Journal of Acoustic Emission, Volume 33, 2016
Contentsi – iii
I–X

Contents, J AE, vol. 33, 2016
EWGAE 2016, Foreword, Contents, Authors Index

33-S1

Stasys V. AUGUTIS, Boris MURAVIN, Edgaras VAŠTAKAS, APPLICATION OF WAVEGUIDES FOR
THE CALIBRATION OF ACOUSTIC EMISSION TRANSDUCERS S1-S8

33-S9

Ireneusz BARAN, Igor LYASOTA, Krzysztof SKROK, ACOUSTIC EMISSION TESTING OF
UNDERGROUND PIPELINES OF CRUDE OIL OF FUEL STORAGE DEPOTS, S9-S20

33-S21

Gabriel BIRCK, Ignacio ITURRIOZ, A VERSION OF THE DISCRETE ELEMENT METHOD IN THE
SIMULATION OF THE ACOUSTIC EMISSION TESTING, S21-S30

33-S31

Antoine BONIFACE, Zoubir M. SBARTAÏ, Jacqueline SALIBA, Nary RANAIVOMANANA,
COMPARISON OF LOCALIZATION STRATEGIES OF DAMAGE IN CONCRETE BY ACOUSTICEMISSION, S31-S39

33-S41

Andreas J. BRUNNER, CORRELATION BETWEEN ACOUSTIC EMISSION SIGNALS AND
DELAMINATIONS IN CARBON FIBER-REINFORCED POLYMER-MATRIX COMPOSITES: A NEW
LOOK AT MODE I FRACTURE TEST DATA, S41-S49

33-S51

Luigi CALABRESE, Massimiliano GALEANO, Edoardo PROVERBIO, Domenico DI PIETRO,Angelo DONATO, Filippo CAPPUCCINI, ADVANCED SIGNAL ANALYSIS APPLIED TODISCRIMINATE DIFFERENT CORROSION FORMS BY ACOUSTIC EMISSION DATA, S51-S60

33-S61

Luigi CALABRESE, Massimiliano GALEANO, Edoardo PROVERBIO, Domenico DI PIETRO,Angelo DONATO, MONITORING OF HYDROGEN ASSISTED SCC ON MARTENSITIC STAINLESSSTEEL BY ACOUSTIC EMISSION TECHNIQUE, S61-S69

33-S71

Manabu ENOKI, Yuki MUTO, Takayuki SHIRAIWA, EVALUATION OF DEFORMATIONBEHAVIOR IN LPSO-MAGNESIUM ALLOYS BYAE CLUSTERINGAND INVERSE ANALYSIS, S71-S76

33-S77

Sebastian O. GADE, Philipp POTSTADA, Benjamin B ALACA, Markus G. R. SAUSE,
MEASUREMENT OF ELECTROMAGNETIC EMISSION AS A TOOL TO STUDY FRACTURE
PROCESSES OF CARBON FIBRE REINFORCED POLYMERS, S77-S86

33-S87

Thomas GUENTHER, Andreas KROLL, LOCALIZATION OF COMPRESSED AIR LEAKS IN
INDUSTRIAL ENVIRONMENTS USING SERVICE ROBOTS WITH ULTRASONIC MICROPHONES,S87-S96

33-S97

Marvin A. HAMSTAD, MODELED TRANSVERSE SURFACE AE SIGNALS FROM BURIED DIPOLE
SOURCES IN A POLYMER ROD, S97-S106

33-S107

Dmitry CHERNOV, Sergey ELIZAROV, Vera BARAT, Igor VASILYEV, FEATURES OF THE AETESTING OF EQUIPMENT ON OPERATING MODE, S107-S116

i

33-S109

Kaita ITO, Manabu ENOKI, HIGH SENSITIVITY DETECTION OF AE EVENTS IN NOISYENVIRONMENT USING STREAM RECORDING AND PARALLEL COMPUTATION, S117-S122

33-S123

Doyun JUNG, Yoshihiro MIZUTANI, Akira TDOROKI, Yoshiro SUZUKI, CHANGE IN B -VALUE BY
AE PROPAGATION LENGTH IN CFRP, S123-S129.

33-S131

Yoshikazu KOBAYASHI, Tomoki SHIOTANI, TWO-DIMENSIONAL Q-VALUE AE-TOMOGRAPHY
AND ITS VERIFICATION ON NUMERICAL INVESITIGATIONS, S131-S140

33-S141

Vendula KRATOCHVILOVA, Frantisek VLASIC, Pavel MAZAL, STUDY OF FATIGUE PROCESSESOF SELECTIVE LASER MELTING AND CONVENTIONAL PRODUCED MATERIALS, S141-S148

33-S149

Gerald LACKNER, Peter TSCHELIESNIG, ACOUSTIC EMISSION TESTING ON LPG TANKS –DEFECT DETECTION CASE STUDIES, S149- S155

33-S157

Zhidong LIU, Hongqiang LIAO, Yuan LIU, THE TIME SEQUENCE CHARACTERISTICS AND
LOCATION METHOD OF AE SOURCE INDUCED BY DEBRIS CLOUD, S157-S164

33-S165

Tomáš LOKAJÍČEK, Richard PŘIKRYL, Šárka ŠACHLOVÁ, Aneta KUCHAŘOVÁ, ALKALI SILICA
REACTIVITY STUDY OF ACCELERATED MORTAR BAR BY MEANS OF ACOUSTIC EMISSION ANDULTRASONIC SOUNDING, S165-S172

33-S173

Sha LUO, Andrea DIAMBRA, Erdin IBRAIM, APPLICATION OF ACOUSTIC EMISSION ON
CRUSHING MONITORING OF INDIVIDUAL SOIL PARTICLES IN UNIAXIAL COMPRESSION TEST,S173-S181

33-S183

Pavel MAZAL, Houssam MAHMOUD, Petr DOSTAL, Michal CERNÝ, Michal SUSTR, Jaroslav
ZACAL, COOPERATION OF MENDEL UNIVERSITY AND BRNO UNIVERSITY OF TECHNOLOGY IN
THE FIELD OF BIOLOGICAL APPLICATIONS OF AE METHOD, S183-S192

33-S193

Marek NOWAK, Igor LYASOTA, Ireneusz BARAN, THE TEST OF RAILWAY STEEL BRIDGE WITH
DEFECTS USING ACOUSTIC EMISSION METHOD, S193-S202

33-S203

Maciej PANEK, Marek NOWAK, Ireneusz BARAN, Krzysztof J. KONSZTOWICZ, ACOUSTICEMISSION DETECTION OF DAMAGE INITIATION IN 1D CFRP NCF COMPOSITES SUBJECTED TO
BENDING, S203-S212

33-S213Matěj PETRUŽÁLEK, Tomáš LOKAJÍČEK, Tomáš SVITEK, ACOUSTIC EMISSION ANDULTRASONIC SOUNDING AS A TOOL FOR MIGMATITE FRACTURING, S213-S222

33-S223

Markus G. R. SAUSE, ACOUSTIC EMISSION SOURCE IDENTIFICATION IN LARGE SCALE FIBRE
REINFORCED COMPOSITES, S223-S232

33-S233

Eric SERRIS, Ana CAMEIRAO, Frédéric GRUY, MONITORING INDUSTRIAL CRYSTALLIZATIONUSING ACOUSTIC EMISSION, S233-S241

33-S243

Tomoki SHIOTANI, Takahiro NISHIDA, Hisafumi ASAUE, Takuya MAESHIMA,QUANTIFICATION OF FATIGUE DAMAGE EVALUATION IN RC SLABS DURING WHEEL LOADINGTEST BY MEANS OF 3D AE TOMOGRAPHY, S243-252

ii

33-S253

Peter TSCHELIESNIG, Andreas JAGENBREIN, Gerald LACKNER, DETECTING CORROSIONDURING INSPECTION AND MAINTENANCE OF INDUSTRIAL STRUCTURES USING ACOUSTICEMISSION, S253-S259

33-S261

Fabian WILLEMS, Johannes BENZ, Christian BONTEN, DETECTING THE CRITICAL STRAIN OF
FIBER REINFORCED PLASTICS BY MEANS OF ACOUSTIC EMISSION ANALYSIS, S261-S270

33-S271

Fengming YU, Yoji OKABE, Qi WU, Naoki SHIGETA, DAMAGE TYPE IDENTIFICATIONBASED ON ACOUSTIC EMISSION DETECTION USING A FIBER-OPTIC SENSOR IN CARBON FIBER
REINFORCED PLASTIC LAMINATES, S271-S278

33-001Johann CATTY, A NEW WAY TO EVALUATE ACOUSTIC EMISSION INSTRUMENTATION:CATS METHODOLOGY, 1-15

iii

EWGAE 2016
32nd European Conference on Acoustic Emission
Testing
Prague, Czech Republic
September 07th - 09th, 2016

Selected Papers fromthe Proceedings
Gerd Manthei, JAE Editor

Czech Society for Non-destructive Testingin cooperation with
Brno University of Technology

Edited by:
Pavel MAZAL and Lubos PAZDERA
Copyright © 2016
Czech Society for NDTAll Rights Reserved

Scientific Committee / Reviewers
Ireneusz Baran
Zdenek Prevorovsky

PolandCzech Rep.

chairmanco-chairman

Dimitrios Aggelis
Anastasopoulos Athanasios
Petr Benes
Jürgen Bohse
Andreas Brunner
Milan Chlada
Crescenzo Di Fratta
Sergey Elizarov
Antolino Gallego
Christian Grosse
Janez Grum
Marvin Hamstad
Catherine Herve
Karen Holford
Lackner Gerald
Jean-Claude Lenain
Gerd Manthei
Marek Nowak

GreeceGreeceCzech Rep.
Germany
Switzerland
Czech Rep.
Italy
RussiaSpain
Germany
Slovenia
USA
FranceUK
Austria
FranceGermany
PolandMasayasu Ohtsu
Kanji Ono
Alain Proust
Edoardo Proverbio
Rhys Pullin
Franz Rauscher
Bob Reuben
Markus Sause
Thomas Schumacher
Tomoki Shiotani
Thomas Thenikl
Pete TheobaldPeter Tscheliesnig
Hartmut Vallen
Shuichi Wakayama
Martine Wevers
Yoshida Kenichi

Japan
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AustriaUK
Germany
USA
Japan
Germany
UK
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GermanyJapan
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Papers are reviewed by members of Scientific Committee

Local Organising Committee:
•
•
•
•
•
•
•

Pavel Mazal
Vaclav Svoboda
Luboš Pazdera
Pavel Turek
Eva Svobodova
Vendula Kratochvílová
Daniela Turkova

chairmanco-chairman
proceedings
exhibition
membermemberaccommodation

Published by:Czech Society for Non-destructive Testingin cooperation with
Brno University of Technology

ISBN 978-80-214-5386-9
II 			

(printed version)

32nd EWGAE

FOREWORD
This publication contains the papers presented at the 32nd European Conference on
Acoustic Emission Testing held in Prague, Czech Republic on September 7th - 9th2016. This conference was organized by the European Working Group on Acoustic
Emission in cooperation with the Czech Society for Non-destructive Testing.
The primary objective of the 32nd European Conference on Acoustic Emission
Testing was exchange of information on research and industrial applications ofscience and technology of Acoustic Emission (AE). The conference brought together
the expertise of scientists and engineers in academia and industry and covered
activities relevant to effective monitoring of infrastructures and technical systems.
European Working Group on Acoustic Emission (EWGAE) was founded in 1972
by a group of researchers and experts. The primary objective of the EWGAE isexchange of information on acoustic emission method with particular emphasis on
scientific and technical development. Secondary objectives are a) Promoting the useof standardised terminology in acoustic emission documentation, b) Providing
information about acoustic emission instrumentation, c) Providing information aboutacoustic emission to interested parties outside the Group, and liaising with otherGroups having common interests and d) Development of common standards foracoustic emission practice (see EWGAE Constitution - www.ewgae.eu/).
Czech Society for Non-destructive Testing (CNDT) is a non-profit publicorganization whose main objective is to promote research, development, practicaluse and other activities in the field of NDT/NDE of materials and structures in allindustrial areas. Through its diversified activity, the Czech NDT Society seeks toassure quality and proficiency in the field of NDT/NDE. The contemporary Czech
Society for Non-destructive Testing (founded in 1993) continues a long-time traditionof the antecedent Czechoslovak NDT Society, established already sixty years ago.
Traditionally, CNDT organizes every year the international “NDE for
Safety/Defektoskopie” conferences, accompanied by tradeshows and workshops.
The society organizes specialized international NDT events, e.g. traditional workshop“NDT in PROGRESS”. Czech Society for NDT was the main organizer of thesuccessful 11th European Conference on NDT 2014 in Prague and 25th EWGAE
conference in 2002. More info: www.cndt.cz.
Proceedings are also published in digital form and papers are also presented at
www.NDT.net.

32nd EWGAE

				

III

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

ALLEVATO Claudio
AET MONITORING OF DAMAGED OR CRACKED CRITICALE QUIPMENT
UNTIL REPAIRS OR REPLACEMENT IS POSSIBLE

1

AUGUTIS Stasys V., MURAVIN Boris, VAŠTAKAS Edgaras
APPLICATION OF WAVEGUIDES FOR THE CALIBRATION OF ACOUSTICEMISSION TRANSDUCERS

7

BARAN Ireneusz, LYASOTA Igor, SKROK Krzysztof
ACOUSTIC EMISSION TESTING OF UNDERGROUND PIPELINES OFCRUDE OIL OF FUEL STORAGE DEPOTS

15

BASHKOV Oleg, POPKOVA Alexandra, SHARKEEV Yurii, BASHKOVA Tatiana
ACOUSTIC EMISSION DURING FATIGUE DAMAGE OFNANOSTRUCTURED TITANIUM

27

BIRCK Gabriel, ITURRIOZ Ignacio
A VERSION OF THE DISCRETE ELEMENT METHOD IN THE SIMULATION
OF THE ACOUSTIC EMISSION TESTING

35

BONIFACE Antoine, SBARTAÏ Zoubir M., SALIBA Jacqueline,
RANAIVOMANANA NaryCOMPARISON OF LOCALIZATION STRATEGIES OF DAMAGE IN
CONCRETE BY ACOUSTIC EMISSION45

BRUNNER Andreas J.
CORRELATION BETWEEN ACOUSTIC EMISSION SIGNALS AND
DELAMINATIONS IN CARBON FIBER-REINFORCED POLYMER-MATRIXCOMPOSITES: A NEW LOOK AT MODE I FRACTURE TEST DATA55CALABRESE Luigi, GALEANO Massimiliano, PROVERBIO Edoardo,
DI PIETRO Domenico, DONATO Angelo, CAPPUCCINI Filippo
ADVANCED SIGNAL ANALYSIS APPLIED TO DISCRIMINATE DIFFERENT
CORROSION FORMS BY ACOUSTIC EMISSION DATA65CALABRESE Luigi, GALEANO Massimiliano, PROVERBIO Edoardo,
DI PIETRO Domenico, DONATO Angelo
MONITORING OF HYDROGEN ASSISTED SCC ON MARTENSITIC
STAINLESS STEEL BY ACOUSTIC EMISSION TECHNIQUE75

CRESPO Saúl, CARRIÓN Francisco, QUINTANA Juan, SEPÚLVEDA Francisco,HERNÁNDEZ Jorge, GASCA Héctor
AE DEFECT EVALUATION OF THE UPPER ANCHORAGE ELEMENTS OFA STAYED BRIDGE

85

DROUBI Mohamad G., FAISAL Nadimul H., STUART Alan, MOWAT John,NOBEL Craig, EL-SHAIB Mohamed N.
ACOUSTIC EMISSION TESTING OF COMPOSITE-TO-METAL AND METALTO-METAL ADHESIVE BOND STRENGTHS

95

32nd EWGAE

IVVDUBOV A. A., SEMASHKO N. A., PRIVALOV V. Yu., KOLOKOLNIKOV S. M.
RESULTS OF STEEL 20 SPECIMENS TENSILE TESTING USING THE METALMAGNETIC MEMORY AND ACOUSTIC EMISSION METHODS105

ELIZAROV Sergey, ALYAKRITSKY Alexander, TROFIMOV Pavel,
BUGANKOV Alexey
THE NEW HARDWARE FEATURES OF A-LINE 32D AE SYSTEMS109ELIZAROV Sergey, BARAT Vera, BARDAKOV Vladimir, CHERNOV Dmitry,
TERENTYEV Denis
FEATURES OF THE AE TESTING OF EQUIPMENT ON OPERATING MODE

115

EL-SHAIB Mohamed, KESHK Omar, SHEHEDA Mohamed
MONITORING OF DEEP GROOVE BALL BEARING DEFECTS USING THE
ACOUSTIC EMISSION TECHNOLOGY125

EL-SHAIB Mohamed, RIAD Omar, SHEHEDA Mohamed
SEEDED FAULT DETECTION ON SPUR GEARS WITH ACOUSTICEMISSION

137

ENOKI Manabu, MUTO Yuki, SHIRAIWA Takayuki
EVALUATION OF DEFORMATION BEHAVIOR IN LPSOMAGNESIUM
ALLOYS BY AE CLUSTERING AND INVERSE ANALYSIS145

FALLAHI N., NARDONI G., PALAZZETTI R., ZUCCHELLI A.
PATTERN RECOGNITION OF ACOUSTIC EMISSION SIGNAL DURING THE
MODE I FRACTURE MECHANISMS IN CARBON-EPOXY COMPOSITE

151

GADE Sebastian O., POTSTADA Philipp, ALACA Benjamin B., SAUSE Markus G. R.
MEASUREMENT OF ELECTROMAGNETIC EMISSION AS A TOOL TOSTUDY FRACTURE PROCESSES OF CARBON FIBRE REINFORCED
POLYMERS163

GUENTHER Thomas, KROLL Andreas
LOCALIZATION OF COMPRESSED AIR LEAKS IN INDUSTRIAL
ENVIRONMENTS USING SERVICE ROBOTS WITH ULTRASONICMICROPHONES

173

HAMSTAD Marvin A.
MODELED TRANSVERSE SURFACE AE SIGNALS FROM BURIED DIPOLE
SOURCES IN A POLYMER ROD

183

HATA Yuto, MIZUTANI Yoshihiro, TODOROKI Akira, SUZUKI Yoshiro
DEVELOPMENT OF A METHOD FOR CLASSIFYING CFRP FRACTURE
MODES BY AE TESTING

193

CHERNOV Dmitry, ELIZAROV Sergey, BARAT Vera, VASILYEV Igor
FEATURES OF THE AE TESTING OF EQUIPMENT ON OPERATING MODE

201

CHLADA Milan, NOHAL Libor, PREVOROVSKY Zdenek
CONDITION MONITORING OF THRUST BALL BEARINGS USINGCONTINUOUS AE

211

ITO Kaita, ENOKI Manabu
HIGH SENSITIVITY DETECTION OF AE EVENTS IN NOISY ENVIRONMENTUSING STREAM RECORDING AND PARALLEL COMPUTATION

217

32nd EWGAE

JIANG Yu, YANG Zhong, XU Feiyun, XU Bingsheng, GALLEGO Antolino
DAMAGE VISUALIZATION AND LOCATION IN METAL STRUCTURE FORCRANE BY ACOUSTIC EMISSION TOMOGRAPHY

223

JUNG Doyun, MIZUTANI Yoshihiro, TDOROKI Akira, SUZUKI Yoshiro
CHANGE IN B-VALUE BY AE PROPAGATION LENGTH IN CFRP

231

JÜNGERT Anne, DUGAN Sandra, UDOH Annett
ACOUSTIC EMISSION TESTING UNDER DIFFICULT CONDITIONS

239

KAMYSHEV Arkady, RAZUVAEV Igor, SUCHKOV Eugeny
INVESTIGATIONS OF THE AE SIGNAL PROPAGATION IN THE VESSELS
AND TANKS IN-OPERATION

247

KOBAYASHI Yoshikazu, SHIOTANI Tomoki
253
TWO-DIMENSIONAL Q-VALUE AE-TOMOGRAPHY AND ITS VERIFICATIONON NUMERICAL INVESITIGATIONSKRATOCHVILOVA Vendula, VLASIC Frantisek, MAZAL Pavel
STUDY OF FATIGUE PROCESSES OF SELECTIVE LASER MELTING AND
CONVENTIONAL PRODUCED MATERIALS

263

LACKNER Gerald, TSCHELIESNIG Peter
ACOUSTIC EMISSION TESTING ON LPG TANKS – DEFECT DETECTION
CASE STUDIES271

LIU Zhidong, LIAO Hongqiang, LIU Yuan
THE TIME SEQUENCE CHARACTERISTICS AND LOCATION METHOD OF
AE SOURCE INDUCED BY DEBRIS CLOUD

279

LIVITSANOS G., SHETTY N., VERSTRYNGE E., WEVERS M.,
Van HEMELRIJCK D., AGGELIS D. G.
CHARACTERIZATION OF FRACTURE MODE IN MASONRY BY ACOUSTICEMISSION

287

LOKAJÍČEK Tomáš, PŘIKRYL Richard, ŠACHLOVÁ Šárka,KUCHAŘOVÁ Aneta
ALKALI SILICA REACTIVITY STUDY OF ACCELERATED MORTAR BAR
BY MEANS OF ACOUSTIC EMISSION AND ULTRASONIC SOUNDING

297

LUO Sha, DIAMBRA Andrea, IBRAIM Erdin
APPLICATION OF ACOUSTIC EMISSION ON CRUSHING MONITORINGOF INDIVIDUAL SOIL PARTICLES IN UNIAXIAL COMPRESSION TEST305

MAHMOUD Houssam, VLASIC Frantisek, MAZAL Pavel, JANA Miroslav
DAMAGE IDENTIFICATION OF PNEUMATIC COMPONENTS BY
ACOUSTIC EMISSION315

MAZAL Pavel, MAHMOUD Houssam, DOSTAL Petr, CERNY Michal,
SUSTR Michal, ZACAL Jaroslav
COOPERATION OF MENDEL UNIVERSITY AND BRNO UNIVERSITYOF TECHNOLOGY IN THE FIELD OF BIOLOGICAL APPLICATIONS OF
AE METHOD

323

MIZUTANI Yoshihiro, MIKI Takehiro, TODOROKI Akira, SUZUKI Yoshiro
MANUAL LAMB WAVE TOMOGRAPHY UTILIZING AE TECHNIQUE

333

32nd EWGAE

VI

MURAVIN Boris, GOTTESMAN Tamara
ACOUSTIC EMISSION COMPARATIVE STUDY OF CARBON/EPOXYCOMPOSITES UNDER LOAD

343

NISHIDA Takahiro, SHIOTANI Tomoki, ASAUE Hisafumi, SAGRADYAN Artur
EVALUATION OF INTERNAL DEFECTS OF REINFORCED CONCRETE
COLUMNS BY MEANS OF AE TOMOGRAPHY

353

NOWAK Marek, LYASOTA Igor, BARAN Ireneusz
363
THE TEST OF RAILWAY STEEL BRIDGE WITH DEFECTS USING ACOUSTICEMISSION METHODVII

OGURA Norihiko, YATSUMOTO Hitoshi, SHIOTANI Tomoki
INTERNAL CONCRETE INSPECTION AND EVALUATION METHODS FORSTEEL PLATE-BONDED SLABS BY USING ELASTIC WAVES VIA ANCHOR
BOLTS

373

ONGPENG Jason Maximino C., ORETA Andres Winston C., HIROSE Sohichi
MONITORING DAMAGE IN REINFORCED CONCRETE STRUCTURE USINGSEESAW EFFECT THROUGH ACOUSTIC EMISSION TEST383

PANEK Maciej, NOWAK Marek, BARAN Ireneusz, KONSZTOWICZ Krzysztof J.
ACOUSTIC EMISSION DETECTION OF DAMAGE INITIATION IN 1D CFRPNCF COMPOSITES SUBJECTED TO BENDING

393

PAZDERA Lubos, TOPOLAR Libor, SMUTNY Jaroslav, MIKULASEK Karel,
CIKRLE Petr
ACOUSTIC EMISSION METHOD IN CIVIL ENGINEERING APPLIED TO
MONITORING THERMAL LOADING CONCRETE SPECIMENS BY A THREEPOINT BENDING TEST

403

PETRUŽÁLEK Matěj, LOKAJÍČEK Tomáš, SVITEK Tomáš
ACOUSTIC EMISSION AND ULTRASONIC SOUNDING AS A TOOL FOR
MIGMATITE FRACTURING409

RAVNIK Franc, GRUM Janez
IDENTIFICATION OF MACHINE COMPONENTS CRACKING WITH SOUNDEMISSION DURING STEEL QUENCHING

419

SAGASTA Francisco, MIZUTANI Yoshihiro, VALVERDE Ignacio,
SUAREZ Elisabet, RESCALVO Francisco J., GALLEGO Antolino
INFLUENCE OF ATTENUATION ON THE ACOUSTIC EMISSION B-VALUEFOR DAMAGE EVALUATION OF REINFORCED CONCRETE SPECIMENS

433

SAUSE Markus G. R.
ACOUSTIC EMISSION SOURCE IDENTIFICATION IN LARGE SCALE FIBRE
REINFORCED COMPOSITES

441

SERRIS Eric, CAMEIRAO Ana, GRUY Frédéric
MONITORING INDUSTRIAL CRYSTALLIZATION USING ACOUSTICEMISSION

451

SHIOTANI Tomoki, NISHIDA Takahiro, ASAUE Hisafumi, MAESHIMA Takuya
QUANTIFICATION OF FATIGUE DAMAGE EVALUATION IN RC SLABSDURING WHEEL LOADING TEST BY MEANS OF 3D AE TOMOGRAPHY

461

32nd EWGAESVOBODA Václav, ŽEMLIČKA František
CONTINUOUS MONITORING OF STORAGE TANK BY ACOUSTICEMISSION METHOD

471

ŠOFER Michal, CRHA Jan, ZENGERLE Hannah
FROM NEAR FIELD TO FAR FIELD AND BEYOND

475

TARASENKO Alexander, BOHAC Petr, JASTRABIK Lubomir
SURFACE ACOUSTIC WAVES IN THE LAYERED SYSTEMS

485

THENIKL Thomas, ALTMANN Daniel, VALLEN Hartmut
QUANTIFYING LOCATION ERRORS

495

TSCHELIESNIG Peter, JAGENBREIN Andreas, LACKNER Gerald
DETECTING CORROSION DURING INSPECTION AND MAINTENANCE
OF INDUSTRIAL STRUCTURES USING ACOUSTIC EMISSION

503

VALLEN Hartmut, THENIKL Thomas, TRATTNIG Horst
EN13477-2 VERIFICATION OF AE INSTRUMENTATION - STATUS ANDPROPOSALS FOR IMPROVEMENT

511

VALLEN Hartmut, THENIKL Thomas, TRATTNIG Horst
OBTAINING WAVE-PATH INFORMATION FROM PULSED SENSOR PAIRS

517

WILLEMS Fabian, BENZ Johannes, BONTEN Christian
DETECTING THE CRITICAL STRAIN OF FIBER REINFORCED PLASTICS
BY MEANS OF ACOUSTIC EMISSION ANALYSIS

525

YOSHIDA Kenichi, KINOUCHI Hiroyuki, YASUDA Takeshi
EXAMINATION OF DYNAMIC BEHAVIOR DUE TO AE METHOD OFMARTENSITIC TRANSFORMATION DURING BENDING DEFORMATION IN
CU-AL-NI SHAPE MEMORY ALLOY SINGLE CRYSTAL

535

YU Fengming, OKABE Yoji¸ WU Qi, SHIGETA Naoki
DAMAGE TYPE IDENTIFICATION BASED ON ACOUSTIC EMISSION
DETECTION USING A FIBER-OPTIC SENSOR IN CARBON FIBER
REINFORCED PLASTIC LAMINATES

543

32nd EWGAE

VIIIINDEX OF AUTHORSAGGELIS D. G.
ALACA Benjamin B.
ALLEVATO Claudio
ALTMANN Daniel
ALYAKRITSKY Alexander
ASAUE Hisafumi
AUGUTIS Stasys V.
BARAN Ireneusz
BARAT VeraBARDAKOV Vladimir
BASHKOV Oleg
BASHKOVA Tatiana
BENZ Johannes
BIRCK Gabriel
BOHAC Petr
BONIFACE Antoine
BONTEN Christian
BRUNNER Andreas J.
BUGANKOV Alexey
CALABRESE Luigi
CAMEIRAO Ana
CAPPUCCINI Filippo
CARRIÓN Francisco
CERNY Michal
CIKRLE Petr
CRESPO Saúl
CRHA JanDI PIETRO Domenico
DIAMBRA Andrea
DONATO Angelo
DOSTAL Petr
DROUBI Mohamad G.
DUBOV A. A.
DUGAN Sandra
ELIZAROV Sergey
EL-SHAIB Mohamed N.
ENOKI Manabu
FAISAL Nadimul H.
FALLAHI N.
GADE Sebastian O.
GALEANO Massimiliano
GALLEGO Antolino
GASCA Héctor
GOTTESMAN Tamara
GRUM Janez

IX

287
163
1
495
109
353, 461
7
15, 363, 393
115, 201
115
27
27
525
35
485
45
525
55
109
65, 75
451
65
85
323
403
85
475
65, 75305
65, 75
323
95
105
239
109, 115, 201
95, 125, 137
145, 217
95
151
163
65, 75223, 433
85
343
419

GRUY Frédéric
GUENTHER Thomas
HAMSTAD Marvin A.
HATA YutoHERNÁNDEZ Jorge
HIROSE Sohichi
CHERNOV Dmitry
CHLADA Milan
IBRAIM Erdin
ITO Kaita
ITURRIOZ Ignacio
JAGENBREIN Andreas
JANA Miroslav
JASTRABIK Lubomir
JIANG Yu
JUNG DoyunJÜNGERT Anne
KAMYSHEV Arkady
KESHK Omar
KINOUCHI Hiroyuki
KOBAYASHI Yoshikazu
KOLOKOLNIKOV S. M.
KONSZTOWICZ Krzysztof J.
KRATOCHVILOVA Vendula
KROLL Andreas
KUCHAŘOVÁ Aneta
LACKNER Gerald
LIAO Hongqiang
LIU Yuan
LIU Zhidong
LIVITSANOS G.
LOKAJÍČEK TomášLUO ShaLYASOTA Igor
MAESHIMA Takuya
MAHMOUD Houssam
MAZAL Pavel
MIKI Takehiro
MIKULASEK Karel
MIZUTANI Yoshihiro
MOWAT JohnMURAVIN Boris
MUTO Yuki
NARDONI G.

451
173
183
193
85
383
115, 201
211
305
217
35
503
315
485
223
231
239
247
125
535
253
105
393
263
173
297
271, 503
279
279
279
287
297, 409
305
15, 363461
315, 323
263, 315, 323
333
403
193, 231, 333,
433
95
7, 343145
151

32nd EWGAE

NISHIDA Takahiro
NOBEL Craig
NOHAL LiborNOWAK Marek
OGURA Norihiko
OKABE Yoji
ONGPENG Jason Maximino C.
ORETA Andres Winston C.
PALAZZETTI R.
PANEK Maciej
PAZDERA Lubos
PETRUŽÁLEK Matěj
POPKOVA Alexandra
POTSTADA Philipp
PREVOROVSKY Zdenek
PRIVALOV V. Yu.
PROVERBIO EdoardoPŘIKRYL Richard
QUINTANA Juan
RANAIVOMANANA NaryRAVNIK FrancRAZUVAEV Igor
RESCALVO Francisco J.
RIAD Omar
SAGASTA Francisco
SAGRADYAN Artur
SALIBA Jacqueline
SAUSE Markus G. R.
SBARTAÏ Zoubir M.
SEMASHKO N. A.
SEPÚLVEDA Francisco
SERRIS Eric
SHARKEEV Yurii
SHEHEDA Mohamed
SHETTY N.
SHIGETA Naoki
SHIOTANI Tomoki
SHIRAIWA Takayuki
SKROK Krzysztof
SMUTNY Jaroslav

32nd EWGAE

353, 461
95
211
363, 393
373
543
383
383
151
393
403
409
27
163
211
105
65, 75
297
85
45
419
247
433
137
433
353
45
163, 441
45
105
85
451
27
125, 137
287
543
253, 353, 373,
461
145
15
403

STUART Alan
SUAREZ Elisabet
SUCHKOV Eugeny
SUSTR Michal
SUZUKI Yoshiro
SVITEK Tomáš
SVOBODA Václav
ŠACHLOVÁ Šárka
ŠOFER Michal
TARASENKO Alexander
TDOROKI Akira
TERENTYEV Denis
THENIKL Thomas
TODOROKI Akira
TOPOLAR Libor
TRATTNIG Horst
TROFIMOV Pavel
TSCHELIESNIG Peter
UDOH Annett
VALLEN Hartmut
VALVERDE Ignacio
Van HEMELRIJCK D.
VASILYEV Igor
VAŠTAKAS Edgaras
VERSTRYNGE E.
VLASIC Frantisek
WEVERS M.
WILLEMS FabianWU Qi
XU Bingsheng
XU Feiyun
YANG Zhong
YASUDA Takeshi
YATSUMOTO Hitoshi
YOSHIDA Kenichi
YU Fengming
ZACAL Jaroslav
ZENGERLE Hannah
ZUCCHELLI A.
ŽEMLIČKA František

95
433
247
323
193, 231, 333
409
471
297
475
485
231
115
495, 511, 517
193, 333
403
511, 517
109
271, 503
239
495, 511, 517
433
287
201
7
287
263, 315
287
904
543
223
223
223
535
373
535
543
323
475
151
471

X

Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

APPLICATION OF WAVEGUIDES FOR THE CALIBRATIONOF ACOUSTIC EMISSION TRANSDUCERS
Stasys V. AUGUTIS1, Boris MURAVIN2, Edgaras VAŠTAKAS3
1

Institute of Metrology, Kaunas University of Technology; Lithuania, Kaunas
Phone: +37067327733, e-mail: vygantas.augutis@ktu.lt 2
Integrity Diagnostics Ltd, Israel, e-mail boris@muravin.com, 3
Institute of Metrology, Kaunas University of Technology; Lithuania, Kaunas
Phone: +37067343761 e-mail: edgaras.vastakas@gmail.com

Abstract
This work presents new type of acoustic emission transducer (AET) calibration methods based on signal
synthesis techniques, applied on different type and shape mechanical waveguides. Based on the results presented
in this work a new type of AET calibration system was developed. The system consists of piezoelectric
excitation transducer, arbitrary signal generator, and different type of waveguides, capacitive transducer, anddata acquisition system and measurement software. Proposed system was calibrated with Polytechinterferometer. Mechanical displacements obtained in waveguides were measured and visualized.
Suggested equipment allowed calibration of AET from tens of kilohertz to several hundred kilohertz range.
Acquired AET pulse and frequency response measurement examples with suggested equipment in bar typewaveguides are presented in work.
Keywords: Acoustic emission transducer calibration

1. Introduction
The majority of acoustic emission transducers (AET) are of piezoelectric type due to theirsimple construction and possibility to perform measurements under complex conditions (hightemperature, environmental pollution, dust, etc.), and they are used in many areas as well
[1-5]. Transducer time and frequency-based characteristics depend on the properties of piezo
ceramics and the construction of the transducer and may vary considerably, depending on theoperating conditions [6-7]. Knowlage of these characteristics is usally significant for theirapplication therefore, the calibration of such transducers is necessary.
The calibration of acoustic emission transducers is defined by the standard E1106-86 [8].
In this standard, a method is described, and recommendations for the equipment allowing
measurement of the AET impulse and frequency responses are provided.
The AET impulse response is measured during the excitation of the AET, using a mechanicalstress of order of microseconds, i.e., impulse, or spike. To prevent the impact of acoustic wave
reflections on the measurement of the impulse response, the standard E1106-86 recommends
using a metal block. The mechanical effects (nanoscale displacements) are induced by breaking
the capillary tube. After its propagation through the metal block, the mechanical stress allows tomeasure the AET impulse and frequency responses while avoiding the impact of acoustic
reflections from the walls of the block on the results of the measurement.
The block size is large in the low frequency range from 20 kHz to 100 kHz, and its mass
can reach several tons. Capillary tubes are used to generate the mechanical impact, whichleads to a problem associated with the manufacture of such tubes, ensuring repeatabilityof production parameters, as well as the limited performance of the calibration system.
The calibration is performed using a wave of Rayleigh type, regardless of the AET operating
conditions. Therefore, the characteristics obtained using existing calibration equipment are

32nd EWGAE ©CNDT2016

S1

correct when the transducer operating conditions are close to those which were present duringcalibration. If, during its real-world application the AET measures, the wave of other type that
propagates in a material with substantially different acoustic impedance, then the transducer
characteristics obtained during calibration are not comprehensive enough. For this reason,a different kind of calibration equipment is necessary, which could provide the possibility tomeasure the AET characteristics using different types of waves and to evaluate thecharacteristics of these transducers under different acoustic impedance.
Considering the shortcomings of the existing calibration equipment, a task to create
a portable AET calibration equipment of a new type, with a greater measurement performanceand allowing the evaluation of AET characteristics using different wave types andwaveguides with different acoustic impedances, was formulated.
One of the main problems encountered was the reduction of the calibration equipment size.
In the equipment described by the standard, a large metal block is used. Block dimensions are
selected so as to reach at least several wavelengths of the propagating wave of minimal
frequency. When reducing the size of the calibration equipment, the introduction of suchblock becomes impossible. For this reason, the bar and plate type constructions of acoustic
waveguides with considerably smaller dimensions were offered. The application of thesewaveguides permits the reduction of the calibration equipment size; however, the acoustic
wave dispersion is unavoidable in the waveguides of such type. The systemes impulse
response obtained in the dispersive waveguides depends on the geometry of the waveguide(length, diameter, cross section shape, thickness – in plate case), and the properties of thetransducer used for the excitation. Because of this reason, it is not possible to obtain
displacements of the order of several microseconds when using the conventional methods of
excitation in such waveguides. Therefore, deconvolution-based methods of mechanicaldisplacement generation have been proposed and practically implemented (implemented
in work method is described in other published work [22]). Based on the results presented inthis work, a new type low-frequency AET calibration stands have been suggested.
2 Waveguide geometry selection criteria
Calibrations stands waveguides measures are determined by several criteria: wave reflection
time and arbitrary displacement form in excitation area. During the calibration of AET, theacoustic waveguides allows avoiding of the wave reflection impact on the measurement result.
It is considered that a minimal delay time for the low-frequency AET calibration (starting with the
AET excitation, until the return of the reflected signal) must be not less than 100 µs. At least
several signal wavelengths from the transducer with minimum frequency excitation must be
accommodated along the direction of wave propagation. It was determined experimentally thatthe dimensions of the bar-type waveguides have to be chosen according to formula (1), while
in case of the plate-type waveguide, the length of a single edge is found using expression (2).
𝑙𝑙𝑙𝑙𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 ≥ 3𝜆𝜆𝜆𝜆𝑚𝑚𝑚𝑚𝑏𝑏𝑏𝑏𝑚𝑚𝑚𝑚 ;(1)

𝑙𝑙𝑙𝑙𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑏𝑏𝑏𝑏𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 ≥ 6𝜆𝜆𝜆𝜆𝑚𝑚𝑚𝑚𝑏𝑏𝑏𝑏𝑚𝑚𝑚𝑚 ;(2)

where l bar – length of the bar-type waveguide, λ max – maximum wavelength used forcalibration.

where l plate – the length of a single plates edge , λ max – maximum wavelength used forcalibration.
To ensure the synchronicity of the mechanical pulse used for calibration across all thetransducer aperture area, the necessary diameter of the bar-type waveguide must be evaluated.

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32nd EWGAE

It was determined experimentally that this diameter has to be selected according to formula
(3).
𝜆𝜆𝜆𝜆
(3)
𝐷𝐷𝐷𝐷𝑏𝑏𝑏𝑏 > 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 ;2

where D b – diameter of bar type waveguide, λ min – minimum wavelength used forcalibration.
3 Visualisation of generated mechanical displacements in bar and plate type waveguides

The excitation signal is calculated using impulse response measured at one point of
the waveguide. Thereafter, the desired arbitrary impact function can be calculated at themeasurement point of the impulse response, by using methods of decomposition or time
reversal mirror [9-21]. The problem arising independently of the excitation method is related
to the continuity of the impulse response on the surface of the waveguide used for AET
calibration. This criteria influences AET calibration uncertanty and needs to be evaluated.
When calculating the excitation signal, none of the examined methods estimates that theimpulse response function, obtained at the adjacent points of the waveguide, may differ fromthe one obtained at the impulse response measurement point.
In order to evaluate the influence of non-continuity of the impulse response on thepredetermined impact function, the bar and plate type waveguide surface vibration scan was
carried out.
The ending of the steel bar-type waveguide with a diameter of 24 mm was scanned.
The measurements were accomplished according to the block diagram presented in Figure 1.
The excitation signal was calculated for the central point according to the modified
deconvolution method presented in previous work [22].

Figure 1. Equipment connection block diagram

The excitation signal frequency band must be selected according to formula (3) (frequency
band is related to λ min ). Measurement results presented in Figures 2 and 3 demonstrate
importance of the following selection. In Figure 3 frequency band was selected according toequation (3). Another case is demonstrated in Figure (2), where frequency bad is intencionallyselected wider than it should be according to equation (3). Measurement results demonstratefrequency bands selection importance for the generation of similar amplitude displacemets inselected area of the bar type waveguide.

32nd EWGAE

S3

a)

b)

a)

b)

Figure 2. Scanned vibrations of the ending of the steel bar with diameter of 24 mm for the excitation signal
frequency band of 270 kHz: a) surface deformation under maximal vibration amplitude b) surface deformation
x-z axis

Figure 3. Scanned vibrations of the ending of the steel bar with diameter of 24 mm for the excitation signal
frequency band of 150 kHz: a) surface deformation under maximal vibration amplitude; b) surface deformation
x-z axis

AET characteristics usally depends from measured wave type. Therefor generation ofarbitrary mechanical pulse waveforms used for AET calibration is necessary not only in bars,
but also in plate-type structures. In order to prove that predetermined displacements may beobtained, not only in a certain area of the bar but in plate as well, a surface scan of the 5 mm
thick steel plate was carried out.
The excitation signal was calculated using the modified decomposition method [22].
Results are presented in figure 4; here, the 2500 mm2 plate area has been scanned, and thegenerated displacemnt is shown in different distances from the excitation source.

a)

S4

b)

32nd EWGAE

d)

c)

Figure 4. Scanned plate surface, where the sinusoid period type mechanical displacement is obtained: a) at 320
mm distance from the excitation source; b) at 340 mm distance from the excitation source c) at 350 mm distancefrom the excitation source d) pulse in the AET aperture area

Measurement results in bar and plate type waveguides demonstrate possibilities togenerate in dispersive waveguides predetermined displacements which can be used for AET
calibration.
4 Acoustic emission transducer calibration
Block diagrams of acoustic emission calibration stand is presented in Figure 5. The AET
is calibrated using arbitrary mechanical displacements generated according to modified
decomposition method [22]. Example of the generated mechanical displacements waveform,
measured with capacitive transducer is presented in Figure 6. Calibration stands waveguides
geometry is selected according to formulas (1) - (3). Both calibration stands allow to measure
AET impulse response, from which AET frequency response is calculated, manucatured
transducer characteristics measuremnt example is provided in figure 7.

Figure 5. AET calibration stands block diagram

Acoustic emission transducer frequency response is determined according to formula (4),impulse response is measured with oscilloscope.
𝑆𝑆𝑆𝑆 (𝑗𝑗𝑗𝑗𝑗𝑗𝑗𝑗)

𝑡𝑡𝑡𝑡
𝐾𝐾𝐾𝐾𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 (𝑗𝑗𝑗𝑗𝑗𝑗𝑗𝑗) = 20log � 𝑌𝑌𝑌𝑌(𝑗𝑗𝑗𝑗𝑗𝑗𝑗𝑗)
�;(4)Where KAET(jω) – AET frequency response, S t (jω) – measured AET impulse response
spectrum, Y(jω) – measured waveguides arbitrary type vibration.

32nd EWGAE

S5

a)

b)

Figure 6. Bar-type waveguides displacement: a) displacement waveform at bar-type waveguides end measured
with capacitive transducer b) calculated frequency responsea)b)Figure 7. AET characteristics measurement example: a) AET pulse response obtained in stand with bar typewaveguide b) calculated frequency response

5. Conclusions
AET calibration stand constructions of a new type are proposed in this paper, offeringcalibration with different types of waves opportunities and reduced size, compared to existing
solutions. These constructions use bar and plate type waveguides. Until now, these
constructions haven't been used for AET calibration, because of limited possibilities togenerate arbitrary waveform displacements. Offered construction application possibilities for
AET calibration are presented by the measurement results.

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32nd EWGAE

References
1. G.Budenkov, O.Korobeynikova, ‘Application of the rod and torsional waves fortesting the extended objects of oil extracting industry’, ndt for safety, pp.31-38,
publisher www.ndt.net, 2007.
2. H.Kwun, Guided wave inspection of nuclear fuel rods, ‘Developments in ultrasonicguided wave inspection’, pp. 1-6, publisher www.ndt.net, 2009.
3. B.Bahr, ‘Automated inspection for aging aircraft, in: International Workshop on
Inspection and Evaluation of Aging Aircraft’, pp. 18–21, publisher Carnegie Mellon
University Pittsburgh ,1992.
4. K.K. Shung, J.M.Cannata, Q.F.Zhou, ‘Piezoelektric materials for high frequency
medical imaging applications: review, journal of Electroceram’, pp. 140-145, springer,
2007.
5. Renaldas Raišutis, Rymantas Kažys, Liudas Mažeika, Egidijus Žukauskas,Vykintas
Samaitis, Audrius Jankauskas,’Ultrasonic guided wave-based testing technique forinspection of multi-wire rope structures’, journal of NDT&E International, vol 62, pp.
40–49, Elsevier,2014.
6. T.L.Jordan, Z.Ounaies, ‘Piezoelectric Ceramics Characterization, NASA LangleyResearch Center’, NASA/CR-2001-211225, pp 1-22, 2001.
7. ‘Effects of High Static stress on the Piezoelectric properties of transducer materials’,
Technical Publications TP-220, pp 1-6, Morgan Electro Ceramics.
8. ‘Standard method for primary calibration of acoustic emission sensors’, ASTM
international, designation E1106 - 86.
9. Mathias.F,’Time Reversal of Ultrasonics Fields – Part I: Basic Principles’, journal
Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.39, No.5,
pp.555 - 566, IEEE ,September 1992.
10. R. Gangadharan, C.R.L. Murthy, S. Gopalakrishnan, M.R. Bhat.’Time reversal
technique for health monitoring of metallic structure using Lamb waves’, Journal ofultrasonics,vol 49, pp703-705, Elsevier, 2009.
11. T. Yamasaki, S. Tamai, M. Hirao , ‘Optimum excitation signal for long-range
inspection of steel wires by longitudinal waves’, journal of NDT&E International, vol34, pp. 207–212, Elsevier, 2001.
12. S. Bahrami, A. Cheldavi, and A. Abdolali, ‘Moving target tracking using time reversal
method ‘,journal of Progress In Electromagnetic Research M, Vol. 25, pp. 39-52,
EMW Publishing, 2012.
13. W.A.Kuperman, W.S.Hodkiss, T. Akai, S.Kim, G. Edelmann, H.C.Song, ‘Timereversal acoustics’ , Marine physical laboratory/SIO and SACLANTCEN undersea
center, pp. 1 – 6, Acoustics, 2000.
14. M.Fink, C.Prada, ‘Acoustic time-reversal mirrors’, Institute of physics publishing,
Topical review, pp.1-38, IOP publishing, 2000.
15. R.Ernst, J. Dual, ‘Acoustic emission localization in beams based on time reversed
dispersion’, journal of ultrasonics, Vol. 54, pp. 1522-1533, ELSEVIER, 2014.
16. H.W.Park, S.B.Kim, H.Sohn, ‘Understanding a time reversal process in Lamb wave
propagation’, Journal of Wave motion, Vol. 46, pp. 451 – 467, ELSEVIER, 2009.

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17. M.Griffa, B.E.Anderson, R.A.Guyer, T.J.Ulrich, P.A.Johnson, ‘Investigation ofrobustness of time reversal acoustics in solid media through the reconstruction of
temporally symmetric sources’, Journal of physics D:Applied physics, Vol.41, pp. 115. IOP publishing, 2008.
18. R.Watkins, R.Jha, ‘A modified time reversal method for Lamb wave based diagnostics
of composite structures’, Journal of mechanical system and signal processing, Vol. 31,
pp. 345-354, ELSEVIER, 2012.
19. L.Zeng, J.Lin, ‘Chirp based dispersion precompensation for high resolution Lambwave inspection’, Journal of NDT&E international, Vol 61, pp. 35 – 44, ELSEVIER,
2014.
20. V.Augutis, M.Varanauskas, ‘Synthesis of acoustic impulses in a solid waveguide,
Ultragarsas’, No. 1(38), Publisher ultragarsas, 2001.
21. A.Benatar, D.Rittel, A.L.Yarin, ‘Theoretical and experimental analysis of longitudinal
wave propagation in cylindrical viscoelastic rods’, Journal of the mechanics andphysics of solids, Vol. 51, pp. 1413-1431, Elsevier, 2003.
22. Vygantas, Augutis; Darius Gailius; Edgaras Vaštakas; Pranas Kuzas, ‚Evaluation ofarbitrary waveform acoustic signal generation techniques in dispersive waveguides‘,
Journal of Vibroengineering, Vol. 17, no. 7, p. 4047-4056, 2015.

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

ACOUSTIC EMISSION TESTING OF UNDERGROUND PIPELINES OFCRUDE OIL OF FUEL STORAGE DEPOTS
Ireneusz BARAN 1, Igor LYASOTA 1, Krzysztof SKROK 2
Cracow University of Technology, Laboratory of Applied Research; Krakow, Poland
Phone: +48 12 628 32 74, Fax: +48 12 628 37 45; e-mail: baran@mech.pk.edu.pl
2
PERN S.A., Plock, Poland

1

Abstract
Considerable quantity of underground in-plant pipelines of crude oil of fuel storage depots and refineries isoperated for more than thirty years. Corrosion damages and material degradation are the main causes forstructural failures of such underground pipelines. Such failures not detected on time and not monitored in time
are potentially the reasons of major breakdowns of the infrastructure of technological underground pipeline
installations in-service and finally are the risks of pollution of the environment. The preventive maintenanceactivities are usually carried out on time driven basis, but evolving defects have to be identified in time to enableappropriate repairs. The primary disadvantages are limited access to pipelines and limited possibilities of testing,due to the necessity for off-line pressure tests of a big part of underground pipeline installation, possible visualinspection and follow-up non-destructive tests (NDT). Based on these facts, we conducted investigations forapplication of AE testing method of underground pipelines during pressure tests using liquid product transported
through pipe. We present the AE investigation on pipeline materials and on real underground pipelines.
Keywords: underground pipelines, AE testing, AE wave propagation in liquid and in pipeline

1. Introduction
A significant part of underground pipelines for transport of petroleum products of thepetrochemical industry has been operated for over thirty years. The manufacturing flaws of
low carbon steel from the period of pipelines construction resulted in various types oftechnological defects, structural and chemical heterogeneity, and often, significant
contamination of the structure in the form of non-metallic inclusions (mainly sulphur
compounds). The long-term operation of such pipelines with hidden defects causes damage
development in the material and welded joints in the form of discontinuities, corrosion
defects, and structural changes in the matrix material. The development of the defects, interms of working loads, can lead to their unstable state, the effect of which may be thepipeline leak resulting in the natural environment contamination. The scale of the problem isshown by the statistics [1], according to which the development of corrosion damage inpipelines of the petrochemical industry, despite the use of better and better protective
coatings, is at the forefront of reasons for their failure (approx. 30%), and consequently,
damage to the natural environment.
Accordingly, in the chemical and petrochemical industries, new solutions in the field of
diagnostic tests are sough, which allow to precisely assess the technical condition of theunderground in-plant pipelines operating for a long time, and to improve their reliability andoperational safety.
It is worth noting that in case of long underground pipelines, it is hardly efficient to use thestandard NDT methods, such as visual testing (VT), ultrasonic testing (UT) or radiographic
testing (RT), because their implementation requires the excavation of the entire pipeline.

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S9

One of the cheapest and simplest methods of testing the underground pipelines is a visualinspection carried out with the use of the CCTV method (Closed Circuit Television).
However, it can only obtain an image of the pipeline interior (internal inspection), and it isnecessary to drain the pipeline, which is difficult to implement. There is also no possibility toobtain other relevant information, including, among others, the wall thickness, the conditionof its outer surface, or the presence of hidden defects in the material [2].
The tests (In-Line Inspection) implemented by intelligent pigs [3], which use the ultrasonic(UT, Ultrasonic Testing) or electromagnetic method (ET, Electromagnetic Testing) of damage
detection, allow to more thoroughly assess the technical condition of underground pipelines.
In this case, it is possible to detect, locate and determine the size of corrosion defects(including stress corrosion cracking), both inside and outside the pipeline, and to detecthidden defects in the pipe wall material (various types of discontinuities). A weak point of
this technique is the necessity to insert a large-size device to the pipeline inside, which is notalways possible to achieve. Therefore, it is mainly used to test the transmission pipelines.
In recent times, a long-range ultrasonic method, which is based on the phenomenon of guided
waves (Ultrasonic Guided Wave Testing), has been used for testing of the underground
pipelines [4, 5]. The test is conducted by generating guided (torsional) waves in the pipe wall,
which propagate to a maximum distance of approx. 200 m (depending on the pipe material
conditions and operating conditions), and the detection and location of damage are possible
by calculating the transit time of reflection of waves from the occurred damage. The
disadvantage of this method is a significant decrease in energy of waves propagating on thewelds connecting individual elements of the pipeline, as well as a result of the material
degradation, especially the corrosion damage, which causes the decrease of the test distanceeven down to 8 ÷ 10 m.
One of the modern NDT methods, which is also used in testing the pipelines is the Acoustic
Emission method (AE). However, it is mainly used to test tightness (leak tests) of the pipeline[6-10], and is also used in case of the intelligent pigs.
It is generally known that AE is a highly sensitive passive test method, which allows to detect
active surface and internal defects in the walls and welds of the tested object under loading.
The main advantages of AE method are the possibility of global testing and monitoring oflarge elements and structures (including those in operational conditions) and above all, thelocation of AE sources generated by active defects/damage with the possibility to determine
the dynamics of their development. Thus, this method is currently considered as appropriate(complementary with other NDT tests) for periodic testing of large equipment, among others,in the petrochemical industry. An additional important advantage of this method is
propagation of AE waves through the operating medium (liquid) in the tested object, whichenables to use AE for testing large objects, such as above-ground storage tanks.
From the above, it can be stated that a prospective solution in the area of diagnostic tests ofunderground in-plant pipelines would constitute the development of the measurement
methodology based on recording of propagation of AE waves through the operating mediumthat are generated by active defects and damage in the pipeline wall material. One of more
important aspects in this case would be the use of the maximum possible distance between AE
sensors in order to reduce the amount of excavation (local access to the pipeline) and thus, thecosts associated with the test realization.
The main components that will constitute the basis for the development of the above
mentioned measurement methodology with the use of AE are:• performance of tests related to propagation of AE waves over a length of the underground
pipeline, selection of the optimal measuring equipment and AE recording settings –determination of the propagation range of AE waves by the operating medium (liquid), as
well as by the pipeline wall;

S10

32nd EWGAE• determination of parameters (amplitude, energy/signal strength, duration, frequency
domain of AE signals, etc.) of the recorded AE signals during laboratory tests, and the ones
generated by actual damage in the wall and especially corrosion damage (concentration ofdeep pitting corrosion) in the pressure testing conditions – loading of the pipeline element
in relation to the conditions of maximum operating pressure, design pressure, proof test
and maximum operating pressure;• verification of the developed measurement methodology in test conditions of in-service
underground pipeline.
The basic research and tests to develop the measurement methodology (diagnostic) with theuse of AE for testing the underground in-plant pipelines are presented in this publication.

2. AE wave propagation tests
In order to develop a new diagnostic methodology with the use of AE to test longunderground parts of the pipelines, it was necessary to determine the maximum distance
between the sensors (mounting place of AE sensors), at which the optimal measurement
sensitivity and possibility to detect defects as AE sources would be maintained. For thispurpose, the tests of propagation of AE waves and measurements of the acoustic backgroundon several sections of the DN600 pipeline filled with crude oil were carried out. The tests
were conducted on the pipelines of different lengths with underground (10 ÷ 70 m) andabove-ground (100 m and more) sections, and sections with the change of their direction(elbows 90°). Measuring points with the mounted AE sensors were located on the exposedparts of pipelines, and their exemplary layout for one of the tested cases is shown in Figure 1.
In order to record AE signals, the AMSY-6 multi-channel measurement system and AE
resonant sensors of VS30-SIC/VS30-V (25÷80 kHz) and VS75-V (30÷120 kHz) types ofVallen Systeme, and R1.5 (5÷20 kHz) resonant sensors of Physical Acoustics Corporation
were used.
As the artificial sources generating AE waves, the following tools were used:• AE sensors of R1.5, VS30-SIC/VS30-V and VS75-V types connected with the internal
generator of the AMSY-6 system,• Hsu-Nielsen 2H/0.50 mm source according to the standard PN-EN 1330-9:2009 [11],• AE sensors of the SE25-P type (DECI company) connected with the Agilent 33220A
function generator.
While testing the propagation and attenuation of AE waves, different ranges of frequency
filters on the measurement channels (3 ÷ 300 kHz, 6 ÷ 300 kHz and 9 ÷ 300 kHz) were used.
It is aimed at checking the difference in attenuation of AE waves, checking the acoustic
background and finding the optimum measuring range, which allows to eliminate noise while
maintaining adequate measurement sensitivity.

Fig. 1. Scheme of the layout of measurement points over the length of the pipeline while testing the propagationof AE waves (underground part of the pipeline presented by the dotted line)

The amplitude of AE signals for the background did not exceed 30 ÷ 34 dB for the range offilters 3 ÷ 300 kHz. For longer distances (above 20 ÷ 25 m), AE waves propagated only by
the medium in the pipeline. It is shown in Figure 2 for tests with AE source in the form of

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Hsu-Nielsen 2H/0.5mm. The amplitude of AE signals recorded at the distance of 70 m fromthe source were respectively 39 dB for R1.5 sensor, 40 dB for VS30-V sensor and 41 dB for
VS75-V sensor. In case of the above-ground pipeline segment and the similar test for the
distance of 70 m between sensors and source, the amplitude was 58 dB.
On the basis of the data collected during testing of the propagation of AE waves, attenuation
curves for the underground and above-ground parts of the pipeline were constructed – thecurves for VS30-V sensor are presented in Figure 3.

a
AE wave throughliquid, 140 cm/msbFig. 2 Propagation of AE waves in the pipeline underground section with a length of 70 m: a – a signal for thesensor at AE source (HSU 2H/0.5mm source), b – a signal for the second sensor (VS30-V sensors)

Fig. 3. Attenuation curves for AE waves in the underground and above-ground pipelines – for AE sensors of
VS30-V

When the distance of the source of AE waves from the sensor is less than 10 ÷ 15 m, AE
signals consisted of several modes of waves propagating with variable speed for Hsu-Nielsen
2H/0.5mm source. Figure 4 shows an example of AE signal, where the source was at the
distance 5 m from the sensor – AE signals of variable speed are visible: longitudinal wave

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(535÷540 cm/ms) propagating through the pipe wall, the Lamb longitudinal wave
(200÷205 cm/ms) propagating along the pipeline wall and the wave propagating throughliquid (140 cm/ms).
The modes and parameters of AE waves identified during testing, which propagate in thepipeline filled with liquid, are important and should be taken into account in the future while
locating AE sources during diagnostic tests.

Longitudinal wave,
535÷540 cm/ms

AE wave throughliquid, 140 cm/ms

Longitudinal Lamb wave,
200÷205 cm/ms

Fig. 4 AE signal recorded at the distance of 5 m from the Hsu-Nielsena (HSU/2H/0.50 mm) source– for VS30-SIC sensor

According to the obtained results, it can be concluded that use of lower frequency filters on
AE measurement channels and low-frequency sensors allows to increase the range of testing
while maintaining the measurement sensitivity – greater range of propagation of AE waves.
It was also found that in case of underground pipelines, the maximum distance between themeasurement points would be approx. 50 m (Fig. 3). In accordance with the fact that
attenuation of AE waves in the exposed pipeline is lower (in relation to attenuation of AE
waves in the underground pipeline), the maximum distance between AE sensors in this casecould be even more than 120 m (Fig. 3).
The data related to attenuation and propagation of AE waves were obtained as a result of thetests, with optimally selected test equipment and recording settings. The results allowed todetermine the maximum distance between AE sensors, for which the optimum measurement
sensitivity is maintained. The information on the modes of AE waves propagating along thepipe wall and through the operating medium (liquid), will be also useful in the interpretationof the results of AE tests of actual in-plant pipelines.

3. AE laboratory tests on pipeline section and material pipe properties
3.1. Mechanical tests of the pipeline steel
In the next stage of the research, the tests of properties of the material sections obtained fromthe pipe of the underground fuel pipeline with the diameter of DN 422÷425 mm (wallthickness 7.5÷8 mm), constructed in 1968, were carried out. The pipeline was operated underthe maximum operating pressure conditions at the level of 0.3 ÷ 0.4 MPa. Design pressure
was 6.3 MPa and proof test pressure was 9.5 MPa. The tests were aimed at determining theeffect of the material degradation on its mechanical properties. A set of material tests, such as:
chemical composition analysis, metallographic tests, tensile testing at ambient temperature,
were carried out.
Chemical composition testing. The chemical composition was analysed using the emission
spectrometry method and to determine precisely the content of sulphur and carbon, the32nd EWGAE

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infrared absorption method was used after combustion in an induction heating furnace. It wastested for standard specimens. The results of the tests and bibliography data of the 18G2A
steel are presented in table 1.
Table 1. Chemical composition of the tested steel and 18G2A steel(PN-EN 10025-1:2007 [12])Materials
Tested steel
18G2A

C
0,17
Max
0,20

Si
0,36

Chemical elements, [%]
S
P
0,040
0,035
Max
Max
1,00÷1,50
0,040
0,040
Mn
1,35

0,20÷0,55

Cr
0,05
Max
0,30

Ni
0,05
Max
0,30

Cu
0,08
Max
0,30

According to the technical documentation, the pipeline, from which the tested sections weretaken, is made of steel of the 18G2A (S355) grade, which was also confirmed by the chemical
analysis of material cut from the pipes.
Metallographic tests were made with the use of the optical microscopy. The surfaces of
unetched and etched (reagent Nital – 4% HNO 3 + 96% C 2 H 5 OH) polished sections withdimensions of 8×15 mm, cut in the circumferential and axial directions, were tested.
The observation of unetched polished sections showed that there are stringers non-metallic
inclusions (manganese, sulphides) along the rolling direction, and their amount results froma slightly increased sulphur content (see Table 1).
The tests of etched sections demonstrated that the pipe material has a fine-grained ferriticpearlitic structure (Fig. 5), which is typical for steel of the 18G2A grade, without distinctive
features of the secondary structure. No significant changes of the microstructure wereobserved.

abFig. 5. Microstructures of etched polished specimens of the pipeline steel,a – in the circumferential direction b – in the axial direction, ×500

Tensile testing used the Zwick/Roell Z100 universal testing machine, in accordance withguidelines of the PN-EN ISO 6892-1 standard [13]. The test specimens were cut out in thecircumferential and axial directions of pipes. The obtained mechanical properties of tested
steel are given in Table 2.
The test results of tensile testing at ambient temperature showed that all the strengthparameters of the tested pipes are in the range of values given in the pipeline technicaldocumentation.
Table 2. Mechanical properties of steel sections
Direction of thesample cutting
Circumferentially
Axially

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R m , MPaR e , MPa

A5, %

Z, %

549
552

371
380

29.35
29.78

49
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3.2. Laboratory pressure tests with AE recording
The specimen (Fig. 6) in the form of the pipeline section (from underground pipeline that has
been operated for over 40 years), on which the pressure test with recording of AE was carriedout. During the pressure test, the specimen was loaded (and unloaded) in stages with the
10 minute load holds and recording of AE. The process of loading the pipeline section lasted
until the loss of its stability (break and leak), which occurred at the pressure of approx.
15.8 MPa. There were numerous corrosion damages (including pitting corrosion) of different
depths on the outer surface of pipes (1 ÷ 2.6 mm, see Fig. 6 and 7).

Fig. 6 Scheme of the test stand and layout of AE sensors and the map of damage on the coating of the pipeline
section (specimen) (1÷6 – VS150-RIC sensors, 29÷34 – VS30-SIC sensors, A ÷ P – inventoried and measured
corrosion damage)

a

b

c

Fig. 7. General view of the test stand with the pipeline section (a) and example corrosion damage (b, c)

For AE recording were used AMSY-6 system and sensors: VS30-SIC and VS150-RIC withintegrated preamplifiers (respectively 46 dB and 34 dB). During the tests on measurement
channels and connected AE sensors, the following ranges of the frequency filters wereapplied: VS30-SIC: 9.3÷300 kHz, VS150-RIC: 95÷850 kHz. The layout of sensors on thetested pipeline section is shown in Figure 6.
During the pressure test AE was recorded. AE testing is primarily aimed at collecting thereference AE signals (with correspondingly wide range of the frequency) typical for
subsequent stages of development of corrosion damages (including pitting corrosion) in thepipeline wall. The high-amplitude AE signals resulting from corrosion damages appeared at
the pressure of 1.6 MPa – location of AE sources was presented in Figure 8. Taking into

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account the attenuation curves (Fig. 3), it can be stated that AE waves generated by corrosion
damages would be detected for distance of 50 m.
In figures 9 ÷ 10 the test results in the form of graphs of changing parameters of AE during
the pressure test are shown. The analysis of the obtained results showed a variable nature ofgenerating AE signals in the loading of the tested pipeline section.

Fig. 8. The location of sources of AE signals along the tested part of pipeline – location of active corrosion
damage for the load of 1.6 MPa

In Figure 9, it is possible to distinguish three characteristic stages, which correspond toindividual stages of the destruction process development of the pipe material:
1. Elastic operation of the pipe (pressure up to 8.5 MPa) was characterised by progressive
increase in AE activity as the test pressure is increased. It is worth noting that in a given
load range with the use of both types of sensors, comparable numbers of AE wererecorded (Fig. 9). The maximum activity of the located AE signals in this case, wasobserved at the time of transition from the elastic operation to the occurrence of local
plastic deformation areas in it. For AE sensors these values respectively reached to:
21 hits/sec – VS30-SIC and 23 hits/sec – VS150-RIC (Fig. 9).
The high energy AE signals, which were possibly generated by the local plastic
deformation in the area of corrosion damages (in places of the wall local thickness loss
– pitting corrosion), were also located. The first such AE signals were detected andsource located at the pressure inside the pipeline at the level of 3.5 MPa, and thegreatest values of the Signal Strength parameter recorded by sensors VS30-SIC.
2. Plastic deformation of the pipe (pressure 8.5 ÷ 13.5 MPa) is characterised bya progressive decrease in AE activity (Fig. 9). At this stage of loading, significantly
fewer AE signals (in relation to the previous stage) and those of a lower amplitude andenergy were recorded (Fig. 10).
3. Strengthening of pipes (pressure from 13.5 MPa) corresponds to a rapid increase of AE
activity. At this stage, a significant increase both in the numbers of AE signals and thevalue of their parameters (Amplitude, Signal Strength) was observed. The difference ofAE activity was visible on particular types of used AE sensors. It is worth noting that inthis case, significantly more signals were recorded with the use of VS150-RIC sensors

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(Fig. 9, b), however, the signals recorded with the use of VS30-SIC sensors arecharacterised by higher values of the Signal Strength parameter (Fig. 10).
After strengthening the material of pipe, the stage of plastic “flow”, on which AE
activity progressive decreases until reaching the moment of failure. The failure of tested
pipe section occurred at the area with innermost corrosion damages (F area on Fig. 6,
Fig. 7, b). It was located between sensors number 3 and 4 (Fig. 8).

abFig. 9. AE-activity for sensors: a – VS30-SIC and b – VS 150-RIC

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a
b
Fig. 10. Signal strength of the located AE signals for sensors: a – VS30-SIC and b – VS 150-RIC

4. AE test of real underground pipelines under operating conditions
In order to verify the results obtained so far and the developed assumptions of themeasurement methodology, the tests with AE of underground parts of the in-plant pipelines(DN500), operated for over 40 years, were performed. The maximum operating pressure ofpipelines were between 0.3 and 0.6 MPa and the design operating pressure was 2.5 MPa.
During AE testing the pipelines filled with crude oil and were subjected to the pressure test
and loaded in two cycles, during which the pressure was raised up to 1.0 MPa with holds theload for 30 minutes. In order to record AE, the AMSY-6 multi-channel measurement system
and AE low-frequency sensors of VS30-SIC were used. During testing, the filters with thefrequency range of 9.3÷300 kHz were used on the measurement channels. AE sensors(maximum distance between the sensors was 40.8 m) were mounted over the length of thetested pipelines – the layout of sensors was shown in Figure 11.

Fig. 11. Scheme of the layout of AE sensors over the length of the tested pipelines

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As a result of the conducted tests, several AE sources, marked as I ÷ IV, were located(Fig. 12). AE sources I ÷ III were identified as the occurrence of leaks during the test on thevalves being close to sensors numbers 10, 16 and 4.
Indicated place and marked as IV was AE source with low activity, low amplitude and energy
and would be graded to severity 1 (minor source in accordance with PN-EN 14584:2013-07[14]) – no further actions shall be necessary, included in the report for comparison withsubsequent tests.

abFig. 12. Linear location of AE-sources along the tested pipelines

5. Conclusion
1. The diagnostic methodology based on tests with AE method of underground sections of inplant pipelines designed for transport of petroleum products is developed.
2. As a result of the preliminary tests, the data related to attenuation and propagation of AE
waves allowed to determine the maximum distance between AE sensors, with themaintenance of the optimal measurement sensitivity. In case of underground pipelines,
the distance between AE sensors is approx. 50 m.
3. It is possible to apply low-frequency sensors for the detection and location of AE sources
connected with the corrosion damages in pipe wall (including pitting corrosion) in case of
AE wave propagating through liquid inside pipelines.
4. The analysis of the test results showed a variable nature of generating mechanisms of AE
in the loading of the tested pipeline section in laboratory test. Three characteristic stages ofAE activity, which correspond to individual stages of the destruction process development,
were identified. It was found that used low-frequency AE sensors are characterised by

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increased sensitivity for detection of the local plastic deformation and material degradation
due to corrosion, which cause local weakening of the pipeline material. These AE sensors
also are suitable for leak testing.
5. It was also found that the high-amplitude AE signals, recorded at the pressure of 1.6 MPa,
was generated by local corrosion damage (pitting corrosion), which are located on thepipeline wall. Taking into account the attenuation curves, it can be stated that AE waves
generated by corrosion damage would be detected from the distance of 50 m.
6. In order to verify the developed diagnostic methodology, the tests of real undergroundparts of the pipelines (DN500), operated for more than 40 years, were performed.
As a result of the tests, several AE sources, identified as the valves leaks, were located.

References[1] Sokólski W. Corrosion Processes Direct Assessment as a Part Of Operational Pipeline
Safety’s, VIII National Conference Corrosion Measurements in Electrochemical Protection
16-18. jul 2004 Jurata, Poland.
[2] Ming-Der Yang, Tung-Ching Su, Tung-Yen Wu and Kai-Siang Huang, Journal of
GeoEngineering, 5 (3) (2010) 99–104.
[3] Sunil K. Sinha, Mark A. Knight Intelligent System for Condition Monitoring ofUnderground Pipelines, Computer-Aided Civil and Infrastructure Eng., 19 (2004) 42–53.
[4] Demma A., Alleyne D., Pavlakovic B., Testing of Buried Pipelines Using Guided Waves,
Middle East Nondestructive Testing Conference & Exhibition – 27-30 Nov. 2005 Bahrain.
[5] Francisco C. R. Marques, Alessandro Demma, Ultrasonic Guided Waves Evaluation of
Trials for Pipeline Inspection, 17th World Conference on Nondestructive Testing, 25-28 Oct.
2008, Shanghai, China.
[6] Pollock A.A., and Hsu S.-Y.S., Leak Detection Using Acoustic Emission, Journal of
Acoustic Emission, 1 (4) 1982 237–243.
[7] Shifeng Liu, Luming Li, Jian Cui, Tie Li Acoustic Emission Detection of Underground
Pipeline Leakage 15th WCNDT, 2000, Roma, Itally.
[8] Anastasopoulos A., Kourousis D., Bollas K., at all. Acoustic Emission Leak Detection ofBuried Pipeline, 19th Intl AE Symposium (IAES19), Kyoto, Japan, 8-12 Dec., 2008.
[9] Bui Van Hieu, Seunghwan Choi, Young Uk Kim,, Youngsuk Park and Taikyeong Jeong,
Wireless Transmission of Acoustic Emission Signals for Real-Time Monitoring of Leakage inUnderground Pipes, Journal of Civil Engineering 15 (5) (2011) 805–812.
[10] Juliano T. M., Meegoda J. N., Watts D. J. Acoustic Emission Leak Detection on a Metal
Pipeline Buried in Sandy Soil, Journal of Pipeline Systems Engineering and Practice, 4(3)(2013) 149-155.
[11] PN-EN 1330-9:2009 „Non-destructive testing – Terminology – Part 9: Terms used inacoustic emission testing”.
[12] PN-EN 10025-1:2007 „Hot Rolled Products Of Structural Steels – Part 1: General
Technical Delivery Conditions”.
[13] PN-EN ISO 6892-1 „Metallic materials – Tensile testing – Part 1: Method of test atambient temperature”.
[14] PN-EN 14584:2013-07 „Non-destructive testing – Acoustic Emission Testing –Examination of metallic pressure equipment during proof testing – Planar location of AE
sources”.

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

A VERSION OF THE DISCRETE ELEMENT METHODIN THE SIMULATION OF THE ACOUSTIC EMISSION TESTING
Gabriel BIRCK1, Ignacio ITURRIOZ1
Department of Mechanical Engineering, Federal University of Rio Grande do Sul, Porto
Alegre, RS, Brazil. Phone: +55 51 3308 3529; e-mail: gabriel.birck@ufrgs.br, ignacio@mecanica.ufrgs.br
1

Abstract
In the present work, events of Acoustic Emission testing in quasi-brittle material were simulated by a version of thediscrete element method. In this numerical approach the solid is modelled by means of a periodic spatial
arrangement of bars with the masses lumped at their ends. The results obtained by numerical approach areevaluated by Acoustic Emission parameters, as b-value, RA value and average frequency, and compared with themoment tensor analysis. The results showed coherent between both forms of evaluation. The numerical approach
was able to simulate Acoustic Emission events and provide more information about the damage process.
Keywords: Lattice Discrete Element Method, Damage evaluation, Acoustic Emission, Moment Tensor

1. Introduction
From a physical point of view, damage phenomena consist either of surface discontinuities in
the form of cracks or of volume discontinuities in the form of cavities [1, 2]. Macroscopically,
it’s, therefore, necessary to identify internal variables that reflect the damage level in thematerial. The most advanced method for a non-destructive quantitative evaluation of damage
progression is the Acoustic Emission (AE) technique. Physically, AE is a phenomenon caused
by a structural alteration in a solid material in which transient elastic-waves, due to a rapid
release of strain energy, are generated. AEs are also known as stress-wave emissions.
In the present work a version of the Discrete Element Method built by bars (LDEM) are employed. Thisapproach belongs to the alternative set of computational methods called by Munjiza [3], "Computational
Mechanics of Discontinua" introduced during the 1960s, which is characterized by the lack ofdifferential or integral equations to describe the model to study the space domain. In the mentioned
approach, the behaviour of the solid is a function of the individual elements, e.g., particles or bars.
Here, a notched beam subjected to the three-point bending test is simulated with LDEM.
The beam is built in quasi-fragile material. Global stress-strain is evaluated and accelerations
in some points of the specimen’s surface are registered. The acceleration registered isinterpreted as data provided by a real device EA.

2. Acoustic Emission
One promisor method for a non-destructive quantitative evaluation of damage progression is
the Acoustic Emission (AE) technique. Physically, AE is a phenomenon caused bya structural alteration in a solid material, in which transient elastic-waves, due to a rapid
release of strain energy, are generated. AEs are also known as stress-wave emissions.
AE waves, whose frequencies typically range from kHz to MHz, propagate through the material
towards the surface of the structural element, where they can be detected by sensors which turn thereleased strain energy packages into electrical signals [4, 5]. Traditionally, in AE testing, a numberof parameters are recorded from the signals, such as arrival time, velocity, hits, count, rise time,
amplitude, duration and frequency. From these parameters damage conditions and localization of
AE sources in the specimens are determined Carpinteri et. al. [6].

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2.1.

Damage process evolution

An effective damage assessment criterion is provided by the statistical analysis of theamplitude distribution of the AE signals generated by growing microcracks. The amplitudes
of such signals are distributed according to the Gutenberg-Richter (GR) law [4, 7], N(≥ A)∝
A-b, where N is the number of AE signals with amplitude ≥A. The exponent b of the GR law,
the so-called b-value, changes with the different stages of damage growth. While the initiallydominant microcracking generates a large number of low-amplitude AE signals, thesubsequent macrocracking generates more signals of higher amplitude. As a result, the bvalue progressively decreases when the damage in the specimen advances, as shown in Figure
1. This is the core of the so-called “b-value analysis” used for damage assessment.

Figure 1. The b-value evolution in experimental test [7]2.2.

Crack classification

The shape of the AE waveforms characterizes the fracture mode [8]. So, to classify active cracks
parameters as Rise Angle (RA) and average frequency are calculated from AE events. The RA
value is defined as the ratio between the rise time and the maximum amplitude. The averagefrequency is obtained from the relation of AE ring-down count and the duration time of the signal,as shown in Figure 2. The AE ring-down count corresponds to the number of threshold crossings
within the signal duration time [9]. From these two parameters, cracks are readily classified into
tensile and shear cracks as illustrated in Figure 2.
The tensile mode of crack (Mode I), which includes opposing movement of the crack sides,
results in AE waveforms with short rise time and high frequency. On the other hand, shear type ofcracks (Mode II) usually result in longer waveforms, with lower frequency and longer rise time.

(a)

(b)

Figure 2. (a) Typical waveforms for tensile and shear events, (b) Relationship between average frequency andRA value

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2.3.

Moment tensor theory

By the generalized theory of AE [10], elastic displacement u(x,t) at location x due to the crack
displacement in y at the time t is represented as [11],𝑢𝑢𝑢𝑢𝑖𝑖𝑖𝑖 (𝒙𝒙𝒙𝒙, 𝑡𝑡𝑡𝑡) = 𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖,𝑞𝑞𝑞𝑞 (𝒙𝒙𝒙𝒙, 𝒚𝒚𝒚𝒚, 𝑡𝑡𝑡𝑡)𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 ∗ 𝑆𝑆𝑆𝑆(𝑡𝑡𝑡𝑡),(1)

𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑙𝑙𝑙𝑙𝑝𝑝𝑝𝑝 𝑛𝑛𝑛𝑛𝑝𝑝𝑝𝑝 ∆𝑉𝑉𝑉𝑉,(2)

where 𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖,𝑞𝑞𝑞𝑞 (𝒙𝒙𝒙𝒙, 𝒚𝒚𝒚𝒚, 𝑡𝑡𝑡𝑡) represents the spatial derivatives of Green’s functions, S(t) is the kinetic
source (time function of crack motion), M pq is the moment tensor components (kinematics of
crack motion) and (*) represents the convolution function. The moment tensor is a symmetricsecond-rank tensor and is comparable to the elastic stress in elasticity (see Figure 3a), whichis expressed as [11-13],

where C pqkl is the constitutive tensor, l is the direction vector of crack motion, n is the normalof the crack surface and ∆V is the crack volume.
If only the first motion of the P-wave A(x) is taken into account and assuming that all of the
moment tensor components have the same time-dependency, Equation (1) can be simplified
as [11-15],
𝐴𝐴𝐴𝐴(𝒙𝒙𝒙𝒙) =𝐶𝐶𝐶𝐶𝑠𝑠𝑠𝑠 𝑅𝑅𝑅𝑅𝑒𝑒𝑒𝑒 (𝒕𝒕𝒕𝒕, 𝒓𝒓𝒓𝒓)[𝑟𝑟𝑟𝑟1
𝑅𝑅𝑅𝑅

𝑟𝑟𝑟𝑟2

𝑀𝑀𝑀𝑀11
𝑟𝑟𝑟𝑟3 ] �𝑀𝑀𝑀𝑀21
𝑀𝑀𝑀𝑀31

𝑀𝑀𝑀𝑀12
𝑀𝑀𝑀𝑀22
𝑀𝑀𝑀𝑀32

𝑀𝑀𝑀𝑀13 𝑟𝑟𝑟𝑟1
𝑀𝑀𝑀𝑀23 � �𝑟𝑟𝑟𝑟2 �,
𝑀𝑀𝑀𝑀33 𝑟𝑟𝑟𝑟3(3)

where C s is the physical coefficient containing the sensor sensitivity, obtained by pencil-leadbreak test [16]; R is the distance between the crack y and the detection point x, and r 1 , r 2 , r 3
are its different direction cosines, respectively; R e (t,r) is the reflection coefficient between
vector r and the direction t of the sensor (see Figure 3b). In the case where the P-wave arrives
vertically to the surface R e (t,r)=2. Equation (3) leads to a series of algebraic equations inwhich the components of the moment tensor M pq are unknowns, where there are 6
independent components.

(a)

(b)

Figure 3. (a) Elements pf the moment tensor, (b) Crack nucleation and AE detection

The source location is determined from the arrival time differences (t i ) between theobservation x i and x i+1 , solving the equations,
𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 − 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖+1 = |𝒙𝒙𝒙𝒙𝒊𝒊𝒊𝒊 − 𝒚𝒚𝒚𝒚| − |𝒙𝒙𝒙𝒙𝒊𝒊𝒊𝒊+𝟏𝟏𝟏𝟏 − 𝒚𝒚𝒚𝒚| = 𝑣𝑣𝑣𝑣𝑖𝑖𝑖𝑖 𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖 ,(4)

where v p is the velocity of P-wave, assumed constant.

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By the analysis of eigenvalues and associated eigenvectors of the moment tensor it is possible
to classify the crack (failure mode), and obtain the crack motion vector (l) and the crack
normal vector (n) [11]. The eigenvalue is decomposed into a shear component X, a deviatoriccomponent Y and a hydrostatic component Z, which, X is the maximum shear contribution, Y
is set as the maximum deviatoric tensile component and Z is the maximum isotropic tensile
component. Three eigenvalues are normalized and decomposed as follows:
𝑋𝑋𝑋𝑋 + 𝑌𝑌𝑌𝑌 + 𝑍𝑍𝑍𝑍 = 1 = 𝜆𝜆𝜆𝜆1 ⁄𝜆𝜆𝜆𝜆1 ,0 − 𝑌𝑌𝑌𝑌⁄2 + 𝑍𝑍𝑍𝑍 = 𝜆𝜆𝜆𝜆2 ⁄𝜆𝜆𝜆𝜆1 ,−𝑋𝑋𝑋𝑋 − 𝑌𝑌𝑌𝑌⁄2 + 𝑍𝑍𝑍𝑍 = 𝜆𝜆𝜆𝜆3 ⁄𝜆𝜆𝜆𝜆1 ,(5)where λ 1 , λ 2 and λ 3 are maximum, intermediate and minimum eigenvalues, respectively.
The cracks are classified, as proposed by Ohtsu [11], into three types of AE sources: X > 60%
as shear cracks; X < 40% as tensile cracks, and 40% < X < 60% as mixed-mode cracks.

3. Lattice Discrete Element Method (LDEM)
The version of the truss-like Discrete Element Method (DEM) proposed by Riera [17], which
is used in this paper, is based on the representation of a solid with a cubic arrangement ofelements which is only able to carry axial loads. The cubic module is presented in Figure 4aand Figure 4b. The mass is lumped at the nodes. Each node has three degrees of freedom,corresponding to the nodal displacements in the three orthogonal coordinate directions.
The equations that relate the properties of the elements to the elastic constants for an isotropic
medium are presented in Equation (6).
𝜂𝜂𝜂𝜂 =9𝜈𝜈𝜈𝜈(9 + 8𝜂𝜂𝜂𝜂)2√3, 𝐸𝐸𝐸𝐸𝐴𝐴𝐴𝐴𝑛𝑛𝑛𝑛 = 𝐸𝐸𝐸𝐸𝐿𝐿𝐿𝐿𝑐𝑐𝑐𝑐 2, 𝐸𝐸𝐸𝐸𝐴𝐴𝐴𝐴𝑑𝑑𝑑𝑑 =𝐴𝐴𝐴𝐴 ,
4 − 8𝜈𝜈𝜈𝜈
2(9 + 12𝜂𝜂𝜂𝜂)
3 𝑛𝑛𝑛𝑛(6)where 𝐸𝐸𝐸𝐸 and 𝜈𝜈𝜈𝜈 denote Young’s modulus and Poisson’s ratio, respectively, while 𝐴𝐴𝐴𝐴𝑛𝑛𝑛𝑛 and 𝐴𝐴𝐴𝐴𝑑𝑑𝑑𝑑
are the areas of the normal and the diagonal elements and 𝐿𝐿𝐿𝐿𝑐𝑐𝑐𝑐 is the size of the basic cubic
module. The resulting motion equations, obtained with this spatial discretization, can be
written in the well-known form presented in Equation (7).
𝐌𝐌𝐌𝐌𝐱𝐱𝐱𝐱̈(𝑡𝑡𝑡𝑡) + 𝐂𝐂𝐂𝐂𝐱𝐱𝐱𝐱̇(𝑡𝑡𝑡𝑡) + 𝐅𝐅𝐅𝐅𝐫𝐫𝐫𝐫 (𝑡𝑡𝑡𝑡) − 𝐏𝐏𝐏𝐏(𝑡𝑡𝑡𝑡) = 0,(7)

𝐶𝐶𝐶𝐶𝑉𝑉𝑉𝑉𝜀𝜀𝜀𝜀𝑝𝑝𝑝𝑝 = 0.5𝐶𝐶𝐶𝐶𝑉𝑉𝑉𝑉𝐺𝐺𝐺𝐺𝑓𝑓𝑓𝑓 .

(8)

where 𝑥𝑥𝑥𝑥 is the vector of generalized nodal displacements, 𝑀𝑀𝑀𝑀 is the diagonal mass matrix, 𝐶𝐶𝐶𝐶 is thedamping matrix, also assumed diagonal, 𝐅𝐅𝐅𝐅𝐫𝐫𝐫𝐫 is the vector of the internal forces acting on the nodal
masses and 𝐏𝐏𝐏𝐏 is the vector of external forces. The operator (•)̇ denotes the time (𝑡𝑡𝑡𝑡)
differentiation 𝑑𝑑𝑑𝑑(•)/𝑑𝑑𝑑𝑑𝑡𝑡𝑡𝑡. If 𝑀𝑀𝑀𝑀 and 𝐶𝐶𝐶𝐶 are diagonal, the Equation (7) is not coupled and, therefore,
the explicit central finite difference scheme can be used to time domain integration. Since thenodal coordinates are updated for each time step, large displacements can be accounted ina natural and efficient manner. This article adopts a relationship between axial force and axial
strain in the uniaxial elements (bar), based on the bilinear law proposed by Hillerborg [18], which
is presented in Figure 4c. The specific fracture energy (Gf) is directly proportional to the area
below the bilinear constitutive law. Another important feature of this approach is the assumptionthat Gf is a 3D random field with a Weibull probability distribution.
The local strain associated with maximum loading in each bar is called critical strain (𝜀𝜀𝜀𝜀𝑖𝑖𝑖𝑖 ).
This value is also a random variable and its variability, which is measured using thecoefficient of variation CV, is related to the Gf parameter by the Equation (8),S24

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The minimum value of 𝜀𝜀𝜀𝜀𝑖𝑖𝑖𝑖 , determined for each specimen bar, is associated with the global
strain for which a specimen loses linearity.
Mesh perturbation was incorporated to improve the performance when the body is submitted
to compression. It is possible to observe in Figure 4c, that the bar failure occurs only whensubmitted to tensile solicitation. In compression, the bars have linear elasticity behaviour [19].
More exhaustive explanations of this version of the lattice model can be found in Kosteski etal. [20, 21]. Applications of the LDEM in studies involving non-homogeneous materials
subjected to fracture, such as concrete and rock, can be found in Riera and Iturrioz [22],Dalguer et al. [23], Miguel et al. [24], Iturrioz et al. [25] and Miguel et al. [26].

(a)

(b)

(c)

Figure 4. LDEM: (a) basic cubic module, (b) generation of a prismatic body, and (c) Bilinear constitutive modelwith material damage

3.1.

AE tests simulated by LDEM

The numerical approach employed in this paper was used to simulate Acoustic Emission tests
in others cases, as presented in Iturrioz et al. [27], where comparison between experimental
and numerical tests performed. In terms of the distribution of AE event amplitudes in timeand frequency, the results were seen to be consistent, as can be seen in Figure 5.

Figure 5. Comparison between numerical and experimental results [27].

4. Applications
A plain concrete beam, with pre-notched and depth equal to half the overall height, wassimulated to a three-point bending (TPB) with LDEM [28]. Standards results as force andvertical deflection were computed and AE test was performed. The scheme and main
geometrical parameters are reported in Figure 6. The material properties are: Young’s
modulus 35GPa; Poisson’s ratio 0.25; density 2400 kg/m3, and specific fracture energy (Gf)70 N/m. The beam was discretized with 168x20x20 basic cubic with length (Lc) equal 5 mm.
Twelve sensors were used to EA test, two sensors per side.

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Figure 6. Schematic representation of TPB test

The statistical analysis of the amplitude distribution (b-value) for three periods of the time ispresented in Figure 7. In Figure 8, the time distribution of instantaneous and accumulated AE
events are plotted, also the reaction (force) by the time.

Figure 7. b-value distribution for three periods

Figure 8. Instantaneous and accumulated AE events, and force by the time

The b-value remains low and about constant for the periods analysed, as can be seen in Figure
7, because the AE are mainly emitted from a specific region of the structure (see Figure 9),
which is expected since the beam has a notch. By the Figure 8, it’s observed that AE eventsoccur throughout all the period. For the period I, shown in Figure 9, the damage is morewidespread than in others, therefore, the b-value is larger.

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Figure 9. Damage distribution in the three periods of b-value analysis

Crack classification for three AE events were performed by AE parameters and by moment
tensor analysis. In Figure 10, crack classification by AE parameters, RA value and Average
Frequency was performed by two sensors. These events were chosen because the amplitude of
the first motion of the P-wave and the beginning of the event are clearly defined.

Figure 10. Crack classification by AE parameters

In Figure 11, it is represented the position and classification for the AE events analyzed bytensor moment analysis. The arrows represent the crack motion vector (l) and the crack
normal vector (n), which are interchangeable [29]. In Table 1, the moment tensor normalized
is presented. Through these figures, the classification by AE parameters and moment tensor
are coherent, that is, the same classification was obtained. It is noteworthy that using moment
tensor analysis it is possible get the orientation and displacement vector of the crack.

Figure 11. AE events representation

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Table 1. Normalized moment tensor for each event
Event
A
X=10.40%
B
X=32.36%
C
X=57.28%

Normalized moment tensor
0.02 0.04 0.10
𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖�� = �0.04 0.07 0.37�
max�𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 �
0.10 0.37 1.00
0.00 0.01 0.09
𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖�� = �0.01 1.00 0.32�
max�𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 �
0.09 0.32 0.33
0.07 0.02 0.13
𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖�� = �0.02 0.90 1.00�
max(𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 )0.13 1.00 0.27

5. Conclusions
The b-value evolution in the damage process is related with the spatial damage distribution.
Furthermore, the classification obtained by the moment tensor analysis showed a demonstrated
consistency when compared with the classification by the AE standard parameters.
Also the damage orientation obtained by the same methodology showed to be similar with thebar and load orientation. However, Suaris and van Mier [30] point out that the eigenvalues
decomposition method to explicitly determine the crack orientation is only valid for cases oftraction and pure shear.
This study has shown the potential applications of the truss-like DEM not only to simulate AE
monitoring analysis, but also to provide a better understanding of the relationships between
the basic AE parameters.
Acknowledgements
The authors wish to thank the National Council for Scientific and Technological Development(CNPq – Brazil) and Coordination for the Improvement of Higher Education Personnel(CAPES – Brazil) for funding this research.
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Shiotani, T.; Fujii, K.; Aoki, T.; Amou, K. Evaluation of progressive failure using
AE sources and improved b-value on slope model tests, Progress in Acoustic
Emission, vol. 7(7), p. 529–534, 1994.
Kurz, J. H.; Finck, F.; Grosse, C. U.; Reinhardt, H.-W. Stress drop and stressredistribution in concrete quantified over time by the b-value analysis, Structural
Health Monitoring, vol. 5(1), p. 69–81, 2006.
Carpinteri, A.; Lacidogna, G.; Niccolini, G. Fractal analysis of damage detected inconcrete structural elements under loading, Chaos, Solitons & Fractals, vol. 42(4),p. 2047–2056, 2009a.

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2009c.
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NDE techniques for crack detection and damage evaluation in concrete: Test method forclassification of active cracks in concrete structures by acoustic emission. Materials
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Ohtsu, M. Simplified moment tensor analysis and unified decomposition of acoustic
emission source: application to in situ hydrofracturing test, Journal of GeophysicalResearch, vol. 96(B4), p. 6211–6221, 1991.
Ohno, K.; Shimozono, S.; Ohtsu, M. Cracking mechanisms of diagonal-shear failure
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Ohtsu, M. Moment tensor analysis of AE and SiGMA code. In Acoustic EmissionBeyond the Millennium, pages 19–34. Elsevier, 2000.
Grosse, C. U.; Ohtsu, M. Acoustic Emission Testing. Springer Berlin Heidelberg,
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RILEM, T. C. Recommendation of RILEM TC 212-ACD: acoustic emission and related
NDE techniques for crack detection and damage evaluation in concrete: Measurement
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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

COMPARISON OF LOCALIZATION STRATEGIES OFDAMAGE IN CONCRETE BY ACOUSTIC EMISSION
Antoine BONIFACE1, Zoubir M. SBARTAÏ1, Jacqueline SALIBA1,
Nary RANAIVOMANANA2
1
I2M laboratory, Civil engineering Department, University of Bordeaux; France;
e-mail: antoine.boniface@u-bordeaux.fr, zoubir-mehdi.sbartai@u-bordeaux.fr, jacqueline.saliba@u-bordeaux.fr 2
University of Toulouse ; UPS, INSA ; LMDC ; France; e-mail : ranaivom@insa-toulouse.fr

Abstract
The monitoring of concrete structures by the acoustic emission (AE) technique allows the detection and the localizationof damage. The source location is determined by a triangulation algorithm based on the arrival time of AE signals recordedby sensors placed at the surface of the material, and the measured concrete wave velocity. The accuracy of AE source
location depends on many assumptions. In this paper, the accuracy related to the choice of the onset detection of AE signals
and to the location algorithm is studied. A statistical analysis of AE location maps based on the distribution of AE events ispresented. The accuracy of AE events location is also evaluated and compared between the studied criteria.
Keywords: acoustic emission, concrete, damage, source location, accuracy.

1. Introduction
The monitoring of nuclear facilities and concrete structures is a major concern in the field of civilengineering to ensure safety and durability. A realistic description of cracking allows a better understandingof the degradation problems in concrete. In the case of a quasi-brittle material such as concrete, macro-cracks
occurs after the development of a micro-cracked area also called fracture process zone (FPZ) [1-5]. The main
objective of this study is to provide further insight in the description of the FPZ evolution in order to preventthe propagation of damage. This is because the accumulation of microcracks causes a local decrease of themechanical characteristics of the material leading to crack propagation.
Micro-cracking is a phenomenon of localized damage causing the release of mechanical energy.
It results from this mechanical event the propagation of an elastic wave in the material. The acoustic
emission technique enables the detection of vibration by piezoelectric sensors located on the material
surface. The main interest of the AE technique is that it allows the monitoring of damage continuously.
It provides also, through the analysis of wave arrival time and triangulation, the localisation of
microcracks.
The localization of AE events by the triangulation technique is mastered [6]. Nevertheless, the qualityof the results is sensitive to different criteria [7]. An important criterion for performing a good locationis the picking of arrival times from the recorded AE signals. Several onset picking techniques have been
proposed in the literature: the simple manual picking technique which is very time consuming, theamplitude threshold picker, the STA/LTA picker (Short Term Average / Long Term Average) and theautomatic onset detection algorithms based on the Hinckley criterion and Akaike Information Criterion(AIC) [8,9]. To determine the position of AE source, the triangulation method is performed based on thearrival times of AE signals recorded by the sensors. Several iterative algorithms, which consist in
generating a sequence of testing and updating of a trial solution based on a linear least-squares algorithm,
were applied to obtain the optimal source locations coordinates: grid search, simplex algorithm, Geiger’s
method [10], … However, even though the principle of location is still the same, the way in which theminimization problem is resolved affects the final result. The simplex algorithm is the most used for its
timeliness. However, this algorithm only allows converging to a local minimum of the minimization
problem and may lead, in some cases, to an incorrect solution.

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Thus, the objective of the following work is to ensure a reliable location of micro-cracks. To achievethis, an experimental investigation is proposed. Three point bending tests were carried out on notchedconcrete beams and the monitoring of the damage is assessed by means of the acoustic emission
technique. Two evaluation criteria for the onset time of AE signal are presented. Their influence on theoverall result of the location is highlighted. Subsequently, three alternative algorithms are also applied
for the resolution of the location problem. The advantages and disadvantages of those methods are
discussed. The accuracy and the possibility of improving the AE technique are also studied.

2. Experimental details
2.1. Materials and Procedures
The dimensions of the tested concrete beams are L x h x l = 500 x 250 x 120 mm. A central notch of
4 mm width is located at the bottom part of the beams and measure 10 mm in height. The compositionof the used concrete is shown in Table 1.
Constituents
cement CEM I 52.5 CE NF
watersand 0-4 mm
aggregates 4-11 mmcoarse aggregates 8-16 mm
BalloyMass proportions
320 kg.m-3
197.5 kg.m-3
830 kg.m-3
445 kg.m-3
550 kg.m-3

Table 1. Concrete composition.
The three-point bending tests were performed using a hydraulic press MTS with a capacity of 100 kN(Figure 1 left). The load was applied with a constant crack mouth opening displacement (CMOD) control
with a slow rate of 0.2 mm.min-1. Figure 1 right shows an example of the load-CMOD curves obtainedwith fracture tests. The load CMOD variations are linear up to 80% of the maximal strength followed
by a nonlinear variation up to the peak load and a stable failure in the postpeak region.

Figure 1. General view of the experimental fracture test setup (left). Load/CMOD curve (right).

2.2. Acoustic emission measurements
The monitoring of the damage is carried out using a network of 8 piezoelectric sensors R15 ofresonance frequency equal to 150 kHz. The two main faces of the beam were instrumented with 4 sensors
so as to perform a 3D localization of acoustic events.
The AE signals were amplified with a 40 dB gain differential amplifier. The signals are converted by
a PCI-card 8 and filtered by a low pass filter of 400 kHz and a high pass filter of 100 kHz. The signal

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detection threshold was set at a value of 35 dB. The signals (or hits) are then grouped by the software
AEwin (Mistras Group) into acoustic events based on their arrival time. Thus a set of signals received
by the sensors (at least four), in a period of 71 microseconds form an event. This time window
corresponds to a propagation distance of 25 cm. The measured wave propagation velocity is equal to3500 m.s-1. This velocity was measured using the normalized pencil lead break procedure.

3. Principals of source localization
Location of micro-cracks is based on the following hypothesis: sources are punctual, sources aretemporally spaced apart from each other so that the acoustic activity resulting from the source does notinterfere with the acoustic activity of the next source, wave propagation velocity is constant, material ishomogeneous and isotropic. According to those hypothesis, the waves travel from a source point tosensors following a straight path with a constant velocity. Figure 2 represents the principal of the AE
technique and illustrates the time of arrival approach:Figure 2. Principal of acoustic emission technique.
As seen in previous parts, the localization process is based on measured arrival time of signals
where “i” denote the order of arrival and “j” the sensor indices. In practice only delays between sensors
are used:. Considering previous hypothesis, a group of delays between sensors
depend on the position of event. The location of micro-cracks is determined by solving an error
minimization problem between the received data by the sensors and those from an artificial event witha known position (eq. 1). This artificial event is determined by an algorithm. The retained position
corresponds to that of the artificial event for which the error criterion is minimum.
,eq. (1)
where “n” is the number of sensors associated to the event. To summarize and considering hypothesis
given above, localization depends on three factors: the velocity of waves, the capability to determine
precisely the beginning of AE signals and the algorithm used to solve the minimization problem. Here,the variation of wave velocity is not considered and is kept constant. In the following parts, only the
influence of the two others factors is studied and discussed.

4. Influence of the onset picking technique4.1. Fixed threshold versus AIC
The determination of the onset time of AE signals has been identified as a source of error for events
location. Indeed, the overall delay measured between the sensors is influenced by the method ofdetermining the onset time of the AE signals. The acquisition of data used in this work has been
performed using a fixed threshold which is the simplest form for onset picking. This technique defines
the onset time of a signal as the time at which the amplitude received by the sensor exceeds a previously
established threshold (35 dB here). The disadvantage of this technique is that it is not adapted to each32nd EWGAE

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signal as small amplitude signals and signal with a high noise level. Figure 3 illustrates this problem; an
important delay between the beginning of the signal determined by the fixed threshold method (magenta)
and the apparent onset time of the signal is observed. An intuitive way to overcome this problem could
be to reduce the gap between threshold and noise. In fact, ambient noise can change during loading due
to experimental environment. Thereby this gap must exist to avoid noise record.
The AIC criterion is widely used in the literature. The idea is to compare the difference between standard
deviation passed and forthcoming at each point of the signal. Eq. 2 gives the exact form of this criterion.
𝐴𝐴𝐴𝐴𝐼𝐼𝐼𝐼𝐶𝐶𝐶𝐶 𝑘𝑘𝑘𝑘 = 𝑘𝑘𝑘𝑘. log[𝑠𝑠𝑠𝑠𝑡𝑡𝑡𝑡𝑑𝑑𝑑𝑑(𝑠𝑠𝑠𝑠(1: 𝑘𝑘𝑘𝑘))] − (𝑁𝑁𝑁𝑁 − 𝑘𝑘𝑘𝑘). log[𝑠𝑠𝑠𝑠𝑡𝑡𝑡𝑡𝑑𝑑𝑑𝑑(𝑠𝑠𝑠𝑠(𝑘𝑘𝑘𝑘: 𝑁𝑁𝑁𝑁))].
eq. (2)

Here “k” is the considered sample and “N” the number of samples on which the computation is done.
The orange curve in figure 3 shows the complete form of this function. The minimum of AIC (green)corresponds to the apparent beginning of signal. The advantage of this technique is the independence
from subjective parameters like threshold. So there is no a priori about what can be the level of amplitude
which correspond to the beginning of the signal. AIC gives the position where there is a modification of
the signal which, in our case, corresponds to the onset time of AE signal.

𝑠𝑠𝑠𝑠(1: 𝑘𝑘𝑘𝑘𝑚𝑚𝑚𝑚𝑖𝑖𝑖𝑖𝑛𝑛𝑛𝑛 )𝑘𝑘𝑘𝑘𝑚𝑚𝑚𝑚𝑖𝑖𝑖𝑖𝑛𝑛𝑛𝑛

𝑠𝑠𝑠𝑠(𝑘𝑘𝑘𝑘𝑚𝑚𝑚𝑚𝑖𝑖𝑖𝑖𝑛𝑛𝑛𝑛 : 𝑁𝑁𝑁𝑁)

Figure 3. Typical acoustic waveform (blue) and results of two different picking techniques.
Fixed threshold (magenta), AIC function (orange) and its minimum (green).

4.2. Results and discussion
Figure 4 shows the damage localization maps obtained with the fixed threshold (top) and the AIC (bottom).
Each blue dot represents one of the 14621 events recorded by the acquisition system. The simplex algorithm
has been used in both cases for minimisation. Figure 4 shows also the histograms of the density of events ineach direction (x, y, and z). The distribution of AE events along the x axis shows that AE events are moreconcentrated around the cracking zone (above the notch) with the AIC which is closer to the reality.

notch

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sensors face 1 (z = 0.12m)
sensors face 2 (z = 0.00m)

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Figure 4. AE events localization maps obtained with fixed threshold (top) and AIC (bottom) with the
distribution of AE events along x, y and z axis.
Another way to compare those two techniques is to evaluate the corresponding errors (eq. 1) obtained
based on the localization algorithm in each case. Comparing the errors event by event is a hard task. This
is why a global analysis of errors is performed. Figure 5 shows the cumulated number of AE events infunction of the error.

Figure 5. Proportion of cumulated AE events inside the beam in function of the error. Comparison
between two criteria of onset time determination of AE signals: fixed threshold and AIC.
Note here that AE events with an error higher than 5 centimeters have not been represented here. Figure
5 shows that for the same error, AIC allows a greater amount of events located inside the beam.
For example: 27% of events are located inside the beam with an error inferior to 2 cm with the use of
AIC against 14.9% with fixed threshold.

5. Influence of the type of minimization algorithms
Analytical solutions do not exist for high order localization. In fact when the number of sensors usedis higher than the unknown location parameters, the system of equations is over-estimated. Computation
techniques exist to solve this problem but localization of only one event means hundreds and hundredsof calculations. This is why algorithms based on least-squares methods can be used to perform a large
amount of process which lead to a solution. Three different algorithms are considered here with the main
goal of finding the position (x, y, z) which leads to the minimum value of error.

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5.1. Simplex
The simplex algorithm is widely used in the literature for the localization of AE sources. Simplex
algorithm consist in:i.
ii.

Defining an initial position ,Calculating the error criterion for this position and around. The number of positions
depends essentially on the dimension of the problem,iii. The best solution (smaller error criterion) from all the calculated solutions is stored,
iv. return to (ii) with the solution of (iii).
The main advantage of this method is its speed. However, it requires a starting position, which cangreatly affect the results. There are several local minimum and only one global minimum whichcorrespond to the sought solution. An initial position near local minimum can lead to a wrong final
solution. Moreover, in its simplest form (without fixed boundary) the algorithm can converge toa solution corresponding to an event located outside the studied structure.

5.2. Adaptive meshing algorithm (AMA)
When using an AMA, the position of an event is not defined from an initial position but from a set of
positions covering the damage referred area. Figure 6 shows an example in 2D of error calculation
between a fictional event on the node of the mesh used and the measured data set from an event which
one seeks the position. Measured delays (dtmeas) between transducers are compared to delays computedat each node of the mesh (dtmesh). The comparison between those two set of data is made with the error
criterion. The final position of AE event is the position of the node with the smallest error.
To increase the accuracy and reduce the cost of calculation time of this technique, three successive
meshes are used to determine the position of a single event. Thus, the first mesh covers a large areasurrounding the sensors with a coarse mesh (centimeter). A new tighter mesh is then used around theposition adopted earlier. This operation is repeated until obtaining a solution in a mesh whose nodes arespaced apart by 2 millimeters. This adaptation of the mesh makes it possible to manipulate matrices ofsmall dimensions in order to make the operation faster.
The main advantage of the adaptive meshing is to avoid local minima problems since many solutions
are calculated and compared to retain the one having the lowest error. The disadvantage of this techniqueis the calculation time required to determine the position of an event. It is between five and ten times
higher than for the simplex algorithm. It should be noted that this disadvantage is not so significant inthis case since the minimization of errors for the location of the set of events (14621 in this case) is of
the order of 1 hour.

Figure 6. Principal of Adaptive Meshing Algorithm (AMA).

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5.3. Genetic algorithm
The different steps of the Genetic algorithms are:
i.
ii.
iii.
iv.
v.
vi.

a set of random positions is built in the damage area,
delays related to those positions are computed and compared to the measured data with theerror criterion (eq. 3),
positions with lowest errors are stored,
couples of solutions are formed from retained positions. Those couples are called “parents”,
new couples of solutions called “childrens” are computed from the parents. For example in 1D
a couple of parents (𝑥𝑥𝑥𝑥 1 , 𝑥𝑥𝑥𝑥 2 ) gives 2 children (𝑥𝑥𝑥𝑥 1 , ,𝑥𝑥𝑥𝑥 2 , ) withand,return to (ii) with childrens as new starting position if the precision criterion is not reached.

The number of initial solutions, the proportion of solutions retained to build parents, the way thatchildrens are computed from parents are parameters which can be changed but the global idea of genetic
algorithm is to defined new solutions by “mutations”. This kind of algorithm is more time consuming
than the two others presented above.

5.4. Results and discussion
Figure 7 shows the AE events localization maps of events related to the use of AMA algorithm (top)and genetic algorithm (bottom). In both cases and in agreement with the results obtained in the previous
section (4), the AIC is used to determine the onset time of AE signals. Those maps are qualitatively
identical. The maximum amount of events above the notch and the distribution of events along the main
axis (x) is similar for the three algorithms (simplex from figure 4, AMA and genetic from figure 7).

Figure 7. AE events localization maps obtained with AMA algorithm (top) and genetic algorithm(bottom) with AIC as onset picking technique.

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In order to compare the three algorithms, the error of localization was considered as earlier. Figure 8
shows the cumulated number of events inside the beam related to the errors for the three algorithms.
For example, when the error is smaller than 8 mm, the simplex algorithm allows a greater proportion of
event located than the other algorithms. In another hand, the genetic algorithm gives better results with
32% of event located with an error inferior to 2 cm against 27% for simplex and 26.3% for AMA.
The fact that simplex seems to allow a greatest number of event located with a small error may be explainedby the ability of this algorithm to find the exact solution when the initial position is well defined. On the other
hand, even if genetic algorithm is able to detect the global minimum of the problem, this algorithm can’t
determine the exact position. Final solution of genetic algorithm will always be really near the exact solution
but not the real solution. In other words if the real solution is “0”, genetic algorithm will find something like
“10−10” but not “0”. Finally AMA is less efficient than the 2 other algorithms for an error less than 2.3 cm.
Contrariwise, AMA gives better results than simplex beyond this value. Due to the way that AMA work, thenumber of possible final solution is finite because it depend on a finite mesh defined by the user. So if the
distance between two nodes of the last mesh is about 1mm and the real solution is between those two nods, itwill not be possible to find the exact solution. However, it’s possible to make AMA final mesh finer to enhance
his accuracy but at the expense of time.

Figure 8. Proportion of cumulated events inside the beam related to error. Comparison between threealgorithms of resolution: simplex (blue), AMA (red) and genetic (yellow).

6. Conclusions
The main objective of the presented research work is to study the accuracy of the location of AE sources
based on different onset picking techniques and minimisation algorithms. The influence of the onset time
techniques on the location maps was analysed considering the amplitude threshold based picking techniqueand the AIC. The results show that the distribution of events around the real fracture is more concentrated
with the AIC and consequently the size of the damage area. Furthermore a quantitative comparison between
the two criteria shows that the localization errors obtained with the fixed threshold are greater. Threelocalization algorithms for solving the optimization problem are compared: simplex, AMA and genetic
algorithms. This study shows that the choice of an algorithm depends on the desired accuracy andcomputation time. Each algorithm has its own advantages and disadvantages. Simplex leads to better results
for smaller error while genetic algorithm gives a global better solution for error higher than 0.8 mm. Simplex
is faster than AMA and event faster than genetic algorithm. To go further a mix of the 3 algorithms could
lead to an optimum localization by dealing with the speed and precision of simplex and the global analysis
of genetic or AMA techniques. The effect of the variation of the velocity during the damage process on thelocalization accuracy will be studied in the future. In addition, for a better evaluation of the accuracy of AE
event location, reconstructed profiles obtained with the AE technique, based on different criteria, and realcrack profile obtained with laser rugosimeter will be compared.

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7. Acknowledgments
This work was conducted as a part of the ANR ENDE national French project. The National Research
Agency is thanked for the financial support and all the partners of ENDE project.

8. Reference[1] Hadjab H., Thimus J.-Fr., « Fracture process zone in notched concrete beams treated by using
acoustic emission », NDT, vol. 12, 2004.
[2] Saliba J., Loukili A., Grondin F., Regoin J-P., « Influence of basic creep on cracking of concrete
shown by the Acoustic Emission technique », Materials and Structures, Vol. 45, p. 1389-1401, 2012.
[3] Jankowski L. J., « Formation of the fracture process zone in concrete », Enginering fracture
mechanics, vol. 36, n° 2, 1990, p. 245-253.
[4] Shah S. P., « Experimental methods for determining fracture process zone and fracture
parameters », Enginering fracture mechanics, vol. 35, n° 1/2/3, p. 3-14, 1990.
[5] Sbartaï ZM, Saïdoun K, « Evaluation de l’endommagement mécanique des bétons par émission
acoustique ». Annales du Bâtiment et des Travaux Publics, Janvier-Mars 2014.
[6] Ohtsu M., Ono K., « AE source location and orientation determination of tensile cracks fromsurface », NDT International, vol. 21, n° 3, 1988.
[7] Ohno K., Kimitaka U., Ueno A., Ohtsu M., « Fracture process zone in notched concrete beam
under three-point bending by acoustic emission », Construction and building materials, 2014.
[8] Carpinteri A., Xu J., Lacidogna G., Manuello A., « Reliable onset time determination and source
location of acoustic emissions in concrete structures », Cement and concrete composites, 2012.
[9] Kurz J. H., Grosse C. U, Reinhardt H-W, Strategies for reliable automatic onset picking of
acoustic emissions and of ultrasound signals in concrete, Ultrasonics, vol. 43, 2005, p. 538-546.
[10]Grosse C., Reinhardt H., Dahm, Localization and classification of fracture types in concrete withquantitative acoustic emission measurement techniques, NDT&E International, vol. 30, n°4, 1997,
p. 223-230.

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

CORRELATION BETWEEN ACOUSTIC EMISSION SIGNALSAND DELAMINATIONS IN CARBON FIBER-REINFORCED
POLYMER-MATRIX COMPOSITES: A NEW LOOK AT
MODE I FRACTURE TEST DATA
Andreas J. BRUNNER
1

Empa, Swiss Federal Laboratories for Materials Science and Technology, Laboratory for
Mechanical Systems Engineering; Dübendorf, Switzerland
Phone: +41 58 765 44 93, Fax: not available; e-mail: andreas.brunner@empa.ch

Abstract
Recently, a rough correlation between cumulative AE signal amplitude and averaged crack area has been derived
from in-situ tensile tests on miniature samples of spruce wood combining AE monitoring and simultaneoussynchrotron-based X-ray computed micro-tomography. This indicated that, at least for the chosen experimental
set-up and equipment, AE tended to be more sensitive than X-ray imaging in detecting formation and propagationof small cracks. An analogous analysis is now presented for quasi-static mode I (tensile opening) delamination
tests performed on carbon-fiber poly-ether-ether-ketone composite laminates with simultaneous insitu AE
monitoring and X-ray radiography using an imaging system with video recording. The data are further used for
estimating the variation in the correlation factor for a given experimental set-up by comparing different approaches
for deriving an estimated value of the correlation factor.
Keywords: Carbon fiber-reinforced polymer-matrix composites, mode I tensile-opening fracture, measurement
sensitivity, acoustic emission intensity correlation with damage size (crack area or diameter)

1. Introduction
Acoustic Emission (AE) monitoring is a non-destructive method with high sensitivity to microscopicchanges caused by, e.g., rapid release of energy from localized sources in materials and structures
which makes it different from many other non-destructive test methods (see, e.g., [1,2]). While AE issuccessfully applied in a wide range of structural health or integrity monitoring using empirical criteria
for the evaluation of the AE signals (see, e.g., [3,4]), it has proven more difficult to extract quantitative
information on the sources of the AE signals, even though it was recognized a long time ago that thetype of source was affecting the recorded AE signals [5].
Recent advances in unsupervised pattern recognition have shown that discrimination between
microscopic damage mechanisms in carbon-fiber reinforced polymer-matrix (CFRP) laminates is
feasible [6]. The identification of the source mechanism (type and orientation) was achieved bydetailed simulations of the whole signal measurement chain from source to signal recording. The mainparts of this simulation comprised model sources simulated as dipole or multipoles in different
orientations and locations with a range of signal rise times, signal propagation in the material withfrequency dependent attenuation and anisotropic wave speeds as well as the frequency-dependenttransfer functions of the AE sensors and the signal recording. For CFRP, this procedure identified thesource mechanisms of the different clusters resulting from pattern recognition as matrix cracks, fibermatrix debonding or fiber breaks [7]. However, identification of the type of a localized model AE
source does not directly yield quantitative information on the size of the damage created by therespective mechanism, e.g., the area or length of a micro-crack in the matrix polymer.

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Combining AE monitoring of load tests on material specimens or structural elements with suitable,
complementary non-destructive test methods [8], in principle, can provide an approach forestimating damage size (e.g., crack area or length) for a correlation with the recorded AE signal
parameters or their respective source properties. This has been demonstrated by combining in-situ
synchrotron X-ray computed micro-tomography with AE monitoring of tensile tests on miniature
spruce wood specimens [9]. After detection of significant AE, the tensile loading was stopped andthe applied load held constant for performing the X-ray tomography measurement. Consecutive
load steps yielded indications of progressive damage, e.g., as growing cracks. Pattern recognitionas outlined in [7] essentially yielded two clusters of AE signals for the wood specimens [10,11]
that could be associated with slower and faster source mechanisms, i.e., with distinctly different
source rise times in the simulations [12]. Tentatively, these were identified as inter-wall cracks
between wood cells for the low and with cell wall cracks for the high frequency cluster. Analysing
the X-ray tomography image slices and identifying the crack locations, a rough estimate of the crack
length increment from one tensile load step to the next could be obtained. This yielded aquantitative, but rough correlation between crack area and AE signal amplitude (measured in mV).
Of course, such an estimation depends on the specific set-up (e.g., specimen material and size/shape)and equipment sensitivity (depending mainly on sensor mounting, AE sensor type and dataacquisition settings), but holding these constant allows for relative comparison among nominallyidentical specimens. Comparing the correlation factor indicated that with the chosen signal
threshold setting, AE tended to be more sensitive to crack formation than the X-ray computed
micro-tomography. This approach is now tried on delamination propagation in CFRP fracture
toughness test specimens for an attempt at estimating the micro-crack size due to a single AE signalof given amplitude.

2. Materials and Testing Details
2.1 Carbon-fiber reinforced epoxy and delamination growth
CFRP composites are increasingly used in many engineering applications, including loadbearing
elements. However, their susceptibility to impact damage and subsequent interlaminar delamination
growth, or more general, their rather weak strength in the through-thickness direction, have spurred
intensive research into the characterization of interlaminar delamination growth under quasi-staticand fatigue loads [13]. There are standardized fracture test methods for the so-called mode I (tensile
opening) [14] and mode II (in-plane shear) [15] interlaminar fracture toughness or delamination
resistance, as well as mixed mode I/II [16], all under constant displacement loading. Analogous test
methods for cyclic fatigue delamination propagation are currently under development [17]. The
evaluation of the quasistatic mode I fracture toughness according to the standard is using so-called
double cantilever beam (DCB) specimens and based on measuring the delamination length
increments due to specific applied loads at given crosshead displacements. The measurement of thedelamination length increments during the load tests is made by visual observation using a travelling
microscope following the propagation of the delamination tip along the edge of the standard test
specimen. In research, methods are developed to replace this either by digital image recording andanalysis or by back-calculating the delamination length from the change in specimen compliancedue to the propagation of the delamination. However, with a required resolution of the crack lengtharound 0.05 mm [14] all these approaches effectively average over many microscopic crack events,
both on the length, as well as on the time scale.
The specimens used in the present research were provided by NASA Langley for a round robin
performed at several laboratories in Europe, the U.S. and Japan. The details of material
preparation and the full range of fracture toughness results are summarized in [18,19]. In the
study reported here, only CFRP laminates made from AS4 type carbon fiber and thermoplastic

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poly-ether-ether-ketone (PEEK) matrix are analysed. The starter crack film insert wasa polyimide film. Two different film thicknesses (7 and 13 µm, respectively) were used in thefour DCB specimens with an average thickness between about 3.08 and 3.15 mm, an averagewidth between 20.05 and 20.09 mm and a total length of 139.5 mm.
2.2 In-situ AE monitoring and X-ray radiography of interlaminar fracture tests
As part of the development of the mode I fracture toughness standard [14] in the framework of
the round robin noted above [18,19] delamination tests on the AS4/PEEK CFRP specimens
were performed with an equipment that allowed for in-situ X-ray radiography and image
recording on video (described in [20]) simultaneously with AE monitoring [21]. For the tests
reported here, the loading equipment was set to a displacement speed around 1 mm/min. Loadand displacement values were recorded with a separate data acquisition system (not as part of
the AE data via external parameter input).
The X-ray radiography used a microfocus X-ray source (focal spot size 5-10 µm at voltages
below 40 kV) in projection imaging mode with an imaging system that allowed for video
recording of the images (Fig. 1) at a sampling rate (image frequency) of 25 Hz (both from
Feinfocus, Germany). X-ray source voltage and current were set constant at 32 kV and
0.116 mA, respectively, for all four tests. Depending on the distance between source and test
object compared to that between source and imaging system, image magnifications betweena factor of about 2 and 5 are feasible with this set-up. For the tests reported here,
the magnification factor was about 2.6. Methylene-di-iodide was used as contrast agent for the
X-ray imaging of the delamination growth (Fig. 2) and injected inside the starter crack witha syringe after slightly opening it (displacement of roughly 3 mm).

Figure 1: Photograph of the microfocus X-ray source (right), the DCB-specimen mounted vertically in the loading
fixture at the center with the AE sensor near the bottom edge, and the X-ray image intensifier on the left. The videorecording equipment attached the image intensifier is not shown (photograph by Empa).

AE signal parameters from the quasi-static mode I fracture test on the CFRP laminates were recordedwith AE equipment type 3000/SPARTAN with one sensor type R-15 (both from Physical Acoustics
Corp.) mounted near the bottom end of the specimen (about 1.5 to 2.0 cm from the edge). Dataacquisition settings were a preamplifier gain of 40 dB, a threshold 30 dBAE, a hit definition time of
500 µs, and a rearm time of 1000 µs. The AE analysis will use plots of AE activity, i.e., the number ofAE signals recorded per unit time, and AE intensity, i.e., maximum AE signal amplitude distributions,32nd EWGAE

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each presented as a function of time (Fig. 3). Video recording of the X-ray images and AE dataacquisition were started simultaneously, within roughly 1 second, in order to synchronize the data.

3. Estimating the Sensitivity of AE Monitoring for Microcrack Formation in
AS4/PEEK Specimens Loaded under Mode I Tensile Crack Opening Load
3.1 Approach for the sensitivity analysis
A detailed study [21] of the AE signals recorded during mode I delamination growth of CFRP DCB
specimens has shown that by defining empirical delamination initiation criteria from the observed
AE activity and intensity, the critical fracture toughness G IC obtained from these is comparable tothat obtained from the analysis procedure outlined in the standard [14]. This confirmed that the AE
analysis is capable of capturing the essential features of the mode I DCB delamination tests not only
in a qualitative manner, but also yields quantitative results. In the previously published analysis of
the sensitivity of AE to yield a measure of the crack length induced by tensile loading of miniaturewood specimens [9], the AE signal amplitudes were correlated with the crack area measured fromsynchrotron-based X-ray computed microtomography. The approach presented here hence firstestimates the total fracture surface generated during the test from the the X-ray projection images,
and then the average AE signal amplitude and the number of AE signals recorded during the mode
I delamination test. It is immediately clear that the correlation between AE signal amplitude andcrack area obtained for this test may and likely will significantly differ from that obtained in thetests on the miniature wood specimens. Major effects causing differences are, e.g., the material type
(CFRP versus wood) and its specific signal attenuation behaviour, the specimen size (30 mm versus
about 140 mm) or more important, the distance between AE signal source and sensor, the different
microscopic damage mechanisms (cell wall delamination or cracking versus polymer micro-crack
formation) as well as the different AE equipment, sensor types (with different frequency transfer
functions) and data acquisition settings used for the AE measurement.
3.2 Estimating the fracture surface area
Getting a first estimate of the fracture surface area created during the test is straight-forward.
Determining the difference between initial and final delamination length, multiplied by the
specimen width yields an approximation which can be considered as a lower bound. The initialdelamination length is well defined and straight across the width of the specimen by the tip of
the polymer insert film. However, the crack tip during delamination growth (Fig. 2) is notstraight and shows small, stochastically distributed protrusions.

Figure 2: X-ray projection radiography image showing the tip of the delamination during delamination growth.
The tip is indicated by the contrast agent (dark/black area) and it can be seen that it is not straight across the
specimen width. The same holds for the tip defining the final delamination length (photograph by Empa).

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It has to be noted that the image with the contrast agent may not in all cases show the exact
location of the tip of the delamination. First, the wettability of the contrast agent may be limited
and may hence not penetrate into the microscopic cracks. Second, there is a minimum amountof contrast agent necessary to yield a detectable contrast difference in the radiography image.
Looking at the grey-scale levels of the image pixels along the direction of propagation of thedelamination yields (coming from the top of the image) a high level (dark) that is slightly
decreasing with increasing length showing some scatter, then a roughly linear decrease andfinally a roughly constant, but low value (bright), again with some scatter. From this, it isestimated that the total delamination length can be estimated to within a few mm. This will befurther considered in the error estimate for the sensitivity correlation.
Fracture surfaces of CFRP laminates in general show some roughness or corrugation that could
vary depending on the scale at which this is analysed. This is also true for the delamination
fracture of the CFRP DCB specimens. Deducing roughness from fracture surface images, suchas, e.g., SEM micrographs is fairly complex in spite of the apparent obvious corrugation orroughness that can be seen (see, e.g., [23] for details). Assuming some type of fractal surface
for the CFRP laminate may be misleading [24]. Even though [24] deals with thermoset epoxy
rather than thermoplastic PEEK, the fractal dimension seems questionable for estimating theadditional crack or delamination area in the thickness direction of the DCB specimens. In theanalysis, this will be considered by simply adding a certain, estimated percentage to the lower
bound area and assessing the influence of that on the sensitivity.
One specimen showed unstable delamination growth yielding a tip outside the field of view of
the imaging system. The other three DCB specimens yield a lower bound of the delamination
area increase of 538, 459, and 432 mm2, respectively, if the change in slope of the grey-scale
image is taken as indication of the position of the crack tip. As discussed above, the tip may
extend somewhat further and the total fracture surfaces may be larger than the planar rejection
due to surface roughens and corrugation. In the analysis, the lower bounds and area values 20%
and 40% larger will be used to estimate lower and upper bounds for the sensitivity.
3.3 Estimating the average AE amplitude
The AE activity and AE intensity (Fig. 3) typically show significant variation in time. The AE
activity first increases roughly exponentially with time, reaches a maximum (close to 1’000 hits
per 5 seconds) and then drops. There are three more peaks in AE activity. In the video recordingof the delamination growth, these phases of high AE activity correlate with faster delamination
growth. The total number of AE signals (“hits”) recorded for the delamination growth period
for the three DCB specimens analysed amount to about 35’620, 32’790, and 51’970. It has tobe noted that these values are estimates that could vary by as much as 30%. The average
amplitudes are estimated to be around 70 to 72 dB AE for all three specimens. From Fig. 3 (left)
the upper and lower bound for the average AE signal amplitude are estimated as 80 and
70 dB AE . Converted to µV, the average amplitudes of 70 and 72 dB AE correspond to about
3’160 and 3’980 µV, and the upper and lower bounds are 7’080 and 2’240 µV, respectively.
The AE signals below the threshold (30 dB AE ) are not recorded and hence do not contribute toboth, total number of AE signals and average AE signal amplitude.
The majority of AE signals recorded lies in the amplitude range between about 60 and 80 dB AE(Fig. 3).

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Figure 3: AE activity (number of hits per 5 seconds, left) and AE intensity (AE signal amplitude distribution, right)
from one of the DCB mode I delamination tests.

3.4 Estimating the average sensitivity of AE analysis
With the estimated lower and upper bounds of the delamination area created during the mode I
fracture test in the DCB specimens, the average AE signal amplitude and the respective upperand lower bound for the average crack area created per average AE signal (with respect to signal
amplitude) can be estimated. The results are compiled in Table 1. Of course, the assumptions
made in the determination of the different quantities are essential, and the result could differ
significantly, if these assumptions were proven to be questionable. One important aspect is AE
signal attenuation, the AE signal amplitudes were used as recorded and no attempt was made atcorrecting for attenuation. CFRP composites can cause significant attenuation and the recorded
AE signals are possibly affected by reflections from the specimen surfaces, since the width andthickness dimensions (nominally 20 and 3 mm) are relatively small.
Table 1: Estimated average and upper and lower bounds for the crack area created in the AS4/PEEK CFRP laminate
by an AE signal of average amplitude

Specimen No.

Estimatedaverage AE
signal amplitude[µV]

Estimated lowerbounddelamination
area [mm2]

Estimated
number of AE
signals [-]Estimated upperbound forsensitivity per
average AE
signal [mV/µm2]

1

-

-

-

-

2

3’160

538

35’6200.00021

3

3’980

459

32’7900.00028

4

3’160

432

51’9700.00038

Using the lower bound of the delamination area for the results presented in Table 1 yields anestimated average upper bound of the sensitivity from three specimens of 0.00029 mV/µm2.
The individual values for the three DCB specimens are within the range between about 0.0002
and 0.0004 mV/µm2 and hence fairly consistent, considering the rough approximations thatwere used for the estimates. Assuming fracture areas larger than the lower bound by 20% and
40% respectively, will yield lower average sensitivities of about 0.00024 and 0.00021 (again inunits of mV/µm2). Using an upper bound for the average AE signal amplitude of 77 dB AE

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or 7’080 µV with the lower bound for the fracture area yields about 0.00061 mV/µm2, and thesame average amplitude with an area that is higher by 40% (as an estimated upper bound) yields
0.00037 mV/µm2. If the lower bound area is increased by a factor of two. i.e., 100%,
the estimated average sensitivity is 0.0003 mV/µm2. This can be compared with the value of
0.0038 mV/µm2 obtained in [9] for testing the miniature tensile specimens made from spruce
wood. The higher sensitivity in the latter case can have several causes as discussed above.

4. Summary and Conclusions
Combining AE measurements on mode I DCB fracture tests with CFRP specimens with insitu
projection radiography and highlighting the delamination area with a suitable contrast agent,yields a rough estimate of the sensitivity of the AE measurement with respect to microcrack
area that can be detected. From the average sensitivity determined for AE monitoring of the
CFRP DCB fracture specimens, it can be estimated that the area of a micro-crack that yields an
AE signal with an amplitude of 70 dB AE or 3’160 µV is 10’900 µm2. This roughly corresponds(if the crack area is considered to be a square or circular shape) to a crack length of 100 µm.
Taking 80 and 60 dB AE as upper and lower AE signal amplitude limit, the crack length range
in the test phase with high AE activity and correspondingly fast delamination growth spansroughly 60 to about 190 µm. These values are consistent with an upper bound estimate ofa micro-crack size of less than 200 µm from the diameter of the protrusions seen forming
stochastically along the crack tip in the X-ray images.
With threshold values of 40 and 30 dB AE , i.e., 100 µV and about 32 µV, the minimal detectablecrack length, estimated from the average sensitivity of 0.00029 mV/µm2 amount to about 20
and 10 µm, respectively. With complementary information on the total defect size, e.g., thedelamination area or fracture surface from other non-destructive test methods, AE analysis canyield rough quantitative estimates of the damage size caused by the microscopic source
mechanism. It is, however, not straight-forward to compare sensitivities estimated in different
tests on different materials directly, since the main effects (material type, specimen size/shape,
AE sensor location relative to the source, and transfer function of the measurement chain) may
have acted in different ways. Nevertheless, this confirms that AE monitoring easily achieves
sensitivity to defects on the scale of a few tens of microns in CFRP laminates, at least inlaboratory–scale applications.
Acknowledgment
The contributions of Mr R Jäggi and of Dr R A Nordstrom for the in-situ AE monitoring andsimultaneous radiography measurements and their analysis as well as discussions with membersof the committee on acoustic emission of the German Society for Non-Destructive Testing,especially Dr. J Bohse, are gratefully acknowledged.
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C R Ríos-Soberanis, Acoustic Emission Technique, an Overview as a Characterization
Tool in Materials Science, Journal of Applied Research and Technology, Vol:9 No. 3
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M E Ibrahim, Nondestructive evaluation of thick-section composites and sandwich
structures: A review, Composites: Part A: Applied Science and Manufacturing, Vol. 64,
pp. 36–48, 2014.

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3.

E Proverbio, Evaluation of deterioration in reinforced concrete structures by AE
technique, Materials and Corrosion, Vol. 62, No. 2, pp. 161-169, 2011.

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S C Lovejoy, Acoustic Emission Testing of Beams to Simulate SHM of Vintage
Reinforced Concrete Deck Girder Highway Bridges, Structural Health Monitoring - AnInternational Journal, Vol. 7, No. 4, pp. 329-346, 2008.

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H N G Wadley, C B Scruby and G Shrimpton, Quantitative acoustic emission source
characterisation during low temperature cleavage and intergranular fracture, Acta
Metallurgica, Vol. 29, pp. 399-414, 1981.

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M G R Sause, A Gribov, A R Unwin and S Horn, Pattern recognition approach to identify
natural clusters of acoustic emission signals. Pattern Recognition Letters, Vol. 33, pp. 1723, 2012.

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M G R Sause, T Müller, A Horoschenkoff and S Horn, Quantification of failure
mechanisms in mode-I loading of fiber reinforced plastics utilizing acoustic emission
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A J Brunner, E Hack and J Neuenschwander, Nondestructive Testing of Polymers andPolymer-Matrix Composites, Wiley Encyclopedia of Polymer Science & Technology(Ed.Arza Seidel), J. Wiley, pp. 1-39, 2015.

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F Baensch, M Zauner, S J Sanabria, B R Pinzer, M G R Sause, A J Brunner, M Stampanoni
and P Niemz, Damage Evolution in Wood – Synchrotron based microtomography ascomplementary evidence for interpreting acoustic emission behavior, Holzforschung,Vol. 68, No 8, pp 1015-1025, 2015.

10. F Baensch, M G R Sause, A J Brunner and P Niemz, Damage Evolution in Wood – Pattern
Recognition based on Acoustic Emission Frequency Spectra, Holzforschung, Vol. 69,
No. 3, pp. 357-365, 2015.
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F Baensch, Damage evolution in wood and layered wood composites monitored in situ by
acoustic emission, digital image correlation and synchrotron based tomographic
microscopy, PhD Thesis, ETH Zürich, No. 22372, pp 1-186, 2015.

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L Vergeynst, M G R Sause, F Ritschel, A J Brunner, P Niemz and K Steppe, Finite element
modelling used to support wood failure identification based on acoustic emission signals,
Proceedings COST – Timber Bridge Conference (Eds S Franke, B Franke, R Widmann),
pp. 141-146, 2014.

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A J Brunner, B R K Blackman and P Davies, A status report on delamination resistancetesting of polymer-matrix composites, Engineering Fracture Mechanics, Vol. 75, No. 9,
pp. 2779-2794, 2008.

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International Organisation for Standardisation, ISO, Fibre-reinforced plastic composites– determination of mode I interlaminar fracture toughness, G IC , for unidirectionallyreinforced materials, 15024, pp. 1-24, 2001.

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International Organisation for Standardisation, ISO, Fibre-reinforced plastic composites– Determination of the mode II fracture resistance for unidirectionally reinforced materials
using the calibrated end-loaded split (C-ELS) test and an effective crack length approach,15114, pp. 1-18, 2014.

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American Society for Materials and Testing, International, ASTM, Standard Test Method
for Mixed Mode I-Mode II Interlaminar Fracture Toughness of Unidirectional Fiber
Reinforced Polymer Matrix Composites, D6671/D6671M-13e1, pp. 1-15, 2013.

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17.

A J Brunner, Fracture mechanics of polymer composites for aerospace applications,
Chapter 8 in ‘Polymer Composites in the Aerospace Industry’ (Eds P E Irving, C Soutis),
Woodhead Publishing, Series in Composites Science and Engineering: Number 50, pp.
191-230, 2015.

18.

T K O’Brien and R H Martin, Results of ASTM round robin testing for mode I
interlaminar fracture toughness of composite materials, NASA Technical Memorandum
104222, pp. 1-120, 1992.

19.

T K O’Brien and R H Martin, Results of ASTM round robin testing for mode I
interlaminar fracture toughness of composite materials, Journal of Composites
Technology and Research, Vol. 15, No. 4, pp. 269-281, 1993.

20.

T de Kalbermatten, R Jäggi, P Flüeler, H H Kausch and P Davies, Microfocus radiography
studies during mode I interlaminar fracture tests on composites, Journal of Materials
Science Letters, Vol. 11, pp 543-546, 1992.

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P Flüeler and A J Brunner, Crack Propagation in Fiber-Reinforced Composite Materials
Analysed with In-Situ Microfocal X-Ray Radiography and Simultaneous Acoustic
Emission Monitoring, Proceedings European Conference on Composites Testing andStandardisation ECCM-CTS, (Eds. P J Hogg, G D Sims, F L Matthews, A R Bunsell andA Massiah), European Association for Composite Materials EACM (ISBN 29506577-10), pp 385-394, 1992.

22.

J Bohse, T Krietsch, J Chen and A J Brunner, Acoustic Emission Analysis andMicromechanical Interpretation of Mode I Fracture Toughness Tests on Composite
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L R O Hein, K A Campos, P C R O Caltabiano and K G Kostov, A Brief Discussion About
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SCANNING, Vol. 35, pp. 196–204, 2013.

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K A de Campos, J Augusto, T A Pereira, L R de Oliveira Hein, 3-D reconstruction byextended depth-of-field in failure analysis – Case study II: Fractal analysis of interlaminar
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2012.

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

ADVANCED SIGNAL ANALYSIS APPLIED TO DISCRIMINATE
DIFFERENT CORROSION FORMS BY ACOUSTIC EMISSION DATA
Luigi CALABRESE 1, Massimiliano GALEANO 1, Edoardo PROVERBIO 1,
Domenico DI PIETRO 2, Angelo DONATO 2, Filippo CAPPUCCINI 2
1
Department of Engineering, University of Messina; Messina, Italy
Phone: +39 090 3977544, Fax: +39 090 3977471; e-mail: luigi.calabrese@unime.it,
massimiliano.galeano@unime.it, edoardo.proverbio@unime.it
2
GE Oil & Gas; Firenze, Italy; e-mail: domenico.dipietro@ge.com, angelo.donato@ge.com,
filippo.cappuccini@ge.com

Abstract
The analysis of three corrosion phenomena (uniform corrosion, pitting corrosion and stress corrosion cracking)has been carried out through the use of acoustic emission technique.
Corrosion attacks have been obtained on three different types of martensitic stainless steel (X12Cr13,
X4CrNiMo16-5-1, 17-4 PH) in a FeCl 3 solution, using conditions set by the ASTM G48 standard. These alloys
are characterized by different mechanical, microstructural and electrochemical properties, for each which lead todevelop specific corrosion forms, albeit subject to the same environmental conditions.
A multivariate statistical analysis approach, based on PCA (Principal Component Analysis), has been adopted toevaluate AE data and to have highlight damage-sensitive features. Specific clusters of variables related to
specific corrosion phenomena have been identified, promoting this approach as a procedure for discriminating
onset of specific corrosion mechanism.
Keywords: acoustic emission, uniform corrosion, localized corrosion, stress corrosion cracking, principal
component analysis

1. Introduction
The health monitoring of components operating in harsh environments is a very sensitive
issue in industrial field. A proper monitoring approach is necessary in order to prevent
unexpected failure (e.g. due to stress corrosion cracking mechanism) and, at the same time, todraw up a proper maintenance schedule that may limit the inactivity maintaining periods.
Considerable efforts have been done in the last decades to identify an affordable technique of
health monitoring of components operating in critical environmental conditions [1].
Corrosion prevention should be achieved by a complex system of monitoring, preventative repairs,
and careful use of materials. A significant industrial relevance have the use of technologies able toguaranty in situ real time monitoring of component during service. These systems allow continuous
monitoring and control of corrosion mechanisms occurring on sensored component [2].
It is difficult however to have and interpret accurate real-time corrosion information in orderto clearly identify and to avoid conditions that cause high corrosion rates.
Several efforts have been focused on on-line non-destructive technique (NDT) that should be
reliable and able to discriminate different corrosion forms simultaneously occurring on thecomponents during their normal use [3] [4] [5]. The acquisition of the AE signals induced bycorrosive processes [6] [7], allows to acquire parameters or values which could be relatedwith specific degradation phenomena that take place on the component [8] [9].
Considering that each corrosion phenomenon generates different peculiar AE patterns, theconcomitant presence of several corrosion phenomena involves the superposition of acoustic
signals making difficult to discriminate each cause that generate them.

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Aim is to give an approach suitable to distinguish the different forms of corrosion that could
occur during the metal degradation, in order to identify dangerous condition duringcomponent operation.
In particular in this work, the corrosion phenomena in three different types of martensitic stainlesssteels (17-4PH, X4CrNiMo16-5-1 and X12Cr13), were investigated performing corrosion tests in
FeCl 3 solution according to ASTM G48 standard. These alloys are characterized by different
mechanical, microstructural and electrochemical properties, for each which lead to develop
specific corrosion forms, albeit subject to the same environmental conditions.
AE data were recorded during corrosion tests to define the peculiar characteristics of the three typesof corrosive attacks (generalized corrosion, pitting/crevice and stress corrosion cracking).
A multivariate statistical analysis approach, by PCA (Principal Component Analysis), was used tohave an integrated statistical approach with damage-sensitive features. Clusters of variables related
to specific corrosion phenomena were identified, promoting this approach as a suitable procedure
for the discrimination between undamaged and damaged conditions of components.

2. Experimental and laboratory practice
2.1 Materials
Table 1: Mean mechanical properties of stainless steel alloys
Stainless Steel

0.2% Yield strength(MPa)17-4 PH
X12Cr13
X4CrNiMo16-5-1

745
618
615

ultimate tensile
strength UTS
(MPa)
1010
760
890

Elongation at
maximum load
ε (%)
24
24
16

Composition [wt%]Table 2: Nominal chemical composition [wt%] of stainless steel alloys
17-4 PHFe balance
Ni 4.43
Cr 15.10
S 0.005
P 0.02
Mn 0.44
Si 0.50
Cu 3.31
Nb 0.22
C 0.042

Stainless Steel
X12Cr13
Fe Balance
C 0.13
Mn 0.43
Si 0.29
P 0.020
S 0.001
Cr 12.18
Ni 0.21

X4CrNiMo16-5-1
Fe balance
Ni 4.8Cr 15.5
S < 0.002
P <0.017
Mn 0.93
Si 0.24
Mo 0.97
C 0.06

Specimens for corrosion tests were made of three different martensitic stainless steel:• 17-4 PH (UNS S17400) stainless steel. 17-4 PH is a precipitated hardening martensitic
stainless steel containing more Cr and Ni than the usual AISI 4xx series, for improved
corrosion resistance, and additional alloying elements for precipitation hardening.
• X12Cr13 is a martensitic stainless steel. It is very popular in industrial field since its
corrosion resistant and good mechanical properties.
• X4CrNiMo16-5-1 is a martensitic stainless steel with good corrosion and oxidation
resistance. This material is tough and strong and it is usually used to produce a variety ofstructural components for the power generation and heat treating industries.

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In table 1 and table 2 respectively details of mechanical properties and nominal chemicalcomposition of the alloys are reported.
Tensile specimens (whose size and shape are displayed in figure 1), were machined byturning with a final surface roughness of 0.3 µm from a forging disk similar to that commonlyused for impeller production for oil & gas industry. In detail, specimens were extracted along
the circumferential direction at maximum diameter in order to get higher mechanicalproperties and toughness.

Figure 1. Dog-bone dimensions for the samples

2.2 Corrosion test set-up
To guarantee the same conditions concerning signal attenuation and acoustic wave
propagation velocity each corrosion test was performed using the same test set-up. All tests
were performed, according to ASTM G48 standard, in an aqueous solution containing 10% by
weight of FeCl 3 . Test was carried out at a room temperature (25°C). Test solution was
prepared by adding 50 g of reagent to 450 mL of distilled water, so to obtain about 500 mL ofsolution. The solution was then added into the test cell at test temperature.
For the execution of tests a dead weight type machine was employed. The stress applied to thespecimens during SCC test was equal to 90% of the 0.2% yield strength of the material. An
appropriate cell was produced by using the well-known design of vessel commonly used for
SCC test with tensile shape specimens (i.e. NACE TM0177 method A). The cell wascomposed of a Pyrex ring and two flanges made of Teflon with a central hole for specimen
accommodation. This set-up conditions were suitable to obtain the activation of SCC
corrosion on 17-4PH, the arise of localized corrosion just like pitting and crevice on
X4CrNiMo16-5-1, and finally to get the uniform corrosion on X12Cr13. For each stainlesssteel three replicas of the test were carried out.
2.3 AE set-up
AE signals were recorded by a 10-channel Vallen AMSY-5 measurement system for the fullperiod of the test. The acquisition was performed by using two sensors placed at the two endsof the sample. However, due to cylindrical shape of the samples a particular solution was
implemented to accommodate AE sensors that need a contact flat surface. In detail two
suitable grips were designed (and produced in 17-4 PH steel) to connect the specimen to theloading machine. The grips have a threaded hole for entering the tensile sample, a hole in theupper part for the connection to the dead weight machine (through a pin) and a large flat
surface for the assembly of the AE sensor. The threaded coupling between specimen and grip
allows acoustic wave generated within the sample during test to propagate to the sensor
without any significant attenuation or interference. A third sensor has been employed asa guard sensor, connected to an independent block and it was used to acquire all the noise
signals that were then removed during the data post processing. AE sensors used werepiezoelectric transducers with high sensitivity (suitable for metal integrity testing), VS150-M
type that worked in 100-450 kHz frequency range with resonance at 150 kHz. In theexperimental set-up, the thresholds of amplitude and rise-time parameters were set

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respectively at 26.7 dB and 2 µs, the amplification was set at 34 dB with a bandwidth (-3 dB)from 2.5 kHz to 1 MHz.

3. Results and Data analysis
3.1 Corrosion phenomena morphology
In figure 2 the corroded surface of each different sample after corrosion test are reported. The
specimens showed only one predominant type of corrosion for each one of the three types ofinvestigated steel. In particular, the X12Cr13 specimen evidenced very intense generalized
corrosion. X4CrNiMo16-5-1 specimen has suffered exclusively pitting and crevice corrosion.
The surface evidenced indeed numerous large pits. At the same time in correspondence of thebottom o-ring of the electrochemical cell extended crevice corrosion was observed. While on
17-4PH specimen the stress corrosion cracking occurred with the formation and propagationof a large amount of cracks.

Figure 2. Micrography of all stainless steel alloys after the corrosion test

3.2 Data Analysis
Before to perform the statistical analysis of data the recorded AE signals have been properly
treated (filtering operations and pre-processing). In particular a three step de-noising approach
was used [10]:1. Spurious events were accurately removed from the population of data. Each acousticalevent identified only by one sensor (and not heard by both sensors) was classified as
spurious event, since not attributable to acoustic event due to corrosive phenomenathat occur on the specimen, and consequently was removed.
2. Delocalized events were removed from the population of data. At first position
identification for all AE events was carried out through a process of localization of
events. Some events, identified by both sensors, were localised outside the area ofinterest (outside the wetted area of stainless steel samples). These events cannot be
attributed to corrosion phenomena and were removed during the pre-processing of data.

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3. Removal of environmental noise. All AE events heard by guard sensor were removed
from the population of data because related to events occurring away from theexperimental set-up thus amenable to environmental noise.
Thus treated signals were subsequently computed to calculate some derived variables, to get
a complete and useful description of the system. In table 3 the direct and derived variables of
AE data were reported.
Table 3. Variables Data-set
Variable name

Variable description

Measurements and value limits

Amplitude

Value of the maximum peak of the waveform.
dB
Indicates magnitude of the waveform.

Duration

Time between the first and the last overshot ofµs
the predefined threshold

Risetime

Time between the first overshoot of the definedµs
threshold and the peak in the AE burst.

EnergyArea under the envelope of the AE burst

CountsNumber of threshold crossing counts (for a
Absolute number
given AE burst).

Eu [10-14 V2s]

Average Frequency
Ratio between Duration and Counts
(AVG)

kHz

Event Frequency

Number of AE events detected in one second

s-1

RA

Ratio between Risetime and Amplitude

Expressed in ms/V, useful toclassify the type of cracks.

Crack Index

Ratio between AVG and RA

Useful to discriminate throughshear and tensile crack.
KhzV/msTime

Duration of corrosion test.

Time is normalized from 0 to
100 for each three tests.

Cumulative

Cumulative value for hits, counts and energy

As is possible to note, a wide variables data-set was defined. These parameters are suitable todescribe the characteristics of the waveforms produced during the corrosive phenomena.
This data-set identifies a multidimensional system (more than 10 dimensions). So it is nearly
impossible to analyse all variables in a comparative way in order to identify a reduced set that
could discriminate corrosive phenomena in an easy way.
To obtain this issue is necessary to use an advanced data-analysis system that is able to deal
with multidimensional data-set in an advantageous way. PCA transform could assist us tooperate a feature selection in order to reduce the system dimensionality, and to help us to givea proper significance to the reduced set of variables [11]. Loadings can be computed ascoefficients used to obtain the principal components. Each principal component is determinedas linear combination of the product of loadings and the respective scaled variables.
In Figure 3 the correlation variables matrix is presented. High values (in brown and blue
colour) correspond to a significant correlation between the variables, i.e. the two variables are
not independent, while values closed to zero indicates that they are mostly independent.

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Figure 3. Correlation matrix, referred to the whole variable data-set

Two dependent variables are likely to provide the same informational content, therefore theycreate redundancy, and they even could lead to mistakes in case of advanced elaboration suchdata-mining and neural networks.
It could be observed that three variables groups could be distinguished. A first group includes
all the time-dependent variables, just like time and the cumulative variables. A second groupconsists of the waveform describers parameters, i.e. Amplitude, Duration, Counts, Average
Frequency and Energy. A third group includes variables that are not clearly attributable to theother two groups i.e. Event Frequency, RA and Risetime, Crack Index.
Pareto graph, showed in Figure 4, represents the variance contribute by each PC in the total
variance observed in the dataset by the principal component analysis. Consequently the
relative variance magnitude was obtained from the eigenvalues associated to each principal
component (autovector). Since the main PCs usually contributes a major portion of thevariance in data, we can compute the most important variables that contribute to these PCs(axes of the multidimensional plane in which the dataset is representable.).

Figure 4. Auto-values for every auto-vector. A total of 85% cumulative value is owned by first three auto-vectors

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This auto-values graph shows that a great amount of variance relies in the first three autovectors (a total of 85% of the total variance is owned by first three auto-vectors), so we canconsider that the great part of the dataset information content relies in first three dimensions.
An analysis delimited to a 3 dimensional space could lead to an efficient evaluation of the system.
A first consideration could be made observing the variables positioning among the spacedetermined by the three auto-vectors (three dimensional space of principal component). In Figure
5 this space is represented with the three projections on the planes identified by the three PC axes.
According to autocorrelation matrix, all variables are gather together in three main clusters.

Figure 5. Events distribution in the three dimensional spaceFirst cluster includes all the time related variables (time and all the cumulative ones)
displaced in the same direction. This common direction could be named as Time-Axes. It
evidently indicates the time evolution of the corrosion phenomena. A second variables group,orthogonal to time axis, includes most part of the waveform descriptors, like Amplitude,
Duration, Counts, Average Frequency and Energy. This variable group could be named
Waveform-Axis. Finally, variables which remain organized in a non univocal way (RA, R andCrack Index) share the same direction but in opposite way (this direction could be named
Event-Axis). Variables data-set identifies three principal axis that could be useful todiscriminate the occurring event that actually happens during the corrosive phenomena: TimeAxis, Waveform-Axis, Event-Axis. First axis indicates the time evolution of the phenomena,second axis indicates the shape of acoustic waveform related to the corrosive phenomena,
finally third axis is related to the occurring corrosion events.
Further information can be obtained analysing the scatter distribution of clustered data in thefirst three principal components relating the shape of the marker to time variable (Figure 6).
Small dot dimension indicates event occurred in the first test stage. Large dot dimension is

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associated to AE events occurred during the final stage of the corrosion test. Is it possible tonote that events are put in an interesting order in the time axis: all three corrosion type start in
the same region (the lower left plane region). This region is characterised by very low
magnitude of time and waveform axes variables.

Figure 6. Hit distribution among auto-vectors space. Marker dimension is time-dependent

Then the corrosion phenomena evolution leads SCC and crevice to move forward the
direction identified as time axis, where the uniform corrosion events are dominant. Thisregion where the large amount of uniform corrosion event persist was coded as “uniform
region”. The combined presence of all corrosion forms in this region could indicate that AE
events related to corrosion activation are quite similar for all corrosion forms and they can be
related mainly to AE events induced by electrochemical activity of stainless steel surface with
FeCl 3 electrolyte (uniform-like corrosion characteristics).
Then, moving on the time axis direction, the combined presence of SCC and pitting/crevice
events can be evidenced (this region was coded as SCC region). This behaviour could be
related considering that, for 17-4 PH SS sample, corrosion phenomena induced by activation,
nucleation and propagation of pits that during time will evolve in crack [12], could haveacoustical waveform quite similar to pitting/crevice events [13] observed on X4CrNiMo16-51 stainless steel corrosion test.
Finally, crevice evolution stimulates a large amount of AE events clustered in the upper sideof the plot (crevice region). This region is populated only by crevice events, and it could be
identified as the region of hydrogen evolution bubbling, as the value of Risetime (the higherones) and values of Duration (the lower ones) suggest us [14].
Furthermore Event-axis could be used to extrapolate more information about thediscrimination phenomena. In particular on the right side of the plot a cluster of AE eventsoccurring in 17-4PH- SS corrosion tests were identified. These events are characterised byhigh amplitude and energy and can be related to crack activation and propagation phenomena(this region was coded SCC crack propagation).

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These considerations could be confirmed through the analysis of bivariate graphs presented in
Figure 7.

Figure 7. Bivariate graphs for most representative variables identified by PCA

Waveforms attributable to uniform corrosion (identified on X12Cr13 stainless steel, blue areas)
are characterized by low amplitude and medium duration, while show a high RA compared withother corrosion forms. AE waveforms due to crevice and pitting (predominant in X4CrNiMo165-1 stainless steel, green areas) are characterized by high amplitude, energy and cover full RA
range. Two clusters related to high and low Duration can be identified respectively related tocrevice/pitting activation and hydrogen evolution bubbling. The waveforms attributable to the
SCC (observed in 17-4PH corrosion test of, red areas) are mainly characterized by low/medium
amplitude, energy and RA. This cluster of data is related to pitting activation. Afterwards few
AE hits with very high amplitude, energy and duration are induced by energetically significant
SCC crack activation and propagation phenomena. Table 4 summarized synthetically the
waveform parameters for each corrosion form.
Table 4. Summarized waveform parameters for each corrosion form
Amplitude

Duration

EnergyRA

Uniform

Low

MediumLow

High

Crevice/Pitting

High

High/Low

High

Full range

SCC

Low/medium
Very High

Low/mediumHigh

Low/medium
Very High

MediumVery Low

4. Conclusions
In this study the issue to discriminate among three different corrosive phenomena was dealt.
Through the use of a multidimensional data-analysis such PCA, specific variable clusters
related to peculiar corrosion phenomena are detected. These clusters are clearly identifiable
and well separated each other in a three-dimensional space, modelled by time-related
variables, waveform-descriptor variables, event-descriptor variables.
Every corrosion event could be identified and assigned to a specific cluster by a triad of variables.
Such information could be in the future used in tests in which these corrosive phenomena occur
simultaneously, in order to determine a clear discrimination among each corrosion phenomenon
and to analyse the contribution of each of them to the specimen damage evolution.

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F. Cappuccini, “Identification of damage evolution during SCC on 17-4 PH stainlesssteel by combining electrochemical noise and acoustic emission techniques,” Corros.
Sci., vol. 98, pp. 573–584, 2015.
L. Djeddi, R. Khelif, S. Benmedakhene, and J. Favergeon, “Reliability of acoustic
emission as a technique to detect corrosion and stress corrosion cracking on
prestressing steel strands,” Int. J. Electrochem. Sci., vol. 8, no. 6, pp. 8356–8370, 2013.
L. Calabrese, L. Bonaccorsi, E. Proverbio, D. Di Pietro, and F. Cappuccini, “SCC
damage evolution on martensitic stainless steel by using acoustic emission technique,”Corros. Eng. Sci. Technol., vol. 50, no. 5, pp. 364–371, 2015.

32nd EWGAE

Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

MONITORING OF HYDROGEN ASSISTED SCC ON MARTENSITIC
STAINLESS STEEL BY ACOUSTIC EMISSION TECHNIQUE
Luigi CALABRESE 1, Massimiliano GALEANO 1, Edoardo PROVERBIO 1,
Domenico DI PIETRO 2, Angelo DONATO 2
1
Department of Engineering, University of Messina; Messina, Italy
Phone: +39 090 3977544, Fax: +39 090 3977471; e-mail: luigi.calabrese@unime.it,
massimiliano.galeano@unime.it, edoardo.proverbio@unime.it
2
GE Oil & Gas; Firenze, Italy; e-mail: domenico.dipietro@ge.com, angelo.donato@ge.com

Abstract
Aim of the work is to investigate by using acoustic emission technique (AE) the hydrogen assisted Stress
Corrosion Cracking (SCC) phenomena, in a martensitic stainless steel. In particular a study based on time
domain approach was carried out in order to analyse the corrosion phenomena by using data variables evolution.
An appropriate set of variables was presented, some of them directly related to the acoustic phenomena, such as
Amplitude, Duration and other descriptors of the AE waveform, while others have been derived from them, suchas b-value and Ib-value, that are descriptors about the time domain distribution of the AE event energy.
Tests were carried out on a X12Cr13 martensitic stainless steel, according to NACE TM 0177 standard, usinga modified sour corrosive environment (5% NaCl, 2.5% Acetic Acid and 10-2M Na 2 S 2 O 3 ).
Keywords: acoustic emission, stress corrosion cracking, time domain series, b-value and ib-value

1. Introduction
In many areas of industrial production, first of all the transport of hydrocarbons and of otherdangerous chemicals, it is of primary importance the definition of effective corrosion
activation and propagation indices, in order to guarantee efficient corrosion monitoring. It isnecessary for the purpose to prevent sudden failures, and to obtain a reduction of
infrastructure maintenance costs [1] [2].
Among all the forms of corrosion, Stress Corrosion Cracking (SCC) results to be one of themost critical and dangerous. That is due to its random nature and the lack of a complete
comprehension of mechanisms at the basis of the corrosion phenomena [3] [4] [5].
Oil & Gas industry, moreover, deals with more and more aggressive environment. So the useof new corrosion resistant alloys [6] or a better identification of the application limits ofalready used metal alloys [7] [1] is fundamental.
For this issue recently the attention is being focused on martensitic and super-martensitic
stainless steel, due to their resistance in moderate aggressive corrosion environments (highCO 2 , medium to high chlorides and low H 2 S) and reduced costs. H 2 S indeed is highly
aggressive so an increasing attention is still given to the identification of materials with highcorrosion resistance in this environmental condition. Due to the high H 2 S dangerousness forhealth, is commonly accepted the use of thiosulphate solution to simulate the sour gas
environment [8] [9].
Among all the monitoring methodologies aimed to evaluate the SCC damage phenomena,
Acoustic Emission (AE) technique plays a key role. The technique allows to identify thegrowing faults during the monitoring conditions and is satisfactory used to detect pitting
initiation and propagation [10], as well as short and long range crack propagation [11] [12].
The data obtained from the acquired transients can be analysed by different methodology
based on uni- and multi-variate approach [13], as well as Principal Component Analysis

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(PCA) or Neural network [14]. But these approaches could lead to difficulty in the datainterpretation, due to the hidden and complex interaction between the AE variables. AE data
analysis could be therefore improved with the use of some additional analysis procedures.
In this work the authors coupled to the multidimensional data analysis the so called b and Ib
value approach already in use in the geophysics field.
The b value analysis is based on the event cumulative frequency-magnitude distribution andwas originally applied on seismic science. It was developed to characterize earthquake data
populations [15], but it was successfully applied in many other field, such as civil engineering[16][17], geotechnics [18]. It took its theoretical basis on the Gutemberg and Richterempirical formula [19]:LogN = a – b(A dB /20)

(1)

where A dB is the event magnitude, N is the number of events with an amplitude higher thanA dB , and b is the so-called b-value.
In order to avoid the problem to define amplitude range and number of AE data, the improved
b-value (Ib-value) was proposed [20]:Ib = (log 10 N(ω1 ) – log 10 N(ω2 ) ) / ((α 1 + α 2 )/σ)(2)where N(ω1 ) is the cumulative number of AE events with amplitude higher than µ-α 1 σ, andand N(ω2 ) is the accumulated number of AE events in which the amplitude is more thanµ+α 1 σ, σ is the standard deviation of the magnitude distribution for each group of events, µ is
the mean value of the magnitude distribution for the same group of events, α 1 and α 2 areconstants.
These two parameters have a slightly different behaviour: b-value shows the material
response to the applied load, i.e. the variation of load as for a stress redistribution [16], whileIb-value shows a better sensitivity to the corresponding fracture processes [2].
Aim of this work is to investigate the hydrogen assisted Stress Corrosion Crack phenomena,
in a martensitic stainless steel, by using acoustic emission technique. The different phases of
the corrosion phenomena have been investigated using an appropriate set of variables ina time domain approach, furthermore b and Ib values analysis was adopted to better identify
critical transients.

2. Experimental part
2.1 Materials
Tests have been conducted on a martensitic stainless steel X12Cr13. It is one of the mostpopular alloy in industrial field since is fairly good corrosion resistance and mechanicalproperties. Chemical composition of the alloy is reported in table 1.
Table 1: Nominal chemical composition [wt%] of X12Cr13 alloy
Fe
Balance

C
0.13

Mn
0.43

Si
0.29

P
0.020

S
0.001

Cr
12.18

Ni
0.21

Specimens were machined with a surface roughness of 0.3 µm from a forging disk used toobtain compressor impellers used in oil & gas industry. To get the higher mechanicalproperties and toughness specimens were extracted along the circumferential direction at
maximum diameter. After heat treatment the specimens were characterized by an ultimate

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tensile strength (UTS) of 760MPa, a 0.2% yield strength of 618MPa and an elongation atbreak of 24%.
Specimens were machined in a dog-bone shape, with a reduced diameter of 3.81 mm in thecentral section (the gauge section with a length of 15 mm), and a diameter of 9 mm in theupper and lower sides having a length of 62.5mm each one (including the junction section).
2.2 SCC test set-up
Tests have been carried out in standardized conditions, according to NACE TM 0177. Corrosive
environment was a modified sour solution (5% NaCl, 2.5% acetic acid and 10-2M Na2 S 2 O3 ). Test
solution preparation was carried out under nitrogen gas bubbling in order to obtain an oxygen freesolution. Test cell was sealed to avoid air inlet for the whole test duration.
SCC set-up was composed by a vessel for tensile shape specimen (NACE TM 0177 method A)
composed by a Pyrex ring and two Teflon flanges (with a central hole for accommodating thespecimen). This SCC vessel was then mounted on a dead weight type machine, to guarantee
a constant stress on specimen. Load was set to the 90% of the 0.2% yield strength of the material.
2.3 AE set-up
AE signals were recorded by a 8-channel Vallen AMSY-6. It was setted with an amplitude
thresholds of 26.7dB, a risetime threshold of 2 µs, and a pre-amplification of 34dB (from2.5kHz to 1MHz). Recording was active for the full period of the test.
Acquisition set-up was then completed by the use of three piezoelectric transducers VS150M. These sensors have a high sensitivity and works in the 100-450kHz frequency range, withresonance frequency at 150kHz. Two sensors were placed at the two ends of the specimen,
while the third one was used as a guard sensor (connecting it to an independent block), its role
was to acquire all the external noise signals, useful for a subsequence filtering procedure.
Sensors were coupled to the specimen with a proper grease layer, without any significant
acoustic attenuation or interference.

3. Results
Test were repeated 5 times, obtaining always a fracture in a time range from 300 to 400h.
Similar results have been obtained by each test showing an appreciable homogeneity inacoustic emission trends. For simplicity and clarity of results each picture, graph and datareported in this paper are related to a single test used as reference.
3.1 Corrosion phenomena morphology
The fracture surface of a specimen after SCC test is reported in Figure 1. The specimen
showed an irregular fracture surface, with a mix of brittle and ductile propagation modes.

Figure 1. Specimen lateral surface (on the left) and fracture surface (on the right). SEM micrographies.

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Several pits with wide eye shape were generated on lateral surface (with a major diameter ofabout 650 µm and minor diameter of about 420 µm), occasionally smaller rounded pits inproximity of them (diameter of about 150 µm) have been observed. Extended crevice
corrosion was observed at the base of the specimen in correspondence to the lower o-ring
position.
3.2 Signal De-noising
Preliminarily a signal de-noising procedure was applied on raw AE data [21]. In particularspurious events, identified by only one sensor or by the guard sensor, were accurately
removed from the population of data. Furthermore, position identification for all AE eventswas carried out through an event localization procedure. All events located outside the gauge
section were removed during data pre-processing. Thus treated signals were subsequently
computed to calculate some derived variables, to get a complete and useful description of thesystem.
3.3 Multidimensional Data Analysis
By using the entirely acquired data-set, it is possible to give information concerning corrosion
phenomena evolution occurring during time. In Figure 2 the cumulative plot of hits during
SCC test is reported.

Figure 2. Cumulative Hits trend

According to cumulative hits plot five regions can be identified and related to specificcorrosion damage stages. A first region was identified as the initiation region, in which
acoustic events are very limited both in occurrence and in amplitude. A second region was
identified as activation region, in which a growing increase in AE occurrence was observed.
When the cumulative trend changed its slope a knee can be identified. This region was called
pre-quiescence. This region was the prelude of a long time period (about one third of the total
duration of the test) during which the amount of acoustic events was very limited and an event
frequency was very low. This region event if acoustically silent was however characterized bya high electrochemical activity [22].

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Then, last region was the re-activation region, in which the acoustic activities re-starts.
The localized corrosion grow up with an increase of pit dimension and crack evolution. In thisphase cracks propagate in large parts of the specimen, increasing progressively theirdimension, until the failure of the sample took place.

Figure 3. a) amplitude (A), b) rise-time (R), c) RA, d) average frequency (AVG) and e) event frequency trends

From the uni-variate trends it is possible to obtain a physical interpretation for each temporal
region. The first region was indeed characterized by a low event frequency (point 1 in Figure3e), at the same time AE hits with low rise-time are not observed (point 1 in Figure 3b).
It seems to be a low-significance region, in which the corrosion phenomena could be related
to initial adsorption of thiosulphate ions and FeS scale formation.
Then, the activation region was characterized by higher waveform frequency (Average
Frequency reach 150kHz value, point 2 in Figure 3d). A slight increase of amplitude
magnitude can be also identified (point 2 in Figure 3a). This region, predominantly
characterized by acoustic events with low and occasionally high intensity events, can beassociated to activation of local surface defects such as pits (low intensity events) [23] andsulphide scale build up and cracking (high intensity events) [24].
Afterwards, a pre-quiescence stage can be clearly identified. This region was characterized byevents with the absence of low-value rise-time (point 3 in Figure 3b) and with a reduction inmagnitude of amplitude (about 40dB, point 3 in Figure 3a). That implies that events with low
RA values, under 200 ms/V, disappeared (point 3, Figure 3c). The simultaneous moderate

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lower average frequency values, suggest a progressive transition from a different AE source
type.
The quiescence region shows a substantial absence of acoustic events. The duration ofquiescence period in some tests lasted over one third of the full test duration. This phase wasmechanically inactive, i.e. characterized by a very low AE activity (point 4, Figure 3e). Thisbehaviour was associated with a change in pit growth mechanisms from thiosulphatepromoted accretion to a load assisted anodic dissolution mechanism [13]. Furthermorehydrogen discharge at the pit tip promoted hydrogen diffusion and trapping in the martensitic
matrix where high stress caused then microplasicity and transgranular cleavage fracture
mechanisms anticipating intergranular brittle fracture. Final phase is the re-activation region,that is the prelude of the final specimen failure. During this phase we can note the reactivation of AE sources. AE hits are characterised mainly by high event frequency (point 5,
Figure 3e), with a relative high mean amplitude (but lower maximum magnitude), (point 5,
Figure 3a). In particular a significant amount of AE signals evidenced an amplitude witha magnitude in the range 40-60 dB. Average frequency and RA values decreased revealingthat the cracking propagation phenomena are shifted firmly in the shear mode [25].

Figure 4. Localization of AE events along specimen length (position is referred from upper specimen side). Bluelines delimit gauge section, green lines delimit fracture region.

Further information can be obtained analysing the AE source colour plot reported in Figure 5.
This plot is a bidimensional representation of three variables: amplitude, position and time.
It shows the localization during time of each acoustic event. Event marker dimension andcolour is proportional to amplitude value of the event. Vertical lines represent the temporalsegmentation as identified in Figure 2, while horizontal lines indicate different specimen
regions. Failure region is evidenced between green lines. Blue lines identify gauge length.
This graph is very useful to visually highlight cluster of events with a higher acoustic
significance, identifying their spatial and temporal localization. By carrying out a comparative
study of data-set values of events in these clusters [13] it was possible to identify them ashydrogen bubbling, FeS build up and cracking, pit initiation and growth, crack opening andfinal fracture (Figure 4).

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3.4 b-value and Ib-value analysis
Data analysis based on b-value and Ib-value could help us to identify the transition froma mechanism to another in a more simple and immediate way. The representation, based on
amplitude values and their magnitude distribution, could give more information that is notpossible to evidence by the conventional amplitude scatter plot. In Figure 5 b and Ib value
trends, calculated separately on the different event populations localised on sample length, areshown.

Figure 5. b-value and Ib-value trends for different specimen spatial regions

Black circles identify all b and Ib values that have been computed on small data population(less than 50 events), that corresponded to a not optimal statistical population of data. Thatleads to consider these points statically not reliable. Plots are related to the different specimen
regions: specimen upper side, gauge section above the fracture, fracture area, gauge section
below the fracture area, specimen lower side, crevice area.
Each location had its own temporal segmentation in order to evidence the b and Ib value trend
during corrosion evolution phenomena on the specific specimen regions. Black vertical lines
indicate temporal segmentation related to each local segment.
A variation in b-value and in Ib-value trends indicates that a change in the corrosion
phenomena on the specimen was occurring. Instead, a constant trend indicates that no
significant variation on corrosion mechanism took place.
Observing the fracture area plot, it is possible to note in particular that quiescence region was
longer that in other sample sections. The re-activation started in a subsequent step respect theother regions. Such evidence could indicate that the evolution of plastic zone at crack tips wasmore intense compared with the other regions. During the reactivation stage the b and Ib trend
evidenced a significant variation. Furthermore, the Ib value was always significantly higher
than the b. Being the Ib-value more sensitive to the fracture mechanisms [16], it could beconsidered a valid precursor for the imminent failure. Afterwards, in correspondence of

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failure events, a significant drop of b and Ib values occurred as a consequence of the presenceof high amplitude value related to failure crack.
Furthermore, a significant increase in b and Ib value trends, can be also observed in the zone e
and f (lower side regions) during the activation phase. This trend could be attribute to H 2
bubble evolution generated in the crevice area (see also Figure 4). These b and Ib peaks canbe discriminated from failure crack propagation considering that these trends are notanticipated by a quiescence stage. Consequently, the quiescence region plays an importantrole to support event discrimination in evolving corrosion phenomena.

4. Conclusions
The different corrosion mechanisms developing on martensitic stainless steel (X12Cr13)
sample during SCC tests in a modified NACE TM0177 solution were evaluated by using
acoustic emission technique.
Acoustic emission data analysis allowed to reveal the different stages of the corrosion
phenomena. The compared analysis between the acoustic waveform parameters and thesource localization allowed to differentiate AE sources.
The use of b and Ib values trends lead to an immediate and simple recognition of the damagetransition on specimen surface. A sudden variation in trend corresponded to a change incorrosion mechanism. A more clear increase in Ib-value than in b-value, anticipated bya quiescent period, indicated the occurrence of a cracking mechanism that lead to the final
failure. These outcomes, as confirmed by further investigation on different kind of stainlesssteel and different test typology, could be of great interest in developing preventative
procedure for failure prediction.
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6.
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P R Rhodes, 'Environment-assisted Cracking of Corrosion-Resistant Alloys in Oil and
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R C Newman and C Healey, 'Stability, Validity and Sensitivity to Input Parameters of
the Slip-Dissolution Model for Stress-corrosion Cracking', Corros. Sci, Vol 49, pp 40404050, 2007.
E M Gutman, 'An Inconsistency in “Film Rupture Model” of Stress Corrosion
Cracking', Corros. Sci, Vol 49, pp 2289-2302, 2007.
M M Hall, 'Critique of the Ford-Andresen Film Rupture model for Acqueous Stress
Corrosion Cracking', Corros. Sci., Vol 51, pp 1103-1106, 2009.
M Ueda, '2006 F.N. Speller Award Lecture: Development of Corrosion-Resistance
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P R Rhodes, L A Skogsberg and R N Tuttle, 'Pushing the Limits of Metals in Corrosive
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S Tsujikawa, A Miyasaka, M Ueda, S Ando, T Shibata, T Haruna, M Katahira,Y Yamane, T Aoki and T Yamada, 'Alternative for Evaluating Sour Gas Resistance of
Low-Alloy Steels and Corrosion-Resistant Alloys', Corrosion, Vol 49, pp 409–419,
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F Zucchi, G Trabanelli and V Grassi, 'Stress Corrosion Cracking of 13% Cr Martensitic
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J Kovac, C Alaux, T J Marrow, E Govekar and A Legat, 'Correlations ofElectrochemical Noise, Acoustic Emission and Complementary Monitoring Techniques
During Intergranular Stress-Corrosion Cracking of Austenitic Stainless Steel', Corros.
Sci., Vol 52, pp 2015–2025, 2010, 1993.
H Shaikh, R Amirthalingam, T Anita, N Sivaibharasi, T Jaykumar, P Manohar, et al.,'Evaluation of Stress Corrosion Cracking Phenomenon in an AISI Type 316LN StainlessSteel Using Acoustic Emission Technique', Corros. Sci., Vol 49, pp 740–765, 2007.
L Calabrese, M Galeano, E Proverbio, D Di Pietro, F Cappuccini and A Donato,'Monitoring of 13% Cr Martensitic Stainless Steel Corrosion in Chloride Solution in
Presence of Thiosulfate by Acoustic Emission Technique', Corrosion Science, in press,
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L Calabrese, L Bonaccorsi, E Proverbio, D Di Pietro and F Cappuccini, 'SCC Damage
Evolution on Martensitic Stainless Steel by Using Acoustic Emission Technique',Corrosion Enginering Science and Technology, Vol 50, pp 364-371, 2015.
S Hainzl and T Fischer, 'Indications for a successively triggered rupture growthunderlying the 2000 earthquake swarm in Vogtland/NW Bohemia', Journal ofGeophysical Research, Vol 107, p 2338, 2002.
J H Kurz, F Finck, C U Grosse and H Reinhardt, 'Stress Drop and Stress Redistribution
in Concrete Quantified Over Time by the b-value Analysis', Structural Health
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G Campanella, L Calabrese and E Proverbio, 'Evaluation of Increasing damage Severityin Concrete Structures by Cluster Analysis of Acoustic Emission Signals', Composites
Part B: Engineering, Vol 43, pp 2676-2686, 2012.
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Acoustic Emissions Accompanying Rock Fracture', Current Science, Vol 89, No 9, pp
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Building Materials, Vol 34, pp 362-371, 2012.
L Calabrese, L Bonaccorsi, M Galeano, E Proverbio, D Di Pietro and F Cappuccini,'Identification of Damage Evolution During SCC on 17-4 PH Stainless Steel by
Combining Electrochemical Noise and Acoustic Emission Techniques', Corrosion
Science, Vol 98, pp 573-584, 2016.
D Nakhaie and M H Moayed, 'Pitting Corrosion of Cold Rolled Solution Treated 17-4
PH Stainless Steel', Corros.Sci., Vol 80, pp 290–298, 2014.
V Smanio, M Fregonese, J Kittel, T Cassagne, F Ropital and B Normand, 'Contributionof acoustic emission to the understanding of sulfide stress cracking of low alloy steels',Corros. Sci. Vol. 53 pp. 3942–3949, 2011.
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Characterization in Metal Plates', Mech. Res. Commun., Vol 38, pp 106-110, 2011.

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

EVALUATION OF DEFORMATION BEHAVIOR IN LPSO-MAGNESIUM
ALLOYS BY AE CLUSTERING AND INVERSE ANALYSIS
Manabu ENOKI1, Yuki MUTO1, Takayuki SHIRAIWA1
1

Department of Materials Engineering, The University of Tokyo; Tokyo, Japan
Phone: +81 3 5841 7126, Fax: +81 3 5841 7181;
e-mail: enoki@rme.mm.t.u-tokyo.ac.jp

AbstractLong-period Stacking ordered (LPSO) Mg alloys were attracted attention due to its high properties. The
materials used in this study were unidirectional solidification (DS) Mg85Y6Zn9, as-cast Mg97Y1Zn2 and as-cast
Mg85Y6Zn9. Compression test and fracture toughness test were conducted with AE measurement. In compression
test of DS Mg85Y6Zn9, kink deformation was generated from the early deformation stage. The number ofmicrocrack was increased from about half of yield stress and it was rapidly increased around the yield point. The
clustering analysis was applied to the extracted AE events. The AE events were divided into two clusters. The
observation results showed that cluster one was considered as the AE events generated by kink deformation andcluster two was considered as the AE events generated by microcrack. The inverse analysis was applied to theextracted AE events. The displacement of height generated by kink deformation was estimated in compression
test. It is considered that the dynamic behavior of deformation and fracture in LPSO-Mg alloys was evaluated by
AE method.
Keywords: Kink deformation, LPSO-Mg alloys, Clustering, Inverse analysis

1. Introduction
A unique deformation mechanism in synchronizes long-period stacking ordered (LPSO)
structures contributes to generate an excellent macroscopic mechanical property [1-6].
Analysis of dynamic deformation mechanism in nanometer scale is expected to develop newmaterials with better performance. Observation by measurement and analysis equipment suchas transmission electron microscope is very useful to understand the deformation mechanism.
However, these analyses are usually applicable to only surface of materials and basically give
static images. Combination with other techniques will be effective to obtain dynamic
mechanism in detail. Acoustic emission (AE) has been used to obtain dynamic information inmechanical deformation which is difficult to be analyzed using other methods.
AE is well known as an elastic wave in materials due to deformation, fracture and so on.
Macroscopic mechanical properties of materials such as strength, elongation, fracture
toughness, fatigue strength and corrosion characteristics are related to dynamic microscopic
behaviors in materials which are mostly sources of AE. One of the advantages of using AE
method is to globally monitor the deformation and fracture behaviors in samples with highsensitivity. Many researches have been done to understand the deformation or fracture
behaviors in various materials including metals, ceramics, polymers and composites byanalysis of AE behaviors. However, electrical or mechanical noises sometime interfere in theeffective AE signals related fracture behaviors. Recently our research group has developed anovel AE analysis system with continuous recording function of AE waveforms in order toavoid noises and extract useful information related to deformation behaviors in materials [7].
Our system also includes advanced waveform analysis methods such as wavelet transformwith high frequency resolution and accurate location algorithm by Akaike information criteria(AIC) picker.

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AE parameters such as amplitude, energy, duration etc. are used to identify AE sources andexplain the dynamic evolution of deformation and fracture behaviors in materials. Also
clustering by AE parameters gives better understanding of aspect of deformation in material
testing. However, it is usually very difficult to identify dislocation movement with continuous
type AE signals. Recently novel cluster analysis of AE signals using frequency components
has been successfully applied to understand the evolution of plastic deformation in Mg alloys[8-9]. In this paper, mechanical properties of LPSO-Mg alloys with different microstructures
were investigated using tensile test, compression test and fracture toughness test. Twomaterials with different composition, Mg97Zn1Y2 and Mg85Zn6Y9, were used for experimentsin this paper. Mg97Zn1Y2 has a volume fraction of α-Mg phase with 75% and 18R LPSO-Mg
phase with 25%, and Mg85Zn6Y9 demonstrates a fully18R LPSO-Ma phase. AE signals during
tensile, compression and fracture toughness tests were recorded and analyzed. Frequency
analysis for each AE event suggested that deformation in α-Mg phase generated a higher
frequency signal and deformation in LPSO-Mg phase a generated a lower frequency one.
Evolution of deformation mechanism during material tests was also analyzed by clustering
techniques using frequency components.

2. Experimental and Analytical Procedures
The materials used in this study were unidirectional solidification (DS) Mg85Y6Zn9, as-cast
Mg97Y1Zn2 and as-cast Mg85Y6Zn9. The surfaces of the materials were observed by ascanning electron microscope (SEM, JSM-6510LA, JEOL). The parallel section surface ofspecimens of unidirectional solidification Mg85Y6Zn9 was shown in Fig. 1(a), vertical section
was shown in Fig. 1(b), as-cast Mg97Y1Zn2 was shown in Fig. 1(c) and as-cast Mg85Y6Zn9
was shown in Fig. 1(d). The LPSO phase in DS Mg85Y6Zn9 was grown to the parallel
direction to solidification direction. DS Mg85Y6Zn9 and as-cast Mg85Y6Zn9 had LPSO single
phase, as-cast Mg97Y1Zn2 had LPSO phase and α-Mg phase. The volume fraction of LPSO
phase was about 25% and α-Mg phase was about 75% in as-cast Mg97Y1Zn2. The DS
Mg85Y6Zn9 specimens of compression test were prepared. The size of compression test
specimen was 5×5×5 mm with cube shape. The as-cast Mg97Y1Zn2 and as-cast Mg85Y6Zn9
specimens of fracture toughness test were prepared.

Figure 1. Surface of specimen. : (a) Mg97Y1Zn2, DS, parallel section, (b) Mg97Y1Zn2, DS,vertical section, (c) Mg97Y1Zn2, as-cast, (d) Mg85Y6Zn9, as-cast.

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Compression test was conducted at constant crosshead speed of 5×10-4 mm/s by a universal
testing machine (AG-5000C, Shimazu Co.) (Fig. 2(a)). The load direction was parallel tosolidification direction. The surface observation was conducted by a video microscope (VHX600, KEYENCE Co.) or high-speed camera (HPV-X2, Shimazu Co., Kirana, Specialised
Imaging Ltd., Phantom Miro M110, vision Research Co.) during compression test. The
maximum frame rate of high-speed camera was 5 Mfps. The surface observation was
performed by SEM and a laser microscope (LEXT, OLYMPUS Co.) after the compression
test. The height and size of kink deformation were measured by a laser microscope. Fourpoint-bending tests were carried out at a constant displacement rate of 8.33×10-3 mm/s withAE measurement. During the tests, the crack path was observed by microscope. The positionsof AE sensors were sides of specimens (Fig. 2(b)). AE sensors were AE254SMH177 (Fujiceramics, Japan). Continuous Waveform Memory (CWM), which was developed by ourgroup, was used to perform AE measurement. The high-pass filter was set at 100 kHz. The
RMS voltage was measured with 200 ms time constant.
AE signal was measured by continuous wave memory (CWM) during both tests. The
sampling rate was 10 MHz, and the range of measurement was 5 V. Two AE sensors (M304A,
Fuji Ceramics Co.) were used in compression test. The AE signal was inputted for the triggerof the high-speed camera in order to observe the generation of kink deformation. The highpass filter of 100 kHz was applied to the AE signal for removing mechanical noise. AE eventswere extracted from filtered AE wave measured in each tests. The energy spectrum of the AE
events was calculated by fast Fourier transform (FFT). The FFT length was 512 samples near
the rising point of each AE events. The energy values of each 19.55 kHz were extracted andSavizky-Golay filter was applied to these energy values for smoothing the energy spectrum.
The all energy values in the filtered spectrum were divided by the maximum energy values offiltered spectrum in each AE events to reduce the effect of AE amplitude. Normalizing wasapplied to the all energy values in the spectrum.
Principal component analysis (PCA) was conducted to the all energy values in the spectrum.
Clustering analysis was applied by using calculated several principal components of each AE
events. K-means method was used for clustering analysis. The value of k in compression test
was decided by average silhouette coefficient. Average silhouette coefficient was calculated.
High average silhouette coefficient means that the data of same cluster was gathered at shortdistance and different clusters were far away with each other. The k value at maximum
average silhouette coefficient was used for analysis.

Figure 2. Schematic figure of system. : (a) compression test, (b) fracture toughness test.

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3. Results and Discussion
The specimens were compressed to eleven different stress levels in compression tests of DS
specimens. The part of generation of kink deformation showed low brightness (Fig. 3). Kink
deformation fraction at each stress levels was calculated by using this difference of brightness.
Kink deformation was started to generate from low stress level and this fraction rapidly
increased near the yield point. The specimen surface was divided roughly, and the number ofgrains introduced kink deformation was calculated. The number of grain introduced kinkdeformation rapidly increased in low stress levels and stabilized after rapid rise. First kinkdeformation generated at each grain in early deformation stage represented by white-colored,
and further kink deformation generated at each grain already introduced kink deformation inlater deformation stage represented by grey-colored.
In the compression tests, the cumulative contribution rate was over 0.7 where the number ofprincipal components was 3. Average silhouette coefficient was calculated at k value fromtwo to seven in this principal components, and it showed maximum value at k = 2. The
clustering analysis was applied to the AE events in compression test at k = 2. Fig. 4(a) shows
the average energy spectrum of each cluster. The energy of cluster one showed high value at
low frequency, and that of cluster two showed high value at high frequency. The cumulative
number of AE events in each cluster was calculated (Fig. 4(b)). AE events of cluster one were
measured from low stress level and dominant under 100 MPa. On the other hand, AE eventsof cluster two were increased around 100 MPa, and the specimen was yielded at the time of
rapid increase of cluster two.

Figure 3. Surface observation of compression test. : (a) 50 MPa, (b) 100MPa, (c) 200MPa, (d)200 MPa and 200 s.

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Figure 4. (a) Energy spectrum of each cluster , and (b) cumulative AE event of each cluster ofcompression test.
The discussion about the classification of AE sources by clustering analysis is effective forunderstanding the deformation behavior. The kink fraction behavior coincided well with the
AE events behavior of cluster one in early deformation stage, and it did not corresponded with
the AE events behavior of cluster one at all in later deformation stage. The kink fraction was
rapidly increased and few AE events of cluster one were measured in later deformation stage.
On the other hand, the number of grains introduced kink deformation accorded with the AE
events behavior of cluster one through the compression test. Our previous papers
demonstrated that the first twin deformations in each grains emitted high AE energy, and thetwin deformations after the first ones emitted low AE energy in the tensile test of rolled AZ31
[10-15]. As well as twin deformation, it was suggested that the kink deformations in early
deformation stage emitted high AE energy and the kink deformations in later deformation
stage emitted low AE energy. Therefore, the AE source of cluster one was considered as the
kink deformation generated in early deformation stage. The microcrack behavior coincidedwith the AE events behavior of cluster two. The AE source of cluster two was considered as
the microcrack. AE event generated by kink deformation tended to have high energy at highfrequency and AE event generated by microcrack tended to have high energy at low
frequency.

5. Conclusion
In this study, compression test and fracture toughness test were conducted for theunderstanding of deformation behavior in DS LPSO-Mg alloys. The following conclusions
were drawn.
(1) Kink deformation was generated from the early deformation stage. The number ofmicrocrack was increased from about half of yield stress and the rapid increase of microcrack
caused the yield in compression test of DS Mg85Y6Zn9.
(2) The classification of AE sources by clustering analysis is effective for the evaluation of
the dynamic behavior of kink deformation and microcrack.

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Acknowledgement
This work was supported by a Grant-in-Aid for Scientific Research on Innovative Areas,"Synchronized Long-Period Stacking Ordered Structure", from the Ministry of Education,
Culture, Sports, Science and Technology, Japan (No.23109001). The Mg alloy samples usedin the present study were supplied by Kumamoto University and Osaka University.
References
1. Y. Kawamura, K. Hayashi, A. Inoue and T. Masumoto: Mater. Trans., 42 (2001) 11721176.
2. Inoue, M. Matsushita, Y. Kawamura, K. Amiya, K. Hayashi and J. Koike: Mater. Trans.
43 (2002) 580-584.
3. T. Itoi, T. Semiya, Y. Kawamura and M. Hirohashi: Scr. Mater. 51 (2004) 107-111.
4. E. Abe, Y. Kawamura, K. Hayashi and A. Inoue: Acta Mater. 50 (2002) 3845-3857.
5. M. Matsuda, S. Ii, Y. Kawamura, Y. Ikuhara and M. Nishida: Mater. Sci. Eng. A, 393(2005) 269-274.
6. X. H. Shao, Z. Q. Yang and X. L. Ma: Acta Mater. 58 (2010) 4760-4771.
7. K. Ito and M. Enoki: Mater. Trans. 48 (2007) 1221-1226.
8. E. Pomponi and A. Vinodradov: Mechan. Systems Signal Process. 40 (2013) 791-804.
9. A. Vinogradov, D. Orlov, A. Danyuk and Y. Estrin: Mater. Sci. Eng. A 621 (2015) 243251.
10. Y.P. Li and M. Enoki: Mater. Trans. 48 (2007) 1215-1220.
11. Y.P. Li and M. Enoki: Mater. Trans. 48 (2007) 2343-2348.
12. Y.P. Li and M. Enoki: Mater. Trans. 49 (2008) 1800-1805.
13. Y.P. Li and M. Enoki: Mater. Sci. Eng. A 536 (2012) 8-13.
14. T. Yasutomi and M. Enoki: Mater. Trans. 53(2012) 1611-1616.
15. Y. Muto, K. Matsumoto, T. Shiraiwa and M. Enoki: J. Japan Inst. Metals, 78 (2014) 381387.

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

MEASUREMENT OF ELECTROMAGNETIC EMISSION AS A TOOLTO STUDY FRACTURE PROCESSES OF CARBON FIBRE
REINFORCED POLYMERS
Sebastian O. GADE, Philipp POTSTADA, Benjamin B ALACA, Markus G. R. SAUSE
Experimental Physics II, University of Augsburg; Augsburg, Germany
Phone: +49 821 598 3407; e-mail: sebastian.gade@physik.uni-augsburg.de
Abstract
In addition to acoustic emission (AE), fracture of solid materials is often accompanied by electromagnetic
emission (EME). These electromagnetic fields arise due to the separation, displacement or movement of charges
and their temporal characteristics and spatial distributions are closely connected to the dynamics of the fracture
processes and the material’s physical properties. The measurement of EME provides a method to directly accessinformation of microscopic failure, such as crack velocity, crack duration, crack wall vibration frequencies orcrack orientation. The use of capacitive sensors to detect EME offers a linear transfer function for the frequency
range of interest. Measurements of EME and AE were performed during interlaminar crack growth ofunidirectional carbon fibre reinforced polymers (CFRP) under mode I and mode II load as well as during fractureof cross-plied CFRP specimens under flexural and tensile loading. Different EME signals are observed andattempts are being made to attribute certain EME signal characteristics to specific fracture processes.
Keywords: electromagnetic emission, acoustic emission, fibre reinforced polymers, fracture mechanics

1. Introduction

Carbon fibre reinforced polymers (CFRP) offer a variety of unique physical properties whichmake these materials favourable for various applications. Especially their mechanical properties
make these materials attractive for engineering purposes. Besides their wide range of application,e.g. in aerospace industry, CFRP materials are still subject of research. Due to their complex
microstructure and anisotropic properties their failure behaviour is complicated and their ultimate
limits are hard to predict. One possible method to study the fracture processes is the acquisition
and analysis of signals generated during these processes. These signals can be acoustic emission(AE) signals, measured as transient displacements at the surface of the material, andelectromagnetic emission (EME) signals, measured in the vicinity of the fracture. Both types ofsignals contain information about their sources, i.e. the fracture processes. The study of AE
generated by fracture is a well-established method but is nevertheless still subject to current
research. On the other hand, the study of EME generated by fracture of composites, though there
has been some research in the past [1-3], has not been progressed this far. EME has been reported
for various materials and failure modes [2,4-7] and numerous theories for the origin of theseelectromagnetic fields were proposed [8-10]. The source mechanisms may well be completely
different for different materials and failure modes, but on a fundamental basis electromagnetic
fields are generated by charges and their dynamics.
Electromagnetic emission, as referred to in this text, is described in terms of electric fields inthe frequency range of kHz to MHz, since these are directly measured as voltage signals by
our capacitive sensors [11].
The different components of CFRP and their fracture processes are likely to generate different
kinds of EME signals (in fact, different EME signal characteristics for the individualcomponents have already been reported [1,12]). Furthermore, in contrast to the emitted

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acoustic signals, the EME signals can be measured almost undisturbed, i.e. are not influenced
by the specimen’s material and geometry. This advantage of EME over AE may prove to beparticularly beneficial in highly anisotropic composite materials.
In the context of a recent research project, we studied EME generation of CFRP materials andits components. In this study we present EME signals generated by interlaminar crack growthof unidirectional CFRP under mode I and mode II load as well as EME signals generated byfracture of cross-ply CFRP specimens under flexural and tensile loading. A discussion of theacquired EME signals is made with respect to the differences when comparing signals
generated by different fracture processes and when comparing the EME signals to theaccompanying AE signals.

2. Experimental set-up and results
2.1 Testing and acquisition set-ups
In order to detect EME signals generated by different failure modes in CFRP a variety ofdifferent testing procedures were conducted to induce CFRP fracture under various loading
conditions. CFRP specimens of different ply stacking sequences were fabricated fromunidirectional Sigrafil CE1250-230-39 carbon/epoxy prepreg laminate.
Most of the applied test fixtures, with the exception of the tensile test fixture presented in
section 2.2.4, were manufactured from non-conductive materials to reduce interfering
influences of conductive materials on the EME acquisition. PMMA was used for most parts
while supports were made from PVC and bolts were made from POM. The mechanical load is
introduced using a tube of pultruded glass-fibre reinforced plastic (GFRP).
Two different types of acoustic emission sensors were used, a KRBBN-PC point contact
sensor and a multi resonant, “wideband differential” (WD) sensor (Physical Acoustics). The
point contact sensor offers an almost linear transfer function and a greater band width but
suffers from a weaker sensitivity when compared to the WD sensor. For the DCB and ENF
tests measurable EME signals could only be detected for macroscopic crack propagation.
Thus, the KRBBN-PC sensor was chosen to measure the accompanying, high amplitude
acoustic emission signals. During fracture of CFRP specimens during flexure and tensile
testing EME signals with a wide range of amplitudes could be measured. Therefore, in order
to measure the accompanying AE signals, the more sensitive WD sensor was chosen. For both
AE sensor types the AE signals were amplified by a 2/4/6 pre-amplifier without use of aninternal bandpass filter. For distinguishability AE signals recorded with the KRBBN-PC
sensor are plotted in blue throughout this text while AE signals recorded with the WD sensor
are plotted in pink.
For the EME signal detection two amplification stages were used. The first preamplifier,which is located inside the shielding enclosure, is a junction field effect transistor (n-channel
JFET 2SK932-22) in a common source circuit with a 10 MΩ input resistor, with a total
amplification of 20 dB. This preamplifier is directly connected to the wire forming thecapacitive sensor. The voltage signal then is further amplified by a commercially available
low frequency amplifier (UBBV-NF35, Aaronia AG). The total gain was adjustable witha potential divider between the two amplifiers. A PCI-2 acquisition card (Mistras corporation,
software: AEwin) was used to record the signals. For flexural an tensile testing, AE and EME
signals were acquired in synchronized mode, while for mode I and mode II testing, AE andEME signals were triggered independently following the settings of Table 1.
Since the field distributions of the EME generated by fracture have been shown to exhibit
pronounced directional characteristics [7,9,13] the positions of the EME sensors were chosen
accordingly.

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All set-ups were electrically shielded by a grounded metallic shielding enclosure. A detailed
description of this acquisition circuit and the shielding measures can be found in [12]. Table 1
summarizes all acquisition parameters used.
Table. 1. Parameters used for EME and AE acquisition
Set-up

DCB

ENF

Flexural

Amplif. AE
[dB]
20
20
40
Amplif. EME
[dB]
40
40
40
Threshold AE[dB AE ]40
40
30
Threshold EME[dB AE ]28
27
35
Sample rate AE[MS/s]
10
10
10
Sample rate EME [MS/s]
5210Band pass*[kHz]
1-30001-30001-3000PDT AE[µs]
1010synchronized
HDT AE[µs]
8080
synchronized
HLT AE
[µs]
300
300
300
PDT EME[µs]
20
20synchronized
HDT EME
[µs]
1200
1200
synchronized
HLT EME
[µs]
200
200
300
( * band pass of acquisition board, AE sensors exhibit smaller band widths)

Tensile
40
46
35
38
10
10
1-3000synchronizedsynchronized300
synchronizedsynchronized300

2.2. Mode I - DCB test
2.2.1 Set-up
Figure 1 shows the DCB test set-up which was manufactured to conform with therequirements of ASTM D5528. CFRP specimens with a [0 7 ] sym ply stacking sequence anddimensions of 155 mm x 25 mm x 3 mm were used. As crack initiation side an ETFE insert
of 60 mm length was embedded at the centre of the laminate plies. PMMA loading blocks
were added using an adhesive. For the detection of the acoustic emission signals, a point
contact sensor is mounted on top of the specimen. A copper wire attached to the upper face ofthe specimen is used as EME sensor.

Fig. 1. Schematic of DCB test set-up.

The load was applied displacement controlled by an universal testing machine (Zwick ZT 5.0)
with a constant cross head velocity of 2 mm/min and is measured by a 5 kN Xforce HP loadcell. The tests were conducted in two stages. The first stage was carried out to induce
a precrack to the specimen, starting from the ETFE insert. In the second stage the precrack
then serves as a natural crack starter, when the crack slowly propagates along the midplane of
the specimen.

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2.2.2 Results
Figure 2 shows exemplary EME and AE signals generated during the first testing stage, i.e.
during the initiation and propagation of the first crack starting at the ETFE insert. The
obtained AE and EME signals show some variation in amplitude and characteristic which is
attributed to variations in crack length and crack propagation. The EME signals all start witha fast rise in amplitude which then slowly decreases, superimposed by oscillations. The rise isattributed to the crack propagation where charges are separated while the characteristic
decrease results from the 1kHz high pass filter of the used acquisition board. The oscillations
are attributed to vibrations of the electrified crack surfaces and the dynamics of the specimen
and EME sensor during and after crack propagation.
Applying a 4kHz high pass filter (6th order Butterworth) removes most of the low frequency
signal components attributed to the charges resulting from crack extension, leaving only theoscillations attributed to crack wall vibration and specimen movement (see figure 2).
A comparison to the corresponding AE signals reveals some similarities between EME andAE signals like similar rise times, frequencies and signal duration.

Fig. 2. AE and EME signals recorded during precracking stage of DCB testing.

In the second stage, starting at the precrack, the crack slowly begins to propagate within thespecimen. During this phase various EME signals could be measured. Three basic EME signal
characteristics were observed. The first kind of EME signal consists of a very fast rise anda slow decay representing the high pass filter of the acquisition board. This signal shape is the
response function of our acquisition set-up to a step-function as input voltage. Figure 3 (left)
shows an exemplary EME signal that consists of several signals of this type occurring in rapid
succession. EME signals with these characteristics are also observed during fracture of carbon
fibres [1,12] where the fast signal rise is attributed to the very short crack length (<10 µm)
and therefore very short crack propagation times (order of nanoseconds [14]). The point
contact AE sensor (with 20dB amplification) did not detect any AE signals that correlate tothis type of EME signals. Only by the more sensitive WD AE sensor with 40 dB amplification
corresponding AE signals were detected (see figure 3, left).

Fig. 3. AE and EME signals recorded during crack propagation stage of DCB testing.

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A second type of EME signal is shown in figure 3 (middle). It resembles the precrack signals withsuperimposed oscillations, but exhibits smaller amplitudes for both, EME and AE signals.
Figure 3 (right) shows a third kind of observed EME signal which exhibits a slower rise than signal
type one. No superimposed oscillations are measured with this type of signal. For the remaining
EME signals the shape could be considered as combinations of these three base types.
2.3 Mode II - ENF tests
2.3.1 Set-up
The ENF test set-up was manufactured according to ASTM D7905. A schematic of the set-up
is shown in figure 4. CFRP specimens with a [0 8 ] sym ply stacking sequence and dimensions of
180 mm x 25 mm x 3.5 mm were used. As crack initiation side, an ETFE insert of 45 mm
length was embedded at one side of the specimens. A point contact sensor was placed at thesurface of the specimens to detect the AE signals. A pair of copper wires is placed near thecrack tip, consisting of a "detecting" wire (attached to the acquisition chain) and a grounded
wire, thus forming a capacitor type EME sensor.

Fig. 4. Schematic of ENF test set-up.

As for the DCB tests, the ENF tests are carried out in two stages, with the load applied
displacement controlled with a cross head velocity of 2 mm/min. The first stage only induces
a precrack while in the second stage the crack propagation occurs. Here, between the twostages, the specimen position is adjusted to account for the new crack tip position and tomatch with the position of the EME sensor.
2.3.2 Results
The EME signals recorded during the precracking stages of the DCB and ENF tests arecomparable in terms of signal characteristics and rise times. As for the mode I tests, the firstrise of the EME signals are considered to contain information about the crack propagation.
When the crack stops its propagation, no new charges are generated and the signal amplitude
starts to decrease. Superimposed oscillations are attributed to microscopic and macroscopic
movement of the charged crack surfaces. Exemplary EME signals are shown in figure 5.

Fig. 5. EME signals recorded during precracking stage of ENF testing.

In contrast to the DCB tests, the crack propagation in the second stage is not entirely stable.
Here, starting at the precrack, the crack initially propagates slowly before unstable crack

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propagation occurs. This unstable crack growth generates strong EME signals which arecomparable to the ones recorded during the first, precracking stage. Figure 6 (left)
exemplarily shows an EME signal of this kind. Another kind, which was also observed during
the DCB tests, is shown in figure 6 (middle). Signals recorded after this unstable crack
propagation are considered to be generated by secondary processes since the macroscopic
crack tip already is out of the detection range of the EME sensor. An example of such EME
signals is shown in figure 6 (right). The kind of EME signal with the step-function like rise inamplitude was not observed during the ENF tests.
In contrast to the distinct and repeatable EME signal types seen in testing of polymers [13] orcarbon fibres [12] the EME signals of composite failure tend to cover a broad spectrum ofdifferent signatures reflecting the complexity of the fracture process seen in composite
materials.

Fig. 6. EME signals recorded during crack propagation stage of ENF testing.

2.4 Flexure test
2.4.1 Set-up
On the basis of DIN EN ISO 14125 a three point bending test set-up was manufactured toinduce fracture of cross-ply CFRP specimens. These tests were conducted on specimens withdifferent ply stacking sequences to obtain a variety of failure modes and crack surface
orientations. Stacking sequences of [0 2 ,90 3 ] sym , [0,90 2 ,0,90] sym and [0 2 ,90,0,90] sym werechosen. The specimens’ dimensions are 95 mm x 15 mm x 2.2 mm. The acoustic emission
signals were detected at the specimen surface by an attached WD sensor. A pair of copper
wires, placed near the region of the load nose, serves as EME sensor. The load was applied
displacement controlled, with a cross head velocity of 1mm/min.

2.4.2 Results

Fig. 7. Schematic of flexure test set-up.

The fracture of the CFRP specimens during the flexure test progresses in multiple steps.
Initial failure occurs in the upmost layer of the specimens directly beneath the load nose.
Here, compressive failure of the 0°-layer occurs. During this first phase of specimen failure
multiple EME signals could be measured. Figure 8 (left) shows an exemplary EME and AE
signal pair of this initial failure.

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Fig. 8. AE and EME signals recorded during flexure tests. Left: Signals from initial failure of first layer. Middle and right:
Signals from final failure (Middle: signal exceeding detection range of amplifier results in signal saturation).

Figures 8 (middle and right) show AE and EME signals recorded during the final failure of
the specimens. The stronger signals (figure 8, middle) may be attributed to the occurrence oflarger cracks, e.g. inter-ply delamination. Other signals, as exemplarily shown in figure 8(right), exhibit smaller amplitudes for both, AE and EME, signals and therefore are attributed
to weaker source mechanisms.
All measured EME signals recorded during the flexure tests exhibit a similar characteristic
shape. An initial rise in amplitude is followed by a slower decrease. The time constant of thedecrease is caused by the 1 kHz high pass filter of our acquisition board. However, the risetimes are assumed to correlate with crack propagation times, which are measured in the orderof 10-6 s to 10-4 s.
2.5 Tensile tests
2.5.1 Set-up
The tensile tests were conducted in accordance with DIN EN ISO 527-4. The tests were
performed in a universal testing machine Zwick 250 with hydraulic grips. The load is applied
displacement controlled with a velocity of 1 mm/min. Here, a test fixture made from nonconductive materials was not feasible to apply. To eliminate influences of the testing machine
and the metallic components of the test fixture on the detected EME signals, a small shielding
enclosure was chosen instead, to only contain parts of the specimen, the AE and EME sensors
and one EME pre-amplifier.
CFRP specimens with a [0,90,0,90] sym stacking sequence were used. At both ends the
specimens were reinforced with end tabs made of cross ply laminates. The specimens
dimensions were 178mm x 15 mm. The specimen thickness was 1 mm at the centre and 4 mm
at the reinforced ends. To ensure the induced failure to occur at the specimen centre (where
the EME sensor is located) additional notches of 2.5 mm length were added (see figure 9).

Fig. 9. Schematic of tensile test set-up. Left: side view of set-up, with shielding enclosure. Right: front view of specimen.

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For the AE detection a WD sensor was attached to the specimen surface. Two loops of copper
wire, forming a kind of capacitor, were placed around the specimens near the centre of thespecimen as depicted in figure 9.
2.5.2 Results
Although the partially conducting CFRP specimens penetrate the shielding enclosure andtherefore weaken its shielding effectiveness [12], numerous EME signals could be detectedduring the fracture tests. Due to the chosen cross-ply stacking sequence different failuremodes occur during testing, i.e. fibre fracture, matrix fracture and various types of interfacial
failure. Microscopic failure arises long before the final failure of the specimens. Figure 10
shows AE and EME signals generated during the early stage of loading.

Fig. 10. AE and EME signals recorded before final failure of specimens during tensile tests.

When final failure of the specimens occurs, numerous AE and EME signal pairs weredetected. These signals exhibit high amplitudes which most of the times exceed the maximum
input voltage of the acquisition equipment which results in saturated signals. Figure 11 shows
exemplary AE and EME signals recorded during this final failure. As seen from the voltage
scale the EME signals are also extraordinary strong, therefore indicating a general relationshipbetween the occurrence of macroscopic damage and strong EME release.

Fig. 11. AE and EME signals recorded during final failure of specimens during tensile tests (partially clipped).

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3. Summary and Conclusion
EME signals could be detected for all tests. Nevertheless, the total amount of detected EME
signals was small when compared to the amount of detected AE signals. This discrepancy ismainly attributed to the weaker sensitivity of the EME acquisition set-up which depends on
various factors but is in particular limited by the considerable dependence of the EME signal
strength on source-sensor-distance.
Both methods, AE and EME, are able to detect the formation of microscopic damage in fibre
reinforced composites. For EME the similarity in the band width is attributed to the vibrationsof newly generated crack surfaces, which also forms the source for acoustic emission.
The EME signal components not accessible to AE may offer further applications. Most EME
theories assume some kind of electrification of the new crack surfaces, e.g. via charge
separation. The build-up of this charge distribution continues as long as the crack propagates.
Therefore, the EME signal rise times are assumed to correlate with crack propagation times,
which should allow for the derivation of the crack tip position as function of time. For known
maximum crack extensions this even allows for the derivation of the time dependent crack
propagation velocities.
For the acoustic emission signals, the analysis of its frequency components, e.g. by applying
pattern recognition techniques, to identify the source mechanisms of the signals in composite
materials is a well-established method. Similar techniques should also be applicable to EME
signals but will require a higher signal yield. In this regard, a combined approach of AE and
EME analysis seems to be the most promising.
The evaluation of the amplitudes of EME signals proves to be more difficult since thedetected EME amplitude not only depends on the source strength but also substantiallydepends on source-sensor-distance as well as source-sensor orientation.
The applied acquisition set-up also demonstrates the close similarity of information of the AE
and EME signals. However, EME acquisition set-ups using capacitive sensors can easily bebuilt to exhibit almost perfectly flat transfer functions. Herein lies a huge advantage of the
EME detection, as acoustic wave propagation is shaped by the propagation medium andpropagation path.
Acknowledgements
This research is funded by the DFG as part of the project ”Relation ofelectromagnetic and acoustic emission to temporal and spatial crack motion on a microscopic
scale in polymers and carbon fibres”.
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S Winkler, ‚Tear detector for mechanical loading test sample - uses capacitive sensor
coupled via impedance converter to electronic evaluation circuit‘ patent DE 4004171
C2, 1993
M G R Sause, ‚In-situ monitoring of fiber-reinforced composites‘ SpringerInternational, Cham, 2016
S O Gade, U Weiss, M A Peter, M G R Sause, ‚Relation of Electromagnetic Emission
and Crack Dynamics in Epoxy Resin Materials‘ J. Nondestruct. Eval. 4 711-23, 2014
M G R Sause, S Richler, ‚Finite Element Modelling of Cracks as Acoustic Emission
Sources‘ J Nondestruct Eval 34 1-13, 2015

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

LOCALIZATION OF COMPRESSED AIR LEAKS IN INDUSTRIAL
ENVIRONMENTS USING SERVICE ROBOTS WITH ULTRASONICMICROPHONES
Thomas GUENTHER 1, Andreas KROLL 1
1
Department of Measurement and Control, University of Kassel; Kassel, Germany
Phone: +49 561 804 2758, Fax: +49 561 804 2847; e-mail: thomas.guenther@mrt.uni-kassel.de,andreas.kroll@mrt.uni-kassel.de

Abstract Compressed air is a widespread but costly energy carrier. Leaks account for 10compressed air consumption in production facilities and their removal offers high potential for cost reduction.
The turbulent flow from a leak causes broadband acoustic emissions. These are exploited for leak detection using
a narrowband ultrasonic microphone that is insensitive to audible noise. A parabolic mirror or an acoustic horn isutilized to enhance the directivity and the received signal power of the microphone. The microphone is mounted
on mobile service robots. In the project Robot}air{ an automated guided vehicle (AGV) and a remotely
controlled micro aerial vehicle (MAV) were used for robotized inspection of production facilities. In order todetect leaks, predefined areas are scanned. A leak is detected and localized based on the sensed peak amplitudeof the ultrasound signal. The corresponding pose of the sensor facing the leak is determined based on theselflocalization of the robot. Leak localization is carried out by triangulation of two sensor poses. Tests wereconducted in an automobile production facility to evaluate the performance of the system. A leak was placed in
an assembly line and multiple measurements were taken from two positions with the AGV and the MAV. The
leaks were successfully detected and localized.
Keywords: air-coupled ultrasound, compressed air, leak detection, leak localization, mobile service robots

1. Introduction
Compressed air is used as an energy carrier in the manufacturing industry. The conversion
from primary energy is costly and losses should be avoided. The losses due to leakage range
from 10project Robot}air{ [3] was to automate routine inspections and gain access to installations,which are otherwise hard to reach. This paper addresses the use of mobile robotic sensing
systems for detecting, localizing and assessing compressed air leaks. An automated guided
vehicle (AGV) with a scanning ultrasonic measuring unit is used for autonomous leakdetection and localization in predefined areas in order to relieve factory workers of themonotonous task of manual leak search. Prior knowledge of the compressed air system and ofcommon leak positions, such as couplings, fittings, joints, etc. [2], is used to narrow down theinspection targets in order to reduce the time demand for an inspection tour. A micro aerial
vehicle (MAV) is used to inspect areas, which are hard or impossible to access by employees
or by the AGV. It is operated manually, due to the often complex structure of the indoor
airspace in industrial environments, e. g. hanging wires or narrow pipes.

2. Leak detection
Compressed air leaks can be detected manually with various methods. Leak detection spray isused to visualize the escaping air [2]. IR thermography can be used to visualize and detect thetemperature gradient between the leaking component, which is cooled down by

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the decompression of the air, and its surroundings. References [4], [5] and [6] provide fora more detailed overview on approaches.
Acoustic emissions play a main role in compressed air leak detection [7]. The decompressionof the compressed air causes turbulences near the leak. Pressure and density variations emerge
from the turbulent flow as broadband acoustic emissions, which can be sensed from a distanceusing microphones. In industrial environments ultrasonic transducers are widely used tosuppress noise in the audible frequency band. Own measurements showed little ultrasonicnoise for most machinery except for presses, air blowers and active ultrasonic devices.
For manual detection hand-held devices are used for leak detection [8].
In the recent years sensor arrays have been used for the detection of acoustic emissions ofcompressed air leaks. With microphone arrays the angle of arrival of a signal can be estimatedwithout sensor movement. Signal processing techniques can also be used to separate thesignal of the leak from interfering signals of other sources and additive noise. A handheld
device with three ultrasonic microphones was used to estimate the direction of arrival using
the time difference of arrival [9]. Simulations for an array with 32 randomly placed ultrasonicmicrophones were conducted in a scenario with multiple leaks [10]. An acoustic camera,
a combination of a microphone array and a camera, was used to localize and visualize
compressed air leaks [11].
Compressed air leak detection with mobile robots is rarely addressed in literature. A similar
leak detection system using an AGV and a single directed sensor for remote gas detection andlocalization was addressed in [6, 12]. In [6] a scanning method (“dynamic raster scan”) isused to optimize scanning time and coverage of the scanned area. In [12] a combination oftriangulation and maximum method is used to reposition the AGV to improve the accuracy
and precision of the estimated leak position. The intelligent automatic measuring system used
for the tests in this paper was investigated under laboratory conditions in [13]. Methods forquick scanning, leak detection and estimation of the direction of the leak were introduced. The
paper showed the feasibility of using the system and discussed boundary conditions, which
influence the accuracy of the methods.

3. Robotic system
The robotic system consists of an AGV, a MAV (Figure 1) and a movable control station.
All subsystems communicate using 5 GHz WLAN. They are controlled using the ROS (Robot
Operating System) framework.
3.1 Automated guided vehicle
The AGV is a vehicle custom made by S-Elektronik GmbH & Co. KG in cooperation with
Fraunhofer Institute for Communication, Information Processing and Ergonomics. It is
maneuvered by two diagonal center pivot plate drives. 2D mapping and self-localization are
realized by diagonally mounted laser scanners SICK S300. On top of the rear side of the AGV
is a landing platform for the MAV. A stereo camera system on a pan-tilt-unit (PTU) Schunk
PW 70, which is used to track the position of the MAV, is mounted in the middle top side of
the AGV. An intelligent measuring system is mounted in the front of the AGV (Figure 2, left).
It consists of a measurement device carrier with several sensors, a PTU and a computer.
The sensor of interest in this contribution is a piezoelectric microphone with a parabolic
mirror produced by SONOTEC Ultraschallsensorik Halle GmbH to focus the incoming
ultrasonic wave towards the sensor. This results in an increased range and a higher directivityof the sensor. The microphone operates in a narrow frequency band with a resonancefrequency of 40 kHz. The ultrasonic signal is converted to an audible signal by a frequency

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mixer, which is recorded with 44100 samples per second and 16 bit resolution using an
uncalibrated sound card. The recorded signal is proportional to the incoming sound pressure.
A camera is mounted behind the parabolic mirror for documentation purposes. The sensors areplaced on the measurement device carrier, which is mounted on a PTU Schunk PW 90. Its twoaxes have a positioning reproducibility of 0.004°. The PTU enables scanning target areas anddirecting the sensors independent of the AGV. The geometric transformations between thelocal coordinate systems of the AGV, the PTU and the sensor result directly from thetechnical design of the devices and the mechanical assembly, and were calibrated manually.

Figure 1. Robotic system consisting of AGV with intelligent measuring system (front)
and tracking system (middle), and MAV with ultrasound measuring module (top back)Figure 2. Intelligent measuring system for the AGV (left); MAV with payload for leakdetection (right)

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3.2 Micro aerial vehicle
The MAV is an Aibot X6 multicopter (Figure 2, right) made by Aibotix GmbH. The MAV wasequipped with six colored LED markers for tracking with the stereo camera system of the AGV.
The payload for compressed air leak detection consists of an ultrasound microphone, a camera
and a light-weight computer. Since turbulence originating from the MAV's rotors is emitting noise
similar to compressed air leaks, an acoustic horn is used to shield the microphone, a model similar
to the one used on the AGV, against this noise. The sensor is placed on the rim of the frame facing
away from the rotor turbulence to reduce the influence of the noise further. The signal acquisitionis similar to the one used for the AGV. The camera points to the same the measuring direction as
the microphone. The sensor pose was measured manually with respect to the pose of the MAV.

4. Leak Detection and Localization
4.1 Automated inspection with AGV
For the AGV an automated leak localization method was developed. In the beginning the inspection
route is taught-in manually using the inspector’s knowledge of the compressed air network and of
fixed and temporary obstacles in the inspection area. Measurement positions are chosen anda measurement area is defined using spherical coordinates with the PTU as origin. The route is
automatically planned and optimized for short, collision-free paths. The inspection area is scannedline by line from each specified measurement point. The level of the ultrasonic signal is recorded asa function of the PTU pan angle and the maximum is calculated for each line. If the detection
criterion, a sufficient ratio between the maximum and the offset of the signal, is met a vertical scanis performed that crosses the found maxima. More details on the scanning method and the detection
criterion are given in [13]. If a leak is detected the sensor is directed towards the leak and its pose issaved for localization. A picture of the leak augmented by the measurement direction and theultrasound level of the leak is saved for documentation.

Figure 3. 2D map of the inspection area with the sensor poses of two successful
detections (green arrows) and the estimated leak position (green sphere)
When a leak was detected from multiple positions, the leak localization is performed. The leaklocation is calculated using triangulation. Two sensor poses are interpreted as rays in thethree-dimensional space, with their intersection representing the position of the leak. Due to

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estimation errors regarding the position of the AGV and the pose of the PTU a directintersection of both rays is unlikely. Therefore, the points on both rays that have the smallest
distance from each other are determined. The point in the center of the line segment between
those two points is the estimated leak position. When the origins of two sensor poses are close
to each other or their directions are nearly parallel, no localization will be attempted, to avoid
scenarios which are prone to large estimation errors. An example of a successful leaklocalization from two measurement positions is shown in Figure 3 in a 2D map.
4.2 Piloted inspection with MAV
The MAV is operated manually. Due to the continuous movement of the MAV, a complete
scan of an inspection area analog to section 4.1 is not possible. Therefore, the operator
manually directs the MAV close towards the target and the potential leak positions.
To support the operator several assistance functions are provided. The audio signal of the leakis transmitted to headphones, such that the operator can perform an aural inspection analog toscanning methods using hand-held devices. Also a live video broadcast can be streamed tosmart glasses or to a monitor, enabling the operator to align the sensor more precisely basedon the visual cues. To account for smaller variations in the MAV pose and the transmission
delay of the audio/video data, a short recording can be triggered while aiming at a potential
leak. When the recording is finished the sound level maximum is automatically searchedwithin the recording. The corresponding picture and the sensor pose are documented. The leaklocalization is performed analogous to the previous section.

5. Industrial case study set-up
An assembly line for car transmissions was used for the case study. Since there were no realleaks present due to recent maintenance, a steel pipe, 21 mm in diameter, with a round drillingof 1 mm diameter was used as an artificial leak. The pipe was mounted on top of a machinein the assembly line (Figure 4) and connected to the common compressed air supply witha pressure of approximately 8 bar.

Figure 4. Picture of a detected leak using the MAV with marked leak position andestimated ultrasound level of the leak (lower left corner)

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The world coordinate system for leak localization is a right hand Cartesian coordinate system
based on the 2D map recorded beforehand by the AGV. The position of the leak wasapproximated by measuring the distance from landmarks in the 2D map and the height of theleak. Small errors resulting from self-localization, tracking of the MAV, and geometriccalibration of the sensors are likely to produce biased results for the estimated leak locations.
Therefore, the focus of the tests lies on the repeatability, i. e. the standard deviation, of theestimates. The results of the detections were validated manually by a visual inspection of theresult pictures with the estimated leak position (Figure 4).
The AGV was positioned in front of the leak at two different positions (Figure 5). 30 scans wereconducted from each position without moving the AGV between the scans to reduce stochastic
errors from the self-localization. For each scan the sensor pose, the pan angle ϕ and the tilt angle θ
are recorded. For both angles the mean and the standard deviation are calculated. As a measure ofoverall precision of the estimated direction the combined standard deviation s ϕ ,θ of the angle pairs(1) is calculated [13]. N is the number of successful detections and i is the index of the detections.

(1)
The results for the estimated leak positions are treated likewise. The mean and standard
deviation of the three Cartesian coordinates x, y and z are calculated and the combined
standard deviation s x,y,z is calculated:(2)
N P is the number of estimated positions and k is the index of the positions.

Figure 5. Inspection positions (green numbered circles) and angular scan range of theAGV (green circle segments) and approximate position of leak (red circle) in the 2D map
The MAV was operated manually to face the leak from some distance (Figure 6). Using the onboard camera and the visual aid, the operator initiated multiple detection attempts from random
positions in front of the leak. The copter poses and the pictures of the leak were saved. Two data

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sets were used to calculate the results. On the one hand all detections were used. On the other
hand only detections where the leak was placed within the marking in the result picture wereused. The combined standard deviation s x,y,z (2) calculated analogous to the AGV-based tests.

Figure 6. MAV searching for compressed air leaks in factory environment

6. Results
In case of the AGV, for both positions all detections were successful and passed the visualinspection (N = 30). The results of the direction estimation are shown in Table 1. The combined
standard deviation sϕ ,θ is in the same range as in a similar scenario in a laboratory environment[13]. The standard deviation of the tilt angle θ is about twice the size than the one for the panangle ϕ .
Using all pairs of sensor poses from the detections 30×30 locations (N P = 900) were estimated(Table 2). The spatial distribution of the positions is visualized in Figure 7.
Table 1. Results of leak direction estimation from two positions
Data setN
ϕ in ° sϕ in ° θ in ° sθ in ° sϕ ,θ in °Position 1
Position 23030-16.95
-3.70

0.12
0.13

-17.36-13.95

0.26
0.24

0.29
0.27

Table 2. Results of leak position estimation for the AGV

Data set
full

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NP
900

x in m18.64

sx in m0.01

y in m20.63

sy in m0.02

z in m2.35

sz in m sx,y,z in m
0.02
0.03

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Figure 7. Spatial distribution of estimated leak positions using the AGV
In case of the MAV, nine detection attempts by the MAV were successfully carried out andrecorded. Three of the detections were rejected after the visual inspection. Due to theirrandom nature not every pair of sensor poses was suited for a successful localization.
The results of the measurements are summarized in Table 3. The spatial distribution of theestimated leak positions is visualized in Figure 8. Outliers can be seen in the visualization ofboth data sets.
Table 3. Results of leak position estimation for the MAV
Data set
full
corrected

NP
18
6x in m18.87
19.36

sx in m0.80
1.15

y in m
21.03
20.51

sy in m1.15
1.64

z in m2.29
2.40

sz in m0.17
0.25

sx,y,z in m
1.45
2.21

Figure 8. Spatial distribution of estimated leak positions using the MAV: raw data (left),corrected data (right)
A comparison of the approximated leak position and the estimated leak positions for all data
sets is shown in Table 4. The Euclidean distance d is calculated in respect to the approximatedleak position.

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Table 4. Comparison of approximated leak position and the mean of the estimated leakpositions
Data set
approximation
AGV - full
MAV - full
MAV - corrected

x in m
19.02
18.64
18.87
19.36

y in m
20.77
20.63
21.03
20.51

z in m
2.20
2.35
2.29
2.40

d in m
0.00
0.43
0.31
0.47

7. Discussion
The results for leak localization with the AGV look very promising. The standard deviation ofboth the direction estimation (sϕ ,θ < 0.3°) and the location estimation (s x,y,z <= 0.03 m) are very
low. In comparison, the location estimates of the leak using the MAV have a much higher
standard deviation (s x,y,z > 1 m) although most result pictures suggest that the sensor poses
point towards the leak. On the one hand the data sample from the MAV is much smaller, suchthat single outliers carry a much larger weight on the result. On the other hand the estimationof the sensor pose is less precise due to an additional error source: The position estimation
using the AGV is mainly influenced by the self-localization of the AGV while staying
stationary, the geometric calibration of the sensor, and the precision of PTU and ultrasonicsensor. The first two bias the leak position and only the latter influence the standard deviation.
The MAV is tracked while moving and so another influence on the standard deviation isadded. A more precise method to localize the MAV is expected to greatly improve the leaklocalization results. The difference of the standard deviations of the estimated PTU angles canbe a result of the geometry of the parabolic mirror, which is not rotationally symmetric. Since
the vertical detection is based on the angle of the horizontal detection the stochastic error of thesecond part of the detection can increase. Although, calculating the standard deviation of theangles from the data of the laboratory experiments [13] for comparison cannot support either
hypothesis. The vertical structures in Figure 7 are a result of using each direction estimate
multiple times. Therefore, the location estimates using all direction estimates from one position
and a single directions estimate from the other position will form such a distribution locatedalong the ray from the latter direction estimate. The results for the leak localization using theMAV do not improve by manually discarding incorrect detections. As seen in Figure 8 both
distributions have a similar spread. Since the corrected data set is much smaller than theoriginal one, the influence of the spread on the standard deviation is larger.

8. Summary and outlook
In the paper it was shown that mobile service robots can be successfully used for localizationof compressed air leaks in industrial environments. Due to the high precision the AGV issuited for autonomous leak inspection, where the estimated leak position can be integrated
into a 3D map of the inspection area. In addition to automatically triggered maintenance thedata can be analyzed for maintenance scheduling. The MAV can be used as a leak detection
tool for areas that are hard to access by foot, but offer a sufficient flight corridor. A roughestimate of the sensor pose and the picture of the leak suffice for manual maintenance. Since
improvements in indoor self-localization and autonomous flight are under way, it can be
expected that a MAV can be used for autonomous leak inspection with a high maneuverability
in the future.

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Acknowledgements
The authors would like to thank the Federal Ministry of Education and Research (BMBF) for
supporting the project Robot}air{ (reference 01IM12007F) under which this work has been
carried out. The contents of this publication are the sole responsibility of the authors.
References
1.

P Radgen and E Blaustein, 'Compressed Air Systems in the European Union', Stuttgart:
LOG_X Verlag, 2001.
2.
C Galitsky and E Worrell, 'Energy Efficiency Improvement and Cost Saving
Opportunities for the Vehicle Assembly Industry', in LBNL-50939-Revision, March2008.
3.
J Hegenberg, R Herrmann, D Ziegner, L Schmidt, T Guenther, A O Müller, A Kroll, T
Barz and D Schulz, 'Forschungsprojekt RobotAir: Praxistaugliches BodenLuftServicerobotersystem für die Inspektion industrieller Druckluftversorgung und die
Verbesserung der Arbeitsumgebungsfaktoren', in Technische Sicherheit, Vol 5, No 5,
pp. 16-22, May 2015.
4.
A Kroll, W Baetz and D Peretzki, 'On autonomous detection of pressured air and gas
leaks using passive IR-thermography for mobile robot application', in IEEE
International Conference on Robotics and Automation (ICRA 2009), pp. 921-926, Kobe,
Japan, May 2009.
5.
W Baetz, A Kroll and S Soldan, 'On gas leak detection of pressurised components byusing thermograms and pattern recognition algorithms', in 8th Int. Conf. on NDE in
Relation to Structural Integrity for Nuclear and Pressurised Components, pp. 503-512,
Berlin, Germany: European Commission, Joint Research Center, Institute for Energy,
29. September - 1. Oktober 2010.
6.
W Baetz, A Kroll and G Bonow, 'Mobile Robots with Active IR-Optical Sensing for
Remote Gas Detection and Source Localization', in IEEE International Conference on
Robotics and Automation (ICRA 2009), pp. 2773-2778, Kobe, Japan, 2009.
7.
A Pollock and S-Y Hsu, 'Leak detection using acoustic emission', Journal of Acoustic
Emission, vol. 1, pp. 237-243, 1982.
8.
Compressed Air Leak Survey Handbook. Brussel: SDT International, 2009.
9.
X Li, H Liu and X Yang, 'Sound source localization for mobile robot based on timedifference feature and space grid matching', in IEEE/RSJ International Conference onIntelligent Robots and Systems, pp. 2879-2886, San Francisco, CA, September 2011.
10. J Steckel and H Peremans, 'Ultrasound-based air leak detection using a random
microphone array and sparse representations', in IEEE SENSORS 2014, pp. 1026-1029,
Valencia, Spain, November 2014.
11. P Eret and C Meskell, 'Microphone arrays as a leakage detection tool in industrial
compressed air systems', Advances in Acoustics and Vibration, pp. 1-10, 2012.
12. G Bonow and A Kroll, 'Gas leak localization in industrial environments using a
TDLAS-based remote gas sensor and autonomous mobile robot with the Tri-Max
method', in IEEE International Conference on Robotics and Automation (ICRA),
Karlsruhe, Germany, 2013.
13. T Guenther and A Kroll, 'Automated detection of compressed air leaks using a scanning
ultrasonic sensor system', in 2016 IEEE Sensors Applications Symposium (SAS), pp.
116-121, Catania, Italy, April 2016.

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

MODELED TRANSVERSE SURFACE AE SIGNALS FROM BURIEDDIPOLE SOURCES IN A POLYMER ROD
Marvin A. HAMSTAD
Dept. of Mechanical and Materials Engineering, University of Denver; Denver, CO, USA
Phone: +1 303 8713191, Fax: +1 303 4450; email: mhamstad@du.edu
Abstract
Finite element modeling (FEM) was used to study the AE type signals generated from internal dipole sources in
a 600 mm long by 8.5 mm polyvinyl chloride (PVC) rod. A pencil lead break on a rod end was used todemonstrate the accuracy of the FEM results. Out-of-plane AE signals (u r ) versus time were obtained from 16
pseudo sensors equally spaced around the circumference at 90 mm and also at 120 mm from the sources. Groupvelocity curves were calculated. Filtering was applied to correspond to expected results from experiments.
Along with the signals, fast Fourier transform and Choi-Williams distribution results were obtained for radiallyoriented dipoles. Three dominant modes in the u r signals were identified: L(0,1) symmetric, F(1,1)
antisymmetric and F(2,1) antisymmetric. The relative dominance of these modes changed significantly with theangles to the sensors. The multiple sensors defined the modal displacement patterns of the circumference alongwith the significant changes in amplitude as a function the angles. Also, the dependence of the signal amplitudesof the modes were determined as a function of the source distance from the axis of the rod.
Keywords: Finite element modeling, dipoles, PVC rod, many sensors, modes

1. Introduction
In the past, extensive finite element modeling (FEM) of wave propagation signals has been
done (by the author and co-authors) using dipole sources (to simulate acoustic emission
sources), for example in steel [1] and aluminum [2] plates. These studies examined the effectsof variables such as source orientation, source location through the thickness, rise time andtype of source (using more than one dipole). The results focused on waveforms, fast Fouriertransforms (FFT), and frequency/time analysis (with superimposed group velocity curves) of
the signals. This author is not aware of such comprehensive studies on rods. This work
represents the initiation of FEM studies with buried dipole sources of the wave propagation
signals in an isotropic rod.
2. Material, Rod Geometry, Group Velocities and Pencil Lead Breaks
To allow use of a current desktop computer, a polymer rod was selected for this initial study.
This decision was made since higher frequencies would experience substantial material-based
attenuation. Thus, very short source rise times and an extremely small mesh size were notrequired to be able to model a long rod (so end reflections would not arrive during the major
direct signal arrival). Hence, a polyvinyl chloride (PVC) rod was selected with diameter of
8.5 mm and length of 600 mm. The rod properties, except for the density (measured at
1500 kg/m3 [3]), were taken from published values [4] that reported bulk longitudinal andshear velocities of 2330 m/s and 1070 m/s respectively.
Due to the guided wave propagation, group velocities were calculated after modifying a codedeveloped by Seco and Jimenez [5]. Figure 1 shows the results for the longitudinal modes aswell as some flexural modes. In referring to these modes, the notation put forward by Rose

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[6] has been adopted. Later it will be established, for the selected signal filtering, that therelevant modes observed were the fundamental longitudinal and flexural modes L(0,1) andF(1,1) respectively and a higher flexural mode F(2,1).
2000
1800

Longitudinal

1400

L(0,1)

1200

L(0,2)

1000
800

L(0,3)

600
400

L(0,4)

200
00 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

Velocity, m/s

1200
1100
1000
900
800
700
600
500
400
300
200
100
0

Flexural n = 2
F(2,1)
F(2,2)
F(2,3)
F(2,4)
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5Frequency, MHz

1,200
1,100
1,000
900
800
700
600
500
400
300
200
100
0

Velocity m/s

Velocity, m/s

1600

Frequency, MHz

Flexural n = 1

Flexural n = 3
F(1,1)
F(1,2)
F(1,3)
F(1,4)

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5Frequency, MHz

Figure 1. Group velocities for a 8.5 mm diameter PVC rod, red rectangles indicate the modes
observed, velocity (m/s) on vertical scale and frequency 0 to 0.5 MHz horizontal scale.
To examine the appropriateness of the selected properties and to examine the accuracy of the
FEM results, axial pencil lead breaks (PLBs) were modeled and experimentally applied to theend of a 1 m long actual PVC rod with the same diameter. For this experiment, conicaldesign broadband sensors (model KRNBB-PC) were placed on the rod circumference at
120 mm from the end PLBs.
3. Sensor Locations, Description of Dipoles, FEM Code and Mesh Structure
In the case of the buried dipole sources, two rings of 16 point-contact “pseudo” sensors wereequally spaced around the rod circumference (spaced at 22.5⁰ increments) at 90 mm and120 mm from the plane of the centers of the dipole sources. These “sensors” provided theout-of-plane displacement (u r ) from mesh points versus time for runs of 280 µs. Initially four
dipole source types were examined. These included single dipole sources aligned in the axial,
radial and tangential directions as well as three simultaneous mutually perpendicular dipolesin these three directions. Since the radial and tangential dipoles results were found to be
similar and included an additional mode not present in the other two dipole cases, it was
decided to focus on radial dipoles with their centers located at a series of distances from therod axis.
The FEM code used was that developed by Gary [7, 8]. It was validated in the past on metal
samples both by comparison with analytical results as well as by experimental results using
PLBs with an absolutely calibrated National Institute of Standards and Technology (NIST)
secondary standard sensor. The FEM mesh structure is shown in figure 2(a) as well as the
directions of the reference angles to the sensors from 0⁰ to 360⁰. The figure also shows theradius at 90⁰ where the dipole sources as well as the PLBs were centered. The mesh structurein the axial z-direction repeated that shown in the figure by a series of slabs of 0.2 mm
thickness. The maximum variable cell size in the r, θ plane was 0.209 mm. The repetition of
the mesh structure in the z-direction resulted in a very efficient FEM code. Each slab had

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3072 cells and the total number of cells was 9.2 x 106 for the rod. For the dipole sources andthe PLBs, the rise time was 2 µs with a cosine-bell time dependency [8]. The time step used
was 0.0429 µs, and the FEM displacement signals were resampled to a time step of 0.1 µs forthe data analysis.
(a)
135°

90° Sources at locations
on this radius

45°

180°

0°

315°
225°

270°

Figure 2. (a) Mesh structure and definitions of angles to the sensors and source locations. (b)Geometry of a radial dipole source in the numbered mesh. Resultants of body forces in blue
cells as well as the center of the dipole (x). Green cells without body force.
The dipole sources were made up of two monopoles (cells with body forces) separated by
two cells without body forces. As shown in figure 2(b), the monopoles, with net force of 1 N,
were two cells wide to preserve symmetry about the radius from the rod axis to the 90⁰
direction. For the dipoles oriented in the other directions, the same two cells without bodyforces were separated by the appropriate monopoles. The centers of the dipoles (relative tothe rod axis) were taken to be located at the mid-points of a line from the center of each of themonopole cells.
4. Results PLB versus FEM
Figure 3(a) demonstrates a comparison between the FEM calculation and the experimental
signal for a PLB (monopole) located on the rod end at 1 mm from the rod axis. For both the
FEM model and PLB experiment the sensor was located on the rod circumference in the 90⁰
direction. The signal is in mV and the FEM result is in nm, since no absolute calibration isavailable for the broadband sensor on a polymer. Since the real sensor does not respond down
to 0 Hz, the FEM signal was high-pass filtered (Butterworth) at 6 kHz (5 pole). It was also
low-passed at 100 kHz (8 pole) to approximate the material-based attenuation of higher
frequencies in the polymer rod. No filtering was done on the broadband sensor experimental
signal. Given the limitations of the comparison (unknown material damping as a function of
frequency, material bulk velocities not measured and lack of absolution calibration of thesensor), the results indicate a reasonable accuracy of the FEM model versus the experimental
axial PLB result for the two fundamental modes that are present. Figure 3(b) schematicallyshows the dipole source plane and the sensor planes.

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10.7

Experiment
FEM

(b)

0

-697

0

0

40

80

120 160
Time, µs

200

Sensor set
Source plane; dipoles
120
250

230

Amplitude, nm

Amplitude, mV(a) 697

8.5
Sensor set

90

600

-10.7
240

Figure 3. (a) Comparison of results PLB experiment (voltage) and FEM (u r ); both scaled byflex peak at about 190 µs for a propagation distance of 120 mm. (b) Geometry of source andsensor positions, dimensions in mm, not to scale.
(a) 8 kHz HP followed by 120 kHz LP
0

22.5°

0

45°

0

0

67.5°

0

90°

0

45°

0

67.5°

0

22.5°

0

Amplitude, pm

Amplitude, pm0(b) 8 kHz HP followed by 80 kHz LP
0°

0°

90°

0
50

100 150
Time, µs

200

250

0

50

100

150 200
Time, µs

250

Figure 4. (a) Preliminary filtered result and (b) final filtered result of u r for radial dipole at 1mm from rod axis at the angles to the sensors for a propagation distance of 90 mm.
5. Filtering for FEM-Based Dipole Results
Since surface PLBs are known to generate larger flexural modes compared to the case ofburied dipole sources, it was necessary to establish a different filtering scheme for the dipolebased results. For the initial filtering a high-pass 8 kHz (6 pole) filter was selected so as torepresent what might be obtained with a near flat with frequency sensor that went down tonear 8 kHz. This high-pass filter was followed by a 120 kHz (8 pole) low-pass filter, but asfigure 4(a) illustrates this selection resulted in a dominance of what will be shown later to be
the F(2,1) mode at 100 kHz. Since it is expected that a dominance at 100 kHz would not bepresent in the polymer material (due to material-based attenuation), a low-pass filter at
80 kHz (8 pole) was selected to reduce the 100 kHz dominance, as illustrated in figure 4(b).
This filter allowed all three modes to be observed in a more amplitude-balanced way for theradial dipole.

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92 µs

(a)
0°

-18.4
28.6

90°

-24.0
28.6

-18.4
28.6

180°

-18.4
28.6

270°

0

40

45°

135°

-24.0
28.6

225°

-24.0
28.6

-18.4
80

120 160
Time, µs

200

240

B, after½ cycle

A

(b)
28.6

Amplitude, pm

Amplitude, pm

28.6

315°

-24.0

280

0

20

40
60
Time, µs

80

100

120

140

Figure 5. Focus on initial arrival region of u r for radial dipole at 1 mm above rod axis at
a propagation distance of 90 mm.
(a)25

(b)

Amplitude, pm

20
15
10
5
0
-5
-10
-15
-20
-25
0

50

100

150
Time, µs200

250Figure 6. (a) Displacement signal, u r , at 0⁰ and 90 mm propagation for source 1 mm off axis.
(b) CWD with superimposed L(0,1) mode. Orange line shows correspondence of initialarrival of signal (a) and group velocity (b).
6. Analysis of Initial Arrival ModeTo allow determination of key aspects of the initial arrival region of the u r signals, the signals
shown in figure 5 at 90 mm from a radial dipole located at 1 mm above the axis were
examined. First as illustrated in part (a), up to about 92 µs, the amplitudes and phase of u r
are identical at all angles to the sensors. Examining the signals at the vertical lines labeled A
and B in figure 5(b), it is clear that as the wave passes the sensors the rod circumference isuniformly vibrating in and out from the static diameter of the rod at a frequency of about
66 kHz (based on the 7.6 µs spacing of lines A and B). Thus, the large number of sensors
allowed a direct determination of the full modal shape of the AE wave. Figure 6 (a) shows theu r signal for the above radial dipole for the sensor at 0⁰ and propagation distance of 90 mm,
and (b) demonstrates the Choi-Williams distribution (CWD) ([9] parameters default values)
for the signal. Due to the match with the group velocity curve for the L(0,1) mode in part (b),it is clear that the initial arrival mode is the L(0,1) mode. Also, the CWD results show thesignal has peak intensity of 61 kHz at 75 µs. Additional runs made as the distance from rod
axis to the center of the radial dipole source varied from 0.2 mm to 3.2 mm demonstrated
only a small decrease (about 1.7 dB) in the u r peak amplitude of this mode from a source near

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the axis to one far from the axis. The small difference may be related to the small change in
geometry (figure 2(a)) of the source cells as the sources were moved further from the axis.

28.6

Max.

(a)
0°

-18.4
28.6

Amplitude, pm

Amplitude, pm-18.4
28.6 22.5°
45°

-18.4
28.6 67.5°-18.4
28.6 90°
-18.4
0

40

80

120 160
Time, µs

200

240

280

42
0
-42
42
0
-42
42
0
-42
42
0
-42
42
0
-42

B (after½ cycle)

A

(b)
90°

Upper
half

45°
0°
315°

Lower
half

270°

0

56

112
168
Time, µs

224

280

Figure 7. Focus on radial displacement from later arrival low frequency mode for a radial
dipole 1 mm off axis and 90 mm propagation.
7. Analysis of Low Frequency Later Arrival Mode
Figure 7(a) shows (in the first quadrant) for a radial dipole (off-axis 1 mm and propagation
distance of 90 mm) at a fixed time (see green vertical line) the amplitude of this mode varies
significantly with the angle to the sensors. Clearly, the amplitude is essentially zero at 0⁰ and
it is a maximum at 90⁰. In figure 7(b), by examining the signal at the vertical lines A and B, it
is clear that the phase of this mode in the upper half of the rod (45⁰ and 90⁰ sensors) is
opposite to that in the lower half (270⁰ and 315⁰ sensors). Further, the out-of-phase
amplitudes are essentially of the same absolute magnitude for the pair at 45⁰ and 315⁰ as wellas the pair at 90⁰ and 270⁰. These out-of-phase pairs are at the same absolute angle difference
from the 0⁰ to 180⁰ line where the amplitude is zero. Also, the time difference of 45 µs from
A to B implies a frequency of about 11 kHz.
In figure 8(a), a rough end-view sketch is shown of the vibration from A to B of this mode
relative to the static circumference of the rod. The 0⁰ to 180⁰ line represents what is called
the neutral axis in static bending. As might be expected, figure 8(b) demonstrates for the u r
signal at 0⁰ and 90 mm and it’s CWD with superimposed group velocity that this mode is thefundamental anti-symmetric mode F(1,1). Based on this CWD result the peak frequency isabout 12 kHz with arrival at about 177 µs. When the distance of the center of this radial
dipole from the rod axis was varied as before, the peak amplitude (pm) at the 90⁰ sensor
depended linearly on the source distance (mm) from the rod axis with a slope of 39 pm/mm.
Further, when the angular dependence of the normalized peak amplitude (normalized by the
90⁰ value) was examined from 0⁰ to 90⁰, it was determined to vary as the sine of the angle tothe sensor locations (from the radius to 0⁰). The character of these two amplitude aspects was
the same in the other quadrants with adjustments per the illustrated modal vibration shape.

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42

(b)

90°

Amplitude, pm

(a)

45°

180°

·0°

·
270°

0

-42
0

50

100

150
Time, µs

200

250

Neutralsurface

Figure 8. (a) End view sketch of radial vibration of later arrival low frequency mode relative
to dashed static circumference. Red at time A and black at time B in fig. 7(b). (b) Signal and
CWD for u r at 90° and 90 mm propagation distance for radial dipole 1 mm off axis. Peakfrequency 12 kHz at 177 µs (orange line).
A
40
0
-40
40
0
-40
40
0
-40
40
0
-40
40
0
-40

B (after½ cycle)0°(b) 14

0°

0

22.5°
Amplitude, pm

Amplitude, pm

(a)

45°
67.5°

-14
40
0
-40
14
0
-14
40

90°

90°

0

180°
270°

-40
0

40

80

120 160 200 240 280
Time, µs

150

176

202
228
Time, µs

254

280

Figure 9. Radial displacement u r signals for later arrival high frequency signal. Radial source
at 1 mm of axis and 90 mm propagation.
8. Analysis of Later Arrival High Frequency Mode
The high frequency region has an interesting behavior with respect to its modal shape. Figure9(a) for angles in the first quadrant demonstrates (use vertical black line to compare) that u r
has the same phase at 0⁰ and 22.5⁰, no amplitude at 45⁰ and opposite phase at 67.5⁰ and 90⁰.
For another set of angles to the sensor, figure 9(b) shows (use vertical lines A and B) thephase is the same at 0⁰ and 180⁰ and opposite at both 90⁰ and 270⁰. The time gap of about
5 µs between lines A and B indicates an approximate frequency of about 100 kHz for thismode. By use of the above observations and similar observations for the other quadrants, themodal shape (end view) of the vibration at the sensors is roughly sketched in figure 10(a) forthe motion relative to the static circumference at the two times A and B. This figure showsthat u r for this mode remains zero at the sensors angles of 45⁰, 135⁰, 225⁰ and 315⁰.

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29

90°

·

(b)

·45°

135°

180°

0°

·

Amplitude, pm

(a)

0

-29

0

50

100

150
Time, µs

200

250

·315°

225°

270°

Figure 10. (a) Sketch of end view of radial vibration of later arrival high frequency mode
relative to dashed static circumference. Red at time A and black at time B in fig. 9(b). (b)Signal and CWD for high frequency arrival u r at 0° and 90 mm distance, radial dipole 1 mm
off axis and CWD peak frequency 101 kHz at 231 µs (green line) for F(2,1) mode.
Additional modeling, examining u r at interior diameters verified that the radii to these fourangles have zero radial displacement for this modal vibration. Figure 10(b) which shows thesignal at the 0⁰ sensor and the corresponding CWD result with superimposed group velocity
demonstrates that this high frequency mode is F(2,1). Also, it is apparent that the frequency
corresponds to the cut-off frequency (see figure 1) of this mode at about 101 kHz. Figure10(b) shows the amplitude slowly increases with increasing time out to the modeled signal
length of 280 µs. When the peak-to-peak amplitude at the 0⁰ sensor of this mode was
examined as a function of the source distance from the rod axis at a fixed propagation time, it
was determined that the amplitude was largest when the source was nearer the axis, and it
decreased in a non-linear fashion as the distance from the axis increased. It was foundwithout an explanation, that this amplitude increased nearly linearly with the horizontal
distance (parallel to the 0⁰ to 180⁰ line) from the source centers (along the 90⁰ radius) to thecircumference of the rod. To examine the angular dependence of this mode a special FEM
run was done with nine sensors equally spaced from 0⁰ to 45⁰. As a function of the angle tothese sensors the normalized (by the 0⁰ sensor magnitude) amplitude decreased directly withthe cosine of two times the angle to the sensor.
10. Analysis Results from Other Dipole Cases
The u r results for the three single dipoles cases (radial, axial and tangential) and the threesimultaneous dipoles (dilatation) (with source positions on the axis to 90⁰ line) are best
demonstrated in figure 11 using the CWD results for first quadrant signals (at 90 mm) withall the source centers off axis at 1 mm. These results illustrate that the radial and tangential
dipoles have very similar intensities of all three modes as the angle locating the sensors
changes. On the other hand, the axial dipole and the dilatation case are similar but only show
intensity of the two fundamental modes. For all four source cases the fundamental modeschange in a similar fashion as the angle to the sensors changes.

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RadialTangential

F(2,1) mode
Axial

Dilatation L(0,1) mode

Frequency (0 to 150), kHz

0°

22.5°
F(1,1) mode
45°

67.5°

90°

Figure 11. CWD results for u r for first quadrant of four different source cases showing modes
and the variation of the intensity as the angle to the sensors changes. All at 1 mm off axis.
11. Conclusions
Based on use of flat-with-frequency pseudo sensors on the circumference of a 8.5 mm PVC
rod; long enough that end reflections do not appear in the u r filtered signals (HP 8 kHz
followed by LP 80 kHz):• For the radial dipole cases, three modes for u r are significantly excited• Modal shapes of the signals for the radial sources vary:o L(0,1) uniform radial vibration about static circumferenceo F(1,1) normalized vibration varies in amplitude in sinusoidal fashion as theangles to the sensors changes from 0⁰ to 90⁰ (or 180⁰ to 90⁰) and it has
opposite phase above and below the 0⁰ to 180⁰ lineo F(2,1) vibration has nodes at lines from 45⁰ to 225⁰ and 135⁰ to 315⁰.
The normalizes vibration varies in amplitude with cosine of two times theangle to the sensors.
• Amplitudes as radial source center moves further from the rod axis:o L(0,1) no significant changeo F(1,1) increases linearly with distance increaseo F(2,1) decreases with distance increase• Different source cases:o Radial and tangential sources similar behavior for all three modeso Axial and three dipole source similar, only fundamental modes• Except for signs, the first quadrant provides examples that translate to other quadrants
The inexpensive use of 16 sensors from FEM modeling and group velocity curves results ineasy mode identification and verification of modal shapes on the circumference. Also, theresults demonstrate that the signals obtained depend strongly on the location of the sensors
relative to the source location in the rod.

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Acknowledgements
John Gary (NIST Boulder, retired) is acknowledged for the finite element runs, Manuela Zelenyak (Universityof Augsburg) for the group velocity curves and Lidewei Vergeynst (University of Ghent) for the PLBs.

References
1. M. A. Hamstad, ‘Lamb Modal Regions with Significant AE Energy in a Thick Steel
Plate,’ Advances in Acoustic Emission – 2007, Proceedings of The Sixth International
Conference on Acoustic Emission, Edited by Kanji Ono, pp. 247 -252, 2007.
2. M. A. Hamstad, A. O’Gallagher and J. Gary, ‘Examination of the Application ofa Wavelet Transform to Acoustic Emission Signals: Part 2. Source Location,’ J. of
Acoustic Emission, Vol. 20, pp.62-82, 2002.
3. Personal communication, L. Vergeynst, University of Ghent, Belgium, 2014.
4. Kaye & Laby, Tables of Physical and Chemical Constants, National PhysicalLaboratory and Online, 2005
http://www.kayelaby.npl.co.uk/general_physics/2_4/2_4_1.html
5. Fernando Seco and Antonio R. Jiménez, Modelling the Generation and Propagation of
Ultrasonic Signals in Cylindrical Waveguides, Ultrasonic Waves, A. Santos (ed.),InTech, doi: 10.5772/29804. 2012.
6. J. L. Rose, Ultrasonic Waves in Solid Media, Cambridge: Cambridge University
Press, 2004.
7. John Gary and Marvin Hamstad, ‘On the Far-field Structure of Waves Generated bya Pencil Break on a Thin Plate,’ J. Acoustic Emission, Vol. 12, Nos. 3-4, pp. 157-170,
1994.
8. M. A. Hamstad, A. O’Gallagher and J. Gary, ‘Modeling of Buried Acoustic Emission
Monopole and Dipole Sources With a Finite Element Technique,’ Journal of Acoustic
Emission, Vol. 17, No. 3/4, pp. 97-110, 1999.
9. J. Vallen, ‘AGU-Vallen Wavelet Transform Software Version R2010.0202,’ VallenSysteme GmbH, Münich, Germany, 2009, Available at
http://www.vallen.de/wavelet/index.html.

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

FEATURES OF THE AE TESTING OF EQUIPMENT ON OPERATING
MODE
Dmitry CHERNOV 1, Sergey ELIZAROV 1, Vera BARAT 1, Igor VASILYEV 2
1

INTERUNIS-IT LLC, Moscow, Russia; Phone: +7 495 3617673; e-mail: chernovdv@interunis-it.ru
2
Blagonravov Institute of Machine Science, Russian Academy of Sciences, Moscow, Russia

Abstract
Composite materials are very widespread in aerospace, reinfinery and chemical industries. The wide application
may be explained by the improved mechanical properties -ultimate tensile strength, flexural modulus and small
specific density.
The nondesctructive evaluation of the composite materials is an actual and complicate problem. Traditional
ultrasonic, thermal and electromagnetic NDT methods have a limited application which is explained byanisopropic properties of the composite materials. This study focuses on the problems of acoustic emission
arrising during the composite materials testing.
In the case of the composite materials AE testing has a set of specific features, for example, high AE activity even at
the beginning of the structure loading, diversity of AE signals waveforms and spectra from the different AE source(fiber breakage, matrix cracking, destruction of the adhesive layer). Novel AE data registration method adapted forcomposite materials was offered in this sudy. This method takes into account the multiform and multiscale character of
AE data. It allows to separate AE impulses from the different sources – fiber and matrix and as result - estimate theintensity of destruction for each component of the composite material separetelly. Authors also proposed improved
criteria and improved location algorithm for the composite materials.
Keywords: AE,

Introduction
From year to year, composite materials find expanding applications in a different
industries - aerospace, oil, machine building, and in medicine and construction. The main
advantages of composites are high rigidity, specific strength and low weight. Despite all theadvantages, composite materials have some drawbacks – the lack of methods for thenondestructive estimation and the low level of maintainability. Thus it is an urgent problem toensure accident free operation of dangerous industrial objects made of composite materials,
and its solution will allow an improvement of the reliability level of devices in service.
One of effective NDT methods within the limits of which such systems are implemented isthe acoustic emission (AE) method. This method is highly sensitive and does not require surface
scanning. Also, the AE method is quite universal and can be used to test both metal and non-metal
objects. Furthermore, multilayer and complex structure of the test material has an effect on the
diagnostic results to a lesser degree as compared with an ultrasonic or thermal testing method.
Destruction of Composite Materials
The initial step in solving this problem is study of destruction mechanisms in composite
materials. The behavior of composites under loading is influenced not only by the presence of
defects, but also by the characteristics of matrix and dispersed phase, the state of dispersed phase(fibrous or granular), the content of this phase in the composite, the direction of applied force, etc.
Methods of composites manufacture may also have a significant influence. The sizes anddistribution of voids and inclusions, the degree of nonuniformity of fibers distribution, thestate of adhesion between the levels are dependent on these methods [3,4].

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Based on achievements of modern science in the field of AE testing of composite
materials, it is necessary to identify the main types of acoustic signal sources occurring in thecourse of applying load of the specific kind. For example, Ono et al. [6] demonstrate thepotentially possible AE sources occurring during destruction of a fiber-reinforced composite.
When the composite stretches in longitudinal direction, a damage accumulation model
includes the stages of matrix destruction, adhesion layer destruction and fiber destruction. In
the initial stages of loading the occurrence of AE sources is associated with brittle destructionof matrix. The process of matrix destruction is characterized by appearance of acoustic signals
with characteristic high-frequency spectral components (up to 700 kHz) and large amplitudes,
which reach values in excess of 55 dB [7]. The AE signals which appear with further increasein load can be associated with the adhesion layer destruction. Such sources are characterizedby signals with amplitude from 60 to 80 dB. Further loading of composite will lead to thefiber destruction. The process of fiber destruction is characterized by acoustic signals, whose
amplitudes can reach 100 dB or more [7, 8].
During application of shear loads, the following stages of composite destruction should
be identified, i.e. matrix destruction and delamination. The initial stage of composite
destruction during application of shear loads is the matrix cracking. AE sources occurring in
the course of matrix destruction both at shear and at tensile loadings are characterized byacoustic signals with similar values of standard parameters. Further increase in loading leads
to composite delamination. During delamination the sources of acoustic signals occur withamplitudes corresponding to the range from 50 to 70 dB. It should be noted that these signals
are low-frequency, with a resonant frequency of about 100 kHz [7, 9]. Thus, based on thevalues of standard AE parameters and the known type of applied load it is possible to identify
the stages of composite destruction and to create new criteria parameters for estimating thestate of structures made of composite materials.
Results of Laboratory Research
To investigate the process of composite material destruction and to specify the diagnostic
model, a series of experiments was carried out which involved compression and stretching ofcomposite material reinforced with carbon fibers.
The first stage of research is study of destruction model using the AE method in the course of
stretching (Fig. 1) of the composite specimen (Fig.2). Fig. 2 shows the specimen of polymer
composite material (PCM) brought to failure using the AE method. The system A-Line 32D by
the company "INTERUNIS" was used as equipment for recording acoustic signals.

Fig. 1 Stretching - tensile machine “INSTRON-250"

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In the experiment AE sensor GT200 were used with a resonance frequency of 165 kHz, as
well as preamplifiers by the company "INTERUNIS" with a frequency bands of 25-500 kHz anda gain of 26 dB. Tests were performed according to a specially designed loading diagram (Fig. 3).

Fig. 3 Loading diagram of PCM specimen

Fig. 4 Values of standard AE parameters recorded during specimen stretching -(a) time dependence of average
amplitude, (b) time dependence of duration, (c) time dependence of AE activity, with superimposed load curve

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Fig. 3 shows the loading diagram, involving execution of four stages when the level oftensile stress increases up to 20, 60, 90 and 120 kN. Duration of force delays at constant load
was determined by activity of AE signals recording – till termination of generation of AE
impulses. At the last stage the load was increased until the specimen lost its bearing capacity.
Fig. 4 shows the results of AE testing of the PCM specimen which were recorded during
specimen testing – the time dependences of average amplitude, duration and activity. On thisbasis, one can determine three characteristic time regions: I - from 130 to 410 seconds, II from 410 to 530 seconds, III - from 530 to 600 seconds. At the initial stage of loading the AE
signals duration is not longer than 5,500 mcs (Fig. 4b, region I), in this case the average
amplitude (Fig. 4a, region II) of such signals varies from 40 to 70 dB. This process according
to the models discussed earlier can be assigned to the process of brittle destruction of matrix.
With further increase in the load there is an increment of AE activity from 100 to 350
impulses per second (Fig. 4b, region II). Also in the region of load increase up to 123 kN an
occurrence of rising trend of the time dependence of amplitude average values (Fig. 4b, areaII) is observed. This process according to the previously discussed models can be assigned tothe process of adhesion layer destruction. At the final stage, there is an increase in all theabove parameters - average amplitude (Fig.4a), duration (Fig.4b) and activity (Fig.4c).
This process can be assigned to the process of fiber destruction.

Fig. 5 Specimen of PCM installed between units of compression machine

The second stage of research is study of the AE signal parameters when the PCM
specimen is exposed to compressive load (Fig. 5). Fig. 5 shows the specimen brought tofailure by compressive load. The system A-Line 32D was also used as the data recording
equipment. In the experiment the same transducers and preamplifiers were used as instretching composite specimens. Tests were performed according to a loading diagram(Fig. 6) which included regions of delay and increase of the compression force level. Fig. 7
shows the results of AE testing of the PCM specimen, recorded during applying compressive
load

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Fig. 6 Loading diagram of PCM specimen

Fig. 7 Values of standard AE parameters recorded during specimen stretching – (a) time dependence of average
amplitude, (b) time dependence of duration, (c) time dependence of AE activity, with superimposed load curve

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Fig. 7 shows the time dependences of standard AE parameters. On this basis one candetermine three characteristic time intervals: stage 0 – from 0 to 84 seconds, stage I - from84 to 392 seconds, stage II - from 392 to 916 seconds and stage III - from 916 to
1,138 seconds. The stage 0 corresponds to pre-loading of the object under test, which isloading by small action wherein alignment of grips and the specimen is checked. At the initialstage - from 20 to 120 kN - acoustic signals of short duration are recorded (Fig. 7b, region I.),in so doing, the amplitude average value does not exceed 50 dB (Fig. 7c, region I). According
to the models accepted, such parameters of AE data correspond to the process of matrix
destruction. Further increase in the load leads to the AE activity increase from 50 to
180 signals per second (Fig. 7c, region II.), and also there is an increment of values both of
the average amplitude parameter - up to 90 dB, and of the duration parameter - up to
20,000 mcs. Such process can be associated with the adhesion layer destruction. At the final
stage there is an increment of all above parameters – average amplitude (Fig. 7a, region III.),
duration (Fig. 7b, region III.) and activity (Fig. 7b, region III.), that may be typical for thefiber destruction. As a result of experimental AE data analysis the key stages of composite
material destruction were identified [1,2].
Data Analysis
During data processing two typical forms of AE signals were determined (Figs. 8 a,b).
The first type signals (Fig. 8a) were recorded from the beginning of internal pressure buildup
and to complete destruction of the object. By the standard parameters values and by the form,
the first type signals correspond to the process of matrix destruction. The second type signals(Fig.8b ) were recorded at the later stages of destruction. By the standard parameters values
and by the form, such signals belong to the process of fibers destruction.

Fig. 8 Form and spectrogram of AE signal corresponding a. to matrix destruction b. to fiber destruction

Both types of signals can be described by invariants of form. The first invariant of form(4) is the ratio of decay time 𝑡𝑡𝑡𝑡𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 to rise time 𝑡𝑡𝑡𝑡𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑑𝑑𝑑𝑑 . On its basis, it is possible to detect
acoustic signals of long duration, which correspond to the process of delamination anddestruction of fibers.

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𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑡𝑡𝑡𝑡𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎1 = 𝑡𝑡𝑡𝑡𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 /𝑡𝑡𝑡𝑡𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑑𝑑𝑑𝑑(4)

𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑡𝑡𝑡𝑡𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎2 = 𝐴𝐴𝐴𝐴 ∙ 𝑡𝑡𝑡𝑡𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑(5)

The second invariant of form (5) is a product of the AE signal amplitude A and the valueof decay time 𝑡𝑡𝑡𝑡𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 . These parameters can be used for filtering noise signals and selecting
AE pulses with a sharp front and a short duration, which belong to the process of matrix
brittle destruction.
AE diagnostics of high-pressure metal composite vessel

180

160

140

120

80

100

60

40

0

350
325
300
275
250
225
200
175
150
125
100
75
50
25
0
20

Pressure, kgf/cm2

To evaluate the efficiency of developed criterial estimations, they were put to an
evaluation test during the object testing to destruction [5, 8]. As the object under test, themetal composite vessel was used which composed of titanium liner and composite shell(Fig. 9), brought to destruction through stepwise increment of load (Fig. 10).

Time, minFig. 9 Metal composite vessel located in protective
bunker 1 – points of TAEs installation

Fig. 10 Loading diagram of object under test

Fig. 10 shows the loading diagram consisting of sections of pressure buildup and delays,
base values of which correspond to 50, 80, 110, 130, 150, 180, 200, 300 kgf/cm2. Furtherpressure buildup resulted in the object complete destruction (330 kgf/cm2).
During the tests, the AE data were recorded both by the industrial system A-Line32D andby the continuous recording unit L-Card. In the experiment AE sensors GT200 were usedhaving resonance frequency of 165 kHz, as well as preamplifiers of the company LLC"INTERUNIS" characterized by a bandpass of 25-500 kHz and gain of 26 dB.
Processing of AE data obtained during tests
Destruction of the object under test consists of two stages – deformation of titanium linerand destruction of composite shell. Titanium liner strain begins at the load of 1 kgf/cm2 andcontinues till vessel destruction. Titanium would be fully subject to plastic strain whenpressure value is 30 kgf/cm2. Once pressure value reaches 30 kgf/cm2 strain intensity

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decreases, as titanium turns into plastic mass. The composite material destruction starts
at pressure value about 50 kgf/cm2.
The first stage of research is matching of the stages of metal composite vessel destruction
with AE data. The main goal of study is identification of characteristic dependences
corresponding to the destruction stages determined earlier.
At the initial stage of pressure buildup there is an intensive straining of titanium liner.
The process of active strain in the form of reduction in the shell thickness occurs in the areaof poles, from pole to equator the material elongates with the cross-section loss. The process ofliner plastic strain is characterized by continuous emission, which is an additional difficultyin data interpretation (Fig. 11). The main difference of such signal from noise flux isa spectrogram form, which allows for revealing the plastic strain against the background noise.

Fig. 11 Oscillogram of liner plastic strain process

With further increase in load the beginning of composite shell straining is observed. The
composite straining begins with the processes of matrix cracking and delamination. These
processes are characterized by specific trend of amplitudes growth (Fig. 12).

Fig. 12 Time dependence of AE-pulse amplitude under loading up to 110 kgf/cm2 with the superimposed
load curve

Fig. 12 shows the amplitude dependence with a characteristic increasing trend.
The increasing amplitude trend characterizes the process of damage accumulation in thecomposite material matrix, as well as the transition to the next stage of object deformation.

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Fig. 13 Separation of signals by two types using the developed criteria (a) criterial plane when loaded
to 120 kgf/cm2, (b) criterion plane when loaded to destruction

Drawing of a criterion plane on the basis of invariants of acoustic waveform will also
permit to separate the stages of composite material destruction. And on the activity
calculation of each of signal types it is possible to assess the intensity of damage
accumulation.
Fig. 13 shows the result of AE signals separation by two types at different load levels.
Under loading to 120 kgf/cm2 (Fig. 13a), the point density raises only in the first cluster that
includes AE signals occurring during the matrix destruction. With further increase in load
(Fig. 13b) the activity of acoustic signals increases in the second cluster, which can be
assigned to the process of fibers destruction.
To evaluate the intensity of damage accumulation, the activity assessment algorithm wasused for each of determined types of AE pulses. The algorithm is an approximation of total
account of the specific type signals (Fig. 14).

Fig. 14 Approximation of total account function - (a) the first type signals, (b) the second type signals

Fig. 14 shows the total account functions of signals of the first (Fig. 14a) and second(Fig. 14b) type. For the numerical evaluation of damage accumulation intensity thecalculation of k-factor was performed after piecewise-linear approximation by function 𝑦𝑦𝑦𝑦 =𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘 + 𝑏𝑏𝑏𝑏. The higher the k-factor value, the higher the damage accumulation intensity. Thus,
within the framework of assigned task it has been possible using the developed criteria toseparate the stages of metal composite object destruction, as well as to evaluate the damage
accumulation intensity.

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Conclusion
As part of the study a large amount of work was carried out. Initially the main stages ofcomposite material destruction which occur at different types of loading were studied.
To solve the problem of evaluating states of composites and matching the destruction stages
and the AE signal parameters, laboratory tests were carried out, which included destruction of
specimens using compressive and tensile loads. Upon experiments performance, the mostinformative parameters of AE signal were determined, on the basis of which the criteria
parameters were developed. Criteria for evaluating the composite destruction stage, as well as
the intensity of damage accumulation were developed.
To check the efficiency of developed method, destructive tests of metal composite vessel
were performed. Application of the developed criteria also allowed for separating the stagesof object destruction.
Referencies
1.
2.
3.
4.
5.

6.
7.
8.
9.

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Ivanov V.I., Vlasov I.E. Acoustic emission testing method. Nondestructive testing:Handbook; volume 7, editor Kluev V.V..: Mashinostroenie. 2005, – 340 pp. (in Russian)
PB 03-593-03. Rules of organizing and conducting acoustic emission testing of vessels,
vehicles, boilers and process piping, 2003 (in Russian)
Fracture Mechanics of Composite Materials. Moscow, Mir 1982, 232 p.(in Russian)
Cherepanov GP Fracture mechanics of composite materials. - M .: Science, Home editionof Physical and Mathematical Literature, 1983. - 296 p. (in Russian)
D.V. Chernov, V.A. Barat and I.E. Vasiliev The development of methodology forassessing the stages of destruction of multilayer composite materials according to theparameters of acoustic emission signals, Electronics, Electrical Engineering and Energy:
Twenty-first int. scientific and engineering. Conf. undergraduate and graduate students,
Moscow: Tez.dokl. Publishing House MEI (in Russian)H. Suzuki, M. Takemoto and K. Ono «A Study of Fracture Dynamics in a Model
Composite by Acoustic Emission Signal Processing», Journal of Acoustic Emission, vol.
11, №1, 1993, pp.117-128
K. Ono and A. Gallego, «Research and Application of AE on Advanced Composites»,
Journal of Acoustic Emission, vol. 30, 2012, pp.180-229
K. Ono, H. Cho and T. Matsuo, “Experimental transfer functions of acoustic emission
sensors”, Journal of Acoustic Emission, vol. 26, 2008, pp. 72-90.
W.H. Prosser, “Advanced AE techniques in composite materials research”, J. Acoustic
Emission, vol. 14, 1996, pp. S1-S11.

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

HIGH SENSITIVITY DETECTION OF AE EVENTS IN NOISYENVIRONMENT USING STREAM RECORDING AND PARALLELCOMPUTATION
Kaita ITO 1, Manabu ENOKI 1
1

Department of Materials Engineering, The University of Tokyo; Tokyo, Japan
Phone: +81 3 5841 7129, Fax: +81 3 5841 7181;
e-mail: ito@rme.mm.t.u-tokyo.ac.jp, enoki@rme.mm.t.u-tokyo.ac.jp

Abstract
Conventional event-by-event recording of AE with fixed threshold voltage is often not effective in noisy
environment such as monitoring of material processing. In such cases, stream recording i.e. continuous recordingof AE waveforms during the whole experimental period is effective because it enables flexible and high efficientnoise reduction and event detection by software. However, analysis of multi-channel AE stream often becomesa very heavy task even for modern CPU because of its extremely huge data amount. In this study, two types ofprocessors for parallel computation i.e. CPU and GPGPU (General-Purpose computing in Graphic ProcessingUnit) are compared in noise reduction process in frequency domain and hit detection with multiple thresholdvoltages of AE streams. Optimum usage of heterogeneous multi-core processor showed high performance foranalysis of AE streams. This method shows great advantage of defects monitoring during noisy environment
such as dynamic material processes e.g. thermal spraying.
Keywords: Signal Processing, Continuous Waveform, Noise Filtering, Multiple Thresholds, Parallel Computing

1. Introduction
Acoustic emission method is one of the few real-time non-destructive evaluation methods for
detection of many types of dynamic phenomenon in solid materials and structures e.g. microcrack initiation and propagation, dislocation movement, friction and leakage. Conventionallyin AE measurement, a threshold voltage has to be set preliminarily. An AE hit is detected
when the voltage signal from AE sensor and amplifier exceeds this preset threshold voltage.
This simple thresholding method needs small calculation load for AE measurement
equipment. However, optimal configuration of noise filter and threshold voltage is very
difficult under measurement environments with high noise level or frequent AE hits.
Therefore, AE stream recording i.e. continuous recording of AE waveform is a reliable
method for successful monitoring in such environments. To this end, an AE measurement andanalysis system called “Continuous Wave Memory” (CWM) has been developed [1, 2]. ThisCWM system includes powerful hardware e.g. computational processor and large scalestorage device for continuous recording of AE streams for several days. CWM system has
been also equipped powerful software for flexible real-time and post analyses of waveforms
which includes frequency domain noise filter and multiple threshold voltages for event
detection [3]. However, continuous recording and analyzing of multi-channel AE waveforms
will induce huge data rate which is too heavy even for multi-core CPU (Central ProcessingUnit) for personal computers. For example, real-time processing is difficult when the data andcalculation amount are huge such as noise reduction in frequency domain.
GPGPU (General Purpose computing on Graphics Processing Units) is a highly attracted
solution for this problem [4]. Historically, GPU is a specialized processor for rendering of

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three dimensional graphics. However, modern GPU has been evolved as SIMD (single
instruction multiple data) processor with hundreds or thousands of calculation cores and it is
not specialized for graphics computing. The total performance of one GPU has been reached
to TFLOPS (Tera FLoating-point Operations Per Second) order meanwhile CPU still remains
in hundred GFLOPS (Giga-FLOPS) order. Software platform for GPGPU also has been
substantiated. CUDA (Compute Unified Device Architecture) of NVIDIA® Corporation is
the most widely used platform for their Tesla® and GeForce® series of processors. The
OpenCL (Open Computing Language) which is an open framework for programing onheterogeneous platform also supports GPGPU. Now, GPGPU has been widely used forscientific calculations. The latest (June 2016) world-wide TOP500 list of supercomputers
includes at least 66 systems with GPU acceleration [5]. Thousands units of commercial GPUs
easily enables PFLOPS (Peta-FLOPS) class supercomputers with much lower cost than theoriginally developed many-core CPU for supercomputers. However, GPU calculation has
some limitations which are not existed in the conventional CPU calculation. At first,
calculation in GPU should be independent each other for better parallelization. If thecalculation is sequential i.e. one calculation depends on the result of the previous calculation,these calculations cannot be parallelized. In such case, conventional CPU with small numberof high performance calculation core still superior to GPU. Actually, some of TOP500
supercomputers equip only CPUs and many-core CPU accelerator units like Xeon® Phi™
from Intel® Corporation. Ultimately, adaptive heterogeneous computing with CPU and GPU
is important for optimal correspondence to many types of problems.
There are some of reports which utilize GPU for processing of AE waveforms. Riha et al.
used GPU to accelerate many individual functions of waveform analysis e.g. event detection,
location of AE events, envelope calculation [6]. However, there are few examples aboutprocessing of AE streams and our previous study is one of the earliest reports [7]. In thisstudy, CWM has been improved to enable real-time processing of multi-channel AE streams
by using heterogeneous multi-threaded programming with CPU and GPU.

2. Experimental Procedure
2.1 Specifications of AE Measurement System
CWM system equipped Intel® Core™ i7-4770S CPU and a NVIDIA® GeForce® GT640
GPU or NVIDIA® Tesla® C2050 GPU. Specifications of these processors are shown in table
1. Hyper-threading technology was disabled in CPU for precise estimation of performance.
Intel® C++ Compiler 2013, GNU C compiler version 4.6 and NVIDIA® CUDA® version 5.5
were used for building the software. In case of GPU calculation, data on the main memory
which is attached to CPU have to be transferred to the GPU memory in advance. Furthermore,
the data have to be transferred again to the main memory after processing in GPU.
CWM system also equipped two of Interface® PCI-3525 dual channel ADC (Analog-toDigital Converter) boards and totally four channels of AE waveforms were continuously
recorded with 10 MHz sampling frequency, 12 bits of resolution and -5 V to +5 V range. The
size of one sampled data was 16 bits and the high-order 4 bits were always zero. Then, thetotal data rate was 80 MB/s (8 × 107 bytes/s). At first, sampled data was stored in the 64 MB
of first ring buffer on ADC boards, then it was sequentially transferred to the 512 MB of
second ring buffer on the CPU memory by direct memory access feature of ADC boards andCPU was not used for this memory transaction. All of signal processing was done on thissecond ring buffer. Finally, the data was recorded to the RAID-0 storage device i.e.
parallelized HDD (hard disk drives). In normal condition, two of Hitachi® 0S03361 type
7200 rpm 4 TB HDD was combined as one RAID-0 device. The read / write speed was about

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250 MB/s maximum and 120 MB/s minimum. It was sufficient speed to record AE streams inreal-time. However, in this study continuous AE waveform for signal processing wasrecorded in advance and read from the storage device. It is because the read/write speed of thestorage device not to become the bottle neck on benchmarking of CPU and GPUs processing.
The continuous AE waveform for testing was extracted from the waveform during plasma
spraying process under very noisy condition [2]. Four of Crucial™ CT500MX200SSD1 type
500 GB SSD (Solid State Drive) was combined to be one RAID-0 device and the maximum
reading speed was about 2.0 GB/s.
2.2 Procedure of Signal Processing
Figure 1 is the schematic diagram of signal processing in CWM. Performance of CPU and GPU
was compared both in the noise filtering part and hit detection parts. Before and after the noise
filtering, STFT (Short Time Fourier Transform) and inverse STFT processes were needed toenable a frequency domain filter i.e. arithmetic operations on time-frequency-magnitude data.
FFT (Fast Fourier Transform) length in this STFT / i-STFT processing was 1,024 (210) samples.
Here, STFT / i-STFT of continuous AE waveform could be parallelized i.e. one result of FFT / iFFT does not affect to the next FFT / i-FFT. Then, the continuous AE waveform simply could be
separated and assigned to each calculation thread. In this study, the length of one section was
262,144 (218) samples. Furthermore, 256 FFT / i-FFT calculation in one section was automaticallyparallelized in GPU by a function in CUDA. Therefore, more number of calculation threads than
the number of cores in CPU and GPU could be prepared in STFT / i-STFT processing.
Meanwhile, in the hit detection part, thresholding of continuous AE waveform is difficult tobe parallelized. When an AE count i.e. threshold crossing was detected, it depended on theprevious result whether this count could be detected as start of an AE hit or not. Therefore,
the number of calculation threads in the hit detection part was limited to (AE channels) × (thenumber of threshold voltages). The number of thresholds is theoretically unlimited in CWM,
however, 2 dB-pitch is empirically sufficient to detect AE hits regardless of noise and signal
levels of continuous AE waveform [3]. Therefore, 32 thresholds are enough to cover thewhole dynamic range of AE waveform. Consequently, the number of calculation threads inthe hit detection part was limited less than 128 in this study. It was much more than thenumber of cores in CPU but much less than GPUs.
Table 1. Specifications of CPU and GPUsCPU
GPU #1Core™ i7-4770S
GeForce® GT640
Cores
4
384
Frequency / GHz3.90 (boost)
0.90
Operations / clock
16
1
Theoretical performance /250
345
GFLOPS
Memory transfer / GB/s
4 (Half-duplex)
Frequency domain noise filter
Continuous
waveform

STFT

Filtering

Inverse STFT

GPU #2Tesla® C2050
448
1.15
1
515
8 (Full-duplex)

Hit Detection
MultipleThresholding

ResultFigure 1. Schematic diagram of signal processing in CWM.

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3. Results
Figure 2 shows the total throughput of CPU and GPUs in the noise filtering part with different
number of calculation threads. As previously mentioned, much more parallelization was
automatically enabled in GPU calculation. Calculation amount of the filtering itself was muchsmaller and ignorable than STFT and i-STFT. Both in case of CPU and GPU calculation, thetotal throughputs were improved as increasing of the number of calculation threads. However,they were eventually saturated in all cases. The maximum throughput of CPU, GPU #1 andGPU #2 were about 70, 260 and 980 MSPS (Mega samples per second), respectively.
Total Throughput / MSPS

1000

CPU

GPU #1GPU #2

4

8
16
32
64
Number of calculation threads

800
600
400
200
0
128

Figure 2. Relationship between the throughput and the number of threads
in the noise filter part of the processing of continuous AE waveforms.

Total Throughput / MSPS

Figure 3 shows the total throughput of CPU and GPUs in the hit detection part with different
number of thresholds. CPU was much faster than GPU when the number of thresholds was
small. However, the throughput of CPU was in inverse proportion to the number of thresholds
whereas the throughputs of GPUs were not so diminished. GPUs showed higher throughputs
than CPU when the number of thresholds was 64 or more.

800
700
600
500
400
300
200
100
0

CPU

4

8

GPU #1GPU #216
32
64
Number of thresholds

128

Figure 3. Relationship between the throughput and the number of thresholds
in the AE hit detection part of the processing of continuous AE waveforms.

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4. Discussion
The throughput of noise filtering process of 4 ch × 10 MHz sampling streams could be donein real-time both by CPU and GPU. In our previous study, a previous generation CPU (Intel®
Core i7-3770K, maximum 3.90 GHz, 4 cores) showed only about 40 MSPS of throughput i.e.
only 60 % of current study. This significant performance improvement may be an effect of
AVX2 (Advanced Vector Extension 2) new instructions of current CPU and its support byIntel® C++ compiler 2013. If AVX2 was disabled or used older generation of FFT library on
compiling, the throughput was about 40 – 50 MSPS with current CPU. Meanwhile, GPUshowed a significant improvement of throughputs than CPU. It is because that AE streams
could be separated in many parts and processed by hundreds of calculation cores in GPU inparallel. It can be said that, STFT extracted the merit of large scale parallel computing byGPU. The bottle neck of STFT / i-STFT calculation in GPU was memory bandwidth between
CPU and GPU [7]. It was only several gigabytes per second, but GPU #2 has four times of
bandwidth than GPU #1 and their throughputs been proportional to their bandwidth.
The throughput of AE hit detection showed a different tendency. Thresholding of AE stream wasvery simple processing but could not be expected to parallelize in GPU because of its sequential
processing i.e. one result affects to the next calculation. Therefore, the performance of one
calculation core decided the total throughput when the number of threshold was small. In suchcases, CPU had an advantage. However, when the number of threshold became large, each CPU
core processed multiple thresholding simultaneously and finally GPU showed the better
throughput. In this type of calculation, memory transaction between CPU and GPU was small andmemory bandwidth between CPU and GPU did not be the bottle neck of calculation in GPU.

5. Conclusion
Analysis of multi-channel AE stream often becomes a very heavy task even for modern CPU
because of its extremely huge data and calculation amount. In this study, two types ofprocessors for parallel computation i.e. CPU and GPU were compared in two types of signal
processing on continuously recorded AE waveforms. In general, GPU calculation showed
better performance than CPU especially in case of large scale calculation for high sensitivity
and automatic AE event detection in noisy environment i.e. noise filtering in frequency
domain and automatic AE hit detection with many threshold voltages. Meanwhile, CPU
showed sufficient performance for sequential processing. Ultimately, proper use of CPU andGPU showed the highest performance for analysis of AE streams.
Acknowledgement
We are particularly grateful for the assistance given by Mr. Dai Miyashita and Mr. Tadayoshi
Okuma about development of software, and this work is supported by MEXT/JSPS
KAKENHI Grant #26820302 and research subsidizing program by Moritani ScholarshipFoundation.
References
1. K. Ito and M. Enoki, “Acquisition and Analysis of Continuous Acoustic Emission
Waveform for Classification of Damage Sources in Ceramic Fiber Mat”, Materials
Transactions. 48 (2007) 1221-1226.
2. K. Ito, H. Kuriki, H. Araki, S. Kuroda and M. Enoki, “Detection of Segmentation Cracks in
the Top Coat of Thermal Barrier Coatings during Plasma Spraying by Non-Contact Acoustic
Emission Method”, Science and Technology of Advanced Materials, 15 (2014) 035007.

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3. K. Ito, H. Kuriki, H. Araki, S. Kuroda and M. Enoki, “Noise-Resistant Method of AE
Events Detection with Multiple Thresholds and Its Application to Plasma Spraying
Process”, Progress in Acoustic Emission, XVI (2012) 55-60.
4. (For example) D. Luebke, M. Harris, N. Govindaraju, A. Lefohn et al., “GPGPU: generalpurpose computation on graphics hardware”, Proceedings of the 2006 ACM/IEEE
conference on Supercomputing, 208.
5. Official website of TOP500, http://www.top500.org/list/2016/06/ .
6. L. Riha, R. Smid, H. El-Sayed and A. Docekal, “GPU Acceleration of AE Signal
Processing Algorithms, Progress in Acoustic Emission, XV (2010) 25-30.
7. K. Ito and M. Enoki, “Real-time Noise Reduction of Continuously Recorded AE
Waveforms using GPU Based Parallel Computing Technique”, Progress in Acoustic
Emission, XVII (2014) 215-220.

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32nd EWGAE

Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

CHANGE IN B-VALUE BY AE PROPAGATION LENGTH IN CFRP
Doyun JUNG 1, Yoshihiro MIZUTANI 1, Akira TDOROKI 1, Yoshiro SUZUKI 1
1
Tokyo Institute of Technology; 2-12-1, Ookayama, Meguro-ku, Tokyo, Japan
Phone: +81 3 5734 3178, Fax: +81 3 5734 3178; e-mail: jdoyeon@ginza.mes.titech.ac.jp,
ymizutan@mes.titech.ac.jp, atodorok@ginza.mes.titech.ac.jp, ysuzuki@ginza.mes.titech.ac.jp

Abstract
In this study, changes in b-values with several fracture modes (matrix cracking, de-bonding and fiber breakage)of FRPs were investigated. When AE sources contain single fracture mode, b-value does not change by
propagation length. On the other hand, when AE sources contain several fracture modes, b-value changes withpropagation length. The change in b-value for multiple fracture modes may occur by change in the proportion ofeach frequency components after the propagation due to frequency dependence of the attenuation.
Keywords: b-value, Carbon Fiber Reinforced Plastic (FRP), Frequency dependence of Attenuation.

1. Introduction
Acoustic Emission Testing (AT) is one of the non-destructive testing methods which detectdamages in mechanical structures by using AE signals caused from damages. Severity ofdamages can be estimated by evaluating AE parameters of detected AE signals. Amplitude
distributions of AE signals are characterized by b-value and the value is also used for theseverity evaluation. Shiotani et. al. [1] and Sammonds et.al.[2] showed that the b-value for
evaluating damages of ground slopes and rocks. b-value is now widely used in the fields ofcivil engineering. On the other hands, Carbon fiber reinforced plastic (CFRP) is a hybridmaterial that contains carbon fiber as reinforcements and epoxy resin as matrixes, and is
widely used in the chemical and mechanical industries. It is known that FRP (fiber reinforced
plastic) have several fracture modes (Matrix cracking, De-bonding and Fiber breakage) andeach mode cause AE with different frequency range (see Figure 1). It is also known that
attenuation of AE changes with the frequency of AE. In this study effects of propagation
length and frequency of AE on b-value are discussed.

Figure 1 Schematic image of defects caused in FRP

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S1232. b-value analysis
The b-value is usually calculated using the cumulative frequency-magnitude distribution data
and applying the Gutenberg-Richter relationship, which is widely used in earthquake
seismology [3]. The relationship can be expressed by a following empirical equation (2)∞N(M) = ∫𝑀𝑀𝑀𝑀 𝑛𝑛𝑛𝑛(𝑀𝑀𝑀𝑀) ........................................................................................................... (1)𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙10 𝑁𝑁𝑁𝑁(𝑀𝑀𝑀𝑀) = 𝑎𝑎𝑎𝑎 − 𝑏𝑏𝑏𝑏𝑀𝑀𝑀𝑀 ................................................................................................... (2)Where M = earthquake magnitude, n(M) = number of the earthquake of magnitude M,
N(M) = total number greater than magnitude M, a and b = empirical constants. As shownin equation (2), the magnitude is proportional to the logarithm of the maximum amplitude
log 10 N(M). Magnitudes of earthquakes are changed with distance from the source tosensor due to attenuation, although, b-value is constant when seismic wave contain single
frequency or attenuation has no frequency dependence. From the equation (2), the b-value
is the negative gradient of the log-linear AE hits-magnitude plot and therefore it
represents the slope of the amplitude distribution (see Figure 2). In terms of AE technique,the Gutenberg-Ritcher formula can be modified as following equation.
𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙10 𝑁𝑁𝑁𝑁 = 𝑎𝑎𝑎𝑎 − 𝑏𝑏𝑏𝑏(

𝑑𝑑𝑑𝑑𝐵𝐵𝐵𝐵𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴
20) ................................................................................................... (3)Both the frequency and amplitude distribution of AE signals generated from source arechanged by characteristics of fractures. For example, micro-cracks generate a large number ofsmall AE. On the other hand, macro-cracks generate small number of large AE. Though,micro-cracks lead b-value for high value and macro-cracks for relatively low value. Whendamage level of structures reaches close to final fracture, fracture behaviour is changed frommicro-crack to macro-crack and then b-value becomes small. This is the reason why b-value
is used to evaluate damage severity of structures.

Figure 2 Definition of b-value

3. AE testing of CFRP plates with Center HollThe specimens used in the experiments are CFRP laminates made from unidirectional prepreg
sheet (Mitsubishi Rayon Japan, PYROFIL#380). The stacking sequences are [0] 8 , [10] 16 ,[90] 16 and cross-ply[0/90] 8 . Plates of 250 mm length and 150 mm width were made using an
autoclave. The pre-cure condition was 85˚C × 2 hours, and the main curing condition was
135˚C × 3 hours under 0.7 MPa pressure. From these CFRP plates, rectangular plate
specimens of 200 mm length as shown in Figure 3 and table 1 were prepared. The GFRP-tabs

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with 2mm in thickness were attached to the specimens of both ends for the tensile test to
avoid causing any damage by a jig. Three AE sensors (Physical Acoustics, Type: PICO) were
mounted on the specimen with different distance from the holes (S1-S3 in Fig. 3). The crosshead speed of tensile tester was controlled as 0.1mm/min. Due to the stress concentration, thespecimens broke at near the center hole (See lower of Fig. 3). Fracture strain and stress areshown in Table 1. Matrix cracking was dominant for the [90] 16 specimen, both the matrix
cracking and de-bonding were observed for [10] 16 specimen. All fracture modes shown in
Fig. 1 were observed for [0] 16 and [0/90] 8 specimens. During the test, AE signals larger than
35dB were detected.

# 1 [90] 16#2 [10] 16#3 [0] 8#4 cross-ply[0/90] 8

Figure 3 Dimensions of tensile test specimen and location of AE sensor (upper) and specimens after the tensile
tests (lower)
Table 1 Specifications of test specimen

l0

4

(mm)
100

σ failure(MPa)11

ε failure
(%)
0.35

200

4

100

18

0.58

16

200

6

100

805

4.1

16

200

6

100

840

4

Test

Layer

Width
(mm)

Length
(mm)

C
(mm)

# 1 [90] 16

16

20

200

# 2 [10] 16

1620# 3 [0] 88# 4 [0/90] 8

8

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4. b-value analysis for CFRP
Figure 4 shows b-value calculated by using detected AE signals by the s1, s2 and s3 sensors in
Fig. 3 (upper) for cross-ply [0/90] 8 specimens. Calculated b-values from detected AE by three
AE sensors are fluctuated during the test in some range, although, the average of b-values are
increased with propagation length. Figure 5 shows centroid frequency of detected AE signals.

Figure 4 Change in b-values during tensile test for [0/90] 8 specimens.

(a) s1 (10mm)(b) s2 (20mm)(c) s3 (30mm)

Figure 5 Histogram of centroid frequencies of AE for cross-ply CFRP [0/90] 8 tensile test.

AE with 50-650 kHz are dominant for s1 (propagation length 10mm), although dominantfrequencies for s3 (propagation length 30mm) are changed as 50-550 kHz. Change indominant frequency with propagation length may change the b-value. Then, we conducted
detailed discussion about the cause of b-value change.
Figure 6 (a, b, c) shows histogram of centroid frequencies of AE for [90] 8, [10] 16 and [0] 8
detected by the S1 sensor respectively. Numbers of hits with logarithm scale are overwrappedin the graph with dotted line. AE in Fig. 6 (a) is mainly caused by matrix cracking. AE in
Fig. 6(b) is caused by matrix cracking and de-bonding. On the other hand, AE in Fig. 6(c) is
caused by all fracture modes described in Fig. 1. By comparing each results, dominantfrequency for matrix cracking, de-bonding and fiber breakage were confirmed
as100~250 kHz, 450~540 kHz and 550~660 kHz respectively.
As we confirmed dominant frequencies for each fracture modes, AE from matrix cracking,de-bonding and fiber breakage caused for cross-ply [0/90] 8 were separated by band-passfrequency filters of 100~250kHz, 450~550kHz and 550kHz~650kHz. Fig. 7 (a,b,c) are resultsof separation for AEs detected for cross-ply [0/90] 8 . The symbols “∆”, “O” and “X” indicate
AEs that detected at different propagation length of 10, 20 and 30 mm. As show in the figure,slope of the each line (negative of b-value) dose not change by propagation length. It is foundthat b-value is not changed by propagation legth when detected AE signals are caused by

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certain fracture mode (When frequency range of detected AE signals is narrow) as not likeFig. 4. In order to discuss about the cause of changing b-value by propagation length in Fig. 4,
the cumulative amplitude distributions of each frequency component according to fracturemodes were investigated.
(a) [90] 16(b) [10] 16(c) [0] 8

Figure 6 Histogram of centroid frequencies of AE for [90] 8, [10] 16 and [0] 8 . Hits with logarithm scale are
overwrapped in the graph with dotted line.

(a) 100~250kHz(b) 450~550kHz(c) 550~650kHz

Figure 7 Cumulative amplitude distributions of each frequency component according to fracture modes(100~250kHz = matrix cracking, 450~550kHz = de-bonding, 550~650kHz = fiber failure).

(a) 10mm(b) 20mm(c) 30mm

Figure 8 Cumulative amplitude distribution for each fracture modes with different AE propagation length.

Figure 8 shows cumulative amplitude distribution for each fracture modes (●:matrix cracking,△ :de-bonding, × : fiber breakage) with different AE propagation length ((a):10 mm,(b):20mm, (c):30mm). AE distribution for total of each fracture modes is also shown in thegraph (symbol ○). Slope of the distribution of all AE sources (○) which propagation length

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10mm (Fig. (a)) is composed with all frequency components of 100~250kHz, 450~550kHz
and 550kHz~650kHz. The negative slope of the line (b-value) becomes 0.06 and the value isdifferent from that of each frequency sorce (100-250kHz: 0.05, 450-550kHz: 0.07 and 550650kHz: 0.049). On the other hands, due to the frequency dependence of attenuation (highattenuation factor for high frequency components) , number of high frequency AE due to fiber
breakage (550-650kHz) is drastically reduced for the results of 20 and 30 mm propagation
lentgh (Fig. (b) and (c)). Though, slope of the distribution of all AE sources (○) which
propagation length 20 and 30 mm are almost decided by 100-250 kHz component (matrix
cracking) and 450-550 kHz (de-bonding) components only. From these result, the change inb-value may be caused by change in the proportion of each frequency components after the
propagation due to frequency dependence of the AE attenuation.

5. Failure mechanism of CFRP estimated by b-value analysis
As propagation length affects b-value for CFRP, we focused on certain propagation length of
10mm and discussed about failure mechanism of CFRP based on b-value analysis. Figure 9 istime-series data of b-value of total AE (O: 50kHz~650kHz) during tensile test (Extracted
symbol □ data from Fig. 4). Change in b-value for each frequency components (each fracturemodes) are overwrapped in the graph (matrix cracking (◊: 100~250kHz), de-bonding (∆:450~550kHz), fiber breakage (X: 550kHz~650kHz)). Dotted points indicate the stress during
the test. Both matrix cracking and de-bonding are dominant fracture modes until 270sec.,
although, fiber breakage is added after 270sec.. When damage level of CFRP specimens
reaches close to final fracture, fracture behaviour is changed from matrix cracking and debonding to fiber breakage and then each b-value changes drastically (matrix cracking: 0.003,
de-bonding: 0.058, fiber breakage: 0.041 and all AE source: 0.048).

Figure 9 Relations between all AE source (50~650kHz), and each AE source according to fracture mode
(100~250kHz, 450~550kHz and 550kHz~650kHz).

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6. Conclusions
In this study, changes in b-values with fractures of FRPs were investigated. We conducted thetensile test with AE analysis for [0] 8 , [10] 16 , [90] 16 and cross-ply[0/90] 8 tensile specimens.
Dominant fracture modes (matrix cracking, de-bonding and fiber breakage) are changed withstacking sequence of CFRP. The AE signals due to each fracture modes can be separated byusing frequency filter of 100~250kHz (matrix cracking), 450~550kHz (de-bonding) and
550kHz~650kHz (fiber breakage). When AE sources contain single fracture mode, b-value
does not chang by propagation length. On the other hand, when AE sources contain several
fracture modes (AEs with different frequency range), b-value changes with propagation
length. The change in b-value for multiple fracture modes may occur by change in the
proportion of each frequency components after the propagation due to frequency dependenceof the attenuation. Additionally, it was found that when damage level of cross-ply[0/90] 8
specimens reaches close to final fracture, fracture behaviour is changed from matrix cracking
and de-bonding to fiber breakage and then b-value for total AE is changed.
Acknowledgement
This work was supported by JSPS KAKENHI Grant Number 16J12078.
References
1.
2.
3.

Shiotani, T., Fujii, K., Aoki, T., and Amou, K., “Evaluation of progressive failure using
AE sourced and improved b-value on slope model tests.”, Prog. Acoustic Emission. VII,7, pp.529-534, 1994.
Colombo, Ing S., I. G. Main, and M. C. Forde. "Assessing damage of reinforced concrete
beam using “b-value” analysis of acoustic emission signals." Journal of materials in civilengineering 15.3, pp 280-286, 2003.
Gutenberg, Beno, and Charles Francis Richter. "Magnitude and energy of earthquakes."
Annals of Geophysics 9.1, 1-15, 1956.

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

TWO-DIMENSIONAL Q-VALUE AE-TOMOGRAPHY AND ITS
VERIFICATION ON NUMERICAL INVESITIGATIONS
Yoshikazu KOBAYASHI 1,3, Tomoki SHIOTANI 2
College of Science and Technology, Nihon University; Tokyo, Japan
Phone: +81 3 3259 0575, Fax: +81 3 3259 0575; e-mail: kobayashi.yoshikazu@nihon-u.ac.jp
Faculty of Engineering, Kyoto University; Kyoto, Japan; e-mail: shiotani.tomoki.2v@kyoto-u.ac.jp
3
C-PIER, Kyoto University; Kyoto, Japan
1

2

Abstract
AE-Tomography is a technique that identifies source locations of AE events and elastic wave velocity distribution in
objects of interest for evaluation of integrity of structures on the basis of the nature in which elastic wave velocity
correlates with deterioration of materials. However, sensitivity of elastic wave velocity is insufficient to reveal thedeterioration on its early stage, and further, elastic wave velocity depends on frequency of elastic wave due todispersion. Thus, Shiotani et al. proposed a method in which Q-Value is adopted as index of the deterioration to detectthe deterioration adequately, and the authors have introduced a tomographic technique that identifies elastic wave
velocity and Q-Value distributions on the basis of the method to visualize distribution of the deterioration. Thus, in thisstudy, the authors propose a new algorithm of AE-Tomography that estimates source locations of AE events, elastic
wave velocity and Q Value distributions simultaneously by using arrival time of AE and gradient of logarithmic
frequency response ratio at receivers on the basis of the tomographic technique. The proposed technique is verified by
performing a series of numerical investigations and its characteristic is discussed in this study.
Keywords: AE-Tomography, Q-Value distribution, Elastic wave velocity distribution, Source location technique,
System identification, Kalman filter

1. Introduction
Japan experienced the period of high economy growth in the 60’s, and many of social
infrastructures constructed during the period, and the structures are playing significant roles inthe society since the time. However, decades have passed since the constructions, and thestructures aged over time. According to the whitepaper of land, infrastructure, transport andtourism in Japan, it was reported that more than 60 percent of bridge decks will be older than
50 years old in 2032 [1]. For keeping serviceability of the structures, i.e. it immediately
supports social activity, maintenance of the structures is an important task, and evaluation of
structural integrity is necessary for the maintenance. The authors have studied on tomographic
techniques, e.g. elastic wave velocity tomography for the evaluations, and the techniques havebeen applied for investigations of existing aged structures [2]. Among the studies, the authors
recently proposed AE-Tomography (Acoustic Emission Tomography) in which elastic wave
velocity distributions and AE source locations are identified by using arrival times of AE atreceivers. AE-Tomography has been applied for the evaluation as well as elastic wave velocity
tomography and successfully visualized deterioration of the structure. Nevertheless, AETomography adopts elastic wave velocity as an index of damage of materials, and it isempirically known that sensitivity of the velocity to early stage of material damage is low, andfurther, in particular cases, its dispersion would cause problems on the evaluation because thedamage level has to be determined by the velocity although it would change by not only damageof the material but also its frequency. For overcoming the difficulty, one of the authors proposeda technique that identifies Q-Value on two- or three- dimensional objects as an index of material
deterioration of the objects [3]. Although the proposed technique assumes that Q-Value andelastic wave velocity is homogeneous on the objects, the authors proposed an algorithm of QValue tomography that identifies Q-Value distribution as well as elastic wave velocity
distribution with consideration of heterogeneity of the distributions on the basis of tomographic

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algorithm [4]. Because the proposed Q-Value tomography is on the basis of conventional
tomographic technique, source information, i.e. signal emission time and location of excitation,
are required, and consequently it leads limitation of application of the algorithm. Thus, in thisstudy, a new algorithm of AE-Tomography that identifies Q-Value distribution, elastic wave
velocity distribution and the AE source locations based on the ray-trace algorithm is introduced.

2. Two-dimensional Q-Value AE-Tomography
AE-Tomography is a technique that identifies elastic wave velocity distribution and AE source
locations by using arrival times of AE at receivers, and two-dimensional Q-Value AETomography is structured on the basis of the conventional algorithm of the two-dimensional
AE-Tomography. Figure 1 shows a conceptual flow diagram of Q-Value AE-Tomography.
On Q-Value AE-Tomography, firstly area of interest is meshed as illustrated in Figure 2. This
mesh consists of triangle and quadrilateral cells, and relay points [5] are installed on the cells
to raise accuracy of the identified AE source locations. It is assumed that elastic wave velocity
and Q-Value are constant in individual cells, and initial elastic wave velocity and Q-Value
distributions are given as initial conditions of the analysis. On the initial conditions, AE
source locations are identified, and then, elastic wave velocity and Q-Value distributions are
updated by using the identified AE source locations on the basis of the tomographic
algorithm. If the updated distributions fulfil a criteria of convergence, the distributions are
adopted as resultant distributions. Otherwise, the procedure in a red square in Figure 1 isconducted on the updated distributions. Thus, the procedure in the red square is iteratively
performed until the criteria is fulfilled. In following subsections, individual parts in the entire
algorithm are introduced.
2.1 Identification of AE source locations
The identification of AE source locations is performed on the basis of ray-trace technique thatconsiders heterogeneity of elastic wave velocity distribution [6]. In a source location techniquethat is adopted here, firstly ray-trace is conducted from the location of a receiver that is assigned
at a nodal or relay points to all of the other nodal and relay points on the mesh for obtaining
first travel times from the receiver to the other points on the mesh. Because arrival times of AE

Figure 1 Flow diagram of Q-Value
AE-Tomography

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Figure 2 A mesh on a cross section

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are known at the receivers, potential emission times of AE at the other points are computed bysubtracting the travel time from the arrival time. Hence, the potential emission time implies that
AE is observed at the arrival time at the receiver if the AE is emitted at the potential emission
time at the point. Thus, every nodal and relay points have a potential emission time for each
receiver on an AE event, and consequently, each nodal and relay points have n potential
emission times on the AE event if n receivers are installed on the area. If the representation of
the elastic wave velocity distribution and ray-paths from the source to receivers are identical to
the real ones, the potential emission times should be the same at a source location of the AE
event. However, a point at which variance of the potential emission times is minimum is
adopted as the source location since the representation is generally not identical to the real ones.
Additionally, an emission time of an AE is approximated as an average of the potential emission
times at the source location.
2.2 Observation equation for identification of elastic wave velocity and Q-Value distributions
Since the source location and emission time of AE are already identified, elastic wave velocity
and Q-Value distributions are identified in the method of Q-Value tomography that was
proposed by the authors [4]. As demonstrated in the conventional elastic wave velocity
tomography, observation equation for first travel time is shown as follows in general on, forexample, a ray-path that is illustrated in Figure 3.
ࢀ࢏࢐ െ ࡻ࢏ ൌ ෍ ࡿ࢑ ࢒࢏࢐࢑(1)

࢑In which, ࢀ࢏ is arrival time of AE of event ࢏ at receiver ࢐, ࡻ࢏ is the estimated emission time of
AE of event ࢏, ࡿ࢑ is slowness, reciprocal of elastic wave velocity, of cell ࢑, and ࢒࢏࢐࢑ is lengthof a ray-path from source of AE event ࢏ to receiver ࢐ in cell ࢑. Thus, the ray-path does not
across cell ࢑, ࢒࢏࢐࢑ is identical to zero. Since the elastic wave velocity distribution is generally
heterogeneous on real structures because damage or insufficient quality of materials cause
decreasing of the elastic wave velocity. If the elastic wave velocity distribution is heterogeneous,
the ray-paths bend because of reflation and diffraction of the elastic wave. This influenceis considered in this study by performing ray-trace on the mesh to obtain the ray-paths.
This is a shortest path problem, and Dijkstra method is used to solve the problem here. For the

Figure 3 Example of ray-path and its travel time

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identification of Q-Value distribution, another observation equation is necessary. The authors
proposed an observation equation for the identification of Q-Value distribution in an algorithmof Q-Value Tomography on the basis of a technique that evaluates integrity of materials byusing Q-Value proposed by Shiotani et. al [3]. In the technique, it is assumed that AE isobserved at receivers on an observation system that is illustrated in Figure 4. The observation
system is described as follows in frequency domain.
��� ��� � �� ��� � ���� ��� �� �������
�

(2)

In which, ��� ��� is acceleration of AE event � at receiver �, �� ��� is acceleration of AE event �
at its source location, ���� ��� is transfer function of cell � on the ray-path from source locationof AE event � to receiver �. It should be noted that ���� ��� is not taken account if cell � is noton the ray-path. Further, �� ��� and ���� are transfer functions of receiver � and dataacquisition system, respectively. Thus, if AE event � is observed at two receivers, namely
receiver �� and �� , for example as shown in Figure 5, ratio of accelerations between the tworeceivers in frequency domain are described as follows in general.
���� ��� �� ��� ∏� ���� � ��� ��� ������� ∏� ���� � �����(3)���� ��� �� ��� ∏� ���� � ��� ��� ������� ∏� ���� � ���

In Equation 3, it is assumed that ��� ��� and ��� ��� are identical to simply the computational
procedure. Q-Value is a parameter that relates with dissipation of kinetic energy while
propagation of elastic wave in media. Q-Value is defined as
�
��
�
� ���

(4)

where � and �� are energy of the elastic wave and dissipation of the energy during a cycle.
A transfer function of elastic wave between two points of which distance is � is given as
follows on the basis of Equation 4 on homogeneous materials.
���� � ��� ��

���
�
��

(5)

In which, �, �, � are frequency of the elastic wave, elastic wave velocity and Q-Value of thematerial, respectively. By applying Equation 5 to compute the transfer functions of individual
cells on the ray-paths, the transfer function of cell � is obtained as follows.

Figure 4 Observation in frequency domain

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���� ��� � ��� ��������
�
�� ��

(6)

Where �� and �� are elastic wave velocity and Q-Value of cell �. Consequently, Equation 3 isrewritten by substituting Equation 6 as follows.
���� ��� ∏� ����� ���
�����
�����
�
� ��� ���� ��
��
��
(7)
���� ��� ∏� ����� ���
�� ��
�� ��
�

�

By computing difference of natural logarithm of Equation 4 with �, following equation is
obtained.
���� ������� �� �� �� ���� ����� � � �� ���� ����� �(8)�� ���� �����Equation 5 implies one observation equation is structured on any combination of two receives
on cross section of interest. Consequently, if n receivers are installed on the cross section, nC2
observation equations are structured by using Equation 8.
2.3 Identification of elastic wave velocity and Q-Value distributions

By using the observation equations, elastic wave velocity and Q-Value distributions are
identified on the basis of identification techniques. In this study, Extended Kalman Filter isadopted as the identification technique. On the algorithm of Extended Kalman Filter,a nonlinear system is described as follows if state vector �� is unchanged while theidentification.
(9)���� � ������ � �� ��� � � ��

(10)

Figure 5 Conceptual sketch of a layout of two sensors for
identification of Q-Value

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in which, �� , �� �� � � and �� are observation vector, mapping function from �� to �� and noise of
the observation, respectively. On this system, elastic wave velocity and Q-Value on individual
cells are used as state values in the state vector. The mapping function is formed by combining
Equations 1 and 7. It should be noted that Equation 9 demonstrates that the elastic wave velocity
and Q-Value of the cells are unchanged while the process of the identification in this study.
Thus, this technique is only applicable for the materials which state is unchanged while theobservations. This implies that AE events that are used as the observation on this technique
should be the secondary AE because occurrence of primary AE indicates fracture is progressing,and it consequently demonstrates state of the material is changing.
On the basis of Equations 9 and 10, Extended Kalman Filter is structured as follows.
(11)������ � ���������� ������ � ������ � �� ��� � �� ��(12)� �� ��� � ������ �� �� � �� ����� � ������ �(13)� �� ��� � ������ �� �� � �� ��� �� � ������
���� � ������ � ������ �(15)

in which, ������ and � ��� are identified state vector at � � � on the basis of the information at� � � and �, respectively. � � is a Kalman gain as shown in following equations.
(14)������ � ����where ������ and ���� are estimated error covariance matrices of the state vector at � � � on
the basis of the information at � � � and �, respectively, �� is an error covariance matrix ofthe observation vector at �. These matrices indicate reliability of initial state value andobservations, these matrices should be given appropriately on the basis of accuracy of theinitial distributions and observations. It should be noted that non-diagonal term of R and P
are set to zero in this study. Further, it is assumed that �� is constant while theidentification. This implies that elastic wave velocity and Q-Value on the individual cells are
independent each other. However, if the correlations are demanded, it can be specified bygiving appropriate quantities. ��� is an observation vector that is described as follows.
��� � � � ���(11)��� ����
�����

By applying Equations 11 and 12 iteratively, the state vector is appropriately identified.

3. Verifications
The introduced algorithm of Q-Value AE-Tomography is verified by performing a series ofnumerical investigations. Figure 6 shows a cross section that is used for the verifications. The
cross section is a square with a side length of 15m and receivers are installed at the apexes of
the cross section. Thus, there are 4 receivers on the cross section in the verification. The crosssection consists of two types of regions. Regions of one of the types are coloured with whiteand regions of the another type are coloured with grey, and the white and grey regions aresupposed intact and damaged area, respectively. For these verifications, two models that aredescribed in Table 1 are adopted in this study. As shown in Table 1, elastic wave velocity and
Q-Value are set to 4000m/s and 50 on the white region, and 3000m/s and 20 on the grey regionin Case 1. This case assumes that the degradation of the grey region is severe, and both of elastic
wave velocity and Q-Value are relatively low due to the degradation. On Case 2, the elastic

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wave velocity on the grey region is raised to 4000 m/s in comparison with Case1. This caseassumes the degradation is still on its early stage and elastic wave velocity is keeping its
quantity in intact condition. Since the observations are necessary for the numericalinvestigations, 100 artificial AE events are generated randomly on the cross section, and theobservations are computed by performing the ray-trace from the generated artificial sources tothe receivers for the arrival time and using Equation 1 on resultant ray-path of the ray-trace,
and assuming straight ray-paths from the AE source to the receives and using Equation 8 on thestraight ray-paths for gradient of logarithmic frequency response ratio. Thus, total number ofthe observation equations is 400 for the arrival time, and 600 for the gradient of logarithmic
frequency response. Consequently, there are 1000 observation equations in this investigation.
As the initial distributions, homogeneous elastic wave velocity distribution of 4000 m/s, andhomogeneous Q-Value distribution of 50 are given.
Figures 7 and 8 show identified elastic wave velocity distribution and Q-Value distribution inCase 1 at 5th iteration and the end of the identification. On the figures, the white circles are
identified source locations and the black circles are real source locations that are generated
artificially. According to the Figures 7 and 8, the identified distributions qualitatively reproduce
the original distributions. On the identified Q-Value distribution, there are high Q-Value areafrom left side to center on the band of 5.5m to 10 m in y coordinate of the cross section. The
low Q-Value areas exist on the upper and lower parts of the cross section and this results shows
the same tendency with the original model that is illustrated in Figure 6. However, the low QValue area penetrates from the upper part to the lower part on the right side of the cross section.
On the identified elastic wave velocity distributions, it shows the same tendency with theoriginal model as well. There are low velocity areas exists on the upper and lower part of thecross section as well. The penetration of the low Q-Value area is caused by the resolution of
the source location technique and limitation of the identification technique. Since resolution ofsource locations is density of relay and nodal points in this source location technique, andfurther, because the resultant elastic wave velocity is not identical to the original one, there are
errors on the source locations. Under this situation, relative error of the source locations
becomes large if the source location and receiver is close, and consequently, resultant Q-Value
distribution is affected by the error. Moreover, since Equation 9 is defined as products of QValue and elastic wave velocity, the equation is fulfilled if the products is identical to theTable 1 Conditions
Case 1Case 2V(grey zone)

3000m/s

4000m/sQ(grey zone)

20

20

V(white zone)

4000m/s

4000m/sQ(white zone)

50

50

Num. Events

100

100

Num. Iteration

200

200

Initial V

4000/s

4000m/s

Initial Q

50

50

Figure 6 Numerical model for the verification

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original value. Thus, in the right side on the band of y=5.5m to 10m, the elastic wave velocity
is overestimated, and consequently, Q-Value would be underestimated. Further, the identified
source locations moved between the middle and the end of the identification. This is because
the elastic wave velocity distribution is updated while the identification, and the source
locations identified more closely to the real source locations due to the update of the elastic
wave velocity distribution.
Figures 9 and 10 show identified elastic wave velocity and Q-Value distributions in Case 2 at
5th iteration and the end of identification. Since the elastic wave velocity distribution is thehomogeneous one of 4000 m/s in the original model in this case, change of elastic wave velocity
distribution while the identification is suppressed and the identified elastic wave velocity
distribution is consequently very close to the original elastic wave velocity distribution. In
contrast, although Q-Value distribution largely changes from the initial distribution, theidentified Q-Value distribution reproduces the original distribution very well. This is because
the accuracy of the identified source locations is high because of the identified elastic wave
velocity distribution is almost identical to the real one. Due to the highly accurate source
locations and elastic wave velocity distributions, actual number of degree of freedom isdrastically reduced since the variables that actually identified is only Q-Value distribution.

Figure 7 Identified Q-Value distribution in Case 1(Left: after 5 iterations, right: after 200 iterations)Figure 8 Identified elastic wave velocity distribution in Case 1(Left: after 5 iterations, right: after 200 iterations)

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Figure 9 Identified Q-Value distribution in Case 2(Left: after 5 iterations, right: after 200 iterations)Figure 9 Identified Q-Value distribution in Case 2(Left: after 5 iterations, right: after 200 iterations)

Consequently, the Q-Value distribution is identified with sufficient number of observations andit was identified more accurately.
These results suggest that the introduced technique identifies Q-Value and elastic wave velocity
distributions more accurately on the early stage of degradation. Because it is known thesensitivity of Q-Value is higher than the elastic wave velocity to the damage, and it is supposedthat the elastic wave velocity is still close the velocity in intact condition nevertheless Q-Valueis already low because of the damage. This characteristic of the proposed method is anadvantage of the policy of the proactive maintenance since finding of the damage on the earlystage is demanded on the policy.

3. Conclusions
In this study, a new algorithm of Two-dimensional Q-Value AE-Tomography that identifies QValue and elastic wave velocity distribution and source locations of AE events by using only
observed AE data at receivers is introduced. The introduced techniques are verified by
performing a series of numerical investigations, and following conclusions are drawn.

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1.

2.

The introduced algorithm was verified on a model that has deteriorated regions in which
both of elastic wave velocity and Q-Value is low as a case that the regions damaged
heavily. The results show that the proposed method identifies Q-Value and elastic wave
velocity distributions adequately.
The introduced algorithm was verified on a model that has deteriorated regions in which
Only Q-Value is low as a case that the regions damaged slightly. In this case, the identified
distributions are consistent with the original distributions as well. Especially, theidentified Q-Value distribution is more accurate than the heavily damaged case and thischaracteristic suggested the proposed technique would be efficient for the evaluation of
structural integrity for proactive maintenance.

Acknowledgement
This study is supported by JSPS KAKENHI Grant Number JPNumber 15K06193 Grant-inAid for Scientific Research (C), Japan Society for the Promotion of Science(JSPS)
References
1.

2.
3.
4.
5.
6.

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Ministry of Land, Infrastructure, Transport and Tourism, 'White paper of land,
infrastructure, transport and tourism in Japan 2013', Ministry of Land, Infrastructure,
Transport and Tourism website: http://www.mlit.go.jp/en/statistics/white-paper-mlit2013.html, 2013
S., Momoki, T. Shiotani, H. K. Chai, D. G. Aggelis, Y. Kobayashi, ‘Large-scaleevaluation of concrete repair by three-dimensional elastic-wave based visualization
technique’, Structural Health Monitoring, Volume 12, No 3, pp 241–252, 2013
T., Shiotani, Y. Takada, T. Watanabe, H. Ohtsu, ‘Damage evaluation of heterogeneousmaterials by Q-value analysis of AE waveforms’, JSNDI, Progress in Acoustic Emission
XV, 1-6, 2012.
Y. Kobayashi, T. Shiotani, ‘Two-dimensional Q-Value Tomography and its verification
based on numerical investigations’, Proceedings of Structural Faults and Repair 2016,
May 2016
Y. Kobayashi, ‘Mesh-independent ray-trace algorithm for concrete structures’,
Constructions and Building Materials, Volume 48, pp. 1309–1317, 2013
Y. Kobayashi, T. Shiotani, ‘Seismic Tomography with Estimation of Source Location forConcrete Structures’, Proceedings of Structural Faults and Repair 2012, July 2012

32nd EWGAE

Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

STUDY OF FATIGUE PROCESSES OF SELECTIVE LASER MELTING
AND CONVENTIONAL PRODUCED MATERIALS
Vendula KRATOCHVILOVA1a, Frantisek VLASIC1b, Pavel MAZAL1c
Brno University of Technology, Faculty of Mechanical Engineering; Technicka 2896/2, 616 69 Brno,
Czech Republic
Phone: +420 54114 3240; e-mail: a xkrato04@vutbr.cz, b vlasic@fme.vutbr.cz, c mazal@fme.vutbr.cz

1

Abstract
The main aim of the study is to compare fatigue processes and its acoustic emission (AE) signal of conventional
produced material and material produced by selective laser melting (SLM) technology. The SLM offers the same
benefits as the rest of the rapid prototyping technologies, the most important is big range of produced shapes, but
without expensive moulds, cores or huge material waste. However the mechanical properties of these materials
are currently worse than those of conventionally produced materials.
AE method was used to get new information about fatigue damage of SLM materials. This study summarizes
results from bending fatigue tests of reference Al alloy AW-2618A (AlCu2Mg1,5Ni) and SLM material withcomparable chemical composition. Acquired AE and fatigue data from both materials were compared. The
fractographic and metallographic analysis were also conducted as complement.
Internal defects and inhomogenities in SLM materials cause different fracture behaviour than we can see atconventional materials. Differences in fatigue behaviour of SLM materials are strongly reflected in AE signal.
The results may be used to improve the production of SLM materials in order to get better material
characteristics. AE data helps to understand the fatigue behaviour of SLM materials in order to predict crack
initiation.
Keywords: selective laser melting, acoustic emission method, fatigue, crack, Al alloy

1. Introduction
Rapid prototyping (RP) technologies, especially those that enable to produce metal parts arenowadays under intensive studies. Selective laser melting (SLM) technology produces partsby adding thin material layers, which are connected by melting by focused laser beam
(Fig. 1a) [1]. This allows producing parts with complicated shapes without huge material
waste or expensive moulds and cores.
As the SLM technology is still under research, achieved mechanical characteristics, especiallyfatigue, are not as good as in case of conventional produced materials. Current studies were
aimed mainly on titanium alloy and steel, aluminium alloys have been overlooked.
For titanium alloy Ti-6Al-4V was possible to get better fatigue life than is usual in castmaterial and equivalent to the cast material with heat treatment [2]. For stainless steel SS
316L and 15-5PH was possible to get only 20-25 % lesser fatigue life than has
conventionally produced materials and fatigue life was strongly effected by surface quality[3]. This is mainly connected with internal defects, which could be more or less similar as
casting or welding defects – porosity, lack of material, but we can also see typical SLM
defects for example unmelted powder in cavities [2, 4].
Those defects could be eliminate by optimizing of process parameter, which are scanningspeed [5], laser power [6], powder grain size and quality [7], building strategy [8], hatch
spacing [9] and layer thickness [10].
Acoustic emission (AE) is one of the standard non-destructive (NDT) methods. It allows to
detect, localize and evaluate defects during their formation, so it is suitable for fatigue process

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monitoring [11]. There is possible not only to detect and characterization fatigue damage
growth [12], but also to distinguish various stages of fatigue phases, to predict the rate ofcrack propagation [13] and to identify sources of AE in metals fatigue specimens and relatethem to damage mechanisms [14, 15].

2. Material and methods
Testing material is aluminium alloy. The results from fatigue testing and AE data of SLM
material were compared with results of reference conventionally produced (extruded) material
from Al alloy AlCu2Mg1,5Ni (2618A) with T6 heat treatment, chemical composition of SLM
material is similar. Chemical composition of both materials is shown in Table 1.
Table 1. Chemical composition (%) of reference and SLM material
Element
Reference material
SLM powder

Si
0,24
0,15

Fe
1,1
1,0

Cu
2,5
2,66

Mg
1,5
1,39

Ni
1,2
1,22

Ti
0,04
0,2

SLM samples were produced by SLM 180 HL machine. At the beginning there were trieddifferent process parameters. Quality of produced samples were evaluated on the basis ofrelative density, the best result was 0,35 %. The final optimal parameters were: laser power
200 W, laser speed 100 mm/s, hatch distance 110 µm and beam diameter 82 µm. Scanning
strategy were meander and it is shown in the Fig. 1b) [17].

a)b)Figure 1. SLM technology principle (a) [16] and meander scanning strategy (b) [17]A set of 26 specimens was made from reference material, 13 pieces of this set were subjected
to heat treatment (T6). 3 sets of 4 pieces were made from SLM material. Two sets were madefrom new powder; the difference was in the distribution of samples on the plate. Samples
from 1st set (identified as 2015) were made one by one, but pieces from 2nd set (identified as2016) were produced together. 3rd set was made from recycled powder.
On the metallographic samples (Fig. 2) we can clearly see layer borders and a lot of defects
near them (red arrows in the Fig. 2). This kind of internal structure has bad influence on
fatigue life.
The bending fatigue tests were carried out by electro-resonance RUMUL Cractronic 8204
testing machine. The fatigue cycle was sinusoidal in stress ratio R = -1, test were conducted inhigh-cycle region in room temperature. Approximate loading frequency was about 52 Hz.

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Figure 2. Microstructure of SLM material in the longitudinal (left) and transverse (right) direction

AE signal was detected by a DAKEL-XEDO and also by IPL system using two piezoelectric
DAKEL IDK-09 sensors with 35 dB preamplifiers. The sensors were clamped on each end of
the specimen by Loctite glue in order to create a two channel linear location system.
Advanced IPL continuous data storage system was used to record all the incoming signals
sampled by 2MHz. This system covers the frequency bandwidth of approximately 20 –800 kHz with a total system gain of 80 dB. The threshold detection of the AE hits was
frequently applied in post processing.

3. Results
All specimens were machined to the shape in the Fig. 3 and fatigue tested in same conditions
at different stress level. The results were compared using standard S-N curves (Fig. 4). SLM
material has significantly worse fatigue resistance than reference extruded material. Extruded
material with T6 treatment has slightly better results than material without treatment. The
results of SLM material fatigue show that the quality of produced material is strongly depend
not only on process parameters, but also on the quality of the used metal powder. Specimens
made from recycled powder have significantly worse fatigue resistance than specimens fromnew powder.

Figure 3. Fatigue testing specimen

The AE measurements were carried only at several stress levels. In order to get an overview
of the AE activity, the number of counts and cumulative number of events was observed, atthe beginning in the reference material (Fig. 5). The beginning of loading (pre-initiation stage)is characterized by high AE activity caused by changes in material microstructure, interaction
and glide of dislocations and persistent slip band formation. This stage is followed by low and32nd EWGAE

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constant AE activity, which takes about 1/3 of the lifetime; micro-cracks start to propagate
here (initiation stage). AE activity significantly increases in the last stage, where the long
crack is growing (post-initiation stage).

Figure 4. S-N diagrams of extruded and SLM materials

The same three stages were observed in the SLM material, but the AE activity is significantly
different (see Fig. 5b). The main differences are in count rate, ratio and duration of the stages.
First two stages, pre-initiation and initiation stages, takes about half of the total fatigue
lifetime, especially the initiation stage is much shorter than in reference material. Thisbehaviour is probably cause by large amount of production defects (pores and shrinkages).

a)b)

Figure 5. AE counts and loading frequency vs. noramlized fatigue cycles of AlCu2Mg1,5Ni at stress amplitudelevel of 229 MPa (a), SLM at stress amplitude level of 122 MPa (b)Significant differences were also observed in the AE amplitude values and duration indifferent phases of fatigue process (comparing the initial phase is given in Fig. 6). In the preinitiation stage of the reference material was observed a lot of events with big scatter ofamplitude value and its duration, most events from the 1st stage of SLM material haveamplitude between 45 and 48 dB AE with low duration.
Reference material initiation stage is characterized by signal with low amplitude and duration,
which are coming from the micro-crack initiation, the same stage of SLM material ischaracterized by higher amplitude with low duration.

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a)

b)

Figure 6. Linear regression of AE events in the initiation stage of AlCu2Mg1,5Ni (a) and same stage of SLM material (b)a)b)

Figure 7. Distribution of signal energy in different stages of fatigue(a) AlCu2Mg1,5Ni, (b) SLM

Percentage of the overall signal energy in the different stages of fatigue process for referencematerial and SLM material is given in Fig. 7. The crack initiation phase (“ini” in the graph)
normally spans the minority of the considered life-cycle in reference material while the preinitiation phase (“pre-“ in the graph) usually covers almost the entire life-cycle.
Unfortunately, this study did not confirm the relationship between phase duration and bending
amplitude. More convincing and consistent results come from SLM material. Here is clear,despite the inhomogeneous structure of SLM, the crack propagation phase (“post-“ in Fig. 7b)spans more than 2/3 life-cycle in all cases of amplitude level. This is caused by manufacturing
defects cause early crack initiation and growth due to cyclic loading. Therefore the initiation
phase as well as pre-initiation phase is very short here. This is also seen in Fig. 5b where theloading frequency is going down very soon and AE hits change its parameters.
The fatigue and AE measurements were supplemented by fractographic study, see Fig. 8 andFig. 9. There is clearly visible a production defects in the Fig. 8 (red ring), the unmelted
powder particles (yellow arrows) shows, that the defect was created during production.

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Figure 8. The fractography of SLM material with marked defect

The red rings in the Fig. 9 and 10 mark the crack origins, which are located in the production
defects. There are the unmelted powder particles in the cavities, so those defects were also
created during the manufacturing process.

Figure 9: The fractographic of SLM material with marked crack origins, stress amplitude level 122 MPa

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Figure 10. The fractographic of SLM material with marked crack origins, stress amplitude level 61 MPa

4. Conclusion
The results from fatigue testing of aluminium alloy AlCu2Mg1,5Ni and SLM material withsimilar chemical composition supplemented by AE measurement and fractographic were
presented in the paper. The results from both material were compared and many differences,
but also some similar behaviour were observed.
All 3 batches of SLM material have significantly worse fatigue life than conventionallyproduced (extruded) material. The AE signal showed the same three stages in both materials,
pre-initiation, initiation and post-initiation stage. The ratio of the stages, AE amplitude andduration is significantly different in SLM and reference material.
The fractographic study of SLM samples showed that the crack origins are located in thesurface defects, which were created during the production. The production parameters for
production of cyclical loaded parts from material AlCu2Mg1,5Ni is not optimized yet andfurther research is needed.

5. Acknowledgements
The authors would like to thank the Czech Science Foundation (GACR) for the funding of the
project GA15-23274S “Design of advanced materials using selective laser melting”. The
project is also supported by NETME Centre (New Technologies for Mechanical Engineering),
Reg. No. CZ.1.05/2.1.00/01.0002 and, in the follow-up sustainability stage, supported through
NETME CENTRE PLUS (LO1202) by financial means from the Ministry of Education,
Youth and Sports under the „National Sustainability Programme I“ and by the project
No. FSI-S-14-2329 “Vibro-isolation system of cosmic carrier payload” of the Faculty of
Mechanical Engineering.

References
1.
2.

J.P. Kruth, P. Mercelis, J. Van Vaerenbergh, L. Froyen and M. Rombouts, 'Binding
mechanisms in selective laser sintering and selective laser melting', Rapid
Prototyping Journal, Vol 11, No 1, pp 26-36, 2005.
Q. Liu, J. Elambasseril, S. Sun, M. Learz, M. Brandt and P.H. Sharp, The Effect ofManufacturing Defects in The Fatigue Behaviour of Ti-6Al-4V Specimens
Fabricated Using Selective Laser Melting', International Fatigue Congress, PTS 1
and 2. Melbourne, Australia 2014, pp 1519-1524, 2014.

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3.
4.

5.

6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.

S148

A.B. Spierings, T.L. Starrand K. Wegener, 'Fatigue performance of additive
manufactured metallic parts', Rapid Prototyping Journal, Vol 19, No 2, pp 88-94,
2013.
H. Gong, K. Rafi, H. Gu, G.D. Janaki Ram, T. Starr and B. Stucker, 'Influence of
defects on mechanical properties of Ti–6Al–4V components produced by selective
laser melting and electron beam melting', Materials and Design, Vol 86, pp 545-554,
2015.
K. Rafi, H. Khalid, T. L. Starr and B. E. Stucker, 'A comparison of the tensile,
fatigue, and fracture behaviour of Ti–6Al–4V and 15-5 PH stainless steel parts madeby selective laser melting', International Journal of Advanced Manufacturing
Technology, Vol 69, No 5-8, pp 1299-1309, 2013.
B. Song, S. Dong, S. Deng, H. Liao and C. Coddet, , 'Microstructure nad tensile
properties of iron parts fabricated by selective laser melting', Optics and Laser
Technology, Vol 53, No 3, pp 451-460, 2014
D. Bourell, A.B. Spierings, N. Herres and G. Levy, 'Influence of the particle size
distribution on surface quality and mechanical properties in AM steel parts', Rapid
Prototyping Journal, Vol 17, No 3, pp 195-202, 2011.
P. Mercelis and J.P. Kruth, 'Residual stresses in selective lasesr sintering andselective laser melting', Rapid Prototyping Journal, Vol 12, No 5, pp 254-265, 2006.
B. Vandenbroucke and J.P. Kruth, 'Selective laser melting of biocompatible metals
for rapid manufacturing medical parts', Rapid Prototyping Journal, Vol 13, No 4, pp
196-203, 2007.
K. ABD-Elghany and D.L. Bourel, 'Property evaluation of 304L stainless steel
fabricated by selective laser melting', Rapid Prototyping Journal, Vol 18, No 5, pp
420-428, 2012.
A. Shanyavskiy nad B. Mukharbiy, 'Fatigue cracking and acoustic emission
regularitires in Metals: Crack origination and growth', Key Engineering Materials,
Vol 592, No 5, pp 735-740, 2013.
I.M. Daniel, J.J. Luo, C.G. Sifniotopoulos and H.J. Chun, 'Acoustic emission
monitoring of fatigue damage in metals', Nondestructive Testing and Evaluation,
Vol 14, pp 71-87, 1998.
Z. Han, H. Luo, C. Sun, J. Li, M. Papealias and C. Davis, 'Acoustic emission studyof fatigue crack propadation in extruded AZ31 magnesium alloy', Materials Scienceand Engineering A, Vol 597, pp 270-278, 2014.
K. Shrama, A. Clarke, R. Pullin and S.L. Evans, 'Detection of cracking in mild steel
fatigue specimens using acoustic emission and digital image correlation', Kex
Engineering Materials, Vol 592, pp 735-740, 2013.
L. Nohál, F. Hort, J. Dovřáček and P. Mazal, 'An experimental investigation ofrolling contact fatigue of steels using acoustic emission method', Insight, Vol 55, No
12, pp 665-669, 2013.
3D Printing Processes: The Free Beginner’s Guide. 3D Printing Industry [online].
2013. Available on: http://3dprintingindustry.com/3d-printing-basics-free-beginnersguide/processes/
O. Koukal, D. Koutný, D. Paloušek, R. Vrána, T. Zikmund and L. Pantělejev,'Research about the influence of process parameters of selective laser malting onmaterial EN AW 2618', Euro PM2015 Proceedings, Reim, France, pp 1-6, 2009.

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

ACOUSTIC EMISSION TESTING ON LPG TANKS - DEFECT
DETECTION CASE STUDIES
Gerald LACKNER 1, Peter TSCHELIESNIG 2
TÜV AUSTRIA, Deutschstrasse 10, 1230 Vienna, Austria; e-mail: gerald.lackner@tuv.at
AT – Consult e.U., Planckhgasse 21, 3400 Klosterneuburg, Austria; e-mail: office@at-consult.at
1

2

Abstract
One main practical application of acoustic emission is testing of metallic pressure vessels at site of installation in
the field. Beginning with the qualification of the employed testing technique and then throughout the whole
period of field testing, people want to know what the intervention on the test object is good for. It is in the very
interest of the stakeholders (manufacturers, owners, operators, authorities as well as testing and inspection
organisations) to verify its capabilities. Practical examples of detected defects taken from qualification tests aswell as from routine tests are able to demonstrate the main advantages and thus support the acceptance of thetesting technique by all parties concerned. This contribution describes by means of case studies a few tests on
LPG tanks where severe defects were found.
Keywords: liquefied petroleum gas (LPG) tank, pressure vessel, field testing, integrity testing, corrosion
detection, leakage detection, crack detection

1. Introduction
The requalification of a pressure equipment after a certain period of service requires usuallya set of inspections/tests in order to assess the structural integrity. Depending on the legal
environment given in the country of installation, the set of inspections/tests to be performed
may consist of external inspection, pressure test, leak test, internal inspection and the like.
Acoustic emission testing (AT) may be employed in the course of the requalification either
alone or in combinations with other tests. It provides information regarding the structural
integrity of the item under test at the same level of safety achieved by conventional
inspections/tests. The following list highlights the most important drivers for its application:•••

Internal inspection is considered to be a high risk activity. Entry into a confined spaceneeds thorough preparation and execution. Serious situations in the course of internal
inspections are reported every year. Acoustic emission testing on LPG tanks makes
internal inspection unnecessary.
Water used for hydrostatic testing is considered to be a hazardous waste and has to bedisposed at specialised facilities. Furthermore, residual humidity may induce or promoteinternal corrosion. Acoustic emission testing does not require to empty or to clean thetank. In most cases it is performed with the LPG of the tank under test. After completionof the test, the tank is immediately available for the consumer.
In the event of an already severely damaged tank, monitoring of the pressurisation withacoustic emission provides on-line information regarding the actual condition of the tankand thus enables the test operator to suspend or even to terminate the test in a safe way inorder to prevent a serious situation to evolve.

Starting from 1993, when the first testing campaign in Austria with more than 1000 tank tests
had been completed, TÜV AUSTRIA and its cooperation partners have been testing
successfully several thousands of LPG tanks in Austria and other European countries till

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today. The core part of the developed procedure consists of the testing technique including theCluster Evaluation Factor (CEF) based on measuring equipment from Vallen Systeme and themobile testing laboratory. The on-board pressurisation unit utilizes for evaporation some partof the liquid phase of the LPG tank under test and feeds back the resulting gas phase ata higher pressure, thus being independent from other pressurisation media like nitrogen.
The applied testing technique is described in detail in several publications [1, 2]. This
contribution focuses on the discussion of test results obtained from different qualification,
demonstration or testing activities at different sites.

2. Case study for crack detection on an above ground tank
A serious incident occurred in Austria in 2009, where a LPG tank failed during a filling
operation inside a gas facility. One head of the tank detached from the cylinder and it was sent
about 100 m away where it hit the ground luckily without causing major damage, neither at
the gas facility nor at the neighbouring facility as shown in figure 1. However, this incident
triggered a follow-up investigation and it was found that both circumferential welds (joggle
joints) were affected by stress corrosion cracking (SCC) due to unspecified maintenanceworks performed on the tank using a corrosive medium. At the other joggle joint a crack witha length of approximately 1 m centred at the 6 o’clock position had been detected. It had tobe shown to the authority that in case of further tanks with similar history, such kind of defectwill be detected by AT while performing the pressurisation. For this purpose a new head waswelded onto the tank. It was intended to pressurise the tank till to burst for obtaining the burst
pressure of the already severely damaged tank. The pressurisation was monitored with AT
during the pressure test section and the results were analysed and described in reference [3].
It could be shown that the degradation was indicated by AT well before the failure of the tank.
The sensor positions and the measuring results are given in figure 1 and figure 2 respectively.
On the other hand side, the tank passed the hydrostatic test (test pressure equal to 1,5 times
the design pressure) without any finding of plastic deformation or leakage.

Figure 1. Set-up of an above ground LPG tank (2700 litre) for crack detection, photo on the top left shows thetested tank with new head welded onto the tank near sensor XD 1, photo on top right shows the affectedcircumferential weld near to the tank leg after the previous failure of the tank head, graph below indicates theincident with the head sent about 100 m away, over the neighbouring road right into another facility.

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Figure 2. Qualification test on an above ground LPG tank (2700 litre), tank was loaded up to 1 MPa (10 bar)
before the test, graphs show CEF and pressure vs. time (1st row left), CEF of located events vs. position along
the cylinder lengths (1st row right), maximum amplitude and energy accumulation of located events vs. time
(2nd row left), waveform and frequency content of an event located near to sensor XD 2 (2nd row right)

One may easily take from figure 2 that the activated degradation process lead to rather highvalues of CEF at an early stage. The test operator had to be alarmed already at about 1,1 MPa(11 bar with value of CEF equal to 2,6) and the test was to stop at the latest at 1,3 MPa(13 bar with value of CEF equal to 2,9). And it is obvious that the obtained location results
based on the linear location using sensors XD 1 and XD 2 (both VS150-RIC, Vallen Systeme)near to the circumferential welds are in good agreement with the location of the SCC affected
weld near to sensor XD 2. The performed test and the gained results were another proof ofcompetence towards the Austrian Accreditation Body, where TÜV AUSTRIA SERVICES
GMBH is accredited as ISO 17025 testing laboratory with the scope of accreditation
comprising the European standards EN 12817 and EN 12819 (requalification of LPG tanks up
to 13 m3 and greater than 13 m3, respectively).

3. Case study for corrosion detection on an underground tank
LPG companies usually perform a service provider assessment prior to awarding a long term
frame contract for performing AT on their tank population. The purpose of this measure is todemonstrate the quality and the effectiveness of the proposed testing service. In many cases
this includes a demonstration on real structures, where tanks with or without defects are to betested and evaluated.
At the site of a gas storage facility we were invited to test an underground tank, which wasburied according to figure 3. The linear sensor setup consisted of sensors XD 1 at thecircumferential weld near to the valve chamber, XD 3 in the area of the valve chamber and
XD 4 at the other circumferential head of the tank. All three sensors were of type VS75-SIC.
The pressurisation was done by a nitrogen blanket on top of a liquid product. The only
information we received from the tank owner regarding the tank condition was, that the tank

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had been tested previously by another testing organisation. Therefore we asked to pressurise
the tank up to 1,1 times the design pressure, which was near to 2 MPa (20 bar). In this way weobtained the maximum amount of information still achievable by AT and at the same time we
kept rather close to the design limits due to risk considerations. All three sensors were
mounted on the 12 o’clock position after removing the soil at their defined location.

Figure 3. Set-up of an underground LPG tank (5000 litre) for detection of degradation, photo on the top shows
the buried tank with the valve chamber box in green and sensors XD 1, XD 3 and XD 4 (VS75-SIC) used forlinear location, photo on bottom right shows some corrosion effects in the area below the valve chamber, photoon the bottom left shows the close-up of the maximum affected area.

Figure 4 indicates that the given active sources lead to a value of CEF above 2,4 at about
1,7 MPa (17 bar) and then increased at about 1,9 MPa (19 bar) to a value of CEF above 2,8.
The tank owner was satisfied with this result and after a final presentation no further
information regarding the technical background was requested. All questions were answered in
sufficient detail and finally we won a frame contract for a three years duration. In order to fulfil
the required number of tested tanks per year we had to produce additional mobile testinglaboratories and we had to train local test operators. Both tasks were handled for the subsidiaryTÜV AUSTRIA TURK by the acoustic emission unit of TÜV AUSTRIA based in Vienna.

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Figure 4. Qualification test on an underground LPG tank (5000 litre), tank loaded before the test till to at least
1,4 MPa (14 bar) , graphs show CEF and pressure vs. time (1st row left), CEF of located events vs. position
along the cylinder lengths (1st row right), maximum amplitude and energy accumulation of located events vs.
time (2nd row left), waveform and frequency content of an event located near to sensor XD 1 (2nd row right)

4. Case study for leak detection on an above ground tank
During a regular field testing route on the Iberian Peninsula, the test operator of QTEC – thePortuguese subsidiary of TÜV AUSTRIA group - came to an above ground tank as shown infigure 5. He applied the usual test set-up procedure and then started with the pressurisation by
feeding LPG liquid phase from the tank under test to the mobile laboratory, where it wasevaporated in the heat exchanger and fed through the filling valve back to the gas phase.

Figure 5. Field test on an above ground LPG tank (2300 litre), photo of the tested tank

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He saw that the value of CEF increased rather quickly as shown in figure 6 and was alarmed.
Additionally he realised that the background noise (given by the measuring parameter RMSstatus) was rising with increasing load (see figure 7). This led him to the conclusion that there
might be a leakage at the tank fittings. A leakage was confirmed at the first pressure hold where
after closing the gas flow to the tank the RMS-status did not reduce, as it is supposed to do incase the increase of noise is caused by the gas flow to the tank from the pressurisation unit.

Figure 6. Evaluation of a field test on an above ground LPG tank (2300 litre), graphs show CEF and pressure vs.
time (1st row left), CEF of located events vs. position along the cylinder lengths (1st row right), maximum
amplitude and energy accumulation of located events vs. time (2nd row left), waveform and frequency content ofan event located near to sensor XD 1 (2nd row right)

He continued the test till to 1,75 MPa (17,5 bar) and performed the final pressure hold phase.
The tank was graded preliminary by class C (value of CEF greater 2,8  red light, the tankwill finally most likely fail the test). Then the follow-up was done to clarify the foundindications. For this reason soapy water was applied to check the fittings for leakage.
Unexpectedly the nozzles and valves did not show any indication for loss of gas phase.
Suddenly the test operator saw that bubbles indicating a leak showed up not at the tankfittings but below at some vertical distance. The photos given in figure 7 document thesefindings. The owner/operator was informed immediately.

Figure 7. Leak indication at an above ground LPG tank (2300 litre), graph left shows RMS-status (red for XD 1
and green for XD 2) and pressure (blue) vs. time, photo on the right shows two leak indications (bubbles ofsoapy water) within parent metal of LPG tank

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The tank was taken out of service and replaced by a new one. It was the first time that suchkind of defect has been seen in the field and the test result prevented an unsafe situation,
which most likely would have evolved in time.

5. Conclusion and Outlook
The described case studies for crack detection, corrosion detection and leak detection strongly
support to use AT in the course of the LPG tank requalification process. It is evident that thelevel of safety is at least the same compared to the conventional approach consisting of, asapplicable and as required, a hydrostatic test and other tests connected to the internal or theexternal inspections. The experience gained by TÜV AUSTRIA group within a period of timeexceeding 20 years and after testing more than 100000 tanks installed all over Europe
highlight the contribution of AT to raise the level of safety in LPG tank operation. It proofs
that AT is the standard test method in many European countries.
The testing technology is applicable to very large tanks with several hundred cubic meters. In
this regard a reference has been set by TÜV AUSTRIA in Italy. The largest cylindrical LPG
tank tested in 2014 was a mounded cylindrical tank with a capacity of 2000 m3 using an
innovative methodology to access the tank shell developed by Blu Solutions [4].
Enquiries from all over the world indicate that our testing service is considered to bebeneficial for our clients of the LPG industry.
References
1. P. Tscheliesnig and G. Schauritsch, ‘Inspection of LPG vessels with AE examination’,
Journal of Acoustic Emission, Vol. 18, pp 138 – 143, 2000.
2. P. Tscheliesnig, ‘Periodic inspection of LPG vessels within Europe’, Proceedings of 28th
European Conference on Acoustic Emission Testing – EWGAE 2008 Cracow, pp 49 – 52,
2008
3. G. Schauritsch, ‘Schallemissionsprüfung an einem rissbehafteten Flüssiggaslagerbehälter –Ergebnisse zum Nachweis der Verfahrenseignung’, Proceedings of 18. Kolloquium
Schallemission, Vortrag #03, 2011.
4. C. Di Fratta, A. Ferraro, P. Tscheliesnig, G. Lackner, V. Correggia and N. Altamura, ‘AT
on Buried LPG Tanks Over 13m3 – an Innovative and Practical Solution’, Proceedings of
31st Conference of the European Working Group on Acoustic Emission (EWGAE),
Contribution Fr.2.A.2, 2014.

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

THE TIME SEQUENCE CHARACTERISTICS AND LOCATIONMETHOD OF AE SOURCE INDUCED BY DEBRIS CLOUD
Zhidong LIU 1, Hongqiang LIAO 1, Yuan LIU 2
1

Institute of System Engineering, China Academy of Engineering Physics, China
Phone: +86 013708120572; e-mail: 411liuzd@caep.cn, 411liaohq@caep.cn
2
Harbin Institute of Technology, Harbin, China; E-mail: 330472801@qq.com

Abstract
The increasing of space debris pose a serious threat to the spacecraft. Space debris may penetrate shield structure
create debris cloud, debris cloud impact the module wall may cause damage and create a large amount of punctate
acoustic emission (PAE) sources, debris cloud AE source is a combinations of these PAE sources. This paper will
present a location method based on AE technology for debris cloud impact position. Firstly, the impact sequence of thedebris and the propagation characteristics of the signals emitted by PAE sources have been analyzed by introducing
the corvonato debris cloud model, the relation of the debris cloud structure characteristics and the debris cloud AE
source time sequence characteristics was established, a concept of most fast ring was proposed, and hypervelocity
impact (HVI) test was carried out to verify the most fast ring concept. Then, the virtual wave front location method(VFLM) was extended to locate debris cloud AE source; location tests were conducted to verify the feasibility of thelocation method. The results indicate that: the most fast ring can be used to represent debris cloud AE source, VFLM
can locate the impact center and the location error is less than 6.1 mm.
Keywords: Space debris, debris cloud, acoustic emission, source location, HVI

1. Introduction
With the increase of human space activities, space debris environment has deteriorated[1], posea serious impact threat to the spacecraft, especially to manned spacecraft[2]. For the purpose ofmonitoring the impacts in real time, a new onboard monitoring system based on AE technology
was assumed to locate the impact position and evaluate the effect of impact[3]. And to reduce theimpact threaten, space debris shield structure was deployed onto manned spacecraft[4]. Whipple
structure is a typical shield structure[5], single layer plate installed onto the outside of thespacecraft module wall, incoming debris would penetrate the plate, then create debris cloud, thedebris cloud would impact the module wall in a certain sequence, create amount of PAE sources.
The wave emitted by the PAE sources propagate in the module wall, arrival at AE sensor ina certain sequence, the waveform of debris cloud AE signal is a mixed waveform. The arrival
sequence of debris cloud AE signal is related to the impact sequence of the particals and AE
signal wave speed, decided the arrival time of the mixed wave, is closely related to the AE source
location problem. In order to develop debris cloud AE source location method, it’s necessary to
study the time sequence characteristic of debris cloud AE source.
In this thesis, corvonato debris cloud model was introduced to analyse the impact sequence ofthe particles, and the propagation characteristics of the signals emitted by PAE sources havebeen analyzed. Finally, VFLM was extended to locate the impact position of the debris cloud.

2. Time Sequence Characteristic of AE Source Induced by Debris Cloud
2.1 Impact Sequence of Debris Cloud
When a projectile impacts a plate, a debris cloud emerges from the back surface of the plate. The
debris cloud may be comprised of solid fragment, liquid droplets, vapor, or a combination of these

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material states. In normal impact case, the debris cloud is axi-simmetric. Debris cloud contains
ejecta veil, rear debris, internal structure and external bubble, as shown in Figure 1. The external
bubble would firstly impact the module wall, the arrival time of debris cloud AE signal is mainly
related to the external bubble. Chi Runqiang studied the debris cloud by numerical simulation,
four measurement points are identified in the debris cloud to represent its velocity distribution[6].
In this thesis, two of the four measurement points are used to represent the external bubble
velocity distribution. Point 1 is at the front of bubbles which the axial velocity and distancemeasured normal to the bumper are the largest, point 2 is at the edge of bubbles which the axial
velocity and distance measured normal to the debris cloud center line are the largest. V1x
represents the normalized axial velocity of point 1, V2x and V2r represent the normalized axial anddiametral velocity of point 2, and V2x≈0.6V1x, h is the distance between the bumper and target
plate. Assume that: all particals well distributed at bubble surface, the debris velocity is constantand the shape of the debris cloud keeps self-similar.
R
v2r

O

v2x
v1x X

θ
rear debris

ejecta veilbump plateinternalstructureexternal
bubblehtarget plate

Figure 1. Schematic of secondary debris cloud structure
Set the intersection point of the bumper and debris cloud center line as original point, therelation of velocity and position of debris inside the bubble shown as equation 1
r tvr vr(1)=
= = tg (θ )x tvx vx
Where r is the distance normal to the debris cloud center line, x is the distance normal to the
bumpe, v r and v x is the diametral and axial velocity of the debris, θ is the spray angle.
Debris With same axial velocity has unified distance normal to the debris cloud center line
and the bumper, form a ring in the space, and impact the target plate at the same time in a ringzone, create amount PAE sources. The bubble would create a series ring with different radius,
the formation sequence of the ring related to the velocity distribution of the bubble.
Corvonato set up a model to describe debris cloud shape use bernoulli lemniscate, as shown inequation 2[7]

η = 2 −(2ξ 2 + 1) + 8ξ 2 + 1(2)

Where ξ、η are shown in equation 3ξ = x xmax(3)η = r rmax
Where x max is Point 1 distance normal to the bumper, r max is Point 2 distance normal to thedebris cloud center line.

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It can be derived from equation 1 and equation 3
vx = ξ vx ,max vr = η vr ,max
Where v x , v r is the axial and diametral velocity of debris partical, v x,max is v 1x ,v r,max is v 2.

(4)

Set the impact time as initial time, the impact time and postion of debris particals shown ash timp = v(5)xr = t v
imp r
Where t imp is the impact time of the projectile, r is the distance between the impact position
and the center of the impact zone.
According to equation 4 and equation 5, the area of the debris cloud impacting zone and theimpacting sequence of the debris particals depend on v 1x and v 2r . As the relative velocity
between space debris and spacecraft is between 0~15 km/s, the average velocity is about 10
km/s[1], while the debris cloud velocity is lower than the space debris impacting velocity,
thus v 1x is less than 15 km/s.
Guan Gongsun[5] and Chi Runqiang [6]studied the shape of the debris cloud produced byprojectile penetrate single aluminium sheet, found that: under the condition that the distance
between bump and target plate is 100 mm, the debris cloud spray angle would be less than 45°.
According to equation 1, it can be derived that(6)v2 r < v2 x ≈ 0.6v1x
2.2 Propagation Sequence of AE Signal Emitted by Punctate AE Sources
Among the entire PAE sources, there is a single PAE source, the wave emitted by which
would firstly arrival at AE sensor. Define the PAE source as most fast PAE Source(MFP).
Assume that: a projectile penetrate the bump at time t imp , then the extern bubble debris impact
the target plate in a certain sequence and create a large amount of PAE sources, the wave
emitted from the PAE source at the position of (α, r) propagate in velocity C P to the AE
sensor located at (x s , y s ), and arrival at the sensor at the time of t P (α, r), shown as figure 2.

Figure 2. Schematic diagram of the propagation of AE signals emitted by the PAE source

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Where C c-s is the AE wave speed along the direction of θ c-s , C p is the AE wave speed along
the direction of θ p . It can be derived thatLp(7)t p (α , r ) = timp (α , r ) +C (θ p )Where L p , θ p are shown in equation 8
Lp = Lc − s 2 + r 2 − 2rLc − s cos α （0 ≤ r ≤ r0 , −180° ≤ α < 180°）
 L p 2 + Lc − s 2 − r 2 θ c-s − arc cos  (0 ≤ r ≤ r0 , 0° ≤ α ≤ 180°)
2 Lp Lc − sθp = 
 Lp 2 + Lc − s 2 − r 2  (0 ≤ r ≤ r0 , −180° < α < 0°)θ c-s + arc cos 
2 Lp Lc − s




(8)

Substitute equation 5 into equation 7

Lp
h
(9)
+
vx ( r ) C (θ p )Equation 9 established the relationship of impact sequence of the debris and propagation
characteristics of the signals emitted by PAE sources. The minimal value of equation 7
corresponds to the arrival time of debris cloud AE signal, its minimal value point correspondto MFP. Equation 9 can be used as a numerical AE source model, simulate debris cloud AE
source both in anisotropic or isotropic plate. This thesis will only concentrate on the debris
cloud AE source in isotropic plate.
t p (α , r ) = t 0 +2.3 Distribution Characteristics of Most Fast Point in Aluminium Plate
Aluminium plate is an isotropic media, wave velocity is irrelevant to the propagation direction.
equation 7 can be simplified as
L
h
(10)
+ p
t p (α , r ) = t 0 +vx ( r ) C
Where c is the wave velocity of the plate.
It can be learned that, when r is unchanged, the minimal value point of equation 10 is α=0.
equation 10 can be simplified asLhrt p ( r ) = t0 + c − s +−(11)Cvx ( r ) C
It can be leaned from equation 11 that, for a given AE sensor array and a given shieldstructure, the value of t p only depends on the value of r and the impacting sequencecharacteristic of the debris cloud, which means the MFP position is also only depend on hevalue of r and the debris cloud impacting sequence characteristic. As the debris cloud is axisimmetric, the MFP position would also be axi-simmetric.
To sum up, it can be learned that, the MFPs distribute on the same ring, MFP is the junctionof the ring and the line defined by the impacting center and AE sensor, shown as figure 3,
define the ring as Most Fast AE Source Ring (MFR). It can be learned from figure 3 andequation 11, for a given impacting case, the difference between impact center signal arrival
time and the MFP signal arrival time is a fixed value, as shown in equation 12

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Figure 3. The most fast ring and HVI AE source of debris cloudtm ,i = tc ,i (rmfr = 0)
t < t m ,i c ,i (rmfr > 0)(12)
dt = t − t = rmfr + h − h
c ,i
m ,i

c v1x vx ( rmfr )Where t m is measured arrival time of debris cloud signal, that is the arrival time of the MFPsignal, t c is the arrival time of the impact center signal, i is the number of AE sensors, dt istime difference between t c and t m .

2.4 Verification Experiment2.4.1 Experiment setup
To validate the MFR concept, HVI test was conducted, shown in figure 4. During theimpact experiment, a bump was placed 100 mm before the target plate in order to obtaindebris cloud. A two-stage light gas gun was utilized to speed up the spherical aluminum
projectile to impact the bump plate, debris cloud was created by the impact. The debris cloud
will keep flying ahead and impact the target plate after a distance of 100 mm. The setting
closely simulates the debris cloud caused by HVI of space debris against the shield impacts
the module wall. Four ultrasonic transducers were deployed on the back face of target toreceive debris cloud AE source, a polyvinylidene fluoride piezoelectric film (PVDF) was
deployed in the center of the front face to obtain the impact time of the debris cloud.

3mm

1mm
bump platetarget plateV
projectile
99mm(a) schematic of panels arrangement (b)schematic of transducers arrangement
Figure 4. Experiment setting of debris cloud hypervelocity impact test

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Table 1. Debris cloud HVI experiment parameters
itemmaterial

size

projectile

AL 2017

Φ3.2 mmbump plate AL 6061-T6 500 mm×500 mm×1 mmtarget plate AL 5A06

500 mm×500 mm×3 mm

2.4.2 Experimental Result and Analysis
During the impact experiment, a projectile was speed up to 1.94km/s, the impact center of thedebris cloud is (5mm, -3.5mm). The waveforms of PVDF signal and AE signal are depicted in
Figure 5.
521Voltage (V)Voltage (V)

0

-5

-10
0

274.35µs
0
-1

400
200
Time (µs)600(a) typical PVDF signal

-2
270

2
1

Voltage (V)10
Voltage (V)276(b) arrival time of PVDF signal

20

0
-10

200
400
Time (µs)

600

t =293.8µs
m

0
-1
-2
-3
-4

-20
0

274
272
Time (µs)-5285

t =299.07µs
c

295
290
Time (µs)300(c) typical AE signal(d) arrival time of MFP and impact center signal
Figure 5. Waveform of collected signals
The arrival time of PVDF signal correspond to the impact time of the impact center, and thearrival time of the impact center signal shown asL(13)
tc ,i = t pvdf + c-s,icWhere t c,i is the arrival time of impact center signal, t pvdf is the impact time of the impact
center, C is measured wave velocity of the target plate which is 5.5 km/s.
The result shows that the arrival time of impact center signal is later than the arrival time ofthe debris cloud signal. The detail is described in table 2.

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Table 2. Comparison between TOA of AE signal from MFP and impact center
item Time
Channel 1 Channel 2 Channel 3 Channel4
tc

299.07 μs

305.11 μs

300.54 μs

313.61 μs

tm

293.80 μs

299.70 μs

295.35 μs

308.55 μs

dt

5.27 μs

5.41 μs

5.19 μs

5.06 μs

It can be learned from Tab2. that, the arrival time of impact center signal is later than debris
cloud signal, and the time difference of each channel is close, the tiny deviation is the test
error. The result fits equation 12, indicate that the MFR concept is valid.

3. Debris cloud AE source location method
As discussed above, arrival time interval of the impact center AE signal is equivalent to thearrival time interval of debris cloud AE signal. It’s anticipated that location methods based on
arrival time interval, such as VFLM[8], could be used to locate the debris cloud AE source.
3.1 Experiment setup
To validate the source location method, debris cloud impact location experiments werecarried out. During the experiments, use the same setting like the previous HVI experiment.
The difference is that PVDF is removed and the arrangement of the transducers is changed asshown in figure 6.

Y
500
S2

S4

X

0
20

25
0

500

0
25

20
0

S3

S1

Figure 6. Schematic of the arrangement of transducers
3.2 Experimental Result and Analysis
The experiment results are shown in table 3, the distance between the impact center and thecalculated position was set as location error. the maximum location error is 6.1 mm, which ismuch smaller than the transducer diameter (~19 mm), indicates that VFLM can be used tolocate the debris cloud impact position effectively.

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Table 3. AE source location result of debris cloud HVI experimenttest number
1
2
3

impact center
location error
4.5 mm, -51.7 mm 1.5 mm, -50.0 mm 3.4 mm
0.6 mm, -55.2 mm 0.5 mm, -56 mm
0.8 mm-3.2 mm, -7.0 mm -9.0 mm, -9.0 mm 6.1 mm
location position

4 Conclusions
This paper take debris cloud AE source as research object, studied the time sequencecharacteristic of secondary debris cloud AE source, extended the VFLM to solve the debris
cloud AE source location problem, and conducted Verification experiments. The results
indicate that:(1) Presented concepts of MFP, MFR, found in isotropic plate, MFP, debris cloud
impact center and AE sensor are collinear;(2) Based on corvonato debris cloud model, set up a equation which can be used todebris cloud AE source in anisotropic or isotropic plate;(3) The location error of the method is less than 6.1 mm, which can satisfy theengineering accuracy requirements, VFLM can be extended to debris cloud impact location.
References
1.

L Udpa and S S Udpa, 'Neural Networks for the Classification of Non-Destructive
Evaluation Signals', IEE Proceedings-F, Vol 138, No 1, pp 201-205, February 1991.

2.

S Q Liu and J Liu, 'Space Environment Support for The SZ-5 Spacecraft', Physics, Vol
33, No 5, pp 359-366, 2004.

3.

Y Y Li and H R Shen, 'Assessing the Short-Term Hazard of Spacecraft Collision
Breakup Debris', Journal of Astronautics, Vol 31, No 4, pp 1231-1236, 2010.

4.

IADC WG3 members, 'Sensor Systems to Detect Impacts on Spacecraft',
http://www.iadc-online.org/Documents, 2013.

5.

Z Z Gong and Z Y Han, 'A Review of Studies on Protection Against M/OD', Spacecraft
Environment Engineering, Vol 27, No 1, pp 24-31, 2010.

6.

G S Guan, 'Hypervelocity Impact Characteristic Investigation of Spacecraft Space
Debris Shield Configuration', Harbin Institute of Technology, 2006.

7.

R Q Chi, 'Research and Modeling of Debris Cloud Produced by Hypervelocity Impactof Projectile with Thin Plate', Harbin Institute of Technology, 2010.

8.

E Corvonato and D Roberto, 'Integral Model for The Description of The Debris Cloud
Structure and Impact', International Journal of Impact Engineering, Vol 26, pp115-128,
2001.

9.

Z D Liu and B J Pang, 'A Method Based on Virtual Wave Front for Acoustic Emission
Source Location in Laminated Plates', Piezoelectrics & Acoustooptics, Vol 32, No 3, pp493-497, 2010.

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

ALKALI SILICA REACTIVITY STUDY OF ACCELERATED MORTARBAR BY MEANS OF ACOUSTIC EMISSION AND ULTRASONICSOUNDING
Tomáš LOKAJÍČEK1, Richard PŘIKRYL2, Šárka ŠACHLOVÁ2, Aneta KUCHAŘOVÁ2
Institute of Geology, Czech Academy of Sciences, Prague, Czech Republic
e-mail: tl@gli.cas.cz
2
Institute of Geochemistry, Mineralogy and Mineral Resource, Faculty of Science, Charles University in Prague,
Czech Republic;e-mail:prikryl@natur.cuni.cz, astastna@gmail.com, s.sachlova@seznam.cz
1

Abstract
Semi-continuous ultrasonic sounding and acoustic emission were used to study the accelerated alkali silica
reactivity (ASTM C1260) of experimental mortar bars with different aggregates. To confirm ultrasonic results,
expansion test and SEM/EDS examination of ASR-related damage were carried out. It was found that highreactivity aggregates pronounced by high mortar bar dilatancy result in significant decrease of P-wave velocity
and high ultrasonic signal attenuation. Acoustic emission can be used for ASR study mainly during first days of
ASTM C1260 study to monitor origin of ASR. Later stage of study is limited by high signal attenuation caused
by material deterioration. Ultrasonic sounding and acoustic emission monitoring can be used as an effective
supplementary tools for the monitoring of internal structure of expanding mortar bars during laboratory tests forthe evaluation of alkali silica potential of aggregates.
Keywords: Alkali silica reactivity; experimental mortar bars; ultrasonic sounding; acoustic emission; swelling
reactions

1. Introduction
Alkali silica reaction (ASR) is one of the critical internal swelling reactions that affect stability ofconcrete structures by cracking (Fournier and Bérubé 2000). Use of non-reactive forms of
aggregates seems to be the most effective way how to mitigate this serious problem (Malvar et al.
2002). Direct search for reactive forms of silica by petrographic methods (Sims and Nixon 2003)
combined with expansion of experimental mortar bars or concrete prisms subjected to ASR
accelerating conditions (Berube et al. 1995, Grattan-Bellew 1997, Thomas and Innis 1999, Rogers
1999, Shon et al. 2002, Haha et al. 2007) presents the most widely employed approach, althoughanalysis of leaching potential of aggregate by chemical tests is also common.
Specifically for tests based on expansion measurement, the precise determination of the onset
and the progression of ASR are fundamental for further classification of tested aggregate. Morerecently, the expansion reading of experimental mortar bars or concrete prisms is supplemented
with direct observation of crack and/or gels associated with ASR by microscopic observation(Lukschová et al., 2009) or by non-destructive testing (NDT) (Rivard and Saint-Pierre 2009).
NDT offers possibility to record change of measured property of test specimen continuously orwith high frequency of readings. Recent adoption of non-destructive techniques to cope with
ASR phenomena both in laboratory and in situ scales cover several methods such as
electric/electromagnetic, seismic and/or acoustic (Stauffer et al. 2005, Saint-Pierre et al. 2007,
Chen et al., 2010, Kodjo et al. 2009, Sargolzahi et al. 2010, Schurr et al. 2011, Leśnicki et al.,2013, Omikrine Metalssi et al. 2013). From non-destructive methods used during tests leading
to the development of brittle damage related to internal stress, recording of acoustic emission

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seems to be most promising as shown on numerous laboratory studies on brittle damage of rockspecimens in rock mechanical tests (Přikryl et al., 2003). We have decided to examine possible
adoption of NDT during the standard ultra-accelerated mortar bar test. By using aggregates
exhibiting wide range of ASR potential, we have focused on the development of test
assemblage allowing quasi-continuous recording of acoustic emission (AE) and after-test
interpretation of the obtained data. These data were correlated to standard expansion values andto direct microscopic observation of microstructural changes (aggregate damage due tomicrocracks, pores filled with alkali silica gel) caused by developing ASR.

2. Experimental
2.1. Material
The experimental study was performed using a set of quartz-rich rocks exhibiting different
microstructural and quartz deformation characteristics (Table 1). Two of selected aggregates(Sample Nos. EMB2 and EMB3) are used in the construction industry as filler for both
concrete and asphalt mixtures as well as gravel basements. Other two samples were sampledfrom natural outcrop (Sample No. EMB4) and from the abandoned quarry (Sample No.
EMB1). For each of the studied materials, chips and blocks several cm to several dm large
were sampled in total amount of 30 – 50 kg.
Table 1. Petrographic and macroscopic descriptions of selected samples.
Sample No.
Sample marking
Locality
Petrographic
classification
Quartz content(vol. %)
Mean/max. grain
size (mm)Q9A
EMB1
Edselequartz frompegmatite

Q0B
EMB2Těchobuz

Q7C
EMB3
Klövsjö

Q3
EMB4
Litohlavy

quartzite

quartz meta-greywacke

chert

100

71

74

35

> 20

0.09/0.46

0.07/0.68

0.01/0.53

2.2. Accelerated experimental mortar bar (EMB) test
A complementary set of EMB was prepared by using same aggregates to perform accelerated
EMB test according to requirements of ASTM C1260-14 (2014) standard. The mortar was
mixed using aggregate fraction of 0.125/5 mm, CEM I 42.5 Portland cement, and distilledwater in the ratio of 2.25/1/0.47 (aggregate/cement/water). Four different mortar bar
specimens (EMB1 – EMB4) were prepared from each sample. After 24 hours of hardening
and 24 hours of tempering, the mortar bars were placed in 1M NaOH solution at 80°C.
Expansion of the mortar bar specimens was measured for 14 days through the test period.
2.3. Acoustic emission monitoring (AEM) and ultrasonic sounding in a temperature
controlled chamber
Experimental mortar bars (EMB) were prepared according to requirements of ASTM C126014 (2014) standard. For each sample, two mortar bars were placed in the 1M NaOH test
solution at 80°C, i.e. same conditions as for the accelerated mortar bar test following ASTM
C1260-14 (2014) standard. Recording of acoustic signals was possible by using a pair ofultrasonic AE sensors, located outside of the heating chamber. Sealed steel 316L waveguides

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were used to enable transmission of ultrasonic signals to AE sensors. As a sensors, a pair of wideband, high-temperature-resistant sensors manufactured by the 3S Sedlak company, was used foreach tested bar. AE was monitored by means of Vallen AMSY multichannel automatic recording
system, which enables also regular ultrasonic sounding of mortar bars during the test. AE wasmonitored continuously during the whole test period, reading of propagating ultrasonic signal (Pwave velocity measurement together with determination of true energy and amplitude oftransmitted signals during the test) was performed with a 10 minutes interval. Concentration andlevel of the test solution was controlled during the test; concentration was checked/corrected every
second day, level of the solution was monitored automatically.

Figure 1. Technical layout of heating chamber equipped with waveguides and ultrasonic sensors.

3. Results and discussion
3.1. Expansion of experimental mortar bars
The studied materials show expansion due to ASR from 0.05% to 0.55% after 14 days of testing(Fig. 2). Based on these data, only one material (sample from pegmatite, EMB1) can beconsidered as non-reactive, whilst the remaining fit to the category of reactive ones. However,their reactivity potential is different as can be seen from expansion values and fromsupplementary measurements by ultrasonic sounding and acoustic emission monitoring as
described below.
0.6

expansion[%]

0.4

EMB1
EMB2
EMB3
EMB4

0.2

0
0

2

4

6

8

time [day]

10

12

14

Figure 2. Expansion curves of mortar bars EMB with different aggregates.

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3.2. EMB Ultrasonic sounding
Ultrasonic sounding data are shown in Figure 3. Dependence of P-wave velocity propagationis shown in Fig 3A. It can be observed that samples with different aggregate activity reflect
different dependencies. P-wave velocity of “fresh” mortar bars was in the range between 3.3
to 4.1 km/s. Starting from these values we can observe increase of P-wave velocities. Aftersome time (0.8 to 3 days) we can observe continues decrease/stabilisation of P-wave velocity.
Samples with higher expansion values show higher decrease of P-wave propagation velocity.
Similar behaviour reflects maximum amplitude of transmitted ultrasonic signal – see Fig3 B.
1

4.4

0.9

4

velocity [km/s]

EMB1

EMB1

amplitude [ ]

EMB2
EMB4

3.6

EMB2

0.8

EMB3

EMB3
EMB4

0.7

3.2

0.6

0.5

2.8
0

2

4

6

8

time [day]

0

10

2

4

6

time [day]

A

8

10

B

Figure 3. Ultrasonic sounding: Changes of P-wave velocity (A) and amplitude of ultrasonic signal (B) for
specimens studied during the first ten days of experiments.

There is observed continues decrease of P-wave amplitude maximum of ultrasonic signal recordedin the course of regular ultrasonic sounding. It can be observed that mortar bars with higher activity
aggregates shows higher decrease of recorded maximum ultrasonic signal amplitude.
3.3. Acoustic emission of EMB
As concerns the AE activity of studied mortar bars, expressed as number of AE hits numbers(Fig. 4), AE energetic parameters (full cumulative energy and/or AE energy rate – Fig. 5) and
1

16000
EMB1
EMB2
EMB3
EMB4

0.8

AE hits rate [ ]AE hits [ ]

12000
0.6

8000

0.4

4000

EMB1
EMB2
EMB3
EMB4

0.2

0

0
0

2

4

6

time [day]

A

8

10

0

2

4

6

time [day]

8

10

B

Figure 4. Basic AE characteristics of studied mortar bars during first ten days of experiment: cumulative
distribution of AE hits (A) and AE hits rate (B).

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AE maximum amplitude radiation of individual events (cumulative AE maximum amplitude
and/or AE maximum amplitude rate – Fig. 6), the studied samples exhibit different behaviour
that can be only partly explained by microstructural changes due to formation of brittle
microcracks associated with ASR. Prior to the assembly of the current test procedure, our
expectation was that materials that are more susceptible to ASR should provide more AE
events due to the development of ASR-related brittle damage. Although this presumption was
confirmed by later direct observation of polished sections prepared from mortar bars, the AE
activity is more complicated. AE rate data are shown normed to maximum value for eachsample and/or data.
1

12000000

EMB1
EMB2
EMB3
EMB4

EMB1
EMB2
EMB3
EMB4

AE energy rate [ ]0.88000000

AE energy[ ]

0.6

0.4

4000000

0.2

0

0
0

2

4

6

time [day]

A

8

10

0

2

4

6

time [day]

8

10

B

Figure 5. Parameters of AE activity of studied mortar bars during first ten days of experiment: cumulative
distribution of AE energy (A), AE rate (B).

In case of AE events, one can observe their continuous increase, mainly during the first daysof the experiment (Fig. 4). Change in the slope of AE activity during the experiment(cumulative number of AE hits, Fig. 4A) is obvious specifically for the specimen EMB4
exhibiting the highest reactivity (black curve on Fig. 4A) and for non-reactive specimen
EMB1 (green line, Fig. 4A). In the case of the most reactive specimen EMB4, AE activity is
the most pronounced during first 2 days of the test and then rapidly diminishes during thethird day (Fig. 4B), although this specimen follows to expand very rapidly during the nextdays (Fig. 2). On contrary, non-reactive specimen EMB1 exhibiting very slow quasi-linear
expansion (Fig. 2) shows very slow continuous increase of AE events during the first 5 days,
followed by non-linear acceleration of AE activity from 5th to 8th day of experiment and very
steep linear increase of AE events from then. Higher AE activity of this specimen can be
explained due to the development of microstructure due to the ongoing hardening of concrete
mixture (Reinhardt & Grosse, 2004). The remaining two samples EMB2 and EMB3 (alsoreactive), show low to moderate quasi-linear increase of AE events (Fig. 4A).
Development of energetic parameters of AE events is even more complicated. The trend offull energy released of AE events (expressed as cumulative number, Fig. 5A) apparently
exhibits similar trend as linear expansion does: two most expansive materials EMB3 andEMB 4 possess much higher increase of full energy compared to less expansive EMB2 andnon-expansive EMB1 specimens. However, focusing on full energy rate (Fig. 5B), one cansee rapid drop of this parameter for the most expansive sample EMB4 (black line, Fig. 5B)
although this material continues to expand during the whole test period. On contrary, non-

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expansive sample EMB1 (green line, Fig. 5B) exhibits change (initiation of rapid increase) infull energy rate from about 7th day of the test. Similar behaviour can be observed also for the
cumulative maximum amplitude release (Fig. 6A) and maximum amplitude rate (Fig. 6B)
which rapidly increases for the same sample from the 7th day of the test. However, its value
remains very low for the most expansive specimen through the whole test, and rapidly drops
from relatively high values to very low one for the second most expansive samples EMB3
(red line, Fig. 6B).
5000000

1
EMB1
EMB2
EMB3
EMB4

0.8

AE amplitude [ ]AE amplitude rate [ ]4000000

3000000

0.6

0.4

2000000

EMB1
EMB2
EMB3
EMB4

0.2

1000000

0

0
0

2

4

6

time [day]

A

8

10

0

2

4

6

time [day]

8

10

B

Figure 6. Parameters of AE activity of studied mortar bars during first ten days of experiment: cumulative
distribution of AE amplitude (A), AE amplitude rate (B).

3.5. Effect of test assembly and conditions on the quality of recorded data
Although the observed behaviour of AE events is linked preferentially to the development of
ASR in the studied material, some additional effects can play role as well. As the individual
mortar bars expand during the test, there was no chance to secure constant contact conditions
between the mortar bar and transducer, resp. wave guide. This fact can have serious impact on
the quality of signal transmitted from mortar bar through the wave guide. Development ofopen brittle fractures, that are filled with test solution or newly formed alkali silica gels during
the test can affect the quality of the signal transmission through the mortar bar; in reality canlead to serious attenuation of signals.
The current experimental set-up prohibited possibility of constant contact conditions due tothe fact that wave guides were fixed to the walls of experimental chamber (in order to prevent
leaching off the test solution from it). As the mortar bar change its dimensions during the test,
the contact conditions at the mortar bar/wave guide could not be constant. This can beovercome by further development of test heating chamber, which will unify sensors contact
conditions. Development of such a test assemblage will be matter of our further research.

4. Conclusions
Ultrasonic sounding and acoustic emission monitoring can be used as an effective
supplementary tools for the monitoring of internal structure of expanding mortar bars duringlaboratory tests for the evaluation of alkali silica potential of aggregates. In general, P-wave
velocity rapidly increases during the first 2-5 experiment days, but then starts to deteriorate
for reactive materials. This can be explained by formation of brittle microcracks, resulting in

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microstructural mortar bar changes – expansion. More reactive materials exhibit faster Pwave velocity decrease compared with less reactive ones. Similar relationship was observed
for the maximum amplitude of ultrasonic signal; the degree of ultrasonic signal attenuation is
proportional to the degree of expansion due to ASR.
Acoustic emission monitoring can be performed by using same sensors that are used forquasi-continuous (readings each 10 minutes) ultrasonic sounding in the recent experimental
set-up. Acoustic emission of materials prone to ASR and of innocuous materials exhibits
considerable difference that is qualitatively and quantitatively dependent not only on thebrittle damage character of the reaction but also on the formation of reaction products – alkali
silica gels. Their presence leads to the significant attenuation of AE signals (specificallydiminishes their amplitude and energy); their reading in expanding specimens can be further
complicated by expansion of mortar bars and change of the quality of contact between
specimen and transducers. All these factors make interpretation of AE more complicatedduring the later stages (from about 10th day) of mortar bar test. Further development ofexperimental set-up (improvements of specimen/transducer contact) could contribute to wider
adoption of AE monitoring during the ASR testing.

Acknowledgement
This study was financially supported through OPPK project CZ.2.16/3.1.00/21516, research
project P104/12/0915 with funding provided by the Czech Science Foundation and by the
Academy of Sciences of the Czech Republic, project RVO 67985831.
References
1. Bektas F., Topal T., Goncuoglu M.C., Turanli L., 2007. Evaluation of the alkali
reactivity of cherts from Turkey., Construction and Building Materials 22, 1183–1190.
2. Bérubé M.A., Duchesne J., Chouinard D., 1995. Why the accelerated mortar bar
method ASTM C 1260 is reliable for evaluating the effectiveness of supplementary
cementing materials in suppressing expansion due to alkali-silica reactivity. Cement,
Concrete and Aggregates 17(1), 26–34.
3. Chen J., Jayapalan A.R., Kim J.Y., Kurtis K.E., Jacobs L.J., 2010. Rapid evaluation ofalkali–silica reactivity of aggregates using a nonlinear resonance spectroscopy
technique. Cement and Concrete Research 40(6), 914–923.
4. Fournier B., Bérubé M.A., 2000. Alkali-aggregate reaction in concrete: a review ofbasic concepts and engineering implications. Canadian Journal of Civil Engineering
27(2), 167–191.
5. Grattan-Bellew P.E., 1997. A critical review of ultra-accelerated tests for alkali-silica
reactivity. Cement and Concrete Composites 19(5), 403–414.
6. Haha M.B., Gallucci E., Guidoum A., Scrivener K.L., 2007. Relation of expansion due
to alkali silica reaction to the degree of reaction measured by SEM image analysis.
Cement and Concrete Research 37(8), 1206–1214.
7. Kodjo A., Rivard P., Cohen-Tenoudji F., Gallias J.L. (2009, November). Evaluation ofdamages due to alkali-silica reaction with nonlinear acoustics techniques. In
Proceedings of Meetings on Acoustics (Vol. 7, No. 1, p. 045003). Acoustical Societyof America.
8. Leśnicki K.J., Kim J.Y., Kurtis K.E., Jacobs L.J., 2013. Assessment of alkali–silica
reaction damage through quantification of concrete nonlinearity. Materials andStructures 46(3), 497–509.

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9. Lukschová, Š., Přikryl, R., & Pertold, Z. (2009). Petrographic identification of alkali–
silica reactive aggregates in concrete from 20th century bridges. Construction andbuilding materials, 23(2), 734-741.
10. Omikrine Metalssi O., Godart B., Toutlemonde F., 2013. Effectiveness of
nondestructive methods for the evaluation of structures affected by internal swelling
reactions: a review of electric, seismic and acoustic methods based on laboratory andsite experience. Experimental Techniques, DOI: 10.1111/ext.12010
11. Malvar L.J., Cline G.D., Burke D.F., Rollings R., Sherman T.W., Greene J.L., 2002.
Alkali-silica reaction mitigation: state of the art and recommendations. ACI Materials
Journal 99(5), 480–489.
12. Přikryl,R., Lokajíček, T., Li, C. and Rudajev, V., 2003. Acoustic Emission
Characteristics and Failure of Uniaxially Stressed Granitic Rocks: the Effect of Rock
Fabric, Rock Mech. Rock Engng. 36 (4), 255–270.
13. Reinhardt, H. W., & Grosse, C. U. (2004). Continuous monitoring of setting andhardening of mortar and concrete. Construction and building materials, 18(3), 145154.
14. Rogers C.A., 1999. Multi-laboratory study of the accelerated mortar bar test (ASTM C
1260) for alkali-silica reaction. Cement, Concrete and Aggregates 21(2), 185–194.
15. Saint-Pierre F., Rivard P., Ballivy G., 2007. Measurement of alkali–silica reaction
progression by ultrasonic waves attenuation. Cement and Concrete Research 37(6),948–956.
16. Sargolzahi M., Kodjo S.A., Rivard P., Rhazi J., 2010. Effectiveness of nondestructive
testing for the evaluation of alkali–silica reaction in concrete. Construction and
Building Materials 24(8), 1398–1403.
17. Schurr D.P., Kim J.Y., Sabra K.G., Jacobs L.J., 2011. Damage detection in concrete
using coda wave interferometry. NDT & E International 44(8), 728–735.
18. Shayan A., Morris H., 2001. A comparison of RTA T363 and ASTM C1260
accelerated mortar bar test methods for detecting reactive aggregates. Cement andConcrete Research, 31(4), 655–663.
19. Shon C.S., Zollinger D.G., Sarkar S.L., 2002. Evaluation of modified ASTM C 1260
accelerated mortar bar test for alkali–silica reactivity. Cement and Concrete Research32(12), 1981–1987
20. Sims I., Nixon P., 2003. RILEM recommended test method AAR-1: detection ofpotential alkali-reactivity of aggregates—petrographic method. Materials andStructures 36(7), 480–496.
21. Stanchits, S., Mayr, S., Shapiro, S., & Dresen, G. (2011). Fracturing of porous rockinduced by fluid injection. Tectonophysics, 503(1), 129-145.
22. Stauffer J.D., Woodward C.B., White K.R., 2005. Nonlinear ultrasonic testing withresonant and pulse velocity parameters for early damage in concrete. ACI Materials
Journal 102(2), 118–21.
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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

APPLICATION OF ACOUSTIC EMISSION ON CRUSHINGMONITORING OF INDIVIDUAL SOIL PARTICLES IN UNIAXIALCOMPRESSION TEST
Sha LUO 1, Andrea DIAMBRA 1, Erdin IBRAIM 1
1

Department of Civil Engineering, University of Bristol, Senate House, Bristol, BS8 1TH, UK,
Phone: +44 117 928 9000; e-mail: sl13796@bristol.ac.uk, Andrea.Diambra@bristol.ac.uk ,Erdin.Ibraim@bristol.ac.uk

Abstract
Soil grain breakage has a significant influence on the performance of the geotechnical system. Due to the natureof the underground environment, it is hard to track the soil grain breakage process in both temporal and spatial
dimensions. Hence, the use of non-destructive Acoustic Emission (AE) technique to characterise the soil
breakage is explored in this work. This study particularly focuses on the individual soil grains, and aims to
distinguish the crack formation of the individual silica sand particles with different sizes and shape under
uniaxial compression. AE parameters and the signal waveforms at each particle crushing point are analysed. It isfound that the AE parameters changing trend match well with the mechanics behaviour in the test, with this, thecrack of the silica sand particle could be detected. What is more, in the frequency domain analysis, the differencein the frequency distribution at critical crushing hit within different silica sand test has been found and thepossible reason are shown.
Keywords: Acoustic Emission (AE), silica sand particle, crack formation, uniaxial compression, frequency
domain analysis

1. Introduction
Acoustic Emission (AE) monitoring technique has been used in various engineering applications
mainly for the assessment of damage and failure of brittle materials [1], evaluation of the responseof retrofitted reinforced concrete elements [2], detection of the onset and position of failure infiber reinforced composite materials [3-6], and monitoring of large bridge structures [7].
In geomechanics, pioneering work of Koerner and co-workers [8-11] and more recently [12-15]
used the AE technique to assess the stability of soil slopes. Correlations between thecharacteristics of the acoustic emission in soils subjected to oedometric compression, triaxial
testing, cone penetrometer tests, direct shear and deformation properties, including particle
crushing have been established by [16-21].
The study of soil breakage phenomena is difficult and complex [22-35]. However, insight into
internal mechanisms can firstly be gained through the study of individual soil particles underloading. This study focuses on the use of AE technique for breakage characterisation of
discrete silica sand grains under uniaxial compression loading. The discussion of the test
results is divided into two parts. In the first part, the observed mechanical response of thesilica sand particle during the uniaxial compression test is presented. The relation between themechanical response and the particle size is also discussed. In the second part, the associatedAE parameters which are recorded during the whole uniaxial compression test are analysed.
Finally, the statistical analyses of the AE parameters at the crushing point are presented andsome correlation between these parameters and the behaviour established.

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2. Material
In this work, Leighton Buzzard sand [26-29] which contains about 96% of Si02 has been selected.
The size of the particles has been defined in terms of the equivalent area diameter da [36] which is thediameter of the circle which has the same area with the projection area of the particle outlet observedin an optical microscope. The equivalent area diameter, da, of the silica particles considered in thisstudy is between 1.47 mm and 2.26 mm. Qic-Pic measurements [37] and optical 2D microscope
analysis [38] have been used for shape description. The latter measurement technique has been used inthis work. The shape descriptors are measured on 2D particle projections of the real 3D particles.
These can only be statistically representative if the 2D projections are obtained from particle
orientations that are randomly oriented in space [38]. While recognising that such data is not normallyattainable using microscopy, in this study, the shape descriptors and da for an individual particle havebeen evaluated based on six microscope images of the particle placed in different positions on the
microscope set up as shown in figure 1. Then the shape descriptors have been evaluated from thesepictures in Matlab, and the average values recorded. The circularity, 𝐶𝐶𝐶𝐶 = (4𝜋𝜋𝜋𝜋𝜋𝜋𝜋𝜋)⁄(𝑃𝑃𝑃𝑃2 ), irregularity,
𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 = 𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 ⁄𝑑𝑑𝑑𝑑𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑐𝑐𝑐𝑐 , and aspect ratio, 𝜋𝜋𝜋𝜋𝜋𝜋𝜋𝜋 = 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 ⁄𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 , [36] average values for 60 silica
particles are given in table 1, where 𝜋𝜋𝜋𝜋 and P (figure 2a) are the area and the perimeter of the particle
projection, respectively, dimax is the diameter of maximum inscribe circle, 𝑑𝑑𝑑𝑑𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑐𝑐𝑐𝑐 is the diameter ofminimum circumscribe, 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is the maximum ferret diameter and 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is the minimum ferretdiameter (all defined in figure 2b). According to the suggested circularity classification [41], themean value of circularity among the 60 silica particle is 0.566, which means this group of silica
sands has a low circularity.

Figure 1. Photos of one silica sand particle in six positions in microscope

𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖

𝑃𝑃𝑃𝑃

𝑑𝑑𝑑𝑑𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑐𝑐𝑐𝑐
𝜋𝜋𝜋𝜋

𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖

𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖
(a)

(b)

Figure 2. Photos of silica sand in microscope and Matlab
Table 1. Statistical analysis of shape descriptors of 60 silica particles

Mean
Variance
Minimum
Maximum

S174

da (mm)
1.889
0.040
1.470
2.260

AR
0.807
0.006
0.560
0.960

IR
0.728
0.004
0.520
0.850

C
0.566
0.034
0.120
0.910

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3. Test setting
The uniaxial compression test on individual particles uses a displacement controlled electromechanical loading frame (figure 3). Each particle was loaded between two rigid steel platens,of which one is fixed to the loading ram that incorporates an LVDT for vertical displacement
measurements and a 5 KN-load cell. The lower platen moves upwards with a speed of
0.05 mm/min.
During the crushing test, two piezoelectric sensors with a bandwidth between 10 kHz and
1 MHz record the acoustic emission signals. The first AE sensor (AE 1), which links tochannel 1 of the AE acquisition system, is fixed within the steel base plate, just below theparticle at a depth of about 1 cm by means of a mechanical system that ensures a constantholding force (figure 3). The second AE sensor (AE 2), which links to channel 2, is simply
placed on the base plate at a distance of about 4 cm from the particle. For both sensors, silicongrease is also used as a coupler. During the crushing test, the resulting vertical force andvertical displacement are recorded, while the AE system allows the acquisition of the acoustic
bursts. The typical AE acquisition system setting parameters are listed in table 2.

Figure.3. Diagram of loading system
Table 2. Typical settings of the AE acquisition system

Sampling rate
Recording length
Preamplifier gain
Threshold of detection

5 MSPS
5K
40 dB
40 dB

4. Results & discussions
4.1 Uniaxial compression test
Although the shape and the size of the silica sand particles are not identical, the observed
uniaxial compression crushing response follows a similar pattern. A typical forcedisplacement response obtained during the compression tests is shown in figure 4. In general,
no visible cracks are observed during the uniaxial loading test (although for some particles,
local crushings possibly of asperities were observed before the final crush, see figure 8), andat some point the sand particle crushes without any warning in a brittle mode. Several small

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fragments are generated and ejected in random directions. The maximum force recorded at the
crushing point is defined as critical failure force, F c. For a particle, the tensile stress, σ ,developed is F c /d2, where d is the particle diameter [32], which in our case, is taken to be d a .
There is some variation of the critical tensile stress among all the tested silica sand particles but
overall the data fits well the results obtained by different authors [26] on similar sand (figure 5)
which show a decrease of σ with the increase of the size of the silica sand particles. It has also
been observed that for some particles, some slight rotation occurred during the loading, and that
may explain the change of the force rate evolution observed in force-displacement curves(figure 4). As discussed by [39], in this case, both the normal and the shear forces contribute to
the particle crushing and affects the observed force-displacement response.
200
180
160

Force(N)

140
120
100
80
60
40

Possible rotation of the particle

20
0
0

0.01

0.02

0.03

0.05

0.04

Displacement(mm)Figure 4. Typical force-displacement line of a silica sand particle under uniaxial compression
500
Y. Nakata et al. [26]data od this study

Tension stress (MPa)

100

10

1

0.1

2

2.5

da(mm)

Figure 5. Critical tension stress versus diameter of the silica sand particles (asterisk) and data from Y. Nakata etal [26] on similar sand (hollow circles).

4.2 AE analysis
During the whole uniaxial compression test, the acoustic emission has continuously beenrecorded by the AE system. We combine the observed mechanical behaviour with the AE
parameters during the whole uniaxial compression test. A representation of an AE signal andsome associated AE parameters like AE Amplitude, AE Duration, Rise Time, AE Counts and
AE Threshold are shown in figure 6. Additional parameters like ASL (Average Signal Level)
which is the average of all the amplitudes of the signal can also be deduced. Examples of AE
burst signals recorded at the critical crushing point for three particles are shown in figure 7.
These three particles have a similar equivalent area diameter, d a , and the shape descriptors of
two of them, designated as silica particle 27 and silica particle 30, are close to the mean valueof the 60 silica sand particles, while the shape descriptors like Aspect Ratio and Irregularity of

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the silica particle 15 are slightly different compared with the averages of the entire sample.
The shape descriptors, critical force and tension stress of the three particles are given in table 3.

Figure 6. Typical AE burst signal and some associated AE parameters
5silica 15

4
3
2

Amplitude(Volt)

1
0
-1
-2
-3
-4
-5
0

200

400

600

800

1000

1200

Time(Microsecond)(a)5
silica 27

4
3

Amplitude(Volt)

2
1
0
-1
-2
-3
-4
-5
0

200

400

600

800

1200

1000

Time(Microsecond)(b)5
silica 30

4
3

Amplitude(Volt)

2
1
0
-1
-2
-3
-4
-5
0

200

400

600

800

1000

1200

Time(Microsecond)(c)

Figure 7. Original AE burst signal recorded at the critical crushing point for three silica particles: (a) silica 15,(b) silica 27, (c) silica 30

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Table 3. Shape descriptors, maximum compression force and corresponding tension stress for the three chosenparticles

Particle
Name
15
27
30

d a (mm)
1.87
1.71
1.89

AR
0.56
0.84
0.81

IR
0.52
0.80
0.73

C
0.72
0.67
0.70

Fc (N)
100.6
100.6
113.1

tension stress (MPa)
28.91
34.24
31.56

In the data recorded by the AE system, the Amplitude of the signals at the critical crushing hit
reaches 99 dB (figure 8), while the Average Signal Level (ASL) ranges from 20dB to 75dB.
The cumulative ASL of all the hits during the tests (figure 9) shows different evolutions forthe three tests. It seems that the Amplitude of the recorded signal is a good parameter that
may allow the detection of the crushing point of the silica particle, while the value of the ASL
and cumulated ASL at the crushing point do not appear to be related to the particle shape andmechanical response information. But the trend of the cumulated ASL matches well with thetrend of the axial force, and a drop in the force is well replicated by the cumulative ASL witha sharp jump. When the axial loading is close to the critical crushing force, the slope of the
cumulated ASL-time relation increases rapidly. To some degree, this could be used as a way
to detect the occurrence of particle crushing.
200

100

Force-Time-15
Force-Time-27
Force-Time-30
Amplitude at critical crushing hit-15
Amplitude at critical crushing hit-27

Amplitude/Average Signal Level (dB)

Amplitude at critical crushing hit-30
ASL at critical crushing hit-15
ASL at critical crushing hit-27

Force (N)ASL at critical crushing hit-30

50

0
50

0

100

150

200

250

300

350

400

0
450

Time (s)Figure 8. Force-time vs. Amplitude-ASL (Average signal level)at critical crushing hit of the three tests
150

2

Force-Time-15

10

4Force-Time-27
Force-Time-30
Cumulated ASL-Time-15
Cumulated ASL-Time-27

1.5

Force (N)

100

1

50
0.5

0
0

50

100

150

200

250

300

350

400

Accumulation Average Signal Level (dB)

Cumulated ASL-Time-30

0450Time (s)Figure 9. Force-Cumulated Average Signal Level-time diagram of the three tests

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4.3 Frequency domain analysis of AE signals
The recorded waveforms at the crushing point (figure 7) are also analyzed in the frequency
domain which is based on Welch's power spectral density estimate method [40] and was
conducted using Matlab software package. The results for the three particles are shown infigure 10. While the maximum amplitudes correspond to different frequencies for all threeparticles, the succession of the peak frequencies appears to match well, especially for thesilica 27 and silica 30. The peak frequencies for silica 15 are displayed differently and thatsuggests that the shape of the particle may affect the AE specific signature. In a bulk soil
formed by an agglomerate of different particles, this may help discriminate between them if
crushing occurs.
10

3

-5

silica 15
2.5

dB

2

1.5

1

0.5

0
50

0

100

150

KHz

10

2

(a)

-5

silica 27

1.5

dB

1

0.5

0
50

0

100

150

KHz

10

1.5

(b)

-4

silica 30

dB

1

0.5

0
0

50

100

150

KHz

(c)
Figure 10. Results of frequency domain analysis: (a) silica sand particle 15, (b) silica sand particle 27, (c) silica
sand particle 30.

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5. Conclusion
This study focuses on the behaviour of individual silica particles under uniaxial compression
loading. The long term objective is the study of crushing phenomena in granular media andthe development of non-destructive detection methods for fracture particle characterisation.
This study uses the AE technique alongside with mechanical loading of particles and it showsthat some AE parameters derived from the analysis of recorded signals can be useful for
tracking of the particle fractures as well as crushing identification. Futhermore, the analysis of
the recorded signals in the frequency domain appears to be related to the particle shape.
Further work is ongoing to estabilsh other criteria for identification of crushing in assemblies
of particles. The study is also extended to a large variaty of particles.

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Dixon, N., Spriggs, M., ‘Quantification of slope displacement rates using acoustic emission
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Wang, Y., Ma, C., Yan, W., ‘Characterizing bond breakages in cemented sands using a mems
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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

COOPERATION OF MENDEL UNIVERSITY AND BRNO
UNIVERSITY OF TECHNOLOGY IN THE FIELD OF BIOLOGICALAPPLICATIONS OF AE METHOD
Pavel MAZAL1, Houssam MAHMOUD1, Petr DOSTAL2, Michal CERNÝ2,
Michal SUSTR2, Jaroslav ZACAL2
Brno University of Technology, Czech Republic
2
Mendel University in Brno, Czech Republicmazal@fme.vutbr.cz

1

Abstract: This paper gives an overview of the common experiments performed in identifying the possible usageof acoustic emission (AE) in selected areas of monitoring life processes of living organisms. The paper
accumulates and partially comments the results of experiments where AE method has been applied to evaluationof response of transpiratory flow responses in various species in different parts of day. The second part isfocused on deepening knowledge of diagnostics and evaluation of the influence of natural conditions to thegrowth of maize. The final part of the paper deals with a continuous monitoring of honey bee (Apis mellifera)colony. Overall colony activity was monitored and data was recorded to describe development of the bee colony.
Keywords: acoustic emission, SAP flow, tree, maize, insects, honey

1. Introduction
Mendel University in Brno focuses in research work on agricultural and forestry sciences.
Brno University of Technology is the second largest technical university in the Czech
Republic with several workplaces focused on the non-destructive testing of materials andcondition monitoring of structures. Both universities are significantly different in its focus,
but some workplaces work closely together. This cooperation exists e.g. between the
Laboratory of Acoustic Emission of Faculty of Mechanical Engineering BUT and theDepartment of Technology and Automobile Transport of Faculty of AgriSciences of Mendel
University and focuses not only on application of AE method in various technical applications(welding, corrosion), there are implemented also the measurements which are aimed to verify
the possibility of unconventional application of AE method in biological sciences.

2. Measurement and overview of results
2.1 Evaluation of transpiration activity of trees
The identification of changes in transpiration activity trees is in the centre of interest ofscientists since the mid-60s. Introduction of AE into the investigation of plants (begin. ofeighties) meant significant progress in identifying sources of AE [1]. Development andapplication of AE occurred largely in the end of the 20th century, and the results were
published in several papers at conferences and in journals [2], [3] and [4].
Our joint research in this area was launched in 2008. We applied the AE method to evaluate
acoustic responses of diurnal changes of sap flow rate in maple tree (Acer pseudoplatanus),
lime tree (Tilia cordata), Douglas spruce (Pseudotsuga menziesii) etc. Sap flow rate was

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measured at breast height by the standard trunk heat balance method for comparison with our
results.
Measuring channel units of multi-channel AE systems Dakel Xedo© were fitted with
piezoceramic sensors of type MTR-15 and magnetic MDK-13. The information from data
files was subsequently processed by software DaeShow©, which enables all basic proceduresof evaluation – ring down counts, AE burst rate, summation of AE counts, RMS etc. Sensors
MTR-15 were fixed on steel blade and magnetic sensors were mounted on the end of
waveguides with conical end (see Fig. 1). Conical wave-conductive sensors appeared to bebetter than blade-form ones.

Steel blade and waveguidewith magnetic AE sensor

Position of sensors on the tree:
on steel blade and on waveguide

Final situation on thetested tree

Figure 1. Arrangement of AE tests on the trees [5]Examples of basic results from tested trees are presented in Fig. 2. Simplest records of AE
activity changes in several days are shown there. AE counts — the amount of overshoot ofsignal over a preset threshold levels during any selected portion of a test. Our measurement
could record up to 16 levels (Counts) of AE signal. In figures there are plotted only selectedenergetic levels and RMS value of the AE signal. The change of AE activity roughly
corresponds to the day’s cycle. It is evident that the AE signal is more active in the earlyevening and partially in the early morning periods.
For evaluation of AE signal sources it is necessary to use more detailed analysis of the captured
signal. It is possible to evaluate changes of rise-time, duration of AE events, peak amplitude, etc.
This could provide another view to estimate the sources of AE signal [5] and [6].
Our results of application of AE for monitoring of changes to the SAP flow showedsignificant changes of selected parameters of the signal in different daily hours on all
monitored trees (maple tree, lime tree and Douglas spruce). The regularity of these changes
and a comparison of changes of AE signal parameters with results of the other standard
methods suggest that they correspond to the daily life cycle of the trees. [7] and [8].
Records of RMS value and individual events in the frequency analysis in measurements ofacoustic emission (AE) have different activities (maximum of records) at regular intervals
depending on the natural processes occurring during daily biorhythm hydration xylem vessels.
A clear correlation can be found between the records of all the curves obtained from available
measurements of temperature fields (Fig. 3). Development of acoustic response activities canbe found in the areas of increased temperature of differential thermocouple with inverted
value (curve 3), which follows its heating and cooling due to sap flow.
The second important activity occurs at the minimum (of the curves 1 and 2) at thedifferential records both in the vertical and horizontal layout. Minima correspond to the sizeof the transpiratory current, respectively to dimensional changes of status (change indiameter) in the aboveground part of the trunk.

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Figure 2. Examples of typical changes of AE records during a day (maple and lime tree) [5]Figure 3. Comparison of sap flow rate with acoustic emission activity – maple [6]Interesting information can be expected from a detailed frequency analysis of AE events inselected time intervals [5] and [6]. Simultaneously a development in the area of acoustic wave
conduction by changing the shape of the waveguides is taking place. The specific shape of the
waveguides affects the signal intensity obtained.
2.2 The maize
The aim of second type of experiments was to monitor AE signals generated by plant todescribe the plant response to various stimuli - being under both water-stressed and wellwatered conditions [9]. The maize has been chosen as a test plant and installed with an AE
sensor at position of its stem to acquire AE signals. The results indicated that great amounts ofAE signals occurred during the daytime whereas small amounts of AE signals appeared
during night. As the results, AE signals generated by tested maize are capable of indicating its

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stress condition. Therefore, using of AE method is interestingly modern technique, especiallyin agricultural field [10].

Figure 4. Schematic plot of test arrangement [10]a)b)Figure 5. Setting up AE device with plant, AE system connected with plant [10]To implement the experiment suitably, experiment was operated inside the greenhouse of
Mendel University at 9.30 AM from 27th March to 3rd April, 2015. The stress conditions oftested maize were visible using AE device showing the AE signal parameters, which are root
mean square and the number of counts values, and environmental factors as following charts.
Similarly to the measurements on trees also the initial measurements in maize indicate a goodsensitivity of AE method on changes in plant nutrition process. AE parameters can be used for
denoting the stress condition of maize, especially in the number of counts and RMS values.
The variation of AE parameter values might occur from the response of transpiration systemof plant due to change of environmental parameters.

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Figure 6. AE parameters (log cnt, RMS) and air temperature, light intensity and relative
humidity values versus a period of time [10]2.3 Acoustic emission and insects
Acoustic emissions can also be used to evaluate the incidence, activities and status of certain
species of insects. It is quite understandably that in focus are mainly wood-destroying insects.
The losses caused by pests of wood used in construction and other industries achieve suchhigh values that the need to develop effective diagnostic methods for detecting of pests
continues to grow. There are supplied instruments for identifying wood-destroying insects e.g.
termites.
Our interest was focused especially on the application of AE for evaluating the condition of
useful and economically important insects, e.g. bees.
2.3.1 Wood-destroying insects
The experiments with evaluation of longhorn beetle (Hylotrupes bajulus) activities were
performed in the laboratories of Mendel University in the past. The preliminary measurements
have shown that damage of wood by beetle larvae is accompanied by the emission of acoustic
signals in the audible and ultrasonic spectrum. The character of monitored events wasmeasured in relation to the function of the power spectral density (PSD) and the total activityof larvae in both day and night mode. According to the PSD function, it is possible todistinguish two main types of AE signals. The first type or second type consists primarily of
frequencies with a maximum of 8 kHz, respectively 30 kHz. Considering the directional

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anisotropy of mechanical characteristics (along and perpendicular to the fibres) caused by theconstruction of wood, can be expected that AE signal emitted by fracture perpendicular to thefibre will have a higher frequency than the signal emitted by fracture along the fibres.
It was interesting to monitor the activity of the larvae during the 17 daily measurements (Fig.
7). Although terrarium maintain a constant temperature of between 24-26 ° C and a relative
humidity of 90%, the activity of larvae for unknown reasons, changed significantly after the
initial slightly elevated stage came a sharp rise and then a significant drop in the number ofmeasured AE events.

Figure 7. AE events sum change during the long-term measurements of beetle larvae activities[11]
During the 24-hour cycle was observed cumulative increase of AE events in order to establish
preferential time zone for the actual measurements in the field. During the reporting period,
there were no typical pattern of distribution of AE events, it was not possible to identify the"active" hours characterized by increased activity of the larvae, or "inactive time" without
tracking events AE [11].
2.3.2 Beneficial insects - honey bee (Apis mellifera)
The initial measuring of activity of honeybees was carried out in 2012. One AE sensor wasplaced on the glass plate, which was stored at the bottom of the hive and the opening of theport. A second sensor was attached to the plate dimensions (30x100 mm), which was inserted
between the frames.
Measurements began in late March and early April. Fig. 8 shows the colony activity indifferent phases on two-channel measuring system (glass and metal).
Sensor on a glass plate (Fig. 9 above) - as shown, thus the RMS correlates with temperature,
which determines the activity of the colony. Between days when the temperature wasunfavorable for the development of colonies is visible low activity on the glass. After
warming activity is increased. When the colony had a chance collecting pollen and nectar (5thday) the activity is even at night. The colony processed brought material. The glass plate mostreacted at elevated temperatures when the activity of bees performing work outside the hive
was the highest. The activity at night is a result of the cleaning instinct, when the space ofhive is cleaned and bee corpses are pulled out of the hive environment.

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a)

b)

Figure 8. Placement of AE sensors in bee hive [12]a) AE sensor on steel plate, b) AE sensor on glass plate in the inlet port

Figure 9. RMS of AE signal and temperature curves for sensors on glass and metal plates [12]Sensor on the metal plate - (Fig. 9 below) the sensor on the plate was not influenced by theoutside temperature, because in the hive bees can create enough heat to their thermal comfort.
This sensor was influenced by its location, so that it was placed on the penultimate a frame
where there is no a large accumulation of bees. In the first phase can be observed that bees
respond positively when the foreign object (plate with sensor) was placed in its space.
In subsequent phases of measuring the colony pulled together into a tight formation to reduceheat loss and activity, thus declined. When the outside temperature increase the colony
responded by loosening of clusters into other aisles, where was placed the sensor with metal
sheet. The last phase of activity measurement is high hives sheet metal backing, when thebees fill the entire space of the hive.

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The vibroacoustic signals are an important part of communication in the honey bees (Apismellifera L.). The aim of next study was to observe the acoustic emission that varies in a beecolony during different weather phenomena (strong winds and hailstorms) and to estimate thenature and the extent of the reactions of the colony by the analysis of the obtained data.
Example of bee colonies response in very bad weather (storms with hail and wind) is shownin Fig.10 [13] .

Figure 10. AE signal (RMS) as result of bee colony reaction on storm [13]Continuation of these experiments was additional measurements aimed at monitoring theactivities of bees exposed to stress factors. There were detected some intensive biologicalprocesses, which were deviated from the standard norms. It was the insertion of formic acidinto the colony [14] . The method of acoustic emission scans disconcerted colonies in theimminent vicinity. To standard hive assembly was inserted glass plate, on which were placed
two sensors. One sensor is placed on top of the glass plate, which intervened to anotherextension. A second sensor was inserted into the bottom edge of the glass plate that was at thebottom of the first extension. The first part was measured normal activity in the colony andthe following day was inserted the carrier Mitegone which wearied the formic acid.

Figure 11. Value of RMS in bee hive with application of intermediate acid [14]The upset of the colony, which was caused when inserted into the evaporator Mitegon
environment hive, when this type of acid reacted more experimental bees in the hive, and it
subsequently, increased expression of excitement that was recorded. Exclusively acoustic
emission, which was used with a suitable methodology for setting and recorded the activity ofcolonies on a glass plate. Glass plate impulses transmitted from individual colonies that haveshown significant differences between both samples. Total RMS samples are very different
from each other on the reaction of acid insertion. General view of the insertion of formic acidin the hive environment is an increased activity of the colony, which manifests enhanced
cleaning soil and intensive ventilation, which was recorded in the Fig. 11. Finally, it is aneffort to encourage the use of application of formic acid, which, when properly handled isbeneficial for the treatment of hives.

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3. Conclusion
Presented examples of the AE method using in biological applications clearly show a positive
reaction of AE signal to the processes occurring in living organisms. The problem is thecorrect interpretation of experimental data.
Application of acoustic emission method for evaluation of growth parameters of the trees cansignificantly extend the knowledge of the nutritional status of trees and the status of forest
stands in the future. A similar situation occurs when evaluating agricultural crops - mostly
tomatoes, corn etc. Also, this application can be useful for assessing the current status of thesecrops, or when deciding on the necessary intervention of growers. Identification of the precise
resources AE signal, of course, requires further detailed studies.
AE method shows its applicability in monitoring the presence or condition of livingorganisms, particularly insects. Also in this case, the application offers various levels of EA,
from the mere registering the presence of wood-destroying insects in design to sophisticatedmonitoring the status and activity of beneficial insects, which will create optimal living
conditions, application of medicaments.
Overview of presented results deals with a continuous monitoring of honey bee colony
depending on the activity, hive space and in the hive. Overall colony activity was monitored
using acoustic emission (AE) method. Data was recorded to describe development of the beecolony. The information gained will be used to represent a more comprehensive view on thelife-cycle and behaviour of honey bees (Apis mellifera).
AE method allows determining, without the immediate presence of the operator both selectplants or insects react to external stimuli. AE method for diagnose the condition of monitored
objects can also be used at more distant sites. This can be very beneficial, especially for beecolonies when the AE signal line may describe the flight activity and intensity field bees thatprovide water and pollen. The method can be used if the colony is located at remote sites or tobeekeepers, which is time busy.
It is obvious that the main task now is to develop the measurement methodology that allows
obtaining comparable results. The collaboration with other professionals is essential toexplain the processes that give rise to acoustic emission signals.
ACKNOWLEDGEMENTS
The project was supported by NETME Centre (New Technologies for MechanicalEngineering), Reg. No. CZ.1.05/2.1.00/01.0002 and, in the follow-up sustainability stage,
supported through NETME CENTRE PLUS (LO1202) by financial means from the Ministryof Education, Youth and Sports under the „National Sustainability Programme I“ and by the
project No. FSI-S-14-2329 “Vibro-isolation system of cosmic carrier payload” of the Facultyof Mechanical Engineering.
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akustické emise", In: Defektoskopie 2008, p. 345 - 352, Brno 2008, ISBN 978-80-214-3759-3.
12. J. Tlacbaba, M. Cerny, A. Pridal, P. Dostal, "Application of the selected physicalmethods in biological research". Acta Univ. Agric. Silvic. Mendel. Brun., 2013. Vol. 61,
No. 1, p. 215 - 220. ISSN 1211-8516.
13. J. Tlacbaba, M. Cerny, P. Dostal, A. Pridal, "Acoustic Emission in the Nest of the Honey
Bee Depending on the Extreme Weather Conditions", Acta Univ. Agric. Silvic. Mendel.
Brun., 2014, Vol. 62, Issue 1, pp. 245-254.
14. J. Tlacbaba, M. Cerny, P. Dostal, "Observing the effect of formic acid on apis mellifera using
acoustic emission method", MendelNet, 2013, p. 888 – 892, ISBN: 978-80-7375-908-7.
4.

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

THE TEST OF RAILWAY STEEL BRIDGE WITH DEFECTS USING
ACOUSTIC EMISSION METHOD
Marek NOWAK1, Igor LYASOTA1, Ireneusz BARAN1
1
Laboratory of Applied Research, Cracow University of Technology,
31-864 Krakow, al. Jana Pawla II 37, Poland, nowak@mech.pk.edu.pl

Abstract
Currently a significant part of operated railway steel bridges in Poland is exploited from the beginning of XX-th
century. Very often the technical condition of them is bad and these objects require the appropriate diagnostics.
Mostly the visual diagnostics is used, and in exceptional cases in some places of objects the other non-destructive
testing are performed. Therefore the opinion about condition of bridge is non completed and it is necessary to study
the effective method, which will enable on more exact diagnostics of steel bridges.
The Cracow University of Technology together with PKP Polish Railway Lines realizing the project for the workout of diagnostic method the steel railway bridges with use of acoustic emission method (AE). During project
realization, the laboratory tests on material from railway steel bridge for collection data were performed. These
measurements were done on standard specimens and on samples with corrosion products. The AE measurementson real object with different crack size were also done. The tests were carried out at a controlled moving train atdifferent speeds and for various weight of railway carriages. The results of acoustic emission tests and analysis
with standard and additional parameters are presented. The results of metallography and numerical analysis ofbridge and defected element are also presented.
Keywords: railway steel bridge, acoustic emission, crack

1. Introduction
Around 250 000 railway engineering structures is used in the European Union member states[1]. In Poland, the main railway infrastructure managing entity is PKP Polish Railway Lines
S.A., which manages 3 500 bridge structures with the total length of over 130 000 metres,
a considerable part of which, i.e. 37% are structures with steel bays [2].
The bridge structures are exploited in the conditions of varying atmospheric environment,
vibrations and varying exploitation stresses caused by the passage of railway units, resulting in
the constant degradation of materials. The negative impact of the environment is especiallysignificant for the steel structures. Simultaneously, the multiple years of exploitation of thePolish bridge structures result in a bad technical condition of many of them, because their age
nears or exceeds the durability assumed at the designing stage.
The danger of defects in the exploitation of bridge structures results in the need to systematiseand extend the used assessment procedures related to the progress of new testing methods.
Based on the report [3] elaborated and issued in 2008 by the European Commission Join
Research Centre, one of the approved testing methods that can be used for assessing thetechnical conditions of this type of structures is the acoustic emission (AE) method.
The incorporation of a new and efficient method based on the system of procedures utilising the
AE method and developed for the purpose of monitoring and determining safe exploitation
conditions for the load-carrying elements of defected bridges featured the foundation ofa consortium comprised of the Cracow University of Technology and the owner of the railway
bridges and line – PKP Polish Railway Lines S.A. The consortium is jointly executing a project32nd EWGAE ©CNDT2016

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within the scope of the Applied Research Programme financed by the National Centre for
Research and Development. One of the project’s stages featured the tests presented in this paper.

2. Laboratory testing
In order to conduct the laboratory tests aimed at developing the diagnostics methodology,
we have acquired structural elements of a steel bridge exploited since the beginning of the XX
century. We carried out the chemical composition test to characterise the aforementioned
material (hereinafter referred to as material I). The test was also conducted in the later stage ofthe project for a section collected from an exploited bridge (material II) constituting the subjectof testing on a real structure. The chemical composition of both steels and the bibliography dataof the content of elements in the cast steel used at the beginning of the XX century to constructbridge structures are presented in table 1.
Table 1. Chemical composition of the tested materials and bibliography data [4]Material
I
II
Cast steel

Chemical elements, [%]
C
0,054
0,05
0,030,35

Mn
0,37
0,50
0,040,75

S
0,029
0,023
0,0040,16

P
0,05
0,04
0,0040,16

Cu
0,20
0,04
0,110,14

Cr
0,02
0,0070,014

Ni
0,15
0,05
0,030,04

Si
0,002
0,02
traces0,18

The next stage for the characterisation of the tested materials was to conduct metallographic
tests of the material’s structure. Exemplary photographs are presented in figure 1.

Figure 1. The micrustrfucture of steel, zoom 500×: a – material I , b – material II

During the microscopic observations conducted using optical light microscopy it was confirmedthat both materials (material I and II) contain a large number of non-metallic inclusions witha globular shape and extended in the direction of the steel section’s rolling. Additional
metallographic tests conducted using the SEM have demonstrated that the inclusions are mainly
sulphur compounds (MnS, FeS) and silicones.
The microstructure of both materials, due to the low content of carbon (approx. 0,05% C), is ferritic
with a minor share of perlite, which is distributed mainly on the grain edges (fig. 1).
The metallographic tests of materials I and II, conducted using the SEM, have demonstrated
ongoing microstructure ageing processes, which consisted in the coagulation of carbides

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and nitrides inside ferrite grains. In both cases, the ferrite grain edges featured inclusions of
cementite Fe3CIII.
The chemical composition and structures tests have demonstrated similarities in both materials
and affiliation to the group of materials used in the construction of bridge structures at the
beginning of the XX century.

3. Laboratory testing on model samples
We have conducted the acoustic emission measurements in laboratory conditions to determine
the emission characteristics of the material acquired from the bridge structure. One of the tests
was a static tensile test. The tests were carried out using the Zwick/Roell Z100 tensile testingmachine with hydraulic grips. The recording of AE signals was executed using two types of
sensors: VS75-V and VS150-M with resonance frequencies of 75kHz and 150 kHz respectively,
AEP4 external preamplifiers with a 34dB amplification and the Vallen AMSY6 AE system.
The tests were performed on flat samples (fig. 2a and b), which included two sensors of each
type, according to the diagram presented in the figure (fig. 2c). The recording of the force anddisplacement values in the AMSY6 system was executed by using parametric channels
connected to output parametric channels of the tensile testing machine.
The bridge structures very often feature extensive corrosive defects, which may impact thegeneration of acoustic emission signals, thus the measurements were conducted on two typesof samples: A – base sample with the surface condition required by the standard [5] (materialIA), B – cut-out from elements with extensive corrosive defect (material IB). The samples wereprepared so that their measurement part included natural corrosive defect including corrosion
products (fig. 2b).

Figure 2. View of the samples for static tensile testing of steel using the AE method: a– types IA, b – type IB and c - diagram of the test standDuring the static tensile test we have recorded signals generated by the material and,additionally, during the loading of the samples with corrosive defect we also recorded signals
deriving from friction, cracking and loosening of corrosion products. The processes also takeplace during the loading of the actual structures with this type of defect and lingering corrosion
products.
Figure 3 presents exemplary tension charts and the activity for both types of AE sensors for the
representative IA and IB type samples. We presented the fragments of charts which included
the range of displacement up to 2 mm, because the part is the most interesting from the point of

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view of the structure’s diagnostics. The eliminated fragment, which characterises the criticalstage of the destruction process, exceeds the NDT scope, because the main purpose of thesetests is to prevent the loss of stability of the structural element.

Figure 3. Activity of signals and the chart of sample tensile testing for material type IA (a, b) and IB (c, d); a, c –VS 75-V sensors and b, d – VS 150-M sensors

The analysis of the obtained results demonstrated substantial differences in the nature ofgenerating the AE signals during the tensile testing of specific types of samples. The only
common feature of the tested samples is the clear peak of the maximum AE activity during thetransition between the material’s flexible operation and creation of plastic deformations. During
the tensile testing of material IA, we registered the highest activity of the located events(54 hits/sec – VS75-V, 55 hits/sec – VS150-M). In this case, the VS75-V type sensors locatedapprox. 880÷900 signals and the VS150-M type sensors recorded the highest number of AE
signals – approx. 1350÷1400.
The AE activity during the tensile testing of material IB amounted to 12 hits/sec with a total
number of 760÷780 signals located by the VS75-V sensors and 6 hits/sec as well as 200÷220
signals located by the VS150-M sensors. It is worth to note the large differences in the numbers

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of the signals located for particular materials and the fact that for material IA, the higher numberof AE signals was recorded by sensors with the higher frequency range of 150 kHz, whereas
during the tensile testing of samples with corrosion products – material IB, we observed an
opposite effect, i.e. more signals were recorded by sensors with the resonance frequency of
75 kHz.
The probable cause for such phenomenon is the fact that in the tensile testing of material IA
samples the AE sources mainly include processes of deformation and destruction of the steel’s
microstructure components, whereas in the case of material IB samples we observed additional
AE sources, i.e. friction, loosening and cracking of the corrosion products.

4. Bridge structure testing
4.1 Structure and testing methodology
One of the bridge structures that was subject to testing using the AE method was the
sandwichstructured steel bridge with the length of 13,5 m, operated since 1915. The entire
structure included extensive corrosive defect and additionally in one of the stringer occurreda crack of approx. 75% of the I-beam’s cross-section. The structure’s diagram and view withthe marked crack spot is presented in figure 4.

Figure 4. The model of bridge structure and crack area of stringer

The tests with AE signal recording were conducted with controlled structure loading bya passage of the Fablok 401Da-466 diesel locomotive with freight cars with varying mass.
During the execution of subsequent measurement sessions, according to the developed program,the crack type defect has undergone gradual repairs and activity aimed at stopping thedevelopment of the existing crack by executing a stop hole at the tip. We realized measurementsfor various sizes of defects. The defect marked as A was characterised by a discontinuity
encompassing approx. 75% of the frame section’s cross-section, B – discontinuity amounting toapprox. 25% of the cross-section, C – discontinuity amounting to approx. 25% of the crosssection finished with a stop hole at the crack’s tip. The fefect, in the form of geometric modelsof the beam element for the FEM calculations, is presented in figure 5. For each type of defects,
a single measurement session embraced an equal number of passages of the train through thestructure with various speeds. Several measurement sessions were conducted and the paper
presents representative results.

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Figure 5. The diagram of defect stringer of bridge for AE test

The testing of the structure was performed using two types of sensors (75kHz and 150kHz)
located in the bridge’s structural nodes. For the purpose of further analyses, in the crack zone we
mounted additional AE sensors, tensometers for deformation measurements and an extensometerrecording the crack’s opening, connected to the AE measuring system, as the parametric channel’s
input. During the bridge loading, we also recorded bridge bending at various measurement points
using potentiometric sensors of displacements with a 100 mm travel.
4.2 Numerical analysis of the stress field in a defected bridge element
The tensometric measurements, structure bending measurements and the determined material
properties were used in the FEM calculations aimed at determining the actual locomotive loadat the structure’s crack spot and then determining the stress field in the defected frame section.
Fig. 6a presents the loading diagram for the cracked node for the FEM calculations and thelocations of the tensometers, whereas fig. 6b specifies the results of the tensometricmeasurements from a single loading session. The charts illustrate the opposite directions ofdeformations on the tensometers placed on both sides of the crack, which is a result of existenceof shear mechanisms in the given zone.

Figure 6. Defected node diagram and an exemplary tensometer record

For each type of defect (A÷C) we created a calculative 3D model, in which we used tetragonal
finite elements of various dimensions. Near the tip crack and stop hole, the element grid has
been condensed to increase the accuracy of the calculations in the given zones.
Figure 7 presents the exemplary results of the FEM calculations for particular defect types.

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Figure 7. Stress distribution by Mises in discontinuity area for A, B and C type of defects

For type A defect, we presented the Mises stress distribution in the entire defect zone and anincreased area of the crack’s tip (fig. 7a). The maximum values, i.e. the cracks concentrated in
the tip in this case, reach the values of approx. 270 MPa. They are similar to the stringer
material’s yield strength. The Mises stresses in the type B defect zone are smaller and amount
to approx. 200 MPa.
The execution of the stop hole in the crack’s tip (type C defect) resulted in a substantial drop in
the stress field’s concentration down to approx. 130÷140 MPa. This activity is often used as
reparatory and is aimed at preventing defect growth.
4.3 AE testing results
The analysis of the obtained results demonstrated substantial differences in the number ofAE signals when loading bridge structures with particular defect types. Figure 8 presents theresults for sensors with the frequency of 150kHz.
When loading of the structure with the type A defect (discontinuity of approx. 75% of thestringer’s cross-section), the maximum activity of the located events amounted to 10 events per
second. We located approx. 650÷700 AE signals in the crack zone for singular sessions andtheir largest number was located in the middle part of the defect (fig. 8a). The values ofamplitudes of the located signals were within the range of 40÷65dB (max. 75dB) and theenergies of these signals – in the range of 20÷10000 eu (max. 100000 eu).
In the case of the type B defect (material discontinuity of approx. 25% of the crosssection),
the maximum number of located events did not exceed 5 hits/sec, with a total number of
130÷140 hits for a single loading session. The signals were distributed evenly along the entire
defect with an increased number in the crack’s tip (fig. 8b). The amplitude and energy of therecorded signals amounted to 40÷60dB (max. 70dB) and 50÷1600 eu (max. 38500 eu),
respectively.
The lowest activity and number of located signals were recorded when loading the structurewith the type C defect (lack of material continuity of approx. 25% of the cross-section, finishedwith a stop hole at the crack’s tip). The values for singular sessions and the 150 kHz sensors
were as follows: activity – 2 hits/sec and total number of 60÷65 hits (fig. 8c). The amplitude of
the recorded signals amounted to 40÷60dB (max. 75dB) and the energy – 500÷2200 eu(max. 29000 eu).

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A

B

C

Figure 8. The location, activity and total number of located events for different types of stringer defects

The next step of the analysis of the recorded signals was to separate the signals located in thecrack tip zone (fig. 9). In the case of loading of structures with the type A and B defects, thetotal numbers of located signals for particular sessions were similar and amounted approx.
25÷30 hits per session.

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They differed however in terms of the values of amplitudes and energies for loading ofstructures with particular defect types. In the case of the type A defect, where the stress values
at the crack tip were higher, the amplitudes of the recorded signals reached the value of 55 dB
and the energy - 1000 eu, whereas for the type B defect (lower level of Mises stresses), thevalues amounted to 50 dB and 260 eu, respectively.
It was concluded that the execution of a round hole in the crack’s tip resulted in a substantial
decrease of the stress values in the given zone (fig. 7c), which in turn resulted in the loweringof the activity of AE signal generation to the value of approx. 5 hits per session. The locationsof signals for particular types of defect in the crack tip zone are presented in figure 9.

Figure 9. Locations of AE signals in the crack tip zone for defect types A, B and C

When conducting the AE method tests, we also realized the measurement of crack edge
displacements using a mounted extensometer, placed transversely to the crack, in its middle
part. It was also used as an input signal for the parametric channel of the AE measuring system.
This enabled correlation of the recorded AE signals with the existing loading of the defect zone.
Fig. 10 presents the exemplary chart illustrating the impact of the mass of the passing train(crack size) on the recorded AE signals. The obtained results indicate that in the studied casethe load increase caused a decrease in the crack opening dilation value – closure of the opening.
Figure 10 also presents the values of amplitudes, energies, signal strength and the Historic Index
parameter in correlation with the load size (extent of displacement of the opening edges).

Figure 10. Amplitude (a), Historic Index (b), signal strength (c) and energy (d) for signals located in crack tip
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We confirmed a substantial increase in the AE signal parameters (maximum amplitude, signal
strength and energy) with an increase in the bridge structure’s loading. The analysis conductedwith the use of the Historic Index also confirmed the influence of the load on the recorded AE
signals.

5. Conclusions
The chemical composition testing and the metallographic testing has confirmed that the material
obtained for the laboratory testing samples and the stringer of the studied bridge are made fromcast steel used for building bridges at the beginning of the XX century.
The laboratory tests demonstrated substantial differences in the nature of the recorded AE
signals during the tensile testing of the samples without (IA) and with lingering corrosion
products (IB).
In the case of samples without corrosion defects, most AE signals were recorded using sensors
with the resonance frequency of 150 kHz, whereas in the case of samples with corrosion
products, most signals were recorded using sensors with lower frequencies.
The FEM calculations demonstrated substantial differences in the value of the reduced Mises
stresses at the crack’s tip for particular defect types and the stop hole in the crack’s tip has
especially allowed reducing the stresses.
The type of the bridge frame section’s defect had an impact on the number and parameters of
the recorded AE signals. Due to the existence of increased stress values (type A defect), we
recorded signals with higher amplitude and energy in comparison to the defect for which thestress values were lower (type B defect). In the case of the type A defect, a large number ofsignals was generated in the middle part of the crack.
The highest values of amplitude and other energy parameters of the signals recorded during theloading of the bridge structure occurred during the most substantial loading. We have confirmed
the influence of load on the recorded AE signals.

References[1] M. Maksymowicz, P.J.S. Cruz, J. Bień, Load Capacity Of Damaged RC Slab Spans Of
Railway Bridges, Archives Of Civil And Mechanical Engineering, 11 (4) (2011) 963-968.
[2] Szadkowski K.: Raport o stanie technicznym kolejowych obiektów inżynieryjnych PKP
Polskie Linie Kolejowe S.A. według stanu na dzień 31 grudnia 2013 roku, Biuro Dróg
Kolejowych PKP Polskie Linie Kolejowe S.A., Warszawa, 2014
[3] Assessment of Existing Steel Structures; Recommmendations for Estimation of Remaining
Fatigue Life, European Commission Join Research Centre, 2008.
[4] J. Hołowaty, B. Wichtowski, Properties of Steel in Railway Bridge Constructed in 1887,
Roads and Bridges, 14 (4) (2015) 271-283[5] PN-EN ISO 6892-1:2010 Metale -- Próba rozciągania -- Część 1: Metoda badania
w temperaturze pokojowej

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

ACOUSTIC EMISSION DETECTION OF DAMAGE INITIATION IN 1D
CFRP NCF COMPOSITES SUBJECTED TO BENDING
Maciej PANEK1, Marek NOWAK2, Ireneusz BARAN2, Krzysztof J. KONSZTOWICZ3
2

1
Wroclaw University of Technology, KMMT, 50-372 Wroclaw, Poland
Cracow University of Technology, Institute of Production Eng., Krakow, Poland
3
University of Bielsko-Biała, IB WIMBiS, ul.Willowa2, Bielsko-Biala, Polandphone +48 33 827 9114, e-mail: kkonsztowicz@ath.bielsko.pl

Abstract
Acoustic Emission was applied to detect microcrack initiation in unidirectional (1D) carbon fiber reinforced
composites (CFRP) subjected to 4-point bending. Materials were prepared from polyester (PES) bonded 1D carbon
fiber fabric and epoxy resin using the RTM technology. Fiber volume content of composites varied from small (35%)
through medium (51%) to high (68%). Samples were cut from 1D composite plates to the size of 120x20x2mm in two
orthogonal directions - along fibers [0] and across fibers [90]. Selected samples had part of their tensile surface(in bending) pre-polished to enable microscopic (SEM) observations. Samples were subjected to monotonic and steploading tests in 4-point bending while Acoustic Emission (AE) sensors attached to samples’ surfaces monitored
the damage initiation. The AE Historic Index proved to be the most reliable AE parameter indicating damage
initiation, which allowed to interrupt loading sequence before failure. Details of microcracks were observed under theSEM on polished composite tensile surfaces loaded in bending. Fiber debonding and breakage occurred
simultaneously and were the first defects to initiate damage in the examined materials.
Keywords: acoustic-emission, 4-point bending, step-load, SEM, debonding

1. Introduction
An important number of damage studies were carried out on unidirectional (UD or 1D) fiber
composites over the past half-century, mostly on glass fiber reinforced plastics (GFRP) andcarbon fiber reinforced plastics (CFRP) materials [1]. Multitude of direct or indirect NDE
methods have been applied in these studies with different degree of success [2, 3].
The majority of NDE techniques are capable of detecting larger defects at the stage of
propagation, closer to catastrophic failure, however the initiating flaws are usually too small
to be detected by available conventional NDE techniques [4, 5].
As pointed out by Lomov et al.[6], one of fundamental issues in composite damage studies isthe definition what “damage initiation” means. Singular fiber breaks may not constitutesubstantial danger to integrity of material, as composites containing broken fibers are known
to sustain long-term fatigue tests [7, 8]. The fiber-matrix debonds also may not be lethal tomaterial as long as they do not coalesce into larger flows [9], whereas the details of the natureof matrix cracks and their relation to matrix cross-linking process are still not quite wellrecognized [10].
Another difficult issue of damage initiation studies is the accuracy of applied experimental
techniques with regard to localization and time of occurrence of initiating defects. In earlier
works [11, 12] significant progress was already achieved using visual examination to describe
flaws of millimiter order of magnitude. Recently, the high resolution scanning electron
microscopy (SEM) and computer tomography (CT) techniques enable flaw detection of
micrometer sizes [3] and yet the details of damage initiation are still not fully recognized.
Due to specific application requirements, many works related to 1D composites were carriedout on multilayer, complex architecture materials and often attempts were made to describe
the flaws of very different nature (i.e. matrix cracks, debonding, fiber breakage, fiber pull-out,

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delamination) – all in one study [13]. On the other pole, many excellent results were achievedin examining single fiber composites (sfc), single bundle or a layer [14-16]. However, owing
to complexity of stress distribution in composite structures, such singular results are hardlytransferable to real materials. There still seems to exist the need for systematic studies ofdamage initiation studies in composite materials of simple fiber architecture, concentrating on
particular type of flaw.
The aim of this work is to describe the microcrack initiation process in 1D non-crimp fabric(NCF) CFRP composites subjected to 4-point bending tests, based on continuous monitoringof damage initiation in function of time of test with use of Acoustic Emission. To this end, thesensitivity of AE Historic Index to the onset of damage is exploited.
Important deformation of fiber reinforced plastic (FRP) composites in bending [17] is turned
into advantage, enhancing the possibility of defect localization in the area of highest risk ofdamage. In the 4-point bending tests it is the area near the center of outer surface, and this wasobserved under the SEM. This simple methodology was already proven successful incompression studies of CFRP composites [18].

2. Materials and Methods
2.1 Sample preparation
Carbon fibre/epoxy matrix composite plates of the size 500 x 500 mm were manufactured by
LZS ILK TU Dresden1 using the RTM technology, with injection pressure of 6 bars,
processing time 8 hrs and resin transfer temperature 600C. The epoxy resin L was used
together with EPH 294 hardener2 to form matrix material.
The 1D composite plates were made of polyester (PES) bonded Toho Tenax E HTS40 noncrimp fabric (NCF) with carbon fibre strands of 12K filaments and linear density 800 tex.
Average tensile strength of single Toho Tenax HTS40 carbon fibres used in this work is equal
to 4300 MPa and tensile modulus to 240 GPa [19]. The number of fabric layers and theamount of resin injected during plate moulding were varied to give final products with small,medium and large fibers volume content: 35vol.%, 51% and 68%, respectively.
Rectangular composite samples of dimensions 120x20x2mm were water-jet cut from each oflarge 1D plates with varying fibre volume content, in both principal directions - across [90]and along [0] fiber strands (Fig. 1).

abFig. 1. a) 1D CFRP rectangular samples water-jet cut along and across carbon fiber strandsb) carbon fiber strands close-up on the backside of 1D PES bonded CFRP sample

1
2

Leichtbau-Zentrum Sachsen GmbH, Institut fur Leichtbau und Kunstofftechnik, Technische Universitaet Dresden, Germany
both manufactured by R&G Faserverbundwerkstoffe GmbH of Waldenbuch, Germany

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2.2 Mechanical properties of 1D CFRP
Flexural strength of the examined CFRP composite materials was determined in monotonic
4 point bending tests [20, 21], with simultaneous monitoring of their AE response [22].
Experiments were carried out using Zwick Z100 electro-mechanical testing machine,
equipped with testXpert v.3.1 software (also by Zwick). The cross-head speed applied in thesetests was equal to 2 mm/min. Dimensions used in 4-point bending experimental set-up as wellas the positioning of AE sensors on sample’s ends are presented in Fig.2a. Five samples per
direction/material were examined.

ab

Fig.2. a) positioning of AE sensors on CFRP sample in monotonic 4-point bending testsb) loading schedule of 4-point bending step-load tests

The cyclic 4-point bending step-load tests [23-28] were performed in similar experimental
set-up geometry, using the servo-hydraulic MTS 810 machine, equipped with FlexTest
software, version 3.5B 1787 and force sensor MTS model 661 19F-01. Five samples per
direction/material were examined. Increasing load was applied following the scheme shownin Fig. 2b, with 10% increasing steps (in relation to rupture stress), followed by 5 min hold.
The load was increased with average rate of 0,01 of fracture stress per minute and recorded
using force sensor MTS 661 19F-01. Unloading rate was on average 0,1 of fracture stress per
minute, down to the level of ~1% of fracture stress, which enabled to avoid testing machine’s
instability. Unloading was also followed by hold period of five minutes. This sequence wasrepeated till final failure of the sample or until interruption of the test by operator.
2.3 Acoustic Emission
The AMSY6 acoustic emission system3 was used for damage monitoring during themonotonic 4-point bending examination of CFRP samples in electro-mechanical Zwick100
testing machine. The AMSY-6 was equipped with Vallen software for parametric andtransient data acquisition and analysis [29]. Loading data were transferred directly to AMSY6 system allowing for correlation with acoustic emission signals obtained from examined
CFRP samples. The AE signals were registered using two broad-band sensors Fuji AE144A(50-700 kHz) attached to sample’s surface (Fig. 2a) with rubber band and vacuum grease asa coupling agent. The Vallen AEP4 preamplifiers had the gain set to 34dB. System thresholdsetting varied from 34 to 46 dB (depending on type of sample), and transient files registration
TR used 2048 samples per set. Standard signal calibration procedure using the Hsu-Nielsen
source preceded the actual measurements [30].

3manufactured by Vallen Systeme GmbH, 82057 Icking, Germany. http://www.vallen.de/

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Damage monitoring during the step-load tests in servo-hydraulic MTS 810 machine was
performed using AMS-3 acoustic emission system (also by Vallen Systeme GmbH), equippedwith Vallen software for parametric and transient data acquisition and analysis. Signals werecollected by Vallen resonant sensors V150-M followed by Vallen AEP3 preamplifiers, withgain set to 34dB. System threshold in most cases was set to 40,7 dB, with 2048 samples per
TR set. Standard signal calibration procedure was applied prior to measurements [30].
2.4 Microscopic observations
Two of each set of 5 samples per material/direction had central part of their outer surface(between inner supports, Fig.2a) pre-polished for microscopic studies. The SEM observationsof damage initiation in these surfaces were performed using high resolution scanning electron
microscope Nova NanoSEM 2004. Uncoated samples were observed in secondary electronmode (SE) in low vacuum conditions (60 Pa) and at relatively low voltage (5kV).

3. Results and Discussion
3.1 The 4-point bending of 1D CFRP
The mechanical response of neat epoxy resin obtained in 4-point bending on same size
samples as the examined 1D CFRP composites is presented in Fig. 3a. It is not quite certainthat the departure from linearity at higher loads may be related only to viscoelasticity of
epoxy resin. It may be also due to described recently microcrack formation similar in nature
to that found in crystalline solids [10].
The samples of 1D NCF CFRP composites cut longitudinally [0] and transversely to fibers[90] practically do not show the non-linearity of force-displacement plots (Fig.3b). The
longitudinal strength of 1D NCF CFRP increases with fiber volume, while transversal
strength does not change with fiber volume (Fig. 3c), as it depends mostly on fiber-matrix
interphase [1, 9].

900
850
800

X
Y

750

σ m [MPa]

700
650
600

100
50
0
30

a

b

35

40

55
50
45
Fiber Volume [%]

60

65

70

c

Fig.3. Force-displacement plots and strength obtained in monotonic 4-point bending: a) neat epoxy resin; b) 1D
NCF CFRP of average fib. vol.- 51%, measured in [0] and [90] direction; c) strength of examined 1D NCF
CFRP in function of fiber volume (X - dir.[90], Y - dir.[0])4

FEI Company, Europe NanoPort, Achtseweg Noord 5, 5651 GG Eindhoven, The Netherlands

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3.2 Acoustic Emission in 4-point bending of 1D CFRP
The AE basic parameters registered during monotonic 4-pont bending tests of 1D NCF CFRP
composites did not reveal any specific pattern, which would be of interest from the point ofview of damage initiation description in these materials. Of all the obtained data, only the AE
Frequency results may be more interesting, as they seem to be indicative of the type of damage
occurring during loading, similarly to results observed in other published works [31-36].
The histograms of AE Frequency vs # Hits (Fig. 4 a-c) obtained for longitudinally cut 1D
NCF CFRP samples point at fiber debonding (~200 kHz) as the dominant damage
mechanism. Interestingly, decreasing number of hits related to fibre breaks (300-350 kHz) canbe also noticed from these results, following the increase of fiber volume content.
This observation may indicate reduced singular fiber damage when more fibers are capable oftransferring the load.
Figs. 4 (d-f) present the histograms of AE Frequency vs # Hits obtained for transversely cut
samples of 1D NCF CFRP composites. It is interesting to note here the increasing role(relative) of debonding and matrix defects following the increase of fiber volume content(~100kHz and ~200kHz). A few signals ~300 kHz in these histograms may come fromincidentally misaligned fibers.

a)38v/o of fib.

b) 51v/oc) 68v/od) 38v/oe) 51v/of) 68v/o

Fig.4. The histograms of AE Frequency vs # Hits obtained during monotonic 4-point bending tests of CFRP
samples with varying fibre volume content, cut in longitudinal [0] direction (a,b,c), and in transversal [90]direction to fibers (d,e,f).

3.2 Determination of damage initiation using the AE Historic Index
The most consistent AE parameter indicating clearly the initiation of micro-damage during
both monotonic and step-loading of the examined CFRP composites was the AE Historic
Index [23, 37, 38]. Fig.5 shows the variations of Historic Index with time of test in monotonic
4-point bending loading. This parameter is sensitive to settings and Fig.5a proves that its
important indications may sometime pass unnoticed in standard AE tests. Fig.5b is the sameplot after re-scaling of vertical axis and it gives the evidence of initial damage appearing
already in the mid-time of bending test (HI ~2).

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At the time of these experiments no significant changes of Historic Index were observed insamples cut transversely to fibers, i.e. in direction [90]. However, the appearance ofmeasuring points at HI values ~1,0 in some of these [90] plots (Fig. 5c) prompted the setting
adjustments, which enabled to find significant HI variations even in neat epoxy resin [10].

a

b

c

Fig.5. The AE Historic Index indications during monotonic 4-point bending for UD NCF CFRP. a) sample cut inlongitudinal [0] direction, b) rescaled HI plot for this sample, c) The HI indication in transversal [90] sample.

The increases of HI values with time of test were much more evident in the case of 4-point
bending step-load tests. As the examples in Fig.6a-c show, the indications of HI enabled tocontrol the increasing-load process and interrupt the loading sequence before macroscopic
failure. The samples were submitted then to SEM observations of microcracks initiation on
their tensile surfaces.

a)

b)

c)

Fig.6. Step-load tests of UD CFRP with 51% fib. vol. in longitudinal [0] direction: a) full test b) interrupted
sample; c) sample cut transversely [90]. Corresponding HI plots below.

3.3 Microscopic (SEM) observations of damage initiation in 1D CFRP
Fig. 7. shows the advantage of damage initiation analysis on the surface subjected to stress as
opposed to standard fracture surface observation, often named “post mortem” analysis [6].
Fig.7a is the SEM micrograph of fracture surface of the sample of examined 1D NCF CFRP
composite cut vertically to fibers and cycled to failure in 4-point bending step-load test.
Tensile surface of this sample is seen on the left side of the picture, vertical to fracturesurface. Not much information concerning the damage initiation phenomena can be obtained
from viewing such fracture surface.

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Fig. 7b is the SEM micrograph of tensile (in bending) surface of 1D NCF CFRP sample cut in[90] direction (vertically to fibers), after the step-loading test was interrupted when the HI
increased noticeably. Debonding crack can be clearly seen in the center of this micrograph on
tensile surface of the sample which was still mechanically undamaged. A few otherdebonding cracks were seen on the same surface, and similar observations were made on
tensile surfaces of other samples examined in these conditions.
Tensilesurface<= tensile =>Fracture surfaceab

Fig.7. a) fracture surface of CFRP [90] sample failed in 4-point bending step-load test; b) debonding crack on
tensile surface after interruption of cyclic step-loading (fibers vertical to stress)

Although Acoustic Emission data indicated the presence of fiber breakages in 1D CFRP
samples cut in [0] direction (longitudinally to fibers), it was much more difficult to find themicroscopic evidence for this type damage mechanism on tensile surfaces of analyzed
materials than in the case of debonding flaws in the [90] samples.
Singular fiber breaks like the one seen in Fig.8a could be rarely found during extensive
microscopic observations of tensile surfaces of 1D NCF CFRP composite samples withdifferent fiber volume content. They were definitely not as numerous as observed in 2D plainweave CFRP composites examined in similar 4-point bending conditions [39], as well as in 3point bending SBS shear tests [40].

<= tensile =>abFig. 8. a) SEM micrograph of broken fibre in a tow parallel to tensile stress, on surface of 1D NCF CFRP sample
cut in [0] direction; b) granularity of epoxy matrix in CFRP may the reason for matrix micro-crack formation
under stress.

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Direct observations of damage initiation in form of epoxy matrix micro-cracks will remain themost difficult feature in direct damage initiation studies in 1D CFRP composites. Due to still
not quite well known processes of epoxy resin cross-linking in the presence of carbon fibers,
the matrix morphology is not fully glassy. The granularity observed in epoxy matrices, of thetype seen in Fig. 8b, may vary with varying carbon fiber content and may be the source ofappearance of microcracks in the boundaries of these domains, but this hypothesis requires
further detailed studies.

4. Conclusions
 Acoustic Emission technique is a very sensitive experimental tool for monitoring of
damage initiation in 4-point monotonic and step-load bending tests of unidirectional(1D) non-crimp fabric (NCF) carbon fiber reinforced plastic (CFRP) composites withvarying fiber volume content
 Acoustic Emission Frequency may be considered as indicative of the type of damage
initiating in 1D NCF CFRP materials
 The Acoustic Emission Historic Index (HI) is a reliable indicator of the time ofoccurrence of damage initiation during the 4-pont bending step-load testing of CFRP Microscopic (SEM) evidence correlates well with AE monitoring and shows thatfiber-matrix debonding dominates damage initiation in transversely [90] cut samples.
Fiber breakages were directly observed in longitudinally cut [0] CFRP composites
before final failure, after interruption of step-load tests
Acknowledgements
The authors wish to acknowledge the financial support of this work by the National ScienceCentre (Poland) with grant No NCN 2011/03/B/ST5/03180. Thanks are due to Dr Jerzy
Schmidt of ARLAB in Cracow University of Technology, Krakow, Poland for helpful
discussions on use of Acoustic Emission technique. Authors also extend their thanks to Dr
Magdalena Ziąbka of AGH University of Science and Technology, Krakow, Poland, forexcellent microscopic (SEM) work, and to Mr. B. Kotecki of AGH University of Science andTechnology, Krakow, Poland, for preparation of samples’ surfaces for microscopic
observations.
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[7] B. Harris (ed.), Fatigue in Composites, CRC Press, Woodhead Publishing Ltd, Cambridge,
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[17] I. De Baere, W. Van Paepegem, J. Degrieck, Comparison of different setups for fatigue
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[19] Toho product specification, http://www.tohotenax.com/products/tenax%c2%ae-carbonfiber/.
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International, West Conshohocken, PA, 2010, www.astm.org.
[21] EN ISO 14125: Fibre-reinforced plastic composites -- Determination of flexural
properties, 1998.
[22] C.J. Hellier, „Handbook of Nondestructive Evaluation”, Chap.10: "Acoustic Emission
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[23] ASTM E2478-11, Standard Practice for Determining Damage-Based Design Stress for
Glass Fiber Reinforced Plastic (GFRP) Materials Using Acoustic Emission, ASTM
International, West Conshohocken, PA, 2011, www.astm.org.
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[27] J.M. Waller, C.T. Nichols, D.J. Wentzel, R.L. Saulsberry, Use of modal acoustic
emission to monitor damage progression in carbon fiber/epoxy composites, in: 37th Annual
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2010, pp. 919-926.
[28] N.B. Mabry, C.Toutanji, H.Seif, M., Acoustic Emission Felicity Ratio Measurements in
Carbon Composites Laminates using Fiber Bragg Grating Sensors, in: W. Ecke, K.J. Peters,
T.E. Matikas (Eds.) Smart Sensor Phenomena, Technology, Networks, and Systems, 2011,
Proceedings of SPIE, 2011.
[29] H. Vallen, "AE testing fundamentals, Equipment, Applications", The e-Journal of Nondestructive Testing, www.ndt.net, Vol.7, No9, Sept. 2002.
[30] N.N. Hsu, F.R. Breckenridge, Characterization and calibration of acoustic-emission
sensors, Materials Evaluation, 39 (1981) 60-68.
[31] P.J. deGroot, P.A.M. Wijnen, R.B.F. Janssen, Real-time frequency determination of
acoustic emission for different fracture mechanisms in carbon epoxy composites, Composites
Science and Technology, 55 (1995) 405-412.
[32] J. Bohse, Acoustic emission examination of polymer–matrix composites, in, J. Acoust.
Emission, vol. 22, 2004, pp. 208–23.
[33] C.R. Ramirez-Jimenez, N. Papadakis, N. Reynolds, T.H. Gan, P. Purnell, M. Pharaoh,
Identification of failure modes in glass/polypropylene composites by means of the primary
frequency content of the acoustic emission event, Composites Science and Technology, 64(2004) 1819-1827.
[34] R. Gutkin, C.J. Green, S. Vangrattanachai, S.T. Pinho, P. Robinson, P.T. Curtis, On
acoustic emission for failure investigation in CFRP: Pattern recognition and peak frequency
analyses, Mechanical Systems and Signal Processing, 25 (2011) 1393-1407.
[35] L. Li, S.V. Lomov, X. Yan, V. Carvelli, Cluster analysis of acoustic emission signals for2D and 3D woven glass/epoxy composites, Composite Structures, 116 (2014) 286-299.
[36] K. Ono, A. Gallego, “Research and Applications of AE on Advanced Composites”, in,30th European Conference on Acoustic Emission Testing & 7th International Conference on
Acoustic Emission, University of Granada, 12-15 September 2012.
[37] N. Ativitavas, “Acoustic emission signature analysis of failure mechanisms in fibre
reinforced plastic structures”, in, PhD thesis, University of Texas at Austin, 2002.
[38] P.H. Ziehl, T.J. Fowler, Fiber reinforced polymer vessel design with a damage criterion
approach, Composite Structures, 61 (2003) 395-411.
[39] M. Nowak, M. Panek, I. Baran, K. Konsztowicz, "Acoustic emission detection ofdamage initiation in plain-weave CFRP loaded in bending", Proceedings of ECCM17,
Munich, Germany, June 2016.
[40] I.J. Baran, M.B. Nowak, J.P. Chłopek, K.J. Konsztowicz, Acoustic emission detection of
microcrack initiation in CFRP under shear stress, 10th Internat’l. Conf. on Comp. Sci. andTechnol. ICCST/10, Lisboa, Sept. 2015, paper 23.

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

ACOUSTIC EMISSION AND ULTRASONIC SOUNDING AS A TOOLFOR MIGMATITE FRACTURINGMatěj PETRUŽÁLEK, Tomáš LOKAJÍČEK, Tomáš SVITEKInstitute of Geology, Czech Academy of Sciences, v.v.i., Prague, Czech Republic
Phone: +420- 224 313 520, E-mail: petruzalek@gli.cas.cz, lokajicek@gli.cas.cz, svitek@gli.cas.cz
Abstract
Migmatite from the Skalka region (Czech Republic) was chosen as an experimental rock material. It has
a macroscopically visible plane-parallel structure (foliation). The foliation was caused mainly by biotite grainarrangement. Four cylindrical specimens of migmatite with sub-horizontal, sub-vertical and oblique foliation
were uniaxially loaded up to failure. A network of 8 broadband sensors was employed for acoustic emission
monitoring and ultrasonic sounding. A grid search method with an anisotropic velocity model was used for AE
hypocenter localization. The source types of successfully localized events were determined from the average firstarrival amplitude. Structural anisotropy of the tested rock material caused the anisotropy of its mechanicalproperties (peak strength, Young’s modulus) as well as a different fracturing in dependence on the angle between
the axial stress and the foliation plane. The combination of tension and shear microcracking together with sliding
in biotite basal planes was found to control the failure of specimens with sub-horizontal foliation. Shearing andsliding were dominant in the failure of specimens with oblique foliation. With greater dip of foliation, the role ofsliding increased at the expense of shearing. Due to the favorably oriented system of microcracks alreadypresent, the shearing and splitting was at the same level during fracturing of specimens with sub-verticalfoliation before nucleation began.
Keywords: migmatite, sample filiation, acoustic emission, ultrasonic sounding, uniaxial loading,

1. Introduction
The process of failure of low-porosity rocks depends on their mechanical properties andactual stress and temperature conditions. At low pressure and low temperature, brittle failure
is most common. This is a progressive process requiring the initiation, growth andcoalescence of cracks (Lockner, 1993). Stress strain behavior of low-porosity crystalline rocks
during laboratory compression experiments is divided into four characteristic stages: crackclosure, elastic region, stable crack growth and unstable crack growth which leads to brittle
failure, (Brace et al., 1966; Bieniawski, 1967; Lajtai, 1974).
The fracturing process of stressed rock begins with crack initiation (σ ci ), which for low-porosity
rocks occurs approximately at 40-50% of peak strength (σ p ) (Cai et al., 2004; Nicksiar andMartin, 2013). After σ ci , dilatancy begins and stable crack growth follows up to the crack damage
threshold (σ cd ), which is approximately at 80% of σ p (Cai et al., 2004). After crossing the crack
damage level, there is unstable crack growth accompanied with nucleation of the fault plane (σ n )at 97-100% of σ p (Rao et al., 2011). The stress drop accompanied with the formation of a macroscale shear failure plane follows after peak stress is crossed.
Throughout stable crack growth, the generation and propagation of tension cracks is supposed
to be dominant (Tapponnier and Brace, 1976; Martin and Chandler, 1994). Because thetension cracks are parallel to the maximum compressive stress, they cause a nonlinear
increase in lateral strain while the axial strain remains linear with increasing stress. In theplane perpendicular to maximum stress, these tension cracks also decrease elastic wave
velocities while increasing elastic wave attenuation, velocity anisotropy and shear wave
splitting (Stanchits et al., 2006). After crossing σ ci , there is an onset in acoustic emission (AE)
activity (Eberhardt et al., 1998). When isotropic material is fractured, random space

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distribution and dominance of tension source types is characteristic for AE events at this stageof loading Stanchits et al., 2006).
The crack damage threshold corresponds to the reversal point in the volumetric stiffness curve(Cai et al., 2004). Unstable crack growth follows after σ cd , already existing tension cracks
connect with shear ones, which also initiate new tension cracks and after reaching peakstrength eventually lead to failure (Lajtai, 1971). There is an exponential increase in AE
activity, AE events cluster around the failure plane and the shear type of sources begins todominate as the nucleation process starts (Lockner, 1993; Stanchits et al., 2006; Eberhardt etal., 1998). The fracturing process is also influenced by grain size (Lei et al., 1992; Eberhardt
et al., 1999), presence of predisposed fault planes (Lei et al., 2004; Petruzalek et al., 2013)
and the loading regime (Zang et al., 2013).
The fracturing process of isotropic rocks is well described, but there are only a few studies ofthe fracturing of anisotropic low-porosity rocks. Shea and Kronenberg (1993) studied the roleof mica concentration and spatial arrangement in rock failure under triaxial loading. Rawling
et al. (2002) found that the initiation of cracks is mostly affected by the orientation of theweakest mineral relative to the axial stress. Kwasniewski (2007) described the dilatancy
process of foliated schist under true triaxial compression. Hakala et al. (2007) thoroughly
investigated the anisotropy of mechanical properties and the fracturing process of migmatic
mica gneiss.
This paper presents a laboratory study of the fracturing process of foliated migmatite under
uniaxial loading and describes a different fracturing behavior depending on the mutualorientation of foliation, microcrack alignment and acting stress.
2. Specimen material and experimental setup
Migmatite, a highly anisotropic rock from the Skalka region in the Bohemian - Moravian
Uplands was used as the experimental rock material. Mineralogical analyses showed a planeparallel arrangement of the clusters of individual minerals in the migmatite specimens. Theserock-forming minerals were quartz, potassium feldspar, plagioclase, muscovite, biotite andamphibole.
The average size of the grains was from 0.5 mm (quartz) to 2 mm (mica). The migmatite had
a distinct, macroscopically visible foliation. There was also found a lineation in the foliation
plane, caused by elongation of biotite - amphibole aggregates. The combination of thefoliation plane and lineation in this plane should correspond to the orthorhombic elastic
symmetry of the rock matrix.
Detailed P-wave velocity distribution and its changes under hydrostatic pressure loading were
measured to determine the elastic anisotropy and primary microcrack orientation (Prikryl
et al., 2007). The P-wave velocity was measured on a 50 mm spherical specimen in
132 independent directions (Pros and Podrouzkova, 1975) under hydrostatic pressure up to
200 MPa. At atmospheric pressure, the minimum velocity (4 km/s) direction wasperpendicular to the migmatite foliation. The maximum velocity (5.9 km/s) direction lay inthe foliation plane in the direction of lineation. The velocity in the foliation plane was within
an interval from 5.6 km/s to 5.9 km/s. The orientation of velocity anisotropy did not change
during the entire hydrostatic loading experiment up to 200 MPa, where most of the
microcracks should be closed. The velocity difference between 200 MPa and atmosphericpressure was caused mainly by the closing of microcracks. The size and nature of theanisotropy of the velocity difference indicated the presence of a microcrack system whoseorientation was parallel to the foliation plane. Velocity measurements returned to their
original values after unloading. A reversible process of closing the microcrack system parallel
to foliation occurred under hydrostatic pressure loading. This experiment confirmed theanticipated orthorhombic symmetry of the rock matrix. The presence of microcracks parallel

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with foliation increases the difference between maximum and minimum velocity (magnitudeof anisotropy) but does not change the type or orientation of symmetry caused by the rockmatrix.
Cylindrical specimens of migmatite 50 mm in diameter and 100 mm in height were uniaxiallyloaded by means of a digitally controlled MTS815 servo-hydraulic loading frame at a constantloading rate (10 N/s) up to failure. Four specimens with different dip of foliation were tested:
sub-horizontal (13°), sub-vertical (81°) and oblique (47° and 67°), as is shown in Figure 2.
Two axial MTS extensometers and MTS circumferential extensometer were attached to thespecimen to evaluate the relative deformations. Eight broadband acoustic emission sensors(WD - Physical Acoustic Corporation, USA) 1.5 cm in diameter were attached to the surfaceof the specimens (Figure 1) and were used for acoustic emission (AE) monitoring as well as
ultrasonic sounding (US).

25

4
2
3

50

1

6

25

5
7
8

50

Fig. 1 Experimental setup showing the location of 8 AE sensors on the cylindrical specimen
A high-voltage sine pulse with a frequency of 200 kHz was used as a source of US. This
corresponds to wavelengths of 2 - 3 centimeters for velocities from 4 to 6 km/s. The AE andultrasonic transmission waveforms were recorded by a multi-channel transient recorder(Vallen System AMSY - 5, Germany). This apparatus was set up in triggered regime, thesampling rate was 10 MHz and the length of recorded waveforms was 2,048 points, eachpoint with 16 bit resolution of the A/D converter.

3. Processing of measured data
For some specimens, the measured relative axial deformation was not correct due to thecontact of axial extensometers with rubber bands which were used to attach the AE sensors.
Loading of the dural calibration specimen showed inflection points in the circumferential
deformation which should not be present and might lead to misinterpretation of experimental
data. From these reasons, the extensometric data were not used in interpretation. The relative
axial deformation was evaluated from loading frame displacement using a dural calibration
specimen with known elastic properties. Apparent Young modulus was calculated from thelinear part of the axial stress-axial strain relation.
A two-step AIC picker (Sedlak et al., 2009) was used to identify the onset times in recorded
AE and US waveforms. The precision of this technique, verified by comparison with handpicked travel times, was ± 0.2 μs. The resultant velocity error should have not exceeded
100 m/s. The measured velocity should correspond to the phase velocity considering the size

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of transducers, length of trajectories between the sensors and frequency range (Dellinger andVernik, 1994).
The ultrasonic transmission was performed in successive transmission cycles at selectedloading levels. Every sensor acted as an ultrasonic wave transmitter while the others acted asreceivers in one particular step. A velocity ellipsoid model was calculated as a least squareapproximation of measured velocities (Petruzalek et al., 2013) for every transmission cycle.
A grid search procedure was applied to locate the AE events. Only strong AE events withclear identification of first arrival time were localized. The velocity ellipsoid was used as ananisotropic velocity model. This model showed smaller localization error in comparison withthe commonly used isotropic velocity model (Petruzalek et al., 2013). The accuracy of
localization of strong AE events was ± 3 mm. That was estimated by the localization of
known transmitting sensor position during ultrasonic transmission measurements and by
localization of sources generated by the pencil lead breaking.
The first arrival amplitude was automatically determined for each recorded waveform of AE
and US. The crack initiation stress σ ci was determined at the point of first decrease in firstarrival amplitude of US. The point where the AE cumulative count-axial stress relation
changed from linear to exponential was defined as the crack damage stress σ cd (Hakala et al.,2007). Source types of AE events were determined based on the average polarity of firstarrival (Zang et al., 1998). Only the source types of AE events located in the middle part ofthe specimen (z coordinate 25-75 mm, see Figure 1) with little error in the localization
process (4 us) were interpreted.

4. Experimental results and discussion
The uniaxial strength and apparent Young modulus (Tab. 1) were determined from the stressstrain data. The uniaxial strength (σ p ) showed highest values for specimens with sub-horizontal
and sub-vertical foliation, while the specimens with oblique foliation had lower values of σ p .
Similar anisotropic behavior of peak strength in dependence on foliation angle was described in(Hakala et al., 2007; Nasseri et al, 1997; Nasseri et al, 2003; Cho et al., 2012). The value ofapparent Young modulus increased with the foliation angle, which is in accordance with results
published by (Rawling et al., 2002; Hakala et al., 2007; Kim et al., 2012).
Tab. 1 Results of uniaxial experiments. Φ – dip of foliation, E – apparent Youngmodulus, pσ – uniaxial peak strength, ciσ – crack initiation stress in MPa and inpercentage of pσ , σcd – crack damage stress in MPa and in percentage of pσ , nσ –nucleation stress in MPa and in percentage of pσ , n – number of AE events detected.
Specimen
vv02
vv23
vv21
vv11

Φ
[ °]
13
47
67
81

E
σp
σ ci
[GPa] [MPa] [MPa]39.1 114.5 51.5
44.2 109.6 76.7
50.7 82.8 62.1
53.4 125.3 43.9

σ ci
[%]
45
70
75
35

σ cd
[MPa]
103.1
98.6
80.3
112.8

σ cd
[%]
90
90
97
90

σn
[MPa]
107.6
104.1
78.7
114.0

σn
[%]
94
95
95
91

n
[-]
4.1*104
2.7*104
0.6*104
7.3*104

The crack initiation stress (σ ci ) was determined at the first decrease of first arrival amplitudeof ultrasonic sounding in the horizontal direction which was most perpendicular to the failure
plane (see figures on the left side of Fig. 3). Specimens with oblique foliation showed higher
σ ci in comparison to specimens with sub-horizontal and sub-vertical foliation. The differenceswere greater when normalized by σ p , because of lower σ p for specimens with oblique
foliation. Cai et al. (2004) summarized previous results of crack initiation stress determinedon various types of mostly isotropic rocks. The crack initiation level was found to be 35-60 %S216

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of σ p . Hakala et al. (2007) determined σ ci of anisotropic mica gneiss depending on the mutualorientation of the loading axis and foliation. Their results showed only slightly higher σ ci for
specimens with oblique foliation (49% of σ p ) compared to ones with sub-horizontal and subvertical foliation (41% of σ p ). The crack initiation in isotropic specimens or in specimens with
sub-horizontal and sub-vertical foliation is associated with microcracks parallel to the
maximum compression stress. In this case, the ultrasonic method used for determining σ ci issuitable and considering its sensitivity, probably more reliable than other methods based on
strain or AE measurements. In tested specimens with oblique foliation, crack initiation was
associated mainly with shear-type microcracks, for which the ultrasonic method is not sosensitive due to their smaller aperture. The very high values of σ ci for specimens with oblique
foliation presented in this paper were caused by an unsuitable method for determining σ ci inthis particular case.
The crack damage level (σ cd ) corresponds to the stress at which the unstable microcracking
begins. In this paper, σ cd was determined at the beginning of exponential increase in AE
cumulative count (Fig. 3). The values of σ cd were found to be 90-97% of σ p , which is inaccordance with the values of σ cd (91-97% of σ p ) published in (Hakala et al., 2007).

Fig. 2 Photographs of fractured specimens and corresponding AE hypocenter distribution
Figure 2 shows specimen scale fractures caused by the failure of loaded specimens. Failure
planes were determined based on AE localization. In specimens with foliation angles of 47°,
67° and 81°, the failure planes lay in the foliation plane. One failure plane developed in thespecimen with a 67° foliation angle. Several parallel failure planes developed in the specimen
with a 47° foliation angle. The development of several extension macrocracks parallel tofoliation led to the failure of the specimen with sub-vertical foliation. One shear plane crosscutting the foliation caused the failure of the specimen with sub-horizontal foliation. Even inthis case, the foliation predetermined the orientation of the failure plane, both planar
structures had the same strike. The described influence of mutual orientation between the

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loading axis and foliation on the failure of stressed specimens is in accordance with the works
of Nasseri et al. (1997), Niandou (1997) and Cho et al. (2012).
The results of ultrasonic sounding, P-wave velocity and amplitude of first arrival reflected theanisotropic behavior of the fracturing process of all tested specimens (Figure 3), which wasmost pronounced in the specimen with sub-vertical foliation (Figure 3c, d).

Fig. 3 P-wave velocity (red color), first arrival amplitude (blue color) and AE cumulative
activity (black color). Figures on the left side correspond to the horizontal
direction most perpendicular to the failure plane. Figures on the right sidecorrespond to the horizontal direction most parallel to the failure plane.
The fracturing process of the specimen with sub-horizontal foliation was dominated bytension-type microcracks up to 94% of peak strength. At 94% of σ p , the nucleation phase

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began accompanied by a sharp increase in shear-type microcracks. The domination of sheartype AE events and continued increase of collapse-type AE events were seen as the specimen
approached failure (Figure 4a).
The specimen with sub-vertical foliation was found to have an equal amount (45%) of tension
and shear-type AE events before reaching nucleation phase at 91% of σp, where thepercentage of shear-type AE sources sharply increased. After nucleation began, the amount ofshear and collapse-type AE sources continuously increased as the specimen approachedfailure (Figure 4b).
The dominance of non-tension-type AE sources was characteristic for the specimens withoblique foliation. Nucleation began at 95% of peak strength and was accompanied witha continuous increase in collapse-type AE sources (Figure 4c, d).

Fig. 4 AE source type versus stress level, tension type- blue color, shear type- green color,collapse type- red color. a) specimen vv02 with foliation angle 13°, b) specimen vv11
with foliation angle 81°, c) specimen vv23 with foliation angle 47° a) specimen vv21
with foliation angle 67°.
The deformation process of migmatite may be explained as a combination of tension
microcracking, shear microcracking, pore collapse microcracking and sliding. Sliding occurs
on the inclined basal planes of biotite (Shea and Kronenberg, 1993). Because the process of
sliding is slow and shear modulus in biotite basal planes is low, it does not cause AE activity.
The attenuation of elastic waves is very sensitive to tension microcracking. The anisotropicchanges of the attenuation reflect a preferential tension microcracking.
Only in the specimen with sub-horizontal foliation did the microfracturing process resemble
the microfracturing of isotropic specimens with dominant tension-type microcracks parallel tomaximum compressive stress up to the nucleation stress level. The other specimens showed
different behavior with an important role being played by non-tension-type microcracking.
Based on optical microscopy and SEM, Rawling et al. (2002) reported that tension-type
microfractures are dominant in triaxially loaded specimens of biotite gneiss independent of
the mutual orientation between maximum compressive stress and foliation. The different
behavior of specimens tested in this study might be caused by the absence of confining

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pressure and by the presence of relatively large amounts of primary microcracks, most ofwhich were parallel to foliation.
In the specimen with sub-horizontal foliation, the tension was active up to nucleation. Whenthe density of extensional microcracks was at the same level as their length, the nucleation
process started and shearing dominated. Even if the dip of foliation was small, sliding was
also present in biotite and caused the anisotropy of the fracturing process. Based on the
results of ultrasonic sounding (Figure 3a, b), the amount of extension microcracks in the
direction perpendicular to sliding was higher than in the direction parallel to sliding. The factthat the failure plane had the same strike as the foliation was also induced by sliding in
foliation planes.
The combination of shearing and sliding led to the failure of specimens with oblique foliation.
Because the process of sliding is slow and shear modulus in biotite basal planes is low, it does
not cause AE activity. The decrease in AE activity (Tab. 1) with increasing dip of foliation
resulted from the increased role of sliding at the expanse of shearing.
Before nucleation stress was reached in the specimen with sub-vertical foliation, thecombination of shear and extension microcracks was found to dominate the fracturing
process. At this stage of fracturing, the relatively high amount of shear-type events wascaused by the interactions between favorably oriented primary microcracks already present.

5. Conclusions
The anisotropy of mechanical properties was determined based on the interpretation ofuniaxial loading tests on specimens with different dip of foliation. The specimens withoblique foliation had lowest peak strength. The apparent Young modulus increased with thedip of foliation.
A new approach based on the decrease of first arrival amplitude of ultrasonic sounding wasused to determine the crack initiation stress. Even if this method is not suitable for specimens
with oblique foliation, it should be very sensitive and probably more reliable than methods
based on AE and strain measurement in the case of isotropic specimens.
Based on the results of ultrasonic sounding, the fracturing process was found to be anisotropic
for all tested specimens.
The mutual orientation between foliation and maximum compression stress determined the failuremode of tested specimens. The combination of tension, shearing and sliding was found to control
the fracturing of the specimen with sub-horizontal foliation. Shearing and sliding were dominant
in the fracturing of specimens with oblique foliation. With greater dip of foliation, we found anincreasing role of sliding at the expense of shearing. Due to the favorably oriented system of
microcracks already present, shearing and splitting was at the same level during fracturing of thespecimen with sub-vertical foliation before nucleation began.

6. Acknowledgements
This work was partially supported by the Grant Agency of the Czech Republic, project No.
16-03950S and by the long-time research development support of the Institute of Geology AS
CR, v.v.i. - RVO67985831.
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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

ACOUSTIC EMISSION SOURCE IDENTIFICATION IN LARGE SCALEFIBRE REINFORCED COMPOSITES
Markus G. R. SAUSE
Experimental Physics II, University of Augsburg; Augsburg, Germany
Phone: +49 821 5983238, Fax: +49 821 5983244; e-mail: markus.sause@physik.uni-augsburg.de
Abstract
Within the last decades many approaches have been proposed to perform source identification in fibre reinforced
composites. Some of them have been validated using skilled micromechanical experiments or by using reference
specimens in combination with imaging methods, leaving strong evidence that these approaches are valid tools toperform source identification tasks. Lately, also numerical methods have been applied to validate the applicabilityof source identification methods to fibre reinforced composites for reasonably small size specimens as typicallyused in materials testing. However, the implementation of the same approach for a real composite structure as usedin an industrial environment is still challenging. The reasons for this are manifold. To name just a few reasons, thefrequency dependent attenuation starts to compromise frequency information with distance of propagation,
directivity effects in a composite laminate may cause distinct differences of frequency spectra when detected at
different angles to the source and changes in material and thickness will influence the guided wave modes. The aimof this contribution is to present and discuss the current limitations of source identification procedures in large
scale composite structures and to highlight the challenges to overcome when attempting to use such approaches.
Influence of signal attenuation, directivity effects, laminate stacking and thickness, presence of existent damage,load configurations and component geometry are discussed and recommendations are given how to estimate theapplicability of a source identification approach for a specific application.
Keywords: source identification, fibre reinforced composites, acoustic emission

1. Introduction
In acoustic emission of fibre reinforced composites our ability to identify the underlying source
mechanisms by signal characteristics has been substantially extended throughout the last
decades. Based on the characteristics of the signals, modern statistically driven approaches suchas multivariate data analysis and machine learning are now able to reliably classify groups ofsimilar signals [1]–[6]. Modern numerical methods have added the ability to model particularsource mechanisms and to obtain corresponding AE signals [7]–[10]. This allows to validate
the origin of particular groups of signals as has been shown for various typical coupon test
configurations, such as tensile testing, flexural testing, fracture mechanics testing and similar
setups [11]–[13]. Despite of these efforts, the direct transfer of established approaches to larger
test pieces made from fibre reinforced polymers is still challenging. For typical structuralcomponents several items differ substantially to the aforementioned test coupons. The mostobvious difference usually is their size. For AE this is linked to three particular challenges. First,
the mean source-sensor distance is likely to increase as the sensor spacing is usually chosenlarger. Therefore, the effect of frequency specific attenuation is expected to be larger. Second,
the influence of dispersion effects increases as well. Third, the sensors are less likely to be
mounted in 1D-like orientation (as being the case for a typical tensile test). This causes AE
signals to arrive from many different angles to the sensor as seen in Figure 1.
Typically, composite structures also exhibit fairly complex stacking sequences. As with theother effects of wave propagation mentioned above, this may readily be expected to have an

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impact on the distinguishability of AE signal groups. Also, the formation of damage within the
propagation path of the AE wave is likely to add some disturbance to the amplitude andfrequency content of the signal. If any of these factors starts to override the intrinsic
characteristics of the AE signals due to their source mechanism, only error-prone source
identification will be the result.

AE source
AE source

AE source
AE sensor

Figure 1. Geometric relation between AE source position and AE sensor position for tensile
test setup (left) and component test setup (right).
In the following the author’s approach to AE source identification is briefly described andsubsequently several factors of influence when applying this to larger structures are elucidated
and discussed.

2. Source identification approaches
A general prerequisite of source identification procedures is a suitable data reduction of theacquired AE signals. This comprises elimination of obvious noise signals, and a suitablestrategy to focus on the AE signals relevant for material failure. One possibility to aid with thelatter is to localize AE source positions and analyze only those signals originating froma specific location (e.g. in the tapered area of a tensile specimen). Subsequently, the detected
AE signals are reduced to a number of features calculated from the signals. This comprises anelementary step of AE analysis, but requires some specific attention when dealing with larger
structures as outlined in section 3.1. A multitude of those extracted features is then used asdataset and is investigated by an unsupervised pattern recognition method to yield groups ofsimilar AE signals, further denoted as “AE signal clusters”.
The overall task of the method proposed in [6] is to detect the most significant clusters of theentirety of AE signals with a minimum of initial assumptions on the cluster structure. Therefore,
no assumptions are made on the exact number of signal clusters or the number of AE featuresor the type of AE features.
Technically, the proposed method is based on a generalization of the clustering approach
introduced by [3] and utilizes a two-stage voting scheme adopted from [14]. Based on a list ofpreselected frequency features, the algorithm calculates all subset feature combinations.
For each feature combination, a clustering algorithm yields the partitions for 2, 3, …, 10
clusters, which are evaluated by cluster validity indices. These statistical measures are used to

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indicate the best partition for the respective feature combination. In the final step, the results ofall subset feature combinations are ranked to yield the globally best partition and the respective
feature combination (cf. Figure 2).
Compared to signal classification methods based on single features, such pattern recognition
methods are computationally intense. But single AE features like peak-frequency or signal
amplitudes have significant dependency on the type of sensor or the details of the specimen
geometry, stacking sequence and material. Therefore, source classification by static AE feature
ranges cannot be generalized beyond certain limits. In contrast, pattern recognition techniques
are adaptive to the problem investigated and do not rely on static AE feature ranges.
However, the algorithm by itself is not able to provide more than groups of similar AE signals.
Based on the hypothesis that similar AE signals may originate from similar AE sources, thefinal step consists of an appropriate labelling of the clusters. This may be achievedby microscopic observations (e.g. relative to hot-spots of clusters at particular locations), bycomparison to predicted onsets using failure criteria for composites (cf. [12], [15]) orby comparison to modelling results (cf. [11]–[13], [16]–[19]). The latter approach, although
being based on modelling results, seems to form the smartest approach to perform such labellingas it does not come with restrictions of specimen type and geometry (other than microscopy).
However, it requires a validated modelling strategy to be meaningful. Then it is feasible to either
validated signals one-to-one or to compare the resulting partitions directly as done e.g. in [11]–[13], [16]–[19].
modeled partition(1) List of

AE featuresfeature 1
known source type(2) Calculate all subset
combinations(3) Compute partition for each feature
combination forclustersfeature 2(4) Rank all partitions based oncluster validity metrics

partitioncomparison

feature 1

Best feature
combination
for global optimum

Global optimumof cluster number

experimental partition

feature 2

Figure 2. Schematic of pattern recognition approach introduced in [6] including validation
procedure using modelling results as proposed in [11], [12].
Regardless how the source identification procedure has been carried out, there is a way to assess
the quality of the partition obtained. Based on the corresponding features, an algorithm has been
recently proposed to convert the corresponding cluster validity measures into a measure of
uncertainty of classification [20]. This is based on the resulting overlap of clusters in theirfeature space and acts as measure of ambiguity of the cluster labels.

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3. Factors of influence
In the following some factors of influence are presented which impact the ability to performsource identification in larger composite structures. In the discussion it is assumed that onlytransient AE signals are to be interpreted and no noise signals are present in the dataset.
3.1 Extraction of features
One technical difficulty which has seen less attention in the context of source identification so
far is the way of feature extraction itself. However, this is of crucial importance and may easily
be much more relevant for source identification than the other items listed below. Typicalcommercial programs either extract features out of the full length of the recorded wave (usuallytaken as default approach) or allow to extract features from some specific time range(e.g. several µs after first threshold crossing). Considering the dispersive nature of guided wave
modes, it may readily be assumed, that the frequency information also changes within the
duration of the wave package. Thus it may not be expected to extract similar information at
a fixed time window of a wave detected at short distance compared to a wave that has travelledsome distance. The signal shown in Figure 3-a is a modeled signals of an inplane dipole source
detected at 100 mm distance in a 1 mm Aluminum plate following the approach taken in [21].
The corresponding feature values taken from the first 100 µs after threshold crossing for thefeatures “weighted Peak-Frequency” and “Partial Power 2” are shown as function of the lengthof the time window used for feature extraction in Figure 3-b.
Here, weighted Peak-Frequency is taken as geometric mean of the classical features “PeakFrequency” and “Frequency Centroid”, while “Partial Power 2” quantifies the fraction of spectral
intensity within the range between 150 kHz and 300 kHz (see e.g. [12] for precise definitions).
It is clearly seen, that before reaching 30 µs window length, the frequency information appears
to be relatively constant. This corresponds to the time window spanning predominantly therange of the detected S 0 -mode as seen in Figure 3-a. This guided wave mode exhibits higher
frequencies in this case, thus turning into higher frequency features (500-600 kHz weighted
Peak-Frequency). In contrast, the A 0 -mode propagates predominantly at lower frequencies.
As soon as the extraction time window starts to include a significant portion of that guided wave
mode, the frequency features start to be affected as well. With increasing length of the extraction
time window this decreases the values and starts to stabilize again for values larger than 75 µs
resulting in almost constant feature values. Consequently, the same AE signal could result in
fairly different AE features just based on the length of the feature extraction time window.

Figure 3. Modeled signal for in-plane dipole detected at 100 mm distance (a) andcorresponding feature extraction using different extraction time windows (b).

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This finding motivates a first assessment on its relevance to source identification procedures.
Figure 4-a present a typical result of the pattern recognition process with accompanying labels
derived from numerical modelling results. The dataset itself was collected during fourpointbending of an unidirectional [0 5 ] sym T800/913 epoxy prepreg material using two WD
sensors as described in more detail in [11], [13]. In Figure 4-a the AE features were computedout of the first 100 µs after signal arrival as determined using the AIC strategy (cf. [22]).
(a)

(b)

(c)

Figure 4. Partition of classified signals using pattern recognition based on 100 µs (a), 50 µs(b) and 25 µs extraction time window (c).

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Keeping the labels, the AE features are re-calculated using 50 µs and 25 µs as extraction time
window. The resulting partitions are given in Figure 4-b and Figure 4-c, respectively. Obviously,
the shortening of the extraction time window results in significant overlap / fusion of theindividual clusters. Correspondingly, some of the AE signals move their positions to those of
the other clusters (labeling of all data points is kept identical to Figure 4-a). Hence, a different
assignment of cluster labels would be expected if the same pattern recognition approach isapplied to the AE features seen in Figure 4-b or Figure 4-c.
Therefore, a source identification procedure based on AE features always needs to reflect thefull frequency information provided by the AE signal, therefore not restricting itself to just the
information given by a single guided wave mode. Suitability of these settings may either bederived from accompanying modelling work, or to some extent, may also be based on theseparation seen between clusters. For the latter, the uncertainty of classification may act asa guideline to select an appropriate length of the feature extraction time window.
However, for larger structures, this becomes increasingly difficult as a constant extraction time
window will not work for the fairly different arrival times of modes as exemplified in Figure 5.
Here AE signals from a study using 0.57 m long double cantilever beams (details in [20]) are
shown as located in 80 mm distance (Figure 5-a) and in 280 mm distance (Figure 5-b). Whereas
100 µs would have been sufficient for the short distance to cover significant amounts of bothguided wave modes, >500 µs would be required for distances >280 mm. Thus adaptive
approaches for feature extraction are required for AE signals travelling to the sensor with fairly
different distances.

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Figure 5. AE signals detected during double cantilever beam tests located at a sourcesensor distance of 80 mm (a) and at 280 mm (b).
3.2 Signal attenuation
In addition to the guided wave propagation in thin composite shells, the attenuation of polymer
based fibre reinforced materials is also of relevance for the identification of AE source
mechanisms. Based on the thermoelastic dissipation effect, higher frequencies are subject tostronger losses of amplitude with propagation distance. Thus, the relative frequency content of
AE signals is expected to change even at short propagation distances (< 150 mm). This effectis well known and was investigated in its relevance to AE source identification in [20]. It was
found that no substantial reduction of AE source discrimination is expected for propagation
distances up to 275 mm. For larger source-sensor distances (up to 500 mm) the study also

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indicates that source discrimination should still be possible given some rise in the uncertainty
of classification. In polymer based composites, for such large propagation distances the signal
attenuation starts to significantly affect the overall detectability of weak AE sources. Therefore,
this allows to speculate if (i) changes to the AE signal frequencies or (ii) the loss of detectability
will be the final limit for successful source discrimination in large composite structures.
3.3 Laminate stacking
On top of the effect of the source-sensor distance, the intrinsic structure of fibre reinforced
composites adds some additional challenges. Previous work has already demonstrated the ability ofthe proposed pattern recognition method to work for different stacking sequences other thanunidirectional materials. In general, the added complexity of cross-ply or quasiisotropic layups canwell be covered by pattern recognition approaches [12], [15]. Also, the frequency feature based
approach was validated for laminates up to thickness values of 15 mm [12]. Special challenges arise
in textile architecture materials such as woven or knitted fabrics. Here the additional level ofhierarchy as introduced by warp and wefts adds further ambiguity in the damage mechanisms, ase.g. matrix cracking may occur within the warp/weft, in between the same, but also in between the
fabric layers. This causes a less distinct separation of the clusters as compared to Figure 4-a for
unidirectional materials, therefore reducing the ability to identify particular failure mechanisms.
However, several successful attempts using mixed amplitude and frequency based patternrecognition have been proposed in literature [23], [24].
3.4 Formation of damage
During mechanical loading of composite materials, a distinct evolution of damage occurs on
several length scales. Therefore, the acoustic properties of the propagation medium will
significantly change during the test. In guided wave testing, the change of the signal
characteristics is actively monitored to detect the formation of damage within the propagation
path between actuator and sensor. In combination with acoustic emission detection this is thenusually referred to as acousto-ultrasonics approach [25], [26]. Hence it is easy to conclude, thatthe characteristics of AE waves propagating through damaged areas will be affected as well.
Therefore, a recent study [12] considered this effect by repetitively pulsing an actuator mounted
on a tensile specimen. Signals were transmitted throughout the test section of the specimen andwere detected with the mounted AE sensors. The result from one measurement using a quasiisotropic stacking sequence for the Sigrafil CE125-230-39 carbon/epoxy prepreg system isshown in Figure 6. The colour code indicates the clusters identified by pattern recognition
methods using the features weighted Peak-Frequency and Partial Power 2. The same feature
extraction applied to the signals originating from the pulser (a source that has constantproperties during the full test) yields the data points in black surrounded by a black ellipsoid.
Choosing a high frequency source for the present case was motivated by the fact that higher
frequencies seem to be affect more from damaged areas than the low frequencies [27]. Despiteof noticeable changes in the pulser signals detected during the experiment, the extracted feature
values stay close together and do not overlap with another cluster. This indicates that the
propagation effect itself will not substantially affect the feature values even when massive
damage forms within the laminate. This has been confirmed with six typical laminate sequences[12]. Additional evidence is brought in by the fact, that the clusters itself are relatively sharp
defined and retain their locations, regardless if only signals from the beginning or the end of theexperiment are used (corresponding to almost undamaged and severely damaged specimens).

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Figure 6. Pattern recognition result from tensile test with superimposed data from pulser
signals during test.
3.5 Directivity effects
Another challenge faced in composite structures when compared to coupons was alreadydescribed in the introduction section. As seen in Figure 1, the direction of propagation from AE
source to AE sensor will substantially differ in large scale composite components whencompared to the frequently used 1D-like arrangements in coupon testing. As the direction ofwave propagation has tremendous effect on the wave velocities and signal attenuation thiscomes with additional challenges for valid source identification procedures. As discussed in[12], this may cause a strong overlap of signal clusters belonging to one mechanism, justbecause of the detection direction. Hence it was proposed to compensate for this effect by
calculating the mean AE features for several principal directions [12]. This has been found towork for test sources applied on unidirectional, cross-ply and quasi-isotropic plates. However,so far no attempts have been made to bring this to the level of composite structures as theaveraging process requires the AE signals to be detected at the principal angles which is hardly
the case in practice. Recently, other research groups also proposed an correction procedure of
the AE feature values as function of propagation distance [28], [29]. For the complexity inchanges to AE features seen in realistic composite structures it still needs to be investigated,which feature compensation technique will perform best to account for this effect.

4. Conclusion
A brief overview on factors of influence to AE source identification as seen in large scale
composite structures was given. For some of them, the limits are well established and can beconsidered in their impact when performing AE testing on this level. Despite of the various
challenges it seems possible to apply source identification in large scale composite structures
within certain limitations. Apart from cleaned datasets it is necessary to consider the influenceof the feature extraction procedure and the directivity effects when judging on the feasibility
for a given application. Without further modification to the feature extraction process it does
seem only feasible to perform source identification as long as the propagation behaviour stays
approximately constant. For general cases, the feature compensation techniques proposed in

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literature will need to be validated on the structural composite level. If successful, these will
form an important contribution to enable reliable AE source identification in large composite
structures.
Acknowledgments
Part of this research was funded by the Free State of Bavaria within the joint research program“ComBo”.
References
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[4]

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[11]
[12]
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[14]

S. Huguet, N. Godin, R. Gaertner, L. Salmon, and D. Villard, “Use of acoustic emission
to identify damage modes in glass fibre reinforced polyester,” Compos. Sci. Technol.,vol. 62, pp. 1433–1444, 2002.
A. A. Anastassopoulos, V. N. Nikolaidis, and T. P. Philippidis, “A Comparative Studyof Pattern Recognition Algorithms for Classification of Ultrasonic Signals,” NeuralComput. Appl., vol. 8, no. 1, pp. 53–66, 1999.
A. A. Anastassopoulos and T. P. Philippidis, “Clustering Methodology for the Evaluationof Acoustic Emission from Composites,” J. Acoust. Emiss., vol. 13, pp. 11– 21, 1995.
C. R. Ramirez-Jimenez, N. Papadakis, N. Reynolds, T. H. Gan, P. Purnell, and
M. Pharaoh, “Identification of failure modes in glass/polypropylene composites bymeans of the primary frequency content of the acoustic emission event,” Compos. Sci.
Technol., vol. 64, pp. 1819–1827, 2004.
V. Kostopoulos, T. Loutas, a Kontsos, G. Sotiriadis, and Y. Pappas, “On the identificationof the failure mechanisms in oxide/oxide composites using acoustic emission,” NDT E Int.,
vol. 36, no. 8, pp. 571–580, 2003.
M. G. R. Sause, A. Gribov, A. R. Unwin, and S. Horn, “Pattern recognition approach toidentify natural clusters of acoustic emission signals,” Pattern Recognit. Lett., vol. 33,
no. 1, pp. 17–23, 2012.
P. D. Wilcox, C. K. Lee, J. J. Scholey, M. I. Friswell, M. R. Wisnom, and B. W.
Drinkwater, “Progress Towards a Forward Model of the Complete Acoustic Emission
Process,” Adv. Mater. Res., vol. 13–14, pp. 69–75, 2006.
J. Cuadra, P. A. Vanniamparambil, D. Servansky, I. Bartoli, and A. Kontsos, “Acoustic
emission source modeling using a data-driven approach,” J. Sound Vib., vol. 341,
pp. 222–236, Apr. 2015.
J. A. Cuadra, K. P. Baxevanakis, M. Mazzotti, I. Bartoli, and A. Kontsos, “Energy
dissipation via acoustic emission in ductile crack initiation,” Int. J. Fract., vol. 199, no. 1,
pp. 89–104, May 2016.
M. G. R. Sause and S. Richler, “Finite Element Modelling of Cracks as Acoustic
Emission Sources,” J. Nondestruct. Eval., vol. 34, no. 4, pp. 1–13, Mar. 2015.
M. Sause, Identification of failure mechanisms in hybrid materials utilizing patternrecognition techniques applied to acoustic emission signals. Berlin: mbv-Verlag, 2010.
M. G. R. Sause, In Situ Monitoring of Fiber-Reinforced Composites, vol. 242. Cham:
Springer International Publishing, 2016.
M. G. R. Sause and S. R. Horn, “Influence of Specimen Geometry on Acoustic Emission
Signals in Fiber Reinforced Composites: FEM-Simulations and Experiments,” in 29th
European Conference on Acoustic Emission Testing, 2010, pp. 1–8.
S. Günter and H. Bunke, “Validation indices for graph clustering,” Pattern Recognit.
Lett., vol. 24, no. 8, pp. 1107–1113, May 2003.

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[15] M. G. R. Sause and A. Monden, “Comparison of Predicted Onset of Failure Mechanisms
By Nonlinear Failure Theory and By Acoustic Emission Measurements,” in 16th
European Conference on Composite Materials, 2014.
[16] M. G. R. Sause and S. Horn, “Simulation of acoustic emission in planar carbon fiber
reinforced plastic specimens,” J. Nondestruct. Eval., vol. 29, no. 2, pp. 123–142, 2010.
[17] L. L. Vergeynst, M. G. R. Sause, F. Ritschel, A. J. Brunner, P. Niemz, and K. Steppe,“Finite element modelling used to support wood failure identification based on acoustic
emission signals,” in COST Timber Bridges Conference 2014, 2014, pp. 141–146.
[18] L. L. Vergeynst, M. G. R. Sause, and K. Steppe, “Acoustic emission signal detection in
drought-stressed trees : beyond counting hits,” in 31st Conference of the European
Working Group on Acoustic Emission, 2014, pp. 1–8.
[19] M. G. R. Sause, J. Scharringhausen, and S. R. Horn, “Identification of failure
mechanisms in thermoplastic composites by acoustic emission measurements,” in 19thInternational Conference on Composite Materials, 2013.
[20] M. G. R. Sause and S. Horn, “Quantification of the uncertainty of pattern recognition
approaches applied to acoustic emission signals,” J. Nondestruct. Eval., vol. 32,
no. 3, pp. 242–255, 2013.
[21] M. G. R. Sause, M. A. Hamstad, and S. Horn, “Finite element modeling of lamb wave
propagation in anisotropic hybrid materials,” Compos. Part B Eng., vol. 53, pp. 249–
257, 2013.
[22] H. Akaike, “Markovian representation of stochastic process and its application to theanalysis of autoregressive moving average processes,” Ann. Inst. Stat. Math., vol. 26, pp.
363–387, 1974.
[23] L. Li, S. V. Lomov, and X. Yan, “Correlation of acoustic emission with opticallyobserved damage in a glass/epoxy woven laminate under tensile loading,” Compos.
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[24] L. Li, S. V. Lomov, X. Yan, and V. Carvelli, “Cluster analysis of acoustic emission
signals for 2D and 3D woven glass/epoxy composites,” Compos. Struct., vol. 116, pp.
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[25] A. Vary, “The Acousto-Ultrasonic Approach,” in Acousto-Ultrasonics, J. C. Duke,
Ed. Boston, MA: Springer US, 1988, pp. 1–21.
[26] K. Ono, “Special issue: Acousto-Ultrasonics,” J. Acoust. Emiss., vol. 12, no. 1–2, pp. 1–
102, 1994.
[27] M. G. R. Sause, “Acoustic Emission Signal Propagation in Damaged Composite
Structures,” J. Acoust. Emiss., vol. 31, pp. 1–18, 2013.
[28] E. Maillet, N. Godin, M. R. Mili, P. Reynaud, and G. Fantozzi, “Lifetime prediction withacoustic emission during static fatigue tests on ceramic matrix composite at intermediate
temperature under air,” no. June, pp. 22–26, 2014.
[29] S. K. Al-jumaili, M. Eaton, K. Holford, and R. Pullin, “A Parameter Correction
Technique ( PCT ) for Acoustic Emission Characterisation in Large- Scale Composites,”
pp. 1–8.

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

MONITORING INDUSTRIAL CRYSTALLIZATION USING ACOUSTICEMISSION
Eric SERRIS1, Ana CAMEIRAO1, Frédéric GRUY1
Mines Saint-Etienne, SPIN EMSE, CNRS UMR 5307, LGF, CS 62362, F-42023 St Etienne, France
Phone: +33 4770290, Fax: +33 4770290 ; e-mail: serris@emse.fr

1

Abstract
Acoustic emission (AE), which has been successfully applied for monitoring a rather wide variety of solids
elaboration processes, was almost never evaluated in the field of industrial crystallization for pharmaceutical orchemical products. Few papers reported that crystallization processes give rise to acoustic emission signals that
could be related to the development of the basic crystallization phenomena. This study is intended todemonstrate new perspectives opened up by the possible use of acoustic emission (AE) as a non-intrusive andnon destructive sensor for monitoring crystallization with a particular focus being put on real industrial
processes. The preliminary results of the study will be reported in this work namely concerning the ability of the
EA to follow the crystallization processes under different operating conditions and different types ofcrystallisation, and the obtained crystals in terms of particle size distribution and agglomeration degree.
Keywords: particle size, crystallisation, monitoring, industrial processes

1. Introduction
We show that acoustic emission (AE) is a very attractive non-intrusive technique formonitoring crystallization processes. AE has been successfully applied in many fields ofmaterial sciences [1] and it was also used in the pharmaceutical industry for monitoring
various chemical engineering processes [2] including fluidized bed granulation [3], fluidized
bed coating [4], powder compaction [5-6], etc. However, few studies deal with the use of AE
to monitoring crystallization processes [7-10]. The objective of this work is to evaluate thepotential for using acoustic emission to monitor batch crystallization. The basic conceptbehind AE monitoring of crystallization processes is that the phase transitions occurring
during crystallization in solution induce physicochemical changes in the suspension. Suchchanges release energy and therefore generate acoustic elastic waves propagating in the liquidmedium [11]. Furthermore, as crystal particles are generated, the elastic properties of thedispersed phase also change. The latter changes affect the acoustic emission caused by theparticle collision impacts and inter-particles and/or particles-wall frictions. In this present
work, -we will only consider particles impacts and the influence of several parameters on it.

2. Materials and Method
2.1 Acoustic emission on crystallisation vessel
As displayed in Fig.1, a 3 L glass vessel equipped with a jacket was used for the experiments.
The jacket is baffled and a centrifugal pump forces the circulation. Stainless –steel baffles anda high efficiency propeller (Mixel TT TM) are used to maintain a good homogeneity of particles
in the slurry. The temperature is set at 20°C and the rotating speed of the impeller is 300 rpm.

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Figure 1 : Experimental apparatus

During this work, we had recourse to system MISTRAS (Massively Instrumented Sensor
Technology for Received Acoustic Signal) developed by Euro Physical Acoustics (EPA) for
the acquisition and the treatment of the signals of EA. The system uses the chart ofdigitalization AEDSP 32/16 which allows digital acquisition high speed and high-resolutionof the forms of wave and gives access to the parameters (Fig.2) of acoustic emission such as
the number of counts, duration, rise time and amplitude (dB).

Figure 2 : Main characteristics of acoustic emission

The sampling rate is of 4 MHz. Two sensors: a WD and a F15 provided by Mistras whose
Eigen frequency are respectively of 450 Khz and 150 Khz are used in this work. They areboth connected to a preamplifier of 40 dB and of a filter [20, 1200] Khz. The PDT (peak time
definition), HDT (hit time definition), HLT (hit lockout time) are of 100 100 and 200 µs,
respectively.
The energy of reference (600 atto Joules) and a threshold of acquisition (30 dB) were given
starting from measurements taking of account the electromagnetic environment, the agitationand the flow of coolant on the level of dual envelope.

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2.2 Powders
Two powders we used in this study, adipic acid and also citric acid. Sigma Aldrich provided bothpowders and the purity is above 99%. In the table 1, the list of the physical properties is given:
Adipic acid
Citric acidDensity
1,36 g.cm3
1,66 g.cm3

Solubility
8 g .L-1
2000 g .L-1

D50
250 µm500 µmtable 1: physic-chemical properties of powders

The solubility is given by the work done by Apelblat et al [12]. The density is obtained byhelium pycnometer (accupyc from micrometrics) and the D50 by laser granulometry using thesirocco device of the Mastersizer 2000 (Malvern).
2.3 Experimental procedure
We only want to study the influence of several parameters of crystallisation on acoustic
emission, that’s why the solutions must be at a saturation state. The first step of experiments
is the dissolution of the two products in order to achieve the saturation stage. Sufficientamount of powder is dropped into the water in order to dissolve all particles. We consider that
this stage is achieved when no particles are observed in the vessel. This stage is at least threehour for citric acid and two hours for adipic acid. The next step of experiment is to dropa certain amount of powder inside the solution and then change the volume fraction, therotating speed or the particle size and records the associated acoustic emission.

3. Results and discussion
This work on acoustic emission on monitoring industrial crystallization will only consider the
impacts of particles and the sensibility of the AE to several parameters such as volume
fraction, rotating speed and also particle size. Previous work [13] shows that crystallization
processes like nucleation and growth is also responsible for AE, but this will not be includedin this work.
3.1 Acoustic emission during dissolution
First we discuss on some acoustic event that occur during the dissolution stage of citric acidand then of adipic acid.
3.1.1. Citric acid
The first experiment on citric acid is recorded after dissolving a large amount of powder (2kg)in 1L of pure water. We show the evolution of AE amplitude during three hours after the
introduction of 500 g of citric acid in order to reach a supersaturated state. This evolution isplotted in figure 3. Two periods (from 0 to 1000s and from 5000s to 6000s) of acoustic
activity is observed. The first one is the dissolution and the particles impacts of the powders.
The other one is not obvious. Normally nothing changes during this period of dissolution.

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Figure 3 : Amplitude of AE (dB) versus time(s).

In order to have an idea of what may happen during this period, let’s have a look on theevolution of centroid frequency versus time.

Figure 4 : Centroid frequency (kHz) versus time (s)The centroid frequency of dissolution phenomena is generally under 200 khz but for thesecond part of ÀE activity the frequency is above 200 khz. In oxalate ammonium
crystallisation in Gherras work [13] is generally near 250 khz for nucleation and/or growth ofparticles. We can consider that, thanking into account the fact that we are above thesupersaturation stage, we have a crystallisation of citric acid during this period.
For this reason and also for the strong viscosity of the solution due to the high concentrationof citric acid, we change to adipic acid system for the following results.
3.1.2. Adipic acid
The first experiment on adipic acid is recorded after dissolving 16g of powder in 2L of purewater. We show the evolution of AE amplitude during three hours after the introduction of 8 g
of adipic acid in order to reach a supersaturated state. This evolution is plotted in figure 5.

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Figure 5: Cumulative counts versus time (s)In this case, we observe in the vessel that the powder is on the surface of the water; thewettability of the adipic acid is not good. That’s why a large number of AE counts arerecorded by 2000s and 3000s when the powder sink into the water and the dissolution occur.
The dissolution and particles impacts are responsible of this AE. In order to have a better ideaof this AE, the evolution of centroid frequency versus time is plotted in figure 6.

Figure 6: Frequency centroid (khz) versus time (s)At the beginning the AE frequency is centred around 175 kHz, and then the frequency
dropped and is between 100 and 175 kHz. This may be the related frequency of dissolution of
adipic acid that is different to the frequency of particle impact. So we can characterize both
AE. In the following results we change the experimental procedure by adding surfactant toovercome the poor wettability of adipic acid.
3.2 Influence of volume fraction
We want now know the sensibility of AE to the volume fraction φ of particles inside thevessel. After dissolving 16g of adipic acid with surfactants in 2L of pure water and waiting for
total dissolution, we introduce different quantity of powder by adding it with a funnel. Afterseveral tests, we decide to introduce from 0,001 up to 0,02 of volume fraction. In table 2, wehave the experimental procedure of the different stages of volume fraction, introduced massof adipic acid versus time.

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Time (s)0-1800s
1800-3600s
3600-5000s

Volume fraction
0,001
0,005
0,01

Total introduced mass (g)
2,72
13
27,5

table 2: experimental procedure

In figure 7, cumulative counts versus time of this experiment are plotted.

Figure 7: Cumulative counts versus time (s)First, we notice that around 100000 counts are recorded when we introduce the particle inside
the vessel. This is the AE of the sliding particles in the funnel. We only want the AE between
those two very emissive events. That’s why we will analyse the AE amplitude versus time of
figure 8.

Figure 8: Amplitude of AE (dB) versus time(s).

We have also the AE of the introduction of particles with amplitude up to 80dB. For the first
volume fraction, AE is recorded in the beginning then weak AE is detected (may be thebackground noise).
3.3 Influence of rotating speed
We want now know the sensibility of AE to the rotating speed of the impeller. Afterdissolving 16g of adipic acid with surfactants in 2L of pure water and waiting for total

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dissolution, we change rotating speed from 300 rpm to 500 rpm. In table 3, we have theexperimental procedure of the different steps of rotating speed versus time.
Time (s)2500-5200 s
5500-7200 s
7200-9000 s
9000-10800 s
10800-12600 s

Rotating speed
300 rpm
400 rpm
500 rpm
400 rpm
300 rpm

table 3 : experimental procedure with evolving rotating speed

In figure 9, cumulative counts versus time of this experiment are plotted.

Figure 9: Cumulative counts versus time (s)We easily notice that the number of counts increases with increasing rotating speed. Withhigher rotating speed, the velocity inside the vessel is higher and the particle impacts are more
numerous and more energetics. The stability of AE is quite good because we can draught
straight line of number of counts versus time. The slope of the line may be in relation with thenumber of particles and the size of particles. This will be discussed in the last part of this work.
3.3 Influence of particle size
In this part, we sieve the adipic acid powder into two different granulometrics classes, big
particles (250-500 µm) and small particles (50-125 µm). After dissolving 16g of adipic acidwith surfactants in 2L of pure water and waiting for total dissolution, we change rotating
speed from 300 rpm to 500 rpm with the sieved particles .In figure 10 cumulative counts
versus time is shown of the experimental part of 500 rpm in order to have the better
differences between the AE of particles. The big particles are represented by the cluster ingreen dots 2 and the small one by the purples ones 3. We have more counts for big particles
and we have also a linear slope for AE counts versus time like in figure 9.

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Figure 10: Cumulative counts versus time (s)The AE is related to the particle size, and we try to assign one AE characteristic by looking at
the variation of centroid frequency versus time plotted in figure 11. In this figure, the big
particles are represented by the cluster in green dots 2 and the small one by the purples ones 3.

Figure 11: Frequency centroid (kHz) versus time (s)We notice that the centroid frequency is lower for big particle and the range is broader (175 to
300 kHz) than those for smaller particles. So we can use AE to estimate particle size
distribution by using may be several sensors and filters in order to have specific response to
precise particle size. In this case, we can monitor crystallisation in order to stop the process
when a certain size is reached.

4. Conclusions
In this study of monitoring industrial crystallization using acoustic emission, several
interesting results are obtained. We can notice that we can distinguish different phenomena
with different centroid frequencies. The crystallisation can be characterized with frequencies
above 200 kHz (figure 4), the dissolution with frequencies down to 100 kHz and for all theparticles impacts (figure6). We can detect particles (with this sensor and this experimental
procedure) when the volume fraction inside the vessel is above 0,05. Further work must be
done in this field to try to detect particle with lower volume fraction. One way can be byincreasing rotating speed because the AE is far higher with a speed of 50 rpm for example.
To conclude we can distinguish the particle size with AE because the AE signature ischanging with particle size.

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This work must be proceed by further investigation with other products, other sensors andalso vessel to better understand all the AE sources and the AE signature.
References
1. A. Marec, J. Thomas, et R. El Guerjouma, “Damage characterization of polymerbased
composite materials: Multivariable analysis and wavelet transform for clustering
acoustic emission data,” Mech. Syst. & Signal Proces., 22, 2008, 1441-64.
2. J. W.R. Boyd et J. Varley, “The uses of passive measurement of acoustic emissions
from chemical engineering processes,” Chemical Engineering Science, vol. 56, Mar.
2001, p. 1749-1767.
3. E.M. Hansuld, L. Briens, J.A. McCann, et A. Sayani, “Audible acoustics in highshear
wet granulation: Application of frequency filtering,” International Journal of
Pharmaceutics, vol. 378, Aoû. 2009, p. 37-44.
4. K. Naelapää, P. Veski, J.G. Pedersen, D. Anov, P. Jørgensen, H.G. Kristensen, et
P. Bertelsen, “Acoustic monitoring of a fluidized bed coating process,” International
Journal of Pharmaceutics, vol. 332, Mar. 2007, p. 90-97.
5. J. Salonen, K. Salmi, A. Hakanen, E. Laine, et K. Linsaari, “Monitoring the acoustic
activity of a pharmaceutical powder during roller compaction,” International Journalof Pharmaceutics, vol. 153, Juil. 1997, p. 257-261.
6. E.Serris, L Perier-Camby, G.Thomas, M.Desfontaines, G.Fantozzi, “Acoustic
emission of pharmaceutical powders during compaction” Powder Technology, vol128(2-3), 2002, 296-299
7. E.L. Lube et A.T. Zlatkin, “In-process monitoring of crystal perfection during melt
growth,” Journal of Crystal Growth, vol. 98, Déc. 1989, p. 817-826.
8. Sawada et al, 1985. Acoutics Emission from Phase Transition of Some Chemicals.
Analytical Chemistry ,57, 1743-1745.
9. Chotard T. J., Smith A., Codet N. , De Baillencourt M. , Fargeot D. and Gault
C. 2005. “Effect of malic and citric acid on the crystallisation of gypsum investigatedby coupled acoustic emission and electrical conductivity techniques” J. of Materials
Science, 41(21), 7210-7217.
10. Betteridge D. et al., 1981, “Acoustic Emissions from Chemical Reactions”. Analytical
Chemistry. 53, 1064-1073.
11. V.A. garten & R.B. head, “Crystalloluminescence,” Nature, vol. 209, 1966, p. 705.
12. A. Apelblat, E. Manzurola, 1987. “Solubility of oxalic, malonic, succinic, adipic,
maleic, malic, citric, and tartaric acids in water from 278.15 to 338.15 K”. J. Chem.
Thermodyn. 19, 317–320.
13. N. Gherras, E.Serris, G. Fevotte, 2012, “Monitoring Industrial Pharmaceutical
Crystallisation Processes Using Acoustic Emission”, International Journal of
Pharmaceutics, 439(1-2), 109-119

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

QUANTIFICATION OF FATIGUE DAMAGE EVALUATION IN RCSLABS DURING WHEEL LOADING TEST BY MEANS OF 3D AE
TOMOGRAPHY
Tomoki SHIOTANI1, Takahiro NISHIDA1, Hisafumi ASAUE1, Takuya MAESHIMA2
C3-b4S14-16, Katsura Campus, Kyoto University, Nishikyo-Ku Kyoto 615-8540, Japan
Phone: +81 75 383 3494, Fax: +81 383 3495; e-mail: shiotani.tomoki.2v@kyoto-u.ac.jp,
nishida.takahiro.6e@kyoto-u.ac.jp, asaue.hisafumi.7a@kyoto-u.ac.jp
2
1 Nakagawara, Tokusada, Tamuramachi, Nihon University, Koriyama, Fukushima 963-8642,
Japan; E-mail: maeshima@civil.ce.nihon-u.ac.jp
1

Abstract
Among maintenance budget of road structures, remediation and replacement of RC bridge deck conquer the major partin Japan, and intensive studies to expand the service life of the deck are carried out. When implementing proper
remediation program of the deck, evaluation of damage progress shall be examined in advance in order to rate thedamage. Observations by naked eyes have so far been conducted for this task; however, this way of investigation is akind of correcting maintenance approach where the repair shall be conducted when the remarkable deterioration was
emerged on the surface resulting in taking the huge repair budget. To decrease the amount of road investment, thedeterioration/ damage shall be evaluated before reaching to the surface from the view point of life cycle cost, beingreferred to as proactive maintenance. Accordingly in this study, internal damage of RC deck due to fatigue failure is
reproduced by wheel loading testing apparatus, and the damage progress was evaluated by 3D AE tomography
analysis. Through the velocity distributions by AE tomography in comparison with visible cracks on the surface byeach damage steps, the applicability of 3D AE tomography is discussed.
Keywords: AE tomography, fatigue damage, RC decks, wheel loading test

1. Introduction
Although a large share has been made by bridge deck replacement among road investment,a principal mechanism of the damage, namely fatigue damage has been discussed when
the deterioration became remarkable as to be observed on the surface. Crack characteristics onthe direction as well as density on the surface have been employed to determine the grade ofthe damage during the fatigue failure. This way of investigation was well implemented for thecorrecting maintenance procedure where the repair shall be conducted when the remarkable
deterioration was emerged on the surface; however, to reduce the repair budget, leading todecrease the amount of road investment, the deterioration/ damage shall be evaluated before
reaching to the surface from the view point of life cycle cost [1]. Bridge owners have long beenpossessing a requirement that the investment could be reduced when internal damage could be
evaluated with NDT approaches and the damage n reasonably quantified. Beside authors’ works
with elastic wave tomography or AE tomography [2][3], X-ray tomography [4] was the only tool
to visualize the internal damage; however, as it takes a large cost and time as well as bringing
harmful situation against human health, as a result, this has not been well employed.
Acoustic emission measurements were then considered that it is the only solution to trace
the damage continuously [5], while as AE monitoring needs multiple numbers of specificsensors, preamplifiers as well as corresponding multi-channel system, this made theapplication only for the very limited numbers of the important structures only. In addition
external environmental situation frequently brought unavoidable noise-signals to record forthat long-term monitoring, so that real-time fatigue damage monitoring with AE technique
has only conducted for experimental purposes [6].

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As different to the AE monitoring, ultrasonic approaches in which signal excitations and theirdetection is necessarily implemented with at least one pair of excitation and receive sensors, areconvenient to explore internal damages. In this ultrasonic technique, the locations of defect can bemeasured by propagation time under the assumption that there exist unique or spatiallyhomogeneous value of ultrasonic wave velocity, whereas in the case that the wave-propagation
length of the media has already been known, overall deterioration can be estimated by the wavevelocity calculated with propagation time. Also it is noted, in order to identify AE sourcessuggesting damage positions/ areas, wave-velocity is necessarily determined in advance of the test,
because the sources are determined based on the arrival time differences among sensors under theunique velocity value e.g., 4000 m/s. AE tomography of which the authors have been developed
for more than a decade enables to determine the both of wave-velocity and AE sources even under
the condition that the damage is evolving or existed within the materials of interest. Relationshipbetween velocity change and fatigue failure progress is, however, not clarified in the RC deck.
In this study, the fatigue failure progress of RC decks is reproduced by wheel loading apparatus,and elastic wave excitations for 3D AE tomography analysis are executed at proper timings
through the test. Finally internal progress of fatigue damage is visualized and quantified by theelastic wave velocity, followed by comparison with resultant surface crack conditions.

2. Wheel loading program
To induce fatigue failure quantitatively, a RC deck specimen was subjected to repeated wheelloading with a steel wheel as shown in Fig. 1. In the apparatus there is a steel wheel of
300 mm in diameter and 400 mm in width and can be applied the load up to 250 kN in thecase of dynamic and 534 kN in the case of static load in the vertical direction. Contrary to theconventional wheel loading machine with a movable loading wheel [7], the foundation where
the specimen is put, is moved in the longitudinal direction within ±500 to ±1000 mm(±500 mm in this test). The repetition rate can be set between 0.897 - 9.97 rpm/ min (8.97 rpm
in this test). The test specimen was 3000 ×2000 ×210 mm in size with steel reinforcement
arranged as shown in Fig. 2. Step-wise cyclic loading was performed as show in Fig. 3.
Firstly the load of 98 kN was repeatedly applied for 100k times, followed by elastic wave
excitations with 35mm dia. steel hammer for 3D AE tomography. Further load repetitions for
200k cycles were applied with load of 127.4 kN. Elastic wave excitations were subsequently
performed after this loading step. Next cyclic loading was conducted for 250k times with
156.8 kN load. The same manner of elastic wave was repeated after this loading step, and thespecimen reached fatigue limit at 235k times during the loading step.

Fig. 1 Wheel lading test machine

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Fig. 2 Configuration of RC deck specimen

Fig. 3 Load application program in wheel loading test(a) Upper surface (unit: mm)(b) Bottom surface (unit: mm)

Fig. 4 Arrangement of AE sensors and impact points. Green lines, red rectangle, and blue rectangle denotewheel load area, static load area, and analysis of interest area, respectively

3 Measurement condition
Damage evolution can be visualized when plotting accumulated AE sources through thewhole failure process. In in-situ structures, however, as there are few cases installing AE
sensors from default condition, cumulative damage interpretation with AE activity cannot be
performed, and therefore tomographic approaches using through the thickness elastic waves
have been employed. In order to artificial excitations, 32 AE sensors of 60 kHz resonant wereplaced onto the four planes namely 10 on the top, 18 on the bottom and 2 each on the twosides. Artificial excitations were made by 35 dia. hammer at designated 18 points on the top
and 22 points on the bottom surface.
The wheel loads were applied in the longitudinal direction shown by the area surroundedby green lines in Fig. 4. In each phase-suspension of wheel loads, the specimen was subjected
to static vertical loads at the central area shown by a red rectangle.
Elastic wave signals due to excitations were amplified at the sensor-integrated pre-amplifierby 40 dB and acquired by AE monitoring system, 48-ch Express 8 of PAC with a 16 bit A/Dconversion rate and a 1 MHz sampling rate.

4 Theory of AE tomography
As shown above in AE tomography (hereafter referred to as AET), both of source locationsand velocity distributions are simultaneously calculated with the following procedure.

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Hereafter, the source location algorithms are demonstrated, leaving detailed tomographic
procedure to the other literature [8].
The source location technique is based on ray-trace algorithm [9]. This algorithm ischaracterized by installation of relay points in each cell as illustrated in Fig. 5. As the raypaths are formed by segments among nodal points in conventional ray-trace algorithm, its
resolution depends on the mesh characteristics, implying that high accuracy source location
requires fine mesh. This leads to increment of the number of degrees of freedom sinceslowness, which is a

Fig. 5 Conventional set of relay points

Fig. 6 Revised ray path in consideration of proposed relay points

reciprocal of velocity, shall be defined in each cell, and consequently makes the identification
procedure more complicated. In this ray-trace algorithm, the relay points between nodes areproposed and a ray-path is formed by segments among nodal, and relay points as shown in Fig. 6.
The resolution of ray-trace is increased without increment of the degrees of freedom by thisapproach. Besides as role of the relay points is relaying the signals, the relay points shall be
distributed uniformly on the surface. However, it is difficult due to the heterogeneous shape ofcross section of concrete structures. To solve this problem, the relay points are installed byusing iso-parametric mapping that is used in the ray-trace algorithm. Since each cell is mapped
into isosceles right triangle, the relay points can be uniformly installed in the mapped cell asshown in Fig. 7. This algorithm does not give exactly uniform distribution of relay points if the
shape of the cell is skewed; however, the distribution is improved by avoiding use of strongly
skewed cells. The source location is estimated by using this ray-trace algorithm.
The procedure of the estimation of source location is briefly described as in Fig. 8. As forthe first step to estimate the source location, the ray-trace is carried out for a receiver as
illustrated in Fig. 9. This procedure calculates travel times t ij from a receiver i to all nodal andrelay points that are numbered as j. Since first travel time T i at receiver i is observed, thepossible emission time of the signal E ij is computed by Eq. 1 at a nodal or relay point j.
𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 − 𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖(1)
The step is applied for all receivers, and then variance of the E ij is computed as follow.
in which

𝜎𝜎𝜎𝜎𝑖𝑖𝑖𝑖 =∑𝑖𝑖𝑖𝑖�𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 − 𝑚𝑚𝑚𝑚𝑖𝑖𝑖𝑖 �𝑁𝑁𝑁𝑁

𝑚𝑚𝑚𝑚𝑖𝑖𝑖𝑖 =S246∑𝑖𝑖𝑖𝑖 𝐸𝐸𝐸𝐸𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖
𝑁𝑁𝑁𝑁

2

(2)

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where N is number of receivers. For the estimation of the source location, the variance σj isevaluated. If the slowness distribution is exactly identical to real slowness distribution, σj must be
equal to zero at the source location and m j must be the emission time. Due to theFig. 7 Mapping to the global coordinate
of set relay points

Fig. 8 Procedure to estimate the source locations

Fig. 9 Mapping to the global coordinate of set
relay points

Fig. 10 Procedure to estimate the source locations andvelocity distributions

discretization error of slowness distribution and insufficient resolution of ray-trace, generally
σ j is not zero even at the source location while the identification procedure of seismic
tomography. However, it is predicted that σ j gets minimum at the source location. Hence, inthis procedure, the source location is determined as a nodal or relay point of minimum
variance σ jmin . Additionally m j is used as possible emission time. It is noted that the accuracyof the estimation of source location is controlled by the density of nodal and relay points
because the source location is assigned to a nodal or relay point in the proposed algorithm.
Furthermore, by applying this technique to the iterative procedure of identification of wave
velocity structure, the source locations are updated in every iterative step, improving theaccuracy of the source location. This approach can be applied for not only AE signals but also
signals that are generated by any excitation point.
Seismic tomography requires source location, emission time and travel time to the receiver.
However, the signals having neither emission time nor source location can be used for seismic
tomography. It is noteworthy that the source location and emission time can be estimatedfrom travel times to the receivers under the wave velocity distribution determined by themethod introduced in previous section. Based on these facts, a procedure of seismic
tomography with estimation of source location is introduced. Figure 10 illustrates theprocedure of seismic tomography with estimation of source location. In the seismic
tomography with estimation of source location, the first step is to estimate the source locationand emission time. If the observed travel times can be separated into groups that are
respectively associated with individual excitation points, the estimation of source location andemission time are carried out for each observed travel time group. The second step is applying
the ray-trace to all estimated source locations. In this step, the ray-trace is carried out for the

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all of estimated source locations, and the travel time among the estimated source locations to
the other nodal or relay points are figured out. Finally by adding the computed travel time tothe estimated emission time, the theoretical travel times at receivers are given by thefollowing equation.
𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 ′ = 𝑚𝑚𝑚𝑚𝑖𝑖𝑖𝑖 + 𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖(4)

In the third step, the slowness distribution is updated to eliminate the difference of thetheoretical and observed travel time by identification technique.
In order to evolve this 2D AET practically, 3D AET has been proposed [8]. Firstly theelement in the 3D AET is expressed in three dimensions different from the one of 2D AET,
where the most different point between 2D AET and 3D AET is the ray-trace technique in thealgorism. In the developed ray-trace technique, the waves in 3D AET are expressed by Eqs. 5
and 6, which are expanded to three dimensions different from Eq. 7 used in 2D AET.
𝑎𝑎𝑎𝑎1 𝑥𝑥𝑥𝑥 + 𝑏𝑏𝑏𝑏1 𝑦𝑦𝑦𝑦 + 𝑐𝑐𝑐𝑐1 𝑧𝑧𝑧𝑧 + 𝑑𝑑𝑑𝑑1 = 0(5)𝑎𝑎𝑎𝑎𝑥𝑥𝑥𝑥 + 𝑏𝑏𝑏𝑏𝑦𝑦𝑦𝑦 + 𝑐𝑐𝑐𝑐 = 0(7)

𝑎𝑎𝑎𝑎2 𝑥𝑥𝑥𝑥 + 𝑏𝑏𝑏𝑏2 𝑦𝑦𝑦𝑦 + 𝑐𝑐𝑐𝑐2 𝑧𝑧𝑧𝑧 + 𝑑𝑑𝑑𝑑2 = 0(6)

For the sake of this developed ray-trace technique, it can be possible to verify the AE
source location and the deterioration of elastic wave velocity in three-dimensions. Thismethod can carry out the calculation, irrespective of input source which may be AE or elastic
wave excitation, and whether input time or position of elastic excitation are known or not.
Accordingly, tomography analysis using the above input sources is named as 'AET'.

5 Results of 3D AET
In order to estimate velocity distributions after each load phases of 100k, 200k, and 250k, ananalysis area was set by a rectangular parallelepiped (blue rectangular in Fig.4) which
consisted of 25 (long axis direction) ×11 (short axis direction) ×5 (thickness direction) nodes
for 3D AET. Results of 3D AET after each load phases are shown in Figs. 11-13.
As show in the figures of the velocity distributions after the first load phase (Fig. 11a-d),
the overall velocity is approximately 3500 m/s or more, and remarkably small velocity
distributions could not be estimated in the internal areas of specimen. It is certain from thethree-dimensional model (Fig. 11d) that velocity in the right side of specimen tends to show
relatively smaller value as 3000-3500 m/s than of the left side.
The results after the load phase of 200k times (Fig. 12a-d) show a characteristic low velocity
area, namely smaller than 3000 m/s in the right side on the surfaces. The difference of large andsmall internal velocity distribution can be more clearly observed in this step than of before one.
It could be regarded in this phase that sound and fatigue damage parts were mixed in the deck.
Velocity distributions after 250k times are shown in Fig. 13a-d. The specimen had reached
its fatigue limit in this load phase. The velocity under 2500 m/s extensively appears in thewhole of specimen. Comparing the results with the velocity distributions after 100k and 200k
times, velocity values were dramatically decreased. Particular low velocity zones could be
confirmed under the wheel running area from three-dimensional model as shown in Fig.13d.

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(a) Upper surface(c) Bottom surface(b) Middle surface(d) Three-dimensional cut-modelFig. 11 Velocity distributions of RC specimen after load phase of 100k times(a) Upper surface(c) Bottom surface(b) Middle surface(d) Three-dimensional cut-modelFig. 12 Velocity distributions of RC specimen after load phase of 200k times

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(a) Upper surface(c) Bottom surface(b) Middle surface(d) Three-dimensional cut-modelFig. 13 Velocity distributions of RC specimen after load phase of 250k times

6 Discussions
The bottom surface cracks overlaid by velocity distributions after the load phases of 100k,
200k, and 250k times are shown in Fig. 14a-c, respectively. After 100k times many cracks
grow in the central area. The area of the right side is more densely cracked than of the left
side. In addition, it appeared to extend toward the four corners direction. Velocity at thisphase shows slightly lower, developing the whole area; however, remarkable difference of thevelocity among areas could not be confirmed. Internal velocity showed the same tendency asin the bottom as shown in Fig. 11, so that it can be concluded that the fatigue damage was not
so progressed intensively at this stage.
In comparison of crack distributions after 200k times and ones of after 100k times,
remarkable dense crack distributions were appeared in the center and right side areas after
200k times. These crack evolution can be confirmed by the velocity distributions as well sincesmaller velocity areas reveal at these dense areas of crack distributions. Specificallyremarkable low velocity zone under 3000 m/s appeared on the right side. It is considered thatlarge scale of fatigue failure might be started around this area.
After 250k times, developed cracks further evolved the overall area of interest, together
with extremely small velocity areas under 2700 m/s. The small velocity areas were also
confirmed in the internal area from Fig.13. It could be concluded that the specimen reached its
fatigue failure limit showed extremely small velocity distribution due to development of
micro- to macro-cracks. In addition, as velocity change rate from 200k to 250k times wasmore rapidly than that from 100k to 200k times, more precise trace of fatigue damage will bepossible to implement the AET between 200k and 250k times. It should be noted that thevelocity is one of the promising parameter relating to the damage obtained by AET but it isalso known that this is not so sensitive to the small damage than e.g., energy parameter [10],and therefore, when one would like to study microscopical behaviour, energy parameters
would be preferably employed to visualize the internal damage. Visualization of elastic wave
parameters before 100k times as well as the phases between 200k and 250k times would bepresented in the follow-up paper with using AET.

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7 Conclusions
The 3D AET has applied to the quantification of fatigue damage progress in RC deck.
The damage was reproduced by the wheel loading apparatus and the tomographic approach
are conducted in every loading phase. As in the result, internal damage was evaluated by 3D
AET using elastic wave velocity distributions. Comparing crack evolutions and the velocity
distributions on the bottom surface it can be suggested that the areas showing dense crack
distribution shows small elastic wave velocity while ones exhibiting sparse crack distribution
show relatively large elastic wave velocity. Thus elastic wave velocities obtained from the 3D
AET can contribute to evaluate the inside damage of concrete, which would be difficult toinvestigate by such conventional technique as external observation. As rapid fatigue damage
had progressed between 200k and 250 k times in this study, more detail investigation on thefatigue damage evolution would be possible when implementing AET between 200k and250k. These results will be presented in the follow-up paper.

(a) 100k times(b) 200k times(c) 250k times (after fatigue limit)

Fig. 14 Overlay map on the bottom surface by velocity distribution and clack situation

Acknowledgment
In this study, experimental works were principally conducted in College of Engineering of Nihon University
under support of Cross-Ministerial Strategic Innovation Promotion Program (SIP). Special thanks shall be sent toProf. I. Iwaki and Associate Prof. Y. Koda with Nihon University as well as Associate Prof. Y. Tanaka with The
University of Tokyo.

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References

1.

D. Frangopol, K. Lin and A. Estes, 'Life-Cycle Cost Design of Deteriorating Structures,'
ASCE, J. Struct. Eng., 0733-9445(1997)123:10(1390), pp. 1390-1401, 1997.
2. Y. Kobayashi, T. Shiotani and H. Shiojiri, 'Damage identification using seismic travel time
tomography on the basis of evolutional wave velocity distribution model,' Engineering
Technics Press, Proceedings of Structural Faults and Repair 2006, 2006 (CD-ROM).
3. T. Shiotani, S. Osawa, S. Momoki and H. Ohtsu, 'Visualization of damage in RC bridge
deck for bullet trains with AE tomography,' Springer, Advances in Acoustic Emission
Technology, pp. 357-368, 2014.
4. J. Otani and Y. Obara edts, 'X-ray CT for geomaterials, soils, concrete, rocks,'
A.A. Balkema, 2004.
5. U. Grosse and M. Ohtsu edts, 'Acoustic Emission Testing,' Springer, 2008.
6. T. Shiotani, D.G. Aggelis and O. Makishima, 'Global monitoring of large concrete
structures using acoustic emission and ultrasonic techniques: case study,' ASCE,
J. Bridge Eng., Vol. 14, No. 3, pp. 188-192, 2009.
7. T. Shiotani, H. Ohtsu, S. Momoki, HK. Chai, H. Onishi and T. Kamada, 'Damage
evaluation for concrete bridge deck by means of stress wave techniques,' ASCE,
J. Bridge Eng., Vol.17, No. 6, pp. 847-856, 2012.
8. T. Shiotani, S. Osawa, Y. Kobayashi and S. Momoki, 'Application of 3D AE tomography
for triaxial tests of rocky specimens,' Proceedings of 31st conference of the European
Working Group on Acoustic Emission (EWGAE), 2014 (CD-ROM).
9. F. Schubert, 'Basic principles of acoustic emission tomography,' J. Acoustic Emission,
22, pp. 147-158, 2004.
10. T. Shiotani, and D.G. Aggelis, ‘Wave propagation in cementitious material containing
artificial distributed damage,’ Springer, Materials and Structures 42, pp. 377-384, 2009.

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

DETECTING CORROSION DURING INSPECTION AND
MAINTENANCE OF INDUSTRIAL STRUCTURES USING
ACOUSTIC EMISSIONPeter TSCHELIESNIG 1, Andreas JAGENBREIN 2, Gerald LACKNER 22

1
AT Consult; Klosterneuburg; Austria
TUV AUSTRIA SERVICES GMBH; Vienna; Austria

Abstract
Beside fatigue cracking, corrosion damages are a main cause for structural failures in pressure equipment, storage
tanks, pipes, ships, road tankers and railway tank cars. Undetected corrosion will lead to catastrophic failure ofstructures and consequently to enormous economic losses. The direct costs only for the US were calculated
approximately with more than 3% of the gross domestic product. The detection of corrosion on time during theregular inspection and maintenance of the structures is very important, but will cause high effort especially forcorrosion attack on hard accessible or coated/insulated surface.
Acoustic Emission Testing (AT) is an integral and non-invasive method, which has been used successfully during
decades for inspection in various industrial branches. Due to this sensitive tool, AT, active corrosion has been
detected / monitored at many different applications.
EC funded projects improved the understanding of acoustic emission related to corrosion process and their
behaviour with respect to AE. Results, especially their AE signature, will be demonstrated concerning active
corrosion and secondary effects of corrosion during loading.
Based on described case studies, future possibilities for integration of AE in risk based inspection system will be
discussed to demonstrate the abilities of AT making our life safer and protect our environment against any kind ofpollution.
Keywords: acoustic emission, inspection, maintenance, active corrosion, secondary effects, monitoring

1. Introduction
Most defects in pressure equipment, storage tanks, pipes, ships, road tankers and railway tankcars are caused by fatigue cracking and corrosion. Corrosion at metallic vessels is usually
a combination of different processes [1]. The primary process is the randomly occurring
chemical reaction anywhere at the structure causing acoustic emission (AE) of low energy andshort duration. For a better understanding of AE during corrosion processes and the secondary
effects several tests were performed within different EC funded projects. Corrosion processes
may happen either at blank metallic surface or at corroded areas, i.e. at brittle scaling layers.
Also secondary effects, like peeling off or breaking of corrosion products, can generate acoustic
emission with high amplitudes or energies and long durations. As a rule of thumb, a loss in wall
thickness of 1 mm caused by corrosion yields to 8 mm thickness of scaling layer. Using
measuring data from laboratory tests as well as from field tests, analysis tools based on patternrecognition were developed for data discrimination. Possibilities in corrosion detection and its
advantages in the application on maintenance and inspection of industrial facilities will bedemonstrated and the future of the methodology will be discussed.

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2. Case studies
In the following paragraphs five representative cases are illustrated:
1) Corrosion testing at flat bottomed storage tanks2) Corrosion in water ballast tanks on ships
3) Corrosion under insulation of a pressure vessel
4) Corrosion between pipes and pipe supports
5) Corrosion at LPG tanks
Acoustic emission testing without pressurization is applied to structures like flat bottomed storage
tanks, case 1), and water ballast tanks on ships, case 2). Acoustic waves propagate in case 1) mainly
and in case 2) partly through the liquid (liquid storage product, sea water). Typical examples forcorrosion detected during pressurization of equipment or components of plants are shown in case3) and case 4). Here secondary effects resulting from corrosion lead to acoustic emission. Peelingoff or breaking of corrosion products and degradation of the structural integrity due to the loss ofwall thickness can be stimulated by pressurisation. The integrity of LGP tanks, case 5), is inspected
regarding corrosion and fatigue cracks using acoustic emission testing.
2.1 Corrosion testing at flat bottomed storage tanks
For identification of source mechanisms of located AE-sources at the tank floor, frequencydomainbased pattern recognition [2-5] was developed during the EU-funded project "Inspection of flatbottomed storage tanks by acoustical methods" (SMT4-CT97-2177). Turbulent leakages as well asactive corrosion processes can be monitored during testing, because they generate acoustic emission.
Thus, degradation of the tank floor can be found. Typically flat bottomed storage tanks are tested andmonitored without pressurization. The AE-sensors are mounted at the outer side of the tank wall andacoustic waves propagate through the liquid product to the sensors. Typical degradation, likecorrosion at wear plates, can be indicated very well by acoustic emission testing (Fig. 1).

Fig. 1: AE-source (wear plate) and related indications from acoustic emission testing

Discrimination of acoustic emission data related to leakage and corrosion, respectively, wasperformed by a classifier developed using reference data from various measurements at tanks.
Choosing appropriate features enables maximum discrimination of measuring data with respect

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to corrosion and leakage (Fig. 2). For verification, the classifier was applied to measuring data
(Leak1, Leak2, Corr1 and Corr2). Different types of layer thickness (Fig. 2) could be
distinguished by specific classifiers developed additionally.

Fig. 2: Results of the developed classifiers applied to measurement data; left: regarding
leakage and corrosion; right: concerning layer thickness

2.2 Corrosion in water ballast tanks on ships
The EU-funded project CORFAT (SCP7-GA-2008-21863) based on the experiences of the EUfunded project "Inspection of flat-bottomed storage tanks by acoustical methods" (SMT4CT972177) and its main topics were detection of fatigue cracking and corrosion regarding thedetection of degradations (cracks, corrosion) on transport products (ships, railway cars andtrucks) by acoustic emission testing. External loads generated by waves during cruising yield
to bending and torsion of the structure. Hence, in areas of high stresses can be stimulated fatigue
cracking as well as peeling off and cracking of corrosion products.
To generate corrosion on ship steel plates, they were applied during laboratory tests to corrosive
environment similar to real conditions [6]. Based on measurement data from acoustic waves
propagating through the metallic plate, a classifier was created to distinguish between acoustic
emission data related to background noise, corrosion and fatigue cracks, respectively. Field tests
at real tankers were carried out during cruising [6]. In the compartments of a water ballast tankAE-sensors were mounted for monitoring acoustic emission (Fig. 3).

Fig. 3: Acoustic emission measurement during anchorage - Location 3D inside one compartment, low levelbackground noise, location of real corrosion sources (purple dots: class “corrosion”)

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Because of good environment conditions during cruising, bending or torsion of the ship wasvery low. AE-monitoring indicated in an empty area of the water ballast tank active AEsources
with amplitudes up to 65 dB AE approximately 1.5 m to 2.5 m below the sensor setup. Thus, two
sensors were replaced from an upper region to the area of the found corrosion for location(Fig. 3). Applying the created classifier to the measurement data of the found AEsources led toa classification of data to the class “corrosion”.
Follow-up inspection of the located areas showed corrosion and corrosion products (Fig. 4).
The biggest corrosion area had a length of approximately 23 cm and a maximum depth of
2.7 mm. Found corrosion products were removed and after cleaning new coating layers werepainted to the surface.

Fig. 4: View of the area with biggest corrosion damages(length 23 cm, maximum depth 2.7 mm) found by acoustic emission testing

2.3

Corrosion under insulation of a pressure vessel

Testing of pressure vessels with respect to structural degradation caused by fatigue cracking
and corrosion is defined by legal regulations and standards. The preferred testing method inmany industrial branches is acoustic emission testing. Severe corrosion under insulation was
found during pressurization of the demonstrated pressure vessel (Fig. 5).

Fig. 5: Indications (middle) of acoustic emission testing during pressurization of a pressure vessel (left)and examples of found corrosion (right) at indicated locations (middle)

Acoustic emission can be effected by structural degradation due to loss of wall thickness andsecondary effects, e.g. peeling off and breaking of corrosion products. The thermal insulation
was removed at the regions indicated by acoustic emission and partly severe corrosion was
found. Corrosion products were removed and after cleaning new coating layers were painted tothe surface and thermal insulation was replaced by new one.

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2.4 Corrosion between pipes and pipe supports
Acoustic emission testing using linear location usually is performed for inspection of thestructural integrity of pipes. This case demonstrate the results of AT during pressurisation ofa pipe. Indications from acoustic emission were related to specific pipe supports (Fig. 6).

Fig. 6: Indications (linear location) of acoustic emission during pressurization of a pipe

Acoustic emission originated mainly by corrosion and/or cracking of corrosion products during
pressurization is described in the following. Nearly during the whole pressurization (13 bar to
24 bar) the cluster evaluation factor (CEF) showed a constant maximum value (Fig. 7).
The values of corrected amplitudes were mainly within a small range (here 50 dB AE and65 dB AE ) for located indications. The values rise to approximately 70 dB AE above a pressure of
21 bar. The event rate for the located events was nearly constant (here from 18 bar to 25 bar).

Fig. 7: Indications (lower part: located events and corrected amplitudes, upper part: CEF) versus
pressure of acoustic emission during pressurization of a pipe

Between the pipe and local pipe supports corrosion as well as corrosion products were
found (Fig. 8) at positions resulted from acoustic emission testing (Fig. 6). Supports

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were demounted and corrosion products were removed. After cleaning new coating
layers were painted to the surfaces. For comparison, other pipe supports without
indication from acoustic emission were inspected, but no corrosion was found there.

Fig. 8: Found corrosion and corrosion products at the inner side of pipe supports

2.5 Corrosion at LPG tanks
Increasing loss of wall thickness rises degradation of tanks. Hence, inspection of LPG tankswith respect to corrosion is very important. Acoustic emission during pressurization of tanks
can originate from crack growth in the base material as well as from peeling off or cracking of
corrosion products. Figure 9 shows the corrected amplitudes of acoustic emission measured
during pressurization up to a pressure of 13 bar with values in the range of 63 dB AE to 83 dB AE .
Indications from acoustic emission testing (Fig. 9) were related to friction and corrosion(Fig.10). Detection of corrosion by acoustic emission testing does not need pressurisation of thetank. Hence, the standard EN 12817 requires for security reasons monitoring of LPG tanks byacoustic emission testing for a period of 5 min before pressurization in order to evaluate any
acoustic emission resulting from active corrosion.

Fig. 9: Indications (left: locations at the surface of the tank (blue area: corrosion); right: corrected
amplitudes versus pressure) of acoustic emission during pressurization

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Fig. 10: Real corrosion found in the LPG tank

3. Conclusion and Outlook
As demonstrated by the discussed cases, monitoring by acoustic emission testing can detect
active corrosion and peeling off or cracking of corrosion products, respectively. Discriminationof measuring data with respect to related sources can be done with classification tools based on
pattern recognition, if relevant reference data will be available. Thus, wave propagation (troughliquid medium or in the metallic plate) has to be taken into account. The knowledge of theorigins of acoustic emission can support maintenance and improve optimization ofmaintenance, repairs and stops in combination with inspection measures, e.g. risk based
inspection. Thus, not only the protection of the environment against disasters and pollution will
be improved but also the competiveness of the industry will be increased.

References
1.

2.
3.
4.
5.
6.

P. J. Van de Loo, D. A. Kronemeijer, 'Acoustic Emission tank testing: How todiscriminate between the onset of corrosion and further stages of degradation?',Proceedings of the European Conference on Acoustic Emission Testing, p174-179, TÜVAustria, Wien, 1998
P. Tscheliesnig, G. Lackner, M. Gori, H. Vallen, B. Herrmann, 'Inspection of
flatbottomed storage tanks by acoustical methods; classification of corrosion related
signals', Journal-AE, Volume 18, 2014
G. Lackner, P. Tscheliesnig, 'Field testing of flat bottomed storage tanks with acoustic
emission – a review on the gained experience', Journal-AE, Volume 22, 2004
I. Baran, G. Lackner, 'Analysis of Corrosion processes and Leak in Aboveground Storage
Tanks with AE Monitoring', 30th European Conference on Acoustic Emission Testing&7th International Conference on Acoustic Emission, University of Granada, 2012
I. Baran, M. Novak, G. Lackner, 'The Acoustic Emission Monitoring System ofAboveground Storage Tanks', 31th Conference of the European Working Group on
Acoustic Emission, Germany, Dresden, 2014
I. Baran, M. Novak, A. Jagenbrein, H. Buglacky, 'Acoustic Emission Monitoring ofStructural Elements of a Ship for Detection of Fatigue and Corrosion Damages', 30th
European Conference on Acoustic Emission Testing &7th International Conference on
Acoustic Emission, University of Granada, 2012

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

DETECTING THE CRITICAL STRAIN OF FIBER REINFORCED

PLASTICS BY MEANS OF ACOUSTIC EMISSION ANALYSIS
Fabian WILLEMS 1, Johannes BENZ 1, Christian BONTEN 1
1
Institut für Kunststofftechnik, University of Stuttgart; Stuttgart, Germany
Phone: +49 711 685 62883, Fax: +49 711 685 85335; e-mail: Fabian.Willems@ikt.uni-stuttgart.de,Johannes.Benz@ikt.uni-stuttgart.de, Christian.Bonten@ikt.uni-stuttgart.de

Abstract
Acoustic emission testing is a quasi-non-destructive testing method to detect and record the ignition andpropagation of irreversible damage in materials under load. In tensile testing of fiber reinforced plastics, firstmicro cracks occur as soon as a critical strain is exceeded. They emit a characteristic sound, which can bedetected via acoustic emission analysis. By increasing the load, the amount of acoustic emissions rises. Even, if
the plastic´s matrix causes a high damping rate, a sudden change in amplitude is indicating the critical strain.
Further, the analysis shows signals of the whole frequency range which can be distinguished into three different
damage mechanisms (matrix cracking, interfacial failure and fiber breakage) by using pattern recognition
techniques. It can be shown that the appearance of micro cracks can be correlated with a specific strain value
which is interpreted as the critical strain.
Keywords: critical strain, micro cracks, failure limit, acoustic emission analysis, fiber reinforced plastics

1. Introduction
Compared to conventional materials for structural components, the deformation behavior ofplastics depends strongly on many different conditions like time, temperature, load etc.
Therefore, all these conditions have to be considered during the design process. Till today, theestimation of the plastics material’s failure limit is more complex than for linear elastic
materials like metal. In most cases, as a failure criterion the principal stress under load iscompared to the tensile strength. As tensile strength values are often measured unidirectionally and do not consider the different influences mentioned above, various reduction
factors for different conditions are used for the valid calculation of plastic parts. Thus, the fullpotential of the material often cannot be exploited. Alternatively, plastics can be dimensionedby means of the critical strain as failure limit. The critical strain is the strain, at which firstmicro cracks occur. There are different estimation methods; however, they are all very timeconsuming. The use of acoustic emission analysis in a slightly adjusted tensile test is
a promising method with the potential to get quick values for the critical strain of a newmaterial. The occurrence and growth of micro cracks lead to an emission of acoustic soundwaves, which are detectable by a piezo transducer. With these signals the critical strain can bemeasured quick and reliable for fiber reinforced plastics

2. State of the Art
The use of the critical strain as a dimensioning factor was invented in the 1970s by Georg
Menges and his group [1, 2]. They found out that an optically detected occurrence of firstflow zones ε F , for lower loads decreased to lower strains, converging towards an asymptotic
value. This is schematically shown in Figure 1. This value was interpreted as the critical strain[1, 3] and its existence could be validated for amorphous transparent and semi crystallinetranslucent specimens as well. It is remarkable that the value for the critical strain differs

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insignificantly for thermoplastics with similar mechanical characteristics [2]. But the mostimportant aspect of the critical strain as a dimensioning factor (the so called “permissible
strain limit”) is that it is mostly independent of






kind of stressing (static, cyclic, etc.) [2, 4]stress distribution (uni- or multiaxial) [3]temperature (assumed no changes in material condition taking place), [2, 5]
time (short-time, long-term) [1, 3]
kind of medium (assumed no change of condition is initiated) [2, 6, 7]
εb at breakstrain ε

σ1

σ2

σ3
σ1 > σ2 > σ3εF growth of first flow zones (crazing)
εcrit = critical strain
time tFigure 1. Determination of critical strain/flow zones by means of creep tests [2]

In comparison to stress based dimensioning methods, the critical strain as a permissible strain
limit provides several advantages, which are summarized in [8, 9, 10, 11]. One majoradvantage of the critical strain is its independence of different boundary conditions, whichallows for a simplified calculation and design procedure [12]. However, there is a remaining
disadvantage, which lies in the measurement of the critical strain being more complex thanthat of conventional stress limits like the tensile or yield strength. Over the past decades,
several different methods were validated to determine the critical strain, of which the mostimportant are listed below.




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Visual examination during tensile testing (quick, but only valid for transparent andtranslucent specimen):
For amorphous transparent thermoplastics the exceeding of permissible strain leads toa silvery glimmer (crazes), which can be recognized with the naked eye [1, 2, 3]. For
semi-crystalline as well as amorphous translucent thermoplastics the exceeding of
permissible strain leads to hazing. With a photo-optical measurement during tensile
testing, the refraction of light can be measured by the changing intensity [13].
Specific fracture energy [13] (time-consuming, valid also for non-transparent
specimen):
During cyclic loading of tensile bars up to different load steps (predefined strain
value), the deformation energy can be calculated for each cycle. The specimen gets
unclamped for 24 h between two load cycles to relax stresses. After a minimum offour load cycles, the specific fracture energy can be calculated by the difference of theenergy loss of the first cycle and a mean value of the subsequent energy losses.
Occurring damages at a specific load step will lead to specific fracture energy greaterthan zero. The lowest load step where first damages occur can be determined as
critical strain.

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

Pin/ball impression method according to Porth [14, 15, 16] (time-consuming, valid for
non-transparent specimen):
This method compares the reduction of tensile strength of different prepared tensile
bars. All tensile bars are prepared with a 3 mm bore hole in the middle. The bore holeof different tensile bars is expanded with a stepwise increased steel pin or ball size.
Afterwards a tensile test is performed. A critical damage level is exceeded, when theinitial strength is reduced by 5 %. The residual corresponding pin oversize is anindicator for the critical strain.

But common to all is that they are either very time-consuming or at limited to certain
materials. A quick, universal and robust determination of the critical strain for a wide variety
of materials is not possible with these procedures. In contrast to that the acoustic emission
analysis can provide a direct detection of micro cracks. In [11] a method based on the acoustic
emission analysis for fiber reinforced epoxy resin is presented. Using a slightly adjusted
tensile test, the critical strain can be quickly detected by means of acoustic emission analysis.
The occurrence and growth of micro cracks lead to an emission of acoustic sound waves,which are detectable by a piezo transducer. With these signals the critical strain can be
estimated for fiber reinforced plastics.
Acoustic emission analysis has also been used since the 1970s for crack characterization ofendless fiber reinforced applications, mostly in aerospace [17, 18, 19, 20, 21]. In addition tothis, different research for thermoplastics has been performed, likewise for unreinforced
thermoplastics [22, 23] and also polymer blends [22, 23] as well as particle and fiber
reinforced thermoplastics [24, 25, 26]. Crack initiation and propagation is correlating with thedetectable signals of acoustic emission analysis. Thereby, different microscopic failure
mechanisms can be distinguished. The signals are analyzed mostly by their amplitude [24, 25,27]. Recent methods are also using frequency based criteria for determination [21, 28, 29].
Cracks emit higher frequencies with increasing stiffness and decreasing density of thematerial [22]. Thus, matrix cracks, interfacial failure (fiber pull-out) and fiber breakage can be
distinguished by frequency in fiber reinforced plastics [19, 20, 21]. Latest methods are alsousing pattern recognition techniques to distinguish between different acoustic emission signal
types [28, 29, 30].
The determination of the critical strain by means of acoustic emission analysis has also beenshown in [25] for short fiber reinforced thermoplastics and particle reinforced thermoplastics. Buta quick, universal and robust method to determine the critical strain for many different materials is
still missing. Therefore, a method based on the acoustic emission analyses is presented.

3. Experimental Set-up and Procedure
3.1. Materials and Specimens
A polycarbonate (PC) from Covestro GmbH, Leverkusen, Germany, available with different
fiber types and contents was chosen for analysis. The following Table 1 lists the specificationof each material.
Table 1. Material specification
Short form
PC
PC-GF20
PC-GF30
PC-LGF35

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Fiber content wt.-%
Fiber type
unreinforced
20
short glass fiber
30
short glass fiber
35
long glass fiber

Classification
Covestro, Makrolon® 3105
Covestro, Makrolon® 8025
Covestro, Makrolon® 8035
Covestro, Makrolon® 8345

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For all tests, standard tensile bars according to ISO 527-2 [31], type 1A, were produced on an
injection molding machine Allrounder 520S 1600-400 of Arburg, Loßburg/Germany.
The processing conditions were kept constant, except for a slight variation of moldtemperature and injection velocity in order to completely fill the cavities. Before processing,
the pellets were predried according to the manufacturer´s processing guidelines.
3.2. Tensile test and acoustic emission analysis
All tensile tests were performed on a tensile testing machine UPM 1476 of Zwick Roell,
Ulm/Germany, at room temperature 23 °C and a relative humidity of 50 % following ISO 291[32]. A strain rate of 0.4 mm/min was chosen. The strain is controlled by sensor-arm
extensometers with an initial distance of 75 mm. The measured values for force andelongation were steadily transferred to the acoustic emission (AE) system during testing.
For the acoustic emission analysis (AEA) the system PCI-2 AE by MISTRAS,
Cambridge/UK, with external amplifiers (40 dB/60 dB) was used. The receiving transducers
are wideband differential sensors with a sensitive frequency response ranging from 100 to
900 kHz. The detectable signals were recorded with the software AEWIN, Version E 5.6. The
sampling rate was set to 10 MHz and the magnification to 40 dB for the reinforced materials
and 60 dB for the unreinforced materials respectively. For the signal acquisition the properties
were set to following values: The threshold to 40 dB for the reinforced materials and to 26 dB
for the unreinforced materials. All other properties were set equal for all materials: peakdefinition time PDT = 20 µs, hit definition time HDT = 80 µs and hit lockout time
HLT = 300 µs. To raise the signal transmission from the specimens to the sensors, theultrasound couplant ZG-F from General Electric, Fairfield/USA, was used. The transducers
were placed with clamps at the surface of the tensile bar between the sensor arms of theextensometers (Figure 2).

Figure 2. Experimental set-up of the tensile test and simultaneous AEA
3.3. Procedure
Before each measurement a sensor response verification test was performed with a pencil leadbreak source according to Hsu-Nielsen described in ASTM F2174-02:2015 [33]. As clamping
the specimens in the tensile testing machine can cause vibrations detectable by the sensors, it
has to be made sure that these have faded away when the test begins. Therefore, a waiting

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period of two minutes was introduced between clamping and testing. Acoustic emissions wererecorded while all specimens were stressed with a constant strain rate until failure. Based on
this data the permissible strain could be detected by a strong increase of recorded signals,
caused by the first micro cracks.

4. Results
Figure 3 shows the stress-strain-curves of the tested materials. It can be seen that the strain atbreak decreases dramatically by adding glass fibers to the Polycarbonate. In contrast to that,
the Young’s modulus increases at the same time. Adding long fibers leads to a higher tensile
strength (about twice as high). This effect can be explained by the ‘theory of the critical fiber
length’: only exceeding the critical fiber length can exploit the full reinforcement potential.
But, at least for the calculating and design process this diagram offers information about
uniaxial tension for the specific testing conditions (for instance: temperature, time, fiber
orientation and length distribution within the tensile bars, etc.), only.
140
140

PC-LGF 35

stressMPastress ininMPa

120
120
100
100

80

80

PC-GF30
PC-GF20

60

60

PC

40

40

20

20

0

0

0

2525
45
65
55 55
45
65
8585
strain in %Figure 3. Comparison of stress-strain-curves of PC with different fiber contents
0

11

22

33

44

strain in %The detailed data determined in tensile tests are summarized in Table 2.
Table 2. Structural properties of the used materials
materialPC
PC-GF20
PC-GF30
PC-LGF35

Young’s modulus in MPa tensile strength in MPa
2339
60.62
4209
53.91
6486
64.45
9730
122.72strain at break in %
77.35
3.12
2.05
2.39

The detected acoustic emissions during continuous testing of a PC-GF20 specimen are shownin Figure 4. Each point in the scatter-plot represents the maximum amplitude of a detectedsignal occurring at a certain strain value. The shown signals have passed the filters, mentioned
in chapter 3.2, only. It can be seen that the first detectable hits are below a threshold of
50 dB AE . When passing a strain value of 0.8 %, the amount as well as the amplitude of thesignals increases significantly. This means that beyond this point irreversible damages occur
within the specimen and thereby induce acoustic sound waves. Lower amplitudes are

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observed at the beginning of the damages. Accordingly, further strain leads to higher
amplitudes. But considering the amplitude only is insufficient, because of the high damping
rate of plastics. However, the sudden change in amplitude is a strong indication for reaching
the critical strain.

amplitude in dBAE

80

70

strain: 0.8 %stress: 31 MPa

60

50

40
0.00

0.50

1.00

1.50

2.00

2.50

3.12

strain in %Figure 4. Amplitudes of AE during continuous testing of PC-GF20
In comparison to that, damages can be observed for PC-GF30 by far lower strains.
The permissible strain that causes an increase in acoustic emissions is reached already at
0.6 %. According to that, the higher glass fiber content decreases the strain at break, which
can be seen in Table 2. Results for PC-GF30 are not presented graphically, because they areclose to that of the long fiber reinforced polycarbonate (PC-LGF35), which are shown in
Figure 5. The increase of acoustic emissions already starts at a strain of 0.5 %, even though
the strain at break of the specimen is slightly higher. However, for this material a threshold of≈ 50 dB AE can be observed as well.
Fiber content and length both influence the conduction of the acoustic waves through thespecimen. This results not only to a rise in the total amount of detected acoustic emissions
with higher fiber content and length, but also to an increase of noise emissions. For nonreinforced PC, hardly any acoustic signals are detected due to high damping. Therefore, thedetermination of the critical strain for unfilled PC is not possible with this method.
100

strain: 0.5 %stress: 48 MPa

amplitude in dBAE

90
80
70
60
50
40
0.00

0.50

1.00

1.50

2.00

2.40

strain in %Figure 5. Amplitudes of AE during continuous testing of PC-LGF35

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In addition, the acoustic emissions that occur are classified by their peak frequency. As stated
before, the peak frequency can be assigned to the different failure mechanisms. The use ofpattern techniques allows to distinguish between different acoustic emission signals. Figure 6
shows the resulting peak frequencies during a tensile test of PC-GF20.

peak frequency in kHz

600
500

fiber breakage

400

interfacial failure
300
200
100

matrix cracks

0
0.00

0.50

1.00

1.50

strain in %

2.00

2.50

3.10

Figure 6. Peak frequencies during tensile testing of PC-GF20
Frequencies below 200 kHz are attributed to matrix cracks. It has to be noted that noise
signals generally have a low frequency and thereby blur with the signals caused by matrix
cracks. In Figure 7 this blurring is more obvious since emitted signals are detected during thecomplete tensile test of the long fiber reinforced polycarbonate (PC-LGF35).
Peak frequencies from 200 kHz to 320 kHz are interpreted as interfacial failure due to a highrate of fiber pull-out. This can be confirmed by SEM images which are shown in eachdiagram. However, the weak fiber-matrix adhesion could only be found in the short fiber
reinforced specimens, whereas the fiber-matrix adhesion is significantly higher in the longfiber specimens (PC-LGF35). All peak frequencies higher than 320 kHz are assigned to fiber
breakage.

peak frequency in kHz

600
500

fiber breakage

400

interfacial failure
300
200
100
0
0.00

matrix cracks
0.50

1.00

1.50

2.00

2.40

strain in %Figure 7. Peak frequencies during tensile testing of PC-LGF35

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The first fiber breakage is observed at a strain of ε ≈ 0.8 % for PC-GF20 and ε ≈ 0.6 % for
PC-LGF35. Finally, these values are correlating with the permissible strain by exceedinga threshold for amplitude.
The difference between the actual beginning of damages and the strain at break for the
discontinuous fiber reinforced plastics is compared in Figure 8. It can be stated that first micro
cracks appear much earlier than the material fails. In comparison to literature [12], which
gives a range of 1–2 % for stiff thermoplastic reinforced materials, the critical strain values
found in this work are lower. Moreover, Figure 8 is showing that even within this materialgroup, detectable differences are found.
3,5

strain in %

3,0

3.0 %

Bruchdehnung
strain at breakfirst micro cracks
Schädigungsbeginn
2.4 %

2,5

2.0 %

2,0
1,5
1,0

0.8 %

0,5
0,0

PC-GF20

0.6 %

0.5 %

PC-GF30

PC-LGF35

Figure 8. Comparison of the detected micro cracks by different reinforced materials

5. Conclusion and Outlook
The appearance of micro cracks has been correlated to a specific strain value which isinterpreted as the so called critical strain. Tough, the acoustic emission analysis is a fast
approach to detect the critical strain of fiber reinforced plastics. The deeper examination of
the signal’s amplitude provides a better indication of the critical strain. The use of frequency
based criteria allows for a better distinction between the occurring fracture mechanisms
within the reinforced materials. Finally, the two methods lead to comparable values for thecritical strain. The total amount of detected signals in general increases with the fiber content
and fiber length.
To validate this method and the determined critical strain values, it has to be verified withdifferent proven methods such as mentioned in Chapter 2. Further investigations on the
acoustic emission analysis have to exclude noise signals more reliably. Since non-reinforced
thermoplastic’s parts are harder to calculate and design, the method should be extended to be
applicable to this group of plastics materials.

6. Acknowledgments
The authors would like to thank Covestro, Leverkusen/Germany for providing the testingmaterials. The findings presented in this work were funded by the German Federal Ministry
for Economy and Energy (BMWi) administered by the Federation of Industrial Research
Association (AIF).¨

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Czech Society for Nondestructive Testing
32nd European Conference on Acoustic Emission Testing
Prague, Czech Republic, September 07-09, 2016

DAMAGE TYPE IDENTIFICATION BASED ON ACOUSTIC EMISSION
DETECTION USING A FIBER-OPTIC SENSOR IN CARBON FIBER
REINFORCED PLASTIC LAMINATES
Fengming YU1, Yoji OKABE1, Qi WU1, Naoki SHIGETA2
1 Institute of Industrial Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505 Japan;

e-mail: houmei@iis.u-tokyo.ac.jp, okabey@iis.u-tokyo.ac.jp, wuqi@iis.u-tokyo.ac.jp 2 IHI Corporation,
Tonogaya 229 Mizuho-cho, Nishitama, Tokyo, 190-1297 Japan; e-mail: naoki_shigeta@ihi.co.jp

Abstract
Recently, the authors have developed a high sensitive fiber-optic ultrasonic sensing system with phase-shifted fiber
Bragg grating (PS-FBG) and employed it for AE detection in composite materials. This research proposed a novel
method to identify the damage types in carbon fiber reinforced plastic (CFRP) laminates from the AE signals
detected by this system. As the PS-FBG sensor was highly sensitive to the normal strain over a broad bandwidth,
the physical quantity of AE signals is much clearer than that of conventional AE sensors. Hence, the signals could
be analysed based on a Lamb wave theory to quantitatively identify the damage types in the CFRP laminates.
The relative amplitude ratio between the two Lamb wave modes of S 0 mode and A 0 mode included in the AE
signal was obtained after the modes were separated by a time-frequency analysis. Especially, we figured out thatthe peak frequencies of AE signals had a correlation with the modes. And then, from the actual AE signals detectedby PS-FBG sensor under three point bending test, the characteristics of the amplitude ratio of the two modes andthe peak frequency were clarified for identification of the three types of microscopic damages: transverse crack,delamination and fiber breaking. Hence, we believe that AE detection using PS-FBG has a great potential forevaluating the damage progress in the CFRP laminates based on the theory of Lamb wave.
Keywords: Optical Fiber Sensor, Composite Material, Guided Wave

1. Introduction
Acoustic emission (AE) detection is a non-destructive testing (NDT) method for evaluating
damage processes in structural composites. Recently, fiber-optic Bragg gratings (FBGs) havebeen applied to AE detection [1-3]. Because of their good flexibility and durability, immunity
to electro-magnetic interference (EMI), and ability to be embedded in composites, FBGs havepotential for extending the application of AE detection to the structural health monitoring ofcomposites [4, 5].
In particular, Wu and Okabe [6] used a special type of FBG, phase-shifted FBGs (PS-FBG), fora balanced sensing system. Due to its high sensitivity and broad frequency bandwidth, thatsensing system was suitable for AE detection in CFRP laminates [7-9]. In this research, we
attempted to find a reliable method to identify damage types in CFRP laminates based on AE
detection using the PS-FBG sensor.
Because the PS-FBG sensor detects only the axial strain over a broad bandwidth, the detection
results possess a high physical reliability. In addition, AE propagates as a Lamb wave in theplate-shaped structure [10, 11]. Hence, the elastic wave theory was applied to studying thedetected AEs in this research.
F. Yu et al. [9] extracted amplitude ratios of the S 0 and A 0 modes from AE signals toquantitatively evaluate the occurrence of transverse cracks and delaminations in CFRP

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laminates. However, because only one parameter was used, the method was not sufficient to
identify damage among the amount of AE events.
The aim of the present paper was to improve the identification method by additionally using
another parameter with highly physical reliability. As an important characteristic in wave
propagation, the peak frequency of AE attracted our attention. This was because the broad
bandwidth [6] allows a PS-FBG sensor to examine AEs with different peak frequencies.
Utilizing the peak frequency and the amplitude ratio simultaneously could improve
the quantitative identification method involving the PS-FBG sensor.
A special configuration shown in Figure 1 was applied to improve the identification method.
In this configuration, the optical fiber without the PS-FBG was glued onto the structural plate.
The segment of optical fiber between the adhesive and the PS-FBG was used as the AE
propagation waveguide. The PS-FBG sensor detected the propagating AE in the waveguide.
The existing research [3] proposed a similar configuration for a strain-insensitive FBG sensor.
In the present paper, we called this special detection configuration a new adhesive method forremote AE measurement (ADRM).
The damage identification method proposed in the present paper depends on correct responses
to the S 0 mode and A 0 mode included in AE. However, the ADRM is different from the normal
detection configuration. Hence, first, we studied sensing characteristics of PS-FBG sensor in
the special configuration. Then, using the ADRM configuration, we clarified the characteristics
of Lamb wave modes in the AE signals caused by transverse cracks, delaminations and fiber
breaks in CFRP laminates. To evaluate the mode characteristics quantitatively, the amplitude
ratio and the peak frequency were extracted from the AE signals. In the section 4, on the basisof the clarified characteristics of Lamb wave modes and peak frequency in the AEs, we
identified the three types of damage among a number of AE events detected under a three point
bending test. Finally, the identification result was verified by finite element (FE) simulation of
AE propagation.

Fig. 1 The sensing configuration of a PS-FBG sensor using the new adhesive method for remote AE measurement(ADRM)

2. Adhesive Method for Remote AE Measurement
Characteristics of Lamb wave modes included in the AE signals or the ultrasonic waveforms
are useful for studying properties of damages in the CFRP laminates. In the normal adhesive
method, the PS-FBG sensor was glued directly on the plate surface in order to accurately detectthe dynamic strain caused by the AE or ultrasonic wave at the adhesive point. Conversely, in
the ADRM configuration, waves propagated through the waveguide of the optical fiber fromthe adhesive point to the PS-FBG. The wave propagation in the optical fiber could influence
the detection of the Lamb wave modes. Hence, the experiment was conducted to investigate thesensing characteristics of PS-FBG sensor in the ADRM configuration.
In the experiment, we used piezo actuator-type macro fiber composite (MFC) to generatea three-cycle sinusoidal wave with a hamming window in an aluminum plate. In the ADRM
configurations, the adhesive point was located 20 cm away from the MFC, and the PS-FBG

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sensor was located 40 and 60 cm away from the MFC to detect the ultrasonic wave, resulting
in ADRM(40-20) and ADRM(60-20) configurations, respectively. In addition, normal adhesive
methods with distances of 20 and 40 cm between the MFC and PS-FBG, respectively, were also
used as references. They resulted in AD(20) and AD(40) configurations, respectively.
PS-FBG sensors in the different detection configurations were used to receive the input signals
with central frequency of 150, 300 and 450 kHz, respectively. The responses of PS-FBG weremodulated by the high sensitive balanced sensing system [6]. Figure 2 shows the results. On thebasis of the theoretical dispersive curve of arrival time against frequency, the A0 and S0 modes
were separated in detection results corresponding to the AD(40) and AD(20) at each frequency.
Referring to the results in AD(20), it was observed that wave components having the samecharacteristics as that of the S 0 and A 0 modes also appeared in the ADRM(40-20) andADRM(60-20).
The results indicated that the waveforms detected by using the ADRM configuration could
reflect the characteristics of the Lamb waves at the adhesive point. In comparing with the resultscorresponding the ADRM(40-20) and ADRM(60-20), we determined that detected wave
components propagated in the waveguide of optical fiber without distortion. Hence, PS-FBG
sensor in the ADRM configuration have a feasible remote sensing of the A 0 and S 0 modes
included in the original AE wave propagating from AE source to the adhesive point.

Fig. 2 Comparison between the response of PS-FBG sensor in the common adhesive method (AD(40) and AD(20))
and ADRM configuration (ADRM(40-20) and ADRM(60-20)) to the ultrasonic wave with center frequency of
150 kHz (from (a) to (d)), 300 kHz (from (e) to (h)) and 450 kHz (from (i) to (l)).

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3. The Characteristics of AE Signals Due to Different Damages
A three-point bending test was implemented to generate damage in the [90 2 /0 2 ] S coupon
specimen (L×W×H= 180×20×1.2 mm3). A T700S/2500 (Toray Inc.) system was used in the
fabrication of the laminate. We glued the PS-FBG sensor in the ADRM configuration on theplate to detect AEs generated during the bending test. The adhesive point was located 50 mm
away from the loading pin. The distance between the PS-FBG and the adhesive point was
200 mm. Furthermore, as a reference to verify that the S 0 and A 0 mode components wereseparated from the AE waves detected by the PS-FBG sensor, the two broad bandwidth PZT
sensors were glued near the adhesive point but on two opposite surfaces [9].
The threshold of the channel connected with the PS-FBG and with PZT sensors were set toeliminate the noise generated by friction between the loading pin and specimen. During
postprocessing, we filtered the AE signals over a frequency range from 150 kHz to 2 MHz toobtain the A 0 and S 0 modes clearly.
As the first step, after each AE event, the cross-sectional surface of the specimen was examined
under a microscope to identify the corresponding damage.
We detected three types of AE signals and showed them in the Figure 3 (a), (b) and (c).
We applied a continuous wavelet transform (CWT) to the AE signals to identify the modes thatwere included in the received wave.

Fig. 3 Continuous wavelet transformation (CWT) results for waves detected by the PS-FBG sensor in the ADRM
configuration: AE generated by (a) transverse crack, (b) delamination and (c) fiber break.

By observing the cross-sectional surface, we identified that the Figure 3 (a) and (b) weregenerated by transverse crack and delamination, respectively. Based on the dispersive
characteristics [9], we qualitatively identified the S 0 and A 0 modes in them. The separations on
the two modes were also verified by adding and subtracting the two AE signals detected by the
PZT sensor pair. The amplitude of the A 0 mode in Figure 3 (a) was found to be larger than thatof the S 0 mode. To quantitatively evaluate this wave characteristic, we calculated the ratio of
the peak amplitude of the S 0 mode to that of the A 0 mode. This ratio was defined as the E/F
ratio. In this research, the peak amplitudes were obtained from the CWT results. The E/F ratio
obtained from Figure 3 (a) was 0.59.
In the Figure 3 (b), the AE signal corresponding to the event of delamination also clearly showed
the separated wave mode components corresponding to the A 0 and S 0 modes. Comparing with
the Figure 3 (a), Figure 3 (b) indicated that the amplitude of S 0 was larger than that of A 0 in the
AE generated by delamination. The E/F ratio obtained from the result was 1.35.
Because of the broad bandwidth of the PS-FBG sensor, it was determined that not only the E/F
ratio but also the peak frequency of AE was useful for identifying damage types in the CFRP
laminates. The peak frequency corresponding to the AE of the transverse crack was 0.29 MHz,obtained from CWT results shown in Figure 3 (a). In contrast, the AE shown in Figure 3 (b)generated by the delamination had a higher peak frequency of 0.80 MHz.

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The results also revealed that the peak frequency was directly related to the relative amplitudeof A 0 and S 0 modes in the AEs, i.e., an AE had a lower peak frequency when the A 0 mode waslarger and an AE had a higher peak frequency when the S 0 mode was larger. That was because
the A 0 mode dominated in the low frequency field of the AE wave, but the S 0 mode was present
in the relatively high frequency field [9, 10]. For example, the A 0 mode was larger than the S 0
mode in Figure 3 (a) and the peak frequency belongs to the A 0 mode in the AE generated by
the transverse crack. In contrast, the peak frequency of the AE caused by delamination belongs
to the larger S 0 mode in Figure 3 (b).
Figure 3 (c) shows one more type of AE detected using the PS-FBG sensor. We also found that
the S 0 and A 0 modes appeared in the corresponding CWT result. However, the amplitude of
the S 0 mode was much larger than that of the A 0 mode. The E/F ratio obtained from Figure 3(c) was 6.2. Because of the strong S 0 mode, the AE signal shown in Figure 3 (c) had a maximum
peak frequency of 0.89 MHz. Because of these characteristics, we believe that this AE wasgenerated by another type of damage.
Previous research [9] has determined that the S 0 mode dominates in AEs caused by sources
whose orientation is parallel to a plate surface and located close to the neutral plane in the
thickness direction. Only damage in the 0-degree ply of [90 2 /0 2 ] S could possibly satisfy thoseconditions. Hence, we inferred that the AE shown in Figure 3 (c) was generated by a fiber break.
In this section, we studied three types of AEs based on Lamb wave theory. The E/F ratio andthe peak frequency obtained from CWT were helpful to quantitatively evaluate thecharacteristics of AE waves. In the next section, we attempt to use both of the parameterssimultaneously to develop a reliable damage identification method.
4. Damage Type Identification Based on E/F Ratio and Peak Frequency
We conducted another three-point bending test for a specimen with the same laminate
configuration [90 2 /0 2 ] S . As a result, 34 AE events were detected using the PS-FBG sensor in
the ADRM configuration. E/F ratios and peak frequencies were calculated from CWT resultsof the AEs.
The E/F ratios and peak frequencies obtained from all of the AE events are shown in Figure 4(a) and (b), respectively. However, both sets of results indicated that it was difficult to identify
the three types of AE using a single parameter. Based on the analysis in section 3, we could
identify that an AE with an E/F ratio of over 4.0 in Figure 4 (a) had been generated by fiber
breakage. However, the differences between the other E/F ratios were too small to distinguishbetween AEs generated by a transverse crack or delamination. On the other hand, the peakfrequencies in Figure 4 (b) separated into two groups. We identified the AEs with low peakfrequencies as being generated by transverse cracks. However, it was still difficult to distinguisha fiber break from a delamination because of their similar peak frequency.

Fig. 4 Damage identification on the basis of a single physical parameter: (a) E/F ratios and (b) peak frequency.

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Hence, we utilized the two parameters, E/F ratio and peak frequency, simultaneously to build
a new damage identification method. First, every AE event was expressed as a vector consistingof the peak frequency and E/F ratio, which was plotted as one point in a 2-D space. Then, weapplied a pattern recognition method—hierarchical clustering—that was based on the minimum
distance between all of the points, to classify the AEs. As a result, three clusters of AE events
corresponding to three types of damages were clearly determined in Figure 5. On the basis of
the AE signal characteristics clarified in section 3, we identified the events in clusters 1, 2 and3 as having been generated by fiber breaks, delaminations and transverse cracks, respectively.

Fig. 5 Damage identification based on utilizing the E/F ratios and the peak frequency simultaneously.

From the classification result, we were able to obtain a quantitative damage identification
standard in the laminates [90 2 /0 2 ] S based on AE detection using a PS-FBG sensor in the ADRM
configuration. When AE signals were filtered using a band filter from 150 kHz to 2 MHz, AE
signals generated by transverse cracks had E/F ratios below 1, in the peak frequency range of
180 kHz to 390 kHz. The AE signals with E/F ratio from 1 to 3, corresponding to delamination
events, had a peak frequency between 410 kHz and 900 kHz. The AEs caused by a fiber break
had a distinctly large E/F ratio (over 4) in the peak frequency range of 750 kHz to 900 kHz.
Quantitative damage identification was made much clearer by combining the E/F ratio and peakfrequency, in comparison with using only one of the two physical parameters. In particular, weused a pattern recognition method to classify the signals, so this method also has great potential
for identifying damage among a large amount of AE events.

5. Validation by FEM
To show the physical reliability of the identification method, we conducted a 2-D finite element(FE) analysis using LS-DYNA to validate the identification result. In this simulation, we mainly
examined the E/F ratio of AEs caused by transverse cracks and delaminations.
In this research, the AE sources were simulated as dipole sources of body force with a cosine
bell shape and rise time of 0.2 s. As an AE source, the source orientation of a transverse crack
is along the in-plane direction. Delamination has a source orientation along the out-ofplane
direction. [10] In addition, as shown in Figure 6, it was found by examination of the crosssection that transverse cracks only happened in the 90-degree-ply. Hence, to simulate transverse
cracks in the cross-ply laminate with a thickness of 1.2 mm, the three dipole sources in the inplane direction were located 0.6, 0.5 and 0.4 mm away from the neutral axis of the plate model,
respectively. In contrast, delaminations happened after transverse cracks grew to reach theinterface between the 90-degree-ply and the 0-degree-ply. Hence, delamination was simulated

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by locating the out-of-plane dipole source 0.3 mm away from the neutral axis. In the three-point
bending test, all of the damage appeared around the loading point in the bottom ply opposite to
the loading surface. Hence, we put the adhesive point 50 mm away from the sources in thelongitudinal direction. Because the PS-FBG was only sensitive to the normal strain, the outputs
were the strain waves along the axial direction in the shell element of the optical fiber model
200 mm away from the adhesive.

Fig. 6 Schematic of the damage (input sources) in Fig. 7 E/F ratio summarized from the numerically the [0 2 /90 2 ] S
generated during a three-point bending simulated strain wave results.
test

Signal processing was applied to the simulated strain wave in the same way as the experimental
data. We obtained the E/F ratios from the simulated strain wave to evaluate the characteristics
of Lamb wave modes quantitatively. They were showed in Figure 7. This result indicated thatsource positions close to the neutral axis increased the E/F ratios. In particular, all of the E/F
ratios corresponding to the simulated transverse cracks were below 1 and smaller than thoseobtained from the simulation result for delamination. The results calculated from the physicalmodel were able to validate the difference between the E/F ratios extracted from the AEs causedby transverse cracks and those caused by delaminations.

6. Conclusions
Using experiment, we verified that ADRM configuration enable us to detect AE wave correctly.
Then, AE detection was conducted under three-point bending test. In this research, using
quantitative physical parameters, i.e., E/F ratio and peak frequency, obtained from AEs on thebasis of Lamb wave theory, we developed a method to identify damage types in a CFRP
laminate. The identification method had good physical reliability because the PS-FBG sensor
detected the pure normal dynamic strain caused by AE over a broad frequency bandwidth. In
addition, because of the ADRM configuration, the characteristics of the Lamb wave modes
were clearly observable even in small coupon specimens and were evaluated correctly using thetwo quantitative identification parameters.
The two AE parameters of E/F ratio and peak frequency enable us to apply a machine learning
tool to the identification of damage types within a large number of AE signals. Furthermore,
owing to the firm physical background of the method, the identification results could also beverified by FE simulation of wave propagation. Hence, we believe that the new method
proposed in this research could be used as an NDT method with great physical reliability for
identifying and evaluating damage types in composites.

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7. Acknowledgments
This research was conducted as a part of a collaborative project sponsored by IHI CO., LTD .
We extend our thanks to all members involved.

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I.M. Perez, H. Cui, E. Udd, Acoustic emission detection using fiber Bragg gratings,
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H. Tsuda, E. Sato, T. Nakajima, H. Nakamura, T. Arakawa, H. Shiono, M. Minato,
H. Kurabayashi, A. Sato, Acoustic emission measurement using a strain-insensitive fiber Bragg
grating sensor under varying load conditions, Opt. Lett. 34(19) (2009) 2942-2944.
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T. Fu, Y. Liu, Q. Li, J. Leng, Fiber optic acoustic emission sensor and its applicationsin the structural health monitoring of CFRP materials, Opt. Laser. Eng. 47(10) (2009)
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H. Guo, G. Xiao, N. Mrad, J. Yao, Fiber optic sensors for structural health monitoringof air platforms, Sensors 11(4) (2011) 3687-3705.
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Q. Wu, Y. Okabe, High-sensitivity ultrasonic phase-shifted fiber Bragg grating balanced
sensing system, Opt. Express 20(27) (2012) 28353-28362.
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W. Qi, Y. Fengming, O. Yoji, K. Satoshi, Application of a novel optical fiber sensor todetection of acoustic emissions by various damages in CFRP laminates, Smart Mater. Struct.
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A New Way to Evaluate Acoustic Emission Instrumentation:
CATS Methodology
Johann Catty
CETIM, 52 avenue Felix Louat, 60300 Senlis, France
Abstract
Acoustic emission (AE) is widely used in order to evaluate the integrity of many types ofstructures, such as pressure vessels, reactors, storage tanks, concrete structures, aerospacestructures. Several regulatory rules exist for AE testing (AT) around the world and allow AE
testing based on two operational modes (zonal location and planar source location methods).
However, the different regulations have not always produced the same rules and requirements,
leading to different results. The method called CATS has been developed by the author, based on
knowledge and experience, to be a universal tool that can evaluate any configuration of AT (astructure, an array of sensors, attenuation characteristics, data acquisition characteristics), with asame methodology. This tool/methodology should help all the users of this technique to measure
in a numerical scale, the degree of performance of an AE testing, giving a clear view on what we
can detect, and where (the degree of coverage of a structure).
1. Introduction
AE is especially useful in the control of integrity of pressure vessels and mechanicalstructures, enabling global and rapid testing of large structures, significantly reducing
maintenance time and shutdown of facilities [1, 7, 8]. Methods have followed several regulatory
rules, codes or standards that have been created, defining the general application rules of thistechnique. For example, in the case of pressure vessels, AT can be applied according to two
operational modes (zonal location and planar source location by triangulation or planar method),
without any major impact on the analysis criteria of data recorded during the test.
In paper [2], we have highlighted, from simulation calculations, that there are significant
differences in performance between the two techniques of location (up to a ratio 5 to 7). The
effect of the acquisition threshold has been also quantified. In Part 2 [3], based on a realindustrial case, we showed the differences in results of testing applied on the same structure withdifferent configurations, all consistent with current rules. We demonstrated that the result of thediagnosis could drastically change, some active AE sources can be missed, or not well evaluated.
Then, the question is raised about the ways to improve the pertinence and reproducibility of the
AE results, from one control to another. We still believe that AE has a great potential to detectemissive areas, but some changes in the AE testing rules may improve the performance of AE
testing to achieve the requirements of safety regulations.
Following the work presented in papers [2] and [3], the author developped a tool that enables toevaluate any configuration of AE testing. The result of the CATS (Control by Acoustic
Technique Universal Scaling) tool is a numerical value between 0 (no detection/location) and 1(full detection/location). This number represents the capability of detection/location of areference source in a specific structure, integrating the following factors:
The attenuation curve,
the sensors network,
Journal of Acoustic Emission, 33 (2015-16)

1©2016 Acoustic Emission Groupthe detection threshold,the type of AE source.
2. AE Testing
AE testing is widely used in the industry to evaluate the health state of a structure. This
technique evaluate the structure by detecting the AE waves emitted by this structure. The
diagnosis is based on the quantification of all the signals recorded during the test. By using
calculation based on the Delta-t (difference of arrival time of the signals on each sensor), we cancalculate the origins of the AE waves. Then, we linked them to the conditions for monitoring it.
3. Required data
3.1. Description of an AE testing
An AE testing is generally applied to metallic or composite structures, such as storage tanks,
reactors. But the CATS method can also be applied on any AE test, on any type of structure: aslong as you have a structure, a sensors network, an acquisition system and their characteristics,
you can apply this tool. Figure 1 gives an overview of an AT in this example; the structure is aspherical storage tank. The sensors network is composed of several sensors regularly installed on
the surface of the structure. The AE test consists in overpressurizing the tank, in order to activate
AE sources linked to potential structural defects; the AE source will generate AE waves that willpropagate in the structure and be detected by one or more sensors. All the sensors are connected
to an acquisition system that records the signals and returns in real time the information to theoperator.
3.2. The structure
As AE testing is generally applied to metallic structures, we will explain the method with an
example of a spherical storage tank. It is important to know the physical characteristics of thestructure (diameter, thickness, position and number of legs, position and number of nozzles), in
order to calculate the most effective sensors network. The structure has AE wave attenuation
characteristics: When an AE wave is created by a physical phenomenon, it propagates into thestructure while losing energy. The original AE wave is attenuated and distorded during thispropagation. Will we be able to detect this wave? Answering this crucial question necessitatesknowing the attenuation characteristics of the structure: this attenuation depends on the type ofmaterial that composes the structure, the thickness, the type of fluid inside/outside the structure,
the presence of insulation layer and also, on the frequency band inside which we will measure
this wave, i.e., the type of AE sensors we use.
To summarize, the CATS method requires:- Dimensions and drawings of the structure,- AE wave attenuation curve (or attenuation curves if different depending on the place on
the structure, due to, for example, changes in thickness).

2

Fig. 1: Principle of an AE test
3.3. The sensors network
The sensors network is defined by the AT engineer, taking into account the characteristics of thestructure, the type of sensors used, and mainly depending on the regulation from which thecontrol is required. Then, we can design for a suitable sensors network, for the same structureand same type of sensor. The CATS method requires:
The exact positions of all the sensors installed on the structure (a drawing of the structurewith sensor positions). We will then be able to calculate the distances between each sensor
and the others.
3.4. The acquisition system
An AE testing necessitates to record the signals coming from all the sensors. These signals are
only stored when their amplitude is above a specific threshold. Under this threshold, the signals
are not recorded and lost. Moreover, an acquisition system is defined by many othercharacteristics that can influence the data recorded: bandwidth, dynamic range, rearm time,
performance in term of Hit/sec, etc. In the CATS tool, we actually do not manage all theseparameters, but nevertheless, the dynamic range (minimum and maximum values of theamplitude that can be measured) has a great importance. The CATS method requires:
The acquisition threshold Athr (expressed in dBae)
The dynamic range.

3

Fig. 2: Input/Output data of the CATS method
Figure 2 gives an overview of the inputs and outputs of the CATS tool. The explainations are
given in each paragraph indicated on the figure.
4. Description of the CATS method
4.1. Principle of the method
The origins:
The CATS method has emerged from 2 basic questions:
What do we have to detect ?
Are we able to detect the requested phenomena within the conditions of the AT? Moreprecisely, can we quantify this capability of detection ?
The definition of ‘Detection’:Before giving answers to these questions, we have to define the term ‘detection’. In the field ofAE, the detection can be done at 2 levels:
The low level [zonal detection/location] (Fig. 3): The zonal detection/location means that an
AE source that generates AE waves will be detected from the moment when a minimum of 1
sensor has picked up the wave, and the amplitude of the signal coming from this sensor ishigher than the acquisition threshold. Then, the position of the AE source is attributed to thezone of the first sensor that has detected the signal. In this zonal mode, the most appropriate
term should be detection. But we also use the term zonal location, due to the fact that the AE
source is attributed to the zone of the first reached sensor. The zone of a sensor named ‘Sx’ isdefined as the part of the structure the closest to this sensor ‘Sx’. The entire structure can bedivided by as many zones as the number of sensors installed on the structure.
The more stringent [linear-planar-3D locations] (Fig. 4): In this case, the AE source will be
more precisely localized on the structure. Depending on the type of algorithm used, the AE
source has to be detected by a minimum of:Linear location: at least 2 sensors,
Planar location: at least 3 sensors,
3D location: at least 4 sensors.
We easily understand that it is more constraining to use this type of location than zonal
location, because it necessitates a more compact sensors network. But the result of the AE test ismore accurate, and can be helpful to precisely identify the AE origins.
4Fig. 3: Principle of zonal detection

Fig. 4: Principle of planar location
5

What do we want to detect?
How can we evaluate the capability of an AE system to select from zonal, linear, planar or 3D
location? Answering to this question requires, beforehand, to answer to an other fundamental
question: What do we want to detect?
This fundamental question is at the heart of this technique, and most of the time, people forgetthat most of the rules of detection actually used are based on an artificial source, called HsuNielsen, from which many criteria has been calculated.
This Hsu-Nielsen method consists in generating an artificial AE wave by breaking a pencil lead.
This method has been elaborated in the 1970’s [5]. In most of the normative documents, thispencil lead has a diameter of 0.5 mm and a hardness (2H). For specific applications, a diameterof 0.3 mm can be used (for example, in the field of composite structures). But the methodologyof the CATS tool could also be applied on different AE type of sources, such as, for example, alaser-generated AE source [6].
In the frame of the CATS tool, we could use any reference source we want, defined by its initial
amplitude (related to the released energy); Nevertheless, it is also important to make this toolclose to the actual practices. Then (in the following chapters) our reference source will beequivalent as the Hsu-Nielsen (0,5mm-2H) source. When this source is generated in a carbon
steel plate, it is characterized by an initial amplitude equal to 98 dBae (+/-2 dBae) when using 100
kHz-300 kHz resonant sensor. This amplitude is the one measured close to a sensor (~10 mm).
Now, we have all the data to evaluate the coverage rate of a sensors network on a structure:
The characteristics of the structure itself,
the characteristics of the sensors network,
the caracteristics of the acquisition system,and the type of source to be detected.
4.2. Steps of calculation integrated in the CATS method
The CATS method necessitates to perform the following steps:
Step 1: The sensors network
Because we have to enter the coordinates of the sensors, this step necessitates knowing exactly
the coordinates of all the sensors placed on the structure.
If it is a linear structure, every sensor ‘i’ will have a coordinate [xi];If it is a planar structure, every sensor ‘i’ will have the coordinates [xi; yi];
If it is a 3D structure, every sensor ‘i’ will have the coordinates [xi; yi; zi].
Figure 5 shows the representations of a sensors network of a planar structure. Each red or white
dot represents the position of a sensor. At the left, the coordinates of each sensor are indicated in
the table.

6Fig. 5: Example of planar (2D) Sensors network. Each dot in the graph represents the position of
a sensor.
Step 2: The attenuation curve
Now, we have to enter the characteristics of the attenuation curve linked to the reference sourcethat has been chosen as seen in Fig. 6: This step necessitates knowing the values of this curve,
obtained from a field measurement realized on the structure itself, or coming from previous
experience, as long as the characteristics of the structure, environment, type of sensor is very
close to the actual case. But we strongly recommend, as required in many normative documents
to always confirm the values by doing field measurements. This will only take few minutes! This
curve needs to be approximated by calculation (polynomial interpolation, logarithmic
interpolation, or any method that will fit the experimental curve). Then, the experimental curve
has to be introduced in the mathematical ‘CATS’ tool.

Fig. 6: Example of attenuation curve.
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Step 3: The type of location
In this step, we only need to choose the location to be evaluated is Zonal, Linear, Planar or 3D
type; Enter the number of Sensors required:
In the case of Zonal location, the minimum required number of sensors for location is 1,
In the case of Linear location, the minimum required number of sensors for location is 2,
In the case of Planar location, the minimum required number of sensors for location is 3,
In the case of 3D location, the minimum required number of sensors for location is 4.
We can also choose a higher value of minimum required number of sensors. For example, if we
consider that the planar location necessitates more than 3 sensors to be more accurate, or tocompensate a complex shape that makes the propagation more hazardous, we can enter 4, or 5.
Below, we will explain the method for the case of a 2D structure.
Step 4: The calculation of distances from each point of the surface to each sensor
This step is the first one of the calculations needed to evaluate the coverage rate of a sensors
network. For that, we need to measure, for each point of the surface (in the case of a planar
configuration), the distances between this point and all the sensors. The objective is to define, foreach point of the surface, which is the closest sensor, the 2nd closest sensor, the 3rd closest sensor,… and for each of them, the distance between the point and these sensors.
In practice, we have to define a ‘resolution’ of calculation: typically, when we have to evaluate aplanar configuration when the distances between the sensors is few meters, we use a step ofcalculation of 0.05 meter or less, meaning that for a surface of a square meter, we calculate thedistance between each of the 441 points of the grid and each sensor. This result is then an arrayof values giving, for each point of the surface, all the distances between this point and eachsensor. An example of grid is shown in Fig. 7, and an example of calculation for a particularpoint in Fig. 8.

Fig. 7: Graph representing the position of sensors (in red), and the position of each point of thesurface for which the distances are calculated (in white). [0.1 m between 2 consecutive points].

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Fig. 8: Graph representing the position of sensors (in red, and white for the 3 closest sensors),
and the position of one particular point of the surface (in green) for which the distances to the 3closest sensors have been calculated.
Once it is possible to represent the surface with these distances, that can help the user tounderstand where are the easiest areas to be localized or detected, and at the contrary where are
the most difficult areas to be localized or detected.
Figures 9 and 10 show the representations of the maximum distances, for a same sensors
network, but using a zonal detection (1 sensor to be reached – Fig. 9), and a planar location (3sensors to be reached, Fig. 10). At the right, the color scale represents the distance (Fig. 9, to thenearest sensor; Fig 10, to the 3rd nearest sensor).

Fig. 9: Mapping representing the distance to the 1st closest sensor, (in red, sensor position;
maximum distance between sensors = 2.5 m).[2]9Fig. 10: Mapping representing the distance to the closest 3rd sensor, (in red, sensor position;
maximum distance between sensors = 2.5 m). [2]Step 5: The calculation of minimum detectable amplitudes
This step necessitates taking into account:
The acquisition threshold Athr (dBae), as it defines the minimum detectable amplitude,
the attenuation curve, andthe previous maps of minimum distances calculated in Step 4.
For each point of the surface, we know, from Step 4, the distance to the 1st closest, or 2nd, or 3rd,closest sensor. In the case of zonal location, the distance to the first closest sensor is required. In
the case where we need to use planar location, the distance to the third closest sensor is required.
Figure 11 explains the calculation of the minimum amplitude that can be processed by planar
location algorithm, for a specific point (in green):
The distance to the third nearest sensor is 1.46 m,
1.46 m gives an amplitude of 61 dBae on the attenuation curve,
It means that an attenuation of 38 dBae is expected at this distance,
If the acquisition threshold is 50 dBae, it means that the minimum amplitude that can belocalized is equal to 50 + 38 = 88 dBae.
Then, by calculating for each point of the surface, we can also represent these minimum
amplitudes, as we have previously represented the minimum distances. Figures 12 and 13 are theequivalent of Figs. 9 and 10, calculated for an acquisition threshold of 50 dBae.
For these calculations, we understand that all the parameters have an influence:
The sensors network,
the structure (attenuation curve), andthe acquisition system (Acquisition threshold).

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Fig. 11: Calculation of the equivalent minimum amplitude (planar location algorithm) vs.
distance (m).

Fig. 12: Mapping representing the minimum amplitude that can be detected (processed) by zonal
location method (maximum distance between sensors = 2.5 m).
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Fig. 13: Mapping representing the minimum amplitude that can be processed by planar location
method (maximum distance between sensors = 2.5 m).
At the right-hand side of Figs. 12 and 13, the color scale represents the amplitude (expressed indBae) .
Step 6: The calculation of the rate of detection/location
This step necessitates taking into account:
The type of AE reference source to be detected, andthe result of Step 5, i.e., the maps of the minimum amplitudes that can be processed bylocation algorithms.
This calculation is, once again, basic: For each point of the surface, we compare the minimum
amplitude calculated in Step 5 with the reference amplitude of the AE source we want todetect/localize.
Example: We use a reference amplitude of a Hsu-Nielsen 0.5 mm-2H source (= 98 dBae – That iswhat we want to detect).
From the maps calculated in Step 5, it means that for each point where the minimum amplitude isless than 98 dBae, the reference source will be detected (zonal location) or localized (planar
location); If the amplitude is higher than 98 dBae, the reference source will not be detected or
localized. Then we can affect a binary value for each point of the surface:
0 = No detection/location ;1 = Detection/location OK.
Figure 14 shows an example of a map where 0 is black and 1 is white. With this type of picture,
it is easy to understand, where are the areas that can lead to difficulties of location/detection(black areas).

12Fig. 14: Mapping representing the areas where planar location method is efficient (white) and not
applicable (black) (sensor’s position in red).
From this type of picture, we can now calculate the fraction of surface where the location
algorithm is efficient. In the example given in Fig. 14, this is 0.46, i.e., that 46% of the surface
satisfies the conditions required to localize a Hsu-Nielsen source. Many examples of thismethod, and benefits in term of understanding are described in the papers [1,2] and [3].
4.3. The scale of the CATS method
Our goal is finally reached. We can now evaluate the capability of an AE instrumentation(sensors network + Data acquisition Threshold) to detect a defined AE source in a specificstructure. It is important to remind the user that this scale is not only one number, between 0 and
1. This scale must specify the type of reference source to be detected/localized, and also thealgorithm of detection/location which is the subject of this evaluation.
The result should be written as follow (2 examples):Example 1:
Zonal location algorithm (= 1 sensor to be reached) / Hsu-Nielsen 0.5 mm-2H
reference source. If the rate is 0.95, we will write the result as follow:
Kcats [1s-Hsu0.5mm] = 0.95
Example 2:
Planar location algorithm (= 3 sensors to be reached) / Hsu-Nielsen 0.5 mm-2H
reference source. If the rate is 0.45, we will write the result as follow:
Kcats [3s-Hsu0.5mm] = 0.45

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We also advise the user of this methodology to complement this result by a drawing of thesensors network, representing the areas where the detection/location is possible or not. It will
help each user of the technique to understand more the performances/limitations of theconfiguration used, and especially the areas where thelocation is not possible.
Then, this tool enables to compare the performance of different sensors networks.
Let’s describe an example:
For the control of a structure, one service provider says that he will place a sensors networkwith a distance of 4 m between each sensor, and assures that he will control 100% of thestructure by zonal location.
For the same structure, another service provider proposes a more compact sensors network(2.5 m between each sensor), saying that it is more efficient, enabling an accurate planar
location.
What is the difference of efficiency between these 2 proposals?
The CATS method will give, in few minutes, the result given in Table 1:
Table 1: Result of CATS Evaluation for 2 different networks and 2 types of location method
Service Provider 1(1sensor/4 m)
Service Provider 2(1sensor/2.5 m)

Kcats [1s-Hsu0.5mm]
0.99

Kcats [3s-Hsu0.5mm]
0.20

1

1

It clearly means that the service provider 2 is 5 times more efficient (0.2 compare to 1) than theservice provider 1 to do a planar location.
The customer can now make his choice with a quantitative comparison given by the Kcats factor.
5. Conclusions
The CATS method has emerged from a fundamental question concerning AE testing:
Are we able to detect the requested phenomena with the conditions of the AE Testing? Moreprecisely, can we quantify this capability of detection ?
This tool has been described in details, step by step, in order to show that the performance of an
AE testing can be easily and quantitatively evaluated. Due to the fact that the required data aremost of the time well-known by AE engineers (sensors network + data acquisition threshold,type of AE source to be detected) we believe that this tool will be used and integrated in the‘good practices’ of this technique. By giving a clear view of the performance of this type ofcontrol, all the users of this technique (AT engineers, technicians, customers, regulatory body)will benefit from this tool. We can also imagine that a minimum level of Kcats parameter
depending on the criticity of the structure to be controlled could be one day defined and required.
References[1] Non Destructive Testing Handbook, Second Edition, volume 5 – Acoustic Emission Testing,
1987, pp 201-202.
[2] J. Catty, “Acoustic Emission Testing – Defining a new standard of acoustic emission testingfor pressure vessels Part 1: Quantitative and comparative performance analysis of zonal locationand triangulation methods”, Journal of Acoustic Emission, volume 27 (2009), pp 299-313.
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[3] J. Catty, “Acoustic Emission Testing – Defining a new standard of acoustic emission testingfor pressure vessels Part 2: Quantitative and comparative performance analysis of zonal locationand triangulation methods”, Journal of Acoustic Emission, volume 28 (2010), pp 11-31.
[4] J. Catty, “Acoustic Emission Testing – Defining a new standard of acoustic emission testingfor pressure vessels Part 3: Study of current analysis protocols and recommendations fordeveloping a new guide for the application of acoustic emission (to be published).
[5] N. Hsu, ‘’US Patent 4,018,084 – Acoustic Emission Simulator’’, April 19, 1977.
[6] C.B. Scruby, H.N.G. Wadley, R.J. Dewhurst, D.A. Hutchins, S.B. Palmer, ‘’A LaserGenerated Standard Acoustic Emission Source’’, United Kingdom Atomic Energy Authority
Harwell, January 1981.
[7] J. Roget, L’émission Acoustique, Mise en oeuvre et applications, 1988.
[8] J.D. Leaird, Acoustic Emission Training Guide; Greensland Publishing Co, 1997.

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