jnd-dec2011

Volume 10

issue 3

December 2011

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vol 10 issue 3 December 2011Journal of Non Destructive Testing & Evaluation

from the Chief Editor

Dr. Krishnan BalasubramaniamProfessor

Centre for Non Destructive EvaluationIITMadras, Chennai

[email protected]@gmail.com

URL: http://www.cnde-iitm.net/balas

The excitement of the Grand Event of ISNT, the NDE2011, to be held inCHENNAI TRADE CENTRE, will bring the ISNT community togetherfor 5 days of science, technology, business, and fun and the Journal ofNondestructive Testing and Evaluation welcomes all delegates. TheEditorial Board joins me in congratulating all the ISNT Award Winnersfor the 2011. This edition of the Journal brings forth the now popularsections BASICS, HORIZONS, PUZZLE, EVENTS, PATENTS, PROBEalong with 4 technical articles. The BASICS is focused on Eddy CurrentTesting Methods that is well compiled by Dr. BPC Rao. TheHORIZONS describes the use of an advanced method of harmonicultrasound that has been shown to be sensitive to micro-structuralchanges including very early damage in materials. The technical articleon indirect methods for inversion of eddy current data employs 2 novelapproaches using the ANN for the characterization of buried defects inmulti-layered components. The application of nano-structuredmaterials for the magnetostrictive generation of long range guidedultrasonic waves in pipes has been discussed in one of the articles andthis novel approach holds great promise for a low profile sensor systemfor NDE of pipes for corrosion. The technical article on the improvedcharacterization of impact damage in Kevlar composite test couponsusing advanced data analysis algorithm using clustering further re-iterates the need for applying advanced data handling algorithms inNDE. The use of Wavelet based analysis for impact echo and lowfrequency ultrasonic inspection of concrete for honeycomb defectsfurther demonstrates the need for signal and image processing tools inNDE. On behalf of the editorial board of the Journal I would like towish all of the readers a very happy and successful 2012.

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vol 10 issue 2 December 2011 Journal of Non Destructive Testing & Evaluation

PresidentShri K. Thambithurai

President-ElectShri P. Kalyanasundaram

Vice-PresidentsShri V. Pari

Swapan ChakrabortyShri D.J.Varde

Hon.General SecretaryShri R.J.Pardikar

Hon. TreasurerShri T.V.K.Kidao

Hon. Joint SecretariesShri Rajul R. Parikh

Immediate Past PresidentShri Dilip P. Takbhate

Past PresidentShri S.I.Sanklecha

MembersShri Anil V. Jain

Shri Dara E. RupaShri D.K.Gautam

Shri Diwakar D. JoshiDr. Krishnan Balasubramaniam

Shri Mandar A. VinzeShri B.B.Mate

Shri G.V. PrabhugaunkarShri B.K.Pangare

Shri M.V. RajamaniShri P.V. Sai Suryanarayana

Shri Samir K. ChoksiShri B.K. Shah

Shri S.V. Subba RaoShri Sudipta Dasgupta

Shri N.V. WagleShri R.K. Singh

Shri A.K.Singh (Kota)Shri S. Subramanian

Shri C. AwasthiBrig. P. GaneshamShri Prabhat Kumar

Shri P. MohanShri R. SampathShri V. SathyanShri B. Prahlad

Ex-officio MembersManaging Editor, JNDT&E

Shri V. Pari

Chairman, NCB &Secretary, QUNEST

Dr. Baldev Raj

Controller of Examination, NCBDr. B. Venkatraman

President, QUNESTProf. Arcot Ramachandran

All Chapter Chairmen/Secretaries

Permanent InviteesShri V.A.Chandramouli

Prof. S. RajagopalShri G. Ramachandran

& All Past Presidents of ISNT

I S N T - National Governing Council

Chapter - Chairman & SecretaryAhmedabadShri D.S. Kushwah, Chairman,NDT Services, 1st Floor, Motilal Estate,Bhairavnath Road, Maninagar,Ahmedabad 380 028. [email protected] Rajeev Vaghmare, Hon. SecretaryC/o Modsonic Instruments Mfg. Co. Pvt. Ltd.Plot No.33, Phase-III, GIDC Industrial EstateNaroda, Ahmedabad-382 [email protected]

BangaloreProf.C.R.L.Murthy, ChairmanDept. of Aerospace Engg, Indian Institute of Science,Bangalore 560012Email : [email protected]@aero.iisc.ernet.in

ChennaiShri R. Sundar, ChairmanDirector of Boilers,Tamil NaduShri R. Balakrishnan, Hon. Secretary,No.13, 4th Cross Street, Indira Nagar,Adyar, Chennai 600 020. [email protected]

DelhiShri A.K Singhi, Chairman,MD, IRC Engg Services India Pvt. Ltd612, Chiranjiv Tower 43, New Delhi [email protected] M.C. Giri, Hon.Secretary,Managing Partner, Duplex Nucleo EnterpriseNew Delhi [email protected]

HyderabadShri M. Narayan Rao, Chairman,Chairman & Managing Director, MIDHANI,Kanchanbagh, Hyderabad 500 [email protected] Jaiteerth R. Doshi, Hon.Secretary,Scientist, Project LRSAMDRDL, Hyderabad 500 [email protected]

JamshedpurDr N Parida, Chairman,Senior Deputy DirectorHead, MSTD, NML, Jamshedpur - 831 [email protected]. GVS Murthy, Hon. Secretary,MSTD, NML, [email protected] / [email protected]

KalpakkamShri YC Manjunatha, ChairmanDirector ESG, IGCAR, Kalpakkam – 603 [email protected] BK Nashine, Hon.SecretaryHead, ED &SS, C&IDD, FRTGIGCAR, Kalpakkam – 603 102 [email protected]

KochiShri CK Soman, Chairman,Dy. General Manager (P & U),Bharat Petroleum Corporation Ltd. (Kochi Refinery),PO Ambalamugal 682 302. [email protected] V. Sathyan, Hon. Secretary,SM (Project),Bharat Petroleum Corporation Ltd. (Kochi Refinery),PO Ambalamugal-682 302. [email protected]

KolkataShri Swapan Chakraborty, ChairmanPerfect Metal Testing & Inspection Agency,46, Incinerator Road, Dum Dum Cantonment,Kolkata 700 028. [email protected] Dipankar Gautam, Hon. Secretary,4D, Eddis Place, Kolkata-700 [email protected]

KotaShri R.C. Sharma, ChairmanQAS, RAPS - 5 & 6, PO AnushaktiRawatbhata 323 303 [email protected] S.K. Verma, Hon. Secretary,TQAS, RAPS - 5 & 6, PO AnushaktiRawatbhata 323303. [email protected]

MumbaiShri R.S. Vaghasiya, Chairman,B 4/7, Sri Punit Nagar, Plot 3, SV Road,Borivile West, Mumbai 400 [email protected] Samir K. Choksi, Hon. Secretary,Director, Choksi Brothers Pvt. Ltd.,4 & 5, Western India House, Sir P.M.Road,Fort, Mumbai 400 001. [email protected]

NagpurShri Pradeep Choudhari, ChairmanParikshak & Nirikshak, Plot M-9, LaxminagarNagpur - 440 022Mr. Jeevan Ghime, Hon. Secretary,Applies NDT & Tech Services,33, Ingole Nagar, B/s Hotel Pride, Wardha Road,Nagpur 440 005. [email protected]

PuneShri BK Pangare, ChairmanQuality NDT Services, Plot BGA, 1/3 Bhosari,General Block, MIDC, Bhosari, Pune- 411 [email protected] BB Mate, Hon Secretary,Thermax Ltd., D-13, MIDC Ind. Area, RD AgaRoad, Chinchwad, Pune- 411 [email protected]

SriharikotaShri S.V. Subba Rao, Chairman,General Manager, Range OperationsSDSL, SHAR CentreSriharikota 524124. [email protected] G. Suryanarayana, Hon. Secretary,Dy. Manager, VAB, VAST, Satish Dhawan SpaceCentre, Sriharikota-524 124. [email protected]

TarapurShri PG Bhere, Chairman,AFFF, BARC, Tarapur-401 [email protected] Jamal Akftar, Hon.Secretary,TAPS 1 & 2, NPCIL, Tarapur. [email protected]

TiruchirapalliR.J. PardikarAGM, (NDTL)BHEL Tiruchirapalli 620 014. [email protected] A.K.Janardhanan, Hon. Secretary,C/o NDTL Building 1, H.P.B.P., BHEL,Tiruchirapalli 620 014. [email protected]

VadodaraShri P M Shah, Chairman,Head-(QA) Nuclear Power Corporation [email protected] S Hemal Thacker, Hon.Secretary,NBCC Plaza, Opp.Utkarsh petrol pump, KareliBaug,Vadodara-390018. [email protected]

ThiruvananthapuramDr. S. Annamala Pillai, ChairmanGroup Director, Structural Design & Engg Group,VSSC, ISRO, Thiruvananthapuram [email protected]. Binu P. ThomasHon. Secretary, Holography section, EXMD/SDEG,STR Entity, VSSC, Thiruvananthapuram 695 [email protected]

VisakhapatnamShri Om Prakash, Chairman,MD, Bharat Heavy Plate & Vessels Ltd.Visakhapatnam 530 012.Shri Appa Rao, Hon. Secretary,DGM (Quality), BHPV Ltd., Visakhapatnam 530012

Contents

Chief EditorProf. Krishnan Balasubramaniam

e-mail: [email protected]

Co-EditorDr. BPC [email protected]

Managing EditorSri V Parie-mail: [email protected]

Topical EditorsDr D K BhattacharyaElectromagnetic MethodsDr T Jayakumar,Ultrasonic & Acoustic EmissionMethodsSri P KalyanasundaramAdvanced NDE MethodsSri K ViswanathanRadiation Methods

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About the cover page:

Editorial BoardDr N N Kishore, Sri Ramesh B Parikh,Dr M V M S Rao, Dr J Lahri,Dr K R Y Simha, Sri K Sreenivasa Rao, Sri SVaidyanathan, Dr K Rajagopal,Sri G Ramachandran, Sri B Ram Prakash

Advisory PanelProf P Rama Rao, Dr Baldev Raj,Dr K N Raju, Sri K Balaramamoorthy,Sri V R Deenadayalu, Prof S Ramaseshan,Sri A Sreenivasulu, Lt Gen Dr V J Sundaram,Prof N Venkatraman

ObjectivesThe Journal of Non-Destructive Testing &Evaluation is published quarterly by the IndianSociety for Non-Destructive Testing forpromoting NDT Science and Technology. Theobjective of the Journal is to provide a forumfor dissemination of knowledge in NDE andrelated fields. Papers will be accepted on thebasis of their contribution to the growth ofNDE Science and Technology.

Journal of Non DestructiveTesting & Evaluation

Published by Shri RJ Pardikar, General Secretaryon behalf of Indian Society for Non DestructiveTesting (ISNT)

The Journal is for private circulation to membersonly. All rights reserved throughout the world.Reproduction in any manner is prohibited. Viewsexpressed in the Journal are those of the authors'alone.

Modules 60 & 61, Readymade GarmentComplex, Guindy, Chennai 600032Phone: (044) 2250 0412Email: [email protected] at VRK Printing House3, Potters Street, Saidapet, Chennai 600 [email protected] Ph: 09381004771

Volume 10 issue 3December 2011

About the Cover pageThe Friction stir welding (FSW) is a newand promising welding process which weldsthe material below its melting temperatureand it has shown superior features such asexcellent joint performance, mechanicalproperties and low energy consumption.This process is effective for joining thematerial which is difficult to weld byconventional fusion welding. Dissimilaralloy material can also be joined with theFSW process The right choice andapplication of the welding parameter isrequired to get sound welds which helps toimproves the mechanical properties ofmaterial. In FSW, both rotational speed andtranslation speed exert a significant effecton the heat input generation and hence themechanical properties of the material.Defects like large mass of flash out causesdue to excess heat input. Also defects suchas cavity or groove like defects are causeddue to insufficient heat input and/orabnormal stirring. It was reported thedefects like porosity, kissing bong, solidinclusion and linear crack like defects inmagnesium alloy. These defects reduce thequality of weld, Hence examination of thesedefects is very much required to optimizethe welding parameter. In the cover page,ultrasonic C-scan image is used forimaging the defects caused due to sub-optimal choice of the rotational speed. Thedifference FSW welds were scanned usinga high frequency focused ultrasound beamof 15 MHz in an immersion mode. Byappropriate time gating, the image showsvarying regions and morphology of damagefor difference combinations of translationspeed and rotational speed of the FSW tool.This study provides the optimalcombination of the two controllableparameters for a defect free FSW processfor Aluminum welding.

(The results are provided by the Centre forNon-destructive Evaluation at IIT Madras)

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Chapter News

Basics

Horizons

NDE Events

NDT Puzzle

Technical Papers

Imaging of Impacted Kevlar Composite Armoursusing Data ClusteringSutanu Samanta and Debasis Datta

Generation of Guided Waves Using MagnetostrictiveNanostructured Sensing Elements for PipeInspectionA.K. Panda, P.K. Sharan, G.V.S. Murthy, R.K. Roy,

S. Palit Sagar and A. Mitra

Detection of honeycomb defects in reinforcedconcrete structures using acoustic pulse-echomethods and wavelet transformsKrishna Prasad M, Herbert Wiggenhauser,

Krishnan Balasubramaniam

Indirect methodologies for inversionof eddy current NDE dataS. Shuaib Ahmed, B.P.C. Rao, S. Thirunavukkarasu and

T. Jayakumar

Probe

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Madras Metallurgical Services (P) LtdMetallurgists & Engineers

Serving Industries &Educational

Institutes for thepast 35 years

24, Lalithapuram street, Royapettah, Chennai 600014Ph: 044-28133093 / 28133903 Email: [email protected]

A-3, Mogappair Indl. Area (East) JJ Nagar,

Chennai 600037 Phone 044-26564255, 26563370

Email: [email protected];

[email protected] www.kidaolabs.com

KIDAO Laboratories

Scaanray Metallurgical Services(An ISO 9001-2000 Certified Company)

NDE Service ProviderProcess and Power Industry, Engineering andFabrication Industries, Concrete Structures,

Nuclear Industries, Stress Relieving

Transatlantic Systems

Electro-Magfield Controls & Services &LG Inspection Services

Plot 165, SIDCO Industrial Estate, (Kattur)Thirumullaivoil, Vellanur Village, Ambattur Taluk

Chennai 600062 Phone 044-6515 4664 Email: [email protected]

We manafucture : Magnetic Crack Detectors, Demagnetizers, MagneticParticles & Accessories, Dye Penetrant Systems etc

Super Stockist & Distributors: M/s Spectonics Corporation, USA fortheir complete NDT range of productrs, Black Lights, Intensity Meters,

etc.

Betz Engineering &Technology Zone

An ISO 9001 : 2008 Company

Call M. Nakkeeran, Chief Operations,Lab: C-12, Industrial Estate, Mogappair (West), Chennai 600037

Phone 044-2625 0651 Email: [email protected] ;www.scaanray.com

Support for NDT ServicesNDT Equipments, Chemicals and Accessories

Call DN Shankar, Manager14, Kanniah Street, Anna Colony, Saligramam,

Chennai 600093Phone 044-26250651 Email: [email protected]

49, Vellalar Street, near Mount Rail Station, Chennai 600088Mobile 98401 75179, Phone 044 65364123Email: [email protected] / [email protected]

International Training Division21, Dharakeswari Nagar, Tambaram to Velachery Main Road,Sembakkam, Chennai 600073www.betzinternational.com / www.welding-certification.com

NABL Accredited Laboratory carrying out Ultrasonic

test, MPL and DP tests, Coating Thickness and

Roughness test. We also do Chemical and Mechnical tests

MetallographyStrength of MaterialsNon Destructive TestingFoundry Lab

Shri. K. Ravindran, Level IIIRT, VT, MT, PT, NR, LT, UT, ET, IR, AE

Southern Inspection ServicesNDT Training & Level III Services in all the

following ten NDT Methods

No.2, 2nd Floor, Govindappa Naicker Complex,Janaki Nagar, Arcot Road,

Valasaravakkam, Chennai-600 087Tamil Nadu, India

Phone : 044-2486 8785, 2486 4481E-mail: [email protected] and

[email protected] Website: www.sisndt.com

OP TECH01J, First Floor, IITM Research Park, Kanagam Road, Taramani,

Chennai 600113 India Phone : +91 44 6646 9880

Dhvani R&D Solutions Pvt. Ltd

Educational CDs -PT, UT, RT, MT, ET, Basic Metallurgyand Mechanical Testing

ASNT Level III Intensive Taining

Call93828 12624

Land044 - 2446 1159

B Ram PrakashA 114, Deccan Enclave,72, T M Maistry Street,Thiruvanmiyur, Chennai 600 041

• Inspection Solutions - CUPS, TAPS, CRISP, TASS

• Software Products - SIMUT, SIMDR

• Training - Guided Waves, PAUT, TOFD

• Services & Consultancy - Advanced NDE, Signal Processing

- C-scans, On-line Monitoring

E-mail: [email protected] www.dhvani-research.com

Classifieds

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BANGALORE

Courses & Exams Conducted : Alsoorganized ASNT Level-III Exam in May2011, Refresher course were alsoconducted.

Other Activities: Conducted ExecutiveCommittee Meeting & the New EC waselected for 2011-2012

CHENNAI

Technical Talk:

28.08.2011- Dr. O.Prabhakar, Professor(Retd) – IIT Madras, School of MaterialsEngineering NTU Singapore delivered atechnical talk on “The Importance of NDEin Automobile components”.

25.9.2011- Mr. P.Vijayaraghavan CentralLab, F&F Division, HAL, Bangaloredelivered a technical talk on “Role of NDTin Aerospace Industry” .

16.10.2011- Dr. T.Jayakumar, DirectorMMG, IGCAR, Kalpakkam delivered atechnical talk on “Role of NDE in CivilConcrete”.

30.10.2011 – Dr. R.Sundar, Director ofBoilers, Tamilnadu delivered a technical talkon “Residual Life Assessment of Boilers”.

6.11.2011 – Dr. M.T.Shyamsunder,Principlal Engineer, NDE Modelling &Imaging Lab, Bangalore delivered a technicaltalk on “Advances in Electromagnetic NDE–Techniques & Application”.

Courses & Exams Conducted :

* UT Level-II (ASNT) course from22.04.11to 30.04.11.

* MT – Level - II(ASNT), forM.M.Forgings Ltd from 24th Apr, 08th,15th ,22nd May 2011

* Surface NDT ( MT & PT) Level - II(ASNT) from 20.05.2011 to 26.05.2011.

* UT L- II (ASNT) course from30.05.2011 to 05.06.2011.

* RT L- II (ASNT) course from10.06.2011 to 16.06.2011.

* UT L-II (ASNT) course was conductedfrom 24.6.2011 to 2.07.2011.

* In-House training on MT Level- I & II(ASNT) course for Brakes India Ltd,Chennai was conducted from24.06.2011 to 28.06.2011.

* RT Level- II (ISNT) course wasconducted from 11.07.2011 to24.07.2011.

* In house training PT Level –II (ASNT)course at BHEL, Ranipet was conductedfrom 12.07.2011 to 15.07.2011.

* MT & PT Level-II (ASNT) course wasconducted from 5.08.2011-13.08.2011.

* RT Level-II (ASNT) course wasconducted from 19.08.2011 to28.08.2011.

* UT Level-II (ISNT) course wasconducted from 05.09.2011 to18.09.2011.

* PT Level-II (ISNT) course wasconducted from 10.10.2011 to16.10.2011.

* UT Level-II (ASNT) course wasconducted from 28.10.2011 to06.11.2011.

Other Activities:

ISNT DAY celebrated on 21.04.2011.

* The Thambithurai Award for BestTechnical Paper presented to Dr. VaidehiGanesan, Scientist, IGCAR

* The PARI award for the Best Memberwas Mr RG Ganesan, Joint Secretary,ISNT Chennai Chapter

EC Meetings: *01.05.2011 *11.06.2011

Annual General Body meeting (AGM)was held on 16.07.2011, 07.08.2011,25.09.2011, 16.10.2011.

DELHITechnical Talk: A technical talk onPhased Array UT by Mr. Peter Renzel ofGE in association Of Kronix wasorganized at IRC, Noida.

Other Activities: One day seminar onNDT was organized on 8th April, 2011 inassociation with Gurgaon College ofEngineering at Gurgaon.

AGM conducted on 9th Oct, 2011 in CSOIat KG Marg. New Delhi.

Election for the year 2011-2012 wasconducted & New Executive committeewas formed unanimously as detailedbelow.:

Chairman - Mr. A.K SinghiVice Chairman - Mr. A.K. PawarVice Chairman - Mr. Kuldeep SinghHon. Gen. Secretary - Mr. M.C. GiriHon. Joint. Secretary - Mr. Daya Ram GuptaHon. Treasurer - Mr. R.S. Mahto

MembersMr. S.K. Goel, Mr. Dinesh Gupta,Mr. Navita Gupta, Mr. F. Sidique,Mr. D.K. Dua, Mr. T. Kamaraj,Mr. Muneshwar Dayal

1st EC Meeting was conducted on 5th Nov,2011 at IRC Nehru Place, New Delhi.Following new members were co-opted asEC member Mr. A. Bhatnagar,

Mr. R.K. Singh, Mr. Ashok Agrawal,Mr. Rajpal Singh.

JAMSHEDPUROther Activities: Annual General BodyMeeting conducted on 29.04.2011 and thenew executive committee was electedunder the chairmanship of Dr. N. Parida,Scientist from NML .Awards :Dr. Amitava Mitra received theprestigious Materials Research Society ofIndia Medal (MRSI Medal).

KALPAKKAMTechnical Talk: Conducted a Technical Talkon X-ray endoscopic inspection of T/TSwelds in heat exchangers by eminent Prof.Dr Uwe Ewert on Friday, 22nd July, 2011.

KOCHITechnical Talk: A technical presentation on“NDT and other technical innovationsbeing implemented in Gas networkingPipelines” was conducted on 17th Aug2011 by senior executives from M/s GAIL.

Courses and Exams Conducted:Certification Programme : Two RTLevel II programmes as per SNT TC IAwere conducted.

One Programme was exclusively for theofficials from Dept. Of Factories andBoilers, Kerala which was held duringthe period 17 /01 /11 to 29 /01 /11.Another ASNT RT level II wasconducted from 2 4 /08 / 11 to 03 / 09 / 11,based on the request from nearbyIndustries.

Other Activities:Committee meetings : Two meetings ofthe executive committee were held duringthe period (on 28/05/11 and 17/08/11).Membership: 5 new members joinedduring the period.

KOLKATACourses and Exams Conducted

Radiography Testing (RT- II) Training& Certification Course from 23rd Mayto 29th May, 2011.

1st Magnetic Particle Testing (MPT- II)Training & Certification Course from15.07.11 to17.07.11

1st Penetrant Testing (PT- II) Training &Certification Course from 22.07.11 to24.07.11.

8thRadiography Testing (RT- II) Training& Certification Course from 01.08.11 to08.08.11.

5th Ultrasonic Testing (UT- II) Training& Certification Course from 05.09.11 to11.09.11.

CHAPTER NEWS

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Other Activities:EC Meetings: 1. April’11 , 2.June,’11.

Membership: *Life member - 135*Corporate Member – 8 *LifeCorporate Member – 7.*Member – 60.*Life Fellow -1.

Annual General Meeting held on 21st.October,2011

New EC Committee being constituted : Sri Swapan Chakraborty- Chairman., Sri.D.Gautam - Secretary., Sri Sandeep Agarwal- Treasurer.

KOTACourses and Exams Conducted:

Leak Testing Level -II Course during June, 2011.Other Activities: New EC Committee has been constituted.Chairman - Shri A. Bahl Hon.Secretary —- Shri S.K.Verma.

MUMBAITechnical Talk: Technical Lecture on 05-09-2011 Speaker: byMr Roman Fernandez, France Topic: “Simulation in NDE”.

Courses and Exams Conducted: Conducted Welding Inspectorexamination at ITT, on 1st May 2011,

Conducted LT Level II Examination for KOTA Chapter on26- 06- 2011 at Kota.

Conducted Welding Inspector examination at ITT, Mahim on28th August 2011.

Conducted Welding Inspector examination for ISNT, TarapurChapter, at TARAPUR on 17th September 2011.

Conducted General NDT Course for ONGC Engineers from 10th

Oct. to 14th Oct. 2011.

NDT Level - III & ISNT Level – III Refresher courses &ISNT Level – III Examination from 31st October 2011 to 25th

November 2011 at Hotel Atithi.

Other Activities:

* APCNDT 2013 committee Meeting was held on 8thApril 2011, 19th April 2011. 25th April 2011.13th May2011. 7th June 2011. 29th July 2011 and 25th August2011.

* EC Meeting was held on 22rd June 2011, 17th August2011 and 20th Oct. 2011.

* Conducted NCB and NGC Meeting on 21ST August atHotel Atithi, Vile Parle, Mumbai.

* NDT Achievement Awards Mumbai meeting held on25th August at Acres Club, Chembur.

AGM was held on 17th Sept.

PUNETechnical Talk: TOFD Technique introduction & PracticalDemonstration on Weld”on 14.05.2011 by Shri Ashok Trivedi .Other Activities: EC Meeting held on 29.04.2011.

TARAPUR

Courses & Exams Conducted

The Certified Welding Inspector Level – II was conductedfrom 07 / 09 / 2011 to 16 / 09 / 2011.Liquid Penetrant Test Level –II conducted from 20/7/2010 to24/7/2010. 50 chapter is planning training on ultrasonic thicknessgauging as per NPCIL requirments.

Other Activities

1. 16th AGM, held on 17.09.2011 in Hotel, Silver Avenue OstwalPark, Boisar. AGM approved chapter audited account reportand annual report for the year 2010-2011.

2. New managing commeetee election was held following memberselected for the post indicated against their names-Chairman-Shri P.G.Behere, Co-Chairman-Shri R.MuraliSecretary-Shri J.Akhtar, Jt. Secretary-Shri D.B.Sathe,Treasurer-Shri V.H.PatilMembers : Shri S. Pradhan, Shri E. MudliyarShri A.P.Kulkarni, Shri P.M.Bhave, Shri N.K.Roy, Shri ChetanMali, Smt Nilima Walinjakar.

TRICHY

Courses and Exams Conducted:

a) Radiographers level I-(In association with BARC-Mumbai)from 11.04.2011 to 29.04.2011.b.) MPT-Level –II- From 16.06.2011 to 19.06.2011.c.) LPT – Level – II – from 13.06.2011 to 15.06.2011.d.) Radiography Level – II from 11.07.2011 to 21.07.2011.

Other Activities:

1. EC meeting was conducted on June 2011

2. Invited Lecture : “Introduction to SafeRad RadiographySystem - an unique & innovative ndt technique” BY Mr.Malcolm Wass U.K. on 11th july 2011.

TRIVANDRUM

Technical Talk: Ms Andreanne Potvin, Product ManagerOlympus NDT corporation made a technical Presentation onLatest Advances In Phased Array Ultrasonics on 28th July2011.

Other Activities:

1. Annual General Body Meeting : was held on 28thMay 2011.

2. Election of office bearers and EC members for the period2011-2013 was conducted

3. MR Kurup Memorial Lecture 2011 was delivered byShri V Srinivasan, Deputy Director, PRSO Entity, VSSC,at Hotel Maurya Rajadhani, Trivandrum on 28th May 2011.

4. Four EC meetings were conducted on the months July,August and September and October 2011.

5. One day National Seminar.

A one day National seminar on Non-destructive Evaluation ofcomposite structures was conducted on 22nd October 2011 at SPGrand Days, Thiruvananthapuram. The inaugural function waschaired by Dr. A. Jayakrishnan, Vice Chancellor, University ofKerala. There were 8 invited lectures covering various area likeUT, RT, Thermography, Acoustic Emission, Holography,Shearography etc.

VADODARA

Other Activities:Executive Committee Meeting held on 25th March , 2011

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among other electromagnetic NDEtechniques, this technique finds largernumber of applications. Thistechnique finds versatile applicationsin power, aerospace andpetrochemical industries. It is notincorrect to say that worldwidealmost all the heat exchangers andaircrafts are inspected using thistechnique. Two main aspects behindthis widespread use are excellentsensitivity to surface as well as sub-surface defects and testing speed ofas high as 10 m/s which no otherNDE technique can match. This isespecially profitable to industries asit enables rapid examination duringmanufacturing stages, while itdrastically reduces the down time ofoperating plant components.

Many developments are taking placein this existing NDE techniqueincorporating the rapid progress inthe fields of microelectronics,instrumentation, sensors, computers,numerical modelling, digital signal &image processing (Fig.1) [2]. Theway EC testing is practised now isdifferent from that it was fourdecades ago. These concurrentadvances in other fields have

Barkhausen emission, micromagnetic,potential drop, microwave, AC fieldmeasurement techniques etc. [1]. Inthese techniques, material underinvestigation is excitedelectromagnetically and themanifestation of electromagneticfields due to material discontinuitiesaffecting electrical conductivity ormagnetic permeability or dielectricpermittivity are measured using asensor, with the exception ofmagnetic particle testing in whichmagnetic particle are used in placeof a sensor [2].

EC technique is the most popular andwidely used electromagnetic NDEtechnique. In industrial scenario,

Eddy current technique is animportant electromagnetic non-destructive evaluation technique thatis widely used in power, aerospace,petrochemical and other industries fordetection of surface cracks and sub-surface damage in components madeof metallic materials. Besides, it isalso used traditionally for assessingthe adequacy of heat treatment ofalloys, as eddy currents are sensitiveto changes in microstructure andstresses, which alter the electricalconductivity and magneticpermeability of the material. Thispaper gives a brief account of basicprinciple, features, applications,limitations of the eddy currenttechnique. It also covers instrumentsand sensors to enable betterappreciation of the technique and itscapabilities.

INTRODUCTION

Early detection and quantification ofdefects, microstructural variations andstresses is utmost important to ensurehigh quality manufacturing and safeoperation of engineering components.The role played by non-destructiveevaluation (NDE) techniques is wellknown from their wide spread useduring manufacture and assemblystages and during service life ofcomponents.

NDE techniques that use some formof electromagnetic excitation aretermed as electromagnetic NDEtechniques and some of these includeeddy current (EC), magnetic particle,magnetic flux leakage, magnetic

B.P.C. RaoNon-destructive Evaluation Division, Metallurgy and Materials GroupIndira Gandhi Center for Atomic Research, Kalpakkam – 603 102, TN, Indiae-mail: [email protected]

Basics

Eddy Current Testing:Basics

Fig. 1 : Recent advances in eddy current testing that are responsible for its enhanceduse by the industry.

8


Basics

enhanced the capabilities of thetraditional EC technique enablingdetection and sizing of incipientsurface defects as well as sub-surface defects, changes inmicrostructures and accumulatedplastic deformation, stress or damagee.g. prior to crack formation etc.Such possibilities allow efficientpreventive actions to be takenavoiding catastrophic failure ofcomponents. Possibilities to inspectionof large areas with automation,elimination of operator fatigue &uncertainty, inspection of inaccessibleas well remote areas, and on-lineinspection ensuring high probabilityof detection and accurate sizing, allhave further enhanced theacceptability of the EC technique bythe industry. This technique richlybenefited by the wisdom andknowledge and contributions fromscientists, engineers and other expertsfrom physics, electrical engineering,material technology, micro-electronics, computers, automationand robotics domains. For betterappreciation of the EC technique andcorrectly choose it for an applicationat hand, it is essential to know thebasic principles, features, capabilitiesand limitations of the technique. Thatforms the objective of this paper.

PRINCIPLE

EC testing works on the principlesof electromagnetic induction. In thistechnique, a coil (also called probeor sensor) is excited with sinusoidalalternating current (frequency, f, ~50 Hz-5 MHz, ~ 100 mA). Followingthe Ampere’s law, this currentgenerates primary magnetic field inthe vicinity of the coil. When anelectrically conducting material isbrought close to this coil, eddycurrents are induced in the materialaccording to the Faraday’s law (referFig. 2) [3].

The eddy currents have very uniqueand interesting properties such as:

Ø They are induced currents that existonly in electrically conductingmaterials

Ø They are always in closed loops,usually parallel to the coil winding(Fig. 2)

Ø They are distorted by defects suchas cracks and corrosion wall lossand by discontinuities such as edge-effect, end-effect as shown inFig. 2

Ø They attenuate with depth (alsoaxially or laterally)

Ø Their intensity depends on materialproperties, electromagneticcoupling (lift-off/fill-factor) andexcitation frequency, but maximumon the surface

These eddy currents also generate asecondary magnetic field, but in theopposite direction to the primarymagnetic field following the Lenz’slaw and this field in turn, changesthe coil impedance, Z which is acomplex quantity with realcomponent, R and imaginarycomponent, X

L. Defects such as

cracks, voids, inclusions, corrosionwall loss, microstructure degradation,localised stresses alter the localelectrical conductivity, σ, andmagnetic permeability, µ, of thematerial and cause distortion of theeddy currents and change the coilimpedance. This impedance change,usually of the order of micro ohms,is measured using high-precisionbridge circuits, analysed andcorrelated with defect dimensions.Alternately, the secondary magneticfield can be detected using aseparate receiver coil or a solid statefield detection sensor. Discontinuitiesor defects that cause maximumperturbation to eddy currents flow,in other words, distortion producelarge change in impedance. Theimpedance change is also affectedby excitation frequency (effect of X

L

or 2πfL) and electromagneticcoupling.

Fig. 2 : Principle of eddy current testing (left) and distortion of eddy current due to crack, edge-effect, surface crack, and sub-surfacevoid (right).

9


Basics

The flow of eddy currents in the testmaterial is not uniform at differentdepths. The eddy currents are quitedenser at the surface as comparedto the deep inside, an effect referredto as skin effect [4]. Theoreticalstandard depth of penetration of eddycurrents, δ, that describes the skin-effect, can be expressed as

(1)

where is f is excitation frequency,Hz, µ

0 is magnetic permeability of

free space, 4π10-7 H/m, µr is relative

magnetic permeability, dimensionless,and σ is electrical conductivity, mho/m. δ is the depth at which thesurface eddy current density hasfallen to 37%. EC techniquecapability, applicability, selection oftest frequency etc. can be readilyunderstood using equation (1). Forexample, depth of penetration ofeddy currents, in other words,interaction of electromagnetic fields,is very low in highly conducting (e.g.Copper) as shown in Fig. 3, ascompared to that of austeniticstainless steel which is lessconducting. Due to the skin effect,with EC test one can readily detectthe surface-breaking defects ascompared to the sub-surface defectsor buried defects.

The locus of impedance changeduring the movement of an EC coilsystem over the test object is calledEC signal. While the amplitude ofthe EC signal provides informationabout the defect severity, the phaseangle provides information about thedefect depth. As depicted in Fig. 4,the impedance change can bedisplayed in a complex plane (real,X – imaginary, Y) as impedanceplane trajectory or as a time-domainsignal viz. X(t) or Y(t). In theimpedance plane, magnitude andphase can be seen; however, thesignal extent or defect length cannotbe seen. On the contrary, in time-domain signals, phase angleinformation that is essential for depthestimation is absent.

Selection of test frequency is veryimportant in the EC tests and ingeneral, it is chosen such that amaximum amplitude signal is formedfor defects and with a decent phaseseparation from the lift-off axis. Asimpler way to determine the workingtest frequency range involvesassuming value of 1 and 2 for δ inequation (1) and calculating theextreme frequencies uponsubstituting σ, µ

0 and

µ

r values of the

test object.

The electrical conductivity is usuallyexpressed as percentage IACS(International Annealed CopperStandard) in which the electricalconductivity of pure copper at 25°Cis taken as 5.8 x107 Siemens/meter.For example, the IACS% value ofaustenitic stainless steel (type 304)is 2.5 with an absolute electricalconductivity of 1.45x106 and that ofAdmiralty brass is 24% with aconductivity of 1.392x107 [5].

The electromagnetic couplingbetween coil and test object is veryimportant. For reliable detection ofdefects, it is always preferable tominimise and maintain uniform lift-off or fill-factor which will bediscussed later in this paper. Failingto do so will result in degradation ofsignal-to-noise ratio (SNR).

Instead of continuous A.C. if theexciter is driven with a repetitivebroadband pulse, such as a squarewave which induces transient eddycurrents associated with highlyattenuated magnetic pulsespropagating through the material, anew technique called pulsed eddycurrent (PEC) technique is formed.The signals reflected from defectsin the object are picked up by asensor. At each probe location, a

Fig. 3 : Interaction of magnetic fields from a coil at different test conditions.

10


Basics

series of voltage-time data pairs areproduced as the induced field decays,analogous to ultrasonic A-scan data.Defects close to the surface willaffect the eddy current responseearlier in time than deep surfacedefects. PEC technique is useful fordetection of hidden corrosion inlayered structures such as aircraftlap-splices and corrosion underinsulation in insulated components.

FEATURES

EC technique is a preferredtechnique for material sorting,determination of hardness, heat

treatment inadequacies, coatingthickness measurements, anddetection of defects in tubes, rods,bars, multi-layer structures, discs,welds, blades and other regular aswell as irregular geometries [6].Some of the attractive features ofEC technique include the following:

§ Ability to distinguish metallicmaterials from non-metallic onesand sorting of materials based ondifference in heat treatment,microstructure and materialproperties (refer Fig. 5)

§ Ability to easily detect tight hairlinecracks which cannot be seen bynaked eye

§ Ability to perform tests on regularas well as irregular geometrieswithout the need for using anycouplant

§ Ability to carry out tests at morethan 10 m/s speed

§ Ability to measure coatingthickness as small as 5µ

§ Possibility to carry out hightemperature testing, even up to1000°C

§ Computer based automated testing,data storage, analysis andinterpretation without the need foran operator

§ Possibility to perform numericalmodelling for optimisation of thetechnique, probes and testparameters [7]

INSTRUMENTS

In EC technique the alternatingcurrent through the coil is keptconstant (~ few hundred mA) andthe changes in the coil impedanceare measured. Since the impedancechange is very small (< micro-ohms),high precision A.C. bridge (refer Fig.6) circuits are employed. The bridgeimbalance is correlated with thedefect or material attributeresponsible. Typical analogue ECinstrument consists of an oscillator(excitation frequency, ~ 50 Hz-5MHz), constant current supply (stepdown from 230 V AC), a bridgecircuit, amplifier, filters, oscilloscope(to display the impedance changesin a 2-D graph or as a vector) or

Fig. 4 : Two types of eddy current signals viz. Impedance plane (X-Y) and time-domain (right) from three surface cracks (a, b, c) in asteel plate.

Fig. 5 : Response on the impedance plane for different metallic materials, enablingmaterial sorting and determination of conductivity and permeability.

11


Basics

meter display unit or decision makingunit.

With the micro-electronic revolutiondigital EC instruments have replacedthe analogue EC instruments. Theseinstruments are smart, high-sensitive,low-cost, automated, modular andefficient (Fig. 7). They are, in manyinstances, interfaced to personalcomputers, industrial computers, andlaptops with possibility for easymeasurements, adjustments, controls,data storage, analysis andmanagement, all performed bysuitable software.

PROBES

EC probe forms the basic linkbetween EC instrument and the testmaterial. Depending on the geometry

of the test material, different probessuch as surface probes (for plates),encircling probes (for rods and tubes)and bobbin probes (for tubes) withcoil configurations shown in Fig. 8are used. Appropriate selection ofprobe coil is important in eddy currenttesting, as even an efficient ECinstrument cannot achieve much if itdoesn’t get the right (desired)information from the coils.

EC probes are induction based andare made up of a few turns of copperwire usually wound around a ferritecore with or without shielding. Everyprobe has an operating frequencyrange and impedance value matchingthe bridge circuit of the instrument.It is desirable to operate the probewithin that range. It is essential toavoid operating near the resonancefrequencies.

The probes are operated in absolute(single coil), differential (two coilswound opposite) or send-receive(separate coils for excitation anddetection) modes. Their design isdependent on the object geometryviz. tube, plate, bar etc. As shown inFig. 8 and the expected type andlocation of discontinuity.

Absolute probes are good fordetection of cracks (long or short)as well as gradual variations.However, absolute probes aresensitive also to lift-off, probe tilt,

temperature changes etc. Differentialprobes have two sensing coils woundin opposite direction investigating twodifferent regions of a material. Theyare good for high sensitive detectionof small defects. They arereasonably immune to changes intemperature and the operator-inducedprobe wobble [5]. The most simpleand widely used probe types are:

· Surface or pancake or pencil probes(with the probe axis normal to thesurface), are chosen for testingplates and bolt-holes either as asingle sensing element or an array- in both absolute and differential(split-D) modes.

· Encircling probes for inspection ofrods, bars and tubes with outsideaccess and

· Bobbin probes for pre-and in-service inspection of heat exchanger,steam generator, condenser tubes& others with inside access. Phasedarray receivers also possible forenhanced detection and sizing.

While pancake or surface probes areused for testing plates and regulargeometries, encircling or bobbin typeprobes are employed for testing tubes,rods, and other cylindrical objects.The EC probes possess directionalproperties i.e. regions of high and lowsensitivity (impedance change).Defects that cause maximumperturbation to eddy currents aredetected with high sensitivity. Forgood sensitivity to small shallowdefects, a small probe should be used.

Fig. 6 : A.C. bridge circuit used tomeasure small changes in EC coilimpedance.

Fig. 7 : Advanced general purpose digital instruments for static as well as dynamic EC tests and for multi-frequency EC inspection ofnon-ferromagnetic and ferromagnetic heat exchanger tubes (Courtesy: M/s. Technofour, Pune).

12


Basics

Similarly, in order to detect sub-surface and buried defects, largediameter high throughput probesoperating at lower frequencies arenecessary. As a general rule, theprobe diameter should be less thanor equal to the expected defect lengthand also comparable to the thicknessof the test object. The sensing areaof a probe is the physical diameterof the coil plus an extended area of4d due to the magnetic field spread.Hence, it is common to use ferritecores as well as shields with high mand low s, to contain the field withoutaffecting the depth of penetration.

Figure 9 shows some typical ECprobes developed for specificapplications [3, 8].

Coupling of magnetic field to thematerial surface is important in ECtesting. For surface probes, it is called“lift-off” which is the distancebetween the probe coil and thematerial surface. In general, uniformand very small lift-off is preferredfor achieving better detectionsensitivity to defects. Theelectromagnetic coupling in the caseof tubes/bars/rods is referred to as“fill-factor”. It is the ratio of square

of tube diameter to square of coildiameter for encircling coils and isexpressed as percentage(dimensionless)

Fill factor = ( D2t / D

2p )* 100 (2)

where Dp is the probe inner diameter

and Dt is the tube outer diameter

[3,5]. Usually, 70-90% “fill-factor” istargeted for reliable inspection.

The encircling probes exhibit reducedsensitivity for shallow and localiseddefects and for such applications,motorised rotating probe coil

Fig. 8 : Basic configuration of coils in eddy current probes.

Fig. 9 : Different types of EC probes.

13


Basics

(MRPC), phased-array, plus-pointetc. are used. The inspection datafrom these probes can be displayedas images which allow easyidentification of circumferentiallocation of defects. For inspection ofirregular and inaccessible regions,flexible and conformal sensors areemployed. The pancake type probesshow reduced sensitivity for sub-surface and buried defects and forsuch needs, integrated probes withcoils for excitation and solid statesensors for reception are veryattractive [2]. Such integrated probesare useful for inspection of rivets andmulti-layer structures in aircrafts andfor detection of deeply located (> 10mm) defects in steel components.

TEST PROCEDURE

General EC test procedure fordetection of defects involvescalibration of EC instrument usingreference standard defects in amaterial with similar chemicalcomposition and geometry as that ofthe actual component. Artificialdefects such as saw cuts, flat bottomholes, electro-discharge machining(EDM) notches are used while wellcharacterised natural defects, cracksin failed or withdrawn componentsare always preferred. Instrument testparameters such as excitation

frequency, gain, phase angle etc. areoptimised for a desired performance.In general, signal phase is rotatedsuch that it is parallel to lift-off orwobble axis and phase separationbetween ID and OD defects isnearly 90 degrees. A suitable ECsignal parameter, e.g. signal peak-to-peak amplitude or phase angle isidentified and an appropriatethreshold is determined forincorporating accept/reject criterion.When defect sizing is required, acalibration graph between signalparameter and defect size isgenerated and used [3]. During actualtesting, any region that produces ECsignals with parameter greater thanthe threshold is recorded defective,while its equivalent size is determinedusing the calibration graph. Similarprocedure is followed for materialsorting, conductivity measurement,microstructure characterisation, andcoating thickness measurement.

TESTING NON-FERROMAGNETIC TUBES

For periodic monitoring of corrosionof tubes in heat exchangers, steamgenerators and condensers in power,petrochemicals, fertiliser and otherindustries, EC technique is employedbecause of its ease of operation,

sensitivity, versatility, speed(~ 10 m/s) and repeatability. Thistechnique can detect wall thinning,cracks, pitting, stress corrosioncracking, hydrogen embrittlement,carburization, denting and crudedeposits etc. Typical EC signals froman ID defect, OD defect and hole ina heat exchanger tube and asurrounding steel support plate areshown in Fig. 10 for absolute anddifferential bobbin probes.

Using phase discrimination, it ispossible to readily distinguish variousdefects as well as support plates.However, single frequency ECtechnique is inadequate for detectionof defects under support plates,baffle plates and in the presence ofprobe wobble. Many a time, it isunder the support plates corrosiondamage takes place. To eliminatesignals from support plates, multi-frequency technique is employed.This technique involves simultaneousexcitation of two or more frequenciesin an EC coil and processing of thecorresponding signals to suppress thecontributions from disturbing sources,similar to solving a system of linearequations [4, 6, 8]. The multi-frequency test procedure is describedin detail in ASME Section V, Article8, Appendix 1.

Fig. 10: Typical absolute (left) and differential probe (right) EC signals for an ID defect (A), OD defect (B) and hole (C) in a heatexchanger tube and for a steel support plate (D).

14


TESTING FERROMAGNETICTUBES

Examination of ferromagnetic tubesis difficult using conventional EC testprocedures due to high relativemagnetic permeability which restrictspenetration of eddy currents andproduces disturbing signals due tocontinuously varying permeability.This disturbance can be eliminatedby employing bias or saturation directcurrent (D.C) which saturates thematerial magnetically and makes thetube material behave non-ferromagnetic, thus, allowsconventional EC testing after whichthe tubes are to be demagnetised.Typical test set up is shown inFig. 11. However, in the case ofinstalled tubes of smaller diameters,saturation units cannot beaccommodated due to limited access.For mildly magnetic materials, partialsaturation using high-energypermanent magnets such Nd-Fe-Bis a possibility. The other techniquepossible is remote field eddy current(RFEC) technique [2].

This technique uses low frequencyexcitation and a separate receiver coilkept at two to three tube diametersaway from exciter coil. The phaselag of induced voltage in the receivercoil with respect to the exciter ismeasured using lock-in amplifiers andcorrelated with wall loss or defectdepth. The advantages of RFECtechnique include ability to test tubes

in microstructure, precipitate size anddistribution, cold work, deformation,dislocation pile-up etc. alter the coilimpedance or induced voltage in apick-up coil. The magnitude andphase of induced voltage orimpedance change are used forquantitative characterisation ofmicrostructures and to estimate thevolume fraction of various phasespresent. Cold worked and annealedconditions, e.g. in stainless steel 316or 304 effect electrical conductivityin opposite directions and strain-induced martensite, being a magneticphase, increases the magneticpermeability. This phase can bedetected using EC technique.Specimens subjected to heattreatments are used to simulate theservice exposed conditions and theexpected microstructures. Usually,EC measurements for microstructurecharacterisation are location-based.In general, absolute probes are usedand analysis is based on impedanceplane signal interpretation. Referencestandards with known electricalconductivity and magneticpermeability are used also forestablishing calibration graph, apartfrom specimens heat treated todifferent ageing conditions throughmeasurement of conductivity andpermeability.

EC technique has been used tocharacterise microstructures intitanium alloy (VT 14 alloy Ti-4.5Al-3Mo-1V) subjected to a series ofheat treatments consisting ofsolutionizing for 1 h at selectedtemperatures in the range of 923-1303 K at an interval of 50 K,followed by water quenching. Thistreatment produces variety ofmicrostructures due to controlled α-β transformation and formation ofvarious phases. The experimentallymeasured EC response for variousspecimens is shown in Fig. 12 alongwith that of the reference standardsviz. Hastelloy-X, Hastelloy-B and Ti-6Al-4V. It has been found that, bothmagnitude and phase angle of

with equal sensitivity to internal andexternal wall loss and linearrelationship between wall loss andmeasured phase lag. RFEC techniquecan achieve inspection speeds of 1m/s. It is used for inspection ofcarbon steel and other ferromagnetictubes in process industry. One recentapplication of this technique is in-service inspection of steam generatortubes of sodium cooled prototype fastbreeder reactors with 23 m longmodified 9Cr-1Mo ferromagneticsteel tubes For this application, acomprehensive RFEC technologycomprising of instrumentation, probes,robotic device has been developedas a solution to the problem of smallerdiameter, expansion bends, supportplates, one-side access andelectrically conducting sodiumdeposits.

CHARACTERISATION OFMICROSTRUCTURES

During manufacturing stages ofcomponents made of alloys, heattreatment is given to ensure requiredlevels of mechanical and physicalproperties and desiredmicrostructures. Likewise, duringservice life of components, it isessential to ensure that there is noundesirable degradation inmicrostructures. EC technique isuseful for assessing these twosituations as it exploits measurementof changes in electrical conductivityand magnetic permeability. Changes

Fig. 11: D.C. Saturation based EC testing of ferromagnetic steel tubes.

15


Basics

impedance change decrease withincreasing solutionizing temperatureup to 1123 K and this is attributed todecrease in α phase (reduction inelectrical conductivity). Beyond1123K, formation of α′ martensitedominates the interactions and resultsin increase in effective electricalconductivity and hence, theimpedance change. Beyond 1273K,magnitude and phase angle reach aconstant maximum, due to 100%formation of α′ martensite.Comparison of impedance magnitudeand phase angle with hardnessmeasurements has established thatEC technique can be implemented inproduction line to quickly assess theadequacy of heat treatment.

APPLICATIONS

A few specific practical applicationsof EC technique are given below forbetter appreciation of the technique.

· Quality assurance of austeniticstainless steel tubes, plates andwelds.

· Inspection of installed heatexchanger/steam generator/condenser tubes (single and multi-frequency)

REFERENCE STANDARDS

Reference standards are used foradjusting the eddy current instrumentsensitivity to enable detection ofdesired size of defects andquantification of conductivity,permeability and material thicknessetc. They are also used for sizingdefects [2]. Some commonly usedstandards in EC testing by ASME(American Society for MechanicalEngineers), BS (British Standards),ASTM (American Society forTesting of Materials) and IS (Bureauof Indian Standards) are:

· ASME, Section V, Article 8, Appendix1 and 2), Electromagnetic (EC) testingof heat exchanger tubes

· ASTM B 244 Method for measurementof thickness of anodic coatings ofaluminum and other nonconductivecoatings on nonmagnetic base materialswith EC instruments

· ASTM B 659 Recommended practicefor measurement of thickness of metalliccoatings on nonmetallic substrates

· ASTM E 215 Standardising equipmentfor electromagnetic testing of seamlessaluminium alloy tube

· ASTM E 243 Electromagnetic (EC)testing of seamless copper and copperalloy tubes

· ASTM E 309 EC examination of steeltubular products using magneticsaturation

· ASTM E 376 Measuring coatingthickness by magnetic field or EC(electromagnetic) test methods

· ASTM E 426 Electromagnetic (EC)testing of seamless and welded tubularproducts austenitic stainless steel andsimilar alloys

· ASTM E 566 Electromagnetic (EC)sorting of ferrous metals

· ASTM E 571 Electromagnetic (EC)examination of nickel and nickel alloytubular products

· ASTM E 690 In-situ electromagnetic(EC) examination of non-magnetic heat-exchanger tubes

· ASTM E 703 Electromagnetic (EC)sorting of nonferrous metals

· BS 3889 (part 2A): 1986 (1991)Automatic EC testing of wrought steeltubes

· BS 3889 (part 213): 1966 (1987) ECtesting of non-ferrous tubes

· Detection of surface as well as sub-surface defects in multi-layeraircraft structures (single frequency,multi-frequency & pulsedtechniques)

· On-line automated saturation basedquality assurance of steel(ferromagnetic) tubes.

· Location of garter springs inPHWRs and measurement of gapin coolant channels

· Detection of intergranular corrosion(IGC) in stainless steels (316, 316Land 304 L)

· Detection of weld centre line inaustenetic stainless steel welds athigh temperature

· Measurement of coating thicknessof SiC on carbon-carboncomposites

· Sorting of materials based onelectrical conductivity and magneticpermeability

· Characterisation of heat treated aswell as degraded microstructures inalloys

· Non-contact detection of metallicobjects, land mines, security metaldetectors

· Monitoring of liquid levels and forposition encoding

Fig. 12: Impedance plane response for various heat treated specimens at 150 kHz.

16


· IS 6398:1983 Code of practice for ECtesting of ferrous seamless pipes andtubes

· IS 11612: 2004 Code of practice for ECtesting of non-ferrous seamless pipesand tubes

· IS 13190: 1991 Recommended practicefor EC examination by rotating probemethod of round steel bars.

· IS15540:2004 Recommended practice forEC testing of installed non-ferromagneticheat exchanger tubing using duelfrequency method.

LIMITATIONS

Like other NDT technique ECtechnique has certain limitations too.But interestingly, most of the originallimitations of the technique in 60s and70s have been overcome by theadvances in instrumentation, sensors,computer and signal and imageprocessing techniques. Some of theimportant limitations include:

· Applicability to only electricallyconducting (metallic) materials

· Inspection of installed ferromagneticcomponents with the exception of tubescan be inspected by remote field ECtechnique

· Difficulty to separate the influence ofone desired variable in the combinedpresence (at the same location beneaththe probe) of several other disturbingvariables such as stress, microstructure,texture, anisotropy etc. thatsimultaneously change conductivity andpermeability.

· Inability to identify circumferentiallocation of a defect when encircling orbobbin coils are used.

· Difficulty in detection of a small defectunder a large defect

· Inability to detect defects at the centreof rods using encircling coils

· Need for skilled personnel forinterpretation of signals and results

SUMMARY

Working on the principle ofelectromagnetic induction, eddycurrent technique is a widely usedNDE technique for detection ofsurface and sub-surface damage.The attractive features of thistechnique include ease of operation,high sensitivity to tight cracks,versatility, extremely high testingspeeds (up to 10 m/s), repeatabilityand reliability. This technique candetect wall thinning, cracks, pitting,stress corrosion cracking, hydrogenembrittlement, carburization, dentingand crud deposits etc. This techniquefinds a lot of applications inengineering industry includingmaterial sorting, determination ofhardness, heat treatment adequacyassessment, material propertydetermination, coating thicknessmeasurements, and detection ofdefects in tubes, rods, bars, multi-layer structures, discs, welds, bladesand other regular as well as irregulargeometries. Successful testingrequires selection of proper instrumentand probes, optimisation testfrequency and use of referencecalibration standards. Whenappropriate standards are used, notonly detection of defects but alsotheir sizing is possible using eddycurrent technique.

ACKNOWLEDGEMENTS

Author expresses special thanks toDr. T. Jayakumar, Mr. S.Thirunavukkarasu, Mrs. B. Sasi ofNDE Division, India Gandhi Centerfor Atomic Research, Kalpakkam,India.

REFERENCES

1. B.P.C. Rao and T. Jayakumar,Discontinuity characterisation usingelectromagnetic methods, J of Non-Destructive Testing & Evaluation, Vo.2,No.2, 2002, pp 23-29

2. B.P.C. Rao, T. Jayakumar, Baldev Raj,Electromagnetic NDE Techniques forDefect and MicrostructuralCharacterization, in Ultrasonic andadvanced methods for non-destructive testing and materialcharacterisation, Ed. C.H. Chen, WorldScientific Publishing Co. (Singapore),June 2007, pp. 247-278.

3. B.P.C. Rao, Introduction to EddyCurrent Testing, Narosa Publishing,New Delhi, India, February, 2007.

4. H.L. Libby, “Introduction toElectromagnetic Non-destructive TestMethods”, Wiley-Interscience, NewYork, 1971

5. V.S. Cecco V.S, G. Van Drunnen andF.L. Sharp, “Eddy current manual: testmethod”, Vol.1, AECL-7523, ChalkRiver, Ontario, Nov., 1983.

6. D.J. Hagemaier, “Fundamentals ofEddy Current Testing”, ASNT,Columbus, OH, USA, 1990.

7. W. Lord, “Electromagnetic methodsof Non-destructive Testing”, Gordonand Breach, New York, 1985.

8. Moore Patrik, Udpa S.S, Non-destructive testing handbook. 3rdedition. Vol.5: Electromagnetic testing,ASNT, Columbus, OH, USA, 2004.

17


Horizon

Dr. CV KrishnamurthyCentre for NDE and Department of Physics, IIT Madras

Nonlinear Ultrasoundand Harmonic Imagingfor NDE

Nonlinear response to ultrasound hasbeen observed in a variety ofmaterials ranging from liquids suchas water, soft matter such asbiological tissues, polycrystallinemetals, amorphous materials such asglasses, composites and porousmaterials such as stones and rocks.The linear response of any materialto ultrasound can be said to arisefrom Hooke’s law irrespective of thenature of the material and has beenwell understood in terms of thefundamental interatomic forces.Experiments on nonlinear responseindicate that in most cases thenonlinearity is not arising from thehigher-order effects of theinteratomic forces. In other words,nonlinearity sets in at loweramplitudes than what would beexpected on the basis of the strengthof the interatomic forces. Unlike thelinear response, studies of theobserved nonlinear response in thesematerials suggest several causes forthe nonlinear response that dependon mesoscale and macroscalestructural features - at themesoscopic level, some of thesefeature dislocation networks andgrain boundaries while others featuremicrocracks; at the macroscopiclevel, cracks are common in mostductile or brittle solids.

It appears that there may not be asingle mechanism for the nonlinearresponse that would be common toall classes of materials. Nevertheless,experimentally, the nonlinear responseis usually described in terms of a

single parameter (the symbol β iscommonly employed) with theunderstanding that this is a “lumped”parameter or some kind of amacroscopic average. It must be keptin mind that certain studies arespecific to observing second harmoniccomponents and the same symbol isused to describe it.

The equation governing thepropagation of a longitudinal wave(of velocity c

o) along the x-axis of

an isotropic solid (of density ρ0) is:

where U is the displacement andCII=ρ

0c2

0 is a linear combination of

the second-order elastic constants,while CIII includes the second- andthird-order constants. The nonlinearparameter is defined as

β = − CC

III

II2

The linear wave equation results forβ = 0. Depending on the sign of CIII ,nonlinear distortion of longitudinalwaves in solids can result in a saw-tooth-like velocity waveform ifCIII < 0 or an N-type wave ifCIII > 0. The majority of solidspossess CIII < 0 and hence β > 0,however, for some materials likeglass or fused silica, β < 0.

If a time-harmonic plane(displacement) wave U=A

1cos(kx–ωt),

where A1 is the amplitude, k is the

wave number, and ω is the angularfrequency. Assuming that thenonlinearity in the solid is small, thesolution to the nonlinear waveequation for this time-harmonic waveis obtained by a power series

U = A0+A

1cos(kx–ωt)

+A2cos[2(kx–ωt)]+. . .

This series can be more explicitlywritten as

U = – 1–8βk2A2

1x + A

1cos(kx–ωt)

+ 1–8βk2A2

1x[2(kx–ωt)]+....

It is noted that the amplitude of thesecond-harmonic displacement isproportional to the acousticnonlinearity parameter and asubharmonic; that is, the staticdisplacement is induced by thematerial nonlinearity. The acousticnonlinearity parameter is determinedexperimentally by measuring theabsolute amplitudes of thefundamental (A

1) and the second-

harmonic (A2) displacement signals,

or

It is to be noted that the discussionneglects the effect of attenuationlosses that may be present in thefundamental and second-harmonic. Ifthe difference in attenuation rates atthe fundamental and the second-harmonic frequencies is large, thena correction factor must be includedin the measurement of β.Experimentally, β is evaluated as theratio of the amplitude of the higherharmonics to the square of theamplitude of the fundamental (all inthe frequency domain).

It is normally assumed that the stressor strain associated with an ultrasonicwave as it propagates through amedium is small enough to be withinthe linear regime of the stress-straincurve of that medium – the regimewhere Hooke’s law is valid. It isnatural to expect that the received

18


HORIZON

signals would only undergo structure-based dispersion and amplitudechanges due to geometric attenuationand material absorption. For smallamplitude vibrations, it is found thatthe restoring force on each atom islinearly proportional to thedisplacement of that atom from theequilibrium position. The validity ofHooke’s law on a macroscopic scalearises out of the linearity at theatomic scale. Considerable degree ofspatial averaging takes place thoughwhen we go from the microscopicscale to the macroscopic scale - asmuch as to alter an inherentlyanisotropic crystalline metal to anisotropic polycrystalline metal!Averaging takes place again whenwe compare a metal that is solidifiedfrom melt with a metal that has beenforged, rolled and cut into acomponent for industrial applications.The processes of forging, rolling etc.,introduces a complex microstructureinvolving dislocation networks andgrains and yet on a macroscopicscale, regular ultrasonic experimentsbarely reveal anything!

Measurements of sound velocity andattenuation performed on a Ti–6Al–4V dog-bone sample show similarbehaviors observed in other fatiguedmaterials such as Al 2024-T4 and410Cb stainless steel. These twoacoustic quantities do not seem tobe representative of the level offatigue damage.

Large amplitude interatomicdisplacements do lead to nonlineareffects, two of which are wellknown, thermal expansion andthermal conductivity of solids. Othernonlinear effects known for sometime now are (i) the hydrostaticpressure dependence of the elasticmoduli, and (ii) acousto-elastic effect- ultrasonic waves travel at differentspeeds depending on whether theyare propagating parallel orperpendicular to the applied stress.These nonlinear effects arecharacterized through restoring forces

expressed in a power series ofdisplacements, with the first termdescribing linear response, the firstand second terms describing second-order effects, the first, second andthird terms describing third-ordereffects etc. These higher-ordereffects can be expressed throughhigher-order elastic constants forcrystalline materials or throughGruneisen parameters for crystallineand non-crystalline materials.However, since the nonlinearitiesinvolved happen to be small, theinteratomic displacement amplitudesrequired are large to be able tomanifest in regular ultrasonicexperiments. Table 1 gives acomparison of the “inherent”nonlinearity parameter manifest indifferent crystalline materials.

It is seen that the nonlinearityparameters are strongly ordered fora given propagation directionaccording to the type of crystalstructure. The relative lack ofdependence on bonding compared tocrystal structure is also apparentfrom the Table. The dependence ofthe acoustic nonlinearity parameterson the crystalline structure suggeststhat the geometry of the local atomicarrangement and shape, rather thanthe strength, of the interatomicpotential are dominant factors indetermining the magnitude of β.

Several techniques have beendeveloped to measure nonlinearity indifferent classes of materials -through the acoustoelastic effect,harmonic generation, resonant modalvibration, frequency modulation, and

Fig. 1 : (Left) Longitudinal velocity of sound – uncorrected for dimensional changes duringfatigue loading (Right) Attenuation as a function of fatigue level for Ti–6Al–4V withduplex microstructure at 10 MHz frequency (from Frouin et al, J. Mat. Res., 14(1998), 1295-1298).

19


HORIZON

wave mixing. Where applicable, theacoustoelastic effect is capable ofmeasuring all three third-order elasticconstants but is difficult to employas it relies on small changes inacoustic velocity. Harmonicgeneration has received the mostresearch effort and relies on thenonlinear material propertiesgenerating harmonics of the inputsignal. This approach is limited bythe difficulty in isolating the sourcesof the measured nonlinearity.Resonant modal vibration, frequencymodulation and wave mixing appearto be attractive for practical use andcapable of quantifying nonlinearity indifferent types of materials.

NONLINEAR RESPONSETHROUGH HARMONICGENERATION

Taking cue from medical ultrasonics,where nonlinear effects of water andsoft matter such as tissue have beenobserved and exploited for imagingwith the harmonic components of thereceived signal, immersion studies onsamples with side drilled, flat bottomand round bottom holes were carriedout. A transducer with sphericallyconcentric elements with the annularshaped outer element as a transmitterat 2.26 MHz and the circular shapedinner element as a receiver at 4.83MHz and radius of curvature of 210mm was used in the tone burst mode.The water path was set to 30 mmfor both blocks. Figure 2 shows the

nonlinear response from flat bottomholes in non-weld and electron beamweld regions of a copper block.

Unlike atoms in the interior of solidmedia, atoms in the vicinity ofinterfaces experience asymmetricstrains. The contact acousticnonlinearity (CAN) is regarded to bedue to the lack of stiffness symmetryfor near-surface strain across theinterface. The compression elasticityis anticipated to be higher than thatfor a tensile stress because the latteris accompanied by weakening (and

even rupture) of the contact betweenthe surfaces. Figure 3 shows the non-linear bulk wave reflection amplitudesstudied for 20 MHz SV-waveincident at 45° on a glass-glasscontact interface as a function ofcontact pressure.

The amplitudes of the first fourreflected harmonics as functionsof contact pressure are shown inFigure 3. For numerical evaluationof Contact Acoustic Nonlinearity(CAN) efficiency for bulk waves,normalized harmonics amplitude isintroduced: N

n = U

nω/Uω. In Table 2the values of N, for CAN calculatedfrom the data of Figure 3 arecompared with the harmonicsgeneration efficiency achievable forconventional (material ) non-linearity(superscript MN).

The latter calculations were carriedout for longitudinal wave of the sameintensity and frequency propagatingin the aluminium alloy with knownthird- and fourth-order elasticconstants. It is seen that CANefficiency for bulk waves (especially

TABLE 1

Comparison of Crystal Structure (Space Group), Bonding, and Range ofValues of Longitudinal Mode Acoustic Nonlinearity Parameters

Fig. 2 : In the echo from a FBH (non-welded zone) the harmonics of 2nd to the 4th order (top)are present, whereas the echoes from an FBH in the weld zone show the presence ofthe 2nd and 3rd harmonics only (bottom), possibly due to a larger attenuation in theweld region.(from Wu and Stepinsky, IEEE Ultrasonics Symposium (2000), 801-804.)

Table 2

(from Solodov, Ultrasonics 36 (1998), 383–390).

From Cantrell, J. Appl. Phys., 76 (1994), 3372

20


HORIZON

for the third- and fourth-harmonics)is much higher than that for materialnon-linearity.

Metals subject to fatigue undergosignificant micro-structural changesleading initially to microcracks andfinally failing often dramatically.Considerable amount of work hasbeen done over three decades ondifferent metals and alloys to identifyprecursors to failure in the contextof NDE. Figure 4 is an example ofa controlled study on Ti-6Al-4Vindicating a dramatic change in thesecond harmonic signal (180%increase in nonlinear factor).

Cantrell developed a model for thenonlinearity in metals fatigued due tocyclic loading based on dislocation

dipoles and monopoles with micro-structural data to corroborate. Thekey features of the evolution of themicro-structure during cyclic loadingin metals are (a) continuousgeneration of large numbers ofdislocations throughout the fatiguelife, (b) to-and-fro motion of thedislocations during cyclic loadingleads to the mutual trapping ofdislocations of opposite polaritymoving on glide planes, (c)accumulating dislocation dipoles self-organize into the vein structurearrangement, (d) increasingdislocation density of vein structurefrom continued cycling results in anelastic instability that drives thetransformation of vein structure intopersistent slip bands (PSBs).Cantrell’s model determines thenonlinearity parameter in terms of thecontributions from the volume fractionof dislocation dipoles and the volumefraction of dislocation monopolesleading to the assessment of thenonlinear parameter as a function ofthe number of fatigue cycles. Figure5 shows a comparison between hismodel calculations and experimentson Aluminum and Steel samples.

The agreement of the modelcalculations with measured dataindicates that (a) the role ofdislocation monopoles and dipoles inevolution of fatigue-generatedstructures is validated, (b) suggeststhat evolutionary time-line of

substructural organization is similarin the two metals (and in agreementwith pure metals from which themodel is derived), and (c) offerspromise that measurements may beused to quantify fatigue damageaccumulation in a variety of metalsat all levels of fatigue.

NONLINEAR RESPONSETHROUGH MODALVIBRATIONS

The vibration response over a certainfrequency range encompassing theconsidered natural frequency isinvestigated using a stepped sineprocedure for increasing levels ofexternal excitation. For each steady- state response, a lock – in virtualinstrument determines theacceleration level at the fundamentalfrequency. The harmonics are storedas well. The apparatus (including a

Fig. 3 : Amplitudes of the first four reflectedSV-harmonics as functions ofcontact load on glass-glassinterface: (o) ω; (p) 2 ω; (∆) 3 ω;(l) 4 ω. (from Solodov, Ultrasonics36 (1998), 383–390).

Fig. 4 : Amplitude of the second harmonic signal versus the fundamental signal for variousstages of fatigue in Ti–6Al–4V with duplex microstructure. (from Frouin et al, J. Mat.Res., 14 (1998), 1295-1298). See Figure 1 for the material’s linear response.

Fig. 5 : Graph of nonlinearity parameter ofaluminum alloy 2024-T4 fatigued instress-controlled loading at276MPa (left) and of 410Cbstainless steel fatigued in stress-controlled loading at 551MPa(right) plotted as a function ofpercent full fatigue life (fromCantrell, Rev.of Prog. Quant.Nondestr. Eval., 28 (2009), 19-32)

21


HORIZON

16-bit A/D converter) is capable ofmeasuring accelerations down to10-2m/s2, which typically correspondsto inferred strains in the order of afew nanostrains. In order to monitorthe resonance peak shift as a functionof the acceleration amplitude,typically 20 resonance sweeps aremade at successively increasing drivevoltages over the same frequencyinterval. Examples of the raw datasets obtained for the first bendingmode on the intact RC beam andafter the first loading step are shownin Figure 6.

It may be noted that the frequencyspan on both figures is markedlydifferent. For the damaged case, wenotice an obvious frequency shift asa function of the excitation level. Atthe same time, the resonance curvebecomes increasingly asymmetric.

Alternately, samples can be studiedby a Single Mode NonlinearResonant Ultrasound spectroscopytechnique which measures theamplitude dependence of theresonance behaviour of a singlemode of the samples. The techniqueis an extension of the simple ring-down measurement for a resonantmode. When a sample is subjectedto a sinusoidal excitation at or nearthe resonance frequency, its responsewill be given by

Ae-αtsin(2πft + ϕ)

an exponentially decreasingsinusoidal signal amplitude when theexcitation is turned off. Thecharacteristic decay of the amplitudeis a measure of the modal dampingratio and the dominant frequency inthe decaying signal correspondsexactly to the resonance frequencyof the sample. By increasing theexcitation voltage and analyzing thering-down, one can investigate thenonlinear effect on both resonancefrequency and damping ratios.

The mode under consideration in thisstudy is the fundamental flexural

mode of a beam, which has a stressconcentration in the middle of thesample and displacement nodes at adistance of 0.224 L from both edges,with L the length of the sample (120mm). Each sample is supported bytwo nylon wires at the node lines. A1000 period burst excitation at a givenamplitude and with a frequency closeto the fundamental flexuralresonance frequency is applied by aloudspeaker (dia 32 mm, focused by

a cone to 20 mm) centered in themiddle of the sample. The responseis measured by a laser vibrometernear one of the edges. The analysisprocedure yields the evolution of thefrequency (f) and dampingcharacteristic (α) as a function ofthe amplitude A in the decayingsignal. If no nonlinearity is presentwe should obtain a constantfrequency and a constant dampingcharacteristic for all amplitudes.

Fig. 6 : Measured response for the intact (a) and damaged state (b) of an RC beam after thefirst loading (from Van Den Abeele and Visscher, Cement and Concrete Research 30(2000), 1453 – 1464).

Fig. 7 : Ultrasonic amplitude C-scan of unidirectional composite (CF/epoxy laminate) exposedfor one hour at three temperatures. Delaminations clearly appear at 290°C whereasno sign of damage is seen at 285°C. The measured value of the interlaminar shearstrength for the same type of samples changes from 121 MPa for nonexposed samplesto 84 MPa at 285°C and to 43 MPa for samples at 300°C. (from Ref [5]).

22


would be connected to damage, theQ-factor would be expected todecrease rather than increase. Theeffect seen in the linear attenuationis regarded not as a measure of themicrodamage, but is believed to berelated to the chemical and physicalchange in linear material parametersdue to the thermal loading.

NONLINEAR RESPONSETHROUGH INTERACTINGWAVES

Non-collinear method is based on thefact that material nonlinearities causeinteraction between two intersectingultrasonic waves. Under certaincircumstances, this can lead to thegeneration of a third wave with afrequency and wavevector equal to

of the nonlinearity, integrated overthe whole sample and has no directinformation on the localization of thedefects.

The nonlinear parameter γ in Figure9 shows an overall increase withincreasing exposure time and heattemperature, up to a factor 10 withrespect to the reference value. As acomparison with a measure of thelinear characteristics of the sample(traditional ultrasound), the values ofthe linear (or low-amplitude) Q-factorof each of the samples are alsodisplayed on the right of Figure 9.Apparently the linear attenuation forheat-treated samples reduces as afunction of exposure time and heattemperature (Q-factor increases). Ifthe linear measure of attenuation

However, when nonlinearity ispresent and related to the thermallyinduced micro-damage, we mayexpect a stronger dependence of thefrequency (f) and damping (α) onthe amplitude for increasing micro-damage. Figure 8 shows the Qfactor (Q = πf/α) indicating that thismaterial parameter is amplitudedependent and that the nonlinearityis higher for samples with increasedthermal damage.

In order to quantify the degree ofnonlinearity, the linear proportionalitycoefficient g between the relativeresonance frequency shift and thestrain amplitude (from using strain-acceleration conversion) iscalculated. It may be noted that gonly represents a global quantification

Fig. 8 : Nonlinear Q-factor for two samples at different heating temperatures and exposure times - (a) 250° C for 45' and (b) 300° C for 60'.(from Ref [5])

Fig. 9 : Nonlinearity g (left) and linear Q-factor (right) for all 21 samples as a function of heating temperature and exposure time. (from Ref [5]).

23


the sum of the incident wavefrequencies and wavevectors,respectively. Theoretically, there areseveral incident wave combinationsthat can achieve this; however,practical material constraints to thetheory lead to the interaction of twoshear waves generating a longitudinalwave as the most useful case. Thisapproach is advantageous offering thepotential for frequency, modal andspatial separation of the nonlinearsignals.

The configuration shown in Figure10 allows single sided measurementsto be made and is employed for laterresults in an immersion tank forscanning measurements. As seen onthe right bottom of Figure 10 there isvery little signal present at the timeof arrival for any wave coming fromthe volume of interaction. This is dueto the lack of interaction as a resultof the single incident wave. Whenboth waves are fired a clear signalappears (right top of Figure 10) atthis location indicative of the materialnonlinearity. If the two signals withonly a single transducer are summed

then the magnitude in the expectedarrival time for the nonlinear signalgives a measure of the underlyingsystem nonlinearity.

NONLINEAR RESPONSETHROUGH FREQUENCYMODULATION

Two methods employing themodulation of ultrasound by vibrationhave been developed, namely, Vibro-Modulation (VM) and Impact-Modulation (IM) methods. Vibro-acoustic modulation (VM) methodemploys forced harmonic vibration ofa structure being tested while impactmodulation (IM) method uses impactexcitation of natural modes ofvibration of the structure.

The impact is produced by aninstrumented hammer equipped withchangeable tips (plastic or metal) anda sensor. This sensor sent an impact-generated triggering signal to a dataacquisition system. The ultrasonicpart of the setup consists of twopiezo-ceramic water-coupledtransducers or piezo-ceramic disks

attached to one side (as shown) orthe opposite sides of the sample. Onedisk is used as a transmitter whilethe other one is a receiver. Staticand dynamic (vibration) stressesapplied to the sample are controlledwith a strain gage attached to theopposite (from a crack) side of asample. Figure 11 shows theschematic and typical results fordefective steel bars.

The VM is based on modulation ofthe ultrasonic signal by low frequencyvibration in the presence of a flawsuch as a crack, delamination or poorquality bonding. The vibration variesthe contact area within a contact-type interface (or alters interfaceopening) modulating the phase andamplitude of the higher frequencyprobe wave passing through thisvariable contact area. The resultingmodulated signal contains newfrequency (side-band) components,which are associated only with theflaw and can be easily detected. Thisfeature is especially advantageous fordifferentiation between integrityreducing defect and other structural

Fig. 10: (left) Schematic of noncollinear inspection system. (right) Received ultrasonic signals at frequency of nonlinear interaction (a) withboth input transducers fired simultaneously, (b) with a single input transducer (from Croxford, Drinkwater and Wilcox, AIP Conf.Proc. (2011), 1335, 330-337)

24


Fig. 11 : (Top) Schematic of the experimental set up for the IM method. It is similar for the VM method except that the hammer is replaced bya shaker. (Middle) Spectra of the probing ultrasonic signals obtained with the IM method: (a) steel pipe without cracks; (b) steel pipewith stress-corrosion cracks.(Bottom) Spectra of the modulated ultrasonic signal obtained with the VM method in steel bars - sample#2-1 has no crack while sample #2-2 has a crack (Donskoy et al, NDT&E Int. 34 (2001), 231–238).

domain - the intact parts of thematerial outside the defect vibratelinearly, i.e. with no frequencyvariation in the output spectrum whilethe defective region generatesresponses with rich spectral content.As the pulse-type modulationproduced by the excitation (pump)wave contains several frequencies,the output signal acquires the

inhomogeneities. The modulationeffect has been observedexperimentally for various types ofdefects: cracks, disbondings,delaminations, and microstructuralmaterial defects.

HARMONIC IMAGING

The nonlinear response of defectshas been dramatic in the spectral

combination frequencies (nν1 ± ν

2)

around the ultra-harmonics ní1. If the

probing wave is strong enough toinduce some nonlinearity in thecontact, the modulation spectrumexpands to multiple side lobes aroundboth, the pump frequency and theprobe frequency (nν

1 ± mν

2). The

magnitudes of the modulation side-lobes are indicators of nonlinearity

25


of the defect and used for qualitativeNDT of cracked flaws in metal parts,concrete and composites.

Figure 12 shows images of thevibration spectra of defects with alaser scanning vibrometry adapted fornonlinear measurements (nonlinearscanning laser vibrometry (NSLV).A piezostack transducer connectedto CW high-power source was usedfor generation of intense ultrasonicwaves (frequencies 20 and 40 kHz,strain amplitudes up to ~10-3). Forplate-like specimens studied, itexcites flexural waves whose out-of-plane particle velocity inducesfrequency modulation of the laserlight reflected from the surface ofthe specimen. After demodulation, thespectrum of the vibrations (particlevelocity output) is obtained over1 MHz bandwidth by FFT.

The nonlinear air-coupled emission(NACE) NDE-application was foundto be particularly beneficial in metalliccomponents where low acousticdamping facilitates the formation ofstanding waves which produce astrong spurious background in thenonlinear scanning laser vibrometry(NSLV).

Air-coupled ultrasonic transducerprovides an alternative to laserDoppler vibrometry for non-contactscanning and imaging applications.Figure 13 shows images obtained bythis technique to detect areas ofimpact damage in CFRP that had apiezo-actuator bonded to it. The air-coupled ultrasonic transducer at450 kHz was used to detect theharmonics of a low frequencyactuator.

The C-scan (transmission mode)displays locations of the impacts andof the actuator (Figure 13 - top). Byusing the 2nd harmonic for imaging,the impacts can be seen (Figure 13-middle). The impact directly on topof the actuator cannot be detectedclearly because of the higherharmonics of the actuator. When the

Fig. 12: (left) NSLV-imaging of fatigue crack in AL cylinder: 4th (left), 9th (middle) and 16thharmonic images.(right-top) photo of the laser welded joint measured; (right –bottom) NACE image of laser welded joint in steel.(from Ref. [6]).

Fig. 13: C-scan of CFRP sample with impact damages (top - transmission mode); C-scan at2nd harmonic (middle);C-scan with an excitation frequency of 320 kHz (bottom)(from Pfleiderer et al (2003), NDT.net - February 2003, 8 No.2)

Fig. 14: Ultra-frequency pair imaging of corner delamination in C/C-SiC-fibre reinforcedceramic: excitation frequency 20 kHz; ultra-frequency pair images at 32.25 kHz(left) and 47.75 kHz (right).(from Ref.[6])

26


the fore-runners of further majordamage, the NNDT is capable ofearly recognition of materialdegradation and “predicting” theoncoming fracture.

FOR FURTHER READING:

1. R. Guyer, P. Johnson, Nonlinearmesoscopic elasticity: evidence for anew class of materials, Phys Today 52(1999), 30–36.

2. Y. Zheng, Roman Gr Maev; I.Y.Solodov, Nonlinear acousticapplications for materialcharacterization: A review, CanadianJournal of Physics 77 (1999), 927-967

3. J.H. Cantrell, Fundamentals andApplication of Nonlinear UltrasonicNondestructive Evaluation, inUltrasonic Nondestructive Evaluation:Engineering and Biological MaterialCharacterization, ed. Tribikram Kundu,(2004) CRC Press.

plate is excited at 320 kHz, only noiseis detected (Figure 13 - bottom).

Figure 14 is an example offrequency-pair generation in a fibrereinforced ceramic that had a cornerdelamination. Excitation was througha piezo-stack and the detection andimaging was carried out with ascanning laser vibrometer to measureboth in-plane and out-of-planecomponents of vibrations.

What makes the nonlinear NDT(NNDT) a unique defect-selectiveinstrument for localising and imagingof nonlinear flaws is that a smallcracked defect (transparent in alinear ultrasonic NDT) behaves asan active radiation source of newfrequency components rather than apassive scatterer in conventionalultrasonic testing. Since the micro-contact (nonlinear) defects are only

4. J.Y. Kim, A.Baltazar, J.W.Hu,S.I.Rokhlin, Hysteretic linear andnonlinear acoustic responses frompressed interfaces, InternationalJournal of Solids and Structures 43(2006) 6436–6452

5. K. Van Den Abeele, T. Katkowski, N.Wilkie-Chancellier, and W.Desadeleer,Laboratory Experiments usingNonlinear Elastic Wave Spectroscopy(NEWS): a Precursor to HealthMonitoring Applications inAeronautics, Cultural Heritage, andCivil Engineering, Chapter 24, inUniversality of NonclassicalNonlinearity – Applications to Non-Destructive Evaluations andUltrasonics, ed. P.P. Delsanto, (2007)Springer Science, NY (USA)

6. K. Pfleiderer, I.Y. Solodov and G.Busse, New opportunities in acousticNDT using frequency conversion bynonlinear defects, in EmergingTechnologies in Non-DestructiveTesting – Busse et al. (eds) (2008)Taylor & Francis Group, London

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Note-1: The above National awards by ISNT are as a part of its efforts to recognise and motivate excellence in NDTprofessional enterpreneurs. Nomination form for the above awards can be obtained from ISNT head office at Chennai,or from the chapters. The filled application are to be sent to Chairman, Awards Committee, Indian Society for Non-destructive Testing, Module No. 60 & 61, Readymade Garment Complex, SIDCO Ind. Estate, Guindy, Chennai-600 032.Telefax : 044-2250 0412 Email: [email protected]

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Introduction

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44 Technical Paper

Journal of Non destructive Testing & Evaluation Vol. 10, Issue 3 December 2011

Imaging of Impacted Kevlar Composite Armoursusing Data Clustering

Sutanu Samanta1 and Debasis Datta2

1Mechanical Engineering Department, North Eastern Regional Institute of Science and Technology (a Deemed University), Nirjuli-791109,

Arunachal Pradesh, India2Mechanical Engineering Department, Bengal Engineering and Science University, Shibpur, Howrah-711103, West Bengal, India

E-mail: [email protected], debasis_datta @rediffmail.com

ABSTRACTExperimental investigation has been carried out to study the behaviour of Kevlar epoxy and Kevlar polypropylene compositearmours subjected to ballistic impact. Immersion type ultrasonic C-scan is performed on impacted armours and extent of damagesof different severity are identified through C-scan imaging of A-scan data. The core damage areas for different impact casesare evaluated from the representative C-scan images and their dependency on the impact parameters are noted. In general thecore damage area of damage is found to be more in case of Kevlar polypropylene composite armours compared to that foundin Kevlar epoxy. However the core damage area is found to increase significantly when the shot remains lodged inside the armourafter impact. The present methodology is able to implement the imaging of the impacted area effectively and without anyintervention of the skilled personnel.

Keywords: Ballistic impact, C-scan, A-scan, Composite armours and Shot lodge.

1. INTRODUCTION

The last two decades have observed a revolutionary attemptin modifying and implementing new methodology anddesign concepts resulting in enhanced and successfulimplementation of laminated FRP composites in almost allareas, thereby gradually substituting traditional monolithicmaterials. Still there are several inhibiting factors, whichhave delayed the widespread use of composites in aircrafts,military armaments and spaceships, where the potentialfor weight reduction is at a premium. A composite maycontain defect/damage in several forms, namely, fibrebreakage, matrix cracks, fibre debonds, fibre pull out andthe delamination cracks. Out of these delamination cracksrunning at the interface parallel to the plane of the plies,are quite common. Such cracks occur when laminates failin flexure mostly under impact loads and are responsiblefor absorbing a significant amount of fracture energy. Sothere is a growing need in military and civil application forcomposite materials that not only have good structuralcharacteristics, but also good penetration resistance andgreater strength after impact. Many researchers haveworked in this area and a considerable amount of literaturehas been published. Hagemier et al [1] inspected boron,glass and graphite reinforced polymer matrix compositesusing ultrasonic C-scan. They observed that through-transmission technique had the advantage of not beingaffected by surface roughness, surface contour etc. Mooland Stephenson [2] inspected boron/epoxy laminates withknown defects using through transmission ultrasonic C-scan with unfocussed probes.

In recent years attention has been given to the developmentof automated C-scan technique. C-scans of tubular

graphite/epoxy specimens were performed by Rogovsky[3]. In this investigation, flaws were simulated in differentlayers of the tube and a method was suggested where thenumber and amplitude of multiple reflections wasconsidered. Jones [4] developed an automatic ultrasonicscanning system which controls the location of probes ina water squirter system. The received signal is digitized toprovide a grey level image. A comprehensive review articleon ultrasonic NDE of advanced composites was presentedby Henneke [5]. The article covers a wide range of works,applied to composite material, which include differentmodes of wave propagation, material characterization,attenuation measurement, C-scan techniques etc. Preussand Clark [6] used the time of flight of ultrasonic C-scanning for detecting, sizing and characterization ofdefects in carbon-fibre composites. Hosur et al [7]presented the results of experimental work on damage ofcarbon fibre reinforced plastic laminate due to low velocityimpact. Both drop weight and low velocity projectileimpacts were carried out. The resulting delamination wasdetermined by immersion type ultrasonic C-scans.

It is observed that there has been a steady increase in theusage of ultrasonic non-destructive testing for detectionof size, shape and orientation of surface and internaldiscontinuities. In recent years, with the advent ofelectronics and high speed computers, the methodologyhas become more powerful and user friendly. Real timedigitisation of the analogue signal has added quantitativeflavour to the methodology. In the present investigation, amethodology for nearly real time evaluation of impactdamage in composite is proposed. The experimental setup and different steps of data grouping methodology arediscussed in the following sections.

45Technical Paper

Vol. 10, Issue 3 December 2011 Journal of Non destructive Testing & Evaluation

2. EXPERIMENTAL

Kevlar-Polypropylene (Kevlar-PP) and Kevlar-Epoxycomposite laminates of different thickness have been usedunder the study. The reinforcing material in the compositelaminates is plain weave Kevlar fabric of size 300mm x300mm. Two types of resin matrices, (i) epoxy and (ii)polypropylene have been used. The experimental set upand data acquisition is discussed briefly in the followingsection.

2.1 Ballistic testing Set-up

The composite laminates were mounted on speciallydesigned holders where two sides were clamped for rigidholding and placed in the line of fire of a 7.62mm calibermilitary rifle. The impact energy was varied by adjustingthe propellant mass in the ammunition. The impact andthe exit (residual) velocities were measured by the foil andcounter method. Each frame consisted of two thinaluminium foil separated by a thin insulating paper boardand were connected to a timer. Two frames were separatedby a distance of 3m and placed as close to the impactedpanel and the velocity was calculated from the time takenby the projectile to travel between two frames at ameasured distance apart. A similar set up was used behindthe laminate panel to record the exit velocity in case ofperforations.

2.2 C-scan and Data Acquisition

C-Scan of composite laminates was done in an immersiontype C-scan set up. The set up is furnished with facilitiesfor automated transducer movement and data acquisition.The set up is comprised of (i) an acrylic glass tankfurnished at the top with lead screws in mutuallyperpendicular directions, (ii) stepper motors and controllersto drive the lead screws which, in turn, hold a commonnut to accommodate the probe holder assembly, (iii) thePCUS 11 ultrasonic board [8], its compatible software[9], (iv) an interfacing computer, and (v) the normal beamlongitudinal wave transducers. The ultrasonic board,installed in the controlling computer, has necessaryfunctions required for ultrasonic measurement, includinghigh voltage pulser, analogue to digital converter, and signalconditioning elements. This board has two connectors forprobe connection. For pulse-echo mode of scanning thesingle transmitter cum receiver probe is connected to theS/E connector and for through transmission mode ofscanning the transmitter probe is connected to the S/Econnector while the receiver probe is connected to the Econnector.

In the present work, impacted Kevlar-epoxy and Kevlar-polypropylene composite specimens were scanned in pulse-echo mode. The probe used had a central frequency of 1MHz and a diameter of 12.5 mm and it remained partiallyimmersed in the coupling liquid. The movement of theprobe in x and y directions are controlled by two steppermotors and the distance between two successive probe

positions was kept at 3mm in the present case. Theultrasonic board seamlessly interacts with the compatiblesoftware that has the capability to condition, gate andzooming of the digitised waveform. For each point scanned,a gated portion of the A-scan trace of the RF signalcovering the backwall echo was digitized at a samplingrate of 80MHz. and stored as an ASCII file in the interfacingcomputer. Desired ultrasonic features were then extractedfrom the digitized waveform for each point and they formeda data set. Such a data set, either for a single feature orfor a set of features, is subjected to clustering for asystematic classification.

3. CLUSTERING OF DATASET

Clustering is an important technique used in discoveringthe inherent structure present in any given data set. It iswidely used in data analysis and pattern recognition. Dattaet al [10] performed immersion type ultrasonic C-scanson through transmission mode. The impact was createdby repeated dropping of weights on GFRP panels. C-scanimage of the specimen was generated by grouping ofsignal amplitude data set by UPGMA linkage method basedon a pre-selected no of cluster. But this is a sensitive taskand may require enough pre-idea regarding nature ofdistribution of object values in the set. Additionally theperformances of such algorithms are also influenced bythe choice of the distance measure (such as Euclidean,Mahalanobis, Hamming, Jaccard, Chebychev etc.) and arerequired to be chosen appropriately according to theunderlying shapes of the data. In order to improve thesedrawbacks an algorithm for data clustering was proposedby Wong et al [11]. This method is based on regulating asimilarity measure and replacing movable vectors so thatthe appropriate number of clusters is determined by thebest performance index. At initial stage a data point ischosen as a reference vector (ci) and those vector (cj)that have high similarity with the reference vector has tobe found. Such similarities are computed with the help ofEq.1.

(1)

where, rij represents the similarity between the referencevector ci and the comparison vector cj, and σ is the widthof the exponential function. The higher value of rij indicatesthe closeness between the two vectors. Then referencevector will be replaced with the average of those vectorswith high similarity with the reference vector. In this wayone time will reach when all the replaced vectors will tendtoward their cluster centres. In the iterative process of thealgorithm, the width of the similarity measure functioncan be changed. The value of this width plays an importantrole in determining how large or small range of data canbe grouped in the same cluster. Each different width valuemay result in different number of clusters and their centres.In the iterative process, the width (σ) is increased by theincrement (dσ), calculated on the basis of similaritymeasure, and for each σ the classification process is

46 Technical Paper


evaluated through calculation of a performance index givenby

(2)

Where the value skd is calculated by

(3)

Where, hk = the number of clusters which is equal to thenumber of different vectors in the final results, mk

d = thecluster centre of the dth cluster which is one of the differentvectors in the final result, nk

d =the number of data whichbelongs to the dth cluster, x= original data. It is obviousthat the large value of sk

d = suggests a compact and wellisolated cluster. That is, the larger is the PI, the morecompact and well isolated is the cluster.

In this way the optimum numbers of clusters along withthe classification results are obtained that correspond tothe maximum performance index. In respect of imagegeneration through clustering, this aspect of Wong’salgorithm might be useful. In an FRP composite an impactprimarily creates delamination, fibre breakage and matrixcracking. The severity of damage is the maximum at theimpact zone and the said zone may be called as the coredamage zone. Damages of lesser severity are expected tosurround the core damage region. In general, 2-4 distinctregions are expected to be present in an impacted compositeand this number may not be judged just by looking at thespread of the data. Thus, the clustering procedure withthe ability to decide an optimum number of clusters wouldnot only be helpful to identify number of distinct zones inthe impacted region, but also be helpful to identify theextent of such zones in a meaningful manner. One or twooutlier points may exist in the classification result but theymight not affect the general prediction. In fact the saidalgorithm is found to be suitable in the image generationprocess.

In the present investigation, the clustering algorithmproposed by Wong et al. and outlined in detail in [11] hasbeen coded in the MATLAB environment and used forclassification of ultrasonic data comprising of single ormulti-attributes. Before using it for the ultrasonic data, thetest data as given in [11] are used and the classificationresults are found to match verifying the correctness ofthe code. The images obtained thereof are used for findingthe core damage region, the area of which is then relatedto the impact and residual velocity of the bullet.

4. RESULTS AND DISCUSSIONS

C-scan images of several impacted regions from fourcomposites laminates are presented and discussed. Theextents of the core damage zones as obtained from theimages are then compared with the respective impact andresidual velocities of the projectile.

4.1 Specimens

Two composite laminates viz. 20 mm Kevlar-PolyPropylene and 20 mm Kevlar-Epoxy each subjected toballistic impacts at several regions have been studied. Threeimpacted regions from each of 20 mm Kevlar-epoxy andKevlar-polypropylene laminate have been C-scanned. Forstriking velocities higher than the ballistic limit, the bulletsperforate the laminates and come out with a residualvelocity. In some cases, however, shot lodging takes place,i.e., the bullet does not perforate the laminate and remainswithin it. Such phenomena are expected to happen whenthe striking velocity is close to the ballistic limit or if thebullet gets diverted from its striking direction whileperforating the plate.

4.2 Dependence of core damage areas ofimpacted regions in the 20 mm thick Kevlarepoxy Composite Plate on impact parameters

To check the dependence of core damage area on impactparameters, three impacted regions in the Kevlar epoxycomposite plate has been chosen. Out of the three, twoare square regions (zone-1, & 3) of sizes 72mm x 72mmand 50mm x 50mm. Rest one is rectangular (zone-2) inshape , size 72mm x 60mm. Zone 2 had to be maderectangular to avoid overlapping with neighboring impactzones. In Figs. 1 to 3, the C-scan images of the aboveregions generated by Wong algorithm are shown. Duringgeneration of each of the images, area of the core damageregion was estimated. The correlation between the damageareas and the impact velocities is discussed in Table 1where variations of the core damage areas for zone-1,zone-2 and zone-3 with the striking velocity, residualvelocity and root of the difference between their squaresare shown.

Table 1: C-scan result of Kevlar Epoxy composite plate basedon Peak Amplitude feature by Wang algorithm

Scanned Striking Residual (Vs-Vr) (Vs2-Vr

2) Core Damagezone Velocity Velocity Area

(Vs) (Vr) by Wang.(m/s) (m/s) (m/s) algorithm

Zone-1 427 159.1 267.9 396.25 190.77

Zone-2 514.7 378.5 136.2 348.78 167.23

Zone-3 521.9 308.5 213.4 421 251.48

It is observed that in general, the core damage areaincreases with the increase in the root of their squaredvelocity difference which is proportional to the loss inkinetic energy of the bullet.

47Technical Paper


Fig. 1 : Peak amplitude based C-scan image of scanned zone-1 byWong algorithm



Fig. 4 : Peak amplitude based C-scan images of zone-1 by Wongalgorithm



48 Technical Paper


4.3 Dependence of core damage areas ofimpacted regions in the 20 mm thick Kevlar-Polypropylene Composite Plate on impactparameters

In Figs. 4-6, C-scan images of an impact region of size72mm by 72mm Kevlar-polypropylene armour for featurepeak amplitude are shown. The images clearly bring outthe damage region in the central portion marked withwhite shade, and these are consistent with those in theactual specimen. It is clear that the images in Fig. 4 and5 are more consistent as it clearly reveals the existence ofdifferent distinct regions around the location of impact.Fig. 6 represents the C-scan image of Kevlar polypropylenearmours where shot lodging takes place. The area of thecore damage region for this case is comparatively larger.

Details of impact information with the areas of the coredamage regions for the case cited above are summarizedin Table 2. It shows that the core damage area is dependenton the root of their squared velocity difference. As thedifference increases the core damage area also tends toincrease. But the magnitude of striking velocity also seemsto play an important role in the core damage area. However,the shot lodging case (zone-3) can be taken as a specialcase where there is an abrupt large increase in the coredamage area. This can be attributed to the embedding ofa bullet of diameter 7.62mm and length 39mm. This isexpected to create a damage that covers a large part ofthe scanned region of 72mm x 72mm. The visible area ofdamage on the plate is also comparatively larger.

Table 2 : C-scan result of Kevlar Poly propylene compositeplate based on Peak Amplitude feature by Wangalgorithm

Scanned Striking Residual (Vs-Vr) (Vs2-Vr

2) Corezone Velocity Velocity Damage

(Vs) (Vr) Area(m/s) (m/s) (m/s) by Wang.

algorithm

Zone-1 477.8 62.5 415.3 473.7 505.96

Zone-2 594.8 440.2 154.6 400.01 464.5

Zone-3 423.2 0 423.2 423.2 2977.7

From the data presented in Table 1 & 2, it has beenobserved that the damage size in case of Kevlar Epoxypanel is less compared to that in case of Kevlar-Polypropylene indicating the localised nature of damage inthe Kevlar-Epoxy armour.

5. CONCLUSIONS

The ultrasonic C-scan technique has been employed forassessment of damage due to ballistic impact on two types

of composite armours. An algorithm suitable for C-scandata clustering has been identified. Performance of thisalgorithm is independent of predetermined number ofclusters. C-scan images generated show that the coredamage area in the impacted composite armours isdependent on the matrix, striking velocity and thephenomenon of shot lodging. Damage of a more localizednature has been observed in Kevlar-epoxy composite platesas compared to Kevlar-PolyPropylene. The damage areafor both the types of laminate is found to depend directlyon the loss of kinetic energy of the bullet. However, thestriking velocity alone may influence the core damagearea. The core damage area increases sharply in cases ofshot lodging.

REFERENCES

1. Hagemaier D.J., Mcfaul H.J. and Moon D., 1971, NondestructiveTesting of Graphite Fibre Composite Structures, MaterialsEvaluaion, Vol. 29, No. 6. pp. 133-140.

2. Mool D and Stephenson R, 1971, Ultrasonic inspection of aboron/epoxy-Aluminium Composite Panel, Materials Evaluation,Vol. 29, No.7, pp. 159-164.

3. Rogovsky A.J., 1985, Ultrasonic and Thermographic Methodsfor NDE of Composite Tubular Parts, Materials Evaluation, Vol.43, pp. 547-555.

4. Jones T.S., 1985, Inspection of Composites Using the AutomatedUltrasonic Scanning System (AUSS). Materials Evaluation, Vol.43, pp. 746-753.

5. Henneke E.G.II, 1990, Ultrasonic Non Destructive Evaluation ofAdvanced Composites, Non Destructive Testing of FibreReinforced Plastics Composites, Vol.2, Editor: John SummerScales.pp. 55-159.

6. Preuss T.E. and Clark G., 1988, Use of Time-of-Flight C-scanningfor Assessment of Impact Damage in Composites, Composites,Vol. 19, No. 2, pp.145-148.

7. Hosur M.V., Murthy C.R.L., Ramamurthy T.S. and Shet Anita,1998, Estimation of Impact-Induced Damage in CFRP LaminatesThrough Ultrasonic Imaging, NDT&E International, Vol. 31, No.5, pp. 359-374.

8. PCUS11 Ultrasonic P/R Board Manual, 1999, Doc # EBD003-1, Fraunhoffer Institute for Non-Destructive Testing,Saarbruecken, Germany.

9. QUT Ultrasonic testing software Manual, Version 4, 1999,Quality Network (QNET) Pvt. Ltd.

10. Datta D., Samanta S. and Maity P., 2004, Automated Imagingof Composite Specimens, Journal of Non Destructive Testingand Evaluation, Vol. 3, pp. 21-27.

11. Wong Ching-Chang, Chen Chia-Chong, Su Mu-Chun, 2001, ANovel Algorithm for Data Clustering, Pattern Recognition, Vol.34, pp. 425-442.

49Technical Paper


Generation of Guided Waves Using MagnetostrictiveNanostructured Sensing Elements for Pipe Inspection

A.K. Panda, P.K. Sharan*, G.V.S. Murthy, R.K. Roy, S. Palit Sagar and A. MitraCSIR- National Metallurgical Laboratory, Jamshedpur 831007, India

*National Institute of Technology, Tiruchirappalli, 620015, India

Email : [email protected]

ABSTRACTThe investigation addresses the generation of guided wave in an aluminium pipe using rapidly quenched magnetostrictive ribbonsprepared by melt spinning technique. The experimentation involved development of a sensing device which has the capabilityof excitation of guided waves as well as sensing the back-wall signals using the magnetostrictive sensor elements. The devicecomprised of a transmitting coil ‘TC’, a receiving coil ‘RC’ and a biasing coil ‘BC’ placed between TC and RC. The sensormaterials used in the study were rapidly solidified nanostructured ribbons of Co36Fe36Si3Al 1B20Nb4, Fe78Si8B14, Fe80Si8B12 andFe40Ni40B20 alloys. The effect of as-spun and annealed ribbons on the sensor output signal was studied. The Fe80Si8B12 ribbonsshowed highest back-wall signal after annealing at 400oC for 15 minutes.

Keyword: Guided wave, magnetostrictive, rapidly solidified ribbon, pipe.

1. INTRODUCTION

The ultrasonic guided wave technique is a promising non-destructive method bearing great potential in the inspectionof bounded mediums such as pipes, plates and rods [1,2].It’s advantages include long range inspection capability,structure health monitoring (SHM) of pipes in relativelyinaccessible regions such elevated location and in buriedstate. The source of the guided waves can be of variousorigins. It can be of piezoelectric crystals, EMAT orMagnetostrictive sensor (MsS) element. The firstcommercially available guided wave actuators were basedon both piezoelectric [3] and MsS [4] transducers. Arrayof piezoelectric transducer and electromagnetic acoustictransducer (EMAT) were commonly used for thegeneration of guided waves. Long range ultrasonic testing(LRUT) technique has the capability of inspecting defectssuch cracks and corrosion from a single sensor positionon a structure over long distances using MsS [5]. Inmany industries pipe corrosion is one of the major problemsfor plant maintenance that might be due to water loggingor environment effect. Long range guided wave (LRGW)technique bears interesting investigations [6-8] on non-destructive detection and classification of pipe integrityusing MsS sensors. The MsS technology is finding wideindustrial applications in various industries including oil,gas, chemical, petrochemical, aerospace, electric powerand civil engineering where the long range cable inspectionand monitoring is beneficial for safety and integrity of thestructure. It is the process of inducing ultrasonic wavesin a solid material with the help of an electrically drivencoil in the presence of magnetic field.

In the MsS technique for the generation of guided waves,the sensor materials used were mostly crystalline materials.The materials used sofar mostly belonged to CoFe-based

crystalline alloys which are not only expensive but alsorequired high bias magnetizing field [9]. In the presentinvestigation, rapidly quenched ribbons of differentmaterials have been used for the generation of guidedwaves and the signals from the back-walls have beenanalysed.

2. ANALYSIS USING SOFTWARE ANDEXPERIMENTATION

2.1 Estimation of guided wave frequency

An infinite number of the vibration modes are theoreticallypossible in the structure due to the mode conversion andreflection from the surface of structure. The propagationvelocity of the wave modes is dependent on frequencyand the wall thickness of the pipes. Dispersion propertiesof Lamb waves, which refer to longitudinal guided wavesin tube, were investigated theoretically using a general-purpose software package called DISPERSE version 2[10]. Exciting Frequency for generation of single wavemode was obtained from dispersion curve (fig-1). For Altube, density equal to 2.696 g/cm3 was fed into thesoftware. The input parameters for cylindrical structureare; pipe inner radius = 15.5 mm, wall thickness = 8.5mm.

If the ultrasonic pulse is considered of many waves withdifferent frequencies, the guided waves will propagate indifferent velocities. The vibration of particles is thecomposite of actions of many frequencies. At least, theirphase velocities are the velocity of the wave-before ofsame frequency, and their group velocities are the ones ofwave packets consisted of different frequencies. Thereare three kinds of modes in the pipes: longitudinal modes,torsional modes and flexural modes, where the longitudinal

50 Technical Paper


modes are subdivided into L(0,1), L(0,2),..; the torsionalones into T(0,1), T(0,2),…; and flexural modes into F(1,1),F(1,2)… the first two wave modes are axisymmetry andlast one is the non-axisymmetry. When frequency isincreased to a certain value, a new mode will appear.From the dispersion curves, there are several modes atone frequency. For the inspections, we usually do notwant to generate all modes simultaneously. Therefore, modecontrol needs to be implemented. With the same frequencyvalue, phase velocities are different for different modes.Therefore, only the mode with the “right” phase velocityis generated. The modes and frequencies can be optimizedfrom the dispersion curve, which can predict thepropagation characteristics for guided acoustic waves inthe structure.

2.2 Experimental set-up

Coils were made by wounding enamelled Cu wire on PVCpipes. The sensing device consisting of a transmitter coil(TC) and receiving coil (RC) was placed close to the endof the test pipe as shown in Fig. 2a. A DC biasing coil(BC) was placed between transmitter (TC) and receivercoil (RC). Initially the individual separate coils were placed

close to each other as shown in (Fig. 2b). Subsequentlya compact coil system bounding the magnetostrictive stripswas made using SWG 22 with 40 windings for TC, 34SWG with 200 windings for DC bias and 34 SWG 180windings for RC (Fig 2a). The windings of receiver weremade in opposite direction of transmitter coil. The counter-wound coil design provides a spatial filter that maximizesthe voltage output when the wave of appropriate frequencyis within the aperture of the sensor i.e. when the strainwave generated from the transmitter passes through theperiodically spaced coils of the receiver. The magneticfield strengths along the circumference of theelectromagnetic coils were measured that generate fieldequivalent to 1.411 Oe/A for cylindrical ac excitation coiland generate field equivalent to 97.41 Oe/A for DC biasingcoil. The magnetic field was measured using a gauss meter.Experiments were carried out with nanostructured alloysCo36Fe36Si4Al 1B20Nb4, Fe78Si8B14, Fe80Si8B12 andFe40Ni40B20 ribbon obtained by melt spinning technique inthe form of stripes of thickness and width of 30-60 µmand 12mm, respectively. Hanning signal with three toneburst waveform was used to excite Lamb waves in thetube during inspection. An APLAB 7645 dual DC Powersupply was used to provide basing current to the DC biascoil. This DC bias coil gives the desired external magnetizingfield to enhance the magnetic anisotropy and thus themagnetostrictive effect in the test structure. The outputvoltage across the receiver coil was measured by Yokogawa4456 digital oscilloscope.

3. RESULTS AND DISCUSSION

3.1 Optimization of DC bias magnetising field

A DC bias and ac excitation magnetization along thedirection of the axis of the coil was required for ageneration of the longitudinal wave mode in the onedirection. A longitudinal magnetization on a magnetostrictivestripe can be achieved by applying the biasing to DC biascoil along the longitudinal direction of the tube. A highcurrent along the circumferential direction can generate anaxial magnetization. At a constant ac excitation magnetizingfield, the DC bias current was varied and noted. The 1st

Fig. 1 : Dispersion curve showing phase velocity for the tube ofOD 48 mm and wall thickness 8.5 mm of length 3.67 mAl tube.

Fig. 2 : Experimental arrangement for the guided wave generationin an aluminium pipe using rapidly quenchedmagnetostrictive ribbons (inset Fig. 5b shows separatecoils of sensors

Table 1: Maximum signal amplitude of the as-spun andannealed ribbons corresponding to the DC biasfield

Composition of As-Spun Ribbons Ribbons Annealed

magnetostrictive at 400oC

Ribbons

DC bias Maximum DC bias Maximum

Field (Oe) amplitude Field amplitude

of Signal of Signal

(mV) (Oe) (mV)

Co36Fe36Si3Al1B20Nb4 34.12 16 16.58 36

Fe80Si8B12 23.19 24 12.16 94

Fe78Si8B14 40.74 50 18.78 34

Fe40Ni40B20 23.19 78 42.96 12

51Technical Paper


reflected back wall amplitude signals were obtained for arange of varying DC magnetizing field corresponding tothe DC bias current. However, care should be taken as tolimit the range of DC current in order to avoid overheatingand damage to the bias coil. The maximum signal amplitudeof the as-spun ribbons corresponding to the DC bias fieldis shown in Table 1.

Fe40Ni40B20 alloy had maximum reflected amplitude valueof 78 mV at 23.19 Oe DC bias magnetizing fields. However,after annealing at 400oC for 15 minutes, the Fe80Si8B12ribbon showed maximum signal amplitude value of 94 mVat a DC bias field of 12.16 Oe.

3.2 Optimization of AC magnetising field

Keeping the optimized DC bias magnetizing field obtainedabove fixed for the as-spun ribbon and 400oC/15minannealed ribbons; the effect of time varying magnetizingfield (AC excitation field) on signal amplitude was obtainedand shown in Table 2.

Table 2: Maximum signal amplitude of the as-spun andannealed ribbons corresponding to the ACmagnetizing field

Composition of As-Spun Ribbons Ribbons Annealed

magnetostrictive at 400oC

Ribbons

DC bias Maximum DC bias Maximum

Field (Oe) amplitude Field amplitude

of Signal of Signal

(mV) (Oe) (mV)

Co36Fe36Si3Al1B20Nb4 5.53 22 3.43 44

Fe80Si8B12 5.53 32 4.77 120

Fe78Si8B14 4.77 84 4.77 46

Fe40Ni40B20 4.77 112 4.77 18

In order to investigate the effect of an alternating current,the 1st reflected back wall amplitude was considered. Themaximum sensor output amplitude value for the as-spunFe40Ni40B20 ribbon was 112 mV at 4.77 Oe ac magnetizingfield. Just as in the case of DC bias field (Table 1), theFe80Si8B12 ribbons showed maximum signal output of120mV at 4.44 Oe. Thus, it was observed that melt spunFe80Si8B12 ribbons revealed higher signal amplitudes afterannealing treatment compared to the other threemagnetostrictive ribbons. After optimising the DC biasfield and the AC magnetising field, guided wave signalfrom the aluminium pipe was observed using anoscilloscope as shown in Fig. 3. Experimental data wastaken from oscilloscope with the as-spun and 400oC/15minannealed magnetostrictive ribbons. The first echo in theFig. 3 is the initial tone burst pulse applied to the transmittingcoil which was electrically linked to receiving coil and theoscilloscope. The second and third signals are the endreflected echoes. The first end reflected signal is the onesignal during the return trip of the elastic wave afterreflection from the other end of the tube. The second end

reflected signal is one detected when the returned wavemade another trip after reflection from the sensor end ofthe tube. The two ends reflected signals, therefore, areseparated by the round trip time from the receiving MsSend to the other end of the tube. The span between the1st and the 2nd back wall was approximately 1.5013 msat an experimental wave velocity approximately4835.16 ms-1.

Fig. 4 shows experimental waveform taken fromoscilloscope with the annealed magnetostrictive alloys at400°C. After annealing at 400oC it was found that themaximum amplitude value for Fe80Si8B12 alloy was 120mV at 4.77 Oe ac excitation magnetizing fields. On theother hand, the maximum amplitude value 18 mV forFe40Ni40B20 alloy is low at 4.77 Oe ac excitationmagnetizing fields as compared to other magnetostrictivematerials. Thus, the reflected amplitude signal wasmaximum for annealed Fe80Si8B12 ribbon instead of theFe40Ni40B20 alloy in DC bias field and AC excitation field.It is known that in the as-spun state the low attenuationin sensor output voltage of FeNiB compared to FeSiBsystem is due to its high stress sensitivity while afterannealing the FeSiB ribbon had enhanced magnetostrictionshowing high sensor output. The waveform recorded at76 kHz shown in Fig. 4 contains the dominant longitudinalwave L (0,2) mode arrival after reflection from the other

Fig. 3 : Signals taken after the optimization of the time-varyingand DC bias magnetizing field of the different compositionof as-spun magnetostrictive ribbons

Fig. 4 : Signals taken for using melt spun ribbon annealed at 4000Cfor 15 minutes.

52 Technical Paper


end of the Al tube. An additional benefit of exciting atlower frequencies is that the L (0,2) mode is primarilyexcited whereas at higher frequencies both theaxisymmetric modes and non-axisymmetric modes areexcited. Based on these results, an excitation frequency of76 kHz was used for the Al tube configuration, and thefirst L (0,2) mode arrival was used in the tube inspection.

4. CONCLUSION

Guided waves could be generated in an aluminium pipe3.67 m long, 8.5 mm wall thickness and 48 mm outerdiameter using melt spun magnetostrictive ribbons ofCo36Fe36Si3Al 1B20Nb4, Fe78Si8B14, Fe80Si8B12 andFe40Ni40B20 alloys. These ribbons are in nanostructuredstate as they are rapidly quenched. A magnetostrictivesensing device was designed for the generation of theguided wave in the pipe. Using disperse software a suitableexcitation frequency of 76 kHz for longitudinal L (0,2)wave mode was determined and used for the pipeinspection. DC bias magnetizing and AC excitation fieldwere optimized and found to be 3-5 Oe and 10 - 15 Oerespectively. The Fe80Si8B12 ribbon annealed at 400oC/15minutes gave high amplitude signal suitable for generationand sensing the back wall reflections. The technique canbe used for the detection of defect in the pipe. Moreover,very low bias magnetizing field ~ Oe was used for themelt spun ribbons compared to magnetizing field ~ kOereported for crystalline alloys.

ACKNOWLEDGEMENT

The authors express their sincere gratitude to Director,NML for permitting to carry out the investigation and alsopermitting to publish the results.

REFERENCES

1. ASNT, Non-destructive Testing Handbook, ultrasonic testing,volume, 7

2. Rose, J. L., Ultrasonic Waves in Solid Media, CambridgeUniversity Press, New York (1999).

3. Alleyne, D. N, and Cawley, P., 1996, ‘The Excitation of LabWaves in Pipes Using Dry- Coupled Piezoelectric Transducers,’J. Nondestructive Evaluation 15, pp. 11–20

4. N.S.Tzannes, “Joule and Widemann effects – the simultaneousgeneration of longitudinal and torsional stress pulses inmagnetostrictive materials”, IEEE transactions on Sonics andUltrasonics, Vol. SU-13, NO. 2, July 1966.

5. H. Kwun and C.M. Teller, “Detection of Fractured Wires InSteel Cables Using Magnetostrictive Sensors,” MaterialsEvaluation, Vol. 52, 1994, pp. 503-507.

6. J. B. Nestleroth, “Pipeline In-line Inspection – Challenges toNDT”, Proceedings, ECNDT 2006, 9th European Conference onNDT, Berlin, Germany, September 25-29, 2006.

7. C. I. Park, S. H. Cho, and Y. Y. Kim, “Z-shaped magnetostrictivepatch for efficient transduction of a torsional wave mode in acylindrical waveguide,” Appl. Phys. Lett., vol. 89,pp. art. no.174103, Oct. 2006.

8. E. Kannan, B. W. Maxfield, and K. Balasubramaniam, “SHM ofpipes using torsional waves generated by in situ magnetostrictivetapes,” Smart Mater. Struct., vol. 16, pp. 2505-2515, Dec. 2007.

9. Y.M.Cheong etal, [12th Asia-Pacific conference on NDT, 2006

10. http://www.me.ic.ac.uk/dynamics/ndt, DISPERSION: DISPERSEversion 2.0.

53Technical Paper


Detection of honeycomb defects in reinforced concretestructur es using acoustic pulse-echo methods and

wavelet transforms

Krishna Prasad M1, Herbert Wiggenhauser2, Krishnan Balasubramaniam1

1 Centre for Nondestructive Evaluation, Department of Mechanical Engineering,Indian Institute of Technology, Madras, Chennai, India,

600036.2 Federal Institute for Materials Research and Testing (BAM), IV.4, Berlin, Germany, 12205.

ABSTRACTHoneycombs/compaction faults occur in the concrete structures due to improper solidification of the concrete, which may reducethe strength of the concrete and also act as a passage for the water/ acids that further corrodes the reinforcements. This paperexplores about the acoustic pulse-echo techniques for the detection of honeycomb defects in a laboratory specimen located atthe Federal Institute for Materials Research and Testing (BAM), Berlin. Since concrete is an inhomogeneous medium, the defectsignals are masked by the material noise due to large amount of scattering/ reflections of acoustic waves. A filtering methodusing the discrete wavelet transforms is applied on the ultrasonic time signals for the better localization of defects.

Keywords: Honeycomb defects, Acoustic pulse-echo methods, Wavelet transforms.

1. INTRODUCTION

The non-destructive testing of concrete using acousticpulse-echo methods operates in the low frequency rangeof around 2-200 kHz, and uses primarily two methods.

(1) Impact-echo method and

(2) Ultrasonic pulse-echo method.

Impact-echo method involves introducing low frequencystress waves into the test object using a low energymechanical impact on the surface and monitoring thesurface displacements caused by the arrival of reflections/scattering of the waves from internal defects and externalboundaries. These reflected waves (Primary waves)produces surface displacements, which are recorded by adisplacement transducer. Here, the analysis of time domain

signal is time consuming and so the analysis is carried outin its frequency domain by calculating the Discrete FourierTransform of the signal. Since the reflected primary wavesare periodic, the depth of a reflecting surface, (d) can becalculated by using the relation d = V/2f [1] where V isthe primary wave velocity and f the frequency. The Fig1.1shows a schematic illustration of the impact-echo method[2].

Ultrasonic nondestructive testing of concrete structuresinvolves the usage of low frequency ultrasonic waves inthe order of 20-100 kHz. Ultrasonic waves are generatedusing the principle of inverse piezoelectric effect, whichtravels into the test object and get reflected whenencounters a material with different acoustic impedance.The same transducer receives these reflected waves andstores as a time vs. amplitude signal. The ultrasonic signalsare analyzed in the time domain and the depth of thereflecting surface can be determined by using the simplerelation d = Vt/2, where t is the travel time.

2. BACKGROUND

Wiggenhauser et al [2] automated the Impact-echo andpresented the Impact-echo data as 2-D and 3-D Impactechograms. Krause et al [3, 4] carried out non-destructivetesting of concrete using Ultrasonic pulse-echo methods.They used a variety of transducers such as single pointtransducers and array of broadband transducers to detectinclusions, defects in tendon ducts and reinforcements.Jansohn and Scherzer [10] used an ultrasonic shear wave-array transducer for detecting the improperly filled tendonducts. They used a normal shear wave transducer withpoint contacts without the need for additional couplingFig.1.1 Schematic illustration of impact-echo

54 Technical Paper


agent. They also reported that ultrasonic devices utilizingshear waves offer advantages with respect to backscatteringin the direction of receiver compared to longitudinal waves.

Reinhardt et al [6] applied the Wavelet Transforms for theAcoustic Emission signals emitted from the defects in theconcrete structures. They decomposed the signals intodif ferent frequency bands using Discrete Wavelettransforms and carried out denoising operations to removethe noise. They also used the Scalegrams to represent theenergy over each scale. Grosse et al [9] carried out filteringoperations using both conventional Fourier transform basedfilter techniques and the discrete wavelet transforms andproved wavelet transforms to be a suitable tool fordenoising the acoustic emission signals because theconventional filtering has some artifacts. Park et al [7]applied the wavelet transforms for non-destructiveevaluation of material degradation of Cr-Mo steels usingUltrasonics. They used Continuous wavelet transforms toevaluate the attenuation factor with the degradation ofmaterial and the discrete wavelet transforms for the noisesuppression of the signal.

3. WAVELET TRANSFORMS

Wavelets are the mathematical functions that decomposethe signal into different frequency bands and then studyeach component with a resolution matched to its scale[8]. This leads to a new concept known as Multi ResolutionAnalysis (MRA) apart from the fixed resolution of ShortTime Fourier Transforms (STFT). Wavelet transformsgive good time resolution and poor frequency resolution athigh frequencies and vice versa.

The Continuous Wavelet Transforms (CWT) are computedby correlating the signal with scaled (dilated) and shifted(translated) versions of the mother (analysing) wavelet.Mathematically it can be expressed as [5]

(3.1)

Where x(t) is the signal itself, t and s are the translationand scale (inverse of frequency) parameters respectivelyand y(t) is the mother wavelet. The wavelets must satisfythe admissibility and regularity conditions which showsthat the wavelets are small waves with finite duration andoscillatory. The information obtained from CWT isredundant and so the time-scale plane is discretized in adyadic manner. This discretized version is known asWavelet series and is mathematically represented as [5]

(3.2)

The value of 0s is found to be 2 and the value of 0τto be 1 for the better reconstruction of the signal. Thiscontinuous wavelet transforms and the wavelet series gives

the time-scale information simultaneously and cannot beexactly used for the localisation of defects.

Discrete Wavelet Transforms (DWT) is based on the subband coding or the pyramidal algorithm. It basically usesa scaling function nothing but a half band low pass filterand a wavelet function nothing but a half band high passfilter. The signal is passed through a series of half bandlow pass and half band high pass filters respectively. Thehalf band low pass filter removes all the frequencies thatare above half of the highest frequency in the signal andthe half band high pass filter removes all the frequenciesthat are below half of the highest frequency. The filteringof a signal corresponds to the convolution of the signalwith the impulse response of the filter. The convolutionoperation in the discrete time case is defined as in theequation 3.3.

(3.3)

Then the signals are downsampled by two to decrease theambiguity with out losing any information. The coefficientsobtained on the high frequency side are known as detailsand the coefficients obtained on the low frequency sideare known as approximations. The approximations areagain passed through the complementary filters leavingthe details aside. The process of filtering and downsamplingis referred to as single level decomposition and thedecompositions can be carried out till a single sample isreached. A one level decomposition can be mathematicallyexpressed as [5]

(3.4)

(3.5)

Thus a single level decomposition halves the time resolutionsince only half the number of samples now characterizesthe signal. However, the frequency resolution is doubledsince the frequency band of the signal now spans onlyhalf of the previous frequency band. The signalreconstruction (synthesis) is done by following the sameprocedure as used for decomposition but in the reverseorder. That is the signals at each level are upsampled bytwo and then passed through synthesis filters g’(k) andh’(k). The important point here is that the analysis and thesynthesis filters are identical to each other and they areknown as Quadrature mirror filters (QMF). However, ifthe filters are not ideal half band, then perfect reconstructioncannot be achieved.

Daubechies invented orthonormal, compactly supportedfamily of wavelets where perfect reconstruction is possible.These wavelets are used in the current work. TheDaubechies wavelet, db7 is used for the present analysis.

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The value 7 represents the number of vanishing momentsand the filter length used was 14.


The layout of the Large Concrete Specimen (LCS)consisting several simulated defects including two regionsof honeycombs is shown in the Fig 4.1 which was obtainedfrom BAM, Berlin.

The LCS has an area of 10 x 4 square meters and athickness of 300 mm, which is designed to incorporatemost of the common defects that occur in concretestructures. Three honeycombs namely K1, K2 and K3 arelocated in the right top corner of the specimen and aredivided into two test areas of 1000 mm x 500 mm and500 mm x 500 mm as shown in the Fig 4.2. Thehoneycombs are of 150 mm diameter and 150 mm length.K1 is laid horizontally, K2 is laid vertically and K3 is laidin inclined direction. K1 and K2 are having a concretecover of around 110 mm and K3 has 112 mm and 156mm at the two corners respectively.

The impact-echo tests are carried out on the specimenusing an Olson instruments impactor unit having animpactor equivalent to a diameter of 3 mm. Unfortunatelythere is no indication of honeycombs except the back wallecho. Even the shadow of the defects at the back wall isnot observed. The B and C-scans of the area where K1and K2 are lying is shown in the Fig 4.3. The reflectionfrom the back wall can be seen in the B-scan at a frequencyof about 7.35 kHz, which gives the thickness as around280 mm for a velocity of 4100 m/sec.

Then the Ultrasonic pulse-echo testing was carried outwith a commercially available shear wave –array transduceras described in the section 2. The centre frequency usedis 55 kHz. The measurement grid is taken as 20 mm x 20mm for both the test areas. The B and C-scans obtainedfrom the analysis of ultrasonic time signals using BAMPEâ

software at the defect depth are shown in the Figs 4.4and 4.6. The Fig 4.5 is a reference figure to analyze theB and C-scans. It is a vertically flipped version of theoriginal test area and is done to have a better visualization.

The shear velocity in the concrete structure was taken as2630 m/sec. The C-scan in the Fig 4.4 shows the K1clearly at a depth of around 80 mm and the direct reflectionis also observed in the corresponding B-scan. The lateralposition of the K1 also matches with the reference Fig4.5. Similarly the Fig 4.6 shows the C-scan at a depth of105 mm. The C-scan does not show any indication of K2and there is a rough indication of K2 in the B-scan, whichis almost nothing to analyze. The Fig 4.7 shows the C-scan at the back wall whose thickness is obtained as 270mm. The shadow of the honeycombs and plastic pipes atthe back wall can also be observed in the C-scan.

The B and C-scans obtained from the same ultrasonictime signals using Synthetic Aperture Focusing Technique(SAFT) are shown in the Figs 4.8, 4.9 and 4.10. TheseSAFT results are presented for comparison with the normalultrasonic time signals and as shown in the Figs 4.8-4.10,these did not show any additional information regardingK2 either in the C-scan or as a direct reflection in the B-scan.

Fig.4.1 Large Concrete Specimen (LCS) showing various types of defects.

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Fig.4.2 Test areas showing K1, K2 and K3.

Fig.4.3 C and B-scans of K1 and K2 from Impact-echo. Fig.4.4 C-scan at a depth of 80mm and its corresponding B-scan

Fig.4.5 Reference figure for the analysis of B and C-scans for K1and K2

Fig. 4.6 C-scan at a depth of 105mm and its corresponding B-scan

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Fig.4.7 C-scan at the backwall (depth of 270mm) and its correspondingB-scan

Fig.4.8 SAFT C-scan from between 67-119 mm

Fig.4.9 SAFT B-scan from over the whole test area

Fig.4.10 SAFT C-scan at the backwall (at a depth of 270 mm).

There is no indication of honeycomb K3 from the secondtest area either in the B-scan or in the C-scan and so theresults are not presented. Similar results are obtained fromthe SAFT also. It is expected that since the honeycombK3 is inclined in its orientation, the transducer is unable toreceive the reflected signal and so no information hasbeen obtained about the honeycomb defect.

The discrete wavelet transforms are applied on theultrasonic time signals as described in the section 3. Anultrasonic time signal obtained from the tests conductedusing shear wave-array transducer is shown in the Fig4.11. The sample size was 1000 and the sampling frequencywas 1 MHz. Thus from the Nyquist’s theorem, the foldingfrequency is 0.5 MHz (500 kHz). Then a seven leveldecomposition is performed using discrete wavelettransform with db7 wavelet.

It is observed from the reconstructions as shown in theFig 4.12 that the level 1(250-500 kHz) and level 2 (125-250 kHz) reconstructions pertain to high frequencies, whichconsists of high frequency glitches or noise. So theselevels can be successfully eliminated. The level 5 (15.62-31.25 kHz) and level 6 (7.81-15.62 kHz) reconstructionspertain to very low frequencies and the analysis of thesefrequency bands is also not appropriate, as these do notcontain any useful information. The level 3 (62.50-125kHz) and level 4 (31.25-62.50 kHz) coefficients representmostly the signal characteristics and the analysis of theselevels can be done individually as usually like ultrasonictime signals.

This is known as wavelet compression in terms of waveletterminology or simply known as wavelet filtering. Thelevel 4-frequency band mostly matches with the centrefrequency of the ultrasonic transducer used whose valueis 55 kHz. The whole work is carried out in MATLABâ

and the reconstructed signals are transferred to BAMPEâ

for analysis. The results obtained in the form of B and C-scans are shown in the Fig 4.13.

The Fig 4.13 represents the B and C-scans at a depth of105 mm. This shows the honeycomb K2 clearly in the C-scan, which was not identified from the analysis of normalultrasonic time signals or from the SAFT results. Thedirect reflection from the honeycomb K2 also can beidentified very clearly.

The intensity variation along honeycomb K2 is plotted asshown in Fig 4.14 before and after performing wavelettransforms This reveals a clear increase in the intensitywhere the honeycomb K2 is located and also shows thesuppression of noise after performing wavelet filteringwhen compared with the original signals.

Then the mean intensity ratios of the defective region(considering K2 only) to non-defective region are calculatedboth before and after performing wavelet transforms. Thisresulted in a 22.20% increase in the intensity ratio afterperforming wavelet transforms.

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Fig.4.11Ultrasonic time signal

Fig.4.12 Reconstruction of wavelet coefficients

Fig.4.12 (a) Level 1 reconstruction. Fig.4.12 (b) Level 2 reconstruction.

Fig.4.12 (c) Level 3 reconstruction. Fig.4.12 (d) Level 4 reconstruction.

Fig.4.12 (f) Level 6 reconstruction. Fig.4.12 (e) Level 5 reconstruction.

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Fig.4.13 C-scan at a depth of 105 mm and it’s corresponding B-scanobtained from fourth level wavelet reconstruction.

Fig.4.14. Intensity plot along ‘K2’ before and after applying wavelettransforms

§ Discrete wavelet transforms based on the principlesof sub band coding were found to be suitable for theanalysis of ultrasonic time signals.

§ Selecting a fourth level frequency band and the furtheranalysis of it revealed the honeycomb defect K2, whichdid not turn up from the analysis of normal ultrasonictime signals or from the SAFT results.

§ The mean intensity ratio of the defective to non-defective region showed a 22.20% increase afterperforming wavelet transforms on the ultrasonic timesignals.

6. ACKNOWLEDGEMENTS

This research work is carried out at the Federal Institutefor Materials Research and Testing (BAM), Berlin, Germanyin collaboration with IITM, Chennai, India through GermanStudent Exchange Service (DAAD). I am also thankful toIZFP group at Saarbrucken, Germany for their cooperationin providing the SAFT images.

REFERENCES

1. Sansalone, M., and W. B Str eett. Impact-echo: Non-destructiveTesting of Concrete and Masonry, Bullbrier Press, Jersey shore,PA, 1997.

2. C. Colla, G. Schneider, J. Wöstmann, H. Wiggenhauser.(1999) Automated Impact-echo: 2-and 3-D Imaging of ConcreteElements, DGZfP Fachtagung Bauwerksdiagnose, 4(5), 307-318.

3. Martin Krause, Frank Mielentz, Boris Milmann, DoreenStr eicher, Wolfgang Müller. (2003) Ultrasonic Imaging ofConcrete Elements: State of the art using 2D synthetic aperture,Proceedings of the International Symposium (NDT-CE 2003),Berlin, Germany, In Print.

4. M. Krause, F. Mielentz, B. Milmann, W. Müller, V. Schmitz,H. Wiggenhauser. (2001) Ultrasonic imaging of concrete membersusing an array system, NDT&E international, 34, 403-408.

5. Robi Polikar. The Wavelet tutorial, Rowan University, http://users.rowan.edu/~polikar/WAVELETS/WTtutorial.html, 1996.

6. Jochen H. Kurz, Hans-Jürgen Ruck, Florian Flinck,Christian U. Grosse, Hans-Wolf Reinhardt. (2003) Waveletalgorithms for non-destructive testing, Proceedings of theInternational Symposium (NDT-CE 2003), Berlin, Germany, InPrint.

7. Ik Keun Park, Un Su Park, Hyung Keun Ahn. (2000)Experimental Wavelet analysis and Applications to UltrasonicNon-destructive Evaluation, 15th WCNDT, Roma.

8. Amara Graps. (1995) An Introduction to Wavelets, Institute ofElectrical and Electronics Engineers, Inc, 2(2).

9. Christian U. Grosse, Hans W. Reinhardt, Markus Motz,Bernd H. Kröplin. (2002) Signal conditioning in acoustic emissionanalysis using Wavelets, The e-journal of non-destructive testing,7(09).

10. Reinhard Jansohn and Jan Scherzer. (2003) Improper filledducts detected by ultrasound reflection, The e-journal of non-destructive testing, 8(4).

5. CONCLUSIONS

Non-destructive testing of reinforced concrete structuresis quite essential to avoid catastrophic failures and thehoneycomb defects play an important role, since thesereduces the solid strength of the structure thus leading tofailure and also acts as a passage for moisture whichfurther corrodes the reinforcements. An attempt is madethrough this research work for the effective detection ofhoneycombs in the reinforced concrete structures. Thefollowing conclusions can be drawn based on this currentresearch work:

§ Effective detection of honeycombs is obtained throughultrasonic shear wave -array transduction where theimpact-echo method did not work out.

§ The wavelet transforms, a recent advancement in signalprocessing is successfully applied in the field ofnondestructive testing of concrete.

60 Technical Paper


Indirect methodologies for inversionof eddy current NDE data

S. Shuaib Ahmed, B.P.C. Rao, S. Thirunavukkarasu and T. JayakumarNondestructive Evaluation Division, Metallurgy and Materials Group

Indira Gandhi Centre for Atomic Research, Kalpakkam, Tamil Nadu- 603 102

E-mail: [email protected]

ABSTRACTThis paper presents two inversion methodologies for inversion of eddy current data. The first methodology uses competitivelearning neural network with cosine similarity algorithm to identify the layer to which an EC image of a defect belongs in atwo-layer structure. The second methodology aims evaluation of depth location and height of subsurface defects using multi-dimensional learning based multilabel radial basis function neural network. The performance and robustness of these twomethodologies have been validated using entirely different type of dataset used for training. The eddy current data required fortraining and validation have been generated using CIVA numerical modeling software. Both these methodologies have shownpromise for evolving artificial intelligence based automated identification and quantification of defects.

Keywords: Inversion, eddy currents, quantification, classification, signal processing

1. INTRODUCTION

One of the important aspects during structural integrityassessment of engineering components using NDEtechniques is quantitative characterization of defects,especially location and size. Among various NDEtechniques, eddy current technique is widely used fordetection and sizing of defects in components made ofmetallic materials. While high-speed couplant-free testingof components for reliable detection of defects is a clearadvantage, the technique suffers difficulties for defectdetection and sizing, due to the combined influence ofseveral variables at a given location. Diffusion behaviorand non-uniform decay of eddy currents in differentdirections result in formation of identical EC signals orimages for defects of same size but located at differentdepths as well as formation of identical signals or imagesfrom different size defects present at same location. Thesetwo are expected to influence reliable quantitativecharacterization of defects and demand the use of multi-frequency EC data and inversion approaches. Extractingquantitative information of defects from measured NDEdata i.e. amplitude, signal or image is called inversion.Two popular approaches for inversion of EC data are i)model based direct inversion and ii) empirical indirectapproach.

In the first approach, scatterer size and location aredetermined following the physics based interactions and

associated mathematical models [1-2]. By this approach,only limited success has been reported, essentially due tocomplex geometry of defect as well as the component. Inthe second approach, empirical relationship betweenmeasured data (signals or images from a variety ofexpected defects) and defect dimensions is established asshown in Figure 1. The heart of an indirect approach isthe inversion algorithm which simulates a mapping betweenthe extracted features and the defect dimensions andlocation. This approach is gaining popularity. In thisapproach, the eddy current signals and images are givenas the input to an inversion module which consists of afeature extraction module and an inversion algorithm thatlearnt the relationship between input and output throughsupervised or unsupervised learning or training.

Signals and images are higher dimensional data. It isexpensive, in terms, of processing speed and memoryrequirement to process and handle higher dimensional data.Hence, instead of providing raw data to the inversionalgorithms, it is a good practice to extract important non-redundant features which describe the signals/imagescrisply. Fourier descriptors, Principal Component Analysis(PCA), features from impedance plane such as phase,amplitude are some of the features useful for inversion ofEC data [3]. For indirect inversion, use of expert systemsand artificial intelligence (AI) methods such as feed forwardartificial neural networks, radial basis function neural

Fig. 1 : Indirect inversion of eddy current data.

61Technical Paper


networks, case based reasoning and support vectormachines etc. have been reported [4-7].

In this paper, two indirect methodologies have beenproposed for inversion of eddy current data. While onemethodology uses EC images as input, the other uses ECsignals as input. This paper gives a brief description of thetwo methodologies and discusses the results of inversion.

2. INDIRECT INVERSION METHODOLOGIES

The paper presents two inversion methodologies for twodiverse problems. The first methodology is based onunsupervised learning using competitive learning with cosinesimilarity, which uses EC images as input to identify thelayer to which a defect belongs to in a two-layeredstructure. The second methodology is based onmultidimensional learning which uses EC signal as input todetermine the depth location and height of a sub-surfacedefect. The EC data necessary for training and validationof the two methodologies have been obtained using CIVAnumerical modeling software [8]. This section presents adetailed description of CIVA and inversion methodologies.

2.1 CIVA model for EC data generation

The success of an inversion methodology depends on theamount of training dataset used in the learning process.Generation of experimental signals or images for trainingpurpose is time consuming and cumbersome. Hence, inthis study, numerical modeling has been used for generationof dataset for both training and validation of the inversionmethodologies. CIVA software has been used for thispurpose. CIVA is a benchmarked NDT modeling softwarebased on semi-analytical methods using dyadic Greensfunctions method [8]. In this method, the interactionbetween defect and electric field generated by EC probeis described with an integral equation (1), which is derivedfrom Maxwell’s equations and solved numerically usingthe method of moments.

(1)

The unknown fictitious current density, JΩ is defined inthe volume Ω containing the defect and depends on thetotal electric field. The solved current density is used forcalculating the probe response or signal for a defect. Theterm J0 in equation (1) is an excitation term that dependson the total primary electric field E0(r) emitted by theprobe in the region Ω containing defect. The dyad

—GΩ

ee

links the fictitious current density to the electric field itcreates inside Ω [9]. The contrast function f(r) is definedby

f rr( ) =

− ( )σ σ

σ

0

0

(2)

where σ0 is the tube conductivity and σ(r) is the flawconductivity. CIVA eddy current module has beenextensively validated through a series of experiments[8-9].

2.2 Competitive learning neural network withcosine similarity

Competitive learning is an on-line method of unsupervisedlearning. It is an efficient algorithm in terms of speed andaccuracy where cosine similarity is used as a distancemetric [10]. The goal of unsupervised learning is toseparate the observed data into clusters or to provideunderstanding about the underlying structure or pattern ofthe data. The outcome of the input data is not providedto the learning algorithm. The important characteristic ofany unsupervised learning algorithm is the similarity measureused to discriminate the clusters.

Competitive learning is an artificial neural network type oflearning process with no hidden layers. The first layer isthe input layer and the second layer is the output layer orthe layer of competitive neurons, as shown in Figure 2.The output neurons compete among themselves to respondto an input, and only a single output neuron which is closeto an input, in the sense of cosine similarity, wins torespond.

Let X be a set of input vectors, each vector x is normalizedto be of unit length ( ||x|| = 1), as magnitude is notrelevant feature to learn for directional data. Thus, thelearning is on unit hypersphere. Let O = O1...OK be aset of weights for output neurons, then learning aims tomaximize the average cosine similarity objective function L

Fig. 2 : Schematic of competitive learning neural network.

L X OT k xx= ∑ ( ) (3)

where k(x) = karg maxXTOk. The weight of output neuron

which closely matches the input neuron is the winner.Once the winner is identified, the weights of winningneuron is updated as

OO

Ok x

new k x X

k x X

( )

( ) ( )

( )

= +

+

η

η

(4)

where η is the learning rate parameter. Each group ofinputs which are activated by one output neuron form acluster.

Figure 3 gives an illustration for the type of clusters ofcompetitive learning on Euclidean and unit hypersphere.In the Euclidean space, clusters will be in the form ofellipsoids scattered throughout the feature space and thedecision boundary will be hyper-planes which vary witheach other in magnitude as well as in direction. On the

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2.3.2 Multi-dimensional learning through multi-label radial

basis function

Given a multi-dimensional training set D =(xi,ci)|1≤i≤n,then multi-dimensional learning attempts to learn a functionf3 that would assign a vector of class label with ddimension for each unseen m dimensional vector of instancegiven by x ∈ X.

f x x x c c cm d3 1 2 1 2:φ φ φ φ φ φ× × → × × × . . . . . . (7)

where φci denoting the sample space of ci assumed to be

discrete, for all i =1, 2, …, d. With, |φc|≥2. φxj denoting

the sample space of feature variable xj for all j=1,2, …m.A wrapper approach is used for multi-dimensional learningfor simultaneous quantification of defect depth and height.This wrapper consists of a problem transformation, amulti-label ranking algorithm and class assigning modulefor each dimension [12].

Multi-label radial basis function (ML-RBF) is multi-labelranking algorithm derived from popular radial basis functiontype of neural network. The architecture of ML-RBF isshown in Figure 4 and it consists of input, hidden andoutput layers [12]. The m dimensional feature vectorsform the input. The hidden layer consists of L sets ofprototype vectors, where L is the number of class. Eachset comprises of kl prototype vectors which are thecentroids of the clusters taken by performing e.g., popularK-means algorithm on all instances corresponding to eachclass. kl is derived from fraction α from the total numberof instances in class l.

The hidden neurons are re-indexed as 1≤j≤K, where Σli=1k1.In addition, a bias prototype neuron Ω0 is present in thehidden layer which is set to 1. The radial basis functiongiven in equation (8) is used for activation of featurevector and the hidden layer.

Ω j i

i j

j

xx c

( ) = −( )

exp,dist

2

22σ (8)

where σ is the smoothing parameter derived from afraction ì of average distance between each pair of prototype

contrary, for hyper-sphere, the features will be embeddedon the surface of unit hypersphere and the decisionboundary will be the hyper-planes which vary with eachother only in direction (with magnitude being 1). The datawhich are represented with unit length and learned withcosine similarity are termed as the ‘directional data’.

2.3 Multi-dimensional multi-label radial basisfunction neural network

Multi-dimensional learning is more generalized form ofsupervised learning and relatively a new concept in theliterature of machine learning and artificial intelligence wheremore than one dimension of classes can be simultaneouslymapped for an input. The following subsection gives thedefinition of multi-label learning and multi-dimension learningand relationship between them for solving an eddy currentinverse problem of simultaneous quantification of sub-surface defect height and depth.

2.3.1 Multi-label learning

Multi-label classification is a variation of supervised learningwhere the task is to learn a function f1 which assigns asubset of label from a set, e.g. L= 1, 2, …, l to eachunseen instance given by a vector of feature x∈X, thedomain of instance and a multi-label training setD = (x i,ci)|1≤i≤n is given with ci⊂ L.

f x x x

L

m1 1 22:φ φ φ× × × → . . . (5)

where φxj denoting the sample space of feature variable xj

for all j=1, 2, …, m.

Multi-label classification is closely associated with multi-label ranking and it is a problem of learning a function f2which produces a vector of real numbers with size equalto |L| as output to each unseen instance of a featurevector x∈ X, and a training set D=(xi,ci)|1≤i≤n is given.

f L Rx x xm2 1 2:φ φ φ× × × → . . . (6)

where R is the set of real numbers. The function f2 isexpected to have the property of ordering the set of labelsL, so that the topmost labels are more related with thenew instance. Overview of multi-label learning and featureselection strategy for multi-label ranking is discussed indetail elsewhere [11]. Fig. 4 : Architecture of multi-label radial basis function neural

network.

Fig. 3 : Clusters on a) Euclidean space and b) unit hyperspherefor two features with M as cluster centers and dotted lineas the decision boundary.

(a) (b)

63Technical Paper


vectors. The output layer is connected with the hiddenlayer through weights. The weights are learned byminimizing the error from the actual label

E = Σ(h–t)2 (9)

where h is the actual output and t is the desired output.

3. IMPLEMENTATION OF THE INVERSIONMETHODOLOGIES

Competitive learning with cosine similarity (CLCS)algorithm and ML-RBF algorithm have been developedusing MatLab. These two methodologies have been testedand validated using the EC data predicted using CIVAmodeling software.

3.1 Methodology for identification of layer withdefect

CLCS algorithm has been used to identify the layer towhich the defect belongs from the EC images of defects.This methodology consists of the following steps:

i) Generation of dataset of EC images at two differentfrequencies

ii) Principle component analysis based extraction offeatures from EC images

iii) Unit length normalization of the featuresiv) Training a competitive learning neural network with

cosine similarityv) Validation and evaluation of the algorithm

A cylindrical two-layer system with 0.45 mm thick stainlesssteel (first layer) and 0.2 mm thick sodium (second layer)as shown in Figure 5 has been considered. Defects areassumed to be present either in stainless steel (SS) or insodium. Unambiguous identification of layer in which thedefect exists has been attempted using the CLCSmethodology.

Dataset of parallelepiped shape defects are modeled inthree locations viz., outer diameter (OD) type, sub-surfaceor inner diameter (ID) type as illustrated in Figure 6. Foreach type of defect, three different depths (0.05, 0.10 and0.15 mm) and lengths (1.0, 1.5 and 2.0 mm) have beenmodeled. With 3 depths, 3 lengths and three locations inSS (layer1) and sodium (layer2), total 54 defects havebeen modeled. Based on the thickness of the two layers,300 kHz and 600 kHz have been chosen as the testfrequencies. Thus, with two excitation frequencies, thereare 108 images in total.

These EC images are subjected to principal componentanalysis for dimensionality reduction and feature extraction.PCA is a linear transformation method where a computationof covariance of input image is performed and the subspacewith larger Eigen values of covariance is retained [13]. Inthis study, three dominant Eigen values from PCA aredetermined for each image. With two excitation frequencies,each defect is associated with two images. Hence, a defectis represented by six feature vectors which are normalizedto unit length. The normalized data is given as input to theCLCS methodology for unsupervised learning.

3.2 Methodology for quantification of subsurfacedefects

The objective of this methodology is to simultaneouslydetermine the depth location and height of subsurfacedefects in SS plates as shown in Figure 7. Multi-dimensional learning through multi-label radial basisfunctions (MD-Learn ML-RBF) has been used. Thismethodology consists of the following steps:

i) Generation of dataset of EC signals

Fig. 5 : The geometry modeled by CIVA to generate EC images ofdefects.

ii) Extraction of features from impedance plane EC signals

iii) Training of ML-RBF by multidimensional learning

iv) Validation and evaluation of the algorithm

EC signals of subsurface defects having different depthsand heights present in a 5 mm thick SS plate have beenpredicted using CIVA. Defects of three different lengths(3, 6, 9 mm) have been modelled. The width of all defectshas been fixed as 0.5 mm. A total of 192 defects havebeen modelled with different heights (0.5, 0.6, 0.7, 1.0,1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 2.0 mm) and depth locations(1.0, 1.3, 1.4, 1.5, 2, 2.5, 2.6, 2.7, 3.0 mm). Forconvenience, the defects have been categorized into threeclasses based on both height and depth. Table 1 givesdetail of the classes and total number of training data setspresent in each class.

Fig. 6 : Parallelepiped shaped a) OD, b) sub-surface, c) ID defectsfor which EC images are predicted using CIVA.

Fig. 7 : Subsurface defects used for testing and validation of MD-Learn ML-RBF methodology.

64 Technical Paper


Table 1: Description of class for quantification of sub-surface defects.

Depth, Class Number Height, Class Numberm m of training m m of training

data data

<1.5 A1 51 <0.7 A2 54

1.5-2.5 B1 66 0.7-1.6 B2 90

>2.5 C1 75 >1.6 C2 48

Ten characteristic features viz. F1 to F10 have beendetermined from the impedance plane of EC signals asshown in Figure 8. These features for each defect alongwith their corresponding classes for both height and depthare given to MD-Learn ML-RBF. The outputs of the MD-Learn ML-RBF are depth and height of the subsurfacedefects. Among the features, F9 and F10 have beenproposed for the first time.

A restricted forward subset selection method has beenused to select optimal number of features [14]. Thismethod works by evaluating the learning algorithm with aset of inputs. Features are sorted in combinations i.e.most contributing to the least.


4.1 Identification of layer with defect

Figure 9 shows the typical EC images of parallelepipedtype defects (length, 1.5 mm, width, 1.5 mm and depth,0.1 mm) in SS and sodium layers at 300 kHz. As can beseen, it is difficult to identify the layer information fromthe images as they are nearly identical. Figure 10a showsthe dominant Eigen values of 300 kHz and 600 kHz,corresponding to set of defects from SS and sodium layers.The unit length normalized Eigen values for these twofrequencies are shown in Figure 10b. As can be seen,there is directionality in the Eigen features of the imagesof the two layers. Thus, use of cosine similarity is beneficialfor this directional dataset.

In order to arrive at an optimal number of features, threedominant Eigen values from 300 kHz and 600 kHz havebeen chosen and ranked based on leave one cross validation

strategy. Table 2 shows the number of occasions ofmisclassifications i.e. where defects in SS are predictedas in sodium or vice versa. It can be observed from Table2 that successful classification i.e. correct identificationof layer with defect for all trained data is possible onlywith two dominant Eigen values from 300 kHz and 600kHz.

Table 2 : Cross validation results with CLCS method.

Input features Number of misclassificationsout of 54 data

300 kHz - Eigen1 and Eigen2 15

300 kHz - Eigen1, 2, 3 13

600 kHz - Eigen1, 2 16

600 kHz - Eigen1, 2, 3 13

300 kHz - Eigen1; 600 kHz – Eigen1 0

300 kHz- Eigen1, 2; 600 kHz – Eigen1, 20

In order to validate the performance and robustness of theCLCS methodology, fresh set of images of defects ofsame size, but of slightly different shape have been fed tothe CLCS method. In contrast to the parallelepiped shapeddefects (shown in Figure 6), elliptical shaped defects asshown in Figure 11 have been used.

In the context of training with parallelepiped defects andevaluating with elliptical type defects, the following threedifferent cases have been studied:

Case 1: Train with all lengths and depths and evaluate forall lengths and depths.

Case 2: Train with all possible combinations of two outof three lengths and evaluate with all lengths and depths.

Case 3: Train with all possible combinations of two outof three depths and evaluate with all lengths and depths.

The classification results for these three cases are shownin Table 3. As can be noted, the CLCS methodology isrobust enough with > 94% success to identify the layerto which the defect belongs, despite change in the shapeof defects used for training.

Fig. 8 : Characteristic features derived from impedance plane EC signals of subsurface defects.

65Technical Paper


Table 3: Performance evaluation of the CLCS methodology.

Test case Number of data set Identification success, %

Case 1 54 100

Case 2 108 99

Case 3 108 94

A special test case wherein an ID defect in SS (layer 1)filled with sodium has been analyzed to further investigatethe robustness of the CLCS methodology. Typical predictedimages of this scenario are shown in Figure 12. As canbe noted from the grey level intensity bar of the images,sodium filling has increased the EC response. This isattributed to the approximately 25 times higher electricalconductivity of sodium over SS.

The results of the CLCS methodology are given in Table4. It can be observed that the CLCS methodology hasshown good performance, despite a few misclassifications.The studies clearly reveal that competitive learning withcosine similarity methodology is reliable and robust andcan be used for identification of layer in which the defecthas formed.

Table 4: Results of CLCS methodology for ID defects in SSfilled with sodium.

Defect depth, Defect length, CLCS methodologym m m m predicted layer

0 . 0 5 1 . 0 SS

0 . 0 5 1 . 5 SS

0 . 0 5 2 . 0 Sod ium

0 . 1 1 . 0 SS

0 . 1 1 . 5 Sod ium

0 . 1 2 . 0 SS

0 . 1 5 1 . 0 SS

0 . 1 5 1 . 5 SS

0 . 1 5 2 . 0 SS

4.2 Quantification of subsurface defects

Figures 13a and 13b show the typical impedance plane ECsignals of defects having different heights and depths. Ascan be seen, there is no direct correlation between theshape of the impedance plane signals and the defectdimensions. Features extracted from these signals havebeen subjected to MD-Learn ML-RBF and Table 5 showsthe results. It is seen from Table 5 that the new featureF9 (ratio of maximum magnitude at two frequencies) hasattained rank 1 with maximum contribution for thequantitative performance.

In order to find the number of optimal input features foreffective quantification of subsurface defects, quantificationaccuracy has been analyzed as shown in Figure 14. Ascan be noted, the accuracy is high and nearly constant forthe first five features while it drops significantlybeyond six features. Thus, an optimal number of first

Fig. 9 : Model predicted images of a defect in a) SS and in b) sodium at 300 kHz.

Fig. 10: Dominant Eigen values at 300 kHz and 600 kHz in a) two-dimensional plane and b) unit hypersphere.

66 Technical Paper


five ranked features have been chosen as input to theMD-Learn ML-RBF.

The MD-Learn ML-RBF methodology has been trained using192 datasets (length of 3, 6 and 9 mm, width 0.5 mm)and validated with the following three unique cases:

Case 1: Evaluation of lengths 4, 5, 7 and 8 mm(interpolation of length).

Case 2: Evaluation of lengths 4, 5, 7 and 8 mm andwidths 0.75 and 1 mm (interpolation of length, extrapolationof width).

Case 3: Evaluation of lengths 2 and10 mm and widths0.75 and 1 mm (extrapolation of length and width).

Table 6 shows the results of the MD-Learn ML-RBFmethodology. In all the validation cases, theMD-Learn ML-RBF has quantified the depth location andheight with 100% accuracy. This reveals the fact that theMD-Learn ML-RBF methodology is robust for simultaneousquantification of defect depth location and height.

Fig. 12: EC images of a) ID SS defect (depth 0.1mm, length 1.0 mm) at 300 kHz and 600 kHz and b) when it is filled with sodium.

Fig. 11: Elliptical shaped a) OD, b) sub-surface and c) ID defectsused for validation.

Table 5: Performance of MD-Learn ML-RBF method ofranking.

Feature Rank

F9-Ratio of maximum magnitude at two frequencies1

F2-Phase at maximum magnitude 2

F8-Phase at maximum reactance 2

F6-Phase at maximum resistance 3

F1-Maximum magnitude 4

F5-Maximum resistance 5

F4-Magnitude at maximum phase 6

F3-Maximum phase 7

F7-Maximum reactance 8

F10-Ratio of maximum phase at two frequencies 9

Table 6: Results of validation of MD-Learn ML-RBFmethodology.

Test case Number of Quantification Accuracy, %data set

Depth Height

Case 1 8 100% 100%

Case 2 8 100% 100%

Case 3 10 100% 100%

67Technical Paper


REFERENCES1. Udpa L, Udpa S.S. “Solution of inverse problems in eddy-

current nondestructive evaluation (NDE)”. J NondestructiveEvaluation 7(1/2) (1988) 111-120.

2. Bowler, J.R. “Thin-Skin Eddy-Current Inversion for theDetermination of Crack Shapes”, Inverse Problems 18 (2002)281- 291

3. Sung-Jin Song, Young-Kil., Shin, “Eddy current flawcharacterization in tubes by neural networks and finite elementmodeling”, NDT&E International 33 (2000) 233-243

4. Rao, B.P.C., Raj, B., Jayakumar, T. and Kalyanasundaram, P.,”Anartificial neural network for eddy current testing of austeniticstainless steel welds” NDT & E International 35:6 (2002) 393-398.

5. Thirunavukkarasu S., Rao B.P.C, Jayakumar T, KalyanasundaramP and Baldev Raj, “Quantitative eddy current testing using radialbasis function neural networks”, Materials Evaluation, 62:12,(2004), 1213-1217.

6. Andrea Bernieri, Luigi Ferrigno, Marco Laracca, and MarioMolinara, “Crack Shape Reconstruction in Eddy Current Testingusing Machine Learning Systems for Regression”, IEEETransactions on Instrumentation and Measurement, 57:9 (2008)1958-1968

7. Jacek Jarmulak, Eugene J.H. Kerckhoffs, Peter-Paul van’t Veen,“Case-based reasoning for interpretation of data from non-destructive testing”, Engineering Applications of ArtificialIntelligence 14 (2001) 401–417

8. Gilles-Pascaud, C., Pichenot, G., Premel, D., Reboud, C. andSkarlatos, A., “Modeling of Eddy Current Inspections withCIVA”, Review of Progress in Quantitative Non DestructiveEvaluation (2008)

9. Reboud, C., Pichenot, G., Prémel, D. and Raillon, R. BenchmarkResults: Modeling with CIVA of 3D Flaws Responses in Planarand Cylindrical Work Pieces. AIP Conf. Proc. 1096, (2009)1915.

10. Zhong, S.,”Efficient online spherical K-means clustering”, IEEEInternational Joint Conference on Neural Networks, (2005) 3180- 3185.

11. Tsoumakas G and Katakis I. “Multi-label classification: Anoverview”, International Journal of Data Warehousing and Mining,3:3 (2007) 1-13.

12. Zhang M.L., “ML-RBF: RBF neural networks for multi-labellearning”, Neural Processing Letters 29 (2) (2009) 61–74.

13. Rao B.P.C, Thirunavukkarasu S, Nand K.K, Jayakumar T,Kalyanasundaram P and Baldev Raj, “Enhancement of magneticflux leakage images of defects in carbon steel using Eigen vectorbased approach”, Journal of Nondestructive Testing andEvaluation (Taylor & Francis), 23:1, (2008), 35-42.

14. Kan Deng, “OMEGA: On-line memory based general purposesystem classifier”, PhD Dissertation, The Robotics InstituteSchool of Computer Science, Carnegie Mellon University,Pittsburgh, PA 15213 (1998)

Fig. 13: CIVA model predicted EC signals of defects at 300 kHz, a) varying height (depth 1 mm) b) varying depth (height 0.5 mm).

CONCLUSIONTwo different methodologies have been proposed forinversion of eddy current data (images/signals). Competitivelearning with cosine similarity method has been able tosuccessfully identify defects between two layers, despitesmall change in defect shape and size. The method hasalso been able to handle complex situations such as defectin stainless steel filled with sodium i.e. an untrained dataset.Multi-dimension learning using multi-label radial basisfunction (MD-Learn ML-RBF) has been able to simultaneouslyquantify both depth location and height of subsurfacedefects in stainless steel plates when five ranked featureshave been used as input. A new parameter called ‘Ratio ofmaximum magnitude at two frequencies’ identified in thisstudy has ranked 1 by the restricted forward selectionalgorithm. MD-Learn ML-RBF has shown capability forinterpolation and extrapolation of depth locations andheights. The two indirect inversion methodologies proposedin this paper are promising for developing artificialintelligence based automated identification and quantificationof defects.

ACKNOWLEDGEMENTSAuthors thank Shri S.C. Chetal, Director, IGCAR,Kalpakkam for encouragement and support during thecourse of this work.

Fig. 14: Result of restricted forward feature selection.

ProbeThe second in the list is Perception (a very tricky thing). It depends upon the mind- the data store

house (It is akin to interpretation which is based on experience) and differs from person to person and

for the same person from time to time. The teacher explained to the class that the equation which he

was about to write is complicated and an un solved mystery. He wanted the students to try and solve it

in their homes and come back to the class the next day and proceeded to write the equation. As he was

writing the equation a boy entered the class. He had not listened to what the lecturer was saying and

thinking that the equation was the home work took it down. Next day when the class commenced no

one except the late comer boy had solved the equation. Except this boy all the other boys were

conditioned by the teacher that they could not solve the equation and hence they could not. This boy

was free of that influence and hence succeeded. We go buy the data that is already available with us

(Our mind).

As brought out in the earlier issue we are neither the body nor the mind. We are a part of the supreme

consciousness (remember the Boson). The base of all material things is that same particle or energy,

only the manifestation is different. This fact cannot be illustrated better than this story. A disciple in the

monastery was upset and hence angry about how he was treated by fellow disciples and wished to

meet the master about the same. He was getting worked up while he was going to meet the master and

to let out the steam he kicked the door. The master was watching this from his room. When the disciple

reached his presence and complained to the master, the master enquired what the door did to him to

receive the kick. He asked the disciple to go and apologize to the door. In the perception of the master

everything in the world is same. So we shall treat all them as equal.

Why did the disciple behave like that? It is his reaction to the incident. All of us are conditioned in that

way because of the past data (our mind). We may think that the response to the stimuli is the right

thing as it is instantaneous. But there is a small time gap between the stimuli and the response. As long

as we retain the response we retain the control with us. We shall become aware of this fact and take

appropriate action and promote harmony in our relationships. The situation can be compared to that of

PT. A drop of liquid on any surface is subjected to 3 forces (Amongst the Surface, the Liquid and the

Air) which decide the surface tension of the liquid. As the surface or the liquid changes the surface

tension also changes. For human beings the changes in the environment brings in various forces into

action creating perceived tension, as things do not go as per our wish. (Just think can the world move if

it has to satisfy every ones wish). A droplet of water on steel surface spreads thin and tries to cover

the whole surface(Wetting ability), whereas on a lotus leaf it assumes the form of a sphere and reflects

the whole universe. But unlike the penetrant particles since the human being is an evolved

manifestation of the same basic energy, it is bestowed with the chance of a choice. We can choose and

act - either spread out and look for defects in others or we can encompass the whole universe and

merge with it. Remember the choice lies in that small gap of time.

I wish to end this soliloquy with a story of Mullah. A farmer approached the Mullah with a complaint

that his house is too small to accommodate his entire family and he does not have privacy even to say

his daily prayers. The Mullah asked him whether he has faith in him and will do whatever he says. The

farmer said yes. Then the Mullah asked him whether he has a goat? The farmer replied in the

affirmative. The Mullah asked the farmer to tie the goat inside his house and come and see him after 10

days. The farmer as agreed did the same and met the Mullah after 10 days. Now the Mullah asks him to

shift the goat outside and come and see him the next day. Next day when the Mullah asks him how he

is feeling the farmer replies that he is on top of the world and very happy. That is in a nutshell is

Perception.

RamRamRamRamRam

68 Perspective


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