An International
Forum For
The AE Scienceand Technology

JOURNAL OF
ACOUSTIC
EMISSION
Vol.36/January-December 2019

Editors: M.A. Hamstad (AEWG) and G. Manthei (EWGAE)

36-00136-001EX
36-00936-03936-05136-071

Receiving Sensitivities of Acoustic Emission Sensors: A data
Compilation
Kanji Ono
Data File for 36-001
Use of Acoustic Emission for Studying Ratcheting Behavior of 304LN StainlessSteel Elbows
C.K. Mukhopadhyay, T.K. Haneef, Suneel K. Gupta,
Vivek Bhasin, S. Vishnuvardhan, G. Raghava and P. Gandhi
Far-field wave propagation signals from pencil lead breaks on the open endof a thick-walled layered cylinder: Part 1 Amplitudes and frequency content
M. A. Hamstad
Far-field Wave Propagation Signals from Pencil Lead Breaks on the Open End
of a Thick-walled Layered Cylinder: Part 2 Modes of wave propagation
M. A. Hamstad
Detection of Defects in Spur Gears by Acoustic Emission Monitoring
Swati Gautam and Naresh Tandon

Proceedings of International Conference on Acoustic Emission - 9
Part I and II

Endorsed by
AEWG
and
EWGAE

Published byAcoustic Emission Group
Encino, CA USA©2019 Acoustic Emission Group

9th INTERNATIONAL CONFERENCE ON ACOUSTIC EMISSION
and
61st ACOUSTIC EMISSION WORKING GROUP MEETING

Organized by: David Kosnik, Didem Ozevin and Valery Godinez,
Compiled by: Didem Ozevin9th INTERNATIONAL CONFERENCE ON ACOUSTIC EMISSION
andst
61 ACOUSTIC EMISSION WORKING GROUP MEETING
June 17-20, 2019
ChicagoPROGRAM COMMITTEEDavid Kosnik, Chair, CTL Group
Didem Ozevin, University of Illinois Chicago
Valery Godinez, MISTRAS GroupEXECUTIVE COMMITTEEDavid Kosnik, Chair, CTL Group
Didem Ozevin, Vice Chair, University of Illinois Chicago
Valery Godinez, Secretary, MISTRAS Group
Gang (Gary) Qi, Past-Chairman, University of Memphis
Thomas F. Drouillard, Archivist
Clinton R. Heiple, Treasurer, Metallurgical Consultant
WEB SITE
Jochen Vallen, Strategic Entrepreneurship Consulting
SOCIETY WEB SITE
www.aewg.org
COVER Photograph
Women in AE Networking Dinner (by Didem Ozevin)Publication AssistanceKanji Ono, University of California, Los Angelesii

WELCOME REMARKS
On behalf of the organizing committee and international advisory board, I am pleased to welcome the global
AE community to Chicago. As a native of the Chicago area, I am particularly glad to share “my city” with you.
Chicago is located in at the southern end of Lake Michigan in the central USA and enjoys direct airline service
from major airports worldwide. While it is a young city by world standards (incorporated 1837), Chicago hasrisen to prominence as a global transportation hub, financial and manufacturing center, and locus foreducation and innovation at the heart of a metropolitan area of over nine million people.
The history of Chicago is intimately connected with technological and scientific innovations: reconstruction of
the city following the Great Fire of 1871 included ten-story Home Insurance Building, the first tall building
with a structural steel frame, and generally considered to be the world’s first skyscraper. In 1900, engineers
reversed the flow of the Chicago River to protect water quality of Lake Michigan and enhance watertransportation links through the region. The 110-story Sears Tower held the title of tallest building in theworld from 1973 to 1998.
While Chicago does experience some lively weather, the appellation “Windy City” refers to the enthusiasm
with which early twentieth century Chicagoans promoted their city. At the time of the AE meeting in midJune, we can expect warm, sunny days with the heat moderated by the cooling breeze from Lake Michigan.
Most attractions within the city proper are accessible from the conference venue and suggested hotels by
public transportation, taxi, or rideshare service – a rental car is not necessary.
In the summertime, the single best way to see the city is by boat cruise on the Chicago River and Lake
Michigan. A number of options are available, from water taxi service to narrated and catered cruises. The
conference banquet will be held on one of the latter.
In keeping with the city motto of Urbs in horto – “City in a Garden” – Chicago has many parks and public
spaces, most notably a series of interconnected parks and paths along the Lake Michigan shoreline, and a ringof forest and prairie reserves around the city limits. Several national and state parks and reserves are locatedwithin a 1-2 hour drive from the city. In addition, nationwide domestic air connections are available from
Chicago’s two airports (ORD and MDW), making Chicago a convenient hub for an extended visit to the USA.
I look forward to seeing you in Chicago!
David Kosnik
Chairman, Acoustic Emission Working Groupiii

ICAE-9 & AEWG-61 Program
MxD at 1415 N Cherry Ave, Chicago, IL 60642
Keynote – Dr. Hoda Azari
Nondestructive Evaluation Program and Laboratory, Manager Asset Management and Construction Team
Office of Infrastructure Research and Development, US Department of Transportation
Federal Highway Administration

Keynote – Professor Joseph RosePaul Morrow Professor Emeritus, Penn State University and CTO of FBS d/b/a Guidedwave

Keynote – Mauricio Calva
Nondestructive Examination Expert, Chevron Energy Technology Company
Session 1-1 Fiber Optic AE Sensors and Dynamic Measurement
Acoustic Modes in Optical Fibers for Lamb Wave and Acoustic Emission Detection
Kara Peters, Junghyun Wee
Fiber-optic acoustic emission sensor systems using fiber Bragg gratings
Ming Han, Guigen Liu, Yupeng Zhu
Development of Optical Fiber AE Sensor by FBG with WDM
Hisafumi Asaue, Tomoki Shiotani, Katsufumi Hashimoto, Iori Yamamoto, Hidehiro Inaba
Multichannel fiber laser acoustic emission sensor system
Caitlin Williams, Meredith Hutchinson, Marriner Merrill, William Pogue, Peter Finkel,
Geoffrey Cranch
Session 1-2 Monitoring Composite Structures with AE
Detection of Contaminants in Unidirectional Carbon Fiber Lay-ups
Fady Barsoum, Isabel McBrayer
Investigating the Structural Response of Composite Repairs under Cyclic Loading
Fady Barsoum, Isabel McBrayer
Damage Detection Through Fretting Emission in Center-Hole CFRP Skin of Cantilever Wing Box Under ConstantMoment Fatigue Loading
Ryan J Neel, Jonathan Awerbuch, Tein-Min Tan, Didem Ozevin
Damage Detection Through Acoustic Emission During Post-Fatigue Residual Strength Test of CFRP Skin of a
Cantilevered Wingbox Under Constant-Moment Loading
Ryan N Neel, Jonathan Awerbuch, Tein-Min Tan, Didem Ozevin
Identifying the Presence and Position of Wheelflat via Acoustic Emission
Okan Topcu
Session 1-3 Monitoring in Service Systems with AE
Development of damage detection method in type-III hydrogen pressure vessel by acoustic emission
Takuma Matsuo, Hiroki Orito, Kotaro Hase
Power Transformers monitoring
Caricia Martinez, Arturo Nunez
Increase uptime and plant reliability deploying asset monitoring solutions
Arturo Nunez
Infrastructure monitoring: solutions and examples
Horst Trattnig, Michael Hauserer
Radar tower inspection using omnidirectional shear acoustic emission sensors
Jason Philtron, Ronnie K. Miller, Steven Owens, Cody Borigo, Ward Fong
Session 1-4 Signal Processing Methods
Noise Tolerant and Wireless AE Measurement System for Process Monitoring
Kaita Ito, Kazuki Takahashi, Manabu Enoki
A deep learning-based two-step localization and characterization of acoustic emission sources in metallic panels
using only one sensor, Arvin Ebrahimkhanlou, Brennan Dubuc, Melanie Schneider, Salvatore Salamoneiv

Sensing and statistical analysis of acoustic emission due to crack rubbing/clapping in thin sheet metals
Roshan Joseph, Victor Giurgiutiu
An Acoustic Emission IoT Framework for Monitoring, Data Management, Diagnostics and Prognostics
Krzysztof Mazur, Brian Wisner, Antonios Kontsos
Application Of Acoustic Emission During Pressure Test Of Austenitic Steel Vessel
Václav Svoboda, František Žemlička
Session 2-1 Sensor Calibration Methods
Progress in Sensitivity Verification of AE Sensors
Kanji Ono
Experimental Characterization of PKBBI Sensor: the Low Power Flat Frequency Response, Broadband Acoustic
Emission Sensor
Hossein Saboonchi, Gonzalez Nunez MiguelTransmitters for the face-to-face sensor sensitivity verification - is their scatter tolerable?
Hartmut Vallen
Far-field wave propagation signals from pencil lead breaks on the open end of a thick-walled layered cylinder: Part 1
Amplitudes and frequency content
Marvin Hamstad
Far-field wave propagation signals from pencil lead breaks on the open end of a thick-walled layered cylinder: Part 2
Modes of wave propagation
Marvin Hamstad
Session 2-2 AE in Materials Science
Acoustic Emission Information Discovery at the University of Illinois
University of Illinois
Mary C Schlembach, William H Mischo
Study of dislocation dynamics of copper single crystals under cycling loading through acoustic emission andmicrostructural characterizations
Gabriel L’Hote, Stephanie Deschanel, Sophie Cazottes, Maurine Montagnant
Acoustic emission monitoring of post-installed shear connectors on steel bridges
Arvin Ebrahimkhanlou, Salvatore Salamone, Arash Ebrahimkhanlou, Amir Reza Ghiami Azad, Kerry Kreitma, Todd
Helwig, Eric Williamson, Michael Engelhardt
On the Use of Acoustic Emission in Vibrations Testing or Progressive Failure Monitoring and Modeling
Brian Wisner, Mira Shehu, Johnathon Kordell, Abhijit Dasgupta, Harsh Baid, Antonios Kontsos
Session 2-3 Additive Manufacturing Studies
Acoustic Emission Monitoring for Additively Manufactured Materials
Brian Wisner, Emine Tekerek, Mir Shehu, Antonios Kontsos
Monitoring Fracture Formation in Additively Manufactured Anisotropic Rocks
Liyang Jiang, Hongkyu Yoon, Antonio Bobet, Laura J. Pyrak-Nolte
Tensile Test of 3D Printed Specimens for Demonstrating the Performance of Acoustic Emission Measurement
Systems
Hironobu Yuki, Tomona Murata
3D-printed anisotropic structure for switching elastic wave propagation
Yoshihiro Mizutani, Ryuichiro Otonari, Takeshi Ashizawa, Akira Todoroki
3D Printing for AE: Acoustic properties of 3D-Printed Materials and Development of Custom AE Fixtures’ Jaimy
Juliano, David Kosnik, Richard Nordstrom, Shawn Anderson
Session 2-4 Monitoring Concrete Structures with AE
Evaluation of Damage Depth Inside Concrete Using Surface Wave Tomography
Norihiko Ogura, Tomoki Shiotani
Self-Healing Degradation of Asphalt Concrete Materials due to Cooling Cycles
Behzad Behnia, Henrique Reis
Application of Surface-Wave Tomography to Sealed Conditions of Repaired Cracks in Concrete
Nobuhiro Okude, Tomoki ShiotaniInternal damage detection of RC bridge decks with uniformly distributed elastic wave sources
Hidefumi Takamine, Yuki Ueda, Kazuo Watabe, Katsufumi Hashimoto, Tomoki ShiotanivSession 3-1 AE Theories and Modeling
Modeling the Acoustic Emissions Produced by the Dynamic Fracture of a Glass Beam
Valentin van Gemmeren, Jurg Dual
Monitoring geochemically-induced fractures in synthetic rocks
Chven A. Mitchell, Laura J. Pyrak-Nolte
Omnidirectional shear acoustic emission sensor for improved location analysis
Jason Philtron, Cody Borigo, Steven Owens, Ronnie K. Miller, Joseph L. Rose
Session 3-2 Quantitative Studies of AE on Composite Materials
Characterization of fiber break by quantitative acoustic emission in a model composite carbon/epoxy
Zeina Hamam, Nathalie Godin, Claudio Fusco, Thomas Monnier
AE Measurements and Analysis on a Layered Pressure Vessel
Adrian Pollock, Miguel Gonzalez, Hossain Saboonchi
Damage identification in composite tubes through acoustic emission monitoring
Neha Chandarana, Emmanuel Ramasso, Constantinos Soutis, Matthieu Gresil
Burst pressure prediction of composite vessels
Markus Sause, Stefan Schmitt, Birte Hoeck, Andreas Monden
Overview of Acoustic Emission in Monitoring Powder Technology
Eric Serris, Ana Cameirao, Frederic Gruy, Jean-Michel Herri, Gerard Thomas
Session 3-3 Testing Metallic Materials with AE
Monitoring of fatigue cracking and damage of metallic materials through Acoustic Emission and Digital Image
Correlation, Stephanie Deschanel, Julien Rethore, Jerome Weiss
Using machine learning to predict welding quality from airborne AE sensor data
Kaiser Asif, Lu Zhang, Sybil Derrible, Ernesto Indacochea, Didem Ozevin, Brian Ziebart
AE measurement during friction stir welding of flame-resistant magnesium alloy with simulated inclusions
Kazuki Takahashi, Takayuki Shiraiwa, Manabu Enoki, Kaita Ito, Eitaro Yukutake
Structural Health Monitoring of Mooring Chains Crack Initiation and Growth using Acoustic Emission
Angela Angulo, Slim Soua, Tat-Hean Gan
Characterization of Microdamage of Flexible Solar Cell under Fatigue Load using AE and LT Techniques
Takahiro Matsueda, Shuichi Wakayama, Kosuke Nakahara, Akihiro Takano
Session 3-4 Civil Infrastructure Applications
Acoustic Emission Measurements of Detensioning and Transport of Prestressed Reinforced Concrete Bridge Girders
Dryver Huston, Robert Worley II, Mandar Dewoolkar, Tian Xia, Daniel Orfeo
Application of Non-Contact Laser Impact with AE Analysis and Tomography Technique for Damage Identification inConcrete, Tomoki Shiotani, Katsufumi Hashimoto, Masaharu Nishikino, Katsuhiro Mikami
Damage Quantification of RC decks by Means of Rain-Induced AE Excitations and Source Location Bias
Katsufumi Hashimoto, Tomoki Shiotani, Kazuo Watabe, Hidefumi Takamine
Development of prestressed concrete bridge tendon damage diagnosis system using acoustic emission techniqueDong-Woo Seo, Ga-Young Kim, Ki-Tae Park, Dong-Hyun Kim, Yoon-Gi Hong
Development of Structural Health Monitoring Technology for Internal Tendon of PSC Bridge Using Acoustic Emission
Technology, Dong-Hyun Kim, Yoon-Gi Hong, Dong-Woo Seo, Ki-Tae Park
Session 4-1 Advances in AE Sensors and Signal Processing
A new time-domain feature (RKM) for Acoustic Emission transient signal processing
Ronnie Miller, Jason Philtron
Applying Location Accuracy Refinement to Simulated Location Testing Results
David Kosnik, Richard Nordstrom
Multi-frequency MEMS Acoustic Emission Sensor
Hanie Kazari, Didem Ozevin
Off-the-Shelf Tools for Generalized, Automated Large-Scale Location Accuracy Refinement
Horst Trattnig, David Kosnik, Richard NordstromviSession 4-2 Advanced Applications of AE
Acoustic Emission Characterization of a Composite Overwrapped Pressure Vessel as a Structural Health Monitoring
MethodBehnoush Golchinfar, Miguel Gonzalez Nunez, Patsi Francis
A comparison study of Vibro-Acoustic Modulation technique and Conventional AE
Behnoush Golchinfar, Majid Ramezani Goldyani, Hossain Saboonchi, Dimitri Donskoy
Use of Acoustic Emission for identifying Gas-Liquid mixing regime in agitated vessel applying machine learning
Giuseppe Forte, Federico Alberini, Mark Simmon, Hugh Stitt
Phased Acoustic Emission Sensor Array for Determining Radial and Axial Position of Defects in Pipe-like Structures
Lu Zhang, Chen Enpei, Gorkem Okudan, Didem Ozevin
Papers not presented
Acoustic Emission in Process of Thermal Decomposition of Potassium Hydrogen Carbonate Crystals,
Shavkat Azimov, Vladislav Petukhov and Abdukholik Lakaev
Acoustic Emission based Crack Tracking for Concrete Structures
Fengqiao Zhang*a,.Yuguang Yangvii

Proceedings of ICAE9Contents
36-S001 Damage Detection Through Fretting Emission in Center-Hole CFRP Skin of Cantilever Wing Box Under
Constant-Moment Fatigue Loading, Ryan J Neel, Jonathan Awerbuch, Tein-Min Tan, Didem Ozevin
36-S009 Damage Detection Through Acoustic Emission During Post-Fatigue Residual Strength Test of CFRP Skin ofa Cantilevered Wingbox Under Constant-Moment Loading
Ryan N Neel, Jonathan Awerbuch, Tein-Min Tan, Didem Ozevin
36-S017 Development of damage detection method in type-III hydrogen pressure vessel by acoustic emission
Takuma Matsuo, Hiroki Orito, Kotaro Hase
36-S023 Noise Tolerant and Wireless AE Measurement System for Process Monitoring
Kaita Ito, Kazuki Takahashi, Manabu Enoki
36-S027 Sensing and statistical analysis of acoustic emission due to crack rubbing/clapping in thin sheet metals
Roshan Joseph, Victor Giurgiutiu
36-S037 Progress in Sensitivity Verification of AE Sensors
Kanji Ono
36-S043 Transmitters for the face-to-face sensor sensitivity verification - is their scatter tolerable?
Hartmut Vallen
36-S051 Monitoring Fracture Formation in Additively Manufactured Anisotropic Rocks
Liyang Jiang, Hongkyu Yoon, Antonio Bobet, Laura J. Pyrak-Nolte
36-S056 Tensile Test of 3D Printed Specimens for Demonstrating the Performance of Acoustic Emission
Measurement Systems, Hironobu Yuki, Tomona Murata
36-S061 Evaluation of Damage Depth Inside Concrete Using Surface Wave Tomography
Norihiko Ogura, Tomoki Shiotani
36-S067 Internal damage detection of RC bridge decks with uniformly distributed elastic wave sources
Hidefumi Takamine, Yuki Ueda, Kazuo Watabe, Katsufumi Hashimoto, Tomoki Shiotani
36-S073 Monitoring geochemically-induced fractures in synthetic rocks
Chven A. Mitchell, Laura J. Pyrak-Nolte
36-S079 Omnidirectional horizontal shear acoustic emission sensor for improved location analysis
Jason Philtron, Cody Borigo, Steven Owens, Ronnie K. Miller, Joseph L. Rose
36-S085 Characterization of fiber break by quantitative acoustic emission in a model composite carbon/epoxy
Zeina Hamam, Nathalie Godin, Claudio Fusco, Thomas Monnier
36-S091 Damage identification in composite tubes through acoustic emission monitoring
Neha Chandarana, Emmanuel Ramasso, Constantinos Soutis, Matthieu Gresil
36-S097 Overview of Acoustic Emission in Monitoring Powder Technology
Eric Serris, Ana Cameirao, Frederic Gruy, Jean-Michel Herri, Gerard Thomas
36-S103 AE measurement during friction stir welding of flame-resistant magnesium alloy with simulated inclusions
Kazuki Takahashi, Takayuki Shiraiwa, Manabu Enoki, Kaita Ito, Eitaro Yukutake
36-S107 Structural Health Monitoring of Mooring Chains Crack Initiation and Growth using Acoustic Emission
Angela Angulo, Slim Soua, Tat-Hean Gan

viii

36-S112 Characterization of Microdamage of Flexible Solar Cell under Fatigue Load using AE and LT Techniques
Takahiro Matsueda, Shuichi Wakayama, Kosuke Nakahara, Akihiro Takano
36-S118 Acoustic Emission Measurements of Detensioning and Transport of Prestressed Reinforced Concrete
Bridge Girders, Dryver Huston, Robert Worley II, Mandar Dewoolkar, Tian Xia, Daniel Orfeo
36-S124 Application of Non-Contact Laser Impact with AE Analysis and Tomography Technique for Damage
Identification in Concrete, Tomoki Shiotani, Katsufumi Hashimoto, Masaharu Nishikino, Katsuhiro Mikami
36-S130 Damage Quantification of RC decks by Means of Rain-Induced AE Excitations and Source Location Bias
Katsufumi Hashimoto, Tomoki Shiotani, Kazuo Watabe, Hidefumi Takamine
36-S136 Development of prestressed concrete bridge tendon damage diagnosis system using acoustic emission
techniqueDong-Woo Seo, Ga-Young Kim, Ki-Tae Park, Dong-Hyun Kim, Yoon-Gi Hong36-S142 Development of Structural Health Monitoring Technology for Internal Tendon of PSC Bridge Using Acoustic
Emission Technology, Dong-Hyun Kim, Yoon-Gi Hong, Dong-Woo Seo, Ki-Tae Park
36-S148 Use of Acoustic Emission for identifying Gas-Liquid mixing regime in agitated vessel applying machinelearning
Giuseppe Forte, Federico Alberini, Mark Simmon, Hugh Stitt
36-S154 Acoustic Emission in Process of Thermal Decomposition of Potassium Hydrogen Carbonate Crystals,
Shavkat Azimov, Vladislav Petukhov and Abdukholik Lakaev
36-S159 Acoustic Emission based Crack Tracking for Concrete Structures,
Fengqiao Zhang.Yuguang Yang
Post-Conference Submission – non-peer reviewed
36-S165 A new time-domain feature (RKM) for Acoustic Emission transient signal processing
Ronnie Miller, Jason Philtron
36-S172 Omnidirectional shear acoustic emission sensor for improved location analysis
Jason Philtron, Cody Borigo, Steven Owens, Ronnie K. Miller, Joseph L. Rose

Pages a to q

Abstracts of papers submitted

Page rConference SponsorsixFor non-pristine structures, existing damage (e.g., caused during fatigue) may generate early
fretting emission, i.e., at low loads, which is unrelated to new damage. In other words, early AE
initiation load may indicate the presence of existing damage. To avoid misguided assumptions,the distinction of fretting-generated emission (FGE) from damage-generated emission (DGE)
needs to receive primary attention. This could be accomplished by targeted waveform analysesof the AE signals. Rate of damage progression is associated with increasing rate of thecorresponding AE-signals. Fracture load is normally anticipated by the increasing rate andintensity of emission. Source localization algorithms, readily available in the commercial AE
instruments, provides means to locate new damage and track its progression.
This study focused on the applicability of AE to detect damage initiation, locate its site, track its
progression and anticipate imminent fracture of a large composite panel that contains both a large
discontinuity (i.e., an open hole) and existing damage caused by an extended fatigue loading.

III. EXPERIMENTAL PROCEDURE
The experimental work was conducted at the Airframe Beam Structural Test (ABST) fixture,
Figure 1, operated by the Structures and Materials Laboratory at the FAA William J. Hughes
Technical Center in Atlantic City International Airport, NJ. The four hydraulic actuators aresynchronized to render the desired loading function. A detailed description of the ABST fixture
and its different supporting systems is provided in Bakuckas et al. [1].
Loading Module

Specimen

Reaction
Module12

Wingbox

1

7

2

6

8

11

3

5

10

4

9

CentralFree-End Actuators Base
Actuators
Structure

Figure 1. General view of the Airframe Beam
Structural Test (ABST) fixture [1].

Figure 2. General view of the test panel instrumented with 6
WDI (no. 1-6) and 6 R15I (no. 7-12) sensors.

The test panel was a 610-mm wide, 1,067-mm long, having 18-plies (3,56 mm-thick), being nearly
quasi-isotropic carbon fiber reinforced plastic (CFRP) panel with a through-the-thickness 79.4 mm
diameter hole at its center, Figure 2. The panel was placed with the 0° plies positioned along thelength of the panel. The details of the panel attachment are described in [1].
The panel was previously subjected to constant amplitude, sinusoidal, cyclic loading, underconstant moment loading, at a frequency of 0.375 Hz and at R-ratio of 0.1, for 165,000 cycles, as
described in [1]. Subsequently, and following post fatigue inspection, the panel was subjected toa residual strength (RS) test. The test included 12 quasi-static loading/unloading cycles,
incrementally increasing the load up to failure, numbered RS-1 through RS-12. Sinusoidal load
profile was applied, with the last loading (RS-12) at an approximate average loading rate of
11.1 kN/min and 15.7 kN/min of the inboard and outboard actuator pairs. Final loading was
terminated prior to catastrophic fracture at 60.7 kN and 86.2 kN, respectively, to ensure that thepanel remained intact upon removal from the ABST test fixture for post-test NDI. This paper
discusses the AE recorded only during the last (RS-12) loading to failure.
The panel was continuously monitored via multiple still and video high magnification camera
systems. Digital image correlation (DIC) was used to continuously monitor panel deformation.
Strain was monitored throughout loading using strain gages at pre-selected sites. Additionally,
non-destructive inspections (NDI) of the state-of-damage were conducted upon each unloading

S10

using flash thermography (FT)and phased array (PA) ultrasonic.
measurements and inspections are discussed in [1].

Results of all theseA 16-channel AE system, manufactured by Mistras Group, formerly known as Physical Acoustics
Corporation (PAC) was used in this program, including data acquisition boards, software (AEWin
and Noesis), sensors and pre-amplifiers. Six WDI and six R15I sensors were installed around thecentral hole. The six R15I sensors were mounted concentric to the central hole, 60° apart:
sensors no. 7,8,10, and 11 at a radius of 168 mm and sensors no. 9 and 12 at a radius of 240
mm, Figure 2. The six WDI sensors were displaced, as needed, for space considerations. Wavevelocity calibration yielded wave velocities of 5,782, 4,795, and 4,484 m/sec along the 45°, 0° and
90° direction, respectively. Noting that the 45°/90°/0° ply ratio of the laminate is 6/2/2,
respectively, an average wave velocity of 5,207 m/sec was used. In this study only the results
recorded with the R15I sensors are discussed.
IV. RESULTS AND DISCUSSION
Free-edge delamination is expected to be the dominant failure at the fwd and aft sides of the open
hole, as was confirmed via visual observations and NDIs conducted at the end of the fatigue test
(to be shown below). Numerous prior studies often revealed that the delaminated fracture surface
contains a large number of broken fibers, fiber/matrix interfacial failures, and extensive matrix
micro-cracking – all of which are sources of AE. This complex damage is further extended during
post-fatigue residual strength test.
a. Characteristics of AE-Signals: The hit amplitude of the AE-signals recorded by all six R15I
sensors are shown in Figure 3, together with selected waveforms and their corresponding
frequency spectra. A large number of AE signals (13.4%) were recorded at load levels below theprevious peak load (i.e., RS-11 test of 78.2 kN). The amplitude of these AE-signals were of 4050 dB (80.9%), 50-70 dB (19.6%) and above 80 dB (0.1%) ranges, normally associated (in smallscale laboratory coupon testing) with matrix cracking, delamination, and fiber breaks, respectively.
Amplitude (dB)
Voltage (mV)

6
5
4

70
60
40

1
0

10

Magnitude (V/Hz)
Voltage (V)

75

150
225
Time (s)AE-Signal 1
Sensor 10

0.8
0.6
0.4

2
1
0
-1
-2
0

150
100

AE-Signal 7
Sensor 10

0
0

300
10

0
3.0
2.0

0.0
0
10
5
0
-5
-10
0

500
1000
Time (µs)
AE-Signal 7 1500
Sensor 10
1000

50

375

20
0

75

AE-Signal 2
Sensor 8

0
0

150
225
Time (s)300

20

AE-Signal 3
Sensor 7

0

375

-20
500
1000
Time (µs)
AE-Signal 2
Sensor 8

0
1.00
0.50

0

75

200
0

150
225
Time (s)AE-Signal 4
Sensor 9

-200
500
1000
Time (µs)
AE-Signal 3
Sensor 7

0
15
10

500
1000
Time (µs)
AE-Signal 4
Sensor 9

5
250
500
Frequency (kHz)AE-Signal 8
Sensor 7

500
1000
Time (µs)
AE-Signal 8
Sensor 7

500
500
250
Frequency (kHz)

80

40

1.0
250
500
Frequency (kHz)100

Loading
Unloading

60

-10
500
1000
Time (µs)
AE-Signal 1
Sensor 10

0.2
0.0
0

9

Load and 5-Hit Event Amplitude vs. Time

Loading
Unloading

12

0

-10

Load and 4-Hit Event Amplitude vs. Time

Loading
Unloading

10
11

3
2

0

0

Magnitude (V/Hz)

7

80

50

Load and 3-Hit Event Amplitude vs. Time

8

Loading
Unloading90

Load (%)

Load and Amplitude of Hits vs. Time
100

0.00
0
4
2
0
-2
-4
0
300
200

250
500
Frequency (kHz)AE-Signal 9
Sensor 9

500
1000
Time (µs)
AE-Signal 9
Sensor 9

0
0

0.15
0.00
-0.15
0
7.5
5.0

250
500
Frequency (kHz)AE-Signal 10
Sensor 8

500
1000
Time (µs)
AE-Signal 10
Sensor 8

2.5

100
500
250
Frequency (kHz)

0
0

250
500
Frequency (kHz)

0
0

500
250
Frequency (kHz)

300

375

200
0
-200
0
25
20
15
10
5
0
0
0.15
0.00
-0.15
0
8
6
4
2
0
0

0

75

150
225
Time (s)AE-Signal 5
Sensor 11

500
1000
Time (µs)
AE-Signal 5
Sensor 11

500
250
0
-250
-500
0
30
20

300

0
375

AE-Signal 6
Sensor 12

500
1000
Time (µs)
AE-Signal 6
Sensor 12

10
250
500
Frequency (kHz)AE-Signal 11
Sensor 10

500
1000
Time (µs)
AE-Signal 11
Sensor 10

0
0
0.04
0.02
0.00
-0.02
-0.04
0
2

500
250
Frequency (kHz)AE-Signal 12
Sensor 10

500
1000
Time (µs)
AE-Signal 12
Sensor 10

1
500
250
Frequency (kHz)

0
0

250
500
Frequency (kHz)

Figure 3. AE-signals recorded during the first post-fatigue RS loading, showing selected waveform at key load levels and theircorresponding frequency spectra.

However, since no new damage is expected to occur at such low load levels, one may conclude
that the majority of this emission resulted from fretting among the fracture surfaces formed during
the prior extended fatigue test. Most of the low-load emission is of low amplitude (45 dB),
S11Development of damage detection method in type-IIIhydrogen pressure vessel by acoustic emission
Takuma Matsuo, Hiroki Orito and Kotaro Hase
Meiji University, Kawasaki, Japan
ABSTRACT
A type-III pressure vessel constructed using an aluminum alloy liner with a full composite
overwrap, is generally used in hydrogen storage tanks at hydrogen refueling stations. However,
the evaluation of the fatigue damages in the aluminum alloy layer of the vessel is difficult by theconventional NDT method, because of the effect of the surface CFRP layer. In order toovercome this problem, a damage monitoring method for a type-III hydrogen pressure vessel
has been developed by using the AE method. The characteristics of the AE parameters that
propagated in the vessel were studied. A small type-III vessel was used as a specimen. Artificial
AE signals were produced by laser ablation using a pulse YAG laser. The pulsed laser was
irradiated on different cross-sectional layers. As a result, the extraction of the AE signal that was
excited in the aluminum alloy layer was successful by using the characteristics of the AE
waveform. However, the AE extraction by using weak AE signals was difficult. Further, an
optical fiber sensor was employed for wide area monitoring. The optical fiber sensor was woundover the outer surface of the vessel. Artificial AE signals could be detected with a constant andhigh signal-to-noise ratio when the optical fiber sensor was wrapped over the vessel.
Keywords: Type-III hydrogen pressure vessel, fatigue, optical fiber AE sensor, monitoring,
waveform classification

1. INTRODUCTION
In recent years, there has been increased attention on using hydrogen as a clean sourceof energy. In Japan, to promote the proliferation of hydrogen vehicles, the development ofhydrogen stations is advancing. Type-III composite pressure vessels are used in hydrogenstations. Figure 1 shows the structure of a type-III composite vessel [1]. The vessel structure
comprises of an aluminum alloy liner which is covered with a Carbon Fiber Reinforced plastics(CFRP) helical layer and a CFRP hoop layer. Although the vessel possesses a high strengthand an appropriate weight, fatigue cracks are generated during its operation.
In order to prevent accidents caused by fatigue, non-destructive inspections andmonitoring of the vessel are important. In standard metal vessels, fatigue cracks are detected
and evaluated by ultrasonic testing on the basis of which fatigue life is estimated. However, in a
Type-III vessel, it is difficult to evaluate fatigue cracks due to the CFRP layer. Therefore, theacoustic emission (AE) method is considered as a suitable alternative. If the progress of thefatigue cracks can be evaluated by the AE method, it is possible to diagnose the health of thevessel. However, there are few cases where the AE method is applied to a type-III vessel
comprising a complicated sectional structure.
In this study, we first evaluate the characteristics of an AE signal propagating through a typeIII vessel. We have clarified the propagation velocity and the attenuation of the AE signal in
addition to its source locating accuracy and examined the application of the AE method to a

9th International Conference on Acoustic Emission

S17

type-III vessel. Further, we have developed a smart accumulator capable of measuring AE
signals in a wide range of type-III vessels using an optical fiber AE sensor.
Aluminum Liner

Helical winding

Hoop winding

Type Ⅲ
composite vessels

Fig.1 Structure of type-III composite pressure vessel

2. CHARACTERISTICS OF AE PROPAGATION IN TYPE-III VESSEL
2.1 Source location accuracy

For evaluating the state of the vessel, it is important to detect where the damage has
occurred. However, in composite materials such as CFRPs, anisotropy of the velocity of
propagation of the material occurs, thus the detection of the source location is difficult. For thisreason, in order to perform a detailed source location detection, a source orientation method
considering velocity anisotropy is required [2]. However, we considered the extent of the impact
of disregarding the anisotropy of the propagation velocity in this test.
Threshold: 10 mV
Sampling interval : 50 ns

Digitizer
Ch.3 ( 300, 150 )485 mmy

2 mm
1.75 mm
6.0 mmx

Ch.1 ( 0, 0 )Fiberdirection

AE Sensor(PAC, R15α)
Ch.2 ( 300, 0 )Hoop winding
Helical winding
Aluminum plate

485 mm

Fig.2 Experimental setup for AE source location in composite plate

Figure 2 shows an experimental setup for the AE source location by the pencil lead breaking
method. A flat plate simulating a type-III composite vessel was used, in which an aluminum alloy
S18

plate with a length of 485 mm, a cross ply CFRP plate, and a unidirectional CFRP plate werelaminated. The total thickness of the flat plate was 9.75 mm. The orientation direction of thefiber of the unidirectional CFRP plate was considered as the y axis and the direction
perpendicular to the fiber was considered as the x axis. An R15 α (resonance frequency 150
kHz) AE sensor was used for the experiment. We set three sensors on the plate. In addition,
artificial AE signals were excited from the aluminum plate by pencil lead breaking. Fig. 3 shows
the AE waveforms detected by each sensor when the AE source was at (x, y) = (150, 200). Thelines in the figure present the theoretical arrival times of the S0 mode of the aluminum plate, theA0 mode of aluminum plate, and the A0 mode of the CFRP plate, respectively. Meanwhile, thesource location was detected using the S0 mode of the aluminum plate.
Aluminum A0 mode
Ch.1

50

123.8μs
Output [mV]

Output [mV]

123.1μs
0

−50
0

100

Ch.2

0

−50
200

400

Ch.3
92.1μs

Output [mV]

50

0

−100

0

200

Time [ μs]4000Time [ μs]Aluminum S0 mode

200

400

Time [ μs]

CFRP A0 mode

Fig.3 Waveforms detected by three AE sensors produced by pencil lead breaking
at x = 150 mm and y = 200 mm
Ch.3

300

Sourcey−direction [mm]Location result

200

100

0
0

Ch.1

100

200

x−direction [mm]

300

Ch.2

Fig.4 Source location results
Fig. 4 shows the results of the detection of the source location. The average location error
calculation of the overall location resulted in an average error of 12 % and a maximum error of
34 %. This error occurred due to the deviation in the arrival time under the influence of thevelocity anisotropy due to the presence of the CFRP layer. The first arrival wave packet was
S19

presumed to be caused by background noise. This result indicated that an AE propagation in
type-III vessels becomes complicated due to the presence of the CFRP layers, thus it isexpected that a highly accurate source location is difficult.
2.2 Characteristics of AE waveform originating from different layers

Further, the micro cracks occurring in the vessel were simulated with an artificial AE
source and the classification of the AE signal was attempted by using a specimen simulating asmall type-III vessel. The experimental setup is exhibited on the left in Fig.5. An opening wasprovided in the central part of the vessel, and the artificial AE signals were excited by irradiating
the cross-section with a pulse YAG laser. The pulsed laser simulated a mode-I type destruction
by point focusing at each layer with a power of 1.47 mJ. The pulsed laser was also used for
simulation of a delamination by line focusing at the interface between the aluminum liner andthe CFRP layer. In addition, as exhibited on the right in Fig. 5, the sensor position was changed
from 25 mm to 125 mm, while the fiber orientation direction of the hoop layer was set to 0 ° and
was increased to 90 ° at 15 ° intervals.
Pre Amplifier (40dB) andHigh-pass filter (10kHz)

Oscilloscope

AE Sensor(PAC, R15α)

0°

15°

Fiber direction of
Hoop Winding

125mm

30°

Trigger

45°

100mm
75mm

60°

φ100mmPulse YAG laser(Energy 1.47 mJ)

50mm

75°

25mm
90°
Pulse
Laser

350mm

45mm

50mm

Fig.5 Experimental setup for detecting artificial AE signals propagated in a small type-III vessel

0

Vp.p

−2

0

100

2

0

Vp.p
−2

200

0

Resonance frequency
150 kHz

Aluminum
S0 modeAluminumA0 mode

(b)

(c)

5

0

Vp.p

−2

100

200

0

Time [μs]Time [μs]

(a)

(b)

Output [V]2

Output [V](a)Output [V]Output [V]2

Resonance frequency
150 kHz

100

0

Vp.p

−5

200

0

Time [μs]

(c)

(d)

100

200

Time [μs]Resonance frequency
150 kHz(d)Resonance frequency
150 kHz

Aluminum
S0 modeAluminum
S0 modeAluminum
S0 modeAluminumA0 modeAluminumA0 modeAluminumA0 modeFig.6 Waveforms and their wavelet contour maps produced at different AE sources

S20

Fig. 6 shows the waveforms and the corresponding wavelet contour maps of each AE
source at the propagation angle of 0 ° and a distance of 100 mm. The AE waveform originatingfrom the aluminum liner had low frequency components. However, it was found that comparedto the waveform originating from the aluminum liner, the waveform originating from using thehoop winding as the AE source had higher frequency components. In addition, the waveforms
having the characteristics of both an aluminum liner and a CFRP layer as an AE source tended
to be detected in a sound source whose interface simulated delamination. They were thus, used
as an AE source. Fig. 7 shows the relationship between the peak amplitude of the AE waveform
originating from the aluminum liner and from the delamination, with the propagation angle at 0°
and 90°. It was found that regardless of the angle and the propagation distance, the peakvoltage of the waveform simulating the delamination tended to be larger than the peak voltageof the aluminum liner used as the AE source. By using these features, there is a possibility of
extracting the AE caused by the fatigue cracks in an aluminum alloy. However, since theattenuation of the AE signal may vary greatly depending on the angle, there is a possibility thata large number of sensors may be required when monitoring a large vessel.
100

100

(a)

(b)

50

Aluminum LinerVpp [V]Vpp [V]

Delamination50

Aluminum Liner

Delamination

0

50

100

0

50

100

Distance [mm]Distance [mm]Fig.7 Change in maximum amplitude as a function of sensor distance at angle at(a) 0°and (b) 90°

3. DEVELOPMENT OF SMART VESSEL BY OPTICAL FIBER AE SENSOR
Based on the results obtained so far, in order to quantitatively evaluate the AE signal
propagating through the vessel, it is considered that a system capable of measuring a wide
range area is necessary. Therefore, we have proposed a system that winds the optical fiber
sensor around the outer circumference of the vessel and constructs a smart vessel using theentire vessel as a sensor, thus monitoring the entire vessel. Figure 8 shows the experimental
equipment diagram. The optical fiber sensor was wrapped around the hoop winding part locatedat the outer surface of the small vessel used in the previous section. A pseudo AE signal was
excited using a pulse YAG laser with an energy of 3.25 mJ. Each sensor was positioned at 60mm from the sound source. Figure 9 shows the detected waveform and the frequency spectrumof the PZT sensor and the optical fiber sensor. The difference between the Signal-to-noise (SN)ratio of the optical fiber sensor and the PZT sensor is small, and it is expected that the AE due
to the fatigue crack propagation can be detected. Thus, by laying this sensor over a wide area, it
is possible to monitor the entire vessel.

S21

PZT sensor

Sensing optical fiber
60mm

φ100mm

60mm

Pulse YAG laser(Energy 3.25 mJ)
356mm
Optical fiber sensor

Fig.8 Experimental setup for comparing artificial AE signals
detected by PZT sensor and optical fiber sensor
1.5

PZT

Output [V]Output [V]

1.5

0

−1.5
0
−6

0

−1.5
0

1.5

×10

250

−6

0.5
1
Time [ms]1.5100Frequency [kHz]

200

×10

FFT coefficient

FFT coefficient

250

0.5
1
Time [ms]Optical fiber sensor

0
0

100
Frequency [kHz]

200

0
0

Fig.9 Waveforms and their frequency spectra detected by optical fiber sensor and PZT sensor

4. CONCLUSION
A damage monitoring method for a type-III hydrogen pressure vessel was developed byusing the AE method. The detection of the source location disregarding he anisotropy of the
propagation velocity was difficult. The extraction of the AE signal that was excited in thealuminum alloy layer was successful by using the characteristics of the AE waveform. An opticalfiber sensor was then employed for wide area monitoring. Artificial AE signals could be detectedwith a constant and high signal-to-noise ratio when the optical fiber sensor was wrapped overthe vessel.
Reference[1] S.W.Kim et., al., Proc. ASME PVP Conf., No. PVP2018-84188, pp. V03BT03A002,(2018)[2] Y. Mizutani，et. al., NDT&E International，Vol. 33, (2000), pp. 101-110

S22

Noise Tolerant and Wireless AE Measurement System
for Process Monitoring
Kaita Itoa, Kazuki Takahashib, Manabu EnokibaNational Institute for Materials Science; bThe University of Tokyo
ABSTRACT

Process monitoring is one of the important applications of acoustic emission (AE) method.
During manufacturing process of materials and structures, microscopic defects affect theirperformance and reliability in some cases. AE method is an effective in-situ NDE technology for
detection of occurrence of such defects. However, there are two problems here. Firstly, AE
process monitoring is often disturbed by strong noise. Secondly, cables installation for AE
measurement is often forbidden due to operational reason at the site, or it is impossible when
the sensors are placed in sealed or rotating parts. The first problem can be suppressed by AE
streaming with digital noise filter in the frequency domain. And the second problem can be
avoided by wireless measurement. In order to solve both problems simultaneously, a wirelessAE measurement system with streaming support was developed in this study. Two channels of
streams with about 4 MHz of sampling frequency is available by a battery-powered computer
board. Acquired waveforms are immediately transmitted to a high performance remotecomputer via Wi-Fi to do heavy processing for noise reduction and event detection. The noiselevel in the acquired waveform was lower than that of the conventional wired system because
the wireless computer board was electrically insulated from the external power line. Furthermore,
the new wireless system can use the same noise filter with heavy calculation as theconventional wired system. As a result, the new system enabled high effectiveness for processmonitoring by high noise tolerance and wireless stream acquisition.
Keywords: AE streaming, Process monitoring, Wireless monitoring, Noise tolerance
1. INTRODUCTION
Monitoring of materials and structures manufacturing process is an important application of
acoustic emission (AE) method because microscopic defects during the process may affect theirperformance and reliability. However, it is a difficult target for AE monitoring due to heavy
vibrations and/or electrical noise from the processing equipment. Under such noisy environment,
it is known that AE streaming i.e. continuous waveform recording with digital noise filter is aneffective solution. Then, the authors have developed an AE measurement system “Continuous
Wave Memory” (CWM) that focused on AE streaming with various analysis functions. [1-2]
However, there is another practical problem at the actual industrial sites. Cabling of powerand communication lines between the sensors, preamplifiers and the main unit of the AE
measurement equipment is often difficult or troublesome at the site. On the contrary, typical
wireless AE measurement systems do not support streaming because of limited computing
performance and wireless communication bandwidth. Therefore, a novel wireless and batterydriven AE streaming server was developed in this research. In this system, the conventional
CWM does not directly connect the sensor but works as a client that receives the AE streams
wirelessly transmitted from the server. Therefore, the full set of function of noise filters andwaveform analyses of CWM is available in this new system.

9th International Conference on Acoustic Emission

S23

2. DEVELOPMENT
2.1 Realization of wireless AE streaming
AE streaming requires the ability to process large amount of waveform data continuously.
The minimum required sampling frequency for AE streaming is 2 MHz because the typicalhighest frequency component in AE wave is about 1 MHz. One sampling data is normally 2
bytes because the typical resolution of analog-to-digital converter (ADC) is between 10 and 16
bits. Therefore, the minimum required data rate of one channel AE stream is 4 MB/s.
Considering the performance of the current battery-driven small computer board, only waveform
acquisition and simple processing such as event detection by threshold crossing are available atthis data rate. It is impossible to perform a heavy processing like a digital noise filter infrequency domain. On the contrary, wireless transmission of AE streams is available for thelatest high speed Wi-Fi (IEEE 802.11ac).
Therefore, the wireless CWM in this study, the battery-driven part works as a simple AE
streaming server i.e. the acquired waveforms are directly sent wirelessly. All of the waveform
analysis is done in the client, which is a high performance computer connected to the power
supply. Figure 1 shows the role sharing of the server and client parts of the wireless CWM.

AE sensor

ServerWireless
AE streaming

Acquisition

Battery

ClientRecordingAnalysis
 Noise reduction
 Event detection
 Event locationPowersupplyFigure 1: Role of the server and client of the wireless CWM.
2.2 AE stream server
An IoT (Internet of Things) board STEMlab 125-14 (Red Pitaya team) was adopted as the
mainboard of the server. A simple preamplifier subboard can be attached to the mainboard.
Figure 2 shows the data and control flow in the server side. Two sensors can be connected tothe server. The range of input voltage is selectable between ± 1 V and ± 20 V and the resolutionof ADC is 14 bits. Due to the narrow voltage range with high resolution sampling and low noiselevel of the on-board circuit, accurate waveform acquisition is possible even when thepreamplifier is not used. The practical maximum frequency of STEMlab 125-14 for streaming i.e.
continuous waveform acquisition without any data loss was confirmed as 3.9 MHz by apreliminary experiment. That is, the raw signal of 125 MHz sampling from the ADC was divided
by 32 in the FPGA (Field Programmable Gate Array). The acquired continuous waveforms were
written in a microSDXC memory card with excellent continuous write performance. The server
supports NFS (Network file system) and the client connects to the NFS server and downloads
the streams data. It is not described in the official manual, but since the STEMlab 125-14
supports the microSDXC memory, hours of waveforms could be cached in the server.
The footprint of the server was 107 mm × 60 mm and the weight excluding the battery was
about 100 g. Since the power consumption during operation was about 3.8 W, the server could
operate for several hours with a small Li-ion battery. Consequently, a wireless AE streaming
server with compact size, light weight and sufficient operating time was developed successfully.

S24

Subboard

MainboardCPU(STEMlab 125-14)

Controlling

Controlling

AE sensors

16 MB/s

500 MB/s

Preamps

ADC(Optional)

Analog(125 MHz, 2 ch)

FPGA(3.9 MHz, 2 ch)

Controlling

Wi-Fi
adaptermicroSDXC

Controlling

IEEE802.11ac

Figure 2: Block diagram of the server part of the wireless CWM.
2.3 AE stream client
The conventional wired CWM could be used as the client after a small implementation ofserver control commands for configuration, starting and stopping of streaming. The full set ofanalysis functions of the conventional CWM which includes digital noise reduction, event
detection and event location is available in the new wireless CWM system.
Various forms of the clients are available. The simplest one is a local client which
communicates with the server via Wi-Fi and analyzing the continuous waveforms (see Figure3(a)). On the contrary, a remote client is also available if there is a broadband router. The
remote client can be built on a virtual machine in data center (see Figure 3(b)). In both cases,
visualization and downloading of the analysis results is available by a local or remote tablet or
laptop computer via Wi-Fi and 4G network since the required traffic is sufficiently small.

(a)

(b)

Wi-Fi

Wi-Fi

Local Client
Wireless
network
adapter

Waveforms

Waveforms

CPU
Results /Remote screen

Internet

RouterResults /Remote screen

Storage

Wi-Fi or 4G

Wi-Fi or 4G

Visualization(Local or remote)

Visualization(Local or remote)

Remote Client
RouterCPU
Storage

Figure 3: Block diagram of the server part of the wireless CWM.
3. VERIFICATION EXPERIMENT
3.1 Noise in actual AE measurement
In order to confirm the performance of wireless AE streaming and noise tolerance, wireless
CWM was tested in an actual materials manufacturing process. In the verification experiment,
two flat plates of magnesium alloy with 2.0 mm thick were joined by friction stir welding (FSW)
process. During the process, AE was monitored by the wireless CWM system. The conventional
wired CWM was also used for comparison. Heat resistant AE sensors (type AE254SMH177 by
Fuji Ceramics) were placed around the welding tool. Channels 1 and 2 were connected to thewireless CWM directly and channels 3 and 4 were connected to the wired CWM via 20 dB

S25

amplifier. A high-pass filter with a cutoff frequency of 100 kHz was applied to the acquired AE
streams to eliminate low frequency noise due to vibration.
Waveforms of typical AE events are shown in Figure 4. There was a large difference innoise level between the two. In the wired channels, electrical noise was likely to affect the ADC
because the all experimental equipment i.e. sensors, preamplifiers and the FSW machine waselectrically connected via the power line to the ADC in the wired CWM. The oscillation waveformof the piezoelectric sensors by an electric pulse is difficult to distinguish from the usual burst
type AE event due to microfracture in the material. On the other hand, electrical noise was notfound in the wireless channels since there was no electrical path through the streaming server.

(b)

Ch.1
Ch.2

0.5

Voltage / V

Voltage / V(a) 1.0

0.0
-0.5
-1.0
-2

0

2
4
Time / ms

6

8
4
0
-4
-8
-2

8

Ch.3
Ch.4

0

2
4
Time / ms

6

8

Figure 4: Typical waveform of an AE event recorded by (a) wireless CWM and (b) wired CWM.
4. CONCLUSION

A novel wireless AE measurement system was developed with streaming support. Twochannels of streaming are available with about 4 MHz of sampling frequency by a batterypowered server and high speed Wi-Fi.

The client as analyzer of the AE stream can use not only a local computer but also a remotevirtual computer. Visualization and downloading of the analysis results is available by alocal or remote tablet or laptop computer via Wi-Fi or 4G network.

Electrical noise was not found in the streams by wireless measurement since there was noelectrical path through the battery-powered wireless streaming server.
ACKNOWLEDGEMENT

This research was supported by JSPS KAKENHI (Grant-in-Aid for scientific research) No.
17K14820. Also, the authors are grateful to Dr. Eitaro Yukutake of Industrial Technology
Innovation Center of Ibaraki Prefecture.
REFERENCES[1] Ito, K., Enoki, M., “Acquisition and Analysis of Continuous Acoustic Emission Waveform forClassification of Damage Sources in Ceramic Fiber Mat”, Materials Transactions, 48 (2007)
1221-1226.
[2] Ito, K., Enoki, M., “Analysis of High Active AE Signal by Continuous Waveform Recorder”,
Key Engineering Materials, 353-358 (2007) 2399-2402.

S26

Sensing and clustering analysis of acoustic emission due tocrack rubbing/clapping in fatigue-cracked thin metal sheets
Roshan Joseph, Victor Giurgiutiu
Department of Mechanical Engineering, University of South Carolina, Columbia, USA
ABSTRACT
Fatigue cracks in metallic structures can cause catastrophic failure to the structure if it is notdetected during its early stage. Rubbing and clapping of crack faying surfaces are a potential
source for acoustic emission (AE) signals. The vibration of fatigue cracked sheet metal samples
were used to induce rubbing and clapping of crack faying surfaces to generate AE signals. The
AE signals generated were captured by multiple piezoelectric wafer active sensors (PWAS)
bonded to the sheet metal sample in a linear configuration, and source localization was performed
to confirm the origin of the AE signals. In some practical cases, the structure would be subjected
to external loading while undergoing crack rubbing and AE generation. The sheet metal sample
was first axially loaded, then rubbing/clapping was induced through the vibration of the specimen
and AE signals were recorded to study the influence of external loading on the AE signals. A large
number of AE signals was captured through various cases of AE experiments. To understand theconsistency or similarity of the signals, it is necessary to perform a statistical analysis of these AE
signals. Pearson correlation was performed for all AE signals in the time domain as well as in thefrequency domain to understand the similarity of AE signals. A 2D plot of time domain as well as
frequency domain correlation was obtained to depict the similarity in the AE signals.
Keywords: Acoustic emission, AE signal processing, Crack rubbing AE, Correlation of AE signals
1

INTRODUCTION

Acoustic emissions (AEs) are the stress waves produced by the sudden internal stressredistribution of the materials caused by the change in internal structure possibly due to crack
initiation and growth, dislocation movement, twinning, phase transformation in monolithic
materials, fiber breakage, fiber-matrix deboning in composites, etc.[1]. AE signal characteristic isstrongly influenced by the source mechanism of the AE. The mechanism of the source issignificantly influenced by the composition of the material[2]–[5]. Many researchers studied AE
due to various kinds of sources. Wang et al.[6] performed acoustic emission due to rubbing in arotor-bearing and source localization. Acoustic emission due to progressive damage in a polymerbased composite[7] as well as clustering of AE signals obtained from failure on carbon fiber
reinforced plastic (CFRP) specimens was studied[8]. Acoustic emission during various fracture
activities was studied to relate the fracture and AE signals [9].
Acoustic emission during fatigue a crack growth event attracted many researchers. The detectionof fatigue crack growth can prevent catastrophic failure of structures. Many researchers havestudied the AE due to fatigue crack growth[10]–[13]. Zhang et al.[14] studied the acoustic
emission signatures of fatigue damages in an idealized bevel gear spline and identified two
different AE signal signatures for plastic deformation and crack jump. Bhuiyan et al. [15]–[17]studied the AE signal signatures recorded by PWAS sensors during a fatigue crack growthexperiment. Roberts and Talebzadeh [10] discussed the correlation between acoustic emission
count rates and crack propagation rates.
The AE signals are generated when a crack growth in metallic structures occurs and several
studies have been reported on this, similar to the works presented in the previous paragraph. It

S27

is not obvious that the crack will be always growing and producing the AE signals. If the fatigue
loading on the structure is not enough for the crack advancement, the crack may not grow. But,
any kind of vibration or fatigue loads which do not cause any crack propagation can produce AE
signals when the crack surfaces rub and clap each other. No research works have been reported
yet to study the signals generated due to rubbing and clapping of crack faying surfaces of thinmetallic plates. This paper discusses AE signal signatures when the faying surfaces of crack rub
and clap each other.
The organization of the paper is as follows. Section 2 discusses the experimental setup andmethods. The experimental set up for generating the fatigue crack in a 1-mm aluminum plate andthe experimental set up for measuring AE signals due to rubbing and clapping of crack faying
surfaces are explained in this section. In section 3, the results of Finite Element Method (FEM)
analysis of the specimen, as well as the AE signal signatures due to rubbing and clapping of crack
faying surface, have been discussed. The paper ends with a summary, conclusions, and futurework.
2

EXPERIMENTAL SET-UP AND METHODS

2.1 Experimental setups
Aluminum 2024-T3 specimen was chosen for manufacturing the test specimen material. The
material properties of the specimen were 73 GPa modulus of elasticity, 2767 kg/m3density, and0.33 Poissons ratio. The aluminum coupons manufactured had dimensions 103 mm width, 305
mm length and 1 mm thickness. 1 mm hole was drilled at the geometric center of the specimen
for initiating the fatigue crack. The hole would cause stress concentration at the edges of the hole,and the crack initiation would happen at the hole. Fatigue loading was applied to the specimen to
generate the pre-crack. For the pre-crack generation fatigue loading from 22 kN to 2.2 kN at afrequency of 4 Hz was applied. Crack initiation started at approximately 40,600 cycles. The crackgrew 20 mm in 400 cycles. After generating the crack in the specimen, four PWAS sensors werebonded on the specimen (Figure 3). One PWAS was bonded very close to the crack at a distanceof 6 mm from the crack. This PWAS was bonded at such a close distance so that it can pick upeven the weak AE signals originating from the crack. Two other PWAS were bonded at 25 mm
distance from the crack in opposite directions, and another PWAS was bonded at 50 mm fromthe crack. The PWAS bonding configuration, the time of arrival, and amplitude of the signals
reaching these PWAS would confirm the AE signal source and geometric spreading of the AE
signals.
The experimental setup for generating crack rubbing and clapping in the specimen is presented
in Figure 2. For AE generation due to crack rubbing, the fatigue crack grown plate specimen was
mounted on a shaker. Continuous sinusoidal excitation was given to the specimen at various
frequencies through the shaker. The vibration of the specimen causes the faying surfaces of thecrack to rub/clap each other. This rubbing and clapping produce acoustic emission signals.

S28

Figure 1 The specimen with hole mounted on the MTS machine for the generation of crack. Cyclic
fatigue loading was applied to generate crack from the holeVibration applicator
25 mmArragnement of PWAS
on the specien

PWAS 1

Function generator

Crack

6 mm

25 mm

50 mm

PWAS 2PWAS 3

PWAS 4

Distance from crack

B&K power amplifier
Shaker attached withsheet metal sample

Mistras AE system

Acoustic pre- amplifier

Figure 2 The experimental set up for generation of vibration-induced crack rubbing/clapping AE
signals. The sinusoidal excitation for vibrating the specimen was generated byfunction generator. Sufficient power for the vibrating the specimen using vibration

S29

exciter was obtained from the power amplifier. Vibration generated AE signals were
captured by using the Mistras AE system.

pre-amplifierBand pass filter(30-700 kHz)

Mistras AE instrument

Turn buckle
Specimen in
the test cell

ShakerFunction
generator

B&K power amplifier

Figure 3 AE measurement test setup in the test cell. Vibration motion is applied to the specimen
in the test cell through a shaker. AE signals collected through PWAS bonded to thespecimen was passed through pre-amplifier and recorded using Mistras AE system
For the generation of continuous vibration, a function generator was used. For obtaining sufficient
voltage of excitation, the signal is amplified using a power amplifier. The amplified sinusoidal
signals are fed to a vibration exciter.
The shaker vibrates the plate in the out-of-plane direction causing rubbing/clapping of crack faying
surfaces and generation of AE signal due to the rubbing/clapping. The PWAS installed on the

S30

specimen sensed AE hits. Four identical preamplifiers with a built-in bandpass filter (30- 700 kHz)
were connected to the sensors. The preamplifier is connected to Mistras AE system. A sampling
frequency of 10 MHz was chosen to capture any high-frequency AE signals. AE hits at plate
resonance were captured and analyzed. At plate resonances, the possibility of crack rubbing, as
well as the threshold of AE signals, are higher, which makes it suitable for AE signal collection
and analysis.
In some cases, the cracked specimen may be subjected to external loading. In such cases, thecrack would be in an open position. Still, the crack rubbing at the crack tips may induce AE signals.
AE signal characteristics in such circumstances need to be studied. A test cell test setup for
simultaneously providing axial load and vibrating the specimen was manufactured in house. In
Figure 3, a schematic of the setup of the test cell and its important features for mounting AE test
specimen and providing axial load is presented. The test cell can arrest two edges of the specimen
securely and load the specimen axially through spring loading. The specimen was connected tothe spring through a turnbuckle as presented in Figure 3. By using the loading bars, the turnbuckle
was turned. Turning the turnbuckle caused extension of the springs and provides thecorresponding load to the specimen. The load can be calculated from the spring deflection andthe spring constant of the spring. The spring deflection was measured from a knob provided at
the spring plate by monitoring its displacement through a measuring scale. After loading thespecimen to a specific load, vibration motion was given to the specimen through the shaker
attached to it. The AE instrumentation for recording the AE signals is also presented in Figure 3.
The AE signals generated by crack rubbing and clapping of the loaded specimen were recorded
using a PWAS located 6 mm away from the crack. The signals were fed to the acoustic preamplifier with a built-in bandpass filter (30- 700 kHz) and then fed to Mistras AE instrument.
Recorded AE signals were post-processed and analyzed.
2.2 Pearson correlation coefficient (PCC) of two signals
A large number of AE signals are generated due to rubbing and clapping of crack faying surfaces.
Pearson correlation analysis was performed to compare the large number of AE signals. The
correlation coefficients of two-time series/frequency series data are calculated using the following
PCC expression.

 ( A, B) 

1 N  Ai   A  Bi  B N  1 i 1   A   B (1)

Where, Ai=time domain signal A, Bi=time domain signal B, µA= mean of signal A, µB= mean ofsignal B, σA= standard deviation of signal A, and σB= standard deviation of signal B. Two separate
correlation coefficients were calculated, using time domain as well as frequency domain data. 2D
scatter plots of time domain PCC vs. frequency domain PCC were constructed.
3

RESULTS AND DISCUSSION

For generating strong AE signals during rubbing and clapping of crack surfaces, there must be
active interaction between the crack faying surfaces. The resonant frequencies of specimen
vibration are appropriate for the generation of strong displacements in the specimens, which cancause active interaction between the crack faying surfaces. FEM modal analysis of the cracked
specimen was performed to find out the resonant frequencies of the specimen. The specimen
modeled and boundary conditions imposed to the specimen for performing FEM modal analysis
are presented in Figure 4. The fixed boundary condition is applied at the support hole of the

S31specimen which was used to connect the shaker attachment to the specimen by arresting all
degrees of freedom. From FEM analysis, the first five resonance frequencies of the specimen
were recognized as 28.2, 37.8, 93.1, 112.3 and 188.4 Hz respectively. It was presumed andobserved that this resonant frequencies and corresponding strong displacement causes strong
crack faying surface motions which generated strong AE signal. We observed strong AE signal
generation at 35 Hz and 180 Hz frequency of vibration of the specimen.
101 mmFixed boundary
condition

304 mm

20 mm crack (tip-to-tip length)
1-mm center hole

4 mm holeat center

6 mm

Pinned boundary
condition
Figure 4 The specimen and boundary conditions given for performing modal analysis of specimen
attached to shaker. Fixed boundary conditions are given at the support holecorresponding to the vibration shaker support.

Vibration excitation was given to the specimen at 35 Hz by using a shaker, and the vibrationinduced the crack faying surface rubbing and clapping. The induced rubbing and clapping of thecrack faying surfaces cause the excitation and propagation of AE signals in the specimen. The
signals were sensed using PWAS sensors bonded 6-mm from the crack. The PWAS sensed AE
signals were recorded using the Mistras AE system. A large number of AE signals were recorded
during a short period of time. The signals collected were used to perform a correlation analysis
by choosing a particular signal as the basis signal. The correlation analysis shows that the signals
are similar. The correlation of AE signals and one typical AE signal collected is presented in Figure
5a and Figure 5b. The AE signal signature is observed to be wideband. Peaks in the frequency
spectrum were observed at 100 kHz. At frequency spectrum between 300- 400 kHz, a peak in
the frequency spectrum by gradual increment and decrement was also observed. The differencesin signals are due to the uncertainty in AE source characteristic at the crack faying surfaces
corresponding to various events. The minor difference in AE signal infers that, even though there
is uncertainty in AE source characteristic which generates the AE signals, the source
characteristics are within a tolerance limit.

S32

To study the effect of the frequency of vibration of the specimen on the AE signal, anotherfrequency of vibration of the specimen was also considered. AE signals received by PWAS when
the plate is vibrated at 180 Hz frequency is presented in Figure 5c and Figure 5d. We observethat the frequency spectrum of the AE signals is wideband frequency spectra. At 180 Hz vibrationof the specimen, a peak frequency of the AE signal spectra approximately at 100 kHz was
observed similar to AE signal recorded at 35 Hz vibration. A gradual increase and decrease of
frequency spectrum between 300- 400 kHz were also observed. AE signals were observed tohave a similar signature.

35 Hz

b

a
180 HzacdFigure 5 AE signals generated due to vibration of specimen at various frequencies a) Correlationof AE signals generated due to the 35 Hz specimen vibration induced crack
rubbing/clapping b) Sample AE signal at 35 Hz vibration c) Correlation of AE signals

S33

generated due to the 180 Hz specimen vibration induced crack rubbing/clapping d)
Sample AE signal at 180 Hz vibration

The crack faying surfaces formed during the fatigue experiment are very rough in texture withsharp peaks and valleys. The vibration of the specimen causes the sharp peaks and valleys ofthe crack faying surfaces to hit each other and generate AE signals. Hence the AE signal
generation source can be considered as a point force excitation source or a point source. AE
signals are generated by rubbing of crack faying surfaces at a point. Even at different frequencies
the source characteristic or the mechanism of ultrasonic AE signal generation may remain thesame since it is a point source excitation. Hence theoretically, it is not very likely that the AE signal
signature may differ at different vibration exciter frequencies. A comparison of the AE signal
signature at 35 Hz vibration and 180 Hz vibration can be observed from Figure 5b and Figure 5d.
Clear similarities in AE signals and signal frequency spectrums are observed in the figure. The
AE signal rise time, as well as the duration of the signal, are observed as approximately the same.
Frequency spectrums of both signals were found to be very similar to broadband nature and tohave very similar peaks and valleys.
To study the effect of external load on the rubbing/clapping AE signal signature the specimen was
mounted on the test cell, axially loaded and vibration excitation was applied. An axial load of 5kN
is applied to the specimen by loading the specimen by turning the turnbuckle and applying thespring load. Four springs are arranged parallel to each other as presented in Figure 12. The
effective spring constant of the springs is 437 N/mm. The spring loading is provided by turning
the turnbuckle between the specimen and the spring. The turnbuckle was turned so that the spring
deforms 11.5 mm, providing 5 kN load to the specimen. The extension of the spring monitors theeffective load through a needle attached near the spring on the spring plate. After loading thespecimen, the specimen was vibrated by the vibration exciter. A higher voltage of excitation was
required to produce AE signals compared to specimen mounted in the test cell without any load.
The AE signals were collected and the correlation of the AE signals is presented in Figure 14.
The signals were found comparable to the AE signals obtained with the no-load case as well as
the AE signal generated due to the unconstrained specimen vibration case. The signal isobserved as wideband in frequency spectra with a higher amplitude at low-frequency range.
Signals had a peak in the frequency spectrum at approximately 100 kHz. A peak in the frequency
spectrum is observed between 300 kHz and 400 kHz. But the frequency response between 100
to 200 kHz has been reduced. The major frequency content is observed below 100 kHz, similar
to the case of unloaded specimen rubbing and clapping AE signals. The signal correlation is foundto be stronger compared to the case of no-load. The reason for such a strong correlation ishypothesized as follows. When the specimen is in a loaded condition, the crack will be in an open
position. The vibration exciter causes relative motion of crack faying surfaces, but the rubbing
between the two surfaces takes place only at the crack tips since all the other location of the crack
is in open position. Hence the point source excitation will be happening only at the crack tips, andthe excitation will be more consistent compared to the excitation when the crack is in the closed
position.

S34

Specimen loaded 5 kNba

Figure 6 AE signal generated due to the vibration of the specimen under axial tension a)
Correlation of AE signals generated due to the vibration induced crack
rubbing/clapping b) Sample AE signal at 35 Hz vibration
4

SUMMARY CONCLUSIONS AND FUTURE WORK

4.1 Summary
A Fatigue crack in Al 2024-T3 specimen was generated by cyclic fatigue loading. Fatigue loadingof 22 kN with an R ratio of 0.1 was applied for the generation of a fatigue crack. The fatigue
loading was continued until the tip to tip crack length is 20 mm. FEM harmonic analysis was
performed to analyze the resonant frequencies of the cracked specimen. A PWAS sensor was
installed on the specimen at a distance of 5-mm from the crack. The specimen was attached to a
shaker table, and vibration excitation was applied at the resonant frequencies of the specimen to
generate AE signals due to crack rubbing/clapping. The AE signals generated were recorded
using the PWAS and Mistras AE system. Correlation analysis was performed on the signals to
identify the change in AE signals and variation in the asperity of the AE signal source. AE signals
at 35 Hz and 180 Hz specimen vibrations were studied. AE signals in the specimen under load
are also studied

4.2 Conclusions
Resonant frequencies of the specimen are suitable for active rubbing and clapping of the crack
faying surfaces. Specimen resonant frequencies do not affect the AE signal signature strongly.
Correlation analysis can conveniently quantify the difference in a large number of AE signals. The
difference in AE signal signature would correspond to the difference in the AE source
characteristics. Close clustering of AE signals is obtained when the vibration AE was generated
in an axially loaded specimen. When the crack is in a closed position the rubbing/clapping
happens throughout the crack length. This will cause more change in the asperity of the AE signal
source as well as AE signal generated from the source. When the crack is in open position, only
the crack tips participate in the rubbing/clapping. This will cause less change in asperity of the AE

S35

signal source as well as AE signals generated from the sources, resulting in a close correlationof AE signals.
5

REFERENCES[1] M. Huang., L. Jiang., P. . Liaw., C. . Brooks., R. Seeley., and L. . Klarstrom., “Using Acoustic
Emission in Fatigue and Fracture Materials Research,” Mater. Res., vol. 50, pp. 1–13,
1998.
[2] T. Ohira and Y.-H. Pao, “Quantitative Characterization of Microcracking in A533B Steel by
Acoustic Emission,” Metall. Trans. A, vol. 20, no. June, pp. 1105–1114, 1989.
[3] D. Healy, R. R. Jones, and R. E. Holdsworth, “Three-dimensional brittle shear fracturing bytensile crack interaction,” Nature, vol. 439, no. January, pp. 3–6, 2006.
[4] C. Kao, F. C. S. Carvalho, and J. F. Labuz, “Micromechanisms of fracture from acoustic
emission,” Int. J. Rock Mech. Min. Sci., vol. 48, no. 4, pp. 666–673, 2011.
[5] R. Joseph, M. Y. Bhuiyan, and V. Giurgiutiu, “Acoustic emission source modeling in a plateusing buried moment tensors,” Proc. SPIE (Health Monit. Struct. Biol. Syst., vol. 10170, no.
May, pp. 1017028-1–8, 2017.
[6] Q. Wang and F. Chu, “Experimental determination of the rubbing location by means of
acoustic emission and wavelet transform,” J. Sound Vib., vol. 248, no. 1, pp. 91–103, Nov.
2001.
[7] M. Bentahar and R. El Guerjouma, “Monitoring progressive damage in polymer-based
composite using nonlinear dynamics and acoustic emission,” J. Acoust. Soc. Am., vol. 125,
no. 1, p. EL39-EL44, 2009.
[8] M. G. R. Sause, A. Gribov, A. R. Unwin, and S. Horn, “Pattern recognition approach to identify
natural clusters of acoustic emission signals,” Pattern Recognit. Lett., vol. 33, no. 1, pp.
17–23, Jan. 2012.
[9] S. M. Cousland and C. M. Scala, “Acoustic emission during the plastic deformation ofaluminium alloys 2024 and 2124,” Mater. Sci. Eng., vol. 57, no. 1, pp. 23–29, Jan. 1983.
[10] T. M. Roberts and M. Talebzadeh, “Acoustic emission monitoring of fatigue crack
propagation,” J. Constr. Steel Res., vol. 59, no. 6, pp. 695–712, Jun. 2003.
[11] T. M. Morton, R. M. Harrington, J. G. Bjeletich, L. Palo, and P. Alto, “Acoustic emissions offatigue crack growth,” vol. 5, pp. 691–697, 1973.
[12] S. Deschanel, W. Ben Rhouma, and J. Weiss, “Acoustic emission multiplets as early
warnings of fatigue failure in metallic materials,” Sci. Rep., no. September, pp. 1–10, 2017.
[13] T. M. Roberts and M. Talebzadeh, “Fatigue life prediction based on crack propagation andacoustic emission count rates,” J. Constr. Steel Res., vol. 59, no. 6, pp. 679–694, Jun.
2003.
[14] L. Zhang, D. Ozevin, W. Hardman, and A. Timmons, “Acoustic Emission Signatures of
Fatigue Damage in Idealized Bevel Gear Spline for Localized Sensing,” Metals (Basel).,
2017.
[15] M. Y. Bhuiyan and V. Giurgiutiu, “The signatures of acoustic emission waveforms fromfatigue crack advancing in thin metallic plates,” Smart Mater. Struct., vol. 27, no. 1, p.
15019, 2018.
[16] Y. Bhuiyan, J. Bao, and B. Poddar, “Toward identifying crack-length-related resonances inacoustic emission waveforms for structural health monitoring applications,” 2018.
[17] M. Y. Bhuiyan and V. Giurgiutiu, “Experimental and Computational Analysis of Acoustic
Emission Waveforms for SHM applications,” in 11th International Workshop on Structural
Health Monitoring, 2017, pp. 1–7.

S36

Progress in Sensitivity Verification of AE Sensors
Kanji Ono
Department of Materials Science and Engineering, University of California, Los Angeles(UCLA), Los Angeles, CA 90095 United States
ABSTRACT
Recent progress in AE sensor characterization studies is summarized. These efforts point topractical approaches for the calibration of receiving sensitivities by directly contacting (face-toface) a reference transmitter and a sensor under test (SUT). This method is for normally incident
waves and the displacement or velocity of the reference transmitter is measured by a laser
technique, similarly to that prescribed by ISO16063-11 for accelerometer calibration. As thereference transmitter, a heavily damped ultrasonic transducer, available from multiple sources, isused. The transmission sensitivities by a laser interferometer and a laser vibrometer showed agood agreement, so either can be used. By coupling an SUT in a controlled manner, its receiving
sensitivity is determined. Over thirty models of AE sensors have been characterized using thepresent method and their receiving sensitivities found to be consistent with those from the HillAdams method, a variation of reciprocity techniques. When one obtains a reference transducer
with its transmission sensitivity characterized, the remaining parts of calibration procedures canbe conducted even outside of metrology laboratory. That is, a practical sensitivity verification ispossible with a traceability to laser-based measurements of displacement or velocity. A variationof the face-to-face method is to insert a buffer medium between a reference transmitter and an
SUT. This is inappropriate because of diffraction loss, which is dependent on SUT size, frequency
and buffer thickness. A parallel method is available for the detection of plate (Lamb) waves andbar waves, again starting from laser-based measurements. It is suggested that a cooperative
effort is undertaken involving multiple organizations to establish national/international standards
for sensitivity verification of AE sensors.
Keywords: AE sensors, laser interferometer, vibrometer, wave modes, sensitivity verification.
1. INTRODUCTION
Efforts to standardize calibration methods for AE sensors have a long history, going back to theseismic pulse method of Breckenridge et al. (1). This seminal work led to two ASTM standards(2,3) for surface wave sensing (also being listed in ISO). These and other early works can befound in (4). At sensor manufacturers, practical sensor characterization has been conducted bydirect contact (face-to-face) methods using a reference transducer that was calibrated byhydrophone replacement procedures (5,6). This approach is for normally incident longitudinal
waves only and has recently been updated by the use of laser metrology methods (6-9). Laserinterferometer or vibrometer can measure surface displacement or velocity without water
immersion needed in the hydrophone calibration. It can also obtain spatial variation of thedisplacement or velocity over the reference transducer. The laser-based calibration methods wereextended to guided wave sensing (10,11). Another approach used is a variation of classicalreciprocity calibration methods developed for reciprocal transducers. This method, proposed by
Hatano and coworkers (12,13), used three transducers and assumed all three to be reciprocal.
This reciprocity condition was shown to be absent for typical AE sensors, invalidating the Hatano
method (14). Hill and Adams (15) relaxed this condition for a sensor under test (SUT), but required
the ratio of transmission and receiving sensitivities of one of the three to be known, mandating

9th International Conference on Acoustic Emission

S37

laser-based measurements. This Hill-Adams version of the reciprocity calibration method
produced results in agreement with those of the laser-based direct contact methods (6,7).
Presently, three methods can provide AE sensor calibration. However, no national standards
agency offers the calibration service for the seismic pulse method, making it desirable for the twoother methods to be standardized. Of the two, the direct contact method is preferred since it isapplicable to guided wave cases and needs fewer sensor coupling per calibration. Below, thebasic calibration procedures are summarized, representative results are shown and finally items
needed to be included in a standardization document are listed.

2. CONTACT CALIBRATION METHODSFor the calibration with normally incident longitudinal waves, a well damped broadband ultrasonictransducer needs to be excited either by short pulses, continuous sinewaves of varying frequency(Chirp) or short-duration, stepped frequency sinewave trains. The surface displacement orvelocity of the transducer is measured using a laser interferometer or laser vibrometer. The input
voltage signals and laser device output are used to define transmission sensitivity. Next, an AE
SUT is coupled to the transmitter surface. The output from the SUT is obtained with the transmitter
excited in the same way as in the first step. The SUT receiving sensitivity can be determined using
the SUT output and the transmitter output since the input voltage spectra cancel out. The
computation procedures of both transmission and receiving sensitivities involve the frequency
domain spectra, utilizing fast Fourier transform (FFT). This procedure was described in (6,7).
For guided wave calibration, a suitable transfer medium is needed. Surface wave calibration
requires a large transfer medium and is excluded here. One needs a long bar with flat surface(s)or a large plate with a thickness similar to intended structures, to which an SUT is to be mounted.
On one end of the bar or plate, a broadband ultrasonic transducer needs to be coupled andexcited as in the normal incidence case. Select a position suitable for SUT installation, measuredisplacement or velocity of the bar or plate either by using a laser device or a calibrated pointcontact AE sensor. Next, install an SUT at the designated position and measure its output withthe transmitter excited in the same manner as in the initial step. The SUT receiving sensitivity canbe obtained by spectral division as above. This procedure was described in (10,11).

3. RESULTS
More than 50 sensors for AE application have been characterized using the two methods given
in Sec. 2 and results for 27 have been reported (6,7,10,11,14). For some, low frequency
responses were also reported and the origins of observed frequency dependence of receiving
sensitivities were discussed (7). Receiving sensitivities of four representative sensors are given
below. Each sensor’s responses for normally incident longitudinal waves are shown in part a) andfor guided waves in part b). The latter is an average of eight spectra corresponding to Lamb andbar waves, symmetric and asymmetric modes and two thicknesses. All of the results below wereobtained using pulse excitation of a broadband transmitter. Limited comparison between resultsof pulse and sinewave excitation (7) indicated good agreement between receiving sensitivities
from the two excitation methods.
Two wideband sensors in Fig. 1 represent a well damped smooth response (V103) and a multiple
resonance type (WD) that gives a series of high sensitivity peaks. Guided wave responses arecompletely different. Two resonant sensors show a series of high sensitivity peaks for normal
incidence case, but enhanced low frequency response to guided waves.

S38

Fig. 1 Receiving sensitivity in dB in reference to 0 dB at 1 V/nm for Olympus V103 (blue) andPhysical Acoustics WD (red). a) Normal incidence (data from ref. 6). b) Guided waves (data fromref. 11).

Fig. 2 Receiving sensitivity for Physical Acoustics R15a (blue) and Physical Acoustics R6a (red).
a) Normal incidence (data from ref. 6). b) Guided waves (data from ref. 11).

Fig. 3 a) Peak amplitude of KRN sensor output vs. position on the surface of Olympus V192
transmitter under pulse excitation. Dotted curve is a parabolic fit, slightly rising at the center.
Maximum and minimum peaks differed by 0.36 dB. b) Frequency spectra of received signals (0dB at 1 mV). At center (blue curve), at 5 mm (red), at 10 mm (green) at 18 mm (purple).
4. PREPARATION FOR STANDARDIZATION
4.1 Transmitter and its characterization
1. Use a broadband ultrasonic transducer having no major sensitivity dips within the intended
calibration frequency range. When one needs to cover up to 2 MHz, select a transducer with its
S39

nominal center frequency of 1 MHz or higher. Our experience suggests Olympus V192 transducer(1 MHz, 38-mm diameter) to be optimal with its smooth output spectrum and anti-resonance dip
above 2 MHz, although several others, notably Olympus V104, function similarly well (14).
2. The transmitting transducer must have its size larger than the size of SUT. Experience suggests
38-mm diameter (V192) gives most uniform displacement output, while 25-mm diameter (V104)
is satisfactory since most AE sensors have sensing areas of 13-mm diameter or less.
3. The use of laser metrology devices for accelerometer calibration is described in an international
standard document, ISO16063-11 (16). This can serve as a useful template for laser uses for AE
sensor calibration.
4. In the guided wave calibration, a transmitter is attached to a buffer medium of a bar or plate.
This can be with glue or by a mechanical holder, but re-calibration is needed after eachattachment.
5. The number and location of laser measurements need to be specified. In our interferometry
works using Thales SH-140 (6,7), positions have to be aligned manually and measurements were
mostly limited to the center point. The output uniformity was evaluated using a point-contact
sensor (KRN BBPCP). Up to 1 mm from the edge of active area of V192 transducer, theresponses differed less than ±0.18 dB, as shown in Fig. 3a. The spectral output was within ±1 dB
band (Fig. 3b). Thus, the center responses have been used in all our tests. Vallen reported on
the uniformity of the surface velocity output using a laser vibrometer (8,9). Many measurement
points allowed him to define the surface responses accurately, averaging on coaxial rings. For a
25-mm transducer, 145 points were used. For well-behaved transducers, the center point only
testing appears adequate, but further study should be made as to the optimal number ofmeasurements needed.
6. In using a pulse excitation method, a step voltage pulse should be avoided since it introduces
an artefact in FFT. An addition of a damping resistor of 5 to 50 ohms in parallel to the transducer
can keep the pulse short (less than several µs).
7. The level of excitation voltage applied on a transmitting transducer depends on the sensitivityof laser devices and on the safe limit from the transducer manufacturer. Peak voltages of pulses
used have been under 400 V, producing up to 10 to 15 nm displacement with Olympus
transducers. For a 500-kHz transducer, it was possible to apply up to 1.5 kV sinewave pulse
without damaging it. However, it is prudent to limit to less than 200 V for those with nominal
frequency above 2 MHz. Some guidelines will be helpful.
8. Proper digitization is needed to record transducer input and output voltages. With advances indigital oscilloscope, offering automatic interpolation, even 8-bit resolution may be adequate.
Whether 12-bit or 14-bit resolution is required needs to be considered. Rate of digitization controls
the resolution of frequency domain signals. We used 2 or 8 ns interval with satisfactory outcome
even for low frequency testing in order to keep the frequency interval identical. A minimum
sampling rate may need to be specified.
9. Temperature of transducer during laser measurements needs to be recorded and reported
since piezoelectric devices are sensitive to temperature. This also applies to SUT.
10. For guided wave calibration, a rectangular aluminum bar of 1:4 ratio (e.g., 6.4 mm x 25.4 mm
or 12.7 mm x 50.4 mm and 1 to 3 m length) worked satisfactorily. An acrylic rod specified in ASTM
E-2075 (17) can be used by mounting a transmitter at the designated SUT position (at one endof the rod) and machining a flat on the side of the rod for SUT installation.

S40

11. Laser characterization of transmitting transducers should be repeated at a regular interval (of1 to 3 years) for commercial usage. For our own transducers, the transmitting sensitivities appear
to be stable over several years with intermittent use.
4.2 SUT (receiver) and its characterization
1. An SUT is to be acoustically coupled to a calibrated transmitter in a mechanically stable manner
under adequate force of 10 to 50 N for the direct contact method (6,7). For the guided wave
calibration, a similar coupling is done at the pre-selected position on a buffer medium (bar orplate). A suitable couplant must be applied to form a thin layer between the SUT and transmitter.
2. Machine oil and petroleum gel have given satisfactory performance, while silicone grease tends
to be too viscous, making it difficult to achieve optimum coupling conditions. Even with petroleum
gel like Vaseline requires 15 to 30 min to reach a stable condition at room temperature andreceiver output continues to increase overnight. On the other hand, low viscosity couplant likemotor oil can run out when uneven force is applied, leaving poor coupling conditions. Solid joint
with a glue is stable, but couplant layer thickness is larger (18) and should be avoided >100 kHz.
3. After SUT-transmitter coupling is established, the transmitter is excited and SUT output ismeasured. Digitization parameters should be identical to those used for transmittermeasurements to allow consistent spectrum operations of multiplication and division (or additionand subtraction in decibel scale). The length of recorded pulse signals needs to be selectedaccording to the lowest frequency to be calibrated (16).
4. A suitable input impedance of digital oscilloscope must be chosen. For most applications, selectthe value corresponding to the input impedance of AE preamplifiers to be used. This is often 10
to 20 k-ohms. For low frequency AE sensors, such values result in severe sensitivity reduction. A
long sensor cable also produces a similar effect. For these cases, it is recommended to use ashort cable and high impedance input of 1 M-ohms as impedance and cable effects can beassessed separately (16).
5. When the frequency domain output is deduced from digitized receiver output, the receiversensitivity can be obtained using the pulse transmitter input and transmitter sensitivity previously
determined. In terms of decibel scale spectra, this operation is subtraction process. The output isin reference to 0 dB at 1 V/nm when the laser interferometer output gives displacement output innm (6). Using a laser vibrometer that outputs in velocity or in m/s unit, one gets receiver sensitivity
in reference to 1 V/m/s. Recent high-end laser vibrometers (e.g., Polytec PSV-400) also provide
internally integrated displacement outputs. This part applies to both the direct contact and guided
wave calibration methods.
6. Receiving displacement sensitivity obtained above can be converted to velocity or traditionalpressure responses and vice versa (6).
7. When a buffer medium is desired in a modified direct contact method (e.g., for elevatedtemperature calibration), it is necessary to re-calibrate using a laser device or compensate forbeam diffraction loss from the transmission sensitivity. Calculation for the diffraction loss is only
available in closed form for identically sized transmitter and receiver, however (19). The buffer
insertion should be avoided for low frequency sensors since the diffraction loss increases sharply
at frequencies below 100 kHz.
8. A final note of caution is needed. The terms, “primary” and “absolute” calibration, have beenused (2, 13). However, all the methods discussed here mount an SUT using a couplant and wehave no means to characterize the couplant at present. It is also expected that the SUT mounting
affects the vibration predicted theoretically or calibrated by a laser device, as noted in (2). Thus,
it is perhaps more appropriate to use the term of sensitivity verification.

S41

5. CONCLUDING REMARKSThis paper summarized information needed for standardization of AE sensor calibration using
direct contact and guided wave methods. While some additional studies are required, thesemethods can verify the receiving sensitivity of AE sensors and are ready for standardization.
REFERENCES
1. F.R. Breckenridge, C.E. Tschiegg, M. Greenspan, Acoustic emission: some applications of
Lamb’s problem, J. Acoust. Soc. Am., 57, 626, 1975.
2. ASTM E1106-12 Standard test method for primary calibration of acoustic emission sensors,
ASTM International, West Conshohocken, PA, USA, 2017, 12p.
3. ASTM E1781/E1781M-13 Standard practice for secondary calibration of acoustic emission
sensors, ASTM International, West Conshohocken, PA, USA, 2013, 7p.
4. D. Eitzen and F.R. Breckenridge, NDT Handbook, vol. 6, Acoustic Emission Testing, ASNT,
Columbus, OH, 2005, pp. 54-60.
5. H.L. Dunegan, Private communication, 2008.
6. K. Ono, Calibration methods of acoustic emission sensors. Materials, 9, 508, 2016.
7. Frequency Dependence of receiving sensitivity of ultrasonic transducers and acoustic emission
sensors, Sensors, 18, 3861, 2018.
8. H. Vallen, Proposal for an absolute AE sensor calibration setup. Proc. World Conf. on Acoustic
Emission, Xi’an, China, 11–13 October 2017.
9. H. Vallen, Obtaining AE sensor sensitivity curves veriﬁed by laser vibrometry, Proc.
International Symp. on Acoustic Emission, XXIV, Sapporo, 5-9 Nov., Japanese Soc. for NonDestructive Inspection: Tokyo, Japan, 2018, pp. 23-28.
10. K. Ono, T. Hayashi, H. Cho, Bar-wave calibration of acoustic emission sensors. Appl. Sci. 7,964, 2017.
11. K. Ono, On the piezoelectric detection of guided ultrasonic waves. Materials, 10, 1325, 2017.
12. H. Hatano, T. Watanabe, Reciprocity calibration of acoustic emission transducers in Rayleighwave and longitudinal-wave sound ﬁelds. J. Acoust. Soc. Am., 101, 1450–1455, 1997.
13. NDIS-2109-91, Method for absolute calibration of acoustic emission transducers by reciprocity
techniques; Japanese Soc. for Non-Destructive Inspection: Tokyo, Japan, 1991; Revised 2004.
14. K. Ono, Critical examination of ultrasonic transducer characteristics and calibration methods.
Res. Nondestruct. Eval. 28, 1–46, 2017.
15. R. Hill, N.L. Adams, Reinterpretation of the reciprocity theorem for the calibration of acoustic
emission transducers operating on a solid. Acustica, 43, 305–312, 1979.
16. International Standard ISO 16063–11. Primary vibration calibrations by laser interferometry,
International organization for standardization, Geneva, Switzerland, 1999.
17. ASTM E2075/E2075M-15 Standard practice for verifying the consistency of AE-sensor
response using an acrylic rod, ASTM International, West Conshohocken, PA, USA, 2015, 5p,
18. K. Ono, Through-transmission characteristics of AE sensor couplants, J. Acoust. Emiss., 34,
1-11, 2017.
19. P.H. Rogers, A.L. van Buren, An exact expression for the Lommel diffraction correction
integral, J. Acoust. Soc. Am., 55, 724, 1974.

S42

Transmitters for the face-to-face sensor sensitivity
verification – is their scatter tolerable?
Hartmut Vallen
Vallen Systeme GmbH, 82057 Icking, Germany, www.vallen.de
ABSTRACTIn this work, “transmitter” (TM) means a device that converts a voltage to a particle motion. This
work deals with differences of receiving sensitivity spectra (Rv) of an AE sensor under test (SUT),
obtained by the face-to-face (F2F) method, when different TM units are used. It is an update ofan earlier work in which a certain set-up of the F2F method was introduced. This work delivers acomparison of Rv spectra for three types of SUT, using alternatively three TM units of same type,
and two FFT time windows of different length. Using the 50 µs long window (W7) presents theresonances correctly in the Rv spectra, but the scatter of the Rv spectra (in terms of deviationfrom an average) is large. Using the 19 µs long window (W1) presents resonance peaks 1-2 dBlower but the scatter is considerable lower. We would like to discuss the trade-off between thevalue of a lower scatter on cost of smoother resonance peaks in the Rv spectrum, and what levelof scatter is considered tolerable.
Keywords: Acoustic emission sensor, sensor calibration, sensor sensitivity verification, scanninglaser vibrometer.
1. INTRODUCTION
In an earlier work [1] we presented pairs of receiving sensitivity spectra of an AE sensor under
test (SUT) using a pair of transmitters (either TMA or TMB, Olympus V104) in a face-to-face (F2F)
setup. The principles and history of the F2F method is described in detail in Professor Ono’s
systematic study of AE sensor calibration methods [2]. We reported deviations of up to 3,5 dB(peak-peak) in a pair of sensitivity spectra obtained for a wideband SUT (Olympus V103), and up
to 7dB (peak-peak) for a 150 kHz resonant SUT (using FFT window W7). Several trials using
different stimulations (sinewave, different pulse shapes) for compensating the deviations failed
the objective. Meanwhile we received two more transmitter units (TMC, TMD) of same type. TMC
fits best and was also used for this work. Since [1] reports in detail (downloadable) the used setup, the following descriptions are reduced to the minimum needed for understanding the results,
due to limited space.
2. MOTION MEASUREMENT BY USING A SCANNING LASER VIBROMETER
The output motion of each transmitter, stimulated by an electrical pulse, was measured by usinga scanning laser vibrometer (LVM) at 161 positions at the transmitter face. The scanned positions
were arranged in 10 rings of 16 positions per ring plus one in the center. One velocity signal
versus time was obtained per circular area. Fig. 1 shows velocity signals of four circular areas
stimulated by three transmitter (TM) units in one diagram per TM. Fig. 1 shows the main pulse at
10 µs and a spike 8 µs later, strongest in graph TMB-W6. This indicates a serious lack of
uniformity of motion distribution at the face of TMB.
Velocity signal outputs from LVM and output signals of the SUT were subject to a window processwith the purpose to exclude a part of the reverberation phase from the Fast Fourier Transform(FFT) process. In this work windows W6, W7 and later W1 are used. W6 decays from 11 µs to16
S43

µs after pulse peak time, W7 from 50 µs to 60 µs, and W1 from 19 µs to 21 µs, smoothly, in the
shape of half a cosine wave, from one to zero.
TMA W6

TMB W6

TMC W6

Fig. 1 – Particle velocity diagrams for the circular areas defined by rings 5, 2, 1, 0 fromtransmitters TMA, TMB, TMC (top to down) using FFT window 6.

Windowing means multiplication of the signal to be processed by the window signal. The legendon the right of Fig. 1 shows the radii of the circle areas the velocity signals R0, R1, R2, R5
represent. The spike at 18 µs, strongest with TMB, indicates some non-uniform motion distribution
over the face of TMB.
3. FACE TO FACE SETUP

Fig. 2 - Block diagram of face-to-face setup

Fig. 2 shows the block diagram of the face-to-face setup. The arbitrary function generator (AFG)
delivers the voltage pulse UT to the TM, causing a transient motion which is converted by the SUT
S44

to the output voltage USUT. Both, USUT and UT, are measured by a transient recorder. Equation (3)in [2] can be converted to:
USUT = UT + Rv + Tv

(1)

Tv = V - UTL(2)

Rv = (USUT - V) + (UTL - UT).

(3)

this leads toEach variable in equations (1) to (3) addresses a spectrum in dB where 0 dB refers to 1 [unit]
given below:
USUT, UT:SUT output voltage and TM input voltage in F2F setup in [V]Rv

SUT’s particle velocity receiving sensitivity in [V/(mm/s)],

Tv

TM’s particle velocity transfer function in [(mm/s)/V]V

particle velocity (measured by LVM) in [mm/s]

UTL

Voltage spectrum at transmitter during velocity measurement in [V].

For more details see [1] and [2].
4. RESULTS
The upper curve triplets in each diagram of Fig. 3 visualize for one SUT three receiving
sensitivities RvA, (red, legend says 1Rv FA5W7), RvB (black), and RvC (blue), stimulated bythree TM units A, B, C of same model (Olympus V104), scaled on the left axis. The right axis
scale is for the lower pair of curves, identified by ΔRvA (red) and ΔRvB (black). ΔRvA and ΔRvB
are deviations-from-average of RvA and RvC, in dB: ΔRvA = RvA – (RvA + RvC)/2 and ΔRvB =RvB – (RvA + RvC)/2. RvB is excluded from the average because TMB causes too large
deviations and is not qualified for this method. Since only RvA and RvC are averaged, ΔRvC
always equals minus ΔRvA and is not shown. The legends of the ΔRv spectra indicate Root Mean
Square (RMS) values of the ΔRvA and ΔRvB over the frequency range 10 kHz to 500 kHz. Table
1 summarizes the RMS and peak-peak values.
Table 1 - Sensitivity deviations ΔRvT of 3 SUT types stimulated by 3 TMs using 2 windows.

SUT

RMS values

peak-peak values

Fig.

Type

ΔRvA

ΔRvB

ΔRvA

ΔRvB

3a)
3b)

V103
VS150

0,44
0,68

0,88
0,72

3,00
5,10

3,60
3,20

3c)

VS900

0,70

0,86

5,00

4,40

0,61
0,15
0,30
0,30
0,25

0,82
0,86
1,05
0,73
0,88

4,37
0,70
1,50
1,60
1,27

3,73
4,30
5,00
3,50
4,27

ØW7
4a)
4b)

V103
VS150

4c)

VS900

ØW1

Table 1 shows a substantial decrease of deviation in the sensitivity spectra when window W1 isused instead of window W7.
S45

dB
-6

8
ΔR

SUT1: V103

-8

7

-10

6

-12

5

-14

4

-16

3

-18

2

-20

1

-22

0

-24

-1

-26
0

20
40
60
80
100
1Rv FA5W7 dB(Vs/mm)
Δ1RvA RMS 10-500: 0,44

a)

120

140 160 180 200 220
1Rv FB5W7 dB(Vs/mm)
Δ1RvB RMS 10-500: 0,88

240

260

dB
14
12
10
8
6
4
2
0
-2
-4
-6
-8
-10
-12
-14
-16

SUT3: VS150

0

b)

50
100
3Rv FA5W7 dB(Vs/mm)
Δ3RvA RMS 10-500: 0,68

150

200
250
300
3Rv FB5W7 dB(Vs/mm)
Δ3RvB RMS 10-500: 1,09

350

dB4
2
0
-2
-4
-6
-8
-10
-12
-14
-16
-18
-20
-22
-24
-26

400
450 kHz
3Rv FC5W7 dB(Vs/mm)

ΔR
11
10
9
8
7
6
5
4
3
2
1
0
-1
-2
-3
-4
500

ΔR
11
10
9
8
7
6
5
4
3
2
1
0
-1
-2
-3
-4
700 750 800 850 kHz
900
2Rv FC5W7 dB(Vs/mm)

SUT2: VS900

0

c)

-2
280 300 320 340 kHz
1Rv FC5W7 dB(Vs/mm)

50 100 150 200 250
2Rv FA5W7 dB(Vs/mm)
Δ2RvA RMS 10-500: 0,70

300

350

400 450 500 550
2Rv FB5W7 dB(Vs/mm)
Δ2RvB RMS 10-500: 0,86

600

650

Fig. 3 – RvT spectra (left scale) and ΔRvT spectra (right scale) obtained from SUT a) V103-64,b) VS150M-6379, c) VS900M-4464 by using TMs A, B, C, and window W7 (50 µs)

Fig. 4 is similar to Fig. 3, but instead of window W7 (50 µs), window W1 (19 µs) was used. Since
in both, Fig. 3a) and 4a), all ΔRv values above 320 kHz were low, the frequency range shown for
the wideband sensor V103 was limited to the range 0 to 350 kHz for better low-end resolution. In
Fig. 4 the red and blue curves are closer together and the RMS values in the legend of the red
ΔRvA are smaller than that of the black ΔRvB.
S46

dB
-8

SUT1: V103

-10

6
ΔR
5

-12

4

-14

3

-16

2

-18

1

-20

0

-22

-1

-24
0

a)

20
40
60
80
100
1Rv FA5W1 dB(Vs/mm)
Δ1RvA RMS 10-500: 0,15120

140 160 180 200 220
1Rv FB5W1 dB(Vs/mm)
Δ1RvB RMS 10-500: 0,86

240

260

dB
12
10
8
6
4
2
0
-2
-4
-6
-8
-10
-12
-14
-16
-18
-20

SUT3: VS150

ΔR
12
10
8
6
4
2
0
-2
-4

0

50
100
150
3Rv FA5W1 dB(Vs/mm)
Δ3RvA RMS 10-500: 0,30

b)

200

250
300
3Rv FB5W1 dB(Vs/mm)
Δ3RvB RMS 10-500: 1,05

350

dB
4
2
0
-2
-4
-6
-8
-10
-12
-14
-16
-18
-20
-22
-24
-26

400
450 kHz 500
3Rv FC5W1 dB(Vs/mm)

SUT2: VS900

ΔR
5
4

3
2
1
0
-1
-2
0

c)

-2
280 300 320 340 kHz
1Rv FC5W1 dB(Vs/mm)

50

100 150 200 250
2Rv FA5W1 dB(Vs/mm)
Δ2RvA RMS 10-500: 0,30

300

350

400 450 500 550
2Rv FB5W1 dB(Vs/mm)
Δ2RvB RMS 10-500: 0,73

600

650

700 750 800 850 kHz
900
2Rv FC5W1 dB(Vs/mm)

Fig. 4 – Rv spectra (left scale) and ΔRv deviation spectra (right scale) obtained from sensora) V103-64, b) VS150M-6379, c) VS900M-4464 by using transmitters TMA, TMB, TMC.

Fig. 5 shows per TM the ΔRvT spectra averaged over three SUTs, a) using W7 (50 µs, the data
shown in Fig 3) and b) using W1 (19 µs, the data shown in Fig. 4).
In Fig. 5a) the deviation spectra in red and blue are as large as in black. Fig. 5b) shows small
deviations in red (ΔRvA) and blue (ΔRvC, equal to -ΔRvA) and larger deviations in black (ΔRvB).
This tells two facts: 1) Transmitters exhibiting poor motion distribution, such as measured with
S47

TMB (see Fig. 1), and 2) the length of the signal’s reverberation phase processed by FFT mustbe limited to a point in time before the signals begin to separate.
3
dB
2
1
0
-1
-2
-3

a) W7 AVG(ΔRvT) (T=A, B, C) over 3 SUTs (1, 2, 3)
0

20

40

60

80 100 120
Avg(ΔRvA)

140

160 180 200
Avg(ΔRvC)

220

240 260
Avg(ΔRvB)

280

300

320

340kHz

2
dB

1
0
-1
-2

b) W1 AVG(ΔRvT) (T=A, B, C) over 3 SUTS (1, 2, 3)

-3

0

20

40

60

80 100 120
Avg(ΔRvA)

140

160 180 200
Avg(ΔRvB)

220

240

260 280
Avg(ΔRvC)

300

320

kHz
340

Fig. 5 – The spectra shown are calculated by “Avg(ΔRvT) = (Δ1RvT + Δ2RvT + Δ3RvT) / 3”, with
T=(A/B/C) and with “ΔsRvT = sRvT – (sRvA + sRvC)/2”, with s=(1/2/3) for the SUT.
a) using window W7, b) W1.

Since amplitude and shape of the stimulation pulse was identical with each transmitter, thedeviations in the sensitivity spectra must be caused by deviations of transmitter properties.
The following treats time domain signals of SUT1 (V103):
15

mV
10

500
mV
400
300
200
100
0
-100

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

2

a)

3

4

5

5

µs6

b)

10
15
20
1FA5-64W7
VFA5W7 x -80

25

30
35
40
1FB5-64W7VFB5W7 x -80

45

50

55
60 µs
65
1FC5-64W7
VFC5W7 x -80

Fig. 6 – SUT1 responses (thick), and velocity signals (thin) vs. time, from
TMA (red), TMB (green), TMC (blue), a) main pulse at 4 µs, b) zoomed reverberation phase.

Fig. 6a) shows the peak amplitudes at 4 µs, about 260 mV of SUT output, 530 mV of velocity.
Fig. 6b) shows the reverberation phase in a x16 amplitude zoom from 5 to 65 µs.
The legends on the bottom of Fig. 6b) are common for both graphs. These legends begin with
“1F” for SUT1 signals (V103), or “VF” for velocity signals (thinner curves, multiplied by 80, from
LVM measurement). Third legend character (A/B/C for red/green/blue curve) identifies the usedtransmitter.
The velocity curves indicate the particle velocity in free air condition, the thicker curves the
response of the SUT on the velocity input. The black (velocity) and green (SUT response) curves
S48

of TMB show at 12 µs a sharp rise, which obviously stems from the spike shown in Fig. 1, TMB-W6
at 18 µs. This behavior disqualifies that TM exemplar.
From 5 µs to 22 µs the thicker red and blue curves (SUT responses) are close together. Then the
curves develop differently. No relation between SUT response and velocity output of LVM can be
seen. It seems, the SUT response is just reverberation of the main impulse.
In the F2F setup the tight acoustic coupling between SUT and transmitter combines the two piezo
elements to one common element with specific resonance properties. These depend, amongst
others, on the combined geometry. In addition to the mechanical load of the SUT on the TM the
specific resonance properties may influence how the reverberation motion develops over time.
The following treats time and frequency domain signals of SUT3, a resonant 150 kHz sensor(VS150M-6379):
300
200
100
0
-100
-200
-300
-400
-500

100
75
50
25
0
-25
-50
-75
-100

mV 0
a)

5 10 15 20 25 30 35 40 45 50 55 µs
60 mV 03FA5W7
3FC5W7b)

5 10 15 20 25 30 35 40 45 50 55 µs
60
3FA5W7
3FC5W7

Fig. 7 – Response of SUT3 on stimulation of TMA (blue) and TMC (red), a) normal scale, b) vertical zoom

Fig 7 shows for SUT3 close curves up to 19 µs after peak pulse time, as with SUT1 in Fig. 6,
and then a different development of the response curves. This must be caused by a differencein properties of TMA and TMC. This fact caused that we introduced window W1 with 19 µs
length, while in [1] window W6 (11 µs) was used.
300
200
100
0
-100
-200
-300
-400
-500

mV 0
a)
300
200
100
0
-100
-200
-300
-400
-500

15
dB

5
dB

10

0

5

-5

0

-10

-5

-15

-10
0
100
5 10 15 20 25 30 35 40 45 50 55 µs60b) 3Rv FA5W1
3FA5W7
3FA5-W1

-20
200
300
3Rv FA5W7

400 kHz 500
3RvC-AW7

15
dB

5
dB

10

0

5

-5

0

-10

-5

-15

-10

mV 0
c)

0
100
5 10 15 20 25 30 35 40 45 50 55 µs60d) 3Rv FC5W7
3FC5W7
3FC5W1-20
200
300
3Rv FC5W1

400 kHz 500
3RvC-AW1

Fig. 8 – W1 (red), W7 (blue); a), c) time domain; b), d) sensitivity in dB(Vs/m),
Green curves: b) RvC - RvA using W7: large deviation; d) RvC - RvA using W1: small deviation.

S49

Fig. 8 d), green curve, proves that using window W1 and stimulation by TMA and TMC results inonly small deviations. Blue and red curves prove that the reduction of peak sensitivity between
use of W1 and W7 is small.
5. CONCLUSION
According to Table 1 the receiving sensitivity spectra, obtained by using two transmitter units(TMA and TMC), three SUT types, a 50 µs long FFT window (W7), exhibit for the same SUT,
deviation-from-average values (ΔRvA) up to 5 dB peak-peak, up to 0,9 dB RMS (root meansquare, corresponds to standard deviation between Rv curves) and an average of 0,61 dB RMS.
By using a 19 µs long window (W1), the deviation ΔRvA is reduced from 0,61 dB to 0,25 dB RMS.
The use of window W1 smooths resonances to some extent. However, the peak maximum of the
150 kHz resonant sensor is only decreased by one to two dB between the use of W1 and W7.
The deviations ΔRvB (when using a third transmitter TMB) are worse due to the TMB’s disturbed
motion distribution (Fig. 1, TMB W6).
We think the transmitter units must be selected for proper reproducibility of SUT stimulation. Only
units with suitable uniformity of motion distribution should be used. A diagram like Fig. 7, showing
a SUT1 response, would allow to discriminate best suited transmitters from others.
By LVM the free surface motion of the transmitter was measured. During stimulation the SUT istightly coupled to the transmitter. It is obvious that the SUT coupling changes transmitter motion
to some extent.
The F2F method stimulates uniform particle motion in normal direction. This corresponds to alongitudinal volume wave arriving at the volume’s surface at the sensor position in normal
direction.
If waves arrive at the sensor in other than the normal direction, or if Rayleigh or plate wave
motions are passing by the sensor’s sensitive area, the particle motion is not uniform andcancellation effects may happen in the sensor’s piezo element and influence the apparent
sensitivity spectrum of the sensor.
Even if reproducibility among TM units would be perfect the behavior of a SUT will change when
it is moved from the F2F setup to a practical application, depending on the acoustic impedanceof the material it is coupled to, but also on the geometry, the conditions of wave propagation andreflection.
The F2F method has potential to obtain a sensitivity spectrum under easy reproducible conditions,undisturbed by any mechanical interaction.
6. REFERENCES[1][2]

Vallen, H. “AE sensor sensitivity verification using a stimulated motion, verified bylaservibrometry”, Proceedings of the EWGAE-33, Senlis, France, 12-14 September
2018, https://www.ndt.net/article/ewgae2018/papers/21.pdf (updated version)
K. Ono, “Calibration methods of acoustic emission sensors,” Materials, 2016, 9, 508.

S50