as they reach the calibration surface where the sensor is located. The thickness of the transfer block is a parameter we need to consider since it defines the radius of spherical waves exciting a sensor. Using a suitable distributed source, plane waves of limited size canbe produced in the so-called near-field region, as is well known in ultrasonic testing. It would be
convenient to use if a single universal transfer function is obtained for a sensor. At this stage, thisis not feasible and we will consider below specific transfer functions for three input wave types,
one for plane wave condition and two for spherical wave conditions. The latter two apply to AE
signals that travel distances of Xo from source to sensor.
The transfer function concept was utilized in fundamental studies of AE source functions andprovided valuable insight to the nature of the origins of AE. A well-characterized sensor, such asa NIST capacitive or conical sensor was used, and sensor outputs were analyzed by a deconvolution procedure, and the AE source functions were deduced. In these studies, the breakage of glass
capillary usually provided the calibration source of a step force of a short rise time of ~1 µs, generating stress waves with a known theoretical basis. See ASTM E-1106, which serves as theprimary standard for all AE sensor calibration (for the surface waves) in the US [1]. Some efforts
to get the transfer functions via frequency domain procedures were reported [2], but no systematic study is known. Manthei [3] used a procedure similar to one at NIST and obtained experimental calibration response to glass capillary breaks for lab-built sensors and a reference sensor(Panametrics V103). The results were in the form of output peak voltage per unit force for the
body waves of normal incidence due to glass capillary breaks. By comparing the predicted73

epicentral velocity signal for a glass capillary break with the observed, he concluded these sensors to be of velocity-sensitive type. At this stage, the velocity was not measured, so the result isuseful mainly for relative calibration. It is noted that the initial single-sided peak has the halfheight width of 1.9 µs (Fig. 5 in Ref. 3). This is comparable to the width expected from the resonance frequency (1 MHz) and a typical glass capillary break of 1 µs [4], though the pulse length
may be extended by the use of plexiglas as the transfer block that attenuates the high frequency
components. His finding is consistent with the expected velocity response near sensor resonance.
Some AE sensors are referred to have velocity response or resistance controlled. Others may
be of displacement response or mass controlled. The third type is acceleration response or stiffness controlled. Such characterization is based on a simple one-dimensional analysis of adamped oscillator, which is used to represent a piezoelectric sensor [5, 6]. In this most simplistic
model, a sensor is considered as a mass (m) connected to a Voigt model (a spring (k) and a dashpot (η) in parallel connection). Taking sensor frame displacement as x and frame-mass relative
displacement as y, we obtain (with a super-dot indicating a time derivative)(2)
Applying sinusoidal vibration at f, we obtain the following:( f / f o )2(3)y=x
1 − ( f / fo )2 + j( f / fo ) / Q
where fo = (1/2π)(k/m)1/2 is the natural frequency and quality factor Q = (mk/η)1/2. In approximation, we have(4)
Equation (4) then predicts an acceleration response (stiffness controlled vibration) at lower frequencies, a velocity response (resistance controlled vibration) near the natural frequency, whiledisplacement response (mass controlled vibration) is expected at higher frequencies. Note that
this analysis was initially applied for a mass-loaded spring seismometer and is useful for a single-mode vibration sensor. We need caution in applying this analysis or results in typical AE
sensors, in which cross-coupled modes are commonly exploited for improved sensitivity. Besides, AE signals are transients and the above analysis is for steady state oscillations.
More advanced transducer analyses are given in Mason [7], where equivalent circuit models
are developed for various boundary conditions. The simplest case is for a free resonator and isrepresented by a series of capacitor, inductor and resistor in parallel to a capacitor. This isequivalent to the above mechanical model.
Once the transfer function of an AE sensor is known, a convolution procedure (Eq. (1)) allows one to simulate the sensor output signals using different input signals. This in turn provides
the insight to original source mechanisms of AE. In order to obtain the transfer function, one
needs a repeatable wave source and a transfer block of adequate size. It is also essential to determine the displacement (or velocity) of the surface on which to install a sensor under test. A
classic setup was used at NIST using a glass capillary break and a capacitive transducer [1, 4]. A
steel cylinder of 90-cm diameter and 43-cm high was the transfer block. In our initial work previously reported [8], we used a setup, consisting of a pulse laser, an aluminum plate (25-mm
thickness) and a laser interferometer. In this work, we added a larger cylindrical aluminum block74

(100-mm thickness x 300-mm diameter) and also used a piezoelectric ultrasonic sensor as atransmitter. Despite the block size limitation, this still provides information useful in understanding AE waveforms encountered in many AE studies. Another significant development is the consideration of plane waves and spherical waves from a point source as input. These required separate approach to get the transfer function of AE sensors specific to an input wave type. It wasalso found that various vibration modes of a sensor are activated over time and depending on the
excitation wave types. This necessitates serious reconsideration of sensor calibration methods inthe future.
Experimental
The initial source of impulse was a pulsed YAG laser and the output was ~2 mJ, focused on
0.5~1-mm-diameter area. Unfocused laser pulses were also used and a 4~5-mm diameter ringlike zone had high beam intensity. In the previous work, laser pulse impinged on an aluminum
plate of 25 mm thickness. This resulted in a sharp displacement pulse on the opposite side, having the peak width of 50 ns at half height and the peak displacement amplitude of 1.4~1.9 nm(Fig. 2a) depending on the laser output. Three additional reflected pulses follow, but these are11, 20 and 24 dB below the initial pulse height. Two larger blocks were added in this study.
These are 300-mm diameter cylindrical shapes of 100-mm or 500-mm thickness. The largest
block turned out to be a wrong geometry and produced extended echo-trains almost submerging
the primary signals. Thus, the present work mainly utilized the 100-mm thick block. Displacement waveform on the opposite surface is given in Fig. 2b for the focused laser beam. The halfheight peak width was 25 ns and was a half that of the 25-mm plate case. The extrapolated basewidth was 48 ns and the main motion of the waveform can be represented by a symmetrical triangular pulse of 48-ns duration. This shape has wide frequency content, well beyond 10 MHz
and frequency at -3 dB point is 13 MHz. Unfocused beam produced similar pulse widths.
Effects of beam spread are examined. Relative changes in the radius of curvature are shownin Fig. 1. From a point source to a typical 13-mm diameter sensor, one expects 35 or 140 ns delay for 100 or 25 mm plate, causing significant changes in the displacements on the sensor surface. Figure 3 shows displacement measured on the 100-mm block from unfocused laser source.
Despite the spread at the source, the peak amplitude decreased and arrival time was delayed. At
12 mm off-center, the peak amplitude was ~1/3 and the peak delay was 200 ns. Assuming a circular symmetry, one can simulate the averaged displacement response over aperture area of different diameter. Using the data of the epicenter (marked 0 mm in Fig. 3), 2-, 4- or 6-mm offcenter, area-averaged displacement was calculated for 2-mm, 6-mm, 10-mm, or 14-mm diameter
aperture. Here, the displacements were summed in proportion to the areas of circular zones of 13, 3-5 and 5-7 mm radii. Results are shown in Fig. 4. The peak amplitude increased with larger
aperture and resulted in 25-times higher peak value for 14-mm size over the peak value of 2-mm
size. Peak width at base also broadened from 50 ns to over 250 ns. While displacement sequenceor locations were ignored, the results can approximate averaged displacement response. Thus,
capacitance sensors with a given area should produce outputs similar to those in Fig. 4. In piezoelectric sensors, however, arrival phases are expected to affect the sensor output substantially.

75

Fig. 2 (a) Epicentral displacement pulse from a focused laser, 25-mm plate. (b) From a focusedlaser, 100-mm plate. (c) From FC500 piezoelectric transducer (2.25 MHz, 19 mm aperture). See
insert for the initial 1.8-µs portion. FC500 excitation is about ten times broader than laser.
In order to enlarge the size of source and to obtain plane waves on the sensing surface, a piezoelectric ultrasonic sensor was used as a transmitter. We used AET FC500, which has nominal
resonance frequency of 2.25 MHz and 19-mm aperture size. It was driven by a negative-goingstep of 1-µs duration (430 V/µs peak slope over 0.7 µs). This produced a displacement waveformon the opposite surface as shown in Fig. 2c. This waveform is much broader than the laserinduced waves. The main motion is a triangular pulse of 240-ns rise and 360-ns trailing times asshown in the insert of Fig. 2c and is limited in its bandwidth. Frequency at -3 dB point is 1.2
MHz. The uniformity of the surface displacement can be seen in Fig. 5, where a PAC Pico sensor
was placed at the epicenter (0 mm), 4-mm or 6-mm off-center. Amplitude was within ±1 dB andthe phase was essentially identical. 2-mm off-center data was identical to the epicentral one and76not shown. Thus, this source can be utilized for plane-wave input to a sensor of 13-mm diameteror less.

Fig. 3 Displacement measured on the 100-mm block from unfocused laser source. At epicenter(0 mm) the peak is the sharpest and highest, broadening and lowering with offset of 2, 4, 6 and
12 mm. With larger offset, peak arrival was also delayed. (x-axis length: 0.6 µs)

Fig. 4 The averaging of displacement response for larger aperture-size receivers. The epicentral
response is used for 2-mm aperture, while displacements measured at larger offset were proportionately summed. (x-axis length: 0.6 µs)
The normal displacement was detected by a laser interferometer (Thales Laser S.A., SH-140;
dc-20 MHz). A sensor under test was placed opposite the laser impingement or FC500 transmitter and its output recorded by a digitizer with 100x signal averaging. Typical sampling frequency
was 250 MHz, but was also at 100 MHz or 1 GHz. Acoustic couplant was silicone grease(HIVAC-G, Shin-etsu).

77

Fig. 5 Similarity of surface displacements produced by AET FC500 on 100-mm block and detected by a PAC Pico sensor placed at the epicenter (0 mm), 4-mm or 6-mm off-center.

Fig. 6 The transfer function of a PAC Pico sensor (s/n 3804) calculated from the epicentral dataof Fig. 5 and surface displacement produced by AET FC500 on 100-mm block (Fig. 2c).
Results
1. Transfer functions
The transfer function of a sensor was obtained from its response to excitation and the corresponding displacement waveform using a frequency-domain deconvolution procedure. Noise in
the sensor response before the arrival of excitation signals was eliminated and cosine taper window was applied first. Note also that digital data was 5-MHz low-pass filtered before the deconvolution was applied. A Butterworth low-pass filter (<30 MHz) was applied on the deconvoluted
transfer function. Some high-frequency noise still remained in some results, making several ofthem irrelevant. The amplitude of transfer functions is in the unit of V/ m·s. The results given invarious figures in this work are in terms of V/m·(time step), with 10-ns time step. In taking the78

convolution integral, this time step is multiplied; thus, the values given here must be reduced by
108 in using them with the standard unit of V/m·s.
a. Pico sensor: The waveform due to FC500 excitation taken by a Pico sensor (PAC, ser. 3804)
given in Fig. 5 indicates initial oscillations of 0.7 µs duration (1.4 MHz) followed by 5 cycles at~2.2 µs period. The latter corresponds to the main resonance of this sensor at 450 kHz. Shown in
Fig. 6 is the transfer function of this sensor, which shows a bipolar pulse of 0.74 µs duration.
Two additional cycles can be discerned in noise. Thus, both the sensor output and the transfer
function exhibit velocity response to unipolar displacement input at 1.4 MHz. The main 450-kHz
resonance of this sensor is, however, difficult to recognize in the transfer function, partly becauseof large high-frequency noise remaining after low-pass filtering. It is also impossible to assign
response type at 450 kHz.

Fig. 7 (a) Waveform and (b) transfer function of a Pico sensor (s/n 3804) obtained using focusedlaser and 100-mm transfer block. (x-axis length: 10 µs)
It is also possible to define a transfer function even when the acoustic input waves are spherical. Figure 7 shows the waveform and transfer function obtained using 100-mm transfer block.
Since the displacement input (Fig. 2b) has a narrow pulse width (25 ns at half height), the waveform and transfer function resemble each other well except for higher noise in the transfer function. The shape is closer to the velocity response with the first negative peak being 3/4 height of
the first positive peak. The main 450-kHz resonance of this sensor is also clearly visible. Since
this was obscure in the plane-wave input case above, this may be due to the spherical nature ofincident waves. This may also cause the broadened first peak (0.45 µs) that has 4~5 times thehalf height peak width shown in Fig. 6b. These two effects are difficult to rationalize on the basisof spherical waves, however.
Effects of the radius of curvature of spherical incident waves are substantial. Previously, we
reported a transfer function using 25-mm thick block [8]. This is shown in Fig. 8. The highest
peak is the third one and it is obvious that multiple incident modes contributed to this complex
transfer function. The main 450-kHz resonance of this sensor is most evident and the first bipolar
pulse can be considered to exhibit velocity response.

79

From the three transfer functions of a single Pico sensor, it is evident that the nature of incident waves strongly affects the sensor response. It also shows that the type of sensor response,
velocity or displacement, for example, must identify the input condition.

Fig. 8 Transfer function of a Pico sensor (red), which accounted for three reflected laser pulses.
First-peak only (blue) curve is essentially same as measured waveform for t<30 µs. [8]Fig. 9 (a) Waveform and (b) transfer function of a V1030 sensor (10 MHz) obtained using
FC500 transmitter and 100-mm transfer block.
b. V1030 sensor: This sensor from Panametrics is a damped ultrasonic transducer and we misidentified it previously as V103 [8]. Current Panametrics V103 has nominal resonance frequencyof 1 MHz, while our V1030 has 10 MHz/0.5” inscription. Using the plane-wave input with
FC500 transmitter through 100-mm block, we obtained the waveform and transfer function asshown in Fig. 9. The waveform is a typical bipolar pulse, indicative of the velocity response with
0.68 µs duration. This is much longer than the 10-MHz inscription implies, but the rise time was
faster than 0.15 µs (which is shorter than the incident wave rise time of 0.24 µs). The transfer
80

function also shows the main feature to be a bipolar pulse of 0.27 µs duration, i.e., the velocity
response. However, it is followed by a 5.5-MHz wave train. The origin of this trailing vibrationis still obscure.

Fig. 10 Waveform of V1030 sensor obtained using focused laser and 100-mm transfer block.

Fig. 11 Waveform of V1030 sensor obtained using focused laser and 25-mm transfer block. [8]Using spherical incident waves and 100-mm block, V1030 sensor produced the output shownin Fig. 10. The main pulse is a negative-going narrow peak having half-height duration of 0.25
µs. It is followed by lower amplitude oscillations. The transfer function contained strong noise at
35 MHz and meaningful features could not be deduced. The previous result of transfer function
obtained using 25-mm radius spherical waves also showed the main peak to be a unipolar pulse,
followed by numerous oscillations (see Fig. 11 [8]). For this large aperture sensor, both sphericalincident waves resulted in apparent displacement response. In spite of a simple structure of averaged displacement for larger aperture shown in Fig. 4, real sensors must be sensitive to the phase
distribution of incident waves. The averaged displacements may be applicable for capacitive
sensors only.
From the present results, the conventional wisdom of using well-damped sensors for highfidelity waveform acquisition needs to be reexamined carefully. Depending on the types ofwaves being detected, appropriate sensor calibration has to be conducted considering the source
types and propagation distances.
c. B-1080 sensor: This is a broadband sensor from Digital Wave. This is twice more sensitive
compared to V1030, in part due to the addition of FET input stage on the sensor. This sensor
81

output indicates the acceleration response to a displacement pulse input, although the two positive-going pulses are not quite symmetric in shape. However, the peak heights are almost identical and the peak positions were symmetric. The transfer function calculation was not successful,again with strong high-frequency noise that could not be eliminated.

Fig. 12 Waveform of a Digital Wave B-1080 sensor excited by FC500-generated plane waves.
d. WD sensor: This is a popular wide-band sensor from PAC with multiple resonances having
multi-element sensing design. Its sensitivity is higher than broadband sensors (V1030 and B1080). Because it has a disc and two ring elements, its response is expected to be complex.

Fig. 13 Waveform of a PAC WD sensor excited by FC500-generated plane waves.

Fig. 14 Transfer function of a PAC WD sensor excited by FC500-generated plane waves.
82

Fig. 15 Transfer function of a PAC WD sensor excited by a laser pulse on 25-mm plate. [8]Under the plane-wave excitation with 100-mm block, the waveform and transfer function
were obtained as shown in Figs. 13 and 14. The first three peaks of the waveform are indicativeof the acceleration response (also for transfer function plot albeit with strong high-frequency
noise) as in B1080 above. In this case, many oscillations follow without any definitive pattern.
The two initial positive peaks have a 1.4-µs period while many pairs of peaks have 1 to 1.8 µs
duration. However, the main resonances of WD sensor are at 200 to 500 kHz range and 2 to 5 µs
periods are not discernible in Fig. 13. Periods corresponding to 1 to 1.6 MHz are present for thetransfer function plot, but again no indication of low-frequency resonances can be found.
The present result differs drastically from the transfer function obtained using 25-mm radius
spherical waves [8]. The previously obtained function is shown in Fig. 15, where two strongest
peaks of opposite signs seem to imply the velocity response. (The velocity response is confirmedon the basis of source wave convolution, as discussed later.) The origin of many oscillations observed in Fig. 15 was not elucidated. Again, the type of incident waves affected the basic character of the transfer function.
e. R15 sensor: This utilitarian sensor from PAC and others of similar designs with 140-175 kHz
resonance have been used widely during the past four decades. This is usually not intended forwaveform acquisition, but its waveform and transfer function are obtained as the representativeof widely used resonant sensors. See Figs. 16 and 17 for the plane-wave input case. The observed waveform shows well-separated pulses corresponding to the front-face arrival of the incident wave (marked A), the back-face arrival (B), the return of reflected wave at the front face (C)and the second arrival to the back face (D). These are spaced at 1.40 µs and the amplitude of Bpulse is 1.7 times that of A-pulse. Other peaks are more irregularly spaced and it is hard to identify its main resonance frequency of 150 kHz from period observation. Broad peaks at ~12, ~20
and ~28 µs may possibly correspond to the main resonance. On the other hand, the first 10 µs(see Fig. 16 insert) contains 13-14 peaks, implying that this sensor has a high-frequency sensitivity and can be used in the same way as a WD sensor above to detect AE activities at different
frequency ranges. The transfer function shown in Fig. 17 has a similar pulse sequence, with a – dpeaks corresponding to A – D peaks in Fig. 16. The pulse sequence A-B-C with the strongest B
suggests acceleration response. However, these three peaks are separated by two-cycle oscillation and the transfer function sequence a-b-c has a large positive peak following b-pulse, making
acceleration designation tenuous.

83

Fig. 16 Waveform of a PAC R15 (s/n 89) sensor excited by FC500-generated plane waves.

Fig. 17 Transfer function of a PAC R15 sensor excited by FC500-generated plane waves.
The transfer function calculated from 25-mm radius spherical wave input is given in Fig. 18
and has less discernible structures under 10 µs, but ~7-µs period becomes visible at longer duration. Its FFT is shown in the lower plot and clearly shows the presence of expected resonancestructures at 155, 250 and 366 kHz, as indicated in the figure. However, two higher resonancefrequencies had no corresponding peaks with a usual face-to-face testing method. The resonanceoscillation in the transfer function appears to need spherical incident waves since it was invisible
in the case of plane-wave input (in both the time and frequency domains).
f. PZT elements: Three PZT discs were similarly excited by plane or spherical waves. These are
PZT-5 elements of 11.0-mm ø (diameter) x 5.4-mm thickness (designated as 175), 18-mm ø x
5.35 (400) and 10-mm ø x 14.4 mm (100), respectively. Disc 175 was taken out of AET AC175
sensor with nominal 175-kHz resonance. Disc 400 and 100 were ordered as 400-kHz or 100-kHz
element. The waveform (left plot) and transfer function (right plot) of Disc 175 with plane-wave
input are shown in Fig. 19. Those of Disc 400 were very similar, while we failed to get the trans84

Fig. 18 Transfer function of a PAC R15 sensor (top) and its FFT (bottom) with peak frequencies
in kHz. FFT used Noesis software.
fer function of Disc 100. Thus, these will be omitted from further discussion. Figure 19 shows
similar features of the waveform and transfer function observed for R15 sensor in the beginning(cf. Figs. 16, 17). For Disc 175, the initial pulse spacing of 1.11 µs continues with regularity.
One distinction is that here each pulse is bipolar, especially for the transfer function plot. This
makes the disc to resonate at 900 kHz rather than the intended frequency of 450-kHz thickness
resonance. The bipolar shapes may be construed as the velocity response, but it is a weak argument and further study must follow.

Fig. 19 Waveform and transfer function of Disc 175 PZT-5 element excited by plane waves using FC500 and 100-mm block. (x-axis length: 8 µs)
85

Fig. 20 Waveforms of Disc 175 PZT-5 element and R15 sensor excited by spherical waves using
a pulse laser and 100-mm block. (x-axis length: 15 µs)
When these same discs were excited by spherical waves, the response was quite different asshown in Fig. 20. Here, 100-mm block and a laser source were used. Left plot is for Disc 175,
while right plot is for R15. In both cases, sharp spikes are followed by decaying output corresponding to the arrival of incident waves at the front or back surface. After a few cycles, these
are replaced by smoother oscillations of respective resonance frequency. In the un-damped Disc
175, sudden rise-and-fall cycles lasted longer and these can be seen even after 12 µs. In R15 withbacking material in the sensor construction, these disappeared after the third cycles. When sharp
steps are smoothed out (by filtering), this response gives rise to velocity-response at the thickness resonance as expected. However, when the main resonance frequency develops from crosscoupling effects (e.g., between thickness and radial modes), it is not possible to interpret the response mode.
2. Source wave convolution
When the transfer function of a sensor is known, we can construct the output signals from
input displacement waveforms by performing convolution operation [8]. In order to show theutility of this procedure, we conducted a series of model computation. The transfer functions
used were those obtained using 25-mm radius spherical waves.
We used three types of displacement waveforms to represent a source function. Type 1 is asingle full-cycle sinewave, Type 2 a half-cycle sinewave from –90 to +90˚ (a smoothed stepfunction) and Type 3 a half-cycle sinewave from 0 to +180˚, respectively. The frequency of the
waves was chosen at 100, 200, 500 kHz, 1 or 2 MHz. Zero-padding was applied as needed to
avoid edge effects. Down-slope was also added for Type 2. For Types 1 and 3, the displacement
waveforms are continuous, but their derivatives or velocity waveforms had discontinuities at the
beginning and end. The velocity waveforms for Type 2 are of a half-cycle sinewave.
a. Pico sensor: The convolved waveforms are given in Fig. 21. For Type 2, a half-cycle sinewave is the prominent feature indicative of possible velocity response, but Types 1 and 3 waveforms indicate oscillations at input frequency. These Pico waveforms thus give no clear correspondence to displacement or velocity input. Considering the findings reported earlier in thiswork, this is not surprising. This sensor responds initially at a higher frequency (Fig. 8), followedby the main resonance frequency of 450 to 500 kHz. In the plane-wave excitation case, a velocity
response is expected at 1.4 MHz at the beginning according to Fig. 5. Thus, we cannot assign
86response type for the slowly developed vibration mode. Amplitude of convoluted waves is given
in arbitrary unit, although the output values are given as obtained. When a sinewave of unity amplitude is given as source input, it represents ±1-m displacement. However, the output is not necessarily proportional to displacement and further evaluation is needed to establish a proper unit
to use.

Fig. 21 Pico-sensor responses to 3 input types.

Fig. 22 WD-sensor responses at 200 kHz.

Pico sensors have been used to evaluate crack-induced AE through source simulation analysis [9-11], where the signal rise time is an important parameter. Thus, we examined the linearity
with respect to the source-function rise time. For Type 2 signals, nominal displacement input risetimes are 0.25, 0.5 and 1 µs for 2, 1 and 0.5 MHz; the corresponding rise times of the convolved
waveforms were 4.60, 4.71 and 4.91 µs, indicating proportional increments with a delay. Similar
relations are observed for Types 1 and 3 signals. This implies that Pico sensors can be used forcomparative rise time studies. However, this sensor appears to have a mixed response to input
displacement and velocity and may cause difficulties in characterizing the nature of source
events. We also need to clarify the origin of the rise time stretching of about 4 µs.
b. WD sensor: The convolved waveforms for 200-kHz sources are given in Fig. 22. Type 1
shows a large dip between two sharp peaks. This waveform resembles the velocity source wave
of Type 1 or the derivative of a full-cycle sinewave displacement. The base duration is extended
to 6.64 µs compared to 5-µs input. Type 2 corresponds to a half-cycle sinewave, with an extrapolated base width of 4.0 µs (1.5 µs longer than 2.5 µs input width). Again, this corresponds to thederivative of Type 2 displacement input. Type 3 is the initial part of Type 1, with the base width
shortened to 4.48 µs (still ~2 µs longer than the input width). The rise time to the first peak was
1.28, 2.30 and 1.28 µs for the three types. Here, the rise time of 1.28 µs for Types 1 and 3 resultsfrom 10 ns effective rise time, while 2.30 µs for Type 2 is due to the smoothed step rise time of
1.25 µs duration. Thus, the WD sensor contributed 1~1.3 µs to the observed rise time at 200
kHz.
Type-2 waveforms using five different source frequencies are shown in Fig. 23. The lower
frequency signals are closer to a half-cycle sinewave, while effects of additional peaks are more
visible at 2 MHz, especially at the trailing part beyond the main peak having minor oscillations.
87Fig. 23 WD waveforms for Type-2 input.

Fig. 24 Rise time for WD and V1030.

The main features of Fig. 22 were also observed at 100 kHz to 2 MHz. In all cases, the WD sensor gives consistent velocity response in the three types of source waves. This finding needs further tests at more frequencies that differ from the sensor resonances (100, 230, 480 kHz) [12],but WD responds basically to velocity signals of spherical wave input. Note that it is difficult toreach this conclusion from visual inspection of the transfer function (Fig. 15).
The observed rise time decreased smoothly with frequency, as shown in Fig. 24, where its
values for Type 2 are plotted against nominal (velocity) input rise time. The observed rise time isalways larger than the nominal value and the difference ranges from 1.20 µs (100 kHz) to 0.63
µs (2 MHz). Because of multiple resonance characteristics of this sensor, this finding is unexpected and surprising, but WD sensor is useful even for rise time studies.
c. V1030 sensor: The convolved waveforms for 500-kHz sources are given in Fig. 25. For
spherical wave input, this sensor gives displacement response, with full or half sinewave output
for Types 1 and 3 and a step-down waveform for Type 2. This reflects the nature of its transfer
function with the negative-going main peak when excited by 25-mm radius spherical waves.
This behavior is expected from its heavily damped construction for ultrasonic testing applications. The waveforms were similar at 200 kHz, but at 1 MHz, an additional peak overlapped andresponse became a mixed one. The observed width of the first full cycle was 4.91, 2.30, 1.51 and
0.53 µs for 0.2, 0.5, 1 and 2 MHz source, showing anomaly at 1 MHz.
Type-2 response of V1030 sensor is shown in Fig. 25. The anticipated step-down behavior
was seen at 0.2 and 0.5 MHz with the “rise” time of 2.33 and 1.25 µs. These compare well with
the nominal values of 2.5 and 1.25 µs. At 1 and 2 MHz, a faster rising component (at 0.30 and~0.6 µs) appears before the main peaks at 0.98 and 0.89 µs (see Fig. 24). The main peak risetimes follow a smooth curve, ending at 0.85 µs for zero nominal source rise time. The origin of
the fast component is apparently due to the main peak (at 0.30 µs) in the transfer function,
whereas the main peak in the convolved waves combines the first two large peaks (at 0.30 and
0.83 µs) in the transfer function. While the overall behavior of V1030 sensor is primarily displacement response, these additional deviations complicate the interpretation and careful waveform evaluation is needed. We obviously cannot assume a heavily damped sensor to be always
well behaving.

88Fig. 25 V1030-sensor responses at 500 kHz.

Fig. 26 V1030 waveforms for Type-2 input.

Discussion
The calibration procedure described here is a straightforward application of current lasersensing technology in measuring the displacement of elastic waves. In combination with piezoelectric or laser generation of planar or spherical waves, we can characterize AE sensors in a newway; that is, the transfer function of a sensor, which should be available in addition to the customary frequency response characteristics per ASTM E-1106. It also became evident that thetransfer function of a sensor depends on the incident wave types as different vibration modes of asensing element are excited in non-unique manners. This resulted in sensor response characteristics (i.e., acceleration, velocity, or displacement) that are dependent on the incident wave modes.
In light of the present study, we must reevaluate the NIST-developed calibration standard(ASTM E-1106 and ISO equivalent) since it relies on circular waves propagating on a steel
block. Depending on the distance from the central glass-capillary break, the wave front changes
producing distance- and aperture size-dependent calibration standard. Through-transmission
calibration method also faces difficulty of spherical wave propagation.
Because most AE sources can be regarded as a point source, AE sensors must detect spherical waves. We have used two thicknesses for the transfer block. Systematic study of effects of
the radius of curvature is needed. Also required is the understanding of how various vibration
modes are excited within the sensing element. In this connection, the results reported here on
PZT elements must be further examined as to how electrical signals are generated in relation to
the position of elastic wave front. Rouby [13] examined long ago how each component of a signal waveform is generated. However, the comparison of R15 and Disc 175 results (cf. Figs. 16,
19 and 20) immediately shows vastly different behavior (input: plane or spherical incident
waves; output: bipolar vs. unipolar). Thus, careful model experiments must be conducted to elucidate mechanisms of signal generation as functions of the radius of curvature of incident waves
and the degree and types of cross-coupled vibration modes.
Conclusions
The transfer functions of representative AE sensors were obtained using the excitation by an
ultrasonic transmitter or pulse laser and displacement measurement using a laser interferometer.
Types of incident waves are critically important in defining the transfer functions as different
89vibration modes were excited. These also affected the sensor response characteristics (i.e., acceleration, velocity, or displacement). The transfer functions and typical source waveforms werecombined by convolution. Results demonstrate the nature of sensor responses and the utility of
the approach used here. This study points to the need of reexamining the standard calibration
method in terms of wave front radius effects. Wider uses of transfer functions can improve ourunderstanding of various AE sources.
Acknowledgement
The authors are grateful to Drs. Al Beattie, G. Manthei and M. Ohtsu for helpful comments
and to Messrs. Allen Green and Hal Dunegan for valuable discussion on sensor calibration.
References
1. NDT Handbook, vol. 6, Acoustic Emission Testing, ASNT, Columbus, OH, 2005, pp. 54-60.
2. B. Allemann, L. Gauckler, W. Hundt and F. Rehsteiner, J. Acoustic Emission, 14, (1996) 119126.
3. G. Manthei, Bull. Seismological Society of America, 95 (5), (2005), 1674-1700.
4. F.R. Breckenridge, C.E. Tschiegg and M. Greenspan, J. Acoust. Soc. Am., 57 (3), (1975),
626-631.
5. T. F. Heuter and R.H. Bolt, Sonics, techniques for the use of sound and ultrasound in engineering and science, J. Wiley, New York, 1955, pp. 9-28.
6. Acoustic Emission, JSNDI, Tokyo, Japan, 1990, p. 14.
7. W.P. Mason, Physical Acoustics and the Properties of Solids. Van Nostrand, 1958, pp. 51-86.
8. K. Ono and H. Cho, Proc. 28th European Conf. AE Testing, Krakow, 2008, pp. 25-30.
9. Y. Mizutani, H. Nishino, M. Takemoto and K. Ono, J. Acoustic Emission, 18, (2000) 286-292.
10. S. Fujimoto, M. Takemoto and K. Ono, J. Acoustic Emission, 19, (2001) 69-74.
11. K. Kagayama, T. Ogawa, A. Yonezu, H. Cho and M. Takemoto, J. Acoustic Emission, 24,(2006), 127-138.
12. M. Eaton, K. Holford, C. Featherston and R. Pullin, J. Acoustic Emission, 25, (2007) 140148.
13. D. Rouby, Proc. 3rd International Symp. on AE from Composite Materials, AECM-3, Paris,
ASNT, 1989, pp. 313-322.

90

COUPLANTS AND THEIR INFLUENCE ON AE SENSOR SENSITIVITYPETE THEOBALD, BAJRAM ZEQIRI and JANINE AVISONNational Physical Laboratory, Teddington, Middlesex, TW11 0LW, United Kingdom
Keywords: Acoustic couplant, sensor sensitivity
Abstract
The removal of air from the interface between a measurement surface and an AE sensor is
crucial to the transmission of ultrasonic energy. The acoustic impedance of air is around 5 ordersof magnitude lower than that of the two contacting surfaces, allowing for very little transmissionof acoustic energy at the frequencies typical of acoustic emission (AE). The use of a couplantcan greatly improve this transmission by around 2 times at 100 kHz and more than 10 times at
500 kHz; a typical gel-based couplant having a high acoustic impedance around 4 orders ofmagnitude higher than that of air. The effectiveness of a given couplant is dependent on its
acoustic impedance, acoustic absorption, application thickness and viscosity. Each of these canhave a strong influence on the sensitivity response of the sensor and can ultimately change theway the sensor responds to different wave modes. This paper considers a number of ultrasonic/AE couplants and compares the sensor response for each couplant to longitudinal and shear
waves, demonstrating that a high-performance ultrasonic couplant can improve sensor sensitivity
significantly above 400 kHz compared with a grease-type couplant.
Introduction
When attaching an AE sensor to a measurement surface, a couplant material is used to remove any air from the interface, introduced due to the microstructure and surface roughness ofthe two contacting surfaces. The reason for doing this is that the acoustic impedance of air ismuch lower than that of the sensor face or material surface and will cause considerable loss intransmission. Piezo-ceramic has relatively high acoustic impedance, as does steel (a typicalmeasurement surface) making them a relatively good match. However, the acoustic impedanceof air is around 5 orders of magnitude lower than that of piezo-ceramic or steel and so introducing a couplant layer with an acoustic impedance higher than that of air, which displaces the air
between the two surfaces, can increase transmission substantially [1]. For AE applications, there
is a range of couplants to choose from depending on the requirements dictated by the application.
The properties of the chosen couplant can have a significant effect on the output generated by thesensor and thus the quality of the measurement. Couplants with high acoustic impedance havebeen shown to produce substantially better ultrasonic transmission than standard couplants [2]and are used in ultrasonic applications to improve the signal-to-noise ratio. For AE and vibration
applications, studies have compared the properties of common couplants and have made recommendations based on transmission and ease of use etc. [3].
Whilst couplants no doubt improve the transmission between the contact surface and the sensor, they do introduce a certain amount of variability [4] and different couplants can provide different transmission properties as a function of frequency [5]. During calibration, this can have aneffect not only on the apparent sensitivity of the sensor but also its frequency response. For thecalibration of an AE sensor, a single couplant is usually selected and the sensitivity of the sensor
as a function of frequency is usually stated using the selected couplant [6-13]. This paper considers the effect of couplant choice on the stated sensitivity of the sensor during calibration.
J. Acoustic Emission, 26 (2008)91© 2008 Acoustic Emission GroupCouplant Requirements
When selecting a couplant for use during an AE measurement or monitoring application, anumber of factors need to be considered, of which acoustic transmission is just one. The mostimportant factors might be long-term stability depending upon length of measurement, removal
and reapplication of the sensor, the mounting surface condition or shape, sources of vibration
which could displace the sensor, mounting surface temperature, etc. There are a number of couplant types to choose from, typically liquid, gel or grease. Other types of couplants based on compounds or adhesives can be used, which provide bonding of the sensor to the surface.
Generally, the acoustic performance of a couplant is dictated by the acoustic impedance,
acoustic absorption and the couplant thickness. In addition to these, the ability of the couplant toforce air bubbles from the contacting layer is extremely important and this often requires a relatively low viscosity. Equally important is the wetting ability of the couplant. This is related to thesurface tension of the couplant and describes the level of molecular contact between a couplant
and the compressing surfaces. This is often related to viscosity and good wetting fluids, likewater, can often result in less air between the couplant and the two compressing surfaces.
Acoustic emission sensors have a dominant response to particle motion normal to the surface
and for this type of measurement the properties described above are most applicable. However, if
detecting particle motion is parallel to the sensor face (shear motion), which is a common requirement in plate structures, the viscosity of the couplant will become a more important transmission property. A high-viscosity couplant or a rigid bond will provide greater transmission ofthese in-plane particle motions than couplants with the conventional properties described above.
Measurements and Results
A number of couplants were selected for testing. Glycerin, propylene glycol, ultrasound gel
and silicone grease are compared in this paper. Glycerin and propylene glycol were selected asthese are considered high-performance ultrasonic couplants, particularly glycerin, which provides a layer of relatively ‘high-impedance’ couplant. Ultrasound gel was also used as this couplant is routinely used for AE sensor calibration at the National Physical Laboratory [9, 10], providing good transmission but only suitable for relatively short measurement periods. The main
advantage of this type of gel is its extremely good wetting properties, which combined with arelatively low viscosity, result in very low air content in the coupling layer. Silicone grease wasalso used in the comparison as this and other similar performing greases are widely used in practical AE applications due to their stability with time and their non-drip consistency. A dry contact was also used for reference purposes to demonstrate the effect of using no couplant. Twosets of measurements were performed. Firstly, a longitudinal sensor calibration was performed
against a velocity interferometer to obtain the sensor sensitivity in V/mm/s as a function of frequency. This calibration was performed for each couplant on the same sensor to show the effectof the couplant on the sensitivity response of the sensor. Secondly, a relative comparison was
performed between each couplant showing how the sensor’s response to a normal incidence
shear wave is affected by the couplant. For the shear-wave measurements, a couplant designed
specifically for shear wave transmission (Panametrics shear-wave couplant) was used in place ofthe glycerin.
The measurements were performed on a 410-mm diameter hemi-spherical aluminium blockwith a flat top of approximate diameter of 50 mm, for mounting the sensor under test. The flat92

top is also polished flat to provide a suitable optical reflection for calibration of the sensor
against a laser interferometer. In this case, the polished surface also acts as an idealised coupling
surface and resulted in reduced variability between couplings. A Panametrics V189-RB
0.5 MHz, 1.5-inch diameter NDT source transducer was bonded with cyanoacrylate to the baseof the hemisphere as shown in Fig. 1. The interferometer was aligned with its optical beam normal to the surface of the flat top along the central axis of the block. The Panametrics V189-RB
transducer was excited with a 1-µs pulse generated by an Agilent 33220A function generator.
Following propagation through the block, the out-of-plane displacement component of the longitudinal wave was measured using the interferometer. The output from the interferometer was
captured at a sample rate of 25 mega-samples per second using a TDS 5054 oscilloscope with thetrigger synchronised with the function generator. A total of 3000 time averages were used tomeasure the interferometer output to improve the signal-to-noise ratio. Once the velocity historyof the surface had been established using the interferometer, a PAC S9208 sensor (with 40 dB
pre-amplification and a 20 kHz high-pass filter) was then coupled to the flat top of the hemisphere, with the selected couplant, in place of the interferometer laser beam. The sensor was chosen due to its broadband response up to 1 MHz. The same procedure was then followed to capture the voltage output from the sensor with 100 time averages being used. This process was repeated for each couplant. To improve repeatability and allow comparison between the different
couplants, the position of the sensor on the surface was fixed using a three-point guide and heldin place with a constant mass of approximately 6 N. Using a constant force does mean that amore viscous couplant might result in a thicker layer and less air removal. To counter this, anincreasing force was applied during installation of the sensor, whilst simultaneously monitoring
the sensor output. Once the apparent maximum output was achieved, the force was reduced tothe holding force of 6 N, ensuring that the sensor output did not drop during this reduction. Thisapproach ensures that the maximum acoustic performance is established for each couplant andreduces variability between re-couplings. It is however acknowledged by the authors, that thismay not represent a realistic scenario for in-situ use.

Fig. 1. Measurement configuration for out-of-plane motion.
For the shear-wave measurements, a Panametrics V152-RB 0.5 MHz, 1.0-inch normal incidence shear-wave transducer was used to generate a shear wave at the sensor after propagation
through the block shown in Fig. 1, with a velocity or displacement component parallel to the surface. This transducer was also bonded to the transmission surface using cyanoacrylate to provide
a rigid bond for optimum shear-wave transmission. For these measurements, only the sensor outputs were measured. In this configuration it was not possible to calibrate the response of the93

sensor to the velocity component parallel to the surface as the interferometer measures only thenormal velocity component of the surface, although calibration to this in-plane component using
optical interferometry has been reported in the literature [10-11].
For both the longitudinal and shear wave measurements, the sensor was coupled a number oftimes using each couplant. This was done to establish both the overall variability of re-coupling
the sensor and to establish the maximum peak-to-peak signal level achievable with each couplant.
The sensor measurement in Fig. 2 shows the longitudinal-wave arrival following a 32-µs
propagation time through the aluminium block. At around 64 µs, a direct shear wave arrives,
which is generated from the edges of the transducer. Although this is difficult to identify fromthe sensor output, it is identifiable from the interferometer measurements (not shown). The direct
echo does not arrive until around 96 µs and is just visible at the end of the time waveform shownin Fig. 2. To obtain the magnitude sensitivity, the quotient of the spectra of the sensor-output
voltage and the interferometer velocity output is calculated. However, to ensure that the sensitivity is calculated for only the longitudinal-wave arrival, the time waveforms are time-gated before the shear-wave arrival as indicated in Fig. 2.

Fig. 2. Sensor output time waveform for longitudinal wave measurements.
Figure 3 shows the calibration results of the S9208 AE sensor for its velocity sensitivity to
the longitudinal wave when coupled using the high-impedance glycerin, propylene glycol, ultrasound gel, silicone grease and a dry contact with no couplant. In each case, the sensitivity is derived from the maximum signal level achievable with each couplant. At lower frequencies, below 400 kHz, the couplant appears to have very little effect on the sensitivity of the sensor, withthe variability observed between 200 kHz and 400 kHz being almost within the variability due tore-coupling which was around 5% to 15% depending on the couplant. However, above 400 kHz
there is greater difference in sensitivity, particularly at frequencies where the sensor exhibits
resonance behaviour, which cannot be explained by re-coupling variability. This shows that theglycerin, propylene glycol and particularly the ultrasound gel do provide the greatest sensitivity.
Conversely, silicone grease provides poorer sensitivity at the higher frequencies, up to as muchas 65% less than the ultrasound gel, for the best coupling achievable with each couplant. This
indicates that ultrasound gel has a lower attenuation coefficient at the higher frequencies than94

Fig. 3. Calibration of S9208 velocity sensitivity for longitudinal wave using different couplants.

Fig. 4. Sensor output time waveform for shear-wave measurements.
silicone grease as both have comparable acoustic impedance. The ultrasound gel could also benefit from good wetting, good air removal properties and a thinner coupling layer. These differences do have implications for couplant choice when providing the calibration sensitivity of thesensor and even more so when performing broadband or high-frequency AE measurements. A
poor choice of couplant could not only have a negative impact on the signal-to-noise ratio, but
could ultimately change the shape of the AE-event waveform being investigated. The dry contact
also shows a large reduction in sensitivity as expected - around a 50% reduction at 100 kHz andsubstantially more at higher frequencies. This is to be expected due to the high acoustic absorption in air (which effectively fills the gaps in the sensor-surface contact) at ultrasonic frequencies
and the very high impedance mismatch. The sensor measurement in Fig. 4 shows a shear-wave
arrival following a 64-µs propagation time through the aluminium block. This wave mode is
generated at the source transducer such that it arrives with its particle velocity parallel to the sensor surface. A small longitudinal-wave arrival can also be seen arriving after a delay of 32 µs.
Although small, it is assumed that a direct echo of the longitudinal wave arrives at around 96 µs
and so the time waveforms are time-gated between 64 µs and 96 µs as shown in Fig. 4.
95

Fig. 5. Response of S9208 to shear wave using different couplants.
Figure 5 shows the response of the S9208 AE sensor to the 1-µs pulse generated by the
V152-RD normal-incidence shear-wave transducer when coupled using the propylene glycol,
ultrasound gel, silicone grease and a dry contact with no couplant. In addition to these, aPanametrics shear-wave couplant, which is specifically designed to have a very high viscosity tomaximise shear-wave transmission, was included in the comparison. It should be noted that Fig.
5 cannot be compared with Fig. 3 to assess the sensors longitudinal and shear wave response as
Fig. 3 is a sensitivity derived from the quotient of the spectra of the sensor and interferometersignals, whereas Fig. 5 only considers the direct output spectrum of the sensor.
The shear-wave couplant clearly provides the greatest transmission of the shear wave in thiscase, but, more interestingly, the dry contact is now not so poor when compared to the use of acouplant. The dry contact is in fact comparable to the silicone grease. The most probable reason
for this is that for good shear-wave transmission acoustic impedance and absorption are nolonger the dominant factors. The transfer of shear forces between atoms is likely to dominate andfor this to be most effective, a rigid contact is necessary. Although the effective contact area forthe dry contact would be significantly reduced by the microstructure of the two surfaces, thesmall contacting area is sufficiently rigid to provide transmission comparable to a low rigidity/viscosity couplant, which provides a full area contact in this case. However, the measurements were performed on a polished metal surface, which should allow for a good dry contact.
The type of measurement surfaces encountered in practice would likely perform very poorly fora dry contact. For the shear-wave measurements however, the couplant effect on frequency response is less clear. The ultrasound gel and propylene glycol provided surprisingly good transmission of the shear wave given their very low viscosity. The silicone grease performed less effectively but has the benefit that it does not drip or dry up with time, which can be a problem
with liquid-based couplants. The silicone grease also had the lowest re-coupling variability ofless than 8% in both sets of measurements. The performance of couplants for shear waves withparticle motion parallel to the surface also represents their performance to symmetric Lambwaves. Similarly the longitudinal wave results presented with particle motion normal to the surface will be representative of couplant performance for anti-symmetric Lamb waves, which
propagate in plate structures [14].
Conclusions
The effect of couplant on the calibration of the velocity sensitivity response of an AE sensor
to a longitudinal wave (out-of-plane) has been considered and shows the effect of the couplant to96

be broadly unnoticeable within the re-coupling variations. However, at higher AE frequencies
above 400 kHz, the properties of the couplant become more important. At these higher frequencies, silicone grease reduced the sensitivity of the sensor by up to 65% compared with a highperformance ultrasonic couplant such as propylene glycol. Although this has implications for theaccuracy of the sensitivity calibrations for AE sensors, it will also have an impact on AE measurements. A poor choice of couplant could significantly reduce signal-to-noise ratio in broadband or high-frequency AE measurements and could lead to distortion of the waveform being
measured.
In addition to this, shear-wave measurements have shown that for the measurement of wave
modes with particle motion parallel to the surface, such as symmetric Lamb waves, there is noreal benefit in using couplants conventionally considered to be ‘high-performance’ ultrasoniccouplants. Given that both planes of particle motion would be encountered in typical AE applications, a balanced choice should be made between the requirement for out-of-plane and in-planeparticle motion and required mechanical/material/chemical properties.
Acknowledgements
We gratefully acknowledge the financial support of the UK Department of Innovation, Universities and Skills (National Measurement System Programmes Unit).
© Crown copyright 2008. Reproduced by permission of the Controller of HMSO and theQueen's printer for Scotland.
References
1. ASTM E650-97 Standard Guide for Mounting Piezoelectric Acoustic Emission Sensors,
2007.
2. Culjat M. O., Singh R. S., White S. N., Neurgaonkar R. R., Brown E. R.: Acoustics Research
Letters Online, 6(3), (2006), 125-129.
3. Colombo S., Giannopoulos A., Forde, M. C., Hasson R., Mulholland J.: NDT&E International, 38, (2005), 187-193.
4. Dugmore K., Jonson D., Walker M.: Composite Structures, 58, (2002), 601-603.
5. Sun L., Kulkarni S. S., Achenbach J. D., Krishnaswamy S.: J. Acoust. Soc. Am., 120 (5),(2006), 2500-2505.
6. Hsu N. N., Breckenridge F. R.: Mat. Eval. 39 (1), (1981), 60-68.
7. American National Standard, ASTM E 1106-07 Standard method for primary calibration of
acoustic emission sensors.
8. Miller R. K., McIntire P. (eds.), Nondestructive Testing Handbook, 2nd ed. Vol. 5, Acoustic
Emission Testing, American Society for Non-destructive Testing, 1987, pp. 120-134.
9. Theobald P. D, Esward T. J., Dowson S. P., Preston R. C.: Ultrasonics 43, 2005, 343-350.
10. Theobald P., Dar F.: Key Engineering Materials, 13-14, (2006), 91-97.
11. Theobald P.: Ultrasonics, 10.1016/j.ultras.2009.03.004, (2009).
12. Goujon L., Baboux J. C.: Meas. Sci. Technol., 14, (2003), 903-908.
13. Hatano H., Chaya T., Watanabe S., Jinbo K.: IEEE Trans. Ultrason. Ferr. Freq. Contr., 45(5), (1998), 1221-1228.
14. Viktorov I. A.: Rayleigh and Lamb Waves, Translated from Russian, Plenum Press N.Y,
1967.

97

LABORATORY EXPERIMENTS FOR ASSESSING THE DETECTABILITY OF SPECIFIC DEFECTS BY ACOUSTIC EMISSION TESTINGFRANZ RAUSCHER
Vienna University of Technology, Institute for Engineering Design and Logistics Engineering,Research Group for Pressure Vessel and Plant Technology, Gusshausstrasse 30, 1040 Vienna,
Austria
Keywords: pressure vessel, experiments, detectability
Abstract
For acoustic emission (AE) testing of pressure vessels during in-service inspection, special
experiments must be performed to get the appropriate information in some cases. Design of such
experiments, especially the loading history, is the focus of this paper. Limitations on the experimental load history, which result from the Kaiser-effect and the goal of the test, are given. Another important point is the generation of an appropriate defect. An example with an initial artificial weld defect and defect growth by pressure cycling is given. The measured low AE activity
during this test shows that experimental verification for AE testing may be necessary, even if
standard procedures are used.
Introduction
In the last decades acoustic emission (AE) testing was developed further [1], and many dangerous defects were found [e.g., 2-4], and probably accidents have been prevented. Frequently,
economic reasons are the determining factor of whether or not AE testing is used during inservice inspection. To get an optimum on safety, the question whether or not defects and failuremodes, which are probable in the specific case, can be detected by AE testing should dominate
the decision. Of course, AE is an integral testing method and able to detect unforeseeable defects, which is a favorable property for inspection strategy.
Finding experimental evidence that a specific type of defect is detectable is difficult. One of
the reasons why the transfer of results from experiments to AE testing is difficult is the complicated load history, which is applied to pressure vessels prior to AE testing. Therefore, this paper
focuses on experimental loading histories for studying the AE characteristics of defects during
AE testing of pressure vessels.
Here, AE testing during in-service inspection, not during pressure testing after fabrication, is
mainly considered. The focus lies on local defects, like welding defects, cracks, pitting, etc., andglobal wall thinning and design failures are outside of the scope of this paper.
Loading History
Loading histories of pressure vessels (Fig. 1) start with the pressure tests after fabrication.
AE testing during the first pressure test is not the focus of this paper. During service, the vessel isusually pressurized with more or less pressure variations, but pressure has to be below maximum
allowable pressure pS, which is the relief pressure of the safety valve. Because of limited tightness of safety valves near the relief pressure, operating pressures are usually below pS. When thesafety valve opens, according to the European Pressure Equipment Directive (PED), the pressure
J. Acoustic Emission, 26 (2008)98© 2008 Acoustic Emission Group

is allowed to exceed pS by 10%. For AE testing the pressure should be raised to a value higher
than the maximum value reached during the operating period before the test, but the maximum
pressure of the first pressure test is usually not exceeded.
Initial defects may be present after fabrication. If they are not detected before the vessel goes
into service, they may grow during the service period. Other defects may be initiated and growduring service. The goal of the in-service inspection is to detect all dangerous defects before theylead to catastrophic failure.

Fig. 1. Typical loading history of pressure vessel; p, pressure; pS, maximum allowable pressure;
ptest, test pressure; pAE, maximum pressure at AE testing.
Plastic Deformation
For the AE generation, plastic deformation is important. First yielding produces AE itself,
and the resulting large deformations activate many secondary AE sources. To see the possible
plastic deformations of vessels, in [5] a finite-element (FE) simulation was performed for an example vessel HSD01 (Fig. 2a). For this vessel, stress-strain curves for the base materials as well
as for the welding materials were available because it was investigated during a European research project. It is a vessel made of high strength steel, and for the allowable pressure, the largest allowable one was evaluated. For details about the analysis see [5].
At the beginning of the simulation, some artificial shrinkage was introduced into the welding
material to simulate residual stresses from the welding process. Afterwards the first pressure test
up to the test pressure ptest = 419 bar (Fig. 2b 1-2) was simulated. The simulation continued with
one operating cycle up to pS = 293 bar (Fig. 2b 4-5) and a second pressure test (Fig. 2b 6). Afterreaching the maximum pressure of the pressure test the pressure was further increased to 486 bar(Fig. 2b 7). Figure 2c shows the von Mises equivalent stress vs. the circumferential strain at the
Point A (Fig. 2a): Due to shrinkage of weld material, large stresses and some initial strain arepresent at the beginning (Fig. 2c 1). At the first pressurization, plastic deformation starts at the
beginning (Fig. 2c 1-2). During unloading and the operating cycle, practically no plastic strain isaccumulated (Fig. 2c 2-5). Also in the second pressure test, almost no plastic deformation occurs,
as long as the maximum pressure of the first pressure test is not exceeded (Fig. 2c 5-6). Only if99the maximum pressure of the first pressure test is exceeded plastic deformation starts again (Fig.
2c 6-7).
Due to yielding the residual stresses are changed in a way that the subsequent unloading andreloading can take place without (or with very small) plastic deformation. Exceeding the firstmaximum pressure leads to considerable new plastic deformations. Like plastic deformation, AE
activity is totally different whether or not the first load limit is exceeded (Kaiser effect - compare
to Fig. 6). This has to be considered when specifying the loading history for verification experiments.
Experimental Load History
When designing experiments for studying AE testing, one has to consider that during AE
testing for in-service inspection the maximum loading of the first pressure test is usually not exceeded. Only the maximum of the operating load, at which defect initiation and/or growth took
place, should be exceeded during the AE testing.

a)

b)

c)
Fig. 2. Example HSD01, plastic strain in spherical vessel with residual stress [5]; a) FE modelwith point A shown, b) simulated cycles; c) circumferential strain vs. von Mises equivalent stressat point A.
100

Therefore, for finding an appropriate experimental loading history, the following procedure(Fig. 3) is proposed:
At first a load LS, which corresponds to the load of the vessel at the maximum admissible pressure pS, has to be specified. This can be a pressure, if a vessel is tested, or a force resulting in an
appropriate stress level, if simple tension specimens are used. At this point the usual safety factors in pressure vessel design have to be considered (usually a safety factor of at least 1.5 to theappropriate proof or yield stress is applied at operating conditions). Starting from this load thefollowing procedure can be applied:a) If an initial defect from fabrication should be simulated, a sample with an initial defectcan be used, or an initial defect can be introduced at the beginning (Defect introduction 1
at Fig. 3).
b) The test itself should start with a loading cycle, which simulates the first pressure test.
The appropriate load Ltest can be evaluated by multiplying the load corresponding to pS(LS) with the appropriate pressure test factor.
c) After this loading cycle, an optional reference cycle up to the load Ltest can be applied.
This cycle gives some reference for the AE of the sample without defect or with the initial defect after the first pressure test.
d) If defect initiation during operation should be simulated, a defect can be introduced after
the first pressure test (Defect introduction 2 at Fig. 3).
e) Now load variations should be applied. In this phase, the load has to be smaller than the
maximum load, which will be reached at the vessel during operation. At least a few load
cycles up to the maximum load, which can be reached at this phase, should be applied.
Simulating defect growth by the investigated mechanism (fatigue, corrosion, etc.) would
be optimal in this phase.
f) Now a loading cycle simulating the AE test can be applied. Here, it is clear that the load
should only be increased up to a value LAE reached during the planed test. If the load israised to a larger value, may be up to burst or fracture, AE above the load LAE would notarise at a comparable AE test. At least AE above the load Ltest is not comparable to AE
during AE testing during in-service inspection.
g) Simulation of further defect growth and further AE tests are of course possible.
Fatigue Cracks Starting from an Artificial Notch as Examples of Defects
Saw cuts (Fig. 4a), which cause U-shaped notches, have been used in connection with gas
cylinders made of high strength steel [5, 7]. These U-shaped notches cause fatigue cracks growing from both sides of the notch into the ligament. Frequently during crack opening, shear cracks
are formed, which connect these two cracks. Increased friction at the crack surface may result.
V-shaped notches (Fig. 4b) cause crack initiation nearly in one plane, but the fabrication may be
more complicated.
Introduction of welding defects in a defined way is difficult. In [6] a special procedure wasused (Fig. 5c) to introduce a welding defect inside of an existing vessel. Here a notch was cut
through the whole wall thickness. Afterwards a strip was pressed into the bottom of the notch.
Onto this strip the weld was placed. The resulting crack-like notches are somewhat similar to
welding defects like lack of fusion at the weld root.
The introduced notches have to be relatively deep to get fatigue crack growth within a practicable number of cycles, and with the usual nominal stresses in pressure vessels. Notches of 1/3 to
1/2 of the wall thickness in connection with cycle numbers of 10,000 full pressure cycles may be
101

Fig. 3. Load history for experimental testing; LS, load according to load at allowable pressure,
Ltest, load according to load at pressure test.

a)
b)
c)
Fig. 4. Examples of artificial defects: a) saw cut (U-notch), b) V-notch, c) artificial weld defect.
necessary. Careful monitoring of defect growth during cycling is needed to do the AE tests at
appropriate time. In [6] online crack growth measurement based on strain gauge measurements
was used.
The pressure cycling with pressure variations as large as possible and with the large defects(both necessary to get crack initiation and growth with a practicable number of cycles) causes
large cyclic plastic deformation in the ligament, which causes brittle material components tobreak. Therefore, AE activity during pressure tests after this cycling may be low. This was seen
in a test with a defect in ferritic weld material as described in the following:
ExampleWithin the following experiment [6] an old vessel ST6599 (Fig. 5a) was tested by means ofpressure tests and cyclic pressurization. The vessel was made of St41KT (0.17%C, 0.018%P,
0.022%S), a material according to ÖNORM M3121. This material corresponds to P265GH of
EN 10028-2. The vessel diameter was 800 mm and the wall thickness 15 mm.
As an initial defect, before the first pressure test, an artificial weld defect of the type given in
Fig. 4c was introduced on the vessel. Therefore, a saw cut was introduced in the original longitudinal weld, a steel strip was placed into this cut, and the weld was placed on the strip (Fig. 5b).
For reference, a second weld with the intention of having no defect (marked “New Weld” in
Fig 5a) was placed on the vessel. In this case only a V-notch, which partially penetrated the wall
thickness was introduced and filled up by welding.

102The testing procedure started with a pressure test, consisting of three pressure cycles (Fig. 6)
up to a pressure of 1.43pS (pS = 32 bar). After the pressure test, the vessel was cycled with sinusoidal pressure variation between 2 bar and pS until crack initiation was indicated (strain gaugemeasurements). Afterwards a pressure test and the next cycling followed. The procedure wasrepeated until the vessel failed at the 6th pressure test through leakage at the artificial weld defect(Table 1).
AE was measured with 5 SE150 sensors with a resonance frequency of 150 kHz and threshold settings between 31.2 and 35 dBAE (Table 2) were used. A 0.5-mm pencil-lead break results
in an AE event of about 84 dBAE at 30 cm distance (distance of the artificial weld defect to thefirst hit sensor). Strain gauges were used for online crack growth detection (Fig. 5a). The indication was based on stiffness changes.
The micrograph (cross section a short distance from the leak) of the defect after the test
(Fig. 7a) shows the crack starting at one side of the steel strip, almost in a single plane, and clear
crack blunting at the 5th and 6th pressure tests. At the fracture surface (Fig. 7b) at least the crack
tip at the last three pressure tests can clearly be seen.

a)b)Fig. 5. Experiment ST6599 [6]: a) Test vessel, b) sketch of artificial weld defect,103Fig. 6. First pressure test with three pressure cycles of experiment ST6599 - events located at
defect region (longitudinal weld defect).
Table 1: Experimental procedure.
Experimental Step
1st pressure test
1st cyclic pressurisation
2nd pressure test
2nd cyclic pressurisation
3rd pressure test
3rd cyclic pressurisation
4th pressure test
4th cyclic pressurisation
5th pressure test
5th cyclic pressurisation
6th pressure test

Pressure
pt = 45,8 bar
2 – 30 bar
pt = 45,8 bar
2 – 30 bar
pt = 45,8 bar
2 – 30 bar
pt = 45,8 bar
2 – 30 bar
pt = 45,8 bar
2 – 30 bar
Leakage at 39,9 bar

Cycles3 Cycles
5064 cycles
3 Cycles4397 cycles
3 Cycles1850 cycles
3 Cycles1222 cycles
3 Cycles
809 cycles
Leakage at first pressurization

Table 2: Threshold settings.
Threshold [dB]
1st pressure test until 8 bar
1st pressure test after 8 bar
Cyclic pressurisation
2nd pressure test
3rd to 5th pressure test
6th pressure test

31.2
35.6
35.6
31.2
31.2
31.2

Waveform recorded
for A > [dB]
43
49.8
49.8
49.8
33.7
31.2

During the pressure tests and cycling considerable acoustic emission activity was aquired
from the investigated weld defect, the “New Weld” and from a position called “Circumferential
weld defect” (Fig. 5a). With radiography in the “New Weld” and “Circumferential weld defect”
also considerable defects were found. Micrographs at the locations with the largest indications
showed insufficient penetration at “Circumferential weld defect” and bad bounding at “NewWeld”. Details of this can be found in [6]; here only signals from the investigated weld defect are
further discussed:

104

a)

b)

c)
d)
Fig. 7: artificial weld defect (after cycling and pressure tests); a) micrograph; b) fracture surface;c) microstructure of ligament; d) microstructure of base material ST41KT
The most important phase during AE examination is the phase of increasing pressure, including the hold periods, of the first pressure cycle of a pressure test – this is the period from start ofthe pressure test until start of pressure release. For this phase of the pressure tests 1 – 6, the numbers of AE events within the investigated region, i.e. the longitudinal weld defect, are given in
Table 3. For comparison also the sum for the whole vessel is given. Figure 8 summarises theacquired events from the investigated defect during the last pressure test until leakage.
Description of the values in Table 3:≥80 dB, located: Here the number of located events with amplitudes larger than 80 dB at thefirst hit sensor is given. The numbers of events that are located within the considered regions are
given. Hsu-Nielson sources (0.5 mm) at the investigated regions reach at the first hit sensor amplitudes between 80 and 85 dB, therefore, amplitudes larger than 80 dB have intensities similaror larger than the Hsu-Nielson source (0.5 mm).
60 dB, located: Here the number of located events with amplitudes larger than 60 dB at thefirst hit sensor are given. The amplitude of 60 dB is 20 dB smaller than the one reached by Hsu105

Nielson sources. Above this amplitude, with the arrangement used, location processing withgood reliability is possible.
TR-page available: Here the number of events is given for which at least for the 1st hit sensor the waveform data was acquired. Only the events, which are assigned to the considered region according to the channel sequence criteria, are considered. This number is important for
further evaluation, because more sophisticated data evaluation is only possible if the waveform isacquired.
40 dB, according to channel sequence: Here the number of events is given which reach at
the 1st hit sensor an amplitude of 40 dB. Only the events, which are assigned to the consideredregion according to the channel sequence criteria, are considered here. The considered amplitudeof 40 dB is about 40 dB smaller than the one which would be reached by the Hsu-Nielson source(0.5 mm).
Table 3: Pressure tests 1 - 6, first pressure cycle, increasing pressure: Number of events at theconsidered regions.

Fig. 8: Longitudinal weld defect; 6th pressure test; channel sequence filter at the top; location
filter at the bottom.

106

During the first pressure test, large AE activity was measured. The acoustic emission during
the pressure tests two to six can be characterised in the following way: There were very few signals with large amplitudes. Only at the 4th pressure test, in the region of the longitudinal weld
defect, one event with amplitude near to the one of a pencil lead break was acquired. In two pressure tests, in the region of the longitudinal weld defect, no events with amplitudes larger than
60 dB were acquired. When considering low amplitude signals the activity decreased until the 4th
pressure test, and increased in the 5th and the 6th pressure test again. This increase of the activity
with low amplitudes correlates with crack propagation at these last two pressure tests.
For the base material, comparable small AE activities can also be found in the literature [e.g.
8]. Here the weld material, which forms the ligament for the considered crack, has a fine-grained
microstructure (Fig. 7 c and d), which may be one of the reasons for the small AE activity.
The applied procedure may possibly not detect the applied type of fatigue crack (pure fatigue, without corrosion) in weld metal of low carbon steel. Most pressure vessels have only asmall number of fatigue cycles and failure modes due to corrosion and interaction between corrosion and mechanical action dominate. When doing experimental verification for AE testing,which focuses on theses failure modes, the corrosion effects have to be included in the experimental testing.
Conclusion
Limitations on the loading history for experiments in connection with AE testing were demonstrated. One experiment following the guidelines was conducted with an initial artificial weld
defect in connection with fatigue crack growth. AE activity during the simulated AE tests was
small. This shows that standard procedures of AE testing are not always appropriate. Analysis ofprobable defects and the possibility to detect these defects by AE testing is necessary. For some
types of defects experimental verification is necessary. In the experiments the investigated defects have to be reconstructed in a proper way. Using fatigue cracks instead of cracks from stresscorrosion or corrosion fatigue interaction may not be sufficient. Procedures of verification experiments, which include corrosion in an appropriate manner, are not established.
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3. Kabanov B. S., Gomera V. P., Sokolov V. L., Fedorov V. P., Okhotnikov A. A.: Use of AE
Method Abilities for Petrochemical Equipment Inspection. Proc. 26th European Conferenceon Acoustic Emission Testing, September 15-17, 2004, Berlin, Germany.
4. Kabanov B. S., Gomera V. P., Sokolov V. L., Okhotnikov A. A. Fedorov V. P.: AE Technology as a Key Element of the Save Operation System at Refinery. Proc. 25th European Conference on Acoustic Emission Testing, September 11-13, 2002, Prague, Czech Republic.
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Aachen, 2005.

107

6. Rauscher F., Bayray M.: Weld Defect in a Pressure Vessel Investigation of Acoustic Emission during Pressure Tests and Pressure Cycling. Bericht Nr. 26, Institut f. Apparate- u.
Anlagenbau, Technische Universität Wien, 2005.
7. Rauscher F.: Gas Cylinders with Longitudinal Cracks – Cyclic Fatigue Life Time after a
Pressure Test. Proceedings ICPVT-10, July 7-10, 2003, Vienna, Austria.
8. Wadley H.N.G., Simmons J.A.: Microscopic Origins of Acoustic Emission. In: Nondestructive Testing Handbook, Vol. 5, Acoustic Emission Testing. American Society for Nondestructive Testing, 1987.

108

INTEGRITY EVALUATION OF COPVS BY MEANS OF
ACOUSTIC EMISSION TESTINGYOSHIHIRO MIZUTANI1, KOUKI SAIGA2, HIDEYUKI NAKAMURA3, NOBUHITOTAKIZAWA3, TAKAHIRO ARAKAWA3 and AKIRA TODOROKI1
1
Tokyo Institute of Technology, Department of Mechanical Sciences and Engineering,
2-12-1-I1-70, Ookayama, Meguro, Tokyo 152-8552, Japan; 2 Japan Aerospace Exploration
Agency, Safety and Mission Assurance Department, 1-1, Sengen 2, Tsukuba, Ibaraki 305-8505,
Japan; 3 IHI Inspection & Instrumentation Co., Ltd. 1, Shin-nakahara, Isogo, Yokohama
235-8501, Japan
Keywords: COPVs, CFRP, impact damage, pressure proof test, integrity evaluation
Abstract
It is important to evaluate the integrity of composite overwrapped pressure vessels (COPVs)
used for space applications. In order to examine applicability of acoustic emission testing(AT) for the evaluation, AE signals were monitored during the pressurization test of a COPV
before and after an impact test. The impact test of 17-J energy was conducted by using adrop-weight impact tester to simulate machine-tool drop. A large number of emissions weremonitored by the AE channel nearest the impact point. In addition, the characteristics of AE
signals detected nearest the impact point were different from those by other channels. Fromthese results, it is assumed that AT can be used for the qualitative integrity evaluation of COPVs.
On the other hand, to realize the quantitative integrity evaluation, it is essential to make database of AE parameters.
Introduction
Composite overwrapped pressure vessels (COPVs) are widely used for space applications,such as accumulators for a space satellite and propellant tanks for a rocket. It is known that
COPVs are likely to suffer complicated internal damages by being dropped or by impacts ofdropped machine-tools. Even when such internal damages occurred, only small damages arecaused on the impact surface in many cases. Therefore, it is difficult to find the impact damages from outer surface by visual testing (VT). Since the internal damages may reduce
strength of the vessels, a reliable nondestructive testing (NDT) for the damages is desired.
Applicability of various NDT methods for COPVs was evaluated by several investigators [1-3],although, the optimal inspection technique is not yet found.
Acoustic emission testing (AT) is one of the candidates for the NDT method of COPVs.
Several standards on AT, such as ASME section V, article 11 [4] for evaluating the integrity offiber-reinforced plastic vessels are available. Only a few papers [5-7] or reports [8], however,appeared in public domain for applying AT to COPVs.
In our previous study [9], the applicability of AT to the integrity evaluation of COPVs wasevaluated by using coupon-level specimens. It was found that Kaiser effect and Felicity effectobserved during a cyclic loading test can be used for evaluating previously induced damages
such as impact damages. The detectable minimum damage size by AT was same or better thanthat by ultrasonic testing (UT).
J. Acoustic Emission, 26 (2008)109© 2008 Acoustic Emission GroupIn this study, as a collaborative research between Tokyo Institute of Technology and Japan
Aerospace Exploration Agency, AE monitoring is conducted during the pressurization test of areal COPV before and after the impact test. IHI Inspection & Instrumentation Co., Ltd. joined
this study in the measurement under the guidance of Tokyo Institute of Technology. Suitable AEparameters for the integrity evaluation are investigated and the applicability of AT is evaluated.
Experimental Setup and Procedure
A thin metal-lined composite overwrapped spherical pressure vessel of 600-mm diameter
was prepared for this study. Reinforcement fibers of the vessel were carbon fibers. The vessel was fabricated by filament-winding (FW) method. Prior to this study, the vessel was pressurized to MEOP (maximum expected operating pressure) 52 times, proof pressure(1.25MEOP) 6 times and nominal burst pressure (1.5MEOP) once. Thus, it is assumed thatseveral damages were already inflicted on the COPV before the start of this study.
Four AE sensors (Vallen-Systeme GmbH, Type VS150-RIC, nominal resonant frequency 150
kHz) were mounted on the outer surface of the vessel at quadrant locations on the equator (seeFig. 1). The programmed hydrostatic pressure cycles were applied to the vessel. The maximum pressure was set as the MEOP and was held for 30 minutes. Outputs of the AE sensors
were amplified 34 dB by preamplifiers and filtered by band-pass filters of 95 to 850 kHz. The
extracted signals were digitized at an interval of 200 ns with 4096 points by A/D converter.
After the first pressurization test, an impact test was conducted with a drop-weight impact
tester. A hemispherical tip of 12.7-mm in diameter was used for the impactor per ISO14623.
Before the impact test, AE sensors were removed from the vessel to avoid sensor damages.
The impactor was dropped to the vessel along guide rails and the impact load was applied at theposition 22.5˚ from the Ch.-4 sensor on the equator of the vessel as shown in Fig. 1. The
weight of the impactor and the impact energy were 2.5 kg and 17 J, respectively. The surface
damage caused by the impact was not evident by VT. After the impact test, the damage
evaluation was conducted by using an ultrasonic flaw detector and UT located an ellipticallyshaped internal damage of about 40 mm in long axis and about 20 mm in short axis. Following
the impact test, AE sensors were remounted on the same positions as before. AE activities
were again monitored during the pressurization test by the same programmed pressure cycles.

Fig. 1 Schematic representation of the pressurization test and the sensor positions.
110Results and Discussion
Figure 2 shows the total number of AE hits for each channel during the pressurization test
before and after the impact test. Before the impact test, the total number was almost identicalamong each channel. After the impact test, the total number of Ch. 4, which was the nearest to
the impact point was more than twice those by other three channels. The result shows that bycomparing the total number of AE hits by each channel, previously induced damages such as
impact damages can be evaluated.

Fig. 2 The total number of AE hits for each channel during the pressurization test, before andafter the impact test.

Fig. 3 AE signal peak amplitude (left) and cumulative AE hits (right) with respect to the elapsed
time. The normalized pressure history is superimposed on the graphs.
Figure 3 shows peak amplitude of AE signal (left) and cumulative AE hits (right) with theelapsed time. The normalized pressure history is overlapped with these graphs. Periods A-D defined in the right are used later in detailed analysis. A large number of significant AE signals
111

(>65dB) were monitored by all channels throughout the first pressurization test, which was
conducted before the impact test (see upper left of Fig. 3). The result reveals that the large
damages occurred all over the vessel at the first pressurization test. During the pressure-holdperiods of the first (before impact) and the second (after impact) pressurization tests, emissions
were continuously detected. Since emissions that continue during pressure-hold generally indicate structurally significant defects, the COPV appears to have significant defects from the
beginning as we mentioned in the previous section. By monitoring the AE peak amplitude andthe cumulative AE hits, a newly induced damage and a damage progression can be monitored.
In addition, there is a possibility to evaluate the existence of significant defects by monitoring
AE during the pressure-hold.
Figure 4 shows the relationship between AE signal peak amplitudes and durations. In general, an AE event with high amplitude and long duration reveals the occurrence or progressionof large-scale damage. Since a large number of such kinds of events were detected during thefirst pressurization test, large-scale damages or progressions occurred during the first test.

Fig. 4 Relationship between AE signal peak amplitude and duration.

Fig. 5 Relationship between cumulative AE hits and AE peak amplitude.
112

Fig. 6 Relationship between cumulative AE hits and AE peak amplitude of Period A to D for AE
signals detected before the impact test. (Periods A to D are defined in right of Fig. 3.)

Fig. 7 Relationship between cumulative AE hits and AE peak amplitude of Periods A to D for
AE signals detected after the impact test.
113

Figure 5 shows the relationship between cumulative AE hits and AE peak amplitude in dB
before and after the impact test. The scale of the vertical axis is logarithmic. When a fracture mode is limited to one, cumulative AE hits in the graph tends to be monotonically decreased with increasing the amplitude. Therefore, the relationship of Ch. 1 to 3 monitored
during the second pressurization test after impact (right of Fig. 5) reveals that fractures withlimited fracture-mode occurred. On the other hand, the relationship monitored during the firstpressurization test before impact (left) and Ch. 4 of the second test reveals that fractures withseveral fracture-modes occurred. Since there was a possibility that the fracture-modes changewith elapsed time, we investigated the transition of the relationship with the elapsed time. Figure 6 and 7 show the results. As strong non-linear relationship was observed for the firstpressurization test, we had assumed the relationship changed with the elapsed time. However,as shown in Fig. 6, the relationship was almost the same throughout the pressurization test.
Therefore, it can be stated that fractures with several fracture-modes occurred throughout thefirst pressurization test. On the other hand, as shown in Fig. 7, the relationship of Ch. 4
changed with the elapsed time and quite different from those of Ch. 1 to 3 at periods A and B.
This reveals that AE related to the impact damage occurred from early stage (periods A and B)of the pressurization test.

Fig. 8 Centroid frequency of detected AE signals with respect to the elapsed time. The normalized pressure history is superimposed on the graphs.
Figure 8 shows the centroid frequency of detected AE signals with the elapsed time. The
distributions of the first test (before impact) and the second test (after impact) appear to be different from each other. In order to examine details of the distributions, centroid frequency’s
histograms for each channel were investigated. The results are shown in Fig. 9. While AE
signals below 400 kHz are dominant for the first test (before impact), AE signals above 400
kHz are observed for the second test (after impact). It is assumed that the origins of AE signals are different for first and second tests. It is also important that in the second test, AE signals with high frequency components (above 500 kHz) are frequently detected by Ch. 4, which
is located closest to the impact point. AE signals, which are related to impact damage are likely
to generate high-frequency components. Since there was a possibility that the distributions
change with elapsed time, we investigated the transition of the histogram with the elapsed time.
Figure 10 to 13 show histogram of centroid frequency of periods A to D.
114

Fig. 9Fig. 10Centroid frequency histograms for each channel.

Centroid frequency histograms for each channel in period A.
115

Fig. 11 Centroid frequency histograms for each channel in period B.

Fig. 12 Centroid frequency histograms for each channel in period C.
116

Distributions of the histogram for the first test are almost identical. This indicates that theorigins of the AE sources did not change with the elapsed time. On the other hand, distributions for Ch. 4 of the second test changed with the elapsed time. It is also noted that the distributions are quite different from those of other channels at periods A and B and also different
from those of Ch. 1 to 3 at periods A and B. This reveals that AE related to the impact damage
occurred from early stages (periods A and B) and this AE consists of high-frequency components.

Fig. 13

Centroid frequency histograms for each channel in period D.

Figures 14 and 15 show the relationship between cumulative AE hits and load histories before and after the impact test. All AE hits were plotted in Fig. 14, while AE hits with above 40
dB peak amplitude were plotted in Fig. 15. If a perfect Kaiser effect is established, cumulative
AE hits should monotonically increase with the load. As shown in the upper plot of Fig. 14,
perfect Kaiser effect was not observed even during the first pressurization test, which was conducted before the impact test. This is due to the vessel may have prior internal damages fromearlier testing. Felicity effect was clearly observed for both the first and the second tests (indicated by arrows). Contrary to Fig. 14, almost perfect Kaiser effects were observed below 30%of the MEOP in Fig. 15. This result indicates that the adequate threshold should be set for an
appropriate damage evaluation. Focusing on Ch. 4 data of Figs. 14 and 15, Felicity ratio
seems reduced after the impact test. This indicates that the damage severity of COPVs can beevaluated qualitatively by Felicity ratio.
In the first part of this paper, we found that AE signals related to impact damage were observed at early stages (periods A and B) and involved high-frequency components (from Figs.
10 and 11) with low peak amplitude (from upper plot of Fig. 7). On the other hand, in the last117

part of the paper, we found that Felicity effects are observed clearly when the AE signals withsmall peak amplitude are eliminated. These results indicate that the effect of the impact
damage appeared throughout the experiment, although adequate AE parameters should be
extracted for observing the effect of the impact damage. When the quantitative evaluation isneeded, it is essential to make database of AE parameters and select appropriate ranges.

Fig. 14 Relationship between cumulative AE hits and normalized pressure. (Upper: The firstpressurization test before the impact test. Lower: The second test after the impact test.)
Conclusion
In this study, the applicability of AT to the integrity evaluation of COPVs was evaluated.
To simulate machine-tool drop, the impact of 17-J energy was applied to the vessel by using
drop-weight impact tester. The programmed cyclic load by hydrostatic pressure was applied
to the vessel before and after the impact test and AE activities were monitored during the test.
In the pressurization test after the impact, the channel nearest the impact point detected a large
number of AE hits. In addition, characteristics of AE signals detected nearest the impact point
were different from those by other channels. From these results, it is concluded that AT canqualitatively make the integrity evaluation of COPVs. On the other hand, to realize the quantitative integrity evaluation, it is essential to make the database of AE parameters.
118

Fig. 15 Relationship between cumulative AE hits above 40 dB of AE signal amplitude and normalized pressure. (Upper: The first pressurization test. Lower: The second test.)
References
1. N. Greene et. al.: Proc. 9th Joint FAA/DoD/NASA Conf. on Aging Aircraft, March 6-9, 2006,
Hyatt Regency, Atlanta, GA, USA.
2. R. Saulsberry: Proc. 9th Joint FAA/DoD/NASA Conf. on Aging Aircraft, March 6-9, 2006,
Hyatt Regency, Atlanta, GA, USA
3. Examination of the Nondestructive Evaluation of Composite Gas Cylinders, Final report,
USDOT, Nondestructive Testing Information Analysis Center, 2002.
4. Acoustic Emission Examination of Fiber-Reinforced Plastic Vessels, ASME Boiler andPressure Vessel Code, Section V, Subsection A, article 11 (1983 and later editions).
5. D. J. McNally: Materials Evaluation, 43, (1985), 728-732.
6. Y. Mizutani, Y. Morino, T. Takahashi, Proc. of 44th AIAA/ASME/ASCE/AHS Structures,
Structural Dynamics, and Materials Conf., April 7-10, 2003, Norfolk, Virginia, USA.
7. Eric v. K. Hill, S.T. Dion, J.O. Karl, N.S. Spivey and J.L. Walker II: Proc. Sixth International Conference on Acoustic Emission, Oct. 29-Nov. 2, 2007, Lake Tahoe, Nevada, USA.
8. C. Wilkerson: NASA Technical Memorandum 108520, 1996.
9. Y. Mizutani, R. Matsuzaki, A. Todoroki: Proc. 19th International AE Symposium, Dec. 9-12,
2008, Kyoto, Japan.
119

STRUCTURAL INTEGRITY EVALUATION OF CNG COMPOSITECYLINDERS BY ACOUSTIC EMISSION MONITORINGOLIVIER SKAWINSKI1, PATRICE HULOT1, CHRISTOPHE BINÉTRUY1
and CHRISTIAN RASCHE2
1
Department of Polymers and Composites Technology & Mechanical Engineering, Ecole desMines de Douai, 941 rue Charles Bourseul, B.P. 838, 59508 Douai Cedex, France;
2
Dynetek Industries Ltd., 4410-46th Avenue SE, Calgary, AB, Canada T2B 3N7
Keywords: AE damage detection, composite materials, CNG cylinders
Abstract
Over the past decades, the increasing use of compressed natural gas (CNG) as an alternativefuel for vehicles has led to the design of attractive lightweight cylinders by the use of fiberreinforced polymers. Nevertheless, the traditional inspection techniques used for steel cylinders
are generally not well adapted to the composite materials. In order to ensure the safety of the users, new inspection techniques has been investigated to give an accurate evaluation of the structural integrity of composite cylinders.
The present work investigates the ability of the acoustic emission (AE) technique to detect
serious damages in CNG composite cylinders. Internal pressure tests with AE monitoring were
performed on CNG Type-3 cylinders (per ISO 11439 and ECE R110), made of an aluminumliner reinforced with a carbon-fiber composite. The experiments were conducted on cylinders
submitted to drop tests from different heights, cylinders submitted to ballistic impact at different
energies and cylinders with longitudinal and transverse notches with different depth. Results of
the AE as a function of the damage type and severity are discussed.
Introduction
In recent years, compressed natural gas (CNG) has become an attractive alternative as a fuelfor vehicles, such as public buses. Compared to oil based fuels, the use of CNG reduces carbon
dioxide and nitrogen oxides emission. Furthermore, the use of fiber-reinforced polymers in thedesign of CNG cylinders has led to attractive lightweight solutions. For the safety of the users,
structural integrity of such composite cylinders must be accurately checked. Nevertheless, the actual inspection techniques, derived from the techniques used for steel cylinders, may not be well
adapted to the composite materials (Bunsell, 2006).
For several years now, acoustic emission (AE) technique has been successfully applied forcomposite materials. By comparison with classical methods, such as hydraulic proof test or visual inspection, acoustic emission technique allows the detection of damages which are not visibleat the surface of the material. Various studies performed on composite coupons with a polymer
matrix have shown that the AE amplitude distribution can be used to clearly identify the main
damage mechanisms (Barré et al., 1994; Hamstad, 1982; Kotsikos et al., 1999). The development of this non-destructive testing method has also led to successful industrial applications forperiodic inspection of glass-fiber reinforced plastic storage tanks (ASTM E1067) and of composite bucket trucks, used for inspecting the high-voltage transmission lines (ASTM F914) (Wevers
et al., 2000).
J. Acoustic Emission, 26 (2008)120© 2008 Acoustic Emission GroupFurthermore, recent works have shown the potential of AE technique for inspection of composite pipes and pressure vessels (Dong et al., 1998, Gebski et al., 2001, Webster, 1999, Wood etal., 2000). The AE technique presents a great potential for the inspection of CNG composite cylinders, as it should not require the removal of the cylinders from the vehicles. This techniquecould be used as an on-board inspection method and structural integrity of the composite part
could be checked by AE monitoring during the filling of the cylinder to its service pressure, forexample.
In the present work, the AE technique has been used during a hydraulic pressure test in orderto check CNG composite cylinders with typical critical defects:
 Damages from low velocity impact (drop test), Damages from ballistic impact (high velocity), Longitudinal and transverse notches inside the composite overwrap.
After the pressure test with AE monitoring, each cylinder was submitted to a fatigue test to
increase their mechanical ageing, in order to estimate the severity of each defect.
Experimental
Specimens: This study concerning AE analysis of fully wrapped aluminum liners was performedon DyneCell® composite cylinders, manufactured by Dynetek (cf. Fig. 1).







ECE approval n°: E1 110R-000039
Working pressure: 200 bar @ 15°C
Nominal internal volume: 76 liter
Nominal dimension: 402 x 864 mm
Nominal weight: 28 kg

Fig. 1. Characteristics of the tested composites cylinders, Dynecell®.
Ten cylinders (1 reference specimen with no defect + 3 specimens submitted to drop test + 3
specimens submitted to ballistic impact + 3 specimens with notches) were tested. Three cylinders, each completely filled with water, were dropped once at a 45° angle onto a concrete surface, impacting one of the dome end (cf. Fig. 2). These three cylinders, referenced J3386, J3416
and J2105, were respectively dropped from a height of 0.472 m, 0.944 m and 1.888 m (corresponding approximately to impact energies of 500 J, 1000 J and 2000 J). After drop test, eachcylinder presented two impacted areas: the first one, the main impact area, corresponds to themain impact, while the second one, the rebound impact area, occurred on the opposite dome after
the rebound of the structure (cf. Fig. 4a).
Ballistic tests were performed on the cylindrical part of three cylinders, referenced J3462,
J2041 and J2043, which were respectively impacted at an energy level of 137 J (velocity = 158m/s), 280 J (velocity = 226 m/s) and 419 J (velocity = 276 m/s) with a steel projectile with aconical head and a weight of 11 g. The ballistic tests were performed by the French Defense Research Agency in Paris (DGA) with a gas gun facility, developed to fire a projectile up to 600
121

45°

h

Fig. 2. Drop test on cylinders.
m/s. Preliminary tests were carried out in order to adapt the velocity of the projectile in order togenerate a non perforating damage.
Two notches, one longitudinal and the other transverse, were made with a Dremel® tool on
three cylinders in the central part along two planes forming an angle of approximately 120°. The
two notches were made with a 1-mm-thick cutter to a length in the bottom of the notch of 50 mm(equal to approximately five times the composite thickness). The notches for the three cylinders,referenced J2112, J2144 and J3419, are respectively 1.25-mm, 2.5-mm and 5-mm deep.
Pressure test: The pressure required to perform the hydraulic test is supplied by a servohydraulic intensifier system. This system is able to control the rate of pressurization accurately
by means of a programmable automatic device. The equipment used for this test is depicted inFig. 3; it includes:
 an 11-kW mono-pump producing a maximum pressure of 250 bars,
 an accumulator,
 a multiplying unit used to pressurize the specimen by quadruplicating the pressure of thepump. This transfer booster enables to transfer the pressure from oil to water, the pipes being pressurized with water, a servo-valve allowing the control of the applied pressure.
The loading procedure for pressure test on the composite cylinders at ambient temperature iscomposed of a pressure cycle described as followed:
 Pressure ramp at a pressurization rate of 1 bar/s up to PTest = 300 bar, Hold period of 4 min at 300 bar, Unload at a depressurization rate of 1 bar/s down to atmospheric pressure.
After the pressure test with AE monitoring, the cylinders were subjected to a hydraulic fatigue
test (5000 cycles between 30 and 300 bar at 5 cycles/minute and room temperature) in order toinvestigate the evolution of each defect and to estimate its severity.
AE equipment: The Euro Physical Acoustics (EPA) DISP® system was used to monitor the AE
signals during the tests, based on two PCI-4 boards, with the following characteristics:
 8 AE channels,
 Frequency bandwidth: 10 kHz-2 MHz, Minimum threshold: 18 dB,
 8 parametric input channels.
122

Climatic chamberP

Pressure
sensorSampleWater

Multipliplying
unit

Temperature

Oil
Control
unit

HydrauliccontrolDataacquisition

Accumulator
Hydraulicpower unit

Fig. 3. Schematic illustration of the pressure test facility.

Fig. 4. Sensors location: a. for cylinders without defect and after drop test. b. for cylinders after
ballistic impact. c. for cylinders with notches (all dimension in mm).
During the pressure tests, four R15 AE sensors, supplied by EPA, (150 kHz resonant frequency, operating frequency range: 50-200 kHz) were used with 2/4/6 preamplifiers. The gain of
preamplifiers was 40 dB while fixed 40-dB threshold level was applied for the tests. Silicone
123grease 500 was used as a couplant at the structure-to-sensor interface. Figure 4 presents the sensors location for the different pressure tests with AE monitoring.
Before the pressure test with AE monitoring, calibration of the sensors is realized throughHsu-Nielsen test in order to simulate an acoustic event using the fracture of a brittle pencil lead.
This generates an intense acoustic signal, quite similar to a natural AE source, such that the sensors detect it as a strong burst. It also allows to check whether the transducers are in good contact
with the cylinder.
Results
In this work, the considered approach aims to relate basic AE parameters (the cumulative
number of hits and counts) to the damage type and severity in order to investigate the structural
integrity of the composite cylinders. Table 1 presents the AE activity recorded during the pressure test on the ten cylinders. By comparison to the damaged cylinders, the cylinder without defect shows a low AE activity and could be then clearly identified by this technique.
Table 1. Results of the AE monitoring.
Cylinder reference
J2045
J3386
J3416
J2105
J3462
J2041
J2043
J2112
J2144
J3419

DefectNone
Drop test, 500 J
Drop test, 1000 J
Drop test, 2000 J
Ballistic impact, 137 J
Ballistic impact, 280 J
Ballistic impact, 419 J
Flaws, 1.25 mm deep
Flaws, 2.5 mm deep
Flaws, 5 mm deep

AE test results
Σ Hits = 769, Σ Counts = 3,666
Σ Hits = 9,780, Σ Counts = 61,690
Σ Hits = 25,688, Σ Counts = 160,053
Σ Hits = 44,232, Σ Counts = 366,804
Σ Hits = 9,784, Σ Counts = 322,891
Σ Hits = 34,729, Σ Counts = 1,184,880
Σ Hits = 79,719, Σ Counts = 2,736,485
Σ Hits = 173,772, Σ Counts = 6,668,306
Σ Hits = 141,975, Σ Counts = 6,542,613
Σ Hits = 160,280, Σ Counts = 6,423,255

Table 2. Results of the fatigue test.
Cylinder reference
J2045
J3386
J3416
J2105
J3462
J2041
J2043
J2112
J2144
J3419

DefectNone
Drop test, 500 J
Drop test, 1000 J
Drop test, 2000 J
Ballistic impact, 137 J
Ballistic impact, 280 J
Ballistic impact, 419 J
Flaws, 1.25 mm deep
Flaws, 2.5 mm deep
Flaws, 5 mm deep

Fatigue test results
No failure after 5000 cycles
No failure after 5000 cycles
No failure after 5000 cycles
Failure after 2924 cycles
Failure after 4376 cycles
Failure after 3216 cycles
Failure after 892 cycles
No failure after 5000 cycles
No failure after 5000 cycles
No failure after 5000 cycles

AE activity recorded during the pressurization of the cylinders with drop impact damages isplotted in Fig. 5. The total number of hits and counts increases almost linearly with the impact
124

energy. Most hits were recorded by the sensor N°4, placed close to the main impact area (for thethree different impact energies, about 80 % of the total hits were recorded by sensor N°4).
5,00E+04
4,50E+04
4,00E+04
3,50E+04
All Channels
Channel 1
Channel 2
Channel 3
Channel 4

Hits

3,00E+04
2,50E+04
2,00E+04
1,50E+04
1,00E+04
5,00E+03
0,00E+00
0

500

1000

1500

2000

2500

Drop impact energy (J)4,00E+05
3,50E+05
3,00E+05

Counts

2,50E+05

All Channels
Channel 1
Channel 2
Channel 3
Channel 4

2,00E+05
1,50E+05
1,00E+05
5,00E+04
0,00E+00
0

500

1000

1500

2000

2500

Drop impact energy (J)Fig. 5. Evolutions of AE hits and counts as a function of the drop impact energy.
AE activity recorded during the hydraulic pressure tests on cylinders with ballistic impact
damages is plotted in Fig. 6a and b. As in the previous case, AE activity increased almost linearly with the impact energy. Most hits (around 70 % of total hits number for the different impact
energies) were recorded by sensor N°2, placed at 50 mm far from the impact point; see Fig. 6a.
Figure 7 shows the evolution of AE hits and counts recorded during the pressure test as afunction of notch depth. As confirmed by these 2 graphs, the severity of the damage (in our casethe notch depth) seems to have no influence on the AE activity. Among all the tested cylinders,
the total number of hits and counts is the highest for the specimens with notches. The highest
number of recorded AE hits or counts was obtained for the cylinder with 1.25-mm deep flaws.
The sensor N°1, close to the longitudinal notch, recorded around 60 % of all the hits.
The cylinders were finally submitted to a fatigue test (5000 cycles between 30 and 300 bar)in order to have an estimation of the defect influence on the structure integrity. Results of these125tests are presented in Table 2. Most of the cylinders withstood the fatigue test without failure.
The cylinder, impacted at low velocity and with an energy of 2000 J, failed approximately after
3000 cycles with a leak before break, a safety issue as required by the standards. All the ballistic
impacted cylinders failed also by leakage during the fatigue test and the number of cycles to failure decreases when the impact energy increases.
9,00E+04
8,00E+04
7,00E+04

Hits

6,00E+04

All Channels
Channel 1
Channel 2
Channel 3
Channel 4

5,00E+04
4,00E+04
3,00E+04
2,00E+04
1,00E+04
0,00E+00
0

100

200

300

400

500

Ballistic impact energy (J)a)

3,00E+06

2,50E+06

Counts

2,00E+06

All Channels
Channel 1
Channel 2
Channel 3
Channel 4

1,50E+06

1,00E+06

5,00E+05

0,00E+00
0

100

200

300

400

500

Ballistic impact energy (J)b)
Fig. 6. Evolutions of a) AE counts and b) hits as a function of the ballistic impact energy.
After the cycling test, significant delamination of a circumferential composite strip was observed on both side of the longitudinal notch for each cylinder with notch defect (cf. Fig. 8).
Discussion
In this work, the severity of the investigated defects can be related to the impact energy forthe drop and ballistic tests and to the depth for the notched cylinders. The experimental results
show that, for the adopted AE sensor location, the acoustic activity increases almost linearly withthe damage severity for the cylinders submitted to drop test and ballistic impact, unlike the cylinders with notches. In the case of impact defects, the severity of the damages may be estimatedby the AE method. For the cylinders with notches, considering the results in terms of AE activity
126

2,00E+05
1,80E+05
1,60E+05
1,40E+05
All Channels
Channel 1
Channel 2
Channel 3
Channel 4

Hits

1,20E+05
1,00E+05
8,00E+04
6,00E+04
4,00E+04
2,00E+04
0,00E+00
0

1

2

3

4

5

6

Notch depth (mm)8,00E+06
7,00E+06
6,00E+06

Counts

5,00E+06

All Channels
Channel 1
Channel 2
Channel 3
Channel 4

4,00E+06
3,00E+06
2,00E+06
1,00E+06
0,00E+00
0

1

2

3

4

5

6

Notch depth (mm)Fig. 7. Evolutions of AE hits and counts as a function of the notch depth.

Delaminatedcomposite strip

Longitudinal
notch

Cylinder after cycling

Cylinder before
cycling

Fig. 8. Delamination of the cylinders with notches after fatigue.
127

(no significant difference regarding the notch depth) and damages, observed after cycling, it
seems that the delamination was the main source of AE during the pressure test with AE monitoring. The fact that delamination propagation occurred in a similar way for the three different
notch depths could explain why no significant difference in AE activity was recorded and why
the number of AE hits or counts could not be representative of the defect severity for the range ofnotch depth considered in this study. The loading of the structures during the pressure test does
not cause the propagation of the matched notch but mostly delamination that is induced by thelongitudinal notch but not influenced by its depth.
The influence of each type of defect on the structural integrity of the cylinders seems difficult
to estimate regarding the results of the AE activity in terms of total number of hits and counts. In
comparison with the cylinders with notches, the AE activity due to ballistic impact damages islower. Nevertheless, regarding the failure of these cylinders during the fatigue test, the impact
ballistic defects are found to be more critical. The difficulty to estimate the gravity of the defects
by considering the total number of hits or counts may also be related to the failure mode, thatwas observed during the fatigue tests. For the cylinders which did not pass the 5000 cycles at 300
bar, the failure occurred by leakage, this kind of failure is induced by liner cracking and may occur even if the composite shell keeps good load bearing properties, which could ensure a sufficient burst pressure if the structure was tight. For this reason, it seems that AE monitoring may
give a good assessment of the composite shell damage level, but may not give sufficient information for predicting the leakage risk.
Table 3. Results of high amplitude AE (80-100 dB).
Cylinder reference
J2045
J3386
J3416
J2105
J3462
J2041
J2043
J2112
J2144
J3419

DefectNone
Drop test, 500 J
Drop test, 1000 J
Drop test, 2000 J
Ballistic impact, 137 J
Ballistic impact, 280 J
Ballistic impact, 419 J
Flaws, 1.25 mm deep
Flaws, 2.5 mm deep
Flaws, 5 mm deep

AE between 80 and 100 dB
Σ Hits = 0, Σ Counts = 0
Σ Hits = 0, Σ Counts = 0
Σ Hits = 2, Σ Counts = 977
Σ Hits = 13, Σ Counts = 14,423
Σ Hits = 58, Σ Counts = 110,987
Σ Hits = 238, Σ Counts = 481,985
Σ Hits = 495, Σ Counts = 1,192,418
Σ Hits = 2014, Σ Counts = 2,265,270
Σ Hits = 1990, Σ Counts = 3,003,495
Σ Hits = 1803, Σ Counts = 2,643,058

The defect effect on the structural integrity could also be evaluated by the high amplitude AE
events. Several works, performed on fiber-reinforced polymer (composite) coupons, have shownthat AE amplitude distribution can be used to identify the main damage mechanisms (matrix
cracking, interface fracture, fiber pull-out, fiber fracture) in composite materials (Barré et al.,
1994; Kotsikos et al., 1999). High amplitude signals, over 80 dB, are generally associated to fiber fractures, which can be considered as the most critical damage mechanism regarding thestructural integrity of the cylinders. Nevertheless, this correlation between the AE amplitude distribution and the damage mechanisms is generally difficult to apply to large composite structuresdue to significant attenuation of acoustic waves in the heterogeneous materials. Table 3 presents
the high amplitude AE activity (amplitude between 80 and 100 dB), recorded by the four AE
sensors during the pressure tests. For the cylinders after drop test and ballistic impact, the128number of high amplitude hits increases with the impact energy. Only a few hits between 80 and
100 dB were recorded for the cylinders after drop test. The highest numbers of high amplitude
hits were obtained for the cylinders with notches and the high amplitude activity does not seemto be influenced by the notch depth. Considering the results of the high amplitude activity, thenotches should be considered as the most critical defect for the structural integrity of the cylinders.
In this study, the results also show that the number of AE events recorded by the sensor
placed close to the defects (i.e., the main source of AE signals) is significantly higher than for theother sensors (60 to 80 % of the total number of hits was recorded by the sensor close to thedamage). Furthermore, from the Hsu-Nielsen tests performed on the reference cylinder, the attenuation coefficient, β, and the wave speed, v, was determined for different directions (cf. Fig.
9). These results show an anisotropic behavior and a significant attenuation of the acoustic signals. Considering this significant attenuation effect for the composite materials and the fact that a
critical damage might not be clearly localized before placing the sensors on the structure (which
was not the case in this study for the created defects), measurements show that the number of
sensors and their position will have an influence on the recorded AE events.
3

4! = 36 dB/mv = 6500 m/s! = 36 dB/mv = 4800 m/s! = 58 dB/mv = 4500 m/s
2

1

Fig. 9. Attenuation coefficient, β, and acoustic wave speed, v.
2,00E+04
1,80E+04
1,60E+04

Channels 1, 3, 4

1,40E+04

Hits

1,20E+04

Channel 1

1,00E+04
Channel 3

8,00E+03
6,00E+03

Channel 4

4,00E+03
2,00E+03
0,00E+00
0

100

200

300

400

500

Ballistic impact energy (J)Fig. 10. Evolution of AE hits as a function of the ballistic impact energy without results from the
AE sensor close to the damage.
129

Furthermore, the influence of the sensor in the vicinity of the damaged area may be estimatedby considering the previous results without taking into account the hits recorded by this sensor.
Figure 10 shows the number of hits, recorded by all the AE sensors except the one close to theimpact point, versus the impact energy for the cylinders after ballistic impact. The total numberof hits increases almost linearly with the impact energy and the defect severity may still be estimated without taking into account the sensor close to the defect. On the contrary, for the cylinders after drop test (cf. Fig. 11), no linear relationship between hits and energy can be establishedwhen the hits, which were recorded by the sensor close to the main impact area, are not taken
into account. For the ballistic impact, the damaged area is mainly restricted to a limited zone
around the impact point. After a drop test, two damaged areas may be identified (cf. Fig. 4a): themain impact area under the impact point, and the rebound impact area on the opposite dome.
Without the hits from the sensor close to the main impact area, the evaluation by AE monitoring
mainly concerns the second damaged area, as shown by the significant number of hits, recordedby the sensor N°3, close to the rebound impact area, for impact energies of 1000 and 2000 J.
1,00E+04
9,00E+03
8,00E+03

Channels 1, 2, 3

7,00E+03

Hits

6,00E+03

Channel 1

5,00E+03
Channel 2

4,00E+03
3,00E+03

Channel 3

2,00E+03
1,00E+03
0,00E+00
0

500

1000

1500

2000

2500

Drop impact energy (J)Fig. 11. Evolution of AE hits as a function of the drop impact energy without results from the AE
sensor close to the damage.
Conclusions
This work shows the influence of critical defects (flaws, ballistic impact, drop test) on the AE
activity of composite cylinders.
For the ballistic and drop test impact, AE activity increases almost linearly with the impact
energy, and in this case, the damage severity may therefore be estimated by the number of AE
hits and/or counts monitored during the AE test. For notches, the damage severity has no influence on the AE activity. It seems that delamination, induced whatever the notch depth is, is themain source of acoustic emission.
For the different investigated damages, considering the total number of hits and counts, andparticularly considering the high amplitude acoustic signals, appears insufficient to estimate thestructural integrity: the highest AE activity was obtained for cylinders with notches, and thesestructures did not fail during the fatigue test. But, this difficulty to determine a basic AE criterion
in order to evaluate the cylinder integrity may also be related to the failure mode. Failure by130

leakage is not acceptable for the integrity of a pressure vessel, but this kind of failure, related to
the liner tightness, can happen even if the composite shell keeps good mechanical properties. For
this reason, even if the proposed AE analysis may give a good evaluation of the damage level forthe composite material, it seems insufficient for the leakage risk prediction.
This work also confirmed the significant attenuation of acoustic signals in composite materials. This result underlines that the number and the position of AE sensors may be a critical point
for large composite structures.
References
S. Barré, M. Benzeggagh (1994), "On the use of acoustic emission to investigate damage mechanisms in glass-fibre-reinforced polypropylene", Composite Science and Technology, 52, 369376.
A. R. Bunsell (2006), "Composite pressure vessels supply an answer to transport problems", Reinforced Plastics, 50 (2), 38-41.
L. Dong, J. Mistry (1998), "Acoustic emission monitoring of composite cylinders", Composite
Structures, 40 (2), 149-158.
P. Gebski, L. Golaski, K. Ono (2001), "Acoustic emission monitoring of fatigue of glass-fiber
wound pipes under biaxial loading", Journal of Acoustic Emission, 19, 285-314.
M. Hamstad (1982), "Testing fiber composites with acoustic emission monitoring", Journal of
Acoustic Emission, 1, 151-164.
G. Kotsikos, J. T. Evans, A. G. Gibson, J. Hale (1999), “Use of acoustic emission to characterize
corrosion fatigue damage accumulation in glass fibre reinforced polyester laminates”, Polymer
Composites, 20 (5), 689-696.
M. Wevers, M. Surgeon (2000), "5.14 Acoustic emission and composites", Comprehensive
Composite Materials Book, vol. 5, eds. A. Kelly, C. Zweben, R. Talreja, J.E. Manson, Pergamon,
pp. 345-357.
C. Webster (1999), "Assessment of the use of acoustic emission as an inspection method for FRP
wrapped cylinders", Transport Canada Publication N° TP 13505E, Powertech Labs Inc.
B. Wood, R. Harris (2000), "Structural integrity and remnant life evaluation of pressure equipment from acoustic emission monitoring", International Journal of Pressure Vessels and Piping,77, 125-132.

131

AUTOMATED METHOD FOR STATISTIC PROCESSING OF
AE TESTING DATA
V. A. BARAT and A. L. ALYAKRITSKIY
Research Dept, Interunis Ltd., bld. 24, corp 3-4, Myasnitskaya str., Moscow, 101000, Russia
Keywords: signal processing, AE data classification
Abstract
Acoustic emission (AE) as a nondestructive testing method allows estimating the conditionof sophisticated industrial objects, detecting defects at their initiation, and preventing development of such defects. The reliability of detection and the accuracy of determination of AE
source location depend on the correct interpretation of the AE testing results. Testing proceduresof different industrial equipment are intended for estimating condition of the objects on the basisof AE parameters, while the AE signals proper are not used in analysis as the primary diagnosticdata.
We have developed the statistical method of analysis to study the whole complex of measured data, both the AE parameters and signal waveforms. This method is based on the two-levelclustering of data, such as AE signals from separate channels, and "groups of signals" formed on
the basis of the primary clustering. The algorithm allows the determination of the quantity of AE
sources, and estimates the degree of their danger without resorting to the preliminary location.
The zonal location is carried out after the determination of AE sources. It avoids the so-called
false locations, which considerably complicate the location map. On the basis of classification
results, one can correct the AE signal arrival time, and such correction makes it possible to improve the accuracy of the AE source location.
The characteristic property of the developed method is data processing in an automatic mode,with minimal operator intervention. In this case there is no need to employ a high-skilled operator. Since the AE signals themselves are used for analysis, the obtained results prove to be morereliable than those obtained when working with the AE parameters. The algorithm built on thedeveloped method has been successfully applied for processing data obtained as a result of laboratory research of the corrosion development, for studying reinforced concrete structures, andalso as an additional instrument for processing data of the industrial AE testing.
Introduction
Each AE event defines a unique process occurring at a certain point of the test object. WhenAE signals propagate from the point of emission to a sensor, the waveforms are complicated due
to conversions to different modes of waves, multiplicity of distribution paths, and due to wave
velocity dispersion by frequency. AE signals contain information not only about the AE source
event for this signal, but in the highest degree about parameters of acoustic and electric path. In
this connection construction of the analytical or even numerical diagnosis model for interpretation of AE signals appears to be an intricate and nontrivial problem, the solution of which cannot
be generalized for the test equipment of different types.
While the data interpretation based on accurate diagnostic models appears to be difficult, thestatistical analysis is a reasonable choice for carrying out the data interpretation and classificaJ. Acoustic Emission, 26 (2008)132© 2008 Acoustic Emission Grouption with the high certainty. At present the statistical methods of data analysis are widely appliedboth in industrial AE systems and in laboratory research and the applications are varied: dataclustering, correlation analysis, and check of different statistical hypotheses.
In this paper the statistical method of analysis enabling the automatic clustering of AE testingdata is presented. This method makes it possible to process bulk data obtained as a result of the
AE testing or during the laboratory research in the absence of a priori information. As a consequence of processing, the data are structured and organized; each cluster formed as a result ofanalysis characterizes an AE source at a definite stage of development.
Method Description
Statistical analysis of AE data is used rather frequently. However, different methods of dataprocessing pursue different aims, and have special features of implementation [4, 5, 7, 8]. Our
approach is based on the idea that the signals generated by the same AE source have a similar
waveform. This similarity may be explained by the similar way of AE wave generation and by
the same acoustic path for AE wave propagation. AE waves from the growing defect propagate
in all directions. At the sensor location point these waves are registered and transformed into AE
signals. With the help of signal analysis we simulate the reverse process – signals grouping andunifying.

Fig. 1. Flowchart of automated method of AE data analysis.
The initial data for algorithm, which realizes this method, can be both the AE signals andtheir parameters computed under data acquisition in on-line mode. When the processing begins,
the initial data represent a bulk of different diagnostic data, and at each step of algorithm the dataconsecutive structuring takes place. At the first step the signals generated by the same AE eventare grouped together. For that purpose we compare the differences in the signals arrival timewith the distances between sensors. This group of AE signals is designated in Fig. 1 with small
figures (circles, rhombs and squares) joined by a brace. Different shapes of these figures represent different measuring channels, by which the signals are registered. Each group characterizes
one AE event.
133

At the second step the groups obtained are classified. Algorithm designed for this purpose
has a hierarchical structure. At the first stage AE signals are classified in accordance with thesignal waveform similarity. In the diagram different classes of signals are marked with different
colors. The groups of signals are classified according to the result of previous classification. The
groups, wherein the signals recorded by the same channel have a similar waveform, are assigned
to the same class. The classes of groups obtained in this way correspond with a high degree ofprobability to the potential AE sources.
When a part of AE signals is defined only by parameters, and waveforms are absent, the“classes of signals” and “classes of groups” are formed on the basis of incomplete diagnosis
information. For classification of AE signals specified only by their parameters, each “class ofgroups” is characterized with a feature set included into the list of impulse parameters to be defined. In this case the classification of AE impulses for which there are no primary diagnosis datais accomplished on the basis of multidimensional empirical distribution function built for the calculated features.
Clustering of Acoustic Emission Signals
Estimation of similarity
For AE signal clustering, it is necessary to determine a distance measure [3]. In order that an
estimation error of AE parameters does not influence on the classification result, the correlation
coefficient r was used as a measure of similarity of two signals; see Eq. (1). The preliminary
analysis has shown that signals relevant to the same AE source and recorded by the same measuring channel have a high correlation coefficient, whose value varies from 0.70 to 0.99 depending on the nature of the AE generating process.
Nr=∑ ( x − x )( y − y )
i =1

i

N

i

N

∑ (x − x) ∑ ( y − y)
i =1

2

i

i =1

(1)
2

i

Using the correlation coefficient as the distance measure of AE signals, an automatic correction of arrival times of signals, which belong to the same cluster, can be carried out, because thecross-correlation function reaches its peak at the time corresponding to a difference of arrival
times of AE signals.
Wavelet analysis
When processing bulk data, the calculation of correlation coefficients for all pairs of signals
requires a long time; to speed up the data processing, a wavelet decomposition may be used,
namely, a signal decomposition by wavelet packets [2].
The decomposition by wavelet packets is one of variety of the multi-resolution analysis, and
it is the signal decomposition on the basis of wavelets (in a general case by the Riss basis) specified for a sequence of subspaces embedded to each other.
w2 n ( x) = 2 ∑ h(k ) wn (2t − k )kw2 n+1 ( x) = 2 ∑ g (k ) wn (2t − k ) ,kwhere g (k ) = (−1) k h(1 − k ) is defined by the type of wavelet function.

134

(2)

Under a discrete wavelet transform the signal is decomposed into a low-frequency component - approximation and a high-frequency component - detailing. At the next level both the approximation and the detailing are subject to further decomposition. So, at j-level of decomposition the 2j coefficients are calculated. In this case the coefficient (j, k) localizes energy within thefrequency range (Eq. 3), where Ω0 is the frequency corresponding to a half of the sampling ratefrequency (Fig. 2)

Fig. 2 Wavelet-packed decompositionΩ⎛Ω⎞ΔΩ = ⎜ j0 k , j0 (k + 1) ⎟
2
⎝2
⎠

(3)

As a rule, the AE signal energy is non-uniformly distributed on the spectrum, and often 3 or
5 coefficients of cluster decomposition are sufficient for location of 95-99% of the energy.
Moreover, the dimension of informational components, as a rule, is at least four times smaller
than the original signal dimension.
This paper offers the assessment method of signal shape similarity on the basis of weighted
sum of correlation coefficient of significant approximations and detailings of signal wavelet- decomposition. The research has shown that the accuracy of this estimation is about 85%.

r = ∑ α ⋅ rij(4)

Figure 3 illustrates the estimation method offered. Figures 3a and 3b show the input signals x
and y. Instead of direct calculation by Eq. 1 we calculate the cross-correlation function between
significant wavelet decomposition coefficients. In this example we have taken only two significant wavelet-decomposition coefficients, С42 (Figs. 3с, е) and C54 (Figs. 3d, f), duration ofwhich is more than one order lower than the duration of the initial signals. Because the calculation rate of cross-correlation function is proportional to the square of signal duration, the use of
Eq. (4) allows the significant reduction in the time of computation of the degree of signals similarity.
Clustering scheme selection
With the clustering of AE signals, the quantity of clusters is to correspond to the quantity of
the assumed AE events. In designing the clustering algorithm, a particular attention was given to
the true determination of cluster quantity. For illustrating this aim, we suggested a two-stage
135

clustering diagram, as shown in Fig. 4; that is, the flowchart of clustering algorithm. At the firststage, a hierarchical agglomerative algorithm with complete link clustering is applied. Complete
link clustering provides the maximum distance between elements of each cluster. This clustering
method allows for avoiding an undesirable merging of clusters. At the second step, the iterative
method providing the controllable merging of clusters is applied.

Fig. 3a and b - Initial signals for correlation coefficient calculation; c and e – significant coefficients, C42 and C54, of wavelet-decomposition for signal 3а; d and f - significant coefficients,
C42 and C54, of wavelet-decomposition for signal 3b.
The distances between centers of mass of the clusters formed are calculated, and two clusters
having the maximum and above-threshold distance between the clusters centers are merged.
Thereafter, the re-clustering is accomplished by the method of k-averages.

Fig. 4. Flowchart of two-stage clustering algorithm of AE signals.
136

When using this flowchart of clustering algorithm, the clustering is flexible and controllable;
it uses two settings – the threshold value for signal addition to the cluster (at hierarchical clustering) and the threshold value for the inter-cluster distance. The setting values are selected on thebasis of information about the test object and the testing conditions.
Clustering of Groups of Acoustic Emission Signals
At the next stage of algorithm, the groups of signals relevant to the same AE event are analyzed. For formation of such groups, we analyzed the arrival times of AE signals and the difference of recording time of the first and last signals in the group. The latter should not exceed thespecified value, which is determined by the ratio of its maximum overall dimension and minimum velocity of acoustic wave propagation experimentally measured for the given test object.
An AE signal beyond the time window is initial AE signal for the next group.
To the same “class of groups” assigned are the groups wherein the signals recorded by thesame measuring channels corresponding to the same classes of signals. The study of algorithm
has shown that for assigning the group of signals to one or another class it is sufficient that theclasses of signals coincide at least for two channels in the group.
The membership of “classes of groups” can be also defined for AE signals, even with no information about the AE signal waveform. For this purpose, each “class of groups” obtained ischaracterized by the values of features listed below representing a median of distribution for eachclass.
− Numbers of three channels with minimum time of signal arrival {n_t1, n_t2, n_t3}− Numbers of three channels with maximum signal amplitude {n_a1, n_a2, n_a3}− Maximum value of signal amplitude in the group Amax
− Ratio of amplitudes of signals recorded by channels n_t2 and n_t1, and also n_t3 and n_t1 {A21
& A31}The signal group belonging to one or another class is defined similarly to the k-averages
method, by the minimum distance between the features, characterizing each “class of groups”
and the group of features to be analyzed.
The “classes of groups” obtained by such a manner characterize the potential AE sources
with a high degree of probability. The quantity of “classes of groups” corresponds to the quantityof AE sources; the numbers of channels, by which the signals with the minimum arrival time arerecorded, define the number of location zone. The quantity of groups in each class and the energy of AE signals included into the group can define the degree of danger of AE source.
Results and Discussion
The practical examples can visually confirm the effectiveness of the present method. To research regularities of AE in concrete, a series of experiments was carried out. On a concrete cubewith a 1-m side length, eight AE sensors were placed, one at each vertex. The AE sensors wereused both for AE measurement mode and as a pulser for the simulation of AE sources.
To illustrate advantages of the present method, two of the experiments were selected. Experiment 1 illustrates the case when the AE sources are not found because of mechanical noise
and impacts. There are two AE sources, a pulser that simulates a growing defect, and a hammer
137

that simulates mechanical noise of equipment. Figure 3а shows the result of volume location of
the AE sources. Indications distributed over the whole field of location are generated by the mechanical noise source or hammer blows. As the hammer impacts are made manually, the relevant
signals have different waveforms. In this case, determination of the arrival time by the thresholdmethod entails the difference in arrival time errors. The errors of arrival time determination differ in their meaning and sign for the signals with different waveforms. This is one of the possible
reasons for the distribution of source locations.
Application of the statistical method of data analysis allows not only forming “the class ofgroups” of AE impulses emitted by the simulator, but also pointing out as an independent classthe signals relevant to the hammer blows. Table on Fig. 5 shows the results of operation; the attributes given in this table conform to the list of “class of group” features. The names of featuresin this table comply with the previous list of features. Each line of the table characterizes one"class of groups"

Fig. 5 Diagram of experiment 1, results of volume location and signal analysis statistics.
In Experiment 2, there are six AE sources in all. Two types of acoustic waves were generated
at three positions on the concrete cube surface by means of a pulser and a Hsu-Nielsen source.
Figure 6b shows the three locations of the AE sources. During the statistical processing of data,application of the present method can give six classes of events corresponding to two different
sources of AE at three points on the test object surface, as shown in the table on Fig. 6. There are
two "classes of groups" in each location zone, which is defined by the channels number {n_t1,
n_t2, n_t3}.
One more useful implementation of the present method is structuring and data compression.
The data structuring is realized due to replacement of the AE signals being analyzed by the"classes of signals" and "classes of groups". Thus, the quantity of the information under analysis
is reduced considerably. For example, during experimental AE studies from pitting corrosion
growth, several hundred thousand signals were recorded; after the statistical analysis of data in
an auto-mode without preprocessing about 20 representative classes of "groups of pulses" describing different stages of corrosion damage development were defined [6].

138

In practical AE applications, it is effective to use automated method of statistical analysis forprocessing of the AE monitoring data, especially when it is necessary to analyze changes in the
structure of AE signals recorded for any length of time, and also in the case of testing sophisticated industrial facilities, for which the construction of an acceptable location scheme appears to
be difficult.

Fig. 6 Diagram of experiment 2, results of volume location and signal analysis statistics.
Additional Possibilities of the Method
The offered statistical method gives comprehensive possibilities to the user, such as AE data
structuring, AE source detection and estimation of their quantity. Besides that our method has
two additional possibilities, which are not directly connected with the final results. They are arrival time correction and some kind of noise filtration.
The arrival time correction is a very important problem. Precision of the arrival time estimation ensures the precise location. As it was mentioned above, the similarity of AE signals is
measured with the help of correlation coefficient.
To calculate the correlation coefficient, it is necessary to calculate at first the crosscorrelation function. The cross-correlation function reaches its peak at the time corresponding to
the difference of arrival times of AE signals. So, we can correct the difference of arrival times ofany two signals belonging to the same class.
At such approach the class of “AE signals groups” is located at one point. For correct location it is necessary only to specify the arrival times of impulses for a single element of the class.
Figures 7a and b shows two signals of acoustic emission. These signals have the similar shape,
but because of different amplitude the signals reach a threshold value at different times. As isevident from Fig. 7с, the error of determination of these signals arrival time difference makes
about 200 µs.
139Fig. 7 a and b – AE signals; c – cross-correlation function of signals of 7a and 7b above.

Fig. 8 Types of noise removed under data structuring: a) band noise; b) stochastic noise.
Additional possibility is filtration of some types of noise. An uncorrelated stochastic noise isremoved automatically because according to the accepted mode of classification it cannot be referred to any class. Some types of the correlated noise, for example, harmonic ones, form theirown classes, and may be excluded from consideration under an expert analysis of the results obtained.
Conclusion
In this paper described is an automated statistical analysis method of AE data, which makes it
possible to structure the AE test data through partitioning into different clusters or the groups ofsignals, characterizing the different AE sources. The key features of the given method are, firstly,
the possibility to process data in an automatic mode with minimum involvement of operator anda minimum number of settings, and, secondly, the possibility to carry out the analysis of the heterogeneous diagnosis information. Based on the results of statistical analysis, it is possible tospecify the quantity of AE sources, to carry out its zone location, and to get additional evaluationof the danger criterion without resorting to the preliminary location.

140

When using the correlation coefficient as a measure of proximity under cluster analysis of
AE signals, we can carry out an automatic correction of signal arrival times belonging to thesame clusters, because the cross-correlation function reaches its peak at the point of time corresponding to the difference of arrival times of AE signals.
References
1. V.A. Barat, A.L. Alyakritskiy. Statistical method for processing of AE signals and their parameters for increasing of reliability of the testing results. Proceeding of the 17th RussianInternational Scientific-Technological Conference on Non-Destructive Testing and Diagnostics.
Ekaterinburg, 2005. (on the CD-ROM)
2. Ingrid Daubechies, Ten lectures on wavelets, SIAM, Philadelphia, 1992.
3. Ajvazyan S.A., Buhshtaber V.M., Enjukov I.S., Meshalkin L.D., Applied statistics in 3 parts,
1989. (in Russian)
4. L.N. Stepanova, A.E. Kareev. Development of a method for the dynamic clustering of AE
signals for increase of accuracy of their localization, Control Diagnostika, 2003, № 6, pp. 15-20.
(in Russian)
5. A. A. Anastassopoulos, T. P. Philippidis. Clustering Methodologies for the evaluation of AE
from Composites, Journal of Acoustic Emission, 13, (1/2), 1995, 11-21.
6. Yu.S. Popkov, A.L. Alyakritskiy, E.Yu. Sorokin, D.A. Terentyev. AE method for determination of pitting corrosion depth and monitoring of defect propagation rate. the Proceeding of EWGAE 2008.
7. Vallen VisualAE™. The standard in Acoustic Emission software. Vallen-Systeme GmbH.
http://www.vallen.de
8. AE-Studio. NPF Diaton. http://www.diatontest.ru/ae_studio.htm

141

TERMITES DETECTION VIA SPECTRAL KURTOSIS AND WAVELET
DE-NOISING OF ACOUSTIC EMISSION SIGNALS
JUAN-JOSÉ G. DE LA ROSA1, ANTOLINO GALLEGO2, ROSA PIOTRKOWSKI3,
ENRIQUE CASTRO2 and ANTONIO MORENO-MUÑOZ4
1
University of Cádiz, Electronics Area, Research Group PAIDI-TIC-168. Algeciras Polytechnic
School, 11202 Algeciras-Cádiz, Spain; 2Univ. of Granada, Dept. of Applied Physics, 18071
Granada, Spain; 3Escuela de Ciencia y Tecnología, Universidad Nacional San Martín, Buenos
Aires, Argentina, 4University of Córdoba, Electronics Area, Research Group PAIDI-TIC-168.
Campus Rabanales. Edificio Albert Einstein, 14071 Córdoba, Spain
Keywords: Higher-Order Statistics, Insect detection, Spectral Kurtosis.
Abstract
In this paper we present the operation results of a portable computer-based measurement
equipment conceived to perform non-destructive testing of suspicious termite infestations. Its
signal processing module is based in the Spectral Kurtosis (SK), with the de-noising complementof the discrete wavelet transform (DWT). The SK pattern allows the targeting of alarms andactivity signals. The DWT complements the SK, by keeping the successive approximations of
the termite emissions, supposed more non-Gaussian (less noisy) and with less entropy than thedetail approximations. For a given mother wavelet, the maximum acceptable level, in the wavelet
decomposition tree, which preserves the insects' emissions features, depends on the comparative
evolution of the approximations details' entropies, and the value of the global spectral kurtosis
associated to the approximation of the separated signals. The paper explains the detection
criterion by showing different types of real-life recordings (alarms, activity, and background).
Introduction
Biological transients gather all the natural complexity of their associated sources, and the
media through which they propagate. As a consequence, finding the most adequate method to get
a complete characterization of the emission implies the selection of the appropriate model, which
better explains the processes of generation, propagation and capture of the emitted signals. This
description matches the issue of measurement termite activity.
This paper presents the improved equipment over previous prototype's performance, based in
the time-frequency domain analysis of the kurtosis, described in [1-2]. The measurement method
is based in the interpretation of the spectral kurtosis (SK) graph, along with the wavelet analysis,
which is thought as an aid. At the same time, we use a simple data acquisition unit, the soundcard (maximum speed at 44.1 kHz), which simplifies the hardware unit and the criterion of
detection. The instruments for plague detection are thought with the objective of decreasing
subjectivity of the field operator [1-5]. On-site monitoring implies reproducing the natural
phenomenon of insect emissions with high accuracy. As a consequence it is imperative the use ofa deep storage device, and high sensitive probes with a selective frequency response. Thesefeatures make the price paid high, and do not guarantee the success of the detection.
Regarding the procedures, the prior detection methods are very much dependent on thedetection of the excess of power in the signals; these are the so-called second-order methods. For
J. Acoustic Emission, 26 (2008)142© 2008 Acoustic Emission Groupexample, the RMS calculation can only characterize the intensity, and does not provide
information regarding the envelope of the signal nor the amplitude fluctuations. Anotherhandicap of the second-order principle, e.g. the power spectrum, attends to the preservation of
the energy during data processing. Consequently, the eradication of additive noise lies in filter
design and sub-band decomposition, like wavelets and wavelet packets.
As an alternative to improve noise rejection and complete characterization of the signals, inthe past ten years, a myriad of higher-order statistics (HOS) methods are being applied indifferent fields of science and technology, in scenarios involving signal separation andcharacterization of non-Gaussian measurements [6]. Concretely, the area of diagnosticsmonitoring of rotating machines is also under our interest due to the similarities of the signals to
be monitored with the transients from termites. Many time-series of faulty rotating machines
consist of more-or-less repetitive short transients of random amplitudes and random occurrencesof the impulses [7].
This paper describes a method based in the SK (related to the fourth-order cumulant at zerolags) to detect infestations of subterranean termites in a real-life scenario (southern Spain).
Wavelet decomposition is used as an extra tool to aid detection from the preservation of theapproximation of the signal, which is thought to be more Gaussian than the details.
The interpretation of the results is focused on the peakedness of the statistical probability
distribution associated to each frequency component of the signal, to get a measure of the
distance from the Gaussian distribution. The SK serves as a twofold tool. First, it enhances nonGaussian signals over the background. Secondly, it offers a more complete characterization of
the transients emitted by the insects, providing the user with the probability associated to eachfrequency component.
The paper is structured as follows: in the following section a review on termite detection andrelevant HOS experiences sets the foundations. Then we make a brief report on the definition of
kurtosis; we use an unbiased estimator of the SK, successfully used in [1-2]. Results arepresented thereinafter. Finally, conclusions are drawn.
Subterranean Termites: Detection project towards HOS
Termite detection has been gaining importance within the research community in the last two
decades, mainly due to the urgency of avoiding the use of harming termiticides, and to the joint
use of new emerging techniques of detection and hormonal treatments, with the aim of
performing an early treatment of the infestation. A localized partial infestation can be
exterminated after two or three generations of the colony's members with the aid of these
hormones, which stop chitin synthesis. A chitin synthesis inhibitor kills termites by inhibiting
formation of a new exoskeleton when they shed their old one. As a direct consequence, theweakened unprotected workers stop feeding the queen termite, which dies of starvation.
The primary method of termite detection consists of looking for evidence of activity. But
only about 25 % of the building structure is accessible, and the conclusions depend very much on
the level of expertise and the criteria of the inspector [1-5]. As a consequence, new techniques
have been developed to remove subjectivity and gain accessibility.

143

User-friendly equipment is currently used in targeting subterranean infestations by means of
temporal analysis of the vibratory data sequences. An acoustic-emission (AE) sensor or an
accelerometer is fixed to the suspicious structure. This class of instruments is based on thecalculation of the RMS value of the vibratory waveform. The RMS value comprises informationof the AE raw signal power during each time-interval of measurement (averaging time). Thismeasurement strategy conveys a loss of potentially valuable information both in the time and in
the frequency domain [1-5]. A more sophisticated family of instruments makes use of spectral
analysis and digital filtering to detect and characterize vibratory signals [4]. Other second-order
tools, like wavelets and wavelet packets (time-dependent technique) concentrate on transientsand non-stationary movements, making possible the detection of singularities and sharptransitions, by means of sub-band decomposition.
Higher-order statistics are being widely used in several fields. The spectral kurtosis has beensuccessfully described and applied to the vibratory surveillance and diagnostics of rotatingmachines [7]. In the field of insect detection, the work presented in [2] set the foundations of thepresent paper. The combined used of the SK and the time-domain sliding kurtosis showedmarked features associated to termite emissions. In the frequency domain (sample frequency
64.0 kHz), three frequency zones were identified in the spectral kurtosis graph as evidence ofinfestation; two in the audio band (which will be also checked in the present paper) and one in
the near ultrasound (roughly equal to 22 kHz). In the present paper the sample frequency was
fixed to 44.1 kHz and the sound card was directly driven by MATLAB. Results are presented in
the user interface, which is shown in Fig. 1; in this measurement situation, the time-raw datacontains alarms and activity signals from termites. This is a clear example of positive detection.

Fig. 1. The graphical user interface, which presents the results to the field operator. The SKgraph is in the bottom-right corner.
The developed virtual instrument also calculates and presents the spectrum (upper-right) andthe raw data (bottom-left). The field operator adds therefore visual information to the classicalaudio-based criterion, which was by the way very subjective and very expertise-dependent.
144

Kurtosis and the SK Estimator
Kurtosis is a measure of the "peakedness" of the probability distribution of a real-valued
random variable. Higher kurtosis means more of the variance is due to infrequent extreme
deviations, as opposed to frequent modestly-sized deviations. This fact is used in this paper todetect termite AE in an urban background. Kurtosis is more commonly defined as the fourth
central cumulant divided by the square of the variance of the probability distribution, which isthe so-called excess kurtosis, i.e. [2, 8-9]:! 4,x = E {x 4 ( t )} " 3(! 2,x ) = C4,x (0,0,0)
2(1)

Ideally, the SK is a representation of the kurtosis of each frequency component of a process(or data from a measurement instrument, Xi). For estimation issues, we will consider M
realizations of the process; each containing N points; i.e. we consider M measurement sweeps,
each sweep with N points. The time spacing between points is the sampling period Ts.
A biased estimator for the spectral kurtosis and for a number M of N-point realizations at thefrequency index m, is given by [2]:^ N ,M
G 2, X&#
4$ (M + 1) M X i (m )
!
N
M $
i =1
(m )=
' 2! .
2!
M '1 $ - M
2*$ +!
X Ni (m ) ()%$ , i =1
"!

.

.

(2)

This estimator is the one we have implemented in the program code in order to perform the data
computation and it was also used successfully in [2].
Regarding the experimental signals, we expect to detect positive peaks in the kurtosis's
spectrum, which may be associated to termite AE, characterized by random-amplitude impulselike events. This non-Gaussian behavior should be enhanced over the symmetrically distributed
electronic noise, introduced in the measurement system. Speech is perhaps also reflected in theSK, but not in the frequencies where termite emissions manifest. Besides, we assume, as astarting point, that non-Gaussian behavior of termite emissions is more acute than in speech. As
a consequence, these emissions would be clearly outlined in the kurtosis spectrum. As a final
remark, we expect that constant amplitude interferences are clearly differentiated due to their
negative peaks in the SK.
To show the ideal performance of the estimator, which has been described in these lines, andalso described in [1-2], we show an example based in synthetics. A mixture of six different
signals has been designed. Each mixture is the sum of a constant-amplitude sine wave of 2 kHz,a constant-amplitude sine wave at 9 kHz, a Gaussian-distributed-amplitude sine wave at 5 kHz, a
Gaussian-distributed-amplitude sine wave at 18 kHz, a Gaussian white noise, and a colored
Gaussian noise between 12 and 13 kHz. Each mixture (realization or sample register) contains
1324 points. Negative kurtosis is expected for constant-amplitude processes, positive kurtosis
should be associated to random-amplitudes and zero kurtosis will characterize both Gaussiannoise processes.

145

A simulation has been made in order to show the influence of the number of sample registers(M) in the averaged results for the SK graph, and to test its performance. Figure 2 shows a good
performance because enough registers have been averaged (M=500).

Fig. 2. Performance of the SK estimator over a set, mixed of synthetics.
De-noising strategy via wavelets. Selecting the optimum decomposition level
The mother wavelet Daubechies 5 has been selected as the most similar mother wavelet,
because of the highest coefficients in the decomposition tree. Given the mother wavelet, to show
the process of selecting the maximum decomposition level in the wavelet tree, we have adopted acriterion based on the calculation of Shannon's entropy (information entropy), which is ameasure of the uncertainty associated with a random variable, X; this entropy denoted by H(X),and defined by Eq. (3):H (X ) = "N! p(x )log [p(x )],
i

10

(3)

i

i =1

where X is an N-outcome measurement process {xi = 1… N}, and p(xi) is the probability density
function of the outcome xi.
We show the strategy via the following example, based on real-life data, like the recordingsof Fig. 1, Fig. 4(a) and (b). The entropy of the approximations and the details are compared foreach level of comparison as shown in Fig. 3. By looking at the entropy evolution graph of Fig. 3,
we see that at level 4, the entropy of the approximations is less than the entropy of the details.
So, level 3 is in a sense, a point of entropy inversion. On the other hand, level 4 exhibits thelowest entropy being at the same time lower than the details' entropy. No improvement isobtained for level 5, where these entropies are very similar (approximations and details’
entropies are almost the same). We can also see that the global difference of entropies increases
towards zero, at level 5, as a complementary indication that further decomposition will notsuppose progress in de-noising. This decomposition strategy is satisfied for the 98 % of the dataregisters, collected at the same place.
146

Fig. 3. Entropy evolution showing the optimum level (4). Level 3 is an inversion point and level
4 exhibits the lowest entropy being at the same time lower than the details' entropy.
Experiment and Results
In this section we describe the experiment and measurement situations. A piezoelectric probe(model SP-1L, Acoustic Emission Consulting) is used in the final version of the instrument, andwas described in detail in [2]. The sensor is connected to the sound card of a lap-top computer
and the acquisition is driven by MATLAB, via the Graphical User Interface (GUI). The userinterface was presented in Fig. 1. The operator can select the acquisition time and the sample
frequency (maximum 44.1 kHz). In the bottom-right corner of Fig. 1, the SK graph is presented.
The user can also examine the raw data and the spectrum. Automatically, the instrument saves
the acquired data (labeling the file with the date). Additionally, the operator can recall the stored
files.
The electronic transducer is presented in Fig. 4, along with its charge-to-volt conversion
modulus (Integrated Circuit Piezoelectric; ICP interface), and the accessory to fix it in the woodthat we used to test the sensor's performance in the lab. A bare waveguide has been used for
insertion into soil. With the aim to have a frequency characterization of the sensor we test its
impulsive response in the lab by emulating a fiber breaking in a piece of wood, where the sensor
was attached via the drill bit. The witness instrument was a high-performance digital storage
oscilloscope (Agilent-54622A). In Fig. 5, we show a photo of the calibration session, where theinstrument’s display shows a single capture (and its associated power spectrum) of the vibratory
and AE signal once it has propagated through the wood. We can appreciate two differentiatedfrequency bands. The first 30 kHz resonant band offer the possibility of sensing AE signals from147

termites (feeding and excavating). Then we find a resonant bump around 100 kHz (only for thesensor SP1-H), which by the way has been useful for ultrasonic purposes by the research team.

Fig. 4. A photograph of the sensors SP1-L and SP1-H, with the mounted accessories (drill bit)prepared to couple it into the a test-piece of wood, in our lab. On the left, the charge-to-volt
converter according to the Integrated Circuit Piezoelectric (ICP) protocol.

Fig. 5. A photograph of the testing lab session with the sensor SP1-H (a high frequency versionof the SP1-L) and a deep memory digital-storage oscilloscope. The acoustic emission event has
been sampled at 400 kHz. Horizontal division = 20 kHz.
Termite sounds from feeding are like sharp pops and crackles in the audio output [1-2]. Hit
rates of 25-100 /s are common in infestation. The key of the SK detection strategy used in thiswork lies in the potential enhancement of the non-Gaussian behavior of these emissions. If thishappens, i.e. if an increase of the non-Gaussian activity (increase in the kurtosis, peakedness ofthe probability distribution) is observed in the SK graph, there may be infestation in thesurrounding subterranean perimeter, where the transducer is attached.
An additional remark (justifying the use of the SK graph) lies in the fact that the kurtosis as a
global indicator, considered as the average of the kurtosis computed for each individual
frequency component, is not a valid tool to target termite activity. This is due to the fact that no148

discrimination is made among the frequency bands of the emissions, which may be originated bydifferent agents.
Hereinafter, in addition to the detection measurement situation presented in Fig. 1, where thedetection is clear, other situations are outlined. In Fig. 6(a) a doubtful measurement case ispresented. Activity evidence (low-level alarms) is outlined only near 6 kHz. Once, the wavelets
have been applied, shown in Fig. 6(b), the enhancement near 6 kHz and 16 kHz confirms thedetection.

(a)(b)
Fig. 6. (a) A doubtful measurement situation without de-noising, where probably low-level alarm
signals predominate. (b) De-noising confirms activity, eliciting the bump near 6 kHz and theenhanced zone near 16 kHz.
The next figures, Fig. 7 (a) and (b), show the measurement’s performance over a background.
In the time-domain, the impulses that are similar to Dirac’s deltas are associated to cracks or
little movements of the sensor while attached in the ground. So it is important to distinguishthese false positives from the impulses induced by termites. De-noising neither add new
information nor reveal termite infestation. The impulses in the time domain are the classicalartificial cracks resulting for the movement of the sensor in the soil.

(a)

(b)

Fig. 7. (a) SK calculation over a background sample register. No activity is seen in thesurroundings of the characteristic frequencies. (b) De-noising does not reveal infestation.
149

The final figures, Fig. 8 (a) and (b), represent another case of detection. This time detection isvery clear and again, we can observe the clear bumps near the characteristic frequencies. The
bump at the left of 6 kHz reflects the presence of alarms but probably far from the termites
whose activity produces the bumps at the right of 6 kHz and 16 kHz. The spectral kurtosis of the
de-noised register exhibits several frequency bumps, not only for the frequencies in the interest
zones (6 kHz and 16 kHz), but also for the rest of the audio signals. These figures have shown
that, despite the fact that audio signals can exhibit non-Gaussian behavior, the non-Gaussian
degree of termite activity is higher.

(a)

(b)
Fig. 8. (a) A clear targeting case with a higher degree of activity. (b) De-noising confirms infestation.

Conclusion
Assuming the starting hypothesis that the insect emissions may have a more peaked
probability distribution than any other simultaneous source of emission in the measurement
perimeter, we have design a termite detection strategy and a virtual instrument based in thecalculation of the 4th-order cumulants for zero time lags, which are indicative of the signals'
150

kurtosis. The instrument is actually in use by a Spanish company. An estimator of the spectral
kurtosis has been used to perform a selective analysis of the peakedness of the signal. It has beenshown that new frequency components gain in relevance in the spectral SK graphs. The main
goal of this signal-processing method is to reduce subjectivity due to visual or listening
inspection of the registers. This means that in a noisy environment, it may be possible to ignore
termite feeding activity even with an ad hoc sensor because, despite the fact that the sensor iscapable of registering these low-level emissions, the human ear can easily ignore them [2]. The
SK signal processing modulus complements human's ear, adding higher-order statistical features,
which improve detection, thanks to the noise rejection action, enhancing non-Gaussian features.
Acknowledgments
The authors would like to thank the Spanish Ministry of Science and Education for funding
the PETRI project PTR95-0824-OP dealing with plague detection using higher-order statistics.
Our unforgettable thanks to the trust we have from the Andalusian Government for funding theexcellence project PAI2005-TIC00155.
References
1. De la Rosa, J. J. G., Gallego, A., Piotrkowski, R., Castro, E. and Moreno, A. (2008). Spectral
kurtosis-based virtual instrument for non-destructive acoustic emission targeting of termites.
Proceedings of The 28-th European Conference on Acoustic Emission Testing, September 17-19,
Kraków, Poland, pp. 94-99.
2. De la Rosa, J. J. G. and Muñoz, A. M. (2008). Higher-order cumulants and spectral kurtosis
for early detection of subterranean termites. Mechanical Systems and Signal Processing (Ed.
Elsevier), 22 (1): 279–294. Available online 1 September 2007.
3. De la Rosa, J. J. G., Lloret, I., Moreno, A., Puntonet, C. G., and Górriz, J. M. (2006). Wavelets
and wavelet packets applied to detect and characterize transient alarm signals from termites.
Measurement (Ed. Elsevier), 39(6): 553–564. Available online 10 January 2006.
4. Mankin, R. W. and Fisher, J. R. (2002). Current and potential uses of acoustic systems for
detection of soil insects infestations. In Proceedings of the Fourth Symposium on Agroacoustic,
pages 152–158.
5. Robbins, W. P., Mueller, R. K., Schaal, T., and Ebeling, T. (1991). Characteristics of acoustic
emission signals generated by termite activity in wood. In Proceedings of the IEEE UltrasonicSymposium, pages 1047–1051.
6. De la Rosa, J. J. G., Piotrkowski, R., and Ruzzante, J. (2007b). Third-order spectral
characterization of acoustic emission signals in ring-type samples from steel pipes for the oil
industry. Mechanical Systems and Signal Processing (Ed. Elsevier), 21(4): 1917–1926.
Available online 10 October 2006.
7. Antoni, J. (2006b). The spectral kurtosis: application to the vibratory surveillance anddiagnostics of rotating machines. Mechanical Systems and Signal Processing (Ed. Elsevier),20(2): 308–331.
8. Mendel, J. M. (1991). Tutorial on higher-order statistics (spectra) in signal processing andsystem theory: Theoretical results and some applications. Proceedings of the IEEE, 79(3): 278–
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Signal Processing Magazine, pages 10–37.

151

ACOUSTO-ULTRASONIC SIGNAL ANALYSIS FORDAMAGE DETECTION IN GFRP ADHESIVE JOINTSANDREAS J. BRUNNER and GIOVANNI P. TERRASIEmpa, Swiss Federal Laboratories for Materials Testing and Research, Laboratory for Mechanical Systems Engineering, Ueberlandstrasse 129, CH-8600 Duebendorf, Switzerland
Keywords: Adhesive joints, GFRP, tensile load, damage accumulation
Abstract
Selected tensile tests on balanced, adhesively bonded double-lap joints (DLJ) made from pultruded glass-fiber-reinforced, polymer-matrix (GFRP) flat profiles have been monitored withacoustic emission (AE) for an assessment of damage initiation and accumulation. The thickness
was 10 mm for the inner and 5 mm for the two outer profiles. Three different bond-line thicknesses of the adhesive bond (nominally 0.3, 0.5 and 1.0 mm), all with an overlap length of 100
mm, were tested. The usual AE signal parameter set was recorded with four AE sensors (eitherof type SE-45H or SE-150M) from three tests each per DLJ type. AE sensors were mounted
above and below the adhesive bond with duct tape using a silicone-free coupling agent. Sensor
coupling was checked before and after the tensile tests to stress levels around 60% to 97% of theeffective ultimate tensile strength (proof-load of 140 kN). The auto-calibration function of theAE equipment (type AMS-3 from Vallen Systeme) was used for checking sensor coupling before
and after the proof-load tests. Transient AE waveforms were recorded during the auto-calibration
with 5 MHz sampling rate. This effectively constitutes acousto-ultrasonic (AU) testing of the
DLJ before and after loading. Preliminary signal classification of the AU waveforms with VisualClass® (from Vallen Systeme GmbH) had indicated that they differed when recorded before
and after proof-load testing. The present paper attempts to identify the AU signal parameters responsible for this, as case study in AU waveform analysis.
Introduction
Previous experiments on the strength of adhesively bonded joints composed of glass-fiber reinforced polymer-matrix (GFRP) pultruded adherends [1-3] indicated that, even using enhanced
mathematical methods on the basis of a fully linear mechanical model, a gap of around 10% between predicted and experimentally determined joint strengths remained. Reasons for this, invoked in [3], were either possible damage occurring by microscopic defect accumulation (e.g.,
micro-crack formation) and/or the resulting nonlinear behavior of the GFRP material at higher
stresses (beyond those obtained on the samples of limited size, i.e., 40 mm x 40 mm).
Acoustic emission (AE) monitoring is, in principle, capable of detecting microscopic damage
accumulation in GFRP composites. AE was hence used to monitor the area of the adhesive bondduring tensile tests on a series of nine full-size adhesively bonded double-lap joints (DLJ) up to aload of 140 kN, including subsequent unloading. The DLJ were then loaded to their ultimate tensile strength, i.e., to tensile failure without AE. AE sensor coupling was checked with the autocalibration function of the AE equipment. This constitutes an acousto-ultrasonics (AU) examination [4]. The present paper investigates whether AU analysis can yield indications of damage accumulation due to tensile loading of the DLJ.

J. Acoustic Emission, 26 (2008)152© 2008 Acoustic Emission GroupExperimental
The balanced double-lap joints (DLJ) consisted of pultruded glass-fiber reinforced polymermatrix (GFRP) flat profiles of 500-mm length, 100-mm width, and 5-mm and 10-mm thickness
for the inner and outer DLJ profiles, respectively. A polyester resin and E-glass fibers were used
for the profiles, with mainly unidirectional rovings towards the center and one and two combined
mats towards the outside for the 5-mm and 10-mm thick profiles, respectively. The combined
mats consisted of chopped strand mats and 0°/90° woven fabrics, which were stitched together.
For the adhesive bond, a two-component polyurethane adhesive (Sika S-Force 7851) was used,
and the nominal bond line thicknesses were 0.3, 0.5 and 1.0 mm. The specimens were cured under ambient laboratory conditions (23±1°C, 50±5% relative humidity) for one week, and thenstored under comparable conditions before testing.
The tensile tests have been performed on a servo-hydraulic test machine (Instron 1346) underdisplacement control (0.5 mm/min). A metal plate has been inserted between the outer two profiles inside the hydraulic grips at the bottom.
For AE monitoring equipment and software from Vallen Systeme GmbH (AMS-3 and VisualAE™) have been used. At least one DLJ for each bond-line thickness has been equipped withfour multi-purpose AE sensors (type SE-45H from Dunegan Engineering Corp.) while the remaining DLJ have been tested with 150-kHz resonant AE sensors (type SE-150M from the same
supplier). Preamplifier gain was set at 34 dB, AE signal acquisition threshold at 50 dBAE (except
for two DLJ with 52 and 65 dBAE in order to eliminate excess noise from the hydraulic grips.
The AE signals were band-pass filtered between 30 and 1000 kHz. Rearm time was set to 1.38
ms. AU waveforms excited with the autocalibration function of the AE equipment (roughly 60
Vpp, 800 ns duration applied to each AE sensor) have been recorded with transient recorders at asampling rate of 5 MHz (200 pre-trigger samples, total 4096 samples).

Fig. 1. Photograph of (left) a double lap joint in the test machine with two AE sensors (above andbelow the adhesive bond) on either side of the joint; (right) side view of the joint with the thicker
inner profile on top and the thinner outer profiles on the bottom.

153

Results
Table 1 summarizes the applied and observed tensile failure loads of the DLJ and the type of
AE sensor use for monitoring the proof load. The average tensile failure load for all DLJ, irrespective of their bond-line thickness is 182.4 ± 28 kN, and 199.1 ± 42 kN, 169.9 ± 23 kN and
178.5 ± 11 kN for 0.3, 0.5 and 1.0 mm bond-line thickness, respectively. Plotting machine load
data versus time (not shown) indicated an overall non-linear behavior of the DLJ, typically starting at tensile loads below 50% of the failure load. Figure 2 shows examples of recorded AU
waveforms from the auto-calibration procedure for checking on sensor coupling. It is evident thatthese signals contain contributions from different wave modes or (multiple) reflections due to thestructure of the adhesive joints. In a preliminary analysis using a waveform classification program (VisualClass® from Vallen Systeme GmbH) such AU waveforms could be separated into
those taken before and after proof loading in most cases (see [5, 6] for sample results and details
on the procedure). A visual comparison of the AU waveforms in Fig. 2 shows some differences
between waveforms recorded before and after the proof load, but also between comparable statesof the two joints (DLJ031 and DLJ101). Qualitatively, the differences among the joints with different thickness of the adhesive layer (DLJ031 with nominally 0.3 mm, DLJ101 with nominally
1.0 mm) seem more prominent than those for each joint taken before and after proof loading (foreach emitter-sensor pair considered).
Table 1: Tensile load cycles, failure load (kN) and proof-load fraction for DLJ with different
bond-line thickness and AE sensor type
DLJ
No.

Load cycle(s)[kN] *

Tensile failureload [kN]

Proof-load (140kN) fraction of
failure load [%]AE sensor
type

031
032
033
051
052
053
101
102
103

0-141-0, 0 to failure
0 to failure
0-140-0, 0 to failure
0-140-0, 0 to failure
0-140-0, 0-100-0, 0 to failure
0-139-0, 0 to failure
0-140-0, 0 to failure
0-140-0, 0 to failure
0-140-0, 0 to failure

155.2
239.2
202.2
143.7
188.7
177.4
190.8
170.8
174.0

90.2
58.5
69.2
97.4
74.2
78.9
73.4
82.0
80.5

SE-45H
SE-45H
SE-150M
SE-45H
SE-150M
SE-150M
SE-45H
SE-150M
SE-150M* except for DLJ 032, the loading to failure was not monitored with AE sensors
In the following, it will be investigated whether the changes in AU waveforms recorded before and after proof-loading (visible in Fig.2 and also evident from the waveform classification[5, 6]) can be traced to specific AU signal parameters. Specifically, signal amplitude, duration,
ring-down counts, (true) energy, and rise-time are investigated and compared. All data presented
are averages of usually four AU waveforms, in a few cases of three or five. There are four sensor
combinations that will pass guided waves through the adhesive bond and, hence through the

154

a)

b)

c)

d)

Fig. 2. Examples of AU waveforms recorded with a multifunctional AE sensor (type SE-45H)
before (on top) and after (bottom) tensile proof-loading of the joints to 140 kN, waves propagating along the side of a) DLJ 031 and b) DLJ101, and from one side across to the other of c) DLJ
031 and d) DLJ101, respectively.
volume of the joint where damage accumulation occurs. The center GFRP-plate close to the topof the adhesive bond had been identified as damage initiation location from AE analysis (AEsource location) and visual observation of the fracture surface after the test [7]. The sensor combinations are from top to bottom (or vice-versa) on each side of the joint (compare Fig. 1) andfrom top left/right hand side to bottom right/left hand side (or vice-versa).
The graphs in Fig. 3 show a clear difference between the two types of sensors (SE-45H and
SE-150M). All AU signal parameters recorded before proof loading to 140 kN yielded lower
values when recorded with the 150 kHz resonant sensor (SE-150M) compared with the massloaded multi-purpose sensor (SE-45H, displacement sensor below 45 kHz, velocity sensor above
70 kHz). AU signal amplitudes (Fig. 3 top) recorded with the 150 kHz resonant sensor seem todecrease more rapidly with increasing thickness of the adhesive than with the other sensor (SE45H). When plotted against the nominal thickness of the adhesive layer of the double lap joints(0.3, 0.5 or 1.0 mm), AU signal parameters typically decreased with increasing thickness of theadhesive, with a few exceptions e.g., in rise time. This was observed for both sensor types, eventhough there was considerable scatter in some cases, which made identification of the trends withadhesive thickness difficult. Figure 3 also indicates the scatter in the data between the different
emitter-sensor configurations. Note that square symbols indicate propagation along the side, triangular symbols across the joint, filled and open symbols indicate different directions of wave
propagation (top-to-bottom or bottom-to-top).
AU signal parameters after proof loading the joints to 140 kN are compared with those fromthe as-prepared state. If plotted against thickness of the adhesive (Fig. 4), it is difficult to identify
clear trends in most cases. This holds for both types of sensors (SE-45H and SE-150M). AU signal energy seems to show the most consistent change after proof loading. For sensor type SE45H, this parameter is increasing after proof loading, while for sensor SE-150M it is decreasing
in most cases (compared with the value before proof loading). AU signal energy is hence plottedagainst the percentage of failure load represented by the proof loading to 140 kN as well (Fig. 5).

155

Fig. 3. Selected AU signal-parameters recorded before proof loading to 140 kN with (left) sensor
type SE-45H and (right) with sensor type SE-150M plotted versus thickness of the adhesive (seetext for more details).

Fig. 4. Selected AU signal-parameters after proof loading to 140 kN compared with those recorded before proof loading with (left) sensor type SE-45H and (right) with sensor type SE150M plotted versus thickness of the adhesive; square symbols indicate values before, diamondshaped symbols values after proof loading (see text for more details).

156

Fig. 5. AU signal-energy after proof loading to 140 kN compared with values recorded before
proof loading (left) sensor type SE-45H and (right) SE-150M, (top) AU signal propagation alongside of joint, (bottom) AU signal propagation across joint; square symbols indicate values before,diamond-shaped symbols values after proof loading (see text for more details).
Discussion
Due to the fairly large scatter in failure loads for each group of joints with the same thicknessof the adhesive (coefficient of variation between about 6% and 20%), it is difficult to decide
whether there is a dependence of the failure load on the thickness of the adhesive layer. Even thereasons for this scatter are not clear. Of course, the sample size (three joints per thickness) is too
small for reliable statistics. Manufacturing effects (e.g., variation in adhesive thickness) andmaybe variability in the lay-up of the GFRP plates are tentative explanations.
Nevertheless, there are indications of a dependence of the AU signal parameters on the
thickness of the adhesive layer of the joints. Essentially, but sometimes with relatively large scatter, the AU signal parameter values tend to decrease with increasing thickness of the adhesive
layer. This would be consistent with the assumption that increasing thickness of the adhesive
layer yields increasing signal attenuation.
The observed lower values of AU signal parameters recorded with sensor type SE-150M
compared with SE-45H quite likely relates to the difference in frequency response. Sensor typeSE-45H has a higher sensitivity in the frequency range between 50 and 100 kHz than SE-150M(about 5-10 dBAE). Fast Fourier transforms of the AU waveforms (not shown here) recorded withthe different types of sensor clearly show higher contributions to the power spectra for the SE45H in the frequency range below 100 kHz than for the 150 kHz resonant sensor.
The proof load of 140 kN amounted to between 58% and 97% of the observed failure load.
This indicates that damage accumulation due to the proof load ranges from low (e.g., DLJ032) to157

severe (e.g., DLJ051). That damage induced by the proof load does affect the AU waves isshown by the examples in Fig. 2. Such an effect had also been suggested by the comparative
waveform classification reported in [5]. This can be interpreted as evidence that damage accumulation does affect AU waveforms, as expected [4]. However, the results presented here indicatethat it is difficult to identify a single AU signal parameter as suitable damage indicator. AU signal energy yields increasing values after proof loading, if sensor type SE-45H is being used. For
the 150 kHz resonant sensor (SE-150M), AU signal energy values after proof loading are decreasing, however with one exception, and the changes are comparatively small. Most of theother AU signal parameters (amplitude, duration, ring-down counts) yield partly increasing andpartly decreasing values after proof loading for both types of sensors, i.e., no clear trend, whilerise time does not seem to change significantly.
As an empirical observation, it can be noted that compressive damage inducing local delaminations in a GFRP-laminate made from woven reinforcement in an epoxy matrix had also shown
increasing AU signal energy transmission with increasing extent of damage [6]. The mechanism(s) responsible for that, however, have not been clarified yet. These experiments were performed with special piezoelectric composite sensor elements with considerable sensitivity in thefrequency range between about 30 and 100 kHz (analogous to the multipurpose sensor type usedin the present investigation). The fact that the 150 kHz resonant sensor used in the present investigation noted a decrease in transmitted AU signal energy after proof loading (except in one case)and that an increase is observed, if sensors with sensitivity at lower frequencies are being usedindicates that frequency-dependent wave propagation effects may play an essential role. Damage
on one hand seems to produce increasing attenuation at higher frequencies (e.g., above 100 kHz),and simultaneously increasing wave energy transfer at lower frequencies. Whether this can be
explained by wave mode changes, by changes in wave interference (the recorded waveforms
clearly contain contributions from different wave modes or reflections, see Fig. 2), by non-linear
or resonance effects or a combination of these, is not clear. Whether modeling of wave propagation would succeed in shedding light on this, is also unclear, due to the rather complex composition of the GFRP-plates and the lack of materials data as a function of damage accumulation.
In principle, it can be hypothesized that the amount of damage in the propagation path of the
AU waves should also be reflected in the signal arriving at the sensor (as long as the geometry of
the test piece and sensor arrangement was kept constant to exclude propagation distance dependent effects). The AU signal parameter analysis presented in this paper does seem to indicate thatthe change of a single parameter will not directly correlate with the amount of damage. It has tobe noted, however, that the only indicator of the amount of damage available here was the ratio
between proof load and failure load. It will be interesting to see whether more detailed and sophisticated methods of analysis, e.g., based on waveform classification or pattern recognition
will, in the future, be able to establish a clear correlation between AU waveform changes andamount of damage. Tentatively, the observation of increasing transmitted AU signal energy(when monitoring test pieces with an AE sensor with sufficient sensitivity in the frequency range
below about 100 kHz) can be taken as an indication of potential damage accumulation that may
need attention and eventually follow-up inspection.
Conclusions
As a case study, acousto-ultrasonic (AU) waveforms recorded during autocalibration forchecking on acoustic emission (AE) sensor coupling before and after tensile proof-tests on adhesively bonded double-lap joints made from glass-fiber reinforced polymer-matrix pultruded158

plates have been analyzed by comparing selected AU signal parameters. It was noted that AU
signal parameters tended to decrease with increasing (nominal) thickness of the adhesive bond(from 0.3 mm to 0.5 mm and finally to 1.0 mm). There was also a clear difference between AU
signals recorded with 150 kHz resonant AE sensors compared with those recorded with multipurpose AE sensor (type SE-45H) with comparatively higher sensitivity in the frequency range
between about 50 and 100 kHz. In spite of indications that waveform classification (with VisualClass® from Vallen Systeme GmbH) succeeded in distinguishing AU waveforms recorded before and after proof loading, it proved difficult to unambiguously identify a single AU signal parameter responsible for this. While for most AU signal parameters investigated, no clear trends
with damage were observed, AU signal energy at least yielded distinctly increasing values whenrecorded with the multipurpose sensor, and simultaneously mainly decreasing values when recorded with resonant sensor after proof loading. The changes in the signal energy, however, did
not seem to quantitatively correlate with the expected amount of damage. It will hence be interesting to further investigate the feasibility of advanced waveform classification and pattern recognition methods for this.
Acknowledgment
The technical support of Mr. D. Völki for performing the tensile load test at Empa is gratefully acknowledged.
References
1. Vallée T., Correia J.R., Keller Th, Composites Science and Technology, 66 (13), 2006, 19031914.
2. Vallée T, Correia J.R., Keller Th., Composites Science and Technology, 66 (13), 2006, 19151930.
3. Vallée T., Keller Th., Fourestey G., Fournier B., Proceedings 13th International Conference
on Experimental Mechanics (ICEM13), Alexandroupolis, 2007, Greece.
4. ASTM Standard E1495, Standard Guide for Acousto-Ultrasonic Assessment of Composites,
Laminates, and Bonded Joints, ASTM International, 2002 (2007).
5. Brunner A.J., Terrasi G.P., Vallée T., Keller T, Proceedings 28th European Conference on
Acoustic Emission (EWGAE-28), Krakow, 2008, Poland, pp. 100-105.
6. Barbezat M., Brunner A.J., Huber Ch., Flüeler P., Journal of Intelligent Material Systems andStructures, 18 (5), 2007, 515-525.
7. Bohse J., Brunner A.J., Chapter 9 in: Delamination Behaviour of Composites, ed. S. Sridharan, Woodhead Publishing, 217-277 (2008).

159

BENDING FRACTURE BEHAVIOR OF 3D-WOVEN SiC/SiCCOMPOSITES WITH TRANSPIRATION COOLING STRUCTURE
CHARACTERIZED BY AE WAVELET ANALYSISTOSHIMITSU HAYASHI and SHUICHI WAKAYAMATokyo Metropolitan University, 1-1 Minami-Ohsawa, Hachioji, Tokyo, 192-0397 Japan
Keywords: SiC/SiC composites, AE wavelet analysis, Bending, Porosity, FFT
Abstract
The fracture behavior of the SiC/SiC composites was examined by 4-point bending tests. The
AE signals detected during the tests were processed by AE wavelet analysis, which extracted thefirst and second major frequencies from continuous wavelet transform (CWT) diagram. Consequently, the two-frequency combination analysis can detect local frequency components andcharacterize a wavelet diagram well. A history of these major frequencies was correlated to initiation and propagation of micro-damages, such as cracks in SiC matrix, crack propagation along
thickness and fiber buckling. Although other micro-damages, such as fiber breaks and pull-out offiber, have not been correlated to the AE signals, the results suggest that the two-frequency combination analysis based on CWT has great potential to characterize the micro-damages of thecomposites.
Introduction
SiC/SiC composites have been studied in order to increase the temperature capability of jet
engine components [1, 2]. An increase in the temperature capability would raise the gas temperature and reduce the need of air cooling, both of which would improve the efficiency of a jet engine. However, active cooling is still needed for the composites to preserve structural strength,
since the temperature capability of the composites is lower than the gas temperature (e.g.,
1600oC at turbine inlet). In order to develop a simple cooling structure with high efficiency, atranspiration-cooling structure was created in the composites by decreasing a volume fraction of
SiC matrix, which was fabricated by four cycles of matrix densification process, “polymer impregnation and pyrolysis (PIP)” [3]. In order to investigate the PIP-cycle effect on the fracture
mechanism, flexural loading and unloading test was carried out in a previous study [4]. However,
the observed fracture behavior could not be correlated to the captured AE signals by examining
AE parameters, such as amplitude and energy. In order to fully understand the bending fracture
behavior and monitor micro-damage evolution under long-term evaluation (e.g., thermal cycling
test, fatigue test), better correlations between captured AE signals and micro-damages are needed.
Many authors have studied frequency analysis based on frequency domain (FT) or timefrequency domain (WT) in order to investigate correlations between micro-damages and AE
signals [5-9]. Many publications on the frequency analysis dealt with CFRPs or GFRPs, whichwere fabricated by materials of quite different properties. As the SiC/SiC composites are fabricated by materials with similar properties (e.g., chemical compositions and mechanical property),
the frequency analysis with a capability to detect minor differences in AE waveforms is required.
In recent studies, the discrete wavelet transform (DWT) has often been applied to characterize
the AE waveform. The DWT decomposes a signal to several scale levels that correspond to frequency range. However, it appears that the scale level of the DWT may give only a rough estimation for characterizing the AE waveforms emitted from the SiC/SiC composites. Thus, thisJ. Acoustic Emission, 26 (2008)160© 2008 Acoustic Emission Group

study applied the continuous wavelet transform (CWT), which can analyze frequency at a finerscale, for the analysis of the AE waveforms of the composites.
The purpose of this study is to correlate the micro-damages of the SiC/SiC composites to the
AE waveforms by analyzing the first and second major frequencies utilizing the continuous
wavelet transform (CWT).
Materials and Experimental Procedures
Materials and Specimens
The SiC/SiC composites used in this study were made of TyrannoTM ZMI SiC (Si-C-O-Zr) fiber of Ube Industries, Ltd. and SiC matrix. The composites have the 3D-orthogonally woven
structure as schematically shown in Fig. 1. The 3D-woven architecture consists of three layers of
X bundles, four layers of Y bundles and Z bundles. Figure 2 shows a cross-sectional view of YZ
plane along “aa”. The material composition near surface is also depicted in the figure. Tensileside is not flat because of the woven structure, which results in a notch-like geometry with stressconcentration. The location of the notch-like geometry indicated by a gray arrow is called a“corner” in this paper.
First, a carbon interface layer derived from the chemical vapor infiltration (CVI) process wascoated on the fibers, then the SiC matrix was infiltrated using methyl-trichlorosilane by a CVI
process and from polycarbosilane by several cycles of PIP process. The porosity was controlled
by varying the number of PIP cycles. The specimens were fabricated with 4 cycles of PIP. Afterthe PIP treatments, the specimens were cut and coated with a thin layer of CVI-SiC. The volume
fraction of fiber, matrix and porosity are 41%, 34% and 25%, respectively. The dimensions of
the specimen were 50 mm length x 4 mm (approx.) width x 2 mm (approx.) thickness. The widthcorresponds to the size of two pitches of the textures.

Fig. 1. Schematic diagram of SiC/SiC weave (Fiber volume ratio; X:Y:Z=1:1:0.2, pitch=2 mm).

Fig. 2. Composite cross section
along “a-a”.

Experimental Procedures
Four-point bending tests were carried out with inner span of 10 mm and outer span of 30 mm
at room temperature in air. Loading was applied by a universal testing machine (Instron, 5505) inZ-direction under a constant crosshead speed of 0.5 mm/min so that the tensile stress was applied161

along X-direction. Deformation of the sample was measured parallel to the Z-axis by the displacement transducer and recorded by a PC-based data recorder (Kyowa, UCAM-500A).
During the test, AE signals were monitored by two wide-band AE sensors (Fuji Ceramics,
M5W), which were attached on both ends of the sample. The AE signals were amplified 20 dB
and the threshold level was set to the value of 3.16 mV at the input of the preamplifiers. The AE
signals were processed by an AE analyzer (PAC, PCI-2 AE system) with AEWinTM operating
software. Recorded AE waveforms were post-processed by the CWT using a commercial software (Weisang, Flex Pro Professional with an option of spectral analysis). Morlet function waschosen as the mother wavelet with non-dimensional frequency ω = 8 [10].

Fig. 3. CWT diagrams of AE waveforms detected during flexural loading, showing characteristic
contour patterns in time-frequency domain.
Results and Discussion
The First and Second Major Frequencies in CWT Diagram
AE waveforms acquired in this study showed characteristic CWT diagrams in the timefrequency domain. As an example, some characteristic CWT diagrams obtained from the bending
test are shown in Fig. 3. Each diagram has two or more characteristic peak frequencies. The sensor used has a slightly higher response between 0.2 and 0.3 MHz and flat frequency response
beyond 0.5 MHz. Thus, a frequency near 0.3 MHz is detected as the dominant frequency in162most of the diagrams. Second characteristic peak frequency of the CWT diagrams often appears
below or above the dominant frequency of ~0.3 MHz. It appears that a combination of several
peak frequencies can possibly characterize and classify the CWT diagram. In this study, the 1st
and 2nd major frequencies were utilized for this attempt.
Figure 4 shows the CWT diagram of an AE waveform, including cross-sectional views taken
along “aa” and “bb” on the CWT diagram. In the CWT diagram, a pair of the frequencies,“FRQ1” and “FRQ2” (white arrows) are the two characteristic frequencies of this WT coefficient
contour. The cross-sectional view along line “aa” is a time history of WT coefficient at the frequency “FRQ1”. Ni and Iwamoto [9] proposed that a WT-time curve at a specified frequency
can identify the occurrence time and duration of the micro-fracture mode of single carbon-fiber
composites. The specified frequency was selected from the peak on FFT plot of the examined
AE signal. However, the WT-curve cannot fully characterize the CWT diagram, because the WTcurve contains information about only one frequency component. The WT-frequency curve takenfrom “bb” line of the CWT diagram exhibits a distribution of WT coefficient indicating “FRQ1”
to be the major frequency. This curve does show that the frequency peak is also located near
“FRQ2”, but it fails to properly account for the intensity at “FRQ2”, even though this is shown
clearly in the CWT diagram. Thus, either WT-time or WT-frequency curve cannot fully characterize the CWT diagram.

Fig. 4. Wavelet diagram with cross sectional views, taken along lines “aa” and “bb”.
Figure 5(a) shows a CWT diagram projected into WT-frequency plots of the same AE waveform (Fig. 4) for a comparison. The projected WT-frequency curves lost time information, but
the frequency and intensity information of two major peaks are preserved. Figure 5(b) shows
FFT of the same signal where the first major frequency in the projected WT-frequency curve isrecognized only as a minor frequency in the FFT envelope due to the time averaging effect. Thus,
FFT analysis fails to detect important frequency information. FFT results also indicate the163highest peak at 0.11 MHz. This can easily be mistaken as a characteristic major frequency of the
AE waveform and implies that FFT-based methods must be used with caution.

Fig. 5. The major and second major frequencies in projected WT-frequency curve of the waveform shown in Fig. 4 (WT-frequency curve not time-averaged.) (a) Projected WT-frequency
curve, (b) FFT spectrum.
Comparison between Projected WT Curve and FFT Spectrum
Figure 6 shows another example where FFT fails to detect the highest peak of the AE signal.
Figure 6(a), (b), (c) and (d) show the WT diagram, AE signal, projected WT-frequency curveand FFT spectrum, respectively. The 1st and 2nd major frequencies are indicated by red circles
and connected by a solid line in Fig. 6(a), (c) and (d). Figure 6(a) and (b) show that a wavefrontof the AE signal has the first major frequency at 0.56 MHz and the second major frequency at
0.32 MHz, and that the frequency component of ~0.3 MHz has long duration. It appears thatthese two frequencies of the wavefront are the characteristic frequencies of the AE signal. The
two frequencies can be clearly recognized by the projected WT frequency curve in Fig. 6(c). On
the other hand, the two peak frequencies of the FFT spectrum in Fig. 6(d) were 0.33 and 0.29
MHz.
Thus, FFT analysis failed to detect the highest characteristic frequency at 0.56 MHz. Since
the frequency component of ~0.3 MHz showed long duration, the frequency component of ~0.3
MHz was estimated to have high intensity due to the time-averaging effect of FFT analysis. This
example again indicated that FFT analysis is likely to fail to detect the higher frequency component of AE waveforms, because high frequency components often exhibit very short duration due
to attenuation.
Figure 7 shows one more example, in which FFT failed to detect the characteristic frequencyof the AE waveform, even though FFT analysis detected a relatively high frequency component.
Figure 7(c) indicates that the AE waveform has the first major frequency at 0.34 MHz and thesecond major frequency at 0.81 MHz. Since the second major frequency component of ~0.8
164

MHz shows relatively long duration, the FFT recognizes the frequency component of 0.79 MHz
as a major frequency. Thus, FFT analysis failed to detect the major characteristic frequency of
0.34 MHz.

Fig. 6. Comparison between projected WT-frequency curve and FFT spectrum, showing thatFFT fails to detect the highest peak of AE signal. (a) WT diagram, (b) AE signal, (c) projected
WT-frequency curve, (d) FFT spectrum.
Mechanical Behavior and Microscopic Damages Observed after Bending Test
Figure 8 shows the stress-displacement curve during the bending test. The curve showed several small fluctuations before the stress reached the maximum value and a drop of stress afterward.
The final bending fracture as indicated by white arrow in Fig. 8 was followed by a sharp decreasein stress.
Figure 9(a) shows SEM micrographs of the most damaged cross section of the specimen usedin the bending test. The horizontal direction is the longitudinal (X-) direction of the specimen,
and the tension side is at the bottom. It can be seen in Fig. 9(a) that fiber buckling was observedin the upper and lower X-fiber bundles. Fiber buckling in the upper bundle was accompaniedwith interlaminar cracks between X- and Y- fiber bundles. Figure 9 (a) also indicates that the upper pore is significantly larger with almost no infiltration of PIP-SiC matrix. Thus, the upper Xfiber bundle was not supported rigidly enough to sustain the compressive stress during bending.
As a result, the fiber buckled in the upper fiber bundle adjacent to the large pore. The fiber165

buckling in the lower fiber bundle can possibly be attributed to the severely damaged upper fiber
bundle. Unfortunately, the complexities of the fiber buckling on the lower side make it difficult tointerpret.

Fig. 7. Comparison between projected WT-frequency curve and FFT spectrum, showing thatFFT fails to detect the highest peak of AE signal. (a) WT diagram, (b) AE signal, (c) projected
WT-frequency curve, (d) FFT spectrum.

Fig. 8. Stress-displacement curve during the bending test.
166

Fig. 9. SEM micrographs of cross section of the specimen subjected to the bending test.
Figure 9(b) and (d) show the 2 corners where the two fiber bundles intersect. It appears thatseveral cracks near corner in Fig. 9(b) were propagated along thickness direction and reached thepore. A crack shown by white arrows in Fig. 9 (d) was propagated into Y-fiber bundle shown by
the gray arrow. A crack initiated from the pore is also observed at the corner as shown by slashedarrow in Fig. 9(c).

167

AE Wavelet Analysis
Figure 10(a) and (b) show the history of the first and second major frequencies, respectively,of the first hit AE signals obtained during the bending test. Relative values of the WT coefficients of the major frequency components are indicated by the circle diameter. In Fig. 10(a), the
1st major frequency components are distributed in a limited frequency band of 250 to 500 kHz.
This range corresponds to the high sensitivity range of the AE sensor-specimen combination,
identified by the through-transmission experiment using a pair of M5W sensors with tone-bursts.
The 2nd major frequencies, shown in Fig. 10(b), are more widely distributed and frequency
components over 500 kHz and below 250 kHz were also observed.

Fig. 10. Frequencies in CWT diagrams. (a) The 1st major frequency. (b) The 2nd major frequency.
The 1st and 2nd major frequency components are shown together in Fig. 11 with connecting
link between the two frequencies, since a combination of the two major frequencies can be considered to represent a contour pattern shown in Fig. 3. A caption above the figure described thebending fracture process, which had been observed in the loading/unloading test [4]. The corner
crack was generated in the CVI-SiC and PIP-SiC matrices on the tension-side surface in a stressrange between 50 MPa and 100 MPa, which grew along thickness direction above 100 MPa andresulted in the bending fracture. The PIP-SiC crack was observed above 100 MPa in the matrixrich region near cavity and on compressive-side surface. Accordingly, in a stress range from 50
to 100 MPa, the cracks in the both CVI-SiC and PIP-SiC matrices were only observed.
The two frequency components of low WT coefficient with widely distributed frequency
range in 50-1050 kHz were observed throughout the test. The signature of the AE signal can beconsidered to correlate with the initiation and propagation of the crack in both CVI-SiC and PIPSiC matrices, since only the AE signals with the signature were observed in the stress range of
50 - 100 MPa while these micro-damages were developed. In the stress range above 100 MPa,
the AE signals with relatively large WT coefficient were detected.
Figure 12 shows the detailed plot of two peak frequencies against time with the axis extended
to the final bending fracture, which shows the AE signals obtained over a stress of about 100
MPa. In order to focus on the characteristics of the damage process, only the combinations offrequency components with relatively large WT were enhanced by linking two frequency symbols by a thick solid line. Black arrows indicate the stress fluctuations that were observed. The

168

Fig. 11. Two-frequency combination and displacement during the bending test. The description
above the figure showed the fracture process obtained by loading and unloading test using asame-lot specimen in the previous study [4].

Fig. 12. Detailed plot of the two-frequency combination and stress vs. time during the bending
test over a stress level of about 100 MPa.
169

open arrows shows where the large drop in stress occurred. It appears that the two peak frequency combinations with large WT formed several characteristic groups, which are indicated bydotted line, called group A, B and C. The AE signals, which were detected above 100 MPa,should correlate to the damages, such as the corner-crack propagation along thickness, fiber buckling and interlaminar crack, while exempting matrix cracks near surface or around pores.
The group A contains frequency components with widely distributed and large WT coefficients. The frequency combination A1 has the 1st major frequency of 340 kHz and the 2nd major
frequency of 810 kHz. The frequency combination A1 may correspond to the initial severe damage in the tensile-side X-fiber bundle due to the corner crack propagation. The micro-damage corresponding to the A1 was accompanied with a stress fluctuation indicated by a black arrow. It
appears that the stress fluctuation can be caused by micro-damages that permit deformation ofcomposites to relax the stress, for example, a rapid propagation of CVI-SiC matrix crack along
thickness or width direction, pull-out of fiber and fiber breakage. Since the pull-out of fiber andfiber breakage can possibly occur after well developed CVI-SiC matrix crack in the fiber bundle,
the A1 may correspond to the rapid propagation of CVI-SiC matrix crack along thickness orwidth direction. Although the combinations A1~A3 contains relatively high frequency component near 800 kHz, the 1st major frequencies of A1, A2 and A3 were different. This may suggestthat the A1~A3 correlate to different micro-damages. From the microscopic observation as shownin Fig. 9(b) and (d), the corner crack in the fiber bundle finally reached upper Y-fiber bundle orpore, which would enhance fiber pullout in the X-fiber bundle, and then result in the stress fluctuation. Since the frequency combination A4 with low frequency component of 79 kHz accompanied by the stress fluctuation, the frequency combination of A4 may correlate to the fiber pullout.
After the corner crack reached a pore or Y-fiber bundle, the crack then developed into Y-fiber
bundle shown by gray arrow in Fig. 9(d) or another crack may be initiated at a corner in the poreas indicated by slashed arrow in Fig. 9(c). Since these crack propagation did not accompany fiber
cracks, the AE signals corresponding to these crack propagation may contain few high frequency
components. Thus, "Group B" with few high frequency components indicated in Fig. 12 may
correlate to the crack propagation into Y-fiber bundle and the crack initiation at a pore corner.
"Group C" in Fig. 12 contains very low frequency combinations with significantly large WT.
The AE signals of C1 and C2 were detected at the final bending fracture with the sharp drop instress indicated by the white arrow. It appears that the final bending fracture was accompaniedwith the fiber buckling, because stress cannot be held any further if such fiber buckling occurred.
Thus, the AE signals of C1 and C2 , which were detected at the final bending fracture, can correlate
to the fiber buckling. The common signature of C1 and C2 is that the 1st major frequencies weredetected at significantly low value of 69 or 89 kHz. Another AE signal C3, of which the 1st major
frequency was 69 kHz, was observed in advance of final bending fracture at 140 s accompanying
a little drop in stress indicated by the black arrow. This may indicate that the fiber buckling was
initiated then. The pattern recognition of the combination will further lead to correlate the AE
signals to more microscopic damages, such as SiC fiber breakage and pull-out of fiber. Further
study will be carried out to find the correlation by the two-frequency combination analysis extracted from CWT.
170

Conclusions
The wavelet analysis of AE signals based on the two-frequency combination extracted fromCWT diagram was applied to investigate the correlation between AE signals and micro-damages
in the SiC/SiC composites with transpiration structure and the following conclusions were obtained.
1) Major frequency components that characterize the contour pattern of the CWT diagram
are extracted with a projected WT-frequency curve and used in two-frequency combination
analysis.
2) The AE signal with low WT coefficient with widely distributed frequency corresponds tothe crack in the CVI- and PIP-SiC matrices.
3) The crack propagation in longitudinal fiber bundle along thickness direction may be correlated to a group of two-frequency combinations, which has high, middle and low frequency
components.
4) The fiber buckling damage was correlated to two-frequency combination, which contains
low frequency component.
References[1] Naslain R.: Journal of Applied Ceramic Technology, 2 (2), 2005, 75-84.
[2] Ohnabe H., Masaki S., Onozuka M., Miyahara K. and Sasa T.: Composites, Part A, 30, 1999,
489-496.
[3] Hayashi T., Wakayama S.: Advanced Composite Materials, 18(1), 2009, 61-75.
[4] Hayashi T., Wakayama S.: Journal of the Japan Society for Composite Materials, in press.
[5] Ageorges C.; Composites Science and Technology, 59, 1999, 2101-2113.
[6] Mizutani Y., Nagashima K., Takemoto M., Ono K.: NDT&E International, 33, 2000, 101110.
[7] Qi G., Barhorst A., Hashemi J., Kamala G.: Composites Science and Technology, 57,
1997, 389-403.
[8] Loutas T.H., Kostopoulos V., Ramirez-Jimenez C., Pharaoh M.: Composites Science andTechnology, 66, 2006, 1366-1375[9] Ni Q-Q, Iwamoto M., Engineering Fracture Mechanics, 69, 2002, 717-728.
[10] Torrence C. Compo G.: Bulletin of the American Meteorological Society, 79 (1), 1998, 6178.

171

ACOUSTIC EMISSION MONITORING OF BRIDGE STRUCTURES IN
THE FIELD AND LABORATORY
RHYS PULLIN, KAREN M. HOLFORD, ROBERT J. LARK and MARK J. EATON
Cardiff School of Engineering, The Parade, Newport Road, Cardiff, CF24 3AA, UK
Keywords: Bridge inspection, laboratory and field investigations
Abstract
Cardiff University, in association with Physical Acoustics Limited, has been actively promoting the use of acoustic emission (AE) to monitor bridge structures as part of any bridge maintenance procedure since the early 1990’s, both in concrete and steel bridge structures. Scepticism
of the capabilities of AE by bridge authorities has led to a large number of laboratory research
projects being completed to demonstrate and validate the technique and support field results.
This paper presents the findings of two field investigations with extensive laboratory support.
One is a box-girder bridge structure where a fatigue fracture was located on an internal diaphragm both on a real structure and a scale model and of the second is a steel-reinforced concrete
structure where regions of possible concrete micro-fracture around steel reinforcing bars was detected. Both investigations demonstrate the capabilities of AE as a diagnostic tool as part of any
bridge maintenance programme.
Introduction
Bridge structures throughout the UK are being used past their life expectancy and well beyond their original design loads. Furthermore, there is an increasing monetary pressure on bridge
authorities to extend the life whilst ensuring public safety. A large aspect of increasing life expectancy is completed by periodic visual inspection and based on findings appropriate repairs or
maintenance can be completed. However, visual inspection can be exceedingly difficult; in thecase of steel box girder bridges special breathing apparatus and lighting equipment is required
and in steel-reinforced concrete structures internal corrosion cannot, in many examples, be visualised externally.
AE monitoring offers a real and practical alternative to visual inspection with the monitoringof steel box-girder structures becoming increasingly accepted [1 - 4] but scepticism still exists
across a large number of bridge authorities even though the UK Highways Agency has produced
an advice note on testing structures with AE [5].
The case of steel-reinforced concrete structures is even more problematic. Fractures identified in box girders can be verified through other NDT techniques but verification of fractures insteel reinforcement is more complex and can require the removal of structural concrete, which
naturally is undesirable. Examples of monitoring steel-reinforced concrete structures can befound in [6 - 9].
One method of promoting AE and demonstrating its capabilities is through laboratory testing.
Verification of results is far less problematic and a greater level of confidence in field results canbe gained. This paper presents the findings of two field investigations, one on a steel box girder
and the other on a steel-reinforced concrete bridge - both tests had extensive laboratory support.
J. Acoustic Emission, 26 (2008)172© 2008 Acoustic Emission GroupExperimental Procedure
Box-Girder Bridge: Cardiff University was appointed to monitor a box-girder bridge in South
Wales. It has a total span of approximately 300 m and is of composite construction, with a concrete road deck supported by four steel box-girder beams. Each box-girder is approximately 1.2
m square and has a maximum span of 31 m. Welded inside the girder at various positions are 10mm thick steel diaphragms of two types. One type provides cross sectional stiffness and maintains torsional rigidity, while the second type also transfers the shear forces carried by the girders
through to the support bearings; it is the former that this study focuses on.
Initially a global approach to AE monitoring of the bridge was completed and several diaphragms emitting high activity were identified. A more detailed description of the global approach can be found in [2]; subsequently a local approach of the most emissive diaphragms wasthen completed [3].
Four PAC WDI sensors were mounted around the outside perimeter of the diaphragm (Fig.
1) so that the sensor face was aligned with the internal edge of the diaphragm. The painted surface was lightly sanded to remove any peaks. Sensors were mounted into position using magnetic clamps. Grease was used as an acoustic couplant. The installed sensitivity of the system
was then verified using the H-N source. AE feature data was recorded for a period of 90 minutes
under service loading at an acquisition threshold of 30 dB.

Fig. 1 Box Girder beam showing sensor attachment and internal diaphragm.
Steel-Reinforced Concrete Bridges : Cardiff University was given the opportunity to demonstrate
the AE technique on a steel-reinforced concrete hinge joints (Fig. 2). Concrete hinge joints arepresent in over 100 bridges in England and a further 12 over-bridges, each containing two hinge
joints, in Wales. They were introduced into bridge decks as a means of simplifying the design,and standardising details on bridges having a range of span and functional requirements. It isthought that the hinge joints transfer shear and accommodate small angular movements but restrict longitudinal movement [10]. It was also assumed the hinges were provided to enable thebridges to cope with possible differential settlement. The disadvantages with hinged joints arethat they are not easily accessible for inspection or maintenance due to their form, and their location over or under live traffic lanes.
173

Previous attempts to investigate the deterioration of hinge joints by visual inspection, which
involves the removal of structural concrete around the joint to expose the reinforcement bars,
have noted particular defects; the majority have cracks running through the throat and a loss ofwaterproofing. Waterproofing failure can lead to chloride rich seepage through the joint that cancause reinforcement bar corrosion leading to eventual failure by yielding.
The joint monitored was of joint was of slab construction. The bridge is an over-bridge on the
M4 in South Wales. The exact positioning of the internal reinforcement bars across the joint was
unknown; however, they were believed to be at 308-mm centres. Grease was used as an acoustic
couplant. An H-N source was used as a source to assess the sensitivity of the sensors.

Fig. 2: Section of a typical hinge-joint assembly, sensor attachment and installed system.
Monitoring was completed for a period of 12hours (7am-7pm) for three days. Heavy vehicles
crossing the joint were logged using time marks for a period of five hours during the initial dayof monitoring. AE feature data was recorded at a threshold of 35 dB.

174

Supporting Laboratory Studies: The laboratory model of the diaphragm was fabricated to halfscale. The upper section of the box section was bolted to a test frame whilst load was applied tothe end section (Fig. 3a) to recreate the torsional loading associated with damage on the bridge
structure. The specimen was cyclically loaded until a substantial fatigue fracture was observed.
AE instrumentation was kept consistent for both the field and laboratory investigation.
Construction of a realistic hinge-joint model containing all internal reinforcing in the laboratory would have been too complex to fabricate, restrain and load so a specimen that demonstrated the capabilities of AE to detect fatigue fractures in concrete and steel-reinforcement bars
was created (Fig. 3b). A circular notch smaller than the inner diameter of the threaded section
was machined at the centre of the bar to initiate a fatigue crack. Two PAC R15I sensors wereconnected to the steel reinforcement bar using waveguides and four PAC R3I sensors were thenadded to the concrete faces as shown in Fig. 3. In both cases grease was used as an acoustic
couplant. Specimens were loaded cyclically until failure.

Fig. 3: Laboratory testing.
Results and Discussion
Figures 4a and b show two location plots from the field and laboratory investigation on thesteel girder. The most obvious difference is the number of events detected, which is considerably
less in the field investigation due to the limited period of monitoring. However, both plots show
regions of high activity in the region of the cut-out. A red line representing the actual fatigue
fracture has been added to Fig. 4b. Other regions of high activity are present in this plot but werebeing emitted from the bolted joint. This was validated using pencil-lead fractures at the bolt
sites and comparing the location with these regions of high activity. Experience of bridge andlaboratory testing has consistently shown that noise is a greater problem in the laboratory due tohydraulic noise and frictional noises between test-rig components.
With limited or no access to the inside of the box girder in the field, it is difficult to quantify
these types of result. However, with the added result from the laboratory work, a greater confidence in identifying the source as a fracture can be made. Furthermore, results from laboratory
studies can be used to aid source characterisation. In simple cases this could entail examining

175

(a) field investigation(b) laboratory study
Fig. 4: Comparison of field and laboratory results from box-girder testing.
amplitude and energy trends, sometimes a more advanced technique such as neural networks
may be appropriate.
The case of box girders is relatively simple, with plate geometries and non-complex propagation paths, a greater level of complexity occurs in steel-reinforced concrete bridge structures.
Varying wave speeds, due to multiple materials, complex propagation paths due to aggregate andmultiple damage mechanisms such as steel fatigue fracture, concrete fracture and corrosion makemonitoring reinforced concrete structures difficult and the collected data difficult to interpret.
Table 1 presents the number of hits within specific amplitude ranges from each day of monitoring the slab hinge joint. The results indicate that there is not a significant difference between
the number of hits on each day of monitoring suggesting that at this site one day of monitoring issufficient to provide the necessary data.
Table 1: Summary of monitoring results.
Amplitude of Hits
(dB)
35-45
45-55
55-65
65-75
75-85
85-100No. of Hits
Day 1 (x 103)
1500
226
58.8
12.9
2.09
0.23

No. of Hits
Day 2 (x 103)
1500
187
40.2
8.4
1.28
0.16

No. of Hits
Day 3 (x 103)
1200
152
37.3
7.3
1.09
0.15

Figure 5 shows the period of logging heavy vehicles, the highest energy emissions from thejoint can be associated with heavy vehicles crossing the joint, however not all heavy vehicles
produced large energy emissions. During monitoring it was visually observed that all vehicles
caused some emissions, these however may be associated with the normal stress loading of thejoint and that vehicles travelling at higher speeds across the joint produced greater energy emissions.
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Fig. 5: Activity of joint with logged vehicles.
Figure 6 shows a planar location plot from three days of monitoring the steel-reinforced concrete hinge joint. The numbered circles represent sensor positions whilst a dashed line shows thehinge position. The plot shows several bands of emission located to the south of the joint and indicate regions of possible damage, likely to be centred around reinforcing bars. These bands occur at larger intervals than the 308-mm spacing of the bars. This implies that the AE is detecting
sources at some bars but not every bar.

Fig. 6: Field testing location results.
After the test data was collected, three new methods of data interpretation were developed. A
method utilising the kernel probability density estimation (KPDE) function [11] was created thatenabled not only the density of signals, but the density of the energy of the signals to be visualised. This is currently not possible using commercial software.
Another technique was developed that expanded on the commercial technique of location
cluster analysis and employed k-means analysis [12] to aid source characterisation, a summary ofwhich is shown in Fig. 7.
The purpose of source location clustering is to automatically identify and associate groups of
events, based on user controlled spatial criteria and a number of signal threshold. In commercial
software, clusters can be organised in terms of their total energy, but there is a limit imposed on
the total number of clusters (100), which was removed in the developed script. A k-means
177

Fig. 7: Flow chart of designed script used to further analyse the recorded data.
analysis of the feature data from the location clusters can then be completed. A k-means analysis
assigns a set of data points to k-centres, or means. The algorithm is popular due to its fast convergence and simplicity. The algorithm is initiated in a random state: each data point is assigned
as a member of one of the k-groups. The set of centres is calculated by taking the mean of themembers of each group, then each data point is re-assigned to a new group according to its nearest center. The process is repeated until no points change groups. The algorithm works exceptionally well on well-spaced groups of Gaussian-distributed data points. In order to make the AEfeature data resemble this, the data is normalised, such as to have unit variance in each feature.
The variance of each group in the feature space, compared with the entire data set can then be
calculated. In theory any regions of high activity that are identified via source location clusteringand have very similar signals, as identified via the level of variance in the feature space, arelikely to be from a repetitive damage mechanism such as fatigue fracture or concrete microcracking.
The resulting plots of the KPDE analysis are presented in Figs. 8a and b. The region of highactivity at approximately (5.5 m, 0.6 m) is clearly visible in Fig. 8a, whilst the remaining bands
that are associated with the internal reinforcing bar are clearly visible. However of more interestis the region of densely populated energy in Fig. 8b, which using the commercial software could
not be visualised. This is a region of low density in terms of events but high energy and it is very
close to the centre line of the hinge section. This suggests that this region is made up of a small
number of high-energy events that could be attributed to concrete fractures. Fatigue fractures of
the reinforcing bar would be likely to occur more frequently and with greater consistency in178energy release under normal traffic loading. Therefore the region is attributed to concrete fractures near to the throat of the hinge.

Fig. 8: Kernel probability density estimation plot (a) hits (b) energy.
To further analyse the regions of high event density on the south side of the joint in Fig. 6,
feature data, including location, from channels 3, 4, 5 and 6 was processed by the developed kmeans analysis based script (Fig. 9). A source cluster area, based on a circle with diameter of 30mm, and threshold of 30 events, was used to analyse the data. A graphical representation of thescript results is shown in Fig. 9, together with a key to the level of variance of the feature data.
Three regions of grouped, low variance clusters are clearly visible and are in close proximity to
the joint. Although the definite position of the reinforcing bars is unknown the distance between
the bars is believed to be 308 mm, and the possible positions of the bars based on the lowest
variance clusters are superimposed. It can be seen that the other grouped regions would also appear to be in close proximity to reinforcing bars, whilst several bars appear to have no active
damage in their vicinity.
Feature data of the cluster with the highest number of events (242) in the low variance group,
which is centred on a reinforcing bar, were compared with that of regions at a similar distancefrom the same sensor, that were not associated with a reinforcing bar position; this revealed little
difference in features. This suggests that the detected signals are associated with damage in theconcrete structure rather than the steel reinforcing bar, which would have distinctly different features. This suggests that the regions detected are due to microcracking of the concrete.
However, further evidence was needed to show that AE was capable of detecting damage inthese complex structures, and thus a laboratory model was fabricated and tested. Figures 10a, b
and c show the results of the laboratory testing of the steel-reinforced concrete specimen. Under
the initial loading, a small amount of concrete fracture occurred around the region of the rebar
notch, and these fractures increased through the duration of its life and further emissions arecaused by localised crushing of loose aggregate. This is evident in Fig. 10a, which shows all located signals using the surface-mounted concrete sensors from the duration of the whole test.
179

Fig. 9: Graphical display of results from k-means analysis based script.

(a)
(b)
(c)
Fig. 10: Laboratory testing of concrete encased steel reinforcement bar.
Figure 10b shows the location of signals from the final 7000 cycles. It was known, based onload history, that this is when the rebar started to fatigue. The plot clearly shows that AE can detect and locate steel fatigue fractures from surface-mounted sensors, again providing confidence
to bridge authorities of the capability of AE. Figure 6c shows a linear location plot against timeusing the sensors attached to the waveguides. The plot clearly demonstrates that the sensors detected the concrete fractures early in the investigation and detected the steel fatigue fractures atthe end of the investigation. Furthermore, results of the laboratory investigations could be used tohelp identify fracture sources or train neural networks.
As discussed previously, monitoring of steel box-girder bridges by AE is less complex thanmonitoring concrete reinforced joints. Current techniques for locating and identifying damage inbox girders are sufficient. However, further validation of field results, using visual inspection or180other NDT techniques, is required to convince bridge authorities of the capabilities of AE. It is
more difficult to promote the use of AE in steel-reinforced concrete structures. AE has been usedpreviously, but validation of results is often impossible as other NDT techniques cannot be usedand visual inspection requires the removal of structural concrete, which is undesirable as it cancause extensive damage to the structure.
Conclusions
Results presented in this paper have demonstrated the capabilities of AE to detect damage inbridge structures. Results have been complemented by laboratory testing that has been required
to support and demonstrate findings to bridge authorities.
References[1] D. C. Carter, R. Pullin and K. M. Holford, Acoustic Emission Studies of a Steel Box Girder
Bridge, 23rd European Conference on Acoustic Emission Testing, (1998) pp. 225-229.
[2] K. M. Holford, D. C. Carter, R. Pullin and A. W. Davies, Bridge Integrity Assessment by
Acoustic Emission – Global Monitoring, 2nd International Conference on Identification in Engineering Systems, (1999) pp. 392-400.
[3] R. Pullin, D. C. Carter, K. M. Holford and A. W. Davies, Bridge Integrity Assessment by
Acoustic Emission – Local Monitoring, 2nd International Conference on Identification in Engineering Systems, (1999) pp. 400-405[4] A. W. Davies, K. M. Holford, R. Pullin, J. Watson., P. T. Cole and S. N. Gautney, Acoustic
emission monitoring trial, Report for The Highways Agency: Maintenance Agency Area 12:
Midlands Links Motorway Viaducts, Cardiff University and Physical Acoustics Ltd. (1999).
[5] Highways Agency Advice Note on NDT (BA86) (2005).
[6] J. R. Watson, S. Yuyama, R. Pullin and M. Ing, Acoustic Emission Monitoring Applications
for Civil Structures, Surrey University’s International Bridge Management Conference (2005).
[7] M. Ohtsu, A Review of AE in Civil Engineering with Emphasis on Concrete, Journal of
Acoustic Emission, 8, No. 4, (1989) 93-98.
[8] M. Ohtsu, The History and Development of Acoustic Emission in Concrete Engineering,
Magazine of Concrete Research, 48, No. 177 (1996) 321-330.
[9] M. J. Ing, R. Lyons and S. A. Austin, Risk-Based Investigation of Steel Reinforcement Corrosion Using the AeCorr Technique, Proceedings of 3rd International Conference on Emerging
Technologies in Non Destructive Testing, Thessaloniki, Greece (2003), pp. 410-418.
[10] J. R. Watson, P. T. Cole, I. Kennedy-Reid and J. Halliday, Condition Assessment of Concrete Half Joints, First International Conference on Bridge Maintenance, Safety and Management, Barcelona, July 14-17, 2002.
[11] C. B. Wilson, Assessment of the reinforced concrete hinges on five M6 overbridges in Staffordshire, Proceedings Institution Civil Engineers, 110, (1995) 4-10.
[12] B. W. Silverman and B. S. Silverman, Density Estimation for Statistics and Data Analysis,
CRC Press (1986).
[13] C. M. Bishop, Pattern Recognition and Machine Learning, Springer, ISBN-10:
0387310738, (2006).

181

ARRIVAL TIME DETECTION IN THIN MULTILAYER PLATESON THE BASIS OF AKAIKE INFORMATION CRITERIONPETR SEDLAK1,2, YUICHIRO HIROSE1, MANABU ENOKI1 and JOSEF SIKULA2
Department of Materials Engineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, 113-8656, Japan; 2 Czech Noise Research Laboratory, Department of Physics, FEEC, Brno University of Technology, 616 00 Brno, Czech Republic

1

Keywords: arrival time detection, Akaike information criterion, multi-layer plate
Abstract
The information of first-arrival time of acoustic emission (AE) signal is important in event
location, event identification and source mechanism analysis. Manual picks are time-consuming
and sometimes subjective. Several approaches are used in practice. New first arrival automatic
determination technique of AE signals in thin metal plates is presented. Based on Akaike information criterion (AIC), proposed algorithm of the first-arrival detection uses the specific characteristic function, which is sensitive to change of frequency in contrast to others such as envelopeof the signal. The approach was tested on real AE data recorded by a four-channel recording
system. The results were compared to manual picks and to other AIC approach. It is shown that
our two-step AIC picker is a reliable tool to identify the arrival time for AE signals.
Introduction
The precise determination of the arrival time of transient signals like AE, seismograms or ultrasound signals is one of the fundamental problems in non-destructive testing and geophysics.
The information of this time is important in event location, event identification and source
mechanism analysis. Manual picks are time-consuming and sometimes subjective, especially inthe case of large volumes of digital data. Various techniques have been presented in the literature
and are routinely used in practice, such as a passing the threshold level, analysis of the LTA/STA(long term average/short term average), high order statistics or artificial neural networks.
Modeling the signal as an autoregressive process (AR) is another approach for onset time determination. It is based on the assumption that the signal can be divided into locally stationary
segments and the intervals before and after onset are two different stationary processes [1]. On
the basis of this assumption, an autoregressive Akaike information criterion (AR-AIC) has beenused to detect P and S phases [1-3] in seismology. For AR-AIC picker, the order of the AR process must be specified by trial and error and the AR coefficients have to be calculated for bothintervals. In contrast, Maeda [4] calculated the AIC function directly from seismogram without
using the AR coefficients. For time series 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 ]))(1)

where k is range through all time of time series. However, the AIC picker does not perform well,
if the signal to noise ratio is low and the arrival is not evident. Further, for AIC picker to identify
the proper arrival, a limited time window of the data must be chosen [3].
Although AE and seismograms are similar to each other for first view, there also exist several
differences. In seismology the signal and noise are usually located in different frequency range.
AE signal and noise are often in the same frequency range and also signal-to-noise ratio is generJ. Acoustic Emission, 26 (2008)182© 2008 Acoustic Emission Groupally not constant during experiment. Kurz et al. [5] successfully applied an adapted automatic
AIC picker based on Maeda’s relation to AE from concrete and used the complex wavelet transform and Hilbert transform as characteristic function instead of the signal. Both these transforms
lead to a certain envelope of the signal. The advantage of the envelope by wavelet transformationis that it can be calculated only for one frequency, while most of the noise of the signal is foundin different frequencies. However, if two or more signals of different amplitude and frequency
superpose each other, the envelope calculated by the Hilbert transform should be used.
In our case, the signal is described by the specific characteristic function, which is used as
input data for AIC. This characteristic function is sensitive to change of frequency in contrast toothers such as envelope of the signal, which indicates only change in amplitude of signal. The
approach was tested on real AE data and compared to manual picks as well as to Kurz’s AIC approach.
Two-step AIC Picker
The performance of the picker depends strongly on characteristic function. The arrival time isindicated by a change in the frequency, or amplitude, or both, in the time series, and characteristic function must respond to this change as rapidly as possible and, ideally, should enhance thechange [6]. The absolute value function CF(i) = |x(i)| is easy to compute and the most widelyused (amplitude threshold picker). The square function CF(i) = x(i)2 enhances the amplitudechanges but not frequency changes. For seismogram threshold picker, Allen [6] used a function,(2)

where K is a weighting constant that varies with sample rate and stationary noise characteristics.
Unfortunately, we discovered that these characteristic functions are not so effective for our case.
We found that our AIC picker succeeds with following function,(3)

where R is a constant specified by trial and error; for our case R = 4. This characteristic function
is sensitive to change of frequency in contrast to others such as envelope of the signal.
During one experiment, an AE measurement system can recognize and record up to several
thousand AE signals. The length of one AE signal and threshold level is defined by researcher inadvance. If we consider the facts mentioned above about AIC pickers, the choice of correct time
window is a crucial factor for the identification of proper arrival. Ideally, the time window starts
in non-informative part of AE signal (noise) and ends in informative part of AE signal (real signal).
Our proposal of the first-arrival detection solves this problem. Figure 1 presents visual description of individual stages of our algorithm. The algorithm shortens the time interval of theoriginal time window in Fig. 1a. The beginning of signal is presumed as non-informative partand it is not changed. The global maximum of the original signal in absolute value is found, tMAX.
This time plus time delay ΔtAM is considered the end of informative part of AE signal. We set the
ΔtAM = 20 µs, which is reasonable for material of our interest. Mathematically, this can be defined for signal x of length N with time step Δt = 0.1 µs as

183imax = i :x(i ) == max(x ), where x = {x(i )| i = 1,..., N }x NEW = {x NEW (i )| i = 1,..., (imax + 200)}(4)where xNEW is results of shortening of time window.

Fig. 1. Visual description of our two-step AIC picker. (a) Definition of new time interval, (b)Characteristic function, (c) Determination of first estimation of arrival time and focus on its
neighborhood, (d) Determination of final arrival time, (e) Result.
184

Our algorithm is two-step process. First, the characteristic function Eq. 3 is computed on
shortened signal (Fig. 1b) and AIC picker based on Maeda’s relation Eq. 1 is applied on this CF.
The global minimum of AIC function determines the first estimation of the arrival time (Fig. 1c).
In second step, we focus on neighborhood of first estimation (Fig. 1c). The time interval ischanged to start at ΔtFB before first estimation and to end at ΔtFA after first estimation. For ourcase, ΔtFA = 10 µs and ΔtFB = 30 µs were found by trial and error. The AIC picker is applied once
again on CF in this shortened time interval (Fig. 1d). The global minimum of recalculated AIC
function defines the arrival time of AE event, as can be seen in Fig. 1e.
Experiment
Acoustic emission is one of methods describing behavior and properties of materials undervarious conditions. Considering the nature of AE, many spurious events can occur during an experiment and represent potential errors in final conclusions. In our case, the localization is used
to eliminate this possible error.
The approach was tested on real AE data. Figure 2 presents measurement set-up of this tensile test of 25-layered SPCC/SUS420J2 thin plate with four sensors, which were located in one
line. The location of AE event is estimated by one-dimensional hyperbolic localization by timesof first arrival. AE measurement system called Continuous Wave Memory [7] was used to recognize AE events by 15-mV threshold and to store every event in 100 µs time length. Continuous Wave Memory sampled data at a rate of 10 MHz by 12-bit A/D converter. The data were
filtered numerically by a 4th-order Butterworth high-pass filter with cut-off frequency of 100 kHz.

Fig. 2. Measurement set-up of tensile test.
Results and Discussion
In the test, the 25 AE events from center region of a specimen were chosen for comparative
investigation. It represents 100 AE signals. Arrival times of these chosen events were determinedmanually as well as automatically using our approach and Kurz’s approach with envelope calculated by Hilbert transform [5]. The signal-to-noise ratio (SNR) of the chosen event varied according to the test stage the individual event occurred.
Figure 3 shows that the localized events obtained with arrival times of our two-step AIC
picker are all situated relatively close to the events localized with the arrival times determinedmanually. The events localized using arrival times determined by Kurz’s AIC picker are not insuch proximity to manually picked events. The explanation is within reach. The mean of differences between manual picks and Kurz’s AIC picker is 2.7 µs, but maximal difference is 18 µs

185(Fig. 5). In case of two-step AIC picker (Fig. 4), the mean is 0.2 µs and the maximum is 2.4 µs
only.

Fig. 3. Localization error: comparison of two-step AIC picker with Kurz AIC picker.

Fig. 4. Histogram of differences between manual picks and automatic picks by two-step AIC
picker and its corresponding signal-to-noise ratios (circles).
We found that 89% of arrival times were determined by our approach with deviation lessthan 0.5 µs, whereas in case of Kurz’s AIC picker it was 41%. The examples of the first-arrival
determination for varying signal-to-noise ratios are shown in Fig. 6.
Conclusion
A new automatic determination technique of the first-arrival times of AE signals is presented
for thin metal plates. Based on Maeda’s relation, the proposed algorithm of the first-arrival
186

detection uses the specific characteristic function. This characteristic function CF is sensitive tochange of frequency in contrast to others such as envelope of the signal.

Fig. 5. Histogram of differences between manual picks and automatic picks by Kurz’s AIC
picker and its corresponding signal-to-noise ratios (circles).

Fig. 6. Examples of first-arrival detection for varying signal-to-noise ratios in first test.
The proposed algorithm shortens the time window of an AE signal so that it ends in the informative part of the signal. The characteristic function is computed on the shortened signal and
AIC picker is applied. The global minimum of AIC function determines the first estimation of
the arrival time. The time window is shortened again and focused on the neighborhood of firstestimation. The AIC picker is applied once again on CF in this shorter time interval. The global
minimum of recalculated AIC function defines the arrival time of the AE event.
187

The approach was tested on a tensile test of a 25-layered SPCC/SUS420J2 thin plate. From
the test, 25 AE events were chosen. Arrival times of these AE events were determined manually
as well as automatically using our approach and Kurz’s approach. Although we and Kurz et al.
use the same Maeda’s equation, the approaches differ in characteristic function. The choice ofcharacteristic function is a crucial factor for the first-arrival detection.
The comparative investigation shows that 89% of arrival times were determined by our
two-step AIC picker with deviation less than 0.5 µs and 96% of arrival times were determined byour two-step AIC picker with deviation less than 1 µs. It shows that the two-step AIC picker is areliable tool for automatic identification of the arrival times for AE signals of varying signal-tonoise ratios.
Acknowledgement
The present research is conducted in LISM (Layer-Integrated Steels and Metals) Project
funded by Ministry of Education, Culture, Sports, Science and Technology of Japan. Also, thisresearch has been supported by Japan Society for the Promotion of Sciences (JSPS).
References
1. Sleemen R. van Eck T.: Phys Earth Planet. Interiors 113, (1999) 265-275.
2. Leonard M. Kennett B. L. N.: Phys. Earth Planet. Interiors 113, (1999) 247–264.
3. Zhang H. et al., Bulletin of the Seismological Society of America 93 (5), (2003) 1904–1912.
4. Maeda N.: Zisin = Jishin 38, (1985) 365–379.
5. Kurz J. H. et al., Ultrasonic 43, (2005) 538-546.
6. Allen R.: Bulletin of the Seismological Society of America 72, (1982) 225-242.
7. Ito K. Enoki M.: Mater. Trans. 48, (2007) 1221–1226.

188

SOME POSSIBILITIES OF AE SIGNAL TREATMENT AT CONTACTDAMAGE TESTS OF MATERIALS AND BEARINGSPAVEL MAZAL1, FILIP HORT1, MARTIN DRAB2 and TOMAS SLUNECKO2
1
Brno University of Technology, Technicka 2, 616 69 Brno, Czech Republic;2
ZD Rpety – Dakel, Ohrobecká 408, 142 00 Prague, Czech Republic
Keywords: contact fatigue, bearing, AE signal, pitting
Abstract
This article compares recent application possibilities of the acoustic emission (AE) method
for more specific identification of the stages of contact fatigue of materials. It focuses also on
examples of results obtained by a new AE analyzer, which allows for continuous AE signal sensing. The results proved that AE techniques enable reliable recognition of running-in period, stabilized run and exact definition of initiation stage of surface damage, leading to pitting. First
results obtained during AE signal sensing in the course of durability tests of axial and radial
bearings in the contact fatigue laboratories of the Brno University of Technology are also presented.
1. Introduction
Endurance of materials against contact fatigue damage is one of the most important parameters for long-term reliable function of all types of bearings, gears and many other movable machine parts. The tests of contact fatigue of materials and bearing durability are conducted using
equipments with test specimens of different form or real test bearing. In most cases the actual
degree of damage is detected by sensing vibrations of the whole testing mechanism. After reaching a predefined level of vibrations, the machine is switched off and the sample or bearing is
examined in detail. Although these testing machines are equipped with sensitive sensors, theresults are not fully satisfactory yet. One potential improvement in the identification of damage
degree is the application of acoustic emission (AE) method. This method can provide detailed
information about periods of changes of damage mechanism. So far, it is difficult to precisely
assign the observed changes of AE signal to the processes in material. One of the reasons is ofcourse the limited knowledge about mechanisms of contact damage, and another one is the range
of information available in AE signal.
An important reason of the loss of decisive properties of all types of bearings is the inceptionof point contact damage, so-called pitting, on some of its elements. Contact damage is caused bycyclically repeating processes in surface layer of material by mutual dynamic load of two bodies.
Damage of surface layers causes the inception of microcracks in places of maximum sheer stress,by progressive separation of damaged surface layers and by inception of holes on the surface(Fig. 1). In the beginning, this fatigue damage results in decrease of functional properties of damaged part; however, emerged surface hole may gradually create a center of fatigue crack, which
successively enlarges to the whole section of the part.
In frame of the project of Ministry of Industry and Trade of the Czech Republic, “Researchof new methods of measuring and evaluation of acoustic emission signal”, a new type of analyzer has been developed that is capable of registering the whole AE signal. In this paper the
J. Acoustic Emission, 26 (2008)189© 2008 Acoustic Emission Groupinformation about the processes of contact damage of materials obtained by standard methods
and by this new analyzer are compared.

a)b)
Fig. 1. Example of contact fatigue microcracks (a) and pitting damage (b) on the surface ofthe specimen from bearing steel (100Cr6) loaded in Axmat testing device.
Compared with classical material testing, contact fatigue is more complicated, as there isalways mutual contact of at least two bodies. In case of development of contact damage, AE signal contains not only “standard information” about the activity of the defect itself, but, taking
into account the change of quality of contact surface, the vibrations of the system grow and noisein the testing equipment grows as well; all this is reflected in the detected signals. We also cannot forget the important influence of the lubrication of the whole system. With respect to thesensitivity of AE method, the importance of quality of setting is apparent – choice of parameters,
sensors, signal amplification, sensor locations, quality of contact between sensor and surface, etc.
[1-3].
2. Experimental equipment
Three basic appliances are used for contact fatigue tests of materials and for tests of bearing
life (principle of all these stations were described in other contributions [4, 5]):a) Axmat station – in this case, 21 balls are placed in guiding ring; they roll away on the surfaceof firmly fixed disc-shaped sample from tested material. This device was modified also for axial
bearing tests. In this case, ring, balls and material sample are replaced by the tested axial bearing(Fig. 2a).
b) R-mat station (Fig. 2b) – two rotating discs roll away on the surface of cylindrical sample.
c) SA 64 testing equipment (Fig. 2c) is devoted for tests of radial bearings durability.
Sensed AE signals are amplified in preamplifier and subsequently processed in AE analyzers.
Our standard AE analyzers (Dakel Xedo®) enable dividing detected signals into 16 elective energetic classes, so-called levels. In order to highlight the AE signal change, it is possible tochoose only suitable levels. The sensing software Daemon® was used for sensing and evaluationof AE signal. After completion of the tests, the signal was processed by software DaeShow®.
The examples of AE signal treatment are presented on Figs. 4, 5, 9 and 11.
In a part of experiments, we had the possibility to use the newly developed AE analyzer
named DAKEL IPL®. It is a new state-of-the-art system for continuously recording and process190

ing data from the AE sensors. Compared to the commonly used AE analyzers, the major advantage of the DAKEL IPL is the capability of continuously sampling and storing the whole AE
signals for as long as there is a disk space to store them. With the biggest currently available fastdisks on the market having the limit of about 1 TB, we can store as long as 17 hours of 5-channel
recording and when using RAID arrays of such disks it can be much more.

a)
b)
c)
Fig. 2 a) device Axmat with axial bearing, b) detail of working part of R-mat device, c) testingstation SE 64 for radial bearings durability tests.

a)
b)
c)
Fig. 3 Typical shapes of the tested specimens a) for Axmat device (diameter app. 40 mm), b)
R-mat (diameter 10 mm) and c) tested axial and radial bearings.
The device has a total of 5 synchronous analog input channels, simultaneously and continuously sampling at 2 MHz. Out of these, 4 channels are generally used to sample the AE signals
and the last channel is used for synchronous monitoring of any external physical parameter thatis used to excite the testing object (such as the loading force, pressure, temperature, etc.). This
comes handy when you need to correlate the AE signals to some external events in time.
The results obtained from DAKEL IPL device are commented later in the text. Some examples of the processed data from contact loading of material (on Axmat testing device) will beshown in Figs. 6-8. First results obtained on radial bearings are collected on Figs. 13 and 14.
3. Experimental results
3.1 Contact fatigue of materials:
Observed changes of the number of overshoots over adjusted levels of the signal during a teston Axmat and R-mat stations are shown in Figs. 4 and 5 [5].
191Fig. 4. Example of observed changes in the number of overshoots over adjusted levels of thesignal during a test on Axmat station (bearing steel 100 Cr6).

Fig. 5. Record of the final part of contact damage test of a bainitic steel on R-mat testing station.
These “simple” and basic records of AE signals enable us to obtain detailed description of
the character of the damage, which emerges either as gradual breaking of the surface layer of thematerial and “leap” extending of pitting or possibly by widening of number of damaged places inmonitored contact trace. When the test is finished, it is possible to determine the type of damage
by visual observation.
Figures 6, 7 and 8 show the examples of latest records, which were obtained by DAKEL IPL,
which is capable of continuously sampling and storing the whole AE signals. Changes of spectral
amplitude (Fig. 6) should be a very important parameter for differentiation of damage stages.
The possibility of displaying the progress of the amplitudes on selected frequencies in time isalso useful (Figs. 7 and 8). In the present case, it is clear that for the identification of contact
damage initiation in steel, it is suitable to observe the change of higher frequencies (in this caseabove ~180 kHz) in time. For comparison, the record obtained during grey cast iron contact fatigue test is also shown (Fig. 8). In this case the mechanism of damage is considerably different
from steel. As a result being a relatively soft material, a deep trail with a plenty of small damages
appears. Gradually the contact pressure decreases and the period of stable damage appears.
192

Records on levels 50 to 70 kHz correspond to this. However, unlike the case of steel, the higher
frequencies do not provide any information about damage propagation.

Fig. 6. a) Continuously sampled signal allows us to create a 3D map of continuously evolving
averaged spectrograms in time during the whole measurement (or any part of it that we choose).
The third dimension on this figure represents the spectral amplitude and is represented by thecolor according to the displayed scale on the right side of the graph. b) This is a horizontal cut of
the 3D spectral map from Fig. 6a at 8 min, 50 min and 100 min in time. This is the common way
to display the spectral amplitudes. Testing device Axmat and tested material – carbon steel.
193

Fig. 7. Displays of a progress of the amplitudes on selected frequencies with time for carbon
steel (the vertical cut of the 3D spectral map from Fig. 6a in this case at 116 kHz, 186 kHz, 232
kHz, and 439 kHz frequencies).

Fig. 8. Record of the amplitudes on selected frequencies with time for grey cast iron is shown forcomparison. Frequencies 59 kHz, 66 kHz, 88 kHz, 155kHz and 468 kHz are presented in thiscase.
3.2 Tests of bearings durability:
Compared to contact fatigue testing, the tests of real bearings present many technical problems. We have conducted a series of successful experiments with axial bearings on a modified
Axmat testing station (Fig. 2a). An example of record of the final part of test of a bearing withpitting on both outer and inner ring is presented on Fig. 9. and Fig. 10 shows details of defects.
194Fig. 9. Example of standard record of AE activity on all levels in the propagation stage of contact
damage of axial bearing damage – final part of the test.

Fig. 10 Overview and more detailed photos of a defect of the outer ring of an axial bearing.
Most recent group of experiments was carried out for durability testing of radial bearings(Fig. 2c). Sensing of AE signal is complicated by position of the tested bearing inside the testingstation, so it is not possible to place the AE sensors to its direct vicinity. In this case we use
waveguides, whose one end touches the outer ring of the bearing, which is fixed in the testing
device and the other end is connected to the sensor. Another sensor is placed on the surface ofthe testing station.
Despite the mentioned complications with AE signal sensing, we have managed to find optimal setting of the measuring chain and to process some records of high-quality and damaged
radial bearings. Example of basic comparison of records from the initial stage of loading of bothbearings is depicted on Fig. 11a.
Notably higher level of signal is apparent in the case of the second (damaged) bearing. Afterdisassembly it was found that some balls in this bearing were “low-quality” (Fig. 12) and so it
was the cause of high AE activity. After further analysis of AE events, differences in their frequency characteristics were found (Fig. 11b). In the case of the high quality bearing the maximum frequencies were ranged approximately between 45 and 120 kHz, while in the case of the
bearing with damaged balls the AE events had maximum on the level at approximately 83-87
kHz.

195

a)
b)
Fig. 11 a) Example of AE signal records from the initial phase of a test of high-quality radial
bearing and from a low-quality bearing (with defect), b) typical examples of AE events and theirfrequency spectra with maximum - Dakel Xedo device and DaeShow software.

Fig. 12 Damaged ball – the “cause” of high activity of AE signal at damaged bearing from Fig.
11 and other defect on the ball surface.
At the end of 2008, the analyzers with continuous sampling and storage of AE signals(DAKEL IPL) were used also for tests of radial bearings on stations SA 64 (Fig. 2c). Because thetested bearing is located inside the testing station, we have to use sensors placed on waveguides.
Here, magnetically held sensors were placed on the surface of the station body. That is why it isnecessary to amplify the AE signal more than usual and it can contain undesirable noise. Despitethat even in this case it is possible to observe significant changes of signal during the initiationand propagation of bearing damage. The changes are perceptible much earlier than they can berecorded by standard vibro-diagnostics. Examples of the records are shown on Figs. 13 and 14.
4. Conclusions
In our experiments, AE method succeeded in distinguishing the characteristic stages of process of material damage by contact loading, which verified the possibility of improving tests ofcontact fatigue resistance of materials.
The situation with AE diagnostics of real bearings damage is much more complicated, as thebearings are composed from many parts which are in contact and especially it is usually not possible to place the AE sensors directly on the bearing surface. For this reason, the signal contains
196

a)
b)
Fig. 13 a) Continuously sampled AE signal (3D map) of spectrograms with time (see also Fig.
6a) during the final part of a real bearing (type 6204) measurement. b) The record of the amplitudes on selected frequencies with time for the same part of the measurement as in a).

a)b)Fig. 14 Examples of horizontal cuts of the 3D spectral maps from various test times of real radial bearings (type 6204): a) bearing No. 3 from Fig. 13. b) bearing No. 2 with different type ofdamage.

Fig. 15 Examples of contact damage of the inner ring of a radial bearing.
197

much more undesirable disturbances, which makes adjusting of measuring chain more difficult.
Nevertheless the examples shown in this paper suggest that it is possible to identify bearings
with damaged parts. After the evaluation of a number of data files, it would be possible to workout simplified way of signal processing of selected signal characteristics that would correspondwith real damage of diagnosed bearing. This simplification will result into one-purpose analyzers
that will be a part of permanent diagnostic systems used on some important bearings, e.g. intransport technology, in technological lines and energetic devices – in order to optimize intervalsof planned maintenance and temporary stoppage of operation connected with it (e.g. turbines
mounting, rolling lines, etc.).
Acknowledgement
This work has been carried out in the framework of the project of MPO CR F1-IM3/136 “Research of new methods of measuring and evaluation of acoustic emission signal” (bearings) andas a part of the solution of the project of Grant Agency of CR nr. 106/06/0343 “Research ofstages of contact damage using the method of acoustic emission” (contact fatigue of materials).
References[1] Yoshioka, T., Takeda, M.: Clarification of Rolling Contact Fatigue Initiation using Acoustic
Emission Technique. Lubrication Engineering, 51(1), 1995, 41-44.
[2] Yoon, D.J. et al.: Early detection of damages in journal bearings by acoustic emission monitoring. Journal of AE, 13(1/2), 1-10, 1995.
[3] Rowland, C.H. et al.: Condition monitoring using acoustic emission on multiple roller bearings in consignia´s letter sorting machines, In: Proc. EWGAE 2002, Vol. 2, p. 131, Prague, 2002.
[4] Zykova, L., Mazal, P., Pazdera, L.: Identification of Contact Fatigue Stages with Acoustic
Emission Method. In: European conference on NDT, Poster 4, 8 pages, 2006, DGZfP, Berlin,
Germany, CD edition, ISBN 3-931381-86-2.
[5] Mazal, P.: The comparison of AE data acquisition at Axmat and R-mat contact fatigue stands.
In: Defektoskopia 2007, pp. 5-11, NDB Bratislava, Tatranska Strba, Slovakia, ISBN 978-80968724-7-3.

198

LASER CUTTING AND ACOUSTIC EMISSION SIGNALSTOMAŽ KEK and JANEZ GRUM
University of Ljubljana, Faculty of Mechanical Engineering, Askerceva 6, 1000 Ljubljana,
Slovenia
Keywords: Burst acoustic emission, laser cutting, PZT sensor, dross
Abstract
The paper presents results of an analysis of acoustic-emission (AE) signals obtained duringlaser cutting of a steel plates, which are frequently used in the production of automotive bodycomponents. In the course of laser cutting, continuous signals, which are related to the quality
achieved of the laser cut, are measured. After laser cutting characteristic AE bursts in thespecimen material, which are results of a thermal influence, can be detected. The presence ofdross is an important indicator of poor quality of laser cutting. The investigation on laser cutting
was conducted on an unalloyed steel DC04 sheet and on austenitic stainless steel X5CrNi18-10
sheet of 1.5 mm in thickness. The same physical mechanism in the cut formation at both steels,
however, resulted in a considerable different cut quality, which was indicated by the AE signals
captured after the cutting process.
Introduction
Laser cutting of unalloyed and stainless steels using oxygen is a well-established cutting
process in industrial applications and capable of satisfying requirements of numerous users interms of production costs and quality [1]. In laser cutting of stainless steel with oxygen, however,the dross formation at the lower edge can hardly be avoided, which results in poorer quality of
the cut made. The cutting mechanism is the same as in cutting of unalloyed steel, but during
cutting stainless steel oxygen will exothermically react both with iron and chromium, and otheralloying elements in steel. The oxidation process, however, is less efficient than in cutting ofunalloyed steel.
The melt solidification on the cut face and in the dross, and subsequent cooling, upon thetermination of the cutting process, results in various phenomena occurring in the material, which
can be sensed as AE bursts. The AE bursts are caused by cracking of the oxide layer due to thedifference in the coefficient of thermal expansion of the oxide layer and that of the sheet material[2]. The AE bursts are produced also by crack and microcrack initiation and propagation.
Microcrack initiation or propagation shows in momentary energy releases at the microscopic
level, which results in elastic waves in the material. A metallographic analysis of the laser-cut
surface will confirm that the point of initiation and propagation of microcracks is usually limited
to the dross and to the lower edge of the laser cut.
The occurrence of AE signals can be attributed to phase changes, i.e. martensite formation
after rapid cooling of austenite [3]. The AE burst signals with lower amplitude values of a
voltage signal may result from the events related to the micro-plastic deformation of the material,
the nucleation and sliding of dislocations [4]. Acoustic emission may result also from bonding
defects during dross cooling, porosity and micro-cavities in the solidified material. An evaluation
should also consider reflection of the AE signals from the specimen walls or other obstacles [5].
J. Acoustic Emission, 26 (2008)199© 2008 Acoustic Emission GroupExperimental Procedure
Laser cutting of sheets was carried out with an industrial CO2 laser Spectra Physics 820. Its
laser beam had a TEM00 power distribution. During cutting the laser focal point was positioned
0.5 mm below the upper sheet surface. The cutting gas used was oxygen with an overpressure of
0.2 MPa. The distance between the nozzle with output diameter of 2.2 mm and the sheet surface
was 1.5 mm. The respective laser-cut sheets with a thickness δ of 1.5 mm were made ofunalloyed deep-drawing steel DC04 (EN 10027-1) and of austenitic stainless deep-drawing steel
X5CrNi18-10 (EN 10027-1). These steel grades are frequently used in the production of bodycomponents in the automotive industry. During laser cutting a sheet was positioned on softrubber supports arranged in segments so as to prevent any noises due to sheet rubbing against the
workbench of the laser system. Figure 1 shows representation of cutting procedure and positionof sensor to capture acoustic emission during and after cutting. Under different laser-cutting
conditions, parallel cuts Lc = 300 mm in length and with spacing of 20 mm were made. For
cutting, laser-beam powers of 430, 500, 570 and 640 W and cutting speeds of 1000, 1500, 2000
and 2500 mm/min were used.

Fig. 1. AE capture during and after laser cutting of flat sheet.
For the detection of acoustic emission a contact PZT sensor VS150-M, a product of Vallen
Systeme GmbH, was used. The sensor makes it possible to measure ultrasonic waves in afrequency range between 100 and 450 kHz. The sensor was connected to an AE measuring
device AMSY4 (Vallen Systeme GmbH), via a pre-amplifier AEP4.
Laser Cutting of Unalloyed Steel DC04
During the laser cutting process a turbulent flow of the cutting gas produces continuous AE
signals. In a continuous signal changes, of the signal amplitude and frequency can be detected. A
lower energy input at the interaction zone caused by an increased cutting speed will result in alower melt temperature and, consequently, in increased melt viscosity. The portion of theoxidized melt will decrease too. When the melt flows out from the cutting front, droplets will
form at the lower cut edge. When the forces due to gas flow cannot exceed the adhesion forces ofthe melt to the sheet surface, the melt leftover will solidify in the form of droplets at the lower
cut edge, i.e. dross will form. The solidification of the molten material persisting at the lower cutedge and cracking of the oxide layer will produce AE bursts in the continuous signal. Figure 2
shows cracked and peeled-off oxide film at laser-cut face that cause distinctive AE bursts. Figure
3 shows that a crack in the oxide film, when the adhesion is strong, can result in a crack at the
sheet base material Distinctive AE bursts increase the amplitude value in the continuous signal.
200

4 µm

Fig. 2. Cracked and peeled-off oxide filmat laser-cut face (DC04).

6,7 µmMore information on the influence of the laser cutting conditions on continuous AE signals is
given in Refs. [6-8].

Fig. 3. Crack in oxide film resulting in
crack in substrate (DC04).

When cutting is stopped, acoustic emission in the form of bursts with appertaining signal
duration can still be captured. The signal duration is a time interval between the first and lasttransition of the absolute signal voltage value across the amplitude threshold set, i.e. 0.1 mV (40dB).
v[mm/min]
2500

2000

1500
1000

(a)
(b)
Fig. 4. a) Images of dross at lower cut edge at cutting with different cutting speeds and power P= 430 W and b) number of AE bursts NAAE above various amplitude levels of AE signals AAE,N
immediately after termination of laser cutting of sheet DC04 with power P = 430 W.
The presence of dross at the lower cut edge is shown by AE bursts after the termination oflaser cutting. Figure 4 shows amplitude distribution of AE burst signals immediately after thetermination of cutting of the flat sheet in the duration of 30 s. The number of the bursts
exceeding the chosen signal amplitude levels AAE,N, i.e. 40 dB, 50 dB, ..., 90 dB, is marked
NAAE. The number of the bursts NAAE at the signal amplitude level of 40 dB is thus equal to the201total number of the bursts NAE,30s in a time period of 30 s immediately after the termination oflaser cutting. It was found that the mutual dependence of the number of bursts NAAE and thesignal amplitude levels AAE,N can be described with an exponential function. A greater distanceof an exponential trend line from the origin of the coordinate system shows the higher AE
activity. Higher AE activity is related to the dross formation at the lower laser-cut edge.

Fig. 5. Number of AE bursts NAAE above various AE signal amplitude levels AAE,N after laser
cutting with cutting speed v = 1000 mm/min.

Fig. 6. Number of AE bursts NAAE above various AE signal amplitude levels AAE,N after laser
cutting with cutting speed v = 2500 mm/min.
202

Fig. 7. Dependence between number of AE bursts NAE,30s and evaluation of dross size O.
A statistical analysis of the height and width of the individual characteristic portions of thedross measured at the metallographic specimens shows a strong dissipation of the dimensions
measured. Consequently, it was decided to evaluate the dross size at the lower cut edge with thegrades ranging between 0 (perfect – no dross) and 5 (poor – large dross). Grade 0 indicates alaser cut without any melt droplets at the lower edge whereas grade 5 indicates explicit dross asshown in Fig. 4 for cutting conditions of P = 430 W and v = 2500 mm/min.
A distinctive dross enlargement can be detected with smaller energy inputs of a laser-beam tothe cutting front. Figures 5 and 6 show the amplitude distributions of AE burst signals in a periodof 30 s after the termination of laser cutting taking into account minimum and maximum cutting
speed used in experiment. With the lowest cutting speed and with all the laser-beam powers, thedross size and the related laser-cut quality are very similar, which is confirmed also by thecloseness of the exponential trend lines. With the highest cutting speed and with the individual
powers used, the quality of laser cutting differs strongly, which is confirmed by the number of
acoustic-emission bursts sensed and the distance among the exponential trend lines.
Figure 7 shows a mutual dependence of a cumulative number of the bursts in a time period of
30 s after termination of laser cutting NAE,30s and the evaluation of dross size O in cutting of theflat sheet DC04. The enlarged dross will show in a higher cumulative number of bursts NAE,30s. A
poorer mutual dependence can be noticed only with lower values NAE,30s < 10, i.e. in the range ofacceptable dross size at the lower laser-cut edge. A circle in Fig. 6 marks number NAE,30s after the
1st and 3rd laser cut, and with a square number NAE,30s after the 2nd laser cut in cutting of a mudguard from a deep-drawn part. A good agreement with the results of laser cutting of the flat sheet
can be stated.
Laser Cutting of Austenitic Stainless Steel X5CrNi18-10
The laser cuts obtained at austenitic stainless steel differ from those obtained at unalloyedsteels. With stainless steels a laser-cut surface does not show pronounced striations. Surface
roughness of a laser cut at stainless steels results primarily from rapid solidification of a thin filmof the melt flowing out during cutting. The laser-cut surface is covered by the oxide layer,
consisting of a mixture of iron (Fe2O3) and chromium oxides (Cr2O3). The portions of the ironand chromium oxides in the oxide layer are approximately equal, which indicates higher affinityof chromium to oxygen atoms in comparison to iron atoms [1]. The ratio of iron to chromium inthe substrate is namely ~ 3.8:1. The chromium content in the substrate is ~ 18 % and that of iron203

~69 %. During laser cutting the hot melt in the cutting front is exposed to the oxygen jet. The
iron and chromium atoms enter an oxidation reaction. Greater affinity of chromium results in ahigher chromium content at the outer layer of the melt flowing out and lower chromium content
below the oxide layer. Below the oxide layer there is a quickly solidified layer of the unoxidized
substrate. This results in a re-solidified melt holding to the substrate and the presence of dross at
the lower cut edge. Larger amounts of the solidified oxides and of the substrate at the cut surface
and in the form of dross at the lower cut edge in comparison with unalloyed steel were confirmed
also by the results of the analysis of the AE bursts after the termination of cutting. Figure 8
shows the amplitude distributions of AE burst signals in a period of 30 s after the termination oflaser cutting of the flat sheet of steel X5CrNi18-10. The results shown refer to cutting with apower P = 430 W and with various cutting speeds. Similarly as with DC04 steel, an exponential
trend line can be attributed to the amplitude distributions. In cutting with different cutting speeds
but with the same power, i.e. 430 W, different qualities of the cut will be obtained. The latter are
indicated also by the appertaining reference images of dross at the lower cut edge. Similarly aswith unalloyed steel, poorer cut quality will show in a larger number of bursts after thetermination of cutting. A reduced cutting speed with the power chosen permits a higher energy
input into the cutting front. This results in scarcer occurrence of dross and a cut of higher quality,
which is confirmed by a displacement of the exponential trend line towards the origin of thecoordinate system in the diagram of the amplitude distribution of AE signals.
v[mm/min]
2500

2000

1500

1000

a)

b)

Fig. 8. a) Images of dross at lower cut edge at cutting with different cutting speeds and power P= 430 W and b) number of AE bursts NAAE above selected amplitude levels of AE signals AAE,N
immediately after termination of laser cutting of sheet X5CrNi18-10 with power P = 430 W.
Figures 9 and 10 shows the amplitude distributions of AE burst signals in a period of 30 s
after the termination of laser cutting taking into account the other cutting speeds. In laser cuttingof the sheet X5CrNi18-10 under the cutting conditions chosen within a wide range of energy
inputs, the occurrence of dross at the lower edge could not be avoided. Also, smaller differencesin the dross size will occur, which is confirmed by the proximity of the exponential trend lines.
The difference in the dross size with the chosen conditions of laser cutting of stainless steel
204

X5CrNi18-10 is less distinctive than in cutting of sheet DC04, which was confirmed also in theanalysis of AE bursts.

Fig. 9. Amplitude distribution of AE burst signals after laser cutting of austenitic stainless steel
with cutting speed v = 1000 mm/min.

Fig. 10. Amplitude distribution of AE burst signals after laser cutting of austenitic stainless steel
with cutting speed v = 2500 mm/min.
Laser Cutting of a Deep-drawn Part
With unalloyed steels, the acoustic emission after the termination of laser cutting of a sheet
makes it possible to very successfully predict the quality of a laser cut under different cutting205

conditions. Poorer quality is noticed with an increased number of AE bursts after the terminationof cutting, which is shown in an increased activity nAE. The increased activity nAE is a result of
the way the AE signals are treated. In the analysis the continuous signals were divided into
defined time intervals of 0.1 s each. This means that the AE activity with the continuous signals
does not exceed a value of 10/s. In case the AE bursts occur after the termination of cutting, thesignal duration is less than 0.1 s because of which the AE activity can be essentially higher.
During and after cutting mud guard out of deep drawn product (Fig. 11) at selected measuring
points, i.e. MP1 and MP2, acoustic emission was monitored with the PZT contact sensor. The
signals captured showed that the activity mightily increase after the 2nd cut. This indicates apoorer quality of the 2nd cut in comparison to the 1st and 3rd cuts (Fig. 12).

Fig. 11. Two mud guards were cut out of deep drawn product made of DC04 steel.

Fig. 12. Amplitude distribution of AE burst signals in laser cutting of mud-guard.
206

Conclusions
AE monitoring in laser cutting with the PZT contact sensors makes it possible to efficiently
control the quality of laser cutting. A good agreement between the quality of a laser cut and thenumber of bursts after the termination of laser cutting of the sheet made of unalloyed steel DC04
was found. The results of AE measurements confirm that more intense acoustic emission after
the termination of laser cutting indicates the presence of larger dross at the lower cut edge.
Intense acoustic emission in the form of bursts is mainly attributed to cracking and peeling-off ofthe oxide layer formed at the surface of the laser cut and dross. The presence of dross increases
the amount of the oxide layer.
With a quantitative way of treating AE signals, the determination of quality of the laser cut ismore demanding in cutting of the sheet made of austenitic stainless steel X5CrNi18-10 under
different conditions than in cutting of unalloyed steel. This is a result of the presence ofdistinctive dross under different laser-cutting conditions used, which is a result of an increased
Cr concentration in the substrate.
References
1. Powell J.: CO2 Laser Cutting; Springer-Verlag, London, 1993, pp. 16 – 22.
2. Beyer E. (edt.), Schneiden mit CO2 Lasern, VDI-Technologiezentrum Physikalische
Technologien, VDI Verlag GmbH, Düsseldorf, 1993, pp. 83-102.
3. P.O. Moore, R.K. Miller, R.K. Hill (eds), Nondestructive Testing Handbook, 3rd Edition,
Volume 6, Acoustic Emission Testing, American Society for Nondestructive Testing, Inc.,
2005, pp. 227-270.
4. Vallen H.: AE Testing Fundamentals, Equipment, Applications, NDT.net, 7 (9), (2002)(http://www.ndt.net/article/v07n09/05/05.htm).
5. Esmail E., Grabec I.: Quantitative Analysis of Acoustic Emission During Martensitic
Transformation of the β-CuZnAl Alloy, Journal of Physics D, Applied Physics, 19, (1986),
605 – 614.
6. Grum J., Kek T.: Quality assessment of laser cut based on captured acoustic emission signals,
4th International Conference on NDT, Hellenic Society for NDT, October, 2007, Chania,
Greece.
7. Kek T., Grum J.: AE signals as laser cutting quality indicators, The 5th Int. Conference on
Condition Monitoring & Machinery Failure Prevention Technologies, 2008, Edinburgh, UK.
8. Slabe J.M., Kek T., GRUM J.: Measurement of acoustic emission and deformations in laser
cutting of deep-drawn sheet parts, International Journal of Microstructure and Materials
Properties, 1, (2006), 383-396.

207

ONLINE MONITORING OF HOT DIE FORGING PROCESSESUSING ACOUSTIC EMISSION (PART I)
ISLAM EL-GALY and BERND-ARNO BEHRENS
Institute for Metal Forming and Metal Forming Machines, Leibniz Universität Hannover,An der Universität 2, 30823 Garbsen, Germany
Keywords: Hot die forging, online monitoring, forging defects, Mg-alloys
Abstract
Online process monitoring systems have been used on forming machines such as forging
hammers or multi-stage presses for a good number of years. These systems typically measureforming forces, punch displacement or frame strains and compare them to learned target values
trying to infer about the tooling or the product condition. In spite of the reached measurement
accuracy, these methods are not physically capable or not sensitive enough to express many
types of the tooling failures or product damage. Over the last few decades, acoustic emission(AE) has proved its strength and reliability as an online monitoring technique, and demonstrated
a high degree of confidence in characterizing various phenomena related to material deformation, phase transformation as well as crack initiation and propagation at various scales.
In this paper, the concept of a forging support system, based on AE as an online monitoring
and analysis technique, is introduced. The proposed support system relies on a damage and failure diagnosis module. This diagnosis module combines different AE analysis and clustering andpattern recognition methods to infer about different types of damage or failures taking place during the forging operation. AE patterns recorded from error-free forgings produced on faultlessundamaged dies serve as a reference for the comparison. In addition, patterns generated by samples with pre-induced damages or artificial defects, or AE patterns collected during the deformation under predefined faulty machine or process settings are used as reference to assist in analyzing the complex patterns, which will be obtained during real forging processes.
Some preliminary results obtained during upsetting of magnesium-alloy specimens are presented. Three different geometries have been machined from AZ31 and AZ80 extruded bars. The
specimens were upset at three constant levels of strain rates and at three different temperatures.
The results reflect the influences of alloy composition, forming speed, and temperature on theobtained AE signals. In addition, different AE patterns could be correlated to the sequence of
deformation and the evolution of damage based on the geometry of the specimen and the induced
stress states.
Introduction
The forging industries are characterized by the existence of many medium-scale companies.
These companies struggle to withstand in the market of today, while they are faced with the influence of high manufacturing and inspection costs, as well as the demanding requirement of
their clients regarding the product quality and safety. Moreover, the current quality concept of“zero defect”, especially in case of safety components, requires 100% inspection in order toavoid defective products. The currently used off-line inspection procedures normally result in ahuge percentage of defectives, which are manufactured in the time span that elapses between the
J. Acoustic Emission, 26 (2008)208© 2008 Acoustic Emission Groupfirst occurrence of the defect and its detection. Furthermore, unscheduled interventions or stoppages of the automatic manufacturing lines add unexpected service and repair costs.
The incorporation of advanced online monitoring techniques into forging lines enables theintegration of quality control/assurance into the production process itself. This helps maintain therequired level of product quality, makes it possible to determine defective product and toolingdamages as early as possible and at the same time provides a means to plan and schedule maintenance tasks [1, 2].
New technological developments suggest the use of AE to be the decisive step toward zerodefect production when tooling and products are completely online monitored [3-5]. However,there are still several challenges to be overcome to make AE a viable option in mainstream
manufacturing facilities.
Acoustic Emission in Metal Forming Applications
During the second half of the 20th century, AE from materials was studied extensively.
However, most studies were hardly complete due to the lack of experimental and theoretical resources [6]. Scientific research has shown that tooling failures in the form of cracking, breaking,chipping or wearing emit strong AE signals. These AE signals can be monitored for process deviations as well as for the investigation of dynamic behavior of materials [7]. Most of the published investigations have focused on correlating the recorded AE signals to the progress of somestandard mechanical or technological tests such as tension, compression, wear, fatigue, or cupping tests [8-17]. Due to the development in the field of applying AE for monitoring sheet metal
working processes, many machine manufacturers offer AE systems. On the other hand, very limited research work e.g. [18] has used AE to monitor real bulk forming processes. Most studies
related to bulk forming processes were carried out by the manufacturer of AE equipment themselves, trying to draw the attention of the forming industries to the AE capabilities as an effective
monitoring and analysis tool.
In a recent industrial oriented study by Skäre [19] information about the deep drawing process (wear of tool, penetration of lubricant, wrinkling of sheet material and cracking of tool or
product) could be obtained with the aid of AE measurements. Adjusting the influence of boundary layer friction based on these measurements, mounting of tools and parts as well as correctionof blank holder pressures can be optimized. It is important to mention here that the process parameters and environmental conditions (forming velocity, temperature, noise, etc.) by deep drawing processes cause little disturbances to the AE measurement when compared to those dominating in bulk forming or forging processes.
Monitoring high temperature forming processes using AE presents a further challenge. In
spite of the direct influence of temperature on damage or transformation progress and consequently on the AE, this topic was avoided by most researchers due to its experimental difficulties
and due to the lack of suitable hardware and software [6].
In different investigations, Hsu, Okajima and Ono [20-22] examined AE during deformationof several materials (austenitic stainless steels, short-range ordering alloys, bcc alloys) at hightemperatures and were able to clarify plastic deformation behavior. In the most examined materials, the AE signal recorded at yield has shown higher signal strength proportional to the temperature increase, followed by considerable decrease at a limiting temperature [22, 23].
209

Through the examination of these studies, it is obvious that most tests were performed underquasi-static strain conditions and to a limited strain (max. 0.5). This means that the results ofthese investigations, despite their convenience for material characterization purposes, do notconsider the most important aspects concerned by most forging specialists. For example, strain
rates of more than 10 s-1 and strains that exceed unity are the usually experienced ranges in mostforging processes. Fundamental studies and investigations, which consider these industrial requirements and which try to build the necessary know-how, are strongly required. Parasad [24]also reported on this requirement in his study about the feasibility of using AE for monitoring
forming processes. Undoubtedly, the current development in both control and data acquisition
technology enables more accurate experimental investigations at higher forming velocities andtemperatures. This will certainly facilitate the AE analysis tasks performed under controlled testing conditions.
Over the past two decades, many studies at the Institute of Metal Forming and Metal Forming Machines (IFUM), Leibniz Universität Hannover, have been concerned with machine healthmonitoring, product quality control, as well as quality assurance throughout the whole process
chains of both bulk and sheet metal forming processes [25-34]. For example, Terzyk [29] has
examined the possibility of monitoring die-forging processes through the analysis of force andsound signals in the audible frequency range (<20 kHz). The investigations showed that someprocess deviations could be determined through the evaluation of AE signals and guided with
3D-force measurements. For example, mass or position deviation of the pre-form, punch tilting
or misalignment of upper and lower die halves could be to some extent detected. Since the deformation action, as well as crack initiation/propagation and phase transformation activities emit
no relevant signal in the examined frequency range, only whole body vibration signals resulting
from tooling/workpiece interaction or generated by machine element could be recorded. Detection of die or workpiece damage could not be realised in this work.
Strache [34] has used both force and AE sensors to monitor stamping (blanking) processes.
The influences of sensor locations and assembly method on the recorded AE signals were examined. Using trend and envelope analysis algorithms, process faults like tooling fractures, sheet
doubling, asymmetrical press loads could be accurately detected. In addition, it was possible torecognize the wear of the stamping tool based on the AE analysis.
Aim of Current Research
Based upon the AE state of the art presented, two main research objectives have been defined: First, test methods based on AE monitoring techniques have not, until now, been standardized. Therefore, the development (and standardization) of AE test methods to monitor standard mechanical tests under manufacturing-process-related conditions, and the constructionof AE reference patterns and maps will present the milestones for the analysis of complex
events encountered during the production process.
 Second, using signal analysis to develop AE method into an online monitoring system forforging process.
This presents several challenges in order to discriminate the AE data sets of interest. Moreover,forging processes are susceptible to high dynamic and thermal stress cycles. The distinction between different AE fatigue events and patterns caused by various mechanisms presents a significant and decisive factor for using AE to monitor forging operations.
210

Fig. 1. Proposed online forging support system based on acoustic emission analysis.
The first phase of the current study, as presented in Fig. 1, will focus on conducting a sequence of AE monitored mechanical tests that simulate mechanical and thermal loading conditions prevalent in forging processes. The AE signals recorded during testing are used to build AE
maps correlating different test parameters to the recorded AE signal parameters (arrival time, risetime, amplitude, duration, counts, hits, energy as well as their distribution and timing). Moreover, AE time patterns will be also recorded to help provide more accurate description for the
progress of the forming process, determination of defect types and locations, as well as the identification of the different damage and failure mechanisms.
In the second phase of the project, and once reference patterns and AE maps are available,
analysis of real complex AE patterns generated during forging operations should be analyzed,
aiming at a complete online monitoring and fault detection of both tooling and products.
Part of the challenge in this project lies in correlating AE patterns to the corresponding generating defects. Another lies in identifying the meaningful AE events from a wide range of simultaneous events, which result from different external effects (i.e. machine elements and hydraulics, or environmental noise). Applying different time and frequency analysis methods as211

well as clustering and pattern analysis techniques to complex processes presents more challenge,
because every manufacturing process has its own unique characteristics and variables, which influence the AE patterns as the damages within the tooling or the product initiate and progress.
Experimental Setup
In order to realize the first objective of this study, a series of upsetting tests were performedon two magnesium alloys (AZ31, AZ80) and an aluminium alloy (Al7075) whose chemicalcomposition are shown in Table 1.
Table 1: Chemical composition of the specimen materials.
Material
AZ31
AZ80
Al7075

Mg%
95.87
90.84
2.4

Al%
3.1
8.3
89.9

Zn%
0.8
0.64
5.8

Mn%
0.23
0.22
0.025

Si%
0.05
0.06

Cu%
0.02
1.45

Cr%
0.2

Fe%
0.15

Ti%
0.015

To study the influence of geometry and the sequence of deformation as well as the damage
mechanism on the generated AE, specimens with three different geometries have been used. The
dimensions and geometries of the specimens are shown in Fig. 2. All specimens have been upset
from 18 to 5 mm in one or two consecutive stages. The cylindrical specimens were used toevaluate the role of standard specimen geometries in characterizing upsetting processes. The
modification in the specimen geometry through the introduction of collar or through recessing
and drilling was performed to develop stress concentration areas, which are more vulnerable todamage. The specimens were upset at three different temperatures (200, 300 and 400°C). To assure nearly constant temperature during the test, the specimens were heated and compressed
within a hardened steel container (Fig. 3c).

Fig. 2 Geometry and dimension of the specimens used for upsetting test.

Fig. 3. (a) Instron high-speed upsetting machine.
(b) Punch with mounted AE sensors.
(c) Specimen in heating/upsetting container.

The upsetting experiments were carried out on a high strain-rate testing machine (InstronVHS 8000, Fig. 3a) to simulate the high strain rates encountered in forging processes. Three212

constant levels of strain rates of 1, 10 and 20 s-1 could be achieved through programming of velocity profiles using closed-loop-control facility available on the test machine. The amplified
punch displacement and force signals were sampled by a data acquisition card (National Instruments NI-6111) at 10 kHz. The AE signals were acquired by a synchronized (NI-6110) card at 5MHz. The data acquisition, processing, and signal analysis were programmed with LabView 8.5.
Two main modes of operation were used: Data acquisition mode: In this mode, minimum amounts of graphical presentation were implemented in order to increase system performance and reduce latency. This module consists
mainly of adjustable band-pass filter and a signal parameter computation module. Based on
adjustable threshold gate, threshold-crossing count, hits, cumulative hits, burst duration as
well as rise and fall time can be calculated. Both filtered signal and derived signal parameters
can be stored efficiently to a single file with the corresponding timed displacement and force
signals.
 Data processing and presentation module: This module is responsible for further data processing and analysis. All recorded data can be reloaded, processed and represented on different graphs.
Results
In this section, results describing different influences from product geometry or material aswell as process parameters including temperature and strain rate will be presented. Due to space
limitation, only interesting waveforms that reflect the similarity or dissimilarity of signals (orpatterns) will be presented. Note that for the purpose of statistical assurance, every test has been
repeated three times under the same test conditions. Only tests with high repeatability are considered. More than 75% of all performed test delivered comparable results with acceptable deviations. To facilitate the comparison, the following abbreviations have been used: Materials(AZ80, AZ31, Al7075), Strain rates (PP1, PP10, PP20), and Temperatures (T200, T300, T400).
One of the difficulties encountered in the current investigations was the influence of background noise level (close to 0.18 volt) caused mainly by the hydraulic drive and generated by thecirculating cooling water inside the punch. Due to the elevated noise to signal ratio, linear scales
have been preferred in order to distinguish between the different signal levels.
Influence of Investigated Materials
Three different trends could be registered for the different materials investigated. In all performed tests, AZ31 has generated signals of higher strength as compared to AZ80 und Al7075.
The registered maximum amplitude has shown a remarkable decrease with the increase of thetest temperature. The recorded signals from AZ31 specimens have longer duration than thosefrom AZ80 under all performed test conditions. This difference decreased with increasing thestrain rate. On the other hand, Al7075 demonstrated a very long signal as compared to thosefrom Mg-alloys. In the most examined Al7075 samples, an exponentially increasing signal wasrecorded. For Mg-alloy specimens, deformed without damage development, the signal envelope
has shown the usually cited shape of an increasing signal followed by an exponential decay. Examples of the recorded signals are shown in Fig. 4.
Influence of Investigated Geometries
All cylindrical specimens deformed without cracking did not emit any useful signal, which
could be used for the specimen evaluation. Signal strength, duration or recorded counts reflect
the aforementioned influences arising from material, temperature or strain rate effects.
213Due to the small specimen dimensions and the small geometrical modification in the specimens with collar, it was hard to get any analyzable changes in the recorded signal. A recognizing
characteristic of the obtained signals from the specimens with collar is that they have a nearlyflat envelope ending with a concentrated, slightly higher, pulse. This can be also seen as a set ofsuperimposed and overlapped signals ending with cracking (Fig. 5).

Fig. 4 Influence of examined material on the generated AE signal from undamaged specimens.

Fig. 5 AE signals from undamaged specimens with collar.

214

Recessed specimens with drilled holes have shown remarkable patterns as compared to theother types of specimens. Due to variations in the dominating stress states during deformation,
the emitted AE signals are consequently changed. The presented example compares a specimen,
which has been upset in a single stage (Fig. 6a) with another, which has been deformed first from18 to 10 mm followed by the second upsetting stage from 10 to 5 mm (Figs. 6b and 6c). It isclear that the developed signal in the single stage experiment (total duration = 5 ms) that ends
with a lower frequency of higher amplitude signal (1 ms) is equivalent to the 2-ms signal for the
first 8 mm of the punch movement followed by the 4-ms signal from the 5-mm stroke in the second upsetting operation. The relatively higher amplitudes in the second stroke are related to thecooling of the specimen. This was caused by preparation time needed for the second stroke.

Fig. 6 AE signals from recessed specimens with drilled ends.
Influence of Temperature and Strain Rate
Comparing all the previously presented signals, it is evident that increasing the test temperature causes AE signals to decrease. It is well known that structural changes and formation of new
slip planes take place in Mg at about 225°C. Therefore, most of Mg specimens upset at 200°C
featured obvious damage. For this test temperature, a limited amount of strain (max. 0.6) has
been applied to avoid complete damage of the specimen although a cracking signal has been recorded.

215

On the other side, increasing the forming speed has proved to increase the emitted AE for theinvestigated materials under the given test conditions. It is clear that this effect cannot be directly
and physically related only to the release of AE from defects, and can be influenced by the impact signals generated by the machine. However, if the generated signals could be classified andanalyzed even with these erroneous influences, the main goal of the research could be achieved.
In order to consider the impact effect during experiments, the impact signals were measured withthe aid of an accelerometer. Results from tests accompanied by impact signals on the body ofthe testing machine were ignored.
A more detailed examination of the signals recorded during the upsetting of the recessed
AZ31 Mg specimens, at strain rate of 1 s-1 and temperature of 300°C, shows five consecutive
stages (Fig. 7). The development of these stages was verified through finite element simulations,although the separation and the splitting stages were inferred from the developed stress states.
In stage (1) crack initiation signals from both the upper and lower corners of the recess sections can be recognized. With an increased strain, an upper ring is separated by shearing along
the cone extending from the outer recess to the inner hole. This shear cracking results in the highcracking signals seen in (2). Further deformation will cause fold-like contact between the upperand lower rings. The resulting friction could be evident from segment (3). As the upsetting progresses, the formed ring expands circumferentially due to the existence of resisting cone in theaxial direction and splits up emitting a strong AE signal (4). This signal is characterized bylonger rise time nearly equal to its decay time. The emitted friction signals proceed till the lower
section of the specimen is separated forming a lower ring. The increased contact area between
the surface of the lower hole and the lower upsetting plate generate excessive friction signal as
shown in section (5).

Fig. 7. Consecutive stages of damage induced during upsetting.
216

Fig. 7 (continued). Consecutive stages of damage induced AE during upsetting. (1) to (5) show
detailed waveforms of 5 stages observed. Specimen details are also given.
Conclusion and Outlook
A series of upsetting tests was carried out in order to correlate the emitted AE signals to different materials and geometries under various temperatures and strain rates. In addition to thereported influences from strain rate and temperature, the geometry of the specimen has proven to
exhibit a great influence on the AE pattern. In a more detailed metallographic examination, thedeveloped cracks and their mechanisms should be investigated, and correlation of the signal
217

parameters to the detected damages should be carried out. More tests should be performed in order to gather enough parameters to feed the proposed AE “Pattern/Parameters/Damage” data
bank. During the next series of experiments, the use of an advanced measurement system will
facilitate the classification and the analysis of the obtained AE signals. Moreover, an attenuation
system will be installed to reduce the hydraulic noise. In addition, the punch will be equippedwith un-cooled hardened end. In combination with guard sensors, the influences of backgroundnoise can be minimized.
Acknowledgement
The authors would like to thank the German Research Foundation (DFG) for the financial
support of this project under the Grant BE-1691/78.
References
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für das Freiformschmieden von hochlegierten Stählen. Konferenz-Einzelbericht: MEFORM
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15. Barschdorff, D.; Haupt, H.: Supervising of metal forming processes by acoustic emission. 9th
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219

NATURAL FIBER COMPOSITES MONITOREDBY ACOUSTIC EMISSION
IGOR MARIA DE ROSA1, CARLO SANTULLI2 and FABRIZIO SARASINI1
University of Rome “La Sapienza”, Department of Chemical Engineering – Materials – Environment, Via Eudossiana 18, 00184 Rome, Italy; 2 University of Rome “La Sapienza”, Department of Electrical Engineering, Via Eudossiana 18, 00184 Rome, Italy

1

Keywords: AE monitoring, mechanical tests, natural fiber composites
Abstract
During the last decade, ecological concerns have resulted in a renewed interest in natural materials, and issues such as sustainability and eco-efficiency have gained more and more attention,
thus controlling the development and introduction of new materials, products and processes.
Plant fibers are currently being evaluated as environmentally friendly and low cost alternatives
for glass fibers in engineering composites and structures. However, serious concerns are still present on the level of mechanical performance that can be achieved with these materials. In thisregard, applying acoustic emission (AE) to monitor monotonic mechanical loading can provide
useful information.
In this work, three case studies on AE monitoring of mechanical tests of plant-fiber composites are presented and discussed. The fibers used as reinforcement are jute (case 1), celery (case2) and phormium (case 3). As a whole, AE data analysis can yield some indications about theeffectiveness of reinforcement, the prevalent mode of failure and the selection of different configurations, to obtain the best mechanical performance.
Introduction
Plant-fiber composites and hybrid laminates including plant fibers are increasingly used forsemi-structural applications, as they show a better end-of-life profile, being intrinsically carbondioxide neutral [1]. However, the mechanical behavior of plant fibers as reinforcement is not easily predicted. In particular, plant fibers are heterogeneous, being cellular structures assembled innature through a hierarchical procedure, and present a hollow, or lumen, of variable dimensions[2]. The cell wall of a fiber consists of a number of layers: the primary wall, which is the firstlayer deposited during cell development, and the secondary wall, which in turn is made up of
three layers, containing cellulose, hemicellulose and lignin in varying amounts. In addition, theintroduction of plant fibers in a polymer matrix may generate compatibility issues, whose consequence may be a large scattering of properties in the final laminate [3].
Real-time monitoring of mechanical tests is a traditional application of AE in composite material studies, which has been hardly ever applied on plant fiber composites, e.g., to contribute inmaterial characterization during post-impact loading [4], or to offer further indications on failuremode during tensile or flexural testing [5]. The results were able, e.g., to confirm concerns aboutscarce impact resistance of these materials [6] and the difficulty in obtaining predictive indications about materials performance, which suggest that specific tools of analysis of AE data
would need to be disposed for these materials [7].

J. Acoustic Emission, 26 (2008)220© 2008 Acoustic Emission GroupIn this paper, three case studies of the application of AE monitoring to the characterization ofplant-fiber composites are presented. Post-analysis of AE data according to a traditional approach based on cumulative counts vs. load, event localization, amplitude and duration distributions allowed clarifying the importance of fiber morphology and compatibility in controlling themechanical behavior of the resulting composites. This can contribute to the selection of fibers
and laminates for the development of plant-fiber composites of engineering significance.
Materials and Test Methods
The experimental characterization was carried out on three different natural-fiber-reinforced
composites. Tests on these were referred respectively as Case 1, 2 and 3.
Laminates used in Case 1 consisted of hybrid composites reinforced with glass and jute fibers. In particular, jute/glass fiber hybrid laminates were manufactured using an RTM procedure.
Plain-woven jute fabric (300 g/m2) and E-glass (Vetrotex VR38, 290 g/m2) were used as reinforcement. The resin was unsaturated polyester (1629 NT from Lonza). Two different configurations (labeled as Q and T) were obtained, whose stacking sequences and characteristics are summarized in Table 1. From the plates, four-point bend specimens were obtained with 150-mm
length, 30-mm width, and 5-(±0.2)-mm thickness. Specimens from T and Q laminates were impacted and then subjected to post-impact four-point bending tests. The impact point was locatedat the centre of the specimens. The impact energy was changed, varying the mass of the hemispherical drop-weight striker (ø = 12.7 mm), which has a constant velocity of 2.5 m/s. Impact
tests were performed on an instrumented impact tower fitted with an anti-rebound device. Fourdifferent impact energies were considered: 5, 10, 12.5 and 15 J. The flexural tests were carriedout in accordance with ASTM D-790 using quarter-point loading configuration. These tests were
performed in a universal testing machine (Zwick Roell Z010) with a support span length of 140
mm and a crosshead speed of 5 mm/min. The strain at the mid-span was determined by means ofstrain gauges. Five specimens were tested for each configuration (T and Q) and for each impact
energy value, as well as for non-impacted specimens, which served as reference materials. Postimpact flexural tests were monitored by AE until final fracture occurred using an AMSY-5 AE
system by Vallen Systeme GmbH. The AE acquisition settings used throughout were as follows:
threshold = 35 dB, RT (rearm time) = 0.4 ms, DDT (duration discrimination time) = 0.2 ms andtotal gain = 34 dB. Four PZT AE sensors resonant at 150 kHz were used: two sensors wereplaced on the surface of the specimens at both ends to allow linear localization, while the othertwo sensors were used as guard sensors in order to discriminate between AE signals and noise.
Table 1. Summary of laminate configurations (case 1).
SampleStacking Sequence
Number of Jute
name
(G = Glass; J = Jute)
LayersQ
4G/1J/2G/1J/2G/1J/2G/1J/4G
4
T
7G/4J/7G
4

Number of Glass
Layers14
14Overall FiberContent [vol %]
50±2
50±2

In Case 2, celery-fiber reinforced composites were tested. Natural untreated fibers from local
celery (Apium graveolens) and P25 epoxy system produced by REA Industries were used. The
average length of the fibers was 10 cm and they were randomly arranged like a mat. The composites were manufactured using the hand lay-up process in a closed aluminum mould. The curing cycle was 7 days at room temperature and under slight pressurization followed by 48 h at
40°C. Two fiber fractions were used, corresponding to about 10 and 20 wt%. Five tensile samples having a length of 230 mm, a width of 20 mm and a thickness of 3.5 (±0.2) mm were221

obtained and tested. In addition, five tensile samples of neat epoxy resin were manufactured toevaluate the effect of fiber introduction on the composite properties. The mechanical characterization of the composites was conducted by longitudinal tension tests (ASTM D-3039) using an
Instron 5584 test machine. The crosshead speed for tensile tests was 0.5 mm/min. All the specimens tested were equipped with strain gauges to determine the Young’s modulus. Tensile tests
were monitored by AE using the same equipment, set-up and parameters previously described.
In Case 3, untreated Phormium tenax (New Zealand flax, harakeke)-reinforced epoxy composites were tested. The matrix was an epoxy resin (Ampreg 26) by SP systems. The fibers were
cut to a length of approximately 2 cm and were randomly arranged in the final composite. The
specimens were manufactured using the hand lay-up process in a closed aluminum mould. The
curing cycle was 20 days at room temperature. Two sets of phormium fiber reinforced laminates
were produced with 10- and 20-wt% fiber content, respectively. Five specimens were tested in
flexure (200 mm x 30 mm x 4.5±0.2 mm) for each laminate and for comparison five specimensof neat resin were also tested. Flexural tests were performed in accordance with ASTM D-790
using a three-point loading configuration. These tests were performed using an Instron 5584 test
machine at a constant crosshead speed of 2.5 mm/min. Span-to-thickness ratio in these threepoint bending tests was 20:1. The strain at midpoint was determined by means of strain gauges.
Flexural tests were monitored by AE using the same equipment, set-up and parameters previously described.
Results
Case 1: Jute/glass hybrid laminates
Here the idea was to demonstrate which one of the hybrid configurations, T (sandwich hybrid) or Q (intercalated hybrid), both with the same amount of plant fibers and therefore with asimilar reduction in weight, would prove more suitable to sustain the application of load. Flexural tests carried out on both configurations demonstrated that T hybrids are superior to Q hybrids at all impact energies by more than 20% both in strength and modulus [8]. Post-impact
flexural and AE test results are summarized in Table 2.
Table 2. Post-impact flexural strength and AE cumulative counts "knee" stressfor Q and T glass/jute hybrids impacted at different energies.
Specimens

Flexural Strength (MPa)σc (MPa)

Q0J
Q5J
Q10J
Q12.5J
Q15J
T0J
T5J
T10J
T12.5J
T15J

209.91±0.37
172.89±7.18160.42±8.98
157.08±10.42
154.57±3.31
269.34±15.47
244.13±0.69
227.43±14.87
212.76±14.20
195.04±18.49

93.66±3.1284.92±9.95
75.40±7.45
71.07±0.99
72.50±10.34
90.33±6.4382.64±4.73
79.85±3.1180.61±2.5654.50±10.13

AE analysis was focused on the measurement of the cumulative counts curve “knee”: thestress value associated with the change in slope of the above curve was referred to as σc and was
supposed to be an acceptable indication of the upper limit for materials service. Values of σcwere comparable both for T and Q configuration, representing in general about 30-40% of the
maximum strength. However, in the specific case of flexural tests after 15-J impact, which was222the highest impact energy applied, T configuration showed a much lower value of σc than Q configuration (54.5±10.1 MPa vs. 72.5±10.3 MPa). It is suggested that the intercalated layers ofglass-fiber composite in Q hybrids offer more gradual degradation to the material with increasing
impact energy. In contrast, on T hybrids, the mechanism of failure is dominated by the breakageof jute-fiber composite core, which takes place by abrupt tearing off of the fibers from the matrix, as noticed elsewhere [9].

Fig. 1. AE localization plots vs. amplitude for Q hybrids taken at σc (left) and at failure (right).
Further characterization on laminates impacted at 15 J, shown in Fig. 1 for Q hybrids and in
Fig. 2 for T hybrids, did indicate that AE events are more concentrated in T hybrids than in Q
hybrids, although damage appears to be more precocious in Q hybrids, as shown by localization
plots taken at σc. AE data confirm therefore the effectiveness of intercalated hybrids in dispersing impact damage close to the penetration site.

Fig. 2. AE localization plots vs. amplitude for T hybrids taken at σc (left) and at failure (right).
Case 2: Celery fiber composites
The introduction of celery fibers in a polymer matrix was attempted, as an example of agrowaste product to be employed in materials. In this case, it could be observed that the largest part223

of localized events is connected with the presence of fibers. This can be shown by comparison
between Fig. 3a, showing AE events for the neat resin and Fig. 3b, showing AE events for the
resin with 10-wt% celery fibers. An issue, which was encountered in fiber introduction due totheir variable diameter, is that it was not possible to obtain a balanced random orientation of fibers in the matrix. As a consequence, resin-rich areas and conversely areas with higher fiber content are heavily present, which affected the mechanical properties of the laminate [10]. Increasing the amount of fiber introduced from 10 to 20 wt%, the negative effect of fiber introduction issomehow attenuated. In particular, the tensile strength and modulus of the neat-resin samples
were 29.1±3.9 MPa and 3.45±0.1 GPa, respectively, whilst the tensile strength and modulus of
the celery/epoxy laminates were 18.67±0.60 MPa and 3.3±0.17 GPa for the 10-wt% laminatesand 24.45±3.65 MPa and 3.7±0.2 GPa for the 20-wt% laminates.

Fig. 3. AE localization plots vs. amplitude at failure for neat epoxy resin (a), for 10-wt% celery/epoxy (b) and 20 wt% (c)The poor mechanical performance of the composite indicates that the introduction of 10-wt%
fibers does not result in effective reinforcement. This would cause the composite to break because of concentration and accumulation of damage in a local region of the sample, ideally situated at the center of the gauge length [11]. In contrast, no preferential localization of AE eventsand hardly any high amplitude event are revealed from Figs. 3b and 3c, even at stress close to
failure: it may be suggested that fibers tend to behave as defects, possible triggers for final failure.
224

The largest majority of AE events detected in celery fiber composites have low amplitudes(less than 60 dB), as shown in Table 3, both in the 20-wt% laminate and in the 10-wt% one. In
general, the introduction of a larger volume of fibers does not result in further composite degradation.
Table 3. Relative distribution (%) of amplitude in neat resin (NR) and celery fiber reinforced epoxy composites at 10 wt% (10) and 20 wt% (20) during tensile loading (I: 0-0.25Fmax; II: 0.250.5Fmax; III: 0.50-0.75Fmax; IV: 0.75-1.0Fmax).
Specimen Interval
10

20

NR

I
II
III
IV
I
II
III
IV
I
II
III
IV

Amplitude interval (dB)

Avg. Loc. Events

35-49

50-59

60-69

70-79

80-89

90-99

80
75
63.02
66.94
100
57.27
58.15
68.45
25
33.33
58.62
62.86

20
11.58
22.27
17.27

7.32
6.51
7.40

4.27
3.78
4.93

0.61
2.73
2.30

1.22
1.68
1.16

20.91
23.35
18.18
25
8.33
13.79
20

12.73
11.01
8.02
37.5
16.68
17.24
2.86

7.27
6.61
3.21
12.5
25
6.90
5.71

1.82
0.88
0.54

1.60

8.33
3.45

8.33
8.57

1.67±2.08
54.67±15.65
158.67±35.08
202.67±22.85
155±13.59
113.5±14.84
93.5±13.4323
7.25±3.96
8.75±2.87

AE data appear to confirm that a reduced number of defects, originated from fiber introduction, are present in the 20-wt% laminate: here, a lower total number of events is detected, of
which a higher percent is between 60 and 80 dB. The larger number of higher amplitude events
may suggest that, in spite of the fact that most fibers in the composite work at very low stresses(ultimate tensile strength for celery fibers is 100±25 MPa, while modulus is equal to 4±1.5 GPa),some fiber-matrix interaction is perceived. This is also indicated by Fig. 4, where no evidence offiber-matrix debonding is shown.
Case 3: Phormium fiber composites
Phormium proved to be a particularly strong and elastic fiber for possible use as reinforcement [12]. In the micrograph in Fig. 5, a fiber bundle is shown, which is extracted from the plantleaf, in which also oriented elementary fibers are visible, indicating the high strength of thestructure, when subjected to unidirectional loading.
When introduced in a polymer matrix, this fiber showed an elastic behavior up to a stressclose to failure. Flexural strength and modulus values obtained for the two laminates, comparedwith those for neat resin, are reported in Figs. 6a and 6b, respectively. The strength of the 20wt% laminate appears to be superior to the 10-wt% one, and to the level measured on the resin.
In this sense, the measurement of σc, as suggested above, represents in some cases a suitable
method to calculate a value for the maximum load in material service, such as for 10 wt%, whereσc was 23.03±0.89 MPa, which is just above 40% of the ultimate load. In contrast, for 20-wt%
laminate, the proximity of the measured σc value to the ultimate load value (in practice, around
80% of it) is likely to hamper failure prediction, as will be shown below. This is likely to be dueto the scarce deviation from elasticity of the behavior of phormium fibers, which results in mostAE activity being concentrated in the last period of loading, indicating a lower rate of premature225

fiber failure. This is consistent with the improvement in composite strength and modulus observed in the 20-wt% laminate with respect to the 10-wt% one.

Fig. 4 SEM image of a part of the fracture surface in a celery/epoxy composite (20 wt%).

Fig. 5. Typical shape of an untreated phormium fiber (SEM image).

Fig. 6 a) Flexural strength and b) Flexural modulus of phormium fiber composites.
As a consequence, alternative parameters to the cumulative counts “knee”, which could provide indications of critical damage development, were searched for, in order to confirm σc as thelimit for material use, or suggest a lower value for it. A different parameter, mean-amplitude hit
frequency or MAHF, defined as the average amplitude in a time interval multiplied by the fraction of hits in that interval with respect to the total hits number, plotted against time, gives in a
repeatable way on phormium-fiber composites a limit for material use which is consistently inferior to that given by σc, as described in Fig. 7. The perceived advantage of MAHF with respect to
σc is that it is sensitive to the increase in average amplitude of the hits that may precede the cumulative counts "knee".
226For the particular sample selected in Fig. 7, the ultimate flexural strength was 71.85 MPa, σc
was 67 MPa, whilst MAHF was only 59.26 MPa, as indicated by the two circles on the curve in
Fig. 7. This indicated that, in spite of the perceived macroscopic elasticity of the composite almost up to failure, increasing average amplitude of detected hits appears to indicate the presenceof significant damage at a lower stress than σc. In our opinion, this suggests that MAHF offers anadditional opportunity for AE analysis to characterize the behavior during quasi-static tests incomposites not showing appreciable plastic deformation, because of the nature of the reinforcement used.

Fig. 7. MAHF and cumulative counts vs. time for phormium-reinforced epoxy composites.
Conclusions
AE proved suitable to analyze the mechanical behavior of plant-fiber-reinforced composites.
These materials, which are increasingly used for environmental reasons, are not easily characterized by other methods, because of the inherent variability in fiber properties, and the not always
effective fiber/matrix compatibility, which leads to an absence of warnings as regards materials
failure. The three case studies reported, pointing to a broad range of plant fiber composites, reinforced with jute (hard bast fiber), celery (elastic stem fiber) and phormium (elastic leaf fiber),
were also deemed capable of confirming the versatility of the acoustic emission technique.
The results also indicated that AE analysis centered on amplitude distribution and cumulative
count vs. load curves can offer some indications on the mode of failure and the maximum service
load for these materials.
References
1.
2.
3.

Joshi S.V., Drzal L.T., Mohanty A.K., Arora S. : Composites Part A, 35 (3), 2004, 371–376.
Madsen B., Lilholt H.: Composites Science and Technology, 63 (9), 2003, 1265-1272.
Eichhorn SJ, Baillie C.A., Zafeiropoulos N., Mwaikambo L.Y., Ansell M.P., Dufresne A.,
Entwistle K.M., Herrera-Franco P.J., Escamilla G.C., Groom L., Hughes M., Hill C., Rials
T.G., Wild P.M.: Journal of Materials Science, 36 (9), 2001, 2107 - 2131.
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4. Santulli C.: Journal of Materials Science, 41 (4), 2006, 1255 - 1259.
5. Czigány T.: Journal of Composite Materials, 38 (9), 2004, 769 – 778.
6. Santulli C.: NDT & E International, 34 (8), 2001, 531-536.
7. Park J.M., Quang S.T., Hwang B.S., DeVries K.L.: Composites Science and Technology, 66(15), 2006, 2686-2699.
8. De Rosa I.M., Santulli C., Sarasini F., Valente M.: Accepted for publication in Composites
Science and Technology, February 2009. doi:10.1016/j.compscitech.2009.02.011.
9. Santulli C., Cantwell W.J.: Journal of Materials Science Letters, 20 (5), 2001, 477-479.
10. Caneva C., De Rosa I.M., Santulli C., Sarasini F.: accepted by International Journal of
Materials and Product Technology, November 2007.
11. Hamstad M.A., Acoustic Emission Beyond the Millennium, Elsevier, 2000, pp. 77-91.
12. Newman R.H., Clauss E.C., Carpenter J.E.P., Thumm A.: Composites Part A: Applied
Science and Manufacturing, 38 (10), 2007, 2164-2170.

228

ACOUSTIC EMISSION FEATURE FOR EARLY FAILURE WARNING OF
CFRP COMPOSITES SUBJECTED TO CYCLIC FATIGUERUNAR UNNTHORSSON, THOMAS P. RUNARSSON and MAGNUS T. JONSSON
Department of Mechanical & Industrial Engineering, University of Iceland, Reykjavik, Iceland
Keywords: Feature patterns, Multi-axial fatigue, Failure warning, Fibre reinforced material
Abstract
The purpose of the study presented in this paper is to evaluate whether the probability distribution of AE features can be used to provide a timely warning of impending failure in CFRP
composites subjected to multi-axial cyclic loading. The probability distribution of each feature,
as a function of lifetime, is estimated from experimental AE data. A warning is given if thevalue of the AE feature is likely to belong to a distribution close to failure.
The paper presents a new AE feature – a trough-to-peak pattern feature. The trough-to-peakpattern feature is compared against hit-based and frequency-based AE features frequently used in
the literature. Of all the AE features studied, only the probability distributions of few trough-topeak patterns can be separated. Furthermore, the evolution curves of these patterns have a salient slope change at around 60% of the lifetime, but the curves of other AE features have only asignificant change in the last 5-10% of the fatigue life. Hence, the results show that valuable information can be extracted from the "rhythm" of AE features.
Introduction
Fatigue in a carbon fiber reinforced polymer (CFRP) composites is a stochastic process influenced by several random factors such as material variations, manufacturing variations and inservice variations. In addition, fatigue is also influenced by the orientations, locations and thetype of the damage mechanisms introduced in the composite. The high damage tolerance ofcomposites means that composites are able to meet their in-service requirements for a prolonged
period of time while damages accumulate and grow.
Acoustic emission (AE) testing is a non-destructive condition monitoring technique, which
can be used for in situ monitoring of composite fatigue. Acoustic emissions are transient stress(pressure) waves, which are generated by the energy released when microstructural changes occur in materials [1,2]. The stress waves travel through the composite and when they reach thesurface, they cause it to vibrate. AE waves can be measured using sensitive transducers, which
respond to surface displacements of several picometers. The AE technique can detect delamination, matrix cracking, debonding, fibre cracking, and fibre pull-out [1, 3-6]. Hence, the highsensitivity of the AE technique may potentially enable early detection of damage.
However, there is no such thing as a free lunch; the high sensitivity of the AE techniquemeans that the measured AE signal may contain a high number of AE transients from sources inboth the composite and the environment. The sources in the composite include damage growth,
rubbing of crack surfaces and friction between the fibres and the matrix due to their different material properties. The varying material properties will result in an anisotropic speed of propagation [7]. In addition, reflection and attenuation of the AE waves add to the complexity. Attenuation can be caused by geometric spreading, dispersion, internal friction and scattering [8].
J. Acoustic Emission, 26 (2008)229© 2008 Acoustic Emission GroupFurthermore, the AE waves from damage growth can be buried in the AE generated by the friction and rubbing of crack surfaces [2]. As a result, multiple AE transients with varying amplitude, duration, and frequency can be emitted in each cycle and simultaneously. The values of the
AE signal features from cumulated damage usually fall in the same range as those that result
from damage growth [9,4]. Hence, a reliable detection of imminent failure while minimizing
false alarms is a challenging task.
In this paper it is proposed that a timely warning about imminent failure can be issued byusing the probability distribution of a suitable AE feature. In order to investigate this, experimental AE data acquired during cyclic testing of 75 prosthetic feet is used and AE features are
extracted from the data. The probability distribution of each AE feature is estimated as a function of lifetime. A failure warning is given if the value of the AE feature is likely to belong to aprobability distribution close to failure. The AE features are evaluated by how well the estimated probability distributions at 50% and 95% of the normalized lifetime can be separated.
Several well established AE features in the literature are studied and also a trough-to-peak pattern feature. The trough-to-peak pattern, which is presented in this paper, is a combination of
two features which were recently proposed by the authors [10] and used for tracking the locations of individual AE sources using a methodology introduced in reference [11].
AE Features
In this study, AE hits are located and determined using a procedure that was described in detail in [12]. Figure 1 shows a flow chart of the procedure. In the first step, the acquired AE signal is processed in order to extract descriptive features for detection. The resulting signal iscalled a detection function, or novelty function, and can be in any suitable domain of interest,
e.g. time and time-scale/time-frequency domains. For detecting and locating hits, the detection
function is input to a peak-picking algorithm that automatically detects and locates hits based on
the trough-to-peak difference of local troughs and peaks. The peaks and troughs that havetrough-to-peak difference below a specified threshold, Ttp, are filtered out. In the final step, thedetected transients are compared against a threshold, TAE, both to locate the hits more accurately
and to filter out weak hits.
In this study, the detection function is the temporal energy of the AE signal in the timefrequency domain. The function is computed using a short-time Fourier transform (STFT). Figure 2 illustrates the procedure. The STFT-based detection function is computed using a segment
size of k = 128 samples and d = 120 sample overlapping. The hits are located by setting thetrough-to-peak threshold, Ttp, to 304 dB V-s and determined by setting the determination threshold, TAE, to 3 mV. With these settings an AE failure criterion equivalent to the 10% displacement failure criterion was designed in [12]. Once the AE hits are determined, the hit-based features can be determined.
AE Hit Patterns
Hit and ring-down counts have been used extensively for interpreting AE data. Both are pulsations in the signal, but on different time scales. The success that has been achieved using these
two features provided an impetus for the authors to investigate whether the information provided
by the timing between the pulses could be used to provide valuable additional information.
In “On Using AE Hit Patterns for Monitoring Cyclically Loaded CFRP” [10], the authors
introduced two features for this purpose and used them for tracking the locations of multiple AE
230

Fig. 1. Flow chart of the AE hit determination procedure.

Fig. 2. Illustration of how the STFT-based detection function is generated.
sources relative to the phase of a reference signal. These two features are the inter-spike interval(ISI) feature and the hit pattern feature. The ISI feature is the time between two sequential hits
and the hit pattern feature is essentially a technique for fusing AE features, extracted from each
AE hit, and for finding and locating patterns which appear within the fused data representation.
In this study, a new feature is made by combining these two features. The new feature iscalled a through-to-peak pattern feature. It is computed by first determining the time from a
trough to a peak (and also from a peak to a trough) for each AE hit, then coding the results andthen counting the occurrences of all patterns found in the coded representation. A trough-topeak interval is a variant of the Inter-spike Interval (ISI). It can also be recognized to include
variants of two commonly used AE hit-based features, namely the rise time and the fall time.

231

Fig. 3. The first step in computing the hit pattern feature is the generation of the coding vector
for the signal segment.
The AE hits are located using the procedure described above, but no determination is performed, i.e. the determination threshold TAE is set to 0 mV. This means that the threshold will
not filter out weak hits. Figure 3 illustrates how the trough-to-peak feature is coded into a coding
vector. The trough-to-peak intervals, in µs, are quantized by using a natural logarithm androunding the result to the nearest integer. Figure 4 explains how the hit pattern feature is computed; by counting how often a certain pattern in the coding vector appears.

Fig. 4. The second step in computing the hit pattern feature is to find and count the number ofoccurrences for each hit pattern in the coded representation of the signal segment.

232

The patterns can be of arbitrary integer length containing an arbitrary combination of codedtrough-to-peak (TTP) and peak-to-trough (PTT) intervals from any number of hits. For example,
consider the following pattern of length 5: [TTP1 PTT1 * * TTP3]. In order to match this pattern
the coded intervals from two hits are required. The asterisk (*) is a wildcard symbol whichmeans that any value is accepted; hence, the required coded intervals are not required to be fromadjacent hits.
Results
The AE data used for this study was acquired during fatigue testing of 75 nominally identicalsamples of a prosthetic foot called Vari-Flex. The foot is made by Össur. In the test, a foot isplaced in the test machine where two actuators are used to flex the foot using a 90°-phased sinusoidal loading. One actuator loads the forefoot and the other loads the heel. The feet were tested
at 1 Hz with 50% higher maximum load amplitude than is used at Össur. All feet were tested
until failure. The failure was defined by a 10% displacement criterion. The failure criterion is aheuristic criterion used in-house at Össur. It defines a failure when a 10% change in the displacement of either actuator, with respect to initial value, is observed. Throughout each test, theAE data was acquired for one full fatigue cycle every 5 minutes. The test specimen, the test
setup, and the experimental procedure used in this study, are described in more detail in references [11, 12, 16].
The goal of this study is to find an AE feature, which can be used to provide a timely warning of impending failure in a CFRP composite subjected to multi-axial loading. The failure isdefined by the 10% displacement-based failure criterion. In order to achieve this objective, experimental data acquired during cyclic testing of 75 prosthetic feet is used.
The lifetime of each foot is normalized to 100% according to the 10% displacement failure
criterion. The probability distribution, of the feature values at each percentage point is estimatedby first computing the feature from all measurements, for each foot tested, and then generating ahistogram of the feature values at the given percentage point of lifetime. All figures in this section have a grey area that represents all values that lie within one standard deviation from themean. Also superimposed on the figures are histograms that show the distributions of the feature
values at 50% and 95% of the fatigue life. The AE features are evaluated by how well the two
probability histograms at 50% and 95% of the normalized lifetime can be separated using an estimated Bayes optimal decision threshold. The threshold is computed using the corresponding
feature computed from all the tested feet. Although the Bayes optimal decision boundary does
not guarantee an error free classification, it gives the lowest error rate [13].
AE Features in the Time Domain
Figures 5a and 5b show the average evolution of the AE hit count and the AE signal’s energy
for all feet tested respectively. The energy of the AE signal, E, is computed using:
1 N 2
E = ∑ x Δt,
R i =1
where x is a vector of length N, containing the discrete values of the AE signal. The samplinginterval, Δt, is equal to the reciprocal of the sampling rate, or 1/fs. The reference resistance, R, is
10 kΩ [14]. As a result, the energy unit is joules (equal to Watts-seconds or volts2-seconds per
ohm).

233

(a) The average evolution of the AE hit countrate.

(b) The average evolution of the signal energy.

Fig. 5. The average evolution of the AE hit count and the signal’s energy. The grey area represents all values that lie within one standard deviation from the mean.

(a) The average evolution curve for the amplitude.

(b) The average evolution curve for the risetime.

(c) The average evolution curve for the ringdown counts.

(d) The average evolution curve for the duration.

Fig. 6. The average evolution of five commonly used AE hit features. The grey area represents
all values which lie within one standard deviation from the mean.
Figure 6 shows the evolution of four AE hit-based features. Each curve is generated by averaging the feature evolution curves from all feet. The points on the evolution curve for each footare computed by extracting the AE hit features from all hits in a segment and then computing
their average. By computing the average, extreme AE hit values may pass unnoticed because afew high values will not alter the mean by much. For this reason, it is of interest to study theevolution of the hit features extracted from the hit with the largest amplitude. Figure 7 shows the234evolution of the amplitude, duration, and the rise time of the hit with the largest amplitude fromeach signal segment. Also depicted in the figure is the amplitude ratio of the two hits with thelargest amplitudes.
Spectral Features
Elliptical bandpass filters, each with 25-kHz bandwidth, are used to divide the original
bandwidth into subbands. The power in the subbands is computed along with the ratio between
the two subbands with the largest powers and the ratio between the subbands with the maximum
and minimum power. The evolution of four selected subbands and the ratios is presented inFig. 8.
The two histograms for each of the features presented in Figs. 5-8 have nearly identicalshapes and overlap almost completely. For this reason they cannot be used to separate the feature values at 50% of the lifetime from the values at 95% of the lifetime. Furthermore, fromthese results it can be deduced that the fall time feature cannot be used for this purpose (in fact,
this was verified). This is because the duration of a hit is the sum of the rise time and the falltime.

(a) The evolution curve for the maximum amplitude.

(b) The evolution curve for the duration of thehit with the maximum amplitude.

c) The evolution curve for the rise time of thehit with the maximum amplitude.

(d) The evolution curve for the amplitude
ratio of the two hits with the highest amplitude.

Fig. 7. The average evolution of AE hit features extracted from the hit with the maximum amplitude and the amplitude ratio of the two hits with the largest amplitudes. The grey area represents
all values that lie within one standard deviation from the mean.

235

Fig. 8. The average evolution of AE features in the frequency domain. The grey area represents
all values that lie within one standard deviation from the mean.
Trough-to-peak Pattern Feature
Figure 9 shows the average evolution of four selected trough-to-peak patterns of length 2 (seeFig. 4 for illustration). These patterns contain the coded values of the fall time of one hit and therise time of the next adjacent hit.
The evolution curves of these four trough-to-peak patterns have a slope change at around
60% of the lifetime. The evolution curves corresponding to the other AE features shown above
do not have a slope change at this percentage point of the lifetime. Instead, the evolution curvesof most of the features shown above change abruptly during the last 5-10% of the lifetime. This
abrupt change, however, is not present in the evolution curves for the trough-to-peak patterns.
236

(a) The average evolution of the number ofoccurrences of trough-to-peak pattern no.
87.

(b) The average evolution of the number ofoccurrences of trough-to-peak pattern no.
94.

(c) The average evolution of the number ofoccurrences of trough-to-peak pattern no.
95.

(d) The average evolution of the number ofoccurrences of trough-to-peak pattern no.
102.

Fig. 9. The average evolution of the number of occurrences of four trough-to-peak patterns
computed from the AE segments. The grey area represents all values that lie within one standard
deviation from the mean.
Furthermore, as one can observe, the histograms of the number of occurrences at 50% and 95%of the lifetime for these patterns can be separated using thresholds.
Table 1 lists the four patterns and the corresponding estimated Bayes optimal decision thresholds. In order to make the patterns more intuitive for the reader, the patterns are augmented byplacing – and + where the troughs and peaks are located respectively.
Table 1. Four trough-to-peak patterns of length 2. The last column contains the estimated Bayes
optimal decision threshold.
Pattern 87
Pattern 94
Pattern 95
Pattern 102

The Pattern[3–3+][3–2+][4–3+][4–4+]

237

Decision Threshold
388. 6
562
64
11. 6

Conclusions
In this paper several commonly used AE features have been studied for the purpose of using
them to provide a timely warning of impending failure. Furthermore, a trough-to-peak pattern
feature was presented and compared against the other AE features. The trough-to-peak pattern
feature is a pattern of an arbitrary length, containing a combination of rise times and fall times of
one to several AE hits. The authors are not aware of any published research on AE where patterns in the occurrences of these two features have been studied.
The evolution curves of the trough-to-peak patterns have a slope change at around 60% of
the lifetime. The slope then remains constant until failure. This slope change is not present in
the evolution curves for the other AE features. This suggests that these trough-to-peak pattern
features are capturing important information from the AE signal, e. g. the slope change may becaused by the formation of a damage that grows until failure. Intuitively, the salient slopechange can be used to provide an early warning about the health of the composite, and the results
support this. The trough-to-peak patterns is the only feature, studied here, which probability distributions at 50% and 95% of the lifetime can be reasonably well separated using a Bayes optimal decision boundary.
The evolution curves studied in this section are all normalized according to the lifetime determined by a 10% displacement criterion. Half of the split-toe foot component for some feet,however, delaminates before the criterion is met. This is represented by a minute increase in thestandard deviation in the last 10% of the lifetime for the energy, amplitude and the subband
powers.
It has been suggested that AE from cumulated damage can provide valuable information
about the material health [15]. This type of AE has mainly been regarded as unwanted and many
attempts have been made to filter it out. In this study, a few AE features are extracted from theAE signals without any attempt to filter out AE from cumulative damage. These features are theenergy, the subband powers and the trough-to-peak patterns that gave the best results.
The promising results obtained using the trough-to-peak patterns strongly encourage further
investigations of ISI and hit pattern features. Patterns made using different coding, different pattern lengths and fusing of different features can possibly be used to extract more useful information from the AE signal.
Acknowledgements
The work of the first author was supported by grants from the University of Iceland ResearchFund, the Icelandic Research Council (Rannis) Research Fund, and the Icelandic ResearchCouncil Graduate Research Fund. Furthermore, the authors wish to acknowledge Össur for bothproviding prosthetic feet for testing and for providing access to their testing facilities.
References
1. M. Wevers, NDT and E International, 30, (1997), 99-106.
2. A. P. Mouritz, Fatigue in Composites, Woodhead Publishing Ltd., Cambridge, 2003, 242–266.
3. M. Giordano, A. Calabro, C. Esposito, A. D'Amore, and L. Nicolais, Composites Science andTechnology, 58, (1998), 1923-1928.
238

4. D. Tsamtsakis, M. Wevers, P. De Meester, J. of Reinforced Plastics and Composites 17,(1998), 1185–1201.
5. H. Nayeb-Hashemi, P. Kasomino, and N. Saniei, J. of Nond. Evaluation, 18, (1999), 127-137.
6. E. R. Green, Journal of Nondestructive Evaluation, 17, (1998), 117-127.
7. L. A. Duesing, Reliability and Maintainability Symposium, 1989, 128–134.
8. W.H. Prosser, SPIE Conference on NDE Techniques for Aging Infrastructure and Manufacturing: Materials and Composites, Dec. 1996, pp. 146–153.
9. Y. A. Dzenis and J. Qian, International Journal of Solids and Structures, 38, (2001), 18311854.
10. R. Unnthorsson, T. P. Runarsson, M. T. Jonsson, J. of Acoustic Emission, 25, (2007), 260–
266.
11. R. Unnthorsson, T. P. Runarsson, M. T. Jonsson, J. of Acoustic Emission, 25, (2007), 253–259.
12. R. Unnthorsson, T. P. Runarsson, and M. T. Jonsson, International J. of Fatigue, doi:10.
1016/j. ijfatigue. 2007. 02. 024, 2007.
13. R. Duda, P. Hart, D. Stork, Pattern Classification, Wiley-Interscience, New York, 2001.
14. M. F. Carlos and H. Vallen, Nondestructive Testing Handbook, ASNT, vol. 6, 2005, pp. 153154
15. J. Awerbuch, S. Ghaffari, J. of Reinforced Plastics and Composites, 7, (1988), 245–264.
16. R. Unnthorsson, University of Iceland, Ph.D. Thesis, 2008

239

IDENTIFICATION OF DAMAGE INITIATION AND DEVELOPMENT INTEXTILE COMPOSITE MATERIALS USING ACOUSTIC EMISSION
D.S. IVANOV, S.V. LOMOV, I. VERPOEST and M. WEVERS
Department of Metallurgy and Materials Engineering, Katholieke Universiteit Leuven,
Kasteelpark Arenberg, 44, B-3001 Leuven, Belgium
Keywords: Textile composites, strength, stress/strain curves
Abstract
Damage in textile composites is closely connected with the internal micro- and mesogeometry of the reinforcement, and reveals features different from classical laminates. This paper
presents a test sequence to characterise damage in textile composites and its dynamics of initiation and development on different scale levels in order to achieve a deep understanding of therelevant phenomena. The proposed test sequence consists of the following steps: 1) Tensile tests
on samples cut in characteristic directions of the textile reinforcement with acoustic emission(AE) and full-field strain measurement on the surface. The test produces stress-strain diagrams
and identifies based on the AE characteristic strain levels: Full-field strain measurement highlights relation between strain concentrations (linked with the damage initiation) and the reinforcement structure. 2) Samples loaded are examined with microfocus-X-ray computed tomography and radiography, revealing the damage pattern and allowing a quantitative analysis of
the damage development. 3) Microscopic examination of cross-sections through the damage
sites, determined with X-ray, identifies the local damage modes. Macro-, meso- and microcharacterisation of damage result in a thorough understanding of the material behaviour. The paper presents and discusses the results of the proposed experimental protocol for carbon/epoxy 3axial braided and quasi-UD composites.
Introduction
Textile composites are steadily gaining positions in aeronautical, automotive, ground transportation and other applications, offering weight reductions over the metal alternative and costsaving against the lay-up of UD tapes. Whilst the design for stiffness of textile composite parts
can use validated predictive modelling, related to the meso-structure of textile reinforcements [15], the reliable design for strength with not-that-exaggerated safety factors has to be based
mostly on empirical information, with modelling approaches being developed [1, 6-11], but still
have not reached a mature state. Validation of the modelling methods asks for adequate experimental characterisation of the damage processes on different scale and hierarchical levels of thecomposite, as the damage phenomena in textile composites are much more complicated than in
UD laminates, and involves processes and parameters on different scale levels: macro (overall
strength and strain-to-failure of the sample and stiffness reduction), meso (damage initiation sites
inside the textile structure of the reinforcement) and micro (local damage mode inside yarns andfibrous plies).
The aim of the paper is to describe a methodology of a systematic study of the damage phenomena in textile composites during tensile deformation. The proposed experimental protocol is
illustrated on two cases of carbon/epoxy textile composites.

J. Acoustic Emission, 26 (2008)240© 2008 Acoustic Emission GroupTensile Test, Damage Monitoring and Post-mortem Characterisation
The proposed suite of tests includes: tensile test with AE and full-field strain-mapping (SM);
X-ray and ultrasonic C-scan examination of the samples after tension up to certain strain levels,
identified by AE and SM results; cross-sectioning and microscopic examination of the samples
in the places defined by the X-ray examination. Full-field measurements of strain fields anddamage identification in textile composites are reported in detail in [12, 13].
Tensile test: The tensile tests are carried out on a standard testing machine (Instron 4505, test
speed 1 mm/min). The samples are cut out of composite plates in the characteristic directions
dictated by the textile structure: (1) test in the direction of one of the yarn systems of the textile(warp/weft for woven fabrics; braiding/inlay yarns for braids); (2) test in the direction transversal
or bias to the direction of the yarn systems in the textile (bias for woven fabrics, perpendicular tothe production direction for braids). In literature, damage in textile composites is often studied
only for the fibre direction, which does not give the full picture.
Acoustic emission: In the tensile test setup described here, two AE sensors are used situated at
the boundaries of the gauge length region. To calibrate the AE sensors, 2H-pencil lead is broken
on the sample surface on the “grip side” of each sensor and signals that occur outside of the sensors are filtered out. The cumulative energy of AE events versus tensile strain is produced. Changes of AE event rates indicate change in damage mechanisms in the sample.
Strain mapping: Strain mapping is a system of measuring strain using digital images taken of asample during loading. The samples are first painted with a black and white random speckle pattern to give a unique pattern of the examined surface, which can then be identified by the strain
mapping software program. Changes in the speckle dot pattern can quantify the deformation of
the sample as the tensile test proceeds. For textile composites, the stress and strain values mayvary significantly over the pattern of weaving, braiding or knitting. Local strain values can beaveraged and global strain levels can be computed, hence the strain mapping system is used also
as an optical extensometer, with a precision of about 0.01% strain.
X-ray, ultrasonic C-scan and microscopy: The AE and SM registration during the tensile test
allows identifying the characteristic applied strain levels for damage onset or change of the damage mode. In subsequent tests, the tensile test stops at these strain levels, and the sample is examined using X-ray and ultrasonic C-scan. The aim of X-ray investigation is detecting very fine matrix cracks that occur within the yarns. An X-ray opaque penetrant (Diiodomethane) is used tofill the cracks and make the contrast higher (only cracks and defects that are connected in some
way to the surface can be seen). Ultrasonic C-scan test is useful for characterisation of the overall damage extent and in revealing overall damage pattern, as previously reported in [15].
Finally, having identified the positions of the cracks in the sample, microscopic study showed afine structure of the damage inside the sample. The sites and direction for the cuts are determinedusing X-ray images of the particular sample.
Materials
The materials used as examples of application of the proposed experimental methodology are
shown in Fig. 1 and Table 1.

241

Triaxial braided carbon/epoxy composites: Braiding yarns in the triaxial braided fabric are organised in a so-called diamond pattern 1/1 (Fig. 1a). If the orientation of inlay yarns (“machine”
direction) is chosen as 0°, then the braiding yarns are directed as ± 45°. Note the uneven placement of the braiding yarns, which produces large open regions in the fabric. The tensile tests
were performed in machine direction (MD), bias direction (BD) and cross direction (CD), as indicated in Fig. 1a. From six to ten tests were performed for each direction.

(a)

(b)
Fig. 1. Textile reinforcements: (a) triaxial braid; (b) quasi-UD woven fabric.

Quasi-UD woven fabric: The textile used for this study is a quasi-UD fabric from Cramer
GmbH. It is produced by weaving flat UD carbon yarns (warp) with sparse, flat, thin glass yarns(weft). The result is an unbalanced plain weave material. This procedure reduces crimp as muchas possible for the warp, however there is still some crimp of the carbon yarns, which are inclined to the fabric plane in average by an angle of 3.8°. The tensile tests were performed in the
directions 0°, 30°, 45°, 60° and 90°, 0° being the direction of the warp carbon yarns (Fig. 1b).
From five to seven tests were performed in each direction.
Table 1. Parameters of the textile reinforcements, matrices and the composites.
Fabric type
FibresYarns
Linear density, tex
Thickness, mm,
Width, mm
FabricSpacing between yarn centre lines, mm
Areal density, g/m2
MatrixYoung modulus, GPa
Poisson coefficient
Strength, MPa
Composite tensile specimens
Number of layers
Thickness, mm
Width x Length between the grips, mm
Fibre volume fraction, %Triaxial braidcarbon T700
24 K
1600
variable
4.21 ± 0.21 / 3.75 ± 0.16
Fig. 2a5.03 ± 0.24; 9.25 ± 0.03 / 20
600
Epoxy
2.7
0.4
70
produced using RTM
4
3.09 ± 0.06
40 x 250
44 ± 1

Quasi-UD woven
carbon T400 /glass EC5
6K / n.a.
400 / 220.311 ± 0.011 / 0.058 ±0.012
1.58 ± 0.12 / 0.62 ± 0.04
Fig. 2b1.53 ± 0.10 / 3.27 ± 0.06
285

10
3.5
15 x 200
46.3

Damage Initiation: Characteristic Strain Levels
When damage is characterised inside a composite by “post-mortem” study of the sample, it isimportant that the expected damage condition of the sample when the test is stopped, is well242

identified. The following stages of the damage development and the correspondent levels of theapplied load (strain) can be introduced: (1) onset of transverse cracks (inter-fibre, intra-yarn failure), (2) onset of delamination on the boundaries of the fibre bundles and (3) onset of fibre failure, starting at delamination, and the ultimate failure of the sample.
This classification could be refined [16], but we propose to keep these three levels as reference points of the damage investigation. An important difficulty is definition what exactly “thedamage initiation” means. As the inter-fibre transversal cracks may be created by coalescence of
the “crescent” debonding on the individual fibres, the definition of “initiation” is rather fuzzy.
We consider the damage initiation strain to be an indication of appearance of a crack, which connects several debonded fibres. In reality such a transversal crack develops fast into a crack
through the whole thickness of the yarn.
Figure 2 shows stress-strain diagrams of two types of composites together with diagrams of
cumulative energy of AE events. At the very beginning of the test, few events of low energy contents occur with low frequency. Then the frequency of events increases sharply and the energy
content reaches higher levels. This is reflected by the increase of the slope of the cumulative AE
event energy curve, which was negligent before. The corresponding first transition strain is calculated by the best fit of the bilinear curve (zero before the transition and linearly increasing afterit) to the AE cumulative energy diagram. We will designate this first transition strain ε1. The AE
events “heard” before could be interpreted as micro-debonding events; the onset of steady generation of higher energy AE events – as appearance of transversal inter-fibre cracks (tri-axial
braid) or delaminations on the boundaries glass/carbon yarns (quasi-UD woven).
For triaxial braid this interpretation is confirmed by X-ray observation of the samples loaded
to a strain just below the first transition strain and after it (Fig. 2a). X-ray images reveal no
cracks before the transition and appearance of few cracks just after it, which further multiply
with the increase of the strain. For quasi-UD woven fabric X-ray examination does not reveal
any crack as they are not connected with the surface of the samples; the damage initiation, detected by AE, was confirmed by microscopic examinations.
For the loading in fibre direction (Fig. 2a) the damage initiation does not affect the slope ofthe stress-strain diagram, as the resistance to the deformation in fibre direction is provided by thecorresponding fibre system (inlay 0° yarns). For the loading in an off-axis direction (Fig. 2b) initiation of damage also corresponds to the transition from the initial linear part of the stress-strain
diagram to the non-linear behaviour.
After the initiation strain ε1 the damage behaviour is characterised by constant increase of AE
events in the same energy range, giving a steady slope or a “jump” to the cumulative energy
curve. This region covers a wide range of strain, until high deformations. At a certain moment a
second “knee” on the AE cumulative energy curve may appear. The corresponding second transition strain ε2 can be again identified by fitting a bilinear curve to the AE cumulative energy diagram. The second transition strain is not that clearly determined as the first. For the case of triaxial braid X-ray observations allow identifying it with the onset of local delamination (Fig. 2a).
Finally, fibre breakage (loading in fibre direction, Fig. 2a) is indicated by the appearance of
extremely high-energy AE events and sharp rise of the cumulative AE events diagram. This happens very close to the final failure of the specimen.
243

Fig. 2 Stress-strain diagrams and AE registration for carbon/epoxy composites: (a) triaxial
braided composite, MD test, 1 – stress-strain diagram, 2 – cumulative AE events energy, 3 – cumulative AE event count; insets – X-ray images; (b) quasi-UD woven composite, 30° test direction, combined results of 5 tests, lines – stress-strain diagrams, points – cumulative AE event energy; inset: micrograph showing early delamination between carbon and glass yarn.
Formal calculation of the transition strains ε1 and ε2 (if the second transition on the AE curve
exists) is performed by the least-square fit of all the cumulative AE energy (CAE) data points (εk,CAEk) by a bilinear function with fitting parameters ε1 , ε 2 ,CAE1 ,CAE2 .

Table 2 lists the characteristic strain levels for the studied materials and different test directions.
244

Damage Development
With the characteristic strain levels identified, the damage investigation proceeds further tostudying the damage structure under loading up to the characteristic levels. This is done using
non-destructive techniques: Ultrasonic C-scan and X-ray investigation, and then doing microscopy at the sections made on the sites identified by the X-ray investigation. The results of thisstudy for the two materials under consideration are only discussed here without illustration.
Table 2. Characteristic strain levels, %.
Material

Test direction

ε1

ε2

εult

3-axial braid

MD
BD
CD
0°
30°
45°
60°
90°

0.29
0.36
0.33
0.16
0.4
0.4
0.4
0.15

0.55
0.45
0.43
n/a
n/a
n/a
n/a
n/a

1.45±0.15
1.25±0.18
1.11±0.03
2.25±0.058
1.79±0.28
1.08±0.23
1.00±0.26
0.80±0.25

quasi-UD

Triaxial braided composite: The crack structure is clearly revealed by X-ray images and is further investigated by microscopy of the cross-sections. Transversal cracks in the yarns, with theplane oriented in the direction of the yarns, start developing at ε1. The cracks are confined in one
ply/one yarn, being therefore limited in width as well as in length. When two yarns with thesame direction of the fibres are in contact, a crack can travel from one yarn to another. FE modelling, reported in detail in [11,17] shows that these cracks are caused either by transversal tension or shear strains. With the increase of strain the cracks multiply and the length increases.
Longer cracks are developed in the yarns, which are oriented at 90° to the loading direction, butthese cracks still have limited length and do not go from one end of the sample to another. At thestrain level ε2 local delaminations start.
The samples final failure for MD and BD tests (in the direction of inlays and braiding yarns
correspondingly) is delamination and consecutive fibre breakage. Straightening of highly
crimped braiding yarns produce out-of-plane stresses, which create large delamination zones.
These delamination zones are smaller for samples tested in MD, as the inlay yarns are lesscrimped. Off-axis CD loading leads to more localized fracture locus coinciding in the direction
and width with the braiding yarns.
Quasi-UD woven composite: In spite of the clear detection of AE events over the ε1 threshold, Xray examination of samples after tensile test up to this strain level did not show any cracks in thesamples, apart from 90° tests, where transversal cracks were revealed. To determine the sourceof AE activity and stiffness degradation, optical microscopy was used. In the cross-sections of
the samples (made in two directions: orthogonal to the carbon warp and glass weft yarns) small
cracks have formed between the carbon and glass yarns in only the surface textile layers within
the composite in the off-axis samples. These cracks, surprisingly, are parallel to the sample surface. The fact that these cracks are parallel to the force, and are located at or near the carbonglass interface indicates that these cracks have formed due to shear between the glass and carbon
yarns. The crack runs between the carbon and glass yarns when the glass yarn is on the outside(at the edge of the composite), but when the glass yarn moves under the next carbon yarn, thecrack is transferred into the second carbon yarn, and continues there until the next carbon yarn245

where it resumes its place between the glass and carbon yarns. This transfer of the crack acrossthe glass yarn is accomplished without breaking the glass yarn itself. The glass interlacing yarns
are very thin and are sparsely distributed in the textile, and the cracks generated in the material
only seem to occur when there is an interaction (overlapping) between the carbon and the glass
yarns at the surface. Therefore the size of the defects is small and they are generally not connected to the surface and thus were not readily observable using X-ray. The samples tested in thefibre direction (0°) fail by fibre failure. Other test directions (30°, 45°, 60° and 90°) produce
clear shear/transversal failure along the fibres.
Conclusions
We have outlined an experimental methodology for study of initiation and development ofdamage in textile composites in tension test. The methodology has been applied to different textile composites: carbon/epoxy triaxial braids and quasi-UD woven. The methodology is based on
identification of the characteristic levels of the applied load. These levels are further used for investigation of damage modes and crack structure in the material.
References
1. Bogdanovich, A.E. Journal of Materials Science, 2006; 41(20): 6547-6590.
2. Bogdanovich, A.E. and Pastore C.M. Mechanics of Textile and Laminated Composites. 1996,
London: Chapman and Hall.
3. Bogdanovich, A.E. and Pastore C.M. Composites Sci. and Tech., 1996; 56: 291-309.
4. Lomov, S.V., Huysmans, G., Luo, Y., Parnas, R., Prodromou, A., Verpoest, I. and Phelan,
F.R., Composites part A, 2001; 32(10): 1379-1394.
5. Verpoest, I. and Lomov, S.V. Composites Sci. and Tech., 2005; 65(15-16): 2563-2574.
6. Zako, M., Uetsuji, Y. and Kurashiki, T. Composites Sci. and Tech., 2003; 63: 507-516.
7. Fish, J. and Yu, Q. Composites Sci. and Tech., 2001; 61: 2215-2222.
8. Karkkainen, R.L. and Sankar, B.V. Composites Sci. and Tech., 2006; 66: 137-150.
9. Tang, X. and Whitcomb, J.D. Journal of Composite Materials, 2003; 37(14): 1239-1259.
10. Greve, L. and Pickett, A.K. Composites Part a-Applied Science and Manufacturing, 2006;
37(11): 1983-2001.
11. Lomov, S.V., Ivanov, D.S., Verpoest, I., Zako, M., Kurashiki, T., Nakai, H. and Hirosawa, S.
Composites Sci. and Tech., 2007; 67: 1870-1891.
12. Ivanov, D.S., Lomov, S.V., Verpoest, I., Baudry, F. and Xie, H., Proc of the European Conf.
on Composite Materials (ECCM-12), 29th August - 1st September. 2006: Biarritz. CD edition.
13. Ivanov, D.S., Baudry, F., Xie, H., Lomov, S.V. and Verpoest , I. Composites part A, submitted.
14. Wevers, M. and Surgeon, M., Acoustic Emission and Composites (Chapter 5.14), in Comprehensive Composite Materials, A. Kelly and C. Zweben, Editors. 2000, Elsevier, pp. 345-357.
15. Truong Chi, T., Vettori, M., Lomov, S.V. and Verpoest , I. Composites part A, 2005; 36:
1207-1221.
16. Benmedakhene, S., Kenane, M. and Benzeggagh, M.L. Composites Sci. and Tech., 1999; 59:
201-208.
17. Ivanov, D.S., Lomov, S.V., Verpoest, I., Zako, M., Kurashiki, T., Nakai, H. and Hirosawa, S.
Proc. of the 8th Intl. Conf. on Textile Composites (TexComp-8). 2006: Nottingham. CD edition.

246

WAVELET ENTROPY AND POWER OF AE SIGNALS AS TOOLS TO
EVALUATE DAMAGE IN COATINGS SUBMITTED TO SCRATCH TESTROSA PIOTRKOWSKI1,2 , ENRIQUE CASTRO1 and ANTOLINO GALLEGO1
1
Departamento de Física Aplicada, Universidad de Granada, 18071 Granada, Spain;
2
ECyT, Universidad Nacional de San Martín, 1650 Pcia. Buenos Aires, Argentina
Keywords: Wavelet power; Wavelet entropy; Damage evaluation; Coatings; Galvanized steel;Corrosion.
Abstract
Wavelet entropy and power were calculated for acoustic emission (AE) signals obtained fromscratch tests on hot dip galvanized samples with different corrosion levels. Wavelet power was
distributed in different frequency bands, according to damage mechanisms. The frequency bands
were automatically obtained by an innovative method that consisted in searching for the relative
minima of the wavelet entropy of signals and the subsequent application of a clustering algorithm. The damage evaluation entailed studying the evolution of the wavelet power in a specificfrequency band, which corresponded to the fracture of the zeta-phase columns of the galvanizedcoating. Results showed damage to increase along with the level of corrosion.
1. Introduction
The early detection of corrosion in galvanized steel used as reinforcement in concrete structures is crucial in monitoring the condition of concrete structures in different environments andunder different load circumstances. The identification of damage processes by acoustic emission(AE) has proven very effective in many applications [1-4]. AE through signal processing can beapplied on-line as a non-destructive technique on remote or inaccessible parts of a structure. Thisapproach is rooted in the broad field of pattern recognition and damage detection in materials
and structures.
The present paper is within a program destined to evaluate the adherence of commercial galvanized coatings with varying depth, working under different load and corrosion conditions (see[5, 6] and included references). Wavelet transform (WT) was applied to AE signals coming fromscratch test (ST) on corroded galvanized steel. The aim was to identify damage mechanisms
through the assignment of a few concise and precise parameters to different coating damage
processes, and then use some of them to evaluate damage.
With this purpose in mind, hot-dip galvanized samples were corroded in a salt chamber andthen submitted to ST. Damage mechanisms involving deformation, fracture and/or extraction of
the different phases that conformed to the corroded coating, briefly denoted in [5] as oxide, chloride, eta phase and zeta phase, were identified. WT results were adequately obtained as timefrequency AE patterns, which revealed that AE power was distributed in five frequency bands.
Consequently, the evolution of AE wavelet power in different frequency bands was carefullycompared with SEM and EDX observations along scratches, which permitted the assignment of
one mechanism to each band. In particular, signals due to corrosion and non-corrosion mechanisms could be easily separated; different mechanisms coexisted along the ST.

J. Acoustic Emission, 26 (2008)247© 2008 Acoustic Emission GroupIn the present paper we present and exhaustively explain a new automated method to assign
frequency bands to damage mechanisms based on wavelet entropy considerations. Once thebands are obtained for different scratches, a damage evaluation criterion is applied. We face theproblem of adherence evaluation in non-corroded and corroded samples; corrosion levels 1, 2
were considered.
2. Experimental
The preparation of hot-dip galvanized samples (50 mm x 20 mm x 4 mm plates) involved theprocedure described in [5]. Samples were hot-dip galvanized at 450ºC for 3 min, after whichsome were submitted to corrosion.

Fig. 1. Transversal cuts of samples. Left: T0; centre: T1; right: T2.
In the present study we compare results for material that was either not corroded (level 0) or
corroded up to two corrosion levels (1, 2). Results are given for one sample of each type since
general results coincide. According to the corrosion level (0, 1 and 2), samples are respectively
named as T0, T1 and T2. The mean coating thicknesses were 152, 158 and 166 µm, in that order.
T1 and T2 were respectively corroded for about 900 h and 1800 h in a salt mist chamber to simulate a marine environment, and afterwards they were cleaned with an ammonia hydroxide water
solution (150/850 ml). Sample T0 was left as cast and was taken as a reference. Figure 1 shows atransversal cut of each sample. The coating of sample T0 appears quite homogeneous. The Zn-Fe
phases, gamma (very thin), delta (well defined and homogeneous), zeta (columnar grains) andeta (almost pure Zn), are observed from steel to Zn (top). The interior part of the coating of sample T1 remained largely unchanged, and only the top of the eta phase was affected by corrosion.
Corrosion also affected the eta-zeta two-phase region of the coating of sample T2. As was statedin [5], corrosion products are a mixture of a Zn oxide and a hydrated Zn chloride.
Two scratches were performed on each sample, according to the procedure described in [5].
STs with AE were performed under controlled conditions using a device that consisted of aloaded probe with a diamond indenter moving linearly along the sample at constant speed, andwith linearly increasing force (0-150 N) over 180 s; the scratch length was about 1 cm . The piezoelectric sensor was in the 100-1000 kHz range, signals were pre-amplified (60 dB), the threshold was at 25 dB and digitization was at 4 MHz/16 bits. In addition to digitizing the signal, theAE system entails the measurement of pertinent hit parameters, where a hit is defined as eachsignal that surpasses the threshold.
We denote the n-th AE signal recorded along the test as sn(t), with n = 1…Nr. The total number of registers (Nr) depends on the sample and the scratch that corresponds to each ST. If Δt =2.5 10-7s is the sampling period, sn(k) corresponds to sn(kΔ t) with k = 1…N ; it thus denotes the248k-th sample of the signal sn(t). In our case, 4000 data were recorded for each AE signal, whichmeans N = 4000 for all signals.
The supra-index n is related with the position along the scratch. If Tn is the instant, at which
the AE signal sn(t) was captured, the corresponding position on the scratch is given by xn = vTn ,where v is the speed of the diamond indenter. At the beginning of the ST time is initialized tozero, and at the end it is set at Te = 180 s. We emphasize that two temporal scales are involved.
One is designated with lower case t to measure time in each register, which ranges within 0 – 1
ms, and the other is designated with capital T that measures time among registers, ranging within
0 - 180 s.
For practical reasons, we use the relative position in %, Xn(%), on the scratch instead of thereal position xn. Thus, the relative position where the signal sn(t) is captured is at Xn(%) = 100(Tn/Te).

a)

b)

c)
Fig. 2. Details of the central part of scratches. Top left: T0; top right: T1; bottom: T2.
SEM and EDX observations showed in [5] that the failure mechanisms in the non-corroded
samples (such as sample T0) were the slippage of the eta phase and the breakage of zeta phasecolumns with eventual ulterior extraction of the zeta phase cracked particles. Other mechanisms
besides the aforementioned appeared when corrosion was considered as for samples T1 and249

T2—namely, the transversal cracking of the oxide phase and the interfacial cracking and extraction of the chloride particles. Figure 2 shows the central part of scratches in the three samples.
For samples T1 and T2, we can observe transversal oxide cracks in the middle of scratches andextracted chloride particles (white and sized in the order of microns) at the borders. The densityof transversal cracks is higher for sample T2.
3. Damage Mechanisms Identified by WT Parameters3.1. Visual frequency bands obtained by wavelet power
AE is a non-stationary process, i.e. the power spectrum changes with time, where short andlong-time phenomena coexist. For this reason a scale-time procedure like the WT [7] is an ideal
tool for managing data. The synthesis and analysis formulas are respectively displayed in (1) and(2) for the register n:
(1)
where
(2)
are the coefficients that constitute the WT; sub-index k corresponds to time displacement (in ourcase as previously mentioned 1 ≤ k ≤ 4000) and sub-index j corresponds to scale, which is associated with frequency (in our case 50 ≤ f ≤ 600 kHz). Moreover, ψjk(t) is the displaced and dilated version of a wavelet ψ(t). The ψjk(t) set is an orthonormal basis of functions with finite energy.

Fig. 3. AE signals coming from scratch 1 of samples: Left (T0; s1432(t)); centre (T1; s704(t)); right(T2; s1096(t)).
Continuous wavelet analysis does not directly provide the time-frequency analysis of the signal; it considers dilation and translation factors for continuous time and time-scale parameters,
where the scale can be correlated with frequency (the correspondence depending on the wavelet
employed). At the same time, the best wavelet basis depends strongly on the physical process
involved. For this reason, we used the AGU-Vallen Wavelet software [8] designed for AE signals and based on Suzuki’s work [9], where the mother function is a Gabor (Gaussian) wavelet.
The Gabor wavelet choice was made for different reasons: it is suited to the AE phenomena andthe scale-frequency correspondence is provided, as is clearly explained in [9]. Furthermore,when WT with different bases (Haar, Gaussian, Symmlet, Daubechies) was applied to signals in250

Fig. 4. WT maps from signals of Fig. 3: a) (T0; X1432=80.3 %); b) (T1; X704=80.9 %); c) (T2;X1096=81.0 %).
previous work [6], we found that the scales where information was pertinent were essentially thesame.
Continuous wavelet analysis does not directly provide the time-frequency analysis of the signal; it considers dilation and translation factors for continuous time and time-scale parameters,
where the scale can be correlated with frequency (the correspondence depending on the wavelet
employed). At the same time, the best wavelet basis depends strongly on the physical process
involved. For this reason, we used the AGU-Vallen Wavelet software [8] designed for AE signals and based on Suzuki’s work [9], where the mother function is a Gabor (Gaussian) wavelet.
The Gabor wavelet choice was made for different reasons: it is suited to the AE phenomena andthe scale-frequency correspondence is provided, as is clearly explained in [9]. Furthermore,when WT with different bases (Haar, Gaussian, Symmlet, Daubechies) was applied to signals inprevious work [6], we found that the scales where information was pertinent were essentially thesame.
Our results after applying the software [8] were obtained as time-frequency patterns where
different colors were assigned to the different power intensities. Numerically, a matrix ofsquared absolute values of WT coefficients was assigned to each signal register; the number ofrows and columns was determined by the register duration and the sampling frequency. Eachmatrix is graphically represented by a wavelet map that represents the AE power distribution in acolor scale, increasing from blue to red, with one color assigned to each rectangle determined bytime-frequency intervals. Figure 3 shows some examples of registers, and their corresponding251

wavelet maps are seen in Fig. 4. We observe that AE power is distributed in a number of frequency bands.
On the basis of visual observation of the total wavelet maps like those presented in Fig. 4, theentire frequency range B0 =[50-600] kHz, was divided into three frequency bands, which were:B1 = [ 50-200] kHz
B2 = [200-350] kHz
B3 = [350-600] kHz.
Due to results such as those shown in Fig. 4, one of the parameters that proved successful in
identifying damage was related with the wavelet power for frequency interval j for each register
defined by:(3)
We used the same parameter defined in [5], the frequency location of the wavelet-power maximum. As different mechanisms coexisted, we studied the evolution of primary, secondary andtertiary maxima occurring in the previously defined B1, B2 and B3 frequency bands. We followed
a special procedure in order to determine the position of these maxima: if the first maximum
corresponded to one of the bands, the second was the one that fell in one of the other two bands;
so that for the third maximum only one band remained as a possibility. Second and third maxima
were only considered if their amplitude was higher than 80% of the amplitude of the first maximum. Primary, secondary and tertiary maxima for the n-th register are respectively named as,and. For the frequency positions of these maxima, it holds that jmax1∈Bm1,
jmax2∈Bm2 and jmax3∈Bm3, where m1, m2 and m3 can take the values 1, 2, or 3. Thus, one, two or
three maxima can be assigned to each register sn(t), respectively located at jmax1; jmax1 and jmax2;
or jmax1, jmax2 and jmax3. The procedure is summarized in the flow chart displayed in Fig. 5.

Fig. 5. Flow chart 1, which summarizes the frequency location of WP maxima.
252

The frequency location of maxima versus the relative position Xn (%) is shown for each register in Fig. 6. The present study includes two levels of corrosion. The important finding is that theprocedure was successful for corrosion levels 0, 1 and 2. In all the cases it can be seen that thedifferent maxima were distributed in narrower frequency bands than the three initially proposed
B1, B2 and B3. A set of five frequency bands was finally obtained, as can be seen in Fig. 6. We
denote them in lower case, as b1 - b5 to distinguish them from the initially defined bands B1-B3.

Fig. 6. Position of the primary, secondary and tertiary maxima of WP. Upper: T0 (Left: scratch 1;
Right: scratch 2); centre: T1 (left: scratch 1; right: scratch 2); lower: T2 (left: scratch 1; right:
scratch 2).
The correlation between the visually obtained b1 - b5 frequency bands and failure mechanisms
established in [5] was based on careful SEM, EDX and XRD observation of the coatings, before
and after the STs. In this way the obtained classification was:253b1: [100] kHz: Deformation by slippage of the eta phase (mechanism A).
b2: [100-150] kHz: Extraction of the cracked zeta particles by zeta-eta interfacial sliding(mechanism B).
b3: [150-160] kHz: Cracking of the columnar zeta phase (mechanism C).
b4: [210-270] kHz: Transversal cracking of the oxide phase (mechanism D).
b5: [≈500] kHz: Extraction of chloride particles by oxide-chloride interfacial cracking(mechanism E).
3.2. Automated frequency bands obtained by wavelet entropy
The classification of AE signals in frequency bands b1 - b5, based on wavelet power considerations, is visually noticeable. Nevertheless, it is not suited for introduction in on-line damage detection algorithms. Moreover, the position and the width of the frequency bands that identify the mechanisms varied somewhat among different samples and even among different
scratches on the same sample. This has to do with the rheological status of the coatings, i.e. thelevel of internal stresses. In [10] Reumont et al. have clearly shown that internal cracks form
during galvanization following internal stresses, due to the different properties of phases; thisalso happens in the corroded coatings. So, the specific value of the characteristic frequency could
suffer some variation for different samples and also for different scratches of the same sample.
For these reasons, an improved automated clustering method, based on entropy considerations, was applied to each ST. Less explained results were previously presented in [11, 12]. For
entropy, we used the Shannon-Weaver definition [13]; (4) is the version adapted for wavelets.
for scale j (frequency interval related with
Formulas (4) and (5) allow us to obtain the entropyj) for each register sn(t).
(4)
where Nj is the number of coefficients involved at level j (in our case Nj is always N) andgiven by:is(5)
The concept of information entropy appears in different fields dealing with complex systems
and with different definitions, including the description of the dynamic behavior of complex systems [14], in deconvolution filters [15], or as an estimator for blind source separation [16].

1432

8

7.8
7.6
0

8.2

8.4
T1
Scratch 1
X704=80.9%8

7.8

200 400 600 800 1000
Frequency (kHz)

7.6
0

8.2

Entropy

Entropy

8.2

8.4
T0
Scratch 1=80.3%
X

Entropy

8.4

T2
Scratch 1
X1096=81.0%8

7.8

200 400 600 800 1000
Frequency (kHz)7.6
0200 400 600 800 1000
Frequency (kHz)Fig. 7. Entropy versus frequency curves corresponding respectively to the signals of Fig. 4.

254

In our case, entropy is a parameter that represents the degree of intrinsic order or degree oforganization of the signal: a relatively low entropy value indicates the existence of a structure
recognizable in the elementary wavelet patterns. Contrarily, a high entropy value indicates thelack of a simple and identifiable structure. We used these concepts to divide the total frequency
band into different bands. When we examine different wavelet power maps as those shown in
Fig. 4, we observe a structure in certain frequency bands, and a quasi-uniform distribution inothers. Entropy takes lower values for the frequencies, for which a structure is found.

Fig. 8. Map of relative entropy minima in the frequency-Xn (%) plane. From left to right: scratch
1 of samples T0, T1, T2. The limits of bands are shown.
Applying (4), for each register located at relative position Xn(%), we obtained an entropyfrequency function; this calculation was carried out for all the frequencies of the entire band B0.
Figure 7 shows as examples the entropy-frequency curves corresponding to the three particularregisters shown in Fig. 3. In these curves we notice several relative minima located for each reg. Note that a number of different values ofare obtained for eachister n at frequencies
in a map of points in the freregister. Thus, it is possible to collect a set of relative minima
quency-Xn % plane. Figure 8 shows these results for scratch 1 of samples T0, T1 and T2. It canbe seen that these points, corresponding to entropy relative minima, are roughly organized infrequency bands for the three samples.
In order to improve the separation in frequency bands, a K-means clustering method was applied to the points, using the subroutine “kmeans” of the statistics toolbox of MATLAB [17](which uses a two-phase iterative algorithm to minimize the sum of point-to-centroid distances[a,b]). We applied the procedure in two-steps in order to eliminate the high frequency dispersionof the [>500] kHz band. First, we clustered all the points corresponding to the entire band B0 in
three bands, thus obtaining (in an automated form) the bands b5, b4 and bl, bl being the lowest
frequency band (note that B0 = b5+b4+bl). Second, the points in band bl were clustered in threebands, thus obtaining automatically the bands b1, b2 and b3 (note that Bl = b1+b2+b3). The wholeprocedure is summarized in Fig. 9, which displays flow chart 2.
Table 1 shows the bands b1 - b5 automatically obtained for both scratches of samples T0, T1
and T2. Note that the automated bands b1 - b5 are now re-defined with respect to those obtained
visually, which were shown in Section 3.1. The values now obtained are seen to be similar, although they vary from one ST to another.

255

Fig. 9. Flow chart 2, which summarizes the bands b1-b5 automatically obtained by searching theentropy relative minima.
Table 1. Frequency bands obtained by the automated clustering of entropy relative minima for all
STs.
ST (sample,
T0, s1
T0, s2
T1, s1T1, s2T2, s1
T2, s2
scratch) /band (kHz)b1[50-100][50-100][50-90][50-100][50-100][50-100]b2[100-150] [100-160][90-130][100-140] [100-140] [100-140]b3[150-200] [160-210] [130-170] [140-190] [140-190] [140-190]b4[200-400] [210-410] [170-380] [190-390] [190-380] [190-380]b5[400-600] [410-600] [380-600] [390-600] [380-600] [380-600]
4. Coating Damage Evaluation
Damage is necessarily related with the number of broken atomic bonds, while AE power isconnected with the strength of these bonds, which could be affected by the corrosion level, but ina way not easily elucidated. Below we propose a method for evaluating damage based on theevolution of the number of cracked zeta phase columns over the ST. It was shown in Section 3that mechanism C, cracking of the columnar zeta phase, which corresponds to band b3, is the one
affecting the deepest region in the galvanized coating over the ST, both in corroded and noncorroded samples. This encouraged us to seek a method suited to evaluate the number of broken
zeta phase columns over STs for different corrosion levels.
Consequently, we calculated the relative wavelet power for each record in band b3,
fined as, de(6)

Figure 10 shows this parameter along the two scratches carried out on samples T0, T1 andT2. Note the presence of very different features for corroded and non-corroded samples, with avery different density of maxima in each case. We considered the location of the maxima (i.e.,
256

!3

x 10
0.012

0.012

T0, s16

T1, s1

0.01

5

0.008

0.008

4

0.006

0.006

3

0.004

0.004

2

0.002

0.002

1

WPb3

0.01

0

0

20

40 60
Xn (%)

80

100

0

0

20

40 60
Xn (%)

80

100

0

!3

6

T0, s22040 60
Xn (%)

80

100

40 60
Xn (%)

80

100

!3

x 10

x 10

T1, s2T2, s2

5
4

4

WPb3

0

5

0.015

0.01

3

3

0.005

0

T2, s10

Fig. 10.

2040 60
Xn (%)

80

100

2

2

1

1

0

0

20

40 60
Xn (%)

80

100

0

0

20

for scratch 1 (upper) and scratch 2 (lower). From left to right, samples T0-T2.

the location of peaks) in the curves shown in Fig. 10 to be directly related with the location ofdamaged zeta phase columns. This is justified by a simple calculation: The width of the columns
is about 6 µm, as can be observed in Fig. 1. Similar values are obtained for the inter-maxima
distance, because the total length of scratches was around 1 cm.
maxima was calculated in small and finite intervals of AE
For this reason, the density ofregisters (i.e., the local density of maxima). To this end we determined the mean number of relative maxima with values surpassing a threshold, set at 0.001, for a given number of AE consecutive registers (we opted for 5). This density was assigned to the five AE registers of the same
maxima versus the relative positioninterval. The results in Fig. 11 illustrate this density ofalong the scratch, for the two STs and the three samples. The features are seen to be very differmaxima
ent for the corroded and non-corroded samples. Clearly, higher values of density ofare obtained for corroded samples than for non-corroded samples, and values increase along withthe level of corrosion.

257

Density of WPb3 maxima

T0, s1T1, s1
0.6

0.6

0.5

0.5

0.5

0.4

0.4

0.4

0.3

0.3

0.3

0.2

0.2

0.2

0.1

0.1

0.1

0

0

20

40 60
Xn (%)

80

100

0

0

20

40 60
Xn (%)

80

100

0

0.6

0.6

0.5

0.5

0.5

0.4

0.4

0.4

0.3

0.3

0.3

0.2

0.2

0.2

0.1

0.1

0.1

0

20

40 60
Xn (%)

Fig. 11. Density ofsamples T0-T2.

80

100

0

0

20

20

40 60
Xn (%)

80

100

40 60
Xn (%)

80

100

T2, s20.6

00T1, s2T0, s2

Density of WPb3 maxima

T2, s1

0.6

40 60
Xn (%)

80

100

0

0

20

maxima for scratch 1 (upper) and scratch 2 (lower). From left to right,

However, results were even more significant when we calculated the cumulative number ofmaxima with values higher than the 0.001 threshold. This parameter is plotted versus
thethe relative position along the scratch in Fig. 12. Note that some steps took place at some Xn(%)
values for scratches on sample T0. It can also be seen that both the qualitative and quantitative
features in Fig. 12 are certainly very different for corroded and non-corroded samples. The quantitative evaluation of damage can thus be defined straightforward by means of this parameter.
The first line in Table 2 shows the total number of the maxima (directly related with the total
number of damaged zeta phase columns) in each ST for samples T0, T1 and T2. It clearly increases in conjunction with the corrosion level.
Somewhat arbitrarily, we defined the critical damage point (CDP) on each scratch as thepoint in position %, at which the number of cumulative maxima is 40. Results for all STs andsamples are shown in the second line of Table 2. Note that the CDP shows a decreasing trend
according to corrosion level.
Thus, the correct ascription of frequency bands to different mechanisms, the selection of thefrequency band that corresponded to the deepest located damage mechanism, and the definitionof an appropriate parameter to quantify damage both permitted the evaluation of damage evolving over STs and allowed us to establish the difference corresponding to corrosion level.
258

Cumulative WPb3 maxima

120

160
T1, s1

T2, s1

90

120

150

60

80

100

30

40

50

0

0

25

50

75

0

100

120
Cumulative WPb3 maxima

200

T0, s1

0

25

50

75

100

160

0

T1, s2
120

150

60

80

100

30

40

50

25

50
X (%)
n

75

0

100

Fig. 12. Cumulative number ofleft to right, samples T0-T2.

50

75

100

50
X (%)
n

75

100

T2, s2

90

0

25

200

T0, s2

0

0

0

25

50
X (%)
n

75

100

0

0

25

maxima for scratch 1 (upper) and scratch 2 (lower). From

Table 2. Total number of WP maxima in band b3 and Critical Damage Point (CDP) for scratches
1 and 2 corresponding to samples T0-T2
ST (sample,scratch)
Total number of
WP maxima inband b3
CDP(position %)T0, s1

T0, s2

T1, s1T1, s2T2, s1

T2, s2

104

90

136

157

171

192

61

55

40

42

42

29

Some of the results have been previously summarized in Refs. [11, 12], but some more details and references regarding entropy calculations and the criterion to evaluate damage are givenin the present paper. We understand that the supply of these details is important. This is because
the present paper renders a twofold purpose. On the one hand it deals in evaluating damage ingalvanized steel analysing AE signals. On the other hand it intends to go further in the route ofsearching for regularities in the evolution of some parameters that connect damage and AE.
These regularities could be useful in a future complete theory that is still lacking.

259

5.

Conclusions

In the line of previous work, specific frequency bands were assigned to different failure
mechanisms applying the WT to AE signals coming from STs on corroded and non-corroded
galvanized coatings. The damage identification procedure was improved by using an entropy
criterion and a clustering method.
Damage evaluation was performed defining new parameters related with the transversal fracture of the zeta phase columns. The parameters were connected with the evolution of certain parameters over STs. They were: the wavelet power in the corresponding frequency band, the density of maxima of this function, the cumulative number of maxima, and a critical damage point,
defined at the position on the scratch, at which a given number of maxima were cumulated. The
CDP showed a clearly decreasing trend in relation with the level of corrosion.
Acknowledgements
This work was partially supported by MEC (Spain), through the financial help for the sabbatical year 2006 of R. Piotrkowski, spent at the University of Granada (SAB 2004-0081); and by
the Spanish I+D National Plan DPI 2006-02970 Project. Hot-dip galvanization was performed at
Galvanizados, S.A., Madrid. Scratch tests were performed at the Elastic Waves Laboratory,
ENDE, CNEA, Argentina.
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[2] A. Iturrospe, D. Dornfeld, V. Atxa, J.M. Abete, Bicepstrum based blind identification of theacoustic emission (AE) signal in precision turning, Mechanical Systems and Signal Processing,19 (2005) 447-466.
[3] Gang Qi, Wavelet-based AE characterization of composite materials, NDT&E International,
33 (2000) 133-144.
[4] Q. Liu, X. Chen, N. Gindy, Investigation of acoustic emission signals under a simulative environment of grinding burn, Machine Tools & Manufacture, 46 (2006) 284-292.
[5] A. Gallego, J.F. Gil, E. Castro, R. Piotrkowski, Identification of coating damage processes incorroded galvanized steel by acoustic emission wavelet analysis, Surface and Coating Technology, 201 (2007) 4743-4756.
[6] A. Gallego, J. F. Gil, J. M. Vico, J. E. Ruzzante, R. Piotrkowski, Coating adherence in galvanized steel assessed by acoustic emission wavelet analysis, Scripta Materialia, 52 (2005) 10751080.
[7] Y. Meyer, Wavelets: Algorithms and Applications, SIAM, Philadelphia, 1992.
[8] AGU-Vallen Wavelet, http://www.vallen.de/wavelet/index.html, 2002.
[9] H. Suzuki, T. Kinjo, Y. Hayashi, M. Takemoto, K. Ono, Wavelet Transform of Acoustic
Emission signals, Journal of Acoustic Emission, 14 (1996) 69-84.
[10] G. Reumont, J.B. Vogt, A. Iost, J. Foct, The effect of an Fe-Zn intermetallic-containing
coating on the stress corrosion cracking behavior of a hot dip galvanized steel, Surface and Coating Technology, 139 (2001) 265-271.

260

[11] R. Piotrkowski, E. Castro, A. Gallego, Wavelet power, entropy and bispectrum applied to
AE signals for damage identification and evaluation of corroded galvanized steel, Mechanical
Systems and Signal Processing, 23 (2009) 432-445.
[12] R. Piotrkowski, E. Castro, A. Gallego, J.M. Vico, J.J. González de la Rosa, Damage identification in corroded galvanized and duplex coatings using wavelet power and entropy, Proceedings of the 28th European Conference on Acoustic Emission Testing, Krakow, Sept. 17-19,
2008, edited by Kanji Ono, pp. 244-249.
[13] C.E. Shannon, W. Weaver, The Mathematical Theory of Communication, University of
Illinois, Urbana, 1964.
[14] R.M. Yulmetyev, N.A. Emelyanova, F. Gafarov, Dynamical Shannon entropy and information Tsallis entropy in complex systems, Physica, A341 (2004) 649-676.
[15] H. Endo, R.B. Randall, Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter, Mechanical Systems andSignal Processing, 21 (2007) 906-919.
[16] K. E. Hild, D. Erdogmus, J.C. Principe, An analysis of entropy estimators for blind source
separation, Signal Processing, 86 (2006) 182-194.
[17] Matlab, version 6.5, The Mathworks Inc, 2002.

261

AE ENTROPY FOR THE CONDITION MONITORING OF CFRP
SUBJECTED TO CYCLIC FATIGUERUNAR UNNTHORSSON, THOMAS P. RUNARSSON and MAGNUS T. JONSSON
Department of Mechanical & Industrial Engineering, University of Iceland, Reykjavik, Iceland
Keywords: Entropy, Multi-axial fatigue, Condition monitoring, Fiber reinforced material
Abstract
The purpose of this study is to investigate whether the entropy of acoustic emissions (AE),generated both by cumulated damage and damage growth, can be used for the condition monitoring of carbon fiber reinforced polymers (CFRP) subjected to multi-axial cyclic loading. The average evolution of four entropies is studied; two in the time domain and two in the frequency
domain. The AE used for studying the average evolution is acquired during fatigue testing of 75
nominally identical CFRP prosthetic feet. The results show that the evolutions of the two entropies estimated in the time domain correlate well with the evolution of the AE hit count. Oneof time domain entropies is computationally simpler than the AE hit count procedure and uses
the whole AE signal. Further research is needed to answer if it is a better alternative to conventional AE hit counting.
Introduction
During the progressive degradation process of a cyclically loaded CFRP composite material
various damage mechanisms are introduced in the material [1, 2]. In each cycle, AE is emitted
from both damage progression and from cumulated damage, i.e. rubbing of delaminated surfaces.
As a result multiple AE transients, with varying amplitude, duration, and frequencies, can be simultaneously emitted from the numerous AE sources within the material. Depending on both
the damage mechanisms and loading, AE from cumulated damage is either separable transientsor inseparable, e.g. due to high degree of overlapping. Throughout the cyclic life the number ofAE signals increases with increasing cumulated damage. The energy of the AE also increases
and for some composites the energy also varies within a cycle.
Features extracted from the AE signal have been used for detection of damage, i.e. delamination, matrix cracking, debonding, fiber cracking, and fiber pull-out [3-7]. AE from cumulated
damage has mainly been regarded as unwanted and many attempts have been made to filter itout, e.g. by thresholding the AE features [8], coupling the AE to the load [5], limiting the analysis to a part of the loading cycle [9, 10], and frequency analysis [11]. The AE waves in the material will reflect and undergo attenuation before being picked up by the AE sensor. The attenuation is due to geometric spreading, dispersion, internal friction and scattering. The values of AE
features from cumulated damage usually fall in the same range as the ones from damage growth[5, 13] and it can be very difficult to distinguish between the two types. Because important AE
events can get buried in the AE signals generated by friction and the rubbing of crack surfaces[11, 12] attempts have been made to filter out AE events from these damage mechanisms [5, 10,13]. Awerbuch and Ghaffari concluded that frictional AE should not be eliminated as it may provide important information about the condition of a composite [10]. They argued that damage
detection could be made easier by using frictional AE, since damage growth, i.e. a materialchange, produces AE only once, but the resulting rubbing of damage surfaces generates AE
many times.
J. Acoustic Emission, 26 (2008)

262© 2008 Acoustic Emission GroupIt is reasonable to assume that both environmental and measurement noise can be kept relatively constant during monitoring. Hence, an increase in the randomness of the AE measurements will mainly be due to increased AE activity, from either cumulated damage or damage
growth. In this study, it is investigated whether the randomness can be used for condition monitoring, i.e. if it can be used to provide early failure warning. For estimating the randomness of
the AE measurements a fundamental concept in Information Theory is used; that is, the entropy.
Entropy and its Estimation
The information entropy was introduced by C. E. Shannon in 1948 [14]. In his paper Shannon developed a method of measuring the randomness of a signal, or its uncertainty. The randomness is information encoded in the signal and the entropy increases with more information.
Shannon recognized that the form of the measure was the same as the entropy in statistical mechanics and, for this reason, he called his measure ’entropy’. Shannon’s formula for the entropyis(1)
The signal’s values are denoted by x and are considered to be discrete random variables. The
possible signal values are denoted by λ, and Pr(x = l) is the probability mass function (PMF) of
X. Consequently, the entropy is a function of the signal’s probability mass function, but not thevalues themselves. Without any constraints, the maximum entropy is attained when all values
are equally probable, i.e. when the signal is white noise. The entropy is the minimum weighted
average number of units, per value, required to encode the signal. The unit of measurement depends on the choice of the logarithm base, i.e. by choosing 2, 10, or e as the base the units will be
bits, hartleys, or nats, respectively. The base can be changed by using the law of logarithm, i.e.
(2)
In practice, computing the entropy can be challenging because the underlying distribution isoften unknown, for instance the AE signal measured during cyclic testing of CFRP. Consequently, the entropy needs to be estimated. This can be done by estimating the probability massfunction using statistical methods or by estimating the entropy directly using data compression[15,16]. A normalized histogram of the random variable can be used to estimate the probability
mass function. By using a histogram to estimate probabilities, the entropy is estimated with respect to a model that assumes that the frequencies of the signal’s values are constant within thesignal segment. The histogram can be normalized to sum to one by(3)
where k is the number of bins and mi is the number of observed signal values that fall in bin i.
The normalized values of the histograms represent the proportion, or probability, of the corresponding signal’s values. In the frequency domain, the frequency can be considered to be therandom variable and the normalized spectrum to be the probability mass function. The spectrum
is normalized by(4)
where Xi is the magnitude of the ith frequency component of the spectrum, e.g. the amplitude if
263

an amplitude spectrum is used. Based on this probability mass function, entropy can be computed using Shannon’s formula. By considering the spectral amplitude to be the random variable, a different entropy can also be defined and computed using Shannon’s formula; the probability mass function of the amplitude intensities can be estimated using a histogram. When theprobabilities are based on discrete Fourier transform, or histograms, the entropy is estimated withrespect to a static model. These two entropies will be referred to as the frequency entropy andthe spectrum entropy, respectively.
The Shannon entropy is properly defined as the minimum entropy over all possible models,i.e. it is an entropy computed using Shannon’s formula and the correct probability mass function.
In other words, it is the theoretical upper limit on lossless data compression that can be achieved
for a given signal [15, 16]. Consequently, the entropy can be used to evaluate compression algorithms to determine whether there is room for improvement. Conversely, compression algorithms can be used to estimate the entropy of data. The compressed data can be written to a fileand the file size then converted from bytes to nats using:(5)
Where File_Size, the size of the compressed file in bytes, is multiplied by 8 to convert to bits.
Equation 2 is used to change from bits (base 2 logarithm) to nats (base e logarithm). The results
are then averaged over all values (samples) by dividing the results with length_of_signal. If theheader of the compressed file is included in the file, then the entropy estimate will be higher. If
the AE signal length is kept constant then the error due to the header will be approximately samefor all computations.
Among the best lossless compression approaches are those based on a scheme known as prediction by partial matching (PPM). The PPM compression scheme is divided into two steps:
modeling, from which the scheme takes its name, and coding. Arithmetic coding is used to code
the output of the modeler. Arithmetic coding is a highly effective technique, which can code
data close to its entropy with respect to the model [16]. The PPM modeler works with the data ina symbol-wise manner and its output is a set of conditional probabilities for the symbols. The
probabilities of the symbols are estimated adaptively and used to predict the next unseen symbol.
For predicting the modeler uses finite context models of k symbols, which immediately precede
the one to be predicted. The number k is also referred to as the model order and is specified by
the user before the data compression is initiated.
During the modeling for each symbol, the modeler begins by looking up how many times thecurrent context of length lc = k has occurred before. If the context has been observed before, followed by the symbol, the symbol can be coded using a probability of nc/n, where nc is equal to
the number of times the context has been observed followed by the symbol and n is the numberof times the context has been observed. If the context hasn’t been encountered before, or it has
only been followed by different symbols, an escape character is passed to the modeler. When themodeler receives the escape character it switches to a context that is one symbol shorter, i.e. to acontext of length lc = k - 1. Again, if the current context has not been observed before, or has
only been followed by different symbols, another escape character is passed to the modeler and it
starts to look for contexts, which are one symbol shorter. This can be repeated until the context
length becomes lc = -1 symbols. When this occurs, all symbols from the alphabet are consideredequally probable. Equi-probability is undesirable since it does not provide an accurate model;264however, it poses no problem for accurate coding. The arithmetic coder is able to proceed eventhough the model is inaccurate; however, a higher number of bits may be required to encode thedata. Intuitively, better compression is achieved with more accurate modeling. Fortunately, thecontext of lc = -1 symbols is only considered at most once for each symbol, and as the modelingproceeds the data statistics improve and lower values of lc become less and less frequent. Everytime an escape character is sent (i.e. whenever the modeler is unable to code a symbol) the probability of observing a novel symbol, when presented with the current context, is updated. By assigning a probability to the escape character the modeling can be improved. For a detailed description of the PPM scheme and examples, the reader is referred to Text Compression [15] andManaging Gigabytes: Compressing and Indexing Documents and Images [16].
Different variants of the PPM compression scheme have been introduced in order to improve
the PPM compression and to speed up calculations. One variant suggested by Howard [17] isreferred to as method D, or PPMD, and estimates the conditional probability of observing a particular symbol given a specific context to be (2nc - 1)=(2n), where nc is the number of times
which the modeler has seen the symbol being preceded by the context and n is the total numberof symbols preceded by the current context. The escape probabilities are estimated by nu=(2n),where nu is the number of unique symbols preceded by the current context and n has the same
meaning as before.
Another variant was introduced by Dmitry Shkarin in Improving the Efficiency of the PPM
Algorithm [18] (in Russian) under the name PPM with information inheritance, or PPMII.
Shkarin presented the PPMII a year later in English [19]. The PPMI uses an additional model inorder to get better estimation of the escape probabilities. In order to overcome the lack of statistical information when estimating the escape probabilities of long contexts, the PPMII allows thelonger contexts to inherit statistics from shorter contexts. The inheritance reduces the computational cost compared with other approaches to this problem [19]. Starting with a code from Nelson [20], Shkarin implemented his improvements, and several other variations, and made themavailable in the public domain under the name PPM by Dmitry (PPMd).
Experimental Procedure
The test specimens used in this study are nominally identical prosthetic feet called Vari-Flex.
The Vari-Flex is made by Össur hf. A total number of 75 Vari-Flex feet were tested. The cyclic
testing was performed in an ISO 10328 Foot/Limb test machine at Össur’s testing facilities. In
the test, a foot is placed in the test machine where two actuators apply a load to the foot using a
90° phased sinusoidal constant amplitude loading at 1.0 Hz. One actuator loads the forefoot andthe other loads the heel. The tests were accelerated by increasing the maximum loading by 50%
and placing a 2° plastic wedge between the heel and the toe components. The wedge is used by
amputees in order to stiffen the foot. The increased load and the use of the wedge result in considerably shorter fatigue tests. Frequently the damage mechanics change depending on the stresslevel, however, both preparatory tests and the fatigue test results show that the damage mechanisms leading to final failure are the same as observed under normal fatigue testing conditions.
All feet were tested until failure. The failure was defined by a 10% displacement criterion.
The failure criterion is a heuristic criterion used in-house at Össur. It defines a failure when a
10% change in the displacement of either actuator, with respect to initial value, is observed.
Throughout each test, the AE data was acquired for one full fatigue cycle every 5 minutes. For a
more detailed description of the experimental procedure the reader is referred to references [2123].
265

Results
Two entropies are estimated in the time domain, one using Shannon’s formula and the otherusing data compression. In order to apply Shannon’s formula the probability mass function of
the signal’s values from each measurement is estimated from a normalized histogram using 216
bins. In other words, the number of bins is set equal to the number of quantized discrete values
from the 16-bit A/D converter used for AE acquisition. In order to estimate the entropy using
data compression, the signed 16-bit integer data is written to a file in ASCII form with no spaces
in between. The file is then compressed using variant H of the PPMd algorithm (PPMdH), as
implemented in an open source software program called 7-Zip. The resulting file size is thenconverted from bytes to nats (see Eq. 5). The model order is determined manually. The bestcompression results are obtained using model order k = 5, or when the maximum context lengthis equal to the maximum number of digits used (omitting the sign). In the remaining part of thispaper, the entropy computed using Shannon’s formula in the time domain will be referred to as
the signal’s entropy, and the entropy estimated using the PPMdH compression scheme will bereferred to as the PPMdH entropy.
In the frequency domain Shannon’s formula for the entropy is used to estimate both the frequency and the spectrum entropies. In order to compute the frequency entropy, the probability
mass function of the frequencies is estimated by first transforming the signed 16-bit integer data
to the frequency domain and then normalizing the one-sided amplitude spectrum to sum to one.
Because of the normalization there is no need to convert the data values to volts. The transformation is made by applying a 221-point discrete Fourier transform (DFT). The number of DFT
points is set to the next power of two higher than the length of the data series, for faster computation. This is done by zero-padding the data to make its length a power of two. The computations
can also be made faster by using fewer points. If fewer points are used, the resolution of thespectrum decreases and less information is provided. Consequently, the entropy also decreases.
If more points are used, the number of frequency bins increases, as does the entropy. This requires zero-padding. Zero-padding the data before applying the DFT results in a frequency interpolation, which means that the added frequency bins will not contain any new information.
As a result, the entropy increase will only be a function of the number of added bins and is thesame for all measurements.
In order to compute the spectrum entropy, the probability mass function of the spectral amplitudes is estimated from a histogram of the amplitude intensities. Amplitudes below the highest amplitude in the one-sided amplitude spectrum of all measurements are quantized with 16
bits. A 216-bin histogram is used to estimate the probability mass function of the quantized amplitudes.
Entropy as a Condition Signature
For evaluating whether the entropies can be used to provide early failure warnings and tocompare them against other AE features, the approach in reference [24] is used. The probability
distribution, of the entropy values at each percentage point is estimated by first computing theentropy from all measurements, for each foot tested, and then generating a histogram of the entropy values at the given percentage point of lifetime. The lifetime of each foot is normalized to100% according to the 10% displacement failure criterion. All figures in this section have a grey
area that represents all values that lie within one standard deviation from the mean. Also superimposed on the figures are histograms that show the distributions of the corresponding entropy at
50% and 95% of the normalized fatigue life. Figures 1a and 1b show, respectively, the average266

evolution of the AE hit count and the signal’s energy for all feet tested. These two figures were
presented in reference [24].

(a) The average evolution of the AE hit countrate.

(b) The average evolution of the signal energy.

(c) The evolution curve for the signal’s entropy.

(d) The evolution curve for the PPMdH entropy.

(e) The evolution curve for the frequency en- (f) The evolution curve for the spectrum entropy.
tropy.
Fig. 1. The average evolution of the AE hit count rate, energy and four entropies computed fromthe AE segments. The grey area represents all values that lie within one standard deviation fromthe mean.
The average evolution of the four entropies for all the feet is shown in Figs. 1c-1f. As one
can observe, the curves are relatively flat from approximately 20% to 95% of the normalized
lifetime, and the standard deviation is high. The curves are therefore not suitable for issuing
early failure alerts. This is verified by the almost perfect overlap of the histograms of the entropy values at 50% and 95% of the lifetime.
267

It is interesting to note that, of the two entropies computed in the time domain, the PPMdH
entropy (shown in Fig. 1d) is lower than the signal’s entropy (shown in Fig. 1c). In order to apply Shannon’s formula, the probability mass function of the signal’s values is estimated using ahistogram of the signal’s values. This estimates the entropy of the signal with respect to a static
model. This is by no means the best model for the data – as can be observed; the PPDdH compression scheme produces a better model. Nonetheless, the evolution curve for the PPMdH entropy shows no additional information.
Table 1 presents the median Pearson and Spearman correlation coefficients estimated between the AE hit count, the energy and the four entropies. Each coefficient is estimated by firstcomputing the corresponding coefficient between the curves from each fatigue test and thencomputing the median of the coefficients obtained from all feet.
Table 1. The median Pearson (left) and Spearman (right) correlation coefficients between the AE
hit count, the energy, and the four entropies.

AE Hit Count
Energy[dB]
Signal’s Entropy
PPMdH Entropy
Freq. Entropy
Spectrum Entropy

AE hitcount
1

EnergySignal’s
PPMdH[dB]
entropy
entropy0.63 / 0.58 0.93 / 0.89 0.92 / 0.89
10.74 / 0.72 0.73 / 0.70
10.99 / 0.99
1Freq.
Spectrum
entropy
entropy-0.24 /-0.19 0.71 / 0.65
-0.44 /-0.52 0.92 / 0.91
-0.28 /-0.31 0.79 / 0.79
-0.24 /-0.29 0.78 / 0.79
1
-0.23 /-0.20
1

The results presented in the table show that the evolution of the two entropies estimated in
the time domain, i.e. the signal’s entropy and the PPMdH entropy, correlate well with the evolution of the AE hit count. Furthermore, the calculations also show that there is a significant correlation between the energy and the spectrum entropy. Since the energy requires less computationand its interpretation is more intuitive the spectrum entropy does not seem to offer any advantage. The frequency entropy measures the flatness of the spectrum. It can be used to detect thepresence of broadband events that may not be detected from the evolution of the energy. Consequently, the frequency entropy can possibly be used to supplement the energy feature.
Summary
In this study the AE hit count, energy and the entropies were extracted from the AE signals
without applying any special filters to filter out any AE, e.g. from cumulative damage. The purpose of the study was to investigate whether the entropies could be used for condition monitoringof CFRP subjected to multi-axial cyclic loading, i.e. for providing early failure warning.
The results show that the entropies studied here cannot be used with the proposed approach
for issuing early failure alerts. This is because they have relatively flat evolution curves from
20% to 95% of the normalized lifetime.
The results also showed that the trending results obtained using both the signal’s entropy and
the PPMdH entropy (file compression) are nearly the same as those obtained using a STFT based
AE hit counting. This suggests that the signal’s entropy might be a better alternative to the AE
hit count for monitoring AE activity because it requires less computational effort, no filtering(the number of hits is reduced by thresholding) and the only tuning required is the choice of the268histogram’s bin size. These results suggest that further work should be done to compare the signal’s entropy against AE hit counts that based on conventional thresholding.
Acknowledgements
The work of the first author was supported by grants from the University of Iceland ResearchFund, the Icelandic Research Council (Rannis) Research Fund, and the Icelandic ResearchCouncil Graduate Research Fund. Furthermore, the authors wish to acknowledge Össur for bothproviding prosthetic feet for testing and for providing access to their testing facilities.
References
1. G. Sims, Fatigue in Composites, Woodhead Publishing Ltd., Cambridge, 2003, 36–62.
2. M. Knops and C. Bogle, Composites Science and Technology, 66, 2006, 616-625.
3. M. Giordano, A. Calabro, C. Esposito, A. D'Amore, and L. Nicolais, Composites Science andTechnology, 58, 1998, 1923-1928.
4. M. Wevers, NDT and E International, 30, 1997, 99-106.
5. D. Tsamtsakis, M. Wevers, P. De Meester, J. of Reinforced Plastics and Composites, 17, 1998,
1185–1201.
6. H. Nayeb-Hashemi, P. Kasomino, and N. Saniei, J. of Nond. valuation, 18, 1999, 127-137.
7. E. R. Green, J. of Nondestructive Evaluation, 17, 1998, 117-127.
8. N. Ativitavas, T. J. Fowler, T. Pothisiri, J. of Nondestructive Evaluation, 23, 2004, 21–36.
9. Y. A. Dzenis and J. Qian, International J. of Solids and Structures, 38, 2001, 1831-1854.
10. J. Awerbuch, S. Ghaffari, J. of Reinforced Plastics and Composites, 7, 1988, 245–64.
11. G. Kamala, J. Hashemi, A. A. Barhorst, J. of Reinf. Plastics and Comp. 20, 2001, 222–238.
12. A. P. Mouritz, Fatigue in Composites, Woodhead Publ. Ltd., Cambridge, 2003, 242–266.
13. Y. A. Dzenis, International J. of Fatigue, 25, 2003, 499–510.
14. C. E. Shannon, Computational Communication Rev. 5, 1948, 3–55.
15. T. Bell, J. Cleary, I. Witten, Text Compression, Prentice Hall, 1990.
16. I. Witten, A. Moffat, T. Bell, Managing Gigabytes: Compressing and Indexing Documents
and Images, Morgan Kaufmann, 1999.
17. P. G. Howard, Ph.D. thesis, Brown University, 1993.
18. D. A. Shkarin, Problems of Information Transmission, 37, 2001, 226–235.
19. D. Shkarin, DCC 2002, IEEE Computer Society, April, Snowbird, Utah, 2002, 202–211.
20. M. Nelson, Arithmetic coding and statistical modeling, Dr. Dobb’s Journal.
21. R. Unnthorsson, T. P. Runarsson, and M. T. Jonsson, International J. of Fatigue,
doi:10.1016/j.ijfatigue.2007.02.024, 2007.
22. R. Unnthorsson, T. P. Runarsson, M. T. Jonsson, J. of Acoustic Emission, 25, 2007, 253–
259.
23. R. Unnthorsson, University of Iceland, Ph.D. Thesis, 2008
24. R. Unnthorsson, T. P. Runarsson, M. T. Jonsson, J. of Acoustic Emission, 26, 2008, 229-239.

269

EXPERIMENTAL SIMULATION AND DYNAMIC BEHAVIOR
OF THE AE DUE TO MARTENSITIC TRANSFORMATIONUSING SHEAR WAVE TRANSMISSION SENSOR
TAKESHI YASUDA, SHINYA KONDO, HIDEO NISHINO and KENICHI YOSHIDAThe University of Tokushima, 2-1 Minamijosanjima, Tokushima, 770-8506 Japan
Keywords: Shear AE wave source, martensitic transformation, source rise time
Abstract
We conducted the waveform analysis of acoustic emission (AE) signals to study the dynamic
behavior of materials deformed or transformed; namely, two types of martensitic transformationin Cu-Ni-Al shape-memory-alloy (SMA) single crystals during tensile deformation that produce
different martensitic structures. The AE source behavior is expected to be of shear. We simulated
AE signals due to martensitic transformation using a shear-wave transducer as the artificial
source. Results of AE waveform analysis in Cu-Al-Ni SMA single crystals are compared withthe simulation results. The martensite with a simpler stacking structure exhibits faster transformation behavior.
Introduction
Acoustic emission is generated by microcracking and phase transformation in materials. AE
waveform analysis has the possibility to determine the dynamic behavior of materials undermartensitic transformation. We have investigated AE waveforms due to two types of martensitic
transformation during tensile deformation of Cu-Al-Ni SMA single crystal [1]. Their detected
waveforms were analyzed by the modified Takashima’s spectrum-gradient method to calculate
source rise time. The different tendency of the source rise time was considered to reflect the dynamic behavior of martensitic transformation. However, this result was not quantitative. It is expected that AE source is of shear nature due to the generation of martensitic habit plane in thismaterial. However, there are few reports on AE source analysis with the shear-type vibration likethe martensitic transformation.
In this research, an experiment is conducted using a shear-wave transducer to simulate AE
sources of martensitic transformation. A Cu-Al-Ni SMA single crystal plate was used as a wave
propagation medium. We changed the rise time of step function voltage to the shear-wave transducer. The induced waves were detected by a wideband AE sensor and recorded with a digital
oscilloscope. S0-mode (zeroth-order symmetric) Lamb waves were analyzed using FFT, and therelation between the source-wave rise time and the obtained m-value from Takashima’s spectrum
gradient method was confirmed. Finally, we detected the AE waveforms during two types ofmartensitic transformation in Cu-Al-Ni SMA single crystals. The results of source rise time during actual martensitic transformation were obtained from the experimental simulation results.
Martensitic Transformations and Habit Plane
The two kinds of martensitic transformation used in this study are β1→β1’ phase transformation and β1→γ1’ phase transformation. Sample Cu-Al-Ni alloy has the composition of 14.1 wt%
Al and 4.1 wt% Ni. It was melted and cast in a high-frequency induction furnace under argon
atmosphere. A single crystal of the Cu-Al-Ni alloy was grown with (001) surface and [100] tensile direction, respectively, using the Bridgman method at a pulling rate of 32 mm/hr.
J. Acoustic Emission, 26 (2008)270© 2008 Acoustic Emission GroupTo generate martensitic transformation by tensile deformation, it was solution-treated at 1273
K for 1 hr. One sample was quenched into water at room temperature (Ms = 250 K, Af = 280 K)
and the other into water at 373 K in order to keep the β1 matrix phase (Ms and Af higher thanroom temperature), respectively. These two specimens were formed to a shape for tensile test
(thickness = 1.25 mm) as shown in Fig. 1. The specimens showed the stress relaxation with appearance of habit plane on surface when tensile stress was applied to each specimen. The optical
microphotographs of habit plane of two kinds of martensitic transformation taken on the front
surface and right-side surface are shown in Fig. 2. The specimen quenched into water at room
temperature shows β1→β1’ phase transformation and one quenched into water at 373 K shows
β1→γ1’ phase transformation.

Fig. 1 Shape of specimen used.

Fig. 2 Habit plane of each martensitic transformation.

Fig. 3 (110) plane stacking figures of martensitic formations from β1 phase, and 18R and 2H
structure [2].
271

Fig. 4 Deformation state due to habit planeand slip plane [3].
These martensitic transformations were confirmed by surface trace analysis. These martensites have the same matrix phase (β1 phase) of the DO3 crystal structure. The transformation wasby shear along <110> directions of the β1 structure [2]. However, β1→β1’ and β1→γ1’ phasetransformations have different structures when they undergo the martensitic transformation. The
accumulating stacking structures are of six kinds of martensitic plane generated in the (110)plane. The β1’ phase has 18R structure and the γ1’ phase has 2H structure. The 2H structure issimpler than the 18R structure as shown in Fig. 3.
Many kinds of martensitic transformation produce the lattice invariant deformation with
twin. This combination of twin is called the martensitic variant. We can assume that the slip
plane is replaced with the habit plane in the case of the shear deformation as shown in Fig. 4 [3].
Its volume change is extremely small under the martensitic transformation because of the existence of shear direction on the habit plane [3].
AE Simulation of the Martensitic Transformation
We simulated the AE waves generated on the martensite habit plane using a shear-wave
transducer (resonance frequency = 2.5 MHz). This experimental setup is shown in Fig. 5. This
shear-wave transducer was attached to the oriented plane of a Cu-Al-Ni SMA single crystal. The
cross-section of the specimens is the same as that of the tensile specimens, and we attached an
AE sensor (M5W) to the surface 20 mm from the shear-wave transducer. Two martensite habit
planes were shown in Fig. 2. In β1→β1’ transformation, the habit plane is inclined by 15° at thefront and 45° at right-side surface. In β1→γ1’ transformation, the habit plane is inclined by 45° at
the front and 51° at right-side surface. The shear wave transducer was put on the corresponding
planes. The voltage was applied to shear-wave transducer using a function generator. We used
impulse or step function. The signals detected by the AE sensor were sent to the digital oscilloscope with 50-ns sampling time after amplified 40 dB through a band-pass filter of 100 Hz to 20
MHz (NF9913). A longitudinal-wave transducer (resonance frequency = 2.25 MHz) was also
used in this experiment for comparison. The experimental setup is shown in Fig. 5.
We characterized Lamb waves due to a shear-wave transducer. An impulse voltage (5 V, risetime = 1.0 µs) was applied. The waveforms obtained were analyzed with the wavelet transform
272

(AGU-Vallen Wavelet software). The waveforms and these wavelet contour maps simulating the
β1→β1’ and β1→γ1’ phase transformations respectively are shown in Fig. 6.

Fig. 5 Experimental set up of the wave transmission sensor to simulate Lamb waves. (a) Experimental setup; (b) β1→β1’ simulation; (c) β1→γ1’ simulation
The curves in wavelet contour maps are the group-velocity dispersion curves of S0-mode
Lamb waves calculated with copper velocity (P-wave = 4361 m/s, S-wave = 2322 m/s). The contrasts in the wavelet contour maps and the group velocity dispersion curves of S0-mode Lambwaves were almost same, implying that the Lamb waves are generated by shear AE source. Considering the range to 8.5 µs and 1200 kHz, we find that the only S0-mode Lamb waves arrived.
Similar results were obtained using a longitudinal-wave transducer as the source in this range.
Beyond the initial stage, the two AE simulation sources had different behavior of the AE waveform amplitude increase. Figure 7 shows AE waveforms of the shear and longitudinal source inthe β1→β1’ simulation. AE amplitude due to the shear source has five-times difference in amplitude between the second and third waves. The corresponding difference in AE amplitude due tothe longitudinal source was sixty times. The third wave is thought that A0-component starting at
8.5 µs and this part is similar in both cases. Therefore S0-component in the longitudinal case ismuch weaker than A0-component and results in the observed difference.
The existence and starting time of A0-component can explained using Two-surface sensing
method [4]. Two same sensors were put on the front and back surfaces of the specimen, respectively. S0-component should detect same wave phase at both sensors. Figure 8 shows Twosurface sensing of simulated AE waveform (β1→β1’ transformation) due to longitudinal wave
transmission sensor as the example. The detected AE waveform at the front and back sensors273

Fig. 6 WT contour map of simulated AE.

Fig. 7 AE waveform due to simulated source. (a) Shear source. (b) Longitudinal wave source.
showed almost similar wave phase until 8.5 µs, but after this part these wave phases reversed
each other. It is thought that this point indicates border between S0-component and A0component.
We need step functions as input applied to the shear-wave transducer to simulate the dynamic
behavior. The rise time of step function was changed from 1.0 to 2.0 µs at 0.1-µs step. We used
“m-value” obtained from Takashima’s method [5] to characterize the AE signals by source risetime changes. The “m-value” is the gradient of frequency spectrum. When the AE waveform includes high frequency spectrum, m-value is lower [5]. FFT analysis with Hanning window was274

Fig. 8 Two-surface sensing of β1→β1’ simulation due to longitudinal wave source.
conducted to the simulated AE waveforms in the 0.1〜1.2 MHz range until 8.5 µs that only includes S0-mode Lamb waves. Figures 9 and 10 show the frequency spectra in varied conditions.

Fig. 9 Frequency response of β1→β1’ simulation.

Fig. 10 Frequency response of β1→γ1’ simulation.
In Figs. 9 and 10, the maximum frequency was constant for each group regardless of the increase in source rise time. As expected, the power spectra decreased beyond the maximum frequency with increasing source rise time.
275

Fig. 11 Relation between rise time and m-value.
Figure 11 shows the obtained m-value vs. source rise time. In each experimental simulation
with a given source, the obtained m-value increased gradually. The gradient values of these relations were obtained by linear regression. This result shows that m-values can be used to estimate
the source rise time, but one must use a unique relation for the geometry and source type.
Dynamic Behavior of Martensitic Transformation
We detected AE waveforms due to two kinds of martensitic transformations (β1→β1’ andβ1→γ1’) in Cu-Al-Ni SMA single crystals during tensile deformation. The martensitic transformation appeared at the center of the specimen. We attached two AE sensors (M5W) separated by
20 mm from the specimen center. AE measurements used a 2-channel AE monitoring system todiscriminate the grip noise. The AE threshold level was 50 dB. Detected AE signals were amplified 40 dB through a band-pass filter of 100 Hz to 20 MHz (NF9913) and recorded at 50 ns sampling time and sampling points of 2500. In this experiment, we detected AE waveforms obtained
until the elongation of 0.1 mm after the beginning of stress relaxation from the appearance ofmartensitic plane on the surface. AE waveform analysis was conducted off-line using the same
analysis as in the simulation. Figure 12 shows the load-elongation curve of each specimen. In thecase of β1→β1’ phase transformation, the martensite plate appeared at about 0.57 mm elongation
with stress relaxation. In the case of β1→γ1’ phase transformation, martensite plate appeared atabout 0.29 mm elongation with stress relaxation. The martensite plates are shown in Fig. 2.
In AE experiments, β1→β1’ and β1→γ1’ phase transformations generated 401 and 174 AE
event counts, respectively. AE signals were classified into two types from the amplitude behavior. This depends on the third-wave amplitude. When this is five-times or less of the secondwave amplitude, the AE is shear type (Event A; circles in Fig. 12). When this is more than fivetimes of the second-wave amplitude, the AE is apparently longitudinal type (Event B; triangles
in Fig. 12). The m-values were obtained by waveform analysis of AE detected by each martensitic transformation. Using the data shown in Fig. 11, we estimated source rise time of eachmartensitic transformation.
Since martensitic transformation as the AE source is due to shear behavior, we only consider
AE Event A (filled circles) in Fig. 12. The average rise times of β1→β1’ and β1→γ1’ phase transformation were 1.4 µs and 0.82 µs, respectively. The β1→γ1’ phase transformation appears tooccur at a higher speed in generating the 2H stacking structure, as it is simpler than 18R stacking
structure of the β1→β1’ phase transformation. With the use of simulation, we can estimate quantitatively the rise time of martensitic transformation.

276

Fig. 12 Rise time distribution of martensitic transformation and load-elongation curves. (a)β1→β1’ transformation. (b) β1→γ1’ transformation.
About the AE of Event B (triangles in Fig. 12), its source seems to have the longitudinal behavior on the basis of the behavior of increasing AE waveform amplitude. There is a possibilityof a combination of shear sources of martensitic transformation producing this mechanism, but
we need to examine this further.
Conclusions
We have investigated the relation between source rise rime and obtained m-value by AE
waveform analysis using shear wave transducer for the quantitative evaluation of two types ofmartensitic transformations in Cu-Al-Ni shape-memory-alloy single crystal.
277

1. In the case that the AE source is shear, AE wave propagation in a Cu-Al-Ni SMA single
crystal thin plate is due to Lamb waves.
2. The relation between the source rise time and m-value was obtained. The change of AE
source rise time appears with decrease of power spectrum in the high frequency range of
the S0-mode Lamb waves.
3. The source rise times of two kinds of martensitic transformation in Cu-Al-Ni SMA single
crystal were estimated by each AE waveform analysis. The source rise time of β1→γ1’
phase transformation was shorter than the β1→β1’ phase transformation.
References
1. T. Yasuda, D. Tani, H. Nishino, and K. Yoshida: J. Acoustic Emission, 24, 2006, 153-160.
2. T. Hunakubo, ed.: Shape Memory Alloy (in Japanese), Sangyou-tosho, 1984, pp. 12-17.
3. K. Tanaka, H. Tobushi, and S. Miyazaki: Mechanical Properties of Shape Memory Alloy (inJapanese), Yokendo, 1993, pp. 10-13.
4. K. Ono, Y. Mizutani and M. Takemoto: Advances in Acoustic Emission-2007, 2007, pp. 336341.
5. K. Takashima, Y. Higo, and S. Nunomura: Philosophical Magazine A, 49-2, 1984, 221-229.

278

IMPLEMENTATION OF ACOUSTIC EMISSION METHOD TO THE
CONVENTIONAL NDT STRUCTURE IN OIL REFINERY
V.P. GOMERA, V.L. SOKOLOV and V.P. FEDOROV
Kirishinefteorgsintez, 187110, Kirishi, Russia
Keywords: pressure vessels, complex testing, oil refinery
Abstract
The article is the addition to the article [1] and describes some results of AE laboratory at an oil
refinery during the past 17 years. Some problems of AE application for testing industrial pressure
vessels and possible solutions of these problems are presented. On the pressure vessel tests, it isshown that AE testing is the essential factor for increasing of the efficiency of complex NDT of
dangerous industrial hardware.
Introduction
NDT is the main means of achieving reliable operation of industrial pressure vessels and piping.
This information is necessary for industry and environmental safety provision of potentially dangerous processes. Usage of new NDT methods, such as AE method, gives a chance to improve traditional NDT structure of industrial objects in essence. Such improvements could include:• Considerable growth of testing volume without increasing testing time;• Testing of previously inaccessible parts of constructions;• Increased effectiveness of traditional NDT methods;• Possibility to obtain new diagnostic information, for example, about flaws growth.
In order to realize this, change is possible only in case of successfully passing complicatedstages of adaptation to industrial conditions. The first stage considers potential advantages of a
new method against other methods in practical inspection, and establishes the scope of correct
applications of this method. Factory management should get conclusive proof that the new
method is really useful for equipment testing.
Kirishi refinery is the largest one in Russia. It processes 22 billion tons of crude oil per year.
More than 70% of production is exported. There are several thousands of pressure vessels at the refinery and they are the main objects for AE testing at the plant.

Fig. 1. Results of pressure vessels AE testing at Kirishi oil refinery.
J. Acoustic Emission, 26 (2008)

279© 2008 Acoustic Emission GroupUsage of AE method in Kirishi refinery has not become widespread for piping, though its periodical testing was successful. Possibly piping is thought to be simpler objects compared to vessels.
So, their validation used less complicated methods than AE. Moreover, from the point of view offactory management, if there are doubts in quality of some parts of piping, it is more rational tochange them at once, rather than assigning their resource by complicated testing methods.
Thus, the complicated testing methods such as AE have the best perspective in solving really difficult tasks, or tasks that can not be solved by other NDT methods.
Problems of AE Applications at the Refinery
1. The feature of AE testing is an opportunity of integral assessment of condition of an industrial object. Meanwhile collected information contains reactions of a large number of control
channels to physical processes of different nature; for example, the formation of cracks, fretting
or friction of construction elements, noise of turbulent flows, etc. The extraction of the informative characteristics of crack formation from the “interfering” processes is complicated task.
2. AE test procedure is connected with the detection of dynamical processes. The analysis ofsuch processes is more complicated, than one of static processes, which are the base of the mosttraditional NDT methods. Therefore the serious problem in AE testing of industrial objects is thecorrectness of usage criteria for assessing the hazard level of defects. These criteria, basically,
are derived for the small specimens for laboratory testing, but we believe that AE testing of pressure vessels requires another type of testing including material testing in fracture mechanics.
Every industrial object (unlike laboratory specimen) is a complicated system with individual features of AE generation during its loading. Many factors influence on AE features, part of which
is impossible to predict a priori. Therefore, direct use of formal criteria and automatic algorithms
does not yield adequate assessment of defect hazard. Automatic algorithms are advantageous in
speed of decision-making, but often unsuitable for solving non-standard tasks.
In the situation described above, the competency of experts is essential. But this practical experience is difficult to be formalized. It accumulates as a result of analyzing a large volume ofexperimental test data of industrial objects. Unfortunately, in such experience prevails subjective
component. The objective component of our practice is the usage of high-quality hardware andsoftware for data collection and analysis. During 16 years we utilized several AE systems, eachof which was one of the best in the world at that time. Our experience has been formed as a result of about 500 pressure-vessel tests. Synthesis of objective and subjective components has
given the results that may be assessed as successful: in our plant AE method becomes establishedand included in the system of basic NDT methods, though it was taken as exotic at the beginning[2-4]. Even so the role of subjective experience is still significant.
3. As a rule, in today's Russia, the development of high technology and expensive NDT
methods is possible only with direct financial support of large industrial companies. In spite ofadvantages of AE method, its implementation is possible only where management has progressive thinking for enhancing the safety and is willing to provide financial support until the benefits overcome the cost. For this reason the implementation of AE method does not depends on its
advantages but depends more on the attainment level of factory management.
4. The problem of “false-negative rejection”. According to testing results of alternative NDT
methods only a small part of active AE sources (15-20%) are considered as indicators of significant defects. As a rule we cannot predict the hierarchy of the relative hazard of defects on the280result of AE testing. All active sources in AE test method are considered to indicate potentially
dangerous defects, until the opposite would be proved. For the testing of dangerous objects thisstrategy is more preferable, than “non-rejection”. Such approach may be compared with inadmissibility of any cracks in pressure vessels, which is declared to be Russian norms [5]. Probably
such approach could be compared to the complication and ambiguity of the hazard assessment of
cracks that is offered today by fracture mechanics. All the more, it is hard to expect that similartasks would be solved explicitly by AE method.
5. The previous problem is only the consequence of other problems: the verification of theresults of AE testing is not just useful, but necessary. Unfortunately, in our applications, we cannot use AE method as in-depth method. AE results must be confirmed with other NDT methods.
On the other hand, AE method is used to assess results obtained with other NDT methods. If any
local defects are first detected with a conventional NDT method during vessel inspection, it is areason for further AE testing. As a result of AE testing, first, AE is used for logging the kinetics
of defect's development for danger assessment. Second, AE determines the location of other defects, if they exist in the structure, but undetected due to their locations being out of inspection
zone of traditional methods.
Efficiency of AE Method for Integral Testing
Complex testing allows us to compensate disadvantages of different methods and to combine
their advantages. Furthermore, combination of test methods increases the probability of detection(POD). Periodic AE testing by loading a vessel by pressure is an example of complex testing due to
AE is combined with such classical method, as is hydraulic test. This is a good illustration of theadvantages of complex testing. It is obvious that hydraulic test of a vessel, combined with AE test,
gives much more information than ordinary hydraulic test.
It is well known that a testing procedure during consecutive applications of several NDT
methods is optimal, if methods with maximal speed of getting information are used first. Probably, AE method has no match with this qualitative characteristic. AE method may be used as thebasis in complex testing due to its performance and other unique properties, such as the capability to integral testing of large constructions and the detection of defects with potential for growth.
It also may be thought as investment to achieve savings in testing of large objects. For example,
if AE testing is applied, it is not necessary to test 100% of welds of large pressure vessels by
conventional methods. Disadvantages of conventional methods (low productivity, large costs ofmaterial and time for metal preparation, etc.) do not allow making the complete testing of large
objects within a reasonable period of time. In complex testing, AE method is first applied for localizing zones with potential defects, and then other NDT methods with lower productivity areused in those local zones to find geometry of probable and confirmed defects. In such way realized the conception of economically efficient 100% test of large objects.
This approach is shown in the following example. Test object - spherical vessel for LPG
storage with inner diameter 10.5 m, wall thickness 16 mm, volume 600 m3. There are 16
longitudinal steel sheets welded with each other and terminated by the hemi-spherical top andbottom caps. Analysis of AE test data showed local zones of the shell (with pointed coordinatesand size), which need additional testing by conventional methods (VT, UT, PT, RT). Also
indicated are the possible type of defects and the degree of their possible hazard (according to thefeatures of detected signals). On Figs. 2 and 3 are shown the results of additional testing.

281

Fig. 2. Drawing of upper part of the sphere tank (10.5-m diameter) with weld marks. Defectzones (1 to 3) are also indicated.

Fig. 3. Drawing of lower part of the sphere tank (10.5-m diameter) with weld marks. Defectzones (4 to 6) are also indicated.
282

Zones 1 to 3 with defects localized by AE and then verified with conventional NDT methods
are shown in Fig. 2:• Zone 1 – a cross crack of 5-mm depth at the weld of upper cap; verified with VT and PT.
• Zones 2 and 3 – zones with relatively high AE zonal activity. Sources were not localized by location algorithms. This could be explained that these are small defects, which increased their
AE activity while load was applied. Probable type of source is the irregularities of metal or
small defects, which are located close to sensors. There were small defects below the UT limits,
but detected with AE.
In two zones, determined with planar location, and in one zone determined by zonal location,
there were no defects verifiable with UT and PT.
To carry out additional inspection for finding the type of active AE sources, it was needed touse the aerial lift, to build scaffoldings and partially dismount service passes (in the upper part ofthe vessel). After the additional inspection, that constructions were used during repair job of detected defects. It is necessary to notice that the probability to find these defect zones by traditional NDT methods was very low; zones with defects (base metal, welds, located in the upper
part of the vessel) are seldom getting in the scope of ordinary testing.
In Fig. 3 are shown zones with defects localized by AE and then verified with conventional
NDT methods:• Zone 4 – a part of weld (900-mm long) in the middle part of the tank: UT was applied and it detected defects of non-allowed dimensions; the defective weld was repaired (shell segment was
localized as a zone of high AE activity around channel #8).
• Zone 5 – area of parent metal on the inner part of the surface with crater type defects, which
had the mechanical nature (maybe they appeared during the tank assembly); the depth is 2-4mm., area to 250 mm2, defects verified with UT and PT;• Zone 6 – weld defect in the middle part of the tank, verified with UT, repaired. Zone registeredwith planar location on the sphere.
In two more zones localized on one of vertical welds, the defects were verified with UT, andweld was repaired. There were also detected two zones of AE activity, but defects were not verified with UT or PT.
Concerning the precision of localization of defect by AE, in real engineering practice, thescraped square for the conventional NDT methods of UT and PT is usually 300 x 300 mm. The
test takes place at this square. Let us consider, for example, Zone 5 (Fig. 3), determined by AE.
Zone is formed with three closely located clusters. The largest cluster (100-cm diameter) contained 12 events, detected by a minimum of three sensors. The cluster was initiated by integral
action of crater defects. They are located, mainly, in the form of three compact groups, localized
on squares with dimensions from 10 to 30 cm2 (in Fig. 3 the dimensions of location cluster are
enlarged for visualization; in real tests, cluster dimensions were smaller). For three compact
groups of defects, at a given cluster size of 30-cm diameter, the coordinates of cluster center calculated by the spherical location algorithm differ from the coordinates of cluster centers of realdefect groups, measured at the object in the following table.
According to Russian standards [6], the precision of multi-channel location shall be better
than greater of two wall thicknesses or 5% of distance between two sensors. According to thefirst criterion we obtain: ∆1=2h=32 mm. On the second criterion (the distance between two sensors varied from 2700 to 3400 mm) ∆2=135-170 mm. So the precision of location is within thelimits prescribed in the standards. The minimal size of inspection for NDT used in refinery283

Coordinates of AE sources
measured directly on object(the centers of defects
groups, Degrees)

Spherical location results(coordinates of the location
clusters centers, Degrees)Longitude

LatitudeLongitude

Latitude

Cluster №1

131.0

-58.0

130.6

Cluster №2

138.5

-58.0

137.0

Cluster №3

146.5

-57.0

145.5

Locationclusters

Absolute difference incoordinates value-57.8

Longitude
Deg
mm
0.4
19

Latitude
Deg
mm
0.2
18

-59.3

1.5

73

1.3

119

-57.6

1.0

49

0.6

55

practice is 300-mm square. Therefore, such precision is satisfactory for engineering applications.
In some cases, the size of preparation zone for additional NDT may be increased to 400 or 500mm square when location uncertainty is suspected.
It can also be used for the planar scheme for locating specific segments of a vessel, because
the sphere diameter is large and the square segment is small relative to the whole surface. It isuseful for detailed analysis of spherical location and is shown on Fig. 4 for Zone 3.

Fig. 4. Results of planar location of AE source activity on the shell segment of LPG storage tank,which was identified as defect group of crater type defect on the inner surface of the vessel.
On the example vessel, it is necessary to test about 490 m of welds. For inspection with PT or
UT (under a code clause of 100% welds inspection), we must prepare the metal surface area of284

more than 147 m2 (covering 150 mm on both sides of the weld). Yet, such methods do not inspect the parent metal of the shell outside the welds. Here lies the effectiveness of AE method forinspection as every part of structure is examined automatically.
By AE testing the whole volume of metal is evaluated with a network of 48 sensors (mesh of
the network is determined with test conditions and test purposes). Scraped area for 48 sensors isabout: 0.01 x 48 = 0.48 m2. Furthermore, for checking AE test results by PT and UT methods, itis necessary also to scrape the surface in the zones, localized with AE method. So in the example
considered, the scraped area for NDT was 1.35 m2. Total scraped area was 1.83 m2. Comparison
with previous evaluation shows a high efficiency of AE testing applied as an integral method for
diagnostics of large pressure vessels.
So, AE testing is a good tool for NDT resource optimization in condition monitoring, when it isnecessary to provide a high level of diagnostics of dangerous industrial objects within limited time
available for inspection.
Usage of AE Information PowerOne of the advantages of AE method is the ability to collect much information, which contributes to its successful usage. It is especially important in cases where the lack of preliminary information about test object could cause non-optimal usage of testing procedures or possible inaccuracies in measuring flaw locations. Unfortunately, such events sometimes take place during testing oflarge industrial objects.
Usually, modern AE equipment collects large quantity of information during a test, which may
be redundant for our conventional practice of vessel testing. For example, we seldom use waveform
analysis. To gain desired results we are often satisfied with other data analysis procedures. However, this redundancy appears as insurance from possible testing imperfections and is a powerfulcompensatory factor when some parts of information is lost (due to non-optimal sensor arrangement, or their insufficient quantity, or existence of background noise).
These circumstances can be illustrated on example of vertical column testing with following parameters: outer diameter – 3200 mm, wall thickness – 36 mm, volume – 426 m2, height – 53.6 m,
insulation layer – 150-200 mm thickness. The column was assembled on site from 3 segments,
manufactured at factory. Segments were assembled with two mounting welds, which were madeduring the column installation. Lower field weld is located at 28.6 m height, upper – at 47.6 m. Lifetime of column - 29 years.
The primary intent of AE testing was the quality control of these welds. AE testing was carriedout twice with an interval of 5 years. Before the first testing it was assumed that there were no serious problems in field welds. Therefore we used a less number of sensors then really needed for thiscolumn. To place several sensors we needed help of steeplejacks (industrial mountaineers). Theirdangerous work could cause inaccuracies in some length measurements. However, these shortcomings in testing arrangement were compensated by information power of AE method. The quantity of
information was quite enough to detect serious flaws in field welds with confidence and to localize
most of these flaws. The results of AE testing became the reason for 100% test of field welds byother NDT methods and consequent repair of several parts of field welds. The second AE testing,carried out 5 years later, had several differences from the first one, which are shown in table below.
285

Conditions
Number of sensors in location map
Distance between nearest sensors
Possibility of planar location
Basic location algorithms(part of weld's length)
Preliminary information about flows
Type of testing
Internal agent
Pressure rise speed
Max. Test pressure
Number of previous repairs(changes of weld's structure)
Interferences from new welding of new
internal facilities of column
ThresholdFirst AE testing
Low weld8170-190 cm
ProblemLinear (⅓)
Planar (⅔)

Upper weld
6200 cmProblemLinear (¾)
Planar (¼)

Second AE testing(5 year later)
Low weld
Upper weld
1711120-140 cm
130-160 cm
Satisfactorily Satisfactorily
PlanarPlanarNo
Hydraulic
Water≈0.064 MPa/min (+ hold)
2.9 MPa

Yes
Pneumatic
Nitrogen≈0.009 (without hold)
2.1 MPa

0

3

Yes

No

45 dB

40 dB

As it could be seen in this table, the outlines of two testing differ considerably. Main advantage of the second testing was using more sensors. Therefore information of flaw coordinates
was more accurate. Probably the second testing was more informative then first one.
It should be taken in account that key task of AE laboratory in an oil refinery could be formulated simply:
1. To detect problem part of construction;
2. To localize with sufficient accuracy the most dangerous flaws existing at the time of testing.
On the base of such statements the results of two testing practically coincide. However, the results of these two testing have differences. These differences gave us data going beyond thescope of key task. For example, we obtained data about flaw growth dynamic in this area. Also,
we got data about the quality of repair done and about the suitability of further repair. For this it
is convenient to use instruments of AE data analysis, which permit us to visualize the structure of
problem areas in whole construction (or in big segments of construction).
Two drawings, taken from results of data analysis, show planar locations of AE sources in thelower field weld area. The drawing is matched with drawing of shell reamer, where positions ofmanholes, welds and repaired areas are shown.
In Figs. 5 and 6 are marked several zones with the following conditions:• Zone 1.1, 1.2, 1.3 – large clusters of non-metallic inclusions and small discontinuities on different depths in base metal.
• Zone 2.1, 2.2, 2.3 (same positions as zone 1.1, 1.2, 1.3 – 5-year later) discontinuities withareas from 4 to 140 cm2 formed. The largest bulge was placed in zone 2-1.
• Zones from 2.4 to 2.8 join to repaired parts of weld. AE activity appeared in these zones
after weld repair. UT instrument detected high concentration of small discontinuities
there. By analogy with variations in zones 1.1 – 1.3, it could be supposed with great probability that in zones 2.4 – 2.8 are forming discontinuities as a result of small flaws fusion.
•

Zone 2.9 – crack in the weld made at a machinery plant (7 mm in depth, 30 mm inlength).
286

Fig. 5. Results of planar locations of AE sources in lower mounting weld area in first testing.

Fig. 6. Results of planar locations of AE sources in lower mounting weld area in second testing.

287

Analysis of the distribution of active zones on these two drawings and the results of additional NDT in these zones led to several important conclusions:
1. The quality of field welds was considerably worse than that made on a machinery plant.
Therefore just the use of field welding limit the safe lifetime of the vessel.
2. As a result of long operation time, the degradation processes of material of welds and of basemetal near welds were actively developed over the last 5 years. The second AE testing showed thatwith time these processes spread to plant-made welds too.
Repair of the weld does not always eliminate the problems. During repair the strain conditions of the metal in repair zone can get worse. It intensifies the degradation process in metal due
to hydrogen embrittlement, which is the main damaging factor for this vessel. In fact, it can raise
the risk of vessel failure. Therefore, the continuation of repair of local flaw zones is not recommended.
In general, similar results were received during testing of the upper field weld. On the basisof complex results the refinery management made decision to change the column. Whole chainof testing and repairs during the 5 year period is as follows:(AE-1) → (NDT-1, 100%) → (Repair-1) → (+ 2 year) → (NDT-2, Local) → (Repair-2) →(+ 3 year) → (NDT-3, Local) → (Repair-3) → (AE-2) → (NDT-4, Local) → (Decision ofcolumn change).
So, in our practice we are using only a small part of information collected by AE system. For thisreason its volume is redundant, but even during solving simple tasks of industrial testing, this AE
redundancy can be useful. It can be used:
1. As additional insurance from testing arrangement faults;
2. As a reserve for solving more high-level tasks than we are solving routinely.
Conclusions
1. Phases for implementation, for demonstrating the capabilities and for proving the efficiency of AE method are successfully finished at Kirishi oil refinery.
2. It is now at a stage of determining the optimal utilization schemes of AE method in complex testing, which combines the capabilities of several NDT methods.
3. We have started the implementation of continuous AE-monitoring technology. The results of trial performance will be used for the assessment of implementing AE-monitoring systems at the new production lines. New equipment will operate in more difficult conditions than
one of the existing production lines. Therefore, it is necessary to develop new technologies fortheir safe exploitation.
References[1] B.S. Kabanov, V.G. Gomera, V.L. Sokolov, V.P. Fedorov, A.A. Okhotnikov, Some Resultsof AE Pressure Vessels at Kirishi Oil Refinery In: Proceeding of the 28th European Conferenceon Acoustic Emission Testing, Krakow, 2008, Poland, pp. 298-303.
[2] Kabanov B., Gomera V., Sokolov V., Fedorov V., Okhotnikov A., AE Testing of Refinery
Structurs, In: Proceeding of the 27th European Conference on Acoustic Emission Testing, Cardiff, 2006, Wales, UK, pp.133-138.
[3] Gomera V., Forker J., Acoustic Emission for industrial testing – some examples and recent
developments, In: Proceeding of the Conference on Non-destructive Testing Equipment and Devices, Moscow, 2003, Russia, no.2.21.
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[4] Kabanov B., Gomera V., Sokolov V., Okhotnikov A., Fedorov V., AE technology as a key
element of the safe operation system at refinery: Journal of Acoustic Emission, 20, 2002, 218228.
[5] PS 03-576-03. Pressure vessels design regulations and their safe operation, § 4.7 (ПБ 03-57603. Правила устройства и безопасной эксплуатации сосудов, работающих под давлением,п.4.7).
[6] PS 03-593-03. Regulations of AE testing of vessels, apparatus, boilers and operation pipelines, § 4.5 (ПБ 03-593-03. Правила организации и проведения акустико-эмиссионного
контроля сосудов, аппаратов, котлов и технологических трубопроводов, п.4.5).

289

AN EXPERIMENTAL ANALYSIS OF FREQUENCY EMISSION ANDNOISE DIAGNOSIS OF COMMERCIAL AIRCRAFT ON APPROACH
S. KHARDI
INRETS-LTE, 25, avenue François Mitterrand, 69675 Bron cedex, France
Abstract
Simple noise level monitoring systems, which are currently used around airports to create anoise map in residential areas, are unable to identify source frequencies and their impact on theenvironment. This article presents dominant frequencies during aircraft approaches of the SaintExupéry Lyon International Airport (France), and analyzes the evidence of impact on the environment. The main objective is the characterization of the most dominant frequencies emitted
during approach operations, and the diagnosis of frequencies having a negative impact on populations living around the airport. A detailed analysis of signal processing concerns for measuring
aircraft flyover noise is presented. A study of the emission angle has been completed and a classification period depending on the noise level has been achieved. Development of a deDopplerization scheme for both corrected time history and spectral data is discussed along withan analysis of motion effects on measured spectra. It is shown that correcting for Doppler amplitude and frequency can give some idea about source directivity. The obtained results provide additional information placing the results of theoretical models in their context, and will help validate and extend certain methods of calculating the propagation of aircraft noise. They have thepotential to quantify environmental quality around the airports. The precise measured frequencies
during the operations can be treated by passive or active control systems developed by aircraft
manufacturers.
Introduction
Among environmental concerns, excessive aircraft noise and its control have become a major
objective of airport authorities. Assessment of aircraft noise depends upon reliable information
including noise measurement. During recent decades, aircraft noise levels have been successively
reduced by 20 dB. Nevertheless, the large numbers of people, who live in communities near airports, are affected by aircraft noise, which has increased tremendously in scope. Decisions havebeen made to enable the choice of possible solutions of aircraft noise control around airports.
Several procedures have been used in the worldwide aircraft operation such as low-noise during
the take-off and landing flight procedures, optimal route distributions, flight route optimization
around airports, etc. Nevertheless, the noise in the vicinity of airports, in particular under the
take-off and landing flight paths, remains high disrupting the quality of life of local residents.
Complaints are increasing despite the withdrawal of the noisiest aircraft, the fleet renewal, andthe international resolutions recommending that airports, faced with the problem of noise, introduce restrictions of operations. These nuisances therefore continue to represent an environmental
problem. Technology solutions and the positive measures taken by airport authorities (restrictions
on use of land, procedures for takeoff and landing, operating restrictions, compensating residents,etc.) failed to reduce their impact near airports because of the growth in air traffic. The aircraft
manufacturers foresee a demand for aircraft to cope with the increased traffic and fleet renewal in
the coming years. The proportion of heavy-lift is progressing towards almost half the fleet. This
growth will differ by two essential characteristics: 1. mass transport anticipating the scarcity of
take-off slots; 2. transport playing on the increasing frequency and flexibility of operations continuing despite the anticipated shortage of oil. All the experts agree that around 2020, taking into
J. Acoustic Emission, 26 (2008)290© 2008 Acoustic Emission Groupaccount the known oilfields and the potential extraction, the production of oil will reach a maximum level and then decrease especially with the growing economic power of China and India.
Whatever the efforts to conserve energy and promote renewable energy, air transport will continue to grow, even with very expensive oil. This problem can only be solved within the framework of a global vision for sustainable development involving new technology engines and fuselages (Julliard, 2003), breakthrough technologies, the design of new procedures and flight paths(Zaporozhest and Tokarev, 1998; Zaporozhest and Khardi, 2004), airspace management, newregulation rules and certification (ECAC, 1997). It is a major challenge for the future of airtransport in the context of economic development linked to compliance with the conditions ofpeople living near airports.
In recent years, increasingly strict regulations on aircraft noise have imposed large economic
penalties on aircraft companies and airlines that fail to comply. As engine technology leads toquieter engines, airframe noise – defined as the non-propulsive component of aircraft noise,
which is due to unsteady flow around the airframe components – has become a major contributor
to the overall aircraft noise levels. The physics behind airframe noise generation is still not fully
understood and must be characterized before reduction techniques can be implemented. R&D
projects confirmed that research in this field is active, and that current technological developments can help reduce aircraft noise levels. The current challenge is to establish an objective assessment taking account of the evolution of air traffic, and a permanent control of noise. This
control is complementary to the actions initiated by the government as the mastering of urban
planning in the vicinity of the airport, assistance for soundproofing, optimization and control ofoperational rules, etc. A compromise should be found between environmental acceptability, thelower cost of design, development, production and exploitation, and increasing the operational
capacity of the airspace. Furthermore, the known absence of a clear link between the certified
noise levels and noise levels measured on the ground justifies the experimental work undertaken
by research and development. Indeed, at the end of each certification, changes have taken place,due to technological leaps facing the aviation industry (Oishi and Nakamura, 2000; Kenzakowski
et al., 2002; Hunter and Thomas, 2003; Kannepalli et al., 2003). In flight, commercial jet aircraft
sources are active (Smith, 1989; Hubbard, 1994; Michel et al., 1998). Their relative importancedepends on the flight segment and the airframe-engine combination. There are many components
in aircraft noise - different parts of the air-frame (flaps, under-carriage etc.), engine fan, and engine jet etc. - whose relative importance changes according to which aircraft is considered andwith the aircraft's operational configuration and mode of flight. Thus, directivity has a muchgreater contribution in aircraft noise source characteristics.
This paper describes spectral characteristics of aircraft noise during approaches, indices used
to quantify the effects of aircraft noise and cumulative noise levels as a result of aircraft operations. The main objective of this work is the characterization of the dominating frequencies defined by the most raised levels and which could be treated by the aeronautical manufacturers.
Protocol for measuring the aircraft noise (ICAO, 1988, 1993, 2004) was strictly respected andused. The work presented in this article makes an additional contribution to a better understanding of the problems of aircraft noise and its control. It gives the results of measurements madefor a year concerning the dominant frequencies emitted by aircraft approaching the SaintExupéry International Airport (Lyon, France). Analysis of the aircraft passage duration, and statistical indices describing the impact on the environment, in particular during night periods, werestudied.

291

Experimental Setup
This section describes the measurement procedures and conditions under which aircraft noise
was recorded. We used approved procedures recommended by the ICAO (1988, 1993, 2004) applied during acoustic tests and analysis of aircraft noise measurements. The measurement campaign of aircraft noise approaching the Saint-Exupéry Lyon International Airport lasted one year.
It was used as a reference year because of changes in the airport infrastructure project (two new
runways) and the predictable fleet renewal (new certification rules and regulations). During oneyear-long measurements, the noise of commercial aircraft of different types was recorded according to annex 16 of the ICAO convention. The noise signals were recorded so that we can assess noise exposure following indices based on A-weighting (LAeq, SEL, L10, L95, Leq, LDN,
LDNL, Lden, etc.). Locations for recording aircraft noise in flight are surrounded by flat terrain
having no excessive sound absorption characteristics (grass fields cut). No obstructions that
could influence the sound field from the aircraft exist within a conical space above the point on
the ground vertically below the microphone, the cone being defined by an axis normal to theground and by a half-angle 80° from this axis. The type of aircraft (engines and their number,fuselage, procedures, loads, thrust changes, etc.) was not recorded. This variable could not becollected in real-time for each flight, since it would require direct access to data from the flightdata recorder (FDR).

Fig. 1: Measuring points under flight path and at lateral locations. The site elevation is between
251 and 263 m.
292

The data were recorded at the four observation points designated in Fig. 1: under flight path 2
km ±400 m lateral, and lateral to a 1600 meter runway and 500 m from the touch axis. Acoustic
data stored under the flight path allows analysis of the frequencies issued without lateral and angular corrections and without the need for multiple systems of expensive measurement stations.
The two side points to ±400 m are used to make an adjustment on the data especially when the
trajectories practiced during the approach deviate from the main axis of the runway due to achange in the runway landing (particular traffic regulation or incident). The last measurement
point is used to control the data when weather conditions change slightly and that the air control
modifies the direction of the aircraft approach operations. Measurements were performed under
the following and stable atmospheric conditions:- no precipitation;- ambient air temperature not above 35°C (95°F)) and not below −10°C (14°F);- relative humidity is between 20% and 95% over the whole noise path between a point of 4
m above the ground and the aircraft;- average wind velocity at 4 m above ground may not exceed 12 knots;- no anomalous meteorological conditions that would significantly affect the measured noiselevels when the noise is recorded.
Ambient air temperature, relative humidity, wind speed, cloudiness and global radiation profiles
were recorded during noise measurements. Measurements are performed under the same atmospheric conditions. Their stability was checked and timetabled. Table 1 shows their fluctuations in
the intervals where stability criteria are met for the whole noise measurements.
Table 1: Changes of meteorological parameters provided by Meteo France. The influence of humidity on the noise levels of less than 0.5 dB for a relative humidity between 30% and 90% at 40°C and at 1 kHz.
Meteorological parameters (per hour)Wind speed (m / s)
Average temperature (°C)
Cloudiness (octas)
Humidity (%)
Global radiation (J/cm3)

Value intervals
1–3
15–35
0–2
35–50
240-290

A SIP 95 sound level meter, a Symphony (01dB Stell©) measurement station, and a DATFOSTEX PD-4 (44.1 kHz sampling frequency) were used to record the acoustic data. The measurement systems are inspected every two years and approved by the French National Laboratory
for testing in accordance with the international standards. The four microphones are positioned to
4 meters above the ground to comply with the requirement of free fields. The ground is flat andconsists of grass shorter without brush, wood or obstacles. The calibrations are performed everyday. The free-field sensitivity level of the microphone and preamplifier in the reference direction, at frequencies over at least the range of one-third-octave nominal midband frequencies from50 Hz to 10 kHz inclusive, is within ±1.0 dB of that at the calibration check frequency, andwithin ±2.0 dB for nominal midband frequencies of 6.3 kHz, 8 kHz and 10 kHz. The output of
the analysis system consists of one-third octave band sound pressure levels as a function of time,
obtained by processing the noise signals through an analysis system with the following characteristics:- A set of 24 one-third-octave bands filters [50 Hz - 10 kHz];- Response and averaging properties in which the output from any one-third octave filter
band is squared, averaged and displayed or stored as time-averaged sound pressure levels;293

- The interval between successive sound pressure level samples is 500 ms ± 5 ms for spectral
analysis with or without slow time-weighting;Analysis system operated in real time from 50 Hz through at least 10 kHz inclusive. Ambient
noise, including both an acoustical background and electrical noise of the measurement system
was recorded for 5 minutes a day with the system gain set at the levels used for the aircraft noise
measurements. The recorded aircraft noise data is acceptable according to international standards,
e.g. the ambient noise levels, when analyzed in the same way, are 20 dB below the maximum of
the aircraft. The reference interval used for defining noise exposure to the residents of the airport,
taking into account human activities, corresponds to the periods of 6h-18h, 18h-22h and 22h-6h.
The exclusion criteria of the recorded data are: strike days and special weather conditions (gusty
winds, stormy rainfall, atmospheric turbulence…). After each calibration, any level deviation
greater than 1 dB lead to the rejection of data for the 24 hours involved. Basic physical properties
of sound pressure were measured: level, frequency distribution, time variation, and time of flight
duration characterizing aircraft passage.
During the final flight phase, aircraft noise source is located well above the receiver position.
Consequences of the atmosphere at high altitudes are less well defined than at low altitudes andtheir effects have not been reported in this paper. They are well studied by Salomons and de Roo(1999) and by Iserman et al. (2004) using the PE method where their limitations were discussed.
Irregularities, which occur in measured spectra due to interference effects caused by reflection ofsound from the ground surface or by perturbations during the propagation of aircraft noise to themicrophone, have been identified and corrections are applied to spectral characteristics, which
are not related to aircraft noise source. No measurements of the reflection coefficient were made.
As specified in Appendix 2 of the Annex 16 of the ICAO convention, narrow-band analysis is
one recommended procedure for identifying these false tones.
According to the above measurement specifications, we identified and retained 15460 turbojet aircraft executing approaches of the airport in the same conditions among 84.5% (+20 T)
equipped by turbojet engines, which land at the airport. It should be remembered that 15% of thetraffic represents propeller aircraft (3–9 T and +20 T) and 0.5% other (-3 T and 3-9 T). It is because the difficulties appear on all the levels of which analysis of the harmonic frequencies, propeller aircraft were excluded in this analysis. The time and frequency signals are analyzed by
DBTrait© software and by specific algorithms developed in a MatLab©/C++© computing environment taking into account the spectral characteristics of the recorded signals. This analysis has
been developed for characterizing source frequencies. It required particular evaluation and selection of the data analysis parameters including the data sampling frequency. Those mainly calculated are noise levels, statistical indices, aircraft passage duration (time of flight TOF), and thepure frequencies and frequency bands in the one-third octave characterized by the higher noiselevels.
Results and Discussion
Aircraft noise varies both in frequency and level during a flight for three main different reasons. First, individual sound generating mechanism each has a distinct frequency dependent directivity, e.g. for high frequencies, jet noise exhibits a ‘zone of silence’ in downstream direction
while for low frequencies the radiation is maximal in this direction. Second, owing to the directivity of the individual sound generating mechanisms, the different sound component contributions dominate the sound radiation in different directions. Furthermore, the relative294

importance of the different sources depends on the flight phase and the engine-airframe combination. The basic propagation model describing sound propagation with particular assumptions(homogeneous atmosphere, heuristic corrections, etc) is unable to assess dominant frequencies.
Thus, the modelling of the source region and the region outside the immediate vicinity of thesource appears to be unfeasible by the most specific methods. Third, the measured noise levels
are inclusively affected by Doppler effect that modifies the frequency contents and the cumulated
energy. Identifying the origin of frequency annoyances of aircraft noise during approach phases
and diagnosis of their source is major. This identification has grown since the 1960s with the development of jets and the expansion of their environmental impact. In spite of the development of
new technologies and initiatives to reduce the emergence of generating high noise levels (passive
and active control, intelligent materials, new technology use, etc.), and successive improvementof aircraft certification on the basis of the ICAO convention, technological break-through andcomprehension of the most emitted frequencies characterized by their high noise levels areneeded.
Thus, the study of dominant frequencies emitted during operations is important because it
should allow manufacturers to focus their R&D on improving the sound-proofing of active andpassive systems that could be implemented in the engine for noticeably achieving a noise reduction. This study also helps to make a diagnosis of frequencies that contribute more to the discomfort or the annoyance of local residents around airports. Frequencies emitted by fan, turbine,compressor, jet noise and aerodynamic noise due to flows around the body of the aircraft (cell,
flaps, slats…) cannot be identified under static conditions. They can all be dominant and dependon the mode of operation or the engaged landing procedure. Another advantage of research carried out in this paper characterizing the dominant emitted frequencies is to reduce the computational time of noise propagation models often conducted in a wide frequency band. In the absenceof data on specific studies on emitted frequencies in operation and the impact of emerging frequencies on the sound quality around the airport, this research work confirms the interest of
dominant frequency bands and pure frequencies characterized by higher noise levels. For a number of reasons, 40 % of the used flight paths do not correspond to the theoretical flight plan published before the take-off. Those changes are generally related to operational conditions (meteorological conditions, power setting, thrust management, etc.) and are confirmed by radar track
information, which samples accurately aircraft position each 0.5 s. These arguments support thiswork.
Because human hearing is not equally sensitive to all frequencies, it is most sensitive to frequencies around 4 kHz. Humans perceive broadband noise by dividing the frequency axis into
bands. Different bands describing the human hearing and its functioning have been developed.
Third-octave bands describe human hearing rather well and have been used in much publishedresearch. When hearing broadband noise without dominant frequencies, the human ear perceives
the central frequencies of all covered third-octave bands. Despite the current knowledge abouthuman hearing and aircraft noise annoyance, the perception of sound is still too complex to beunderstood as adequately as needed for accurate prediction of perceived noise during aircraft operations. To complete the understanding of the impact of aircraft noise, we have also analyzed thepure frequencies.
A further point having an impact on measurements, which has to be highlighted, concerns
aircraft directivity. It is asymmetric and it may be expected that asymmetry should be changed in
the future due to the effect of modifications such as chevrons, scarfed inlets … and should become available in coming years. Assessment of the directivity of a moving source is295generally a complex problem because directivity functions may be described in various coordinate systems: fixed to the aircraft, fixed to the flight track or to the ground. Directivity and spectral content measured at a reception point could be particularly altered by the influence of theforward speed. The Doppler effect changing the frequency content of the signal and the distribution of energy in time transforms the true radiated directivity into an apparent directivity as observed at a fixed receiver on the ground. Removing Doppler effects from recorded sound levels
is a possible technique but requires very complex procedures. Because of this complexity, it
seems more realistic to describe the source by its apparent spectral components measured at a
fixed receiver position, as is done for noise certification. Measurement recording under the flight
path, avoiding angular corrections, allows access to frequencies at the reception point. Of course,
knowing the speed of each aircraft and making adjustments due to the Doppler effect, we obtainthe frequencies emitted by the sources. Geometric calculations performed thereafter allow a correction of Doppler effect. Knowing the frequencies recorded at the receiver, we can assess thefrequencies emitted by aircraft sources according to the emission angle θj (Fig. 2) and the indicated aircraft speed. To remove Doppler effect, we first consider an aircraft as a source in motion
where the receiver is placed under the track in Xj position. The aircraft height Z is considered
above the reference (X-Y) plane, generally taken to be the ground plane, with the measurement
microphone at 4 m above this reference plane.

Fig. 2: Illustration of emission angles under flight path. Height of the receiver and the windspeed are neglected.
Because of Doppler effect, the measured sound under the flight path is perceived differently
by the receiver because of aircraft speed and angular effects. Analysis by Miyara et al. (2001)
gave at the reception point the observed frequency fd as:

where θj is the emission angle between the flight path and the sound direct radius.
296(

the aircraft glide slope). Mach number M = V/c (V and c are respec-tively the speed of the aircraft and the sound). Vw the wind speed supposed to be parallel to theground. f is the main emitted turbojet engine frequency. Geometrical calculation allows the recovery of the pure frequencies emitted by the source.
For a given time t and Xj,

where Xi is the relative distance when theaircraft is at a height Zi. Then,and. For Xj, we have divided the lateralinterval into two parts for the purpose of calculation. For Xi ∈ [0, Xmax] (example of Xmax = 8000
m corresponding roughly to the lateral distance when almost all aircraft are aligned with respect
to the axis of the main runway of the Saint-Exupéry Lyon International Airport; Zmax = 419 m),
we can write:∀ Xi ∈ [Xj, Xmax].
In addition,
where

,

,

,

, then∀ Xk ∈ [0, Xj]. Figure 3 show the emission angle θj behavior de-pending on the lateral distance for four Xj values under the flight path.

,.
In order to obtain the aircraft speed, two methods can be used. Either the aircraft speed ismeasured by the on-board instruments or assessed by experimental data. The first solution required a reliable device ensuring a perfect data synchronization between on-board and ground
instruments. It was not possible to perform that synchronization with all aircraft landing at the
airport. The second could be effective and robust. It is based on the frequency measurements andgeometrical calculation. In order to obtain V, two conditions were chosen: x large and positive
close to 0 and π. This results in two equations from whichand x large and negative, yielding
the frequency f could be eliminated and the system solved for V supposed constant andthe observed frequency before the aircraft over flight andafter the over flight.
For x ~ +∞, then the observed frequency
For x ~ -∞, then the observed frequency

withthe main engine frequency,

297

is

If we take into account the height of the receiver h1 (4 m), h2, relation between the previous
variables and angles (Fig. 4), f and fd can be easily written using the following :Fig. 3: Emission angle behavior under flight path.
Aircrafth2

Receiverh1
h1

Xj
Virtuel reception point

Fig. 4: Interference between direct and reflected sound waves responsible for the so-called“comb-filter” effect (or Lloyd's mirror effect).
The interference arises between two sound waves as a combination of a direct and a reflected
wave. Analysis by Smith (1989) showed interference patterns caused by ground reflections, andfrequencies shown in the Fig. 5 used the following expressions:
298

and
the path length difference between the direct and the reflected sound wave; c is the soundspeed,
andare respectively the frequency at which a constructive and destructive interference occurs.
As shown in Fig. 5, the main objective was to show that the frequencies, which will be obtained in the following sections, cannot in any case to be confused with those, which correspondto the frequencies resulting from the constructive interference. The effect of destructive interference between the direct and the reflected sound waves at the microphone is not considered in thiswork because the unvested frequencies are considered destroyed. This is the reason why thiswell-known effect (comb-filter effect) has not been considered in this paper. Nevertheless, astudy by Miyara et al. (2001) shows that effect could be used in the estimation of the aircraft's
altitude. For outdoor experiments, without any habitation, this effect does not occur. If we wantto take this into account, as part of measures for assessing sound quality, we can use the resultsof Fergusson et al. (1994), Schulten (1997), and Miyara et al. (2001). They provide the conditions where Doppler patterns and comb filter are superimposed on the spectrogram and must be
considered. They particularly show how to obtain a directivity of a single aircraft over-flight.
Other research has been carried out by Lo et al. (1999), who developed a model describing the
temporal variations of the destructive interference for an aircraft approach. As far as we are concerned, the free field condition is filled and this combined effect neglected.

Fig. 5: Model of frequency interference.
Aircraft Sound Spectra
This section describes the method for analyzing the spectral content of recorded aircraft noiseof different aircraft, representative for the fleet of passenger aircraft in the year of 2007. Different types of commercial aircraft have been studied, mainly aircraft with jet engines. Approach
events are captured and noise recorded according to annex 16 of the ICAO convention.

299

Over the last two decades, many techniques have been developed for spectrum analysis ofdiscrete time series. It is clear that aircraft noise on approach is considered as unsteady states.
Estimation of the power spectral density or the spectrum of discretely sampled deterministic andstochastic processes is often based on procedures employing the fast Fourier transform (FFT).
This spectral analysis is computationally efficient and produces acceptable results in the large
class of signal processing. The most important limitation is that the frequency resolution is able
to distinguish spectral responses of two or more signals. A further limitation is due to the implicit
windowing of data that occurs when processing with FFT, which manifests itself as leakage inthe spectral domain. Time-frequency distributions (TFD) can be used to study non-stationary
signals of aircraft noise. They give a spectral representation of the signal, which is function oftime. When the signal dynamics change slowly, and high frequency resolution is not required,
TFD are usually obtained by computing the spectrogram. When high time and frequency resolutions are needed, TFD such as the Wigner-Ville or the Choi-Williams distributions are preferred(Griffin, 1991; Papandreou and Boudreaux-Bertels, 1993; Hlawatsch et al., 1995; Li, 1997).
Thus, efficient time-frequency representation analyzing the nature of non-stationary aircraft
noise signals is needed (Orfanidis, 1988; Lo et al., 1999). This is performed by mapping a onedimensional signal in the time domain, into a two-dimensional time-frequency representation of
the signal. A variety of methods exist in the open literature, based on the Wigner-Ville distribution (Jones and Baranuik, 1994 and 1995). A separate analysis of a time domain or a frequency
response is not sufficient to assess the behavioral aspect of the aircraft noise levels. Timefrequency distribution (Kwok and Jones, 2000; Biscainho, 2001; Boashash, 2003) that associates
for each instant a frequency representation of the signal can be effective in assessing aircraft
noise frequencies corresponding to the raised levels. The Wigner-Ville distribution (Baraniuk,and Jones, 1993; Czerwinski and Jones, 1994; Cohen, 1995) chosen for this paper is defined as:

where s is the analytical signal of the signal time measurements and s* the complex conjugate. In
addition to the time-frequency representation as found in the spectrogram, integrating the distribution for a given time (respectively frequency), gives the instantaneous power (respectively thepower spectrum). The instantaneous frequency can be assessed by the marginal moment
and its dual the group delay. A valuable property of the

Wigner-Ville distribution is that it satisfies the marginal conditions. For time-series X(n) (Oppenheim and Schafer, 1989; Orfanidis, 1988), the expression of the discrete-time Wigner-Ville
distribution, WV(n,f) is:

where hN(k) is a data-window, which performs a frequency smoothing. While Fourier spectra areperiodic with period equal to the sampling rate, WV(n, f) is periodic in frequency with period
equal to half the sampling rate. This may cause a spectrum disturbance, which can be removed
either by over-sampling or by using the corresponding analytic signal. The distribution is negatively affected by important cross-terms, which limit its practical use. Cross-terms may be adequately reduced by smoothing the distribution over time. The resulting smoothed Wigner-Ville,
SWV(n, f) is:
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The time-resolution depends on the time-window gM(m), and it is lower in SWV than in WV.
Moreover, SWV does not satisfy the marginal conditions. To reduce the cross-terms satisfying
the marginal conditions, a larger class of time-frequency distributions, such as the Choi-Williams
distribution, which includes the Wigner-Ville as a special case, could be suggested. When thesignal is composed of several components interference occurs between their time-frequency patterns due to the bilinear nature of the distribution. To reduce this effect the transform issmoothed in frequency – in our case by a Hanning window over 512 points. The software toolbox (MatLab© and C++©) has been used for aircraft-noise signal processing that offers a more
informative description of signals such as the predominant frequency emitted during aircraft approach. Figure 6 shows a typical time-frequency spectrum of aircraft noise measured during
three approaches. It gives an illustration of a Wigner-Ville distribution used in this paper showing the distribution of energy in the sound across frequency.

Fig. 6: Time-frequency spectrum of three aircraft approaches.
Figure 7 reveals the two major components in ground-perceived aircraft noise: broadband
noise and some tonal components. As previously mentioned, interference pattern resulting fromdirect and reflected noise is not considered.
The amplitude of the broadband noise in third-octave bands is determined based on the average energy that remains in each band after removing all tonal components from that band. An
automatic search of maximum levels was achieved and pure frequencies or frequency bands
were made from recording spectra. Identification and counting of frequency bands were made byusing specific routines developed under MatLab© and C++©. After normalization compared tothe total number of sound signatures, we obtained the result shown in Figure 8 to show what the301third-octave bands perceived level sensor with the highest levels. The major observed bands are630 Hz, 800 Hz, 1000 Hz, 1250 Hz and 1600 Hz, whose noise levels are the highest. The thirdoctave bands 1.25 kHz and 1.6 kHz are dominating. Their origin could be either the airframe of
the aircraft (fuselage, wing, drift, landing gear and high lift devices), which upon landing with anengine rpm in slow motion, may have a higher contribution from 10 dB above the noise of theengine; or engines, particularly the combustion chamber and the turbines that are emitting
broadband sounds between 1500 Hz and 5000 Hz. At this stage, one cannot attribute these frequency bands to a defined source but to the whole (nacelle - components - engines).

Fig. 7: Cumulate noise levels (1/3-octave bands).

Fig. 8: The dominant frequency bands.
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The pure dominant frequencies emitted by sources, corrected for the Doppler effect describedabove were evaluated for speeds of approaches between 56 m/s to 150 m/s. These are the frequencies 800 Hz, 1000 Hz, 1142 Hz and 3500 Hz. Figure 9 shows these frequencies. It should be
noted that the frequencies 800 Hz, 1000 Hz and 1142 Hz are almost equal in energy compared to3500 Hz. Their maximum intensities and their width at half height are: 800 Hz (77 dB, 32 Hz),
1000 Hz (75 dB, 38 Hz), 1142 Hz (79 Hz, 38 Hz) and 3500 Hz (62 dB, 34 Hz). The 1-kHz frequency observed was highlighted by Cremezi (2000) and Cremezi and Legros (2000) by modeling the propagation of noise emitted by aircraft in a complex and turbulent atmosphere. The
study by Miyara et al. (2001) has also highlighted the frequency of 1 kHz during aircraft overflights.

Fig. 9: Spectral analysis showing dominant frequencies.
The observed frequencies in our experiments, not recovered by Cremezi calculations, are dueto the fact that the engine was ignored or unknown in its modeling. A method of re-trajectory
simulation has subsequently been used by Cremezi to determine noise levels and the associatedspectra. Among the difficulties, resolution required a large number of approximations simplifying the overall problem. Cremezi confirmed that the model parameters are still quite sensitive,
and interference generated by turbulence may introduce significant differences with the experimental results. Moreover, the absence of the directivity of the source in motion is an additionaldifficulty when making approximate calculations. The experimental results presented in this article, will certainly provide additional useful information, thus placing the results of theoreticalmodels in context. Theoretical approach has difficulties due, in part, to simplifying approximations. Experimental approach is handicapped by the lack of data from the black box of each aircraft. It can therefore be objectively argued that the combination of the two approaches contributes to a better understanding of emitted frequency problem.
The frequency of 3500 Hz is characteristic of the fan noise. This noise, known to aircraft engine manufacturers, accounts for up to 7% of the noise on approach. However, we have no explanation for the observed frequency of 1142 Hz. It could originate from the engines. Finally,certain frequencies were not observed. They correspond to the low frequency noise (50 Hz - 500Hz), which may occur under certain weather conditions and engine operation. They result from303

the mixing of hot jet at the exit reactors (severe turbulence). The frequency of 63 Hz, observedby Miyara et al. was not found in this study, due to limitations of our system of measurements at
frequencies below 200 Hz. Regarding noise in the environment, it is essential to remember thatthe observed frequency bands 630 Hz, 800 Hz, 1000 Hz, 1250 Hz and 1600 Hz and 800 Hz frequency pure, 1000 Hz, 1142 Hz, and 3500 Hz, are emitted during phases of approach and arecharacterized by the highest noise levels. This may steer some research on the nuisance of aircraft noise around airports and assesses the relevant areas to be developed taking into account thetype of aircraft and flights, while preserving the performance gained for flight safety and useful
lives of the engines.
On the one hand, broadband noise arises from the combustion chamber during the combustion process, from the turbulence in the jet of engines and from air flow around the aircraft nacelle. On the other hand, tonal components occurring between 1000 and 7000 Hz are particularly
emitted by the turbine and the compressor for the turbojet engines. In addition, the latter can begenerated by flows over cavities such as the landing gear box and flows on the level of the flaps.
Also, it has been shown that from time to time the tonal components do not appear in a narrowfrequency region but in a large interval of the spectrum. It can occur when tonal components
have very close values or when the source frequencies have undergone fast changes around an
average frequency. This phenomenon is significant for aircraft manufacturers because of active
and passive controls implementation. Nevertheless, it has no meaning for the psycho-acoustic
community because human hearing is often unable to distinguish close frequencies. In addition,even a clear directivity towards the front of aircraft, time-frequency spectra recorded from aircraft approaches did not show discrete tones due to rotational speed of the engine axis.
Aircraft Time of Flight
The study of aircraft noise in the environment supposes knowledge of the relevant objectives
and tools for characterizing its quality. For a long time, a lot of work has been directed towards
statistical indicators (Nelson, 1987; CEC, 1999; Koppert, 2000). Many authors continue to suggest indicators even if Lden is nowadays considered as a reference. Literature review shows differing visions which confirm the continuation of research in this field. Impact of aircraft noisearound airports is often associated with the duration of exposure to noise events. Thus, analysisof the time of flight (TOF) completes those indices in the context of an overall vision, whichmust implicitly take into account air traffic morphology. For one-year measurements, we therefore determined classes corresponding to the TOF, by time intervals. These classes are divided
into five: 1. C1 (TOF is between 20 and 30 sec), 2. C2 (31 - 60 sec), 3. C3 (61 - 90 sec), 4. C4(91 - 120 sec) and 5. C5 (121 - 125 sec) shown in Figure 10 which TOF varies from 20 s to 125 s
for all selected flights. 88% of the TOF are between 31 and 90 sec (51.6% between 31 and 60 s;
36.3% between 61 and 90 s). Raney and Cawthorn (1979) established an average TOF of 60 s,
and Cremezi (2000) gave an average time between 40 and 50 s.
Noise levels represented by the index SEL ranged from 54 dB (A) to 109.3 dB (A). 2% of
approaches (SEL varying from 62 to 101 dB (A)) have not been accepted (over flight, passagesof light vehicles, heavy agricultural or construction sites …) for social and economic activities
around the airport.
We calculated, in turn, the full width at half maximum FWHM as an index of each aircraft
acoustic signature to explore future opportunities allowing to progress in the field of the indicators, which objectify the environmental impact of aircraft noise around airports. Thus,
304

Fig. 10: Percentage of classes related to the aircraft over flight duration.
FWHM is an expression of the extent of a function, given by the difference between the two extreme values of the independent variable, at which the dependent variable is equal to half of its
maximum value. It also can be used to estimate the width of a spectral line. A first assumption
supposes that the distribution, which makes it possible to model the signal, is of Gaussian type.
The scientific literature from the solid state physics through spectroscopy and astrophysics
evokes the notion of FWHM. The common thread in all those disciplines is the profile of the distribution (for example, crystallite size, flight time, angular size of stars, etc.) (Warenghem et al.
(1978), Fried (1994)).
Indeed, a signature must have a theoretical sound profile characterized by its width at halfheight characterizing its energy. As soon as the constructive interferences mentioned are present,
we cannot logically attribute the total signature to one aircraft noise. The study of changes in
FWHM gives a clear idea of the energy characteristics for each aircraft passage. FWHM is madeaccording to the following three steps: 1. Calculation of the maximum measured value during thepassage of each aircraft divided by two (Imax / 2). 2. Identification of the two instants t1 and t2
where we find Imax / 2 sides of the maximum of this passage; 3. Calculating the width at halfheight FWHM-t2 = t1. In our case, the FWHM values are between 7 s and 70 s, with an averageof 31 s.

Fig. 11: Comparison between the numbers of events occurring during the night by 24 hour.
NE24h(90 dB(A) < Nmax) = 0.1 % and NE22h-6h(90 dB(A) < Nmax) = 0.05 %.
305

We also analyzed the following noise indices: Leq24h, Leq6h-18h, Leq18h-22h, Leq22h-6h, LDN,
LDEN, SEL, and DEN LDNL depending on the day of the week. Three groups can be distinguished: SEL whose levels are distributed around 100 dB (A), [DEN, LDNL] around 75 dB (A)and the group [Leq24h, Leq6h-18h, Leq18h-22h, Leq22h-6h, LDN, LDEN] whose levels are between 48
and 56 dB (A). The problem is to define the upper and lower limits for each of the indices andtheir ability to objectify the impact of air traffic on the environment. This result confirms that wemust guide the analysis of indices to study each event such as the aircraft passage. It would benecessary to introduce the type of aircraft and flights. Thus, for night flights (22h-6h), we have
performed an analysis of events (maximum levels produced by the passage of each aircraft) representing 15.1% of the total number of aircraft because people neighboring the airport are sensitive to those flights. The effect on health has already been demonstrated in the past. In this paper,
we classified noise levels and we compared them to their corresponding period of 24 h. Events
producing maximum noise levels belonging to a specified interval were recorded. Figure 11
shows the percentages of the numbers of events (NE). 0.1% of events whose maximum levels areabove 90 dB (A) for periods of 24h and 0.05% for periods of night (respectively 8 and 1 aircraft)are not represented.
The analysis shows that the majority of NE is situated between 50 and 80 dB(A). Almost
every percentages of night are slightly higher than those obtained during the 24 h except for
those, which are located in the interval 60-70 dB(A). The differences vary from 1 to 3%.
In conclusion, we have fewer aircraft at night but that generate maximum levels comparable
to those of 24 h or even in days. This finding could lead policy makers, managers of airports, airlines … to recommend that pilots use approach procedures, which generate less noise, in particular, at night.
To broaden the scope of this analysis, we reviewed in the next paragraph quiet and noisy periods. In addition, before examining the quiet and noisy periods, we made an initial separationaccording to the time and number of aircraft on approach. The traffic distribution providing additional information useful in interpreting the results is: 61% of aircraft approaching between 6h
and 18h, 24% between 18h and 22h, and 15% between 22h and 6h. This traffic distribution certainly has an effect on the analysis of the noisy and quiet times of the day. The analysis of thenoisiest and quietest periods was carried out every 30 minutes; they are defined in the following
manner: first, we define a cumulative duration of one minute corresponding to the time step andcalculate levels minute by minute. Then, for each 30 minutes the highest and lowest values arecompared to background noise. We show in Fig. 12 periods and percentages corresponding to thelowest observed levels. 60% of the lowest levels are achieved during the period between 0h and
2h, which is followed by the 2h-4h period (26%), then 14% for 4h-6h and 22h-0h periods.

Fig. 12: The quietest periods.

Fig. 13: The noisiest periods.
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The silent period is between 0h-2h. The average difference between the levels recorded during this period and L95 (~ 41 dB (A)) is 1.7 dB (A). In addition, the period 14h-16h represents
49% of the highest noise levels of the day (Figure 13). It is followed by the period 10h-12h(25%), and then by periods 8h-10h and 16h-18h (respectively 15% and 11%). The average difference between the levels recorded during the noisiest and L95 is 36.8 dB (A). This gap is almost
22 times higher than for the quietest period.
The average noise level of the most silent period is 45 dB (A) and 77.3 dB (A) for the mostnoisy. Periods 6h-8h, 12h-14h and 18h-22h do not appear, according to the definition, among thenoisiest or the quietest period of a day. These periods are characterized by intermediate noiselevels between the two provided previously. When one counts the number of hours corresponding to the quieter periods (S), intermediate (I), and most noisy (B), there is an equi-partition, i.e.
8 hours for each type of period. Clearly, in terms of sound annoyances, periods (I), or a part,
might be confused with periods (B). It is known that the impacted ground surface decreases with
the noise level. Thus, it is essential that this information be considered in the analysis of eventsas it gives objective knowledge of a sound situation.
Conclusion
Time-frequency analysis (Wigner-Ville distribution) has been applied to process experimental data. Computing and interpreting time-frequency distributions of aircraft noise is quite a long
operation. A procedure was tested using one-dimensional variables. This work is directed towards the search of the dominant pure frequencies and the aircraft passage times when approaching the Saint-Exupéry Lyon International Airport. This research provides aircraft manufacturers
with frequencies emitted during approaches, which should be treated at the source. It also allows
the diagnosis of frequencies responsible for the noise annoyances surrounding the airport.
Measurements of aircraft noise in the vicinity of the airport were conducted during one year.
They served as a reference year because of changes in the airport infrastructure project including
the two new runways, planned for 2015, renewal of fleets conditioned by the evolution of aircraft
certification and regulation, etc. A geometric study of frequency emission according to the angles and indicated aircraft speeds on approach has been finalized. Doppler corrections have been
made and diagnosis of the effects of destructive interference examined. Dominant pure frequencies were observed and their analysis reveals an agreement with theoretical works. Their origin
could come from the airframe of the aircraft (fuselage, wing, drift, landing gear and high lift devices), which upon landing with an engine rpm at idle up to 55% for some aircraft can have ahigher contribution from 10 dB above the noise of the engine. Next, we analyzed the noise levels
over time and aircraft passage times. We identified the quietest and noisiest periods.
The relative sensitivity of acoustical indices confirms the need for further research including
the morphology of air traffic, the type of aircraft and flight configurations. One must take account fleet renewal, introduction of engines and cells’ breakthrough technologies, etc. We must
focus more on analysis of individual temporal events considering the impacted ground surfaces.
This is a result of environmental situations conditioned by the emergence of new aviation technologies. Experimental results presented in this paper could validate and extend calculation
methods. They provide additional useful information placing the results of theoretical models incontext because of strong approximations for calculation needs. This work has been performed
for turbojet aircraft with the aim of analyzing the frequency characterized by higher levels. Nevertheless, it is clear that additional research is needed to achieve this goal by performing noise307analysis of commercial propeller aircraft. Moreover, it is certain that one of the main objectives
supplementing this work is the addition of the aircraft typology (type, model, dimensions, engine
and their locations, thrust, EPR, fuel consumption, flight configurations, performance, etc.).
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Biscainho L.W.P., Diniz P.S.R. and Esquef P.A.A. (2001). ARMA processes in sub-bands withapplication to audio restoration, Proceedings of IEEE International Symposium on Circuits andSystems, Sidney, 2 (2001), pp. 157–160.
Boashash B. (2003). Heuristic Formulation of TFDs. in Time Frequency Signal Analysis andProcessing: A Comprehensive Reference, ed. B. Boashash, Elsevier, Oxford, pp. 29-57.
Commission of the European Communities (CEC, 1999). DG XI. Working group on noise indicators. Position paper on EU noise Indicators.
Cohen L. (1995), Time-Frequency Analysis, Prentice Hall, New Jersey.
Cremezi C. and Legros C. (2000). Application de la méthode GF-PE à une source élevée enmouvement. In Actes du 5ème congrès français d’acoustique, EPFL, Lausanne (Suisse).
Cremezi C. (2000). Etude de la propagation du son dans l’atmosphère: application au bruit desavions en phase d’approche et de décollage. Thèse de Doctorat.
Czerwinski R. N. and Jones D. L. (1995). Adaptive cone-kernel time-frequency analysis. IEEE
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European Civil Aviation Conference (ECAC, 1997). Doc 29 (2nd edition).
Ferguson B. (1992). “A ground-based narrow-band passive for estimating the altitude and speedof a propeller-driven aircraft”. Journal of the Acoustical Society of America, 92 (3), 1403-07.
Ferguson B. and Quinn B. (1994). “Application of the short-time Fourier transform and the
Wigner-Ville distribution to the acoustic localization of aircraft”. Journal of the Acoustical Society of America, 96 (2), Part 1, 821-827.
Fried D.L. (1994). Atmospheric turbulence optical effects: understanding the adaptive-optics implications. Adaptive optics for astronomy. NATO-ASI, ed. D. Alloin and J.-M. Mariotti, Kluwer.
Griffin C. (1991). A comparison study on the Wigner and Choi-Williams distributions for detection. Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91, IEEE
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Hlawatsch F., Manickam T.G., Urbanke R.L. and Jones W. (1995). Smoothed pseudo-Wigner
distribution, Choi-Williams distribution, and cone-kernel representation: ambiguity-domain308

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Hubbard H. H. (1994). Aeroacoustics of flight vehicles, vol. 1: Noise sources, Acoustical societyof America, New York
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ICAO (1988). “Recommended method for the calculation of the noise level contours in the vicinity of airports”. Circular 205-AN/1/25. International Civil Aviation Organization. Montreal,
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310

NEW DEVELOPMENTS OF SOFTWARE FOR
A-LINE FAMILY AE SYSTEMSSERGEY ELIZAROV, АNTON BUKATIN, МIKHAIL ROSTOVTSEV
and DENIS TERENTYEVInterunis Ltd., bld. 24 corp 3-4, Myasnitskaya str., Moscow, 101000, Russia
Keywords: acoustic emission software, source location, statistical division criterion, waveletanalysis
Abstract
The universal A-Line software of Interunis is an integral part of acoustic emission (AE)systems and is designed for solving various problems of data processing and visualization, andfor full-scale control of the system hardware component. Simultaneously with development of
new generations of the A-Line AE systems the software has been improved, is based on the upto-date information technologies and programming methods, incorporating real AE applications
experience of internal and external users of the A-Line family AE systems. This paper briefly
describes the significant functional features of program features implemented recently; a methodof approximate location of AE sources using a free-form sensor array on surfaces of thin-wallvessels, information statistical criterion for division of AE sources according to the type andmethod for evaluating the distance from a sensor to an estimated source on the basis of waveletanalysis of AE impulse taking into account the dispersion dependencies of group velocities of
Lamb waves.
Approximate Location by Free-form Sensor Array (ALFS).
One of principal disadvantages of the classical methods of AE source location on thin-wallvessels is their sensitivity to distortions of the wave front formed by several Lamb waves
possessing the propagation velocity dispersion. Often, the source coordinates are computed withan error of the difficult-to-define magnitude. The new method attempts to increase confidence
level and accuracy of the AE signal source location by taking into account an influence ofvarious factors on the signal propagation velocity and AE wave front distortion.
The backbone of the suggested method is an assignment of a range (or several ranges) of
velocities containing the velocities of basic modes of elastic waves on the structure. This velocity
range can be additionally extended for taking into account the wave front distortion due to thetest object design features (for example, hatches, branch pipes, welds, etc.) and the anisotropic
properties of structure material. The AE source location is calculated on the principle that thewave velocity, whose the front arrival time is recorded by each of the sensors, lies within theselected velocity range.
When describing this location method, it is convenient to operate with a concept of wave
“packet”: a set of the AE signals recorded by different sensors mounted on the test object. The
packet’s critical feature is its duration, i.e. the time interval between the first and last impulse inchronological sequence as its components. If the packet duration is less than the characteristic
magnitude dependent on the object geometric parameters and the evaluation velocity of elastic
waves propagation in the object material, it can be expected that the whole set of impulses has
been emitted during a single AE event. The ALFS method consists in calculating for each such
J. Acoustic Emission, 26 (2008)

311© 2008 Acoustic Emission Groupwave packet the object region comprising all possible points where the event could be capable togenerate this wave packet at the given arrangement of sensors and the given velocity range ofelastic wave propagation over the object surface. For this purpose the object surface is modeledwith a discrete mesh of finite number of points. For each wave packet, the ALFS method
calculates all nodes of this mesh, near which the given AE event could theoretically occur. The
list of all such nodes is the location region description for the given wave packet.
The location is approximate because for each packet not one point on the object is indicated,but a region. However, the calculation of a single location point is impossible without an error.
This approximate method is more accurate than the point location, since the desired source of
event is within the obtained region with a high probability. Intersection of the location regions
obtained for the different packets allows the source to be located more precisely, provided thatthe several packets of signals have been received from the same source as a result of the several
discrete AE events. When the object regions are displayed in A-Line program the overlay
sections of location regions are different in color depending on how many regions are overlappedon this section.
The size of the region thus determined can be reduced and so the accuracy of source locating
can be additionally increased by the following several methods:• Proved reduction of the selected range of elastic wave velocities, for instance, at the cost of
the more accurate measurement of velocities of the wave modes used and exclusion of somemodes from the consideration due to impossible recording of their arrival to the sensors.
• Research of the intersection of regions computed for different wave packets. If the regions
are sufficiently strongly intersected, that is the regions intersection area exceeds the definite
percentage of their combination, appropriate wave packets can be assigned presumably to thesame source, while the intersection of regions found for wave packets relevant to this source
can be assumed as the source location region.
• Selection of the optimum sensor number for calculating each region of the source location,i.e. the usage of data from the optimum sensor number.
• For some sensors the velocity ranges of acoustic waves can be specified individually, taking
into account that the certain wave modes can, or on the contrary, cannot arrive to one or othersensor.
To obtain the best results, the operator can change all the above-mentioned parameters of themethod on-line. Figure 1 shows results of ALFS implementation compared to the classicaltriangulation location method on the spherical tank surface.
••••

We summarize the basic advantages of ALFS over the classic point location methods:
The method does not impose restrictions on the sensors’ geometrical order and arrangementon the test object.
Specified is the velocity range of elastic wave propagation on the test object surface that ismore realistic than its assignment with the constant or the selection of scalar values.
The location is carried out by a random number of any impulses in the wave packet (fromone or more), and the maximum number of impulses used for location is specified as analgorithm parameter.
The location is carried out with regard to the real geometric shape of an object. This
minimizes the distortions and errors, but requires the more accurate description of the object
geometric shape and assignment of all relevant parameters.
312

Fig. 1 Results of ALFS implementation (two lower pictures) compared to the classical triangulation location method (two upper pictures) on the spherical tank surface.
••

The location result for one AE wave packet is a region on the object wherein the source ispositioned with a high probability.
The location result for a number of wave packets is a set of the regions. The intersection ofsuch regions with each other makes possible to define a small section on the object wherein
the source is positioned with an even higher probability.
The authors have obtained the Russian Federation patent on the above method.

Information Statistical AE-Criterion
By virtue of a number of the AE characteristic features both of the random process and theirreversible process, the statistical methods of AE information processing appear to be the mostacceptable. Such methods can reveal the most typical regularities of process development, on the
one hand, and average the influence of statistical spikes, on the other hand. When the behavior of
not one attribute of the AE test data is analyzed, but two or three as a whole, we can increase theefficiency of identifying phenomena occurring within the test area. From the classic thresholdprinciple of recording, the AE data are not a continuous signal but a sequence of values of
impulse parameters (amplitude A, energy E, duration Dur, etc.), which are obtained directly
during the experiment or tests and contain information about a process or a set of processes
generating AE. Statistical consideration of these parameters enables to define the criterion of
separation of the underlying processes; in particular, we utilize a significant relative change inthe statistic characteristics of the impulse parameter distributions. In other words, the moment oftransition from one stage of damage development in the test region to another stage can be
determined by the moment of violation of AE data flow. To provide access to the information
about the set of processes occurring in the test object, the whole sequence of AE impulses313

arriving channel-by-channel is divided into samples. In this paper, we have used the samplingwith the fixed number of impulses n. The critical characteristic of the histogram obtained is its
pattern. Thus, at the stage of dispersed accumulation of micro-cracks the AE impulse flow isusually considered as the Poisson flow, and the main crack growth is accompanied by thedeviation from this distribution [1]. One more analysis of the amplitude distribution pattern
forms the basis of the widely currently known “Ib-value” criterion [2].
Recently, the Scientific & Training Center of Welding and Control and Interunis haveapplied one more evaluating characteristic, the distribution entropy Sн [3]. This allows forevaluating the degree of disordering in the histogram under consideration. The evaluation of dataobtained during the model experiments, and also during the industrial testing has shown that therelationship FP = Pmod(SнP) can be selected as an example of the identification parameter. ThisFP function is dependence of the histogram distribution characteristic Pmod, representing themost probable value of the random parameter P in the sample, on the relative entropy of this
distribution SнP. The computational results for different recording channels are plotted on a chartin different colors in the indicated coordinates, whereby the diagnosis diagram is formed. The
source can be identified by the group position of points in the diagram field. In Fig. 2a, b and cseveral examples of resultant diagnostic diagrams for the amplitude distribution are presented:
for the loading of a defect-free object (Fig. 2a), loading of an object with active AE source (Fig.
2b) and cyclic loading of standard specimen loaded to failure (Fig. 2c). In Fig. 2a, registered AE
signals concentrated into zone I. This zone is defined by entropy value SнA interval [0.3…0.7],
and most probable amplitude value (Amod) increases threshold level on [0…5] dB. Actually, thesignals fulfilling this zone appear to be noise of loading object, distributed exponentially. Zone II(Fig. 2b) stands out against a background of zone I by Amod and entropy value, taking place in
SнA interval [0.5…0.8]. This zone appears because the signals with increased amplitude begin toprevail. Entropy in turn increases because the dispersion of amplitude becomes larger.
Zone III corresponds to the leakage signals, defines by SнA interval [0.0…0.3]. In Fig. 2czone III appearance explains by plastic deformation following by the large number of low
amplitude impulses. Besides zone IV, the last one, corresponds to the critical crack appearance
before specimen failure. Finally, note that in all cases of the process-type identification(accumulation of dispersed micro-damages, growth of main cracks, leak) by the cumulative
attributes (AE signal amplitude mode; entropy of amplitude probability distribution), no
preliminary filtration of data has been carried out.
Some Aspects of Wavelet-Analysis Software A-Line OSC Processing
In this part of paper one version of the application of continuous wavelet transform forevaluating the distance to an AE signal source is described. This is implemented in thespecialized program for waveform processing. The AE signal in-time smearing caused by thegroup velocity dispersion which noticeably reduces the accuracy of arrival time determination,
and as consequence, the AE sources coordinates, has resulted in the development of various
methods for the additional processing of signal waveforms. One of the methods of such analysis
is the use of the signal time-frequency transform, and the wavelet-spectrograms rather long-usedin AE are the most known and convenient among them [4]. To visualize the waveletspectrograms, the use is made of the traditional color diagram on which the X-axis corresponds
to time, and Y-axis corresponds to frequency, while the energy density distribution is displayedby the different color (or black-white) tones: violet and dark blue (white) tones – minimum
density, red and yellow (black) – maximum.
314

Fig. 2a Diagnostic diagram for the defect-free object loading.

Fig. 2b Diagnostic diagram for loading of an object with active AE source.

Fig. 2c Diagnosis diagram for cyclic loading of standard specimen loaded to failure.
315

In a separate window calculated and displayed are the dispersion curves for the specified values of object thickness, and CL and CT are velocities of the volume longitudinal and transverse
waves in the object material. Just here it is possible to select the desired mode of Lamb waves(usually A0 and S0) for calculating and displaying, and to indicate the magnitudes of soundspeed in liquid, possibly filling up the object. To suit user’s convenience, the program offers thetabulated velocity values for a number of hard materials and liquids. From the plot containing thedispersion curves V(f), we can obtain the dependences of the arrival times of signal different frequency
components to the AE sensor located at the distance L from the source [5].

Fig. 3 Wavelet-spectrogram window with evaluation results for L, t0 and d and results of waveletspectrogram section plot.
Figure 3 presents an example of the wavelet-spectrogram window with the superimposed
picture of dispersion curves for the detected waveform. The imposition parameter values L and t0 (t0– time of signal emission relative to the time of waveform onset) are selected on-line by moving
the scaling markers (two light-gray verticals) with the mouse in the wavelet-spectrogram316

window. The left marker corresponds to the fastest Lamb-wave arrival (mode S0 at ƒ→0), andthe right one to the arrival of high-frequency components (all modes at ƒ→∞). The horizontal
scaling marker allows adjusting obtained curves onto frequency scale, so the wall thickness dvalue may be corrected if it is not known in advance. As a rule, when selecting the parameters, it isconvenient to be guided by the characteristic frequency in the wavelet-spectrogram, in which the valuesof group velocities of modes A0, S0 and A1 are equal. Under marker movement at the upper right of
the appropriate window displayed are: t0, L and d, which correspond to the current variant of thedispersion curves superimposed. Moreover, in some instances, it is useful to know the time ofwave arrival through the liquid inside the object, and this time is displayed by the appropriatelycolored vertical line.
When it is difficult to carry out the correct superimposition of dispersion curves (in case ofhigh noise at the small distance between the source and AE sensor or if a great number of themodes in signal is present [6]), we can apply the wavelet-transform for making more precise thearrival time by determining the energy density maximum in the spectrogram on the preselected
frequency. The point of time conforming to this maximum can be applied for AE source location
instead of the usually used threshold intersection time or the time of signal amplitude maximum[5, 6]. The A-Line OSC Processing program offers the plotting of the movable wavelet-spectrogram
sections by means of the constant frequency and constant time lines, and the appropriate envelope
spectra complete the picture in the waveform windows as shown in Fig. 3. The application of thenormal wave velocity on the maximum-conformable frequency allows the AE-source
coordinates determination error to be reduced.
References
1. Kuksenko V.: Model for the transition from micro- to macro-fracture, Mechanics of Jointed
and Faulted Rock, 1995, Balkema, Rotterdam. p. 997.
2. Shiotani T., Nakanishi Y., Luo X., Haya H.: Damage Assessment in Railway Sub-Structures
Deteriorated Using AE Technique, Proc. 26th EWGAE Conf. Berlin. 2004, pp. 225-232.
3. Ship V., Muravin G., Samoilova I., Dorokhova E.: The application of complex information
parameter to acoustic emission for diagnostic during the stage of fracture, Nondestructive
Testing Evaluation, 13, 1997, 57-71.
4. Suzuki H., Kinjo T., Hayashi Y., Takemoto M., Ono K.: Appendix by Hayashi Y., Wavelet
Transform of Acoustic Emission Signals, Journal of Acoustic Emission, 14 (2), 1996, 69-84.
5. Hamstad M., O’Gallagher A, Gary J.: Examination of the Application of a Wavelet
Transform to Acoustic Emission Signals, Journal of Acoustic Emission, 20, 2002, 39-81.
6. Cole P., Miller S.: Use of Advanced A.E. Analysis for Source Discrimination Using
Captured Waveforms, 3rd MENDT - Middle East Nondestructive Testing Conference &Exhibition, 27-30 Nov. 2005, Bahrain, Manama.
http://www.ndt.net/article/mendt2005/pdf/30.pdf.

317

APPLICATION OF ACOUSTIC EMISSION IN OPTIMIZING THE
DESIGN OF NEW GENERATION CASTINGS OF HIGH-VOLTAGE
ELECTRIC DEVICES
JAN PŁOWIEC1,2, WOJCIECH L. SPYCHALSKI1,2, HUBERT MATYSIAK1, and
KRZYSZTOF J. KURZYDŁOWSKI1
1
Warsaw University of Technology, Faculty of Materials Science and Engineering, Materials
Design Division, 141 Woloska St, 02-507 Warsaw, Poland;2
Materials Engineers Group Ltd,, Warsaw, Poland
Keywords: sand casting, pressure test, Al alloys, optimization, design verification, FEM
Abstract
The technological progress in power systems requires the application of new solutions thatwill meet higher specifications than the currently used, in all parts of equipment design. This also
refers to the external castings of power devices, especially those operating over a long time underhigh-pressure SF6 dielectric gas. Increased requirements include a 20% increase of the SF6 operating pressure and the same increase of the destructive test pressure, and simultaneous reductionof the casting weight. In this paper we present test results of casting materials and strength calculations for the implemented changes in design. The optimization and design verification activities included acoustic emission (AE) measurements performed during a hydraulic pressure test.
The results obtained allowed to determine the locations and pressure values, at which the plasticstrain began to occur, and the position of the final failure.
Introduction
Technological progress in power engineering has been observed over recent years and requires the implementation of solutions meeting higher requirements than those used at present inall devices and elements in this area, taking into account also the external castings of these devices, which are operated under multi-year influence of high-pressure SF6 gas as the best available dielectric. Higher requirements consist in 20% increases in working, control and destructive
SF6 pressures, as well as the reduced weight of castings [1]. Increased requirements in relation to
castings can be met through the implementation of structural modifications and new manufacturing processes, with simultaneous increase in their strength and tightness adapted to higher pressure. Users of the castings demand also the delivery by manufacturers in ready-to-assemble
commercial condition, i.e. as fully machined castings accompanied with the certificate confirming their strength, gas tightness matched to higher pressure, gas permeability checked with helium and resistance to control and destroying pressure checked in hydraulic test. Correct functioning of such castings requires appropriate strength and gas tightness to keep required working
pressure of filling medium for 30 years. An example of such a casting is shown in Fig. 1.
To meet such rigorous requirements, a series of examinations was carried out during structural and manufacturing-process optimization, including examination of microstructure, material
properties, modeling process and behavior in working conditions [2]. Hydraulic pressure tests
with simultaneous recording of AE signals on the basis of the accepted criteria were some of thesignificant examinations during optimization and checking of the finished products. The AE examination was carried out during increasing and maintaining the pressure until the part was
J. Acoustic Emission, 26 (2008)318© 2008 Acoustic Emission Groupdestroyed. Vallen Systeme AMSY-5 multi-channel signal recording system with R2007.0904
software was used for AE examination. Because of non-magnetic properties of the cast material,
the sensors were attached with assembly tapes. Figure 2 shows the arrangement of the sensors on
the casting [3]. The examination was aimed at correlating AE results with test pressure value,
and also at locating the source locations, where because of the complex shape of the element thecrack could develop the earliest.

Fig. 1. Example of an aluminum casting, an element of high-voltage electric device.

Fig. 2. Example of sensor arrangement on examined T-connection.
Results
Figure 3 shows AE signal amplitude vs. time, and reflects the increasing AE activity withpressure during a pressure test [4, 5]. The locations, at which the AE signal sources occur
initially, are the local stress concentrations resulting from the cast geometry. The pressure
applied during examination was much higher than the working pressure of 3 bar. The maximum
pressure achieved during examination was 38 bar. Thanks to holding the pressure at various
levels, the occurrence of Felicity effect was revealed, determining the level of structural damage,at the pressure amounting to 25 bar for one of these casts.
319

Example of such an effect can be seen on Fig. 3, where the first occurrence of Felicity effectis marked. A large number of the AE counts can be seen within area 2 as compared to area 1.
Although the AE signals from both groups were recorded during the same pressure-hold level of
25 bar, higher AE intensity (higher number of counts of higher amplitude, energy or longer
duration) and activity (higher number of hits, counts and signals of higher energy) during thesecond pressure hold at the same level shows the existence of significant structural damage. This
example indicates the occurrence of Felicity effect, which determines the presence of localized
plastic deformation.
Subsequent analysis fully confirmed the occurrence of the Felicity effect (Fig. 4). Already at
25 bar, significant structural damages were found in the material, determining its possible use.

Fig. 3. Amplitudes of the AE signals recorded during the time when the pressure was held during
the pressure test performed on a T-connection casting, including the pressure change pattern andthe Felicity effect analysis.
Other parameters used to evaluate the damages presented in this article are RA value and themean frequency for an AE hit, Fa, defined in JCMS-III B5706 [6]. The RA value is the ratio ofrise time to amplitude, expressed in ms/V [7]. The Fa value is the ratio of AE counts to the duration. By using these two indices we can determine whether the nature of a crack is a result of
tensile or shear forces. Ohtsu and coworkers [8] linked the Fa vs. RA relation to the tensile/shear
nature of concrete cracks, but recently Takuma [9] used these indices successfully to evaluate thedegree of tool wear, which demonstrates that these parameters can be successfully used for otherapplications [10].
320Fig. 4. The pressure during the pressure test performed on a T-connection vs. the AE counts,
showing the Felicity effect. Insert shows the start of AE activity at 21 bar (Felicity ratio of 0.84).

Fig. 5. Relations of Fa frequency indexes and RA values.
In connection with the above, the next analysis was aimed at comparing the relations of the
Fa mean frequency and RA values. The relation between these indices is shown in Fig. 5. There
321

are two areas isolated in the figure. The first area, B1, is characterized by high Fa frequency values, and low RA values. The other, B2, presents low Fa values, and RA values are higher than in
B1 area.

Fig. 6. Total changes of AE signal amplitudes recorded during pressure hold-ups in the course of
the pressure test of the tee.
We can assume that the AE signals coming from the cracks have high amplitudes at very low
rise time values. B1 area isolated in Fig. 5 is distinguished by such AE signals. While the AE
signals that come from crack friction show low amplitude with high values of the rise time. The
signals of such parameters are grouped in B2 area.
Figure 6 presents the total AE signal amplitude changes recorded during pressure changes in
the course of the pressure test of the tee. The signals of B1 area, resulting from the cracks andfeaturing high Fa frequency value and low RA value, marked in Fig. 6 by a green line, are thedominant signals during all the pressure holds. B2 signals, coming from the crack friction andfeaturing low Fa values and higher than B1 area RA values, marked in Fig. 6 by a red line, are
not recorded as intensively as the crack signals, and appear in the early part of the pressure holdstages. It is worth noting the sudden rise of B1-type signals (from the cracks) after exceeding thepressure of 30 bar. Note that typically this rise occurs mainly at a fixed pressure at the level of 35
bar. Figure 7a presents localized events recorded in one of the cast areas with a crack drawn (inred). Compare with the crack visible on Fig. 7b. The localized AE events occurred along thecrack.
It is noteworthy that the location, at which the plastic deformation was located, and at which
the final crack occurred, is only a one of a few locations where stresses determined with the finite elements method [11] are concentrated (Fig. 8). Use of the AE monitoring allowed us toverify the numerical calculations based on assumption of uniform cast material and ideal mapping of the assumed shape.
322

Fig. 7 a) Planar location of the AE events recorded during the entire pressure test of aT-connection in the area of 1, 2, 7 and 8 sensors; b) Picture of the T-connection with attachedsensors and a clearly visible crack at center.

Fig. 8. Reduced stresses at 20-bar pressure with indicated positions of concentrated stresses.
Other examination results, presented on Figs. 9 and 10, show the change of amplitude, signal
duration and AE counts for various test pressure ranges. The ranges 5-10 bar, 10-15 bar and 2530 bar were selected for the presentation. It can be seen that, in initial testing phase within the 510 bar range, the recorded signals were of low amplitude, no higher than 50 dB. Analysis of theircharacter showed that they are caused by plastic deformation. Because the source of the signals
was not found, plastic deformation in this pressure range was assumed in a significant part of thecast material. An analysis of the AE signals occurring in the subsequent pressure range of 10-15
bar shows a distinct increase of amplitude of the recorded signals. We also observed distinct
323

localization of signal sources, which in most cases were located at the central part of the casting,at the place where the cracking occurred in the final phase of the test. The last results in the 2530 bar range (the cracking occurred at 31 bar) showed the further growth of both the signal amplitude and the number of located sources. The amplitude distribution within the 25–30 bar area,
as shown in the Figure 10, reveals very high amplitude signals at the level of 100dB, which obviously proves propagation of the crack.

Fig. 9. Signal duration vs. amplitude. Green: 5-10 bar; Red: 10-15 bar; Blue: 25-30 bar.
It has to be noted that just before the cracking, at the 28-31 bar range, a relatively small number of AE signals were recorded. This low AE activity probably results from the relatively low
ductility of the material. Along with the pressure rise, resulting from plastic deformation of thematerial, there is a decrease in the number of mobile dislocations whose movement is the sourceof the AE signals. Figure 10 also allows one to observe simultaneous rise of the mean amplitudeof the AE signals with the rise of the pressure value. In addition, the rise of AE signal amplitude
ranges can also be observed.
Conclusions
The presented results show that the AE method can be an efficient tool for optimization of
structure and the manufacturing methods of pressure devices. Using the AE measurements during the pressure tests allowed us to determine the particularly strenuous spots and the beginningof plastic deformation in the examined castings. Knowledge of locations and pressure ranges, atwhich the plastic deformation was initiated allowing us to implement the structural changes
aimed at decreasing the stress concentration. No phenomenon of plastic deformation localization
was found after optimization was performed. This is significant for achieving the long-term operation without a failure.
324

Fig. 10. AE counts vs. amplitude. Green: 5-10 bar; Red: 10-15 bar; Blue: 25-30 bar.
Acknowledgement
This work was supported by the Industrial R&D grant 6ZR92006C/06775 of Polish Ministryof Science and Higher Education.
References
1. Kopejtkova D., Molony T., Kobayashi S., and Welch I.M.: A twenty-five-year Review ofExperience with SF6 Gas Insulated Substations CIGRE Session, Paris 30 Aug.-4 Sept. 1992,
paper 23-101.
2. Materials Engineers Group Ltd., Aluminum casts for energetic – Report for WKS Rzeszow,
Warsaw 2007.
3. Materials Engineers Group Ltd. Internal Procedure: Guide for Acoustic Emission Examination of Pressure Vessels, Warsaw 2007.
4. Płowiec J., Spychalski W.L., Matysiak H., Michalski J., Proc. 28th European Conference
Acoustic Emission Testing, Krakow, Poland, 17-19 Sept. 2008, pp. 274-279.
5. Preparation and implementation of high-voltage chambers casting technology for power application – industrial project, cooperation with ZM WSK Rzeszow (grant 6ZR92006C/06775
of Polish Ministry of Science and Higher Education).
6. JCMS-III B5706 (2003), Monitoring method for active cracks in concrete by acoustic emission. Federation of Construction Materials Industries, Japan
7. Ohtsu, M., Isoda T., Tomoda Y.: Acoustic emission techniques standardized for concrete
structures, J. Acoustic Emission, 25, (2007), 21-25.
8. Tsuji N., Uchida M., Okamato T., Ohstu M.: Application of acoustic emission technique toevaluation of cracking in concrete structures, Progress in Acoustic Emission X, JSNDI, 2000,
pp. 189-194
9. Takuma, M. Shinke, N., Nishiura, T. and Akamatu, K., J. Acoustic Emission, 24 (2006) 52-66.
10. Ono K.: Structural integrity evaluation using acoustic emission, J. Acoustic Emission, 25,(2007), 1-20.
11. Zienkiewicz O., Taylor R., The finite element method. 4th ed. McGraw-Hill, London, 1994.
325

CUMULATIVE CONTENTS
J. of Acoustic Emission, Volumes 1 - 26, 1982 - 2008
Volume 1 (1982)
Number 1
Page 1An Acoustic Emission Study of the Intergranular Cracking of AISI 4340 SteelA. Nozue and T. Kishi

Page 7

Acoustic Emission Behavior of a Low Alloy Steel

Page 21

An Acoustic Measurement of Boiling Instabilities in a Solar ReceiverPage 29

A Broadband Acoustic Emission Transducer
Mark B. Moffatt, Theodore J. Mapes and Arthur T. Grodotzke

Page 35

A Simple Tape Recording System for Acoustic Emission

Page 37

Earthquakes as Acoustic Emission - 1980 Izu Peninsula Earthquake ln Particular

Page 45

AE Literature

Page 67
Page 68
Hardy, Jr.

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

P. G. Bentley and A. Plevin
Kiyoo Mogi

T. F. Drouillard

Conferences and Symposia
The Fifth International Acoustic Emission Symposium K. Ono
Third Conference on AE/Microseismic Activity in Geologic Structures and Materials

H. R.

Page 69
Page 72

The Tenth European Working Group on Acoustic Emission
Future Meetings

Number 2
Page 73

Reciprocity and Other Acoustic Emission Transducer Calibration Techniques

Page 81

Production Acoustic Emission Testing of Braze Joint

Page 87
Breckenridge

Acoustic Emission Transducer Calibration by Means of the Seismic Surface Pulse

Page 95

Acoustic Emission Testing of Filament-Wound Pipes under Repeated Loading
Leszek Golaski, Maciej Kumosa and Derek Hull

Page 103

Source Mechanism and Waveform Analysis of Acoustic Emission in Concrete

Masayasu Ohtsu

Page 114

Acoustic Emission in Aircraft Structural Integrity and Maintenance Programs

J. M. Rodgers

Page 121

AE Literature
T. F. Drouillard
Conferences and Symposia
The 23rd Meeting of AEWG
K. OnoThe 24th Meeting of AEWG
W. F. Hartman and J.W. Whittaker
AEWG AWARDS
K. OnoThird National Conference on Acoustic Emission
K. OnoCARP - Sixth Meeting
J. Mitchell

Page 141
Page 144
Page 145
Page 146
Page 147

1

K. OnoRoger Hill

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

Page 148
Page 149

The XIth EWGAE Meeting
B. Audenard
BOOK REVIEWElastic Waves in Solids
Roger Hill

Number 3
Page 151

Testing Fiber Composites with Acouatic Emission Monitoring

Page 165
Vessels

On the SPI/CARP Recommended Practice for Acoustic Emission Testing of Fiberglass Tanks andC. Howard Adams

Page 173

Some Details on the NBS Conical Transducer

Page 179

Low Temperature Behavior of Liquid Couplants used in Acoustic Emission Experiments
J. Baram and J. Avissar

Page 183

Acoustic Emission Behavior of Nickel during Tensile Deformation

Page 191

Instrumented Impact Testing of Structural Fiber-Reinforced Plastic Sheet Materials and theSimultaneous AE Measurements
S. I. Ochiai, K. Q. Lew and J. E. Green

Page 193

On the Sensitivity of the Acoustic Barkhausen/Magnetomechanical Acouatic Emission Effect
A. E. Lord, Jr.

Page 195

AE Literature
Thomas F. Drouillard
Conferences and Symposia
1982 ASNT Spring Conference
K. OnoFifth Internatl Conf. on NDE in the Nuclear Industry
K. OnoThe Institution of Metallurgists Meeting on AE A. P. G. Rose
Review of Progress in Quantitative NDE
K. OnoThe 24th Meeting of AEWG
K. OnoThe Sixth International Acoustic Emission Symposium
Symposium on Structural Faults: Inspection and Repair
First International Sympoaium on AE from Reinforced Composites
Sixth Internatl Conf. on NDE in the Nuclear Industry
BOOK REVIEW
AE in Geotechnical Engineering PracticeRobert M. Koerner
Acoustic Emission
Clinton Heiple
New Products and Services
AE Events of Interest / Letter from the Editor

Page 211
Page 211
Page 211
Page 213
Page 214
Page 215
Page 219
Page 220
Page 220
Page 220
Page 221

Number 4
Page 223

Marvin A. Hamstad

Thomas M. Proctor, Jr.

S.-Y. S. Hsu and K. Ono

In-Flight Acoustic Emission Monitoring of a Wing Attachment Component
S. L. McBride and J. W. Maclachlan

Page 229

Effect of Crack PreAence on In-Flight Airframe Noises in a Wing Attachment Component
S. L. McBride and J. W. Maclachlan

Page 237

Leak Detection Using Acoustic Emission

Page 244

Acoustic Emission from Glass/Polyester Composites; Effect of Fibre Orientation
F. J. Guild, B. Harris and A. J. Willis

Page 251

Changes in Acoustic Emission Peaks in Precipitaion Strengthened Alloya with Heat Treatment
C. R. Heiple and S. H. Carpenter

Page 263

A Miniature Optical Acoustic Emission Transducer
D. C. Emmony, M. W. Godrrey and R. G. White

Page 266

Classification of NDE Waveforms with Autoregressive Models
2

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

Ronald B. Melton

Page 271
Page 294
Page 300
Page 300
Page 301
Page 302
Page 302
Page 302
Page 303
Page 303
Page 303
Page 303
Page I-1

AE Literature
Thomas F. Drouillard
Conferences and Symposia
The l1th Meeting of the European Working Group on Acoustic Emission Roger Hill
ASTM Subcommittee E 7.04 on Acouatic Emiaaion Alan G. Beattie
Meetings of Japanese Committee on Acoustic Emission
The 10th World Conf. on Non-Destructive Testing
Conference on Periodic Inspection of Pressurized Components
1983 ASNT Spring Conference
Symposium; Nondeatructive Methods for Material Property Determination
First International Symposium on AE from Reinforced Composites
Review of Progress in Quantitative NDE
25th Meeting of Acoustic Emission Working Group
BOOK REVIEW
Progress in Acoustic Emission Adrian A. Pollock
ANNOUNCEMENTS
AE Events of Intereat / Letter from the Editor
Index to Volume 1

Volume 2 (1983)
Number 1/2
Page 1
Rotating Machinery Diagnosis With Acoustic Emission Techniques Ichiya Sato, Takao
Yoneyama, Soji Sasaki, Toshitaka Suzuki, Tomoaki Inoue, Tsuguaki Koga and Takashi Watanabe
Page 11

In-Field Experience in Condition Monitoring of Rotating Machinery by Demodulated ResonanceAnalysis
G. Buzzacchi, M. Cartoceti, C. De Michelis and C. Sala

Page 19

Acoustic Emission from Environmental Cracking of a High Strength Titanium Alloy
S. Yuyama, T. Kishi, Y. Hisamatsu and T. Kakimi

Page 29

Detection of Corrosion Fatigue by Acoustic Emission

Page 39

Effect of Overaging on Acoustic Emission Behaviour of 7075-T651 Aluminum During Crack
Growth
S.L. McBride and J.W. Maclachlan

Page 47

AE Source Identification by Frequency Spectral Analysis for an Aircraft Monitoring Application
L. J. Graham and R. K. Elsley

Page 57

A User's Perspoctive of Small Computer-Based Acoustic Emission Equipment

Page 64

Magnetomechanical Acoustic Emission: A Non-Destructive Characterization Technique ofPrecipitation Hardened Steels I. Roman, S. Maharshak and G. Amir

Page 67

Acoustic Emission Couplants I: The ASTM Survey

Page 69

Acoustic Emission Couplants II: Coupling Efficiencies of Assorted Materials
A. G. Beattie, J. A. Baron, R. S. Algera and C. C. Feng

Page 71

AE Analysis During Corrosion, Stress Corrosion Cracking and Corrosion Fatigue Processes
S. Yuyama, T. Kishi and Y. Hisamatsu

Page 96

Acoustic Emission, Principles and Instrumentation

Page 129

AE Literature

Page 143

Conference and Symposia

Thomas F. Drouillard

3

P. Jax and B. Richter

M.A. Hamstad

A.G. Beattie

A.G. Beattie

Page 143
Page 146
Page iPage ii
Number 3
Page 151

The 25th MEETING OF AEWG
A.G. Beattie
AEWG Awards
AEWG XXV
Editorial
S.L. McBride
A Note from the Editor
Kanji Ono
Acoustic Emission Source Kinematics Based on the Moving Dislocation Theory
Masayasu Ohtsu

Page 159

Characterization of Concreto Damages by Acoustic Emission Analysis
Marie-Christine Reymond and Andre Raharinaivo

Page 169

Effects of Solute Distribution on Acoustic Emission Behavior of Solid Solution Alloys
S.-Y. S. Hsu and Kanji Ono

Page 179

Acoustic Emission During Fatigue Crack Growth in 7075-T6 Aluminum at 20°C and 120°C
S.L. McBride and J.W. Maclachlan

Page 187

An Acoustic Pressurementer to Determine In-Situ Soil Properties
Arthur E. Lord, Jr., and Robert M. KoernerPage 191

An Acoustic Emission Data Acquisition and Analysis System Using an Apple II Microcomputer
P.E. Wilson and S.H. Carpenter

Page 195

Acoustic Emissions in Geological Materials

Page 221
Page 239
Page iPage ii

AE Literature Thomas F. Drouillard
Conferences and Symposia
AE Event of Interest
M.A. Hamstad
Editorial
Davis M. Egle/ERRATUM

Number 4
Page 247

Resonance Analysis of Piezoelectric Transducer Elements

Page 261

Acoustic Emission Evaluation of Metal-Elastomer Junctions

Page 267

Partial Discharge Detection in Bushings by an Acoustic Emission Method

Page 272

Acoustic Emission as a Measure of Material Damage under Thermal Cycling
Ryszard Zuchowski and Leszek Korusiewicz

Page 275

Acoustic Emission Sensors

Page 281
Teti

Mechanical Properties and Acoustic Emission in Laser Welded HSLA Steel

Page 289

Acoustic Emission Detection of Crack Initiation during Dynamic Fracture Testing of HighStrength Materials
S. I. Ochiai, M. C. Cheresh and J. E. Green

Page 292
Page 319
Page 319
Page 327

AE Literature T.F. Drouillard
Conferences and Symposia
12th EWGAE Meeting
L.M. Rogers
Cover Photos

Page I-1

Index to Volume 2

Arthur E. Lord, Jr. and Robert M. Koerner

M. Ohtsu and K. Ono
M. Sorel and C. Schepacz
J. Skubis

C.M. Scala

4

G. Dionoro and R.

Volume 3 (1984)
Number 1
Page 1

Acoustic Emission Due to Crack Growth, Crack Face Rubbing and Structural Noise in the CC-130
Hercules Aircraft S. L. Mcbride and J. W. Maclachlan

Page 11

Determination of the Source of Acoustic Emission Generated during the Deformation ofMagnesiumMark Friesel and Steve H. Carpenter

Page 19

Thermal Restoration of Burst Emissions in A533B SteelI. Roman, H. B. Teoh and Kanji Ono

Page 27

A Generalized Theory of Acoustic Emission and Green's Functions in a Half Space
Masayasu Ohtsu and Kanji Ono

Page 41

Acoustic Emission Charachterization of the Mechanical Strength of Sintered SAE-316 StainlessSteel
I. Roman, M. Watad and A. Mittelman

Page 46
Page I
Page ii

AE Literature Thomas F. Drouillard
Editorial S. Vahaviolos
Meeting Calendar

Number 2
Page 51

Description of Compound Parameters of Particle-Filled Thermoplastic Materials by Acoustic
Emission Techniques Jorg Wolters

Page 59

A New Method of Acoustic Emission Transducer Calibration
Masayasu Ohtsu and Kanji Ono With Appendix By F.R. Breckenridge and T. Watanabe

Page 69

Pattern Recognition Analysis of Magneto-Mechanical Acoustic Emission Signals
Masayasu Ohtsu and Kanji Ono

Page 81

An Investigation of the Acoustic Emission Generated during The Deformation of Carbon Steel
Fabricated by Powder Metallurgy Techniques
Yue-Huang Xu, Steve H. Carpenter and Bruce Campbell

Page 90
Page 100
Page 100
Page 104
Page 104
Page 105
Page 106
Page 107
Page iPage ii
Page ii

AE Literature Thomas F. Drouillard
Conferences and Symposia
AEWG-26 (Abstracts)
AEWG-27/AEWG AWARDSAEWG Chairman's Letter Davis M. Egle
CARP-8/JCAE/ASNT Methods Committee
Other Conferences
Book Review Arthur E. Lord, Jr.
AE Events Of Interest
2nd Internat'l AE Conf/EWGAE-13/7th AE Symp.
Ultrasonics 85/ASME Symposium

Number 3
Page 108

Monitoring of Metal Cutting and Grinding Processes by Acoustic Emission

Y. Kakino

Page 118Classification of Acoustic Emission Signals from Deformation Mechanisms in Aluminum Alloys
D. Robert Hay, Roger W.Y. Chan, Douglas Sharp and Khalid J. Siddiqui

Page 130

Effects of Interfacial Segregation on Acoustic Emission Behavior of A533B Steel
H.B. Teoh, Kanji Ono, E. Kobayashi and I. Roman
5

Page 144

Magnetomechanical Acoustic Emission of Ferromagnetic Materials at Low Magnetization Levels(Type I Behavior) May Man Kwan, Kanji Ono and M. Shibata

Page 158

Technical Note
Acoustic Emission during Nickel Electroplating of Copper
I. De Iorio, F. Langella and R. Teti

Page 164
Page 172
Page 172
Page 173
Page 174
Page 117
Page 157
Page 175

AE Literature Thomas F. Drouillard
Conferences and Symposia
EWGAE-13
5th Colloquium on AE D. Schumann and W. MorgnerBook Review
Kanji Ono
Meeting Calendar
Available Books on AE
Cover Photograph

Number 4
Page 176

Measurement, Detection and Analysis of Longitudinal and Transverse AE Waves Emitted Near
A Crack
C. Duytsche, P. Fleischmann and D. Rouby

Page 182

Three-Dimensional Crack Location by Acoustic Emission
C. B. Scruby and G. R. Baldwin

Page 190

Magnetomechanical Acoustic Emission of Ferromagnetic Materials At High Magnetization Levels(Type II Behavior) May Man Kwan, Kanji Ono and M. Shibata

Page 204

Technical Note
A Report on the Pulsed Acoustic Emission Technique Applied to Masonry

Page 212
Page 224
Page 224
Page 226
Page 233
Page 239
Page 242
Page 243
Page 181
Page 189
Page iPage ii
Page ii

AE Literature Thomas F. Drouillard
Conferences and Symposia
AEWG-27 A. G. Beattie
EWGAE-13 (Abstracts)
7th Int'l Ae Symposium Kanji Ono
ASTM A. G. Beattie/PROC. AE Workshop R. H. Jones et al.
ASME Symposium D. A. Dornfeld/4th Conf on AE/MA H. R. Hardy, Jr.
Cover Photograph/Announcements
Meeting Calendar
Available Books On AE
Ae Events Of Interest
Editorial
Kanji Ono
Thomas/Hochwald Award for Dr. Tim Fowler

Page I-1

Index to Volume 3James D. Leaird

Volume 4 (1985)
Number 1
Page 1

Acoustic Emission Monitoring of Flaw Growth in A Graphite-Epoxy Experimental Wing Segment
John Rodgers

Page 9

Acoustic Emission Measurments Using Point-Contact Transducers

Page 19

Relationship of Acoustic Emission to Internal Bond Strength of Wood-Based Composite Panel
Materials
F. C. Beall

Page 31

Laboratory Leak Detection in Gas and Liquid Storage Tanks Using Continuous Wave Acoustic
EmissionA. E. Lord, Jr., R. M. Koerner and R. N. Sands
6

C. B. Scruby

Page 41
Page 61
Page 61
Page 65
Page 66
Page 67
Page 68
Page 40
Page 69
Page 70
Page iPage ii

AE Literature Thomas F. Drouillard
Conferences And Symposia
AEWG-27 (Abstracts)
Kanji Ono
ASTM E-7.04
Alan G. Beattie
EWGAE-14 (Provisional Programme)
WCNDT-11/ Other Conferences
AE in Brazil
Call for Papers (2nd Int'l Symp AE from RP/8th Int'l AE Symp)
EWGAE-14
2nd Internat'l AE Conf
AE Events of Interest
ASME Boiler and Pressure Vessel Code/Activities at AEWG-27

Numbers 2 and 3
Pages S1-S332
Proceedings of the Second International Conference on Acoustic Emission, Oct. 28 - Nov. 1, 1985
S1

Characterizing Fracture Types in Rock/Coal Subjected to Quasi-Static Indentation Using Acoustic
Emission Technique, A. Wahab Khair

S7

Applications of Statistical Inference to Improve an Evaluation of Rockburst Danger in
Underground Coal Mines, Stanislaw Lasocki

S11

Field Determination of Prestress (Existing Stress) in Soil and Rock Masses Using Acoustic
Emission,A. E. Lord, Jr. and R. M. Koerner

S17

Directional Acoustic Emission Activity in Response to Borehole Deformation in Rock Masses,
Robert J. Watters and Amir Soltani

S19

Acoustic Emission during Dissolution of Salt, H. Reginald Hardy, Jr.

S21

Kaiser Experiment in sawcut Rock, J. D. Leaird, J. Dunning and M.E. Miller

S26

Acoustic Emission Technique for Solid Propellant Burn Rate Control, V. Lalitha, S. K. Athithan
and V. N. Krishnamurthy

S30

AE Montioring of Jet Engine Breech Chambers, Nitin Dhond and Davis M. Egle

S32

Acoustic Emission Studies for Detection and Monitoring Incipient Cracks in a Simulated AeroEngine Mount under Fatigue, S. C. Pathak and C. R. L. Murthy

S35

Post-Test Selective Screening of Acoustic Emission Data - How Helpful Is It?, B.C. Dykes

S38

Comparison between Experimentally Detected Surface Motions Due to a Disbonding and
Simulated Waveforms, Shigenori Yuyama, Takuichi Imanaka and Masayasu Ohtsu

S42

Attenuation and Dispersion in AE Waveforms: a Comparison of Theory and Experiment,
R. A. Kline and S. S. Ali

S46

The Effects of Transducers on the Decay of a Diffuse Energy Field,
H. A. L. Dempsey and Davis M. Egle

S50

The Generalized Theory and Source Representations of Acoustic Emission,
Masayasu Ohtsu and Kanji Ono

S54

Experimental Studies of Diffuse Waves for Source Charcterization, Richard L. Weaver
7

S58

Effect on Flaw Location by the Wave Shape of Acoustic Emission Propogating in a Limited
Medium,Yukuan Ma

S62

Applications of Quantitative AE Method; Dynamic Fracture, Materials and Transducer
Characterization,
Wolfgang Sachse and K. Y. Kim

S64

A Case for Acoustic Emission Surveilence of Operating Reactors, William F. Hartman

S69

Characterization of Acoustic Emission Signals Generated by Water Flow Through Intergranular
Stress Corrosion Cracks, T. N. Claytor and D. S. Kupperman

S74

On-Line Acoustic Emission Monitoring of Nuclear Reactor Systems - Status and Future,
P. H. Hutton

S77

Acoustic Leak Detection in Nuclear Power Plants, John W. McElroy

S78

On the Application of Acoustic Emission Analysis to Evaluate the Integrity of Protective Oxide
Coatings,
H. Jonas, D. Stover and R. Hecker

S82

Utilization of Acoustic Emission for Detection, Measurement and Location of Partial Discharges,
Jerzy Skubis, Jerzy Ranachowski and Boguslaw Gronowski

S86

Acoustic Emission Examination of Power Plant Components, G. Tonolini, G. Villa and S. Ghia

S90

Acoustic Emission from Aluminum Alloy 6061 Strengthened by Whisker and Particulate Silicon
Carbide,James R. Kennedy

S94

Acoustic Emission during Phase Transformation in Alloys GCr15 and Fe-32Ni,
B. Q. Zhang, C. K. Yao and R. M. Tian

S98

Acoustic Emission Studies of Structural Relaxation in a Metallic Glass,
G. L. Goswami, P. K. K. Nair, P. Raj and G. P. Tiwari

S102

Effects of Secondary Phases on Acoustic Emission in 316 Stainless Steel and a Nimonic Alloy
PE-16 during Tensile Deformation and Fracture,
Baldev Raj, T. Jayakumar, D. K. Bhattacharya and P. Rodriguez

S106

Burst-Type Behavior of Structural Steels, O. Y. Kwon, I. Roman and Kanji Ono

S111

Acoustic Emission Behavior of an Advanced Aluminum Alloy,
I. Roman, Kanji Ono and C. H. Johnson

S116

Acoustic Emission Produced by the Deformation of Uranium, C. R. Heiple and S. S. Christian

S119

An Investigation of the Acoustic Emission Generated during the Deformation and Fracture of
Molybdenum, J. B. James and S. H. Carpenter

S123

Manufacturing Process Monitoring and Analysis using Acoustic Emission, David A. Dornfeld

S127

Acoustic Emission of Flexible Disk Magnetic Media Systems,
Ming-Kai Tse and Armand F. Lewis

S131

Acoustic Emission Monitoring of Drilling, M. W. Hawman

S132

Vibro-Acoustic Emission - A Conventional Means of Inspection using AE Technology,
8

J. R. Webster and T. J. Holroyd
S134

Weld Penetration Monitoring Using Acoustic Emission, J. Maram and J. Collins

S135

Using Acoustic Emission Measurements to Establish the Quality of Bonding of Fine Wires to
Microchips,
S. H. Carpenter, D. R. Smith and J. H. Armstrong

S137

Real-Time Aircraft Structural Monitoring by Acoustic Emission, S. Y. Chuang

S138

Aircraft Structure Surveillance in-Flight Using Acoustic Emission, P. H. Hutton

S142

In-Flight AE Monitoring, G. G. Martin and I. G. Scott

S147

In-Flight Monitoring for Incipient Cracks in An Aero Engine Mount: An Approach ThroughPattern Recognition, C. R. L. Murthy, M. A. Majeed, S. C. Pathak and A. K. Rao

S151

Acoustic Emission Monitoring of Aircraft Structures, S. L. McBride and J. W. Maclachlan

S155

Real-Time Acoustic Emission Monitoring Requirements For Cold Proof Testing of the USAF
F-111 Aircraft, J. M. Rodgers

S157

Leakage Test by Acoustic Emission Testing (AET) on Flat Bottom Tanks,Peter Tscheliesnig and Heinrich Theiretzbacher

S161

Acoustic Emission of Offshore Structures; Attenuation - Noise - Crack Monitoring,
Steinar Lφvaas

S165

Acoustic Emission Monitoring of a Node in An Off-Shore Platform,
A. B. M. Hoff and M. Arrington

S166

Acoustic Emission Monitoring of a Bellows and Wye Section on a Fluidized Catalytic Cracking
Unit,
Thomas Gandy, Martin Peacock and Bobby Wright

S170

Acoustic Emission Modulus Determination and Source Location in Unidirectional Fibre
Reinforced Polymer Composites, A. M. G. Glennie, T. J. Gulley and J. Summerscales

S174

Acoustic Emission Monitoring of Iosipescu Shear Test On Glass Fibre-Epoxy Composites,
M. K. Sridhar, Iyer Subramaniam, Chandra Ajay and A. K. Singh

S178

Fracture Mechanisms Characterization in Discontinuous Fibre Composites using Acoustic
Emission Amplitudes, J-M. Berthelot

S182

Testing Stress Corrosion of Glass Reinforced Plastic With Acoustic Emission Monitoring,Leszek Golaski and Andrzej Figiel

S186

Analysis of Fatigue Damage in CFR Epoxy Composites by Means of Acoustic Emission: Setting
up a Damage Accumulation Theory, M. Wevers, I. Verpoest, E. Aernoudt and P. De Meester

S191

Characteristics of Acoustic Emission Generated from GFRP during Tensile Test,
Kusuo Yamaguchi, Hirotada Oyaizu, Yasuaki Nagata and Teruo Kishi

S195

Acoustic Emission during Load-Holding and Unload-Reload in Fiberglass-Epoxy Composites,
M. Shiwa, M. Enoki and T. Kishi

S199

Acoustic Emission Monitoring of Composite Damage Occurring under Static and Impact Loading,
D. S. Gardiner and L. H. Pearson

S203

Fatigue Crack Closure Study, Guozhi Lu
9

S207

Amplitude Distribution Analysis of Acoustic Emission during Fatigue Testing of Steels Used in
Offshore Structures, R. Visweswaren, M. Manoharan, G. Jothinathan and O. Prabhakar

S211

AE Monitoring of Corrosion Fatigue Growth; Secondary AE Sources, Christian Thaulow

S215

Slow Strain Rate Stress Corrosion Cracking of Compact Tension Specimen and Measurementwith Acoustic Emission, X. Q. Zhu and J. Z. Xiao

S220

Temperature Dependence of Inclusion-Fracture-Related Acoustic Emissions in 7075-T651
Aluminum,
S. L. McBride and J. Harvey

S224

Asssessment of Fatigue Damage with Acoustic Emission, M. Nabil Bassim

S228

Monitoring the Wood Cutting Process with Acoustic Emission, Richard L. Lemaster and David A.
Dornfeld

S232

Acoustic Emission Characterization of Wood Fiber Hardboard, Henrique L. M. dos Reis

S236

Detection of Western Hemlock Wood in Very Early Stages of Decay using Acoustic Emissions,
Masami Noguchi and Koichi Nishimoto

S240

Application of AE to Mechanical Testing of Wood, Keiichi Sato, Takeshi Okano,Ikuo Asano and Masami Fushitani

S244

Effect of Moisture Conditioning on AE from Particleboard, Frank C. Beall

S247

In-Process Acoustic Emission Monitoring of Laser Welds,
J. W. Whittaker, T. M. Mustaleski and K. D. Nicklas

S251

Monitoring of Thin Welds by Acoustic Emission, G. L. Goswami and P. R. Roy

S255

Defect Detection in Stainless Steel Uranus 45 Tig Welded Joints by Acoustic Emission,
Vincenzo Dal Re, B. Birolo and F. Cipri

S259

Classification of Acoustic Emission Signals Generatied during Welding,Roger W. Y. Chan, D. Robert Hay, Khalid J. Siddiqui and Douglas R. Sharp

S263

Acoustic Emission Behavior of Metal Matrix Composites,
C. Johnson, Kanji Ono and D. Chellman

S269

Detection of Crack Initiation and Propagation Using Acoustic Emission, W. G. Reuter

S270

Fracture Toughness Measurement of a NiCrMoV Steel by Acoustic Emission, Vincenzo Dal Re

S274

Microcrack Initiation and Acoustic Emission of A533B Steel in Fracture Toughness Tests,
Takanori Ohira and Yih-Hsing Pao

S278

Quantitative Evaluation of Microcrackings during Fracture Toughness Testing of Al2O3 by AE
Source Characterization, S. Wakayama and T. Kishi

S282

Three Dimensional Location and Quantitative Evaluation of Cracking Size in Ti Alloy by Acoustic
Emission Source Characterization, T. Kishi, H. Ohyama and K. H. Kim

S286

The Features and the Mechanism of AE Generation from Fatigue Cracks of SUS304 Piping
Components, Kusuo Yamaguchi, Hirotada Oyaizu and Akio Yamashita

10

S290

Acoustic Emission Study in Arctic Sea Ice in a Field Laboratory, N. K. Sinha

S294

Acoustic Emission Monitoring of the Main Shaft in the Hydroelectric Power Plant,
H. Imaeda, H. Kimura and A. Yasuo

S296

Acoustic Emission Applied to Reinforced Concrete Wall, M. C. Reymond and M. Diez

S300

Damage Process Characterization in Concrete by Acoustic Emission, J-M. Berthelot and J-L. Robert

S304

Detection of Fatigue Cracks in Highway Bridges with Acoustic Emission,
David W. Prine and Theodore Hopwood II

S307

Laboratory Acoustic Emission Investigation of Full Size ASTM A-588 Bridge Beams,
Al Ghorbanpoor and Donald W. Vannoy

S311

Acoustic Emission Made Audible by Time Dilation of Digitally-Recorded AE Signals,
Nelson N. Hsu and Steven E. Fick

S312

Magnetoelastic Resonance Spectroscopy, Wolfgang Stengel

S316

Discrimination of Fracture Mechanisms via Pattern Recognition Analysis of AE Signals during Fracture
Testing, Kanji Ono and Masayasu Ohtsu

S321

Some Design Concepts for an Accurate, High Speed AE Signal Acquisition Module, T. Kevin Bierney

S325
the

Advanced Acoustic Emission Monitoring System by Distributed Processing Waveform Microdata andSystem Configuration, Kusuo Yamaguchi, Takashi Hamada, Hatsuo Ichikawa, Hirotada Oyaizu, Teruo
Kishi and Hisashi Ishitani

S329

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

S330

Authors Index

Number 4
Page 71

Evaluation of Pattern Recognition Analysis of Acoustic Emission from Stressed Polymers andComposites
R. M. Belchamber, D. Betteridge, Y. T. Chow, T. Lilley, M. E. A. Cudby and D. G. M. Wood

Page 85

The Use of Acoustic Emission to Measure the Ductility of Hardened Surface Layers
J. Roget and D.P. Souquet

Page 93

The Detection of Longitudinal Rail Force via Magnetomechanical Acoustic Emission
M. Shibata, E. Kobayashi and K. Ono

Page 103

Application of Acoustic Emission on Railroad Car Cushioning Devices
V. Godinez and W.D. Jolly

Page 107

Methods of Calculating Attenuation and Dispersion Effects on Acoustic Emission Signals
R. A. Kline and S. S. Ali

Page 115Classification of Acoustic Emission Signals Generated during Welding
Roger W.Y. Chan, D. Robert Hay, Victor Caron, Michel Hone and R. Douglas Sharp

Page 124
Page 124
Page 124
Page 125
Page S333

Conferences and Symposia
The 2nd International Conference on AE/28th AEWG Meeting
29th AEWG Meeting/AE Primer/The 8th International AE Symposium
AE Training Course Announced
Extended Abstracts from The Second International Conference on AE
11

Page I - 1

Index to Volume 4

Volume 5 (1986)
Number 1
Page 1

Nondestructive Monitoring of Installed Refractories by Acoustic Emission

David A. Bell

Page 7

Improving the Reliability of Critical Parts by Acoustic Emission Surveillance
during Proof Testing
Edward Goliti

Page 15On the Applicability of Amplitude Distribution Analysis to the Fracture
Process of Composite Materials
Luis Lorenzo and H. Thomas Hahn

Page 25

Detection, Measurements and Location of Partial Discharges in High Power Transformers using
Acoustic Emission Method
Jerzy Skubis, Jerzy Ranachowski and Boguslaw Gronowski

Page 31

Is it Time for Acoustic Emission Surveillance of Operating Nuclear Reactors?

W. F. Hartman

Page 39

Fracture Toughness Measurement of a NiCrMoV Steel by Acoustic Emission

V. Dal Re

Page 45

Uncommon Cries of Cast Iron Elucidated by Acoustic Emission Analysis
Winfred Morgner and Hartmut Heyse

Page 51
Page 53
Page 54
Page ii
Page 50
Page iPage 6
Page 14
Page 59
Page 60

The XVth and XIVth EWGAE Meetings Roger Hill
ASTM E7.04 Meeting A. G. Beattie / French AE Codes / E. German AE Colloquium
The Second International Conference on AE from Reinforced Plastics
29th AEWG Meeting / AE Primer
Meeting Calendar
Appreciation K. Ono / European Perspective Roger Hill
Available Books on AE
EWGAE Provisional Form
Montreal Conference Information / Cover Photo
AE Training Courses

Number 2
Page 61

Prediction of Lumber Checking during Drying by Means of Acoustic Emission TechniqueShigeru Ogino, Koji Kaino and Masahiko Suzuki

Page 67

On the Acousto-Ultrasonic Characterization of Wood Fiber Hardboard
Henrique L. M. dos Reis and D. Michael McFarland

Page 71

Effect of Moisture Conditioning on Acoustic Emission from Particleboard

Page 77

Acoustic Emission During the Deformation and Fracture of Molybdenum at Low Temperatures
Jay B. James and Steve H. Carpenter

Page 85

Acoustic Emission Produced by the Deformation of Uranium
C. R. Heiple and S. S. Christiansen

Page 95

A Discussion of the Basic Understanding of the Felicity Effect in Fiber Composites
M. A. Hamstad

Page 103
Page 110Page 111

AE Literature - ConcreteThomas F. Drouillard
Book Review
M. A. Hamstad
The 29th Meeting of Acoustic Emission Working GroupThe Second International Symposium on Acoustic Emission from Reinforced Composites
XVth Meeting of The European Working Group of Acoustic Emission (EWGAE)
The 8th Int'l AE Symposium / The 30th Meeting of AEWG
12Frank C. BeallPage 66
Page 94
Page 102
Page i

Acousto-Ultrasonics: Theory and Application
Meeting Calendar
The Second CARP Symposium
Cover Photogragh
AE Events of Interest

Number 3
Page S1Page S42
Page 113

Page 114
Page 116
Page 121
Page 122
Page 123
Number 4
Page 124

A SPECIAL ISSUE ON AEWG AND EWGAE MEETINGS - EXTENDED ABSTRACTSThe 29th Meeting of Acoustic Emission Working GroupXVth Meeting of The European Working Group of Acoustic Emission (EWGAE)
The 29th Meeting of Acoustic Emission Working Group (AEWG)
XVth Meeting of The European Working Group of Acoustic Emission (EWGAE)
The 8th International AE Symposium/ The 30th Meeting of AEWG
Acousto-Ultrasonics: Theory and Application
Program of The 29th Meeting of Acoustic Emission Working Group
Abstracts of The 29th Meeting of Acoustic Emission Working Group
Program of XVth Meeting of The EWGAE
Program of The 8th International AE Symposium
The 2nd International Symposium on AE from Reinforced Composites M. A. Hamstad
The Generalized Theory and Source Representations of Acoustic Emission
Masayasu Ohtsu and Kanji Ono

Page 134

More Recent Improvements on the NBS Conical Transducer

Page 144

Nondestructive Evaluation of Adhesive Bond Strength Using the Stress Wave
Factor TechniqueHenrique L. M. dos Reis and Harold E. Kautz

Page 148

A Note on the Prediction of Fatigue Life of Metal Structures by Use of the Felicity EffectJ. W. Whittaker

Page 152

Vibro-Acoustic Emission in Plastic Bars

Page 156

Acoustic Emission Testing of Glass Fiber Reinforced Plastic Components

Page 162

Correlation of Acoustic Emission to Microstructural Sources
James Mohr and Amiya K. Mukherjee

Page 172

Acoustic Emission Characterization of Pinch Welds

Page 184
Page 185
Page 186
Page S69
Page 155
Page 143
Page 171
Page 185
Page 151
Page 161
Page iPage ii

XVth EWGAE Meeting/ 8th Int'l AE Symp.
DGM Symp. on AE/ 16th Symp. on NDE/ Prog. QNDE/ AE in Sci. & Tech.
30th AEWG Meeting/ Acousto-Ultrasonics/ XVIth EWGAE Meeting
9th Int'l AE Symp./ 3rd Int'l AE Conf./ 3rd Int'l Symp. on AE - Composites
Instruction for AEWG-EWGAE Extended Abstracts
Abstract; XVth EWGAE Meeting
Meeting Calendar
Call for Papers - 30th AEWG Meeting and AEWG Short Course
Registration Forms for 30th AEWG Meeting and AEWG Short Course
Call for Papers/ Announcements/ AE Training Courses
Available Books on AE
Progress in Acoustic Emission III
AE Events of Interest
Acoustic Emission of a Kouros Kanji Ono

Page I - 1

Index to Volume 5
13

Thomas M. Proctor, Jr.

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

Volume 6 (1987)
Number 1
Page 1

Fracture-Induced Acoustic Emission during Slow Bend Tests of A533B Steel
H.B. Teoh and Kanji Ono

Page 13

Real-Time Monitoring of Multi-Pass Welding by Acoustic Emission K. Ishihara and K. Yamada

Page 19

Application of Pattern Recognition Concepts to Acoustic Emission Signals Analysis
C.R.L. Murthy, B. Dattaguru and A.K. Rao

Page 29

Punch Stretching Process Monitoring Using Acoustic Emission Signal Analysis--Part 1:
Basic Characteristics Steven Y. Liang and David A. Dornfeld

Page 37

Punch Stretching Process Monitoring Using Acoustic Emission Signal Analysis - Part 2:
Application of Frequency Domain Deconvolution
Steven Y. Liang, David A. Dornfeld and Jackson A. Nickerson

Page 43

Modeling Concrete Damage by Acoustic Emission

Page 61

Pattern Recognition Analysis of Acoustic Emission from Unidirectional Carbon Fiber-Epoxy
Composites by using Autoregressive Modeling
Masayasu Ohtsu and Kanji Ono

Page 73

A New Approach to the Use of Acoustic Emission Peak Amplitude Distribution as a Tool of
Characterizing Failure Mechanisms in Composite Materials
A. Mittelman and I. Roman

Page 84
Page 79
Page 80
Page 81
Page 83
Page 83

Waveform Digitizers Kanji Ono
30th AEWG Meeting/8th Int'l AE Symp.
M. Ohtsu
Int'l Conf. of NDE with AE Technology
Letter to Editor/An Erratum/ AE Training Courses
31st AEWG Meeting and AEWG Short Course
Progress in Acoustic Emission III

Number 2
Page 85

J.M. Berthelot and J.L. Robert

Acoustic Emission Wave Characterization: A Numerical Simulation of the Experiments on
Cracked and Uncracked Specimens T. Aizawa, T. Kishi and F. Mudry

Page 93

Flaw Growth in Alumina Studied by Acoustic Emission
M.A. Hamstad, P.M. Thompson and R.D. Young

Page 99

Acoustic Emission Characteristics in Concrete and Diagnostic Applications

Page 109

Measurement of the Maximum Applied Loads to Automobile Components by Acoustic Emission
Technique Tatsuhiko Yoshimura and Shigeto Kano

Page 115

Effects of Heat Treatment on the Acoustic Emission Generated During the Deformation of 7075T651 Aluminum Alloy Zu-Ming Zhu and S.H. Carpenter

Page 121

An Overview of Acoustic Emission Codes and Standards

Page 125

Acoustic Emission Applied to Pressure Vessels

Page 133
Page 133
Page 135
Page 136

Third Symposium on AE W. Morgner
Fourth European Conf. on NDT and 16th EWGAE
AE Codes/Standards Activity within ASME and ASNT
Prof. S.H. Carpenter, 1987 University Lecturer

Number 3
Page 137

Application of Acoustic Emission to the Field of Concrete Engineering
14

Masayasu Ohtsu

J.C. Spanner, Sr.

Brian R.A. Wood

J. R. Mitchell

Taketo Uomoto

Page 145

A Method of Rapidly Estimating the Fatigue Limits by Acoustic Emission
Tatsuhiko Yoshimura and Shigeto Kano

Page 151

Preliminary Investigation of Acoustic Emission from Wood During Pyrolysis and Combustion
Frank C. Beall

Page 157

Preliminary Investigation of the Feasibility of Using Acousto-Ultrasonics to Measure Defects in
Lumber
R.L. Lemaster and D.A. Dornfeld

Page 167

Development and Application of Acoustic Emission Methods in the United States - A Status
Review
P.H. Hutton

Page 177

Acoustic Emission Produced by Deformation of Metals and Alloys - A Review: Part I
C. R. Heiple and S.H. Carpenter

Page 205
Page 205
Page 206
Page 166
Page 156
Page 176
Page 208

30th Meeting of AEWG D.M. Egle and R.A. Kline
A Brief Note on the Kaiser and Felicity Effects R.A. Kline and D.M. Egle
The Third Int'l Workshop on Composite Materials / The Sixth (Japanese) Conference on AE
31st Meeting of AEWG
Available Books on AE
Cover Photograph
AE Training Courses/Announcements

Number 4
Page 209

The Application of Acoustic Emission Techniques in High-Temperature Oxidation Studies
A. S. Khanna, B. B. Jha and Baldev Raj

Page 215

Acoustic Emission Produced by Deformation of Metals and Alloys - A Review: Part II
C. R. Heiple and S.H. Carpenter

Page 239

Acoustic Emission During Deformation and Crack Initiation of Pipeline Steels
M. W. Drew, B. R. A. Wood and R. W. Harris

Page 249

Acoustic Emission Waveform Analysis to Identify Fatigue Crack Propagation in a Mirage Aircraft
C. M. Scala and R. A. Coyle

Page 257

The Detection of Hydrogen-Assisted-Crack Formation in U - 0.8% Ti Alloy Electron-Beam
Weldments
J. W. Whittaker and M. W. Richey

Page 261
by

The Detection of the Fracture of Autoclaved Aerated Concrete during Autoclave Curing Process
Acoustic Emission

Satoshi Teramura, Koichi Tsukiyama and Hideaki Takahashi

Page 267
Page 273
Page 238
Page 256

31st Meeting of AEWG - Abstracts
Meetings on AE and related topics
9th Int'l AE Symp/World Meeting on AE
Cover Photograph

Page 273

INDEX to Volume 6

Volume 7 (1988)
Number 1
Page 1Page 9Evaluation of Fracture Toughness of Autoclaved Lightweight Concrete by Means of Acoustic
Emission Technique, Satoshi Teramura, Koichi Tsukiyama and Hideaki Takahashi
Identification of Crack Propagation Modes in 304 Stainless Steel by Analysis of Their
Acoustic Emission Signatures Daniel R. Smith Jr. and Steve H. Carpenter
15

Page 21

A New Sensor for Quantitative Acoustic Emission Measurement

Page 31

Acoustic Emission Propagation and Source Location in Small, Spherical Composite Test
Specimens
J. W. Whittaker, W. D. Brosey, O. Burenko and D. A. Waldrop

Page 41

A High Fidelity Piezoelectric Tangential Displacement Transducer for Acoustic Emission
Thomas M. Proctor, Jr.

Page 49

Acoustic Emission from Fatigue Cracks in Chrome-Molybdenum Steel Cylinders
P.R. BlackburnPage 57
Page 20
Page 40A
Page 48
Page 30

31st Meeting of AEWG
Meeting Schedule
World Meeting on AE
Instructions to Authors - World Meeting on AE
Books Available

Number 2
Page 59

Chung Chang and C. T. Sun

Acoustic Emission Measurements on PWR Weld Material with Inserted Defects using Advanced
Instrumentation P. G. Bentley and M. J. Beesley

Page 81

Acoustic Emission Measurements on PWR Weld Material with Inserted Defects
C. B. Scruby and K. A. Stacey

Page 95

Characterization of Acoustic Emission from Thermally-Cycled Lithium Hydride
J. W. Whittaker and D. G. Morris

Page 103

Apparatus for Coupling an Acoustic Emission Transducer to a Rotating Circular Saw
Richard L. Lemaster and David A. Dornfeld

Page 111

Measurement of Density Profiles in Wood Composites Using Acoustic Emission
Richard L. Lemaster, Michael F. Gasick and David A. Dornfeld

Page 119

Acoustic Emission during Intergranular Stress Corrosion Cracking of Iron
M. A. Friesel and R. H. Jones

Page 129
Page 129
Page 80
Page 94
Page 102

XVIIth Meeting of EWGAE - Program
The 9th International Symposium on AE - Program
Meeting Schedule
World Meeting on AE
Books Available/Training Courses

Number 3
Page 135

Acoustic Emission from Porous Films of Aluminum Oxide during the Application of Electrical
Voltage J. Sampath Kumar and S.P. Mallikarjun Rao

Page 139
Page 140
Page 141

Third Domestic Conf. on Subsurface Acoustic Emission (Sendai, Japan) - Program
Acoustic Emission (UK) - Abstracts
Seminar on Acoustic Emission (India) - Abstracts

Page S1

Extended Abstracts of Presentations at AEWG(I) Seminar on ACOUSTIC EMISSION
Acoustic Emission during Tensile Deformation and Fracture in Austenitic Stainless Steels
Baldev Raj and T. Jayakumar

Page S13

Detection of Breakaway Oxidation of Zircaloy-2 by Acoustic Emission Technique
B. K. Gaur, A. K. Sinha, B. K. Shah, P. G. Kulkarni and R. Vijayaraghavan

Page S18

Magnetomechanical Acoustic Emission Behaviour of Some Structural Steels
16

S. G. Savanur and C. R. L. Murthy
Page S29

A Study of Acoustic Emission Activity in Granites during Stress Cycling Experiments
M.V.M.S. Rao

Page S35

Acoustic Emission Studies on Adhesive Potted Inserts of Honeycomb Sandwich Panels
T.S. Sriranga and R. Samuel

Page S40

Location of Weld Defects by Acoustic Emission

Page S43

Thermally Induced Relaxation Behaviour in a Metallic Glass
G. L. Goswami, S. K. Jha and G. P. Tiwari

Page S47
Page S48

Meeting Schedule/Training Courses
Announcements

Number 4
Page 145

G.L. Goswami and P.R. Roy

Acoustic Emission During Quasi-Static Loading/Hold /Unloading in Notched Reinforced
Fiber Composite Materials S. V. Hoa and L. Li

Page 161

The Acoustic Emission Generated during the Plastic Deformation of High Purity Zinc
S. H. Carpenter and Chung-Mei Chen

Page 167

Evaluation of Concrete Structure Deterioration via AE Observation of Core Tests
Masayasu Ohtsu, Tatsuro Sakimoto, Yutaka Kawai and Syuro Yuji

Page 173

Diagnosis of Rotating Slides in Rotary Compressors using Acoustic Emission Technique
Ichiya Sato, Takao Yoneyama, Kouichi Sato, Toshiyuki Tanaka and Hiroaki Hata

Page 179

AE-Monitoring Systems for the Detection of Single-Point and Multipoint Cutting Tool Failures
Thomas Blum, Ippei Suzuki and Ichiro Inasaki

Page 185

Direct Measurement of Water Hammer Pressure by AE Source Wave Analysis
Mikio Takemoto and Yasuhisa Hayashi

Page 193

Acoustic Emission from an Industrial Applications Viewpoint

Page 201

Downhole AE Measurement Technique and Its Application to Geothermal Fields
Hiroaki Niitsuma

Page 211

Acoustic Emission Source Location in Fiber Reinforced Plastic Composites
D. J. Buttle and C. B. Scruby

Page 225

The Acoustic Emission Source Mechanism for Fatigue Crack Propagation in 7075 Aluminum
S. L. McBride, P. Bowman and K. I. McRae

Page 231
Page 231
Page 192
Page 200
Page 210
Page 224

Conferences and Symposia
Third International Symposium on AE from Composite Materials/EWGAE Meeting
Meeting Schedule
Books Available
Progress in AE IV, Proc. The 9th International AE Symposium
The World Meeting on AE/ASNT Recognition/Codes and Standards Committee

Page I-1

Index to Volume 7

Trevor J. Holroyd

Volume 8 (1989)
Numbers 1 and 2
Pages S1-S338 Proceedings of the World Meeting on Acoustic Emission, March 20-23, 1989
17

S1

"Acoustic Emission Technology using Multi-parameter Analysis of Waveform and theApplications to Fracture Modes and Growth Recognition in Composites",
K. Yamaguchi, H. Oyaizu, J. Johkaji and Y. Kobayashi

S4"Acoustic Emission Detection of Crack Presence and Crack Advance During Flight",
S. L. McBride, M. D. Pollard, J. D. MacPhail, P. S. Bowman and D. T. Peters

S8"Time-frequency domain (3-D) Analysis of AE Signals using Simple Instrumentation
Techniques", S. V. Subba Rao, K. V. Srincivasan and M. Annamalai

S12"Improving Acoustic Emission Crack/Leak Detection in Pressurized Piping by Pattern
Recognition Techniques", R. W. Y. Chan, D. R. Hay, J. R. Hay and H. B. Patel

S16"An Efficient Unsupervised Pattern Recognition Procedure for Acoustic Emission Signal
Analysis", M. A. Majeed and C. R. L. Murthy

S20"Solving AE Problems by a Neural Network", I. Grabec and W. Sachse

S24"The General Problems of AE Sensors", Y. Higo and H. Inaba

S28"Acoustic Emission Transducer Modelling Using System Identification Techniques",
S. Kallara, P. K. Rajan and J. R. Houghton

S32"Design of 3-Dimensional AE/MS Transducer Arrays", M. Ge and H. R. Hardy

S38"Simultaneous Velocity Tomography and Source Location of Synthetic Acoustic Emission Data",
S. C. Maxwell, R. P. Young, and D. A. Hutchins

S42"Use of Mechanical Waveguides and Acoustic Antennae in Geotechnical AE/MS Studies",
H. R. Hardy, Jr., F. Taioli and M. E. Hager

S49"Linear Location of AE Simulated Sources on Steel Pipelines with Waveguides", B. Q. Zhang

S53"AE Application and Recent Results on Nuclear Components", P. Jax and V. Streicher

S57"Acoustic Emission from Steel Structures", A. Nielsen

S62"AE Role in the Diagnosis and Prognosis of Defects in Industrial Plant Steel Components",
F. Tonolini

S66"Structural Integrity Evaluation Using Acoustic Emission Techniques",
B. R. A. Wood and R. W. Harris

S70"On-line Application of Acoustic Emission Analysis", W. Morgner

S71"AE - For when you absolutely cannot afford a Failure of a Critical Pressure Boundary",
H. L. Dunegan

S75"Periodic Inspection of Compressed Gas Cylinders and Transport Vessels by Using Acoustic
Emission Testing", H. Barthelemy

S79"Locating Fatigue Cracks by Acoustic Emission Testing", P. M. Horrigan,
J. F. Finn, F. R. Tuler and J. H. Smith

S84"A New Acoustic Emission Measurement System and its Application to the Local Monitoring of a
Crack in a Pressure Vessel", B. Tirbonod and L. Hanacek

S88"An Approach for the Integrity Assessment of M250 Maraging Steel Pressurized Systems",
T. Chelladurai, R. Krishnamurthy and A. R. Acharya
18

S93

"Detectability of Defects in Reactor Pressure Components by Location and Interpretation of
AE-Sources", C. Sklarczyk and E. Waschkies,

S97"Application of Acoustic Emission Technique during In-service Pressure Vessel Inspection",
S. Liu, G. Shen, Y. Wan and Q. Duan

S101

"Acoustic Emission Leak Monitoring in Pressurized Piping", H. B. Patel and A. W. Cook

S103

"Application of Acoustic Emission Technique in the High-temperature Oxidation Studies –A Review", A. S. Khanna

S105"Acoustic Emission during Phase Transformation in Cr12MoV Steel and Fe-33Ni-4Ti-10Co
Alloy", B. Q. Zhang, C. K. Yao and C. G. Jiao

S109

"Effect of Pre-exposure to Water on the Acoustic Emission Behavior of 2091-T3 Al-Li Alloy",F. M. Zeides and I. Roman

S114"Acoustic Emission from Weld-seam of 16MnR Steel during Stress Corrosion",
B. Q. Zhang and J. Q. Sun

S118"The Influence of Crack Front Geometry on Acoustic Emission During Fatigue of Al2024-T351",
F. A. Veer and J. Zuidema

S122"Acoustic Emission Monitoring of Incipient Crack Propagation and its Growth Rate in EN-24
Steel under Fatigue", S. C. Pathak and C. R. L. Murthy

S126"Acoustic Emission during Tensile Deformation and Fracture in Austenitic Alloys",
B. Raj and T. Jayakumar

S131"Relationship between Acoustic Emission and Flaw Size in Si3N4 Ceramics",
Y. Mori, M. Nishino, K.-I. Aoki, T. Kishi and K. Kitadate

S135"A Comparison of the Acoustic Emission Generated from the Fracture and Decohesion ofGraphite Nodules with Theoretical Predictions", S. H. Carpenter and Z. Zhu

S140"Influence of Grain Size on Frequency Spectra of AE Signal Generated during Tensile
Deformation in an AISI Type 316 Stainless Steel",
B. Raj, P. Kalyanasundaram, T. Jayakumar, P. Barat and P. Rodriguez

S145"Evaluation of Fatigue Crack Growth Rate of Carburized Gear by Acoustic Emission Technique",Y. Obata, H. Kobayashi, K. Aoki, T. Yamaguchi and K. Shibata

S149"Acoustic Emission Investigations in Poland", J. Ranachowski, Poland "Comparative Studies on
Acoustic Emission Generated During Lüder's Deformation in Mild Steel and Portevin-Le
Chatelier Effect in Austenitic Stainless Steel",
B. Raj, T. Jayakumar, P. Kalyanasundaram, P. Barat, B. B. Jha, D. K. Bhattacharya, P. Rodriguez

S154"Development and Future Aspects in AE Source Characterization", M. Enoki and T. Kishi

S158"AE Source Modelling-Comparison of Inversion and Forward Techniques",
D. J. Buttle and C. B. Scruby

S162"Source Inversion of Acoustic Emission for the Determination of Crack Kinematics and Kinetics",
M. Ohtsu

S166"Acoustic Emission Analysis and Ultrasonic Velocity Imaging in the Study of Rock Failure",
S. D. Falls, T. Chow, R. P. Young and D. A. Hutchins
19

S170

"Thin-Film Acoustics: Line and Point Sources Generation and the Testing of thin Films",
K. Y. Kim and W. Sachse

S175"Acousto-Ultrasonics: An Update", A. Vary

S179

"Theoretical Basis of Acousto-Ultrasonics", M. T. Kiernan and J. C. Duke

S184

"Fracture of Boron Particles in 2219 Aluminum as a Known Acoustic Emission Source",
C. R. Heiple, S. H. Carpenter and S. S. Christiansen

S188"Surface Analysis by Tribo-acoustic Emission", M.-K. Tse and P.-Y. Gu

S192"Acoustic Emission Measurements of Rubbing Surfaces",
S. L. McBride, R. J. Boness, M. Sobczyk and M. R. Viner

S197"Vibro-Acoustic Emission Rubbing Sources", J. R. Webster

S201"Acoustic Characterisation of Small Particle Impact", D. J. Buttle and C. B. Scruby

S205"Estimation of Impact Force between Rough Surfaces by Means of Acoustic Emission",
I. Kukman and I. Grabec

S209"Acoustic Emission Monitoring of Magnetic Hard Disks during Accelerated Wear Testing",
J. C. Briggs, M. M. Besen and M.-K. Tse

S213"Applications of Acoustic Emission Techniques for Diagnosis of Large Rotating Machinery and
Mass Products", I. Sato, T. Yoneyama, K. Sato, T. Tanaka, M. Yanagibashi and K. Takigawa

S217
A.

"Quality Inspection of Rolling Element Bearings using Acoustic Emission Technique", V. Bansal,
Prakash, V. A. Eshwar and B. C. Gupta

S219"Stress Wave Sensing - Affordable AE for Industry", T. J. Holroyd

S223"Cavitation Monitoring of Hydroturbines with True-RMS Acoustic Emission Measurement", O.
Derakhshan, J. R. Houghton, R. K. Jones and P. A. March

S227"Monitoring of the Cutting Process by Means of AE Sensor", D. A. Dornfeld

S231"Tool Monitoring by Acoustic Emission", J. Roget, P. Souquet, M. Deschamps and N. Gsib

S236

"MONPAC - An Acoustic Emission based System for Evaluating the Structural Integrity of Metal
Vessels", T. J. Fowler, J. A. Blessing and T. L. Swanson

S238"ICI's Perspective on Acoustic Emission Monitoring", S. Hewerdine

S239"MONPAC - Condition Monitoring for Static Plant - Case Histories", P. T. Cole

S240

"Acoustic Emission Monitoring of a Large Pressure Vessel during a Pneumatic Re-Qualification
Test", M. Peacock

S241"Summary of Experiences with MONPAC Testing by MQS/Dunegan Testing Group",
R. K. Miller

S242"Microseismics and Geotechnical Applications", M. Ohtsu

S246"Acoustic Emission Phenomena in Geological Media", R. A. Kline, A. S. Khan, Y. Xiang, J.
Shlyapobersky and F. Irani
20

S250

"Acoustic Emission/Microseismic Activity at very low Strain Levels", B. H. Armstrong

S254"Focal Mechanism Determinations for a Sequence of Mining-Induced Seismic Events Recorded at
a Hard Rock Mine", T. I. Urbancic, S. Talebi and R. P. Young,

S258"Effects of Porosity on Acoustic Emission Signatures", R. J. Watters and D. M. Chuck

S262"Acoustic Emission Monitoring and Analysis Procedures Utilized during Deformation Studies on
Geologic Materials", X. Sun, H. R. Hardy, Jr. and M. V. M. S. Rao

S266"PDP 11/34 Based Microseismic Monitoring System for Kolar Gold Fields, India",
G. Jayachandran Nair

S268

"Fracture Mechanism Studies of Carbon/PMR-15 Composites by Acoustic Emission",
J. S. Jeng, K. Ono and J. M. Yang

S272

"Assessment of Fatigue Damage in Carbon Fibre Reinforced Epoxy Laminates with the Energy
Discrimating Acoustic Emission Method",
M. Wevers, I. Verpoest, P. De Meester and E. Aernoudt,

S276"NDE Procedure for Predicting the Fatigue Life of Composite Structural Members",
M. J. Sundaresan, E. G. HennekeII and A. Gavens

S280"Detection of Impact Damage in Composite Structures by Use of Thermally-activated Acoustic
Emission", J. W. Whittaker and W. D. Brosey

S284"Characterization of Failure Modes in Glass Fibre Reinforced Plastic Composites",
M. N. Raghavendra Rao and C. R. L. Murthy

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

S292"Analysis of AE Signals in Time and Frequency Domains Coupled to Pattern Recognition to
Identify Fracture Mechanisms in CFRP", A. Maslouhi and C. Roy

S297
N.

"Some New Results in the Damage Identification in Kevlar-Epoxy Composites", D. S. Rajan, N.
Kishore and B. D. Agarwal

S301"Monitoring Initiation and Growth of Matrix Splitting in a Uni-directional Graphite/Epoxy
Composite", S. Ghaffari and J. Awerbuch

S306"Correlation of Internal Bond Strength of Particleboard with Acousto-Ultrasonics", A. T. Green

S311"Acoustic Emission during Contact Drying of Southern Pine Veneer", F. C. Beall

S314"Nondestructive Evaluation of Adhesively Bonded Joints using Acousto-Ultrasonics",
S. Tanary, A. Fahr and Y. Haddad

S317"AE Research on Adhesion in Composite Materials", H. G. Moslé

S318"Acoustic Emission Testing of Flexed Concrete Beams Reinforced with Bonded Surface Plates",
D. P. Henkel and J. D. Wood

S322

"Acoustic Emission Investigation into some Concrete Construction Problems",
J. D. Leaird and M. A. Taylor

S326"Correlation of AE and Fracture Intensity during Impact Indentation of Coal Block",
S. J. Jung and A. W. Khair
21

S330

"Acoustic Emission Monitoring of Pressure Vessel Preventing Catastrophic Failure", S. F. Botten

S334

Limiting State Prediction from AE Signals for Large-size Structures under Static, Cyclic andThermocyclic Loads, V.A. Strizhalo and V.A. Strelchenko

S337

Authors Index

Number 3
Page 1The MONPAC System
Timothy J. Fowler, James A. Blessing, Peter J. Conlisk and Terry L. Swanson

Page 11

Acoustic Emission Monitoring of a Large Pressure Vessel during a Pneumatic Re-qualification
Test
M. J. Peacock

Page 21

ICI's Perspective on Acoustic Emission Monitoring S. Hewerdine

Page 25

A Summary of Experiences with MONPAC™ Testing by the MQS/Dunegan Testing Group
R.K. Miller, R.G. Tobin, D.J. Gross and D.T. Tran

Page 31MONPAC - Condition Monitoring for Static Plant - Case Histories

Page 35

Detection of an Impulse Force in Head-Disk Media Contact using Small Piezoelectric Transducer
Kenji Mochizuki, Isamu Sato and Takefumi Hayashi

Page 41

Investigations of Sensor Placement for Monitoring Acoustic Emission in Machining
David V. Hutton and Qing Huan Yu

Page 47

Use of Plane-Strain Compression for the Diagnosis of Acoustic Emission Source during Plastic
Deformation F. Zeides and I. Roman

Page 51

Transverse Cracking and Longitudinal Splitting in Graphite/Epoxy Tensile Coupons as
Determined by Acoustic Emission Steven M. Ziola and Michael R. Gorman

Page 8Page 9Cover Photographs
Meeting Calendar -- 33rd AEWG Meeting, 10th IAES, Kumamoto Workshops

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

Third International Symposium on AE from Composite Materials (AECM-3)
EWGAE Meeting/Future Meetings/Errata
The Status of EWGAE Panel on Standards J. Roget /Codes and Standards Committee
Abstracts of Third International Symposium on AE from Composite Materials (AECM-3)
Selected Abstracts of AECM-3 (continued)

Number 4
Page 65

Phillip T. ColeA Review of International Research Relative to the Geotechnical Field Application of Acoustic
Emission/ Microseismic Techniques H. Reginald Hardy, Jr.

Page 93

A Review of Acoustic Emission in Civil Engineering with Emphasis on Concrete
Masayasu Ohtsu

Page 99

Application of Acoustic Emission for the Evaluation of Microseismic Source Location Techniques
M. Kat and F. P. HassaniPage 107

Acoustic Emission Characteristics of Unstable Slopes
A. Chichibu, K. Jo, M. Nakamura, T. Goto and M. Kamata

Page 113

Experimental Studies on the Effect of Stress History on Acoustic Emission Activity –A Possibility for Estimation of Rock Stress
Sumio Yoshikawa and Kiyoo Mogi
22

Page 125

Acoustic Emission from Phase Transformations in Au - 47.5 at. % Cd
C. R. Heiple and S. H. Carpenter

Page 135
Wood

The Effect of Same-side and Through-Thickness Transmission Modes on Signal Propagation in
Richard L. Lemaster and Stephen L. Quarles

Page 92
Page 143
Page 200
Page 210

Meeting Schedule
Abstracts of Third International Symposium on AE from Composite Materials
EWGAE Meeting/AEWG -33 Books Available
Progress in Acoustic Emission IV, Proc. The 9th International AE Symposium

Page I-1

Index to Volume 8

Volume 9 (1990)
Number 1
Page 1

Origin of Acoustic Emission Produced during Deformation of Beryllium
C. R. Heiple and S. H. Carpenter

Page 9Optimal Waveform Feature Selection Using a Pseudo-Similarity Method
K. J. Siddiqui, Y.-H. Liu, D. R. Hay and C. Y. Suen

Page 17

The Effect of Same-Side and Through-Thickness Transmission Modes on Signal Propagationin Wood Richard L. Lemaster and Stephen L. Quarles

Page 25

Defect Detection in Rolling Element Bearings by Acoustic Emission Method
N. Tandon and B. C. Nakra

Page 29

Monitoring of a Pressure Vessel (ZB2) by means of Acoustic Emission
H.-A. Crostack and P. Böhm

Page 37

A Procedure for Acceptance Testing of FRP Balsa Wood Core Pressure Vessels
P. Ouellette, S.V. Hoa and L. Li

Page 45

Page 69
Page 72
Page 73
Page 8Page 31
Page ii

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

Number 2
Page 75

Composites
Felicity Ratio Behavior of Pneumatically and Hydraulically Loaded Spherical Composite Test
Specimens
J. W. Whittaker, W. D. Brosey and M. A. Hamstad

Page 84

Correlation of Felicity Ratio and Strength Behavior of Impact-Damaged Spherical Composite Test
Specimens J. W. Whittaker, W. D. Brosey and M. A. Hamstad

Page 91

Delayed Acoustic Emission: A Rheological Approach
P. Fleischmann

Page 97

Acoustic Emission Analysis of the Accumulation of Cracks in CFRP Cross-ply Laminates under
23

N. Rochat, R. Fougeres andTensile Loading

J.-P. Favre and J.-C. Laizet

Page 103

Analysis of Acoustic Emission Events from Single Fiber Pullout Experiments
W. Mielke, A. Hampe, O. Hoyer and K. Schumacher

Page 109

Digital Signal Analysis of Acoustic Emission from Carbon Fiber/ Epoxy Composites
Kanji Ono and Kenji Kawamoto

Page 117

Acoustic Emission: A Micro-Investigation Technique for Interface Mechanisms in Fiber
Composites
D. Rouby

Page 123

Acoustic Emission Characterization of the Deformation and Fracture of an SiC-Reinforced,
Aluminum Matrix Composite Oh-Yang Kwon and Kanji Ono

Page 131

Burst Prediction by Acoustic Emission in Filament-Wound Pressure Vessels
Michael R. Gorman

Page 140

Critical AE Problems for the Researcher

Page 142

Quality Inspection of Rolling Element Bearing using Acoustic Emission Technique
Vibha Bansal, B.C. Gupta, Arun Prakash and V.A. Eshwar

Page 147
Page 152
Page 153
Page 154
Page 122
Page 130
Page 154

Adrian A. Pollock

Conferences and Symposia
XIX Meeting of EWGAE, 10th International Acoustic Emission Symposium
1st Symp. Evaluation of Adv. Materials by AE
Intl Joint Meeting at Kumamoto
Korean Working Group on Acoustic Emission (KWGAE), AE Training Courses
Meeting Schedule
34th AEWG Meeting
Cover Photograph

Number 3
Page 155

Wood
Frank C. Beall, Topical Editor
Anecdotal History of Acoustic Emission from Wood

Thomas F. Drouillard

Page 177

An Experiment on the Progression of Fracture (A Preliminary Report)
Fuyuhiko Kishinouye (Translated by Kanji Ono)

Page 181

Acoustic Emission from Drought-Stressed Red Pine (Pinus resinosa)
Robert A. Haack and Richard W. Blank

Page 189

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

Page 197

Nondestructive Evaluation of Adhesive Bond Strength of Finger Joints in Structural
Lumber Using the Acousto-Ultrasonic Approach Henrique L. M. dos Reis,
Frank C. Beall, Michael J. Chica and Dick W. Caster

Page 203

Determining the Abrasiveness to Tools of Wood-Based Composites
with Acoustic Emission Richard L. Lemaster

Page 209
Page 215

Lumber Stress Grading utilizing the Acoustic Emission Technique Keiichi Sato,
Hajime Takeuchi, Katsuya Yamaguchi, Naoto Ando and Masami Fushitani
AE Literature - Wood Thomas F. Drouillard and Frank C. Beall

196, 214, 223
Page 226
Page 188

Conferences and Symposia
10th International Acoustic Emission Symposium (abstracts)
Intl Joint Meeting at Kumamoto/ KWGAE/ 34th AEWG Meeting, AE Training Courses
Meeting Schedule24Page 180
Number 4
Page 227

Fuyuhiko Kishinouye (biography) / Cover PhotographDetection of Irradiation Effects on Reactor Vessel Steels by Magneto-Acoustic Emission
Oh-Yang Kwon and Kanji Ono

Page 237

New Algorithm for Acoustic Emission Source Location in Cylindrical Structures
Dong-Jin Yoon, Young H. Kim and Oh-Yang Kwon

Page 243

Characterization of Fatigue of Aluminum Alloys by Acoustic Emission, Part I Identification of Source Mechanism D. J. Buttle and C. B. Scruby

Page 255

Characterization of Fatigue of Aluminum Alloys by Acoustic Emission, Part II Discrimination Between Primary and Other Emissions D. J. Buttle and C. B. Scruby

Page 271

Acoustic Emission Source Location Using Simplex Optimization
M. P. Collins and R. M. Belchamber

Page 277

Acoustic Emission during Lumber Drying

Page 283

AE Source Orientation by Plate Wave Analysis
and William H. Prosser

Page 289
Page 270
Page 276A

Conferences and Symposia
10th International AE Symp.
Cover Photograph AE Inspection of Monorail Trains
Meeting Calendar

I-i

Index to Volume 9

S. Ogino, K. Kaino and M. Suzuki
Michael R. Gorman

Volume 10 (1991/92)
Number 1/2
Page iCurrent Research and Future Trend of AE Applications to Civil Engineering and GeologicalTechnology
Masayasu Ohtsu, Topical Editor

S1-S12

Variety of Acoustic Emission Waveforms Produced by Discrete Crack Growth in Rock
Steven D. Glaser and Priscilla P. Nelson

S13-S17Signals

Estimation of Maximum Stress in Old Railway Riveted I-Girder Bridges using Acoustic Emission
Hisanori Otsuka, Hiroshi Hikosaka, Hiroyuki Miyatake and Syouzou Nakamura

S18-S21

Another Look at Booming Sand

Marcel F. Leach and Gottfried A. Rubin

S22-S28

Expected Acoustic Emission from Around a Shaft in Intact Rock

S29-S34

Acoustic Emission Monitoring during Microseismic Activity Caused by Mine Subsidence
Brian R. A. Wood and Robert W. Harris

S35-S41

Acoustic Emission/Microseismic Activity Monitoring of Salt Crystallization for Stone
Conservation
M. Montoto, R.M. Esbert, L.M. Suárez del Río, V.G. Ruiz de Argandoña and C.M. Grossi

S42-S48

Acoustic Emission Monitoring during In-situ Heater Test of Granite
T. Ishida, K. Kitano, N. Kinoshita and N. Wakabayashi

S49-S54

Using Acoustic Emission Testing in Seepage Investigations
25

B.J.S. Wilkins and G.L. Rigby

Andrew R. Blystra

S55-S58

Acoustic Emission of Penetration Experiments to Judge Soil Condition
S. Naemura, M. Tanaka, S. Nishikawa, M. Nakamura, K. Jo and T. Kishishita

S59-S62

A Laboratory Investigation of AE from Coal

S63-S76

Determination of the Initial Stresses on Rock Mass using Acoustic Emission Method
K. Michihiro, K. Hata, H. Yoshioka and T. Fujiwara

S77-S89

U.S. Bureau of Mines Research on the Kaiser Effect for Determining Stress in Rock
Michael J. Friedel and Richard E. Thill

S90-S96

Evaluation of Joint Properties of Anti-washout Underwater Concrete by Acoustic Emission
Measurement
Kazuya Miyano, Tatsuo Kita, Yuji Murakami and Takako Inaba

S97-S103

Automated Determination of First P-Wave Arrival and Acoustic Emission Source Location
E. Landis, C. Ouyang and S. P. ShahS104-S109

Application of Acoustic Emission Techniques in the Evaluation of Frost Damage in Mortar
Hisatoshi Shimada and Koji Sakai

Number 3/4
Pages 1-11

R. W. Harris, B. R. A. Wood and T. Flynn

Acoustic Emission of the 45HNMFA Structural Steel during Low-Cycle Fatigue
J. Siedlaczek, S. Pilecki and F. Dusek

Pages 13-17

Parameter Estimation in Acoustic Emission Signals
C. E. D'Attellis, L. V. Perez, D. Rubio and J. E. Ruzzante

Pages 19-23

Acoustic Emission Technique at Proof Tests of Nuclear Pressure Vessels in Hungary
Peter Pellionisz and János Geréb

Pages 25-29

Effects of Wave Velocity Change on Magnetomechanical AE in Sintered Iron
Noboru Shinke and Yoshitugu Ohigashi

Pages 31-33

Origin of Acoustic Emission in Naturally Aged Aluminum-Lithium Alloys
F. Zeides and I. Roman

Pages 35-41

Analysis of Artificial Acoustic Emission Waveforms Using a Neural Network
Hironobu Yuki and Kyoji Homma

Pages 43-48

Source Force Waveforms: The Use of a Calibrated Transducer in Obtaining an Accurate
Waveform of a Source Thomas M. Proctor, Jr. and Franklin R. Breckenridge

Pages 49-60

Acoustic Emission Monitoring of a Fatigue Test of an F/A-18 Bulkhead
C. M. Scala, J. F. McCardle and S. J. Bowles

Pages 61-65

Maximum Curvature Method: A Technique to Estimate Kaiser-Effect Load from Acoustic
Emission Data
M. Momayez, F. P. Hassani and H. R. Hardy, Jr.

Pages 67-70

Acoustic Emission Measurements on Bridges

Pages 71-82

Acoustic Emission Monitoring of the Sensitivity of Chemicals to Impact
Timothy G. Crowther, Adrian P. Wade and Nancy Brown

Pages 83-89

Study of Acoustic Emission Generation in Sliding Motion
Musa K. Jouaneh, Richard Lemaster and Frank C. Beall

Pages 91-95

The Role of Acoustic Monitoring as a Diagnostic Tools in Nuclear Reactors
26

H. Hick, H. Willer, E. Winter and F. Simacek

Shahla Keyvan and Ron King
Pages 97-101

Acoustic Emission Produced by Sliding Friction and its Relationship to AE from Machining
S. H. Carpenter, C. R. Heiple, D. L. Armentrout, F. M. Kustas and J. S. Schwartzberg

Pages 103-106

Comments on the Origin of Acoustic Emission in Fatigue Testing of Aluminum Alloys
C. R. Heiple, S. H. Carpenter and D. L. Armentrout

Pages 107-111

Acoustic Emission of Wood during Swelling in Water
Stefan Poliszko, Waldemar Molinski and Jan Raczkowski

Pages 113-116

Characterization of the ASL Parameter

Pages 117-121

Acoustic Emission from Bubbles in a Water Column,

Pages 122-124

In Memoriam, Dr. Raymond W. B. Stephens (1902-1990)

12, 18, 24, 30, 34
42, 66, 90, 96, 102
Page I-i

J. W. Whittaker
Mark A. Friesel and Jack F. Dawson

Conferences and Symposia
Progress in Acoustic Emission VI, Proceedings of The 11th International AE Symposium
36th Meeting of Acoustic Emission Working GroupIndex to Volume 10

Volume 21, 2003
21-001

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

1

21-014

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

14

21-029Analysis Of Source Location Algorithms, Part Ii: Iterative Methods
Maochen Ge29

21-052

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

52

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

70

21-A01

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

A1

21-095

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

95

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

112

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

120

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

131

21-07021-11221-12021-13127

21-142

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

142

21-149

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

149

21-157

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

157

21-166

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

166

21-176Development Of A Novel Optical Fiber Sensor For Ae Detection In Composites
Isamu Ohsawa, Kazuro Kageyama, Yukiya Tsuchida And Makoto Kanai

176

21-187

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

187

21-197

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

197

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

206

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

213

21-223

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

223

21-230Operation Monitoring Of Roll Cover By Acoustic Emission
Juha Miettinen And Pekka Salmenperä

230

21-20621-213

Contents21.Pdf
Auindex21.Pdf
Ausnotes.Pdf

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

Iaes16.Pdf

Iaes16, 16-th International Acoustic Emission Symposium

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

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

Wavelet Transform Freeware And Introduction By M. Hamstad

Volume 22, 2004
22-S01The Kaiser-Effect And Its Scientific BackgroundHans Maria Tensi

S122-001Modal-Based Identification Of Acoustic Emission Sources In The Presence Of Electronic Noise
M. A. Hamstad And A. O’gallagher

1

22-A01*

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

A1

22-022

Experience With Acoustic Emission Monitoring Of New Vessels During Initial Proof Test
28

Phillip Cole And Stephen Gautrey

22

22-030Quantitative Damage Estimation Of Concrete Core Based On Ae Rate Process Analysis
Masayasu Ohtsu And Tetsuya Suzuki

30

22-039

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

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

49

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

22-071

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

71

22-077Detection Of Incipient Cavitation And Best Efficiency Point In A 2.2mw Centrifugal Pump Using
Acoustic Emission
L. Alfayez And D. Mba
7722-083

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

83

22-091

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

91

22-102

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

102

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

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

119

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

127

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

138

Basic Principles Of Acoustic Emission Tomography
Frank Schubert

147

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

159

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

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

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

29

173

22-190

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

190

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

201

22-208

Acoustic Emission Examination Of Polymer-Matrix Composites
Jürgen Bohse

208

22-224

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

224

22-236

Acoustic Emission Measurement System For The Orthopedic Diagnostics Of The Human Femur
And Knee Joint
R.P. Franke, P. Dörner, H.-J. Schwalbe And B. Ziegler236

22-243Rods And Tubes As Ae Waveguides
Kanji Ono And Hideo Cho

243

22-253

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

253

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

264

The Effect Of Waveguide Material And Shape On Ae Transmission Characteristics - Part 2:
Frequency And Joint-Time-Frequency Characteristics
Joanna Sikorska And Jie Pan274

22-20122-26422-274

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

Jae Index Folder*

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

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

* Indicates The Availability In CD-Rom Only.

Volume 23, 2005
23-001

23-02523-037

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

1

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

25

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

3730

23-047

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

47

23-064Development Of An Optical Micro AE Sensor With An Automatic Tuning System
Hiroshi Asanuma, Hironobu Ohishi, And Hiroaki Niitsuma

6423-072Development Of Stabilized And High Sensitive OpticalFiber Acoustic Emission System And Its Application
Hideo Cho, Ryouhei Arai And Mikio Takemoto

72

High Precision Geophone Calibration
Masahiro Kamata

81

Development Of Heat-Resistant Optical Fiber Ae Sensor
Pornthep Chivavibul, Hiroyuki Fukutomi,
Shin Takahashi And Yuichi Machijima

91

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

96

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

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

113

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

119

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

129

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

136

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

142

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

150

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

156

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

16423-081
23-091

23-09623-102

23-11323-11923-12923-13623-14223-15023-15623-16431

23-173

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

173

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

181

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

189

Ae And Electrochemical Noise Analysis For Fracture
Study Of Hard Surface Film
Akio Yonezu, Hideo Cho, Takeshi Ogawa And Mikio Takemoto

196

23-206The Origin Of Continuous Emissions
Kanji Ono, Hideo Cho And M. Takuma

206

23-215

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

215

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

224

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

233

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

243

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

249

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

260

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

272

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

277

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

28523-181

23-18923-19623-22423-23323-24323-24923-26023-272

23-27723-28532

23-292

23-29923-310

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

292

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

299

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

310

23-318

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

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

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

I-1 - I-4
I-5

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

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

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

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

1224-022

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

2224-044

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

4424-052

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

24-067

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

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

Utilization Of Cascade Optical Fiber Ae System For Source Location Of Lamb Waves Through A
Cross-Ply Cfrp Plate
33

331

Takuma Matsuo, Hideo Cho And Mikio Takemoto84

24-097 Elastic Waves From Fast Heavy-Ion Irradiation On Solids
Tadashi Kambara, Yasuyuki Kanai, Takao M. Kojima, Yoichi
Nakai, Akira Yoneda, Yasunori Yamazaki And Kensuke Kageyama
9724-104

Acoustic Emission Rate Behavior Of Laminated Wood Specimens Under Tensile Loading
Andreas J. Brunner, Martin T. Howald And Peter Niemz

104

24-111

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

11124-119

Acoustic Emission Of Sensitized 304 Stainless Steel With Simultaneous Hydrogen Charging
S. H. Carpenter, Kanji Ono And D. Armentrout
11924-127

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

127

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

139

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

14524-13924-14524-153

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

24-161

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

24-173

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

173

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

24-187

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

187

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

196

24-19624-205

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

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

34

24-222

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

222

24-228

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

228

24-234

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

Contents24.pdf
AUindex24.pdf
AusNotes.pdf

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

JAE Index Folder*234

I-6 - I-8

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

* indicates the availability in CD-ROM only.

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

25-021

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

25-033

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

25-042

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

25-051

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

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

25-080

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

25-092

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

25-107

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

35

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

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

25-132A SIMPLE METHOD TO COMPARE THE SENSITIVITY OF DIFFERENTAE SENSORS FOR TANK FLOOR TESTING
HARTMUT VALLEN, JOCHEN VALLEN and JENS FORKER

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

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

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

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

25-172

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

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

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

25-194

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

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

25-224

ACOUSTIC EMISSION SOURCE LOCATION ON AN ARBITRARYSURFACE BY GEODESIC CURVE EVOLUTION
G. PRASANNA, M. R. BHAT and C. R. L. MURTHY36

25-231

PROBABILITY OF DETECTION FOR ACOUSTIC EMISSIONADRIAN POLLOCK

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

25-247

REAL-TIME DENOISING OF AE SIGNALS BY SHORT TIME FOURIER
TRANSFORM AND WAVELET TRANSFORM
KAITA ITO and MANABU ENOKI

25-252

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

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

25-267

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

25-276

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

25-286

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

25-294

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

25-308

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

25-316

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

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

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

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

25-348

MAPPING OF WHEEL FLANGE RUBBING ON RAIL USING AE:
LABORATORY TEST
37

N. A. THAKKAR, R. L. REUBEN and J. A. STEEL
25-355DAMAGE ASSESSMENT OF GEARBOX OPERATING IN HIGH NOISYENVIRONMENT USING WAVEFORM STREAMING APPROACH
DIDEM OZEVIN, JASON DONG, VALERY GODINEZ and MARK CARLOS

25-364

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

25-373IMPACT IMAGING METHOD TO MAP DAMAGE IN CONCRETE BRIDGEDECK SLABSSTEPHEN D. BUTT, VIDYADHAR LIMAYE and JOHN P. NEWHOOK

Contents25
AUindex25
AusNotes
In Memoriam
EWGAE27

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

JAE Index Folder*

I-8 – I-10
I-12

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

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

Volume 26 (2008)
26-001

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

1-13

26-014

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

26-023

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

26-032

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

32-39

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

40-59

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

60-71

26-072

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

72-90

26-091

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

91-97

26-098Laboratory Experiments for Assessing the Detectability of Specific Defects by Acoustic Emission
TestingFranz Rauscher
98-10826-109Integrity Evaluation of COPVs by Means of Acoustic Emission Testing

26-04026-06038

Yoshihiro Mizutani, Kouki Saiga, Hideyuki Nakamura, Nobuhito Takizawa, Takahiro Arakawa
and Akira Todoroki
109-11926-120

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

Automated Method for Statistical Processing of AE Testing Data
V. A. Barat and A. L. Alyakritskiy

132-141

26-142

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

26-152

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

152-159

26-160

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

26-172

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

26-182

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

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

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

26-208

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

208-219

26-220Natural Fiber Composites Monitored by Acoustic Emission
Igor Maria De Rosa, Carlo Santulli and Fabrizio Sarasini220-228

26-229

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

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

26-247

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

26-262

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

172-181262-269

26-270Experimental Simulation and Dynamic Behavior of the AE due to Martensitic Transformation
Using Shear Wave Transmission Sensor
Takeshi Yasuda, Shinya Kondo, Hideo Nishino and Kenichi Yoshida270-278

26-279Implementation of Acoustic Emission Method to the Conventional NDT Structure in Oil Refinery
V.P. Gomera, V.L. Sokolov and V.P. Fedorov
279-289

26-290An Experimental Analysis of Frequency Emission and Noise Diagnosis of Commercial Aircrafton Approach3926-31126-317

on Approach
S. Khardi

290-310

New Developments of Software for A-line Family AE Systems
Sergey Elizarov, Аnton Bukatin, Мikhail Rostovtsev and Denis Terentyev

311-317

Application of Acoustic Emission in Optimizing the Design of New Generation castings ofHigh-Voltage Electric Devices
Jan Płowiec, Wojciech L. Spychalski, Huber Matysiak and Jakub Michalski
318-325

Contents26
AUindex26
AusNotesContents of Volume 26 (2008)
Authors Index of Volume 26
Policy/Author’s Notes/Meeting Calendar/DVD/Subscription Information

I-1 – I-3
I-4
I-5 – I-7

JAE Index Folder*
Cumulative Indices of J. of Acoustic Emission, 1982 - 2008
PhD Thesis of Runar Unnthorsson, University of Iceland** indicates the availability in CD-ROM only.
Cover illustration is from 26-160 by Toshimitsu Hayashi and Shuichi Wakayama.
This figure shows the WT diagram, AE signal, projected WT-frequency curve and FFT spectrum. While WT
identifies the first major frequency at 0.56 MHz and the second major frequency at 0.32 MHz, FFT shows the 1st
and 2nd major frequencies of ~0.3 MHz as these frequency components have long duration. This clearly shows that
FFT analysis failed to detect the highest characteristic frequency at 0.56 MHz.
In using FFT, it is essential to be cognizant of this shortcoming.

40

Authors Index, Volumes 1-26 (1982-2008)
Satoshi Abe
A. R. Acharya
C. Howard Adams
E. Aernoudt
B. D. Agarwal
Dimitrios G. Aggelis,
Mamoru Aizawa
T. Aizawa
Chandra Ajay
Kensuke Akamatu
Y. Akematsu
M. Akiyama
Morito Akiyama,
L. Alfayez
R.S. Algera
S. S. Ali
Bernhard Allemann
A.F. Almeida
A. L. Alyakritskiy
Masashi Amaya,
J. F. R. Ambler
G. AmirA. A. Anastassopoulos,
Naoto Ando
E. Andres,
M. Annamalai
K.-I. Aoki
K. AokiKazuaki Arai
Ryouhei Arai
Takahiro Arakawa
D. Armentrout
Baxter H. Armstrong,
J. H. Armstrong
M. Arrington
Ikuo Asano
M. Asano,
Hiroshi Asanuma
J. Asquith,
S. K. Athithan
Y.H.J. Au,
B. Audenard
J. Aviassar
Janine Avison

19-045
8-S88
1-165
4-S186
8-S29725-069, 25-308
21-149
6-85
4-S17424-05221-22316-S150
25-10722-077
2-69
4-S42, 4-107
14-11915-S107, 15-S108
26-132
13-S35
5-S16
2-64
13-011, 18-021, 18-217, 18-224, 20-229, 22-059, 23-318,
25-033
9-209
18-155
8-S8
8-S131
8-S14524-11123-07223-243, 26-109
10-97, 10-103, 15-43, 16-S10, 24-119
8-S250, 12-117
4-S135
4-S165
4-S24019-13423-064, 23-129
18-211
4-S26
18-196
1-148
1-179
26-0911

J. Awerbuch

8-S301Friedrich-Wilhelm Bach,
Baldev Raj

25-331
4-S102, 6-209, 7-S1, 8-S126, 8-S140, 8-S149,
11-43
3-182
23-11919-2098-S217, 9-142
1-179
17-S37, 23-173, 24-044, 24-222, 25-341
8-S140, 8-S149
26-13222-127, 25-042
23-113
18-87
2-69
18-61
8-S75
12-1494-S22425-21518-131, 19-241, 20-188
24-022
4-S244, 4-19, 5-71, 6-151, 8-S311, 9-197, 9-215,
10-83, 12-i (1/2), 12-55
1-21, 1-300, 2-67, 2-69, 2-95, 2-143, 3-224, 3-239, 4-65,
5-53, 5-172, 15-63, 15-S111, 21-095, 23-299, 23-331
7-59
26-208
12-45
4-71, 9-271
5-1
15-S117
26-0601-35, 7-59
11-85
15-S117
4-S178, 4-S300, 6-43, 11-11
12-27
8-S2094-71
25-224
4-S102, 8-S149, 11-43
5-S69
14-S47
12-55
4-S321
26-120G. R. Baldwin
Rao Balusu
R.H. Bannister
V. Bansal
J. Baram
Ireneusz Baran,
P. Barat
V. A. Barat
Michel Barbezat,
Roy Baria
A. Barron,
J.A. Baron
M.W. Barsoum,
H. Barthelemy
B. L. Baskin,
M. Nabil Bassim
Matthew Baxter,
M. Bayray,
Mulu Bayray
Frank C. Beall,
Alan G. Beattie
M. J. Beesley
Bernd-Arno Behrens
S. Béland,
R. M. BelchamberDavid A. BellS. H. Benabdallah
Petr Benes
P. G. Bentley
Avraham Berkovits
C. C. Berndt
J. M. Berthelot,
Yves H. Berthelot,
M. M. Besen
D. Betteridge
M. R. Bhat,
D. K. Bhattacharya,Frank S. BiancanielloThomas Bidlingmaier
Jacek M. Biernacki,
T. Kevin Bierney
Christophe Binétruy

2

B. Birolo
Jan Bisschop
Michal Blahácek
P.R. Blackburn
M. J. Blanch
Richard W. Blank
J. A. Blessing
Thomas Blum
Andrew R. Blystra
T. Boczar
P. Böhm
Jurgen Bohse
Yasuyuki Bokoi
R. J. Boness
R.W. Bosch,S. F. Botten
S. Bousias,
P. Bowen,
S. J. Bowles
P. Bowman
Marcelle Brachet
John Brandon
Franklin R. Breckenridge
J. C. Briggs
W. D. Brosey
Nancy Brown
C. Brun,A.J. BrunnerAndreas J. Brunner,
G. Budenkov
R. Budzier
C. Buelens,Аnton Bukatin
E. Bulatova
O. Burenko
Maria Burger
Stephen D. Butt,
D. J. Buttle
G. Buzzacchi

4-S25520-153
18-279, 20-163, 20-274, 22-138
7-49
20-2299-181
8-S236, 8-1
7-179
10-S4917-S7
9-29
15-S108, 16-S233, 16-S343, 19-001, 22-208
16-S196
8-S192, 15-S117
18-2938-S33025-03313-S0810-49
7-225, 8-S4
2-159
17-49
1-87, 3-59, 10-43
8-S2097-31, 8-S280, 9-75, 9-84
10-71
18-15515-S108,22-127, 24-104, 25-042
17-S13, 17-S51
20-172
18-34
26-311
17-S13
7-31
22-10225-3737-211, 8-S158, 8-S201, 9-243, 9-255
2-11

CARP Aerospace/Advanced Composites Subcommittee
11-C1
Giuseppe Campanella,25-324
Bruce Campbell
3-81
Claudio Caneva,
25-080Nicholas J. Carino
5-S24
Mark Carlos
15-S104, 15-S107, 15-S109, 18-189, 18-248, 18-272, 25-355
John M. Carlyle
4-S329Victor Caron
4-S259, 4-115
Steve H. Carpenter,
1-251, 2-191, 3-11, 3-81, 4-S119, 4-S135, 5-77, 6-115,
3

Damian Carter
M. Cartoceti
Dick W. Caster
Enrique Castro
J. Catty,
M. Cerny
J. Cerv
Roger W.Y. Chan
C. Chang
Chung Chang
C. Chapelier
T. Chelladurai
D. Chellman
Chung-Mei Chen
Jihua Chen
M.C.Cheresh
M. Cherfaoui
Michael J. Chica
A. Chichibu
Chang-Sheng Chien,
Y. S. Chin
Pornthep Chivavibul
Robert Chivers
T. Chow
Y. T. Chow
Min-Hwa Chung,T. Chelladurai,
M. Cherfaoui
Akiyoshi Chichibu,
Milan Chlada
Frantisek Chmelík
Hideo Cho
N. S. Choi
A-A. R. Choudhury
S. Y. Chuang
S. S. Christiansen
F. Cipri
M.A. Clark
T. N. Claytor
Roger B. Clough
D. M. Chuck
P. ColePhillip T. ColeC. E. Coleman

6-136, 6-177, 6-215, 7-9, 7-161, 8-S135, 8S184, 8-125, 9-1, 10-97, 10-103, 11-5, 12-141, 13-S01,
15-43, 16-S10, 24-119
17-49
2-11
9-197
26-142, 26-247
18-205
17-S2020-025
3-118, 4-S259, 4-115, 8-S12
4-S62
7-21
5-S52
8-S88
4-S2637-161
19-001
2-289
5-S66, 22-071
9-197
8-107
15-S118
11-71
23-091, 24-076
10-1238-S166
4-71
13-1
12-111
11-1, 18-144
11-S47, 12-S1
17-S57, 20-134
17-S29, 20-108
16-S115, 21-112, 22-119, 22-224, 22-243, 23-072, 23-196,
23-206, 23-277, 24-012, 24-067, 24-084, 24-127, 24-161,
25-107, 25-124, 25-157, 25-172, 25-267, 26-072
16-S324
16-S125
4-S137
4-S116, 5-85, 8-S184
4-S25515-S107
4-S69
5-S69
8-S25815-S109
8-S239, 8-31, 18-61, 18-180, 18-232, 19-191, 22-022
5-S16
4

J. Collins
M. P. Collins
Peter J. Conlisk
A. W. CookDavid B. Cook
R. Daniel Costley, Jr.,R. A. Coyle
J. Crha
H.-A. Crostack
Timothy G. Crowther
M. E. A. Cudby

4-S1349-271
8-1
8-S101
17-83
12-27
6-249
17-S45
9-29
10-71
4-71

C. E. D'Attellis
V. Dal Re
Wojciech Darski,
B. Dattaguru
A.W. Davies,
Jack F. Dawson
V. G. Ruiz de Argandoña
Igor Maria De Rosa,
Juan-José G. De La Rosa
S. De Bondt,
I. De Iorio
P. De Meester
C. De Michelis
L. M. Suárez del Río
L. Delaey
H. A. L. Dempsey
O. Derakhshan
J. Derenne,
A. Deruyttere
Wendy Desadeleer,
M. Diez
Seth-Andrew T. Dion,John J. Ditri,
Nitin Dhond
G. Dionoro
Jason W.P. Dong
Jason Dong,
P. Dörner,David A. Dornfeld

10-13
4-S255, 4-S270, 5-39
25-341
6-19
18-232
10-117
10-S3525-080, 26-220
26-142
11-95
3-158
4-S186, 8-S272, 13-79, 15-S105, 18-34
2-11
10-S35
11-95
4-S46
8-S22318-29911-95
22-2534-S29625-187
12-23
4-S30
2-281
16-S1
25-35522-2363-242, 4-S123, 4-S228, 7-103, 7-111, 8-S227,
6-29, 6-37, 6-157
see Reis18-96
13-31, 13-45, 13-56, 14-S61, 15-S109, 16-iv, 16-S333,
21-052, 21-070, 21-A01
26-1896-239
1-45, 1-81, 1-121, 1-195, 1-271, 2-129, 2-221, 2-292, 3-46,

Henrique L. M. dos Reis,
R.D. Douglas,
Karyn S. Downs,
Martin Drab
M. W. Drew
T.F. Drouillard,

5

Q. Duan
Qingru Duan
R. Dubiel,
J. C. Duke
H.L. Dunegan
J. Dunning
C. Divaker Durairaj
F. Dusek
A. G. Dutton
C. DuytscheJ. Dvoracek,
B.C. Dykes
Yuris A. Dzenis
Mark Eaton,
J. Eberlein
Davis M. Egle
Gernot Eilers
J. Eisenblätter
Islam El-Galy
T. El-Raghy,Sergey Elizarov
R.K. Elsley
T. ElyD. C. Emmony
C. Ennaceur,
Manabu Enoki,

3-90, 3-164, 3-212, 4-41, 5-103, 9-45, 9-155, 9-215,
11-53, 12-71, 12-79, 13-42, 14-1, 16-v
8-S97
16-S243
18-15
8-S1798-S71, 15-53, 15-S106, 16-v
4-S22
17-15
10-1
20-229
3-176
18-81
4-S35
15-S112, 20-016, 20-099

R. M. Esbert
V. A. Eshwar
Sam Evans,
W. T. Evans

25-140, 25-215, 26-172
20-1722-ii (3), 3-104, 4-S30, 4-S46, 6-205
19-100
15-S119, 16-S85, 19-100, 19-153, 24-179, 24-196
26-20818-61
26-311
2-47
16-S101-263
22-071
4-S195, 8-S154, 13-S29, 15-S90, 15-S120, 16-S269,
21-142, 23-292, 23-310, 24-139, 25-247, 26-182
10-S35
8-S217, 9-142
25-21511-71

A. Fahr,S. D. Falls
Daining Fang
Ahmed M. Farahat
J.-P. Favre
Carol Featherston,
V. P. Fedorov
G. Fernando
F. Ferrer,
Steven E. FickAndrzej Figiel
Chantal Filion
F. Finck,
P. Finkel,

8-S314, 12-39, 15-S122
8-S166
11-85
11-S37
9-97
25-14020-218, 26-279
20-22918-155
4-S311
4-S182
16-S186
22-083
18-61
6

R.D. Finlayson,
J. F. Finn
P. FleischmannPeter Flüeler
T. Flynn
Jens Forker,
D. S. Forsyth
R. Fougeres
Timothy J. Fowler
H. Frackiewicz,
R.P. Franke,
Michael J. Friedel
M.A. Friesel,
L. Froyen,
Yoshiaki Fujii
Taisaku Fujioka
Tetsuro Fujiwara
T. Fuketa,
Hiroyuki Fukutomi
Roy D. Fultineer, Jr.
Masami Fushitani,
Takashi Futatsugi

18-61, 18-189, 18-272
8-S79
3-176, 5-S42, 9-91, 19-229
22-127
10-S5918-258, 25-132
15-S122
5-S42, 9-91
8-S236, 8-1
12-14922-236
10-S77
3-11, 3-239, 7-119, 10-117, 18-61, 18-189
11-95
23-11915-S31
10-S63, 11-S65, 15-S40, 15-S113
11-21
23-091, 24-076
15-S103
4-S240, 9-209, 13-S42
23-249Laurent Gaillet
M. Gakumazawa
Antolino Gallego
Thomas Gandy
D. S. GardinerJohn Gary,

26-032
16-S150
26-142, 26-247
4-S166
4-S19912-157, 14-103, 15-S115, 16-S251, 17-37, 17-97, 19-258,
20-039, 20-062
7-111
14-119
7-S13
18-180, 19-191, 22-022
8-S27715-S105, 8-S32, 21-014, 21-029
17-S37, 19-285, 20-083
12-171
18-21
10-19
8-S301
4-S86
4-S307
18-96, 18-211
10-S1, 25-316
1-81
4-S1701-263,
4-103, 18-272, 25-355

Michael F. Gasick
Ludwig Gauckler
B. K. Gaur
Stephen N. Gautrey
A. Gavens
Maochen Ge
P. Gebski
D. Geisse,
B. Georgali,
János Geréb
S. Ghaffari
S. Ghia
Al Ghorbanpoor
J.D. Gill,
Steven D. Glaser
T. G. GlennA. M. G. Glennie
M. W. Godfrey
Valery Godinez,

7

L. Golaski
Douglas E. Goldsack
Edward Goliti
V.P. Gomera,
Javier Gomez,
Carlos M. Valdes-Gonzalez,
M. Gori,Michael R. Gorman
G.L. Goswami
J. Goudiakas,
Per A Gradin,
Igor Grabec
L. J. Graham
A. T. GreenJ. E. GreenP. Gregson,
David W. Greve,
Arthur T. Grodotzke
Boguslaw Gronowski
D.J. Gross
S. Gross,
C. U. Grosse
Christian Grosse,
C. M. Grossi
Janez Grum
N. Gsib
P.-Y. GuF. J. Guild
T. J. Gulley
M. Gullikson
Dawei Guo
Hua GuoYanfeng Guo
B.C. Gupta

1-95, 4-S182, 17-S37, 19-285, 20-083, 24-187
16-S186
5-7
18-111, 20-218, 26-279
13-S21
12-117
18-167
8-51, 9-131, 9-283, 13-S01, 14-i (3/4), 17-29, 23-037
4-S98, 4-S251, 7-S40, 7-S43
18-15513-97, 26-023
8-S20, 8-S205, 11-79
2-47
4-124, 8-S306
1-191, 2-289
18-23925-115
1-29
4-S82, 5-25
8-25
18-23914-S74, 22-083
25-316
10-S3526-199
5-S60
8-S188
1-244
4-S17026-02314-S19, 16-S222
23-11916-S317
8-S217, 9-142

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

9-181
8-S314
8-S42
5-15
20-265
15-S404-S325
9-103
2-57, 2-i(3), 5-95, 5-110, 5-123, 6-93, 9-75, 9-84,
11-33, 12-157, 13-31, 13-45, 13-56, 14-103, 14-S61,
15-1, 15-S108, 15-S109, 15-S115, 16-S222, 16-S251,
16-S333, 17-37, 17-97, 19-258, 20-039, 20-062, 21-052,
21-070, 21-A01, 22-001, 22-A01, 23-001, 23-047,
24-234, 25-092, 25-194, 26-040
8

L. Hanacek
Kotaro Hanabusa
Mineyuki Hanano
L. D. Hall,
V. Hänel
J. J. Hanley
Christian Hansmann
Yoshio Harada
H.R. Hardy, Jr.
Robert W. Harris
W. F. Hartman
J. Harvey
H. Nayeb-Hashemi,
Toshiyuki Hashida,
F. P. Hassani
Hiroaki Hata
Hajime Hatano
D. Robert Hay
J. R. Hay
Hiroshi Haya
Tomoharu Hayano
F. Havlícek
M. W. Hawman
Takefumi Hayashi
Toshimitsu Hayashi
Yoshie Hayashi,
Y. Hayashi
Yasuhisa Hayashi
Jianmei He
R. Hecker
C. R. Heiple
D. P. Henkel,
Edmund G. Henneke II
B. Herrmann,
C. Hervé,
Stefan Heusermann
S. Hewerdine
Hartmut Heyse
H. HickKazuo Hiekata
Christopher Higgins,
Y. HigoHiroshi Hikosaka
Eric v. K. Hill,
Roger Hill
Takayasu Hirakawa
Yuichiro Hirose

8-S84
22-159
11-S7519-20914-115
11-27
26-01423-181
3-242, 4-S19, 8-S32, 8-S42, 8-S262, 8-65,
10-61, 15-S105, 15-S118, 16-S277
6-239, 8-S14, 8-S66, 10-S29, 10-S59, 15-S113, 17-121
1-144, 4-S64, 5-31
4-S220
12-1
13-S68, 24-215
8-99, 10-61
7-173, 10-S63
15-S115
3-118, 4-S259, 4-115, 8-S12, 9-9
8-S12
22-039, 23-260, 24-205
24-067, 25-107
17-S45
4-S131
8-35
26-16019-03521-1317-185, 14-69, 15-S108
20-194
4-S78
1-221, 1-251, 4-S116, 5-85, 6-177, 6-215, 8-S184,
8-125, 9-1, 10-97, 10-103
8-S318
8-S277, 14-53
18-167
18-125, 22-071
16-S85
8-S238, 8-21
5-45
10-67
19-04525-316
8-S24, 16-S150, 16-S196, 19-085
10-S1325-187
1-73, 1-149, 1-294, 5-51, 5-i(1), 10-124, 16-S125
23-15626-1829

Y. Hisamatsu
Koji Hisamatsu
S.V. Hoa
A. B. M. Hoff
Karen Holford
T. J. Holroyd
Kyoji Homma
Michel Hone
K1-Jung Hong,
Theodore Hopwood II
K. Horikawa,
Keitaro Horikawa
Siegfried Horn
P. M. Horrigan
Filip Hort
J.R. Houghton
Martin T. Howald
O. Hoyer
Nelson N. Hsu,
S.-Y. S. Hsu
J. Hu
Christian Huber
J. H. Huh
Derek Hull
Patrice Hulot
Donald H. Humes
Wolfgang Hundt
U.-D. Hünicke
David A. Hutchins
David V. Hutton
H. Hutton

2-19, 2-71
11-S1
7-145, 9-37, 11-65
4-S16517-49, 18-232, 22-166, 25-140, 25-215, 26-172
4-S132, 7-193, 8-S219
10-35
4-S259, 4-115
13-S61
4-S30419-02221-206, 21-223
26-001
8-S79
26-1898-S28, 8-S223, 14-61
24-1049-103
4-S311, 5-S24, 5-S28, 5-S29, 13-23
1-183, 1-237, 2-169
16-S150
22-127
15-S80
1-95
26-120
17-29
14-11920-172
5-S29, 5-S34, 8-S38, 8-S166
8-41
3-239, 4-S74, 4-S138, 6-167

Hatsuo Ichikawa
Y. Ichimura
Makoto Ichinose
Hassane Idrissi,
Chikako Ikeda
Junji Ikeda
R. Ikeda
Yukifumi Ikeda
Sei Ikegaya
H. Imaeda
Takuichi Imanaka
Hidehiro Inaba,
Takako Inaba
T. Inamura,
Ichiro Inasaki
Tomoaki Inoue
Yoshiki Inoue

4-S32519-14220-001
18-299, 18-307, 26-032
24-22824-173, 24-228
21-131, 22-119
23-272
23-096
4-S294
4-S38
8-S24, 19-196
10-S9019-085
7-179
2-1
11-S8910

Akichika Ishibashi
T. Ishida
Takeshi Ishigohka
K. Ishihara
Hisashi Ishitani
C. Ishiyama
Toshiro Isoda,
Ken-Ichi Itakura,
Kaita Ito
D.S. Ivanov
V.I. Ivanov,
N. Iverson,
K. Iwai
K. IwakiKeisuke Iwaki
Y. Iwata
S. Iyer,Takeshi Izuta,

11-S65, 15-S40, 15-S113
10-S4224-111
6-13
4-S32516-S150, 16-S196
25-021
13-S54, 13-S75, 19-109, 23-119
24-139, 25-247
26-24018-14425-36421-197
21-16623-260
16-S142
18-18913-S42

Laurence J. Jacobs,
Jay B. JamesP. JaxT. Jayakumar
J. S. Jeng
Hee-Don Jeong,
B. B. Jha
S. K. Jha
Kyung-Young Jhang
C.Y. Jian,
C. G. Jiao
Takehiko Jibiki
K. Jo
J. Johkaji
C. H. Johnson
Malcolm J. S. Johnston,
W.D. Jolly
H. Jonas
L. E. Jones
R. H. Jones
R. K. Jones
Magnus T. Jonsson,
Young-Chan Joo
P. A. Joosse,
G. JothinathanMusa K. Jouaneh
S. J. Jung,

12-27
4-S119, 5-77
2-29, 8-S53
4-S102, 7-S1, 8-S126, 8-S140, 8-S149, 11-43
8-S268
13-S616-209, 8-S149
7-S43
16-S261
13-S68
8-S10524-173
8-107, 10-S55
8-S1
4-S111, 4-S263
12-117
4-103
4-S78
20-2293-239
7-119, 8-S223
25-252, 25-260, 26-229, 26-262
15-S1, 16-S212
20-2294-S207
10-83
8-S326

B.S. Kabanov,Koji Kagayama

18-111, 20-218
23-277, 24-127
11

Kazuro Kageyama,Kensuke Kageyama
Hideshi Kaieda
Katsuyuki Kaiho
Karl-Ulrich Kainer
Koji Kaino
K. Kajiyama
T. Kakimi
Y. Kakino
Małgorzata Kalicka
S. Kallara
P. Kalyanasundaram
T. Kamada,
M. Kamata
Masahiro Kamata
Tadashi KambaraPeter Kamlot
Makoto Kanai
Yasuyuki Kanai
Yasuhiro Kanemoto
Elijah Kannatey-Asibu, Jr.
Shigeto Kano
C-S. Kao,
Justin O. Karl,
Bo Karlsson
M. KatChiaki Kato
Tatyana Katsaga,
K. Katsuyama
Kunihisa Katsuyama
Harold E. Kautz,
Tatsuya Kawada
Yoshiaki Kawaguchi,
Yutaka Kawai,
Teppei Kawakami
Kenji KawamotoTomaž Kek
James R. Kennedy
Jiri Keprt
Shahla Keyvan
A. Wahab Khair
A. S. Khan,
A. S. Khanna
S. Khardi
E. W. Khokhlova,
Jens Kiehn
M. T. Kiernan
S. Kihara,
Tadashi Kikuchi

13-S89, 19-045, 21-176
24-09723-12924-11120-108
5-61, 9-277
19-022
2-19
3-108
24-187
8-S28
8-S140, 8-S149
19-1348-107
23-081
24-097
16-S8521-17624-097
15-S40
15-S118
6-109, 6-145
25-36425-18726-0148-99
19-05325-294
11-S1911-S275-144, 12-65
24-21512-127
7-167
21-1499-109
26-1994-S90
26-06010-91, 14-97, 15-79
4-S1, 8-S326, 15-S105, 16-S53
8-S246
6-209, 8-S103
26-29012-14920-108
8-S176
18-68
11-S47
12

Cindi Kilkenny
Byoung-Geuk Kim
Byung-Nam Kim
Dal-Jung Kim
K. H. Kim
K. Y. Kim
Kyung-Woong Kim,
Sang-Hyo Kim
Isao Kimpara,
H. Kimura
Ron King
Tetsuo Kinjo
N. Kinoshita
Teruo Kishi
Fuyuhiko Kishinouye
Takahiro Kishishita
N. N. Kishore
P. Kisnomo
Tatsuo Kita
K. Kitadate
K. Kitano
Shigeo Kitsukawa
Kiyoshi Kiuchi
R. A. Kline
W. Knabl
Markku Knuuttila
E. Kobayashi
H. Kobayashi
Satoshi Kobayashi
Takao Kobayashi
Yoshifumi Kobayashi
Y. Kobayashi
Miroslav Koberna
R. M. Koerner
Tsuguaki Koga
Takao Koide,
Masami Koike,
Takao M. Kojima
B. Koktavy,
J. G. Kolaxis
V. Kolovos,
Hidemichi Komura
Shinya Kondo
Johannes Konnerth
Shigeo Konno
Ja-Ho Koo
T. M. Kooistra

16-S186
15-S120
12-S24, 15-S90, 16-S269
16-S261
4-S282
4-S62, 8-S170
13-1
15-S11
13-S89, 19-045
4-S294
10-91
15-19, 14-69
10-S42
1-1, 2-19, 2-71, 4-S191, 4-S195, 4-S278, 4-S282,
4-S325, 6-85, 8-S131, 8-S154, 12-127, 13-S29,
15-S90, 15-S120, 16-S269
9-177, 9-180
10-S55, 11-S47
8-S297
15-33
10-S908-S131
10-S4223-23319-0534-S42, 4-107, 6-205, 8-S246
20-257
19-1623-130, 4-93
8-S14521-149, 23-150
21-001
23-181
8-S1
11-61
1-220, 2-187, 2-195, 4-S11, 4-31
2-1
13-S47
19-20224-097
17-S100
17-69
18-217
19-19626-27026-01423-23315-S90, 16-S269
20-23813

Atsushi Korenaga,
M. Korenska,
Leszek Korusiewicz
I. Kosiková
T. Kossivas
V. Kostopoulos
A. Kotolomov
D. A. Kouroussis,
Torsten Krietsch
R. Krishnamurthy
Vladimir Krivobodrov,
J. Krofta,
J. Królikowski,
D.A. Kronemeijer,
Joseph Krynicki
Takafumi Kubo,
I. Kukman
P. G. Kulkarni,
Yusuke Kumano
J. Sampath Kumar
M. Kumosa
M. Kunieda
D. S. Kupperman
Yu Kurokawa
Krzysztof J. Kurzydlowski
Yasufumi Kusano,F. M. Kustas
T. Kusu
May Man Kwan
Oh-Yang Kwon
H. Kwun,19-196
18-29
2-272
17-S100
20-22920-265
17-S51
18-217, 18-224, 20-229
16-S233, 16-S343
4-S26, 8-S88
13-87
18-279, 20-274
12-14918-17415-S116
13-S42
8-S205
7-S13
23-1297-135
1-95, 16-S10
19-134
4-S69
23-224, 24-145
25-166
13-S75
10-97
21-1423-144, 3-190
4-S106, 9-123, 9-227, 9-237, 13-1, 13-S83, 15-S1,
15-S19, 16-S212
11-27

J.F. Labuz,
G. Lackner,
J.-C. Laizet
A. Laksimi,
V. Lalitha
Michal Landa
E. Landis
R. J. Landy
Terence G. Langdon
F. Langella
Stanislaw Lasocki
Robert J. Lark
Ralf Laschimke,
A. Lavrov
Marcel F. LeachJames D. Leaird,

25-36418-167, 20-179, 22-201
9-97
18-125, 22-071
4-S26
17-S57, 20-025, 20-163, 22-138
10-S97, 15-S104
1-7
20-108
3-158
4-S7
22-166, 26-172
22-10220-292
10-S18, 11-19, 16-S186
3-204, 4-S22, 8-S322, 12-117
14

R. D. LeblancChong Soo Lee,
Joon-Hyun Lee,K. A. LeeP. Y. Lee
Sang-Ho Lee
Seung-Hwan Lee
Sekyung Lee
Seung-Seok Lee
Weon-Heum Lee
L. Legin
Xinglin Lei
Jack Leifer
Constantina Lekakou
D. J. Lekou
A. M. Leksowskij,
A. Lemascon
Richard L. Lemaster,J. C. Lenain,
K. H. Leong
Armand F. Lewis
Luidmila Lezvinsky
Steinar Lfvaas
Bang xian Li
Jihui Li
L. Li
Y. P. Li
Zhengwang (Z. W.) Li
Z. W. Li
Steven Y. Liang
T. Lilley
S. Lim,Jae-Kyoo Lim
R. Lima
A. Limam,Vidyadhar Limaye,
C. K. LinDan Lindahl
J. Liöka
S. LiuShifeng Liu
T. LiuX1-qiang Liu
Y.-H. LiuDavid A. Lockner
Manuel Löhr,
T. Lokajícek
Thomas Lokajicek

15-S122
13-S61, 15-S80
13-S8315-S80
15-S117
15-S11
15-S1915-S120
15-S11
16-S261
18-14423-102
4-S12723-02520-22912-149
5-S66
4-S228, 6-157, 7-103, 7-111, 8-135, 9-17, 9-203,
10-83, 12-55
18-161
23-025
4-S127
16-S35
4-S161
16-S243
24-001
7-145, 9-37
23-29221-213, 23-233
19-118
6-29, 6-37
4-71
19-13416-S309
15-S11718-30725-37315-S117
19-162
17-S108
8-S97
15-S124
15-S117
15-S123
9-9
14-S8822-19017-S100
22-09115

S.V. Lomov
M. I. López Pumarega
Arthur E. Lord, Jr.
Luis Lorenzo
E. Lowenhar
M.G. Lozev
Guozhi Lu
Krystyna Lublinska,
P. Lukác
K. Lundgren
Xiu Luo
Xun Luo
D. Lupascu

26-24017-61
2-187, 2-195, 3-107, 4-S11, 4-31, 5-152
5-15
15-S109
15-S104
4-S20325-16617-S29, 20-108
5-S29
22-039, 23-260, 24-205
23-11917-S78

Yukuan Ma
J. D. MacPhail
Yuichi Machijima
A. Machová
J. W. Maclachlan
Eric Madaras
M. R. Madhava
Morihiko Maeda,
S. Maharshak
A. Maie
Ian Main,
M. A. Majeed,
Z. J. Majewska,
Zofia Majewska
S.A. Majewski,
Arup Maji
O. Makishima
Ajit Mal
M. Manoharan
Ll. Mañosa
Gerd Manthei
Theodore J. Mapes
J. Maram
H. Marcak,
P. A. March
K. Marsh
G. G. Martin
Hiroaki Maruyama
A. Maslouhi,
Y. MasuiS. Mata
O. Matal
Kristian Mathis
Takuma Matsuo

4-S58
8-S4
23-09120-0251-223, 1-229, 2-39, 2-179, 3-1, 4-S151
23-037
8-S28819-202
2-64
21-213
13-S21
4-S147, 8-S16, 12-107
12-S7
16-S105, 18-1
12-S7
15-S116
21-166, 25-308
14-S19, 16-S222
4-S207
5-S49
15-S119, 16-S85, 19-100, 22-173, 24-179, 24-196
1-29
4-S134
12-S7
8-S22315-S104
4-S14223-2338-S292, 12-45, 15-S122, 16-S299
23-18917-23
17-S6520-10824-067, 24-084, 25-124, 26-072
16

K. Matsuura
Kimitoshi Matsuyama
Huber Matysiak
S. C. Maxwell
Masami Mayuzumi
P. Mazal
H. Mazille,
D. MbaStuart L. McBride
J. F. McCardle
J.D. McColskey
John W. McElroy
D. Michael McFarland
K. I. McRae
M. Mediouni
Ronald B. Melton
P. G. Meredith,Philip Meredith,Jakub Michalski
K. Michihiro
W. Mielke
Juha Miettinen
Hamish D. S. Miller
M. E. Miller
R. (K.) Miller
O. Minemura
J. R. Mitchell
J. Mitchell
A. Mittelman
S. Miwa,
Makoto Miwa,
Kouitsu Miyachika,
Kazuya Miyano
Hiroyuki Miyatake
Noriko Miyoshi,
Junichiro Mizusaki
Souichi Mizutani
Yoshihiro Mizutani
Kenji Mochizuki
Mark B. Moffatt
K. Mogi
James Mohr
Waldemar Molinski
Florian Moll
M. Momayez
Keiichi Monma

21-12011-S65, 15-S40
26-317
8-S38
23-224, 23-285, 24-145, 25-238
17-S2, 17-S20, 17-S70, 18-75, 26-189
18-299, 19-229
19-209, 22-077
1-223, 1-229, 2-39, 2-179, 3-1, 4-S151, 4-S220, 7-225,
8-S4, 8-S192
10-49
15-1, 15-S108, 15-S111, 15-S119
4-S77
5-67
7-225
22-071
1-266
12-S12
13-S2126-317
10-S63
9-103
21-230
5-S1
4-S22
8-S241, 8-25, 15-S104, 15-S107, 15-S108, 18-61,
18-189, 18-272
16-S75
6-135
15-S109
3-41, 6-73
19-142
13-S42
13-S47
10-S9010-S1325-10724-21520-19416-S115, 18-51, 18-286, 19-035, 20-194, 23-224, 23-285,
24-145, 25-179, 25-238, 26-109
8-35
1-29
1-37, 8-113, 16-S45
5-162
10-107
20-108
10-61
15-S11317

M. Montoto
Antonio Moreno-Muñoz
Bryan C. Morgan
L. Morgan
Winfred Morgner
Y. Mori
Yoshiki Morino
Hiroyuki Morishima
Hirokazu Moriya
D. G. Morris
K. Morofuji
Zofia Mortimer
W. J. Moś cicki,
H. G. Moslé
F. Mostert
Andre Moura
F. MudryAmiya K. Mukherjee
M. C. Mumwam
Kohei Murakami
Yuji Murakami
K. Murayama
Boris Muravin
Gregory Muravin
S. A. F. Murrell,
C. R. L. Murthy,
T. M. Mustaleski

10-S3526-142
15-69, 15-S103
16-S125
3-172, 5-45, 6-133, 8-S70
8-S131, 16-S45, 20-248, 21-197, 22-091
20-19420-14523-113, 23-129, 23-142, 24-196
7-95
21-21316-S105, 18-1
12-S7
8-S317
18-18923-102
6-85
5-162
16-S6523-215
10-S9016-S75
16-S35
16-S35
12-S12
4-S30, 4-S147, 6-19, 7-S18, 8-S16, 8-S122,
8-S284, 12-107, 25-224
4-S247

S. Naemura
T. Nagamachi
Takuo Nagamachi
Koji Nagano,
S. Nagano,
Jyothi Nagaraj
Kenji Nagashima,
Yasuaki Nagata
M. M. Nagl,
G. Jayachandran Nair
P. K. K. Nair
Hidehumi Naito,
Yoichi Nakai
Hideo Nakajima
Hideyuki Nakamura
M. Nakamura
Syouzou Nakamura
Yasuhiro Nakanishi
Hiroyasu Nakasa
Eisaku Nakashima,

10-S5523-18922-15911-S1, 13-S54, 13-S75
19-085
14-97
19-011
4-S191
11-71
8-S266
4-S98
12-S2424-09724-111
23-243, 26-109
10-S55, 8-107
10-S1320-145, 22-039, 23-260
16-S2519-20218

M. Nakano
Noritaka Nakaso,
Gen Nakayama,
B. C. Nakra
H. Nayeb-Hashemi,
O. Nedzvetskaya
Priscilla P. Nelson
Katsumi Nemoto
Y.S. Neo,K. P. Nerz
John P. Newhook
Eng T. Ng
K. D. Nicklas
Jackson A. Nickerson
A. NielsenPeter Niemz
Hiroaki Niitsuma
Akira Ninomiya
T. Nishida
S. Nishikawa
Koichi Nishimoto
Shigeto Nishimoto
Hideo Nishino
M. Nishino
Satoshi Nishinoiri
V. N. Nikolaidis,
Minoru Nishida,
H. Nishino,
Takako Nishiura
Osamu Nishizawa
Masami Noguchi
M. Noguchi
C. Nojiri
Hiroaki Noma,
Richard Nordstrom
S. Norgren
Václav Novák
Marek Nowak
A. Nozue
Akira Nozue
J. Nuffer
Staffan Nyström,
Y. Obata
S. I. Ochiai
Satoshi Oda,
A. O’Gallagher

21-197
13-23
25-157
9-25
12-1, 14-85, 15-33
17-S13, 17-S51
10-S1
23-142
18-96
5-S56
25-373
19-275
4-S247
6-37
8-S57
24-1047-201, 11-i(4), 11-S1, 23-064, 23-113, 23-129, 23-142,
24-19624-111
21-187
10-S55
4-S23619-202
23-189, 24-153, 26-270
8-S13123-310, 24-076
17-69, 20-229
12-S1818-51, 18-102, 18-286, 19-011, 19-035, 19-075
24-052
11-S27, 23-102
4-S236
11-21
16-S150
25-10715-S108, 16-S204
26-02320-16324-044, 24-222, 25-341
1-1
21-14917-S78
13-97, 26-023
8-S145, 16-S45, 20-248, 22-091
2-289
13-S47
14-103, 15-S115, 16-S251, 17-37, 17-97, 19-258, 20-039,
20-062, 21-052, 21-070, 21-A01, 22-001, 22-A01, 23-001
19

Takashi Ogata
Takeshi Ogawa
Steve L. Ogin
S. Ogino
Eu Seok Oh
Yoshitugu Ohigashi
Takanori Ohira
Hironobu Ohishi
A. OhmoriTatsuma Ohnishi
Tadashi Onishi,
Kentaro Ohno
Isamu Ohsawa,
Shiro Ohta
M. Ohtsu

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

M. OnoeIrving J. Oppenheim,
Hisanori Otsuka
P. Ouellette
C. Ouyang
H. Oyaizu
K. Ozawa
Didem Ozevin,24-07623-156, 23-196, 23-181, 23-277, 24-127, 24-161, 25-124
23-025
5-61, 9-277
16-S277
10-25
4-S27423-06421-20619-10925-23823-047
13-S89, 19-045
15-S401-103, 2-151, 2-247, 3-27, 3-59, 3-69, 4-S38, 4-S50,
4-S316, 5-124, 6-43, 6-79, 6-99, 7-167,
8-S162, 8-S242, 8-93, 10-i (1/2), 11-i(4), 11-S37,
11-S57, 11-S65, 11-S89, 13-S14, 15-S31, 15-S40, 15-S50,
15-S60, 15-S70 16-S65, 16-S95, 18-S1, 18-S7, 19-118,
19-142, 19-184, 20-001, 21-157, 22-030, 23-047, 23-136,
23-272, 25-021
4-S28225-051
13-S14
16-S75
4-S240
18-111, 20-218
11-21
17-15
1-7, 1-67, 1-69, 1-141, 1-145, 1-146, 1-183, 1-211, 1-213,
1-214, 2-169, 2-247, 2-ii(1/2), 3-19, 3-27, 3-59, 3-69,
3-130, 3-144, 3-174, 3-190, 3-233, 3-ii(4), 4-S50,
4-S106, 4-S111, 4-S263, 4-S316, 4-61, 4-93, 5-124,
5-i(1), 5-ii(4), 6-1, 6-43, 6-84, 8-S268,
9-109, 9-123, 9-177, 9-227, 9-270, 11-117, 12-177,
14-35, 14-69, 14-S19, 15-19, 15-S95, 15-S105, 15-S108,
16-S115, 16-S134, 16-S289, 17-S37, 18-51, 18-102, 18-286,
19-011, 19-035, 19-063, 19-075, 19-091, 19-285, 20-083,
21-112, 21-120, 22-119, 22-243, 23-173, 23-206, 24-044,
24-119, 24-187, 24-222, 25-001, 25-179, 26-072
5-i(4)
25-115
10-S139-37, 11-65
10-S97
8-S1
21-16625-35520

Jie Pan
Yih-Hsing Pao
Krystian Paradowski,
Philip Park
Young-Jin Park
H. B. Patel
S. C. Pathak
J. Pavelka
J. Pazdera,
L. Pazdera,
Martin Peacock
L. H. Pearson
Peter Pellionisz
R. Pensec,
L. V. Perez
Marianne Perrin
D. T. Peters,
S. Peteves
J. Petras,
L. Petras,
J. Petrasek
Christian Pfleiderer,
T. P. Philippidis,
Rodney G. Pickard
C. Picornell
Aleksander Pilarski,
S. Pilecki,
J. Pininska,
Rosa Piotrkowski
A. Plevin
Jan Płowiec
T. Pocheco
Stefan Poliszko
M. D. Pollard,
Adrian A. Pollock
Elizabeth L. Porter
O. Prabhakar
Arun Prakash
A. Prateepasen,
C. W. Pretzel
D. Prevorovsky
Zdenek Prevorovsky
David W. Prine
Thomas M. Proctor, Jr.
William H. Prosser
A. ProustJose Pujol
R. Pullin
M. I. López Pumarega

22-264, 22-274
4-S27425-16615-S11
15-S11
8-S12, 8-S101
4-S32, 4-S147, 8-S122
17-S100, 22-091
18-81
18-29
4-S166, 8-S240, 8-11
4-S19910-19
18-125
10-13
26-032
8-S4
20-265
18-75
18-81
17-S8312-141
13-11, 17-69, 20-229
15-79
5-S49
12-23
10-1, 12-149
18-8
17-61, 26-142, 26-247
1-35
26-317
14-85
10-107
8-S4
1-237. 1-303, 9-140, 10-122, 15-S107, 15-S108, 25-231
17-121
4-S207
9-142, 8-S217
18-196
5-172
20-285
17-S57, 18-279, 20-134, 20-274, 20-285, 22-138
4-S304, 15-S106
1-173, 5-134, 7-41, 10-43
9-283, 14-S1, 15-S106, 15-S115, 17-29, 17-37, 23-037
17-S83, 18-161, 19-229, 20-229
17-11122-166, 26-172
17-61
21

K. K. Purushothaman,

12-111Gang Qi
Jie Qian
Stephen L. Quarles

17-111, 19-275, 24-001
20-016, 20-099
8-134, 9-17, 9-189

M. RaabAmani RaadJan Raczkowski
Andre Raharinaivo
Baldev Raj,

20-27420-30010-107
2-159
4-S102, 6-209, 7-S1, 8-S126, 8-S140, 8-S149,
11-43
4-S98
8-S297
8-S28
4-S82, 5-25
4-S147, 6-19
7-S29, 8-S262
7-135, 11-101
8-S28426-12017-S92, 18-118, 20-188, 22-049, 24-022, 26-098
4-S232, 5-67, 5-144, 9-197, 11-107, 12-15, 17-83
14-11925-331
14-S74, 22-083
18-96, 18-211, 25-348
4-S26915-S1072-159, 4-S296, 5-S63
6-257
17-S702-29
10-S2223-025
18-41
14-S47
4-S300, 6-43, 11-11
15-S107
9-91
17-S78
1-114, 4-S155, 4-1, 25-286
20-257
4-S102, 8-S140, 8-S149, 11-43
2-319
4-85, 5-S60, 5-S66, 8-S231, 8-34
19-1342-64, 3-19, 3-41, 3-130, 4-S106, 4-S111, 6-73, 8S109, 8-47, 10-31

P. RajD. S. Rajan
P. K. Rajan
Jerzy Ranachowski
A.K. Rao
M. V. M. S. Rao
S. P. Mallikarjun Rao
M. N. Raghavendra Rao
Christian Rasche
Franz Rauscher
Henrique L. M. dos Reis,
F. Rehsteiner
Wilfried Reimche,
H. W. Reinhardt
R.L. Reuben,
W. G. Reuter
L.E. Rewerts
Marie-Christine Reymond
M. W. Richey
J. Richter
B. Richtor
G. L. Rigby
Carlos R. Rios
L. Rippert,
Steffen Ritter
J. L. Robert
R.A. Roberts
N. RochatJ. RödelJohn M. Rodgers
P. Rödhammer
P. Rodriguez
L. M. Rogers
J. Roget
K. Rokugo
I. Roman

22

Igor Maria De Rosa,
A. P. G. Rose
Joseph L. Rose,Z. Rosecky
Sabine Rosner
J. N. Rossettos,
Мikhail Rostovtsev
R. Rothea
D. Rouby
C. Rowland,
C. RoyP. R. Roy
Gottfried A. Rubin
D. Rubio
Thomas P. Runarsson,
J. E. Ruzzante

25-080
1-213
12-23
20-02522-110, 25-149, 26-014
12-1
26-311
19-2293-176, 9-117
18-87, 18-239
8-S292, 12-45
7-S40, 4-S251
10-S18, 11-19
10-13
25-252, 25-260, 26-229, 26-262
10-13, 17-61

H. Saadaoui,
Wolfgang Sachse
S. Sagat
Kouki Saiga
Masahiro Saito,
Naoya Saito
Koji Sakai
Norio Sakaino
K. Sakamaki
Kiyoshi Sakamaki
N. Sakata
Yasunori Sakata
Tatsuro Sakimoto
Takumi Sakuma
C. SalaH. M. Sallam
Pekka SalmenperäPeter Sammonds,
A. Sampath
R. SamuelR. N. Sands
A. S. Sankaranarayanan,
N. Saniei
Mary Sansalone
Carlo Santulli
Fabrizio Sarasini,
Hiroaki Sasaki
Soji Sasaki
S. SaseL. Hanumantha Sastry
Akihiro Sato,
Ichiya Sato

12-45
4-S62, 8-S20, 8-S170, 11-79
5-S16
26-10913-S6815-19, 16-S289
10-S104
15-S50
16-S134, 18-68
21-206, 21-223
16-S75
15-S70
7-167
16-S196
2-11
14-85
21-23013-S21
5-S12
7-S35
4-31
12-111
15-33
5-S24
26-22025-080
16-S25
2-1
17-15
11-101
19-2022-1, 7-173, 8-S213
23

Isamu Sato
Kazuhiko Sato,
Kazuhisa Sato
K. SatoKeiichi Sato,
Kouichi Sato
Takashi SatohMarkus G. R. Sause
S. G. Savanur
M.W. Scaife,
C. M. Scala
C. R. Scales,
G. Schauritsch,
Christian Scheer,
R. Schelling
C. Schepacz
Jerzy Schmidt
H.J. Schoorlemmer,
Frank Schubert
M. Schulz
Daniel SchultheißK. Schumacher
Thomas Schumacher,
D. Schumann
H.-J. Schwalbe
J. S. Schwartzberg
Ian G. Scott
C. B. Scruby
Petr Sedlak
C. Seguf
Kazuyoshi Sekine
G. P. Sendeckyj
Hirofumi Sentoku
U. Senturk
M. SetoMasahiro Seto,
B. K. ShahS. P. ShahR. Douglas Sharp
V.V. Shemyakin,G. Shen,Gongtian Shen
H.W. Shen
Ping Shen
Nanling Shi
K. Shibata
M. Shibata
Mitsuhiro Shigeishi,
Hisatoshi Shimada

8-35
13-S54, 13-S75, 19-109, 23-119
24-215
16-S45
4-S240, 8-S213, 9-209, 13-S42
7-173
23-10226-001
7-S18
18-2112-261, 6-249, 10-49
25-051
18-13825-331
20-2382-267
23-173, 24-222
18-180, 20-238
22-147
20-17226-001
9-103
25-316
3-172
22-236
10-97
4-S142
3-182, 4-9, 7-81, 7-211, 8-S158, 8-S201, 9-243, 9-255
26-182
5-S49
23-233
11-33
16-S1915-S117
11-S1911-S27
7-S13
10-S973-118, 4-S259, 4-115
19-172
8-S97
16-S243
15-S105, 18-189
15-S123
16-S317
8-S1453-144, 3-190, 4-93,
11-S57, 13-S14, 15-S50, 15-S60, 16-S65
10-S10424

Shigeo Shimizu,
Tatsuya Shimizu,
Takayuki Shimoda
J. Sikula
T. Simo
Masayuki Shimojo
Noboru Shinke
Takuo Shinomiya
Tomoki Shiotani
M. Shiwa
Donald A. Shockey
Ménad Sidahmed
Khalid J. Siddiqui
J. Siedlaczek
Joanna Sikorska
Josef Sikula
F. Simacek
A. K. Singh
A. K. Sinha
N. K. Sinha
Petr Sittner
Olivier Skawinski
C. Sklarczyk
A. Skraber,
Jerzy Skubis
A. Slimani
Daniel R. Smith Jr.
J. H. Smith
M. Sobczyk
T. Sogabe
V.L. Sokolov,
Amir Soltani
Nobukazu Soma
Jun-Hee Song
M. SorelM. Ben Souda
P. Souquet
J.C. Spanner, Sr.
L. M. Spasova,
Th. Spies
Nicholas S. Spivey,
Wojciech Spychalski,
M. K. Sridhar
K. V. Srincivasan,
T.S. Sriranga
K. A. Stacey
J.A. Steel,
Wolfgang Stengel

19-196
13-S6820-19417-S100
17-S6516-S196, 19-085
10-25, 16-S134 , 23-164, 24-052
20-145
15-S50, 16-S95, 18-248, 19-118, 19-142, 20-145, 20-153.
21-166, 22-039, 23-260, 24-205, 25-069, 25-308
4-S195, 21-197
21-00120-3003-118, 9-9
10-1
22-264, 22-274
22-091, 26-182
10-67
4-S174
7-S13
4-S29020-16326-120
8-S93
18-1442-261, 4-S82, 5-25
5-S42
4-S135, 7-9
8-S79
8-S19221-120
18-111, 20-218, 26-279
4-S17
23-12916-S309
2-261
11-11
4-85, 5-S60, 8-S231
6-121
25-051
15-S119, 19-100, 19-153, 24-179
25-18725-166, 26-317
4-S174
8-S8
7-S35
7-81
18-96, 18-211, 25-348
4-S31225

D. Stöver
H. Strauven,
V. Streicher
V. A. Strelchenko
V. A. Strizhalo
S.A. Strizkov
C. E. Stuart,
George Studor
V. Suba
S. V. Subba Rao
Iyer Subramaniam
H. N. Sudheendra
C. Y. Suen
Katsuhiro Sugawara,
J. Summerscales
C. T. Sun
X. SunJ. Q. Sun
M. J. Sundaresan
M. Surgeon
Hiroaki Suzuki
Ippei Suzuki
M. Suzuki
Masahiko Suzuki
Tetsuya Suzuki
Toshio Suzuki,
Toshitaka Suzuki
P. Svadbik,
V. Svoboda
Terry L. Swanson
Grzegorz Swit
Tatsuo Tabaru,
A.N. Tafuri
V.L. Tahiri
F. Taioli,
H. Takagi
Hideaki Takahashi,
K. Takahashi
Shin Takahashi
Katsutoshi Takano
Atsushi Takashima,
Kazuki Takashima,
S. Takashina,
Nobuo Takeda,
Mikio Takemoto

4-S78
13-79
8-S53
8-S3348-S33419-172
12-S1223-037
17-S208-S8
4-S1748-S2889-9
13-S544-S170
7-21
8-S262
8-S1148-S27715-S105, 18-34
11-117, 14-35, 14-69, 15-19, 15-S108, 16-S178, 16-S289,
24-012
7-179
9-277
5-61
22-030, 23-272
13-S892-1
18-29
17-S2, 17-S83
8-1, 8-S236
24-18725-10715-S104
16-S299
8-S42
16-S1426-261, 7-1, 13-S68
16-S324
23-09124-111
19-10912-S18, 13-S08, 16-S150, 19-085
18-10212-1277-185, 11-117, 14-35, 14-69, 15-19, 15-S108, 16-S115,
16-S178, 16-S289, 18-51, 18-102, 18-286, 19-011, 19-035,
19-063, 19-075, 21-120, 21-131, 22-119,
26

Hajime Takeuchi
K. Takigawa
Masanori Takuma
S. Talebi
Okiharu Tamura
H. Tanaka
M. Tanaka
T. TanakaToshiyuki Tanaka
S. Tanary,
N. Tandon
Daiki Tani
Yoshihiro Taniyama,
O.B. Tarutin
Masayuki Tateno,
A. Taylor,
M. A. Taylor
C. M. Teller
Hans Maria Tensi
H.B. Teoh
Satoshi Teramura
Giovanni P. Terrasi
Christian Tessier
R. Teti
Lawrence W. Teufel
Kazuhiko Tezuka
N. A. Thakkar,
Christian Thaulow
Heinrich Theiretzbacher
W. Thelen
Pete Theobald
Richard E. Thill
P.M. Thompson
D. D. ThorntonPeter G. Thwaite
R. M. Tian
B. Tirbonod
Anil Tiwari
G. P. Tiwari
R.G. Tobin
Akira Todoroki
S. Tomecka-Suchoń ,Yuichi Tomoda
H. Tonda,
F. Tonolini
G. Tonolini

21-120, 21-131, 22-119, 22-224, 23-072, 23-156,
23-196, 23-181, 23-215, 23-277, 24-012, 24-067, 24-084,
24-127, 24-161, 25-124, 25-157, 25-172, 25-179, 25-267
9-209
8-S21316-S134, 23-164, 23-206, 24-052
8-S25416-S178
21-166
10-S55
8-S213
7-173
8-S314, 12-39
9-25, 17-23
24-15325-15715-S120
11-S75
18-2398-S322
11-27
22-S01
3-19, 3-130, 6-1
6-261, 7-1
26-15226-032
3-158, 5-156
15-S124
23-12925-348
4-S211
4-S157
14-11526-091
10-S77
6-93
23-33114-vi (3/4)
4-S94
8-S84
14-53
4-S102, 7-S43
8-25
26-10912-S7
15-S31, 21-157, 23-272, 25-021
13-S08
8-S62
4-S86
27

V. Torra
T. Toutountzakis,
D.T. Tran
H. Traxler
D. Tsamtsakis,
P. Tscheliesnig
Ming-Kai Tse
A. Tsimogiannis,
Yukiya Tsuchida
N. Tsui
Nobuyuki Tsuji
Yusuke Tsukahara,
Koichi Tsukiyama
F. R. Tuler

5-S49
25-033
8-25
20-257
13-79
4-S157, 17-S108, 18-138, 18-167, 20-129, 20-179, 22-201,
25-276
4-S127, 8-S188, 8-S209
18-21, 18-224, 22-059
21-17621-21315-S6013-23
6-261, 7-1
8-S79

F. Uchida,
Junichi Uchida
Atsushi Uchiyama,
Farid A. K. M. Uddin
Toshiyuki Uenoya,
Takateru Umeda
Runar Unnthorsson,
Taketo Uomoto
T. I. Urbancic
E. Uria,

18-102, 19-075
23-181
13-S4223-13613-S95, 15-S112
20-24825-252, 25-260, 26-229, 26-262
6-137
8-S254
13-79

S.J. Vahaviolos
S. Vajpayee
Carlos M. Valdes-Gonzalez,
H. Vallen,
J. Vallen,
P.J. Van De Loo,
D. R. V. Van Delft
Koen Van Den Abeele
Gert Van Dijck,
S. Van Huffel,
J. G. M. Van Mier
R. Van Nieuwenhove,
Donald W. Vannoy
D. Varchon
Alex Vary,
F. A. Veer
Vasisht Venkatesh
Vincenzo Venturi,
E. Verbrugghe
I. Verpoest
A. Vervoort
R. Vijayaraghavan,

3-i(1), 4-S329, 15-S109, 18-189, 18-248, 18-272, 19-172
5-S12
12-117
18-167, 18-258, 18-265, 22-102, 25-132
18-265, 25-132
18-174, 20-238
20-22922-25322-253
18-41
20-15318-2934-S30720-285
8-S175, 12-i (1/2), 12-71, 12-79, 14-53
8-S118
14-61
25-32411-1
4-S186, 8-S272, 26-240
20-292
7-S13
28

G. Villa
M. R. Viner
A. Vinogradov
A. Yu. Vinogradov
P. Vionis
R. Visweswaren
L.E. Vlasov,Aaron C. Voegele
J. von Stebut,
Y. Vougiouklakis

4-S86
8-S192
17-1
16-S158
18-217, 20-229
4-S207
18-150
17-83
18-25820-265

Toshiya Wada
Adrian P. Wade
Haydn N.G. Wadley
N. Wakabayashi
Shuichi Wakayama,

23-150
10-71
5-S69
10-S42
4-S278, 12-S24, 16-S170, 21-149, 23-150, 24-173, 24-228,
26-160
12-S1
12-S1
7-31
25-187
8-S97
19-02214-i, 14-v, 14-vi (3/4), 14-S47
11-107
8-S93
15-S104
3-41
20-001
13-S42
3-59
2-1
23-11918-232
4-S17, 8-S258
15-S104, 19-172
4-S54, 5-S40
18-29
4-S132, 8-S197
11-S75
14-S74
4-S186, 8-S272, 13-79, 15-S105, 18-34, 18-41, 20-206,
20-292, 22-253, 26-240
1-263
1-147, 4-S247, 5-148, 6-257, 7-31, 7-95, 8-S280,
9-75, 9-84, 10-113
10-S22
10-67
1-244

Teruyuki Waki,
Akimasa Waku,
D. A. Waldrop
James L. Walker II,
Y. Wan,Y. Wang,
Alexander Wanner
Kris A. Warmann
E. Waschkies,
G. Washer
M. Watad
Hiroshi Watanabe
Naoaki Watanabe,
T. Watanabe
Takashi Watanabe
Yoshinori Watanabe
J.R. Watson,
Robert J. Watters
D.J. Watts
Richard L. Weaver
Z. Weber,J. R. Webster
Qiang Wei
B. Weiler
M. Wevers,
R. G. WhiteJ. W. Whittaker
B. J. S. Wilkins
H. Willer
A. J. Willis

29

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

2-191
ICAE Banquet
20-206, 22-253
10-67
3-239
20-2383-51
6-125, 6-239, 8-S14, 8-S66, 10-S29, 10-S59, 15-S113,
17-121
8-S318
4-71
5-S52
25-115
4-S166
11-5
25-11523-129Y. Xiang,
J. Z. Xiao
Yue-Huang Xu

8-S246
4-S215
3-81

H. Yamada,K. Yamada
M. Yamada
Minoru Yamada
Yoshiaki Yamade,
Hiroshi Yamaguchi
Katsuya Yamaguchi
Kusuo Yamaguchi
T. Yamaguchi
Koji Yamamoto
Hirofumi Yamasaki
Akihiko Yamashita,
Akio Yamashita
T. Yamauchi
Yasunori Yamazaki
Tinghu Yan
W. YanM. Yanagibashi
Masa-aki Yanaka,
Takahito Yanase
J. M. Yang
Xuanhui Yang
C. K. Yao
Takeshi Yasuda
A. Yasuo
Daisuke Yasuoka
J.J. Yezzi, Jr.

18-51
6-13
21-21323-233, 23-243
12-12724-111
9-209
4-S191, 4-S286, 4-S325, 8-S1
8-S14519-19624-111
13-S894-S286
11-21
24-097
17-49
15-S109
8-S213
13-23
23-096
8-S26815-S123
4-S94, 8-S105
24-153, 26-270
4-S294
15-S60
15-S10430

Akira Yoneda
Takao Yoneyama
Akio Yonezu
Dong-Jin Yoon,
Kenichi YoshidaToshikatsu Yoshiara
Sumio Yoshikawa
Tatsuhiko Yoshimura
Hiroshi Yoshino,
H. Yoshioka
Takeo Yoshioka,
R.D. Young
R. P. Young
R. Paul Young
Y. Youssef,
Qing Huan Yu
Hiroo Yugami
Syuro Yuji,
Hironobu Yuki
Kunihiro Yuno
Shigenori Yuyama,
J. Zaloudek
F. Zeides
Bajram Zeqiri
Kornelija Zgonc,
B. Q. Zhang
Fan Zhang
Zhizhen Zheng
X. Q. Zhu
Z. ZhuZu-Ming Zhu
Zuming Zhu
B. Ziegler
J. Zietek,
Steve Ziola
B. Zogala,
Daihua Zou
Ryszard Zuchowski
J. Zuidema

24-0972-1, 7-173, 8-S213
23-156, 23-196, 23-277, 24-127, 24-161
9-237, 13-S83, 15-S11
16-S142, 18-68, 19-022, 21-206, 21-223, 22-159,
23-189, 24-153, 26-270
19-202
8-113
6-109, 6-145
12-S1
10-S6319-196
6-93
8-S38, 8-S166, 8-S254
5-S29, 5-S34, 25-294
12-39
8-41
24-215
7-167
10-35
11-S892-19, 2-71, 4-S38, 13-S14, 15-S107, 16-S75, 18-248,
19-118, 19-184, 21-187, 21-213, 23-233
17-S65
8-S109, 8-47, 10-31
26-091
11-79
4-S94, 8-S49, 8-S105, 8-S114
18-144, 20-300
15-S123
4-S215
8-S135
6-115
16-S31722-236
12-S7
8-51, 14-S12
18-15
5-S1
2-272
8-S11831